5_Regression_time_NN.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Import libraries:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "from imports import *"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nS6lIBYBMuaj"
      },
      "source": [
        "# Individual coordinate reconstruction (t,x,y,z)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "image = np.load(\"/data/vidales/images/image_realistic_4comp_1411_corrected_bunch.npy\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**hit_nuvT variable loading**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "table = pq.read_table('saved_archives/ophit_variables_y_corrected_bunch.parquet')\n",
        "nuvT_c_bunch = ak.from_iter(table.column('nuvT_c_bunch').to_pylist())\n",
        "del table"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler(feature_range=(0, 1))\n",
        "nuvT_c_bunch_np = nuvT_c_bunch.to_numpy()\n",
        "nuvT_c_bunch_scaled = scaler.fit_transform(nuvT_c_bunch_np)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Model architecture definition**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {},
      "outputs": [],
      "source": [
        "def periodic_mse(y_true, y_pred, T):\n",
        "    return tf.reduce_mean(tf.square(tf.math.mod(y_true - y_pred + T/2, T) - T/2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {},
      "outputs": [],
      "source": [
        "def create_cnn_model(x_train, conv_dim):\n",
        "\n",
        "    from tensorflow.keras import layers, models\n",
        "    from tensorflow.keras.callbacks import ModelCheckpoint\n",
        "    \"\"\"\n",
        "    Creates a CNN model based on the provided architecture.\n",
        "    \n",
        "    Parameters:\n",
        "    - input_shape: tuple, the shape of the input data (excluding batch size).\n",
        "    \n",
        "    Returns:\n",
        "    - model: A compiled CNN model.\n",
        "    \"\"\"\n",
        "    # Input layer\n",
        "    input_shape = (x_train.shape[1], x_train.shape[2], x_train.shape[3])\n",
        "    input_layer = layers.Input(shape=input_shape)\n",
        "    \n",
        "    # First convolutional block\n",
        "    model = layers.Conv2D(32, conv_dim, padding='same')(input_layer)\n",
        "    model = layers.BatchNormalization()(model)\n",
        "    model = layers.LeakyReLU(alpha=0.1)(model)\n",
        "    model = layers.MaxPooling2D((2,2), padding='same')(model)\n",
        "    \n",
        "    # Second convolutional block\n",
        "    model = layers.Conv2D(64, conv_dim, padding='same')(model)\n",
        "    model = layers.BatchNormalization()(input_layer)\n",
        "    model = layers.LeakyReLU(alpha=0.1)(model)\n",
        "    model = layers.MaxPooling2D((2,2), padding='same')(model)\n",
        "\n",
        "    # Third convolutional block\n",
        "    model = layers.Conv2D(128, conv_dim, padding='same')(model)\n",
        "    model = layers.BatchNormalization()(input_layer)\n",
        "    model = layers.LeakyReLU(alpha=0.1)(model)\n",
        "    model = layers.MaxPooling2D((2,2), padding='same')(model)\n",
        "\n",
        "    # Fourth convolutional block\n",
        "    model = layers.Conv2D(256, conv_dim, padding='same')(model)\n",
        "    model = layers.BatchNormalization()(input_layer)\n",
        "    model = layers.LeakyReLU(alpha=0.1)(model)\n",
        "\n",
        "    # Fifth convolutional block\n",
        "    model = layers.Conv2D(512, conv_dim, padding='same')(model)\n",
        "    model = layers.BatchNormalization()(input_layer)\n",
        "    model = layers.LeakyReLU(alpha=0.1)(model)\n",
        "    \n",
        "    # Flatten the output and pass through dense layers\n",
        "    model = layers.Flatten()(model)\n",
        "    model = layers.Dense(1024, activation='relu')(model)\n",
        "    model = layers.Dropout(0.3)(model)\n",
        "    model = layers.Dense(512, activation='relu')(model)\n",
        "    model = layers.Dropout(0.3)(model)\n",
        "    model = layers.Dense(256, activation='relu')(model)\n",
        "    model = layers.Dropout(0.3)(model)\n",
        "    model = layers.Dense(128, activation='relu')(model)\n",
        "    model = layers.Dropout(0.3)(model)\n",
        "    \n",
        "    # Output layer\n",
        "    output_layer = layers.Dense(1, activation='linear')(model)\n",
        "    \n",
        "    # Create the model\n",
        "    model_def = models.Model(inputs=input_layer, outputs=output_layer)\n",
        "    \n",
        "    model_def.compile(optimizer='adam', loss=lambda y_true, y_pred: periodic_mse(y_true, y_pred, T=19), metrics=['mse'])\n",
        "\n",
        "\n",
        "    return model_def"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**model use embeddings as suggested by ChatGPT**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {},
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from tensorflow.keras import layers, models\n",
        "import tensorflow as tf\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "def create_periodic_embeddings(time_values, embedding_dim):\n",
        "    \"\"\"\n",
        "    Creates periodic embeddings (sinusoidal) for time values.\n",
        "\n",
        "    Parameters:\n",
        "    - time_values: np.ndarray, input time values of shape (N, 1) or (N,).\n",
        "    - embedding_dim: int, the number of embedding dimensions (must be even).\n",
        "\n",
        "    Returns:\n",
        "    - time_embeddings: np.ndarray, periodic embeddings of shape (len(time_values), embedding_dim).\n",
        "    \"\"\"\n",
        "    # Ensure the embedding dimension is even\n",
        "    assert embedding_dim % 2 == 0, \"Embedding dimension must be even for sine and cosine pairs.\"\n",
        "\n",
        "    # Flatten `time_values` if it is 2D\n",
        "    if len(time_values.shape) > 1:\n",
        "        time_values = time_values.flatten()  # Convert shape from (N, 1) to (N,)\n",
        "\n",
        "    # Compute the divisor term\n",
        "    div_term = np.exp(np.arange(0, embedding_dim, 2) * -(np.log(10000.0) / embedding_dim))\n",
        "    \n",
        "    # Initialize the embedding array\n",
        "    time_embeddings = np.zeros((len(time_values), embedding_dim))\n",
        "    \n",
        "    # Compute sine and cosine embeddings\n",
        "    time_embeddings[:, 0::2] = np.sin(time_values[:, None] * div_term)  # Sine for even indices\n",
        "    time_embeddings[:, 1::2] = np.cos(time_values[:, None] * div_term)  # Cosine for odd indices\n",
        "    \n",
        "    return time_embeddings\n",
        "\n",
        "from tensorflow.keras import layers, models\n",
        "\n",
        "def create_cnn_model_with_periodic_output(x_train, conv_dim, y_train, embedding_dim):\n",
        "    \"\"\"\n",
        "    Creates a CNN model that integrates periodic embeddings for the output (time) variable.\n",
        "    \n",
        "    Parameters:\n",
        "    - x_train: np.ndarray, input training data.\n",
        "    - conv_dim: tuple, dimensions for the convolutional layers.\n",
        "    - y_train: np.ndarray, time labels (target variable).\n",
        "    - embedding_dim: int, dimension of the periodic embeddings.\n",
        "    \n",
        "    Returns:\n",
        "    - model: A compiled CNN model.\n",
        "    \"\"\"\n",
        "    # Input shape\n",
        "    input_shape = (x_train.shape[1], x_train.shape[2], x_train.shape[3])\n",
        "    input_layer = layers.Input(shape=input_shape)\n",
        "\n",
        "    # Periodic embeddings for time\n",
        "    time_embeddings = create_periodic_embeddings(y_train, embedding_dim)\n",
        "    time_embeddings_input = layers.Input(shape=(embedding_dim,))\n",
        "\n",
        "    # First convolutional block\n",
        "    masking_layer = layers.Masking(mask_value=0)(input_layer)\n",
        "    model = layers.BatchNormalization()(masking_layer)\n",
        "    model = layers.Conv2D(32, conv_dim, padding='same')(model)\n",
        "    model = layers.BatchNormalization()(model)\n",
        "    model = layers.LeakyReLU(alpha=0.1)(model)\n",
        "    model = layers.MaxPooling2D((2, 2), padding='same')(model)\n",
        "\n",
        "    # Second convolutional block\n",
        "    model = layers.Conv2D(64, (3, 3), padding='same')(model)\n",
        "    model = layers.BatchNormalization()(model)\n",
        "    model = layers.LeakyReLU(alpha=0.1)(model)\n",
        "    model = layers.MaxPooling2D((2, 2), padding='same')(model)\n",
        "\n",
        "    # Third convolutional block\n",
        "    model = layers.Conv2D(128, (3, 3), padding='same')(model)\n",
        "    model = layers.BatchNormalization()(model)\n",
        "    model = layers.LeakyReLU(alpha=0.1)(model)\n",
        "    model = layers.MaxPooling2D((2, 2))(model)\n",
        "\n",
        "    # Fourth convolutional block\n",
        "    model = layers.Conv2D(256, (3, 3), padding='same')(model)\n",
        "    model = layers.BatchNormalization()(model)\n",
        "    model = layers.LeakyReLU(alpha=0.1)(model)\n",
        "\n",
        "    # Flatten the output\n",
        "    model = layers.Flatten()(model)\n",
        "\n",
        "    # Combine with time embeddings\n",
        "    concatenated = layers.Concatenate()([model, time_embeddings_input])\n",
        "\n",
        "    # Dense layers after concatenation\n",
        "    model = layers.Dense(1024, activation='relu')(concatenated)\n",
        "    model = layers.Dropout(0.3)(model)\n",
        "    model = layers.Dense(512, activation='relu')(model)\n",
        "    model = layers.Dropout(0.3)(model)\n",
        "    model = layers.Dense(256, activation='relu')(model)\n",
        "    model = layers.Dropout(0.3)(model)\n",
        "    model = layers.Dense(128, activation='relu')(model)\n",
        "    model = layers.Dropout(0.3)(model)\n",
        "\n",
        "    # Output layer\n",
        "    output_layer = layers.Dense(1, activation='linear')(model)\n",
        "\n",
        "    # Create the model\n",
        "    model_def = models.Model(inputs=[input_layer, time_embeddings_input], outputs=output_layer)\n",
        "\n",
        "    # Compile the model\n",
        "    model_def.compile(loss='mse', optimizer='adam', metrics=['mse'])\n",
        "\n",
        "    return model_def\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 4-component realistic representation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Training and set separation**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "8825\n"
          ]
        }
      ],
      "source": [
        "test_size = int(np.floor(0.30*np.shape(nuvT_c_bunch_scaled)[0]) )\n",
        "print(test_size)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [],
      "source": [
        "# prueba (los mapas originales están invertidos en y)\n",
        "# Invert along the 59 dimension (axis 1)\n",
        "inverted_image = image[:, ::-1, :, :]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "x_train dim.: (20592, 59, 70, 16)\n",
            "x_test dim.: (8825, 59, 70, 16)\n"
          ]
        }
      ],
      "source": [
        "x_train, x_test = inverted_image[:-test_size], inverted_image[-test_size:]\n",
        "print('x_train dim.:',x_train.shape)\n",
        "print('x_test dim.:', x_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [],
      "source": [
        "y_train, y_test = nuvT_c_bunch_scaled[:-test_size], nuvT_c_bunch_scaled[-test_size:]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {},
      "outputs": [],
      "source": [
        "model = create_cnn_model(x_train, (7,7))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "2024-11-29 12:15:37.581176: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 5442877440 exceeds 10% of free system memory.\n",
            "2024-11-29 12:15:41.875923: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 5442877440 exceeds 10% of free system memory.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "1287/1287 [==============================] - 26s 20ms/step - loss: 0.2196 - mse: 0.2196 - val_loss: 0.1754 - val_mse: 0.1754 - lr: 0.0010\n",
            "Epoch 2/100\n",
            "1287/1287 [==============================] - 19s 15ms/step - loss: 0.1782 - mse: 0.1782 - val_loss: 0.1747 - val_mse: 0.1747 - lr: 0.0010\n",
            "Epoch 3/100\n",
            "1287/1287 [==============================] - 17s 13ms/step - loss: 0.1766 - mse: 0.1766 - val_loss: 0.1748 - val_mse: 0.1748 - lr: 0.0010\n",
