MLP-GAF-Timeseries.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "19ae9026",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Init Plugin\n",
      "Init Graph Optimizer\n",
      "Init Kernel\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.axes_grid1 import ImageGrid\n",
    "from pyts.image import GramianAngularField\n",
    "import pandas as pd\n",
    "import yfinance as yf\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import warnings\n",
    "import os\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f1a93603",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1673, 5)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock_symbol = 'FB' # Stock Symbol Google / Alphabet\n",
    "# last 5 years data with interval 1 day\n",
    "data = yf.download(tickers=stock_symbol, period='10y', interval='1d')\n",
    "s = pd.Series(list(range(0, len(data))))\n",
    "data.set_index(s, inplace=True)\n",
    "data_training_size = int(len(data)*0.7)\n",
    "data_test_size = len(data) - data_training_size\n",
    "data_training, data_test = data.iloc[0:data_training_size,:], data.iloc[data_training_size:len(data),:]\n",
    "data_training = data_training.drop(['Adj Close'], axis=1)\n",
    "data_test = data_test.drop(['Adj Close'], axis=1)\n",
    "sc = MinMaxScaler()\n",
    "training_data = sc.fit_transform(data_training)\n",
    "training_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e088042d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_gaf(timeseries):\n",
    "    data = dict()\n",
    "    gadf = GramianAngularField(method='difference', image_size=timeseries.shape[0])\n",
    "    data['gadf'] = gadf.fit_transform(pd.DataFrame(timeseries).T)[0]\n",
    "    return data['gadf']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f37139ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1613, 60, 5), (1613,))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "window = 60# check that window is less than shape[0]\n",
    "x_train = []\n",
    "y_train = []\n",
    "for i in range(window, training_data.shape[0]):\n",
    "    # take from current minus 60 first dimension, empty seconds dimension to grab all 5\n",
    "    x_train.append(training_data[i-window:i])\n",
    "    y_train.append(training_data[i, 0]) # zero second dimension only to grab which result should be\n",
    "x_train, y_train = np.array(x_train), np.array(y_train)\n",
    "x_train.shape, y_train.shape\n",
    "\n",
    "x_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5db2a690",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gadfImage = create_gaf(x_train[0]) # first image\n",
    "plt.imshow(gadfImage, interpolation='nearest')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2a2f9283",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1613, 60, 60)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gadf_training = []\n",
    "\n",
    "for i in x_train:\n",
    "    gadf_training.append(create_gaf(i))\n",
    "gadf_training = np.array(gadf_training)\n",
    "\n",
    "gadf_training.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "aa7f3ea5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metal device set to: Apple M1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-11-17 02:03:45.033486: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
      "2021-11-17 02:03:45.033800: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten (Flatten)            (None, 3600)              0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 3600)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 30000)             108030000 \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 30000)             0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 10000)             300010000 \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 10000)             0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 10001     \n",
      "=================================================================\n",
      "Total params: 408,050,001\n",
      "Trainable params: 408,050,001\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = keras.models.Sequential([\n",
    "    keras.layers.Flatten(input_shape=(60,60,1)),\n",
    "    keras.layers.Dropout(0.5), # 50% dropout\n",
    "    keras.layers.Dense(30000, activation='relu'),\n",
    "    keras.layers.Dropout(0.5), # 50% dropout\n",
    "    keras.layers.Dense(10000, activation='relu'),\n",
    "    keras.layers.Dropout(0.5), # 50% dropout\n",
    "    keras.layers.Dense(units=1)\n",
    "])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "62de6d80",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/150\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-11-17 02:03:46.061111: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)\n",
      "2021-11-17 02:03:46.076457: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n",
      "2021-11-17 02:03:46.382712: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "51/51 [==============================] - 29s 552ms/step - loss: 1789.1721\n",
      "Epoch 2/150\n",
      "51/51 [==============================] - 25s 480ms/step - loss: 0.8662\n",
      "Epoch 3/150\n",
      "51/51 [==============================] - 26s 507ms/step - loss: 0.2414\n",
      "Epoch 4/150\n",
      "51/51 [==============================] - 28s 547ms/step - loss: 0.1646\n",
      "Epoch 5/150\n",
      "51/51 [==============================] - 26s 504ms/step - loss: 0.1487\n",
      "Epoch 6/150\n",
      "51/51 [==============================] - 27s 530ms/step - loss: 0.1224\n",
      "Epoch 7/150\n",
      "51/51 [==============================] - 24s 470ms/step - loss: 0.1074\n",
      "Epoch 8/150\n",
      "51/51 [==============================] - 23s 458ms/step - loss: 0.1070\n",
      "Epoch 9/150\n",
      "51/51 [==============================] - 24s 468ms/step - loss: 0.1010\n",
      "Epoch 10/150\n",
      "51/51 [==============================] - 24s 461ms/step - loss: 0.1007\n",
      "Epoch 11/150\n",
      "51/51 [==============================] - 23s 457ms/step - loss: 0.1052\n",
      "Epoch 12/150\n",
      "51/51 [==============================] - 24s 462ms/step - loss: 0.0986\n",
      "Epoch 13/150\n",
      "51/51 [==============================] - 25s 484ms/step - loss: 0.1000\n",
      "Epoch 14/150\n",
      "51/51 [==============================] - 28s 547ms/step - loss: 0.0900\n",
      "Epoch 15/150\n",
      "51/51 [==============================] - 25s 497ms/step - loss: 0.0904\n",
      "Epoch 16/150\n",
      "51/51 [==============================] - 24s 476ms/step - loss: 0.0934\n",
      "Epoch 17/150\n",
      "51/51 [==============================] - 24s 464ms/step - loss: 0.0905\n",
      "Epoch 18/150\n",
      "51/51 [==============================] - 23s 457ms/step - loss: 0.0914\n",
      "Epoch 19/150\n",
      "51/51 [==============================] - 24s 461ms/step - loss: 0.0886\n",
      "Epoch 20/150\n",
      "51/51 [==============================] - 23s 458ms/step - loss: 0.0891\n",
      "Epoch 21/150\n",
      "51/51 [==============================] - 24s 468ms/step - loss: 0.0871\n",
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      "Epoch 61/150\n",
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      "51/51 [==============================] - 25s 482ms/step - loss: 0.0730\n",
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      "51/51 [==============================] - 23s 449ms/step - loss: 0.0706\n",
      "Epoch 66/150\n",
      "51/51 [==============================] - 24s 464ms/step - loss: 0.0721\n",
      "Epoch 67/150\n",
      "51/51 [==============================] - 36s 710ms/step - loss: 0.0705\n",
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      "Epoch 69/150\n",
      "51/51 [==============================] - 27s 528ms/step - loss: 0.0704\n",
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      "Epoch 73/150\n",
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      "Epoch 74/150\n",
      "51/51 [==============================] - 24s 468ms/step - loss: 0.0662\n",
      "Epoch 75/150\n",
      "51/51 [==============================] - 26s 518ms/step - loss: 0.0674\n",
      "Epoch 76/150\n",
      "51/51 [==============================] - 27s 524ms/step - loss: 0.0651\n",
      "Epoch 77/150\n",
      "51/51 [==============================] - 26s 504ms/step - loss: 0.0695\n",
      "Epoch 78/150\n",
      "51/51 [==============================] - 26s 513ms/step - loss: 0.0692\n",
      "Epoch 79/150\n",
      "51/51 [==============================] - 25s 498ms/step - loss: 0.0677\n",
      "Epoch 80/150\n",
      "51/51 [==============================] - 27s 528ms/step - loss: 0.0678\n",
      "Epoch 81/150\n",
      "51/51 [==============================] - 24s 468ms/step - loss: 0.0657\n",
      "Epoch 82/150\n",
      "51/51 [==============================] - 24s 469ms/step - loss: 0.0671\n",
      "Epoch 83/150\n",
      "51/51 [==============================] - 24s 468ms/step - loss: 0.0651\n",
      "Epoch 84/150\n",
      "51/51 [==============================] - 24s 471ms/step - loss: 0.0691\n",
      "Epoch 85/150\n",
      "51/51 [==============================] - 25s 493ms/step - loss: 0.0659\n",
      "Epoch 86/150\n",
