time_series_forecast.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3aeacc5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from CovidTemporalPrediction import CovidTemporalPredictor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "45983772",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_state_name = \"Texas\"\n",
    "temporal_predictor = CovidTemporalPredictor(state_name=test_state_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "245a4125",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NGwY/Pz8AQGRkJC5duoT169fjjjvugEajMQletVot3N3dZYGxRqMRfwYAd3d3i691c3MDgBq311Z2dlGTziz7+LgjP78E+nqauqaxYZ+YYp9UYV+YYp+YKigoE3/Wlevr9bZyY8DviCn2iamb7ZPaXJQ6ZLCsUCjEQLlS27ZtceDAAQBAaGgoMjMzZdszMzMRHByM0NCKVVwyMjLEuuTK0ozK7ZZeW917G5du1MRgEGAwNO0KNL3eAJ2OO6sU+8QU+6QK+8IU+6SKQXKiFwSB/XIDvyOm2Cem6rNPHLLoZenSpZg6darssYSEBLRt2xYAEBUVhdjYWHFbamoqUlNTERUVhdDQUISHh8u2x8bGIjw8HCEhIYiOjsbVq1eRlpYm2145wDAqKgrHjh2DcGMOH0EQcPToUURFRdXTpyUiIuIAPyJH5ZDB8rBhw3D48GGsXLkSV65cwbp167Bp0yY8+uijAIDJkydj8+bN2LhxIxISEjBnzhwMHToULVq0ELcvXrwYBw8exMGDB7FkyRJMmTIFANCiRQsMGjQIs2fPRkJCAjZu3IgtW7bgwQcfBACMHDkS+fn5WLhwIRITE7Fw4UKUlJTgrrvusk9nEBEREZHdOGQZRo8ePbB06VIsW7YMS5cuRfPmzbFkyRLExMQAAGJiYrBgwQIsW7YMeXl5GDhwIN58803x9dOmTUNWVhaefvppqFQqTJgwQZapXrRoEebNm4dJkyYhODgYb7/9Nnr06AEA8PLywmeffYbXX38d33//PTp16oTPP/8cHh4eDdoHRETkZASzPxKRnSkEgWsG1YeMjAJ7N6HeSOc0ZM1UBfaJKfZJFfaFKfaJqZyCMjz/8V4AQIcIX8x9qJedW2Rf/I6YYp+Yutk+CQ72rvE5DlmGQURERETkCBgsExEROQCBxRdEDonBMhERERGRBQyWiYiIHABHEBE5JgbLREREDoZxM5HjYLBMRERERGQBg2UiIiIiIgsYLBMRERERWcBgmYiIyAGwTpnIMTFYJiIicjSMnIkcBoNlIiIiByBw7jgih8RgmYiIiIjIAgbLREREDoZLXxM5DgbLREREREQWMFgmIiIiIrKAwTIREZED4Pg+IsfEYJmIiIiIyAIGy0RERA6AiWUix8RgmYiIiIjIAgbLREREjkBatMw0M5HDYLBMRERERGQBg2UiIiIHwGQykWNisExEREREZAGDZSIiIgfDLDOR42CwTERE5AgYIRM5JAbLREREREQWMFgmIiJyAEwsEzkmBstERERERBYwWCYiInIAgmRREoFpZiKHwWCZiIiIiMgCBstERERERBYwWCYiIiIisoDBMhERERGRBQyWiYiIHAAH9RE5JgbLREREREQWMFgmIiJyAIJsWRKmmYkcBYNlIiIiIiILGCwTEREREVnAYJmIiMgRsPKCyCExWCYiInIwnBmDyHE4RLCs1WoxevRoHDx4UHwsLi4O999/P2JiYnDnnXdi48aNsteMGTMGnTp1kv137tw5AIAgCFi8eDH69++Pvn37YtGiRTAYDOJrc3Jy8MwzzyAmJga33XYbNm/eLHvv06dPY+LEiYiKisL48eNx8uTJevz0RERETCwTOSq1vRtQVlaG559/HufPnxcfy8jIwOOPP47Jkyfj3XffxalTpzB37lwEBwdj6NCh0Ov1uHTpEr799lu0bt1afJ2/vz8AYNWqVdiyZQuWL18OnU6H2bNnIzAwENOmTQMAzJ07F6Wlpfjuu+8QHx+PV155BW3atEGPHj1QXFyM6dOn45577sG7776L9evXY8aMGfjrr7/g4eHRoH1DRERERPZl12A5MTERzz//PASj+03bt29HUFAQZs2aBQBo3bo1Dh48iF9//RVDhw5FSkoKysvL0aNHD2g0GpP3XbNmDWbOnInevXsDAF544QUsXboU06ZNw5UrV7Bz507s2LEDERER6NixI+Li4rBu3Tr06NEDW7duhUajwZw5c6BQKDBv3jzs3r0bf/zxB8aNG1f/nUJERE6JpRdEjsmuwfKhQ4fQr18/PPfcc4iOjhYfHzx4MDp37mzy/MLCQgAVQXZYWJjZQDk9PR2pqano06eP+FivXr1w9epVXL9+HfHx8QgLC0NERIRs+2effQYAiI+PR69evaBQKAAACoUCPXv2RFxcnFXBslKpgFKpqPXzGxOVSin7P7FPzGGfVGFfmGKfmFKpqs4ZCoUCarVz9w2/I6bYJ6Yaok/sGiw/8MADZh+PiIiQBbNZWVn47bff8MwzzwAAkpKS4OLighkzZuDkyZNo06YN5syZgx49eiAjIwMAEBISIr4+KCgIAJCWloaMjAzZNgAIDAxEeno6gIoSkPbt25tsl5aJ1EZAgKcYcDdVPj7u9m6Cw2GfmGKfVGFfmGKfVMko0Io/q1QK+Pt72rE1joPfEVPsE1P12Sd2r1muSWlpKZ555hkEBQXhX//6FwDg4sWLyMvLw8SJEzFz5kx8//33eOSRR7B161aUlpYCAFxdXcX3qPxZq9WipKREtq1yu1ZbcZCqaXttZWcXNenMso+PO/LzS6DXG2p+gRNgn5hin1RhX5hin5gqKioTf9bpDcjJKbJja+yP3xFT7BNTN9sntbkodehguaioCE8++SQuXbqEdevWwd294qrhzTffRGlpKby8vAAA8+fPx9GjR7F582bccsstACoC48oyjcpA193dHRqNxiTw1Wq1cHNzA4Aat9eWwSDAYGjaBWh6vQE6HXdWKfaJKfZJFfaFKfZJFemsTYIA9ssN/I6YYp+Yqs8+cdiil8LCQkybNg3nz5/H6tWrZbNeqNVqMVAGKmq72rZti/T0dISGhgKAWI4h/Tk4OBihoaHIzMyU/a3MzEwEBwcDgMXtxqUbREREtsQBfkSOySGDZYPBgKeffhopKSn45ptv0KFDB9n2hx9+GMuXL5c9/+zZs2jbti1CQ0MRHh6O2NhYcXtsbCzCw8MREhKC6OhoXL16FWlpabLtlQMMo6KicOzYMXGGDkEQcPToUURFRdXjJyYiIiIiR+SQZRg//PADDh48iE8//RQ+Pj5iZtjFxQV+fn647bbb8PHHH6Nz585o06YN1qxZg4KCAtx3330AgMmTJ2Px4sVo1qwZAGDJkiV49NFHAQAtWrTAoEGDMHv2bMybNw8nTpzAli1b8O233wIARo4ciSVLlmDhwoW4//77sWHDBpSUlOCuu+6yQ08QEZFTYpaZyGE4ZLC8bds2GAwGzJgxQ/Z437598c0332Dq1KkoKyvDW2+9hczMTERFRWHVqlViaca0adOQlZWFp59+GiqVChMmTMDUqVPF91m0aBHmzZuHSZMmITg4GG+//TZ69OgBAPDy8sJnn32G119/Hd9//z06deqEzz//nAuSEBERETkhhWC8IgjZREZGgb2bUG/UaiX8/T2Rk1PEAQY3sE9MsU+qsC9MsU9MXUorwIKvDwMAWoV64/X/q1ov4MylbBw5m4HRt7SGv7fpGgNNEb8jptgnpm62T4KDvWv+G3VpGBERETWc9zfEAQCSrxfi5Yd72bcxRE7GIQf4ERERkanEq3n2bgKR02GwTERE5AAEjuojckgMlomIiBwMA2cix8FgmYiIyBEwPiZySAyWiYiIiIgsYLBMRETkAASLvxCRPTFYJiIiIiKygMEyEREREZEFDJaJiIgcANfTJXJMDJaJiIiIiCxgsExEROQQBDM/EZG9MVgmIiIiIrKAwTIREZEDYM0ykWNisExEREREZAGDZSIiIiIiCxgsExERERFZwGCZiIiIiMgCBstEREQOQJCM8ONgPyLHwWCZiIiIiMgCBstEREQOgMlkIsfEYJmIiIiIyAIGy0RERI5AsPgLEdkRg2UiIiIHJnC0H5FdMVgmIiJyYAyVieyLwTIREZEDECz9zMwykV0xWCYiInJgjJWJ7IvBMhERkQNgBpnIMTFYJiIicmAMoonsi8EyERGRA2OsTGRfDJaJiIgcGINlIvtisExEROTABE4eR2RXDJaJiIgcgGBh7jhmlonsi8EyERGRA2OwTGRfDJaJiIgcgOWYmNEykT0xWCYiInJgBsbKRHbFYJmIiMghMComckQMlomIiByYgUXLRHbFYJmIiMjBCBZ/IaKG5hDBslarxejRo3Hw4EHxseTkZEydOhXR0dG4++67sWfPHtlr9u3bh9GjRyMqKgpTpkxBcnKybPvXX3+NwYMHIyYmBi+//DJKSkrEbWVlZXj55ZfRu3dvDBo0CF999ZXstTX9bSIiIluzlEDmctdE9mX3YLmsrAyzZs3C+fPnxccEQcBTTz2FoKAg/Pjjj7j33nvx9NNP49q1awCAa9eu4amnnsK4cePwww8/ICAgAE8++aR4QNm2bRuWL1+OBQsWYPXq1YiPj8f7778vvv+iRYtw8uRJrF69Gq+//jqWL1+OP/74o1Z/m4iIqCExVCayL7sGy4mJiZg0aRKuXLkie/zAgQNITk7GggUL0K5dO8yYMQPR0dH48ccfAQAbN25Et27d8Oijj6JDhw545513cPXqVRw6dAgAsGbNGjzyyCMYNmwYevTogTfeeAM//vgjSkpKUFxcjI0bN2LevHno2rUr7rjjDjz22GNYu3Ztrf42ERFRfZNmk5lYJrIvuwbLhw4dQr9+/fDdd9/JHo+Pj0eXLl3g4eEhPtarVy/ExcWJ23v37i1uc3d3R9euXREXFwe9Xo8TJ07ItkdHR6O8vBwJCQlISEiATqdDTEyM7L3j4+NhMBhq/NtEREQNiWUYRPaltucff+CBB8w+npGRgZCQENljgYGBSEtLq3F7fn4+ysrKZNvVajX8/PyQlpYGpVIJf39/uLq6ituDgoJQVlaG3NzcGv92bSmVCiiVCqte01ioVErZ/4l9Yg77pAr7whT7xJRCoZD9rFab76PKx5s6fkdMsU9MNUSf3HSwXJmxbdu2LTw9PW3RJpSUlMiCWQBwdXWFVqutcXtpaan4u7ntgiCY3QZUDDSs6W/XVkCAp+zA1xT5+LjbuwkOh31iin1ShX1hin1SxcOjQPxZpVLA37/inFouyM8llY87C35HTLFPTNVnn1gdLKempmLevHl49tln0bFjR0yYMAGJiYnw9fXF119/jc6dO990ozQaDXJzc2WPabVauLm5iduNg1etVgsfHx9oNBrxd+Pt7u7u0Ov1ZrcBgJubW41/u7ays4uadGbZx8cd+fkl0OsN9m6OQ2CfmGKfVGFfmGKfmCourjo36fUCcnKKAAC5eSWy51U+3tTxO2KKfWLqZvukNhefVgfL77zzDgoKChAQEIDff/8d165dw7p16/DTTz/h/fffN5mGrS5CQ0ORmJgoeywzM1MsjwgNDUVmZqbJ9s6dO8PPzw8ajQaZmZlo164dAECn0yE3NxfBwcEQBAE5OTnQ6XRQqys+fkZGBtzc3ODj41Pj364tg0GAoYmvUarXG6DTcWeVYp+YYp9UYV+YYp9U0Ruq+kEQBLFfyo36x9n6i98RU+wTU/XZJ1YXeBw4cAALFixAREQEdu3ahcGDB6Nnz56YNm0ajh07ZpNGRUVF4dSpU2JJBQDExsYiKipK3B4bGytuKykpwenTpxEVFQWlUonu3bvLtsfFxUGtViMyMhKdO3eGWq2WDdiLjY1F9+7doVQqa/zbREREDYoD/Ijsyupguby8HL6+vhAEAfv378ctt9wCADAYDGKm9mb17dsXYWFhmDt3Ls6fP4/PP/8cx48fx4QJEwAA48ePx9GjR/H555/j/PnzmDt3LiIiItCvXz8AFQMHV65cie3bt+P48eOYP38+Jk2aBHd3d7i7u2Ps2LGYP38+jh8/ju3bt+Orr77ClClTavW3iYiI6oWlRUkathVEZMTq6LZLly744YcfEBwcjPz8fAwZMgRarRZffPEFIiMjbdIolUqFTz75BPPmzcO4cePQqlUrfPzxxwgPDwcARERE4L///S/efvttfPzxx4iJicHHH38sDqgbNWoUrl69itdeew1arRYjRozA7NmzxfefO3cu5s+fj0ceeQReXl545plnMGLEiFr9bSIioobExDKRfSkEKydwPH78OJ544gnk5OTg8ccfx6xZszB//nz89ttv+PLLL1mucENGRkHNT2qk1Gol/P09kZNTxJqpG9gnptgnVdgXptgnpo5fyMJH38cDAMICPbDw8f4AgNSsIsz74qD4vK9eus0u7Wto/I6YYp+Yutk+CQ72rvlvWPumPXr0wJ49e1BYWAgfHx8AwCOPPIJnn30Wfn5+VjeSiIiILGNmmci+6jSDs1KpxNmzZ7FhwwYUFhZCr9fDy8vL1m0jIiJyGpaWuGasTGRfVmeWCwsLMW3aNMTHx0OhUGDgwIFYvHgxrly5glWrViE0NLQ+2klEROSUuNw1kX1ZnVn+4IMPoFAo8Ndff4kLdcyePRsajQaLFi2yeQOJiIicGmNlIruyOljeuXMn5syZgxYtWoiPtWvXDq+99hr2799v08YRERE5O8bKRPZldbCcnZ2N4OBgk8d9fHxQXFxsk0YRERFRBZZhENmX1cFy9+7d8fvvv5s8vnbtWnTp0sUmjSIiInI2lgb1MVYmsi+rB/jNmjULjz76KI4fPw6dTodPP/0USUlJOHXqFFauXFkfbSQiInJaAgsxiOzK6sxyz549sWHDBnh4eKBVq1aIi4tDs2bNsHbtWnG5aSIiIrKOpZCYmWUi+7I6swwAkZGRnPmCiIioATBYJrIvqzPLWq0WK1aswOXLlwEA8+bNQ0xMDKZNm4acnBybN5CIiMiZsQyDyL6sDpYXL16MVatWobCwELt378bPP/+MGTNmoKioiNlmIiKiupKN8OMSfkSOwupg+Y8//sAHH3yArl27YseOHejbty+eeOIJvPLKK/j777/roYlERETOi2UYRPZldbCcm5uLdu3aAQD27t2LgQMHAgD8/PxQWlpq29YRERE5CYsD/JhaJrIrqwf4tWzZEidOnEBWVhZSUlIwePBgAMD27dsRERFh8wYSERE5M2aWiezL6mD5sccew6xZs6BUKtG/f39ERkbi448/xscff4y33367PtpIRETU9FkIirmCH5F9WR0sjx07FpGRkUhJScGtt94KoGJVv5UrV2LAgAE2byAREZEzY6xMZF91nmc5MjJS/L0yaC4rK4NGo7FNy4iIiJyUYOFnImp4VgfLOTk5WLFiBc6dOwe9Xg+g4hZReXk5EhMTceTIEZs3koiIqKmzGBQztUxkV1bPhvHGG29g06ZN8Pf3x5EjRxAaGoqioiLExcVh+vTp9dFGIiIip2WwdwOInJzVmeX9+/fjvffew9ChQ3H27FlMmzYNkZGRePXVV5GYmFgfbSQiImryLA7kY2KZyK6sziwXFRWhU6dOAIC2bdsiISEBAPDQQw/h4MGDtm0dERGRk+NsGET2ZXWwHBoaiqtXrwIAWrdujbNnzwIA3N3dkZeXZ9vWEREROSOudk3kMKwOlkeMGIG5c+ciNjYWt9xyC37++Wf88ccfWLZsGVq1alUfbSQiInJazCwT2ZfVNcvPPfccysvLce3aNdxzzz0YMWIEnn32WXh7e2PZsmX10UYiIqImz2LJMmNlIruyOlh2dXXFK6+8gvLycgDAggUL8MQTTyAkJARqdZ2mbSYiIiIJQVJ8wViZyL6sLsPIzs7GlClTsHz5cvGxCRMmYPr06axZJiIisjGWYRDZl9XB8sKFC1FSUoJRo0aJj33xxRcoKCjAe++9Z9PGEREROQvLi5I0ZCuIyJjVwfKePXvw5ptvomPHjuJjXbt2xeuvv46///7blm0jIiJyegYGy0R2ZXWwrNfrzd4ScnFxQUlJiU0aRURE5HQsllswWiayJ6uD5T59+uCDDz5AYWGh+FhhYSGWLl2KPn362LRxREREzo4ly0T2ZfX0FXPnzsWDDz6IW2+9Fa1btwYAXLp0CX5+fvjyyy9t3T4iIiKnwLwykWOyOlhu2bIltm7dit9++w3nz5+HWq3G5MmTcc8998DNza0+2khERORUpNlkzoZBZF91mhjZ29sb999/v63bQkREREYYKxPZl9U1y0RERFQPLK3gx0IMIrtisExEROTAmFkmsi8Gy0RERA7AYgaZwTKRXVkdLCclJdVHO4iIiMgMA1PLRHZldbA8atQoTJo0CRs2bEBBQUF9tImIiMjpMCYmckxWB8tbt25F//79sWLFCgwaNAizZs3Cnj17OLUNERFRPWBmmci+rA6W27Zti1mzZmHnzp349NNPodFoMGvWLAwdOhQffvghrly5YpOG/fTTT+jUqZPJf5GRkQCAf//73ybbdu7cKb7+66+/xuDBgxETE4OXX35ZthR3WVkZXn75ZfTu3RuDBg3CV199JfvbycnJmDp1KqKjo3H33Xdjz549NvlMREREVmOsTGRXdZpnGQAUCgVuueUW+Pn5ITAwEN9++y2+/vprfPnllxg8eDBef/11hIWF1blhd999NwYPHiz+rtPp8Mgjj2Do0KEAKmqn33//fQwYMEB8jq+vLwBg27ZtWL58Od5//30EBgZi7ty5eP/99/Haa68BABYtWoSTJ09i9erVuHbtGl588UWEh4dj5MiREAQBTz31FDp27Igff/wR27dvx9NPP42tW7ciPDy8zp+HiIiotmSLktivGUSEOs6GkZ6ejs8//xyjR4/G+PHjERsbi3nz5mHfvn34448/UFpaiqeffvqmGubm5obg4GDxv19++QWCIOCFF16AVqtFSkoKunfvLnuOq6srAGDNmjV45JFHMGzYMPTo0QNvvPEGfvzxR5SUlKC4uBgbN27EvHnz0LVrV9xxxx147LHHsHbtWgDAgQMHkJycjAULFqBdu3aYMWMGoqOj8eOPP97U5yEiIqoLljkS2ZfVmeVHHnkEhw8fRkBAAO69914sW7YMbdu2Fbd7enpi8uTJmDt3rs0amZubiy+++AJvvfUWXF1dkZCQAIVCgRYtWpg8V6/X48SJE7JgPTo6GuXl5UhISIAgCNDpdIiJiRG39+rVCytWrIDBYEB8fDy6dOkCDw8P2fa4uDir2qxUKqBUKqz/sI2ASqWU/Z/YJ+awT6qwL0yxT0wpFArJz4BaXdE3xucSlUohe25Txe+IKfaJqYboE6uDZW9vbyxfvhxDhgyBSqUy+5yoqCgxU2sL69evR0hICEaOHAkAuHDhAry8vDBnzhwcOnQIzZo1wzPPPIMhQ4YgPz8fZWVlCAkJEV+vVqvh5+eHtLQ0KJVK+Pv7i1loAAgKCkJZWRlyc3ORkZEhey0ABAYGIi0tzao2BwR4NvmDmY+Pu72b4HDYJ6bYJ1XYF6bYJ1Xc3V3En5VKBfz9PW887ip7nq+fJ1RNNBljDr8jptgnpuqzT6wOlpcvX17jc5o1a4ZmzZrVqUHGBEHAxo0b8dhjj4mPXbhwAaWlpRg0aBCmT5+Ov/76C//+97/x3XffISgoCABkwXDl71qtFoIgmN0GAFqtFiUlJRZfa43s7KImnVn28XFHfn4J9HqDvZvjENgnptgnVdgXptgnpopLysWfDQYBOTlFAIATiRmy5+XkFEKlbPqZRX5HTLFPTN1sn1RelFanVsHylClTav1H16xZU+vn1saJEyeQnp6OUaNGiY89+eSTePjhh8UBfZGRkTh16hS+//57PPfccwBgEtxqtVq4u7tDr9eb3QZU1ElrNBrk5uaabHdzc7Oq3QaDAIOhadeZ6fUG6HTcWaXYJ6bYJ1XYF6bYJ1UMhqp+qCgZNEAQBPwTnyp7Xnm5AYL5G7tNEr8jptgnpuqzT2oVLIeHh9utpOCff/5B7969xcAYAJRKpex3oGJKu8TERPj5+UGj0SAzMxPt2rUDUDGTRm5uLoKDgyEIAnJycqDT6aBWV3z8jIwMuLm5wcfHB6GhoUhMTJS9d2ZmpklpBhERUX3j2D4i+6tVsPzuu+/WdzssOn78OHr27Cl77KWXXoJCocA777wjPpaQkICOHTtCqVSie/fuiI2NRb9+/QAAcXFxUKvV4hzNarUacXFx6N27NwAgNjYW3bt3h1KpRFRUFD7//HOUlpaK2eTY2Fj06tWrIT4uERE5KzOBsd7MHUoG0EQNq1bB8qZNm3D33XfD1dUVmzZtqva5Y8eOtUGzqpw/fx5jxoyRPXbbbbdh1qxZ6NevH2JiYvDrr78iNjYWCxYsAAA88MADeO2119CxY0eEhIRg/vz5mDRpEtzd3cU2zp8/H2+//TauX7+Or776Sgy8+/bti7CwMMydOxdPPvkkdu7ciePHj8sCcyIiooZgrpyPU8kRNaxaBcsvvfQSBg8ejMDAQLz00ksWn6dQKGweLGdmZsLHx0f22IgRI/D666/j008/xbVr19ChQwd8+eWXiIiIAACMGjUKV69exWuvvQatVosRI0Zg9uzZ4uvnzp2L+fPn45FHHoGXlxeeeeYZjBgxAgCgUqnwySefYN68eRg3bhxatWqFjz/+mAuSEBFRvTIXApvNLNd/U4hIQiHwErVeZGQU2LsJ9UatVsLf3xM5OUUcYHAD+8QU+6QK+8IU+8TUnhOp+Oq3MwCAQB8N3n9yIApLyjFz6T+y5306awg0rk1/hB+/I6bYJ6Zutk+Cg71rfI5N556xdi5iIiIiqmBuiWvzmWXmuIgaktXzLCcnJ+O9997DuXPnoNfrAVTUT2m1WmRnZ+P06dM2byQREZEzMl+zbIeGEDkxqzPLCxYswNmzZ3HnnXeK8x937doVmZmZmD9/fj00kYiIyBmYRsF6g+ltZQbLRA3L6szy0aNH8cknn6Bfv374559/MHz4cPTo0QMffvghdu3ahUmTJtVHO4mIiJxGZUBsfnErRstEDcnqzLJWq0XLli0BAG3atMHZs2cBVEzHFh8fb9vWEREROTHOhkFkf1YHy82bN8e5c+cAVATLZ85UjNw1GAwoKiqybeuIiIichLnyCnOZ5SUb4qDTcyYEooZidRnGfffdhzlz5mDRokUYOnQopkyZgvDwcOzduxedOnWqjzYSERE5JXOZ5UtpBfgn/hqG9YywQ4uInI/VwfL06dOh0WggCAJ69OiBJ598Ep9++inCwsKwaNGi+mgjERGRUzJYGM2XV6Rt4JYQOS+rg2WFQoGpU6eKv0+fPh3Tp0+3ZZuIiIgI5jPLAGfEIGpItQqWN23aVOs3tPVy10RERM7AXPxrfjYM4HDCdWTmlWDisPbw89LUb8OInFytguWXXnpJ9rtCoYAgCHBzc4NarUZhYSFUKhX8/f0ZLBMREdmIpWA5LbsYadnFyC8ux/P/im7YRhE5mVoFywkJCeLPW7ZswcqVK/HOO+8gMjISAHDp0iW8+OKLGD16dP20koiIyAlZKsOodOpidgO1hMh5WT113OLFizF//nwxUAaA1q1b45VXXsFnn31m08YRERE5C8FMIbKlzDIRNRyrg+X8/HxoNKb1UQaDAaWlpTZpFBEREQE6BstEdmd1sNyvXz8sWLAAKSkp4mNJSUl44403MHToUFu2jYiIyKkxs0xkf1ZPHTd//nxMmzYNd9xxB3x8fCAIAgoKCtCjRw+8+uqr9dFGIiIip8Rgmcj+rA6WQ0NDsXnzZuzbtw/nz5+HQqFAZGQk+vfvD4VCUR9tJCIiavKkJcuV9cs1DfAjovpndbAMACqVCoMHD8bgwYNt3R4iIiK6gZllIvuzumaZiIiIGgYzy0T2x2CZiIjIAQiSNfwqfzJwXWsiu2OwTERE5KCYWSayPwbLREREjsBMXMyaZSL7Y7BMRETkoJhZJrI/BstEREQOwFxYzMwykf0xWCYiInI0N2JkvcFg33YQEYNlIqqd9Oxi6PQ8cRPVF3MTXzCzTGR/DJaJqEb7T6Zh7ucH8NHGeHs3hcgpVIbIrFkmsj8Gy0RUoy+2nAYAnL6UY+eWEDmJG2nm2syzzFINovrFYJmIiMghmAbGlZllV7Xl07VOx+wzUX1isExERORgxBX8bgTLLtUEy+UcS0BUrxgsExEROQBpxUXlz3r9jcyyi8ri68p1DJaJ6hODZSIiIgelvREIu7lWEywzs0xUrxgsExEROajKrLGr2nKwrGNmmaheMVgmIiJyUOU6PQDAxaWammUGy0T1isEyERGRgxEEAYIgiIGwi4oD/IjsRW3vBhARETmrc8m5uJRWgGExzWUD/IpKdZi59B8UleoA1DR1HINlovrEYJmIiMgODIKAd9ceBQCUaXXQuMpPyZWBMgC4VDcbBjPLRPWKZRhERER2YJAsZX3gdDoEM4uSVKq2DIOZZaJ6xWCZiIjIDqTBskKhqPa5rtUM8NMxs0xUrxw6WP7rr7/QqVMn2X8zZ84EAJw+fRoTJ05EVFQUxo8fj5MnT8peu2XLFgwfPhxRUVF46qmnkJ2dLW4TBAGLFy9G//790bdvXyxatAgGQ9XBJicnB8888wxiYmJw2223YfPmzQ3zgYmIyGnoZcEyzK12LWJmmch+HDpYTkxMxLBhw7Bnzx7xv7feegvFxcWYPn06evfujZ9++gkxMTGYMWMGiouLAQDHjx/HvHnz8PTTT+O7775Dfn4+5s6dK77vqlWrsGXLFixfvhzLli3Dr7/+ilWrVonb586di4KCAnz33Xf497//jVdeeQXHjx9v8M9PRERNl0Eyok+B6jPLnDqOyH4cOlhOSkpCx44dERwcLP7n4+ODrVu3QqPRYM6cOWjXrh3mzZsHT09P/PHHHwCAb7/9FnfddRfGjh2LyMhILFq0CLt27UJycjIAYM2aNZg5cyZ69+6N/v3744UXXsDatWsBAFeuXMHOnTvx1ltvoWPHjpg4cSLGjBmDdevW2a0fiIio6ZFmlpXVx8rVLkrCAX5E9cvhg+XWrVubPB4fH49evXqJNV4KhQI9e/ZEXFycuL13797i88PCwhAeHo74+Hikp6cjNTUVffr0Ebf36tULV69exfXr1xEfH4+wsDBERETIth87dqx+PiQRETkl45rlaqow4MKp44jsxmGnjhMEARcvXsSePXvw2WefQa/XY+TIkZg5cyYyMjLQvn172fMDAwNx/vx5AMD169cREhJisj0tLQ0ZGRkAINseFBQEAOJ2c69NT0+3qv1KpQLKmlIFjZTqRu2cqpoaOmfjTH2iruakLeVMfVIT9oUp9ol8UJ9SqYCymkF+bq6WM8t6g1Dr/bIx4XfEFPvEVEP0icMGy9euXUNJSQlcXV3x0UcfISUlBW+99RZKS0vFx6VcXV2h1WoBAKWlpRa3l5aWir9LtwGAVqut8b1rKyDAs8bRzY2dj4+7vZvgcJyhT/z9Pa16vjP0SW2xL0w5c59ohapzhIuLEq4ay6dkXx93vP5Yf2zccQ6nL2bLtqlcVFbvl42JM39HLGGfmKrPPnHYYLl58+Y4ePAgfH19oVAo0LlzZxgMBsyePRt9+/Y1CV61Wi3c3NwAABqNxux2d3d3WWCs0WjEnwHA3d3d4msr37u2srOLmnRm2cfHHfn5JdCzVg6Ac/VJdnZhrS4EnalPasK+MMU+AXJyisWf9XoBZWU6i8/VaXVo18wLLz3YE1Pe2i7bVlBYhpyconprp73wO2KKfWLqZvukNheaDhssA4Cfn5/s93bt2qGsrAzBwcHIzMyUbcvMzBTLJ0JDQ81uDw4ORmhoKAAgIyNDrEuuLM2o3G7ptdYwGARZPVpTpNcbWCtnxBn6pFxnqPZ2sTFn6JPaYl+YcuY+0ZbrxZ8VgGwKU2MqpcJiP5WV65t0Hzrzd8QS9omp+uwThy16+eeff9CvXz+UlJSIj505cwZ+fn7igDvhxrQ7giDg6NGjiIqKAgBERUUhNjZWfF1qaipSU1MRFRWF0NBQhIeHy7bHxsYiPDwcISEhiI6OxtWrV5GWlibbHh0dXc+fmKiRaNrXgEQNxprZMKqrSebUcUT1y2GD5ZiYGGg0Grzyyiu4cOECdu3ahUWLFuGxxx7DyJEjkZ+fj4ULFyIxMRELFy5ESUkJ7rrrLgDA5MmTsXnzZmzcuBEJCQmYM2cOhg4dihYtWojbFy9ejIMHD+LgwYNYsmQJpkyZAgBo0aIFBg0ahNmzZyMhIQEbN27Eli1b8OCDD9qtL4gciXRuWCKqO6tW8KtuNgzejieqVw5bhuHl5YWVK1fi7bffxvjx4+Hp6Yn7778fjz32GBQKBT777DO8/vrr+P7779GpUyd8/vnn8PDwAFARaC9YsADLli1DXl4eBg4ciDfffFN872nTpiErKwtPP/00VCoVJkyYgKlTp4rbFy1ahHnz5mHSpEkIDg7G22+/jR49ejR0FxARURNmvIJfddeh1U0dx8wyUf1y2GAZADp06CBbWU+qR48e+Pnnny2+dty4cRg3bpzZbSqVCnPnzpWt6icVGBiIFStWWN9gIicgMLNMZBPWZZarWZSEwTJRvXLYMgwickyMlYlsw5qaZWlm+baezWXbWIZBVL8YLBORVRgsE9mGNZllabA8cWh7/Ou29gj0qZjStEwyqwYR2R6DZSKyisDpMIhsQi+58lQqFbISJ3eNvOxCOsBP46rCnX1bokMLXwBA0tV87IhNqefWEjkvBstEZBVmlolsw9Jc/G6uKiydOVj2mIuZmmUXyfK+a/86Z9vGEZGIwTIRWYXBMpFtyGuWFbJ7NmqV/PRsbjaM6uZeJiLb4Z5GRFZhGQaRbRiMpo6rjlpl+gQXFU/hRA2BexoRWYWZZSLbsGaAn7nt1c29TES2wz2NiKzCeZaJbEO6GqZCAXEp+ZqyzJWYWSZqGNzTiMgqDJWpPvx5OBnvfhuLjNwSezelwRjXLFuLmWWihsE9jYiswsQy1YcNO87jXEoeVmw+Ze+mNBjjmuXK3xSoXeDMAX5EDYN7GhFZh9Ey1aNLqfn2bkKDudnMsrqmZf+IyCYYLBORVSxMDUtkE8709TLJLFt5Icp9kahhMFgmIiKyA72l2TBqmTDWM1omahAMlonIKpwNg8g2ZJllM9sn394BADB2UJsaX09E9Udt7wYQUePSULFyuU6P99fHwV2jxn8m9qhTTSeRI5NmhqW7VeU3/Y4+LTCgWzN4ubtYeL2h/hpHRCJmlonIKg2VWd557BoSr+bhxIUsnLyQ1SB/k+zDWe9WSOdZttQHlgJlAPDQyPNdb64+jMw855l6j6ihMFgmIqs0VFhTWKIVfy4p0zfQXyV7cM5Q2SizLEju2tTyLsrA7mGy3y+mFuDbP8/ZqnlEdAODZSKyij2ygILThlPOwWkzy5Jg2VCHPnB1UeHhOzvJHkvLKr7pdhGRHINlIrJKw4U1kuyac8ZSTsNJY2XTzHId3kNlNNcyFyohsj3uVURklYYKbDicz3k466wOgoWaZWu++ybBMhcqqTdX0gtw8HS6035fnRlnwyAiq9inDIOaMmaW6/4dV6kURr8zB1Yf9AYD5q86DAAoLtNhWExzO7eIGhL3KiKySoNllqUxgJMGU86iLvW6TYHBqAyjcueyZpZElVJ+GtfrDTh2PgNlWg6KtSVtedU0fTuPptixJWQPDJaJyCr2CGs4wK9pc9JY2ahmuW6dYFyGceV6If774wms/O30TbWN5DjNu3NjsExEVmmoMgzp8r/OGkw5C2e9GDLOLNtigF+lI2cz6tgqMkd6DHLOb6tzY7BMRNbhAD+yMWe9GNLf5NRxgGnNMjUAJ/2+OjMGy0RkFWetL6X646yzC5jWLFv/HsY1y1Q/nHUucKrAvYyIHJN0mmWep5o0Zw1E5LNhSKaOs6JA1lIZBtmWgWUYTo3BMhFZhfMsk605aWJZdpemzjXLLMNoEM56QUcVGCwTkVXsMRjLWQeAOQvjQMRZyjIszYZhTfirZhlGg5AN8GPg7HS4lxGRVRrsPKHgctfOwvg7Va43mH9iE2NSs1wHSpZhNAgegpwbg2Uisoq1J/W6Zgm5JonzMM7U6Z0yWBaq7qBYtSgJg+WGwGyyc2OwTERWseakcfFaHp76YBdWbuECCWSZ8QwrOicsw6jrR3aWkhV7k35F2eXOh8EyEVnFmvPEkrWxKCrVYe/JNKszM9IqDE5X17QZ//Pq9c7x722QF8LWbeo4DvBrEMwsOzcGy0RkFWtOGrmFZeLP5bqKW+vFpeX45OcT+OPgldr/TaZymjTjiyG9wfnKMKSf2JrwNyzQE7dGhUNtJmjmRabtmFzYkFNhsExEVrHmPCGtpywt1wMAftp9AUfOZuD7nYkoKdNZfK10rlnGyk2bSWbZSf7BjWfDqOunnnpXJOY93Nv0/Z2k9rshMD52bgyWicgq1mSWlZKAt1RbESxfSisQH9PqLJ/MpX/HWYInZ2U6wM85/r0tzYZhzaIklTSuKpPHyqvZv8g60u+oc3w7SYrBMhHVG4Uks1x2I1iWhgFlWsuZZdmAGgbLTRozyzdfE6txMRMsO8lFR0NgZtm5MVgmIqtYE8eozAXLRtnmH3clYeE3R2T1zRV/x3aBBDk21ixX7Fc38zXXuJiezo+dy6j7G5KMvGbZfu1oqrLySvHHwSsm5wFHwWCZiKxT5zIMHQRBQGpWkfhYUUk5ftt/GUlX87Fu+3mLf8ZZMo3Oyllnw5B9r2/yI7uaySyv2Xb25t6URPJY2Tm+nw1p4TdH8P3ORHzwXZy9m2KW2t4NIKLGxZqcn3EW+XDCdRSVVpVe5BVrxZ+lQTRgnHXjyakpKiopR+zZ63AzCvSc5eLI9O5J3T+3WsXcV33i3a36lVtYcS5IySiq4Zn24dB7V3p6OmbOnIm+ffti8ODBeOedd1BWVpGif+utt9CpUyfZf99++6342i1btmD48OGIiorCU089hezsbHGbIAhYvHgx+vfvj759+2LRokUwSG775eTk4JlnnkFMTAxuu+02bN68ueE+NJGjs+KcIV2Kt6xcjxWbT8m25xeVW3ytNJBgzXLTc/ZKDu5/ZSuWbjyOVb8nyLY5yywOlhYl4czJjoczxzk3h80sC4KAmTNnwsfHB2vXrkVeXh5efvllKJVKvPjii0hKSsLzzz+P++67T3yNl5cXAOD48eOYN28e3njjDURGRmLhwoWYO3cuPvvsMwDAqlWrsGXLFixfvhw6nQ6zZ89GYGAgpk2bBgCYO3cuSktL8d133yE+Ph6vvPIK2rRpgx49ejR8RxA5GGsyLLKp427ULEvlFVmuT5OvmMWzU1OzcE2s+PP1nBLZNqfJLEuSNAIEBmEOjMcg5+awmeULFy4gLi4O77zzDjp06IDevXtj5syZ2LJlCwAgKSkJXbp0QXBwsPifu7s7AODbb7/FXXfdhbFjxyIyMhKLFi3Crl27kJycDABYs2YNZs6cid69e6N///544YUXsHbtWgDAlStXsHPnTrz11lvo2LEjJk6ciDFjxmDdunX26QgiB2PNKUM6dVWpmZkv8ouqyjCMs2nyzLIVf5TsTqc3YO1f57D9SHLdXu80wbL5qePqmloe1CPM5DFnGSxZ3xgrOzeHDZaDg4Px5ZdfIigoSPZ4YWEhCgsLkZ6ejtatW5t9bXx8PHr3rpqgPSwsDOHh4YiPj0d6ejpSU1PRp08fcXuvXr1w9epVXL9+HfHx8QgLC0NERIRs+7Fjx2z7AYkaKWsyy1pdVTbZfGZZK/lNHiEILMNotP4Xm4IdsSlYt/08MvNKTLbraiizcMYBfraoif2/uyKxdOYgRLevOm+WaRks25otAufEq3n44e8k5Bdra34y2Z3DlmH4+Phg8ODB4u8GgwHffvst+vfvj6SkJCgUCqxYsQK7d++Gn58f/u///k8sybh+/TpCQkJk7xcYGIi0tDRkZFRMpSPdXhmQV24399r09HSr2q9UKmT1mk2J6sZAEhUHlIicqU+USiXU6po/p0qlhLa8KkAuLDGtTy4ornpMqYTF9xUUlrc1Bk31+5GRW4J1f51Dn86huKVbM/Hxy+mF4s8