            "Epoch 4/100\n",
            " 301/1287 [======>.......................] - ETA: 11s - loss: 0.1747 - mse: 0.1747"
          ]
        },
        {
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "Cell \u001b[0;32mIn[28], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m y_pred, history\u001b[38;5;241m=\u001b[39m \u001b[43mtrain_and_predict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx_test\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_test\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m16\u001b[39;49m\u001b[43m)\u001b[49m\n",
            "File \u001b[0;32m~/AI_nuvT/functions/_5_Regression_AI.py:90\u001b[0m, in \u001b[0;36mtrain_and_predict\u001b[0;34m(model, x_train, y_train, x_test, y_test, epochs, batch_size)\u001b[0m\n\u001b[1;32m     87\u001b[0m callbacks \u001b[38;5;241m=\u001b[39m [checkpoint, early_stopping, reduce_lr]\n\u001b[1;32m     89\u001b[0m \u001b[38;5;66;03m# Train the model\u001b[39;00m\n\u001b[0;32m---> 90\u001b[0m history \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     91\u001b[0m \u001b[43m    \u001b[49m\u001b[43mx_train\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     92\u001b[0m \u001b[43m    \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     93\u001b[0m \u001b[43m    \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mepochs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     94\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     95\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallbacks\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     96\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mx_test\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_test\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     97\u001b[0m \u001b[43m    \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\n\u001b[1;32m     98\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    100\u001b[0m \u001b[38;5;66;03m# Load the best weights\u001b[39;00m\n\u001b[1;32m    101\u001b[0m model\u001b[38;5;241m.\u001b[39mload_weights(weights_file)\n",
            "File \u001b[0;32m/opt/anaconda3/envs/ROOT_TF/lib/python3.9/site-packages/keras/utils/traceback_utils.py:64\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     63\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 64\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     65\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:  \u001b[38;5;66;03m# pylint: disable=broad-except\u001b[39;00m\n\u001b[1;32m     66\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
            "File \u001b[0;32m/opt/anaconda3/envs/ROOT_TF/lib/python3.9/site-packages/keras/engine/training.py:1221\u001b[0m, in \u001b[0;36mModel.fit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1219\u001b[0m logs \u001b[38;5;241m=\u001b[39m tmp_logs  \u001b[38;5;66;03m# No error, now safe to assign to logs.\u001b[39;00m\n\u001b[1;32m   1220\u001b[0m end_step \u001b[38;5;241m=\u001b[39m step \u001b[38;5;241m+\u001b[39m data_handler\u001b[38;5;241m.\u001b[39mstep_increment\n\u001b[0;32m-> 1221\u001b[0m \u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mon_train_batch_end\u001b[49m\u001b[43m(\u001b[49m\u001b[43mend_step\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1222\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstop_training:\n\u001b[1;32m   1223\u001b[0m   \u001b[38;5;28;01mbreak\u001b[39;00m\n",
            "File \u001b[0;32m/opt/anaconda3/envs/ROOT_TF/lib/python3.9/site-packages/keras/callbacks.py:436\u001b[0m, in \u001b[0;36mCallbackList.on_train_batch_end\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m    429\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Calls the `on_train_batch_end` methods of its callbacks.\u001b[39;00m\n\u001b[1;32m    430\u001b[0m \n\u001b[1;32m    431\u001b[0m \u001b[38;5;124;03mArgs:\u001b[39;00m\n\u001b[1;32m    432\u001b[0m \u001b[38;5;124;03m    batch: Integer, index of batch within the current epoch.\u001b[39;00m\n\u001b[1;32m    433\u001b[0m \u001b[38;5;124;03m    logs: Dict. Aggregated metric results up until this batch.\u001b[39;00m\n\u001b[1;32m    434\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    435\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_should_call_train_batch_hooks:\n\u001b[0;32m--> 436\u001b[0m   \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_batch_hook\u001b[49m\u001b[43m(\u001b[49m\u001b[43mModeKeys\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTRAIN\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mend\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlogs\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[0;32m/opt/anaconda3/envs/ROOT_TF/lib/python3.9/site-packages/keras/callbacks.py:295\u001b[0m, in \u001b[0;36mCallbackList._call_batch_hook\u001b[0;34m(self, mode, hook, batch, logs)\u001b[0m\n\u001b[1;32m    293\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_batch_begin_hook(mode, batch, logs)\n\u001b[1;32m    294\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m hook \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mend\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m--> 295\u001b[0m   \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_batch_end_hook\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    296\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    297\u001b[0m   \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    298\u001b[0m       \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mUnrecognized hook: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhook\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. Expected values are [\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbegin\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mend\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
            "File \u001b[0;32m/opt/anaconda3/envs/ROOT_TF/lib/python3.9/site-packages/keras/callbacks.py:316\u001b[0m, in \u001b[0;36mCallbackList._call_batch_end_hook\u001b[0;34m(self, mode, batch, logs)\u001b[0m\n\u001b[1;32m    313\u001b[0m   batch_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime() \u001b[38;5;241m-\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_batch_start_time\n\u001b[1;32m    314\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_batch_times\u001b[38;5;241m.\u001b[39mappend(batch_time)\n\u001b[0;32m--> 316\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_batch_hook_helper\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhook_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    318\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_batch_times) \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_batches_for_timing_check:\n\u001b[1;32m    319\u001b[0m   end_hook_name \u001b[38;5;241m=\u001b[39m hook_name\n",
            "File \u001b[0;32m/opt/anaconda3/envs/ROOT_TF/lib/python3.9/site-packages/keras/callbacks.py:354\u001b[0m, in \u001b[0;36mCallbackList._call_batch_hook_helper\u001b[0;34m(self, hook_name, batch, logs)\u001b[0m\n\u001b[1;32m    352\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m callback \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcallbacks:\n\u001b[1;32m    353\u001b[0m   hook \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(callback, hook_name)\n\u001b[0;32m--> 354\u001b[0m   \u001b[43mhook\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    356\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_timing:\n\u001b[1;32m    357\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m hook_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_hook_times:\n",
            "File \u001b[0;32m/opt/anaconda3/envs/ROOT_TF/lib/python3.9/site-packages/keras/callbacks.py:1032\u001b[0m, in \u001b[0;36mProgbarLogger.on_train_batch_end\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m   1031\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mon_train_batch_end\u001b[39m(\u001b[38;5;28mself\u001b[39m, batch, logs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m-> 1032\u001b[0m   \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_batch_update_progbar\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogs\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[0;32m/opt/anaconda3/envs/ROOT_TF/lib/python3.9/site-packages/keras/callbacks.py:1104\u001b[0m, in \u001b[0;36mProgbarLogger._batch_update_progbar\u001b[0;34m(self, batch, logs)\u001b[0m\n\u001b[1;32m   1100\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mseen \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m add_seen\n\u001b[1;32m   1102\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m   1103\u001b[0m   \u001b[38;5;66;03m# Only block async when verbose = 1.\u001b[39;00m\n\u001b[0;32m-> 1104\u001b[0m   logs \u001b[38;5;241m=\u001b[39m \u001b[43mtf_utils\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msync_to_numpy_or_python_type\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlogs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1105\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprogbar\u001b[38;5;241m.\u001b[39mupdate(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mseen, \u001b[38;5;28mlist\u001b[39m(logs\u001b[38;5;241m.\u001b[39mitems()), finalize\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
            "File \u001b[0;32m/opt/anaconda3/envs/ROOT_TF/lib/python3.9/site-packages/keras/utils/tf_utils.py:554\u001b[0m, in \u001b[0;36msync_to_numpy_or_python_type\u001b[0;34m(tensors)\u001b[0m\n\u001b[1;32m    551\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m x\u001b[38;5;241m.\u001b[39mitem() \u001b[38;5;28;01mif\u001b[39;00m np\u001b[38;5;241m.\u001b[39mndim(x) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m x\n\u001b[1;32m    552\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m t  \u001b[38;5;66;03m# Don't turn ragged or sparse tensors to NumPy.\u001b[39;00m\n\u001b[0;32m--> 554\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap_structure\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_to_single_numpy_or_python_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[0;32m/opt/anaconda3/envs/ROOT_TF/lib/python3.9/site-packages/tensorflow/python/util/nest.py:869\u001b[0m, in \u001b[0;36mmap_structure\u001b[0;34m(func, *structure, **kwargs)\u001b[0m\n\u001b[1;32m    865\u001b[0m flat_structure \u001b[38;5;241m=\u001b[39m (flatten(s, expand_composites) \u001b[38;5;28;01mfor\u001b[39;00m s \u001b[38;5;129;01min\u001b[39;00m structure)\n\u001b[1;32m    866\u001b[0m entries \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mzip\u001b[39m(\u001b[38;5;241m*\u001b[39mflat_structure)\n\u001b[1;32m    868\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m pack_sequence_as(\n\u001b[0;32m--> 869\u001b[0m     structure[\u001b[38;5;241m0\u001b[39m], [func(\u001b[38;5;241m*\u001b[39mx) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m entries],\n\u001b[1;32m    870\u001b[0m     expand_composites\u001b[38;5;241m=\u001b[39mexpand_composites)\n",
            "File \u001b[0;32m/opt/anaconda3/envs/ROOT_TF/lib/python3.9/site-packages/tensorflow/python/util/nest.py:869\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    865\u001b[0m flat_structure \u001b[38;5;241m=\u001b[39m (flatten(s, expand_composites) \u001b[38;5;28;01mfor\u001b[39;00m s \u001b[38;5;129;01min\u001b[39;00m structure)\n\u001b[1;32m    866\u001b[0m entries \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mzip\u001b[39m(\u001b[38;5;241m*\u001b[39mflat_structure)\n\u001b[1;32m    868\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m pack_sequence_as(\n\u001b[0;32m--> 869\u001b[0m     structure[\u001b[38;5;241m0\u001b[39m], [\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m entries],\n\u001b[1;32m    870\u001b[0m     expand_composites\u001b[38;5;241m=\u001b[39mexpand_composites)\n",
            "File \u001b[0;32m/opt/anaconda3/envs/ROOT_TF/lib/python3.9/site-packages/keras/utils/tf_utils.py:550\u001b[0m, in \u001b[0;36msync_to_numpy_or_python_type.<locals>._to_single_numpy_or_python_type\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m    548\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_to_single_numpy_or_python_type\u001b[39m(t):\n\u001b[1;32m    549\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(t, tf\u001b[38;5;241m.\u001b[39mTensor):\n\u001b[0;32m--> 550\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[43mt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnumpy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    551\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m x\u001b[38;5;241m.\u001b[39mitem() \u001b[38;5;28;01mif\u001b[39;00m np\u001b[38;5;241m.\u001b[39mndim(x) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m x\n\u001b[1;32m    552\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m t\n",
