Wavenet-GitHub.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/wsu/el7/pre-compiled/python/3.7/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/wsu/el7/pre-compiled/python/3.7/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/wsu/el7/pre-compiled/python/3.7/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/wsu/el7/pre-compiled/python/3.7/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/wsu/el7/pre-compiled/python/3.7/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/wsu/el7/pre-compiled/python/3.7/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
      "/wsu/el7/pre-compiled/python/3.7/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/wsu/el7/pre-compiled/python/3.7/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/wsu/el7/pre-compiled/python/3.7/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/wsu/el7/pre-compiled/python/3.7/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/wsu/el7/pre-compiled/python/3.7/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/wsu/el7/pre-compiled/python/3.7/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "#!/usr/bin/env python3\n",
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "Created on Sat Jun 27 15:20:31 2020\n",
    "\n",
    "@author: parinazfarajiparvar\n",
    "\"\"\"\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "#matplotlib inline\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "import tensorflow as tf\n",
    "tf.logging.set_verbosity(tf.logging.FATAL) # suppress unhelpful tf warnings\n",
    "\n",
    "from keras.models import Model\n",
    "from keras.layers import Input, Conv1D, Dense, Activation, Dropout, Lambda, Multiply, Add, Concatenate\n",
    "from keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "   Day Number     Date  Year  Month  Day      Time      371.4      373.5  \\\n",
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       "4           1  6012019  2019      6    1  00:04:00  59.000000  64.000000   \n",
       "\n",
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       "\n",
       "       392.1      392.4      392.8      393.5  393.9  \n",
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       "4  58.500000  50.666667  61.800000  59.714286   57.0  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "'''Reading data'''\n",
    "df_data = pd.read_csv('/wsu/home/gc/gc40/gc4042/nima projedct/data/RDS_NA Remove.csv')\n",
    "df_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "ave_5min = df_data.groupby(np.arange(len(df_data))).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22512</td>\n",
       "      <td>175.0</td>\n",
       "      <td>11302019.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>59.925758</td>\n",
       "      <td>65.131818</td>\n",
       "      <td>60.066667</td>\n",
       "      <td>58.365368</td>\n",
       "      <td>64.590043</td>\n",
       "      <td>...</td>\n",
       "      <td>61.997835</td>\n",
       "      <td>54.600866</td>\n",
       "      <td>67.012121</td>\n",
       "      <td>58.345455</td>\n",
       "      <td>57.152201</td>\n",
       "      <td>58.198485</td>\n",
       "      <td>53.675000</td>\n",
       "      <td>56.405447</td>\n",
       "      <td>59.641558</td>\n",
       "      <td>58.003030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22513</td>\n",
       "      <td>175.0</td>\n",
       "      <td>11302019.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>59.880303</td>\n",
       "      <td>62.480303</td>\n",
       "      <td>59.154545</td>\n",
       "      <td>59.613636</td>\n",
       "      <td>65.583658</td>\n",
       "      <td>...</td>\n",
       "      <td>60.064935</td>\n",
       "      <td>52.418506</td>\n",
       "      <td>68.190909</td>\n",
       "      <td>57.939394</td>\n",
       "      <td>56.211219</td>\n",
       "      <td>58.581818</td>\n",
       "      <td>54.986039</td>\n",
       "      <td>58.188492</td>\n",
       "      <td>59.500108</td>\n",
       "      <td>57.174242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22514</td>\n",
       "      <td>175.0</td>\n",
       "      <td>11302019.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>48.083333</td>\n",
       "      <td>56.824242</td>\n",
       "      <td>56.657576</td>\n",
       "      <td>54.441017</td>\n",
       "      <td>65.907359</td>\n",
       "      <td>...</td>\n",
       "      <td>60.619913</td>\n",
       "      <td>50.877561</td>\n",
       "      <td>67.806061</td>\n",
       "      <td>55.927273</td>\n",
       "      <td>56.281746</td>\n",
       "      <td>59.433333</td>\n",
       "      <td>53.359524</td>\n",
       "      <td>57.912626</td>\n",
       "      <td>59.394697</td>\n",
       "      <td>56.777273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22515</td>\n",
       "      <td>175.0</td>\n",
       "      <td>11302019.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>54.309091</td>\n",
       "      <td>55.115152</td>\n",
       "      <td>57.142424</td>\n",
       "      <td>54.861472</td>\n",
       "      <td>68.306277</td>\n",
       "      <td>...</td>\n",
       "      <td>58.710606</td>\n",
       "      <td>48.250000</td>\n",
       "      <td>68.425758</td>\n",
       "      <td>55.157576</td>\n",
       "      <td>57.827020</td>\n",
       "      <td>58.245455</td>\n",
       "      <td>49.212121</td>\n",
       "      <td>58.545094</td>\n",
       "      <td>56.939394</td>\n",
       "      <td>53.540909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22516</td>\n",
       "      <td>175.0</td>\n",
       "      <td>11302019.0</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>58.237500</td>\n",
       "      <td>58.787500</td>\n",
       "      <td>58.500000</td>\n",
       "      <td>60.758929</td>\n",
       "      <td>66.178571</td>\n",
       "      <td>...</td>\n",
       "      <td>59.766667</td>\n",
       "      <td>48.326786</td>\n",
       "      <td>65.083333</td>\n",
       "      <td>54.433333</td>\n",
       "      <td>56.294444</td>\n",
       "      <td>57.075000</td>\n",
       "      <td>51.350000</td>\n",
       "      <td>55.251786</td>\n",
       "      <td>57.596429</td>\n",
       "      <td>53.583333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22517 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Day Number        Date    Year  Month   Day      371.4      373.5  \\\n",
       "0             1.0   6012019.0  2019.0    6.0   1.0  60.346970  64.050000   \n",
       "1             1.0   6012019.0  2019.0    6.0   1.0  63.512121  63.377273   \n",
       "2             1.0   6012019.0  2019.0    6.0   1.0  61.966667  62.037879   \n",
       "3             1.0   6012019.0  2019.0    6.0   1.0  62.416667  63.203030   \n",
       "4             1.0   6012019.0  2019.0    6.0   1.0  61.425758  61.263636   \n",
       "...           ...         ...     ...    ...   ...        ...        ...   \n",
       "22512       175.0  11302019.0  2019.0   11.0  30.0  59.925758  65.131818   \n",
       "22513       175.0  11302019.0  2019.0   11.0  30.0  59.880303  62.480303   \n",
       "22514       175.0  11302019.0  2019.0   11.0  30.0  48.083333  56.824242   \n",
       "22515       175.0  11302019.0  2019.0   11.0  30.0  54.309091  55.115152   \n",
       "22516       175.0  11302019.0  2019.0   11.0  30.0  58.237500  58.787500   \n",
       "\n",
       "           374.6      375.3      375.8  ...      386.6      387.1      387.8  \\\n",
       "0      60.148485  58.499026  67.228355  ...  56.782576  56.268579  60.703030   \n",
       "1      60.148485  56.832035  70.529221  ...  57.262987  58.925830  60.610606   \n",
       "2      59.225758  57.590368  66.934199  ...  57.427706  56.440476  60.659091   \n",
       "3      57.671212  56.679004  69.415152  ...  58.913853  56.885931  62.150000   \n",
       "4      58.900000  56.315693  72.017532  ...  59.522511  59.013131  60.227273   \n",
       "...          ...        ...        ...  ...        ...        ...        ...   \n",
       "22512  60.066667  58.365368  64.590043  ...  61.997835  54.600866  67.012121   \n",
       "22513  59.154545  59.613636  65.583658  ...  60.064935  52.418506  68.190909   \n",
       "22514  56.657576  54.441017  65.907359  ...  60.619913  50.877561  67.806061   \n",
       "22515  57.142424  54.861472  68.306277  ...  58.710606  48.250000  68.425758   \n",
       "22516  58.500000  60.758929  66.178571  ...  59.766667  48.326786  65.083333   \n",
       "\n",
       "           388.5      389.5      392.1      392.4      392.8      393.5  \\\n",
       "0      58.145455  67.409307  58.977273  54.995022  60.063167  59.879870   \n",
       "1      59.509091  64.435606  62.157576  58.506061  58.764033  62.219805   \n",
       "2      59.245455  70.440657  58.568182  58.492424  60.623377  61.135065   \n",
       "3      58.024242  70.873160  57.704545  57.236364  60.386183  61.913095   \n",
       "4      58.925758  66.178355  58.290909  55.653030  59.265224  62.417532   \n",
       "...          ...        ...        ...        ...        ...        ...   \n",
       "22512  58.345455  57.152201  58.198485  53.675000  56.405447  59.641558   \n",
       "22513  57.939394  56.211219  58.581818  54.986039  58.188492  59.500108   \n",
       "22514  55.927273  56.281746  59.433333  53.359524  57.912626  59.394697   \n",
       "22515  55.157576  57.827020  58.245455  49.212121  58.545094  56.939394   \n",
       "22516  54.433333  56.294444  57.075000  51.350000  55.251786  57.596429   \n",
       "\n",
       "           393.9  \n",
       "0      56.833333  \n",
       "1      58.092424  \n",
       "2      58.465152  \n",
       "3      57.850000  \n",
       "4      56.569697  \n",
       "...          ...  \n",
       "22512  58.003030  \n",
       "22513  57.174242  \n",
       "22514  56.777273  \n",
       "22515  53.540909  \n",
       "22516  53.583333  \n",
       "\n",
       "[22517 rows x 36 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ave_5min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2abbc3bbac18>]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "input_data = ave_5min.loc[:,'371.4'].values\n",
    "plt.plot(range(len(input_data)),input_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "past_window = 50\n",
    "future_window = 10\n",
    "X = []\n",
    "Y = []\n",
    "for i in range(len(input_data) - (past_window + future_window)):\n",
    "    X.append(input_data[i:i + past_window])\n",
    "    Y.append(input_data[i + past_window:i + past_window + future_window])\n",
    "\n",
    "X = np.array(X)\n",
    "Y = np.array(Y)\n",
    "Y = np.expand_dims(Y, axis =2)\n",
    "X = np.expand_dims(X, axis =2)\n",
    "X_train = []\n",
    "Y_train = []\n",
    "X_test = []\n",
    "Y_test = []\n",
    "\n",
    "X_train = X[:int(len(X) * 0.9)]\n",
    "Y_train = Y[:int(len(Y) * 0.9)]\n",
    "X_train = np.stack(X_train)\n",
    "Y_train = np.stack(Y_train)\n",
    "X_test = X[int(len(X) * 0.9):]\n",
    "Y_test = Y[int(len(Y) * 0.9):]\n",
    "X_test = np.stack(X_test)\n",
    "Y_test = np.stack(Y_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_4 (InputLayer)            (None, None, 1)      0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_199 (Conv1D)             (None, None, 32)     64          input_4[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_200 (Conv1D)             (None, None, 64)     4160        conv1d_199[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_201 (Conv1D)             (None, None, 64)     4160        conv1d_199[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_103 (Activation)     (None, None, 64)     0           conv1d_200[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_104 (Activation)     (None, None, 64)     0           conv1d_201[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "multiply_49 (Multiply)          (None, None, 64)     0           activation_103[0][0]             \n",
      "                                                                 activation_104[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_202 (Conv1D)             (None, None, 32)     2080        multiply_49[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "add_52 (Add)                    (None, None, 32)     0           conv1d_199[0][0]                 \n",
      "                                                                 conv1d_202[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_203 (Conv1D)             (None, None, 32)     1056        add_52[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_204 (Conv1D)             (None, None, 64)     4160        conv1d_203[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_205 (Conv1D)             (None, None, 64)     4160        conv1d_203[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_105 (Activation)     (None, None, 64)     0           conv1d_204[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_106 (Activation)     (None, None, 64)     0           conv1d_205[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "multiply_50 (Multiply)          (None, None, 64)     0           activation_105[0][0]             \n",
      "                                                                 activation_106[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_206 (Conv1D)             (None, None, 32)     2080        multiply_50[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "add_53 (Add)                    (None, None, 32)     0           conv1d_203[0][0]                 \n",
      "                                                                 conv1d_206[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_207 (Conv1D)             (None, None, 32)     1056        add_53[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_208 (Conv1D)             (None, None, 64)     4160        conv1d_207[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_209 (Conv1D)             (None, None, 64)     4160        conv1d_207[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_107 (Activation)     (None, None, 64)     0           conv1d_208[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_108 (Activation)     (None, None, 64)     0           conv1d_209[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "multiply_51 (Multiply)          (None, None, 64)     0           activation_107[0][0]             \n",
      "                                                                 activation_108[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_210 (Conv1D)             (None, None, 32)     2080        multiply_51[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "add_54 (Add)                    (None, None, 32)     0           conv1d_207[0][0]                 \n",
      "                                                                 conv1d_210[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_211 (Conv1D)             (None, None, 32)     1056        add_54[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_212 (Conv1D)             (None, None, 64)     4160        conv1d_211[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_213 (Conv1D)             (None, None, 64)     4160        conv1d_211[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_109 (Activation)     (None, None, 64)     0           conv1d_212[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_110 (Activation)     (None, None, 64)     0           conv1d_213[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "multiply_52 (Multiply)          (None, None, 64)     0           activation_109[0][0]             \n",
      "                                                                 activation_110[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_214 (Conv1D)             (None, None, 32)     2080        multiply_52[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "add_55 (Add)                    (None, None, 32)     0           conv1d_211[0][0]                 \n",
      "                                                                 conv1d_214[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_215 (Conv1D)             (None, None, 32)     1056        add_55[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_216 (Conv1D)             (None, None, 64)     4160        conv1d_215[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_217 (Conv1D)             (None, None, 64)     4160        conv1d_215[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_111 (Activation)     (None, None, 64)     0           conv1d_216[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_112 (Activation)     (None, None, 64)     0           conv1d_217[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "multiply_53 (Multiply)          (None, None, 64)     0           activation_111[0][0]             \n",
      "                                                                 activation_112[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_218 (Conv1D)             (None, None, 32)     2080        multiply_53[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "add_56 (Add)                    (None, None, 32)     0           conv1d_215[0][0]                 \n",
      "                                                                 conv1d_218[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_219 (Conv1D)             (None, None, 32)     1056        add_56[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_220 (Conv1D)             (None, None, 64)     4160        conv1d_219[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_221 (Conv1D)             (None, None, 64)     4160        conv1d_219[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_113 (Activation)     (None, None, 64)     0           conv1d_220[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_114 (Activation)     (None, None, 64)     0           conv1d_221[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "multiply_54 (Multiply)          (None, None, 64)     0           activation_113[0][0]             \n",
      "                                                                 activation_114[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_222 (Conv1D)             (None, None, 32)     2080        multiply_54[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "add_57 (Add)                    (None, None, 32)     0           conv1d_219[0][0]                 \n",
      "                                                                 conv1d_222[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_223 (Conv1D)             (None, None, 32)     1056        add_57[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_224 (Conv1D)             (None, None, 64)     4160        conv1d_223[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_225 (Conv1D)             (None, None, 64)     4160        conv1d_223[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_115 (Activation)     (None, None, 64)     0           conv1d_224[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_116 (Activation)     (None, None, 64)     0           conv1d_225[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "multiply_55 (Multiply)          (None, None, 64)     0           activation_115[0][0]             \n",
      "                                                                 activation_116[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_226 (Conv1D)             (None, None, 32)     2080        multiply_55[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "add_58 (Add)                    (None, None, 32)     0           conv1d_223[0][0]                 \n",
      "                                                                 conv1d_226[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_227 (Conv1D)             (None, None, 32)     1056        add_58[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_228 (Conv1D)             (None, None, 64)     4160        conv1d_227[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_229 (Conv1D)             (None, None, 64)     4160        conv1d_227[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_117 (Activation)     (None, None, 64)     0           conv1d_228[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_118 (Activation)     (None, None, 64)     0           conv1d_229[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "multiply_56 (Multiply)          (None, None, 64)     0           activation_117[0][0]             \n",
      "                                                                 activation_118[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_230 (Conv1D)             (None, None, 32)     2080        multiply_56[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "add_59 (Add)                    (None, None, 32)     0           conv1d_227[0][0]                 \n",
      "                                                                 conv1d_230[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_231 (Conv1D)             (None, None, 32)     1056        add_59[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_232 (Conv1D)             (None, None, 64)     4160        conv1d_231[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_233 (Conv1D)             (None, None, 64)     4160        conv1d_231[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_119 (Activation)     (None, None, 64)     0           conv1d_232[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_120 (Activation)     (None, None, 64)     0           conv1d_233[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "multiply_57 (Multiply)          (None, None, 64)     0           activation_119[0][0]             \n",
      "                                                                 activation_120[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_234 (Conv1D)             (None, None, 32)     2080        multiply_57[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "add_60 (Add)                    (None, None, 32)     0           conv1d_231[0][0]                 \n",
      "                                                                 conv1d_234[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_235 (Conv1D)             (None, None, 32)     1056        add_60[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_236 (Conv1D)             (None, None, 64)     4160        conv1d_235[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_237 (Conv1D)             (None, None, 64)     4160        conv1d_235[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_121 (Activation)     (None, None, 64)     0           conv1d_236[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_122 (Activation)     (None, None, 64)     0           conv1d_237[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "multiply_58 (Multiply)          (None, None, 64)     0           activation_121[0][0]             \n",
      "                                                                 activation_122[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_238 (Conv1D)             (None, None, 32)     2080        multiply_58[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "add_61 (Add)                    (None, None, 32)     0           conv1d_235[0][0]                 \n",
      "                                                                 conv1d_238[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_239 (Conv1D)             (None, None, 32)     1056        add_61[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_240 (Conv1D)             (None, None, 64)     4160        conv1d_239[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_241 (Conv1D)             (None, None, 64)     4160        conv1d_239[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_123 (Activation)     (None, None, 64)     0           conv1d_240[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_124 (Activation)     (None, None, 64)     0           conv1d_241[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "multiply_59 (Multiply)          (None, None, 64)     0           activation_123[0][0]             \n",
      "                                                                 activation_124[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_242 (Conv1D)             (None, None, 32)     2080        multiply_59[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "add_62 (Add)                    (None, None, 32)     0           conv1d_239[0][0]                 \n",
      "                                                                 conv1d_242[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_243 (Conv1D)             (None, None, 32)     1056        add_62[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_244 (Conv1D)             (None, None, 64)     4160        conv1d_243[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_245 (Conv1D)             (None, None, 64)     4160        conv1d_243[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_125 (Activation)     (None, None, 64)     0           conv1d_244[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_126 (Activation)     (None, None, 64)     0           conv1d_245[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "multiply_60 (Multiply)          (None, None, 64)     0           activation_125[0][0]             \n",
      "                                                                 activation_126[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_246 (Conv1D)             (None, None, 32)     2080        multiply_60[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "add_63 (Add)                    (None, None, 32)     0           conv1d_243[0][0]                 \n",
      "                                                                 conv1d_246[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_247 (Conv1D)             (None, None, 32)     1056        add_63[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_248 (Conv1D)             (None, None, 64)     4160        conv1d_247[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_249 (Conv1D)             (None, None, 64)     4160        conv1d_247[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_127 (Activation)     (None, None, 64)     0           conv1d_248[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_128 (Activation)     (None, None, 64)     0           conv1d_249[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "multiply_61 (Multiply)          (None, None, 64)     0           activation_127[0][0]             \n",
      "                                                                 activation_128[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_250 (Conv1D)             (None, None, 32)     2080        multiply_61[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "add_64 (Add)                    (None, None, 32)     0           conv1d_247[0][0]                 \n",
      "                                                                 conv1d_250[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_251 (Conv1D)             (None, None, 32)     1056        add_64[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_252 (Conv1D)             (None, None, 64)     4160        conv1d_251[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_253 (Conv1D)             (None, None, 64)     4160        conv1d_251[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_129 (Activation)     (None, None, 64)     0           conv1d_252[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_130 (Activation)     (None, None, 64)     0           conv1d_253[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "multiply_62 (Multiply)          (None, None, 64)     0           activation_129[0][0]             \n",
      "                                                                 activation_130[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_254 (Conv1D)             (None, None, 32)     2080        multiply_62[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "add_65 (Add)                    (None, None, 32)     0           conv1d_251[0][0]                 \n",
      "                                                                 conv1d_254[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_255 (Conv1D)             (None, None, 32)     1056        add_65[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_256 (Conv1D)             (None, None, 64)     4160        conv1d_255[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_257 (Conv1D)             (None, None, 64)     4160        conv1d_255[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_131 (Activation)     (None, None, 64)     0           conv1d_256[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_132 (Activation)     (None, None, 64)     0           conv1d_257[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "multiply_63 (Multiply)          (None, None, 64)     0           activation_131[0][0]             \n",
      "                                                                 activation_132[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_258 (Conv1D)             (None, None, 32)     2080        multiply_63[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "add_66 (Add)                    (None, None, 32)     0           conv1d_255[0][0]                 \n",
      "                                                                 conv1d_258[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_259 (Conv1D)             (None, None, 32)     1056        add_66[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_260 (Conv1D)             (None, None, 64)     4160        conv1d_259[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_261 (Conv1D)             (None, None, 64)     4160        conv1d_259[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_133 (Activation)     (None, None, 64)     0           conv1d_260[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_134 (Activation)     (None, None, 64)     0           conv1d_261[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "multiply_64 (Multiply)          (None, None, 64)     0           activation_133[0][0]             \n",
      "                                                                 activation_134[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_262 (Conv1D)             (None, None, 32)     2080        multiply_64[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "add_68 (Add)                    (None, None, 32)     0           conv1d_202[0][0]                 \n",
      "                                                                 conv1d_206[0][0]                 \n",
      "                                                                 conv1d_210[0][0]                 \n",
      "                                                                 conv1d_214[0][0]                 \n",
      "                                                                 conv1d_218[0][0]                 \n",
      "                                                                 conv1d_222[0][0]                 \n",
      "                                                                 conv1d_226[0][0]                 \n",
      "                                                                 conv1d_230[0][0]                 \n",
      "                                                                 conv1d_234[0][0]                 \n",
      "                                                                 conv1d_238[0][0]                 \n",
      "                                                                 conv1d_242[0][0]                 \n",
      "                                                                 conv1d_246[0][0]                 \n",