FJeVoFugpe11JNfNrAwAUQE5hGQJ8NFApm1afSRlfA1YOiFUoFHX+vvu7qDAoKgxxiZkAAJ3B0Gj3HUfab6RzxitscDx6+5uKMqT0nGL8Z2JUrV/nSH1SX6zt24boE4cNlo29//77OH36NH744QecOnUKCoUCbdu2xUMPPYTDhw/j1VdfhZeXF+644w6UlpbC1dVV9npXV1dotVqUlpaKv0u3AYBWq0VJSYnF11ojIMCzTqswNSY+Pu72boLDcYY+8fTUwN/f0+L2/CItPNzUUKuU4tzKALAr7prJcwskWRWVSil7XxfXqsOTq6u62r/ZWDS178eC1UeQmJyL2LMZGDW4nfi4p0fVMdTd3fT7UlJD5nj/6XR8/NMJDOwRjpce6VPtcxszaTZZoVDA9cZ3XqlU3NT3PSig6rUad9dGv+84wn7jXSApGbvJfx+p2LMZdXovR+iT+lLXvq3PPmkUwfL777+P1atX48MPP0THjh3RoUMHDBs2DH5+fgCAyMhIXLp0CevXr8cdd9wBjUZjEtxqtVq4u7vLAmONRiP+DADu7u4WX+vm5mZVm7Ozi5p0ZtnHxx35+SVOM2q9Js7UJwWFpcjJMT+9z6W0fLzx1WG0DPXGm4/3k2WWzcnJrxrgp9cbZO9bWlqVdS4u1lr8m41BU/1+JCbnij9L/330+qp/95zcYuTkFCG3sAznknMR3T4IqRmFqM7RhOsAgL3HryErq7DJHkul5SY6vQFlZRXfeYNBuKnvu06SuU/PKISnmQVLGgNH2m/yJOVEBv3N/fsYl5VZ816VfZJwIROrfjuNgd3DMDgqvM5tcUTW9u3Nfk9qE5w7fLD85ptvYv369Xj//fdx5513Aqi4Aq8MlCu1bdsWBw4cAACEhoYiMzNTtj0zMxPBwcEIDQ0FAGRkZIh1yZWlGZXbLb3WGgaD0OTrLPV6A3S6pnPitwVn6BOdzvJn/HprAvQGARdT85FbUFbjan+C0S/S99VJAomm0q9N5XOYI/1cKoV85UadzoCFq48gPacEI/u1RNfWAbV+36sZhQgLbNyZUUtkU8cZBDHTrABu6nuillxcFJeUN/rvnCPsN9K/L0C4qfZIS9LUKmWd3uv9dUdxPacEpy/lYEDXZjW/oBGpa9/W5/fEoS83ly9fjg0bNuCDDz7AqFGjxMeXLl2KqVOnyp6bkJCAtm3bAgCioqIQG1s1LVFqaipSU1MRFRWF0NBQhIeHy7bHxsYiPDwcISEhiI6OxtWrV5GWlibbHh0dXT8fkpyOQRAa9Qh1S4NbrmYU4sK1fPF3a5ctLTfKCMgG+HEoeqMiXSBj2Y/HEXs2A+k3pof74+AVFJVanl/bmKMuUmALeqPZMGz1LXdzrVrkpbSGuztUO/IFZG7uvYol33/pv5U1jKdbpPrlsJnlpKQkfPLJJ5g+fTp69eolZn8BYNiwYfj888+xcuVK3HHHHdizZw82bdqENWvWAAAmT56Mhx9+GNHR0ejevTsWLlyIoUOHokWLFuL2xYsXo1mziquxJUuW4NFHHwUAtGjRAoMGDcLs2bMxb948nDhxAlu2bJEteEJUV3qDAW9+fQQFJeV449G+8HJ3sXeTrCYIFRkwhUIBbbke3/51DuGBnjh849Z5JWuD5TKjk7r0zoyzzLvbmAmCgMy8UgT5ukGtlpdNfPzzCdnvxWU1DPCTyM4vtUn7HJFs6jgbLqGskdT7l5mZhYasZ8sV/KSrmFYGywaDgMKScvh4ulp6GdmRwwbLO3bsgF6vx6effopPP/1Utu3s2bNYunQpli1bhqVLl6J58+ZYsmQJYmJiAAAxMTFYsGABli1bhry8PAwcOBBvvvmm+Ppp06YhKysLTz/9NFQqFSZMmCDLVC9atAjz5s3DpEmTEBwcjLfffpsLkpBNJFzOxZXrFfWafxy8gglD29XwCsez90Qq1mw7i8nDO6C4VIc9x1PNPi+3wPKgWH9vDXIK5MG08Umd8yw3Lj/tvoDf9l/GPbe0hrKGwc1FZmZGscT4IqqpEATBptlKKQ9NVbbS3Cw0ZD35Cn43948lvVisDJb/++NxHL+QhVmTotG1Te3LlKhhOGywPH36dEyfPt3i9uHDh2P48OEWt48bNw7jxo0zu02lUmHu3LmYO3eu2e2BgYFYsWKFdQ0mqgXp/LI1DX5zVPFJWQCAL349jdt7RVh8njSz3DzYE1clt9M93VxMg+Vy4zIM6c/MLDu63/ZfBgD8uu8SRg1oVe1zf913qdbv21Qzo8alRYKkDuNmZ1JyUavg6+mKvCItMnJ5u94WbHkMKpZllivCsMrj6kcb4/HFnGE2+1tkGw5ds0zU1DhiyHc+JReHE65bPBlUd5Lw87J8y1AaDHsblZt4uZtep+v0BiRdy8P3/0vElfQCWTDBMozGpaZFR7Tltb9V0FRrbo0HgNv6ejDYv2IaLQbLtiH9xt7sP5W0Zl9jVLPMY51jctjMMhHVv8KScrzz7VEAwMzxPRDdIcjkOdWdxKsLeqSZZePabC8P80H2wjUVA29PXsxC82Av8XEO8HM8fxy8YnGbzoYr8DXVzLJxUJRXpMWJC1k2e/9gX3ckpuQxWLYRW2aWSySZZbWFaRELS8qRlVeKVs28bfZ3qe6YWSZqSNLjrQNMHSsdUX3gdJrZ51Q38MjcEtaVcgurapaNg+OaBjamZBQhMSVX/L2pT8PY2Jy5nIPvdyZa3G7LOXGbarBs7judmlUMwDaHhhAxs1zq1GVMJVYMJq2OrAtvsjulA/zMHdoEQcArXx7EG18fxvEk211ANRaO+H1lsEzkxFSSrIbeQjawuuPWX0eSLW7LLagms2ymDMNYlmTBEgbLjuVqNYuKKBUKm2aWm2oZRn3fbg/xqwiWy8r1yC+ybgXapmLrgct4+sPd1R6nasumNcuSAF5vMJi8t/Tf7Lv/na/x/ZranTdHLEVhsExkJwpHSC1LVNaZ6vQGHDufIS5FXdfjcJZkyi+TmmU366bMc8Bjp1OraQCazobTlxQWN83ZHKq9ALTBoSHYr2rp3+tOWooRdz4TAoBTF7Nv+r2k/1w3eziSzrNcsRiNfLv0O+/mWnNioaklEywlbuyJwTJRA7LlXKq2IF0IRH/jiL31wGX898cTWLIhTpxT+WZ5eRjXLFsXLBdbsYgF1b/qVp8WINQ6s9wuwrfG51xOL2iSt6LrO3tWOcAPcN5BfgU3ps2rbVlQYUk5Tl/KNht8CvLpeW6qXdIyDL1BMMkMF0qOd+6amhctccSyhZthy4ttW2GwTOTEpEuDVl7N74hNAQBcuV6xIp8tDsOmZRjWBctJ1/Kb9OIUjY2immhZEIAjRgvUWOJtYaCnsa9/P1Or5zmSsnI99p1Mtfi9rS4baIt7Tj4eLtC4VARazrraW+GNu2O1vTB559tYLN4Qhz8PmynbsGE8Kp06zmAQTL4L0syyey0yy45YtnAzmFkmcnYOdgyQZZZv/Bwe6Ck+dimt4KazFmqVwmRJV88ayjDMLQErXUqb7KumRUcqtQnzqXZ7ZTBXE71BwK/7LuHjn0/YbMBWfVu//Ty+3HIGr648ZHZ7fdeZKhQKsRTDGTPLeoNBDEp1tQwmKwdYmhu8Wl9lGHqDYBLsSheScdc4YRmGA34eBstEDUh6DLjJdQdsQpZZNnOAKiopv+n5X13VKngYHfDNBcNS5jLP1iyRTPWrtl9djUv1pxjjOWYtUauU+Hn3BcSezcBTH+5uFKvS7Y6/BsDybAwNERAE+7kBAPafSsfzH+/FPzfa5AyKSnViUGuLTGV9DfCrqFmWv3dBibRmueZ9xBGDy5thy9l0bIXBMlEDcrRRy9LMcmX2RTodXGGp9cGycdbR1UVpMnWccZBknD0xGyyXMlhubGrKHNc2s2y82uO3f56tc5scRfUD/GxzJR0iqVvOKSjDqt8TbPK+jYG0lEFvgxpY2cxxN3EYFwRBdizTC4LJAGbpcvC1+VPMLNc/BstEDcjRrpjLJZnlygNuibbqQF5UorN6UKLxgBRXtQqebsaZZfnvgT4a2e/mM8uOn01s6gShor6yticz1xqCYbda3GI25+i5jDq9zpE0REAQIpkRw9lI7z7Yoq9tlVkuK9fL2mO2ZtnKtusNAnR6Q5OZk7y2ZTMNicEyNVmCIDjc7VpHu2KWLkusv3E7UDoYqKi03Op6R+MAycvDBWqV/FBjnFEM9HEzeY0xZpbt778/nsB/lv2DqxlFtXq+i1pZbZK0psyypZkAVMrGf+qq7i6TrSq0urQJMHmsrInOW21MeuyvTea1pmDYVncFjY9jen31Ncu1SbCUavWY+9l+vPDJXoc759WGcd87WlIJYLBMTdhXW8/gP8v+QezZ2o3MbwiOdrtMmlnW6Q34afcF2fbjSVlY8PURk9epVZZP58Y1dtJbwZZeH+hrFCybGQDImmX7KtXqEJeYiaJSHXYcTanVa9QqRbWBbU31mJbmmFVVN3ddI9EQF86h/h4YGtNc9pizzCojDzhrEyzLfzcOjuUzx1l+P4Mg4MDpNFzNNH9BaRIsm6lZlra9NtMwHjqTjqz8MhSV6vDP8cZXl278CR0tqQQwWKYmbO+JNAgC8PHPJ+3dFJFeclB0iAF+kgNxuc6A3/ZfrtXrfDwtT/llnC0M8fcweY7xohbSzLJCYX4E+IFT6cgvds6VyByBttz6bI9KpYSqmgurmgb4WQqmy/UG/Hk4GftPml+ivTGQXjhHtvSTbbPlseH+29qjbXjVrCRZThIsF0iOFbWpWTZ+jnFQaylA3rgzEXM/24/jSZkAgC37LuHzX07jg+/iZM/TluuxO/4a/jmeKnvcIFQ/dVxt2p4nWaHRRdX4wjrjz++IU8fVrWCMyME5Wga3kqO062JqPk5ezEZqVlX2Q2fFrS8fD1dk55eZ3WYc4ISaySwbk9YoC4Ll6ZJ2xV3DPbe0rnU7yXbqcvterVRCrVSg8puiUMgzdDWVYVgMlnUGbNhRsQxwsL872jeveXETRyM9FgyNaY6EK7n18ndcXVR4dmIUZi79BwCQV+gcF5yy7GwtjrvGGdyCYq3Jccn4Z53egD8OXYEgAB9tPI4Vzw/Bpn8uAqgYUFmu0+OPQ8n463Ayikt1Zks5cgrK8OVv8nnEC0utq1mWLmfuaeUc9o7AuFtsMSDT1hgsU5PkqHVb0gOfvSbGKC7VYcmGOJOyBmlJRk2qyywb1yz7e1cM3hvZtyX+OHQFfSJDTF5jHBxbqlXNyiuBTm+AIAhwUdduJgWyDW0dgmWVSiErmXB3Vcu+dzVnlms+RV3PKXb4YFkQBJO7KdJjQX1nAz3c1OKFiqMeG21Nlp2tRabSOJAtKC5HWCBw9koODp5ON3vMy8orlR3Hdx67Ktt+PacEPxuVtplzLjn3ptpeIHl+o8wsG/W9Iw7wY7BMTZKj3q6XHvgaahq5snI9ft59AW3CfNCncwj2n0ozW/9basVIap9qVl4zzgZWBjzjhrRFdIcgtG7mXeNrmgWYlm4AFbf1X1yxHzq9AW8+1q/adpBtldWhDMNVrYRKcvJ216jkwbJL9aeg2swxW3m7PPl6Icp1BlnJgaPQGwSTOn1pZtlFXb8BjlKhgJe7CwqKy50mWC6wdkYJM5lloGJxmSvXC2XbKp+ZkiF/fO8JeYmFpbrlmmhrmP/emPR854hLRdeEA/yI7KRAclvKeCYGe5IGyA1VkvHD30n483AyPvvlFF7/6hCSjQ78dVHdqlLGAU5l9lCtUqJjCz+z04kZP9YixDSgBoC9x1ORU1CGguJyLP/xhE0XCqDq1SWz7KJWyjLLxpniutYsSxWX6ZCdX4rXvzqEt9YcQXpOsdXtrG/msoNnJdnEhjhGVZYUSLOQtlBSpsP/jqY4XL/Lp1+rTc2y+YVBjANloOIzz//qkMl4mBSjWWKuSYLllx7sWXOjzbar5rZL67MdpdTPGsYf0RFrlh0niiCyoXzpbal6ztpYw3h+zYawI7Zq5oKrGUU4nJBe42tmjOkq+/2B4R1kv2tcLfepceBraRW35sEVy2oH+bqZ1K56SOZl7hjhi4gbz5VmXBKv5uHM5RyL7SDb+vNwco3PMR6o5qpWQSkNljXmL6QsqU0ZRkmZDsfOZ4q/x551vDmYjbN9SVfzZINp1UbHKOOSDVvwvhEs2zqz/NfhZHz75zmsNKq7tTdrSxmMs5kFxeUo11m+QDQXRBv7Ze8l8efwIM86TQlYm7aXlFW10xEDzZoY32XlbBhEDUQ6PVJ105w1NGmAbK8DgvTAakl4kKfsdy93F1mGsLqBWWpl9XMqV3puYhTGD2mLOQ/EmA2anv9XNPp2DsEjd0VaDKqupN98lpxqdi2zCHGJmTU+z9uoLMbFRWlSsyxV89RxtSvDkAaAxgvgOALjAGbFZnlGsiHqTCv/bQpsXKJWOQ97ah1LDuqLcRlGTXeh9Ebbf959wWTlyLpSq5TwdFNXe0fOEmvLKhwx0KyJ8b+NI5aSMFimJuNKegG+35mI/GItTl7MFh8v1xmQW1iGj38+IaspKy4tx5dbTuPvuKvm3q5eNFRmWW8w3NSiLJWD8ioZZ/iqW5lNaTQHrqVAN8DHDaMGtEaQr7vZgLprmwA8cW83hAV6ws3C37NUGrD/VFqD/rs2dbWdbsx09UalbJ5lkxKdmpbDrmUZhjSL6IixgnS/P3MpG1lGM8mYZJbroQ2VC/3YOrNccmOsQ1GpzqoZdeqTTm9AidG4jJrGiJjLyB49V/MF4qgBrdCuefV18n5erlAoFNBKMtVhgebHZVhql0EQkHA5RzbzhdnnG6qe74izSphjvM86Ynbc8S7Biepo8YY4FJaU41pmkWx0cZlWjzV/nEVcYiZiz2ZgQNdmWPnbGew/VTFH676Tabi1R7hJkFcfpAcv40yGrWTkluDN1UfQIsQLYwe3qdN7mKs7VikV4oG4uiDHuB9dXVQw1HDwqzloMn+okk6xBFTckj9wKg3f/HkOANDM3wORrfyrfW+qmaXMp6ebGjq9IE4r56KS/zu6qFWyeZY93Ixrlqs/BRlnos0pLtXJVjUwDpIcgfQW/9aDV0y2uzTA3a/6rFmulF+kRYDRapz2UGTmgsBgEFBdAt9cRrZy7mRLVEoFhkSHIyu/FElX8y0+r7JPpNPTdWsTiNSsmuu8K9t14FQavtxyBsF+7vjq1REWn19QrMWmfy5gR2wK3DVqvPlYvxqPr/ZmMsDPAa94GSxTk3D2So6YMTmelCXbJgCyW8jxiZlioFyprFxfp1tk1jI0QGZ5856LKCwpx5nLOdXW9HZrG4CTF7JNHlcpFVCrlNC4qMQgyM1VXnvqaqEOufL1UkqFAgaTNZrkqquBBkyD6dAAD6RnF6OoRB4YfbrppOyuQtK1PAbLNqC1MK1g8yBPpGYXVwXLRhlSF7VSFsh6GK3M6KpWQgHTFbwq1XaAX6m26ntQGbxpy/VQKOAQUwxWnvwFQcBZM/Mpm7SxHmLnyprlotJyGAyCzZID0ll08osdI1guMBMs6/QCqpt8xdzxuLq5r6fc2QkdInwR5Otu8c5XpRA/07nmw4JqmVm+0a41f5wFUJEMeWXFXovPl9ZJF5XqkJJRiHbhjje1onQ6RdNFSRwvI84yDGr0dHoDvtxyutbPP2s0pyUALPvhOD74Lk6sv6svDVGGkVfDbToAuKVbMzQzs7IeUBWg+Hi6yB6rbc1yXU7C1S2JLG0TUBGABdwoEykyyixLA2WgYoAZ3TxL5S4qlVIWIBuXE7iqlbLvvJfRggkqlbLamSCMyzDM1SNfSSuQ1a6XlFXUMM/+dB9eXLFfFkjbS+W8sVqdwWypQkMMQq4swxAEIDWrCDtiU2xSkiHtX0dZ8MRcZrmmbKW1dbI92gWiebAXANOpLnu0C5T93rVNAADg4REdoVQoMH5I21ov2X4tswizP9knu2CNP19zeUglnRXz5zeUbYeu4Ln/7sGJCxWJLeObrJxnmageHEm4blIDWB1zmZ3KAHrlltN46aFeACqC8LjzmegQ4QtfL43Ja+qiIQb41eYg7OnmAk9387t/ZYDi66VBRm5FraqLWikboV9dsFzbk4A1pEGTt4eLGHRJg2VzI9eNgzeqG0ur96lVSlmJhnHQ56pWyUqPTIJlpQIqlQKWZqUzrpWPbOVvMtuFAPm+VFKmw85jV8VygxMXss0uhNOQKjNlxksoV2qIQcjSwZef/XIKKRlFiE/MxKx/Rd/U+8oyy7W4UG8I5kpNajreWqqTvTUqDBeuFZjMqewnOScMiWmOY+czIQB4fHQXBPq6Qac34OfdF6A3COjTueL7N6xnBAb1CIOLWoXd8ddq/XluZoly45UJHcHOY1eRX1yOQ2fS0b1toOlsGA7YZp5JqNFLvnEQ83RTw1VysnZ1UcLHw3Tpz8vpBRbf62JagVg/tfPoVXyy6STmrzpss0VOGqIMw9wy1J1b+aNv56qAwdNdbfFOb2Ug/NAdHaEAEOCjgb+3xiSzbGl2K2U9THslvc3p7e4qLukqLcPILzI9QdZlbmAyZakMQ61SyAJkF5VS9r1yMcose5tklhXVZpZdjYJv4xpmc+VAJWV65BVW7QP1PRQhLbsYm/dcrHbmhMo+ML4TUsm4D+plgJ+k7yvnAz55MbtWd6KqI61Zvtn3shVzGXPprf0yMwswSY/H3dtWZIYjgj3xwPCOJrX2U++KlN1B07io8OKDPfHSgz0R6FtRhqJWKTFxWHvcf3sH2TGxsuSmoRaHKXewkgaDIIjnqMpVY01rlh2rzQCDZWqkLqcVYOGaI/hoYzyy8iquur08XBEWWDXlWbCfO3p1si6jVK4z4IsbJR3HzldksPKKtGK92M2S3l6y5QC/4lIdysr1yMwrkU2EX8nfW4NWkpXz3FzV4i1EY5XBcstQb7z3xAAseLQfVEqlycnBUumEqh6yZG6SenIXtVK8HS8NPsydqL/7XyL+MDOgimonNasI5TqD2eACqCijkH4vXNRKk9p2aRDialRWoVIqZVlVb6OLW+NMtbQcR61SINhMLWji1TzZKoHaOqw8aI1F645i856L+O+Pxy0+RwyWLQRIKqXC4sWnrRhfqFQ6dCYdgiBgw47z+O5/581OsVZQrMWrXx7EJz/LFwIyCILsu+EwmeVqyjB2Hk3Bkx/swqZ/LkAQBOw7mYoXV+zDoYTr4nPHDm6DmeN74OWHe8HVRSUr/1GrlLg1Kvym21hdX3Vs4YeRfVve9N8A7FOGoTcYcDE132zQW1BcLpYiHTpzHcWl5aazYbAMg8g2PvvlFNKy5SOJPTQqhAZ4iJnjIB83jOjbAjuPWTeF2IFT6biYWoB0yfsfPZeBMq2+VlNZVcdWmWWDIODjn04gO78M993aFp/9crIiYyEIZqdI8vVyldXv6vQGxHQIwog+LZCeXYx4yaBIaUASJAlGVEZBkLeHi9lsWn2UYbSTLGGcklGInh2DAVRklisHiuQVmc/sfb8zEedTcnH/7R3MBldk3oFTafj819MI9XdHlxs1l0DFlFeVo/grMsnyYFka9BlnljVq42BZnln28XCV3UI3CZYlU9N5uLnAx8MVVyG/OCwsKZeValjK5tpK7o063Utplu9Y1VSGoVAooFIqxFvm9bEoiZeZu2wAEJtwHf5eGnHRmR7tgtDZaFDspn8u4mpmEa5mFuF6TglCb9Tolmn1ssGZtroDd7MKzZRhbDt0Bfff3kGcKeeXvZdkg+H+lpwn3DVqtAmrOuZIM8u2OryF+ls+Fj00oiOOSIL3m2GPzPLGnRWrxg7vFYEH7ugo25ZtVFLy8c8n8aDRc1iGQWQDOr3BJFAGKrKl0syyv48bQv090E1yopeq7piXbub9cwrrNkF9WbkeO49dxdXMIrPBssEgIOlannhLqjZSM4tw7HwmLqcX4KON8Sgp0yO/SCuuXBjk6ybLhvh6amRZOxdVRQ3y/bd3wP23G63OZ6Ee2Tiz/O+x3eCiVpoc9JVKBcYMbA0A+Ndw+UGwOt3aVvw7DY02zdpIT1wxHYLFz2YQBLFmMreawUXHzmdiy75LtW5LY3H6Ujbia7FYSF1UzqSSnlOCnUcrAgkfT1fZrDEeGjUESbjkolKa3HKWBsvGwa9SqZBdXPl4Gi1qYvR86XfT001t8vxK0n3J3J2WhlbZB+YynpXqo3xJSuOiEudPd1ErxZ9zC7VIvJonPi/XzHEuNauqD6U1yqVGdxwcZYBfYYlpO/539CpWbU2o1euNL/g9NFXHTltdyAyOCsegHmG4rWdz2eMj+7VERLCXzbKrlZllvcHQYKvGVl54bZesHlvJOFg+czmnUSxKwswyNQoGQcD3/0uEh0aNfl1DzT7HQ6OWBW6VO+BT47pj74lUeGjU+PzXqlkzAnw0soGB/buE4sBpy0tB5+SXmox6ro1f917C1gOXoVYpEdMhSHy88mD424HL+Hn3BQzoGorH7+lq6W1k/o6rfnCIl7uL7Dafr6crYjoEo3mQJ0q1egzqESZuMy6bsJQ9l2eWVWjf3BfLZg5GXrEWL63YX/U8hQL3DmqDW6PD0bFNEHJza55LFACeGtsdiVfz0LGFn8k2pVKBmRN64PCZ6xh3a1tZ3XlRSTncNWqcvVL90tfXsuwfNNlSek4xFm+IAwC8+khv2QWFLZirqdS4KGV3HoyXr3ZRK6GQfk/URmUYZmqMZZnlGoJl6RRrHm5qk7INtUppMtvE33HXMKJvS4T4u9d7QKo3GKBSKs3OG1um1ePr3y0Ha/U9z7tCocDM8T1wJb0At3Rvhp92X8DvB67gem6JbClzcwGVtGZd+r0wnmnEUTLLlRclxt8H4ylDLTEOlqWJB1t9hdQqJR69uzOKS3X439GqrPYdvVsAMJ+wASpWV7XmAnDV7wno0MIP7649Cm8PF7w+tU+14wTqm7kxNY1hURJmlqlROHYuE38eTsamPRcx97MDZp/jplHJgtmIGzW5GhcVbusZge5G0/n4G80HeveAVibvKV1lKftGyYEgCDh6LgMffB+Hy9Xceq209cBlABUZcWnGy2AQUK6rGDENAPtPpeNKegEMBgFx5zMRe7bqNlx+kRapWUUQBAGnLmZjh5krdikvdxfZCa5lqBdc1Eq8Ma0v3n2ivyw7aLzghKXM8piBbUyeo3FVmcwxqlQqoFAoEOLvYVUWRuOqQtc2ARan0YpuH4TH76kYaS49eRWV6pCVV4pDZyr6a1jP5ohs6Wfy+oycimkBD5xKw5INx5CeXQyd3oDier5NX1+kC+8Yzy1uC+YCH42LSvb98DCam1ytNs4sK2V1i+am8pNerBnPlmE8/7C0vtnNRQUfo+W1Ld3afvnzA5j50T+yW9tnr+Tg819O4aqFwENbrkfK9cJql0k2noElt6Ciz4yzgnq9gC37L4m/m5vTvT7Kl4y1auaNwVHhUCmVFvdzcxdJ0oGyBZKs7c1mlpOu5eH3g5ct1sRbYhCEagcTVpZh+Fq481ATldExUVqGYeu1pIwvICsvmiz9merKNyx5+5tY5BdpcTWjCPGJWYhPzKxx+e+6qmmlRHMze3BREiIbuZRmfnWklqFe4hyr7ho1WoZ6Y9SAVsjKKzUZhOHp5gIfT1cx4xpgtKRzWKAH3DVqcXT3zAk9ENUuEDMW/w2dvmIxgQOn0nDqUlUGM7dAi7kP9YS32hWFJeXYfzINXVr7w9PNBeU6A5Kvy6cbkt6C0hsEfPOnfODg/FWHZb+//FAvuLoo8e7aoyjV6tE+wheXUk0DdI2rCl1bB+DouYpaTeMTb+VFhFKhMEmNGE+v5mlhIFDfziHwcnepyNBJSzJMBm3V/0lf2sbsglK88XVVv0W1C0KOmexFfnE5MvNKxLsLn/96Gnq9AVczi/DqI73RMrRiAKROb0BBcbnJkt/GLqcVIPbcdYzo09IkyGuMKi/mKgM5cwOQXF1UskDCzSjoc1ErEeTrJn7v1Sp5zbJLDZll4+WyjS/kpM/VuKpNvqsBPm4Wg9/iMh0+2XQST93XHcF+bnhv3TEAFTPgvDO9v+y567afw/YjFRekj9/TBQO6NpNtT7qWh9W/n0XvyGDZ45l5JQj0dTPJjG365wKuSI4FD97RAV9uOSN7Tn3UKVfHmmBZGhRL64GNV0ssLtOhXGeo1bzRgiBg4ZrYiteV6jB+SLtatRsAvvz1NA6cTscT93ZF387yO43Z+aVi/bifl2uN064pFQqTAM+kDEMSLFuaRrGujLO8lX/63kFtkHA5B2Xletn0byF1CJal/6Yf/3wCQMX5Lbp9kKWX1JnxtH1J1/IQ6u8hHiOzzYxzMZk6zgGDZWaWqVEwt1iIp5saLUOqZniozHKNH9IO08d0NXvADpdkigO85ZlllVKJGWO6QIGKAXGdW/pDoVAg8EYGes+JVFmgDFQMNnvqw914/L2dmPzKVny66SS+2VYRAH+z7SzeWnNE9vxUya21snI99hxPrfZzn0vJxQ+7ksSTVWJKntlFDVQKhay0Qi8IeGx0Z2hcVJh8e4dqT8TGB+twSd23lEKhQNc2ASaD5Iyn92qIW3yekpXgjC8eOrfyk5UK+HlVZZfmfFpVLnIxNR9XrhdCbxDw043sPgC8t+4onv94L85cki9wUlaux+lL2WL/r9p6Blv2XcayHyzPglCfFDacYEwQBLz9TSz+s2wPkq5V1K+am4pP46KSpdbcXU2D2+ljusLPyxXDYipqMaWBo/EAP0AemBhPDWe8D7du5g2VUgFXFyWGRoeb/P3aZBL/OpKM99YdFX9Pzy7GX4eT8cF3ccgtLENxqU4MlAHgi19PY/+pNPyy96JYorB4fRxSMgqx6Z+Lsveu3L/PpeTKHq8MlF3VSrwzvT9u6RYGY9J+aIi4ubbB8tkrOcjMK5VtFwQBJWU6k8wyUDFzRm0USQY7SstAamIwCGK53Oe/nEa5To/jSVnYeyIVBoMgG5tgqaZ9sORYWTGPvvx5psFyw10MVyYiIoK98NHMQXjh/hjZ9tA6lAKac+ZS9WVrdZVTIL84WbgmFi9/fkA8hxvXLAOm2XpHXMGPmWVyeBm5JWLGVEqtVqJVM2/sOVERcNZm8vUgP3fgxqIk/j5VmcM2YRVBd492QZj/aF94uqnFjOmkYe2xbvv5ajMU0mzDoTPX0SbsitguKWkZRuWMAub4erkir1CL3yS3bqujUilkt6QFg4BbuoWhf9dmNdZpGmfvmgebD5YtMQ7EGyLLKi3D2Huyqp8Hdm8GF7UKfpKscOtmPrLlzs2pPIDnFWmRdLXiLsbqbWfx7owB4nO+/j0BB0+no0e7QIwa0EoMgBKv5qFMq8fhhOs4dCYdE4e1R4sQ89Py2ZIt5yItLCkXs3EL18RiZL+WZjNoGheV7Pawm0ZtMhtG8yBPLHlqoPi9qCmzLGWcqTZerKNlqDcW/fsWaFxU8HBTI85oJTPjoMecc2ZW8Fy/4zyAin/jycM7mGz/4sbdCHdXNe7o08JidvFKeiEupxXgw+/jzW5/enx3i8FOfdcsG7MYLEsyg3qDASt/k2fAC0rKsfSH4zh1MRsDuzczfjnyimq35PX1nKoEiPExyFjC5Rz4eLggxN8DGXlVrzMIAt74+ohYw3vgdDpOSVbxjGzpj2M3viODuochxN8dnu4uGNQ9DFqdASeSsnD/7R3wx6ErOCgZr1JdzXJ9kx6vVUqlyRzPAT5uUKsUsvPd8/dHY8mN8Qu1JR33UVxajr/jrqF5kCeibmSbcwrK4O3hYnXyw9xdvcKScry5+ggG9wjDhWumd4mN6+QdcSEVBsvkkA6cTsOWfZfRr3MIfpZkbx4f3UWcB1mlVMjmDq7N7TFp5srLzQVzJsfgcMJ1jL6ltfi4caAT0zEYUR2CcD45V7x1CwDBfm7iCnfGvvtfYo1tsWTxk7fg590XsPdkGkrKLH+mScPa4/udFX9HpVTIVuSrPPbUZkCT8UnaUma5thoiWHZ1UcFVrYRWZ5ANGLlvcFsA8uVn/W4sqFLdrb2UjCIUl+rw3H/3iI/lFpShpEyHv49dRUzHYPFkejwpy6RGOOlaHn7YlYT8Ii1OXjyED58ZBF9PV5xLzsXB0+m4d3AbJKYW4Pi567i7fyubZN+lWb26jHLX6Q3YczwVLUK8TE7Iluam9nJ3kd16N65Zrgx6LN3JMPd9lE6nZhyUmHsfaXmMm1FmWTqXsFqlQOswHySm5KG2jidlyY4FxtbvOG92kGKlv49dtTiIbGh0OLq1CTS7Daj/2TCMGZdPRbcPQlxiJmLPZWD99vO4kJqHjhF+sqwyAGTnlYrf/93xpgkBc7XEx5MyUVBcjlu6NRP/Ta9LBv5WdxEVd+463v4mFt4eLlj85C1IuS4vs5EOdpMGyhOHtcPw3hG4kJqP0jIdHr6zo6wGfsaYrjAYBCiVCrRu5i0Llo2PifbILIt/22gf89Co4e3hKk7bObx3BLq2Nj/jU3WupBfAIAg4czkHX/12Rny/8UPaIrdAix1HU9CxhR9eerBnje9VWFKOpBuDsy3NGlVYUo7fLRxXjM/djrgoCYNlckif/1IREEsD5QAfDfp0DhGD5Tv7tETbMB+0CvVGWnYxbu8VUeP7tmvug79uVEYE+rqhYws/RBrNKWqOUqFAp5b+cNeoxAB22qguOHkx2+ZTkvl5azC8dwvsPSk/6U4a1h4bdyZCADB7cgzahvmIwbKPhysCfdzEmuxxt7a16m8+endn7D+Vhn5dQm96LmlLNc+2FuznblKfWhmoSwfBFJaUI8jXDek5pqU8Uk9/tFv2u94gYO1f57DvZBr+PFL9beJzybmyGt9Pfz6Bx+7pgo82xqNUq5fN9W0wCBg72Py/z/XcEgT6aCwu+CIlDZbrUke590Qq1twoGZo9Ocbi8/y8XMVp+Tzd1bJ5iysyzZanhqsNaYmVtfWYxrNxSIMahUJhcSGO6tQ0WHJ1DQsUWRqsVtPtc+lgQXPLNduacWZZOg/zXze+75V3WYCKsoCUjEJcSDXNDCpQNSDNuNY9O78UH22sKFXy8XQVV8fLkOyPeYVafPBdHEb2a4kuRoHfl5tPAqjok6uZRUi8mlurzze4RzgUCgVmjLE8w5C05EHKdOo4+2SWAZhcyLppVPD2rAqWQ/3rVpZRqtUjOb0QSzfGyzK5P+6qKkk7l5wLg0GATm/A+at5aBXqDS93FyRfL8Tav87hlm7NcD6lIiGg0wtQKIB+XczPVlUd46SQI9YsM1gmh2NplG5JmR5qlRIvPdgTl9IKMKxncyiVCrw6tTe05Xq4udb8de4dGYIR1/KhVinRIcLX6rZJL3gDfdxwzy2tsCM2udoMMAC0b+4rm8v0xQdiZFnqSr6erlAqKjLmt/VsLk4ppFAAI/q0wK1RYXDXqMXsTKcWfki6lodH7oqEWqXEG//XB3lFWnGwWm0N6hEmq3m+GQ012C0s0MMkWHa9EQCESE4gof7uKNPqxWC5XXMfXMssqvHfTG8QsO/GBUtNo/y37Lss+/1cSh6+/j3BbE3ntsPJGDWgNVzUSuQXaeHhpoZapcQ/8dew6vcE9O4UjCfv6w5BqAhDLWUcpRle44FWtbFTMl3VhWuWs6+tQr2RW1gRQFbUilcFOcZNMxcsP3FvV6zYfAodb+xvk2/vgPU7zqPHjdlppLO2WHviN65xlt5dEQShTt/Ful789u8aigOnLE89WVOJSL4kQK5u6WxbkQbLalX1FxZe7i5oGVoRLJsL5L09XFCi1aNcZ5BllgVBwOe/nBJ//9+NWXz+PHRFNm0nULH0dtK1PHz83BDZ49J9L/l6odmLGYUCGBIVLk6pOahHmFX/9hFGdxON72g0aBmG0S5kfFHj7lqRWa56ft3vSEgHRvfqGIxYM+WOjy3aCQ+NWlwVc87kGPx1JBnnknNNSpoEAdXuA5YYT0HoiFPHMVgmh5GVVwqtTi87EEhNGFKRjevYwk82F69SoahVoFz5XONFOKzh5+UqBl2+Xq5Qq5R48YGeOJ6UhW2Hky0uZ9uqWUX2u7CkHH07h6BTS390axuAkxfkg8jmPFCV4evbOVQMlluGeEOpVJjcDpw9OQalWr2YffD10sDXq/pZHOpbXbKLdWFc3yrl763BmIGtcSmtACP7tcTfx67ixIWKk2yIn/uNEgvbjWo3N13SaQsDaMq0ejz5wS4E+brhek4JmgV64LlJUVh1Yw7eI2czUFaux9vfxEKrM2DWpCizKw9KTzAlVk69BcgzVueSzQfL7hq1rOzB090Fd/ZtKdaBGmdLjWdWASq+xy1CvBDkW1HHOrx3BDq19EMLMxd05qZUq45xGYZ04KcgQHYscdeoEezrJtaad27lj1KtDhfNzC5jLVcXJfp3aVZtoODnad/90pi0nMRVrZIFl61CvRER7Cne3QrxdzeZ01rKzVUNVxcVMvNKZZnlo+cycU5SBpNdUGaxnhuoSIjsO5mK/SfTkHy9EM2DvWSLpFhaVCTU3wNTRkbioTs74XJagUmmuCa+nq5iqZa52XxcLdR31wfji2PjwN1dI1+Mx1al7r0jQ8wGywBky8cvWm+a5LlZxhf7jphZ5mwYZDeCIKBUq0NqVhGy8krxysqDmPfFQcxc+o/4nObBnpg2qjP+7+5IDDaaCs4epo3uAhe1Er07BYt1py1DvTH21rZY+8ZIswNeAKBtmA9m/SsKowa0wuQbq9pNGdFJzFgMiQ7HF3OGylYg7NjCD4O6h0GlVGDCUPPTKlUE0M55zVs524IlYwe3xbMTo+Dp5oJ+XULFk+DA7mFo1axqAY9nJ/aQvW7isHYYP6QtlAoFfDxcTE6eI/q0kP3ew2j+buOMlvF0aEDFySA9pwQCKgZ6SmfpACrmgk6+Xoj07GJ8tLEiuDAIApb9cByvf3UIWXmlRiupWZ9ZlgYAZy5nm31OoI+b7GTt6aZGxxZ+mDelF957YoBJ1ktt4cwdFugp1osqFAq0DPUWL6oqL15v6WZ+36mO8UWyNANoEAR5oO+mlmUQvT1c0FwSVNVUM1zdVIKebi4I9JUPantsdGfZ77UZfFjJ0qqjtiQtt9K4yoNlL3e1bJCsj4drtZlaVxeVGMBVZpbzi7XY9M8F2fOMp9I058stZ3DqUg7yi8vFVSRr0i68Yn9WKhRoE+ZTpwv2954YgN6dgvHwnZ2sfq0t1TSFoLvGKLNso1p36ZoCDc34DhxrlsnhlGn11WZzrVGu0+N/R69i38k0PDC8Azq1lNcCF5aUY9M/F9A8yBMnLmTXOEMBADw7IcrkJGRP7Zv74r//GWz2YKxSKWUnX6Ci9q1P5xD06xJ6YyBJVZAW5OeOj2YOggIKi7fS/u/uSEy9K7LBR8o3Bm3CfLDoiQH49q9zOJ6UhV6dgi0+N8jXHS/cH42C4nJ0aR2AVs28ER7kCQWAbm0DMah7mDh7SY+2gWge7IXbekZApVTgh11JsqnEerQLlE11NWpAK9mt4eG9I2RTig2Nbm5xYIsla/86J/6cmlWMr39PwC3dmon7zJptZ2W1wtLMTH6xFpv3XMSV9AKkZBShXbgPOrfyR4CPGw6fuY6TF7MREewpq3OurFn083LF7b0ixLrF8CAP2brwlcFxu/CqEqYxA9tg+U8Vc7fW9g6P1F39W6JLa39xUObDd3bC2j/PWbxAlDIebOdhlFkOkMx4IwiCbPCum6sKvpJsb1T7QGTmlVoM6NqF++DIWfOZNw83NYKMZoDw8XQVB6ECkP2tmjw1rnutn1tX0gsdVxeVrGZZoVTIzgneHi7VniPUktl48ou0yM4vxeINcUizsAqdVPsIX5SU6izOj23Oiw/E4NNNJ8XSFeNzTV0E+Ljhyfvqv99vlotaKcvy2+rcYO7uVUMpMbrY52wYjUhZWRneeOMN/Pnnn3Bzc8Ojjz6KRx991N7NshmDIOBiaj6+/PU0MvNK8fy/ohHZyh/5RVr8fvAyotsHoVNL/4oVzsp08HZ3gbbcgF/3XUJKRiEGRjdHSbEWZ6/kIPl6ITKNMl3vrTuG9hG+cFUr0a1NILq09scvey+ZnQKuOtKTnaOo7pacdBaG6PZBmDmhh8XnAqhxIJdCoWiQOVdv1jPjumP1trO4d2DrBv27QX7umDGmK05dzEaX1tWfMKUnVE83F9kgyCkjO6FzK3+4uqjEC57KkoCodkGyYNnTzUU2dVN4kHz2kBF9WsiC5dt6RsiCZX9vjawmNSLYEykZ8kDB+GSxO/4azkvm7q0sKamUklGEjNwSXEkvxOY9F5GSURXwnb6UY1IScsnCypO+nhpZiVP/rs1wUvK3zJ3CYjoE4cUHYhDk616nE7dCoZDdNh8W0xy3dGtmcWoz49dKGdeWSudSNwjymW7yi8plF69urmo8ProL/jySjBYhXvD30uDo+QyxtKK6AXoeGrXJwFgfD1fcO7gNNu5MAmD+DoMlnu4u0OnqN7sm7d9Qf3dZ5lhlNDhSrVLKSlyMdWrpJx7/zyXn4u1vY8VZakb2a4kJQ9rh6Y92m63hHz2gNXq0C8RLK/bj+o3BnjEdgsRSHwB4YXI0dhxJwbHzmQj1d0fHFn6479a2WPvXeQR4axDdwfaLazgy6YWL1kaLpBiXNNmSi1opmzbVmHH5IjPLjciiRYtw8uRJrF69GteuXcOLL76I8PBwjBw50t5Ns1rlgLkLqfn4dNNJs2uzL1p/DCH+7uLcl9sOJSO6fRDOp+SiqFSHIF83FJaUiwe72iyvWzllk6XaTXPmPdwL67afx8XUfDx4R8cGX9XqZklvZRU20mWU6yKmYzCiOwTZ5d/LXaNG78iQm3oPtUqJARbKALq2CZCt7OjprsZzE6Pw/oY4hPi7w12jFgexRQR7VQRd93TBl7+eRo92gQj0dUNogAfSs4vhrlHhlSm98fzHewFU1LLf2aeFuKogAAT5uplM1wVUPy93mVaPF1fst7i9tiJCPNG+uS/G3doWapUSUe0CEeCtEWvn2zc3HRSruDFTjC1JA7kJQ9vhp10X8NCdHat9jaebWnYh66pWyuZSFwRBduenZaiXrNRAEAREhHjh0buryicycktwABXBspe7i+x7oHFViTNfVAaS0oFQvp6uuKN3C7hr1GjTzMfhjmXuGjX6dg5B4tU8PDSiI4pKqrJ7SqVCFjwpFJBlM9uEecPXU4O4xEz07xqKicPaY7PkArHyHDNhaDvc3b8VgIp/U3PBcuUMKG3CfcRguUOEHzq18MOG/yXitt4t0KNdEJr5e6BZoAdujaqY5WJIdHMM6hEGpULhcH1b37wkwXJhqWkJ1h29W4gzmtRWffahu0aNcp3lQdKnLspjBA7waySKi4uxceNGfPHFF+jatSu6du2K8+fPY+3atQ4ZLJeU6cQgt6RMh4y8EhSWlEPjosKRhOs4eyUXyhrmmQXkk8QDkJVJmDt5mxPq717jFF1dWvvj9KUcdIjwxXOTorB4QxwuXMvHoB5haNfcF7P+FYXUzGK0a+5T7fs4IuksDMNrMZVdU9KUT1izJ0fj7W9i4e+tgb+3BkG+7njzsX7w86qYvaRv51AE+7mLU9YN6NoMkS39xWzdiw/EYMuBK+jdMQj+3hq89GBP7DmRilujwmXTpYUHeeL5f0WLwXTvyBC0DPGSrTDo5e4iW2Wtpn3u4Ts7iatKApCVBgAV9fK7bswi4O3hCoVCIZtruGWoN+ZN6QU3V7VdlvW+u38r3N4rwmKmefLwDtgVdw3TRlUEudHtgxCfmIkn7+suy46OGtAavp6umDSsPS6m5mNEn5ayDLy546O0Xl2tUmLsoDbiAibtwn3EREDlXYinx3XHovXHEOTrBm8PVyiVCgyNrr623p6euLcbBEGAQqGAl3tV0DWiTwvZoFFXFxXahvsgsqUfdAYBT93XHW6uKiSnF6JtuE9Ffb/RanlTRnaSffYJQ9th5W9n4OvpKpsxo3Lg59DocJy9koPWzXwwqEcYPN3U6BUZgg5tgpCfV4wAHzdMHNpe9jdqM8ViU9A+wlc2X7iPZ9X3ujIr++zEKHzx6ync1b8V7u7fCtn5peKAPTdX8xcqDcXdVYX8aqpsjBf8asgBlbXFYNmMhIQE6HQ6xMRUzUzQq1cvrFixAgaDAcpa7KBKpeU6VFsqKNbihU/21moaLHNG39IazYM9cSmtAFczChHk644zl7KRnlMCpUKBXp2C0ba5D05dzIabqwq3RjVHVIcgJCTnISUtH7dGhyMzrxQ5+aXw93FD8yBPJFzOQXZ+KSJCvBAe5Ikt+y7hp10XoFAAs/4VLa4QVGnmhB44dTEb/bs2g1qtdIgZHaylujHYz02jxoLH+iItqxh9O4c6da1xZZ+oGmD56/rWPsIPHz4zCBpXFTQ36nKlC+IAQAdJ+QIABEuC4NBATzw3uSfy80ug1xvQpU0AukgGcT03KQo/776A+25ti2B/d7w2tQ92xKZg1C2t4OXmgr+OJItTdo0Z1BolZXrsPZ6K0QNbo1enYLzyxUHkFJShfYQvWoZ4iZng/l1CEdMxWAyWe3UKxn23tsUrXxwEUDGN3vgh7bDvZBp0OgOGRIebndHC1pljwLrvh7k2VbqrfyvcdSN7CQDP/isKhcXlYvD21uP9cDmtAAO6NYNapcRoSalQK3XVv+FtvSJM/s7AHmH46cYgtQHdmsHLwwXlegPcNWrkFZaJwbK3hwvUaiW6tQvEOzP6w89LA1crbmur1Urc1b8lfj9wBVPu7myXfcZL7Yp3nhiA/MIydG4dgLJyPfy8XFFcpsPIfi3hplHj5Sm9Za+JlJQ+SQdPPn5PF5MB2bdGh8PdTY2IYC+8JLkLUjmrTde2gVj27K2y14QHe0GlVDjEMaSyNESlVFT7fayLqXdF4uvfExDi5272vZ8Z3wPbDyejb5dQqFTKipVob/B0r/ju9ewUjE86DhGTFtPv7YpN/1zE3hOpuKtfSxSUlGPr/oopLicMbYfd8dfEBNnA7hXn3v9M7IGNO5MwcVg7LL0xNzZQMU4jp6BMrOcf1rO5bOrJlx7qiXe/rVpC3tjoga2xckvVSpBR7YNw5nI2tOVVF+1KhQItQ73QNtwHA3uEWdXHDXGuUQiWJrV1Ytu2bcOCBQuwd+9e8bGkpCTcfffd2L9/PwICah6pXHm1Xt9yC8owbeFfFuuWfL1cMbxPS2hc1SjT6nBrTATCbtRYqlVKi6OGM3JKKiY/t8HAPwC4nJYPg0FAm3DT27hEVL3UzCK8/+0ReLq5YN6jfU0G0mXmluDCtTz06RwKhUKBpJRcXLyWhyE9I+CiVuGrX08hKSUXLzzUC35eGqzZegZpWUV4YlwP+HppcCk1H2VaHTq1qv9ZGBxNYnIusgtK0beL+TKcyqXQjZdwPpGYiVdW7IVBAF6e2hcDuls3T/lPO89j1ZbTeGBEJ0y+MxKCICA9uxihAR4Oc5emtEyHcr2hVucBQRCw7cBlBPm5o3fn6hem+H3/JXzyQzzuGdwW08c6/qA6ACgs1mL74SvoFRlqdtrDm2EwCDh7OQctQr1kJRaWCIKAD9YdRWpmERbMGFCrFQYNBgHJ1wsQEeINlVKB9Oxi7D9xDaEBHugZGWpy5yYxORfbDl5GgI8b7r6lNRQKBU5dyESpVo9beoQjPasIydcLMaBbGJRKBeLOXce5K7k4dyUHHVr4oVmgJ65lFKJnZAg6tQpAYnIuPlgfC7VKiTdn3AKFQgGVUnEjiyxArVI6zPfeHAbLZmzatAlLly7Fzp07xceSk5MxfPhw7Nq1C82a1TzFUVZWYYNlFQuKtbiWWVHP6OqiRJCv240aIQPcXFU2/wKqVEr4+LiLWTJin5jDPqnCvjDVFPokt6AMBkEwCaRr/frCMvhJ7qI1hT6prfwiLbw9XKo9PzlTf9QW+8TUzfaJv79njc9hGYYZGo0GWq28GL3ydze32h0UDQYBhgaaWNvdVS3OMykSABeV8kahfP20Q6831PuI7caGfWKKfVKFfWGqMfdJZQ13Xdvv5WZ+1ovG3Ce15aFR1/r85Az9YS32ian67BP7FwI5oNDQUOTk5ECnqxrwkJGRATc3N/j4NL5BZ0RERERUNwyWzejcuTPUajXi4uLEx2JjY9G9e/daDe4jIiIioqaBkZ8Z7u7uGDt2LObPn4/jx49j+/bt+OqrrzBlyhR7N42IiIiIGhBrli2YO3cu5s+fj0ceeQReXl545plnMGLECHs3i4iIiIgaEINlC9zd3fHee+/hvffes3dTiIiIiMhOWIZBRERERGQBg2UiIiIiIgsYLBMRERERWcBgmYiIiIjIAgbLREREREQWMFgmIiIiIrKAwTIRERERkQUMlomIiIiILGCwTERERERkgUIQBMHejSAiIiIickTMLBMRERERWcBgmYiIiIjIAgbLREREREQWMFgmIiIiIrKAwTIRERERkQUMlomIiIiILGCwTERERERkAYNlIiIiIiILGCwTEREREVnAYJmIiIiIyAIGy2SWwWCwdxMcAleDJ2twv6nA/YZqi/tMFe43jovBMom+/PJLPP/88wAApZJfDQAoLCy0dxMcTmpqKgCe5CpxvzHF/cYU95sq3GfM434j50j7DL+lhEOHDuHOO+/E119/jYEDB9q7OQ7h2LFjuP/++/Gf//wHr732GuLj4+3dJIewadMmLFy4EIWFhU5/kuN+Y4r7jXncbypwnzGP+40pR9tn7N8CsqsPPvgAU6ZMwfjx47F9+3aMGzfO3k2yu4SEBLz22mvo168f7rvvPhQVFWH69Ok4evSovZtmN3q9HgBw8OBBxMbGYseOHXZukX1xvzHF/cYU95sq3GfM434j56j7DINlJ1V5W8Pb2xuhoaGYPn063NzcTJ7nTDVUlZ81Li4ORUVFeOKJJ3DPPfdgyZIl6Ny5M5YuXYrExETZc52FSqWCVqvF2bNn4ePjgx07duDKlSsAnKsvuN+Y4n5jGfcb7jOWcL8xz1H3GQbLTiY5ORlarVa8rXHfffdBpVJh9+7dAIA///wTn3zyCbZu3YqSkhIoFAp7NrdBVPZJ5WfNyMhA27ZtxStcAHjhhRdw8OBB/P777yguLm7y/VLZJ1J//vknysrKMG3aNBQWFmLLli0A0OT7AuB+Yw73G1Pcb6pwnzGP+41cY9ln1Hb7y9SgkpKSMHfuXOTk5MDLywv3338/xo4di4CAANx22234/vvvsX37dhw8eBAtW7bE6tWrsWHDBsyePRvdu3e3d/PrhXGfTJo0CZMnT0bHjh2xdu1apKSkIDIyEgBQVFSEwMBAHD9+HGfOnEGvXr3s3Pr6YdwnkydPxqhRo+Dp6Qk/Pz/07NkTkyZNwoULF3D06FEcO3YMMTExEAShSR7Qud+Y4n5jivtNFe4z5nG/kWts+wwzy07ik08+QYsWLfDRRx9h5MiR+Oqrr/DFF1/AYDCgZ8+euHbtGi5fvow1a9bgk08+wR9//IGioiL8+uuvyM/Pt3fz64Vxn6xatQrLly/HnXfeiTZt2mDp0qXYunUrCgsLsWHDBtx3331IT0/HmTNnADTNW2PGfbJy5Up89dVXKC4uxqBBg/Dmm28CAEaPHg2lUonffvsNer2+yZ3wK3G/McX9xhT3myrcZ8zjfiPX6PYZgZq85ORkYcCAAcKxY8fEx9asWSNMmTJF+P7774WsrCxhxIgRwpIlSwS9Xi/odDpBEARh48aNwogRI4SCggI7tbz+WOqThx56SPjll1+E69evC7NmzRJuvfVWoVu3bsJDDz0kCIIg/Pe//xXGjRtnp1bXL0t9MnXqVOHrr78WBEEQDAaD+P34/PPPhSlTpgjbtm0TtzUl3G9Mcb8xxf2mCvcZ87jfyDXGfYZlGE4gKCgIXl5eyMzMFB+79957kZqaip9++gnDhg3Dpk2b4O7uLntdt27dkJWVJd4maUqq65N169ahV69eWLJkCZKSkqBWq9GqVSsAQHBwMNzc3FBQUABvb297Nb9eWOqTtLQ0bNu2DUOGDEHr1q1hMBigUqlwzz334NSpU9ixYwf69OkDf3//JnVbmfuNKe43prjfVOE+Yx73G7nGuM+wDKOJEKq5RVNYWIju3bvj4MGDYiG9j48Pbr/9dnh5eWH16tVwd3dHcnIydu3ahezsbADAtm3b0L9/f7Ro0aJBPoOt1bVPvL29sW7dOgBAQEAA8vPzkZaWBqBi5HKXLl2a1IGrkqU+ue222+Dt7Y2ffvoJAODi4gJBENCsWTPcdtttuHz5Mn744QcATWvQkrPuN9Vx1v2mLseSprrf8FxjHs83ck1tn2FmuQkwGAwQBAEqlUr8XTqJd1BQENq3b4/Y2FgcO3YM/fr1AwD07NkT3bp1w7lz55CdnY2//voLK1asQMuWLREUFISDBw/ijTfesMtnulk30yddu3bFuXPnkJ6ejkuXLuHTTz9FcXExgoKCEB8fj8WLF9vlM90sg8GA0tJSeHh4mL0qr65PunTpgvPnz+PKlSto2bKl+Prhw4fj6NGjaNOmjT0+0k25mf5oyvtNXfukqe43er0epaWl8PT0FH+vPK4AzrXf3ExfNNV9Bri5fmmK+43BYIDBYIBarRZ/r+3511H3GWaWG7lVq1Zh3LhxePbZZ7FmzRoA8uVDK6/axo0bh9LSUuzatQu5ubkAKq7MBg4ciMTERJSUlODRRx/Fxx9/jLvvvhtdu3bFjh07MGbMmAb/TDfLFn2SlJQErVaLfv364aWXXsLIkSPRvn17/PrrrxgwYECDf6ablZ2djddeew3btm0DYHpVXps+uXDhgjhnqlKphMFggIeHB1599VUMHz684T6MDdiiP5rafmOLPmlq+83q1asxZswYPPPMM5g9ezZKSkpkQZAz7Te26Iumts8AtumXprTffP3115g4cSJeeOEFfPnllwAqvveVmebGus8wWG7EfvjhB6xfvx4PP/wwQkJCsHbtWsydO1f8Uup0Ori6ugKomBB+2LBhOHPmDLZu3Sq+R2RkJK5fvy5O+t2nTx88+uijeOaZZxAQENDwH+om2apP0tPTkZKSIv7+6KOPYtasWY2yT4CKTMeff/6Jffv2iZ+rsk/0en2t+iQ1NRXp6eniY5UXINITQ2Nhi/5oSvsNYJs+aUr7zYoVK/Ddd9/hySefxB133IHTp09j1qxZKCsrA+Bc+42t+qKp7TO26pemsN8IgoBFixaJ59/AwED8/PPP+Oabb8TtjXmfYRlGI7Zr1y70798f48ePx/jx43H+/HmMGzcO0dHRGDduHFxcXHD16lXMnTsXxcXF+PDDD5GZmYkffvgBvr6+GDJkCHbu3IlOnTqhc+fO9v44NsE+Me/MmTMoKirCxYsXsWvXLjz44INi5lClUiE1NRUvvvhijX3SqVMnO38S27BVf/A70jT7RK/X4+DBgxg9ejRGjRoFABg4cCBGjBiBCxcuoHPnzk6z39i6L5rC9wNgvxgrLS3F0aNHMWXKFIwdOxa33347XnzxRXE6wMqA99q1a3jppZca3T7DYLkRSUlJQUBAADw8PFBcXAwXFxdEREQAqLhq69ChAx577DGsXbsWkZGRyM7OxgsvvIAhQ4bgqaeeQosWLTB16lT4+fnhtddeQ0REBC5duoSnnnoKfn5+9v1wdcQ+MSXtE4PBgLKyMqxfvx4TJ05EeXk59u7di6ioKHTr1g2CIGDHjh148cUXMXToUDz55JNNrk/YH6bYJ6akfZKWloYLFy7goYceErd7e3sjIiICcXFx6Ny5M7Zv395k+4R9YR77RU7aH6mpqVCpVOLCMq6urjh79ixiYmKQkJCAyMhIsT8a5fm33ieno5v2448/CkOHDhUmTZokjBw5Ujh06JAgCILw0ksvCTNmzBC0Wq2g1+vF548ePVpYtmyZcPjwYeH06dNm3/PixYvC//73PyEtLa1BPoOtsU9MmeuTsrIyQRAE4bXXXhOOHj0qnD9/Xpg4caKwfPlyoaSkRBAEQYiPjxdOnjxp9j0bc5+wP0yxT0wZ98nBgwcFQRCE9957T9i/f78412tKSorQvXt34e+//xYEoWn2CfvCPPaLnLQ/7rzzTrE/Dhw4IBQVFQlZWVnC1KlTheHDhwv33Xef0LlzZ+Gbb74RDh48KCQkJJh9T0fvDwbLDu6ff/4R7r77buGHH34QTp48KcyaNUsYN26cEBcXJyQmJgqdOnUSJ/YuLy8XBEEQfvrpJ+GWW24x+6VrChPgs09MmeuTiRMnCrt27RIEQZBN9v/hhx8KU6ZMEbeZ09j7hP1hin1iytKx5MCBA4IgVB0/BEEQ9u7dK/Tt29fiyV4QGnefsC/MY7/ImeuPCRMmCP/884/seQcOHBBKSkqEsrIyYf369cLgwYOFI0eOmLxfY+kPDvBzcH///TdCQ0Mxfvx4dO3aFUuWLEFJSQn279+Pdu3aYejQoViyZAkAiNO03HfffXBxccH27dsBQBxVCjSu+TwtYZ+YMtcnhYWFSEhIAAB4eXlBr9cDAKZMmQKDwYDdu3cjKysLgOmcmI29T9gfptgnpiwdS+Lj4wFUHT8A4Pjx4wgKCkLHjh0tvl9j7hP2hXnsFzlz/VFUVITTp08DqJrtom/fvnBzc4Orqyvuv/9+BAQE4H//+x+Axnn+ZbDsYJKSksSCeJ1OB61Wi2bNmkGouAsAAGjfvj3i4uIAAC+99BLi4uLw/fffi19Ag8GALl264Pr16wDk06Y1RuwTU9b2CVAxSMtgMCAgIABjxozByZMnsX//fgCN54BlCfvDFPvEVF36BADKy8uxbds2REVFif1QUFCApKSkBm2/LbEvzGO/yFnbH5WzXeh0OgBVgXGXLl2QkZEBoHGefxtfi5uov//+G7feeiueffZZjB07Ftu2bYNarcatt96K4cOHo6ysTNwBU1JSxOUwW7dujWeffRYrVqzAX3/9hbKyMhQXFyMlJUUstG+s2CemrO2TyhWxKjOGldsmTpyIkJAQbN26tVEfzNkfptgnpm62T65du4YLFy6Isx589dVX6NOnDzZv3izLkjUG7Avz2C9yde0Pg8GA1NRULF++HGfPnoVCoUBxcTHOnj2LIUOG2PMj3RTOhmFHwo2VaUpLS/HZZ59h0qRJGDNmDNavX49vvvkGZ8+excyZM8XVgAwGAwoKCpCWloa2bduK7zNt2jRkZGTgww8/xOrVq5GVlQUfHx906dLFjp+ubtgnpmzRJ5XzUyoUCnE1pbFjx+KXX36Bh4eHPT+e1dgfptgnpmzZJ5mZmQgICMDp06fx3nvvIScnBx999BFGjhxpz49Ya+wL89gvcrboD6VSibCwMPz999/YvHkzunfvjkuXLsHFxQVRUVF2/oQ3oSEKo8mUdFDA0aNHhcGDBwupqaniY99++61w7733Cr///rvs+UeOHBG6d+8upKSkCIJQVRyv1WqF06dPC2vWrBHWr1/fUB/DptgnpmzVJ1KNZUCFOewPU+wTU7buk8OHDwudOnUSoqKihKVLlzbAJ7Ad9oV57Bc5W59/s7KyhL///lv46KOPhLVr1zbUx6g3zCzbwRdffIF//vkHoaGhuPXWW9G3b18UFRWhpKREfM4dd9yBixcv4vPPP8edd94pDiL4/fff0bx5czRv3hxAxUTge/fuRa9evdC5c+dGO7E5+8SULfukpKQEe/fuRZ8+feDr62uXz3Oz2B+m2CembNknxcXF2Lt3L1q3bo1XX30V