Darts_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0b9156a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-04-26 13:34:02 prophet.plot ERROR: Importing plotly failed. Interactive plots will not work.\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from darts import TimeSeries\n",
    "from darts.dataprocessing.transformers import Scaler\n",
    "from darts.models import NaiveSeasonal,NaiveDrift,TFTModel,NBEATSModel, TCNModel,TransformerModel\n",
    "from darts.metrics import mape, rmse, r2_score\n",
    "from darts.utils.statistics import check_seasonality, plot_acf\n",
    "from darts.utils.timeseries_generation import datetime_attribute_timeseries\n",
    "\n",
    "from darts.datasets import SunspotsDataset, AirPassengersDataset\n",
    "\n",
    "import sys\n",
    "import time\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import logging\n",
    "logging.disable(logging.CRITICAL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "e6643577",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"C:\\\\Users\\\\bduser\\\\Desktop\\\\Harsh_Thesis_work\\\\Bitcoin_price_with_sentiments.csv\")\n",
    "\n",
    "df.drop(['Unnamed: 0'],axis=1,inplace=True)\n",
    "\n",
    "df['Price'] = df['Price'].str.replace(',','')\n",
    "df['Price'] = df['Price'].astype('float')\n",
    "\n",
    "df['Open'] = df['Open'].str.replace(',','')\n",
    "df['Open'] = df['Open'].astype('float')\n",
    "\n",
    "df['High'] = df['High'].str.replace(',','')\n",
    "df['High'] = df['High'].astype('float')\n",
    "\n",
    "df['Low'] = df['Low'].str.replace(',','')\n",
    "df['Low'] = df['Low'].astype('float')\n",
    "\n",
    "\n",
    "df['Vol.'] = df['Vol.'].astype(str)\n",
    "for i in range(len(df['Vol.'])):\n",
    "    if \"K\" in df['Vol.'][i]:\n",
    "        df['Vol.'][i] = df['Vol.'][i].replace(\"K\",\"\")\n",
    "        df['Vol.'][i] = float(df['Vol.'][i])\n",
    "        df['Vol.'][i] =df['Vol.'][i]*1000\n",
    "\n",
    "    elif \"M\" in df['Vol.'][i]:\n",
    "        df['Vol.'][i] = df['Vol.'][i].replace(\"M\",\"\")\n",
    "        df['Vol.'][i] = float(df['Vol.'][i])\n",
    "        df['Vol.'][i] = df['Vol.'][i]*1000000\n",