      "                                                                 conv1d_250[0][0]                 \n",
      "                                                                 conv1d_254[0][0]                 \n",
      "                                                                 conv1d_258[0][0]                 \n",
      "                                                                 conv1d_262[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_135 (Activation)     (None, None, 32)     0           add_68[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_263 (Conv1D)             (None, None, 128)    4224        activation_135[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_136 (Activation)     (None, None, 128)    0           conv1d_263[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dropout_4 (Dropout)             (None, None, 128)    0           activation_136[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv1d_264 (Conv1D)             (None, None, 1)      129         dropout_4[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "lambda_4 (Lambda)               (None, None, 1)      0           conv1d_264[0][0]                 \n",
      "==================================================================================================\n",
      "Total params: 186,657\n",
      "Trainable params: 186,657\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n",
      "(20211, 50, 1)\n",
      "(20211, 10, 1)\n",
      "Train on 16168 samples, validate on 4043 samples\n",
      "Epoch 1/20\n",
      "16168/16168 [==============================] - 65s 4ms/step - loss: 40.7673 - mean_squared_error: 2008.4955 - mean_absolute_error: 40.7673 - mean_absolute_percentage_error: 64.6596 - val_loss: 5.1638 - val_mean_squared_error: 42.4189 - val_mean_absolute_error: 5.1638 - val_mean_absolute_percentage_error: 8.8419\n",
      "Epoch 2/20\n",
      "16168/16168 [==============================] - 43s 3ms/step - loss: 8.5458 - mean_squared_error: 116.0417 - mean_absolute_error: 8.5458 - mean_absolute_percentage_error: 14.6554 - val_loss: 3.7882 - val_mean_squared_error: 23.1326 - val_mean_absolute_error: 3.7882 - val_mean_absolute_percentage_error: 6.1780\n",
      "Epoch 3/20\n",
      "16168/16168 [==============================] - 43s 3ms/step - loss: 6.0998 - mean_squared_error: 62.9162 - mean_absolute_error: 6.0998 - mean_absolute_percentage_error: 10.6757 - val_loss: 3.3262 - val_mean_squared_error: 20.3242 - val_mean_absolute_error: 3.3262 - val_mean_absolute_percentage_error: 5.5478\n",
      "Epoch 4/20\n",
      "16168/16168 [==============================] - 43s 3ms/step - loss: 5.6622 - mean_squared_error: 54.8459 - mean_absolute_error: 5.6622 - mean_absolute_percentage_error: 9.9357 - val_loss: 3.2049 - val_mean_squared_error: 19.3364 - val_mean_absolute_error: 3.2049 - val_mean_absolute_percentage_error: 5.3670\n",
      "Epoch 5/20\n",
      "16168/16168 [==============================] - 43s 3ms/step - loss: 5.4440 - mean_squared_error: 50.6613 - mean_absolute_error: 5.4440 - mean_absolute_percentage_error: 9.5464 - val_loss: 3.0347 - val_mean_squared_error: 18.1118 - val_mean_absolute_error: 3.0347 - val_mean_absolute_percentage_error: 5.1251\n",
      "Epoch 6/20\n",
      "16168/16168 [==============================] - 43s 3ms/step - loss: 5.2244 - mean_squared_error: 46.5839 - mean_absolute_error: 5.2244 - mean_absolute_percentage_error: 9.1395 - val_loss: 2.7369 - val_mean_squared_error: 14.8621 - val_mean_absolute_error: 2.7369 - val_mean_absolute_percentage_error: 4.6081\n",
      "Epoch 7/20\n",
      "16168/16168 [==============================] - 44s 3ms/step - loss: 4.9629 - mean_squared_error: 41.6150 - mean_absolute_error: 4.9629 - mean_absolute_percentage_error: 8.6479 - val_loss: 2.5671 - val_mean_squared_error: 11.9490 - val_mean_absolute_error: 2.5671 - val_mean_absolute_percentage_error: 4.2078\n",
      "Epoch 8/20\n",
      "16168/16168 [==============================] - 44s 3ms/step - loss: 4.7153 - mean_squared_error: 37.2019 - mean_absolute_error: 4.7153 - mean_absolute_percentage_error: 8.1339 - val_loss: 2.0135 - val_mean_squared_error: 8.9070 - val_mean_absolute_error: 2.0135 - val_mean_absolute_percentage_error: 3.3585\n",
      "Epoch 9/20\n",
      "16168/16168 [==============================] - 44s 3ms/step - loss: 4.5987 - mean_squared_error: 34.9834 - mean_absolute_error: 4.5987 - mean_absolute_percentage_error: 7.8701 - val_loss: 2.2406 - val_mean_squared_error: 9.8190 - val_mean_absolute_error: 2.2406 - val_mean_absolute_percentage_error: 3.6613\n",
      "Epoch 10/20\n",
      "16168/16168 [==============================] - 44s 3ms/step - loss: 4.4522 - mean_squared_error: 32.5734 - mean_absolute_error: 4.4522 - mean_absolute_percentage_error: 7.5441 - val_loss: 1.9611 - val_mean_squared_error: 8.6341 - val_mean_absolute_error: 1.9611 - val_mean_absolute_percentage_error: 3.2499\n",
      "Epoch 11/20\n",
      "16168/16168 [==============================] - 44s 3ms/step - loss: 4.2933 - mean_squared_error: 30.1821 - mean_absolute_error: 4.2933 - mean_absolute_percentage_error: 7.1480 - val_loss: 2.0670 - val_mean_squared_error: 8.7821 - val_mean_absolute_error: 2.0670 - val_mean_absolute_percentage_error: 3.3882\n",
      "Epoch 12/20\n",
      "16168/16168 [==============================] - 44s 3ms/step - loss: 4.2879 - mean_squared_error: 30.0476 - mean_absolute_error: 4.2879 - mean_absolute_percentage_error: 7.0953 - val_loss: 1.8957 - val_mean_squared_error: 8.2547 - val_mean_absolute_error: 1.8957 - val_mean_absolute_percentage_error: 3.1950\n",
      "Epoch 13/20\n",
      "16168/16168 [==============================] - 44s 3ms/step - loss: 4.2032 - mean_squared_error: 28.9330 - mean_absolute_error: 4.2032 - mean_absolute_percentage_error: 6.9267 - val_loss: 1.8603 - val_mean_squared_error: 7.9426 - val_mean_absolute_error: 1.8603 - val_mean_absolute_percentage_error: 3.1073\n",
      "Epoch 14/20\n",
      "16168/16168 [==============================] - 44s 3ms/step - loss: 4.1599 - mean_squared_error: 28.3457 - mean_absolute_error: 4.1599 - mean_absolute_percentage_error: 6.8229 - val_loss: 2.0159 - val_mean_squared_error: 8.5631 - val_mean_absolute_error: 2.0159 - val_mean_absolute_percentage_error: 3.3173\n",
      "Epoch 15/20\n",
      "16168/16168 [==============================] - 44s 3ms/step - loss: 4.1165 - mean_squared_error: 27.6789 - mean_absolute_error: 4.1165 - mean_absolute_percentage_error: 6.7289 - val_loss: 1.9142 - val_mean_squared_error: 8.0492 - val_mean_absolute_error: 1.9142 - val_mean_absolute_percentage_error: 3.1727\n",
      "Epoch 19/20\n",
      "16168/16168 [==============================] - 44s 3ms/step - loss: 4.1229 - mean_squared_error: 27.8849 - mean_absolute_error: 4.1229 - mean_absolute_percentage_error: 6.7424 - val_loss: 1.8256 - val_mean_squared_error: 7.8223 - val_mean_absolute_error: 1.8256 - val_mean_absolute_percentage_error: 3.0614\n",
      "Epoch 20/20\n",
      "16168/16168 [==============================] - 44s 3ms/step - loss: 4.0900 - mean_squared_error: 27.3700 - mean_absolute_error: 4.0900 - mean_absolute_percentage_error: 6.6800 - val_loss: 1.8894 - val_mean_squared_error: 7.9627 - val_mean_absolute_error: 1.8894 - val_mean_absolute_percentage_error: 3.1384\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2abbcd4ffcf8>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "from keras.callbacks import EarlyStopping\n",
    "\n",
    "# convolutional operation parameters\n",
    "n_filters = 64 # 32 \n",
    "filter_width = 2\n",
    "dilation_rates = [2**i for i in range(8)] * 2 \n",
    "\n",
    "# define an input history series and pass it through a stack of dilated causal convolution blocks. \n",
    "# Note the feature input dimension corresponds to the raw series and all exogenous features  \n",
    "history_seq = Input(shape=(None, 1))\n",
    "x = history_seq\n",
    "\n",
    "skips = []\n",
    "for dilation_rate in dilation_rates:\n",
    "    \n",
    "    # preprocessing - equivalent to time-distributed dense\n",
    "    x = Conv1D(32, 1, padding='same', activation='relu')(x) \n",
    "    \n",
    "    # filter convolution\n",
    "    x_f = Conv1D(filters=n_filters,\n",
    "                 kernel_size=filter_width, \n",
    "                 padding='causal',\n",
    "                 dilation_rate=dilation_rate)(x)\n",
    "    \n",
    "    # gating convolution\n",
    "    x_g = Conv1D(filters=n_filters,\n",
    "                 kernel_size=filter_width, \n",
    "                 padding='causal',\n",
    "                 dilation_rate=dilation_rate)(x)\n",
    "    \n",
    "    # multiply filter and gating branches\n",
    "    z = Multiply()([Activation('tanh')(x_f),\n",
    "                    Activation('sigmoid')(x_g)])\n",
    "    \n",
    "    # postprocessing - equivalent to time-distributed dense\n",
    "    z = Conv1D(32, 1, padding='same', activation='relu')(z)\n",
    "    \n",
    "    # residual connection\n",
    "    x = Add()([x, z])    \n",
    "    \n",
    "    # collect skip connections\n",
    "    skips.append(z)\n",
    "\n",
    "# add all skip connection outputs \n",
    "out = Activation('relu')(Add()(skips))\n",
    "\n",
    "# final time-distributed dense layers \n",
    "out = Conv1D(128, 1, padding='same')(out)\n",
    "out = Activation('relu')(out)\n",
    "out = Dropout(.2)(out)\n",
    "out = Conv1D(1, 1, padding='same')(out)\n",
    "\n",
    "# extract the last 60 time steps as the training target\n",
    "def slice(x, seq_length):\n",
    "    return x[:,-seq_length:,:]\n",
    "\n",
    "pred_seq_train = Lambda(slice, arguments={'seq_length':10})(out)\n",
    "\n",
    "\n",
    "model = Model(history_seq, pred_seq_train)\n",
    "model.compile(Adam(), loss='mean_absolute_error')\n",
    "model.summary()\n",
    "# feeding data:\n",
    "first_n_samples = df_data.shape[0]\n",
    "batch_size = 2**10 \n",
    "epochs = 20\n",
    "\n",
    "#es = EarlyStopping(monitor='val_loss', mode='min', verbose=1)\n",
    "print(X_train.shape)\n",
    "print(Y_train.shape)\n",
    "lagged_Y_train = Y_train[:,:-1,:1]\n",
    "encoder_input_data = np.concatenate([X_train, lagged_Y_train], axis=1)\n",
    "model.compile(Adam(), loss='mean_absolute_error', metrics=['mse', 'mae', 'mape'])\n",
    "history = model.fit(encoder_input_data, Y_train,\n",
    "                    batch_size=batch_size,\n",
    "                    epochs=epochs,\n",
    "                   validation_split=0.2)  \n",
    "\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Mean Absolute Error Loss')\n",
    "plt.title('Loss Over Time')\n",
    "plt.legend(['Train','Valid'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = model.predict(X_test, batch_size=batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mape=5.418583\n"
     ]
    }
   ],
   "source": [
    "from keras import backend as K\n",
    "\n",
    "tf_session = K.get_session()\n",
    "mape = tf.keras.losses.MeanAbsolutePercentageError()\n",
    "MAPE_error = mape(Y_test, preds)\n",
    "\n",
    "print('mape=%f' % MAPE_error.eval(session=tf_session))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_prediction(sample_ind):\n",
    "    plt.figure(figsize=(10,6))   \n",
    "    \n",
    "    plt.plot(range(1,past_window+1),X_test[sample_ind,:,:])\n",
    "    plt.plot(range(past_window,past_window+future_window),Y_test[sample_ind,:,:],color='orange')\n",
    "    plt.plot(range(past_window,past_window+future_window),preds[sample_ind,:,:],color='teal',linestyle='--')\n",
    "    \n",
    "    plt.title('Encoder Series Tail of Length %d, Target Series, and Predictions' % past_window)\n",
    "    plt.legend(['Encoding Series','Target Series','Predictions'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JmKGeZzG1ge92+8CtHYoJBlL/Upm+4zBDSZ8DG619D8uyT8diPvw3YTIpz2OGf7CGv2/GDClvVubqsVx4MedOA+Z8PB0zpcsSa/9zMOfoC5gPQCdmaDhlO/Krr3sfeByTCVqFGbLraXjxVkzt0heY+quXaDsOSUy90SjMebwRUzieGgL9i/UcG5RSnWX3QpgA/F3Mh/dHmAzLOdb+/wFcDDyM+dLyOeZvMtvM23XWc35XKdWEuTo2lXkcA7yKGQaei3l9X7a2dXd878WcX2utx73dxf06sIL36dY+1mHKErIe3s1i/49gjtXL1nvGHMwFDzm1rbVuwFyZ/FPM67cAU6QP5n2qXim1KSNIz3Q7Zt68DzDnzApMjWA2fgh8Yb1WlwM/kexY/+NIJosxQiCEENlRSr0M/EdrfWOPdy5uPwKYYufdM4rIe7P944Bf6Yx50wYTqzRhPjA1lyF8IUqFBGRCiF6llNoHkwVZDnwXk62a3E090qCklBqCmVNrFqYe6GXgT1rrq/uwW0KIPiI1ZEKI3rYtZii7FhOUnVpqwZjFhZlUeSfMsOKfaJvLSwhRYiRDJoQQQgjRx6SoXwghhBCijw2EIUsvZtqC1eQ+fYAQQgghRG9yYeai+xDryulsDISAbCpts6ALIYQQQgwEB2LmRMzKQAjIVgNs2hQkkci/3q2uroING5pt65RoI8e2uOT4Fo8c2+KS41s8cmyLp9Bj63Q6GDKkHKz4JVsDISCLAyQSyYICstQ+RHHIsS0uOb7FI8e2uOT4Fo8c2+Kx6djmVGYlRf1CCCGEEH1MAjIhhBBCiD4mAZkQQgghRB8bCDVkQgghRL8Uj8fYtKmBWCzSq+2uW+ckkZD1wYsh22PrdLrw+yuoqKjG4XAU3K4EZEIIIUSeNm1qwOcLUF4+3JYP5Wy53U5iMQnIiiGbY5tMJonHYzQ1bWbTpgZqa7cpuF0ZshRCCCHyFItFKC+v6tVgTPQ9h8OB211GTU0dkUirLfuUgEwIIYQogARjpcvhcAL2TD8iQ5ZCCCHEIHH00Ufi8XjweLzp22688RZGjBhZ1HZXr/6a00+fzquvvsX69Q1cc83l3Hnnfbbtv7Gxkd/97jcsXrwIh8OB0+nivPMuYK+9pua8r1NOOZ777nsYr9dnW//sIAGZEEIIMYhcf/1N7LDDTn3W/tCh9bYGYwAPPPB7ttlmGFdfPQOHw8GWLZtpacltqDAWi+F2u3n00T/Y2je7SEAmhBBClIBp06Zw5pk/4513ZrFlyxbOPfd/OeSQwwCYN+9T7r77dkKhEADnnvsL9t57XxYunM9tt91Ca2sLPp+f88+/kPHjJwLwxz8+x3PP/YG6uqHsuede6XYys2U9tTtr1lvcf/89eL1eDj30cO6//x7+/vd3CAQCW/W9oWEte+65V3p4uLq6hupqsy0ajXL//fcwd+5HRKMxdtxxR375y0sJBALMmHE1gUCAFStWsHnzJh5++EmmTZuSbmP58qXcfvutbNmymWg0yjHHHMcPfnAUra2tXH/9VSxduhiXy83o0WO47rrfFO/FQQIyIYQQwjbvfbaaf32a0xKGWZu2+wgO2G1Ej/e7/PJL0kOWLpeLhx56Ir2tvLycBx98nE8/ncuVV17KIYccRmPjFi677CJmzLiZ3Xbbg3g8TjAYJBqN8utfX8yll17J1Kn7MHv2B/z61xfz7LMvsWzZUh5//GEeeeQpamvruOWW7oOVztrdtGkjN998A/fd9wjbbTeaZ599qsvHH330sVx++SW8+ebf2HXXPZg27aD0cOVTTz1GeXk5DzzwOAD33HMHTzzxCGeddS4A8+Z9xl133Y/f799qn7FYjKuvvpyrrrqeMWPGEgoFOe206eyxxx589dVimpqaePLJ5wEzZFpsEpCJovvLv5cytNrHvhOH93VXhBBi0OtuyPKww44AYOLE3Vi/voFwOMy8eZ8xduz27LbbHoAJ4qqqqvjqq0WUlZUxdeo+AEyZsjdlZWUsX76Mjz/+iP33n0ZtbR0AP/jB//D222902afO2p0//zPGjVNst91oAL773R9w550zO338lCl786c//YU5c2bz6adzueKKX3HccdOZPv0U3nvvHYLBILNm/QOAaDTCTjvtnH7sIYcc1iEYA1ixYjnLli3hqqsuS98WjUZZunQJO+20M8uXL+V3v7uJPffci/33n9blc7OLBGSi6P45dxV+b5kEZEKIQe+A3bLLYvUVj8cDmKALIB6Pk0x2fpVgMpns9ApSh4MuH5Nru7lcoRoIlDNt2sFMm3YwSo3n8ccfZvr0U0gm4Ze//FWXBf6BQMdgDMxzqK6u6VBTlpqH7Kmnnmf27A/5z3/e4/777+axx57B6/V2ui87yLQXouhaI3FWNjTTGOzdmayFEEL0bLfddmfp0iXMm/cpYIKlxsZGxowZSyQSYc6c2QDMmTObWCzGdtuNYfLkKbz//nts2rQRgL/85eWc2504cTe0/pyVK1cA8Nprr3R53w8//A/BYDNgAqkvvtDpK0enTTuIZ599inDYFPmHQkGWLl3SY/ujR4/B5/Px+uuvpm9btmwpwWAz69atxel0cdBBh/C///tLNm/eRFNTcYctJUMmii4cNTMeL1y2iX0mDOvj3gghxOCWWUMG8KtfXc4uu0zo8v5VVdXMmHEzd945k9bWFhwOJ+ee+wumTt2HGTNu3qqo//rrb6KsrIyddtqZ6dN/yjnnnEZtbR377Zf7kF5tbR0XXngpF130C2pqath//4Nwu934fB2no1i0aBF33jkznZnbdtvRXHDBxQCceOIpPPTQfZx++kk4nU7AwamnnsHYsdt3277b7eamm2Zyxx2/4+mnnyAeT1BbW8sNN9zEV18t4t577wIgkYhz4omnMHRofc7PMReOXNOOfWAssGTDhmYSifz7Wl9fSUNDk22dEm26O7aJRJLTb34bgAN3H8FPvzO+N7s2KMi5WzxybIurFI7vmjXLGD58TK+3O1iWTgqFggQC5QC8+uqf+ctfXub3v3+oT/uU67Ftfw44nQ7q6ioAtgeWZt1u9l0UInfhaDz984KlG3OuGRBCCDF4Pf/8M7z99lvE4zGqqqq55JLL+7pLfUYCMlFUrRETkG1bX8HKhmbWbW5h2JBAD48SQghRCk4++TROPvm0vu5GvyBF/aKoIlaGbNLOQwFYsHRTX3ZHCCGE6JckIBNFlRqyHDOsgtoqLwuXbuzjHgkhhBD9jwRkoqhSQ5Zej4sJY2pZuGxTQRdnCCGEEIORBGSiqFJDlt4yF+PHDiHYGmP5usF91ZUQQgiRqx6L+pVSY4GXMm6qAaqAnYEngB2BMLAIOEtr3dDJPh4FDgfWWzc9r7WeUUjHxcAQzgjIJowZApg6srHDq/qyW0IIIUS/0mNAprVeCkxK/a6Uus16XBK4WWs9y7r9t8BvgK4ul/iN1vquAvsrBph0QOZxUV3hZVR9OQuXbuQ7+/b+vD1CCDGYnXHGyUSjUWKxKCtWLGf77XcEYNw4xWWXXVX09hOJBI888gAnn3wabnfn4cULLzzDn//8Ig6Hk1gsyrRpB3POOefl3NZ9993NuHGKQw89vNBu9xs5TXuhlPIAJwBHaK03ArMyNv8HOMe+ronBIBxpy5ABTBhTy6y5q4jG4pS5XX3ZNSGEGFQeeOAxAFav/prTT5/eYY3GbMTjcZxOZ17zRaYCshNPPKXTgGzevE/54x+f44EHHqeiooJ4PM6SJYvz6uNZZ52b8+P6u1znIfs+sEprPSfzRqWUExOM/bmbx/6fUuos4CvgUq31whzbFgNQatmkdEA2dghvzF7BopVbGD+2ti+7JoQQJaOhYR3XXHM5oVCQSCTCgQcekg5q7r//HtatW0tzcxOrVq3kvvseQevPufXWm3A6XUyZMpW33nqD22//PWPGjGXp0iXcccetbNmymVgsxrHHnsC3v/09br31JgDOPPMUHA4H99zzQHoWfoB169ZRUVGJ328W+3a5XOy0087p7e+99y5PPPEIkUgEj8fDL37xS8aPn8iHH/6X3//+Tmvty4X89Kdn8MYbr7P77ntw1FFHE4lEuO++u/n004+JRKLsvPM4LrzwUnw+Hy+++AIvvPAMbncZANdffxPbbTe6tw57TnINyE4FHu7k9juBZqCrIclfA6u11gml1EnA60qpHbTW8S7u34G1DEFB6usrC96H6FxXx9ZtBWKjRtbgcjrYv9LHXX/6jKUNQQ6aKsOW2ZJzt3jk2BbXYD++69Y5cbvbro/zrPoDnpWPF6WtyLYnERl1fPr3zHYzuVxmPcfM7UOG1DBz5p34/X6i0SjnnXc2c+Z8yN5774PT6eCTTz7m0UefpLq6hnA4zDXX/Jobbvgtu+++B2+99QbPPvsHXC4HEOfaa6/g+utvZPToMQSDzZx88glMmjSJiy++lD//+UUefvhxvF5vh35NmzaNZ555gqOPPpI999yLyZP34lvf+g4+n4/ly5fx5JOPcMcd9xAIBPjyyy+46KILeOmlV3G5nCxa9AWXXHIZl1xyKQBvvvk6Tqc59o888ih1dbU88siTANx++608/fRjnHHGOdx992288MLLDB1aTzgcJplMdnncMmVznxSn02nLeZ51QKaUGgkcDExvd/stmAL/I7XWnS7+pLVelfHz40qpmcC2wLJs25e1LPuv7o7txi0tlLmdbNzQnL5t+5FVzF6wlm9P3a63ujigyblbPHJsi6sUjm8ikdhq3UNXPEGxloiOx9va6m69xXg8ASS32h6JxLjrrpnMn/8ZABs2rOfzzz9n8uSpJBJJ9tvvAMrLq4jFEixa9BXl5RVMmLAbsViCgw8+DL8/QDyeZNGixSxbtoTLLrskve9YLMZXXy2mvn649XsCl6tj3zweH/fd9ygLF87nk0/m8tJLf+JPf3qe++9/jPfee48VK1Zw5pmnpu8fjUbZsGEj8XiCMWPGotSE9HNKJtuO/bvv/pPW1lb+/ve/WY+LoNR4YrEEkydP5ZprrmTatIPYf/8DGTFiZI/rVOa6lmUikdjqPM9YyzInuWTITgFe1VpvSN2glJoB7AV8V2sd7uqBSqlRqaBMKXUEEAdWdXV/MXiEI/H0cGXKhDFDeOXfSwm2Rin3lfVRz4QQwn7hkccTHnl8z3fsZU8//QQtLS088MDjeDwebrjhGiKRto9tv79tSbvu1hxOJpPU1g7ttD4tFov12A+Hw8GECbsyYcKu/OhHx/Dd7x7O0qVLSCaT7L//tC4vPsjsX2d9uvjiXzNp0uQO22666Vbmz5/Hxx/P5txzz+DSS69g6tR9e+xnX8hlHrJTyBiuVEpNBC4DRgL/VkrNVUq9mLF9rpVVA3hMKfWZUuoT4HLg+1rrnl85MeCFo3G8ZVufZhPG1pJMwufLNvdRr4QQorQ0NTUxdGg9Ho+HtWvX8O9/v9vlfbfffgeamhqZN89k02bNeouWllB6m8vl5O9/fz19/yVLFhMKhXC73fh8PoLB5k73u3Tpkq2K+JctW0IiEae+vp59992P999/j6VLlwAmyFq4cH5Wz23atIN55pknCYdNgBkMNrNs2VJisRirV3/NrrvuxvTpP2XKlL354gud1T77QtYZMq31uHa/zwe6vAxDaz0p4+fBc12qyEk4Gsfr2fo022FkFd4yFwuWbWQvVd9HPRNCiNJxzDHHccUVv+KnPz2eYcOGM3ny1C7v6/V6ufLK67nppuvw+fxMmbI31dXVlJdX4Ha7ufnmmdx++6089dSjxOMJ6urquO663wBw7LEncu65Z+D1+joU9be0hLjzzpls3rwJr9eL0+ni6qtnUF1dQ3V1DZdddhUzZlxFJGKm7thjjz0ZP35ij8/tpJNO5cEH7+WMM07C4XDgcDg59dQzGTZsONdddyXBYDMOh4Phw0dw5JFHFXooi8aRLNZgt33GAkukhqz/6u7Y3vrsXIKtUa44ees//tue/4S1m1q48cz+mTruT+TcLR45tsVVCsd3zZplDB/e+xco5VrnlKtQKJgOpj788D/cfPMNPP98dxMpDB65Htv250BGDdn2wNKs282+i0LkzgxZdpxvbMKYIXz61QY2NrZSW+Xrg54JIYToyj/+8QbUgzAqAAAgAElEQVTPP/8skMTj8XDVVbK4TrFJQCaKKhyNU1PR8fLnCdYcZAuWbmLa7iN6u1tCCCG68b3vHcX3vtd/h/cGI1lcXBRVOJrA5+mYIRtVX05VoIwFyzb2Qa+EEEKI/kUCMlFU4UgMTydDlg6Hg/Fja1m4dBMDoI5RCCGEKCoJyERRhaOJTmvIwNSRbQlG+Hp9sJd7JYQQQvQvEpCJokkmk0Si8U6HLGHrOjIhhBCilElAJoomFk8STyQ7HbIEqKv2MWyInwVLpY5MCCHscvTRR3L88T/i5JOPY/r0Y3jzzb/Zss/FixcBcOGF/8uqVSu7vf8778xiwYJ56d8//3wB11xzecH9GMzkKktRNOGoWTu+qyFLgPFja3l//hpi8QRul3w/EEIIO1x//U3ssMNOfPHF55x99mlMmbIPNTU16e3xeByXq+v35u7ccssdPd7n3Xdnscsu45kwYVcAdtllAldddX1e7ZUKCchE0UTSAVnXgdaEMUOY9fEqlqxuZOdta7q8nxBCiNyNG7cLgUCA1177M7Nnf8iQITUsWbKESy+9giFD6rjttptZu3YN4XCYww8/gpNOMot7f/LJx/zud7/B6/UyceJuW118dfTRR3LzzTPZYYedaGhYx223/ZaVK1cAcPjhRzBu3C7861/vMHv2B7zyysv85CdmdYC7776dhx56AoC//vUvPP30EzgcDkaO3JaLL76MIUNqee21V3jjjdeprKxi8eKvqKys4Prrb6aubiifffYJM2feTCKRJBaLcfLJp/LNb36r9w9qkUhAJoomnSHrooYMYJcxQ3AAC5dukoBMCDEoHPXScx1u+/5O4zh110mEolGOf/XFDtuP3WUix+4ykQ0tLZz2t1c6bD9l4h4ctbPKuS9z5swmEongdrv57LO5PPro04watS0A55//M0455XQmTZpMNBrlF784h/HjJ7DHHpO56qrLuPLK65g8eQpvvfUGL7zwbKf7v/baK9hvvwOYMeO3AGzevJmamhqmTTuIXXYZz49+9JN0P1IWL17EvffexUMPPcnQoUN54IHfM3Pmb7n22hsBWLhwAY899jTDhg3nppuu54UXnuWss87lqace45hjjudb3/ouyWSS5ubO18wcqCQgE0XTGul5yLLCX8aY4ZUsWLqR70/bvre6JoQQg9rll1+Cx+OlvLycGTNuoqGhgd12m5QOxlpaWvj444/YvHlz+jGhUJClS5cyZEgdPp+PyZOnAHDYYd/kt7/tOFN/KBRi3rxPmTnz7vRtmcOiXZkzZzb77XcAQ4cOBeAHP/ghp5xyfHr77rvvwbBhwwGYOHFXPvzwvwBMnjyFJ598lDVrVjN16r5MnLhrroelX5OATBRNJIsaMjBXW/7tg+W0RmL4PHJKCiEGtpeOOqbLbYGysm631/n93W7PVqqGLOW1114hEPCnf08mEzgcDh588HHc7q3fd7/88ouC2+9OMmnmosyU+avH40n/7HS6iMfNZ8kxxxzPAQccxIcf/pfbbruZqVP35cwzf1bUvvYmqaIWRZPNkCXA+LFDiCeSfLFic7f3E0IIYY9AoJw99tiTJ598NH3b2rVr2LBhPWPGjCUcDjN37hwA3n77zU6HBwOBALvuujvPPfeH9G2pjFt5eXmXQ4p77TWV999/jw0b1gPwyisvMWXK3j32efnyZYwatS1HHfUjfvzj41i4cH7Wz3cgkHSEKJpshiwBRm9TAcC6TS1F75MQQgjjyiuv4447buWkk0ydVyBQzqWXXkld3VCuvnpGuqh/8uSp6SHEzvZx6603MX36MTidLr75zSM48cRTOOKI7zBjxjW8/fZb6aL+lB122JGzzjqXCy441yrqH8VFF13WY39feOEZ5sz5iLIyN2VlHi644CJ7DkQ/4RgAy9aMBZZs2NBMIpF/X+vrK2loaLKtU6JNV8f23U+/5pHXPuems/ejvsbfySONeCLBGTfP4qhp20sdWSfk3C0eObbFVQrHd82aZQwfPqbX23W7ncRiiV5vtxTkemzbnwNOp4O6ugqA7YGl2e5HhixF0USi5oTuKUPmcjrxelyEwrHe6JYQQgjR70hAJoom2xoygIDXTahVAjIhhBClSQIyUTStkTgOwOPu+TQL+NySIRNCCFGyJCATRROJxvGUuTpc3twZkyGL9kKvhBDCXgOgFlsUSTKZAHr+jMuGBGSiaMLReFbDlQDlvjLJkAkhBhy320Mw2ChBWYlJJpPEYlE2b16Px+OzZZ8y7YUomnAk3u06lpn8XjehBgnIhBADy5Ah9Wza1EBzc+/Oo+h0Okkk5CrLYsj22DqdLvz+Cioqqm1pVwIyUTThaLzHKyxTAj4p6hdCDDwul5uhQ0f0erulMKVIX+mrYytDlqJocgrIvG5awjESkvYXQghRgiQgE0WTSw1ZwOcmCbSG48XtlBBCCNEPSUAmisbUkGWfIQMIheVKSyGEEKVHAjJRNLnVkJUBSB2ZEEKIkiQBmSiacDSR05AlSEAmhBCiNElAJoomvyFLCciEEEKUHgnIRFEkk8n0TP3ZkAyZEEKIUiYBmSiKSCxBEvDlOmQpGTIhhBAlSAIyURThqJm+ItshS78nlSEr7lWWv39pHq//d3lR2wD463+Xce/L84rejhBCiMFBZuoXRRGOmIDMk+XSSU6nwyyfVOQM2bwlG4knijv5bGMwwsvvLiESS3DE3o1sP6KqqO0JIYQY+CRDJooi1wwZmML+YtaQRWMJWsIxWooc9L3+wXKi8QRlbif/nLuqqG0JIYQYHHrMkCmlxgIvZdxUA1RprWuVUuOAx4A6YANwktb6y0724QLuAL4FJIHfaK0fLLz7or9KBWTZ1pBB8dezbApFgOJeONAYivCPOSvZZ8IwylxO/rNgLcccunO6Rk4IIYToTI8ZMq31Uq31pNQ/THD2B2vzvcDdWutxwN3AfV3s5gRgJ2BnYD/gaivQE4NUasgy5wxZEbNXjVZAVswM2d8/WEE0muB7+43l0MmjiEQTvD9/TdHaE0IIMTjkNGSplPJggquHlVLbAJOBp63NTwOTlVL1nTz0J8ADWuuE1roBE9T9OP9ui/4ulSHLdtoLKH6GrDFoLhgoVtDX3BLlrTkrmTp+G0YOLWfs8CrGDq9k1serSMqi6UIIIbqRaw3Z94FVWus5wHbWz3EA6/+vrdvbGw0sy/h9eRf3E4NEXkOWXjctRVzLsikjQ1aMAOnvHy4nEolz5P5j07cdsucoVq0P8uXKLba3J4QQYvDItbDlVODhYnSkJ3V1FQXvo76+0oaeiM60P7Ye70YARgyrpn6IP6t91NUGCH25vmivUxwzdBhPJKmqCeDz2FfX1RSK8NZHq9h/j5FMmjAifft3D9yR599exPsL1nHA5Py/g8i5WzxybItLjm/xyLEtnr44tll/IimlRgIHA9Otm1YAo5RSLq113CrcH2nd3t5yYAzwofV7+4xZjzZsaCZRwHQF9fWVNDQ05f140bXOju36jUEAmptaIJbdEKEjkaQlHGPN2i24nPZfALw6o4/LV25mSKXXtn2/9O5iWsIxjthr2w7HYt+Jw/nn3FX8z7KxVAU8Oe9bzt3ikWNbXHJ8i0eObfEUemydTkdeSaRcPvVOAV7VWm8A0FqvA+YCx1nbjwM+tmrE2nseOEMp5bRqzI4C/phzb8WAke+0FwAt4XhR+pSqITNt2FdHFmqN8sbslew1rp5tt+n4R3jIpJHE4kne+3S1bW0KIYQYXHINyNoPV54NnKeU+gI4z/odAKXUa0qpKdavTwCLgS+B/wDXaq0X59tp0f9FonFcTgdulyPrxxR7+aTUVZZ2t/Hm7JW0hGMcecDYTrePqq9g3HY1zJq7ioQU9wshhOhE1kOW1tQW7W/7HNini/t/J+PnOHBOPh0UA1NrxCws7nDkEJClMmRFutKyKRihwl9Gc0vUtgxZqDXG3z9cwZ47D2X0sK5rDg7ZcyT3/3kBC5ZsZNcd6mxpWwghxOAhM/WLoghH43izXDYpJZ0hK9J6lltCEYbVmgsM7ArI3vpoBaFwjO8fsH2399tr3DZUBsp4+2OZuV8IIURHEpCJoohE43hzvIox4CsDIFiEDFkimaQ5FGX4kABgz2z9LWGTHZu001DGDO/+ipwyt5Npu4/gk0Ub2NjYWnDbQgghBhcJyERRhCN5ZMi8xashC7XGiCeSDKs1AZkdGbJ/zFlJsLXr2rH2Dp40imQyyTuffF1w20IIIQYXCchEUZghy+yvsITMIUv7A7LGoCnoH1rtw+lwFBz0tUZi/O2DFey+Yx3bj6jK6jHb1PiZuEMt73zyNfFEoqD2hRBCDC4SkImiyCcg83pcOBzFyZClZumvKveYJZoKbOPtOatobolmnR1LOXTSKDY3R/hk0YaC2hdCCDG4SEAmiiIcTeDNYdkkAKfDYZZPKkaGLGQuFKgq9+D3ugoasgxH4rz+wXJ23b6WHUdW5/TY3XeqY0ilV4r7hRBCbEUCMlEU4Ugs5wwZWAuMF2E9y9SQZVXAg99b2CLmHy9qoCkU5bv7jcn5sS6nk4P3GMn8JRtZtymUdx+EEEIMLhKQiaIIRxP5BWTesqJcZdkYjOBwQIW/zFrEPP82tjSb4G67bfJb6+zAPUbidDiYNVeK+4UQQhgSkImiCEfjOQ9ZArbUd3WmKRSh0l+G0+kwGbIC2gi2xnA4wOfN/fkBDKn0sufOQ/nXp6uJxqS4XwghhARkoggSiSTRWL4ZsuLUkG0JRqgsNwt7B3yFZciCrVHKfWU4c1iFoL1D9hxFc0uUj/S6vPchhBBi8JCATNgun4XFU/xFy5BFqQqYgKzQGrJQa4xyX26T3rY3fuwQhlR6mbtofUH7EUIIMThIQCZs1xaQ5X56BQoMlrrSGIxQlcqQed20RuIkEvkt9B1siaZXFciX0+FgeG2Ahs0ya78QQggJyEQRpAOyPGrIyn1uwtE4sbi9tVWNoQiVARNEpVYEaI3kF/gFW2OU+wvLkAHU1/hZv6Wl4P0IIYQY+CQgE7YLR/IfskxlnuwctoxE47RG4lsNWUL+KwKkasgKVV/joykUzTswFEIIMXhIQCZsV0gNWSp7ZWdhf2PGLP2QsURTnkFfsCVacA0ZmAwZwHoZthRCiJInAZmwXSFDlv4Cg6XONKVm6W+XIcvnSstEMkkoHCu4hgzaArKGzTJsKYQQpU4CMmG7cMTUfxWSIbOzsD89S78NGbLWcIxkEipsyJANrfYB0LBFMmRCCFHqJCATtgtHTaCT79JJYG+GrG3ZJJPVKiRDllpFwI4MWYW/DJ/HJRkyIYQQEpAJ+4WjJkPmySMgSxXLh1rtW88yVUOWmhi2kKL+oNUvO66ydDgcDK32s14CMiGEKHkSkAnbpa6y9OWzdFIRhiybQlG8Hlc6YxewIUNmx1WWYK60lCFLIYQQEpAJ20UKuMrSU+bE5XTYPmSZGq4EcLuceNzOvNoItlgZMhtqyMCai2xzC8lkfpPUCiGEGBwkIBO2a43GcbucOJ25r/XocDgKXtqovcZQJH2FZYo/z/Us0xkyv10ZMj+RWCJd5yaEEKI0SUAmbBeOxvMarkwJ2LyeZWMwmr7CMt2G100oHM95X6naNvsyZNaVljIXmRBClDQJyITtIpF4XutYpti9nqVZNqljQNaSx4UDwZYYHreTMnf+AWemodXWXGSyhJIQQpQ0CciE7Vqj8byusEwp97kJhe25yjKRTNIUilBVvvUQoz/PDFmwNZqemsMO6bnI5EpLIYQoaRKQCduFo/G8CvpT/L4y2zJkzS1Rkkk61pB58xsWNQuL21M/BmZqkOoKjyyfJIQQJU4CMmG7SKTAGjIbhyyb2s3Sn24jz6L+UGuUcq99GTIwhf2SIRNCiNImAZmwXTiaKGjI0s6i/kZrHcv2NWT5XsnZ3GJvhgygvtonNWRCCFHiJCATtmstcMgy4HUTjSWIxnKv8Wqv/TqWmW3E4gmisURO+wuFo7ZNCptSX+NnU2OYWDy3vgghhBg8JCATtotE43gLnPYCyKvovr3UskmZE8NC/utZBltithb1gwnIksAGmbFfCCFKlgRkwnbhSIEZslRAZsN6lk2hCE6Ho8MwY3qJphwCslg8QTgat33IMn2lpQxbCiFEyZKATNiu0KssA97UAuOF15E1BiNUBspwOrZeNcDvyz1D1raOpf0ZMpDJYYUQopRJQCZsFYsniCeShU0M68s9e9WVxmC0Q0E/5LeIeds6lvZmyGoqvbhdDtbLlZZCCFGyJCATtgqnFhb35J9FyidY6kpTKEJ1eccAKpBHDVmoSBkyp8NBXbVMfSGEEKUsq08WpZQPmAkcDrQC7wM3AC9l3K0GqNJa13by+KuBnwFfWze9p7U+N/9ui/4qHLECsn6SIdsSjFA/pLrD7f48asiaU+tY2lxDBmZNSxmyFEKI0pXtV/2bMYHYOK11Uik1TGu9FpiUuoNS6rYe9ve41vrC/LsqBoJ0hqzAaS/ArqL+aIdZ+iEjIMshC5fqj91XWQLUV/tZ8nWj7fsVQggxMPT4yaKUqgBOArbVWicBrGAs8z4e4ATgiGJ0UgwcbUOW+QdknjIXbpez4AxZOBInHI13mIMMwOd14SDHov6W1JBlMTJkfoKtMUKtUQJF2L8QQoj+LZtxpR2BDcBVSqnZSqlZSqlp7e7zfWCV1npON/s5Vin1qVLq70qp/fLtsOjf2oYs8w/IwJqtv8AastQcZJWBjgGO0+HAl+N6lsHWKA7aMnh2altkXIYthRCiFGXzyeIGdgA+1lpfpJTaB3hFKbWT1jo1xnIq8HA3+7gXmKG1jiqlvgm8rJQar7XekG1H6+oqsr1rl+rrKwveh+hc6tguWx8CYNg2lQUd78qAh3iysNdso7Vs0nYjqjvdT2WgjKTDkXUbCYeDgL+MYcOq8u5TV8ZZk+CGu3jOcu4Wjxzb4pLjWzxybIunL45tNgHZMiAGPA2gtf6vUmo9MA6YrZQaCRwMTO9qB1rrNRk/v6GUWgHsCvwz245u2NBMIpHM9u4d1NdX0tDQlPfjRdcyj+269c0AtATDBR1vb5mTTY2tBe1j2arNACRj8U7343G72LSlJes21m8KEfC6inIeuZNm2aTFyzcxbsTWbwRy7haPHNvikuNbPHJsi6fQY+t0OvJKIvU4ZKm1Xg+8DXwTQCk1DtgGWGTd5RTg1e6yXUqpURk/TwLGAjrn3op+z7YhyzwX/87UZGXIOivqh9yHRUOtsaLVdwV8ZZT73DL1hRBClKhsi2HOBh5WSv0OiALTtdabrW2nAP/b/gFKqdeAK7XWs4EblFJ7AXEgYj1+TfvHiIHPjqsswQRLDQWu7di2sHjnQVTA62ZjY/ZtBFuiVBThCsuUoTUyF5kQQpSqrD5dtNaLgUO62Daui9u/k/Hzyfl0Tgw89gVkZbQUOO1FYzCC3+uizN15X/xeV45F/TFqq3wF9ak79dU+VjQEi7Z/IYQQ/ZfM1C9sFY7EcQBlBUwMCyZ7FWyNkUzmXzfYGIp0umxSWxtlOa5lGS3KpLAp9TV+NmxpIVHAcxZCCDEwSUAmbBWOxvGUuTos5p2rgM9NPJEkEkvkvY+mULTTOchS/D6TIcsm6Esmk4RaY7Yvm5RpaI2fWDzJ5qZw0doQQgjRP0lAJmwVjsYLWjYpxY71LBuDkS4L+k0bZSST0GpdiNCd1kiceCJZlElhU+prUnORSR2ZEEKUGgnIhK3C0XhBs/Sn2LGeZWMoQlUnk8Km+L2mn9kMWxZrYfFM9dV+ANYXeDGDEEKIgUcCMmGrcCRecEE/tGXIWvLMkCUSSZp7GrLMYYHxYHody+JlyOqqfTiQDJkQQpQiCchs9Pp/l/P+/NKezSMStSkgswKfYJ5XWja1RElC90X9VrYrmwxZsMX0o8JfvAyZ2+WktsorAZkQQpQgCchsNGvuKv792eq+7kafarWK+gtV6JBlU3oOsiwyZFlk4YLWfYq98PfQan/B868JIYQYeCQgs1GoNUZTS2FzZw104UgCnx01ZAUW9W+xFhbvroYsPSyaw5BlMWvIwEx9IRkyIYQoPRKQ2SSZTNISjqWX6ylVdg1Z5lLf1ZlsMmS5BGRtRf1FzpDV+NjSHCES7fnKTyGEEIOHBGQ2icQSxBNJmluiBU1mOtDZNWRZ5nbicTvzLupvtALjbGrIsgn6mlujuF0OPDZM6dGd+hq50lIIIUqRBGQ2SWVQorEEkWj+k5kOdOFo3JYhS7AW/w7nl3FsDEZwOR3dDjGWuV24XY6sArLUwuKOAie87Unb1BcybCmEEKVEAjKbZH6oN1n1S6UmmUwSidiTIQNTQB/MO0MWoTLQcwDl97qzysIFW6JFrx+DzMlhJUMmhBClRAIym2R+qJdqYX80liAJtszUD6bGK9+i/qYeZunfqo2sivpjRa8fA1Pz5nE7pbBfCCFKjARkNtk6Q1aaAVmrVYhuR1E/pIYs88+QdVfQn+L3umkJ91xAH2ztnQyZw+FgqFxpKYQQJUcCMptk1jo1t5TmkGXEWhPSjqWTwGSv8i7qD0a7LehPt5FlnVqwJUa5v/gZMoD6ap8MWQohRImRgMwmmYFDc4lmyMI2Z8j8eWbIkskkTaEI1TZmyELhaPqqzGIbWuNn/ZaWkr5aVwghSo0EZDZJBQ4OR+nWkNk9ZFnuMzVkuQYmrZE4kViCyvKeM1p+r5tQD8szxRMJWsJxKnqhhgzM1BetkTjNJXoeCSFEKZKAzCah1hhul5OqgKdka8hSQ5a2TXvhLSORTNIayW2S1Kb0LP3ZFfX3lCELpZdN6p0MWepKS5mLTAghSocEZDZpCccIeF1UBMpKdtqLsDX/mn3TXmQ/k36mxqAJiLMp6g943YSjcWLxrueOS0290Xs1ZGYuMinsF0KI0iEBmU1C4Rh+XxmV/rKSHWqyu4Ys3/UsG3PIkPmtoK+7LFxvrWOZMjQ9F5kEZEIIUSokILNJqDVGwOumIuCRgMzGon7IfT3LVEBW2c3C4imBLNbMDLb0zjqWKT6Pm8pAmVxpKYQQJUQCMpuEwjECPjeV/rKSrSELF2HaC8gjQ5bFwuLt2+hueo1Uhqy3asjAFPZLhkwIIUqHBGQ2SWXIKgNlBFuiJBKlN2WB3Rmy8nSGLLcAtykYJeB143b1fHr7s8iQhXq5hgxgaLVP1rMUQogSIgGZTVrCMfxeNxX+MpJAcw9TKQxG4Wgcp8OB22XPAtwBa4gw1/UsG0MRKrPIjkFGQNZdhqyld2vIwGTINmwJE0+U7kL1QghRSiQgs0l6yNIqJC/FyWHDkThej6vHBb2z5feaTFuus/U3BiNUZ1E/BtldyRlsjeHzuHA5e+/Ppb7GTyKZZFNjuNfaFEII0XckILNBNBYnGktYRf0mECjFwv5wNG7bwuIALqcTr8eVV1F/rhmy7gOyaK8V9KfUV8uVlkIIUUokILNByJpYNFXUD5TkXGQmILOnfiwl4HXnXNTfFIpmNeUFtGXheqoh683hSjAZMoAGmRxWCCFKggRkNkgtvWOK+k0gUIrLJ4UjRQjIclzPMhZP0NwSzeoKS2jLwnWXIWtujfZqQT/AkCovTodDMmRCCFEiJCCzQWrpnVRRP1CSU1+Eo3HbprxIKc9irclMqaHiqixryKDnLFyoNdarU16ACRTrqr0SkAkhRImQgMwGqWkZAj43ZW4nPo+rNIv6o4kiZMjKchqyzGUOsnQbXnf3NWQtvV9DBjC02i/rWQohRImQgMwG6cWnrQLxCn8ZzS2lV0MWKUINmd+b25Bl2yz92Qdk3bWRTCatov7ezZCBTA4rhBClRAIyG6Q+zFPzZlUGSnO2/taI/UOWAV9uRf1NOSwsvlUbXQRkkViCWDzZ6zVkAPU1PppC0ZwXVxdCCDHwSEBmg5Z2GbLKgKc0i/qLdJVlSzhGIpndygdbgtkvLJ7i72bIsi8mhU1JXWm5dmOo19sWQgjRuyQgs0EoHMPpcOCx5uCq8JfRXILTXhRjyDLgc5MEWq0LJ3rSFIrgdjnS01lkw99NUX962aQ+qCFLBWRrNgR7vW0hhBC9K6uv/UopHzATOBxoBd7XWp+plFpq/Z6qPL5Ea/23Th4fAB4B9gJiwIVa678U3Pt+IjVLf2qG+spAWcllyBKJJJFYIh2U2iWQsZ5lNlc6NoYiVAY8Oa0WkMrCJZPJDo9LLSzeFxmyOmty2HWbQuw4rKLX2xdCCNF7sv2UuRkTdI3TWieVUsMyth2ttZ7Xw+MvBJq01jsppXYG3lVK7aS1bs6jz/1Oi7WweEqFv4xINFGUIbz+KrWwuM9jb+AS8JrMVKg1BtU9378xmP0cZCl+r4u4FVC2f71S62gG+iBDVukvw+N20rBJCvuFEGKw6zGdoZSqAE4CrtBaJwG01mtzbOcnwL3WY78EZgPfznEf/VYoHMOfkUEpxfUsI1ZAZufSSZCRIcuysL8xFMmpfsy0YYKtzurI0jVk/t7PkDkcDmqrfBKQCSFECcjm03NHYANwlVJqtlJqllJqWsb2p5RSnyql7lFK1XSxj9HAsozflwPb5dfl/ifULkOWWj6plNazTGXIPEUo6ofulzbK1BSK5DQpLGQsYt5ZQNaHNWQAdVVeGjZLUb8Qwl6O8Fqq5h6Hs2VZz3cWvSKbr/1uYAfgY631RUqpfYBXlFI7AQdqrVcopbzAbcBdwInF6GhdXeE1NPX1lTb0pKNIPEF9bSC9/22bTUG/s8xdtDb7m0CFqXfaZmiFrc857jTfGVxZHMtkMkljMMrw+tz6MGIbE/B4/J4Oj0s6HTidDrYbVZNTXZpdRg2r4oMFawbNefSxXsempjDfmNJ/vo8NlmPbX8nxLZ6Cju3iF6HhVbyuCBz6N+iD97f+rC/O22wCsmWYQvynAbTW/1VKrcfUk822bgsrpe4B/tzFPpYDY4AG6/fRwP4PbLAAACAASURBVNu5dHTDhmYSieymPuhMfX0lDQ1NeT++O03BCK5tSO8/HjFZlZVrtrBdnb8obfYn9fWVrFlrnnu4JWLrcW61iurXrm/ucb+h1hixeAIX5NSHqNXG6jWN1LXLrjVsDBHwulm/vm/KHQMeJ5ubwny9ejNl7oFfj/jgy5/RFIqy25iukum9q5jvC0KObzEVemzL13xMAGDNGzR+eh/hkSfY1reBrtBj63Q68koi9ThkqbVejwmevgmglBoHbAOsVkpVW7c5gGOBuV3s5nngLOu+OwNTgddz7m0/1X7IshTXsyzWkKXf68YBWa1n2RRKLZuU35BlZ8OioT5YWDxTXZXJPG5sDPdZH+yyJRhh+dpmNjeHiScSfd0dIUqaq3khsfLxRGv2pUJfiiO8rq+7VPKyrcA+G7hMKfUZ8AwwHfACs5RSnwLzgHHAz1IPUErNVUqNtH79LVCjlFoE/AU4U2s9KL42xeLmasrMKRkCPjdOh6Oklk9qjaSusrQ3IHM6HPh6WPw7ZUse61hCW1F/ZwFZsCVKRR9MeZGSCsg2NA78NS3nL9kAQDIJW5pL529DiP7I3fw5sYoJNI2/E0c8RIW+uK+7VPKy+qTRWi8GDulk057dPGZSxs9B4Me5dm4gSBWCZ2bInA4HFX53iV5laf+wWiDL9SzTGbIcr7Lsqag/l3Ux7VZrzUW2YRAsMj5v8cb0zxsbw9RawaYQopfFmnG1LqN11HTiFYrQDhdR/tUMwg0/IVI/aCZAGHBkpv4CpT7E/d6tY9uKgEeGLG2S7XqWjXlmyLxlLpwOR6dtBFujfTLlRUptpReHY+BnyBKJJPOWbGT0NqauYmPTwH4+Qgxk7uDnAMQqJgAQGnsBsYoJVCz8Pxyxxr7sWkmTgKxAbQuLb/2hXekvrdn6w/0gQ9ZoBcAVOdZ8ORxmqaXOMmSh1hjl3r6rIXO7nAyp9A34GrJla5toboly0CRTxTDQn48QA5mr2QRk8fJdzA1OD00T7sQZ/pryL6/uu46VOAnIChRq7ThkCVARKEsPoZWCsFVD5vXYf0plmyHb3Bym3OfG7cq9D/5Ogr5EMmkCsj7MkAFsM8Q/4DNkny3egAOYsss2eD0uNg7w5yPEQOYOLiTp9BEPbJ++LVY9lZbR5+Bf+SDuTe/3Ye9KlwRkBQp1sbROpb+s5CaGdbucuJzFCchawt0fy2gszpwvGhi3XX7TKQR8blraBX0t4RhJ+mbZpEz1QwIDPiCbt2QjY0dUUhXwUFvpZWOTZMiE6Cvu5gXEyseBo91ScTteTtw3msqF50F8YL/nDEQSkBUo1ElRP5gasuaWKIlk/nOnDSRm3c7inE4Bb1l6xvyufLBwHU2hKN/Ya9s82+iYIUsvm9SHV1mCyZBtbGwdsOdSsDXKV6u2MHH7OgBqq3xskhoyIfqMq/lz4hXjO25wV9A0/jbcwS8ILLml9ztW4iQgK1BXRf2V/jKSyezXYBzowpG47VNepAR8bloj8W7nrvrHnJWMqAswYcyQvNrwe90dasj6etmklPoaP7F4kqbgwBwCX7h0E8kk7LZDLWAuVJAaMiH6hiO6BVd4FbHyTgIyIDr0cFpHHEtg6a24mub3cu9KmwRkBQq1xnAAPu/WwUhlIDU57MD8EM1VOBovyhWW0JZ9bAnHO93+1ddbWLK6icP22jbv5Y0CnQZkfbeweKb62gAA6wfosOVnizfg97rZYWQVAEMqvTQGI8TiMjmsEL3NZV1h2WmGzNI87kaS7hoqF/wckp2/7wr7SUBWoFA4ht9rJoLNVBEordn6w9FEUa6whLYrWLu60vKtj1bi87jYb+LwvNvw+zobsuw/GTIYmFcmJpNmuouJY4ek6wtrq3wkgU1SRyZEr3M3LwQglrrCshNJTx3N6ibKGj8isHQmjkgDJLIY7UkmcLYsw9PwV/xLbqXys9MZ8v4BDH1rGL4V99v1FAatvv3qPwiEWmMdprwAqPSbubBKpbDf1JAVOUPWyfDvluYwHy5cx6F7juowbJxrG63hOIlkMh1cp5Zr6usasvohJkM2ECeHXbU+yKamMLvuUJe+rbbKC8DGxtZ0sCmE6B2u4EKSzgAJ/5hu7xcefjThNc9RvuhayhddC0DCXUOirJakp878X2b+d8QacQcX4Gr+HGe8bd3fuG9ba2g0Qfmi6wkP/zHJsvzKSkqBBGQFagnHOhT0Q9uQZckEZJE41RXFmdE+FfAGO1nP8p+ffE08kcy7mD/F73WTBFrDsfRVlc1dXEHb2yr8Zfi9rgF5pWVqdv5dt69N31Zbaa3PKRkyIXqdWTJJgaOHATKHg8bdH8Pb8DqO6HqckQ04ohtxRv8/e28e3sZ93/m/BjeIgyB4STzES9LolmzJkuNDvhO7TpzYztlcbdJs2jTd57fpL9sjmybZ/WW37XaPbpM43W6SJmmbuM7hdWLHdnzb8SHrFnVA4iGJIiWSAA+QuIGZ3x84RJEACYAgMQS/r+fxYwqYGXw5BAbv+Rzvjw9d1IcucgXd9Cl0US+qvoq4fSvhpo+SsG8hbt9CwrYJ1VgNgH7qBDVv3kJV/38nsPE/LcNvuTIRgmyRBMOxrJGZqwPGV08N2dKlLFOzJmdFyOIJhZeODLKtw82aVJ1V0a9hvpoWvfp6MUxGHUZD+TP7bqdlRXp3dff7aK6zXTMmaWaETCAQLC/66dPEau/Mc+MqImseWvRrJhzbiaz9ENaBbxNq/Tco1tZFH7MSKf83zQonGElkTVmajHrMRv0qqiFb+pTl7Bqvw2dHmZiOctcio2NwtUt2ZuNAIBQve/1YmlqnZcWlLCPRBGcHJtjW6b7mcYvJQJXZIGrI8iShKPRcmuTJN84zMDK94PYCQS6k2Bj66BXi8xT0LxWBrv8Aqoqt9+vL/torBREhWyShSIwqsz3rc3arcfUIsmgC8xLaXsDcCNlzhy7R4LKyvas2225FvsbVv1cgHCt7/ViaWqeF3sHJci+jIM5cHCeeUK+pH0tT4xTWF7lQVZUrY0FO9o9x6vw4noHxzI3Cy0eH+Nqn9i6qXlKwejFML9xhuVQo1nWE1n0W64VvEGz7PAnHtmVfg9YRn+pFEozEseb40rZXrR63/qWMkJlNeiTp2gjZhStT9Fya5MN3rp/T4VoMWSNkYQ1FyKotBMJxwtE4FtPK+Nh2941hMurY2FI95zm3wyIGjM8gHI1z5JyXU+eTIiwdPax3Wdi7uZGt7W5MRh1/+5PjPPpCD79zX+4OOYEgF/pMh+XyCzKAYMcfYxn8IbZzX8F//U8L2zkRQhe5gjJj3FOlsTKu7BpFUVRCkUTWon5IFvZPhyq/hiyeUEgo6pI59eskKekTNiNC9vzhS5iMOm7ZsbYkr3E1LXpthKxBI12A6bornz9Cc93K+Nie6PexaV0NRsNcoe52mum/7C/DqrTJd588zUHPKDaLgc3tbra017Cl3T3n/XffvjaeevMC122oY+f6ujKtVrBSMQROo+gdKJbFl3kUg2p0E+z4AvZzf4Fx7BVi7v357RifxnX4feinT+O7vR90S9NAVm5EDdkiCEWzj01K41glKctwerD4EkXIIJlSDKTE0nQoxlunhrlp65qSdUCmo5wzI2TJweIaiZCliuJXSh3ZyHiQkfHQNd2VM3E7zEyHYkRjwnQyFk9wvNfH/p1r+dt/eyufe982bt/VnPVm4L23dNBSb+d7vzqzahqGBKUjOTJJhhJkFYol1PpZEpYWbOe+DGoe5tBKhOpjH8U4eQBdYgrD1LGlX2SZEIJsEaQjNrlSlo4qE1OrIGUZTqUSl6qGDJLzLNM1ZK8eGyIWVxZtdXHt8ec2DgRCMexaSVmmBNlK6Uzs7k/aXWzPUj8GZLouRWE/nL00STSucN2GenS6+b8ojQYdn3nPFgKhGD98xoO6QuebCspDcqj4lvIuQm8l0PUljP4jmId/Pv+2ShzniU9jGnuR6fVfA8A48dYyLLI8CEG2CK4OFs/+pW23GolEE8TilR0FCKcihUsdIQtG4iiKyguHB9m0zkVLffZmimIw6HWYDLqMyI7FE0TjStYO2nLgspvR66QV40XW3TdGvctCQ032lK/bkbK+EIKM7j4fBr3EpnX5GWa2Nth5360dHPSM8tap4SVenaBSkKJedDEvCXv56w8jaz9M3L4VW8/XQMkR6VUVHKf/CPPIE0zLf0Wo49+RsLZjnDiwvItdRoQgAyamI3z7/3ZnhEW+BDPGobmL+qHyxyctS8oyVUN2tMeLzx8uidXFbKzmq+OTMoPFNZKy1OkkahzmFSHIYnGF0xfG2dZZm3O2aM0Ki/gtJd39Y2xocRUUYb5vXxvrm6v5p2fPinMoyIvMyKQydFjOQdIT2PA19KHzWC59d+7zqort7J9hGfpnAp1/TmjdHwAQq96LYeJNqNDIsBBkwJA3wIHTI1y4MlXQflcjZLlqyFbH+KTlSFmmZ00+f+gSbqeZXRtKX9BcZckiyDQSIYOUOewKqCHruTRBJJZge0duO5IaESEDkinbwdHAHK+2hdDpJD797s3EFYXv/eqMSF0KFkQfSAqyRJk6LGcTrb2HaM1+bH1/hRS/tsGnqu8vqbr4CMF1nyPY+SeZx2OufeijV9CFLy73cpcFIchIpoMAxqcL+3IILlhDJiJkpcJmMTAxHeH0hXHuuK45M6i6lFjNBkJpQRZKz7HURoQMUuawKyAacqJ/DL1OYlObK+c2ZqMeu9XI+Ar4fZaS7n4fANvmEa+5aKyp4kN3buBk/xgvHRks9dIEGkYXvgTBSwXtY5g+nZxFaS5NZ/qikSQCG/4jupgP6/m/zTxsvfgtbH3/hVDTxwhs/M/XNCDEXPuAyq0jE4KMq4JsYqqwrqXQQhGytCCrcOuLyDKlLFU1Weu1f2fTkryG1WzIiOz03Eyt1JAB1FabGZ+KklDy6EwqI919Pja0VC/ol+Z2mFd9hKy7b4xqu4mWeltR+9++q4ltHW4efbGH4bFgiVcn0Cq2s1+Gl+4vaB/99Olk/VgZOyxnE6++nnDjQ1Rd+Ca6yBXMQ/+M3fOnRBoeYHrz/5ozbzNh24Kit2OcFIKsYrGa9ZiMOiYKjZClBJnVnF2IXJ1nWekRsuR5MC1ll2UqUrVvSwOOqiUaYj4jQhbUWA0ZJFOWiqoWfOOwnIxPRbg0GsjZXTmTlTCfMxZPoCxROlBRVE6dH2Nbuztnrd1CSJLE7/7WZox6Hf/nyVOaF+uC0nAhvgMmjqOfPpXfDqqKIXC6bIaw8xFY/xegxnAe+yiOk39I1H0H/u3fAV2WGzqdgXj1DRgqtLBfCDKSFzWX3Vy4IAvHsZj0OdNnNosRCZiudEGWEjGWJYyQuZ1mJAnu3r10Q2mzpyy1EyGrS3uRaVjEZFJweQiyGqdZ07YX8YTCl/7hLX7xm/NLcvz+K34C4Xhe52o+ahxmPvZOmd5BP0+/VZm1NYKreC6O8/UX21HQYb7yk7z2kaIj6GLjmuiwnI1S1Umo5VMYJ98mXr2HyV3/Ajpzzu1jrr0Ypk5AvPLmugpBliIpyAqLPAQjsXlTWjqdhM1a+eOTlqOGbOf6Ov7rH9xE2xrHkr3G7KJ+CTQ1M9C9AjoTC0nBuR1mAuF4JuWtNY6c8+KdDHN2YGJJjn+ybwwJ2JrDPLcQ9m1pZO/mBh5/tZ/LvsDiFyfQJONTER55vBuTYy1qw51Yrvw0r45DQyqSFreX2YMsB8GuLzG9/mtMXvcY6Oe/dsSq9yGhYJw8uEyrWz6EIEvhspuKipAt9IXtqDJWvKN2WpAZl2h0EiTHJ6UFyVJhNRuIxRVicYVAOCm2SzEns1TUajxCllAUTp0fY3tHbruLmbgdKYGp0ZmWrxxNFsoPjEwvSRfjiX4f7WsdmdKGxfKRuzagKCoHz4yU5HgCbRGLK3zr5yeIxBQ+/9B29B0fQR/qx+A/suC+hkB5Z1guhGp0Eer4d6jGhb344tV7UJEqso5MCLIULruZialIQRfeUCSes6A/jWNVRMjimI16TYmXYqjKDBiPJ8cmaajDEpK2InarEZ9fm2m+/stTqRRcfhGf9HzOsSX4fRRVxTsZKnr/kYkQJ8+P43YmRzxNBkp7UxUIx+gb8rO1iO7KXFTbzaxb48hMSRBUFj9+/hy9Q34+df9mmuts0PogqmTMK22pnz6DYnSjmuqXYaVLi2p0kbBvrshOSyHIUtQ4zETjSqaGKB+CeQgye5VpFRT1J5ZssPhyMlOQTYdj2KzaSVemqXVaNDvP8o3uKxj0Elva8xNkS2kO+/irffzZ379ZdPru1WNDSBI8fFsXkIySlZLT58dRVdheoP/YQmzrcNM76M80pQgqg9eOX+bFI4Pcu28dN2xqSD5oqiFadw/m4Z8tOBPSMJ0q6F/hN81pYtX7MEy+nd8szBXEyv8WLRFXvcjyvxMOhuML2iLYrcaKn2cZjsYxLWH92HJhnTHPMvm31VaEDJJRJS3WkPmDUV47cZl3bF2Tdwquxr405rCjEyGefmuAhKLy5BsXCt4/nlB47fhldnTWsqMrGcG6VGJB1t3vw2o20NnkLOlxt3W4UVSVMxfHS3pcQfm4cGWKHzzjYdM6Fw/f1nnNc5HGh9FHhjBOvJn7AKqKPnCahBYc+ktEzLUPXXwSfcBT7qWUFCHIUrjsSSuFQurIkinL+b98HFVGpoOxinbSjkQTS+rSv1ykxXUwEicQimmqwzJNrdOC1x/W3PvppcODxOIK79y7Lu99jAYdTpuJ8RLXkD32Ui86CfZubuDNk8OMThSWujze62MyEGX/riZsFiNup5mB0dIJMlVVOdE3xpa2mpIbHHc1V2M26UXaskKYDsX4xs9O4LQZ+f33bZvzfonU34eqs2K+8ljOY+giQ+jifm2MTCoRlWoQKwRZCpcjbQ6bnyBTVZVgJI7VMr8QcViNKKltK5VQJL6klhfLRTpCFgrHCWiwhgygttpCJJrQ1PspGkvw/OFL7OiqTda2FECNw1zSGrKzAxMcPDPCfTe28aE7N6DTwa8KtIJ4+egQLrspEx1rqbeXNEI25AsyPhVha4nTlZA0Tt68robuPp/mRLugMBRF5e//bzeTgQh/+OB2nNn8Fw12IvX3YR5+HJTs1wT9tLZGJpUCxdqJYqybPzK4AhGCLIXLlhJkeUbIwtEEqkoeEbLUPMsKriOLRBMVkrJM/g7BSJyAhmvIAE3Vkb1+8gpTwRj3FhAdS1NKt35FVfnRc+eocZi5d986ahxmbtm+lteOD+Xtd+abDNPd5+OWHU2ZaERrg53LviCxeGnqVU72pccllV6QQdJGwzsZZqTAyKBAW/z81T5Onh/no/dspGNt7tR2ZM370cV8GMdfzvq8IXAG0MhQ8VIhScRc+zBUWKelEGQpzCY9VrMhby+yzNikhWrIVsE8y3SX5UonLa7H/OG8xHY5qK3WlvWFoqo8c2CAtjUO5HW5Z1fmopRu/a+fuMKF4Snef3tX5v14341tKAo8cyC/KNmrx4cA2L/j6ry/1gY7CUUtmb9Xd/8Ya2urqKu2luR4s0l3uXb3ibTlSuXw2VGefOMC+3eu5bZdzfNuG629G8XgxJKj21I/fQrF1IBqKl1HrxaIufZhCPYiRb3lXkrJyCsEIMuyBfgfwN1AGHgD+DPgh0AXEAF6gM96PJ7RLPv/Y2rf9Jl7zOPxfH2xiy81hXiRpbuYFuyytFb+PMtwJEGja+ULMotZjwR4U9EnLUbIrprDasP64niPj+GxIJ99YGtR43/cTjPhaCKvBpn5CEfj/PTlXjqbnOzb0ph5vN5lZd+WRl46Osj972ibd+yWoqi8evwyWzvc1LmuiqWWejsAl0anWde4OGPiaCyBZ2CC23YtzTxWSA4dr3dZONk/xl27W5bsdQRLw5WxIP/nl6doX+Pgo/dsXHgHvYVow3swjfwCNv/POS73hunTlRUdSxFz3QiAceIA0YbfKvNqSkO+EbK/JinENno8nu3AlwEV+GuPxyN7PJ4dQC/wl/Mc4y89Hs+u1H+aE2NAQeOTMnMsF/gSSQ8Yr+SUZaV0WeokCYvZgDeV6rFrsIbMWWXEoNdpJmX59IGL1DrN7NlUnL9Rqcxhn3zjApOBKB+5a8McP7z739FGLKbw64MD8x7jRJ+P8anIHLHU6LZi0Ou4NLL4CNnZgQlicYVtJfQfy8bWjlpOXxwnnqgsW4DVwNNvXURV4Q8f3I7RkN91Ndz4MLq4H5P3uWufUFX0AQ9xm/ZGJi2WuGMXqmSsKIPYBQWZLMt24BPAlz0ejwrg8XiGPR7PmMfjeWnGpm8CbUuyymUibQ6bD/lGyBzW5B15JVtfJH3IVr4gA6gy6xlNiZ3FRGyWCkmSqHWaNZGy7Bvyc3Zggnv2tBbdLZg2h13MTEvvRIhnDgxw49ZGupqr5zzfVGfjerme5w8NzuvP9fLRIZw2EzvX113zuF6no7nOxsDIVNFrTNPdP4ZBrysqvVsI2zrcRKIJegcnl/R1BKVFUVWO9XjZ3unOlCfkQ8x9G4qxdo5JrC48gC4xXVGWFxn0FuLOXRVV2J/PVbQL8AFfkWX5oCzLL8myfMvMDWRZ1gF/ADwxz3G+IMvyCVmWH5dlWZPvjhpHcp6lkkd3UqaGbAFBZjbpMRl0FRshU1WVSDSO2VQZ5YhWszEjym0lGmlTamqrLZoQZM8cuIjVbODWncWn32ocabf+4n+ftM3F+1Mmrtl49zvaCUXivHD4Utbnx6ciHO/1cfP2NRj0c9/LrQ12BkYXHyHr7h9Dbq1e8huYzW016CRJ2F+sMC5cmWIyEJ1zU7AgOiORxvdhHv0VJK6+T7U+w3KxxKr3YfAfBqUySoLyCQEYgE7giMfj+aIsy/uAX8iyvN7j8fhT2/wdMA18I8cxvgRc9ng8iizLnwCelmW50+Px5D1VuLbWnu+mOamvn7/+o2WNk4SiYqkyU23PPW0eQGdMlsq1NrsW3NZpNxNV1AVffyUSiSVQVHC7qiri96t2mLmU8pxa1+yidokKrwtl5rltbnBw6MxwWc/3FV+AQ54RHrx9PetaFp4/l4satw1JgnCiuM/HyT4fb58Z4SPvlJG7cqdN6+sd7N7UwHOHLvGRezdjmXEjVV/v4IWjQyiqyvvu2EB93dxrzabOWl47cRmDxUiNo7iZqqPjIYa8Ae59R/uy/O02tdfgGZgo++ey3K+/nFzxBahxWooW3M8cuoROgjv2ti34vQKzzq36Cbj0HerDL0H7h5OPjfYDUNN2A5gq8O8Quh0ufoN6fQ/U7Svpocvxvs1HkF0A4sCPADwez1uyLHuBjcBBWZb/BtgAvMfj8WQtWPB4PIMzfv6BLMv/A2hJHTsvfL5pFKV4X536egejo/OnHAwkj99z3rdg8e6IN/mlHZwOE12gYL/KrMc7Hlzw9Vci6cHp8Wi8In4/w4zyo3Agwmi0/H5fs9+7VSY9Y/4IQ5cnMRrKE5l89NdnkSSJm7Y0LvrvXm0zcemKv+DjKKrKIz89Ro3DzP5taxbc/117Wjl0ZoSfPn+Wd97QCiTP7fCIn1+9fp7NbTUYVTXrcWpSDR7HzgyzNc/RULN55Viyg7O9wbYsnxW5pZrHX+2n94Ivu4fVMpDPdbdSuOwL8JXvHuDu3a188M71RR3jjWNDdDZXEw1FGV3ge2Xuud2J29xE/Nw/4bfdD4Bj+ChG81rGJvVA5f0ddLod1ALT518gpJYuCrjY961OJxUVRFrwau7xeLzAi8A9ALIsbwQagB5Zlr8O7Abe5/F4chaByLLcPOPndwEJYDDX9uUiYw6bh/VFMBLHZNRlTW/MxlFlqtgB45FYMshZMTVkqboxg16n2UaFtBdZqR3u82U6FOPV45fZt6Uxk3JcDEnri8JryN7ovsKFK1O8/7auvCZFrG+pZtM6F0+/deEaT7FT/WP4/OF5Ox9bGpIX14Hh4g1iu/t81DjMBZvnFsu2zlpUkr+fYGlRVZV/ee4c8YTKG6euFBU8GPOHuTgyza5C05VpJB2RxocweX+NFJsAkkPFExVY0J9GMa8hYW3HOHGg3EspCfneXv8+8OeyLJ8Afgx8HGgG/hxoAl6XZfmoLMs/T++Q+nf6Cvf9VP3YMeA/AA94PJ7yhx5mUcj4pGA4nnF2XwiH1ZiJJFUakVjyi60SRifBVbd+LVpepKlNFcL7ymR98fLRQSKxBO8qwgg2G8WYw4ajcX7yci8da53s29q48A4p7r+pnYnpKL85cTnz2MvHhrBbjVy3IXfK01FlwmU3ZdLZhZJQFE6dH2drh7soe5BiaGt0YLMYOLnKBZmiqLx58krJjH2zceScl5P9Y2xuq2FyOlrULNFjPUlXqKIFGRBZ8zCSGsU08ktQFQyBMxVbP5YmVr03aRBbAZMp8vrW8Xg8fcDtWZ7KeWXxeDy7Zvx8d8ErKwPVBbj1J+dY5velba8yVm6ELFqZETItjk1KkzGHLYP1RSyu8NyhS2ztcNPasPi6TkhGyI73Jkf95CtWnnrzIpPTUf7wwe1zbC7mY0tbDR1rnTz15gVu3bmW8akwR895uWt3y4Lp35aG4kco9V+eIhiJL5k7fzZ0OomtHW66z48VdG4rDc/Fcf73L07xvokQD9zcUfLjR2IJfvTcOZrrbXz+oe184Zu/4c1Tw2wpMLV9tMdHg8vK2tqqotcSd15PwtqBZfgnxGpuQlJCldlhOYOYax+WK/+KLnwRxbqijR6EU/9MjAYddqsx75RlvrYIDquRUCSxpHdo5aLSUpaZCJkGLS/SpIvKS+VwXwhvnRpmcjpa1JikXNQ4zETjCoF5LClmMhmI8syBi+zb0sj6LDYX8yFJEu++qQ3vZJi3Tg3z/NsDJBQ1L6PW1no7Q75AUd5e3X0+JImCv6QXy9YON5PTUS6VoEN0pTI8nvQV/NVbF/EHSp+p+NWbF/D5w3zsno1YTtVIlwAAIABJREFUzQZ2b6znkGeUWDzvnjUi0QSnL4yzc33d4oSzJBFe8zDGsZcxjb0KUJEeZDOppEHjQpDNIl8vsmA4nvdoncw8ywqMkmUEWaWlLDUcITMadFTbTXiXWZCpqsozBy7SUm9nS3vxnZWzuTp9IL/f53iPl1hc4b59xYnCnevraKm38eQbF3j2rQtsbKlmbe3CdV0tDXbiCZUrY8GCX7O7f4zOtc7M5I7lIt2AsJrTliMTIfQ6iVhM4Re/OV/yYz/1ZvLmQF6X/EzcuLWRUCTO8d78z/nJ82PEEwq71i/eMDiy5v1IaoKq8/8NgIS9sgVZwrYFRW/PyyB2OBjgjaHs1jdaQAiyWdQ4zIznU0NWQIQsfRGuSEFWaSnLFRAhg2Rh/3JHyLr7xxj0BnjX3taSpr/caS+yPOvITvT5cNlNRadMdZLEu29q57IvyGVvYMFZgWnSr1do2nI6FKP/sp+ty5iuTON2Wmius9Hd71v219YKo+MhGmqs3LpzLS8dHWR4vHBBnYsfP3cOvV7ig3dc7arc3FaDs8rIW6eu5H2co+e8WM0GNrQu3jA4Yd9C3LYZfeg8CUsrqiH3YPKKQGcgXr0HwwKF/a9cusgdj/6Qz/76SSIJzZWwA0KQzSHfeZYFFfVnBoxXXmF/paUsM4JMo6awadxOy7IX9T/91kVcdtM1syJLQTpCNp6HwEwoCifPj7Ots3ZRonCP3ECjuwqb1chuOb+xT2vcVeh1EgMFFvafOj+Gqia7HsvB1g43ZwcmM5/V1cbIRIh6l5X33tKBXi/xs5f7SnLcYz1ejvZ4eeDm9mu6jfU6HTdsbuRoj2/eyRBpFFXleG/SnT+frv18iKx5P1D56co0Mdc+DFMnIJ77szkVjVBrsfLoux/GrNfmDbcQZLNw2c34A1ESSu46EVVVCyzqzz9lOeYP84NnPHl9kLVAxaUsU5ExLY5NmkldKkKmLkNnUUJR+OnLvZy+MM49e1pL9qWRptpmQq+T8oqQ9Q76CUXibF+kuNHpJP7wwW185dM35m1vYtDraKqzFTzT8vDZURxVRjrWlseYc1uHm3hC4ezARFlev5yoqsroRIgGlxWX3cw7b1jH22dG6L/sX3jneYjFk4X8a2uruGdP65znb9zSSDyhcPjs6ILH6h/y4w/GCnfnn4fwmoeAZLRsNRCr3oeEgtF/6JrHh6aneKLnLAD3d27ghQ9+jM21pTvPpUYIslm4HGZUFfyB3OIpGldIKGpBRf0AU3mMT3rxyCAvHRnk8VdLcxe31EQrNUKm4RoySM6AjMWVvN5Ti2FiOsJ/+/FRnnzjAvt3ruXuLF8+i0Wnk3DZTXmlYLv7fegkia0lqGFrqbezucA0Yku9vaCZlpFYgqM9XnbLDUXP+1wsG1tdGA06uvtWXx3ZVChGOJqg3pWcuHHfvnXYrUYee7FnUTczzxwYYGQixG/fvTHrDUpnk5N6lyWvtOXRHi86SVr0TcZMlKouJnf8kNC6PyjZMbVMvHoPKtI1cy2fPd/HHY/+kC++/BxT0eTNnlGv7e8pIchmkY8XWb6DxdPYrAYkFk5ZqqrKgdPDSBI8f/gSF4e176wcjibQ6SQM+spoqa93Wdm3pbEs9T6FkLG+WMI6stMXxvnq996m77KfT9+/md+5b/OSTQaocVjyGjB+om+MzmYnVWUSzK0Ndiamo3mXH5zo9RGNKdywqWGJV5Ybk1HPxlbXqqwjG011WNbXJAWZ1WzggZvbOXNxghN9xZ0P32SYX75+nt1yfc7rhCRJ7NuyhlMXxplcoATmWI+XDS3VJW/4iDa+F8VS/JzZlYRqdJGwb8Y48RbRRIK/+M3LfOypx2l2OPjVwx/BYVq8gfVyIATZLFz2hb3IgunB4nlGyPQ6HVUWw4Ipy/NXphidCPOB29djsxj54bOevAadl5NILIHFpK8YjyODXsdnH9jKGnfxXkDLQdqtfym8yBRV5Revn+dvfnwEm8XAlz+xh5u3ry3568zE7TQv6NY/GYhy4cpUSSMJhZIp7M/TRuLAmRGcVUbkEhRrL4at7W4u+4JlsUopJyMTSUHW4Lo6k/b265ppcFl57KXeohz1H33hHAAfvnPDvNvduKURVU2+B3LhnQxxaTRQ0nTlaiVWvQ914hDvffxRvn3sEJ/evounHvoIna7SdYQvNUKQzeKqIMt9BxxKRcjyLeqHZB3ZQumlA6eH0eskbt25lg/c0UXvoP8aR3EtEo4kCjoPgtKwVBGyqWCU//nYMX7+Sh/7Njfy5U/uobm+NAaw8+F2WBibisybRjqZivBs7yxf9LKlgE7LSDTB8R4vuzc1oNOV94ZlW+qcda8y+4vR8RASUO+6OhDeoNfx0G2dDI4GeL07/05ISNpTHPSMcv9N7ZnPYC6a6mysa7Dz1qnhnNsc60m+p3dtEIJsscRc+zAnxnlPczXfede7+S+33onFsLK+m4Qgm4XTZkSSmDd9komQFSBEHAu49SuqyttnRtjW4cZmMXLz9rWsb67msRd7NW2X4Q9GMzNABctHldmA2aQvqSDrGZzkq997mzMXxvn4u2Q+854tWEzLc0GrcZqJJ+aviTvRN4azysi6xvIUx0OyAcFZZWQgD0F2rNdLNK6wt4zpyjTNdTZcdtPqE2QTIVwOM0bDtbVDezY10L7Gwc9f7cvUwS5EPKHwL78+S4PLyr1786ul3Le1kb4hf06rjaM9XhrdVZqPyGudqWiEw4lkR+kXWkZ5T9fGMq+oOIQgm4Vep8Npm9/6IhhJfmkU0om30DzLvkE/Y/4IezcnLQV0ksTH3rmRQDjGz17RboH/ZCBKtV0IsuVGkiRqnZaSpSxfOjLIX/3zYfQ6iS99fA93XNe8rGlod3r6QI6B6YqicrJ/jK0dtQWNSloKWhvseVlfvH1mhGqbiQ0t5U1XQvL9srXDzenzY0Wl6VYqacuL2egkiQ/csZ7xqQjPH1rYKDQWV/jZy31c9gX5yN0b5gi8XOxLXc+zRclCkTiei+MlMYNdzcQVhc88+yQPPPM6Xl0zxonXy72kohGCLAs1dvO8gixUYFE/JCNkU/NEut46PYzRoLsmdL2u0cFdu1t4+cjgotu0l4qpYDST5hUsL0lz2MV7kYUicf7luXNsbHXx1d+9gbY1yx+BcqcGpuf6ffqv+JkOxdjeVf5mi5YGO0PewLzWOOFonOO9PnbL9WVPV6bZ1lFLIByn/4o2ryVLwUjK8iIbm9tq2NFVy5NvXMiZhYhEEzx74CJ/8u3XefrARW7c0lhQvZfbaWFjq4s3Tw7PScef7B8jnlAXNUx8taOqKn/6ygu8cPE8X71pP/Y1t2Me+QVSrPDh7lpACLIsJMcn5Y5mFVrUD2C3mpgOxrLWyCiKysEzI+zorJ1Tj/W+Wzpx2kz88BmP5u5sVVXFH4heY4ooWD5qneaSpCyTXwwKD9zcXrbuxbRbf65Sge6+MSSujgIqJy31dmJxhZFUB182jvf6iMXL2105my3tNUjAyVVifxGJJZicjmY6LLPx/tu6CEXiPPnG+WseD4bj/PL183zxkdf58Qs9rHFX8ccf3sVn3lO4r9eNWxu5Mhbk4vC1UdVjPV5sFgPrWwqbx1ppvDl0CX8kWT/6xZef41f9PXnv+8ixQ/zg1HH+6Lob+PiWHQTXfQ4pEcBy6R+XbsFLiBBkWXA55o+QBcNxDHop77A1JMcnJRSVUGRuvYJnYILJQJQbNs+9eFdZDHzwzvWcvzLFK8eG8n695SAUiRNPqKKGrEzUVluYDsUy46uK5fC5UexWY1m/GBxpc9gcArO7z0dHkzMzF7acpDst56sje/v0CNV2baQr0ziqTLStcdB9fnUIMm+WDsvZtDTYuWn7Gp4/dAnvZIipYJSfvdLLFx95nZ+90kdnk5M//9hu/v1vX8/WdndRafw9cgN6nXRN2lJRVI71+tjeVVs2fzot8N3uozz4fx/jLw/8hqlolKMjw3zyV0/wF795mWhi/uvaG0OX+Nrrr/BA10a+dOMtACQc24m678A68G1QVt5knNX7TpgHl93EdChGLJ49JVGIS3+a9Pik6dDcN8nbp4cxG/Xs7Moeur5xSyOb1rn46cu9+DU0fmkykFyLSFmWh8xQ7hx1V/kQTygc6/Gxa31dWb8YdJJEjcOc1a1/OhSjb8jPNo14w62ttSVHKOUQZKFInON9PvbI5e+unM22Tjd9g36CYe02CpWKtOVFthqymTx4ayeSJPG3jx3ni4+8zpOvX2Brew1f+Z0b+H8+sHPRNyp2q5HtnbW8dXo4k+XoHZpkOhRbtenKWCLBF19+jj995QXuWtfBn+27GafZzC8f+hCf3r6Lbx9L2lcMTOVOr9+wpomv3LSfv7vrXdfUlQbbPo8+chnzlZ8ux69SUoQgy0JaYEwGskfJgpE41gJTO1fnWV57IYwnFA56Rtm5vjbn+CFJkvjoO2XC0QQ/eam3oNddSvxpQSYiZGWhFF5knoEJQpE412mg7d6dY2D6yf4xVCir/9hMjAYda2qrclpfHOv1ai5dmWZbRy2KqnL6wsqssSmEtClswzwpS0i+7961t5XLviC7Nzbwn35vH597cHtJayn3bWlkfCqSGV91tMeLXiexrUMb7+nlxBcK8YFf/JTvn0ymGr9/3wMZ41az3sB/ufVOvvOud3N2fIz3P/ET4rNqNQem/AwHpjHodHxu1x6shmu/i2O1dxO3bcJ68ZugcR/P2awsk45lYqYXWV313A9zMFxMhCyZapld2H/mwjjToVimuzIXzXU27rmhlaffusj+HU2aqDtIi0vRZVkeMoJsEXVkR896MRl0bNFA9MntNHNuYHLO4yf6fNgsBjrWOsuwquy01ts5eyn7bMi3T4/gsps08RmdTWeTE4tJz4m+MXbL2hOMpWRkIoTVbMCWR63vg7d2cu/etiWbYbtrfR1mo543Tw2zqa2GYz0+Nra6ND8zFyAcjxOMx3Bb5he2+RJJxLk0PcU377qXD8jZa/Le07WRbXUNDE1PYdDpUFSVuKIQjMX4yC9/hkmv57kPfCx7x7UkEWr7PI5Tn8c4/gox920lWfdyICJkWciMT8pRYByMxAv+IKXHYkzPipAdOD2C1azPy+zygZvbqXGY+eGznnk7vJaLSREhKysuhwmdJBUtyFRV5fC5UbZ2uDUxi9TtsDAxHbmmeUVRVbr7x9ja4dZU+q+lwc6YP0JgVuovFIlzom+MPZsaym7PkQ2DXsfmthpO9vuWZTB9ORmdCNPgsuZV9yVJ0pKKI7NJz/Ub6zjkGeGyL8CQd+W48//1269z64++z1N9+RfbZ+Oty4MkFIUmu4PffOSTOcVYmo5qFzc3J/3evnHkbd77+KP8ztNP0D85wX+6+fZ5P1/hNR9EMdVjvfB3i1rzciMEWRbSAmM8R2F/MBwv2J0+LcimZtSQxeIKh8+Oct2G+rwaBCwmAx++awMDI9O8eHiwoNdfCvyBKJIETpsQZOVAr9NR4zDhmyzO+uLC8BTjUxGu31hf4pUVR43DTEJRr6mTHBiexh+IaiZdmaalPrtj/9EeL/GEwt5N80e8y8n2zlp8/ghXxrKblVYKSQ+y+d30l5N9W9YQCMf5p2fPAqwI/7FgLMZDGzbTaLPxO08/we//+kl8odzdxdmYjkb5r2+/wQM/f5Tvdh8FkqnJQuiodnF2fIzXhy7x329/Z0ao5URvIdTyGczeZ9FPewp6rXIiBFkW7FYjep2Us9OymKJ+i0mPQa+7JkJ2sn+MYCTO3izdlbnYI9ezua2GX7x+vux3uP5gFEfqXAnKQ666q3w4fNaLJKGZO/VsXmTpAdDbNCbIcs20fPv0CDUOM53N2kmvzibdHNFdwfYXiqLinQjNa3mx3Gxpr8FuNXL6wjhra6toqNG2O39cUdj8vUd45nwvzzz82/zJ3pv4Re85bv3x93nl0sUF9z/lG+Xfv/w827//9/zXt9/gwQ2b+NiW7UWt5T1dG3nxgx/n0Xc/xIc25Wc9Emr9PVSdJVlLtkIQgiwLOkma14usmJSlJElJc9gZguzAmWFsFgNbCvBWkiSJvZsbmArG5vVBWg78gShOW/ltCFYztdWWolOWR86NsrHFlYnelpuMW/+M36e7z0dbo4Nqjb3PXHYTdquRgZGpzGPBcJzu/lR3pQbTlWnqXFYa3VWcSM0GrUTGpsIkFHVey4vlxqDXZayNVsLsyr6JcULxOK0OJ0a9nj/ecyPPvv+jtDmraayyZd1nZinNV19/hR+d6eb+zg08+dCHeeTu++YU4BfCOmc1d6xrz3t71VRHeO1vY7n8I6Sot+jXXU6EIMuBy5F9fFIsniAWVwqOkEFyfFLaEToaS3DknJfdcj0GfWF/hs6mZLFw31B5Hbf9gagmfKFWM7VOC+NTkYJNg0fGgwyOBrhOI+lKmBEhS9VuBsMxegb9mcHYWkKSJFrqbQyMXI2QHevxEk+oWf0Etca2DjdnL04Qiy/Ow06rZDosNSTIAPbvaMJk0Gk6pZ3mpG8UgK21V68RW+vqeeqhDyO7kxHrL736Ir/sPcf5yQn+4xuvsPMH/8DgVPIm5S9vvZNjn/w3fOOue7lhTdOyjmJLE2r7HJISwTrwD8v+2sUgBFkOXDnGJwVTxq7FFIDaq4yZGrLjvT4i0QQ3LNBdmY3mOhtmo778giwY1VzkYrVR77KSUFQuzojU5MORc8k7Ri3YXaSxW40YDbpMhOzU+XEUVdVc/VialgY7g97pjBh++8wIbqeZzibtpivTbO90E40rnM3S1VoJjKasYBbyIFtu2tY4eOSPbyvLeLJC6faOYtTp2FBz7Q1RWlhNR6O8eXmQTz3zC/b+83d55Ogh9jSuJZxITrLpdNWUrDOzWBK2jUTq7k0KskR5M0r5IARZDpKCbG7KMm2oWGhRPyS/cNIpywNnRnBWGdm0rnAnb51OomOtg96h8l5M/YGYSFmWmd1yPVazgSdeO1/QfkfOjtLaYNfUF5YkSbhnmMOe6PNhNRvo0mg9VmuDnWhMYXQiRDAcWxHpyjRyaw0GvUR3haYtR8ZD6HVSxjxZS5QjUlQMJ32jbKypxaTP3nBmN5l4+uGP8P/dfDt/uvcmDn/89/jH+x6gy1WzzCudn1Db59HFvFgu/2u5l7IgQpDlwGU3EYrE54ylSY8+KiplWZWcZxmOxjne42X3poai3dE7mpwMjEyXLeUQiSaIxBJCkJUZm8XIffvWcbTHS89gfgLdH4hybnBSU9GxNDUOM+NTYdS03UV7jWZHy8wcoXTk3MpJV0LShmFDi6tiC/tHJkLUVVs0ZZWy0nh4wyY+u/P6ebcx6vX8m53X84U9N7LWrs2oX6zmVmKOnVgvfgPU8ttFzYc2r3QaIGMOO8utPxhJRriKSVk6rEaCkTiHPKNE4wr7ikhXpulcW01CUbkwnHue3lIymbImcIoasrJz954WnFVGfvZyb16dt8d6vKgqXLdBO/VjaZJdoxEGvQHGpyKa666cSVOtDUlKCrK3z4xQ6zTTqSHz2oXY1ulm0BsouktXy4yOa6vDciXyAXkLH960tdzLWDwpo1hDwIPJ++tyr2ZehCDLQdqLbLY5bDCczI8XFyFLdpi8eGSQGod5UU7e6TqVctWRpccmOW3a6NBbzVhMBu6/qZ0zFyc4lcdInCPnvNQ6zaxrtC/D6grD7