      "51/51 [==============================] - 29s 576ms/step - loss: 0.0662\n",
      "Epoch 87/150\n",
      "51/51 [==============================] - 28s 540ms/step - loss: 0.0619\n",
      "Epoch 88/150\n",
      "51/51 [==============================] - 27s 533ms/step - loss: 0.0657\n",
      "Epoch 89/150\n",
      "51/51 [==============================] - 27s 529ms/step - loss: 0.0644\n",
      "Epoch 90/150\n",
      "51/51 [==============================] - 27s 525ms/step - loss: 0.0650\n",
      "Epoch 91/150\n",
      "51/51 [==============================] - 25s 496ms/step - loss: 0.0641\n",
      "Epoch 92/150\n",
      "51/51 [==============================] - 27s 522ms/step - loss: 0.0663\n",
      "Epoch 93/150\n",
      "51/51 [==============================] - 24s 467ms/step - loss: 0.0638\n",
      "Epoch 94/150\n",
      "51/51 [==============================] - 25s 497ms/step - loss: 0.0624\n",
      "Epoch 95/150\n",
      "51/51 [==============================] - 26s 506ms/step - loss: 0.0633\n",
      "Epoch 96/150\n",
      "51/51 [==============================] - 27s 528ms/step - loss: 0.0628\n",
      "Epoch 97/150\n",
      "51/51 [==============================] - 23s 450ms/step - loss: 0.0610\n",
      "Epoch 98/150\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "51/51 [==============================] - 23s 444ms/step - loss: 0.0619\n",
      "Epoch 99/150\n",
      "51/51 [==============================] - 23s 458ms/step - loss: 0.0592\n",
      "Epoch 100/150\n",
      "51/51 [==============================] - 23s 455ms/step - loss: 0.0577\n",
      "Epoch 101/150\n",
      "51/51 [==============================] - 24s 465ms/step - loss: 0.0576\n",
      "Epoch 102/150\n",
      "51/51 [==============================] - 23s 446ms/step - loss: 0.0570\n",
      "Epoch 103/150\n",
      "51/51 [==============================] - 24s 474ms/step - loss: 0.0572\n",
      "Epoch 104/150\n",
      "51/51 [==============================] - 23s 458ms/step - loss: 0.0599\n",
      "Epoch 105/150\n",
      "51/51 [==============================] - 23s 445ms/step - loss: 0.0600\n",
      "Epoch 106/150\n",
      "51/51 [==============================] - 23s 451ms/step - loss: 0.0611\n",
      "Epoch 107/150\n",
      "51/51 [==============================] - 26s 514ms/step - loss: 0.0591\n",
      "Epoch 108/150\n",
      "51/51 [==============================] - 24s 467ms/step - loss: 0.0572\n",
      "Epoch 109/150\n",
      "51/51 [==============================] - 23s 451ms/step - loss: 0.0598\n",
      "Epoch 110/150\n",
      "51/51 [==============================] - 23s 452ms/step - loss: 0.0619\n",
      "Epoch 111/150\n",
      "51/51 [==============================] - 24s 461ms/step - loss: 0.0638\n",
      "Epoch 112/150\n",
      "51/51 [==============================] - 23s 447ms/step - loss: 0.0602\n",
      "Epoch 113/150\n",
      "51/51 [==============================] - 23s 442ms/step - loss: 0.0585\n",
      "Epoch 114/150\n",
      "51/51 [==============================] - 23s 455ms/step - loss: 0.0570\n",
      "Epoch 115/150\n",
      "51/51 [==============================] - 23s 444ms/step - loss: 0.0547\n",
      "Epoch 116/150\n",
      "51/51 [==============================] - 23s 457ms/step - loss: 0.0606\n",
      "Epoch 117/150\n",
      "51/51 [==============================] - 23s 443ms/step - loss: 0.0554\n",
      "Epoch 118/150\n",
      "51/51 [==============================] - 23s 444ms/step - loss: 0.0541\n",
      "Epoch 119/150\n",
      "51/51 [==============================] - 23s 454ms/step - loss: 0.0567\n",
      "Epoch 120/150\n",
      "51/51 [==============================] - 23s 446ms/step - loss: 0.0546\n",
      "Epoch 121/150\n",
      "51/51 [==============================] - 23s 449ms/step - loss: 0.0552\n",
      "Epoch 122/150\n",
      "51/51 [==============================] - 23s 458ms/step - loss: 0.0577\n",
      "Epoch 123/150\n",
      "51/51 [==============================] - 25s 490ms/step - loss: 0.0564\n",
      "Epoch 124/150\n",
      "51/51 [==============================] - 25s 484ms/step - loss: 0.0539\n",
      "Epoch 125/150\n",
      "51/51 [==============================] - 24s 477ms/step - loss: 0.0539\n",
      "Epoch 126/150\n",
      "51/51 [==============================] - 28s 558ms/step - loss: 0.0545\n",
      "Epoch 127/150\n",
      "51/51 [==============================] - 25s 495ms/step - loss: 0.0571\n",
      "Epoch 128/150\n",
      "51/51 [==============================] - 24s 479ms/step - loss: 0.0534\n",
      "Epoch 129/150\n",
      "51/51 [==============================] - 24s 471ms/step - loss: 0.0499\n",
      "Epoch 130/150\n",
      "51/51 [==============================] - 24s 466ms/step - loss: 0.0540\n",
      "Epoch 131/150\n",
      "51/51 [==============================] - 23s 457ms/step - loss: 0.0518\n",
      "Epoch 132/150\n",
      "51/51 [==============================] - 25s 487ms/step - loss: 0.0551\n",
      "Epoch 133/150\n",
      "51/51 [==============================] - 25s 483ms/step - loss: 0.0512\n",
      "Epoch 134/150\n",
      "51/51 [==============================] - 25s 482ms/step - loss: 0.0504\n",
      "Epoch 135/150\n",
      "51/51 [==============================] - 24s 477ms/step - loss: 0.0515\n",
      "Epoch 136/150\n",
      "51/51 [==============================] - 23s 456ms/step - loss: 0.0523\n",
      "Epoch 137/150\n",
      "51/51 [==============================] - 23s 454ms/step - loss: 0.0508\n",
      "Epoch 138/150\n",
      "51/51 [==============================] - 23s 441ms/step - loss: 0.0588\n",
      "Epoch 139/150\n",
      "51/51 [==============================] - 24s 467ms/step - loss: 0.0493\n",
      "Epoch 140/150\n",
      "51/51 [==============================] - 23s 453ms/step - loss: 0.0504\n",
      "Epoch 141/150\n",
      "51/51 [==============================] - 23s 449ms/step - loss: 0.0530\n",
      "Epoch 142/150\n",
      "51/51 [==============================] - 24s 463ms/step - loss: 0.0521\n",
      "Epoch 143/150\n",
      "51/51 [==============================] - 23s 446ms/step - loss: 0.0530\n",
      "Epoch 144/150\n",
      "51/51 [==============================] - 23s 460ms/step - loss: 0.0502\n",
      "Epoch 145/150\n",
      "51/51 [==============================] - 23s 452ms/step - loss: 0.0496\n",
      "Epoch 146/150\n",
      "51/51 [==============================] - 23s 452ms/step - loss: 0.0518\n",
      "Epoch 147/150\n",
      "51/51 [==============================] - 23s 459ms/step - loss: 0.0527\n",
      "Epoch 148/150\n",
      "51/51 [==============================] - 25s 482ms/step - loss: 0.0519\n",
      "Epoch 149/150\n",
      "51/51 [==============================] - 23s 448ms/step - loss: 0.0482\n",
      "Epoch 150/150\n",
      "51/51 [==============================] - 23s 444ms/step - loss: 0.0505\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x14ede47f0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(\n",
    "    optimizer='adam', #already does a batch aproach, re listen the podcast to remember later\n",
    "    # adam algorithm is stochastic gradient decent, using batch but different from SGD uses adam algorithm to optimize\n",
    "    loss = 'mean_squared_error'\n",
    ")\n",
    "# model.fit(gadf_training, y_train, epochs=100, batch_size=32)\n",
    "model.fit(gadf_training, y_train, epochs=150, batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "db80f2ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>155.399994</td>\n",
       "      <td>159.460007</td>\n",
       "      <td>155.009995</td>\n",
       "      <td>158.779999</td>\n",
       "      <td>19180100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>159.559998</td>\n",
       "      <td>160.490005</td>\n",
       "      <td>157.949997</td>\n",
       "      <td>159.419998</td>\n",
       "      <td>17592000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>158.509995</td>\n",
       "      <td>158.660004</td>\n",
       "      <td>153.279999</td>\n",
       "      <td>154.919998</td>\n",
       "      <td>21675100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>155.860001</td>\n",
       "      <td>157.350006</td>\n",
       "      <td>153.550003</td>\n",
       "      <td>154.050003</td>\n",
       "      <td>19761300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>154.759995</td>\n",
       "      <td>157.339996</td>\n",
       "      <td>154.460007</td>\n",
       "      <td>154.779999</td>\n",
       "      <td>15424700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>332.489990</td>\n",
       "      <td>333.190002</td>\n",
       "      <td>325.510010</td>\n",
       "      <td>327.640015</td>\n",
       "      <td>21872600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>774</th>\n",
       "      <td>329.820007</td>\n",
       "      <td>332.459991</td>\n",
       "      <td>327.000000</td>\n",
       "      <td>327.739990</td>\n",
       "      <td>12376600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>775</th>\n",
       "      <td>330.179993</td>\n",
       "      <td>341.859985</td>\n",
       "      <td>329.779999</td>\n",
       "      <td>340.890015</td>\n",