    "\n",
    "df['Vol.'] = df['Vol.'].astype(float)\n",
    "\n",
    "df[\"Change %\"] = df[\"Change %\"].astype(str)\n",
    "for i in range(len(df[\"Change %\"])):\n",
    "        df[\"Change %\"][i] = df[\"Change %\"][i].replace(\"%\",\"\")\n",
    "df[\"Change %\"] = df[\"Change %\"].astype(float)\n",
    "\n",
    "df['Date'] = pd.to_datetime(df['Date'])\n",
    "\n",
    "df.set_index(\"Date\",inplace=True)\n",
    "\n",
    "df.sort_values(by='Date',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "9366a507",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Vol.</th>\n",
       "      <th>Change %</th>\n",
       "      <th>sentiment_scores</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>13,444.90</td>\n",
       "      <td>13,850.50</td>\n",
       "      <td>13,921.50</td>\n",
       "      <td>12,877.70</td>\n",
       "      <td>78,430.00</td>\n",
       "      <td>-2.93</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>14,754.10</td>\n",
       "      <td>13,444.90</td>\n",
       "      <td>15,306.10</td>\n",
       "      <td>12,934.20</td>\n",
       "      <td>137,730.00</td>\n",
       "      <td>9.74</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>15,156.60</td>\n",
       "      <td>14,754.10</td>\n",
       "      <td>15,435.00</td>\n",
       "      <td>14,579.70</td>\n",
       "      <td>106,540.00</td>\n",
       "      <td>2.73</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>15,180.10</td>\n",
       "      <td>15,156.50</td>\n",
       "      <td>15,408.70</td>\n",
       "      <td>14,244.70</td>\n",
       "      <td>110,970.00</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>16,954.80</td>\n",
       "      <td>15,180.10</td>\n",
       "      <td>17,126.90</td>\n",
       "      <td>14,832.40</td>\n",
       "      <td>141,960.00</td>\n",
       "      <td>11.69</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Price      Open      High       Low       Vol.  Change %  \\\n",
       "Date                                                                      \n",
       "2018-01-01 13,444.90 13,850.50 13,921.50 12,877.70  78,430.00     -2.93   \n",