            "File \u001b[0;32m/opt/anaconda3/envs/ROOT_TF/lib/python3.9/site-packages/tensorflow/python/framework/ops.py:1149\u001b[0m, in \u001b[0;36m_EagerTensorBase.numpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1126\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Copy of the contents of this Tensor into a NumPy array or scalar.\u001b[39;00m\n\u001b[1;32m   1127\u001b[0m \n\u001b[1;32m   1128\u001b[0m \u001b[38;5;124;03mUnlike NumPy arrays, Tensors are immutable, so this method has to copy\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1146\u001b[0m \u001b[38;5;124;03m    NumPy dtype.\u001b[39;00m\n\u001b[1;32m   1147\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1148\u001b[0m \u001b[38;5;66;03m# TODO(slebedev): Consider avoiding a copy for non-CPU or remote tensors.\u001b[39;00m\n\u001b[0;32m-> 1149\u001b[0m maybe_arr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_numpy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# pylint: disable=protected-access\u001b[39;00m\n\u001b[1;32m   1150\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m maybe_arr\u001b[38;5;241m.\u001b[39mcopy() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(maybe_arr, np\u001b[38;5;241m.\u001b[39mndarray) \u001b[38;5;28;01melse\u001b[39;00m maybe_arr\n",
            "File \u001b[0;32m/opt/anaconda3/envs/ROOT_TF/lib/python3.9/site-packages/tensorflow/python/framework/ops.py:1115\u001b[0m, in \u001b[0;36m_EagerTensorBase._numpy\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1113\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_numpy\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m   1114\u001b[0m   \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1115\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_numpy_internal\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1116\u001b[0m   \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:  \u001b[38;5;66;03m# pylint: disable=protected-access\u001b[39;00m\n\u001b[1;32m   1117\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_status_to_exception(e) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28mNone\u001b[39m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "y_pred, history= train_and_predict(model, x_train, y_train, x_test, y_test, epochs=100, batch_size=16)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Prueba time embeddings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "2024-11-29 11:56:14.396943: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 4354143360 exceeds 10% of free system memory.\n",
            "2024-11-29 11:56:18.608700: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 4354143360 exceeds 10% of free system memory.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "515/515 [==============================] - 14s 21ms/step - loss: 0.9289 - mse: 0.9289 - val_loss: 0.1776 - val_mse: 0.1776\n",
            "Epoch 2/20\n",
            "515/515 [==============================] - 8s 16ms/step - loss: 0.1863 - mse: 0.1863 - val_loss: 0.1764 - val_mse: 0.1764\n",
            "Epoch 3/20\n",
            "515/515 [==============================] - 8s 16ms/step - loss: 0.1781 - mse: 0.1781 - val_loss: 0.1764 - val_mse: 0.1764\n",
            "Epoch 4/20\n",
            "515/515 [==============================] - 8s 16ms/step - loss: 0.1781 - mse: 0.1781 - val_loss: 0.1756 - val_mse: 0.1756\n",
            "Epoch 5/20\n",
            "515/515 [==============================] - 8s 16ms/step - loss: 0.1768 - mse: 0.1768 - val_loss: 0.1758 - val_mse: 0.1758\n",
            "Epoch 6/20\n",
            "515/515 [==============================] - 8s 16ms/step - loss: 0.1763 - mse: 0.1763 - val_loss: 0.1756 - val_mse: 0.1756\n",
            "Epoch 7/20\n",
            "515/515 [==============================] - 8s 16ms/step - loss: 0.1767 - mse: 0.1767 - val_loss: 0.1758 - val_mse: 0.1758\n",
            "Epoch 8/20\n",
            "515/515 [==============================] - 9s 17ms/step - loss: 0.1762 - mse: 0.1762 - val_loss: 0.1759 - val_mse: 0.1759\n",
            "Epoch 9/20\n",
            "515/515 [==============================] - 8s 16ms/step - loss: 0.1762 - mse: 0.1762 - val_loss: 0.1757 - val_mse: 0.1757\n",
            "Epoch 10/20\n",
            "515/515 [==============================] - 8s 16ms/step - loss: 0.1765 - mse: 0.1765 - val_loss: 0.1769 - val_mse: 0.1769\n",
            "Epoch 11/20\n",
            "515/515 [==============================] - 8s 16ms/step - loss: 0.1992 - mse: 0.1992 - val_loss: 0.1815 - val_mse: 0.1815\n",
            "Epoch 12/20\n",
            "515/515 [==============================] - 8s 16ms/step - loss: 0.1796 - mse: 0.1796 - val_loss: 0.1760 - val_mse: 0.1760\n",
            "Epoch 13/20\n",
            "515/515 [==============================] - 8s 16ms/step - loss: 0.1771 - mse: 0.1771 - val_loss: 0.1756 - val_mse: 0.1756\n",
            "Epoch 14/20\n",
            "515/515 [==============================] - 8s 16ms/step - loss: 0.1769 - mse: 0.1769 - val_loss: 0.1756 - val_mse: 0.1756\n",
            "Epoch 15/20\n",
            "515/515 [==============================] - 8s 16ms/step - loss: 0.1758 - mse: 0.1758 - val_loss: 0.1756 - val_mse: 0.1756\n",
            "Epoch 16/20\n",
            "515/515 [==============================] - 9s 17ms/step - loss: 0.1756 - mse: 0.1756 - val_loss: 0.1756 - val_mse: 0.1756\n",
            "Epoch 17/20\n",
            "515/515 [==============================] - 8s 16ms/step - loss: 0.1758 - mse: 0.1758 - val_loss: 0.1757 - val_mse: 0.1757\n",
            "Epoch 18/20\n",
            "515/515 [==============================] - 8s 16ms/step - loss: 0.1761 - mse: 0.1761 - val_loss: 0.1757 - val_mse: 0.1757\n",
            "Epoch 19/20\n",
            "515/515 [==============================] - 8s 16ms/step - loss: 0.1756 - mse: 0.1756 - val_loss: 0.1756 - val_mse: 0.1756\n",
            "Epoch 20/20\n",
            "515/515 [==============================] - 8s 16ms/step - loss: 0.1762 - mse: 0.1762 - val_loss: 0.1756 - val_mse: 0.1756\n"
          ]
        },
        {
          "ename": "ValueError",
          "evalue": "in user code:\n\n    File \"/opt/anaconda3/envs/ROOT_TF/lib/python3.9/site-packages/keras/engine/training.py\", line 1621, in predict_function  *\n        return step_function(self, iterator)\n    File \"/opt/anaconda3/envs/ROOT_TF/lib/python3.9/site-packages/keras/engine/training.py\", line 1611, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"/opt/anaconda3/envs/ROOT_TF/lib/python3.9/site-packages/keras/engine/training.py\", line 1604, in run_step  **\n        outputs = model.predict_step(data)\n    File \"/opt/anaconda3/envs/ROOT_TF/lib/python3.9/site-packages/keras/engine/training.py\", line 1572, in predict_step\n        return self(x, training=False)\n    File \"/opt/anaconda3/envs/ROOT_TF/lib/python3.9/site-packages/keras/utils/traceback_utils.py\", line 67, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"/opt/anaconda3/envs/ROOT_TF/lib/python3.9/site-packages/keras/engine/input_spec.py\", line 199, in assert_input_compatibility\n        raise ValueError(f'Layer \"{layer_name}\" expects {len(input_spec)} input(s),'\n\n    ValueError: Layer \"model\" expects 2 input(s), but it received 1 input tensors. Inputs received: [<tf.Tensor 'IteratorGetNext:0' shape=(None, 59, 70, 16) dtype=float32>]\n",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "Cell \u001b[0;32mIn[20], line 22\u001b[0m\n\u001b[1;32m     12\u001b[0m history \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mfit(\n\u001b[1;32m     13\u001b[0m     [x_train, time_embeddings],\n\u001b[1;32m     14\u001b[0m     y_train,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     17\u001b[0m     validation_split\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.2\u001b[39m\n\u001b[1;32m     18\u001b[0m )\n\u001b[1;32m     20\u001b[0m \u001b[38;5;66;03m# Make predictions on the test set\u001b[39;00m\n\u001b[0;32m---> 22\u001b[0m y_pred \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_test\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[0;32m/opt/anaconda3/envs/ROOT_TF/lib/python3.9/site-packages/keras/utils/traceback_utils.py:67\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     65\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:  \u001b[38;5;66;03m# pylint: disable=broad-except\u001b[39;00m\n\u001b[1;32m     66\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[0;32m---> 67\u001b[0m   \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28mNone\u001b[39m\n\u001b[1;32m     68\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     69\u001b[0m   \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
            "File \u001b[0;32m/opt/anaconda3/envs/ROOT_TF/lib/python3.9/site-packages/tensorflow/python/framework/func_graph.py:1129\u001b[0m, in \u001b[0;36mfunc_graph_from_py_func.<locals>.autograph_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1127\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:  \u001b[38;5;66;03m# pylint:disable=broad-except\u001b[39;00m\n\u001b[1;32m   1128\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(e, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mag_error_metadata\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m-> 1129\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mag_error_metadata\u001b[38;5;241m.\u001b[39mto_exception(e)\n\u001b[1;32m   1130\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1131\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m\n",
            "\u001b[0;31mValueError\u001b[0m: in user code:\n\n    File \"/opt/anaconda3/envs/ROOT_TF/lib/python3.9/site-packages/keras/engine/training.py\", line 1621, in predict_function  *\n        return step_function(self, iterator)\n    File \"/opt/anaconda3/envs/ROOT_TF/lib/python3.9/site-packages/keras/engine/training.py\", line 1611, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"/opt/anaconda3/envs/ROOT_TF/lib/python3.9/site-packages/keras/engine/training.py\", line 1604, in run_step  **\n        outputs = model.predict_step(data)\n    File \"/opt/anaconda3/envs/ROOT_TF/lib/python3.9/site-packages/keras/engine/training.py\", line 1572, in predict_step\n        return self(x, training=False)\n    File \"/opt/anaconda3/envs/ROOT_TF/lib/python3.9/site-packages/keras/utils/traceback_utils.py\", line 67, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"/opt/anaconda3/envs/ROOT_TF/lib/python3.9/site-packages/keras/engine/input_spec.py\", line 199, in assert_input_compatibility\n        raise ValueError(f'Layer \"{layer_name}\" expects {len(input_spec)} input(s),'\n\n    ValueError: Layer \"model\" expects 2 input(s), but it received 1 input tensors. Inputs received: [<tf.Tensor 'IteratorGetNext:0' shape=(None, 59, 70, 16) dtype=float32>]\n"
          ]
        }
      ],
      "source": [
        "# Assuming x_train and y_train are your data\n",
        "conv_dim = (7, 7)\n",
        "embedding_dim = 16\n",
        "\n",
        "# Generate periodic embeddings\n",
        "time_embeddings = create_periodic_embeddings(y_train, embedding_dim)\n",
        "\n",
        "# Create the model\n",
        "model = create_cnn_model_with_periodic_output(x_train, conv_dim, y_train, embedding_dim)\n",
        "\n",
        "# Fit the model\n",
        "history = model.fit(\n",
        "    [x_train, time_embeddings],\n",
        "    y_train,\n",
        "    batch_size=32,\n",
        "    epochs=20,\n",
        "    validation_split=0.2\n",
        ")\n",
        "\n",
        "# Make predictions on the test set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {},
      "outputs": [
        {
          "ename": "ValueError",
          "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "Cell \u001b[0;32mIn[21], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m y_pred \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_test\u001b[49m\u001b[43m,\u001b[49m\u001b[43mtime_embeddings\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[0;32m/opt/anaconda3/envs/ROOT_TF/lib/python3.9/site-packages/keras/utils/traceback_utils.py:67\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     65\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:  \u001b[38;5;66;03m# pylint: disable=broad-except\u001b[39;00m\n\u001b[1;32m     66\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[0;32m---> 67\u001b[0m   \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28mNone\u001b[39m\n\u001b[1;32m     68\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     69\u001b[0m   \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