       "      <td>25387200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>776</th>\n",
       "      <td>344.339996</td>\n",
       "      <td>353.649994</td>\n",
       "      <td>343.200012</td>\n",
       "      <td>347.559998</td>\n",
       "      <td>25076600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>777</th>\n",
       "      <td>343.829987</td>\n",
       "      <td>346.649994</td>\n",
       "      <td>340.869995</td>\n",
       "      <td>342.959991</td>\n",
       "      <td>18114100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>778 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Open        High         Low       Close    Volume\n",
       "0    155.399994  159.460007  155.009995  158.779999  19180100\n",
       "1    159.559998  160.490005  157.949997  159.419998  17592000\n",
       "2    158.509995  158.660004  153.279999  154.919998  21675100\n",
       "3    155.860001  157.350006  153.550003  154.050003  19761300\n",
       "4    154.759995  157.339996  154.460007  154.779999  15424700\n",
       "..          ...         ...         ...         ...       ...\n",
       "773  332.489990  333.190002  325.510010  327.640015  21872600\n",
       "774  329.820007  332.459991  327.000000  327.739990  12376600\n",
       "775  330.179993  341.859985  329.779999  340.890015  25387200\n",
       "776  344.339996  353.649994  343.200012  347.559998  25076600\n",
       "777  343.829987  346.649994  340.869995  342.959991  18114100\n",
       "\n",
       "[778 rows x 5 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "past_window_days = data_training.tail(window)\n",
    "df = past_window_days.append(data_test, ignore_index = True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "28755e19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((718, 60, 5), (718,))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs = sc.transform(df) # we transfor the data into an array and then gather the window sizes with the prediction\n",
    "\n",
    "x_test = []\n",
    "y_test = []\n",
    "for i in range(window, inputs.shape[0]):\n",
    "    # take from current minus 60 first dimension, empty seconds dimension to grab all 5\n",
    "    x_test.append(inputs[i-window:i])\n",
    "    y_test.append(inputs[i, 0]) # zero second dimension only to grab which result should be\n",
    "x_test, y_test = np.array(x_test), np.array(y_test)\n",
    "x_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ef08acbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(718, 60, 60)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# transfor flattened data into gaf\n",
    "\n",
    "gadf_test = []\n",
    "\n",
    "for i in x_test:\n",
    "    gadf_test.append(create_gaf(i))\n",
    "gadf_test = np.array(gadf_test)\n",
    "\n",
    "y_pred = model.predict(gadf_test)\n",
    "# un scale the values to normal amount of google shares\n",
    "scale = (1/sc.scale_[0]) # scaling label -> amount that use scaler to scale\n",
    "# every single value of scale_ is the factor that was use for every single value in the scaler\n",
    "# sc.scale_[0] is the scaler factor\n",
    "# we do the 1/scale to do the reverse and have the original numbers\n",
    "sc.scale_, sc.scale_[0], scale\n",
    "y_pred_final = y_pred*scale\n",
    "y_test_final = y_test*scale\n",
    "\n",
    "gadf_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f0b27765",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AVKpUCYsXLwYAfPjhh+jWrRsaNmyIvHnzJul8RoxR1apVUaVKFXTu3DnONr/99hvKlSuHoKCg/0SPn58f2rdvj4ULF/6XfOGHH37AL7/8gqpVq+K3337D999/DwD4/vvvsXHjRlSpUgU1atTA0aNH/zt2lixZsGzZMnz77bf/fb6kMHDgQBQtWhRVq1ZFYGAg5syZg/Tp02PBggV4++23ERgYiKCgILdn0xMEQRAEIRXy7beAG7ILA6AwypMHKF6c4sdg+HCes04dIFMmYPt2oHVr4LffKI4El6G01p5ug8uoWbOm3rNnT6xlx48fR4UKFTzUIiG1Ib83QRAEQUihHD5Miw0AuKP/HBgIFCwIrFrF+Z9+AipXBurXj7vtnDlAnz7AypUUSUKCUErt1VrXtF4uFiNBEARBEARBcERMDDB0qPuOf/8+M89ZZvB96SXboggAOnQASpQAunUDDh1yX7tSGSKMBEEQBEEQBMERU6cCmzcDZcpw3sgc5yr27weio2MLI0dkycL23L0LLF3q2rakYtwmjJRSM5RSV5VSR+ysH6GUOmB6HVFKRSulcpvWhSilDpvW7bG1vyAIgiAIgiAkCzNmULS8/z7nL150zXG1BoYNAz74gPM143h32ScgAMifHwgJcU1bBLdajGYCsOv0qLUer7UO0loHAXgHwD9a6xsWmzQ1rU/AL0QQBEEQBEEQXEhMDN3c6tcHihblstBQ1xx7wQJg4kRg/XqKnEKFErZ/8eKsdSS4BLcJI631ZgA34t2Q9AIw111tEQRBEARBEIREcfYsY4CqVAGKFOEyV1iMbt8G3n6bNYsA4NGjhB+jeHGxGLkQj8cYKaUyg5alvywWawBrlFJ7lVKDPdMyQRAEQRAEIdVz+DCnVaoAhQsDStkXRh99BEyZYp4PC6N16cQJwKI2JADgxRd5nKVLga5dgZ9/TnjbSpQALlxgfJKQZDwujAC0B7DNyo2uvta6OoA2AF5VSjWyt7NSarBSao9Sak9ERIS725oo/Pz8EBQUhMqVK6Nbt264f/9+oo/Vv39/LFiwAABr+hw7dszutps2bUpUPZ/ixYvj2rVrNpdXqVIFQUFB/9UKCgkJQaZMmRAUFITAwEDUq1cPJ0+ejLNvTEwMXnvtNVSuXBlVqlRBrVq1cM5k+v3ss88S3EYDy+/D0TYlSpRAUFAQqlevjh07dtjc7v3338e6desS3RZBEARBEFIghw5RDFWqBKRPT5c3W8Jo6VLGCr38MtCjB93k6tallalCBeD1183bBgezgOvo0UDDhsCff1IcJZTixYEnTyjAhCTjDcKoJ6zc6LTWYabpVQALAdS2t7PWeqrWuqbWuqa/v79bG5pYMmXKhAMHDuDIkSNInz49pliOJACITqTKnzZtGipWrGh3fWKFkSM2btyIAwcO4MCBA6hXrx4AoFSpUjhw4AAOHjyI559/3qbQmTdvHsLCwnDo0CEcPnwYCxcuRM6cOQEkTRg5y/jx43HgwAGMGzcOL730Upz10dHR+Oijj9C8eXO3t0UQBEEQBB/iyBGgVCkgc2bOFykCnD8fe5uzZ2kBqlwZaNUKWLgQeO45WnNqm7qxGzeat//9d4qtgQOT1rbixTkVdzqX4FFhpJTKAaAxgMUWy7IopbIZ7wG0BGAzs50v0rBhQwQHB2PTpk1o2rQpevfujSpVqiA6OhojRoxArVq1ULVqVfz0008AAK01hg4diooVK6Jt27a4evXqf8dq0qQJjIK2q1atQvXq1REYGIhmzZohJCQEU6ZMwbfffougoCBs2bIFERER6NKlC2rVqoVatWph27ZtAIDr16+jZcuWqFatGl566SUkpejvnTt3kCtXrjjLw8PDUbBgQaRJw59cQEAAcuXKhVGjRuHBgwcICgpCnz59AADffPMNKleujMqVK+O777777xizZs1C1apVERgYiOeeey7OOcaMGYP+/fsjxkEV6EaNGiE4OBgALWAfffQRGjRogD///DOW9Wn37t2oV68eAgMDUbt2bURGRtq9RoIgCIIgpGBCQoDSpc3zVaowvXZUFBMzABRFUVG0Eq1aRWvQw4dApkxMrPDNNxRTYWHMRDd7NtC0KTPLJQVDGE2fnrgYJSEWad11YKXUXABNAORVSoUC+ABAOgDQWhsmk04A1mit71nsmh/AQqWU0b45WutV7mpnchIVFYWVK1eitalC8a5du3DkyBGUKFECU6dORY4cObB79248evQI9evXR8uWLbF//36cPHkShw8fxpUrV1CxYkW88MILsY4bERGBQYMGYfPmzShRogRu3LiB3LlzY8iQIciaNSuGDx8OAOjduzfeeOMNNGjQABcuXECrVq1w/PhxjB07Fg0aNMD777+P5cuXY+rUqXY/Q9OmTeHn54cMGTLg33//BQCcOXMGQUFBiIyMxP379/9bbkn37t3RoEEDbNmyBc2aNUPfvn1RrVo1jBs3DhMnTsSBAwcAAHv37sUvv/yCf//9F1pr1KlTB40bN0b69Onx6aefYtu2bcibNy9u3Iid12PkyJG4ffs2fvnlF5h+OzZZunQpqlSp8t98xowZsXXrVgAUlwDw+PFj9OjRA/PmzUOtWrVw584dZMqUCdOnT7d5jUqUKGH3fIIgCIIgeBnR0YCfn/PbX7oEBAaa5596ium7q1Wje92vvwLbtwOvvQaUK8dtnnkGKFuWKb6zZjUXat2xg7WQgoOBESOS/lnKlqXVado0nmPQoKQfMxXjNmGkte7lxDYzwbTelsvOAgi0tX1S+d//AFP/22UEBQEWRg2bGBYRgBajF198Edu3b0ft2rX/61SvWbMGhw4d+s9icfv2bZw+fRqbN29Gr1694Ofnh0KFCuHpp5+Oc/ydO3eiUaNG/x0rd+7cNtuxbt26WDFJd+7cQWRkJDZv3oy///4bANC2bVubFh+DjRs3Im/evLGWGa50AF3mBg8e/J/IMAgICMDJkyexYcMGbNiwAc2aNcOff/6JZs2axdpu69at6NSpE7JkyQIA6Ny5M7Zs2QKlFLp27frfuS0/48cff4w6deo4FHQjRozAJ598An9/f0yfPv2/5T169Iiz7cmTJ1GwYEHUMhVZy549OwD710iEkSAIgiB4KXPnUrisWEELTu/ewN69tOzUqRP//k+eAJcvM+mCgbHfkSMUOP/+Czx+HPt4fn7A7t2MSQLYYcyYEdi6lfspBXTokPTPpxSLz/7+OxM8CEnCbcJIMGPEGFljdP4BusxNmDABrVq1irXNihUrHFpAjH3j2wZgAoQdO3YgU6ZMcdY5s78zPPvssxgwYIDNdRkyZECbNm3Qpk0b5M+fH4sWLYojjOy58Tn6jLVq1cLevXv/s5TZYvz48ehqI6jR8hrEdy5710gQBEEQBC9l0CDg3j0KkuXLgSVLmDyhe/fYcULTpzNO6NNPY+9/+TJd3yxd3ipVArJk4XEfPgTGj+fy2lYh8aaBVQAUSI0asQ0ZMwL16rEdrkApoGRJ4MwZ1xwvFeMNyReSje++AzZtcu0rPmuRs7Rq1QqTJ0/GkydPAACnTp3CvXv30KhRI/zxxx+Ijo5GeHg4NloG7pmoW7cu/vnnn/+yvBluZtmyZUNkZOR/27Vs2RITJ078b94Qa40aNcLs2bMBACtXrsTNmzcT/Tm2bt2KUqVKxVm+b98+hJkypsTExODQoUMoVqwYACBdunT/fe5GjRph0aJFuH//Pu7du4eFCxeiYcOGaNasGebPn4/r16/H+owA0Lp1a4waNQpt27aN9XkTS/ny5REWFobdu3cDACIjIxEVFWX3GgmCIAiC4KVUqsTpzJl0d6tbF3jrLSZFMPoSN24Ab7wBfPEFawtZYhRytbQY+fkBbdoA7dszhmjZMsDf31zjyB4dOwKnTzP9t41Y6SRRqhSFnZAkxGLkJQwcOBAhISGoXr06tNbw9/fHokWL0KlTJ2zYsAFVqlRB2bJl0bhx4zj7+vv7Y+rUqejcuTNiYmKQL18+rF27Fu3bt0fXrl2xePFiTJgwAT/88ANeffVVVK1aFVFRUWjUqBGmTJmCDz74AL169UL16tXRuHFjFDWqOjuJEWOktUb69Okxbdq0ONtcvXoVgwYNwiNTYGDt2rUxdOhQAMDgwYNRtWpVVK9eHbNnz0b//v1R2zTqMnDgQFSrVg0AMHr0aDRu3Bh+fn6oVq0aZs6c+d/xu3XrhsjISDz77LNYsWKFTauYs6RPnx7z5s3DsGHD8ODBA2TKlAnr1q2ze40EQRAEQfBSjMFeo7RH375MnQ0Ax4/T4vPOO4AxsLpuHdCli3n/S5c4tRRGANNra824ookTmYkuPu+bZ58FXnmFlqLnn0/a57KmVClgwwa2yUVeQKkRlZQMZN5GzZo1tZGlzeD48eOoYPwBBMHNyO9NEARBELwErZliOyCAsUAA8NNPQPPmFBI//wz89htjfkaOBMaNAzp3pludwfffM0j92jUgTx7b53n0CEiXDkjjhCPWiBFAjRpAz55J/nixmDgRGDYMCA8Hcubk+9dfZ/pwIQ5Kqb1a65rWy1OVK50gCIIgCIKQSrhxgxahfv0oXABmlytWjHE+e/cyS9zAgcDbbwMtW9ItLirKfIzQUCBDBsBODDMArndGFAGMR3K1KAIYYwTQnW7LFmapq1IFiIhw/blSMCKMBEEQBEEQhJSHER9UsSLQoAFdzCpXZoxQuXIUD0+eAEbG3x49gKtXYxdivXSJbnTe7p5Wpgyn//zDRBMGltYvIV5EGAmCIAiCIAgpj4sXOQ0IAN59F/jwQ2aTAyiWDMuQUWPomWeAHDlYfBWgK97OndzW2yldGmjXDvjgA7a/WjXWUFq4MPnaEBNjjsmyXGZKnOULpAphlJLiqATvRX5ngiAIguBFXLjAaUAA44ref9+87qOPGDs0fjwLsAJ0r2vXDli1iqLoxAng3Dmgbdtkb3qCUYr1mgoVYtruBg2ATp2AXbvMlrOEcPMm8MknwIMHzu/z3Xd06bM839SpQN68QM2ajNPyclK8MMqYMSOuX78unVbBrWitcf36dWTMmNHTTREEQRCE1M3RoxQGr77K+QIF4m5TujTw7bfA8OGxl9etC1y5ws79smVc5gvCCGAc1IIFTL7Qtq05u9533wGrVwPR0c4f6/vvgTFjmKzCGbRmMovHj4F588zLjxzhtFgx+8krvIgUn5XuyZMnCA0NxcOHDz3UKiG1kDFjRgQEBCCdEeApCIIgCELy8uQJUL06C7MOGEA3uP79nd9/1y6gTh3gjz+AsWNpRdq3z23NdQsxMeZkEM8/D8yaxfe//spEFPERFQWUKEFxWKgQEzpkyOB4H+N7S5sWCAoCTLUg0bEj9z90KLGfxi3Yy0qX4usYpUuXDiVKlPB0MwRBEARBEAR3M306rRQLF7JTnlACA5nBrm9fCoTkjNFxFZYZ8j77jC6FmzYB69c7J4w2b6Yo6t0bmDOHwrBuXcf7LFnCpBYjR/Kc4eFAwYLm5BU+Qop3pRMEQRAEQRB8GK1pAbLk1i1g/nzG01iyciXd5Dp0SNy5MmRgAdaoKCZjSOxxvIXChZllr2tXiiOt+Vqxwr5rneH+NmAAp87EKG3ZwoQPzzzDecODKzSUMV4+gggjQRAEQRAEwXv59VdaHwYPppsYwGKsPXoAlSoBt29zmdbA9u3m1NyJ5fvvmahh4ULvT9PtLE2b0nIUEkKh1LYthaUtgoOBbNnokgjEL4wePQL+/Rdo2JBudGnSUBg9ecJ4LbEYCYIgCIIgCIIL2LCB059/ZiwLQNctgJ3yjz+m69vx48x8Vq9e0s7XuTPji9KnT9pxvInGjTndvJlFbQFm37PF6dO