9957L7y8vOzyueqCfWEe+0XO1seRffv2oXfv3hgyZEijLr2QYs1yAyorK8OcOXPwyy+/YMyYMTAYDPjkk0/w2WefYcyYMdiwYYP43JCQEIwcORJKpRKrV68GUPElPHToEIYOHQoAWL9+Pfr3748ff/yxURbMA+wTc+qzTxrjIC32hyn2ian66JN+/frh559/RkhICB588MFGEwSxL8xjv8jVR38MGDCgUR9HLGmc0UQjlZmZicTERLz88suYMGEC3nnnHTz00EP47bffoNFokJycjFOnTonP79SpE2655Rbs378fubm5cHNzg8FgQFZWFu6//3589NFHmD9/Pj799NNGmwlin5iqzz7x8fGx4yerG/aHKfaJqfrokwULFuCTTz5pdMcS9oV57Bc5nn9rj8FyA7py5QpOnz6NPn36AKiYYsXf3x/NmzdHu3btUF5ejl9//VV8vre3NyIjI1FcXAydToeMjAwkJibit99+Q69evXDw4EHcd9999vo4NsE+McU+kWN/mGKfmGKfVGFfmPf/7d1dSJN7AMfx36xEGKUY9GI0KBiyCMfqIrqYtRGV3WQEXQRBL2JKF10EWhAUUaQkDCkpF7uJuotW9EKvUyoTutEQZLWRtiiELjQmBBZ7zsU5TZ7z7CEPp/acjt/PXVuN//O96dd4eqSLGT1mjnuWi6impkbhcFiZTEYrVqyQy+VSaWmp0um0Nm/erGw2q56eHt26dUvbt2+XJFVVVWlwcFBfvnzR8uXL1dbWptraWlVWVjp8NT8HTaxoYkYPK5pY0WQaLQqjixk9Zo6xXERut1vt7e2mxzANDQ1p5cqVKi8v17Zt2zQxMaGOjg4tWrRIXq9XL168UDAY1MKFCyVJ9fX1Dp3+16CJFU3M6GFFEyuaTKNFYXQxo8fMMZaLbP78+aZfDwwMqKamRpK0ZMkSNTU1aWJiQsePH5fL5VI2m9WZM2d+y+eczhRNrGhiRg8rmljRZBotCqOLGT1myOln181mmUzG8Pv9Rjwez782OTlpGIZhjI+PG0+fPnXoZM6hiRVNzOhhRRMrmkyjRWF0MaOHPf6DnwOMv36e+vDwsCRp06ZNkqRoNKq1a9cqHo+roqJCwWDQsTMWG02saGJGDyuaWNFkGi0Ko4sZPX6M2zAc8P35g6lUSj6fT48fP9aFCxeUy+V08eJFhUIhh09YfDSxookZPaxoYkWTabQojC5m9PgxxrKD5syZo4GBASWTSTU3N6uxsdHpIzmOJlY0MaOHFU2saDKNFoXRxYwe9lzG9+/fUXSJREKvX7/WgQMHVFpa6vRx/hNoYkUTM3pY0cSKJtNoURhdzOhhj7HsIMMw/nc/EvLfookVTczoYUUTK5pMo0VhdDGjhz3GMgAAAGCDp2EAAAAANhjLAAAAgA3GMgAAAGCDsQwAAADYYCwDAAAANhjLAAAAgA3GMgBA1dXVunHjxox+78ePH3X37t1ffCIA+G9gLAMA/pHW1lY9e/bM6WMAQFEwlgEAAAAbjGUAmGXGxsbU3NysQCCg2tpa3b59O/9eLpdTd3e3tmzZotWrV2vNmjVqaGhQJpORJO3Zs0cvX75UPB5XOByWJE1NTencuXMKBoMKBALatWuXnj9/7si1AcDPxlgGgFnk27dvamho0Pj4uK5evarOzk7FYrH8+1euXFEsFtPRo0f14MEDdXV1aXR0VG1tbZKk8+fPKxAIqK6uTtevX5ckHTt2TH19fero6FA8HlddXZ2amprU29vrxCUCwE811+kDAACKp7+/X6lUSo8ePZLH45EknT17VvX19ZIkj8ej9vZ2hUIhSdKyZcu0detW3b9/X5JUUVGhefPmqaysTJWVlXr37p3u3LmjmzdvyufzSZL27dunZDKpWCymjRs3Fv0aAeBnYiwDwCzy5s0blZeX54eyJPl8PpWVlUmSwuGwXr16pc7OTo2MjGhkZETpdFqLFy8u+HnDw8OSpN27d5te//r1qxYsWPCLrgIAioexDACziMvlUi6Xs7w+d+6ffx1Eo1F1dXVpx44dWr9+vfbu3asnT57YPirOMAxJ0rVr1+R2u03vlZRwpx+A3x9jGQBmEZ/Pp2w2q1QqJa/XK0kaHR3V5OSkJOnSpUs6dOiQGhsb838mFovlR/Hfff+MT58+adWqVfnXI5GISkpKdPjw4V91KQBQFPyzHwBmkXXr1snv96ulpUWDg4MaGhpSS0tL/lvgpUuXqq+vT+l0Wm/fvlUkEtHDhw81NTWV/wy3260PHz5obGxMXq9XoVBIJ06cUCKR0Pv373X58mV1d3ebbvUAgN+Vy7D7ugAA8L80Pj6u06dPq6enR2VlZTp48KCi0aiOHDmi6upqnTp1SslkUm63W36/Xxs2bNDJkyeVSCRUVVWl3t5etba2yjAM9ff3a2pqSpFIRPfu3dPnz5/l8Xi0f/9+7dy50+lLBYB/jbEMAAAA2OA2DAAAAMAGYxkAAACwwVgGAAAAbDCWAQAAABuMZQAAAMAGYxkAAACwwVgGAAAAbDCWAQAAABuMZQAAAMAGYxkAAACwwVgGAAAAbPwBeMIiffPMnk4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = temporal_predictor.plot_input_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "2c56d706",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Texas model setup\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_26a15_row9_col1 {\n",
       "  background-color: lightgreen;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_26a15\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_26a15_level0_col0\" class=\"col_heading level0 col0\" >Description</th>\n",
       "      <th id=\"T_26a15_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_26a15_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_26a15_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
       "      <td id=\"T_26a15_row0_col1\" class=\"data row0 col1\" >123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26a15_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_26a15_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
       "      <td id=\"T_26a15_row1_col1\" class=\"data row1 col1\" >Texas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26a15_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_26a15_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
       "      <td id=\"T_26a15_row2_col1\" class=\"data row2 col1\" >Regression</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26a15_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_26a15_row3_col0\" class=\"data row3 col0\" >Data shape</td>\n",
       "      <td id=\"T_26a15_row3_col1\" class=\"data row3 col1\" >(865, 7)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26a15_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_26a15_row4_col0\" class=\"data row4 col0\" >Train data shape</td>\n",
       "      <td id=\"T_26a15_row4_col1\" class=\"data row4 col1\" >(778, 7)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26a15_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_26a15_row5_col0\" class=\"data row5 col0\" >Test data shape</td>\n",
       "      <td id=\"T_26a15_row5_col1\" class=\"data row5 col1\" >(87, 7)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26a15_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_26a15_row6_col0\" class=\"data row6 col0\" >Ignore features</td>\n",
       "      <td id=\"T_26a15_row6_col1\" class=\"data row6 col1\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26a15_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_26a15_row7_col0\" class=\"data row7 col0\" >Numeric features</td>\n",
       "      <td id=\"T_26a15_row7_col1\" class=\"data row7 col1\" >3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26a15_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_26a15_row8_col0\" class=\"data row8 col0\" >Date features</td>\n",
       "      <td id=\"T_26a15_row8_col1\" class=\"data row8 col1\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26a15_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_26a15_row9_col0\" class=\"data row9 col0\" >Preprocess</td>\n",
       "      <td id=\"T_26a15_row9_col1\" class=\"data row9 col1\" >True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26a15_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_26a15_row10_col0\" class=\"data row10 col0\" >Imputation type</td>\n",
       "      <td id=\"T_26a15_row10_col1\" class=\"data row10 col1\" >simple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26a15_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_26a15_row11_col0\" class=\"data row11 col0\" >Numeric imputation</td>\n",
       "      <td id=\"T_26a15_row11_col1\" class=\"data row11 col1\" >mean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26a15_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_26a15_row12_col0\" class=\"data row12 col0\" >Categorical imputation</td>\n",
       "      <td id=\"T_26a15_row12_col1\" class=\"data row12 col1\" >constant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26a15_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_26a15_row13_col0\" class=\"data row13 col0\" >Low variance threshold</td>\n",
       "      <td id=\"T_26a15_row13_col1\" class=\"data row13 col1\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26a15_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_26a15_row14_col0\" class=\"data row14 col0\" >Fold Generator</td>\n",
       "      <td id=\"T_26a15_row14_col1\" class=\"data row14 col1\" >TimeSeriesSplit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26a15_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_26a15_row15_col0\" class=\"data row15 col0\" >Fold Number</td>\n",
       "      <td id=\"T_26a15_row15_col1\" class=\"data row15 col1\" >3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26a15_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_26a15_row16_col0\" class=\"data row16 col0\" >CPU Jobs</td>\n",
       "      <td id=\"T_26a15_row16_col1\" class=\"data row16 col1\" >-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26a15_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_26a15_row17_col0\" class=\"data row17 col0\" >Use GPU</td>\n",
       "      <td id=\"T_26a15_row17_col1\" class=\"data row17 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26a15_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_26a15_row18_col0\" class=\"data row18 col0\" >Log Experiment</td>\n",
       "      <td id=\"T_26a15_row18_col1\" class=\"data row18 col1\" >False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26a15_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_26a15_row19_col0\" class=\"data row19 col0\" >Experiment Name</td>\n",
       "      <td id=\"T_26a15_row19_col1\" class=\"data row19 col1\" >reg-default-name</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_26a15_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "      <td id=\"T_26a15_row20_col0\" class=\"data row20 col0\" >USI</td>\n",
       "      <td id=\"T_26a15_row20_col1\" class=\"data row20 col1\" >09c4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fa5b3a78d60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temporal_predictor.setup_model(train_fraction=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "0d090c3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Texas: Training on different models\n"
     ]
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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       "<table id=\"T_9133a\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_9133a_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_9133a_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
       "      <th id=\"T_9133a_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
