diff --git a/last_challenge.ipynb b/last_challenge.ipynb new file mode 100644 index 0000000..1229419 --- /dev/null +++ b/last_challenge.ipynb @@ -0,0 +1,3042 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 158, + "id": "6d04c52f", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import seaborn as sns\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import scipy.stats as st\n", + "\n", + "#Import or Export to my SQL\n", + "# Name of database: 'match_project'\n", + "\n", + "from sqlalchemy import create_engine \n", + "import pymysql.cursors\n", + "import os\n", + "# import getpass\n", + "import urllib.parse\n", + "\n", + "import requests\n", + "import config\n", + "\n", + "from datetime import datetime\n", + "import locale\n", + "from bs4 import BeautifulSoup\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.neighbors import KNeighborsRegressor, KNeighborsClassifier\n", + "from sklearn.preprocessing import MinMaxScaler, StandardScaler, OneHotEncoder\n", + "from sklearn.metrics import mean_squared_error" + ] + }, + { + "cell_type": "markdown", + "id": "fbe6c35c", + "metadata": {}, + "source": [ + "Part 0: getting the data, cleaning and wrangling" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3852c3c9", + "metadata": {}, + "outputs": [], + "source": [ + "# importing the consumption data\n", + "df = pd.read_csv(\"data/measurements.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a9fd96ad", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(388, 12)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# First looks at the dataframe, shape, null values, dtypes... to anticipate on corrections\n", + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "5f5ba2ae", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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distanceconsumespeedtemp_insidetemp_outsidespecialsgas_typeACrainsunrefill litersrefill gas
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418,54,54621,515NaNE10000NaNNaN
.......................................
383163,73924,518NaNSP98000NaNNaN
38416,14,3382531ACSP98100NaNNaN
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distanceconsumespeedtemp_insidetemp_outsidespecialsgas_typeACrainsun
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1124,23021,513NaNE10000
211,25,53821,515NaNE10000
312,93,93621,514NaNE10000
418,54,54621,515NaNE10000
.................................
383163,73924,518NaNSP98000
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388 rows × 10 columns

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" + ], + "text/plain": [ + " distance consume speed temp_inside temp_outside specials gas_type AC \\\n", + "0 28 5 26 21,5 12 NaN E10 0 \n", + "1 12 4,2 30 21,5 13 NaN E10 0 \n", + "2 11,2 5,5 38 21,5 15 NaN E10 0 \n", + "3 12,9 3,9 36 21,5 14 NaN E10 0 \n", + "4 18,5 4,5 46 21,5 15 NaN E10 0 \n", + ".. ... ... ... ... ... ... ... .. \n", + "383 16 3,7 39 24,5 18 NaN SP98 0 \n", + "384 16,1 4,3 38 25 31 AC SP98 1 \n", + "385 16 3,8 45 25 19 NaN SP98 0 \n", + "386 15,4 4,6 42 25 31 AC SP98 1 \n", + "387 14,7 5 25 25 30 AC SP98 1 \n", + "\n", + " rain sun \n", + "0 0 0 \n", + "1 0 0 \n", + "2 0 0 \n", + "3 0 0 \n", + "4 0 0 \n", + ".. ... ... \n", + "383 0 0 \n", + "384 0 0 \n", + "385 0 0 \n", + "386 0 0 \n", + "387 0 0 \n", + "\n", + "[388 rows x 10 columns]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = df[['distance', 'consume', 'speed', 'temp_inside', 'temp_outside', 'specials', 'gas_type', 'AC', 'rain', 'sun']]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "3d33f539", + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "Can only use .str accessor with string values!", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[17], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m df\u001b[38;5;241m.\u001b[39mdistance \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mdistance\u001b[38;5;241m.\u001b[39mstr\u001b[38;5;241m.\u001b[39mreplace(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m'\u001b[39m)\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m x : \u001b[38;5;28mfloat\u001b[39m(x))\n\u001b[0;32m 2\u001b[0m df\u001b[38;5;241m.\u001b[39mconsume \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mconsume\u001b[38;5;241m.\u001b[39mstr\u001b[38;5;241m.\u001b[39mreplace(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m,\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m'\u001b[39m)\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m x : \u001b[38;5;28mfloat\u001b[39m(x))\n\u001b[0;32m 4\u001b[0m df\n", + "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\generic.py:5989\u001b[0m, in \u001b[0;36mNDFrame.__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m 5982\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m 5983\u001b[0m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_internal_names_set\n\u001b[0;32m 5984\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_metadata\n\u001b[0;32m 5985\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_accessors\n\u001b[0;32m 5986\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_info_axis\u001b[38;5;241m.\u001b[39m_can_hold_identifiers_and_holds_name(name)\n\u001b[0;32m 5987\u001b[0m ):\n\u001b[0;32m 5988\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m[name]\n\u001b[1;32m-> 5989\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mobject\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__getattribute__\u001b[39m(\u001b[38;5;28mself\u001b[39m, name)\n", + "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\accessor.py:224\u001b[0m, in \u001b[0;36mCachedAccessor.__get__\u001b[1;34m(self, obj, cls)\u001b[0m\n\u001b[0;32m 221\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m obj \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 222\u001b[0m \u001b[38;5;66;03m# we're accessing the attribute of the class, i.e., Dataset.geo\u001b[39;00m\n\u001b[0;32m 223\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_accessor\n\u001b[1;32m--> 224\u001b[0m accessor_obj \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_accessor(obj)\n\u001b[0;32m 225\u001b[0m \u001b[38;5;66;03m# Replace the property with the accessor object. Inspired by:\u001b[39;00m\n\u001b[0;32m 226\u001b[0m \u001b[38;5;66;03m# https://www.pydanny.com/cached-property.html\u001b[39;00m\n\u001b[0;32m 227\u001b[0m \u001b[38;5;66;03m# We need to use object.__setattr__ because we overwrite __setattr__ on\u001b[39;00m\n\u001b[0;32m 228\u001b[0m \u001b[38;5;66;03m# NDFrame\u001b[39;00m\n\u001b[0;32m 229\u001b[0m \u001b[38;5;28mobject\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__setattr__\u001b[39m(obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_name, accessor_obj)\n", + "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\strings\\accessor.py:181\u001b[0m, in \u001b[0;36mStringMethods.__init__\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m 178\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, data) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 179\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01marrays\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mstring_\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m StringDtype\n\u001b[1;32m--> 181\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_inferred_dtype \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate(data)\n\u001b[0;32m 182\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_categorical \u001b[38;5;241m=\u001b[39m is_categorical_dtype(data\u001b[38;5;241m.\u001b[39mdtype)\n\u001b[0;32m 183\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_string \u001b[38;5;241m=\u001b[39m \u001b[38;5;28misinstance\u001b[39m(data\u001b[38;5;241m.\u001b[39mdtype, StringDtype)\n", + "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\strings\\accessor.py:235\u001b[0m, in \u001b[0;36mStringMethods._validate\u001b[1;34m(data)\u001b[0m\n\u001b[0;32m 232\u001b[0m inferred_dtype \u001b[38;5;241m=\u001b[39m lib\u001b[38;5;241m.\u001b[39minfer_dtype(values, skipna\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m 234\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m inferred_dtype \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m allowed_types:\n\u001b[1;32m--> 235\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCan only use .str accessor with string values!\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 236\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m inferred_dtype\n", + "\u001b[1;31mAttributeError\u001b[0m: Can only use .str accessor with string values!" + ] + } + ], + "source": [ + "df.distance = df.distance.str.replace(',', '.').apply(lambda x : float(x))\n", + "df.consume = df.consume.str.replace(',', '.').apply(lambda x : float(x))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "5edce80c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "distance float64\n", + "consume float64\n", + "speed int64\n", + "temp_inside object\n", + "temp_outside int64\n", + "specials object\n", + "gas_type object\n", + "AC int64\n", + "rain int64\n", + "sun int64\n", + "dtype: object" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def inside(x):\n", + " if x isintance(stri" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "f40d6852", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Javier\\AppData\\Local\\Temp\\ipykernel_22700\\3400076421.py:1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df.temp_inside = df.temp_inside.str.replace(',', '.').apply(lambda x : -1 if x is None else float(x))\n" + ] + } + ], + "source": [ + "df.temp_inside = df.temp_inside.str.replace(',', '.').apply(lambda x : -1 if x is None else float(x))" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "9719af76", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Javier\\AppData\\Local\\Temp\\ipykernel_22700\\333457368.py:1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df.temp_inside = df.temp_inside.apply(lambda x: mean(df.temp_inside[df.temp_inside != -1]) if x == -1 else x)\n" + ] + } + ], + "source": [ + "df.temp_inside = df.temp_inside.apply(lambda x: mean(df.temp_inside[df.temp_inside != -1]) if x == -1 else x)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "6a9b1a68", + "metadata": {}, + "outputs": [], + "source": [ + "value = df.temp_inside.mean()\n", + "df.temp_inside.fillna(value, inplace = True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "45964311", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.temp_inside.isna().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "ccaa049a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\Javier\\AppData\\Local\\Temp\\ipykernel_22700\\350768019.py:1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " df['AC'][334] = 1\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " distance consume speed temp_inside temp_outside gas_type AC rain \\\n", + "0 28.0 5.0 26 21.5 12 E10 0 0 \n", + "1 12.0 4.2 30 21.5 13 E10 0 0 \n", + "2 11.2 5.5 38 21.5 15 E10 0 0 \n", + "3 12.9 3.9 36 21.5 14 E10 0 0 \n", + "4 18.5 4.5 46 21.5 15 E10 0 0 \n", + ".. ... ... ... ... ... ... .. ... \n", + "383 16.0 3.7 39 24.5 18 SP98 0 0 \n", + "384 16.1 4.3 38 25.0 31 SP98 1 0 \n", + "385 16.0 3.8 45 25.0 19 SP98 0 0 \n", + "386 15.4 4.6 42 25.0 31 SP98 1 0 \n", + "387 14.7 5.0 25 25.0 30 SP98 1 0 \n", + "\n", + " sun \n", + "0 0 \n", + "1 0 \n", + "2 0 \n", + "3 0 \n", + "4 0 \n", + ".. ... \n", + "383 0 \n", + "384 0 \n", + "385 0 \n", + "386 0 \n", + "387 0 \n", + "\n", + "[388 rows x 9 columns]" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# removing the specials column\n", + "df = df.drop('specials', axis = 1)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "id": "83be7890", + "metadata": {}, + "source": [ + "Part II: EDA" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "9a40e073", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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distanceconsumespeedtemp_insidetemp_outsideACrainsun
count388.000000388.000000388.000000388.000000388.000000388.000000388.000000388.000000
mean19.6528354.91237141.92783521.92952111.3582470.0798970.1237110.082474
std22.6678371.03317213.5985240.9946666.9915420.2714840.3296770.275441
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25%11.8000004.30000032.75000021.5000007.0000000.0000000.0000000.000000
50%14.6000004.70000040.50000022.00000010.0000000.0000000.0000000.000000
75%19.0000005.30000050.00000022.50000016.0000000.0000000.0000000.000000
