usd_to_inr_models.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "def adj_r2_score(r2, n, k):\n",
    "    return 1-((1-r2)*((n-1)/(n-k-1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "exchange_data = pd.read_csv('USD_INR.csv')\n",
    "exchange_data[\"Date\"] = pd.to_datetime(exchange_data[\"Date\"])\n",
    "ind_exchange_data = exchange_data.set_index([\"Date\"], drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Real DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Change %</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-08-10</th>\n",
       "      <td>64.165</td>\n",
       "      <td>63.898</td>\n",
       "      <td>64.175</td>\n",
       "      <td>63.855</td>\n",
       "      <td>0.48</td>\n",
       "      <td>Test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-09</th>\n",
       "      <td>63.860</td>\n",
       "      <td>63.780</td>\n",
       "      <td>63.860</td>\n",
       "      <td>63.710</td>\n",
       "      <td>0.26</td>\n",
       "      <td>Test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-08</th>\n",
       "      <td>63.692</td>\n",
       "      <td>63.750</td>\n",
       "      <td>63.785</td>\n",
       "      <td>63.615</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>Test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-07</th>\n",
       "      <td>63.840</td>\n",
       "      <td>63.710</td>\n",
       "      <td>63.865</td>\n",
       "      <td>63.648</td>\n",
       "      <td>0.26</td>\n",
       "      <td>Test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-04</th>\n",
       "      <td>63.675</td>\n",
       "      <td>63.670</td>\n",
       "      <td>63.786</td>\n",
       "      <td>63.572</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>Test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Price    Open    High     Low  Change % Class\n",
       "Date                                                      \n",
       "2017-08-10  64.165  63.898  64.175  63.855      0.48  Test\n",
       "2017-08-09  63.860  63.780  63.860  63.710      0.26  Test\n",
       "2017-08-08  63.692  63.750  63.785  63.615     -0.23  Test\n",
       "2017-08-07  63.840  63.710  63.865  63.648      0.26  Test\n",
       "2017-08-04  63.675  63.670  63.786  63.572     -0.05  Test"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ind_exchange_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reversed and Flipped DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Change %</th>\n",
       "      <th>Class</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980-01-02</th>\n",
       "      <td>0.00</td>\n",
       "      <td>Train</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-01-03</th>\n",
       "      <td>-0.63</td>\n",
       "      <td>Train</td>\n",
       "      <td>7.95</td>\n",
       "      <td>7.95</td>\n",
       "      <td>7.95</td>\n",
       "      <td>7.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-01-04</th>\n",
       "      <td>1.26</td>\n",
       "      <td>Train</td>\n",
       "      <td>8.05</td>\n",
       "      <td>8.05</td>\n",
       "      <td>8.05</td>\n",
       "      <td>8.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-01-07</th>\n",
       "      <td>0.00</td>\n",
       "      <td>Train</td>\n",
       "      <td>8.05</td>\n",
       "      <td>8.05</td>\n",
       "      <td>8.05</td>\n",
       "      <td>8.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-01-08</th>\n",
       "      <td>0.00</td>\n",
       "      <td>Train</td>\n",
       "      <td>8.05</td>\n",
       "      <td>8.05</td>\n",
       "      <td>8.05</td>\n",
       "      <td>8.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Change %  Class  High   Low  Open  Price\n",
       "Date                                                \n",
       "1980-01-02      0.00  Train  8.00  8.00  8.00   8.00\n",
       "1980-01-03     -0.63  Train  7.95  7.95  7.95   7.95\n",
       "1980-01-04      1.26  Train  8.05  8.05  8.05   8.05\n",
       "1980-01-07      0.00  Train  8.05  8.05  8.05   8.05\n",
       "1980-01-08      0.00  Train  8.05  8.05  8.05   8.05"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame = ind_exchange_data.sort_index(axis=1 ,ascending=True)\n",
    "data_frame = data_frame.iloc[::-1]\n",
    "data_frame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = data_frame[[\"Price\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11769aa50>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1121bbf10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11cf67f90>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cf4fa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "split_date = pd.Timestamp('01-01-2010')\n",
    "\n",
    "train = df.loc[:split_date]\n",
    "test = df.loc[split_date:]\n",
    "\n",
    "ax = train.plot()\n",
    "test.plot(ax=ax)\n",
    "plt.legend(['train', 'test'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = MinMaxScaler()\n",
    "train_sc = sc.fit_transform(train)\n",
    "test_sc = sc.transform(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Non-Shifted"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ANN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train_sc[:-1]\n",
    "y_train = train_sc[1:]\n",
    "\n",
    "X_test = test_sc[:-1]\n",
    "y_test = test_sc[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda2/lib/python2.7/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "import keras.backend as K\n",
    "from keras.callbacks import EarlyStopping\n",
    "from keras.optimizers import Adam\n",
    "from keras.models import load_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "7712/7712 [==============================] - 8s 1ms/step - loss: 2.3960e-05\n",
      "Epoch 2/200\n",
      "7712/7712 [==============================] - 8s 1ms/step - loss: 6.2432e-04\n",
      "Epoch 3/200\n",
      "7712/7712 [==============================] - 9s 1ms/step - loss: 1.7799e-04\n",
      "Epoch 00003: early stopping\n"
     ]
    }
   ],
   "source": [
    "K.clear_session()\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Dense(12, input_dim=1, activation='relu'))\n",
    "model.add(Dense(1))\n",
    "model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "early_stop = EarlyStopping(monitor='loss', patience=2, verbose=1)\n",
    "history = model.fit(X_train, y_train, epochs=200, batch_size=1, verbose=1, callbacks=[early_stop], shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The R2 score on the Train set is:\t0.856\n",
      "The Adjusted R2 score on the Train set is:\t0.856\n",
      "\n",
      "The R2 score on the Test set is:\t0.787\n",
      "The Adjusted R2 score on the Test set is:\t0.787\n"
     ]
    }
   ],
   "source": [
    "y_pred_test_ann = model.predict(X_test)\n",
    "y_train_pred_ann = model.predict(X_train)\n",
    "print(\"The R2 score on the Train set is:\\t{:0.3f}\".format(r2_score(y_train, y_train_pred_ann)))\n",
    "r2_train = r2_score(y_train, y_train_pred_ann)\n",
    "print(\"The Adjusted R2 score on the Train set is:\\t{:0.3f}\\n\".format(adj_r2_score(r2_train, X_train.shape[0], X_train.shape[1])))\n",
    "print(\"The R2 score on the Test set is:\\t{:0.3f}\".format(r2_score(y_test, y_pred_test_ann)))\n",
    "r2_test = r2_score(y_test, y_pred_test_ann)\n",
    "print(\"The Adjusted R2 score on the Test set is:\\t{:0.3f}\".format(adj_r2_score(r2_test, X_test.shape[0], X_test.shape[1])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.save('ANN_NonShift.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Process the data for LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_tr_t = X_train.reshape(X_train.shape[0], 1, X_train.shape[1])\n",
