notebooks/main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LIME for ECG Time Series Dataset Example\n",
    "\n",
    "This notebook demonstrates how to use LIME technique for time series datasets by employing the modules in src."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "# Update the path below according to your project structure\n",
    "sys.path.append('../src')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import Required Libraries\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading and Preprocessing the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mahdihabibi/Library/Python/3.9/lib/python/site-packages/urllib3/__init__.py:34: NotOpenSSLWarning: urllib3 v2 only supports OpenSSL 1.1.1+, currently the 'ssl' module is compiled with 'LibreSSL 2.8.3'. See: https://github.com/urllib3/urllib3/issues/3020\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from data_preprocess import preprocess_data, split_data\n",
    "from visualization import plot_class_distribution, plot_sample_signals\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed 1 rows with null or infinite values.\n",
      "Training dataset after preprocessing:\n",
      "   0        1        2        3        4        5        6        7     \\\n",
      "0   3.0  0.39475  0.42683  0.40271  0.36694  0.40403  0.42741  0.42354   \n",
      "1   2.0 -0.25097 -0.24359 -0.17080 -0.16245 -0.17461 -0.17786 -0.17280   \n",
      "2   4.0  0.76671  0.77335  0.76703  0.76831  0.77436  0.77349  0.77428   \n",
      "3   3.0 -0.11454 -0.12602 -0.15173 -0.18220 -0.18592 -0.18366 -0.18393   \n",
      "4   3.0 -0.29191 -0.30657 -0.31069 -0.30988 -0.31269 -0.32061 -0.32349   \n",
      "\n",
      "      8        9     ...     1630      1631      1632     1633      1634  \\\n",
      "0  0.45237  0.42515  ...  0.42560  0.423450  0.439390  0.45879  0.452610   \n",
      "1 -0.17083 -0.17054  ... -0.23849 -0.241870 -0.244640 -0.24521 -0.243480   \n",
      "2  0.77122  0.76613  ...  0.75079  0.753830  0.759440  0.76402  0.769820   \n",
      "3 -0.16847 -0.15904  ... -0.02097 -0.023981 -0.037699 -0.05316 -0.049396   \n",
      "4 -0.31899 -0.33060  ... -0.23937 -0.254440 -0.262070 -0.26039 -0.258970   \n",
      "\n",
      "       1635      1636      1637      1638      1639  \n",
      "0  0.419910  0.375330  0.366500  0.391920  0.390370  \n",
      "1 -0.243640 -0.247380 -0.249610 -0.249050 -0.250440  \n",
      "2  0.773550  0.773380  0.771790  0.763780  0.757610  \n",
      "3 -0.026982 -0.033078 -0.067727 -0.087037 -0.098574  \n",
      "4 -0.262170 -0.269980 -0.278390 -0.280100 -0.280270  \n",
      "\n",
      "[5 rows x 1640 columns]\n",
      "Removed 0 rows with null or infinite values.\n",
      "Testing dataset after preprocessing:\n",
      "   0         1         2         3         4         5         6         7     \\\n",
      "0   1.0 -1.100900 -1.136300 -0.269700 -0.287390 -0.252010 -0.216640 -0.181270   \n",
      "1   3.0  0.277960  0.293750  0.323010  0.312740  0.266580  0.233220  0.243510   \n",
      "2   4.0 -0.259780 -0.257880 -0.258900 -0.248390 -0.227680 -0.229630 -0.252780   \n",
      "3   3.0 -0.100410 -0.068534 -0.051683 -0.066385 -0.089782 -0.084242 -0.056820   \n",
      "4   3.0  0.083505  0.079466  0.075540  0.083133  0.079600  0.061685  0.060065   \n",
      "\n",
      "       8         9     ...      1630      1631     1632      1633      1634  \\\n",
      "0 -0.163590 -0.092842  ... -0.234330 -0.252010 -0.30507 -0.393500 -0.499620   \n",
      "1  0.256340  0.225550  ...  0.250930  0.276540  0.28621  0.267300  0.245100   \n",
      "2 -0.256260 -0.256170  ... -0.258200 -0.259100 -0.25693 -0.239240 -0.227960   \n",
      "3 -0.056761 -0.080574  ... -0.169920 -0.178470 -0.16660 -0.145190 -0.129140   \n",
      "4  0.078609  0.095152  ...  0.003916  0.023833  0.05540  0.079133  0.073154   \n",
      "\n",
      "      1635      1636      1637      1638      1639  \n",
      "0 -0.58804 -0.658790 -0.747220 -0.871020 -1.030200  \n",
      "1  0.22467  0.200620  0.208800  0.257250  0.284180  \n",
      "2 -0.23463 -0.235500 -0.228220 -0.236620 -0.254950  \n",
      "3 -0.16031 -0.201710 -0.175870 -0.125520 -0.112450  \n",
      "4  0.05773  0.056222  0.056939  0.057024  0.070183  \n",
      "\n",
      "[5 rows x 1640 columns]\n"
     ]
    }
   ],
   "source": [
    "# Path to the dataset \n",
    "file_path_train = '../data/ecg_train.csv'\n",
    "file_path_test = '../data/ecg_test.csv'\n",
    "\n",
    "# Loading and preprocessing the training dataset\n",
    "ecg_train = preprocess_data(file_path_train, sep=',', header=None, split=False)\n",
    "print(\"Training dataset after preprocessing:\")\n",
    "print(ecg_train.head())\n",
    "\n",
    "# Loading and preprocessing the testing dataset\n",
    "ecg_test = preprocess_data(file_path_test, sep=',', header=None, split=False)\n",
    "print(\"Testing dataset after preprocessing:\")\n",
    "print(ecg_test.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 187 entries, 0 to 186\n",
      "Columns: 1640 entries, 0 to 1639\n",
      "dtypes: float64(1640)\n",
      "memory usage: 2.3 MB\n"
     ]
    }
   ],
   "source": [
    "# Check info for train dataset\n",
    "ecg_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 40 entries, 0 to 39\n",
      "Columns: 1640 entries, 0 to 1639\n",
      "dtypes: float64(1640)\n",
      "memory usage: 512.6 KB\n"
     ]
    }
   ],
   "source": [
    "# Check info for test dataset\n",
    "ecg_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training features:\n",
      "      1        2        3        4        5        6        7        8     \\\n",
      "0  0.39475  0.42683  0.40271  0.36694  0.40403  0.42741  0.42354  0.45237   \n",
      "1 -0.25097 -0.24359 -0.17080 -0.16245 -0.17461 -0.17786 -0.17280 -0.17083   \n",
      "\n",
      "      9        10    ...     1630     1631     1632     1633     1634  \\\n",
      "0  0.42515  0.62191  ...  0.42560  0.42345  0.43939  0.45879  0.45261   \n",
      "1 -0.17054 -0.17127  ... -0.23849 -0.24187 -0.24464 -0.24521 -0.24348   \n",
      "\n",
      "      1635     1636     1637     1638     1639  \n",
      "0  0.41991  0.37533  0.36650  0.39192  0.39037  \n",
      "1 -0.24364 -0.24738 -0.24961 -0.24905 -0.25044  \n",
      "\n",
      "[2 rows x 1639 columns]\n",
      "Training targets:\n",
      "0    3.0\n",
      "1    2.0\n",
      "Name: 0, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Split train dataset into one column as targets/classes and rest as features\n",
    "ecg_train_x, ecg_train_y = split_data(ecg_train, target_column=0, split=True)\n",
    "\n",
    "print(\"Training features:\")\n",
    "print(ecg_train_x.head(2))\n",
    "\n",
    "print(\"Training targets:\")\n",
    "print(ecg_train_y.head(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "4.0    0.278075\n",
      "2.0    0.245989\n",
      "1.0    0.240642\n",
      "3.0    0.235294\n",
      "Name: proportion, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Check for Class Imbalance\n",
    "print(ecg_train_y.value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plots the distribution of classes using a bar chart\n",
    "plot_class_distribution(ecg_train_y, title=\"ECG Signal Class Distribution\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plots one sample signal from each class in the dataset\n",
    "plot_sample_signals(ecg_train_x, ecg_train_y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing features:\n",
      "      1        2        3        4        5        6        7        8     \\\n",
      "0 -1.10090 -1.13630 -0.26970 -0.28739 -0.25201 -0.21664 -0.18127 -0.16359   \n",
      "1  0.27796  0.29375  0.32301  0.31274  0.26658  0.23322  0.24351  0.25634   \n",
      "\n",
      "       9        10    ...     1630     1631     1632    1633     1634  \\\n",
      "0 -0.092842 -0.11053  ... -0.23433 -0.25201 -0.30507 -0.3935 -0.49962   \n",
      "1  0.225550  0.21010  ...  0.25093  0.27654  0.28621  0.2673  0.24510   \n",
      "\n",
      "      1635     1636     1637     1638     1639  \n",
      "0 -0.58804 -0.65879 -0.74722 -0.87102 -1.03020  \n",
      "1  0.22467  0.20062  0.20880  0.25725  0.28418  \n",
      "\n",
      "[2 rows x 1639 columns]\n",
      "Testing targets:\n",
      "0    1.0\n",
      "1    3.0\n",
      "Name: 0, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Split test dataset into one column as targets/classes and rest as features\n",
    "ecg_test_x, ecg_test_y = split_data(ecg_test, target_column=0, split=True)\n",
    "\n",
    "print(\"Testing features:\")\n",
    "print(ecg_test_x.head(2))\n",
    "\n",
    "print(\"Testing targets:\")\n",
    "print(ecg_test_y.head(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of ecg_train: (187, 1640)\n",
      "Shape of ecg_train_x (features): (187, 1639)\n",
      "Shape of ecg_train_y (labels): (187,)\n",
      "Shape of ecg_test: (40, 1640)\n",
      "Shape of ecg_test_x (features): (40, 1639)\n",
      "Shape of ecg_test_y (labels): (40,)\n"
     ]
    }
   ],
   "source": [
    "# Check the shape of arrays\n",
    "print(\"Shape of ecg_train:\", ecg_train.shape)\n",
    "print(\"Shape of ecg_train_x (features):\", ecg_train_x.shape)\n",
    "print(\"Shape of ecg_train_y (labels):\", ecg_train_y.shape)\n",
    "\n",
    "print(\"Shape of ecg_test:\", ecg_test.shape)\n",
    "print(\"Shape of ecg_test_x (features):\", ecg_test_x.shape)\n",
    "print(\"Shape of ecg_test_y (labels):\", ecg_test_y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building a CNN Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from data_preprocess import prepare_for_conv1d_training\n",