            "File \u001b[0;32m/opt/anaconda3/envs/ROOT_TF/lib/python3.9/site-packages/keras/engine/data_adapter.py:250\u001b[0m, in \u001b[0;36mTensorLikeDataAdapter.__init__\u001b[0;34m(self, x, y, sample_weights, sample_weight_modes, batch_size, epochs, steps, shuffle, **kwargs)\u001b[0m\n\u001b[1;32m    246\u001b[0m _check_data_cardinality(inputs)\n\u001b[1;32m    248\u001b[0m \u001b[38;5;66;03m# If batch_size is not passed but steps is, calculate from the input data.\u001b[39;00m\n\u001b[1;32m    249\u001b[0m \u001b[38;5;66;03m# Default to 32 for backwards compat.\u001b[39;00m\n\u001b[0;32m--> 250\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m batch_size:\n\u001b[1;32m    251\u001b[0m   batch_size \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(math\u001b[38;5;241m.\u001b[39mceil(num_samples \u001b[38;5;241m/\u001b[39m steps)) \u001b[38;5;28;01mif\u001b[39;00m steps \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;241m32\u001b[39m\n\u001b[1;32m    253\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_size \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(math\u001b[38;5;241m.\u001b[39mceil(num_samples \u001b[38;5;241m/\u001b[39m batch_size))\n",
            "\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()"
          ]
        }
      ],
      "source": [
        "y_pred = model.predict(x_test,time_embeddings)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K2OTDnpfN88F"
      },
      "source": [
        "## Visualización de resultados"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Access each model's history\n",
        "model_history = history.history\n",
        "\n",
        "# Plotting the validation loss for all models\n",
        "plt.figure(figsize=(6, 6))\n",
        "\n",
        "# Plot each model's validation loss with smooth lines, subtle markers, and refined styling\n",
        "plt.plot(model_history['loss'], label='train', color='blue', linestyle='-', markersize=8, alpha=0.8)\n",
        "plt.plot(model_history['val_loss'], label='test', color='orange', linestyle='-', markersize=8, alpha=0.8)\n",
        "\n",
        "# Enhance plot appearance\n",
        "#plt.yscale('log')  # Use logarithmic scale on y-axis\n",
        "#plt.ylim()\n",
        "plt.xlabel('Epochs', fontsize=14, fontweight='bold')\n",
        "plt.ylabel('Validation Loss (log scale)', fontsize=14, fontweight='bold')\n",
        "plt.title('Model Loss (4-Dropout Layers)', fontsize=16, fontweight='bold')\n",
        "#plt.ylim(0.1,0.3)\n",
        "# Refined grid with lighter lines\n",
        "plt.grid(True, which='both', linestyle='--', linewidth=0.5, alpha=0.5)\n",
        "\n",
        "# Add legend with better positioning and larger font\n",
        "plt.legend(fontsize=12, loc='upper right', bbox_to_anchor=(1, 1), borderaxespad=0., frameon=False)\n",
        "\n",
        "# Add background color for plot to make it stand out\n",
        "plt.gcf().set_facecolor('whitesmoke')\n",
        "\n",
        "# Show plot with a tight layout to avoid clipping\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Initialize a copy of the scaled combined data\n",
        "y_pred_inverse= np.copy(y_pred)\n",
        "y_test_inverse= np.copy(y_test)\n",
        "\n",
        "y_pred_inverse = scaler.inverse_transform(y_pred)\n",
        "y_test_inverse= scaler.inverse_transform(y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 1600x600 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Assuming y_test_inverse and y_pred_inverse are numpy arrays\n",
        "fig, axs = plt.subplots(1, 2, figsize=(16, 6))\n",
        "\n",
        "# Scatter plot\n",
        "axs[0].scatter(y_test_inverse, y_pred_inverse, color=\"black\",s=5)\n",
        "axs[0].grid()\n",
        "axs[0].plot([0, 20], [0, 20], color=\"r\")\n",
        "axs[0].set_xlim([0, 20])\n",
        "axs[0].set_ylim([0, 20])\n",
        "axs[0].set_ylabel('Predicted [ns]')\n",
        "axs[0].set_xlabel('Real [ns]')\n",
        "\n",
        "# 2D Histogram\n",
        "im0 = axs[1].hist2d(y_test_inverse.flatten(), y_pred_inverse.flatten(), bins=(100, 300), cmap='viridis', cmax=30)\n",
        "fig.colorbar(im0[3], ax=axs[1])\n",
        "axs[1].grid()\n",
        "#axs[1].plot([0, 20], [0, 20], 'r')\n",
        "#axs[1].set_xlim([0, 20])\n",
        "#axs[1].set_ylim([0, 20])\n",
        "axs[1].set_ylabel('Predicted [ns]')\n",
        "axs[1].set_xlabel('Real [ns]')\n",
        "axs[1].set_facecolor('#440154')\n",
        "\n",
        "# Adjust layout and show the plot\n",
        "fig.title('Real vs Predicted nu interaction time')\n",
        "fig.tight_layout()\n",
        "plt.show()\n",
        "#plt.savefig('/tmp/your_image.png', dpi=300)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7P0vOulbN_dx"
      },
      "source": [
        "**Metrics computed**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MSE_nuvT: 7.9410\n"
          ]
        }
      ],
      "source": [
        "testScoreMSE_nuvT = mean_squared_error(y_test_inverse, y_pred_inverse)\n",
        "print('MSE_nuvT: %.4f' % np.sqrt((testScoreMSE_nuvT)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s_dr6DJnOBkT",
        "outputId": "ce1bfc87-a07b-4168-d65c-2d18245098e4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RMSE: 7.959\n",
            "R²: -0.001\n",
            "MAPE: 1518.90%\n"
          ]
        }
      ],
      "source": [
        "def mean_absolute_percentage_error(y_true, y_pred):\n",
        "    y_true, y_pred = np.array(y_true), np.array(y_pred)\n",
        "    return np.mean(np.abs((y_true - y_pred) / y_true)) * 100\n",
        "\n",
        "testScoreMSE_nuvT = mean_squared_error(test_nuvT_compressed_4comp, nuvT_pred_compressed_4comp)\n",
        "r2 = r2_score(test_nuvT_compressed_4comp, nuvT_pred_compressed_4comp)\n",
        "mape = mean_absolute_percentage_error(test_nuvT_compressed_4comp, nuvT_pred_compressed_4comp)\n",
        "\n",
        "print('RMSE: %.3f' % np.sqrt((testScoreMSE_nuvT)))\n",
        "print('R²: %.3f' % r2)\n",
        "print('MAPE: %.2f%%' % mape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "**Gaussian fit of the residuals between prediction and actual data to check the quality of the results**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 678
        },
        "id": "o5AxbEK2Outl",
        "outputId": "d608bab8-7b5e-4dda-90f0-5c9ad9b82bdd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "------------------\n",
            "Media nuvT = 5.580523491402194\n",
            "Sigma nuvT = 52.49821478635204\n",
            "------------------\n"
          ]
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Gaussian fit of the difference between the prediction and the real value\n",
        "\n",
        "from scipy.optimize import curve_fit\n",
        "\n",
        "# Define the Gaussian function\n",
        "def func(x, a, x0, sigma):\n",
        "    return a*np.exp(-(x-x0)**2/(2*sigma**2))\n",
        "\n",
        "# Variables de los histogramas\n",
        "diff_nuvT = nuvT_pred_realistic.flatten()-test_nuvT\n",
        "\n",
        "fig, axs = plt.subplots(figsize=(10,6))\n",
        "\n",
        "# Valores de X\n",
        "axs.title.set_text('nuvT')\n",
        "counts_nuvT, bins_nuvT, patches_nuvT = axs.hist(diff_nuvT,120, range=[-400,400], label='Data')\n",
        "\n",
        "step_nuvT = (bins_nuvT[1]-bins_nuvT[0])/2\n",
        "bin_centers_nuvT = bins_nuvT[:-1]+step_nuvT\n",
        "\n",
        "ini_nuvT = 0\n",
        "fin_nuvT = 119\n",
        "axs.axvline(x = bin_centers_nuvT[ini_nuvT], color = 'k', label='Edge\"s fit')\n",
        "axs.axvline(x = bin_centers_nuvT[fin_nuvT], color = 'k')\n",
        "\n",
        "popt_nuvT, pcov_nuvT = curve_fit(func, bin_centers_nuvT[ini_nuvT:fin_nuvT+1], counts_nuvT[ini_nuvT:fin_nuvT+1])\n",
        "\n",
        "\n",
        "ym_nuvT = func(np.linspace(bin_centers_nuvT[ini_nuvT], bin_centers_nuvT[fin_nuvT],100), popt_nuvT[0], popt_nuvT[1], popt_nuvT[2])\n",
        "axs.plot(np.linspace(bin_centers_nuvT[ini_nuvT], bin_centers_nuvT[fin_nuvT],100), ym_nuvT, c='r', label='Fit')\n",
        "axs.set_xlabel('Bias [ns]')\n",
        "axs.legend(prop = { \"size\": 7 })\n",
        "\n",
        "fig.supylabel('Count')\n",
        "fig.tight_layout()\n",
        "\n",
        "# plt.savefig('/tmp/CNNx2+4T+N_Distrib_I.png', dpi=300)\n",
        "\n",
        "print('------------------')\n",
        "print('Mean nuvT =',popt_nuvT[1])\n",
        "print('St. Dev. nuvT =',popt_nuvT[2])\n",
        "print('------------------')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UDrQSbjuCg7w",
        "outputId": "c7cb838f-6b17-4d07-d600-a9e2ce54bb1c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Gauss nuvT\n",
            "115.17707820363492\n",
            "24.574550175010334\n"
          ]
        }
      ],
      "source": [
        "print('Gauss nuvT')\n",
        "print(np.std(diff_nuvT))\n",
        "print(np.mean(diff_nuvT))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Visualización de las representaciones intermedias\n",
        "\n",
        "Se visualiza las características intermedias aprendidas por la CNN. Se puede observar como la entrada se transforma al pasar através de las capas. Se selecciona una imagen aleatoria del conjunto de entrenamiento y se visualiza durante su transformación. Cada fila corresponde con una capa y cada imagen en la fila con un filtro."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras.models import Model\n",
        "\n",
        "def visualize_intermediate_layers(model, x_train, sample_index=0):\n",
        "    \"\"\"\n",
        "    Visualizes intermediate layer outputs for a specific sample from the training data.\n",
        "    \n",
        "    Parameters:\n",
        "    - model: The trained Keras model.\n",
        "    - x_train: The training data.\n",
        "    - sample_index: Index of the sample from x_train to visualize.\n",
        "    \"\"\"\n",
        "    # Get the outputs of all intermediate layers\n",
        "    successive_outputs = [layer.output for layer in model.layers[1:]]\n",
        "    visualization_model = Model(inputs=model.input, outputs=successive_outputs)\n",
        "    \n",
        "    # Select a sample image from x_train\n",
        "    sample_image = x_train[sample_index:sample_index + 1]  # Shape should be (1, height, width, channels)\n",
        "    \n",
        "    # Pass the image through the model to get intermediate representations\n",
        "    successive_feature_maps = visualization_model.predict(sample_image)\n",
        "    \n",
        "    # Get layer names for labeling\n",
        "    layer_names = [layer.name for layer in model.layers[1:]]\n",
        "    \n",
        "    # Plot each layer's feature map\n",
        "    for layer_name, feature_map in zip(layer_names, successive_feature_maps):\n",
        "        if len(feature_map.shape) == 4:  # Only consider convolutional layers (ignore dense layers)\n",
        "            n_features = feature_map.shape[-1]  # Number of filters\n",
        "            size = feature_map.shape[1]  # Spatial dimensions\n",
        "            \n",
        "            # Create a grid for visualizing each filter's feature map\n",
        "            display_grid = np.zeros((size, size * n_features))\n",
        "            \n",
        "            for i in range(n_features):\n",
        "                x = feature_map[0, :, :, i]\n",
        "                \n",
        "                # Normalize the feature map values for better visualization\n",
        "                x -= x.mean()\n",
        "                x /= (x.std() + 1e-5)\n",
        "                x *= 64\n",
        "                x += 128\n",
        "                x = np.clip(x, 0, 255).astype('uint8')\n",
        "                \n",
        "                # Resize or pad `x` if it doesn't match the expected dimensions\n",
        "                if x.shape[1] < size:\n",
        "                    x = np.pad(x, ((0, 0), (0, size - x.shape[1])), mode='constant')\n",
        "                elif x.shape[1] > size:\n",
        "                    x = x[:, :size]\n",
        "                \n",
        "                # Put each filter's activation in the display grid\n",
        "                display_grid[:, i * size : (i + 1) * size] = x\n",
        "            \n",
        "            # Display the grid\n",
        "            scale = 20. / n_features\n",
        "            plt.figure(figsize=(scale * n_features, scale))\n",
