combmodel.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "856acb84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.03012963]\n",
      " [0.03008561]\n",
      " [0.02971147]\n",
      " ...\n",
      " [0.98833552]\n",
      " [0.98382381]\n",
      " [0.99647869]]\n",
      "[[0.03012963]\n",
      " [0.03008561]\n",
      " [0.02971147]\n",
      " ...\n",
      " [0.54425911]\n",
      " [0.55427295]\n",
      " [0.55735413]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from keras.models import Sequential\n",
    "from keras.layers import LSTM, Dense\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# Load the data\n",
    "df = pd.read_csv('G.csv')\n",
    "data = df.reset_index()['Close']\n",
    "\n",
    "# Preprocess the data\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "data1 = scaler.fit_transform(np.array(data).reshape(-1, 1))\n",
    "data = []\n",
    "\n",
    "for i in data1:\n",
    "    if ((np.isnan(i)) != True):\n",
    "        data.append(i)\n",
    "data=np.array(data).reshape(-1,1)\n",
    "# data = np.array(data).reshape(-1,1)\n",
    "# print((data))\n",
    "# Split the data into training and testing sets\n",
    "train_size = int(len(data) * 0.8)\n",
    "train_data = data[:train_size, :]\n",
    "test_data = data[train_size:, :]\n",
    "\n",
    "print(data)\n",
    "print(train_data)\n",
    "\n",
    "# c = 0\n",
    "\n",
    "# print(c)\n",
    "#     else:\n",
    "#         print(\"no\")\n",
    "#     print(type(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fab5450a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare the data for training\n",
    "def prepare_data(data, look_back=1):\n",
    "    X, Y = [], []\n",
    "    for i in range(len(data)-look_back):\n",
    "        X.append(data[i:(i+look_back)])\n",
    "        Y.append(data[i+look_back])\n",
    "    return np.array(X), np.array(Y)\n",
    "\n",
    "look_back = 100\n",
    "train_X, train_Y = prepare_data(train_data, look_back)\n",
    "test_X, test_Y = prepare_data(test_data, look_back)\n",
    "\n",
    "train_X = np.reshape(train_X, (train_X.shape[0], train_X.shape[1], 1))\n",
    "test_X = np.reshape(test_X, (test_X.shape[0], test_X.shape[1], 1))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9744f4d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm (LSTM)                 (None, 100, 64)           16896     \n",
      "                                                                 \n",
      " lstm_1 (LSTM)               (None, 100, 32)           12416     \n",
      "                                                                 \n",
      " lstm_2 (LSTM)               (None, 16)                3136      \n",
      "                                                                 \n",
      " dense (Dense)               (None, 1)                 17        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 32,465\n",
      "Trainable params: 32,465\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Build the LSTM model\n",
    "# model = Sequential()\n",
    "# model.add(LSTM(64,return_sequences=True, input_shape=(look_back, 1)))\n",
    "# model.add(LSTM(32,return_sequences=True))\n",
    "# model.add(LSTM(16))\n",
    "# model.add(Dense(1))\n",
    "from tensorflow.keras.models import load_model\n",
    "model = load_model('combmodel.h5')\n",
    "model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1eee1acb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "42/42 [==============================] - 15s 260ms/step - loss: 1.3214e-04\n",
      "Epoch 2/100\n",
      "42/42 [==============================] - 10s 249ms/step - loss: 3.7608e-05\n",
      "Epoch 3/100\n",
      "42/42 [==============================] - 10s 248ms/step - loss: 3.5915e-05\n",
      "Epoch 4/100\n",
      "42/42 [==============================] - 11s 251ms/step - loss: 3.9509e-05\n",
      "Epoch 5/100\n",
      "42/42 [==============================] - 10s 249ms/step - loss: 3.7684e-05\n",
      "Epoch 6/100\n",
      "42/42 [==============================] - 10s 245ms/step - loss: 3.4669e-05\n",
      "Epoch 7/100\n",
      "42/42 [==============================] - 10s 246ms/step - loss: 3.5842e-05\n",
      "Epoch 8/100\n",
      "42/42 [==============================] - 10s 247ms/step - loss: 3.4649e-05\n",
      "Epoch 9/100\n",
      "42/42 [==============================] - 10s 249ms/step - loss: 3.8192e-05\n",
      "Epoch 10/100\n",
      "42/42 [==============================] - 10s 246ms/step - loss: 3.6802e-05\n",
      "Epoch 11/100\n",
      "42/42 [==============================] - 10s 243ms/step - loss: 3.5983e-05\n",
      "Epoch 12/100\n",
      "42/42 [==============================] - 10s 247ms/step - loss: 4.3556e-05\n",
      "Epoch 13/100\n",
      "42/42 [==============================] - 10s 248ms/step - loss: 4.2470e-05\n",
      "Epoch 14/100\n",
      "42/42 [==============================] - 10s 246ms/step - loss: 3.3573e-05\n",
      "Epoch 15/100\n",
      "42/42 [==============================] - 10s 249ms/step - loss: 4.1592e-05\n",
      "Epoch 16/100\n",
      "42/42 [==============================] - 10s 248ms/step - loss: 3.4991e-05\n",
      "Epoch 17/100\n",
      "42/42 [==============================] - 10s 247ms/step - loss: 3.7210e-05\n",
      "Epoch 18/100\n",
      "42/42 [==============================] - 11s 266ms/step - loss: 3.5295e-05\n",
      "Epoch 19/100\n",
      "42/42 [==============================] - 11s 254ms/step - loss: 4.2856e-05\n",
      "Epoch 20/100\n",
      "42/42 [==============================] - 10s 247ms/step - loss: 3.5269e-05\n",
      "Epoch 21/100\n",
      "42/42 [==============================] - 11s 256ms/step - loss: 4.2417e-05\n",
      "Epoch 22/100\n",
      "42/42 [==============================] - 10s 246ms/step - loss: 3.5094e-05\n",
      "Epoch 23/100\n",
      "42/42 [==============================] - 10s 248ms/step - loss: 3.9052e-05\n",
      "Epoch 24/100\n",
      "42/42 [==============================] - 10s 248ms/step - loss: 3.3381e-05\n",
      "Epoch 25/100\n",
      "42/42 [==============================] - 11s 252ms/step - loss: 3.3876e-05\n",
      "Epoch 26/100\n",
      "42/42 [==============================] - 10s 242ms/step - loss: 3.0934e-05\n",
      "Epoch 27/100\n",
      "42/42 [==============================] - 10s 246ms/step - loss: 3.6771e-05\n",
      "Epoch 28/100\n",
      "42/42 [==============================] - 10s 249ms/step - loss: 3.3164e-05\n",
      "Epoch 29/100\n",
      "42/42 [==============================] - 10s 247ms/step - loss: 3.1125e-05\n",
      "Epoch 30/100\n",
      "42/42 [==============================] - 10s 244ms/step - loss: 3.0974e-05\n",
      "Epoch 31/100\n",
      "42/42 [==============================] - 10s 241ms/step - loss: 3.3066e-05\n",
      "Epoch 32/100\n",
      "42/42 [==============================] - 10s 244ms/step - loss: 3.6948e-05\n",
      "Epoch 33/100\n",
      "42/42 [==============================] - 10s 245ms/step - loss: 3.1456e-05\n",
      "Epoch 34/100\n",
      "42/42 [==============================] - 10s 248ms/step - loss: 3.2423e-05\n",
      "Epoch 35/100\n",
      "42/42 [==============================] - 10s 249ms/step - loss: 3.2294e-05\n",
      "Epoch 36/100\n",
      "42/42 [==============================] - 11s 256ms/step - loss: 3.3853e-05\n",
      "Epoch 37/100\n",
      "42/42 [==============================] - 10s 249ms/step - loss: 4.5551e-05\n",
      "Epoch 38/100\n",
      "42/42 [==============================] - 11s 251ms/step - loss: 4.3436e-05\n",
      "Epoch 39/100\n",
      "42/42 [==============================] - 10s 250ms/step - loss: 3.4980e-05\n",
      "Epoch 40/100\n",
      "42/42 [==============================] - 11s 252ms/step - loss: 3.1073e-05\n",
      "Epoch 41/100\n",
      "42/42 [==============================] - 10s 248ms/step - loss: 3.0245e-05\n",
      "Epoch 42/100\n",
      "42/42 [==============================] - 10s 250ms/step - loss: 3.3908e-05\n",
      "Epoch 43/100\n",
      "42/42 [==============================] - 10s 248ms/step - loss: 3.5267e-05\n",
      "Epoch 44/100\n",
      "42/42 [==============================] - 10s 243ms/step - loss: 4.4650e-05\n",
      "Epoch 45/100\n",
      "42/42 [==============================] - 10s 246ms/step - loss: 3.8383e-05\n",
      "Epoch 46/100\n",
      "42/42 [==============================] - 10s 248ms/step - loss: 3.1262e-05\n",
      "Epoch 47/100\n",
      "42/42 [==============================] - 10s 248ms/step - loss: 3.1859e-05\n",
      "Epoch 48/100\n",
      "42/42 [==============================] - 10s 247ms/step - loss: 3.8298e-05\n",
      "Epoch 49/100\n",
      "42/42 [==============================] - 10s 243ms/step - loss: 3.4109e-05\n",
      "Epoch 50/100\n",
      "42/42 [==============================] - 10s 250ms/step - loss: 3.0329e-05\n",
      "Epoch 51/100\n",
      "42/42 [==============================] - 10s 247ms/step - loss: 3.5791e-05\n",
      "Epoch 52/100\n",
      "42/42 [==============================] - 10s 243ms/step - loss: 3.3696e-05\n",
      "Epoch 53/100\n",
      "42/42 [==============================] - 11s 253ms/step - loss: 2.8456e-05\n",
      "Epoch 54/100\n",
      "42/42 [==============================] - 10s 245ms/step - loss: 2.9336e-05\n",
      "Epoch 55/100\n",
      "42/42 [==============================] - 10s 246ms/step - loss: 3.4871e-05\n",
      "Epoch 56/100\n",
      "42/42 [==============================] - 11s 251ms/step - loss: 3.4289e-05\n",
      "Epoch 57/100\n",
      "42/42 [==============================] - 11s 250ms/step - loss: 3.1353e-05\n",
      "Epoch 58/100\n",
      "42/42 [==============================] - 10s 247ms/step - loss: 2.9591e-05\n",
      "Epoch 59/100\n",
      "42/42 [==============================] - 11s 252ms/step - loss: 3.1368e-05\n",
      "Epoch 60/100\n",
      "42/42 [==============================] - 10s 248ms/step - loss: 3.4600e-05\n",
      "Epoch 61/100\n",
      "42/42 [==============================] - 10s 249ms/step - loss: 3.1455e-05\n",
      "Epoch 62/100\n",
      "42/42 [==============================] - 10s 246ms/step - loss: 3.0313e-05\n",
      "Epoch 63/100\n",
      "42/42 [==============================] - 10s 244ms/step - loss: 2.9030e-05\n",
      "Epoch 64/100\n",
      "42/42 [==============================] - 10s 248ms/step - loss: 2.9394e-05\n",
      "Epoch 65/100\n",
      "42/42 [==============================] - 10s 246ms/step - loss: 2.9449e-05\n",
      "Epoch 66/100\n",
      "42/42 [==============================] - 11s 251ms/step - loss: 2.9592e-05\n",
      "Epoch 67/100\n",
      "42/42 [==============================] - 11s 253ms/step - loss: 2.9313e-05\n",
      "Epoch 68/100\n",
      "42/42 [==============================] - 10s 244ms/step - loss: 3.3430e-05\n",
      "Epoch 69/100\n",
      "42/42 [==============================] - 10s 244ms/step - loss: 3.5581e-05\n",
      "Epoch 70/100\n",
      "42/42 [==============================] - 10s 245ms/step - loss: 2.8779e-05\n",
      "Epoch 71/100\n",
      "42/42 [==============================] - 10s 245ms/step - loss: 3.3389e-05\n",
      "Epoch 72/100\n",
      "42/42 [==============================] - 10s 246ms/step - loss: 3.0477e-05\n",
      "Epoch 73/100\n",
      "42/42 [==============================] - 10s 246ms/step - loss: 3.2970e-05\n",
      "Epoch 74/100\n",
      "42/42 [==============================] - 10s 247ms/step - loss: 3.1125e-05\n",
      "Epoch 75/100\n",
      "42/42 [==============================] - 11s 255ms/step - loss: 3.0126e-05\n",
      "Epoch 76/100\n",
      "42/42 [==============================] - 10s 244ms/step - loss: 3.0378e-05\n",
      "Epoch 77/100\n",
      "42/42 [==============================] - 10s 250ms/step - loss: 3.5206e-05\n",
      "Epoch 78/100\n",
      "42/42 [==============================] - 10s 249ms/step - loss: 3.5611e-05\n",
      "Epoch 79/100\n",
      "42/42 [==============================] - 11s 252ms/step - loss: 3.1232e-05\n",
      "Epoch 80/100\n",
      "42/42 [==============================] - 11s 250ms/step - loss: 2.8872e-05\n",
      "Epoch 81/100\n",
      "42/42 [==============================] - 11s 250ms/step - loss: 3.9218e-05\n",
      "Epoch 82/100\n",
      "42/42 [==============================] - 11s 262ms/step - loss: 3.7037e-05\n",
      "Epoch 83/100\n",
      "42/42 [==============================] - 12s 292ms/step - loss: 3.3094e-05\n",
      "Epoch 84/100\n",
      "42/42 [==============================] - 10s 248ms/step - loss: 3.0828e-05\n",
      "Epoch 85/100\n",
      "42/42 [==============================] - 11s 256ms/step - loss: 3.0348e-05\n",
      "Epoch 86/100\n",
      "42/42 [==============================] - 10s 249ms/step - loss: 3.1446e-05\n",
      "Epoch 87/100\n",
      "42/42 [==============================] - 11s 255ms/step - loss: 2.8320e-05\n",
      "Epoch 88/100\n",
      "42/42 [==============================] - 11s 251ms/step - loss: 2.7158e-05\n",
      "Epoch 89/100\n",
      "42/42 [==============================] - 10s 248ms/step - loss: 3.0035e-05\n",
      "Epoch 90/100\n",
      "42/42 [==============================] - 11s 255ms/step - loss: 2.7996e-05\n",
      "Epoch 91/100\n",
      "42/42 [==============================] - 10s 250ms/step - loss: 3.2259e-05\n",
      "Epoch 92/100\n",
      "42/42 [==============================] - 10s 245ms/step - loss: 3.7478e-05\n",
      "Epoch 93/100\n",
      "42/42 [==============================] - 10s 250ms/step - loss: 3.3470e-05\n",
      "Epoch 94/100\n",
      "42/42 [==============================] - 11s 258ms/step - loss: 2.9532e-05\n",
      "Epoch 95/100\n",
      "42/42 [==============================] - 10s 249ms/step - loss: 3.3184e-05\n",
      "Epoch 96/100\n",
      "42/42 [==============================] - 10s 248ms/step - loss: 3.0094e-05\n",
      "Epoch 97/100\n",
      "42/42 [==============================] - 10s 245ms/step - loss: 3.6839e-05\n",
      "Epoch 98/100\n",
      "42/42 [==============================] - 10s 246ms/step - loss: 3.8494e-05\n",