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" + ], + "text/plain": [ + " distance consume speed temp_inside temp_outside \\\n", + "count 388.000000 388.000000 388.000000 388.000000 388.000000 \n", + "mean 19.652835 4.912371 41.927835 21.929521 11.358247 \n", + "std 22.667837 1.033172 13.598524 0.994666 6.991542 \n", + "min 1.300000 3.300000 14.000000 19.000000 -5.000000 \n", + "25% 11.800000 4.300000 32.750000 21.500000 7.000000 \n", + "50% 14.600000 4.700000 40.500000 22.000000 10.000000 \n", + "75% 19.000000 5.300000 50.000000 22.500000 16.000000 \n", + "max 216.100000 12.200000 90.000000 25.500000 31.000000 \n", + "\n", + " AC rain sun \n", + "count 388.000000 388.000000 388.000000 \n", + "mean 0.079897 0.123711 0.082474 \n", + "std 0.271484 0.329677 0.275441 \n", + "min 0.000000 0.000000 0.000000 \n", + "25% 0.000000 0.000000 0.000000 \n", + "50% 0.000000 0.000000 0.000000 \n", + "75% 0.000000 0.000000 0.000000 \n", + "max 1.000000 1.000000 1.000000 " + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# EDA looking at the spread of data per column etc...\n", + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "756b5e8d", + "metadata": {}, + "outputs": [], + "source": [ + "def cat_stats(df, column):\n", + " \"\"\"Function to generate the statistical data for categorical columns\"\"\"\n", + " print(f'****** Brief analysis of {column} *****')\n", + " frequency_table = df[column].value_counts()\n", + " # Calculating the proportion of each unique value \n", + " proportion_table = df[column].value_counts(normalize=True)\n", + " display(frequency_table, proportion_table)\n", + " \n", + " mode_col = df[column].mode()\n", + " display(f'mode = {mode_col}')\n", + " \n", + " # Plotting a count plot for the 'MSZoning' column from the dataframe 'df', using the \"Set3\" palette for coloring\n", + " sns.countplot(data=df, x= column, palette=\"Set1\")\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "f49fe115", + "metadata": {}, + "outputs": [], + "source": [ + "# to keep as long as not working via the function_file.py (Javier) -ot gives an intermittent error sometimes\n", + "# it works other times no, so was kept to run the code\n", + "def num_stats(df, column):\n", + " \"\"\"Function to generate the statistical data for numerical columns à la describe with mode in addition\"\"\"\n", + " print(f'****** Brief Analysis of {column} *****')\n", + " \n", + " mean_col = df[column].mean().round()\n", + " median_col = df[column].median()\n", + " mode_col = df[column].mode()[0]\n", + " variance_col = round(df[column].var(),2)\n", + " std_dev_col = round(df[column].std(),2)\n", + " min_col = df[column].min()\n", + " max_col = df[column].max()\n", + " range_col = max_col - min_col\n", + " quantiles_col = df[column].quantile([0.25, 0.5, 0.75])\n", + "\n", + " print(f'mean= {mean_col}, median= {median_col}, mode= {mode_col}')\n", + " print(f'var = {variance_col}, std_dev = {std_dev_col}, min = {min_col}, max = {max_col}, range = {range_col}')\n", + " print(f'quantiles : \\n{quantiles_col}')\n", + " \n", + " # Plotting a histogram for the column of the 'data' dataframe\n", + " # 'bins=30' divides the data into 30 bins for more detailed granularity\n", + " sns.histplot(df[column], kde=True, bins=30, color=\"blue\")\n", + " plt.show() # to show the plot as it goes!\n", + " sns.boxplot(data = df[column], color=\"lightgreen\")\n", + " plt.show() # to show the plot as it goes!" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "6967ed2d", + "metadata": {}, + "outputs": [], + "source": [ + "numerical = ['distance', 'consume', 'speed', 'temp_inside', 'temp_outside']\n", + "categorical = ['gas_type', 'AC', 'rain', 'sun']\n" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "1851cf33", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "****** Brief analysis of gas_type *****\n" + ] + }, + { + "data": { + "text/plain": [ + "gas_type\n", + "SP98 228\n", + "E10 160\n", + "Name: count, dtype: int64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "gas_type\n", + "SP98 0.587629\n", + "E10 0.412371\n", + "Name: proportion, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'mode = 0 SP98\\nName: gas_type, dtype: object'" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "****** Brief analysis of AC *****\n" + ] + }, + { + "data": { + "text/plain": [ + "AC\n", + "0 357\n", + "1 31\n", + "Name: count, dtype: int64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "AC\n", + "0 0.920103\n", + "1 0.079897\n", + "Name: proportion, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'mode = 0 0\\nName: AC, dtype: int64'" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "****** Brief analysis of rain *****\n" + ] + }, + { + "data": { + "text/plain": [ + "rain\n", + "0 340\n", + "1 48\n", + "Name: count, dtype: int64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "rain\n", + "0 0.876289\n", + "1 0.123711\n", + "Name: proportion, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'mode = 0 0\\nName: rain, dtype: int64'" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "****** Brief analysis of sun *****\n" + ] + }, + { + "data": { + "text/plain": [ + "sun\n", + "0 356\n", + "1 32\n", + "Name: count, dtype: int64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "sun\n", + "0 0.917526\n", + "1 0.082474\n", + "Name: proportion, dtype: float64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'mode = 0 0\\nName: sun, dtype: int64'" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for col in categorical:\n", + " cat_stats(df, col)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "146658b8", + "metadata": {}, + "outputs": [], + "source": [ + "# Quick analysis categorical:\n", + "# Type of gas: 59% vs 41%, likely to be comparable groups\n", + "# AC is about 8% of the time, is it meaningful?\n", + "# Rain is about 12% of the time, is it meaningful?\n", + "# Sun is about 8% of the time, is it meaningful?" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "d427102f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "****** Brief Analysis of distance *****\n", + "mean= 20.0, median= 14.6, mode= 11.8\n", + "var = 513.83, std_dev = 22.67, min = 1.3, max = 216.1, range = 214.79999999999998\n", + "quantiles : \n", + "0.25 11.8\n", + "0.50 14.6\n", + "0.75 19.0\n", + "Name: distance, dtype: float64\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "****** Brief Analysis of consume *****\n", + "mean= 5.0, median= 4.7, mode= 4.5\n", + "var = 1.07, std_dev = 1.03, min = 3.3, max = 12.2, range = 8.899999999999999\n", + "quantiles : \n", + "0.25 4.3\n", + "0.50 4.7\n", + "0.75 5.3\n", + "Name: consume, dtype: float64\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "****** Brief Analysis of speed *****\n", + "mean= 42.0, median= 40.5, mode= 42\n", + "var = 184.92, std_dev = 13.6, min = 14, max = 90, range = 76\n", + "quantiles : \n", + "0.25 32.75\n", + "0.50 40.50\n", + "0.75 50.00\n", + "Name: speed, dtype: float64\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "****** Brief Analysis of temp_inside *****\n", + "mean= 22.0, median= 22.0, mode= 21.5\n", + "var = 0.99, std_dev = 0.99, min = 19.0, max = 25.5, range = 6.5\n", + "quantiles : \n", + "0.25 21.5\n", + "0.50 22.0\n", + "0.75 22.5\n", + "Name: temp_inside, dtype: float64\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "****** Brief Analysis of temp_outside *****\n", + "mean= 11.0, median= 10.0, mode= 8\n", + "var = 48.88, std_dev = 6.99, min = -5, max = 31, range = 36\n", + "quantiles : \n", + "0.25 7.0\n", + "0.50 10.0\n", + "0.75 16.0\n", + "Name: temp_outside, dtype: float64\n" + ] + }, + { + "data": { + "image/png": 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SESIAQDJCBABIRogAAMkIEQAgGSECACQjRACAZIQIAJCMEAEAkhEiAEAyQgQASEaIAADJCBEAIBkhAgAkI0QAgGSECACQjBABAJIRIgBAMkIEAEhGiAAAyQgRACAZIQIAJCNEAIBkKg6RrVu3xuzZs2Ps2LGRy+Xiueee6/F4lmXR1tYWY8eOjREjRsSMGTPi7bffPt95AYBBpOIQOXbsWFx99dWxevXqsz7+6KOPxsqVK2P16tWxY8eOqK+vj1mzZsXRo0crHhYAGFyGVfqNLS0t0dLSctbHsiyLVatWRWtra8yZMyciItatWxd1dXWxYcOGuPfeeys97ICTZVn565Mfn0w4CQD93emvE6e/fgxmFYfIuezduzc6Ozujubm5vC2fz8dNN90U27Zt+9QQKZVKUSqVyuvFYrEa4/Wpjz/+uPz1nif3JJwEgIHk448/juHDh6ceo+qqcrFqZ2dnRETU1dX12F5XV1d+7Gza29ujUCiUl8bGxmqMBwD0E1U5I3JKLpfrsZ5l2RnbTrd06dJYvHhxeb1YLA74GLnooovKX0+YPyGGXOQPlQA4u5MfnyyfPT/99WMwq0qI1NfXR8R/zow0NDSUtx88ePCMsySny+fzkc/nqzFSMqeH15CLhggRAD6Tc/2P+2BSlVfFpqamqK+vj46OjvK27u7u2LJlS0yfPr0ahwQABqCKz4h89NFH8e6775bX9+7dG2+++WaMGjUqLrvssli0aFGsWLEiJkyYEBMmTIgVK1bExRdfHHfddVevDA4ADHwVh8jOnTtj5syZ5fVT13bcc8898X//93+xZMmSOH78eCxYsCA+/PDDmDp1arz88stRW1t7/lMDAINCxSEyY8aMc/6Ncy6Xi7a2tmhra6v0EADAIOfKSQAgGSECACQjRACAZIQIAJCMEAEAkhEiAEAyQgQASEaIAADJCBEAIBkhAgAkI0QAgGSECACQjBABAJIRIgBAMkIEAEhGiAAAyQgRACAZIQIAJCNEAIBkhqUe4EJy8uOTqUeAfiHLssg+ySIiIjcsF7lcLvFE0D9ciK8TQqQP7XlyT+oRAKBf8dYMAJBMLsuyLPUQn6ZYLEahUIiurq4YOXJk6nEqkmVZdHd3px4D+pXu7u5YtmxZREQ8/PDDUVNTk3gi6H9qamoG7NuWn+f121szVZbL5SKfz6ceA/qtmpoa/0bgAuatGQAgGSECACQjRACAZIQIAJCMEAEAkhEiAEAyQgQASEaIAADJCBEAIBkhAgAkI0QAgGSECACQTFVDpK2tLXK5XI+lvr6+mocEAAaQqn/67te//vX44x//WF4fOnRotQ8JAAwQVQ+RYcOGOQsCAJxV1a8R2bNnT4wdOzaampriu9/9bvz973//1H1LpVIUi8UeCwAweFU1RKZOnRrr16+Pl156KZ588sno7OyM6dOnx+HDh8+6f3t7exQKhfLS2NhYzfEAgMSqGiItLS1x++23x1VXXRXf+MY34oUXXoiIiHXr1p11/6VLl0ZXV1d52b9/fzXHAwASq/o1Iqf7whe+EFdddVXs2bPnrI/n8/nI5/N9ORIAkFCf3kekVCrF3/72t2hoaOjLwwIA/VRVQ+TBBx+MLVu2xN69e+P111+PO+64I4rFYtxzzz3VPCwAMEBU9a2Zf/zjH/G9730vDh06FF/+8pfjuuuui+3bt8f48eOreVgAYICoaog8/fTT1Xx6AGCA81kzAEAyQgQASEaIAADJCBEAIBkhAgAkI0QAgGSECACQjBABAJIRIgBAMkIEAEhGiAAAyQgRACAZIQIAJCNEAIBkhAgAkIwQAQCSESIAQDJCBABIRogAAMkIEQAgGSECACQjRACAZIQIAJCMEAEAkhEiAEAyQgQASEaIAADJCBEAIBkhAgAkI0QAgGSECACQjBABAJIRIgBAMkIEAEhGiAAAyQgRACCZPgmRxx9/PJqammL48OExefLkePXVV/visABAP1f1ENm4cWMsWrQoWltb44033ogbbrghWlpaYt++fdU+NADQz1U9RFauXBk//OEP40c/+lFceeWVsWrVqmhsbIw1a9ZU+9AAQD9X1RDp7u6OXbt2RXNzc4/tzc3NsW3btjP2L5VKUSwWeywAwOBV1RA5dOhQnDhxIurq6npsr6uri87OzjP2b29vj0KhUF4aGxurOR4AkFifXKyay+V6rGdZdsa2iIilS5dGV1dXedm/f39fjAcAJDKsmk8+evToGDp06BlnPw4ePHjGWZKIiHw+H/l8vpojAQD9SFXPiNTU1MTkyZOjo6Ojx/aOjo6YPn16NQ8NAAwAVT0jEhGxePHiuPvuu2PKlCkxbdq0eOKJJ2Lfvn1x3333VfvQAEA/V/UQmTt3bhw+fDgeeeSROHDgQEycODE2bdoU48ePr/ahAYB+ruohEhGxYMGCWLBgQV8cCgAYQHzWDACQjBABAJIRIgBAMkIEAEhGiAAAyQgRACAZIQIAJCNEAIBkhAgAkIwQAQCSESIAQDJCBABIRogAAMkIEQAgGSECACQjRACAZIQIAJCMEAEAkhEiAEAyQgQASEaIAADJCBEAIBkhAgAkI0QAgGSECACQjBABAJIRIgBAMkIEAEhGiAAAyQgRACAZIQIAJCNEAIBkhAgAkIwQAQCSESIAQDJVDZHLL788crlcj+XnP/95NQ8JAAwgw6p9gEceeSTmz59fXr/kkkuqfUgAYICoeojU1tZGfX19tQ8D/1OWZdHd3Z16DCJ6/B78TvqPmpqayOVyqcfgApPLsiyr1pNffvnlUSqVoru7OxobG+M73/lO/OxnP4uampqz7l8qlaJUKpXXi8ViNDY2RldXV4wcObJaY3KBKJVK0dramnoM6LeWL18e+Xw+9RgMAsViMQqFwmd6/a7qGZGf/OQncc0118QXv/jF+POf/xxLly6NvXv3xq9+9auz7t/e3h7Lli2r5kgAQD/yuc+ItLW1/c9Y2LFjR0yZMuWM7b/97W/jjjvuiEOHDsWXvvSlMx53RoRq8tZM/3Lqd/FpZ0jpe96aobd8njMinztEDh06FIcOHTrnPpdffnkMHz78jO3//Oc/Y9y4cbF9+/aYOnXq/zzW5/lBAID+oapvzYwePTpGjx5d0WBvvPFGREQ0NDRU9P0AwOBStWtEXnvttdi+fXvMnDkzCoVC7NixI37605/Gt771rbjsssuqdVgAYACpWojk8/nYuHFjLFu2LEqlUowfPz7mz58fS5YsqdYhAYABpmohcs0118T27dur9fQAwCDgs2YAgGSECACQjBABAJIRIgBAMkIEAEhGiAAAyQgRACAZIQIAJCNEAIBkqnZn1d5w6oOBi8Vi4kkAgM/q1Ov2qdfxc+nXIXL06NGIiGhsbEw8CQDweR09ejQKhcI598llnyVXEjl58mS8//77UVtbG7lcLvU4QC8qFovR2NgY+/fvj5EjR6YeB+hFWZbF0aNHY+zYsTFkyLmvAunXIQIMXsViMQqFQnR1dQkRuIC5WBUASEaIAADJCBEgiXw+Hw8//HDk8/nUowAJuUYEAEjGGREAIBkhAgAkI0QAgGSECACQjBABknj88cejqakphg8fHpMnT45XX3019UhAAkIE6HMbN26MRYsWRWtra7zxxhtxww03REtLS+zbty/1aEAf8+e7QJ+bOnVqXHPNNbFmzZrytiuvvDJuvfXWaG9vTzgZ0NecEQH6VHd3d+zatSuam5t7bG9ubo5t27YlmgpIRYgAferQoUNx4sSJqKur67G9rq4uOjs7E00FpCJEgCRyuVyP9SzLztgGDH5CBOhTo0ePjqFDh55x9uPgwYNnnCUBBj8hAvSpmpqamDx5cnR0dPTY3tHREdOnT080FZDKsNQDABeexYsXx9133x1TpkyJadOmxRNPPBH79u2L++67L/VoQB8TIkCfmzt3bhw+fDgeeeSROHDgQEycODE2bdoU48ePTz0a0MfcRwQASMY1IgBAMkIEAEhGiAAAyQgRACAZIQIAJCNEAIBkhAgAkIwQAQCSESIAQDJCBABIRogAAMkIEQAgmf8Htdp/gGHdnl0AAAAASUVORK5CYII=", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for col in numerical:\n", + " num_stats(df, col)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cf058363", + "metadata": {}, + "outputs": [], + "source": [ + "# Brief analysis:\n", + "# Distance: mean is 20 km vs 14.6 for the median, when the mode is 11.8 => right skewed (some very high values, outliers)\n", + "# Consume: mode at 4.5L/100km , median is at 4.7, and mean at 5 ! closer to a normal distribution, but not perfect, with \n", + "# some right skewed too with bursts of high consumption\n", + "# speed: median, mode and average are close (40.5 for median, 42 for the two others), varies on both sides with some low and\n", + "# high values but not completely off\n", + "# Temperatures (inside and outside): kind of 'normally distributed' but flatter." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "797fda92", + "metadata": {}, + "outputs": [], + "source": [ + "df_E10 = df[df['gas_type'] == 'E10']\n", + "df_SP98 = df[df['gas_type'] == 'SP98']" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "37fb1755", + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Creating histograms for each numerical column in 'df1 numerical'\n", + "df[numerical].hist(figsize=(10, 10), bins=60, xlabelsize=10, ylabelsize=10); # makes it automatically to fit the figure!" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "3eac96af", + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Creating histograms for each numerical column in 'df1 numerical'\n", + "df_E10[numerical].hist(figsize=(10, 10), bins=60, xlabelsize=10, ylabelsize=10); # makes it automatically to fit the figure!" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "4896d378", + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Creating histograms for each numerical column in 'df1 numerical'\n", + "df_SP98[numerical].hist(figsize=(10, 10), bins=60, xlabelsize=10, ylabelsize=10); # makes it automatically to fit the figure!" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "e938803a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "( distance consume speed temp_inside temp_outside\n", + " count 160.000000 160.000000 160.000000 160.000000 160.000000\n", + " mean 21.096250 4.931250 43.506250 21.917429 10.118750\n", + " std 20.307234 0.900956 14.077949 0.653602 6.392185\n", + " min 1.700000 3.700000 14.000000 21.000000 -5.000000\n", + " 25% 12.075000 4.400000 35.000000 21.500000 6.000000\n", + " 50% 15.400000 4.800000 42.000000 21.500000 9.000000\n", + " 75% 21.200000 5.300000 51.000000 22.500000 14.250000\n", + " max 130.300000 10.800000 88.000000 25.000000 27.000000,\n", + " distance consume speed temp_inside temp_outside\n", + " count 228.000000 228.000000 228.000000 228.000000 228.000000\n", + " mean 18.639912 4.899123 40.820175 21.938007 12.228070\n", + " std 24.179598 1.118408 13.170122 1.177840 7.271373\n", + " min 1.300000 3.300000 16.000000 19.000000 -3.000000\n", + " 25% 11.800000 4.200000 32.000000 21.500000 7.000000\n", + " 50% 14.150000 4.700000 39.500000 22.000000 11.000000\n", + " 75% 18.150000 5.225000 48.000000 22.000000 17.000000\n", + " max 216.100000 12.200000 90.000000 25.500000 31.000000)" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_E10[numerical].describe(), df_SP98[numerical].describe()\n", + "# close average consumption for lower speed\n", + "# conso E10/SP98 = 1.0065577 vs speed E10/SP98 = 1.0658026 ... => higher consumption for SP98?" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "e2dfb1a3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting consumption vs speed, for both type of gas\n", + "sns.scatterplot(data=df_E10, x= 'speed', y = 'consume')\n", + "sns.scatterplot(data=df_SP98, x= 'speed', y = 'consume' )\n", + "### my comments: is orange (SP98) a bit above for the same average speeds ?" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "4fa8013b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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dO7dDXzsYo04d9LxWFThGTAjxXCIiImqbsIKZgoIC/Oc//+nwYOarr77CVVddhSuvvBIA0LdvX7z99tv45ptvAj6nvr4e9fX17scWi0X2cSXHa5DXPxnbAuTMlFfX+3mWdC45nktMREREcgormJkyZQrmz5+PAwcOYPDgwT4JwFOnTpVlcGPGjMHLL7+Mw4cPY8CAAfjPf/6DHTt2YOXKlQGfs3TpUjzxxBOyvH4gpjgNlk3LxcNr9uObI5WYP6YnxmcqkCBYYUpKQp1Gj8LzemLjwXL3c/L6J2P5tFzmyxAREcksrN1MCkXgZRQ5E4BFUcQjjzyC5cuXQ6lUwuFwYMmSJVi0aFHA5/ibmcnIyOiQ3kxmqx062yloPp4LoaSw+UTOODgm/xlHGpNgsTUgQadGcjzrzBAREbVWh+9m6qwE3HfffRf/+Mc/8M9//hMDBw7Ed999h3nz5iEtLQ0zZszw+xytVgutVtsp4zMJtcDHcwHPQAYAijdBuX4usq9dDfRgewciIqKO1O5Gkx3pwQcfxMMPP4wbbrgBADB48GAcOXIES5cuDRjMdKra076BjEvxJum8nsEMERFRRwormHnyySeDnv/jH/8Y1mBaslqtPktaSqUy4luz3epCJBeHOk9ERETtFlYw88EHH3g9bmhoQGlpKVQqFXJycmQLZqZMmYIlS5YgMzMTAwcOxL59+/D8889j5syZsty/3XQhcnBCnSciIqJ2CyuY2bdvn88xi8WCW2+9Fb/97W/bPSiXVatWYfHixbjnnntQXl6OtLQ03HXXXbIFS+1m6AHkjJOWlFrKGSedJyIiog4V1m6mQL7//ntMnjwZP//8s1y3bLeO6M3kxXwcWDvHO6DJGQdMXQWYesv/ekRERF1Ah+9mCqSqqgpms1nOW0Y/U2/g2tVSsm+dRVpaMvRg4i8REVEnCSuYeeGFF7wei6KIkydP4u9//zsuv/xyWQYWU/RJDF6IiIgiJKxgZsWKFV6PFQoFevTogRkzZgQtaEdEREQkt7CCmdLSUrnHQURERBSWwH0J2sBiseDDDz/EwYMH5bgdERERUauFFcxcf/31ePHFFwEANpsNF154Ia6//nrk5uZizZo1sg6QiIiIKJiwgplt27bhkksuASAV0BNFEVVVVXjhhRfwpz/9SdYBEhEREQUTVjBjNpvRrVs3AMCGDRswbdo0xMXF4corr0RRUZGsAyQiIiIKJqxgJiMjA1999RVqa2uxYcMGTJgwAQBQWVkJnU4n6wCJiIiIgglrN9O8efNw0003IT4+Hn369EF+fj4Aaflp8ODBco6PiIiIKKiwgpl77rkHF110EY4ePYrx48e7O1tnZ2czZ4aIiIg6lay9maJRh/dmAgBbpUc7AxPqNEk4Ua+D2dYAo16NZIMGpjhNx7w2ERHRWajDezM5HA688cYb2LRpE8rLy+F0Or3OFxYWhnPb2GQ+Dnw0Gyhp/p7V2QWwDV+Cm94ug9XuQF7/ZCyblou0RH0EB0pERHR2CisBeO7cuZg7dy4cDgcGDRqEIUOGeH11GbZKn0AGAJQlhThn96OYP6YnAGBbUQUeXrMfZqs9EqMkIiI6q4U1M/POO+/gX//6F6644gq5xxNbak/7BDIuqtJCjB/xGJY0Pd5WVIGKGjuXm4iIiGQW1syMRqNBv3795B5L7KmzBD2tcdR4Pa6ua+jI0RAREXVJYQUz999/P/785z/jLM8dDk0XPCHJroz3epygU3fkaIiIiLqksJaZduzYgc2bN+PTTz/FwIEDoVZ7f0i///77sgwu6hl6ADnjgOJNPqcaswqwsaw5MTqvfzKS47nEREREJLewgpnExET89re/lXsssUefBExdBayd4xXQOLILcGj4Eqx4uwyAFMgsn5bLfBkiIqIOwDozcvCqM2NEnaYbTtTrYLE1IEGnRnI868wQERG1RYfXmXE5ffo0Dh06BEEQMGDAAPTo0aM9t4td+iTpq4kOQHbkRkNERNSlhJUAXFtbi5kzZyI1NRV5eXm45JJLkJaWhttvvx1Wq1XuMRIREREFFFYws2DBAmzduhXr1q1DVVUVqqqq8NFHH2Hr1q24//775R5jl2O22lFcXoN9ZZUoPl3DYntERERBhJUzk5ycjH//+9/ubtkumzdvxvXXX4/Tp0/LNb5265ScGRmdqLJh4Zr92F5U4T7GdghERNTVtOXzO6yZGavVil69evkc79mzJ5eZ2sFstfsEMgDbIRAREQUTVjBz8cUX47HHHkNdXZ37mM1mwxNPPIGLL75YtsF1NRU1dp9AxsXVDoGIiIi8hbWbaeXKlZg0aRLS09MxZMgQCIKA7777DlqtFp9//rncY+wyLCHaHbAdAhERka+wgpnBgwejqKgI//jHP/Df//4XoijihhtuwE033QS9nnkd4TKGaHfAdghERES+wgpmli5dil69euGOO+7wOv7aa6/h9OnTWLhwoSyD62qS4zXI65+MbX6WmtgOgYiIyL+wcmb++te/4txzz/U5PnDgQLz88svtHlRXZYrTYNm0XOT1T/Y6znYIREREgYU1M3Pq1Cmkpqb6HO/RowdOnjzZ7kHFPK/2BibAkOxVITiYtEQ9Vk0fiooaO6rr2A6BiIgolLCCmYyMDHz55ZfIysryOv7ll18iLS1NloHFLPNx4KPZQElh87GccVJDSlPvVt3CFMfghYiIqLXCCmb+8Ic/YN68eWhoaEBBQQEAYNOmTXjooYe6dgVgW6VvIANIHbXXzgGuXd3qGRoiIiJqnbCCmYceegi//vor7rnnHtjtUu0TnU6HhQsXYtGiRbIOMKbUnvYNZFyKN0nnGcwQERHJKqxgRhAELF++HIsXL8bBgweh1+vRv39/aLVauccXW+os7TtPREREbRZWMOMSHx+P4cOHyzWW2KcL0fsp1HkiIiJqs7C2ZlMAhh5Ssq8/OeOk80RERCQrBjNy0idJu5ZaBjSu3UzMlyEiIpJdu5aZyA9Tb2nXkrvOjFGakWEgQ0RE1CEYzHQEfRKDFyIiok7CZSYiIiKKaZyZiSXtaJNARER0tmIwEytkaJNARER0NuIyUywI1SbBVhmZcREREUUBBjOxoDVtEoiIiLooLjN1hHbmtpitdlTU2GGpa4BRr0ZWnTl41Mk2CURE1IUxmJFbO3NbTlTZsHDNfmwvqnAf23VHBlKCPYltEoiIqAvjMpOc2pnbYrbafQIZAFhX3AhHdoH/J7FNAhERdXEMZuTUztyWihq7TyADACt2lOO/w5dAZJsEIiIiH1xmkpMrd0VjAEbeDaQPBxrrAZUOOLYbsFWhzlKBE/U6mG1SPkyyQQNTnAYAYKlr8Htbq92B694uw5Z7/4KeSgvbJBAREXlgMCMnnVEKZKatBr5+Gdj2bPO57Hxg0LVQr58D29DFuOntMljtDuT1T8ayablIS9TDqFMHvLXV7kC1EI+eyUGzZ4iIiLocLjPJqE7TDc6JS6VApmSL98mSLcCGRVCmnI9zdj+K+WN6AgC2FVXg4TX7YbbakRyvQV7/ZL/3zuufjOR4Tcd+A0RERDGIwYyMTtTr0JA6zDeQcSnZDKQPh6q0EOMzm3/024oqUFFjhylOg2XTcn0Cmrz+yVg+Lde9HEVERETNuMwkI7OtAYZaC3rlPeCbL7PrJcBeKx0DoHHUeD23uilfJk1bh9enJMJpE+DQGlGn7gZBn8hAhoiIKAAGMzJK1KvRI64nsOcb33yZaauBNbcDKi0AoEEVD8DsvsSoV7tr1ChLCqEEoAagc+1YAvsvERER+cNlJhllaCwQPnnAf77M1y8DE58Cju2BI7sAp53Nhe7y+icjTVvH/ktERERh4MxMe7RoW6BqtEt5Mf6UbAEmLEHjfz/DoeFLUCPEA2jOh9HZjzYHMv62dtsquQ2biIjIDwYz4fLXtuDWj4M+Rayz4PUeD2LF22V447YUbFowFsnxTXVmjnnUqPG7tbsAuOrFVrVEICIi6kq4zBSOQG0LNPFBn9aoScCSwpOw2h3obtAgp2d8c2Kvq7/SyLsDbO0u5HITERGRHwxmwhGobUH1SSnZ1w8xOx8HzVLg4rdmjKEHkFMgLS0F2trdipYIREREXQ2DmXC42ha0tHYOcPkyn4BGzM5H3cTnMXNNWeCaMfok6bnhvjYREVEXxZyZcOiM/o/XngbenAL84QuppkydGaLWhHpNIoqtcXj3zl7NOTJ+iKIIwRgiJybQaxMREXVRDGbCYeghdawu3uR7LiUX0JmApL4AAAGADsCgbsFvabbaITp1MB3fBSE73+9Sk5hTAEHLYIaIiMgTl5nCoU+SCtnljPM+7ipwF8YW6ooaOyyWKgifPQKMmOWbe5OdD2HiUqC+OvxxExERnYWifmbm+PHjWLhwIT799FPYbDYMGDAAq1evxrBhwyI7MFNv1F31ChTWCqDeAuiMcOqToVMrgYrD7tozMCS3Krix1DWgV0O1tDy15nZpV9PIu5vqzGiBY3uAMz8BCe3omm2rlL7stdKXPhGI78X6NUREFNOiOpiprKzE6NGjcemll+LTTz9Fz549UVxcjMTExEgPDSeqbFi4phjbiyoAAHGaX/HpbQpkbl8IwXOnk2u2JkR9GKNODbs1QXpgr/WuMeNy47vh58yYTwC/lgDbn/Fewmrl+IiIiKJVVC8zLV++HBkZGXj99ddx0UUXoW/fvhg3bhxycnIiOi6z1Y6Fa/a7AxkAmD+mJ9K3PeQdyACtbkeQHK/BjxYNnNkF/i/IzodY/YuUr9NWtkrgp42+gUwbxkdERBStojqYWbt2LS688EJcd9116NmzJ4YOHYpXX3016HPq6+thsVi8vuRWUWP3CmQAYHymAsrSAK0MWlEfxhSnwdABWTh2ydMQWwY02fkQ8x6E0G98eEtCtaeBhF6sX0NERGelqF5mKikpwUsvvYQFCxbgkUcewe7du3HfffdBq9Xilltu8fucpUuX4oknnujQcVnqGnyOaRwhEnNbUR8mNVGPOkUSHFc8B2VDjbTcpE2AQxUHqyIBR6pViLPXINkQeHt3wNdurG/3+IiIiKJRVAczTqcTF154IZ566ikAwNChQ/HDDz/gpZdeChjMLFq0CAsWLHA/tlgsyMjIkHVcRp3a55hdmRD8Sa3JdTEfh/aj2V5LVY1ZBTh00RKs+PoMbhyZidmvfI0L+yRh2bRcpCXqWzdgnRGwVoS+hoiIKAZF9TJTamoqzj//fK9j5513HsrKygI+R6vVwmg0en3JLTleg7z+yV7HNpY50ZgVIN8lZ1zoXBdbJcQWgQwAqEoLcc7uRzEiTYnXd5Ri5pgsbCuqwMNr9sNstbduwIYeQPUvAVsttGp8REREUSqqg5nRo0fj0KFDXscOHz6MPn36RGhEElOcBsum5XoFNCt2lON43nKI4daeqT3tmzzcRFVaiPGZCmz/6QyGZiQCALYVVaCippXBjD4J6DceuORB34CmHbVxiIiIokFULzPNnz8fo0aNwlNPPYXrr78eu3fvxiuvvIJXXnkl0kNDWqIeq6YPRUWNHdV1DUjQqZEYr4Fw7WopmbZOqj0DQ4/AgYKtsvlaZyOgMUi1ZdJHAAoVYOgOOBoAWxWS9SrEaZSob3S6n17dlLtjttqlont1DTDq1f5zakxpgEYPTF7RXGdGlyglBuuTvMfShvo4REREkSaIoihGehDBrF+/HosWLUJRURGysrKwYMEC3HHHHa1+vsVigclkgtls7pAlp7CZjwMfzW7uvn3T+4DTDnz9sveuo+x8YMQsiHvfwo9DF+O0kIxb39gDACi8fyx0aqXPNvG8/slty6lpORaA9WeIiCii2vL5HdXLTAAwefJkHDhwAHV1dTh48GCbApmoZav0DR7iuvkGMoD0+OuXIaScj3P3PIp4sQaAFLAYtCqfQAZA23Jq/I0FYP0ZIiKKGVG9zHTWqj3tGzwIQnMg415uGt7UzkAHxPeEcu+bGGBsxK47MtBDXQ+n7QgK+mpwYd/+GJRmQn2jEzq1Et+WVeK1HaWoqLGH3sLtbywurvozXG4iIqIoxmAmAhqtZt8fvKuBpMYATFstzdJ4tjTILgBmrEPCF3+E8fCnAAAlgBnZ41A2ZhmueGMfrHYHAGB0v+54YfpQ1Nb71sPxEaq+DOvPEBFRlIv6ZaazjdlqR0WD1veEtqlOzci7Ayw3FQIbHoaQMtDrsKJkE9K3L8T8MT3dx7786Qxe/7IURr1vPRwfoerLsP4MERFFOQYznayixo51xY2+NWmqT0qzL+nDA7cdKNkinW/BtXXb05c/nUGdx86ngAw9pGRff1h/hoiIYgCDGZn9YqnDf09asLv0Vxz6xYJa82mpW/WpA0DZLvQRj0KvVqBoxFPeAc3aORAnLQNC7S0L0JYgXWfDowWpiNMo3ccstsbQA9YnSbuWwq2PA2m2qbi8BvvKKlF8uqb1xfyIiIhkwJwZGZWdqcWiDw7gy5/OIE6jxJob+yBOWwFsf9Y926ICcFN2AQ73Xoq/pz+GS0c8Bo2jBnZlPDKcTij1icFfROVniQqAym7GbSf/ilHTl+C6t8tgtTtg1LXy7TX1BtpSH8fDiSpb+7eGExERtQNnZmTyi6XOHcgAwPwxPXFOzdcQPAIZF6GkEP13LUKjw4n8N45j1N/NyH/jOI5YNRBP/zdw24HsfODYnoDHXa0P5o/piTH9uiPJ0IZmlPokIHkAkH6h9M9Wzsi0e2s4ERFROzGYkUllrd0dyADA+EwFFMaUgPkvihLvPJe8/slQJaSgIXMsRH9tB7LzgcuXAae+9z0+Yhaw6yUAUv7MlBwVnvrtYPQy6tr/jQVRUWP3CWRc2tRugYiIqB24zCQTS513forGUQ04/ee3uPSOa8CH94xCgk6NeJ0Kxytrsb60ETcPzEL8Fc9DaLQBdVXSdm0IwKFPgN5DgfyHgaoyacnp2B5gze1Se4Imyep6qLobQg/aVglHdTmcNjMcWiPq1N0g6BND16Zxf8/Bt35XhzhPRF0c26iQTBjMyKRlfopdmQAoagNcLVHHmfCb5CScqLLhgff+g4cuPwdPbziMFwuV2De7H7Sf/w9Qsrn5Ca5ZmNoK4F+3BLxvRYMWzipb8JwV83GIH82GsqQQSgBqAKqsAhzPWw5rUgZSW5HvYtQF3/qdEOI8EXVhbKNCMuIyk0ySDBqM6dfd/XhjmRMOyy+B81+atj175p2UW+oxpl93zB/TE+oND3gHMoC7tQGcjQHv25hVgHXFjcFzVmyVED+a7dOlW1VaiN7bFmLf4dJW5bskx2u8Ood7yuufjOT4NuTsEFHXwTYqJDMGMzLpZdThqd8Odgc0K3aU43D8Rf7zXzy2PXvmnSxcsx+LJw/ElBwVFC0DGZeSLYAmHpi8EmKL7dSNWQU4dNESrNhRHjxnpfa0TyDjoiotxPlGe6vyXUxxGiyblusT0OT1T8byabmtXq4ioi6mNW1UiNqAy0wyMunVWPrbwaixO1Bd1wCFXg2rPhmGySuknBZ7LaBLBBJ6udeFPfNOKmrsWPzhfrx7RfC3RVSq4bBWApc/C9FRh7oaC3516PD5ESdWNG3LBoLkrIRoUaBx1KC8lfkuaYl6rJo+FBU1dlTXNSBBp0ZyvIaBDBEFxjYqJDMGMzIJVm/F0C1wFV3PvJMBPePx7vWpQNXRoK8l1FVB9c/fAZBmY85cshyljYlYseNbdyADBMlZCdGiwK6Mb1O+iymOwQsRtQHbqJDMuMwkg/bUW/HMO/ng932B9fMh/Lyt1bVmVKWFSN++EFZLBWaOyXIfD5qzYujhs0Tl0phVgB8tGua7EFHHYRsVkhlnZmQQrN7KN0cqIdqqAGtV0/bDREAbD9RbgDoLTDoT/jItGw9/rEKcohFC/iIpwfe8qwBBAA59DHz5grRE5drNtOZ2r9dQlRZi4Ag7dAmpALxzVsxWKf+lpr4BfeIakOCohLKhFsIVz0L85AEIxZvc92ls2s10QVJGh8+0uMZlqWuAUa9GsoGzO0RdhquNyto5Uo6MSxvaqFAUiKKt9QxmZBCo3kqcRon3pmfC9PFdUrKbxgBMW+3TFTv+nCuxatISYP0C76S47HzgkgelwMZhBw6u9akp46J11CAhTo1NC8a6c1ZcS197j1TivemZSCh8BMrSpsRijQHCxKUQJ/wJjvpaNKoTUKfphsQ21JkJF1sgEFF72qhQFIiyrfVcZpJBoHor88f0xLm7H2neOTTybp9ABgDQ6zxpealldn/JFmD7M8DRrwHRCWx71m8gAwBxCSZ0N2iQ0zPePSPjChhc43AHMoB0n3X3Qfj8f6Dq0Q+61HOR2L1np8zIsAUCEQEIq40KRYEo3FrPYEYGgeqtjM9UeAcQ6cP9tzdIHw4h2FbshBTA6QSyL/V/TXY+VGqtV56L59KXzzg8dfI2SLZAICKKcVG4tZ7LTDJw1Vt57KPvcVGqEuMzFdA4qtHLqATyHpD6JtlrgcYA7Q0CHfc872yQZnYgegdETXk0usZqKJtmVcxWO3612hGnUWL+mJ5IT1AA178FqHTAsd3N43HpwG2QLXNjzLbgwQpbIHQ9zJ8iijFRuLWewYxM0hL1eHlqChTr7oOws0Xey7TVUq6LSuv/yYGOe5wXtQkQ3v09MGyGFNQ01nv1ZhJvl2ZeXPkot4/OwnvTM6XlpZ0tWiK4xuMKaDpoG6S/3Jh//mFE0OewBULXwvwpohgUhVvrGczIxVYJ5br7fKfeXLMoI++WAo/sfN+lpmN7IGZf6n+pKTsfqD4FVP8CsWAxhHX3+VzSmFWAz484MExf5/5gmDuqB87d84jv8pLneLY922HbIAPlxuwsOYMx/bpjh0eHcRe2QOhaQuVPrZo+lDM0RNHItbXecyeaS4S21jNnRi7B1hBLtkj5MrteAkbMgtiyhswvByFMXgkxp8D7eHY+cMkDQEIahM8WAekXojHL+xpXC4MH1pehsrY5H6WHwgJlsDyc9OEdug0yUG7MaztKcevoLFzCFghdHvOniGKUa2t9y1pBEdxaz5kZuYRYI2xUm1B+1b/QACPMw5+GaYQN6sYadOuWDF1iU3uDCUuk+jPORkCbAKj0gL1GWlIaeTdQXw1h8go0NtYBAqBQKKFssOE8uxm77syEUm3B1tt6Q91YjZ5aW/Dx6hLROPVFOOuqgcrdEPQmOPTdoTP6bxzZVoG2q1vtDtz39j58dO9oKAShU1sgMDcjugT6HXFh/hRRFIuyrfUMZuQSYo3wWL0e+X8/DsDsdXzTgt8gRx/f9EgE/nGNlNOy7RmfRF9hyHQoty6RmlfWlEPYLl0jaAwwTlsNsfBl9CnZItWzufWToOMRtUYIH90DjcfsjSK7APbJL0DTLaP133cAgbarA1JAoxAE5PSMD3iN3JibEX2C/Y4AzJ8iinr6pKjZTs9lJrkEKc/tyC7AxjKnz3HPHBFH1TEpp2biU/5r0ZRsAT5+ABhxD4Syne5ABoC7fo3g+fjkdwFbIog54yAe2+2zDKUsKYRy/X2os/if+m+LQNvVgc7PjWFtm+gUTb8jRBTbGMzIJcgaomPyC9h90uF1uGWOiKKuCsJnjwCpv/FfiwYASjYDSpVUd8bzmpb1a9KHA589IrU+aBnQZOcDVzwDxWeP+H0JZUkhFNb2BzOu7eotP6wikRvD3IzoFE2/I0QU27jMJCdTb9Rd9YoUDNRbgLhEKNVx0NSZ8crYBuDyLIhqA8QGK1BfA2XjMdRZknCiXoesOjOgjgMEBXDju01br5vqwux9Exj+BykQqa8BEtKAO7cCRRukvk2N9dLS0si7pUBGbZC2Xa+5XTrWcit3zWn/lYSb7qEW7XAe3QOHxohfYUKtIh7dw8gvSUvUY9X0oaiosbc5N0aO/BbXPc7U2vHarcPxbVklXttR6tVZHGBuRiS153eEiMiFwYyMpLyMYmwvqkByvAY77uwO9dp73XktmLYaQoslJHV2AepHPAV06w7MWAdsWCTNwLgMuLz5+JalzcddO51SLwAUquaeT9uelYIhQApYtj3rO9Dzr/E95tE3Stj2LARI03bdswpQcdESPPBJLZ64alCb80tMcW3/YJIjv8XfPUb3644Xpg/FfW/v8wpomJsRWeH8jhAReeIyk0xa5mW8Ni0T2g0LvPNY/OTCKEsKcc7Xj0BUaoEND3sHMgCQMsg3wAGa+jY9C1SfkJa4PO/tqmfjT844OPRJcGa3WA4LMD5VaSHO2f0oLkpVdkp+iRz5LYHu8eVPZ/D6l6WYOSbLfYy5GUREsY/BjExa5mWcZ7I3BzJA4L5MABQlhVDYawL2bfIJZFxcfZsEwfu5TfVsfAKaphoAKlMa7Feu9A5ogoxPVVqI8ZmKTskvkSO/Jdg9vvzpDIZmJAJgbgYR0dmCy0wyaVkzQ6hvUXcmVP+lQHVqWtO3qWX+S8t8Ga0R0HcDVBqg5hegwQqLMwG1BaugGHEG3Q0KGJROCEH6N2kcNQA88ktslR71BUyAIVmWLXpy1B4JdY8EnRqbFoxlbgZRODrob5+oPRjMyKRlzQxR26LuTKj+SzqT/+Ot6NsEjcH3uGe+zN07gS+eAA43157pkTMO+okrcKxWjfRNiyCUBu/fZFfGAzDDqFcD5uO+7d9dlR9NvYOPNwQ5ao+Eukd3g6ZTa9wQnTU68G+fqD24zCSTljUzDpo13m0LguWxZOdLAYm/88f2ANkFvsddz6s+BVGhArIvDXzNsT1Ayvleh4XiTTAc3YJzvg7Qv+nrl5u6dEstEzaWOZHXPxlp2jrf/5gBUo+OtXOk/2trBzlqj7B+CVEHsFV26N8+UXswmJFJy5oZ9649DseVf4Z4y1rg+reAjIuBcX/0X/dlxCygwQZcvsw3KDn1A3Dls74BjWffJkGAOOnpwPf+7BEpJ6YFRUIvKEL0k3Jmj8Ohi5Zg90kHlk/Lhc7+a+AeVMWbpOlnD2arHcXlNdhXVomS0zVSQb6Kw8Cxb4CKIp//AHr+HOM0Sswu6IfVMy7E6hkX4n+u9A7IAmlz/RJbpc+YPMddfLqGhfUC4M+pCwnWf87P3z5RZ+Iyk4xcNTPO1NqRgjNQfjwPgucf/4ArgCufBypLpeUbV92XNbcDN74nBTQDrwZGzmquC1P9C2A9A2SOAPIXSn2bXMtKRZ9JdWamvQZ0ywb6XOxbU8a1VOQv9yZEPk6j2oSKCf+HOGUCnr2uKb/kWIgeVFaz+5fKc3t0nEaJ96ZnQr3nEe+EZj9T1K6fY6W1AYs/PIAXC39yn2vtFu1W1y/xM20u5oxD1ZhlmPJ6iXsLN1sf+GKLiC4mRP+5kOeJOhCDGZmZ4jQwCbXAe/N8/y/m8CdAoxVIv9C3/os+Edj2tP8dRbeslWrMeNaZ8SQA2LAQ6H0B8M/f+b/GX+5NiHycY/V6nHCoMTDNIwgI0YOqokELfdP/nXt+0M0f0xPn7vazpOWaor52tU8S4eKPvsf2n854HXNt0V41fWjI5N2Q9UsCTJsLxZvQ27kQ88csxpLCk21+3a4g1BZ6/pzOQiH+9kOeJ+pAXGbqCMGmY5uWb7xk5wMKdeA2Bj9vg5gTJG/m2B4IxYUQ+471e4noyptpebz6F4gt6800acwqwA8WDXaWnPHeDm3oATFAD6rGrAKsK25ERY3dZ3v0+EyFbyDj4meKulNaEAR5n1zb0Tvkdc8CbBHRBQXpP4eccdJ5oghhMNMBnDZz8As8l3dyxgFjFwL2Gqly7/VvATf+C8h7oHk5addLcExcBtFf3syIWdI2agCiSuvzH5vGrALUTXwejSd/9DouZo+D0G88Gif/2Sc4EbMLgEnLcWmGAt2VVtTWe291Fq94xmcsjuwCFI18Cit2lKO2vgFmm/eHmcZRHfxnUl/jlbuS7jyGtTP6YettGdh5sxFbbuuNRwtSEadRApCpBUGIaXHXdnRPbH0gkWMLPfNtYkyQ/nOYuorbsymiuMwksxNVNijsWqQEu6hbNnD93wFdovTvogPiuhb5NS22RyvhRM1ly5FQU+I/JwZAo9YEzbWrYTf/AqfNglP1Gmwsc+Kvr5bgrosexPhhC6Fx1MCujIfa1BMKMQmp3fSovOIlCLUVMIo1EBx1EH7eBtXf8qGy1+LmrAI4fvNnAEnu/BLFsa+batjcBVEE7Anp2HxCCa3YHXGaYzDqNThRZfP6lu3KhMA/D41B2pr+3kz3TIkWwODsfAgjZgEfSd/jbVkFGDV9Ca57uwz6pqCmXUJMi7u2o3ti6wNJe7fQM98mRpl6S0vC7jozRmlGhoEMRRiDGRm58gjyMtS4PbvA/06h7Hzg+F7AfFTKm5nyAvDDh96BDNC85DTybmmXjfUMFHHpaPzqr1CV+t7XkV0Ap14qXmUTDfik7BTW7z+BL5tyTpYUnsSSpmtH91NhaGY19h89ilXTh0KhT8KXxWcw8eBSKFosBalKC6H8ZB5w9V+880uacn4EAMqsAhxNXYwt35XgxRsvwOIPv8eQzESM7tfd/foby5y4LavA79gxcSnw8QO+uSueP4Ntz0qtFfAonpm8BN+WVSHFqGtfXoZr2rx4k88p13Z0T9zW3cy1/X2bn6WmUD8n5tvEOH0SgxeKOlxmkpErj2DFjnI0XP5M67ZKJ/QKnl/TNw8YMQtCgw1xohWHLlqCxizfJR7n5BegM0pbkU1xGuQP6IE5Bf1xST/v7cmj+3XHbaOz8NqOUndugylOgwl9lAFzWoTiTdKOqhD5JV/+dAYmvRrbf6rAaztKcdvoLIzu1x0AsGJHOQ5dtASOlktlOeOAjOGtzjFSlRbi0nQB/7v+x/bnZQSYNhdzxuF43nKs2FHuPsbWB97avP3dA/NtiEhunJmRkWceQUODHdrLHpce2GulztYlW3y3SodqV6DUAEq1tCSljsP5SbVA/kMQJ/4JoqCAXVTApkqEQpcEzxrCqYl6KBUCHr3yPByvsqG+0QmtSoF9R6tw39v7AACPFqSit+MYcKwGKpVWytNpyr/ByLulIKKxXmpxADTn8LQ8d2w3tKIVAFBd1wgAsNoduO/tfZg5JgszR2ehvtGJXxUq1Ez+K/T2X4F6aYraqU+GzvJz8J9Bi5+RufIMrHaHPPkrfqbNBUMPJIoGrJudFnxbdxfX6u3vLciRbxOLzFY7yqvrUWVrgEGjhEGrQqJezd8rij4x2LKCwYyMjDq1u56KYdOj3rMNrlkZF9e26FDtCurNwL9nSvkzX7/s3bwyOx/ixOcw/uX9OD/V6JVv4MpJuHVUX9z+5jdet3SN8dzdj0C5s0Ubg2vfACACu/7ivX08pyDwuex8dBs0HXEaC4y65l8pq93hrhETp1HihelDMfv9Uo/t1qeR19+M1yYnBv9FbPEzqm/KZZEtf8XPtLkJ4IdMK4Tc/u6HHC0rYs2JKhsW/ns/tv/UPCM1ul93zCnojz7d4pDKPCGKFjHasoLLTDJKjtfg2clSkOA3B8bVIsBzq3T1LwG3OruvG3m3byADKadE99n9eG1apjvfwGy1e+Uk7Dta5V7qcQlY86VkC1B9QgpWWm4TLy4MfK5kC9QbHsSzkzORZPDfSmDmmCy8/mWp37oxnx9xhP4ZNPFsrcD8ldjU1dpNmK12n0AGkDq4ryoswpbDp7mTi6JDDLesYDAjo1C5J545MNj1khTt9hsP+xUrfevIeG67Th8esAaNULIF55mk/xC68g08cxJa5q4AIWq+JKQErncT5JyipBAT+ijRy6jzm0sxKrs5GbilB9aXwX7FSt8tny22njdmFXi1VuDMSWxqT75NLKqosfsEMi5f/nQGPRO0zBOi6BDDLSu4zCQzVUOIeioqLaBNAO7c4t7S+N+jlRg8/k8QRv0CqPRSborluJRnc/VLgCZ4h2dVQw0ykvQ4WmnDr7V2iBDx2q3D8W1ZJV7bUeqTu5KmPyq9hp/cFzgafV/AdW18L6kOjjpOaqugUAENVvdzVY3SFnF/uRQt6854stodOGRNQG7LLZ9aI1BfDefNH6FRnYAqIRF6RXxzawWKWeHm28SiUDlC9Y3OszZPiGJMDLesYDAjt1AlveO6AckDvA7Fa9WAoAaS+gIbHgEue0xaknLNgtz07+D3FBux9Q99MfZvP6O6vhEz35CWZUb3644Xpg/FfW/v8+pv9OPcHGiacnBa5r5g0LVS8NJUuwYagztfx+faEbOAD++Wrs3OB4bc6D7dMpeiuNy3AJ0ng1YN6ON9k8wSUqAAoAHQs+mLzg7h5NvEolA5QlqV4qzME6IYFMMtK7jMJDet0XdLtkt2vnS+heR4DaDSQVw/H0gZCGx42Hs5Jy456D0FdTyEjxdg3YxsiKLoPvXlT2fw+pelmDkmy31sdL/uaFQaIHoGSy4lWyBuWARxokcPqKZ8HX/XunOAXI8/eTDgmmpXy5Mgcgn2uz+6X3eUV9fz95+iQwy3rGAwI7f6amnGwqfGTAFw+TI01tvwn6Pe5dtNcRoI9RYpwddffoxCGfSe0CZA6DMSJsGKUaZfvcr/7yurwtCMRADAmKYaM6rGmuZkYo1B2pLd1EpBGHk3xD5jgHOulM4Hydfx6TPlsabaslQ9gIB5Ek9eNQg/n6