        "            plt.title(layer_name)\n",
        "            plt.grid(False)\n",
        "            plt.imshow(display_grid, aspect='auto', cmap='viridis')\n",
        "            plt.show()\n",
        "\n",
        "# Use the function to visualize intermediate representations of your model\n",
        "# Assuming x_train is your training data and create_cnn_model has been defined\n",
        "#model, _ = create_cnn_model(x_train, conv_dim=(3, 3), maxpool_dim=(2, 2))\n",
        "#visualize_intermediate_layers(model, x_train)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABjQAAACgCAYAAACrOoX9AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAfg0lEQVR4nO3de3RV9Zk38OeEXBAKUdASUi5Cx3oBigqtCih9LaWkoFVrqVrFKb05aguiLnTaaR3XqnhZFTt10NLpdHRZxakiWqkXWhV1REUQ70U7Ui4CpV6aRJFLkv3+8b5mNUVkJ3Kyd8jns1b+yD7P2b/v2ccn+xwe9zmFJEmSAAAAAAAAyLGSrAMAAAAAAADsioEGAAAAAACQewYaAAAAAABA7hloAAAAAAAAuWegAQAAAAAA5J6BBgAAAAAAkHsGGgAAAAAAQO4ZaAAAAAAAALlnoAEAAAAAAOSegQYAAJCZhx56KAqFQtx2220fWPdf//VfUSgU4k9/+lP7BAMAAHLHQAMAAMi9iRMnxpIlS6Jv375ZRwEAADJSmnUAAACAXdlvv/1iv/32yzoGAACQIVdoAABAJ3TJJZdEoVCIZ599Nr785S9HZWVl9OrVK2bMmBENDQ2xcuXKmDBhQvTo0SP233//uPLKK5vvu2XLljj//PPj0EMPbb7fUUcdFXfeeecO6/z617+OI444IiorK6Nbt24xePDgmDp16gdmq6uri89//vPRp0+fePLJJyPi/T9y6jOf+UwMHTo0li5dGkcffXTz/i+//PJoampqsc8XXnghxo8fH926dYv99tsvzjnnnFi4cGEUCoV46KGH2n4gAQCAdmOgAQAAndjkyZNj+PDhcfvtt8c3v/nNmD17dpx33nlxwgknxMSJE+OOO+6IY489NmbOnBnz58+PiIitW7fGm2++GRdccEEsWLAgbrnllhgzZkycdNJJceONNzbve8mSJfGVr3wlBg8eHPPmzYuFCxfGD37wg2hoaNhpnnXr1sWYMWNi9erVsWTJkvj0pz/9gfk3btwYX/3qV+P000+Pu+66K2pqauLiiy+Om266qblmw4YNMXbs2Fi5cmVcd911ceONN0Z9fX2ce+65H/LoAQAA7clHTgEAQCf2rW99K2bMmBEREePGjYv7778/rr322pg/f36ceOKJEfH/roS4++6741e/+lWcdNJJUVlZGb/85S+b99HY2Bif/exn46233oprrrkmpkyZEhERjz32WCRJEtdff31UVlY21//jP/7j+2ZZsWJFTJw4MT7+8Y/HggULolevXrvM/8Ybb8Rvf/vb5sHHuHHj4qGHHoqbb765Ocfs2bPjzTffjIcffjgOOeSQiIioqamJCRMm+JJxAADoQFyhAQAAndikSZNa/H7wwQdHoVCImpqa5m2lpaXxD//wD7F69ermbb/+9a9j9OjR8ZGPfCRKS0ujrKwsfvGLX8RLL73UXPOpT30qIv7fVSD//d//Ha+99tpOc9x3331x9NFHxzHHHBOLFi1KNcyIiKiqqtrhKo5PfvKTLbIuXrw4hg4d2jzMeM+pp56aag0AACAfDDQAAKAT+/vBQXl5eXTr1i26du26w/YtW7ZERMT8+fNj8uTJ8bGPfSxuuummWLJkSSxdujSmTp3aXBMRccwxx8SCBQuioaEhpkyZEv369YuhQ4fGLbfcskOOBQsWxLvvvhv/9E//FBUVFanz9+7de4dtFRUV8e677zb//sYbb0SfPn12qHu/bQAAQH4ZaAAAAK1y0003xaBBg+LWW2+NE044IY488sgYOXJkbN26dYfaL37xi/H73/8+amtr46GHHop+/frFaaedFkuWLGlRN3v27KipqYmampq4//77d2ve3r17x5///Ocdtm/cuHG3rgMAABSXgQYAANAqhUIhysvLo1AoNG/buHFj3HnnnTu9T0VFRYwdOzauuOKKiIh4+umnW9zetWvXmD9/fkyaNCmOP/74D9xXa40dOzaef/75ePHFF1tsnzdv3m5bAwAAKD4DDQAAoFUmTZoUK1eujLPPPjseeOCBuOGGG2LMmDHRt2/fFnU/+MEPYurUqfGrX/0qFi9eHHfeeWecd955UVZWFmPHjt1hv2VlZXHLLbfEaaedFieffPL7fjRVW0yfPj169eoVNTU1ccMNN8S9994bU6ZMiT/84Q8REVFS4m0RAAB0BKVZBwAAADqWr33ta7Fp06a4/vrr4z//8z9j8ODBcdFFF8W6deviX//1X5vrjjjiiHjqqadi5syZ8Ze//CX23nvvGDlyZDzwwAMxZMiQ9913SUlJ/OIXv4gePXrE6aefHu+880584xvf+FB5q6urY/HixTF9+vQ466yzolu3bnHiiSfGpZdeGmeeeWbsvffeH2r/AABA+ygkSZJkHQIAAKC9fetb34pbbrkl3njjjSgvL886DgAAsAuu0AAAAPZ4l156aVRXV8fgwYPj7bffjrvvvjv+4z/+I77//e8bZgAAQAdhoAEAAOzxysrK4qqrrop169ZFQ0NDHHDAAXH11VfHtGnTso4GAACk5COnAAAAAACA3CvJOgAAAAAAAMCuGGgAAAAAAAC5Z6ABAAAAAADkXrt/KXhTU1OsX78+evToEYVCob2XBwAAAAAAciRJkqivr4/q6uooKdn5dRjtPtBYv3599O/fv72XBQAAAAAAcmzt2rXRr1+/nd7epoHGnDlz4qqrrooNGzbEkCFD4pprromjjz461X179OgRERHHVJwYpYWyXdbf/uxTqfb7pU+OTFXX0aR9/BGtPAZluz727/nOo0+kqvvpmCPSr9+B3L5sSeraL404KnVtU/3bqeruePm51Ps88RPDUtd2JLetfCZ17ckHDt/t61/+fLoeiIi4aOie2QfF+O+wdH/DbYgkyToBZG571d5ZR+j0ujzzSqbrF+09TwfSOPyArCN0eiXL/pDp+vNffDp17UmHHFbEJBkZpgeyVlK7OesIRbFyRq/UtQde/WYRk+xaU2W3TNcnIp7L9jVRsZy/PP055seHZ3eOaUi2xyONdzXPD3am1QONW2+9NaZPnx5z5syJ0aNHx89+9rOoqamJF198MQYMGLDL+7/3MVOlhbIoLZTvsr5nj3Rf85FmXx1R2scf0cpjkGKY9J7uPbrs/vU7kGI9B00pn4PWrZ/+ee1Isj4GH/EcFOU5KC2paGsc2HMYaEAkpV2zjtDpdcn4dXzR3vN0IAV9kLmSjF/HZ/2eJ3NdvDfIWkmXxqwjFEXJXun/vpZm/N9hkz7I3p749zUiunewc8yuvqai1V8KfvXVV8fXv/71+MY3vhEHH3xwXHPNNdG/f/+47rrr2hwSAAAAAADgg7RqoLFt27ZYtmxZjB8/vsX28ePHx2OPPfa+99m6dWvU1dW1+AEAAAAAAGiNVg00Xn/99WhsbIw+ffq02N6nT5/YuHHj+95n1qxZUVlZ2fzjC8EBAAAAAIDWavVHTkXs+DlWSZLs9LOtLr744qitrW3+Wbt2bVuWBAAAAAAAOrFWfSn4vvvuG126dNnhaoxNmzbtcNXGeyoqKqKiwpfaAAAAAAAAbdeqKzTKy8tjxIgRsWjRohbbFy1aFKNGjdqtwQAAAAAAAN7Tqis0IiJmzJgRZ5xxRowcOTKOOuqomDt3bqxZsybOOuusYuQDAAAAAACIQpIkSWvvNGfOnLjyyitjw4YNMXTo0Jg9e3Ycc8wxqe5bV1cXlZWVcWzXyVFaKG91YHaTsrKsE3R6TfX1WUeATJUOGph1hFZZ+D93pqqbOPqLRU7SOaU9/hEd7Dlo/cuwzCx87K7UtRNHHV/EJJ3XnvocbK/eJ+sInd79t92QurZm8JFFTJKNe159PHVtsR5/4+EHFmW/pJe2DyYMGFnkJNmY8MzrqWvvHb7v7g8wXA9kraR2c1H2u/akvqlr+8/fUJQMWRo1/6XUtY9+bUQRk5DKMyuLstt71zyVqm5PPcekffx19U2xzydejdra2ujZs+dO61p9hUZExNlnnx1nn312W+4KAAAAAADQaq36Dg0AAAAAAIAsGGgAAAAAAAC5Z6ABAAAAAADknoEGAAAAAACQewYaAAAAAABA7hloAAAAAAAAuWegAQAAAAAA5J6BBgAAAAAAkHsGGgAAAAAAQO4VkiRJ2nPBurq6qKysjGO7To7SQvku6+959fFU+60ZfOSHjZZLaR9/RCuPQVlZ6tKqRenqNn4u/fIdyT0rH0ldW3Pg0alrm+rrU9Xdt35F6n1+vvrQ1LUdSdbHIOv186AYx6B00MC2hYE9Sfu+DINc2l69T9YROr0uy1dmuv7LPz84de0nvvlSEZNkp/HwA7OO0OmVPPlCpuvfu+ap1LUTBowsYpKMDNcDWSup3Zx1hKL406l9U9fuf8uGIibZtabKbpmuT0Q8k+1romK5cdXi1LVTBo0tYpIP1pBsjwcbbo/a2tro2bPnTutcoQEAAAAAAOSegQYAAAAAAJB7BhoAAAAAAEDuGWgAAAAAAAC5Z6ABAAAAAADknoEGAAAAAACQewYaAAAAAABA7hloAAAAAAAAuWegAQAAAAAA5F5p1gHIv/XvVKaqK4naIifJxsNbsk4AAAB7rv/97C9T19bEkUVMAtlZsXVr1hFgj9RtY5J1BMhcfdOe1Qeu0AAAAAAAAHLPQAMAAAAAAMg9Aw0AAAAAACD3DDQAAAAAAIDcM9AAAAAAAAByz0ADAAAAAADIPQMNAAAAAAAg9ww0AAAAAACA3DPQAAAAAAAAcs9AAwAAAAAAyL1CkiRJey5YV1cXlZWVcWzXyVFaKG/PpflbZWVZJ+j0murrs44AmSodNDDrCK2y8H/uTFU3cfQXi5ykc0p7/CM62HPQvi/DPpSFj92VunbiqOOLmKTz2lOfg+3V+2QdodP7yi/uS11722H7Fy9IRu559fHUtTWDjyxKhsbDDyzKfknv/ttuSFU3YcDIIifJxr1rnkpdW5RjMLw4PXDvb36VunbCcV8tSoaOoqR2c1H2+9uH70hd+4VjTixKhixtGN83dW2fJ2qLkkEftMIzK4uy27R/Yzv7Oaauvin2+cSrUVtbGz179txpnSs0AAAAAACA3DPQAAAAAAAAcs9AAwAAAAAAyD0DDQAAAAAAIPcMNAAAAAAAgNwz0AAAAAAAAHLPQAMAAAAAAMg9Aw0AAAAAACD3DDQAAAAAAIDcM9AAAAAAAAByr5AkSdKeC9bV1UVlZWUc23VylBbKd1l/z6uPp9pvzeAjP2y0XEr7+CNaeQzKylKXrp42LFXdwJ88l379DuSVn308de0B3/7f1LVN9fWp6u5bvyL1Pj9ffWjq2o4k62OQ9fp5UIxjUDpoYNvCwJ6kfV+GQS5tr94n6widXpflKzNdv/uij6SufedzbxcxSXYaDz8w6widXsmTL2S6/r1rnkpdO2HAyCImychwPZC1ktrNWUcoirrhH01d2/OZTUVMsmtNld0yXZ+IeCbb10TF0lHOMQ3J9niw4faora2Nnj177rTOFRoAAAAAAEDutWqgcckll0ShUGjxU1VVVaxsAAAAAAAAERFR2to7DBkyJH73u981/96lS5fdGggAAAAAAODvtXqgUVpa6qoMAAAAAACgXbX6OzReeeWVqK6ujkGDBsUpp5wSr7766gfWb926Nerq6lr8AAAAAAAAtEarBhpHHHFE3HjjjXHffffFz3/+89i4cWOMGjUq3njjjZ3eZ9asWVFZWdn8079//w8dGgAAAAAA6FxaNdCoqamJL33pSzFs2LAYN25cLFy4MCIibrjhhp3e5+KLL47a2trmn7Vr1364xAAAAAAAQKfT6u/Q+Fvdu3ePYcOGxSuvvLLTmoqKiqioqPgwywAAAAAAAJ1cq79D429t3bo1Xnrppejbt+/uygMAAAAAALCDVg00Lrjggli8eHGsWrUqnnjiiTj55JOjrq4uzjzzzGLlAwAAAAAAaN1HTq1bty5OPfXUeP3112O//faLI488Mh5//PEYOHBgsfLFpJdrUla+VbQMWTrksdNT1w6MPxYlQ8Vfi7LbDuMjj3fLdP3NTdsyXR8AAIpp2YuDUtceFM8VMQkAe5pu67dkHQHYzVo10Jg3b16xcgAAAAAAAOzUh/oODQAAAAAAgPZgoAEAAAAAAOSegQYAAAAAAJB7BhoAAAAAAEDuGWgAAAAAAAC5Z6ABAAAAAADknoEGAAAAAACQewYaAAAAAABA7hWSJEnac8G6urqorKyMY7tOjtJCeXsuzd8qK8s6QafXVF+fdQTIVOmggVlHaJWF/3NnqrqJo79Y5CSdU9rjH9HBnoP2fRn2oSx87K7UtRNHHV/EJJ3XnvocbK/eJ+sInV7Vj1elrv3L/9lWxCTZWD9vUOra6lPSH6vWaDz8wKLsl/Tuv+2GVHUTBowscpJsjFn+duraRw//yO4PMFwPZK2kdnNR9ru9qjJ1bdnG2qJkyFLDfj1T15ZsayhiElJ5ZmVRdnvvmqdS1e2p55i0j7+uvin2+cSrUVtbGz177rx3XKEBAAAAAADknoEGAAAAAACQewYaAAAAAABA7hloAAAAAAAAuWegAQAAAAAA5J6BBgAAAAAAkHsGGgAAAAAAQO4ZaAAAAAAAALlnoAEAAAAAAOSegQYAAAAAAJB7hSRJkvZcsK6uLiorK+PYrpOjtFC+y/otv+mTar9dj/vzh42WSy/POjR17ScuXpF+x2VlqUvrJhySqq7nvS+mX78DefOEoalrey14PnVtU319qrr71q9Ivc/PVx+aurYjyfoYZL1+HhTjGJQOGti2MLAnad+XYZBL26v3yTpCp9dl+cpM1//DT4alrj1o2nNFTJKdxsMPzDpCp1fy5AuZrn/vmqdS104YMLKISTIyXA9kraR2c9YRiiLpVpG6trB5axGT7FpTZbdM1ycinsn2NVGxdJRzTEOyPR5suD1qa2ujZ8+eO61zhQYAAAAAAJB7BhoAAAAAAEDuGWgAAAAAAAC5Z6ABAAAAAADknoEGAAAAAACQewYaAAAAAABA7hloAAAAAAAAuWegAQAAAAAA5J6BBgAAAAAAkHul7b1gkiQREdGQbE9V3/DO1nR1ybY2Z8qzpi1bUte26hj8/+ch1X63p8uwpz4HjduK8xw0peyBuvqmVqyfbp8dTdbHIOv186Aox6Ap3d932KO14nwMe6qGhvSvtSiOJOPX8U3vFuk9TwfSqA8yV5Lx6/hO/56j0XuDrJXsoc9B0pi+tpDxMWhq7JLp+kTEnvj3NTrOOea9tZNdvE8uJLuq2M3WrVsX/fv3b88lAQAAAACAnFu7dm3069dvp7e3+0Cjqakp1q9fHz169IhCoRAREXV1ddG/f/9Yu3Zt9OzZsz3jwB5Nb0Hx6C8oDr0FxaG3oDj0FhSP/oLiyGtvJUkS9fX1UV1dHSUlO/+mjHb/yKmSkpKdTlh69uyZq4MIewq9BcWjv6A49BYUh96C4tBbUDz6C4ojj71VWVm5yxpfCg4AAAAAAOSegQYAAAAAAJB7uRhoVFRUxA9/+MOoqKjIOgrsUfQWFI/+guLQW1AceguKQ29B8egvKI6O3lvt/qXgAAAAAAAArZWLKzQAAAAAAAA+iIEGAAAAAACQewYaAAAAAABA7hloAAAAAAAAuZf5QGPOnDkxaNCg6Nq1a4wYMSIeeeSRrCNBhzJr1qz41Kc+FT169IiPfvSjccIJJ8TKlStb1CRJEpdccklUV1fHXnvtFZ/5zGfihRdeyCgxdEyzZs2KQqEQ06dPb96mt6DtXnvttTj99NOjd+/e0a1btzj00ENj2bJlzbfrL2i9hoaG+P73vx+DBg2KvfbaKwYPHhyXXnppNDU1NdfoLUjn4YcfjuOOOy6qq6ujUCjEggULWtyeppe2bt0a3/nOd2LfffeN7t27x/HHHx/r1q1rx0cB+fNBvbV9+/aYOXNmDBs2LLp37x7V1dUxZcqUWL9+fYt96C3Y0a7OW3/r29/+dhQKhbjmmmtabO8ovZXpQOPWW2+N6dOnx/e+9714+umn4+ijj46amppYs2ZNlrGgQ1m8eHGcc8458fjjj8eiRYuioaEhxo8fH++8805zzZVXXhlXX311XHvttbF06dKoqqqKz33uc1FfX59hcug4li5dGnPnzo1PfvKTLbbrLWibt956K0aPHh1lZWVxzz33xIsvvhg//vGPY++9926u0V/QeldccUVcf/31ce2118ZLL70UV155ZVx11VXx05/+tLlGb0E677zzTgwfPjyuvfba9709TS9Nnz497rjjjpg3b148+uij8fbbb8ekSZOisbGxvR4G5M4H9dbmzZtj+fLl8S//8i+xfPnymD9/frz88stx/PHHt6jTW7CjXZ233rNgwYJ44oknorq6eofbOkxvJRn69Kc/nZx11lktth100EHJRRddlFEi6Pg2bdqURESyePHiJEmSpKmpKamqqkouv/zy5potW7YklZWVyfXXX59VTOgw6uvrkwMOOCBZtGhRMnbs2GTatGlJkugt+DBmzpyZjBkzZqe36y9om4kTJyZTp05tse2kk05KTj/99CRJ9Ba0VUQkd9xxR/PvaXrpr3/9a1JWVpbMmzevuea1115LSkpKknvvvbfdskOe/X1vvZ8nn3wyiYhk9erVSZLoLUhjZ721bt265GMf+1jy/PPPJwMHDkxmz57dfFtH6q3MrtDYtm1bLFu2LMaPH99i+/jx4+Oxxx7LKBV0fLW1tRER0atXr4iIWLVqVWzcuLFFr1VUVMTYsWP1GqRwzjnnxMSJE2PcuHEttustaLu77rorRo4cGV/+8pfjox/9aBx22GHx85//vPl2/QVtM2bMmPj9738fL7/8ckREPPPMM/Hoo4/GF77whYjQW7C7pOmlZcuWxfbt21vUVFdXx9ChQ/UbtEJtbW0UCoXmK3n1FrRNU1NTnHHGGXHhhRfGkCFDdri9I/VWaVYLv/7669HY2Bh9+vRpsb1Pnz6xcePGjFJBx5YkScyYMSPGjBkTQ4cOjYho7qf367XVq1e3e0boSObNmxfLly+PpUuX7nCb3oK2e/XVV+O6666LGTNmxD//8z/Hk08+Gd/97nejoqIipkyZor+gjWbOnBm1tbVx0EEHRZcuXaKxsTF+9KMfxamnnhoRzl2wu6TppY0bN0Z5eXnss88+O9T4Nw9IZ8uWLXHRRRfFaaedFj179owIvQVtdcUVV0RpaWl897vffd/bO1JvZTbQeE+hUGjxe5IkO2wD0jn33HPj2WefjUcffXSH2/QatM7atWtj2rRpcf/990fXrl13Wqe3oPWamppi5MiRcdlll0VExGGHHRYvvPBCXHfddTFlypTmOv0FrXPrrbfGTTfdFDfffHMMGTIkVqxYEdOnT4/q6uo488wzm+v0Fuwebekl/QbpbN++PU455ZRoamqKOXPm7LJeb8HOLVu2LH7yk5/E8uXLW90neeytzD5yat99940uXbrsMOHZtGnTDv+XA7Br3/nOd+Kuu+6KBx98MPr169e8vaqqKiJCr0ErLVu2LDZt2hQjRoyI0tLSKC0tjcWLF8e//du/RWlpaXP/6C1ovb59+8YhhxzSYtvBBx8ca9asiQjnLmirCy+8MC666KI45ZRTYtiwYXHGGWfEeeedF7NmzYoIvQW7S5peqqqqim3btsVbb7210xrg/W3fvj0mT54cq1atikWLFjVfnRGht6AtHnnkkdi0aVMMGDCg+d83Vq9eHeeff37sv//+EdGxeiuzgUZ5eXmMGDEiFi1a1GL7okWLYtSoURmlgo4nSZI499xzY/78+fHAAw/EoEGDWtw+aNCgqKqqatFr27Zti8WLF+s1+ACf/exn47nnnosVK1Y0/4wcOTK++tWvxooVK2Lw4MF6C9po9OjRsXLlyhbbXn755Rg4cGBEOHdBW23evDlKSlq+xevSpUs0NTVFhN6C3SVNL40YMSLKyspa1GzYsCGef/55/QYf4L1hxiuvvBK/+93vonfv3i1u11vQemeccUY8++yzLf59o7q6Oi688MK47777IqJj9VamHzk1Y8aMOOOMM2LkyJFx1FFHxdy5c2PNmjVx1llnZRkLOpRzzjknbr755rjzzjujR48ezf+XUGVlZey1115RKBRi+vTpcdlll8UBBxwQBxxwQFx22WXRrVu3OO200zJOD/nVo0eP5u+ieU/37t2jd+/ezdv1FrTNeeedF6NGjYrLLrssJk+eHE8++WTMnTs35s6dGxHh3AVtdNxxx8WPfvSjGDBgQAwZMiSefvrpuPrqq2Pq1KkRobegNd5+++344x//2Pz7qlWrYsWKFdGrV68YMGDALnupsrIyvv71r8f5558fvXv3jl69esUFF1wQw4YNi3HjxmX1sCBzH9Rb1dXVcfLJJ8fy5cvj7rvvjsbGxuZ/4+jVq1eUl5frLdiJXZ23/n44WFZWFlVVVXHggQdGRAc7byUZ+/d///dk4MCBSXl5eXL44YcnixcvzjoSdCgR8b4/v/zlL5trmpqakh/+8IdJVVVVUlFRkRxzzDHJc889l11o6KDGjh2bTJs2rfl3vQVt95vf/CYZOnRoUlFRkRx00EHJ3LlzW9yuv6D16urqkmnTpiUDBgxIunbtmgwePDj53ve+l2zdurW5Rm9BOg8++OD7vs8688wzkyRJ10vvvvtucu655ya9evVK9tprr2TSpEnJmjVrMng0kB8f1FurVq3a6b9xPPjgg8370Fuwo12dt/7ewIEDk9mzZ7fY1lF6q5AkSdJOsxMAAAAAAIA2yew7NAAAAAAAANIy0AAAAAAAAHLPQAMAAAAAAMg9Aw0AAAAAACD3DDQAAAAAAIDcM9AAAAAAAAByz0ADAAAAAADIPQMNAAAAAAAg9ww0AAAAAACA3DPQAAAAAAAAcs9AAwAAAAAAyD0DDQAAAAAAIPf+LzwfYqqSl2hwAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 2000x125 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 2000x125 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 2000x62.5 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 2000x62.5 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 2000x62.5 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 2000x31.25 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 2000x31.25 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 2000x15.625 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 2000x15.625 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 2000x7.8125 