0uuXKBWTOHL8w2ruX16FhQ6ZDr1iRwig8nGLVhyxGKT7GSBAEQRAEQfBhdu9mp3v7duCHH4Dff6cI+vxzWna+/prb3bjBqVGXSDBToQKQPTvrMoWHc9maNRQu1lax06eBGjW4PCAgfmG0ZQunxvdesyZd9Yz9xGIkCIIgCIIgCAng1i3gxx+BiAjg3j0uu32b9YRatWKHe+5cYNIkruveHahd27z/ypUUAOXLJ3vTvZ40aZg1bscOur1ly8a4rQMHYm/35And7UqX5rwjYXT8ODP+rV0LlCsH5MvH5Y0aAVevmlN9+5DFSISRIAiCIAiCkDBu36bL1MaNrjvme++x9lC+fCwKeuGCOYi/dm3glVf4fu5cYN06FhNt1ozZ0MaPZ8d/1arYWdkEM089BRw8SEH0xhvMvmek8jY4e5ZJGcqU4bwjYfTzz4z/Wr+eFj2Dvn0plIxjizASBEEQBEEQUizHjtFi4Irip+vW8Tg//8wU26NGAQ8fMjnA338zU1zt2kw1/fAh0LMnBRHA9ND79rFQ686dQNGiSW9PSsUy5faAAfyuZ83id7prF1CrFlC5MtdXqMBpQAAQFgacP8/kF48emY+xZo35vaUwSpeO17JdO7o+5srlto/kakQYCYIgCIIgCAkjMpLTBw9Y6+bx48QdZ88eoHVripuiRZkR7vPPmRHt11/56tkTyJGD21sXGs2SBahaNfGfIzXRogXw3Xe0xBUvDgwaxLisb74Bnn6aLowjRgCLF5tdFAMCmLp8wgSKnYMHufzSJeDoUaBUKVrojOQOBg0bAkuXAsOGJecnTDKSfEEQBEEQBEFIGDdvmt/36cPYlOefT/hxNmyg69bBg+YYFYDxQ6NG8b2Pda69lrRpgddfN883bQr4+wNjxtAdcevWuG5vJUtyOm8ep6GhFE1r13L+zz+BTJmAYsXc3/5kQCxGgiAIgiAIQsIwhNHevexcJzbW6MgRZi2zFEUAxdDcucx4VqNG0toq2CZtWqYmj4mhW52tWKC6dSmajDgjY7p2LQvhBgWlqGQXIowEQRAEQRCEhGEIowoVmIVs0ybOh4cze9zp084d58gRc1yLJZkz04WuQQOXNFewQ9++TMv98su212fPHluYXrpEIbV2LV3zUkoBXBMijARBEARBEISEcfMmkDEj3aiaNGFwfkgIXeP27mXq7PiIjmYSB1vCSEgeGjRgbFHTpva3sVwXGkq3x4gIoGVL97cvmXGbMFJKzVBKXVVKHbGzvolS6rZS6oDp9b7FutZKqZNKqWCl1Ch3tVEQBEEQBEFIBDdumLONGZaDd95hEgUA2L/f8f7Hj3P/R49EGHmaPHkcr+/RgwkuKlakMPrnHy43MgOmINyZfGEmgIkAZjnYZovWup3lAqWUH4BJAFoACAWwWym1RGt9zF0NFQRBEARBEBLAzZtmYVSuHPDZZxRGBtaFQ62ZM8ec2U5iiLybatVoJerThynR//0XKFIEKFTI0y1zOW6zGGmtNwO4kYhdawMI1lqf1Vo/BvAHgA4ubZwgCIIgCIKQeCyFEcB6QkYGM4CpnK1TeN+9C0yZwvTP69YxFunIEaBKleRps5A0ChdmjNG//5rTeacwPB1jVFcpdVAptVIpVcm0rDCAixbbhJqWCYIgCIIgCK4iKooj/+PHJ3xfa2GUJg3QpQvfBwUxfffRo+b1WjMW6eWXWSdn1y6gTRugUiUIPkJAAF0fz50TYeQG9gEoprUOBDABwCLTclvpLbS9gyilBiul9iil9kRERLi+lYIgCIIgCCmRU6cYMzJyJIXMwYPOF2q1FkYA8N57wKuvAl9/zXnLzHQrVzIpAwDMmsXMZk8/nfTPICQflhkC69TxXDvciMeEkdb6jtb6run9CgDplFJ5QQtREYtNAwCEOTjOVK11Ta11TX9/f7e2WRAEQRAEIcVw6JD5fYMGtPR89plz+9oSRtmzAxMnAtWrc/6ihQPQtGkM8lfKXBy0WrVEN13wANWrM8bo7bdZ3ygF4jFhpJQqoBSTnyulapvach3AbgBllFIllFLpAfQEsMRT7RQEQRAEQUhx3L9PC5GfH/Dii0y/DADz5sW/b1QUcOdOXGFkkCMHkCWLuRhoRASwdCnw/PNAmTLAgwcM3M+RwzWfRUg+6tQBxo0D0qf3dEvcgtuy0iml5gJoAiCvUioUwAcA0gGA1noKgK4AXlZKRQF4AKCn1loDiFJKDQWwGoAfgBla66M2TiEIgiAIgiAklKgoIHduc6rsadO4/LvvgDfeAA4fBn79lYKpQoW4+9+6xWnu3LaPrxRjlwyL0c8/85wvvMC6RadOMfWzIHgZbhNGWute8ayfCKbztrVuBYAV7miXIAiCIAhCqub4cYoiAMic2by8QwcKo8aN6Sq3fz+wfn3c/Y3Yobx57Z/DEEaPH9O9rkULJlowMtfZElyC4GE8nZVOEARBEARBSE4si68OHWp+X6IE8NtvQKZMjDnasAHYujXu/l98AeTMCbRta/8cRYrQlW7lSiA8HHj9dS4vVYpTsRgJXogII0EQBEEQhNTE/v0UP1FRwHPPxV7Xty9r1axaBfj7Ax99ZF4XHQ0MGcJ02//7n+MYoYAACqIFCyiiWrbk8nLlOJXaRYIXIsJIEARBEAQhNbF/PxAYyMQL9siSBRgxghnkunVjzNHatcBPPzEl96hRjs9RpAhrF/3+O/DMM0C6dFzepg2wejVQr57rPo8guAgRRoIgCILgzYSE0B1JEFxBZCSwb59zqbJffhlo1gzYsQPo3x/o1QvImpUFYTNkcLxvEYvKK506md+nSUPrkbJVtlIQPIsII0EQBEHwZtq354j7zZuc37TJXEBTEBLKBx8Ad+8ydXZ8ZM0KrFvHJAp9+jAbXfv2dMOLj0aNgOHDgZkzgS5dktpqQUgW3JaVThAEQRAEF3D9OqerVwM9e9K9ac8eFlgUdyQhITx6BEyaRFFUp47z+ylFK9Hx47QiOUOmTNxHEHwIsRgJgiAIgjdTtCiny5ezY7pnD+fff99zbRJ8k+PHmT67deuE71uwILB3L9CwoevbJQhegggjQRAEQfBmrl7ldMECZgzz8wMGDAD++YedXF9Aa2DZMnNhUMEzHDzIadWqnm2HIHgpIowEQRAEwVvRGrh8mYKoSRO+nzyZxTKjooCTJz3dQuf49VfGpowc6emWpHyiopj57bPP4q47dAjImBEoUyb52yUIPoAII0EQBEHwViIjgQcPgKAgZqa7dAkYNAioXJnrDx/2aPOc4tEjYNgwZiObN4+fR3AfU6eyBtHo0cDGjbHXHTzI305aCTEXBFuIMBIEQRAEbyU8nNMCBWIvL1eOnVtfEEbnzpmzoN25AyxZ4ukWpWy++AKoXx/Ilw+YPp3LtOb3v2WLuNEJggNEGAmCIAiCt3L5MqfWwih9eqB8ed8QRsHBnL7wAq1GvtBmX+XmTeDCBaBDBwogw9Vyzx5g1iygcWNa7wRBsIkII0EQBEHwVuwJI4AuUUePJm97EsOZM5yWK0crxpUriT/WrVvAb78x6YTWwK5dnArk2DFOK1Xi933yJL+fX35hbNGff9ItUxAEm4iTqSAIgiB4K46EUdGiwN9/s+OrVPK2KyEEBwPZswN58wL58ydeGF25ApQsCdy/D0RH8/M3awa8/TYwbpxr2+yrGMKoYkXg7FnGqIWHM7arUycgRw7Ptk8QvByxGAmCIAiCtxIeDqRLB+TKFXddgQK0nNy8mfztsmbNGrpw2SI4GChdmuItKcJo5UqKIgDYsAHYto3vv/gCuHgxccdMaRw7BmTOTNFYrhyXLV8O3LhBESkIgkNEGAmCIAiCtxISwk5uGhuP64IFOTWsSp4iIgJ45hngrbdsrzeEEUBhlNj2/vMPkDs30LUrhdH27eZ1GzYk7phJ5csvKUCioz1zfmuOHqW1KE0aszD6/XdOa9b0XLsEwUcQYSQIgiAI3srZs3Qfs4XhXmdkrktubt9mOugff6QwWLaMWecsiY6muCtVivMFCtBilJi4oH/+ARo1Apo3Z9ryVauY0CFzZuDAgaR+moSjNTBlCnDqFJMbeJp164CtW4HAQM4HBPC72byZ8UWVKnm2fYLgA4gwEgRBEARv5dw5oEQJ2+s8bTGqXRt4+mngww+BDBmAhw+BXr2AvXvN21y/zoKjhQpxPn9+1jWyFlD2CA+nuDp3jq/GjYF27czrGzRg9rX9+xPe/piYhO9j8PgxMH8+2wTQzc+TPHoE9OhBAfrRR1yWJg3Qrx/fZ8wotYsEwQlEGAmCIAiCNxIZCVy7Fr/FyN3CaOhQoEwZFg41iIoCTp8GevYE2rQBZswAWrakdaJ9e7YbAK5e5TRfPk7z5+c0PBx4/30ed9o0iqz162OfNySEgqpQIeC119ix79QJKFwYOH8e+OADutUFBdFiZGmFiowEfvqJ9ZOsuXcP6NIFyJMn8d/dxx/zs2fKxOyAnhZGRhzRV1+ZRShgFkm9e3umXYLgY4gwEgRBEARvxLBG2BNG2bOzY/7rr8DgwXRlczWPHlEQBQcz1bNBRASFSKNGwIoV7HivXs0CotevAwMHcr21MDLE3Jw5FBeXLwODBgG7dwPvvRdb3BhWoKtX+dn69QOKFeOyokVpqcqWjcLo9m26tBltfu01YMgQts/aOvXbb8zmd+sW3c8Sw+rVPO+//9KCtXcvxaInePVVftaCBeMmWPD3p0j99lvPtE0QfAwRRoIgCILgjZw9y6k9YaQUhcbhw8DPPwN9+gBPnri2DYcO8ZiZMpmLhQLmzHLWacSDgoDPPgMWLwbmzrVvMfr4Y6bvXrSI86VKATt3mjPNAcDx45yeOgW8+Sb3sUXTpix427Qpt8uWDZg5k5ar/fvjpvLev59pq3PnBt55h5/hxg3nv5M7d4B9+yiIqlRh26OjgdBQ54/hKh48YIxXRATw7ru23eXy5OH3IwhCvIgwEgRBEARvxCiMai/GCKClBGDszZ07tGC4kt27Oe3blymx793jvOGCZggdS954g0Jo3Tr7FiOAVqVmzYATJyhW8uQBPvmEQub8eS4PCKC73ddfx3YRs6RsWX7unDlpGWnaFJg8mRauPn24zDI5w4EDQPXqQJMmFDNXrtAF0Fm2baMQatKE88WLc2pY+Gxx+DBdBhMiwJxh3z5OFy+my6MgCElChJEgCIIgeBs7dtBCUqaM7RpGBkZHe/JkwM8PGDaMdX0srTtJYfduumO1aMH506c5tWcxAhj0X6EC23D1Kudz5+Y6f39g4kRauMaO5bJy5WjlGTqULmrvvMPMc5s3A+XLO9fOoCC6sy1eTLe7IUOYEGLcOJ7z6acp5qKjKVICA4FWrcz7b9ni/Hcyfz6PXbcu5w3h6kgYvf46XQYrVAB++IGicNy4xGXns2TnTk7r1EnacQRBAOBGYaSUmqGUuqqUOmJnfR+l1CHTa7tSKtBiXYhS6rBS6oBSygtyYAqCIAhCMnH/Pi0defLQ6qKU/W2XLGGAfYUK7OwfOACMGsVsbQ8eJL0tu3YBtWqZa+IYgsuRxQjg9idO0MXL3z92HaZXX6UwsHbvGjoUqFePLmEXLtBqVKGC823NlAl49lkWxDUICACWLmUR3PnzWYj2wQMKqRdeAI4cYRySM8Lo8WPgjz8Y0/Xqq0yFDZjrTIWExN1HayaC2LEDqF+fbXz9dWDePArA6tUZK5UY9u2jK2KJEvavgyAICcKdFqOZAFo7WH8OQGOtdVUAHwOYarW+qdY6SGstFckEQRCElMHp08ym5ihV9MSJtD7MmMFOtyPatwfGjOH7n39mbM3q1Qy4nzUraW0NDweOHaObXpkyFGjr11NYXL4MZM0KZMlie9/y5WnNOnbM7EYXH3nz0k3t008pHgBmoEsqgYGs4fPOOyxEC1CQpE3L5Q0bUmTcvcu6TB072o7V+vprpiMvXBgYPdq8PF06CjBri1FMDLPoZc/OVOYjRlCInThBF8jRoylkp1p3f+Lh2WcphGrUYN0iw6VPEIQk4zZhpLXeDMCuM63WervW+qZpdieAAHe1RRAEQRC8grFjaeExMqjZYtMmpoBu3Dhhx65eHXj+ebq9Va9OoZQUjIxtLVrQ0lGpEo/ZtCmtObbc6AwMC9PWrbQYJZRPPqEQGTgw4fvaols3WuK6dAEWLmTSBIOGDelit2YNXe4WL6aAsebECcY5nTxpdg00KFHCLIyio4GXXqIL5OLF5m0aNaKYLFeOFqZPPmHK8suXaY2y5t49c0yXwaNHtIBdvQoMH852/vhj4r4TQRDi4C3Vvl4EYFkEQANYo5TSAH7SWidwOEUQBEEQvIxbt4C//uL7fftsx89oTfe1Dh0Sfx6lmNTg+++ZQjq+wp5Dh7ITb531bd06WnECTZ7u27YxoYEhVurXt39My8/mrMXIkvTpmWHOVbzyCr/bkSPNLnAGdetSqAwZYl62bx9QrVrs7c6dYwY66/0BJmBYs4bfd79+zMjXuTPF0P37FFO2YsVKlGC7LlwASpeOva5dOwrSFSs437Gj2YXxzz9Zw0kQBJfi8eQLSqmmoDB622Jxfa11dQBtALyqlGrkYP/BSqk9Sqk9ERERbm6tIAiCICSSRYvoUgWYs4kB7BhHR/P92bOsA5TUYPpKlWiFCA52vN3x48CkScA33zAWxiA6mi55zZub44OyZwdefBH43/84f+GC/eMWK2YWRLaERHLj789YHlttyZ6dMUcREXRLy56diRysCQmxnyGwenW6Hg4YQFE0bhxF8Ouv04Vv5kzb+9lL3HD5Mi2H69bRxe/yZcaTGVkHa0qUgSC4A48KI6VUVQDTAHTQWl83lmutw0zTqwAWAqht7xha66la65pa65r+iTHXC4IgCEJycOYMRUb16hRGV6/S9S1rVsatLF0KTJnCbWvbfew5R+XKnB496ni7b75hm+7fpyBo3hxYuxbYvp2Z5zp2jLvPRx9x+uKL9o/r5wesWsV6QQl1CfQEjUzjr4MH01JkKVwBiszQUHNqbmuaN+f099+Btm2Bt9+2vZ01lqm+hwxhyvFhw/hbABjrtHEjRZGRwS5PHnOhW0EQXIrHXOmUUkUB/A3gOa31KYvlWQCk0VpHmt63BPCRh5opCIIgCK4hLIxxObVqMSvZgAFMZvD887TOPPsst8uXjxafpFChAl3qjh5lXI01WjN+Ze5coH9/xgKdOsXkEN27s3OfPr05WYEl2bKxwx6fi161aswG5yirnrfQvz8TVnTqxBTlkyfzMxoZ7i5e5Hdmz2JUoQJQsCCtRr16OX/egAB+j/Pn87eQPTutS/XqMfHGtWsUmGfP8txp0gAVK/rGdyoIPog703XPBbADQDmlVKhS6kWl1BCllOHE+z6APAB+tErLnR/AVqXUQQC7ACzXWq9yVzsFQRAEIVkIC2Pwfrt2jDdasYLWl59+ooXm998ZTH/2bOyU04khc2Z2pL/8knV7rOnXj1aqe/coCjZtorvWypV03Zo9m+3Mls328eMTRQa+0oEPDAR++w3ImJHJJR4+BJYvN683XN3sWYyUAlq2NKcMdxY/P1oM16+nu9/33/P7X7OGCSNat6aIXruWgnXjRhaKFQTBLSid1OJiXkTNmjX1nj1S9kgQBEHwQgID2bFevJivVavYEbau5+MqevVi3Z2qVYGDB2OvMwRL8eJmFz+DefMYb9OvHy0YqY2oKLqqBQaaEx9Mm8YCrSEh9t3Yrlyhu12NGgk7X2AgcOgQrYY1azLhhdZ057t82Wy1O3Ik6ZZEQRAAAEqpvbZKAnlLVjpBEARBSDmcPQt89RVfRsB/WBhdpABmnUtK5jlnmDHDbH2IiWGnPibGnGb7mWeYiS6NlfNIjx7ubZe3kzYti79++inFTv78TGKRLp3jukr58yeu0Oqff9JyZ2TBq1mT1y0oiNerWDHGFYkoEgS34/GsdIIgCIKQ4vjlF8apfPop5x89YrxIoULJ14ZMmRgr9PgxU4CXKkVhZtRQGjCAiSCEuHTpQqvN8uUsxnr0KOsPOetCmBDKlo2dGnzePGDZMlr1/PzoRmekeRcEwa2IMBIEQRAEV5MlC6eTJ9NSZNSfSU5hBJhr4xgJGCIigGPH+N4owirEJTAQKFKEmffy52dChuSy2JQoAZQsaZ4vU8Z+bJMgCC5FhJEgCIIguJrbtzl99IgxPnPmcN5TwigsjBYkgHEzSsUtKCqYUQpo2JDvHz2iS52RAl0QhBSLCCNBEARBcDW3bzMuZO9ept9+910uT25hVKSIOcOdUZh1wQLGrRhCSbDN11+z+K0RIyYxPoKQ4hFhJAiCIAiu5tYtFussXx7YsYOJDrJlS/7CnH5+dMtSCnj1VS578gSoUyd52+GLFCgAvPIK0KwZ58ViJAgpHhFGgiAIgmCPf/8FcuUyJywwePSIhTijo23vd/s2kCMH3+fIwWD60FCKpeSmZk2gcePYGdVGj07+dvgqAwcCTZrEjvsRBCFFIum6BUEQBMGau3eB995jNrBbt5gZrGxZ8/rZs82B+W3axN3fUhgBtNh4qibQjBlmAffVV8CdO0CVKp5piy/y7LMJK9oqCILP4pQwUkplAlBUa33Sze0RBEEQBM+zfDmLrxqEhsZev2kTpxs22BdGZcq4rXkJwrKA7Ftvea4dgiAIXk68rnRKqfYADgBYZZoPUkotcXO7BEEQBMH17NkD7Nxp3wXOYMcOTt94A/D3Zx2b2bMZn/P4MfDPP1y/caPt/W/dim0xEgRBELweZ2KMPgRQG8AtANBaHwBQ3F0NEgRBEIT/OHuWrl/x8egRM779/rv9bTZtAmrVAurWNafPtoXWFEaNGgHffAM89RSwdCnQty/w3HNAhgzAhQuM2dm3D9i6Ne4xrF3pBEEQBK/HGWEUpbW+7faWCIIgCIIlMTHMnvbhh/Fve/IkEB4ObNtme31EBK0/RYowjba1pefOHaBjR2DNGqZn3rWLggiIXQh13jwKnkKFgOnTgdy5GZh/+rR5m+hoIDJShJEgCIKP4YwwOqKU6g3ATylVRik1AcB2N7dLEARBSO2cOwdcuwYcPhz/tsePc3rmDKdaA+PHc/7iRYqbgweBb78F6tePa+XZuRNYvJgWoYcPuaxpU04ts7kBjD26dAlo1QpYv55CaO9e8/rISE49kYFOEARBSDTOCKNhACoBeARgDoDbAP7nxjYJgiAIAoUMAAQHx7+ttTC6cAEYORIoXRoYPhy4fx/Yvx/o0oXC6PRp4OpV8/5GOu6rV4GKFYETJ4DWrbnMqPmzbBktTf36mfcrXx5IkwY4dsy87LbJyUIsRoIgCD5