       "      <th id=\"T_9133a_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
       "      <th id=\"T_9133a_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
       "      <th id=\"T_9133a_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
       "      <th id=\"T_9133a_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
       "      <th id=\"T_9133a_level0_col7\" class=\"col_heading level0 col7\" >TT (Sec)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_9133a_level0_row0\" class=\"row_heading level0 row0\" >gbr</th>\n",
       "      <td id=\"T_9133a_row0_col0\" class=\"data row0 col0\" >Gradient Boosting Regressor</td>\n",
       "      <td id=\"T_9133a_row0_col1\" class=\"data row0 col1\" >7562.3922</td>\n",
       "      <td id=\"T_9133a_row0_col2\" class=\"data row0 col2\" >172171142.0241</td>\n",
       "      <td id=\"T_9133a_row0_col3\" class=\"data row0 col3\" >12467.7048</td>\n",
       "      <td id=\"T_9133a_row0_col4\" class=\"data row0 col4\" >-0.3298</td>\n",
       "      <td id=\"T_9133a_row0_col5\" class=\"data row0 col5\" >1.7706</td>\n",
       "      <td id=\"T_9133a_row0_col6\" class=\"data row0 col6\" >2.4446</td>\n",
       "      <td id=\"T_9133a_row0_col7\" class=\"data row0 col7\" >0.0300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9133a_level0_row1\" class=\"row_heading level0 row1\" >lightgbm</th>\n",
       "      <td id=\"T_9133a_row1_col0\" class=\"data row1 col0\" >Light Gradient Boosting Machine</td>\n",
       "      <td id=\"T_9133a_row1_col1\" class=\"data row1 col1\" >7757.6789</td>\n",
       "      <td id=\"T_9133a_row1_col2\" class=\"data row1 col2\" >173573651.8997</td>\n",
       "      <td id=\"T_9133a_row1_col3\" class=\"data row1 col3\" >12494.8525</td>\n",
       "      <td id=\"T_9133a_row1_col4\" class=\"data row1 col4\" >-0.3176</td>\n",
       "      <td id=\"T_9133a_row1_col5\" class=\"data row1 col5\" >1.6085</td>\n",
       "      <td id=\"T_9133a_row1_col6\" class=\"data row1 col6\" >2.8661</td>\n",
       "      <td id=\"T_9133a_row1_col7\" class=\"data row1 col7\" >0.0233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9133a_level0_row2\" class=\"row_heading level0 row2\" >rf</th>\n",
       "      <td id=\"T_9133a_row2_col0\" class=\"data row2 col0\" >Random Forest Regressor</td>\n",
       "      <td id=\"T_9133a_row2_col1\" class=\"data row2 col1\" >7671.3129</td>\n",
       "      <td id=\"T_9133a_row2_col2\" class=\"data row2 col2\" >174291479.6932</td>\n",
       "      <td id=\"T_9133a_row2_col3\" class=\"data row2 col3\" >12549.2475</td>\n",
       "      <td id=\"T_9133a_row2_col4\" class=\"data row2 col4\" >-0.3462</td>\n",
       "      <td id=\"T_9133a_row2_col5\" class=\"data row2 col5\" >2.0998</td>\n",
       "      <td id=\"T_9133a_row2_col6\" class=\"data row2 col6\" >2.1222</td>\n",
       "      <td id=\"T_9133a_row2_col7\" class=\"data row2 col7\" >0.0733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9133a_level0_row3\" class=\"row_heading level0 row3\" >et</th>\n",
       "      <td id=\"T_9133a_row3_col0\" class=\"data row3 col0\" >Extra Trees Regressor</td>\n",
       "      <td id=\"T_9133a_row3_col1\" class=\"data row3 col1\" >7633.8377</td>\n",
       "      <td id=\"T_9133a_row3_col2\" class=\"data row3 col2\" >175887373.6168</td>\n",
       "      <td id=\"T_9133a_row3_col3\" class=\"data row3 col3\" >12619.8420</td>\n",
       "      <td id=\"T_9133a_row3_col4\" class=\"data row3 col4\" >-0.3652</td>\n",
       "      <td id=\"T_9133a_row3_col5\" class=\"data row3 col5\" >2.1101</td>\n",
       "      <td id=\"T_9133a_row3_col6\" class=\"data row3 col6\" >1.7686</td>\n",
       "      <td id=\"T_9133a_row3_col7\" class=\"data row3 col7\" >0.0667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9133a_level0_row4\" class=\"row_heading level0 row4\" >dt</th>\n",
       "      <td id=\"T_9133a_row4_col0\" class=\"data row4 col0\" >Decision Tree Regressor</td>\n",
       "      <td id=\"T_9133a_row4_col1\" class=\"data row4 col1\" >7791.3952</td>\n",
       "      <td id=\"T_9133a_row4_col2\" class=\"data row4 col2\" >177204861.6873</td>\n",
       "      <td id=\"T_9133a_row4_col3\" class=\"data row4 col3\" >12668.5700</td>\n",
       "      <td id=\"T_9133a_row4_col4\" class=\"data row4 col4\" >-0.3775</td>\n",
       "      <td id=\"T_9133a_row4_col5\" class=\"data row4 col5\" >2.1300</td>\n",
       "      <td id=\"T_9133a_row4_col6\" class=\"data row4 col6\" >1.9052</td>\n",
       "      <td id=\"T_9133a_row4_col7\" class=\"data row4 col7\" >0.0200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9133a_level0_row5\" class=\"row_heading level0 row5\" >ada</th>\n",
       "      <td id=\"T_9133a_row5_col0\" class=\"data row5 col0\" >AdaBoost Regressor</td>\n",
       "      <td id=\"T_9133a_row5_col1\" class=\"data row5 col1\" >7535.2397</td>\n",
       "      <td id=\"T_9133a_row5_col2\" class=\"data row5 col2\" >180203549.5605</td>\n",
       "      <td id=\"T_9133a_row5_col3\" class=\"data row5 col3\" >12515.5064</td>\n",
       "      <td id=\"T_9133a_row5_col4\" class=\"data row5 col4\" >-0.2682</td>\n",
       "      <td id=\"T_9133a_row5_col5\" class=\"data row5 col5\" >1.4362</td>\n",
       "      <td id=\"T_9133a_row5_col6\" class=\"data row5 col6\" >3.0733</td>\n",
       "      <td id=\"T_9133a_row5_col7\" class=\"data row5 col7\" >0.0233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9133a_level0_row6\" class=\"row_heading level0 row6\" >knn</th>\n",
       "      <td id=\"T_9133a_row6_col0\" class=\"data row6 col0\" >K Neighbors Regressor</td>\n",
       "      <td id=\"T_9133a_row6_col1\" class=\"data row6 col1\" >7726.9717</td>\n",
       "      <td id=\"T_9133a_row6_col2\" class=\"data row6 col2\" >186498313.3333</td>\n",
       "      <td id=\"T_9133a_row6_col3\" class=\"data row6 col3\" >12782.1361</td>\n",
       "      <td id=\"T_9133a_row6_col4\" class=\"data row6 col4\" >-0.3216</td>\n",
       "      <td id=\"T_9133a_row6_col5\" class=\"data row6 col5\" >1.5258</td>\n",
       "      <td id=\"T_9133a_row6_col6\" class=\"data row6 col6\" >2.4667</td>\n",
       "      <td id=\"T_9133a_row6_col7\" class=\"data row6 col7\" >0.0167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9133a_level0_row7\" class=\"row_heading level0 row7\" >dummy</th>\n",
       "      <td id=\"T_9133a_row7_col0\" class=\"data row7 col0\" >Dummy Regressor</td>\n",
       "      <td id=\"T_9133a_row7_col1\" class=\"data row7 col1\" >7807.4230</td>\n",
       "      <td id=\"T_9133a_row7_col2\" class=\"data row7 col2\" >213558990.6667</td>\n",
       "      <td id=\"T_9133a_row7_col3\" class=\"data row7 col3\" >13182.2617</td>\n",
       "      <td id=\"T_9133a_row7_col4\" class=\"data row7 col4\" >-0.2504</td>\n",
       "      <td id=\"T_9133a_row7_col5\" class=\"data row7 col5\" >1.2185</td>\n",
       "      <td id=\"T_9133a_row7_col6\" class=\"data row7 col6\" >2.7523</td>\n",
       "      <td id=\"T_9133a_row7_col7\" class=\"data row7 col7\" >0.0167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9133a_level0_row8\" class=\"row_heading level0 row8\" >llar</th>\n",
       "      <td id=\"T_9133a_row8_col0\" class=\"data row8 col0\" >Lasso Least Angle Regression</td>\n",
       "      <td id=\"T_9133a_row8_col1\" class=\"data row8 col1\" >8761.8132</td>\n",
       "      <td id=\"T_9133a_row8_col2\" class=\"data row8 col2\" >221797453.3499</td>\n",
       "      <td id=\"T_9133a_row8_col3\" class=\"data row8 col3\" >13757.2925</td>\n",
       "      <td id=\"T_9133a_row8_col4\" class=\"data row8 col4\" >-0.4507</td>\n",
       "      <td id=\"T_9133a_row8_col5\" class=\"data row8 col5\" >1.6194</td>\n",
       "      <td id=\"T_9133a_row8_col6\" class=\"data row8 col6\" >4.1519</td>\n",
       "      <td id=\"T_9133a_row8_col7\" class=\"data row8 col7\" >0.0200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9133a_level0_row9\" class=\"row_heading level0 row9\" >lar</th>\n",
       "      <td id=\"T_9133a_row9_col0\" class=\"data row9 col0\" >Least Angle Regression</td>\n",
       "      <td id=\"T_9133a_row9_col1\" class=\"data row9 col1\" >8770.3965</td>\n",
       "      <td id=\"T_9133a_row9_col2\" class=\"data row9 col2\" >221871662.1940</td>\n",
       "      <td id=\"T_9133a_row9_col3\" class=\"data row9 col3\" >13761.7155</td>\n",
       "      <td id=\"T_9133a_row9_col4\" class=\"data row9 col4\" >-0.4525</td>\n",
       "      <td id=\"T_9133a_row9_col5\" class=\"data row9 col5\" >1.6123</td>\n",
       "      <td id=\"T_9133a_row9_col6\" class=\"data row9 col6\" >4.1577</td>\n",
       "      <td id=\"T_9133a_row9_col7\" class=\"data row9 col7\" >0.0200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9133a_level0_row10\" class=\"row_heading level0 row10\" >en</th>\n",
       "      <td id=\"T_9133a_row10_col0\" class=\"data row10 col0\" >Elastic Net</td>\n",
       "      <td id=\"T_9133a_row10_col1\" class=\"data row10 col1\" >8412.6334</td>\n",
       "      <td id=\"T_9133a_row10_col2\" class=\"data row10 col2\" >222453418.5676</td>\n",
       "      <td id=\"T_9133a_row10_col3\" class=\"data row10 col3\" >13629.2249</td>\n",
       "      <td id=\"T_9133a_row10_col4\" class=\"data row10 col4\" >-0.3906</td>\n",