UzWbh7rSda4abV+DMBk1LPGXYVnYIKT/WPcsKlxxaSjALanxvac7K+8KNnoREhT6fiVxnAwQM/4GEqFmAdHGh8iYW7SvAWGEGQ5SEfIZpvDBiMpQVbgLEsAeyqa1DfkX3StSY3DTI3DTF+Z6siuCjIRIdMCt+9qxu00Lxgli0QTnDw/xnUb6jUpHtwOC6oKr524Qku9jRqNT4FobbBzdmCChLJy0pVpmuttuOwmuitMkE2HYgQjcc11WK4kHvOc4qYf/SMTkQqJnuqMhNb9Pqaxl9BPHS/3anIiBFkOatIpy1leZKFUhMxqLnzUzEy/p71bFn/x7mxyli9CJlKWmsJo0PHemzvovzyV6aDMRnf/GLG4oim7i5mkrS+Gx4KaTlemSTv211VbaF8BnXMzkSSJrR1uTp0fK9g2RcuMTmjT8mIlcdI7ylqbvexdkqUk3Pw7qHobVRe+Ue6l5EQIshxYzXpMRt0c64tgJI5Okoqa/ZdOWdZVW0pSa9LZ5MQ7Gb5m1MxyISJk2uOm7WtodFfx81f7cn7BHjk3is1iYGOr9gZfw1VzWNB2ujJNS6qwf8+mBk1GHBdiW0ctgXCc/svltdApJWnDbFFDVjynfKPX+I9VAqrRRajp45iv/ARdeKjcy8mKEGQ5kNJu/VkEWZXFUNTFNy1ebijRxTst6oqJksUTCv/4qzMMegMLb5wFfyCKzWIo2NRWsHTodToevLWDwdEAb50envN8QlE41uNlR1edZjsXa1IRsmQXoDZF40zkVhfXb6zn9uuay72Uotja4UaCikpbpiNkooasOMLxOOcmxtlWV1mCDJJGsaDDOP5auZeSFW1elTVCNi+yULjwOZZpnFUm/u37d/Dum9pLsDpoX+NEJ0lFCbIzF8d55dgQR8+NFvXawqVfm+zZ1EBrg53HX+0jnri2xfvswCSBcJzrN2rP7iJNldmAxaRnS1vNihD7VrOBzz+0fcWmx+xWI+1rnRXlRzYyEaLaZioqiyGAs+M+4orC1goUZIq1nbFbjhJZ83C5l5IV7V/xyojLPnd8UjASx1qE5UWaXevrijKVzYbZpKe53kZ/EYX9x84lL8A+f/YB6gsh5lhqE50k8dD+TkYnwrx2/PI1zx05O4rRoGNbh3ZTgZIk8fvv3cYH71hf7qWsGrZ1uOkb8hMIxxbeeAUgLC8WR7vTxffve4CbmlrLvZQlQbG0gKRNsS4E2TykU5Yzu9aCi4iQLQWdTU76Lk8V1J6sqipHU7YCxXoQ+YPCpV+r7OiqZX1zNU/8pp9oLGkcrKoqR86NsrXdjdmkzYtRmh1dtTS6q8q9jFXDtk43qpocVVUJjEyEVmzEUgs4zWbu61hPfZX4DC43eQkyWZYtsiw/IsvyOVmWT8iy/L9Tj2+UZfkNWZbPpv6/Icf+elmWvynLcq8syz2yLP9eKX+JpcJlNxONKRl3foBQRGOCbK2TUCTO8Fgw730GvQF8/jA6SSpekAWiVIuUpSaRJImHb+tkYjrKC4cHgaR5qc8fYZcG3fkF5aWzyYnVbKC7b+WnLWPxBBNTESHIFsETPWc5NjK3BlWw9OQbIftrIAxs9Hg824Evpx7/NvBNj8ezEfgm8Pc59v8osB7YALwD+Kosy+3FLnq5cDmSgmOmF9liU5alphiD2LTp5nUb6opKWcbiCsFIXJjCahh5XQ1bO9w89eYFQpE4h8+OIpFMmQsEM9HrdGxpr6G7fyyvSQ9axjsZRkUU9BeLqqr8vy//mh+c0q5XVyWzoCCTZdkOfAL4ssfjUQE8Hs+wLMsNwPXAj1Kb/gi4XpblbJWAHwL+wePxKB6PZxR4HPhAKX6BpSTjRTZTkGksZbm21obFpC9IkB3t8dK2xkFnczK6FkqZ3ebLVFBYXqwEHtrfyXQoxq/fHuDIOS/rW6rF30yQlW0dbsanIgwV2XWtFYTlxeIYmp5mIhKpyIL+lUA+EbIuwAd8RZblg7IsvyTL8i1AKzDo8XgSAKn/D6Uen8064MKMf1/MsZ2mmD0+KZ5QiMQSRc2xXCp0OomOtfkbxPqDUfoG/exaX5fxfCo0bTkZEKawK4GOtU6u31jPU29dYGBkWpOzKwXaIN3osdLtL0aEKeyi6PaNALCtdmVNnagU8lEWBqATOOLxeL4oy/I+4Bcsc4Srtnbxc/fq6wtz0nY4kx/qmJrcdzIVKauvtRV8rKVk2/o6fvZiD05X1YKt3sffvogK3L5nHbF40hYhLukK+n3Ojybvote1uDL7ael8VCLFnt9PvXcbf/Q3LwJw141t1Ndpb35luRHv3eQ5aG20c/bSJB+7v7TnYznP73QkgdWsp7PNvSKNegul1Of2/Onkjf1+uR2HWdtjy5aaclwX8hFkF4A4qdSkx+N5S5ZlLxACmmVZ1ns8noQsy3qgCRjIcoyLQBvwdurfsyNmC+LzTS9qvEd9vYPR0amC97OaDQwOTzE6OsXIeLJwXoklijrWUrHGZSGhqBzqHmJDi2vebV89cgmX3YTTrGM8mmxz7780Tltd/h01A5eTNhtKNM7o6FTR51aQH4s5v1V6idt2NTPkDWBUVfF3moV4715lU2sNLx0dZHBoAtM8N3YXh6d49IUe7riumT2b5o+kLPf5vTg0Sa3Titc7vWyvWS6W4tweuHiJdmc1YX+UMMs/AUYrLPbc6nRSUUGkBQWZx+PxyrL8InAP8KwsyxuBBuAscBT4CPBPqf8fSdWIzeYx4DOyLP8MqAXeB+wveLVlYKYX2dXB4tpJWQJ0NiUdzfuG/PMKslhcobt/jBu3NGYmERTTaSnGJq0sPv7OjasiWiBYHNs63fz64ACegYmsY6sUVeXZAwP87JVe4gkVo0G3oCBbbkYmQqyttZV7GSuWv7vzXq4EK1/MapV8uyx/H/hzWZZPAD8GPu7xeCZSj/+RLMtngT9K/RsAWZafkmV5T+qfPwT6gHPAm8B/9Hg8fSX6HZaUmeOTgqnB4loq6geotpmodVroXaCO7OzABJFogp2pTjudTqLGYcI3WVinpT8Qw2zSCyfsFYIQY4J82NjqwqDX0d03t45sfCrCf/vxUf71xR62d9Zy/cZ6+ob8murKVFSV0YmwqB9bBFVGI53VNeVexqolL2WREk+3Z3n8DLAvxz6/NePnBPAHxS2xvLjsZs4OTAAzBJlFe3YPnU1O+hZw7D/a48Vo0LG57eoHzu20MD5VYIQsGMVZpb1zIBAIisds1COvc6XGKF21lDx4ZoTvP31oFVVHAAAWbUlEQVSGWELhk/fK7N/ZxKvHL3P47CjD4yHWaMTEd2IqQjyhiA7LIjkz5uVfPaf4zPbrWGsXdZXlQDj1L4DLkUxZKqqaSVlazdqLDHU2OfH5I5nGg9moqsqxHi9b2mquiWy5nRZ8RaQsRbpSIKg8tnW4uewL4psME4rE+c6Tp/jW49001Fj56u/u5bZdzUiSRFfK/7B3sPCxbUvF1aHiljKvZGXyxtAg3zhykLiGop6rDSHIFsBlN5NQVKZDsYxfV5VZe9GhhQxih7wBvJNhds5yanc7zYz5IwWNXkpGyIQgEwgqjW0dbgCeevMCX/3eAV7vvsK7b2rjzz62+5pI2No6G1azgR4NCTJhebE4TnpHqTabaRHRsbIhBNkCZMxhpyIEw3EkwKLBCFlbowO9TqLvcnZBlp5dubPrWkFW60x2aE4F8u+o8QeiVIsImUBQcTTV2ahxmHnxyCCKAn/y29fz0P4uDPprvyp0kkRnk1NzETKdJOF2ighZMZz0jbK1tl7UnJYRIcgWIGMOOx1Njk0yG9Bp8A1rMuppqbfnjJAd6/HR1uigxnGtt0zaHDbfEUoJRWFaDBYXCCoSSZJ44OZ27trdwtc+tZeNrbm7ttc3VzM4Gih40sdSMTIeorbaPEc8ChZGUVVO+7xsrRXm0eVEvHMXwGVPCo+J6WSETGuWFzPpbHLSf9k/x69tKhild3CSnevntrK7nUmBlq/1xXQwhgo4RMpSIKhIbtvVzEfv2bjgta6r2YkKOaPyy83oREjMsCySkWAAs14vRiaVGSHIFqDadnWeZSgVIdMqnU1OwtEEl33XzqM73utDBXZtmDtYura6sPFJ6bFJImUpEKxuOtdWI6Gdwn5heVE8a2x2znzqD/igvKXcS1nVCEG2AEaDDrvVmExZhmOa8yCbSa7C/mM9XqrtJtY1zi3WrDIbMBv1eacs/WKwuEAgIGmQ3VRno3ew/BGyYDjOdCgmLC8WgSRJGHRCEpQTcfbzwGU3J4v6I9oaLD6bRncVVrPhmhRCPJF059/ZVZe19k2SpFSnZX4RsqlActySEGQCgaCrOVnYX0iX9lIwKjosF8WXXn2R//zma+VexqpHCLI8cDlMjE9HCEW0HSFLdz7NjJB5BiYIRxPsWj83XZmm1mlhLE9z2HTKUtheCASCrqZqgpE4V3zBsq5jJONBJgRZMTzV38PFqfJHOlc7QpDlQXp8UjASx6rhCBlA51onl0aniUQTABw7l3Lnb889DsPtNBeUsjToJU2a4woEguVlfUtyjm6568hGhSArmvFwiMHpKVHQrwGEIMsDl92MPxAlFEloOkIGyToyVYXzV5Jz5o72eNk8y51/Nm6nBX8gSiyuLHj8tEu/8KoRCASN7ipsFgO9C4xtW2pGxkM4qoyabrrSKid9owDC8kIDCEGWBzUOM+kSCa0Lso50Yf9lP0O+IN7J8LzpSkimLIG8Zlr6A8KlXyAQJEmWSVSXvbB/dCIk6seK5KQ3aRq+TUTIyo4QZHmQ9iIDNJ+ydFaZqHdZ6Bvycyzlzr+ja67/2EzcKbPYfNKWYo6lQCCYSVezkyFvgGA4VrY1jIyHRIdlkVgNBm5qaqGhylbupax6hCDLA5f9qru9FudYzqazqTojyNY12hccJeIuwIvMHxSCTCAQXKWruTppEJtjSshSE08ojE2Fqa8WgqwYPrF1B4+/74PlXoYAIcjy4hpBpvEIGSQL+8enIvRcmlwwXQlXI2QLCTJFVZkKxkTKUiAQZOhc60SCsg0a902GUVVoEBGyglFVFbXMliWCqwhBlgdOm5F0DbvWa8jgqkGsCuzMQ5AZDXqcVcYFU5bBcJyEoooImUAgyGA1G2iut9NbpgiZsLwontNjXjZ/7xFeHrhQ7qUIEIIsL/Q6XUaEaL2GDGBdox29TqLaZqJtzVx3/my4nZYFI2QZDzKb9tO2AoFg+VjfnPQ/LIdB7Mh4yhRWRMgK5qR3lLFwmEabqB/TAkKQ5Uk6bbkSImRGg54btzZy5/XNWd35s5E0h50/QuZPz7EUKUuBQDCDruZqQpE4l72BhTcuMaMTIUwGnZivWwQnfaOY9XrWu9zlXooA0L660Ag1djMXmFoxhqifvr+wIbE1TjPd58dQVTWnx9iUmGMpEAiy0NWcMogd8tNcb1/W1x6dCFHvsgpvxCI46fWyyV0nZlhqBPFXyJNapwWbxYC+Qt+4tU4LkWiCUCSec5urKUshyAQCwVUaa6zYrUZ6Li1/Yf9ISpAJCkNVVU76Rthau3CdsWB5EBGyPPmtd7Sxd0tDuZexZKTNYX3+CFWW7DVi/kAUnSRhs4oaMoFAcBUpNUd3uR374wmF0fEQW9tFyq1Q4orCh+St7F3bVO6lCFIIQZYnNQ4zNQ7zwhuuUGqcaXPYMK0N2VMO/kAUR5Ux77o0gUCweuhqruZ4r4/pUAz7Mt20nTo/TjSusKkt96xeQXaMej1fuWl/uZchmEFl5t8EBZMZnzRPp6Vw6RcIBLlYn6ojW06D2IOeEaxmvYiQFYCqqnzp1Rf5zokj5V6KYBZCkAmAZF2YXifN60UmXPoFAkEuOtY6kCToXSaD2HhC4cjZUXatr8NoEF9l+aCqKl95/RX+4cQRLvrLO39UMBfxLhYAySHBNQ7zvF5k/oBw6RcIBNmxmAy01tuXzbH/zMVxAuE4e+TKre0tJaqq8vU3X+Pbxw7xe9t38VWRrtQcQpAJMtTOYw6rqmoqQiYK+gUCQXa6mqvpu+xHUZbeIPbgmRHMJj3bOkW6Mh/++u03+F9H3uaTW3fw9VvuEDYhGkQIMkEGt9OSM2UZjiaIxRWRshQIBDnpanYSiSYYXGKD2ISicPisN5WuXBnekOXGZbbw0c3b+Kv9dwkxplFEl6Ugg9tpZnwqgqKo6HTXfmDTLv0iZSkQCHKRMYgdnMzZrV0KzlycYDoUE+nKPBgOTNNos/PZndfPa/wtKD8iQibIUOu0oKhqxgB2JunHxHgSgUCQiwaXFUeVcckL+w+dGcFs1LNdpCvn5dvHDnHjv3yP0z4vgBBjGkcIMkEGd8Ycdm4dmRibJBAIFkKSJLqaqulZQuuLhKJw6OwoO9fXYjKKdGUu/uH4Yf7iNy9z57p2NtQI4boSEIJMkMGdMofNVtifTlk6RMpSIBDMQ1ezk+GxYOYmrtScHZhkKijSlbkYDkzzu08/wZdee4l7O7r49t2/JWZVrhDEX0mQIW0OO5alsH8yI8hEl6VAIMjN+hmDxpeCg2dGMBl1bO+qXZLjr3T+6XQ3z13o5z/ceAvffdd7MOpFFHGlIIr6BRmsZgNWsyFrytIfTI5DMeiFhhcIBLlpX+NEJ0n0Dk5yT4mPrSgqh86OsqOrDrNIV2a44J9kJBjghjVNfP66PTy4XqbTJcZJrTTEt6vgGtzO7OawYmySQCDIB7NJT2uDfUkK+89dmsAfiLJHri/5sVciCUXhfx87zG0//j5fePHXKKqKWW8QYmyFkleETJbl80A49R/AnwBTwLdmbNYAXPF4PNdn2f8fgbsBb+qhxzwez9eLWrFgSUmaw85NWfoDUZwiXSkQCPJgfXM1r524TCKhlPS4B8+MYjLo2CHSlZwaHeWTP/85B4cvc9e6dv7mtnvQiS7KFU0hKcv3ezye7lmP7Ur/IMvy48Br8+z/lx6P5xuFLE6w/LidlqzDgf3BKO1rHGVYkUAgWGl0NTt5/vAlzl/24zSXJrWoqCoHz46wvbMWi2l1V9t0e0e596f/gt1o5Ft338fDGzYJS4sKoCTvalmWG4B3Ap8txfEE5cPtMDMdihGJJa6p0UhGyETKUiAQLIy8LpkyO3hmmDt3NpXkmD2XJpmcjrJnk+iu3Fpbx1/s38+DbTL1VVXlXo6gRBRSQ/bPsiwfl2X5W7Isu2Y99wngWY/HMzzP/l+QZfmELMuPy7K8ufClCpaDdKfl+NTVtGU0liAcTYgaMoFAkBc1DjNdzU5eP3a5ZMc8eGYEg16kKyHp9/al/fuFGKsw8o2Q3erxeAZkWTYD/xP4BvCxGc//LvBn8+z/JeCyx+NRZFn+BPC0LMudHo8nke9Ca2sXP4ajvl6k3Baic11SiCUkKXO+hseCADSvceY8h+LcLi3i/C4d4twuDbfvbuU7T5wkLulYW2db1LEUReVIj5c9mxtY1yIK1tOI9+7SUY5zm5cg83g8A6n/R2RZ/hbwRPo5WZZvBGqBp+bZf3DGzz+QZfl/AC3AhXwX6vNNoyhqvpvPob7ewejoVNH7rxZ0SrIIt+/iOM01VgD6h5LdUpKiZD2H4twuLeL8Lh3i3C4dcrMTgF+/0c99N7Yt6lg9lybxTYZ5aL9b/L1SiPfu0rHYc6vTSUUFkRZMWcqybJNluTr1swR8GDg6Y5NPAT/weDzxeY7RPOPndwEJYDDX9oLyUeMwIwFjM1KWfjHHUiAQFEhdtZX1rS4OekYWfayDnhEMeold6+tKsDKBQJvkEyFrBH4qy7Ie0AOngM8ByLJsBT4IvGP2TrIsHwV+y+PxDAHfl2W5EVAAP/DAfAJOUD4Meh3VdtM15rBTwRggXPoFAkFh3Lyjie8/eQrvZIi6amtRx1BUlYOeEbZ11GI1r+7uSkFls+C72+Px9AHX5XguBMwu8E8/t2vGz3cXu0DB8uN2Wq4xh50UETKBQFAEN+1Yy/efPMVhzyjv3LuuqGP0X/Yz5o/w0P7OEq9OINAWwqlfMAf3LHNYfyCK1azHaBCjSgQCQf401dlZ12DnoGe06GMcPDOCXifSlYLKRwgywRxqU+OTVDXZRCE8yAQCQbHsluvpGZy8xkonX1RV5eCZUbZ2uKmyiJIJQWUjBJlgDm6HhWhcYTqUrB0TcywFAkGxpI1cD58tPEp2/soUPn+YPbIwgxVUPkKQCebgTpnDptOW/qCIkAkEguJYW2ujuc7GwTOFd1u+nUpXXrdRpCsFlY8QZII51FabATKF/SJCJhAIFsNuuZ6zAxOZBqF8mA7FeOXoEDu6arGJdKVgFSAEmWAObkcyQubzh4knFALhuBBkAoGgaPbIDajAkQLSlk+81k8oGhfdlYJVgxBkgjk4qowY9DrGpiIZDzIhyAQCQbE019todFflbRJ72RfgxSOD3Lazieb6xY/NEwhWAkKQCeYgSVKm0zLt0i9qyAQCQbFIksQeuZ4zFyaYCi6ctnzsxV6MBh3vvVVExwSrByHIBFlxOy34/OFMzYfTJmo4BAJB8eyRG1BUlSPnvPNud/rCOEd7vNz/jjZhRi1YVQhBJsiK22lmzB/J3M2KlKVAIFgM6xrt1FVbODSPSayiqDz6/DlqnWbeeUPrMq5OICg/QpAJslLrtDAxHcmYOYqUpUAgWAySJLFnUwOnzo8RCMeybvN69xUujkzz8O1dYjKIYNUhBJkgK26nBVWFi8NTmAw6LCZxcRQIBItjt1xPQlE5miVtGYkm+OkrvXQ2Odm3ubEMqxMIyosQZIKsuJ1JL7L+y1M4bSYkSSrzigQCwUqnc60Tt9OcNW359IGLTE5H+fCdG8T1RrAqEYJMkJVa51UvModIVwoEghIgSRK7NzbQ3T9GKBLPPD4+FeFXb11gz6YG1rdUl3GFAkH5EIJMkJW0OSwgOp0EAkHJ2C3XE08o/397dxYiZ5nFYfyp7miWjraTno4ajYlJ7CNIjCYRjCYuoHij6DjqjCB6I26DXg0IiiiCEKIguKEggkQRBUW9kXihouKCM6OjYZijE2LiFpdERUHjmC4vvq9jI7ZbfZ03VT4/KLrr7arqw+HlrX99W/HvDd/vtnzk2Q2MjrY564SFBSuTyjKQ6UdN3bOfgWlTAC95Iak5iw4cZHDmnjt3W27a8gUvvLGFk5bPZfY+0wtXJ5VjINOExnZbeskLSU3pa7VYNjLMGxu2sv2bHTz41FsMTN+DU1fMK12aVJSBTBOaNRbIPIZMUoOWxWy++XaUtU8m/938GaevPJgZfoG4fucMZJrQ2JmWbiGT1KSRuYPsNWMPXli/hf2HZnD8EXNKlyQVZyDThIbcQiZpEvT39bF0ZBiAs09cxJR+34qkKaUL0O7rgOEB+lothj3QVlLDTjtmPgvm7M2ShUOlS5F2CwYyTWjxgiHWXLpi57FkktSUWXtPY9Xh7qqUxridWBNqtVqGMUmSdgEDmSRJUmEGMkmSpMIMZJIkSYUZyCRJkgozkEmSJBVmIJMkSSrMQCZJklSYgUySJKkwA5kkSVJhBjJJkqTCDGSSJEmFdcOXi/cD9PW1On6hJl5DP87eTi77O3ns7eSyv5PH3k6eTno77rn9v+Z5rXa7/Zv/6S6yEniudBGSJEm/wirg+V/64G4IZFOBo4APgB2Fa5EkSfop/cD+wCvA9l/6pG4IZJIkST3Ng/olSZIKM5BJkiQVZiCTJEkqzEAmSZJUmIFMkiSpMAOZJElSYQYySZKkwrrhq5M6EhEjwL3AELAVOD8z3ypbVXeKiJuAPwPzgcWZub4et8cdioghYC2wkOpCgv8DLs7MjyPiaOAuYDrwNnBeZn5UqtZuFRGPAgcDo8CXwOWZ+ZrztzkRcS1wHfX64NxtRkS8DXxd3wCuzMx19rdzETENuBk4iaq/L2bmRSXWhd/DFrI7gdszcwS4nWry6rd5FDgO2PSDcXvcuTawJjMjMw8HNgCrI6IF3Af8re7vs8DqgnV2swsyc0lmHgncBNxTjzt/GxARS4Gjgc31fedus87KzCPq2zr725g1VEFsJDMXA9fU47t8XejpQBYRs4GlwAP10APA0ogYLldV98rM5zPznfFj9rgZmbktM58ZN/QSMA9YDnydmWPfh3YncM4uLq8nZObn4+4OAqPO32ZExFSqN63LqD5cgHN3stnfDkXETOB84JrMbANk5oel1oWeDmTAXOC9zNwBUP98vx5XM+xxwyKiD7gUeBw4iHFbJDPzE6AvImYVKq+rRcTdEbEZuAG4AOdvU64H7svMjePGnLvNuj8iXo+IOyJiH+xvExZS7Y68NiL+ERHPRMRKCq0LvR7IpG50K9UxTreVLqTXZOaFmXkQcBVwY+l6ekFErACOAu4oXUsPW5WZS6j63MK1oSlTgAXAq5m5HLgSeASYWaKYXg9k7wAHREQ/QP1zTj2uZtjjBtUnThwC/CUzR6mOx5k37u9/BNqZua1QiT0hM9cCJwLv4vzt1PHAocDG+uDzA4F1wCKcu40YO1QkM7dTBd9jcW1owibgW+pdk5n5MvAJ8BUF1oWeDmT12SavAefWQ+dSJeGPy1XVW+xxcyLiBmAZcEa98AL8E5heb0YHuAR4qER93SwiZkbE3HH3TwO2Ac7fDmXm6syck5nzM3M+Vcg9hWoLpHO3QxExEBGD9e8t4K9Uc9a1oUP1bt6ngZNh5xUDZgNvUmBdaLXb7Z9/VBeLiEOpTl39A/Ap1amrWbaq7hQRtwBnAvtRfYrYmpmH2ePORcRhwHqqheCrenhjZv4pIo6hOsNnGt+f2v5hkUK7VETsCzwGDAA7qMLY3zPzX87fZtVbyU6tL3vh3O1QRCwAHgb669t/gCsy8wP727m6v/dQXd7i/8DVmflEiXWh5wOZJEnS7q6nd1lKkiR1AwOZJElSYQYySZKkwgxkkiRJhRnIJEmSCjOQSZIkFWYgkyRJKsxAJkmSVNh3spoYW2XaRcMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_prediction(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xoU08mImWZnkWTWv30ZAHns1laA949x93RO5WtDTOcaBJbzvFQ5s+hfZAb0aLeDyPlnpBTz3/Ci2d2yK0WVPeEIp27dSjXY/3oJUnOaW3vxftGn0B7aFmREvLuslgZHq1j4F/okVsKtHSZcOl9n6HpgUqRNMzvcSZcXCh6Xemol3HTWjianf68TX9O9YLIQaKwnWhOdUfoT2Q96BFQr6ot/8+8G3g/9BeRI6j/SY9jZD9VP/OHwkh2tFmhbojhFnA62gp2P1o5/dlfdtQ4/sntOurVj/ug0H2Ow/dIb9Db6MOTRLgcWrVg/b/jjZWL+v3jL1okwK86ltKWY82I/cutPN3FE3IDtp9KkkI0dzP8e7PQ2h13XaiXTPlaJo7T7gBKNTP1feBW1QUK7gwuFz+iOIrFIrJjhDiZWC7lPKBYXf2rx0RaILguf2E1mPZ/63Af8l+db0mEros4Aiw2Jv0uUIxGVBOlkKh8AlCiKVo0Yoy4BNoUaWFQ+h7JiRCiDi0mk+b0PQ1LwMvSin/XwDNUigUAUBpshQKha9IR0sjx6M5WndPNgdLx4RWyDcXLaX3ImdqTSkUikmEimQpFAqFQqFQ+AElfFcoFAqFQqHwA4FMF4aiTcGvxvup8AqFQqFQKBRjiQmtVtou9BnDwxFIJ2sxZ6ppKxQKhUKhUIwHLkar2TcsgXSyqgGamztxOkemC0tIiKKxccgF4BWjQI2v/1Bj61/U+PoPNbb+RY2v/xjt2BqNBuLiIkH3XzwhkE6WA8DpdI3YyXIfr/Afanz9hxpb/6LG13+osfUvanz9h4/G1mOJkxK+KxQKhUKhUPgB5WQpFAqFQqFQ+AHlZCkUCoVCoVD4AeVkKRQKhUKhUPgB5WQpFAqFQqFQ+AHlZCkUCoVCoVD4AeVkKRQKhUKhUPgB5WQpFAqFQqFQ+AHlZCkUCoVCoVD4AeVkKRQKhUKhUPgB5WQpFAqFQqFQ+AHlZCkUCoVi3LGvsJ4n3pb09NkDbYpCMSiBXCBaoVAoFOOIjw5WsflgNVnJUYjMOPIzrMRGhgTElrd3llFY0crJqjbuv2kusVGhAbFDoRgK5WQpFAqFwiM276+ipqmLitoO3t9bCUBqfAQi00p+hhWRYSU+JszvdtjsDk5Wt5GfYaWkpo2f/XMP/3HzPNISI/3et0LhDcrJUigUCsWwdPfaOVXdzo1X5LFqQRqlte0Ulrcgy1rYeayWD/dXAZBkDeOGS3JYOjPFb7acrGrD7nCxdkkG1qhQHnrhIL94Yg9f/eQcRGac3/pVKLxFOVkKhUKhGJYTFS04XS7m5iZiNhnJSYslJy2WdUuzcDpdlNd1IMtb+OhgFU+9U8j83ERCQ0x+saWwohWAvHQrUeEWvn/HIh58/gC/fXY/d39iBstmpvqlX4XCW5TwXaFQKBTDcqy0GbPJQEF2/HnbjEYDWanRrFmcwWfWFtDRbWPzwSq/2VJY3sLUpEiiwi0AJFrD+e4di5ieFstfXjnKG9tLcblcfutfofAU5WQpFAqFYliOlTaTOzWWUMvQ0anc9Fjy0mPZuLMMu8PpczscTidFla3kZ1jP+jwyzMJ/3jKfJTOSeWFTMU9uLMTh9H3/CoU3KCdLoVAoFEPS0W2jvLaDAg/1Tlcty6KxrZedx2p9bktZbQe9fQ7EOU4WgMVs5N5rZrFuWSYf7Kvk4X8forfP4XMbFApPUU6WQqFQKIZElrXgAgqyPHOy5uYkMDUpkje3l+H0cdqusLwF0PRYA2E0GLhpZS53rMnn4MlGnvugyKf9KxTeoJwshUKhUAzJ8bJmQixGpqfFeLS/wWDgqqVZVDZ0crCo0ae2FJa3kGwNJy566LpYly1MZ870hNNOmUIRCJSTpVAoFIohOV7aTF66FbPJ80fG4hnJJMSE8caOUp/Z4XS5OFHRSl5GrEf7Z6dGU9XYqVKGioChnCyFQqFQDEprZx+VDZ3M8DBV6MZsMrJ2SQZFFa0+iyZVN3TS0W07T/Q+GNmpMbhcUFbX7pP+FQpvUU6WQqFQKAZFljUDeCx678/F89KICrfwxnbfRLPc9bEGEr0PRFZqNAAlNcrJUgQG5WQpFAqFYlCOlTYTHmoiKzXK62NDLSZWXZDOweJGKuo6Rm1LYXkLsVEhJFnDPdo/LjqU2KgQSqqVk6UIDMrJUigUCsWgHC9tJj/disk4ssfF5QvTCbWYeHOU2iyXy0VheQsiw4rBYPD4uOyUaEprlZOlCAzKyVIoFArFgDS19VDb3O21Hqs/UeEWLp2fxo6jdTS0dI+4nYbWHprbez3WY7nJSo2murGTnj77iPueaMimRk62NgfajEmBR2sXCiHCgAeBVUAP8LGU8l4hRIn+/x591+9IKd/2g50KhUKhGGOOu/VYo3CyANYszuC9PRW8vauc21bnj6gNt3g+f5D6WIORPUUXv9d2eO2gTUR6HXbWb3iGlt5eVmVO46cXrSTHqhbV9heeLhD9KzRHKl9K6RJC9F9e/UYp5WHfm6ZQKBSKQHKstJnIMDPpyd7rsfoTHxPGslkpfHSgivUrsomJCPG6jcLyFiLDzKQlRXp1XLYufi+taQ9qJ8vlcvHLnduIiQrjS7MW+a2fD8vLaOnt5YY8wY7qKqJCtHNR2tZKUngEERaL3/qejAzrZAkhooA7gXQppQtASun7tRIUCoVCETS4XC6OlzZTkBWH0QsN1GCsW5rF1kM1vLe7gusvme718YXlLeSlW722xRqli9+DfIahwWCgvL2NV/bv5uqMXDJjPKsF5i2vFBcSGxrKHy6/ErPReHo8v/7+2xxtrOeOmXO5e/Z8pkZH+6X/yYYnmqwcoBH4kRBitxBikxDion7bnxJCHBRCPCqECN7XBIVC4RdcLhdvbi+lon70s8cUwUN9aw+Nbb0jKt0wEGmJkSzIS+T9vRVe66NaO3qpbe4ecSRqWmrMuBC/f2/ZRRgNBn6+fYvf+rhv7kJ+c+kqQkymsxzW/1q6govTM3lk/24uePKvfP7t12jp6RmiJYUneJIuNAPTgX1Sym8JIZYCrwohcoGLpZTlQohQ4PfAw8Dt3hiQkDC6MHRSkvK2/YkaX/8xUca2uqGT5zcV8+6eCn7z9UtIjosItEnAxBnfQLG3uAmAFQvSzxvLkY7tp9fN4Ft/+Ig9RU1cd2mOx8fJqjYAls5NG1HfM6cncOAdSVRMOOGhnqpkxp6kpGi+tXw5P9m8mW9dsoILMzJ83sflSdFcPsDn65MKWD+3gNKWFh7ZtYtfb9vGqvwcvrJkic9tCCRjfV/w5GorBezAvwCklDuEEA1o+qzd+me9QohHgVe8NaCxsQOnc2QLiCYlRVNfH/xvJ+MVNb7+YyKN7c7D1QC0d9n44Z+28d+3LyIiLLAPsok0vsPhcrn4zTP7mZoUyadXjUxUPhC7j1QTGxlCqMF11liOZmwTIiyIDCsvfnCCpSLR42V6dh+uIcRiJDrEOKK+E2NCcblg75HqoNZlAXxrxQr+vHsPX3/jLV6/4VM+bfuZ40fIjI5h+dTBnbcITHxr/jJumT6DzJjYCfU7Gu19wWg0eB0YGvYKl1I2AB8AqwGEEPlAMlAthIjVPzMAnwL2e2mzQqEY5xRVtBIeauLrN86lpqmLR186hN3hDLRZk4Zjpc0cK21m26Ean427y+XimK7H8qYmlSesW5ZFc3svWw5We3xMYUULuVNjvVo7sT/9xe/BTlRICA9dvpZfXXKFT9vtczj4wdZNPH38iEf7+0sTNtnw9Ir9AvBdIcQh4BngDiAU2CSEOAgcBvKBL/nFSoVCEbQUVbaSkxbLrGnxfObKAo6WNPPPtyUu18gi1ArveHNHGQYDdPXakWW+WSOwpqmL1s4+CjJ9H/WZMz2e/AwrL24+SUe3bdj9O3tsVNR1eF26oT/WqFCsUSGU1LSNuI2x5PLMbGYlJgH47He0uaKU1t5ersnxLNrZ0tPDF955nTdOFvmk/8mKRzF9KeVJYOUAmxb41BqFQjGu6OqxU1nfyQUiGYCL5k6hobWbV7aWkGQNZ/3y7MAaOMEpq23nyKkmrlmRzVs7y9hbWM+safGjbvdYqVYfazRFSAfDYDBw++p8/t/fd/Hih8XceWXBkPufqGjFBaNO82WnxgT9DMP+2BwOvvr+28xMSORrC0evi3ql+ATRISFcmpHp0f4xoaFsrigDDFw1PXfU/U9WVMV3hUIxYk5WaQ/A3PQzqYVrL5rGhbNS2bD5JB8fqQmccZOAt3aWERpiYs3iDOZMS2DviXqcPoh8HC9tJiEm1OM1Ar0lPTmKKxal8+H+Kk5VDx1dOlHegsloYHpazKj6zE6Npqaxa9xUfreYTHTZbfx+z07qujpH1Vafw8Gbp4q4MjuHUJNnekmjwcBlGdl8UFaCw6nS/yNFOVkKhWLEFFW2YjDAtClnHoAGg4G7riqgINPK3984hixTy3f4g4bWbnYerePSeWlEhFlYmJ9Ea0cfp6pGlxJzulwcL2uhINP3eqz+XHvRNGIiQ3hyY+GQjmFheQvTpsQQYjGNqr+s1GhcaJXfxws/uvASehx2frXz41G1U9LaQojRxDW53k2MWJ01nebeHnbXeq6fU5yNcrIUCsWIKapsJSMp6rxp8WaTkS/fMIckazgPv3iI6sbRvYkrzmfjrnIMBm3JGoC5uQmYjAb2nqgfVbsVdR10dNtGvZTOcESEmbn58lxOVbcNKoLvtTko8VGldrf4fTylDHOscdw9ex5PHjvEscaGEbeTH5/Awc/cy6rMaV4dd1lmFiaDgXdLT42478mOcrIUCsWIcDidFFe1nZUq7E9