FvMJIa31faz1aa13L9HpPa/0wORonCIIgpFLq1KGIAShyHj1yvL0hTM6fZ4KEkBDzuvnzgddeAwIDOd+gAaeLFpm3sXSFa9Eitvtc3bpMptC2Ld3mlDKvy5ABKFWKwmzjRgqxI0e4ToSRIAiCT+FMVrq1SqmcFvO5lFKr3doqQRAEIfVy/z5jfAxiYuhWZ8m+faw1ZHD8OAVLdDTFkbH9X3+xBtG4ceZt69QB6tUD3nyTyR0ACiN/fwqdzp3jtsmRyKlQgcLs3Xdpsfr0Uy4XVzpBEASfwhlXurxa61vGjNb6JoB8bmuRIAiCkLoxxIollhad0aOBGjWA/v05f/MmXeHq1uV8cDAtRkoB7doBzZvT3c3Azw/4+Wfg3j1zPaLTp2kNunOH2egSQsWKFEY7dzKxg5HqWyxGgiAIPoUzwihGKVXUmFFKFQOg3dckQRAEIVVjxBS9+aY5e5yxLCIC+OwzZpf76y9g82bGEkVHA++/z2369gV+/BEICIhd3NSSgABOb96k6925c0DZsva3d0SVKpzWqgWsXGle7u+f8GMJgiAIHiOtE9uMBrBVKWWqZodGAAa7r0mCIAhCqsawDo0ZQ6uLv78565uRTW7GDGDAAGaRMxIttGoFfPkls9FFRAANG9o/R7ZstBzdvElRFB0NlCmTuPZ27063uZYtgbRpmUnv0CGgWLHEHU8QBEHwCM4kX1gFoDqAeQDmA6ihtZYYI0EQBME9BAcDefNSbCjFpAfLlwOPH9NClDEj3d0+/5yiqEQJ4IMPuO+IEUDPnnyfz4HXt1I8/s2b5ux3huUnoaRNCzzzDKcA3emMVN+CIAiCz2DXYqSUKq+1PqGUqm5aFGaaFlVKFdVa73N/8wRBEIRUx+nTsa03nTsDM2cCWbIAUVGMBUqfHujdm/FIbduyKKvBs88CEyYwXsgRuXJRGO3bR1FTqZI7Po0gCILgIzhypXsTdJn72sY6DeBpt7RIEATBl7h5k+5eaZwJ2RScIjiY4segRQtadyIjKY46deLyNGnMcUWWNGnC9NyDBjk+T65cwI0bfFWuzIx0giAIQqrFrjDSWg9WSqUB8J7WelsytkkQBMG7iYlhjEuGDOx8587N95GRwMcfAy+/zO20BlavBp5+OnFB/amRBw+AixdjW4wyZmTWt+zZKYziI21a4Pvv49/OsBidO8fsdYIgCEKqxuEQp9Y6BsBXiTmwUmqGUuqqUuqInfVKKfWDUipYKXXIwmUPSqnWSqmTpnWjEnN+QRAEtxATw5o4gwYB/foBhQoBjRuzaGilSsArr9Cta+NGYNkyoE0b4LvvPN1q38FI1V26dOzlBQs6J4oSQq5cwNGjTNRQvXr82wuCIAgpGmey0q1RSnUB8LfWOiFpumcCmAhglp31bQCUMb3qAJgMoI5Syg/AJAAtAIQC2K2UWqK1PpaAcwuCILieM2fo1nXuHNChA1C+PPD88yzwCQCPHjG25d13aSm6f5/Lb91K+LkiI5k5LbVhpOW2FkbuIFcu1jICWItIEARBSNU4I4zeBJAFQJRS6iEABUBrrbM72klrvVkpVdzBJh0AzDKJrZ1KqZxKqYIAigMI1lqfBQCl1B+mbUUYCYLgOR4/Blq3ZkD/zJnMfGYdk5IhAzB8ODBnDtNLR0RwudH5dpa1a5n6+ZNPWMw0NZHcwsggsam6BUEQhBRDvMJIa+2uIcvCAC5azIealtlaXsdNbRAEQXCOdevYaf/7b3Pwvz1q1ACmTTPPh4XZ39YW06dz+t57QO3atFKlFk6fZrprS9HiLizPUbiw+88nCIIgeDV2Y4yUUmWUUouVUkeUUnOUUq5+aigby7SD5bYPotRgpdQepdSeCGN0VhAEwdX8+Sezz7VtG/+2NWqY35cp47wwioxkHZ5584D+/YEiRYAPP2QSh5TIw4dMv21JcHDyWW9y5+Y0a1bJKigIgiA4TL4wA8AyAF0A7AcwwcXnDgVQxGI+AKyVZG+5TbTWU7XWNbXWNf39/V3cREEQUj0xMUDHjsDs2Zw6k13OEEZNmgB16sQWRg8fAmPGAFeuxN3vl1+Ar0z5bl58EXj7bWD7duDw4SR+CC9j40bg33+BoCB+ToM1a4CdO4Fy5ZKnHYbFKE+e5DmfIAiC4NU4EkbZtNY/a61Paq3Hg7E/rmQJgH6m7HRPAbittQ4HsBtAGaVUCaVUegA9TdsKgiAkP0eOAIsXA61aOR/vU7Mm8PPPwMKFzFoXFma2+ixbxtih+vWByZOBYsWA337junnzmMjhzBlmuatfn8tPn3b95/IU777L9OVPPQWcPAnMnctYLK2BPn2AokWBDz5InrbkzMmpCCNBEAQBjmOMMiqlqsHs2pbJcl5rvc/RgZVScwE0AZBXKRUK4AMA6Uz7TgGwAsAzAIIB3AcwwLQuSik1FMBqAH4AZmitjybq0wmCICSEBw+YQMHSrWrTJk4nTWKn3RmUAgYO5PvChZm44fp1IG9eWksAip9XXqHVol8/ioPt24FPPwVKluQ2JUpwaqSw9nWOH2eq8969+Xlz5QLmzwd+/RVo3x64dg34/HPz53Y3hlh19roKgiAIKRpHwigcwDcW85ct5jWApx0dWGvdK571GsCrdtatAIWTIAiC+zh6lCmxixZltrny5dlZnzMHCAwEwsOZdrtEicR3ngsV4tTfn+5j69cDzzwDfPEFcPkyUK8eX2+9xRimvn3N++bIwTiYc+eS/lk9zd27wP/+xxpP339PkQjQGrZ8uXm+bt3ka1OjRvzeR4xIvnMKgiAIXotdYaS1bpqcDRGEFMtLLzGrWNeunm6JYMn9+yzq+fgxaxJlykQh9OABO/CA2Vo0YEDiz2MIIwAYO5buY4MGAZUr8wUw/fegQcA338QVYCVK+K7FqH9/fs/z5zO9+dq1rPNkiCCALoPTpwOlSlEIGjWhkoN06cwxXYIgCEKqx5k6RoIgOCIsjIU9Y2KAggU5Im4QGQlMncoRfxFG3sXq1RRFlSsD+/cDFy4AbdowWcKHH3Kbd9+lpeOFFxJ/nsBAoHt3WotWrKCbXseOsbcJCgJ277a9f4kSwKFDiT+/J/n1V04jIymKXn8deNXKUaBuXYql6dNZu0mywwmCIAgeQoSRICSW338HChRgLMn581zWuzezlwHAsWPm5Tt2MC1xWvnLeQStGfdjyYIFdFPbv5/r1q+niHn8GPjoI6B5c8b7JJUsWZhUYcYMZmDr0YPWEWcpWRJYsoTC21dFw4QJ/F4bNoy7ztJ1ztKNUBAEQRCSGemlCUJiCA2lFSF9euDePbpBhYays337Nq0QEyYAfn7c/u5djvpXr+7ZdqdEliwBvvsOaNYsdta46GhaIfLnpwWobVtmgwNo5Vu0COjVyyxWW7Y077t+vetdujp3BlaupOhKCCVKUFRkz854nIIFXdsud/Hggfm9cV1sxQ8VK2Z+36ePe9skCIIgCA5QOp7CgUqpj7TW71vM+wGYpbX2uidYzZo19Z49ezzdDCE18NZbjAcBmMUsIoJZxqpVi7utnx876Z9+StcsIencusUU2idOAF9+SWtKpkwswnr8OLBlC7BrF5MbWKI1X337UsQeO5Yw640nCA5mPaRLl5jee8gQT7fIOc6dM2fXM7D3vDl+nEkvChRwf7sEQRCEVI9Saq/Wuqb1cmcsRkWVUu9orT9XSmUA8CcAh6m6BSFFExXF2IkuXYB9+4BatZjZLCiIHe7ixYG//6Zg2r8fqFKF6997DyhSBHjuOU9/At/kr79ohciaFRg8mGIUYK2fN95gDFe7dlxWoAATXrRqxdiWf/4BQkIYR/Thh8w6N2aM94siAChdGrh4kdPly31HGBmi9K+/WLupYkX72yZnwgVBEARBsIMzwmgAgNlKqXcANAWwUmv9rXubJaQ4IiNZxPKll8zuZb7Kjh2sSdOzJ/DTT0DGjOZ1RqHOsWOZ/jlvXqBSJSZgaNyYy/38mJ65eHGPNN/jXLvGTrORkc0Zxo41J0QAaJlbsoSiM3NmitVcuWiZ27WLgsdwkevTh26MgYHADz8Av/xCITV2rEs/lltRiq6A06ZRHGbKxOW2Yqe8hfBwTkuWZHyVIAiCIHg5doWRUsoyGOJ7AD8B2AbgH6VU9fgKvApCLH74gRaT9OmZrEBrpvHNksXTLXPMiRMMeC9enNaHR48Y45EuHWNSsme3vV+aNEDOnMAff1AYZc5M97tevdhRb9AA2LzZezu17uTtt5mMICwMOHAAuHEjbpY2w+VKKboofvIJhWitWsDDh8Dw4fwtGaRLB8yaRcFQrlzcc1auzFijb76hkB092ve++7ZtGbe2aROz502ZQtfMU6dip7/2FgyLkbjHCYIgCD6CI4vR11bzNwFUNC2Pt8CrIMTi4UNOV62i5aRrV4qOt97iaP7s2axhkpxs3my7U26gNevb3L5Nt6xNm2iV2LGDHVN7osgSyxTdXbqwY68UsHUrv4s2bVzxSXyLbduYsKJ+feDIES4bPdqcGAFgHaEtW8xWkvTpKWocJR4w3OhskSYNa+n07g106wbkyeOSj5KsNG5Mgb18OV3PXn6Zy7dt4+/U27h8md+7v7+nWyIIgiAITiEFXoXk4dIlTletoivQuXPMtvX551w+bBitMmPGcPQ/ORgzhuKsQweKlXXrgM8+A2rUAMaPBw4f5mg8wNTc770HjBrFznzp0gk/X7p0dPN6/BgoX55ioFUr303BnFC0Bq5eZYFTgN/j668zY9+nn9Ky1qsXRfQPP3Cb/fuB1q2B115Leja2Ro0YqxNPwhmvJWNGphBfvjy2VWznTu8SRrNm8T8eHg7ky+f7rrOCIAhCqiHeGCOl1GcAvtRa3zLN5wLwltb6PTe3TUhJhIRQGDx+zCKXo0YBQ4dyFH/1anNsTunSQL9+7m+P1uyY37gB/PgjR+LffZexQ5s2McB99myKlhkzaGEwLBJ16iTt3OnTM76lXz+mjO7cOamfxjb378cuNmtvm5UrgWefTbogjYlhdrFChWhZs+a99yg8AaaszpqVlqGoKArUQYMYO3T6NLd5/XWgalVgwADXub0p5XsudJa0acPYqtWr6YZaoQKFkTfx/POcNm7sO6nFBUEQBAEAtNYOXwD221i2L779PPGqUaOGFryUkiW17tVL60WLtG7aVOsrV8zrTp7UeuhQrStX1rpcOa2fPHF/e8LDjcTNsV/TpmmdPr3WOXNyvm1b95w/KkrrAgW07t3bvOzmTa1LldJ6xoykH3/6dK3TptV6/nzOHz+uddWqWh86ZN4mJobnB7Ru0ULr27cTfz7LY+XNq/Wbb2pdpYrW0dFcHx2tdaFC5u/55s3Y+1+8yP2M9TlyaP3oUeLbk1LZvZvfT4YM/H6HDtU6Sxb+nryBJ09i/58GDfJ0iwRBEAQhDgD2aBtawpmsdH5KqQxa60cAoJTKBCCD25SakPKIjqYLU/fudPmxdvspW5ZB5YsXM97nmWdovfjrr9gZ31yJEdtikCkTULgw0L8/rUj//MPReWP029X4+QENGzKOxuDHH5lo4P33GQuTIZF/s8OHaX1Rii5oUVFMl3zoEONSliwBzp6lJebIEaB9e1qNGjUCtm+P38pki9mzmQK7f3+mMjdqPJ07xwxxW7cy2UKvXnSvypkz9v4BAcCGDUxzDgBPPRU7uYJAjLTWjx7xe23aFJg4kZn2Bg70bNsA/n4N8uUzWwgFQRAEwRewpZYsXwBGAtgK4EUAL5jej4xvP0+8xGLkpVy8yNHjyZPj37ZPH/No85Il7mvTt9/yHBUq0FIRHq51WJj7zmeLH35gG0JCtL53j+0oXpzL3npL6+3btX74kNaYRYto7YmJsX2s8+e1DgzUescOrV95hRaFtWu1zpXL/H1WqGB+nzat1sWK0bIUHa31nDlcvnx54j5L48Zaly/PY3XubD7Pn39qfemS1tWqaZ05s9aRkYn8soT/MH4jb77J77tRI1rYvOG7XbiQbZs9m5ZgQRAEQfBCYMdiFG/Ut9b6SwCfAKgAZqX72LRMEJzj/HlOnanbM306rQs5cpitB5s2AU8/zRiVsDDggw9okdBJCKI/coQpjv/6i9aSAgWSPx6iYUNOV64Efv6Z9X1++43Wnq+/Zq2jb7+lNaZjR8bbVKjAbHrWfP45cPAg8Mor3L5rVwbqX71KS9yzzzIuZds2bjtkCN+/8ALjqDp0YN2frVsT9hkePwbWrKGFrWdPHmvyZFqo/PyYtKJ2bcYNzZ/PuCIhaVSqxGnJkvy+R4xg5sQDBzzaLDx8COzdy/ft29MSLAiCIAi+hC21ZP0CkB9AewDtAORzZh9PvMRi5IU8eaJ1mzZa+/nRquEsffsyzufgQa3bt+f+gNYFC5qtEb/8wtiKOXO0fuYZrX//3bljx8RoXbQoj+tJoqK0rlhRa6Vo4WnUyNy+FSsYc1W+PL+HevVocStThjEl+/aZjxMayrio0qX5vWTMqPWuXQlvT506Wjds6Pz2MTFa9+hhvh5Hj8ZeX6kSl+fPz+souIaRI/m9rlzJ+fPnOf/jj55tl2EpzJTJs+0QBEEQhHhAYi1GSqnuAHYB6AqgO4B/lVJdHe8lCCbmzKFFZOJEoGhR5/d74w3GGdWpQ0vHK68wRXF4OOOR6tZljEz+/Ixr2bQJ6NuX6Z3jY+9e4MIF92WDcxY/P2YU++ADWlu++47LlWJ8U58+zNYWGclaPkOG0DKTLRtjha5f52ceOJAZ4dasAZYupYWuVq2Et6dBA6YTnz+flqD4+PZbFmp96SXGuFSsGHt9pkycfv89rV2Ca6hdm78d4/suUoQW1kOHPNemhw/NFt633vJcOwRBEAQhCSgdjzuSUuoggBZa66umeX8A67TWgcnQvgRRs2ZNvWfPHk83Q3j0iPV/KlViiutjxxiEn9A0yZcvM33z5ct0+4qJAebOZUf75k3gjz+Y+js0lGmv69QBqlenQHDEu+8CX35JN7PcuRP9Md3OqVMUg4MHAz/9ZF4+axaTQpQpY05tPWAA04onhQ0bWMg2Jobn3bLFXJxTa9bPuXCBQmjJEhZK7dgR+PNP29f233+5z9ixvp0i29vQmtehWDHzsoYNuTyhrpCuYt06/naWL2fyFEEQBEHwYpRSe7XWNa2XO5OVLo0hikxcB+K3NAmpBK3ZKYqIYF2VJUsYt3P7Ni1EFy4AI0cmrmNcoACPNXcus5SlSUOrBsAO+7BhfBm8/jozuoWHx40X2ruX1pTmzXnMpk29WxQBjNHYtCmu9advX8YZTZ9OQVSgQOzvIbE8/TSv2/r1jFFq2ZIiaf16XoPXXuN2M2bw+6xVi5Yie9e2Tp2k13wS4qJUbFEE0CL3++/8P7pThE6ZAhw9SrH73HMszrtiBbB2LS28jRu779yCIAiC4GacsRiNB1AVwFzToh4ADmutR7q5bQlGLEbJxLVrFBe9ejHQ/5VXzOuyZQM6daLlZvJkIDiY7m1Vqri/XQcPAkFB7LgPGGBerjUtTwcPMsFAVBRTY7/8svvb5C60poWnQYPEp/V2xBtvmF37Jk7k95U1K1C/Pl0Z33gD+Phj95xbSDhTpvD3HBISVzS5kjJl+J+uWpVunlFRtBr++y9d++Kz1gqCIAiCF2DPYhSvMDLt3BlAAwAKwGat9ULXNzHpiDBKBjZvZgazW7doDTh1imJk0iQKppo1zbElWgP37iVfJjKtWQ+nfn3GyRjs3MmYpFGjmO0tKgq4dCn5s9D5Eo8e0X3x9dfNNZ9+/pnxTPfu0TooeA/bt/N3v2QJM8K5mp9+Yn2qHTvMyyZMAO7fB95+m/OrV9PKKAiCIAheTqKFkVLqC6312/Et8wZEGLmZ8+c5Kly0KPDii+wQlSnDdNDlynm6deSFF9g5vHbNvOzFFymUwsJo/Th3Dpg61XNt9CXGj6crZIUKtApky+bpFgm2uHOHCRhq1KCr26uv8tq5wq1Oa6YGDwnhfJMm/H/t28ckEJ06USyvXSuxZIIgCIJPkBRhtE9rXd1q2SGttdelmRJhZEV0NK07DRuycxsZyVHdkycZH5BQl5tPP2UtoTNn2FHyRsvBV1+xrsuNG0CuXMyulj8/6/j8+qunW+d7PHlCK0G9enRDFLyXEiXM4gUALl6kBTUpPHrEOLfWrSm8SpVi5sLoaCB9em5jJGxPI6GngiAIgm+Q4OQLSqmXAbwCoKRSysgDqwBkBbDNyZO2BvA9AD8A07TW46zWjwDQx6ItFQD4a61vKKVCAEQCiAYQZavxKRKtgXfeofAYPDhpx9q7l1mili+ne9v48czgBgAbNzJpgrMjvFoz9XaDBmwb4H2iCGDHDQDOnuXo+bp1dPvr1s2jzfJZ0qUDGjXydCsEZ6halcKoVCkOXly6lHRhNGqUOdbs9GkONvj58WWglFiKBEEQhBSBoyG+OWBR1yWmqVHgtYbWum98B1ZK+QGYBKANgIoAeimlYhU60VqP11oHaa2DALwD4B+t9Q2LTZqa1qcOUQQwYcEXXwD/+x/TVCeF1as59fcHxoyhKJoxgx2dDRuABQucP9amTUy73bt30trkbgzRduYMY4m++ooj3S1aeLZdguBujAQnQ4ZweulS0o+5bx+nEybwPiJWQ0EQBCEF40gYPQFwSWvdS2t9HkBGAJ0BNHHy2LUBBGutz2qtHwP4A0AHB9v3gjnzXerk7l3W2Kldmy5gX37JWJjp02mxSSirV9Nq0r8/YxAyZWImuaFDmTDhrbfoDmeLdeuAN9+kmLp5k0H3pUoB/fol5RO6H0MYbdrEbFkbN7IQqWRPE1I6vXtTFPXqxXlXCKOQEKaHHzo06ccSBEEQBC/H0fDfKgAvAjitlCoNYAeA2QDaKaVqa61HxXPswgAuWsyHArBZ1EQplRlAawCWT18NYI1SSgP4SWudcqPlIyPpt79yJevIfPcdkwT8/DPw4AH9+VetYmpsw68/PkJDmY1t1CjW7hk/nrV7Mmbk+gkTGHs0caI5qxTAuISICLOF5eJFZp4LDaVY8kb3OUuyZQPy5aPlLXNm4JtvYqfuFoSUSsWK/N3HxNAFMqHCSGveawyr0OPH/N+XKOH6tgqCIAiCF+LIYpRLa33a9P55AHO11sNA17i2ThzbltO5PbNHewDbrNzo6puSPrQB8KpSymagg1JqsFJqj1JqT0REhBPN8kK6dKF4+fprppWuW5c1Se7e5fpRo+j2NnAgxcm5c3GPsXs3BY3BxIns6AwcyDS+desyY5tBgwYs6DlpEl3OHj1imt+iRZmwIV06jjwvWAAcP87iqA0buvd7cBVGvMOIEay3IwipiTRpgEKFEi6MPvmE2SUfP+b8hQsUWYYVVhAEQRBSOI6EkaWIeRrAWgAwucXFOHHsUABFLOYDAITZ2bYnrNzotNZhpulVAAtB17y4jdR6qta6pta6pr+/vxPN8kLOnQNatQK2bmXqa4Bi5qmn6Ab3+efA++8Dv/1GS461BeSLL+h+V6sWj7F8OQtyduoEFC9OkbN9OwWYJcOG0SI0bx7w++/AsmVMvx0aCrRpA3z2GVC6NOOSfKk+iWFVGzjQs+0QBE9RuDAwdy6TjkRHO7fPkiVMWrJ0KeeNARixGAmCIAipBEeudIeUUl8BuASgNIA1AKCUyunksXcDKKOUKmE6Rk8AcSL3lVI5ADQG0NdiWRYAabTWkab3LQF85OR5fY+ICAqR+vXNy5SimDEYM4b1SU6fBv75B6henSmp27alCGrXjq5zhlWnfHlaoBzRvj1jkIYNY7apwEDgr7/YjiFDKKpOn3Z8DG9k+XImikhqRi5B8FUKF6YgWrCA9cfis/pERpoTLUydykGUs2c5LxYjQRAEIZXgSBgNAvA6gOIAWmqt75uWVwTwVXwH1lpHKaWGAlgNpuueobU+qpQaYlo/xbRpJwBrtNaWWQDyA1io6BKVFsAcrfUqpz+VL/HkCeOK8uaNu84yBW7atOzkhIbS3e3YMaBOHYqiYsWAP/5gR2jFCm7bqlX8xTj9/LhfkyZ0m/njDyZYSGo2PE9TpYo5Q5cgpEYsrURG3TFHbN9Ot7mnnwbWrGHSkrNnaW0uVMi9bRUEQRAEL8GuMNJaPwAwzsby7QC2x93D5jFWAFhhtWyK1fxMADOtlp0FEOjMOXye69c5tSWMbBEQwBHdEiWYTOHHH1l800iK0LNnws5fujTd6aQOiSCkHCpXBv7+m+/PnLGdrn7RIiZrePZZ4M8/OaAybx7dcocMYaxRrVqxaxYJgiAIQgpGilJ4mmvXOE1IfJRl7Iwr0uiKKBKElMXo0cBzz1EgnTkTd73W3Ob4cVqIMmfmIEvevIwpbNaMFqTJk5O/7YIgCILgIUQYeRpDGDlrMRIEQYiP9OlpDS5RIrYwWrAA+P574OOP6Y47bZrZ7TZHDm7TpAmX793LdYIgCIKQShBh5GlEGAmC4C5KlYotjJYtY+bKUaMohnr1orXImgEDpP6XIAiCkOqwm65bKZVXKfWBUuo1pVRWpdRkpdQRpdRiU8FXwRUYtYdEGAmC4GoMYWQkYzh+nNN//6W7nC1RJAiCIAipFEd1jOYAyACgDIBdAM4C6ApgGYBp7m9aKsGwGOXJ49l2CIKQ8mjUCLh3jwWjtab7nIEv1SYTBEEQhGTAkTDKr7V+F8BrALJqrcdrrU9orX8GkDNZWufrXLjA2kNjxtBvf/ZsBjRbcu0affuNoqSCkMKZNQsYPJj9dMHNdOkCvPoq8PPPwMGDwN27QIYMXCfCSBAEQRBi4SjGKBoAtNZaKXXNal2Mje0FS6Ki2PG4eBF4+JD+/H37AqtXA8OHA19+CZw4wQDnUqU83VpBSBYiIlhP+M4dhrDUrevpFqUCWrYEJk0Cli7l/OefUyDJfUdIRmbNYoWJIkU83RJBEAT7OBJGJZVSSwAoi/cwzZdwe8t8nTlzgJMnOTqbLRtw+jTw0UfsoKxaxcKuZcpwW1vpdAUhBfLtt/TsypqVydFEGCUDpU0hoYYw6ts3YeUBBCGJ3LgBPP88ULgwa5QLgiB4K0rb8WdRSjV2tKPW+h+3tCgJ1KxZU+/Zs8fTzSDVqjHg+c8/gfv3OX/pElCsGJdv3szCrP36USB9+KGnWywIbqd+fU4rVQLmzwdu3fJoc1IHDx6YkywUKsT7kCAkI3v2sFYwQGcKqRksCIKnUUrt1VrXtF7uyGJ0Tmt9wY1tSrns3w8cOABMnAiUK2deXrgwC7LevQs0bMhls2d7pImCkNw8eQLs28c8AAUKALdvA5GRNKgKbiRTJiAggEP1YqITPIClU8T27ebHnyAIgrfhKPnCIuONUuov9zclBfHLL0ym0KtX3HXffcfiiYKQyjh8mOF2deqY4wwuXvRsm1INhjudCCPBA5w9a36/caPn2iEIghAfjoSRsnhf0t0NSVGULQu89hqQO7enWyIIXsOuXZyKMPIARqIFLxZGMTHAzJlMniekLM6eBfLlo9OEpUgSBEHwNhy50mk774X4GDrU0y0QBK/jyBFmpi9WDFCmYRcRRsnEU08By5YB1at7uiV2GTKEWcWbNwfWrvV0awRXcuYMtXmaNEBIiKdbIwiCYB9HFqNApdQdpVQkgKqm93eUUpFKqTvJ1UBBEFIGN24wGZpSzAGglH1hNHcu8PXX5vm7d5niW0gkL77ILztjRk+3xCY3bwIzZvD9vXuebYu34st1v86eBUqWBEqUAM6d83RrUhcnTwK//uobyW9jYlj6UbDPkyeebkHKx64w0lr7aa2za62zaa3Tmt4b89mTs5GCIPg+N28CuXLxfbp0QMGC7Kt/+inQtWvsbSdNYmpvg759gY4dk62pKQ+l+KV7KWvXMlln2bKSztkW77zDxKa+2Cl6/Jj/81KlgOLFeX198XP4Ko0aAf37A1WrAmvWeLo1jvnqK6ByZeauEuJy4gQTFw0f7umWpGwcWYwEQRBchqUwAhhntHUry3v99RdwwSIH5smTQFgYO1CPH7PjvHevb4+aC/ZZuZK/jS5deN2joz3dIu9hzRpg3DjGXi1ZEv/23kZwMC0BZcvSYhQTQ6F0/TrvAfPne7qFKZfISODqVXr3FywIjB3r6RbZJyIC+OQTvv/7b8+2xVuIiqJr8V9/8Z7YvTuzuX79NZcJ7kGEkSAIycLNm0DOnOb5gADWPY6J4fyyZZxeu8aX1uwk79rFUmB37gBXriR7swU3c/48H/Jt2pjLvIWHe7pV3sP779MNrWhR4IcffE80njzJqSGMALrTTZ1K69GkSZ5rW0rHKFn21FPAoEFMle6tLnVLl1LIFSkCLF7s6dZ4B2vXAuvXs/LLv/8ys+vPPzOv16pVnm5dykWEkSAIycKtW7EtRgMHAs8/z5t/6dJ8MALmjhQAvPBC7Jonp04lS1OFZOSVVzj99FPvzVYYEcF2ursg8YoVLIT6+eec37GDHaI33wTefpt1wV94wb1tcDXG/7lcObrSAfwf//gjkDYtP5MkZHAPhltqQADQpw89aufO9Wyb7HH8OJAhAxP6HjpEAZDa+e03TjdvZhWYtGmBTp2AGjXoQSG4BxFGgiC4Ha3jutK1bs30zE2aAC1bAtu20XpkKYw2bODUGGm2XOcp7t4Vlz5XoTXwzz+MgShe3HuFUe/ewOTJwPLlids/JobxAfEllvj7b2DPHuDdd9nxeeMN/meef57C7M03gVmzfCuBwalTjIvInp3XN18+4MMP2WkfP57brFjh0SamWCyFUUAAEBgIbNrk0SbZ5fhxWhWHDAFatQIGD07dbpaPHgGLFrHCQkwMy182bkyvixo1mOX10SNPtzJlIsJIEAS3c/8+44UshZElQUF0owgJYQcyrUUhgfffB/bv52iiJy1Gly7RDaVQIY6+RkV5ri0phcuXKRbKleO8NwqjCxeAdev4/vz5xB3jk0+AChUo/gz3JlscPw5UqsQ8Ge3a0Vo0ZQqQNSvXG5UgfKnDePKk+fqmTQu8+irjXkqW5Ps0acR10l0Y/6PChTlt0ADYudM7710nTvA/kjUrvQeeeopeBStXerplnmHvXuDBA2DECOD11xkjZliLa9Tg8/TwYc+2MaUiwkgQBLdz8yan9oRRYCCnX3xBF5tatczbNmzI+kdlynBEfeFC97fXmocPOeJaujRF3ty5EhvhCgyhW6YMpzlysGPkTcJo40bz+8RYLM+eBT77DGjalP+D776zvZ3WFEYNGgDt21M0Dh/OgGuDEiWA2rWBP/5IeDs8haUwAoCXX2aMxMiRFID+/hI76C5CQ/n9Gln669fnQMShQ55tlzUPH9IKWr4859Olo/gvXhx49lnHwvniRVpzrTMd3r3L/5OnOX6csbLPP5+wZ8bWrZzWr897RlgYLdcAhREg7nTuQoSRIAhux4jNsEy+YEnlypxOncoA/AULzNaDmjU57d6dI82vveZeV7bHj80JIQwsO+rDh7NOqrf66vsSp09zaggjpTgy6k0d5T17KNYaN06cxfKjj2gV+f13/oYnT+b7jRvZWRowgAkVIiIonMqXZ6r6X38Fvvwy7vH69mU64337kvzR3E5kJLPPlSplXubvz//x4MGcz5/fu653SiI0lAM6BvXrc2p0ur2FU6d4z61QwbysSBFgwgRatwzLyN69dMH+8ks+B+7fp4vpK6/Q5ey998z37rfeooCIjEz+z2PwzTd8tpUrRxfYkSP5P3eGbdvoWpgvX9x1xYtzcGHPHpc2VzAhwkgQBLcTn8Uoc2bz+/ffp7ta6dJ8UBpiaswYpikNDXV9rJHWHM37+mu67FnWUALMwmjOHI7+d+tGN6eVK6UmS1I4fRpIn54Z1wzy5nW+85Ac7NlDIVyhAn93higPCWEg9D//2N/3/HmKoMGD+Zv+/HMKn+eeA55+mhbQmTOZktsY3a5Qgd9Hv34Uitb07UsLwE8/ufqTuh7DbdBw5TLw8zN/tvz5KZQE12MtjIoU4X3V22K6jhzh1FIYWc4b/42FC4HVq5mIZMIEWmGXLWOM6sOHTOAyZQozmM6eTVc0ww02uQkPB0aNYgytUnSRffAA+OCD+PfVmsLIELLWKCUJGNyJW4WRUqq1UuqkUipYKTXKxvomSqnbSqkDptf7zu6bWti0ib63guDLxCeMAPNDoFMnTidMMGeqM2jenFNXP+yGD2f8hlE4zzqGw6ixVKsWR/+NgrTPPAN07uydPvu+wOnTtCb4+ZmX+ft7jzB68oTWmZo1OXp78yZdWgCm0F20iB0fe/folStpDTJig4oVMwvqKVMouP39aSndtYvbWHcOrcmVi5anuXO9P/janjCyJF8+sRi5i9BQs+XdoHNnpoC+ccMzbbLFzp0cHKtUKfZyf3/+3o3/15kztCgfO8b/zNmzjFWbP59WpRYtmLCkZUu6DKZLl/iEKUll8mQ+F6ZO5YDKzp3A//7H5bNmOd73zBlaWuvWtb+NJGBwH24TRkopPwCTALQBUBFAL6VURRubbtFaB5leHyVw3xTNnDkcVWzThiMNAEcpvemG5ixHjtA3fts2T7dE8ATOCKMVKzjKlj495wsViu2CA/AhWLKka4XRjRv04R4wgC4mnTuzKKWlu55hMTJGX0uXpqXgo484Ytm0qXTuEsPJk2Y3OgNvEkbHj3MkukYNc6etfHl2cubMoSUJsN/5CgujkDayKgIUga1bAy+9RGvoCy9QYI0cSQFmOcJvj+7d6SJkZG30VpwRRq5ypTt1igMnZ88C8+aZn5kG167RIpxaBjHu3+e9zfr31LUrvwNvKha8Ywf7B5ZJdwBaRsqXN1uMgoOBKlU4eDBoEO8TZ84wNlEpWon69qXF6N13gY4d+VxJ7iyiu3YBX30FdOjAZ1jBgnTHHT+e95GZMx3vb7jI1aplfxtJwOA+3Gkxqg0gWGt9Vmv9GMAfADokw74pgiVL6EpRqRLF0LhxvNHXrAlUrGgeXfQV5s8Hdu9mYLG3dHqE5MMZYZQ9O9P6xkfz5ozPcFUHZ/16+qUPGkSrVYsW7FBYZiC7cIEj20YQMwA0akT3vt9+Y0fZSD0sOMetW+zwGDFkBnnzmgv8ehpDEJcqBTRrBvz5J5Alizkg/N13GT+wfr3t/S9fptCztIhZM3YsR5E/+YQeAmmceCo3a8bR8Geeoeupt2JY1woVsr9N/vzsxN+9m/jzaE2r3Pr1HKTo2dNsNTB45hlahC2TaaRkLFN1W1KzJu/D//6b/G2yxYMHtMras45UqECLkda0MJcubf9Y/v7A9Om0KH36Kfsb4eHJO2i1Zg2fIQUKxHV39fPjYPe//zp+fu3ezWeNtQXNEiMBw+7dSW+zEBt3CqPCACxzC4WalllTVyl1UCm1Uill/Ayc3TfF8sYbHBnZvp2ZSL74gq/r12k6ff55z458XbjAG6yzbn47d5rfp4YinefOub8YpC9x6xZH9HLkSPqxmjfniKCrHgirV7Ndxuic0VG3DGy9eDF2HIwlffvSqjt3Lt2mBOfYuZOdnXr1Yi/39+dI6J07nmmXJcYgjiFuunallTAigte9Y0f+Hv/5h0k7rAkP52ixIzJkYO2W0aMpupwhY0ZztrqPP/YOEWmLS5f433L0ufLn5zQpcUa7d7NQdPbsfDaVLEnr79Ch/H5WrzbfLxzFhKUk7AkjpWilDQ5O/jbZYs8e9mXsCaPy5Sls0qQBbt+Oa2F2hJHUx4hhcjda0wIcEMBBDluJE+rX50DAzp20KNkqZLt7N0tYpEtn/1zFi/M8qUXoJyfuFEY2wkZhffveB6CY1joQwAQAixKwLzdUarBSao9Sak9ECjFF3LpFd4AePYBs2TgSnT49RxbLlAFmzKAg+fVXz7Xxl18Y+BefryzAzuLOneYOUEp3OYqJYQ2GN9/0dEu8hxs32EFyZjQ8Pp5+mg93V7nTrV/PEXjDjaNKFXZW589np/fZZ9mxsvbVt6RPH46Ob97smjalBrZto9ioUyf2cn9/Tr3hdm4pjAwGDaL7yqxZbH+7duzo9OsXN5vh5cvOWUETw8SJwLBhfO+t99RLlxy70QHmzmNSPoOR3XDMGE5//JGusTNn0qLWsSMFatWqqacjaQgjW/etUqW8RxgZosVwS7WmTx8OFBs4shhZE58wMhKjHDzo/DEdsW8ff/MjR9ofSDP6QQ0b0jNoxAizRwXAge+9e+la6Ail+HwyPB4E1+FOYRQKwPIvGQAgzHIDrfUdrfVd0/sVANIppfI6s6/FMaZqrWtqrWv6Wz69fBjjTxoUxGmhQuxwDR0KfP89b/JVqrhHGMXE0F/ekVuD1vTlBegbHx9Hj9IfvmNHzl++nNRWejfHjnH0c/Vq7x3JTW5c2UHMk4cP0bVrk36s6GiOMFu6LGTIwP/an3+yE2W4nDhyA2zfnj7kxv9CiJ/t21m/yiheapA3L6feIowyZIjdRqXY4TKyqrVuzQ75vHlxU2g7YzFKLDlz0l0MoIU6ObhyJbZ7Wnw4I4wMi1FShJEhAl56ia7nrVoxnmjcOKBLF8aJjRpFd7pdu5LmtucrGN+Jre+/dGne92xZOZObkydpUbTnblmoENNeG5lLreNOHZEvH1+24nDmz2d2yGnTKEJ++y3p38fSpbwvPPOM/W2KFGE4RNq0fM7cvh27ttm6dRxoadMm/vM1b85BxwMHktZuITbuFEa7AZRRSpVQSqUH0BNArHA/pVQBpfh4UUrVNrXnujP7pmT27+e0WjXzsqAgZulq04Z/vM6dOeJ67Zprz/3mmxwB7d3b/ijEokUcoatRgzECEyc6diEy/rRG2711dNNVGAkmwsJSh9ugM7h65LxZM1ohE9JJs8W1a/ydW7s8vP8+R5c//5yuHg0b8j9nj8yZuX7BAnbChPg5eZLfsTXG+Jar722JISKC7bGVNtuSnj05PX2ag0A//cRR4CtX3GcxAsxJHZJDGEVG8rP06uX8PgkRRkkZMLt0iW502bIx8x/AgYy336ar0sSJFE316tFtyyhwumwZO8cp8T978SIHkTJliruudGne90JCkr1ZcTh5khkf4/uP7d/P2kRlyybs+JUrx7UYPX7MeLOaNfk9VatGi29QEMMVEoPWTL9ft25sC7Mt9u2j+JkwgR4JP/5oThaycCF/y08/Hf85mzbldMuWxLVZsI3bhJHWOgrAUACrARwHMF9rfVQpNUQpNcS0WVcAR5RSBwH8AKCnJjb3dVdbvY0DB/gAMh4Ytnj2Wd7Yli1z3XmDg2mRqlaNIx/587OQmmUs0+XLQP/+FEVLlnCkZdgw24UIDYyHdunSHA1O6cJo61bzw8jbs0YlF5cvO/49J5Snn2YcSlILFRq/Reu2Zc9Oy+3IkRzh27wZaNvW8bH69OHonzdle/JWoqP5m7DVafY2VzpnHBEMgRIczE74kCG810VFuc9iBDDOAEgeYTRhAqfWKfTtYVxjR4kXAD7r0qdPWifdul6PJblyAa++SsufkQr95El2Jtu3pzuVMRiZknD0nRjuaN7gTnfyJBOYxEfZssz05iiRiS0qVaIXh6X3xty5FERjx/IetHkzs0yeOcP7eEI5coS/o8OHGXsYHxkymOOH3niDg0CzZzNhxJw5fNYY2VkdUbAgn1WGK6ngGtxax0hrvUJrXVZrXUpr/alp2RSt9RTT+4la60pa60Ct9VNa6+2O9k0NxMSws2dpLbJFtWqMN3rtNdfFNRgPh2nTaGauXZsjbq+/bt5myRIGRc+YwQfezp0MSP7wQwa1WmbyMjh3jttmzMiHYEp3pduxg9axggX5XnC9xahBAz5Y7GUDcxZ7wigxNGvGDsfnn4sLZXxERLDjbKvT7E2udNeuOSeMMmViJzQ4GFi8mMsMi7s7LUZZstDa6W5hFBUV293HmWtz9SqvcXwWIz8/CrwzZxLfPkciwJLixdnhPHEitsBLiSmPHX0nhjuap4XRw4cUxM4Io8RSogRdJy3jeFav5ndjuKulT09L6CefcJ1l4p34uHGDtcz69WMfJyEWVQBo3JgDzSNHAu+9RyvQF184t69SvJZJ+e8IcXGrMBISzqpVzo1aKMVOob8/Rxxc0RE7eJAPqYoVgW7dGGs0fDjNvP368Ty//MIR9CpVzO348Uc+cJo04dQ69iMkxDyiatSsiIkxp8JNSURF8fNWqMCRKqP+Qmrm/n2zG46ryJKFLgurV5uXaZ3w/4GRCcsVwsjPjw+2Awf4Pxbs4yiNc5Ys7GB4gzBy1mIEUBRv28aYtI8/Ni93p8UI4L3V3cJo40Z+F0YQvDOpnp2pYWSQ1M6ds8IobVoOKJ44wUG92rUZP5bQrGVPnnj/4Iej7yRfPloaTp5M3jZZY9SLc6cwMpIgGEW6Af5fbLnvvfQS3TG//db5448ZQ9HVujUTKeTMmbD2KUXL1c2b/K8sWOA40Y81pUuLMHI1Ioy8jAkT+OcwUrE6okgRjjLs20crz8svJy0g/dAhpsa0rNUybhz/7LNnc8Rw505zVjADf39aiz78kJ3fr76Kfdxz52ILoz176NtbtCjwyiv2HzAPHtBnd8IE738IGVy6xFHSYsVi119IzRhWGVePnLdvz9/s+fPMEJY/P7/3FSsS3jZXufn16sV4o5UrXXO8lIojYaQUl3vDwElEhNmCFR+WHZSOHc0jx/G5kiWVEiXc3zGaN48C