      "Epoch 99/100\n",
      "42/42 [==============================] - 10s 246ms/step - loss: 3.2446e-05\n",
      "Epoch 100/100\n",
      "42/42 [==============================] - 10s 245ms/step - loss: 2.8924e-05\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1a496a6eeb0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the modeldwqefqqqqqqq\n",
    "model.fit(train_X, train_Y, epochs=100, batch_size=64, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "10c67d7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 0.00 MSE (0.01 RMSE)\n",
      "Test Score: 0.00 MSE (0.02 RMSE)\n",
      "84/84 [==============================] - 5s 46ms/step\n",
      "19/19 [==============================] - 1s 51ms/step\n",
      "[[ 50.40673]\n",
      " [ 50.33074]\n",
      " [ 50.06138]\n",
      " ...\n",
      " [500.50568]\n",
      " [502.7038 ]\n",
      " [512.4131 ]]\n",
      "[[ 48.875   ]\n",
      " [ 48.474998]\n",
      " [ 47.790001]\n",
      " ...\n",
      " [505.200012]\n",
      " [514.299988]\n",
      " [517.099976]]\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "train_score = model.evaluate(train_X, train_Y, verbose=0)\n",
    "test_score = model.evaluate(test_X, test_Y, verbose=0)\n",
    "print('Train Score: {:.2f} MSE ({:.2f} RMSE)'.format(train_score, np.sqrt(train_score)))\n",
    "print('Test Score: {:.2f} MSE ({:.2f} RMSE)'.format(test_score, np.sqrt(test_score)))\n",
    "\n",
    "# Make predictions\n",
    "train_predict = model.predict(train_X)\n",
    "test_predict = model.predict(test_X)\n",
    "# scaler.scale_\n",
    "# Inverse the scaling\n",
    "scale_factor = 1/scaler.scale_\n",
    "train_predict = scaler.inverse_transform(train_predict)\n",
    "# train_predict1=train_predict1*scale_factor\n",
    "train_Y = scaler.inverse_transform(train_Y)\n",
    "# train_Y=train_Y*scale_factor\n",
    "test_predict = scaler.inverse_transform(test_predict)\n",
    "# test_predict1 = test_predict1*scale_factor\n",
    "test_Y = scaler.inverse_transform(test_Y)\n",
    "# test_Y = test_Y*scale_factor\n",
    "\n",
    "print(train_predict)\n",
    "print(train_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3f8a7785",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the results\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(train_Y.flatten(), label='Actual')\n",
    "plt.plot(train_predict.flatten(), label='Predicted')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.plot(test_Y.flatten(), label='Actual')\n",
    "plt.plot(test_predict.flatten(), label='Predicted')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0f867f65",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('combmodel.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "18759d17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.07840884]\n",
      " [0.07840884]\n",
      " [0.07843621]\n",
      " ...\n",
      " [0.94342309]\n",
      " [0.94730792]\n",
      " [0.94615891]]\n",
      "[[0.07840884]\n",
      " [0.07840884]\n",
      " [0.07843621]\n",
      " ...\n",
      " [0.75268113]\n",
      " [0.75339239]\n",
      " [0.74518491]]\n"
     ]
    }
   ],
   "source": [
    "# Load the B data for testing\n",
    "df = pd.read_csv('B.csv')\n",
    "data = df.reset_index()['Close']\n",
    "\n",
    "# Preprocess the data\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "data1 = scaler.fit_transform(np.array(data).reshape(-1, 1))\n",
    "data = []\n",
    "\n",
    "for i in data1:\n",
    "    if ((np.isnan(i)) != True):\n",
    "        data.append(i)\n",
    "data=np.array(data).reshape(-1,1)\n",
    "# data = np.array(data).reshape(-1,1)\n",
    "# print((data))\n",
    "# Split the data into training and testing sets\n",
    "train_size = int(len(data) * 0.8)\n",
    "train_data = data[:train_size, :]\n",
    "test_data = data[train_size:, :]\n",
    "\n",
    "print(data)\n",
    "print(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8dcdeeab",
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_data(data, look_back=1):\n",
    "    X, Y = [], []\n",
    "    for i in range(len(data)-look_back):\n",
    "        X.append(data[i:(i+look_back)])\n",
    "        Y.append(data[i+look_back])\n",
    "    return np.array(X), np.array(Y)\n",
    "\n",
    "look_back = 100\n",
    "train_X, train_Y = prepare_data(train_data, look_back)\n",
    "test_X, test_Y = prepare_data(test_data, look_back)\n",
    "\n",
    "train_X = np.reshape(train_X, (train_X.shape[0], train_X.shape[1], 1))\n",
    "test_X = np.reshape(test_X, (test_X.shape[0], test_X.shape[1], 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bbf75439",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 0.00 MSE (0.01 RMSE)\n",
      "Test Score: 0.00 MSE (0.02 RMSE)\n",
      "79/79 [==============================] - 3s 40ms/step\n",
      "18/18 [==============================] - 1s 40ms/step\n",
      "[[ 11.850275]\n",
      " [ 12.13931 ]\n",
      " [ 12.220837]\n",
      " ...\n",
      " [140.30074 ]\n",
      " [139.76614 ]\n",
      " [139.89835 ]]\n",
      "[[ 13.005   ]\n",
      " [ 12.995   ]\n",
      " [ 13.23    ]\n",
      " ...\n",
      " [139.630005]\n",
      " [139.759995]\n",
      " [138.259995]]\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "train_score = model.evaluate(train_X, train_Y, verbose=0)\n",
    "test_score = model.evaluate(test_X, test_Y, verbose=0)\n",
    "print('Train Score: {:.2f} MSE ({:.2f} RMSE)'.format(train_score, np.sqrt(train_score)))\n",
    "print('Test Score: {:.2f} MSE ({:.2f} RMSE)'.format(test_score, np.sqrt(test_score)))\n",
    "\n",
    "# Make predictions\n",
    "train_predict = model.predict(train_X)\n",
    "test_predict = model.predict(test_X)\n",
    "# scaler.scale_\n",
    "# Inverse the scaling\n",
    "scale_factor = 1/scaler.scale_\n",
    "train_predict = scaler.inverse_transform(train_predict)\n",
    "# train_predict1=train_predict1*scale_factor\n",
    "train_Y = scaler.inverse_transform(train_Y)\n",
    "# train_Y=train_Y*scale_factor\n",
    "test_predict = scaler.inverse_transform(test_predict)\n",
    "# test_predict1 = test_predict1*scale_factor\n",
    "test_Y = scaler.inverse_transform(test_Y)\n",
    "# test_Y = test_Y*scale_factor\n",
    "\n",
    "print(train_predict)\n",
    "print(train_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "48674721",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the results\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(train_Y.flatten(), label='Actual')\n",
    "plt.plot(train_predict.flatten(), label='Predicted')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.plot(test_Y.flatten(), label='Actual')\n",
    "plt.plot(test_predict.flatten(), label='Predicted')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "626b3f39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.0313568 ]\n",
      " [0.03187823]\n",
      " [0.03155826]\n",
      " ...\n",
      " [0.8661222 ]\n",
      " [0.8661222 ]\n",
      " [0.86113534]]\n",
      "[[0.0313568 ]\n",
      " [0.03187823]\n",
      " [0.03155826]\n",
      " ...\n",
      " [0.6934045 ]\n",
      " [0.69561027]\n",
      " [0.69225374]]\n"
     ]
    }
   ],
   "source": [
    "# Load the data for J training and testing\n",
    "df = pd.read_csv('J.csv')\n",
    "data = df.reset_index()['Close']\n",
    "\n",
    "# Preprocess the data\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "data1 = scaler.fit_transform(np.array(data).reshape(-1, 1))\n",
    "data = []\n",
    "\n",
    "for i in data1:\n",
    "    if ((np.isnan(i)) != True):\n",
    "        data.append(i)\n",
    "data=np.array(data).reshape(-1,1)\n",
    "# data = np.array(data).reshape(-1,1)\n",
    "# print((data))\n",
    "# Split the data into training and testing sets\n",
    "train_size = int(len(data) * 0.8)\n",
    "train_data = data[:train_size, :]\n",
    "test_data = data[train_size:, :]\n",
    "\n",
    "print(data)\n",
    "print(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "22cc8b92",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare the data for training\n",
    "def prepare_data(data, look_back=1):\n",
    "    X, Y = [], []\n",
    "    for i in range(len(data)-look_back):\n",
    "        X.append(data[i:(i+look_back)])\n",
    "        Y.append(data[i+look_back])\n",
    "    return np.array(X), np.array(Y)\n",
    "\n",
    "look_back = 100\n",
    "train_X, train_Y = prepare_data(train_data, look_back)\n",
    "test_X, test_Y = prepare_data(test_data, look_back)\n",
    "\n",
    "train_X = np.reshape(train_X, (train_X.shape[0], train_X.shape[1], 1))\n",
    "test_X = np.reshape(test_X, (test_X.shape[0], test_X.shape[1], 1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "48620687",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "42/42 [==============================] - 9s 210ms/step - loss: 1.5557e-04\n",
      "Epoch 2/100\n",
      "42/42 [==============================] - 10s 246ms/step - loss: 1.8210e-04\n",
      "Epoch 3/100\n",
      "42/42 [==============================] - 10s 244ms/step - loss: 1.3533e-04\n",
      "Epoch 4/100\n",
      "42/42 [==============================] - 10s 245ms/step - loss: 1.5124e-04\n",
      "Epoch 5/100\n",
      "42/42 [==============================] - 10s 242ms/step - loss: 1.1457e-04\n",
      "Epoch 6/100\n",
      "42/42 [==============================] - 10s 246ms/step - loss: 1.2399e-04\n",
      "Epoch 7/100\n",
      "42/42 [==============================] - 11s 254ms/step - loss: 1.0748e-04\n",
      "Epoch 8/100\n",
      "42/42 [==============================] - 11s 250ms/step - loss: 1.0561e-04\n",
      "Epoch 9/100\n",
      "42/42 [==============================] - 10s 245ms/step - loss: 1.1814e-04\n",
      "Epoch 10/100\n",
      "42/42 [==============================] - 10s 244ms/step - loss: 1.0719e-04\n",
      "Epoch 11/100\n",
      "42/42 [==============================] - 10s 241ms/step - loss: 1.2601e-04\n",
      "Epoch 12/100\n",
      "42/42 [==============================] - 10s 246ms/step - loss: 1.3201e-04\n",
      "Epoch 13/100\n",
      "42/42 [==============================] - 10s 244ms/step - loss: 1.1670e-04\n",
      "Epoch 14/100\n",
      "42/42 [==============================] - 10s 240ms/step - loss: 1.1100e-04\n",
      "Epoch 15/100\n",
      "42/42 [==============================] - 10s 246ms/step - loss: 1.1105e-04\n",
      "Epoch 16/100\n",
      "42/42 [==============================] - 10s 243ms/step - loss: 1.2286e-04\n",
      "Epoch 17/100\n",
      "42/42 [==============================] - 10s 246ms/step - loss: 1.2405e-04\n",
      "Epoch 18/100\n",
      "42/42 [==============================] - 10s 248ms/step - loss: 1.0988e-04\n",
      "Epoch 19/100\n",
      "42/42 [==============================] - 10s 247ms/step - loss: 1.0758e-04\n",
      "Epoch 20/100\n",
      "42/42 [==============================] - 10s 243ms/step - loss: 1.0757e-04\n",
      "Epoch 21/100\n",
      "42/42 [==============================] - 10s 241ms/step - loss: 1.0766e-04\n",
      "Epoch 22/100\n",
      "42/42 [==============================] - 10s 239ms/step - loss: 1.3266e-04\n",
      "Epoch 23/100\n",
      "42/42 [==============================] - 10s 245ms/step - loss: 1.0628e-04\n",
      "Epoch 24/100\n",
      "42/42 [==============================] - 10s 246ms/step - loss: 1.0249e-04\n",
      "Epoch 25/100\n",
      "42/42 [==============================] - 11s 262ms/step - loss: 1.0783e-04\n",
      "Epoch 26/100\n",
      "42/42 [==============================] - 11s 251ms/step - loss: 1.1007e-04\n",
      "Epoch 27/100\n",
      "42/42 [==============================] - 10s 248ms/step - loss: 1.0574e-04\n",
      "Epoch 28/100\n",
      "42/42 [==============================] - 10s 242ms/step - loss: 1.1372e-04\n",
      "Epoch 29/100\n",
      "42/42 [==============================] - 10s 247ms/step - loss: 1.0522e-04\n",
      "Epoch 30/100\n",
      "42/42 [==============================] - 10s 245ms/step - loss: 1.0430e-04\n",
      "Epoch 31/100\n",
      "42/42 [==============================] - 10s 248ms/step - loss: 1.0721e-04\n",
      "Epoch 32/100\n",
      "42/42 [==============================] - 10s 244ms/step - loss: 1.0460e-04\n",
      "Epoch 33/100\n",
      "42/42 [==============================] - 10s 245ms/step - loss: 1.0684e-04\n",
      "Epoch 34/100\n",
      "42/42 [==============================] - 10s 241ms/step - loss: 1.1035e-04\n",
      "Epoch 35/100\n",
      "42/42 [==============================] - 10s 248ms/step - loss: 1.0359e-04\n",
      "Epoch 36/100\n",
      "42/42 [==============================] - 10s 248ms/step - loss: 1.1979e-04\n",
      "Epoch 37/100\n",
      "42/42 [==============================] - 10s 250ms/step - loss: 1.0527e-04\n",
      "Epoch 38/100\n",
      "42/42 [==============================] - 11s 255ms/step - loss: 1.0110e-04\n",
      "Epoch 39/100\n",
      "42/42 [==============================] - 11s 268ms/step - loss: 1.0923e-04\n",
      "Epoch 40/100\n",
      "42/42 [==============================] - 10s 248ms/step - loss: 1.0798e-04\n",
      "Epoch 41/100\n",
      "42/42 [==============================] - 11s 251ms/step - loss: 1.0857e-04\n",
      "Epoch 42/100\n",
      "42/42 [==============================] - 10s 245ms/step - loss: 1.1777e-04\n",
      "Epoch 43/100\n",
      "42/42 [==============================] - 10s 246ms/step - loss: 1.3212e-04\n",
      "Epoch 44/100\n",
      "42/42 [==============================] - 10s 240ms/step - loss: 1.1087e-04\n",
      "Epoch 45/100\n",
      "42/42 [==============================] - 10s 248ms/step - loss: 1.1209e-04\n",
      "Epoch 46/100\n",
      "42/42 [==============================] - 11s 255ms/step - loss: 1.1427e-04\n",
      "Epoch 47/100\n",
      "42/42 [==============================] - 10s 247ms/step - loss: 1.1632e-04\n",
      "Epoch 48/100\n",
      "42/42 [==============================] - 10s 246ms/step - loss: 1.0814e-04\n",
      "Epoch 49/100\n",
      "42/42 [==============================] - 10s 248ms/step - loss: 1.0068e-04\n",
      "Epoch 50/100\n",
