model_daily_sentiment_noclose.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "df155c11",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dl_/miniconda3/lib/python3.9/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.1\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n",
      "2022-08-03 15:11:50.633505: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n",
      "2022-08-03 15:11:50.633547: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"
     ]
    }
   ],
   "source": [
    "# Load libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from matplotlib import pyplot\n",
    "from pandas.plotting import scatter_matrix\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.linear_model import ElasticNet\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "\n",
    "#Libraries for Deep Learning Models\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from tensorflow.keras.optimizers import SGD\n",
    "# from kerastuner.tuners import BayesianOptimization\n",
    "\n",
    "\n",
    "from keras.layers import LSTM\n",
    "from keras.wrappers.scikit_learn import KerasRegressor\n",
    "import tensorflow as tf\n",
    "\n",
    "\n",
    "\n",
    "#Libraries for Statistical Models\n",
    "import statsmodels.api as sm\n",
    "\n",
    "#Libraries for Saving the Model\n",
    "from pickle import dump\n",
    "from pickle import load\n",
    "\n",
    "# Time series Models\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "#from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
    "\n",
    "# Error Metrics\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# Feature Selection\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import chi2, f_regression\n",
    "\n",
    "\n",
    "#Plotting \n",
    "from pandas.plotting import scatter_matrix\n",
    "from statsmodels.graphics.tsaplots import plot_acf\n",
    "\n",
    "# Ignore all GPUs, tf random forest does not benefit from it.\n",
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "34a5bf38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dt              datetime64[ns]\n",
       "BTC_AdjClose           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_btc = pd.read_csv('BTC-USD.csv', parse_dates=['dt'], dayfirst=True)\n",
    "df_btc['dt'] = df_btc['dt'].apply(lambda x: pd.to_datetime(str(x), format='%Y-%m-%d'))\n",
    "df_btc = df_btc[['dt','BTC_AdjClose']]\n",
    "# df_btc = df_btc.set_index('dt')\n",
    "df_btc.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "302dc6c7",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['TSLA_AdjClose', 'TSLA_Volume', 'AAPL_AdjClose', 'TSLA_14-high',\n",
       "       'TSLA_14-low', 'TSLA_%K', 'TSLA_%D', 'TSLA_%R', 'TSLA_RSI', 'S&P',\n",
       "       'DFF', 'DGS3MO', 'DGS6MO', 'DGS10', 'BAMLH0A0HYM2', 'VXIBMCLS',\n",
       "       'USEPUINDXD', 'DCOILWTICO', 'TSLA_EPS_diluted', 'TSLA_P/E',\n",
       "       'sentiment_vader', 'total_msgs'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tickers = ['TSLA'#, 'AAPL'\n",
    "            ]\n",
    "\n",
    "\n",
    "\n",
    "df = pd.read_csv('combined_dataset_daily_sentiment_TSLA_AAPL.csv')\n",
    "df['dt'] = df['dt'].apply(lambda x: pd.to_datetime(str(x), format='%Y-%m-%d'))\n",
    "df = df.set_index('dt')\n",
    "\n",
    "df = df[['TSLA_AdjClose', #'TSLA_Open', 'TSLA_High', 'TSLA_Low',\n",
    "       'TSLA_Volume', 'AAPL_AdjClose', 'TSLA_14-high', 'TSLA_14-low',\n",
    "       'TSLA_%K', 'TSLA_%D', 'TSLA_%R', 'TSLA_RSI', 'S&P', 'DFF', 'DGS3MO',\n",
    "       'DGS6MO', 'DGS10', 'BAMLH0A0HYM2', 'VXIBMCLS', 'USEPUINDXD',\n",
    "       'DCOILWTICO', 'TSLA_EPS_diluted', 'TSLA_P/E','sentiment_vader',\n",
    "       'total_msgs']]\n",
    "\n",
    "# set daterange for model\n",
    "# end_date = '2021-08-01'\n",
    "\n",
    "# df = df.loc[:end_date]\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "eac48266",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TSLA_AdjClose</th>\n",
       "      <th>TSLA_Volume</th>\n",
       "      <th>AAPL_AdjClose</th>\n",
       "      <th>TSLA_14-high</th>\n",
       "      <th>TSLA_14-low</th>\n",
       "      <th>TSLA_%K</th>\n",
       "      <th>TSLA_%D</th>\n",
       "      <th>TSLA_%R</th>\n",
       "      <th>TSLA_RSI</th>\n",
       "      <th>S&amp;P</th>\n",
       "      <th>...</th>\n",
       "      <th>DGS6MO</th>\n",
       "      <th>DGS10</th>\n",
       "      <th>BAMLH0A0HYM2</th>\n",
       "      <th>VXIBMCLS</th>\n",
       "      <th>USEPUINDXD</th>\n",
       "      <th>DCOILWTICO</th>\n",
       "      <th>TSLA_EPS_diluted</th>\n",
       "      <th>TSLA_P/E</th>\n",
       "      <th>sentiment_vader</th>\n",
       "      <th>total_msgs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dt</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-11-05</th>\n",
       "      <td>6.300000</td>\n",
       "      <td>10244500.0</td>\n",
       "      <td>17.928806</td>\n",
       "      <td>6.316000</td>\n",
       "      <td>5.372000</td>\n",
       "      <td>98.305106</td>\n",
       "      <td>88.586524</td>\n",
       "      <td>1.694894</td>\n",
       "      <td>65.094551</td>\n",
       "      <td>1417.26</td>\n",
       "      <td>...</td>\n",
       "      <td>0.15</td>\n",
       "      <td>1.72</td>\n",
       "      <td>5.57</td>\n",
       "      <td>19.64</td>\n",
       "      <td>225.43</td>\n",
       "      <td>85.64</td>\n",
       "      <td>-0.7440</td>\n",
       "      <td>-8.467742</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-06</th>\n",
       "      <td>6.230000</td>\n",
       "      <td>11620000.0</td>\n",
       "      <td>17.874533</td>\n",
       "      <td>6.316000</td>\n",
       "      <td>5.404000</td>\n",
       "      <td>90.570181</td>\n",
       "      <td>88.485070</td>\n",
       "      <td>9.429819</td>\n",
       "      <td>62.733128</td>\n",
       "      <td>1428.39</td>\n",
       "      <td>...</td>\n",
       "      <td>0.15</td>\n",
       "      <td>1.78</td>\n",
       "      <td>5.49</td>\n",
       "      <td>19.60</td>\n",
       "      <td>195.50</td>\n",
       "      <td>88.62</td>\n",
       "      <td>-0.7440</td>\n",
       "      <td>-8.373656</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-07</th>\n",
       "      <td>6.308000</td>\n",
       "      <td>8572500.0</td>\n",
       "      <td>17.190607</td>\n",
       "      <td>6.410000</td>\n",
       "      <td>5.404000</td>\n",
       "      <td>89.860859</td>\n",
       "      <td>92.912049</td>\n",
       "      <td>10.139141</td>\n",
       "      <td>64.287757</td>\n",
       "      <td>1394.53</td>\n",
       "      <td>...</td>\n",
       "      <td>0.14</td>\n",
       "      <td>1.68</td>\n",
       "      <td>5.63</td>\n",
       "      <td>20.49</td>\n",
       "      <td>358.00</td>\n",
       "      <td>84.50</td>\n",
       "      <td>-0.7440</td>\n",
       "      <td>-8.478495</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-08</th>\n",
       "      <td>6.262000</td>\n",
       "      <td>6370000.0</td>\n",
       "      <td>16.566753</td>\n",
       "      <td>6.410000</td>\n",
       "      <td>5.404000</td>\n",
       "      <td>85.288295</td>\n",
       "      <td>88.573112</td>\n",
       "      <td>14.711705</td>\n",
       "      <td>62.628464</td>\n",
       "      <td>1377.51</td>\n",
       "      <td>...</td>\n",
       "      <td>0.15</td>\n",
       "      <td>1.62</td>\n",
       "      <td>5.67</td>\n",
       "      <td>20.85</td>\n",
       "      <td>241.14</td>\n",
       "      <td>85.07</td>\n",
       "      <td>-0.7440</td>\n",
       "      <td>-8.416667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-09</th>\n",
       "      <td>6.064000</td>\n",
       "      <td>4315000.0</td>\n",
       "      <td>16.853561</td>\n",
       "      <td>6.410000</td>\n",
       "      <td>5.404000</td>\n",
       "      <td>65.606391</td>\n",
       "      <td>80.251848</td>\n",
       "      <td>34.393609</td>\n",
       "      <td>55.936108</td>\n",
       "      <td>1379.85</td>\n",
       "      <td>...</td>\n",
       "      <td>0.15</td>\n",
       "      <td>1.61</td>\n",
       "      <td>5.77</td>\n",
       "      <td>20.82</td>\n",
       "      <td>278.07</td>\n",
       "      <td>86.08</td>\n",
       "      <td>-0.7440</td>\n",
       "      <td>-8.150538</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <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",
       "      <th>2022-07-26</th>\n",
       "      <td>776.580017</td>\n",
       "      <td>22273600.0</td>\n",
       "      <td>151.600006</td>\n",
       "      <td>842.359985</td>\n",
       "      <td>675.099976</td>\n",
       "      <td>60.672029</td>\n",
       "      <td>75.098009</td>\n",
       "      <td>39.327971</td>\n",
       "      <td>57.026327</td>\n",
       "      <td>3921.05</td>\n",
       "      <td>...</td>\n",
       "      <td>3.06</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.06</td>\n",
       "      <td>25.24</td>\n",
       "      <td>30.93</td>\n",
       "      <td>99.83</td>\n",
       "      <td>8.3024</td>\n",
       "      <td>93.536811</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-27</th>\n",
       "      <td>824.460022</td>\n",
       "      <td>29370000.0</td>\n",
       "      <td>156.789993</td>\n",
       "      <td>842.359985</td>\n",
       "      <td>675.099976</td>\n",
       "      <td>89.298121</td>\n",
       "      <td>75.937677</td>\n",
       "      <td>10.701879</td>\n",
       "      <td>63.699169</td>\n",
       "      <td>3921.05</td>\n",
       "      <td>...</td>\n",
       "      <td>3.06</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.06</td>\n",
       "      <td>25.24</td>\n",
       "      <td>30.93</td>\n",
       "      <td>99.83</td>\n",
       "      <td>8.3024</td>\n",
       "      <td>99.303818</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-28</th>\n",
       "      <td>842.700012</td>\n",
       "      <td>28241000.0</td>\n",
       "      <td>157.350006</td>\n",
       "      <td>849.900024</td>\n",
       "      <td>675.099976</td>\n",
       "      <td>95.881001</td>\n",
       "      <td>81.950384</td>\n",
       "      <td>4.118999</td>\n",
       "      <td>65.873169</td>\n",
       "      <td>3921.05</td>\n",
       "      <td>...</td>\n",
       "      <td>3.06</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.06</td>\n",
       "      <td>25.24</td>\n",
       "      <td>30.93</td>\n",
       "      <td>99.83</td>\n",
       "      <td>8.3024</td>\n",
       "      <td>101.500772</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-29</th>\n",
       "      <td>891.450012</td>\n",
       "      <td>31771000.0</td>\n",
       "      <td>162.509995</td>\n",
       "      <td>894.960022</td>\n",
       "      <td>675.099976</td>\n",
       "      <td>98.403525</td>\n",
       "      <td>94.527549</td>\n",
       "      <td>1.596475</td>\n",
       "      <td>70.890890</td>\n",
       "      <td>3921.05</td>\n",
       "      <td>...</td>\n",
       "      <td>3.06</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.06</td>\n",
       "      <td>25.24</td>\n",
       "      <td>30.93</td>\n",
       "      <td>99.83</td>\n",
       "      <td>8.3024</td>\n",
       "      <td>107.372568</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-01</th>\n",
       "      <td>891.830017</td>\n",
       "      <td>39014300.0</td>\n",
       "      <td>161.509995</td>\n",
       "      <td>935.630005</td>\n",
       "      <td>675.099976</td>\n",
       "      <td>83.188123</td>\n",
       "      <td>92.490883</td>\n",
       "      <td>16.811877</td>\n",
       "      <td>70.926774</td>\n",
       "      <td>3921.05</td>\n",
       "      <td>...</td>\n",
       "      <td>3.06</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.06</td>\n",
       "      <td>25.24</td>\n",
       "      <td>30.93</td>\n",
       "      <td>99.83</td>\n",
       "      <td>8.3024</td>\n",
       "      <td>107.418339</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3555 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            TSLA_AdjClose  TSLA_Volume  AAPL_AdjClose  TSLA_14-high  \\\n",
       "dt                                                                    \n",
       "2012-11-05       6.300000   10244500.0      17.928806      6.316000   \n",
       "2012-11-06       6.230000   11620000.0      17.874533      6.316000   \n",
       "2012-11-07       6.308000    8572500.0      17.190607      6.410000   \n",
       "2012-11-08       6.262000    6370000.0      16.566753      6.410000   \n",
       "2012-11-09       6.064000    4315000.0      16.853561      6.410000   \n",
       "...                   ...          ...            ...           ...   \n",
       "2022-07-26     776.580017   22273600.0     151.600006    842.359985   \n",
       "2022-07-27     824.460022   29370000.0     156.789993    842.359985   \n",
       "2022-07-28     842.700012   28241000.0     157.350006    849.900024   \n",
       "2022-07-29     891.450012   31771000.0     162.509995    894.960022   \n",
       "2022-08-01     891.830017   39014300.0     161.509995    935.630005   \n",
       "\n",
       "            TSLA_14-low    TSLA_%K    TSLA_%D    TSLA_%R   TSLA_RSI      S&P  \\\n",
       "dt                                                                             \n",
       "2012-11-05     5.372000  98.305106  88.586524   1.694894  65.094551  1417.26   \n",
       "2012-11-06     5.404000  90.570181  88.485070   9.429819  62.733128  1428.39   \n",
       "2012-11-07     5.404000  89.860859  92.912049  10.139141  64.287757  1394.53   \n",
       "2012-11-08     5.404000  85.288295  88.573112  14.711705  62.628464  1377.51   \n",
       "2012-11-09     5.404000  65.606391  80.251848  34.393609  55.936108  1379.85   \n",
       "...                 ...        ...        ...        ...        ...      ...   \n",
       "2022-07-26   675.099976  60.672029  75.098009  39.327971  57.026327  3921.05   \n",
       "2022-07-27   675.099976  89.298121  75.937677  10.701879  63.699169  3921.05   \n",
       "2022-07-28   675.099976  95.881001  81.950384   4.118999  65.873169  3921.05   \n",
       "2022-07-29   675.099976  98.403525  94.527549   1.596475  70.890890  3921.05   \n",
       "2022-08-01   675.099976  83.188123  92.490883  16.811877  70.926774  3921.05   \n",
       "\n",
       "            ...  DGS6MO  DGS10  BAMLH0A0HYM2  VXIBMCLS  USEPUINDXD  \\\n",
       "dt          ...                                                      \n",
       "2012-11-05  ...    0.15   1.72          5.57     19.64      225.43   \n",
       "2012-11-06  ...    0.15   1.78          5.49     19.60      195.50   \n",
       "2012-11-07  ...    0.14   1.68          5.63     20.49      358.00   \n",
       "2012-11-08  ...    0.15   1.62          5.67     20.85      241.14   \n",
       "2012-11-09  ...    0.15   1.61          5.77     20.82      278.07   \n",
       "...         ...     ...    ...           ...       ...         ...   \n",
       "2022-07-26  ...    3.06   2.81          5.06     25.24       30.93   \n",
       "2022-07-27  ...    3.06   2.81          5.06     25.24       30.93   \n",
       "2022-07-28  ...    3.06   2.81          5.06     25.24       30.93   \n",
       "2022-07-29  ...    3.06   2.81          5.06     25.24       30.93   \n",
       "2022-08-01  ...    3.06   2.81          5.06     25.24       30.93   \n",
       "\n",
       "            DCOILWTICO  TSLA_EPS_diluted    TSLA_P/E  sentiment_vader  \\\n",
       "dt                                                                      \n",
       "2012-11-05       85.64           -0.7440   -8.467742              0.0   \n",
       "2012-11-06       88.62           -0.7440   -8.373656              0.0   \n",
       "2012-11-07       84.50           -0.7440   -8.478495              0.0   \n",
       "2012-11-08       85.07           -0.7440   -8.416667              0.0   \n",
       "2012-11-09       86.08           -0.7440   -8.150538              0.0   \n",
       "...                ...               ...         ...              ...   \n",
       "2022-07-26       99.83            8.3024   93.536811              0.0   \n",
       "2022-07-27       99.83            8.3024   99.303818              0.0   \n",
       "2022-07-28       99.83            8.3024  101.500772              0.0   \n",
       "2022-07-29       99.83            8.3024  107.372568              0.0   \n",
       "2022-08-01       99.83            8.3024  107.418339              0.0   \n",
       "\n",
       "            total_msgs  \n",
       "dt                      \n",
       "2012-11-05         0.0  \n",
       "2012-11-06         0.0  \n",
       "2012-11-07         0.0  \n",
       "2012-11-08         0.0  \n",
       "2012-11-09         0.0  \n",
       "...                ...  \n",
       "2022-07-26         0.0  \n",
       "2022-07-27         0.0  \n",
       "2022-07-28         0.0  \n",
       "2022-07-29         0.0  \n",
       "2022-08-01         0.0  \n",
       "\n",
       "[3555 rows x 22 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c6a86e18",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TSLA_AdjClose</th>\n",
       "      <th>TSLA_Volume</th>\n",
       "      <th>AAPL_AdjClose</th>\n",
       "      <th>TSLA_14-high</th>\n",
       "      <th>TSLA_14-low</th>\n",
       "      <th>TSLA_%K</th>\n",
       "      <th>TSLA_%D</th>\n",
       "      <th>TSLA_%R</th>\n",
       "      <th>TSLA_RSI</th>\n",
       "      <th>S&amp;P</th>\n",
       "      <th>...</th>\n",
       "      <th>DGS10</th>\n",
       "      <th>BAMLH0A0HYM2</th>\n",
       "      <th>VXIBMCLS</th>\n",
       "      <th>USEPUINDXD</th>\n",
       "      <th>DCOILWTICO</th>\n",
       "      <th>TSLA_EPS_diluted</th>\n",
       "      <th>TSLA_P/E</th>\n",
       "      <th>sentiment_vader</th>\n",
       "      <th>total_msgs</th>\n",
       "      <th>BTC_AdjClose</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dt</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-11-05</th>\n",
       "      <td>6.300000</td>\n",
       "      <td>10244500.0</td>\n",
       "      <td>17.928806</td>\n",
       "      <td>6.316000</td>\n",
       "      <td>5.372000</td>\n",
       "      <td>98.305106</td>\n",
       "      <td>88.586524</td>\n",
       "      <td>1.694894</td>\n",
       "      <td>65.094551</td>\n",
       "      <td>1417.26</td>\n",
       "      <td>...</td>\n",
       "      <td>1.72</td>\n",
       "      <td>5.57</td>\n",
       "      <td>19.64</td>\n",
       "      <td>225.43</td>\n",
       "      <td>85.64</td>\n",
       "      <td>-0.7440</td>\n",
       "      <td>-8.467742</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-06</th>\n",
       "      <td>6.230000</td>\n",
       "      <td>11620000.0</td>\n",
       "      <td>17.874533</td>\n",
       "      <td>6.316000</td>\n",
       "      <td>5.404000</td>\n",
       "      <td>90.570181</td>\n",
       "      <td>88.485070</td>\n",
       "      <td>9.429819</td>\n",
       "      <td>62.733128</td>\n",
       "      <td>1428.39</td>\n",
       "      <td>...</td>\n",
       "      <td>1.78</td>\n",
       "      <td>5.49</td>\n",
       "      <td>19.60</td>\n",
       "      <td>195.50</td>\n",
       "      <td>88.62</td>\n",
       "      <td>-0.7440</td>\n",
       "      <td>-8.373656</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-07</th>\n",
       "      <td>6.308000</td>\n",
       "      <td>8572500.0</td>\n",
       "      <td>17.190607</td>\n",
       "      <td>6.410000</td>\n",
       "      <td>5.404000</td>\n",
       "      <td>89.860859</td>\n",
       "      <td>92.912049</td>\n",
       "      <td>10.139141</td>\n",
       "      <td>64.287757</td>\n",
       "      <td>1394.53</td>\n",
       "      <td>...</td>\n",
       "      <td>1.68</td>\n",
       "      <td>5.63</td>\n",
       "      <td>20.49</td>\n",
       "      <td>358.00</td>\n",
       "      <td>84.50</td>\n",
       "      <td>-0.7440</td>\n",
       "      <td>-8.478495</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-08</th>\n",
       "      <td>6.262000</td>\n",
       "      <td>6370000.0</td>\n",
       "      <td>16.566753</td>\n",
       "      <td>6.410000</td>\n",
       "      <td>5.404000</td>\n",
       "      <td>85.288295</td>\n",
       "      <td>88.573112</td>\n",
       "      <td>14.711705</td>\n",
       "      <td>62.628464</td>\n",
       "      <td>1377.51</td>\n",
       "      <td>...</td>\n",
       "      <td>1.62</td>\n",
       "      <td>5.67</td>\n",
       "      <td>20.85</td>\n",
       "      <td>241.14</td>\n",
       "      <td>85.07</td>\n",
       "      <td>-0.7440</td>\n",
       "      <td>-8.416667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-09</th>\n",
       "      <td>6.064000</td>\n",
       "      <td>4315000.0</td>\n",
       "      <td>16.853561</td>\n",
       "      <td>6.410000</td>\n",
       "      <td>5.404000</td>\n",
       "      <td>65.606391</td>\n",
       "      <td>80.251848</td>\n",
       "      <td>34.393609</td>\n",
       "      <td>55.936108</td>\n",
       "      <td>1379.85</td>\n",
       "      <td>...</td>\n",
       "      <td>1.61</td>\n",
       "      <td>5.77</td>\n",
       "      <td>20.82</td>\n",
       "      <td>278.07</td>\n",
       "      <td>86.08</td>\n",
       "      <td>-0.7440</td>\n",
       "      <td>-8.150538</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <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",
       "      <th>2021-08-26</th>\n",
       "      <td>701.159973</td>\n",
       "      <td>13214300.0</td>\n",
       "      <td>146.921646</td>\n",
       "      <td>729.900024</td>\n",
       "      <td>648.840027</td>\n",
       "      <td>64.544717</td>\n",
       "      <td>71.687598</td>\n",
       "      <td>35.455283</td>\n",
       "      <td>53.984633</td>\n",
       "      <td>4470.00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.34</td>\n",
       "      <td>3.20</td>\n",
       "      <td>20.68</td>\n",
       "      <td>54.90</td>\n",
       "      <td>67.42</td>\n",
       "      <td>1.9191</td>\n",
       "      <td>365.358748</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46942.21875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-27</th>\n",
       "      <td>711.919983</td>\n",
       "      <td>13833800.0</td>\n",
       "      <td>147.977219</td>\n",
       "      <td>729.900024</td>\n",
       "      <td>648.840027</td>\n",
       "      <td>77.818848</td>\n",
       "      <td>73.098073</td>\n",
       "      <td>22.181152</td>\n",
       "      <td>57.017706</td>\n",
       "      <td>4509.37</td>\n",
       "      <td>...</td>\n",
       "      <td>1.31</td>\n",
       "      <td>3.17</td>\n",
       "      <td>19.41</td>\n",
       "      <td>69.52</td>\n",
       "      <td>68.84</td>\n",
       "      <td>1.9191</td>\n",
       "      <td>370.965548</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49058.66797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-28</th>\n",
       "      <td>711.919983</td>\n",
       "      <td>13833800.0</td>\n",
       "      <td>147.977219</td>\n",
       "      <td>729.900024</td>\n",
       "      <td>648.840027</td>\n",
       "      <td>77.818848</td>\n",
       "      <td>73.098073</td>\n",
       "      <td>22.181152</td>\n",
       "      <td>57.017706</td>\n",
       "      <td>4509.37</td>\n",
       "      <td>...</td>\n",
       "      <td>1.31</td>\n",
       "      <td>3.17</td>\n",
       "      <td>19.41</td>\n",
       "      <td>147.47</td>\n",
       "      <td>68.84</td>\n",
       "      <td>1.9191</td>\n",
       "      <td>370.965548</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48902.40234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-29</th>\n",
       "      <td>711.919983</td>\n",
       "      <td>13833800.0</td>\n",
       "      <td>147.977219</td>\n",
       "      <td>729.900024</td>\n",
       "      <td>648.840027</td>\n",
       "      <td>77.818848</td>\n",
       "      <td>73.098073</td>\n",
       "      <td>22.181152</td>\n",
       "      <td>57.017706</td>\n",
       "      <td>4509.37</td>\n",
       "      <td>...</td>\n",
       "      <td>1.31</td>\n",
       "      <td>3.17</td>\n",
       "      <td>19.41</td>\n",
       "      <td>105.19</td>\n",
       "      <td>68.84</td>\n",
       "      <td>1.9191</td>\n",
       "      <td>370.965548</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48829.83203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-30</th>\n",
       "      <td>730.909973</td>\n",
       "      <td>18604200.0</td>\n",
       "      <td>152.478256</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>648.840027</td>\n",
       "      <td>99.890425</td>\n",
       "      <td>80.751330</td>\n",
       "      <td>0.109575</td>\n",
       "      <td>61.802974</td>\n",
       "      <td>4528.79</td>\n",
       "      <td>...</td>\n",
       "      <td>1.29</td>\n",
       "      <td>3.16</td>\n",
       "      <td>20.02</td>\n",
       "      <td>77.75</td>\n",
       "      <td>69.28</td>\n",
       "      <td>1.9191</td>\n",
       "      <td>380.860806</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47054.98438</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3221 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            TSLA_AdjClose  TSLA_Volume  AAPL_AdjClose  TSLA_14-high  \\\n",
       "dt                                                                    \n",
       "2012-11-05       6.300000   10244500.0      17.928806      6.316000   \n",
       "2012-11-06       6.230000   11620000.0      17.874533      6.316000   \n",
       "2012-11-07       6.308000    8572500.0      17.190607      6.410000   \n",
       "2012-11-08       6.262000    6370000.0      16.566753      6.410000   \n",
       "2012-11-09       6.064000    4315000.0      16.853561      6.410000   \n",
       "...                   ...          ...            ...           ...   \n",
       "2021-08-26     701.159973   13214300.0     146.921646    729.900024   \n",
       "2021-08-27     711.919983   13833800.0     147.977219    729.900024   \n",
       "2021-08-28     711.919983   13833800.0     147.977219    729.900024   \n",
       "2021-08-29     711.919983   13833800.0     147.977219    729.900024   \n",
       "2021-08-30     730.909973   18604200.0     152.478256    731.000000   \n",
       "\n",
       "            TSLA_14-low    TSLA_%K    TSLA_%D    TSLA_%R   TSLA_RSI      S&P  \\\n",
       "dt                                                                             \n",
       "2012-11-05     5.372000  98.305106  88.586524   1.694894  65.094551  1417.26   \n",
       "2012-11-06     5.404000  90.570181  88.485070   9.429819  62.733128  1428.39   \n",
       "2012-11-07     5.404000  89.860859  92.912049  10.139141  64.287757  1394.53   \n",
       "2012-11-08     5.404000  85.288295  88.573112  14.711705  62.628464  1377.51   \n",
       "2012-11-09     5.404000  65.606391  80.251848  34.393609  55.936108  1379.85   \n",
       "...                 ...        ...        ...        ...        ...      ...   \n",
       "2021-08-26   648.840027  64.544717  71.687598  35.455283  53.984633  4470.00   \n",
       "2021-08-27   648.840027  77.818848  73.098073  22.181152  57.017706  4509.37   \n",
       "2021-08-28   648.840027  77.818848  73.098073  22.181152  57.017706  4509.37   \n",
       "2021-08-29   648.840027  77.818848  73.098073  22.181152  57.017706  4509.37   \n",
       "2021-08-30   648.840027  99.890425  80.751330   0.109575  61.802974  4528.79   \n",
       "\n",
       "            ...  DGS10  BAMLH0A0HYM2  VXIBMCLS  USEPUINDXD  DCOILWTICO  \\\n",
       "dt          ...                                                          \n",
       "2012-11-05  ...   1.72          5.57     19.64      225.43       85.64   \n",
       "2012-11-06  ...   1.78          5.49     19.60      195.50       88.62   \n",
       "2012-11-07  ...   1.68          5.63     20.49      358.00       84.50   \n",
       "2012-11-08  ...   1.62          5.67     20.85      241.14       85.07   \n",
       "2012-11-09  ...   1.61          5.77     20.82      278.07       86.08   \n",
       "...         ...    ...           ...       ...         ...         ...   \n",
       "2021-08-26  ...   1.34          3.20     20.68       54.90       67.42   \n",
       "2021-08-27  ...   1.31          3.17     19.41       69.52       68.84   \n",
       "2021-08-28  ...   1.31          3.17     19.41      147.47       68.84   \n",
       "2021-08-29  ...   1.31          3.17     19.41      105.19       68.84   \n",
       "2021-08-30  ...   1.29          3.16     20.02       77.75       69.28   \n",
       "\n",
       "            TSLA_EPS_diluted    TSLA_P/E  sentiment_vader  total_msgs  \\\n",
       "dt                                                                      \n",
       "2012-11-05           -0.7440   -8.467742              0.0         0.0   \n",
       "2012-11-06           -0.7440   -8.373656              0.0         0.0   \n",
       "2012-11-07           -0.7440   -8.478495              0.0         0.0   \n",
       "2012-11-08           -0.7440   -8.416667              0.0         0.0   \n",
       "2012-11-09           -0.7440   -8.150538              0.0         0.0   \n",
       "...                      ...         ...              ...         ...   \n",
       "2021-08-26            1.9191  365.358748              0.0         0.0   \n",
       "2021-08-27            1.9191  370.965548              0.0         0.0   \n",
       "2021-08-28            1.9191  370.965548              0.0         0.0   \n",
       "2021-08-29            1.9191  370.965548              0.0         0.0   \n",
       "2021-08-30            1.9191  380.860806              0.0         0.0   \n",
       "\n",
       "            BTC_AdjClose  \n",
       "dt                        \n",
       "2012-11-05       0.00000  \n",
       "2012-11-06       0.00000  \n",
       "2012-11-07       0.00000  \n",
       "2012-11-08       0.00000  \n",
       "2012-11-09       0.00000  \n",
       "...                  ...  \n",
       "2021-08-26   46942.21875  \n",
       "2021-08-27   49058.66797  \n",
       "2021-08-28   48902.40234  \n",
       "2021-08-29   48829.83203  \n",
       "2021-08-30   47054.98438  \n",
       "\n",
       "[3221 rows x 23 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set daterange for model\n",
    "end_date = '2021-08-30'\n",
    "\n",
    "df2 = pd.merge(df, df_btc, how='outer', on='dt')\n",
    "df2 = df2.set_index('dt')\n",
    "df2['BTC_AdjClose'].replace(np.nan, 0, inplace=True)\n",
    "df2 = df2.loc[:end_date]\n",
    "df2.reindex()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfaa962f",
   "metadata": {},
   "source": [
    "## Standardize Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "12ba95fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# scaler = StandardScaler().fit(df)\n",
    "# scaled_data = pd.DataFrame(scaler.fit_transform(df), columns = df.columns, index = df.index)\n",
    "# scaled_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d6e50899",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['TSLA_AdjClose', 'TSLA_Volume', 'AAPL_AdjClose', 'TSLA_14-high',\n",
       "       'TSLA_14-low', 'TSLA_%K', 'TSLA_%D', 'TSLA_%R', 'TSLA_RSI', 'S&P',\n",
       "       'DFF', 'DGS3MO', 'DGS6MO', 'DGS10', 'BAMLH0A0HYM2', 'VXIBMCLS',\n",
       "       'USEPUINDXD', 'DCOILWTICO', 'TSLA_EPS_diluted', 'TSLA_P/E',\n",
       "       'sentiment_vader', 'total_msgs'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2267f136",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.012729401238700786"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:          TSLA_AdjClose   R-squared:                       0.904\n",
      "Model:                            OLS   Adj. R-squared:                  0.904\n",
      "Method:                 Least Squares   F-statistic:                 3.347e+04\n",
      "Date:                Wed, 03 Aug 2022   Prob (F-statistic):               0.00\n",
      "Time:                        15:11:52   Log-Likelihood:                -21054.\n",
      "No. Observations:                3555   AIC:                         4.211e+04\n",
      "Df Residuals:                    3553   BIC:                         4.212e+04\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=================================================================================\n",
      "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------\n",