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABkEAAABGCAYAAAB7RzO+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAA9hAAAPYQGoP6dpAABvh0lEQVR4nOz9d5hkR33o/7/r5NO5e3py2Ak7O5tXK+0qZwklQAIjwIBINhiMjbG5NmCbe4k22DjABYG5xiAwIJIQQRISQhnFXe2utDnPTo49nbtP/v3Rsu6PS7gWXB6+yPV6Hj16pvuE6u2uc6rq86k6IoqiCEmSJEmSJEmSJEmSJEmSJEmSpOcZ5TddAEmSJEmSJEmSJEmSJEmSJEmSpF8HGQSRJEmSJEmSJEmSJEmSJEmSJOl5SQZBJEmSJEmSJEmSJEmSJEmSJEl6XpJBEEmSJEmSJEmSJEmSJEmSJEmSnpdkEESSJEmSJEmSJEmSJEmSJEmSpOclGQSRJEmSJEmSJEmSJEmSJEmSJOl5SQZBJEmSJEmSJEmSJEmSJEmSJEl6XpJBEEmSJEmSJEmSJEmSJEmSJEmSnpdkEESSJEmSJEmSJEmSJEmSJEmSpOclGQSRJEmSJEmSpOehm266CSEE4+Pjv5bj33///Qgh+Na3vvVrOf5vwhve8AYGBwd/bcd/05vexMaNG8lkMti2zZo1a/iLv/gLlpaWfm3nlCRJkiRJkqT/6rTfdAEkSZIkSZIkSZL+K6jVavzBH/wBq1evxrIsdu7cyd/8zd9wxx13sHv3bgzD+E0XUZIkSZIkSZKed2QQRJIkSZIkSZKk30qNRgPbtn/TxfhPu/nmm3/i70svvZRkMsnb3vY2fvzjH3PppZf+hkomSZIkSZIkSc9fcjksSZIkSZIkSfov4kc/+hGXXXYZqVSKWCzGeeedxz333PMT2xw7dow3vvGNjI6OEovF6O3t5cUvfjF79+79vx6/XC5z5ZVX0tnZyeOPP87o6ChXXnnlT21XrVZJp9P80R/90X+67IODg7zoRS/i29/+Nlu3bsWyLD7wgQ8AMDc3x1ve8hb6+vowDIOhoSE+8IEP4Pv+f/r4P8v4+DhCCG666aafek8Iwfvf//5f6fgA7e3tAGiazE+TJEmSJEmSpF8H2dKWJEmSJEmSpP8CvvzlL/O6172O6667ji9+8Yvous5nP/tZrrzySu666y4uu+wyAGZmZmhra+OjH/0o7e3tFAoFvvjFL3LWWWexe/duxsbGfubxp6amuOaaa3Bdl0cffZTh4WHe/va386d/+qccPXqU0dHRZ7f90pe+RLlcfk5BEIBdu3Zx8OBB3vve9zI0NEQ8Hmdubo4zzzwTRVH4H//jfzAyMsKjjz7Khz/8YcbHx/nCF77wy/+j/Zr4vo/jOOzZs4f//t//O+effz7nnXfeb7pYkiRJkiRJkvS8JIMgkiRJkiRJkvQ8V6/Xecc73sGLXvQibr311mdfv+aaazj99NP5q7/6Kx5//HEALrzwQi688MJntwmCgBe+8IVs2LCBz372s/zTP/3TTx1/z549vPCFL2RkZITvfOc75HI5AN74xjfy3ve+lxtvvJGPf/zjz25/4403cskll7B+/frn9DkWFhY4cOAAa9asefa1t771raysrLB//34GBgYAuOyyy7Btmz//8z/nL/7iL57zeX6dHnvsMc4555xn/77mmmv42te+hqqqv8FSSZIkSZIkSdLzl1wOS5IkSZIkSZKe5x555BEKhQKvf/3r8X3/2f/CMOSqq65ix44d1Go1oDVL4W//9m9Zv349hmGgaRqGYXD06FEOHjz4U8e+6667uOCCC7jwwgu5++67nw2AACSTSd74xjdy0003PXv8e++9lwMHDvDHf/zHz/lzbN68+ScCIAC33XYbl1xyCT09PT/x2a6++moAHnjgged8nl+nTZs2sWPHDh544AE+8YlPsHv3bl7wghdQr9d/00WTJEmSJEmSpOclORNEkiRJkiRJkp7n5ufnAbj++ut/7jaFQoF4PM473/lObrzxRt797ndz0UUXkc1mURSFN73pTTQajZ/a7zvf+Q6NRoM//MM/xDTNn3r/7W9/O5/61Kf4yle+wh/8wR/wqU99ir6+Pq677rrn/Dm6u7t/5mf7/ve/j67rP3OfpaWl53yeX6d4PM62bduA1qybs846i7PPPpvPfvaz/Nmf/dlvuHSSJEmSJEmS9PwjgyCSJEmSJEmS9DyXz+cB+OQnP8nZZ5/9M7fp7OwE/vezQ/72b//2J95fWloik8n81H7//M//zNe//nWuvvpqbr31Vq644oqfeH/16tVcffXV3HjjjVx99dV873vf4wMf+MAvtfyTEOJnfrbNmzfzN3/zNz9zn56enud8nv9gWRYAjuP8xOvLy8u/9DH/T9u2bUNRFI4cOfL/7JiSJEmSJEmSJP1vMggiSZIkSZIkSc9z5513HplM5j+1DJUQ4qdmdNx+++1MT0+zevXqn9resiy+/e1vc8MNN3Dttdfy9a9//admebzjHe/giiuu4PWvfz2qqvLmN7/5V/9Qz3jRi17EHXfcwcjICNls9v/ZcaEVGLIsi6effvonXv/ud7/7/+wcDzzwAGEY/sx/W0mSJEmSJEmSfnUyCCJJkiRJkiRJz3OJRIJPfvKTvP71r6dQKHD99dfT0dHB4uIiTz31FIuLi3zmM58BWkGFm266ibVr17J582aefPJJPvaxj9HX1/dzj6/rOjfffDNvetObuP766/nSl77Eq171qmfff8ELXsD69eu57777uOGGG+jo6Ph/9tk++MEPcvfdd3PuuefyJ3/yJ4yNjdFsNhkfH+eOO+7gX/7lX35h2X8RIQQ33HADn//85xkZGWHLli088cQTfPWrX33Ox7rtttv413/9V6699lpWrVqF53ns3LmTj3/846xevZo3velNv1QZJUmSJEmSJEn6xWQQRJIkSZIkSZL+C7jhhhsYGBjg7//+73nLW95CpVKho6OD0047jTe84Q3PbveJT3wCXdf5yEc+QrVa5fTTT+fb3/42733ve3/h8RVF4d/+7d9IJpPccMMN1Gq1nxjYf8UrXsH73//+X+qB6L9Id3c3O3fu5EMf+hAf+9jHmJqaIplMMjQ0xFVXXfUrzw75x3/8RwD+/u//nmq1yqWXXsptt93G4ODgczrO6tWrMQyDD33oQ88+o2VwcJDf//3f5z3veQ/pdPpXKqckSZIkSZIkST+biKIo+k0XQpIkSZIkSZKk57dt27YhhGDHjh2/6aJIkiRJkiRJkvRfiJwJIkmSJEmSJEnSr0W5XGbfvn3cdtttPPnkk9x6662/6SJJkiRJkiRJkvRfjAyCSJIkSZIkSZL0a7Fr1y4uueQS2traeN/73sdLXvKSn9omCAJ+0eR0IQSqqv5K5fh1n8P3/V/4vqIoKIrySx9fkiRJkiRJkqRfnmyJS5IkSZIkSZL0a3HxxRcTRRFLS0u8//3v/5nbjIyMoOv6z/3vsssu+5XLcdlll/3Cc4yMjPzSxx4fH/+Fx9Z1nQ9+8IO/8meQJEmSJEmSJOmX80s9E+TTn/40H/vYx5idnWXDhg18/OMf54ILLvi52z/wwAO8853vZP/+/fT09PCud72Lt771rb9SwSVJkiRJkiRJ+u23d+9eHMf5ue8nk0nGxsZ+pXMcPnyYSqXyc983TZNNmzb9Usd2XZenn376F27T09NDT0/PL3V8SZIkSZIkSZJ+Nc85CPL1r3+d1772tXz605/mvPPO47Of/Syf+9znOHDgAAMDAz+1/cmTJ9m4cSNvfvObectb3sLDDz/M2972Nm6++WZe9rKX/T/7IJIkSZIkSZIkSZIkSZIkSZIkSf//nnMQ5KyzzuL000/nM5/5zLOvrVu3jpe85CV85CMf+ant3/3ud/O9732PgwcPPvvaW9/6Vp566ikeffTRX6HokiRJkiRJkiRJkiRJkiRJkiRJP99zejD6hz/8YZ544gmefvppbrnlFs4991z+7u/+jiuuuIJHHnnkZ+7zgx/8gEOHDiGE+InXVVXF8zx0Xf+/njcMQ2ZmZkgmkz91HEmSJEmSJEmSJEmSJEmSJEmS/muJoohKpUJPTw+K8vMff/6cgiD33HMPAP/6r//Kpk2b+Ou//muuuOIK3vjGNzI3N/cz9ykUCkBrHd5UKgXAjh07uPbaa1laWqK7u/un9nEc5yfWBZ6enmb9+vXPpaiSJEmSJEmSJEmSJEmSJEmSJD3PTU5O0tfX93Pff05BkK985Sv09vYyMjLCli1b+MIXvkBHRwdTU1P/1xkaHR0dZDIZAHK5HMDP3ecjH/kIH/jAB37q9e1ffQsLJwZIjkNsISRx3yEmPt1PczlGbELDWooIdYg0QXBxkeixDCIENxNhzwucDCge6HVQGxEoUBqNGPpOnYmr4rgdPnrcJZVssipd4LzsMW6890qSxxVUN6KZFcQWIhQvwo8JnJTAS0eEBoRdTcKqjggEUdzHjLtwIIniwcBtSyyf0UZpFLSaID4bkTrRYOpyGy8TQgSRFmFNa4QbK7h1g7Z8lf7UCvsfHeG6yx9jb6mHw6e6EXWVKO4jBLQ9ZLB8gUvkC4x5nfg0GJWI1737Np6uDvCjH28hdRyqF9QJZ2Kcdc5Bdt25nr77q4Sawjn/+CQPfPAcpq6AzkcUMk8tE+kataEki6erWIuCSAEnA/5Qg10X/jvbv/pm8k+FhJrATQn0WkRhvSCIhaCA0V5nsK3A4aO9oIecu/YYT31vPZ1PNmhmDQrrVE6/8gB75vow7k0SWwiZvRC6HgatHjJ5jaB31RJZq0HVM5h9pBetDqEOWhPMlQitGREYrfOXNngoto86bWGMlYl/N0mlT4GtZXxPYdWnIrT5IpPX97P1xfsBePj4CMYJi+5HHXxbpdatUuuF0ISRLy+zcF4bsfmA5Q0abluIsaRglCF99SxLP+6m2R6iBLD680uceE07XY/6BKZCYb2Kt6ZOUNXRCxqJDQWKk63fvN7WQDmQwF6MaNtXY2lznHovKA2Blw1JHxKUL2rAtI09J6hsdDFmdDJHI2pdCoEBTkfAgy+6kUoU8jtf/FNCMyIYaDLas8B8NUGpFCP0VLQlnUgBuhsknohhLYckJhqoTZ96b5zpywUEkNuroHggwggRguJHLG5VMJcFXirC2FDiv439iBNOO1+760JymxZZ2dVO9kiEFxO0P7LEsTe0EeQ8jCmDjicDYg8d5Pj/HCEsmhCCXlHQywLfhkiAiCB9PMQq+ESqgBACW6GRVygPt95v2xvx++/+DmmlwV/e+SriUwpOJiK2eQX3sRxWIaLWLYi0CL0ssAoRxXURelHBW1unI1thqZjAX7TpfBwUL6LZpkAEiWmfD/7z53jzLW8ldRxUN0JEYBZ8Zs/X6XrUp9qj4aYEoQZuLiS/Cxp5BSWIMCoRIoD5S316uwvEPhgj1FUWtsepjHnkntTIHG0ycaWFtSjIHvaY364Tra7R+Q2ThW0qKJA5HBEpgpW10P2IT6QIljdopM5ZoPB0O2pTkD4WElt0cZMaC9tU9DUVgr0p7MWIwBQEJqTGAxaudum8w0B1Wq+HmkB1I5a2KKj11r+P1qT1HSjgWwIRtb6PSAUEWEsRvi2oDIfEB8uM5ReYraaYPdSBXlawFyJUFwKjde0tD6oMXDNO3dOZeqqHgbuarIxarJzuI5oKalMhNi3ouXOGUy/rofuROrU+i5UxQWzTCm9bfT8f+/5LUYarOCULK9Ngfec8S3+/iti+GQ69r5vOjhKluoU/nmTo2xVW1iVJH68T2BqlYYPi+hC7u0bfR0NOvDyN6KvjlU3UmsKLLtzJXd87k94Ha1QHbFZeVCP1wwRtd58Ez+PUm8awty+zMp3GWFEZuL2KlzFRGwGNDgP3NUWqDYPef9GYOd+m2euBEdLVWaRxVyeVoZDkCQW9FlEehNwZC1Tu70RzQARR65quCrwEeMkIa0kQnw1JjldRTs4y9/Ixar0RXY8F6DUfJ6OT2jXDzIv6qfWH9G6co+KYNHa2kTpzkYX5NKKpkjyiYi+FVPsU3FzEyPYJju8YIL8rJDFVp9YbIzFeZfrSNL4FIgStDulxHzepUrisCfMm6SOC5JRHpVcnv6fMsbcbaKcsOnf4qE6IseMIi6/cyMrpHkKLMMZNVn1vhfnzs4y+/Aj75rqI9rd+i33Xj7PvcD+JYxqxixd5Qc8hvnHodIY/5nDid7MAREBoRoRmSNeDAsWHwlqV5oCHnnAIFmK07RHYix4TL4GNI9OMF7OIH2fQ6hGxxYDYdJ1aX5y5a13Cusbaf14E1yNKJaj3pyhs0Ak16L9jhWZPnEZep94hqI14xE/oZI75LGxXEa6g6wmPhdN10sdbdazSb9Joa5Wrub2KUCJWfTxCmZiHjjZqq1IsvaZO5ttxGu0qZinEKIcUbqijKCHuvjT5p0PMkkel36R4aYMzB8d5YnwQ7VCMzLEAe8lj5jwLxYNGv49eVEmeBLMU0mhTqIxEvPqSh3h4aYTFH/QRWFDv9xm61ae42qQ8DEE8wJ7SSJ8MWd4kCKyI5AmFrq8foHrBGhQ34rT/vgdb9fjmj89m+NsNZs6Po7qguNDsjHCzAcO3eKh1j2abzfJGnWZHiAhb14f8HsjeN87U7w4TbC+j7ErRf8cSx29oo/d+j1qnztL2ALSIvttb161gcwVdD6nPJhCuQIRgzSv4sdZ9OzYXYb9qFlP1Ob6vD72k4GZCetYsULu9E8VvtfH+45oUmIL0cR/Fj4gEoICTVlm5qsHrNzzG7X97MW5KwU0JYgshi9tAqwqsBYHqta5ljY4IvSJInzuP/60O4vMekRCYD+1n8k9PQy+Dk4sQgSB1KiJ1ok7t3VXmZrLEjxrYixHNNsFFL3+SeSfJ0eV2ypMp0ocU2ndXKP91k+jreRrtCtXhABIe3bfpJE5VOfJ7cdSaQmhFrFs/ybv6fsAbf/AHiECgNVrtXi8RohdV0icjfBtq3SBCQf+PqkxdksAdbZDPVhEiYn4hTc8PNBL3HeLIR0ZRbZ9owSJzWND+6DLHX9NGYgLMUoRRCtAaAeOvBUWJyP3Iwk0LGu0R+adDVCeksE4nMRkSqVDrVkCAWYyIlNY14z/atsNfmuHoW7rRyq22gpeK0IYrNBdjZPepxBYCal0qtX4QPvTd06T0Z3WaD7ehNaC0weMFm/fz9Cc302xTKG3ysLMNGgUbBGgFjcQEZI65mAs13HyMube4uE0dc7+N1mj9JmPzrWtesyNstTdmDWIzcMkbnuDO75xFsztgy8aTHF1uJ/eFGLVuDRFBZQiMQuse6GQFzY4Ia16gNSIiFeo9cMmlT2GpLvd840ya7RGKA9kjEXo1xIu12iaBCXotIj4fUBjTiM9GLJ0Z0NlfYGExzerPOKxsTNL2/YPMvWo9jc4IrdJq60dq63dd7xJ4yQi1Kejc4TF3po4SgPpMvleoQudOB3OuQnU0Q2lQo94XQs5BP2HT/ZiDvuIQxDQK622GX3mM+U8MkXxqlsi28PJxar0WhQ2C1PHWMdt2FVFqdQpndWMv+yxtNIgtRIggopFXqKzzaH9EI7dziaNvakMrKfT+uMnsWRb29mWKpzKkDykofqv8yVMRxbUQn4JGXhAZEYkJqF5SxTAC9B+mSZ9wASivMsgeaSC8gPLqOCIEoxywcLrOe17+Lf7u69eTnGj1pyK1dZOovaRCo2qgzlhY8626XFr3TB/JDIkf1Wk74BJYClotoLDORHVa7dfAEIRGq43ipyKEI9Careue4oPWiCic5ZFpq1JcSpB/REd56RLuD/JUzm0Qi7nUayaKEmLuipM94jF3jobX4YGnoKebDH4y5Nhr4iSPqHTsrKBWHY68OYteUElORBQ2RXSOLrJYSGHFXHr+UWHmgiSNzpDQDukeWCbxIZvycIL0wRKRrbG4NUFlKOIdV96BG+ocrndy/4lRrCfjqBcWqBzN0v3jAD+mUBpSaAy7dHSWcAOVcsmGZRO9rBBYEWpDYBZb1wEliGhmFRrnVAkCBXNfjOErTrI+Nct3v38uAFsvP8TJz64h1AS1bkHqZMDyZgVroXWNctMQqRHtewLUP1jg1X1P8NEnrsacMHFzAUZnHbehYx+2sJZafep6uyA9HrJ4OqiOgIE6qWQT9/42AgMCE1QXcgd8VCfgvZ/4Im+5/U18+oWf531Hr2VlTzuJU9D+nYPM3LCe2kDIaacfxw00pr8xiBcXfPhNN/Hfdr4SZdLC6/T4/kWf5lVPv4GE6VC9t5PcIY96h8bb//ybfOkPX8jc2XFqq3zQIow5ndzBgGabQnmkVeZIjTA7a5h6QMPR8GfihLGAWL6B09TI3mOTmmhS6zFpZhXcBDj5EHNJITETkd1fxo/rzJ4bQ69CfD6g2qsSmK32H4M1jKcSKB5U1ngIOyByFVAjOu7Xn20vijBCcVvXnpW1Os3T6ugHYliFiPLqiCARkNulobrgpFtjGqEVEZt9pu1+SiG/r4k5vkRzqJ3JK3SU7ga5O22MYkB5lU7X/Yuc+MsYXl0nccggORmgBBErazSSk63vLbRD7EmN/h+scPRPYqQydbgvS/q4hxdXmD9bMHpTgZnL8lQ2OWwemiKmecx8dISt79vN7fdvx55tXXcTMw7GbIXi5jbOfMeT3HFkI31fUTAe2kewfR2BpVIcNRFhhJcQfPz3/hd/tv8V1KaTJE6qJKYCrIJHYKosbtHpfrRBZcCiPNi6/ljzgvNevYt7frgVvdJqw+UOOrzgoz/mm5+7DN9ujR9ECnhr66gnbIa+vsj8xe0Uz3Ax4y6Z78aJVAg1QSMv8GOtsSLFa133RdTqN7lpcDp9MAMyTxp0PFGmvDrJ3KU+QotI7zAITIGXhORERO4Hh2ieMYL1zllWvthPbN6j2muwdGaAWldIHRVkjjuUB0wqA4LEVMTKxpD0qhI9qTKHpjqx99p4mQiv16GvcwU3UKm7BvW6QfpBm+oA2POCjsfLCC/g5CsyCB9eeNUTfPfAZtrvMsnunGXxwh5y+yocfYPNxZsOsXe5h8qeHANnT3H8cA96e51gOk7ilEJsLkCvBUSqYGmTjp+MUBqC+EyEmxKoLoy+/AjdZonv79vC6i+4iDAisDWmz7dx2wLsWZW+u4sc+b0E2b1qq/8ftv49Gy8t0/unKyxdPkgzJ1AdiM0HpPYvs3huO7GFgEa7SiMv6L2/jNNm08yp1LoVQu2Zdk45wnz1PDHdZfzHq8geDqm3Kwy+9Dh79w+26vS8huKCt75O5h6bRl6QmGttV1nnYmeaNMoWOAr9d0bEdk8ibJOFS3rY/oan2PUvW/ASrd9B+8OLhB9vcnJ3P6ljUOsROD0exryG1+8gCgaDtzvoB6fwV/eQ/fA0bqAyV01Rv7+dzDEfEUbEdpxg9hVjaE6E6rTGX0JVoAQRgS5YubDJuSMnAHjkiXWIjib6oRj2ckSoC5pZCOIRwzcX8f+uRqEeo/50lt6HHIw9Jzj8oVFesW0H98yM4d7bRs83j1O6cAijHDB1uQqAsawQJCKsOUF8LiB1rIKfNFnabFNe46N4CmHKI5Zy8FyFYCFGJCJ6741YGdNwsxFeKgA7YOwfilTXtjHz4oCorpLdp2KuhCSmGhx7nUn7wxq1HoF59jId/z2i2ZfCPrbIqet7afT7JI9qeAmIT0fUuwRarTUmUdngMjywwKmne7AWFbwYeG0B+IK2kQLh99to5EWrf3vSZ3mDhl5rjekorSYIfqzVD2B1jfZvWVS7NapnNThn6AQ7pgfwp+IMfq9JacRm6Syf3C6N8hBYi4LYYti6ByYEZjnCWvKY32aieiACcNoiQiMiNqWQuHye+aUUxlGbSIXuxxzmzzBpdgUojoKxIgjs1v15w1VH2DPRR/yxGJ2Pl5g7N02jKyLUI0Qo0EuCxHSEmxREAs579S5+eHQd+btaY2qVAaU1jpNs7aM2BOnjsLw5YuBOj8BQqAxoOFnBqi+PM/m7g3Q9UmN5U4zKcIRaF4hQEJoRfjJEeAK12brfdz3iUxrWcVOt8UaRdjEP2VhnLVM5nKP7x62xzplLI0TMRz9lkphq1Ws/JnATEJ+LyO0uUhlNUe9Q8RKta2/bAZ/4o0fx1wwwd06c+oYm5jGLUGuN+2WOhQg/orhGxWkLUHxB6qjAqEUQPtMvSQtWNgd0r1pmVWoFgJOlHIuFJBv7Z3j68ACdD6mkD5WY+/MGB97wKZLJ5M8OSjzjOQVB8vk8qqo+O+ujVCoB0Gw26ezs/Jn75HI5pqen2bp1K81mk/Xr13PRRRehaRptbW0/c5+//Mu/5J3vfOezf5fLZfr7++ls83AetfHy8Hd//Wk+9Lo38OotT/Dluy7CcgSqGYHVGlitzXcgBiBo81DNEG3ZwkmBk4qISgJ7XmAWQ0xXwRk0SRRU/CZUV/tcNXqQHYVVfOHWl8Iah2pSw55SSc5EqEREJoSmQIkJSERE8RAijfS0TiSgcXqdj2z7Pv/UeTnle7ooXNhLrV9APERvKrg9UIlbWK5Aq4LiQOpUiP/qAs0H2rHOLeKLGAd399B+JKJ8TQ73M8OoVyqs3z6BJkJCBKe6s2TvzuKmod7nU+qP6PmRwlC2yv88vpFY1UbEIHB16Al5/PAWYppKY8TALAXcevcLuPajj/CNndtxBnSKSZtQBycjyMy0Bh+ilyzzrtF7eaIygpLwOPfq4+xyNpLf52GgMH1FiL6sMfrNKitrE5RH4kweb2fdR3Yy/pUxHju5iZSi43XpGG5EflxQ0bJ4s+3EFbB8n9U/CDhxvQ4BdK9e5rr+o6yzprlp9jwmB3TChkBtCERJoDQjFBHR6FbIP+VQOcNGTznE99pcvm4PH774CdZ+/49QlvLovTXsf1ri+P2jNLt8XrlqLz8qbUBxk5BVqKw3KY9GiO4mERAVTE7+fi/0NxA/TqDYQMbHzUD39yJ828ZZpyAiFZoKhUv7GbmzCjsPIM5YT3VdnMCOiBSVzD6dptNL2gXfAtePoWmC+poI/foalVMGalVBbKoS1wPafmTR/gULpelSHUzgt3sEFQvFhFgdPFXgGyF3i9XcvbIebyN03W5SCmz+4KLbWQ4SfOnU2aw82NX6Dfb7bBqdZaHH59q+vdz8pcvoeqyOGWoopopaU2gOCeJTrUcCebYgthhg+iq6Bo0RF8u2+NDOV2JP6ITdAXUlTbxqo+ohixcEWF4vUWeAVrMQSUFzEBJ3h4z+L53x62KIAIQqCHOgO60bh1GOIAlOvNVZbbYJjDLEF0Pa7vMwp0tEp6b4Eq+k0q9i5CJqmwOE7dM40c2q3U0iRVBfY+J1+gR1FXVaoWd3wNw5AmFFLDkWga8jOiJWztQZ+coKE5tyZC+aY7ka4/d2/hHqmoCaZhHprcGH3D5Y87VFjr2hnY6dIcpKxOw5Kje94jP8ceFtKM8MEEZExOYDrBWbuSjOqNVgeVuC2kafxIRFdX1EdkbBbppYzQjV0vCHIoiShH+6gnVvJ7G5iEa/oN4V0bkzQtM0ljbruH0By5N9pBcE579pJ2clT2AJj4cqa/juzq0YhztIlCIwoNkHXiZEWTDoWVWhuD6LtRxhVCNUYOlMwdhZ4xy/dwjhQhADIUAJIoQqqPZHrYGBuiBSIT4ZsLBOYc1Zk/x+/0N0qBX+4uD1vPeld/LR77+Ueqa1bWw+orSl1Wg9cngNancdZXXEZCxN18YFrH/voNrfaqiShvlrh0kvRvg9OiKmoBqCpgj4VukC4iWbTaMn2bvQTRCaRFaMqZfHSG1ci9YMWapaUNVIrqis/9w+jr1mFQffkaNtt0rvbScxwiGqpTjjr4vQVtUIAhutYqGqgh/ObcXfEjEba0Org340RvvRCqUXrEV1Irx+wViXQ1/nIsVPDbB8fjvxuZCZ7YLTzj7KXC1F8Eg72ocniRZ1Yir0ZCvMrOTwxgxec9nDFL0Yu5d7KcxmWZzqJ2EoqCGEduu+ZdQiAltgbimR/UoCxYsob2knY8cJ8hYDj3ksbrHxY2CUoLJhtDV43ISppT42rJ7myjfcw6cPXMi69QWOTHSR3KmTeXqeZDHH0iaLQtSG1bAJcxGlfKzVUFBNnO0eYt7EWlDQVGgMgb0UEoUalq8j4hGV9TZ6NWLm/S6iYiESBsvnCpy2gHUTPZS3GiiGRvyEjr0YMfs3MWolwVNPb8SeUwgNKJ/pc2BuhE1bp6nd2cv4aV0oo+P09Tsc/ZMOksc06j0haKB6AqMOuhqiRBEdJwTTAzpXbTrCHQc3EExZNOIW3Y9FTB1fR3MwJIVC164CE+9TKT3Z0QqgTsdp2x/Q3DxAaVAjc8IjvuywErPQAzj2J+3ET2nEZyJiDbCGa6ykTNz+OFoDdE9QW2MSdEUsbXLp/2YCt0+j2RdgLajY2Sa1qSSaXsffMsriaRbVgRCWYoRZAUlBpQ+8ZIiqQTpVZylrUR8VMBeSLAYET+XYYdv03GGgVwOaOYPi6XFEKkKtCTQREnSH1CyV9IM+jaSOXYVH6xuYrObontdJ3H2Awks24vRHVDeA1ldHCRXcnKD92nkWJ7rpv0Unfvc+ovWrid5eI/XaIo+W19GfKiFSBosXx+ja54FoBRHCvIKfcZl8m4r1dJa+H5aoBhbeYJ013QscPNlDY8igfW87/Tt8ykt5fFsgjkzQvs1gsjuLOaNj1SO6z5wluruD7JxCxWin3h+QHS0x2rbIzMdXM79dITQjtGUFbTmkevcwN7/777l891+gCoHX4ZLKCJq6heZDaLQGi1UHYrM+qCqo4CbVVtA2KfjLc+/k+wtbKJ4RfyaAD40YJMaK1E+lcBKtToQIQTUi+p50OLG5k6veeYDH5wYI7s6TfDKJO6ISljSsJYGXiFh+gYd7oI3uTI3ioSSmLwiz4HdG/ODENnq6V3jZ+kN80Tsb45BJaavF35z2Od6tvwzjB1nyB6EyDHMvDFj3VxPoQTuZcYGTE4RbY7x531t4/3V38NGnr8RzNKIVg7Z9CsX1EQ0UGt0Bdm8Vz9WoTreRLEJl3qRQTRPGAxQrYuk8nWRhHd0jVeYns2iKRvFCj+qVOeJP2tRHIy5/weMEKDzyP7ezee1JZispGq90qU8nSIyrkAzRXZ/Ukkas6tPMaoi40gqApVr3OBGAGYJRhpNvHSa5IHjTm2/na5NnMHewg0gPUU0LI1IgGaEjSM9CcsInyiSoHsvQNR5Q71CJ97rsbw6RXha03XUY8eYtlMds7JLKFdfs5LZHTsc6rlDcahOaabKHfdYMTGCpHuMPr2FpW8j5Zxxk/h2rKJ9mYwQCP91gYM0yMytpDl5okHulQd9Lj3JwsQ/z0QypvccgPcTiaQqaL4hXI6xGQJRW6X3/o8z8xblobsTy9oALthzivofOom/rDI1Rg/RhBasYEpmghBFWEOHpGlEc/ATU45CdC1neLOgeW+TMjnnmuhrsefUa7HmB1j+AppqQAD8J1KDRJbAXIoJ2CNt8oroKKZ1ERUVtRpilENWLaGZUJn8ftOkUHU+GpFYEfpcgN1CBB9JMXq8z+F0LN6dT3QiHHt1Emx2wfNVqRNT63kQE7UcjElMOE1dapJfaMAsJli82MdoD8t/QWdyikD0UkZsJ2XzDSZ4srWXlrF561y2w/FgX9kodb5VO+o4eEl0CXY1QQojVBX4nROmQqKqQLLaSLmrrIozDNmdcu4+jTgdh3qAwpuLkQ5pDNtkjIbXVCtq2FS4aOMSth7ZQtnJEGRMnEihua2BMa0RwMIaeiEhPQHzOZ3mDzg9f/jGuffyteNNxkiVBvORSS9mURlXcjojkOISGoDEUEmR9hr8cMXWpieYIdB8iDZQIgjwkpmyahSSmBvXVAvuePmobItq6alQbcexcyI6zvshm+w/Y8PpjxCttjKXnOTd5jP/+nd+leDok5wTVcxyq2zN036Oy4Z/Gmb1uiOqLa2iTcQrHBvjra2/ln770O8z+dYnUrQa9uxzKQ3FKfRHJtIZqmtQ3mhTWq2TOnqfxVAdNK83DhRGGU0uMDNYYn8mRySh03hmhNAMWt8VxN7tcNDbB/qUu/nrsLt7zw98lf1hBb0QsbBOoCAIDvBgYxQgjgs6BRY4f6KH96gW2dS2w7Kbx1ijEjhks/OMGwl6NWr/AS4RUEgrnXr2Pmb9azfJ6CxFA6mRIesdxXvgPh/jY3a+g95FW4PTsC/bxL/0/Yttn/5TmYEh2BmILLrV1Js3zi2SVEPPmLOo+g9KQSn1dgD2jYpUhfdJDc6HaG+Nt+95MrGLTkfK5ds0xvkqGppaCjWOEeYszzj/CnonVrYDHBR6/c9ou3n3otbQ/GMOLw8oqlVuDsxntrTFZTtNYqzOz2sDoq9GfafC6L93Hx750PUrcx8w1cEyNxS6Djh0QFhSctgi/w2Gkv87xh1cRDjZJLdhUB0JG+pbQRMjJ/TnGL9Sx5jUiAX48JDOuUDzdRdcMirHWYI5iCzQX6qtBDSBMQLSmwV+d8SAfm/wdBr8yj3swTWEsQXk0gu4mmmrhpAVeSqA2IT4bYPkeXp9JrK1JbUTH8lR6nwyYuirE7dVJTEXYDhAI6pkQV1FQB+qwxmFqJEXXY0nSj06y7WKH6Wqa+ZfECOoWoqGwEvZhjAuUeIQzFOH2C7SmwBluEuZtRK+DfcrGdgQn39SJURREXRHVsxX89iT5fT7r/9ccRz+SJXebQW5khcv6TrGzNMgtN/0LZ93+Z9Ab0hgKaADL1QTZp9KkT3rccWo72pKJIRw0oRPF40xeZZA63uojhrrgrbvfQv4+A+fKBnXDIMgrlCNo9Hkkjum4fTrNQZWgOyTSI/71jZ9muylYfehMYgc1vBx47y5xggHMl1SJ7s+TPRXQzCrYfUUaT2QpXtBP+VyPZBaax/ME7a0gvgAsH7xQ4OYizFmB1QjxEoIgLlBNsKoRqachPe5Q2dJOvUtBJeD15z7El8sXYS8IVAOiVISyZjULFydo+2KclZc6nDYyzrFyHv+OXurb6zBnUx+2wAbLEziDMP7aT7PtfX/IYtSNsjFi8+8eYWNyhs/vOo/puQSYIaKuYiyrZF47TXl/N9V2yM1kMQouyrqAtnSN8bAPgiQkVURHO4mKzvgNeZJzCntObSd3wKX78AIHBgcZe/cuxj9wJvFFgaJGFM6B0RsncAfbCdtMwh6XwFHwQ53kTEi9U+HQfZs5ec4y1599gEcfOhMRRMSnmiTKJiurfcbOmmDxyjjWjjh+hyA2F1HvFgxcfYLZSgql6pCdEZRMk8ACMwpQTs1hndkHaXD6FZprHMb78sQnW0kYpCCyWgPEvgvR7YNw/TyaZhFmI2rnO5yo96FhIxwBKfBiEYl8g/IWi+Q41H63jGV41KYyeEsJ1DYX0ClsV/E615GaaJKdVTke9FHZbJHdutgahwz6ENoir3zxk9xy+3lkjoJxVEdrhBj3hag1h/KaFM1VGwg1wfhcnI58mcJUHjOj4HdEZA5XaV6wgeqZIal9Blq5lTAnFCACr01gTds8om4ila9BXie5L0Z5s4tXVdHLCrEFQc9tyyx+TMfUEnxo3Z3Etzv8+Vkvp+3j60lNWXzLvgC0iHjGQMu3s3SBxdpPzmK6vQRWhBkJRBE69tVZOCNOdV0crQno8OYX3MvXjp3B5s4ZAEIEyljE0ZV2nCNtOEMRyZMK1vGQxTOgti1JZVBB6C56oCNigkYW3J44iVkQsQgDQe1UDyJTZfbKBLHNKbKzIdk5g2qPgmKBpkeEbVAbDSACo2wy5wje9Tt384+3XUvbUxHhjGDpMofidA9ZXZCbjkCA22siEoJGT0j6qCBItJJEFR8sF/oH5zl0VRZ7ViUKVT41dh+nn3gHyoDP4kU5qkM+a9csMjU3AJkQ1xaYQWt/v10QtsHa/7aPmYfXk3sSkuMNIlWwsD3Gptce4IlTqyAyCboU7HmFideoCEdBSbfGApx2Da2sEl8CNWZy4cZpHqhtoPNwCsWyGLqnzvRFMdxsiJeEYpcgNisITbjv4XOwq4LGENQGA6KYT3q3SWnABUfBmNRYvsQju1ujus6m3ilornLJ5Kssl8dobPaYOMtGEJFN1rm87wgxxeWWk1uoTqTQfAWhtcZ3mgMRjTURffcGBMd1FrfE0VUo1dqhF1RTp9mpct0FO7nnlu3YSxG1IYFeaSXZ2mcWqO7L0hzsZuCOIkrMZvZcn3VDM5SusIiqYzT7LERCQasYGELBSUe0bVmg3ugkPhfi9UKst0pzPo7bpyKKrX5hoyvCnhMocY+lZgdq3KQzVqFKBjWhcXj3eoZ+7BPfPYk/O0fHGzwO8PMnW/yHX+rB6GeccQY33ngj1113HSsrKywvL3Pdddf9zAejv+lNb+L222/n9ttvx3Ec/v3f/53PfOYzrF+/nv379//Mc/yfy2H9RxCk/x8+RGYyjtpsZXXlDvm8+iO388mDF+MdTqE6gmBtDa9iIDwFs6NOc8VCqalcef4e7jq0jmjFQG0qrewgTxBqEfEZSE4GuCmF4qiCvnWFWsUivtvmFW+4l1ONNu45uBZ1ziCIhyhNhSDrocZ8DMMnl6gzc7iD7HABVYnoiFfZnJ4GoOjHeHRmkHLFJmyq4CnExzXUJpz56qe47/gomfvsVhZUr6DzCY+FbTrNrgAAtaLQd8YMhTt6qXdHKKtqdGYqNH2N5UICFk3CWEj/0CJ/PnwX/2v6IqZLaf54zf18bO8LUHcn0c5aoXIqzar1s0zs7UbtbnD92t3cvOtMzGkdNxOSOKUSn2l1xlbWqAzcvsL4S7IgIDAjEhsKXDNwgOlmhsfu3ERgRXjtHvZJg44nPRbOaB0nyPmYCQenYKOmXIgE+R+YxBZ8ltcZJGYCyq+u0JMqM1dJ0mjqJGIOxZU46R0WpbGA07acYCi+zG13nkU41CCasQiNCL2iYC22IruBFaHVW5mwAGpT4KVCkidV1EZrpk6zPSJa1WCgo8DS7X2kr5ql1LBo7stgFgQD155k4ntD+PHWwItejnCvKJNP1Ch+v4fyGQ667RF7KIGThT961ff5zKELCQIFz9Uw99kkJ0O8uGDlXIeeziKm5uP4GkvlOE7BpmdwibWZBRQRcaLSRhAqLN3TQ6hDo8+nf2iRF/bs41+fPh9rr029JyDeX6FatNEWDcxlgR9vZcoFVkT/5ln+bPBu/tutr29lZvY7nD96DF2E3Hd4DeZxi9CMWgPkWYeLho9xqNhBw9VxfQ1D81mdW2LnsUEo6SRXlUhaDgsrSewn4kArKFc8zSPZUaWyHCe9x0C/cglD85mZaCN2UsdajqgMwwUX7+W+I2vQZkzMJYFRjvCvKVKtWoRVHb2oopdbNyRERPL0ZTbmZ1lsJjg03UVf+wqT+7owlxWc9hDFFXQ+EeImFKxXzVFsWKgiIm030dWAE7N5Qkels7uIpfksVePUijYbhqfZf7yX9q4STU+jNp7mhec/yf1Tq3H2Z3Dbfbr6C7TZdS7NH+ai+CH+bvpqJspZCuUY+lMJzn3JUzxw32bMsRJRJGicShKpEUZXHXcuhrmk4mZCRk+bJIoERw/1QgBR0keoEeqciV4WNLsDcrsURASlUVhzzjhL9Tjz48/MgEv4mLZHc9lGxHyo6CR6y6xpW6QZ6Jy8a4iRq07g+Bolx2K5mCCatQjbPMSKjlYTeH0ufd0FJk+0k+quUK1apFN1Go5Bc9FGSXlk0jWqT7WhV8SzWd6KD14SfKv1mxIBaE2BCFqZGNUBQbPbAzUi94RO6pTH3DkGbiZEPBM08WMRii/wOjxEXUXNObxn6514kcr//Mp1rWuGHeHHIkIrJL9DRWtELG8WeJ0eBILUAZ3atgaKGuBVTNBCYqkm9aUY6f065TUBJD0UPSQKBWFZJ31Qo7wmILIDiATCDNi4aobx7w0TnFvCMjxKR3Lkd8HClS6xhENtOYZwFSIR0f64SrVfYJ+1hOtrBIHCQG6Fk48OoHgCpz2AEFCgbdUKK4dzfPGln+YTMy/g+EoblZpF+q44yxe5XLVhPz/YvYnsLg17OWTuPEF8UkEErUzHSG1loEYaNNtDsvtbs3MAkuNNkn87zcF7R4kEON0e7b1FCqU4+dstAkNQ727Ve7UpUDzou+IUV3Qc5F/2nY9yJI7T7SPqKnpVkDkMyUkHpRlQGbLJPTjJ1KdSKCIiiAS6GlAsxlGnLLy8T6q9ShAq1CdbMxWTo0W6U2UmVrLUqyaJVANnb4bBcyc5cqQHvaRiFAUD31+msDXLwgtcIl9BLejo1WfuuSWTjkdUFi72eMXWnViKxzePbmVj1yw7DgxDKBB2q5cZVbXWjEkrpKuvQHe8zMF7Rmn2t9J31KJGmPc4e/QEu2f6yCTqzC+kiepaq5dqhJiTBvZiK4Cp11rtgd6rT3F85wB+qvU9iliAlXBwGjphTUeJe6h6gFe0sKc1mu0B1ryKn2jNKNMa0GyL+OSrPscHj72YwsNdEEJj2OX6rU9yyyNnYk+rGCVQnYhGp+DtN3yXjz15BWFFBytEXdbJHIHKKnjR1Y/z0NwIy0fbiJSI0047wVNPjqB0toLupunRbBikHrMpbXOImmprkGbZJn1AwyxGlFYLEhMRRi1iZUxh4MIJJh4aoH2Pz9SlrcadXlJQfEGoR/Td6zL+Yp0wFqDUVMJEwOoveRx7rY5W0hD9dS5ffZh77tqK2+2xenCedqtKwYkxU05xaf9R9he7aXg6M8faMRdUvGTE4B0Oc2daRCq4qQi/w6Onp3U9PXrPMM0BlzVDc4yl53liYRULx9tQa0qrHgRgLgva9nks/H6D9ptizJ6r0nXGHDP7OrFnFVT3f88C8WPQ6Pfoul9l/uxWoMMstNpsa154lJlqmtJjHfixqJVd1RTkNy/QdHUajo7vq63BDNulNpGCtIcZd2lPVVmuxuj5Z52Tb4MoEIS11jPphO0TTzWfve4nxpVWQsgZNbYNTPDooRHwBfETOtZSRGFzRNtIgaWZNKtH5phYyhKOxxk4Y5qpx3pRvNYsWrctwOysEx1o3Uu6z5zFDxWKdRvPUzEMH0W0Ejssw2N+PsOqmwXLGw28OHSeP8NoepGjpXZOnWrHzja4YOA4P9y/HmPGQATQvidk+jLQCwpeNsToqBO3HQrTGV511mOMWAt8ceIcpg92EgnI7xKsrIewt4lh+jh1HfuQhRK06hOAvRSRmPKZeJGCknUw98YITXBXN/id9XvYW+zh+Fw7fk3HzjYAiN+RpJkTVId9Oh9WSE45VHtNlNct0PQ0isdzdK9dwNR8Th7uZv2GCY7OtxOeiiMC8NIBXUPLvKTvaf796Jk0TyYJYiFa2sUvGWzdcJKnnhxpFTBqtfmMsiA+HbG8GURvA3HKZvWXlqgPZZi8XIV2B3XSQq8ImusbWAds7KWIyqpWlrpehnpPROKUIHbtHHMHO0gdbX33f/TW7/D5D1/L4rbWvVOrt5Jx/ESr7Tl00Tj98RUenhrGvi1FJGBlY8SV5+9hrpFkz95hYhMqg1ef5PBjg4hVdZKJBuVKjMBXiHwFK9nq5zh1HYoGIgKlo0nqAbsVYOiNCBIh+ILMIYFvC/x4K7vcz3moK3qro7/Ymt3pZFvJS/m9EbMXRZgLaisTdkuVs1eNs2+xG0MLWDqYJ31E4CUE1YGQl13wOLfs3QplHZKtaVkj/QucerwPe30R2/BYPJwntFt9DVvzmCqm8TyV3rYSU0/2EPU3ST/QGjivroJorIaxI4EIITHd6lu4cQU3LbjojU9w2/3bMAoKStCq42oTnCx0njdDqWHheBqbu2d44ugQ8XSD6mIcQgF6a2aIOW2gby7SkypzfD6PMmGz8dxj1DyTIye6UUsqekVBqz8z4yts/b6jgQbxmENlKkWkh9htDaJIcEbvJGsTc3x+z7l8+4LP8In5y9mUmGbWTXP7iQ0oO1PU1zVRtIhYvInvqzQWYlgLGi+97scU/RgPTIzgHkthLQpqgwFr/q1KdTjByhqVxpjDJWsP89D4MIGvcsbgBPvmujHvTRHYoF20THBvGwhodEYEvU0UNSJcsCACvbeGrgf4uzOop5WoFW2MaR1zRRBY0HPZJKWmhR8oqEqEqfloSsjEXA4iaL/HbCViDCoYZxd4/erH+Nzh86hXTCKvNSshma3zx2MP8NXJMzl1ogPhCUh5JNINBJC2m0xOt2FM62gNQd9lE0zeO0Cjz6dtl4pZDJm5NOIdF/6QaSfLLfu2woqB1lnnU9tu5i13vxGMkHi2QaNmwLJJmPZIPWXyr+/4BLcUt/ONJ7aT3qdT3t4kvtei3hti9VeIWy5LiylUI+CswXH23byezDGP0pDOmW/YzYO3b20lJjRaM4LCniZXrz3AgzefQXVLk86OEt3xMjHN48hKOwOpFaaraeZOte5VSm8d2/Ko1SyYNQlSAbnuEroaUvlxR+v3o0GzO0Bra6DtSxBsqpJL1fADlUIxjjJtodUEzQEXa6J1fW6OOnz6/C/zhz96PbEJjea6BlbMJQwV0vEG1aZJrWhjJhzCUOCVTIwFDaMicE6v4pVNjEUNtS5ojLhoi62ZI9C6t2o1QXR6mcayzbo104wv54j2ptArsPolRxmIr/Dd3aeBqzC8Zo63DDzAB/a+CM9TiU7F0RqCZq9HpqNCrW7yug2PA/CNE1sxv5ehsDninVfczv5aL/eeHMWbiSPaHc4cHGfPbC+uo9OeK/PqgZ38fvoov3v8Wvad6kGICM0IiCLwl20iPcTMNFF3J0mfDFk4QxCfEoy9/DD77xgjMRVR7RX0XD7J8jf7iL1knukTeexZjVBrzST2u1zi+02a7RF+KsBYUlEdwVd/759523vegZsQFE4LiU+q/MHrb+cTd1yDUWy1pb12n0S+xlCuwFhynlsePROlrtCzeQ4vVFg42E5ot7K/hS9QGwp6pTXAt+13n+bASieFnR0ofquv0PGooLBB4GVbg7ZaxkU3fJyGTlQySB9SyT/VYOpPfHqyJU4c7aJ3aIm8XWPfE8NE3U1CT0FdMojUiDAegBFy9cb93HlwPZGjku8ucU3/fhbdJHc+ugURtLK2jVIrY752Tp3AVxDLBsIX0NPkHafdyz/ffxVWZw3fU7Efj5M75HHqxQpDt/hMXWaw5aIjHFrqQP1hlkYnpI+12tAi4NkZOfXNDda+Z56Df9nP+o0TeIHKkSM9bN3QmnHZFq8DMLuSQt2bQKu3ruHF0136+5dpeDreXXnci8ooO1M0OkPsVRWG25bpixV5+qNbWjOgh8Br8xGewlsuvJd/vfNylP4aQoBbsNBXVOLTAns5pDii0BhxSeVqVCZTqDWF4e2THJ3uwBg30eoCc6UVJDFXWklo1b6I7KYl4oaLF6gkDIdCI0ahmIBn2okJy6Hu6jiOju+qrXHCvIN2qJXMGditMZWLth3gaLGdmYUMUeOZKZ7P0Asq8ZlnZh6srZKMOYzlFhgv54jrLiXHYmEhDTWN7MAKxVKc0FERVQ1rQaHR75PZp1EdiAhiIXpRIf90hOJHxP94isMnuhE1DTIuZszDeiBJePkKF/UdY8lJ8NjxISJfQY+5DHcss1BNUDmYo/cBn6lLW/fCcH2VgXwrg358vo1o1kL4raTwwIT6kMeGNVPsP9RP4oRGsy1CrKqxunOJg+Pd6LMGoRnR/mRrxkXlhjLKj7JUhlozCvx0QKyjRjbeYP7pTqzREls6Z9iWHscJdb74jRcQGBEdu0Pmz1QIBxqESyaxaZVVXzzO0T8ZfnZ2Ah0O/3jWN/kfn3kdzXyElw1Q0y7bVk3gBioH7l+N4rRm1/qZAH2lNdtIrwpCozWr+T/6z042erYf4iXg7Gv2suzEqbgmU0/24He6XLruMHsWe2g4Br6vIAR4DZ2orrJu3RRHZjsIygZ62iERb9JwDIKjCd587Q/59OOXYCRcTNNDuSdLaasLEcSzDfKJGpbmMVNOUZlLMrx6DsfXWNzRSe8DLn0fOsqBz2/Ae2GRfKKGF6goIuLUeDvZrjKlcoywoj9bD5p5eMcrv8vRRieHK50s1eOUaja26VI8lSGyQpLtVXKxBvWvdpN73QSHD/eiNBXCWEC2u8y7xn7IB7/8Khq9PokTGvmnPZo5lfKggh+PGPx+jcXT4qRfNsPcw72om0vUqyZRVQMrpLOrSM0x6EhWOXmwG0KB0t7k9IFJ1iXn+PFiq53u+BpuoBLTPeqejvlvOaauiNDTDn7BYtuWY/TYJb63fzPxfRaRgObmBj35Ik1fa43lVS2iQKDqIX1f1Kj26BTXgt/eGktKHNVbYy0Xr8BDWRJTIYEp0BZqPPaD/0GpVHr2URw/y3OaCQLwzne+k9e+9rUcOnSIw4cPc/XVV/O1r32Nt771rUBrFsf09DRf+tKXAPjrv/5rbr75Zr785S/z5je/mW3btiGEIBaL/dxz/LzlsBBQ63lmerkaUkBDERGXDBzjcXMVDVfn/N5xputpZssp4qbLUgR+QmVNbI7jPXkaHTqKiJgrJnHmYkR2SKhr5J92MUoCEZpUGllMvbU8QLdeZGtsnOXhGLvdQVAitM46GmCbHvlEjXarylx7it5UmYxRxw9VDpS7cUOVTrtCb7qEpoYEoaA9XmO2O4m7J8sqexlVHWllIIStbOBIExhFcFMKQcYHRWGhnMDpDUGA52iYmk9MdyEHBQUyiQZntY9zgbVEsWsH75+4ljatyuaeGXYsj2A4OmpTsFBOEKmQTDRYa8+0llRQdLJDK6yILJGqolVBq0GkKehV0CutKXsrIsfqNfN4kYo32iBwFRQtxE1HGGWPSNXRywpaXcePaQgrwrI8dDWgtNqmmTNo5iMCSyVpOQwml7E1DzdUOT07yY+0MWoxi0iPmK2lKDSfmZ4aCLo2LqArIbMrKarxGKERkuiu4hxKozZbjQKzCH6pNXW22teahkwEmh7QZtWY6I5It17CT0T4iYiFWqLV2bBag2CKK2jUDXo6Z1hM9KDo4f/+7YWwq7yKMBREEQgR4aYjoimBFxck0g2G00uYSkAj0MnbNSasDCPpJTrNMk6oMZBYYTS2wE3pboJYRKy9Rt6uklCbvHbj43wpOouOXIWB1ApP1gbw2zxia2rUSzZCiYgnm8R1l5TSJOpr4HkKbbkqGb3BWGyO4915TjU6WoOECYeY5bJnsYfuZIVS3WYgu8Lq5CJ7V3pYu2qW2XKK0zun6LNXmMlmuMcdo/1HJnojxJnQqVXTWCWF9EmfSPdQlRA14dHsUPFjAj8ZEtcckqkGlVDgZVQIod9ucnrXFE8vdlNvMwkicJZs8gNFLu09woC5zLiRp+HrnJM/yaObQyZ39KI0W1OZvZhCo0MQeTrdyQptVo2M0aAR6AyOFjhaaqcvUURXAjJmg1LS4oK2Yyw3YnTGqrihyuQqQUprsjq3xMmNEcLRKdVsFBEx4eSo2BYbkq0BKcfTKA56zDZShCr4vkomUSfoU2gWLNy6gci5uL5JZEbUPYOaqxOpEYqjEDkqkRKheODHW5kdXlI802mKyBgNji3kEQkfzfDJpep0xKucUNvoyxQ5PtfO2vwCq2IFdi4PUB9xWazHqTQs6hUTSjqqL7hs3SEenR6ktmKzqmeZizqP8p2GhW145DrraEqIY7pUDA/H07i45xiPaAGVhkUEKEpItRgjmamTNjwMtRVorTRNqoeyGJUQa1FF+DoihPiCT+zwAt4LerAHKqRjDVxfY2kxiTADcsk65UqMbLpGv77MnJ/GXd8gqOggIoQdEIs71LvSeClQGwJ1wgDAKEWEu21EBEF/iN7TJBNv4Do65TUKkR0gIoGqhrTnqszPdFJa56MkPQQg1IhcusZ0OYWTiVAjQbEUx15U0JwAdc7EW+WjL2mtpTIGmyye1arKg4kqdc+g6WskdIf0aUssFxJk0nVUJUJTA/qTRZZiGb60dB4lx6bWMAlmYpjlkMhVeHByBH1ZwyxHGOWA1ZvmmOpP4zQNDNPDNt1WY+pIkkiBlQ0RitcaRKsMxLjELrNnqAllnVhbnXO7TvKYGMQsGYSGIFJbU17NcohnCxK6gyJCettKTAyr5DM1mp5GdSHOiqJR73xmmaXOCOinP3OKnFnDVj0yeoPZfIrdiT6ihkHMdGmz61QTNaaXMihKiCIiEpaDooQMZwuEF64wmljgeDpPVLZbSxJaGk5GQZ03CXua0BPQrGt0ZaqU9IDyUAqhhhyv5hmKL7O+c46SY4MWYacaDORWcAKNrFmnGejENJe03qQWGDQHXMykg+9paCmHwXyBscQ85XaL7dlTfNfbhJYPieleq852aCyfzBJprToYJjw2pGcpbIgRRQJVCcnHWvfmg4VOagmDtngdISKKtks5YaNpAY4fIzVSxPE0ykULXIV6ZDKQXGF6MNcKNMVd1tqzRGpEozfASykYxVbn597CWvo7V1hOtM5bA0ojOol1K3SZJS7pOcqdnkYEvKRjN0+19/HCsX24YavpteTE2aP2cebAJI8fGMH3NIggsFq/1UiLMCsRIowwynDkcA+pEjhJFTKta6/jajQrJviCmfNNREcDGq1l91IdVebPzELgE/Y02dAzz2p7gbuHG9iGz8u6d7G7OsCJUmtWblarsz13iiPVDhbbEzQtAy3mU++w0KtQHg0h75BMNMlajVZ7Z20dSw9o+DpzzRRCRERqhJ/zEUZIEEFg6mSPCPxjSUrDApSI+WKS/Loliv021ZqBaoQEVQ3RVEl01FjYnoKMi9AimrqBVlHositoSsiOVRkQYMRdUvEmQ6nWs+fqvoGmBFiqj64E7NO7qTZMdL0VbEjFmky+IE1nbo6E7uJHyrOdjo3ZWe44uKG19FbumSneixY7olWtL0Npdab8OJB1WVpMoiddRlOLqCLkqKcykFihfJpFcHsbwTP12PXjpOci1CaE2wU9iRI5u44mWuV0QxU31AgjQTOrM3N+Fj8ZEiZ8clYNW3Vb7b0QTN0npTVJZBrU6hpKXaHSpxKpPm57axAl8FUcT0dNuTyyOEy9zSBjNVjqq+K6GpWhGIPbJqk4JhmrwfDgMo9mBgkigSEianUT/3gMo6KitdWJwtZyWV4ypC1bpddcIUwL8laN6Vqa1aklar7B/uQ6Agu0svrM0iKtJSjXZhYoeRbTYwGntU0TIljqbSVc9LUVWTB8mg0DSw9ag5SKS6NuQIeDiAR+UwMtIms0sFdVqBVbSUNRLgLFwF5oBSicooGmwPL2PIoXoXQ1yaZrlIwAHzhvYJyd9gDuIylE0FoCMjETEKka2SMOoRKidjWoBDaRAiPGPPMXhGAGZNuqpCyHctNsJRos2eTM1iCQrgbUu1r3+8RQkYvTBzlmdnG4q4OaEeOG7sf4wnaVuO5gqAHVpIkiIiquSbtdpexazOgpKo6KvqARdYXUO1sDd367B65CbmCFYpgjtAJEzMewPWKGj5dRcR2daqo1kKZnmgDMx2IMjM4z05aiWTIxlYgwUuhKVvBClcVOh3rFJnEqorIm4FQ9hzpnEuoRuXyFcs1iLLXAiYE8mzpmyRk1fuyrRJEgZ9UZTS7QbldpBho9dolT3W0otO5zkR6iZVxSsSZ+kKDRHqG6CmYxRIStdTgHrSVWbZnhxJEujBUVPxZhDVRoLsfI21VyVo2E7vA7+V08fmCEbKxBot9BFREJwyFv1XikOcZwpshpmSkavs5k2WAksUQjMIiPOeyb6cadjbWW1tu0jK17LJYTDORWSBpNnm4Y9LevUGxYFKYzHDA7sVUPVQu5u7aeo8V2RmML5PUq+WSNqb44A90FvFBhbilN6CnEOmvULZuiH6Po2nSlK5zq13Fdm47hZRq9OWrdKo3egI6O1soJ/fkiiohIag6uo5OoP7PMyY42jLCVka42BUbcRVFC2jcWKDctNDVgYSlF/mREWUmTLLeSt6yVEDchGE0tQgo0EeBH6rP/n1lO4xVNQg1iCwGhLliq2JxtH+fOzAZmlBQCSFoOXfEyccXBVH2MbBPfU9ENH0v3KT6dpzbQRFQ0tLpAr8GR492kqsC0RqhGuEkFa07wzcnTeXn/Ltb0zTMZz5CKNbEUj7GxaY4c7GsFQIDQClFKOrXeiBNuB6fqOQhayxGdOTLOjsIajIJCM2+gqSHanIHaEBzL5BFBa0zAWgm5+9EtJEug1VoBzNCDYNLijsJWcpUIfdKknjboylfI6TXqvs6G1CxpvUmpZtNQbFQBttHq059wOrCSDkOZApoSsmtTHK+mo8V80vEmpu4zP6KTsVqJG73JEkJEFOZbS6AQCpqDLkqxdZ+vhWYr0H7eCme2zTFRzbJQSbAl30qWXOmI0WOXaAQ6h4udnBLtqI6ObXmEoYILKLbPOcPjPBqshkBg5+uoaohzOE17oo7T0Gn4eispc9iA4xZ9sSKNQMdKO3huqyybjFkuGjjOeDXHwYaOHwnOGjtB09dZMuNktRpxxaEnVWYmmyU9XCCMFIJI0JaqsaJEdKRaiRNBoBBUNeb9NLebG+k3lpmtpEikGmTsJqbmU3FMSNVRlZD+ZJHHy8OAQRD3qa5S6LFLHPR4Zlk7QaFuo3gwPdGGkvRoxlpLhykxn6GuZWYTKXQgcnTCkopZgH+YvZJmTtDMtYJ2zbzC3UvrWtnNnQFK3iGbaKAqEZ1WhYTqIFIuQUxhY262dU08o8rxQhtt8TpNX2PxSJ5ocxXvqSTT9TTlugUKeIkINecQ6jaB1Wp/GkWFxECRpqfR3lahoMcojdnUuy3iZoEgbAUZHV/DDVXCToe1vfOt776r1S811IAgVLj76NpWwo8eYmg+Xqiyc6Efe05tzWQuR5jlAK0REeoxrMsWKRo2hhEwmCtQDw3iPRXa4nUKdZtGV4yC0In3FFnakiF3+jyv7NjBHdpmHlyVwU8FLHRHWFOthJBIFURKK4DVHOtGzTlkjAZuqBLvrHFh21EA/EjFUj2avkajmmglfaShq3eFjblZDhY7mRgJ2dC+xOGtCoYSETNdap7BD/ZsoiOuYJYi6g0FT42IwoiSbxPaIe3pGpbmM6dENDWLeqRR7xH4sRA71eTM7gkeCQYJQ4WsVWdt/xwHm71k71dRnQjjmWXRwkaEm1JYm5vHfybDw48UokigtwUkdJeTizk62qoYSR8/VFlqxFk+2kVYaS3HaRZby1SVY4IOs8KuZh923MFIB1iGB8DcqbbWbyMOblvIQKbCqVPtvGboCRYbCZJGkzarRs6uU3VNRtOLlDLWs23WpXqcjekCU/eP4sUV/EYr2cpedPDiGquTSxR7bArlGOlEk+tX7ebfjl+OESqM19ooNGJETRW0iPMGT7IpMc0T5iBPdMVZ3GJBT510psqmtln6rBVUEdIdK3Ek00GhHEPMJVB8EEbI8cU88Y4a4mi6tUy/2QqmZ9uqrIRJEFAcNVrJFQ0Db6MPIVhlDT8DPZkypurjrF+mO1lmJL5IUmniRSrBxiociVMaVFGGK/RkKkzOdbUSINb1ggClqRCpEW3ZKjHhEJgQrmqSS9WIGx5Xte3jh8sbYG2VZlNHN30ylkvJS6O0OTjzFkpnE6GEBEfjGCWB6gr0SoRZCvBthQeeXM+Zpx3FDxX8ztaSeCXPojtZob29CoAXqpQ9CzdQubz9EGXHYsW0Gc0vsTUzST00+MbimUw7WTL5KvlEDVP1OTjcmv6Rba+QjTXotCvkzSrdsTKHzA467QqnKllCEyr9Bp1mhUfGIra1z9NhVvAjlSAS+AMK67LzPOwO4SoRnqVSDw2sja32g6aEpPQmnqniBQoZu0nf5hLFpk1Md8mYDfYMd9OnuWR7SxRPZdCXNVZIcVfHxtbMxWZr1Y5ap0ZgQmNtE0LB7LkJqiMBl7VN8N2xDIGjsbZ/jrpnoCohY+kFeswiXqSyVI2TsBz6k0WSmoMXtvpTqggZzi6T0escLHcRRoJKl0q8o0TCcpgvmZRdix67BFEr+c1LhKzpmafbLlPyLDqsKnONJHXfaPXrxy0gA0KnuWSAgPSJgOX1Ku12k5nukEhVCHTwVqnwg/8ziPDTnnMQ5JWvfCWf+cxnePDBB1EUhd27d3PHHXewalWrozg7O8vExMSz2w8NDXHHHXfwZ3/2Z9x444309PTw4he/mCNHjvzcc/yfy2GVSiUGBgZQ7CJGtsL6/DzrErPcfHgbtUrA78V+xMsHdJaDBAmlSdWw2G/38sTKIG2pJTrNKseWklyS2MMV8YN0qSr/llrH51fOZbBzmcV4ApZDxMIyyWMGSUWhMdpBqCssljVenqkTpO7mae0VeCWLLauO89RcD1Hk0RWfJ+032NTm49cVEpRY8WyOLmSpzic4nEwx0rVITizTn1zhVbnH2d6nsunEGzi+FMctevh+E7MUEpkq5WwEFQ8qEKUcxLJF1VIw8st4izGiSkDCL9Jvr2CmfR51hxhNLnCO2As1g9XeBEHF5WghzTnGPgp9gmOHe9HLTZy9BmhNasWQu6eGEU4dt93hPX238nn1Ag7qXYiiQf5JqORV7BN1tEaIXvPR5zV+56VzfCXwuX6wzvdPbaQ2ncTqWsQLNPRZBXsxRK+01pGsd6ikrl8iobuUtpSYLybxlm2Cniab9Gn0Ro2ooWMrIcPBKU6PK9yRS2DFqyzNK7hVG5EpES1qfO70m1gJNe5OrudH5lomZtq4Ir+bH8ydQ6iBWoHsnhrl4RhNHcodrec2hAqsMhYorAj61h6nRy3hBHnqqYCxrkWOPDJI9rRJDDVgdjlNNbARx6B3cJbHYoOEZZdAba1PY09H3LNnACGC1tRFfIJYgCgpqIpCcQGChMNQYpY6OkVi5JOLODWNw5UUbqBiqT4Xx3fz1cEN2LpHX6JI2q8zXojznvxTlFdFVHybRlPDoEI60+CbY9/gj09djaEEXJ7bz7FmF4tllZcOPMbOwgAxxeM0cZjr1SLd7bN82rkYgG67jK363PvAFq698gn2H7mYpdUKp5sFThxYy99d8A0mY3nqoU6vv8J51lOMrTvB9//tUoxCg1w5jptS0ash1q5JVipx4oYLTZMo2aRvZJljJ7qYLph06QsMd7vEVJclJ8Hhk5184cI7+LR3Jl1GmYtjh3nzgdfwlaF/5ym3jb3FflYcQZ+YZX14jJd3PczvuO8gdaKV1R0BlbaIykGbvs2znGUcxhIuj9RGuSK3h6aylbTXIKPX6dcjVCMkqvtclXmSRTeJqflc2ltkf6GHNeYptnUe5jtTm5mfydIwTe6YH2K5V2FNYh7dqaG5Fmv6Fjl62yr8fAP2a8z3GrxobDcnE20cuXcE8/Qi5biNWDGY2pFuDfIk6yQPGTiZVmamshxQH/KwT+hUcy5qTaAsQ6Pq4xxTGd44SdUzSAQOg2KOZGqFNq3GcSfOKqYxmx4nT8S5aPVTPDI5iNeMEMUQrewS6fCPmYf483qDA6KTy1IHuFbdyx6rjYavs96a48HZ1UDImuwE+xe7eG/8CfyhR2mEHkIIdFRurqziLPsk3WqAJhRUBE4UcNm+tyNKAYquoTdaneuw5uNpEUGqxEu6nuC65FOYIuSNjRvYmp9CASq2gaV4PLXcQYDg1UMP8PjKEEv1OAoRWbPO4R7B+WuPsv/L60mNuzTzOm5S0PGxJ9BW9XH8DT0MrJ5h0FwhsroYXbfII5ODuDUTFYfu7DwzboqR/imcQGPiVDtqRcXeVGZ6Vw9BTxG1HCDGdeKH67gKxA4LivGQ9j3NVnb5yAq/s3UP3zx5OnOLOr3JFYasKnjwvt67+X58KxXf5LLsQc63pvhhfZjHvR7uuWMt9NfxyoL0IReXEGVW4B43UXwHlwhFBHy373vQBzuaAV1ag34twWJQ49LZPwQBZ4+doOjazFWSbO+cYH7FZjR/ipPk6LMKbOYI+4IMwYoOqoJZUxFhhNYIIK2R9FZ4fLYLpVGnM+YRi1y2ZgpUkgaMgBtq+KFCTHM5ONzJRv04nWqJvFolr1RIxh0e7B7j1sktVFZ0+tQyf9T5EP/QvJKVRowlV8ENPBJanbXaSf4qf4gvlLvoT+SZrPViTEdMn2FSHq3Q9oTGcsLl9JEJMnor6D/Us8TNlW3gqhycTOHlXF7cvoMP7bwOjCYb+k5ydXovk26OFyQOUAotwkhwwOnjsNvJ9WM/5o6J9TSKMbaMjnNO4jhRU6E9XMBsljk/s5+MVsdSfFb8GC9K7eGftCvot1dY9uJUPRPqDT7Q93UOOd34kYYmWgPf/ZlplrwkKa3BdCPDYDKgO1/maK2DU1qW1/Q8weWxYxxw23j/wRfz3ckx8toiHZkYC0EKHB+35hK6Ddq6y4TtsLKQxJjV2XnfAH91zbfZYw7wxOIqHMUlNeLw4cHv8ERlmB5thrXxGJoIGPNOMZCa5j2xHxNTDCb9KnOGjbnBx0Ph0fKbiQKfyPXxAh1z2iXqMTAmG5SGLShD760+tR6NudMCVmdm6YhVKLs201GKDW1zVFeZrIoVuGdqDbVikmF7ivLFS3j39ZO9YJ5zrX0YjSZ/OfZtbjxxMfFGkT2TpzE3b5JvrzC7YjAWm+VbJ8aABgPt81iaz4IxgBcIegZn6EsUWW7G6WOZB0+M8PLBh7lrZh1TkzEWrSwdyQrabIrU5mWyVp0oEswaKarJFNYxn+ZFZcKjSTig8oNXfY2TfoMdzX7WGPN8c+VMfnh8DMtv8MKzdvK1vdtIZurUYgaOa3G++hQFM449WOZoqZ32WJXf73qQA04fKhFo0KZVyCpVGpGJmRjjYNBJTPdYqdg0PHjhpffRCFufs12t4EYqpTDGan2e7y+NIbyARjZEqapkdgqIBOWLqiRTDcJEK2iih+AsxBnbPE6pFJFlmbO7log7dV7WfpLbj1wCRGgdrWudNe2jeBHVUkiXPU9Cc/AilTFrll3VQTQRUA8NulLzrL5igVuntrAmvUixZnOyFsP1XaIVj5WKiZP1GDBnOZF2aeo6DNSJxjP0rVkgbTY5UWij+XSSreceYdeja5jstVjTs8AZmSM8vdRDbaDOv3R/lVfsfw0qdT7a+wikHnm2vX1HzeYj4moKhRyDiWmOHOrDGyiSydYYMhbxqh7dYpZN5hGO0cmwtsh0lGWXNkTUBHMZAieikomodkRcrO/ms7MXsjo5ScZbJqZ4XN5W4kQ9z7C9wvYOlyU3wWQ9y9SCTUEX+EWPbHsFP1CplGIY8zqT3SYv6tzBw8owGbPBmsQC3z94Di4RfgjGuCA0PObO9EjuN9icP0YYCc7sKrIpPsmQvkRP93q+mTkPe05gzASo8w6pho52coWZJYOB5DT1YYMgEtw8sYXPnXMjf7jn1Vzf8Th/nB1nMajxwdlLOax0kPGWW1nQ6iLjYwFO0eI0axpRq9MVznJe7iBORuUalrmm73t8q5ph2suSNytMOm3P9r52N/txyjZhzUFZCmi0+0RtFexcg7ZYg8WVJBdk9qNuC9lb6qHNqrE+McvO4ipimtu69rcppLQm/fYK1cAk0e9wQeIw98bW86A+wtJKkuklg5rbWncjbRRYziexTuhAk8ef7CF50sHJQr3LJ66VqJRDzu/eR9pv0CaqvKJzlidLq6jXDHStynp1GV0NCALBaD7FdCnN2ZvHGbaXaIYa986PUWwmcGIezrBArSutjPsAtgaHeG3fUa6rXMtko51kZ5VHNn6LVx2/jEYFNCWgLznL6d4c8fgynWKB8/NHWfSSlH2bVyR28ESync5oHqVRo40lCilByi2we3GUTw7fwt+6l/PQyii+Ifho/1fYbqn8w/JqANxIYzmucH1mJ59fOQ99PEF5NsaDI91c0v80n7rnXNBDDmppzs8c4VVtD/C94DT69SKLbpzZUylUJyR92gqh4XDHk8Ok0g3W5ecZ7ZngaKKdl7bv5mOXXoMIBHqiTpYCpxYtHN9jKLnMo8f7iY5D03AIPej9TokTL0uhtOKdpP0qvckir8ju5OHKKE8srCIaB3OyTO9eBy9tEugKsakKQUwn9foCq8wlOtUyJ7x25p00dS9G1liiMNnBSr9HVAet6CKOKSxsVKmWQsKGw1h+gdNSkxS9GCtlwaEDGfJdxdYa9UsJnBNxOh6vU5lVMUsOhODkBOYhMGY9Yscj6p0KriFIPRVQms+g/X6VTcZxhN1D1TH47vQ6/jJ/C2899jbUpsDJRQQpB21RJXPOPDsWujg6lyCqO9QTHmdoh5kd0lkY7yWYDilaGvmnmxjlkDkzQa7YoGGEKFUY+dcKs+dniC2HNHKtLN5EISR9tMrUJSk67vOYSKsYbRXwmuSCZSaWLXL6MiNxlekwQxAKqkWFV40+xv+yzqfXLnFVbCcKEdeOPcqMnyGn1jjW7GR3qZ91q05yuNjJzESGZG8Rw6sSOHGy/SuUDubYdu5h9mg9OHWDk8U4wqtz48AXOOR28rna+TSLFi8cepTjbicbktP0a3UWAoO74puY0rbStGP0aQss63H8lMJZnRP8Q34n58Sz1Eo2V3fuYnv8BO9ZeBmaUyNneczNmwzmCmzpWuTQnlHWcZxb5k9nTfIUhWaM2fk4P0wP8jLjIR4wxwg6HPpjK3ww92M+VdjCcbWd8UIciCOaDUr9Ff6q5y4emRvlRCWNJmr02yXmVxJUqwmiaRV0D3zB0ZMdfOqMs1ncH2PLaSc4P3McL1J51B3izOw4R6qdvDrxGF1jm/huuBn9uI22uUS1HKLNN1AKPrTpLB036Sw2yDwkUK5d5rzOExwodbE+PUdGq3NFx35+WN3AD6Y3sGBkCdMqD98/SDhWRbF9YrpD7/oSTz/Wh1FvklizwkhuCTfQ8EKFI3MJ9voZOuPz9CeLDIWTnGrm+ELnA7y6dgUvzD4NwMcOXcNHV3+Nv9x7A0d/3IHwAZp4WZe0qBM4gqgeYRQU4tMR1171OF86eBaDXYsk4jrd+TLnZ45w49FLGD+SRslU8GoORRd6UzO8NXsn036OrkwRH5VCEGfSbeNbP7wI3fVpdoZU4yHTusnC/hgdR+vE5lz0QgOl2gDXo3uXoOdFK6ix1tQgx9d4cLKPN/X9iGUvwZ3l9VTzEdU8DGlLZK8+xT/23QU+POb3EOSKjHYv8IL2Q3zKuaQ1CG0ErWeLHtKZOiOiJz6LV3Np+Dqj8QmiWoN8sMxThW7O6ZhkdXqC+4+cQ71TpdEl2GSdZJMYpyJCXnD2bqqByX9b9x3+59zlHFzsxNcFgzdVqfaYpA6WaZhpGrEIvSb42m1nEGkO9VJAT26ZgdwM40aOOTPJuX0neXR6iKRocJa2j3rap92sEgaC8xNzzGXOI3bEpNlmYU84NNtN9GaIZulcauziB4XNNAONmNagS5SJ2y7D9gKf3H858XyRdqOKj8pCvZeO+0s0OnSctEBZidC9CFdRiZ1TxKu5WLpPp12h3yriRYLi4TRq08dNAUYDt+qS2BFw3tn7+FpxA0VMOmJVBmPTdKeLjBpzNHWddrXCoF6hR40x6df50xNvJqxbNPKtJUqDZhMvptPuz/PC3Bx7tH4qrsmLlCfZs62NXTN9TAcGTQ+ikouxrPLS0x9iRJQJNIelnMLSaXHObJ/i+txO0kqTUmhRCmJstQ/yeDTCjJ3hUfc01BoEVZfGnMbvXvA430ydh8jWGDAXUZs+V7Sd4G5nLaoIMU4LKNRjeCeTvPj8R/ju7tYs0jDjozk1Fuox3jVyK3cVN3FkMckuL48baryoZwff23c2zS1VLu042kq+mM+hFCNOXgTCaSIqrWd69euzPLXcgbN6mRcN7KPkWcw101wRjfOFpa28ZuBBZp0MJc+iHhjsMQTX9O3lttomXjKwG1PxuPnQhWiLrQk72b0lRN0hTNgohTgrgwI/8OjNzOCFClMLJm12nVdl7yOhuOxqDqAbPpvMGZb8GK/NF7hTbGJYWWIsOEFSafItYy1PTua5ILePmOpS9GKItXWePtbPmcOHCSMFK3Dp8sucrhfoTPWQUptMO+twzSorqzT2zWXpGJ5gkCnaghoBgnpg0J5aoF2p8ONmNzE1oKu9TDFp86N13+OFB68hb9dwA1ipQ0wrs7xs8qmN3+D22hjH6p0koyZH1iYplyJe330f/zx7OcakBSW411sFqSrGvEbTjKitD9HLCi8bfpS5Zopyl0WnWWVVMMkfjszw8Ueu4O3rb2NnYwgn1HhNbDe9WoKvVPK8uKPJRmuKmOLwvZXTuXtiFcWqjbtikd28zNu7HuAfg8vw/BQz7Rk6WMH0fULP4uDeHCt9ILw6TSOElMN1yUcIUdjv9HKZtp+FWIqVIEE1MHnsWAI1Nkr2uEOQsvBSBuZyE/cqnajRIMoUqfgWIoRVIyc4AfzfFrt6TsthRVHE29/+dm699Vbuv/9+RkdH/7O7/oTrr7+eQqHAvffe+5/a/sSJE4yMjPxS55IkSZIkSZIkSZIkSZIkSZIk6flpcnKSvr6+n/v+c5oJ8kd/9Ed89atf5bvf/S7JZPLZB6Sn02lsu/U02P9zOayPf/zjDA4OsmHDBlzX5ctf/jK33HILt9xyy3/6vLlcay39iYkJ0un0cymyJEn/H/Ifz/eZnJz8hev0SZL0/12yHkvSbz9ZjyXpt5+sx5L020/WY0l6fpB1+TcriiIqlQo9PT2/cLvnFAT5zGc+A8DFF1/8E69/4Qtf4A1veAPw08thua7Ln//5nzM9PY1t22zYsIHbb7+da6655j99XkVRgFawRf6YJOm3XyqVknVZkn7LyXosSb/9ZD2WpN9+sh5L0m8/WY8l6flB1uXfnP/MpInnFAT5z6ycddNNN/3E3+9617t417ve9VxOI0mSJEmSJEmSJEmSJEmSJEmS9CtTftMFkCRJkiRJkiRJkiRJkiRJkiRJ+nX4rQiCmKbJ+973PkzT/E0XRZKkX4Gsy5L020/WY0n67SfrsST99pP1WJJ++8l6LEnPD7Iu/3YQ0X9mjStJkiRJkiRJkiRJkiRJkiRJkqTfMr8VM0EkSZIkSZIkSZIkSZIkSZIkSZKeKxkEkSRJkiRJkiRJkiRJkiRJkiTpeUkGQSRJkiRJkiRJkiRJkiRJkiRJel6SQRBJkiRJkiRJkiRJkiRJkiRJkp6XfiuCIJ/+9KcZGhrCsizOOOMMHnrood90kSRJAj7ykY+wffv2/1979xdSd/3Hcfzl/DcndmoTd3aShkJgpVtL6d8soz9SzSSCYptToavFXJ4GpbSiCNbWol2UtbGIbla4GxcrSDorOyG5FPWUzZqDTGvNrHBnNts8et6/m9++cHLbj1/Y8Xv0+YBz4ef75sv3c/Hk4PlwVFlZWcrJydHDDz+s48ePx8yYmV588UX5fD5lZGTorrvu0rFjx2Jmzp8/r61btyo7O1uZmZmqrKzUzz//HM+tAPivnTt3KikpSX6/31mjYyAxnDx5Ups2bdKyZcu0ZMkS3Xjjjeru7nau0zLgblNTU3ruueeUl5enjIwM5efn66WXXlI0GnVm6Bhwly+++EIPPfSQfD6fkpKS9MEHH8Rcn61mx8bGVF1dLY/HI4/Ho+rqap0+ffpf3h2wMFyu40gkooaGBhUVFSkzM1M+n081NTX65ZdfYu5Bx+7n+kOQgwcPyu/3a/v27ert7dUdd9yhBx54QMPDw3P9aMCCFwwGtWXLFh09elSBQEBTU1MqLy/X2bNnnZndu3drz549ampqUldXl7xer+677z6Nj487M36/X4cOHVJzc7Pa29v1559/qqKiQtPT03OxLWDB6urq0v79+7Vq1aqYdToG3G9sbExr165VamqqPv74Y/X39+u1117TlVde6czQMuBur7zyivbt26empiZ999132r17t1599VW98cYbzgwdA+5y9uxZrV69Wk1NTRe9PlvNbty4UaFQSK2trWptbVUoFFJ1dfW/vj9gIbhcxxMTE+rp6dHzzz+vnp4etbS0aGBgQJWVlTFzdJwAzOVuvvlm27x5c8xaQUGBNTY2ztETAbiU0dFRk2TBYNDMzKLRqHm9Xtu1a5czc+7cOfN4PLZv3z4zMzt9+rSlpqZac3OzM3Py5ElbtGiRtba2xncDwAI2Pj5u1157rQUCASsrK7P6+nozo2MgUTQ0NFhpaeklr9My4H7r1q2zxx9/PGbtkUcesU2bNpkZHQNuJ8kOHTrk/Dxbzfb395skO3r0qDPT0dFhkuz777//l3cFLCx/7/hiOjs7TZINDQ2ZGR0nCld/E2RyclLd3d0qLy+PWS8vL9eXX345R08F4FLC4bAkaenSpZKkwcFBjYyMxDScnp6usrIyp+Hu7m5FIpGYGZ/Pp8LCQjoH4mjLli1at26d7r333ph1OgYSw+HDh1VSUqJHH31UOTk5WrNmjd5++23nOi0D7ldaWqpPP/1UAwMDkqSvv/5a7e3tevDBByXRMZBoZqvZjo4OeTwe3XLLLc7MrbfeKo/HQ9fAHAiHw0pKSnK+cU3HiSFlrh/gcn7//XdNT09r+fLlMevLly/XyMjIHD0VgIsxM23btk2lpaUqLCyUJKfTizU8NDTkzKSlpemqq66aMUPnQHw0Nzerp6dHXV1dM67RMZAYfvjhB+3du1fbtm3Ts88+q87OTj355JNKT09XTU0NLQMJoKGhQeFwWAUFBUpOTtb09LR27NihDRs2SOI9GUg0s9XsyMiIcnJyZtw/JyeHroE4O3funBobG7Vx40ZdccUVkug4Ubj6EOSCpKSkmJ/NbMYagLlVV1enb775Ru3t7TOu/ZOG6RyIj59++kn19fX65JNPtHjx4kvO0THgbtFoVCUlJXr55ZclSWvWrNGxY8e0d+9e1dTUOHO0DLjXwYMHdeDAAb3//vu64YYbFAqF5Pf75fP5VFtb68zRMZBYZqPZi83TNRBfkUhE69evVzQa1VtvvfU/5+nYXVz957Cys7OVnJw840RsdHR0xkk6gLmzdetWHT58WG1tbcrNzXXWvV6vJF22Ya/Xq8nJSY2NjV1yBsC/p7u7W6OjoyouLlZKSopSUlIUDAb1+uuvKyUlxemQjgF3W7Fiha6//vqYteuuu07Dw8OSeE8GEsHTTz+txsZGrV+/XkVFRaqurtZTTz2lnTt3SqJjINHMVrNer1e//vrrjPv/9ttvdA3ESSQS0WOPPabBwUEFAgHnWyASHScKVx+CpKWlqbi4WIFAIGY9EAjo9ttvn6OnAnCBmamurk4tLS367LPPlJeXF3M9Ly9PXq83puHJyUkFg0Gn4eLiYqWmpsbMnDp1St9++y2dA3Fwzz33qK+vT6FQyHmVlJSoqqpKoVBI+fn5dAwkgLVr1+r48eMxawMDA1q5cqUk3pOBRDAxMaFFi2J/RU9OTlY0GpVEx0Cima1mb7vtNoXDYXV2djozX331lcLhMF0DcXDhAOTEiRM6cuSIli1bFnOdjhNE/P8X+/+nubnZUlNT7Z133rH+/n7z+/2WmZlpP/7441w/GrDgPfHEE+bxeOzzzz+3U6dOOa+JiQlnZteuXebxeKylpcX6+vpsw4YNtmLFCjtz5owzs3nzZsvNzbUjR45YT0+P3X333bZ69Wqbmpqai20BC15ZWZnV19c7P9Mx4H6dnZ2WkpJiO3bssBMnTth7771nS5YssQMHDjgztAy4W21trV199dX20Ucf2eDgoLW0tFh2drY988wzzgwdA+4yPj5uvb291tvba5Jsz5491tvba0NDQ2Y2e83ef//9tmrVKuvo6LCOjg4rKiqyioqKuO8XmI8u13EkErHKykrLzc21UCgU89nX+fPnnXvQsfu5/hDEzOzNN9+0lStXWlpamt10000WDAbn+pEAmJmki77effddZyYajdoLL7xgXq/X0tPT7c4777S+vr6Y+/z1119WV1dnS5cutYyMDKuoqLDh4eE47wbABX8/BKFjIDF8+OGHVlhYaOnp6VZQUGD79++PuU7LgLudOXPG6uvr7ZprrrHFixdbfn6+bd++PeZDFjoG3KWtre2ivxPX1taa2ew1+8cff1hVVZVlZWVZVlaWVVVV2djYWJx2Ccxvl+t4cHDwkp99tbW1OfegY/dLMjOL3/dOAAAAAAAAAAAA4sPV/xMEAAAAAAAAAADgn+IQBAAAAAAAAAAAzEscggAAAAAAAAAAgHmJQxAAAAAAAAAAADAvcQgCAAAAAAAAAADmJQ5BAAAAAAAAAADAvMQhCAAAAAAAAAAAmJc4BAEAAAAAAAAAAPMShyAAAAAAAAAAAGBe4hAEAAAAAAAAAADMSxyCAAAAAAAAAACAeYlDEAAAAAAAAAAAMC/9B27UxBhT9U8EAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 2000x7.8125 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "visualize_intermediate_layers(model_compressed_4comp, x_train_compressed_4comp, sample_index=3)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "bW565vHZTGgm",
        "Xl6QksnQLMsr"
      ],
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.9.10"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}