    "\n",
    "# Prepares the dataset for Conv1D training:reshapes features and one-hot encodes labels.\n",
    "X_train, y_train = prepare_for_conv1d_training(ecg_train_x, ecg_train_y, num_classes=4)\n",
    "X_test, y_test = prepare_for_conv1d_training(ecg_test_x, ecg_test_y, num_classes=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X_train (features): (187, 1639, 1)\n",
      "Shape of y_train (labels): (187, 4)\n",
      "Shape of X_test (features): (40, 1639, 1)\n",
      "Shape of y_test (labels): (40, 4)\n"
     ]
    }
   ],
   "source": [
    "# Check the shape of arrays after preparation for Conv1D\n",
    "print(\"Shape of X_train (features):\", X_train.shape)\n",
    "print(\"Shape of y_train (labels):\", y_train.shape)\n",
    "\n",
    "print(\"Shape of X_test (features):\", X_test.shape)\n",
    "print(\"Shape of y_test (labels):\", y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv1d (Conv1D)             (None, 1637, 64)          256       \n",
      "                                                                 \n",
      " max_pooling1d (MaxPooling1  (None, 818, 64)           0         \n",
      " D)                                                              \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 818, 64)           0         \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 52352)             0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 100)               5235300   \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 4)                 404       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 5235960 (19.97 MB)\n",
      "Trainable params: 5235960 (19.97 MB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Build the Model\n",
    "from model_training import create_cnn_model\n",
    "\n",
    "input_shape = (X_train.shape[1], 1)\n",
    "num_classes = y_train.shape[1]\n",
    "model = create_cnn_model(input_shape, num_classes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "6/6 [==============================] - 0s 32ms/step - loss: 1.0000 - accuracy: 0.6548 - val_loss: 0.2113 - val_accuracy: 0.8947\n",
      "Epoch 2/10\n",
      "6/6 [==============================] - 0s 13ms/step - loss: 0.0772 - accuracy: 0.9702 - val_loss: 0.0710 - val_accuracy: 1.0000\n",
      "Epoch 3/10\n",
      "6/6 [==============================] - 0s 13ms/step - loss: 0.0088 - accuracy: 1.0000 - val_loss: 0.0330 - val_accuracy: 1.0000\n",
      "Epoch 4/10\n",
      "6/6 [==============================] - 0s 13ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.1158 - val_accuracy: 0.9474\n",
      "Epoch 5/10\n",
      "6/6 [==============================] - 0s 13ms/step - loss: 3.7533e-04 - accuracy: 1.0000 - val_loss: 0.1975 - val_accuracy: 0.9474\n",
      "Epoch 6/10\n",
      "6/6 [==============================] - 0s 13ms/step - loss: 3.4215e-04 - accuracy: 1.0000 - val_loss: 0.2429 - val_accuracy: 0.8947\n",
      "Epoch 7/10\n",
      "6/6 [==============================] - 0s 13ms/step - loss: 1.6473e-04 - accuracy: 1.0000 - val_loss: 0.2606 - val_accuracy: 0.8947\n",
      "Epoch 8/10\n",
      "6/6 [==============================] - 0s 14ms/step - loss: 1.6174e-04 - accuracy: 1.0000 - val_loss: 0.2688 - val_accuracy: 0.8947\n",
      "Epoch 9/10\n",
      "6/6 [==============================] - 0s 14ms/step - loss: 2.0013e-04 - accuracy: 1.0000 - val_loss: 0.2678 - val_accuracy: 0.8947\n",
      "Epoch 10/10\n",
      "6/6 [==============================] - 0s 14ms/step - loss: 1.0048e-04 - accuracy: 1.0000 - val_loss: 0.2644 - val_accuracy: 0.8947\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x2d15fcc70>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the CNN model\n",
    "model.fit(X_train, y_train, epochs=10, batch_size=32, validation_split=0.1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 0.949999988079071\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "loss, accuracy = model.evaluate(X_test, y_test, verbose=0)\n",
    "print('Test Accuracy:', accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /Volumes/D/GitHub-Portfolio/LIME-for-Time-Series/models/my_ecg_model/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /Volumes/D/GitHub-Portfolio/LIME-for-Time-Series/models/my_ecg_model/assets\n"
     ]
    }
   ],
   "source": [
    "# save the trained model\n",
    "model.save('/Volumes/D/GitHub-Portfolio/LIME-for-Time-Series/models/my_ecg_model')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 2ms/step\n",
      "Shape of probability_vector (number of rows in test dataset, number of classes): (40, 4)\n",
      "Predicted Classes: [[9.60822523e-01 6.12199510e-05 2.42851593e-06 3.91139463e-02]\n",
      " [2.52706966e-09 3.01634600e-07 9.99942780e-01 5.68527867e-05]\n",
      " [1.91989326e-04 3.92525834e-07 2.44749092e-08 9.99807656e-01]\n",
      " [7.13336021e-08 1.77099213e-09 9.99999881e-01 1.72920949e-08]\n",
      " [1.00496669e-08 4.49996378e-08 9.99995470e-01 4.54836027e-06]\n",
      " [1.45962664e-09 4.84392415e-10 1.00000000e+00 1.05418507e-09]\n",
      " [1.65135336e-10 1.00000000e+00 6.45926734e-09 6.98477276e-09]\n",
      " [9.23755169e-01 2.25992167e-07 9.64754143e-09 7.62445852e-02]\n",
      " [4.78263047e-08 7.84193510e-09 2.86911721e-08 1.00000000e+00]\n",
      " [1.85972418e-11 1.00000000e+00 1.76686671e-12 3.34028860e-10]\n",
      " [1.28480046e-10 2.81508622e-10 1.00000000e+00 8.64587402e-10]\n",
      " [3.43647992e-11 4.64087879e-09 1.00000000e+00 6.33716146e-09]\n",
      " [6.93971214e-10 1.00000000e+00 1.19663246e-09 5.54597535e-10]\n",
      " [2.56602326e-11 8.05677264e-05 9.99919057e-01 3.33960315e-07]\n",
      " [4.09765841e-12 1.40440034e-06 9.99998569e-01 1.85678517e-09]\n",
      " [2.68664626e-12 5.92051265e-07 9.99999404e-01 5.60581581e-10]\n",
      " [1.45083834e-10 1.00000000e+00 2.31665360e-12 1.03373747e-10]\n",
      " [1.92854449e-03 2.53574655e-08 1.77965234e-07 9.98071373e-01]\n",
      " [9.99992371e-01 7.49833191e-08 1.40075196e-08 7.53624954e-06]\n",
      " [1.75509194e-05 5.67366499e-07 1.71170186e-07 9.99981761e-01]\n",
      " [5.75178056e-12 7.99070321e-09 1.00000000e+00 3.53480384e-10]\n",
      " [1.08223963e-08 4.24744354e-12 8.26829924e-11 1.00000000e+00]\n",
      " [1.58513214e-09 1.00000000e+00 3.78702153e-10 2.12086099e-10]\n",
      " [2.09211208e-08 9.99999881e-01 3.22218792e-12 6.07174329e-08]\n",
      " [1.39201997e-10 1.00000000e+00 1.72949381e-11 6.18063309e-11]\n",
      " [4.53221044e-10 9.99810874e-01 1.82236894e-04 6.85924715e-06]\n",
      " [7.79082338e-14 1.42818954e-14 2.18857853e-12 1.00000000e+00]\n",
      " [5.97884834e-01 2.04728849e-07 2.80592083e-08 4.02114868e-01]\n",
      " [2.34721422e-12 1.00000000e+00 1.75995828e-12 3.57248779e-11]\n",
      " [4.78398788e-12 3.36837697e-12 9.99999642e-01 3.86274593e-07]\n",
      " [4.12745714e-01 6.17928981e-06 3.55329007e-06 5.87244511e-01]\n",
      " [2.08516343e-10 1.95862633e-08 9.99998569e-01 1.45827448e-06]\n",
      " [1.37318004e-11 1.00000000e+00 6.78838305e-11 5.62415080e-11]\n",
      " [9.99999762e-01 1.44857424e-07 9.14470666e-10 6.89930602e-08]\n",
      " [1.99626471e-09 1.00000000e+00 4.88152407e-10 1.39822065e-09]\n",
      " [1.13154725e-04 1.75399056e-10 3.34039019e-09 9.99886870e-01]\n",
      " [9.99966025e-01 1.54409180e-10 4.62772286e-11 3.39679209e-05]\n",
      " [1.65344693e-09 1.00000000e+00 8.73837180e-09 9.39602796e-09]\n",
      " [9.99996901e-01 5.92177002e-12 4.17270428e-12 3.06636412e-06]\n",
      " [7.08991685e-11 1.00000000e+00 1.36242773e-09 2.70711920e-09]]\n"
     ]
    }
   ],
   "source": [
    "# Create the probability vector for each instances in test dataset\n",
    "probability_vectors = model.predict(X_test)\n",
    "print(\"Shape of probability_vector (number of rows in test dataset, number of classes):\", probability_vectors.shape)\n",
    "print(\"Predicted Classes:\", probability_vectors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LIME for Time Seris Domain"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1) Select an instance for explanation and predict its class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of instance_ecg: (1639, 1)\n"
     ]
    }
   ],
   "source": [
    "id_ecg = 15                          # Just for an example: choose an index from rows\n",
    "instance_ecg = X_test[id_ecg, :]     # Extract the selected instance from the test dataset\n",
    "print(\"Shape of instance_ecg:\", instance_ecg.shape)  # Check the shape of the selected instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 11ms/step\n",
      "Probability vector of the selected instance: [[2.6865847e-12 5.9204558e-07 9.9999940e-01 5.6057192e-10]]\n"
     ]
    }
   ],
   "source": [
    "# Predict the class of the selected signal by trained model\n",
    "probability_vector = model.predict(instance_ecg[np.newaxis, :])\n",
    "print(\"Probability vector of the selected instance:\", probability_vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top predicted classes: [2 1 3 0]\n",
      "Predicted Class for the selected instance: 2\n"
     ]
    }
   ],
   "source": [
    "# class labels\n",
    "class_labels = [0, 1, 2, 3]\n",
    "\n",
    "# Import and call the analyze_prediction function\n",
    "from lime_explanation import analyze_prediction\n",
    "\n",
    "top_pred_classes, predicted_class = analyze_prediction(probability_vector, class_labels)\n",
    "\n",
    "print(\"Top predicted classes:\", top_pred_classes)\n",
    "print(\"Predicted Class for the selected instance:\", predicted_class)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Generating Perturbations in the selected ECG Signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lime_explanation import segment_ecg_signal, generate_random_perturbations\n",