    "X_tst_t = X_test.reshape(X_test.shape[0], 1, X_test.shape[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "7712/7712 [==============================] - 20s 3ms/step - loss: 1.3438e-05\n",
      "Epoch 2/200\n",
      "7712/7712 [==============================] - 19s 3ms/step - loss: 1.1315e-04\n",
      "Epoch 3/200\n",
      "7712/7712 [==============================] - 18s 2ms/step - loss: 4.7274e-05\n",
      "Epoch 4/200\n",
      "7712/7712 [==============================] - 18s 2ms/step - loss: 2.9131e-05\n",
      "Epoch 5/200\n",
      "7712/7712 [==============================] - 17s 2ms/step - loss: 2.3614e-05\n",
      "Epoch 6/200\n",
      "7712/7712 [==============================] - 18s 2ms/step - loss: 1.9989e-05\n",
      "Epoch 00006: early stopping\n",
      "The R2 score on the Train set is:\t0.978\n",
      "The Adjusted R2 score on the Train set is:\t0.978\n",
      "\n",
      "The R2 score on the Test set is:\t0.903\n",
      "The Adjusted R2 score on the Test set is:\t0.903\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import LSTM\n",
    "K.clear_session()\n",
    "model_lstm = Sequential()\n",
    "model_lstm.add(LSTM(7, input_shape=(1, X_train.shape[1]), activation='relu', kernel_initializer='lecun_uniform', return_sequences=False))\n",
    "model_lstm.add(Dense(1))\n",
    "model_lstm.compile(loss='mean_squared_error', optimizer='adam')\n",
    "early_stop = EarlyStopping(monitor='loss', patience=5, verbose=1)\n",
    "history_model_lstm = model_lstm.fit(X_tr_t, y_train, epochs=200, batch_size=1, verbose=1, shuffle=False, callbacks=[early_stop])\n",
    "\n",
    "y_pred_test_lstm = model_lstm.predict(X_tst_t)\n",
    "y_train_pred_lstm = model_lstm.predict(X_tr_t)\n",
    "print(\"The R2 score on the Train set is:\\t{:0.3f}\".format(r2_score(y_train, y_train_pred_lstm)))\n",
    "r2_train = r2_score(y_train, y_train_pred_lstm)\n",
    "print(\"The Adjusted R2 score on the Train set is:\\t{:0.3f}\\n\".format(adj_r2_score(r2_train, X_train.shape[0], X_train.shape[1])))\n",
    "print(\"The R2 score on the Test set is:\\t{:0.3f}\".format(r2_score(y_test, y_pred_test_lstm)))\n",
    "r2_test = r2_score(y_test, y_pred_test_lstm)\n",
    "print(\"The Adjusted R2 score on the Test set is:\\t{:0.3f}\".format(adj_r2_score(r2_test, X_test.shape[0], X_test.shape[1])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_lstm.save('LSTM_NonShift.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predictions made by each Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda2/lib/python2.7/site-packages/keras/models.py:318: UserWarning: Error in loading the saved optimizer state. As a result, your model is starting with a freshly initialized optimizer.\n",
      "  warnings.warn('Error in loading the saved optimizer '\n"
     ]
    }
   ],
   "source": [
    "model_ann = load_model('ANN_NonShift.h5')\n",
    "model_lstm = load_model('LSTM_NonShift.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1984/1984 [==============================] - 1s 652us/step\n",
      "1984/1984 [==============================] - 2s 887us/step\n"
     ]
    }
   ],
   "source": [
    "score_ann= model_ann.evaluate(X_test, y_test, batch_size=1)\n",
    "score_lstm= model_lstm.evaluate(X_tst_t, y_test, batch_size=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANN: 0.002102\n",
      "LSTM: 0.000275\n"
     ]
    }
   ],
   "source": [
    "print('ANN: %f'%score_ann)\n",
    "print('LSTM: %f'%score_lstm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_test_ANN = model_ann.predict(X_test)\n",
    "y_pred_test_LSTM = model_lstm.predict(X_tst_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "col1 = pd.DataFrame(y_test, columns=['True'])\n",
    "col2 = pd.DataFrame(y_pred_test_ANN, columns=['ANN_prediction'])\n",
    "col3 = pd.DataFrame(y_pred_test_LSTM, columns=['LSTM_prediction'])\n",
    "col4 = pd.DataFrame(history.history['loss'], columns=['Loss_ANN'])\n",
    "col5 = pd.DataFrame(history_model_lstm.history['loss'], columns=['Loss_LSTM'])\n",
    "results = pd.concat([col1, col2, col3, col4, col5], axis=1)\n",
    "results.to_excel('PredictionResults_ANN-LSTM_NonShift.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### ANN's Prediction Visual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UNFVTXun0+TecuymXK95dzMlHZfqMu/dL9Tedc8h3JeEmIcwb/CN/6Uu7ZipSeXjPVj59Y/q2ZfuT4zyuyO5rnXdcB75asdszbvXkM3hh5mbembfdE09RFRkp8Zw9oAOnHNWaQU/8Qt8O6WHn1Af0iqEGmT59OhdffDEAF198MdOnT/f0ZWVlMWTIED7++OOgcx944AGeeeYZXeGtAWCOY6h0alPSkfLOvO0+1fCmzleFb+ZuUhvBz11wrM8qzfwGbqaq+JKbT+1B7/bppCTEMfPOkTx9fv8q5zuM1YrZXPT9LcNJS7R5VonhTElmmiXbOKlHK4rKG4ZVoHGuGMK82ceC/Px8fv31V9asWYMQAqfTiRCCG2+80TPmgQce4K9//Ssnn3xywPwePXowYMAAPvvss9oUW1MNzI+fhrKZWJ955ueNLNyWzwfXDAUgIc53BXb+8R15/Id1HCpVyqNTi+SQ11r6z9EMenymT9uwbhnceGp3z3kPUyBaKIZ1U3sMZw/owCVDOgPQt0MzAHq2SQOgW2ZK8MkhaJZkI7uWy49WF71iqCG++OILrrjiCnbs2EF2dja7du0iKyuLnJwcz5hevXrRu3dvvv/++6DXePDBB3n22WdrS2RNdTFphgqtGGqEhdvyPcfmN/ETjAe02ebfMUTiOlDxDI+d09czTgiYeuWQqKup9WidSvZTZ3FC95Yc26k5x3byluO9bGhnvrt5OCN6ZlZxhUDSE20UNpAVg1YMNcT06dM599xzfdrOP/98pkyZ4tP24IMP+igLM3369GHgwIExk1FTM5jdVR3alFRt7h/by3M8tm87z7F5r2C04X3kdk99eHzvsClJ/jakM69fdjxz7zmVTY+P9QlEqwmEEPTr2CzqeelJcRSWVfoU6qqvNE5TUh0wZ86cgLZbb72VW2+91aft2GOP9dlHcMczuPnqq69iIZ6mBjE/lxx6Tygq3J5GrVLjuXZEN540vJJKTWU5zcFoVxlxBhNHdKOwvJKLh3QKew+LRTCmr1phWOpRzEl6og2HS1Ja4QzqVVWf0CsGjSZKzI+aCkf9f/urT1QYbqITR3TzSSOx1pTkrqCskgGdmrNq8hmeMRkp8Tx+Tr+oTUL1iXQjriGSvE11TUwVgxDiXSHEASFE0KgOIcTZQohVQoiVQoilQojhsZRHo6kpuoq9CFx6xRAlbsXgdvV84SLlYbS3oBynS1LhcLEtt4TWaQmkR1MruS7MM1JCReSbye7vY/bAqq/EesUwDRhTRf8s4Fgp5QDgauDtI7lZQ7DdHSlN4TvWd1oWb2JOwl1cZ/1BB7hFyY9r9gJec9w5x3Xw9BWUVfLJkp0cKLJ7vIIi4uB2mNIBtswMP7YmWfEhTGkPBcH3DP1JSVB7HWNe+J2C0vqtHGKqGKSUc4GQOWellMXS+6RLAar91EtMTCQ/P79RPzillOTn55OYmBh+sCZmpNvVw22QZSP2Sq0YouE+I2dRsck7x71qOFhSwb4ClfZi9DFtAieH4oe7oLIEln9Qc4JGwqpP1ecH50U0vKPJzXbNnvqdbbXODXZCiHOBJ4HWwFlVjLsOuA5UpLA/HTt2JCcnh9zcwOyIjYnExEQ6duxY12I0adyZGixIVuYc5sQeraqeoAnAvIHv9jjakV/CL+v2A1W7pPow2eQd5KplV9BE4955GyMa3qN1Kted3I03524LW4a0rqlzxSCl/Br4WghxMvAYMDrEuDeBNwEGDRoUsCyw2WxkZWXFUlSNBgCXVE81gYwoLYImkK6tvMFhHQwlcM173kyqweobhKVZLb8wWY0MrwlGmovCvfD97bDpJxj1MIy4K2DKRYM78ebcbVz7/lJ+ueNkT7BcfaPe/FYbZqfuQgj9+qWp17iNRwLpSZ2giQwhoHtmCmf188YtdM8MH4kcQM4y+OZG37bK4DmUYkbOEvVpLwRHBfynl1IKALMeVauZYt+04i2SvenC31+ww3MspeTTJTvZllvsM76uTON1qhiEED2EEa0ihBgIxAP5Vc/SaOoWty4Q6JQY0SClxCJUjEFVQWr+6aoDcDnh7dNg5UfetoxuUFEcek4wivZH5VXkO3cfFOyCZkZcxa5FwcftWuxzaq4jYT7+esVu7v1yNac995unbf6WPLLun8EfW/KqJ+MREGt31enAAuBoIUSOEOIaIcQkIcQkY8j5wBohxErgVeAi2Zh3jzWNAhdeU5JOohc5FU4XTpcMGotwzXCvGdhcVyEoC14NbEtIA3sUisHpgOeOgu/vjHyOmW3GA3zg39Xne+ODjysL9L1Z/OAoAF6ctZnBT8xka24xd372p6e/xO5ASslj368D4PNlkXk91SSx9kq6RErZTkppk1J2lFK+I6V8XUr5utH/tJSyj5RygJTyBCnlvFjKo9HUBLOM8o9HWXJ0HEMUlFeof6tgacofGt+bW0f15JPrhoWtc8AvD/meX/IJxKeFXzGUF0L+VnVcahgmVn0SenxV7FmhPo8eG7z/jMfVZ9H+gK7WaV6vwtwiO+e8Mt+n/8qpi8m6f4aniNHXK3bXult0vdlj0GgaGu3EQZ0rKQrKjKppoUpi3nn6UdHFL7jpMRriU8AeuhocAFPHwstGLrKPL/C276tGVTWnXSmjVt66zpx4C1z3G9ywQB0ntYCivWEvVWT39aZakn0oYMwNHy4j51Bp9HJWE60YNJoocZn+bCod9dvtsD7hzoeUdKSFjdoZ9RhO/geceCtYbZCQqmz+818Ehz1wjssJ+w0F8MbJsNdruuH1k2DOU7D4rchlsBdDcgbEmWpPdxwM7QdAG6P+dGpbFQTnjDyYrUNzr5tu2/REPr5WpSKfuf5AQN3qWKIVg0YTBVJKnyhM4ahlT5h6iJSSL5blUGb45ucW2XlyxnpP+guACS/P82ysVqvinXnr0V4Efc6D0x6EMx5TbYnNoOwQ/PIwLHwtcH6pydZvVgpu5jwJM+5Wxwc2QPb8wDFmKkrUvgbA+Oeh9zlq5WImb6NaWcx7IWD6iJ6tfFJ5u/lo4lDP8YcTh3Bi91bcfYZalfy+OY8L31jA7I0HAubVNFoxaDRR4O+eqhUDXD1tCXd//ieP/7CO/YXlDH5iJm/M3cZ8w5umwuFi9W5vpG8oU1JIFr8FjzSHShUVTUWx96HsJs4UEFcaxLExyCZwSF4bCtPGwUvHKZfTVZ8HjqkognjDzXbQ1XDhe8qcZabvX9Xn7McDSg1/cM1Qvr3pJI5p51vqs1VaAoseGMWXN5xIj9bqO958Wk/6G2m+F28/SM7B2JuUtGLQaKKgwuHCivdNWNS273w9ZPZGtRn/0aKdDJ0yy9NebNjOD5dW+Iz31EfI36pMPFWx/Xf46T5j/GYVF1C8P9BcFG+q6rb4rUA31NIgiiGzF4x5OvB+bg5uU59fTfSuWP78BLb+qq7vrwj8OfsV7/Hrw4OauL684QQWPTCKFy8ewMThWaQmxNEmPZHju7TwGffyJcd5jtPDufPWAFoxaDRRUOFwYTEpBlzB7ccul2zUebsiwb2ncNgvm2ibXT/BtzepjeA5T4a+QHGucgN1p7oo3AuvDVPH0s9L59hLvMeOcvjxH963dCnVxrM/Ny2CQVf5toVyO333TLV6+Pp6+OBc2L3MVxkFIy7B9/zx1lCw26cpOV4pgrMHdOCf43uHvFRzU2CcVgwaTT2jwunCYtplkEHeAh1OF90emMG/f44sh05dU1rh4D//28irs7dEHUx1+ycrQvbd++Vqnv15I3lFvv9GnWfdoDZlAZZODX1xe6HveeFur5lo5L2+fS27w2RTYroVH6q39MnNlBnK/X92x1rfeXEJcNlXMOpfvu3HX+l7HiyAze8hH5R4v6ju50M//KvCbH5LrYUiP1oxaDRREGBKclYEjLEbm66v/7a11uQ6Ej5cuIOXft3CMz9v5NK3F/HBguyI5tkdTr5ZuSegvb+p7OUrs7dw6dvqoXrzqT14+4pBvoNL8wJrKbhcsHI6lPglxNzujQqmVY+IZPQhtQ2kdwhs7zEKhlyrjs97Gx7Kg34Xhr+eJYIH9AO74a5N0GFQ+LFVYDPl5Dq+c4sqRtYMWjFoNFHgv2JYunU/B0t8lYNbMTQUS5LFLz3FQ9+uDTHSl6Ighe0TbRY+uGZokNEqTsFdw9mHJX5lWNb/F76ZpMw3blLbwtqv1XFmL6rF9XNVsqZhN8F5fq6pCWlqxdH/AuX+mtpatSdleMfEJcEDe+CKb41zP1NRKNLawLWzYNA1vteLku1PjmP7k+Oql2AwSrRi0GiiwH+PIR4HT85Y7zPG3sBiG6rKW1QVxX6K4Zh26Sz95+k0S7Kx7tEzffouG9Y59ANtxt0w3bRH4B+odvMyaGZ60z/hptBCXTw9RPvHkKbqQDNmCvQPsyJIMXJ59hgFE15SpqV/7lMbzu6VQrDVR1UkZ0D5YbUiqgZCiGr/X0VLnafd1mgaEgeK7D4rBptw8PtmX7t8JMV7io18OGnRlK+MEf6Pmt5+LpShMNdp3vzEWOIs3gdXcnwcr182kEkfLgfgplPDmH42zvAe+28st+oB3U5RG74AvUJsEAP0GgfXzoa3TvW2XfebCjyLhqQWMGk+tOwBtkQ4/u/evi4nwdh/+254R3TNDPXd7AXq+vUYrRg0mijYvL/IZ4/BhgOXn83I7givGAY88j8cLkn2UyFrU9Ua/hav5snBldX9X63iy2W72fSE8vC56ePlnj5bkLoUY/q2q/r7dRwCOUb2UavJLGOOOehzrvrscTr8/hwc8xf15l0VHQaqvYIeo5RHk9ssFC1t+wZvFwKGXh/99dzKoPRgvVcM2pSk0URBcnycnympMohi8JqSPl60M+h16lMdB/8EbaGqi01fvIsKpyvADXfi8CgLZDXrrBTB37/ztqWYyrC4PY/i05QZB6DTUBj9CEx4MbJ79L9AKZDqKoVY4FZoZYG5kOobWjFoNFHgdAXuMeQVV+A0PejNK4YHvq69/DbVpdxIbrfx8TGc0buNJ7VFKLLun8GCrd7o4ltG9Yzuhs4KOPYiZaJpdbRqK9ztjWwu2gfpHeGBHEg0zFoWCwy/PfxqoT7j3ngOFmxXz9CKQaOJAqdLYhW+piSAtabi7pHsMbj5fXPd1ygvr3RhswoS4qwkx1s9WVDNFJb7Bqn9vHYfQ7MyaJUaH76wjpnSg1C8z+uydc3/vH05S6C8AFZ/7rvZ3FhIMTLHfnyB+p71GK0YNJoocEpVoMdNolAPzLNf9SZdi8Yr6fJ3FocfFGPKK50kxqkAqqT4uKCmpG25vikmisodSKkK3EfFr0bSO/dGclJzuPADdVxZCtvnquOjzgyc29BJNbnqrvmq7uSIAK0YNJoocLp8A9yGWVSVLSm9ZT73FJT7zPlpTdU5+f/+bt0qh2K7gxQjmjbJZqWswuuG+uuG/ewrKGfzfl8X0iXZB1mcfZD4uCgT4i19V31aTauMdseqz12LYOHr6rgqz6OGSnwKHHe5OvaP6q5naMWg0USB04WPYiiS3nw5Jcab9rJsXxuy22XzYEkFXe/7gY8W7SDFlOLgt025AYnmaguH00VesZ1WaSoXT2qCldJKJ06XJLfIztXTlnL7pytYmn3Ix2S008jwOXdTNU1hPUzlO93xBb8/BzuMIo7hEtQ1VP7ysvr85WFY8k7dylIFWjFoNFHgdLm8pqS4RM8eA3g3ce0OFz1bp3LOgPaevq73/cDAx34B4MGv1+BwSc49zmtHf+x73yC52uBAUTk9HvyRORtzyUxV7qKZaQlICXnFdk/FsIXbDvLp0l10aZlM15a+ieNev+z46G7aph9kdINTH/C2BYsg9s8x1FgwB6jNN9VpcLng+X6+NailhBUfQUXtVW5zoxWDRhMFPiuGuARswqsY3Lb5SqcLm9XCrVV469gdLjpleB+yXy7P4ftVgXmHYsnuQ96U4a0MxeA2KV01dQmTv1vnMz7RZuX7W0fQp73yFLp+ZDfG9G0b3U0dZdD+OLD4maB6n+09Hnyt2ntorIx7Vn0W7oE9K5UCWPI2FOyEpe94N+a3zYFvbwyeGTbGaMWg0USBU0r6WYw8/X4rhvEv/Y6UkgqnJD7OQrfMVG48pXvIa8VbfWOOb/501KDLAAAgAElEQVQ4dKbSI+EfX/zJNdOWUFbh9FRVO1BUzrmv/eEZ41YIWa2UCWfd3kL+3HXY5zqlFQ5SE+IY168dACN7ZkYvTKg6BuOeMw4EnPVs9NdtSLgT9rkc8OZI+PQy+PEeb//nV8LPD8IH56jzvSujKg9aE2jFoNFEgdPl4nyrYQePSyDepBhKKpy8MHMzczflkmukmq4qd/6h0krG928XU3kBPluaw6wNBzjm4Z+4+M0FANz44XKfMQ4jf89xnVt4lIObx87uA8Ca3WrD9IaR3flo4lBO7NGKqKkoCW4mSs1USewmHw7sa+xs+tn3fN03sOAV37Zp41Vho1pCKwaNJgp8goTjkujawsaZfbxuiC/O2gzA7sPKTJPulwvphG4tPceTRnbnmb8ey6y7RsZOYD+W71QP3qU7fKNvHx7fx3Pcq61v2czkeLWacNcotlgEJ0WrFFwuZSKpKG68G8vVJUSxJwDGPqM+dy1UyqGW0IpBo4kCn0AvWyJHtUzgkiGdA8a1TU8EID3Jm44s+6mzuOqkrp7zlinxJMVb6Z6ZysThWT6eStVh8/4iVueED5zamlsMQJxFkP3UWWQ/dRbxcd5HgdMvXce4fu0497gOvHRxiER0Tgd8ezPsMUxhh3fCU50hb4t3zMvHqYI50tV4N5aj4fTHoHWfwDTc7pTeoDLFDr3O21a0B9Z+UyviacWg0UTBlv3F3pP4VHA5iLME/hm9fKmq0eu/YujZRr2Nj+jZyicNdXycBbvDRWF5ZbXTdp/+/FwmvDIv7LhRz6mCN6HyNT1zwbGe42X/HE1SvJXnLxpAl5Yh3vS/vg5WfABvnqK8al7opyJ7l5hqHhzK9h7rFQOcdCvc+Efgfkp6B/hnriru02ucaut2Clxu1KJY/x21gc6uqtFESIXDxeLsg5BoNMQlgL2Ik3q0DBibYphf/PcYslqlsOGxMSTE+SqThDgrDpek/+T/MahLC7644cQakdk/MM3MnacfFbS9WZINm1VQ6ZRkpMQHHePDmi+9x0tNvvnulYF/xSK9YvDS93z1U7Qf9iyHVoYnW5pfQaPup6nPNV+oqPBw9SSOkJiuGIQQ7wohDggh1oTo/5sQYpXx84cQ4thg4zSa+kCvh370bbDGg7MCIQT9OnjLWbZOS6Dnn0/Dig9JTwx890q0WQMKriTYvH+KZvv/H1vzKLYHVkrzZ8sBrwJYlaP2Ecornfz3z+AusNlPneXrTrv5F9i5CHarTekNj41lzSNnhi8Mk7M0dN8fL6sSnf55gep5yuk6Ia0NHB2hW+rW2bGVhdibkqYBY6ro3w6MlFL2Bx4D3oyxPBpNtThcWkGA5cVqC3AjHNYtg8UPjsa26BX49iZaG3sN4d68g/Wvzing0rcW8d4f2WHl22tKwzF1vhr/7vztvPzrloCxAfEV+Vvho7/Cu2eoAjeL38JqEZEVnX97VOg+p12V6PzmBnXe7ljo+1dVJ0FTfczBgTEipqYkKeVcIUTXKvr/MJ0uBDrGUh6Nprp46xubtMPOhapg/a7FPHFuX6bMWM+0q4b4zEtNiOPp8/t5PHpCESwZ3d4C5dm0bEf4/P2JNu/Gtbtewr9/2uhpO39gR75cnsPyh04PVEIO39xOzLjb62tfHSbNh6+vh/2GocBdne3ybxp22uy65p6tapWaGFmFvSOhPm0+XwP8GHaURlNLbMst9jxkb/1EedwkYFohlBh5gt45nf4dm/PJdSf4PKDdXDS4M73aVv3H3DrNmxbCHUfgNiEl2sL/mZpTfX+zcg8uv+XNsxf0J/ups4KvXCrLA9siwb92cYdB6uHVti9c+yukmWI0EtK1UjhSUlrVilKAeqIYhBCnohTDvVWMuU4IsVQIsTQ3t+5z2GsaN39syeO0534j6/4ZPPXjBlYY/v9thOntfUiI8o7+m63ByFnmk0StY4tk5t17Klee2JW8YhUcd7hUKaGl2eFXDO48Te4tgW4PeGsoX3li16r3CiojzMVTnOubLnrd197jh/Lh2lneSmxxCXCLO7V2C7hxYWT30NQL6lwxCCH6A28DZ0sp80ONk1K+KaUcJKUclJlZjVB8jSYKtuV56w+8/ttWT2bR/unGQ3TM09B1ePDJZtOMu76AP2+fBj/c6dPUsUUy6Uk2iu0OXC7pybhaVfS0G3fVuLevGOTTHh9nYfJf+gSb4qWyrOp+N19cpX6K9hnnV3v7rEGs0vEpKpr53uzGWXinEVOnikEI0Rn4CrhcSrmpLmXRaMzE+xW3P9vIlPrihf1UQ9u+vjUFzJvQ5gftexOqvpHf6iI9MQ4pobjCwYdGvej9hYGmnkMlvmm63SsG/72Ke844uur7A1QaSvDMKSoddlxS8HHZv6vPt05THkxuxjwd/h6aBkWs3VWnAwuAo4UQOUKIa4QQk4QQk4whDwMtgdeEECuFEFX4vmk0tUe8X5yB0yVplRqPFSP4zBLnW1Pgj5e8x5G+gQMsesPn1B0Qt35PIQeNh39RucOndOjGfUUc99gvvDRrM1JKps3fzpQZKm13QpyV7272rmSy830rrwXFLe/R46DjYJUB1VmFi2zhbvhyIgy8Qp0fyUa1pl4Sa6+kS8L0TwQmxlIGjaY62KyBisFqEeAyKQaz+WTWozDiLnXs7+XjrPRdXZj56V4YeLknGnjdXpWo7qI3lU2+ebKNw6WV3PjRcn6751TA+7D/zy+b+M8vvgvtRJuFfh2bse7RM7n3y9XcfFqP8F/WvccQn+KNSq4o9k197V/AvjRPfaa2DUyhrWnw1Pkeg0ZTH7FafDdrHS6pUl+4jDfpYA/DPJVAj32rfNsfMyWcO7gdig/49k/xFvS5bJhv3qXl/1Srkh35pZ4oZnfq7GC4vaKS4+N4+ZLjaNcshFnITKmxuZ2QDgmGKarCWGnMfRZWfe7Ng+SmshSWvw/F+8JfX9Pg0CkxNJoguPxs/2WVTmPF4FYMQf50SvOBnnBggzq3JXvfxpe8o/LgTL9InXc4HvatUUFgoAq1DJ5IV1M+oq9vPNEnn9L8LXn0bJMWMhL6jtFHBXWXrZIVH8H23yC5JdgSvVHJpfmqeP2vj6nzHqeDMK4tq5fLSdNw0CsGjSYIT/7oW2rzh1V7VdbRqhSD+y3baVeBSFfNMF3gTq9ScI896gxT/12wZwVxVgt92qczsHNzjuusHtJPnNsXgFZGrEORkeG1fbNEz/Tsp87ittGhK8YFxeVSFcKyf4c0Y9XSzIgxnfOkqiDmZosqS+qT/RPgztovSaqJPVoxaDRB2HUwcAN59+Ey3z0Gf767XXkZOewQl6hKWIaKdcjdoJSHmTdPAeCHW0fw1Y0neZpH9FDu2eVGEFtxuQMhoH1zZSa6cFCYhAFbZ8PMR3zbXC5fRRVvlBlt3kV9bpwBH53vO0c6IWsEXPCeOh9yPaS3R9P40KYkjSYaPIohiMmmYKfKC7R1NtjVJjJZJ8PiNwLHAuxaAifdHlgU3i+NtzvB3sZ96ppFdgep8XG8cfnxbM0tYUhWmIhid4nIDd9DeSHcvRFWfQKb/+cds2el+kxppUxIxfu9feOeVWky3PQaD6P+BcdfWfV9NQ0WvWLQaKLBXW3LbW8/4wnoZ0qB/Od03w1Z/zQQrXt7j898Ak65X+UQam5sOpcFRjmnGRlav16xh/2F5fy+OY8Kp4uWqQnhlYKZvE1e2VZ/7tt382Lv8aCrffs6n+B7bo2DEXfqFBeNmLArBiHEy/hkDvNFSnlrjUqk0dQjrj+5G2/M3eZtcHsUJRs1GE68GexFsPoz34nxRnlMc4rpyQWqYM2LRnb5LiepDd/up8KIu+G7W1UMgR/J8XH0bJ1KepKNoVNmhRZ262yVPnvMFN92i823fOSMf8DWX70y+dPBN3qatn1V8Fu3U0PfW9OoiGTFsBRYhipPMhDYbPwMALR7gqbRIgTcP+4Y7jnTFD386+PqM8EUYWxLDpw88h716V+6sVln6HwiDL8DUkwFfmyGW+nK6bD6i4DLtW+e5Nl0BhjYOUi21g/OgYWv+gbYSRlYUziUactNz9He40nz1ecJN0Gb3sHHaxodYVcMUsr3AIQQVwKnSikrjfPXgf9VMVWjabBkpMRzVj+VHfTqk7J45ueNDBXrg7tqWqzqQT/veW9bs07q078ojcUCVwdJIhxneBjNNhRPv7+qT4cdhBWbVbDJKCsab7Xw8bXDfOeb3Wt3LvBW/CoNkX7s+KtgwN+C9wHc9qeKys7sFXqMptESzR5DeyDNdJ5qtGk0jY6DJRWeTKXutNefJhg+/b3GB04w5xe6Zib0Oddoj6A0JgRPp1xZDo+3ht+e9onEfumSAYHxCuZVwre3qM/9a+GZ7sHvN/556DQ4tDwtusKYJ4Mnx9M0eqJRDE8BK4QQ04QQ04DlwJSqp2g0DY8tB9Sb+fsLdgAghGDVw6aqY8FKU/Y9T31eN0c9cM1prsc8BeeGMd+07R/YVrRXfc77D3EmxXDK0a0Dx5rTcLjl+/Ryb1tLIzXG5d+ofYVwJTs1TZqIXweklFOFED8CQ42m+6SUOh5e0+iodKp4gb+lLofd7aBZR9LNXjzBYhha9Qy+kQsw7IbwN/VXNg47lBj5iBDsNCXDCxrdbK6psH+1Wi0c3OptmzRfbXRrNBEQ8YpBqEofo4FjpZTfAvFCiCFhpmk0DQ6HU9KSAp5wPKtqID/bEw7v9A7oNDT05OoiBDywF876jzovyfVWiHNVsmFfUdXz3aakLkZg3P+d6O0bcr1WCpqoiMaU9BpwAuDOmFoEvFrjEmk0dczaPQVY/D203WUqj70U+l8YOKkmiE/2VkArO+SjjJItlSEmGbjTcQy70bc9uSWM+3cNCqlpCkSjGIZKKW8CygGklIeACHfWNJqGw31frfbWXXATZ9RkPvOJ2KaZdpuUCnarlNwG749RabvTE+NUGu/3z4HNM1XngQ2w5C11HJ+sPKTcVERQj0Gj8SMal4NKIYQVI9hNCJEJhM7/q9E0QOwOpRBswi+DaVXJ82oSm5Fd1ZzHCOj3v4t57oK1DO6aoTalt81WP5m9VN4lz/xkGD1Z1YZ4sqPa+NZooiSaFcNLwNdAayHEE8A8tFeSppFRVqEUQzx+isFdujNUwZ2aorVf3MCt3joI5yevpHPLZCg77O03KwXwJuZLSFOb4YOuipGgmsZMNF5JHwkhlgGjAAGcI6XUOXc1jQqHS+0tJGH37Thg/KpbYqwY4r31GBh5