      "Intercept      -140.1489      2.377    -58.949      0.000    -144.810    -135.488\n",
      "AAPL_AdjClose     5.9662      0.033    182.948      0.000       5.902       6.030\n",
      "==============================================================================\n",
      "Omnibus:                      156.665   Durbin-Watson:                   0.013\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              476.813\n",
      "Skew:                           0.132   Prob(JB):                    2.89e-104\n",
      "Kurtosis:                       4.775   Cond. No.                         114.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.formula.api import ols\n",
    "from statsmodels.stats.stattools import durbin_watson\n",
    "\n",
    "#fit multiple linear regression model\n",
    "model = ols(\"TSLA_AdjClose ~ AAPL_AdjClose\", data=df).fit()\n",
    "display(durbin_watson(model.resid))\n",
    "\n",
    "#view model summary\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0124a72c",
   "metadata": {},
   "source": [
    "## Lagged Returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2b4edd0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# lagged returns\n",
    "\n",
    "return_period = 5\n",
    "\n",
    "Y = np.log(df2[tickers[0]+'_AdjClose']).diff(return_period).shift(-return_period)\n",
    "\n",
    "Y.name = Y.name +'_pred'\n",
    "\n",
    "lagged_data_0 = [ tickers[0]+'_%K', tickers[0]+'_%D', tickers[0]+'_%R',tickers[0]+'_RSI']\n",
    "lagged_data_14 = [tickers[0]+'_14-high', tickers[0]+'_14-low']\n",
    "\n",
    "try:\n",
    "    lagged_data_1 = [tickers[1]+'_14-high', tickers[1]+'_14-low', tickers[1]+'_%K', tickers[1]+'_%D', tickers[1]+'_%R', tickers[1]+'_RSI']\n",
    "    lagged_data = lagged_data_0 + lagged_data_1\n",
    "except:\n",
    "    lagged_data = lagged_data_0\n",
    "\n",
    "response_variables = [tickers[0]+'_AdjClose']\n",
    "\n",
    "X1 = df2.loc[:, ~df2.columns.isin([*lagged_data, *response_variables])].select_dtypes(exclude='object')\n",
    "X1_lagged = np.log(X1).shift(-return_period)\n",
    "X2 = df2.loc[:, df2.columns.isin([*lagged_data_0])]\n",
    "X2_lagged = np.log(X2/100)\n",
    "X3 = df2.loc[:, df2.columns.isin([*lagged_data_14])]\n",
    "X3_norm = np.log(X3)\n",
    "X = pd.concat([X1_lagged, X2_lagged, X3_norm], axis=1)\n",
    "\n",
    "X.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
    "X.replace(np.nan, 0, inplace=True)\n",
    "Y.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
    "Y.replace(np.nan, 0, inplace=True)\n",
    "\n",
    "dataset = pd.concat([Y, X], axis=1).dropna().iloc[::return_period, :]\n",
    "Y = dataset.loc[:, Y.name]\n",
    "X = dataset.loc[:, X.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fae325c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TSLA_AdjClose_pred</th>\n",
       "      <th>TSLA_Volume</th>\n",
       "      <th>AAPL_AdjClose</th>\n",
       "      <th>TSLA_14-high</th>\n",
       "      <th>TSLA_14-low</th>\n",
       "      <th>S&amp;P</th>\n",
       "      <th>DFF</th>\n",
       "      <th>DGS3MO</th>\n",
       "      <th>DGS6MO</th>\n",
       "      <th>DGS10</th>\n",
       "      <th>...</th>\n",
       "      <th>TSLA_P/E</th>\n",
       "      <th>sentiment_vader</th>\n",
       "      <th>total_msgs</th>\n",
       "      <th>BTC_AdjClose</th>\n",
       "      <th>TSLA_%K</th>\n",
       "      <th>TSLA_%D</th>\n",
       "      <th>TSLA_%R</th>\n",
       "      <th>TSLA_RSI</th>\n",
       "      <th>TSLA_14-high</th>\n",
       "      <th>TSLA_14-low</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dt</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-11-05</th>\n",
       "      <td>-0.038180</td>\n",
       "      <td>15.277608</td>\n",
       "      <td>2.824562</td>\n",
       "      <td>1.857859</td>\n",
       "      <td>1.687139</td>\n",
       "      <td>7.229730</td>\n",
       "      <td>-1.832581</td>\n",
       "      <td>-2.407946</td>\n",
       "      <td>-1.897120</td>\n",
       "      <td>0.476234</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.017094</td>\n",
       "      <td>-0.121190</td>\n",
       "      <td>-4.077550</td>\n",
       "      <td>-0.429329</td>\n",
       "      <td>1.843086</td>\n",
       "      <td>1.681200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-10</th>\n",
       "      <td>0.016356</td>\n",
       "      <td>15.408819</td>\n",
       "      <td>2.784583</td>\n",
       "      <td>1.860041</td>\n",
       "      <td>1.687139</td>\n",
       "      <td>7.210324</td>\n",
       "      <td>-1.832581</td>\n",
       "      <td>-2.525729</td>\n",
       "      <td>-1.966113</td>\n",
       "      <td>0.457425</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.421497</td>\n",
       "      <td>-0.220000</td>\n",
       "      <td>-1.067299</td>\n",
       "      <td>-0.580960</td>\n",
       "      <td>1.857859</td>\n",
       "      <td>1.687139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-15</th>\n",
       "      <td>0.068344</td>\n",
       "      <td>15.344281</td>\n",
       "      <td>2.849564</td>\n",
       "      <td>1.894617</td>\n",
       "      <td>1.729884</td>\n",
       "      <td>7.235482</td>\n",
       "      <td>-1.832581</td>\n",
       "      <td>-2.525729</td>\n",
       "      <td>-1.966113</td>\n",
       "      <td>0.506818</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.294239</td>\n",
       "      <td>-0.177324</td>\n",
       "      <td>-1.366877</td>\n",
       "      <td>-0.573397</td>\n",
       "      <td>1.860041</td>\n",
       "      <td>1.687139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-20</th>\n",
       "      <td>-0.026717</td>\n",
       "      <td>14.581676</td>\n",
       "      <td>2.868268</td>\n",
       "      <td>1.901212</td>\n",
       "      <td>1.769173</td>\n",
       "      <td>7.250742</td>\n",
       "      <td>-1.832581</td>\n",
       "      <td>-2.302585</td>\n",
       "      <td>-1.966113</td>\n",
       "      <td>0.530628</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.050773</td>\n",
       "      <td>-0.054995</td>\n",
       "      <td>-3.005679</td>\n",
       "      <td>-0.416595</td>\n",
       "      <td>1.894617</td>\n",
       "      <td>1.729884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-25</th>\n",
       "      <td>0.051262</td>\n",
       "      <td>15.775817</td>\n",
       "      <td>2.892094</td>\n",
       "      <td>1.925416</td>\n",
       "      <td>1.797079</td>\n",
       "      <td>7.255718</td>\n",
       "      <td>-1.832581</td>\n",
       "      <td>-2.525729</td>\n",
       "      <td>-2.040221</td>\n",
       "      <td>0.482426</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.391076</td>\n",
       "      <td>-0.213370</td>\n",
       "      <td>-1.128027</td>\n",
       "      <td>-0.518998</td>\n",
       "      <td>1.901212</td>\n",
       "      <td>1.769173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <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",
       "      <th>2021-08-10</th>\n",
       "      <td>0.010062</td>\n",
       "      <td>16.632804</td>\n",
       "      <td>5.000417</td>\n",
       "      <td>6.592908</td>\n",
       "      <td>6.441329</td>\n",
       "      <td>8.404696</td>\n",
       "      <td>-2.302585</td>\n",
       "      <td>-2.813411</td>\n",
       "      <td>-2.995732</td>\n",
       "      <td>0.254642</td>\n",
       "      <td>...</td>\n",
       "      <td>5.923457</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.758902</td>\n",
       "      <td>-0.186342</td>\n",
       "      <td>-0.215442</td>\n",
       "      <td>-1.771897</td>\n",
       "      <td>-0.469982</td>\n",
       "      <td>6.588844</td>\n",
       "      <td>6.441329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-15</th>\n",
       "      <td>-0.052838</td>\n",
       "      <td>16.512965</td>\n",
       "      <td>4.994295</td>\n",
       "      <td>6.592908</td>\n",
       "      <td>6.475186</td>\n",
       "      <td>8.398786</td>\n",
       "      <td>-2.407946</td>\n",
       "      <td>-2.995732</td>\n",
       "      <td>-2.995732</td>\n",
       "      <td>0.231112</td>\n",
       "      <td>...</td>\n",
       "      <td>5.870619</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.806474</td>\n",
       "      <td>-0.132391</td>\n",
       "      <td>-0.128901</td>\n",
       "      <td>-2.087458</td>\n",
       "      <td>-0.461912</td>\n",
       "      <td>6.592908</td>\n",
       "      <td>6.441329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-20</th>\n",
       "      <td>0.044479</td>\n",
       "      <td>16.352820</td>\n",
       "      <td>4.995442</td>\n",
       "      <td>6.592908</td>\n",
       "      <td>6.475186</td>\n",
       "      <td>8.410986</td>\n",
       "      <td>-2.407946</td>\n",
       "      <td>-2.813411</td>\n",
       "      <td>-2.813411</td>\n",
       "      <td>0.300105</td>\n",
       "      <td>...</td>\n",
       "      <td>5.915097</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.798775</td>\n",
       "      <td>-0.947746</td>\n",
       "      <td>-0.927374</td>\n",
       "      <td>-0.490392</td>\n",
       "      <td>-0.709786</td>\n",
       "      <td>6.592908</td>\n",
       "      <td>6.475186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-25</th>\n",
       "      <td>0.027337</td>\n",
       "      <td>16.738898</td>\n",
       "      <td>5.027022</td>\n",
       "      <td>6.594413</td>\n",
       "      <td>6.475186</td>\n",
       "      <td>8.418210</td>\n",
       "      <td>-2.525729</td>\n",
       "      <td>-2.995732</td>\n",
       "      <td>-2.995732</td>\n",
       "      <td>0.254642</td>\n",
       "      <td>...</td>\n",
       "      <td>5.942434</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.759072</td>\n",
       "      <td>-0.262266</td>\n",
       "      <td>-0.303794</td>\n",
       "      <td>-1.466665</td>\n",
       "      <td>-0.553381</td>\n",
       "      <td>6.592908</td>\n",
       "      <td>6.475186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-30</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.001096</td>\n",
       "      <td>-0.213796</td>\n",
       "      <td>-6.816315</td>\n",
       "      <td>-0.481219</td>\n",
       "      <td>6.594413</td>\n",
       "      <td>6.475186</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>645 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            TSLA_AdjClose_pred  TSLA_Volume  AAPL_AdjClose  TSLA_14-high  \\\n",
       "dt                                                                         \n",
       "2012-11-05           -0.038180    15.277608       2.824562      1.857859   \n",
       "2012-11-10            0.016356    15.408819       2.784583      1.860041   \n",
       "2012-11-15            0.068344    15.344281       2.849564      1.894617   \n",
       "2012-11-20           -0.026717    14.581676       2.868268      1.901212   \n",
       "2012-11-25            0.051262    15.775817       2.892094      1.925416   \n",
       "...                        ...          ...            ...           ...   \n",
       "2021-08-10            0.010062    16.632804       5.000417      6.592908   \n",
       "2021-08-15           -0.052838    16.512965       4.994295      6.592908   \n",
       "2021-08-20            0.044479    16.352820       4.995442      6.592908   \n",
       "2021-08-25            0.027337    16.738898       5.027022      6.594413   \n",
       "2021-08-30            0.000000     0.000000       0.000000      0.000000   \n",
       "\n",
       "            TSLA_14-low       S&P       DFF    DGS3MO    DGS6MO     DGS10  \\\n",
       "dt                                                                          \n",
       "2012-11-05     1.687139  7.229730 -1.832581 -2.407946 -1.897120  0.476234   \n",
       "2012-11-10     1.687139  7.210324 -1.832581 -2.525729 -1.966113  0.457425   \n",
       "2012-11-15     1.729884  7.235482 -1.832581 -2.525729 -1.966113  0.506818   \n",
       "2012-11-20     1.769173  7.250742 -1.832581 -2.302585 -1.966113  0.530628   \n",
       "2012-11-25     1.797079  7.255718 -1.832581 -2.525729 -2.040221  0.482426   \n",
       "...                 ...       ...       ...       ...       ...       ...   \n",
       "2021-08-10     6.441329  8.404696 -2.302585 -2.813411 -2.995732  0.254642   \n",
       "2021-08-15     6.475186  8.398786 -2.407946 -2.995732 -2.995732  0.231112   \n",
       "2021-08-20     6.475186  8.410986 -2.407946 -2.813411 -2.813411  0.300105   \n",
       "2021-08-25     6.475186  8.418210 -2.525729 -2.995732 -2.995732  0.254642   \n",
       "2021-08-30     0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "\n",
       "            ...  TSLA_P/E  sentiment_vader  total_msgs  BTC_AdjClose  \\\n",
       "dt          ...                                                        \n",
       "2012-11-05  ...  0.000000              0.0         0.0      0.000000   \n",
       "2012-11-10  ...  0.000000              0.0         0.0      0.000000   \n",
       "2012-11-15  ...  0.000000              0.0         0.0      0.000000   \n",
       "2012-11-20  ...  0.000000              0.0         0.0      0.000000   \n",
       "2012-11-25  ...  0.000000              0.0         0.0      0.000000   \n",
       "...         ...       ...              ...         ...           ...   \n",
       "2021-08-10  ...  5.923457              0.0         0.0     10.758902   \n",
       "2021-08-15  ...  5.870619              0.0         0.0     10.806474   \n",
       "2021-08-20  ...  5.915097              0.0         0.0     10.798775   \n",
       "2021-08-25  ...  5.942434              0.0         0.0     10.759072   \n",
       "2021-08-30  ...  0.000000              0.0         0.0      0.000000   \n",
       "\n",
       "             TSLA_%K   TSLA_%D   TSLA_%R  TSLA_RSI  TSLA_14-high  TSLA_14-low  \n",
       "dt                                                                             \n",
       "2012-11-05 -0.017094 -0.121190 -4.077550 -0.429329      1.843086     1.681200  \n",
       "2012-11-10 -0.421497 -0.220000 -1.067299 -0.580960      1.857859     1.687139  \n",
       "2012-11-15 -0.294239 -0.177324 -1.366877 -0.573397      1.860041     1.687139  \n",
       "2012-11-20 -0.050773 -0.054995 -3.005679 -0.416595      1.894617     1.729884  \n",
       "2012-11-25 -0.391076 -0.213370 -1.128027 -0.518998      1.901212     1.769173  \n",
       "...              ...       ...       ...       ...           ...          ...  \n",
       "2021-08-10 -0.186342 -0.215442 -1.771897 -0.469982      6.588844     6.441329  \n",
       "2021-08-15 -0.132391 -0.128901 -2.087458 -0.461912      6.592908     6.441329  \n",
       "2021-08-20 -0.947746 -0.927374 -0.490392 -0.709786      6.592908     6.475186  \n",
       "2021-08-25 -0.262266 -0.303794 -1.466665 -0.553381      6.592908     6.475186  \n",
       "2021-08-30 -0.001096 -0.213796 -6.816315 -0.481219      6.594413     6.475186  \n",
       "\n",
       "[645 rows x 25 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "38097496",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1728x1728 with 25 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset.hist(bins=50, sharex=False, sharey=False, xlabelsize=1, ylabelsize=1, figsize=(24,24))\n",
    "pyplot.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7ee05d84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Correlation Matrix'}>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation = df.corr()\n",
    "pyplot.figure(figsize=(15,15))\n",
    "pyplot.title('Correlation Matrix')\n",
    "sns.heatmap(correlation, vmax=1, square=True,annot=True,cmap='cubehelix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bda90a3a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4k/iIELw+HdTJAAc6Gg53UtvAQHhLUT3XP7amy7bl17RYVY4mMxDefwgl1YFa/J+71gOXgT/WSCB8jMitbOL37+zotcelodUsjXZYgXB3B2vLAH9Zq5raeGNT8YBefCoa3KzLq+n1PrvKGo7IBa+prcM6uRRLRlgc45bmVPLAZ/tYn197VF93e0n9YTVghQhU0+zhq/9dyXNr+laqt2xP1Ze+o/LHL2/hbx8OzmywpsIa4zPeXT6wk/HUBmRwyw8jK1zXYlyra5o9rMyt5qsPrmTN/t7bDoOt0d0+6B0c3THbkHBoQeSKfcZSWKs7LYn19Mo8fvLyFuv3NzeV8N+luVQ2tlHe0Mbo5Ehiw50U+gPhTYV1tLZ7KartPsMJ/oxweGBp9IFr0TD/Gr+B5d0lda0s/OPHh5QcMsv448Jd1nOXHqSkv6bFQ3yEiwR/+XZVpwmzzGrNXWWNh9WODQyEH/p8H5/vruSznOAOhNpmD1oHr7FsjnMurAkOkPsrsOPyy1qBKoHwMeK1jcU8/MV+68Lvbvfy6a4KKzD2+rTVmxcV6iQzLpwwp53tnU62a/bXMO13Sw67AVHX4uHWp9ZR0ejmxXWF3Pn8JrYUdT/WocHdzpzff8SHO8r7/PwPLN3HLU+u6/Hva/bXcPY/vuCz3ZV9er4V+6r63AFQEvDZFNf1fCI+Uq57dDX/XbrvqL/ul81v39zO79/ZMdibccRVNBoX5CM5IUdntc0eLrx/Of/4aM9Re82+8AWcB8XxpaSuFa1hXx8mb/H5NF97ai3//nTvUdiywdHc1kFFY9thly4erkJ/MDLQ1WW1zR5iwozS1vKGQ58wy8y0NbjbqfA/z8YjvG7r4Xr4i/1c9J/lx1z1g/lZuhy2Q1oX1uyM3VhQF3T7KxuKeWFdoZV9NMvhd5Y10truJSU6hKz4cCsjbI6bdXf4KK5r5ZvPrO8SmHsCxgg3BZRGA6REh2JTxjFU1dRGu9fHnoomSurdfJrTNcvc1uHl8eX7aff6WLGvim8/uyEo6WR22sRHuIgIcRAV4rC+az2pa/EQG+4kIdLYxuqmNt7cXGJNwGpmhOtb2w9reIAZCNttyuqs2l/VTH1LO48u24/W2mobBHY+mRlhj9cX1Obtr9aAzvAv6zhhCYSPEeZ4hLIGN2X1bq7470puemItL28o4qmVecz9/UdUNrYR6rThctiw2RSLxibx1uYS3O1e6+DLrWyi3dt1fIOnw9evk/K6vFqW7ChnVW6NdRD1NNZhW1E9lY1tfLyz74FwaZ2bBnd7jz1l72wpsZ77YKqa2rjmkdXcu6RvPetmJ4HLbjvqGQd3u5dle6t4cV1hvx/7xZ5KKyg6FPWt7fz2ze3H3MX5UL2/razX0qoviwr/rKtHMxDeXtJAh0/z8vrCHmdW31/VzPaSw5sIpL+eX1vIwj99MmAlceLoqfRP5pJfffDOx8qmNtztPgr6cN/jlfk5FNS0HPLYuw6vj399vIcfvbSZNzYVH9JzmFm6gV6ForrZw4Q0o3LtcAIBM4tZ39phjRPeepgTEB2q5rYOfv/OjoOef3Irm/B0+ILGxB4LGvyBzLCY0H6fQ7XWbPAHwpuL6qwsY4fXZ5X/vrrBaCOaFQDm/VOiQ8mMD7e+a+Z1o9XjZV1eDe9tK+vyWbV7NREhdhw2RX1rO20dB65DsWFOIlwOals8nPbXz/jfqnwrG7qpm06Sj3dW8Lu3drBsbxVLtpfzzpZSqwMIDoxnj/MH3snRIQetYqhpNjLCZrD+7tZS7nhuo7WkUmCH7S7/usQF1S1dJrU6GDMQHpsSZd22v6qZVzYUcdfbO9hd3mQFwLX+6okOr4/d5Y1MSY8B+nbO7UlLQHuxYYACYZ9P85OXt7A+v+fKjr0VjUetCm5IBsIf7SincgCm9H9zcwm7ygbmRGcFwvVu/vJBDjnljaRGh/LE8jzu/2Qv1c0eNhXVExXqtB5z/cLh1La08+s3tjHtd0v4eGc5df6DprRTgHf7sxuY9rsl3PrUOupaPLR6vN1+Bl6fRmttlayU1rVaMyu/sam421LJbf6T2oaCvn9pK5va0Jqgk5vJ59O8v91Y43dXHy7O+yqa0BpeWlfYY5C3t6LRmm3QDOynZcYc9dLogpoWf1akuV+NvNL6Vq5/bA33fnDoZXRLd1fyxIq8Pp1ctNasy6s56LjUnaUN3PrUuqNeQtvoNnpZC2tbDqvsZyCV1LWyYm/vY6cOhdkzfSTXKuzMbKhUNXm4d8luvvnM+i5jjb77wia++/ymo7ZNAKtyq2l0dwzamorHG3e7l2seWcXDn+ce0uPLG9wDVoZa6f8e59ccPANqXn8KeymdHAivbijq13VrIJmZ4LYOH+WH2MH57rYy7v1wN29tKeFXr2/r93m4vrXdatwOZGl0h9dHfWs7E9KiAWMo1KEys5j1re1W+6a/M/FqrQek03vN/hoe/mK/VSLck0J/u2J7ST33f7KHe5fkHPZrDwQzOEuNCe13mWtedQvVzR7mjYinxeO1Ok72VjbR1uEj1Gnj1Q3F+AJW4zCPrZToULLiwymuM8p0zUpGd4fXarMFZmC9Po3Xp3HZ7USEOKzvT1y40f6NCXMSHmJnd3kTDe4OCmpare9J52w1HMiO5lU1k+c/7gKXYTIDSPP5U6JDDxoI17W0ExvuIiEiBMAaN22+98DPd6c/Tvj6U+v47Vv9q2JraG3H5bBZY5dHJ0eyv6rZaneXN7itTnKzoyivuoW2Dh9nT04FYP9hVJ30VBrd7vWx8iDHQU/yqpt5YV0hb24q6fE+P391G9/+34ZDev7+GnKBcEldK197ah2PLd9/WM+TW9nEnc9v5Pfv7Dzsberw+qyDs6zezd7KJuZkx/OtU0exo7TBygjtKKknOtRhPW7ByATGpkTy4roiWjxe9lY0Wb1Hncc3bCuuJy02lM9yKrnpibWc9Y/PueSB4FnmXt9YzNTffsDzawut2ZRL692UNbiJDXfS4O7go26yvuZJbU9FU5cJA3piBuEt3QSum4rqKG8wst+7Oy1sXt/aTml98AXN/Owa3B28tbnrgbWxoJav3L+cm59Ya1wQa1tx2BQzsuIoqXMfkZnwCmtaup3lMjCY+Gx337OZb24qQWv4eFf5IW9vvv+1q5sP3gn04rpCLntwJe9sKe31fh/uKGfJjnJW96Ox/MOXNvPEYR5/+yqN99Lu1VbHRqvHy78/3cs5//yCvQM88Utf3PfxHr7+1LoBH9feuTS6w+vjl69vPexlGXqzvaSBtJhQUqNDeXDpPt7bVsaNj6+hyt+bXVbvZnNhHfurmukY4LW4i+taue/jPd1+jubF/3haQ33Fvqpey18PZY3Nvrrn/RyW763mlQ19m7m0osEd1AC87+M9XPnQSrYV17O7vNHK6BwKMyNcXNuKp5sO0EDm9aek7vDGt/W6PY1t/PjlLfynh/Lr1zYW9fkYe3JFHk+tzOvX6+cFdIT2lLHZXlLPPz7a3eM55bFl+xmRGMFjN86hwd3B2wc5X3dm7s/J6dEU17UOWKWFGbBmJ0QQ6rQd1jKFjYGBsD9gya9uCRo7eTBLdpRz8j2f9loiuqOk4aDPaZ6Diw/SQWP+fVNBHQ99nntIFWADxZyPxd3upaG1A5fdRkJkiBXUFNa08NNXthx0yInZgf61k0YCBwLOrf6qva+fNJLiular/QbG+wd/RjgunHavZntJvdU+dXu8VultYLWbWYXkdCgiQxxWG3hMshEMxoQ7iQhxWMmN+tZ2q3KgtN7d5fu225pBucU61swsLRglxSEOmzUbdWp0aK/l/D6fprbFQ3y4y5rZunNZuFlGHOKwsdP/WqX1rWwPOKf8d+k+/vFR78mN+tZ2YsKcnDkxhTMnpnDhtGGUN7SxLs/YH/k1LVZCyQzozez6yWOSCHXarLbfoQhsoweWRn+wvYyrHl51SFlbs6Jjbw/DZOpb21lfUEtZg7tLe/9IGHKB8FL/mNO9FU2U1bu56N/LD6n86r9Lc9Ealu+t6lN2+d4lOVz47+X89s3tQRf2prYO8mtaaPcat5U1uCmsaSEzPpyLZ6QT4bIzNiUSmzIa/YEZYaUUPzl7PKeMS8JpV9S0eKwLRWl9K69vLOamx9fQ1uGltMHNRdPT+dsV09hYUEdBTQuFNa1Wxu9/q/P57gubaPZ4WZ1bbfWeFvszwmdPSiUxMoT3t5V1eW/biuuJDXei9YETX2+01tZn1t2F94PtZTjtistnZ3ZZz+2ut3dwyX9WBAWDuVXNuOw2xqZE8uiy/UGfb12Lh5ueWEuHz+gR3lbcQEldK6kxoWTGheHx+qzGfV+527386+M9XXpUa5o9FFQbAfD5/1rGD1/a3OWx5jp6yVEhfLqr74HwaxuLcTlsVDV52FRU16/tNeX7Gz0HyyzurWjit2/usH7ujXnyX7Gvb5nQ/OpmXl5fxEv9WFKgqqmNP70XPGty4HaZF7Y/v7+Lv3yQw87SBj7c0f+S6cCshdaal9YVWiVTYMzw2lsgsLO0gWaPl2aPlz++u7PLJDitHi+vrC/qd9BT2ak0end5E8+sKuDGx9ccVmDSm+0l9UxOj+HXF0zkG4tG8r+vzaO8wc0tT6ylxdPBh/4OsQ6fDlqCrKi2hSsfWnlYWbYX1hTwtw93dwkOmto6rO/bsTxbfovnwLIgWmu+8fR6bn5irfX9/duSHP78/i7r/n//aA/z//ixVV6ote62E62m2dNrR+OHO8o5+x+fW+elTYV1PLZ8P0lRIewqawz6LvfkO89t5DvPbbR+31PRhE/DrU+t49x/fsF3X9jU6+N9Ps23/7ehy2Q6cOD48ml6nSQHDgRo7V49IMvvdOfVDUV0+HS3mdCyejc/eHHzQRuppmdW5XP32zv7lXXMr27GblPWz915fk0h//hoT7fL/G0oqGVTYR03nZDNgpEJjEqK4H+r8/v8+nDgcz5tfArAgFVamN+1+AgXqdGhfLyrgt+8sa3bCiN3u5f/e2sHU37zAS91EzCaAU5Da3tQoLq9UyfFF3sqWfSXT7vN5O0oacDr0z2eLxvc7Vz07+Vc/J/lvQbtZglqb8sutng6rImT3tlaSoO7g/KGti7HtKfDx91v7+Cut3f0+5rw1uaSXq+5S7aX8dUHV+D1aX726lYue3AlC//0CbvLG4kKNcbAmkHN82sLeH5t4UGHli3bU0lMmJPF45OJj3Cxzl/Wuq24ngiXnctnZwKwPq/WOgeZ45CTo4wxwgDvBbQhW9sPBMKBgacZ2LnsNiJDHNY+HZUcCRgZ4cgQh/V9qG/1BH03NhUGX39y/NeLfZVN1rknsJrTLHNWyjgek6NDqWg0kiTdnasa3R34NMSGOwlx2IkKOZCgMrfVHCM8LTOWnLIG/8zSHeRVN+Nu9/Losv388b1dPL+m904SMxA+Z0oaD10/m5FJxmdgjrfOCXgf5nfs5fVFJEeFMD4tiuyECCtZdChaA+beCewsMV9/aU4FdS0e/u+tHcy++yM29uHab3Yw9tS+XLG3ymrHHyymqGh0U9/S/XWxoLqlT0MBh14gnGMEwvsqm/hiTyWbCut4a0vP6XkwTpKBDbuyejevbixi4agEfBre23bwXtgX1xWys7SBJ1bksb/K2PnL91Yx7XdLeOQLIztmU8YXo6bZQ1Z8OFGhTp64eS4PXDvLOolEhzmDnnfxhBSeuGkuceEu6prbrXESpfVuluwo49OcSrYU1aM1ZMWHc/7UYbzyzYX86KxxgNHIzqtq5u63d3LSmEROGJ3A3som60SfX91MVZOHtJgwFo9PZmlOZVBvfnNbB7lVzVw2MwOlui9L6aw5oBewtZsLwNKcSuZkxzM7Ox6vT/PJzgr+9uFufD7NlqI6SuvdQcFgXlUzWQnhfOe0MeSUNwb1vr61pZS6lnYeum4WNgVLdpRRXNfKsNgw0uOM2QEL+1ke/e7WUu79cDevbQwek/X7d3Zy9j8/54cvbaG+tZ1Pcyq7dJLkVTcTH+Hi3ClprNhX3ade7Z2lDewqa+Q7p47GYVN81I9JyQKZjS0zI9zu9fGjlzYHLVAPRqNOo0mOCjno2JJcf4/ewUpk/vjeTi75z3Je8QfAu8oa+5x9eGldEQ8u3ceq3ANZ58ClPsxSy09zKjh9QjLDE8K7XAgPZnVuNXP/8LE1zm5zUT0/enkLr/r38Yc7yrnx8bU9lrj5AhrUVY1tLNlRHtSoq2pq48qHV/GDlzbzbD8XuDcvrGaHjVUB0drB93oITH7x2lZ++ooxi6e73duvLHWLxzimJ6ZFc+6UNH52zgROGJ3Iv66aydbier7x9Hpe21CE0240GnL95zN3u5dvPrOBVbk1h1UKaFaYdA4qdpY2oDUoZXQGPOcPmI8lu8sbmXXXR9a5obKpjUZ3B/sqm3loaS4F1S38+7N9PLpsP43udrw+zfNrCqhraef6x9YYHZibipnz+4+CspFVTW2c+ffPuenxtT3uy092lbOrrNGaX+GNTUbn2T2XTQU4aNVGW4eXjQV17Cw5MFt/bmUTWfHhlNS7iQhxsKmwjkZ3O3srmrrtqa9p8fDO1tJur6kVAefCg51XAgONwOBlfX4t5/7zi8MeL6+15oW1xvFZWNu1sfTqxiJ82vjM+pKRLm9w4/H6uP+Tvk/ulVfdzOT0GBw2FfR5tHg6+M9ne/F0+KyG4tpuVlh4YU0hkSEOLp2ZgVKKq+ZmsbGgjkL/mOODZd3hQOn54vHJwMErLQKHTfWmOiAQnpEVR3FtK0+uzO+2I/2FtYU8tnw/HT7Nu1u7tqUCS6PrW9tJ98/qGzhOuK7Fww9e3Ex+dQvLuxmeYjbaO8/sa9pSWI/H6yOvqplvPLO+x/dlBsK9dXiYw60y4sKChn4Fdri4273c+PgaHlm2n0eX7eeify/v8/VQa80vX9/G917YhLvdi8+n+fP7u4Imjvw0p5K1ebUU1bawq6yRiWnR1DR7+CyngugwI5tqvt7HOytQCp5amddjcF3Z2Ma7W8u4aPowbDbFKeOS+HB7Oa0eL9tKGpg4LJr02DDCXXa+8H/+/j4eokIcRIQ4rDbsaxuMc2NChAt3uw+3p+eMsMthIyLEbpVNLxyVwLlTUpk7Ip5wl926f11LOw3udqJDHbjstqB2qLvda10z1+bV0O7V2BRB7R5j4iuX9XtKdAjtXs0L6wo5+Z5PrXOQu93L/Z/ssa555vjg+MgDjzW31ewEGJMcSXlDm9U29/kTaL9/ZwfhLjvlje4u83AEnuPNQNiUnRgedN+csuDM9p7yRj7fXcn1C4bjtNsYnhB+WEsoBU2WFfAdNTuMlu6u5GevbuXJlXlUNbXx+e6DJ0W2FRvX+PKGtm47dz/LqSTKvy+7G/Ntcrd7OecfXzDr7g/50Uubgz43n09z/r++4NpHVx+0o2lQA2Gl1NlKqRyl1F6l1E+7+btSSt3n//sWpdTMvj62OxrjC6iUcSHe7A+mPt9dSVVTG2/3EBA/+Nk+vvrgSmuQ+1ubS2j3av5w8RTGpUTx+kZjaaH9Vc1Wxi9QXYuH8oY2Th6TBEBJnRt3u5dfvr4Nr09by0lMSY+xygzMk8ac7HhGJUUy2t8TFhVQGh0oLtxFbYuHulb/Ivb1bvb4T7xmOXNWgvGcs4bHMdEcu9PYxt3v7MBpV/zlsmmMT41mb0WTdeEwe4jTYkI5fWIKjW0dQWPGjCWOYP7IBMYmR/HWlpKgC1G718ffPtwdlG0LDA47l0aXN7jZVdbIyWOTGOefHOD7L27mvo/3sLW4nlx/SWzgDNX7q5rJTojg/KlpzMmO468f5FgB5msbihibEsmisUnMHRHPm5tL2FvRREZsGBlxxudxsAu71joom/KBf/zy0k6zE5bWt9Li8fLRznLmjzSC+M4TmBjbGs5lszJo6/D1abH1f32yhzCnnWvmD2fuiHje3VpqnTjrWjxWWd6PX97MXW/3vASXWYpnZoR3ljbw0voi/v3pXioa3Pz+HaNnek9FI+NSo5k4LLrXE6jWmtwqI6uxrbi+x6C+xdPB/1YVsKGgjvs/3UuEy47Xp63j72DMi3NgT+O+iibGJEfictjIr26hqNYoeVo4KpHpmbFsLqynosHN7c9uYOKv3+fv3QRMbR1ebnt6PWvzaqzv7K9e30ZZvdvqbCita6W4rtUKOLf0UCpZWNtiXTCqmtqoaHBTWm+UmXo6fNzy5DpyyhpIjw3jxXWFPQYzDy7dx3v+xuDKfdXUt7Rb5U5m49/cJ98+dTTr8mu7lIH7fJq3Npfw8voi8qubOfmeT3moH2NEd5Y2ojVMGhYddPsZE1P4w8VTWJ1bw4aCOi6cng5gHZN/eHcnW4vrOWVcEsv3Vvd5+ZC9FU38+OXNTPvdEjYX1lnlz52PSzMwXDAygV1lDdy7ZDf/XbrPusitzavhj+/u7Hep9rOrC7jw/mVdxne3eDr6NQzB59P89JUttLZ7+dTf4brf/9lkxofxz4/3cPtzG/BpI0j5aGc5q/dXU9HYxh2Lx1DX4uG/S3N5bnUh7V5trcOptTGxSFVTG+vza7uUoplBnNmB8MJa4/v1wbYyTh6TxAmjEgl12ljVKUtb39LOPe/vsm7fUdKAx+uj0T+bcX1LO1VNHq6Zl8WnPzyF+6+egdeneWdLKef+8wsW/PETbn0qeOZ/MyOxo6SBBnc7v3trO5v9DZnKxjbG+K9jgRnQj3aUc7G/k8z8vAtrW6xGZmAg/MqGInaUNnTphOxNd8fauvxacquaOW18cpeZrLXWvLy+yJhZ193B9pJ63t5SwqsbirqtHnO3e2lwdxDusvPSusI+V0PkV7cwKimCjLiwoED43a1l3PN+Dkt3V1qlg53HaXd4fSzZUcbpE5KJ8GejzIlx8qqbuf/TvZz1j8+t997obufVDUVdOhAKa1qJDnUwOT0Gl93Gvl4ywlpr7n5nJyf++dNuhyAFsiYfCnfxt8unsfOusxmeEM7/VhltnRaPMelUXYuH/OoWwl12Lp2Vzpr9NbR7gyf2bGg9EAjXtngYmRRBSnSIleUD+MsHOdQ0ewh12rr9/M3vW2VjcNWPyby2XDt/OFuL6nrsRDDPxb1lhM3OhXP84zPNsZ2Bmbvn1hSwYl81f750Cg9eO5NdZY1Wp6vPp3ltY8+VQ0W1rdS3tlPe0Mbjy/P44UubeeCzfTyxIs/63Mx26OaieorrWjlncioZcWH4tNGGjAxx0OzxUlhjBMq3nzqalOhQrn54dbeVbP9bnY/H6+OGhdkAXD47k8a2Dp5bU8COkgYmp8dgsylGJ0eyZn+1/30b14+UGGPN37TYUOw2RVmDm1tOHEFSVAit7V6rDRjYLrQCYbuNiBBHUGb5P9fMIi0mjMiALGxdq5EESowKYWpGjDWGu8Hdzp5yYw6ZUUkRuNuN5507Ip791c3W52VkhA8EmynRxja/uakEnz7Qcffkijz+umQ3T68yKi/M5Z3Mc1Wo02Z1XDe5O4gMcZAYGUJ9a3tQ5eHDX+Ti03DFnEy0JqgKoabZw4l//pRHvjCu2V0C4YQI6+dwl73LWOfHV+QR4rBx1dwsaz/sr2qmqa2DbcX1fJpT0a8lVls8Xuv9NQaURpfUGdu8pbie97aV8a1TRjEyKeKgE2hqrdlWUk+a/3vROSustWbp7kpOHpPEhGHRvc4Q/9HOcqqbPcwcHsdL64uCPoviulYa3B2sz6/t9jsdaNACYaWUHfg3cA4wEbhKKTWx093OAcb4/90KPNCPx3bR0tZBY1sHZ05MwevTvL/NaOyaH9Ttz25kT3kjT6/K51evb7NOlGvzjF7hlf4Gw5IdZUxIiyY7MYIr5mSyoaCOH7+8hfPu+4Kbn+zaY2/22JwyzgiEy+rdPLkij/1VzZw50ShJSo0OZVRypNXjkhkX3Osz2j82IjqgNDpQXIST2pYD5SHFda1Wg/kT/8y6ZnANkBRlDPCvaGhjS1E9Z01KJTUmlDHJkbjbjYkuokIdmG8lNSaUE0cnEuKwBY0TNpdUmpQezbdOHUVVUxvXPrraKjv5cEc59328hzcCBsUHB8LBB6RZun7ymCRGJEbgtCsrwHh5vVHK5rQrKxD2+TR51S2MTIpAKcVvLphEbYuHP723k/1VzWwoqOMSf4/5mRNTya9uwafhstkZZMWHY1MEXfz/tiSny/rG//pkL/P+8DE1zcYkY0t3V2K3KZbvrebNzSVc+sAKPB0+apo9TBoWzRWzM7nvqhlMzYjhFX/v5/K9Vby1uYS8qhayEyOYnB7DrOFxPLMqv9vGdluHl32VTXywvYx3t5Zx26JRxEe4uHFhNnnVLfzrY2NZm4c+z+U3b27n129s592tZTy6bD/3fNA1G9fc1mF97mavuHnSeH97GT9/bRsPf7GflbnV7Ck3gszshAjyq5t7DNqqmjw0ujs4Y0KKkT3pphwSjNmdm9o6mJIeg0/Dt04dDRyYURKMC9a7W0u7ZF/aOrxWNmRDQC/v3oomxqZEkRUfTl5Vs3XhO2G0EQiXNbj50ctbWLKjnDEpUfzz4z1WtqGwpoXaZg/vbS3j/e1lPL0yn/UFtaTHhtHu1fzs1S3W96uswc2KvVU0tXVw3tQ0ciubux1LFdgrm1/dYq1vuLGgjj+/v4vNhXX8/fLp3HbKKHaVNXZZ9sz04NJ9PLumgIpGN1c/sop7PjBKaF12m9WIzatqJikqhKvnZWG3KV5eHxwU5FY10+DuoMOnueXJdVQ0tvWpUsNkluhO7BQIA1w5N4svfnIqvzh3Aj85ezwxYU72VzXzWU4FT63M55YTR/DPK2YQ7rLzn896z45pbQSOZ/x9KW9sKqHF08Ejy/ZbJXLm+qambcUNJEWFcMLoRMob2qhqaqOtw2dlwb725Dr++3ku/+pHVg7giRX72VxUz9WPrOaGx9awq8wI4k740ydBc0nsrWjinx/t4c/v7+o2S/jaxmI2FNSREh3C6txqq3MU4KHrZrNgVAJbiuq5em4Ww2JCeWdLKW9tLiHcZeebi0Zx3pQ0XlhbyJq8GsYkR7J8bzWf5lTwwfYyPt5VwQ/PHEtMmNOqIAKjRHzKbz9gVW41u8oaiQ13sqGgjv+tLqCk3s05k1NxOWzMHh7Pin1V1rkmt7KJM/6+lP98to/fvbXDmBE24Duyp7yJff6sx6ikSEYkRjAnOx6Xw8bv391Ju8/HorFJfLSzPChoMdfjNDLTpTy+PI+L/rOcZ1blU9HYxoS0aCJcdraVNLBkexlaaz7cUc7Ggjp+8NJmnltrBEpFta3MzY5HqQMVO1prPvMPJ+lLB2JBdQtn/+NzxvziPe54bmPQefaFtUY29bunjwGCM6GbCuvIrWzmjtOM89Rv39zO7c9u5Psvbua2brKFZgboe6ePJT0ujK8/ue6gQxbc7V5K691kJ0SQlRARNIHYloAOevOcvabTNWn1/hpqW9o5e3KadZvZ0V1Q08KGglr2VzVbjcyX1xfx/Rc3M/+PHwet7lBY20JWQjh2myI7Mdyae6E7z6zK59Fl+4kMcfCbN7f3OHbP3e6lKiAjrJTCblNcPTeLNXk17C43vhsPf7GfT3ZVUOKv0DphVCLNHi//+mQvk3/7gdXxZU7m5fVpSuvcRIc5SYkODRris2JfNYsnJDN7eDzr8+uobfYEdR6YgYx57dtR0hBUxrmxsI4xyZFMzYjFpw9kfN/YVMzsuz+yslZmgN9bRtgMks+fOgyHTXHzCSOICnVYgbu73ct/PtvHvBHxXDEni7MmpTI5PZqnV+ahtdFB/L0XNhvXpfyaoGEVcKDNlRQVwp/f38WrG4s5bXwy7V5tlSub5x2z0350ciQnjUkEjDakGUSalRsXzUjng++dzAXThvHKhuAgfMXeKp5Ykccp45Ksstx5I+IZnhDO/729A69Pc5G/U3R0UqQVbM4aHgcY2VUAp93GnOw4Lp+dwS/OnUCo0447qDT6QDBodkQ4/aXRpogefjarBaJDnZw6PpmtxfVsKqxjwR8+5vbnjAmXzpqUat3/7EmpaA03Pr6Gu97eQa1/4iuTuc3mcVfe4KauxWMt57bK396I9U+uZa4lfMKoRGsoR6PbaEObyyvlBiQVVuXWkBwVwqnjjEqMwLHrX+yppLiulbvf2clbm0u6BMIRIQ5SokOI8ndgmcGpw6aoa/GwfG8Vp45LJiHSeA8zh8fh8w9bvPmJtdz0+FpO/etnNLjb2VXW0G0FRaBWj5eIEDuRIQ6a2jpYursSd7uXsoZWkqJC0BrCnHZuOmEEE9OiDzpTen51C43uDr4yfRhwIBC+5/1dPL0qn+V7qylrcLN4QjIzMmPZWlTfpXP7jU3F3P/JHl5aV0RqdCj3Xz0Dm4L3/G1In09bnZuLxyfz9pZSPu9lKdbBzAjPBfZqrXO11h7geeDCTve5EHhKG1YBsUqptD4+tovGtg4cNsUNC7IBI3MzJjmSDp/mM38P/jtbS/nHh0aPzysbivF0+NjsP/Es31tNtb9X/gx/AHvjwmwumZnOS+uL8GlNbmVzly+CeZE9eUwSSkFJfStr9tcwNiWSey+fRlSIgzEpkaT6e6EgOGgFrIxwdK8Z4QOTSbR4vHT4L/x7KpoIcdhI8h8YYEwPD0bWpaKxjUz/641JibTuM9t/IgMjIxzmsnOyf8kmsyb/nS2ljEqKIDU6lAunp/Pat05Aa6PUCLDKQwMbGoGBcOeStM93V5IUFcKEtChcDhsT06KZlhFDYqSL1/09ppfNymRvRRN7KxopqTcmXjF7ySanx/D1k0by3JpCrnt0NTYFF/oPuKvnZfHnS6ew9EensHBUIqFOO9mJEdYFKq+qmfs+2RtUurq9pJ77Pt6Dx+sj119O7273cf2C4bS2e/n+C5tYn19LeYOb2hYPk4fF8OfLppIcFcqlMzPYWdrA9pJ6fvbqVr77wibKGtyM8G/r9QuGs7+qmWUBJ6IOr4/fvrmdib/+gMX3LuUbT68nLSaUW082Jqg4c1Iql83K4P5P97K1qJ53t5aycFQCa36+mM2/OZNr5mXx4NJ9XcZpBGYcavyl0TlljdgUVnYKYNmeKir8mZvshHCaPd4ey8nMi+0lM9OJDXfyu7d2dDt286V1RWTFh/PM1+bxo7PGcdMJ2YxJjrQa3luL6jnnH1/wrf9tsPaxaUN+He52H+mxYWwsqMXnMyYWKqhpYZR/GwtqWli5r5rESBdjUyKZlhkLGJ0qX52VwYvfmM+MrFhuf3YDtz61jlP/+hmXPbjCCnKW7q5kU0Ediyck84Mzx/JpTqX1nShvcFNc14pSWBd7s6znzc0lVkMjMBAOPP5fXl/EY8v3c+38LM6ZksZXpg7D5bBZZZkA728r5YHP9tHoNo7fvOpm9lU0ozVW8D4mJZLaFo+/46eZEQkRJEWFcOq4JF7dUMSq3Gqr8WBmRBIjQ6yLTF/WbjWVN7ixKUiLCev27ynRoXz95JEkRYUwMimCnLJGfvbqVsalRPGjs8YRE+7kayeN5O0tpbyxqZimtu4zq/sqm3l+bSGXzsxg+U9P4+QxSUFVOZ0zwttL6pk0LNpaRiIqxIFNGZ1Mtz9rZFoXj0/mX5/s4YHP9gVVKLy1uYQ/vberS6fOvsomdpc38dNzxvOjs8axpaiOrz+1jlfXF1Hb0m4dF20dXq58aBV//2g3D3y2z1omJNBnuytJjQ7ljsVjqGhsI6+6hf1VzbgcNsamRPHYjXP461en8eOzx3PulDQ+3lXB82sLOXNiCmEuoyHR2u5FKXjkhtmMSorgF69t4y8f5DAqKYJvnjKa6+YP5/3tZVz50EqK61rZWdZIh0/z9w934+nwccdpY4gKcfDL17fhsClOn2BcpxZPSGZ3eRMX/2c5eVXN3LtkNy0eLzedkM3O0gY2FtaxsaDWGuu2t6LRyvSPTDLOV6FOO7Oy4mh0d7BobBJXzsnEp4OHKZidNS0eL8+vLSQx0sWkYdE8v9bo3EmOCmF4QgQvry/i1qfXs3p/DfurmpmTbVQpPb+mEK/PmABvRFIEadGhFAVUJ5XUu5maEcPO0gY+2lHeaznpX5fkkF/dwvlT03hzcwn/9HceNrrbeWdLKRdMG8aEtGicdhVUtrrMP/vrNfOGMyopgg0FdUzNiOE7p41mR2kDZfVuNhXW8eiy/Xy0o9ya8XlcqrGPmz0dVjanJ2bF1fCEcLITwsmvarG+m2YG/U1/1nVudjy5lc1UNbWxt6KJC+9fxl+X5BDmtLNobJL1nClRobjsNgpqWqyMoJnt31/VTGSIg+SoEJ5YkWc9Jrey2WprjEqKtIa6gHFeDgz4XtlQzNSMGF66bQGN7nYW/PETzvnnF0HHVFNbB6f85TPuetso040LyLJdNisDl93G48vzrBLp/OoWa6jSglEJKGVM0Ob1aauipCHgOC5rcBMb5iQhwmUN8WlqM8ZdThoWw8wsYzzmN55ezxX+Sd6a2jqsUu2qpjZ8Ps0vX99KdbOH9fm1aK3ZWFDLjKxYsv2dCWYp7RMrjHJPc+iPWRpd0+zpMatWWNNCiMPG1IwYlv74VL46O4PxqVHsLmvif6vzufaR1VQ2tvG9M8YCxlwv18/PZnd5E6v311iZtufXFvCHd3fxya4Kcsoa2VXWwMc7y9laXI/TrnjgmplcPCOdt24/kX9dNQOHTbFiXzUtng4rGDM7jkYlR3LCaCMQjgp1EOlvR765qYTshHBGJkYQHerk9AlGhYTZkfPcmgKufmQ1ceEufn7uBOs9KmV0bCgFf79iunXNHR3Qfpw53LgtJepAu/a5r8/nnsumYbMpwjoFwhXdZISdDluPwW+4KyAQbjEywtFhTqttfsdzG2n2eMmvbsHlsHGKP+gMCfh59f4a/rc6n6rGNuIDAuFk/zabHZ5lDW6eWplPY1sH6bFhlPgzuGamNC0mjLhwJzOHG+fHFk8HTW1GRti8j3k+NecFOGlMkjU8rySgU+nz3VXEhjuZmhHDvUtyugTCANMyYpk3IsHKXIPREVbR0EZhTQtjA/bD9AxjP5idkedPTaO8oY2lOZX85JWtXdZU7qzF4yXMaQTCGwtqueGxNby0vojSOjeLxxtD0W45cQTxES4mDou2Kha8/uOs87A5sxLw3MlpuBxGFUqrx8vDX+Ry19s7+MuSHBIjjeGD0zNjaW33BiUO3O1efv3Gdv66ZDdLd1dy8cx0kqNCmTcigbe2lHLJAyv41v82WJ16v794CpnxYfzh3Z4nNh7MQDgdCBwlXuS/rS/36ctju2h0dzBzeJx10IIRjIQ57cSFOxmfGsV/l+ZS3ewhKSqE3721nU9zKvB0+Ihw2Vmxr4qPd1Xg01iZXJtN8edLp/KnS6bw5u0nYrepLjPt5vgnKMiMDyMxMoTSOjd51c2MTIwkKtTJ4zfN4ZfnTbRKBaJDHcSEB3/xzZKyzmOETXERLmqbPdS3tFu9U+ZzAWTGh2MzB20ACREh2NSBhekz/Afk6KQDa5XNGRFv/WyWt3z39DHUtbbzp/d3sq+yiXX5tVw+O9OaZCArPpxQp42c8kbKG9xWhjcwEA4cCxJYGq21ZvneKk4ak2g93+M3zeWZr81jemYcjW0dOO2K75w2mnCXnb98kENelXHCHpF4oFzke2eMZUJaNHHhLh69YY7VoA912rliTlZQz9+4lCirEWSu/xYYyPz6je24HMZhUlDTwqc5lUSFOPju6WNx2W1WZ0NlUxu1ze3WOnQAX5k2DKdd8Zs3thtrRfobDNn+bT17ciqRIY6gySO+89xGnliRx6Uz0/nb5dO496vTeOrmuYQFjIf59QUTiQt3ccfzG8mrbuGCacNIjjbKju70Zzg2Fhgz+v7r4z34fJoCf8ZhVFKE1SjYVWaUNBmflZPM+DCrlHtMSiTD/dv5xqZizv/XF116Ds1G04S0aJ65ZR4er49LH1gR1BNf2+xhZW41F89IJybMybdPHU24y8Gs4XGszzcC29+/uwOP18fwhHAe/iI3qGG1Yl8VNgW3nDjCP9ayidc3FuPzl+4OT4iwMpILRhnfm4n+xi3ATSdkE+Kw8+TNc/nqrExj9tCxSeyvamZLUT1zs+Opb22n2eNl1vA4blyYzeR0IxM6zZ9ZLqptJSUqlJlZsQBsLa7j/k/2cMdzG62xsLvKGxnmP0bM0iC7TfHRznJCHDa+d7rR6IkJd3LelDRe21hsVX88tiyPf3+618qAFte2WsGFWYo3PjUanzZKwPZXtVjjhK5fkE11s4crH1rFVQ+votHdzsaCOqJDHXzrlFGA0TOfVx08u3NVUxvPri5gbV5Nl+CwutlDXLjLumD3ZkRiBOvyaymtd/PL841efoA7ThvN7OFx3Pn8Jib/5gN+8+b2Lo81J4v77uljSIwMYfGEFKsCZZL/gmry+Ywy/LEpUdaQifOmpjElI5bHlhsZ3bsunMw/r5rBCaMT+fP7u7j0gRW427384d2dfOe5jTy4dF/QkAo4kDH5yrRhfPvU0dxz2TQKa1r5k38yqw35dbR6vLyzpZSqpjaeuGkO0zJj+duHu3G3e8mvbuaCfy2joLqFDfm1zMqOY96IBADW7K8m1z8Uwm5TOO02LpuVQUyYk2vnD+eUsUl87/Sx/PYrkwDj+7ZgZAKnjUtmeEIEf79iOpWNbeyrbOb7Z4zDblN8Z/FofnrOeNbl1fLUyjxrPKI5/vekMYm8/72TuemEbO5cPMa6jtywIJu/XT6NgpoWrnp4Fe9sLeWmE7L5wZnjiHDZeXx5HhsL6jh5XBLRoQ72Vjaxr7IJp11ZnaRgjNEzn2+8f3jNrtJGfvjSZv796V4rUAAjoFs4KpHTxiWzvaQBd7uP5OgQZmfHWRVJO0oayK0yroWXzcpga3E9n++ppN2ryYgLIyM+3Co1Nb8v91w2lRCHja89tY4z//55t8HwztIG3txcwk0nZPP3K6Zz6cwM/vnxHrYV1/PW5lJa271cMScTp93GiMSIoOEFa/JqGJ8aRVyEixNGJ6IU3HXhZC6YZnSoPrMqn8seWMFdb+/gW89usLI5KdGhjEqKZE52vDWfQYfXx7WPrOZ3bwV//81AIzM+nEnDomls62DZ3iraOrzsKG1AqQPrh149zyhxXLu/hs9yKthcVM/GgjpOHZ8UdF2w2RTpcWHsr2y2suhmFZtZNXX+1GHWkIvKxjYKalqY5m8oj0qKJL+mBU+Hj7YOL1c/soq7/Eu9aK3ZU97IjMxYJqRF8/JtCzlncio7SxtoaO2wHvPUyjwrWE2PDSPEcWD7EiJDuHxOBi+vL+QLf2dDfnUzJXWtpMeGERvuChqOYXYWdB5DGBvuJCEyxMoI5/iHZ01Mi2aGP/u1Jq8GrY3JNQPL8Csb23jVX7kBRpYuv7qF2pZ2ZmTFWVn1/KpmciubrEoas3OktrndGvva09KLRbWtZMSFoZQiPdb4f2xKFOvya/jFa9uob23nh2eOZf7IBOsxX5k+zKi221FuBbH7KputYRC7Shv5y/s53PbMej7ZVc641ChmZ8fz9yumMyUjhogQB9MyY1m5r9pqE9mUMR+LTRkdLieMMr7LUaEOK6DcVdbI4gkpVnvLTCjsr2pmbV4Nv3p9G4vGJvHuHScFrWMLcOvJI1nx09M4b+qBqoQxVuWiw2pLJgcEa+brgFFG3NrutbLPdS3tVua782RZpoiA73tkiPFzTJgTj9dHeUMbMWFOxiRHkhkfRkFNC2dMTOHy2RksHp/MKH+H3vCEcLITI3jw2ln88rwJuNuN4SCBbTczUWSqaHCzs7SBEYkRnD4h2brdbEveefoYnr91gZXMqmgw5oaIDD0QCJud0eYQhpPHJjLM3zY1Oz+01nyxp5ITRydy3pQ08vzZ087t/vuumsH9V88g2X8eVcrYd9tLGvBpGJF0oD0cE+5kdHKktSTpr8+fSEKEi8eX72dzYR11Le29dpS3tnsJcxmdJ+Z43Q35tVQ3exgWG8anPziFH5xptG/MIZc7Sxt4fWMxz6wq4OlVedZzaa15bHke6bFhTBwWzcjECPZWNLEu3xi77enwsbmwjmvnDyfU39Hncth4aX0hy/dW8YMXN/PcmgLqW9u5ZGY6qdGhXOGfpO3cqWnsr2pmc2EdS3dXsruskZgwJynRIfzorPFBZdOdDWYg3F0rq3O3RE/36ctjjSdQ6lal1Dql1Dp3u5dFY5OICHFYQeeMrDh+c8FE/vrVaVw4PZ3Wdi8JES6e+/o83O1efuKfcOb6hdnkV7fwz4/2kB4bFnTCdtptXDk3i7EpUSwclcA7W0uDGpc5ZY2MS4lCKUVaTCjFda0U1rQy3N+YnZ0dz7jUKKt3xzwZBxqbEsXIxIhuyxXBWP+stsVDY1sH4/2L2CuFlRHonGG22xSJkSFWearZ2IkJd1qNlHn+QDjCdWBWvEnDYrjlxBE8t6aQb/9vA3ab4uKZ6UHPOyY5it3ljVbAcuq4JPZUNFHX4uGRL3KDxkO0erzsKGlgT3mjEUy2tDPVf6IAo8ctKtRp9S6OSopkWGwY3z51NB9sL+cv/kAkMBAOddp5786TeOs7J3Lq+AMnre6MTYmyZvH7eJfRSN5b0URbh5dtxfWsz6/lzsVjUMq4MOeUGRNDxIQ5uf200VYjJa+qGY/XFzTOJC7CxWnjk1mXX0uY087vL5qC3aas706Iw87JYxP5eKexJNLO0gbe21bGHYvHcM9l07hkZgaXzspgTKcLUHSo8dr7/eNzA0t+kiJDSIhwsausgadX5nPvh7vZVFRnjQ+emRUX0IBoZHxqFA9cM5Pnbp3PnOx4K/s7JjnKylzfu2Q324obuO7R1SzZfiBo3++frXtYbBiT02N49ZsLSYwKCSqNN3v/5o080KkCRnBW39rOzrIGNhfWc96UNL59ymh2lTVy7aOrufaR1XR4fXyaU8H0zFhO9mc+Hlu+n7ve3sHCUQmcMSGF7MQI2jp8OOw2bjlxhLX/52THc8bElKAhBX++bCqbf3Mmj904h1+cN5ERiRH87YppOPwtm1nD43DYbdx/1Uzuvmgy80fGU15v9LCmx4WREBlCemwYD3+xn78u2U1MmJO8amMd491ljUwcFkNcuNPKZJjHz5VzsqwyJTA635raOnh1gzEmcnuJkbVY78/k+jRdyngm+I9pY/K6Nqsz5eSxSSz7yan88ZIpbC6s47pH17ByXxXTs+K4fsFw3rz9BK6am0W7V1sNy9pmD1c9tIqfv7aVrz64MqjUFqC6qc0q5zqYkf7tmJIew4n+jAOAw27jP9fM5PZTR3P6hBSeXpXfpXT+05wKxqZEWmP1F/sbGFnx4UxIMwLhJdvL+O/SfVblx4jECDLjw/jFuRO4/bTRLByVQIvHy5T0GL4ybRiRIQ6evmUej94wm70VTXzl/mU89Hku187PYmRiBPcu2R1U1vzBtjKmZcYyzD8Bz+kTkpk0LBp3u4/Txifj8fpYn1/LkyvzGZkUwaKxSfz07PGU1rt5dnUBL68vYmtxPfd9sofiulZmD49jVFIEiZEuVuca2c7Ac5MpOzGCx2+ayx2LxwR1zD1581wevG4WAFMzYvndhZO4eEa6Nd4wxGHntkWjyIoPp6C6JaikLsRhBHXpsWH85oJJfGfxGOtvNpvikpkZPHbjHGpbPESGOLjlxBHGZEuzMnhrcwnFda3MzIpjdHIke8qbrImynPYDzYTrFgznj5dMYdHYJLLiwwlz2lm9v4ZXNxSxNKfSCoTNYOGE0QnMzo63OjiSokL4zQWTWPPzxcRHuFifX0tVUxsjkiK4cLpRSvpHf899Rlw4mXHhVgfR53sqGZ8axfjUaN64/QTuunASxXWtPO6v7Kht9vDy+iK01vzrkz1EhTr4xsmjjCEzX5lIXLiTX76+jb8uyWFyulFpBDAmJYotRfU8tmw/FQ1u1ufXMtd/7N6xeAzPfm0+0zJjGZMcSXpsGP/+bC9KGR04ng6fNXTDbJTOH5lATnkj1U1tPPRFLsv2VvHs6oKgCgUzo5QeG8ZFM9LJjA/j9+/sZHtJA+1ebZVMhjhsnD05FYdNsa2knn2VTcSFG53nvz5/UpfvVWZ8OKtyq/H6NBEuO6tyjY6u/OpmhidEcNakFDp8mo93lVuVI7OzjcqvUckReH3GeWJpTiWN7g42+icdLK5rpdnjZax/vOu0zFjOn2p0DJQ2tPKDlzYz/w8f88Bn+zhlXBKf//hU3rnjxC7bd9uiUWgNHq+PuHAnOeVNVDd7SI812j63nDiCm07IJj02zOoAaWjtCJqVNzbMRaI/ENb6QOZ44rBoZmbGWfvil+dNYPX+Gh5blgdAYqSLqqY2Ps2pID02jBlZseyvbLY+hxlZsSRFhhDuspNf08JrG4uxKZiaEcMXe4zzcU2Lx6rO62mcsBEIB7e3xqVG4dPG8Lj3v3syt582JujvoU47Wf5On7L6VmOSKJedxMgQwpx2dpY1sKmwjnavMSnjlIA2kmnByAS2FtdbcyzMzja+w1nx4YQ47MRFuPjZOeO5bFZm0Oe5OKCNZAbCedXN3PX2DobFhnHfVTOCOlxMRns2uGrI/GxSY0LJSjCSIuZtnYW57Ljbg8eDm8MMzFVUXP7lk0zdZYfNz6KswU10qAOlFIv9s6DffMII7rlsGg9cO8toS4Y4yIo/kIi4ZGaG9XxxAcmnEIfd+j3cZae8oY386haGx4dbHYB2m7ISTYmRIUFt+PIGN41tHUSFOq11hs3EwaKxSYQ4bJw0xujIigt3WufxnPJGKhqN+YTM4xLokhEOddoJddqtc05smJP4CJeVXR+ZGPyZm534UzNiSI4O5bTxyUFDYdbk1bC3ookX1xVanT6mVo+XcKedqFAH5qXTTHClxYRisymrg8OMTzYW1FkTWa7OPdDZ/smuCjYX1vGd00bjtBvfjS3F9SzNqcRhU3z71FFEhTi4Zt5wY59EuLhg6jBe21DMnc9v4pUNRfzurR2MSorg3q9OY+XPTrPaQudOTmXSsGjOn5pGa7uXD3aUMco/ZPKCqWncdWHX86VpMAPhIiAz4PcMoPMMDD3dpy+PBUBr/ZDWerbWejZglRKNTo7EaVeMSYnkyrlZLJ6QwlmTjIPnwunpjE6O4oYF2dS1tJMRF8al/mDP4/Vx7+XTgnq2Al0wbRj51S08vSqfx5bt5zvPbWRXWSPj/BeQtJhQNhXW4fH6rEDDZJ5UOo8PBuOk8ckPT7EukJ3FhbusL6k5SUFWfDhT/Bf7zLiuZY7J0SHWem4ZAX83JyGaNMx4bGpMaND7/f4ZY7lqbiY55Y2cNj7ZKiMxjU2JIqeskY92ljNpWDRnTkqlxWNkZu5+ZyevbCgmxJ9lbfF08Ks3tvHz17ZaE5EMT+jacJzhv8CZvZK3nDiCkYkR5FUZY7lSOvXg9dW41Ci0Ng7c1bk1ZCeE0+HT7Clv4plV+YQ57Vw5N4u06FAKqlvYU95kbcMdi8dwh/+CZpbSxoUHBxCX+k+0Z01K4ep5WWz+zZnWOBuAxeNTqGhsY1tJPa9tLPaX7g8/6HZfPS+L4QnhnDwm0epxBOPiNCEtmp2ljVZg9c6WUvZVNJEQ4SI7MYKmtg6KaluoavIwLtUY6z4+NdrKDIQ6baT7Z9W224wx2tcvGE5KdCivB0z+lVvVzHB/tguMRtgLty5Aa82r/rHRW4rqUYouF26zN/zJFXm0tnuZkRXLhTOGMSwmlPX5tSzbW8WTK/PZVtzAOZPTGJkYQVpMKM+tKSTEaeevXzXKqy6ekc5dF03m4x8sYnpApcfjN83h31fPpDPzgnLLiSP45AeLyIgLZ97IeIbFhFqzkWYnRnDt/OGkRofi8frYUdJgHR9TM2KobGxj0dgkfnz2ODwdRsl8blUz41IjSYwMsca0XTk3i8TIEKus3TQjK45pGTE8uSKP3Komazxx4ORry/dWW/vVprA6Q8yLV+C5Iy0mjKvmZvGfa2ayq6yBvOoWZmTG4rDbmJoRa/WEm6VCdzy/kfyaFh6+fjbTM2O7rDFrLiXRF+Z23bZoVJdzYnJ0KD88axz3XTWdjLgwfvH6NqsEq9Hdzpr9NUEdVSnRoZw0JpGTxiSSERdGeaObP/iXoTIn/huRaFzYvn7ySDLiwjl9QjIOm+Ln504IqnhZPCGFq+Zmsbu8ictnZ3DXhZP53hljySlv5J/+dVkrGt1sLqq3qnvAOH5+ce4EZmTF8vuLJ+OwKf70/k42F9Zxw4JslFIsGJXAzKxYnl6Vzzv+0nXzM5w1PA6lFCeOTmTJjnLyq5sZkdh9Q7A7LoctKPC8Zt5w/n7F9KD3BkZnqVlWmh4bhk3B+LRoHPbeL+kzsuJ4/tYFPHrDbCsA/9X5E3nw2pn88rwJXDYrgzHJUeytaGJPeVPQuQqMDMhVc7Ow2Yxxn2NTo3hzs9HhWdbgprbZQ5jTzij/4xaOSmTm8DgrME6OMipXlFKMS4niU/93fkRiBAmRIVw0I53d5U1EhTiYkBbFmJRIyhrcVDS62VRQZ3UujU+N5roF2Zw+IYX/fp5rjd/74Uub+e/nuby/rYyr52VZGfHoUCd3LB7DpsI6Orw+7rtyhvV9nZIeQ0VjG//39g6ue3QNLR4vc/xBRGJkCAv8WXClFIvGJaG1UeZrXouX763GZbdZ4wXNc9vTq/L5x4d7mJIeQ1uHL2iCqZK6Vpx2RVJkCCEOOz8528hY/OyVrYDRWQYwMimSUKed0cmR/g7jJsYkR3HquGRSY4Kvu2Bc583zz1emp1PT7GF7SQNFta1kJ4QzLSOW1OhQ3t9Wxvr8Wlz2A9d5s/G8r7LJWo+4vKGN0vpW6/gLzAqar19a72ZrUR3NbcbER987fSyhTntQB48pIy6ca+cPZ2RSBGdNSrU6TM2OqItnZPCbCyYZQWFARjgjoCM/JtxJYqQLj9dHg7uDHaUNxIU7SYsJJSbcyTcWjeTuiyZz48JssuLDrWNzZlacUWFR0cT41ChGJkayv6qZjQV1RLjsjEk2khXDE4x2xeubijlhdCIXz0gnr7qFwhpjacTJ/mtZd5NsfrC9jO0l9VYywnTWpFRuXJjNP6+Y0WOlTUZcGIU1rZTWuxkWE8q9l0/j71dMY2xqFB/vrKA64Lw8uZtA+KQxiXh9mof9EyOe4U+CjAo4hm89eRRzR8RbQWRUiMMKmM3P1uzM3VZcz0XTh3UJwnqTGReGy2EjNSaMmDAnn//oVC6e0X2xZqjDTqvHG1QVaJZHH5gsy25tq1JY6/wCRPhLowM/CzNz+s1TRvHHS6YwP6ADXinFry6YGHQ9jo9wWZOZdb7mpUSHEhXiYEZWrLWs6fCECCb4A+G4cGeXa57ZDi1rcNPkbicqsDS6qhml4FunjuLTH55i3T4sNoySula+9uRaLvr3cgBOHJNoTGDnbyf3tA/MzHVchCsokM/u1Pk6I8toP5sl4af7r3mzhxvVOR/uKOeify/nxy9v4frHVgdV2bS0dxDmsgd1SJhDYDp3hCRHhZIYGcI9H+yiuK6Vi2ekU93ssYZoPfxFLpnxYVw6y2gXXzYrg8rGNp5cmce0zFh+eOY4Vv18sZWMA6PztdnjpbbFw/dOH4vdpvjaSSNRSgV9/gmRIbxzx0n8+gJjuqi6lnbr+qWU4jr/kNjudD/g9OhYC4xRSo0AioErgas73edN4Hal1PPAPKBea12qlKrsw2O7cNiUlbq/dKYxbiOwdGdkUiRP3TyXGf7ek9tPG80rG4qYPzKB0clRPH3LXCakRZMY2XPQdcmMdJZsL+PXbxilUDZlZHgOBMJhVklk5y9rSozxvJ2zt30R16ncVykjq2eWqmR285xGANuA066CxnGcOyWN1JhQQp1Gj2TnL3uo084fL5nKbYtGERvW9WI3LjWSVzYUUdXUxrdPHW1dPM2Jo6qa2hibEsnu8iZa2r1U+zPB5jjW7jLi0zJjiA51WL1koU477955EjalrJPFoTDHUvzz4910+DTfPGUUP3llK6tyq3ljUwlfmWZcCLISwlmTV0NjW0fQOGozc2Y2FDqfTE8dn8y187O4bn42QNDJxPy7TRljdd7aUsIp45KCsoc9CXHYef1bJ+Cwd72ojk+N4qlV+VYv3Bubiml0d3DWpFRr+1bsNbJzE1IPXLCn+jtNRiVFYrMpbCgy4owSo6+fNJLqJg9biusAo5dwdW61VXFgSooKYeGoRN7bVsrPzhnP5sI6RiVFBq1/Dcb3MSMuzJr9dWZWHCEOOx9872RsSrHoL5/y5/eM8tTzpqZhsymWfO9kqpo8JEa6rOeLDHFw3fyuHQeBx3VPzJPony6ZSqO7o8tFzSxzamzrsALh86amUdPs4b4rZ7DbX0759hZjgoYp6bFsyK+zxuRfMDWNr/jLKTu7bkE2P3xpM48tz7NuW7GvGodN0eHTtLZ7OXlsItuKjZl8zfH9ZgVH53MHGOPHX75tIfcuybEmogCsi8G+yibSikP5Yk8VPz93PGdMTKGgpoW73t5BXlWz9ZzVTR7rYn8wp09I4dmvz2NBQJlfZ+EuB98/Yyzff3Eza/NqmDcygWV7qujwaU7r1LH31M1zUUr5M3sHZjo31y0e2el9zxoez5bfnhk0Xsz0mwsmsmhsIqf7S//Om5LG0t2V3PfJXpx2m9WQNycxNC0cnchr/uz29MxY1uXXcvLYJK6Yc6Dv9boFw/neC8ZMlNMyY9lcWEeY0259bneePpZ3t5bR7tVdtnkgDI8PZ11eLV6fZkJaNOdMTu0StPYksMMIjIqmwEmXxqRE8sK6QqPkfm4mvRmfEmWNaS1rcFuN9dn+ia7M687EYdFsK26wMhhgXBPN0l3zM/rrV6fx50unojCy2OY8Fc+vKaTZ47UadKYfnDmWc+/7gn9/utc6l/zpvV3YbYrrOzV8rplnzMlw/tRhQZ/VzSeMYNHYJN7ZUsr9/slw5o4IrmAxXTQ9nU92VvDNRaOtJVP2VzVbpbBgnEfDXXb+8dEeEiJcPHHTHK55ZDVPrMijvrWdi2ekU1rXSkp0qNXBcd6UNNYuqOHJlfkkRoZw0pgkokIdjPNfayakRbNiXxXudl9QKWpngW2Ha+Zl8dyaAl5cZ4y7Hp4Qgc2mOH9qGo+vyCM1OpTJ6dHWcAZzLPj24no+2lluTXyzubDOujaPTT5wvTCr6oprjZn1bz5xBLedPCqoxLQ7vz5/Ij6teWTZfqtSwAyETZnxYXyyy8g6NbqNiRbNSfxiwpzW0JfqpjZ2+JfvMT//n51zYCzrt04ZxU9f3Up8hIsRSRF8llNJVbOHRWOTiA5z8sqGIpbvq2JaZqwVoGYnhPNpTgXudh/fOXWMVY32wXbjeB6bEoXLbqOo04RZu8sb+c5zG5maEcudi4MzvinRodYQiJ5kxoezLr+WiBA7qTGh1jH59uZSXvDPtfLHS6bw9Mr8bhMic0fE+zvBG0iJDrGGAI7qJiNrtkNO9peeBhqeEMGHO8rxaZgxPK7LY3vjsNu4aPow6zwYWBbdWajLTluHURqdHBVCRWObtbrIgcmylFUCHe60B3UIjk6JJCHCZWU74UDAmBIdas2aHMhc6zjQvBHx7Cpr7JLEmO2ft0ApxfvbSmn2eMmMD2dsSiRKdU16wIEhhGZpdFSowwpQ61rarXWHA7/vw2LDWJVbTaO7g9PGJ3P6hBTr71PTY1iXX9tzIOxvt8eHu6yOp8RIV5f7LxqbxPjUKKs9ctKYREYkRnD9wmw+2FbGO1tLUQq+cfJI/vt5LiV1rby4rpCSercxRthlJ1Qb35OkqBBrnp+02K7797JZGWwrrufmE7MZnRTFaxuLWZVbzZiUKHaVNXLelDSrs3fR2CRmZsWyoaCOhaMSUEoFZf0BpmXEcP7UNCYNi+Gbp4zi5hOzu7QnO38m2Qnh5FW3BHUC9WbQMsJa6w7gduADYCfwotZ6u1LqNqXUbf67vQvkAnuBh4Fv9fbYg71mVKjTOpAumpHOL87rOtH0yWOTrA85NtzFu3eexG/8PQwnjUnqNQgG40Rw31UzuGDaMH5x7gQ+/P4iY6Ic/0ktLaAXt3PJXFJkCDedkG2NReqPwAAsMcrFWRNTOXtyKtOzYjlxdGLQpBoms1GSHhsWdIK5dv5w/nb5dABuXDicS2Z236M3PCGiy1hmgHH+jLRRCpRsBY5en7Y+v4y4cJQyAipzxr8NBbUoFZydNoW7HCz76WlWyQQYwfDhBMHme3DZbazKrWFOdhyXzswg3GXn3iW7cXd4ufGEbMBoYJilUIGlPk5/JsAcA925EeC027j7oilWR0hn8f5xaOZsuYGlOgcTF+Hq9oQwIS0aT4ePdq/mvClpVDV5cNpt/PzcCdb4cXOtv8DtMieOGRPw/haNTeLiGelk+qsLCmtaqW328MamYhrcHVzZzcXm3CmpFNa0sr2kgc1F9VaA3dn8kQm0ezVJUSHWPo8KNdY4vHhGOh6vj9nD46yLQlSokxGJEb2eBA9FZnx4t0MOUgKO1fRYo4F5/tRhvPCNBcSEO63GuzmpzZSMGBL9x1RydEiPVSNgLK0R7rLz/JoCQp1GGVyLx+vvNDAuBCMSIzl/WhrzRyaQ6G90r8yttsYDdWdyegyP3zQ36AIQE2YMd9hX0cSjy/YT4bJb+82sgvkgoOS9utnT59Jou02xcFRir+8VDoyHN2f7fXtLKQkRLmtmUZP5PJ3PAe9tLSXCZQ/qKTZ1FwSDcX44e3KalSW12RT3XDqV86akcf+ne3l1QzHJUSFW52h3vnXqKL5+0ggevn6WFTAAnDM5jfgIFzYF9351Kg6bYlpmjHWBH5EYYWUeRiUfgUA4wajs2FfZRHpsKL88f6I1TONwnTkxlbMnpXL/1TO45cSRvd7XzHzZbcpfHdFMXIST31wwkZe/udC63+zhRmAZuP/MTIxSwZ2fdpuyrkfmsj5PrcwDugbxE9Ki+cq0YTz8xX6qmz3cfIIxPOLsSalWhYfJ5bDxfxdO7hLkuhw2JqRFc/tpo8mIC2NEYkTQJDSB5o6IZ9XPF5OVEE5kiMOaFyDw/k67zcqy/f7iySREhnDt/OHsrWjiLx/k8NTKfErq3EGNYaUUv7twMo/fNIe/fnUqdpviiZvm8sOzxgHG2LvyhjbqW9uDzs+dWUOcwpxMGhbNyKQIa+12cyKob586mqhQh1HKH5ANjAo1xtM9smw/LR4vPz1nPE67YmNhHTnljaREhwRd75OijHlGthQZJbuZceEHDYLBOA4ddhvDA4L2zvsqKz6cqqY2mtqMSYcCO/KNybKM71F5Q5u1Tm53LpmZQVpMKCMSI0iKDMHj9eHp8DEqOdJqf+VWNlvJDzC+i+52H3ab4oyJKYxKiiQmzGmVgxqVVeFBqx4A1iSbj94wu8dzUm8y48JpdHewu7wpaOJU8xgLcdg4bXwyz3xtXpeOAzC+Q9/0zwsxIjGC8WlRJEa6rCqKQKkxobgctm7bmiMSI6wqJbPUvD/uuWwaN/mPw96EOY2McGu717qemRlhT9A6wsZn2TlAOnVcMut/dYY14RT0vKpKb8xhV+mdrjl3XzSFv10xnZToEOvzGB4fTrjLQXZCRLff9agQB2FOO+UNbmuyLEdAtUhsNwHtsJhQf0c8/OmSKUHncfP62HMgHJgRNranc1k0GMH2+9892Wq7hrscfPrDU/jKtGFWcumCqcM4098eKKprZcW+albuqz5QGh1ibMNlsw60UVO7OU/+9JzxPPO1eZw2PoXM+DCGxYSyKreGev9kvoFtF6UUPzxrHA6b4rQehjEqpbj/6pnWd7sv7T+zomdkUt+uvYOZEUZr/S5GsBt424MBP2vg23197MF0F2AdTE8zp/Ym3OXgX1fNsH6/+6Ip1s9mFiIsoL7fZC7/cygCD8qYMJc1zgzgma/N6/Yx5ut3ly02dR7L0hfmZDbxES6m+3tah8WEUtbg5p7LpnDzE+tIjgoh3Gk0/s1Srk92VTAsJqzHbN6hnOQOxmm3MTLJmHDpnsum4bDbGJ8axYaCOq6YnWn1bAb2tI9JDg5qkyJDrPWWE/pYUhro/qtnWpNknDEx5eAPOIjAsqwfnz2OjQW1fGfxGFJjQimuM/b5RzvKGZkUEZR9DnXa+fsV04PK3/7vwsnWz+bY7a3F9Ty1Mp/xqVHMye56oTxjYio/f20b//pkD1VNbV0ar6b5IxN4eX0RMzJjuwRSl8/O5NFl+7moh7KqoyGw06q7c0d8hMtaPig+wsWwmFArYE2J6rknHIyL+jmT03hlQxET0qLx+jRbiurJjDdKy7YW1zMyMYLL/VlIs1SsptnDpTMzuh2z1ZvRSZEs3W2M4bx2/nDrWMqIC2dKegzvbSvjG4tG0e41lk7ra2l0X4W7HJw3JY23tpTwo7PH8dHOcq6Yk9ljKa95Tpo3Ip6txfXUtrQzJT3moAH3wdhsiu+fOZZ3t5WyMrear87K6PU5Txufwmnjux6ToU47PzprHPurmhmdHMVdF00OatiDMXRiRlYsM7P635g8mOH+oKbDp7s04A5XVkJ40PWjN2ZZ7ekTkvlgezk5ZY3Mzo6zxrCZvn7ySMamRAVPVOgPhDPiej7nhzrtTM0wsiJx4U7rfQf63uljeWdLKXERLn5+7nimZcZYDaH+CHXa+d/X5lkT9fTF6JQoSurdXYbm3H7qaBaNTbKyetfMy+LMiSlc9+gacsoaKK5r7fbcGZjpC+wkCuyo63z9CWRep7L9QwgWjU3icX/ViTnkKC7CxY/PGs/PX9va5XMalRTJin3VfGPRSE4em8SEtGg2Fxplz50nS3LabSRFhbA2L3iekb4yt0cpupR5m89lZoHTYkJRCrQ2EhQdPmMfrc+voa3DZw0H68zlsPH0LXMBFbS+6ejkyKAy2xkBAZ/ZUF84KsFqV01Mi7YmgYyPcPHVWZn8/t2d/pnsY2j3+nhzcwlnTEjpU0VXd8xrTH1rO6kB7U7zOJmcHhM0bKI7505O5YG0aOZkxxMd6mTdL8/o9n7xES42//rMbq8j5jE2Kqn7RMdACXXacHf4aPF4mZgWisOmrCWUApdP6ikQNgWeU6LD+h/SnDY+mc9/dGq3lYhAl5mZwZiwNKSbfaGUIiU6hOK6Vlo8Xmt27vgIlz8j3PW6anZqzBsR3yWDftr4ZJ5YkddjlWhgRtjMPHc3J0VvzpiYwttbSvn+GWMJcRrvqbi2lYLqFhrbOnA5bIS77Na1+pIZ6TzyRS5hTnuP+8SklGLeyAS+2FNlzcTe+XNeOCqRrb89q99tmt6cOCbRaFv1cF7obFAD4aHI/NIPTwg/7EZdoMDxAX0d05HkP+g6T+xwuFKiQ0iMDGHR2CSr3Gjh6EQa3e2cOi6ZK+dkcuakFD7aWUFVU5s1eU1pvbvXEssj5ZfnTaTd57NOIDOy4thT3sQPzhpr3ce8MMf5xycFSgwIhPvSI95ZTJjTGjMxEEYnR+LwzyA6PCGCFT9bHLCtxva1tnutMUSBzAlQujPJHwg/8Nk+dpQ2cPdFk7v9DsdHuLh4RrqV/TPHHne2YFQCNtV9GeKYlCg+/eEp3Y6XP1qSIkOsoQ3dBRtKKWt5FTNIMyseOs862Z1LZ6XzyoYipvjXAtxSVE9GXDhhLgdbi+uDZn502m1Ehzqw2RS/OG9CL8/avcnp0azMrWbByASrZ9V03tQ0/vTeLnIrm6ySuUNtzPXmq7MzeGFdIdc+spq2Dl+PZeNg9DTPGxHPLSeO4KHPc1mXX9vvC3xPRiVFcsaEFJbsKLfGTB2KwNK77srwXA4bi7s5xgZCYEDYXXboaJmTHceTN88lwmXng+3ltLZ7u+1ESY8N65KxNgOrnqobTLOy41iXX8v0bjrMwAj67rpoMjFhThx2GxdOP/TOs+7mp+jNmORIPt9d2WWejLkj4oPOa0opkqNDGZ8WxercGqqa2vq13wKHKgQOzenMPF+a1SpmIGxMvHRgv1w1N5OxKZFdOmmumz+cmVlxfN+/tM/0zFieX1sI2hgO0FlqTJhVGt/dPCS9MRvEKVGhXQI883prriUcG25MdNTg7iA23GnNlmqW1vdUcQVYEyZWBKxTOzo5EqftwGtOD8gIm+eZ86YcKEGfOCzaeq3YcBeXz4nnbx/u5l8f7+XcqWlUNLipafb0WD3XF4EdCYGdsGZjvqcO5UAOu413vnNilzkFutNT4GG+/yPRgRf0+k47Xp+m0d1BhH8srTn2tD0gIxxpBcLdb29glvVQkiVKqR6DYOgUCPv3UU9z9YBxfTEnYDOzlwkRLnIrm63McCDzPNBd22veyAR2/N/ZPY4rjw5zkBjpIish3Oq0GNHHLKgpIy6cV/zVO16fxmFT7ChtoNE/hNPT4SPUZWdGZiy7yhoYnRzJqKRIOi020aNpGTG8trHY6kjq7nw/kEEwGKtATM+M7XW/BpJA+CgzSwkGqlFnCgzAujvYumNmhA8lU94bpRQv37YgaJv++tVpaK1RSvGnS6cCEO7aYU3WZTKXhTmaThyTGPT7D88cx9dPGhnUuDFPgOaEGoHMcliHTQXNxjhYQhx25o9M6LZxEBjg9LeRHhPmJDshnJW51YxMiuh2vI3pnkunMnlYNKv31/Q43jQ9Now3vn1ijw27/jZKB5rDbiMx0hi71Ll0zzQyKdIKhAFrLG/nhnF35o9I4OsnjeCiGenW0jAZcWHWTJSdzxHfP2MsI5MiDylb+70zxnL9guxuszaXzEjnLx/k8PL6IutifCiVDQczOzueH5wxlns/3E16bFivDS27TfHCNxYAxtjpgQyEAX5w5jiU6jo++HhhDi3RumtZ6dFkZh0D15vtbuxcdyJCHCwen8zC0Ym93m/O8Hj+S26X8cGBuuuIOBrMUsO+dHyBEbC9sckYSpHWj/0WH+EiNTqU5raOLpVkgWLCnZw6LskqM5w/MoEQh43hCRFB1y2lVFBZtOmcKWmcExAAXj0vi/1VzWwqrOOE0V07qdOiQ9ns/7m/lQmRIQ4SI0MY1s04QzOgN9cPNZeUbHB3EBPmtAKDdXnGcKq+jAW0hq1EhVgB07CYUJwOW9CQt7nZ8fzzyumcG/A5BF7DzEqgy2Zl8PSqfGtZmoQIl1VmeygCO30Dg6+4CBcPXjuzz4FpX4Lg3piltbO7qVgYSGbFSH1rO2FOe1Ag7AlYPsnMOvZUbh7usltza/RnYq++Mqs9UqJDgqpcejIpPYaP/ddzsz1oXrO7OzeeNCaRGxYM58Lp3XcM97aMoVKK9797MlGhDqqbPDjt6rA6MOw2RWpMKKs6rf0b7nRw9uQ0q8LlR2eNs2b2PhhzMjNzYslDmQOpv8xJ7/pq8FvtQ0xqTCghDluXJXEOV1SIo98nA3MNs+7KzQ5Xd5P5dA4gw132oKWUAGtq+8EU5rJ36aEyD97R3QRtZvATF+Ea0Cz/4eipHD7CZbdKXQInmeirKRmx5FW38LuvTOp1fLbNprjxhBHceJCxQlN6GD98rEiNCcWn6fECaI5BMU/25tjavjSMjeyuMf+AORlNRlw4M6fFMjIpsst8BAf7LHsT7nIQHt/96T45OpRFY5N4dUMx8/wVGUciEAb4zuIxjE+LJsqf3e4L87Pt63ifvhiXGsV/r5s9YM93tIU67aRGh1Ja7x7UQNiUHBViBeZ9DYQBHr1xzkHvM39UAqdPSOl1kqjBYo7XPdhQCNO4gOt+ejcBYG/mj4ynrrX9oNeYx2+aa/0c6rRzw8Lsfu2TQONTo3n6lu6vJXCgpDklOqRPExR2dvGMYd2O+0+MdBHmtB8IhMOcxIQ5qXS2Wefi2HCnf8xheJ8ySuZ1OnCOj6vnZXUJsGw21aWqIHAMsll99+Ozx/lnuA8nr7qZtJiume3+iAl3EhXqoNHdEZQRBoImszvSJqdH88A1M49YNYsp8JpqLCPkspZfMzPCzoCMcOeJRk1KKWLDnVQ1ebqstzsQzO94XwO4wBUyDpRGG9+97trmseEufhcwBK2/zHbCsNgwtv72rD4F670ZFhtmZW9N4Z2Or/58NyakRaMUbCqsIyU6ZMCzvwNBAuGjzGm38dq3TiAzfuCzsLHhLlo9HX0+GU9Oj+Y/18wckHGphyLMZSfXv6RLWozRqDsSQflAiI9wccOC4d1OLpEYZTQy4g+xsXE0KaUYmRjBjKy4gy610p1vnDyS+SPjOWnM8ZlJ66/Zw+OpiHf3+PcTRyfycmKRNd4vKarvGeFAZq/wCaMTiAp1HvWx0V+dlcE3d23gXf+yKX2dLOtQ9Pd8s2iskeEyl7ERhqz4cKqbPAedwPFocPqrJyob24LWUh8IkSEOHrnh2Oy0mJEVxy/Pm2BNMnMwgVU6/S1p/8tXp/W5HDHQz8/t/1CKvjIDtkMdwtLdhKVgXKfGp0WxyV92HR1qBMKBgUSCf9xlX5MKMWFOQp22oLHOfZ0DxVxu0+vTVjY5KtTJmZOM9b27m2zxUGTEhbOztKHbpbGOFqVUUFXAkRIYsIX6M8Lmcloecx1huw17iNHx0zkYCxQT5g+EQwc+pEmMNDr5+pqkmZx+4LtgTnxpdiz3tVrzUB1uEAyQERvGGuv5bLjbfYcVvEaEOBiRGEFuZTPDj4FEV3ckEB4EA3XS7Cwu3Imrm+V0eqKUCir/OdrCXXZrdsCZw+N4Z0vpUSmbOBTmrJ7dSbQywkf2JDdQXrxtgbWOc39NTo/pdg3DLytzTbqeTM2I5ZMfnmL9PmlYDD86a1yfG8amqFDnYfUKH66T/OP5P9hhlPmZPdjHgqSoEB7rQ+ZwqJk1PI4Onz7sUsiBkhodSmVj2yHNk3C8Mte07Kv02DCiQhw0tnX0eyLOw8k2HilmwNbfibL64vtnjOW6R40meXSYg5GJkfgC5jFLiAxhX2WztQTiwdhsiqdunndIQyxcDhtjkqMob3Af0eMtMy6M3eWNx0Tn1pEWOFlZmNNuZfghuDTa5m9W9ZQRhgOZ1iOREXbabXxz0aguQ+h6khodSkKEi+pmj7XNvZVGH2vMDrrEyBCSokLYWdoQtK8OxeRhMUYgfIwmuiQQ/hKJi3D1Op7gWBPmPPD1u2ZuFnHhzl4nvThWmSVXAz3T7pFyJGbfFga7TfHtU0cP9mb0W2SIg8nDotlcVI9Ndb/Mgzi2/Mi/tM6xIiU6lK3F9cdFY2+wKKUYmxpFTlnjEcleHW1mMN/fibL64qQxSZwxMYUPd5QTE+bk1xdMtCbWhAMTP3aezbo3Pa0P3RenjEuyZrE+Uk4Zl4xSvY8L/bIIcx3o2Al3GRnh2hYPPp8OmizL7LTvbYbi2HAX4S77Eess+vHZ4/t8X6UUk9NjWLq78kBGOPLoZIQHQuCEvomRLnaWNvSaje+LyenRvLm5RAJhceR97cQRuPux9MNgCzy4JqXHHHTSlGOVWQ4rDUBxPJs7Ip7NRfXG+rhDoCF2vDtW5iMwpcbIebAvzp+axsjEiGNu/x2K7IRwHDbFuD4uU9Jf91w6lXWza63ZdwMTU2bWtD+B8OHoTzB0qK6elzVg64Ef60IdXccI+zQ0uNsDlk8y1hS/el5WrxMbJkWGHNHhPP01OT2apbsrifSvvXs8JUvMSe+y4sOtku7DHddrVhF2N3fQsUAC4S8Rc7zK8cIMhJXimJht+VCZF+QjNcGQEEfDvBEJPPzFfhKOobJocfwws4PHyxCRwXLTYUx6d6xJjg5l6Y9PJS36yIxpjYtw9TinQHZCBFGhjgGdRE8cPaGurmOEAWqaPbR7fUGZ8T9cPKXX5/reGWO5vrnr8l6D5co5Wdj9awqDsQzSny6ZMijLg/aXOYlfVny41blwuKXR80ck8LfLp3HmxGMzRjl+ow9x3DN7maJC+j6D7LEoKSqExeOTWTDq+MxoCwEwJzsepY6PXmtx7Llg6jDavT5riUAxNAzWrOXX+ZecOZTZqsXgC8oIO+3WRE+1LR48Xh8uu63PVROpMaGDOsFYZ5nx4Xz/zANDV+w2xZWDtMRbf2XFR3DWpBROn5CCV2ucdtXvpdE6s9kUl8zMGKAtHHgSCItBY2aEY46DcRO9sdtUn5YBEeJYFhPuZMHIhB7XfRaiN1kJ4Xz39LGDvRliiHDabSQMgUmlvqwCy23D/cs6AtQ0G6XRrmNwcrihwOWwBS0tuP13Z/e6VOaXgQTCYtCY6/cdiUXQhRD997+vzftSjF0UQghx7Ap12gJ+tlvtwFp/afSXPfg6XgyF/fDlf4fimGWOO5BZjIU4NkgQLIQQ4kgLWj7JdWCMcG2LB0+H75hcLkx8Ock3TQwaqzRaMsJCCCGEEENCaKd1hM3y6JoWD+1ePSQykeLYIN80MWjCJBAWQgghhBhSQgIC3TCnHaUU8eEuapvNjLBUJ4mjY1ACYaVUvFLqQ6XUHv//cd3cJ1Mp9alSaqdSartS6s6Av/1WKVWslNrk/3fu0X0HYiDIGGEhhBBCiKFFKWWVR5tJkdhwpzFZlldKo8XRM1jftJ8CH2utxwAf+3/vrAP4gdZ6AjAf+LZSamLA3/+utZ7u//fukd9kMdAi/Ce/aAmEhRBCCCGGjFCnDaUOZIfjI1zUthiTZYVIabQ4Sgbrm3Yh8KT/5yeBizrfQWtdqrXe4P+5EdgJpB+tDRRHXpgEwkIIIYQQQ06Y026VRQPERQSWRksgLI6OwfqmpWitS8EIeIHk3u6slMoGZgCrA26+XSm1RSn1WHel1eLYZ84SmBIlawEKIYQQQgwVof5A2BQf7vJPliXLJ4mj54h905RSHymltnXz78J+Pk8k8ArwXa11g//mB4BRwHSgFLi3l8ffqpRap5RaV1lZeWhvRhwRwxMiePVbC1k8IWWwN0UIIYQQQhwloU570OzR8REu6lraafF4JSMsjhrHkXpirfXpPf1NKVWulErTWpcqpdKAih7u58QIgv+ntX414LnLA+7zMPB2L9vxEPAQwOzZs3W/34g4omZmSTJfCCGEEGIoCXXaaPceCIST/NWBZfVuhsWGDdZmiSFmsLpc3gRu8P98A/BG5zsoY9DAo8BOrfXfOv0tLeDXi4FtR2g7hRBCCCGEEAMoIsRBuKtrIFzd7MElGWFxlByxjPBB/Al4USl1C1AAfBVAKTUMeERrfS5wAnAdsFUptcn/uJ/7Z4i+Ryk1HdBAHvCNo7r1QgghhBBCiEPy3dPH0tbhtX5PCpgvRtYRFkfLoATCWutqYHE3t5cA5/p/XgZ0eyRora87ohsohBBCCCGEOCJmDQ8eGpcUeSAQlsmyxNEi3zQhhBBCCCHEoEmMDMwIS3gijg75pgkhhBBCCCEGTZjLTlSIUagqGWFxtMg3TQghhBBCCDGozHHCMlmWOFrkmyaEEEIIIYQYVIlmICwZYXGUyDdNCCGEEEIIMajMCbNkjLA4WuSbJoQQQgghhBhUZmm0BMLiaJFvmhBCCCGEEGJQJUlptDjK5JsmhBBCCCGEGFRmabTLrgZ5S8RQIYGwEEIIIYQQYlAlRrkAyQiLo0e+aUIIIYQQQohBlRQZCsgYYXH0yDdNCCGEEEIIMajGpUZx9bwsFoxKGOxNEUOEY7A3QAghhBBCCDG0uRw2/nDxlMHeDDGESEZYCCGEEEIIIcSQorTWg70NR41SqhLIP4ynSASqBmhzxMCQfXJskv1ybJL9cmyS/XLskX1ybJL9cmyS/XLskX0SbLjWOqnzjUMqED5cSql1WuvZg70d4gDZJ8cm2S/HJtkvxybZL8ce2SfHJtkvxybZL8ce2Sd9I6XRQgghhBBCCCGGFAmEhRBCCCGEEEIMKRII989Dg70BogvZJ8cm2S/HJtkvxybZL8ce2SfHJtkvxybZL8ce2Sd9IGOEhRBCCCGEEEIMKZIRFkIIIYQQQggxpAzpQFgplamU+lQptVMptV0pdaf/9nil1IdKqT3+/+P8tyf479+klLq/03O9r5Ta7H+eB5VS9sF4T8e7gdwnAc/5plJq29F8H182A3ysfKaUylFKbfL/Sx6M9/RlMMD7xaWUekgptVsptUspdelgvKcvg4HaL0qpqIDjZJNSqkop9Y9BelvHtQE+Vq5SSm1VSm3xX/sTB+M9fRkM8H65wr9Ptiul7hmM9/NlcQj75Qyl1Hr/cbFeKXVawHPN8t++Vyl1n1JKDdb7Op4N8D75vVKqUCnVNFjv51gxpEujlVJpQJrWeoNSKgpYD1wE3AjUaK3/pJT6KRCntf6JUioCmAFMBiZrrW8PeK5orXWD/wB/GXhJa/38UX5Lx72B3Cf+57sEuAyYqrWefBTfypfKAB8rnwE/1FqvO8pv40tngPfL7wC71vqXSikbEK+1ljUID8FAn8cCnnc98D2t9edH4318mQzUPlFKOYASYKLWusofcLVorX971N/Ul8AA7pcEYCMwS2tdqZR6EnhKa/3x0X9Xx79D2C8zgHKtdYlSajLwgdY63f9ca4A7gVXAu8B9Wuv3jv67Or4N8D6ZD+QDe7TWkYPxfo4VQzojrLUu1Vpv8P/cCOwE0oELgSf9d3sS44uG1rpZa70McHfzXA3+Hx2ACxi6PQyHYSD3iVIqEvg+cPeR3/Ivt4HcL2LgDPB+uRn4o/9+PgmCD92ROF6UUmOAZOCLI7flX14DuE+U/1+Ev+M7GiMwFodgAPfLSGC31rrS//tHgFS1HKJD2C8btdbmcbAdCFVKhfiDt2it9UptZN6eMh8j+meg9on/b6u01qVHcfOPWUM6EA6klMrG6GVcDaSYXxD//30q3VRKfQBUAI0YWWFxGAZgn9wF3Au0HKltHIoG4lgBHldGqeevpExqYBzOflFKxfp/vEsptUEp9ZJSKuUIbu6QMUDHC8BVwAt6KJdxDZDD2Sda63bgm8BW/Jlh4NEjub1DxWEeK3uB8UqpbH/W/iIg88ht7dBxCPvlUmCj1roNI1ArCvhbkf82cRgOc5+IABIIY2UOXwG+G5DZ7Tet9VlAGhACnHaQu4teHO4+UUpNB0ZrrV8b6G0bygboWLlGaz0FOMn/77qB2r6hagD2iwPIAJZrrWcCK4G/DuAmDkkDdW3xuxJ47vC3amgbgGuLEyMQngEMA7YAPxvQjRyCDne/aK1rMfbLCxhVE3lAx0Bu41DU3/2ilJoE/Bn4hnlTN3eTzrzDMAD7RAQY8oGw/6L2CvA/rfWr/pvL/eUcZk1+RV+fT2vtBt7EKFUQh2CA9skCYJZSKg9YBoz1j00Vh2igjhWtdbH//0bgWWDukdnioWGA9ks1RuWE2XH0EjDzCGzukDGQ1xal1DTAobVef0Q2dogYoH0yHUBrvc+fnX8RWHhktnhoGMBry1ta63la6wVADrDnSG3zUNDf/aKUysC4hlyvtd7nv7kIo5PVlIEMJThkA7RPRIAhHQj7SzIfBXZqrf8W8Kc3gRv8P98AvHGQ54kM+BI6gHOBXQO/xV9+A7VPtNYPaK2Haa2zgRMxxg6dMvBbPDQM4LHiUP4ZVv0n9PMBmdH7EA3g8aKBt4BT/DctBnYM6MYOIQO1XwJchWSDD8sA7pNiYKJSKsn/+xkYY/XEIRjIY0X5VyBQxqy53wIeGditHTr6u1/8w2veAX6mtV5u3tlfqtuolJrvf87r6ft5TwQYqH0igg31WaNPxCih2Qr4/Df/HKPm/kUgCygAvqq1rvE/Jg9jcgwXUAeciZFNeRujJNoOfIIxs6eU5fTTQO0TrfWOgOfMBt7WMmv0IRvAYyUf+BxwYhwrHwHf11p7j9Jb+VIZyONFKTUceBqIBSqBm7TWBUfrvXyZDPR5TCmVC5yrtZYO1kM0wMfKbRiz4LZjnNNu1FpXH7U38yUywPvlOWCa/zn+T8vKHYesv/tFKfVLjCECgVn4M7XWFUqp2cATQBjwHvAdmeug/wZ4n9wDXI0xvKMEeEQP0Znvh3QgLIQQQgghhBBi6BnSpdFCCCGEEEIIIYYeCYSFEEIIIYQQQgwpEggLIYQQQgghhBhSJBAWQgghhBBCCDGkSCAshBBCCCGEEGJIkUBYCCGEEEIIIcSQIoGwEEIIIYQQQoghRQJhIYQQQgghhBBDigTCQgghhBBCCCGGFAmEhRBCCCGEEEIMKRIICyGEEEIIIYQYUiQQFkIIIYQQQggxpEggLIQQQgghhBBiSJFAWAghhBBCCCHEkCKBsBBCCCGEEEKIIUUCYSGEEEIIIYQQQ4oEwkIIIYQQQgghhhQJhIUQQgghhBBCDCkSCAshhBBCCCGEGFIkEBZCCCGEEEIIMaRIICyEEEIIIYQQYkiRQFgIIYQQQgghxJAigbAQQgghhBBCiCFFAmEhhBBCCCGEEEOKBMJCCCGEEEIIIYYUCYSFEEIIIYQQQgwpEggLIYQQQgghhBhSJBAWQgghhBBCCDGkSCAshBBCCCGEEGJIkUBYCCGEEEIIIcSQIoGwEEIIIYQQQoghRQJhIYQQQgghhBBDigTCQgghhBBCCCGGFAmEhRBCCCGEEEIMKRIICyGEEEIIIYQYUiQQFkIIIYQQQggxpEggLIQQQgghhBBiSJFAWAghhBBCCCHEkCKBsBBCCCGEEEKIIUUCYSGEEEIIIYQQQ4oEwkIIIYQQQgghhhQJhIUQQgghhBBCDCkSCAshhBBCCCGEGFIkEBZCCCGEEEIIMaRIICyEEEIIIYQQYkgZ1EBYKXW2UipHKbVXKfXTbv6ulFL3+f++RSk1s9Pf7UqpjUqpt4/eVgshhBBCCCGEOJ45BuuFlVJ24N/AGUARsFYp9abWekfA3c4Bxvj/zQMe8P9vuhPYCUT35TUTExN1dnb24W+8EEIIIYQQQohj3vr166u01kmdbx+0QBiYC+zVWucCKKWeBy4EAgPhC4GntNYaWKWUilVKpWmtS5VSGcB5wO+B7/flBbOzs1m3bt2AvgkhhBBCCCGEEMcmpVR+d7cPZml0OlAY8HuR/7a+3ucfwI8B3xHaPiGEEEIIIcQQVtfiYUdJw2BvhjgCBjMQVt3cpvtyH6XU+UCF1nr9QV9EqVuVUuuUUusqKysPZTuFEEIIIYQQQ9D/vb2DSx5YTqO7fbA3RQywwQyEi4DMgN8zgJI+3ucE4CtKqTzgeeA0pdQz3b2I1vohrfVsrfXspKQupeFCCCGEEEII0YW73cuS7eW42318uKN8sDdHDLDBDITXAmOUUiOUUi7gSuDNTvd5E7jeP3v0fKBea12qtf6Z1jpDa53tf9wnWutrj+rWCyGEEEIIIb60Pt9dSVNbB0674q3NnfN14ng3aJNlaa07lFK3Ax8AduAxrfV2pdRt/r8/CLwLnAvsBVqAmwZre4UQQgghhBBDx7tbS4kNd3LpzAyeXJHH35bkUN/aTlJUCN88ZTR2W3ejOMXxYjBnjUZr/S5GsBt424MBP2vg2wd5js+Az47A5gkhhBBCCCGGIK01H++q4OxJqVw2K4PHlu/n/k/3EhHioNHdwbjUaM6YmDLYmykOw6AGwkIIIYQQQghxrKlsaqPR3cHk9BgmpEWz7CenERfuxGm3ccKfPuHZ1fkSCB/nBnOMsBBCCCGEEEL0yXNrCjj3n18w7w8fsbei6Yi+Vn51CwDDE8IBSI8NI9zlwGm3ccWcTD7bXUlRbcsR3QZxZEkgfJzLr27m9Y3FdHhlOWUhhBBCCPHltKmwjl+8thWbDTq8mlufXkfDEVzSKK+qGYDshIguf7tybhYKeHpVfpe/tXg62FXWgKdD2ubHOgmEj2Pvbi3lvPuW8d0XNnHefcvIr24e7E0SQgghhBBiQLV7ffz0lS0kR4Xy7Nfn8+9rZpJX1cxDS3OP2GvmV7dgtynS48K6/C09NoxzpqTxv1UF1Lcawfjeika+/b8NTP7NB5z9jy+48qGV1DR7jtj2icMngfBx6sW1hXz72Q2MSYnkL5dNpbS+lZ+8sgVjfjEhhBBCCCG+HD7LqWRXWSO/On8i0aFO5o9MYP7IBD7YXnbEXjOvupmMuDCc9u7DpW+dMoqmtg6eWpFHTlkjF/97BUt3V3LLiSP45XkT2F7SwKJ7PuXmJ9by36X7KKyRMupjjUyWdRx6YW0BP311KyeNSeKh62YR6rTT7tX8/LWtvLqhmEtnZQz2JgohhBBCCNEjr0+zv6qJ0clRB73ve9tKiQlzcuakA5NTnTExhd+9tYPcyiZGJkUO+PblV7cwvJuyaNOkYTGcNj6Zez/czX8+20dUqINXv7WQjDhjTPHcEfE8t6aQ1bnVfLKrgr98kMMNC7P5xbkTsMmyS8cECYSPYRWNbv62ZDe7yhoJd9m5YNow9lc189DnuSwam8R//UEwwJVzMnllQxE/eWULZQ1uvnHySBw99GAJIYQQQggxWErrW7nz+U2s2V/DD88cy8ljk6hu8nDq+OQu9/V0+PhoRzlnTkoNys6agfCHO8r5+kkR/PKNbYxNjuTGE0Yc9vZprcmrbmZmVmyv9/v75dN5dk0Bq3Kr+cnZ460gGGBqRixTM4zHF9e18s+PdvPosv2kx4Zx84mHv43i8EkgfIzaUlTHtY+sxt3uY97IeIprW/nZq1uxKTh/ahp//eo0KwgGsNkUj904h5+/tpW/fJDDO1tK+dsV0xifGj2I70IIIYQQQohgdz6/ie3F9ZwwOoG/LtnNX5fsBuBPl0zhyrlZQfddsa+KBncH50xODbo9Iy6ciWnRvLiukLIGN8+uLiDEYePsyWmkxoQe1vbVNHtodHf0mhEGiAl38s1TRvHNU0b1er/02DD+fOlUqps8/Pn9XZwwOpFxqQfPhIsjS1KGx6DyBjffeHo9UaFO3vvuSTx9yzw+/sEi3v7Oiaz/5Rncf/XMoCDYFBPm5P6rZvCfa2ZS2dTGFf9dxbbiesCYXVom0xJCCCGEEIMpv7qZNftr+PZpo3nq5nncuXgMv7lgIovGJvHz17aycl910P2fXV1AVIiDE8ckdnmuOxaPpqzezePL8zhlXBJen+bfn+497G3M8y+dlJ0YfpB79p1Sij9eOoXoMCfXPbqa/VXSLh9skhEeRHsrGvnxy1tYOCqRtNhQNhfWsamwjj0VTYQ4bLx820JG+cc8KKWYnB5z0OdUSnHulDSmpMdw5UOruOqhVdx0QjaPLNuP16e566LJXD4780i/NSGEEEIIMcg+2F7Gy+uL+POlU4mPcA325gDw2sZilIKLpqdjtym+d8ZYAC6fnck5//yCX72xjXfvOAmXw8ayPVUs2VHOj84aR4ijaxLo7MlpzMmO58Md5Vw4PZ2739nB82sLuPXkkWTGH3oQu7eiEYARiQM79jg5KpT/fW0eVz60iq8/tY4l3z1ZxgsPIgmEB0l9aztff2o95Q1uNhbWoTXEhTuZlhnLuVPSOGNiCpOGHTzw7UlmfDgv3raAO57byH2f7GViWjSx4U5+/PIWMmLDWDi6a6+aEEIIIYQ4/mmt+dN7u/jv58byQjOzCg9avnsk5Fc3c++S3eytaCIzPowTxyTx8voiFoxMYFhs8LJEESEOfnPBRG55ch0/fXUL41OjeHJFPlnx4dzSy5jahMgQq5z69tNG89L6Iv71yR7uuWzaIW/3hvw6YsOdDD+MYLonY1Oi+O1XJnHHcxv5aKcx9lkMDgmEB8mvXt9GUW0Lz359PhlxYXg6fGTFh6PUwPUKpceG8cKt8/k0p5IFoxJw2BSL713KH9/bxRvfPkF6oIQQQgghvoTu+3gv//08l2vmZbGrrJEX1hZw26KRA9rOPJgdJQ1c/9ga2jq8zMyKY0NBHR9sLwfgJ2eP7/YxiyekcPnsDF5aX4TWMCEtmrsunNTtkMDupMWEce284Ty5Mo/qJg9KKa6el0lyVChjU6JwOfo2KnR9QS0zs+KOWFv53Mmp3BMXxn8/z5VAeBBJIDxIfnDmWM6clMKc7Pgj+joOu40zJh6Yav4HZ47l+y9u5tWNxVzWaZmljQW1PL+mkAZ3O5PTY/jmolESLAshhBBCHEd2ljbw9492c8nMdO6+aDKvbSzm+y9uZlVuDQtGJRyVbahuauPmJ9bitCuev3Uho5Oj8Po05Q1uwl12YsN7LtO+57Jp/O4rk2lwt5MS3f9Jr755yihe3VjE7opGWj0+PtppBN/TM2N5/tb5Bw2q61va2VvRxMUz0vv92n3lsNv4+kkj+c2b29ngD7rF0SeTZQ2S4QkRnD912FF/3YumpzNreBy/eWMbeyuarNtfXFfIxf9ZwbtbS8kpa+QvH+TwvRc3Ud/Sjs+nafF0HPVtFUIIIYQQ/fPs6gJcDhu/Pn8iSinOmZxGVKiDF9YWDOjrrM+v4cV1hazYW4XW2rq9uqmN776wiZoWDw9fP9taJ9huUwyLDes1CDaFueyHFAQDJEWFsP6XZ/DFj09j2U9O5Ymb5vCr8yeyqbCOn7+29aCP31BYC3DEg9NLZ2UQ7rLz3OqB3S+i7yQjPMTYbIr7r57Befct4/ZnN/D2d06kw6e5d0kOM7JiefqWeUSGOPjPZ3u55/0c3ttahtNuZIXfvfOkg04jL4QQQgghBkdzWwevbSzm/KlpVsAZ5rJz8Yx0nl9byG9bPH0KRA9m2Z4qrn10tfX7lPQY5o6Ip6zezSe7KnB3ePnjxVP6NNHrkWD3VzSGOu2cMi6ZU8ZBVVMbD3y2jzsXj+m1Pbshvxa7TTEt88hue2SIgwunD+P1jSX86oKJRIc6j+jria4OOSOslLqkt38DuZFiYKXFhHH3RZPZVdbIaxuLeWZVPuUNbfzorHFEhhh9I986ZTRvf+dEblg4nMtmZWBTip+/tjWox08IIYQQQhw73tlSSlNbB9fMC16L94o5mXg6fLy+sfiwX6OysY0fv7yZUUkRLP3RKfzxkin4tOaZVfmsz6/lK9OG8eH3Tu6yHvBgu2qOsT0f7azo8T4+n+ajnRVMGhZNuOvI5wuvnJNFa7uXNwZgv4j+O5w9fIH//2RgIfCJ//dTgc+AVw/jucURds7kVKZmxPD7d3fS6O7gxNGJLBwVPJP05PQYqydvdEoUv3p9G69uKObSTmOLhRBCCCHE4PtkVwXpsWFdynonDYthakYMD32eS1uHjxtPyO52OaLudHh9vLO1lJfXF1Hd5CGn3Fha6OXbFjA8IYLhCRFcNTcLrfVRnYyrv7ISwhmbEslHO8p7nIX6na2l7Cxt4G+XH/qM0/0xNSOGaRkxPLg0l8vnZNLu1SzNqSQtNlTGDR8Fh5wR1lrfpLW+CdDARK31pVrrS4FJfX0OpdTZSqkcpdRepdRPu/m7Ukrd5//7FqXUTP/tmUqpT5VSO5VS25VSdx7q+xiqlFL8+Kzx1LW0s3h8Mv/5f/bOOjyu4+rD710SsyzLlpnZcYxJHIaGkzbUMHxNmiZp0jZlTDHlNszYBhpmjiFmZibJYoZlvN8fF/YuamWvbdma93n82F68O3fmN2fOOXPmuuOTvv7aWUOYMbSIP3y4lVaH9zBdpUAgEAgEAoEgFYIhmWV7WzlpVEncBelPzh2HzWLi/o+38+TCvSl/7n3vb+GeV9dT3eaivCCT204Zwcf3nMy0qIVab14Ea5w1vj8rK9vodPljnvMHQ/z9sx2MK8/jkuMOXaEsI5Ik8cOvjaW2w833/7eeOX/6kjtfXst3X14nsjAPA+koljVMluV6w/8bgTHdvUmSJDPwCHAeMAG4WpKkCVEvOw8Yrf65DXhMfTwA3CvL8nhgDnBnnPcKumHu6FKW/ewMnrh+erf7Ekwmifu/MRmHN8C9r29ga10Xv31/Cx9urE/6PoFAIBAIBALBoWdLXSedbj8njSqN+/xJo0pZ8KPTmTOimDfX1qS00Kppd/HqymqunjWYefeexrM3zeQn545jTP+8dF/+YeHM8f0JhmQ+29oQ89xXO5upanXxvbNG63uMDwdKVmYJH21qYMKAfP5v7nBqO9zsMhS1FRwa0rEQXiBJ0qeSJN0kSdKNwIfA/BTeNwvYLcvyXlmWfcCrwCVRr7kEeFFWWA4USpI0QJblelmW1wLIsmwHtgGHx3VzjDGgICtlD97o/nn88oIJLN7VwvkPLuK5JZV8/7X1bKvvOsRXKRAIBAKBQCBIxpLdrQAxW92iuXz6YCpbXaypau/2M5/8ai+SBN89Y/QxcaTmtMGFjCvP45H5u/EHQxHPvb2ulqJsK2eO75/g3YcGSZL4+xVT+evlU3j51tl862QlbXv+9sR7mQXp4aAXwrIs3wU8AUwFjgOelGX5uym8tQKoNvy/htjFbLevkSRpGDANWIHgkHPjicP49Pun8JNzx/HWHSeSn2nl7lfWEQqJ9A2BQCAQCASCI8XSPS2M7Z9Hv7yMpK87b1I52TYzb66tSfo6ly/A/1ZV8/VpFQwszErnpR4xTCaJH54zlspWF/9bFV5i2D1+Pt/ayIVTBmI1H/7TZQcWZnHljMFYzCYGFGQxrjyPBTuaD/t19DXScqdlWX5LluXvq3/eTvFt8dxK0auppK+RJCkXeBP4nizLccOSkiTdJknSakmSVjc3iw6VDkb2y+U7p43k+CFF/OTcsexqcrCptvNIX5ZAIBAIBAJBn6TJ7mHZnlZOG9uv29fmZFiYO6qUFfvakr5uxb42vIEQF04ZmK7L7BWcOb6MGUOL+NW7m7n3tQ04vAHe21CHNxDi0mm9I8H0tLFlrKpsw+6J3cssSB8HvRBWj0vaJUlSpyRJXZIk2SVJSiVXtgYYbPj/IKAu1ddIkmRFWQS/JMtywgrVsiw/KcvyDFmWZ/Tr1704CHrGmeP7I0kwf4dI3xAIBAKBQCA4Ery+uoZASOaqmYO7fzEwtjyPqlYX3kAw4WsW72rBZjExa3hxui6zVyBJEk/fOINbTx7BO+trueap5fz+g61MG1LI8UMKj/TlAXD62H4EQjJLdrfEPBcIhlhV2UaLKF570KQjIvxX4GJZlgtkWc6XZTlPluX8FN63ChgtSdJwSZJswDeB96Je8x5wg1o9eg7QKctyvaRsan0G2CbL8j/T8BsEB0hxjo0pgwpTSt+obnPxs7c2ql63xMIrEAgEAoFAIEiNYEjmlZX7OXFkCSP65ab0nlFluQRDMvtanAlfs2hXM7OHF5NpTe2YpaOJwmwbPz9/PP+66jg213ZSlpfJUzfM6DWVr48fWkRepiWuff3gvN1c8fgyZvzhC857YBGvrtx/BK7w2CAdJ0U3yrK8radvkmU5IEnSXcCngBl4VpblLZIk3a4+/zjwEXA+sBtwATerbz8JuB7YJEnSevWxn8uy/NFB/RLBAXH62H488OUu2pw+inNscV8TCsnc+9oGVla28crKauaOKuXZm2Zisxz+fRgCgUAgEAgExwpfbmukpt3NT88bl/J7RpcpVZ93NToYVx4bv2rs8rCz0cFlxw9K23X2Ri6eOpChxdkMKMykNDf53urDidVs4uTRpczf0RRxPvP+VhePL9zDmePKmDW8mPc31vHTtzbR6fbz7VNHHuGrPvpIx0J4tSRJ/wPeAfQYfbJ0ZcNrPkJZ7Bofe9zwbxm4M877FhN//7DgCHDa2DL+/cUu5m9v4rLp8QXzpZX7WVnZxv3fmEwwJPPLdzbz87c38bfLp/Qa75tAIBAIBALB0UQoJPOvL3YxtCSbcyeWp/y+Ef1yMEkkPKJn4U4lEjl3dPIK1McCUwcXHulLiMtpY8v4aFMD2+rtTBiYjzcQ5CdvbsRikvjj1ydTXpDJt04ewT2vruP+j7czdXAhc0aUHOnLPqpIRzguHyVaew5wkfrnwjR8ruAoYUpFAcNKsnk5QWrG0t0t/O79LZw8upRvzhzMdXOGcvcZo3hjTQ0fiHOIBQKBQCAQCHqMLMu8ubaGbfVd3HPmaCw9qHacaTUztCSHXY32uM9/urmBisIsJgxIZbej4FBw2hilttErK/fT6fZz50vrWLa3ld9dMonygkwAzCbl6KVMq4lPNseejSxIzkFHhGVZvrn7VwmOZUwmiRtOGMbvPtjKpppOJg8qADSBruW+97YwvDSHh68+Xo/+3n3maBbuauFX725m9ohiyvIyj+RPSMhv39/C8r1tTBiQz+8vnUi2LR1JFAKBQCAQCAQHztr97dz33hY21nQyrjyPi6f2vLLz6LLcuBHhLo+fRbtauOGEoSJr7whSlp/JVTMG85/lVby+phpvIMTvLpnI5VHZl5lWMyeOLGXe9iZ+c9EEcc96QDqqRo+RJOlLSZI2q/+fIknSLw/+0gRHE5fPGES2zczD83cRVM8U/t0HW/nh6xuYMCCfF2+ZTUG2VX+9xWziH1dMweUL8su3N6NkwfcuNtd28tySSgDeXFvD/R9tP7IXJBAIBAKBoM/zyeYGLntsKc12L7+/dBJvfOfEHkWDNUb3z6WyxYkvEMIfDNHh8gEwb1sTvmCI8yannmotODT8+bLJ/OL88cwZUcK7d57EDScMi/u608f2Y3+bK2nxM0Es6QhvPQX8CHgCQJbljZIkvQz8IQ2fLThKyM+0cvupI/nn5zu58dmVjCrL5fmlldx04jB+feEETKZY79Sosjx+eM4Y/vTRdt5dX9drzm7T+PcXO8nPtPC/b8/hgS928czifcwdXcrXDHtwAsEQQVkmw3LsVVQUCARHP8GQjCzLWMymiIIrAoHg6OXFZZUMLsrmo3tOJjfjwE35seX5BEIyG2o6+HBjPS8uq2T28BL2t7non5/BtMFFabxqwYEgSRK3njKCW08ZkfR1p40tA7awYEdzypXDBelZCGfLsrwyanINpOFzBUcZd585muIcG/d/tI3Fu1s4a3wZv0qwCNb4v7kj+GhTA3/6aBvnTirvNSX6N9d28sW2Ju49ewz5mVZ+9LWxrNzXxl0vr+UHZ4/FapZ4Z30t2+rtZFvNPH/LLKYPFROGQCA4NLQ4vLQ6fORnWZCQ9P1hiXD7gjy2YDf/WV5Fu8uPzWzCHwpx1vj+3H7qSI4fUigWxQLBUUhjl4dle1v57hmjD2oRDHDmuDJyMyw8sXAvS3a3MK48nzanj0FFWVw9a0hS+03QuxhcnM3IfjnM39HELXOHH+nLOWpIx0K4RZKkkYAMIEnS5YCogNRHuW7OUK6ZNYR2l3KUUneGltkk8dPzxvHNJ5fz8or93DJ3OJtqOmmyezhzfP/DdNWxPPnVXnIzLNx40jBA2X/x32/N5lsvrOIvnygp0hMG5HP7qSP4cGM9tzy/imdvmikWwwKBIG3IssyWui7eXFvDSyv24wuE9OeunT2EP1w6KUZj/cEQ2+q7+Nlbm9hS18XZE/ozcWA+Hn8IbyDIm2tq+HxrI0OKs7luzhCumT1UN6ZlWSYkK7osEAh6Jx9srEeWOaA9wdHkZFj4+rQK/rO8CoC/Xj6FSRUFB/25giPD6WPLeHF5FS5fQNS0SZF0tNKdwJPAOEmSaoF9wLVp+FzBUYrJJFHSg7PY5owo4cSRJTw4bxcbajr4YGM9IVnmxVtmcfLofofwSuNT0+7iw0313HLSMPIzw/uaC7Ks/O+2E6jtcGM2SQwoyESSJK6aMYSrnlzGZY8t5cxxZZw4qpTr5gwR6dICgaDHyLLMl9uaeHF5Feuq2rF7A5gkuHz6IE4e3Q+HN8D6/R28tGI/o8tyuemksOd/6Z4W7nxpLe0uP3kZFp67aSanjyuL+PwfnD2GT7c08vrqav700XbeWlvLMzfNZEttJ3/4cBtleRm8ctscrAew31AgEBxavIEgr67cz6SKfEaVpSf99do5Q/jP8iqmDy0Si+CjnNPHlfH04n0s29N6RINJRxMHtRCWJMkMfEeW5bMkScoBTLIsx6/DLhAk4dcXTeCnb25iwY5mvj6tgo01HXz/f+v55HunHPYDzl9cVoUE3HxSbGqJySQxuDg74rEhJdl8/oNTeWT+bj7aVM+X25uobnNx38UTD9MVCwSCYwFvIMiPXt/IexvqGFiQyaXTKpgyqIAzxpVFOBevmjGYNpeP332wlf75mcwcXsyLSyt5bOEehpXkcN/FEzlhRAll+bHp03mZVi6fPojLpw9i4c5mbv/PGk768zwABhZksrqqnYfn7eb7Z485bL9bIBCkxh8/3MauJgdP3zAjbZ85rjyfX14wnpnDitP2mYIjw4xhRWTbzMzf0SQWwikiHWi1XkmSLLIsByRJmifL8hlpvq5DwowZM+TVq1cf6csQpMCOBjsXPLiIb84azB8unXzYvjcYkjnh/i85bnAhTx7gRPO797fy7JJ9PHj1tLSkLgkEgmMfbyDIt15YzaJdLfzoa2P59ikjklaBdfuCXPfMCtZUteuPXTBlAH/6+mQKsqwJ3xfNpppOPt/WyMSB+Zw+toyfvrmRdzfU8e6dJ4nokEDQS1i4s5n7P9rG9gY7t548nF9cMOFIX5Kgl3Lri6vZVt/Foh+frm+d2Vzbyf0fb2PWsBLuOWt0t58RDMnsarKzu8lBQZaV0WV5rK/u4M8fb2N4aQ53nD7qqHOcSJK0RpblGMP+YCLCK4HjgXWSJL0HvA7oNbtlWX7rID5b0McZW57HNbOH8NKK/VwxfTAluTYGFmQd8sINy/a00mT3HlQF65+eN4711e3c8+o6qlqc3Hn6KFFwQiAQJKTZ7uW+97awaFcLf71sClfOHNzte7JsZp69cSbPLd1Hjs3C3NGljB+Q3+PvnjyoQD/7HeA3F0/kq13N/PKdzbz1nROFdgkER5g9zQ7u+O8a+hdk8vtLJnL1rCFH+pIEvZgzx5Xx+dZG1lV3cPyQIrbUdXLJI0sIyTKrKtu5evZgyvIy2VLXSavDx3FDCnn6q720OH0MLsomN8PMc0sq2RvnGKax/fPYUtfFrS+u5qsfnx6xffBo5WAiwmtlWT5ekqTnDA/LgATIsizfko4LTCciInx00Wz3curf5uPyBQHIy7Rw+tgy/m/ucKYOLjwk33nvaxv4bEsDq3551kFVsHb7gvzsrY28s76OcyeW848rp5JzkNUdBQLBsYXLF+CPH27j1VXVBEMyPztvHN8+deSRvizeXlfD9/+3gUkV+ZTlZVKcY+OK6YOYPaLkSF+aQNCn8AdDXPzwEho63Xx0z8kMKMg60pck6OU4vQFOuP9LTh7Tj0euOZ6fvKFst/nvt2ZzxeNL+fq0Qbj9AT7a1ACAxSQRlGUKs6y0u/wAjC7L5dZTRjBxYD52T4Bt9V0EQzI3nDCMnY12LnxoMd89YxT3njP2SP7UHnEoIsJlkiT9ANhMeAGscWCra4HAQL+8DJ6+YQY7Gu3YLCY2VnfyyZYGvtjWyLx7T+v2+JCeUtfh5uPN9Vw4ZcBBH+OUZTPzr6uOY1JFAX/6aBu/ec/C36+YmqYrFQgERyP+YIgvtzXS6vRhNZl4aP4uatrdXD9nKNfNGcqY/nlH+hIBuPS4CvY0OVlT1U5jl4c1Ve28saaGWcOKuXLmYC47vkIcvSQQHAZeXFbFtvounrh+ulgEC1IiJ8PC1bOG8PTifWxv6OK9DXVcctxApg8t4rzJA3hzbQ05NjP3nDmakWW5LNnVwpUzBzN9aBEOb4DGLg9Di7MjtubMMThBJ1UUcOGUATyzeB/XzRlK/zi1KI4mDiYiXA88RuQCWEOWZfl3B3NhhwIRET762d/q4qx/LeS8SeU88M1pPXqvLMvsb3MxpDg7woirbnNR3+nh4fm7WV3Zxif3nMKQkuwkn9Qz/vrJdh5dsIfHr5vO1yb2FwakQNADOlw+/ru8iuvnDKMgu/enYQVDMn/7dAerKtsYXZbLeZMHMGFAPot3N/Pnj7fT2OXVXzt+QD6/uWhChJHRG3H7gry0ooqXV+5nb7OTb84czB+/PlkcsyQQHEJaHF7O+PsCpg4u5MVbZgnbQZAydR1uTv3bfCRJwhcI8d5dJzFlUCF1HW7eXV/HFTMGHVQh2qpWJ+f86yvOGFfGY9dNT