    "from visualization import plot_segmented_ecg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+jBkzxnJ4xNnOPitBS4wbN44PP/yQrVu3MmXKlFq35+bmMnnyZG666SaeeOIJJk2axNq1a/npp5+4+uqrLfc7u2+cu6hiXQYMGMCyZcsoLy9n9+7drFu3jrfeeos+ffrUWrgxLCyMvLw8zGZzjYF6U47LP9cjjzzCiRMnWL16NYMHD8bNzY3S0lI+++wzq7OEEEJYRxaOE0IIUacOHTrg4uLCxx9/jMFgqHX7iRMncHd3p2vXrkRFRREcHExqaiqxsbGW/8LCwnj11Vc5dOgQOp2OIUOGsHnz5ho5v/zyS43p2s01YsQITp8+TXBwcI0atm3bxjvvvINOp2ty1rl7IkeMGIFer0dRlBrZR48eZfny5VRUVDB48GA8PDwsx1JX+/XXX61+vqao3quZl5dXo6bc3FyWLl1q0x8H/vOf/3DNNdegKAqenp5MnDjRcpx3WlpanW3YvXs3PXv25JprrrEM0DMyMjh69GiDq5mfKyoqisDAwFqv4zfffMOsWbMwGo2MGDGChIQEYmJiLK9D//79Wb16NT///HNLmm4xbtw4evfuzZIlS8jLy6t1+6uvvkpFRQVXXHEFUHn8/bBhw1i0aBFJSUl1Zh47dqzB51y9ejUTJ06kvLwcNzc3Ro8ezXPPPQeced3PNmLECCoqKvjll18s1ymKwqZNm5raTIvdu3dz0UUXMXLkSMs6BL///juAVdtPCCGE9WRPuhBCiDrpdDqefvpp5syZwzXXXMPNN99Mjx49KC0tZdu2baxdu5b777/fMoX8wQcf5KmnnkKn0zFx4kQKCwtZsWIFGRkZlino9913HzNmzOC+++7j2muvJS0tjaVLlwLUuXCWNa6++mo++ugjbr/9du655x4iIiL4888/WbVqFbfccguurq5NzvLz8yM7O5stW7YQExPD+eefz/Dhw5k9ezazZ8+mR48e7N+/n//+97+MHz/eMqV69uzZvP7663h6ejJq1Ci2bNnSpEG6n58fe/fuZfv27fTt27dJNUZHRzN16lSefPJJTp06Rf/+/UlMTGTJkiV07tyZbt26Nbm9jRk1ahTvv/8+8+fPZ+rUqRiNRt555x0CAgIYNWpUnW0YMGAAK1asYOXKlQwaNIjk5GTefvttysvLGzz+/1w6nY57772XZ599luDgYCZNmkRiYiL//e9/ufnmm/H392f27NnceOON3H333UyfPh13d3fWrVvHpk2b+O9//9voc8THx9ea4VGtU6dOhISE4OLiwiuvvMK//vUvrrnmGmbOnEmfPn3Izc3lyy+/ZOvWrTz88MOWWQVarZbXXnuNOXPmMG3aNK677jpGjRqFj48PSUlJfPfdd5YF2OrbVqNGjWLx4sXMmTOHW265BZ1Ox6effoqbm1uttQ+gcu2AsWPH8sQTT5CdnU3Hjh1Zv349R44csfrva8CAAWzYsIF+/foRHh7Onj17WLlyJRqNxqrtJ4QQwnoySBdCCFGvCRMm8Nlnn/Huu+/y1ltvkZubi5ubG3379mXJkiWWFaQBrrvuOry9vXnnnXdYt24dXl5eDBkyhMWLFxMZGQlU7v194403WLp0KbNnz6ZTp048+eSTPPjgg01eNbs+Xl5erF27lldffZVFixZRVFREp06dePjhh/nXv/5lVdbVV1/Nli1bmDNnDvfddx+zZs1i5cqVLF26lLfffpucnBzCwsK4/fbbmTNnjuVxd999N15eXnzwwQd88MEHDB48mMcee4ynn366wee7+eabOXDgAHfddRcvvfQSoaGhTarzpZde4u233+bTTz8lPT2d4OBgLr30Uh544AGrZg405vzzz2fx4sW89957zJ07F41Gw9ChQ1mzZo1lPYFz23D33XeTl5fHmjVrWL58OREREVx55ZWW1ecLCwtrLMzWkJtvvhkvLy/effdd1q1bR3h4OHfddRd33XUXULnS/9q1a1myZAnz5s1DURR69+7N8uXLmTx5cqP51avx12XBggXcdtttAMTExLB+/Xref/99PvnkEzIyMvDy8iI6Opp33nmH8ePH13hsWFgYn3zyCV9//bVlscXCwkKCgoIYNGgQK1asYNKkSfUOoPv06cNbb73F8uXLeeihhzCZTPTv35/33nuvxrT5sy1ZsoSXX37Zsmd/8uTJTJ8+vcapApvi5Zdf5rnnnrPsue/WrRvPPPMM3377Lbt27bIqSwghhHU0iqz+IYQQopVs3ryZ8PDwGou7HTt2jMsvv5wVK1Y0aUAlhKjbqVOn2LdvH5MnT66xEOB9991HSkoKX331VRtWJ4QQoqlkT7oQQohW88cff7Bx40YeeeQRunfvTkZGBm+++SZRUVGMGzeurcsTQtW0Wi3z589n8uTJXHvtteh0OrZu3cpPP/1U4zz0QgghHJvsSRdCCNFqysrKWLp0KT/++COZmZkEBAQwfvx4Hn74YTp06NDW5Qmhejt27GD58uXEx8dTUVFBjx49uP322y2nTxRCCOH4ZJAuhBBCCCGEEEI4CDkFmxBCCCGEEEII4SBkkC6EEEIIIYQQQjgIGaQLIYQQQgghhBAOwulWd9+7dy+KouDq6trWpQghhBBCCCGEcAJGoxGNRsPgwYMbva/T7UlXFAW1rJWnKArl5eWqqVfYnvQBIX1ASB8Q0geE9AEhfUD9rBmHOt2e9Oo96LGxsW1cSeP0ej3x8fH07NkTLy+vti5HtAHpA0L6gJA+IKQPCOkDQvqA+sXFxTX5vk43SHdWZqOR5I8+BqDrLTehtcF0fzVkSo2Om6mGGu2RqYYa7ZGphhrtkamGGu2RKTXaNvPU2k8w5uRg7tXLJnlqabezbm9pt7TbUfLslSka53TT3Z2VYjKR9vW3pH39LYrJ5DSZUqPjZqqhRntkqqFGe2SqoUZ7ZKqhRntkSo22zcz8biOm7TtRKipskqeWdjvr9pZ2S7sdJc9emaJxMkgXQgghhBBCCCEchAzShRBCCCGEEEIIByGDdCGEEEIIIYQQwkHIwnFCCCGEEEK0EyaTCaPRWHnZYEATHAxAmcGAzhb5KshUQ43WZhoMBsv/tdq697O2dY3OztXVFZ3ONq+QDNKFEEIIIYRQOUVRSE9PJz8/v8Z1XrfeAkDyqVNoNBqbPI+jZ6qhRmszzWYzLi4upKWl1TtIb+saBQQEBBAeHt7i10kG6UIIIYSol77MSE5BGZFhvm1dihCiAdUD9NDQULy8vNBoNChmM/qqPXtekZFo6hncWUMNmWqo0dpMk8mEwWDA3d293r21bV2jM1MUBb1eT2ZmJgAREREtytMoiqLYojC1qD6JfGxsbBtX0ji9Xk98fDwxMTF4eXm1KEsxmylNTQXAs3Nnm/3ROnqm2mtsbh9Qe7vbc6a1eU3pA+2x3e0l0xZ55/aB1mx3TkEpD73+O7mFZTx80xAmDI1sUZ49anSUPHtm5h5L4MSJE/QdPw5vHx+HrNHRM9VQY0OZjX0WmEwmjh49SmhoKMFVU5OhcuBgLi8HQOvmZrM9q46eqYYarc00mUyUlZXh4eFR/yBdJe1uz3JycsjMzKR37961tpM141DZk+4kNFotXl26OF2m1Oi4mWqo0R6ZaqjRHplqqNEemWqosaHMn3Ykk1tYBsDSdXv5fd8pUjOKGdCrA7OuisXNte4vimpotxpqrM70jOyMtrjIJoNANbXbWbd3czKrj0E/dwCv0WjQubvbpDY1ZaqhRntkqqHG9q76b9BoNLbo+HQZpAshhBCiTtsPnLZcrjAp/H0oA4DTOSX4ebsx89K+bVWaEKIOspdTiLZlq79BGaQ7CbPRSOr6LwHofO3VaF1dnSJTanTcTDXUaI9MNdRoj0w11GiPTDXUWF9mQbGBxLRCAJY/OpFPfjpCYUk5nUJ9+N+fSXz3xwmun9wbD/faXyXU0G411FideXr9lxizsjD36mWTPLW021m3ty0zFbOZ8rx8ANwCA2w2Jd/RM9VQoz0y1VCjaBoZpDsJxWQi5dPPAOg07UqwwQeJGjKlRsfNVEON9shUQ432yFRDjfbIVEON9WUePJEDQJdwX7qE+/HYzOGV91UU9h3J4nROCX/GnWbSsNrHqauh3WqosToz/YuvKi/ffqtN8tTSbmfd3rbONOblApUDLFuxV+bC117ju82bG7zfkSNHmDFjBp06deLll19u9Rrry9y5cyczZ85k8+bNdO7cucWZRqORtWvXctttt7WoxujoaF566SWuvvrqOu8fHR1db1avXr3Y8O23lswKdzdWf/AB//vf/0hNTcXHx4eBAwcye/Zs+vXrV6v+jz/+mO+++46kpCQMBgORkZFceOGF3HHHHfj6NrwY6bfffstHH33E0aNH0Wg0REVFcd1113HjjTda7jNp0iSmTZvGvffe2+jrYiuNvZ62IoN0IYQQQtRyoGqQHtujQ43rNRoNE4dF8vGPh/ll18k6B+lCCNEcj9x9N48tXGjZWztu3Dgef/xxLr300jaurPV99913vPTSS1YP0pujvtfYxeXMUDGvoIBZ996Li4sL9957LzExMRQUFLB69WpuuukmVq5cyciRI4HKhQ5vv/120tPTmT17NsOHD8fNzY0DBw7wxhtv8Msvv/DZZ5/h4eFRZz3r16/nhRde4IknnmDo0KEoisK2bdt4/vnnyc7OZu7cuZb7ubfT4+VlkC6EEEKIWg4czwZqD9IBJg7tzMc/HiYuIZtifTk+Xm6tXZ4Qoh3y9fbGOySkxpRqX19fQkJC2rCqttGaJ+Bq6DVWzGYAXl6xAoPBwLp16/Dz87PcvnjxYm677Taefvppvv/+e7RaLa+++irHjx9nw4YNNU5F1rlzZ4YPH85FF13EF198wc0331znc3788cdcc801XHvttZbroqKiyMjIYM2aNZZBelBQUIvb7qjkoAIhhBBC1GAwmkhOLwIgumtgrdvDg73pFOKDWYH9CdmtXZ4QQlBSUsKCBQsYNmwYQ4cOZf78+ej1esvtiSdPct/ChQwZOpRx48bx8MMPk5WVVW9eaWkpTzzxBGPHjiU2NparrrqKn376yXK7oih8sH49U//1LwYNHsyVV17Jt99+W2+eoiisWrWKyZMnM3DgwDrvn5yczEPPPst5113HyFGjeOihh8jJyeHLL79kwYIFQOX06p07dwLw+++/c+211zJgwAAuvPBCXn/9dcqrTo8GkJ6ezkPPPsv4a6/l/AkT2LBhg3Uvaj1y8vL49c8/mTljRo0BOlTOrnr22Wd5/fXX0Wg0lJSU8MUXX3DbbbfVea7w4OBgvv76a2644YZ6n0+r1bJ3714KCgpqXD9r1izWrVtn+fekSZN44403LP/esGEDl1xyCbGxsVx33XWsWbOmxnT+6Oho1q9fz2233caAAQMYN24cy5Yts9xuNpt5++23ufjii+nfvz9Dhgzhzjvv5OTJk01/sWxEBulCCCGEqCH5dCFms4KftxvB/nVPRxzUu3KvS9xxGaQL4agURaHMaK78r7yCMoMN/iuvaHKmPfcG//TTT4SGhvLll1/yyiuvsHHjRlatWgVARmYmdzz2GJEdO7L+88956623KC4u5oYbbqgxkD/b0qVLOXLkCCtXrmTjxo2cd955PPjgg6RWnbd+yeuvs37jRh695x6+/eYbZs6cydNPP83atWvrzFuyZAmffPIJTz75JBs2bKh1/8LCQm6ZMQOD0cjbL77I+++9x8mTJ3nggQe49NJLefzxxwH4448/GDx4MFu3bmX+/Plcd911fPfddyxcuJD//e9/PProowBUVFRw1113kV9YyMqXX+b1JUt49913bfJaHzlxApPZzJAhQ+q8vWvXrkRHR6PRaNi/fz+lpaWMGTOm3rzIyMgaU+nPdeedd3Lo0CHOO+88Zs2axcqVK9m/fz++vr507969zsf8+uuvPPbYY1x77bV8++23XH311SxevLjW/f7zn/8wbdo0vv/+e2655RbeeOMN/v77bwDWrFnDu+++y/z58/nxxx9Zvnw5SUlJDa59YC8y3V0IIYQQNSSmVe69iOrkX+/pZPr3COb7bYmWBeaEEI5FURQeW76Nw8l5VdfE2+FZGs6M6RbEf+aOs8up4QYMGMCDDz4IQJcuXRg7diwHDhwA4NNPPiEsOJhH774b76goNFotr7/+OqNGjeKHH36oc9GvkydP4u3tTWRkJH5+ftx///0MHz4cf39/9Ho9H3zwAS88+ijjR4zAu0sXunbrxqlTp3j33XdrTdvW6/WsXr2a1157jQkTJlhqPPv+GzdupKSkhJfmzcPP1xfvqCief/55y5Tx6oXVqqehv/3220ybNo0bbrgBnU5Hly5deOaZZ7j11ltJTU0lMTGRYwkJfP3OO0RGROAdFcVLL73EVVdd1ehruXDhQp577rla18+fP5/rr7uOwqLKmVXn7kWvS05O5WfCuVPRp06dSkpKiuXfQ4cO5Z133qkzY8qUKYSHh7NmzRq2bdvGli1bAOjWrRsvvvgiQ4cOrfWYd999lylTpnDHHXcA0L17d5KSkli9enWN+1111VVceeWVANxzzz28++677Nmzh+HDh9OlSxf+85//MHHiRAA6derElClT+OGHHxptt63JIF0IIYQQNRw/VTVI7+hf7336dQ8GIOl0IcWlRnw8W74KtRDCttrzadO7detW49/+/v6cOnUKgEOHDnE8OZlx11xT4/h2g8HA8ePH68y76667uOeeexg9ejQDBgxg7NixXHHFFfj6+rJ//34MBgNPLFqEVqOxZFZUVFBeXk5ZWVmNrISEBAwGAw8//DDas57/7PsfPXqUbl274nfWKud9+vShT58+ddYXHx9PXFwcX3/9teVHj+qZCsePHychIQF/f38iz5piHhMTU+/ibGe77777uOiii2pdXz3QDvSv/CzIz89vNCswsPIQqXOnqr/11lsYjUag8jj2xrIGDRrEoEGDMJvNHD58mC1btvDRRx9x11138fPPPxMcHFzj/gcPHqzVhuHDh9capPfo0aPGv319fS11TZo0iX/++YelS5eSmJhIYmIiCQkJhIWFNdpuW5NBupPQuroyYPF/LJedJVNqdNxMNdRoj0w11GiPTDXUaI9MNdRYV2biqTN70usT6OdBxw7epGWXEJ+Yw/C+4a1ao6Pl2TOz9/PPkJSYiNat5Qv0qandzrq9bZWp0Wh4ec44Sosrp3dr3dxtskdbURTM5YYmZbq76Zr2nBoNntWnLWtijTqdrt7bzIrCyBEjePKJJ9C6udWoob5Tfw0ePJgtW7awbds2tm/fztdff82bb77JO++8g5eXFwBLFi2ie/futTLdzvnbrB48v/7660RFRdV6Ljc3t8rp3la022w2c+utt3LNNdfUantISAjHjx/HbDbXymtoWnm14OBgunbtWudtiqIw5PzzcXFxYc/evQwaPLjWfbZv384HH3zAc889R//+/XFzc2Pnzp0MHDjQcp+OHTtaLnt7e9c7SE9PT+ftt9/m7rvvJjw8HK1WS9++fenbty8XXHABl19+OX///TdTpkyp8TgXFxfMVYvcNeTcbVXdRoCVK1eyfPlypk2bxujRo7ntttvYvHkz33//faO5tiaDdCeh0enw7dXT6TKlRsfNVEON9shUQ432yFRDjfbIVEON52YqimJZNK5bx4anNvaLCiYtu4Q9RzJrDNLV0G411Fid6d0jCm25ocYewZbkqaXdzrq9bZmp1Wrx9vOxWZ6Fh21nzmg0GnRN2OPbVL169WLjxo107tbNMijLz8/nscce4/bbb2fUqFG1HvPf//6XoUOHMnnyZCZPnsyCBQu47LLL+PHHH3n44YdxcXEhPTubyRdfbHnMmjVrSEhI4Nlnn62RFRUVhYuLC2lpaZap0+fev2fPnnz++efojUbLDwcHDx7kzjvv5Kuvvqr140avXr1ITk6ma9eulkH6zp07WbNmDU8//TQxMTEUFRVxIiWFXr16AZCUlERxcXGLXkuNRkNgaChTpkzhww8/5Prrr8fH50yfMpvNvPXWW6Snp9OhQwc0Gg3Tpk1j9erVXHnllbX2QpvNZjIyMup9Pjc3Nz7//HMiIiKYNWtWjduqp9t36FD7rCN9+vThn3/+qXHd3r17rWrrW2+9xZw5c2o877vvvtuqK+1Xk4XjhBBCCGGRX2yg1FCBRgMRwd4N3ndU/8pplX/uT8Nsbv0vMUIIUZebbrqJoqIiHnnkEQ4fPszhw4d58MEHiYuLo3fv3nU+JiUlhYULF7J9+3ZOnTrFjz/+SFpaGoMHD8bX15cbb7yRpUuX8s0335CSksL69etZtGgRoaGhtbKacv8rrrgCf39/Hn30UQ4fPsyBAwdYuHAhvXv3Jjw83LL3/sCBA5SVlXHHHXewadMmVqxYQWJiItu3b2fBggUUFRUREhLCyJEjGThwIPPmzWPfvn3ExcUxb968GtPt61NUVERWVlad/1UPUB977DF0Oh3Tp0/n559/JiUlhV27djF79mz27dvHiy++aPlhYcGCBURFRXH11Vfz0UcfkZCQQEpKChs3buT6669n+/btlnOqnysoKIg777yTpUuXsmTJEuLj40lJSeHXX39l7ty5jBw5kmHDhtV63F133cUPP/zA+++/T1JSEl988QUfffRRo20/W0REBNu2bSMhIYETJ06wZMkSfvrppxor6LcWVexJz8/P57XXXuO3336juLiY6OhoHn744To3kKib2WgkbUPlVI2OV1xmk2lZasiUGh03Uw012iNTDTXaI1MNNdojUw01npuZ3380ACEBnri51j+dFGBwdAjeHi7kFhqIT8qlX1Rwq9TobNsmY8P3VGRmYq7aO9bSPLW021m3ty0zFbMZY9Wxwa7+/jaZjaGGzM6dOvH+ihW8vmIF06dPR6fTMWTIENasWVPvubUXLlzIf/7zHx599FHy8/Pp1KkTjzzyiGWRsfmPPYafpydLlywhMzubiIgI7rvvPu6888468xYsWEBgYCBLly4lMzOz1v09PT15Z9UqXnz+eW684QY8PD2ZMGECjz32GACjRo1i4MCB3HjjjSxatIiLL76Yl19+mffff5+3336bgIAAJk2axCOPPAJUzpp46803efbpp/nX7bfj4enJ3XffbTlOvyEvvvgiL774Yp23bf/zT3y0WgJcXVn36aeseucdFi1aRHp6On5+fgwZMoR169bVOJbe09OTNWvW8MUXX/DNN9/wxhtvUFJSQkREBKNGjeK5554jJiam3noeeOABunXrxmeffcbatWspKyujY8eOXHLJJdx99911Pua8887j2Wef5e233+bVV1+lf//+TJ8+3aqB+iuvvMKzzz7LNddcg7e3NwMHDuSZZ57h6aefJi0trcaUfXvTKG2x/95K//rXv8jKymLhwoUEBwfz4Ycf8sUXX/DVV1/VeZxHQ+Li4gCIjY21R6k2pdfriY+PJyYmxvJrWnOZysrYcUPlypOj1q21yZQiNWSqvcbm9gG1t7s9Z1qb15Q+0B7b3V4ybZF3bh+wd7tLH32ZpV8dYmCvDjx/z9hGH7vkkz38siuFq87vwR1T+9fKa8/bprUzB7y/Ct96BhiOUqOjZqqhxoYyG/ssKCsrIzExke7du9dYKEwxmyk5cQLAssp5S6khUw01WptpMpkoKyvDw8Oj3uPx27rGtvTXX3/RoUOHGmPDt956i/Xr17Np06ZWq6O+v0WwbhzqmK/yWZKTk9m2bRtPP/00w4YNo3v37jz55JOEhoayYcOGti5PCCGEaFdO55QA0LFD045jHRxdOXVTzpcuhBCirfzxxx/ccccd7Nixg7S0NDZv3swHH3xgmQmhNg4/3T0wMJCVK1fW+MVBo9Gg0WgoLCxsw8qEEEKI9ie9epAe0vDx6NVie1ROcU88VSCnYhNCCNEm5s6di16vZ968eeTm5hIREcFtt91W7+EIjs7hB+l+fn6cf/75Na778ccfSU5O5vHHH29WpqIo6PV6W5RnV6WlpTX+3xKms87fqNfr0TXhFAXtIVPtNTa3D6i93e0509q8pvSB9tju9pJpi7xz+4C9230qq3Il4CAflyZ9Vnq6QniQJ+m5pew9nMbQ6BCn2TatnVlWVoauhd9f1NhuZ93eZ2c29llgMBgwm82YTCZMJtOZG846qtVsNtf4d7OpIVMNNVqZWX2EsqIoNbexA9XYlnQ6HQsWLGDBggW1bqv39bIDk8mE2WymtLS01inhFEVp8mkQHX6Qfq49e/awYMECLrroIiZMmNCsDKPRSHx8vG0Ls6OkpKQWZyhnrUp45MgRNDY416oaMttLjdb2gfbS7vaY2dy8hvpAe2632jNtmVfdB+zd7vScYsCFkoJ04uNzmvT4iEAN6bnwx+4EvMzZTrdtWiszOTkZzenTNstTS7uddXvXldnQZ4GLiwsGg+GcwDMDqrKysiafg7zhIlWQqYYam5lZaxu3MK9R9shsxwwGAxUVFZyoOo7/XHWdp70uqhqkb9q0iUceeYQhQ4awePHiZue4urrSs6dtz2tpD6WlpSQlJdGtWzc8PT1blGUqK2N/1eXo6GibLW7i6Jlqr7G5fUDt7W7PmdbmNaUPtMd2t5dMW+Sd2wfs3e5yowJaGDmkHx5uDa/uXi3bcJq9xw+QUaglJibGabZNa2d27doV74AAm+Wppd3Our3Pzmzss8BgMJCWloa7u3vNxaoUhep97x4eHjYbtDl8phpqtDJTURQMBgPu7u71741VS7vbORcXF7p06YK7u3uN6xMSEpqeYeui7OWjjz7ihRdeYMqUKfznP/9p8q8QddFoNC1eLb01eXp6tnx197NWYvTy8rLNB4kKMttLjdb2gfbS7vaY2dy8hvpAe2632jNtmVfdB+zdbgBfLzeCAnyb/PghMR2BAySmFYLWFS8v59o2rZXp4eEh3wccJK+tMuv7LNBqtZb/zl75Wzlruq1Wq7XZat+OnqmGGq3NrJ6yrdFoGlzdvS1rFGf+Fj09PWut7t7Uqe6gklOwffzxxzzzzDPMmDGDJ554wqoGnstZT8GmmEwUHqqc4u/XNwZNPX/c7S1T7TU2tw+ovd3tOdPavKb0gfbY7vaSaYu8c/uAPdsdn5zLi7/kEBUZyOsPTrAq47ZnfySnoIyX54yjb9cAp9g2rZWZuXcfycnJ9Lv4Irx9mrbqfmvX6OiZaqixoczGPgtMJhNHjx4lNDSU4ODgM3mKgqm08jh3nadHi75DqylTDTVam9mkU7CppN3tWU5ODpmZmfTu3bvWdrJmHOrwg/TExESuuOIKJkyYwMKFC2vc5uHhga9v03/pB+cdpAt1kj4gpA+I1uwD329L5K0v9zOqfzhP3D7Sqsc+/95Odh5M584r+3PleT3sVKFzkvcB0ZQ+cPr0afLz8wkNDcXLy0sGU+2MyWSyTHevb5Au2k71wuSZmZkEBAQQERFR6z7WjEMdfrr7jz/+iNFo5Oeff+bnn3+ucdu0adN4+eWX26gyIYQQon3JyqtcOTwk0PqBYI/OAew8mE5Car6NqxJCNEV4eDgAmZmZbVyJsAez2UxFRQUuLi5oZcq5wwoICLD8LbaEww/S77nnHu655562LkP1zBUVZPxY+SNH2MUXonVp+aZXQ6bU6LiZaqjRHplqqNEemWqo0R6Zaqjx7EzdrpNolVBCAqxfrLRnZ38Ajqfmq6LdaqixOjPrp5+pSE9H6dXLJnlqabezbu/mZmo0GiIiIggNDcVoNFrysv/4E4AO48bYrEZHz1RDjdZmlpaWcuLECbp06VLvQrJtXaOzc3V1tdksB3mVnYRSUcGJle8AEDp5ItjgD0wNmVKj42aqoUZ7ZKqhRntkqqFGe2SqocazM6MAXdR0QgKbM0gPACA1sxh9SZnDt1tN2yb1/TUAmK+/ziZ5amm3s27vlmbqdDrLQMFUVkbKm28D0GnCeTZbgd7RM9VQo7WZ1efcrrWCvwPVKGxH5koIIYQQoobm7EkP9PMgyM8DRYGktEI7VCWEEEI4BxmkCyGEEKKGDs0YpMOZvekn0gpsWI0QQgjhXGSQLoQQQoga/LzdmvW46uPSE2WQLoQQQjSbDNKFEEIIYeHupsPVpXkL3/So2pN+/JQM0oUQQojmkkG6EEIIISx8PJu3Fx2gR9We9LTMIluVI4QQQjgdGaQLIYQQwsLXq/mD9GB/TwJ93TErNixICCGEcDJyCjYnoXV1JebJxy2XnSVTanTcTDXUaI9MNdRoj0w11GiPTDXUWJ1TceNdfLXlOL4+LTu9To/OAewuLKVo2r8Y0T/cYdutpm0T9ejDpKSkOF27nXV7S7ul3Y6SZ69M0TiNoihO9Xt3XFwcALGxsW1cSeP0ej3x8fHExMTg5eXV1uWINiB9QEgfEK3VB77depxVXx9g7MCOzJ85vNk5a384zKc/H2HSsEgenD7EhhU6L3kfENIHhPQB9bNmHCrT3YUQQghBsd4IgF8LprvDmRXej6fmt7QkIYQQwinJdHcnYa6oIGvL7wCEnH8eWpeWb3o1ZEqNjpuphhrtkamGGu2RqYYa7ZGphhqrM3X//EVsYRY+7lEtyuoZGYBWMeN/bC+pP5rpOHmCQ7ZbTdsmZ8vvVKSdRunVyyZ5amm3s25vabe021Hy7JUpGievspNQKipI+O9yADqMHQM2+ANTQ6bU6LiZaqjRHplqqNEemWqo0R6ZaqixOrPztm/oDGR5XtKirCA/D4K8Xbj0+J8kr/iTiPPHOWS71bRtTr61CgDztCttkqeWdjvr9pZ2S7sdJc9emaJxMt1dCCGEEBa+ni1bGEij0RDVyd9G1QghhBDORwbpQgghhLDwaeEx6QBRHWWQLoQQQjSXDNKFEEIIYeHr2fJBer+oYMvlMkNFi/OEEEIIZyKDdCGEEEJYeHu1/Dy4fc8apL/z7YEW5wkhhBDORAbpQgghhJMzmcyWy342GKTrtBrL5S17UvnnWFaLM4UQQghnIYN0IYQQwsmVlBotl71buHBcXd784h8MRpPNc4UQQoj2SNbQdxJaV1ei5z1suewsmVKj42aqoUZ7ZKqhRntkqqFGe2SqoUaAonKFr8LPw8NVxxgP9xbnVddoKDfht6WIU1klLP5oF4/fNgKNRtN4QAOZ1ZdtVaOt8uyZ2e3+uZw6dcrp2u2s21vaLe12lDx7ZYrGaRRFUZrzwOPHj7Nt2zYyMzOZMWMGKSkp9OnTBx8fH1vXaFNxcXEAxMbGtnEljdPr9cTHxxMTE4OXl1dblyPagPQBIX1AtEYfOJSYw2PL/iA82ItVj19o0+yDJ3L491t/UmEyM+OSGK6b3KvZA3VnJe8DQvqAkD6gftaMQ63ek242m3nqqaf44osvUBQFjUbDJZdcwooVKzh58iQfffQR4eHh1lcthBBCiDZRrK+c7m6L06+dq19UMLdeFsO73x7kw//Fk19sYNZVjv9DuRBCCNFWrD4mfcWKFWzYsIHnn3+ebdu2Ub0j/tFHH8VsNrNkyRKbFylaTjGZyN72J9nb/kQx2ea4QDVkSo2Om6mGGu2RqYYa7ZGphhrtkamGGgEKi0qJLk6iV0GiXdo9dXwPZl4ag0YDG7aeYNU3cejLjI0HNZBp6xptwV6ZeTt2YjoU73TtdtbtLe2WdjtKnr0yReOs3pP+xRdfcN9993HNNddgOmtDxcTEcN9997F48WKbFihsw2w0cuSVVwEYtW4tOp3OKTKlRsfNVEON9shUQ432yFRDjfbIVEONAEVFpUxL/x3SwWy80fbt9vDgusm90Wk1vP/dIb79/QTb/klj4tBIMnL19OsexKVju6Mo8OvuFEpKjYzoF46Plxs+VQvZFRbq62x3hcmMi876dXDVsm3MRiNJS5dVXr7sUpvkqaXdzvi3KO2WdjtSnr0yReOsHqRnZ2cTExNT521hYWEUFha2uCghhBBCtJ5ifTmhrfA8V0/sRecwX975+gCnc0pY/8sxALbuO0VxqZEivZFvfj8OwKpvDqDTarh2Ui8G9grh+bd/576qHJNZIflUAR/9EM/fhzIY1DuEBbcOx8tDFjUSQgihflYP0rt27cqWLVsYM2ZMrdv++usvunbtapPChBBCCNE69GUVrfZcI/qGM6BnB9b+cJjtcacpK6+goLicj344XON+Gk3lYHzdpqOs23QUV/OZc7nf+MT3GLVnBuT7jmbx3Hs7uXhkVzLzSjlvcCfW/3KMvEIDs68dQF6hgW370+jTNZAOAZ50CfeltNS66fZCCCFEa7F6kH7rrbfy1FNPYTQamThxIhqNhuTkZHbu3Ml7773H/Pnz7VGnEEIIIexEb2jdAauHmwt3TO3PHVP7oygKn/58lO/+OIGiKFw+LoobLozGbFb4ZVcKK9bvw6zUff72qI7+XDiyC2s2xnPgeA4HjucA8OH/4i33+evZdHRaDSZzzZPZuJqNPFx1Oel0IUHB8OVvCWTllaLTatBqNaCBoX3COH9wJ1mRXgihemXlFbi66NBp5f3M0Vk9SL/uuuvIzc3lzTff5JNPPkFRFB566CFcXV258847mT59uj3qFEI4mSJ9Oek5JXSL8MPVpe7jn8oMFWTk6gkJ9LT5NFdFUTidXUJJYYnlutzCUkI8PMjKK8XX2xUPNxdOZRVTWlZBj87+8iVeWG17XBo7D6ZzzcReRIb5tlkd+tLW25N+Lo1Gw/SLopl+UXSN63VaDReP6kpooCcHE3O4bFhHDt2xBoDZ1wxk2MAuBPhWntO9R6cAXv14Nxm5+jqfw2RWCPb3oKDYQIWp9plnH/3v7zX2zJ/tt92p/O/PRAb0DKFDgCfHU/O5ZEw3unf0b0mzW+y3PansPpzB9Iui6djBsU9/K4StVJjMmM0Kbq7NOy76eGo+3/x+nHKjmeF9w5gwpDOlhgq8PV3JKzIQ6OuORqMhr6gMXy+3Gutd1LXYZXZ+KX8fSic9R8/g6BD8fdzpFuGHRqPBWGGmWF9OoJ8HAAXFBrw8XHB10VFmqGD9L8fw0JiIqMr6+MfD6E1aCvXlmEwKoUFejBvYkahO/pQbTWzZm8aGrZn4/llClwh/TCYzuYVldAr1Ycqobvh5u1GUf+Y7i8msoAN2HDjNL7tSyMjVk5hWQJ+uQTx/z5hmv4aidVg9SAe4++67ufnmm9mzZw8FBQX4+fkxcOBAAgICbFyeczObFTLyjbimFtC7mxsebs3aXG2qWF+OVquxDKAKS8pJTCugf1QwukYW+jGbFX7Ylsi+o5kYK8wE+npQUGIgJMCTGy6MJqjqTU9RFJLTi3Bz1ZJXaOCH7UkM7NWBoTFh6AvPfGHTlxnxcHGtd8BXfZ+T6UV06+inqtf7r4Pp7Dqcwah+EQzpU/eRpdUfLo0NZktKjWTk6vFw0xHRwbvOgefq7w4xuH9nhvQJJTWziJJSI1pt5QfSgeM5GIwmrp7QE5NZ4ZOfDtMpxIfhfcMJ9HWv80Nhy54UjpzWM3l4F3p3CeS33SksXbePCpOZkf3CeeL2EVUfeCbyCg2EBnlxMr2QJ978k/xiA57uOgZHh3Je3xBLZmGxgUAPD8u/i0uNHD2ZR99uQXi4V25bk1nhxx1JpGWVcMmYbhgrzOQXlbH7cCYHTuSQkJJfY2/b3S9tRnF1o8KkEBbkRVQnf7bHnQbg/64ZwKVjujf42qpFqaGCtd8dpHr1kfyiMoI9PNj2Txpxx7OZNCyS7h3r/vFEURRSM4sxmRXSc0rYFZ9B3+5BRPie6XdFJeVkZpYCEODjTmiQFyaTmfikXMKCvAkJ9KTCZOZIch5eHi5oNBoM5RXodFoqTGaOpxaw90gm2opyzq/KXLDiD/wCfBkWE0Zmnh5Pdxd6dg6gQ4Ann/50hMTTBcy6KpbtcafJzi9j7MCORIb5oCgQGebLgePZ9IwMQCkz2PnVBWOFmdXfH2R3fCansooB2J+QzfJHJ+Hp3jbvO6053d1ag6NDGRwdiqmszHLdxGGR6DzcLf+O6R7EqscvwKzAyfRCvt+WyMShkQT7e7B13yl6RwYysHcIJrNCmaGCvUczyUzPhxU1nysyzIcpo7sBlZ9BWXmlbPjjBIcSczmUmGu53+a/TzKodyheni5cOro7BmMFP/91ElNZGeOr7rPoo12YXdwwmRTGDoygQ4AnW/acIq+ojCvH92BArw7kFJQR5OeBwWhi+ef/UFBiYOyAjpzMKCIlowhPdxcuH97J8rzvf3+Y4JAg3Fy1rNlYOVsg+XQh107qxc9/ncTDTYdWq8FkUjArCiazgrurDk93FwpLyhkdG8GkAa2x+oAQtqUoCj/tTObdbw+g0Wh4/NYR+Pu6U2404aLTWg6P2fTXSU5lFhPqo2NI1WNXfR1HZOdgEk8X8uOOZEvmtv1pvP7p3hrPc+mYbvSKDOC/n+0jNNCLudcNJNDPg6PJeaz+ei+zq+63ZU8Ke5MK+W13quWxX/6WAECnEB9CAz05lpJPSZmRqyf0pLjUyI87kukW4cdNF0fzwfeHOJVVUuM7xle/JdT6sfCrqswasso5lJRn+efuw5l8+/sJoOYMoWdW7aBLl2C++yOxxsPjk3J555sDzL52IHEJ2azZeIjunfy5YHgXTCaF8A5eBPp6cPRkHp7uLnT0r1lTWnYx+rIKenTyJyuvlLyiMnpFBlbOPjrnfuk5egb3DqGk6jOmeiHQzFw92QWluLpo0VA5a0kDmBWFzqG+6LQajp7M45ddKRSWlDNldDc6BHhiMpkxVpjZHncaTw8XBvTsgK7q+2evLoG4t6MfHpr9bcDHx4fzzjvPlrWIsxQUG/j3WztJOl0EZNApxJvXH5pgk4HjrsMZRHUNJSTQ03KdyaywYetx9h7JomdkANdO6mX5slhhMnPiVAFmRcFsVojuElhrgJ1bWIqLEQJ9zwyKtu47xeuf7EEBbpnShzEDOvLEm9vIzCtlYK8OPH3XaHRaDTsOpLNlTyparYZe4V6WxYse/u/vJGaVUZff9qQysFcIJpNCQbGBIyfzat0ONd+sbn3mRzRu7nSN8KN7R38mDu1M7y6BfPP7cfYfy0an0xCflIu+rIIO/h7cPKUPE4dGsvtwJn8dSmdU/wiGxYTVeJ7l6/cxaXRPwoK8+eSnw+QWlnHhyK4E+3nQJdwXLw9XivTl6LQaDp7IYd+xLG68MBrfes5FXPmDQyFms0Kx3siGrQlkZufjuUPPqNiOTB0fRXGpkbSsYrqE+1GsN/L+dwfZuu8UAD9uT+LB6UNqvKGWlBr56c8UPv6x8njPmy7uw/hBnQgN9CTpdCFZ+aUcPJ6Dh5sOFxct6385ZvnC3r2jH8H+nmg1Gjw0FVSvRPH9thN8syOFruF+JJ2ue7HIDVtP0CHAk5SMIgDe/ioOH09XRsdGcO3kXiQlZ1nuu+zzfzBqXdn4ZxKdQ31IzSy23LbzYDrrfzlG947+vP3VftJz9PTpGsjh5DPbvNRg4s/9p/l730nL9r7jhZ/p1CmYqE7+FOnLOXQih5KyCkIDPTlvcGeSTheSW1DGibQCAMtiVY2p3guXkauvsdfuq98SuHhUtxZNIcsrKuPQiVyG9Altk8GascLMJz8d5qedyegLSyyD9Ofe+4vRQ7pajhn+flsibq46uob7EtujA6P6R3DgRDb7jmZRXGrkxKmCGrk/7kiu8bc46+VNlJrPfJDG9uhAem4JWXmVA/eYbkEcSc7FXHuHZw2uZqNlkJ6Qko/xVAk7D6bXe/8n395uufzXobrvd3ad1dKyi1n381FKSo1cNLIr7m46ftiehMmscP3k3vSMDKhx//ScEgL9POr9svDutwf4flvNL01ZeaV898cJrpvcu9767am1p7vbg0ajQaeB7h39mXvdIMv1Z7+mOq0Gb09Xxg3shCk6mB1Vg/QlD04graCcwb1DLT/iVbtoZFf2Hs3ih+1J5BaWodFU/qhR3YfO/pLuajZaBul/HUy3fOE+t7/9fSgDT3cdpQYTIYGelBkqKKo6V/2+o1k17rvrnzPva7/tScOorXl7Ylohiz7a3aTXaFd8Bvm5UVQP+ytMZtrPV1qhZmXlZ34o3LInlV/3Z9Kjsz+Do0Nxd9OxfvOxGu/v/377zwbzXM1GyyD9p53JGP9Os9zWIcCT7h39+PtQRq3HbfwzyXI5I1df43PD1XymxurvLQChgZ7odFqy8koxKwqnsootP8ACfPHrmYF20ulCXlz9d711dwn3JTLUl77dg/gz7jQHT+RYbtNooIOfCwou6HRaPN1dCPb3IL/YQGpmMYbymqdHi0/KYf/Jyu9oF47oQpdwP8rKK/j4x8P8b3sSqZnFJKTmU2qo4HByHv+raruri5Y+XYOIO54NwNDuAVxYlfnvt/7kQErl97rwYC8y80oxmxW6RfhZxhVjYjtSXmFi1ddxVJgUIsN8SMsqwdVFy5xrB3IwMZefdiTV+xnv6qLFRael1HDm9a7vs33tWZd7RQaw6N7xje4EVAuNUn2i8wbMnDnTqtA1a9Y0u6C6mM1mli1bxueff05RURHDhw/nqaeeIjIy0uqsuLg4AGJjY21ao61t3XuKVz7ahU4Lpqq1cp6ZNZoh0c37BfzkqTx+W/01KRlF/OUSib+fJ3ddGYuHu45d8RkcOJHDyfQiy/0Dfd3pGOJDSKAnx07mcSrrzPSZ2B4deGbWKHJzS9j3xf9ITCvg57JQzBot9984hKS0Ao6czOPQiZwGv2QPiQ5Fo6n8BbCaVjHTr6jy18CDvlGg03Hd5F6YzQq74jPoGu5HamYRCakF9cXi4+mKq4uWvCJDrTyzxro/XHc3XY03vUvGdOP6CVFsXfMNew5nst+7e72Z3p6uDOzVgZ0H0mscC6nVarhlSh9G9Y8gMswXc0UFaZt/Iz27hM+z/Tl0sv62xXQL4nhqPuUVZrQaary+vl6uli95TWl3gI87+cV17zU8O8tSdz2ZWq2GYH8PFLOCq4uO8goTOQV1/7jSUJ6np5vll1aoPNZ0YO+Qun9FrqLTali54AJOZhSxPe40O/en0iXjSIPtPpdL1Qddkb4cADcXLd07+TNxaCTD+4ZhKjfy2ZKPASjpNZDMwnIuHtmVlV8foMJk5tbL+vLFL8coLjUyvG8Y998wGH8f93qfz1xRQdaW3wHwHjGahNNFdO/oz+a/U/j05yOUGiro3yOYF+4Zi1arQa/XEx8fT0xMDF5eXo1mhpx/HloX6wf4OQWlvLfhIL/vrfzBx02rMD2siLjj2ezz7GbV346LTouPpysmsxl/H3cKissxV1QQlX0UqNw2Pj4eaDRQUFzeYJaHmw4PNxfc3HRUVJjQarXotBouHNEFXw8dHof34uftxsnQaLKKyvl1d2rVcytk55fWyosM8yG2Rwd2H87EZFYoKTXW+CJQ3S893V1QBgzDrNESl5BNeYW5VhaAVgMx3YPx8nDhmom9yMjVs+STPQT7e/CvK/qxc/8pvI/+g06nITOyL+VmjWUQNnl4JH27BwPwxmf76NjBm7fmT641e+XcPmCL7X2uO5/7gdCTB7n+gt4MuOaSFmfao0ZbZ1qbV/11yazAnsMZZOWXsu9olmU2zYShnenbJQDXg5UDZmPfIVSgJTu/lK3/nEKr0dAp1AcfD1f++OdUnZ+PfbsHkZJRTJdwX8YN7MjaHw6j1xuY5JqOjgoO+ffGYNZQajBx+bjuuLqc2aM+om84Q/pUfq5qNZXH1FeYzOw5nEleURkRwT5s2Zta473XEDOYmy7tR5+uQZZj/lMyiigpM9K7jj1jtnot2yJTDTU2lNmUz4K2rrE5FEXhtz2pvPPlP3TNPEJokBe/GsPr/Mxx0Wm5/oLebN13ipSMInRaDUH+HphMZ96fw4K8Cfb3ID2rkMATcZRXmMnrHktabhndO/oxeVgkE4dF4unuwoatJ6gwmekW4c+hxBwKSsr5YXsSAAN6dsDb05XDSbmYqqbXXzy8M+M0afx9KJ1vCoII8Pfijiv6E9uzg+X584sMbN13igqTGY1GQ2JaAbsPZ+Dp7sKQ6FBSM4tJOl3IkD6hzLoqlqTUPJJ//AVvD1cGXjOFoKCah65k55fy6se78XR34V+X9SbjVGK9feB0dgkn0/KJTD9McamRdZm+7Duex5TRXblzan/LZ8u6n4/UWKjT18uV0CAv8goNmBWF/KKa3w1b+l26PmFBXpXfjxWl6v1QwWA0U1K1qKePpys9Ovvj4+XGweM5KFS+aZYbzXh7umI2my3f0at3wqx8/EKHPt7emnFokwbpM2bMqPHvvXv3otFoGDRoECEhIeTn57Nv3z4URWHixIm89tprzSy9bsuWLeOjjz7i5ZdfJjw8nEWLFpGamsqGDRtwc6t7j2R91DJIN5sVtu9PwVSSzk/7y/knIYf7rh/EhSPrXj1fX2bkwPEc3N10VH+/q6hQ+DMujVNZxRxOyq3zOLyzebjpuGJ8FFv2niLznOP63N10eLm7kFf1h9vB34OcwjIa6z2DeocwbmBH3ttwEH1ZBRHB3lx5fg/e+nK/5T4uOi1XjI/C003HH/vTyCs00DnUhwlDOzOgZwc6h9Y8TtNkVvjr4GlyCw246DQoSuWbqbGi8ticgb1C0GjgWEo+5UYTkWG+bNmbSq/OgWTkllBSVsHhpFx2HDhNWdUf95TR3egdGUCArzt9uwfz085kPt98zDJw6xZR/x7jajHdgggP9mJ/QjaGchPFTVg5ODLMFx9PV1Iyiiz312k1+Hq7YTYr9I8KxN/NQKHRg237z/yK6OaitQwagv09eOqOUUSG+fDfz/ax/1g2Wq0Gd1cdpQYjuYUGvD1duXNqP/KKDPy88ySncyp/dNFqNfh6uRLg405F1RSiqyf2YsrobuQXlbE97nTlNCKTmaPJ+cQdz6ZLuC9zrxtEdn4pSacLGdQ7hE4hNT9Uftudwkc/HGZwdCg3XRxtWYjpyMk8vvw1gYxcPVqtBk83HQajiWkTenLzlBjiErL4aedJCooNzLoqlpBAT5Z//g9HU/IwmxV6dA5g6vgodh5M5+jJPCYOjbRMTa2290gmx08V0Ld7ECdOFfDLrhQ6dvBhVGw4oYFevLj6L3RaDZeO6Y6XpytDokMJ8vMgLiGb8GAvOoY07djOjFw9ZYYKukb48dmmo5aFqoL8PLhuci9G9Y9g39Estu1PQ19mJLprEEOiQ9BqKwdpR5LzSEwrtPSxc82fOZyxAzs2+4uZNdb9fISPfzxsGTSMH9SJqyf2pGfnAPYnZPH+d4co0RuJ7dmBWdNi0QC/7z1FTkEpfx/KILfq2L3hMWH4+bgxqn8EoYE1a60wmVn381H2Hc1k7MBOTB0fhVar4VhKHkdP5hMS6EnvyEBOZRXz5/40jp8q4LrJvRjaJ6x2wU2kKAoJqfn4eLrh4aYjITWf2J4dasxIKik1su9YFn27BXEyvYgKs5lFH+6q8YMRVP44GRLoycETOZiqZhSZzGZ2HKh/z31Dbrwwmpun9AEqDy+46cmNVJgUVj1+AeHB3jXu2xp9YPq/N1JcamT5oxPpEu5nl+dojxRF4UhyHjqdhl6RgU1+XGJaAf8cy6ZXZAAZuSUoSuWU/XOPLU/PKSH5dCH9uvlx5MjhWn3AbFbY8Efll+fLxnZv8HzxZrPCfa/+SvJZP8hX83TX0S3CH6PJTEJKPlC5V+qWKTH07hKATz2zv0TraY33gdZwOCmXDVtPUFxqxGA0UaQvr7GTqNqAnh3QajWkZBRhNit0CvXhritjLbPj9hzOpH+PYIL9Pet4lkpK1QzQ6kOldFpNo2vHHEnOpdRQQWzPEIcb7NmyDxxOziX5dBGuLhrGxHa0zCAqN5p47t2dxB3P5tpJvRgdG8Hh5DxcdBpcql7Hnp0D8PN2569D6XSL8CM82Muywy0lo4idB9LRG4wM7BXC9IuiiUvIJjTIi49+OExCSj5hQV48cONg+vfoUKsuk8nM7/tOYTKZOW9w5yYfN282K1SYzA5/nL3NB+lnW716NZ9//jnvvvsu4eHhlutzc3OZNWsWY8aM4aGHHrKy5PqVl5czatQoHnnkEW666SYACgsLGT9+PC+88AKXX365VXlqGaTDmT/G348o/LL7lGVhnT/2pVVOWe/kT9/uQXy4MZ6950yPq8uQPqFMGlp5LOmyz/8hLbsYnVZLZJgP5w3uzNA+oQT7V067+zPuNEX6cnbFZ+Dn7cadU/sT6OfBvqOZPPPOTiqqfrWMCPZm4rBIYroFsvr7QxxPLSA00JMrxkfh7+POuIEdcXXRkVdURkJKPv17dLD8evnZpqP0jAzgziv71xrktQaD0cT//kwkJNCLsQM61rpdX2bk70MZdArxoWdkAP8cy2L5+n84nV2Cr5cr91w9gBF9w/n5r5P4+7gxftCZ1X+rpw3HJWRzwYgu6LQaTufoGR0bwda9p/g7Pp2UjOIaz+fr5UZ010DumNrP8sNEdR/o06cP328/xV8H0xk3qCOXjOnO6g0HSUjN556rB9Cjc0CdbTRWmNl7JJNekQGWhUsAktMLOZSYy6BeIUR08K7zsfZSbjSxP6FysB8aWHksstqnJimKwp4jmaz6Oq7GrBNruLloueuqWDLz9Hy++RjB/h68PGccfp4au34x+31vqmWqbGiQF1eOj2LqeT1s/jxqkpmrZ/eRzMoVvjWVe2oG9w6p88vdocQckk8Xsj8hmz/+OTOdcmCvDqRll9Cna1DljAyTmYzcUvYnZDE4OpTrJ/eusZfyode3cCwln3m3DGP84E41nsPeX84VReGqeRswmxVWP3VRg196RduwVR9IyShi018nGdCrA13D/Xj32wPEHc+uNaPl7NXwPdx0PHXnKGJ7dKhcBd/PAz9vGbS3NrUN0s1mhW+3niAhJZ/YnsH8vvcUWfmlnM6u/RnpotMw/aI+5BWW8d22RDqFePPq/efXeUYHZ9aafcBkVmz+I0VJqZH4pFz6RwXXOqzIWdh1kD5u3DieeuopLrroolq3bdq0iSeeeIKdO3daE9mg/fv3c9111/HDDz/QvfuZRZmmT59O7969eeaZZ6zKU+Mg/cBpN9b/WrUghIsWYz3TLj3dXfD1qpzqDWAwmukfFUyfbkFEdvCiU2EKGo2GwMGD0Oia/0tTek4J/xzLpkeED0FZSQAEDh6EwaRw4HgO/aKCm308rWIykbd3nyWzJXXaI6+svIL441mE5CTj5eHaoszUzCL+OZaNi0bB8+Qx+nQLImT4kBp5zX1DtnW77ZGphhqtzSw3mvjf9iS+/yPRMlvhmok96Rjiw087k8krLEMxmxmkqVykzH/gQIb2i+BYSj4dO3hbfiR78PUtpGYWE9XRn+fuGsaxY0ca7APNaXdmnp4jyXm8/uleyo0mrr+gNzMuibHcLtvbukyzWeGVD3exbX8at0zpww0XRluV99aX+/l+WyJXnd+DO6b2r3Hbue8Dtm53qaGCGxZsIEqfxvxbhxM2YqhTbG811Fidmb59JykpKfS7/FK8fVt2FoBzazSj4a9D6ejLKqgwmYnuGoSftxurvzvIr1XH27u56ogM8+F4agE+nq48e/foGjMH1PBaqqHGhjLV8n0gO7+Uj36IJyElv85ZG1B5ONsV46PwcNfh5qqrPJ7Zz53c3XtJySii16TReHt71PlYW9So1sym9IG2rlE0zJpxqNUjqbKyMuob15eUNG8PUkPS0yunEkZERNS4PjQ01HKbtRRFQa+v+zQtjqS0tPJ4Sn+vM38MxgozWq2G/t0Dycov43SOHnc3HbdfGs24gRGWAfq5TGVl7J/3EgAD3l+FzqP5b35+nhrGDwipzLzvxRqZfbv6opjK0dczhbcxprIy4p+rmdkSts4D6Bnmzv75i1qcGeSjY+LgsMrX8T8rOQZ4npNX3Qeq/99U9mi3GraNI2ReOCyCC4dFkHS6CJPZTI9OladpGhcbYsnbf/tdsAsGTF2FuaKcHhFewJn3pSduHcy85Ts4kVbAkk/3ceEA9wb7gLU1bt6VyrvfHcZUdQiMv7cbl43uXON9Uba39Zn3XtuXWy7uQaCvu+W1bGpe17DKL1yHk3JqfT6d+z5g63bnFpbhopi47vQvHH/5F7ydZHurocbqzBOLXgVAf/74Fn9BrqvGgT0CzrmXmXuuiuH2S3vz1Dt/k3S6iONVa8EUlxp57t2dPHn7UDqFeNeb2VLOvL3rynTU7wPfT5pNYXnl4XNms0Jy+plZghoNBFWd0iymW+WPOjqdhpsu7IW/T83ZGMUFBRx+ofJ7qnnsQPSaundINadGNW7vujSlD7R1jaJhiqI0+XS9Vg/SR40axWuvvUavXr2IioqyXH/w4EFef/11zj///AYebb3qjnjusefu7u4UFNS/wFZDjEYj8fHxLa6ttQS6F9M9zJ1CvYnBPbzo1dGTsABXwIv0PB+83HX4eRWScKz+Y6aV8jOD5iNHjqCx8lh+tWa2lxqTkpJsnmktZ9w2tsiML0yr8e+m5l0yxJfP/shl15EccvLduVmrwUVX9xu7NTUmZpSx5pdsFAUCvHWYFZg6wo/E40ebndkUjrht7JV59nrBTc3TVK2unpBawIGDh+qcZlj9PmDrdmcV1Fw/w1m2txpqPDczOTkZzenTNstrSo03jfclIc2VChOEBbqyflsOWQUGHl32J6P6+BLZwQ13pcJyrmdHfS3VuL3rymzr7wMms8If+3IYWfXvw0l5tU4b5uqi4YKB/vTt4omvZ+0fldJSjpN2znWyvZue2VAfcJQaRf2aup6a1YP0J554gptvvpnLL7+cyMhIAgMDycnJITU1lV69evH4449bXWxDPKp+rSkvL7dcBjAYDHh6Nu+4OVdXV3r27GmT+uyptLSUpKQk+vfpwfDB/eu8T0yd19ZmKiujeqm26Ohom/2y5uiZaq+xug9069bNqv6u9na358ym5sXEQL8+ebzwwR4SMwys/b2Af98+zHKO0eZkFhSXs3TDdhQFxg+MYM41/er9RVe2d+u2O9qs8N6mHEoNFfgGR9I1/MyU5nPfB2xdoy4lHzhzGjFn2d5qqPHczK5du+IdEGCzvKbWOPCsmZkD+pWx8pt49h7NZtuhyunMZ5+6cO3WQob068RV53Vr0Xojsr1rZjrC9wGTyczSz+LYc6jIMki/dHRX+kaHoSgKx08VkpxezGVjutCna9MXUrR1nfbIc4TMpvSBtq5RNCwhof4zFp3L6kF6REQE33//PV9++SW7d++moKCATp06cffdd3PllVfi6mrbRR6qp7lnZmbSpUsXy/WZmZlER0c3K1Oj0ahi0Y1qnp6eLa7XpD3zQenl5WWbP1oVZLaXGq3tA+2l3e0x05q8ITFePDzdxJJP95F4uphH3tjO9Iv7cMk5K9o3lKkoChUmhZyCUhZ9/A95ReVEhvlw3w1DGly4RbZ367e7Z+cA4o5ncyq7jJio2qvaV78P2LpGs1JzEUtn2d5qqPHcTA8Pjzb/PuDl5cUzs8awdd8pfv7rJKVlFRhLS6Fy6RySTxeRkHGcrfvTuXxcdy4fG9XkU7nZsk5757VVZmt/H6g+BW5OQSmbd6VwJDkPz7Nmdd0+NdaSOXaQVdE2rdPeeY6U2VAfcJQaRd2aOtUdmjFIh8rOcfPNN3PzzTc35+FW6dOnDz4+PuzcudMySC8sLOTQoUPccsstdn9+IYRoS4N6deCWCSGs+TWbvCIDK7+KY0h0KGFBjX9JMxhNPPvODvYnZFuu8/F05bGZw512ZVVH1jOycpB+LCWfC0bUfbpNeygpa/x0kUKcTaPRcN7gzpw3uDNQuadtx/aVAPh4uZFXpnA6u4RVXx9g9+FM5s8c3uwFZUXbST5dyLLP93E4Oc9ynbeHC/dOGwDPfdyGlQnR/ln9jvn11183ep+rrrqqGaXUzc3NjVtuuYXFixcTFBREp06dWLRoEeHh4XWuMC+EEO1N5w5uLH94PE+s/JvMXD13vvAzw2LCeGzGsHoH2/lFBhZ9tKvGAD0syIvn7h7T6qfdE03Ts3PlIoPVC3S1Fr0M0oUNvf/kRaTmlfPbnlS+/f04ew5n8uRbf3LZuO6EBHjSLyrYqr1JonUUFBvYfTgTg9GE2Vz5I8t3f5zAZFbwdNcxsFcIgb4eXDu5F8GeWna0dcFCtHNWD9Lnz59f5/UajQadTodOp7PpIB3gvvvuo6Kign//+9+UlZUxfPhw3n33XZtPrRdCCEfl5+3GgluH8+h/f6fCVDn98IX3/2L84E6Yy8o4+6RM+jIjT779J0mnC9FpNQzqHYKHmwu3Xd6X8GAZoDuqnpEBAJxIK6DCZMalBcfzWqOktKJVnkc4j64Rftx6WV9G9A3n8Te3ceRkHkc+rtwbO6p/OOMGdmJYTJicB9tBpGYWMX/5HxQU1z4zz8h+4dxz9QA6BJw5BtpUVtaa5QnhlKwepG/evLnWdXq9nl27drFq1SqWL19uk8LOptPpePTRR3n00Udtnu0sNC4uRM2603LZWTKlRsfNVEON9shsSV7PzgGsevxCdh5MZ+XXcew7lsW+Y1loFTODOozAxUXL+6/8it4IRfpyAnzdef7uMXSN8GvVOlsjTy2Z1uRFBHvj7eFCSVkFKRlFdO/o3yo16suMmDQ6To28hPEDOznN9lZDjdU5nW+fSXp6OlqVtTumexDPzhrNDzuSyCkoIz4xhx0H0tlxIJ0OAZ68PGdcvYfuOPP2bo127z2SSWJaIVqthq+3JFBQXE5IoCc9Ovmj0Whw1WkZN6gjo/pH1Jr5oOZ2t/dMNdQomkaj1HfS82ZYs2YNP/zwAx9/7LjHqVhzEvm2ptfriY+PJyYmRlUL3QnbkT4g6usDccez+WlnMiWlRgzlJuKOV55WrVpIoCfzZw6ndxfrVtgVbeuJN7exPyGbe68fxEUjK49Lt/f7wNtf7ee7PxK5bnIvZl7a1+b5ouXay2fB0ZN5bNh6gv0J2eQWltEtwo9F946XNTKawJZ94H/bk1ix/p8a13UK8eY/c8fj7+PeomxhP+3lfcCZWTMOtem7YnR0NK+++qotI4UQQtQhtkcHYnt0sPw7LbuY09klxFUdg379Bb3x8pCppGrTs3MA+xOySUjNtwzS7U1fVjnd3Vv6i7Cz3l0CefjmoWTnl/Lgki0knS7k+fd34uHmQlZeKVeM746XhysDenbAx0vOxWxrBcUGVn93iE1/nwRgSHQoHu46An09uG5yLxmgC+FAbDZILy8vZ/369QQHB9sqUtiQYjJReCgeAL++MWh0OqfIlBodN1MNNdoj0141ep9Ooicw5BLnarejZ1qb17NzAAAJKfmtVmNJqRGNYsY3M5mCOIPTbG811FidWXQoHlNyMkozTz17bl5bt7tDgCfzbx3OE29u459jZxa3XLpuHwDB/h48ctMQIkszbFanI7S7tTPzCsv4859TGBKOYiiv4LsUDYWlJgCmnhfFnVP7N2sRP0dvt1pqtEemGmoUTWP1IH3SpEm1/qDNZjN5eXkYDAYee+wxmxUnbMdsNHLg3wsBGLVuLTob/IGpIVNqdNxMNdRoj0w11GiPTDXUaI9Ma/OqF49LOl2IscKMq0vtxeNsXaO+rAIXxYT3J29ywEaZ7XHbtGVmwnMvVl6eOMEmeY7Q7n5Rwbw8Zxzfb0vERaflxKkCTqRVntkgp6CMp1ds4cHjnwAw6MMP8PbzafUa1ZZZpC/n110pjOwfgYtOw4NLtlBcUMzDJypfx9Ko6XTrFMzsawYS0z2oTWpsrUw11GiPTDXUKJrG6kH6iBEj6vzVzcfHh4kTJzJmzBibFCaEEEI4m/BgL8vicamZ9S8eZ0tynnTRVvp0C6JPt8rBoqIo5BaW4enuwuK1u9l3INVyv8dXbOPZuRNkOnYdFEWhwqRw9GQeb3y2l1NZJaz65oDl9o6BZ45dvmNqf6ac1xtdK505QgjRfFYP0l9++eUGb09PTyc8PLzZBQkhhBDOSqPR0DXCj0OJuSSfLmyVQbqcJ104Ao1GQ7B/5Wm+Hr1lGJ985wYnKm9LyShi2ef7ePy2uncUOatd8Rm8+cU/ZOaV1nm7j6cr828dTtrudwGYMrqbDNCFUAmrB+kxMTGsW7eOAQMG1Lpt165d3HXXXezdu9cmxQkhhBDOxjJITy9qleerXjhOCEfh6e7CrZf1Zceayn+76DTsOJDOVY9+y8RhkfTpWrn3PSWjCE8PF/p0DWJYTFgbVtz6jp7M4/n3dmIyV57WQ6OBC4Z34YrxUWzdd4qBvULo2z0YbUU5aW1cqxDCek0apL/33nvo9XqgclrN559/zu+//17rfnv37sXNTVbjFEIIIZqra3jlee2T0wvt/lyKoqAvMyL7JoUju2NqLG9uOIzJrLD57xQ2/51S6z4DenagS5gvA3uHMLJfOIUl5fh6uaHVtr/efSqrmEUf7cJkVhjVP5w7r4zFw01nORzg7Bk4JvkNTghVatIg3WAwsGzZMqByOtLnn39e6z5arRZfX1/+7//+z7YVCiGEEE6ka7gvAMmn7T9IL68wU2FSkJOvCUd2wYgujBvenZ//SuajHw5TbjSh1WiI7hpIudFEQmoB+xOy2Z+QzXfbEvF0d6HUUEFUJ3+ev2cMvio9nVtCSj4HU4sZMyACP08Nx9JK2XniML/tPU2poYLQQE/uv2GwnK5OiHaoSYP0//u//7MMvvv06cO6desYOHCgXQsTQgghnFHXiMo96Zl5pXY/XlxfWpkvh/kKR+ft6cpV5/fksrHd0Wm1KIBOq8FsVljxxT/8se8UPToHcOB4NqWGyt3HJ04V8OCSLVw8qivR4d6WrIIiA0EeHm3UkqZ74s0/MGhc+fTnw8R0DWTP0RwgB6hcHf/RW4bKAF2IdsrqY9IPHz5sjzqEnWl0OrreOsNy2VkypUbHzVRDjfbIVEON9shUQ432yGxOnq+XG0F+HuQWlnEyo4guITUHE7assXpld3d3tzZvd2tnqqHG6pyON91IZmYmGherv7bVmaeWdteV6epSM1+r1TD3ukHMvW4QAMdS8ohPysXf253Fa3eTkatnzcZ4tIqJYcFDAHjz9d8ZOTCSkf3CGd43DLMCqRlFmBWFTiE+FJcaCfJrfBDf0naXlBr5estxsvL1hAV5M3lYJAeOpbOtqk4jlYu8lRpM7DlaeT75sQPCGRYTwcShnZu0CJzat7ej5KklUw01iqbRKIqiNHanBQsWMHv2bCIjI1mwYEHDgRoNL774os0KtLW4uDgAYmNj27iSxun1euLj44mJicHLy6vxB4h2R/qAkD7gnJ56+0/2Hs1i7nUDGT8g1G594OjJPB5e+jshgZ689++LbJotbEfeB5rn263H2bD1BOk5lesqxXQLotRQQVITDiUZFhPGPVcPICzIC5PJXGNAbDKZycjV4+KiRavRYKww0yHAE1cXLXlFZXyz5Thdwn0Z0S8CH8/Kg0mqTzFnKDdhNJkJ8vNg8Ue72XMks97nnzKqK8Niwti8K4WTp/MJci9hyvmDpA84KXkfUD9rxqFN+kl2586d3HrrrZbLDZFTYwghhBAt0zXCj71Hs0hOL2L8gFC7PU9J1XR3bw85Kl20P1PH92Dq+B6YTGbyiw0E+XlgMiv8tjuFbftPsys+o97H7orPYPYrvxDs70FGTgkDeoYwNCaUDgGevP/dITJz9TXu7+/jxoi+4fxzLOusU6LtZUDPDvh6uXEyo5CUjOJaz6PVwJXn92TP4QyS04vQajWcN6gT990wGFeXyh8GLhrZFb0+hPj4eJu9NkIIx9akQfovv/xS52WhHorJRPGJRAB8orrbZLqKGjKlRsfNVEON9shUQ432yFRDjfbIbG6eZYX3Ovb42bLG6tOveblrKTqWYJNMW9dor0w11FidWXL8BOZTaSjR0TbJU0u7bZWp02kJ8nGjOOE4AJOHdmfy8C5s2ZPKyYwiDp7IIcjPgzGxHdFoITLUl7e/iiPueDans0sA2Hcsi33HsiyZWq0GrWIm3JCLWVE4rQTx818nLbe7ueooN5rYn5Bd4zHurjp0Wg3FVT+Q3TSlDzdcEM3tl/elwqSgVczok5IoSzyBi5P1c2f9+3bGdovGtfzgJqEKZqOR/Y88BsCodWvR2eAPTA2ZUqPjZqqhRntkqqFGe2SqoUZ7ZDY3r1tE/adhs2WN1cek+7hqHKLdrZmphhqrM4/+e2Hl5XFjbZKnlnbbdXt7eDBhaGS993/h/8bwZ9xpjqfm0yXMl6TThfy2J5XCknIuGdONW6bE4KZUsOOGmwEwzv8PaYUVBPl5MHZgR3y93DicnMtfB9Nxd9PhotVywYgultOkHU7OpcxQwcBeIUDlTFRXFw2msnKn7efSbudpt2hckwbpkyZNavI0do1Gw6ZNm1pUlBBCCOHMOof5oNFAQXE5BcXldnue6tXjZbq7EDVpNBrGDujI2AEdLdfdellfzGbFcny6qezMScjHD+6M7pwV4/t0DaJP16A68+u7XgghoImD9BEjRsix5kIIIUQr8XBzITzYm9PZJZzMKLLbtLeS0qrp7h4ysU6Ixmg0GnQ6+T4shLC/Jn0qv/zyy/auQwghhBBn6Rbhx+nsElIyS+geYJ/nqN6T7iV70oUQQgiH0ayfzg0GA19//TV//fUXBQUFBAcHM3r0aC6//HJcbHAOTyGEEMLZdQn3ZXvcaVIyiukeYJ+9dyWWQbp8dgshhBCOwupP5bS0NGbOnElqaiqRkZEEBweTlJTEN998w+rVq/nggw/w9/e3R61CCCGE06he4f1kRjFE+9rlOSyru8sgXQghhHAYVn8qv/DCC5jNZr766itiYmIs1x84cIB7772XV155hRdeeMGmRQohhBDOpnqF99TMYsyKj12eo/o86TLdXQghhHAcVg/Sd+7cybPPPltjgA7Qv39/HnjgAV566SUZpDsgjU5H5I3XWy47S6bU6LiZaqjRHplqqNEemWqo0R6ZLcmL6OCNi05LWbmJghKTXWq0HJPu5U64g7S7tTLVUGN1Tvg108jKykJjg0MK1dRuZ93e0m5pt6Pk2StTNE6jKIpizQPGjh3LU089xcUXX1zrts2bN/P444+zc+dOmxVoa3FxcQDExsa2cSWN0+v1xMfHExMTg5eXV1uXI9qA9AEhfcC5zV30C8npRdw8oQNTJw+2eR+Y9dImTmeX8PKccfSLCrZptrAdeR8Q0geE9AH1s2YcqrU2fObMmbz22mucOnWqxvUFBQW89dZbzJw509pIIYQQQtQhPNgbgPySikbu2TylVceke3vKdHchhBDCUVg9byopKYm8vDymTJnC0KFDCQsLIy8vj927d1NaWoqHh4dlT7pGo+GDDz6wedHCeorZTGlqKgCenTuj0Vr9+4wqM6VGx81UQ432yFRDjfbIVEON9shsaV5YUOXekrziM4N0W9ZYvbq7p5sW/cmTNsm0dY32ylRDjZbMlFTMmVko0dG2yVNLu511e0u7pd0OkmevTNE4qwfpqampRFd9SJhMJtLS0gDo27ev5T7VM+itnEkv7MhcXs7eex8EYNS6teg8PJwiU2p03Ew11GiPTDXUaI9MNdRoj8yW5lUP0vOLzxyTbqsajRUmjBVmADy1ikO1uzUy1VBjdebheQsqL48cYZM8tbTbWbe3tFva7Sh59soUjbN6kP7hhx/aow4hhBBCnMOyJ90O091LSs9kerjLKdiEEEIIRyHzFYQQQggHFVZ9TPpZe9JtpXpld093F3Rajc3zhRBCCNE8Vv90furUKZ577jn27NlDUVFRrds1Gg2HDh2ySXFCCCGEMwsN9ASgtNyMvqwCWy7oW308ureH7EUXQgghHInVn8z//ve/2bdvH9dccw0BAQF2KKm206dPs2jRInbu3El5eTkDBgxg/vz59OrVq1WeXwghhGgLXh6u+Hq5UqQ3kplXSocgP5tl66umu3vJyu5CCCGEQ7F6kL5v3z6ef/55LrvsMnvUU0t5eTmzZs0iICCAt956Cw8PD9544w1uvfVWvvvuO4KCglqlDiGEEKIthAR4UKQ3kp1fatPcM3vSZZAuhBBCOBKrj0kPCQnB09PTHrXUadeuXRw9epTFixcTGxtLr169WLRoEXq9nl9++aXV6hBCCCHaQpBf5Uq6eUUGm+ZWH5PuJdPdhRBCCIdi9Sfz3XffzRtvvEF0dDSdOnWyR0019OrVi5UrVxIWFma5Tlt1fr7CwkK7P397odHp6HjVVMtlZ8mUGh03Uw012iNTDTXaI1MNNdoj0xZ5gX7uAOQWGmxaY0lZ5XR3bw9Xh2y3vTPVUGN1Tujll5KTk4PGpeU/qKip3c66vaXd0m5HybNXpmicRrHyZOY5OTnccsstJCUlERgYWGuvukajYdOmTTYt8lzvvfcer7zyCt98843lnO1NFRcXh6Io9OzZ007V2U5paSlJSUl069atVWcvCMchfUBIHxDrfj7Cl7+fZPzAMOZeO8BmuZ//cpz1v57gguGduGtqX5vlCtuT9wEhfUBIH1C/hIQENBoNsbGxjd7X6p9kFyxYQEpKCuPGjaNDhw7NKvBsqampTJ48ud7bt2/fXuO4859//plXX32V2267zeoBejWj0Uh8fHyzHtsWkpKS2roE0cakDwjpA87LZCgBIC0z36afXalp+QCUFheo6jPRmcn7gJA+IKQPqJubm1uT7mf1IP2vv/5i4cKFXHfddVYXVZewsDA2btxY7+3+/v6Wy5988gnPPfccU6dOZd68ec1+TldXV6fbk66YzZTn5ADgFhyMRmv1cgSqzFR7jc3tA2pvd3vOtDavKX2gPba7vWTaIq/YnAY78zBU6IiJibFZjVsOHwSKiewUTp/org7XbntnqqHG6szCU6dITU2l+4ABeHl7O2SNjp6phhobymzP3wfskamGGq3NbC/fB5xZQkJCk+9r9SDdz8+PiIgIax9WL1dXV3r06NHo/RYtWsQ777zD7bffzmOPPYZGo2n2c2o0GrxsebJZO/P09GxxvaayMvbd9xAAo9atRefh0eK61JDZXmq0tg+0l3a3x8zm5jXUB9pzu9WeaYu8sA6Vp13LLzbi5eVlsxoNFZVHuwX4euLh4uJw7bZ3phpqtGTOexwA9/dXyfcBB8lrq8z2+H3AHplqqLG5mWr/PuDMrBm/Wv1TyPTp01m5ciXFxcXWPrTZqgfojz32GPPnz2/RAF0IIYRQk6CqheOKS40YjCab5cp50oUQQgjHZPWe9NOnT3Pw4EHGjRtHVFQUPj4+NW7XaDR88MEHNitw586dvPPOO8yYMYMrrriCrKwsy21eXl54t3DalxBCCOHIvD1ccNFBhQnyCssI8bbN6rpynnQhhBDCMVk9SE9MTKRv3zOrwJ67OLyVi8U36rvvvgPgww8/5MMPP6xx29y5c7n33ntt+nxCCCGEI9FoNPh66sgrNpFTUEaIjX6clvOkCyGEEI7J6k/mcwfK57L1IP25557jueees2mmEEIIoSZnBuml0NE2g3TLedJlursQQgjhUGy2PF9mZibLli1j0qRJtooUQgghBODnVTnFPbewzGaZ+qpBupdMdxdCCCEcSovnuG3dupVPP/2ULVu2UFFRQefOnW1RlxBCCCGq+HpWDtJzCmwzSK8wmSmvWoTO28MFMNskVwghhBAt16xBem5uLuvXr+ezzz7j1KlT+Pj4MG3aNK688kqGDRtm6xqFDWh0OsIvmWK57CyZUqPjZqqhRntkqqFGe2SqoUZ7ZNoqr3qQnltQZpPMklKj5bKnuwsas8kh223PTDXUWJ3T4cLJ5OXlOV27nXV7S7ul3Y6SZ69M0TiNYsVB5Dt27GDdunVs2rQJk8nE0KFD2bVrFx988AEjRoywZ502ExcXB0BsbGwbV9I4vV5PfHw8MTExqjqvu7Ad6QNC+oDQ6/V8/sNu1m/LpV9UMC/PGdfizNPZJcx6aRMebjo+f+lyG1Qp7EneB4T0ASF9QP2sGYc2aU/66tWrWbduHYmJiXTt2pXZs2czbdo0vLy8GDFihJy3XAghhLCjs/ek20KJZWV3OR5dCCGEcDRNGqS//PLLREdHs2bNmhp7zIuKiuxWmLAtRVGoKCwEwMXPzyY/rKghU2p03Ew11GiPTDXUaI9MNdRoj0xb5flUD9KLyjCbzZiqPn+bm1l9+jVvTxeb1lnNmbZNa2QaCwtRSkpscgYdNbXbWbe3tFva7Sh59soUjWvSIP2yyy5j8+bN3H333YwePZpp06YxceJEe9cmbMhsMPDXzH8BMGrdWnQeHk6RKTU6bqYaarRHphpqtEemGmq0R6at8nw8Kk/GYig3UVJYwv5bW5ZZUlpzZXdHbbc9M9VQY3XmgbvnVF5+fxV4t+wUfGpqt7Nub2m3tNtR8uyVKRrXpEH6q6++SnFxMRs2bODLL7/k3nvvJTAwkAsuuACNRiO/qAghhBB25O6qxcNNR1m5ibwiQ4vzLHvSZbq7EEII4XCafJ50Hx8fpk+fzueff86GDRu48sor+eWXX1AUhccff5ylS5eSkJBgz1qFEEIIpxXo6w5Avg0G6WeOSW/xmViFEEIIYWNNHqSfrVevXsyfP58tW7bwxhtvEBUVxapVq7jiiiuYOnWqrWsUQgghnF6ArxsA+UUtXzxOX1Y53d3bU/akCyGEEI6mRT+hu7i4cOGFF3LhhReSnZ3NV199xVdffWWr2oQQQghRJcCnck96blE5IS3Mqj5PuqzuLoQQQjieZu1Jr0uHDh2466672Lhxo60ihRBCCFHFMt290IZ70mW6uxBCCOFwbDZIF0IIIYT9BFQN0vOKWz5Il/OkCyGEEI5LfkJ3EhqdjtBJEyyXnSVTanTcTDXUaI9MNdRoj0w11GiPTFvmBVYdk55XVN7iTH1pzfOkO3K77ZWphhqrc4LOG0d+QYHTtdtZt7e0W9rtKHn2yhSN0yiKorR1Ea0pLi4OgNjY2DaupHF6vZ74+HhiYmLw8vJq63JEG5A+IKQPiOo+YHQL5YXVe4gM82XFvEktynxk6e8cOZnHE7ePYFT/CBtVKuxF3geE9AEhfUD9rBmHNmlPelpamlUFdOzY0ar7CyGEEKJhgVULx+XZ4Jj0EjlPuhBCCOGwmjRInzRpEhqNpsmh8fHxzS5I2IeiKJgNlefW1bq7W7U91ZwpNTpuphpqtEemGmq0R6YaarRHpi3zqheOK9aXU1ZcgquLrtmZ+nPOk+7I7bZXphpqrM40lZWhlJdji8mPamq3s25vabe021Hy7JUpGtekQfqLL74oG0TlzAYDO264GYBR69ai8/Bwikyp0XEz1VCjPTLVUKM9MtVQoz0ybZnn7emCi06Lxmhg980zW5R57nnSHbnd9spUQ43Vmftvv6vy8vurwNvbIWt09Ew11GiPTDXUaI9MNdRoj0w11CiapkmD9KuvvtredQghhBCiARqNhkA/d/JzDC3KMZnMlJWbAPB0l/VjhRBCCEfTrE/n/fv3s3PnTsrPmnqlKAp6vZ7du3fz2Wef2bRIIYQQQkCQrwf5OYUtytAbKiyXq/ekCyGEEMJxWD1IX7t2Lc8//3ydx0VptVrGjRtnk8KEEEIIUVOgn3uLM0qqTr/m5qrDRadtcZ4QQgghbMvqT+ePPvqI8847j507d/Kvf/2L66+/nn379rF06VLc3d2ZOnWqPeoUQgghnF6gX8uPBawepPt4ylR3IYQQwhFZPUhPTU3lpptuwt/fn/79+7N79248PDy4+OKLmTVrFmvWrLFHnUIIIYTTC7LBIL1YXzVI93JrcZYQQgghbM/qQbqrqyseVav6de3aleTkZIzGyg/8oUOHkpSUZNMChRBCCFEp0NcGg3TLnnQ5Hl0IIYRwRFbPdYuJieHXX39l5MiRdO/eHbPZzD///MOwYcNIT0+3R43CBjRaLcFjRlsuO0um1Oi4mWqo0R6ZaqjRHplqqNEembbOC/Jzx4yWlJCeDOoV0qzMIn05AL5n7Ul39HbbI1MNNVbnBIwcTmFhkdO121m3t7Rb2u0oefbKFI3TKHWtANeATZs2MXfuXK6++mpefPFF7r33Xg4ePMhFF13Ehg0bGDBgAG+++aa96m2xuLg4AGJjY9u4ksbp9Xri4+OJiYnBy8urrcsRbUD6gJA+IM7uA2m55Ty4ZAtBfu58sHBKs/LW/3KMD74/xOThkTxw4xAbVyvsQd4HhPQBIX1A/awZh1r9c8gFF1zAW2+9RY8ePQB49tln6datG59++ilRUVE8+eST1kYKIYQQogmqj0nPLzJgMlv1G7tFcdWedB9POSZdCCGEcETNWtp1woQJTJgwAYDAwEDee+89W9YkhBBCiDr4e7uh0YBZgcJiQ7NWey+qWjjO10uOSRdCCCEcUbMOLFAUhUOHDrFr1y7+/vvvWv8Jx2MqK2Pbldew7cprMJWVOU2m1Oi4mWqo0R6ZaqjRHplqqNEembbO0+m0BHtqmZ+whkMzbm5WZnFp9Z70M4N0R2+3PTLVUGN15t7pMyh79kWna7ezbm9pt7TbUfLslSkaZ/We9P3793P//ffXWCROURQ0Go3l//Hx8TYt8my7du1ixowZrF69mpEjR9rteYQQQghH1NIV3uUUbEIIIYRjs3qQ/tJLL+Hi4sJLL71EeHg42lZc5a+oqIh58+ZhNptb7TmFEEIIRxLg27LBdbFlursM0oUQQghHZPUg/eDBg7z22mtccMEF9qinQU8//TSRkZGcOnWq1Z9bCCGEcARBzTgO/WxF1dPd5Zh0IYQQwiFZvRs8ODgYnU5nj1oa9M0337B3714ef/zxVn9uIYQQwlEEtHi6uwzShRBCCEdm9Z70m266ibfffpuRI0e22jn6UlNTeeGFF1ixYgXe3t4tzlMUBb1eb4PK7Ku0tLTG/1vi7IUe9Ho9OhscMqCGTLXX2Nw+oPZ2t+dMa/Oa0gfaY7vbS6Yt8s7tA94eZ35ftzazwmSm1GACQEeF5bPQEdtt70w11HhuZllZGboWfn9RY7uddXufndmevw/YI1MNNVqb2V6+Dziz6vXbmsLqQXpycjLHjx9n7Nix9OrVCw+Pmr/oazQaPvjggybnpaamMnny5Hpv37ZtG48++ig33HADw4YNIzU11dqSazEajXZd3M7WkpKSWpyhlJdbLh85cgSNW8uPRVRDZnup0do+0F7a3R4zm5vXUB9oz+1We6Yt86r7QElBYbMzi8tMlssnExPQajU2r9MeefbIVEON52YmJyejOX3aZnlqabezbu+6Mtvj9wF7ZKqhxuZmqv37gLNza+Lr16xBep8+fSz/VhSlxu3n/rsxYWFhbNy4sd7bP/30U0pLS7n33nutK7QBrq6u9OzZ02Z59lJaWkpSUhLdunXD09OzRVnm8nISBw0EoHtMDFob/IGpIVPtNTa3D6i93e0509q8pvSB9tju9pJpi7xz+4DWLYv9Xp1wd9VyuZWZp7JKgNN4e7jQr19fm9Z5NmfZNq2VmTCgP6X6Urp27463n59D1ujomWqosaHM9vx9wB6ZaqjR2sz28n3AmSUkJDT5vhrF2lF1K5s0aRKZmZm4ulYeO6coCqWlpbi7u3PVVVfx7LPPWpUXFxcHQGxsrM1rtTW9Xk98fDwxMTGtdmiBcCzSB4T0AXFuH8jM03PH8z/jotPwxctXWPaGN8XhpFwefWMrYUFevPPEhXasWtiSvA8I6QNC+oD6WTMOtXpPemv78MMPqaiosPw7IyODGTNm8PzzzzN27Ng2rEwIIYRofcF+Hui0GipMCnlFZQT7N32vWlHVonG+smicEEII4bCsHqT36dOn3gPeNRoNXl5edOnShVtvvZUrr7yyxQV26tSpxr+rV5YPCwsjODi4xflCCCGEmuh0WoIDPMnM1ZORq7dqkF5cWnmOdB85R7oQQgjhsKw+Bdv8+fNxdXUlKiqKOXPmsHDhQubMmUN0dDQAV155JZ07d2bBggUNHmsuWpeprIzt19/E9utvqrFKY3vPlBodN1MNNdojUw012iNTDTXaI9NeNc7c/Q4PHf+YzIx8qx5bvSfdx7PmnnS1tNvZaqzO/Oe2Oyh7aZHTtdtZt7e0W9rtKHn2yhSNs3pP+v79+xkzZgxvvvlmjT3qc+fO5f7776eoqIj//ve/vPLKK7z//vtceumlNi24c+fOHDlyxKaZzsJsMDhlptTouJlqqNEemWqo0R6ZaqjRHpn2qNHFVHkYWFqedadjKtZX7kn3rWNPuhra7Yw1VmaWN34nq/LU0m5n3d7SbkfMU0umGmoUjbN6T/qvv/7K9OnT65zyfu2117Jp0yYAxo8fb9UKdkIIIYSwTla+defMPjPdXY5JF0IIIRyV1YN0T09P0tPT67wtLS3Nsgq72Wy2XBZCCCGE7WVbuSf9zHR3OSZdCCGEcFRWD9IvuOACXnvtNcse82q//PILS5YsYfLkyZSXl7N+/XpiYmJsVqgQQgghasps9nR3+RFdCCGEcFRWH5P+2GOPkZSUxNy5c3F1dSUgIIC8vDxMJhNjxoxh/vz5bNq0ic2bN/POO+/Yo2YhhBBCUDndXVGUes+6cq7i6j3pMkgXQgghHJbVg3Rvb2/WrFnD9u3b2bFjB3l5eYSHhzNixAiGDRsGwKBBg/jpp58IDw+3ecFCCCGEqGSsMJNfbCDQ16NJ9y/SyynYhBBCCEdn9SC92ujRoxk9enSdt3Xs2LHZBQk70Wjw69/PctlpMqVGx81UQ432yFRDjfbIVEON9si0Y42Hk3JR0JCVV9rkQXpJ9cJx55yCTU3ttlmmGmqsyvGJ6UOJXo9Ga/VRinXmqaXdzrq9pd3SbofJs1emaJRGURSlsTvNnDmThQsX0qNHD2bOnNlwoEbDBx98YLMCbS0