L2R0854veh2OGQ/lQbyf2vRVY9sfF1v5NE2CSHIlmWP6DwDTzX1SyoOBszSahonDqRTDXXF+SebcuZBqo4zlOf8H2+eqBHsA/9gO/87yJrMrN1YMXYbDjnnq+Ib5sZdL02SIZMWwDLWvYI6IcZ9LoFuwSRpNQ8ThUttmJ1jWBR9QG4FhAy5VP26SMwDhTeXtNiUd/3elGE77Z+xl0jQpIsmVlFUbgmg09QH3iiFRhHEPrXWkMiWd8bjK0Apw1Jmhg+o0miMgKhcLIUQLoCcq0yoAUsq5NS2URlNXuPcYPJjrNtclbfqqaObPrlDV1cCb2lujqWGiiXyeCMwFfgYeMT4nx0YsjaZucJuSXO5CPA/uhbH1IECs6wjlabTRVEfaoutsaWJDNL9ZtwGDgR1SylOB44DcmEilafQs3n6QN+duDT+wlnGbkgoy+kO3U1Tj0BB1m2sTq827xwBqQ1qjiRHRmJLKpZTlQgiEEAlSyg1CiKPDT9NoArnwjQUAXHVSlk9K6brGHeDWIn8FFJjyCw270Vu1rS5wR16DWsHospqaGBKNYsgRQjQHvgF+EUIcAvbERixNU+HXDQc4s0/buhbDw7bcEoQ7oN+cynrMk3UjkBurKfvM4GvrTg5NkyDiVzUp5blSysNSysnAQ8A7wDmxEkzTuHFXR/tz1+EwI2uX9XuLSHJ7JPWuR7/e5pxHem9BE2Oi2XweJoRIA5BS/gbMRu0zVDXnXSHEASHEmhD9vYQQC4QQdiHE3dEIrmngGOEAFY76lW7r3fnbyXIvhDsPq3pwbWLeX9BoYkw0rx7/BxSbzkuMtqqYBoypov8gcCvwbBRyaBoBlYZCcEoZZmTtIQ1Zfkh4UDXE1aMaBifcXNcSaJoQ0ewxCCm9f8VSSpcQosr5Usq5QoiuVfQfAA4IIc6KQg5NI8BdJc3pHzdQh+SXVPg21CfF0LI7XDlDbzpraoVoVgzbhBC3CiFsxs9twLZYCeaPEOI6IcRSIcTS3FztJdvQqTTcQt0BZXnF9qqG1wob9oapklbXdD0JWh9T11JomgDRKIZJwInAbuNnKHBdLIQKhpTyTSnlICnloMzMzNq6raYGKSir5KyXfmfjviIOl6m3c6dTsirnMIMen8k3K3bXqXxumTw4615ZaTR1QTRptw8AF8dQFk0j5+VZm1m7p5ApM9azv1A9dJ1Skp2vUk78vHYf5xzXoc7kKy73L87jDD5Qo2nkhF0xCCGuFUL0NI6F4WlUIIRYJYQYGHsRNY2Ft+epaN29BWXkFinF8MWyHF6bvQWAH9fso7BcuYpOnb+drvf94CmcUxtsPlDs2yDrl8eURlNbRLJiuA3lXQRwCXAsKtX2ccCLwIhQE4UQ04FTgFZCiBzgX4ANQEr5uhCiLbAUSAdcQojbgd5SSu2b18gwbzJv2u/7AN6wz2vbP+eV+Zx8VCbT/sgGIL/ETsf45KDXdLkk36/ey1n92mG1HHk67HfmbfcGtwH0v/CIr6nRNEQiUQwOKaU7B/F44H0pZT4wUwhRZXYxKeUlYfr3AR0jklTToHGnmvAiicOJw+9XcFteCdvyvMFchWUOaBH8mn95dR5rdhdSWFbJZcO6HJF85ZVGrWcMOU97CBKbHdE1NZqGSiSbzy4hRDshRCKqrOdMU19SbMTSNDb8TUJXWX9iS+IVNKdqT6CdB0OnvF6zWy0s3WapI+FwqXr3iXMrhljXdtZo6jGRKIaHUeaebOC/Usq1AEKIkdSiu6qmYVNW6asYxloXA3CudV6V8yZ9uCzstds2O/J4A/fexotnGXmb6jJhnkZTx4RVDFLK74EuwDFSSnP2rqXARe4TIcTpNS+eprFQXum7kbs5aQAAx1s2Vet6UkqaJam3+uR4bx3mz5fuYv3e6LeoCsuUYuhUul41tBtQLbk0msZARHEMUkqHlPKQX1uJlNK8i/h0jUqmaVSU+60Yjmmv7PdJVASMnTg8i29uOslzHiw6+t8/b6TAeJhXOFzkFdvZllvMPV+sYuyLv0ctn3vF0GvBXaohvX3U19BoGgs1maaxFqqkaxoqblNSxxZqW8riVArBbdM/Z0B7ju+idpn/Ob43Azo1594xvYDgifb+b463yE+F08Wgx2dy2nO/hbz/H1vzmLspdMT8oRK/Gs8J6eG+kkbTaKlJxVB/kt5o6h3uFcMNp3THIqBbhqovYMXJA+N68dg5ffn8+hPY8sRYzxybVb1rVDh9FYP/CiJYKgv3CsDNpW8t4op31b5Gdl4Jxz36P7YYcQtSSp7+aQNxmALcrFGVQ9doGhU6sbumVnB7JZ0o1rDtX8NJt6oHt004GdSlBWnzpmDJ30ScqZpbQpw69l8xHCr1NT99sHBHwP1+WrPPc2zK/Uil08WMNXs5VFrJh8a8HfmlHCiy01kcUIPOeb26X1OjaRTUpGLIrsFraRoZZZVOWlFA1oxL4f2zoVDlRbLiopmrAOb9B96b4DMn3q0Y/FYMHwZRBP7844tVnmN3wj6AQyUVJNvUZvWGfYW4XJL8EuXuetMJRg4u7ZGkaeJUWzEIIU4XQvziPpdSnlczImkaI/ZKF0sTb1Ane1ZAnvJG6iOyabPyJdVevB9K8tXxwe2cMe9iMij01G5w88LMzQD8a0Jvn3azd5IZsyIZMmUWk79bB8DCbQeZuzmXg8b+wqAMIx5CB7ZpmjiR5Eo6TQixSQhRLIT4UAjRWwixFHiK8IV6NBoAdh8u8204lA1Akqgg9c93ve0v9IPvboOXBtCiYC3jrQsCVgxuTuvVmgmWP2hm1I/KTEvw9PXroB7uecV2Hv1+XUi5rpy6hIe/VQUGu8w0kgUn6o1nTdMmkhXDc6j02i2BL4CFwAdSyuOllF/FUjhN42FJ9sHIBlaWwLJpnlMBXDV1SdCh7Ve+yMvxr/BW/HMAtEr1KgaXsa/w4Nerw95yb0G5b0N8SmSyajSNlEgUg5RSzpFS2qWU3wC5UsoXYy2YpnHhH/kcKaMty3xWGxv2eYPXbPOeAWCgUKalFy4awDXDszizTxv2F9rZdbCUvOLAOIngmDydUttUS1aNprEQiWJoLoQ4z/2Dyr5tPtdowrJxX5Bo5MxeYeeNsK7xyac05gUVvHbdyd2gvyoPUnTsNfz35pPolJHMQ6M70jyugrxiOyP+PZtlOw4FXPPjiUOZdddIn7YkjP2F0ZMhLiFgjkbTlIjEWfs3YEKIcwloc5KmSsornTgq7GBOadRxCPQ8HWY/EXZ+W6Ee7uv2eJWLtWgPrP8YgBbN0mnRoRlUlsNTnZhsSeVT3gy4zj1nHs3grhkMycoIiIWYeHxzWAsk6ZrKGk1YxSClvKo2BNE0XqbOz/a+kbuxxIH0i4nsfALsXAD3ZkNSCxzL3ifuu1vITFU5kca95E11ce9602LVYYevroXVnwOQ5PIruAPccbyNa9rvIDGrBwBWi2D7k+OY8IpK3d3MXQIkKUSOb42mCRFWMQghrqiiW0opP6hBeTT1kF/W7WdVzmHuOuPoas1/+qcNtHMrhlEPw6xHYfA1sF95A9FhEJz1HGR0U/ENxsM5Lk1lOi0qLvHkRQpK8QGPUnDTVewlW7YD4K0rBnH6rxNg+kZ4YI9SSG+dhhj7FEOz2rJmdyGXFRmeURndqvUdNZrGRCSmpMFB2gTKnNQB0IqhkXPt+0sBuOW0np6gs2hJFobnT/MuMLlAHeeo69L3fGhvZDM1u4oatv54Kpnyw/rQF1/9WUDT581eYfBhZabKSImHvI2q4+B2cFao829v5v7b1nDBwLYkvnmp6m91VLW+n0bTmIjElHSL+1gIIYC/Afei3FbDG4g1jYbdh8twuiQ9WqdGPMedIynZvWIwu4JWGkV4bCHqKcSp9gRRSW6xnTF92rJ0x0EW9f8Blld938zy7Z7jVJspv2POYrCqPE0U7ibu8HZ6pRkyDbkO4uIj+l4aTWMmotc/IUScEGIisA4YDfxVSnmRlHJVmKmaRsRpz81h9H9CZzANhru6WppwKwFT/ebKssA2M8aKIYFKhnXLoNzhpH3zJKzLp6r+CX5e0+nBq8R2+/w078n3d8C3N3nPv70JNvygjrsOD/t9NJqmQCSRzzehFMLxwBgp5ZVSyo0xl0xT73DvFbuC1EcIRak7eZ5lrWowm2ocbsUQokKssWK4Nu4HnC5VTCfRZkp7kdHdd3zvv3hltSqlcpJlNbZDWwnJzgXww53qOKV11V9Go2kiRLJieBlIB4YD3wkhVhk/q4UQesXQBPFPaV0VJRUqlXUmBZDaFtLbeTuPMR7kbfsHn2ysGIZaNvD0TxtYvvMw5QUHvP0tusAJN3vPj79SfR49DuG004FcBluieIdJ1YpBo4HINp+zYi6FpkGxYudhTu0V2UO01K5WDAMsW7x7Cm76Xwh9zgtd+yAucO/htqLnwYpSMs07w5lPwLEXq43szKPVxvZvz8DGGcxPvA1nShso8bvIibfAUWNh2jjfdl21TaMBItt8Dp/jWNOk2FNQFn6QQV6xHZAcbcnBP5QBqLogjl/Ool5iJ6OsK9SJy7RqadtP/bgxbSBbS/Z723ufA8deAj3PAIsF0tpD0R7V16xzaJOWRtPEiGSPoUgIURjkp0gIEX3VdU2DIw4Hbcn3nB+MMP+Q3eHk9k9Xcpl1ZvVunJgOHb3e0k/ZTNHMpflBJhiIIL/WZ78GE16Ao8copQBw13oYeZ/3XhqNBohAMUgp06SU6UF+0qSU+q+pkVLpdPGXV+Yxd1Mud8Z9wcLEW2hFAWkJceSXRKYYFmxVD+9Lrb+qhlB7CVXRYzQAd8V9RpsE0yrhgmmh5wye6Ht+1n/guL8Fj2rufyF0OQku/ih62TSaRoou7akJSn5xBatyCrjj05WcbFWpq7tY9tMxxRFQWjMU7tTXvS2GNbI6BXA2fA/ALXHfsLXlKartjCegz7mh59iS4IpvvedV3bdld7hqBrToGr1sGk0jJaaKQQjxrhDigBBiTYh+IYR4SQixxfB0GhhLeTSR4zQe6odKK7AYKak/yfqRH0svxWUvrWqqh0Mlft5LRoqLqDjpdgBWu7riqqwAWwqceHOYSUB8mvc4sXn099VomjCxXjFMA8ZU0T8W6Gn8XIeuCFdvsBsRyy4JVqlcTm27FwGQUpEX0TVmbdiPDYe3YczT0QvS76+UJbZmk+ykopkjLaLT+hjvsfY20miiIqaKQUo5F6iqdNfZwPtSsRBV+6FdFeM1tYTdVGc5Dt8iO80q9kZ0jbIKJymYPJhSWlZLFqclgfOtv3NMyWIoORB+AkB8MvQ4XR1HUPdBo9F4qes9hg7ALtN5jtEWgBDiOiHEUiHE0tzc3FoRrilTYVIMVnxrLvc8GFlaDIdLMqT9keceKkusZuDZJZ/A/bu9XkgajSYi6vovRgRpC5pvQUr5ppRykJRyUGZmZozF0visGITviqHCXo70r6UQhKJyB2dXGnmIht1U9eAqKE827U1UtensjzUOEiJP+KfRaBR1rRhygE6m847AnjqSRWPC7nBygXUO/cVWWphKa653dSJTHOZgGJfVgtJK1u86wFlFX6iGZsET3EVCh9amF4FzXq/2dTQaTWTUtWL4L3CF4Z00DCiQUkZmwNbEFHuli2dsb/LfhIfYJr3bPpb0tmSKw+wtKK9y/uXvLiJTFHgbBlxabVks2+eogzb9Qqfo1mg0NUas3VWnA8TmE3oAABn4SURBVAuAo4UQOUKIa4QQk4QQk4whM4BtwBbgLeDGWMqjiRyzKcmGE1fzrjBxFu0zW5GMnc0HioLO23WwlGvfX8qqnAJaYSiG0x+FpCNwGR1yvfos0otJjaY2iCSJXrWRUl4Spl8C1Tc+a2KGvdLrZtrLsgvZ4TzoOIjEpGTicZBXFNyUNOLfsz3HIy1/qoMuR1jnoOtJ6tMRLNmSRqOpaeralKSpp5SW+qYkFUYxHastkXhR6anMVhXJwlAe5gR31cFmxC5IV9XjNBpNjaAVgyYoJSXFvg1GkjnLwa20Fwdx2P1zWQdyRvdEVfzmSMtluvcVjPoMGo0mtmjFoAlKYaFf4tyMbupz10J1ejholhMfsnZ8HnlAWlWkZEKLLDj71SO/lkajCYtWDJqg5BcU+Da4o4eNimk2e1UB7XCRdXaV/VERlwC3rYReZ9XcNTUaTUi0YtAEpcB/xeCuV3DK/Tiw0qpkc8AcKSWdxH5+ib+Hp21v1YKUGo0mFsTUK0nTcKks99tDSG6lPhNSOSyakRwkkZ7d4eKz+MdoJ0yriTFPxVBKjUYTC/SKQRMUm/2wb0Mzbworu0gkzulb3vPt37fR66GfaMMhb2NySxg6CY1G07DQKwZNAA6ni1TnIfXaMGkepPvmNTzssFFUWIjTJbFaVLqrx39Yr/pIIQPDo2noDSCCpcPSaDT1Gb1i0ASwaX8xncUBXMKqNp2TM3z6y0ggiXLyir0BZ2mJQd4xOh4fa1E1Gk0M0IpBE8CUGevpIvZTnNAWrLaA/vaZGSQLO2/N3eZpS7RZAXCYF6EVkVV602g09QutGDQe5m7K5cI3FtCjdSotKSS5ZdDSGLRqkUESdp/86AkWyKCQCuLAYiiTjoNiL7RGo6lxtGLQePj3zxtYvP0g6/cW0kIUY01pEXRcfFIqx1h2YS7JcKH8ieWJk+go8uC4v8HkgurVeNZoNHWOVgxNnJnr9vPBwh0AlFeqXER5xXbaiEOIpBClOLfMBGDxH7M8BXsGynXe/q2/xk5gjUYTc7RXUhNn4vtLATi6TRqpCerXoWXeUlomFMLhncEnjbwXfrqXiXEzyLq/G6cencn5LlOdhAkvxlpsjUYTQ/SKQQPAhW8sYM+u7TwSN5WBFiOq+ZgJwQf3vxCAHkLVR5i9MZfDDtMm9ZGm2dZoNHWKXjFoPPwtbhZ/j/uFZa6eqqHHqOADkzMoaNGXA3neXx+X0+n9bTrSbKoajaZO0SuGJs5wy2petb0AQDyVABwjDBNSFWmumx1aw2nWlWSJvWRQSAthVHS7OzCHkkajaVjoFUMT58P4JwHoJu5lq2wPQLIwAtfiwtdXnp1wFwDznH042OJYMlJbx0ZQjUZTa+gVgwaAYyy7GG9d5NtoDW0SKh9yi8/5cOtaWrTS7qkaTWNAK4YmztL4IaE7E9JCdsWNuC2gTVTqSGeNpjGgFUMTp4LAlBceLNaQXXEpGYGNrvB1oDUaTf1HK4YmTjkhNpjdFdtCYVYat68GWwqc90bNCabRaOoMvfncxHFgocSSRso/d4LFAk91gfLDcNGHkV+keWd4cE/shNRoNLWKXjE0cVxOB+XWFKUUAAZeoT6bdYrsAraU2Aim0WjqDL1iaOJIZyXEmfYZRk9WKS9s4V1VuW8nCP1uodE0NrRiaMJIKbE47bjMbqkWKySkRnaBxGaxEUyj0dQpMX/dE0KMEUJsFEJsEULcF6S/ixBilhBilRBijhCiY6xl0ihKK5y0EEVUxgdPr63RaJomMVUMQggr8CowFugNXCKE6O037FngfSllf+BR4MlYyqTxsj2vhBYUU5moFYNGo/ES6xXDEGCLlHKblLIC+AQ4229Mb2CWcTw7SH+9YfP+Ig6XVtS1GDXG+JfnkS5KcMVrk5BGo/ESa8XQAdhlOs8x2sz8CZxvHJ8LpAkhAirECCGuE0IsFUIszc3NjYmw4Tj9+bmMf3lendw7FhwjdtBGHKZMJNW1KBqNph4Ra8UggrRJv/O7gZFCiBXASGA34AiYJOWbUspBUspBmZmZNS9pBNxm/ZIJhZ8GtK/dU8Dm/UV1INGR8bztNQDSy3PqWBKNRlOfiLVXUg5gdojvCPhEQkkp9wDnAQghUoHzpZQFMZYralwuyR22LwEor3yNRJs38ve8l34lgQpWPXVRXYlXLVpbCgFon+yvqzUaTVMm1iuGJUBPIUSWECIeuBj4r3mAEKKVEB5n+PuBd2MsU7WwO1ye4yEPfempdfzrhv18FD+FVYnXkVuk0lWX2B3c/PFy9haU1YmskbLN0gUAy6n317EkGo2mPhFTxSCldAA3Az8D64HPpJRrhRCPCiH+Ygw7BdgohNgEtAGeiKVM1aXY7rVurUq8lm/mLmXRtnw+eP8tBlk2AfDRpx8BcNW7Czlx3WN898N/g16rvmCXVnYlHQOdh9W1KBqNph4R8wA3KeUMYIZf28Om4y+AL2ItRzAmf7WCRStW8ONjV4cdu2pnPuZClyPmXcGsU79havwznrbbc+7gyql9OW3361wa9yulG+dTYj+flIT6GUeY6CrHYdUbzxqNxpcmnc/g6OWP8KP1Dig9GHasffcqn/NWlXuoyFkeMG7ajtOZFPcdoCqh5a6cETCmvhAvy3HGacWg0Wh8aVqKYf13MG08VCrb/yjrCgCkPbxHkaXskM/5NttRlKz6Iey8+D1LqiFo7HG6JEnYcWnFoNFo/GhaiqEkD7J/96wQbIZX7LIFs9mQXbXLprPSdyO5W+Umz8qgIq0zdhE86VxZReWRSh0TyiqdJAk7UisGjUbjR5NSDGsOKRdTR3EeO/fspYUoBmDQ4tvoNa0P2ItDT64IXbYy/q7VzO56h+fcftxVbDlLbZu03PhJDUhe8xSVV5JOqU6Ep9FoAmhSiuEQKmvobzM+pfObgRXKnAW7Q092lAOwt/lA5vfzOk7t7jQBAFtmd09bwil3033QaHJlOs1dh6g8XMV164DNG9ey54PrSRNlkBSkRKdGo2nSNCnFkN6iDQDW3QuD9hfnVWFOMgrdt5v4KUm9x3qaXW36ApDR/Xjv2NQ2CCFYc+pUAPavmkV9wvXRhRyf9y0ALfucWsfSaDSa+kaTUgzW1FYAdCB4rqXSA9tCzhXGigFbEs1btva0dxx3DwAJqab0TlZV+Ca9XQ8AKkKtGCrLYfMv4AhMzLd51jSY3Kxq81Y1kaZMJa16Da/x62s0moZNk1IM8anKbNKTnQCscmXx48j/8t6A6QC0m/P/7Z17fFTVtce/Ky8ChPBMJEAw4aGIoAIBRUUUQQUVUSnKB0WtF/tS672196qo1fqkaqtWlI+9UrFCUS9aqVYFFdHWBwLykKIQFBB5iA+QV0Ie6/6xd5IzyUzChMwkMuv7+cxnzqyzzz6/WefMWXP2Pnvt6yNuWxkYUpqT0bxqxjNJcv0Wndu24J6S8fyt/58r17Vt25692ozyneHnQ94w8xqYOZadbz0cumL/Xnq+80sASgoXgDZcyoqirWvplRTIa5iSFrmwYRgJSUIFhvTmofMT59/0ISNPG8pJg4dUGefdAmXVcvh9uZSCHa9QSjIkp5DhB6wtL6/qV2jdIpUb75rGmNEXVNo6ZKazRruQuW1RWD2Hr38WgG+3rA+xr/n7A5XLqc9dQsmzVxzoV6yTD2ffWbn8RXnjJCM0DKNpk1CBoVObqkdK35YCWqW7f/7dsjLYpf6xzXcfRu/tCmWBx0z/dBpZJZvZKe4JnuapydzTfwHlP3611v1lpqeysvlAOuxeA/v3RCyXsmcr/O8I+PYzKNrJESvvD1mfuvoF+NdD0XzV8KjSe8dbAFxf8hM2jn/z4Os0DOOQI6ECQ0py1dfNuXJm5XJSkjB+/+TKz1KyB75eW2P7b9Jy3HoRbhzdn3552TXKVCetfT5JlMOe0H6Nonl3VC7nbp0PmxbBw/0om3pi+Irm3wq765iH4tmJ8PTYiKv3frmK9uwA4Jab7uSko7rWqd8wjMQjoQIDwG9KLuM7zaBn58NC7LdeNYG8olmVAaJkV82L8O7m1ecYqpvUDDdtpu7bEWJPf/f+cMVJ3uWejFpW3p2bS6o1Id3fw3VI7/ii5oYL74N/vwiF891APqjRN/HvVcsB+EXJtbRuaX0LhmGEJ+ECw6ael3JG2gyQ0DmEBuW3Y/29ZzP+tP4A6Fs1p57O3R/5qaVIlLd0dxUlW1eHXb+0vEdY+5ZuF7Kp+3jOKJ5Ss84Vz1UzlMOCqr4DCl+H2RPg9jYQKFv6jdP/q6smRfMVDMNIMBIuMDxx+UA+nDw84vrUTHcnkbbpPcDlFKpgRc+fR72/4qy+7NVmlHz+boh9XXkOL5WdwPfassY2q8tz6TpsEtMuLeDCM4fVrHThvaF9INurBZ0XfgKfvARA0eqqfpCkHRvYrc3Jz+0S9fcwDCNxSLjAUBdfayZfa6b7MHsCzDi3cl3a0edEXV/vLu35XDuyc9v6EHum7CU7K4snykayoTybs4vvZpO6cRa3llxB95z2pKcmc1SnthxRNIO3y/ry97ITWFvemaSyYlgYuJN4zPVLzCodVllHBclrXoZXbwJVWuzeyNbkjkiSHXbDMCJjV4hqDOuVzQOlP3IfPnmJ5A3vAHBHySUMOeKwWrYMz9GdWtNa9tDpq7dh6VPw8Rz279xGO75HMrLZlzuUofsfZPiwEbxY5i7wD086s3Lq0JN7dOCcfnlMLLmRa0qu5b7ScQCUblwEM8fByqqpLJZqT36+341/mFE6gjllQ0gt2wfvT4X3HqF18Wa+axZ9P4lhGIlF05xBphHp3KY5fXM7wNZQe/9j+tarvrSUJLqI7wyee42zAQiUtchiyrnH8NHGHYwd0IX8N8bxYtlJPN+5Ko9TUpLwwLhjGZTfjmFHZTPoLni9rB/D1y90Bda+Vln2qqtv4oJpH5BXNAuAG1L+WiVk3s3kAp+1PKle38MwjMTB7hjC8H3WgBq23M71/6f9aOnosPZmzTPonpXB2AGuzX/i4HzWaC4t05JDyokIFw/qSnardB6d0J9F5TUTAL5ZdhyZLdKZNel4zj22E0tvGcGqnPNrlCvNzKv39zAMIzGwwBCGdeUdK5c3lGfzeOnZtOhxSr3r+31p+LEF+3pdEPL5ttFHs+7uUUi1J6aCjOqbQ2Hr0LEO+zSNx3Nup2PrdI7p0oY/ju9Hu5ZprCvN5uaSKzi7+O7Ksultom8OMwwjsbDAEIZhvbIZWDSVU4r/wND9D3J36QQ6tav59NCBUi4pDCyayg5tyS0ll1faW7XMCCknIiQnRQ4Kldsd1q1y+eTiBzmq+EmG9cmtUW78oFyeLhvBKs1jt7pR36lda94NGYZhBLE+hjCM7JvDz2gL/knVwd3a0yKt/q56+dohjHzoHY4rfhwQTklaQUf5ll45repV3/G9upK3elaI7T9O7laj3LiBuSz4dDvjCnLp8/R0AF7v1L1GOcMwjCAWGCJw1/l92L6rmOuGH3HQdfXq2IqxA7rwf0s2cfVpPZi04FeAsD65fjds6alV290xpg+XHN81bPNTs5Rkpl8+kPLAWIxgvijDMIxwWGCIwITjD2+wukSE3114DEN6duC0Xtk8sqDwoOobc1xnPtq4g5+e2p3ObeqeszkpSfjDRceyeUfRQd35GIaRGIg2YK7/eFFQUKCLFy9ubBn1Zs6STbTPSOPUI+tOwmcYhtFQiMgSVS2oq5z9fWwELhxgKSkMw2i62FNJhmEYRggxDwwicpaIfCoihSJyQ5j1XUVkgYh8JCIrRGRUrDUZhmEYkYlpYBCRZGAqMBLoDYwXkd7Vit0MPKuq/YCLgUdjqckwDMOonVjfMQwCClX1M1XdD8wGzqtWRgGfzpTWwOYYazIMwzBqIdaBoTMQnG5sk7cFuQ24REQ2Af8ArglXkYhcJSKLRWTx9u11THFpGIZh1JtYB4Zw+R2qPx87HnhSVbsAo4C/iEgNXar6uKoWqGpBVlZWDKQahmEYEPvAsAkIJvHpQs2moiuBZwFU9T0gHeiAYRiG0SjEOjB8CPQUkXwRScN1Ls+tVmYjcDqAiByFCwzWVmQYhtFIxHzks3/89EEgGZiuqneJyG+Bxao61z+l9CcgA9fM9N+qOq+OOrcDG+opqQPwdT23jTVNVZvpip6mqq2p6oKmq+1Q0nW4qtbZFv+DTIlxMIjI4gMZEt4YNFVtpit6mqq2pqoLmq62RNRlI58NwzCMECwwGIZhGCEkYmB4vLEF1EJT1Wa6oqepamuquqDpaks4XQnXx2AYhmHUTiLeMRiGYRi1YIHBMAzDCCGhAkNdKcBjvO9cn158tYisEpFfevttIvKliCzzr1GBbW70Wj8VkTNjqG29iKz0+1/sbe1EZL6IrPXvbb1dRORhr2uFiPSPoa4jA35ZJiLfi8h1jeEzEZkuIl+JyMcBW9Q+EpHLfPm1InJZDLXdJyKf+P2/ICJtvD1PRPYFfDctsM0Afx4Uev3hUtocrK6oj11D/24j6HomoGm9iCzz9nj6K9I1Iv7nmaomxAs3wG4d0A1IA5YDveO4/xygv19uBazBpSK/Dbg+TPneXmMzIN9rT46RtvVAh2q23wE3+OUbgCl+eRTwCi4P1gnAB3E8fluBwxvDZ8ApQH/g4/r6CGgHfObf2/rltjHSdgaQ4penBLTlBctVq2cRMNjrfgUYGQNdUR27WPxuw+mqtv4B4NZG8Feka0Tcz7NEumM4kBTgMUNVt6jqUr+8C1hNzUyzQc4DZqtqsap+DhTivkO8OA+Y4ZdnAGMC9qfU8T7QRkRy4qDndGCdqtY24j1mPlPVt4Fvw+wvGh+dCcxX1W9V9TtgPnBWLLSp6jxVLfUf38flKYuI15epqu+pu7o8Ffg+DaarFiIduwb/3damy//rHwf8tbY6YuSvSNeIuJ9niRQYDiQFeFwQkTygH/CBN13tbwWnV9wmEl+9CswTkSUicpW3HaaqW8CdsEB2I+gKcjGhP9bG9hlE76PG8t2Pcf8sK8gXN2PiQhEZ4m2dvZ54aIvm2MXbZ0OAbaq6NmCLu7+qXSPifp4lUmA4kBTgsRchkgHMAa5T1e+Bx4DuwHHAFtxtLMRX70mq2h83094vROSUWsrG3Y/iEjCOBp7zpqbgs9qIpKMxfDcZKAVmetMWoKu6GRP/C5glIplx1BbtsYu3z8YT+gck7v4Kc42IWDSChoPWlkiB4UBSgMcUEUnFHfCZqvo8gKpuU9UyVS3HJROsaPqIm15V3ezfvwJe8Bq2VTQR+fev4q0rwEhgqapu8zob3WeeaH0UV32+0/EcYIJv7sA31Xzjl5fg2u+P8NqCzU0x0VaPYxc3n4lICnAB8ExAb1z9Fe4aQSOcZ4kUGA4kBXjM8G2XTwCrVfX3AXuwff58oOJJibnAxSLSTETygZ64zq6G1tVSRFpVLOM6LT/2+694muEy4MWAron+iYgTgJ0Vt7kxJORfXGP7LEC0PnoNOENE2vomlDO8rcERkbOA/wFGq+regD1L3FzsiEg3nI8+8/p2icgJ/lydGPg+Dakr2mMXz9/tcOATVa1sIoqnvyJdI2iM8+xgetF/aC9cL/4aXNSfHOd9n4y7nVsBLPOvUcBfgJXePhfICWwz2Wv9lIN84qEWXd1wT3osB1ZV+AVoD7wBrPXv7bxdgKle10qgIMZ+awF8A7QO2OLuM1xg2gKU4P6RXVkfH+Ha+wv964oYaivEtTNXnGvTfNkL/XFeDiwFzg3UU4C7UK8DHsFnRmhgXVEfu4b+3YbT5e1PAj+tVjae/op0jYj7eWYpMQzDMIwQEqkpyTAMwzgALDAYhmEYIVhgMAzDMEKwwGAYhmGEYIHBMAzDCMECg3FIIyJdRORFn2VynYg8JCJpInK5iDzSBPSNEZHegc+/FZHhjanJMCwwGIcsfsDQ88DfVLUnbsRqBnBXjPaXUo/NxuAyaAKgqreq6usNp8owoscCg3EoMwwoUtU/A6hqGfCfuME/LYBcEXlVXK7/30DlSPCXRWS5iHwsIhd5+wCfRG2JiLwWSFHwlojcLSILgcnicvkn+XUtROQLEUkVkUki8qGvd45fdyIuB9R94nL9dxeRJ0VkrN/+dJ+8baW4hHPNvH29iNwuIkv9ul7xdKpx6GOBwTiUORpYEjSoS0q2EUjB5emZgEvo9iMRKcClJ96sqseqah/gVZ+/5o/AWFUdAEwn9K6jjaoOVdXbcSNkh3r7ucBrqloCPK+qA1X1WFw65StV9V3c6N9fq+pxqrquokIRSceNxL1IVft6vT8L7PNrdYkPHwOuPzg3GUYoFhiMQxkhfFbJCvt8Vf1GVffhmpxOxqUWGC4iU0RkiKruBI4E+gDzxc3sdTOhCdSeqbZ8kV++OLCuj4i8IyIrccHo6Dq0Hwl8rqpr/OcZuAlmKqhIsLYEN5mMYTQY9WkTNYwfCqtwuW4qEZcyORcoo2bQUFVdIyIDcDlq7hGRebiMs6tUdXCE/ewJLM/127UDBgBvevuTwBhVXS4ilwOn1qG9rmkii/17GfY7NhoYu2MwDmXeAFqIyEQAnyXzAdxFei8wQtx8us1xncD/EpFOwF5VfRq4HzcF5KdAlogM9vWkikjYf/yquhuXFfQh4CXfrwFuqsYtvllqQmCTXX5ddT4B8kSkh/98KbCwHj4wjKixwGAcsqjLEHk+rv9gLS5DZxFwky/yT1y2z2XAHFVdDPQFFvkmo8nAneqmlBwLTBGR5b78ibXs+hngEkKbmG7BzcY1H3fRr2A28Gvfydw9oL0IuAJ4zjc/lQPTMIw4YNlVDcMwjBDsjsEwDMMIwQKDYRiGEYIFBsMwDCMECwyGYRhGCBYYDMMwjBAsMBiGYRghWGAwDMMwQvh/i4Gp560IXAcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a30014990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(y_test, label='True')\n",
    "plt.plot(y_pred_test_ANN, label='ANN')\n",
    "plt.title(\"ANN's_Prediction\")\n",
    "plt.xlabel('Observation')\n",