+OVHzoSRYQPOjX6oK/sMCIWwscG//hsBw/N2827d54UkyLtD4YSHvtx/8fbeGLhXiYMyOfWU4Yzd1Q/3ttQx18+2a6fz/nbiydy44nD0nq9Hn+QCx9azO4mB/mZFiZVFHDDCUM5d9KAtH6PQHCsEQzJ3Pz8Kr7a2czl0wf1qqwKjz9Iq9NHRWFWxGPfe3U9n2xpYMqgAva1OLF7AvrzUwcX8r0zRzOyXy7NDg/TBhcdVXtwZVnmn5/v5KF5u7nhhKH87pJJcV/X6fITkmWKcmyH+QoFPcXjD/L80kreW1/Hj84dy+ljy7p/k+CQ0ub0sWhXM3/5eDvNDi8f3n1yr8kWERw9rKlq54mFe8iwmnno6p4FjlLh0QW7+esnO3jy+umcM7E87Z+fbg7FQnidLMvpb9lDiFgIHxs4vAFO+NOXnDm+jH+rUeFOt5/v/HcNS/e00j8/g4euPp5Zw8MV7V5esZ+fv72Js8aXUdnqYneTQ3/utLH9uHb2UOweP5ceV3FIDNNWh5fPtjayqbaT5Xta2dvi5O4zR/P9s0YLL+9hoNXh5dMtjSzf20qH28+FkwekVJAoHsGQLCJhhwltop06uJAN1R28cuscThh5+KKnjV0e6js9jB+QF3EueE27i+ufWcm+FidDS7K58/RRTK4o4DfvbWHlvjZ+deEE/m/ucLyBIAt2NNPU5aFfXgbnTCg/qha+ifjjh1t5atE+/nHFVC6bPkh/vNXh5TsvrWVVZRtmSeKiqQP55QXje3SuvODw8qPXN/D6mhpsZhNzRpbw4i2zjvQl9Tm8gaCuL59uaeC7L6/DFwwxsl8O/7jyOI47RDVRBIKDwR8McdFDi+lw+fn8B6eQ18sLZx2KhXCxLMttB31lhxGxED52uO+9Lby0ooolPz0Dty/It/+zhj3NDm48YRjztjfRbPfyym1zmFRRQE27i7P/+RUzhhXx3E0zMUkSC3c2s73BzsxhRUwfWnRYF6PeQJBfvL2ZN9bUcP2codx38cQ+v7AKBEMs3NnM8UOK0hpFanf6eODLXbyycj/eQIjy/EwyrCaqWl088M3juOS41KuDB4IhfvLmJj7aVM85E/tz6bQKZg0rxukLsHJfG6PL8hhbLrz26cIfDHHC/fOYODCfx6+bzjn/XkiOzcJHd5+c9sWkLxDi8YV7WLizGVmWGVaaQ227m1WVbYRkyLSa+MOlk7l8+iB2Ndq5/pmVOH0Bbj91JJ9vbWR9dQcAVrPE36+Y2qN+dTQSCIa47pkVrK3q4JmbZnDy6H44vAGufnI5u5rsfOfUUXS6/by0oooBBZnccdoo9jQ7mL+jibPG9+ees0ZHOBYER4YVe1u56snlfPvUEUhIPLVoL6t+cRbFBg2WZZnFu1uYMqiwR8dyCbpn7f52/vrJdtbu7+AfarbLva9tYMLAfH514QSmDCpImOEmEPQG1ld38PVHl3Dt7CGH9ajTAyHtC+GjEbEQPnbY2+zgjH8sZGz/POo63EgSPHzN8Zwyph91HW4uf2wp3kCIp2+cwb+/2MWqyjY+/8GpEWmMRxJZlvnzx9t54qu9DCnO5rZTRnD1rCF9dkH8wBe7+NcXO7GYJG49ZQQ//tpYdjc5+GBjPcGQzAVTBiQ9GkaWZX7+9mbmbW/kmzOH8PVpFSzb28pfPtmO3RPgsuMruOnE4YwfkIcvGOKGZ1aydn87z988i5NGlXZ7fbIsc/er63l/Qx2nj+3H2v0ddLr9Ma87c1wZt582kmmDC5OeASvonk+3NPDt/6zh6RtmcNaE/ry7vpZ7Xl3fYwdGKjz05S7+8flOpg0pxGoyUd3uoiw/k7mjSpg4sID/LKti2d5WThnTj001HZhNJl68ZRYTBuYjyzJfbGvC6Q0wY1gRg4rSV1+gN9Pp8nPVk8uobHXyqwsn8OaaGjbUdPLUDdM5Y1x/QEnN+9YLq2h3+TGbJCZVFLChuoNpQwr57//NTlpJv6nLQ5bN3OujDEcrnS4/lzyyGH9Q5vMfnMLeZicXPrSYP319MtfMDh/P89/lVfzync2MK8/jv9+afVD7CgVh3lhTw8/e2khRto1+eRlsqesCYOLAfF761mwKs8W2AsHRwR8+2MrTi/dxz5mjybIpDs4LpwzodXNhr1wIS5J0LvAAYAaelmX5z1HPS+rz5wMu4CZZltem8t54iIXwscVfPtnOyn1t9MvN4BcXjGdwcXjQ7Wl2cOXjy2h1+gD49YUTuGXu8CN1qXGRZZlPtzTw1KJ9rKlqZ9qQQp6/aVbKBYH8wRAPfbmLz7Y28si1xzOyX27E83aPn/c31NPu8jG8NIeTRpb2ymJD+1tdnP2vhZw0qpTCbCtvra1lSHE2+9tcmCSQJIlgSOb+b0yOe36iLCvFiR5dsIeJA/N1gwJg1rBifnfpRMaVRy6iO91+rnh8KfUdHl67/YSYRXab08eOBjvHDS4ky2bm9dXV/OiNjfzoa2O58/RReANBvtrZwt5mB2aTxLQhRSze1cLzS/fR7vJjNUtcO1uJ9h/rVLY4MZskBhVlpTWz4ubnVrK1voslPzkDi9lEKCRz4UOLsXv9fP79Uw+6srtGZYuTc/79FWeP788j18Yve+ELhLj/422s2NtGToaZv10+lWGlOWn5/qOZZruXO15aw6rKdqxmiYeunhZT+8DhDdDm8FGYYyU/08oHG+u4+5V1nDy6H09cP12/j21OH7XtblqcXnUsVWIzm7h0WgVnjivjsYV7KMyy8uh1xx/10eSadhf3vbeVZoeX44cUcvcZow/rfmp/MMTNz61ixb5WXr51DjOHFSPLMmf8YyH5WVZe//YJWM0Sn2xu4O5X1zFhYAE7GroYW57PO3ecqI/zzbWd3P3qOrz+EOPK87hm9hDOHN//sP2Oo5EOl4/fvr+Vt9fVctKoEh69djoZFhP/+GwHI/vlcvn0QcKJKjiqCARD/OC1Dby3oS7i8U+/d0qvypLrdQthSZLMwE7gbKAGWAVcLcvyVsNrzge+i7IQng08IMvy7FTeGw+xEO5b7Giw88nmBs4YV8bkQQVH+nISIssy76yv5Uevb+TcSeU8dPU0fMEQn21p5JQx/eKmo1W1Ornn1fWsr+4g02qiONvGG985kYFqxFuWZf7vhdXM296kv2dAQSbv3nUSZXlHvtT9vhYnr67aT027m6W7W/AGQsy79zT652fw8LzdvL+xjiumD+bSaRWYTRI/eG09C3c2888rp/L1aeE9icbI+tWzhvCnr0+itsPNp1saKc/P5PzJ5QkXZ3Udbr7x6FIA3roj3HbavlSAaUMK+c6pI7n39Q2MH5DPq7fOSZqW6/IF+HhTA/N2NPHhxno9mnm4qe90Y5KktB5rYPf4mbe9iQ6Xn2/OGkyGxczjC/fw54+3A3DOhP48eu3xaTHiFu1q5oZnV3LX6ZHnFC7e1cJ1z6zge2eN5ntnjTno75FlmeufWcmG6g6+uPfUo/4YiCNBKCTz/sY6+udnplz9+tWV+/npW5sY0z+XO04bxbaGLp5etI9gKGyPXDVjMDIy766vwxsIUZBlpdPt54LJA3jo6mlH7V5rjz/IFY8vY1+Lk8kVBazY10peppXvnzWa6+YMTcv48QaCtDl9CY/b+fW7m3lxWRV/u3wKV8wI10p4Y00NP3x9AyeMKMHlD7KhuoNx5Xm8etscPt/ayI/e2Mhj1x7PeZMHsK2+i28+uZzcDAuzhxezZE8LjV1eHr/ueFEIMg71nW5yMyxc/dRyttfbufP0Udx1xiiR+iw4JggEQ7y/sY7jBhdhMUl8sa2Rm04c1qtq4PTGhfAJwH2yLH9N/f/PAGRZvt/wmieABbIsv6L+fwdwGjCsu/fGQyyEBb2ZR+bv5m+f7uDcieXsbnawu8nB0JJsHr9uuh6x9AaCPLu4kofm7cJikvjTNyYzvDSHq55YzpRBBbz0rdlIksRrq6v58Rsb+dl547j+hKGsrmzn2/9ZwwR1v2VepoXGLg/98zPTFllLlWV7Wrn9v2tw+QL0z89kxtAirp0zlJnDihO+x+MPcsvzq1i+t5UHr57GhVMGsrfZwS/f2czSPa3ccMJQ7rtoYo+N4+0NXVzx2DLK8jN45bY5LNvTyj2vrufcieWcNKqE33+wDV8wxICCTF65dU7KUUBfIMTFDy+m3eXjix+celjTO99dX8vP3tpEboaF9787Ny2LuxV7W7nz5bW0OJQMi8kVBZw+royH5+3izPH9GdM/l0fm7+GK6YP4w9cn9Thi1+nys2xvKx0uHw5vgEcX7KE018Y7d55Eti0yffbuV9bxyeYGPvneyYyIyoLoKVq69e8vmcj1Jww7qM8S9IwFO5r40RsbabYrx0ldMX0QZ0/oT0luBhWFWfo58R0uH0v3tHLCiBLeWFPDHz/axt1njOIHBgfJkeDzrY28v6GO/5s7POb0Ao1mu5eXVlTh8Yc4aVQJM4cVc+/rG/hwYz1P3TCDsyf0Z0eDnd9/sJXFu1v4v7nD+dWFEw7qujz+IDc+u5IV+9oY0S+Ha2YNIdtmYcGOJva3uQDY3mDn26eM4Gfnj495/3NL9vG7D7Yyrjyfa2YP4ZszB2M1mwiGZL72768Ux+2dJ/H1R5di9/h54/YTGVycjT8Y4uKHl9Du9PHFvaeSmyTtva/xv1X7+cmbm8i0mvAHZZ6+YQanjxPVuQWCw0lvXAhfDpwry/K31P9fD8yWZfkuw2s+AP4sy/Ji9f9fAj9BWQgnfW88xEJY0JsJhmR++c4mFu5oJsNq5sYThvLYwj24vEGeuH46uZkWfvzGRrY32DlrfBm/u2SSHsXUqmLf/43JnDqmH1/711eMHxgZwfxwYz3ffWUtFpPigfYFQ5hNEj87bxzfOnnEIf99nS4/972/hbfX1TKiXw4v3DwrIp29O1y+ADc8s5LVVe2cMqYfy/e0kmE18ZNzx3Ht7CEH7HlcsbeVm59fhVmSsHsDTB9axMu3zibDYmZNVRu1HR7OnViOzdIzz/366g4ufWQJ3zltJD85d9wBXVtP6HT7+dun2/nv8v0cN7iQnY12xvTP4+VbZ8csJnvCvO2N3PbiGoYUZ3P/NybT7vLz87c30eb0Ma48jze+cyK5GRb++flOHvxyF6PKcpkxtAir2US2zczw0hzOnVQed8+b3ePnjx9u4/U1NRHRwNLcDP737Tkx6f4ATXYPZ//zK4aWZPPG7Sf2+L5o7GtxctljSxlSnM2b3zmxz+7PP5L4AiEqW51IwOgUjoeRZZmfvLmR11bX8LWJ/emXl0H/vEwunzEoYfTzUODxBzn1b/P1M6GvmzOEu05X9sdpGTxf7Wzmtv+sxhsIYZYkAiGZ3AwLDm+An58/jttOGRnxu3717mZeWrGf/912QsSJBz3lzpfW8uGmev5v7nA21nSwqrIdgCHF2Yzpn4s/KDO6LJefnT8+YZ93egNx929/tqWB2/6zRo/OP3/zTE4zHLe0bn8733hsKVfNGMyfL5tywL/hWMLpDXDa3xdQkmNjZL9czp1UzkVTBx7pyxII+hy9cSF8BfC1qMXsLFmWv2t4zYfA/VEL4R8DI7p7r+EzbgNuAxgyZMj0qqqqQ/vDBII0Ut/p5tqnV7C32QlASY6Nv10xRS9GoxEKyVzz9HJWV7YzpCSbhk4Pn9xzCkNKIhea+1qcvLyiCkmSGNUvl0+3NPDl9ib+etmUlI4TWrK7hZp2F5ccVxE3khwIhlhV2Y4kwaiyXL2wSk27i5ufW0Vlq5NbTx7Bd04beUBRUrcvyIPzdvH0or2cN2kAv7xwfFpSvVfua+P+j7dx/qQBXDtnyEEtHI384H/r+WBTPV98/9SYe5EuGjo9fPs/q9lQ0wnAbaeM4EdfG8uX25q446U1zBxWzLM3zUxamCgR2xu6uOzRpQzvl8PLt84hX71nsizT5Q6Qm2mJMKbnb2/iL59sp83pwx8M4fQF8QVCjO2fx2u3nxCR5t/p8nPpo0uoanVywwnDuGjqAAYWZpGTYSHHZkm6MP1kcwO3/3cN508u55pZQ5kzorhHKaWNXR4ue2wpLl+QN24/4aAjy4LDh8cf5OdvbWJ9TQdtTh8dLj+ZVhO/vnBiRJEnUPrpoUjNe3rRXv7w4TaevmEGy/e28vTifQCYTRJ3nzGa/vkZ3Pf+FoaX5vLINdMYUJDF59saeX11NZceVxFx5JSG0xvgvAcWYTZJfPK9k/WsiqV7Wnh60T6mDy3iphOHJR3Hu5scnPXPhdx95mh+cLaydWBzraILEwfmp6UtvtjayC/e2cQ5E8r5/aWx50j/7dPtPDJ/D3+4dBLXzRl60N93tPPvL3by7y928dYdJ3L8kKIjfTkCQZ+lNy6ERWq0QJACrQ4v72+oI8tm5oxxShQkHp1uPz99cyMfb25I2QjxBULc8vwq1u5vZ8GPTotZVHr8QUySRIvDy8/e2sTCnc0ADCrK4mfnjefM8WU0dXmpaXexsrKNN9bUUNPuBsAkwYxhxYwqy+WddbWYTRJPXj8jLefA+gKhA44EHk4aOj2c8Y8FFGRZ+dvlUzlxZEla9jbua3HSPz+DHQ12vvvKOtqdPu44fRRzRhQzfWg4mvTu+lq+/7/1DCjI4kdfG8uFUwawrd7O9oYubBYT508ekHCPmssX4IIHF+PyBXj3zrl6qmpPCIVkFqqRsWlDinjxlllkWs3IssydL6/lsy2NvHjLLE5MoXJ3NH//dAdPfLUHf1BWFh4XTeS8yd3vTex0+bnyiWXUtLt49bYTenX9AEH3VLe5+Pnbm1i8u4XHrp3OGePKaHZ4eWl5FS+t2M+1s4dw95mjybSacfuCPLpgN1/tasEkwe8unhT3/odCcsJx6vEHmfuXeYwrz+e/35oNwJqqNrbWdbFiXxsfbKwHYGz/PF66tWdVlr/aqeyN//G5Y/nOqSP5+2c7eGT+HgqzrXS4/EwcmM/bd5yUUPv++flOHpq3i+U/O/OQ7ncPhWQktZBhNMGQzLdeWMXi3S28fcdJTKqIP76CIZkvtzXS4vBRXpCBSZKYv72JDKuZn503Lq0ODFmWCYbkw16Eqsnu4bS/LeC0sf149Nrph/W7BQJBJL1xIWxBKXh1JlCLUvDqGlmWtxhecwFwF+FiWQ/KsjwrlffGQyyEBcc6siyzv83F0JLUK9pWtjg5658LuWrmYP74deUcuM21nXz7P2uo7XBjNUuYTRImSeIHZ49hdP887v9oG9sb7BGfI0kwc2gxN544jMJsKyv2tjJ/RzM7GuycMqYfv75wwiGLivZmNtd2cufLa6lqdTGwIJOHrjme6UMPLDKwta6L+97bwsrKNmxmE75giLK8DJ66YUbCfYor9rZy3/tb2VbfRV6GBbs3oD83uiyXC6coaXrb6rvY0WhXioxNGcDS3S18sqWBV26dk3IRpES8t0GpFHzuxHJ+c/EEnli4l+eXVvKTc8fxndNGdv8BCXD5Any1s5kHv9xNVauTz9Qj0uo63Czf20qHy09Jro3y/EwKs23Udrj400fb2d/q4vmbZx7QAlzQ+/D4g1z1xDI9K0Jj2pBC1u3vYPbwYv52+VRufG4l+1qczB5eTGWrE28gxP9uO0GvbCrLMo8u2MPjC/fw+HXT4x6t9tqqan785kZevnU2J46MfX5VZRtZVjMTBuQfkNPr2/9ZrZ+pvnRPK9+cOZj7Lp7IF9sauevldQkLxWlVn8vzM3nltjk9/t500u70cd4Di8jJMPPuXXPJsZkJhGTd6baz0c4dL61ld5Mj4n1mk3JCwO8vncT1BxFNDoVk1u5vZ2t9F9vqu1i6p5XGLg9/vXwqFx+GtOS9zQ7mbW9iQ00nH2+q5/MfnMpwUWVeIDii9LqFMOhVof+NcgTSs7Is/1GSpNsBZFl+XD0+6WHgXJTjk26WZXl1ovd2931iISwQxOe+97bwn+VVvHjLLPIyLdzw7EpybBaumT0EuydAh8vHd04bqS+wgyGZd9fXUt3mZkBhJgMLsphUkR93H+ihSk88mnB6A3y6pYEHvtxFp1spMDOqrGfpuMv2tHLbi6vJsJr51snDaXV4yc+0cvPc4d0WpgmGZD7f2shHm+qZObyYk0eVsrPRzl8/3cHuJgeSBMNKchjTP5dt9Xa9qE46ixI9s3gfv/8gXNj/phOH8esLJ6QlQl7d5uJr//6KSRUF3HjCMH7xziY6XLHnPIOSzfDnb0xh7mixCD6WaHP6eHNNDd5AkOKcDKYOLmDiwALeXFPDva9vINNqwmoy8cT10zlxVClVrU6ufGIZwRC89u05DCzM4lfvbOb1NTVk28zYLCbevfOkCKeiLMuc98AiAD6+5+RDomv1nW5+9c4WdjXZOXdiOT85d5w+Ru55dR0fbqzn/e/OjTnybWNNBxc/vCThMXOHm6W7lQrvg4qyKc21sb66g2tnD2VoSTYPfrmLDKuZX184gWlDCmmye/EFQowvz+fuV9exbG8rn3//lB45dI3c994Wnl9aCUBhtpXjhxTR7vKxvrqDf1+V/nPIjdR3KqcR1Hd6AEXn+sIxegJBb6dXLoQPN2IhLBDEp8Pl46onllPZ6iQYkumXl8Grt805YENEEJ/9rS6+8dgSyvIyef+7c1Mq0FTV6uTR+Xt4bU01I/vl8uIts/QiaenAGwgSCkGWTdmTGArJ1HW6sZlNlKU5vXJNVRsbqjsZW54XN9p2MLy2upqfv7WJQEhmWEk2D119PBVFWbQ6vDR0eehyB8iwmDhpVKn+WwV9gz9/vJ0Xl1Xy/M2zIgpR7W5ycNUTy3D5guRkWGhxePnuGaO47PhBXPLIEiQJLj9+ECsr2xhfno8/GOKtdbX89fIpXDmj+5oK6abd6ePsfy1kQEEWb99xop7qK8syNzy7kvX7O1j8kzN6zXnxK/e18dO3NuL1h5g5rIj3N9YTDMmM7Z/H0zfOiFsssb7TzUl/nsedUUenpcqeZgfn/OsrLj2ugh9+bQzl+ZlIkoTbF+TG51aysaaD9+6ay5gUirP1lGBI5tJHlrCvxcnj102n0+3n9HH90lZzQiAQHDhiIYxYCAsEyWhxeLnjv2sZUpLNLy8YHze6Kzh4tDThZAXK/MEQTyzcw4IdzazZ347FJHHjCcP47pmj454rLVBocXiZt72JM8eVUdKDvZmCYx+PPxi3wN/uJjsvLK2i1enlqplDOHVMP0BZUN3z6jo213YxdXAhe5ocyLLMpdMq+PVFE3p8RFi6+HhTPd95aS1XzRjMby+ZSIbFxJtra/nh6xu476IJ3HTS8CNyXanQ1OXBZJIoybEljaZf/8wKqlpdLPzRaQB8uqWBqYMLU6oM/u3/rGbxrhYW/vj0mP3ZTV0ezn9wMVazxPfOGs0V0wf3OCPF5QvwwtIqdjXayc+yctcZo/Tv0dLmH7x62mFJwRYIBKkjFsKIhbBAIDjyyLLMNx5bSk27m0U/Pj3GOPf4g9zx0lrmbW9i6uBCThvTj2tmDzmkxW8EAkEswZCMwxOgINuKLxBCRj5iC2Ajf/54O48v3EN+pgWbxUyLw8ukinzeueOkw14Q6lDwxpoafvj6Bt6640TW7+/gdx9spSDLyl8um8y5kxIXxFtT1cZljy3jB2eP4e4zR8d9zfrqDn75ziY213Zx79lj+G6C18Wjus3FFY8vo6HLQ0VhFs12LzkZZp68YQYTB+Zz+t8X6NH6vr4dSCDobYiFMGIhLBAIegdL97RwzVMr+NvlU7jCkGIZDMnc/t81fLGtkT9cOolrZ4vjRwQCQSxLdrfwwcY6fAGZ2cOLOW9y+QEdSdcbsXv8zPjDFxTn2Giye5k7qpR2l4+NNZ1cMX0QJ40qZdbw4ogtIrIsc8Xjy6hqUyLJydKRZVnmrlfW8fmWRj68e25KZ1gD/ObdzbyyspqXbp3NzGHF7Gq0c9t/1mD3BBg/II9Fu1p4/fYTmDnswM+BFggEhwaxEEYshAUCQe9AlmXO/tdX5NiUqqoaf/xwK08t2sdvLprAzb04xVEgEAgOJa+vruazrY1k28z84dJJZFjM/PPznTz51R5CMljNEtfNGcrPzhuPzWLikfm7+dunO/jT1yfHnCcdjxaHl7P/uZBsm4W/Xj6F3AwLTy/ex6aaDoaW5PD7SyZFnHLQ6fZzwv1fct6kAfzjyqn64zsb7Vzy8BLc/mDK3y0QCA4/YiGMWAgLBILewwtLK/nNe1t4986TmDq4kJdWVPGLtzdz4wlD+e0lk4705QkEAkGvw+7xU9Pu5sVllbyyspq5o0rJy7Tw8eYGLpwygH9fdVzK6eHrqzu46+W11LS7Aci2mTlldD+W7G5hVP9cXv/2CfpnPbZgD3/5ZDsffHduzNnIqyrbaHP6+NrE8vT+WIFAkDbEQhixEBYIBL2HLo+fuX+ex/B+udx+ygjuemUdJ48u5ekbZhwT+/wEAoHgUPLqyv38/O1N5GVauXb2EO49Z2xKlfiNdLr9LNjRhEmSOGFkCaW5GXpBw+vmDOGXF0xge4OdK59YxkkjS3ju5lmH6NcIBIJDiVgIIxbCAoGgd/HhxnrufHktAFMHFfDfb80+Zvb5CQQCwaGm0+UnO8OMNc3Ow9++v4XnllSSn2nBGwhRlp/Be3fOpShHnKYgEByNJFoIi8PNBAKB4AhxwZQBbKodyabaDh69ZrpYBAsEAkEPOFRnJv/moomcNb4/766vJctq5sYTh4lFsEBwDCIiwgKBQCAQCAQCgUAgOCZJFBEWG9EEAoFAIBAIBAKBQNCn6FMRYUmSmoGqg/iIUqAlTZcjSA/invROxH3pnYj70jsR96X3Ie5J70Tcl96JuC+9D3FPIhkqy3K/6Af71EL4YJEkaXW8sLrgyCHuSe9E3JfeibgvvRNxX3of4p70TsR96Z2I+9L7EPckNURqtEAgEAgEAoFAIBAI+hRiISwQCAQCgUAgEAgEgj6FWAj3jCeP9AUIYhD3pHci7kvvRNyX3om4L70PcU96J+K+9E7Efel9iHuSAmKPsEAgEAgEAoFAIBAI+hQiIiwQCAQCgUAgEAgEgj6FWAgLBAKBQCAQCAQCgaBP0acXwpIkDZYkab4kSdskSdoiSdI96uPFkiR9LknSLvXvIvXxEvX1DkmSHo76rE8kSdqgfs7jkiSZj8RvOtpJ5z0xfOZ7kiRtPpy/41gjzWNlgSRJOyRJWq/+KTsSv+lYIM33xSZJ0pOSJO2UJGm7JEmXHYnfdCyQrvsiSVKeYZyslySpRZKkfx+hn3VUk+axcrUkSZskSdqozv2lR+I3HQuk+b5cpd6TLZIk/fVI/J5jhQO4L2dLkrRGHRdrJEk6w/BZ09XHd0uS9KAkSdKR+l1HM2m+J3+UJKlakiTHkfo9vYU+vUdYkqQBwABZltdKkpQHrAEuBW4C2mRZ/rMkST8FimRZ/okkSTnANGASMEmW5bsMn5Uvy3KXOsDfAF6XZfnVw/yTjnrSeU/Uz/sGcDkwRZblSYfxpxxTpHmsLAB+KMvy6sP8M4450nxffguYZVn+pSRJJqBYluWWw/yTjgnSrWOGz10DfF+W5a8Ox+84lkjXPZEkyQLUARNkWW5RF1wuWZbvO+w/6hggjfelBFgHTJdluVmSpBeAF2VZ/vLw/6qjnwO4L9OARlmW6yRJmgR8KstyhfpZK4F7gOXAR8CDsix/fPh/1dFNmu/JHKAK2CXLcu6R+D29hT4dEZZluV6W5bXqv+3ANqACuAR4QX3ZCygdDVmWnbIsLwY8cT6rS/2nBbABfdfDcBCk855IkpQL/AD4w6G/8mObdN4XQfpI8325BbhffV1ILIIPnEMxXiRJGg2UAYsO3ZUfu6TxnkjqnxzV8Z2PsjAWHABpvC8jgJ2yLDer//8CEFktB8gB3Jd1sixr42ALkClJUoa6eMuXZXmZrETeXtTeI+gZ6bon6nPLZVmuP4yX32vp0wthI5IkDUPxMq4A+msdRP07pdRNSZI+BZoAO0pUWHAQpOGe/B74B+A6VNfYF0nHWAGek5RUz1+JNKn0cDD3RZKkQvWfv5ckaa0kSa9LktT/EF5unyFN4wXgauB/cl9O40oTB3NPZFn2A98BNqFGhoFnDuX19hUOcqzsBsZJkjRMjdpfCgw+dFfbdziA+3IZsE6WZS/KQq3G8FyN+pjgIDjIeyIwIBbC6JHDN4HvGSK7PUaW5a8BA4AM4IxuXi5IwsHeE0mSjgNGybL8drqvrS+TprFyrSzLk4GT1T/Xp+v6+ippuC8WYBCwRJbl44FlwN/TeIl9knTNLSrfBF45+Kvq26RhbrGiLISnAQOBjcDP0nqRfZCDvS+yLLej3Jf/oWRNVAKBdF5jX6Sn90WSpInAX4Bvaw/FeZlw5h0EabgnAgN9fiGsTmpvAi/JsvyW+nCjms6h5eQ3pfp5six7gPdQUhUEB0Ca7skJwHRJkiqBxcAYdW+q4ABJ11iRZblW/dsOvAzMOjRX3DdI031pRcmc0BxHrwPHH4LL7TOkc26RJGkqYJFlec0hudg+QpruyXEAsizvUaPzrwEnHpor7hukcW55X5bl2bIsnwDsAHYdqmvuC/T0vkiSNAhlDrlBluU96sM1KE5WjUGIrQQHTJruicBAn14IqymZzwDbZFn+p+Gp94Ab1X/fCLzbzefkGjqhBTgf2J7+Kz72Sdc9kWX5MVmWB8qyPAyYi7J36LT0X3HfII1jxSKpFVZVQb8QEBW9D5A0jhcZeB84TX3oTGBrWi+2D5Gu+2LgakQ0+KBI4z2pBSZIktRP/f/ZKHv1BAdAOseKpJ5AIClVc+8Ank7v1fYdenpf1O01HwI/k2V5ifZiNVXXLknSHPUzbyB13RMYSNc9EUTS16tGz0VJodkEhNSHf46Sc/8aMATYD1why3Kb+p5KlOIYNqADOAclmvIBSkq0GZiHUtlTpOX0kHTdE1mWtxo+cxjwgSyqRh8waRwrVcBXgBVlrHwB/ECW5eBh+inHFOkcL5IkDQX+AxQCzcDNsizvP1y/5Vgi3TomSdJe4HxZloWD9QBJ81i5HaUKrh9F026SZbn1sP2YY4g035dXgKnqZ/xOFid3HDA9vS+SJP0SZYuAMQp/jizLTZIkzQCeB7KAj4HviloHPSfN9+SvwDUo2zvqgKflPlr5vk8vhAUCgUAgEAgEAoFA0Pfo06nRAoFAIBAIBAKBQCDoe4iFsEAgEAgEAoFAIBAI+hRiISwQCAQCgUAgEAgEgj6FWAgLBAKBQCAQCAQCgaBPIRbCAoFAIBAIBAKBQCDoU4iFsEAgEAgEAoFAIBAI+hRiISwQCAQCgUAgEAgEgj6FWAgLBAKBQCAQCAQCgaBPIRbCAoFAIBAIBAKBQCDoU4iFsEAgEAgEAoFAIBAI+hRiISwQCAQCgUAgEAgEgj6FWAgLBAKBQCAQCAQCgaBPIRbCAoFAIBAIBAKBQCDoU4iFsEAgEAgEAoFAIBAI+hRiISwQCAQCgUAgEAgEgj6FWAgLBAKBQCAQCAQCgaBPIRbCAoFAIBAIBAKBQCDoU4iFsEAgEAgEAoFAIBAI+hRiISwQCAQCgUAgEAgEgj6FWAgLBAKBQCAQCAQCgaBPIRbCAoFAIBAIBAKBQCDoU4iFsEAgEAgEAoFAIBAI+hRiISwQCAQCgUAgEAgEgj6FWAgLBAKBQCAQCAQCgaBPIRbCAoFAIBAIBAKBQCDoU4iFsEAgEAgEAoFAIBAI+hRiISwQCAQCgUAgEAgEgj6FWAgLBAKBQCAQCAQCgaBPIRbCAoFAIBAIBAKBQCDoU4iFsEAgEAgEAoFAIBAI+hRiISwQCAQCgUAgEAgEgj6FWAgLBAKBQCAQCAQCgaBPIRbCAoFAIBAIBAKBQCDoU4iFsEAgEAgEAoFAIBAI+hRiISwQCAQCgUAgEAgEgj6FWAgLBAKBQCAQCAQCgaBPIRbCAoFAIBAIBAKBQCDoU4iFsEAgEAgEAoFAIBAI+hRiISwQCAQCgUAgEAgEgj6FWAgLBAKBQCAQCAQCgaBPIRbCAoFAIBAIBAKBQCDoU4iFsEAgEAgEAoFAIBAI+hRiISwQCAQCgUAgEAgEgj6FWAgLBAKBQCAQCAQCgaBPIRbCAoFAIBAIBAKBQCDoU1iO9AUcTkpLS+Vhw4Yd6csQCAQCgUAgEAgEAsFhYM2aNS2yLPeLfrxPLYSHDRvG6tWrj/RlCAQCgUAgEAgEAoHgMCBJUlW8x0Vq9DGCyxdAluUjfRm9Fl8ghC8QOtKX0atx+QJH+hJ6NR5/kGBIjLFEBEMyHn/wSF9Gr0bodHJ8gRD+oNDpZAidTo7Q6eQIne4eodPJOdZ0WiyEjwFaHV5m/OEL5m1vOtKX0mv5yZsbueOlNUf6MnotK/e1MeW+z6jrcB/pS+m1XPDgIh6et/tIX0av5bkl+zjtbwuO9GX0WlocXqb//gsW7Gg+0pfSa/nRGxu446W1R/oyei3L97Yy9befUd8pdDoR5z+wiEfnC51OxLOL93HG3xcc6cvotTTbvRz/+89ZuFPodCLufX0Dd7187Oi0WAgfA2xvsOPyBalucx3pS+m1rKlqp7pNGA+JWFPVTiAkU9/pOdKX0ivpcPnY0+ykul2MsUSs3d9OQ5fnmPIUp5Nt9V24/UHRh5Kg6LRon0SsqWrHH5RpEDodlzanj70tQqeTsaaqnbpOj4iaJ2BrfRcef4jqdmEvJmLtMWZPi4XwMcCeZgcAHpH6GxePanx6AiIdKBFaH/KKlKm47Gl2AoiUsiTsaRJtlIw9TapOi/aJi8cfpLbDjVfMYwnR53q/aKN47BXt0y3hPiR0KB6aTgtbKD4uX4DaDvcxZU+LhfAxwF7VSHf7jp2OmU6qWl3IsmifZGgGhFuIf1z2CuMhKcGQzL5WVYdEG8Vlb4um08JIj8e+FqfQ6W7YKxxySdFtIdE+cQkEQ1S1KtFy0Ubx2dui2kJCh+KyT53HPMdQ+4iF8DGA8PAlZ49Y5CVFlmU94inaKD6ifZJT2+7Wi9F5xEIvLkKHkiPaJzmKTos2SoawhZJT0+7Gp25dEQu9+GiZTWKMxedYtIXEQvgYQEvlOJY6ZjoRKYnJaXX66HT7ATE5JkI3QEX7xEVrHxA6lAiROp4cYYAmp9nhxe5RKkYLHYqP0OnkGHVa6FB8hLMpOcfiekMshI9ynN4AdWrhDCH+8dGEzR+URSGfOGjCBmJyTER4chT9Jx5iIZwchzdAQ5fQ6WRofcgXCIlCPnHQHAUgxlgijsVoVToROp2cLo+fJrsXELZQIox1CkLHiE6LhXAvxR8M8fySfd0u3LR8feh7wrZwZzNb6jq7fZ02OULfEremLg9vrqnp9nXG9ulrfeiNNTU02ZNXYPUHQ+xX91X1pf4DsLm2k69SOEYiwsDqQws9XyA1nd7bhw3QBTua2FrX1e3r+mq0qrHLw1trU9Hpvtk+AK+vrqZZXaAkwhcIsb+tb+5/3VzbyaJdKei00ZnSB3U60K1O9832AZi/o4lt9anodLiNjpXChmIhfBjpyQHdK/a2cd/7W1m1ry3u89p+PG1yzLCY8PiDhEJySkZHb6SnB5j/8p1NPLZgT9znAkElqiDLMnubHWRYlK7u9gepanVi9/gP+noPN7Is96iN3lhbw72vb6Arzm+VZVnvQxHt4wvh9AYiHCxHEz1pnw6Xjx++voF319XFfV5rn/1tLgIhmQyLSZ8ct9V3HZVRq56OsUfm7+a+97bEfS4UCmdY7Gl26n3I4w/SZPfQ2HV0HvHSkzZavreV+97fypqq9rjPh8eYMp4yLCbcfUynf/H2Zp74KrlOh0Iyew19qE/p9JoafvDaBpzeQNzPMvahsE4HcXgDVPYBnW5z+vjRGxt5d31t3OfDOu0kqOq0x6DTR2PUqqdj7KF5u1LS6b0tkbZQk91DUx/Q6aV7Wrjv/a2s3d8R93mjLQRhnQ6G5JQWh72RHuv0W5t48qu9cZ8z6vS+qD5U2eLEEUe7jibEQvgwcefLa/npm5tSfn27yweAK45XanNtJ5N+8ym7Gu3saLBjNkmM6Z+Hxx9i4a5mzn9wEbsa7Wm79sPB4l0tTP3tZ3SovzsVOpz+hF67yx5byl8/2U5NuxunL8j4AfkAeP0hvvnkcv71+a60XPfhQpZlTv3bAv67Yn/K7+lwKUZkvOp+/1tVzYl/nofHH2RHo52R/XKxmiU8gSDPLN7HRQ8t7tZ72tt4dMFuLnxoccqvb1fbJ94Yc3gDHP/7z/lgYx07GpSxNG5APp5AkMYuD+c/uIj3N8RfQPdWGjo9TPj1p6yv7kj5Pe0uX9z2Afjlu5u56bmVyLLMzkY749Qx5vEH+flbm/nuK+vScdmHlTteWsPP3+65TsfToU01ik7vbrKzvcGOxSQxqiwXjz/Igp1NnP/gInYbtiUcDSzc2czU335Gpyv1BWpHkj709UeX8rdPd1DT7sbtD0b0oSufWMYDXxx9On3yX+fz8srUdbrdqfahOFHMV1ZWc9JfNJ3uYnT/XMwmRaefXrSXix5efNQ55B6Zv5uLH16S8uuTjTG7x8+0333GR5vq2R6h0yHqO92KTm88unS6vtPNhF9/yoYe6XRiW+gX72ziludXEQrJ7Gx0GMZYiJ+9uYm7Xz36dPr2/6zhF+9sTvn1HfpcH7tg21jTwaTffMqeZgc7GuxYzRIj++Xi8YeYv72J8x5YFJHRczQwf0cTx/3uc732Syq0u/xx2wfgkkeW8I/PdrC/zYXHH4rQ6SueWMaDXx5dOh2NWAgfJrbVd7Fmf/yoQTw6tOJF/iCyLEd06P1tLnzBEPO2N7Gqso1JFQUUZFlx+4M0dynpQzuOsoXw9oYuujwBdqVoGAaCIezegG48dLr9ER6wylYXn25pYFWlElGfO6oUUBY9TXYvOxqPLi+fyxdkf5uLdQkiT/HQnApufxBfIBQhcpWtLlocXlZVtrG2qp3pQ4vItJpx+xQvscMboK7j6PIUb6+3s6WuK+WUJmP7ABFjrM3hw+EN8OmWRlbuayPTauL4IYW4fUFaHF5kGd3wOlrY1+LE7Q+yric65PLr7eP0BiJSgKtanSzb08qaqnY6XH7mjioBlPZstnt0B8LRxLZ6e8Lobjw6k+h0VZsTXzDE/O3NrKpsY/KgAvIzrUrEXNXpnUeZTu9QdXp3c2rX7Q+GcPqCeipvrE47+WxLAyt1nVb7kC9Is9171M1jDm+AmnY3a6s6Un5Ph6FQYbROV7U6abZ7WV3ZztqqDqYPKSLLasbtC9FkV4pn1XW40/0zDinb6rvYVNuZcnq3toiJp9OtDh9OX1CZ6/e1kWU1M22wotOtDh+yzFGnQ/uaFZ3uicOyM4lOV7a4WLK7hTX72+l0h3VaydzxHnXtA7CtoYu1B2ALeeLodGWrYk8v2NHMyso2pgwqJC/TokbMj1adttPp9kdsp0iGNxDE7Q/qNVCidbqq1cVnWxtjdNrlC9Bs9x51tlA0YiGssnRPC9c8tfyQRcE6XH72t7lSTtPpUgeqxx/k1VXVzPjD5zy7eB+yLOspVPN3NLG+uoM5I4r1RYyWopDulKlAMMS1Ty9n6e6WtH6uhub11c64644utXqmxx/E7Qsy+09fcMOzK2lxKMLl8gWobHXx9rpaCrKsHDe4EFAEMRiSqWxJ7Xt6wgtLK/nlO6lHk3qC3j5tqV93p96HQvzmvS2c9Od5zN/RBIQ9o08t2ofTF2TOiBKyrGY8/iBOrzKhaufCpouGTg8XPbSYhs5Ds8DW2qi6PbU26jSMsY01HUz97Wf8+t3NeANBnGr7LN/byvK9rcwYWkx+phVvIKRXbj0UaYm/eHsTLy6rTPvnQtgYSHWMgaJDmsF61ZPLOO+BRbpR4PQGCcnw4LzdAJw+tgxQDFaHN0Cn269Hu9LF0t0tXPv0odPpdpeP/W2ulNPKtMioxx/k5ZX7mfGHz3l+iaLTLnUczdvexIbqDmWM2cx6+wBp34IQCIa4+snlLNvTmtbP1dCyKPanqEPGMebyBZj1xy+48blVtDq8Shv5guxtcfLu+lqKsq1MGVQIKOmwITn97QPw/JJ9/Prd1KNJPUFbtFUfgE57A0F+9c5m5v5lPgtUndZ06MlFe3H7FZ3OtCp9SLMDKtOs03Udbi56aPEh29qgtVFNe2oL+C7DPLa+WtHp38TR6WV7W5kxrIh8dRGj63Sa2wfg529v4j+HSKd7OsZA6UMedRFz+ePLOP+BcFagyxcgJMNDqk6fZtBppzdAu8vfowyPVFi8q4Xrnl5xyLIV2p091Gm3Zi+G+O/yKmb+4QteXFap6rTy3LztjWys6WTOiGKybJotpOl0eu1FfzDEN59cxoq9h0qnVVvoAHTa4VV0+qbnVtHm9ClrDl+A3U0OPthYT3GOjckVhYDiiIJDYws9u3gfvzlEOh1N0oWwJEnHJ/tzWK7wMLG6sp2le1ppcaTXcANlj0aHy4cvENI9TN1h7JgNnR78QZnffbCVDzfV656/5Xvb8Adl5gwvOeQDt93lZ8nuVpYn2LN8sHQcoIHl9ofo8iiTwKJdLdzz6jp8gRD+oCKQi3a1MGt4MdkZZgD9/tZ1utNecGTRrhY+3dKY1s/U6Gn7QGS0qrHLQ7vLzy3Pr2Jfi1NPVdQKIc1Wxd9opKdb3LbWd7KptpPNtd0XODsQtN+7P8WFnnGMNaoRuheXVfGvz3fp7aN5O7XJEQzifwgMrC+2NbJwR/dFTw4ELfLUUyPdG1CqQzZ0etnd5OCbTyoLUbehD1UUZjG6LA9QIluHypmysrKNJbtbaUvzAhsUndYMymZHz3Ta7Q/SqOr0fe9v5ePNDbqzadneVgIhWXc2Gdsn3WOszelj2d5WVuw7NAaWrkOtqS1ijO3T4VL60lc7m/ne/9bjC4arQy/a1cLs4SXk2CyAQac73HgDh0KnG9L6mRoHpdO+EA1dHtqcPm5+fhX7W10xOj1reDFZNlPEXJ92na5TIrapFKI8EDrcB2aku1VbCOCFZVU88MUuXYMau7zsbHQojgJVpzWNSLctBPD51kYW7jw0QQGtfXrah7SslMYuD7uaHFz91HKCITmiDw0qymJ0WS5ARODkUOj04t0th0SngyGZLk8Aly9Ia4qfH9GHujz4giF+/e4WPt3SoLfPkt2tBKN0+lDZQq0OH8v3trHiENnTna6e2ULGwFuHy4c3EGKhqtPeQAjN3/DVzmbmjCgm2xZpT9e0u/R91uli0a5mPtt6aOzpaLqLCP8jyZ+/H9pLO7xoHT5dA9fu8evpKXav4pGD1MUtnMoRwu0PIknK41WtLt2IAjBJMGNYEVlWZXO/w3dovKBa+6QrwhMIhiKKOPXUk661j9cf1IVMkpT2iU6N1YQN0CPGstyziSYVHF4lAtbTIgWJMO6X1tqn2e7tQepvZDTGJCm/u6bdFZF+N7osl9LcDIORfmiiVZqHvq0H+8CToWUDaGhe0AONVoEynva3OWP2ysTrQ5WtzrQXYnF4AmlrH1mWI/pQT9tHS2sFpTqkW+1DbU4fTm84GgNhRwpwSI10R5r7kFGnuzx+fcJPWYcM0SqXL4jJqNOGvmk2ScxQtx94/CG97dKt0/ZDrtM960NhDQrF6LTLG63TyiIPwmMsJPfMcZMKdm+Adqf/kOi0NsYaujwpO1r1rIKAomeSUacNbTS2fx4lBp12HCKnd9gWSk+UMEannT1zFhjnerdfuTZJUt7vTGGur2p1pu1eazg8Af1eHyzROt1TW0hLa1X+raTWS5KySHH5AhH787WMAiAyq+AQ6XS62sju8etZQNHbBFNBcy54VHtRs6f3t0XaQhaTxPSh6vYDY2Ap7fa08hvStd4IBEMRhQV7OtcbbUW3Qaer21wxRfy0zCaI0ukUM/FSxeEN6BHpdJAsSyrpQliW5dOT/DkjLVfXS7CnceC+uaaG6X/4Qq/AZkw76XnEU+mYhVlWLCYJpzeA2zBwJ1cUkJdpjRm4PRG27726jr9+sj3pa9JpgFa3ubjo4SWc/8Ai/bGeekGj2wegX24GTm8Al2GyBKKieeFIT6oLva92NnP63xckLCSg4fAGCIRk3Rg9UIIhmT9/vJ3jfve5fiSC1j6QuuB0GdvIH6JfXgag7CEyTgZzRij7PQ405c4XCHHhQ90Xj0qnM2VVZRtz/zKPe19frz/W46wCw94zzWjtl5eBwxvpXMm0mpgyqFA3sLQ+5PGHaOzm6CWNxxfu4ZbnVyV9TTAk4/QF09I+do+fW19cw6w/famnOHYa2ieVycVocCh9KKj3IYcvoBvtoPQhq1nCbJJw+cKL5FR1qLrNxdy/zOu2yF86HZavra5m+h++4OlF+4Bw/4EDc6a4/UGKc2yYJFSdDhugUwYVkJNhIctmikqNTt14uPuVdfz90x1JXxPW6YNfxOxvdXHhQ4u58MFwAbqeGum6Bhn2CYd1OjzGAOaMDBvpkTqd2nct2NHEGX9f0K2j0OEJ4DM4eQ6UYEjm/o+2cdzvPmeJumWowzBmUk397TS0kdsfpF+uOsbUNjLOY4BhrlezClLUaW8gyAUPLuLDjfVJX5dOZ8qKva3M/cs8fvjGBv0xPXMn5TGmXI8y1yuLIa0PabaQJCntMmVQQYxOa3VBUuHRBbv51gvJdToQVIIT6WifLo+fW19czew/fakf42d0NvVYp31BPP6Q3oec3qC+MAZFpzMsJiRJzdxRx0CqtlBVq5O5f5nH7qbudDp9C73XVlUz/fdf8MxiTacNtlBPdUid60tytDEWjHAUTB1cSLbNQqaWYdnDeQzgrpfX8s/Pkut0Otcbmk5f9FCsTh+QPW3QaYc3EGELQaSzyajTqbbR/O1NnPGPBd06Cu2egOKAP8jMzUAwxB8/3MrVTy1P+JqU9whLkjRJkqQrJUm6QftzUFfXy0iXgTVveyP3vr4BXyCkH49h7OwH4qFx+YJk2yzkZFj0RUyOzczXp1Vw1cwhAOGBq06OrU5f3GNzomm2e3l3Qx3rEpSV17Crwnaw4h8IKlWbt9V3UdPu1gdgT73EkQaocu9KczNU4Vfa4ILJAzhlTD/GlefrA7fZkPqe6sB9aUUV+1qceoGbROhe0INso4fm7eLxhcpxI1v0PmQw0lNMd9GMMq8/iNsXoDQ3UvynVBQwc1gRl04bCCiGhNcf6nE60NI9LWyu7WJzN6l06XKmVLe5uPapFbQ4fPoYUzyiAf35VIiO5oHWh8KLmMuOH8Q1s4Zis5j0lDtjH0rFgJBlmf8sq2J1ZfI0KG3STYfx8L1X1/PFtkZ8gZBeMEPTIW8g1O2ZnBC5MOx0+wnJ6H3I6Q3g9AU4Y2wZUwcXcvrYMiRJIstqpt3l1zNg9qXYV99ZV0tNu5udjcmLe4SN9INb6H2xtZEfv7FR0en6WJ1OdR91Z5QnPctmJifDgkNtn7wMC5ccN5CrZgwGMOzDV35Hi8Ob0hFBTXYP72+s67aATrqcTYFgiKueXMb2Bjv721z6NYZrFaSmDZoDzxsIRowxhzeg7827cMpATh3TjzFleQep0/vZ2+LUoxSJSFcbPfDlLp5Qnd2aDnVEzPU9ayMtM0UfYz6ljaYMKmTG0CIumVYBoGYV9NzpvXR3K1vqUtfpgzXSq9tcXPeMotPb1PbxBcLzS6pjLLp9IDzXa/bO5ccP4trZQ7CaTbrTu6c6HQqpOt1NESbtO9OxiLnnlXV8sa0JbyCkH7FmPM0gla16xkCLdk2lBmeK0xfkzHGKTp82tp+u08a04lSdKe+sq6Om3c2ubnRau8c9OQUkHp9taeDHb27EFzTqtKEgYap9KCozJSfDTI7NrNvT+ZkWLp4ardMhHOq9brJ74x5vFk1Dp4cPNtazLlWdPkiHpT8Y4sonFJ2ubA1Hb3u6EI6XuaPZQtr/L5wykNPH9mN0Wa7usOzpGAPFnt7b3AOdPsg2euDLXTy1aB/Xzxma8DUpLYQlSfoN8JD653Tgr8DFB3V1vQxH1ER/oLy7vo6ibCuzhxdTq1ZzbD8AD5ax0JHHHyTTaiLHZsbhVbx4WTYL/7rqOK6ZrS6ELcrANRpVqUyQn21tQJbRvfOJ0BcxB2k8rK5qp7bDzfmTywH0ipeaYKaa+mtsH81LXJqXgS8Y0gf1JcdV8OItszCbJH3gGgdfKuLv9AZYoO7ZTHQEiEY6nCmyLPPu+jpOGlVCfqYl3D7OnjlT/MGwoGnp9cZFjMsXoCQ3g9dvP5HpQ9VIgy0y0lDd7o6oPpmITzYr++26jcSkyQD9ZHMDvmCI8yeXU9fhUfbgH0C6VDwvqCb+2qL0R18by68vmgAQk3IHpFR0bWNNJ7Ud7m49m9oY6/IEDqoYVIfLx4KdzfoYq23XdKhnbRRRRdsZaWDZ1X35kyoKePfOk/RIcabVHNU+qU2OH6t9qNusizQ5U97dUEdJjo1Zw4upVTMsDiYirEXzsqxmcmwWXGrEPMtm5oFvTuObs1SdVqN52u+A1Iy5T7c0KjrdTfvY06TTKyvbqO/0GHRai1Ypv7exy5tS6q+edeEzjLG8DLyBkF7w8NLjBvLCLbMwJdDpVNISHd4AC3f2TKcPZq5XdLqWk0eXkpdhCc/1zp45LL2BoF7kSIlWhSjNi3RY9su18cZ3TuT4IUVAeCGs/Y79ba6U9OLjzUokuHudTo8t9NGmevxBmfMnl1Pb4VZSgN0HbgsZM3dK8zL0iDnAj84dyy8vVHQ6wxJPp7vvQ+trOqjv9HTbf7SgQIfbf1DFoNqdPhbG0emOHgZOjDqtL4TVPtTl8eMLhJhcUci7d56k6/eB67TSh7ptI12HDm4R8+6GOkpzbcwcVnTA7QNRgROfotPZemApQLbNwoNXT+PKmcpCOFPbami0p1PQIa32QCpZKXDwttDKfW00dHli7OmebtHQ2ydKp12GLRjfmFbBczfPQpIkMq2RW1ggtfaxe/x8pe6tPxz2omZPnzKmH7+/dFLC16UaEb4cOBNokGX5ZmAqkHHAV9cLcXQTaXD5Alz95HLWJjl6xBdQjjQ6a3x/hpXk6JOj1snK8jKoStHzZkzl0AZqToZmYAXIUYs/aWQZNq/nZyoFR1Lx0Hy8SRu43af9QqSxGM1jC/Zw/0fbkn7OZ1sasVlMXDtb8c7o4ub2U6aKd0rib0hrdepeYhsQHpw5tnAb6R4sNRKWn2lJqX0W7GjGG9AMldSM0ERttLfZwdcfXZLUSN3T7GBfi5NzJ5ZTUZQd0T7ZNjO5GZYeT45a+nhEpMEX1AseaGgpdw5vgPxMC8GQ3G16XyAY0sU/1ckxmYfvB6+t5/XV1Uk/57OtDYwfkM8JI0rwBUO0OLx6m5flZaRcnb0zImKu7O8szrHh9IUjwsY2yjL0IZvFhM1iSkn8P1KNB39QTupYMB5Kb1zYG/liayP/9/yqpClz87Y3EQzJ3HTicCSJsA65wmMslcVXl9HAiloIa5GKWB0yRYyxypbu9+dVtTp1b3+3zgJNhxKMIac3wDefXJY0cuoNBJmv6vTQ4my9fTQjvSwvo+cOSzXimWWzkJNhVvdQB8nJsES8PtNqRpaVjJ2e6XRqBmgqkZhH5u/mzx8n3wrz2ZZGMow63aE6C9w+vQ/VpLBFQ6/Wqu4xh1idzraF2ygrjk6nYqTP396kF2tJ2ZmSoA/tbnLwjUeXJDXAdjU5qGp18bWJ5VQUZek62eH2kZthIdtmZn9b96nRRp326g5LpX1cBiPdSLROB0Ky3ocT4Q+G9MIzB9s+AN//33reWluT9HM+29rIxIH5zB5egjcQosXh0+dtTadTSf01Vo12+4NYTBKFWVZl/6va33OMfcgW7kOZVhM2syklZ4rm0PUFQkkXuNoYk+XI+2fksy0NfOuF1Ul/35fbmwjJcPNJw4GwTncYdDoVHYpYCKv2q9aHtHEUo9NWc4wt1N292Nvs0I/J6TZw0o2zyeENcNUTy9hY05HwM7yBIAu2N3H2hP4MKc6JaB8I96FUiN7CkqXaUk41PTw7TvsEQzLtLr+u06k4vVN2FKQQNHlk/u5utyx+tqWBTKuJa2YpOl2jO5yUPiTLdKsNEDmPefSIcLROx7eFQNPp7ttn3vYmfEFNpxO3kSzLKei0nW88uiTpXLejUcloOndiedLrSnUh7JZlOQQEJEnKB5qAESm+96igu5z9pbuV8vwr97XhDQR5e11NjHCs2NeK3RPgnInlDCzMotmueM21CXXKoMKUJkeISmvVIg16yl1Q74gaxmjVhIHKYdfdGVgdLqXCKPQg2umKv3ldlmWeW7KPT9RF0dLdLTEiLssyn21tYO6oUkb3VyoX1qpVQV2+oH50RiriZlwoaINYi0ppgzMrzsDVBvWkioKUxP8TQ3XRZG3kC4T0BXOigfvOeiUFfXt9F41dHuZvb4p5jVZ1+uwJ5VQUZkZkFRRl2xhcnJ1a+xgWm5oXtDDbilndZ+7yxi6EM6wmdd9QgEkVBQDsa0meArWysk1f2B6sh6++081ba2v1yM4HG+siFoeg3NvVVe2cM6E/AwuzAKUPaWI4ZVChkvqbQtVfozNF8xLrixivthAOG1hGL2h+poWhxdl6OlsyPt2cWh+yG6KEidropRVVfLm9CYc3wLb6LjbEWfB9uqWB/vkZzBhaRL/cjAgv8aSKAr3QTHcYozdteqRBmRyb9DEWtdCzmCPGmN0b6Da975MU2we6jwgv2d3C8r1trEqi08v2tOLwBjhnotKHmuxefIGQbkQqOt2ztE23L6TqtIlcVafdvkBSnZ44UBtjyftQu9OnVxftPqtA3ZuXoH1CIZnnllTymaprSxLo9OdbGzl5dCmjyjSdViILHn+ohzqtXIdW6RXQ9y9qfSjCwLLF1+nuMOp0Mh3yBoK6IZZorn93fS1r93ewo9Gu6PSOODqt9tmzVR2qMxjphdlWhqSo08a0Vs3pXZJjQ5LCtRxiHJY2s74FSNPpvd200cp9bfqckKqRnigoUNfh5u11YZ1+f0NdTNpok93D2v3tnDOhXNfpug63PldMGVSI259a6m/0NrGwLRROtzeOM+MYy8+0Mrg4i33d6LQsy/oiBpI7C4zZHInm+pdW7OeLbY24/UG21nXFXfB9uqWBAQWZzBhaRGmUTk9W72tP53pt3GtjLJ4tBMpcZhxjXZ5At1kkkWPs4Jwpi3e1sGJfGyv3teHxB3lnXW2MTi/d3YrTF+Qc1RZq7PLgD4b0cTtlUGGPt0G5fdFzveL0jh5jmXF1Orkt1OrwsjJlnU6+3giFZJ5dvE8PMizelciebuSU0f0YWZYDKGPM7VfOI++JTms2tD8o63ZIdB/KjuNs6rFOpzjXewMhAqozKlEbvb1O0emdjQ7qO936kXNGPt3ciCTBWRPKkl5Xqgvh1ZIkFQJPAWuAtcDKFN97VNBdWqtWtKjN6WP+9ia+/78NbKiJ3Gvz2ZZGsqxmTh5dSkWRIv71nR59EE6uKKDF4Y0x7qMxprXqRrrmwVIHbnSkwSj+JTkZTK4o4MON9UkjY3uanQRDMhWFWd0uYrTB4QuE4nbgHY12muxe2tSJ7c6X1/Kwem6dxrZ6OzXtbr42sT+lORnYLCZqO9y6MTBlkCI4qUTNO+NEq6INLGMbaYV8tIF7yph+NHZ5u90PtLPBToU6kScbuEZDINHA1fpQq9PHC0sr+daLq2OOBvlsayNTBxdSXpBJRWGWHhHudPkpyLIypDgrpShkdETY5VfEPkc3ouJHGlqdXmQZpg8tIttm5oMNyQur7FQ9xBWFWQed1rpol5Iy0+b0Ud3m4q6X1/HOutqI13y5TUkR1SIxoC2EI/tQKlEkY3q9yx+5v9PlD5BhMWHWygATOTnmZFiYPrSIJbtbkkaOPP4gla0uvQ8lG2dGXYinQ95AkOV72/Tn//TRNn7+duS51R5/kK92tnDOhHJMJomKoixDxFPxEg/Iz0xtjLkSjzE90hDHSNfG2Klj+gGKQyMZOxrtDCjIBHqQ1ppQp5U+1Or08eU2Rac3RR3X9dnWRrJtZk4apei0LCt7uzrcfiQJJlXk09jl7bY/G9NatYq/WVYz2WpqtNMbTJK546U0L4MJA/L5cGN9UofcnmaHrtOpto+yZST2tdsb7LQ4vPr+wDteWsujCyJ1ektdF7Udbs6ZWE6/3AysZona9nhjrKfRqsisgnjRqgxLZMrdqWP6Ud/pYU1V8v31qep05CIm/kLvK4MOPbekkm+9sDomk+OzrY1MG1JI/3xVpw1bfAqzrQwuzu7xPOZSCx1l2Szk2MILvWidzlR1GmDGsGKyrN3r9A6DTqeatplYp8O20P5WF999ZR3vro8c419sbVJ0elJ//b5EOix7Ptd71NToTJuZ3AyzntaaZTVjMui00RbKVXV60a6WpJEjjz9EdZs7JZ22dzPXe/xB/fiyVoePP3y4lV+8HXkeqtsXZNGuZs6Z0B9JitTpTrefsvxMyvMzezzXR48x3RaKnuvj6nT3c/3AtOl0uA99vrWR7/1vvV4PReOzrQ3k2MycOKqEiqIsQqpOd7r9mCSYODA/pdRfj7owBDUzxbCFxeEN4PTGsYUMOl2Wn8G48jw+6Eandzc5CMkp2kJq+7gMRQSNbK3votXp0+2A7/x3DY+pdWM0NtV2Ut/p4ZyJ5ZTlZWIxxdfpqh7YQhC7z1x3WBp0OjNq+8GpY/pR2+FOmjELylyvj7EkGZbG+kaJ12SaTnt1nY7eIvLZ1gaOH1JEWV5m0utKaSEsy/Idsix3yLL8OHA2cKOaIn1U4fIFufLxZXE7XnepHMbJUTtztC4q5WBHo53JFQVkWs0MLMzUX9Ph8pOXaeGEkUp13ugFYjTGTukxRISzbVrKXSDWg6UfXRIiJ8PM/80dzq4mh+61jf89ym/VDCxZlrnx2ZVxI5XdGenaOYd2VVjaXX7qOiPbZ6daEXb60GJMJomBBUrEU/MSj+iXw6iyXJ5bUtltAZmI/YvqPSuJSgcyeom1AhGa4XrdnKEUZVv1yt6J6HD7IibH/63az49e3xDzuu7ap9Pl16N37S6lDwVDckwBrp0NdmYMVfaCDSzMwu4N0OXxKxHhHCuzhpewt9nJR5uST1pdURFzWSZyoZcgNVprn7L8TK6aOZj3NtRR35k4i0Fz8gwoyFTO9XN4ufjhxXE9td1NjlofanP69Aqa0WNsZ6ODbJuZ8QPywhHhdrc+bueOLiXLaubh+bu7jfZH7D3TCh3ZLPgCIbrUVHQjWcYxZrNwy9zhuP1B/ru8KuF3aPchbKQH+P0HW3kqTr8zGunxdGhNZbvuaVZ0yBPTPjXtLtz+IDOGhftQbbtbP6KjMNvGzOHFfLS5odvKn1paKxgiwjFe4lhPutaHThhZwoyhRTyzeF/SPYydLj/FOTaybWbcvgBrqtq48ollcc+PtesRz0SLGK0PefVq2dFttMug09p9qelw0eHykZ9p1auoPzK/Bzqt7q3StrA4VGdTdMRc0ySPP0SuqtM7Gu1JdVozbLRFTCgkc8OzK+N6wI1GeryFjNY+nW4/Tm+ATref2o7IyueaTs8YWoTJJDGgIEvVaeXzRpXlMqJfDs8u2detU7crjk5rWQXNcbIKonX62jlDKchKRaf94THmD/LKyv385I2NMa+L2H4Qp33anT49etfq9NHU5VF0Oqq43M7GSJ3udPtxqPNeUbaNWcOK2dXk4JPNyXXa2Ie06Hm2TZvrA3HnemP7lOs6XaufsRsPzclTrup0Swo6nWjhqO3xa3X49Kr5sTptJzfDwtj+eeGFsMFIP1nV6Yfm9Uyntehdts2ip4fH6rRi1iq2UFinX1qxP0n7hG0hUGzF376/hacXdaPTceay1ZXt+v1pd/losntj2qe63YXHH2L6sGL1ezMNOu2nKNvKzOHFfLixXi922F37QLIxlrgPnTSqlOOHFPL04r1JU8I73H5KcjOU/bO+IKsq27jqiWVxz49NFvGUZVnXIcUWUvpQdArvzkYHUwYVkmExR2R/tbt8FGRZmT2iGFnumU7rtRxUWyhh1oXVaE9b+L+5w9neYNcXX4naB8L2dCgkc/0zK+Jqe6QOxc5lX+mnhfixe/zYvYHY9Ybq3Jo5rAizSaJct6eVNh9dlsvw0hyeWbKv20JfqfQhYxuZTBIZFhMefwiTpOh0fqYlrl0T8T0uf8QYe2lFFT99M45OR9hCse3T5vTpDu42p5/GLg+BkByTCbiz0a7bQsnoSdXoCkmSTgSGAIWSJJ2S6nt7Cy5fQC8CYiQ6H72q1ckmQ7S3us2lh/3bnD69ZHj053gDIV1wBhVmA5r4K2mts4YX882Zg3nyqz2s29+uLxqii2p0xKRLGfc0xE/lMC76cjIsXDBlAAMKMpMaENr3DCjM1CeVhTub9XRpI8aO2eLw8snmyGizUSB2NTkStE/kvsuKoiy9fQCKsm389fIp1He6+dNHyt6IP3ywlSe/ivSEQaJoleJ8aI4TEYZwRC9TTV+8/oRhfLGtMelE0+HyM6Aw7AVdsrs1Ir1Dwx61iFlf3RFhZCzZ06JX0m11+HSPfkNXuI1kWcYbCEa0D6h9yO2nMNvGjScMZcqgAn7x9ibanL6EiwZjWqvWPlpKWYfLTyAkx2YVGPpUboaZW04ajgw8v6QyafvkZVrIy1QMk52NDjbWdMbdo6kZ6R1u5bxlzSsMSurk4t1hZ5OWMhdt3GntI0kS+ZlW8tSCYpqQjyrL5ecXjGfRrhZeXVVNKKQ6d+IsGqLPFtTaB6DZ7osbMQ+3j4Ux/fM4bWw/XlhWGXfRpv1WIKIPzdveFPd6tCI1Shv4WbizOcIz+pVhjCk65KPd5Y9w7GnGjXatgwqzqOv04PAG8AdlirKt/OKC8eTYzNz72gZCIZlXV+7nx2/EOnfi9aHwQli5L8naKCfDwq2njKCm3R2RVhevjQqzrWTbzLh8QdZUtStFQOLptMGZUtniZLMh2lvV6tT3PmvtA/F1Wh9jetqmRzdA54wo4coZg3hswR42VHckXDQYNUiLCGdajdGqYEzEPNPYPjYLF00dSHl+Jk/FMbiN7QNKH3L5Ajh8Ab5KRaftik4bFxrGMafpdEOUo0vb4qHd2wo19VebLwqzrfzt8inUdbj1mhC/e39r3EVDR1Kd9qjtEN/hlKPOedfPGcpnWxsTZnnIskynQafdvgBLdrdEpLpq2KPSWuPptNZcbQ4fLU5Nh8JtpOh0SF/Aazqt6VBhto2bThrGpIp8fvH2ZtqdPlari4YYnY7TPllW5Xe3On3IMrH7F21hs01zegdDMs8vrYzbPgCdqpMnN8OCyx9kZ4OdjTWdbKyJrSBtrNbanU53ZwtJkkR+loVctaCYpimj++fxs/PHsXBnM6+trk66aIiu7q+1D6g6nZFkjGWYGVeezylj+vHckiQ67Yqv01qhzHjto7SRj/k7miIc98b2anX69AwM43d7VZ3OtoZ1qLbDjV09grEo28avLhhPlqrTsizz8or9cRcN8bPjomyhOFkF4TaycNspI6huc+upuInaSNFpi67TK/a16QtZjVBIxqGdgODysy9Gp11Uq1sEFVso+VyvtQ+EA0tF2TZOHFnK5dMH8eiCPWyq6aTJ7uHihxfH1C4wjjFvwJgarfwOly8QGzGPmusvPm4gZXkZSXW602hP+4J0efws2tXC8jg6bY9jT0fotOpskmUlczN++6hzvaGN6gwZlkU5ij1d0+7Wa0Lc994W/RiqyDZKNtfHpkYbvzfHpozv6+YM5ZMtDQmzPLS9y5H2dEtc2yBijDl9rNvfHnFfF+826LTTG3euD4Zk/EGZbGvkdccj1arRfwGWAL8EfqT++WEq7+1NBBPknHv8hnx0p4/fvr+V7/1vnf68ZrRWFGbR6vTpJcOjBcDrD+qpXeUFmUiSsnm9XRUQgF9cMB6zSeLTLY1sVyejDVH7RzRhM0nhqtFGD5YzTlqrtn8RlIFrNZu4+LiBrKxsS5gerQmE5m3TFh/xvJxGoX9zbQ23/3etnlbs8AZYsa9NFystotCYYOBqbaSJv+bxKciycvyQIi6dVqFHPD/b2hh3MtLSY0BZNEhqoSNA9wrFRqtMevsAXHZ8BbIMq/bFT7vz+IN4AyG9fVy+AA5vALs3EJMqZ2yfVoePb72wmn9+vlN/bN72JvIylAVjmzo5QqS4BUIyITmyfSDsSS/MsmIxm/j1hRNod/lZvreVlfviLxo0MTRJ4RTAbLUPae0TvX8x2kgfXJzN7OHFcYVc/x53eHJ0egNJowlaG8ky/OPzHdz47Er99eurO+hQvYXtLp8uvg3RYywQ0quCam2keUHNJom8DAvXzR7CmP65fLSpHrtHce5E/wZtv6MyxjQvsYXcDO1YAG83zibl35cdP4gWh4/dTfGdKdFjzOULYvcE4no5jZPjlrpObnx2JW+srlHbTGbBjia9TzTbvbrn1qhD+hizhp0pyhFKyuRUmG2lLC+T754xmg1qNeuvdjXHde5EjzGAohwrJilxRDjagDh7fH/yMy1J+1CHy0dhlo0sdSHsSFBUzeUL6s6kNqeP+97fwvf/t15/XstkqSjMihxjMTod7kPapKxlFRRkKxryqwsnYJIkPt3SwLb6rriLBqNOu/WIsFKN1KUWpIu3N08jJ8OCzaLo9Iq9bQkjY9pYGliopAdqk35HnNReYx96fU01t/93LWtUnbZ7/Kza1x6j0zEGlupY0XUoymFZmGVj+tBiLjnOqNMNKem0xSTpc2HC/Yvq92pOqcumD0KW0fffReP2K/t+tTHm9CpO3XjV16MXwv/3/Cr+9UWUTmcqhl27y0eLpkOd4UiDtsc4nk63u3wUZlmxmk38+sKJtDp9rNjXyop9bazY1xaTAWTsQ9oYy4rS6exonbZEjrHBxdnM6kanI5xN6hwG8SN2WhsFQzL//HwnNz67Uk/1XLe/nU41+t7mSmILBcK2kCRJEXO9xSSRYzNz3eyhjCrL5cNNDXS64y8atLRWkxSu7q+1D6g6bU28yDPO9S0Ob8KaDrE6HcDhCSRon/C421jTyc3PreLNNWGdnm/U6a5wIUfjvdcWxRnWcB8yHqFUkG2lLD+Tu04fxfrqDuo6PXy1sznuoiGeTkfbQrE6FDmXnT2hnLyM5Drd6Va2Z2VZzTjV9oHYPuT0BfRFSrvTx6/f3cy9r4UdrfOidTrRXO8P6e0zMMoWKlA1RDvRQdFpxZ7elKJO52aY9ey4pO1js5BhMXPx1G502h3W6UBI1jMDk9lCAK+u2s/t/13LWvUI0y6Pn9VVsfZ0PFsIwlXSNZ1uNzgsZw4r5uKpA3Wd/nxrY9xMoug+ZDObKMhSdTqRPa1+b6o67fAGCIbk8BjzBrB7lKyk6EyE6H34tzy/ige+2KU/Nn97EwVZVnJsZt3ZBJFrDp9uC3W/zE01InwpMFaW5fNlWb5I/XPQxydJknSuJEk7JEnaLUnST+M8L0mS9KD6/EZJko5P9b3xCBkGpxGtHH6W1Uyby8fm2k5dtLyBIE8s3MukinymDy2iPYkX1BcI6QaozWKiLE8pgKBF8wDyMq0UZtvocPl0gYyuEKulLJfmZkSUe1cWwsGUIsIAZXmZapGS+GmEWrpUf7XIVJM60OKJv8MbLvyiRX+1ye+pr/biC4S48USlct0udeDavYGIAa95QbU20gqKaZGBIlW8BxVl0+VRBoexnSKv3ad7rDpcvkgvcZcHkxQ2VKLbyNg+kHgvlD45FmiRhmBC8dcWdFlWMzsalX14WvtUt7l4d30tF04dQL/cDNpc4WiV0QiNETYtbbPdpWcVaO2jXYMmvtFtpPWp4pwMXYi1aFWzel3xKklq5OptlJH0qBrturJsZqV91LEUb/+dwxPZhxSjXhlLj8zfTV6mhW8cX4E/KOtexXheUON9rShUKrZqjgJJknTDy9g+nVHto6VsluZm4A/Kav82hQ2sLg/ZSSLmOYb2gcSFZfRFjL63KoDD60/gbFL6UKbVpEddtNTDL7Y1sb3Bzk0nDgOUfaPafFwf0YciFzEDC5Q+pHnkC/U+lKVen58Ol58uTyBmMup0+SPGGChe4RybpVsvsdZGJpNEaV5G0nN/dWeKVVlA2g1RXyPGMdbu8rG5tksfYx5/kKcW7WNyRQHThhR2m1WgTY4ZFjP98jKo7XDR6VYiwqDptJV2tX0gMkKutR1oOh2KqOXgUPcvdhdpAKUPBQzFpOK1j9kk6X1N0+l44zK+Tiv36qmv9uILxup0lycQkTrnjTIgBhZm0Wj36AZRUY7SRoOKlJTgUEiJyEa3j9Jm4T7Urup0OOvCi8UkYTNH6rS2zSc3aoylrNOG46mi51ZjH9qm7sPT5vr9rS7e31DHRVMHUppro9UZztwxbg+J59AFJd3V2Ie0Mdbu8uvGeDKd1uYUrZCPptOp6VBm0uOOlGheHGdTAh0K96HmCOfLw/N3k59p4evTKvAFQuxXdTp6+0y0Tg9UU3+165AkSalhUJilzPGJ2scwxnyBEE61f2tzl6LTqdlCiX4vhG0u4x5YuzcQ9/UOTwBJtS/COq30k8+2NrKz0aHr9C7D9pP6JHO9tjjQdDpmrncqc5k23iLbyBcxxpTfrdQDSXWuN6s6naxgljbXZ+tzffy6OsYx1ub0saWuS5/HPP4gTy/ay9RBBRw3uFCxhRJGhMMOy0yrmdJcm55VoLVPfqaVgiyrMtervz16zGuPl+Zm6NX9M9VtUCll7qhtV5avHM+ZaDtIh+rkia6jEa9N4+m0putPLtyLPyjH6HSHyx+xdz16rq8ozKKhy6MvCosMc32H269vj4pX6bwzSqczo2whm9mENUqn9Yiw1j55kX0wXvuAYYypWajxqq/bDX1oc10n7S6/PsYqW5yqTg+gJDeDdsNcn8wWSkaqC+G9gDXF16aEJElm4BHgPGACcLUkSROiXnYeMFr9cxvw50FmzwAAj4hJREFUWA/eG4Nm6