From db837c1a3734a2c045a275bfa43736be4ea84fdc Mon Sep 17 00:00:00 2001
From: Jesper Olsen <43079279+jesper-olsen@users.noreply.github.com>
Date: Mon, 23 Mar 2026 14:53:36 +0800
Subject: [PATCH 1/4] Fix demos.ipynb compatibility with Keras 3 / TensorFlow
 2.x

- Replace deprecated lr= with learning_rate= in Adam optimizer
- Replace lambda loss with named function decorated with
  @tf.keras.utils.register_keras_serializable() to fix model
  save/load serialization
- Switch model save format from .h5 to .keras
- Replace deprecated K.function() activation visualization with
  multi-output tf.keras.Model approach
- Fix activation reshape to use correct 12x12 spatial dims
  (Wraparound2D pads 10x10 input before conv)
---
 demos/demos.ipynb | 254 ++++++++++++++++++++++++++++++----------------
 1 file changed, 165 insertions(+), 89 deletions(-)

diff --git a/demos/demos.ipynb b/demos/demos.ipynb
index 7c1e0cb..77b3125 100644
--- a/demos/demos.ipynb
+++ b/demos/demos.ipynb
@@ -66,34 +66,101 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model: \"sequential_12\"\n",
-      "_________________________________________________________________\n",
-      "Layer (type)                 Output Shape              Param #   \n",
-      "=================================================================\n",
-      "wraparound2d_12 (Wraparound2 (None, 12, 12, 1)         0         \n",
-      "_________________________________________________________________\n",
-      "conv2d_4 (Conv2D)            (None, 10, 10, 10)        100       \n",
-      "_________________________________________________________________\n",
-      "reshape_12 (Reshape)         (None, None, 10)          0         \n",
-      "_________________________________________________________________\n",
-      "dense_42 (Dense)             (None, None, 10)          110       \n",
-      "_________________________________________________________________\n",
-      "dense_43 (Dense)             (None, None, 10)          110       \n",
-      "_________________________________________________________________\n",
-      "dense_44 (Dense)             (None, None, 2)           22        \n",
-      "=================================================================\n",
-      "Total params: 342\n",
-      "Trainable params: 342\n",
-      "Non-trainable params: 0\n",
-      "_________________________________________________________________\n"
-     ]
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ wraparound2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Wraparound2D</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ reshape (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Reshape</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)        │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)        │           <span style=\"color: #00af00; text-decoration-color: #00af00\">110</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)        │           <span style=\"color: #00af00; text-decoration-color: #00af00\">110</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>)         │            <span style=\"color: #00af00; text-decoration-color: #00af00\">22</span> │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ wraparound2d (\u001b[38;5;33mWraparound2D\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m1\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m10\u001b[0m)     │           \u001b[38;5;34m100\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ reshape (\u001b[38;5;33mReshape\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m10\u001b[0m)        │             \u001b[38;5;34m0\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m10\u001b[0m)        │           \u001b[38;5;34m110\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m10\u001b[0m)        │           \u001b[38;5;34m110\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m2\u001b[0m)         │            \u001b[38;5;34m22\u001b[0m │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">342</span> (1.34 KB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m342\u001b[0m (1.34 KB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">342</span> (1.34 KB)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m342\u001b[0m (1.34 KB)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -107,41 +174,44 @@
     "except:\n",
     "    pass\n",
     "\n",
-    "loss = lambda x, y : tf.keras.losses.categorical_crossentropy(tf.reshape(x, shape=(-1, num_classes)), \n",
-    "                                                              tf.reshape(y, shape=(-1, num_classes)), \n",
-    "                                                              from_logits=True)\n",
+    "@tf.keras.utils.register_keras_serializable()\n",
+    "def loss(x, y):\n",
+    "    return tf.keras.losses.categorical_crossentropy(\n",
+    "        tf.reshape(x, shape=(-1, num_classes)), \n",
+    "        tf.reshape(y, shape=(-1, num_classes)), \n",
+    "        from_logits=True\n",
+    "    )\n",
+    "    \n",
     "model = initialize_model((wspan, hspan), layer_dims, num_classes=num_classes)\n",
     "# model = initialize_model((wspan, hspan), [10, 10, 10, 10], num_classes=num_classes, totalistic=True, bc=\"periodic\")\n",
-    "model.compile(optimizer=tf.keras.optimizers.Adam(lr=1e-2), loss=loss)\n",
+    "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=1e-2), loss=loss)\n",
     "\n",
     "model.summary()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x64c871978>]"
+       "[<matplotlib.lines.Line2D at 0x11884d190>]"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 720x576 with 1 Axes>"
+       "<Figure size 1000x800 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -155,7 +225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -164,20 +234,18 @@
        "Text(0.5, 1.0, 'Observed Output')"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
-       "<Figure size 864x288 with 3 Axes>"
+       "<Figure size 1200x400 with 3 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -214,14 +282,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 345,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
     "### Save and load a model\n",
-    "model.save('path_to_my_model.h5')\n",
+    "model.save('path_to_my_model.keras')\n",
     "del model\n",
-    "#model = tf.keras.models.load_model('path_to_my_model.h5', custom_objects={'Wraparound2D': Wraparound2D})"
+    "model = tf.keras.models.load_model('path_to_my_model.keras', custom_objects={'Wraparound2D': Wraparound2D})\n"
    ]
   },
   {
@@ -233,47 +301,69 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
-     "ename": "IndexError",
-     "evalue": "index 2 is out of bounds for axis 2 with size 2",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-28-2dda4f3cddf6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m         \u001b[0mlayer_im\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmin_max_scaler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_outs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0mpattern\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_outs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mwspan\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhspan\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-28-2dda4f3cddf6>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mj\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m         \u001b[0mlayer_im\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhstack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmin_max_scaler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_outs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m...\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0mpattern\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_outs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mwspan\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhspan\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mIndexError\u001b[0m: index 2 is out of bounds for axis 2 with size 2"
-     ]
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Convolutional filters')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
     "import tensorflow.keras.backend as K\n",
     "\n",
-    "inp = model.input                                           # input placeholder\n",
-    "outputs = [layer.output for layer in model.layers]          # all layer outputs\n",
-    "functor = K.function(inp, outputs)   # evaluation function\n",
+    "_ = model(X_test)\n",
     "\n",
-    "layer_outs = functor([X_test, 1.])\n",
+    "layer_outputs = [layer.output for layer in model.layers]\n",
+    "activation_model = tf.keras.Model(inputs=model.inputs, outputs=layer_outputs)\n",
+    "layer_outs = activation_model(X_test, training=False)\n",
     "\n",
-    "\n",
-    "\n",
-    "# Plot activations of different neurons in different layers \n",
     "all_layer_activations = list()\n",
+    "min_max_scaler = lambda x: (x - np.min(x)) / (np.max(x) - np.min(x) + 1e-8)\n",
     "\n",
-    "min_max_scaler = lambda x : (x - np.min(x))/(np.max(x) - np.min(x))\n",
-    "# min_max_scaler = lambda x : (x - np.mean(x))\n",
+    "# Layers 1-4: conv2d, reshape, dense, dense_1\n",
     "for j in range(1, 5):\n",
-    "    if j==1:\n",
-    "        layer_im = np.hstack([min_max_scaler(layer_outs[1][0][..., i]) for i in range(10)])\n",
-    "    else:\n",
-    "        pattern = np.reshape(layer_outs[j][0], (wspan, hspan, -1))\n",
-    "        layer_im = np.hstack([min_max_scaler(pattern[..., i]) for i in range(10)])\n",
+    "    out = layer_outs[j][0].numpy()  # first batch item\n",
+    "    out_shape = out.shape\n",
+    "\n",
+    "    if len(out_shape) == 3:\n",
+    "        # Conv2D: (12, 12, 10) — already spatial\n",
+    "        pattern = out\n",
+    "    elif len(out_shape) == 2:\n",
+    "        # Dense/Reshape: (144, N) -> reshape to (12, 12, N)\n",
+    "        n_channels = out_shape[-1]\n",
+    "        pattern = out.reshape(12, 12, n_channels)\n",
+    "\n",
+    "    n_channels = pattern.shape[-1]\n",
+    "    layer_im = np.hstack([min_max_scaler(pattern[..., i]) for i in range(n_channels)])\n",
     "    all_layer_activations.append(layer_im)\n",
     "\n",
-    "        \n",
     "plt.figure()\n",
     "plt.imshow(np.vstack(all_layer_activations))\n",
     "plt.title(\"Activations of hidden layers given \\\"Glider\\\" input\")\n",
@@ -282,25 +372,11 @@
     "plt.imshow(np.squeeze(np.dstack(model.layers[1].weights[0].numpy())))\n",
     "plt.title(\"Convolutional filters\")"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -314,7 +390,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.9"
+   "version": "3.12.10"
   }
  },
  "nbformat": 4,

From a7a7ba35b15b4cdd3aa5461fc9faa8073ca28c0b Mon Sep 17 00:00:00 2001
From: Jesper Olsen <43079279+jesper-olsen@users.noreply.github.com>
Date: Mon, 23 Mar 2026 16:30:17 +0800
Subject: [PATCH 2/4] - Fix trailing comma bug in logit_to_pred() in
 train_ca.py that turned   the return value into a tuple, causing the
 "Observed Output" plot to   render as all black

---
 train_ca.py | 7 ++-----
 1 file changed, 2 insertions(+), 5 deletions(-)

diff --git a/train_ca.py b/train_ca.py
index bcef5fd..f2dcded 100644
--- a/train_ca.py
+++ b/train_ca.py
@@ -91,15 +91,12 @@ def initialize_model(shape, layer_dims, nhood=1, num_classes=2, totalistic=False
     #model.add(tf.keras.layers.Reshape(target_shape=(-1, wspan, hspan)))
     return model
 
-
 def logit_to_pred(logits, shape=None):
     """
     Given logits in the form of a network output, convert them to 
     images
     """
-    
-    labels = tf.argmax(tf.nn.softmax(logits), 
-                                axis=-1), 
+    labels = tf.argmax(tf.nn.softmax(logits), axis=-1)
     if shape:                 
         out = tf.reshape(labels, shape)
     return out
@@ -211,4 +208,4 @@ def get_config(self):
         return config
     
     def call(self, inputs):
-        return tf.nn.convolution(inputs, self.filters, padding=self.pad_type)
\ No newline at end of file
+        return tf.nn.convolution(inputs, self.filters, padding=self.pad_type)

From 515afca87ea732099e2adc874e5186a60be1a8e3 Mon Sep 17 00:00:00 2001
From: Jesper Olsen <43079279+jesper-olsen@users.noreply.github.com>
Date: Mon, 23 Mar 2026 16:34:49 +0800
Subject: [PATCH 3/4] save output