       "2018-01-02 14,754.10 13,444.90 15,306.10 12,934.20 137,730.00      9.74   \n",
       "2018-01-03 15,156.60 14,754.10 15,435.00 14,579.70 106,540.00      2.73   \n",
       "2018-01-04 15,180.10 15,156.50 15,408.70 14,244.70 110,970.00      0.15   \n",
       "2018-01-05 16,954.80 15,180.10 17,126.90 14,832.40 141,960.00     11.69   \n",
       "\n",
       "            sentiment_scores  \n",
       "Date                          \n",
       "2018-01-01              0.07  \n",
       "2018-01-02              0.06  \n",
       "2018-01-03              0.04  \n",
       "2018-01-04              0.06  \n",
       "2018-01-05              0.05  "
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "b888e2e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Price               0\n",
       "Open                0\n",
       "High                0\n",
       "Low                 0\n",
       "Vol.                0\n",
       "Change %            0\n",
       "sentiment_scores    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "b2dd1056",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create time series object for target variable\n",
    "plt.figure(figsize=(10,6))\n",
    "series = TimeSeries.from_series(df[\"Price\"]) \n",
    "series.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "e9e08efe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zZszgsssuS3CrREQkXhS6pCoUuirBO5E+Kysrwa0REZF4U+iSqlDoqgTN6RIRqdtycnLc2/pdIJWl0FUJ3tDl8/kS3BoREYk3VbqkKhS6KkEr0ouI1G0KXVIVCl2VoOFFEZG6rTKhyzsUKQIKXZWiFelFROq2ioaud999l5SUFN555514NEtqCIWuSlClS0Skbqto6Bo3bhx+v59zzjknHs2SGkKhqxJKm9OlrSBEROoGb+hasGABRUVFZZ7fqFGjWDdJahCFrkrwVrq8wUuVLxGRusEbuq6++moeeeSRMs9PSUmJdZOkBlHoqgRv6PL+4Hlvi4hI7RX6ef/www+XeX7jxo1j2RypYRS6KsE7kV6hS0Sk7gn9vN+/f3+Jc/bu3eve1pqO4qXQVQmqdImI1G0VCV3r1693b2dkZGjer7gUuirBO5FeoUtEpO4J/bwPN6d3x44d7u2cnJywwSxS27dv5/rrrw8KdlJzKHRVgipdIiJ1W0U+7/ft2xd0f/v27VF7/z//+c88/vjjHH/88VF7TYkfha5K8IauvLw897hCl4hI3ZDo0GVZFgCrV6+OagVN4qN+eScYY5oCs4A+wFHAGuB9oBFQCIy3LGutMaY38FzgNW+3LGuWMSYVeBVoA0y3LOv+wGs+GHit9YHn51EDaCK9iEjdVpHP+9AwtGvXrqi8t9/vZ+fOne79efPmccIJJ0TltSU+KlLpygbGAu8G7hdgB6XjgPuAmwPH7wUuA0YBdweOXQl8bFnWscBwY8yBxpgBQAfLsoYCy4Gzo/KVxIGGF0VE6rZwn/ehE+VDK12h9yO1efNmtm3b5t7fvXt3VF5X4qfcSpdlWQXAdmOMcz8XSA88nI8dwsAOUisBjDE7jDGtgaOBvwQen4ld3WoLzAgc+wwYD7zhfU9jzFXAVQATJ07kxBNPjORrq5T8/HzS09PLPGfPnj3uv85tgPT09HKfW1NUpB/qCvVFMfVFMPWHrS72Q7iq1apVq2jQoIHbF5s3bw56fP369VHpJ2do0bFhw4Zq2f918fvCq2PHjqU+Vm7oKo0xpgFwB3BF4JB3MZLdQBrQAtgT5timkGNBLMuaAkwJ3I3Ltbbp6elldhRAmzZtAEhOTg463rRp03KfW1NUpB/qCvVFMfVFMPWHrS72Q716JQeIGjVqxNKlSyksLGTs2LHuqIijYcOGUemnRYsWBd1PTk6ulv1fF78vKiri0IUdip61LOv3wH3vBlQtgExgJ9AM2BU4thZIChzznlcjaE6XiEjdFu7zfteuXZx88skArF27tsScrqysrKi8d2iVLVrDlhI/EV29aIy5DVhjWdbbnsNbjDE9jDHNgDTLsjKAecBJgcdHAt+HHBsFzImo5QlQ0Tldq1evprCwMK5tExGR2MvJySnz2MaNG90w1KyZXV+IRjiaN28eF110UdAxha6ap0KhyxjzCXZQet4Y80fgTuAEY8xXxpj7Aqf9HXgR+DzwOMDzwFhjzBzgK8uy0i3L+gnYbIz5FugNvBe9Lye2KrI46rRp0zj44IOZMGFC3NsnIiKxFa7S5Q1dhYWFbqWrbdu2AEyePJlff/21Su979NFHu7edIc5oVdAkfio0vGhZ1skhh14Jc85yYGjIsSzg9DDn3hx6rCaoSKXrwQcfBGDq1Km88MIL8W2giIjEVLjqUnZ2tns7Pz/fPadt27asWrWKffv2ceihh0ZtO6ADDzyQ9evXq9JVA2lx1EqoSOjSD4GISO0Vrrrk3eDau+2PU+mKtk6dOpXaFqneFLoqoSIT6bVCsIhI7RXuD2vvBPesrKygSlcsOFcG6o/8mkehqxIqUunylplFRKR2CVdd8q4S7w1d7dq1CzovmsOLoNBVEyl0VYIzkT4nJycoXHlvq9IlIlI7FRUVVajSVdrwYrS2A3JC19y5c7UqfQ2j0FUJTqVr06ZNQX+xOH/5+P3+oL+CioqKEBGR2qG0kYzSKl2hoWvr1q0Rv3dqaqp72xle3LNnD6NHj474NSX+FLoqwQld69evDzq+YsUK+vTpw4MPPkheXvHe3d7JldGWn58f1Z3rRUSkbKVNXA8NXaVVurz7JlZF586d3dvz5s3TCEsNotBVCU7oCh2X/+yzz1i+fDm33HJL0PFolZJD/fLLL3Tp0oUOHTrwzTffxOQ9REQkWGlzqLyf9Xv37nX/4Ha2jnNEGrr8fr8brGbOnFlii52vv/46oteV+FPoqoTQ/bQ6dOhQ5vmxCl0zZ85k06ZNFBYW8uijj8bkPUREJFhplS7vZ/3ixYvZt28fbdq0KfE7ItLhxby8PIqKikhOTmbkyJFBQ40ACxcujOh1Jf4Uuioh9EqUnj17lnl+rEKX93U/+ugjlZZFROKgIsOL3333HQDHHnssTZs2DTov0iknzmd8SkoKAE2aNAl6PDOzxmxhXOcpdFVC+/btg8q6sQpdBQUFfPzxx6WGKe/VKoWFhfqBExGJg4oMLzqOPfZYGjRowI8//sjpp58OhN9CqCJCQ1dycjKnnHKK+7h+B9QcCl2VdMghh7i3+/btW+a5kV7Ke/bZZzN27FieeeaZsI+H/oDv2bMnovcREZGKcypdJ598Mo8++ih/+ctfgPChq3///gAMGDCAI444Agi/WXZFhIYugOnTp/P+++8DCl01iUJXJQ0bNgyAxo0bc9ppp4U9x9lZPpIfsB07dvDBBx8A9g9VOKFhLpZXSYqIiM2pdLVo0YIbbriBAQMGAOE/671XGDZs2BCIXqXLkZaWBgQPb0r1VqENr6XYn/70J/x+P+PHj3fDVaguXbqwZMmSiFanX758uXu7e/fuYc8J/atKoUtEJPacSpczp6pRo0alnussYOo9L5qVLoCWLVsCqnTVJKp0VVLz5s2544476NSpU4nJjKmpqbRo0YLBgwcDlf8Be+uttzjuuOPc+6VN2nQqXc6VMRpeFBGJPafS5Vw9WFbo8j4W60qXQlfNodBVBfXr1w/6wfrmm2/YtGkTBxxwAFD50HX++ecH3S9t0qYTupyd5lXpEhGJPWeUoSKhyyvWla6dO3dGbV9HiS2FriryrpfStm1bGjduXOUfMEd5a8I4oUuVLhGR2Fu0aBFQfEFVRUOXU+mKduhq3LgxDRs2JDc3N6LpLBJ/Cl1V5A1dzl8d0Qpd4SpdO3bscLf/UaVLRCQ+/H4/P/zwAwBHHnkkYIcer27dugFw0003BR13fidEOrzo/C4IDV0+n6/EEGN+fj7vvvsuO3bsiOi9JLYUuqpo7dq17m3nB9D5AavqXx7hKl2jRo1ybztbTKjSJSISW+vWrWPbtm2kpaW54Sq00nX99dfz008/ce+99wYd9/5OiGQY0KmwOX9oe4VOpp8yZQrjxo0L+l0h1YdCVxU5S0icccYZ+Hw+oDh8xaLS5d3uwbl6UpUuEZHYcqpcgwcPdj/rQ0NX48aN6d+/P8nJyUHHneHF2bNnc9ZZZwH25/att94a9Id7OH6/n48//hiAMWPGlHg89PfAt99+C2hroOpKoauK/vOf//D000/z9ttvu8diNafL+xfSoEGD3C0mVOkSEYmt0KFFKLkdT4sWLcI+1xvOnAVN7777bh544AF3EdXSbNiwgdWrV5OWluZeGe/ltMH5I71r167uY7Haik4ip9BVRf369eOaa66hQYMG7rFYhS5v5Wv69OmqdImIxMn8+fMBgoJPmzZtgtZr9O5Y4uVUurxWrlwJ2Fejl/W7YvXq1QAceuihJCUllXjcmeflTLb3TmtxgqJUHwpdMRCt0JWbm0tBQYF737s+V/v27VXpEhGJE2delbOlD9gT2b2LWPfo0SPsc8Nd5eidVP/NN9+U+r7r168Hgle493IqXU7o8u5Ykp6eXurrSmIodMVAJBPpveHKy1vdckrFTglboUtEJPZycnLIysqiQYMGtG7dOugxb6AqbQmJ0ON33nknn332mXt/1apVpb53eaHLqXQ5vyu8oasivxtK+90jsaHQFQORVLpKC2jeH6TzzjsPsFfFh+LlKkpbRFVERKrO+wevM4necfDBB5f7/NDhxbvvvjvo/oYNG0p9bmUrXd6gFbpPb6iFCxfStGlTrr322iqPzEjFKHTFQCShy/mBCeXM67r33ntZunQpUFzpUugSEYk9J3Q5yzN4Pfzww5x66qlBF1OFKm8RVSdYlfVYLCpdn376KTk5OTzzzDN069ZNwSsOFLpiIJIlI8qrdHnLz06ly/kLp7SV60VEpOpCp3Z4tWnThg8//JBjjjmm1OeHm0gPuMtHlFXpWrduHRB+jS4oe05XeZWujRs3urc3b97MihUryjxfqk6hKwaiUelq164dUHxlovfqyNDQpUqXiEjs7Ny5Eyh9SYjyhAtdPp+P++67Dyg9dO3cuZPffvuN5ORkevbsGfacqlS6nCsjnSHTfv36MXPmzDKfI1Wj0BUD0Qhdbdu2BYqrWN4fHueql0aNGlGvXj3y8vLIz8+vUptFRCS8soYXK6JevZK/alNSUjjwwAMB+yrDoqKiEud89913+P1+Bg8eXGLLIUdZla7MzEy+++67UrcfWrNmDQAjR450j5100klkZWXx7LPPctJJJ2k7oShT6IqBSK5eDA1dHTt2BIrDlrcM7Gz34PP5VO0SEYmxsoYXI9W4cWMaN25My5Ytyc/Pdz/XvZzV5Z2dT8JxKl1ZWVn8/vvvQQFr1qxZDB06lEsvvZSBAwfyyCOPuI8VFha6Q5cnnHBC0Gu+9tprXHPNNcycOZNTLnuHrP2V37pIwlPoioGqVLpatmzJ7NmzS4Qub/nZuwqyM5le87pERGIjFqHLCUtO9cwZwvRy5lj169ev1Ndxfh+88cYbQWuGeb311lv89NNPQRtxb9myhfz8fNq1a1fiCsz//ve/9o02FzB/51UMuqKQdVsUvKJBoSsGnH238vPzmTZtWoWe44SuYcOGcfzxx7urHO/Zs4d9+/a5P/THH3+8Ow8ANK9LRCTWnEAU6fBiOM5wYVmhy7ly8aCDDir1dZzw5uVdJT+U8we68zslLS2NAQMGAPbc4UaNGjFv3jz75H2LaJ2yjRUb63HlZD+7s/x8Os8f0abdYlPoigHvOi5nnHFGhZ7jhC7nB8gbupyy8wEHHMDs2bPp0qWL+zwtGyEiEluJqnQ5IxylXbkIJfd/BPjjH/9Y6vk///wzUDyK0qxZM7p3785PP/3EunXr6NOnj3vuUYc3Y+kb7WjZFGZa0