kmIX7b5qHyWjgwecO0NbZN6r+XC4XHd02n6TDxjsd3TY2H6hi6cwU4mPCAG28CzKt7C1sGJVY+rgunveHHutcls1MIT/DygubigcUwZ+sbMXhdJGfMfqZbrFRocRFh1I6TsTvbv7zgmXEhITwPzu3juh4p8uF0+XCZDRiMnr3yI8NDeMmMZPE8OCofTceCd6qbAqFIqiprO+kt89B7tTBH4BJ1nC+duM8fvX0Xh587gBLZiQTHmrW/5jO/DvErEfDXDS09lDf0kNDazcNrT00tOh/t/bQa3OQn2Hlm5+aP+Lp/BOBD/ZW0GdzcuWSs0XMC/OTeGJjIVWNXUxN9G6NPzfHS5tJjgs/7bz5k7NE8JtPcudacdZ2Wd6CAcid6ptZjlkp0eMqkgUQFxbOY2uupiAhcUTHbyov5RsfbOSZ9TdQEO99G3+4fO2I+lVoKCdLoVCMiKLKVoAhnSyA6Wkx3HvNLB577SjPbyr2qo+wEBOJseEkx4UzMzseoxHe3lnOSx+d4saVnlcMn0j02Ry8t6eCuTkJ5y3cPD9Pc7L2FtaPyMlyOJ3I8maWzEjxlbnD4hbBv7u7nIvnTjlL33eiopWMlCifFbfNTo3mQFED3b32oK78fi6XZmQBWjTXHZXylFeLC2nr6yM7ZuSOqt3ppLmnh6QIFdHylvFzlSkUiqCiqLIVa1QICbHDRzwW5ifx6H9cQq/NQXevg+5eu/anz37W/10uSIwNI9EaRmJsOJFh5vPE1929Dt7YXsqMrDiflCsYb2w7XENbl411S8+fih8XHUpOWgx7C+tHVD6jrLaD7l6Hz9Yr9JRrL5rGzmO1PLmxkO/duQijwYDd4aS4spVL5qX5rJ/sKZr4vbyuI+grv59La28Pt762gRvFDO6ePd+jY2wOB2+cLGJt9nTCzCN/3K954SnSo2L451XXjriNyYpysiYAbZ19lNS0U1rTRmltBwbgzisF0REhgTZNMYEpqmgld2qsxzPQDAYDYSFmwkLMxEWHjrjfW1flUVTZymOvHeXHdy8hNnLyXOdOp4u3dpYxbUr0oE7CgvwkXthUTGNrj0cOcH+O6/Wx/C16Pxe3CP6xV4+y5WA1l8xLo7SmnT6706fOUFaqFiUrqW4bd05WTEgoISYTv975MVdNyyU1cvjlXT6qLKe5t8frWYXnckFKGs/JI/TY7aNy1iYjk1fUME5p6+zjQFEDr2w5xR9eOMh/PrKV+/93C79//gAbPjpFZX0HB4ob+f3zB+juHR/1YBTjj+b2Xhpae4ZNFfqDUIuJL1w7i+5eO3997ejQDsTzAAAgAElEQVSIhPA2+/is+7O3sJ665m7WLc0a1LldmK9VCh/JLMNjpc2kJUYGxHE9VwRfWK4J8PN86AzFRoYQFx1KSe340mWB9pLywMWX0+Owc+trG2jv6x32mFeLC4myhHBZRvao+l6dNY0uu52PqypG1c5kRDlZ44iKug6+/cdtPPTCQV7ecoqapi5EhpVbLs/lO59ewMP3X8ID913IF6+bRWlNBw+/eGjcPkwUwU2xW481iqVORkN6UhS3XpHHkVNNvL2zzOPj7A4nT2yUfPnBzVTWj596SaDpcd7cUUayNfy0IzUQqfERpCVGsq/QOyfL7nBSWNHCjDFOFbpxi+C7euy8uPkksryF1PgInzt82anR42INw4GYYY3gHxfPRDY38tm3XqXP4Rhy/+tyBT+88OJRR59WTM0gzGRSpRxGgIr7jSM+3F+FC/j2rQvISo0eVLi5IC+Ju64q4G+vH+MvrxzhC9fN8nrqrkIxFEWVrVjMRjJTvFuR3pdcOj+NIyVNvPjhSfIzrOSkDR1Va27v5Y8vHT4t2N91vI6pSYGz31sKy1s4Vd3GHWsFRuPQKdqF+Ym88XEZHd02osItHrVfXNlKn81JQVbg0mj9RfAWs5Fls1J93kdWajT7T4w/8buhr4HYfTfyyba9PLT8Jf5wREsFpkQMPsHh0oys06L50RBhsXDR1EzeKT3Jzy5a6dcitRMN9eQdJ9jsTrYfrWFhfhIFWXHD3hxWzJnCrVfksaewnn+8pRbrVfiWospWpqVGB7SMgsFg4LPrCrBGhfLnl4/Q1TN4evxERQs/eXwX5XUdfPG62eSmx7K/aOTFHQPBmzvKiI6wsGL28I7HwvwknC4X+0949h0dTifPfVBEdISFGVmBnUzgrgSv6bF8n47OPl35ffxEs4zdpVh3rcHcfhCAOxIqeO/m20mJiBz03v7mqaJRFTA9l/sXLeXBy9b4rL3JgnKyxgkHihro7LGzYo7nb3arF2dwzYpsthys5vkPipWjpfAJfTYHpTXtAUsV9icyzMJ9186iqa2Xf7x1/Lxr3OVy8d6eCn719D5CQ0x8785FLC5IZkFuImW1HTS19QTIcu+oqO/gYHEjqxale7S8TFZKNPExoez1MGW4cVc5p6rbuW11vs/KJYyUiDAzn16dT3io2S8On1v8Pl5Shqb2w1h3rsbY10Drotdwmq2YW3YSYjLR53Bw78bXeezg3rOOsTudfOODd3ho7w6f2bFkShorpmaoKJaXKCdrnLD1UDVx0aHM9PKmc+1F07hiYTpv7Szjje2lfrJOMZkoqWnH4XQFRPQ+ELlTY7n+kmnsOl7HR/2WZ+mzOfi/14/x1DuFzJ4Wzw8/cwHpenpwfp5WlHGfh5GeQPP2jjJCLEYuW5ju0f4Gg4EFeUkcKWmit29o3U5NUxcvfXSKBXmJLC5I9oW5o2ZxQTIP33/xqGahDsZp8fs4cLIszVux7l4HBiMti9/CFrccm3UJltadAJgMBmxOJ9/fsolXigpPH7e1spzGnm7W54xuVuG57Kut4fHDB3za5kRn/CSkJzGtHb0cOtnEumWZw2oxzsVgMHDr6jw6e2z8+8OTRIZZWLlgqp8sVQQrdoeT+pZuapq6qG3qpqapk5qmbmqbuggPNfPjuxdjMXu2AK9b05QzNWaYPceOdcuyOFrSzNPvFJIzNRaXycQDT+6ltLada1Zkc81F0zD2ewOfkhBJSnwE+4sauGKRZ45LoGhq62H70VouWzDVY30VaCnD9/ZUcOhkIxcM4jw5XS4ef+MYZpOR29eIoIpS+NOW7NTgr/weUvcaMYfuwhGWSevCl3CGa2tU2mOXENqwEYOtBZPFyh9Xr+OmV/7Nl959k8TwcJZPzeCV4kIizBYuz8z2qU2vnizkzwf28sn8AqJDfO8AT0SUk+UDem0OKuo7hhXejpSPj9TidLlY7oEWYyCMBgN3f2IGXb12nnhbEhFmHtOKzorAUNvUxXMfFFHZ0ElDS89ZpQ6iIyykxEeQmx7LHlnP1sM1rJzvmfNdVNFKanxEUNVhMxoMfH79TH70fzt5+N8H6ep1YHc4+NqNc5mfO/BSIgvyEnlnVzldPfaAp8iGYtP+KlyuMwtBe0p+RixR4Rb2nqgf1MnatK+SwopW7lpX4JeoUbCSHeTi97CKx4k6dj/22IW0zn8eV0jC6W222CUAmFt3YUtcTbjZwhNXXcvVLz7LnW++wobrbuKNk0WsyZ5OuNlzp9wTVmdN5+F9u/mwvIyrc/J82vZERaULfcC/3j3BL57YQ0vH8HVLvMXlcrH1UDU5U2OYkjCytcgAzCYjX7xuNnnpsTz26lEOn2r0oZWKYKO8roMHntxDYXkLmSnRXHVhFvdcPYPv3bmI/73/Yh762sV89/ZFfOm62WSnRvPWjjKczuE1ey6Xi6LK1qBJFfbHGhXKPVfPpLa5G2t0KD/8zOJBHSyA+bmJOJyuoP8tFFW0kJUaRaI13KvjTEYj83ITOFDUiN1xfimXhtZunt9UzKzsOC6aO8VX5o4LslJjglP87nLB4Z8Rfexr9CVcQcuiV89ysADssYtwYTydMgRtfcNn1t9AWlQUu2uq6bT1sd4PTtAFKVOICQnl3dKTPm97oqKcrFFS3djJloPVuFycLp7nS0pq2qls6GTFnNHfBEMtJr524zxSEyL4x5tyREUcFcFPcWUrv3xqLyaTke/eoTlSN1wyneWzp5CTFktk2Jm3W4PBwFXLsqhr7maPByLp2uZuOrpt5KYHn5MFMGd6Aj+5ewm/+/olpMQPvc5a7lQt0uPpDLxA4HK5KK3tOC3W9paF+Ul099qRZWffm1wuF/98S4ILPnNlQVClCceC7NRogKBLGUYW/hcc/AE9U26hbf4zYDr/xdpljsYRNRNLy86zPs+IjuGDm+/grtnzOHr3F1mTPd3n9llMJi7LyOLdshL1/PCQSe9kHT7VOKoZRi9uPonFYiTUYkL6wcnaeqgai9nIEh8JUiPCzHziwiwa23rOu/Eqxj/HSpr4zTP7iYqw8N+3L/Qo+rkwP4mUuHDe2F467AzUogrPFoUOJOnJUYR5kAIyGg3My03gYPHAkZ5goL6lm+5eO1kjrEc2KzueEIvxvFmG2w7XcPhUEzeuzPE6QjYRiIkMIT4mNLhmGDp7Cat5AWZ8k/ZZfwbj4Kk+m3Up5rY94Dp7UoO7HmKUJYRQk3/SoKuyptPrsFPR3uaX9icak9rJ2neint89e4DfP39gRDfZk1Vt7JH1rF2cQV56LIU+dlpsdic7jtayMD+JiDDf5dYX5iURHmpi26Hq4XdWjBv2najnwecPkmgN479vW0hirGcPT6PRwJVLMymtaeeYvnbdYBRVthIZZiY1Yego0XhhQV4SXb12TvjhBckXlNZqVemzRxjJCrGYmDMtgb0n6k9HHlo7ennmvRPkpcdy2cLJOwkmKyWaU8HkZBlDabzkBCz4NRiGfjTbYhdjtLdh6jg+Rsad4bq8fI7d9UUyY4L3RSuYmLROVl1zF3997RjxMaFU1HfyppflDVwuFy9s0or3rV2Sici0UtnQSXtXn89sHEltLE8IsZhYXJDCbllPT59a33AisP1IDY+8eJiM5Ci+8+mFxEZ5J2JePjuV2MiQYX8HRZWt5EyNPWum3nhmVnY8ZpORfUFamLS0ph2T0UBa4sj1mAvzk2jt6ONUlRZ5eHJjIb02J59dVzBhzuNIyE6NprapK7jWeB3GuXJj18Xv/XVZY0WoyYxZrSDiMZNypPpsDh7dcBijAb7z6YUsmZHMq9tKqGzo9LiNIyVNHC9r4erl2YSHmk+v6F5Y3uozO7eMsDaWJ6yYk0qvzcEe6f0isorgYtO+Sh579Sj5GbF881PzvZrm78ZiNrF6cQZHSpoHTaF09tioaugkJ4hThd4SGmJiZnYc+080BGWx3tKaNqYmRWIxj/xWPTc3AZPRwN7CenYfr2NPYT3XXTxtVBNpJgLZU7ToYNCJ3z3AEZGD05IQECcLYFN5KRf963Hqu7oC0v94YlI6WU+9U0hZXQf3XD2TJGs4n16VT1iImcffPObRDCuny8ULm4pJjA07Pe192pQYLGajz8TvLR29HD7ZxPLZqV7XxvKE3KmxJMeFs1WlDMc1b24v5Z9vS+bkJHD/TfNGNR195fyphIeaBi1aW1ypRULyJpCTBVoph4bWHirrPX/JGgvcone3SHukRIZZKMi0sut4HU9ulGSlRrN2iXflICYiWfq4nqoef04WBgO22CWYWwLjZMWFhlHY3MT7ZSUB6X88MemcrI8OVvHRwWquXp7FPH16d0xkCLeuyqO4so339lYM28auY3WU1XZw/cXTT79hmk1GctJifOZkfXykBqfL5ZNZhQNhMBhYPjuV42UtNLR2+6UPhX956aOTPL+pmCUzkvnKDXM8Wm5lKCLCzKycP5Xdso665vPfUIsqWzAaDEybEjxFSH2B+z4QbCnDxrYeOrptZKWMzskCLWXY0NpDZ4+du9YVqAXjgZgIXfw+DiNZADbrEsxdJzD0jX0JkjlJySRHRKpSDh4wqX5pZbXtPLmxkBlZcVx30dnTW5fNTGFuTgL//rCY+pbBnQ67w8mGzSdJT4pk6cyzC3rmZ1gpq2sfcqFaT9BqY9WQOzWW1GGmoY8Gd3HTbYdr/NaHwj9U1nfwytYSVsxO5d71s3y2UPPqxRmYjAbe2ll+3raiilYyUqIIDRmdMxdsWKNCmZ4Ww/4TwZU6L63RRO8jLd/QnwX5SVjMRq5enk2mD5y2iUJ2akzQlXHwlDO6rF1j3rfRYGBVZjYflJdicwy9bNNkZ9I4WV09Nh7dcJiocAv3XTPrvBScwWDgzrXashIDLTTrZvOBKupauvnkpTnntSEy43C5tDf+0VBS005VQ6fPBe/nkhgbTkGmlW2Ha4JSj6IYnE37qzCbDNx0ea5P08nWqFCWz05ly8FqWjvPTOJwOJ2crG6bcKlCN/NzEzlV3U5zu+8LCo+U0tp2jAYD6Umj105Zo0L57ZdXcM2K7NEbNoHI0sXvo30xDgS22IW4DCbMAdJlrZ2WQ1tfLx9Vnv9CpjjDpHCyXC4Xf3v9GI1tPXzx2tnERA68HEh8TBg3r8zhaEkzWwbQKvX02Xllawn56bHMzUk4b/v0tBhMRsOo62Vt0WtjLS7w/9I3K+ZMoa65+/R6dAr/09Nnp6xm5DVmevscbDtcwwUimRg/LG1z5dIsHA4n7+4+c/OsqOukz+YM2iKko2WBvmD0gSBKGZbWtJOWGDHqNLCbqHDLpCs6Ohxuvdt4FL9jisQeNScgkSyAyzOzuSl/BvFhYQHpf7wwKZyst3aUse9EAzddljvsQ+LSBVPJT4/l2feKzlsm553dFbR19nHjZbkD3qxCLSamTYkZVb0sm93BzqO1LMpPGpP11BaJJEItJiWAHyPsDie/e/YAX/vtpiHT0kOx81gt3b12vy30nRofwUKRxAd7K09Pbz9RoV3TwVyEdDSkJUaSZA1jf5A4WS6Xi9KattPibIV/cOsLi6vG50um3boYS+tucI59JC7UZOaRVeuYn+zfjMt4Z8I7WbKsmRc+LOaCgmRWX5A+7P5Gg4HPXjWDPruTpzYWnv68o9vGWztKWZCXOOSDRmRaKalpp7dvZHnq/UWNem2ssVlLLCzEzAUiiV3H6+izTe7centXn0/rnA3E0+8UUlTZigt4a2fZiNrYtL+StMRI8vwYVbpqWRZdvXY+3F8FaPWx4mNCiY+ZmG+tBoOBBXlJHC1pDoracS0dfbR1+Ub0rhicqHAL6UmRHB+mCO9Y0Gdz8MKmYhq9mIhki12CwdGJqfOoHy0bmuKWZgqbgnv9z0AyoZ2slo5e/vjyEVLiIrhrnefrc6XGR3DdxdPYo9eVAXhtWwk9fQ5uuGTo9aDyM6w4nC6KRvhmtFWvjTUjK25Ex4+E5XOm0N3rYG+QCX/Hmj+9fISf/mM3XT02v7S/aX8lm/ZXcdWyLFYtzuSjA9W0ermoeElNG6eq21k5P82vqZ9pU2IoyLSycVcZNruT4iBdFNqXzM9NxO5wcuRUU6BNOV2rbKSV3hWeIzLjOFHZGvClld7ZXc4b20u90gXa3OL3AJVycDidrN/wDL/ZvT0g/Y8HJrST9eb2Mnr67Hz5+tle1w9auySDrJRonnynkLLadt7fW8Hy2alMTRp6DbHcqbEYDIwoZdjS0cuhk41+q401GCLTSkJMGNsOTd5Zhk6Xi1PVbTS09vD4W9LnEwGKKlp5amMhs6fHc8Ml0/nkZbk4nE427vZONLppXxUhZuPpmaH+5KplWbR09PHm9lIa23onVBHSgcjLiCUyzMy+IFgwurS2HQOQkTyyNQsVnlOQGUefzcmp6sCtxdfe1ccb20uZl5NAbrrV4+Oc4dk4Q5IDVpTUZDRy9fR8NpYU02nzz8vpeGdCO1lrFmfwgzsvGNYxGgiT0chdVxXQ2W3jgaf2Aobzyj4MRHiomayU6BHVy/r4cA0uF2OWKnRj1GtmHSlpCqrZVWNJQ2sPPX0OMpOj2H28js0HqnzWdnN7L49sOERCTNjpma1pSVEsLkjmg72VHkfOunrs7Dhay5KZKT5dy3IwZk2LJzM5ile3lQD4NT0ZDJiMRubmaAtGO5yBjWqU1rSTmhAx4cplBCMi04oBApoyfG1bKT19Dm5cmePdgQEuSgpwfZ6gy25nY0lxwGwIZia0k5UQGzYiB8tNZko065Zl0tvn4PKFU0mI9UyPkp9hpbiqDZvdc42Ty+Viy6Fqv9fGGozlc1JxubQiqJORcn0h3tvXCmZlx/H0uyeorO8Ydbs2u5NHNxyip8/BVz45h8h+ztFVy7Lo6XPw3t5Kj9rafrSGXpuDy/wkeD8Xg8HAlcsycThdhFiMpI/itzReWJCXREe3jaKKwAqhS2vbR13pXeEZUeEW0pOjOD6KCUujob6lm/f3VrBizpQRPa9s1iWYu09i6AuM3GPplKmkRkayoUgGpH/g9OLnwciEdrJ8wfrl07jrqgKuu3iax8eITCt2h9Or5RqOlDRR3djFJfPSRmLmqEmJiyA3PZath6onZc2s8jo9PZMUxT1XzyQ8xMSfXj5C7ygmA7hcLp56R1Jc1cY9V884z0nJTIlmbk4C7+wqH3aihMvlYtO+SrJSosf04bu4IJlkazh5U2N9VvA0mJk1LR6zyRDQWYatnX00t/cq0fsYUpAZR1FlKzb72EcwN2w+idFo4LqLPH/G9Od0UdKWwJRyMBoMXJsr2FJRTrc9MCnDLpuNo43BqSme+HfNUWIxG7l4bhphIZ5ruvL0nLo39bLe3lFGbFTIeVXkx5IVs1Opbuwan2t5jZLyug6S47X0TGxUKPesn0llQyfPvndixG1u2l/F5gPaEk6LRPKA+3ziwiw6um3DpieLK9uoqO9k5QL/Ct7PxWQ08p3bFvL59bPGrM9AEh5qpiArjn0BXDDaLXpX5RvGjoJMKza7k5NjXMqhtKad7UdrWbM4Y8Qzd20xC3AZzAHTZQF8dcFi9t5xD+Fm/8sY+tPQ3UWXzUZUSAj5cefXrgwGlJPlB9zTggvLPMvxl9W2c6SkmVWL0k+vhRgIFhekYDEb2Xp48tXMKq/rOEtkPHtaAuuWZrJpfxW79Bmm3lBY3sLT7xQyNydhSC1fXrqV/Awrb+0sG3J20wf7KgkLMQXECY+LDh20gO9EZEFuInXN3VQ3nr9+41jgXktPLX8zduTruiw5hilDl8vFcx8UERVuYd3SrJE3ZArHHj03YJXfAZIjIrGOcVHSivY2rtnwLF9+700AzEG6HmdwWjUBEBlxFFW2eTQteOOuckItJr8Vl/SUiDAzC/OT2Hm0NiBh80DR3WunobXnvJlc118ynelpMTz+5nEavCgc2tTWw6MvHSYxNox7188cdqbo1Rdm0dzey8eDrCHZ0W1j1/E6Lpyd6lVEVTEy3AtGByplWFrTTkpcuNczohUjJzLMQmZKNMc9fDH2BUdONXGstJmrl2ePuvC0LXYJltY94AzcDL9dNVWs3/As9V3+fzkpam5i/YZnqevq4r55i/ze32hQTpafyM+00mtzDLvCe1NbDzuO1nLxvClniaIDxYrZqXT22INqeRF/U6EL3M91sswmI/ddMwtw8edXjnjkMNe1dPPIhsP02hx85ZNzPZoFOGtaPJkpUbyxowyn8/wU1dZD1dgdTlbOD6wTPlmIjwkjKzWafYWB0XiU1rSrVGEAEJlWiiq9m7A0UpwuF89vKiYxNswnE1ns1iUYnN2YOw77wLqREWmxsKO6ktdOjlxi4QkH62tZv+FZeh0ONlx3E8umBPd9UTlZfiI/Q9NlDVfK4b09FThdLtZckDEWZg3LzOx4rFEhk2qZnfI63ckaYGZPkjWcz1xZQHFVGy9vOTXg8e1dfby/t4JfPLGH//rTx5TWtPP5q2cyNdGzhX0NBgNXX5hNbVMXe855sLsF77lTY1XNpDFk6YwUiqvaOHxqbCtZd3TbaGzrUU5WACjIisPucFJc6f96WduP1FBe18ENl073iUTEFrsUIKClHGbEJyLiEnip6Ljf+rA7nXx+4+tEWCy8dv0tzEkcWOsaTCgny0/ERoaQGh8xZFHS7l47m/ZXcoFIJtEaPobWDY7RaODC2akcOtlEc3tPoM0ZE8rrOogINRMfEzrg9iUzUrhk3hTe+Lj0dDXwXpuDHUdreej5A3zj4a08ubGQ7j47n7x0Or/8woUszE/yyoaF+Umkxkfw+sclZwmuj5c2U9vczcoFgZl1Olm5YlE6KXHhPLWxcExT56dF70qPNebkp1sxGPB7ytBmd7Bh80myUqNZMsM3GktnWDqO0CkBFb8bDAauyxNsr6qkqsM/k6fMRiN/W7ueV6+/henWsVsVZTQoJ8uP5GdYKaxoHTAFBPDRgSq6ex1cuTRzjC0bmhWzp+B0ufhwb0WgTRkT3KL3oWbt3boqn9SECB577SiPvXqU+/93C39+5QhldR2sXpzBj+9ewk8/t5RPXJjtcT21/hiNBtYty6SstoPD/ZZ1+WB/FZFhZi4YZHaiwj9YzEZuW51PbXP3iNeYHAlueYGKZI09EWFaIWl/18t6b08ljW293LQyB6OvZgobDNhjlwRseR031+cKXMDLRYXD7usNLxQe44EdWwGYnZhEWtT4+X0oJ8uPiEwr3b3205qf/jicTt7ZXU5+hvX0SvDBQlpiJNOmxPDervIJXzPL6XRRUd8xbCou1GLii9fOpqfXzv6iBpYUJPPtWxfw6y8t5+bLcn2SyrtwVirxMaG8rldYb+3oZV9hPSvmTCHEoip/jzWzpyewSCTx+rYSryY+jIaSmnYSY8OCQp85GSnIiuNkVSt9o6iPNxSdPTZe/7iE2dPjmZkd79O2bbFLMPWUYuit9Wm73jDdGscdM+eQEe27Z9o/jxzkS+++ya6aKvoc/tfL+RrlZPkRkTF4vazdx+tpbOvlyiXBFcVyc9GcVEqq2yirHX3V82CmrqWbPpvTIycpPTmKX35xOb//6gruumoGBVlxvnsTRRPar12SSWFFK4XlLXx0sBqH08Wl81WqMFDcekUeGOBfo6iX5g1lNarSeyApyLRid7goqvRPvazXPy6lq8fOTStzfd62zaoXJQ1gyhDgtytXc3VOnk/aeuzgXr754buszprG05+4nhDT+HvZVE6WH4mPCSMxNuw8XZbL5eKtHWWkxkcwNzc4C6gtmZmC2WRkywQXwFe4Re8pnkWiYiNDsJj990O/ZF4aUeEWXvu4hA/3VzEjK44pCZ4J6BW+Jz4mjGtWTGPfiQa/z7jt6rFR19KtUoUBJC/ditFg8EvKsLG1h3d3V3Dh7FS/TGKxx8zHZQgJeMoQoLmnm0MN3tcX7M8j+3bzvS2buGpaLn+/8hrCzOOzpIlysvyMyLAiy1vOSrvJshZKa9tZuyTDp5EQXxIZZmHZ7FS2H6mZ0DWzyuo6MBoMHs8E9DehFhNrFmdw+GQTjW09Aa+dptAWmp+SEMFT7xT6LY0EUKpHjZXoPXCEh5rJnuKfelkvfXQSgOsvHrw48agwhmKPmYeldYd/2veCu956la+8++ao2kiLiuKTeQU8tuYT4zKC5UY5WX4mP8NKR7eNqn7Vo9/aWUZ0hIXls1MDaNnwXLE4k84eOweLJ27NrIq6DlITIvwanfKWyxdOJSzERExkCAvyEgNtzqTHbDJy++p8Glp7eGN7qd/6cc8szFSRrIAiMq2cqmobdj1Rb6hu7GTbkRquWDR1RBNjPMUWuwRz2z5w9vmtD09Yn5PHsaZGjjV69+xwuVwcb9KOuT6vgD+uvgrLOHawQDlZfkdknl0vq6qhk4PFjVyxMD2oHuwDsUAk6zWzBq5EPhEor2sPuvpTEWEW7rtmFvd8YsakWJR5PDAjO54lM5J5Y3sZdc3+qWhdWttOfEwoMRGTZwmjYGRGZhwOp291Wa9sLSHEbGLdslEsn+MBNutSDM5ezO0H/drPcFydk4/RYODlIunxMS6Xix9u/ZArnnuSww3BudjzSFB3cD+TZA3HGhVy2snauKsMi9nIZQuDPw1k0mtmHSxupLUzsG9G/qCzx0ZjWy/pScGRKuzPvNxEZk8PTr3eZOWWy/MwmQw8/e4Jv8y6La1pV6nCICA3PRaT0eCzlGFlfQc7j9ZyxaJ0vzvQ9tjgEL+nRESyYmoGG4qkR78Vp8vFdza/z58P7uWzs+YxK2HiRPCVk+VnDAYD+RlWZFkzrR29bDtcw0VzphA9Tt5W3TWzth+ZeNGs06L3ZPVgUwxPXHQo1100jYPFjew74dsUenevndqmLiV6DwLCQnRdVqlvnKxXtpYQEmIak3qIzrA0HGHpAa387uaGXMGp1hZk89CrJjicTr7xwUYeP3KAryy4gJ9dtHLImoXjjfEp1x9niMw4dh6r44WncAgAACAASURBVJn3i3A4XKxZHBxL6HhCWmIk09Ni2HKomjWLMybUxV9WN/CahQrFYFyxKJ0th6r517uFzJoWT6iP6peV13XgQoneg4WCzDje3F5GT599VIuyV9R1sOt4HVcvzyIqfGxqn2mLRe8ek76G4prcfJZPzSArJpZXiwvptNnotPXR0Wejw9bH4tQ01mRPZ0OR5OnjR/jPC5bx7cUXTqhnDCgna0xwr2O442gtC/OTSImPCLBF3rFizhSeeFtSVtsxod60y+s6iAq3YI0aH1FFReAxm4zcsUbwP0/t5bVtJXzy0hyftOsWvasaWcFBQWYcr39cyomKVuaMIm3/8tZThIeaWLN47OohOqJnE1b7IgZ7Oy5z4K6n6JBQokNCcblcfH7j6zj7pQ3NRiNfmLeQNdnT+WReAfFhYVyeOS1gtvoT5WSNAWkJEUSFW+jotrF2yfiJYrlZMiOZf717gi2HqieUk1XhwXI6CsW55GdYuXBWKm/tKGP57FSf1DErqWknNiqE2KiB189UjC2ndVmlzSN2sspq29kj67lmRfaYRbEA7JEzADB1Hsceu3jM+h0Mg8HA+zffToTZQlRICFGWEEJNptP3XYPBMGEdLFCarDHBYDBwgUhiVnYcuVNjA22O10SGWViYnzihamY5nE4qGzpVqlAxIm6+PJcQi4lXt5b4pL2yWiV6DyZCLSamp8WMqijpy1tOER5qHnN5iD1Kc7LMHcfGtN+hmJmQRHaslcTwCMLM5kn1YqucrDHizisL+MYt88ftxbVizhQ6e+x+r3o9VtQ2dWOze7acjkJxLrGRIcyZHs+JitFP8+/tc1DV2KlShUGGyIyjtKad7l6718eW1rSz70QDaxdnEDHG61A6w7NxGcMxdRwd034VA+NRulAIEQY8CKwCeoCPpZT3CiHygX8ACUAjcKeUcmwW+RqHjFcHC2BWdrxeM6uaCwqSA23OqClXonfFKMlOjWHnsTrauvpGNTW/vL4Dl0uJ3oONGZlWXttWQmF5C/NyvSsp8PKWU0SEmll1QQDkIQYj9qiCoIpkTWY8jWT9Cs25ypdSzgF+oH/+J+ARKWU+8AjwZ9+bqAgGjEYDy2dP4dDJJlo7egNtzqgpr+vAZDSQFiTL6SjGH9OmaE5RSXX7qNpxi94nkt5xIpAzNRazyYD0MmV4qrqN/UUNrF2aSURYYGTPjqiZKpIVJAzrZAkhooA7gR9IKV0AUspaIUQysBD4l77rv4CFQogkfxmrCCwr5qTidLn4+EhtoE0ZNeV1HUxJiFAV1RUjJjMlGgNQUtM2qnZKa9qJjrAQF61E78FEiMXE9LRYjnlZlPTlLaeIDDOzalG6nywbHnvkTEx9tRj6hq5RpfA/njxhctBSgT8SQuwWQmwSQlwEZACVUkoHgP53lf65YgIyJSGSnLQYth6u9kvF67EkGJfTUYwvwkPNpCZEjD6SpYvex7OcYKJSkGmlrLadrh6bR/sXV7ZysLiRK5dmEh4auMn79qgCAMydxwNmg0LDk6vADEwH9kkpvyWEWAq8CtzkCwMSEkb3oEtKUiF2f3Lu+K5dPo1HXzhAW6+TXL3+12A4HE66e+1EBVl1+9aOXlo6+iiYlhjQ60ddu/5lLMa3IDueAyfqR9xXn81BVUMny+ZMGVfXw3iydTQsmzuVV7aWUNvWx5KM+GH3f3jDYWIiQ7hl7YxROVmjHt/IJbAPrJyEpCtH19YEY6yvXU+uglLAjp4WlFLuEEI0AN3AVCGESUrpEEKYgDSg3BsDGhs7cDpHFhVJSoqmvn50b5GKwRlofGemx2A2GXltczG3rckf8Diny8WuY3W89NFJ2rps/OZLywP6Vncux0qaAIiPtATs+lHXrn8Zq/FNjQvng7ZeCk82jCjdd6q6DYfTRVJ06Li5HibTtZsQacZsMrLjUBXTkofWbxZVtLJX/v/27jw+qvLu+/jnzEwyk5msJIEk7AgcVlkEV7Rure2tbb3v+lj1EbXaaqvPfXeztVq3KrRCW7VuFXdF6trW2mrr0upt7SYUQUA4gCQQ1gRIIHsy55znj0kiSAJZZuZMku/79eKFmTlz5peLMXy5znV+VwX/57SjqN3fQG0P3zMu4+tmkx/IoWnncmrzBsafVVf0dmx9PqPbE0NHvFxoWdZu4C3g0wCtdxQOBtYDK4ALWw+9kNhsV//ZPlsOEW7rmfXhoT2zXNdlxcbd3PrYUha9vIZo60zW6tK9HlXbMd1ZKPEyuigb6Pm6rI3bYi0gRhdnx60miZ+0gJ+xQ7O7tFn0S+9uIjucxukzvFuL1c4wsDMn4tcdhp7r6vTC14HHTNP8OdACzLUsq9o0za8DT5qmeTNQRWyBvPRzc44u5r21FazcuLu9ncO6zVX8+p2P+GjbfgbnZnDlFyYxyxzMt+99lxUbdjM7hdo+lFfUkhNJJzuSWpcxpe8ZPiQTn2FQuqOGGeO6f8/P+i3VFOSEyM8JJaA6iYcJI/L43bul/OiJpYSDASKhAOFQgHAwLfZ7KEBzi8OHZVV8+fSxBNPjs59lb0Ujkwju+g24Lmi9n2e6FLIsy9oEnNrB4+uA4+Jck6S4SSMHkZcV5N1VO8jPCfGbdzaxpnQveVlBLvmsyZypxe137R19VD6rNu3BcVx8vtT4H728dTsdkd4KpvkpKYj0aCbLcV2s8mqmje353niSeCdNLaaiuoGa+hbqm1qo3t1EfWOU+qboQbP5uZnpnDpjqIeVHiyaOZGMbdX4mnbihIq9LmfASp2FMtJnxHpmFfHKPzbzwUd7yMxI48unj+W0GUNJTzv4X3HTxhbwjzW72LhtX/tG2V6K2g7b99QxefSRF7GKdMWo4ixWbNiN67rdukNw++46ahtaMIfnJbA66a38nBBfPWdSh8+1RO32wBXJSCOYlhqzWBDrlQXgr1urkOUhhSzpkU9NK2HVR3uYOb6QT88e3unC9imj8/H7DFZu3J0SIWvnnnqitquZLImb0cXZvPvBDvbsb6QgJ6PLr2trcmmO8P7/C+mZtICfnEx/Sm7s/fEehh/Skn+6x9UMXOrEKD1SkJvBrZcfyxfmjD7snYPhUIDxw3NZkSJ7HrYteh+mkCVx0rbnYHf7ZVlbqsjPDlKg9ViSAG56AU56oRa/e0whSxJu+tgCduypZ1dVvdelUF5ZS8BvUDQo7HUp0k8MK8zE7zMo7ca6LLd1Pdb44XlqQioJE82cREDb63hKIUsSbtq42OaqKzd4P5tVXlFLSUFE2+lI3KQFfAwbnNmtmazte+qpqW9hgi4VSgJFIxNjXd9d58gHS0LobxpJuMG5GZQURFLikqHuLJREGF2cTdnOGpwubje1vrXvktZjSSLZmZMw7Dp8jVu8LmXAUsiSpJg2Np8NW/d1eQ+wRNhX18z+umaGDx4YW4JI8owqyqKhKUplVUOXjl+3pZq8rCCFuV1fKC/SXR8vfte6LK8oZElSTB9bgO24rNrkXff38orY5RzNZEm8tS1+78q6rLb1WOaIXK3HkoSyI7GNorX43TsKWZIUR5XkkJmRxkoPLxlqOx1JlJKCCGkBX5fWZe3cW8/+umbMFGhpIv2bm5aDHRqmxe8eUsiSpPD5DKa1dn+3HW8WYZZX1JKXFSQzI82T95f+K+D3MWJIJmU7jjyT1dYfa8IINSGVxItGJupyoYcUsiRppo0toK4xysat+zx5/61a9C4JNKoom827anGcwy9+t8qryclMZ3Ce1mNJ4tmZk/DXWeBEvS5lQFLIkqSZPHoQAb/hyV2GLVGHHXvqFbIkYUYVZdHUYrNjb+f94FzXZd2WKszhWo8lyRHNnIjhNuNv2OR1KQOSQpYkTUYwgDkijxUe9MvasacO29F2OpI4o4uzAQ57ybCiqoF9tc26VChJ076HoS4ZekIhS5Jq+tgCdlU1sGNPXVLfV4veJdGKBoUJpvsPu/h9nfpjSZJFI+NxMbT43SMKWZJU08bmA7By456kvu+KDbuJhAIMydN2OpIYPp/ByCFZlB2mjYNVXk12JF3bOkny+MPYGaO1+N0jClmSVAU5GQwrTG7394qqepavr+TUGUPx+bQORhJnVFEWWypqidqH3kHrui7Wlmqtx5Kkiy1+10yWFxSyJOmmjS1g49Z91DYkp/v7m8u24vMZnD5zWFLeTwau0cXZtEQdtu8+9HJ4ZXUDVTVNulQoSRfNnIi//iOwG70uZcBRyJKkmz6uAMd1WbUp8ZcM6xtb+OsHOzhu0hDysoIJfz8Z2EYVxzq/l+08dF1WW38sU4veJcnszEkYro2/foPXpQw4ClmSdKOLs8mOpCel+/v/rtxOU4vNZ2YPT/h7iQzOzSAjGOjwDsN1W6rJCqdRkq/1WJJc0dY7DLUuK/kUsiTpfIbB0Ufls2rT3g7XrsRL1HZ4c9lWJo7MY8QQbQotiWcYBqOKsij9xEyW67qsL69ivNZjiQfs8FG4RppClgcUssQT08cW0NAUZX15dcLeY5lVQVVNk2axJKlGF2eztaKWlujH/4DYva+RPfub1B9LvOFLxw6Pxa82DkmnkCWemDxqEAG/L2F3Gbquy+vvlVM0KMzUo/IT8h4iHRlVlIXtuGytrG1/rH09ljaFFo9EMycSqNNMVrIpZIkngul+Jo7MY+XG3bju4fd664kNW/dRtrOGz8wejk+XZySJ2he/H7AuyyqvIjMjjZLCiFdlyQBnZ07C31AG0dojHivxo5Alnpk+Np/K6ka27+l8r7eeeu29LWRmpHHClKK4n1vkcPKzQ2SF0yg9oPO7taWa8cNzFfjFM+2L3+vWeVzJwKKQJZ6ZNrYAIO53Ge6qqmfFht2cOmMowTR/XM8tciSxxe/Z7Z3fd+9rYPe+Rl0qFE9FMycC4K9VyEomhSzxzKDsECOGZLJ0XUVcLxm+uXQrfr/BGTOHxu2cIt0xqiiLbbvraGqxD+iPpZAl3nEyRuH6MrSHYZIpZImnTplWwuadNXy0rfP93rqjrrGFv67aznGThpCTqeaj4o1RxVm4LpTvqsUqryYSCjBMm5OLlww/0YhJQNvrJJVClnjqpCnFREIBXl+6JS7n+98V22lucfjM7BFxOZ9IT4wqygagdMd+1ms9lqQIO3MifvXKSiqFLPFUMN3PKdNL+Pf6SnZXN/TqXLHmo+VMGpXHcM0aiIfysoLkZqbz/oZKKqobtB5LUkI0cxL+ph0YLXu9LmXAUMgSz50xcxgGBm/+e2uvzrN0XQXVtc2axZKUMKoom3Xar1BSiN26+D2gxe9Jo5AlnhuUHWLWhEL++sF2GpqiPTpHW/PR4vwwU8YMinOFIt3X1i8rIxjQzKqkhLY2DrpkmDwKWZISPjN7BA1NNu+u2tGj168vr2bzLjUfldQxuji2Lmv8sBx8Pn0mxXtOcChOIFuL35NIIUtSwpiSbMYOzeHNZeU4TvfbObz2Xnms+ehkNR+V1DC6OJu0gI8pY7Stk6QIw8COTNBMVhIpZEnK+Mzs4VRWN3Z7P8Mtu2pYuXE3p88cSrqaj0qKyMxI446rTuC0GerXJqkjmjkp1isrAduZyaEUsiRlzBhfQH52iNeXlnf5NQ1NUR783RqyI+mcccywBFYn0n15WUFdKpSUYmdOxNeyF6O5wutSBgSFLEkZfp+PM2cNi62v2llzxONd1+XJP61jV1U9X//iZLLC6UmoUkSk72rfw1Cd35NCIUtSyslHlxBM93epOenbK7bz3toK/vPkMbpFXkSkCz7eKFrrspJBIUtSSjgU4OSji3lvbQVVNU2dHrd5Zw3PvLmeKWMG8R8njExihSIifZebXoiTXkigeqnXpQwIClmScs6cNRzHcXnr/Y6bk9Y3RnngpVVkhdP52jmT1LJBRKQbmgZ/gWDlKxgtVV6X0u8pZEnKGZybwfRxBbz9/naaW+yDnnNdl8dfXcve/U1844tTtA5LRKSbGodeiuE0Etz5gtel9HsKWZKSPjN7OLUNLfx9zc6DHn9z2Vb+vb6S8049irHDcjyqTkSk74pmT6clazoZW59UK4cEU8iSlDR+eC4jh2TxxtJy3NYfAh9t28fzb21kxrgCPjN7uMcVioj0XY1DLyFQu4rA/ve9LqVfU8iSlGQYBp+ZPZwde+pZXbqX2oYWHvzdavKyglx+9kQMrcMSEemxpqL/g+vLILTtSa9L6dcUsiRlzZ44mJzMdF5/bwuP/uFD9tU1841zpxAJpXldmohIn+am5dA05D9j67KitV6X028pZEnKCvh9nDFzGGvKqlj50R6+fPq49k13RUSkdxqGXorPriW067del9JvKWRJSjt1xlDCwQDHThzM6TO1B5yISLxEc48nGhmvS4YJFPC6AJHDycxI446vn0AkFNA6LBGReDIMGksuJXPDD/HXrsXOnOh1Rf2OZrIk5WVmpClgiYgkQGPJhbhGmmazEkQhS0REZIBy0wtoGnwOoR3PgNP5VmbSMwpZIiIiA1jj0EvwtVQRrPi916X0OwpZIiIiA1jLoNOwQyMIbXvK61L6HYUsERGRgczw0Th0Lul738ZXX+p1Nf2KQpaIiMgA11hyMS4+QtsXe11Kv6KQJSIiMsA5oaE0F3ya0LanwYl6XU6/oZAlIiIiNA69FH/zTtJ3v+51Kf1Gl5qRmqZZBjS2/gK4zrKs10zTdIFVgNP6+FzLslbFu0gRERFJrOaCs7DThxDa9iTNg//j8Afb9fiaduGERyenuD6qOx3fz7Msa3UHj59oWZZ2lxQREenLfGk0lfxfMsruxte4HSdUcvDzrktg/3JC2xYT3PUihl3PnlPW46YXeFNvH6DLhSIiIgJAw9C5GDiEti9pf8xo3kPG5vvJ++eJ5L13GqEdz2BHJmC4UfwNZd4V2wd0ZyZriWmaBvAucINlWdWtj79tmmYA+CNwq2VZahkrIiLSBznho2jOO4XQ9sW0ZE8nY9ti0itfwXBbaMmeSc3Eu2ka8iX8DZvI+9en8DXt8LrklNbVkHWyZVnlpmkGgbuB+4CLgRGtj2cDi4GbgBu7U0B+fmZ3Dj9EYWFWr14vh6fxTRyNbWJpfBNHY5tYno/vxK/D3y8i9/0vQTAfxl8DR11OWu5U0oAsgIZYfMgJ7AWv6+2GZI9tl0KWZVnlrb83mab5APDyJx7fb5rmI8B3ulvAnj21OI7b3ZcBscGqrKzp0WvlyDS+iaOxTSyNb+JobBMrJcY34zOER3+PaNZUmgs/B74gtAAH1uVmUGAEaNhTSp3X9XZRb8fW5zO6PTF0xJBlmmYECFiWta/1cuEFwArTNPOARsuyGlovF54HrOhB3SIiIpIqfOnUj73p8McYPpxgEb6m7cmpqY/qykzWEODXpmn6AT/wIXA1MAFY1NrGIQ34O7HLhSIiItLPOcFirck6giOGLMuyNgEzOnhqB3B03CsSERGRlOcES/DXfuh1GSlNLRxERESk22zNZB2RQpaIiIh0mxMqwWfXYkT7xsJ3LyhkiYiISLc5wWIAzWYdhkKWiIiIdJsTjG2742vUHYadUcgSERGRbvt4JkshqzMKWSIiItJtdusG0rpc2DmFLBEREek+fxgnkItfM1mdUsgSERGRHnGCxfgaNZPVGYUsERER6REnVKw1WYehkCUiIiI9YgdLtCbrMBSyREREpEdim0TvAifqdSkpSSFLREREesQJlmDg4Guu8LqUlKSQJSIiIj3S3pBU67I6pJAlIiIiPeKEtLXO4ShkiYiISI/Y2lrnsBSyREREpEfc9EJcI4BfM1kdUsgSERGRnjF8rXcYKmR1RCFLREREeswJFitkdUIhS0RERHrMCZbo7sJOKGSJiIhIj9nav7BTClkiIiLSY06oBJ9dgxGt8bqUlKOQJSIiIj3mBNUrqzMKWSIiItJjjnpldUohS0RERHrs45kshaxPUsgSERGRHrNDbfsX6nLhJylkiYiISM/5wziBXPyayTqEQpaIiIj0ihMsUhuHDihkiYiISK/Eur5rJuuTFLJERESkV5xQidZkdUAhS0RERHrFDhbja9oFTtTrUlKKQpaIiIj0ihMswcDB11zhdSkpRSFLREREeqW9IakuGR5EIUtERER6xQlpa52OKGSJiIhIr9jtM1m6w/BAClkiIiLSK256Ia4RwK9eWQdRyBIREZHeMXyxhqSayTqIQpaIiIj0WqwhqWayDqSQJSIiIr3mBEs0k/UJClkiIiLSa3awWPsXfoJCloiIiPSaEyrBZ9dgRGu8LiVlKGSJiIhIrzlB9cr6JIUsERER6bX2ru+NWpfVRiFLREREes0JFgFqSHoghSwRERHpNTuk/Qs/SSFLREREes8fwQnk4NdMVjuFLBEREYkLR20cDqKQJSIiInGhru8HU8gSERGRuHBCJQpZB1DIEhERkbiwg8X4mneBa3tdSkpQyBIREZG4cIIlGK6Nr6nC61JSgkKWiIiIxEV7Q1LdYQgoZImIiEicOCFtrXMghSwRERGJC1szWQdRyBIREZG4cNMLcY0AfvXKAhSyREREJF4MH06wSDNZrQJdOcg0zTKgsfUXwHWWZb1mmubxwCIgAygDLrYsS7cUiIiIDFBqSPqxLoWsVudZlrW67QvTNA3gaeAyy7LeNU3zRuAO4PI41ygiIiJ9hBMswV+31usyUkJvLhfOAhoty3q39esHgfN7X5KIiIj0Vbb2L2zXnZmsJa2zV+8CNwAjgM1tT1qWtds0TZ9pmoMsy9rb1ZPm52d2o4RDFRZm9er1cnga38TR2CaWxjdxNLaJ1efHt3IUlNdQmAukpdb3kuyx7WrIOtmyrHLTNIPA3cB9wG/jUcCePbU4jtuj1xYWZlFZWROPMqQDGt/E0dgmlsY3cTS2idUfxjcYHUQ2sHfbeuzIeK/LadfbsfX5jG5PDHXpcqFlWeWtvzcBDwAnAVuAkW3HmKZZALjdmcUSERGR/qW963uj7jA8YsgyTTNimmZO638bwAXACuDfQIZpmnNaD/068HyiChUREZHU5wTV9b1NVy4XDgF+bZqmH/ADHwJXW5blmKY5F1hkmmaI1hYOCatUREREUp4dauv6rpB1xJBlWdYmYEYnz/0dmBrvokRERKSP8kdwAjn41ZBUHd9FREQkvtSQNEYhS0REROLKCRZr4TsKWSIiIhJnTqhEM1koZImIiEic2cFifM27wLW9LsVTClkiIiISV06wBMO18TVVeF2KpxSyREREJK7aG5IO8DsMFbJEREQkrpyQGpKCQpaIiIjEma2ZLEAhS0REROLMTS/ENQL4GzWTJSIiIhI/hg8nWKSZLK8LEBERkf4nFrI0kyUiIiISV05wKL7GbV6X4SmFLBEREYk7OzwGf0MZOFGvS/GMQpaIiIjEXTQ8DsNtwde42etSPKOQJSIiInFnR8YBEKjb4HEl3lHIEhERkbizw2MB8CtkiYiIiMSPm56PkzYIf71CloiIiEhc2ZHxmskSERERibdoeBwBzWSJiIiIxJcdGYevuQKjpdrrUjyhkCUiIiIJYYdjdxgO1HVZClkiIiKSEG1tHAbquiyFLBEREUkIO2M0rhHAX7/R61I8oZAlIiIiieFLw84YNWAbkipkiYiISMLY4XFakyUiIiISb3ZkHP76j8C1vS4l6RSyREREJGHs8DgMpwlfwxavS0k6hSwRERFJmGhkPMCAbEqqkCUiIiIJk/A2DtFa0iteTcy5e0khS0RERBLGTcvHCeQmrI1DxtbHyF55ITjNCTl/byhkiYiISOIYRmzxe936hJw+ULsGJ1gMvvSEnL83FLJEREQkoezI+IRdLvTXrsPOnJCQc/eWQpaIiIgkVDQ8Dn/zTozo/vie2HUI1FlEIwpZIiIiMgB9vPg9vuuyfA2bMZx67MjEuJ43XhSyREREJKHscGvIinMbh0DdOgCiulwoIiIiA5EdHo2LL+6L3/21a2Pn1+VCERERGZB8QeyMUXFv4xCoW4sdHIqblhPX88aLQpaIiIgknB0ZRyDOdxim8p2FoJAlIiIiSWCH2zaKduJzQtduvbMwNRe9g0KWiIiIJIEdGYfhNOBr3BqX8/kayjCcRuxMhSwREREZwNrvMIzT4vdArQWk7p2FAAGvC/gk245SVVVJNHrkPYgqKnw4TpymHeUQyR5fn89PRkYmmZk5GIaRtPcVEZHEi0bGAxCo30ALZ/b6fP661L6zEFIwZFVVVRIKhYlEio74F20g4CMaVchKlGSOr+u62HaUmppqqqoqGTRocFLeV0REksNNL8QJ5MRte51A7Vrs0HDcQFZczpcIKXe5MBptJhLJ1kzGAGMYBoFAGrm5+TQ3N3pdjoiIxJthYIfHxq2Ng79uHXbEjMu5EiXlQhaggDWAGYYPcL0uQ0REEsCOjIvPmizXJlC3nmgKL3qHFLxcmGrOO+/zpKenk54ebH/sJz/5GcXFJQl93x07tvPVr87llVf+zO7dlfzoRzdy772L4nb+/fv38/Of38GmTRsxDAOfz89///e3OeaY2d0+12WXXcSiRY8RDIbiVp+IiPQ/dngcoR3PQrQWApk9Po+/vhTDaUzp9g2gkNUl8+YtYMyYsZ69f0FBYVwDFsDDD/+SwYOHcOut8zEMg337qmlo6N5lumg0SiAQ4IknfhXX2kREpH/6ePH7RqLZ03t8Hn/rnoWp3IgUFLJ6Zc6cWVx55dW8887b7Nu3j2uu+R9OPfUMAFav/oD77/8F9fX1AFxzzTc59tjjWbt2DXff/TMaGxsIhTL41reuZeLEyQD8+tfP8/zzvyI/v4AZM45pf58DZ7WO9L5vv/1nHnroAYLBIKeddiYPPfQAr7/+DuFw+KDaKyt3MWPGMe2XZnNycslp3ZWgpaWFhx56gJUrl9Pc3MJRRx3Fd797PeFwmPnzbyUcDlNeXk51dRWPPfY0c+bMan+PLVvK+MUv7mTfvmpaWlo4//wLOfvsL9DY2Mi8ebdQVrYJHe6/YQAAGDhJREFUvz/AiBEjuf32OxL3hyMiIinHjrS1cdjQq5AVaN2zMJrCdxaCQlaX3Hjjde2XC/1+P48+urj9uUgkwiOPPMUHH6zg5puv59RTz2D//n3ccMP3mD9/IVOnTsO2berq6mhpaeGHP/w+119/M7NnH8eyZe/xwx9+n+eee4nNm8t46qnHePzxJQwalM/Pfnb4ANLR+1ZV7WXhwh+zaNHjDB8+gueeW9Lp68877wJuvPE63nzzNaZMmcacOae0XypcsuRJIpEIjz22mGjU4YEH7mHx4se56qprAFi9ehX33fcQGRkZB50zGo1y6603csst8xg5chT19XVcccVcpkw5mrKyUmpqanj66ReA2OVKEREZWOyMMbgY+Ot7d4ehv24tdmhEry45JkPKh6y/rdrBux/s6PA5wwC3F2uk5xxdzElTi4943OEuF55xxlkATJ48ld27K2lqamL16lWMGjWaqVOnAbFglp2dzUcfbSQtLY3Zs48DYNasY0lLS2PLls28//6/OfHEOQwalA/AF7/4n7z11hud1tTR+65Zs4rx402GDx8BwNlnf5F7772rw9fPmnUsv/nNH1i+fBkffLCCm276ARdeOJe5cy/jb397h7q6Ov73f/+C60JLSzNjx45rf+2pp55xSMACKC/fwubNpdxyyw3tj7W0tFBWVsrYsePYsqWMn/98ATNmHMOJJ87p9HsTEZF+yh/CyRjZ68Xvgdp1Kd2EtE3Kh6xUl56eDsSCFIBt27idJD/XdTu8czIWFruXFjt73+7cmRkOR5gz51PMmfMpTHMiTz31GHPnXobrwne/+wOOO+64DvtkhcOHBiyIfQ85ObmdrtFasuQFli1byj//+Tceeuh+nnzyWYLBYIfHiohI/9TrNg5OFH/deprzz4hfUQmS8iHrpKmdzzalajPSqVOPZsGCeaxe/QFTphzdfrlw5MhRNDc3s3z5MmbOnMXy5cuIRqMMHz4Sx3H51a+eoqpqL3l5g/jDH37X7fedPHkqP/nJ7WzdWs6wYcN59dXfd3rs0qX/ZNKkKUQimbiuy/r1Vvsdk3PmnMJzzy1h2rRpBALp1NfXUVFRwahRow/7/iNGjCQUCvGnP73CZz97NgCbN5dRUFBAXV0d2dk5nHLKqRx77PGce+5nqanZTzBY2O3vU0RE+q5oZDwZVX+PbRRtdL+TlL+hFMNt1kxWf3HgmiyAH/zgRiZMmNTp8dnZOcyfv5B7772LxsYGDMPHNdd8k9mzj2P+/IUHLXyfN28BaWlpjB07jrlzv8I3vnEFgwblc8IJ3b+cNmhQPtdeez3f+943yc3N5cQTTyEQCBAKHdpaYePGjdx7713tM2jDho3g29/+PgAXX3wZjz66iMsvn9s6M2Zw+eVfO2LICgQCLFhwF/fc83OeeWYxtu0waNAgbrvtDj76aCMPPngfAI5jc/HFl1FQoIAlIjLQ2OFxGE49vqbtOKFh3X59+52FKd6+AcDo7mWqOBoFlO7ZU4vjfFzDzp2bKSoa2aUTpOpMlpfq6+sIhyMAvPLKy/zhD7/jl798tEfn8mp8u/MZ6KsKC7OorKzxuox+S+ObOBrbxBoI45u29x1y/30O1TN/R0v+ad1+fXjTQiIfzaPytO3dWvje27H1+Qzy8zMBRgNlXXlNt2ayTNO8BbgVmGpZ1mrTNF1gFdD2N/Fcy7JWdeecEl8vvPAsb731Z2w7SnZ2Dtddd6PXJYmIiLSzw21tHNb3KGT5a9dih0am/J2F0I2QZZrmTOB4YMsnnjrRsqzauFYlPXbppVdw6aVXeF2GiIhIh5xgEY4/k0AP2zgE6vrGnYXQxb0LTdMMAvcDV6ON5URERKSnDKN1D8MehCwnir9uA3aK71nYpqvL+m8DnrYsq7SD5942TXOFaZo/aQ1jIiIiIp2yw+N61MbB37Apdmdhind6b3PEy4WmaZ4AzAZ+0MHTIyzLKjdNMxtYDNwEdGsRUOsisnYVFT4Cga7f0tmdY6X7vBhfn89HYWFW0t832QbC9+gljW/iaGwTa0CMb+EU2Pk8hXl+CISPfHybhjIAsofPgkHdH6dkj21X1mR9CpgAlJqmCTAMeM00za9YlvU6gGVZ+03TfAT4TncL+OTdhY7jdPmONt1dmFheja/jOP3+7pqBcAeRlzS+iaOxTayBMr5BRpAN7N3yPnbW0V1+XXj7csIY7G4eBt0cpzjeXdhlRwxZlmXdAbRvpGeaZhlwDrDNNM0My7IaTNMMAOcBK7r17iIiIjLgRFs3ig7UbehWyPLXrcPJGAn+bsx+eag3zUgnAIta2zikAX8ndrmw3/ja1y6lpaWFaLSF8vItjB59FADjx5vccMMtCX9/x3F4/PGHufTSKwgEOv6jevHFZ3n55d9iGD6i0RbmzPkU3/jGf3f7vRYtup/x401OO+3M3pYtIiJyWHb4qNhG0d1c/B6oXUu0DzQhbdPtkGVZ1qgDvux6/OyDHn74SQB27NjOV786t9M9+Q7Htm18Pl+39hRs0xayLr74sg5D1urVH/DrXz/Pww8/RWZmJrZtU1q6qUc1XnXVNd1+nYiISI/4wzih4fi708bBacFfv5Hmws8lrq4407Y6PVRZWcGPfnQj9fV1NDc3c/LJp7YHlYceeoCKil3U1tawbdtWFi16HMtax513LsDn8zNr1mz+/Oc3+MUvfsnIkaMoKyvlnnvuZN++aqLRKBdc8H/53OfO4c47FwBw5ZWXYRgGDzzwcHs3d4CKigoyM7PIyIht2Oz3+xk7dlz783/7219ZvPhxmpubSU9P55vf/C4TJ05m6dJ/8ctf3svkyVOxrLV85Stf4403/sTRR0/j3HPPo7m5mUWL7mfVqhU0NTUzbtx4rr32ekKhEL/97Yu8+OKzBAJpAMybt4Dhw0cka9hFRKSfsCNj8dd1/Q5Df/0mDLelz/TIgj4QsoLbf0Vo+9MdPmcY0JtdgRpLLqap5KIevTY7O5uf/vQXZGRk0NLSwre+dTVLl/6L2bOPA2Dlyvd55JGnyMnJpampiVtv/SHz5i1g6tRp/OUvb/Lcc7FZsWg0ym233citt/6YESNGUldXyxVXzGXq1Gl85zvX8fLLv+Whh54gGDy0O8bxx5/IM888xXnnfZ7p02cyY8YxnHXW5wgGQ2zZspnFix/nzjvvIxwOs3HjBn7wg+/w4ouxTaM3blzPtdf+gO9+9zoA3njjT+3nXbz4cXJz83jsscVEow733nsXS5Y8yRVXXMX999/Ns8++REFBAU1NTahtmoiI9EQ0PI5Q9ZLYX+RduNrjr1sLgN1H2jdAHwhZqcq2He677y7WrIntIrRnz242bFjfHrJOOOEkcnJyASgrKyUzM5OpU6cBcPrpZ/KTn4Tbn9u8uYybb76+/dzRaJSyslKKiooPW0M4HOahh55k7do1rFy5gpdf/i0vvfQiDz30JP/61z/YurWcq6/+avvxLS0t7NtXDcDIkaOYNGlKh+f929/eobGxkb/85XVcF1pamjHN2DXwmTNnM3/+LcyZcwonnngyxcUl3R47EREROzIOn12Lr2kHTujIf5cEatfiYhCNjE9CdfGR8iGrqeSiTmebvGzh8Mwzi2loaODhh58iPT2dH//4RzQ3N7U/n5Hx8Z0Prut2uibLdV0GDSrocL1XNBo9Yh2GYTBp0hQmTZrCl750PmeffSZlZaW4rsuJJ87pdIH+gfV1VNP3v/9DZs2adcj4LlhwJ2vWrOb995dxzTVf4/rrb2L27OOPWKeIiMiB7HAsLPnrN3QpZMXuLBzVZ+4shK53fJdPqKmpoaCgkPT0dHbt2snf//7XTo8dPXoMNTX7Wb06Nuv19tt/pqGhvv05v9/H669/fLmutHQT9fX1BAIBQqEQdXUdbw1ZVlZ60EL3zZtLcRybwsJCjj/+BP7xj79RVhZr0u+6LmvXrunS9zZnzqd49tmnWy8HQl1dLZs3lxGNRtmxYztTpkxl7tyvMGvWsaxfb3XpnCIiIgeyI20bRXdt8Xugdi3RPrKdTpuUn8lKVeeffyE33fQDvvKVixgypIiZM2d3emwwGOTmm+exYMHthEIZzJp1LDk5OUQimQQCARYuvItf/OJOlix5Att2yM/P5/bbY63JLrjgYq655msEg6FDFr43NNRz7713UV1dRTAYxOfzc+ut88nJySUnJ5cbbriF+fNvobk51oZi2rQZTJw4+Yjf2yWXXM4jjzzI5ZfPBcAwfFx++ZUMGVLE7bffTF1dLYZhUFRUzOc/f26vxlFERAYmJ1iC488krfqfNA7/6hEObm69s/Ds5BQXJ4bbm5XjvTMKKP1kx/edOzdTVDSySyfoSx3f6+vr2gPS0qX/ZOHCH/PCCy97XNXheTW+3fkM9FUDpauzVzS+iaOxTayBNr6R9T8kvPleqmf8hpaCzvs0+mvXMugfx7F/ysM0FX+5R+8Vx47vo4GyrrxGM1lJ8pe/vMELLzwHuKSnp3PLLfO9LklERMRTdUfdRPruN8j68BqqTvgnblpeh8cFamN3FupyoXTonHPO5ZxzdGlNRESknT9EzZRF5L53BpnrrqVm6qMdH1a3Fhdf+2L5vkIL30VERMQz0ewZ1I+5jtDOFwju/E2HxwRq12GHR4M/lOTqekchS0RERDxVP+q7tGQfQ+a6b+Nr2nnI8/66tdh9aM/CNgpZIiIi4i1fgJopD2HYDWR++P8O3s7FacJf/1Gf2k6njUKWiIiIeM6OjKN23G0Ed79OaNuT7Y/76z/CcO0+tZ1OG4WsLjjvvM9z0UVf4tJLL2Tu3PN5883X4nLOTZtiG2Nee+3/sG3b1sMe/847b/Phh6vbv1637kN+9KMbe12HiIhIqmgcfiXNg04lsv4GfPWxZtp99c5C0N2FXTZv3gLGjBnL+vXr+PrXr2DWrOPIzc1tf962bfx+f4/O/bOf3XPEY/7617eZMGFi+36DEyZM4pZb5vXo/URERFKS4aNm8gPk/eMEstZ8g32zXsFf23Zn4Tivq+s2haxuGj9+AuFwmFdffZlly5aSl5dLaWkp119/E3l5+dx990J27dpJU1MTZ555FpdccjkAK1e+z89/fgfBYJDJk6dyYBPY8877PAsX3sWYMWOprKzg7rt/ytat5QCceeZZjB8/gXfffYdly97j97//HV/+cqzL/P33/4JHH10MwB//+AeeeWYxhmFQUjKM73//BvLyBvHqq7/njTf+RFZWNps2fURWVibz5i0kP7+AVatWctddC3Ecl2g0yqWXXs6nP/3Z5A+qiIhIKyc0jFpzIdlrriJj8/0E6tZhh8f0uTsLQSGr25YvX0ZzczOBQIBVq1bwxBPPMHToMAC+9a2rueyyrzJ9+kxaWlr45je/wcSJk5g2bSa33HIDN998OzNnzuLPf36DF198rsPz33bbTZxwwknMn/9TAKqrq8nNzWXOnFOYMGEiX/rSl9vraLNp00YefPA+Hn30aQoKCnj44V9y110/5bbbfgLA2rUf8uSTzzBkSBELFszjxRef46qrrmHJkic5//yL+Oxnz8Z1XWprO94jUUREJJmaii+gqfIVIhtvww1k0ZJ3otcl9UifCFnnvvT8IY99Yex4rpw+k/qWFi565beHPH/BhMlcMGEyexoauOK13x/y/GWTp3HuOLPLNdx443WkpweJRCLMn7+AyspKpk6d3h6wGhoaeP/9f1NdXd3+mvr6OsrKysjLyycUCjFz5iwAzjjj0/z0p4d2fK+vr2f16g+466772x878JJkZ5YvX8YJJ5xEQUEBAF/84n9x2WUXtT9/9NHTGDKkCIDJk6ewdOm/AJg5cxZPP/0EO3fuYPbs45k8eUqXx0NERCRhDIOaiXczqPo4fM2VRPvgonfoIyErFbStyWrz6qu/JxzOaP/adR0Mw+CRR54iEDh4WDdsWJ/Q2lwXDMM46LEDv0xPT2//b5/Pj23bAJx//kWcdNIpLF36L+6+eyGzZx/PlVdendBaRUREusJNL6Bm4r3krLyAaPZMr8vpkT4Rsl469/xOnwunpR32+fyMjMM+Hy/hcIRp02bw9NNPcNllsd3Ed+3aSSAQYOTIUTQ1NbFixXKmT5/JW2+92eGluXA4zJQpR/P887/ioosuAT6+XBiJRDq9nHfMMbNZsuRJ9uzZTX5+Ab///UvMmnXsEWvesmUzI0aMZOjQYYTDYf74xz/0YgRERETiq3nwf7Bnzhqc0FCvS+mRPhGy+oqbb76de+65k0suia2bCocjXH/9zeTnF3DrrfPbF77PnDm7/fJdR+e4884FzJ17Pj6fn09/+iwuvvgyzjrrP5g//0e89daf2xe+txkz5iiuuuoavv3ta1oXvg/le9+74Yj1vvjisyxf/m/S0gKkpaXz7W9/Lz4DISIiEidOxnCvS+gx48C73JJsFFC6Z08tjvNxDTt3bqaoaGSXThAI+IhGncRUJ56Nb3c+A31VYWEWlZU1XpfRb2l8E0djm1ga38Tp7dj6fAb5+ZkAo4GyLr2mx+8mIiIiIp1SyBIRERFJAIUsERERkQRQyBIRERFJgJQMWR4uxhePua4DGEc8TkREJNWlXMgKBNKpq9uvoDXAuK5LNNpCdfVu0tP73v5UIiIin5RyfbLy8gqpqqqktrb6iMf6fD4cRy0cEiXZ4+vz+cnIyCQzMydp7ykiIpIoKRey/P4ABQXFXTpW/UQSS+MrIiLScyl3uVBERESkP1DIEhEREUkALy8X+iHWpr43evt6OTyNb+JobBNL45s4GtvE0vgmTm/G9oDX+rv6Gi/3LpwD/NWrNxcRERHpgZOBd7tyoJchKwjMBnYAtldFiIiIiHSBHygGlgJNXXmBlyFLREREpN/SwncRERGRBFDIEhEREUkAhSwRERGRBFDIEhEREUkAhSwRERGRBFDIEhEREUkAhSwRERGRBPByW51eMU1zPPAkkA/sAS6xLGuDt1X1TaZp/gz4EjAKmGpZ1urWxzXGvWSaZj6wGDiKWPO6jcBVlmVVmqZ5PLAIyADKgIsty6rwqta+yjTNl4DRgAPUAv9tWdYKfX7jxzTNW4Bbaf35oM9u75mmWQY0tv4CuM6yrNc0tvFhmmYIuAs4k9gY/8OyrCuT/XOhL89kPQjcb1nWeOB+Yh9K6ZmXgFOAzZ94XGPcey6w0LIs07Kso4GPgDtM0zSAp4FrWsf3HeAOD+vsyy61LGuaZVkzgJ8Bj7U+rs9vHJimORM4HtjS+rU+u/FznmVZ01t/vaaxjauFxMLVeMuypgI3tT6e1J8LfTJkmaY5GJgJPNP60DPATNM0C72rqu+yLOtdy7LKD3xMYxwflmXttSzr7QMe+icwEpgFNFqW1bb/1YPA+Ukur1+wLGvfAV/mAI4+v/FhmmaQ2F9EVxP7BwPos5tIGts4ME0zE7gEuMmyLBfAsqxdXvxc6JMhCxgObLMsywZo/X176+MSHxrjODNN0wd8A3gZGMEBM4eWZe0GfKZpDvKovD7NNM1HTNPcAswHLkWf33i5DXjasqzSAx7TZzd+lpim+YFpmg+YppmLxjZejiJ2KfAW0zSXmab5tmmac/Dg50JfDVkifdG9xNYM3ed1If2NZVlftSxrBHAD8FOv6+kPTNM8AZgNPOB1Lf3UyZZlTSM2xgb6uRBPAWAM8L5lWbOA64DfAJnJLqSvhqxyYKhpmn6A1t9LWh+X+NAYx1HrzQXjgC9bluUQW98y8oDnCwDXsqy9HpXYL1iWtRg4DdiKPr+99SlgAlDaukh7GPAaMBZ9dnutbYmGZVlNxILsSejnQrxsBqK0Xha0LOtfwG6ggST/XOiTIav1TosVwIWtD11ILLFWeldV/6Ixjh/TNOcDxwDntv5ABfg3kNE6hQ3wdeB5L+rry0zTzDRNc/gBX38e2Avo89tLlmXdYVlWiWVZoyzLGkUsuJ5FbKZQn91eME0zYppmTut/G8AFxD6v+rkQB62XWd8CPg3td8oPBtaT5J8Lhuu6Rz4qBZmmOYHYbZh5QBWx2zAtb6vqm0zTvAf4L6CIWNrfY1nWZI1x75mmORlYTex/7obWh0sty/pP0zRPJHZnS4iPb9Xe5UmhfZRpmkOA3wERwCYWsK61LGu5Pr/x1TqbdU5rCwd9dnvBNM0xwK8Bf+uvD4H/sSxrh8Y2PlrH+DFirRpagB9alvXHZP9c6LMhS0RERCSV9cnLhSIiIiKpTiFLREREJAEUskREREQSQCFLREREJAEUskREREQSQCFLREREJAEUskREREQSQCFLREREJAH+P/jBUZtrD7IsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_prediction(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_prediction(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_prediction(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}