MUMXHVSGFKcjccfosnuxe4N4A0EeWn5fmo73Pz4a+MozrFFRfOSi79W7KhD9RJrFGVbFQNUW8REXY/WKfrnZyppm4ZCR75giM44+xejU10AStSFZSJx61T3Whi9qxA/J9/uCTCkWBFlY/phi8PL04v2cv7kck4bq1Rm22k4bD0yWhU7cAG21isDXWujomwrsqwMqC5PIK5HrdPtp39+eCGrRGKM0TwLkiRFvMeYyqH9P8tqTjg5dhgcElazhMsfDKf3uuIbWEOKsyPaB+Cfn+/EJEncc+YYinJstDrip3LokRjVAC3NzcBmNrGjUSnCoEVStL87XH59gRctPp1uP7laBNpgYGXbDBHhbhYxoBpoSRYxHaqXONtmVoQtyd4ghze2D7U6fazc18a87U3ccdoohpbkAOE+VN/pifDAev1BbMYxVhRO29S8xKBMAh2GRUy89gH0PqQb6TZjpCFyjBnHtmaka/vSExrp+h5qpa/bPQE8fqXyZbRn2eENkGMzU5KTEe5DDh/BkMzfPt3OiNIcbjppGDazKWKMxXemhKN5gF6IRB9jqjZ0uMOOpq4Yh1zkGINwen3irIvw/7X2K1F1Mx7anrjCbGuciHCUw9Kg09o+xy6Pkp3x3+VV1Ha4+el54yjJsUV4ieOlbUbrdF2HR4/maRRm2+h0JzbSjX1Ii1Zlqn3I4w8l2L8Yb4wp9yKhDqmF8ox9U3k8vpEeq9Nemu1enl68jwumDOAUtThORB+Kk1WgLVAHFSoFxbbpOm3T2yckK33D7g3EdVga+1C706caoGGd1tJnI9ooymGZbTOTYTElbR9QTg0wmyQ9cydeG2mOuiHF2VQaNAiULBWzSeKeM0fH6HTEGIty6JblKfPDtno7soyeVaAZoooOaUZ65PV0uZWtJTkZ5sgtLLbUxliuoQ8lXcS4FUdhti189BLEn+sdXr/eh4xttHxvKwt2NHPn6aMYUqI8r/Wh2KyCqMydoizqOt16MTEN3RZS26fTnVynO1z+WJ1OxRbqTqddkTrd6VbO341rC3kD5NoslOTYInQ6EAzxt093MKKfotNWsxQxxiKdKbFZF2DQ6eywLaRdT4dLqfVhj3KYRY8xQD8nN5xVEN8hZ5LC/y7OsSWtYhzO/jLrmU3GttMw2tMOr1KNusPlJxAM8Z9lVdR1evjJebH2dKwzJRhrT3e4aXf6o3TaSoc7sbNJe7y8IBOXV8kw0NrH5QsqzpUktlCuwRZS2jhxYElrHwgXmYo7xuLotFaT4JnF+7ho6kDmjupGp/2xDrmQjH7EVYFuT9sIqhFqpy8Y0z6yrNzb8oJwH9LqpUBYp6PRxpnWPrkZFmxmU8IihPoxsvlKtqxbbXuljRKvyYzzGMA/Pt+J1Wzi7jMUnW5x+PTn4s1jRh1KRKoLYRewXpKkJ9QI7YOSJD2Y4nsTMQvYLcvyXlmWfcCrwCVRr7kEeFFWWI6yN3lAiu+NIaQanokWMYPVjqnR7vTzwrJKZg0v5uTRpZTk2HB4A7phFZuqEDVwi7L1irZFBvEvzLKpHqwEEWFXWPy1QkeZBk+6PygnjcRo6Z1F3SyEw5Oj8lmaZzyuF9QbYGBhpp4+AcrAfWNNDU5fkHvPGasbdJoHCyJTyrxqipNF/ZCw+HdiM5t0AdEmAW0ARLePltYaYWAZIsJK+3Q/cEER/9ZuDKyCbCUdyDhwo9tUmxSMfajVqXgp31lfyw0nDKW8IJPiHBv7W116el3ctFa1D5lMEgMLM9lSFxnNy7SaybSa6HD5dAGJ9qhpaUxGAzJb3cvhDyrjIGZvXhwDojjHikN1CiVqo4hIg3rsULSwBUMyLl8wZoy1OZQK2qW5Nm46cZjuvNH6kNsfjIiUeQ1ZF6CIf5cnQE27Szc8QblnHS6/fh3JFjGg9CGt0BEofSjai64V8jG2j/adbQmOa9IMPC0FV/MSe9XqlUYcngC5mRZ9HIHSz5btaWVno4PvnT0Gq9lEcY4tcozFnRwj95lvVfuQpgmaIdFuMELjOQtiDCybEo3R+lCiaEyOLVzJtSg7sZHuUPfEFWaFIw3aueCJIg2xOu3jhWWVnDCihJNGlVKck4HdE9DHVmzaZqyRrul0oaEPFWZZaXf6dUdl9KLK2IfCEfNwtCquTseJNGj3JJEOaQt0TeM1nY7XpnZvgIqiLKQonX59TTUuX5AfJtDpxihPusUkYTFHGulb6zrJsJj069D6kK7Trlid9gVCEc4mbXuG1j7REXNjG2ntI0lSNzqtpmxn28i2ajqk9aH4RrqxD7U5vbQ5fby7vo4bTxhG//xMSnJsVLY49S1TDUkculpBMX2MqfNXplU5a1zRac0hF6tDhdlWMi1mffxpbRTW6eT78EGZx+yeQNzCRVobhfd3BgwL4cg29QdDePyhWJ12enlhaSX98jK4MY5OO33ByOwvQ9YFQEVhNh0uP7Ud7khbKDvSFopeZMR1WNqS20Jmk6Q7S3OjdTqJLQSxOu32x1b11XS6yKDTrU4fS/e0srvJwQ9UnS7KjtTpxng6bdWcTUp7R/chTY8i2iiZTrvCOm2c62P3mYeDApojqijbpjt+orF7A4RkZXGlZX8ltIUS6bTLz/NLKzlpVAknjiylOMdGp9tv0OnIOTTGYanqtLYPX6Mwy6qMMae2EE6s08YxptnJgZCcsi0E6Fki0Wgne2RFL4QT2NNaxohGm9PH62tqcPuD3Hv2GN15E2lPR9qLNotJv39Gnc6ymnV7V1sQa06t6P7j9gfxB+UonbZEzGPR7aO0UeQWFkmSKMqx6ovSaLT+W2TU6QSZKfHm+laH4jR5f0MdN544jDJVp/c2O/Rs37gOyzRGhN8Dfg8sRTk+SftzMFQA1Yb/16iPpfKaVN4bg5ZSEu0VNHofjDTbvdS2u5k+tEi9yUrH1BbCjZ3eiDSVaC/opIH57G9T0qUKjAM3yguaKF2qf36G/u9smzliAZc0NdqWWkRYiaLZ4niw4g/c/CxrxGKj1eGlus1FUbaVkf1ydcOoztAZo1PKMizhCMCY/nlYzRIbazopyLbqj2sipxVHcfmCEQuxLt3Dl6G3lxY10I67ibcQjjawQPWCdrMQVox0SzeRhtg+1Ob0UdPu1o8jAijOtiVtH4j0YE2sKND3JkZmFURGPGMWegZR1vpQls0U8duT7YtJ2QuqGunZVqXasjbpRBt88doHlEmlut3FhIEFZNnM+hgztlFDlJFuFDbtHM2NtZ0x7eMwnGEb7SjQ2svYhzINKXcQGzFXHotcCCtpfomrGOvpUlpaq6H6bLw2ys2INLBanD6q1SIRWh8qyrElbR8IG1j5mcqZphtrI/uQNsY6jWmJ0W3k9lOaa8NiksJ9yOCQg3ip0ZGTIyjRmO4iMQU9jDQYUaqyesJjTDVYNJ2Ol1UQ0YcGKmcg2j2BiGhVYbaNDrc/cdqm7uHOSNI+3Y+xkm4iwsr8ERtpiBeBdXiVcW8cC60OH9VtbkpybAwvzdE1PFKHIg0IY/voOl3bGRnNU9tZ02lHVO2EeGNMO/1AW6jH1emo1GjoRqfd4T1x4S0aCbIKdAMrbIQqOq2MseO1MZadbIzFGliTKvLDY0xtI0mS9Iin5tzujLqeDjUrK9Og05m2yD4UU/E3IqsgHM3TPi8e2tYRrVqwtl0qZn9nnPYBpX5IdbuLiQPzybSmotOhmPYB5ciXgqxIW8juCehRwURprdF9KJktBEaHnLYQVu5JMlvIZjbFHBkD8bdB5WZYIm0hdR4Dw1yfEz3XJ45W5WdZGFSUpfchrY2KDNlfegZhHKd3v7wMTBJROmTIzkmgQxE6nSQirPXfwmzVFvInsYU88ftQY5eH+k4304eE20eWwwvg+k53lE5HOr0nDixgb7MThzcQlVVgi2yfOM4mk6Rk2CXS6XhVtTVibKGE2V8+vX0g7ExJlGFZkG0lPzN8DZo9XZqbwbDSHP03RoyxrsisAuMYG9s/D4spjk5H2dP2qNoJuk5rWReaLRTn3GAj0bYQKG2UKCKs3R9Fpy369gOIbSPNqWbsQ61On36c6PSUdDrSFkpGqscnvQC8QngB/LL62MEgxXksehd6otek8l7lAyTpNkmSVkuStNrji+8xsutGeuTA3dVkJxCS9Q5SYjBQy/KU/QJGA0+JVoWb9JrZQ2JSXJR/2yJSEuN5sHIzlOidpgsxBlaCc4TB4AWNsxA2eoy1iHA40uDRH4+3eT3aSNeOktI8SRazSf+9muHfEJX6a2yf0twMrp41JG77ABFn6Bn3eOqpLur3yrLy+yVJ0j1X0Qa6sY2M7VgUlVIWCIZ050anYeBm28w4DR6seJEGsxrB1QiGZL3QgdZGxbmRfSjuwDWI2x2njTRca7Qn3W9YeMb2oYIsJdIQ7kOWiN8eW0kyshopxHpBZVnW+1B0uhSgH3uUyMMXPcZa1WOAyvOV/hI9xiCeMyV8nSeOLOG4wYXIMhFe4iI9q8CZsH0gsg9FO5vieUEz9UiD8pzZJFGYFekF9QVC+oQeTpeyRLQPxKtVECA300qxYSwYjwHS2iO6jZLtXwS46/RReh8o0CMNVvV6vPqZkNH7qPU+ZFX6kEWNtERMkAkK+RjbsShbWcRobRIKyfqCSbsPig6Fj0SBOA5LPa01sg/tbLQTDMn0V1O7NINFa5/oFMdonb52zpCINDINxWGZOHNHT2u1GcaYLVqnu0+NjhetitBpV5ROa2PM5YvZL5hIp5u6PLoGWc0m3QDT+lR0SpnRAO2Xl8FVMwcjy5HtoxnrETrt7kanbZpOW+K2D8Q30qMjwkadjnRYdhNp8ASwmCT93GpQIh5asbvyBDod7SiASIflHaeFx1hktMqWtJZDWKdN+vuN0SpIHhE2pkZDOKvAqNNanRCj07s5QbTKnsDZpM31iWwhiF3oGdtn7qhSpg4qUPuQMTtO+fd+tXJ3tKMgkU5HLvK6zyqwqMV/Eo2xTrdPyfyKGmPabzfi8MZGhLX2kaTwWevFOanP9ZIkcefpxj6ktIum141dHv1EAKO9qKW1GnXaZjZhMYd1WpIiC6zFax8I20LxdFpfxGSFt7DYE+p0/D60s9FOSMag05Ht4/GHHenaCRrGeew69YgeiOxDBVHOpujtB51uP/nqtoCwTlsi5/roE0YiKrOrY0wd18aoeTydjraFOhJsg8rLiMz+0m0h1emTYTGTF1UnIdkYK8vP5IoZg2NsIa0vGXXamGVnTB2H8BgzmST9t0S3D4T7UKTDMtIWiq/TmtM7XJsini1kM5v0cQ+oRT8jdbokWqe7kgeWEpFq1ejTgF0o+3IfBXam4fikGmCw4f+DgLoUX5PKewGQZflJWZZnyLI8w2xWbliiPcLa/sQR/ZS/t6r7NfrnxwrbZDUSpYlbKCTjC0Ya6XmZVm4/VVnIREYalCIs7QkMrBaHTxc2jSzDHliIrSQZd++ZZmCpQvXVzmbG/PJjvv7oEtZXd9Cpp0upk6NDW+zE7he0ewPkZVr1zxzRL0cfuP0NnVVro4rCLIpzbLEGVlSawp2njyLDYtL3nUF4ctxnOC7D2EbadRq/N9p4SjXSUJITjlbJssxpf1/A7Pu/5OF5u8IDV50gu9x+PaU5kZdYG5ixfSjWgJhcUUCT3as7HaLTpUDxgl4weQBApCc9y0qnO5waHW1gtTi8+nUbf7/RqEolqyA6Ivzogj1M/M0n3PzcSva2OCLSpcDoBY0/xsryM8m0KgKXbTPT1KXsX9QdBXHGWHRKmVHYJEnih+eMVdokYnJUPkfrQ/HaByL7UJbNHGFUxd0XE9cLatPbp77TzaTffMqZ/1zIBxvrwpF5a2T7xG8jP/kGA2tEvxzaHMoYK8216cUqtDaymiXG9M+jIaIaaaz4f+P4CoaVZKtZE2b1vcoRYsYxZryeLo+yR64gO6xD0Y6kTGs4AyO6zaLbx1gM6jsvrWHa7z7nR69v0O9DOK01XI00xmHZnU7nxer0pCidDgSVEwKM7ZOfaeXbp45QryPx/sXo62nWxphRp62Ri5juqmpD7P7FBTuadJ3eWNORMNIQitovqB0vlZdpCet0aQ6tTi8NXR59HlO+U/n3oKIsCrKsSbMuAO46fTQ2S7iSqNY+EKXThjaKO8asmqNS/TvO0RbxHJbG/YuyLHPq3xYw5/4veWT+7phIQ7vLFz4FIk7WRV6mRde1VHR6UkUBTXaPbtDFizRMqijgvEnlynXE2b+YKDW6xRGumG78/cl0SBuP0fs7IWxQPjxvNxN/8wm3PL+KvWphPaORnihapS1iyvMzsZlNDChQ9LpRLcJTFken9TEWodORfUiSJO6No9Oa1ml9KF77QNRcH+1s6slcr7ZPXYebib/5hDP/sYCPNtWHFzFxdDp67rB7NFtI+R26Tnd6KM3N0LcUaG1ks5gY3T83wf7FcBtdPn0QQ4qz9VM/lOeVDIrIMRa+ni63ciyeUYcyo1JWs6zhbSrh9olMHdfaRyscCfDt/67h+N99zo/f2KAv/pSihmrWhSd+nw7b09l6+4BRp+OPMQj3oegTNECxM247RdHpgginty1im1hsH/Lq2RD6748aY6mcEKE5x7Tvmb9d0elvPLqETTWdep2SaFsoEJIjzncP67Q1PNeX5tDm9NHQ5dXbx/idQ4qzycu0RG5h8cfa0989YxQ2symm1gWQcK5PrtOWiP8byYzjTFGK/oWdGSf/dT4n/PlLHl2wW19wa06JNqcvXMA4jr2YF2ULQfI12aSKAho7vbrT4VAUy/oHcI4sy6fKsnwK8DXgXym+NxGrgNGSJA2XJMkGfBMlBdvIe8ANavXoOUCnLMv1Kb43hqAcnhz/s7yKH7y2HjB4sNSBe/KoUgC21kdOjsZGnxhlYIWPVIjsMDedOIzvnjGKU8eU6Y8VZtvwBUJ6moOxE/iDIRbtambmsKLIhXBUOlD0/kXjzdYFUC0G1aaKmHYA9bb6Ll5bXZ1wjzAoG+4veHARVa1OvAFlr1eeun+xNNfGuPI8WtXKyEavjWaAlebaKM/PjJMuFXnd/fMz+ceVU/mOMfIZJyJs9Bgt2NGM1SwxZ0SJ/li05yo6Yg6xE4T2XVr7dLj81LS7sXv8/PuLXbQ6fdjMJr04h7F92p0+fvXOZp5YqJxxbFcjMZqBZexDkhSOkBsjKhMrCgiEZL1yciIP1i8uGM/dZ45mRGlO+LpzoiraGvrQ3mYHla0uZgwrjojydptVYAsbqdrEGR0R3lTTidkkMX9HM2+vqwWU/qz1R+M+82V7Wrni8aV4/OFq0rkZFkpyMhg/II/iHFvYS6z2oWybWd/fNX5APpIUWwkwOtXlpFEl/PKC8VwxY1C4faK8oN5AKKLi4vwdTYwqy9W9oEDEPnOIjcRobaj9Dg0lWqX87p2NDnzBEHUdbp5atE9fxJhNEplWU0QfanF4ufrJ5frxbLozRdWZk0eV4vQFqWp1xXU2leRkMKAgM6JoX3TBNVCiIf+86jh+ccH4iN9SmG2NGGNGA0s7imimoQ+FnQCRaYdG4k+OkRHPzbVdBEIhXl9Tw6rKNv1asjPMuLyRe89eXFbJD1/foLcPJNbp8jiRhkkDlZRMTWujj77RuPnE4Xz3jFGcMrqfoX1suP1BveKpsX18gRCLdjYzc2hxRPQ01sCKzrqIdVhmWZVtHeH2UXR6a10Xr6+uMRTLihxj2m+/4MFF7G914Q2E8AdlfZ95v7wMxvTPo9WhRvMMfV0bHyW5Sh+KjTREtk95QSb/uGIqt3ej08Y2mre9CZvZxOzhYZ2OHj/xI8KxRrpRp9ucPmo73HR5/DzwxS7aHD4yLCY1nc8cqdMuH794e5N+Fr22v9M4xrR2VNInIwtdgdKH/EFZj7bGW8QA/PLCCdxz5miGlRh0WjXS40Wrdjc52N/mYsawooiIXXdzvT6PGfZ3RkeEN9UqOj1vexPvrNd0On49kKW7W7jyceWMY22M5WVaKc6xMWFAPiU5GexoUAqBaXO9slhTvnvCAHWMRR1dkhFlPJ88upRfnD+ey6eHYxiFUX0oek/u/O1NjOmfqy/AtfaJzGyK7UPavUmU/bWj0Y4/KFPb4eapRXv1gn0Ws7Kv29iHmu1evvnkMv3YmXA0LzzX270BqttdkbaQek9Kc2yU52clLbgGinPyX1dN5efnR+p0UbYtaoyF+9C8HY0AEfZitDMyXsQ8XtZFdAbh5tpO/KEQr62uYXVVWKf1Yzy1eiBOH88v2aefRR/OsMzR2wdidboojk5rOpTIFrpl7nDuOn0Up4wuNbSPFacvqAdIjJlN3kCQRbtamD40yhaK2iaWTKe1sZhjM2MzmyLGGCgFzt5YU61kx2XZ9HkxQqfrFJ2ubnPh8YcIhmRdh8rzMxlVlkurQ83cidBpda7PtcXR6VhbaGBhFn+/cqru2NXaB7rRaYuJWcOKY36/5tSNFxGOm7mTbdXt2RaHj/pOD51uxZ5uc/r0vcvZUTrd5vLxs7c26WfRa1kXmk5r8/LW+i7MJkl35BZH6bQvGNL776HYI2yVZXmH9h9ZlndykFWkZVkOAHcBnwLbgNdkWd4iSdLtkiTdrr7sI5SK1buBp4A7kr23u+/UPLrtTh+fbK7nnXW1Efs+BxVl8c8rp3LnGaOwmKSUDKx61YOVqNEzreaIQlJgSFVo0dKBwp1y6Z5WOlx+zp88QN+MDrHRvOj9i5IkxTUgig0Rzzq1UMW48nz2Njv0vcvRqRygnAu5pa6LedubdMHLzbBw95mj+ddVx1GSk0GT3UuLwxsRaTAa6eXxBm6cTnnhlIGcPi7sKMjLtGCSwu0D4YiwLMt8uLGeuaNKKTN8b4z4J/VgRe5fdPqUybe2QzGWTxxZSiAks6UuvHc5y2aOTJdSi2C9ubYGUNI28zItnDiyhN9dMpHLpiuLsq31XZTkZISjeaqhJUkwYUAeYBT/+B6sgYVZ/ODsMRFe3YIsGw2dHn3CMEbMtTPjzp9cHpNVkJssqyBuJEaLCKt9qNPNjKHFWM0S66s7ADVdyhqZ+uv0Bfl0SwOrKtvZUtepR65yMy386RuT+fG54yjJzYhxNkmSpItfeUEmJTnRKWWxRrokSXzr5BGMK8/XH9MyDCL7kE+/xhX72tQxFhm9y7Sa9IJwcffFRO09U9ooHBGuM/ShyhanHmlQPt8S0Yc2VHeybG8rH2xQ7peW1nrljMH8+6rjmKBqzNb6rvgLYXVybLZ79T0/iYz044cUce3soRGPFWZb444xgA831tM/P4PpQ4pionhGR1ui9onWIFAMrEAwREOXhxNHKoaMsQ9plceNe4Q/2lTP2+tqI44uG1Kczd+vmMpdZ4zGbNDpZA5LfYwl0Oksm6LTRUl02tg+S/a00OUJxO1DyfYvmg3HBRmLQRmjVbUdHkpyFGfjnmaHvnc5Xtrmp1sUnZ6/oym8iMmwcM9Zo/nXlcdRnGujsctDq9OrHyGjtJEyrktzbZQXZEbuPfPHOiwBLpo6kNPHhnU6P8uKFK3T6lwWCsl8vKmek0d3o9MpjrGSHBt2r1IMqq5D+f0njizFFwyxraFLv1cxOu308c66Wt5aqywG7d4AuRlWThpVyu8vmcjXjw/rtDGap0XpTZLikIPwQi+RkV5RmMX3o3S6MNtKXYdHd8AY53pNp8+bNCA2c8fYh6Ir/ibIuoCwU72u083MYcVYTAadjrOFxe4N8NnWRlZWtrGlrksfY7mZFv582WR+dK5iu4THmHIvtQJmoMxPJTm2bp0pkiRx6ykjGFueF26frMgxBuFstKYuD6uq4ut0dsS8lriWQ4wtZIgIg0Gn3X494yo7qg+tr+5g+d42PtwYqdNXzRzMA988jrHlRp2OYwupzqaI7K8Ec/30ocVcM3tIxGMFWcl0uoHy/EymDS7S7T9tUafN9ckKh0b2ofA+an8wRGMcnS5QsxeiK49/tLmBN9fWKk5vtQ8NL83hb5dP4a4zRiNJ4YVwWZxtUNGBpXgOXe23/fBrYyNSf7XocFinw46CJbtbsHsCXDAlyhayWpLqtNVs0ou6RheDajf0odJcxdm4q8mh712OZ09/slnR6QU7mvTjWnMzLHzvrDH848qplOQq2ZOtTl9ERLjE0IfKC7LiZFjG3tuLpw7UT3ABQ7EsQx/Stv0pOt3AKaP7UZoXbtMezfWG8VeUY9NPcjCOMV8gxHaDTkfbQu1OH++ur+VNVae1MTZ3tKLTlxw3EFD6UL/cDD0TTRtjZpPEuAEJnClx1gLRpLoQXi1J0jOSJJ2m/nmagy+WhSzLH8myPEaW5ZGyLP9RfexxWZYfV/8ty7J8p/r8ZFmWVyd7b3eEI8I+9jU7CclKdMLuCWCzmMiwmPnG8YMoy1Oq+3a4lHN2tQIKWmEcgHHl+ZhNkp6q0JON2ZqHRi82oJ7xBfDRxnpyMyycMqZf5FEkUfti4ldxUztmZmLxH1iYxbCSbLbUdsWkSxlTStZWtQPKgfH65JhhYcLAfE4e3U+vUmnc8wFhA6I0TzGwYtJaU9m4bpIoyLJGnNemGRAbajqp7XBzwZSB2Mwm/X5oCzF9j3CcSIPWPnlGL6ihKmN44CoRjI3VnYZFjDmiffY0KQaqJoBaNM9qNnGDWnkUFMNQ2/MBYWEryrbpZwQ2xDhTuh+4RdmR7WP08H24qYHpQ4sYUJAVk1UQEa2KiTTEtk+Bauwa+9Dg4iwGF2ezsTpcHCZuH9qv9KEN1Z0RRvqpY/oxfkA+JeoYA+J70lUDIhXxj6YwaowZ2+jTzQ3IMlw4ZUBM+xj3L8YdY7b4Y6zV0D5mk8Ss4cV0uv3sb3Xp+7yU829j22djTQeg7RG2UJafyaXTKvTFSofLH3chXJqbQf+CTEJyeLtA9NE3ydAKioXbx6e32YKdzZw3aQAmkxSTGh32kCc2QOMZ6W1On24MGscYaOlSFoKGNLI2p499LU6CqlPK4Q2QaVXOM7x8+iD65WXohVJMBp027h+bMCAfk4RBp1OfHDVtMBaFMep0XoaFk8eURvSh2LTNxJkpERHPODo9tCSHTYZCedpnxR1jNR0Ri5iJAwuYO7qUUtUwkWUiIsIlhj6kZO4Y0+tjIw3xMJsk8jMjdUhbiK2v6aCu08MFUwZEGPsxqdHx2icq80BrH+3za+PqdHgRY2yfnY12nL4gOxvtetp9XoYFm8XE9ScM0xcuik7HOgqKc2z6Ga9hnU495a4waowZs78+2lTPzGFFlKvpx8Y2SlbUMN7+Tm2e0lJY6zo8DC7OjtDpAkMKdlwdqu7Qx15uhoXTxpYxrjyfktywTkfqUNiZ0j8/M+aoxFTaJ3qMGa/tY1WnL5g8IKZ9IvcvJnGmRESrIseYxSQxc1gx7S4/NW2usJGeUKc79WvNzbRQXpDJJcdV6DZPtE6Hx5iN/gWZBEOynoYafYJG0jbKiT/X2z1+vtrZzPmTI3U60xo5tpI5m3LjOL3b1C1vIZkYnS5Q08eNKdTtETrdhcOrFC81mySumDGYfnkZFKs6bTZJlKrfY3Q6TlCzv2KdTT23p9tdYXv6w40N5GVamDuqX0xQIFlqdLI2Cjss3VQUZjK0JDus0waHZXydDtvTeZkWJlUUcNKoUkpyMgx7dWOdKYpOdx8UiIfFbCIv0xI5xlTH/brqdhq6PIotFJWVAqRkC0UXXFN+e3x7WluUZ0Xp9PYGOy5Vp93q/vPcDAsZFjPXnzBMzwjpcPkj1htaYKk4x6bXfdB06FCkRn8H2ALcDdyj/vv2pO/opVhMEm1On15pbEN1hxLNiwr9a6F35QxZpZm0wjigeLWGFGezvUHxcvVkY7bRm2Uzm3Tjzx8M8enWBs4aX0am1RwxARqPT4LkHhqjt9RYbbOuw0NFYRbDSnP0CS96H6kmzNp5dhuqO8IeLIPxX2rYpG70YOmpHDkZDCvJ1s9Gg9QXMcbP0Qx6zYD4aFM9VrPE2RP6RxxnE12tNnnV6PhGujZwT1JTeeyG6oTRbaS1jywrKURayl309SduHxuDi7ORJNiuns8ZNtJTNyBAaSNtEbO32cG2+i7OV/cVa79ZK3SkiboxMqW3TxxhM5skJS3R5cPjD9Li8FFRmMXwksg+lB2nD2n7OaKNdA2jRzieJ70018bQkmy219vD+z78KRpYOZHtA+E+9OGmekaV5TKmf17M5Gj8/clSymIq2qqLpNoOt5Lq1C8XUPuQwUjXsJgkvX12Nzvo8vj1lDu9fXKTt09Jro3hahpmuA8p7RN9Nms8onVIM7C+3NaILxDigimRfSjeOdzRJBtj7YYxNrp/Hv3yMrB7A3q6lDHqYzFJag0CxXBcrxrpuRmRiUhaH9LOkIVwYRzt8cHF2WxrCLcPpLiIMeyzsplN+IPKEWC+QIhPtzRw9oT+ZFjMuvZAvEI+8Q0I4/5OrY2MRnqsTtuSj7HqDt3QMbZRcYIxpo2Pkhwbw0pzaHF4I472SqV9lOuy6u0D4cIrH26sx2Y2cZaq0+FoVeT4SdUALelGpwsMkQYN4xjTnN7aIiZe+0REzA3zmLbXcXt95FyfmYJOG/fD2swmPZq3u8nB9ga7rtOarkQXOrKapYhz0xO1j1aost3lw+0L0uZUdHpYSXaUTke2Dxh1OtJIj9dGkQvhcHr98NIcttd3GXQ6tbn+/9t78zjJyvre//Ottau6e3qbvWcfYIZ9hhmYAYwIiCgqjKIgbnhz81MT9UZI+AVyTUSjP0m8LvHm3iREzdXcRIFIRtyCu0QCKjgDA8q+9wwwW8/WPdPV3c/vj3OeU6dOnb3Oqaru+rxfr3lNd3XVqec8y/d8t+f79Dn6B6g65L7z4C6csKAHxy/ord3fqZVz29YCJ64nRPTY5PT+cSzs68Jqc++hIafdn/W6fx5/+RAOmXLabW46+8daYz1FS07/1jaH7Cdo+GGvn2J/1v/wty9hYmoarz9tYc09120T85XTtv6xFe3T1XnXLOzF3B4jG6Pb3Lbk7J/dNtmh5VCPQ5/Wc2h+b9HKmMibBhpg9NuSgVI8fdrRPxOTxhFgxyan8P3fvIjXnLTQaLdv5o67Qy6bkRpZaBSDshnCAyWstMnpvlKALlQjpz3kkKvT25DTOgsTCK8LAfVyWsuhbz+4C4VcBheeOB8Z27FjVV3Ix2HpIocsh+WRiuWwdNOnvXQhy+lt7hHW1KyxXpsuZNOn9ZaUR6xnfcKp0UqpY0qpzyql3gzgvwL4kVLK/bCoNmd4oGRt0AYMz/UhhxEDVDverjwAxsTsKebQlc9i08pB/OLpfZiaVtEULJvw1/vdDoxVatKijWvZF66jyp3HwrWf36nbu9cRaVhp22vaX86jkK0WvdFnkeliI0/tOWIJxV4X7yEAhyddR4SL1t6we5829peE9RID1Qfkov4u6/gWe1q0VnSd3s9w0apaBRSA5Rwp5jJYs6DX8oD1uRgxwwMlq38A01lwtFb4F3LVyqyuEfOeIvpKeZy4cA7ufWqv1T9AuDnU55hDWrDZ06KBqrLmNFCMI0wcBTRcHo6A4XHdd2TCSjlZbCrpVlscxV6cc0j3D+AQ/j3VtBbteAJqvaCbVg3hxYNHrbNKnRV/veguZK2HkH2N2dOi7f3jdv9+WQXOfeZT0woHxyfNNdZV0z9uwn9xf3UOKQX88ql9UMrbUeAWMZ/XU8T6ZQPIZ6U6h1wKaHhhN/Tsc8ieFg1UHTPO/nGLxHg5CgDz+APz4Tjc32Uph279MzxQqqlc/8ALB8wiNe4Klr1/AKPveruqcvqXT+/D9LSK7bDUc2h0vFKTFg2gzpNulz1e+8zt+zv1feiKrVU5Xa262lfOmw6Oav8AtXJaG4i1a6y6rlyjVb1FbFpp7A37xdNaDoV3WOo+WtzfhWxGMDpWqUmLntNVK6edxeaiVPcHqoZwKZ/FGpuB5GbEOOX0gy+M4tDRSk3/FHNVhbgmEmPLbOovG2nq99r6R382iAGHnNaZTfa0aMDWP446Fv4Rc8daKBvP+p0H9BqrldP9DiW9Tk6b/QO4OyFyGamRSVoHmNdTxKZVg9h54Cie32d8d1g5PacrZ+ke9jVmT4u29w/gkpnitn/RLTW6bBSDOnRsEjtHj7rqQkBtny/q77L6Z1oBvzR1GS9HgVdm0xnL+005bepClXBZF/Z2AU45XU2LBuCSueM+TwCPzJ0euy5kjKORQajltNaFqp9xyukHXxjFQRd9etDSp+vldF8pj0LOqCXwC0tON6ZPj45P1KRFA/VzKNBhadYcqJXTxVo53VeqqQnQXy7UPA+ca+yJ3YetqG7NGusJnkNan/6lpU/X78P3QgdOhgdKyIhRnd2eFt2r5XSdIRwvsLT3yDGMjI6ju2DIaT2ObkGBJU59+oUDdc4Uva8YcNgbPVVH+EB3AWsW9NboQkCCqdEi8lMRmSMigwC2A/hHEflsmM+2G0sHqsrFqrndeNCMVnl5sJwK1mB3wZq0Z68ewqGjk/jNzoNWafso6UAArEW0f2yiJi0aqH2o64IqQefk1j0czYjwwaMVHDo2aXqJa40YEbGiyPN7i5bXSBdn+s8njYnl6Ul3i+Z1F3Dy4jnoLeZwj/n5oxGUdN1H/WVDERkdm6hJi7bfM1BfpMe14m/A/sURMxKTyYhVmdbt4ajnUCGXwXB/CQ++cMCsqu2eVWCPCOtxtM+h+5/bj6OVKdscCqNg1c6hA+aRV/a06Jr+qfPwhesfwIiK2CMx9YZw3rV/AGMOPbN3zDqn07nvD0DNng/7vQ31FHC2WRDtnqf2mkcqhFPSRcR6cFfXWKUmLdreP/afw0Sr7A9Re9VfrWAtGyxbe42dWQXlQtb6jFbEfv7EHvO7vaJ57nuES4Us1i8dwD02Z0r4h6PxXbmMYLi/hNGxibq0aPs9Ox98JbeKv46osXG/OXTlM2a6lKEELOorYYVp6PW5GDHucrpSPzfNfpzvkNMD3QUrVfrs1UM4MF7Bb3YdtArxRFWwrDl0ZKImLRqori1Abz+onpPrJYc85fT4JI5MTBnOlKFaI8YupxfM6bIcPavmdkMpWHPALofmBkWruos4dbgPPTVyOkKkQR871V1Af8mISNrTou39Yv8/TnV/oKqkL+7vMuW0MU/saa0aPYeKOaP6saVg1clpU0m3yWldGGeouzqH7ntmP45NTkWaQ/ZK/yuGui2Hrj0tuqZ/IkTMveaQXU6vdMhpvzX21O4j2HXgKERqv1c/x+zRPABW1eRaOb0HUy4naHghItb61/N9dGyiJi0acBgxVvaX97M+KDNFP+t1VhZQ3WtqHwOrunidnLYbMbbj2jwym8qFHE5f0m+t0Si6kD2at6ivC/vHKnVp0fY+cu7/9av421PjtDPm/D6bnF7cV33W95XqHZb1cvqAtf3AjpbFzsDSgF2fXjWE0bEKHnnxUFUXiph1UZXTlZq0aAA1mTtdzm1iHnOozi4wgwL7xyo4Wpk2daFqH/SX8shkqpmKi/q6LN3GKadrnd7uDkt75s5pS/pQLmQtOR0lc0eP3aDpeNg/VqlJi7buueB0pvjJodp1qK8PGP2vHbpuctquOyw1j9nSp4lYDksvOW3rn16zaJ9+Pmg5PTE5nUpqdJ9S6iCANwP4R6XUBgCvDvnZtmKp7WyzS9ctxvP7xvHsvrHQCtarT1yA155seJh01eJ7n9pr2yMcrITaj6BYYU6QPYeP1aRFA1XvDFDdv2jtrfKo4uZm0B+ZmMJTu42KcU4jxqmkGyXdjdcuWzcMwCg4ANQK/7m2aN5c2yLesHwApy/tx9pFc5DLZnDWykH8wq6kh400mH3UX8qb53lWatKiNXXRKmtPg3cqh1dq9Mj+cWs/mFYgrEiDXcEy59CKoTLWLevH9ufdnSnVok/V/hERvOG0xTjPdHacvWoIE5PT2P78aGwv6IqhMpQyvLH2tGj7PZcLwf2j06WdCtaAmQ6kMwN0arRxHSNdqubhaDvndct6Yw7955N70VPM1ShSWvjbI+aAUV30wrXz0VPMYfW8bszrLZprLLyzCaj20Qqbl9ieFm3vH6B+32Lo/Z2mArXn8DHsOmDMoUIuY3mDqwpENWtBf+aMZQNYNliurjGb8O8x9zICtcJ/2WAZZ60YtDzEm1cP4aGRAzh4tBIpItxnOZvy1lFBzrRoex85vcTuEeH6hyNgRqsOT2BkdMyqPKrlkKuzyZxDIkaRpmf3juH5/ePea8xFTr/mZENO1MrpeNsP9BzaffhYTVo04IgIFxz7zD2i5m79c+jYJJ42K3sO10WrtJJuXHdOV856zU9Oa495Pis1FTY3LB/AuqX9WLuwF7lsBmeuGLBlpoSL5gFVZ0p/KW+c5zleqUmLtt8zYJdDPg65nH9E2FVOW9Gqejm0cm431i/rx/bn9xtZBR5Ob7scEhG84fRFNXL62OQ0Hnj+QOyI8IqhMqamFbY/P1qTFm30T22ho7KPwzJvZnA514LeZ67ltN2Z0lvMIZcNI6f3oKdYn63g7B8AeMXx8/DqExegXMjiuPk9mNtTwL1P7bPOVg2fVeCQ02OVmrRoe/8AVSW67Pusdy8cChhVoF88eBSL+7vQlc9isek07ncYeoacNl7bsHwASwZKrnJ6Tle1grY9WrV8sBtnrhiw5PTZppw+dLQSSRfScqivnMdAuYADYxN1adH2e6571rvqivVVtXUBtH2HDUfBYLfhaF3pkNMllzmUMeX0U3uOYGR03DMi7CanL9b69GqbnLacTVGDAsYcevnQ0Zq0aKBWTus9zF5OJcDoo3pnUxEHj07i2b02fXrILaugqk/reeWU070uTu9CNlMjM7ScXrOwF/lsBmeuGKzJIIwcWCrljcDSeKUmLbp6z7Vyx18X8gssHbOCAkDVQdFXrnemLBnQcroH65aa+rTLNijtlLLrQpY+vcaQ05tXDWG8MoUHXxhNPjUaQE5EFgG4AsC3Q36mLdELd7i/ZD3knnj5cH00z2Phvu+81bjBLG+/YE4XVs3txj1P7Y1Uqtu+H04rg9/b8WJNWjTgiAg7PMVuXr7uYs4zdVAfybG4vwt9pbz1urvwN/62blk/Tlw0B4+/fNj4m0tE2OklXj7UjW9+4Fzr75tXDeGpPUfw0sGjkRSsfpuSriMNzrRoez/UH59U3z86VcjeR32lPDJS3b+4uN8Yb+3lc0vb1AfFr5rbg80rBzEyOo7xypTLwnV3pnzmitPx1o3GERJnrhxERoB7noyqpFe/a7k5h/7lF88BqKZFA/XpUn79Y/w96zKHithn7vkQMeZ9tX9qvegArCJgALBl3TC68hk8/vLheieNFYmp9RJfeOICfOk9Z0JEIGIck3XPk9HWGFDto4V9XSjkMnjipcP4pS0tGqg1hJ0ph24KVncxh5ztAQpUDfpHXzSO5HAKf2cf9XRV19iqed3YtHLQWmO9DsXEbYtGVz6LW99/Nk5f2g8A2LxqENMK+NXT+yJ5iXX/9OmHo7nG7GnRgEs0z6fQUbdlqDnWgrk/b+foUUvxtFKjXdKltLNpuL9kZcg88fJhFy+xe6Th91+1Gje8zpDTi/qMvZL31sjpYAWrK5+xlCi9xr774K6atGigPnNH34tIrfKl6SnmrFQ0jV4LVTldMrNhqoamvq6+ht6jecZyw6B97KV6Oa3n5vzerho5vXJuN7Z+4FzLwNy8aghP7j6Clw8ejeawtGfulIyKqs60aMBbDrkpoPo1uxzqtxXtGzFrXQCoi1aVapwp1XNMN60cwvP7xnFscto14wWoT9v87BXrrOr/m1YOQSw5Hb4wpj293imndVo0UJ/Z5Jf2Cxhr0E1n0Zk7GVNOayPGUkBtkRg3Of3YS4ddonn1EXMAuOikBfji1RstOb3JlNNRIuZAdW4v7i8hnxU89tLhmrRowBkRDo5WdZuRotr9ncZ9PPLiIUxNKxdnimON2c/jnteDTSuHqmvMxYB09lGpkMVt7z8Hpy4xKiJvXjWEqWllZhZEj+ZZQYHxSl1aNBAt66LHZY0BhjNFFzqydCGfLSx6jS0ZKON3zCONnnB51nsFlj5w/nH4k9euBWDI+mWDZUOfjmDElAtZyxGh5cF3HtxVkxYN1Gbu2IMiGXH/Hnd92ugDXSNmuL+EQXMbDlB9llmBpWL1LNyNKwZwwoIeVzk91+qfYo0TavW8Hmz9wLmWHNm8agiPv3wYuw8dC70PH6iOXV85b0SEj0zUpUUDbnJI95P7GgNQ83mt1+yzRYQBe2CpXl+skdOrBvHs3jFUplRd38910YUA4HNXrsOb1ms5PWiT0+Gf9e5Stp6Pwziq6OdKqV+JyCoAj4f8bFuhUzkML/EAvvyejfjqPc/iwhMX1LxPK1hOQ9jJplVD+PaDO/GuzcbRJF0uBqobA+U8xg9MWRPkWw/urEmLtl8rmxFroXcXjTRDeyqp5o8uOsE6pqH6PbUKllYglg+Vse/IRJ0C0W0zhFfN7cYXr96If7jrKbywf6wmotBfLlgPWz90NOZXz+zDscq0q2Lohj3ScOTYFH7+xG4crUzjmotOqHmfp6HnIvxftWY+PvmmU6xzDwGzAFrZKF3/8qFj1sJdbnmwapV0EVgPiJXzuvH2TcuRzWTwze0jONv0aGrCzKG+Uh4nLZ6D+57dh7NWGJ+Psn+xkM1gsemF/taDO2vSooH6aJ6lPLiktQKGkb5ybk/Na4PdhiPihf3jRup8LoNFfSUUbEWJnAXaugtZ5HMZLBsq47b3nYO/v+tJq6qfRmcSLOwLmkOD+NYDO/HE7kM19xSEXUkfKOfx3Yd2YdqWFg1Uo+ATU9M2I6O2z+y8fdMynLakr+ZhpTMoqmusqkD8x+N7qkaMTcnV83vV3G686+zlWDZYxl2P78bJw9W5CRgKxJ7Dx2rSpJ2csWwAhWwGvzJTN8PLIPv2gzwOHjXSot9+1rIao6ku0uCzxpYMlPBXl5+G19qcMfq79h6ZwLHKlKWAO7cfuKVtrpzbjQ3LB/Clqw05/Wqb9xrw3nvmZPOqIXzvoRdx1VnLau7JDxHBQDmPlw4es4z2bz24syYt2n4tXegIMMb48LHJmn7U/PHFa6zjrqz7KNcbwoDRR6Njo5jjMIS7i9WI8EpTTt9811PYdeBoTYREzzOn8uBEy+n7nt1vyOmI+xf7y3kcHC/g7if34GhlGn988Zqa91m1Chy1HNzm0AVr5+P/e9OpWGs7ZkcXQHvxwFHsOXzM5mxyd1hmBFWHy9xuvGPTMuSygm9u32lFnjReTm87fWWjnsN9z+7DGXrvfISKtl35DBbNqcppe1o0UM0kqzu2xGMtf+7KdVg1r1ZOayPmhdFxLJjThbyZSpvPiusam9tTQClvFOW0y2mtI2gGQ8vpIXznwV14as9h856jyaGBbsMh950dO2vSooHqcTaT0yqUHHrnpuVYv3TANbJdv8bK+PkTNiU9X9WF7GvsHZuXYflQGf/x+G6cvLiv5vsGu4vYb55F7MWG5UY9h189YzgsI/ePuU3swHgFdz2+G+/ctNwhp52ZO95ZBcsGy/jLy0/Fa0+pldNDZk2Zw0cnLd1Up7W61Uuxy+mNKwbxxXdvxD/d+ywuXLug7rpAsD69edUgfvCbl3CFGSgI00d6G9TuQ8dq9Gl7WjRQdUratxj2FLM4WqnNgND8v69di6lph5w214I9sCQiWDm3GztGDljGm32OWs/6ed340tVn4ua7nsJLB4/WBHQGIvQPANz/7L5YgaWBcgED5Qnc89ReHK1M4/rXra15n6cccnF6X3iiIadPWFCVQ/msURtn5+g49h6ZqOpCHs6mfFase149txvv2rwchVzGkNPmvWq8sgrs6H3Cv3p2P9Yt6YMILNvJj1CGsFLqNgC32X5/CsDlYT7bbiyzeR8A4IK1C3CBY9EC9mievwKxuK8Lh45OWlXjoqQl7jxw1BIyRyvT2LLOedaZOSnz1Q373cWca6QKgBUhsqM9cQ/tPIBCNmPt1Vg5txtPvHzYUtx0lKe3K4fB7gIKuQwW95eQzQhuvPTkuuvqasJBCtYicyHsOzIR+lgOwK5g6XN+p+vSogG3vVXe0aqufLbuLFUANeckaiVA7wkasBauqcAVclYEYdXcbmQzgrdvWlZ39h9gL7jmL9wW9ZXw/L4xHJucQj4rrk4OJ5aXuJy3hNzRynSNFx2o9xLrc3K9IsLua6GIqWmFR186aPVP1tz3oY3AXNaInk2YEZeB7gLmm5HeU5f04W/efkb9dV32fLihFVq9byny/sVyHv2lAl46eKwmLVrTlTcM4fpolZuhV66JpADViNJDO7UhXFUOjO8v1Fy3x+YlXjWvB3O68vjQhcfjQxceX/d9g91FzO+t+FYX7cpnzWJLx6JFGsr2/jF+dqZFA1VPurMAjduRCiKCK85cWvf6UHcBz+4dw/6xCcvocmYVdLtE81abyv6FJy6oc1bq6wLh1tiB8QqOTIRPuQNgzRu7nL5k/aKaz9vXlqa7mHOVQQCwzkVOW0r6zgMo5DJWhGDV3G48s+dIXW0IHa0q5jJY3Gfsw/r4ZafUXVdXEw7snxo5HSHSYEWrCjhQNvbNOdOiAb9oVX0flQpZV3k62F3Aw7tqHbraaTfgcFjWrLG5PchlM3jHpuXu8t+SQwHP+v4u7DLPbi+ErMzeV672j36mhZHTZUc/OfFaC5UphcdeOmT1Ty6bwbLBcvUUhlwG+aygMqXQU8zXHDviJaeHIuhCQPUcz6hyqL9kZBXsPnSsJi1aU8pnrSrzgH8xKH10lB29xnaYRswSj4iwlvu9tqyL1fO6Macrj/924fH4by5yeqi7gMPHioFyeqBsOCuipLXao3n9pTyU0nK61oi19nfWRczr+0dEcOWZ9WtsoLuA5/ePYc+hY3iFGeFd6aELAbXRPAB49UkL6tY+4J7W6saivhL2j1UwHiOrYPehWjn9+lMX11Rcd/YPYMydMfOZ4MRNTmt9Z8fIAXTlM9acWjHUjef3jVmOCUtfNLO/SvksFphZOX+xpV5OawMyUBcy5+y+I5Voc6gmq6BgyGlHWjRQ3fdbpwu5POvLhZynnLZ0oYHa7Dg9h+xp+1pOr5zXHUpOO7MKnCzuLxkRc1MXCiOnQxnCItIFo1r0yQCsViilfjfM59uJRf1d2LJucU1akhtnrx7Cu89ejrNWDvq+T0+UfWZJ86hpiXN7ilb0wPlw1EajPaWju5B1jVR5oR9yD+88aBTwMRfqH7zqOGtvBlArPC8+ZaFVBdSPD7/6+LrooRMtjA8dnYx4LEc1NfqI6WRwpkUD9VWR/c7O82K4v4S7Ht9t/QwYUbaPX3ay5dm0K6CnDM/BeSfMsx4UXrzx9MXISG0asxt6/KMooPlsBr3FnGkIV69/iSMS5+wfvX8xav8Axhyye+k/ftkpNY6NciFrGMJdObxlw5Lgh96cLrz/vNV1896JXmN79RoL60nvrs4hrWy5fVdXPouDRyfrlXSPqLmTUsEwRHW6lM4YuPyMJSjmM9Z2DPsa+53j5+KhkQM1+0DdeM85y62jKfzoLmaNORQhXcrae1YqWOvNmRYNVD3pdcWyPAw9N4YHSvjmA0akR/dPuZDD/7xqvaVw1FS07S/hsnWL8TpHxMLJOcfNxbvPXo6NKwZ836eV26hyWq+t+XOKKOWzGK9M1c0hZyRGf1+UNbbYtsZWDHVbD+8PnL+6pg+0fOst5vC6Uxdi+VDZNeps59qLTrAcCl5oOW3IoQh7z7qr0bwD48bPzrRowN5HtXvIo8ohXaxI99eG5QP42KUn4/w1883rVdP1Thvuw3knzLOO7/DisnWLkbdlt3jRbcnp8P1TzBnzwCmnnfqHs9CRPic3yrPeLqffaCso+Yktp9ZVx69MGUXDLt+wxDJg/a77vvNW1ch+N6py2jilIvw+c9s2KB85XTQNYedJEWH7SI+DdnovMvvrLRuWoFzIWXsVa+X0PPxm18GauipuvOecFdaxOn70FHOGLlSJE82r9o8zLRqwVfxtQBdaMlDCtx805LSeT93FHL5w1Xqcsay/7npLB0q49PTFgc/wV5hyesNyfzmt5ZA+8jPqs2zhnC4Ucxkcc3EUOHUhwDDIjhQmEZYlppP7N7sOYuXcqpz+0AXH4Y2nV9ec3SF3yamLsGpeTyg57XTSO+m25HQl2nGkpgHf311An9m3zrRowK26v3d2nBdLBsq4+0lTTptBjLNWDuJjl56MV5ly2r7GTl/Sb8jp1QFy+vRhdOWy1mksXnQXc3h6z5FI/RNWk/knAI8AuBhGmvQ7APw25Gfbit5iHp9/2/rA983pyrt62J3ohauFYFglvb+cR1c+g6581noA29Oigfr9r4CRbms/GDuIJQNlfP7Kdfjzbz6ENbZFdtz8Hhw3v6oc2VMhLj19MS61LWov3nX2isD36DSUIxENPXvKnfbY2atFa5yb+3UE3J4eGMQntpyCa2/dju3Pj1rpZpmM4N22+7MXCuovF/CV3z0r8LprFvZizcI1ge/rLmbN/gmvYAHaQ1yw7tWZFg3UR8wBo4+i9M9FJy3AH154PP7XT56oSVd0poKX81mMwqjs++FXn+C8TB2ZjNSl5rhhGTHWwzHa3qq+UsFyRrzhtPoHttNTPGRmRHhFzd24+V0bcO2tD2BqWlkPl75yvsazaU+NPm1JP/72nRsCr+sWoXfDcKZM4djkVM0Zyn7012QVGD/bq0Vr7NWu9ecyUt0vFYb3nbcaO0eP4t+2jWDNwmr6t115sM/R3q4c/jqEnO4rxZXT4ZX0ciGLYi6LgXIeuaNSkxYN1B/hBhhZAuOV2rQ6P5YOlvG5K0/Hn3/zYYec7sVx86u/2+XQZeuGrQIsfrw7hJwu5Y2zjQ/HdFj2lfLWGnNmFOjrG/8bfaSzKKLIoU9uORXX3rodD7wwakWgshnB1eessN5TtilwA93h5PTahXOwduGcwPd1F3ORn2OATmmtZu4406IBr2d9oWY7UhCvOXkh/pspp9f4yelCzjjippjFtReFk9N6v70feo3tjWzEVLO/dB+5Gd3aiWJ/1hdzmZptOX6ICP7h3Rtx7a3bjaPqzPb2lws1ka2yLTX69KX9+N/vCJbTblFQN+xzaE6A40Vj7x+9XuzVojVdDjk9UDbkdNjnAWDUVtg1Oo6t23fWzKFLPeV0Hl+4KoScLoeT093OORQhg1AXhBsoF3BkYrImLRrw1oUq0+Hl9LKhMj57xen46B0P1+hCxy/orclgsBvCuhBdEO85d2Xge/RcP3x0EhNxAkulvDWHXHUhR0aKltN+W7OcfPJNp+DaWx/Ajheqjn4vOW3Uuggnp09aPAcnLQ6W0z06KBBBnw5rCB+nlHqriFymlPqKiPwLjD3DM46we5/Coje8742opK9dOMeq7njmigHM7SnW7Ydw7n8FgP9+yYlWMYqwbFk/HCio7ZGGJBExqlvq433C9s+qeT0o5DI4fn4vBruLWDFUrkuLBuo9WGevGsIt792MU4b76t7rxdLBMr7+3rOx78gE5vW6p385zylOkp6isQ86SsVfwJhDC/uM84hPWNBj7VO3U3SZQ1+8emNNEZcgshnBNRedgKvPWeF7/27HniSBrq4YdY2duKgXc7pyWNjXhfXLBnD42KSrx9UZ8XzH5uU497i5yGfDj8XGFYP4wbWvxLhHmhVQWywraXq67Ep6eEfBor4urDEf4gvmFPHWjUvq3ueMNAx0F/CN3z8HJy4Kfihp5nTl8bkr1+GG1631WWPG9Z1FbpKgXk6HU57XLOzFiweNVM8NKwaxqK+r7rP6mWKX33/2hpMiy+k3rV+Ci05aCL+4QTmlNSYi6C7msH9sAkqFd+iumtttyek5pTxWzu12fdZY1f3N9p+z2pDTYRQbzbKhMm55n7+cTksGAcaz0YrmRZifaxb2YulACf2lPI6f34N3ushpt2jVl68+M5IRk80Irr3oBLwnQE5X51B4R1YYLEM4YtbFmoVz0FfKY35vEeuX9eNoZaouLRqoymm9zt61eTleefw8a3tXGM5cMYgfXHOe79qsVvxN41kfPaugr5THgjlFnLCgF8cv6PGR07X9M9RTxL/+/jk1NVGCmNNlBIr+9JITPVNQtS5UyGVqUo+TwJLTEefQ2oW9lmzfuGIAwwOlura56dN//saTrCrnYXnzGUtw0UkLkPFJuXXWQkiKjFktPqqjYPVcU59e0IPuYhar5nbXpUUD9dlNrzhuLm557+ZQjkLN8qFu3BpSTqe2xiJmXYRtRcX8f1RETgHwIoAV0ZvYWrJmdcMkqUsHCrlw7XtNvCLUukCEXcEKKljhRdCCTFVJL+Yip0sN95fw2CdeZ/3+0+vOd32fc2+Vrl4ZlWxGPBctkPbDMYuJqWkcOjoZWgEFDINW8/1rznN9j1ukwR5hikKQVzAtZ4GOCFfXWLg+umDtAjx448UAgPeftxrvP2+16/u6Ctm6QkdRHCmaYi7r27Y0nSndhRz2HBozz+8M1z+ZjOCeGy60fv/Fn77a9X3ViHm13euX+ae4eeG3v0crcs7jW5Igrpy+5qITrAJ9/9Mj8lHImvvuC+nL6bSV9KhZF0sHy5acPglzrBRlJ045lMmkK6d7upI18gBjDh2bnMaRY5Ohn2MA8OX3nGn9/INr3eW0W3q9mzEYhiA5ndaz3tomFnEOXXTSAjzw0dcAMLZs/cGrjnN9X6mQrSl01NuVjyWnu/JZ3yJMaTpTuos5jIyOR3JY5rKZGtkcJKftcuiMFOS0XstJB02A6r7vfREdlte+Zg2uNX922+duXKs2owBAXQZdWJwpxU6s7K8U5HR3MRu5f5YNVeX02oVzPDPNnBmW6cnpFHWhYg7jlSmMTYQ//SBsK24WkQEAfwbgDgA9AP48XjNbR1COfhzqUu4ipEyFwTj4O9lrumFP20yaWgUr6f6pF25pkObD0T6Hko6EdbnsM0+LtLx8+mESNa01LF25TOKZIm6UU3Y2HT42CaXCZ12Exc2TngYZ81iqtPoHiK6kh0FEao7ESxOdWpx0NA8wFIiojoKwNGsOWY6ClBQsQMvp5J/zQLS9eHFJK6ugx+lsSnisu3LR9kzHJV05ncXhYxVUJlVqulDaayybMTJ2UjHyCrVzKMmIsyGnM6GrdTdCKWU5lJ6crs9uSgNnVeokiaNPh60a/UXzx58BWBWnce1ANuEoA2DfF3MsdKnuKDRLwUpzYnYXs9h7JFqqS1icEeG0SLd/qnMoaeXEiubN4DlUzBnnBKY2hyIWpWnke4CUnCldORyZmERWJHlHgaPQUZqUC1nPqviNYJfT2YxESqcMQykgypQU9j2wSWOk3Ok1luz1nedTpkX1GMDkv6fXNoeCIkJRaaactvooYZmnj3VMS053FWa+LmRsYZkyHJYJy2lrjTXJWZCKnO6qrrGwJ2hEoTQL9OneGjk9M/VpK6sgRaf33iPHAgsgakL1oogsEJEvicj3zN9PEpH/GrehrSKTgh5npW2a3oekU/rKhSzKKSwmJyWr2mZKHqyUonmliJUj41K2IjFpLtzkIw1W4Y8UHlp132WmrkXZWxuWmjmUdB+l9FB3+x4gvXSgw0cncbQSPh0oLLrdXscBJUmpkE354Zh81gVgtDsN48vte4AmpEYnLqfr0zbToJrZlE7EHEgnc0e3u1nZX6V8NnFnkHEiQTaVrAvAaHcz+qeUUr0UwF55PNo+8zBUHRzNeJblUs/cSfo5BhjtTsM4rf+eNFOjc5GL84alWc/6tHUhINocCtuK/wPgHwH8d/P3xwDcAuBLkVrYYtKICOtCPqNjldDehyjceOlJmN8bb79ZFPSCTUPB6u3KYXTM2GaetHB70/phDPUUEvfQOykXsxBBYOn2OOi+Hx2rJK6A9pXz+MvLT/Xcu5ckPcVcauPQU8zhBbO4XNIKxO+fd5zlYU0TLfTT6KOeYg6T0wqTE9Eqj4dh08ohfOT1JwYefZEEac0h/XAcHasEHmcWh49fdnLgcWFJ0NuV3hzqLmax35LTyc6hN58xjPm9xdSVUF3pPR2HrnHt/WOVxBXQge4Cbnrzqa7nAydNT1culf4BjPW7U58jnHAf/cH5q635mSZVOZ3Cs76Qw8TkNCaQvC60edUgPvL6E61jjtKkp5hLRRfS8mH/WMU6vzpJPn7ZybH3BUchzTlkFDVMS04vwcK+UuqBpe6UdSFAy+lki2XNVUrdKiI3AIBSalJEopXEbAPSMFTtUYA0Ig1hj09plDecugjFbCYVIWH3UCbdRwv7unDFxqWJXtONfDaDL7xtfSrGgF05TGMOXXlm/aHnafD//M4qvDbg3Ne42D2HSTsLTl0SveBKHE5b0o+PX3YyXuk4ficJavonYQWrkMvg936nOTti/vyNJ1nOxSSpldPJP+SbJaffeNpidOWzqRjd9ihq0n20qK+EtzZBThdzWXzhqvU4M+Bc6TjYldo05PTbzmqOnH7vK1e5HnGVBD1dOeCA8XPSfXTakv5Er+fFuqWGnH7F8SnI6RTnUDGXbZqc/ugbTwp9/FMUelLWhZrhaAKAS9cNo7uYSyWI1ZtiHy3uL+EtG+orkidNVz6Lv37bOmxaGb0QVxBx1lhYQ/iIiAwBUAAgIpthibuZQ5SjCMKSy2asA7yTVtCbyUB3AVecmY6i0p2ikt5M3hjibOU4pGnENBPnWXpJMhvmUNZxNnWSdKfoKGgm56xOXvkEjDlTyGaMqtozuH8GugupOf56UnbqNotLU5LTaTssm8UJC3pdj5FLgtnQR5TTwZxzXFpy2jgpZXJaJZ5R0EwGuwupOf7sc6gZdSnS4rJ14c5Xjoo98Ba2f8IawtfCqBa9WkTuBjAPwFsitm/W0tuVw7HD6expmA3UeNJnsPBPi7S9oLMB9pE/s8WISZPuYhYTY8nvzZstzBYlPS1qM5v4rHdjtjh104L9448+z/zAeIVy2oPZ4GxKkzi6ou+7RORMEVmolPo1gPMA/CmAYwC+D+CF2C2dZeiJyUnpDheuP1RAg6Eh7M9siJinTVVOs3/c4BzyJ83tGbMF7SxI4wSN2QB1oWB6qE/70pPyNp+ZTpxtUEEz7e8BTJg/nwOjWNb/ArAfwM2RWzhL0cKfC9cdKlj+2I+xYP+4o4VbIYXK7LMBOgqCoYLlT9p7YGc6NGKC0fvz0jhBYzZAZ0owPXRY+sI55E+cPcJB78oqpfaZP18J4Gal1DeUUn8G4Lg4jZyNVIU/F64baW7unw3kshnrXDX2jzu6kA/7xx0+HIOxFCz2jytM/fWnkMugYMof9o87NGL8YWp0MNrpTTntDh1y/hRzWSsbJewcCjSERUT3+oUAfmz7W/qHcc0QqGD5w9TfYJhe749OB6Ly4A6zLoJharQ/lNPBMKvAH8uIYf+4kvYpI7MB6kL+0JkSTNRnfdBM+xqAn4nINwGMA/gPABCR4zADq0anBReuP2kfXTIbsAy9GVwFME24xvxJ81iO2YI9bZPUw9ToYOj09sfK3GH/uNKb4hFls4VeZlj60sPis4FEdVj6RnWVUp8UkR8BWATg+0opZf4pA+BD8Zs5u9CdPpNLmaeJXfh3ceG6QiXdH90/nD/uxDkyoNPoKVBO+zFbjuVIE91HXVTSXdEOXfaPO/agAJ9l7lg1d9g/rjA1OphEDWEAUErd6/LaY1EbNpvpYTqQL3bhX8iyj9yoCn8qEG5w75k/2YyglM9ivDJFOeQBswr86aGcDqSH+xd96WbE3JdcNoNiLoNjk9N8lnnALSz+2FOjKafdqcqhZFKjSQi4cP3RCzeXEeS4cF3h3jN/6CUOxsoqYB+5wqwLf3RaayGbQSbDir9u0CHnD/snmF7KaV96Kad9seuKrMzuTlR9uiUzTUQGReQHIvK4+f+Ax/teKyKPisgTInK97fUbRWRERLab/y5pXuvroRHjDxXQYNhH/rB/gqES6g/34fvDQkfBMKvAH+pCwXAO+cOsAn84f4KJqgu1qievB/AjpdTxAH5k/l6DiGRhnFn8OgAnAbhKRE6yveVzSql15r/vNqPRXrCAhj+lfBYZoQLqB7MK/KGRFwwNGX+oQPjDrItgaOj5wzUWjLXO+CxzhbqQP90FOnSDiKoLtUpaXQbgK+bPXwGwxeU9ZwF4Qin1lFJqAsDXzc+1HTxH2B8RQXcxx4ejD710pvhCBTQY9pE/7B9/MhlBdyHL55gPPRH3nnUa1IWCYXaTP72U077kshmU8ln2jw9Rq9e3qicXKKV2AYD5/3yX9wwDeN72+wvma5oPisiDIvJlr9TqZkEvaDA9NIR94RzyJ2rxg06ESro/zCoIhg5Lfyin/WF2XDDsI3+4xoKhnPanWsC4xanRIvJDEXnI5V/YqK7bLnB9fNPfAlgNYB2AXQA+49OO94rIfSJy3+7du6PcQmgYaQjGWLhUQL1gOpA/XGPBUIHwh3vPgukp5lDg/PGEcsgfyqBgdB+x4q873azlEEhPkZk7fkSVQ4HHJ8VFKfVqr7+JyEsiskgptUtEFgF42eVtLwBYavt9CYCd5rVfsl3rHwB826cdNwO4GQA2btyovN7XCIzEBNNTzGFapdL9swKmA/nTlc8gmxH2jw89xRwyYlRnJ/XQiAmmpyvHSqQ+MPXXn3I+CxH2jx89xRxP0PChV6e1Uk570tOVQ57zx5OoJ2i0qifvAHC1+fPVAL7p8p5fATheRFaKSAHA28zPwTSeNW8C8FCKbQ1kwZwuFHMZDPeXWtmMtmbFUJn948PyoTIK2Qzm9xZb3ZS2RESwfKiM4QHOIS9WDHVjeKBEQ8aDhX1aTpdb3ZS2ZflQN5ZQTnui5fQ8ymlXMhnB8sEyllBOe7JiiP3jx8K+LhRyGfaRD8uHurFkgM8xL1YMdaOQy2BuTzg5LaoFUToRGQJwK4BlAJ4D8Fal1D4RWQzgi0qpS8z3XQLg8wCyAL6slPqk+fo/wUiLVgCeAfA+vefYj40bN6r77rsv8fsBgLGJSZTyWSqhHkxMTgMA0+58GJuYRLmQWpLGjOdoZQr5rBEZJvVMTStUpqbRxcwUTyin/aGcDoZy2h/KaX8op4PhGvOHcjoYtzkkIvcrpTY639sSQ7hVpGkIE0IIIYQQQghpL7wMYboTCCGEEEIIIYR0FB0VERaR3QCebeAScwHsSag5JBk4Ju0Jx6U94bi0JxyX9oNj0p5wXNoTjkv7wTGpZblSap7zxY4yhBtFRO5zC6uT1sExaU84Lu0Jx6U94bi0HxyT9oTj0p5wXNoPjkk4mBpNCCGEEEIIIaSjoCFMCCGEEEIIIaSjoCEcjZtb3QBSB8ekPeG4tCccl/aE49J+cEzaE45Le8JxaT84JiHgHmFCCCGEEEIIIR0FI8KEEEIIIYQQQjqKjjaERWSpiPxERH4rIg+LyB+arw+KyA9E5HHz/wHz9SHz/YdF5G8c1/p3EXnAvM7fiUi2Ffc000lyTGzXvENEHmrmfcw2El4rPxWRR0Vku/lvfivuaTaQ8LgURORmEXlMRB4RkctbcU+zgaTGRUR6betku4jsEZHPt+i2ZjQJr5WrRGSHiDxoPvvntuKeZgMJj8uV5pg8LCJ/1Yr7mS3EGJeLROR+c13cLyIX2K61wXz9CRH5gohIq+5rJpPwmHxSRJ4XkcOtup92oaNTo0VkEYBFSqlfi0gvgPsBbAHwHgD7lFI3icj1AAaUUn8iIt0A1gM4BcApSqkP2q41Ryl10Fzg/wrgNqXU15t8SzOeJMfEvN6bAbwFwGlKqVOaeCuzioTXyk8B/LFS6r4m38asI+Fx+RiArFLqIyKSATColOIZhDFIWo7Zrns/gGuUUnc14z5mE0mNiYjkAOwEcJJSao9pcI0ppW5s+k3NAhIclyEA2wBsUErtFpGvAPiqUupHzb+rmU+McVkP4CWl1E4ROQXAnUqpYfNavwTwhwDuBfBdAF9QSn2v+Xc1s0l4TDYDeBbA40qpnlbcT7vQ0RFhpdQupdSvzZ8PAfgtgGEAlwH4ivm2r8CYaFBKHVFK/RzAUZdrHTR/zAEoAOhcD0MDJDkmItID4FoAn0i/5bObJMeFJEfC4/K7AD5lvm+aRnB80lgvInI8gPkA/iO9ls9eEhwTMf91m47vOTAMYxKDBMdlFYDHlFK7zd9/CIBZLTGJMS7blFJ6HTwMoEtEiqbxNkcpdY8yIm9f1Z8h0UhqTMy/3auU2tXE5rctHW0I2xGRFTC8jL8AsEBPEPP/UKmbInIngJcBHIIRFSYNkMCY/AWAzwAYS6uNnUgSawXAP4qR6vlnTJNKhkbGRUT6zR//QkR+LSK3iciCFJvbMSS0XgDgKgC3qE5O40qIRsZEKVUB8PsAdsCMDAP4Uprt7RQaXCtPAFgrIivMqP0WAEvTa23nEGNcLgewTSl1DIah9oLtby+Yr5EGaHBMiA0awrAih98A8GFbZDcySqmLASwCUARwQcDbiQ+NjomIrANwnFLq35JuWyeT0Fp5h1LqVAC/Y/57V1Lt61QSGJccgCUA7lZKnQHgHgD/I8EmdiRJPVtM3gbga423qrNJ4NmSh2EIrwewGMCDAG5ItJEdSKPjopTaD2NcboGRNfEMgMkk29iJRB0XETkZwF8CeJ9+yeVtdOY1QAJjQmx0vCFsPtS+AeCflVK3my+/ZKZz6Jz8l8NeTyl1FMAdMFIVSAwSGpOzAWwQkWcA/BzACebeVBKTpNaKUmrE/P8QgH8BcFY6Le4MEhqXvTAyJ7Tj6DYAZ6TQ3I4hyWeLiJwOIKeUuj+VxnYICY3JOgBQSj1pRudvBXBOOi3uDBJ8tnxLKbVJKXU2gEcBPJ5WmzuBqOMiIktgPEPerZR60nz5BRhOVs0ScCtBbBIaE2Kjow1hMyXzSwB+q5T6rO1PdwC42vz5agDfDLhOj20S5gBcAuCR5Fs8+0lqTJRSf6uUWqyUWgHgFTD2Dr0q+RZ3BgmulZyYFVZNgf4GAKzoHZME14sC8C0ArzJfuhDAbxJtbAeR1LjYuAqMBjdEgmMyAuAkEZln/n4RjL16JAZJrhUxTyAQo2ruHwD4YrKt7Ryijou5veY7AG5QSt2t32ym6h4Skc3mNd+N8HKP2EhqTEgtnV41+hUwUmh2AJg2X/5TGDn3twJYBuA5AG9VSu0zP/MMjOIYBQCjAF4DI5rybRgp0VkAP4ZR2ZNpORFJakyUUr+xXXMFgG8rVo2OTYJr5VkAdwHIw1grPwRwrVJqqkm3MqtIcr2IyHIA/wSgH8BuAP9FKfVcs+5lNpG0HBORpwBcopSigzUmCa+V98OogluBIdPeo5Ta27SbmUUkPC5fA3C6eY2PK57cEZuo4yIiH4GxRcAehX+NUuplEdkI4P8AKAH4HoAPsdZBdBIek78C8HYY2zt2Avii6tDK9x1tCBNCCCGEEEII6Tw6OjWaEEIIIYQQQkjnQUOYEEIIIYQQQkhHQUOYEEIIIYQQQkhHQUOYEEIIIYQQQkhHQUOYEEIIIYQQQkhHQUOYEEIIIYQQQkhHQUOYEEIIIYQQQkhHQUOYEEIIIYQQQkhHQUOYEEIIIYQQQkhHQUOYEEIIIYQQQkhHQUOYEEIIIYQQQkhHQUOYEEIIIYQQQkhHQUOYEEIIIYQQQkhHQUOYEEIIIYQQQkhHQUOYEEIIIYQQQkhHQUOYEEIIIYQQQkhHQUOYEEIIIYQQQkhHQUOYEEIIIYQQQkhHQUOYEEIIIYQQQkhHQUOYEEIIIYQQQkhHQUOYEEIIIYQQQkhHQUOYEEIIIYQQQkhHQUOYEEIIIYQQQkhHQUOYEEIIIYQQQkhHQUOYEEIIIYQQQkhHQUOYEEIIIYQQQkhHQUOYEEIIIYQQQkhHQUOYEEIIIYQQQkhHQUOYEEIIIYQQQkhHQUOYEEIIIYQQQkhHQUOYEEIIIYQQQkhHQUOYEEIIIYQQQkhHQUOYEEIIIYQQQkhHQUOYEEIIIYQQQkhHQUOYEEIIIYQQQkhHQUOYEEIIIYQQQkhHQUOYEEIIIYQQQkhHQUOYEEIIIYQQQkhHQUOYEEIIIYQQQkhHQUOYEEIIIYQQQkhHQUOYEEIIIYQQQkhHQUOYEEIIIYQQQkhHQUOYEEIIIYQQQkhH0VJDWEReKyKPisgTInK9y9/fISIPmv/+U0ROt/3tGRHZISLbReS+5racEEIIIYQQQshMRZRSrflikSyAxwBcBOAFAL8CcJVS6je295wD4LdKqf0i8joANyqlNpl/ewbARqXUnqY3nhBCCCGEEELIjKWVEeGzADyhlHpKKTUB4OsALrO/QSn1n0qp/eav9wJY0uQ2EkIIIYQQQgiZZeRa+N3DAJ63/f4CgE0+7/+vAL5n+10B+L6IKAB/r5S6OegL586dq1asWBGjqe3B6FgFLx48isrUNPLZDBbO6UJ/Od/qZhFCCCGEEEJIW3L//ffvUUrNc77eSkNYXF5zzdMWkfNhGMKvsL18rlJqp4jMB/ADEXlEKXWXy2ffC+C9ALBs2TLcd9/M3E68ddsIbrh9B+ZWpqzX8vksPvLmU7Fl/XALW0YIIYQQQki6bN02gk/f+Sh2jo5jcX8J1128hjowCYWIPOv2eitTo18AsNT2+xIAO51vEpHTAHwRwGVKqb36daXUTvP/lwH8G4xU6zqUUjcrpTYqpTbOm1fnCJgxfPrORzFuM4IBYLwyhU/f+WiLWkQIIYQQQkj66IDQyOg4FICR0XHccPsObN020uqmkRlMKw3hXwE4XkRWikgBwNsA3GF/g4gsA3A7gHcppR6zvd4tIr36ZwCvAfBQ01reAnaOjkd6nRBCCCGEkNkAA0IkDVqWGq2UmhSRDwK4E0AWwJeVUg+LyPvNv/8dgD8HMATgf4sIAEwqpTYCWADg38zXcgD+RSn17y24jaaxuL+EERejd3F/qQWtIYQQQgghpDkwIETSoJV7hKGU+i6A7zpe+zvbz78H4PdcPvcUgNOdr89mrrt4DW64fUeNN6yUz+K6i9e0sFWEEEIIIbVwL2f7MdPHhAGh5jPT50wYWpkaTSKwZf0wPvXmUzHcX4IAGO4v4VMslEUIIYSQNoJ7OduP2TAm1128BqV8tuY1BoTSYzbMmTCIUq6FmmclGzduVDO1ajQhhBBCSLtz7k0/do3cDfeXcPf1F7SgRWS2jElaEco4153t0dLZMmc0InK/ub22hpamRhNCCCGEkNkD93K2HzNpTJwG5vlr5+Enj+xOzeDUkU+99VBHPgF4fk+cz8RtW6uM7Zk0ZxqBqdGk49i6bQTn3vRjrLz+Ozj3ph/PujQPQgghpFV47dnkXs7WMVPGxC0d9//e+1yq6blxqlE3o4J1q1OTZ8qcaRQawqSjaLVgIYQQQtqBtJzC3MvZfrRqTKLOMTcD00nSBmecyGczoqWNGNtJrO1OWcdMjZ5hzPY9CWnjJ1jYj4QQQjqBNFM79eepq7QPrRiTOHMsrCGZpMHZX85j/1il7nWvyOfWbSPIiGDKpcZSktHSuMZ2Umu7U9YxDeEZRLP2JLQDaRn8nbLngcwO6PgiZHbS6rWdtlN4y/phyqo2o9ljEmeOeR2R5Pa+JNi6bQSHj07WvZ7PimvkU+vhbkZw0tFSr75QMApZecmMJNd2J6xjGsIziE6JZqZp8Lf6HLpWKz9k5tBKx5eepyOj48ianu9hztdZDWVT82gHp/ZMcQq3y7xsl3bMJLwMWr85dt3Fa/DhW7b7XjdJg/PTdz6KynS9UdtdyIU2MgEgK5L4kabXXbymRk7Y8ZMZM2VttwvcI9wGhM3lb8XkbkVhqTSLELRyzwP3J5MoNKMYhxv2eQrA8nxzvs5eKJuaS6vWtp2ZUAjHbV5++JbtWP/x7zd1bkZZHyzGabB12wjE429+c2zL+mEMlPOefx/uLyVqcHrpzwfG61Ol/d4/rVRNm5KYB1vWD+NTbz4Vwx79NV6Zwo13PFz3PTNhbbcTjAi3mCie4WZHM1vltU7T4G/lnodOieg3g3bzzodpT9Q2t8qr61eshPN1ZhF2zjVDNrXbmm0lSa7tuP3qFm1qt0I4XrJo/1ilqRH0sOujHSL9aeI21wB3ferTdz6K+jgrIADOXzsP5970Y885+9E3nuw6N5OOuALR9eow709yHujU5JXXf8e1P0fHKxg1jXb9PZdvGMY37h9p67XdTtAQbjFRFJBmPri2bhvBH936QN0+CC/hn6SCk7bB36o9D0xXSYZ2UzbCtCdOm73WQV8p76tENErQfOR8nRlEmXNpy6ao87/VRnPa35/UM+4jW3fgn+99zlKQo8jCdiiEE9TPfvOvmU65sOtjNju73dbwdbc9AAhQmVI1r33sWw+7Fp8CjP2tdiPNbc7GmZvNcgiFeX8a8yDs3unxyhR+8shufOrNp6a2tlstn5OGhnCLiaKAxH1wRT2c3K8YgLNtcQ8i97uHqIJppizKVu5PjtpHaUQ4k2pHuykbYdoTp81u6yCfERyZmKzzAAPJOQGCHrh+8zXNtThT1rkfzbyHKHMuKdnkdX9R2uL1TLnv2X2+z62oeLW1GUZ7Ek7trdtGaoxgjU6XDNM3STuFo/RFmH4OkkVhHDVJrLmw68OrPSOj4zj3ph8H6l7tjNsadttbW5lWnkYwYOylDSMLoszNRpzjTr26r5SHCHDNLdvx6TsfrRujMHq43zxYef13Io/91m0jOHKsvqCXFztHx1ML+LRbICIJRHkYO7ORjRs3qvvuu6/Vzajh3Jt+7Cno+80FOTpWiS00nZPWjXxG0NOVs75nbGLSV5AN95dw9/UX+Lbf/p6g9rilvLgV63HrDwBNS6Fxti3qwyzsvSf5nXG+N8z7o1zTPpYC1ChucdrhNZcFwOeuXNd0Y8krZQmAVWDqmlu2e6aJPX3T6z2v7Rx3r7Xptd7i4CczSvksLt8wjG8/sMsyxgfKeXz0jScDaHwthjVO4ly71aR5D279FmXOBbUtrGPM6xpR2uL1TIkiO4Lwa6uWVU7c1lirZDrgrzsAwOevXNf06K5bX1y+YdjV+AujOwTpL0FyL6q+ERQcCLpO0Jg4iTOHW+UQ9HvOhcXv+Q3En7NR9VAvkpLRYeZBFDkRpMM7SVIfcJJUX7cCEblfKbWx7nUawq0lyiQPUkyAei+V10M9Ls7F6yUcvRT8KIsoqG9K+Sy68hlXwyArgs9ccXriD4iwRqLXgypORPTGOx62jA6373S+Rxsm+rpRBVeY94e9Zpj5HbUdWY/z+/pLeRybnK77Lmd/JE3QQ89vnkZ9eAQZ3UlHy5xVo89fOw+3/PL5ukhAPivoKeYausekjJN2JWg+x63O7dVvUeecV+ZQWAeWn0wA3CvIurUlisIdd/z92rpzdNzz+wWIbMylRVA/RWlDEsZVVAeGl3MEMGS2dnifv3ZejePNeR2/dsY1tt0M+DDR3LSNllY6BKMa+U6GQ+ikcR2nXtdz6qFB87yR9Wy/dl8pjyMTk1bKuBdhrtsM50oUmqWDpAENYbSnIQwEL2Y7/aU8bry0vpBAPiM1ezWAYO9bVNyMy6iCI8hwtgsTrwPLwxI1ohGGoPtN8kEVxht+3cVrcN1tD7gaJp9+y+m+RRa8nBVh3h8mCuqnKDbSDsC4P+dc91L89XcoR9uSIozy42ak+0VLvAj7UNRzDoi/z6rPloGhf/bLFPHCa3yjRLv9jJOwY9poVkXcLBD9uaiSLKwDJ6wBAiQbhXDKeD+58bkr17lez+0eoyh+fhkVftkFXsezaEM3jMx6x+ZlrqnJmmd8Mj3CEFdptxNmbST1zIrqwAC8j9exE0aWecktP4eGnjdRZWpYORPFeAkrw5J2vHj1m1dU3M954UeSUX57e8LIKLvxHeTQi6or+bUlnxHks4KxyrTvfTida06irqug7QhpObyctGPGFg1htK8hrAk74QfK+dAKqVf0LCp+aa9+Cg6A0Mqu35lpcTEMkCmMO4RRHCME8B+jd25ehq/94nnPaOX2j74GQHhhFFbR8XqPdlx4PZT7S3l0F3N1WQVuRdL0dwVFhDVhvP5u17Xj9x3OdP7z187D/733uYBvqm2bX5QeiGdA+nmmnWnb56+d51rZ0U/p27ptxNXx4UWjqehJECfbw44A6A+Qec754FSSvbIqtAzwOy/Zra3aEPrEllM925REf4Zx5kV5boTNjIjjwPL7jM4mCBPZ8+pvt3uMsgVHAJyzehC/fu5AoHMxzLj5zUu93uM4P8Mo7V5z2o0wW1bccMvu8TOaohp//aV8qPY72+JcByuGSvjPJ/dFNtDi6EZRjLSoRmMYwyGuoebVxqCMO6fsiRMR9kpF93NGhXFuBQVKwqx3oLGtfpoozkgvvMY/rOFpD8Z4ObOdUeq4qflhn2vtlrFFQxjtYwh7KTSNpp6khVZivIxGr4eyW5TaK3Ltl/7oRxRBE/RZP2Va08gYff7KdQDq91EC7kpqUnty3Erphx0brzaG9cYG7TdvxDDzi8KHYaCcx+tPW1TXN1Hbaccv/XVaqdDr3U8JXv/x78eKzGrcHphhagME4RX1buThrq975NhkaOMfqPZff0CKmpf8sMsCL4XLzdhJMqNFo+WvM/qo17aX883tOm7j7ibnosgevz3idoKyNoYdDp+wTqO4TkS39kVV/PtLeRwYd4882qNRbg4tN+dbGJnqNheCcMqfMN9lN0ic1amdeD1nksKeMZaGsy5qO4KIqyfYHeZRrxs12ymsoz3OfNP4bVFLor5MEGH1w2GfdZGkMzJMO+I4jfWz6L5n98UaqzjbccLIxzgOmjSJZQiLyKDfRZVS+xps1GsB/DWALIAvKqVucvxdzL9fAmAMwHuUUr8O81k32sEQ9pvU5XwGlWkVuK+gmXiltTmVkCjKuVskMklhkhRensxGHsRBXmi7sdmowaNxU8gaMXqc0X4/YZjPiKcB4xYlcjoEwniOm+1A8ntohH1oNZJe1ug68UtVjYtOxQfCRdLD3oPf3uN2IErKnx0BEjGUo8ylKCnKUdeUc/zjrke/6KVXBDQpJ6J2jNkdvmEUPb/vcW5P8nNMTimFML4er++MEuHMCAK/SxvPfSEjt87nTJLPcr3OWh0sCBvhakRG64JRXs4Sv4wgZ6ZbXymPickpKz3XvlbCtjHu8yqoKFnUCGXaYx8mGytqDYuoeBX81G1yyxTR2Ukblw/GTl0HwgWCnCRZp6AZxDWEn4bR5wJgGYD95s/9AJ5TSq1soEFZAI8BuAjACwB+BeAqpdRvbO+5BMCHYBjCmwD8tVJqU5jPutEOhnDQYs6I8b/fQypOhMQPP0Hnt5enlM9gsLsYK4obpZCWppzPAEDgnosk8Uof9jLOkqCRKLfX9Zz93agxZReaEMBNjHgpZiLA565YB8D/od4fsC9VC9kV138n9n3ExX7/zjRBrxTQRknK8I+yPy8MTgPCmY7lpohFqYngFXFrF3Smh9eWAieNeu8bwc9Ysu+jd4vABmFXepJwavpFaZzYayIA6SvNmqwI5pSiOWoa2a4UNH5hzxpNk3I+g2I+m5jzym4cxZ1XQXUGorYjiEbmn8DQrZx6jv5+vzN6w6DXStKFVO24OYvdsgqiGF/NCJT4peB7BQ/6S3kAqHve+23n8LtWUFZVszJKGylGp0m7UGlUvAzhjN+HlFIrlVKrANwJ4I1KqblKqSEAbwBwe4NtOgvAE0qpp5RSEwC+DuAyx3suA/BVZXAvgH4RWRTys21J0Ll30wqY05VHKZ91/Xspn8WNl56Mnq7kjoD2S3e67uI1nm0er0zHWnheZ1Ned/Eaz/sGAAVB0efvaTA6XsGI+QC1n5c2UM6n9p1JC3u3/m707GJ9VqCCuxGcz3orbEoZZ/R9+Jbtvs6c0fGKb/ErfSZfK7Dfv26nniO3/PJ5HJkIf+ZfWPrMB26Us0adlPJZnL92XmIPTAHw+tMW4Z/vfc5aJ/b+GB2v1Ch0+8cquO5fH8D5a+eFuv5oikawJHSd6257ANf9azgjGDDW9413PIxv3D/SdAPfr436LyOj4/jG/SO4fMOwpeSFQZ+VunXbiKd8KeUzvjLeeb0bbt+Bj33r4UCDvDKl8Ee3PoCPbN2BdR/7ftOMwSllyIFsJvxsaiQLwO+zWikO279eZKWxlTFWmU40g8OujMd5bmk9Jupn8xnBQDkPgWG8RNlL2cg4KLg7+8crU/jwLdsb7tvKlLLOx21krnjNE21MOiPBbk6/yrRCuZDD0ze9vu4zTrzGLysC8WlPFLSuq6O/dt3Pq99Hxyt1RvBAOY93bF5W17/6+Xv4aL1+kM8KROB5xrJmy/ph3H39BXV9FuZM7Sg4v9eN89fO832O7h+r4Ibbd2DrtpFE25Y0ofYIm1b0Bsdr97lZ1qG/WOQtAF6rlPo98/d3AdiklPqg7T3fBnCTUurn5u8/AvAnAFYEfdaN3t5etWHDBr+3pM7z69+LqWKf/5uUwtwnvo39y16JqcIc6KB8duIgBp67Cz17H8Ezm/7YCK1FIHvsAAaeu6v2uuLiC1Gq5rtCtTksSqHnxV9j7rM/BgAcHlprtUcmx6EkC2QLxnvd7k+pyPedOHrNtLodbjj7Z3oSmakJTOdKNWN6eGgt9q56LVQ2JYNeKXjOr4avi/bs+0YIM6+nJzH3ye+hZ+8jeG7DBzCdL0e6fmZyHOU9v8WR+ad5jrtUxpCZrljrUQBM50rwGkupjEHlStHHQ00DkNaMY8i+cPtcYu1tBzkWQPbYASzddnONjA61pqcngekpQ44771Ep4+8Z05Ebpg+i9FXa/ep1/bTkXVhMnUHL9up4IXR/yFQFQ0/9O/Yc94bk+zBoXDz+ruegxvW55fysUpCpCahsId4zT6ka2Wf/2X49N+r0mVxX6+aEH0phxS/+Bw4PrcWe414fvY1KAVMTdWtczyFn//jqkGZbgnAbP/v3JaHT6PnWqM7rJjuzEwdR2vcEDi9cH/1ZGqKPEtXTPb63bn5nC1VZbr4/zDpuFT/72c9cI8Jhw4p7ROQjAP4vDKvsnQD2NtgmN6notMq93hPms8YFRN4L4L0AUCwWo7QvFQaeuytwsWYnDqJn7yOewla/x3fSOyakTFUsAa6v+8ymP/b8+FRhDvYve6XV5sQejiI4Mv80dB3eCQA1faHCKPZBD1O/90VVkrzeH1J50w9jb8Up5LXCtMn8W8+Lv8b44HE2pTVrGUxTxT7sXfVaAEDP3kdwtGcxDi88w/d6UNNAJobXWARWznSSSlXca80AA1qmKv4P8UwO+5e9Ej17H8HgMz/yliNufS4Cma5gbO6J3kbwVAVDz/zIVe54KSECQMXp07jKYQLzSSbHsez+/4XDQ2txZO6J6Rllvo1o33mo0YaUfmYcHlqLvSsuDHZ8ZHK1ypEdEcDPiPH6TFha1a8iyafyhMWU+3rd6v9DGwUOx/f+Za9MXqGGi3wzZbI2EJxOKa2z2NH35jQu9DPPafjuX/ZK7DnuDdi/7JUYeO4uDD3173UBBqeeVDj4PI71r7TkmsqXraF1PkM1bmtD5cvu6VL63l0MyWaRnTho9U/k57TWC3LFmte0c1H3ea0j1b8tYXAbe7tTIpRO44N9vllOJCcuThe373LKTqD6DPV69qmAfjo8tNbXJnC1LRrUe+xj49QBXPV1j+/x7M82IWxEeBDARwG80nzpLgAfa6RYloicDeBGpdTF5u83AIBS6lO29/w9gJ8qpb5m/v4ogFfBiAj7ftaNdtgjDPgX/nDudfK7hte+qbDHgoQ9IkPBfM4n+KDXe2+TSmHrLmTxyTf5Fy1I6igpP/Q+rTCFLaL2qS6K4LWvUFecDFttOcr4z3T0vsxrbt0eqc+zGcFUQnvx/QhbaMa+39teuVHP7UbG6/MBx724VfINe2RVGMK2vZHz0fWetP1jlbac282QUWGJWwhsNtNO4wN4V+WNsl/QWU3crSCPfd3EQcs3u97jVhix0TNOwx4/5Xy/XaaF2bff6Nqw93nY2gJJkc8KrjxzaV0NgDDy0G9PvNv+1iD0czmpPaRx98lq3eoTW04NffSV3h7ndRyos0hUErU9ggpPua2huAW0nOulkfa3S9Esrz3CoSLCpsH7hwm36VcAjheRlQBGALwNwNsd77kDwAdF5OswimUdUErtEpHdIT7btmxZP+x6FqAuMnHNLdutPRx+Dwl7tTo3Y9cpoO17XLesHw51XqJeQJGMhxDKgtu+ikbQtorfPomrNtUL/yRxLvZzb/qx7x7YOEawPrfU7SiVGy+tVnL2u0ctzMLsKbF/h35QBh1J40Y+Ky2thr64v2StJb+q7cV8tqZwBxC9Cq7XMVQD5TxOWtTreublmLmnOOg8w4wIVl7/Hat9zvnm106/dTls6x8vtNwCqspfUjgNfK+Ht5tCHeU7YCu+1qzZ6Fc53Y7fObG6ym8zDbFdB8ax4vrvWMVd0jSCozg3+h0F2MJ+ptHnjXYw+zkioxoCjTKtlKthF1Ze6f2zTl1Bp91pea+L38WR/fo7AODYZG29ALs+YpcvcXC7BzvjlSnceMfDNd/h/M5zb/pxKLmg61Ms7i9hdGwi0pjb+yPomZQ0AuO59M+/eK5O/1DwL6QoAD5zxem4xqNQaBx5/I7NyxItpBR3n6wC8JNHdgMwnvdh5kC5kHPVoe3jm0TbNGHWtNsailMUza3IVdz2Cxqra9IMfA1hEfm8UurDIvItuOgNSqlL436xUmpSRD4IoxBXFsCXlVIPi8j7zb//HYDvwqgY/QSM45P+i99n47alVbgpllpJsxutAFwN2k+9+VRfL4ubQaQ3wNu/O6njDsJGJDVhlLqwip++L6+qmf2lPD6x5VRsXD6Y+DmfgLvwCxIcYe/NGcl33ofTex70vQJYBW2iCEj9oHQaImGqhOYygvm9Xa6e+jCEOe5Dt8V5BJmb4hEl8hD23GRnNoDXd7hlhOwfq1gFxPyiu3q+OJ1agP+4C/wL7ehCR2GjMEHOlqjYC6F4KSL6gWp3JLodM+FV0V3B+4zspBFz8HS7gpSRrEiN993LKIgafRIA56wexMM7D0VWVPV6S7vwVJSzSuNE4vJZwY2XnuxZbdevMrnb+d+AtyMySiXwJDIS7OsmTH/YT3rIiljPTTdHhzLfbzfuR8crVhEpryr5bg5FXdXWTx+JQ9Rzu0fHK9i6bcTz+6Io+wrh1obXcZEa5zPJ6xQGN7RzxnkEIeAeqQwKbOwcHXc9Ys1utDZabdr+nEy6mnAjVdP12IedAyOj4/j0nY/WHFfld1+NVnTXelvUPgsT8HJy+OgkPvath3HNLdute4rT/jScHWkQdHzSBqXU/SJyntvflVI/S61lKdAuqdFu+B0uDrgL3KB0A69y8/boS5g2hMGrxHvQ9fwiAfZz3cIsZJ1mE3TmsSaJ81jtxmGU9DSv8ySDrhmGpM4f9sLtbEw9Tn7HStnPR4x6/JRXlFUbyM5MiEZT7NzwS2OLmvoTdq0FbU2wf29SxyeEOfLAb+3o44TsY+DXLuf69Lv2My5yy0mrzxp1nuWpjQWvbJQox7IAwWdxeskQL7mgjb0kHYNhiXIOp1s/fWTrDt/0fPtc9jsHNOiscidJHGHyzs3LQmUoeUXA46QueqXFRiVOqmNUfcSOl9MrbiRVO1+cBkzSRwpFXdtAeAePn47QyDFTYVLY3dZRVz4TqHcklSLrtf6C+s7P+RT3rOqwY9xIvznbGJWgZ0YQfuef24+/cltT7WQEx0qNVkrdb/5vGbwiMgBgqVLqwcRb2cF4eaH8vFNef9OT3mua+x1fFOfB4oxm2BfdsJk6dGSi/prOfZHO81idiyjI82tPfw1jCDXioXOmKrvh1Z/2B4tfZDcOW7eNuJbmD4M9Bc5PKfby6t99/QW+ZxzaMxGiKhyVaRXoWdc0mmLnhVcam1cqlB9hvc46NdErmme/jtt8ixN1cqYsuuG1duwp1s6UQ6/xvnxD7Xj5rcswUWuvdLUghcPL2RIFfaayXWGwH0cUVLchDM75Hdbx89E3nuzrJIx6FJlbf3k5AdzOmLa31SvdEvCPIOl0RrfPOPce+j0bvOSR17PSS8aEXdfD/aWazB6v+S4Atn/0NQCCxznMd1emFL72i+cbdnjESZP0WtcK9evaT3HX2TBd+UxsY35kdLzGgaKv6absA0YNkrGJqUiyNO4ZqvZ56jcv/AyiuLrN+Wvn1cmIow4HjNc6AvwdE37PySjO66Atf/a2uckbr3aOTUxi67aRyHpw2KwGv34Lu483bnqy2zMjSjBivDJlyY1Gnl3tSthiWT8FcCkMw3k7gN0AfqaUujbNxiVNJ0SEgzxiQd6roIIZboqP0wh2fr/fnsk4Dwo/z34anlcv4hYvSFNwxI2GOe8lTgrm0ze93lfAOveBus0Tv4IsYSIHzSCJMU0qauk2bmEjsVGvbSfqGgza9xtl7oVZ614RJK/idVoWAe6Kiv21sYlJzxTb7R99ja8sb3XBEL+5GzQn3RxRQLRtBl5t8XK8OZ2lzu9oNHvA3pYknilh1rXbdZOYM83MhIgzl8Oua6B5e2adeM03INpeSy0LGiXqvPDT34LwK2AadrydDgy/wIb9M1HWXhJrxatgrX0OOouoaSemG43qJh/ZuiPy1hB9H1H7W5NkBuhMoaFiWQD6lFIHReT3APyjUuqjIsKIcIIEbboPG4Xy27sXxoPj3LccpBS6RW2d36+jeQDq9kUGRZ682hjUjjjXieojD+OdSys66UVcj6Hzc86+CUqb1JGTLeuHPaPC9uiK3xh6CWiv6Eyzce5RtRe3A8LNy7jZF27XcWubppGHnd9ciroGt6wf9nSQ+M09t7aH8cB7rTuvuVku5Fwj2c42Ad6Kmy5WFye7JypxnTF+8shvTur7C+qbKO23f5ebbCnls3URKmf0xy8zIQpxnyluVYeD0gfdrhv0/A8z3mFlildtirCnGMTJgAHCr2v9c1x0qn+cFNCdo+Ou0bOoslrLgkaJUowpqOCZX1/o9e2VmRFWdsXRd4Jq2YRtSxT5qrNAnIawPbstihGudZOoctnLkHWbu85xd463/V7couROGtFBGt3b326ENYRzIrIIwBUA/nuK7elYwjyIwywwL2EQlErj1aaoio/X9x8YN5QAL8ETx4hNYhHar+Ml5LwUh3YxzOx4KYZBhbnc7sXeN35pk04B7ZWCGWS0aaI8/NPE76Hmlp513W0P1GQ++D2MnEqhl/feL6W3v5QPXAONPOyC5nfUNTjsMTf95p5XxC+uUTnqkW0QVYkCvOWx1xpMSl6ESQ2Mg3NOppkC5+WwdRanClKSk5QVUeez2zjYU+C95IZ2ntn/5jenwo63W1qomzLtlv7r9boT5zaoqCS5rt1kZtxaJRq3NRq1MGAYueyG3/MmjO7n1k5dK8B+PJZf9DDqFoEkiGrYJiVf4xjUfvImqlx2M2RL+ay1pSPIqA6al0G6tZe8D1sZPmqBzXYmrCH8cRgVmu9WSv1KRFYBeDy9ZnUmfg/isA/pKEIijbRdv+9vRqSkEbyEnJfi0I4l4b3uQaf7uKWG5rMSeC9+BrZTMWo0Yp9UxL8Rgh5qXpkPTvweRm7ZF24GCOCeERIm6qCv71Xkyy1TQ18/6fkdx2hJ2qhM6npRI6tJ9mfUCEoU4joYoz5LvGT+tFI1KYZBEapWygqvcfjJI7s901b9ZIpX30cZb7doplvfeNWmsO9ZDnMOb1yC1mEYw9Upzdy2Wtmzd4Irajd+7E1YuewkzNwIIki/CnOdVjiho8rkpNoY51kQlMkWRS4Hreug8QozL4Pe4/UdYauxJ+WEbTVhzxG+DcBttt+fAnB5Wo0i8QkrJNKKKvh9fyu8jVHwE3JJF7VKizCKoV9FSC/8DOwgIy/ufbSyf4MeUlGUo6RS6Bt1LHgZ02G8z0kQx2hJWjFrhqKXtnHWbg7FOM+SsEpomPe1SlZEHYe4DoxGxtsvqyuscy6NORy0Dv2KB3llN9m3NzhxW5NhK9z6pd/77V+PQhLOrSScfK1wLEWVyUm1Me6zwGvtRF2njcrxMDVA4urWTjng50SaDWnSoQxhETkBwN8CWKCUOkVETgNwqVLqE6m2joTGmfbSlc/47klKK6oQJKTaIeXVj6iKA9D8glhBJJFZ4PY5oLVR2mYS9JCKUogqCUdPEo4FwHv8nH/Xe/XSMIajXDPpedeseZymcZZ26nVU4jxLwiqh7bJNwo2o4xBX8W3VeIeZw43sVQe816FfwcVpj+hU3OhXEH5zMKl1noRzK6m10mzHUhyZ7OawcW43SON7/fBap31m1lXY94dd10HbnhqVk242hVch03bJ6oxL2NTofwBwHYC/BwCl1IMi8i8AaAi3AUF7DdxIM6rgZ0wC7lVZz73pxzPSwEorst6OtDpK20yCHlJuDyGvqurtoLQDwc6cdp3HSc+7mT6P2804jPMsCauEtrMDLuo4xFV82228NY3KDL91GFRPoJmOgWbMwZkazU2KuDI5zTlo/46wBTDdtp4dMY9lcn6m0XXtHO+oVaP98LIpBsr5wEKoM5Gwxyf9Sil1pohsU0qtN1/brpRal3YDk6Sdj09qhDjl5NvliI+kjq1oFe3SjyRZwszLOFXV2xXO45lFO2WhdPLcaeT8UyD8s66dxluT5rj79RXgnlk2U3QGN2a6HtQq0pY9Ucdl/ce/71nY0nn0XLO2JMXBq1/7S3kcm5yesfO00eOT9ojIapj1CUTkLQB2Jdg+0gBJV79rJmkWfmkG7bZfjyRDGO96UObDTILzeGbRTlHtdnmWtIIo49BIxK6dxluTdlYZ0PgpGjOFmRzNTRs/YzHt51ZU/dTrVILR8YpVlyVOIbRmsnXbiOe2rwPjFXzuynWzbp6GNYQ/AOBmAGtFZATA0wDekVqrSCSSrn7XTGa6At5u+/VIcrTjQyotOI9JXNrlWTITmE0yJW2ZkUati3ZmNt5TowSlPqc9B5M62slJuwZ7dH97sbi/NCvnaSbMm5RSTymlXg1gHoC1AF4F4BUptotE4LqL16CUz9a8Frb63d3XX4Cnb3q95wHiaeMlsGaKAh637wlpJziPSSO0w7OENBfKDJI2fhFZIP05GFU/dWuPF+0Y7PE7m3g2r21fQ1hE5ojIDSLyNyJyEYAxAFcDeALAFc1oIAlmy/phfOrNp2K4vwSBsT9ipuTsz/SH6Uzue0I0nMeEkChQZpC0CXM2cppzMKp+6taegbJ31eh2w884n81r27dYloh8E8B+APcAuBDAAIACgD9USm1vRgOTZLYWy5rptGvBAEIIIYQQ0nzaoRBfo/rpTCqE1g79nSZexbKCDOEdSqlTzZ+zAPYAWKaUOpRaS1OEhjAhhBBCCCHtzUwyIv2YKcGe2dLfXsStGm2VQFNKTYnI0zPVCCaEEEIIIYS0P7OlEN9MKTA1W/o7KkER4SkAR/SvAEow9gkLAKWUmhPrS0UGAdwCYAWAZwBcoZTa73jPUgBfBbAQwDSAm5VSf23+7UYA/w+A3ebb/1Qp9d2g72VEmBBCCCGEEEI6B6+IsG+xLKVUVik1x/zXq5TK2X6OZQSbXA/gR0qp4wH8yPzdySSAP1JKnQhgM4APiMhJtr9/Tim1zvwXaAQTQgghhBBCCCFAyOOTUuAyAF8xf/4KgC3ONyildimlfm3+fAjAbwHM7vg8IYQQQgghhJDUCdojnBYLlFK7AMPgFZH5fm8WkRUA1gP4he3lD4rIuwHcByNyvN/ts6R9mSkFBAghhBBCSDpQHyStIrWIsIj8UEQecvl3WcTr9AD4BoAPK6UOmi//LYDVANYB2AXgMz6ff6+I3Cci9+3evdvrbaTJ6Op0I6PjUABGRsdxw+07sHXbSKubRgghhBBCmgD1QdJKUjOElVKvVkqd4vLvmwBeEpFFAGD+/7LbNUQkD8MI/mel1O22a7+klJpSSk0D+AcAZ/m042al1Eal1MZ58+YleYukAT5956M1JdoBYLwyhU/f+WiLWkQIIYQQQpoJ9UHSSlqVGn0HgKsB3GT+/03nG0REAHwJwG+VUp91/G2RTq0G8CYAD6XbXJI0O10O7fZ7nRBCCCGEzHzsqdBeZ9dQHyTNoFXFsm4CcJGIPA7gIvN3iMhiEdEVoM8F8C4AF4jIdvPfJebf/kpEdojIgwDOB3BNk9tPGmRxfynS64QQQgghZGbjTIX2gvogaQYtiQgrpfYCuNDl9Z0ALjF//jmM84rdPv+uVBtIUue6i9fghtt31KTDlPJZXHfxmha2ihBCCCGEpIVbKrQT6oOkWbQqNZp0OLoaIKsEEkIIIYR0Bn4pzwJQHyRNhYYwaRlb1g9T0BFCCCGEdAiL+0sYcTGGh/tLuPv6C1rQItLJtGqPMCGEEEIIIaSDuO7iNSjlszWvMRWatApGhAkhhBBCCCGpw61xpJ2gIUwIIYQQQghpCtwaR9oFpkYTQgghhBBCCOkoaAgTQgghhBBCCOkomBpNCCGEEEIIaSlbt41w7zBpKjSECSGEEEIIIS1j67YR3HD7DoxXpgAAI6PjuOH2HQBAY5ikBlOjCSGEEEIIIS3j03c+ahnBmvHKFD5956MtahHpBGgIE0IIIYQQQlrGztHxSK8TkgQ0hAkhhBBCCCEtY3F/KdLrhCQBDWFCCCGEEEJIy7ju4jUo5bM1r5XyWVx38ZoWtYh0AqKUanUbmoaI7AbwbAOXmAtgT0LNIcnAMWlPOC7tCcelPeG4tB8ck/aE49KeJDIumdKcwWzP4LBkcwU1NTkxdXjfyPT4wX0JtK8T4VqpZblSap7zxY4yhBtFRO5TSm1sdTtIFY5Je8JxaU84Lu0Jx6X94Ji0JxyX9oTj0n5wTMLB1GhCCCGEEEIIIR0FDWFCCCGEEEIIIR0FDeFo3NzqBpA6OCbtCcelPeG4tCccl/aDY9KecFzaE45L+8ExCQH3CBNCCCGEEEII6SgYESaEEEIIIYQQ0lF0tCEsIktF5Cci8lsReVhE/tB8fVBEfiAij5v/D5ivD5nvPywif+O41r+LyAPmdf5ORLJu30n8SXJMbNe8Q0QeauZ9zDYSXis/FZFHRWS7+W9+K+5pNpDwuBRE5GYReUxEHhGRy1txT7OBpMZFRHpt62S7iOwRkc+36LZmNAmvlatEZIeIPGg+++e24p5mAwmPy5XmmDwsIn/VivuZLcQYl4tE5H5zXdwvIhfYrrXBfP0JEfmCiEir7msmk/CYfFJEnheRw626n3aho1OjRWQRgEVKqV+LSC+A+wFsAfAeAPuUUjeJyPUABpRSfyIi3QDWAzgFwClKqQ/arjVHKXXQXOD/CuA2pdTXm3xLM54kx8S83psBvAXAaUqpU5p4K7OKhNfKTwH8sVLqvibfxqwj4XH5GICsUuojIpIBMKiU4hmEMUhajtmuez+Aa5RSdzXjPmYTSY2JiOQA7ARwklJqj2lwjSmlbmz6Tc0CEhyXIQDbAGxQSu0Wka8A+KpS6kfNv6uZT4xxWQ/gJaXUThE5BcCdSqlh81q/BPCHAO4F8F0AX1BKfa/5dzWzSXhMNgN4FsDjSqmeVtxPu9DREWGl1C6l1K/Nnw8B+C2AYQCXAfiK+bavwJhoUEodUUr9HMBRl2sdNH/MASgA6FwPQwMkOSYi0gPgWgCfSL/ls5skx4UkR8Lj8rsAPmW+b5pGcHzSWC8icjyA+QD+I72Wz14SHBMx/3Wbju85MAxjEoMEx2UVgMeUUrvN338IgFktMYkxLtuUUnodPAygS0SKpvE2Ryl1jzIib1/VnyHRSGpMzL/dq5Ta1cTmty0dbQjbEZEVMLyMvwCwQE8Q8/9QqZsicieAlwEcghEVJg2QwJj8BYDPABhLq42dSBJrBcA/ipHq+WdMk0qGRsZFRPrNH/9CRH4tIreJyIIUm9sxJLReAOAqALeoTk7jSohGxkQpVQHw+wB2wIwMA/hSmu3tFBpcK08AWCsiK8yo/RYAS9NrbecQY1wuB7BNKXUMhqH2gu1vL5ivkQZocEyIDRrCsCKH3wDwYVtkNzJKqYsBLAJQBHBBwNuJD42OiYisA3CcUurfkm5bJ5PQWnmHUupUAL9j/ntXUu3rVBIYlxyAJQDuVkqdAeAeAP8jwSZ2JEk9W0zeBuBrjbeqs0ng2ZKHYQivB7AYwIMAbki0kR1Io+OilNoPY1xugZE18QyAySTb2IlEHRcRORnAXwJ4n37J5W105jVAAmNCbHS8IWw+1L4B4J+VUrebL79kpnPonPyXw15PKXUUwB0wUhVIDBIak7MBbBCRZwD8HMAJ5t5UEpOk1opSasT8/xCAfwFwVjot7gwSGpe9MDIntOPoNgBnpNDcjiHJZ4uInA4gp5S6P5XGdggJjck6AFBKPWlG528FcE46Le4MEny2fEsptUkpdTaARwE8nlabO4Go4yIiS2A8Q96tlHrSfPkFGE5WzRJwK0FsEhoTYqOjDWEzJfNLAH6rlPqs7U93ALja/PlqAN8MuE6PbRLmAFwC4JHkWzz7SWpMlFJ/q5RarJRaAeAVMPYOvSr5FncGCa6VnJgVVk2B/gYArOgdkwTXiwLwLQCvMl+6EMBvEm1sB5HUuNi4CowGN0SCYzIC4CQRmWf+fhGMvXokBkmuFTFPIBCjau4fAPhisq3tHKKOi7m95jsAblBK3a3fbKbqHhKRzeY1343wco/YSGpMSC2dXjX6FTBSaHYAmDZf/lMYOfe3AlgG4DkAb1VK7TM/8wyM4hgFAKMAXgMjmvJtGCnRWQA/hlHZk2k5EUlqTJRSv7FdcwWAbytWjY5NgmvlWQB3AcjDWCs/BHCtUmqqSbcyq0hyvYjIcgD/BKAfwG4A/0Up9Vyz7mU2kbQcE5GnAFyilKKDNSYJr5X3w6iCW4Eh096jlNrbtJuZRSQ8Ll8DcLp5jY8rntwRm6jjIiIfgbFFwB6Ff41S6mUR2Qjg/wAoAfgegA+x1kF0Eh6TvwLwdhjbO3YC+KLq0Mr3HW0IE0IIIYQQQgjpPDo6NZoQQgghhBBCSOdBQ5gQQgghhBBCSEdBQ5gQQgghhBBCSEdBQ5gQQgghhBBCSEdBQ5gQQgghhBBCSEdBQ5gQQgghhBBCSEdBQ5gQQgghhBBCSEdBQ5gQQgghhBBCSEfx/wM2zQtAL8A56wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "res = sm.tsa.seasonal_decompose(Y,  period=52)\n",
    "fig = res.plot()\n",
    "fig.set_figheight(8)\n",
    "fig.set_figwidth(15)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3f21df99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TSLA_AdjClose_pred</th>\n",
       "      <th>TSLA_Volume</th>\n",
       "      <th>AAPL_AdjClose</th>\n",
       "      <th>TSLA_14-high</th>\n",
       "      <th>TSLA_14-low</th>\n",
       "      <th>S&amp;P</th>\n",
       "      <th>DFF</th>\n",
       "      <th>DGS3MO</th>\n",
       "      <th>DGS6MO</th>\n",
       "      <th>DGS10</th>\n",
       "      <th>...</th>\n",
       "      <th>TSLA_P/E</th>\n",
       "      <th>sentiment_vader</th>\n",
       "      <th>total_msgs</th>\n",
       "      <th>BTC_AdjClose</th>\n",
       "      <th>TSLA_%K</th>\n",
       "      <th>TSLA_%D</th>\n",
       "      <th>TSLA_%R</th>\n",
       "      <th>TSLA_RSI</th>\n",
       "      <th>TSLA_14-high</th>\n",
       "      <th>TSLA_14-low</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dt</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-11-05</th>\n",
       "      <td>-0.038180</td>\n",
       "      <td>15.277608</td>\n",
       "      <td>2.824562</td>\n",
       "      <td>1.857859</td>\n",
       "      <td>1.687139</td>\n",
       "      <td>7.229730</td>\n",
       "      <td>-1.832581</td>\n",
       "      <td>-2.407946</td>\n",
       "      <td>-1.897120</td>\n",
       "      <td>0.476234</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.017094</td>\n",
       "      <td>-0.121190</td>\n",
       "      <td>-4.077550</td>\n",
       "      <td>-0.429329</td>\n",
       "      <td>1.843086</td>\n",
       "      <td>1.681200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-10</th>\n",
       "      <td>0.016356</td>\n",
       "      <td>15.408819</td>\n",
       "      <td>2.784583</td>\n",
       "      <td>1.860041</td>\n",
       "      <td>1.687139</td>\n",
       "      <td>7.210324</td>\n",
       "      <td>-1.832581</td>\n",
       "      <td>-2.525729</td>\n",
       "      <td>-1.966113</td>\n",
       "      <td>0.457425</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.421497</td>\n",
       "      <td>-0.220000</td>\n",
       "      <td>-1.067299</td>\n",
       "      <td>-0.580960</td>\n",
       "      <td>1.857859</td>\n",
       "      <td>1.687139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-15</th>\n",
       "      <td>0.068344</td>\n",
       "      <td>15.344281</td>\n",
       "      <td>2.849564</td>\n",
       "      <td>1.894617</td>\n",
       "      <td>1.729884</td>\n",
       "      <td>7.235482</td>\n",
       "      <td>-1.832581</td>\n",
       "      <td>-2.525729</td>\n",
       "      <td>-1.966113</td>\n",
       "      <td>0.506818</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.294239</td>\n",
       "      <td>-0.177324</td>\n",
       "      <td>-1.366877</td>\n",
       "      <td>-0.573397</td>\n",
       "      <td>1.860041</td>\n",
       "      <td>1.687139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-20</th>\n",
       "      <td>-0.026717</td>\n",
       "      <td>14.581676</td>\n",
       "      <td>2.868268</td>\n",
       "      <td>1.901212</td>\n",
       "      <td>1.769173</td>\n",
       "      <td>7.250742</td>\n",
       "      <td>-1.832581</td>\n",
       "      <td>-2.302585</td>\n",
       "      <td>-1.966113</td>\n",
       "      <td>0.530628</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.050773</td>\n",
       "      <td>-0.054995</td>\n",
       "      <td>-3.005679</td>\n",
       "      <td>-0.416595</td>\n",
       "      <td>1.894617</td>\n",
       "      <td>1.729884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-25</th>\n",
       "      <td>0.051262</td>\n",
       "      <td>15.775817</td>\n",
       "      <td>2.892094</td>\n",
       "      <td>1.925416</td>\n",
       "      <td>1.797079</td>\n",
       "      <td>7.255718</td>\n",
       "      <td>-1.832581</td>\n",
       "      <td>-2.525729</td>\n",
       "      <td>-2.040221</td>\n",
       "      <td>0.482426</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.391076</td>\n",
       "      <td>-0.213370</td>\n",
       "      <td>-1.128027</td>\n",
       "      <td>-0.518998</td>\n",
       "      <td>1.901212</td>\n",
       "      <td>1.769173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <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",
       "      <th>2021-08-10</th>\n",
       "      <td>0.010062</td>\n",
       "      <td>16.632804</td>\n",
       "      <td>5.000417</td>\n",
       "      <td>6.592908</td>\n",
       "      <td>6.441329</td>\n",
       "      <td>8.404696</td>\n",
       "      <td>-2.302585</td>\n",
       "      <td>-2.813411</td>\n",
       "      <td>-2.995732</td>\n",
       "      <td>0.254642</td>\n",
       "      <td>...</td>\n",
       "      <td>5.923457</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.758902</td>\n",
       "      <td>-0.186342</td>\n",
       "      <td>-0.215442</td>\n",
       "      <td>-1.771897</td>\n",
       "      <td>-0.469982</td>\n",
       "      <td>6.588844</td>\n",
       "      <td>6.441329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-15</th>\n",
       "      <td>-0.052838</td>\n",
       "      <td>16.512965</td>\n",
       "      <td>4.994295</td>\n",
       "      <td>6.592908</td>\n",
       "      <td>6.475186</td>\n",
       "      <td>8.398786</td>\n",
       "      <td>-2.407946</td>\n",
       "      <td>-2.995732</td>\n",
       "      <td>-2.995732</td>\n",
       "      <td>0.231112</td>\n",
       "      <td>...</td>\n",
       "      <td>5.870619</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.806474</td>\n",
       "      <td>-0.132391</td>\n",
       "      <td>-0.128901</td>\n",
       "      <td>-2.087458</td>\n",
       "      <td>-0.461912</td>\n",
       "      <td>6.592908</td>\n",
       "      <td>6.441329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-20</th>\n",
       "      <td>0.044479</td>\n",
       "      <td>16.352820</td>\n",
       "      <td>4.995442</td>\n",
       "      <td>6.592908</td>\n",
       "      <td>6.475186</td>\n",
       "      <td>8.410986</td>\n",
       "      <td>-2.407946</td>\n",
       "      <td>-2.813411</td>\n",
       "      <td>-2.813411</td>\n",
       "      <td>0.300105</td>\n",
       "      <td>...</td>\n",
       "      <td>5.915097</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.798775</td>\n",
       "      <td>-0.947746</td>\n",
       "      <td>-0.927374</td>\n",
       "      <td>-0.490392</td>\n",
       "      <td>-0.709786</td>\n",
       "      <td>6.592908</td>\n",
       "      <td>6.475186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-25</th>\n",
       "      <td>0.027337</td>\n",
       "      <td>16.738898</td>\n",
       "      <td>5.027022</td>\n",
       "      <td>6.594413</td>\n",
       "      <td>6.475186</td>\n",
       "      <td>8.418210</td>\n",
       "      <td>-2.525729</td>\n",
       "      <td>-2.995732</td>\n",
       "      <td>-2.995732</td>\n",
       "      <td>0.254642</td>\n",
       "      <td>...</td>\n",
       "      <td>5.942434</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.759072</td>\n",
       "      <td>-0.262266</td>\n",
       "      <td>-0.303794</td>\n",
       "      <td>-1.466665</td>\n",
       "      <td>-0.553381</td>\n",
       "      <td>6.592908</td>\n",
       "      <td>6.475186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-30</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.001096</td>\n",
       "      <td>-0.213796</td>\n",
       "      <td>-6.816315</td>\n",
       "      <td>-0.481219</td>\n",
       "      <td>6.594413</td>\n",
       "      <td>6.475186</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>645 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            TSLA_AdjClose_pred  TSLA_Volume  AAPL_AdjClose  TSLA_14-high  \\\n",
       "dt                                                                         \n",
       "2012-11-05           -0.038180    15.277608       2.824562      1.857859   \n",
       "2012-11-10            0.016356    15.408819       2.784583      1.860041   \n",
       "2012-11-15            0.068344    15.344281       2.849564      1.894617   \n",
       "2012-11-20           -0.026717    14.581676       2.868268      1.901212   \n",
       "2012-11-25            0.051262    15.775817       2.892094      1.925416   \n",
       "...                        ...          ...            ...           ...   \n",
       "2021-08-10            0.010062    16.632804       5.000417      6.592908   \n",
       "2021-08-15           -0.052838    16.512965       4.994295      6.592908   \n",
       "2021-08-20            0.044479    16.352820       4.995442      6.592908   \n",
       "2021-08-25            0.027337    16.738898       5.027022      6.594413   \n",
       "2021-08-30            0.000000     0.000000       0.000000      0.000000   \n",
       "\n",
       "            TSLA_14-low       S&P       DFF    DGS3MO    DGS6MO     DGS10  \\\n",
       "dt                                                                          \n",
       "2012-11-05     1.687139  7.229730 -1.832581 -2.407946 -1.897120  0.476234   \n",
       "2012-11-10     1.687139  7.210324 -1.832581 -2.525729 -1.966113  0.457425   \n",
       "2012-11-15     1.729884  7.235482 -1.832581 -2.525729 -1.966113  0.506818   \n",
       "2012-11-20     1.769173  7.250742 -1.832581 -2.302585 -1.966113  0.530628   \n",
       "2012-11-25     1.797079  7.255718 -1.832581 -2.525729 -2.040221  0.482426   \n",
       "...                 ...       ...       ...       ...       ...       ...   \n",
       "2021-08-10     6.441329  8.404696 -2.302585 -2.813411 -2.995732  0.254642   \n",
       "2021-08-15     6.475186  8.398786 -2.407946 -2.995732 -2.995732  0.231112   \n",
       "2021-08-20     6.475186  8.410986 -2.407946 -2.813411 -2.813411  0.300105   \n",
       "2021-08-25     6.475186  8.418210 -2.525729 -2.995732 -2.995732  0.254642   \n",
       "2021-08-30     0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "\n",
       "            ...  TSLA_P/E  sentiment_vader  total_msgs  BTC_AdjClose  \\\n",
       "dt          ...                                                        \n",
       "2012-11-05  ...  0.000000              0.0         0.0      0.000000   \n",
       "2012-11-10  ...  0.000000              0.0         0.0      0.000000   \n",
       "2012-11-15  ...  0.000000              0.0         0.0      0.000000   \n",
       "2012-11-20  ...  0.000000              0.0         0.0      0.000000   \n",
       "2012-11-25  ...  0.000000              0.0         0.0      0.000000   \n",
       "...         ...       ...              ...         ...           ...   \n",
       "2021-08-10  ...  5.923457              0.0         0.0     10.758902   \n",
       "2021-08-15  ...  5.870619              0.0         0.0     10.806474   \n",
       "2021-08-20  ...  5.915097              0.0         0.0     10.798775   \n",
       "2021-08-25  ...  5.942434              0.0         0.0     10.759072   \n",
       "2021-08-30  ...  0.000000              0.0         0.0      0.000000   \n",
       "\n",
       "             TSLA_%K   TSLA_%D   TSLA_%R  TSLA_RSI  TSLA_14-high  TSLA_14-low  \n",
       "dt                                                                             \n",
       "2012-11-05 -0.017094 -0.121190 -4.077550 -0.429329      1.843086     1.681200  \n",
       "2012-11-10 -0.421497 -0.220000 -1.067299 -0.580960      1.857859     1.687139  \n",
       "2012-11-15 -0.294239 -0.177324 -1.366877 -0.573397      1.860041     1.687139  \n",
       "2012-11-20 -0.050773 -0.054995 -3.005679 -0.416595      1.894617     1.729884  \n",
       "2012-11-25 -0.391076 -0.213370 -1.128027 -0.518998      1.901212     1.769173  \n",
       "...              ...       ...       ...       ...           ...          ...  \n",
       "2021-08-10 -0.186342 -0.215442 -1.771897 -0.469982      6.588844     6.441329  \n",
       "2021-08-15 -0.132391 -0.128901 -2.087458 -0.461912      6.592908     6.441329  \n",
       "2021-08-20 -0.947746 -0.927374 -0.490392 -0.709786      6.592908     6.475186  \n",
       "2021-08-25 -0.262266 -0.303794 -1.466665 -0.553381      6.592908     6.475186  \n",
       "2021-08-30 -0.001096 -0.213796 -6.816315 -0.481219      6.594413     6.475186  \n",
       "\n",
       "[645 rows x 25 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "eefe2d70",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fig, axs = plt.subplots(1,1, figsize=(12,8))\n",
    "\n",
    "# axs.plot( dataset['TSLA_AdjClose_pred'], label = \"actual value\", color = 'blue' )\n",
    "# #axs.plot( (np.exp(s['LM_predicted'])-1)*100, label = \"LR predicted value\", color = 'red', marker='D')\n",
    "# axs.plot( dataset['TSLA_%K'], label = \"LSTM predicted value\", color = 'green')\n",
    "\n",
    "# axs.set( \n",
    "#        xlabel='Date', \n",
    "#        ylabel='percentage change in 5 days',\n",
    "#        title='Feature engineering')\n",
    "# axs.xaxis.label.set_size(15)\n",
    "# axs.yaxis.label.set_size(15)\n",
    "# axs.yaxis.label.set_size(15)\n",
    "\n",
    "# axs.title.set_size(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "83c8a2f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dataset.to_csv('dataset_tsla_aapl.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "67952dab",
   "metadata": {},
   "outputs": [],
   "source": [
    "end_date = '2021-07-05'\n",
    "\n",
    "\n",
    "\n",
    "X = X.loc[:end_date]\n",
    "\n",
    "Y = Y.loc[:end_date]\n",
    "dataset = dataset.loc[:end_date]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "07b6fade",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(633, 28)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "effe4460",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Specs</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DGS10</th>\n",
       "      <td>7.620062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DFF</th>\n",
       "      <td>3.189416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VXIBMCLS</th>\n",
       "      <td>2.997578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USEPUINDXD</th>\n",
       "      <td>2.571015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TSLA_Volume</th>\n",
       "      <td>2.366126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_msgs</th>\n",
       "      <td>1.839248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TSLA_RSI</th>\n",
       "      <td>1.541056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TSLA_EPS_diluted</th>\n",
       "      <td>1.481475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BTC_AdjClose</th>\n",
       "      <td>1.439718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DGS6MO</th>\n",
       "      <td>1.205102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Score\n",
       "Specs                     \n",
       "DGS10             7.620062\n",
       "DFF               3.189416\n",
       "VXIBMCLS          2.997578\n",
       "USEPUINDXD        2.571015\n",
       "TSLA_Volume       2.366126\n",
       "total_msgs        1.839248\n",
       "TSLA_RSI          1.541056\n",
       "TSLA_EPS_diluted  1.481475\n",
       "BTC_AdjClose      1.439718\n",
       "DGS6MO            1.205102"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bestfeatures = SelectKBest(k=5, score_func=f_regression)\n",
    "fit = bestfeatures.fit(X,Y)\n",
    "dfscores = pd.DataFrame(fit.scores_)\n",
    "dfcolumns = pd.DataFrame(X.columns)\n",
    "\n",
    "#concat two dataframes for better visualization \n",
    "featureScores = pd.concat([dfcolumns,dfscores],axis=1)\n",
    "featureScores.columns = ['Specs','Score']  #naming the dataframe columns\n",
    "featureScores.nlargest(10,'Score').set_index('Specs')  #print 10 best features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6d7e2ae",
   "metadata": {},
   "source": [
    "# Based on best features, we drop TSLA_high, low, and open"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b68fb520",
   "metadata": {},
   "outputs": [],
   "source": [
    "# X = X.loc[:, ~X.columns.isin([''])].select_dtypes(exclude='object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f2fd2a58",
   "metadata": {},
   "outputs": [],
   "source": [
    "validation_size = 0.05\n",
    "\n",
    "#In case the data is not dependent on the time series, then train and test split randomly\n",