    "plt.ylabel('INR_Scaled')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### LSTM's Prediction Visual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rt5bGn5jdHTxZOP01jc5v/ZzLd6sKAPg87VpGfnkuAE/6rwvPOb1XHuf+oR8Af92/H7/963AOH9a9Trkz8VLu7gRtukY6/zgdbimEGzdCVhcksxMXjGnL9D+PDk/5JI69ojnSMgPcPr6+hiFop+k+Ao68q8HLrrjiCg4++GD23XdfDjvsMM4999yYegy1cdJJJ/HGG2+w1157MWbMGNLS0nZEaiUpRCkGv590tzOFsuz6VPqDMf+GX/+ex9nP/MABg7vEzLvuLetveokMYE+zynoR+8rA4QR3JuIv5STnV7wT3I8gsf9NHIRIs20Q73luJouo3chTB3PE5H+y5uROyN5HA5Dmsr4zn7BXL95esCE89ddph/HgrBVkzPMScGaAM+oVmtHBkqWKNl1Iz/mCY4+bzqTBXRl7x2cM79V2x/+hmhDdMSSZc889l6VLl3LyyScze/ZsJk6ciNdbv1/zKaecwhtvvMErr7zCaaed1gSSKgkTvWPwVtQxUUmEp79dE1MN79k5VuGbr3+3DMH3nzwqpjhS32wDw46Hvc+BfS62OtOykW053Od+nL84P63xjKv2i3yzH+VYXVOIz/+FfHCZde0rx/gsw7jHFXlFfnjpfmSnu/EHQ2TiJeCyj66Oug+GHA0DJsfes2AVFOfCvP/SLtPNHwZ2pqSy6VKD7wwtc8ewA9/sk0nPnj0577zzOO+88xg+fDiLFy9m773jnE1G0b17d9xuN5999hnTp0/nu+++ayJplfqIjngO+iqADrVNVRLg3pnLmbu6gBfPnwBAmiv22/6JnddRmfYSN1WeAQieYAV42sTexB2xL3ST2LiBif07ct7YDjCfxLizB6cBuc5T+EtZkDOn3EIwrT3De7UDYFC3bDLw4kq3ZRj/V+unOt2GwcafYeUsGHs+7TLc5DRx+dEdRXcMSeaTTz7B77e+DW3evJmCggJ69UqseMett97K3XffjdOpRxXNiWhvpIC35rm20nDmri4IX0d/S9+nfyd4/1LOYAZ9xPJgcpVtCscPhHFGah7sO7gHAL07WOm4nz1nPBnBWozcu+0T2/ZG7BjXuF+n6+q3GP7SKEb1iRz/njlhN/bu4aFtdru6P9RJz1i/V30BD+9N23Q3xbpjaH2Ul5fHGJqvvPJKcnNzufzyy0lPTwfg3nvvpXv3ug1bVey7775JkVPZOaIVQ9C/a+bCSSVuAnjw89qAT5m6el9yTReOHN4jPF51vu8iwCPFl0KplRyvG9u4e3QeLAPyq6XL7hxJZzhi9VO8dviBjDvwIILG4HY6YEMcN9SrlkOgEqZHefxNr8X7r2Ib5C2HZw5HgDZdhoKnW90ftGO/yHVxLqMr5/JKRc8dStrX1KhiaERCofgeKg888ECta2bPnh3TnjRpEpMmTaoxLzqVt5JaHET+OwcDqhgaQjBkeMdzC8MdObAB7nX9ymn+m8MRzRAJRusu2+hUGlEAJw5xse+yW61G/0mxNx75ZxAnvDsVgAkLboC0dTj2PgcqKmDG1da8EafAr3aq9OyoL2juNuAvg/Ja0nDf3Te2nbcM2u+e8OcGOHHZVczicsp9h9EmrXm/evUoSVEaSLRiCPh3vQRpqcQXCFlKwWYf5xLASmRXRVGFn9F92vPJJeNj1p62R3qkMW5K7I2dLhgd5aRRnAuf3gT/7gX3DYz0H3VvTaFuKYSTIgGlTP6n5fFUH2W1B+eFOfJe2OOP4eZ/PdMTytuUapKqGETkGRHZKiJxozpE5FgRWSQiC0Vkvojsl0x5FKUxEBNRDCHdMTQIXyDertqwqaiSYMjgC4TI2VrMqaGPyfIVxE5bEeVtlFYzy0C99BoL6XHsAg4nDDoMxvwF/v4r7H8ldNnDHqx25NP/IBh7vnXtTMCFfMIFcMKTMOGicFe0B1ZzJdk7hueAI+oY/xwYZYwZDZwHPLUzDzOtrAZva/u8zQUTpRjUxtAwPl68qUZflXtpUYWfV39cx7jyrzmt4GF4/pjIpOyelncPQJ+JO/bws9+zXI0P/gf8+ZXYMYcT/vQQtN/NamfakdAZUTFHu/8BTnsV9vyT1Y4yeNeJOwOOvIsNA0+n0GRxxIPfUFTevJVDUhWDMeZroNacs8aYUhN5u7VhJzIap6enU1BQ0GpelsYYCgoKwkZtpemI3jEEfHqU1BCuf7tm4Om/3M9zmfNtCst8bC6qJF2qKdtLfoKsqAjjvc6o/QEnPx+///gnIruMA66GoUfFn1dFhq0Yuo+EybfAgIPh3BngToeQbQ9xZ9Z9j+q3bNuJtpQDhsUbm3e21ZRbQETkeODfQFfg6DrmXQBcAFbKiOr07t2b3Nxc8vJqZkdsqaSnp8d4QSlNgwlF0jqv2rqd4SmUpaVwpftNPi+4k8+WbGFU9eObzgOh516wya6dXt3FNJphx4H7dfjfKZG+M96CQYc0TKA2neD016HPeCuief+rImP9DrSOk6L7EqBjpy4gIdpQGTc1eHMi5YrBGPMO8I6IHADcBsT9L2iMeQJ4AmDs2LE1tgVut5t+/frVWKcojU7UrjSNXcMvfVfg/OetCLT9nVGxIe36WL/3OAZ+ehZ6jIbOg+q+0YCDYfiJMPY8KMq12jvC4MPj9zvdcEztnoa1km4dS7WlnL++MJ/PrjiAQd2yd0y2JNNsvJLsY6cBItI51bIoSp1EHSURbN5nxc0NEfBhn81f8lO4f4uJnOW3k6jo4FNftH53s5NPHnht/Q9xuq3gsr77wag/g6OZvOZsw3fV53vh+7XhIWMMr/24jtV5sYkCU3U0ntJ/MREZKHakh4iMATxAQd2rFCXFmGDUpRqfE8UYg0OECk8nGHW6dURkE4p6FXV12TuGE56yjpAAsrvBtCIYWutpc/Mno2rHYCmGjm084aF3Fmzgurd+5eD7vwr3zVmZT78bZvDdylpiK5JIst1VXwG+B4aISK6InC8iU0Vkqj3lRGCxiCwEHgVONa3FeqzsukTtGIzuGBLGFwwRDBncxg8u+6W4zyUA9JBCOmMZZP88xAkd+sLIk1MkaZKwdwxPnDIAgOmfr2DcHbNYlVfKla//Ep5W5g1gjOG2D60Yjzd+ym1yUZNqYzDG1JkW1BhzN3B3MmVQlMZm7qo8TqzyVFTFkDCVvhBd2E6mvwCCtm3m8Dvg+0cAuHas0H/PwcjrH8HABhqLdwUyreI/7d87hw48xjbaklfi5bhH5sRMO+fZH/gxZ1u4/c6CDdxz0kgrtUcT0UwO3xRl1yGmpKceJSVMhT/INS67MuGGqFSnZ1plMU8Z2ZGxxi6Z2X9Sk8rWJGRGzKdHOX8IX5d4Yx0YopVCFRe99BO525ouYaMqBkVpIM6olBh9iuPXCFZqUu4LcIrLPkN3RUUNd+xv/S7ZHEmON/DQphWuKfBkwmgrBiOD+PEvvdpH0od3b5vO//5qpSKftXRrjbrVyUQVg6I0AGMMjqgdQ4WjYUFOLRFjDG/+lEuF7ZufV+Ll3zOWxqS/+OPD38YYVunQN3Ldxq7U9sFl8OUd1nVUfYUWxZ8eBuBm98uc2L1mrqWXp0wIX780ZTz7DujM1YdZmWO/WZHPKY9/z5fLE8jRtJOoYlCUBhAImfBRUgAnEtQ4hvOe+5Gr3/iF2z9awpbiSsbdMYvHv17NHNubxhcI8esGy7CcE7JTVdsvSCB+3iOnp2ZfSyCq9Of9jofZo0ek1OdQWUeXwCbm3TiZty7al4FdrRiHSw4exMjeluH6hzWF5BYm/0hJFYOiNABfIBTOrurFjYTU+PzlcivbwMvz1jHhzs/D/aX22fn28ogdxoubwt2PqJnMLrtHtXZiNUt2SRy2z09aFm9dtA/zbpzME3/qyidp19Pmsb3p1jadvXfvACVbYFp7WPg/Hj5tr/DythkJ5mjaGRGT/gRFaUH4AqGwjcGPB4eJrxhCIdNq8nbVRlWNhe1R2UQz8SLxcgxNviVyfe2amLraLY6/28mmN/1C5qOj6RbaymFLb46MF9uJBhe9BhiYcQ3tMyM7qKZQDClPiaEouxK+YIjjnJZ7oV/cSKjmUVIgGGLgTR9z0aQBXHfE0KYWscGU+wI8NnsVaW4ne/Vpz74DE08+8PdXF9Q6dt1bv7K+sIJ9B3QK96WLl4w2cdJAjD4dtq+zdhJVmU1bKm2jdkdF6+Gx/aFye6TvgaFWSu+gbaD2lZIZjNSryGqCIj+6Y1CUBuALhBjlWA1YisERRzF4baPrY1+talLZdpSX5q7loS9Wcu/M5Zz+1Dxe/D4noXXeQJB3F26s0V91Hg7wyJcrOf2peQBcctBAOrgDpGfWkh9o0vUw8aL4Yy2ZeDvLYKzXkvuxfdlDrBQae+/WIekiqWJQlAbgC0Y8bcoCTgpLyigsi41lqFIMu8pJkqPasc0/3vstoXUlcQrbp7sdvHj+hDiz4cpDBuEKVDQ4XXWLxxuVgrt6rYnT37B+l25mRvt7WPPvo3A4kn/MpopBURpAtAtmJW5cBPn3jKUxc7yB5p1SuTo7Wpi+NEoxnOD4mgO7VjD/5kNp5/Cycq836EIkUGtGt//iuK0DYCx//tbOgddBVncrpXc0B90YuT7xaRh8mJX+G5CKbcja2CjpZKGKQVEawNaSyBa/nHQ8EuCbFbFJzrz+eOUrYyn1BihpJrV/q6uFPaNcKOuiqk7zOc5PeMDzGM/5ryFr2zL4dy9cS9/hjeGR6N49i76JLNQdg6UArl4OE6bG9me0hxty4cKvrdThYKX/Puo+6/r7/zSJeKoYFKUBrNhSEr5un90GF0FC1c6MvHHrGscy+l+fMmLap/XOawqqn3i1z4zv9XLD24sYfNPH4fbF/7Oivqe5XwBAKgrhsT+Ex/v26kHOXUeTc1e1jKiqGCIccK3lhfXXL2DfS6HbcEjLhh6jYj2zxk2xfi//CPJXJl0sVQyK0gAyPRGPkJC4alEMQbqwjSzK+d+8dXHvEwg1HwOEPxiryGqrLvbKD+vxBUOJu+GW54O/AgLV8knpUVIEh8Pywuq1Nxx2e0wAXAzRSuLHJ5MvVtKfoCgtiGAo8hL1eNJwEyS/1Ecw6kXvDYT4Mf1iPk+7mhvfabr8NjuKz1vJf9wPsmr4szze7V383so65/e7YQbfr7LKptzreqz2iT8+BQ+OtFwyo6kquqPsGFndkv4IVQyK0gCiFUC/bh1w26U9f4sq7l5lY+gm26mPb1akvkZ5VvEqjnL+gHPlZxxe9DoPl/y9xpziavaQmb9tZkK/jpzs+rrmDbtHvfjLtsLDY6zrw++Ecz+GLoMbU/zWQ1ViwX0vS/qjVDEoSgOI3jE4lr7HIMcGjnD8wLGPRrxFGuKVdNbTP9Q/Kcl4A7FHQ/1NzeOv1XllMe2SykB8d9zd9oXzP4uciUcz4SLYfd+dEbV1c+abVhU7pwa4KUrzIlDzmOUxz4MYY0U8A2wsip3zyeJNdd7yL8+kVjn4vbUnZfti2RY2F1XGGN0Bfswp5MecaiUnD7gWzvvYyox69P0w6LDIWM8xzaf2slIv+l9KURpAekVUymO7LGUVZbbR9qc1sWXLp75kee8Ulvnoe/1HvDxvLXt6tjDZ8RMAX/2eF5NorikJBEOUl5fW6A+GDHklXs57bj5/f20B83O20T09wCixPGLWFZYz0REVv/GPfDj4ptibnPIieLKh+0g47dVkfgylkdFcSYrSANK9lk0guN/VOHuOCpelBKj0B2mX4SYYiMQ6dKaIfNrR9/qPwn03vbOYnPQrwAN9K/8HwG0fLuX+U0Y10aew2FpSyfg7PmeSYytUy3KdX+pl4/YKAOauLmTu6kJeavsE+/Ete1VaZSlf8dwRWeCM4+LqTocbm75esbLz6I5BURqACdr2g/4H1hgLu3n6I0dJ89MTy/3z1s+5fLioZt6hZLJhm/XiT8PerQw5ivKM7pSadM599kemfbAkZv6AoJUj6tIOc2lLlM3BrkqmtBxUMShKQzCWF5LD4YReY8Ld42QZxzz0jeXjH4xftjEe2UTO9y/5X+2ZSneGa9/8hfOf+5EKXzCc0mNrSSXH/+c7ANKrFMOht1E8+GQy8bJkUxG/rI/1quoRtBTXeRXP8ZD7EQp7TbYGDpmWFLmV1KGKQVEagAlZuwJxuqESZIsbAAAgAElEQVRd73D/re7nKPMFeXDWChas3pzw/X5Nj+O908i8Pj+Xz5dtZY9bPuHPT3wPwN9eitSqThfbFdWdTvfOHXGIIY2Ie+ptxw4LFyeqYmLbPDr26AuZnSCra9I/g9K0qGJQlIZQVcpTYiNU93Csw0WA6Z+vIL1aoffORGIc9unfiep8f2LTlQf9eZ21C5i/NpLgLlyY3pUBnjYAZBI5Dsv0uOgpsR5I6WUboXijprdooahiUJQGUOGzv0nHSV0wRKwI3wmOZTH9VXaGnLuO5qqBGxgs6yO1j4EeH53NlP360cZTSzqEBFmxpYRfc4vqnbcqz/JCcjmEnLuOZtrkbiAOK4Gb/aJvI5ayeMV9O38qfJYp/eME6/3+cc2oZqVFoIpBURrAlu220TWOYqj65t21KuK53wHhsQudH8DCVxj79Xl8mnYdobS20K5PePyALS/gDYQorvTvcNruQ//va/74yLf1zpt8/1eAna/p+//AylnQpov1mezqaR0owU2AfZxLcM+5j3Ncs8DhhrR2dd1aaSGoYlCUBPEFQqzZan8jd9T09M4Qy4jrwU/I4YFDbw2P3eB+Bd6NpFju194JPUeH2wes/y99zEZGTvuUM56c12gyVw9Mi+bKQwbBzBtg4wJoY9sJsq2ykxe6P2KwRO0G1n4LGDj7XfBkRfpvrDt4T9k1SapiEJFnRGSriCyuZfwMEVlk/3wnIk3ryK0oDWDoPz7GWWWElZo7hnOcMwFo7zGIywM996JoxHlx7yV5y6wz/Si+TLsKiD3//25VPqXe+m0QK7dGFMCiXGvHUukP8v4vGxks6znWEbuTyLnraC7zvB/pqKqz3LYnAMc4vuejtGoBa6GA5Yl19P1We/SZmim1hZLsHcNzwBF1jK8BDjTGjARuA55IsjyKskNsL/cRMkQUQ5yjpMnOBVzaYymnDc9CxPrT8gzYr/abrvm6zhiAX3OLOP3JeTz/XU698m2KSsPx7Bxr/jNz1vDwFyuZ4bmB6Z7/cL5zBhc73+WyyYNg40L44rYoWazjpbiZO6N2PgDs8ScYdToceG29cim7JkmNfDbGfC0ifesY/y6qORfoXdtcRUklVfWNI4rB/tNp1yfGAHvVttuIqmhJRnbH2m86fgrsfa7l7vnt/wGQRTmlWN/CNxVZAWg/Re0gaiPdHVFUVfUS7vlkOQAusWT+h/sla8KhT8LTh8Xe4OTnrd8i8IfLYc70yFi3YbFzPZlw/H/rlUnZdWlONobzgY/rnaUoTcTqvNLwS/ayV63gswypcu1Mt35f+hNcuTTecouM9pHrcz+Gi6MS5u1xLLTpDIdMY9tBd1nT8dGvs+UyWnWElO6u/8/U6w/hwU8Hinl34UZCdRUCCgVhvW3HuHKplbFz2HFRMldTZn1tI/oh/6pXDqVl0CxyJYnIQViKodZ9t4hcAFwAsNtuuzWRZEpr5buV+Zz+lPXynHrgABbY/v/dpRAjTqRNZ2uiK61mQXewir0DpEcpht33hfLCSLvTgPBlh7ZWneUz9u7G079Zymd7ueUaOz+n/h1DpT/Iw+6HOdw5n76VL9P/xhnhsW2ennTwRaXbuDXqxW/bFGIYNwVm/dO6Pvzf4PLAP7fHVhFTWjQpVwwiMhJ4CjjSGFNQ2zxjzBPYNoixY8c2n7qISotkdX4kF9BjX62iXYabogo/l7jes4okR9sYqnYP0VQllUuv5t6Z2REu+Ara71btHmkAnLPyUkaHuhAKHRbOuNo2I34N5mi8gRBHO+db8ymnGGvXke4iVilE48mO358W5XU0xDYRqlJoVaT0KElEdgPeBs4yxvyeSlkUJRqPM/ZP49jRPTnfOSP+ZBE4qJoHz+725re6YgDLTTWz2nGNrVzaezcyyfELpb4AL81bR1e2sb24uMYttpXFpumu9EVSWExy/BK+vnW/WryG/jYPrlwSfwysNNrnfAQd+9c+R2mxJNtd9RXge2CIiOSKyPkiMlVEqhy6bwE6Af8RkYUiMj+Z8ihKonhcsX8awZCJGG9Hnlr34pu3wu77WNe1FXevTjUFsnRjMWVlpfyQfjEXB16MKR26fHMJe932GQ99vgJjDM/NWcMDMxaFx+9v95olpqzilLnHEZeuQyG9be3yON3Qtw6PKqVFk2yvpNPqGZ8CJD+LmKI0ELezpmII46xWvABg2PHw5R3WN3H7WCjM0Q/EGqHjUc3z59Qn5tJHLNvCua6ZHPjyz3x1zUEA5BRYx1wPfPY7D3xmbbTbUwr2iZa7bTeWXHE4Wx7Yj3DKo/a7wfZ1cPZ70H9S3bIorZ6U2xgUpTnidAjdKCSfdgRxMqowymEuXlGazoMs7554jDu//gdWM2A7CdIBK6eRzzhZW1DOii0lDOqWHU6dHU04dTbAlsVk4qNfZZS31N/maTCakjDNyV1VUZoNDl8x89IvYVX6WTzlvpceZVEv2XhBYDuLCFz6M8HJlktoR0p44I9WLiWPWLmT5qy0MpzGi4ROt9Nx4LCV1p09IoOudFUKSoNQxaAocfjPp5Ez+0OcC1icH/UtfcKFyXlopwE42/UCYGJPJwOz/FGDhs7Z1hFVSaXV37NdxBtq9uUTrYvj4gSe3aDlNZWGoYpBUeKwcXtlTHursW0Ely2MH7fQWNi2iIePHwA/PR/u7sJ2Kv2WciqtDNBXNtO7nWXrOGVsbwjY8lb3guo9Pv7Rl6LUgSoGRYmDm9jjGldVKoxkKgWIvNjXzrEzmlqc7PyK5Zstt1Vf+XZmp13J/wJX8NY5e3JPj6/g2SNtwdPh6pUw/ESrvS0nufIqLRI1PitKHFwSWxPBid2Ok267UanyeKqKPPZkga+Ua92vM/bnU5iyf3+WrFxtyVi4gr1fHR273pUOWV3gxKctT6ehxyRXXqVFUu//5SLyMFasZ1yMMZc1qkSK0gxwEazWrpY8L1l0HxHbvmIx3N0XgL9lfMqEO30Ml3xIq7kUgDQ7mlkE9r8qaWIqLZtEjpLmAz9heUmPAVbYP6OBHSs1pSjNnBj3T2Cww86gmmzFEB0Qd8ZbMUdXoyut3E3ZYmVdxRmlHcZfACc8CV33SK58Squg3v/LjTHPA4jIOcBBxhi/3X4M+DSp0ilKirgq7b2Y9vHOOdZFopHMO8MJT1k2hoGTrfZ1OXB3X76ptJLudXDYhuY+4yHnG+v6qHuTL5fSamiI8bknEJ11K8vuU5QWxx5mZfyBpkgmN/Jk+OODkWdldMAvbtKw3FSnTrQzu1YpjjFnJ18mpVXRkH3xXcACEfnSbh8ITGt0iRQlxazcWspAKax/YhPiNn6muj7grsBp7FGVf2+vs2HCRZYnkqI0IgkrBmPMsyLyMTDB7rreGLM5OWIpSurwB2umnEg1FaSTQSX7OH7D/ekdVmd6W41RUJJCwkdJIiLAIcAoY8x7gEdExidNMkVJEYFglBPeuc2jqOCboQPZZrJ4xXNHpFOVgpIkGmJj+A+wD1CVMbUEeLTRJVKUFPPbxiIWhfpRmdndqrp289ZUi0RQXLQjUjyIQ29LnTBKi6chimGCMeZi7ES+xphtQJz8w4qya3P9278SwEllu0FWR/U02ingkOF9cIi9k+m7P/xBw4eU5NEQ47NfRJzYwW4i0gVofoexirITeANWaM4Yx0rYFOWZdNx/oU3XFEkFvTtFFdUZpyVMlOTSEMXwEPAO0FVE7gBOAm5OilSKkiIqfEEk3ved0ac3vTDRRBcHGlZLVTZFaSQa4pX0soj8BEwGBDjOGLO0nmWKsksRCBk8dgK9kgF/jAncSSn+8lRLoLQiEsmVFF21fCvwSvSYMaZ5OXwryk4QCJpwIFmg59gUSxOFtzjVEiitiER2DD9h2RWiQz6r2gbonwS5FCUlBEIhZqddAYCjOQWODTgYfnwq1VIorYREciX1awpBFKU5EAgaOopVaznkbEZOd0OPhtFnQo+RqZZEaQU0KFWkiHQABmFlWgXAGPN1YwulKKkiEIoEt7XLbjYWBovjNGxIaRoSVgwiMgW4HOgNLAQmAt8DBydHNEVpegKhiEeSw19Wx0xFabk0JMDtcmAcsNYYcxCwF5CXFKmUFs8Pawp54utVqRajBjHpMBSlldKQo6RKY0yliCAiacaYZSIyJGmSKS2aUx7/HoBz/9APt7P5lB6vCnADIKR1qJTWSUP+InNFpD3wLvCZiLwHbEyOWEpr4Ytlqc9DFM3qvKjjI6OB/UrrJGHFYIw53hiz3RgzDfgH8DSgIZjKDnH0iB4A/LJ+e4oliWXpxqJIY9SfUyeIoqSQhqTdnigi2QDGmK+AL7HsDHWteUZEtorI4lrGh4rI9yLiFZGrGyK4sotjR8X4As3rW/mL39l2j4NvhrRm5pWkKE1EQ46S/guURrXL7L66eA44oo7xQuAy4L4GyKG0AHqULeNx9wOYoC/VooQxxuCy02Hg0FoHSuulIYpBjDFhlw1jTIh6jNd2jEOtKTOMMVuNMT+CnYNAaTWcmfcAhzvnM2D796kWJUxBmY8sK6s8uDNSK4yipJCGKIbVInKZiLjtn8uB1ckSrDoicoGIzBeR+Xl56iW7q5PjGgBAn9IFAOSXelMpDgDLNpUw2LHeanQamFphFCWFNEQxTAX2BTbYPxOAC5IhVDyMMU8YY8YaY8Z26dKlqR6rNCJFFX6Ofugblm8uIdd0AqBz5ToW5W5n7O2zeHfBhpTKt73Cx0SHnTC4i3piK62XhnglbTXG/NkY09X+Od0Y07x8DZVmzcOfr+C3jcXcOWMpXq+9QzCGnAIrpfTM3zanUDoorQxwqetdq5HWtu7JitKCqVcxiMhfRWSQfS22p1GRiCwSkTHJF1FpKTz17RoANhVV4PdZiiGvqIz/fGlVSvt48WaKKy1z07Nz1tD3+o+o8DVdkNmKrVG+FZ6sJnuuojQ3EtkxXA7k2NenAaOwUm1fCUyva6GIvIKVT2mIiOSKyPkiMlVEptrj3UUk177XzfYc/arWAglGJaf7fUspbtv7x0mIZZtLwmPHPTKHae//xr8+WAJAQVnttodQyPD+Lxtj7r0zvPjt7wCYHqPB0XyisRWlqUkkJUbAGFPlNXQM8IIxpgCYJSL31LXQGHNaPeObsZLyKS2cqlQTw2QNK00v0rHcVJ0SAgx/c77Hh6F9WJ3fjdX5kejj4ooAdIh/zz89+i2LNxRTXOHnzIm775R8lf4gvcVyapCJF+3UvRRlVyeRr0UhEekhIulYZT1nRY2pT5+SEBW+IGn4+CjtJp50309X2QaAkyAdKeFa9+v8z3NHjXXrCmsvabl4g1XVLK9k5z2atpf7yaLCaqS33+n7KcquTCKK4RZgPtZx0vvGmN8ARORAmtBdVdm1qfAH+cBzEwAHOH+lu1jhLRn4aCvWDqG35COE7P5KLna+yyUvzav33t3b7XylteJKP1liKwaNeFZaOfUqBmPMh8DuwB7GmL9GDc0HTq1qiMihjS+e0lKo9IcY7Ii4o45w5AAw3JHD7LSrwv13uJ5hoORyqetdrnG/zvHOb+PezxhDuww3YMj0OMP9b8xfz9JNDa+PXFzh5++ut6yGKgallZOQhc0YEzDGbKvWV2aMiU6RcXejSqa0KCr9iXkXne76gllp1/I31/sAZOKNa1y+Z+ZyOlauIyf9DPqs/B/5pV5W55VyzZuLOHL6Nw2Wr7jSz3jHcquhikFp5TSotGc9SCPeS2lhVCSoGKpjsBLtZUTtCgD+O3sVc9NuB2DM4tvpO3/POu/z3ap8AkHDAYPjB0duK4vKyuJps0OyKkpLoTF98rT0lVIrie4YqnOr+3l8wdgMrFU7CJ+xvtfkpdX0SKqKh6ji9CfncfYzPwCQk1/GXrd+yko7bsEYwz0fW+6xoZ5jIKvrDsmqKC0FddZWmoQKb6BG3wfOQxJaG8iPLQG6rdxydf0qNAqAl0rHhscGSi67y2Y+WRyJoo7K/Yg/GGLG4k1sK/fz0ty1AKwtKKei1KoL4RhxUkIyKUpLpjEVQ04j3ktpYXi91dxO++7PBlNHzqt9L8XYp5MBX0XM0Etz1zJcVnOWy/Kc9ojfdnstZlbatXyVdiXXvrkoPN8fVcd5W5mPTLd1LLVsczGhkKGgzBv2jCK93Y5+REVpMeywjcH2QrrWGHMogDHmhEaTSmlxBCstxRBs0x1n2WYIBQlVr6k89BhY9iHcnAcuD/N8/Zk4/wqCvtiaDQ/OWkFO+s3hdhp+nnTfz8HOhXGf/dLctfSRLfSWfMbfGemfu7qQr1fk4Q8a2leVGtEYBkWpXzGIyMHAY0BPrHrPdwIvYBmba0YkKUoc8rZZTm3+vafg/Pp2GHc+zpz3wQH+vpNwH30PtN8dygvA5QHA4bHiJwP+yjrvnYGvhlI4sLtlY8gv9XLrh0uY6bmfIY5chlY+i0F43nM39/hP5ZxnoUe7dK52zbQWdti5CGpFaQkkcpR0P1Z67U7Am8Bc4EVjzN7GmLeTKZzScli2bgsA6V36wbQiGHESrpC1E5BBh1pprt3p0K5XeI3DZQWu3fXhopo3jOIM1+c1+v5W9ggAN73zKwBDHLkA9JY8BspGJjqWMt39KACbi8o50Wm7uGodBkVJ6CjJGGNm29fvikieMabO5HmKUh3jt+0EUZXRDh3cFlaDyxM/ctnpSQOguDSSO2nZ5mIOdvxc7/Mm+H9kfWE5+aWxx1BdpCh83ceRRzpeMrBSaphBhyNauU1RElIM7UUk2n4g0W3dNSiJsKWg0NqfRr14d8u2N6yu+C9jp71jSCPycj/iwW/ISbdLhI85G35+IbKgTVcoi5QI2f+eLwH4k2NOuO+VavmY/ur8iBmhCQDIiJMb9qEUpYWSyFHSV8Afo36i28ckTzSlpVDpD9IzaKfDyIhKlVq1i3ClxV3nTLMUQ7ad3G7JxmqpLvY4NrbdeyzVaUcpD3kerVW2q9xv8nnaNVajTada5ylKa6LeHYMx5tymEERpuTw7J4fRsgqvcZHWfVRkQOzvJbVEGg/u1RmABz3/Af7NUQ9VS3VRfV2/A2D5DAAqjGXAPsv5WeKCZnZOfK6itGAS8Uo6u45hY4x5sRHlUZohny3ZwqLc7Vx12I7VQb77k2Xc7/bhy+hKWnQBnCPvtozNA+PnX3RH2R6KKiwvo5ESFezWa2+4fBFMH2m1x/0VnG5WrFjOoN+f4CTnV3hxxxfKmQbBaum6M3XHoCiQmI1hXJw+wTpK6gWoYmjh/PWF+QBcevAgPK4di4k80fkNVPc6zeoKh91e+yJn5Ijpzo+WAjDdbXkbmS5DEZfHci+9aTNUbAenC8ZNYVDl/fA73Od+PP59z3oXBhwE06oFs2X3aOjHUpQWSSJHSZdWXYuIAGcA12G5rWocQytiw/YKgiHDwK6J10Ou9AfDNRYaTFSW07xSL1f3zaHfZsvtVUo2Rea5M2KM2uEjqupMmAqH/MtyiwU48Dr4yk4K3KGflvNUFJuEIp9FxAWcA1wFzANOMsYsT6JcSjPCSZAOlHLw/bMxBnLuOjrhtXklXqY6P9yxB3syWdPnOPqtf5eJ/TtyyLfPR8Yqi2pf54w1ZhtxIOd9Cj1Gxhq6D7rR+v3V3bFGcUVp5dT7FUlELgaWAHsDRxhjzlGl0Lr4u+st5qdfRAdjeQWF4tRHqI1yX5ADHFaAmuk+osHP7rX7YABGrnmGjGCUV9LJz9eyAhj/15hm4PB7oc+4+N5Po06D3uPhhCcbLJuitFQS2Ts/DLQF9gM+EJFF9s+vIlJ3SKrSIpjksNJN7C7WMU71lNZ1UeYLsI/TSmktnoYXwHFtXgDAxDWP8K7Xdkfd/yoYdlzti5xuOOudcNOdWUdivI79YMpn0FkjnhWlikSOkvolXQqlWVNVgekw53zucTzBwpzPmLRnrzrXVFHujUqUl5a4bSL87LbWc7zGhWCoNG7SJ99S/0JP1LPS2zb4uYrSmknE+Ly2KQRRmi9OrJf7Ra4PAFi8NRcSVAz5JVEpsw+4psHPlj8+iP/nl3greIBV5tOVmdjC9lHJ8DRjqqI0iETiGEqIX51NsOIY9OtYC8dVzavIW7S1lpmxeANBbnp9HsdVhSP0juf5XA8ibHN0ZJxjOYMcGzDBBCvIZneLXHcb1vDnKkorJpEdg1ZGb4X4gyFO/O93XH3YEHoTWzeh/dYfsMJY6ub7VQVkRgcvyI6VBa8kjUGO9dYtGlJB9sJvYOPPO3SEpSitGXXcVuJSUOpjUW4RV7y2EJfE7hjSyjfVsiqWkDFkSd21FBJhmX8HazD3GAl7n7PTz1eU1kZSFYOIPCMiW0VkcS3jIiIPichK29NpTDLlURInaNdJ3lbuw1Vtx5AeKI63pAbbyvx85LFjBY79zw7L0rlzVA4jdStVlKST7B3Dc8ARdYwfCQyyfy4A/ptkeZQE8fqDXOF6kwNkQaTsJbDCOZA2gW0J3ePbJWvIFDsfUfW8RA2gfXbUaaamxlaUpLPDNZ8TwRjztYj0rWPKscALxhgDzBWR9iLSwxiT2FmFkjS8gRCXu6xSGwtCA9lLVgJQ4mxH+wQVg7uiMNIYfcYOy9Jzy2wANnn60mMH7RSKoiROqm0MvYD1Ue1cu68GInKBiMwXkfl5eXlNIlxrxheI2BU8+CnMGgR/m0claRBIzG6QEdhuXRz9QK01FxJhyeC/AZAdTEwhKYqyc6RaMcT7+hfX7cQY84QxZqwxZmyXLl2SLJbi9UfsCsMca6loNwC6DiWvAiTow5j6vYP6l1lRy/QYVffEeijqaKXScBPYqfsoipIYqVYMuUCfqHZvYGOKZFGi8PkqYtoZmdY5v8+4cEuQwjJfvGVhisr9lBYVWI0uO1bHoYoDhlnBap5U/9+qKK2EVP+pvQ+cbXsnTQSK1L7QPPBXxh4XZbSxYgEGdvLQW/LZVFT3cdJZz8wjiwq2mayY9Nk7gtNjRTtLbem0FUVpVJLtrvoK8D0wRERyReR8EZkqIlPtKTOA1cBK4Engb8mUR0mcoK88pu1q1xOAvYpmAbBlxY9x160vLOevL8xnUW4RfSTPsknsLFUlPIcetfP3UhSlXpLtlXRaPeMGuDiZMig7RqDaUZKr+54xbe/2+Gkx9r/nSwDaUMHBzoWNI0xmR5g6BzoPapz7KYpSJ7o3V+JSURG7Y5AsK/o4cJ61Y0gr21Dr2jR8POh+tHEF6j58pzybFEVJnKTuGJRdl4rystgOO421q/cYykwa7Ut+j7vuAMcvvOC5O9niKYqSRHTHoMSlrKRa2ovs7tZvh5MSsnD4y2sugppK4ZwZSZBOUZRkoopBiUt5cb51kdYWxGmd89t4JQ1HsCLuOq+JbEK9g46Gvn9IqpyKojQ+epSkxCVYYkeXT/0W2vWJGfNKGs440c/GGMpIJ60qt9JuE5MtpqIoSUB3DEpc2vk2E8IB2T3AEfu/ic+RhjNYUzF4AyFMVDC7u8eIpMupKErjo4pBiUvnwGZKPF3A5akx5nek4wrFKoanvlnN0H98EtPniKM8FEVp/qhiUGoQCIZoH9pOhadT3PH8Sie+ilKCoUi+pNs/WgpAqcmITKx2BKUoyq6BKgalBr9vKaWdlBHwtIs7XoGHdHzkl0ZqLOydvoHH3Q+wu2MruQNOhyuXWbEHiqLscqhiUGpw54yltKUMryt+jqO9u8IAxyae//LXcN8l8iaHO+cD0Nu3Gtr2aBJZFUVpfFQxKGG+/j2PUx7/nqGd3fR3bKZP9/jpzbuHtgDQt3h+uM8r6ZEJWZoWXVF2ZVQxKGHumbmMH9YU0n31WwC4C5bFnec84XEADtj4dLjP74hSDMdMT56QiqIkHVUMrZxZS7bw4ty1APSp/J3PPNfgqbRiGGTyP+Mv6jkGgJWlnnDBnqAzKo9RRofkCawoStLRALdWzpQXrOOgId2yOSw0h0GODfgq51pfGdr1jr/I6WJhaAAGod8NMzhoSBeOrQp4c3pqxD0oirJroX/BrZw/Oebwiec6Tnn8OzaV+AEYKHbmVGfNGIYqRjtWsb9zMUNlHb8uX0l6oJhSVwe4IbcpxFYUJYnojqGV85DHSo/9uudWVoZ6AZAmdm3lBNJcf5J2PQDfBIdTnNGbLE2NrSi7PLpjUAAY71jO6a4vYjvr2DEUDD0zpr2/c3GtcQ+KouxaqGJo5XwRHF37YFVJzTiU/+GamtPd7sYQSVGUFKOKoZUj7vTaB521v+iz23eu0dctuLkxRFIUJcWoYmjleNy1HxfVRVZmZqRx/BMAyIHXNoZIiqKkGDU+t3LKxE56d+UyKM+n4rGDycAHZ71b5zqXM+o7xahTYfgJde4wFEXZddAdQyvHaQLkO7tauY26j8Ddtps10GVIA2+kSkFRWgq6Y2jliAkSEme47TrlOVg+A9r2rH/x4f8GT2b98xRF2aVQxdDKMcEAxhn1v0HvsdZPIuzzt+QIpShKStGjpFaOK1hByFmHZ5KiKK0OVQytGGMMWaFifJ72qRZFUZRmRNIVg4gcISLLRWSliFwfZ3x3EflcRBaJyGwRqSVzm9LYlPuCtKOMQJoqBkVRIiRVMYiIE3gUOBLYEzhNRPasNu0+4AVjzEjgVuDfyZRJibAmv4wsqcB44ldqUxSldZLsHcN4YKUxZrUxxge8Chxbbc6ewOf29ZdxxpsNK7aUsL3cl2oxGo2/PPwR3WUbxpOValEURWlGJFsx9ALWR7Vz7b5ofgFOtK+PB7JFpFP1G4nIBSIyX0Tm5+XlJUXY+rjhwSe4bvrT9U/cRXjQbWVWdZVvTbEkiqI0J5KtGCROn6nWvho4UEQWAAcCG4BAjUXGPGGMGWuMGdulS2pqCr+ZdiuPe2uYSfhtYxErtpSkQKKdY6hjHQDpgeIUS6IoSnMi2YohF+gT1e4NbIyeYIzZaIw5wRizF3CT3VeUZLkaTCgU0WeV/mDM2P89Mp23H7q6qUXaaUocVprsroPHp1gSRQETUxYAABM+SURBVFGaE8lWDD8Cg0Skn4h4gD8D70dPEJHOIlIlxw3AM0mWaYfwBkLh6+f/dVa41vEXy7bwlOd+rnO/Sl5ROQBl3gDXvjibTdua9y5io3QHwDn55hRLoihKcyKpisEYEwAuAWYCS4HXjTG/icitIvIne9okYLmI/A50A+5Ipkw7Sqk3crp1oesjvnv3Ceatyif48mnh/jVPnWWNP/0196w6ltWv1KxZ0Jxwm0pyMkdoniNFUWJIehyDMWaGMWawMWaAMeYOu+8WY8z79vWbxphB9pwpxhhvsmWq4q43vuamW65LaO7inJgTMP7wy7Ws37iJQ50/hfvGl8xi6tOzuW/Ledacra9QVtFkH6fBeEI+Qk4txakoSiytOvL50F+v5A7HY1C8qd65vg2LYtplznaE1s+rMe+x9cfSXbaF20XfNV8vpjQqCboyUi2GoijNjNalGBa+AtNHgdc6++8jlturCVTWu9R4y2LaeY7OlC+ZWf8zE1A6qSAYMqQZH0bzJCmKUo3WpRiCPtiWAxXbAXDbXrE/zf2Kpeu21Lk05K+Iaff1r+Ic16cAbB98EmvTh8ZdV1HNg6m5UOEPki4+jEsVg6IosbQqxbC40AqrCJQVsja/BCfWS3vsD5ezxzODwVdW61rjq6h1rP3pT7O0x/HhduXEy/n9Tx8AkLn8ncYQvdEpqfSTTTmkt0u1KIqiNDNalWLYZtoA8OnMD9j9kd60ldiXfbCo9mMfE7CMyIVZg5g77JZw/4Y+xwDg6dw33Jc+cQqD9tqfkBF6BDfiLy1srI/QKCxbs57v3nvS+vwZHVItjqIozYxWpRjadugKQMa6L+OOlxRsqH1xwFIiHS/8kLQ9jw53O9r2sPr77RWZm9UNEWHO/s8DsHnhpzsjdqOz5ZnTOXH1PwDoPDDBojyKorQaWpVicLWxvh13oDTueFnBxrj9AFJloHal065LpOxl9z9auwdP26g0HS7LBbRNzz0A8G6vQ+HUwi8LfuCX56+CUKj+yQ1kgCPyOTuNOLTR768oyq5Nq1IMnqyOAIxmWbjv52E38mXfKwDI/Pq22hfbR0m4M8jK8IS7Jb0tAL06WqmrV3eZHB5r36kbAeMgULw57i3nfv4O5dO6k//rZzXGgu9cxKg1T+HftDiBT5Y4FRUV9Jb8SIenTaPeX1GUXZ9WpRjSMmMNrSWX/86Yk6+jzxGWYujg3QCmeo4/wBgkUEEIAaeHrLSapbLbZbphWhH9L3473Ne5XSZ5tMdZuCquPBO/OYdMKihd+G5M/9xvPmWMYyUAxS+cQdnanxvyMetk1jP/aLR7KYrSMmlViqFnh9hgruwO3QDo37VtpPNf7amsFp+w5OETOarwRbykgQgZbidv97+V349+s87ntU13s9gziq6FP8VXODa+oLHcaO055tN/hsc6edfR5tmDEvl4CZFfUBC+Xjyp+QbfKYqSOlqVYnA5Ix839+iXwtcOh/BFcHS4nf76KVAe8STas9CqI7TNZdkRRIQTzr6cwePqP58v6zSCdqHtUBZbQ6KoKJJAtvP6mTB9FGVf3Me2Be+zj3NJzRtNa1encgEsmcsKah0uLdnOuSFrR1N8yXKGTzqpXvkVRWl9tCrFEE3vvQ6PaWef/QovBSL2AX/B2hprStO7N/g5ziyr5pCpjM0kLg8OC193DFpn/m2+uZ0O750V7veaakdWhauhso6M5Pf0g3v716pAfvn5h/B1284N/yyKorQOWp1i+FeP//K441RweWL6xw3qyZm3v80n458FoGx7zapm/sxuDX5elXHaV7Y9pr+tsdJy1Hj523zf40zu6vF/sZ0Pj4G7diO4cVGN+Ssf+lOksS3H+l1eGOPVVJm/BoB1pySQykNRlFZLq1MM/7zwdC685Ylax93ZnQFo8+GFgJVTqIqeZXGOeOpB2lqVTMvXLYjp9xsnAAvM/7d37kFSVXce/3wZnsPDYWAwvN/BgALCqMhDjbIorAoG5RGILyqWqWyi2dXVXaIRy8TCxJR5qWtKohITH4VsyLoRiEbUoIaHIFCIgCHKwiKCKxieM/PbP86ZsXumZ4YepnvG6d+nqqtv/+655377d2/fX99z7vmdgSm36zGomNvmzGLh6BeqrNu3qlLfRmkJA/avqPi4f+Nyjt7bD+7ry8HXHq6w28fhLqhn39TpOxzHcSAHA0Nt7LPwD7/F0ZAh9VjCBD0fjJqXdn3dBhVzwNrw8dbkTKzvWxfWn3IRh6xq2uulpcUUjZ5N6xZ59O7dr8r6TusehNLjFZ9fe2Nl0vrCl26l1dHQ17B/8ysV9uYHd3KQtqhNQdrfw3Gc3MEDQyXGDB3E38rCCOlDz96ILb6hYt3QsZelXd+Q7h35wLqgTz9L0nespIz2OkzzNh14tdM0tpZ1Z8GIxey0cLcyfNpcWrcMk+eMHdiFfzntT7xYeiYLS8azo+xU8uw4R1+8t6K+scuDrkdLJrLLCpP232XvSnh5PpjR9vAu9rXwvgXHcWrGA0Mluhe04c2uswDI3/Rb8jeHZptX+94EUtr1tWzejNbNSum7/1XY/TZ8+A4HDx+lIwcpaVPI7FnXsH7yMq6//EJWlA4DoH1M3QHhiakfTT+TvZc9wcW3PckDJVMB+Gj7W7DqUY5+tKOi7KVfHsPt3ATA86Vns7h0DG1KPoGXfwAbF9Hx2B4+bd21Tn5xHCd3SN3zmeO0ya86GrioW98619efnWHhP8YB0AlAkNeuiP5F7ehf1A6A7SPvYPaqs1nYfUjS9pKYcXYvACbM/DZ/fmYFY/a8BM+/RGJDlA2/mlsGGd965TzmTT6dlQ/eAOUJYxfNYQCwtt3oOn8Px3FyA79jSMFO61LF1qFjUYqSJ8a6sqr9BAD5rZKfjLpjynAev+c2VMOdyaQzurKrdf8q9r+rLV/odApDexTws6+OoLBtS17TWVXKNSvolaZ6x3FyDQ8MKegzourAtYIBVS+yJ8p1x29PaT82YFLSZ0nkNau9uWrLqZdWsf3+nCer2IaM+UfWlfXjnuOzKmytOvestX7HcXIbDwwpmDi0Gzceu5kVpUPpd+TXzOy2lPyC9McwlPObb0/k7uNh4NobZV+qsPcfOKhO9Q0Z8Fmz1gMlX2HYkUeYNuGCKuWmndWTn/Z7hOKZn80f0aGw7t/DcZzcwPsYqmHc5Ot56+As3hv/xZOu67QvtOfA8K9z/Vuncs6o8xm1NjxF1CKvbnHZOnTj/uNXsqJsGFdNnsy6c3qlbH5q1TyPBdeeRVmZcdXRO7k8byVTB194Ut/FcZymjweGaph1Tu96q0sS900dyu8HdubLp3XhitfnUagD1DWF3ZTh3fne+9/hoQv6072gTa3lmzUTX502nV3/N5n8Vi3quFfHcXIFWW2J2RohxcXFtnr16oaWUWcWrdlJp3YtuWBQ1U5ux3GcTCFpjZnVOm2j3zE0AFNH9mhoCY7jONXinc+O4zhOEhkPDJIukbRF0jZJVZ7blNRL0p8kvSXpbUmTUtXjOI7jZIeMBgZJecAvgInAYGCmpMGVin0XeMbMzgRmAA9mUpPjOI5TM5m+Yzgb2GZm75nZMeApYHKlMgaUz615CrArw5ocx3GcGsh0YOgOfJDweWe0JXIXMFvSTuC/gW+lqkjSDZJWS1q9d+/eVEUcx3GceiDTgSFVfofKz8fOBB4zsx7AJGChpCq6zOwRMys2s+KiorrnLXIcx3FqJtOBYSeQmJynB1WbiuYAzwCY2etAa6BzhnU5juM41ZDpwLAKGCipr6SWhM7lJZXKvA9cBCDpS4TA4G1FjuM4DUTGRz7Hx08fAPKABWb2fUl3A6vNbEl8SumXQDtCM9O/mtmyWurcC/ytjpI6Ax/VcdtM01i1ua70aazaGqsuaLzampKu3mZWa1v85zIlxskgafWJDAlvCBqrNteVPo1VW2PVBY1XWy7q8pHPjuM4ThIeGBzHcZwkcjEwPNLQAmqgsWpzXenTWLU1Vl3QeLXlnK6c62NwHMdxaiYX7xgcx3GcGvDA4DiO4ySRU4GhthTgGd53z5hefLOkTZJuiva7JP2PpHXxNSlhm3+LWrdIujiD2nZI2hD3vzraCiUtl7Q1vneMdkn6adT1tqQRGdQ1KMEv6yQdkHRzQ/hM0gJJH0ramGBL20eSronlt0q6JoPafijpnbj/xZIKor2PpMMJvns4YZuR8TzYFvWnSmlzsrrSPnb1/butRtfTCZp2SFoX7dn0V3XXiOyfZ2aWEy/CALvtQD+gJbAeGJzF/XcFRsTl9sC7hFTkdwG3pCg/OGpsBfSN2vMypG0H0LmS7T7g9rh8OzA/Lk8C/kDIgzUKeDOLx+9/gd4N4TPgPGAEsLGuPgIKgffie8e43DFD2iYAzePy/ARtfRLLVarnL8C5UfcfgIkZ0JXWscvE7zaVrkrr7wfubAB/VXeNyPp5lkt3DCeSAjxjmNluM1sblw8Cm6maaTaRycBTZnbUzP4KbCN8h2wxGXg8Lj8OTEmwP2GBN4ACSV2zoOciYLuZ1TTiPWM+M7NXgP0p9peOjy4GlpvZfjP7GFgOXJIJbWa2zMxK4sc3CHnKqiXq62Bmr1u4ujyR8H3qTVcNVHfs6v13W5Ou+K9/GvDbmurIkL+qu0Zk/TzLpcBwIinAs4KkPsCZwJvR9E/xVnBB+W0i2dVrwDJJayTdEG2nmtluCCcs0KUBdCUyg+Qfa0P7DNL3UUP57nrCP8ty+irMmLhC0rho6x71ZENbOscu2z4bB+wxs60Jtqz7q9I1IuvnWS4FhhNJAZ55EVI7YBFws5kdAB4C+gPDgd2E21jIrt4xZjaCMNPeNyWdV0PZrPtRIQHj5cCz0dQYfFYT1eloCN/NBUqAJ6NpN9DLwoyJ/wz8RlKHLGpL99hl22czSf4DknV/pbhGVFu0Gg0nrS2XAsOJpADPKJJaEA74k2b2HICZ7TGzUjMrIyQTLG/6yJpeM9sV3z8EFkcNe8qbiOL7h9nWlcBEYK2Z7Yk6G9xnkXR9lFV9sdPxUmBWbO4gNtXsi8trCO33X4zaEpubMqKtDscuaz6T1Bz4CvB0gt6s+ivVNYIGOM9yKTCcSArwjBHbLh8FNpvZjxPsie3zVwDlT0osAWZIaiWpLzCQ0NlV37raSmpfvkzotNwY91/+NMM1wO8SdF0dn4gYBXxSfpubQZL+xTW0zxJI10dLgQmSOsYmlAnRVu9IugS4DbjczA4l2IsU5mJHUj+Cj96L+g5KGhXP1asTvk996kr32GXzdzseeMfMKpqIsumv6q4RNMR5djK96J+3F6EX/11C1J+b5X2PJdzOvQ2si69JwEJgQ7QvAbombDM3at3CST7xUIOufoQnPdYDm8r9AnQCXgS2xvfCaBfwi6hrA1CcYb/lA/uAUxJsWfcZITDtBo4T/pHNqYuPCO392+Lrugxq20ZoZy4/1x6OZafG47weWAtcllBPMeFCvR34OTEzQj3rSvvY1ffvNpWuaH8MuLFS2Wz6q7prRNbPM0+J4TiO4ySRS01JjuM4zgnggcFxHMdJwgOD4ziOk4QHBsdxHCcJDwyO4zhOEh4YnCaNpB6SfhezTG6X9BNJLSVdK+nnjUDfFEmDEz7fLWl8Q2pyHA8MTpMlDhh6DvhPMxtIGLHaDvh+hvbXvA6bTSFk0ATAzO40sz/WnyrHSR8PDE5T5kLgiJn9CsDMSoHvEAb/5AM9Jb2gkOv/e1AxEvx5SeslbZQ0PdpHxiRqayQtTUhR8LKkH0haAcxVyOXfLK7Ll/SBpBaSvi5pVax3UVw3mpAD6ocKuf77S3pM0pVx+4ti8rYNCgnnWkX7DknzJK2N607LplOdpo8HBqcpMwRYk2iwkJTsfaA5IU/PLEJCt6skFRPSE+8ys2FmdjrwQsxf8zPgSjMbCSwg+a6jwMzON7N5hBGy50f7ZcBSMzsOPGdmZ5nZMEI65TlmtpIw+vdWMxtuZtvLK5TUmjASd7qZnRH1fiNhnx9ZSHz4EHDLybnJcZLxwOA0ZUTqrJLl9uVmts/MDhOanMYSUguMlzRf0jgz+wQYBJwOLFeY2eu7JCdQe7rS8vS4PCNh3emSXpW0gRCMhtSifRDwVzN7N35+nDDBTDnlCdbWECaTcZx6oy5too7zeWETIddNBQopk3sCpVQNGmZm70oaSchRc6+kZYSMs5vM7Nxq9vP3hOUlcbtCYCTwUrQ/Bkwxs/WSrgUuqEV7bdNEHo3vpfjv2Kln/I7Bacq8CORLuhogZsm8n3CRPgT8g8J8um0IncB/ltQNOGRmvwZ+RJgCcgtQJOncWE8LSSn/8ZvZp4SsoD8B/iv2a0CYqnF3bJaalbDJwbiuMu8AfSQNiJ+/Bqyogw8cJ208MDhNFgsZIq8g9B9sJWToPAL8eyzyGiHb5zpgkZmtBs4A/hKbjOYC91iYUvJKYL6k9bH86Bp2/TQwm+QmpjsIs3EtJ1z0y3kKuDV2MvdP0H4EuA54NjY/lQEP4zhZwLOrOo7jOEn4HYPjOI6ThAcGx3EcJwkPDI7jOE4SHhgcx3GcJDwwOI7jOEl4YHAcx3GS8MDgOI7jJPH/lRhDgpDzGJkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a30399950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(y_test, label='True')\n",
    "plt.plot(y_pred_test_LSTM, label='LSTM')\n",
    "plt.title(\"LSTM's_Prediction\")\n",
    "plt.xlabel('Observation')\n",
    "plt.ylabel('INR_Scaled')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}