       "      <td id=\"T_9133a_row10_col5\" class=\"data row10 col5\" >1.6000</td>\n",
       "      <td id=\"T_9133a_row10_col6\" class=\"data row10 col6\" >3.5971</td>\n",
       "      <td id=\"T_9133a_row10_col7\" class=\"data row10 col7\" >0.0200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9133a_level0_row11\" class=\"row_heading level0 row11\" >omp</th>\n",
       "      <td id=\"T_9133a_row11_col0\" class=\"data row11 col0\" >Orthogonal Matching Pursuit</td>\n",
       "      <td id=\"T_9133a_row11_col1\" class=\"data row11 col1\" >8307.8111</td>\n",
       "      <td id=\"T_9133a_row11_col2\" class=\"data row11 col2\" >228300082.1486</td>\n",
       "      <td id=\"T_9133a_row11_col3\" class=\"data row11 col3\" >13742.8764</td>\n",
       "      <td id=\"T_9133a_row11_col4\" class=\"data row11 col4\" >-0.3977</td>\n",
       "      <td id=\"T_9133a_row11_col5\" class=\"data row11 col5\" >1.5226</td>\n",
       "      <td id=\"T_9133a_row11_col6\" class=\"data row11 col6\" >3.2883</td>\n",
       "      <td id=\"T_9133a_row11_col7\" class=\"data row11 col7\" >0.0167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9133a_level0_row12\" class=\"row_heading level0 row12\" >ridge</th>\n",
       "      <td id=\"T_9133a_row12_col0\" class=\"data row12 col0\" >Ridge Regression</td>\n",
       "      <td id=\"T_9133a_row12_col1\" class=\"data row12 col1\" >9497.8566</td>\n",
       "      <td id=\"T_9133a_row12_col2\" class=\"data row12 col2\" >235755419.5658</td>\n",
       "      <td id=\"T_9133a_row12_col3\" class=\"data row12 col3\" >14494.4512</td>\n",
       "      <td id=\"T_9133a_row12_col4\" class=\"data row12 col4\" >-0.7075</td>\n",
       "      <td id=\"T_9133a_row12_col5\" class=\"data row12 col5\" >1.6743</td>\n",
       "      <td id=\"T_9133a_row12_col6\" class=\"data row12 col6\" >5.0861</td>\n",
       "      <td id=\"T_9133a_row12_col7\" class=\"data row12 col7\" >0.0200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9133a_level0_row13\" class=\"row_heading level0 row13\" >lr</th>\n",
       "      <td id=\"T_9133a_row13_col0\" class=\"data row13 col0\" >Linear Regression</td>\n",
       "      <td id=\"T_9133a_row13_col1\" class=\"data row13 col1\" >9532.1131</td>\n",
       "      <td id=\"T_9133a_row13_col2\" class=\"data row13 col2\" >236092876.1744</td>\n",
       "      <td id=\"T_9133a_row13_col3\" class=\"data row13 col3\" >14513.0559</td>\n",
       "      <td id=\"T_9133a_row13_col4\" class=\"data row13 col4\" >-0.7155</td>\n",
       "      <td id=\"T_9133a_row13_col5\" class=\"data row13 col5\" >1.6757</td>\n",
       "      <td id=\"T_9133a_row13_col6\" class=\"data row13 col6\" >5.1119</td>\n",
       "      <td id=\"T_9133a_row13_col7\" class=\"data row13 col7\" >0.0233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9133a_level0_row14\" class=\"row_heading level0 row14\" >lasso</th>\n",
       "      <td id=\"T_9133a_row14_col0\" class=\"data row14 col0\" >Lasso Regression</td>\n",
       "      <td id=\"T_9133a_row14_col1\" class=\"data row14 col1\" >9514.9759</td>\n",
       "      <td id=\"T_9133a_row14_col2\" class=\"data row14 col2\" >236143312.6240</td>\n",
       "      <td id=\"T_9133a_row14_col3\" class=\"data row14 col3\" >14512.9911</td>\n",
       "      <td id=\"T_9133a_row14_col4\" class=\"data row14 col4\" >-0.7148</td>\n",
       "      <td id=\"T_9133a_row14_col5\" class=\"data row14 col5\" >1.6752</td>\n",
       "      <td id=\"T_9133a_row14_col6\" class=\"data row14 col6\" >5.1066</td>\n",
       "      <td id=\"T_9133a_row14_col7\" class=\"data row14 col7\" >0.0167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9133a_level0_row15\" class=\"row_heading level0 row15\" >br</th>\n",
       "      <td id=\"T_9133a_row15_col0\" class=\"data row15 col0\" >Bayesian Ridge</td>\n",
       "      <td id=\"T_9133a_row15_col1\" class=\"data row15 col1\" >9386.6415</td>\n",
       "      <td id=\"T_9133a_row15_col2\" class=\"data row15 col2\" >236659192.3494</td>\n",
       "      <td id=\"T_9133a_row15_col3\" class=\"data row15 col3\" >14480.2222</td>\n",
       "      <td id=\"T_9133a_row15_col4\" class=\"data row15 col4\" >-0.6870</td>\n",
       "      <td id=\"T_9133a_row15_col5\" class=\"data row15 col5\" >1.6915</td>\n",
       "      <td id=\"T_9133a_row15_col6\" class=\"data row15 col6\" >4.9246</td>\n",
       "      <td id=\"T_9133a_row15_col7\" class=\"data row15 col7\" >0.0200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9133a_level0_row16\" class=\"row_heading level0 row16\" >par</th>\n",
       "      <td id=\"T_9133a_row16_col0\" class=\"data row16 col0\" >Passive Aggressive Regressor</td>\n",
       "      <td id=\"T_9133a_row16_col1\" class=\"data row16 col1\" >8302.1175</td>\n",
       "      <td id=\"T_9133a_row16_col2\" class=\"data row16 col2\" >241518288.2945</td>\n",
       "      <td id=\"T_9133a_row16_col3\" class=\"data row16 col3\" >14040.4438</td>\n",
       "      <td id=\"T_9133a_row16_col4\" class=\"data row16 col4\" >-0.4466</td>\n",
       "      <td id=\"T_9133a_row16_col5\" class=\"data row16 col5\" >1.7175</td>\n",
       "      <td id=\"T_9133a_row16_col6\" class=\"data row16 col6\" >2.1670</td>\n",
       "      <td id=\"T_9133a_row16_col7\" class=\"data row16 col7\" >0.0200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9133a_level0_row17\" class=\"row_heading level0 row17\" >huber</th>\n",
       "      <td id=\"T_9133a_row17_col0\" class=\"data row17 col0\" >Huber Regressor</td>\n",
       "      <td id=\"T_9133a_row17_col1\" class=\"data row17 col1\" >8199.3291</td>\n",
       "      <td id=\"T_9133a_row17_col2\" class=\"data row17 col2\" >246315776.2199</td>\n",
       "      <td id=\"T_9133a_row17_col3\" class=\"data row17 col3\" >14115.0889</td>\n",
       "      <td id=\"T_9133a_row17_col4\" class=\"data row17 col4\" >-0.4483</td>\n",
       "      <td id=\"T_9133a_row17_col5\" class=\"data row17 col5\" >1.5624</td>\n",
       "      <td id=\"T_9133a_row17_col6\" class=\"data row17 col6\" >2.4624</td>\n",
       "      <td id=\"T_9133a_row17_col7\" class=\"data row17 col7\" >0.0233</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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    {
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     "output_type": "stream",
     "text": [
      "Error evaluation for test data\n"
     ]
    },
    {
     "data": {
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       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_eac8e_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_eac8e_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
       "      <th id=\"T_eac8e_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
       "      <th id=\"T_eac8e_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
       "      <th id=\"T_eac8e_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
       "      <th id=\"T_eac8e_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
       "      <th id=\"T_eac8e_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_eac8e_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_eac8e_row0_col0\" class=\"data row0 col0\" >Gradient Boosting Regressor</td>\n",
       "      <td id=\"T_eac8e_row0_col1\" class=\"data row0 col1\" >7260.8881</td>\n",
       "      <td id=\"T_eac8e_row0_col2\" class=\"data row0 col2\" >69380805.5489</td>\n",
       "      <td id=\"T_eac8e_row0_col3\" class=\"data row0 col3\" >8329.5141</td>\n",
       "      <td id=\"T_eac8e_row0_col4\" class=\"data row0 col4\" >-0.9120</td>\n",
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       "      <td id=\"T_eac8e_row0_col6\" class=\"data row0 col6\" >8.2086</td>\n",
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   ],
   "source": [
    "temporal_predictor.train_model(error_metric=\"MSE\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c41697d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_41ebb_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
       "      <th id=\"T_41ebb_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
       "      <th id=\"T_41ebb_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
       "      <th id=\"T_41ebb_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th id=\"T_41ebb_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_41ebb_row0_col0\" class=\"data row0 col0\" >Gradient Boosting Regressor</td>\n",
       "      <td id=\"T_41ebb_row0_col1\" class=\"data row0 col1\" >3245.8091</td>\n",
       "      <td id=\"T_41ebb_row0_col2\" class=\"data row0 col2\" >30561184.4805</td>\n",
       "      <td id=\"T_41ebb_row0_col3\" class=\"data row0 col3\" >5528.2171</td>\n",
       "      <td id=\"T_41ebb_row0_col4\" class=\"data row0 col4\" >0.7870</td>\n",
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\n",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = temporal_predictor.plot_model_fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b5e33a40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x550 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = temporal_predictor.predict_and_plot_future()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}