      "42/42 [==============================] - 10s 247ms/step - loss: 1.0684e-04\n",
      "Epoch 51/100\n",
      "42/42 [==============================] - 10s 243ms/step - loss: 1.0694e-04\n",
      "Epoch 52/100\n",
      "42/42 [==============================] - 10s 243ms/step - loss: 1.0100e-04\n",
      "Epoch 53/100\n",
      "42/42 [==============================] - 10s 244ms/step - loss: 1.0066e-04\n",
      "Epoch 54/100\n",
      "42/42 [==============================] - 10s 248ms/step - loss: 1.0401e-04\n",
      "Epoch 55/100\n",
      "42/42 [==============================] - 10s 244ms/step - loss: 1.0784e-04\n",
      "Epoch 56/100\n",
      "42/42 [==============================] - 10s 245ms/step - loss: 1.1426e-04\n",
      "Epoch 57/100\n",
      "42/42 [==============================] - 10s 245ms/step - loss: 1.0170e-04\n",
      "Epoch 58/100\n",
      "42/42 [==============================] - 10s 246ms/step - loss: 1.1621e-04\n",
      "Epoch 59/100\n",
      "42/42 [==============================] - 11s 252ms/step - loss: 1.2988e-04\n",
      "Epoch 60/100\n",
      "42/42 [==============================] - 10s 245ms/step - loss: 1.1188e-04\n",
      "Epoch 61/100\n",
      "42/42 [==============================] - 10s 247ms/step - loss: 1.0477e-04\n",
      "Epoch 62/100\n",
      "42/42 [==============================] - 10s 246ms/step - loss: 1.0721e-04\n",
      "Epoch 63/100\n",
      "42/42 [==============================] - 10s 248ms/step - loss: 1.1218e-04\n",
      "Epoch 64/100\n",
      "42/42 [==============================] - 10s 249ms/step - loss: 1.0079e-04\n",
      "Epoch 65/100\n",
      "42/42 [==============================] - 10s 248ms/step - loss: 1.1272e-04\n",
      "Epoch 66/100\n",
      "42/42 [==============================] - 10s 246ms/step - loss: 1.1208e-04\n",
      "Epoch 67/100\n",
      "42/42 [==============================] - 10s 247ms/step - loss: 1.0956e-04\n",
      "Epoch 68/100\n",
      "42/42 [==============================] - 10s 246ms/step - loss: 1.1288e-04\n",
      "Epoch 69/100\n",
      "42/42 [==============================] - 10s 251ms/step - loss: 1.0334e-04\n",
      "Epoch 70/100\n",
      "42/42 [==============================] - 10s 250ms/step - loss: 1.1983e-04\n",
      "Epoch 71/100\n",
      "42/42 [==============================] - 10s 247ms/step - loss: 1.1474e-04\n",
      "Epoch 72/100\n",
      "42/42 [==============================] - 10s 245ms/step - loss: 1.1129e-04\n",
      "Epoch 73/100\n",
      "42/42 [==============================] - 10s 246ms/step - loss: 1.1192e-04\n",
      "Epoch 74/100\n",
      "42/42 [==============================] - 10s 246ms/step - loss: 1.2024e-04\n",
      "Epoch 75/100\n",
      "42/42 [==============================] - 10s 244ms/step - loss: 1.0599e-04\n",
      "Epoch 76/100\n",
      "42/42 [==============================] - 10s 249ms/step - loss: 9.8184e-05\n",
      "Epoch 77/100\n",
      "42/42 [==============================] - 10s 245ms/step - loss: 1.1159e-04\n",
      "Epoch 78/100\n",
      "42/42 [==============================] - 10s 246ms/step - loss: 1.0600e-04\n",
      "Epoch 79/100\n",
      "42/42 [==============================] - 10s 248ms/step - loss: 1.0373e-04\n",
      "Epoch 80/100\n",
      "42/42 [==============================] - 10s 245ms/step - loss: 1.0480e-04\n",
      "Epoch 81/100\n",
      "42/42 [==============================] - 11s 251ms/step - loss: 1.1122e-04\n",
      "Epoch 82/100\n",
      "42/42 [==============================] - 11s 251ms/step - loss: 1.3692e-04\n",
      "Epoch 83/100\n",
      "42/42 [==============================] - 10s 244ms/step - loss: 1.2766e-04\n",
      "Epoch 84/100\n",
      "42/42 [==============================] - 10s 246ms/step - loss: 1.0975e-04\n",
      "Epoch 85/100\n",
      "42/42 [==============================] - 10s 245ms/step - loss: 1.0236e-04\n",
      "Epoch 86/100\n",
      "42/42 [==============================] - 10s 247ms/step - loss: 1.0955e-04\n",
      "Epoch 87/100\n",
      "42/42 [==============================] - 10s 247ms/step - loss: 1.2066e-04\n",
      "Epoch 88/100\n",
      "42/42 [==============================] - 10s 247ms/step - loss: 1.0223e-04\n",
      "Epoch 89/100\n",
      "42/42 [==============================] - 10s 249ms/step - loss: 1.0595e-04\n",
      "Epoch 90/100\n",
      "42/42 [==============================] - 11s 253ms/step - loss: 1.0287e-04\n",
      "Epoch 91/100\n",
      "42/42 [==============================] - 11s 251ms/step - loss: 1.1079e-04\n",
      "Epoch 92/100\n",
      "42/42 [==============================] - 11s 252ms/step - loss: 1.1036e-04\n",
      "Epoch 93/100\n",
      "42/42 [==============================] - 10s 245ms/step - loss: 1.0187e-04\n",
      "Epoch 94/100\n",
      "42/42 [==============================] - 10s 246ms/step - loss: 1.0965e-04\n",
      "Epoch 95/100\n",
      "42/42 [==============================] - 10s 244ms/step - loss: 1.0390e-04\n",
      "Epoch 96/100\n",
      "42/42 [==============================] - 10s 244ms/step - loss: 1.0621e-04\n",
      "Epoch 97/100\n",
      "42/42 [==============================] - 10s 246ms/step - loss: 1.0209e-04\n",
      "Epoch 98/100\n",
      "42/42 [==============================] - 10s 247ms/step - loss: 1.1753e-04\n",
      "Epoch 99/100\n",
      "42/42 [==============================] - 10s 246ms/step - loss: 1.0624e-04\n",
      "Epoch 100/100\n",
      "42/42 [==============================] - 10s 247ms/step - loss: 1.1936e-04\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1a4a19a8f10>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the modeldwqefqqqqqqq\n",
    "model.fit(train_X, train_Y, epochs=100, batch_size=64, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "700db642",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 0.00 MSE (0.01 RMSE)\n",
      "Test Score: 0.00 MSE (0.01 RMSE)\n",
      "84/84 [==============================] - 4s 48ms/step\n",
      "19/19 [==============================] - 1s 47ms/step\n",
      "[[ 38.059082]\n",
      " [ 38.38903 ]\n",
      " [ 38.62052 ]\n",
      " ...\n",
      " [385.5627  ]\n",
      " [391.04556 ]\n",
      " [391.88702 ]]\n",
      "[[ 41.34708 ]\n",
      " [ 41.433578]\n",
      " [ 41.680721]\n",
      " ...\n",
      " [389.299988]\n",
      " [390.450012]\n",
      " [388.700012]]\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "train_score = model.evaluate(train_X, train_Y, verbose=0)\n",
    "test_score = model.evaluate(test_X, test_Y, verbose=0)\n",
    "print('Train Score: {:.2f} MSE ({:.2f} RMSE)'.format(train_score, np.sqrt(train_score)))\n",
    "print('Test Score: {:.2f} MSE ({:.2f} RMSE)'.format(test_score, np.sqrt(test_score)))\n",
    "\n",
    "# Make predictions\n",
    "train_predict = model.predict(train_X)\n",
    "test_predict = model.predict(test_X)\n",
    "# scaler.scale_\n",
    "# Inverse the scaling\n",
    "scale_factor = 1/scaler.scale_\n",
    "train_predict = scaler.inverse_transform(train_predict)\n",
    "# train_predict1=train_predict1*scale_factor\n",
    "train_Y = scaler.inverse_transform(train_Y)\n",
    "# train_Y=train_Y*scale_factor\n",
    "test_predict = scaler.inverse_transform(test_predict)\n",
    "# test_predict1 = test_predict1*scale_factor\n",
    "test_Y = scaler.inverse_transform(test_Y)\n",
    "# test_Y = test_Y*scale_factor\n",
    "\n",
    "print(train_predict)\n",
    "print(train_Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "45bdeb54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lJSXsXElJCZmZmXTu3Jm4uLhKX1PqJ/33FJGmZvZ/RnNq4Uf8fug1dLsw/A+5XzK30evVyLEm6/wH8esJ/+WcYScCsOyevvSxrAucnJhb6zFXVnnP7/1VuQWle/fubNu2LfizYsWK4LlHHnmEKVOmMHXqVJYsWUJ6ejrDhg0jPz8/WGfcuHHMmjWLmTNnsmDBAgoKChg5cmTYmAYREZGmqvQYlP1ZLNEf210tf3HO92dQkptNzuxJoeQEYNeftRFmravyGBSbzRa2/sU+pmnyxBNPMGHChOBaGK+++ippaWm8+eabXH311eTm5jJt2jRmzJjB0KFDAXj99ddp3749c+fO5ZRTTjnAryMiItLA7evXiJKglNnFs1fc412JaGt2N8xpyFVuQVm3bh0ZGRl07tyZCy64gPXr1wOQmZlJVlYWw4cPD9Z1Op0MGjSIhQsXAoF1OzweT1idjIwMevToEawTjcvlIi8vL+xHRESkMTIopwXFKD9Bicb0N8weiiolKP369eO1117jiy++4MUXXyQrK4sBAwawa9eu4DiUfYMz9yk9RiUrKwuHw0Hz5s3LrBPN5MmTSU1NDf60b9++KmGLiIg0GKEEJXKbFIu16nNbPJuWHGhIMVGlbzpixAjOOeccevbsydChQ/n000+BQFfOPsZ+N9Q0zYiy/VVU58477yQ3Nzf4s3nz5qqELSIi0nDsm7tShTEo5dmT30S6eEpLTEykZ8+erFu3LjguZf+WkOzs7GCrSnp6Om63m5ycnDLrRON0OklJSQn7qYhWLm0c9N9RRJoal2vv1OCoXTzgNav26C4sbphTjQ8oQXG5XKxZs4a2bdvSuXNn0tPTmTNnTvC82+1m3rx5DBgwAIA+ffpgt9vD6mzbto2VK1cG6xyofUvDFxXFaPdiqVH7/juWXvJfRKSxWZK5i9e+/ZWdBS5GmvMAsPkiEwvDMPBTfq/E/sz91khpKKo0i2f8+PGcccYZdOjQgezsbB544AHy8vIYM2YMhmEwbtw4Jk2aRNeuXenatSuTJk0iISGBiy66CIDU1FTGjh3LrbfeSsuWLWnRogXjx48PdhnVBKvVSrNmzYILiyUkJFTYxST1j2maFBUVkZ2dTbNmzYJ7+oiINEbLpt3ENbZPeP2vJ7hkb1nqruUR9SyGsXcBt8oPfDW9nhqJsa5VKUHZsmULF154ITt37qR169Ycd9xxLFq0iI4dOwJw2223UVxczHXXXRdcqO3LL78kOTk5eI3HH38cm83GeeedF1yobfr06TX6ANrX3bQvSZGGq1mzZlGntYuINCbX2D4B4JJ144JlG9KG03K/ehaDJtOCUqWVZOuLyq5E5/P58HgaZuYogW4dtZyISJMwMTWi6MOh8zlrYK+wss27i2jxZCcSDVelL/3HoVfT5cJHDjjEmlCVlWQb9WaBVqtVDzgREWmQhvSI3IXYMMBXxeGjDbUFRZsFioiI1ENJcY6IMqvFIMUortJ1TJ8SFBEREamG3/2RrSVEWdbeUo1JH4a/YQ51UIIiIiISYz6iDEewRI7CqM6k1Cax1L2IiIjUPG+0x3GUfXcq04Ky1WwRfhmNQREREZHqiDrwtZpdPI+Yo/nW14vl/oMBDZIVERGRato/7fBjRN8ssBJdPNeMHMibXaewOGFw4NpKUERERKQ6rPt2MN4r6pgUIjfkBfjN3z7suEPrVF74v760aZYYKNAYFBEREamOHpYNYcf+Mh7P+7egeAw703q+wVvm8FAdm3PfC0CzeERERKQatu3cFVHmxB217v5jUOymh//8vRfxTmewzIjbu73MvllAakERERGRqlq8dlOl65Y1SNbnD3URORKbA2BYArvAG6bGoIiIiEgVxfmKKl3XUsZTO86bG3xtOPfucbNvFpBaUERERKSq4szKL11fVgtKS3+pbiLr3q6dfWNQ1IIiIiIiVeUrya903bISFCtRWkn2JioWTTMWERGRqvIV51W6rsWAIa7/sMVsBcCPjuMC5ZgRdY1gC0rD7OKJXOhfRERE6ozfVRBRNs93JIOi1DUMgzOGDGbw3DSOs6whP/VoPgSMaAmKVYNkRUREpJrMKF08WSk9y6w/bmg3vNhY4O9Jvt8BRG9B2TcGxaJBsiIiIlJpn/wD5twD7sgWlN6dWpf71smjepIcZ+M/5/baWxKtBUVdPCIiIlIFnt2bsC99GYCEFudGVoiyUWBpFx7bgfP7tseyd2nZ8sagWNTFIyIiIpUxf8X64OsTd/8v4rzF6qjwGpZS695HH4OyL0FpmC0oSlBERETqWLflkyPKXKY9dGCtWgdHtMnHFlvgempBERERkYr9+g7tcxZFFOeRGHy9bwZOZb3qC2wWON8XGly7b6l7taCIiIhIxd6/MmpxoSWUoFiq2IKyp9t5jHBNZnbPJ4JlwS6eKIu4+f0mO7K2VOkz6poSFBERkbriK7u7pciSFHxt2KrWgvLEBb25ZfQ53PO33sEyiy2QoDT37YKPbgyr/8Hz99D6v93544NJVfqcuqQERUREpBbd9NbPXD59CV5XMfmP9iqznsuaHHxtsVStBSXRaWPYEWnE2UOzfyylu4l+eg22LAsejtr+FABdlj9cpc+pS0pQREREaonX5+ejX7by9W/ZvD3rPZKLy+5WcdtCLSgWW8WzeCriSWofXvDupVHrmRt/OODPqg1KUERERGqJ128y1vopb9kfYN223eXW9dhDLSjWKo5BiSajVbPwgtxNUesVZC4+4M+qDVqoTUREYs80wfRXuEBZQ+M3Te62vwGAteij8us6QgmKUQMtKC2TKncNL1Ub71JX1IIiIiIxt+PFUZQ80Qe8rliHUqN8/tACasd6lpRb1+9MCb62VnGQbDQOW/gjfmvcIcHXLjPUPmFkrz7gz6oNSlBERCSmNu8uovXWr4nLy4RN9XM8RHWVTlD2t8Tfjb/MlsHjeH9h8LWliuugROPcL0FJ8Ya6mOb7jwy+brZ6BhSV3/0UC0pQREQkpjbvDj2YMRpXF095Ccrf3RMZ7Ho8eOy1JgRfW+w134IS780Lvk6y7xdXbv1bE0UJioiIxJa/1EJiRrRF2xsunxk9QZnv68nye4bxznUnckzJsxxT8gzbO44MnrfWQAuKw2rhH+5rWeLvFrgmvuC9tpme8DiLcw/482qaBsmKiEhs+UIPS79pNKq/nKO1oKzwd+Imy10sT3DQu4ODRy8fTocWCWT++XuwjtV+4INkDcNgbdrpXLatDyvjrtgbkAcsVuz7JSiFBXmkRLlGLClBERGR2PKHHpY+Gn+C8kb67bx7zgnB40HdWgPwZ2bpRdZq5vH80Q3Hs2jdVpi5LyA32OOwmp6wHQY9JYVR3x9LSlBERCS2fO7QS79ZTye9Vo/fH1n20LUXRu/KMkKPZFsNJSg2q4XEuLhQgc9D8Z8L6WmsD6vnrYcJSmNKVEVEpAGy5G8NvrZ9++96OaOkurzRMpQyxtmYllBqZq3BoTh2ux2vufdx73NTMHNs8NxuM7B6rdelBEVERCTMrz9+HXxt2/wD/jf+HsNoapa/jEGy0ZilFqmzWGouQ3HYLHj2dZj43LT2hBLCfCN5b3FRjX1eTVGCIiIiMWV3h88gsfy1NEaR1Dzffg0oWxMOK7OuWcUNAivLbrVQQKCbJ3/PzrBzu2xpAPhdSlBERETCpCU23keRz2/iMQMtI1tTetHyhjll1jWN2klQHDYL2WZzAAqyM8PO7bC1BaDTr4+Dp6RWPr+6Gu9vhYiI1Ht/bVxPSuGGKCd+CuzP08CZmFgJNKNkXPkuzoRyJvOWHpvS4uAai6FFggMngZlSbT8PjT8pJA6XvVQ8306qV1sNKEEREZGYyM4v4aBXejOw5NvIky+eBKs/rPOYKrLps8coePgI2L2+4sqA6TexGHsTrQo2Qjy+Syv6eKYxpuWb4Ewut25VxDus2PFGlOe37oPXXyop+v5JmHV1jX3ugVKCIiIiMfH71j3lnnf99FbdBFIFHRbfT1LxX+TMnly5N5ilBqEY5T9yk+PsfD/xb7xy/WkHEGF0L/kir7m53UhKPOELtrFqVo1/dnUpQRERkZhItvnKPb+juH518eSXhB7mrkouI2ZWsZsqzm6t0Rk8+1iSWkeU9RxxFRYzyjToekIJioiIxMSmzRvKr2A98OXea5LHF0o2LBV01+xjUroFJXb7DB3ZOSPseEWnS4lz2Ig3i2MUUcWUoIiISEw4v7q73POlFy6rD0ovW++PtgBbNGFL3ccuQSls1jW8IDHQovIDR8YgmspRgiIiIjFxhGVj+RVqaV2Q6grrrvGX3z0Vek/9aEFxtOgYdmxJ7wFAfqdT2eyP7P6pD5SgiIhITOSb8eWe91vqVxdP6cYQfyXHboSPQYldgnLyYW342ndU8Di5Y+D1xLN68KW/b3jlerIeihIUERGJicMtm8s976Vy4zzqiq9UsmFWsovHpFSCEsMWlDYpcZQQSvhatQ6sINs62UmL1P3WZinZU4eRlU0JioiI1Dnv/mvAR3HIn6/WQSSV5w9rQvFC/nb45e3yFzerJ2NQAFKsobVQEuJDrVd+W3hLlvnjC3UWU3mUoIiISJ0rdFVuDEd9UnrjP7/fT9EzJ8Ksq/DNe6y8d4VeVrAOSm2z+qMnUm57+KJwxoLyvk/dUYIiIiJ1rtDtpcQMn6Uz2FU/HoxlMV0Fodemn4SSLAByf/movHeFXsawiwfASRkJirNFlMLCWo6mYkpQRESkzhUWlxBnhK9iev05p0RWzNtWRxFVzDd7QvB16TEonnLGypj1qIsnHnfUciMxchaP7/uptR1OhZSgiIhInSsuyA07zjOS+Xvf9hH1fJ/dVlchVcizYVHooNQsnvJ3Ia4/LSj3ef8PgKe9Z4eVO1IiE5Qdu3bWRUjlOqAEZfLkyRiGwbhx44JlpmkyceJEMjIyiI+PZ/DgwaxatSrsfS6XixtvvJFWrVqRmJjImWeeyZYtWw4kFBERqc9MM2x3YldRHhBoffig7c3sPPf9qG/L2/p7nYRXGZZS40nCZvGUs15L+Er3sU1QTjplFN1LptHyjH+HlScmN4uo687bUUdRla3aCcqSJUt44YUXOPLI8FXoHnnkEaZMmcLUqVNZsmQJ6enpDBs2jPz8/GCdcePGMWvWLGbOnMmCBQsoKChg5MiR+HwNb9CUiIhEZ5bkBp7Qfh/FUwfieW1U8Ny+BKXESODsq+/n4O7HRr2Gtx5tFWMNS1BCz6v0nKVlvicskYlxC8pVJx7MtxPO4KJ+HcLKk5KSIup69v73iaVqJSgFBQVcfPHFvPjiizRv3jxYbpomTzzxBBMmTGDUqFH06NGDV199laKiIt58800AcnNzmTZtGo899hhDhw6ld+/evP7666xYsYK5c+fWzLcSEZGY+mXZQoyHOrBz6lBWLV9E/K6V2DO/hq8fBMDjCiwG5jHKX4zNZ3XWeqyVZZTqrum846tKvSdsHZQYt6AYhkHr5Cj30xa5YJ7p80TWq2PVSlCuv/56Tj/9dIYOHRpWnpmZSVZWFsOHDw+WOZ1OBg0axMKFCwFYtmwZHo8nrE5GRgY9evQI1tmfy+UiLy8v7EdEROqvPz/+DwCtdi1l+Z+luvDnPwKEHoB+o/zF2NJzf4HCXbUTZGW48vG/eQH88nZYC0qlmfVnDEpZzCibMhpmA0xQZs6cybJly5g8eXLEuayswJSrtLS0sPK0tLTguaysLBwOR1jLy/519jd58mRSU1ODP+3bRw6kEhGR+sPrDXWBtC38LeJ8yqZAC0QrX3aF1zJfHFxjcVXVlo/+jeX3z2HWVViNchKU3L+iFoftxRPjFpSylN6Ucbo30HhgaWgtKJs3b+bmm2/mjTfeIC4ursx6xn5ZommaEWX7K6/OnXfeSW5ubvBn8+byl0cWEZHY8pZ6vJy8YUrE+b4bX4z6vie9oyLKjD2bai6wKvpzRWjmTjuj7JktnnfHRj/RAFpQDIvBV77e/OZvT6vDBgbKTG8F76p9VUpQli1bRnZ2Nn369MFms2Gz2Zg3bx5PPfUUNpst2HKyf0tIdnZ28Fx6ejput5ucnJwy6+zP6XSSkpIS9iMiIvVXTmr3ar3v4+ZjOKbkGV7wnh5+YndmDURVdYMsv1Sqnn3LD2Wcqf8JSlpKHGM94xnhnoxpTwDA4m9gLShDhgxhxYoVLF++PPjTt29fLr74YpYvX87BBx9Meno6c+bMCb7H7XYzb948BgwYAECfPn2w2+1hdbZt28bKlSuDdUREpGFrFle9h/EHNwzk5RvOID7jiLByz6//q4mwKs9TjP/7pw/4MpXc9Dimumek8u+zevDSmGMxrIHuHos/9i0o5a0uEyE5OZkePXqElSUmJtKyZctg+bhx45g0aRJdu3ala9euTJo0iYSEBC666CIAUlNTGTt2LLfeeistW7akRYsWjB8/np49e0YMuhURkYbJWs2/wJOcNnq2S+XlhBMYzX+C5fZvH4DB/6yp8Cr013t3cdBvLx/wdcy9A2v9GPV6ZdTR/TsB8NnawIDZdiVrYeFU6Hc1WO3lvLP2VClBqYzbbruN4uJirrvuOnJycujXrx9ffvklycmhzYgef