llSXuZsE1AdHHlCF3S37fm05yC/rh0QI8uMUNFMSCGW1ZwmUludWaplsyo+4DLngCqT0llco/tAf42Dsr0EVANX4Ipr5bhwj5Jzdup65rK5vurOxPinkgfAVyyAFCooF+dD33TEpGr/H+NVoXXbh0OURShFAQo7U15PQGWkITsAmDKSmD8E6ETvlqOt84SsFT98mm5XnkSeo0S35ZV4YoXtsNqd7ivY0n78LFNQHRKS9TjxelDUV5dD7OtAXEaJQwaFRLjWGeGokyMtqxgMCM3nVHKIXE2AF885jOrIZQU4hzxUcwfsxhLCk+6y7cb9YnSBf7qzmgTgt5Tyj53Qhh4jbv0PwB3+f+KiX/ByFe+dV9+eF6O9C8BlpCEkkJg/XzpFxoigmoxXocmIWCp+oVN32tOz3iYrXbMfnsfS9rLiG0ColtXyRGis0AMtqzgMpPcXGuOQZZnXOX/AY/y7Updc02VlstJ1SelY6GWfBJ6eS/7NL1WkkezxLz+yWjQd5faCgS7n2vJKNgaaosaMMgZh2plUqtK1bOkvfz4MyWiroozM3JzrTn+ckB6HGALtFa0Ik6jxMwxWahvdKDRYYZ63OOAvRrIuhS45H6gdCuw903glx+BiU8B1srAW6p3vQRoEqTS09PfkQocNS0ROW3SdrpLmpZ6DCY97JNfgOL0DxCCfS91Fmnn1dRV0v1cDRk1Bmk8qb8BqsqAG/8lLX31G48jluC7MlxbUGUtaR/FpbfNVqnuj6WuAUa9GsmGjvu/867aJoCIiMFMRzD1lhKBg2xr7j5oOv7vxn74244SvLajFN/P7Q98vMB7pmTAFcDMz4CP7wcKn5S2aAfaJj1ttVTz5Y0rpMcz1gFvTgFqT0OhN2L1jD7o1yPeXTZd0y0Dzsaa4MGMaxue5xpqfY0UMHz8ALBubvO1OeOAfpeF3GLqOi9bSfsoLr3d2fkrXbFNABERwGWmjpPQC5i4NOC2ZtWGh2C1VODLn85g/pieED5+wPe6lPObApymar3OxuDbpJ2NzY83PAxMXQVHdgEOVGrw5s6fkRjn/WGmSEhp/TY8fZI0S9MtSwpkApS7TtPWtWoLtixbtaO49Hao/JWO2GHE7e9E1FUxmOko+iQgI3BOiqJkEwYapQ+08ZkK315OgG9Oi6AMnjMjKL0ei8Y0WCeuwOvfVvovEx/ONrwQ5a519l9bVapelpL2UVx6OxL5K12tTQARkQuXmTqS3Rr0tFa04tGCVKRrA1zXcttzoy3462nipNkU14e4vRbf1aVh3mXdA3flbes2vFaUu05LD12q3my1w2Z3YN5l/fHIledJW8YVArq3JackiktvRyp/pSu1CSAicmEw05GClX7WGNCte0/M3P0AlH1n+b+m5TZtf9u2PTkdXrky0Bpx1+q9WDd7TPDntWUbXivLXQfbhhosl6RNH7pRXHo7kvkr3AJMRF0Nl5k6UpBtzeLEpVBveFDqXu1vOzbgezzQdYB0/Odt7lwZZOdDrDPj2cmZ8uZKtLPctay5JFFcepv5K0REnYfBTEcKkpMiZAyHwpXvsesl/+0KTn0vtStwHQ90navp466XpNwZYxpw+TIo3puBCX2U8v5fejvLXcuaSxLFpbeZv0JE1HkE0bMz4VnIYrHAZDLBbDbDaOzEZQdX7ZP6GkDfDRCdQEOtVMlXnyjlc6we33x9y/oxSVmAww7UWwFdAqBUQ7TXAFojBJUWaKyT7qExSLVmKn8GPrpHes1bP5G6WedeB5x3lbRlW58IxPfy/wHfxjotZqsdoq0KOvuvUDZUQ6EzQpnQs1XBw3+OVmL3wVKMz1RA46iGXZWAjUecWLGjHFa7Ax/eMwq/yWxjEOI1/ugqve2qM8P8FSKitmnL5zdzZjqCZ+0Tz1oznjuRblnn/Rx7rXftmBvfBf49U3rupueAX36AMGMdsGUpkL8I+OwR7/t51pbRJgCTlvvWo/FXf6WNdVr857s4sWxaJtJaUTrl3LhqDDr5JJQ7N7uPuXpIXfd2WXi5JFFcepv5K0REHY8zM3KzVQLvzWwODvIeAI5947ulOtBxoKl1wYXSv7uumf6OFJyMmOW/1ozreXkPAmoDsOlx/9fkjJN2L+mTfMca6LomgfopAdLyScjeP7ZKiO/N9LsNvTGrAJ+ftwSjB/Xjhz8REbXp85s5M3JrWfskUP8jV/5LToH3cc/8F8/nJqRKCcCm9OC1ZnSJgFIdtOdSXdUvKDldA2f1qcB1Wo7ukoKd8v8CZbuAUwcQbz2Ggr5axGmUPpdvK6rAmVq79JyKw1IQVlEkPXYdO30IwshZUiCnMXg9X1VaKH9+DxERdQkxtcy0dOlSPPLII5g7dy5WrlwZ6eH417K2SctaMS72WmDN7cAfCgGFAvbaKqg0OigOfiQdt9d6P9deKy05mY+HeP0qwHom6CWVv1bApnBCUJT5v8C1NLbeo/owAGV2Pm695EGM7N0H0/55BFa7w30uTqNECs4A781rDpA0BmD6v4Dtz3kHTa72C67vs4mqoTr490ZERORHzMzM7NmzB6+88gpyc3MjPZTgWtY2CVYbxl4LKBRA8gAc1Z+P6gZBynFxfcB7PtfQQ1peCtpMCYAmPmQ9mrgEE87d/QiEQPcaeXfTUtZm7+MlWyBsfwYDanZj/pieXqfmj+kJ/afzvIOWkXcD25/xnf1xtV8Yebf38QjWhSEiotgVE8FMTU0NbrrpJrz66qtISgqe6FlfXw+LxeL11ala1j4JVhvGoxZKcrwGKrU2cF2ZBqsUBISqNVN9Mug1zuwCQKkNXt8m0NIYAJRsgdLYC+MzvX91puSofHNhQtwH6cObH0e4LgwREcWumFhmuvfee3HllVfisssuw5/+9Keg1y5duhRPPPFEJ43Ml1k0QHfFSmg+ngehZJOU+zJttXTS84M9ZxwwZSVQWwGcKYZJZ4Kz0Szly7iu9Xyu+aj0z0D3yy4ArnwWeG2iFPj4uUbMLkDj5c9A2WCXloAC3StUSnijHclGFbbc2hsaRzWU+kT0MKike7pmlTQGIC5Z2pXVWC9tHz+2W3pN1zWuZbQoqAtDRESxK+p3M73zzjtYsmQJ9uzZA51Oh/z8fPzmN78JmDNTX1+P+vrmXBOLxYKMjIxO2c3k2ra890gl5o/pifGZCiQIVpiSkqHSG4F6S3MtFJUe+OQh4PAn7ueLt6yD8M4N3vVm1HFSN2xjGvDXPOnCljVpVFrAlCnVsnl5dPM1o+4DBlwOVJ+SlqeO7QF2vQQxfQSEEXdKOSuAn3tlAC+NCvyN3rkV4hdPeM/E5BQAF3ncc9pq4Ou/ei9VuZKbXbkys3ZIrxdFdWGIiCg6nDV1Zo4ePYq5c+fi888/h06na9VztFottNoQPYw6QMsy/UsKT2JJ07m8/kqsmt4XpuQU6UCALdHCz1shpg+H4Fkbpok4ey+QnQ+hZItvTRrXlmxP9lrA2QB88ZjPUo9QUggRTggj75bu43EvMWcchKkvSPf0u/37UuDkd75LSsWFUjDlyoPxt33c9Xjk3cDxfdLOLAYxRETUTlGdM7N3716Ul5dj2LBhUKlUUKlU2Lp1K1544QWoVCo4HI7QN+kkbSrT33L7tsuulyCMmAUx23u7tiO7AHDYIQRpZSDGdQca6r3PB8lZEUq2QOyb53WsMasA9itWAg22wG0TLl8qFezzp2QL0Hds6FyZvmO5rERERLKJ6pmZcePG4cCBA17HbrvtNpx77rlYuHAhlErfeieRYqlrCHq+2vN8y+3bLk3bte23fQGHKECot0DUGvGjWYthtpPA2jlSEHDZE1JejNYozYbUVgCiiDKxF7oVLEHcBAGKhlqITmfQzU92QYtT07dC3VgDuzIeG8ucGGFNQK5QLi0Fjbxb+nItPx3bA5wp9tpO7UOtA1QhasWodX6rCxMREYUjqoOZhIQEDBo0yOuYwWBA9+7dfY5HmjFEGX6vMv3BtiDba3HSbEf+G656MhWI0yjxw72ZUiDTcvnGlYdS+L+o/s1iXP5KGd6/ZxSMCWoof/0JvYKM6ZRdh7Gvu17HDADY9JsBgMLou5TlcuO7Qb9P6BODn2/tNURERK0U1ctMsSQ5XuPTIdklr38ykuM9Zitabt/2IOaMw48W75mNmWOyIGoMEAPloXz9MoRe5+Oc3Y/i2cmZMOrUWLhmP36p10AMsEVbzM6H2endTMk9ziDjQ/Uvgc+5tlcHez63YBMRkcxiLpjZsmVLVFb/NcVpsGxark9Ak9c/Gcun5XqX6dcnSbMsLT/wc8ZBmLoKQwdked1naEYihPpqKfnXn6aaLarSQozvo0BdgwPbiypgUtQHzLMRRsyCSdG868trnEHGh37jA59z5cEEez5zZYiISGZRvzW7vTq70aTZaseZWjvinTVIFKugaqiGQp8IGJJ9P8RtlVIysGu7tmvGovY0nHXVEHVJEBvr4ayrhlpwQHj98sAvfP1bwL9ugf22jfhB6I/f/mUndt5sRNpHv/Pdet20Rbvh9x/hoNAPmXENSHBUQmmvBnSm5rH6G5/rewh2Ltj3x0CGiIha4azZmh2LTHEamBpOAx/N9t6x5JqV8Ex8dc1iuJiPS8879jUU01YDhU82Lyvd+F7wF1Y1LRlpjTAqpPwcuzIhcO4LAHVcInLV1uBjDRR8tBx7uNcQERG1U8wtM0U9W6VvcAAAxZuk3Ui2ytDPc/dG2tJ8XnQEb2MgOuDILkCDvrs7f2djmRONWQX+n5MzDtAmhDdWIiKiKMJgRm6BasgAUpBQezr08/zVaVGoAtd+GTELoiYe1okrUG7Xu/N3dp904NBFS3wDGtfMS70lvLESERFFES4zyS1QDZlg522VgPXX5seN9b7XNFiBD+/2X/tlze3ATWtQ++sJpCbVAbZeSEtMwrPXDcGZWjt+vfwlj/wdU3PuyvFvgbwHPPJpWvRPCvW9EBERRQEGM3ILVkPG33lXnszIu5qPqfy0Y1Bpg+a/CGo9Ut4dKz1omnkxmXo37aKKB5DiZywm4Ng3vq0Rpq2WAqRQ3wsREVEU4DKT3NpSY8UzT+bYnuYlJM9/d/F3zCU7H/DclNaanBdbJfDxAwHr1mDiUtaDISKimMBgRm5tqbHimSez66XmnBjPf3fZ9RJwyQNAi75N7grAnstUQOicl2C5PSVbgIzh3IlEREQxgctMHcHUG7h2degaK545KU19mdw5MY5G4IpnAacDsNdIy0yHPgYyR0hLUi1zZq5+yXccwXJeQuXD2K2t/36JiIgiiMFMR2lNjZWWOSktc2Jm7wF6niv9e8VhYPPSwPfyl2cTLOelrbk9REREUYrLTJHUlvyaYNdm50szNMGe357XJiIiimIMZiKpLfk1wa7Ne1DKqQn2/Pa8NhERURRjb6Zo0JYeRkH6OYXVA4n9k4iIKAqxN1OsaUsPo0DXhhuAsH8SERHFOC4zERERUUzjzExH8Vq+MQGGZM6AEBERdQAGMx3B1aLAsyidK7HW1Dty4yIiIjoLcZlJbp4tCjy1psUAERERtRmDGbkFaxMQqsUAERERtRmDGbmFahMQ6jwRERG1CYMZubFNABERUadiMCM3tgkgIiLqVAxm5MY2AURERJ2KW7M7gqk3cO1qtgkgIiLqBAxmOgrbBBAREXUKLjMRERFRTGMwQ0RERDGNwQwRERHFNAYzREREFNMYzBAREVFMYzBDREREMY3BDBEREcU0BjNEREQU0xjMEBERUUxjMENEREQx7axvZyCKIgDAYrFEeCRERETUWq7PbdfneDBnfTBTXV0NAMjIyIjwSIiIiKitqqurYTKZgl4jiK0JeWKY0+nEiRMnkJCQAEEQ2n0/i8WCjIwMHD16FEajUYYRktz4HkU/vkfRj+9R9Dvb3yNRFFFdXY20tDQoFMGzYs76mRmFQoH09HTZ72s0Gs/KX56zCd+j6Mf3KPrxPYp+Z/N7FGpGxoUJwERERBTTGMwQERFRTGMw00ZarRaPPfYYtFptpIdCAfA9in58j6If36Pox/eo2VmfAExERERnN87MEBERUUxjMENEREQxjcEMERERxTQGM0RERBTTGMy0wV/+8hdkZWVBp9Nh2LBh2L59e6SH1GU9/vjjEATB6yslJcV9XhRFPP7440hLS4Ner0d+fj5++OGHCI747Ldt2zZMmTIFaWlpEAQBH374odf51rwn9fX1mDNnDpKTk2EwGDB16lQcO3asE7+Ls1uo9+jWW2/1+bsaOXKk1zV8jzrO0qVLMXz4cCQkJKBnz564+uqrcejQIa9r+HfkH4OZVnr33Xcxb948PProo9i3bx8uueQSTJo0CWVlZZEeWpc1cOBAnDx50v114MAB97mnn34azz//PF588UXs2bMHKSkpGD9+vLtXF8mvtrYWQ4YMwYsvvuj3fGvek3nz5uGDDz7AO++8gx07dqCmpgaTJ0+Gw+HorG/jrBbqPQKAyy+/3Ovv6pNPPvE6z/eo42zduhX33nsvdu3ahY0bN6KxsRETJkxAbW2t+xr+HQUgUqtcdNFF4qxZs7yOnXvuueLDDz8coRF1bY899pg4ZMgQv+ecTqeYkpIiLlu2zH2srq5ONJlM4ssvv9xJI+zaAIgffPCB+3Fr3pOqqipRrVaL77zzjvua48ePiwqFQtywYUOnjb2raPkeiaIozpgxQ7zqqqsCPofvUecqLy8XAYhbt24VRZF/R8FwZqYV7HY79u7diwkTJngdnzBhAnbu3BmhUVFRURHS0tKQlZWFG264ASUlJQCA0tJSnDp1yuv90mq1GDt2LN+vCGnNe7J37140NDR4XZOWloZBgwbxfetEW7ZsQc+ePTFgwADccccdKC8vd5/je9S5zGYzAKBbt24A+HcUDIOZVqioqIDD4UCvXr28jvfq1QunTp2K0Ki6thEjRuCtt97CZ599hldffRWnTp3CqFGjcObMGfd7wvcrerTmPTl16hQ0Gg2SkpICXkMda9KkSfh//+//obCwEM899xz27NmDgoIC1NfXA+B71JlEUcSCBQswZswYDBo0CAD/joI567tmy0kQBK/Hoij6HKPOMWnSJPe/Dx48GBdffDFycnLw5ptvuhMW+X5Fn3DeE75vned3v/ud+98HDRqECy+8EH369MHHH3+Ma665JuDz+B7Jb/bs2di/fz927Njhc45/R744M9MKycnJUCqVPlFteXm5T4RMkWEwGDB48GAUFRW5dzXx/YoerXlPUlJSYLfbUVlZGfAa6lypqano06cPioqKAPA96ixz5szB2rVrsXnzZqSnp7uP8+8oMAYzraDRaDBs2DBs3LjR6/jGjRsxatSoCI2KPNXX1+PgwYNITU1FVlYWUlJSvN4vu92OrVu38v2KkNa8J8OGDYNarfa65uTJk/j+++/5vkXImTNncPToUaSmpgLge9TRRFHE7Nmz8f7776OwsBBZWVle5/l3FETEUo9jzDvvvCOq1Wpx9erV4o8//ijOmzdPNBgM4s8//xzpoXVJ999/v7hlyxaxpKRE3LVrlzh58mQxISHB/X4sW7ZMNJlM4vvvvy8eOHBAnD59upiamipaLJYIj/zsVV1dLe7bt0/ct2+fCEB8/vnnxX379olHjhwRRbF178msWbPE9PR08YsvvhC//fZbsaCgQBwyZIjY2NgYqW/rrBLsPaqurhbvv/9+cefOnWJpaam4efNm8eKLLxZ79+7N96iT3H333aLJZBK3bNkinjx50v1ltVrd1/DvyD8GM23wf//3f2KfPn1EjUYjXnDBBe7tctT5fve734mpqamiWq0W09LSxGuuuUb84Ycf3OedTqf42GOPiSkpKaJWqxXz8vLEAwcORHDEZ7/NmzeLAHy+ZsyYIYpi694Tm80mzp49W+zWrZuo1+vFyZMni2VlZRH4bs5Owd4jq9UqTpgwQezRo4eoVqvFzMxMccaMGT4/f75HHcffewNAfP31193X8O/IP0EURbGzZ4OIiIiI5MKcGSIiIoppDGaIiIgopjGYISIiopjGYIaIiIhiGoMZIiIiimkMZoiIiCimMZghIiKimMZghoiIiGIagxki6nD5+fmYN28eAKBv375YuXJlRMdDRGcXBjNE1Kn27NmDO++8s1XXMvAhotZQRXoARNS19OjRI9JDIKKzDGdmiEhWtbW1uOWWWxAfH4/U1FQ899xzXudbzrY8/vjjyMzMhFarRVpaGu677z4A0tLUkSNHMH/+fAiCAEEQAABnzpzB9OnTkZ6ejri4OAwePBhvv/2212vk5+fjvvvuw0MPPYRu3bohJSUFjz/+uNc1VVVVuPPOO9GrVy/odDoMGjQI69evd5/fuXMn8vLyoNfrkZGRgfvuuw+1tbUy/qSISC4MZohIVg8++CA2b96MDz74AJ9//jm2bNmCvXv3+r323//+N1asWIG//vWvKCoqwocffojBgwcDAN5//32kp6fjySefxMmTJ3Hy5EkAQF1dHYYNG4b169fj+++/x5133ombb74ZX3/9tde933zzTRgMBnz99dd4+umn8eSTT2Ljxo0AAKfTiUmTJmHnzp34xz/+gR9//BHLli2DUqkEABw4cAATJ07ENddcg/379+Pdd9/Fjh07MHv27I76sRFRe0S6bTcRnT2qq6tFjUYjvvPOO+5jZ86cEfV6vTh37lxRFEWxT58+4ooVK0RRFMXnnntOHDBggGi32/3ez/PaYK644grx/vvvdz8eO3asOGbMGK9rhg8fLi5cuFAURVH87LPPRIVCIR46dMjv/W6++Wbxzjvv9Dq2fft2UaFQiDabLeR4iKhzcWaGiGRTXFwMu92Oiy++2H2sW7duOOecc/xef91118FmsyE7Oxt33HEHPvjgAzQ2NgZ9DYfDgSVLliA3Nxfdu3dHfHw8Pv/8c5SVlXldl5ub6/U4NTUV5eXlAIDvvvsO6enpGDBggN/X2Lt3L9544w3Ex8e7vyZOnAin04nS0tKQPwci6lxMACYi2Yii2KbrMzIycOjQIWzcuBFffPEF7rnnHjzzzDPYunUr1Gq13+c899xzWLFiBVauXInBgwfDYDBg3rx5sNvtXte1fL4gCHA6nQAAvV4fdFxOpxN33XWXO3/HU2ZmZlu+RSLqBAxmiEg2/fr1g1qtxq5du9wf+pWVlTh8+DDGjh3r9zl6vR5Tp07F1KlTce+99+Lcc8/FgQMHcMEFF0Cj0cDhcHhdv337dlx11VX4/e9/D0AKPIqKinDeeee1epy5ubk4duwYDh8+7Hd25oILLsAPP/yAfv36tfqeRBQ5XGYiItnEx8fj9ttvx4MPPohNmzbh+++/x6233gqFwv9/at544w2sXr0a33//PUpKSvD3v/8der0effr0ASDtfNq2bRuOHz+OiooKAFLAtHHjRuzcuRMHDx7EXXfdhVOnTrVpnGPHjkVeXh6mTZuGjRs3orS0FJ9++ik2bNgAAFi4cCG++uor3Hvvvfjuu+9QVFSEtWvXYs6cOe346RBRR2EwQ0SyeuaZZ5CXl4epU6fisssuw5gxYzBs2DC/1yYmJuLVV1/F6NGjkZubi02bNmHdunXo3r07AODJJ5/Ezz//jJycHHd9msWLF+OCCy7AxIkTkZ+fj5SUFFx99dVtHueaNWswfPhwTJ8+Heeffz4eeugh9yxQbm4utm7diqKiIlxyySUYOnQoFi9ejNTU1PB+KETUoQSxrYvcRERERFGEMzNEREQU0xjMEBERUUxjMENEREQxjcEMERERxTQGM0RERBTTGMwQERFRTGMwQ0RERDGNwQwRERHFNAYzREREFNMYzBAREVFMYzBDREREMe3/A1EO1Eb//gKuAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting the consumption and distance, does not seem to show differences...\n", + "sns.scatterplot(data=df_E10, x= 'distance', y = 'consume')\n", + "sns.scatterplot(data=df_SP98, x= 'distance', y = 'consume' )" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "1e37055f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting the consumption and outside temperature, does not seem to show differences...\n", + "sns.scatterplot(data=df_E10, x= 'temp_outside', y = 'consume')\n", + "sns.scatterplot(data=df_SP98, x= 'temp_outside', y = 'consume' )" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "39934749", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Correlation heatmap for the dataframe looking at both gas population\n", + "df_new = df[numerical]\n", + "\n", + "corr=np.abs(df_new.corr())\n", + "\n", + "#Set up mask for triangle representation\n", + "mask = np.zeros_like(corr, dtype=bool)\n", + "mask[np.triu_indices_from(mask)] = True\n", + "\n", + "# Set up the matplotlib figure\n", + "f, ax = plt.subplots(figsize=(10, 10))\n", + "# Generate a custom diverging colormap\n", + "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n", + "# Draw the heatmap with the mask and correct aspect ratio\n", + "sns.heatmap(corr, mask=mask, vmax=1,square=True, linewidths=.5, cbar_kws={\"shrink\": .5},annot = corr)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "c9c2aece", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Correlation heatmap for the dataframe looking at E10 or SP98 gas population\n", + "df_new = df_SP98[numerical]\n", + "\n", + "corr=np.abs(df_new.corr())\n", + "\n", + "#Set up mask for triangle representation\n", + "mask = np.zeros_like(corr, dtype=bool)\n", + "mask[np.triu_indices_from(mask)] = True\n", + "\n", + "# Set up the matplotlib figure\n", + "f, ax = plt.subplots(figsize=(10, 10))\n", + "# Generate a custom diverging colormap\n", + "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n", + "# Draw the heatmap with the mask and correct aspect ratio\n", + "sns.heatmap(corr, mask=mask, vmax=1,square=True, linewidths=.5, cbar_kws={\"shrink\": .5},annot = corr)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "b8892929", + "metadata": {}, + "source": [ + "PART II: AB Testing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f231f588", + "metadata": {}, + "outputs": [], + "source": [ + "# Idea for AB testing, for the same average speed, conso SP98 is more or less conso E10 ??\n" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "id": "7c8dfc20", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " p_value = 0.37736070294426416\n", + "We are not able to reject the null hypothesis\n" + ] + } + ], + "source": [ + "numerical = ['distance', 'consume', 'speed', 'temp_inside', 'temp_outside']\n", + "categorical = ['gas_type', 'AC', 'rain', 'sun']\n", + "\n", + "\n", + "#Set the hypothesis\n", + "\n", + "#H0: consumption E10 <= consumption SP98\n", + "#H1: consumption E10 > consumption SP98\n", + "\n", + "significance_level = 0.05\n", + "\n", + "t_stat, p_value= st.ttest_ind(df_E10[numerical]['consume'], df_SP98[numerical]['consume'], equal_var=False, alternative = \"greater\")\n", + " \n", + "print(f' p_value = {p_value}')\n", + "if p_value > significance_level:\n", + " print(\"We are not able to reject the null hypothesis\")\n", + "else:\n", + " print(\"We reject the null hypotesis\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "c616e152", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " p_value_mw_con = 0.897931485573079\n", + "We are not able to reject the null hypothesis\n" + ] + } + ], + "source": [ + "# same but trying with Manwhitneyu\n", + "\n", + "#Set the hypothesis\n", + "\n", + "#H0: consumption SP98 <= consumption E10\n", + "#H1: consumption SP98 > consumption E10\n", + "\n", + "significance_level = 0.05\n", + "u_stat, p_value_mw_con = st.mannwhitneyu(df_SP98[numerical]['consume'], df_E10[numerical]['consume'], alternative = \"greater\")\n", + " \n", + "print(f' p_value_mw_con = {p_value_mw_con}')\n", + "if p_value_mw_con > significance_level:\n", + " print(\"We are not able to reject the null hypothesis\")\n", + "else:\n", + " print(\"We reject the null hypotesis\")" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "2bffed92", + "metadata": {}, + "outputs": [], + "source": [ + "# Trying the same but before removing the outliers for average speed: => not better than without the outiers.