uLg6A2NjYNq6kcXq9nvj4eGJiYvDy8mrrckQbkD4gpA+I+vrAvDe2Ep+Uy2MzhzFuYKdGcxRFYdq8DZjMCu8/eREdAjztWbawIXkfENIHhPQB9bNmHNqkn2TPHscritLgf2azuZllCyGEEKKpQgMrv6Rl5jbtNGylhgpM5srPczkmXQghhHBcTZru/uGHH9Z5WQghhBBtIzSock94U1d4rz5HuotOi7urzm51CSGEEKJlWnxwU0FBAXFxcRQVFdmiHmEnprIyds64nZ0zbsdUVuY0mVKj42aqoUZ7ZKqhRntkqqFGe2Tas8bua17G1WwkM69pe9LPPv3auavBq6ndzlRjdWbcrNmULV7idO121u0t7ZZ2O0qevTJF45q8cNz+/ftZsWIFU6ZM4aqrrgIq96ovXryY8vJy3N3duffee7njjjvsVatooYrCQqfMlBodN1MNNdojUw012iNTDTXaI9NeNVb/yp7V5D3pDZ9+TS3tduQ8u2XaeEeIatrtrNtb2u2QeWrJVEONonFN2pN++PBhZsyYQXx8vGWhgri4OF588UUiIyN54403mD17NkuWLGHTpk12LVgIIYQQZzR1T7rl9Guecvo1IYQQwpE1aU/622+/TZ8+fVi9ejWenpXHwK1ZswaAxYsX06dPHwCys7P58MMPueCCC+xUrhBCCCHOpi+roLjUWPu0auco1lfuSfeVc6QLIYQQDq1Je9L//vtvZsyYYRmgA/zxxx9ERkZaBugA48aN49ChQ7avUgghhBC1VA+4s5qwN736mHRZ2V0IIYRwbE0apOfn5xMeHm759/Hjx8nLy2PkyJE17ufp6Ul5ebltKxRCCCFEnUICq1Z4b8Jp2Ir0DR+TLoQQQgjH0KRBekBAADk5OZZ/79ixA41Gw+jRo2vc7/jx4wQFBdm2QiGEEELUqUNA00/DVn0KNpnuLoQQQji2Jh2TPmLECD777DMuuugiTCYTX3zxBe7u7owfP95yn/LyctauXcuQIUPsVqxoAY0Gn549LJedJlNqdNxMNdRoj0w11GiPTDXUaI9MO9cYEugN5DRp8TjLdPe6jl1XWbudpsaqHK+o7pSWlaHRtvjMuapqt7Nub2m3tNth8uyVKRqlURRFaexOx44d44YbbiAwMBBFUUhLS2POnDnce++9AHzxxResXbuWo0eP8vnnnxMTE2P3wpsrLi4OgNjY2DaupHF6vZ74+HhiYmIsq+oL5yJ9QEgfEA31gW9/P86qbw4wdmBH5s8c3mDOE29uY39CNg/fPJQJQzrbs2RhY/I+IKQPCOkD6mfNOLRJe9J79erFZ599xnvvvUdOTg533XUX06dPt9z++uuv4+LiwvLlyx16gC6EEEK0J9XHpDdp4bjSBvakCyGEEMJhNGmQDtCzZ09efPHFOm9bv349ISEhaG0xDUsIIYQQTRISWLk3pUnHpFtOwSaDdCGEEMKR2WRUHRYWJgN0B2cyGNh11z3suuseTAaD02RKjY6bqYYa7ZGphhrtkamGGu2Rae8aO3jpAMgvMlBuNDX4uCJ9/QvHqa3dzlJjdebBex+kbOlyzE7Wbmfd3tJuabej5NkrUzSuyXvShcopCobMLMtlp8mUGh03Uw012iNTDTXaI1MNNdoj0841+ni64O6mw1BuIju/lI4hPnU+pMJkptRQAYB3XdPdVdZup6mxKqc8O7vqonO121m3t7Rb2u0wefbKFI2S3d9CCCGESmk0GkKrz5XewHHpJVXHo4Mcky6EEEI4OhmkCyGEECoWEtD4celFVceje3m4oNPJR78QQgjhyOSTWgghhFCxkCbsSbes7F7H8ehCCCGEcCwySBdCCCFULCyock96Rm4Dg3TLonEy1V0IIYRwdDJIF0IIIVQsPNgbgPTsknrvU336NTkeXQghhHB8srq7s9Bo8IzsbLnsNJlSo+NmqqFGe2SqoUZ7ZKqhRntktkKNEdWD9Ab2pFeffq3e6e4qbLfD5dkx06NTJwzlBjRO1m5n3d7Sbmm3w+TZK1M0SqPY5Hwe9vfuu+/y8ccfk5WVRc+ePZk3bx6jRo2yOicuLg6A2NhYW5doc3q9nvj4eGJiYvDy8mrrckQbkD4gpA+IxvpAsb6c6U/+D4DPX7wMD/fav79/8uNhPv7pCBeP6src6wbZu2RhY/I+IKQPCOkD6mfNOFQV091XrFjBsmXLePjhh/n2228ZNGgQ//d//0dKSkpblyaEEEK0KR8vN8s09vr2plcvHOcrC8cJIYQQDs/hB+l6vZ5Vq1bxyCOPcOmll9KtWzeeeOIJIiMj2b17d1uXJ4QQQrS58A5VU95z6j4uvfoUbLJwnBBCCOH4HH6Qvnv3bkpLS7nsssss1+l0Or799luuuuqqtitMZUwGA3vm3s+eufdjMhicJlNqdNxMNdRoj0w11GiPTDXUaI/M1qoxvGqF9/ScuvekVx+T7u1Z9550tbbbkfLsmRn/yHwMb67E7GTtdtbtLe2WdjtKnr0yReMcfuG4xMRE/P39OXLkCK+//jpJSUn07NmTBx98kCFDhrR1eeqhKJSmpFouO02m1Oi4mWqo0R6ZaqjRHplqqNEema1UY/UK7xn17EkvsZwnvZ496Sptt0Pl2TGz7NSpqovO1W5n3d7Sbmm3w+TZK1M0qs0H6ampqUyePLne2++//37Kysp46qmnePjhh+nYsSPr1q3j1ltv5euvv6ZHjx5WP6eiKOj19a+C6yhKS0tr/L8lTGVllst6vR6d2ewUmWqvsbl9QO3tbs+Z1uY1pQ+0x3a3l0xb5J3bB+rK9PPSAZCRW1Ln51t+UeVj3F3q/vxzxHbbO1MNNZ6bWVZWhq6F31/U2G5n3d5nZ7bn7wP2yFRDjdZmtpfvA85MUZQmn6WjzQfpYWFhbNy4sd7bN2/eTFlZGY8//jjnn38+AP369WPv3r189NFHLFy40OrnNBqNxMfHN7vm1paUlNTiDKW83HL5yJEjaNxavniQGjLbS43W9oH20u72mNncvIb6QHtut9ozbZlX3QfqytQXVn5pO5WRV+fnW27V7dnpKcSXptu1Tnvk2SNTDTWem5mcnIzm9Gmb5aml3c66vevKbI/fB+yRqYYam5up9u8Dzs6tia9fmw/SXV1dG9wbfvDgQQCio6Mt12k0Gnr06EFqamqzn7Nnz57NemxrKi0tJSkpiW7duuHp6dmiLFNZGfurLkdHR6Pz8GhxfWrIVHuNze0Dam93e860Nq8pfaA9tru9ZNoi79w+UFemu18hn/6eQ6lRS0xMTI3HGyvMGIyVn5dDBsTUOeXdEdtt70w11HhuZteuXfEOCLBZnlra7azb++zM9vx9wB6ZaqjR2sz28n3AmSUkJDT5vm0+SG/MsGHD0Gg07Nu3jylTpgCVUwUSEhIYPXp0szI1Go2qzi/o6enZ4npN2jNrBHp5ednmj1YFme2lRmv7QHtpd3vMbG5eQ32gPbdb7Zm2zKvuA3VldgqrvK6g2IC7uwc63Zn7ZOdX7kXXaTV0CPJDq6091c6R222vTDXUeG6mh4eHfB9wkLy2ymyP3wfskamGGpubqfbvA86sqVPdQQWD9I4dO3LNNdfw/PPP4+npSZcuXfjwww9JTU3lpptuauvyhBBCiDbn7+OOTqvBZFbIKzLQIeDMXpaC4srVeP283eocoAshhBDCsTj8IB3g6aefZtmyZfz73/+moKCAvn378t577xEVFdXWpamHRoN7aIjlstNkSo2Om6mGGu2RqYYa7ZGphhrtkdlKNWq1GoL8PcjKKyW7oPScQXrl8YT+Pu6tV6dsG5tmunXoQLnRaNVemIby1NJuZ93e0m5pt8Pk2StTNEqj2OR8HuoRFxcHQGxsbBtX0ji9Xk98fDwxMTGqmp4vbEf6gJA+IJraB+a9sZX4pFzm3zqcsQM6Wq7/ZVcKSz7Zw6BeITx3z5jWKFnYmLwPCOkDQvqA+lkzDtU2eg8hhBBCOLwg/8rjBHPya56exzLd3UdW5BVCCCHUQAbpQgghRDvQwb9yintOQVmN66sH6QENTXcXQgghhMOQQbqTMBkM/PPwPP55eB4mg8FpMqVGx81UQ432yFRDjfbIVEON9shszRo7BFTuSc8uOHdPeuPHpKu53Y6SZ8/MI088heGd9zGfdb7iluSppd3Our2l3dJuR8mzV6ZonCoWjhM2oCgUJxy3XHaaTKnRcTPVUKM9MtVQoz0y1VCjPTJbscZgv7r3pOdX7UlvcOE4FbfbYfLsmKk/kVh50Wy2SZ5a2u2s21vaLe12mDx7ZYpGyZ50IYQQoh0IrtqTnlNrT3r1IF2OSRdCCCHUQAbpQgghRDsQfNYx6WefuCWvqHKQHugrx6QLIYQQaiCDdCGEEKIdCPKr3JNurDBTWFJ57LLZrJBXWDn9PbDqdiGEEEI4NhmkCyGEEO2Aq4vWsoJ7btXAvEhfjslcuVc90FcG6UIIIYQayCBdCCGEaCeqj0vPrjpXevVg3c/bDVcX+cgXQggh1EBWd3ciLn5+TpkpNTpuphpqtEemGmq0R6YaarRHZmvWGOznyXEKyK5a4T2vsPJ49KAmTHVXc7sdJc9umb6+VJgqbJenlnY76/aWdjtknloy1VCjaJxGUZxrLf24uDgAYmNj27iSxun1euLj44mJicHLy6utyxFtQPqAkD4grOkDK774h//9mcQNF/bmlikxbPormaXr9jEkOpRnZo1upYqFrcn7gJA+IKQPqJ8141CZ+yaEEEK0E8H+Vadhy6/ck55btSc90E9WdhdCCCHUQgbpQgghRDvRwXIatspj0qtXdm/KdHchhBBCOAY5Jt1JmAwGDj37AgB9n3oCnXvL96qoIVNqdNxMNdRoj0w11GiPTDXUaI/M1q6xek969THpuUVNG6Srvd2OkGfPzGPPvoBBr8f87EJo4TRXNbXbWbe3tFva7Sh59soUjZNBurNQFAoPHLRcdppMqdFxM9VQoz0y1VCjPTLVUKM9Mlu5xuCqPem5VXvScwuaeI50lbfbIfLsmFkcf7jyotlskzy1tNtZt7e0W9rtMHn2yhSNkunuQgghRDtRvSe9pKyCUkMFmXmVg/UO/jLdXQghhFALGaQLIYQQ7YSXhyveHpWT5BLTCiznSe8c6tuWZQkhhBDCCjJIF0IIIdqRzmGVA/Jt+9Pg/9u796iqyvyP4x9A8BKaoKLNciwEQUmu3n8llpI6o85yyiZTbExT0xmbvIIM422WknltdBWZjqwUZ5oarZlpRlumZjWEoKUWXkAwrRWgeSFFjlye3x/8PD9PkIQeZB94v9ZiLfez9/me7+N+9sP+svfZR5VX1+9q7lmfKQEAgFqgSAcAoAHp9H9F+p7MrxyWAQCAa6BIBwCgAbnvnlaSpO+Kr0mSQjq3qc90AABALfF090bEvQ6+MsEVYpKjdWO6Qo51EdMVcqyLmK6QY13EvNM5dg9o67AcFtj2B7b88TFvBfvGmTG9VFHhvKcqu06/G+v+pt9WjOcqMV0hR9TMzZjG9Sz9I0eOSJJCQ0PrOZOaFRcX6+jRo+rWrZta3Ob3osI1MQbAGEBtx0BFhdHjCe/qWmm5/Hxb6LV5MXJ3d7sDmaKuMA+AMQDGgOurTR3KlXQAABoQd3c3JU17QG/tztaw//GnQAcAwMVQpAMA0MAEdfJRwvje9Z0GAAC4BRTpjUTFtWs69sJySVLX+Dly9/JqFDHJ0boxXSHHuojpCjnWRUxXyLEuYrpCjnURkxydG/PkshW6dvmyKv6QIN3mba6u1O/Gur/pN/22Sry6iomaUaQ3EqaiQhcOHLT/u7HEJEfrxnSFHOsipivkWBcxXSHHuojpCjnWRUxydG7Mos8OOS2mK/W7se5v+k2/rRKvrmKiZnwFGwAAAAAAFkGRDgAAAACARVCkAwAAAABgERTpAAAAAABYBEU6AAAAAAAW4WaMMfWdxJ108OBBGWPk5QJfH2CMUWlpqTw9PeXm5na7wVRSWChJaubnJ91uPFeJ6eI53vIYcPF+N+iYtYz3o8ZAA+x3g4nphHhVxkAj6Xedx3SFHL8Xs2m7dnJzv83rKy7Y78a6v2+M2aDPB+oipivkWMuYDeZ8oBG7du2a3NzcFBUVVeO2ja5I//TTT2WMkaenZ32nAgAAAABoBEpLS+Xm5qbIyMgat210RToAAAAAAFbFZ9IBAAAAALAIinQAAAAAACyCIh0AAAAAAIugSAcAAAAAwCIo0gEAAAAAsAiKdAAAAAAALIIiHQAAAAAAi6BIBwAAAADAIijSAQAAAACwCIp0AAAAAAAsgiIdAAAAAACLoEgHAAAAAMAiKNItqKKiQn/605/Uv39/RUREaNKkSTpz5kx9pwUnuXjxoubPn6/o6GhFRUXpySefVGZmpn39008/reDgYIefcePG2dfbbDYtWrRI/fr1U2RkpGbNmqXz58/XR1dwGwoKCqrs5+DgYG3btk2SdPToUcXGxioiIkIDBw7U66+/7vB65gnXlp6eXu3+Dw4O1qBBgyRJr7zySrXrb5SamqpBgwYpLCxMY8aMUVZWVn10B7X06quvOszrknOO+ZpiwFqqGwe7d+/WY489psjISA0cOFDLli1TSUmJff2BAweqnRfS09Pt26SlpenRRx9VeHi4hg4dqnffffeO9Qm1U90YSExMrLJ/Bw4caF/PXNBIGFjO2rVrTZ8+fcyePXvM0aNHzYQJE8zgwYONzWar79TgBE8//bQZPny4ycjIMLm5uWbRokUmLCzMnDx50hhjTL9+/czWrVtNYWGh/efChQv218fHx5uYmBiTkZFhDh06ZEaOHGnGjh1bT73Brdq7d68JDQ01BQUFDvv66tWr5vz586ZPnz5m3rx5Jicnx7z11lsmNDTUvPXWW/bXM0+4NpvN5rDfCwsLzXvvvWeCg4Pt+/l3v/udmTNnTpXtrtu2bZsJCwsz77zzjsnOzjZz5swxvXv3Nt9++219dQs/wpYtW0zXrl1NbGysvc0Zx/yPiQHrqG4cZGRkmG7duplXXnnF5OXlmb1795ro6GgTHx9v3yY1NdXExMRUmReuj4OcnBwTGhpqVq1aZXJycsyGDRtMSEiI+e9//3vH+4ibq24MGGPMqFGjzKpVqxz2743zOnNB40CRbjE2m81ERkaa1NRUe9ulS5dMWFiY+ec//1mPmcEZTp06ZYKCgkxmZqa9raKiwsTExJg1a9aYc+fOmaCgIPPFF19U+/r8/HzTtWtXs3fvXntbbm6uCQoKMgcPHqzz/OE869evNyNGjKh2XXJysnnwwQdNaWmpvW3lypVm8ODBxhjmiYboypUr5uGHH3Y4Gf/Zz35mNm3a9IOvGTx4sHnxxRfty6WlpWbAgAEmOTm5LlPFLcrPzzdTpkwxERERZujQoQ4n5s445muKAWu42TiYNWuWGT9+vMP227dvN/fff7+9AFuwYIF59tlnfzD+H/7wBzNq1CiHtpkzZ5oJEyY4sRe4HTcbAxUVFSYiIsK899571b6WuaDx4HZ3izl27JiuXLmifv362dtatWqlkJAQZWRk1GNmcAYfHx+tX79eoaGh9jY3Nze5ubmpqKhIx48fl5ubm/z9/at9/YEDByRJffv2tbf5+/urffv2jA8Xc/z4cQUEBFS7LjMzU71791aTJk3sbX379tWpU6d07tw55okGKDk5WVevXlVcXJwk6dq1azp16pQ6d+5c7fbffvutTp065TAGmjRpop49ezIGLOqLL76Qp6en/vGPfyg8PNxhnTOO+ZpiwBpuNg4mTJhgnwOuc3d3V2lpqS5fvizp5r87pMpxcOM4kSrHwYEDB2SMcVIvcDtuNgZOnz6t4uLiH5z7mQsajyY1b4I7KT8/X5J0zz33OLT7+fnZ18F1tWrVSgMGDHBo27lzp7788kslJCToxIkTatmypRYvXqyPP/5YLVq00NChQzVt2jR5eXmpoKBAPj4+atq0qUMMxofrOXHihHx8fDR27Fjl5eXp3nvv1dSpUxUdHa38/HwFBQU5bO/n5ydJ+uabb5gnGpjz588rJSVFs2bNUuvWrSVJOTk5Ki8v186dO7VkyRLZbDb16tVLc+bMcdjP1Y2BY8eO3eku4EcYOHCgw+dKb+SMY76mGG3btr39TuC23WwchISEOCyXlpYqJSVF3bt3l6+vryQpOztbPj4+evTRR1VQUKCgoCDNmDFDYWFhkirHQYcOHRzi+Pn56erVq7pw4YI9DurPzcbAiRMnJEmbN2/Wvn375O7urujoaM2YMUMtW7ZkLmhEuJJuMVevXpUkeXl5ObQ3bdpUNputPlJCHTp48KDmzZunwYMH66GHHtKJEydks9kUFhamDRs2aOrUqXrzzTeVmJgoqXJ8fH9sSIwPV1NWVqbc3FxdunRJ06dP1/r16xUREaHJkycrLS1NJSUl1c4BUuWDA5knGpatW7eqZcuWeuKJJ+xt10/UmjdvrpdeeklLlixRbm6unnrqKZWUlDAGGhhnHPM1xYBrKSsr09y5c5Wdna0FCxZIqiywvvvuOxUXFysxMVEvv/yy2rZtq9jYWOXk5EiqfhxcX7527dqd7QRq7cSJE3J3d5efn5+Sk5MVHx+vjz76SNOmTVNFRQVzQSPClXSLadasmaTKifT6v6XKg6p58+b1lRbqwK5duzR79mxFRUVpxYoVkqTFixcrLi5Od999tyQpKChInp6emjFjhubOnatmzZpV+0uW8eFamjRpovT0dHl4eNiP8+7duys7O1sbN26sdj9f/8XaokUL5okG5u2339bIkSMd9uXIkSMVHR3tcNWrS5cuio6O1u7du9WpUydJVU+6GQOuyRnHfE0x4DouX76s559/Xvv379e6devsV8nvueceZWRkqHnz5vL09JQkhYaGKisrS5s3b9aiRYvUtGnTKuPg+jJzg/VNnTpVY8aMkY+Pj6TK88B27drpV7/6lY4cOcJc0IhwJd1irt++UlhY6NBeWFio9u3b10dKqANbtmzR9OnT9fDDDys5Odn+F84mTZrYC/TrunTpIun/b2G7ePFilcmX8eF67rrrLodfsFLlvi4oKFCHDh2qnQMkqX379swTDcixY8d05swZjRgxosq679+W6ufnp9atWys/P58x0MA445ivKQZcQ2FhocaOHavPPvtMGzdurPIRuVatWtkLdKnyM+sBAQEqKCiQVHkeWd04aNGihVq2bFn3HcBtcXd3txfo1914Hshc0HhQpFtM165d5e3t7fB9l0VFRcrKylKvXr3qMTM4y9atW/XHP/5RY8eO1apVqxxuSRo3bpzmzZvnsP2RI0fk6emp++67Tz169FBFRYX9AXKSlJeXp4KCAsaHC8nOzlZUVJTDcS5Jn3/+uQIDA9WrVy8dOHBA5eXl9nWffPKJ/P391aZNG+aJBiQzM9O+T2+0evVqDRkyxOFBT1999ZUuXLigwMBAtWnTRv7+/g5joKysTJmZmYwBF+SMY76mGLC+S5cu6de//rXOnz+v1NTUKsfyvn37FBkZ6fCd2GVlZTp27JgCAwMlST179tT+/fsdXvfJJ58oKipK7u6c9lvd3LlzNX78eIe2I0eOSJICAwOZCxoRjlaL8fLyUmxsrFasWKH3339fx44d04wZM9ShQwcNHjy4vtPDbcrLy9PSpUv1yCOPaMqUKTp37pzOnj2rs2fP6rvvvtOQIUP0zjvv6C9/+YvOnDmjf//733rxxRc1ceJEeXt7q3379ho2bJgSExOVnp6uw