---
 demos/demos.ipynb | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/demos/demos.ipynb b/demos/demos.ipynb
index 77b3125..dee9adc 100644
--- a/demos/demos.ipynb
+++ b/demos/demos.ipynb
@@ -197,7 +197,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x11884d190>]"
+       "[<matplotlib.lines.Line2D at 0x118bcec00>]"
       ]
      },
      "execution_count": 4,
@@ -206,7 +206,7 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0MAAAKTCAYAAADbidN0AAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAARENJREFUeJzt3QuUVeV9P+53YBjuF5FwJyJqQKNACpEfibktiejKSjXpBbNMMNTqqklbDSteaAI20YREG2uT0JDQkmhtI01WamxiSVISbV1BMRCrTRFB5Kbc5SJ3mTn/9b7+zzgDM8PMMGfOZT/PWpuz9zn77LNnz+bM+Zz3fb+7KpfL5QIAAEDGdCn2DgAAABSDMAQAAGSSMAQAAGSSMAQAAGSSMAQAAGSSMAQAAGSSMAQAAGRSdagAdXV14ZVXXgl9+/YNVVVVxd4dAACgSOJlVF977bUwfPjw0KVLl8oPQzEIjRo1qti7AQAAlIjNmzeHkSNHVn4Yii1C+R+4X79+xd4dAACgSPbv358aSvIZoeLDUL5rXAxCwhAAAFDViuEzCigAAACZJAwBAACZJAwBAACZJAwBAACZJAwBAACZJAwBAACZJAwBAACZJAwBAACZJAwBAACZJAwBAACZJAwBAACZJAwBAACZJAwBAACZJAwBAACZJAwBAACZJAwBAACZJAwBAACZJAwBAACZJAwBAACZJAwBAACZJAwBAACZJAwBAACZJAwBAACZ1K4wtGDBgjB69OjQo0ePMGXKlLBixYoW19+7d2/49Kc/HYYNGxa6d+8e3va2t4VHH320/vG//uu/DlVVVY2mcePGtWfXAAAAChOGlixZEmbPnh3uuOOOsGrVqjBhwoQwffr0sGPHjibXP3bsWPjgBz8YNmzYEH74wx+GNWvWhEWLFoURI0Y0Wu/tb3972Lp1a/30xBNPhHL085//PLz3ve8NN910U7F3BQAAaEF1aKN77703XH/99WHWrFlpeeHCheGnP/1pWLx4cbj99ttPWj/e/+qrr4Zf//rXoVu3bum+2Kp00o5UV4ehQ4eGcrdv377w3//936l1CwAAqJCWodjKs3LlyjBt2rQ3N9ClS1pevnx5k8955JFHwtSpU1M3uSFDhoQLL7wwfPnLXw61tbWN1lu7dm0YPnx4GDNmTLjmmmvCpk2bmt2Po0ePhv379zeaSsWAAQPquwYCAAAVEoZ27dqVQkwMNQ3F5W3btjX5nPXr16fucfF5cZzQ3Llzw9e+9rVw11131a8Txx1973vfC0uXLg3f+ta3wksvvRTe8573hNdee63Jbc6fPz/079+/fho1alQoFcIQAABUaDe5tqqrqwuDBw8O3/nOd0LXrl3DpEmTwssvvxzuueeeNO4ouuKKK+rXHz9+fApHZ511VvjXf/3XcN111520zTlz5qRxS3mxZahUApEwBAAAFRiGBg0alALN9u3bG90fl5sb7xMryMWxQvF5eeeff35qSYrd7mpqapoMFLHi3Lp165rcZqxIF6dSFFuqotiqFYNg7EYIAACUnjZ9Uo/BJbbsLFu2rP6++IE/LsdxQU1597vfnUJNXC/vhRdeSCGpqSAUHThwILz44otpnXKTD0O5XK6kxjIBAACNtbnZInZPi6Wx77///rB69epw4403hoMHD9ZXl5s5c2bqxpYXH4/V5GKp6RiCYuW5WEAhFlTI++xnPxsef/zxVH47Vp37yEc+klqSPvaxj4VyE1usevbsmeYVUQAAgAoaMzRjxoywc+fOMG/evNTVbeLEianwQb6oQqwC17BrWBzL87Of/Sx85jOfSeOB4vWFYjC67bbb6tfZsmVLCj67d+8Ob3nLW8Ill1wSnnzyyTRfjmI3v8OHDwtDAABQwqpysT9XmYvd0WL3tHiNn379+hV7d9KYqOeffz489thj4X3ve1+xdwcAADJjfxuygdH9BaCiHAAAlD5hqACEIQAAKH3CUAEIQwAAUPqEoQKW1479FAEAgNIkDBWAliEAACh9wlABCEMAAFD6hKECEIYAAKD0CUMFHDO0d+/eQmweAADoAMJQAVuGFFAAAIDSJQwVgG5yAABQ+oShAhCGAACg9AlDBb7OUC6XK8RLAAAAp0kYKmDLUG1tbTh48GAhXgIAADhNwlAB9OzZM3Tr1i3NqygHAAClSRgqgKqqKuOGAACgxAlDBeJaQwAAUNqEoQJxrSEAAChtwlCBKK8NAAClTRgqEGEIAABKmzBUIMYMAQBAaROGCsSYIQAAKG3CUIHoJgcAAKVNGCoQYQgAAEqbMFQgxgwBAEBpE4YKxJghAAAobcJQgegmBwAApU0YKhBhCAAASpswVCDGDAEAQGkThgrcMnTs2LFw5MiRQr0MAADQTsJQgfTp0yd06fLG4d27d2+hXgYAAGgnYahAYhDSVQ4AAEqXMFRAwhAAAJQuYaiAXGsIAABKlzBUQMprAwBA6RKGCkgYAgCA0iUMFZAxQwAAULqEoQIyZggAAEqXMFRAuskBAEDpEoYKSBgCAIDSJQwVkDFDAABQuoShAjJmCAAASpcwVEC6yQEAQOkShgpIGAIAgNIlDBWQMUMAAFC6hKFOaBk6dOhQeP311wv5UgAAQBsJQwXUr1+/+vl9+/YV8qUAAIA2EoYKqLq6OvTt2zfN7927t5AvBQAAtJEwVGDGDQEAQGkShgrMtYYAAKA0CUMFprw2AACUJmGowIQhAAAoTcJQgRkzBAAApUkYKjBjhgAAoDQJQwWmmxwAAJQmYajAhCEAAChNwlCBGTMEAAClSRgqMGOGAACgNAlDBaabHAAAlCZhqMCEIQAAKE3CUIEZMwQAABUUhhYsWBBGjx4devToEaZMmRJWrFjR4vp79+4Nn/70p8OwYcNC9+7dw9ve9rbw6KOPntY2y61l6LXXXgt1dXXF3h0AAKC9YWjJkiVh9uzZ4Y477girVq0KEyZMCNOnTw87duxocv1jx46FD37wg2HDhg3hhz/8YVizZk1YtGhRGDFiRLu3WY4tQ7lcLuzfv7/YuwMAAPz/qnLxU3obxFabd77zneGb3/xmWo6tHaNGjQp/8Rd/EW6//faT1l+4cGG45557wvPPPx+6devWIds8UQwZMXTs27cv9OvXL5SaXr16hcOHD4eXXnoptX4BAACF0ZZs0KaWodjKs3LlyjBt2rQ3N9ClS1pevnx5k8955JFHwtSpU1M3uSFDhoQLL7wwfPnLXw61tbXt3ubRo0fTD9lwKmXGDQEAQOlpUxjatWtXCjEx1DQUl7dt29bkc9avX5+6x8XnxXFCc+fODV/72tfCXXfd1e5tzp8/PwWM/BRbkUqZaw0BAEAGq8nFLm+DBw8O3/nOd8KkSZPCjBkzwuc+97nUfa695syZk5q98tPmzZtDKVNeGwAASk91W1YeNGhQ6Nq1a9i+fXuj++Py0KFDm3xOrCAXxwrF5+Wdf/75qdUndpFrzzZjRbo4lQthCAAAyrxlqKamJrXuLFu2rFHLT1yO44Ka8u53vzusW7euUVnpF154IYWkuL32bLPcGDMEAAAV0E0ulsCOpbHvv//+sHr16nDjjTeGgwcPhlmzZqXHZ86cmbqx5cXHX3311XDTTTelEPTTn/40FVCIBRVau81yZ8wQAACUeTe5KI752blzZ5g3b17q6jZx4sSwdOnS+gIImzZtStXg8mJxg5/97GfhM5/5TBg/fny6vlAMRrfddlurt1nudJMDAIAKuM5QKSr16wx95StfSa1lsaVr8eLFxd4dAACoWAW7zhDtY8wQAACUHmGoExgzBAAApUcY6gTGDAEAQOkRhjqBMAQAAKVHGOoExgwBAEDpEYY6ecxQBRTvAwCAiiAMdWIYqq2tTReTBQAAik8Y6gQ9e/YM3bp1S/N79+7tjJcEAABOQRjqBFVVVcYNAQBAiRGGOolrDQEAQGkRhjqJ8toAAFBahKFOIgwBAEBpEYY6iWsNAQBAaRGGOokxQwAAUFqEoU6imxwAAJQWYaiTCEMAAFBahKFOIgwBAEBpEYY6uYDCvn37OuslAQCAFghDnUTLEAAAlBZhqJMIQwAAUFqEoU4iDAEAQGkRhjqJMUMAAFBahKFObhk6evRoOHLkSGe9LAAA0AxhqJP06dMndOnyxuHeu3dvZ70sAADQDGGok8QglO8qJwwBAEDxCUOdyLghAAAoHcJQJ1JRDgAASocw1ImEIQAAKB3CUCcShgAAoHQIQ53ImCEAACgdwlAn0jIEAAClQxjqRMIQAACUDmGoEwlDAABQOoShTmTMEAAAlA5hqBNpGQIAgNIhDBUhDO3Zs6czXxYAAGiCMNSJRowYkW43btwY6urqOvOlAQCAEwhDnWjMmDGhW7du4dChQ2HLli2d+dIAAMAJhKFOVF1dHc4777w0v3r16s58aQAA4ATCUCcbN25cun3++ec7+6UBAIAGhKFOdv7556dbLUMAAFBcwlAn0zIEAAClQRgqUsuQbnIAAFBcwlAnGzt2bLrdvn276w0BAEARCUOdrE+fPmHkyJFpXusQAAAUjzBUBIooAABA8QlDRaCIAgAAFJ8wVATCEAAAFJ8wVAS6yQEAQPEJQ0VsGVq/fn04evRoMXYBAAAyTxgqgqFDh4b+/fuHurq6sHbt2syfhAAAUAzCUBFUVVUZNwQAAEUmDBWJIgoAAFBcwlCRKKIAAADFJQwViZYhAAAoLmGoBMJQLKQAAAB0LmGoSMaMGRO6desWDh06FLZs2VKs3QAAgMwShookBqFzzz23vnUIAADoXMJQESmiAAAAxSMMFZEiCgAAUGZhaMGCBWH06NGhR48eYcqUKWHFihXNrvu9730vXWS04RSf19AnP/nJk9a5/PLLQ1bC0OrVq4u9KwAAkDnVbX3CkiVLwuzZs8PChQtTELrvvvvC9OnTw5o1a8LgwYObfE6/fv3S43kx7Jwohp/vfve79cvdu3cPWekmZ8wQAACUQRi69957w/XXXx9mzZqVlmMo+ulPfxoWL14cbr/99iafE8PP0KFDW9xuDD+nWifv6NGjacrbv39/KEdjx45Nt9u3bw979uwJZ5xxRrF3CQAAMqNN3eSOHTsWVq5cGaZNm/bmBrp0ScvLly9v9nkHDhwIZ511Vhg1alS48sorw+9+97uT1nnsscdSy1IMCDfeeGPYvXt3s9ubP39+6N+/f/0Ut1uO+vbtG0aMGJHmtQ4BAEAJh6Fdu3aF2traMGTIkEb3x+Vt27Y1+ZwYbmKr0Y9//OPw4IMPpguMvutd72p0bZ3YRe6BBx4Iy5YtC1/96lfD448/Hq644or0Wk2ZM2dO2LdvX/20efPmUK50lQMAgDLpJtdWU6dOTVNeDEIxAHz7298Od955Z7rv6quvrn/8oosuCuPHjw/nnHNOai269NJLm+xSVyljimIRhf/8z/9URAEAAEq5ZWjQoEGha9euaYxLQ3G5teN94sVG3/GOd4R169Y1u86YMWPSa7W0TqXQMgQAAGUQhmpqasKkSZNSd7a82O0tLjds/WlJ7Pr23HPPhWHDhjW7TuxCF8cMtbROpXCtIQAAKJPrDMWy2osWLQr3339/6toVix0cPHiwvrrczJkz05ievC9+8Yvh5z//eVi/fn1YtWpV+PjHPx42btwY/vRP/7S+uMItt9wSnnzyybBhw4YUrGKRhXPPPTeV7M5KGHrxxRcbVcgDAABKbMzQjBkzws6dO8O8efNS0YSJEyeGpUuX1hdV2LRpU6owlxdLRsdS3HHdWDo6tiz9+te/DhdccEF6PHa7e/bZZ1O42rt3bxg+fHi47LLL0niiShkX1JLY+hWvwxTLg8dugW9/+9uLvUsAAJAJVblcLhfKXAwSscR2rCwXg0W5iRevXbFiRfjBD34Q/vAP/7DYuwMAAJnIBm3uJkfHM24IAAA6nzBUAlSUAwCAzicMlVDLUCxIAQAAdA5hqMRahmKpcgAAoPCEoRIQLzJbXV0dDh06lK6xBAAAFJ4wVAK6deuWrquUbx0CAAAKTxgqEYooAABA5xKGSoQiCgAA0LmEoRLhWkMAANC5hKES6yanvDYAAHQOYajEWoa2b98e9uzZU+zdAQCAiicMlYi+ffuGESNGpHkV5QAAoPCEoRKiqxwAAHQeYaiEKKIAAACdRxgqIVqGAACg8whDJUQYAgCAziMMlWA3uZdeeikcOXKk2LsDAAAVTRgqIUOHDg39+/cPdXV1Ye3atcXeHQAAqGjCUAmpqqrSVQ4AADqJMFRiVJQDAIDOIQyVGEUUAACgcwhDJUYYAgCAziEMlWg3uTVr1qRCCgAAQGEIQyXm7LPPDjU1Nam09saNG4u9OwAAULGEoRJTXV0d3va2t6X51atXF3t3AACgYglDJUhFOQAAKDxhqAQpogAAAIUnDJUgYQgAAApPGCpBuskBAEDhCUMlaOzYsaGqqirs3r077Ny5s9i7AwAAFUkYKkG9evUKZ511VppXUQ4AAApDGCpRusoBAEBhCUMlShEFAAAoLGGoRAlDAABQWMJQidJNDgAACksYKvGWoY0bN4aDBw8We3cAAKDiCEMlatCgQWmK1qxZU+zdAQCAiiMMlUHr0PPPP1/sXQEAgIojDJXBuCHXGgIAgI4nDJUwFeUAAKBwhKESppscAAAUjjBUBt3kXnjhhXD8+PFi7w4AAFQUYaiEvfWtbw29evUKr7/+eli/fn2xdwcAACqKMFTCunTpEsaOHZvmVZQDAICOJQyVOBXlAACgMIShEqeiHAAAFIYwVOJUlAMAgMIQhsqom1wulyv27gAAQMUQhkrceeedlwop7N+/P2zdurXYuwMAABVDGCpx3bt3D+ecc06aV1EOAAA6jjBUBlSUAwCAjicMlQEV5QAAoOMJQ2VARTkAAOh4wlAZ0E0OAAA6njBURi1Dr7zySqoqBwAAnD5hqAz0798/DBw4MM1v2rSp2LsDAAAVQRgqEyNGjEi3L7/8crF3BQAAKoIwVCZGjhyZbrds2VLsXQEAgIogDJUJYQgAADqWMFRmYUg3OQAAKGIYWrBgQRg9enTo0aNHmDJlSlixYkWz637ve98LVVVVjab4vIZyuVyYN29eGDZsWOjZs2eYNm1aWLt2bXt2reLHDOkmBwAARQpDS5YsCbNnzw533HFHWLVqVZgwYUKYPn162LFjR7PP6devX9i6dWv9tHHjxkaP33333eHrX/96WLhwYXjqqadC79690zaPHDnSvp+qAukmBwAARQ5D9957b7j++uvDrFmzwgUXXJACTK9evcLixYubfU5sDRo6dGj9NGTIkEatQvfdd1/4/Oc/H6688sowfvz48MADD6Rr6jz88MNNbu/o0aPpejsNp0onDAEAQBHD0LFjx8LKlStTN7b6DXTpkpaXL1/e7PMOHDgQzjrrrDBq1KgUeH73u9/VP/bSSy+Fbdu2NdpmvK5O7H7X3Dbnz5+f1slPcbtZ6Sa3Z8+ecOjQoWLvDgAAZCsM7dq1K9TW1jZq2Ynicgw0TRk7dmxqNfrxj38cHnzwwVBXVxfe9a531Y99yT+vLducM2dO2LdvX/20efPmUOli6IvdByNFFAAAoAyqyU2dOjXMnDkzTJw4Mbzvfe8LP/rRj8Jb3vKW8O1vf7vd2+zevXsah9RwqnSxq6GucgAAUKQwNGjQoNC1a9ewffv2RvfH5TgWqDW6desW3vGOd4R169al5fzzTmebWaG8NgAAFCkM1dTUhEmTJoVly5bV3xe7vcXl2ALUGrGb3XPPPZfKaEdnn312Cj0NtxkLIsSqcq3dZlYorw0AAB2nuq1PiGW1r7322jB58uRw8cUXp0pwBw8eTNXlotglLn5oj0UOoi9+8Yvh//2//xfOPffcsHfv3nDPPfek0tp/+qd/Wt/96+abbw533XVXOO+881I4mjt3bhg+fHi46qqrOvBHLX+6yQEAQBHD0IwZM8LOnTvTRVJjgYM4Fmjp0qX1BRA2bdqUKszlxepnsRR3XPeMM85ILUu//vWvU1nuvFtvvTUFqhtuuCEFpksuuSRt88SLs2adMAQAAB2nKhcv9FPmYre6WG0tVpar5GIKjzzySCpNHlvlnn766WLvDgAAlHU2KHg1OTqOliEAAOg4wlAZhqFYae/1118v9u4AAEBZE4bKSCxtHiv6xZ6NW7duLfbuAABAWROGykgsTBGr7EVbtmwp9u4AAEBZE4bKjHFDAADQMYShMg1DL7/8crF3BQAAypowVGbiBW0j3eQAAOD0CENlRjc5AADoGMJQmRGGAACgYwhDZdpNzpghAAA4PcJQGRdQqKurK/buAABA2RKGyszQoUPT9YaOHz8eduzYUezdAQCAsiUMlZlu3bqlQBTpKgcAAO0nDJUh5bUBAOD0CUNlSEU5AAA4fcJQGRKGAADg9AlDZUh5bQAAOH3CUBnSMgQAAKdPGCpDwhAAAJw+YajML7yay+WKvTsAAFCWhKEyNHz48HR76NChsHfv3mLvDgAAlCVhqAz17NkznHnmmWl+y5Ytxd4dAAAoS8JQmTJuCAAATo8wVKaU1wYAgNMjDJUpLUMAAHB6hKEyJQwBAMDpEYYqoLw2AADQdsJQmY8ZUk0OAADaRxgqU7rJAQDA6RGGyjwMxYuuHjx4sNi7AwAAZUcYKlP9+vULffr0SfPGDQEAQNsJQ2VMVzkAAGg/YaiMCUMAANB+wlAZU14bAADaTxgqY8prAwBA+wlDZUw3OQAAaD9hqIwJQwAA0H7CUAV0k1NaGwAA2k4YqoCWoe3bt4djx44Ve3cAAKCsCENlbNCgQaGmpibNv/LKK8XeHQAAKCvCUBmrqqpSXhsAANpJGCpzymsDAED7CENlTkU5AABoH2GoQsKQinIAANA2wlCZ000OAADaRxgqc7rJAQBA+whDZU4YAgCA9hGGKqSb3NatW0NtbW2xdwcAAMqGMFTmhg4dGrp06RKOHz8eduzYUezdAQCAsiEMlbnq6uoUiCIV5QAAoPWEoQowZMiQdKtlCAAAWk8YqgBvectb0u3OnTuLvSsAAFA2hKEKMHjw4HSrZQgAAFpPGKoAWoYAAKDthKEKIAwBAEDbCUMVQDc5AABoO2GoAmgZAgCAthOGKoAwBAAAbScMVQDd5AAAoO2EoQpqGTp06FCaAACAAoWhBQsWhNGjR4cePXqEKVOmhBUrVrTqeQ899FCoqqoKV111VaP7P/nJT6b7G06XX355e3Ytk/r27RtqamrSvAuvAgBAgcLQkiVLwuzZs8Mdd9wRVq1aFSZMmBCmT59+ygt+btiwIXz2s58N73nPe5p8PIafrVu31k/f//7327prmRXDY76rnDAEAAAFCkP33ntvuP7668OsWbPCBRdcEBYuXBh69eoVFi9e3OxzamtrwzXXXBO+8IUvhDFjxjS5Tvfu3cPQoUPrpzPOOKPZ7R09ejTs37+/0ZR1+a5ypwqlAABAO8LQsWPHwsqVK8O0adPq7+vSpUtaXr58ebPP++IXv5haLq677rpm13nsscfSOmPHjg033nhj2L17d7Przp8/P/Tv379+GjVqVMg6FeUAAKCAYWjXrl2plWfIkCGN7o/L27Zta/I5TzzxRPjHf/zHsGjRoma3G7vIPfDAA2HZsmXhq1/9anj88cfDFVdckV6rKXPmzAn79u2rnzZv3hyyTjc5AABom+pQQK+99lr4xCc+kYLQoEGDml3v6quvrp+/6KKLwvjx48M555yTWosuvfTSJrvUxYk36SYHAAAFDEMx0HTt2jVs37690f1xOY7zOdGLL76YCid8+MMfrr+vrq7ujReurg5r1qxJoedEcVxRfK1169Y1GYY4mW5yAABQwG5ysXzzpEmTUne2huEmLk+dOvWk9ceNGxeee+658Mwzz9RPv//7vx8+8IEPpPnmxvps2bIljRkaNmxYG3+c7NJNDgAACtxNLpbVvvbaa8PkyZPDxRdfHO67775w8ODBVF0umjlzZhgxYkQqchCvQ3ThhRc2ev6AAQPSbf7+AwcOpCpzf/AHf5Bal2Jr0q233hrOPffcVLKb1tFNDgAAChyGZsyYka5lM2/evFQ0YeLEiWHp0qX1RRU2bdqUKsy1Vux29+yzz4b7778/7N27NwwfPjxcdtll4c477zQuqA10kwMAgLapyuVyuVDm4nWGYontWFmuX79+IYtii1psTevdu3dqbQMAgCza34Zs0OaLrlLaLUOxy+KhQ4eKvTsAAFDyhKEK0bdv31TgIordGAEAgJYJQxWiqqpKRTkAAGgDYaiCqCgHAACtJwxVEBXlAACg9YShCiIMAQBA6wlDFWTw4MHpdseOHcXeFQAAKHnCUAXRMgQAAK0nDFUQYQgAAFpPGKrAbnKuMwQAAKcmDFUQpbUBAKD1hKEKopscAAC0njBUgd3kDh48GA4dOlTs3QEAgJImDFWQvn37hpqamjRv3BAAALRMGKogVVVVusoBAEArCUMVRkU5AABoHWGowqgoBwAArSMMVRgV5QAAoHWEoQqjmxwAALSOMFRhdJMDAIDWEYYqjG5yAADQOsJQhdFNDgAAWkcYqjC6yQEAQOsIQxVGNzkAAGgdYahCu8kdPHgwHDp0qNi7AwAAJUsYqjB9+/YNNTU1aX7nzp3F3h0AAChZwlCFqaqq0lUOAABaQRiqQCrKAQDAqQlDFUgRBQAAODVhqAIprw0AAKcmDFUg3eQAAODUhKEKpJscAACcmjBUgXSTAwCAUxOGKpBucgAAcGrCUAXSTQ4AAE5NGKpAuskBAMCpCUMV3E3u4MGD4fDhw8XeHQAAKEnCUAXq27dvqKmpSfM7d+4s9u4AAEBJEoYqUFVVla5yAABwCsJQhVJEAQAAWiYMVSjltQEAoGXCUIVSUQ4AAFomDFUo3eQAAKBlwlCF0k0OAABaJgxVKN3kAACgZcJQhdJNDgAAWiYMVSjd5AAAoGXCUIXSMgQAAC0Thio8DB04cCAcPny42LsDAAAlRxiqUP369Qs1NTVpfufOncXeHQAAKDnCUIWqqqrSVQ4AAFogDFUw5bUBAKB5wlAFU1EOAACaJwxVMBXlAACgecJQBdNNDgAAmicMVTDd5AAAoHnCUAXTTQ4AAJonDFUw3eQAAKB5wlAF000OAACaJwxVMN3kAACgg8PQggULwujRo0OPHj3ClClTwooVK1r1vIceeihUVVWFq666qtH9uVwuzJs3LwwbNiz07NkzTJs2Laxdu7Y9u0YTYejAgQPh8OHDjg0AAJxOGFqyZEmYPXt2uOOOO8KqVavChAkTwvTp08OOHTtafN6GDRvCZz/72fCe97znpMfuvvvu8PWvfz0sXLgwPPXUU6F3795pm0eOHGnr7tFAv379Qk1NTZrfuXOnYwMAAKcThu69995w/fXXh1mzZoULLrggBZhevXqFxYsXN/uc2tracM0114QvfOELYcyYMSe1Ct13333h85//fLjyyivD+PHjwwMPPBBeeeWV8PDDDze5vaNHj4b9+/c3mjhZbIXTVQ4AADogDB07diysXLkydWOr30CXLml5+fLlzT7vi1/8YhrMf91115302EsvvRS2bdvWaJv9+/dP3e+a2+b8+fPTOvlp1KhRbfkxMkVFOQAA6IAwtGvXrtTKM2TIkEb3x+UYaJryxBNPhH/8x38MixYtavLx/PPass05c+aEffv21U+bN29uy4+RyTAUf3cAAMCbqkMBvfbaa+ETn/hECkKDBg3qsO127949TZyaMAQAAB0QhmKg6dq1a9i+fXuj++Py0KFDT1r/xRdfTIUTPvzhD9ffV1dX98YLV1eHNWvW1D8vbiNWk2u4zYkTJ7Zl92jmdxYpoAAAAKfRTS5WJps0aVJYtmxZo3ATl6dOnXrS+uPGjQvPPfdceOaZZ+qn3//93w8f+MAH0nwc63P22WenQNRwm7EgQqwq19Q2aV8Y0k0OAABOs5tcLKt97bXXhsmTJ4eLL744VYI7ePBgqi4XzZw5M4wYMSIVOYjXIbrwwgsbPX/AgAHptuH9N998c7jrrrvCeeedl8LR3Llzw/Dhw0+6HhFtJwwBAEAHhaEZM2akLlfxIqmxwEHsyrZ06dL6AgibNm1KFeba4tZbb02B6oYbbgh79+4Nl1xySdpmDFOcHmOGAACgaVW5eKGfMhe71cUS27GyXLzQKG967LHHUrfE2GVx9erVDg0AABVtfxuyQZsvukp50U0OAACaJgxlpJvcq6++mq4RBQAAvEEYqnADBw6sr/q3Z8+eYu8OAACUDGGownXr1q2+gp/y2gAA8CZhKAOMGwIAgJMJQxmgvDYAAJxMGMpQy1C8PhQAAPAGYSgDdJMDAICTCUMZoJscAACcTBjKAN3kAADgZMJQBugmBwAAJxOGMkAYAgCAkwlDGRozpJocAAC8SRjKAC1DAABwMmEoQ2HowIED4ciRI8XeHQAAKAnCUAb0798/VFdXp/ndu3cXe3cAAKAkCEMZUFVVpbw2AACcQBjKCOOGAACgMWEoI4QhAABoTBjKCOW1AQCgMWEoI7QMAQBAY8JQRghDAADQmDCUEcIQAAA0JgxlhDFDAADQmDCUEVqGAACgMWEoI4QhAABoTBjKWDe5Xbt2hVwuV+zdAQCAohOGMuLMM89Mt6+//nrYv39/sXcHAACKThjKiJ49e4bevXvXtw4BAEDWCUMZYtwQAAC8SRjKEOW1AQDgTcJQhmgZAgCANwlDGSIMAQDAm4ShDIahnTt3FntXAACg6IShjF5rCAAAsk4YyhDd5AAA4E3CUIYIQwAA8CZhKEOU1gYAgDcJQxmiZQgAAN4kDGUwDO3ZsyccP3682LsDAABFJQxlyMCBA0NVVVWa3717d7F3BwAAikoYypCuXbumQBQprw0AQNYJQxlj3BAAALxBGMoYYQgAAN4gDGWM8toAAPAGYShjtAwBAMAbhKGMEYYAAOANwlBGw9DOnTuLvSsAAFBUwlBGxwwprQ0AQNYJQxmjmxwAALxBGMoYYQgAAN4gDGWMMUMAAPAGYSijY4YOHz4cDh06VOzdAQCAohGGMqZPnz6hpqYmzSuiAABAlglDGVNVVaWrHAAACEPZpLw2AABoGcokFeUAAEAYyiRhCAAAhKFMUl4bAADaGYYWLFgQRo8eHXr06BGmTJkSVqxY0ey6P/rRj8LkyZPDgAEDQu/evcPEiRPDP/3TPzVa55Of/GQa2N9wuvzyy/1+CsSYIQAACKG6rQdhyZIlYfbs2WHhwoUpCN13331h+vTpYc2aNWHw4MEnrT9w4MDwuc99LowbNy6VdP7JT34SZs2aldaNz8uL4ee73/1u/XL37t39fgpENzkAAGhHy9C9994brr/++hRoLrjgghSKevXqFRYvXtzk+u9///vDRz7ykXD++eeHc845J9x0001h/Pjx4Yknnmi0Xgw/Q4cOrZ/OOOMMv58C0U0OAADaGIaOHTsWVq5cGaZNm1Z/X5cuXdLy8uXLT/n8XC4Xli1bllqR3vve9zZ67LHHHkutRWPHjg033nhj2L17d7PbOXr0aNi/f3+jidbTTQ4AANrYTW7Xrl2htrY2DBkypNH9cfn5559v9nn79u0LI0aMSCGma9eu4e///u/DBz/4wUZd5D760Y+Gs88+O7z44ovhr/7qr8IVV1yRAlZc/0Tz588PX/jCF/z+2kk3OQAAaMeYofbo27dveOaZZ8KBAwdSy1AcczRmzJjUhS66+uqr69e96KKLUje62KUuthZdeumlJ21vzpw5aRt5sWVo1KhRfp9tDEOx9a2uri617gEAQNZUt/VDdGyp2b59e6P743Ic59Oc+GH73HPPTfOxmtzq1atT604+DJ0oBqX4WuvWrWsyDMXxRQostN+ZZ56ZbmMr3969e1ORCwAAyJo2NQnEanCTJk1KrTt5sWUhLk+dOrXV24nPiV3mmrNly5bUajFs2LC27B6tFINkv3796rs+AgBAFrW5f1TsnrZo0aJw//33pxaeWOzg4MGDqbpcNHPmzNSNLS+2AP3iF78I69evT+t/7WtfS9cZ+vjHP54ej13nbrnllvDkk0+GDRs2pGB15ZVXppakhqW36VjGDQEAkHVtHjM0Y8aMsHPnzjBv3rywbdu21O1t6dKl9UUVNm3a1GgMSgxKn/rUp1JrT8+ePdP1hh588MG0nSh2u3v22WdTuIpdtoYPHx4uu+yycOedd+oKV+AwFANq/F0CAEAWVeVivesyFwso9O/fP1Wty3f/omUf+tCHwqOPPhr+4R/+IVx33XUOFwAAFaEt2UAZsYxyrSEAALJOGMooY4YAAMg6YSjjYciYIQAAskoYyijd5AAAyDphKKN0kwMAIOuEoYzSTQ4AgKwThjJKyxAAAFknDGV8zFCsw37s2LFi7w4AAHQ6YSijBgwYELp0eePXv3v37mLvDgAAdDphKKNiEDrzzDPTvPLaAABkkTCUYcprAwCQZcJQhimiAABAlglDGaa8NgAAWSYMZdjw4cPT7ZYtW4q9KwAA0OmEoQw766yz0u3GjRuLvSsAANDphKEMy4ehDRs2FHtXAACg0wlDGaZlCACALBOGMiwfhrZu3RqOHTtW7N0BAIBOJQxl2ODBg0OPHj1CLpcLmzdvLvbuAABApxKGMqyqqkpXOQAAMksYyjjjhgAAyCphKOOEIQAAskoYyjhhCACArBKGMk4YAgAgq4ShjHPhVQAAskoYyrh8GIqltWtra4u9OwAA0GmEoYwbPnx4qK6uDsePH08XXwUAgKwQhjIuBqGRI0em+Y0bNxZ7dwAAoNMIQyiiAABAJglDCEMAAGSSMIQwBABAJglDCEMAAGSSMIQwBABAJglDhNGjR6ejsGHDhpDL5RwRAAAyQRgijBo1Kh2Fw4cPh127djkiAABkgjBE6N69exg2bFg6Eq41BABAVghDJGeddVa6FYYAAMgKYYhEGAIAIGuEIRJhCACArBGGSIQhAACyRhgiEYYAAMgaYYhG1xpSQAEAgKwQhmjUMrRnz56wf/9+RwUAgIonDJH06dMnDBw4MM1rHQIAIAuEIeoZNwQAQJYIQ9QThgAAyBJhiHrCEAAAWSIMUU8YAgAgS4Qh6glDAABkiTBEPdcaAgAgS4QhTmoZ2rZtWzhy5IgjAwBARROGqBevM9S7d+80v2nTJkcGAICKJgxRr6qqyrghAAAyQxiiEUUUAADICmGIRoQhAACyQhiiEWEIAICsEIZoRBgCACArhCEaca0hAACyQhiiyZahLVu2hOPHjzs6AABULGGIRoYOHRpqampCbW1teOWVVxwdAAAqVrvC0IIFC1J3qh49eoQpU6aEFStWNLvuj370ozB58uQwYMCAdEHPiRMnhn/6p39qtE4ulwvz5s0Lw4YNCz179gzTpk0La9eubc+ucZq6dOkSRo0aleY3bNjgeAIAULHaHIaWLFkSZs+eHe64446watWqMGHChDB9+vSwY8eOJtcfOHBg+NznPheWL18enn322TBr1qw0/exnP6tf5+677w5f//rXw8KFC8NTTz2VQlPc5pEjR07vp6NdFFEAACAL2hyG7r333nD99denQHPBBRekANOrV6+wePHiJtd///vfHz7ykY+E888/P5xzzjnhpptuCuPHjw9PPPFEfavQfffdFz7/+c+HK6+8Mj32wAMPpC5aDz/88On/hLSZMAQAQBa0KQwdO3YsrFy5MnVjq99Aly5pObb8nEoMPsuWLQtr1qwJ733ve9N9L730Uti2bVujbfbv3z91v2tum0ePHg379+9vNNFxhCEAALKgTWFo165daWD9kCFDGt0fl2Ogac6+fftCnz590sD8D33oQ+Eb3/hG+OAHP5geyz+vLducP39+Ckz5KT/GhY4hDAEAkAWdUk2ub9++4ZlnnglPP/10+NKXvpTGHD322GPt3t6cOXNSwMpPmzdv7tD9zTrXGgIAIAuq27LyoEGDQteuXcP27dsb3R+XY0nm5sSudOeee26aj9XkVq9enVp34nii/PPiNmI1uYbbjOs2pXv37mmisC1DmzZtSl0bq6qqHGoAALLdMhS7uU2aNCmN+8mrq6tLy1OnTm31duJz4rif6Oyzz06BqOE24xigWFWuLduk44wcOTIF2FjNr7kqgQAAkKmWoSh2cbv22mvTtYMuvvjiVAnu4MGDqbpcNHPmzDBixIjU8hPF27hurCQXA9Cjjz6arjP0rW99Kz0eWx1uvvnmcNddd4XzzjsvhaO5c+eG4cOHh6uuuqqjf15aoVu3bun4b9myJWzcuPGk8VwAAJDJMDRjxoywc+fOdJHUWOAgdmVbunRp/Qfm2LUqtirkxaD0qU99Kn2wjhdUHTduXHjwwQfTdvJuvfXWtN4NN9wQ9u7dGy655JK0zXhRV4rXVS7+zuKFV2PoBQCASlOVi4NCylzsVherysViCv369Sv27lSEa665JvzLv/xLuiDuLbfcUuzdAQCADs8GnVJNjvIzZsyYdPvCCy8Ue1cAAKAghCGa9Hu/93vpNpZDBwCASiQM0aT8OKH//d//DYcOHXKUAACoOMIQTYoVAWNFudra2vDb3/7WUQIAoOIIQ5yydWjFihWOEgAAFUcYolnvfOc7060wBABAJRKGaJaWIQAAKpkwRLMmT56cbtevXx927drlSAEAUFGEIZo1YMCAMHbs2DT/m9/8xpECAKCiCEO0yLghAAAqlTBEi4wbAgCgUglDtDoM5XI5RwsAgIohDNGiCRMmhG7duoWdO3eGjRs3OloAAFQMYYgW9ejRIwWi6Omnn3a0AACoGMIQp6SIAgAAlUgY4pQUUQAAoBIJQ7Q6DMVrDR0/ftwRAwCgIghDnFK88Grfvn3DoUOHwurVqx0xAAAqgjDEKXXt2jVMnjw5zSuiAABApRCGaBVFFAAAqDTCEK2iiAIAAJVGGKJNYejZZ58Nhw8fdtQAACh7whCtMnLkyDB06NBQW1sbfvvb3zpqAACUPWGIVqmqqjJuCACAiiIM0eaucirKAQBQCYQhWk0RBQAAKokwRKvlrzW0bt268OqrrzpyAACUNWGIVhs4cGA477zz0ryucgAAlDthiDZx8VUAACqFMESbKKIAAEClEIZodxGFXC7n6AEAULaEIdpk4sSJobq6Omzfvj1s3rzZ0QMAoGwJQ7RJz549w/jx4+tbhwAAoFwJQ7S7iMJTTz3l6AEAULaEIdrsve99b7r97ne/63pDAACULWGINvvjP/7jcOGFF4bdu3eHuXPnOoIAAJQlYYg2iwUUvvGNb6T5hQsXht/+9reOIgAAZUcYol3e//73h6uvvjrU1dWFP//zP1dmGwCAsiMM0W733HNP6N27d/j1r38dHnzwQUcSAICyIgzRbiNHjqwfM3TLLbeE/fv3O5oAAJQNYYjTcvPNN4e3ve1t6SKsX/jCFxxNAADKhjDEaenevXv4u7/7uzQfb3/3u985ogAAlAVhiNN2+eWXhyuvvDLU1taGv/zLv1RMAQCAsiAM0SH+9m//NrUS/fKXvww//OEPHVUAAEqeMESHOPvss8Ptt9+e5mfPnh0OHjzoyAIAUNKEITrMbbfdFkaPHh22bNkSPvOZz4TXX3/d0QUAoGQJQ3SYnj17hvvuuy/NL1q0KFxyySVh7dq1jjAAACVJGKJDxUIKDz30UBgwYEBYsWJFmDhxYgpGuVzOkQYAoKQIQ3S4GTNmhGeffTZ84AMfCIcOHQo33HBDuOqqq8LOnTsdbQAASoYwREGMGjUq/Od//mf4m7/5m1BTUxMeeeSRcNFFF4VHH33UEQcAoCRU5Sqg/9L+/ftD//79w759+0K/fv2KvTuc4H/+53/CNddcU39B1ssuuyxMnjw5jB8/Pk3nnXdeqK6udtwAAOjUbCAM0SmOHDkS5syZU19goaF4faILLrggBaPzzz8/nHXWWWmKlemGDBkSunTRgAkAQOsIQ5SsOJboiSeeSLf5qaVrEsUudm9961tTMBo2bFjo3bt3k1NM/YMGDUrTmWeemaZCtzYdOHAgbN26NU2vvPJK/fy2bdvCGWecEcaOHRvGjRuXbocPHx6qqqoKuj8AAARhiPJRV1cXNmzYkEJR7E63bt26sHHjxnTfyy+/nB5vr1jRLoajGExi61OcYrg6cWoupMQepDGoxdDz2muvNbqN3zjE4hCt1adPn/pwFINavnfqibdR3J/mpuaeE4Nf/HkHDhyYpvgz529jM3HDn+nE58eWt65duzY5xcfaE+Litg8fPlx//OJtfj4+lj/2DX8ncT6+Xrw+1fHjx+tv8/NRr1690hTLuOfnu3Xr1mgf4/bjeROfV1tbm6YTf8aGx5PKcPTo0bB58+b694/8bfyCIv4feMtb3hIGDx5cP8XlOMXzruH53nA+/r/KL+fn2/t/ArIsvofHvwnxfTm+f/fo0cP/IwpKyxAV8+YZA1H+g02sRtfwQ3V+Pk7xpN+9e3fYtWtXePXVVzutlHdslYqtPrHVKn8bu/bF/VizZk14/vnnw/r169MH8nIWPwDmPwTm55sTj338YNpZv4O4L/EDbT74tPZYn/jBN7/c8Lbhh978fMPbpqaGz2su0Lb0YToGuaamhuG1pcDc1OvlA2VTU8OA3FQwby4otxTOW3LicTrxmOUDcH6Kv8/8bQwkTU3x8e3bt3faOZc/P1p6/MSwn5/iYy19GXIqJx67ls6t1pxv7f2yoy3LbdHSl1MNp/z/ida81onHp6nj1pZ9OdVjDfe54W1T9zW3/6d672nusaaef+Kxa+pYNpzi/7X8F5ENz7cTz7sTf6a8+P8xflkYw09+ivedKAai/Bdb+YB04ms0XG6N9hybhj/ziceguduoufejE/e5Nefaicexted1U/Oneqy1ck18edra/9t/+7d/G971rneFcglDRq1TsuI3/rF7XJzaIr5Z7dmzpz4cxfljx46lD+jxNj/ll08VdmKrTpz69u3b6DZ+uxznTyW+xosvvlgfjmKQa+7N+lR/tJr7oxhfY+/evSkIxp+34W1bWrCak/8D0R7xD13DLo3xw2D+2J94G18j/t7zf1Qazkfxj2r8eeKU3594G+9v78+Ub3WiaU19iCnVfYkfqOL7RX7MYbyNX1LEFt0dO3akKX6p0nA+/v4bBuk4neoP/qlCd9xmHCcJNC/+H4lT/FtFZdm3b18oJwooQIXLf2hr7tup+MHvxA+D+W/iG35reOI3iC192xS/EY+BMQahQhTAiPsSP3Dmv32MYaphV6aGt/H189/oNfx2r+Fyc98ANny9E29b+pa1uXUabqO5n6thS1XDKR7vpr4VP9UU5YPliVP++EQnfosZnXisWmp9a+23j819u9/St635Y5I/N09sNYpGjhyZur11RBe2ht8WN+xu2fA1W/o9xnUafvHScIrPbaolpzXfGp/q93zi+dnwvo52qv1tzTfTbW1Rak+rwYnHoqnj1tTrt+W4NbWdln7Hp/rdt7Ylqbnf8YnrtNQy1tR7TcPW/+b+vzb8cu7EnyH+f41fTDRs9clP8f9zDEANW47y8/H+1r7eqX4PrT02Lf38TXWdzc/n/26e2KW74d/O5n5/rW1xbGq99vyMLR2vllqUova8V0UXX3xx+sK4mLQMAfXiG3dL4htbw9aXchD3Od/tKI6Vgo4+v/IfeuI5BnSc+EVMa3pVQGdRsxgAAMgkYQgAAMikdoWhBQsWpMGpsfLHlClTwooVK5pdd9GiReE973lPKvEbp2nTpp20/ic/+cmT+rFefvnl7dk1AACAwoShJUuWhNmzZ4c77rgjrFq1KkyYMCFMnz49VeZpymOPPRY+9rGPhV/96ldh+fLlYdSoUeGyyy5LJZMbiuEnf9HKOH3/+99v664BAAAUrppcbAl65zvfGb75zW+m5VjtIwacv/iLvwi33377KZ8fq2zEFqL4/JkzZ9a3DMWywA8//HAodMUIAACgcrUlG7SpZSiWBV25cmXq6la/gS5d0nJs9WmNWEIxliEcOHDgSS1IsQzf2LFjw4033piuEdOcWEY3/pANJwAAgLZoUxiKF7CMLTtDhgxpdH9c3rZtW6u2cdttt6WL4DUMVLGL3AMPPBCWLVsWvvrVr4bHH388XHHFFc1e1G7+/Pkp7eWn2DIFAADQFp16YZGvfOUr4aGHHkqtQLH4Qt7VV19dP3/RRReF8ePHh3POOSetd+mll560nTlz5qRxS3mxZUggAgAACtYyNGjQoHQRuu3btze6Py4PHTq0xef+zd/8TQpDP//5z1PYacmYMWPSa61bt67Zq9vH/n8NJwAAgIKFoXgl7kmTJqXubHmxgEJcnjp1arPPu/vuu8Odd94Zli5dGiZPnnzK19myZUsaMzRs2LC27B4AAEDhSmvH7mnx2kH3339/WL16dSp2cPDgwTBr1qz0eKwQF7ux5cUxQHPnzg2LFy9O1yaKY4vidODAgfR4vL3lllvCk08+GTZs2JCC1ZVXXhnOPffcVLIbAACgJMYMzZgxI+zcuTPMmzcvhZqJEyemFp98UYVNmzalCnN53/rWt1IVuj/8wz9stJ14naK//uu/Tt3unn322RSuYnntWFwhXocotiTF7nAAAAAlcZ2hUuQ6QwAAQEGvMwQAAFAphCEAACCThCEAACCThCEAACCThCEAACCThCEAACCThCEAACCThCEAACCThCEAACCThCEAACCThCEAACCTqkMFyOVy6Xb//v3F3hUAAKCI8pkgnxEqPgy99tpr6XbUqFHF3hUAAKBEMkL//v1bXKcq15rIVOLq6urCK6+8Evr27RuqqqpKIo3GYLZ58+bQr1+/Yu8OZcJ5g/MG7zmUOn+rKIfzJsabGISGDx8eunTpUvktQ/GHHDlyZCg18ZctDOG8wfsNpczfKpw3VOL7zalahPIUUAAAADJJGAIAADJJGCqA7t27hzvuuCPdgvOGQvJ+g3OHzuQ9h0o7byqigAIAAEBbaRkCAAAySRgCAAAySRgCAAAySRgCAAAySRgCAAAySRjqYAsWLAijR48OPXr0CFOmTAkrVqzo6JegjM2fPz+8853vDH379g2DBw8OV111VVizZk2jdY4cORI+/elPhzPPPDP06dMn/MEf/EHYvn170faZ0vOVr3wlVFVVhZtvvrn+PucNzXn55ZfDxz/+8fSe0rNnz3DRRReF3/zmN/WPx6Ky8+bNC8OGDUuPT5s2Laxdu9YBzbDa2towd+7ccPbZZ6dz4pxzzgl33nlnOlfynDdE//Vf/xU+/OEPh+HDh6e/Sw8//HBoqDXnyauvvhquueaa0K9fvzBgwIBw3XXXhQMHDoTOIgx1oCVLloTZs2enOuqrVq0KEyZMCNOnTw87duzoyJehjD3++OMp6Dz55JPhF7/4RXj99dfDZZddFg4ePFi/zmc+85nw7//+7+EHP/hBWv+VV14JH/3oR4u635SOp59+Onz7298O48ePb3S/84am7NmzJ7z73e8O3bp1C//xH/8R/u///i987WtfC2eccUb9OnfffXf4+te/HhYuXBieeuqp0Lt37/S3KwZssumrX/1q+Na3vhW++c1vhtWrV6fleJ584xvfqF/HeUMUP7/Ez7uxMaAprTlPYhD63e9+lz4X/eQnP0kB64YbbgidJl5niI5x8cUX5z796U/XL9fW1uaGDx+emz9/vkNMk3bs2BG/Zss9/vjjaXnv3r25bt265X7wgx/Ur7N69eq0zvLlyx3FjHvttddy5513Xu4Xv/hF7n3ve1/upptuSvc7b2jObbfdlrvkkkuafbyuri43dOjQ3D333FN/Xzyfunfvnvv+97/vwGbUhz70odyf/MmfNLrvox/9aO6aa65J884bmhI/q/zbv/1b/XJrzpP/+7//S897+umn69f5j//4j1xVVVXu5ZdfznUGLUMd5NixY2HlypWp+S+vS5cuaXn58uUd9TJUmH379qXbgQMHptt4DsXWoobn0bhx48Jb3/pW5xGpVfFDH/pQo/PDeUNLHnnkkTB58uTwR3/0R6lr7jve8Y6waNGi+sdfeumlsG3btkbnVP/+/VM3b3+7sutd73pXWLZsWXjhhRfS8v/8z/+EJ554IlxxxRVp2XlDa7TmPIm3sWtcfJ/Ki+vHz9CxJakzVHfKq2TArl27Uh/bIUOGNLo/Lj///PNF2y9KV11dXRrzEbuwXHjhhem++KZRU1OT3hhOPI/iY2TXQw89lLrfxm5yJ3Le0Jz169en7k6xC/df/dVfpfPnL//yL9P7zLXXXlv/vtLU3y7vOdl1++23h/3796cv47p27Zo+33zpS19K3Zki5w2t0ZrzJN7GL2oaqq6uTl8Sd9Z7kDAERfyW/3//93/Tt23Qks2bN4ebbrop9aeOxVmgLV+6xG9cv/zlL6fl2DIU33di//0YhqAp//qv/xr++Z//OfzLv/xLePvb3x6eeeaZ9OVdHCTvvKHS6CbXQQYNGpS+PTmx6ldcHjp0aEe9DBXiz//8z9MgwV/96ldh5MiR9ffHcyV2udy7d2+j9Z1H2Ra7T8ZCLL/3e7+XvjGLUyyuEQelxvn4LZvzhqbECk4XXHBBo/vOP//8sGnTpjSf//vkbxcN3XLLLal16Oqrr07VBz/xiU+kIi2xIqrzhtZqzftLvD2x0Njx48dThbnO+vwsDHWQ2OVg0qRJqY9tw2/k4vLUqVM76mUoc3F8YQxC//Zv/xZ++ctfprKlDcVzKFZ9angexdLb8YOL8yi7Lr300vDcc8+lb2fzU/y2P3ZZyc87b2hK7IZ7Yvn+OA7krLPOSvPxPSh+4Gj4nhO7R8W++t5zsuvQoUNpzEZD8Qvf+Lkmct7QGq05T+Jt/AI4fumXFz8fxXMtji3qFJ1SpiEjHnrooVQh43vf+16qjnHDDTfkBgwYkNu2bVuxd40SceONN+b69++fe+yxx3Jbt26tnw4dOlS/zp/92Z/l3vrWt+Z++ctf5n7zm9/kpk6dmiZoqGE1OecNzVmxYkWuuro696UvfSm3du3a3D//8z/nevXqlXvwwQfr1/nKV76S/lb9+Mc/zj377LO5K6+8Mnf22WfnDh8+7MBm1LXXXpsbMWJE7ic/+UnupZdeyv3oRz/KDRo0KHfrrbfWr+O8IV/l9Le//W2aYqy499570/zGjRtbfZ5cfvnluXe84x25p556KvfEE0+kqqkf+9jHcp1FGOpg3/jGN9IH2ZqamlRq+8knn+zol6CMxTeKpqbvfve79evEN4hPfepTuTPOOCN9aPnIRz6SAhO0FIacNzTn3//933MXXnhh+rJu3Lhxue985zuNHo/lb+fOnZsbMmRIWufSSy/NrVmzxgHNsP3796f3l/h5pkePHrkxY8bkPve5z+WOHj1av47zhuhXv/pVk59rYqBu7Xmye/fuFH769OmT69evX27WrFkpZHWWqvhP57RBAQAAlA5jhgAAgEwShgAAgEwShgAAgEwShgAAgEwShgAAgEwShgAAgEwShgAAgEwShgAAgEwShgAAgEwShgAAgEwShgAAgJBF/x8RN/UBnecqMAAAAABJRU5ErkJggg==",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzoAAAKTCAYAAADR1X0mAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjgsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvwVt1zgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAO8VJREFUeJzt3QmUFOW5P+B32HEBJCiI4p6IKxJcAmqMSuQqMVFjYtQIF43GJQYlibsYYxRNlGgMikvUmKui3uuGcT0oRq4LIEG9ijsKQRH8q4AgDDD9P1X3zFwwgMx0z1R1z/OcU9bS1d0f1eXM/Pr76q2qQqFQCAAAgArSIusGAAAAlJqgAwAAVBxBBwAAqDiCDgAAUHEEHQAAoOIIOgAAQMURdAAAgIrTKspATU1NvP/++7H++utHVVVV1s0BAAAyktwGdMGCBdG9e/do0aJFeQedJOT06NEj62YAAAA5MXPmzNh0003LO+gkPTm1/5gOHTpk3RwAACAj8+fPTztBajNCWQed2uFqScgRdAAAgKovuaRFMQIAAKDiCDoAAEDFEXQAAICKI+gAAAAVR9ABAAAqjqADAABUHEEHAACoOIIOAABQcQQdAACg4gg6AABAxRF0AACAiiPoAAAAFUfQAQAAKo6gAwAAVBxBBwAAqDiCDgAAUHEEHQAAoOIIOgAAQMURdAAAgIoj6AAAABVH0AEAACqOoAMAAFQcQQcAAKg4gg4AAFBxBJ16mDt3buyzzz6x9957N94nAgAAFK1V8S/RfNTU1MTf//73dLlQKERVVVXWTQIAAFZBj049tGnTpm65urq6Pk8FAACakKBTD23btq1bFnQAACC/BJ0GBp0lS5Y0xucBAACUgKBTDy1btkynhKADAAD5Jeg0sFdH0AEAgPwSdBpYkEDQAQCACgo6SXnlgw8+OLp3756WV77vvvvWuP8999wT3/72t2PDDTeMDh06RN++fePRRx+Ncu/RUYwAAAAqKOgsXLgwevXqFaNGjVrrYJQEnYceeiheeOGF2HfffdOg9I9//CPKkaFrAABQgTcMPfDAA9NpbV155ZUrrV9yySVx//33x9ixY6N3795RbgQdAACowKBTrJqamliwYEF07tx5tfsk17+seA3M/PnzIy8EHQAAyL8mL0Zw+eWXx2effRY//OEPV7vPiBEjomPHjnVTjx49Ii8EHQAAyL8mDTq33357XHjhhXHXXXfFRhtttNr9zj777Jg3b17dNHPmzMgLVdcAACD/mmzo2pgxY+InP/lJ3H333dG/f/8v7TWp7TnJG1XXAAAg/5qkR+eOO+6IIUOGpPOBAwdGOTN0DQAAKrBHJ7m+5q233qpbnz59ekydOjUtLrDZZpulw85mzZoVt956a91wtcGDB8dVV10Ve+yxR8yePTvd3r59+/T6m3Ij6AAAQAX26EyePDktC11bGnrYsGHp8vDhw9P1Dz74IGbMmFG3//XXXx/Lli2LU045JTbeeOO6aejQoVGOBB0AAKjAHp1vfetbUSgUVvv4LbfcstL6+PHjo5IIOgAAkH9NXl663NVWXauurs66KQAAwGoIOvWkRwcAAPJP0KknQQcAAPJP0KknQQcAAPJP0KknQQcAAPJP0GlgMYIlS5Y0xucBAACUgKDTwB4dVdcAACC/BJ16MnQNAADyT9CpJ0EHAADyT9CpJ0EHAADyT9CpJ0EHAADyT9CpJ1XXAAAg/wSdelJ1DQAA8k/QqSdD1wAAIP8EnXoSdAAAIP8EnXoSdAAAIP8EnXoSdAAAIP8EnQZWXauurm6MzwMAACgBQaee9OgAAED+CTr1JOgAAED+CToNDDrLly9PJwAAIH8EnQYGncSSJUtK/XkAAAAlIOg0sBhBQtABAIB8EnSKCDoqrwEAQD4JOvVUVVVVF3b06AAAQD4JOg2g8hoAAOSboNMAgg4AAOSboNMAgg4AAOSboNMAtdfoKEYAAAD5JOg0gB4dAADIN0GnAQQdAADIN0GnAQQdAADIN0GnAQQdAADIN0GnAQQdAADIN0GnAVRdAwCAfBN0GkCPDgAA5Jug0wCCDgAA5Jug0wCCDgAA5Jug0wCCDgAA5Jug0wCKEQAAQL4JOg2gRwcAAPJN0GkAQQcAAPJN0GkAQQcAAPJN0GkAQQcAAPJN0GkAQQcAAPJN0GkAVdcAACDfBJ0G0KMDAAD5Jug0gKADAAD5Jug0gKADAAD5Jug0gKADAAD5JugUEXSqq6tL/XkAAAAlIOgUUXVtyZIlpfgMAACAEhN0GsDQNQAAyDdBpwEEHQAAyDdBpwEEHQAAyDdBpwEEHQAAyDdBp4hiBKquAQBAPgk6RfboFAqFUn8mAABAkQSdIoJOYunSpcV+BgAAQIkJOkUGHffSAQCA/BF0GkDQAQCAfBN0GqBly5bplFCQAAAA8kfQKbLymqFrAACQP4JOA7mXDgAA5Jeg00CCDgAA5Jeg00CCDgAA5Jeg00CCDgAA5JegU2TQUXUNAADyR9BpIFXXAAAgvwSdBjJ0DQAA8kvQaSBBBwAA8kvQaSBBBwAA8kvQaSBBBwAA8kvQKbIYgaprAACQP4JOA+nRAQCA/BJ0GkjQAQCA/BJ0GkjQAQCA/BJ0GkjQAQCA/BJ0igw6ihEAAED+CDpFVl1bsmRJKT8PAACgBASdBjJ0DQAAKijo/P3vf4+DDz44unfvHlVVVXHfffd96XPGjx8fX//619NwsM0228Qtt9wS5U7QAQCACgo6CxcujF69esWoUaPWav/p06fHwIEDY999942pU6fGaaedFj/5yU/i0UcfjXIm6AAAQH61qu8TDjzwwHRaW6NHj44tt9wyrrjiinR9u+22iwkTJsQf/vCHGDBgQJQrQQcAAJrxNTrPPvts9O/ff6VtScBJtq9OcoH//PnzV5ryRtU1AABoxkFn9uzZ0bVr15W2JetJePn8889X+ZwRI0ZEx44d66YePXpE3qi6BgAA+ZXLqmtnn312zJs3r26aOXNm5I2hawAAUEHX6NRXt27d4sMPP1xpW7LeoUOHaN++/WpDRG2QyCtBBwAAmnGPTt++fWPcuHErbXv88cfT7eVM0AEAgAoKOp999llaJjqZastHJ8szZsyoG3Y2aNCguv1PPPHEeOedd+KMM86I1157La655pq466674vTTT49yphgBAABUUNCZPHly9O7dO50Sw4YNS5eHDx+ern/wwQd1oSeRlJb+29/+lvbiJPffScpM33jjjWVdWjqhGAEAAORXVaFQKETOJRXakuprSWGC5NqePEh6sZKAl1yDlIQ7AAAgP9kgl1XXyoFrdAAAIL8EnQYSdAAAIL8EnQYSdAAAIL8EnSKDzvLly9MJAADID0GnyKprierq6lJ9HgAAQAkIOkX26CSWLFlSis8CAAAoEUGnBD06gg4AAOSLoNNAVVVVbhoKAAA5JeiUYPiaa3QAACBfBJ0iKDENAAD5JOiU4Dod1+gAAEC+CDpF0KMDAAD5JOgUQdABAIB8EnSKIOgAAEA+CTpFUHUNAADySdApgmIEAACQT4JOEQxdAwCAfBJ0iiDoAABAPgk6RRB0AAAgnwSdIihGAAAA+SToFEGPDgAA5JOgUwRV1wAAIJ8EnSLo0QEAgHwSdIog6AAAQD4JOkUQdAAAIJ8EnSKougYAAPkk6BRBjw4AAOSToFMEVdcAACCfBJ0i6NEBAIB8EnSKIOgAAEA+CTpFEHQAACCfBJ0iqLoGAAD5JOgUQTECAADIJ0GnCIauAQBAPgk6RRB0AAAgnwSdIgg6AACQT4JOERQjAACAfBJ0iqBHBwAA8knQKYKqawAAkE+CThH06AAAQD4JOiUKOoVCoVSfCQAAUCRBpwRBJ7F06dJiPwsAAKBEBJ0SBZ3q6upSfB4AAEAJCDolCjrJ8DUAACAfBJ0itGzZMlq0+N9DKOgAAEB+CDpFUnkNAADyR9ApkqADAAD5I+iUKOgoRgAAAPkh6BRJjw4AAOSPoFOkNm3apHPFCAAAID8EnSLp0QEAgPwRdIok6AAAQP4IOkUSdAAAIH8EnSKpugYAAPkj6BRJjw4AAOSPoFMkVdcAACB/BJ0i6dEBAID8EXSKJOgAAED+CDpFUowAAADyR9Apkh4dAADIH0GnSIIOAADkj6BTJFXXAAAgfwSdIunRAQCA/BF0iiToAABA/gg6RVJ1DQAA8kfQKZIeHQAAyB9Bp0iKEQAAQP4IOkXSowMAAPkj6BRJ0AEAgPwRdIqkGAEAAOSPoFMkPToAAJA/gk6RBB0AAMgfQadIqq4BAED+CDpF0qMDAAD5I+gUSdABAID8EXSKpOoaAADkj6BTJD06AACQP4JOkQQdAADIH0GnRFXXli1bFjU1NaX4TAAAgCIJOiXq0UksWbKk2JcDAACyCjqjRo2KLbbYItq1axd77LFHTJw4cY37X3nllbHttttG+/bto0ePHnH66afH4sWLo9KCTnV1daZtAQAAGhh07rzzzhg2bFhccMEFMWXKlOjVq1cMGDAg5syZs8r9b7/99jjrrLPS/adNmxZ//vOf09c455xzKmroWkKPDgAAlGnQGTlyZBx//PExZMiQ2H777WP06NGxzjrrxE033bTK/Z955pnYc88946ijjkp7gQ444IA48sgjv7QXqFxUVVXVhR1BBwAAyjDoJEOzXnjhhejfv///vUCLFun6s88+u8rn9OvXL31ObbB555134qGHHoqDDjpote+TBIb58+evNOWZoAMAAPnSqj47f/TRR7F8+fLo2rXrStuT9ddee22Vz0l6cpLn7bXXXlEoFNLqZCeeeOIah66NGDEiLrzwwiin63Q+++wzPToAANBcqq6NHz8+LrnkkrjmmmvSa3ruueee+Nvf/hYXXXTRap9z9tlnx7x58+qmmTNnRp65lw4AAJRxj06XLl2iZcuW8eGHH660PVnv1q3bKp9z/vnnxzHHHBM/+clP0vWddtopFi5cGCeccEKce+656dC3VQWHFauZ5V1tW1VdAwCAMuzRSa5F6dOnT4wbN65uW3KTzGS9b9++q3zOokWL/iXMJGEpkQxlqwR6dAAAoIx7dBJJaenBgwfHrrvuGrvvvnt6j5ykhyapwpYYNGhQbLLJJul1NomDDz44rdTWu3fv9J47b731VtrLk2yvDTzlTtABAIAyDzpHHHFEzJ07N4YPHx6zZ8+OXXbZJR555JG6AgUzZsxYqQfnvPPOS0swJ/NZs2bFhhtumIaciy++OCqFqmsAAJAvVYUyGD+WlJfu2LFjWpigQ4cOkTd77713TJgwIe6+++44/PDDs24OAABUrLXNBo1eda05MHQNAADyRdApAVXXAAAgXwSdEtCjAwAA+SLolICgAwAA+SLolICqawAAkC+CTgno0QEAgHwRdEpAMQIAAMgXQacE9OgAAEC+CDolIOgAAEC+CDoloBgBAADki6BTAnp0AAAgXwSdEhB0AAAgXwSdElB1DQAA8kXQKQE9OgAAkC+CTgkIOgAAkC+CTgmougYAAPki6JSAHh0AAMgXQacEFCMAAIB8EXRKQI8OAADki6BTAoIOAADki6BTAoIOAADki6BTAqquAQBAvgg6JaBHBwAA8kXQKQFV1wAAIF8EnRL36BQKhVK8JAAAUARBp4RBJwk5y5YtK8VLAgAARRB0SliMoLZXBwAAyJagU8IenYSgAwAA2RN0SqBVq1bRosX/Hsrq6upSvCQAAFAEQadElJgGAID8EHRKRNABAID8EHRKRNABAID8EHRKXHlNMQIAAMieoFMienQAACA/BJ0SBx1V1wAAIHuCTono0QEAgPwQdEpE0AEAgPwQdEpE0AEAgPwQdEpE1TUAAMgPQadEFCMAAID8EHRKxNA1AADID0GnRAQdAADID0GnRAQdAADID0GnRBQjAACA/BB0SkSPDgAA5IegUyKqrgEAQH4IOiWiRwcAAPJD0CkRQQcAAPJD0CkRQQcAAPJD0CkRVdcAACA/BJ0S0aMDAAD5IeiUiKprAACQH4JOiejRAQCA/BB0SkTQAQCA/BB0SkTQAQCA/BB0SkTVNQAAyA9Bp0QUIwAAgPwQdErE0DUAAMgPQadEBB0AAMgPQadEBB0AAMgPQadEFCMAAID8EHRKRI8OAADkh6BT4qCzbNmyqKmpKdXLAgAADSDolDjoJKqrq0v1sgAAQAMIOo0QdJYsWVKqlwUAABpA0ClxMYKEoAMAANkSdEqkqqoqOnfunC6/+eabpXpZAACgAQSdEjrooIPS+b333lvKlwUAAOpJ0Cmhww47LJ3fc889USgUSvnSAABAPQg6JTRgwIBo3759TJ8+PV588cVSvjQAAFAPgk4JrbPOOnHggQfW9eoAAADZEHQacfgaAACQDUGnxAYOHBitW7eOV155JV5//fVSvzwAALAWBJ0S69SpU+y///7psuprAACQDUGnERi+BgAA2RJ0GsF3v/vd9AaikyZNihkzZjTGWwAAAGsg6DSCrl27xl577ZUu33fffY3xFgAAwBoIOo3E8DUAAMiOoNNIDj300HT+9NNPx5w5cxrrbQAAgFUQdBrJ5ptvHn369Imampp44IEHGuttAACAVRB0mmD42n/913815tsAAAClCDqjRo2KLbbYItq1axd77LFHTJw4cY37f/rpp3HKKafExhtvHG3bto2vfe1r8dBDD0VzCTrjxo1LjwEAAJDToHPnnXfGsGHD4oILLogpU6ZEr169YsCAAau9DqW6ujq+/e1vx7vvvhv/+Z//Ga+//nrccMMNsckmm0Sl69mzZ2y33XaxdOnS+Nvf/pZ1cwAAoNmod9AZOXJkHH/88TFkyJDYfvvtY/To0bHOOuvETTfdtMr9k+0ff/xxWmZ5zz33THuC9tlnnzQgNQeqrwEAQM6DTtI788ILL0T//v3/7wVatEjXn3322VU+J7kQv2/fvunQteT+MjvuuGNccsklsXz58tW+z5IlS2L+/PkrTeUedB5++OFYtGhR1s0BAIBmoV5B56OPPkoDShJYVpSsz549e5XPeeedd9Iha8nzkutyzj///Ljiiivit7/97WrfZ8SIEdGxY8e6qUePHlGuevfunVZg+/zzz+PRRx/NujkAANAsNHrVtaS88kYbbRTXX399Wm75iCOOiHPPPTcd8rY6Z599dsybN69umjlzZpSrqqoqw9cAACDPQadLly7RsmXL+PDDD1fanqx369Ztlc9JKq0lVdaS59VKLtBPeoCSoXCrklRm69Chw0pTOasdvjZ27NjV/psBAICMgk6bNm3SXpmkXPKKPTbJenIdzqokBQjeeuutdL9ab7zxRhqAktdrDpJjkwzvS3qnDF8DAIAcDl1LSksn5aH/8pe/xLRp0+Kkk06KhQsXplXYEoMGDUqHntVKHk+qrg0dOjQNOEmZ5aQYQVKcoLlIerOOOuqodPnmm2/OujkAAFDxWtX3Cck1NnPnzo3hw4enw8922WWXeOSRR+oKFMyYMSOtxFYrKSSQ9GKcfvrpsfPOO6f3z0lCz5lnnhnNybHHHht/+MMf0uFryT2HkuuWAACAxlFVKBQKkXNJeemk+loy9Kucr9fZfffdY9KkSWnVuaRnDAAAaJxs0OhV11i5V6f2JqplkC8BAKBsCTpN6Ec/+lG0a9cuXnnllZg8eXJTvjUAADQrgk4T6tSpU12p6aRXBwAAaByCTkbD1+644474/PPPm/rtAQCgWRB0mti+++4bm2++eXrx1L333tvUbw8AAM2CoNPUB7xFi7p7Dhm+BgAAjUPQycC///u/R1VVVYwbNy7efffdLJoAAAAVTdDJQDJ0bb/99kuXb7nlliyaAAAAFU3QybgoQRJ0ampqsmoGAABUJEEnI4ceemh6R9f33nsvnnzyyayaAQAAFUnQyUj79u3jqKOOSpcVJQAAgNISdHIwfO2ee+6JTz/9NMumAABARRF0MtSnT5/YaaedYvHixTFmzJgsmwIAABVF0MlQUmLaPXUAAKD0BJ2M/fjHP45WrVrFpEmT4sUXX8y6OQAAUBEEnYxtuOGGcdhhh6XL5513XtbNAQCAiiDo5MBvfvObtFfnwQcfjCeeeCLr5gAAQNkTdHJg2223jRNPPDFd/sUvfuEGogAAUCRBJycuuOCC9AaiU6dOjb/+9a9ZNwcAAMqaoJMTXbp0iXPPPTddTuaLFi3KukkAAFC2BJ0cOfXUU2OLLbaIWbNmxRVXXJF1cwAAoGwJOjnSrl27uPTSS9Plyy67LD744IOsmwQAAGVJ0MmZH/7wh/GNb3wjFi5cGMOHD8+6OQAAUJYEnZypqqqqG7Z20003xcsvv5x1kwAAoOwIOjnUr1+/OPzww9My07/85S+zbg4AAJQdQSenkmt1WrduHY899lg88sgjWTcHAADKiqCTU1tvvXVahS2R9OosW7Ys6yYBAEDZEHRy7LzzzovOnTvHK6+8ErfeemvWzQEAgLIh6OTYBhtsEOecc05duenly5dn3SQAACgLgk7OnXDCCdGpU6d444034v7778+6OQAAUBYEnZxbf/3145RTTqnr1SkUClk3CQAAck/QKQM///nPo127djFx4sR46qmnsm4OAADknqBTBjbaaKM49thj68pOAwAAaybolIlf/OIX0aJFi3j00Udj6tSpWTcHAAByTdApE1tttVX88Ic/TJd/97vfZd0cAADINUGnjJxxxhnp/M4774x33nkn6+YAAEBuCTplpHfv3nHAAQdETU1NXHHFFVk3BwAAckvQKTNnnXVWOr/ppptizpw5WTcHAABySdApM9/61rdit912i8WLF8fVV1+ddXMAACCXBJ0yU1VVFWeeeWa6PGrUqFiwYEHWTQIAgNwRdMrQIYccEl/96lfjk08+iRtuuCHr5gAAQO4IOmWoZcuW8atf/SpdHjlyZFRXV2fdJAAAyBVBp0wNGjQoNt5445g1a1bcfvvtWTcHAAByRdApU23bto2hQ4emy0lRgkKhkHWTAAAgNwSdMnbcccelgWfKlCkxadKkrJsDAAC5IeiUsS5dusQPfvCDdPnaa6/NujkAAJAbgk6ZO+mkk9L5mDFj4uOPP866OQAAkAuCTpnr27dv7LzzzukNRP/yl79k3RwAAMgFQacCbiB68sknp8ujR49WlAAAAASdynD00UfH+uuvH2+88UY88cQTWTcHAAAyp0enAqy33npxzDHHpMuKEgAAgKBTcUUJ7rvvvnj//fezbg4AAGRKj06F2HHHHWOvvfaK5cuXx4033ph1cwAAIFOCTgX26lx//fWxbNmyrJsDAACZEXQqyPe///3YcMMNY9asWTF27NismwMAAJkRdCpI27Zt49hjj02XFSUAAKA5E3QqzE9/+tP03jqPP/54vPnmm1k3BwAAMiHoVJgtt9wyDjzwwHT5uuuuy7o5AACQCUGngosS3HzzzfH5559n3RwAAGhygk4FSnp0Nt988/j444/j7rvvzro5AADQ5ASdCtSyZcs44YQT0uWrr746CoVC1k0CAIAmJehUqOOPPz7atWsXkydPjgkTJmTdHAAAaFKCToVK7qczaNCgdPmKK67IujkAANCkBJ0Kdvrpp6fzBx54QKlpAACaFUGngvXs2TMGDhyYXqNz1VVXZd0cAABoMoJOhRs2bFhdqemkChsAADQHgk6F23fffWOXXXaJRYsWuYEoAADNhqBT4aqqqup6dZJS09XV1Vk3CQAAGp2g0wwcccQR0b179/jggw9izJgxWTcHAAAanaDTDLRp0yZOPfXUdHnkyJFuIAoAQMUTdJqJE044IdZZZ5148cUX48knn8y6OQAA0KgEnWaic+fOMWTIkHTZDUQBAKh0gk4zctppp6XFCR566KGYNm1a1s0BAIBGI+g0I9tss01873vfS5evvPLKrJsDAACNRtBpZmpLTd96660xd+7crJsDAACNQtBpZvbaa6/YbbfdYvHixXHttddm3RwAAGgUgk4zvoHoqFGjYsmSJVk3CQAASk7QaYa+//3vx6abbhpz5syJO+64I+vmAABAyQk6zVDr1q3jZz/7WV1RgkKhkHWTAACgpASdZur444+vu4HoU089lXVzAAAg+6CTXNuxxRZbRLt27WKPPfaIiRMnrtXzxowZk14jcsghhzTkbSnxDUQHDx6cLv/hD39wbAEAaN5B584770wvZr/gggtiypQp0atXrxgwYEB6vceavPvuu/HLX/4y9t5772LaSwkNHTo0nY8dOzbeeustxxYAgOYbdEaOHJkOexoyZEhsv/32MXr06HQI1E033bTa5yxfvjyOPvrouPDCC2OrrbYqts2UyLbbbhsHHXRQeo3O1Vdf7bgCANA8g051dXW88MIL0b9///97gRYt0vVnn312tc/7zW9+ExtttFEcd9xxa/U+Scnj+fPnrzTROE477bR0ngTVefPmOcwAADS/oPPRRx+lvTNdu3ZdaXuyPnv27FU+Z8KECfHnP/85brjhhrV+nxEjRkTHjh3rph49etSnmdRDElJ32GGH+Oyzz9LPCQAAKkGjVl1bsGBBHHPMMWnI6dKly1o/7+yzz057F2qnmTNnNmYzm7WkOERtr84f//jHWLZsWdZNAgCAorWqz85JWGnZsmV8+OGHK21P1rt16/Yv+7/99ttpEYKDDz64bltNTc3/vnGrVvH666/H1ltv/S/Pa9u2bTrRNJLrp84666x477334v77709vKAoAAM2mR6dNmzbRp0+fGDdu3ErBJVnv27fvv+zfs2fPePnll2Pq1Kl103e/+93Yd99902VD0vKhffv2ceKJJ9bdQBQAAJrd0LWktHQyFO0vf/lLTJs2LU466aRYuHBhWoUtMWjQoHToWSK5z86OO+640tSpU6dYf/310+UkOJEPJ598crRu3Tq9pmry5MlZNwcAAJo26BxxxBFx+eWXx/Dhw2OXXXZJe2YeeeSRugIFM2bMiA8++KC4VtHkunfvnn62iauuusonAABAWasqJDdRybmkvHRSfS0pTNChQ4esm1OxktLhu+66a3r9VHK9ThJ+AACgHLNBo1Zdo7wk11/tvffeaeW1a665JuvmAABAgwk6rKS21PR1110XS5cudXQAAChLgg4rSariJddbJTeHffzxxx0dAADKkqDDSpLrc2qLEtx2222ODgAAZUnQ4V8cddRR6fy+++5LS4cDAEC5EXT4F7vvvntsvfXWsWjRorj//vsdIQAAyo6gw7+oqqqKo48+Ol02fA0AgHIk6LDG4WuPPvpozJ0711ECAKCsCDqs0rbbbpveV2f58uVx9913O0oAAJQVQYfVMnwNAIByJeiwWkmZ6eR6nWeeeSamT5/uSAEAUDYEHVare/fusd9++6XLd9xxhyMFAEDZEHRY6+FrhULB0QIAoCwIOqzRYYcdFm3bto1XX301XnrpJUcLAICyIOiwRh07dozvfOc76bJ76gAAUC4EHdZ6+FpynU5NTY0jBgBA7gk6fKkDDzww7dn55z//GU8//bQjBgBA7gk6fKl27drF4Ycfni4bvgYAQDkQdKjX8LW77747lixZ4qgBAJBrgg5r5Zvf/GZ6X51PP/00HnnkEUcNAIBcE3RYKy1btowjjzwyXTZ8DQCAvBN0qPfwtbFjx8bChQsdOQAAckvQYa3tsssuscUWW8TixYvjySefdOQAAMgtQYe1VlVVlZaaTjz88MOOHAAAuSXoUC+1Qeehhx6KQqHg6AEAkEuCDvWy3377RZs2beLdd9+N119/3dEDACCXBB3qZd11101LTScMXwMAIK8EHerNdToAAOSdoEODg85TTz2lzDQAALkk6FBvPXv2jM033zyqq6uVmQYAIJcEHepNmWkAAPJO0KHo63SUmQYAIG8EHYoqMz19+vR44403HEUAAHJF0KFB1ltvvdh7773TZWWmAQDIG0GHBlNmGgCAvBJ0KEmZ6UWLFjmSAADkhqBDg2233Xax2WabxZIlS5SZBgAgVwQdGkyZaQAA8krQoSjKTAMAkEeCDkWXmW7dunW888478eabbzqaAADkgqBDUdZff31lpgEAyB1Bh6IpMw0AQN4IOpQs6IwfP16ZaQAAckHQoWjbb7999OjRIy0znYQdAADImqBD0ZSZBgAgbwQdSsJ1OgAA5ImgQ0nsv//+aZnpt99+O50AACBLgg4lKzO92267pctPP/20owoAQKYEHUpmr732Suf//d//7agCAJApQYeSB50JEyY4qgAAZErQoWT69euXzl977bX46KOPHFkAADIj6FAyX/nKV2K77bZLl5955hlHFgCAzAg6lJThawAA5IGgQ0ntueee6dx1OgAAZEnQoVF6dCZPnhyff/65owsAQCYEHUpqq622im7dusXSpUvTsAMAAFkQdCipqqoqw9cAAMicoEPJuXEoAABZE3Ro1KBTU1PjCAMA0OQEHUquV69esc4668Snn34a06ZNc4QBAGhygg4l17p16/jGN76RLiszDQBAFgQdGoUbhwIAkCVBh0a9cWhynQ4AADQ1QYdGkQxda9GiRUyfPj1mzZrlKAMA0KQEHRpFhw4dYuedd06X9eoAANDUBB0ajfvpAACQFUGHRqMgAQAAWRF0aPSCBFOnTo0FCxY40gAANBlBh0az6aabxuabbx41NTXx/PPPO9IAADQZQYdGZfgaAABZEHRokuFrEyZMcKQBAGgygg5N0qPz3HPPxbJlyxxtAACahKBDo9phhx2iY8eOsXDhwnjxxRcdbQAAmoSgQ+OeYC1aRL9+/dJlNw4FAKCpCDo0OgUJAABoaoIOTRp0CoWCIw4AQKMTdGh0u+22W7Ru3To++OCDePfddx1xAAAanaBDo2vfvn18/etfT5efeeYZRxwAgEYn6NAk9thjj3Q+adIkRxwAgEYn6NAkdt9993Q+ceJERxwAgEYn6NBk1+kkpkyZEkuXLnXUAQDIX9AZNWpUbLHFFtGuXbt0SNKavqW/4YYbYu+9944NNtggnfr37+9b/WZom222iU6dOsWSJUvi5Zdfzro5AABUuHoHnTvvvDOGDRsWF1xwQfrtfK9evWLAgAExZ86cVe4/fvz4OPLII+PJJ5+MZ599Nnr06BEHHHBAzJo1qxTtp4xuHFrbq2P4GgAAuQs6I0eOjOOPPz6GDBkS22+/fYwePTrWWWeduOmmm1a5/2233RYnn3xy7LLLLtGzZ8+48cYbo6amJsaNG1eK9lOG1+koSAAAQK6CTnV1dbzwwgvp8LO6F2jRIl1PemvWxqJFi9JrNDp37rzafZLhTfPnz19povwpSAAAQC6DzkcffRTLly+Prl27rrQ9WZ89e/ZavcaZZ54Z3bt3XyksfdGIESOiY8eOdVMy3I3yVzt07ZVXXokFCxZk3RwAACpYk1Zdu/TSS2PMmDFx7733poUMVufss8+OefPm1U0zZ85symbSSDbeeOM0tBYKhfT6LgAAyEXQ6dKlS7Rs2TI+/PDDlbYn6926dVvjcy+//PI06Dz22GOx8847r3Hftm3bRocOHVaaqAyGrwEAkLug06ZNm+jTp89KhQRqCwv07dt3tc/73e9+FxdddFE88sgjseuuuxbXYsqaoAMAQFNoVd8nJKWlBw8enAaW5I/WK6+8MhYuXJhWYUsMGjQoNtlkk/Q6m8Rll10Ww4cPj9tvvz29907ttTzrrbdeOtG8KDENAEAug84RRxwRc+fOTcNLElqSstFJT01tgYIZM2akldhqXXvttWm1tsMPP3yl10nuw/PrX/+6FP8GykjSI1hVVZWeJ8n582VDHgEAoCGqCsmV4TmXlJdOqq8lhQlcr1P+dthhh3j11VfjgQceiIMPPjjr5gAAUEbWNhs0adU1SLhOBwCAxibokFnQmTRpkqMPAECjEHTItEenDEZOAgBQhgQdmtxOO+2U3ivpk08+ibffftsnAABAyQk6NLnkfky9e/eu69UBAIBSE3TIhIIEAAA0JkGHTLhxKAAAjUnQIdMenSlTpsTSpUt9CgAAlJSgQya22Wab6NSpUyxZsiRefvllnwIAACUl6JCJFi1aGL4GAECjEXTIjIIEAAA0FkGHzIPOpEmTfAoAAJSUoEPmlddeeeWVWLBggU8CAICSEXTIzMYbbxw9evSIQqGQVl8DAIBSEXTIlOt0AABoDIIOmXLjUAAAGoOgQ6b06AAA0BgEHTLVp0+fqKqqihkzZsTs2bN9GgAAlISgQ6Y6dOgQ2223Xbo8ceJEnwYAACUh6JC5fv36pfPx48dn3RQAACqEoEPmDjjggHT+6KOPZt0UAAAqhKBD5vbff//0Op1XX301/vnPf2bdHAAAKoCgQ+Y6d+5cV2b68ccfz7o5AABUAEGHXBgwYEA6N3wNAIBSEHTI1XU6SY/O8uXLs24OAABlTtAhF/bYY4+01PTHH38c//jHP7JuDgAAZU7QIRdat24d++23X7ps+BoAAMUSdMjd8LXHHnss66YAAFDmBB1yV5DgmWeeifnz52fdHAAAypigQ25stdVWsfXWW8eyZcti/PjxWTcHAIAyJuiQK4avAQBQCoIOueJ+OgAAlIKgQ67su+++0bJly3jrrbfinXfeybo5AACUKUGHXEnupdO3b9+6m4cCAEBDCDrkjuFrAAAUS9AhtwUJxo0bl1ZgAwCA+hJ0yJ0+ffpE586d03vpTJw4MevmAABQhgQdcicpRtC/f/90+dFHH826OQAAlCFBh1xyPx0AAIoh6JDroJMMXfvkk0+ybg4AAGVG0CGXevToEdttt13U1NSkRQkAAKA+BB1yy/A1AAAaStChLIJOoVDIujkAAJQRQYfc2meffaJNmzbx3nvvxRtvvJF1cwAAKCOCDrm17rrrxl577ZUuP/TQQ1k3BwCAMiLokGuHHnpoOr/hhhsMXwMAYK0JOuTaoEGDYr311otp06bFE088kXVzAAAoE4IOudahQ4cYPHhwunz11Vdn3RwAAMqEoEPu/exnP0vnY8eOjXfffTfr5gAAUAYEHXKvZ8+e0b9///Tmoddcc03WzQEAoAwIOpSFU089NZ3feOONsWjRoqybAwBAzgk6lIWBAwfGFltsEZ988kncfvvtWTcHAICcE3QoCy1btoxTTjmlrihBoVDIukkAAOSYoEPZOPbYY6N9+/bx0ksvxYQJE7JuDgAAOSboUDY6d+4cP/7xj9NlpaYBAFgTQYeyLDV9zz33xD//+c+smwMAQE4JOpSVnXfeOb75zW/G8uXLY/To0Vk3BwCAnBJ0KNtS09dff30sXrw46+YAAJBDgg5l55BDDolNN9005s6dG3fddVfWzQEAIIcEHcpOq1at4qSTTkqXlZoGAGBVBB3K0vHHHx9t27aNyZMnx8SJE7NuDgAAOSPoUJY23HDD+NGPfpQuDx8+3A1EAQBYiaBD2TrnnHOiXbt28dhjj8Wf//znrJsDAECOCDqUra997Wvx29/+Nl0eNmxYvPfee1k3CQCAnBB0KGunnXZa9OvXLxYsWBDHHXecIWwAAKQEHcpay5Yt4+abb4727dvHuHHj4rrrrsu6SQAA5ICgQ0UMYRsxYkS6/Mtf/jKmT5+edZMAAMiYoENFOPXUU2PvvfeOhQsXxrHHHhs1NTVZNwkAgAwJOlSEFi1axE033RTrrLNOjB8/Pq699tqsmwQAQIYEHSrGNttsE5dddlm6fMYZZ8Tbb7+ddZMAAMiIoENFOfnkk+Nb3/pWLFq0KIYMGWIIGwBAMyXoUJFD2NZdd914+umn46KLLlJyGgCgGRJ0qDhbbrllXH755enyr3/96zjmmGPSIgUAADQfgg4V6ac//WmMHDkyvc/ObbfdFn379o233nor62YBANBEBB0qUlVVVZx++unxxBNPRNeuXePll1+OXXfdNR544IGsmwYAQBMQdKho3/zmN2PKlCmx5557xrx58+J73/tenHfeebF8+fKsmwYAQCMSdKh43bt3T3t2fv7zn6frF198cRx44IExd+7crJsGAEAjEXRoFtq0aRNXXXVV3H777elNRR9//PHo0aNHHHHEEfHggw/G0qVLs24iAAAlJOjQrBx55JHx/PPPR+/evWPJkiVx1113xcEHHxybbLJJDB06NCZPnqwcNQBABagqFAqFyLn58+dHx44d02ssOnTokHVzqADJaT916tS49dZb016eOXPm1D223XbbxXe/+93o1atX7LTTTrHttttG69atM20vAAD1ywaCDs3esmXL4rHHHou//vWvcd9998XixYtXOiZJyEnCz84775xOSfDZeOON02t/kopurVq1avbHEACgIoLOqFGj4ve//33Mnj07/db76quvjt133321+999991x/vnnx7vvvhtf/epX47LLLouDDjqo5P8YKFZyjiVh57nnnktLUr/00kuxYMGCNZax3nDDDdPQk4SfJPh06tQpnZJzdsXlZEquD1pxateuXbRoYQQpAEDmQefOO++MQYMGxejRo2OPPfaIK6+8Mg0yr7/+emy00Ub/sv8zzzyTlvgdMWJEfOc730mHCSVBJyn5u+OOO5b0HwOllvzv8d5779WFnmSaPn16fPDBB+lUijLVSdhp3759tG3bdrVT0quUFFRI5l9cTqakV+mLU3Kz1Nr5itOK25KQteJjK64ny7XTF9dXnJKwt6r1Nc1rpy+uf9n2NT2eWN3+q3tsxe21ywBAMw06SbjZbbfd4k9/+lO6XlNTk1avOvXUU+Oss876l/2TqlYLFy5MK1vV+sY3vhG77LJLGpZWJblIPJlW/Mck7yHokCfJuf/RRx/F+++/n4aeZJ5c65Ocp8n06aefrjRPps8//zwWLVr0L8PjyJ9VBaA1haOG7FPKx764vT6vVZ/XWdV6qZ/3Za/T0NdsrLaurcb4dxQTzpv635in5xX73HJ/zcZ83by8X1bvWcntTBx99NFx0kknRbkEnXpdXFBdXR0vvPBCnH322XXbkm9W+/fvH88+++wqn5NsHzZs2ErbBgwYkA4PWp2k9+fCCy+sT9OgySXnftKLmUxJcK9vSKoNPbVTbcBf1ZSUv06m5P/B2uXaKbnG6ItT7fakx6l2XjvVridtWHH+xW2106rWk+9HVtxWu177WO362sy/uLyq9Sxk+d4AkEd77rlnlJN6BZ3k2+vkD5nkOoQVJeuvvfbaKp+TXMezqv2T7auTBKkVw1Ftjw5UUkhad91104m1s2Lw+eK0psdX99iK21e3vDb7rWneVI992f6r23dt1ot5nVI/78seW9N7FPO6jf3YmjT1+zXW65bL8xrrdbP40qQx3jNPX/7kqS15bE9jtfVrX/talJNclouqvS4BoNaKQ8EAAL5Mvco9denSJb0o+cMPP1xpe7LerVu3VT4n2V6f/QEAAJo06CSVnvr06RPjxo2r25aMpU/W+/btu8rnJNtX3D/x+OOPr3Z/AACAJh+6llw7M3jw4Nh1113Te+ck5aWTqmpDhgxJH09KT2+yySZpQYHE0KFDY5999okrrrgiBg4cGGPGjInJkyfH9ddfX3TjAQAAShJ0knLRc+fOjeHDh6cFBZJqU4888khdwYEZM2asdAPEfv36pffOOe+88+Kcc85JbxiaVFxb23voAAAA1Fe976OTBTcMBQAA6pMN6nWNDgAAQDkQdAAAgIoj6AAAABVH0AEAACqOoAMAAFQcQQcAAKg4gg4AAFBxBB0AAKDiCDoAAEDFEXQAAICKI+gAAAAVR9ABAAAqjqADAABUHEEHAACoOIIOAABQcQQdAACg4gg6AABAxRF0AACAiiPoAAAAFadVlIFCoZDO58+fn3VTAACADNVmgtqMUNZBZ8GCBem8R48eWTcFAADISUbo2LHjah+vKnxZFMqBmpqaeP/992P99dePqqqqzBNkErhmzpwZHTp0yLQtlA/nDc4d/Mwh7/yuolzOnSS+JCGne/fu0aJFi/Lu0Un+AZtuumnkSfIhCjo4b/AzhzzzuwrnDZX6M2dNPTm1FCMAAAAqjqADAABUHEGnntq2bRsXXHBBOgfnDY3NzxycNzQVP2+otHOnLIoRAAAA1IceHQAAoOIIOgAAQMURdAAAgIoj6AAAABVH0AEAACqOoFMPo0aNii222CLatWsXe+yxR0ycOLHxPhnK0ogRI2K33XaL9ddfPzbaaKM45JBD4vXXX19pn8WLF8cpp5wSX/nKV2K99daL73//+/Hhhx9m1mby59JLL42qqqo47bTT6rY5b1iVWbNmxY9//OP050n79u1jp512ismTJ9c9nhRWHT58eGy88cbp4/37948333zTwWzGli9fHueff35sueWW6Tmx9dZbx0UXXZSeK7WcNyT+/ve/x8EHHxzdu3dPfyfdd999saK1OU8+/vjjOProo6NDhw7RqVOnOO644+Kzzz6LpiLorKU777wzhg0bltYInzJlSvTq1SsGDBgQc+bMadxPiLLy1FNPpSHmueeei8cffzyWLl0aBxxwQCxcuLBun9NPPz3Gjh0bd999d7r/+++/H4cddlim7SY/Jk2aFNddd13svPPOK2133vBFn3zySey5557RunXrePjhh+PVV1+NK664IjbYYIO6fX73u9/FH//4xxg9enQ8//zzse6666a/u5LgTPN02WWXxbXXXht/+tOfYtq0ael6cp5cffXVdfs4b0gkf7skf+8mX/SvytqcJ0nIeeWVV9K/iR588ME0PJ1wwgnRZJL76PDldt9998Ipp5xSt758+fJC9+7dCyNGjHD4WK05c+YkX5EVnnrqqXT9008/LbRu3bpw99131+0zbdq0dJ9nn33WkWzmFixYUPjqV79aePzxxwv77LNPYejQoel25w2rcuaZZxb22muv1R6cmpqaQrdu3Qq///3v67Yl51Lbtm0Ld9xxh4PaTA0cOLBw7LHHrrTtsMMOKxx99NHpsvOGVUn+Trn33nvr1tfmPHn11VfT502aNKlun4cffrhQVVVVmDVrVqEp6NFZC9XV1fHCCy+kXXK1WrRoka4/++yzjZlDKXPz5s1L5507d07nyXmU9PKseC717NkzNttsM+cSaW/gwIEDVzo/nDeszgMPPBC77rpr/OAHP0iHyvbu3TtuuOGGusenT58es2fPXul86tixYzr02u+u5qtfv34xbty4eOONN9L1F198MSZMmBAHHnhguu68YW2szXmSzJPhasnPqVrJ/snf0EkPUFNo1STvUuY++uijdExr165dV9qerL/22muZtYt8q6mpSa+xSIaW7Ljjjum25IdCmzZt0v/xv3guJY/RfI0ZMyYdFpsMXfsi5w2r8s4776RDkJJh1eecc0567vz85z9Pf8YMHjy47mfKqn53+XnTfJ111lkxf/789Eu2li1bpn/fXHzxxekQo4TzhrWxNudJMk++hFlRq1at0i9/m+pnkKADjfjt/P/8z/+k35TBmsycOTOGDh2ajmFOip3A2n6ZknxTeskll6TrSY9O8jMnGS+fBB1Ylbvuuituu+22uP3222OHHXaIqVOnpl/KJRecO2+oNIaurYUuXbqk33p8sTJWst6tW7fG+mwoYz/72c/Si+6efPLJ2HTTTeu2J+dLMhTy008/XWl/51LzlgxpTAqbfP3rX0+/7UqmpFBFcpFnspx8Q+a84YuSSkfbb7/9Stu22267mDFjRrpc+/vJ7y5W9Ktf/Srt1fnRj36UVuk75phj0mInSdVQ5w1ra21+viTzLxbtWrZsWVqJran+fhZ01kIyDKBPnz7pmNYVv0lL1vv27duYnw9lJrleLwk59957bzzxxBNp+c4VJedRUiFpxXMpKT+d/GHiXGq+9t9//3j55ZfTb1Zrp+Sb+mQoSe2y84YvSobFfrF8fXLdxeabb54uJz9/kj8mVvx5kwxZSsbG+3nTfC1atCi9RmJFyZe5yd81CecNa2NtzpNknnyxm3yZVyv52yg515JreZpEk5Q8qABjxoxJK0nccsstaRWJE044odCpU6fC7Nmzs24aOXLSSScVOnbsWBg/fnzhgw8+qJsWLVpUt8+JJ55Y2GyzzQpPPPFEYfLkyYW+ffumE6xoxaprzhtWZeLEiYVWrVoVLr744sKbb75ZuO222wrrrLNO4T/+4z/q9rn00kvT31X3339/4aWXXip873vfK2y55ZaFzz//3EFtpgYPHlzYZJNNCg8++GBh+vTphXvuuafQpUuXwhlnnFG3j/OG2kqg//jHP9IpiQwjR45Ml9977721Pk/+7d/+rdC7d+/C888/X5gwYUJaWfTII48sNBVBpx6uvvrq9A/UNm3apOWmn3vuucb7ZChLyQ+CVU0333xz3T7JD4CTTz65sMEGG6R/lBx66KFpGII1BR3nDasyduzYwo477ph+EdezZ8/C9ddfv9LjSQnY888/v9C1a9d0n/3337/w+uuvO5jN2Pz589OfLcnfM+3atStstdVWhXPPPbewZMmSun2cNySefPLJVf5Nk4TltT1P/t//+39psFlvvfUKHTp0KAwZMiQNUE2lKvlP0/QdAQAANA3X6AAAABVH0AEAACqOoAMAAFQcQQcAAKg4gg4AAFBxBB0AAKDiCDoAAEDFEXQAAICKI+gAAAAVR9ABAAAqjqADAABEpfn/ObIVfee6w18AAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 1000x800 with 1 Axes>"
       ]
@@ -240,7 +240,7 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1200x400 with 3 Axes>"
       ]
@@ -316,7 +316,7 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1000x800 with 1 Axes>"
       ]
@@ -326,7 +326,7 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1000x800 with 1 Axes>"
       ]