\n", + "# The function to remove the outliers\n", + "def removing_tukeys_outliers(data,column):\n", + " Q1 = data[column].quantile(0.25)\n", + " Q3 = data[column].quantile(0.75)\n", + " IQR = Q3 - Q1\n", + " \n", + " # Define bounds for the outliers\n", + " lower_bound = Q1 - 1.5 * IQR\n", + " upper_bound = Q3 + 1.5 * IQR\n", + " \n", + " # Identify the outliers\n", + " not_outliers = data[(data[column] > lower_bound) & (data[column] < upper_bound)]\n", + " \n", + " return not_outliers" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "id": "ec9ea36c", + "metadata": {}, + "outputs": [], + "source": [ + "#new_df = removing_tukeys_outliers(df,'speed') => not better than without the outiers.\n", + "\n", + "# trying with speed range from 30 to 54\n", + "new_df = df[(26 <= df.speed) & (df.speed <= 54)]" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "id": "fa86f237", + "metadata": {}, + "outputs": [], + "source": [ + "new_df_E10 = new_df[new_df['gas_type'] == 'E10']\n", + "new_df_SP98 = new_df[new_df['gas_type'] == 'SP98']" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "id": "4e082321", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " p_value_new = 0.02757441286993821\n", + "We reject the null hypotesis\n" + ] + } + ], + "source": [ + "#Set the hypothesis\n", + "\n", + "#H0: consumption SP98 >= consumption E10\n", + "#H1: consumption SP98 < consumption E10\n", + "\n", + "significance_level = 0.05\n", + "\n", + "t_stat, p_value_new= st.ttest_ind(new_df_SP98[numerical]['consume'], new_df_E10[numerical]['consume'], equal_var=False, alternative = \"less\")\n", + " \n", + "print(f' p_value_new = {p_value_new}')\n", + "if p_value_new > significance_level:\n", + " print(\"We are not able to reject the null hypothesis\")\n", + "else:\n", + " print(\"We reject the null hypotesis\") # means consumption SP98 is less than E10 for 30<=speed<=54" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "id": "b3b03a8f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(4.643478260869565, 4.824347826086957)" + ] + }, + "execution_count": 150, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "new_df_SP98['consume'].mean(), new_df_E10['consume'].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "id": "db442802", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(161, 115)" + ] + }, + "execution_count": 151, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "new_df_SP98['consume'].count(), new_df_E10['consume'].count()" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "id": "6fcc30ab", + "metadata": {}, + "outputs": [], + "source": [ + "test_E10 = new_df_E10[['consume', 'speed']].groupby(['speed']).mean()\n", + "test_SP98 = new_df_SP98[['consume', 'speed']].groupby(['speed']).mean()\n", + "test_E10.reset_index(inplace=True)\n", + "test_SP98.reset_index(inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "id": "5f1a3091", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 153, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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2i2jq4H1yXFvJAaj4mBBX8JWcaC0bZtBMK58U5PgK7368vaghwEdCSPBo7eqByTI40NWaHMBal0MrOYQ4xrsdT7B8yOb1b0MZBTk+snA0C3J2FNVLL+qEDHV8Z1VMmEqqs3FFLM2vIsQp2x45E7NYkEMrORTk+Mzk7DhEa1Vo7uyhQWmEWDS0u5+qAqzzq2glhxD7dF1GtOtZ8z9pJYdGO1CQ4ytqpQJzRiQCoLocQjhej5PoYo8cjhce66gmhxC7KlpYj5ykKA0y48IAAM0U5FCQ40sLR6UAoCCHEK6x3TqB3B22DQEJIf3xouPMuHDER7APEU0U5FCQ40t8xMPh8hbaFUIIbOZWubmSE2t50aaaHELs40XHmfHh0oeIdr1xyI8XoiDHh7LiI5CfHAmTWcSuc7TLihBeeOxqjxyO+uQQ4hwvOs6MC0d0mApKhQAAaB7ivXIoyPEx3v14exGlrAhxdzgnR31yCHHONl2lUAgBS1kZTWY0dRhgDpJdxRTk+NgCPuLhTD0NFyRDXqOH6Srqk0OIc3wlJys+AgCQEMk+GPh7G3lZUyemPv8Npjz/jV/v1xEKcnxsVl4iNCoFqlq7cb6+PdCHQ0hANXR4WXhMNTmE2CWlq+LDAUBayfH3NnJed8f/ZgONghwfC9coMTMvAQCw7SzV5ZChrVHqk+Nu4TF7wezuMaO7Z2gXUhLSV6fBKKWleJDDV0v9vY2cD+B1txeWr1CQ4wd8xANNJSdDmcFolmpq3K3JidZaCympLoeQ3qSZVWEqxISxDwSBWsmxtolw74OMr1CQ4we8LmdvcSN9CiVDFq8NUAjW3VKuEgSBuh4T4kCFzc4qjgcZ/l7JqeertdG0kjNkjEqNQlpMGPRGM/aVNAX6cAgJCL6MnRCphcKyKuMOa5BDdTmE2OIrOVnx1iAnPjIwu6soXTUECYIgNQaklBUZqviLrbv1OBwPcihdRUhvfXdWAUBCgIIcnq5K9vDvXG4U5PgJT1nRiAcyVHm6fZyz7rCiIIcQWxXN/dNVgQpyGjzsheUrFOT4ybwRSVAIQFFdO6osUTchQ0mDh3OrOF7H00o1OYT0UtnMhnNmxtsJcvyc3qV01RAVF6HBpOw4ALSaQ4YmvssjwcNdF3E0v4oQuyrtFB4n2BQe+7MRbUMbD3IoXTXk8K3kNOKBDEWN7d69+NHuKkL6MxjNqLMEFrYrOXwLudEsQtdt9MuxdBlM6DCwHcSUrhqCFo5mQc6OogYYTeYAHw0h/uXp3CqOanII6a+6tQuiCISpFb1604SplYjUKAH4bxs5T1VplArEhKn8cp8DoSDHjyZlxSE2XA1dtxFHK1oCfTiE+FWD5YXW0yZh0pBOWskhRGJbdCwIvVsz8G3k/moI2GCzWtv3WAKFghw/UioEzBvBt5LTiAcytEidUD1dyQmnmhxC+pKmj9tsH+f83RCwMcgaAQIU5PjdQtpKToYoT+dWcXx+FdXkEGJlr9sx5++GgA1BNtIBoCDH7+ZbmgIerWjxe7ttQgKl02BEV493BYm0hZyQ/ux1O+b8vY082LaPAxTk+F16bDhGpUZBFIEd5yhlRYYGvoqjVSmkYkh38d1VbXojFe4TYlHZwnrk2A1yIvy9kkPpKgJKWZGhx/YTnqcFibE2Qz39tSWWkGBnr9sxlxAVmHQVreQMcdKIh6J6vzZpIiRQ+IuspyMdAEClVCBay7al0pBOQgCTWURNazeA3j1yOP+v5ARXI0CAgpyAmJGbgDC1ArU6Pc7UtgX6cAjxOalHjpcFibHUK4cQSa2uG0azCJVCQEp0WL+f+3t+lZSuopWcoS1MrcSs/EQAlLIiQ0NDB/uEl+Dh3CqOeuUQYsXHOaTHhUGp6J8GlkY7UOEx8Tc+4mEbBTlkCPB2+zhHvXIIsZJ2VsX175ED2Gwhb/f930uPySy1d/AmLS03CnIChNfl7C9pRqeBiihJaLM2ApQpXUUrOYSgws70cVs8PdymN8Jg9O2ORJ4SUwjWuVnBgIKcABmeHInMuHAYTGbsLW4K9OEQ4lON0kgHL9NVNKSTEIm96eO2YsLUUhrL1ymr+jZrStpe6ixQKMgJEEEQpNUcSlmRUNfQ7v3uKsCmJocKjwmxbh93sJKjUAiIt/zN+Lr4mH+QCaadVQAFOfLpaAR2rAK+e97lqyy0dD+m4mMS6hplKkiUanJoCzkh0kpOloOVHMCaOvJ1kNPQFnxFxwAFOfIxdgHfPgvsXAWYTS5dZc6IJCgVAoobOlDe1Onb4yMkQMxmUZY+OQBtISeEE0XRZqSD/cJjwH/byIOxRw5AQY58otMBhRowGwFdpUtXiQlTY+qwOACUsiKhS9fdA6OZNb1M8LZPDtXkEAKApYD1RjMEAUiL7d8jh/N/kEMrOaFJoQTistnp5gsuX41vJaeUFQlVvB4nOkwFrcqzuVUcLzzW0UoOGeJ4qio1OgwaleO3cn8FOY1BOLcKoCBHXnE57HtLmctXWTiaBTm7zjeih4YOkhDU1CFfF9S4CN4nh4IcMrRVDlB0zPkryKnnbSK8XK2VGwU5cornQY7rKzkTMmKREKlBu96IQxeafXRghAROo4wvfnFSnxwDzGaa+0aGLj593NH2cU4qPPZxsX4wTiAHKMiRV9ww9t2NdJVCIWDeCMsuqyJKWZHQ02D5BOltPQ5grckxi0A7NdEkQ5i16Nh5kMOL/Zt9nq5iH2aSqSYnhMW5v5IDAAv5VPKzDXIfESEBZ+127P2LX5haiTA1e9mi+VVkKBuoRw7njy3kZrNobfhJu6tCWHwu++7GSg4AzLf0yymsbJUq1AkJFXLNreKsvXIoyCFD10Ddjjl/1OS0dPXAZEkfe9vVXG4U5MiJr+S0VQNG14OVlOgwjE2PAQDsKKLVHBJaGjvkLUiU6nJoSCcZwlxNV9lOIhdF39Sx8Q/nseFqpzu9AiG4jmawi0wC1BEARKC1wq2rWlNWVJdDQot1pIM8n/CoVw4Z6lq7etCmZzVpGS6u5PSYROk6cgvWRoAABTnyEgTrak5zqVtXXTDKWnxMu0ZIKJFrAjkXR12PyRDHV3ESIzWI0KicXjZMrUSEhvWnamr3zeqn3B9k5ERBjtz4Dis3i4+n5yQgQqNEQ7sBJ6t1Pjiw0LfrfANG/XYTPt7vep8i4nuNMvbJAaw1Oa00v4oMURXNlu3jA6SqOKkux0d/M3xuVbDtrAICHOQ899xzEASh11daWprDy2/durXf5QVBwOnTp/141APgvXLcLD7WqBSYMzwRAG0l99S6g5UwmMz4cF95oA+FWPSYzFJaSa6aHGl+FaWryBDlatExJwU5PlrJ4XV3wZiucr7O5Qfjx4/Ht99+K/1bqRy47fuZM2cQExMj/Ts5Odknx+YRD7oecwtGJePbU3XYdqYeD148QuYDC32HylgzxcLKVrTrjYjSBvzpPeTx3hwKwdqt2FtSTQ6lq8gQJXU7djfI8dlKTvCmqwL+LqBSqZyu3tiTkpKCuLg4ly6r1+uh11t3Oul0Pk4FedD1mONzrA5eaKY3aTc1tutR0tABADCZRewvbcIlo1MCfFSk0aYRoFIhyHKbvCanlYIcMkRJKzmupqsifNsQMFiHcwJBUJNTVFSEjIwM5OXl4ZZbbkFxcfGA15kyZQrS09OxaNEibNmyxellV65cidjYWOkrOztbrkO3L86zdBUA5CZFIicxAkaziN3nG2U+sNB2sM9IjD30+AUF3iNHzt4Z1pocCnLI0MSDnKz4CJcuH+/jXjm0u8qBmTNnYu3atfj666/x5ptvoqamBnPmzEFjo/03qPT0dKxevRrr1q3D+vXrMXr0aCxatAjbt293eB8rVqxAa2ur9FVe7uN6DV543NkA6Nvdvjpfzdl2tk7Oowp5By2pqnjLp/w9xRTkBAOeq5djpANHfXLIUFfhabrKZ0FOcM6tAgKcrrryyiul0xMnTsTs2bMxfPhwvPvuu3jsscf6XX706NEYPXq09O/Zs2ejvLwcL7/8MhYsWGD3PrRaLbRaPz7w4XFAWCzQ3crqclLHuXX1BaOS8d6eCzTiwU0HS1mQc8/cPLzyzVkUVrZC192DmDB1gI9saLNuLZUvyKE+OWQo6zQYpWDF7d1VPghyRFG0ruQEWbdjIAjSVbYiIyMxceJEFBUVuXydWbNmuXV5v/BwhhUAzB6eCLVSQFlTJ0otNSbEOb3RhGOVrQCAqyZlICcxAmYROFDaFOAjI40+yNXb9snxVQdXQoJVlSVVFa1VSQH/QHxZeNyuN0JvNAMAkqIpXeWUXq/HqVOnkJ6e7vJ1Dh8+7Nbl/SLe8x1WUVoVpuXEAwC2Ufdjlxyv1MFgNCMxUoPcxAjMzmdb8amuKfCsNTlypqvYbRmMZnT3mGW7XUIGA1cHc9ry5UoO/xuP0CgHbEwYCF4HOd3d3R5f94knnsC2bdtQUlKCvXv34oYbboBOp8Odd94JgNXT3HHHHdLlV61ahc8++wxFRUU4ceIEVqxYgXXr1uHnP/+5t7+GvLwoPgZYygqgEQ+uOmQpOp6aEw9BEDDb0m9oN9XlBJw0t0rGlZxIjRIqy04tqsshQ4216Dg4gpxg3lkFeBjkmM1mPP/888jMzERUVJS0I+p3v/sd3nrrLZdvp6KiArfeeitGjx6N5cuXQ6PRYM+ePcjJYUFCdXU1ysqsqyEGgwFPPPEECgoKMH/+fOzYsQMbNmzA8uXLPfk1fMeLdBVgnWO1u7gReqNJrqMKWXxnFV8Bm2VZyTlRpaNtxgHmi5ocQRCsKSuqyyFDjLtFx4B1C3lbtxEGo7yrnw0yj22Rm0dBzh//+EesWbMGf/rTn6DRWH+xiRMn4p///KfLt/PRRx+hqqoKBoMBlZWVWLduHcaNsxbqrlmzBlu3bpX+/etf/xrnzp1DV1cXmpqa8MMPP2Dp0qWe/Aq+5WHXY25sWgySorToNJikglpinyiKONAnyEmNCUN+UiREEdhXQnU5geSrTqhUfEyGqkoP0lWx4WrwNlUtMtfl1LfLO7ZFbh4FOWvXrsXq1atx22239epQXFBQEFwjFgLFdiXHg8JIhULAgpFsYOc2GvHgVHlTFxra9VArBUzMjJXOn2lZzaGt5IHliz45gDXIaaV0FRlirCMdXOuRA7D3lHjLak6jzCkrX2wukJNHQU5lZSVGjOg/dsBsNqOnhz5ZSb1y9Dqgu8Wjm1g4mtfl0FZyZw5cYCs1EzJjEaa2BtxSXQ4VHwdMp8GITgNLt8q9lM2Lj2klhww1nqzkANaGgHJ3PQ7mRoCAh0HO+PHj8cMPP/Q7/z//+Q+mTJni9UENepoIINIyUsDDlNW8EUkQBOBUtQ51Os+Lu0Mdr8eZbklVcbPyEwAAp2p0si/PEtfwVRyNSiH7iJK4cBrtQIYeg9GM2jb2fuBO4THgu23kfG5VsK7kePTK8+yzz+InP/kJKisrYTabsX79epw5cwZr167Fl19+KfcxDk5xw4COOpayypjs9tUTo7SYkBGLwspWbC9qwA3TsuQ/xhDQt+iYS4kOw/DkSJyv78DekiYsGe/efDTiPb6TIylSA0GQZ24VFxtBQzrJ0FPT2g1RBMLUCrfbMvDiY7l3WFnr7oIzyPFoJefqq6/Gxx9/jI0bN0IQBDzzzDM4deoUvvjiC1x++eVyH+Pg5GXxMWDdZUVbye3TdffgTG0bALZ9vC9KWQWWNNLBB8vYfH4VpavIUFLR3AkAyIgLd/uDA/87lDvIkUY6BGm6yuM15CVLlmDJkiVyHkto8XIbOcD65by25Rx+KKqHySzKNsU5VBwpa4EoAsMSIpASHdbv57PyE/H+njIqPg6QBh8VHQO2k8gpFUmGjooW97ePc75ayWlok78Xlpy8bgbY3t4OnU7X64vAq67H3JRhcYjSqtDc2YPjlrEFxKrv1vG+eL+c0zVtPhtMRxxr9EGPHI765JChiBcdu1uPA/imIWB3jwlteiMAIDmUgpySkhIsW7YMkZGRiI2NRXx8POLj4xEXF4f4ePtvOEOOl12PAUCtVGDuCPZGTSmr/g4NEOQkRWkxMiUKALCvhFZz/M2XW0upTw4Ziiq9WcnxQZDDt6NrlArEhAffSAfAw3TVbbfdBgB4++23kZqaKntRYUiwXckRRcDDx2jBqGR8faIW287W4xeLRsp4gIOb0WTG4TLnQQ7A6nKK6tqx+3wjrpgQZDPOQhx/AZRzbhXHt5DT7ioylFhXclzvkcP5Isixpqrk31wgF4+CnGPHjuHgwYMYPXq03McTOmKyAAiAsQtorwOiUz26mQUjWfHx4fIW6Lp7EBPm2tTZUHemtg0dBhOitSqMSo12eLnZ+YlYu/sC9hRT52N/s7Z790FNjrSSQ2lIMnRUtLDCY3d75AA+CnKCfKQD4GG6asaMGSgvL5f7WEKLSgPEZLLTXhQfZydEID85EiaziF3nqDEgx1NVk4fFOS3I5p2Pz9S2SekT4h/+qMnpMJhkn8VDSDAymUVUt7AeOd6kq5o7DRA96MRvT7AP5wQ8XMn55z//iQceeACVlZWYMGEC1OreqwsFBQWyHNygF58D6CpYXU72RR7fzIKRySiu78C2s/WUcrEYqOiYS4jUYExaNE7XtGFvSROWTqTHz1+k/hk+2F0VHaaGILBMcGtXD5Kjg/dFlhA51LV1w2gWoVIISI3pv5t0IHysQ49JRJveKEtWoCHI51YBHgY59fX1OH/+PO6++27pPEEQIIoiBEGAyUSTswGw4uMLO71ayQHYiIc1u0qx/WyD9BgPddZOxwkDXnZWfiJO17Rh9/lGCnL8RBRFn67kKBUCorUq6LqNaO0yUJBDQh6vx0mLDfOonUi4RolwtRJdPSY0dxhkCnJCNF11zz33YMqUKdi9ezeKi4tRUlLS6zuxiPe+Vw4AzMpLhEalQGVLF87Xd8hwYINbra4bFc1dUAgsXTUQvpV8N/XL8RtdlxFGM1sST/BB4TFAxcdkaOE7qzzZPs7JXZfDV3KCdfs44OFKzoULF/D555/bHdJJbPBBnV5sIwdYBH5RbgJ2nGvAtrP1GGHZFj1U8VWcMWkxLs1EmpWfAEEAztW1o75NT5/6/YCnqqK1ql6DU+UUF6FGWRNtIydDQ0Wz+9PH+0qI1KCypUu2ICfYJ5ADHq7kXHrppTh69KjcxxJ6ZOh6zNGIBytH86ociYvQYExaDABQ92M/4dvHfTHSgaNeOWQoqfBw+rgt+Vdygj/I8Wgl5+qrr8ajjz6KwsJCTJw4sV/h8TXXXCPLwQ16PF3VWgGYTYDC80+0C0Yl4382nsLekkZ095h89ul4MHC16NjW7PxEnKrWYU9xI66elOGrQyMW/BOeL3rkcDxdRUM6yVAgpas82FnF+SpdFcw1OR4FOQ888AAA4A9/+EO/n1HhsY3odEChBsw9gK4KiMv2+KZGpUYhLSYMNbpu7CtpwgLLys5Q091jwgnLiAt3gpxZ+Ql4e2cJ1eX4ifXFz3ef8HivnFbqlUOGgMpmz3vkcFKQI8PfjNFkRnNn8O+u8ihdZTabHX5RgGNDobQGNl6mrARBwIJRSQCGdsrqaHkLjGYRKdFatwrwZuYlQhCA4voO1Om6fXiEBLD2yPHlZGJpfhWt5JAQJ4qivIXH7d4HOU2dBogioBB8t7lADl4P6CQDkKn4GIC0erO9aOgGOQctoxym58a7tZU+NkKNcemsLodWc3yPFx77YgI5RzU5ZKho7DCgu8cMQQDSY70PcpplWMlpaDNIt+nJlnZ/8ShdZS9NZeuZZ57x6GBCkozFx/NGJEEhAGdr21HV0oUML3Kzg9XBUhbkTB3m/iDY2fmJOFHF6nKunZwp96ERG77skcNRTQ4ZKniPnJRoLTQqz9cmeEPARhlqcqQeOT78ICMHj4KcTz/9tNe/e3p6UFJSApVKheHDh1OQYyve+2nkXFyEBpOy43C4rAU/FNXj5hnDvL7NwUQURZuVnIGbAPY1e3gi/rmjhOZY+YEv51ZxVJNDhgpvpo/bklZyZAhypI7m0cGbqgI8DHIOHz7c7zydToe77roLP/rRj7w+qJASZzONXAYLRibjcFkLtp9tGHJBTnFDB1o6e6BVKaTUkztm5CVAIQAlDR2oae1GWqz7rdGJa/gnxSSf7q6imhwyNFRK28c975EDyLu7iqergrnoGJCxJicmJgZ/+MMf8Lvf/U6umwwN8bnsuwzpKsBal/NDUT2MpqE1mJCnqiZlxXm0ZBsTpsaEzFgA1C/H1xr9sJJDNTlkqJCj6BiwBjm6biN6vHz/GCzpKlkLj1taWtDa2irnTQ5+vPBYVwUYvZ+CPSkrFrHhaui6jThaMbQea6kJYK779TicNOLhPAU5vsK2lrLAw5c1ObGWlRxddw/MZnmmKhMSjCr49nEv01Wx4WrwGmFvi4/r20M4XfXqq6/2+rcoiqiursZ7772HK664QpYDCxmRyYA6AujpZE0BE4d7dXMqpQLzRiRhQ2E1tp+td6tXzGB34AKrpZnmQdExNzs/Eau3F2NPCQU5vsJ7cAiCtdDRF/hKjigCbd1GKeghJNTI0e0YYINt4yI0aOowoKnDgJRoz1P2jYNgAjngYZDzf//3f73+rVAokJycjDvvvBMrVqyQ5cBChiCw1Zz600BzqddBDgAsGMWCnG1n6/Ho5aO8P8ZBoLnDIA0nnepFYDc9Nx5KhYALjZ1Ddoear/F8f3yEb7eWalVKRGiU6DSY0NJloCCHhCw5uh1zCZHWIMcbPF0VzMM5AQ+DnJKSErmPI7TF5bAgR+a6nGMVLWjpNEhbafHDn4HKg8D1bwHq0CqqPVzOUlX5yZFeNZ6KttTlHC1vwe7zjbh+WpZch0gspO3jfmgQFheuZkFOZw9yEn1+d4T4na67B23dRgDer+QAQEKEPMXH1h2UwZ2ukqUmR6fT4bPPPsOpU6fkuLnQEy/vDqv02HCMSo2CWQR2nGtgZ5p6gK0vAqe/BIq3ynI/weSApejYm1QVN9tSl0PFx77hzxe/WOqVQ0Ic31mVEKlBhMajdYle5NhGLorioElXeRTk3HTTTXjttdcAAF1dXZg+fTpuuukmFBQUYN26dbIeYEiIk69XDrdgJFvN2XbG0v249jhgshQ2l++V7X6CBS86nu5F0TE3K5/12KHOx77R6Ie5VVyctMOKeuWQ0CTV48iUWo+P9L4hYGtXD4yWYv+QXMnZvn075s+fD4A1BhRFES0tLXj11Vfxxz/+UdYDDAl8h5VM6SoAWDjaOuJBFEWg4oD1hxX7ZbufYNBjMuNoRQsA94ZyOjIjNwFKhYCK5i6UN3V6fXukN6lJmD/SVZY6nFZaySEhqlKmnVVcogwrOXy1NiZMBa1KKctx+YpHQU5raysSEtin4a+++grXX389IiIisGzZMhQVFcl6gCFBxq7H3IzcBISpFajV6XG2tp3V4nCVBwGTUbb7CrSTVTp095gRF6FGflKU17cXqVWhIIv65fiKX1dyIqhXDgltUrdjGepxAHlWcuoHSSNAwMMgJzs7G7t370ZHRwe++uorLF68GADQ3NyMsLDQKniVBU9XdTYA+nZZbjJMrcTMPFZbsu1sXe+VnJ5Olr4KETxVNXVYPBQy7dax1uXQiAe5NfhhbhUXG26pyaEgh4QouUY6cAmR7IOBN31ypNXaUA1yHnnkEdx2223IyspCRkYGLr74YgAsjTVx4kQ5jy80hMcBYWzlAK3lst3sQssuqwOnS4BGywpaxhT2vXyfbPcTaFITQBl7As0ebi0+FkVqJCcnf0wg56yjHagmh4QmXnjsbbdjLsHyd9nU4fkHg4a2wdEIEPAwyHnwwQexe/duvP3229ixYwcUCnYz+fn5VJPjiC+Kjy1BTk+ZZRUnPhcYdSU7XREaQY4oitYmgDIGOdNy4qFWCqhs6UJ5U5dst0tsm4T5YyWHD+mklRwSmuRqBMhZt5B73oFfWq0N8pEOgId9cgBg+vTpmD59eq/zli1b5vUBhay4YUDNMVmLj4cnRyIzLhwT2iyrOJnTgeyL2OkQ2WFV2dKFWp0eKoWASVlxst1uhEaFSVlxOHChGXuKGzEs0bvBd8TKH3OrOGl3FRUekxDUZTBJtTNZcfK8RiVE8cLjHoiiCEFwvwRgMKWrPApyTCYT1qxZg++++w51dXUwm3sP+vr+++9lObiQwgd1yriSIwgCFoxKxuTD59kZWdOBzGkABNaTp60GiE6T7f4CgaeqxmfEIFwjbxX/rPxEHLjQjN3FjbhpRrastz1UdfeY0GEwAfBXnxzaXUVCF6/HidKqEBPufY8cwLqSYzCZ0a43IjrM/U7hUuFxqKarHn74YTz88MMwmUyYMGECJk2a1OuL2MHTVTKu5ADAwpGJmKw4x/6ROR0IiwFSx7N/h0BdjlR07IMZXVSXIz/+qVOtFBCtledF2Zk4KjwmIcy26NiTFRd7wjVKhKvZB8ZmD+ty+BbykF3J+eijj/Dvf/8bS5culft4Qle8b4KcuckdiBbaYBCVqNUORzYAZM1gu6vK9wLjrpH1/vxNagKYkyD7bU8dxupyqlu7caGxE7lJkbLfx1AjpaoitbK9KDtj7ZNj8HjpnZBgJXfRMZcQqUFlSxcaO/QepeqtQU6IruRoNBqMGDFC7mMJbVLhsTyjHbjo+qMAgJNiDraXtLEzs2ey74O8KWCH3ohT1ToA8hYdc+EaJaZks9ulfjnyaPTj9nHAGuT0mER0WtJkhISKCt4I0AdBDuD5NvLBMtIB8DDIefzxx/GXv/yFlvjdEWep+dC3Al3N8t2upT/OEfMIbD9rGfHAi4+rDgNGzyvoA+1IeQvMIluqTYv1Tf+lWZaUFY14kEeDH4uOASBcrYRGyV7GqPiYhBq5e+RwUkPAdveDnA69EV097APFYAhyPEpX7dixA1u2bMGmTZswfvx4qNW9C5fWr18vy8GFFE0kEJkMdNSz4uNwmVYmKq1Bzs5zjegxmaFOyAciEoHORqD6GJA9Q5778jNf9Mfpa1Z+Al79Dth9vpHSHTLgNTn+GOkAsOL72Ag16tv0aOk0yP5mQEggVcq8fZxLiPC8ISD/IBOuViLSD3V33vJoJScuLg4/+tGPsHDhQiQlJSE2NrbXF3FA7uJjo4EFMQBKwsaiXW/E4bIWQBCArMG/lfyAH4KcqcPioVEpUNemR0lDh8/uZ6ho