4r33OADgjCU8McIoMffKJ0/su2Xaonx5CqE9ezi4UqmS8xajCxfYAU6fnvd3b80G9vgxrZ32RLlSHAA9diz28pgYeonMn588zy3j/OXLu+8ctoRRSIjZDdWS7Nk5KLxqlXOff+5cDgwPG8Z7/kcfJa6NzzxD758pU2zHhDmiVCn2sRJbvmXiRD6bU6LVNLGIMPIioqLoFte5s+P89Zb07cub3+DB/FP16ZO44MF79yiwrIOh/fwYPxQZaa7W3LVr3P0LFAA++IA3iDVrzLEfT55w5MoQRgUK8HMePcrjTJ5s22y9ahVHXrp04Q3DV0ZEDFdCQxjdvWvODuTLHDrEuLYpUxL+wDRcJ10tjDqYSj7PmsVBgeLF6bs9YoTzx7hyhZ0cV9RXAnisunUlbXd8xFf4s1Il3iM8yaNHvO85azEytmvQgAHfM2fS0lKypNuaCIDnOn+e8W3uYuVKxj3kzs3zWWffs4UhjJwRhiVL0kX7xo2Et+3yZd6TnLEYAeyEnz3LgbennuLnOXyY8aCjRjkWO4MH89nWujWtLbZcx70BY8DHkbXSljDauZPP+x49GOvibvbu5T2zYkX3ncNaGD14wN+M0Sexplo1/g6Ne5Q99u5lzaKGDR3HWDuDUozvbtcu4fuWLs0+VWIGkvbvZ5/tyhWKYYGIMPIijh2j25F1XQ9HZMnCTuuaNbQW3bjBukcJ4do1BqheusT0ubbInJlFZS9dchyE/uqrHMHo1o3xSBcu8EFj3ISyZeO0dWuzuXr79rjHmTyZI7XffMP5f/7hKOWgQd7hYmMP40FZvLg50DcluNNNn06f/JdfNsdQOIu7hFGZMnygfvwx/zc//UQL5LFjzgdyX7lCt5L4MiIlhMaN+Z+09GkXYhMWxu/cnqWucmV2PD2ZTthWDSNHNG7M6bhxnKZPT/did2PEo7orZW9EBK+XUVelWjXnzhUWxoE1Z6yxRsxLYgbA7BUytYdRBgOgiA0M5GBis2aM7Th71vZ+GzbQdXfsWGatBLx3lD08nFNHwqhSJboSW2Z//Osv1prz83M8uBMdze9qxoykFbXevZuDsRkyJP4Y8eHvz+MbwsF4NtgTRsbgcHzXduRIZv1bsMC5RAnuIinxYsb/OH9+cf+2RISRF7FrF6fxFfayJm9eoEUL5rRv2jThWbG2bKFlp3lzih9HFCrkuBOZIwfTeWfKxBF9o3aRcRMyOhkDB/JBVrSoOb21wYMHFHmdOwP/+x//tJs28UY0bRpH5O/cSdhnTC4MYVS0aMoSRhs28LdVvjzjDJYvd35fdwkjgDFvuXPTvbNaNbNod7Z9V664NlsewA6y1knPmJeSCQujILVnGa9UiaOgicm2tHZtbLeZxGIk9nBWGLVqRct7/fpJP3dCMISRuzKrGXX1jEGzoCD+p+NLonPpEv/zzmQRS0rnLqHCqHNndggvX+Y+/fvTTdxIj2xYunbv5nPK6Pj/8APvFa++ai4c6svCyLDSGFYjrSmMWrfmM9bammTJxo20rr34Ir+XhHL3Lq0s//xDt3p3kiYNww6Me4IhjGy50gHm+Gkjpbs9jh3jf9661ENyY6QvT4z79unTjLsbMoSeO0bMbWpHhJEXsXs33ccSUtnZmrZt2RFPSDDutm0cUVm2zDWd18qVWb/nyy/p3tGhg/nmN2gQA3fbt+d8/fo8v6V71vr1FEft2lGENWrEVJhbt/LhdfYsfd29kfPn+fDMmJE3zDx5nHM78WYiIuiT36IFrTIPH/LaONsRCw/ndXQ2ViMh1K7N34ORYapMGbrlrFvn3P7uEEY1anAa34M1NRMW5tjFKr6K9fY4fpxFT4sVS1rGwqNH2WGuVClhVh+jCGVyUrAgf8PJKYwsl9vDmRpGBmXKcFBtzZqEt88QMs4KozRp+FmM/322bMy8OnFi7OO98w4t5cHB7DAuX27OPJYtGzvWnhJG9tyZly3jfScxwig4mM+v9u3Nrqz2zrN4MQc/K1dOXKH5hQsZyxQT435hBMQWRkbfyJ7FKFcu/pYc3b/v3qWwTkpfzVUUKkQB/+23CXfhPn2a34NRKHrnTte3zxcRYeQl3LrFm9pTTyXNrccYMU9IAPq2bUCtWq41Z6dNy1iPCxdoQTIqx6dLF9si1qgRO0mW7f3hB4oKo0NiacWaPp03gcmTzQXRvImQEHOBQaU4orRyZdLcDTzNP/9w+vTTvF7Hj/N6vvgiOwpPnjjef/dudnzSpnVP+zJnNndIleJv2Vm3IncIoyxZaDE8ccK1x01u3n6bHYQFC1x/7LAwx522cuXYgU2oMDLiIDNlossTQAu60VF0FuP3M38+O0reTrVq7huAOXiQnS9jYMMQSPH9xxIijNKn5wDa4sUJd58MDeX1Tup1Mtp66RJFgSGsDx3iwFxUFDBggHn7KlU8I4xq1zZ3ZK0ZMICuxcZglKN7W5EiHIg1fjeGhbthQ4omo0CxNVrzP9WyJWOuDh5kkdz4YnIsMURH2rRmF1R3UrSo2VJ06hR/b44GgatWdSz8DZdPw9Lpab7/npZZ6+RbV64Af/5pv4Dx6dN8NhvJL6QeEhFh5CWMGMFRqcRmNTEoU4Y3PGv3NFtERVF07N5NX2tP8PzzHIF87jmKw+3b+eceMcIs1Nq25UjWtm0cpWvVypzAwduwFEYAExZERPj2SMyePRS0hstOzpwURfv3A7/9xhuvPR4+pLBq1SpZmgqAHbeQkPiD0Y1sVs6ONCeE8uV9WxiFh/Nhe/kyA4xdLexv3KA11R4ZM3I0OiF1wKKj+Xt89ll2Drdsoajr0IGuQQlx0zp9mh1LdydOcBUNGlBEuiP+8tCh2El5cubkf8aRqxVAgZGQjHzduvEZkFCXoNBQipqkxglmy8ZBjbAwWo8yZmRn8+BBxtLUqWN2jwY4iHnsmOP7nzvYvZsWcevO7t27jBcKDub/19/f8WCUUvxfGDG+27ZRXBqlJgDbz9gTJzjg2a4d0Ls3B6Jmz+ZgpbMcOABUr87PkBxWl9q1zQOwM2fSXTCNg95vjRq8tvfu2V5vCCNvsBgBHBgsVSq22/6ZM2xf9+6srWeN1vytlClDd/S8eSkaBRFGXsO2bRQAtWol/Vg1ajBjSnxs2cKMXk2a0G3EE2TKRPesmzf5gBk7ljf0l1+OvV3RokC9enxvjFi6wlVp2jSKMFekJr15kzcay05EmzZ8OCXEgudtHDzIEUTLANPPP2fcV7lyjms+bN1Ky15yCyMg/tHc0FC6ctjzNU8KhjDy1VTt779P4fjuu8zM5up6J7dvx1/v4+mneV989Mi5Y+7cyQ5h794Uc+nSsQ4bQMHQsKHzhRuDg3nPsSxd4M20aMGpsy6kCeHChbgCsUgRx9k2793jNXbWYgTQAlGyJF3YEmI1craGUXwoxfYePcrnYu/ejN+YMYPLLK1FAPDmm3QFf+EFJn+x5OpVChhXY3ke62ttxLeePcv/gTP1s+rX52e7eZP/tfr1KRgMYWTLPdOIha5Xj4Mbu3bxvzpvnnP3O635TAkKci7+zBX068f7TdeujE/+5BPH29eqxWeDPfdUY5DFWyxGQNwsg99/z3tno0b0wrGOyw4P5/+0TBnOly0rwshAhJGXcPOm64L4atTgDzy+BAVLltAqs2iRewusxUetWuxIDh5MH/PhwzlyZ4+SJTlC4gph9OqrrLtkWZAxsRij25bWt+zZefP05RvOoUNxsxVmykQXiFdeYWfTXsd57tzky85lYAjT+GIg4gvCTQpGqnYjXiEphIRwNDa5aqbMm2ceMDBq8RidIVcQE8NOc3wp0p9+miPKzlpblyyhGGrdmiPf9eqxs5g1K4+RLh2XtWvHTsTw4bxGtggO9p7RYGeoUYOf2TqzVFiY/SxrzvDgAZ9N1pafgADHv23DrSohwih9eiZBOH6cv0FnuXTJdVbfwoXpsXD/PtMYly7NDmS2bExhbUnGjKzdd/dubJGiNZMO1avn+rgvy6Qi1kmWDGF05w7FjjPCyBhsnDOHAzmNGnG+QAG6Ci5aFHef3bv5fLbsM/TsSSurMy7M4eEcqLDMDuhusmal4M6Xj/c2I8GCPYwBanviNjiYFhZXlXlwBRUq8Bo8fszBrBkzeP/++mtaYi0tejExTKgEmEVwmTK+3U9xJSKMvACt6VqSO7drjmcEfzu6KWvNgPVmzczxP55CKXMcUbVq7Gw7Ik0a3tji6/g6g5E+fNw43jySwrZttA5ZZxUsVsz59NHexrVr7ORY17cyMGoJ2coCd+gQ3RZeftmx0HU1hQvzv+SsMLJ0fXQVhs92UjMS7tnD4NhXXkmYW1lS+PprurF98gk7P9myuXb0++5d3n/isxg1asT/ujNJFO7fZ0B348bmzophRalZk/eLAwc4+HLsGEe6v/mGI+S2ygX4mjDy86PgmzWLGaYMevSgpSyxac+NzHPWneyAAFpq7FkIElLc1RKjVtKmTc5tHxPjemEE8DkUFER3OYCuYrZ+r8bvzVJArFxp/q++9FLC23DypP37hmXdJOt4EMt1584558ZYuzYF6Tvv8Dncs6d5XffufKZZC+Ddu9nHsLT2dOzI/Y0kOI4wBjTtlQZxFyNH8p7/wgvxb1ugAK2i9gaETp/2vvtDxYoMMQgO5u/v3j3WuaxZk9bYb77hQMfGjRxEef11iiJDDJcty2e9vcGi1IQIIw8SEcGO5/37fHC5Whg5chs5f54+qG3auOacSeWtt5itbs8e54RaYCBvsElxVbp1i/UrevbkDSOp4mjrVj5QrTNTFSvmvYUA48M6I5U1xYqx02nrgThrFkfpP/jAfe2zhVL0X4+vM3/+PLctUsT1bQgK4u8gMRmbDLTm/8IgOUbzgoP5vT3/PEV+mjQcPf33X9edw4j9im+0NUcOdkzjizl5/JhtPH06dqfHEEbGQEXu3IwdOXuWbsSrVrHTV7++efQUoIXk+nWzi4mvMGkS72XTpvE7Dg7mPSksLPHJauwV4g0IMLvL2SKxwihNGnOmUmeIiKDLp6uEkSEgu3fn9M03aUkxsqhaky4dxdzSpWaL7qxZ7Fh//DH/SwkpvPn4Md2OO3a0/WwzLEZ16sRNKGI9+GYvQYMlWbIAo0fTwtC8eex7YbdunE6aFLt9Bw7EdfnPm5f/M2dq4RjuXoalwlupVcv2M0Rruml7W/stswzu3Mlnm3HvGzWK7p1Tp/IeoRQH48eMMcdaGWm/JQGDCCOPcfcuzbpPP22u9u0qYZQvH0esHdVyMUSTMSLmaYxsdY4CIi2pWtW56tTWXL9ufrgYAZTduzMI9Ysv2KFNTGHOx485umSrhkmxYnyAW/uh+wJGxiJHbg/t2rGjaV3X5OJFfnZPZPWqW5fC2dHoV0gIO27uKM6XMyddcebM4cP1gw9i14gwMjtFRdk/xqZNTL/63XcUKcnxwDJcmCzdhurXp/U5qRZVA+M48VmMAHY69+xxnFVu9Wp2Bn791ez6B3Ck9P33WSLAFi1b8h6QJo058yJg7ghaBtr7Atmy0TobHc1R4alTzQkk/vc//k8TivG9WwsjQ/DYizMyhFFCki8Y1K9Pq4kziSSMe3lCBZg9jAQzXbpwmjFj/G7mbdpwgNNwI9u5k6PwRoZY60xhjpg1iwM2p07ZjpE8f56Wmho14v4nzp+P/X07e73feYeWLaNorUG5chwg+fJL4O+/uWznTj7rDBc8S1q35gCK0Z+xx/HjjCN2lHzFG6hVi/cH689z+TL7Efa8KDxFxYoMjdi+ndehQgXz4FPjxuwXvvcen0Nr1/L5bHmfN1zKfXUQ15WIMPIAN2+akx0cPmxOienKDmS3bnzY20q3CTA5Q7p08fvaeivGTSmhcUb9+3NkDDAHUJYuzTSxv//OB/rQoQlvz/79jIewJYyMG44rik4mNzt3MkbKUQ2i/v3ZGfv559jLXRUUnRjq1uUIriOrUUiIe+KLDEaMYOe/SRNmmxw/3rxu9Wq6Ib77rv39f/+dnd3Bg5MvTm3uXMbIWY4ct2jB79JZ96b4cNZiBJg7d46Sl/z+O3+flqIIoOAZO9axy0uOHFxvpAU/epSjqL16JW/CEFdhlHvo1Im/tx49GP/Spg3/Cwm1sBsDT7Zc6QD7wigsjJb/7NkTdj7AHKNpy2oUGUkr6qJFsYsoG4ImqQwfzvt0QqyFxvNk/Xp2ms+fp0WncmVajhIijGbPppBNk8Z2mvzz5ykCixRhLJHlYNv582YxX7my8+7L6dIBU6bYzkz7/fc8VpcuvDcsWcKBJMMaa0mbNrxPLF7s+HzHj/vGoINhbbF+hhiC1duEUcaM7H+sX09hVKeOeV2aNBSud+9S8NasGffZbMy7Ii7W1xFh5AF69OCN3biZG6OVrrIYAbSCxMRwBMoWe/ZQFLmydlFy4myAvSW3brFDevo0H9yGMCpZkiNYffrQ5DxnDjtFlm5M8WE8xO1ZjADfG4nRmr7K8VkVy5bl6Pu337IzZnS+PCmMjDY7iss5d8498UUGefLQPfP+ff7PZswAPv2UwsD43U6ZYrsO1OPHjJnp0IGJLpIjY9CRIxQG1gKjTh12shLSwXNEQixGVapwFNxetrX799lZ696dHbzEULmyWRgZAy3vvpt8GbNcSfr0dMMCaLGcNYsxam3bUlQk1MIeHs7v1Xp0P75OVEJqGFlTvTq/++3beQ+2bPNnnzFWolMn3qfXrGFHz1X/47RpE+5aW6AAf0Pr1pldTg2B2qwZrXfOcu4crTGNGzNLq7WQPX+e2RKNGjyWVqPQULb96lXXub7myEFPiDJl6KK5eDHQtKk5NteS2rUpeByl7daa1l1fEEZGSIK1MDLuEd44qNysGdt3/Xpcq54RNtGtm+3U9vny8fcvwkiEUbITHs4b6Hvvmc3Thg+9K4VRpUrs3I8ZE7fwn9a0GCVHxWl3kSMHH4YJsRgtXWruhO7YwY5moUKxR9ZGjGAnYM0aPoCddX/bto0dEFuZgIyHtq8lYAgN5e/VGXfLr75iB2XkSP6+o6PZofGUMMqVi+6k9oTRtWscGXb3w23gQP6/Fy+mS8Z779HFyXBljYykq9fUqXxvMGECLctGMHTZshT07sxMN3cuO6Rdu8ZebmQVXLo08UH8liTEYqQUH/D2ruP27bTUJsZNzKByZQ6SPHjAe4JS3pWGN6FMm8Y4qh9+MItFoyOa0GQgYWHshFu7OBcsyO/JkStdYoVRpkxs79SpvAfPn8/lISEcfOnVi9fss8+Sv0aaPVq0YFuWL2fn0hj0rFWLXhvOFBiOiuL3Wbw4/4MnTsROv3zvHjvp1aubnzOG+7LWdD309+fLOs41KaRPz+98wwb+Tzp1sr2dUkwSs3u3/aQFV6/yvuYLwihHDj7TbFmMChXyTlfAZs04zZaNiRcsad+eiVksk7NYkiYNf1cijEQYJTt//82bWPfu7DDnz2/OuuRKYaQUXUxy5qQVxJL163lzatjQdefzBFWrxhZGly+zU24vFmLePD6sM2RgR/Wvv+Kmkc6eHVi2zOxvvWZN/O3Q2lwDwhaFCvFhuWcPO8muyKaXHBhpki1N8vaoUoWuVkWKUIxfucIHvat8/xND3br8DLbch4zPVreue9tguDC0asUYhPLl2dHbs4cjdyNHMjbmpZdodbt7l52K4cOBzp3No3xly1IADBninlg1rYE//uCD1VbZgFdeoU/69OlJP1dCLEYAr9H587Y7l+vX87+VlHtZ5coUnCdOUBgVLcrOua+SOzcHaSxJrDAKD7cdJ5Q+PZ9djoRRYuKLDKpXNwtoo8joO+/w//TFF7QkHTtGMWuvo56c9OrFmjE//8z/u/H7MQSSM2m7Q0M5oFS8OD+TUrHd6das4T2gY0ezMDL+E/fu8fz+/q76RLHp3ZuDJh06sLi3PZ5/nv/rceNsrzcyQBqJAryd2rV5PzaeIVozntZbB5Vr12aCmZMn49Zgy5KF1jxHz+TChUUYAQC01inmVaNGDe3tNG+udYUK5vn27bXm303ru3ddf74vvuCxt27V+uxZrUeP1jooSOv8+bV++ND150tOPvuMn23ZMq0//VTrbNk4nzmz1h9+qHVMjHnbS5e0TpNG63fe0bpePW7n56f1yZO2j/34sda5cvH14YeO2xEczONNmWJ/m0GDzNcZ0Pqjj7SeN0/rRYu0vn5d66NHtT5yROtnn9W6c2ethw3jMk/y3nv8jhLyO/n+e36+X3/ldPFi97UvPn7+mW04dSruutGj+dnu3UveNhn/R0DrL7/kb/TECa3nzOGy0aO1fuMNrTNk0Pr2bfN+YWFad+rEbcaOdX27/v2Xx54xw/b6mBitGzbUOksWrZcsSdq5Pv2U53L2d7V9O7f/+++462rV0rp+/aS1x/j/jh2rdc2aWrdokbTjeSMxMVrnyKH1K684v09UlNZFi/J3Z4saNbRu3dr2udKl03rkyEQ1VWut9bffmv8ndevyPwzwnmS0bdYsrffuTfw5XElMjNbly7ONa9eal9++zWWffBL/MTZu5Lbr1nG+QQM+q7Xmf6V9ez6PnjzR+upVbvvDD1x/7pzj/68rCA7mczE+xoxhWw4ejL38yROtK1XSulQp3+l7/PADP8vFi5w/ejT+Z70v06ULf8cGT57weVSmjPk7SEkA2KNtaAmPixlXvrxFGN2/b7vD9eiR1pkyaf366+ZlH3xgfgBYduRdxe3bWhcowE4goLVSnI4b5/pzJTd37mgdEGD+/tq2ZaetWzfOt2ql9fDhWq9Zo/VLL3HZyZNar16tdZ8+Wv/4o+Pj//ST1iVLUkQ6ujYzZ/LYhw/b3+bRI61ffVXriRO17tkztkiyfGXLxptQpkx8CI4a5bmHf8eOsW+SznDxIj9HrVqcerLjcuQI2zBhgtahoVqfOUMRHRKiddOmWlevnvxtunhR6+LF2Undvz/2uj59tM6YkW1+5hnb+3ftSuF/9apr2/X661qnT6/1zZv2t7l0iR21vHnZMU0sI0bwczrLw4cUip06aR0dbV723HP8rj7/PPFtMejQgdcE4P80JfLUU1pXqcJ7kda8hufP8z6qtdbHj1Oka83flyHi//zT9vE6dNC6cuW4yw0xMH584tv6zz/mwavs2c33WE8PFjlizhyte/eO+6woVYodzvgwPmNwMOeNAYTLl7Vu147v332X66KjtU6blgN9Wmu9axfXJ3XQwhVcv651njwUtJb3iZEj2ca//vJc2xLKjh2x22z8J1KiSNBa69deYx/E4M8/zX2Tb77xXLvchQijZKRvX46mhYbGXm6MfC5YYF62eLH5h+cuLl/mTWncOP6hw8PNHQxfZ/NmduosRUlMDEfeCxdmZ8/4fp97LuHHnzaN+x4/bn+bQYO0zpnT+e/04UOtBw6k8PrnH44mTp/Om67x4D93jpatdOl4/kaNkr9TUKaMcw90awxRBGh95Yrr2+Us0dGxr7/xMr5To1PhLVy8aG7b1Km2t9m/n+unTXPdeR8+ZEemW7f4t507l+ffvj3x5xs8mIM1CeHzz3neLl20nj9f68aNOf/BB86NYsfHoUPm38d33yX9eN7IvHnmAaT9+7WuXZvzadNqXayY+fMHBVF8A1rXqWN/UOjVV3nfs+bsWe47fXri2/rwodZDhlBEAxykyZo1aYLcU3TrpnW+fFrfuOF4uw8/5MClIVz37tX/WTItRZFBQIDW/fvz/YoV3GbHDte3PzEYHgOVK2v99tta9+vH+Zdfds8AsLt48ID/j8GDOUiZMSOtyikVQ/gZgyXGb7J0ad5zUxoijJKRpUt5Ey9VKrY7zLhxcTuLxgi7O4VRaubmTboo7NuXuP0NN5vJk+1vU7Gi1m3aJO748XHrltZffaW1vz8frkeOuOc81ty/zxviBx8kfF/joZgnj+cF+G+/8eY+eTLdTNas0bpHD36uJ0882zZbnDnDzqDxYLImJoYWp3btHB8nOpodqZIled9ZsYKjymPGxBYS16+bO5+rVsXfvhs36JI6erTzn8ma7t21LlcuYfvExFAcZcjAtubMyRF2V7JkidZFiniPe5Y7mDjRbJX082NH6O23OZj3xRcUhXXrsjO/ZIn936HWZrFq7QK+ezeXL1qU9PZu2mR+PjZqlPTjeYKdO9m5LlWK36vhKmdN//4UOwbR0bzvG5///PnY2zdrpnXVqnw/axa3OX3aPZ8hocTEaP3HHxTfadPynjF8uGsGMZKbqlX1f942ffrEvQ4pid9/jz0Q3LMnnyGjR/Manj3r2fa5GhFGycyWLfwhDRhgXtamTdwOQUyMCCNvJiaGD6sePWyvv35dO+1DnhROnNC6YEGKo+R4+O3bx881f37i9r92zbPWopTM669TIERG2t9m8mRevxIlzPcXwwowZAi3uXOHo59GHIezo/ENGzL2JCwsce1v1YqWiMRw9SqFizviMVMLFy/SvXTFiqQd57ff9H/uyZasWsXlW7Yk7fha0yU9Xz4eb9iwpB/PU8yerXWTJubPUrAgLXMREVwfHU3hZB3fNnUq/+tt28Y9puFqd+WK1l9/zfeOXGE9RXS078QU2WLpUg4eHDrk6Za4n82b+TtauZLzQUF06z59moNRAQFmb4YTJziY4oti18CeMJKsdG6iQQNmzpk5k9lzHj1iOk+jGJyBUsAHH7CQmuB9KMWsbHv32l5vpBG2l5HOVZQrx3oYDx8Cb77p3nMB5joYlSsnbv88eWxnNxOSTsOGvJ8YdbhssWwZa4+cOcPMVf37M5NYnz6sndS1KzMw7tnDzFfbtjlft2fcONbJaN6cRSYTyq1bzqXqtoW/PzOWOVu8UohLQACLWBsZDxOLkd3KOjPd9eucuiKdcebM5ro41hlEfYnevXn/vnCBGU9r12YWxBYtmO1x7lz+V194IfZ+gwYxzf9ff8U9ppGaeeNGlh9Imzbx/yt3kiaN79ZLBFgKYNw476xb5GqMbIFHjzJT58mT7HuULs107XnzsuD4jBn8Db/9NosSpzTcKoyUUq2VUieVUsFKqVE21vdRSh0yvbYrpQIt1oUopQ4rpQ4opfa4s53uYuRI3thfeMFc6NFWzYUPPwReey3Zmyc4SWAgH1p378ZefugQiwymTWuuku1OypVjytqlS80V311FZCTTNRt1aqZNYy2s8uVdex4h6RjFHY0aJtY8ecJBmGbNKOwLFAB++YXb//wzO8Z//cWUwGvWsKq9rYJ/9qhXj0VVT56k4NLa+X1PnDAXlxZ8G6NGmbUwunGDU1fVeencmefwhrTcSSVDBmDsWBZ4X7iQg6ZFi7LmTMGC/C9akzmzbWFRowYHN9atYw2jvHkT9j8WBGvy5GEa/qNHKdgfPDD3AapVA1at4vs33+QgbaVKrCcWHe25NruDtO46sFLKD8AkAC0AhALYrZRaorW2KFmGcwAaa61vKqXaAJgKwLJqSlOt9TV3tdHd5M1LS9CoURQ/AKtGC75F1ars/B05wmKnWgMDBrD+DMCbgysL6jnitdeAL79kAdAGDVxzzEePWBtjwwagUSOK9717WQ9BHrTeR/78nF65Ynv97t0U8caIsoHRuZoxgyJ40qS4tS6c5emngc8/5+DPV1+xJlt0NB+s9n4zT54AH3/M/8rbbyfuvIL3EJ/FyJV1+TxZD81dtG7N2m9btvB/U6uWuSCvM6RNS4vTihXcN29etzVVSEVUqkRhZBQXthwczZ+fA7QnT/L5MmgQi5D/8w+fCfZ48IA1rGJiuJ91wWhvw23CCEBtAMFa67MAoJT6A0AHAP8JI631dovtdwIIcGN7PMKLL7KYa48evHFlzerpFgkJpWpVTg8epGXou+8oit58kyIpOQvlZs7MUfoJE9gxzp+fHc7Zs4G2bRNW4C8yEpg1i0UyN2xgkdG5c4HNm+k++NxzbvsYQhKITxitX09xYm8QpkULvpLKG2/QDW/kSOf3yZYN+Okn9xWiFJKPzJmBXLmAsLDYy69fp0tXWnf2LlIIdesmrch0+/a0/q5Zw0E7QUgqlSpx8GzbNgoYo0ixQcOGFEZt2vD3lzkzi5Y7EkajRgE//MD3T56kbmFUGMBFi/lQxLYGWfMigJUW8xrAGqWUBvCT1nqq65uYPGTLxlEdwTcpXpzX8Jdf+Oc+doxWlfHjPfMHHzwY+PZbtmfUKHZQJ00yu2KMHRv/aG10NKu1L1/O+fHjgeHDKfrOnwfKlhVrkbeSNSsfRo6EUVCQ61yZ7JE2LUcKDxxge/z8GD/kiPLl3d8uIfnInRu4eTP2suvX5RonF888w+mDBzLYILiGypWBe/eA336jKMqWLfb61q3Z9zBEUbt2wN9/08PE1mBIeDgHw3r0AIYNcz6W1ZO4UxjZ6lbZ9EZXSjUFhZGlc1B9rXWYUiofgLVKqRNa68029h0MYDAAFC1aNOmtFgQrlKIQWrCA1qPffuOf3FOjHuXLA40bA1OnAqVKURQ99xxdWn78ke4Y33zj+Bg//0xR9NVXvMGVKcPlGTPSVC54L0rRamRLGN27x4QgyRWzmDGjjFSnZnLmjCuGRRglH/7+7GxevMhBMkFIKoaL/oULjO+zxoj5M2JdO3emxWj3btvWz8mTaSX69FP2V3wBd3btQgEUsZgPABBmvZFSqiqAaQA6aK2vG8u11mGm6VUAC0HXvDhoradqrWtqrWv6y5CJ4CbmzweiouhO17dvwnzB3cGQIcC5c2xL9erA9Ol0h+vdm6MzERH2971+nTFvDRvSHVCsQ75H/vy2ky9s3coEGtbZLwXBHeTIYVsYuTK+SHDMDz8wkYO1y5MgJIaKFc3Pj3r14q43EvoYGC5069fH3VZruvk3a+Y7oghwrzDaDaCMUqqEUio9gJ4AllhuoJQqCuBvAM9prU9ZLM+ilMpmvAfQEsARN7ZVEByilHeZgLt0AcaMoSvFb7+Zhdo773B05rnnYmeKiY5mNrDTp4EKFZje9csvRRD5KvYsRvv3c5qUuAVBcBaxGAlCymPePJaRad8+/m39/Zm515Yw2rABOHuWZSJ8CbcJI611FIChAFYDOA5gvtb6qFJqiFJqiGmz9wHkAfCjVVru/AC2KqUOAtgFYLnWepW72ioIvka6dMBHH3Gk0Kg9APD9pEnA6tXmzF8xMUysUKsW0LIlky7s3i0uUL6MPWF04QJH67NnT/42CamPnDmB27fN81pz0EWEkSD4Lrlz06vE2aylzZox69wPP7C/ATAlfatWFE6+lmrfrXljtNYrAKywWjbF4v1AAANt7HcWQKD1ckEQ4mfQINZY+vprcwaz6dN5swsJAQYOFLcLX6dAAXZAo6JiB7xevMi6KIKQHFhbjI4e5cCL1KkShNTDK68wEc/rr7OP8fXXFFZly7IAsa8N1ElCTUFIgXzzDS0KRirlTp2A0aOZie6ttzzbNiHp5M/P0fmICGYjNLhwgVkUBSE5yJmTCT+ePKEVe+lSLm/b1qPNEgQhGSlVil4o//sfM+aeP8947GnTzIOzvoQII0FIgaRLB8yZA3TowJoDb77JTsypU/HuKvgAlsU1rYVRo0aeaZOQ+siZk9Pbt1mnb9kyoEYNoFAhjzZLEIRkRimKoitXGKNUrx6TQfkiXl5mSRCExJI2LYMeP/rI3IERUgbFinF6/rx52Z07dGsSVzohuciRg1PDnW7/fhHmgpBaSZOGWehOnWKG1EyZPN2ixCEWI0EQBB/DcJezFEYXTeW0ixSJs7kguAVjwOXWLbp2PnwIZMniyRYJguBJ/PzMdRF9FbEYCYIg+Bg5czKg1ZYwEouRkFxYCqOoKIqjDBk82SJBEISkIcJIEATBBylWjBmADAyRJMJISC4MYfThh3SdAUQYCYLg24gwEgRB8EGKF49tMTp9mnUnJPBdSC4MYbRtG9C6Nd+LMBIEwZcRYSQIguCDFCsWVxiVKcMAWEFIDiyTuvj7cyrCSBAEX0YeoYIgCD5IsWJMk2xkBDt1igX1BCG5yJrV/N7IlCjCSBAEX0aEkSAIgg9SqhSnZ84w8P3MGRFGQvJiaZ2MjORUhJEgCL6MCCNBEAQfpHx5Tk+coEvdkycijITkJySEv7vbtzkvwkgQBF9GhJEgCIIPUqoUa0acOEE3OkCEkZD8FCsG5M1rdukUYSQIgi8jBV4FQRB8kPTpKY5OnGDHFPD9wnqCb5Ihg7jSCYKQMhBhJAiC4KOUL09hlC8fM4QZAkkQkpOMGVncFRBhJAiCbyOudIIgCD5K+fJ0ozt+nG50Snm6RUJqJGNG83sRRoIg+DIijARBEHyUSpWAx49ZYFPiiwRPIcJIEISUgggjQRAEH6VJE04fPxZhJHgOEUaCIKQURBgJgiD4KEWLmtN2izASPIWlGBJhJAiCLyPCSBAEwYdp0YJTEUaCpxCLkSAIKQXJSicIguDDDB4MXLvGeCNB8AQijARBSCmIMBIEQfBhKlcG5szxdCuE1IwII0EQUgriSicIgiAIQqIRYSQIQkpBhJEgCIIgCInGEEZKAWnFD0UQBB9GhJEgCIIgCInGsBJlyCBFhgVB8G3cKoyUUq2VUieVUsFKqVE21iul1A+m9YeUUtWd3VcQBEEQBM9jWIzEjU4QBF/HbcJIKeUHYBKANgAqAuillKpotVkbAGVMr8EAJidgX0EQBEEQPIwII0EQUgrutBjVBhCstT6rtX4M4A8AHay26QBgliY7AeRUShV0cl9BEARBEDyMCCNBEFIK7hRGhQFctJgPNS1zZhtn9gUAKKUGK6X2KKX2REREJLnRgiAIgiA4jwgjQRBSCu4URrZCMLWT2zizLxdqPVVrXVNrXdPf3z+BTRQEQRAEISlYJl8QBEHwZdyZWDMUQBGL+QAAYU5uk96JfQVBEARB8DBiMRIEIaXgTovRbgBllFIllFLpAfQEsMRqmyUA+pmy0z0F4LbWOtzJfQVBEARB8DAijARBSCm4zWKktY5SSg0FsBqAH4AZWuujSqkhpvVTAKwA8AyAYAD3AQxwtK+72ioIgiAIQuIQYSQIQkrBrTWqtdYrQPFjuWyKxXsN4FVn9xUEQRAEwbsQYSQIQkrBrQVeBUEQBEFI2YgwEgQhpSDCSBAEQRCERCNZ6QRBSCmIMBIEQRAEIdGIxUgQhJSCCCNBEARBEBKNWIwEQUgpiDASBEEQBCHR+PkB6dKJMBIEwfcRYSQIgiAIQpIICOBLEATBl3Frum5BEARBEFI++/cDmTJ5uhWCIAhJQ4SRIAiCIAhJIkcOT7dAEAQh6YgrnSAIgiAIgiAIqR4RRoIgCIIgCIIgpHpEGAmCIAiCIAiCkOoRYSQIgiAIgiAIQqpHhJEgCIIgCIIgCKkeEUaCIAiCIAiCIKR6RBgJgiAIgiAIgpDqEWEkCIIgCIIgCEKqR4SRIAiCIAiCIAipHhFGgiAIgiAIgiCkepTW2tNtcBlKqQgA5z3dDhN5AVzzdCMEAHItvAm5Ft6DXAvvQa6F9yDXwnuQa+E9pMRrUUxr7W+9MEUJI29CKbVHa13T0+0Q5Fp4E3ItvAe5Ft6DXAvvQa6F9yDXwntITddCXOkEQRAEQRAEQUj1iDASBEEQBEEQBCHVI8LIfUz1dAOE/5Br4T3ItfAe5Fp4D3ItvAe5Ft6DXAvvIdVcC4kxEgRBEARBEAQh1SMWI0EQBEEQBEEQUj0ijNyAUqq1UuqkUipYKTXK0+1J6SilZiilriqljlgsy62UWquUOm2a5rJY947p2pxUSrXyTKtTHkqpIkqpjUqp40qpo0qp103L5VokM0qpjEqpXUqpg6ZrMda0XK6Fh1BK+Sml9iullpnm5Vp4CKVUiFLqsFLqgFJqj2mZXA8PoJTKqZRaoJQ6YXp21JVrkfwopcqZ/g/G645S6n+p8VqIMHIxSik/AJMAtAFQEUAvpVRFz7YqxTMTQGurZaMArNdalwGw3jQP07XoCaCSaZ8fTddMSDpRAN7SWlcA8BSAV03ft1yL5OcRgKe11oEAggC0Vko9BbkWnuR1AMct5uVaeJamWusgixTEcj08w/cAVmmtywMIBP8jci2SGa31SdP/IQhADQD3ASxEKrwWIoxcT20AwVrrs1rrxwD+ANDBw21K0WitNwO4YbW4A4BfTe9/BdDRYvkfWutHWutzAILBayYkEa11uNZ6n+l9JPiAKwy5FsmOJndNs+lMLw25Fh5BKRUAoC2AaRaL5Vp4F3I9khmlVHYAjQBMBwCt9WOt9S3ItfA0zQCc0VqfRyq8FiKMXE9hABct5kNNy4TkJb/WOhxghx1APtNyuT7JgFKqOIBqAP6FXAuPYHLdOgDgKoC1Wmu5Fp7jOwAjAcRYLJNr4Tk0gDVKqb1KqcGmZXI9kp+SACIA/GJyM52mlMoCuRaepieAuab3qe5aiDByPcrGMkn95z3I9XEzSqmsAP4C8D+t9R1Hm9pYJtfCRWito01uEQEAaiulKjvYXK6Fm1BKtQNwVWu919ldbCyTa+Fa6mutq4Mu768qpRo52Fauh/tIi/+3cz8vVtVhHMffn5wpKoKgJAYMlBAX1aKEwNxIvyAEgzAwKCRatKj+gNrkJqiINkHtBEELpigSobGgH6sgKYxp+rEJqcnUtRGl+bS433AWRRDO/c695/2C4X7vd87iGR7mXD73POfA7cDrVXUb8CttVOtf2ItVluRyYBfw1n8d+g97U9ELg9GltwzcuOL9BuBkp1qG7HSSOYD2eqbt259VlGSWUSg6VFXvtG170VEbTfmE0Ry4vRi/7cCuJCcYjVbfleQg9qKbqjrZXs8wuo/iDuxHD8vAcruaDfA2o6BkL/q5H/iyqk6394PrhcHo0jsGbE6yqSXvPcDhzjUN0WFgb1vvBd5bsb8nyRVJNgGbgc871Dd1koTRrPi3VfXKil/ZizFLsj7JtW19JXAP8B32Yuyq6pmq2lBVGxl9HnxUVY9gL7pIcnWSa/5eA/cBX2M/xq6qTgE/JdnStu4GvsFe9PQwF8foYIC9mOldwLSpqvNJngKOAuuA/VW11LmsqZbkTWAHcH2SZeA54AVgPsnjwI/AQwBVtZRkntHJ9zzwZFX92aXw6bMdeBRYbPe2ADyLvehhDjjQnhJ0GTBfVUeSfIa9WCv8v+jjBuDd0fc4zABvVNVCkmPYjx6eBg61L5J/AB6jnbPsxXgluQq4F3hixfbgzlOpmoqRQEmSJEn63xylkyRJkjR4BiNJkiRJg2cwkiRJkjR4BiNJkiRJg2cwkiRJkjR4BiNJ0sRIcl2S4+3nVJKf2/psktd61ydJmlw+rluSNJGS7APOVtXLvWuRJE0+rxhJkiZekh1JjrT1viQHknyQ5ESSB5O8lGQxyUKS2Xbc1iSfJvkiydEkc33/CklSTwYjSdI0ugnYCTwAHAQ+rqpbgd+AnS0cvQrsrqqtwH7g+V7FSpL6m+ldgCRJq+D9qjqXZBFYByy0/UVgI7AFuAX4MAntmF861ClJWiMMRpKkafQ7QFVdSHKuLt5Qe4HRZ1+Apara1qtASdLa4iidJGmIvgfWJ9kGkGQ2yc2da5IkdWQwkiQNTlX9AewGXkzyFXAcuLNrUZKkrnxctyRJkqTB84qRJEmSpMEzGEmSJEkaPIORJEmSpMEzGEmSJEkaPIORJEmSpMEzGEmSJEkaPIORJEmSpMEzGEmSJEkavL8ACnZzGA3cjHUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,5))\n",
    "plt.plot(y_test, color='red', label='Real FB Stock Price')\n",
    "plt.plot(y_pred, color='blue', label='Predicted FB Stock Price')\n",
    "plt.title('FB Stock Price Prediction')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('FB Stock Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3be3aef7",
   "metadata": {},
   "source": [
    "# LOSS - Mean Squared Error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a95c32cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23/23 [==============================] - 1s 37ms/step - loss: 0.6493\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.6493444442749023"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(gadf_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d262246",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9 (tensorflow)",
   "language": "python",
   "name": "tensorflow"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}