/xxbDYb5513HsXFxQwZMoTp06djtdavrbVFRKR6rOXNAtn5R4XvP3vAEbBhv0JXPjiTo1WvcYlr3q76mNaSPIjbbwjC3jEoZj0dILs/b0Kr0Ot5j2Lrf33MYjngBOXbb78NOzYMg4kTJzJx4sQy3xMXF8fTTz/N008fePOZiIjUP5ZyBln6d6ytsDWhY4uEiDJz93qMtr0OMLLKKcJJMyreMG+72Yw0Y8/eN+2KSFDM8KVk672kVu2Cr22unJiOm6nPLU4iItJA2X3FZZ9c+V6F73faIx9POzetPZCQKq9gBxnG7kpV9Vnj2WXubdXxlkSc3zfNuKG0oKQk1U0LVWUoQRERkRp3ZsE7ZZ6zrHqP73yBcYtLmp8etY7DGvl4yttaNwnKrpcipzoDnO6axJXuW8ILDQu+vbsZe0sKoryrYXXx2J2Rq8rGihIUERGpUX5/xd0aabbAw7xFt/5Rz1sMgwW+wFTl2fZhgcLcOthU1u+n5Z5fw4pust/H31t9wEt3XM7oUWeFnTMtNtrs7eLx/xIlKWsAs3hKczjK33qgLtX4IFkREWnaXF4/Ff0d3sm/GQzwp7aLer5Zgp1n2z3K834X53k+gN3g9db+2hzrtmbTdb+ypyaMC77esH8LQ6ml+gs3LGX/x/u+WTwNpQWl9JCTH464h+jpY91QC4qIiFQot8jD+18tYHdufoV1S9wVr6HhMAJL4bdt0zbqecMweOOq/rx2zeDQU7O2B5zuXo9nzeywos984bssO+ISw479hoU3vScDkJ3SA/K2wW+fwd5djI0GNgal9C3u07df7AJBCYqIiFTCK2+9yajvTqfF4+0qTBT+/GVepa9rs5X9GDIMI/gD1H6C8lRvjvj+5rCio2/9KOzYGR+eoJiGlaT0QwAwSvbge/IomHkhrAh094Rm8TSMBKV9iwSuc9/EY55zsXeK7eKp6uIREZEKxWd+EXpiFO2GxJahkz4PYIA1UOGTnzbSt5LXNSyVeQzV/sN9T2EJzaKUpzcL79KJc4THaxpWLPGBdxoluVh9gZk8u3+dTYteF5RaB6VhSHLauPeOCditFgxLbNsw1IIiIiIVsqcdHjrwuYIvPW4Xux7uRe7UE4MP477tE/d/e5kslVqgc18LSu2NON257INK1XPawuM1DQuWhOYAWNx5wfISr5/s6aNp+fvMQL0G0oICgb15WiTGfrCsWlBERKRCNqNUcuANJShz589nhPsvcP8F7kJwJmH3Ra4HUhaLpRIJSi0vFrb2u/c59OurK1XXZg2PxWZ6cCQFEhS7JzQ+p9Wmz3GYbtrsK6inGwXWZ2pBERGRCtksoU4Ks1SCkpNTakGzol0A+F2BKcQuSzzZZrNyr1uZFhRz36OqFsaguEqKOPSry6Kee77TkxFlSU4bW8zQcvDtitcSn9wCgHhvbrDcYbrD3hdvlrNwnUSlBEVERCpkKzWKwusJJSjxnlCC4srbAYDpDiwRvzG5Dx+kji73uoZRmRaUfS9qNkHZuXsX+ZMPLfP8VWPGRJQlx9l53HNuWFlCamA8Tmv/jhqNr6lTgiIiIhUq3cXjdYe6cBwlu4Kv83dnBV64iwDw2xMotqWWf+HKdPEEW1BqdgzKwhdvoZWRV+Z5o4xumfQUZ9hxYvr+K6dITVCCIiIiFbKWkaD4S3X3uLeuBOC0rU/tfQ/8lXYS8309eds7OPqFjUo8hmqpBSW5ILPMc95yHo9Oe3ji0iI5nt/9B9VYXBKgBEVERCpkNX3B16UTFMNfqjxvG0WFoRaJNgVruP30njzUajJ7hk2JfuHKJCh7MxSjhseglDezZvE5P5Z5Lt4e3urTPMFBEXFhZX5Tg2IPlBIUERGpmFm6BcUVvbwoj/Wb/goeZ7Y4gZZJTj67+QSuHnRI9OtWaRZP3a0mMqBntzLPlU49JiffhdVi4N9vLI2/AU0rrq+UoIiISMX80VtQSpf7S/Iozg8Nmk07699hl7jFfU3kdavQglLTs3iqvTZJqaRs0NljASISlIa07kl9pQRFREQqVuqh7POUSlBKdf1Y3AW49iYoWy1tadsmNB0XoPcZ13Ku657w61ZiFo9RSy0oZV3t1qSHyo+n1DsHHBL4jr79lhWzGz7kwGihNhERqZBRKhHxe0p38YTK7Z5crDl/AOC2p0RcY3T/TrT0nQhzSxVWoovHrKW9eJxE7o68wt6Te667ovx4osThN6wNZz37BkItKCIiUrFSiUhS5hfB16UHybYvWkP/lRMB8NiiL3ef0GK/2S5V6eKpYZYoGUWzeDupCfYK3llGgiI1SgmKiIhUqHQLSostc6KWh7FG38uleXLCfheuSoJSs00UhYkdIj+pErlQjtE8okwJSs1TgiIiIhWy+L3hBX4/mGbZCUoZD+yITej27gRcrlrq4rHgjSgzKtFac8Z5l/OyeSazjwiNVdl/DEppT/r/znsnfla9IJswjUEREZEKGaV2MAbg/ubQ79oyV3c1LdEfLy2THFzonsAIy2K+OOhG3qjUh9fOXjwRSReVa0E5rG0zut77GlZLqHJ5LShjLriIZkccX60YmzK1oIiISIUsfndk4Y/PYfdH37nYLGPwa4LDxg2XX07+kId45coTKvXZRi118VjMQILyS/c7Qp9VyeEupZMTALOcBMVaiQ0RJZJaUEREpELWaAkKMLTg4+hvMMp+vBzfpRXHd2lV5vnIa9VugmJzhLqdorWqVEZzcss8Z7VHH48j5VMLioiIVKisBKUsSZ6dNfbZZmUWaivcBdt+rdJ1rXsTFKcztPmf1Vdc5fgAPJ6y709cSlq1rtnUKUEREZEK2aqYoByU+1PNfXglNgt0Pdodnj8Bc+vPlb7svhYUhz2UoOQ421UnQvy+UGwr/J2Crz2GA0vz9tW6ZlOnBEVERCpUUQvKNfGP8rO/Sy19euBRVdZmgUVuL04z0PKRt+brSl91XwuKYbPzT89V7DaT2NT5vOpFWGq357Vnfcqjx/1I8fjN2O/cCNaK1lWRaDQGRUREKmQ3y05QVrc8hf/eeCW3/rclvbMuq/HPDi11H33GUE5ODsHVVezxlb7uvh2arVYHA869mXE/ncsTJ/WufqB7nduneq0wEk4tKCIiUiGbGVgW/p3OD0SedAZWjR133qm18+F78xOjjB6en75+L/jaY4tcYr8s1r3roFjsdv7Wux2vje0XuU5LpUPUOvc1TQmKiIhUyLZ335rDOrSNOGc6kgBo3yKBs1z386u/M093eKLmPjy42mz0JMBfHJpBk/TTfyt92X1dPBars4KaFVOCUvOUoIiINGG+/B3smfMfyN9eZp2SPdvpbmwAwBmfEHHe2JugANw+9mKeP+xlLvj7RTUYZfmzeGxmaBG5uJ0roDinUle1720VsjkOPEGJtq+PHBiNQRERacL+ePZcDi1eTu7qD0m9eUHUOr++fAPH7n0dFxeZoPgTWgdfD+jSigFVWeOkMipYByW1eHN4gbsQ4iP3yynN5zdx7G0VsjkrP26lLDbKWPJfqk0tKCIiTdihxcsBSM1ZEXiwR7NnY/BlfEJkguJsWcvTaCvo4hm4853wAq8raj1+mgGbfgTA7fXjMAJdPHZH3AGHmEIZ906qTQmKiIgAULjolYiy5cuXcKxlbfA4PkoLSmpKaq3GFVLJbhSfhw0fP0T+o0dBfhYAW5d/CR/dAC8PB8Dl9eEkMDOpJlpQPjP7A7DG0u2AryUB6uIREREAPMUFEWX5742DUlvJ2J2RrQ2O+MRajCo0zbhL3uJK1fd7XXRaNhmA4hkXEGc1+S2/HRml6pR4/KQbeQDYaqAFpd+lDzF1di/OOvv8A76WBChBERERAJr9MBlX/gacp02ChBYAwW6QfexRWhuccbWboGTkLi/zXInHR76ZSmsjNJPH43axb9hrfHZgZdmTKXUNTzF/Za4lfe+hkVI6damevoe0pe/1txzwdSREXTwiIk2Y1wx/DDhXvkXu/GdLnQ/fiddqj2Omd3BYWW23oGzJKnuG0eZ1y4PJSbEZWMPEXVzBeBB3IQV5uwHIsTQHR+3GL9WjBEVEpAlxFxeS9cK5FPz4KgA5JEXUyctaH3ztJTxBwZGIb+STXO++KVhkdUSOS6lJ3tKPqlJTjTduzaLrOycHj3cRWKQt74fIsTRhPEV4i/MBcFtqN3apPiUoIiJNyA/vTiF96xySPg8kGBvM9Ig6LmsoafGUSlDWtj4V7HFcfFwneh1aat+dKiwvXx3+0G6B4A90OX0/8xE6vnBoWL08M9ASsmfD8vIv6CnGv35e4KX1wMefSO3QGBQRkUbONE2e+vh7OqdY2f3XNgaVOueyN2f/JTzMwp2sn3YZLXuPxFvqMZFwxPDga8NS6u/b2k5QSndDeUso8Rkc/9uDEfVcRmDkiR1vxLnSPK5C3J5AnRTPrpoLVGqUEhQRkUZu+eY93PzT6QD8L/7vwXLzx+eDe+yU1jXr08CLze/j5bhguS0u1LIS2sAPsNVuK8Sxh7SGfUuxeN2s3pTN0VHqeY3AI+0vsxXd+KvM63mKC7F5AzOWNnX8Gz1qOF6pGeriERFp5PJLQi0KGewMvjY+v40kf1657/UZob9jHfGhBGVXXIdQpdLJSi1IdZZ6VK3+ALerKGo9n2EHoF182TsvA/h+eh27JzAGxRZfV2u4SFUpQRERaeSsllACYbGG/7OfbJafoJQeJFt6ts65g47mNNck7un0Rg1FWTZL6T6oT28hccfPUet59yYoXd1ryr1e8pqZJHkCs3gsSTW8LL/UGHXxiIg0cqVTkjjCu3RSCLVG/GZ05jAzM+y8228JXiAuKbS/zSGtk3j73qtIctb+Y6Sg/22k/P5+8Dhu+09R61mr0JDT17sMgPg2hxxQbFJ71IIiItLIWY3Q1FynWRx2rrkRGItReN1y/ux5a8R77aUWYbO37hJ2LjnOHj4WpZZYW3YOOz74j1ej1uvozYxaHs2+3YdbH3xk9QOTWqUERUSkkbNa/KHX3uKodRJTWmLY7BHlp3nnArAnvn3MFjRz7NctZcUftd56f/iU6fd8J1R47bj4yHVgpH5QF4+ISCNnLbXJnt0XPUHBkYTFGpmgJFACgMWMnhTUBYetcn9LT/H+nYHWVcHjzn+fxINzvqBDWkt2rVtM2+YJfJidxpuOSaE31fIUaak+JSgiIo2cUSpBSfHtiV7JYomaoOyTc8LEveu01r3KJij3XHcZeS89QooRGFdz9JFHcvSRgS6cYvcYnDYLGavWw3ulEhSro8bjlZqhLh4RkUbO9IdmwbQ0y16YzLBF/s3qMwNjTNp36VnzgVWSzWLwp79tuXWWHvcMR7VvxoPdP+E/nvN486jXw87HO6xYLAaWuP3SrDoYQyPVoxYUEZFGzvSXv7Lqr61O50jAGmUMyr4BthZb7FoaDMPAUsa4E4CcM16hb59RADx8Xh/yznyWlLjorUHHHdKKS93/ZLrjP2z2t6Z9rUQsNUEJiohII+f3meWezzhhNABGed0dltg+LhINV5nnUnudGXZcVnICgTVhXrj/Lp79YjjHHdZeCUo9pi4eEZFGrnQXTzTWvWNPbFG6eEKVyn7o14Vf/GWvV2IpL+4oHDYL151+LEcfUn63kcSWEhQRkUauoi4eqz3QclLusu8xbkG503NFTD9f6l6VEpTnnnuOI488kpSUFFJSUujfvz+ff/558LxpmkycOJGMjAzi4+MZPHgwq1atCruGy+XixhtvpFWrViQmJnLmmWeyZcuWmvk2IiISwe8vv4tn39iThOZpZVeKcYLyj78dz9vewTGNQepWlRKUdu3a8dBDD7F06VKWLl3KySefzFlnnRVMQh555BGmTJnC1KlTWbJkCenp6QwbNoz8/PzgNcaNG8esWbOYOXMmCxYsoKCggJEjR+Lzld8EKSIi1VNhC0pyIDFJTUwop1Jsu3gu7tcRT6l9gQA8zuaUnDghRhFJbatSgnLGGWdw2mmn0a1bN7p168aDDz5IUlISixYtwjRNnnjiCSZMmMCoUaPo0aMHr776KkVFRbz55psA5ObmMm3aNB577DGGDh1K7969ef3111mxYgVz586tlS8oItLUmf7ADBgPNr43evNzm7PDzjtbB5aSt5e33ogltgkKgEn4lGD7DYuIO/m2GEUjta3aY1B8Ph8zZ86ksLCQ/v37k5mZSVZWFsOHDw/WcTqdDBo0iIULFwKwbNkyPB5PWJ2MjAx69OgRrBONy+UiLy8v7EdERCrHNAMt1H6sDLjnG3pfF76Xzb79dOyW8ATgPd/A0EE9WNBsDQeHFziTYxOI1IkqJygrVqwgKSkJp9PJNddcw6xZszjiiCPIysoCIC0tvA8zLS0teC4rKwuHw0Hz5s3LrBPN5MmTSU1NDf60b6+JYSIilZVXUAiAE1cwGVnp7wTA74l9g/Vspfa82WK2wnviXYG61sPBEvs5Faf/3/jwAns5XVLS4FX5N+7QQw9l+fLlLFq0iGuvvZYxY8awevXq4Pn9d7Y0TbPC3S4rqnPnnXeSm5sb/Nm8eXNVwxYRabJWff9JRNnmEa/yRtJlNB8dak2xW0P/Dic6HfztpP58OWI+aTd8WSdxVqR/lzbB14XEaRXYRq7Kw7IdDgddugS23O7bty9LlizhySef5PbbbwcCrSRt24bmlmdnZwdbVdLT03G73eTk5IS1omRnZzNgwIAyP9PpdOJ0OqsaqoiIAM1btIL9GqlH9D8K+j8RVmYv1YLit9hw2CwM79er9gOsJIvF4BHP+Vxg/Zr8iz6he6wDklp1wG12pmnicrno3Lkz6enpzJkzJ3jO7XYzb968YPLRp08f7HZ7WJ1t27axcuXKchMUERGpvjbJgT/w1if3KbeerdQYlCJ7i1qNqbq6nXsvH580m+6HHRbrUKSWVakF5a677mLEiBG0b9+e/Px8Zs6cybfffsvs2bMxDINx48YxadIkunbtSteuXZk0aRIJCQlcdNFFAKSmpjJ27FhuvfVWWrZsSYsWLRg/fjw9e/Zk6NChtfIFRUSaOtMXmGZsGuX/k28tlaD4ktJrNabqOrv3QbEOQepIlRKU7du3M3r0aLZt20ZqaipHHnkks2fPZtiwYQDcdtttFBcXc91115GTk0O/fv348ssvSU4OjbR+/PHHsdlsnHfeeRQXFzNkyBCmT5+O1Wot62NFRORA7F0HxbSU/++sYRh85+vBCdaVGMddVxeRiZTJME2z/CUG66G8vDxSU1PJzc0lJSWl4jeIiDRhH708iTM3Pcy65ifS9eaPy637/W9/4dqzjZOP61tuPZHqqMrzW7sZi4g0ckawBaXif/KPP+wgQN0oEnuxn9guIiK1a99S94a60qXhUIIiItLIHbrlPaByLSgi9YUSFBGRRmz1X7l0swR2jE91bYtxNCKVpwRFRKQR27EntHeZ05Mbw0hEqkYJiohIIxZnuIOv998NWKQ+U4IiItKYeYqDL00a3KoS0oQpQRERacQ8rlCCsm+6sUhDoARFRKQR8xSFxp2YJ94ew0hEqkYJiohII3bI19cEX7ccMDqGkYhUjRIUEZFGavOuAjpYdsQ6DJFqUYIiItJILV+5Ivh69wWfxDASkapTgiIi0gjNX/oLZ3xzavC4Rdf+MYxGpOqUoIiINELZH/4rvMCqZe6lYVGCIiLSyHi9Xs61zo91GCIHRAmKiEgjM3/FH7EOQeSAKUEREWlkDG9J2PGGFgNjFIlI9alTUkSkkXB5ffy+KQuH3x1W3ummT2MUkUj1KUEREWkkPv3kfUYtvyKszHXNjzhjFI/IgVAXj4hII9Fq2ZNhxzutbXCmHxajaEQOjBIUEZFGwmGEbwbYzLc7RpGIHDglKCIijUSRGd6ZY0O7F0vDpQRFRKSRMIxYRyBSc5SgiIg0EikWV6xDEKkxSlBERBqBzJ2FxPuLAPjN0gWAEiMuliGJHBAlKCIiDVxOoZs3Hx/PEZaNADQ/62EWdfsnrrHzYhyZSPVpHRQRkQZu4+4iJtjfDB63Tj+ItF7/KucdIvWfWlBERBq4vKLwsSeWuJQYRSJSc5SgiIg0cP+d8Xp4gTM5NoGI1CAlKCIiDdwtlpnhBY6k2AQiUoOUoIiINHCFzrTwAov+aZeGT7/FIiINnc9dcR2RBkYJiohIA5fiywm+zhkwIYaRiNQcJSgiIg3IH9n5vPjVSko8PgBM0yTeLAYgf9gUmg+/LZbhidQYJSgiIg3Ic088wJXfHc83Mx/DNE3cPj8OPABY2nSLcXQiNUcLtYmINCCPOf4LwIg/H+SfD9vpmGzyNyOQoNid8bEMTaRGKUEREWmg/lMyEUqAvbsYK0GRxkRdPCIijYRh0+aA0ngoQRERaSzsakGRxkMJiohIY6EWFGlElKCIiDQWNmesIxCpMUpQREQaCJ/fLL+CWlCkEVGCIiLSQHy7NrvMcx5sYLHWYTQitUsJiohIA2F155d5zo63DiMRqX1KUEREGgi7qU0BpelQgiIi0kD4PCWxDkGkzihBERFpIMpLUHIv+rQOIxGpfUpQREQaiC079kQt39zudFK7DazbYERqmfbiERFpILxLpgf/1d5uaYPnoH4cdNHTtI9rFsuwRGqFEhQRkQbiMtsXwddp/1oLFjWCS+Ol324RkYZIyYk0cvoNFxGJsWK3jzXb8jDNsleKNU2T+f4jAcg9cWIdRSYSO0pQRETq2F97ivnH28v5dcseAG5/YRbLnrmUH5b9VOZ7ClxeEgjM4olr1bkuwhSJqSolKJMnT+aYY44hOTmZNm3acPbZZ7N27dqwOqZpMnHiRDIyMoiPj2fw4MGsWrUqrI7L5eLGG2+kVatWJCYmcuaZZ7Jly5YD/zYiIg3AlFkLuGrVaD597g4A7tzxTy6xfUXy5zeU+Z49RR6SKQLAmdSsLsIUiakqJSjz5s3j+uuvZ9GiRcyZMwev18vw4cMpLCwM1nnkkUeYMmUKU6dOZcmSJaSnpzNs2DDy80NLNI8bN45Zs2Yxc+ZMFixYQEFBASNHjsTn89XcNxMRqad6bP+Qwy2buNP+Fttyi2lr7Aagp291me956vV3OdSy9w85Z0pdhCkSU1WaxTN79uyw41deeYU2bdqwbNkyTjzxREzT5IknnmDChAmMGjUKgFdffZW0tDTefPNNrr76anJzc5k2bRozZsxg6NChALz++uu0b9+euXPncsopp9TQVxMRqZ8SExLY21vDnz9+SttS5zyuYlzYSXRYMQwjWH7Xrrtg32Fyep3FKhIrBzQGJTc3F4AWLVoAkJmZSVZWFsOHDw/WcTqdDBo0iIULFwKwbNkyPB5PWJ2MjAx69OgRrCMi0phZrKG/Dc3NS8LOrV36Ddf9ewpPvx1aGfabtdk0NwpClZLSaj1GkVir9joopmlyyy23MHDgQHr06AFAVlYWAGlp4f/nSUtLY+PGjcE6DoeD5s2bR9TZ9/79uVwuXC5X8DgvL6+6YYuIxJx9T2bw9W+Zmzih1L/Ey35awmu2KfAb/LH+J7ocfAhfvTaJk+ylLmCx1l2wIjFS7RaUG264gV9//ZW33nor4lzpZkkIJDP7l+2vvDqTJ08mNTU1+NO+ffvqhi0iEnPprvXB11faPgs7d9rOl4KvXe9di9vr5wH7K8Ey79GX1X6AIvVAtRKUG2+8kY8++ohvvvmGdu3aBcvT0wP9ovu3hGRnZwdbVdLT03G73eTk5JRZZ3933nknubm5wZ/NmzdXJ2wRkZjbuKuQVArLPN/aCLUQdy/8kT3F7rDztmH31lpsIvVJlRIU0zS54YYbeP/99/n666/p3Dl8Ln7nzp1JT09nzpw5wTK32828efMYMGAAAH369MFut4fV2bZtGytXrgzW2Z/T6SQlJSXsR0QC9hS5ySl0V1xRYm7R+l0M+s+3OKncf69sWwaFrlKzG5t1hPjmZb9BpBGp0hiU66+/njfffJMPP/yQ5OTkYEtJamoq8fHxGIbBuHHjmDRpEl27dqVr165MmjSJhIQELrroomDdsWPHcuutt9KyZUtatGjB+PHj6dmzZ3BWj4hUjsfnZ8oTD9PTu5KRt71GfJwz1iFJOd76fh0fOv5FZ8v2StW3+t0UlrjxmwYWw4Sxcyp+k0gjUaUE5bnnngNg8ODBYeWvvPIKl156KQC33XYbxcXFXHfddeTk5NCvXz++/PJLkpOTg/Uff/xxbDYb5513HsXFxQwZMoTp06djtWrgl0hV/J6Vx/3uRwHI/H4GnYdcEeOIpDyHFS2ml2V9RHl+XFs2H/MvjvjuegCKHS2Id++mpX8nyzNX0cPYuwS+M6kuwxWJKcMsb/OHeiovL4/U1FRyc3PV3SNN1rbcYoZP/oQVcYGkZGXXa+lx8UMxjqrqNu0q4svVWVxyXEfi7I37j5Tnn3uMq7ffHzz+s/OFHJL5FkXnvkFCj5Hg94HFyi9r19Hrrb4AZJnNSTdy8GNguWe3NgmUBq0qz2/9pos0UJM++IkFzpuCx9binTGMpvruePF90r+8lunvfRTrUGqdzRr+T+4hf7sX7twSSE4gOH04LqlFsE66EZhQ4DLilJxIk1LtdVBEJLZSfv8fqfai4LHh88Ywmuq7rHAaw6w/wW+LgL/FOpxalWr3hxckp0OU5RWSExMiylyWeOJrKzCRekjpuEgDYZom2fkleHyBh9zV1o/DK/gbZoLS3bIh1iHUGdNVajXYK7+OmpwAJMdF/u3otWgAtDQtakERaSD++9Uqfvv6DRb4e/L0lcNpbkkCdgTPG35P7IKTSrG6A2uc/JlxJocc1KfMeomOyH+ak/z5UWqKNF5KUETquUe/WMv2vBLaLJ/Kk453ACh4LY7tZnOwwAbbwXTyrm8wLSilV43+LSuPZMpfZboxsXqLAy8cieXWs1ii3JNeF9RCRCL1lxIUkXrM5fWxat67DLYs51jrb8HyJKOEJGMbAEXOVuBdj9fjKusy9cauAhcDH/gEG34++edpPPPaWzxt7KrSNd5Zspnn3v+CTWYbPrxxED0OSq2laGuexbc3QbFHjjGpSNzpDW+GlsiBUIIiUo89P289rzj+U24dV3wbKITued+x8vtP6HH8yDqKruo+/SmTNXGXA3DzY9cxyvo9lJ5Z7PcHZ6qUeHxk5ZbQqVV4a8O6jx/hG+drAMz67/H0+Hf4Xjb1iWmarNmWz8GtE4mzW7H6SgCwOKo23NU/ahoWq73iiiKNiAbJitRju9b9WGGdTUlHBV9v/+KxWozmwJimiaNwa/D4SceznGT9JbySOzSI9L73lzFlyoN88eOvwbIpX65lguW14PHfrN+TnVdSe0EfoI9+2cppT33HYXfPpsTjw7Y3QTEcFbegbBrxGjua98a84issR55b26GK1DtKUETqsS1ZFS+JXuwPNYQOsfxESWFeObVj55lv/uDt+b+WWyd7Z2Atl/U7Cui24lGecjyDZW5oc7zd856LeM/nj1xCdl5xzQZbQ95fuJrvHDfzov0x3l26Gas/0A1ndVacoHTodxatb/4Wo13f2g5TpF5SgiJST837fQftPBsjyk0j/P+2jlKLegFsWvFdrcZVXe/P+ZZZzvJ34r3imU94/6ctjJ0yk8tsXwAwzPM1AG6vn3/Y/hfxnjG2Ofw65/WaD7gGnOxbQHvLDoZZl5Gw6i3s/kALirWCQbIiogRFpN66f+Z87rO/GlHuG/MZ2095Ho81Ac/f3+CkEwaFnf/g4w9xeX0R74u1l+1lj6XZ5G8NwEfOu/n9vX/zjfPWiDortuzBTvSZSkNXjGd15l81E2gNstpDa5f8bfPDOPa2oNjiqj5IVqSpUYIiUk/1KlkctdwWn0pa/wuwT/gLe/eRNG/bCfcF71BiCTz0brO/zQV3P832ejQ2w+83aVvWbJ2bfyXJCHXR3GGfGVHlzx0F3Db9C1KMYvxYWHXpbxF1ds+8usbirTHO0F4jFsPkUDYAYK9EF49IU6cERaQObd5dxJDHvuX1RZFdN/uYpsnVM5Zyl/3NsHLfmc9hDrod2hweKCi1L4vjsFN4o8UNweNZzns5fdL7bNhZyNINu2v2S1RDiddHEXHBY9/J9+JzNsPT9ypo3pEWRkGZ791itOXypz/mWf8DAOyJa0f3Tm0j6g101b+urQRr+NL2zfd+z/jE5GjVRaQUTTMWqUNPfbmKCXvuZdnH3TD7vRBcsKy0ddkFdF/7NK1socGuJamHEHf0ReVe24xrHna8NO5ajnnUJNkoot8xxzF5VM+a+RLVUOLxU2DGBx/Q1gHXw4m3UJm9i9uZ27jLfIlDrVsAcDfvGjhx3SIo2gXTTw9VdhdBJWbI1Blv9FasuP3GDYlIJLWgiNShrgWLOdm6nH/a3yErtyhqnT+zC7jJ9kHw2GzWgbjLK97pt3lKUkTZkrjr+No5nvOWX1rdkGtEscdHAXsTh+NvBlvF+8os73h58PUp1qXB1236nLX3xeHQaSB54/4InnPtKrtlCsDj89Ppjk/pdMenuL3+cuvWCG8Zi+e1PKT2P1ukgVOCIlKH0puFZm+8/b+3o9a5//2lYcfGuBWQ2q7Ca48YOrTMc70tf5R5ri4Uu30ksTchOyxyIbm5HcZFlBV2OSPqtSx9/i/sOCmlFRv8aYHPydtZbhzLN+/hYutcXrL/h+W/1f49Mfaue+It9U9tvq0FxDer9c8WaeiUoIjUIa/bHXx91qboS5cP9JcaHDui/FVkS0tokcGff/uk7Ar+2M3sKfH4SDT2dnc4Ilt6Bo+5l687h8/c6X/cwMgLXTUvYgdgi8WgyAi0zrgKcsuNo8jt40H7ywy1/ox/8YtMmfM7eSW1uMmiJ/Cdd1laB4vykzrX3ueJNCJKUETqkOkuDL7ubNkOphlR55i0Ugf9rqrS9a0Zvcv+7KKq7XlTHSWe6ElQicdHEntn6jgjB4jarBZOHnNP8Ng85kosdgdfdRwXXrGMrpFiS6BlylWYU258haWSkY3rf+f5r1bx4Cdryn1PdX24/C82ZgcGKOcmdwmWWxI0/kSkMpSgiNSwXQUuitxl7Czsyg879O3ZHHZsmiYb/soCILPDOVX+7PRmoT1e8rqciWtUaB2V4pysKl+vKv79yWqOuGc2a7PyI865SkpwGnvviTOyBSXokveg3zUYpzwIQFF8aLZOSaeToyY3AC5roAXFU1T+Krqb/gottT/MupS1cZeSvu6NYNkf2fnsKXJHe2uV7CpwMXHmfMbZ3gfA0iqUWCV0OOqAry/SFChBEalBe4rcDHz4G0Y+vYASj4/cYg9mqVYSwxM+MDZ/Z/jiYj9tygmuoNq8Rasqf36c3Uput3PxW+ykDB6H88izWec/CIDla2t3zMVbC9ZwkrGMhz9ZHnGupLBU14ujnCm2XYbCiIeDg2g3FIQ2yPMfNbrMt7mtgRYUX3H5XTzvzv8p+Hrf1OZ/uP7LhS8s4r1lWxjz+Cxuf3VOudeojCe/Whe2yJ7dkYB39Me4Dj+HlBOvO+DrizQFmmYsUoN+3ZLLhf5PaJ6Tz5ETi3D7TJw2C/ee0Z2L+nXA8BSG1b9t+pecc1FnTumeDsBr367mUQIPzmZd+1crhtTznoWSyZDUBoCulkASlLR0KgwdVd2vVqGH7C9ypvUHXt3wK3ACAK98n0mR20dHAt1LLiMOp7Xy/+ycPewkeCXwOv6g7mXW89iSwAX+chKUF+b/yX/sz0c9N2HL1by2cRifO16nKCsOV8lgnHHVX47e2L2eM60/BI9tSc2xHXIitkNOrPY1RZoaJSgiNai4pIh77DMA6OP7nXbWHbzsG8GzH2Tx8S+HMyI3fIzEJNtL3PWGycB7/8XSjTkYaz/F7tg7juOIs6sXhM0ZTE5KO9gXmIK7dU8xrZKcOGw124C674E8xjYHTBO3z+TFj+dzkLETm+FjpAPy7K1pXcF1Smvf8WAYOxeKdmG0PrTMel5roGtr/urNfPD5b9x+6qFha8xs3l3ErM+/4Cpn9FakHpYNPGJ5EYAUirnluXeY8o/LqhBpuK5sCjtulpJa7WuJNFVKUERqUMHu0DiPAdbVAEy0vMZEXmPzltaYENax2srI4wXH4xxzbxfiDTfznc8CsKn1YDpEWcStOmZ3vpNTMyfzu6sZ7/zvF2YuDSx49s34wXRuVTub1pmuPPK9cTzneIJelvXBcm/yQVW/WPtjKv48qwOAy62f88SCBG7Lv4H/nHdU8PyEWSv43HlnpT9ySu44XK4LcDrjK64czX4DkhPTu1bvOiJNmMagiNQg1+5tZZ5rb9lBB8uOqOeWxF3PfOc/gsepReUvOFYVZlKg++hoyx/E//wi/3NM5HfnaB5+7oVqX3NXgYvPVmzD4wssdpZb5GGe78jg+eI9O8gr8YYlJwBWa2XWjq06nyWQoNgMP+Pt7/LPVWcxfuZStueV8OaPm/D/+U1Yfc+pj1Z4zdfe+5A/d5S9BH95rCWB2Tteazyc+hB0GVKt64g0ZWpBEalBnvzKzZTxYcFK2SuZ+hMju2iqy4wLdS/cu7f7CeC/vonguQbslW8lWLBuJ2NeWYzP7yeREm4ecRRXDerC6m15dLGEBvxu3LwJd3rkIN/UDrWz3P7arHzOCI2npY2xh7vXnMEpyx+mpyWTq6ylBr5e8RX2dn0hoye8fEqZ18xYM41bVu/mwweqPqjV4d4DwLZul9D+uGur/H4RUQuKSI3auCGzUvU+aje+3PPNhtxSE+EAcFjPPmWe8+/ZUqVr/WPaF0yzPsSz9idZFTeWXfNe4ItVWazL2kM6oU0J13z0GPmFka0Pzv5VW9elstoZkS1TqUYRi+Ju5EXHFE60rgAgt8cYaNc3UKHDcXzlC183xn/qI/wQFxjge7p1MR/a7qTQVcaU8f2Mm/kz5z3/Q2AX6b1dPLakqs/EEpEAtaCI1JA/dxSQ4N4F9ignz3oGepwDJbmQ0BLzs8+gjNygJK41cd3K/su+qjq3b1/mOXdJ6T2GK3aX/Q0GW38JHt/pf55OM06kDXv4v7jQdOpR1gXMWP8H+9aC9Q28FesJ/yhzHZMDZRqV6zqK63ZS+LHDBvvWlmvfD0uv8yj5cTGU2uPv95VL6N2n/BlVK7bksvWXr/g/2xz+Nuki5joDqwEnNK+5ljCRpkYtKCI15I/sAsbb3wVgZ5dzyLSVWvW09yWBrpTkdLDaSWvfpYyrgHnkhRHLuR8IwzDg+iXQ9RTMFofgu+i94Lm/siteXdbnN3F7/WzaVUSGEVl/ifNaFsddH1H+x/eBRcoKLclYh95Ta8kJQM+LHgi+/tR3bJn1nC07hh0ffML5oYOxX0J8cywpGWF1kv76rtzP/nXLHs6YuoApjucYaV3EwribSDACmwSmtEiv7FcQkf2oBUWkBpimSdbO0BTi5m064BtwM7x2IrkHncj+k0wHHHk4BZ80x46XPzpfTAvPdlodPwb/b58RP7Tys00qrXU3uPgdDKB0W8Ons17lyp6DiHeU3QJx6SuL+W5dYBO+dxyRS/O3NqKv3rpvobJ8I5HamSsU0uPwI2BiYA2UrlPPhbL2DGzWIeyw7YljITUe2h8XLBs49GyY9lTw2OMuf2XZL1dm8aL9MdoZkR9qdDq+cl9ARCIoQRGpAVO//oMZc5YxZm9/ifX4G0lLbAm3ZZIaF7kGhmGxkHT7GvC56V76fNeTIurWhi1mK9oZO7nJ9gHH/PsMZt50Kut3FHLyYW2wWkKtN6ZpsuOPn7jcugo3No61rC3/wmc9y85P76OVd3uwKN1Xu0vs7+/gUfdgvvgNhhkaO+K1JWIktcGa0DK8ssUSaN0qxdq+D/z9VXh3DACmq/yZPG9/u4zxccsiyn19r8Aa5b+9iFSOEhSRA+T3m3w092sWx90GQL6RRHLi3gdheRvD2eOrNIOmJiWf/m/4LDC7ZIl1LP0em4oNH1fSin+dfgRXnHAwAAUuLw/ZX+Qoy58VX7TnedD7YjCT4aPQsvTubiNx1Mq3iM6WcSTcvh5MP3x+Oxx6GrbOJ4LVUfmus+5n8+XcCxieM7PCBOVK26fhBSMege2rsJ48oZrfQERACYpItRW4vDz11Tp25LuY47wtWO5PTCvnXfVD6rEX4dk8D/uKmQD8GHdD8Nyjs//Otp5P0rZZIjmFnjKTk21HjKXt6mkA7D7pIVoMCiQ8rXqfAQX/gjZHgKcYR8cBtfxtotjXcjGq+mu9mPZAx5ThCU9Q9u2tZBgGpmlyofXr4Dl/QmssfS4N7iUkItWnQbIi1fTesi3M+e571i7/Pqw8Kamc3XrrEfs5z/NXp8gdk8fb32X8I08DsLucnX1bHnlq8HXz1qVWiDUMOPGfcNjp0PNc2G/QaUNhOgL/HS3uUILi9voZ+fQCLpu+BIASj581ZmBci2vwPVhu+knJiUgNUYIiUk3F+Tl847yVz5x3hZVb//5KjCKquoP+9u+o5XfZ3iSvxMOWnXn4zejdIo7OA+DUh+GoSzBqcFp0feGxJgDwV3Zo8Ovfnv2eblmfcvSfz3DLzJ/JKXLTfO/mjo4OfSAuJSaxijRG6uIR2Y/H58durTh3b1m0PrLw2oXQ8pDI8voq9SAY8wlFi14mYe37weLulo18+N087vtqByPj9pu5c9qjcPiZ4EyC466p44DrzopdBmcAQ60/g99PgcdPq6zveNzxHAALV/3OG81f4TIjHwAjQYuyidQkJSgie638K5eRTy8AYOIZR3Dp8Z3Lf4MryvTatO61EFkt63wCha2ODUtQANZ99x5/swZaTwqMRJJuWw3OlMDMlybgzGO6wt4tfIp3buC2OTncZXsjeH6AdTU/zn+E5rZAgsL+M4RE5IAoQRHZ6x9vLyeFAu62vc7Xn/ameeLVnHVU2bvvzv11A3/fNz1lzCfQ+tC6CbQWOGyRScdgltLX/jsASWYhxDer46hiq/vxpwcTlH9On8PaHINDneHL//7DHlr0rtwZWyJSZU3jTyGRSkgq3syvcVfxd9t8nnM8yXNvf8TGXYVl1m9tBBYG+8F3BHQ+AZIa7rLmCVEWautr+T0GkdQfhs3JSn8nAKYW3cb9tunBcyXnzIh8gwbHitQoJSjSZJmmGZwyCnCOf27Y+dnOO3h42ut0uuNTLn5pETmFoRktJR4f19k+BKCfrYLFyxoAu9XCuuaDAChpfwLZZrPwCsOiD6Zt7HpYNgRfH29dFXiRfiRxPc7A3aluFtUTaaqUoEiTZJomF7ywiPOfX4TfH0hSsl2RPZ6XFkzjH7Z36Z45nd7//pLft+fz4fK/OOzu2bQkMPbA7DK8TmOvLV2vngHD/k3cRTPYYOsUfvL4m2ISU6y5oi0xN3oWGAaO/3sP/96BseZxkXsRiciB0RgUaZJ2Frj5MXN34HWhC4fVQgqRK4Yea1kbXN79Eutcznj8QQqJ4wTLapyGBwDriMl1F3htiksNJiK5h5wNvy8PlF/5TcxCijXvua/h+N+FGJSayZS4d7aOxYrlljWw/huMgwfHJD6RxkwJijRJe4rc3GV7g0GWX7jyhUf5ZYefDxyhMReutKNxbv8p7D0dLDuY5niU7sYG4o1SC5g171RHUdedfsedAPtuR3LbmMYSS4k9RsARu2Hl/+DrB2D/NW5sDmiEa8CI1AdKUKRJ2lng5qq9e6j03v0Z7S2poSXdB9+F8+jRMOXwiPftP3DUn3Yklsru79KApHQ6GvpeDklpkNJ0ExQgMK36yPMCPyJSZ5SgSJO0u8AVfD3R/lr4yUG3BZZrv2c3bFwIX06Abb9EvY5lyL9qM8zYsVhg5OOxjkJEmjANkpUmI7fYw8q/AlOD8/N2l11xX4uIxRqYPnz1fLj8C4rb9gur5k3rpeZ9EZFaogRFmoy73l/ByKcX8L9lWyjZvTV6pcu/jF7e4Tjix34cOk5KxzZiUs0HKSIigLp4pAlZuGIt/7J9yIv/28Tfj0iIrHDzr9C8Y9kXsDlh7Fxw58MhJ9deoCIiogRFmob/LdvCk/ZnONG6gqMsf/LamuEEl7hI7RBY26K85GSf9sfUapwiIhKgBEUaPY/Pz8SPVrLSugIIzMTpag/sqZLd8QzaXPZ6LMMTEZEoNAZFGrUSj4/B//mWkz3zw8pTjSIA2rQ7JBZhiYhIBZSgSKO2amsef+0p5hhLGfvlHHtl3QYkIiKVogRFGjWXx8c/bTMZbZsbvUJqu7oNSEREKkVjUKRRMk2TT37dxs1vLWN93EeRFQ4bCSfcWveBiYhIpShBkUbp1YUbmPjxalqU3gCwWYfANGGLNbThm4iI1EtV7uKZP38+Z5xxBhkZGRiGwQcffBB23jRNJk6cSEZGBvHx8QwePJhVq1aF1XG5XNx44420atWKxMREzjzzTLZs2XJAX0SktLcWb+Zc6zx+irsmVDhuBSSnKTkREWkAqpygFBYW0qtXL6ZOnRr1/COPPMKUKVOYOnUqS5YsIT09nWHDhpGfnx+sM27cOGbNmsXMmTNZsGABBQUFjBw5Ep/PV/1vIlJKb8taHrU/Hzw2bfExjEZERKqqyl08I0aMYMSIEVHPmabJE088wYQJExg1ahQAr776Kmlpabz55ptcffXV5ObmMm3aNGbMmMHQoUMBeP3112nfvj1z587llFO0t4kcuPSC8Fk7xrkvxygSERGpjhqdxZOZmUlWVhbDhw8PljmdTgYNGsTChQsBWLZsGR6PJ6xORkYGPXr0CNbZn8vlIi8vL+xHpCwlHh+JxaX22rn8SzjstNgFJCIiVVajCUpWVhYAaWlpYeVpaWnBc1lZWTgcDpo3b15mnf1NnjyZ1NTU4E/79u1rMmxpZDbsKqSDsR0A89SHoEO/Ct4hIiL1Ta2sg2Ls265+L9M0I8r2V16dO++8k9zc3ODP5s2bayxWaTxyiz0Uurz8mV1Id8sGAIy0HrENSkREqqVGpxmnp6cDgVaStm3bBsuzs7ODrSrp6em43W5ycnLCWlGys7MZMGBA1Os6nU6cTmdNhiqNjMvrY8hj89hZ4CKFQn6N2xk40fbI2AYmIiLVUqMtKJ07dyY9PZ05c+YEy9xuN/PmzQsmH3369MFut4fV2bZtGytXriwzQREpz7bcYv7ILiC3oJB2RjadjW0AFDhaQ1xqjKMTEZHqqHILSkFBAX/88UfwODMzk+XLl9OiRQs6dOjAuHHjmDRpEl27dqVr165MmjSJhIQELrroIgBSU1MZO3Yst956Ky1btqRFixaMHz+enj17Bmf1iFTWM9/8wX++CMzYedY+ldOsi4PnjFZdYhWWiIgcoConKEuXLuWkk04KHt9yyy0AjBkzhunTp3PbbbdRXFzMddddR05ODv369ePLL78kOTk5+J7HH38cm83GeeedR3FxMUOGDGH69OlYrdYa+ErSVHy7Npv/fPEbRxvr2GimhSUnAInte8UoMhEROVCGaZpmrIOoqry8PFJTU8nNzSUlJSXW4UgMvLpwA/d+tIrzrd/wsP3FyArNO8Oln0LqQXUfnIiIRFWV57f24pEGxTRNhkyZx/odhQyzLI2enABcPU/jT0REGrBamWYsUlve+HET63cUMtyyhBcdU6JXOv0xJSciIg2cEhRpUBZn7maoZRkvOB6Pet6f0AqOuaKOoxIRkZqmBEUaFEthNi85HgsvHHwXnPcaJLTEMuy+2AQmIiI1SmNQpMHw+U0K1v8Ijr0FyW3hpp/Bvnen4iPOillsIiJSs5SgSL3386YcHvvyd7q0SaKDkQ1Afus+JF/3FVSwhYKIiDRMSlCk3nt+3noW/LGTBX/sZL5jNgCOzscpORERacSUoEi95vL6mLtqC5Ntr7DG7EAHyw4AnKnpMY5MRERqkxIUqbfmrN7Ola8tZYRlGRfavgk/2fPvsQlKRETqhGbxSL115WtLAZN/2V8PP3Ho6ZDSNup7RESkcVALitRL2fklpLGbH+NuiDx5zNi6D0hEROqUEhSpd75as52xry5lin1m+IkuQ+GcaRDfLCZxiYhI3VGCIvWKaZqMfXUpAL2MP0MnTp8CvS4AR2KMIhMRkbqkBEXqhSK3l5veWs72vBKOMDbwhmMSzY2CwMkrvoJ2fWMboIiI1CklKFIvvPnjJuau2Q7Ah46XQskJQOtDYxSViIjEihIUiQnTNLn29Z9onmhn8qgj2bqnhIusX3GSZTm9LOtDFVPagTM5doGKiEhMKEGRmHjo89+YvSoLgDtOPRyjYBuT7NPCK50zDbr/LQbRiYhIrClBkZh4fn6glcSKjytnLMWy8afQJoAA3UZA91Fg0VI9IiJNkRIUqXN+vwmYbIi7GIB+mVM50RrYBNBjTcA+YQtYrDGMUEREYk0JitS5AreXcyzfBY9LL8Zm6TRAyYmIiGipe6l7uUUezrJ+H/WctdPxdRyNiIjUR0pQpE6Yprm3awf2FHloY+yJrDR0IvS/vk7jEhGR+kldPFInXvouk4dn/8Z71w5gT7GHI4y8wImB/wBPMaQfCb0vjm2QIiJSbyhBkTrx4GdrAHj0y7Ws3ZbHQvIDJ469ClIyYhiZiIjUR+riKcdnK7axYN3OWIfRKKRQwD9s77L9rw2UFORgM/yBE/EtYhuYiIjUS2pBKcML8/9k0me/AfD7AyNw2JTLVVeJx8ddtje5wPYt5/i+Y5p1ROikPS52gYmISL2lp24Z9iUnAJdNX8zOAlcMo2mYtuQU8c93f+Gwu2czwLIKgHbGTu61zwhUSO8Zw+hERKQ+UwtKFCUeHykU8mvclQD0+OMl+j6wi/vP6s49H67iioGdmXD64RiGEeNI6xef3+TqGctweX0MPrQN//5kdfDcHpLowI7wN5wyqY4jFBGRhkIJShR/ZBdwre2j4PHKuCu42v0P7vnQBAxeWpDJGb0y6NW+WcxirAyvz4/VYlDi8XPPhyvp1CqR03u2pWPLhFpJrtbvKAjuSPzdup0kU0Qfy1put73N4ZZN4ZWHToTOJ9Z4DCIi0jgoQSklO6+EGYs28vTXf/CqfWPYuecdj/OtrxeDrb+wyt+Re966n/duO7tK11+1NZdv1+7gyhMOrvUxLYszd3PRi4s4ukPzwPGG3QD854u13DKsGzcN6Vqjn/f8vD+Z/Plv/N36LWdYfmCOvw//tk+PrNh9FKQdAcePq9HPFxGRxsUwTdOMdRBVlZeXR2pqKrm5uaSkpNTYdX/alMOoZxdiw8sy5zWkGkXQ5zJY9kpE3Ze9p3Lopc/wv2VbuOz4ThzZrlnY+QKXl0KXl7SU0CDQTnd8CsBBzeL5/o6Tqx2ny+vDaQssBz//9x3838uLObFbay4d0JFWSU56HpTKkfd9SX6JN/ie/pZVtCSPT/39MLGQHGfjhzuHkOSsmRw18N1C++tEaN8PTpkM7frUyOeJiEjDU5XntxKUUky/n9nP3cqIHS8HCuJbwPh14C3GfLovRkFWsG6h6WSwawo7CLRQHNQsnr/2FAMwdmBnpi3IxGG18NGNx3NYeiDGfQnKPt+OH0ynVomVjs/r83P3h6t4a/EmTujaihtP7sp5z/8QtW4yRcxwTOIoy3omey7kTvtbwXM/+g9jtPtO7ht1NBce26HSn1+Wd5Zs5vX3Z/GR8+7wE1YHHHctDLgJElsd8OeIiEjDpgSlulbNgncvDby22OCCt6Db8MDxzj/g98/hmCv5bfLxHOb/g2e9Z9KMfH42u/KubxAQGtdhx0sHYzt/mhkclp7Ci//Xl/sefZT/2h9no5nGHZ4rWWIexk93D6NFoqNS4d3z4Upe+2EjqRSQRDF/0Zpm5POI/QXSjd184TuGrWZLPvb3Z4LtDS6zfVHu9T4+/n+cMWxY1HPb80oY9exC2reI5/Wx/bBZLfj8Jh6fnzh7aDO/tVn5nPLEfF63P8hA66rQBS55H7oMqdT3EhGRpkEJSnX5fTDrGkhOg2OugOadolbLnj+NNl/fUuZl/uc7kXOt8wF4xHM+z/rOAuAV+8OcZP0lWG+i5/+Y1/wcvhk/uMLQXvpuPQ98uoahlmU8bH+BFIq40nMLd9te5xDLtsp/x1KWJgyk160fY7dGjoeZ/Pkanp+3HoDz+7bnXyMP56RH57GzwEWrJAfvX3s8HVomcNqT37F7WyYLnDcHFl87qC9c9LZaTEREJIISlANhmlDRDBd3If7HDsfiyq3UJX2mwfu+EzjR+itp+22Sd7vnSvr+7WZ6tW9Gt7TkiPdu3l3ECY98A8C51nk8an++Up8JQEZvuPxLWPwCdOwf2O/GlU/+0yeQXLyFH3xHcInvbjq2TODWYYcyokc689btoG/H5vSc+CWjrV9ysLGNF72ns5VWgEk7Yyc7zRRKcALgxM0HjnsCs3SadYAbfwarxl6LiEgkJSh1YePCwIP/oL7gTILfv4C1n1X8PnsC3LIGvnsUFj6N3zQY5n6EP82DeGhUT15akEnLRAcP/q0nrZOdXPzSIlb+lcdo65ehWTHNO0HOhtA1T30IbHFQsge2LofVH0BcM/i/DwJJyn6KN/5E/CsnAXCO616WmYcC8N9Ljuaa138CoIuxhbnO24LvOct1P/+0vR3sxvnBdwQXeibwT9vbXG/7CDOuGcZln0Fa96rcRRERaUKUoMSaKz/QElO0E75/EpZNDwy4TU6Hk/8Fh50OpsmP9x5HP8tv7DBTuNdzKV/4j8GHNexSdrx86LibIyyBac+m1YFxxyYo3Akz/gbxzWHMx5FLxpfXEuT3k3tfO1KNwmDRQ54L+K/vDPaNo7nH9hqX22ZX/juf8ST0ubTy9UVEpMmpyvNbbfG1wbm3qyYuJfDgPuPJyDqGQV7znpD7G62NPJ51PMVOM4UkitlDEv/yXM4S/6HcaXszmJwAGIPvBHs8NGsPNy4tO4byuqksFgq7nE7qn+8Ei+6wzyTbbMZxljUMsK6inbF3k8RDhsCfX4XeO3oW5nePY2yYHyo7qG9gfRMREZEaohaUGCpY/gFJH4ypXOUWB8ONP1U8Pqay8rNg2jDYs6nsOoeNhPNfD6wDs/RlGPgP6HFO4NwH18PGBTDsfjjirJqJSUREGjV18TQUfj8sfx02/wg/v152ve6jYMTDkNSm5mPY9CM070ju9PNJ3bU8VJ7aHq5ZAPHNav4zRUSkSVKC0hCZJhRsh6Q0WPkevDcWel8S2FAvLrX2P3/7aniuf+B1Wg8YPat2EiIREWmylKBI9aydHZiJdPLdkNQ61tGIiEgjo0GyUj2Hnhr4ERERibHa3VJXREREpBqUoIiIiEi9owRFRERE6h0lKCIiIlLvKEERERGRekcJioiIiNQ7SlBERESk3lGCIiIiIvWOEhQRERGpd5SgiIiISL0T0wTl2WefpXPnzsTFxdGnTx++++67WIYjIiIi9UTMEpS3336bcePGMWHCBH7++WdOOOEERowYwaZNm2IVkoiIiNQTMdvNuF+/fhx99NE899xzwbLDDz+cs88+m8mTJ5f7Xu1mLCIi0vDU+92M3W43y5Yt44477ggrHz58OAsXLoyo73K5cLlcwePc3Fwg8EVFRESkYdj33K5M20hMEpSdO3fi8/lIS0sLK09LSyMrKyui/uTJk7nvvvsiytu3b19rMYqIiEjtyM/PJzU1tdw6MUlQ9jEMI+zYNM2IMoA777yTW265JXjs9/vZvXs3LVu2jFr/QOTl5dG+fXs2b96s7qMaontas3Q/a57uac3TPa15jeGemqZJfn4+GRkZFdaNSYLSqlUrrFZrRGtJdnZ2RKsKgNPpxOl0hpU1a9asNkMkJSWlwf4C1Fe6pzVL97Pm6Z7WPN3TmtfQ72lFLSf7xGQWj8PhoE+fPsyZMyesfM6cOQwYMCAWIYmIiEg9ErMunltuuYXRo0fTt29f+vfvzwsvvMCmTZu45pprYhWSiIiI1BMxS1DOP/98du3axf3338+2bdvo0aMHn332GR07doxVSECgO+nee++N6FKS6tM9rVm6nzVP97Tm6Z7WvKZ2T2O2DoqIiIhIWbQXj4iIiNQ7SlBERESk3lGCIiIiIvWOEhQRERGpd5SglPLss8/SuXNn4uLi6NOnD999912sQ6qXJk6ciGEYYT/p6enB86ZpMnHiRDIyMoiPj2fw4MGsWrUq7Boul4sbb7yRVq1akZiYyJlnnsmWLVvq+qvEzPz58znjjDPIyMjAMAw++OCDsPM1dQ9zcnIYPXo0qamppKamMnr0aPbs2VPL3y42Krqnl156acTv7XHHHRdWR/c0ZPLkyRxzzDEkJyfTpk0bzj77bNauXRtWR7+nVVOZe6rf0xAlKHu9/fbbjBs3jgkTJvDzzz9zwgknMGLECDZt2hTr0Oql7t27s23btuDPihUrguceeeQRpkyZwtSpU1myZAnp6ekMGzaM/Pz8YJ1x48Yxa9YsZs6cyYIFCygoKGDkyJH4fL5YfJ06V1hYSK9evZg6dWrU8zV1Dy+66CKWL1/O7NmzmT17NsuXL2f06NG1/v1ioaJ7CnDqqaeG/d5+9tlnYed1T0PmzZvH9ddfz6JFi5gzZw5er5fhw4dTWFgYrKPf06qpzD0F/Z4GmWKapmkee+yx5jXXXBNWdthhh5l33HFHjCKqv+69916zV69eUc/5/X4zPT3dfOihh4JlJSUlZmpqqvnf//7XNE3T3LNnj2m3282ZM2cG6/z111+mxWIxZ8+eXaux10eAOWvWrOBxTd3D1atXm4C5aNGiYJ0ffvjBBMzffvutlr9VbO1/T03TNMeMGWOeddZZZb5H97R82dnZJmDOmzfPNE39ntaE/e+paer3tDS1oABut5tly5YxfPjwsPLhw4ezcOHCGEVVv61bt46MjAw6d+7MBRdcwPr16wHIzMwkKysr7F46nU4GDRoUvJfLli3D4/GE1cnIyKBHjx6639TcPfzhhx9ITU2lX79+wTrHHXccqampTfY+f/vtt7Rp04Zu3bpx5ZVXkp2dHTyne1q+3NxcAFq0aAHo97Qm7H9P99HvaYASFGDnzp34fL6IjQrT0tIiNjQU6NevH6+99hpffPEFL774IllZWQwYMIBdu3YF71d59zIrKwuHw0Hz5s3LrNOU1dQ9zMrKok2bNhHXb9OmTZO8zyNGjOCNN97g66+/5rHHHmPJkiWcfPLJuFwuQPe0PKZpcssttzBw4EB69OgB6Pf0QEW7p6Df09JittR9fWQYRtixaZoRZRL4P9A+PXv2pH///hxyyCG8+uqrwcFc1bmXut/hauIeRqvfVO/z+eefH3zdo0cP+vbtS8eOHfn0008ZNWpUme/TPYUbbriBX3/9lQULFkSc0+9p9ZR1T/V7GqIWFKBVq1ZYrdaIzDI7OzvirwOJlJiYSM+ePVm3bl1wNk959zI9PR23201OTk6ZdZqymrqH6enpbN++PeL6O3bs0H0G2rZtS8eOHVm3bh2ge1qWG2+8kY8++ohvvvmGdu3aBcv1e1p9Zd3TaJry76kSFMDhcNCnTx/mzJkTVj5nzhwGDBgQo6gaDpfLxZo1a2jbti2dO3cmPT097F663W7mzZsXvJd9+vTBbreH1dm2bRsrV67U/YYau4f9+/cnNzeXxYsXB+v8+OOP5Obm6j4Du3btYvPmzbRt2xbQPd2faZrccMMNvP/++3z99dd07tw57Lx+T6uuonsaTZP+Pa3zYbn11MyZM0273W5OmzbNXL16tTlu3DgzMTHR3LBhQ6xDq3duvfVW89tvvzXXr19vLlq0yBw5cqSZnJwcvFcPPfSQmZqaar7//vvmihUrzAsvvNBs27atmZeXF7zGNddcY7Zr186cO3eu+dNPP5knn3yy2atXL9Pr9cbqa9Wp/Px88+effzZ//vlnEzCnTJli/vzzz+bGjRtN06y5e3jqqaeaRx55pPnDDz+YP/zwg9mzZ09z5MiRdf5960J59zQ/P9+89dZbzYULF5qZmZnmN998Y/bv39886KCDdE/LcO2115qpqanmt99+a27bti34U1RUFKyj39Oqqeie6vc0nBKUUp555hmzY8eOpsPhMI8++uiwqV8Scv7555tt27Y17Xa7mZGRYY4aNcpctWpV8Lzf7zfvvfdeMz093XQ6neaJJ55orlixIuwaxcXF5g033GC2aNHCjI+PN0eOHGlu2rSprr9KzHzzzTcmEPEzZswY0zRr7h7u2rXLvPjii83k5GQzOTnZvPjii82cnJw6+pZ1q7x7WlRUZA4fPtxs3bq1abfbzQ4dOphjxoyJuF+6pyHR7iVgvvLKK8E6+j2tmoruqX5PwxmmaZp1114jIiIiUjGNQREREZF6RwmKiIiI1DtKUERERKTeUYIiIiIi9Y4SFBEREal3lKCIiIhIvaMERUREROodJSgiIiJS7yhBERERkXpHCYqIiIjUO0pQREREpN5RgiIiIiL1zv8DcLYMbCCswQkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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EEHunvM5HZmh1TiNrM8EJgGpxYu7mpcXR9o9Jsr2I2+1myJAh9O/f3xCcxHPggQdSUlKCxWJhyJAhhq+srCwARowYwYoVKwzXNX0ebejQoTidTj744IO4xxvnnASDkW/y3Nxc+vTpw6ZNm2L60TipduTIkXz33Xd4PJ429UMIIcTeqajzkaHUtHjOdi0r/FizODq7SwmRAKUXmz59OpMmTeLUU0/l3XffZcuWLSxfvpw//elPfP311wBcc801PP300zz99NNs2LCB2267jbVr1zZ7T4fDwR//+EduuOEGnnvuOX7++WdWrFjBggULAMjJycHpdPLOO++wa9cuqqr06Hzu3LnMmzePBx98kA0bNrB69WqeeeYZ7r//fgDOPfdcTCYTF110EevWreOtt97i3nvv7eRPSAgh9l/l9T6ylOoWz/lSPSD8WLMmnkHvTBKg9GKKovDWW29x5JFH8tvf/pZhw4ZxzjnnsGXLFnJzcwE4++yzufXWW/njH//IhAkT2Lp1a6sTU2+55Rauu+46br31VkaMGMHZZ59NaWkpABaLhYceeognnniCgoICTjnlFAAuvvhi/vGPf7Bw4UJGjx7NlClTWLhwYTiDkpSUxOuvv866desYP348N998M3fffXcnfjpCCLF/q6jzkUFsgPK1Ooxl6jiKB5/Fq8HDIgcsPStAUbReODmgurqa1NRUqqqqSElJMRxraGhg8+bNDBw4EIejZ6WrROLk/6cQQrTPvLd/4ITPz2WsaRM+zYxNCVI8/RHMY88kO8lOUNU4+08P8LL9zwD4c8divfzjTu1TS7+/m9o/JskKIYQQ+5mKOh+ZoSGeC/1/ZGpRfy497Gx94QhgMSv4iMx1NPWwIR4JUIQQQoh9UHltZJnxbRccz8ChReHgpJEvKgxQbImVxehsEqAIIYQQ+6C6+hqcig+A4QMHgCV22qk/Kgww9bAARSbJCiGEEPsg1aNnTzQUsCXFPSd6iAdZZtw1euHcXxGH/H8UQoj2MXn1GiiqLRlM8X/de7WogRSrBCidqrG4WX19fTf3RHSExv+PrRWtE0II0YRPz6CotuTmT4nOoJhtnd2jhOxzc1DMZjNpaWnhuh0ulwulB5XuFW2jaRr19fWUlpaSlpYWLpkvhBCidZqmYfHXghWwtxSgRIUBSs/6ObvPBShAeJfdxiBF9F5paWmGXZOFEEK0zhtQcWt6BlpxpjZ7niGDovSsQZV9MkBRFIX8/HxycnLw+/3d3R3RTlarVTInQgjRDrXeAMmKHqCYHc0XRAsS9TPW1LN+3u6TAUojs9ksv+CEEELsd2obAiSjb86qtBCgGPSwDErP6o0QQggh9lqtN0AyocUidglQhBBCCNED1HkDJCl6BqWlSbIGPWyIRwIUIYQQYh9T76lnmukb/Umbh3gkQBFCCCFEJymr9fLFi7czyFSiN7R1iMfUs6alSoAihBBC7EMeX/ozN1heijRYW95j55nAsZRqaXDwJZ3bsQTtVYAyb948FEVhzpw54bYLL7wQRVEMX4ceeqjhOq/Xy+zZs8nKysLtdnPyySezffv2vemKEEIIIYDyOh87yYw0WOzNnjv76CH8OTCLD05YBu6sLuhd27U7QPnqq6948sknGTNmTMyx4447juLi4vDXW2+9ZTg+Z84cFi9ezKJFi/j000+pra1l5syZBIPB9nZHCCGEEMCu8iryKQPgI+VgGHlqs+dee8wwvrxpGr8+ZEDXdC4B7QpQamtrOe+883jqqadIT0+POW6328nLywt/ZWRkhI9VVVWxYMEC7rvvPqZPn8748eN54YUXWL16Ne+//37734kQQgixn9M0jYZdGzArGtWai3/2vwMsze+xoygKOSk9a5PARu0KUK688kpOPPFEpk+fHvf40qVLycnJYdiwYVxyySWGkvMrV67E7/czY8aMcFtBQQFFRUUsX7487v28Xi/V1dWGLyGEEEIYVdT7yfDtBKDG3Y+/nDq6m3vUfglP2V20aBErV67k