9OMEci6OeuWQEOWrlRxvGgI2BOBv3BsehWHvvPOO3McxNMTnsJWXFpnqcvjk8fB45GRPwNFj1dh+th4X5SWwlNXZTYO2KaDZLOKwH4KcMLUSU7LjsLekCbuLG5Gf7P1srKHMn3OrOGvXYwpySOgwGM2o1XUDALLi5e3jxUc7eNIQsGEQ1eMAHq7kcPX19dixYwd27tyJ+vp6uY4pdMnd9ZgP5cychgWjUwAA2/rW5ZTvAwZh7VRRXTva9EZEaJQYkxbt0/uybiWnOVbeaujgnVD99ymP5leRUFTT2g2zCGhVCtl3McmxkhPSQU5HRwfuuecepKenY8GCBZg/fz4yMjJw7733orOzU+5jDB1xw9h3udJVfChn5nQsGJkEACisbEV9mx7ImAoISqCtGmitkOf+fEUUgbI9wLr7gFUFwIVd0iiHKcPioFJ6FYsPiA/r5HU5xHPSTBs/vgDGhtP8KhJ6KlosO6tk7JHDebWS0+a/sS1y8Ojd47HHHsO2bdvwxRdfoKWlBS0tLfjvf/+Lbdu24fHHH5f7GENHvNwrOZYgJ2s6UmLCMCk7DgDwwd4yQBMBpFmGpQZrXY6hAzi4BnhjPvD2EqDwPywA3Pmqteh4mO9SVdzkYXHQqhRoaNfjfD3V5Xiqvk2PM7WsjUF+sv96Dkm9cmglh4QQXxUdA9aVnGYP/mYG00gHwMMgZ926dXjrrbdw5ZVXIiYmBjExMVi6dCnefPNNfPLJJ3IfY+iQCo/LvE8hdTUDjbzT8TQAwD1zcwEAa3eXorvHFLz9chrOAZt+A7wyFvjiYaC2EFCFAWMtjQvPf4+TpVUAfNPpuC+tSinV/dBWcs99cbQKJrOISdlxyEn0Y5ATTqMdSOjxVdExYF3J4RsF3DGYGgECHgY5nZ2dSE1N7Xd+SkoKpaucic0GIADGLqC9zrvb4vU4CflABOsGvGxiOjLjwtHYYcAnByuCa1inyQic3gCsvQ54bRqw93XWMyg+D1j8R+CxU8BNa9nvY9Ijv2U3BAGY4oeVHIDNsQKoKaA31h9madHrp2b69X6lwmNaySEhxFfdjgHrSo6u24gek3mAS/cmpauiQ3glZ/bs2Xj22WfR3d0tndfV1YXf//73mD17tmwHF3JUGiDG8gbg7Q6rCmvRsXTzSgXunZcHAHhrRwlMmZb+ODWFgCFAwWd7PbD9ZeDVycBHPwaKtwAQgFFXALetA35xCJjzCxaoCQIw9moAwBXK/RiVEi3VW/gaLz7eS3OsPFJU24bjlTqoFAKuKsjw633HWgYOUk0OCSUVPkxXxYarwct83O2V02AzumUw8GgL+apVq3DllVciKysLkyZNgiAIOHLkCLRaLTZv3iz3MYaW+BxAV8FqT7xp0ldpLTq2dfOMbPzluyKUNHTgm0oNrohOZ8XHVYeB3LleHLgbRJGlyPa9CZz8DDBZ/ojCE4CpPwGm32Odyt7XmKuBnX/BpYrDODBM3m2TzhRkxSJMrUBDuwFFde0YlerbHV2hZv3hSgDAxaNTpJbx/sLTVbSSQ0KJNV0l/+ugUiEgLlyN5s4eNHf0ICXa9Y7yPMhJHgQTyAEPV3ImTpyIoqIirFy5EpMnT0ZBQQFefPFFnDt3DuPHj5f7GEML32HVXOr5bYiidWdVVu8gJ1Krwu2z2H2s/qGYDesE/NMvx9QDHHwX+McC4K3LgcJ/swAncxpw3RssJXX5HxwHOACQOQ2NikRECd1YEn7G98dsoVUppSGglLJyj9ks4jNLkOPvVBVgO6STghwSGsxmEdWtvlvJAazzqxrd2GGlN5qg6zYCGDyFxx6t5KxcuRKpqam4//77e53/9ttvo76+Hk8++aQsBxeS5Oh63FwCdDUBSo11B5WNO2fn4s3tJThU1oLynInIxuesX46vff0UsG81O63UAhNvAGbcB2ROdfkmuk0iNvVMw+3KzSho/wHAbb45VjtmD0/EjnMN2FvchDtm5/rtfge7PcWNqG7tRkyYCpeOTfH7/cdRnxwSYura9OgxiVAqBKT6qPYlIVKD8/UdaHajVw5v9qlSCH4rJfCWRys5//jHPzBmzJh+548fPx5vvPGG1wcV0uTYRs7rcdImAqr+fwApMWH40RT2ifr9SkuBuK+bAvZ0AUc+ZKcXPgk8fhq47u9uBTgAcKKqFRtMbPUpqnQzK1j2k/EZMQCAc3XtfrvPUMBTVcsKMqBVKf1+/7GWlZyuHhPbVUjIIFdp6ZGTHhvmsz5hfCWnyY2aHGtHc82gmfPn0aNXU1OD9PT0fucnJyejurra64MKabbbyD3loB7H1v0LWAHympJYiAoN0NkANBV7fp8DKfoGMLSxHWQLfyPt+HLXgdJm7DOPQbsiGkJnI1C2W+YDdSzXsu35QlMHzGYqPnZFl8GETYXsbz4QqSoAiNaqoLC83uooZUVCgFR07MOBs1KQ48Yk8sHW7RjwMMjJzs7Gzp07+52/c+dOZGT4d2fFoMNXclorALOHnzod1OPYGpESjcvGpkAvqlEWNspyPR/2yzm+jn0ffx2g8PyTx8ELzTBBicqUi9kZp77w+tBclRUfDpVCQHePGXVt7vePGIo2n6xBh8GEYQkRPp0x5ozCZumc5leRUODLnVUcD3Lc2V1VP1SCnPvuuw+PPPII3nnnHVy4cAEXLlzA22+/jUcffbRfnY4zzz33HARB6PWVlpbm9Drbtm3DtGnTEBYWhvz8/MGXHotOBxRqwNwD6Krcv75RD9SwyeO228ft+emC4QCAb9stgZWv+uXo24GzX7PTE673+GZEUcShMtbpWDne0hjw9Jd+m72lUiqknhSljdT52BXrDrFU1XVTMgO6fB1LO6xICOE7q7J8uJITH8ELj91fyRksE8gBDwuPf/3rX6OpqQkPPvggDAb2AIWFheHJJ5/EihUr3Lqt8ePH49tvv5X+rVQ6zumXlJRg6dKluP/++/H+++9j586dePDBB5GcnIzrr/f8zdWvFEogNosVD7dcAOKy3bt+7XG2Yyk8gTXOc2JGbjwmZ8dhf+VI3KsBUO6jlZyzX7EGhwn5QPpkj2/mQmMnGtoN0CgVyJ6xDNgeCegqgapDAwZ0cslJjERpYycuNHZIDQKJfXW6buwoYgNhl08JTKqKi43QAI2daHGz5wchwciXIx04Hqg0uxHk8Jqc5FBfyREEAS+99BLq6+uxZ88eHD16FE1NTXjmmWfcvi2VSoW0tDTpKzk52eFl33jjDQwbNgyrVq3C2LFjcd999+Gee+7Byy+/7MmvETjeFB/bNgEc4JOzIAj42YJ8HDKPBACIdScAfZv79zkQKVW1fMBjcobPq5qYFQttWCQwajH7gR9TVrmJrCdFaSN17h7I50erYBaBqcPikJvkvzEO9tBoBxJKfNkjh3O4ktPZBOx5A6g53u86Q6Ymh4uKisKMGTMwYcIEaLWe/dJFRUXIyMhAXl4ebrnlFhQXOy6O3b17NxYvXtzrvCVLluDAgQPo6bH/4qbX66HT6Xp9BZw3xceVA9fj2Fo8Pg3hiZmoEJMgiGbrOAi5dLUA5ywrcV6kqgDgB8uqgFTbYel+jJOf+y1lxWculTZQumogPFX1o6lZAT4S6pVDQocoiqhoZh+yfDHSgeMdi6WVnM4m4Ls/AKsmAl89yeYK9iEFOYOkESDgZZDjrZkzZ2Lt2rX4+uuv8eabb6KmpgZz5sxBY6P9Zmw1NTX9ZmalpqbCaDSioaHB7nVWrlyJ2NhY6Ss72830kC/Ee9Erp2LgnVW2lAoB983Lw0EzKz42XZC5Luf0BpY+Sx4LpI7z+Gba9UZ8faIWAHDlBEtd1ojLWS+gpvNA/Wk5jnZAuUm0kuOK0zU6nKrWQa0UcHVB/52W/kZdj0moaOowoLuHzZNKj3O9E7G74i1DOk0dTRC/ex5YVQD88ApgsLTQqD0BmHvPtZK2kA+SkQ5AgIOcK6+8Etdffz0mTpyIyy67DBs2bAAAvPvuuw6v07e4kc8ZclT0uGLFCrS2tkpf5eXlMh29F+I8TFd1NrE3fMCt/jM3TMvGaRXra9Rw+gf37nMgPFU1YblXN/PV8Rp09ZiQnxSJydlx7MywGCD/EnbaTykrvpJzobGDZlg58allFefSMSmIiwj8pzqaX0VCBU9VpURrfdp3KkHRgcdV/8b3ql9A+OFl1gIkdSIblKzUsDrL1t7vl0MuXSW3yMhIaWSEPWlpaaipqel1Xl1dHVQqFRIT7ReJarVaxMTE9PoKOE+7HlceYt8ThrvVhyZco0RWwcUAgMi6QxA93breV0cjULyVnR7vXZCz7iCbYL18ap9dOjxl5acgJys+HAoB6DSYpO2SpDeTWcRnRyypqimBT1UBtJJDQofPi447m4Dv/4iIv03BL1SfIVrogiFpPHDzv4CfbQfGXcveYwCgwfpebDKLaOrgE8gD/8HGVUEV5Oj1epw6dcpuo0GATT//5ptvep23efNmTJ8+HWr14GgxDcCartJVsS3hrnKzHsfWFZcuQpeoQZTYgaNHDrh9fbtO/RcQTUD6JCBphMc3U9Hcid2WeVH96jtGLwUEBds27828LxdpVUpkWLZtXqCUlV27zjegVqdHXIQal4xxvFHAn6gmh4SKEkv7CtkbAVqCG6wqALb/L2BoQ5GQg58ZHsXJa74Exl5l7XGWbOmt1mCdH9jUYYBZZHtLEoJg9dZVAQ1ynnjiCWzbtg0lJSXYu3cvbrjhBuh0Otx5550AWKrpjjvukC7/wAMP4MKFC3jsscdw6tQpvP3223jrrbfwxBNPBOpX8ExkMqCOACCypoCucrMex1ZibBRqoljNzKEdX7l9fbuOr2ffvVzF4cMdZ+cn9v/DjkwEcizT00996dX9uCqXio+d4qmqqwrSAzLGwR4e5NBKDhnMugwmvLebrfDL1lyzqxn4/n+Av0ySghukTgBufh+Pxf8VX5tnoLmzz/icpNHse701yOGDPOMjND4bNeELAT3SiooK3HrrrRg9ejSWL18OjUaDPXv2ICeHrXRUV1ejrMy6AykvLw8bN27E1q1bMXnyZDz//PN49dVXB0+PHE4QrNPIXU1ZiaJ1Z1SWZz1j4kfPA8BSVmdrvdxK3lYDlO5gp8f/yOObEUUR6y1vmtdPc5D68HPKKseyjZxWcvrr0Bux6ThLGS8Pgl1VXGw41eSQwe/tnSWobu1GZlw4br1omHc3xoObVQXA9j8Bep0U3OBnPwBjr0ZcJCts7reNPImv5JyVzmpos6SqBlEjQMDDZoBy+eijj5z+fM2aNf3OW7hwIQ4dOuSjI/KjuBy2Y8jV4uOmYuvk8dQJnt3l6HnAodcwVVGEf2wvxss3TvLodgAAJz4DIAJZF1nTbx44Ut6C4oYOhKuVuGKCg27XY5YBm37NOja31QLRqfYvJxNpJYe6Hvfz9QlWIJ6XFIkpvEA8CNBKDhnsGtr1eH0r21jyqyWjEab2cJXUbGYrNrtfY4ENwN4zFj4JjLmq19idxEgHDQF5uspmJWcwFh0DQVaTM6S4u5LDV3HSCuxOHndJFpvuPVJRia1HzqCmtduz2wGAE5ZUlZe7qtYdYum6KyakIUrrIOaOzbJ0PBaBMxu8uj9X8MZ2tJLT36eW1OJ1kwM7xqEvPtahrdsIo8k8wKUJCT5/+bYI7XojJmbG4ppJXsyAPPIvYOsL1pWbm95jKzfjruk3VzA+0kFDwMSRAAT2wbqDtWexjnSgIIe4wt2uxy4M5RxQZJJUNT9BLMI7u0o8u52WMsscLAEYd53Hh6M3mvDFUT7BeoDUhx9TVtaux7SN3FZNazd2nGMveD8K8BiHvniQAwC6bqOTSxISfM7VteODfaw046mlY6FQePEB4qyl5nLWQw6DG87hSo4mwjpyyLKa09A+ONNVFOQEirvbyCs9LzruJfsiAMBUxVl8sKcMbd0eLO+f+JR9z5kLxHjeCO77U3Vo7epBWkwYZg8fYE7UGEuQU7Kd5Zp9KDshAoLAVgWaKf0h+e+RSogim4k2LNF37eY9oVYqpJVA2mFFBpuXvjoNk1nEZWNTBn4tdMZkZK+RADDxeofBDedwJQewFh838CCH0lXEHfFujHYw6oGaQnbaw6JjiSXImastRpveiI/3e9Ac8bhcqSrrBGvlQJ9ckkawrspmo3XiuY+EqZVIj2EFeVSXw9gWiAdLb5y+rJPIqfiYDB57ixvxzclaKBUCfnPlGO9urPIgS1OFx7s0LJlvBW+29zeTzHdYseJjHuQMpuGcAAU5gcNXcjrqAcMAb6Q1lsnjEYlAfJ5395vFgpwCnIMCZry9owQ97tQwNJ4Hqo8AgpI1jfJQY7seW8/UAQCun+pi6sOPKashPcPK3P/5cLJahzO1bdCoFFg2MfBjHOyRio9pJYcMEmaziBc2ngIA3DIjGyNSor27wfPfs+/5FwOKgQuXEywrOU12V3J698qRRjpQuoq4JDwO0May0wOt5kipqoEnjw8oZSygiYba1ImZkbWoau3Gl8eqXL8+X8XJX8hqfDz0+dEqGM0iCrJiMTLVxT9sHuSc+27gwNBLQ3KGlSgCa64CXp0kFRtyvDfOZWNTEBsRnI03pYaAlGIkg8QXx6pwtKIVkRolHrlslPc3WLyFfR9+qUsXdxrk8JUcS9djSlcR98VbdlgNVHzsRRPAfhRKKeX1s3w29fsf24pdL7CVdlV515tI6o3jTq+VtIlsBczYxQIdH7KdYTVk1J4ASn9gQff2l6WzjSYzPjvCAuFgTVUBQBzvlUPpKjIIdPeY8Kev2CrJAwuHIznay+Chq8X6XsFn/g2ABzmtXT39dyXylZzWcoj6NmklJ8nb4/QzCnICydXiY2mcg5f1OFz2TADAHE0xIjRKnK5pww9F9qe491J7Eqg7CSjUrN+Ch87WtqGwshUqhYCr3dkqKQh+S1lZd1gNoZUc28d0/z+lMRo7zjWgoV2PhEgNFo4KjjEO9sRSuooMImt3l6KypQupMVrcNz/f+xss/YGN2Ukcad0ZNYC4CI2UHOi3ySIiAYhgq/XtVadhsARBfEfWYEFBTiDF57LvztJVnU2sESBg6RUjA0tdjqZqP26ewf4YVm8vHvh6fBVnxGUs3eYh3hvnkjEp0icJl/Eg5+xXgNF3n9iH5ErOqc/Z97BYwNzDuqXC2hvn6oJ0aFTB+5JBQzrJYNHcYcBfvz8HAHhi8WiEa2QYj3LevVQVACgVgvR346z4uLPyJAAgWqvyvElhgATvK9ZQwFdynA2e5E0AE0ewink58F47zSW4b0o0lAoBO8414Hhlq+PriKLNrirPU1UmsyjNqnIrVcVlXQREpbIdBHyrpA/w0Q4tnT1DI/3RcM6ySqdizcMAoPDf6LxwCF+fYGMc+g1PDTI0pJMMFn/9/hzauo0YkxYt33gUXnQ83LVUFSdtI293XHxsrD3N/jnIUlUABTmB5UrXYznrcbjwOCCZbVXMbC/EUstumTd/cLKaU30UaDoPqMKB0Vd6fNc7z3k5wVqhYGMeAOC071JWERoVUix/0EOi8zFfxclbyIrKJ9wAANB9+TS6e8zIT47EpKzYAB7gwKgmhwwGFxo78N6eUgDAb5eNHbh9hiuaSoDmEvYhJXeeW1eVGgI6WclRNrLi48HWCBCgICewpK7HTtJVlTJ0OrbH0i8H5fvwswUsH/zlsWpUtnTZv/zxdez7qMWANsrju11vSVVdMynD8wnWPGV1egNgNnl8LAMZUjOseJDDH9tLnwYUaqTV78QcxXEsnxJcYxzsoZocMhj86asz6DGJWDAqGfNHylTjxndVZc8EtO5tQ4+PcNYQkK3khLWy1FpiJK3kEHfwlRx9q/0uvraTx+Wqx+GyrEHOhMxYzBmeCJNZxNs77Ix6EEVrl2MvUlVt3T346oQME6xz57O6kY56y3gJ3xgy08hbyoGqwwAEa0F5Qh7aC+4AAPxG9SGunRScvXFs8WaAtIWcBKuDF5qxobAaCgF4aqmXjf9sSf1x3EtVAda+N/1GOwDSSk50ZxlUMCIpmlZyiDs0kUCkJZK3V3zcVMyCH6XW48njDll2WKHqEGDqwU8tqzkf7Svrv9xfsR9oLQc0UcDIxR7f5abjNfKkPpRqYJQlZebDXVZ8UKdbKzmmHp+uLvkEfwxz5gBR1k+W/4m4Be1iGAoUJciu3hygg3MdNQMkQcPOpghRtDb+u3FaNsakxchzXyYjUGypT3Sj6JjjKzl2e+XEZAKaKChFE3KE2kHXIwegICfwpOJjO3U5vB4nfRKgkjmCThwBhMUBxm6g5hgWjkrGmLRodBhMePqz47375vCC49FLAXW4x3fJU1XXT83yPvVhu5XcR0M0c6UdVi6u5DSXAi/lAZ/+zCfH4zM8yBl7jXSWKIr44HgXVhstKzvf/YEFcEGM1+S0dvXQYFUSOAfeAf4nFTi9sdfZXx2vwcELzQhXK/HYYhka/3FVh1g2ICwOyJjs9tWdNgQUBCBpJABghFBJQQ7xgLPiY1/V4wCsgFeqy9kPQRDwwvKJUCkEfHmsGh/xmVZmkyypqvKmTuwpboIgyDTBevilgDqCrTBVH/X+9uzg6SqXRzuc+AwwtAGF/2GFgINBWy1QtpudHmvtfXS8Uoeiuna8J1wFc0QyK2o8uCYwx+givpJjMoto19MkchIgpzcAohnY/Zp0lsFoxotfsR1K9y/IR6plNp4s+NZxF0c59JXgrPAYkAZ1DheqqPCYeCDehZUcuetxOKkuh9W1TB0WjyeWsCf0c5+fwNnaNuDCLqC9htXAeLAUyvFt43OGJyIjzvPVIIkmgvXrAXyWsuJBTmOHATpXprWft+nCfPg9nxyT7E5/CUBkz7FYa53U+sNs1W3uuBwoLn6SnbntJUDfHoCDdE2YWgmtpY8P9cohAVPPuhjjwk6pDOFfey/gQmMnkqK00kYP2Xi4dZxzuoUcAJLZqtMIRRWt5BAPOOp63NNtnTzuqyCHr+RU7JfO+un8fCwYlQy90YyH/nUIxmOWXVVjr/Y4ZSaKItZbgpzlco4F4OkVHwU50WFq6ZNL2UApK0MHULbH+u/D/2K58mBnJ1XVYzLji6NsjMPyqZnAtLuAhHxW6L37bwE4SNdRrxwSUIYOoNWmvrLwP2jt6sFfvmNbsB+7fBQitSr57q+71fr67UHRMTDAFnJA2mFF6SriGUcrOTWFrOtsRKK1M7LcMqcBgoKlfHTsTU2hEPDnmyYhOVqLkroWdB/zvgHgobIWlDR0IEKjxBUT0uQ4cmbUYjZiouGM9dOTzHJc3UZeuoNNio/NZq3Q22uAIveLdd/dVYrV28+j0TIMz6c6m1greMBa4wTgh6J6NLQbkBipYVtclWrg0t+xH+56FWiv9/2xecjaK4eCHBIADWd7//vox/j7liK0dPZgREoUbpouc0PN0h2WUQ4jrO8lbrLdQm6vlq0rdjgAlq5KjJQxQPMTCnICTVrJKetdQCttHZ/u/eRxR7RRQOp4drp8n3R2UpQWq26ejLmKE4gytUKvSQByF3h8N7zg+IoJafJ+igmLZY3rAJ+nrAYsPuYDQ0dcBky+lZ0+9K5b93WiqhXPfn4CL2w8jdkrv8ejHx/BobJm3xXRnv0KMBvZzr3E4dLZfHjqNZMzoFZaXiLGXQdkTAEM7cD2//XN8cjA2iuHGgKSAOAfttIK2K7YhjPYs2srALZlXKWU+S3Xi63jHN9CbjCa0WnovzO0QZ2JHlGJSEGPKH2tx/cTKBTkBFpsNgCBTdbusPmE7MuiY1s2/XJszR2RhF9lsXkln+qno7TZs5WF7h6TlPrwaIzDQKTGgF/Kf9uwaQg4UPGxlBe/FJh6JztdtFlaIXPF0XI2VkOjVMBgMuPTw5VY/vdduOqvO/Dx/jJ02XkB8srJPg0AAei6e7D5JHsh65VaVCiAy37PTh942zpPLchY5/DQSg4JgHpWXIzsi6TO8FfhB8wZnohLRqfIf3+2rzseCrepZbO3w6q+y4xSka3AC31XqgYBCnICTaUBYiyTuG1TVr4uOuZ4v5yK3kEOjHqMb90GAFhvmIlffHgYBqPZ7Zv//nQddN1GZMSGYXZ+ordH29/oZQAE1syupVz2m3dpJaelDGgsAgQlW1lKGgnkzGU7LA7/y+X7KqxsAQDcOz8Pn/98Lm6YlgWNSoETVTo8ua4Qs1Z+hz9+edL13V7O6NusL5A29TibCqthMJoxIiUKEzL79PHIXwgMX9RreGew4b2N9pc0BfhIyJDEV3KSx6A0k+1WvFa5C09dMUr+juHNpezDhgejHGwJgiDV5dgLchra9DgnWt6j6inIIZ7oW3zc0ci27AJ+CHJmsO9VR1ixM3fuOwh6HUyRaSgKm4DCyla8ZNkC6Y51B1mq6ropmVDIMaOlr6hk1sQO8MlqjkujHXiqKmsGS6EBwFTWLRiH1wJm14LDQsuA1ImZsSjIisPLN07C3hWLsOLKMchOCEdrVw/+uaMEF7+8FXe+vQ/fnqyFyexhKqtoM2DSQ0wYjtPmTHx2uBIvbjotTUZePtXBGIfLnmPfj3/CnjNBhs9h++ZkLToNg6Dwm4QWy0qOmDQSvz2eimYxCilCCyYYjsh/X3zreNZFQJh3jQXjnQQ5jR0GnBMtbT8afFP76EsU5AQDqfi4lH2XJo+PZMM0fXrfeazrsrmnd7+ZE6zgWDlxOf504xQAwFs7SvDdKddzsg3temw9y1Jwsk3atYePIvBBXQ4Pcura9I7fNPnW8RGLrOeNu5YFPC1lQMnWAe9HbzThTE0bABbkcPGRGvxs4XBsfeISvH3XdFwyOhmCAGw7W4/71h7Awv/dgte3nrffyMuGKIqo1XVj65k6vLHtPA5/vRYA8I/68bjiLzvwyMdH8Ma286ho7oJGpcB1kx30MkovACbexE5/+9yAv5e/TcqKRXZCOLp6TPjuVF2gD4cMJT3d0mv4D81J2FnSho3ibPazY/+W//683Dpuy1lDwIY2Pc6ZaSWHeMO2+BjwXz0OwIqa+/TLgaHT2q1zwvW4fFwq7p6bCwB4/D9HUd3qYIhnH58fqYLJLGJSdhxGpHg+1HNAvIndhV2y7/yJjVBL25Ltpqx6tVS3CXLU4UDBzez0wYELkM/WtKPHJCIuQo2s+P59hJQKAZeOScU7d1+ErU9cjJ8uyEdchBoVzV146avTmLXyOzz27yM4Ut6CToMRR8pb8PH+Mjz3+Qncsno3pjz/DWa+8B3uemc//m/TMYzSsQaAG4wXIVqrwvSceNw+axj+eN0EbPzlfOe9jC79LdvVVrzF+kIbJARBwNUF7AWZ14IR4heN5wDRDDEsDr/fwl6HRP6B4NQXbHu5XMwmoISVE3hTj8M5DXLa9YN6JWfw7QcLRfF90lX+qsfhsi8CzmywBjlFXwM9Hawbs+UYfnPlGOwvbcLxSh0e/vAIPrh/5oA7BdZJYxxk6HDsTNwwIH0yUH0EOLMRmHanrDefmxiJI50tuNDYgbHpfZaFKw+ylurh8f1bqk+9A9i3mnVA7WgAIpMc3odtqmqg3H1OYiSeWjoWj10+Cp8frcJ7uy+gsLIV6w9VSjuj7FEIQH5yFG6MOorIKj26IjLw+i/vQWZ8hHv1AvG5wIz7gL2vs9WcvItZYXKQuHpSBv6+9Ty2nqmHrrsHMWHqQB8SGQosqaqG8Dycr+5EQqQG11x1OVD5R/bafmYTMPEGee6r6jDrkRMWy3Y9ekmaX2WnV05DhwHFomVAb2cjK6eI9EF9pY8EzyvTUMZHOzRf6D153B8rOUDvpoCiaJ1VNX65tH1dq1LitVunIkqrwr7SJrxqqd1w5HSNDieqdFArrZ+sfcp2lpXMci3FxyUNdlZyeKrKXkv1tIlAxlSWCjz6odP74EHOBJtU1UDC1ErcND0bX/xiHj57aC6WT82ExrJLIilKg3kjknDfvDy8fOMkfPmLeTj5hyvw7WML8bNktmsuvOA6ZCVEelYQueAJQBPNUpyW1GawGJMWjZEpUTCYzNh8YvBteR3UzGbg458AH/7Y5Vq0kGEpOi40sJ1IP12Qj5hwjXVF99jH8t0XX0HNW+jRKIe+pIaADtJVXQhDZ7gl0BlkO6woyAkGPF3VWsGeQN0tgCpM/snjjmRMYRX67bVA7QlrE7s+DQBzkyLxPz9ix/TX74uw63yDw5vkKwqXjkmRitp8iu8QKt7KPuHIKEca1GlnuZkXHdumqmzxAuSD7zodJHrcZiXHE5Oz4/Dnmybj4NOX4cDTl+HA05fj/ftm4umrxuGGaVmYkBmLMLWSTUc+Y0lF2mwdd1tkEjD3YXb6++ftTl0OFEEQcPUkFlh/Tikr/yrdDpz6nK0MB2mbAZ+xrOTsa0sGAOuW8QJLyurcd/Kl02XYOm5LGu3gIF0FAPr4EZYzBlfKioKcYBCTwWoczD3WlYj0SazTrD+ow1nzKgD47vdsMnniCLYS0ce1kzNx0/QsiCLwyEdHpD8AW0ZLjxfAxwXHtpJHsfbj5h7grPudhp3JTbIM6uwb5HQ2sQnAgOMXm4k3AOpItsWcD8LsQ2804XSNjl3cwyCHY6MonLReL93OgsDIFGv7AE/NfhCISmXFlkE2vJMHOTvPNfinezRhbOvPBtmbodcsKzknjelIiNRgJK9DTBrJVnRFE3B8nff3062zjnKQoegYGGAlh8+0sgzqHGzFxxTkBAOF0jockaeKMv2UquL4G57tKo6DNMZz14zHiJQo1LXp8cR/jsLcZxvzzvONqG/TIz5C7ZsGWI7wlQmZ0yfWlZw+6arirawXTvJYINZB3ZE2GpiwnJ0+tNbuRXjRcWy4/aJjWfEgeswy75e5NZHAxb9hp7e9xHrvBIm8pEhMzIyFySxi0/GaQB/O0NDR0Dtd7KNRK0HJ1AM0nQcAFJmzcFFuQu+WGXKmrEp3sE7lCcNlG/njaAu5wWiW5sBp0sayMwdZ8EpBTrDgxcd1J9j3LD8VHXO8Xw43frnDi0ZoVPjbj6dCq1Jg65l6/HNH72Vp3hvnmkkZUo2IX0y8kX0v2sxecGXCt5FXt3aju8em67CrS8a8A/KJz4Culn4/dqfo2CtmEyuCBoBx1zi/rKum/ISt+nU2ALtek+c2ZXL1JFZDQCkrPzn6IVtJ5YZSkNNUDJiN6BLCUY0EzMpP6P3zCdezZqFVh4CGIu/uS8at45zUDLBP4TEPepQKAeHpliCHVnKIR3jxMReolRwASBkPpIxxevHRadF49mo29+pPX53B4bJmAEBbdw++PsE+OfstVcWljGW7rMxGeZaFLeIj1IgOYxsRy5osqzmiaH2xGTFAkJM1HUgZx0Z3FP6n3489KTr2SNkeNjokLA7InS/PbSrVwKJn2OldfwXagqfQd5ml4H1/aZPLbQ9CQXOHoXcw7g+iaE1VjbiMfR9kn/i9YqnHKTJnAhAws29396hkax8tb3vmyFyPA1hXclo6e2A0WQvGeTlCYqQGCv6e0Fom73Z4H6MgJ1jw4mOANefrG/T4WmwWEG3ZBTXB8SqOrVsvysaygnQYzSJ+8eFhtHb1YGNhNfSWsQAFWT5+07ZnkmU45gC7mdwhCEL/GVb1ZwBdJSsQz5k70A1YV3PsFCDzomOfP16nLLOqRi+Vt95r7DWs1UBPB7D9T/Ldrpcy48IxPSceoghsOFYd6MPxi/KmTsx58Xvcs2a/f++4bDerO1NHApc8xc6rP+u02D6kWFatzpgyEBehxujU6P6XsU1Zefq4NF9gaTFBKd8HFbCZb3wRuaXLuhrHg5ykKC3bNh5hCd68XY3yIwpygoVtbjVzmu8mjzuz8NdsS+K0u1y6uCAIWLl8IrITwlHR3IWn1hdi3UFecOxgLICvTbyB7RSrOgzUuT+GwpF+M6z41vGcOaxweyAFN7GpxLWF7NgsDEaz3U7HshNFa72EXKkqThCAy//ATh9cAzSel/f2vXDNZEtjwCES5Hx7qhZdPSbsOt+Iwgp5dxk6xQvPJ17PNjEoVCzoba3w3zEEkmUl55yY2b8ehxu9FNBEsZ45fQYiu6yYj3KY4fUoB1sqpQKxluG2tnU5vOiYTyqXio8H0TZyCnKChe1Kjr9TVdz0u4E7P3fatK6vmDA1/nrrVKgUAjYUVmNfaRMEAfjRFB83AHQkMgkYuZidlnE1p98MK3eXjCMSrMHFIesOlLO1bTCYzL4vOq48xFaeNFFAvny5fEnuPPa4m41sS3mQuHJCOhQCcLS8xX4LgBCz63yjdPqDfRecXFJGXc3Ayf+y01PvYquECcPZv4dKysqyklMkZvZPVXGaCOvmCE8LkH2QquISIvoXH/OVnGS+YzN5FPs+iOqtKMgJFvE2QY6/i469NDk7Dk9eYa3hmTs8CemxPt4l5MykW9j3Yx+zYlsZ9FrJ6ekGSneyHzjqj2MPT1kVfgLo29lJfxUdn7K8CY1cDKjDfHMfi54FIAAnPmUjNoJAcrQWc4azoP3LEF/NMZlF7C22Bjn/PVKFdr0fhpQe+zdrO5E6Acicys6T3gwHzyd+j5lNEC3pmyIxs3/RsS3eM+fEevd7S5lNQLF8oxz6sjfaoaHNkq6KtgQ50koOBTnEXZHJrM9LVGrgVnK8cO+8PFw2lm0Xv31WzgCX9rFRV7Di2rZq63wXL+Um2azklO1iRcTRGazY2eUbmQck5AOGdhYIwE9Fx7apKm8aAA4kbYK1+eFnDwZNcSLfZRXqs6xOVumg6zYiWqtCflIkOg0mfH7Ex7+zKFpTVVPvtKbZky0fegbRm6HHmkshmPToEjVo06ZjTJqTNFLeQvYa39UMnPvWvfupOsIaxWrlGeXQl71t5Lw5YJKUrrIEr1STQ9wmCMC93wD/b7esuVZ/USgEvHH7NHz3+EJcMSEtsAej0lq7NR/9SJab5OmqqpYuGIssL07DL3WvdkoQrEGAJWXF6yZ8Wo9Te4JtcVWFWVN5vrL4eSAmC2guCZop5VeMT4daKeB0TRvO1gZPLx+58Q7kF+Ul4Mcz2caFD/eV+fZOKw4AdSfZc4uvUgA2jeOGQJBj+R3PixmYnpcMpb16HE6htLa6cDdlxVNV+QsApfxjJ+01BLTuruqTrmo8z4YTDwIU5AST8LhBNfisL5VSgeHJPpw27o7JP2bfT30hS5O6pCgNIjVKmEXAdNZSdDzQ1nF7Jv2YFWVW7EdP1XH/FB3zVZzhiwCtj/9/wmKBay39cvatti6vB1BshBoLR7FW+6G8msPrcWYPT8TyqVnQKBUorGz1bQHyoTXs+/gfsdcvbhDWbniMbx8fKFXF8WDwzCb3RtD4sB4HsD/aob5vuiomC1BHsH5IzSU+OQ65UZBDQlPmNNakrqcTOPm51zcnCAJyEiORgmZom04DEDwr4I1OZek0AK273pKKjrMTfFjDxLeO+zJVZWv4JcD0e9jp//48KDoh8zEPXxytghiC25p7TGbsL20CAMwZnoSESA2unMhWVD/w1WpOt87aoZ3Xm3GJIwEIQFeTrI05g5GZ76wyZ2KWo6JjW2kFLJ1n0rv+2qRvAyosO7J8FORIKzmdTtJVCgUbUwEMmgCWghwSmgTBWoAs0y6r3KQILFAeY//ImMJ2THnCskU/+sw6aGHwbdFxwzmWTlCogNFX+OY+7Ln8edbrqbUM2Py0/+7XgcvGpiJMrUBpYyeOV+oCfTiyO1bRgk6DCfERaoxJYz1abr2Ipaw+P1LpmwLkwv+wDxFJo4Fhs3r/TBNh7fVV714rB113z6Bq3thddRIAUKEehrHpLpQaCIJ1NcfVlBUf5RCfJ9soh77i++yuMptF6XSveXiDrPiYghwSugosQU7pD0CL959mcxIjsUBhCXJGuLGrqq/hlwIxWdD2tGKJ4oBvi475Kk7eAiA83nf305c2Crj27+z0wTXWae0BEqlVYdHYVADAF8dCL2W16xxLVc3KT5R6tMzMS0B+UiQ6DCbfpOl4K4Rpd9qvTUt2vy5HFEXc9MZuLPjTFmw9UyfDQfqY2Qx18zkAQGTmeOf1OLZ4XU7pDtd6Cfk4VQUACVG9g5zmTgNMlrmEfOcVgEG3c46CHBK64rKtXUFlGIyXl6DFPEUh+4c7W8f7UiiBKbcDAG5Rfu/jehyeqpK5AaAr8uYDMx9gpz//hd25Xf50dYE1ZdV3qOxgx+tx5gy3pksEQZBWcz7YK3PKquowUH0UUGqsHyb6knbiuP5meKyiFadr2tBjEvHgvw7haHmL98fqS7oKqE1dMIhK5I0c7/r14oZZOqWLrKXEQM5bmgD6Msjps5LDGwHGR6ihVtqECrSSQ0gQ4WMejnzodYv5sShBgtCOdkSweVReMBT8GGZRwBzlSUyJbPLqthxqKbd0VxbY1PFAWPQM2zavqwS+/m1gjsHi4tHJiNaqUN3ajYOWWWuhoLvHJP0+s4f3buR5/TQfFSDzOVVjr3a8WYJvI3djJWdjIetlpFQI6DSYcM+a/ShpCI5WBPaYalkqrljMwMwRqe5dWUpZDTDLqqWMjcwQlOyDg4/Y9skRRRGNtiMdbPEVuoaiQTG2g4IcEtrGXcN2AzSdZ9tdvZDbshcAsMs8Dj1QenVbZ7vjsN1cAABIL+4/tFMWfFdVzhwgKsU39zEQTSRw3esABODI+8CZrwJzHADC1EosHs+KcUNpl9WhsmYYjGakRGsxPDmy188SIjVSS4cP98u0mqNvt64+OBsB42a6ShRFqWHji8snYkJmDBo7DLjj7b2oa+v24oB9p674KACgVMjEOFfqcWyNu5athNWdAGqOO74cX8XJms52L/oID3L0RjM6DSbU8+3jUZo+F8xnNX6GdvbhJchRkENCmzbauqvIywLkqAq2HXqbqQCVzd4VRhZWtuJDE9udJRz5F2DqGeAaHvBHA0BXDJsFzH6Inf7iYaDTRytXLuCNATcWVveatjyY7bbZOm6vgJ2nrP57WKYC5BOfAoY29mbnbEgkT1e1VbGdWAM4VtGKypYuhKuVuKogA+/cdRGGJUSgvKkLd7+z3z/dm92kK2fBSXfcSKiUbr6dhscDo5aw087S6cW+T1UBQIRGCY2K/Q5NHQYpXdVvJUepZv/3wKDYYUVBDgl9PGV1fB1g1Ht2G906CBVssvM2c4F1hpWHCitb8Z15KjpU8UBHHXD2a69ur5+2WjYZGgh8kAMAlz7N3vTaa4BNTwbsMOaOSEJ8hBoN7QbsthmBMJjttlOPY2tWvswFyFKH4zucN8MMjwOiLI1BXeiQu8GSqlo0NgXhGiWSo7VYe89FSIzU4ESVDg+8dxAGY3AFpqpG9iYfmeVGPY4tPpm88BP7I2jMJqB4Kzvt4yBHEIRe28gdpquAQdX5mIIcEvryFrARDN0twFkP0yUl2wGzEbXqTFSIKdZp5B46XtkKI1Sozl/OzrAZ2imL018CEFm/oNgseW/bE+pwlrYSFEDhv62rTP4+DKUCSyeGzpiHDr0RRyzFuXOG2x+sa1uA7HUH5NoTQOUBlq6YfNvAl5d24jjfRi6KIjZYUlVXFaRL5+cmReKdu2cgQqPEjnMN+NUnR4OmaNxsMiNZz4agZo3ycMzCyMUsBdVWxXZa9VV9hI2A0MYCGVM9P1gX8W3kjR0G63DOaGdBDq3kEBJ4CqW1yO+Ihymr82wLdHn8bADwaiXHYDTjdDVrkBcx09I079y3rm0ldVWwpKpsZU0H5j7CTn/xSMCaxPHGgF8dr4HeKM8AV1m5kbrcX9oEo1lEVnw4shMiHF6OFyAfq2jF8UovCpB5wfHopa7Vebm4E+eoJVUVoVHi4tG9b7cgKw6v3z4NKoWA/x6pwspNpzw5ctmdKz6HGHTCKCowYswkz25EpWXdogH7Bch863jefJ+McuiL198026SrEiM1/S8o1VsF/zZyCnLI0MBTVue+Adrr3b++5cWmI2shAKDUix0fZ2vbpE7H6fnjWV2DaAYO/8vj2+yls4n1BgICs3XcmYt/A6SMAzobgA2PB+QQZuQmIDVGC123EdvPBlk33vJ9wIs5wKbfuHRxqR5ngE67CZEaLJngZQfkni7gmGUW3LQ7nV+Wc/HNcIOld9GisakIU/cv6l84Khl/uoEV6r/5Qwne3F7s2v37UPGpgwCAOnUm1FovOpbzlNXJ/7LH2Nb5rey7j1NVnG1DwAaX0lW0kkNIcEgZw7oUm42sNscdjeeB5lJAoYZ2JAtyvElXHZcmj8ewQlE+tPPwe/bz8u46+xX7PVPGA4nDvb89Oam0lrSVEjj5mXUsgB8pFQKWTbT2zAkaJiNb4erpYM8FF+rHeF3RnBEDjxO49aJsAKwAucOTIt6T/2WzlmKHAfkuvulKQY7jdJUoithYWAMAWDbR8XDf5VOzsOJKti39fzaewn+PBHZnT8sF1jOrO87Lv7HsWewxNbSxeVacvg0oZzs6MdyDETIesN1G3sgLj52lqzrqA7qRwBUU5JChY5JlaOfRD9y7Hl8yHjYL2elsKb28udPj3TmFUpBj2Q469hogLA5oLbduF/UGn4czLshWcbiMycCCJ9jpDY8D7f7vbHvNZBbkfHOyFp2GINm1s/9Ntp0YYNtzS7Y7vXhrZ48UMM/Ot1+PY2t2fiLyvClA5qmqqXewGUau4OmqlgtAj/1t4EfKWxymqvr66YJ83DM3DwDwxH+O4ociD1ZlZWA2i1A1stWp8AwPi445hQIo4JPJbVJWpTvZIMz4XOtuJh+zDXKkLeT20lXaKDasE3Cr2WMgUJBDho4J17OCyeqjQO1J16/HRxIMvxTpMWHQqBToMYmobvWsdwcPcqROx+ow65wtbwuQ9W3WoCyY6nH6mv8EkDaRDXD84hG/NxWblBWL7IRwdPWY8P3pIBgf0FYLbHmBneZvHqc3OL3K3pJGmEUgPykSabFhA94FK0BmqzluFyDXnwXKdrHC8SkuFBxzUSmssFY0A43n7F6EFxxf5iBVZUsQBDy9bCyuKkhHj0nEA+8d9K7GyENFde3INpcDAJLzC7y/wYmWmsFz3wAdll1/fto6bosHORcaO6WdbHYLj4FBM2meghwydEQmAiN5X4qPXLuO0WCtbxl+KRQKAcMsBZ6eFB/bFh33GufAU1ZnNnq3slG0mU03ThjOal+ClUoDXPcGoFADZzYM3PVVZoIgSGMePj8SBCmrb54B9DqWUr3q/9h5ZzYCZserhTxVNdvB1nF7rp+aBbVSwFF3C5B58D1yCRCT4fr1BMHa+dhO/QZLVbEgh+96G4hCIeCVmyZhzvBEdBhMuOudfbjgZUsHd+0pbsQIgaXLVKljvb/BlDFA+iSWZj5hSeH6YV5VXzzIKapjr1FRWpXjwFMqKqeVHEKCx2RLAfKxf7tW/1Kxj6UOIpKANPaJLTeRBznu1+XwouOYMJUULAEAUscDmdPZi9zeN9inI0++jlm6J4+7xnkPk2CQNgG42NIzZ9OvAF21X++ep6y2nqmHrtuyo6l8P/DRbV53x3bLhd2WoFsAlr4C5C8ENNFAey1Qdcjh1az9cQZOVXGJUVossXR9dnk1x6gHjlhSvM46HDuS5PgT/+HyFlS1diNSo8TFo5NdvkmtSol//GQaxqXHoKHdgDvf3icVyvrDiaLzSBTaIEIAEkfKc6O8APnYv9lIloazbOXMWcNFmfHCY2sjQDupKm6QrOT4fk8aIcFk5GLWabStmjXZGmiauE2qitch5CSy1vkXPNhhddymHqdfd9ppd7IeJD+8wr68EcypKltzH2VpmarDwBe/BH78b78FZ6NTozEyJQpFde3YfKIWN+QbgQ9uYim08n3AAzuAaDfnEbnLZAQ2WuqTpt4BZE1jp0dexjoLn95gd05aQ7sep2vYp+1Z+Qlu3eWPLxqGL49V479HqvDU0rGI1A7wNnD6S/aYRGcAIy5z674AOB3vsPEYbwA4cKqqr+gwNdbcMwPXv74LpY2duGfNfnx4/6yBfx8viaKIZkvRsSEqC1qN4637bplwPbD5afbB6sDb7LzM6aypop/0HeGQaG9nFTdIdlgFzUrOypUrIQgCHnnkEYeX2bp1KwRB6Pd1+rTzRlOESFRaYMIN7LQrYx4s/XFsgyFvVnKkepwsOzNoJlzPdlqEx3v3NfZqvzQOk4VSxdJWSg1LtR2RaRu9CwRBkHrmfH34PFvB6bLsFOmoA9bfL89uN2f2/xOoPc7+3xY9az1/tGWg6pmNdq+2x5KqGpMW7fyNyI7ZwxORmxiBdr3RtQJk3uF4yu2e9WqR0lW90xpmszVVtazAtVRVXynRYXj37ouQEKnBsYpW/L9/HUKPj8d1nKtrR6q+FACgTpMhVcVFpwH5ll1Uu15l3/2YqgKsKzmc05Ucqai8HDB41xzVl4JiJWf//v1YvXo1CgpcK+A6c+YMYmKsw9CSk11f5iQEk25lO1lOfclm6oQ5GKzXXs+KlAHriw9sVnI8qAM43rfo2JYmErhX5vEOg0HKGOCS3wLfPgt8tQLIv9hvXZqvnpSBP39zBj8q+x9AcRyITAGW/4MFPCXb2Irawl/75s7b64At/8NOL3qm9zTvkZezIvn606yFQZ9WALbzqtzFOyCv3HQaH+4rwy2Wbsh2NRVbdnkJwNSfuH1fAHqPADAZpUDJNlW1cJTnr+H5yVF4+64ZuHX1Hmw/W48nPzmGV26aZHeOlxxs63EUKWPkvfGCm9kHK7Nlx5+fto5z8RHqXv+22yOHi0xiwXlXMysqT5ehANsHAr6S097ejttuuw1vvvkm4uPjXbpOSkoK0tLSpC+l0ruJ0GSIyZzK8ujGLuDU544vx2fGpE7slbbI5UFOU6dbLeZ7TGacqrFTdEyAOb8Asmaw4tv//txvu63ykiLxfPxXWKrYC5OgAm5+j316XvZndoGtK+2323egx2TGwQtNrnVS/uZZ9vumTwam9mmuFx4H5M5jp+3ssvKkHsfW9dNcLEA+tJZ9H7EIiHMSDDkTmw2oI9h26OZS6Wy+inPZOPdTVX1Nzo7D32+fCqVCwPrDlXjpK9+lUPaUNElBjrRKJZcxy9hjBQDaGDaWxY9USgViw62BjtMgRxAGRfFxwIOchx56CMuWLcNll7me650yZQrS09OxaNEibNnivK+IXq+HTqfr9UWGOEGwFiA7G/Mgpap6LxlnxIVBpRBgMJpRo3N9G/nZ2jYYjHaKjgkbvXHd64AqjG2d3fSk051Fsjm9ET/peg8A8M+Yh9jEdIA9PybfxrY+f3LvgF2ye0xmfLSvDJe8vBXXv74bT60/7vx+y/ZY+zUte4X9/n05SFlVt3ahuKEDCgG4KM+9ehwuKUqLxZYC5I/2OyhANvVYu3D3DcLcoVAAiSPYaUv9Rq9UlYu7qgZyyegUvHQ9W014Y9t5bDkjf2sAURSxt7gRIxU+CnK0UcCYq9jp3Pls4ref2fbFcZquAgZF8XFAg5yPPvoIhw4dwsqVK126fHp6OlavXo1169Zh/fr1GD16NBYtWoTt2x03zVq5ciViY2Olr+zsbLkOnwxmE28CIAAXdgDNF/r/XBRttnD2Lk5WKRXSnCB3xjsUVjgpOiZA0kjrCsq+fwBf/MK3NTH1Z4D1PwUAvGu8HC/WzUSNbe+jpf/L3sTaa4BPf2o36OoxmfHxfhbc/GZ9ISqaWVv+z45UorKlq9/lAbCUzQbbYuP+hcUAgNFXsu9le3oFWXwVZ0JmbK9P3e66zZKm+uxwlf0OyGc2sdqkyBTrsXiKBwOWN8PD5S2obu1GlFaFBV6kqvq6YVoW7p6bCwB4ZfMZiDKvCJ6v74ChvQmpQgs7g7/Jy+mSp4Bx1wGXrJD/tl0Q3yvIGaDey8XZZIEUsCCnvLwcDz/8MN5//32EhQ3cyAoARo8ejfvvvx9Tp07F7Nmz8fe//x3Lli3Dyy+/7PA6K1asQGtrq/RVXl4u169ABrO4bDb0DgCOfdz/57XH2RZedYT1070NT4qP+zUBJP1NuY0VIgsK4PD7rPjXjYGVLutqBj68lbXSz5mHjRm/hCgCXx6zKcTVRAI3rgFU4Szg3fl/0o9sg5sn17HgJilKi6eXjcXMvASYzCLe2VFi/74PvAXUFrIu14uec3yMcdmsdwpENqrDwpt6HFuz8q0FyL1+b473xplym/crCn0+8VsbAKZ4narq6+eXjECkRonjlTp8faJW1ttm9TiWxyomE9BGy3r7AICEPOCmd1mzzABIsA1yHDUC5AbBoM6ABTkHDx5EXV0dpk2bBpVKBZVKhW3btuHVV1+FSqWCyeTaJ7hZs2ahqKjI4c+1Wi1iYmJ6fRECwGbMw4f9a0D41vHc+WxHVh+eFB8f7zvOgdg3+VbghndYo8Dj64B/3+FwJIBHzCaWgmo6z2YG3fQulk1hqxpfHOvTqydlLLDM8iHq+/+BsWQn/r2/HJe+0j+4+eHXl+C++fl4YCErEv5of7m1/w7XXgd8z4uNf9e72NiePikrURSxy8t6HE6hEKSi4w/29fnw11Jm/RvgjSq9YfOJ32wWsem4ew0A3ZEYpcXdltEP//fNWbfq5gayt6TJJlU1WrbbDSYJNjus7I50sMWLyhvPsRXKIBSwIGfRokUoLCzEkSNHpK/p06fjtttuw5EjR1wuJj58+DDS0+X/QyFDwNirAXUk20FSsb/3zwboNmpdyXEtyKGiYzeNvw645QNAqWVv8B/eAhhk6mr77XOs3koVDtzyLyAyCVdOSIdCAI6Wt6Cs7+rc5NtgnngzIJrQtPYnWLluB8qbupAUpekV3IRr2GvWwlHJGJEShXa9ER/3DR6+fQ7Qt7IVmml3D3ysY5ay7+e3AIZOlDd1obKlCyqFgBm5rm3UcOYGXoBc3oITVTYFyIfeAyACeQvkmZskpavO4nBZk09SVbbun5+P6DAVztS24ctCeZpMSvU4QgU7Q+56nCAR785KjoOi8mASsCAnOjoaEyZM6PUVGRmJxMRETJgwAQBLNd1xh/VTxKpVq/DZZ5+hqKgIJ06cwIoVK7Bu3Tr8/Oc/D9SvQQYzbZR1iCXv6AqwN9Oy3ey0g2aBOUl8Jce1dBUvOo4OUyEnkYqOXTJqMXD7JywQLd4CvH892/LvjWP/tvYgue7v0rbX5GittDLyhU3qpsdkxr8PVuDKc9fhnDkDKWIj/hq+Gk8vHY0ffn1pr+CGUygE3DePrSS8s7PE2relbK+1D9BSB8XGfaVOYKtNxi7g/PfYXdwAgO0mitB43wEkKUqLxeP6dEA2GVmqEPCu4NhWQh7bEt/Tge0HjwAALpdhV5UjsRFq3D+fBWervj3r8TBdWyUNHahr02OUwvL8CNGVHL56o1EpED1QY0U7ReXBJuC7q5yprq5GWZm18t9gMOCJJ55AQUEB5s+fjx07dmDDhg1Yvnx5AI+SDGp8MOaJ9daUSOlOwGRgby78D7gPvo28tLHDpeJG2/44VHTshrwFwB2fAdpYFniuvQbobPLstqoOA5//gp2e/zgwoffrxjWWxoBfHK1iwc2Bcix6ZRt+/ckxnGkW8Vv14zAqtJgnHsJ9yg39ghtb103JRFKUBlWt3WwXkckIbHyc/XDKT4DsGa4dsyCwbcUAcGajTarKu3ocWz+eaS1A7jQYgXPfAm1VQHiCfJ2zlWo2Tw1A6anDAHyTqrJ199xcxEeoUVzfgc9kmE+2t4Q978aqLStDIb6Skxylde21ysnYjmAQVEHO1q1bsWrVKunfa9aswdatW6V///rXv8a5c+fQ1dWFpqYm/PDDD1i6dKn/D5SEjtz5rICwu9Va3Gm7ddzBH3lmXDiUCgHdPWbUtQ08M4eKjr2QfRFw1xdARCILVNYsY1O73dFexxr8GbuBUVcAlzzd7yJLxqdBrRRwuqYNF//vVvz6k2Moa+pEUpQGv106Fu88eRdUy/7ELvzt79noBwfC1ErcMTsXAPDmD8UQD7wF1BSyidyXPefesVtSVuLZr7DnHNsWPdvLehxbs/MTkcMLkI9WWzscT/6x3Xo0j1mKjxO7ShGtVWH+SPl+B3uiw9T4maU+6i/fnfW6E/Ke4kZEogvJJsvW9CQf7KwKAtnx4QCALMv3AfEVrQbHtbGBFFRBDiF+p1BaB+PxMQ/SvCrHc600KgUy49iLgCvbyAsrWZqFio49lD4JuGsjEJUG1J0E3rkSaK1w7bpGA/DxTwBdJWsCuXy1NIfMVmyEWuq8W9nShcRIFtxs//UluH9BPksPTb2TjQURTcAn9zhdVbp9Vg7C1ApUV5bD9O3z7MxLf8c6xbpj2BwgLA5CZyOGdRyHVqXAlGFx7t2GEwqFgFtmsNWczbsPAEWWrttyFBzbsqx8jBAqZWkA6Io7ZucgKUqL8qYu/OeAi88XO1g9ThOG851VkSlAhGc9ioLdRXkJ+L+bJ2Hlchd3dwX5DCsKcgiZZGkMWPQNUHkQaCwCBCVLlTjBa2sGqsvpMZlxqpoFObSS44WUMcA9m1gasek88PaVrGjcGVFkAzDL97CU160fstUUBx67fDTmjUjCU0vH4IcnbYIbThCAq1ex1EtrOfDfhxx2Z06I1OD6qVl4UvURVD1tbIr99Hvc/72VKrb6BGCx8gCm5cTLHiDcMC0LKoWAuXUfsgaIeQtkrzkxJ7I3wxGKStkaAA4kQqPCgxez1Zy/fl+E7h7P+i5daOxEja4bY5WhXY8DsLEfP5qShfzkKNeuYLuN3E+dyt1BQQ4hyaNY+3TRBHz+MDsva+Dpv7Z1Oc5Q0bGMEvJZoJMwHGgtY4FOnZMBvQfesvR7EYAb3mINB50YlxGD9++biZ8uGO64sFcbzfrn8J1fe/7u8PYeHNGIm1TbAAAVc553rdjYHkvK6nLFQcxxc+q4K5Kjtbh+dBhuUVo6yM97TPb7OGVkgc1IoQrzR/k2VWXrxzOHIS0mDNWt3fhoX9nAV7Bjb4mlFiqWFX6Haj2ORxKGsw+FhjagTZ6dbHKiIIcQwLqaU1vIvjtJVXGuruRI/XEyqOhYFrFZwN2bgJRxrBvxmqXWQaq2Sney8RAAq4MZebl8x5BeAFzxAjv9zTNAxYH+lzGbkLmT1f7827gQfzvnebGwOf9S6KFGrqIWlyQ2e3w7zjwU+S3CBQOOi8PRmTVP9tv/rDwCZlFAvNAGrd43v4M9YWolfn4p20Dwt63n0WVwfzVnTzFLS07Q1LAzQnglx20qDds9BwRl8TEFOYQAwITrWfM5zsHWcVuuruRIRcdZlKqSTXQqcNcGIGMK0NkIrLm6dyFwSxlrImg2shqauQ/LfwzT72Xt981G4D93sy7Ktg68DdQUwqiJwUvGW7D+UAUa2wcuUrfnVJMZO03jAQBjWh2PsfFYtw7Z59i28dd6rsaXx2pkvXmzWcTnJ5tRIVpWcOqdrL75wE3Ts5EVH476Nj3e21Pq1nV5fxwAyOixjIChlZzegnhQJwU5hACsiHDUEnY6LI69eQ4gN8k6v8rZNnJedEz1ODKLSADu+C8wbDZrsLf2OqBkO2DoBD76MdDZwOpgrvmrw11yXhEE4JpXgfhcljqznZ7e0QB8z4qNlYt+h8ysYdAbzXhvj505aS7Yfb4Rm81sxpWyz8BOWRxcA6G7Fc3hOfjaPB1rdpW6Nknd1Zsva0atTo9SIYud4ctP/Hv/Aay9FuholM7SqBT45SKWqnxjWzHa7c3qcqC8qQtVrd2IUvYgrN3S3JFWcnoL4kGdFOQQwl30UzYzaeKNLtVOZCdEQBCADoMJDe0Gu5ehomMfC4sFbl8H5F8C9HQA/7qRNQ2sKQQikljXZI0P66DCYi31ORrg9JfsDRYAvn2WtSVIK4Aw417cZ2lM997uCx4Vv+4+34jvTFMhQgCqDgE6GWsfjHpg998AAMoFjyFco8bJah0e/vCILE30AOusKlMi34njo0/8RgPw/R+B4q3A/n/2+tHyKZnIS4pEU4cBa3Y6mCtmxx5LPc4VqW0QIALh8UCkbzo1D1q0kkPIIJC/EHj8DHDFSpcurlUpkRHLtpE7mmFVVNtORce+pokEbv0IGL2U9cEp28W66978Hhty6WsZU4DFf2SnNz8N7F1t7Ri8jHU2XjohDZlx4WjsMODTw5Vu3bzRZMbekibUIw5dKZYVRjlXc458wGqbYjIRM+PH+MdPpkGjVOCrEzV4/D9HYfJy9pPZLLKGiADSRkxiZ/rqE3/ZbkBv6Yp9aG2vKfYqpQKPXMZWc1ZvL0Zrl2uDX/da6nEuTrC0C0ge45uVwcEsiBsCUpBDiK2oFLcmLkspKwfFx1R07CfqMOCmtUDBLay2atmfgZw5/rv/i37KugObe4BNv2LnTb6NNTIEe4O9e24uAOCfPxS7NTTyeJUO7XojYsPVCJtg6UAsV5BjNgE7/8JOz/45oNJg/shk/P22qVApBPz3SBV++2mhS129HTlwoRl1bXpEh6kwYtw0dqav3gxtprVDV2HteWVxVUEGRqZEQddtxFs/DNB+wGKPpR6nQEtFxw7xXYsddf1r0wKMghxCvDDQNPJjlS0AqOjYL5RqYPk/gBXlwDSZZi65ShCAa14D4lhTPWhjgct+3+siN8/IRrRWhfP1Hdh6ts7lm951nm1bnpmXAMXYq9iZxdu8n+MFACc/A5pLWArGpvnfZeNSseqWyVAIbJr677846XGgs8EyC2zxuDSoUy0Fu21V8hy/LVEEzmxipxMtb7q8e7OFUiHgscvZqsPbO0vR3GE/zcyVN3VKA1EzjJbt51R03F9YDBDNxqIEW+djCnII8YJ1Grn9lRzqdBwAahfb0cstPA646T0gczpwzV+AqN51G9FhatxyEUufvbnd9ZqQ3bbzqpJHsXlq5h42Y8obogj88H/s9MwH2MBaG1cVZOBPN7D00ppdpfjT12fcDnRMZhGbjrMVkGUFaewximIDQWV/M2woYgGbUgP8yFIbdfYrQNd7btWS8WkYlx6Ddr0R/9jufDWHz6uamBULVaOl3oRWcuwL0uJjCnII8YKzlRwqOh6CMiYD938HjP+R3R/fNTcPSoWA3cWNUirTGb3RhP2l7I12zgjL9uvRlnl93qaszn3H+kKpI1m6zY4bpmXh+esmAABe33oer31/zq27OFDaJKWq5o2wBH3Sm6HM28jPWlZxcucBWdPYOAzRZK2PslAoBDy+mB3Du7tKUe9k9hzfOj4nNwZoPM/OTKIgxy6p+JiCHEJCBu+VU2JnG7lUdKxVISeBio4JG+zKRxr804WakKPlrejuMSMpSoORKZaVFj6V/OxmwORa8axdO/7Mvk+7y+kcpp/MysFvl44FALzyzVmXjpvbYCk4XjI+DRqV5e3GV2+GZyz1OKOuZN+n3cW+9ylABoBLx6RgcnYcunpMeH3reYc3yXdWLUzSsYBJEw3EZMh73KFCCl6Da4cVBTmEeGGYJXhp6zaipbP3G45UdJwZC4WCio4Jc79lO/mXx6pR3drl9LK8HmdWfqK1cD1rBtvCrG8FSnd4dhBle4ELO1mR9uyHBj7mBflSLcsfN5zC+y70++mVqrKdVWU760gunU1sPhlg7Xc17hrW86q1HDj/fa+LC4J1Nef9vRfs/j9UtnShvKkLSoWAibadjmkDgX20kkNI6AnXKJEWEwagf+dj6nRM7JmYFYuZeQkwmkWs2Vnq9LK7pHocm1lPCqU0sNPjlNUOSy3OpFuA2EyXrvKLS0fggYVs2OXTnx3HuoPOp3rvL21CfZseMWEqzB1hc/zJPngzPPctGyyaMg6Iz2HnqcOByT9mp/sUIAPAvBFJuCg3AQajGX/b0j8Nx1NVEzJjEd5iqR+iomPH+P9r8wWgx3nw7k8U5BDiJUczrAptVnIIscVXcz7YV+aw+26XwYTDZWw77pzhfeZe8ZTV6Y3uT36uPWmpXxHcGnchCAKevGI07pqTCwD41SdHpSZ/9vDeOIttU1WA9RN/cynQ0+3esTvCd1Xx4I+beqf1530aKAqCgMcsqzkf7y9HeVPvv1/eH2dWfoK1mJaKjh2LTGYrZxCBRvdqt3yJghxCvGRbl8NR0TFx5tIxKchPjkRbtxEf7y+3e5mDF5rRYxKRHhvWv5Fk/sWAOoL1gqk55t6d71zFvo+7ZsCp7H0JgoBnrhqHm6dnwywCD390GN+erO13OZNZxMZCvqsqvfcPo1JYp2jRLM+boanH2g+nb5CTMoaN/bBTgAywNOC8EUnoMYn46/e9d3vxepxZeYk2QQ6t5DgkCDapyOBJWVGQQ4iXcpL4So41yCmqbYeeio6JAwqFgHvnscnNb+8osTs+gdfjzB6e2L+RpDocGH4pO316g+t33FwKFH7CTs971N3DBsCO/YXlE3Ht5AwYzSIe/Nch7Chq6HWZ/aVNaGi3pKpsU22A5c3QEizIkbIq283qkyISgazp/X/upAAZgLSas+5QpfRBpaa1GxcaO6EQgOnDooFGnq4a5f3xhjIeNAfReAcKcgjxUp40jdy63M2LjsdnxlDRMbHr+qlZSIjUoLKlC1+d6D/12249ji3blJWrdv2VrWrkX+LSEFpHlAoBL984CYvHpcJgMuP+tQewz9JTBrDOqlrSN1XFyTkGgO+qGrnE/sy5cddaCpDLgPNb+v146rB4XDomBSaziL98