From 603df60cacc295f6c954a7496458a99ce6a884b8 Mon Sep 17 00:00:00 2001
From: Jesper Olsen <43079279+jesper-olsen@users.noreply.github.com>
Date: Tue, 24 Mar 2026 10:07:17 +0800
Subject: [PATCH 4/4] - Add @tf.keras.utils.register_keras_serializable() to
 Wraparound2D and   SymmetricConvolution so models save/load without
 custom_objects - Fix get_config() in both custom layers to correctly
 serialize padding   and other constructor arguments, replacing stale
 unrelated fields - Change default padding in Wraparound2D from 2 to 1 to
 match actual usage - Use dynamic spatial reshape in activation visualization
 to avoid   hardcoded grid dimensions - Remove unused numpy import from
 train_ca.py - PEP 8 formatting cleanup

---
 demos/demos.ipynb |  19 ++---
 train_ca.py       | 199 +++++++++++++++++++++++++++-------------------
 2 files changed, 129 insertions(+), 89 deletions(-)

diff --git a/demos/demos.ipynb b/demos/demos.ipynb
index dee9adc..bc87c1f 100644
--- a/demos/demos.ipynb
+++ b/demos/demos.ipynb
@@ -197,7 +197,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x118bcec00>]"
+       "[<matplotlib.lines.Line2D at 0x118e08bf0>]"
       ]
      },
      "execution_count": 4,
@@ -206,7 +206,7 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1000x800 with 1 Axes>"
       ]
@@ -289,7 +289,7 @@
     "### Save and load a model\n",
     "model.save('path_to_my_model.keras')\n",
     "del model\n",
-    "model = tf.keras.models.load_model('path_to_my_model.keras', custom_objects={'Wraparound2D': Wraparound2D})\n"
+    "model = tf.keras.models.load_model('path_to_my_model.keras')\n"
    ]
   },
   {
@@ -316,7 +316,7 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1000x800 with 1 Axes>"
       ]
@@ -326,7 +326,7 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1000x800 with 1 Axes>"
       ]
@@ -349,16 +349,17 @@
     "\n",
     "# Layers 1-4: conv2d, reshape, dense, dense_1\n",
     "for j in range(1, 5):\n",
-    "    out = layer_outs[j][0].numpy()  # first batch item\n",
+    "    out = layer_outs[j][0].numpy()\n",
     "    out_shape = out.shape\n",
     "\n",
     "    if len(out_shape) == 3:\n",
-    "        # Conv2D: (12, 12, 10) — already spatial\n",
+    "        # Conv2D: already spatial (10, 10, 10)\n",
     "        pattern = out\n",
     "    elif len(out_shape) == 2:\n",
-    "        # Dense/Reshape: (144, N) -> reshape to (12, 12, N)\n",
+    "        # Dense/Reshape: (100, N) -> reshape to (10, 10, N)\n",
     "        n_channels = out_shape[-1]\n",
-    "        pattern = out.reshape(12, 12, n_channels)\n",
+    "        spatial = int(round(out_shape[0] ** 0.5))\n",
+    "        pattern = out.reshape(spatial, spatial, n_channels)\n",
     "\n",
     "    n_channels = pattern.shape[-1]\n",
     "    layer_im = np.hstack([min_max_scaler(pattern[..., i]) for i in range(n_channels)])\n",
diff --git a/train_ca.py b/train_ca.py
index f2dcded..79de175 100644
--- a/train_ca.py
+++ b/train_ca.py
@@ -1,13 +1,12 @@
 import tensorflow as tf
-import numpy as np
-
 import collections
 
+
 def periodic_padding(imbatch, padding=1):
-    '''
-    Create a periodic padding (wrap) around an image batch, to emulate 
+    """
+    Create a periodic padding (wrap) around an image batch, to emulate
     periodic boundary conditions. Padding occurs along the middle two axes
-    '''
+    """
     pad_u = imbatch[:, -padding:, :]
     pad_b = imbatch[:, :padding, :]
 
@@ -17,37 +16,40 @@ def periodic_padding(imbatch, padding=1):
     pad_r = partial_image[..., :padding, :]
 
     padded_imbatch = tf.concat([pad_l, partial_image, pad_r], axis=2)
-          
-    
+
     return padded_imbatch
 
+
+@tf.keras.utils.register_keras_serializable()
 class Wraparound2D(tf.keras.layers.Layer):
     """
-    Apply periodic boundary conditions on an image by padding 
+    Apply periodic boundary conditions on an image by padding
     along the axes
-    padding : int or tuple, the amount to wrap around    
+    padding : int or tuple, the amount to wrap around
     """
 
-    def __init__(self, padding=2, **kwargs):
+    def __init__(self, padding=1, **kwargs):
         super(Wraparound2D, self).__init__()
         self.padding = padding
-    
-    def get_config(self):
 
+    def get_config(self):
         config = super().get_config().copy()
-        config.update({
-            'vocab_size': 0,
-            'num_layers': 1,
-            'units': 0,
-            'dropout': 0,
-        })
+        config.update({"padding": self.padding})
         return config
-    
+
     def call(self, inputs):
         return periodic_padding(inputs, self.padding)
-    
-def initialize_model(shape, layer_dims, nhood=1, num_classes=2, totalistic=False, 
-                      nhood_type="moore", bc="periodic"):
+
+
+def initialize_model(
+    shape,
+    layer_dims,
+    nhood=1,
+    num_classes=2,
+    totalistic=False,
+    nhood_type="moore",
+    bc="periodic",
+):
     """
     Given a domain size and layer specification, initialize a model that assigns
     each pixel a class
@@ -56,124 +58,156 @@ def initialize_model(shape, layer_dims, nhood=1, num_classes=2, totalistic=False
     num_classes : int, the number of output classes for the automaton
     totalistic : bool, whether to assume that the CA is radially symmetric, making
         it outer totalistic
-    nhood_type : string, default "moore". The type of neighborhood to use for the 
+    nhood_type : string, default "moore". The type of neighborhood to use for the
         CA. Currently, the only other option, "Neumann," only works when "totalistic"
         is set to True
     bc : string, whether to use "periodic" or "constant" (zero padded) boundary conditions
     """
     wspan, hspan = shape
-    diameter = 2*nhood+1
+    diameter = 2 * nhood + 1
     model = tf.keras.Sequential()
     model.add(tf.keras.layers.InputLayer((wspan, hspan, 1)))
-    
+
     if bc == "periodic":
         model.add(Wraparound2D(padding=nhood))
-        conv_pad = 'valid'
+        conv_pad = "valid"
     else:
-        conv_pad = 'same'
-    
+        conv_pad = "same"
+
     if totalistic:
         model.add(SymmetricConvolution(nhood, n_type=nhood_type, bc=bc))
-        model.add(tf.keras.layers.Reshape(target_shape=(-1, nhood+1)))
+        model.add(tf.keras.layers.Reshape(target_shape=(-1, nhood + 1)))
     else:
-        model.add(tf.keras.layers.Conv2D(layer_dims[0], kernel_size=[diameter, diameter], padding=conv_pad, 
-                                         activation='relu', kernel_initializer=tf.keras.initializers.he_normal(), 
-                                         bias_initializer=tf.keras.initializers.he_normal()))
+        model.add(
+            tf.keras.layers.Conv2D(
+                layer_dims[0],
+                kernel_size=[diameter, diameter],
+                padding=conv_pad,
+                activation="relu",
+                kernel_initializer=tf.keras.initializers.he_normal(),
+                bias_initializer=tf.keras.initializers.he_normal(),
+            )
+        )
         model.add(tf.keras.layers.Reshape(target_shape=(-1, layer_dims[0])))
-    
+
     for i in range(1, len(layer_dims)):
-        model.add(tf.keras.layers.Dense(layer_dims[i],  activation='relu',
-                                        kernel_initializer=tf.keras.initializers.he_normal(), 
-                                        bias_initializer=tf.keras.initializers.he_normal()))
-    model.add(tf.keras.layers.Dense(num_classes,  activation='relu',
-                                    kernel_initializer=tf.keras.initializers.he_normal(), 
-                                    bias_initializer=tf.keras.initializers.he_normal()))
-    #model.add(tf.keras.layers.Reshape(target_shape=(-1, wspan, hspan)))
+        model.add(
+            tf.keras.layers.Dense(
+                layer_dims[i],
+                activation="relu",
+                kernel_initializer=tf.keras.initializers.he_normal(),
+                bias_initializer=tf.keras.initializers.he_normal(),
+            )
+        )
+    model.add(
+        tf.keras.layers.Dense(
+            num_classes,
+            activation="relu",
+            kernel_initializer=tf.keras.initializers.he_normal(),
+            bias_initializer=tf.keras.initializers.he_normal(),
+        )
+    )
+    # TODO - use softmax or no activation instead of relu?
+    # model.add(tf.keras.layers.Reshape(target_shape=(-1, wspan, hspan)))
     return model
 
+
 def logit_to_pred(logits, shape=None):
     """
-    Given logits in the form of a network output, convert them to 
+    Given logits in the form of a network output, convert them to
     images
     """
     labels = tf.argmax(tf.nn.softmax(logits), axis=-1)
-    if shape:                 
+    if shape:
         out = tf.reshape(labels, shape)
     return out
 
+
 def augment_data(x, y, n=None):
     """
     Generate an augmented training dataset with random reflections
     and 90 degree rotations
-    x, y : Image sets of shape (Samples, Width, Height, Channels) 
+    x, y : Image sets of shape (Samples, Width, Height, Channels)
         training images and next images
     n : number of training examples
     """
     n_data = x.shape[0]
-    
+
     if not n:
         n = n_data
     x_out, y_out = list(), list()
-    
+
     for i in range(n):
         r = tf.random.uniform((1,), minval=0, maxval=n_data, dtype=tf.int32)[0]
         x_r, y_r = x[r], y[r]
-        
-        if tf.random.uniform((1,))[0]<0.5:
+
+        if tf.random.uniform((1,))[0] < 0.5:
             x_r = tf.image.flip_left_right(x_r)
             y_r = tf.image.flip_left_right(y_r)
-        if tf.random.uniform((1,))[0]<0.5:
+        if tf.random.uniform((1,))[0] < 0.5:
             x_r = tf.image.flip_up_down(x_r)
             y_r = tf.image.flip_up_down(y_r)
-            
+
         num_rots = tf.random.uniform((1,), minval=0, maxval=4, dtype=tf.int32)[0]
         x_r = tf.image.rot90(x_r, k=num_rots)
         y_r = tf.image.rot90(y_r, k=num_rots)
-        
+
         x_out.append(x_r), y_out.append(y_r)
     return tf.stack(x_out), tf.stack(y_out)
-        
-    
-    
+
 
 def make_square_filters(rad):
     """
     rad : the pixel radius for the filters
     """
-    m = 2*rad + 1
-    square_filters = tf.stack([tf.pad(tf.ones([i, i]), [[int((m-i)/2), int((m-i)/2)], 
-                                                        [int((m-i)/2), int((m-i)/2)]]) 
-                               for i in range(1, m+1, 2)])
-    square_filters = [square_filters[0]] + [item for item in square_filters[1:] - square_filters[:-1]]
+    m = 2 * rad + 1
+    square_filters = tf.stack(
+        [
+            tf.pad(
+                tf.ones([i, i]),
+                [
+                    [int((m - i) / 2), int((m - i) / 2)],
+                    [int((m - i) / 2), int((m - i) / 2)],
+                ],
+            )
+            for i in range(1, m + 1, 2)
+        ]
+    )
+    square_filters = [square_filters[0]] + [
+        item for item in square_filters[1:] - square_filters[:-1]
+    ]
     square_filters = tf.stack(square_filters)[..., tf.newaxis]
-    
+
     return square_filters
 
+
 def make_circular_filters(rad):
     """
     rad : the pixel radius for the filters
     """
-    
-    m = 2*rad + 1
 
-    qq = tf.range(m) - int((m-1)/2)
-    pp = tf.sqrt(tf.cast(qq[..., None]**2 + qq[None, ...]**2, tf.float32))
+    m = 2 * rad + 1
 
-    val_range = tf.cast(tf.range((m+1)/2), tf.float32)
-    circ_filters = make_square_filters(rad)*val_range[..., None, None, None]
-    rr = circ_filters*(1/pp)[None, ..., None]
+    qq = tf.range(m) - int((m - 1) / 2)
+    pp = tf.sqrt(tf.cast(qq[..., None] ** 2 + qq[None, ...] ** 2, tf.float32))
+
+    val_range = tf.cast(tf.range((m + 1) / 2), tf.float32)
+    circ_filters = make_square_filters(rad) * val_range[..., None, None, None]
+    rr = circ_filters * (1 / pp)[None, ..., None]
     rr = tf.where(tf.math.is_nan(rr), tf.zeros_like(rr), rr)
     return tf.stack([make_square_filters(rad)[0]] + [item for item in rr][1:])
 
+
+@tf.keras.utils.register_keras_serializable()
 class SymmetricConvolution(tf.keras.layers.Layer):
     """
-    A non-trainable convolutional layer that extracts the 
+    A non-trainable convolutional layer that extracts the
     summed values in the neighborhood of each pixel. No activation
     is applied because this feature extractor does not change during training
     parametrized by the radius
     r : int, the max neighborhood size
     nhood_type : "moore" (default) uses the Moore neighborhood, while "neumann"
-        uses the generalized von Neumann neighborhood, which is similar 
+        uses the generalized von Neumann neighborhood, which is similar
         to a circle at large neighborhood radii
     bc : "periodic" or "constant"
     TODO : implement the "hard" von Neumann neighborhood
@@ -181,9 +215,9 @@ class SymmetricConvolution(tf.keras.layers.Layer):
 
     def __init__(self, r, nhood_type="moore", bc="periodic", **kwargs):
         super(SymmetricConvolution, self).__init__()
-        
+
         self.r = r
-        
+
         if nhood_type == "moore":
             filters = make_square_filters(r)
         elif nhood_type == "neumann":
@@ -192,20 +226,25 @@ def __init__(self, r, nhood_type="moore", bc="periodic", **kwargs):
             filters = make_square_filters(r)
             warnings.warn("Neighborhood specification not recognized.")
         self.filters = tf.squeeze(tf.transpose(filters))[..., None, :]
-        
+
         if bc == "periodic":
-            self.pad_type="VALID"
+            self.pad_type = "VALID"
         else:
-            self.pad_type="SAME"
-        
+            self.pad_type = "SAME"
+
+        self.nhood_type = nhood_type
+        self.bc = bc
+
     def get_config(self):
         config = super().get_config().copy()
-        config.update({
-            'num_layers': 1,
-            'units': 0,
-            'dropout': 0,
-        })
+        config.update(
+            {
+                "r": self.r,
+                "nhood_type": self.nhood_type,  # needs to be saved in __init__ too
+                "bc": self.bc,  # needs to be saved in __init__ too
+            }
+        )
         return config
-    
+
     def call(self, inputs):
         return tf.nn.convolution(inputs, self.filters, padding=self.pad_type)