4cPa+bMmerdu7ciIiLqu3v4kQICAtS5c2ctXrxYmZmZOnnypJKSkvTZZ59p6tSpeuyxx3T58mX9/ve/V05OjrZt26aUlBRNmTJFEvNEQ5KVlaXg4OAq7Y888oi+/vprLVy4UHl5ecrIyND06dMVFRWl/v37S6p8EvSmTZu0fft25eTkKCEhQSUlJRo1atSd7gZukzOO+ZpiwPqSkpJ05swZLV++XL6+vvbzg7Nnz6q8vFxRUVHy8fFRXFycPv/8cx0/flxxcXG6ePGivbAbN26cDh8+rBUrVujkyZP685//rB07duiZZ56p387hRxkyZIjS0tK0bt06nT59Wh988IESEhI0fPhwBQQEMBc0Im6G72OwnPLycq1atUrbtm1TSUmJevXqpfnz56tjx471nRpuU3JyslavXl3tul/+8pd64YUXlJqaqtTUVJ05c8b+OaTJkyfb/wJeXFyspUuXaufOnZKk6OhoJSYmVrk9CtZ27tw5rVy5Uh9++KGKiooUEhKi2bNnq2fPnpKkw4cPa8mSJcrKylK7du00YcIExcbG2l/PPNEwTJo0Sd7e3tXOC2lpaXrppZd0/PhxeXl5adCgQQ7PrJCkjRs36vXXX9fFixfVvXt3JSYmqlu3bneyC7gF8fHx+vrrr7V582Z7mzOO+ZpiwFpuHAfl5eWKjIz8wQd7vf/+++rYsaNOnz6tFStWKD09XTabTT169FBcXJzD07z37dun5cuX69SpU+rYsaOmT5+un//853eqW6iF6uaC//znP1q/fr1yc3PVsmVLjRgxQs8//7z9o5HMBY0DRToAAAAAABbB7e4AAAAAAFgERToAAAAAABZBkQ4AAAAAgEVQpAMAAAAAYBEU6QAAAAAAWARFOgAAAAAAFkGRDgAAAACARVCkAwBgEfHx8QoODr7pz7hx47Rt2zYFBwfrq6++qpc8nfn+a9euVXBwsBOyAgCgYWhS3wkAAIBK06ZN0+jRo+3LL7/8srKysrRu3Tp7m7e3t3x9ffXGG2/Iz8+vPtIEAAB1iCIdAACL6NSpkzp16mRf9vX1lZeXlyIiIqps6+vrewczAwAAdwq3uwMA4GK+f7t5fHy8Jk6cqDfeeEMxMTEKCwvT6NGjlZeXpz179mjEiBEKDw/X448/rqNHjzrEyszMVGxsrMLDw9W7d2/FxcXp/PnztconPT1dwcHBSktL04QJExQeHq4HHnhAy5cvV3l5uX07m82mpKQkPfDAA4qMjNS8efNks9mqxLtZTuXl5Ro1apT69OnjkGd8fLwiIiKUm5tbq9wBALAainQAABqATz/9VFu2bFF8fLySkpJ08uRJTZ48WUlJSZoyZYpWrVqlb775RrNnz7a/JiMjQ+PHj1ezZs20Zs0aJSQkaP/+/XrqqadUUlJS6xxmz56tHj16KDk5WcOHD9eGDRv05ptv2tfPmTNHf/vb3zRlyhStWbNGly5dUkpKikOMmnLy8PDQCy+8oOLiYi1btkyStGvXLm3fvl1z585V586db+0/EAAAi+B2dwAAGoArV65ozZo1CggIkCTt379ff/3rX5WSkqJ+/fpJkr788kstW7ZMRUVFatWqlVauXCl/f3+9+uqr8vDwkCSFh4dr2LBh+vvf/66xY8fWKofHH39cv/nNbyRJ/fr1065du7R3716NHj1a2dnZ2rlzpxYuXKgnn3xSktS/f3+NGDFCOTk59hg/JqfAwEBNnz5dK1euVExMjBYuXKiHHnpIY8aMub3/RAAALIAr6QAANAB33323vUCXpLZt20qqLHCva926tSSpqKhIV69e1aFDhzRgwAAZY1RWVqaysjL99Kc/VUBAgD7++ONa5xAZGemw3KFDBxUXF0uqvIVdkgYOHGhf7+7uriFDhtiXa5PTxIkTFR4erueee07GGC1durTW+QIAYEVcSQcAoAHw9vautr1FixbVthcVFamiokKvvfaaXnvttSrrmzZtWuscmjVr5rDs7u4uY4wk6dKlS5IkHx8fh23atWt3Szl5eHjoF7/4hQ4dOqSwsDC1adOm1vkCAGBFFOkAADRCd911l9zc3DR+/HgNGzasyvrmzZs79f2uF+fnzp3TT37yE3v7xYsXbymns2fPau3aterWrZv27NmjHTt2aOjQoU7NGQCA+sDt7gAANELe3t4KCQlRbm6uQkND7T9dunTR2rVrlZ6e7tT369u3ryRpx44dDu179uy5pZzmz58vDw8PpaSkaNCgQVq0aFGtn0oPAIAVUaQDANBIzZw5Ux999JFmzZqlDz74QLt379YzzzyjtLQ03X///U59r3vvvVdPPPGEVq9erfXr1+vDDz/U7Nmzdfz48Vrn9Pbbb2v37t1KSEhQ69atNX/+fNlsNi1cuNCpOQMAUB8o0gEAaKQefPBBbdy4Ufn5+Xruuec0d+5ceXh4aNOmTYqIiHD6+y1YsECTJk3Sli1b9Nvf/lYlJSV69tlna5VTQUGBlixZogEDBmj48OGSKh9QN2PGDO3cuVP/+te/nJ43AAB3kpu5/kQXAAAAAABQr7iSDgAAAACARVCkAwAAAABgERTpAAAAAABYBEU6AAAAAAAWQZEOAAAAAIBFUKQDAAAAAGARFOkAAAAAAFgERToAAAAAABZBkQ4AAAAAgEVQpAMAAAAAYBEU6QAAAAAAWARFOgAAAAAAFvG/yKrXT5UMbbIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Segmentation using the fixed number of slices\n",
    "num_slices = 40 \n",
    "slice_width = segment_ecg_signal(instance_ecg, num_slices)\n",
    "\n",
    "# plot the segmented ECG signal\n",
    "plot_segmented_ecg(instance_ecg, slice_width)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of random_perturbations array (num_perturbations, num_slices): (150, 40)\n",
      "Example Perturbation: [0 1 0 1 0 1 1 1 0 1 1 0 1 1 0 0 0 1 1 0 1 1 0 0 1 1 0 1 0 0 0 1 0 0 1 0 0\n",
      " 0 1 1]\n"
     ]
    }
   ],
   "source": [
    "# Perturbation\n",
    "num_perturbations = 150\n",
    "random_perturbations = generate_random_perturbations(num_perturbations, num_slices)\n",
    "\n",
    "# Example output\n",
    "print(\"The shape of random_perturbations array (num_perturbations, num_slices):\", random_perturbations.shape)\n",
    "print(\"Example Perturbation:\", random_perturbations[-1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Apply the Perturbation to the ECG signal and Display\n",
    "from lime_explanation import apply_perturbation_to_ecg, perturb_mean\n",
    "from visualization import plot_perturbed_ecg\n",
    "\n",
    "# Choose the perturbation function\n",
    "perturb_function = perturb_mean  \n",
    "\n",
    "# Apply a random perturbation to the ECG signal\n",
    "perturbed_ecg_example = apply_perturbation_to_ecg(instance_ecg, random_perturbations[-1], num_slices, perturb_function)\n",
    "\n",
    "# plot the original and perturbed ECG signals with highlighted slices and deactivated segments\n",
    "plot_perturbed_ecg(instance_ecg, perturbed_ecg_example, random_perturbations[-1], num_slices, title='ECG Signal with Perturbation')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Predicting Outcomes for Perturbed ECG Signals with the ECG Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 12ms/step\n",
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      "1/1 [==============================] - 0s 8ms/step\n"
     ]
    }
   ],
   "source": [
    "from lime_explanation import predict_perturbations\n",
    "\n",
    "## Predict the class probabilities using the trained ECG classifier\n",
    "perturbation_predictions = predict_perturbations(model, instance_ecg, random_perturbations, num_slices, perturb_mean)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4: Calculating Distances and Assigning Weights to Perturbations for ECG Signals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of Cosine Distances Array: (150,)\n"
     ]
    }
   ],
   "source": [
    "from lime_explanation import calculate_cosine_distances\n",
    "\n",
    "# Calculate cosine distances between each perturbation and the original ECG signal representation\n",
    "cosine_distances = calculate_cosine_distances(random_perturbations, num_slices)\n",
    "print(\"Shape of Cosine Distances Array:\", cosine_distances.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of Weights Array: (150,)\n"
     ]
    }
   ],
   "source": [
    "from lime_explanation import calculate_weights_from_distances\n",
    "\n",
    "#Applying a Kernel Function to Compute Weights\n",
    "kernel_width = 0.25  # This can be adjusted based on your specific needs\n",
    "weights = calculate_weights_from_distances(cosine_distances, kernel_width)\n",
    "\n",
    "# Now we have the weights for each perturbation for further analysis\n",
    "print(\"Shape of Weights Array:\", weights.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of perturbation_predictions: (150, 1, 4)\n"
     ]
    }
   ],
   "source": [
    "# Check the shape of perturbation predictions\n",
    "print(\"Shape of perturbation_predictions:\", perturbation_predictions.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5: Constructing an Interpretable Linear Model for ECG Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Segment Importance Coefficients: [ 9.22297320e-09  1.26698970e-08  5.53387647e-09  6.25533323e-09\n",
      "  3.76481671e-09 -7.37439494e-09  4.47139829e-09 -9.75092713e-09\n",
      "  9.34897782e-09 -1.30896053e-08 -1.62798052e-08 -3.36454635e-09\n",
      "  3.38380385e-09  9.61862652e-08 -5.15851413e-09  1.13056193e-08\n",
      " -8.69844710e-09 -2.70965415e-09  8.65957832e-08  1.13721921e-07\n",
      "  8.01311368e-08  1.67043378e-08  1.42398119e-08 -8.02115202e-09\n",
      "  2.02637425e-08  1.40631131e-08 -5.69910648e-10 -3.74319659e-08\n",
      "  3.73196837e-09 -1.87477095e-10  5.36113266e-09 -5.55803849e-09\n",
      " -2.12973755e-08 -3.97073569e-09  1.00583040e-08  1.76368396e-08\n",
      "  1.91694705e-08  2.01435378e-08  2.12903579e-09  4.57058127e-09]\n"
     ]
    }
   ],
   "source": [
    "from lime_explanation import fit_explainable_model\n",
    "\n",
    "# Constructing the Explainable Model for ECG Signals\n",
    "segment_importance_coefficients = fit_explainable_model(perturbation_predictions, random_perturbations, weights, target_class=top_pred_classes[0])\n",
    "\n",
    "# The importance coefficients for each segment\n",
    "print(\"Segment Importance Coefficients:\", segment_importance_coefficients)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6: Extracting Insights from the Linear Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top Influential Signal Segments: [27 20 18 13 19]\n"
     ]
    }
   ],
   "source": [
    "# Isolating Influential Signal Segments\n",
    "from lime_explanation import identify_top_influential_segments\n",
    "\n",
    "number_of_top_features = 5\n",
    "top_influential_segments = identify_top_influential_segments(segment_importance_coefficients, number_of_top_features)\n",
    "\n",
    "# The indices of the top influential segments\n",
    "print(\"Top Influential Signal Segments:\", top_influential_segments)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mahdihabibi/Library/Python/3.9/lib/python/site-packages/numpy/core/fromnumeric.py:3432: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/Users/mahdihabibi/Library/Python/3.9/lib/python/site-packages/numpy/core/_methods.py:190: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the LIME Explanation: Highlighting Key Segments\n",
    "from visualization import visualize_lime_explanation\n",
    "\n",
    "visualize_lime_explanation(instance_ecg, top_influential_segments, num_slices, perturb_function=perturb_mean)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "\n",
    "As seen from the plots above, we could identify the most influential segments of the ECG signal (yellow segments) from our analysis. These highlighted segments showcase the most important parts of the ECG signal that significantly impact the model's decision-making process. This visual representation helps to demystify the model's reasoning, highlighting specific signal segments that are critical in influencing its predictions."
   ]
  }
 ],
 "metadata": {
  "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": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}