    "# seed = 7\n",
    "# X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=validation_size, random_state=seed)\n",
    "\n",
    "#In case the data is not dependent on the time series, then train and test split should be done based on sequential sample\n",
    "#This can be done by selecting an arbitrary split point in the ordered list of observations and creating two new datasets.\n",
    "train_size = int(len(X) * (1-validation_size))\n",
    "X_train, X_test = X[0:train_size], X[train_size:len(X)]\n",
    "Y_train, Y_test = Y[0:train_size], Y[train_size:len(X)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3dcd1862",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_folds = 10\n",
    "seed = 7\n",
    "# scikit is moving away from mean_squared_error. \n",
    "# In order to avoid confusion, and to allow comparison with other models, we invert the final scores\n",
    "scoring = 'neg_mean_squared_error' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6aa15898",
   "metadata": {},
   "outputs": [],
   "source": [
    "models = []\n",
    "models.append(('LR', LinearRegression()))\n",
    "models.append(('LASSO', Lasso()))\n",
    "# models.append(('EN', ElasticNet()))\n",
    "models.append(('KNN', KNeighborsRegressor()))\n",
    "models.append(('CART', DecisionTreeRegressor(max_depth=5\n",
    ")))\n",
    "models.append(('SVR', SVR()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3c7e427d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Boosting methods\n",
    "# models.append(('ABR', AdaBoostRegressor()))\n",
    "# models.append(('GBR', GradientBoostingRegressor()))\n",
    "\n",
    "# Bagging methods\n",
    "models.append(('RFR', RandomForestRegressor(max_depth=5\n",
    ")))\n",
    "models.append(('ETR', ExtraTreesRegressor(max_depth=5\n",
    ")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "60f14f42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR         cv_mean: 0.003, cv_std: 0.002, train: 0.003, test: 0.009\n",
      "LASSO      cv_mean: 0.004, cv_std: 0.003, train: 0.004, test: 0.005\n",
      "KNN        cv_mean: 0.008, cv_std: 0.011, train: 0.003, test: 0.007\n",
      "CART       cv_mean: 0.014, cv_std: 0.028, train: 0.003, test: 0.006\n",
      "SVR        cv_mean: 0.005, cv_std: 0.003, train: 0.004, test: 0.007\n",
      "RFR        cv_mean: 0.005, cv_std: 0.005, train: 0.003, test: 0.006\n",
      "ETR        cv_mean: 0.005, cv_std: 0.004, train: 0.003, test: 0.007\n"
     ]
    }
   ],
   "source": [
    "names = []\n",
    "kfold_results = []\n",
    "test_results = []\n",
    "train_results = []\n",
    "for name, model in models:\n",
    "    names.append(name)\n",
    "    \n",
    "    ## K Fold analysis:\n",
    "    \n",
    "    kfold = KFold(n_splits=num_folds) #, random_state=seed)\n",
    "    #converted mean square error to positive. The lower the beter\n",
    "    cv_results = -1* cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring)\n",
    "    kfold_results.append(cv_results)\n",
    "    \n",
    "\n",
    "    # Full Training period\n",
    "    res = model.fit(X_train, Y_train)\n",
    "    train_result = mean_squared_error(res.predict(X_train), Y_train)\n",
    "    train_results.append(train_result)\n",
    "    \n",
    "    # Test results\n",
    "    test_result = mean_squared_error(res.predict(X_test), Y_test)\n",
    "    test_results.append(test_result)\n",
    "    \n",
    "\n",
    "    print(f'{name: <10} cv_mean: {cv_results.mean():.3f}, cv_std: {cv_results.std():.3f}, train: {train_result:.3f}, test: {test_result:.3f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cf436a27",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|--- feature_12 <= 4.23\n",
      "|   |--- feature_18 <= 6.36\n",
      "|   |   |--- feature_12 <= 3.95\n",
      "|   |   |   |--- feature_18 <= 6.31\n",
      "|   |   |   |   |--- feature_27 <= 3.35\n",
      "|   |   |   |   |   |--- feature_0 <= 18.76\n",
      "|   |   |   |   |   |   |--- feature_21 <= -0.08\n",
      "|   |   |   |   |   |   |   |--- feature_22 <= -0.07\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 17.59\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_1 <= 2.60\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 <= 16.06\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_0 >  16.06\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_1 >  2.60\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_1 <= 2.88\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_1 >  2.88\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  17.59\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_1 <= 2.57\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [-0.04]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_1 >  2.57\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 3.51\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  3.51\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |--- feature_22 >  -0.07\n",
      "|   |   |   |   |   |   |   |   |--- feature_10 <= 1.02\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_4 <= 2.55\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [-0.03]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_4 >  2.55\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [-0.00]\n",
      "|   |   |   |   |   |   |   |   |--- feature_10 >  1.02\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [-0.11]\n",
      "|   |   |   |   |   |   |--- feature_21 >  -0.08\n",
      "|   |   |   |   |   |   |   |--- feature_23 <= -0.30\n",
      "|   |   |   |   |   |   |   |   |--- feature_27 <= 3.19\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_22 <= -2.51\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 <= 2.61\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [-0.03]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_3 >  2.61\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [-0.03]\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_22 >  -2.51\n",
      "|   |   |   |   |   |   |   |   |   |   |--- value: [-0.02]\n",
      "|   |   |   |   |   |   |   |   |--- feature_27 >  3.19\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [0.05]\n",
      "|   |   |   |   |   |   |   |--- feature_23 >  -0.30\n",
      "|   |   |   |   |   |   |   |   |--- feature_14 <= 4.61\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [-0.12]\n",
      "|   |   |   |   |   |   |   |   |--- feature_14 >  4.61\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [-0.09]\n",
      "|   |   |   |   |   |--- feature_0 >  18.76\n",
      "|   |   |   |   |   |   |--- feature_10 <= 0.63\n",
      "|   |   |   |   |   |   |   |--- value: [0.24]\n",
      "|   |   |   |   |   |   |--- feature_10 >  0.63\n",
      "|   |   |   |   |   |   |   |--- value: [0.18]\n",
      "|   |   |   |   |--- feature_27 >  3.35\n",
      "|   |   |   |   |   |--- feature_4 <= 3.31\n",
      "|   |   |   |   |   |   |--- value: [-0.25]\n",
      "|   |   |   |   |   |--- feature_4 >  3.31\n",
      "|   |   |   |   |   |   |--- feature_0 <= 18.87\n",
      "|   |   |   |   |   |   |   |--- feature_0 <= 18.77\n",
      "|   |   |   |   |   |   |   |   |--- feature_17 <= -1.62\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 18.36\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_17 <= -2.13\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_17 >  -2.13\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  18.36\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_23 <= -0.83\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [0.19]\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_23 >  -0.83\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
      "|   |   |   |   |   |   |   |   |--- feature_17 >  -1.62\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 <= 18.59\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_12 <= 3.01\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 21\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_12 >  3.01\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 22\n",
      "|   |   |   |   |   |   |   |   |   |--- feature_0 >  18.59\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_10 <= 0.93\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
      "|   |   |   |   |   |   |   |   |   |   |--- feature_10 >  0.93\n",
      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [-0.14]\n",
      "|   |   |   |   |   |   |   |--- feature_0 >  18.77\n",
      "|   |   |   |   |   |   |   |   |--- value: [0.24]\n",
      "|   |   |   |   |   |   |--- feature_0 >  18.87\n",
      "|   |   |   |   |   |   |   |--- feature_17 <= -2.47\n",
      "|   |   |   |   |   |   |   |   |--- value: [-0.13]\n",
      "|   |   |   |   |   |   |   |--- feature_17 >  -2.47\n",
      "|   |   |   |   |   |   |   |   |--- value: [-0.13]\n",
      "|   |   |   |--- feature_18 >  6.31\n",
      "|   |   |   |   |--- feature_13 <= 5.17\n",
      "|   |   |   |   |   |--- value: [-0.28]\n",
      "|   |   |   |   |--- feature_13 >  5.17\n",
      "|   |   |   |   |   |--- feature_25 <= 5.79\n",
      "|   |   |   |   |   |   |--- value: [-0.03]\n",
      "|   |   |   |   |   |--- feature_25 >  5.79\n",
      "|   |   |   |   |   |   |--- value: [-0.07]\n",
      "|   |   |--- feature_12 >  3.95\n",
      "|   |   |   |--- feature_19 <= 9.12\n",
      "|   |   |   |   |--- feature_17 <= -3.09\n",
      "|   |   |   |   |   |--- value: [0.27]\n",
      "|   |   |   |   |--- feature_17 >  -3.09\n",
      "|   |   |   |   |   |--- feature_10 <= -0.31\n",
      "|   |   |   |   |   |   |--- value: [0.17]\n",
      "|   |   |   |   |   |--- feature_10 >  -0.31\n",
      "|   |   |   |   |   |   |--- feature_2 <= 4.88\n",
      "|   |   |   |   |   |   |   |--- value: [0.18]\n",
      "|   |   |   |   |   |   |--- feature_2 >  4.88\n",
      "|   |   |   |   |   |   |   |--- value: [0.18]\n",
      "|   |   |   |--- feature_19 >  9.12\n",
      "|   |   |   |   |--- feature_17 <= -1.44\n",
      "|   |   |   |   |   |--- value: [-0.12]\n",
      "|   |   |   |   |--- feature_17 >  -1.44\n",
      "|   |   |   |   |   |--- value: [0.05]\n",
      "|   |--- feature_18 >  6.36\n",
      "|   |   |--- feature_3 <= 6.13\n",
      "|   |   |   |--- feature_23 <= -0.31\n",
      "|   |   |   |   |--- feature_26 <= 5.13\n",
      "|   |   |   |   |   |--- feature_4 <= 4.86\n",
      "|   |   |   |   |   |   |--- value: [0.09]\n",
      "|   |   |   |   |   |--- feature_4 >  4.86\n",
      "|   |   |   |   |   |   |--- value: [0.09]\n",
      "|   |   |   |   |--- feature_26 >  5.13\n",
      "|   |   |   |   |   |--- feature_17 <= -1.80\n",
      "|   |   |   |   |   |   |--- feature_3 <= 5.72\n",
      "|   |   |   |   |   |   |   |--- value: [0.15]\n",
      "|   |   |   |   |   |   |--- feature_3 >  5.72\n",
      "|   |   |   |   |   |   |   |--- value: [0.18]\n",
      "|   |   |   |   |   |--- feature_17 >  -1.80\n",
      "|   |   |   |   |   |   |--- feature_18 <= 7.73\n",
      "|   |   |   |   |   |   |   |--- value: [0.20]\n",
      "|   |   |   |   |   |   |--- feature_18 >  7.73\n",
      "|   |   |   |   |   |   |   |--- value: [0.19]\n",
      "|   |   |   |--- feature_23 >  -0.31\n",
      "|   |   |   |   |--- feature_18 <= 6.91\n",
      "|   |   |   |   |   |--- feature_20 <= -0.04\n",
      "|   |   |   |   |   |   |--- feature_22 <= -2.56\n",
      "|   |   |   |   |   |   |   |--- value: [0.06]\n",
      "|   |   |   |   |   |   |--- feature_22 >  -2.56\n",
      "|   |   |   |   |   |   |   |--- feature_11 <= 1.43\n",
      "|   |   |   |   |   |   |   |   |--- value: [0.11]\n",
      "|   |   |   |   |   |   |   |--- feature_11 >  1.43\n",
      "|   |   |   |   |   |   |   |   |--- value: [0.09]\n",
      "|   |   |   |   |   |--- feature_20 >  -0.04\n",
      "|   |   |   |   |   |   |--- feature_18 <= 6.73\n",
      "|   |   |   |   |   |   |   |--- value: [0.14]\n",
      "|   |   |   |   |   |   |--- feature_18 >  6.73\n",
      "|   |   |   |   |   |   |   |--- value: [0.12]\n",
      "|   |   |   |   |--- feature_18 >  6.91\n",
      "|   |   |   |   |   |--- feature_4 <= 5.31\n",
      "|   |   |   |   |   |   |--- value: [-0.03]\n",
      "|   |   |   |   |   |--- feature_4 >  5.31\n",
      "|   |   |   |   |   |   |--- feature_20 <= -0.23\n",
      "|   |   |   |   |   |   |   |--- value: [0.04]\n",
      "|   |   |   |   |   |   |--- feature_20 >  -0.23\n",
      "|   |   |   |   |   |   |   |--- value: [0.01]\n",
      "|   |   |--- feature_3 >  6.13\n",
      "|   |   |   |--- feature_16 <= 6.93\n",
      "|   |   |   |   |--- feature_5 <= 5.85\n",
      "|   |   |   |   |   |--- value: [-0.13]\n",
      "|   |   |   |   |--- feature_5 >  5.85\n",
      "|   |   |   |   |   |--- value: [-0.13]\n",
      "|   |   |   |--- feature_16 >  6.93\n",
      "|   |   |   |   |--- feature_19 <= 9.09\n",
      "|   |   |   |   |   |--- feature_9 <= -2.26\n",
      "|   |   |   |   |   |   |--- value: [0.01]\n",
      "|   |   |   |   |   |--- feature_9 >  -2.26\n",
      "|   |   |   |   |   |   |--- value: [-0.06]\n",
      "|   |   |   |   |--- feature_19 >  9.09\n",
      "|   |   |   |   |   |--- feature_0 <= 17.74\n",
      "|   |   |   |   |   |   |--- feature_27 <= 6.18\n",
      "|   |   |   |   |   |   |   |--- value: [0.07]\n",
      "|   |   |   |   |   |   |--- feature_27 >  6.18\n",
      "|   |   |   |   |   |   |   |--- feature_17 <= -1.66\n",
      "|   |   |   |   |   |   |   |   |--- value: [0.04]\n",
      "|   |   |   |   |   |   |   |--- feature_17 >  -1.66\n",
      "|   |   |   |   |   |   |   |   |--- feature_18 <= 7.08\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [0.02]\n",
      "|   |   |   |   |   |   |   |   |--- feature_18 >  7.08\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [0.02]\n",
      "|   |   |   |   |   |--- feature_0 >  17.74\n",
      "|   |   |   |   |   |   |--- feature_23 <= -0.35\n",
      "|   |   |   |   |   |   |   |--- feature_17 <= -1.87\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 <= 18.73\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [0.14]\n",
      "|   |   |   |   |   |   |   |   |--- feature_0 >  18.73\n",
      "|   |   |   |   |   |   |   |   |   |--- value: [0.13]\n",
      "|   |   |   |   |   |   |   |--- feature_17 >  -1.87\n",
      "|   |   |   |   |   |   |   |   |--- value: [0.15]\n",
      "|   |   |   |   |   |   |--- feature_23 >  -0.35\n",
      "|   |   |   |   |   |   |   |--- value: [0.09]\n",
      "|--- feature_12 >  4.23\n",
      "|   |--- feature_8 <= -2.59\n",
      "|   |   |--- value: [-0.41]\n",
      "|   |--- feature_8 >  -2.59\n",
      "|   |   |--- value: [-0.25]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "regr = tree.DecisionTreeRegressor()\n",
    "model = regr.fit(X_train, Y_train)\n",
    "text_representation = tree.export_text(regr)\n",
    "print(text_representation)\n",
    "\n",
    "# fig = plt.figure(figsize=(25,20))\n",
    "# _ = tree.plot_tree(clf, \n",
    "#                    feature_names=iris.feature_names,  \n",
    "#                    class_names=iris.target_names,\n",
    "#                    filled=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3b81c6aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig = pyplot.figure()\n",
    "fig.suptitle('Algorithm Comparison: Kfold results')\n",
    "ax = fig.add_subplot(111)\n",
    "pyplot.boxplot(kfold_results)\n",
    "ax.set_xticklabels(names)\n",
    "fig.set_size_inches(15,8)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "aab5d8f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compare algorithms\n",
    "fig = pyplot.figure()\n",
    "\n",
    "ind = np.arange(len(names))  # the x locations for the groups\n",
    "width = 0.35  # the width of the bars\n",
    "\n",
    "fig.suptitle('Model Comparison')\n",
    "ax = fig.add_subplot(111)\n",
    "pyplot.bar(ind - width/2, train_results,  width=width, label='Train Error')\n",
    "pyplot.bar(ind + width/2, test_results, width=width, label='Test Error')\n",
    "fig.set_size_inches(15,8)\n",
    "pyplot.legend()\n",
    "ax.set_xticks(ind)\n",
    "ax.set_xticklabels(names)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "883d56f3",
   "metadata": {},
   "source": [
    "# First ARIMA Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b5a5a09c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_ARIMA=X_train.loc[:, [\n",
    "       #'TSLA_Open', 'TSLA_High', 'TSLA_Low',\n",
    "       'TSLA_Volume', 'AAPL_AdjClose', 'TSLA_14-high', 'TSLA_14-low',\n",
    "       'TSLA_%K', 'TSLA_%D', 'TSLA_%R', 'TSLA_RSI', 'S&P', 'DFF', 'DGS3MO',\n",
    "       'DGS6MO', 'DGS10', 'BAMLH0A0HYM2', 'VXIBMCLS', 'USEPUINDXD',\n",
    "       'DCOILWTICO', 'TSLA_EPS_diluted', 'TSLA_P/E','sentiment_vader',\n",
    "       'total_msgs']]\n",
    "X_test_ARIMA=X_test.loc[:, [\n",
    "       #'TSLA_Open', 'TSLA_High', 'TSLA_Low',\n",
    "       'TSLA_Volume', 'AAPL_AdjClose', 'TSLA_14-high', 'TSLA_14-low',\n",
    "       'TSLA_%K', 'TSLA_%D', 'TSLA_%R', 'TSLA_RSI', 'S&P', 'DFF', 'DGS3MO',\n",
    "       'DGS6MO', 'DGS10', 'BAMLH0A0HYM2', 'VXIBMCLS', 'USEPUINDXD',\n",
    "       'DCOILWTICO', 'TSLA_EPS_diluted', 'TSLA_P/E','sentiment_vader',\n",
    "       'total_msgs']]\n",
    "tr_len = len(X_train_ARIMA)\n",
    "te_len = len(X_test_ARIMA)\n",
    "to_len = len (X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "715e255b",
   "metadata": {},
   "outputs": [],
   "source": [
    "modelARIMA=sm.tsa.ARIMA(endog=Y_train,exog=X_train_ARIMA,order=[1,0,0])\n",
    "model_fit = modelARIMA.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "490433a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.009546995902460418"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error_Training_ARIMA = mean_squared_error(Y_train, model_fit.fittedvalues)\n",
    "predicted = model_fit.predict(start = tr_len -1 ,end = to_len -1, exog = X_test_ARIMA)[1:]\n",
    "error_Test_ARIMA = mean_squared_error(Y_test,predicted)\n",
    "error_Test_ARIMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "baf0ba3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_results.append(error_Test_ARIMA)\n",
    "train_results.append(error_Training_ARIMA)\n",
    "names.append(\"ARIMA\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3427cb16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compare algorithms\n",
    "fig = pyplot.figure()\n",
    "\n",
    "ind = np.arange(len(names))  # the x locations for the groups\n",
    "width = 0.35  # the width of the bars\n",
    "\n",
    "fig.suptitle('Comparing the performance of various algorthims on the Train and Test Dataset')\n",
    "ax = fig.add_subplot(111)\n",
    "pyplot.bar(ind - width/2, train_results,  width=width, label='Train Error')\n",
    "pyplot.bar(ind + width/2, test_results, width=width, label='Test Error')\n",
    "fig.set_size_inches(15,8)\n",
    "pyplot.legend()\n",
    "ax.set_xticks(ind)\n",
    "ax.set_xticklabels(names)\n",
    "pyplot.ylabel('Mean Square Error')\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a5c1bc4",
   "metadata": {},
   "source": [
    "# LSTM and Time Series Based Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b38484e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "seq_len = 2 #Length of the seq for the LSTM\n",
    "\n",
    "Y_train_LSTM, Y_test_LSTM = np.array(Y_train)[seq_len-1:], np.array(Y_test)\n",
    "X_train_LSTM = np.zeros((X_train.shape[0]+1-seq_len, seq_len, X_train.shape[1]))\n",
    "X_test_LSTM = np.zeros((X_test.shape[0], seq_len, X.shape[1]))\n",
    "for i in range(seq_len):\n",
    "    X_train_LSTM[:, i, :] = np.array(X_train)[i:X_train.shape[0]+i+1-seq_len, :]\n",
    "    X_test_LSTM[:, i, :] = np.array(X)[X_train.shape[0]+i-1:X.shape[0]+i+1-seq_len, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0ea5cbd3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-03 15:12:04.356980: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n",
      "2022-08-03 15:12:04.357011: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)\n",
      "2022-08-03 15:12:04.357027: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (DESKTOP-LJ3SJ1H): /proc/driver/nvidia/version does not exist\n",
      "2022-08-03 15:12:04.357169: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "def create_LSTMmodel(neurons=12, learn_rate = 0.01, momentum=0):\n",
    "        # create model\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(50, input_shape=(X_train_LSTM.shape[1], X_train_LSTM.shape[2])))\n",
    "    #More number of cells can be added if needed \n",
    "    model.add(Dense(1))\n",
    "    optimizer = SGD(lr=learn_rate, momentum=momentum)\n",
    "    model.compile(loss='mse', optimizer='adam')\n",
    "    return model\n",
    "LSTMModel = create_LSTMmodel(12, learn_rate = 0.01, momentum=0)\n",
    "LSTMModel_fit = LSTMModel.fit(X_train_LSTM, Y_train_LSTM, #validation_data=(X_test_LSTM, Y_test_LSTM)\n",
    "                validation_split=0.5,epochs=330, batch_size=72, verbose=0, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "257b7653",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "bda241d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.plot(LSTMModel_fit.history['loss'], label='train')\n",
    "pyplot.plot(LSTMModel_fit.history['val_loss'], label='test')\n",
    "pyplot.legend()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c03974de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19/19 [==============================] - 0s 1ms/step\n",
      "1/1 [==============================] - 0s 11ms/step\n"
     ]
    }
   ],
   "source": [
    "error_Training_LSTM = mean_squared_error(Y_train_LSTM, LSTMModel.predict(X_train_LSTM))\n",
    "predicted = LSTMModel.predict(X_test_LSTM)\n",
    "error_Test_LSTM = mean_squared_error(Y_test,predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "711830e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_results.append(error_Test_LSTM)\n",
    "train_results.append(error_Training_LSTM)\n",
    "names.append(\"LSTM\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3f36c79a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compare algorithms\n",
    "fig = pyplot.figure()\n",
    "\n",
    "ind = np.arange(len(names))  # the x locations for the groups\n",
    "width = 0.35  # the width of the bars\n",
    "\n",
    "fig.suptitle('Comparing the performance of various algorthims on the Train and Test Dataset')\n",
    "ax = fig.add_subplot(111)\n",
    "pyplot.bar(ind - width/2, train_results,  width=width, label='Train Error')\n",
    "pyplot.bar(ind + width/2, test_results, width=width, label='Test Error')\n",
    "fig.set_size_inches(15,8)\n",
    "pyplot.legend()\n",
    "ax.set_xticks(ind)\n",
    "ax.set_xticklabels(names)\n",
    "pyplot.ylabel('Mean Square Error')\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a5a3fa65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.009293484259228532,\n",
       " 0.004848399627421507,\n",
       " 0.007090962833889892,\n",
       " 0.006469291039250078,\n",
       " 0.007074333038542134,\n",
       " 0.005519450467279655,\n",
       " 0.006614864903958294,\n",
       " 0.009546995902460418,\n",
       " 0.04089855658300086]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e56ca2ff",
   "metadata": {},
   "source": [
    "# Tune ARIMA Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a08f8386",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ARIMA(0, 0, 0) MSE=0.0025124\n",
      "ARIMA(0, 0, 1) MSE=0.0025046\n",
      "ARIMA(0, 1, 0) MSE=0.0612995\n",
      "ARIMA(0, 1, 1) MSE=0.0501192\n",
      "ARIMA(1, 0, 0) MSE=0.0025044\n",
      "ARIMA(1, 0, 1) MSE=0.0025114\n",
      "ARIMA(1, 1, 0) MSE=0.0572773\n",
      "ARIMA(1, 1, 1) MSE=0.0485456\n",
      "ARIMA(2, 0, 0) MSE=0.0025037\n",
      "ARIMA(2, 0, 1) MSE=0.0025122\n",
      "ARIMA(2, 1, 0) MSE=0.0574455\n",
      "ARIMA(2, 1, 1) MSE=0.0542901\n",
      "Best ARIMA(2, 0, 0) MSE=0.0025037\n"
     ]
    }
   ],
   "source": [
    "#Diable the warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "#Grid Search for ARIMA Model\n",
    "#Change p,d and q and check for the best result\n",
    "\n",
    "# evaluate an ARIMA model for a given order (p,d,q)\n",
    "#Assuming that the train and Test Data is already defined before\n",
    "def evaluate_arima_model(arima_order):\n",
    "    #predicted = list()     \n",
    "    modelARIMA=sm.tsa.ARIMA(endog=Y_train,exog=X_train_ARIMA,order=arima_order)\n",
    "    model_fit = modelARIMA.fit()\n",
    "    error = mean_squared_error(Y_train, model_fit.fittedvalues)\n",
    "    return error\n",
    " \n",
    "# evaluate combinations of p, d and q values for an ARIMA model\n",
    "def evaluate_models(p_values, d_values, q_values): \n",
    "    best_score, best_cfg = float(\"inf\"), None\n",
    "    for p in p_values:\n",
    "        for d in d_values:\n",
    "            for q in q_values:\n",
    "                order = (p,d,q)                \n",
    "                try:\n",
    "                    mse = evaluate_arima_model(order)                    \n",
    "                    if mse < best_score:\n",
    "                        best_score, best_cfg = mse, order\n",
    "                    print('ARIMA%s MSE=%.7f' % (order,mse))\n",
    "                except:\n",
    "                    continue\n",
    "    print('Best ARIMA%s MSE=%.7f' % (best_cfg, best_score))\n",
    "    \n",
    "# evaluate parameters\n",
    "p_values = [0, 1, 2]\n",
    "d_values = range(0, 2)\n",
    "q_values = range(0, 2)\n",
    "#warnings.filterwarnings(\"ignore\")\n",
    "evaluate_models(p_values, d_values, q_values) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "73bbc6af",
   "metadata": {},
   "outputs": [],
   "source": [
    "# prepare model\n",
    "modelARIMA_tuned=sm.tsa.ARIMA(endog=Y_train,exog=X_train_ARIMA,order=[2,0,0])\n",
    "model_fit_tuned = modelARIMA_tuned.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "500ab05d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.009617635965499773\n"
     ]
    }
   ],
   "source": [
    "# estimate accuracy on validation set\n",
    "predicted_tuned = model_fit_tuned.predict(start = tr_len -1 ,end = to_len -1, exog = X_test_ARIMA)[1:]\n",
    "print(mean_squared_error(Y_test,predicted_tuned))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e4af0398",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.009617635965499773"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error_Training_ARIMA = mean_squared_error(Y_train, model_fit_tuned.fittedvalues)\n",
    "predicted = model_fit_tuned.predict(start = tr_len -1 ,end = to_len -1, exog = X_test_ARIMA)[1:]\n",
    "error_Test_ARIMA = mean_squared_error(Y_test,predicted)\n",
    "error_Test_ARIMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "370efb14",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_results.append(error_Test_ARIMA)\n",
    "train_results.append(error_Training_ARIMA)\n",
    "names.append(\"ARIMA_tuned\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "83dac621",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "29f3f78e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compare algorithms\n",
    "fig = pyplot.figure()\n",
    "\n",
    "ind = np.arange(len(names))  # the x locations for the groups\n",
    "width = 0.35  # the width of the bars\n",
    "\n",
    "fig.suptitle('Comparing the performance of various algorthims on the Train and Test Dataset')\n",
    "ax = fig.add_subplot(111)\n",
    "pyplot.bar(ind - width/2, train_results,  width=width, label='Train Error')\n",
    "pyplot.bar(ind + width/2, test_results, width=width, label='Test Error')\n",
    "fig.set_size_inches(15,8)\n",
    "pyplot.legend()\n",
    "ax.set_xticks(ind)\n",
    "ax.set_xticklabels(names)\n",
    "pyplot.ylabel('Mean Square Error')\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "116d6d2e",
   "metadata": {},
   "source": [
    "# LR Model - Stock, lagged return plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "9bd27694",
   "metadata": {},
   "outputs": [],
   "source": [
    " # Full Training period linear model\n",
    "    \n",
    "LRtest = LinearRegression().fit(X_train, Y_train)\n",
    "train_result = mean_squared_error(LRtest.predict(X_train), Y_train)\n",
    "# train_results.append(train_result)\n",
    "    \n",
    "# Test results\n",
    "test_result = mean_squared_error(LRtest.predict(X_test), Y_test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "16c009ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.00417254,  0.02007297,  0.39685403, -0.36644656,  0.18733865,\n",
       "       -0.23537181,  0.20424196, -0.0118134 ,  0.00447359, -0.01065719,\n",
       "        0.01922132,  0.02382523, -0.02502691,  0.00790986, -0.00085195,\n",
       "        0.05834071,  0.00638517,  0.00184077, -0.00705978, -0.00080688,\n",
       "       -0.01088535, -0.00253158,  0.01126153, -0.02650327,  0.39685403,\n",
       "       -0.36644656,  0.18733865, -0.23537181])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LRtest.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ad1ac4cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.4762107357962078"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LRtest.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "8a0d4aed",
   "metadata": {},
   "outputs": [],
   "source": [
    "res_LR = LRtest.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "0eaacb6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TSLA_AdjClose_pred</th>\n",
       "      <th>LM_predicted</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dt</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-01-27</th>\n",
       "      <td>-0.028582</td>\n",
       "      <td>-0.001138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-01</th>\n",
       "      <td>0.014681</td>\n",
       "      <td>0.030549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-06</th>\n",
       "      <td>-0.048775</td>\n",
       "      <td>0.016932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-11</th>\n",
       "      <td>-0.019206</td>\n",
       "      <td>0.035159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-16</th>\n",
       "      <td>-0.018916</td>\n",
       "      <td>0.044880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-21</th>\n",
       "      <td>-0.145506</td>\n",
       "      <td>-0.028915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-26</th>\n",
       "      <td>-0.033570</td>\n",
       "      <td>0.023998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-03</th>\n",
       "      <td>-0.148604</td>\n",
       "      <td>-0.008691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-08</th>\n",
       "      <td>0.208803</td>\n",
       "      <td>0.059827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-13</th>\n",
       "      <td>-0.060261</td>\n",
       "      <td>0.025406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-18</th>\n",
       "      <td>0.013685</td>\n",
       "      <td>0.054847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-23</th>\n",
       "      <td>-0.067870</td>\n",
       "      <td>0.104315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-28</th>\n",
       "      <td>0.067251</td>\n",
       "      <td>0.071127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-02</th>\n",
       "      <td>0.013837</td>\n",
       "      <td>0.049816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-07</th>\n",
       "      <td>0.045181</td>\n",
       "      <td>0.038827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-12</th>\n",
       "      <td>0.052448</td>\n",
       "      <td>0.110030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-17</th>\n",
       "      <td>-0.027532</td>\n",
       "      <td>0.094449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-22</th>\n",
       "      <td>-0.020992</td>\n",
       "      <td>0.085155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-27</th>\n",
       "      <td>0.006647</td>\n",
       "      <td>0.110445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-02</th>\n",
       "      <td>-0.053667</td>\n",
       "      <td>0.062689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-07</th>\n",
       "      <td>-0.130873</td>\n",
       "      <td>0.041568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-12</th>\n",
       "      <td>-0.022388</td>\n",
       "      <td>0.094055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-17</th>\n",
       "      <td>0.006997</td>\n",
       "      <td>0.102864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-22</th>\n",
       "      <td>0.082524</td>\n",