y96c95ZY63GiOysBkwFQhQOxw7w/3lCWRCs5hIQce71ypKJjSlURB8LUStw+ixXJvvlDSa8WBG3dPdJzaHbfehxu5BI2ULa2kBV7DqS9zpqymf+YV8cOAGqlAn/98RQsHJWMrh4T7lmzH0fKW/o0AEy3f2W50hqiaO2Pw3dV9TvQcGDSrez0wXfsXoTvHPvv0SqcrW2TRjnMyk+09vNJHgUo6C3TKamonFZyCAkZvF6iubMHrZZt5FR0TFxxx+wcaFQKHC1vwYEL1pk/+0ubYDKLyEmMQGacgw7OkYms3gSwFt46s+d1NsA0cxqQO1+Go2dDav/xk2mYlZ+Adr0Rd769D+/uKkVDux6x4ereu6psyTW1uvEc0FTMtsLz9J090xwXIAPs7/SK8WkQRWDVt2elouOZeQk2QQ7V4wyIr9A1nrObGgwECnII8VKkVoXkaC0A4EJTB4w2RccFWXEBPDIS7JKitLh+KtvC/abNiIFd52xGOTgjpay+dH657lZg/z/Z6XmPydrrJUytxD/vnIEpw+LQ2tWDP3x5EgCwZHwq1EoHbzH8E3/jOcBkf3eZS3hwlzuPzU9yJGUskD2LpeqO9C9ABoBHLx8FQQA2FtaguKGD1ePkJlgDMdpZNbC4YYAqjKX3mksDfTQAKMghRBa2M6yK6qjomLju3nlsO/k3p2qlwldejzPbUT0Ox0c8XNjFGuI5cuBtQK9jKyj8OjKK0qqw5u6LMD7DGmgsneggVQUAsdlsd5i3b4Z86vjoKwe+7PS72feDawFz/0Lv0WnRuLrA2s14XEYMYsPV1pUcGucwMIXSOhw1SFJWFOQQIgOpLqehQ0pVUdExccWIlChcOiYFosh2WjV3GHCqhq0Ezs4fYCUnIY81wBNNQNE39i/T0wXs/js7Pe8Rn9WVxIar8d69MzF1WBymDotznKoC2DEkjmCnPd1h1dkElPXpcuzMuGvZ1vXWsn4dkLmHLxsJ/ic7My+RBUO8MzOlq1wTZIM6KcghRAa2KzmFFVR0TNxz33y2nfw/B8vx1YkaiCIwMiVKSoM6xVNWZxxsJT/yAdBRB8RkARNvlOmI7UuI1GD9g3Ox/sG5jlNVHA8aPH0zPPcdC+6SxwLxuQNf3oUC5OHJUbjbMkT16kkZLCAydrHJ5q7cB5Gv3komFOQQIgPbHVZUdEzcNTs/EeMzYtDdY8YLG08BcKEeh+Ppp6Jv+3cQNhmBnX9hp+f8AlCqZTpiGXj7iZ/vqhp9hfPL2eI9c85sAtr6b9sHgKeXjcWJ3y/B5Ow467EljgSUKs+Oc6ihlRxCQg/veny+vp06HRO3CYIgjXpo62aFuAPW43AZU4DoDKCnAyjZ3vtnJz4FWi6wRnlT75DzkL3nzUBHUw+bVwX073LsjG0Bsp0OyAD7vwhTW/rtSDurqB7HZbYrOe6OHPEBCnIIkcEwm23keqMZUVqVFPgQ4oplBelIj2XDXgXBMjPJFYJgLby1TVmJonUQ58z/B2iCrAhe6npc5P6bYdketmMsIpFNZXeH1AH5XbsFyL1QPY77EocDV7wI3LiGghxCQkVsuBoJkRrp3+MzqOiYuEetVEjDLydkxCIuQuP8CrakupxN1jfuos1A3QlAEwVcdJ+8ByuHhDxAoQIM7YCu0r3r8l1VIxfb73LszPjrWAFySxlQbL8AWWLbCJC4RqUFZv0/YMSioGieGPgjICRE2A5RLMiiVBVx391z8/DrK0Zj5fKJ7l0xdz6gjQHaa4HKg+w8vooz/W4gPF7eA5WDUg0kDGeneTDhKh7kuLKrqi91OFBwCzt9cI3jy4kiDeYMARTkECIT2/QUFR0TT2hUCjx48Qj3nz8qDTDiMnb6zAbgwm42uFKpAWY9JP+BykUqUnVjJ07DOdZEUKEGhi/y7H5dKECGrgowtAGC0hqMkUGHghxCZGIb5FDRMfE724GdO/7MTk+6FYhx0pQv0DyZRs53VeXOdd7l2JnUcUD2TMBsBI78y/5l+OpS4nAWRJJBiYIcQmSSm8TSVVR0TAJi5OVsdaPhDKvHERTA3IcDfVTOeTK1+uzX7Ls7u6rs4as5Bx0UIEupKtpZNZhRkEOITGbkJiA6TIVlE9Op6Jj4X1gsm+HEjbuWrUIEM3d7qnQ1sxEWgPdBzrjrAG0s22JfvKX/zxuoHicUUJBDiEwy4sJx6HeX46UbCgJ9KGSo4ikrAJj3aOCOw1WJIwEIQFcT0NEw8OWlLsdj2O4sb2gigElOCpB54EUzqwY1CnIIkdGArewJ8aXxy9mb8rS7gPRJgT6agWki2ORqwLXVHD513JNdVfZMu9NyuxuBtlrr+aII1LHO05SuGtzoFZkQQkJFZCLw833A1X8J9JG4jgcRA20jNxmBc5YhpKNcmDruitTxQNZFlgJkmw7IHfVAdwsAAUgaKc99kYCgIIcQQkjgJFnqcgYa6Fhu6XIcHg9kXyTf/U+/m323LUDmAVd8LuurQwYtCnIIIYQEjqvTyHmqypMux87YFiCXbO19LFR0POhRkEMIISRwkl3cRi7X1vG+NBHApJvZaV6ALAU5NM5hsKMghxBCSODwdFVbFdCts3+ZxvNAYxGbdTXCwy7HzvCeOac3sAJkaWYVreQMdhTkEEIICZzwOCAqjZ1uKLJ/GZ6qypnD+gHJrVcB8r+oEWAIoSCHEEJIYPG0kKPxDtJATpl2VdnDV3P2/gPoqGOnkyhdNdhRkEMIISSwkpxsI+9qYcNGAWC0zPU4tsb/iBUgt1sGdsZmA9po390f8QsKcgghhASWVHxsZxv5uW9ZGilpNJCQ77tj0EQABTf1PyYyqFGQQwghJLB4QGEvXSXtqpKpy7EzvGcOQOMcQgQFOYQQQgKLBxTNpUBPt/V8k5FNVAeA0T6sx+F4ATIApNMMulBAQQ4hhJDAikphu6ZEM9B4znp++V42XiE83hp8+NoNbwPL/gxMuME/90d8ioIcQgghgSUI1p40tikrvqtqxOWAUuWfY4nLBmbc67/7Iz5FQQ4hhJDA49u1bYuPeZDjy11VJKRRkEMIISTw+k4jbzzPhnYqVMBwH3Q5JkMCBTmEEEICjxcf82nkfFfVsNmsKzIhHqAghxBCSODxlZzGc2xX1VnLKAd/7KoiIYuCHEIIIYEXmw2oIwCTAag5ClzYxc6Xe+o4GVIoyCGEEBJ4CgWQOIKd3vM663KcOBJIHB7Y4yKDGgU5hBBCggPfRn58PftOu6qIl4ImyFm5ciUEQcAjjzzi9HLbtm3DtGnTEBYWhvz8fLzxxhv+OUBCCCG+xaeRiyb23ZdTx8mQEBRBzv79+7F69WoUFDhvo11SUoKlS5di/vz5OHz4MJ566in88pe/xLp16/x0pIQQQnzGdl5UWByQPTNgh0JCQ8CDnPb2dtx222148803ER8f7/Syb7zxBoYNG4ZVq1Zh7NixuO+++3DPPffg5Zdf9tPREkII8RmergKAkX7sckxCVsCDnIceegjLli3DZZddNuBld+/ejcWLF/c6b8mSJThw4AB6enrsXkev10On0/X6IoQQEoQS8ljzP4B2VRFZBDTI+eijj3Do0CGsXLnSpcvX1NQgNTW113mpqakwGo1oaGiwe52VK1ciNjZW+srOzvb6uAkhhPiAUg3MuB/ImUtBDpFFwNYCy8vL8fDDD2Pz5s0ICwtz+XqCIPT6tyiKds/nVqxYgccee0z6t06no0CHEEKC1ZUvBvoISAgJWJBz8OBB1NXVYdq0adJ5JpMJ27dvx2uvvQa9Xg+lUtnrOmlpaaipqel1Xl1dHVQqFRITE+3ej1arhVarlf8XIIQQQkhQC1iQs2jRIhQWFvY67+6778aYMWPw5JNP9gtwAGD27Nn44osvep23efNmTJ8+HWq12qfHSwghhJDBJWBBTnR0NCZMmNDrvMjISCQmJkrnr1ixApWVlVi7di0A4IEHHsBrr72Gxx57DPfffz92796Nt956Cx9++KHfj58QQgghwS3gu6ucqa6uRllZmfTvvLw8bNy4EVu3bsXkyZPx/PPP49VXX8X1118fwKMkhBBCSDASRF65O0TodDrExsaitbUVMTExgT4cQgghhLjAk/fvoF7JIYQQQgjxFAU5hBBCCAlJFOQQQgghJCRRkEMIIYSQkERBDiGEEEJCEgU5hBBCCAlJFOQQQgghJCRRkEMIIYSQkERBDiGEEEJCUsBmVwUKb/Cs0+kCfCSEEEIIcRV/33ZnUMOQC3La2toAANnZ2QE+EkIIIYS4q62tDbGxsS5ddsjNrjKbzaiqqkJ0dDQEQej1M51Oh+zsbJSXl9NcKw/Q4+c9egy9Q4+f9+gx9A49ft5z9BiKooi2tjZkZGRAoXCt2mbIreQoFApkZWU5vUxMTAw9Ob1Aj5/36DH0Dj1+3qPH0Dv0+HnP3mPo6goOR4XHhBBCCAlJFOQQQgghJCRRkGNDq9Xi2WefhVarDfShDEr0+HmPHkPv0OPnPXoMvUOPn/fkfAyHXOExIYQQQoYGWskhhBBCSEiiIIcQQgghIYmCHEIIIYSEJApyCCGEEBKShlyQs3LlSsyYMQPR0dFISUnBddddhzNnzvS6zF133QVBEHp9zZo1K0BHHHxef/11FBQUSI2aZs+ejU2bNkk/F0URzz33HDIyMhAeHo6LL74YJ06cCOARB5eBHj96/rln5cqVEAQBjzzyiHQePQfdY+8xpOehc88991y/xyctLU36OT0HnRvo8ZPr+Tfkgpxt27bhoYcewp49e/DNN9/AaDRi8eLF6Ojo6HW5K664AtXV1dLXxo0bA3TEwScrKwsvvvgiDhw4gAMHDuDSSy/FtddeK/0B/+lPf8Kf//xnvPbaa9i/fz/S0tJw+eWXS3PDhrqBHj+Ann+u2r9/P1avXo2CgoJe59Nz0HWOHkOAnocDGT9+fK/Hp7CwUPoZPQcH5uzxA2R6/olDXF1dnQhA3LZtm3TenXfeKV577bWBO6hBKD4+XvznP/8pms1mMS0tTXzxxReln3V3d4uxsbHiG2+8EcAjDG788RNFev65qq2tTRw5cqT4zTffiAsXLhQffvhhURRFeg66wdFjKIr0PBzIs88+K06aNMnuz+g5ODBnj58oyvf8G3IrOX21trYCABISEnqdv3XrVqSkpGDUqFG4//77UVdXF4jDC3omkwkfffQROjo6MHv2bJSUlKCmpgaLFy+WLqPVarFw4ULs2rUrgEcanPo+fhw9/wb20EMPYdmyZbjssst6nU/PQdc5egw5eh46V1RUhIyMDOTl5eGWW25BcXExAHoOusrR48fJ8fwbcgM6bYmiiMceewz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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting the consumption and speed, does not seem to show differences...\n", + "sns.lineplot(data=test_E10, x= 'speed', y = 'consume')\n", + "sns.lineplot(data=test_SP98, x= 'speed', y = 'consume' )" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "id": "83e719cc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((28, 2), (29, 2))" + ] + }, + "execution_count": 154, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_E10.shape, test_SP98.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "id": "0feb7aac", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.23047979797979806" + ] + }, + "execution_count": 155, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "difference_consumption = test_E10.consume - test_SP98.consume\n", + "difference_consumption.mean()\n", + "# on this speed range the difference of consumption is of 0.26 l/100km for the range 30-54 km/h" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4e015689", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ae9780f7", + "metadata": {}, + "outputs": [], + "source": [ + "Part III: Machine Learning, can I predict the consumption" + ] + }, + { + "cell_type": "code", + "execution_count": 213, + "id": "b2fc174c", + "metadata": {}, + "outputs": [], + "source": [ + "# Let's see if we can infer the consumption\n", + "# Need to create a train and a test groups\n", + "# First we need to one hot encode gas_type (0 for SP98, 1 for E10)\n", + "df_enc= df.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 214, + "id": "75557faa", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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388 rows × 9 columns

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" + ], + "text/plain": [ + " distance consume speed temp_inside temp_outside gas_type AC rain \\\n", + "0 28.0 5.0 26 21.5 12 1 0 0 \n", + "1 12.0 4.2 30 21.5 13 1 0 0 \n", + "2 11.2 5.5 38 21.5 15 1 0 0 \n", + "3 12.9 3.9 36 21.5 14 1 0 0 \n", + "4 18.5 4.5 46 21.5 15 1 0 0 \n", + ".. ... ... ... ... ... ... .. ... \n", + "383 16.0 3.7 39 24.5 18 0 0 0 \n", + "384 16.1 4.3 38 25.0 31 0 1 0 \n", + "385 16.0 3.8 45 25.0 19 0 0 0 \n", + "386 15.4 4.6 42 25.0 31 0 1 0 \n", + "387 14.7 5.0 25 25.0 30 0 1 0 \n", + "\n", + " sun \n", + "0 0 \n", + "1 0 \n", + "2 0 \n", + "3 0 \n", + "4 0 \n", + ".. ... \n", + "383 0 \n", + "384 0 \n", + "385 0 \n", + "386 0 \n", + "387 0 \n", + "\n", + "[388 rows x 9 columns]" + ] + }, + "execution_count": 214, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_enc.gas_type=df_enc.gas_type.apply(lambda x: 0 if x == 'SP98' else 1)\n", + "df_enc" + ] + }, + { + "cell_type": "code", + "execution_count": 215, + "id": "c28f162b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "gas_type\n", + "0 228\n", + "1 160\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 215, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_enc.gas_type.value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 216, + "id": "139c3fc3", + "metadata": {}, + "outputs": [], + "source": [ + "# Perform train_test split to predict consume and normalizing.\n", + "features = df_enc.drop(columns=[\"consume\"])\n", + "target = df_enc[\"consume\"]\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.20, random_state=0)\n", + "\n", + "#I normalize the data...\n", + "normalizer = MinMaxScaler()\n", + "normalizer.fit(X_train) # VERY IMPORTANT we apply the fit of the normalizer to the X_train, not all the X data...\n", + "\n", + "X_train_norm = normalizer.transform(X_train) # and here we apply the normalizer to transform the data\n", + "X_test_norm = normalizer.transform(X_test) # (both X_train and X_test populations)\n", + "\n", + "#X_train_norm = pd.DataFrame(X_train_norm, columns = X_train.columns)\n", + "#X_test_norm = pd.DataFrame(X_test_norm, columns = X_test.columns)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 238, + "id": "6d86846d", + "metadata": {}, + "outputs": [], + "source": [ + "# Model selection we will be using KNN as our predictive model.\n", + "\n", + "knn = KNeighborsRegressor(n_neighbors=9)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 239, + "id": "f7301cd4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
KNeighborsRegressor(n_neighbors=9)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "KNeighborsRegressor(n_neighbors=9)" + ] + }, + "execution_count": 239, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "knn.fit(X_train_norm, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 240, + "id": "3cb65823", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.27267976660870374" + ] + }, + "execution_count": 240, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "knn.score(X_test_norm, y_test) # 0.08446696 for 100 neighbors, 0.1659455 for 50 neighbors, 0.1972028 for 25n, 0.272679 for 9n" + ] + }, + { + "cell_type": "code", + "execution_count": 241, + "id": "f0f44c78", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.2555096549540998\n", + "1.1204952721694545\n" + ] + } + ], + "source": [ + "#Evaluating the model\n", + "pred = knn.predict(X_test_norm)\n", + "\n", + "MSE = mean_squared_error(y_test, pred)\n", + "RMSE = mean_squared_error(y_test, pred, squared=False)\n", + "\n", + "print(MSE)\n", + "print(RMSE)" + ] + }, + { + "cell_type": "code", + "execution_count": 242, + "id": "51a8d1e9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "corr=np.abs(df_enc.corr())\n", + "\n", + "#Set up mask for triangle representation\n", + "mask = np.zeros_like(corr, dtype=bool)\n", + "mask[np.triu_indices_from(mask)] = True\n", + "\n", + "# Set up the matplotlib figure\n", + "f, ax = plt.subplots(figsize=(10, 10))\n", + "# Generate a custom diverging colormap\n", + "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n", + "# Draw the heatmap with the mask and correct aspect ratio\n", + "sns.heatmap(corr, mask=mask, vmax=1,square=True, linewidths=.5, cbar_kws={\"shrink\": .5},annot = corr)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "37e859e6", + "metadata": {}, + "source": [ + "Making a second model, without some features: temp_outside, temp_inside, rain, sun and AC\n", + "Keeping only speed and distance..." + ] + }, + { + "cell_type": "code", + "execution_count": 261, + "id": "c857b6aa", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.541967536079291" + ] + }, + "execution_count": 261, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Perform train_test split to predict consume and normalizing.\n", + "features = df_enc.drop(columns=[\"consume\", \"AC\",\"rain\", \"sun\", \"temp_outside\", \"temp_inside\",'speed'])\n", + "target = df_enc[\"consume\"]\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.20, random_state=0)\n", + "\n", + "#I normalize the data...\n", + "normalizer = MinMaxScaler()\n", + "normalizer.fit(X_train) # VERY IMPORTANT we apply the fit of the normalizer to the X_train, not all the X data...\n", + "\n", + "X_train_norm = normalizer.transform(X_train) # and here we apply the normalizer to transform the data\n", + "X_test_norm = normalizer.transform(X_test) # (both X_train and X_test populations)\n", + "\n", + "#X_train_norm = pd.DataFrame(X_train_norm, columns = X_train.columns)\n", + "#X_test_norm = pd.DataFrame(X_test_norm, columns = X_test.columns)\n", + "\n", + "# Model selection we will be using KNN as our predictive model.\n", + "knn = KNeighborsRegressor(n_neighbors=9)\n", + "\n", + "knn.fit(X_train_norm, y_train)\n", + "\n", + "knn.score(X_test_norm, y_test) \n", + "\n", + "# was 0.272679 for 9n Model 1 (all features), 0.484984424 for Model 2 (distance+speed), \n", + "# 0.523775 for Model (only distance+gas_type)" + ] + }, + { + "cell_type": "code", + "execution_count": 270, + "id": "21ab1aed", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "R-squared score: 0.1367616403767018\n" + ] + } + ], + "source": [ + "#Trying with only the linear regression:\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.metrics import r2_score\n", + "\n", + "# Perform train_test split to predict consume and normalizing.\n", + "features = df_enc.drop(columns=[\"consume\", \"sun\"])\n", + "target = df_enc[\"consume\"]\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.20, random_state=0)\n", + "\n", + "# Normalize the data\n", + "normalizer = MinMaxScaler()\n", + "normalizer.fit(X_train) \n", + "\n", + "X_train_norm = normalizer.transform(X_train) \n", + "X_test_norm = normalizer.transform(X_test) \n", + "\n", + "# Create and train a linear regression model\n", + "model = LinearRegression()\n", + "model.fit(X_train_norm, y_train)\n", + "\n", + "# Make predictions\n", + "y_pred = model.predict(X_test_norm)\n", + "\n", + "# Calculate R-squared score\n", + "r2 = r2_score(y_test, y_pred)\n", + "print(\"R-squared score:\", r2)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "78b526f4", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}