66+/jnv8+OOP58wzz6R///5s3ryZW265haOPPpqVK1dit9spKSnBZrPFZF5yc3MpKSmJe8958+bx5z//OdGuCiGEEPsVbyCIAx8AfXKyIc3ZzT1qv4QClG3btnHNNdfw3nvv4XDETwmdffbZ4cdFRUVMnDiR/v378+abb3L66ac3e29N05rddfjGG2/k2muvDT+vrq6msLAwka4LIYQQ+zx/QMNKQH9ibn5opzdIKEBZuXIlpaWlTJgwIdwWDAb5+OOPeeSRR/B6vTF73+Tn59O/f382btwI6DsN+3w+KioqDFmU0tJSJk+eHPd17XY7dnvzs5CFEEIIAX5VxaqEFpz08gAloTko06ZNY/Xq1axatSr8NXHiRM477zxWrVoVd2O+srIytm3bRn5+PgATJkzAarWyZMmS8DnFxcWsWbOm2QBFCCGEEK3zB9VIBqWFybG9QUIZlOTkZIqKigxtbrebzMxMioqKqK2tZe7cuZxxxhnk5+ezZcsWbrrpJrKysjjttNMASE1N5aKLLuK6664jMzOTjIwMrr/+ekaPHt3spFshhBBiv1W1A759ASb+BpJyWjzVH9Cw49ef9PIMSofWtTWbzaxevZrnnnuOyspK8vPzOeqoo3jppZdITo5sVjR//nwsFgtnnXUWHo+HadOmsXDhwrgZGCGEEGK/9s9fQek6KF4Fv/5Xi6f6ojMo+3uAsnTp0vBjp9PJu+++2+o1DoeDhx9+mIcffnhvX14IIYTYt5Wu0/+7aWmrpxqGeMzWzutTF5C9eIQQQogeqrzOF3niar0UfSCoYVX2jQyKBChCCCFEF2ho8BBcdi+UrGnzNXe98X3kiTuz+RND/EEVG/vhKh4hhBBCJK7OG+D5e67G/NFf4e+Ht/m60l82Rp5YWi+6ti/NQZEARQghhOhkn2zcw6TAl6FnWpuvc9dujjzxVoPa8qa6egZl31jFIwGKEEII0cm2V9STrVQmdI2maUwNfh5p2LUGnpwCqtrsNTJJVgghhBBttmlPHdlUJXTNlpIyTjB/YWwsWQ0Nlc1e45dJskIIIYRoqx92VmFS2j60A/DuV2txK97YA1rLGRSbzEERQgghRGsq631U7dhobAwGWr3u8w3FAKhak410g744Z+v8ARVreBWPDPEIIYQQohlL1u1iirLS2OirafU6r1fPnlTjMh4I+pu9xh+M2s3Y0rs32ZUARQghhOhEn28qY7rpG2Ojt/UARVH1TImPJpkQtfnsi28fWsXToXvxCCGEEMLI4wsyyFRsbGxDgNI4lONr+qs63hCPvwG+fR537RBsyr6xikcCFCGEEKIT+QJRWY1GbQhQTEE/mMGvmSF6Gkq8IZ4v/g7v38a5JhdrKNDbenkGRYZ4hBBCiE7kC6rYQwFKjRaqBpvAEI8fC2d7b4kciBOg7Fj1HgA2tV4qyQohhBCidd6oDEqZlhJqrG7xGlXVMGt6oOHHwhfaCLaouaGDxgCl4j9X02fPZ+HnKdTrD2SIRwghhBDN8fsD2BR96W85yQxgF3zxJHgqoPBQyCuKvUaNVITNTkuGUj1QAfQMiqbBklth+UOkN7m2v6lUf2Du3at4JEARQgghOpEaiExqDRdR27ZC/wKYG1thNnq5cGZKEtMzcwn8bNYPBn2w7UtY/lDLLyxDPEIIIYRoVrAh/PA7dXDs8V3rYpr0gmt6gKJY7WQl2fATClDUALs3fhlzTYxePsQjAYoQQgjRmaIyKI8HT449/vgk+PEtQ5M/qIaXCytmGxazEh7i0QJeln/+KQCPBU7m6cBx8V9XMihCCCGEaFZArwirmu2cNf0wlgQPjD3n/bmGp76oXYkVsw2r2UQglEGpqvUwxvctAD+qhazT+sd/XcmgCCGEEKI5SlAPUDSzHZfNTANxMht71oO3NvzUULLebMVmNuHT9AyKY+mtDDTtAmDqEVOo0xzxX1gyKCLarspa6j68D4q/6+6uCCGE6AFMjZVfzTacNjP1zQUUz5+mr84hdlfi6AyKo74EgApLNoccejifqqOp0JJi7yd78YhGtd4Aj/ztZtwf/wWeOLK7uyOEEKInaMygWPQMiideBgVg+5ew4R1oqApVn40EKBazQqDJwtv3R9xJXqqLGlxM9j7Eid47jfeTIR7R6MfiaiaZYmdjCyGE2D9pmoYpFKBgtuG0WmggNrPxTOBY/cG/zoH7R6HWlxuGeKxmE77GVTwhg/oXYjYpHDcqj6TkVEq0JhVRZIhHNPp5d61xvwVfffd1RgghRLfzB7XIahyLA5fNTL0WG6B8ow6NPPHV4Nr+CVYlkkGxmU0xGZQDhw8E4PHzD2TFjdNQHRKgiGasL6klS4kquFO1vfs6I4QQotv5gpEy90oLQzw7tCzDc81Xb8igRC8zbqQ4M/T/Kgpmk0J6ktN4U1PvrsUqAUoH2lBSzSAlakvtyl+6rzNCCCG6nS8Q2ShQsdhx2sx4mgzxqBmDYwIUS82OyBwUix2r2aTvahwSsLjAYgx0Mtw2lgbHEtBMbDj8QVAUejMJUDpQdeUeUhRPpKFya/d1RgghRLfTA5RIRViXzYInaojHY8/EdP7L+JzZhutstTsMuxLbolbxAARsaTGv5fEH+Z3/9xzmfQjz2F91/JvpYhKgdCBToMmcExniEUKI/ZovoGJTQnMTzbFDPM5TH4KMgbjsxmyIvW47VoKh62KHeDRn0y0CYdqIXLzYOOqgsQzOjrPsuJfp3QNUPU2gwfi8IXYDKCGEEPsPXzBoGKpx2szURw/xWPV5I0FVM1yXtfsLzm78DR2qg+KPXsUTJ0C58qjBzBiZy6iClI58C91GMigdyBQqZxzmremejgghhOgRvAEVO6FCbRY7LqvZuMzY6gL0wmyX+66hWMuIvUk4QInkFMzuzJjT7BYzRX1SUXr53JNGEqB0oMZyxmESoAghxH7NWHDNjsVsMi4ztukByolj8nlbPYSp3vtjb2K2YrMohjkolqTYAGVfIwFKBzEU42nkq41/shBCiP2CHqCE5qCEVt144mRQbjjuAG47aSR+xUa15jLexGzDYorsxQNgcsXJtOxjJEDpIN6AikPxGdo0b3U39UYIIURP4Auq2BsnyVr0PXjiBShJdgu/OWwgRX1S2a2lGm/SZC8eIO4clH2NBCgdxBu11r2R1iBDPEIIsT/bsqOYqyz/05+EKrtG1zNpDFoaZbpt1NCk4Fp4iCdqXYsEKKKtvIEgjtBEqPCukpJBEUKI/daPJdWkffCHSENod+EpY6PK2tuNy4Ez3HF2IDbb9SGe6ABFhnhEW3n9kQxKhRJKz3lroXwzvHQB7Py2G3snhBCiqz369kpOMq+INIT2Z7v7nEPwXvwxXPZZOGhplJVkI2YNjmLaL4d4pA5KB/EGIuOMlUoaaDswBRuo//t0XL49sPtHuOqr7u2kEEKILrNry48Yoo0afSsURVGw9x0b95rMJBsKxpoo+GqxWRT8WvQQj2RQRBtFD/HUmiMTnFy+PfqDPRu6o1tCCCG6gaZp4I9UF9+hZcJhV7d6XabbzmvBycbGhiqsZhNqdLQjGRTRVtGTZP0WNx6/DWfUqh7NmRGbthNCCLFP8gVVHOilJzaZB7L1zPfoU5DT6nVuu4WFwWPZoWUxy/IeI83bSRl5Kha/CZcSVcpCAhTRVl5/ZJmxZnFQixMnkQBF8ZTrpe8dqc3dQgghRG9Tvgl2r4fhxxuaG3xq+HfAgPxsBh3QenACYLeaCGDhbfUQ3vYdzNBMO0vcmVhrGnATtRmted//9S1DPB3EGwhGttS2OqjVHLEnlW/u4l4JIYToLDUNfnhoPPzrHPjpfcMxjz+IM5RBMdlc8S6P6/AhWRwxNCv0TOGw4QUA2Mwm3EpD8xfugyRA6SC+qP0WlFAGpVGNFnq86aPu6JoQQohOcOWLUaszt35uOFbvC+BsHJKxutt8T6vZxPMXHcKXN03j9lOL+MOxwwEwmxTeC04EwJ86YK/63VtIgNJBojeEsthdvBw8kj1aCh+oE7gvcKZ+0od3gKeiG3sphBCio3y8YXfUM+PKG48/iIvGAKVJ4bU2yElxcP6h/XHb9aGcJLsF94AJXJX5D8yXf9beLvcq+/4gVheJXmY8ZkAuL9imMLvhPKaNyGHhm+uYY3mFNLUWakr2i8lNQgixX9FUw9MGf2RlJwkM8TRHURReuvTQ8OP9gQQoHcTv9ZAS+mZMS0nhyel6Ki6oavx35XZqyp2kKbXhQj1CCCF6u6isSZMAxeNTo4Z49j5Agf0nMGkkAUoH2LN+BWctORGzObSldlRlQLNJYXBOEvXloTbZ4VgIIbpebakeRCTnddgtDfuvaS0N8XRMgLK/kTkoe2lXdQP1/zwfsxaINDbZ/MltM1NPqM1X14W9E0II8c/lP8O9Q+G+4eDvuJUwjqhSEk0zKPW+QLgOigQo7SMByl568/ti+pl2GxubBCgum4U6LZRB8csQjxBCdDZN0/Avuw++fpp7Xvs6cqCutMNeI5whAQh4Dcca/MFIYbV2TJIVEqDstQ07ymIbm2ZQ7GY84QyKDPEIIURnm/vcW1g/+gu88XuSlagCZx04D9AZXdm1SXbc4wtGinV2wCTZ/ZEEKHupvPin2MYmu1O6bBbqaJyDIhkUIYTobF/9uCX8uIA9kQMNlR32Gs7oDEqTPz49fjVyXIZ42mWvApR58+ahKApz5swJt2maxty5cykoKMDpdDJ16lTWrl1ruM7r9TJ79myysrJwu92cfPLJbN++fW+60m2qq6piG+3Jhqcum5n6xiEemYMihBCdStM0kqPKwvczRQ3rNMT5mf3dS7BpacKvYwxQmmRQ/MEOX8Wzv2l3gPLVV1/x5JNPMmbMGEP7Pffcw/33388jjzzCV199RV5eHscccww1NTXhc+bMmcPixYtZtGgRn376KbW1tcycOZNgMNj+d9JNTPHmlDjTDE/dNkvUJNla+OF1+PD2mFnfQggh9p7HH9TLOoQUKlHzBJsEKLvWfASLL4XnTknoZ7KmaYYNYZvOL2yQVTx7rV0BSm1tLeeddx5PPfUU6emRomOapvHAAw9w8803c/rpp1NUVMSzzz5LfX09L774IgBVVVUsWLCA++67j+nTpzN+/HheeOEFVq9ezfvvvx/39bxeL9XV1YavniCoalhUT+wBR5rhqctupp6oSbIvnQ8f/01K3wshRCeo8vhJjQpQ+ipRGRRPZfihqmq8tGhh1LG2V/r2BzXDJFmtyRCPvopH5qDsjXYFKFdeeSUnnngi06dPN7Rv3ryZkpISZsyYEW6z2+1MmTKF5cuXA7By5Ur8fr/hnIKCAoqKisLnNDVv3jxSU1PDX4WFhe3pdsfxe6ChioaozaAMmuxY7LZZqA9tHlhXXhw5EC/VKIQQYq9U1vtJJTLk0leJnoMS+bm7rriaQ00/RI5V72jza3j8wcgyYkDzNp0kG12oTVbxtEfCAcqiRYtYuXIl8+bNizlWUlICQG5urqE9Nzc3fKykpASbzWbIvDQ9p6kbb7yRqqqq8Ne2bdsS7XaH+XJzOdvvn4r24Hgayncal5k1MpkNT102M3WhIZ6dP62KHNi0DJ6YAju+6cQeCyHE/qWy3t9kiCd6Dkql/t/vXmLnJ89xoLIxcqyq7XMhDcuIIWaSbIMvQBKhmisyxNMuCVWS3bZtG9dccw3vvfceDoej2fOaluPVNK3VEr0tnWO327Hb7XGPdbULnljGesePAGQ+MZorLQWtXuO2W/CEhniGEhVcrXxG/+/CmXDzzg7vqxBC7I+qPH7SojIoBUp55GBDJSuXvcGEjy5lBkD0r50EAhTDMmKImSRrrt1JilKPqpgxpXZz1r+XSiiDsnLlSkpLS5kwYQIWiwWLxcKyZct46KGHsFgs4cxJ00xIaWlp+FheXh4+n4+Kiopmz+nJshXjsMwQU+uBhctmpk5rPqDDLyt7hBCio1R5fIY5KNH8dRV898GLzVyYQIDSZIjH5K8DVa8mq2kazlI9M96QMULmoLRTQgHKtGnTWL16NatWrQp/TZw4kfPOO49Vq1YxaNAg8vLyWLJkSfgan8/HsmXLmDx5MgATJkzAarUazikuLmbNmjXhc3qybCoTviY6g9Kshp4x8VcIIXq7yno/acQPUKoq9jAF47D6JjW0P0+CAYphiAdgz3oAdlV7GezTH9v6H9zmewqjhIZ4kpOTKSoqMrS53W4yMzPD7XPmzOHOO+9k6NChDB06lDvvvBOXy8W5554LQGpqKhdddBHXXXcdmZmZZGRkcP311zN69OiYSbc9jS+gkq1UJnxdqxkUgJLVMOCw9nVMCCFEWKXHT6oSPzPtrd7DQEXP8h/nvYtxli00BE08YHsMauLPg4ynoekQD8Avn0POCFbvqOIA5RcALH3Ht+9NiI7fzfiGG27A4/FwxRVXUFFRwSGHHMJ7771HcnKkeNn8+fOxWCycddZZeDwepk2bxsKFCzGbzS3cufuV1XnDQzy7tdSY4R5d7DwaV3QdlOZ4yls+LoQQok2qPH4ylJq4x1K9xZgUvd7JKdOnUtQvi0efWagfTGCfHo8/SDJ67ZOAZsKiqPDLCpj4WzbsquEoJZQVT259nqKIb68DlKVLlxqeK4rC3LlzmTt3brPXOBwOHn74YR5++OG9ffkutafGF86gbKaAbOIEKKbYj9RsUmiwJLV8cxniEUKIDlFW7Wl2OD4pFFQELW4unzaCOm+A3ZpeGkKr3RXnT8z4PP4gBaHly++oBzHT/AWU6kuWd9d4yWr8AzYpu93vY38ne/EkYE+tNxyUbDf3jX/SsGPjNqv29LjtYVITRQghOkR99R5sSsuVydVQQU233UKdNQMApaEK/A1teo2KOh99QgHKWnUgAJpX/0OzvLaBDEJ/dLolQGkvCVASsLvWS04og7LLalw29kDgdO4NnAUnx88KWV0p+LUWhrC8kkERQoiOoFXrBTHLSQlnR2JEbUlic2fg1ULZ77rd8c9vYntFXXj58g+a/vtA8+h/aGaWf6sP+YAEKHtBApQE7K7x0k/ZpT+29yegRT6+seMO4fRr5oMrI+61aS47lbibv7kM8QghxF5TVQ1rvf5zutqaxR4tJe55pqif1VnJdvYQCmSqd0DdnrjXRKspK8au+NFQ2GYZAIDircJX8iO37bkOAL8tDczW9r+Z/ZwEKAnYvqeagYoemVcmDaKGyNr2o0YPYFB28/NMUl1W/C1N+ZEhHiGE2GtldT6y0Ots+Z05zS5QMLvSwo+zkuyRTMvTx8LfBsOS21p8nUC5vkqnwZlD0BEaItJU/rXwkfA5isXW3rchkAAlITUlP2FTggTMTvzuAqq1qOI7rRTiSXNaUVv6uL0SoAghxN4qrWkgtzFAceUyIrTcF2Cj2idyojMyLzA72U6QJkPwmz/W/7vzW6gri3kdc42+b4+a3AeH040vNISv1EaWKlvq274qSMSSAKWNNE3DXKbv2eBPG0yyy27IoGBtYfgGSHNZ0bTI/PCY+SgyxCOEEHuttDoyV7Daksk76kEAbHaPpSp6mD0qQMlKsvO1OsxwHy3QANu+hCenwhNHGo75g2p4V2RLcg5pbhvVoXuPNG3t2De0H5MApY3K63zk+fV9dKy5w0lxWBmgRBX1SevX4vVpLhvBqI97lTbYeELjEE/x9/DLFx3SZyGE2N/srvGSrOhLiUcMLOQx+0UsHXgtr416gCotKkAJreIBPYPycOA0LvZdx699N+uN/gZY+6r+uNpYYba0xosLDwA2ZzKpTms4oz5SiQpQTnmsQ9/b/kYClDbatKeOzFDhHUtqPnaLiVItDYAGV0Gra91TnFa0qOd/8P+OHVombwQP0Ru81RAMwLMn6V/e+EWGhBBCNK8hEMSOH4DU5GTe/9PpTJ11G86kVKqjs95RGZTpI3IZO6SQoqPPCQcaWsDDNxt/IZ7ahshOxYo9iTSnLXzvxvL3N7hvh/Hndfj7259IgNJG5XU+Uhp3x3SmMWlwJjcGf8f36cdg/937rV7vsJgIRE2S3aLlc5j3YR4KnA6A1lClR+kNlRD0tnmpmxBCiAhfQMUWClAwRyappjisWAlETswYFH6Yl+rgnxcfyjXThuJV9Gs0n4fi0l2R84ORa+t8AVxKqF6KzU2qy0q1Zhzmv/HX8WtiibaTAKWNqj1+UkJpQxxpHDook2fnXsOYa/6Lktqn5YtD/ui/BI9mo/jQW8NtNY0TbRuq+f77byMny5wUIYRImDegYmsMRCyRFTxOm5ldWlQZiIFH0pSiKGjm0DWBBpJDwzj6jSM/k+u9wXAGBXtoiAfjQon0XGOtLJE4CVDaqLohQGpjBiU0dumwtn3voLxUB99owyjyLiD/uOv426/GsGDWROot+h5Fiurnmy+WRi748kl49FAo+7mD3oEQQuz7fAEVuxLKoEQt8x2Q6eaJwEyeCRyL9/KvQIlf1F6x6DvPm4MN4cm2gCFAicmgRM1BAVBNVkNwJNqnwzcL3FdVefykNO6O6WimMmELDh+SxdVHD2FEvl406MyJenRtdSRT5k8mU6lhWH3UFuCr/qn/97t/wdF/2qu+CyHE/sIXVMNzUDDbw+1jC9O45ddHU9TnNOxZLay6tDpp3KR4YPRCiKhaVfW+yBwUbEmk2axsj1ohFLSnYmomABJtJwFKG1V7/FEZlMQDFEVRuHbG8Jj2FIeFbb5sMpUaDtVWx26GvPXzdvRWCCH2T15/9BCP3XDspLGt7yysRAUo4UwMGAKUOm+QnKghnjSHjdVacvi4Zk/8d4SIJUM8bVTdEDUHJWoPh72VnWxnu6avAGrcAtxgx9cQ8HXY6wkhxL7MFwxGJsk2CVDawmyxEdTiZD+aZFDcSmh+is1NZpKNMiIl9ZUO/B2xP5MAJVrVdnjrBv2riZp6H8k0TpLtuOi4MMPFdi2n+RMCDVC8qsNeTwgh9mW+QPwhnrZy2Cw0EKdEfZMMiht9OTG2JDLdNsOeP9Fl9EX7SYASzd8AXz6hz/towuepxtyY4ejIACXdxXYtq+WTfpFhHiGEaAtfQMUWZ5JsWzmt5lYDlKYZlHS3jbKoXZNNUTVWRPtJgBItKZTJ8FbrZYw9FeFDWqissWqy6ZOoOkhhhpNNWn7LJ239HDRNhnqEEKIVvmD8ZcZt5bCa8BLZgbiscW5JdAbFF8QdNQfFajZRFr1rcgf+Ebs/kwAlmj0ZLHrwUfXo0Wjzi6D0RwCU0DdnsIMnPxVmuPhajZ08C7BDyww9WAn//Q3cfwDUl3fo6wshRK+jabDuNSjfHHPIOMSTeAbFYTXToEWu+0kL1bmKqk1V3+CPBCg2ffVO9BwUTG0vQSGaJwFKNEUJZ1FSa39G8dXCe/q+DKbQGnjNntLs5e3RN92JDyslWmxK8IPggfqDulJYuxjqy+Cn1qvWCiHEvizw00fw7wvgoXExx7zRlWTbMUnWYTXjjRri+VkNZbjr94TbGhoasCiq/sSWpL9u9LBQMGr1j2g3CVCaSso1PNU2f0IwqKL59SXGJnvLuxYnKi/Fgctm5le+ufw3eCT3+M8KH/tQmxDewjvMlYEQQuyvVFXjyZffijT4PYbjAb8/Ejy0c4gnemPX9VpoI9iaSE0ULapoW2MGxUAClA4hAUoTXqdxwqoS9NLgbQinDJUOnH8Cen2UA/KS2a5lc73/MkqiSjGXpIyhlCaZFVXt0NcXQojepLrBz4bqyBwRSlYbjmtBb+RJO4Z47BYzTiL3+EkL1U6pjdqXx6dv5hqwuOIP5+SOSvh1RSwJUJooVdNi2rzeBhyhyj0ma8eXL/7rqUXYLCYOGZjBl9oIAIptA0hJy2BX06Gf6H98Qgixn2nwqziUqAUDO1Yajmv+qJ+R7RziadyRGAj/0ahFZVCcDfpwT8AR+YP2kiMGcrL3r3xUcAkcdHHCrytiSSXZJnaW19B0iyef1xOuKNjRGRSAUQWprLhxGqqmMfH2ciY3PMSVRx9E/k4vJdubBCgBCVCEEL3Pz+u+pv8HV2CZcj2MOav1C5rh8QcNGQ72bDCeEPoZqSkmFFPiv+KcVnNkAixQGvojUfFWg68ebC7cvlCA4o7UsLrhuAP4viiP0X3SwCJ/+3cE+RSj+AIqn1SkhZ83VhP0N0QyKJ21AVSG20a6S09H7iSLCcP6kpfqCP/jCAs0xLlaCCF6rm3l9ez41xwsZevhlUv26l4eXxAnURmUUAmIsFCWWTPZmt0QsCUOqwlXVICiOJLxNK7qqS1B0zTsXj1AMUXNWbSaTUzon4FNgpMOIxmUKMt/3sOTDdNxuQKszzyau0qvxIkPn8/b6QEKgNmkcN+ZY6n3BTggL4X8lDIUmsw5kQBFCNHLrNxawVCluvUT28DjD+KIGoKJrk8CRDIo7Zh/AhBQtcgkWyA3xUlpZRr9lVLY8ik1uMnQ9BpZtvTW9/YR7ScBSpQjh2bz4mVHUlpzCF9+vQ0fFpz4CPg8kXX1nTAHJdoZE/qGH+elOngoeBRnmD8hqXFr77YM8fjq4PPHYMRJkHNAJ/VUCCHaprLehy+q+Bm1uyEpu1338vqbZFAaKo0nBPVjWjv/mKz1BgzPc1Mc7KpMpz+l8NpsUoCrQr85LSm5sTcQHUZyUVFMJoWJAzI4YXQ+NrMJXyh+8/saIpOyOjGD0lRuioN12gCKvAt4KTBVb2xDgLJh0U3w0e3w2CGd20EhhGiDX8o9ZBGV6Shd1+57NZ2Donkq9WW9mr4VidIYoLQzg1LT4OcO/7n6k+lzyUm2U6qlxT85SQKUziQBSjNsFlM44g/4vJEMShcGKKP7pDJzTD7ZyY5wsNRagLJuZzU1P33WBb0TQoi22VleTYESKXQWM7E1AR5/EGfUKh6l/Ge0e4fByxcBYGpc6diOFTwAE/tn8FTwRCY1PAyHzSEnxcHu5gIUd/uyQKJtZIinGTaLCZ9mAQUCvqhJsp2wiqc5FrOJR849kFe+2U75K6H0aCtzUHZWekhGyiwLIXoGVdUYseX5yGarEDuxNQEeXxAXxj/UFE85rHkZ7YwFmFT9Z7XSzgDl5LEFKAqML0wHRSE3xR67WKFR+oB2vYZoG8mgNMMelUEJ+r04lPaXTt77vpgjm1e1kkExmxVUTf63CiF6hrvf+ZHfqi8bG5vOG0lAQ0A1zkGJ4vM17FWZe9CH+k8Z14d+mS4AcpIdcYd43sy/EnJGtOs1RNvIb7Jm2Mwm/KEEU9DfgD28iqfrMiiN7BYT3sZlbq0UalOAIIkvrRNCiM6wZmspyYpejv6JwIl6Y/TKm+UPwxdP6I83LYMf39Qfb/8a1r8dc7+GpsuMo/irSsPD8aYO+mMyN8XOrqYVvYG1fdtfy0W0jQzxNEOfg9IYoHhx7GVUvjfsVlOb56A0+FVcEncKIXqIYGgHdlUxs0MLVV4NBSg/bd7CkPf+pLeNPBWeO1l//IdN8I9p+uPLP4fckeH76XNQ4v8cDFSXhDMoSgfNF8xNiZ9BuWiKZE86m/wma0b0JFk14I1kULpwDkoj4xBPy3NQGvxBw0ZXBAPxT/z2n/DJfR3UQyGEaIZHrxkSsCZTpYU21muohNX/5Y6nXgif9t5//h5+/Mu65ZHrS7433s4fjPw8bkKtKYlU/e6gPyazm1nFk5nU9X+s7m8kg9IMm9msT5IFVJ8XV7fOQTFFtvJuJYPi8QdJig5QfLXgTDOcU1nnJe1/V+hPhp8otVKEEJ1C0zRMDZVgAc2RTnVtKEDZ/DFs/pgnrZEJ/dlbXgv/ybz4fy9zTeNvpyZLkmMqyUa/Xs0u8tADovbWWWnKYTVTSVKH3EskRjIozYge4tGiMyjdMQfFasKrtS2D4vEFMUdXn337Bn2MN8qsv38QdUFFR3VTCCEMGvwqblXf+dfszqBGM/78tCrB8OPxpp/Cjw9W1kdOKlljuMYbiAzxvBE8hF1aGl+rwwDwV+2ir7JbPzFtQEe9DUDhGt8VlGt6oOLrM6kD7y2aIwFKM4xDPL7IHJROriQbTyKreDz+oGEnTr5/Cd77E6iRHwQVeyK7cuKv78iuCiFEWKXHR5pSC+gBSsCW0qbrxph+jjzZ8TV4a8NPozMo9wXO4kTrP/hc1eeoNFTspDAcoPTrgHegUxT4n3o4B3kf50LfHzCd+68Ou7dongQozTBkUIJdsxdPc+yWRCbJBiN9NRyIzJpPpybSLhkUIUQHCwRV6v93Hc63riYNPbhQnOnUKu42Xe9uutfO10+Hn3r8QRyhOijPXHokfztzLLu1VADUmqgMSnr/DngnuvfmHMlRw7MJYmapOh6Lu5m6KKJDSYDSDLvZhD80BwW/NzzxqjsCFIfVHJ6DokUP8ajBcHnnRvpfF3GCmKhAJF2J/DVCaIa9EEJ0lJv//SWub/9B2vp/M8kUmkPiTKe4ofny87/x/YFPg6MMbUuCB+oP1vw33Obz+bCFhoYG5GaRk2wPV3q11hWTr5TpJ3ZgBmVobjJPX3gQt8wcyXO/PbjD7itaJgFKM/SlvaFhlaCv2zMojUM84QCleifcMxAW/w6+ewm+fAqqizlg1xukKHGGbUKVG72BYPgvGr1dAhQhRMda9l2klP1BptB8Emc6dUR+fm5vXHIMvD7wFoYefgYejD9fXw9O1h8Ufwd1eql8xReVAba5yU62hzMoBfU/YlFUgiYrJOV15FtCURQuOnwgRw6T8vZdRQKUZkRvFkjQ12W7GcejF2prDFBCgdK2L/XU5/cvweJL4a3rCT5+GL/adgd5Spxhmwa9rbLeT4YiQzxCiM6TptSFHzcWacOZDlFFJP8ZmE5laNnxScefSLrLRh3GVZI7lFzWqaGhmk1LAXB59SEcvy0NLHYy3XbKSAMILxDwJg8Ak/x66+3k/2AzbFFZC8UwB6XrV/FYzCYCplA2x9+Apmk8+eHamPPMnrLmb/L1M+Cro6LeR1p0gCJDPEKIDqRpGunRP2MaOdO5etpQPgyOI2B2UTLwdI7y3sdbkxZBzgjcdjP1mjFAGVSQwxdqqAxC8SrY8hlZ9foEWr9L30nYbFIIuHKMr5U9vKPflugGUgelGTZLpNS9JVCPqXGjq26ogwKgmUOvG/RS5fGzvWQXjSNQbfLjG/DeLVSMuJn06CGe1f+Gn5bABa9CwbgO7LEQYn/U4FdJpS72QFI2vx8zlN0HvYXFqXG32cUXm8s4dFAmAC6bhcomQzwHDingu52F+pPlD8Pyh5kbOqYk50ZunZJGbbmDJEUfAnfmS5XXfYFkUJphM5vChdrswahf6N1QSRYiAYoSaKCmIUAS+j/ElwJTWau2cbb61wuorPcZJ8mCPszz7//ryO4KIfZT0UuLDdzZKIpCTpob7EnYLCaOGJqN1az/GnLbzNQ3GeIZ2jeXDWrfuK9jS+8Tfhw9DwVAkeKT+wQJUJoRXQfFEdT/GtBQwNz8LPROFZqcqwQaqG3wkxQa163FGSkf3QYV9X7SiJN+rdzaId0UQuzfKuv9xixtI3fLk0tddgv1mjGDMqgghw1a/ADFnBKZBJuT3CSznTsK0ftJgNKM6CEep6r/Y1PNdr1iTzdQQpNzFTTqPfUkoQcoIwf0oQZXm+6hmaxU1PuMNQaiNVR3SF9pqILnToGnj4N/ngU1uzrmvkKIHq+y3k9qvAyKK7PF61xxMigZqak4ktL5Wc2PvSA50paVZOcztQiAjXknQo4M8ewLJEBpRnRxNKeqZ1BUc9ev4AmzRoKQ+vq6cAZFcSS3OUBBDVBX78FFM+Xy92yI354ATdNYsuBWfcb9L5/Dxnfhk3v3+r5CiN6hst5nLGXQyNzypDmXzYwnKkAJmp1gMjE8L4mr/Fcz23cVP6hRtU2SInNQhuclcXvgfC6y3k3GeU8j9g0ySbYZNnOkvLxLCw3xdNMEWQCL1U5QUzArGp76WpJDGRSzM5lqLRKgPBs4hm1aDlu0PA4zreE3lnfDxxQ0Uqt+wK00E6DUlMRvT8DPu+uwlnwL5qhGb5wfVkKIfVKlx09GvAxKK9w2C3VRQzyq1YkZOH18X+7eVcsPNf25gCWRC/pMCD88ZWwfBmQexQF5KTht0T98RG8mGZRm2CyRSbJuLVT4rBuKtDVy2Mw0hKrJ+urrwkM8VmeqIYNSMOAAtgz7Dcee/hvuNf2W4Q0LeTBwWvj47zZcQkq8GfYAtSXw4R3w98PbXR9l6Y8lHGT60djoymjXvYQQHccXUPnmlwqCqtb6yXuhobKUSabYMgitcdmbDPFY9J9rZ0zoy5c3T+eyKYN5LjCDGs3J64NuhbTC8Kkmk8L4fukSnOxjJEBphsMamYMSHhLpxgDFaY2kP70NdSSFqsVaXalUR+0Qesykifxj1kGcObGQNX8+lo9vOp7NRdfwB/+l4XNSQsNDDwVONbyGWl2Cf+ULULIafv6oXf1cvX5j7BwXb5xJuUKILnXr/9Zw+mPLeeazzZ32GkFVI3P1E6QonnARtrbSMyhRqyRtxqHrITlJvKFOYoz3KQZOu7gjuit6OAlQmuFsMh4KdEsV2UYOaySD4vVEMij2pDTjHJSovyoURSE3xcH1xw7nP8GphmV4AE8Hjmem93YeCZwCwPc//ohaG9poa/vX8NMH4G9mOKgZvrJfYhulWq0Q3W7RV9s43fQxAz+8vNOGXT/euJvMSj17su6Aq7ncd41+YMbtrV7rtJrZSaT8vdJkSP34ojzOOaiQpy88hKI+qU0vF/ughAKUxx9/nDFjxpCSkkJKSgqTJk3i7bffDh+/8MILURTF8HXooYca7uH1epk9ezZZWVm43W5OPvlktm/f3jHvpgPZzCa8ijEgMXVjgOK0msPl7gPe+khBoqQ0aqLmoJAau0FW33QXx43Ko1xLNrS/eOV0Ljn7dLZpehVG364NkU0RVzwKL5wOH/wlfof8DTGrflRVw1K3E4Bv1CE8FzhGPyABihDdqt4XQEHlftvfmaatgB9e75TXWb2tkhEmvWTB5CnHctp5V7D14nUweXar15pMCg3OyMocU32p4bjbbuGuM8Zw1AE5TS8V+6iEApS+ffty11138fXXX/P1119z9NFHc8opp7B2bWS88bjjjqO4uDj89dZbbxnuMWfOHBYvXsyiRYv49NNPqa2tZebMmQSDwY55Rx1EURSCVmOKUemmIm0A9qgMytDd75GtVAHgTkmnOpRBCZod4M6Ke31Gko1KksLPVZONkYVZjMxPoTS0E+gIJU72Y8WjsW2aBs+fBvOLoDbyQ6Ry6/c8bH4AgIGDR/CBqu9EqjVUJvJWhRAd7KstFYxVNkUaWllR0147t28iQ6lFVcyQPYIZo/Lo37dP6xeG5KRGfsaaaqU8wf4uoVU8J510kuH5HXfcweOPP86KFSsYNUovjGO328nLi7+LZFVVFQsWLOD5559n+vTpALzwwgsUFhby/vvvc+yxx8a9zuv14vVG5jVUV3dQvY5WaFYXjXsEAphs3RegRM9BmVT+arjdnZzGFjUPVVPwZo/G1UydlgyXzZBBUa0uTEBOsiMcoIQ39WrFms/fpuiX5fqTzR9DSgHq/64io/zn8DnJuf2p+lEfg9bqK+ie6jFCCIAnP/6ZqeZVkYagv9lz94ZWov+x6kkZiLsdGeecZDtIwlWEtHsOSjAYZNGiRdTV1TFp0qRw+9KlS8nJyWHYsGFccskllJZG/sJeuXIlfr+fGTNmhNsKCgooKipi+fLlzb7WvHnzSE1NDX8VFhY2e26HshoneSnduYrHaqJBi/2rx+rOYMiwkfwu5VEs573U7PUZbhsVUQGKFnpvKU4LNea0Fl7ZGFpomsa6t/4efu4vXsuuf12FKSo4AbDYk/BaU/QnMsQjRLcpr/Px2U9ljFWi/o36m1nJtxc8viD2Gj0La8ke2q575KbYeT0YmhbQZ2JHdU30UgnXQVm9ejWTJk2ioaGBpKQkFi9ezMiRIwE4/vjjOfPMM+nfvz+bN2/mlltu4eijj2blypXY7XZKSkqw2Wykp6cb7pmbm0tJSfM1OG688Uauvfba8PPq6uouCVIUmxvqoxq6YSfjRs6oIZ5Gn0x7lSNMJhb+5iDgIJQWqtxmuG1sJ2oOik0PUBRFwerOgGaKy2pmmyFEWbm1ggNNG8PPf1m3gr6erU3jGCgYDw4P+MDkr9P/YuuktLIQonn1vgAARaYtkUZ/27KlbXuBcrA42FYRpK+iT7K3Zw1q1636Z7q50X8xq9Qh3HLWTR3XR9ErJRygDB8+nFWrVlFZWcnLL7/MrFmzWLZsGSNHjuTss88On1dUVMTEiRPp378/b775Jqeffnqz99Q0rcVfrna7Hbu964ukmexNlsl1Y6E2fRWP8fXTM/XJYi19do0y3DZWa5E5KIo98tjuTMLbYMGuBGKuU4JefZmwXQ9uPv9hC1cqxeHjAys/D+/0XKKls0IdgW3kiZww7FjMrmXgC53oqQQtCPVlsk+GEF0oqGpkUxGetwZ0XIBSWwoPjYfsA9h2+IsUKqGMeXobNzBtYtbkASxZt4uU4XMgte1zV8S+KeEhHpvNxpAhQ5g4cSLz5s1j7NixPPjgg3HPzc/Pp3///mzcqP/FnZeXh8/no6LCmPIvLS0lNzc33i26ldXuxK9FFf7pxkmy0YXaGo0aUNDm65sO8ZiiApQ0t41qIsHYsuAY48XVkYCkZss3mBQtXHvFhB6c/KJmc27yQhxnP8NRZ1wOikKK20F5Y1B07xC4bzg8PlmvsyKE6BL+oMYoU5PNQP318U9O0PdvPA6+WtjxNdvL68IZFNJiVxO2RZLdwqtXHsY109s3RCT2LXtdB0XTNMME1mhlZWVs27aN/Hx96diECROwWq0sWRIpV1xcXMyaNWuYPHny3nalw7msTSsbduMcFEvsHBTFntzM2bEy3DbKiQ5QIo/TXFYqo7IrS9WxHNzwaGSDrootoGlomoZl9zoAvlBH8osa2Z00u99w3v39kRxXlBeu5jgo280KdWRsZ1b/p8397iyaquJ/6Tfw6KH6cukP/gp/P0JPVwuxDwmqWmz1aN/eByjeQJAdaz8JP9+zp5TCcIDSvgyKENESClBuuukmPvnkE7Zs2cLq1au5+eabWbp0Keeddx61tbVcf/31fP7552zZsoWlS5dy0kknkZWVxWmn6aXWU1NTueiii7juuuv44IMP+Pbbbzn//PMZPXp0eFVPT6LvrhkVlHRnJdmYjbTsYG77CF2G28b36uBIQ2XkL6pUp42qqAzKtIPHUWnJZI02UG948UxY/Dt2VXtx+/QfQKb0fqzTBoSvsWcPxGo2fjtNHpzFx2qTbAxA2c+xbV3liyfg37NY9dgFWH94BXb/wPblL+kbGpZ8D2tf6b6+CdEJAqqKhSZlHDogg7JyawXjTJF/yw27t5CmhAKhtC5ayCD2aQkFKLt27eKCCy5g+PDhTJs2jS+++IJ33nmHY445BrPZzOrVqznllFMYNmwYs2bNYtiwYXz++eckJ0f+Wp8/fz6nnnoqZ511Focddhgul4vXX38ds7nn7aHgspup16IyKN1ZSdZiHOJRrUktnB3neqsZjzVqcnJUsKVnUCIByuEHjuX304fxQfDAyPnfv8S2klJyqAQgJauPYWdRU5x5JYcOyuSd4EHUaE2Gxn75PKG+d5TPf9oDb98A615l/J43wu1Jn0ZVuazY0vUdE6ITBVUNi9I0QPHw5eZyPnriWvyvXq3XNkrQZxt2kRO1Jtheqe+GHrAmheesCbE3Epoku2DBgmaPOZ1O3n333WaPN3I4HDz88MM8/PDDibx0t3DZLD1miMdpM9OgRQIUzZrYPhcAJ4zO56hv7+P/Wf7FsSfeF25Pc1qN5fJT8jlyWDKPvzMGv2bGGvrhZvrp3fBEu/TcQpb+kMPV2iusd09g5IGzYl4vw23jjMPHMPrTf7DFcV7kQH1Z567q0TRQAzH3f/rD75kU5/Q0tTLypPj7zumTEN3EH9SwoBraVm8p5uyvP2OzYwEUA5N+l/Dk9R07d2BWIoFNRp2+x0/QlZP46gsh4pC9eFrgtPacIR6H1UQDUb9w2/EXyq0zR9Jv6BhKT3waCsaF29NcVmMKOCmXkfkpVJPEr303s0XVJzBP+Op6ppq/A6Cw3wDK00dzdtq/SLn4tZiNvRrdMnMkg7LiZHs8lQn3vy12VnpY/8QFaH8bAjXGpev+mj2tXq8Vf9exSzCF6GZBVcOCcYWeWl1MElH7bAUS23MLIBg1eR6gT0CvgaIk97wFD6J3kgClBS5bkyGebg1QjMuMo5cJt1Wqy8qzvz2YCw41TmBLddpwRJfMNVtRFIUHzxnH19oBXOb/fcy97GkFLL3+KP4z5zj6ZrSczUl327jOdxklWtQQUyeVv79m0bcML3kdpaESPrnPcEzxlMWcv0YdAMCPaiG7tDT9uo/u6JS+CdEd9DkoegalcaL9WNMm/mG7N3KSqsa7tEWmOuNeOUOUHQBYUiRAER1DApQW6JNke8gcFKtxiMfk6Lgx3hSnBSexf0GdMq4PH10/lU2mAVzju8J4MCkXs0lpUw2WP588ig8d0zjU+yjbtdBeQQ1VLV/UTl9tiVrCvvvH8ENN0zA3xFa0Pcf3Jx4K/oq/Z97AXH9omGrt/zqlb0J0h0BQwxzKkEYP5R5iivz7SLSybFDVsDcYM5KDTXpGxZQkAYroGBKgtMBlsxgLpDrSuqknerAUvUzY3IEBioLC08HjAVCHHW84NjDLzbzTR/OZOtp4UTObEsZT1CeVb245hlPGFVDVOBm3k4Z4kh2Rydba7vXhx/W+IMlq7B5Ot55xKGMvuIsrzzmNz7TQe6z6RZYbi31GUNWwhgKUai3+UCy+xAKU8jofWVozW1gkZcdvFyJBEqC0IDfFQTq1kYb+h3VfX5IdrIpeJtyBm32N75fGL1lHMrf/QkxnPRdz/ORxBXjtmXwQHA9Ag7sPmBJbdaUoCqlOayRA6aQhniFpkX4ptbvAq///q6j3ka7Uxpx/1kGFTBmWzdDcZLKzstkcmm9D8apO6Z8QXS2gRjIotTRTbDLBAGV3jZdspVK/v9bk14hkUEQHkQClBYcNycR/2HXUJQ1APW9xQnVHOprJpFBBSqRh19oOu7fDambJ749k7m9OA4st5rjVbOKYkblc6r+WW/wXUnr0/Ha9TqrTGqm30kkbCFoDTX7Qvj8X/B4q6vykKzVAZBxeKzrDcOqYvmmR2i/bV3ZK/4ToakFVDS8zjlny38gXG7zH5ffArrWUVnvICq3o26j1NZ4jAYroIBKgtEBRFI487le4r/8O09Cju7s79M90RWqPDJraofdubS7J3FNGMeWAfNb0OYv8cce06zVSnVaqOzmDonlrjA1fPQVfPKFnUNCPvWg9jfsHPoFyymOGU0f3SeUztUh/snIhBHwI0dvpy4z1AGXUoGYKqHlr9T23WvPvWfD4ZKwb3yIZfbXb4uBhlGppAHgsKdD3oI7othASoPQmRw3P4QLfjfzFfwFMu7VLXzvFYeXpCw9i8RWHxVSMbStDBqWDJ8mqwSCB/83hAv+/Yw9u+xL3+v+GJwX+5piJXDvrnJhJz5MGZ7I4eDi7tDSo3g6blnZoH4XoDvoyYz1ASU/PjH/SklvQ5hXClk+bvc8vZfWwUa91NWTjP3AreoBSMGgk5/pu4llmUnrWm+DK6Ng3IPZbUk+nF/njcQcAcNzo43rlD4FUp5UtnTRJ9u7Hn+TGPc9wcuj5JjWfnVoGh5vXwvo3mcCb4XBcccX/IT0iP4UDB+WzatsQjjV/bdgOQIjeKqBGFWpzpjd7noIGiy+H38ffzPMP//2Ol0KP7d4KkkL/oC44chS5B49kbOH/UZDWfRuqin2PZFB6EafNzNyTR3HwwN4XnEBoiKdxmWMHDvHUeQPUFG80tPXrk8+9gbPjX5Cc1+y9pgzPplgLfb41xc2eJ0RvEQiqkUJtVhf/nvyGYWuLaFoL3/M/747M73L6K8KF3syOFI4fnS/BiehwEqCILpPqil7F03FDPBt21TBQMf5gNTuS8Sb1iX9Bcn6z98pPdbCrMUCp3tlRXRSi2xgyKCYLSsaAcIHCphTV32zRtkx3ZAK9Xa0PD/HQjqKRQrSFBCiiy6Q6rdSFtg7QvG1cNdAGP5bUMFzZZmhT7Mm40vPDK3YMWlhlkJviiFS8lQBF7AOi56BgthjngsVTsTluc6bb+G8pjVBGxSYBiugcEqCILuO2W8J7G2ltXdbYBj8WVzPEtMPYaE+mb4aLHVqcgnIt/MWXn+qgBD2D0lK6W4jeIhC9m7HJwtjCtMhqunhqS+M2OxRj7SVT40aBsnOx6CQSoIgu47ZZqNMaMyiJFYaKa80rsOJxfimrI4smQ0b2ZEb3SeV/wcSK6+WmONglGRSxD9HnoDQGKFZyUxwcNWZg8xfUx+5ZBaB5YysxA5JBEZ1GAhTRZcwmBb8lNEk2wcqVTS38dBP89zfwzv9jStkibErQeIItif+bNIDvB1/K1b6r2nxfh9VMgyMHAMVXC798AcFAK1cJ0XNFV5LFpC/czBt7LJrFwY9qnLoo9WXw3Uvwv6uMFasbYrOeQbOjWwtYin2bBCiiSymhv7ZM/lrQtHbf58G3IpVeL6x7OvaE5DxsFhPP/OZgMg49l91aapvvnZqWESmI9/QM+Kx9lXOF6AmChkmyoa0ghh+HcuMO/jNkXvi8Taq+uq1sTwksvhS+fR5Wvagf1DTwxRZyU62SPRGdRwIU0aUUuz72ragBCLa/UmuBrT6mrUxJ427L5awu+iOMO1d/HUVhRH4yl/nmsE3NhrNfaPXe4wpTeS04OdLw4e2w4b1291WI7mRYZmyOmuhqtnDL/50Es95g6bFLWKJOAODdL6O20SjbqAcnz5/GM77rY+6t2VqYyyLEXpIARXQpxRY1oW4vhnkyiB0PT8su5I9/uovRv7rJMHHvjAP7Mu6w49jw689gxEmt3vu4onz+E5xibHzxTChZ0+7+CtFdmi4zjjHwCBy5g6nQ9H8zWb7tkWMN1ewo3gmbPop7b8WRErddiI4gAYroUk6HLbL0t50reQJBFZsvdrNBkzt+hViL2cQtM0cybUTbNjGbNCiTKnMax3vnGQ/88nnCfRWiu+lDPKEMiinOsnv0Gifl6AHKMCUqQPnmWf799780e29FaqCITiQBiuhSbrslXAuFdtZCqfT4yVBix8OVZgKURNksJr6++ZhIRdlG27/ukPsL0ZX0ZcZN5qA0ke62hTMoA0y7DMd+b1rU7L3NkkERnUgCFNGl3PbIUuP2DvFU1EV2JjZoZo+d9kh1WXGnZhsbt3/ZYfcXoqsYlhmb42dQ0pxWKrTEsyFKC3v7CLG3JEARXSrJFpVBaaiCZX/Tl/I2+vaf8MjBUL6p2XuU1/nIUPTsy0fBsZEDWvwS3e1lbrJrs9ZMASsherJAdCXZeHNQ0IdBq2xtGwL9IXpp8qApzZ8oxF6SAEV0KZfdHK4my5dPwke360t5AY83AP+7Avash4/vbfYeFfWRDMo666iom8epGrsX6n3G+ieKr7bZfUqE6KkMpe6bmYMCcNOvZ3CN74rw841qH3xa7JDQ08HjI0+GHtth/RSiKQlQRJdKihri8W/+LHJA03jy+YWG580pr4vMQTlnyjhuyf87xcNnweS2F2RrC5vZxAW+/8eS4IRIY5xaEEL0ZMZCbfHnoAAcdUAOP2Qfxyp1EACrkqdSinEIZ3HBtbwWnMwX6gGsHfB/0EHzvoSIRwIU0aWiJ8ma/FFzUHZ+yzXbr4s895Q3e4+Keh95il6OOzO3kL/+7tfk//qhDt8T5J5fjeUby3gu8V+HVwulxr0SoIjeJRBUsbYyB6XR/WeN4ypu5MMD/syKggsoiZoofpt/FhN/9QeOGtWPuZn3MuDXUsBQdC6pUSy6VPSGgWYlkiX5cckCDog+sfKXZu+xo6Ke/kpopUHGoE7ope7woVmsnnss5zy5gtpiJ3ZqoKEa2l6UVohuF6/UfXOK+qTy8dwzMZkUvnj7h8jO3sDlJx5CXoaLv18woYU7CNFxJIMiulSS3UxlnNUC9s3vA/CNOgQArfKXZod5ynfvJEXxoKFA+oBO6yuAyaSQ7LBQo4X2EJIMiuhlgqqGVWl9Dkojk0kBID/FwRYtL9yeN/zQTumfEM2RAEV0qaE5yXymjoppH6iUAPCJOgYITUitjzPMo6qcs0tPLfvc+WB1dF5nQ5IdFmpx6k8kQBG9jD+oYW66F08bDMtN5snATG7wX8KLh70DmYM7qYdCxCdDPKJLjchP4TvbOOo1Oy7FG3O8Ju0ANlfnMtC0C777V8zE18WvvsRp6goALJbW/xrsCMkOa1SAUtUlrylERwmqaqvLjOOZNDiTBy6cQiB4JEcdkNNJvROieZJBEV3KbFIYOyCP+wK/YpU6mA+D44zHc4bzWPAU/cnnjxqO1TT4+Wzlt+HnpqHTO7u7ADLEI3o1Qx2UVibJRlMUhaMPyGXGqDysZvlVIbqefNeJLnfBpP7823oKp/r+yn2Bs8LtAcXGqUcfwbvqwXpDzU5DQLC7xmuoIKsccW2X9DfZYaWmMYPSELtJoRA9WTCBSbJC9CQSoIgud9TwHFb+6Ri+veUYtmqR1HFDcj9G9M2kIDeXstC+IJRvDh8vq/ORpehDLNXjLoXUvl3SXz2DInNQRO8UCGqRZcYSoIheRAIU0S1sFhPpbhuKPbLZmDVJr7nQP9PFL1qo7HZFKECpLyfpmycYEFpenJJV0GV9lUmyojcLqKpkUESvJN+tolt5/EGw6Y9tQ6YCMCDTzdYNOYznJ6jYAsCel65kxNa3GNG4CMGdHXOvzpLisFIbnoMiQzyidwkGVWxK4nNQhOhukkER3e4M7208GTgxPKekf6abrY0ZlPLNfLpyFVlb3zJe1IUBSrLDEpmDIgGK6GWCwWDkiWRQRC8iAYroVo+edyArteFkn3EPWPUgYECmi5/VPvoJ69/i8Nfj7JjahQFKUvQcFJkkK3obNWrTSwlQRC8i362iWx07Ko+NdxxvWMY4NDeZT9QiVE3BVLsr3P6Lmk0/0279SZdmUKzUIMuMRe+kqf7IEwlQRC8iGRTR7ZrWWMhOtpOf35dvtKHhtst91zDTdyf1mh2PPQeScrusf8kOC7WhDIoqAYroZZToDIrMQRG9iAQookeaOjyb+YEz+EI9gGdcv+XeW2/htEkjOcXyOLW/+Qgsti7rS5LNQq2iZ1C0BqkkK3oXLShDPKJ3ku9W0SOdd2h/vt4yhScd05h78ijcDitzTx4FJ49CUZQu7YvJpKBa9bosimRQRG8TyqBoirnL/+0IsTckQBE9Up80J/++bJKhrVt/uNqTwQemgAeCfkmVi96jMUAxWZDwRPQmMsQjRBuYHJGCcpT+0H0dESJRjXNQlLbvZCxETyABihBt4HI68GiheS9PHAGV27q3Q0K0kakxgyJZP9HLSIAiRBskOyx4iJqYu+XT7uuMEImQDIropSRAEaINkh1WMpTaSEMXriISYm80ZlBk3pTobSRAEaINkh1N5pN7KrqnI0IkSNNCAYosMRa9jAQoQrSBL6AaG+rLu6cjQiRAVTUs4QBFMiiid5EARYg22FnlMTbUl3VPR4RIQEDVMBMKrs0yB0X0LhKgCNEGBw/I5Eb/RZEGyaCIXiCoalgUfTdjRTIoopdJKEB5/PHHGTNmDCkpKaSkpDBp0iTefvvt8HFN05g7dy4FBQU4nU6mTp3K2rVrDffwer3Mnj2brKws3G43J598Mtu3b++YdyNEJ7n0yEFUjzqfP/gv1RskgyJ6Ab+qYkEPUDDLHBTRuyQUoPTt25e77rqLr7/+mq+//pqjjz6aU045JRyE3HPPPdx///088sgjfPXVV+Tl5XHMMcdQUxMpDz5nzhwWL17MokWL+PTTT6mtrWXmzJkEg8GOfWdCdCCnzcycaUMp00IF2zySQRE9XzCohQMURVbxiF4moQDlpJNO4oQTTmDYsGEMGzaMO+64g6SkJFasWIGmaTzwwAPcfPPNnH766RQVFfHss89SX1/Piy++CEBVVRULFizgvvvuY/r06YwfP54XXniB1atX8/7773fKGxSio6S5bFRo+p48mmRQRC8QUKMCFFnFI3qZds9BCQaDLFq0iLq6OiZNmsTmzZspKSlhxowZ4XPsdjtTpkxh+fLlAKxcuRK/3284p6CggKKiovA58Xi9Xqqrqw1fQnS1NJeVCpL0J3USoIieL6CqkUmyMgdF9DIJByirV68mKSkJu93OZZddxuLFixk5ciQlJSUA5ObmGs7Pzc0NHyspKcFms5Gent7sOfHMmzeP1NTU8FdhYWGi3RZir1nNJjS7PsSj+OtAVVu5QojuFQhqWGlcZiyreETvknCAMnz4cFatWsWKFSu4/PLLmTVrFuvWrQsfb7rjrKZpre5C29o5N954I1VVVeGvbdtkHxTRPSzO5MgTf133dUSINghGLzOWIR7RyyQcoNhsNoYMGcLEiROZN28eY8eO5cEHHyQvLw8gJhNSWloazqrk5eXh8/moqKho9px47HZ7eOVQ45cQ3cFqcxHUQsG0TwIU0bMFVA2rIqXuRe+013VQNE3D6/UycOBA8vLyWLJkSfiYz+dj2bJlTJ48GYAJEyZgtVoN5xQXF7NmzZrwOUL0ZG6HlTqc+hNvbcsnC9HNjHNQJIMiepeEvmNvuukmjj/+eAoLC6mpqWHRokUsXbqUd955B0VRmDNnDnfeeSdDhw5l6NCh3HnnnbhcLs4991wAUlNTueiii7juuuvIzMwkIyOD66+/ntGjRzN9+vROeYNCdCS33UIdDlKoB58EKKJnC0QtM5YARfQ2CX3H7tq1iwsuuIDi4mJSU1MZM2YM77zzDscccwwAN9xwAx6PhyuuuIKKigoOOeQQ3nvvPZKTI+P28+fPx2KxcNZZZ+HxeJg2bRoLFy7ELGWYRS+QZDdTpzlAQYZ4RI8XVCVAEb1XQt+xCxYsaPG4oijMnTuXuXPnNnuOw+Hg4Ycf5uGHH07kpYXoEdw2PYMCSAZF9HgBQyVZmYMiehfZi0eIBLjtFuo1CVBE7yBDPKI3kwBFiAS47WZqGzMoMklW9HAyxCN6MwlQhEiA226hvjFAef1qKFndvR0SogWBqN2MJUARvY0EKEIkIMlu0SfJNvr74RDwdV+HhGiBzEERvZkEKEIkwDBJttH2r7qnM0K0Qp+DInVQRO8kAYoQCWisg2LgremezgjRCr3UvQzxiN5JAhQhEuC2myObrzUKerunM0K0wi+TZEUvJgGKEAlw2y1kUW1sDEiAInqmYPQcFAlQRC8jAYoQCXDZzGzS8o2NEqCIHspQB8UsAYroXSRAESIBqU4rC4PHMt9/BlvVHL0x0NC9nRL7td01Xh58fyMlVbHfh1IHRfRmEqAIkYD8VCfzzzuU74dcxjfaUL1RMiiiG93w3++Y//4GzvvHiphjfkMdFFlmLHoXCVCESNAJo/OZMiwbnxb6gS+TZEU3+mJzOelUk1e2AjTNcCwYlDkooveS71gh2sFuNeMlFKBIBkV0o5xkO/fW3M9E0wZY1RfGnx8+FlBlDorovSSDIkQ72C0mfI3xvQQoohvlpDj04ARg6V2GY/ocFCnUJnonCVCEaAe7RTIoomfISbZHnlRtMxzTMyihuj0yB0X0MhKgCNEOdotJ5qCIHsFts1CipUca6svDDwNBDbNkUEQvJQGKEO1gt5okgyI6lqbBzx9BbWlClwU1jUotKdJQ/F3kmGGzQAlQRO8iAYoQ7WAzS4Ai9kKT1TYAn7z5PDx/Kiw8MaFbGWqdAOz8NvzQuMxYAhTRu0iAIkQ72K1mfOEARQq1ibap9wW4bv4z1N4xEFYuDLcXV3mo/eI5/cmeDQndMxC9ISCgFa8KPzYWapM5KKJ3kQBFiHawW6IyKEFf93ZG9Bpvfl/MxRX3kRSogNevCbf/WFyDi6hMnK++zffUh3HUyPPiteHHhlL3kkERvYwEKEK0g2GSrGRQRBuV1ngpVHbHtK8rriZfKYs0VGxp8z0DwahhHABfbfihIXgxmRPtrhDdSgIUIdrBWKhNMiiibTaWVJOkRAW0oUzJhu17GKQUR9oTCFCazkFR/J7wY3/0MmOzDPGI3kUCFCHaQR/i0VPmmmRQRBuVFW8yNpT/DMCeXduwKJFhmoQyKE3moCiBSIASlGXGoheTAEWIdtADFBsAmqziEW1QVe/ngPKPjI17Nur/rS9r0r6+zfc1VIsFTKoPgnrWJKBqWKVQm+ilJKQWoh3sFjPe0BwUyaCIaJv31FG3/B/UVJQyOEUjZ9psSMln6fpdnGlqEqCUriOonoaloZxQvAuAunNVm/96DKiqIYMCgL8ezCk0+IO4G4eU7EmxFwvRg0kGRYh2sJoV/ErjXjwyB0XoGvxB7vn7Aoq+uZVJmx8h57tH4bMHAVj/zTKGmXbgU2zc5T8HAHXb11R7/KRRA8A2cvUb7Vrb5vo6MXVQAELzUKo9PlKp09scqXv57oToWhKgCNEOiqKgmUN7oEgGRYS8+MUvnO39r7Fxx0pKqxvos+UVAOoGnci31vEAqDtWUlbbQIaiBygl7hFUaEmYVL8epLRBIG6Aok++9TfURua2SIAiehkJUIRor1CAosgcFBHy0dptTDKtM7SpQT+vfbeTYcovAKSPPwl34RgaNCsWXzX1JRtIDwUo1uRsNmp99AvbOFFWDQYxK3plWlVT9MbGlTwNVXq7yQpW1168MyG6ngQoQrSXxaH/Vwq17Zc0TePV9z6g7oGD4Pv/4A+q+Ld/i13xE3BkcoZ6l35e5S/sqPSQERrGISmXMf2zWKMNBCDl0zsYoei7EKvODKoa99VpqGxTP9RgJHtSi1N/EMqgmEL3UG0poCh78W6F6HoySVaI9rLYIBBaNaGqYJJ4f3+ydmc1uz/+B27LBnjlYl4uLeRkTZ8Ea+5/CFpxf6gGs6eMmuqq8DAOrizGFaaxSh3MRNMGBuz+kAGNNdRcmVTh1h97KtvWETUQfliDkxTqwwGK2VcNJtBkeEf0QhKgCNFOmsUZeRLwgM3dfZ0RXa64qsEw92Pix79hiGUnAMrgo0nzZlNd5SRF8WCp/iUyWdWVybikVF5RB8fc05KURZUW+j5qYwYF1R9+WKs5QQH8HgJBFVugBmygONPa8Q6F6F7yJ58Q7WS2u/FpoT9968u7tzOiy5XXecNzRwCGmPTgxDPmApj4W/qmO9mhZQOQXb0Ok6KhoYAznTSXjWDhITH3tKVkRwUoVW3riCGDEppn4q+nrqKEJ6zzATBJgCJ6IQlQhGindLedSpL1Jx4JUPY3e2p9ZFJtaFMzh+I8ZT6YzIzMT+FHrRCAMQ1fARC0p4JZT1zff8mJ/CVnvuF6c98Dmx/iCfqh9AfQNEOzFhWg1Gp6Vs/vqeXlh64Pr+AxOWWIR/Q+EqAI0U7pLisVjRMaJYPSqy3+fB3lfxsP79zU5mvK63xkRmVQAExHXBfe8+bA/umsUocAMFldqZ/gygyfa7eYqcuZyDZVz7KoSfkkp0WGeLSoIZ4Gf5D1T1wAjx0KG94xvKai6sNMQczUo68s215ahkuti5wkw4+iF5IARYh2SnfbqCQUoEgGpdfyBoJ8/cZTZNRtghWPxmQomlNW6yVD0TMo60fNQT3ubhhzdvj4kOwkNlqGAeBW9KXoJneW4R7nHtKPqwOz2ZAyCdOFb5DqtFIdGqZR6yvC51330jcML31bf7LiccM9GjMommLGEypHa1EbKNYiwRBV29v0noToSWSSrBDtlOGyUaGFhngkg9K7qEH9y2JjW7kHN5EN9qjdBcl5rd6irNZLRmiIZ/gxF0NaoeG4yaRA/miCxUq4TknTAGVsYRrP3noFbtvVYFJwaBr1Jv17Sosa4tmy9gtCyRHIGWG4hxLUJ8lqihmPpp8U9NbjUKKWv7uMrytEbyAZFCHaKc1tiwzxeCrinvPtLxUsevwv1C/6rT6HQHS5b37eScUHD0LFVgBe/XYHb/z5JIJ39YeKLWwtqyNfiQow92xo0309tZXYlNAqHnf8ACAvI5ViojIZ7syYc1IcVswmvUaJoij6PBUIr+IprWlggimqT36P8Qaa3gfNZMETimJUXz1OogoITru1Te9JiJ5EAhQh2inDFTXE00wG5bTHlnPOrvtw/fgyrPtfF/ZOAGzYVcPXz1xP+ie3wj9/RVDV+O877zNT+QxzoB5+ep/Ne+rop5RGLmpjgEKdfk3Q4gKrM+4pfdNd7NCigpeMQa3etnFJsNlbDarKtvJ6spSoFT2h1T2bSmvY88h0Fmh/BhoDlNCOg746nOgZlA/7XAbp/dv2noToQSRAEaKdDJNkm5mDYiMqa9LWuhaiwyxbv5uzzaEdhPdsYNmGUo6viwoUd65ia1k9/ZVdkbY9G1u9r6pqmD1lAGgtDJ/0TXOyPTpAyRrW6r2dKXqWRUGDmp2U1/kjVWghHKCcef