       "      <td>0.090641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-27</th>\n",
       "      <td>-0.011078</td>\n",
       "      <td>0.063505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-01</th>\n",
       "      <td>-0.040645</td>\n",
       "      <td>0.087727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-06</th>\n",
       "      <td>0.017934</td>\n",
       "      <td>0.109581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-11</th>\n",
       "      <td>-0.008265</td>\n",
       "      <td>0.086009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-16</th>\n",
       "      <td>0.026044</td>\n",
       "      <td>0.084806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-21</th>\n",
       "      <td>0.079008</td>\n",
       "      <td>0.167590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-26</th>\n",
       "      <td>0.008964</td>\n",
       "      <td>0.110339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-01</th>\n",
       "      <td>-0.027426</td>\n",
       "      <td>0.110790</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            TSLA_AdjClose_pred  LM_predicted\n",
       "dt                                          \n",
       "2021-01-27           -0.028582     -0.001138\n",
       "2021-02-01            0.014681      0.030549\n",
       "2021-02-06           -0.048775      0.016932\n",
       "2021-02-11           -0.019206      0.035159\n",
       "2021-02-16           -0.018916      0.044880\n",
       "2021-02-21           -0.145506     -0.028915\n",
       "2021-02-26           -0.033570      0.023998\n",
       "2021-03-03           -0.148604     -0.008691\n",
       "2021-03-08            0.208803      0.059827\n",
       "2021-03-13           -0.060261      0.025406\n",
       "2021-03-18            0.013685      0.054847\n",
       "2021-03-23           -0.067870      0.104315\n",
       "2021-03-28            0.067251      0.071127\n",
       "2021-04-02            0.013837      0.049816\n",
       "2021-04-07            0.045181      0.038827\n",
       "2021-04-12            0.052448      0.110030\n",
       "2021-04-17           -0.027532      0.094449\n",
       "2021-04-22           -0.020992      0.085155\n",
       "2021-04-27            0.006647      0.110445\n",
       "2021-05-02           -0.053667      0.062689\n",
       "2021-05-07           -0.130873      0.041568\n",
       "2021-05-12           -0.022388      0.094055\n",
       "2021-05-17            0.006997      0.102864\n",
       "2021-05-22            0.082524      0.090641\n",
       "2021-05-27           -0.011078      0.063505\n",
       "2021-06-01           -0.040645      0.087727\n",
       "2021-06-06            0.017934      0.109581\n",
       "2021-06-11           -0.008265      0.086009\n",
       "2021-06-16            0.026044      0.084806\n",
       "2021-06-21            0.079008      0.167590\n",
       "2021-06-26            0.008964      0.110339\n",
       "2021-07-01           -0.027426      0.110790"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = Y_test.to_frame()\n",
    "s['LM_predicted'] = res_LR\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "ce990aaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1,1, figsize=(12,8))\n",
    "\n",
    "axs.plot( (np.exp(s['TSLA_AdjClose_pred'])-1)*100, label = \"actual value\", color = 'blue', marker='D')\n",
    "axs.plot( (np.exp(s['LM_predicted'])-1)*100, label = \"predicted value\", color = 'red', marker='D')\n",
    "\n",
    "axs.set( \n",
    "       xlabel='Date', \n",
    "       ylabel='percentage change in 5 days',\n",
    "       title=f'Comparison actual and predicted percentage change for {tickers[0]} stock')\n",
    "axs.legend()\n",
    "axs.xaxis.label.set_size(15)\n",
    "axs.yaxis.label.set_size(15)\n",
    "axs.yaxis.label.set_size(15)\n",
    "\n",
    "axs.title.set_size(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad3c8bd9",
   "metadata": {},
   "source": [
    "# LSTM Model - Stock, lagged return plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e524d330",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 10ms/step\n"
     ]
    }
   ],
   "source": [
    "res_LSTM = LSTMModel.predict(X_test_LSTM)\n",
    "# res_LSTM.tolist() # works for javier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "47c0a3d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TSLA_AdjClose_pred</th>\n",
       "      <th>LM_predicted</th>\n",
       "      <th>LSTM_predicted</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dt</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-01-27</th>\n",
       "      <td>-0.028582</td>\n",
       "      <td>-0.001138</td>\n",
       "      <td>-0.237678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-01</th>\n",
       "      <td>0.014681</td>\n",
       "      <td>0.030549</td>\n",
       "      <td>-0.270895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-06</th>\n",
       "      <td>-0.048775</td>\n",
       "      <td>0.016932</td>\n",
       "      <td>-0.255112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-11</th>\n",
       "      <td>-0.019206</td>\n",
       "      <td>0.035159</td>\n",
       "      <td>-0.234881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-16</th>\n",
       "      <td>-0.018916</td>\n",
       "      <td>0.044880</td>\n",
       "      <td>-0.254192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-21</th>\n",
       "      <td>-0.145506</td>\n",
       "      <td>-0.028915</td>\n",
       "      <td>-0.249269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-26</th>\n",
       "      <td>-0.033570</td>\n",
       "      <td>0.023998</td>\n",
       "      <td>-0.242217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-03</th>\n",
       "      <td>-0.148604</td>\n",
       "      <td>-0.008691</td>\n",
       "      <td>-0.235002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-08</th>\n",
       "      <td>0.208803</td>\n",
       "      <td>0.059827</td>\n",
       "      <td>-0.220894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-13</th>\n",
       "      <td>-0.060261</td>\n",
       "      <td>0.025406</td>\n",
       "      <td>-0.211632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-18</th>\n",
       "      <td>0.013685</td>\n",
       "      <td>0.054847</td>\n",
       "      <td>-0.207868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-23</th>\n",
       "      <td>-0.067870</td>\n",
       "      <td>0.104315</td>\n",
       "      <td>-0.224731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-28</th>\n",
       "      <td>0.067251</td>\n",
       "      <td>0.071127</td>\n",
       "      <td>-0.212563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-02</th>\n",
       "      <td>0.013837</td>\n",
       "      <td>0.049816</td>\n",
       "      <td>-0.192978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-07</th>\n",
       "      <td>0.045181</td>\n",
       "      <td>0.038827</td>\n",
       "      <td>-0.157255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-12</th>\n",
       "      <td>0.052448</td>\n",
       "      <td>0.110030</td>\n",
       "      <td>-0.210289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-17</th>\n",
       "      <td>-0.027532</td>\n",
       "      <td>0.094449</td>\n",
       "      <td>-0.154163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-22</th>\n",
       "      <td>-0.020992</td>\n",
       "      <td>0.085155</td>\n",
       "      <td>-0.169732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-27</th>\n",
       "      <td>0.006647</td>\n",
       "      <td>0.110445</td>\n",
       "      <td>-0.192534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-02</th>\n",
       "      <td>-0.053667</td>\n",
       "      <td>0.062689</td>\n",
       "      <td>-0.160184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-07</th>\n",
       "      <td>-0.130873</td>\n",
       "      <td>0.041568</td>\n",
       "      <td>-0.163869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-12</th>\n",
       "      <td>-0.022388</td>\n",
       "      <td>0.094055</td>\n",
       "      <td>-0.268574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-17</th>\n",
       "      <td>0.006997</td>\n",
       "      <td>0.102864</td>\n",
       "      <td>-0.214823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-22</th>\n",
       "      <td>0.082524</td>\n",
       "      <td>0.090641</td>\n",
       "      <td>-0.139290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-27</th>\n",
       "      <td>-0.011078</td>\n",
       "      <td>0.063505</td>\n",
       "      <td>-0.173416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-01</th>\n",
       "      <td>-0.040645</td>\n",
       "      <td>0.087727</td>\n",
       "      <td>-0.140043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-06</th>\n",
       "      <td>0.017934</td>\n",
       "      <td>0.109581</td>\n",
       "      <td>-0.096767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-11</th>\n",
       "      <td>-0.008265</td>\n",
       "      <td>0.086009</td>\n",
       "      <td>-0.131715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-16</th>\n",
       "      <td>0.026044</td>\n",
       "      <td>0.084806</td>\n",
       "      <td>-0.155102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-21</th>\n",
       "      <td>0.079008</td>\n",
       "      <td>0.167590</td>\n",
       "      <td>-0.146728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-26</th>\n",
       "      <td>0.008964</td>\n",
       "      <td>0.110339</td>\n",
       "      <td>-0.186596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-01</th>\n",
       "      <td>-0.027426</td>\n",
       "      <td>0.110790</td>\n",
       "      <td>-0.197788</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            TSLA_AdjClose_pred  LM_predicted  LSTM_predicted\n",
       "dt                                                          \n",
       "2021-01-27           -0.028582     -0.001138       -0.237678\n",
       "2021-02-01            0.014681      0.030549       -0.270895\n",
       "2021-02-06           -0.048775      0.016932       -0.255112\n",
       "2021-02-11           -0.019206      0.035159       -0.234881\n",
       "2021-02-16           -0.018916      0.044880       -0.254192\n",
       "2021-02-21           -0.145506     -0.028915       -0.249269\n",
       "2021-02-26           -0.033570      0.023998       -0.242217\n",
       "2021-03-03           -0.148604     -0.008691       -0.235002\n",
       "2021-03-08            0.208803      0.059827       -0.220894\n",
       "2021-03-13           -0.060261      0.025406       -0.211632\n",
       "2021-03-18            0.013685      0.054847       -0.207868\n",
       "2021-03-23           -0.067870      0.104315       -0.224731\n",
       "2021-03-28            0.067251      0.071127       -0.212563\n",
       "2021-04-02            0.013837      0.049816       -0.192978\n",
       "2021-04-07            0.045181      0.038827       -0.157255\n",
       "2021-04-12            0.052448      0.110030       -0.210289\n",
       "2021-04-17           -0.027532      0.094449       -0.154163\n",
       "2021-04-22           -0.020992      0.085155       -0.169732\n",
       "2021-04-27            0.006647      0.110445       -0.192534\n",
       "2021-05-02           -0.053667      0.062689       -0.160184\n",
       "2021-05-07           -0.130873      0.041568       -0.163869\n",
       "2021-05-12           -0.022388      0.094055       -0.268574\n",
       "2021-05-17            0.006997      0.102864       -0.214823\n",
       "2021-05-22            0.082524      0.090641       -0.139290\n",
       "2021-05-27           -0.011078      0.063505       -0.173416\n",
       "2021-06-01           -0.040645      0.087727       -0.140043\n",
       "2021-06-06            0.017934      0.109581       -0.096767\n",
       "2021-06-11           -0.008265      0.086009       -0.131715\n",
       "2021-06-16            0.026044      0.084806       -0.155102\n",
       "2021-06-21            0.079008      0.167590       -0.146728\n",
       "2021-06-26            0.008964      0.110339       -0.186596\n",
       "2021-07-01           -0.027426      0.110790       -0.197788"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s['LSTM_predicted'] = res_LSTM\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "15629d2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1,1, figsize=(12,8))\n",
    "\n",
    "axs.plot( (np.exp(s['TSLA_AdjClose_pred'])-1)*100, label = \"actual value\", color = 'blue', marker='D')\n",
    "axs.plot( (np.exp(s['LM_predicted'])-1)*100, label = \"LR predicted value\", color = 'red', marker='D')\n",
    "axs.plot( (np.exp(s['LSTM_predicted'])-1)*100, label = \"LSTM predicted value\", color = 'green', marker='D')\n",
    "\n",
    "axs.set( \n",
    "       xlabel='Date', \n",
    "       ylabel='percentage change in 5 days',\n",
    "       title=f'Comparison actual and predicted percentage change for {tickers[0]} stock')\n",
    "axs.xaxis.label.set_size(15)\n",
    "axs.yaxis.label.set_size(15)\n",
    "axs.yaxis.label.set_size(15)\n",
    "\n",
    "axs.title.set_size(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2baee675",
   "metadata": {},
   "source": [
    "# LSTM with hyperparameter tuning\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "585fd9f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def buildmodel(layer, densenumber, denselayersize, optimizer, dropoutrate):\n",
    "\n",
    "    layer1 = tf.keras.layers.Input(shape=(X_train_LSTM.shape[1], X_train_LSTM.shape[2])) #seq length, input size\n",
    "    # layer2 = tf.keras.layers.Embedding(input_dim=6,output_dim=64,input_length= 15000)(layer1)\n",
    "    layerX = tf.keras.layers.LSTM(50, activation = 'relu') (layer1)\n",
    "    for f in range(densenumber):\n",
    "        layer6 = tf.keras.layers.Dense(denselayersize,activation=\"relu\")(layerX)\n",
    "        layer7 = tf.keras.layers.Dropout(dropoutrate)(layer6)\n",
    "        layerX = tf.keras.layers.BatchNormalization()(layer7)\n",
    "    output = tf.keras.layers.Dense(1, activation=\"linear\" )(layerX)\n",
    "    model = tf.keras.Model(inputs=layer1,outputs=output)\n",
    "    model.compile(loss=[\"MSE\"],optimizer=optimizer) \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "eccec5a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "dictionary =  dict(layer1= [ \"LSTM\"], \n",
    "                  densenumber = [2, 4, 6, 8],\n",
    "                  denselayersize = [8, 16, 32, 64],\n",
    "                  optimizer =[\"adam\", \"rmsprop\", \"SGD\"],\n",
    "                  dropoutrate = [0.2, 0.4, 0.8]\n",
    "                  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "1d45331f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "iterate = 0\n",
    "model_dict = {}\n",
    "for layer in dictionary[\"layer1\"]:\n",
    "    for densenumber in dictionary[\"densenumber\"]:\n",
    "        for denselayersize in dictionary[\"denselayersize\"]:\n",
    "            for optimizer in dictionary[\"optimizer\"]:\n",
    "                for dropoutrate in dictionary[\"dropoutrate\"]:\n",
    "                    tf.keras.backend.clear_session()\n",
    "                    tf.random.set_seed(1)\n",
    "\n",
    "                    model= buildmodel(layer, densenumber, denselayersize, optimizer, dropoutrate)\n",
    "                    \n",
    "                    model_history = model.fit(X_train_LSTM, Y_train_LSTM, #validation_data=(X_test_LSTM, Y_test_LSTM)\n",
    "                                            validation_split = 0.5,epochs=80, batch_size=72, verbose=0, shuffle=False)\n",
    "                    predictions=model.predict(X_test_LSTM)\n",
    "                    # y_val = Y_test_LSTM\n",
    "                    # predictions2 = pd.DataFrame(predictions)                        \n",
    "                    \n",
    "                    # Y_3 = pd.DataFrame(y_val)\n",
    "                    # correlation = pd.concat([Y_3, predictions2], axis=1).corr('spearman').iloc[0,1]\n",
    "\n",
    "                    hist = model_history.history\n",
    "                    error_Training_LSTM = mean_squared_error(Y_train_LSTM, model.predict(X_train_LSTM))\n",
    "                    predicted = model.predict(X_test_LSTM)\n",
    "                    error_Test_LSTM = mean_squared_error(Y_test, predicted)\n",
    "                    \n",
    "                    # test_loss = model.evaluate(car_test_features_norm[features], car_test_labels, verbose=0)\n",
    "                    test_rmse = np.sqrt(error_Test_LSTM)\n",
    "                    \n",
    "                    model_dict[iterate]= dict(layer= layer, densenumber = densenumber, denselayersize=denselayersize, optimizer= optimizer,dropoutrate= dropoutrate, \n",
    "                    predictions = predictions, history = hist, MSE_Training = error_Training_LSTM, MSE_Test = error_Test_LSTM, RMSE_Test = test_rmse\n",
    "                    )\n",
    "                    iterate += 1    \n",
    "\n",
    "                    \n",
    "                   \n",
    "              \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "bc44cbd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_pd = pd.DataFrame(model_dict).transpose().sort_values(by=\"MSE_Test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "028a4907",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>layer</th>\n",
       "      <th>densenumber</th>\n",
       "      <th>denselayersize</th>\n",
       "      <th>optimizer</th>\n",
       "      <th>dropoutrate</th>\n",
       "      <th>MSE_Training</th>\n",
       "      <th>MSE_Test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>LSTM</td>\n",
       "      <td>4</td>\n",
       "      <td>64</td>\n",
       "      <td>rmsprop</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.004326</td>\n",
       "      <td>0.004295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>LSTM</td>\n",
       "      <td>4</td>\n",
       "      <td>64</td>\n",
       "      <td>adam</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.004536</td>\n",
       "      <td>0.004424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>LSTM</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>SGD</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.005137</td>\n",
       "      <td>0.004495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>LSTM</td>\n",
       "      <td>4</td>\n",
       "      <td>64</td>\n",
       "      <td>SGD</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.004423</td>\n",
       "      <td>0.004498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>LSTM</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>SGD</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.004208</td>\n",
       "      <td>0.004505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <th>37</th>\n",
       "      <td>LSTM</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>adam</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.056795</td>\n",
       "      <td>0.057482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>LSTM</td>\n",
       "      <td>2</td>\n",
       "      <td>64</td>\n",
       "      <td>adam</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.021731</td>\n",
       "      <td>0.129865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>LSTM</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>adam</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.030892</td>\n",
       "      <td>0.144176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>LSTM</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>adam</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.177115</td>\n",
       "      <td>0.192829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>LSTM</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>rmsprop</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.026056</td>\n",
       "      <td>0.248865</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    layer densenumber denselayersize optimizer dropoutrate MSE_Training  \\\n",
       "67   LSTM           4             64   rmsprop         0.4     0.004326   \n",
       "65   LSTM           4             64      adam         0.8     0.004536   \n",
       "15   LSTM           2             16       SGD         0.2     0.005137   \n",
       "69   LSTM           4             64       SGD         0.2     0.004423   \n",
       "8    LSTM           2              8       SGD         0.8     0.004208   \n",
       "..    ...         ...            ...       ...         ...          ...   \n",
       "37   LSTM           4              8      adam         0.4     0.056795   \n",
       "27   LSTM           2             64      adam         0.2     0.021731   \n",
       "46   LSTM           4             16      adam         0.4     0.030892   \n",
       "36   LSTM           4              8      adam         0.2     0.177115   \n",
       "129  LSTM           8             32   rmsprop         0.2     0.026056   \n",
       "\n",
       "     MSE_Test  \n",
       "67   0.004295  \n",
       "65   0.004424  \n",
       "15   0.004495  \n",
       "69   0.004498  \n",
       "8    0.004505  \n",
       "..        ...  \n",
       "37   0.057482  \n",
       "27   0.129865  \n",
       "46   0.144176  \n",
       "36   0.192829  \n",
       "129  0.248865  \n",
       "\n",
       "[144 rows x 7 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_pd[['layer','densenumber','denselayersize','optimizer','dropoutrate','MSE_Training','MSE_Test']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9958e3f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_pd.sort_values(by=\"MSE_Test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f6639cce",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model_predictions = model_pd.iloc[37]['predictions']\n",
    "best_model_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e23a6cc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model_pd.to_csv('lstm_hyperparameter_tuning.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cafc7648",
   "metadata": {},
   "outputs": [],
   "source": [
    "for count, model in enumerate(model_dict): # should be model_dict\n",
    "    x_arr = np.arange(len(model_dict[model]['history']['loss'])) + 1\n",
    "\n",
    "    fig = plt.figure(figsize=(16, 6))\n",
    "    ax = fig.add_subplot(1, 2, 1)\n",
    "    ax.plot(x_arr, model_dict[model]['history']['loss'], '-o', label='Train loss')\n",
    "    ax.plot(x_arr, model_dict[model]['history']['val_loss'], '--<', label='Validation loss')\n",
    "    ax.set_title(label= (f\"#: {count} dense: {model_dict[model]['densenumber']}, denselayersize: {model_dict[model]['denselayersize']}, optimizer: {model_dict[model]['optimizer']}, dropout: {model_dict[model]['dropoutrate']}\"))\n",
    "    ax.legend(fontsize=10)\n",
    "    ax.set_xlabel('Epoch', size=15)\n",
    "    ax.set_ylabel('Loss', size=15)\n",
    "\n",
    "    # plt.savefig(f'{model}.png')\n",
    "\n",
    "    # ax = fig.add_subplot(1, 2, 2)\n",
    "    # ax.plot(x_arr, model['history']['MSE_Training'], '-o', label='Train acc.')\n",
    "    # ax.plot(x_arr, model['history']['MSE_Test'], '--<', label='Validation acc.')\n",
    "    # ax.legend(fontsize=15)\n",
    "    # ax.set_xlabel('Epoch', size=15)\n",
    "    # ax.set_ylabel('Accuracy', size=15)\n",
    "    # plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "992d5440",
   "metadata": {},
   "source": [
    "# Use Parameters from Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9468d5e9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "f55666b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.keras.backend.clear_session()\n",
    "tf.random.set_seed(1)\n",
    "LSTM_tuned = buildmodel(layer=[ \"LSTM\"], densenumber=4, denselayersize=64, optimizer='rmsprop', dropoutrate=0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "0205b2f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 144ms/step\n",
      "19/19 [==============================] - 0s 2ms/step\n",
      "1/1 [==============================] - 0s 12ms/step\n"
     ]
    }
   ],
   "source": [
    "model_history_tuned = LSTM_tuned.fit(X_train_LSTM, Y_train_LSTM, #validation_data=(X_test_LSTM, Y_test_LSTM)\n",
    "                                            validation_split = 0.5,epochs=80, batch_size=72, verbose=0, shuffle=False)\n",
    "predictions_tuned = LSTM_tuned.predict(X_test_LSTM)\n",
    "# y_val = Y_test_LSTM\n",
    "# predictions2 = pd.DataFrame(predictions)                        \n",
    "\n",
    "# Y_3 = pd.DataFrame(y_val)\n",
    "# correlation = pd.concat([Y_3, predictions2], axis=1).corr('spearman').iloc[0,1]\n",
    "\n",
    "hist_LSTM_tuned = model_history_tuned.history\n",
    "error_Training_LSTM_tuned = mean_squared_error(Y_train_LSTM, LSTM_tuned.predict(X_train_LSTM))\n",
    "predicted_LSTM_tuned = LSTM_tuned.predict(X_test_LSTM)\n",
    "error_Test_LSTM_tuned = mean_squared_error(Y_test, predicted)\n",
    "\n",
    "# test_loss = model.evaluate(car_test_features_norm[features], car_test_labels, verbose=0)\n",
    "test_rmse_LSTM_tuned= np.sqrt(error_Test_LSTM_tuned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "9adf4cfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.03064647],\n",
       "       [-0.01636474],\n",
       "       [-0.02229575],\n",
       "       [-0.02495533],\n",
       "       [-0.01528767],\n",
       "       [-0.01738651],\n",
       "       [-0.01800048],\n",
       "       [-0.02107024],\n",
       "       [-0.00877144],\n",
       "       [-0.00839131],\n",
       "       [-0.01122422],\n",
       "       [-0.00655604],\n",
       "       [-0.00877311],\n",
       "       [-0.01052022],\n",
       "       [-0.011363  ],\n",
       "       [-0.00602959],\n",
       "       [-0.00927912],\n",
       "       [-0.01338334],\n",
       "       [-0.00924314],\n",
       "       [-0.01225671],\n",
       "       [-0.01559512],\n",
       "       [-0.01223695],\n",
       "       [-0.00481373],\n",
       "       [-0.0076411 ],\n",
       "       [-0.00990836],\n",
       "       [-0.00693691],\n",
       "       [-0.01264947],\n",
       "       [-0.01310575],\n",
       "       [-0.01381342],\n",
       "       [-0.00510243],\n",
       "       [-0.00190647],\n",
       "       [-0.00508878]], dtype=float32)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions_tuned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "6ad63efc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, 2, 28)]           0         \n",
      "                                                                 \n",
      " lstm (LSTM)                 (None, 50)                15800     \n",
      "                                                                 \n",
      " dense (Dense)               (None, 64)                3264      \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 64)                0         \n",
      "                                                                 \n",
      " batch_normalization (BatchN  (None, 64)               256       \n",
      " ormalization)                                                   \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 64)                4160      \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 64)                0         \n",
      "                                                                 \n",
      " batch_normalization_1 (Batc  (None, 64)               256       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 64)                4160      \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 64)                0         \n",
      "                                                                 \n",
      " batch_normalization_2 (Batc  (None, 64)               256       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 64)                4160      \n",
      "                                                                 \n",
      " dropout_3 (Dropout)         (None, 64)                0         \n",
      "                                                                 \n",
      " batch_normalization_3 (Batc  (None, 64)               256       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 1)                 65        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 32,633\n",
      "Trainable params: 32,121\n",
      "Non-trainable params: 512\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "LSTM_tuned.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a499d9a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "s['LSTM_tuned_predicted'] = predictions_tuned\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5fb1c49",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axs = plt.subplots(1,1, figsize=(12,8))\n",
    "\n",
    "axs.plot( (np.exp(s['TSLA_AdjClose_pred'])-1)*100, label = \"actual value\", color = 'blue', marker='D')\n",
    "axs.plot( (np.exp(s['LM_predicted'])-1)*100, label = \"LR predicted value\", color = 'red', marker='D')\n",
    "axs.plot( (np.exp(s['LSTM_predicted'])-1)*100, label = \"LSTM predicted value\", color = 'green', marker='D')\n",
    "axs.plot( (np.exp(s['LSTM_tuned_predicted'])-1)*100, label = \"LSTM tuned predicted value\", color = 'black', marker='D')\n",
    "\n",
    "\n",
    "axs.set( \n",
    "       xlabel='Date', \n",
    "       ylabel='percentage change in 5 days',\n",
    "       title=f'Comparison actual and predicted percentage change for {tickers[0]} stock')\n",
    "axs.xaxis.label.set_size(15)\n",
    "axs.yaxis.label.set_size(15)\n",
    "axs.yaxis.label.set_size(15)\n",
    "\n",
    "axs.title.set_size(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3158b90c",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ddd5d801",
   "metadata": {},
   "outputs": [],
   "source": [
    "error_Training_LSTM_tuned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec44e4d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_results.append(error_Test_LSTM_tuned)\n",
    "train_results.append(error_Training_LSTM_tuned)\n",
    "names.append(\"LSTM_tuned\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e8bf2ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(test_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab42c7dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(train_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7788dfb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "len(names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "612051de",
   "metadata": {},
   "outputs": [],
   "source": [
    "# compare algorithms\n",
    "fig = pyplot.figure()\n",
    "\n",
    "ind = np.arange(len(names))  # the x locations for the groups\n",
    "width = 0.35  # the width of the bars\n",
    "\n",
    "fig.suptitle('Comparing the performance of various algorthims on the Train and Test Dataset')\n",
    "ax = fig.add_subplot(111)\n",
    "pyplot.bar(ind - width/2, train_results,  width=width, label='Train Error')\n",
    "pyplot.bar(ind + width/2, test_results, width=width, label='Test Error')\n",
    "fig.set_size_inches(15,8)\n",
    "pyplot.legend()\n",
    "ax.set_xticks(ind)\n",
    "ax.set_xticklabels(names)\n",
    "pyplot.ylabel('Mean Square Error')\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7851d713",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08893eab",
   "metadata": {},
   "outputs": [],
   "source": [
    "return_period = 5\n",
    "\n",
    "baseline_pred = Y_test.diff(return_period).shift(return_period) #.dropna(inplace=True)\n",
    "baseline_test = Y_test.diff(return_period)\n",
    "baseline_pred\n",
    "\n",
    "# base_line = mean_squared_error(baseline_pred, baseline_test)\n",
    "# base_line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc5f7107",
   "metadata": {},
   "outputs": [],
   "source": [
    "baseline_df = pd.DataFrame(baseline_test).merge(baseline_pred, on='dt',how='outer')\n",
    "baseline_df.dropna(inplace=True)\n",
    "baseline_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "409ed411",
   "metadata": {},
   "outputs": [],
   "source": [
    "base_line = mean_squared_error(baseline_df['TSLA_AdjClose_pred_x'], baseline_df['TSLA_AdjClose_pred_y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f88c034",
   "metadata": {},
   "outputs": [],
   "source": [
    "base_line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac2425be",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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