/rZO35ihxFz94pZgsNoSEezVePMzTvZWqRBCeid5IARYh2SnfbqGhcZvz9S7BrXcw5edFDB2ow5rjoXDurPKQq9eHnL3+6htPNn0RO+PZ5XNs/oVDZHWlrQ4BSVucjW9UDFFNan2bP65PuZKchQBne6r1PPHAgK9WhAAQ/fZCKel+kCi2gNejzXhoDkEYms4WK8KTtsvAQj0lW8IheSgIUIdopw22jTEuJNCydZzjuD6r0UfZEGmpLEV1rw47dhuc5W17FqfgM80Ju3PP/sCuRTJfaZIinpt6D/7NH9BokISVVDeSGgk9TSvMBSt90J8HoH7NtGGo5ZVwBT1t/DYDy1T+Y/PpRnGD+Mny8cfmxG+Mu2orJSq0lAwBL/Z7IHBTZJFD0UhKgCNFOWUl2fnSM40c1tHpj0zIIRopm7apuoICyyAW1uxBdxxsIouxcZWi70KTXEKkZdwn3+X9lOPYj+uZ9pqpt4NOzLvW+AE/+7Y9Yl9yM9sSR4XOLqzyRibUp+c32IT/VyXfRJe1DNVJaYreYGXHYybwRPBQTKn0VY5DVmEFpGqBgMhN06TVVnL4yXI0ZlmbmxwjR00mAIkQ7mU0Kr845hhN88/S5KN4q2LEyfHxnZQMFkkHpcv6gym2LV/Hc809zpPql4Vh/k/7/YNhRF1B46lxeDx4aPrYxZRLljXOKyn4CYNUvlRwa+BoAJejjq88/YvcHD+Pd9m1k+K6FDIrNYuK6K65i6xH3wuXL2/wejhyWzQp1RNxjireaoKrhVpoEKGYrilsPUFLUykgGxSYZFNE7SR0UIfZCToqDYXmprC4byJHm1VC+Cfrpe6wUV3kMQzxqbSkmTQNfLdiTu6vL+7zF3+4g+etHuMT6n7g/4YKp/TCn9uXkcUHmfjWLQ3etI1upprbvEfxc+TUZygYo+R7yx7ClrJ6RUYHAuHfOwKoEOQmgcQfilIIW+1PUNw36XpLQe0h1WqnW4s8dMQW91Gz+Ms4QjwVLSi7sAQe+yMofGeIRvZRkUITYSxP6p0cmy9ZHhnQ27KphpGlr+HlDRTEf3HU66j2DoXxzV3dzv1BV7+fn3bXMsbxsaF+rRuZ+mLP08vMOq5k7L/s1NRd/gXrpJ4yYdAKfBkcD4P/0YVBVNuyqCRdjA7AqcSY6J7ccoLRHisMa2ZMnjuTnZ8QO8ZgtpKakUqs5AMgOVbmVAEX0VhKgCLGXCjNclGuxAcr6HXs4QPkl/NzasIdRDd9iCnph19qu7uY+75VvtjPxL2/x5sdfhDfJAwiYHbwbPChyYkZkTojJpDCosABTwRjGFaaxrt+vqdZcWMt+hHWL2bKzlH4m4xyQp52zjC+cPqDD30uyw0K11nJg4VaMBdsUk4XsJDt7tCYbA0qAInopCVCE2EuGkvdR9VC8O9ZgU4J4NX1ipJUAeaGaFXhrmt5GxLHw000se/Fu2Pltq+e+8X0x91if4FP7NYZ2U3p/vtQOiDRkDqY5s44ezz8CJwAQfOVyTix+OOac/jOu5GH1DOrNyagzH4Sk7Da+m7azmE14TEmGtofUs9is5oafp1FnOK6YLBRmuNhD0wBFJsmK3kkCFCH2kl4PpbGirJ5B2VXdwOAGPUuyzjYav2Y2XiQBSquqPH6+eWsBUzbcCU9ObfHcH0uqWV9SwzTTNzHHTBY7P9ui6o+0UFjtsCGZ/DjwfH5QCzGrXs5UPjAc19L6M238cC6/9Slcf9qGaeKFibylhFQpkSGeVebRnHrNg1zo/2O4Lb3JTsqYzAzKdlOiZRjbZZKs6KUkQBFiL2W4rVGbBuoZkhWbyjjapP/V/3PKwbHzCbzV7ItUVeMP//qCT174617Ps/mptIajzK1nTuq8Ac544D1uqptHSpNhDwDsKbw8ezqr8s4kmFMEw45t9l6KovDIhVP4S/4jfK8ODLffZ7+C1X1/jfLbdwGwWMygKIm/qQTUEAksxvbLoF+mi7z+kUxQptLkeyjoY2CWO7LsvZEM8YheSlbxCLGX0l02yptMkv3qx63catIry27NOoKq3f8jK/oXyj6aQVmxuYz8NU9yhPW/aE8tQPnjlnbfa8OuWoY0qQESz/YKD+eZ3+fEUDEzDYVNk+9i8/JXOMq0CvOMv9Iv00W/y/7Rpte1WUzc8auJ3PKvO5le9zrHTRjGdcfM6fSApClf4746hMreAzarBa9mwa4ESKfJ91DAS7LDyk7HYAjN5dUUM4rZhhC9UUIZlHnz5nHQQQeRnJxMTk4Op556KuvXrzecc+GFF6IoiuHr0EMPNZzj9XqZPXs2WVlZuN1uTj75ZLZv377370aIbpDhtlEZqp+h1ZehaRrqTx9gU4LUJw/EnzaE6pgMyr4ZoNR5g0wxfweA0swOz221YVcNfaPryDRjZ5XHUAoeRwqDZ1zGtNvexXzTduhzYMKvPSg7iX9efQK/ufFx8mf8vsuDEwDDK2p6gGK3mPChz2nKaDrEE9BX9fgyI/VTNJOlW/ouREdIKEBZtmwZV155JStWrGDJkiUEAgFmzJhBXZ1xstZxxx1HcXFx+Outt94yHJ8zZw6LFy9m0aJFfPrpp9TW1jJz5kyCQdmrRPQ+KQ4rlaEMilJXyq7Pnuc4rz4UYBt5AukuK1VNa1p4a6BqO6x5BVS16S17rfI6Lz4tqlpq6Bdre6zfWRmp1gqGKr3RdlZ6cBHZl0YJbaanKApYem/2IF5cYbeY8YYClFFRS9gBCOifwUlHHsIuLQ2AYGbrmxMK0VMlNMTzzjvvGJ4/88wz5OTksHLlSo48MlIG2m63k5eXF/ceVVVVLFiwgOeff57p06cD8MILL1BYWMj777/Pscc2Pz4sRE9kMingzIBQnJH3/mzyQnNiLSNOIG13bE2L8vIyAvOPIIdyONUL437dxb3uHKXVXvpE/+1fuwuS4/8saMnOSg+2rR9BdHzhrwNzatxzD2xDpmVfYLOYwgFKjFCAcmxRPhuD/2JPyTpGTTmrC3snRMfaq0myVVX6XyoZGcZZ40uXLiUnJ4dhw4ZxySWXUFoaKfG9cuVK/H4/M2bMCLcVFBRQVFTE8uXxS0F7vV6qq6sNX0L0JDvrFdaoAwxtu5JHQb/JpDptMRmUjdt26sEJwHpjhrE321XTYJy8WbG1+ZObsWVPHVff/RgLbX8zHvDVxT1/Z2WDcb+aabcl/Jo9UV6Kg92NNU2GHQeEhni0Zv6uDESySEPHTmbUsReDIyX+uUL0Au0OUDRN49prr+Xwww+nqKgo3H788cfzz3/+kw8//JD77ruPr776iqOPPhqvV//HU1JSgs1mIz093XC/3NxcSkpK4r7WvHnzSE1NDX8VFhbGPU+I7jJlWDYn+24noEX+SblOnQ8mE2mu2AxKPyVq40CLvau62elKq72RWi8AFVsSvscnP+3hV+aPYw+ENvBrakd5fWRLgTMXwmHXxD2vt3nsvAnckPEQP0y6Fw75HdCYQWlm2CrQEL9diF6q3QHKVVddxffff8+//vUvQ/vZZ5/NiSeeSFFRESeddBJvv/02GzZs4M0332zxfpqm6WPGcdx4441UVVWFv7Zt29bebgvRKf588ihUTJF6KEBy7iAAPUBpkkExzK2oiR+Y90YVVVWkK7WRhvKf23Sdpmks37ibyspyfi6tZaBJ/0xeyPt/lGihP2Z8tXGvra7cTVLjfjnDjgOTOe55vc3IghSeueZURhx7SXgXZH2SbCSD8lfzFZELgt6mtxCiV2tXgDJ79mxee+01PvroI/r27dviufn5+fTv35+NGzcCkJeXh8/no6LCOMO/tLSU3NzceLfAbreTkpJi+BKiJxmQ5ebEMfnGRrdeECw32UFNKIMSU7ANUPeRfXle/XYHWcXLDG3qno1tuva/K7fz7sK/kvbAQJJ++ZBByk4AKtyDqQvtLYM/NoMSVDVstfoKwKAre5+vmho9SRYgLyuzG3sjROdKKEDRNI2rrrqKV155hQ8//JCBAwe2ek1ZWRnbtm0jP1//4T1hwgSsVitLliwJn1NcXMyaNWuYPHlygt0XoufIT3FgImrVSigjmO62ccrxx6MpJr40jYm5TqneAf7en55f/O0ObrS8CMCWUEl2f8kPbbr28WU/82frswDM3vOX8EZ3dckD8RAaAoszB6W0poF8TZ9/Ykrrt1f97w1sFpNhlZS2jwdkYv+WUIBy5ZVX8sILL/Diiy+SnJxMSUkJJSUleDx69cba2lquv/56Pv/8c7Zs2cLSpUs56aSTyMrK4rTTTgMgNTWViy66iOuuu44PPviAb7/9lvPPP5/Ro0eHV/UI0RtlJNkwE3/J8OTDj0a5/ifudl0Xc0xBo/jZC6G+PPbCNlLV9i/n7ShV9T4KFL1Q3RPp1wJgqdgEauvlA3z1keEbOz4AVHcOVlcKdYQyKHEClJ2VnvD8EyVt35+bZm+yikexNb/jsRC9XUIByuOPP05VVRVTp04lPz8//PXSSy8BYDabWb16NaeccgrDhg1j1qxZDBs2jM8//5zk5OTwfebPn8+pp57KWWedxWGHHYbL5eL111/HbN43xo7F/klBYX7gVwBo486PPcGdidWdQY0W+av3ZXUKAPnb30Z770/tet31JTVM+8u/+fiFO6Ch+1a4+T3V4V2ErX3G4dWsmFUvVP7S4nW7a7wUetbFtJtyR+G0mfFozWdQdkSv4NlfMihRAcoJBw7mX4Gj9Cdjzu6mXgnRORKqg6K1UnTJ6XTy7rvvtnofh8PBww8/zMMPx+4UKkRvNb5fGr8OHsM36lBen3lp3HNG901jQ0lfJij63IyjfnsHj/7jHq60vEbg54+bq3DRovlLNnCXej+H/PQjvL0NTvv7XryLvRCqHKua7RTm5bBpTR4jlG2wZyNkND8c/PPuWkYocYKYcefirrVQ1zjEE2cOys5KDwMbV/DsBwFK0zkofXKyOOWmF9B2rEAZcFg39kyIjiebBQrRQQ4dlMlj503k/jkXNrt0eEzfVIqjdpvNKBjMl30uJKgpWGu2QdWOdlWWPcT0o/7gu3+1fGIn0TQNk7dSf+xIY1C2m5+1Av3gnvXNXwj8UlZPf8W4kklL6wcjT9EzKOEhnthVPFvL6iJLtlP3jyEeQx0UqxOXKwll6PR9foKw2P9IgCJEBzp+dD5Dc5ObPT6mbxp1UUM82FwUDerDWm0AAIuf/DOeOwrhs4fa/JouW/cMjWqaxntrS3jotc+o2PkTyZoeQCjOdAZlJ/Gz1kc/b/eGFu+ztbyO/kqomONJD8LVq1AuXw4WO26bhbpmhng0TWPNxp8YYQqVHSgY12HvraeyWUz4oxPfVpmDIvZdEqAI0YUGZbnZ1PcUALTcUQAUprvC2YbT6l7CGayFJbcA8NWWcm67bz67Xrq62UqqVR6/saGL9vZ57bud/P2Ff/HblafjXHBkeC6I4kqnMN3JFvT35GtlJc/W6AxKxmB9OMiuB3kue3QGxTjEs2lPHYOqvgIgmDu6XSX1exu7xRTe2RgAm6v7OiNEJ0toDooQYu+YTAo3XvZb2DEWJV2fl5HmslKsJcU9/y+vr+P1mrnwA2huK8rM+2LOKatpMjejenuXzMd4/bud3GJ9QS+SpsI91qcAPYNiMZuoSR4MHjCVtVwLZXtZ1K7FTeaq5CTb+UrTfwkHa0qIzhV9+0slB5v04SPz4KM65k31cDaLCQtRq6IsMqwj9l2SQRGiO/SZAC59Lkqq0xZTCr+Rt74q8uTrBXF39PXVNNkorx373yRq8546Pv9hK6OVTbEHnXrlV2+SPifE6qsEvyfufVRVw7tnC1YliGq2Q3KB4fiIvBS2OoYDYF77Mr6tX4SPldY0kNdYkTdj0F6+o97BbjFjUqIyZCb5ES72XfLdLUQ3i1cKH+CTH7YzuTqyg7iCBlXG1S6apmGp22W8sKGKzuINBFmybhdH3buU31v+G15WbODQN7izu1MjlXObqfGyaU8tQ/36HBUld1TML1yTSSFrxBHh57ZnZkCtPl9lT42PnMZ9f5KbVPHdR9mtJizN1NoRYl8jAYoQ3SzNZaUyzhDPS8//nbnW54yNTTbfq/L4ydCM20Z0ZoByzzvref2FB/nEdg0XW94GwKs1WRwdCkbS3XYqG/cm8sQPUFZtq2Kc6ScAlL4T457zu+mj+UodFmko1ee07Kn1kqNU6m3J8bfJ2NdYTEqzxQCF2NdIgCJEN0tz2iK/yKMcYoqdXFpbbJzPsXZnNUOV7caTOjFAWfDpZh6yPUqhaXe4bf0pb/ChemDkpNDEzXS3jXIttKKpvizu/b7bVhkOUOgTP0ApSHOy9eDbIg3l+rBSWU09WYTea9K+P0EW9L2HzLRemVeIfYEEKEJ0M4fVFHeIZ0how7wqcyb/DEwDIOn9G9j88m0Q0HeufXdtCceZvzJe2FAFmtbh+/vU+wLk0iQTcuw8xhx4KAf+v3f5z7D7qOt/NBxxPaBnhiqIBCiBoMpD73zP+g+fDwdRa7ftZpQSmjPTTAYFwNHvQJ4JHKs/qdA3VwzU7MasaGgo4M7uuDfag2kgGRSx35AARYhupihK3EmyQ0x6ZkQpOo2ftMjk0YGrH4D1b6GqGt+sWcsEk55VeTmoz9X44Jt1rPrbCah394eynzusn99tq2Kcqcn9+kwAIM1l48xzL8b9m8WQqtc/yXDZqGgcuqov543vi7F9ejfDP74KXr6EBn8Qdq3BrvgJOtJbnOhamO5iqxYaxgllUEyhuTdBZxaY948FiaP7pFLl6t/d3RCiS+wf/6qF6OHizUFp3NHXnZZNnUuB6HIn1cV8t72SCfWfgRVqcyaycUcfMMO0mtci5237AjIHx9xbVTU+/WkPRX1SyXDb2tTHTXtqGRMVoGgmK0re6GbPT3NFD/GUsxMP55s/1J9vfJcvNpczXtMr4Jr6Tgjv/hxP33QnW0IBilq2CTWoYm/YDTb2m/knAFaziV/9/kH4MBtGndbd3RGiU0kGRYgeoLllxgBmdyY3X30VPxWeQVn4F/4e3l27ixPM+rJb//CTqI5zj6UrvmDNaw9C7W5D++vf7+Txhc+w6/7DoPi7NvWxpKohUvH14N+hXPVVi4XCMtw2yhuHeJbeyZSf7qGGSN2Oe55/lVusLwCgNDP/JPpeJdZQ5mDPBu57axXnhYIdU9bQNvV/X6E4UuCEe6D/pO7uihCdSgIUIXoAl8MRfrxGHWA86EwnNdlN+dH38lxght5WX8a6nVUUKfp8DPOw6YZdkhtNLVlI0Te3wisXG9rf/L6Y56x3MSK4AV48p019LK5qIF8JTXYdcFiLGwACpLuskSEeYNT2l+ijRCbLzuFFAAK2FJj4mxbvpSgKuf2GUaxlYFL91H67mGPMKwliwjT1j23qvxCid5EARYge4D+XTeabvLOoyx7Ps8EZxoPONAAy3FbKSNHb6suoryrDreiTZd05g+JmUMI2LTU8TXJYsCqh1SA1O9vUx13VDRQ0BhgpfVs9PzfVgdfUfIblGPM3AFjOfrZNZeoPHZzFl+oBAEwJfKY3JuVCzohWrxVC9D4SoAjRAwzPS+bAy55Cu/h9tmtNVqQ49Yqz6S4bFaEhHq1uD0rNDgACjgzMdhcBW/ObFDZlbmG+R3N2VdaRQ6jmSmrrAUqKw8q551+MN2UAzzKToBb7mqojDQYcEXtxHJMGZ/KFqgcj000rAb2svhBi3yQBihA9SJLdQr01w9gY+iWc6rSG53RU7C5mlO97AJQUfdWMaktt8+uU1fnwaVE723hrW71GrS7Wl/WarG1e1jty+AHYr/2OjwfMYYeWFXPcNPgoMFvjXBlrTJ9UvnFOQo0KdBSXBChC7KskQBGih1GSc4wNoT17LGYTfrv+CznDsyVcZdaUrmczktMyW76xGinwVVbjwRS9K24rG/pVefyk+fQdh7WUgoT3gBmY5aaYOP0beGSb72EyKUwsOoCvtOHhNsmgCLHvkgBFiB5mbXmTf5bRQzfO2F/yiktvGzKw5Umr0fvhBGrLjPvoNFnl09Qr32xnrClUf6QdOyUPzHbHLUbHoKkJ3Wdi/wzWqVF1QELzc4QQ+x4JUIToYaaPjNr4bthxhmyFKSl2mAT0IY9x/TIo1dLCrQ/bLuYt5fDIabWhDIimodSVGm/hrW6xTy9+vokLze/qT0ad2tpbiDEoK8kQoGw5422Y9UbCuxDnpToo1qKGwCSDIsQ+SwIUIXqYP80cyYqBVxHodzic8qjhWHZaUqQWSqMjrgNgQv90tkVNsJ19030sHXUnP6h6xmP+v16nrt5DjTdAqtpkg8EWApR6XwDKf6LQtBvN6oKx5yb8ngZkuQxDMwNGT4aBbZscGy0/1UGxFpVFkgBFiH2WBChC9DB90pwcOusOLL99E9zGjMng7CS2aJEluZWnLwrXI8lMslMwJVRPxKFPmM1KsoezKr+vupvAQxP49puvGRazwWDzAcpPpbUMQl+KrGQf0GJxtubkpTjwFf2a/2VejPabtxO+vlFuimRQhNhfSKl7IXqRwdluNmv5TECf1JqWU2g4nn/U5ZCeDP30KqOZSfZI7RQgtWEH2R9ey6/NTXY89tY0+5obdtUyWCnWn7SzaquiKDzw6wnAhHZd38hhNRszKI62r1wSQvQukkERohcZnJ1kXK6bnG88wWSCA/8vHEhkJUXthxMyMrCOYaYdBE12FjZWpm0xQKlhsClUzK0HlJXfRVTWRJWdfYXYV0mAIkQvMjDLTa0WKYvfuAS5OdlJ9pgApVFd/qGRbEQLc1A276ljUGMGJbP7A5RAdOK3HSuKhBC9gwQoQvQibruFpBHTAdBQWtwBGCAr2U551BDPRrVP+HGg78HUEJpP0kwGZen6Upat380ARV8BROaQveh9x3jhokO4PXc+5UffC/0O6e7uCCE6icxBEaKXueb8M2BrAUpKQavnZjXJoOzuO50BO17AqgRRhh5H7SeLAdAaqmka6vxUWsPFz3zOZebXSVdClWbTB3TQu2i/w4dmcfjQ33Z3N4QQnUwCFCF6o/6T2nRamtNKmRbJoBw6ZiR7ZnxMjlaOs+94qnkHALWhCnOTazfsquUK82tca/2vfo4zE5M9CSGE6AoSoAixDzOZFMMQjykpm5wBRQA4AK9JL56mNcQO8ZTVejncvDr8XEnvH3OOEEJ0FglQhNjHGSbJ2lMMx1R7MgQxTJL9blslf/znJwz2/cgALbKRn9KOHZCFEKK9JEARYh/XYIkalrHYDccUewrUg+KrYcnq7SQvuwUt4OeSukrOMH+CYdzH7+maDgshBBKgCLHPe2fOVH7439kMN5dg6jfZcCw/Lw82gTnoZddLV3OM5QMAqkxxqsUe89eu6K4QQgCgaJqmtX5az1JdXU1qaipVVVWkpKS0foEQIq731pYw4KWjGWba0fKJN2xuteaKEEK0JpHf31IHRYj92NThOax2H9riOVq/SRKcCCG6nAQoQuzHbBYTJ59/DarJiseSZijkBsB1G1AuWNw9nRNC7NckQBFiP2ftMxbTtT/wzRmf8r9gZI6K6syE5FywOruxd0KI/ZVMkhVCQFI2+Vm1bNeyw01Kcl43dkgIsb+TDIoQAoCCNCdbtEhQogS93dgbIcT+TgIUIQQADquZadOOY7OWrzcMmtqt/RFC7N9kiEcIETZ7+nACk79AXfMfTCNmdnd3hBD7MQlQhBAGFlcqHHxxd3dDCLGfkyEeIYQQQvQ4EqAIIYQQoseRAEUIIYQQPY4EKEIIIYTocSRAEUIIIUSPIwGKEEIIIXocCVCEEEII0eNIgCKEEEKIHkcCFCGEEEL0OBKgCCGEEKLHSShAmTdvHgcddBDJycnk5ORw6qmnsn79esM5mqYxd+5cCgoKcDqdTJ06lbVr1xrO8Xq9zJ49m6ysLNxuNyeffDLbt2/f+3cjhBBCiH1CQgHKsmXLuPLKK1mxYgVLliwhEAgwY8YM6urqwufcc8893H///TzyyCN89dVX5OXlccwxx1BTUxM+Z86cOSxevJhFixbx6aefUltby8yZMwkGgx33zoQQQgjRaymapmntvXj37t3k5OSwbNkyjjzySDRNo6CggDlz5vDHP/4R0LMlubm53H333fzud7+jqqqK7Oxsnn/+ec4++2wAdu7cSWFhIW+99RbHHntsq69bXV1NamoqVVVVpKSktLf7QgghhOhCifz+3qvdjKuqqgDIyMgAYPPmzZSUlDBjxozwOXa7nSlTprB8+XJ+97vfsXLlSvx+v+GcgoICioqKWL58edwAxev14vV6Y163urp6b7ovhBBCiC7U+Hu7LbmRdgcomqZx7bXXcvjhh1NUVARASUkJALm5uYZzc3Nz2bp1a/gcm81Genp6zDmN1zc1b948/vznP8e0FxYWtrf7QgghhOgmNTU1pKamtnhOuwOUq666iu+//55PP/005piiKIbnmqbFtDXV0jk33ngj1157bfi5qqqUl5eTmZnZ6n0TVV1dTWFhIdu2bZPhozaQzytx8pklTj6zxMjnlTj5zBLT3s9L0zRqamooKCho9dx2BSizZ8/mtdde4+OPP6Zv377h9ry8PEDPkuTn54fbS0tLw1mVvLw8fD4fFRUVhixKaWkpkydPjvt6drsdu91uaEtLS2tP19ssJSVFvkkTIJ9X4uQzS5x8ZomRzytx8pklpj2fV2uZk0YJreLRNI2rrrqKV155hQ8//JCBAwcajg8cOJC8vDyWLFkSbvP5fCxbtiwcfEyYMAGr1Wo4p7i4mDVr1jQboAghhBBi/5JQBuXKK6/kxRdf5H//+x/JycnhOSOpqak4nU4URWHOnDnceeedDB06lKFDh3LnnXficrk499xzw+dedNFFXHfddWRmZpKRkcH111/P6NGjmT59ese/QyGEEEL0OgkFKI8//jgAU6dONbQ/88wzXHjhhQDccMMNeDwerrjiCioqKjjkkEN47733SE5ODp8/f/58LBYLZ511Fh6Ph2nTprFw4ULMZvPevZsOYLfbue2222KGlER88nklTj6zxMlnlhj5vBInn1liuuLz2qs6KEIIIYQQnUH24hFCCCFEjyMBihBCCCF6HAlQhBBCCNHjSIAihBBCiB5HAhQhhBBC9DgSoER57LHHGDhwIA6HgwkTJvDJJ590d5e6zccff8xJJ51EQUEBiqLw6quvGo5rmsbcuXMpKCjA6XQydepU1q5dazjH6/Uye/ZssrKycLvdnHzyyWzfvr0L30XXmTdvHgcddBDJycnk5ORw6qmnsn79esM58plFPP7444wZMyZchXLSpEm8/fbb4ePyWbVs3rx54bpTjeQzM5o7dy6Kohi+Gqudg3xezdmxYwfnn38+mZmZuFwuxo0bx8qVK8PHu/Rz04SmaZq2aNEizWq1ak899ZS2bt067ZprrtHcbre2devW7u5at3jrrbe0m2++WXv55Zc1QFu8eLHh+F133aUlJydrL7/8srZ69Wrt7LPP1vLz87Xq6urwOZdddpnWp08fbcmSJdo333yjHXXUUdrYsWO1QCDQxe+m8x177LHaM888o61Zs0ZbtWqVduKJJ2r9+vXTamtrw+fIZxbx2muvaW+++aa2fv16bf369dpNN92kWa1Wbc2aNZqmyWfVki+//FIbMGCANmbMGO2aa64Jt8tnZnTbbbdpo0aN0oqLi8NfpaWl4ePyecUqLy/X+vfvr1144YXaF198oW3evFl7//33tZ9++il8Tld+bhKghBx88MHaZZddZmg74IADtP/3//5fN/Wo52gaoKiqquXl5Wl33XVXuK2hoUFLTU3V/v73v2uapmmVlZWa1WrVFi1aFD5nx44dmslk0t55550u63t3KS0t1QBt2bJlmqbJZ9YW6enp2j/+8Q/5rFpQU1OjDR06VFuyZIk2ZcqUcIAin1ms2267TRs7dmzcY/J5xffHP/5RO/zww5s93tWfmwzxoO8XtHLlSmbMmGFonzFjBsuXL++mXvVcmzdvpqSkxPB52e12pkyZEv68Vq5cid/vN5xTUFBAUVHRfvGZVlVVAZCRkQHIZ9aSYDDIokWLqKurY9KkSfJZteDKK6/kxBNPjNkWRD6z+DZu3EhBQQEDBw7knHPOYdOmTYB8Xs157bXXmDhxImeeeSY5OTmMHz+ep556Kny8qz83CVCAPXv2EAwGwzsuN8rNzQ3vNyQiGj+Tlj6vkpISbDabYcfqpufsqzRN49prr+Xwww+nqKgIkM8sntWrV5OUlITdbueyyy5j8eLFjBw5Uj6rZixatIiVK1cyb968mGPymcU65JBDeO6553j33Xd56qmnKCkpYfLkyZSVlcnn1YxNmzbx+OOPM3ToUN59910uu+wyrr76ap577jmg67/PEtqLZ1+nKIrhuaZpMW0ioj2f1/7wmV511VV8//33fPrppzHH5DOLGD58OKtWraKyspKXX36ZWbNmsWzZsvBx+awitm3bxjXXXMN7772Hw+Fo9jz5zCKOP/748OPRo0czadIkBg8ezLPPPsuhhx4KyOfVlKqqTJw4kTvvvBOA8ePHs3btWh5//HH+7//+L3xeV31ukkEBsrKyMJvNMdFdaWlpTKQoCM+Eb+nzysvLw+fzUVFR0ew5+6LZs2fz2muv8dFHH9G3b99wu3xmsWw2G0OGDGHixInMmzePsWPH8uCDD8pnFcfKlSspLS1lwoQJWCwWLBYLy5Yt46GHHsJisYTfs3xmzXO73YwePZqNGzfK91gz8vPzGTlypKFtxIgR/PLLL0DX/xyTAAX9B+WECRNYsmSJoX3JkiVMnjy5m3rVcw38/+3csUsycRzH8e8TmmiEEAQqgdjU0FRtQUOuTm3SELgJDkFTLU1BU1tLEC21urRFlDVFgyddKShUtPQHBIUtn4aHrq58qIby6Hm/4Kbfj0M/6PHhuO9lMpZIJHx5PT4+2tHRkZfX+Pi4hcNh357b21s7Pz//lZlKslKpZOVy2Q4ODiyTyfjWyexjkqzdbpNVB9ls1lzXtVqt5h0TExM2OztrtVrNhoeHyewD7XbbGo2GJZNJfmP/MDk5+e71CM1m09LptJl14Tr2pUdqf7HnMePNzU3V63XNz8+rr69P19fX3f5oXXF3dyfHceQ4jsxMa2trchzHG7teXV1VPB5XuVyW67rK5/MdR82Ghoa0v7+varWq6enpXzuiVywWFY/HValUfGON9/f33h4ye7G4uKjj42NdXV3p7OxMS0tL6unp0d7eniSy+ozXUzwSmb21sLCgSqWiy8tLnZycKJfLqb+/37umk9d7p6enCoVCWllZUavV0s7OjmKxmLa3t709P5kbBeWV9fV1pdNp9fb2amxszBsR/R8dHh7KzN4dc3Nzkv6Omy0vLyuRSCgSiWhqakqu6/rO8fDwoFKppIGBAUWjUeVyOd3c3HTh23y/TlmZmba2trw9ZPaiUCh4/7XBwUFls1mvnEhk9RlvCwqZ+T2/nyMcDiuVSmlmZkYXFxfeOnl1tru7q9HRUUUiEY2MjGhjY8O3/pO5/ZGkr91zAQAA+F48gwIAAAKHggIAAAKHggIAAAKHggIAAAKHggIAAAKHggIAAAKHggIAAAKHggIAAAKHggIAAAKHggIAAAKHggIAAALnCXTfLLB6ztc4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the results\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(train_Y.flatten(), label='Actual')\n",
    "plt.plot(train_predict.flatten(), label='Predicted')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.plot(test_Y.flatten(), label='Actual')\n",
    "plt.plot(test_predict.flatten(), label='Predicted')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3490c919",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('combmodel.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95dc84ec",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}