O50Pyf/1c9zH5b31UlpFLqqidJC17/+9S8++eQToHgFei8tGyEiEluxmtMFpYeu/fv3k5GRQYMGDdyr2cMJrXQ1bNiQP/3pT6Wev3jxYqB4wr1zNXz//v3p0KFDUOg6+OCDaZfm41+X24WE3Dz7+F+e9lNUpGpXJBS6qglnHS7nh8tbHr766quB4qqWlypdIiKx5YQuJ6BE4qOPPgq6X16ly7l46sADDwx79aPDW+l6/fXXWbp0aal7QELxkKW30uV12GGHubd79eoFwITT4ISBxefsy4Zf1pX6FlKG+uWdYIxpCswC+gBHWZa11BhzLnA9kANcYlnWBmNMb+C5wGveblnWLGNMKvAq0AaYblnW/YHXfBA4ClgPjLcsKy8GX1tCLViwgCOOOKLCP6TOhqdDhw4Fwo/Jq9IlIhJ/znqJZc2VKs/YsWM5+eST3ZGL8ipd5a1E7/BWugYPHuxOpj/++OP58ssvS5zvLAFRWujq3bu3e/uyyy4DoH59H59Mhve/hafe9/PdEvhpJfTpWmbTJIyKVLqygbHAuwDGmAbAjcBw4PbAfwD3ApcBowBnY6krgY8tyzoWGG6MOdAYMwDoYFnWUGA5cHZ0vpTqxflroSJXMO7bt4+FCxeSlJTEkCFDgIqHLlW6RERiywld4T6DK8O5yApKVrqcappjy5YtgD2Xtyze9bu8E+4//PBDrr/+et5//302b97MW2+9BUBGRgZQcnjRMWrUKG644QY++eQT9yp6gIbJPs4b4eOkI+yhxovv8bN4lYYYK6vcSpdlWQXAdmOMc6gHsCxQnZpjjHkocLyDZVkrAYwxO4wxrYGjgb8EHp+JXd1qC8wIHPsMGA+8EYWvpVpxViDOzc3F7/eX2CTVa/Xq1RQUFHDooYe6P9ThQle44UVVukREYsupCkUzdDlhyZknFlrpci6gatWqVZmv2aZNG8455xwOOOCAoJ1LUlNTeeyxx4LOg/IrXfXr1+fRRx8t9f0GeEYuH37bzyv/KP13m5RUbugKowXg3VsgKfCvt+d3A2kh53qPbQo5FsQYcxVwFcDEiRM58cQTI2hm5eTn55Oenh7V12zYsCG5ubn8/vvvpa4mDLBy5UrA/iFx2hBuYdV69eqVaKOzivGmTZui0v5Y9ENNpb4opr4Ipv6w1ZV+cAJKVlZWqV9vRfqisLDQve33+0lPT3eXX3jhhRdYtmwZU6ZMoWXLlqxduxaApKSkcl/XWfS0rPOc996yZQvp6els2rQpqB0V1bWVD2gPwO8bcklPL7moa135viiNt0IYKpLQtRPwRmPnu8i7h0ELINNz7q7AsbXYIa1ZyHlBLMuaAkwJ3I1L/TI9Pb3MjopEo0aNyM3NpVWrVmWu7+L8ddK2bVu3Dd4fTkf79u1LtLFDhw6A/YMZjfbHoh9qKvVFMfVFMPWHrS70g9/vd0cSevXqFVRN8qpIX3ivfmzdujUdO3YMWpj0+++/58033+Tf//43BQUFAHTp0iWqfbx79246duzo/o7p3LlzpV6/I/D9M36OvsbPvryGYZ9bF74vIhXJ1YurgN7GmGRjzDHAksDxLcaYHsaYZkCaZVkZwDzgpMDjI4HvQ46NAuZE3PpqrqIr0ztj694fyIoOLzrlbmfOQXW2f/9+TjjhBO68885EN0VEpEKys7MpLCykYcOGpQauigo3p8vZf9Exf/58oHi4MS2txGBQRJxhyoyMDPx+f6nDixVxUGAFi00ZUWlanVKh0GWM+QQ7KD0PnA88CnwN3BP4D+DvwIvA54DzW/V5YKwxZg7wlWVZ6ZZl/QRsNsZ8C/QG3ovS11LtVDR0hbscOdzcgXDHvIuoVnfvv/8+X375JXfffbdWNBaRGiEaVy46ws3pOuSQQ4LO+fbbbyksLHTndEVrFfxGjRrRpEkTCgoK2Lt3b5VCV9uWkJQE23dBbp4+yyujQsOLlmWdHObw2yHnLAeGhhzLAk4P83o3V7yJNZfzQ1XR0OWtdCUlJbFmzRrGjRuHZVlA+NDlBDXvzvLV0fz587nooovc+1u3bqV9+/YJbJGISPmiNYkeii+wguJKl8/n47333mPcuHEUFRWRl5fH119/7U54j1alC+xq1759+8jIyHB/Z0QSupKSfHRI87NxO2zeAV06RK2JtZ4WR42hyla6Qlc77tKlC/3793fvhxterCmh69///nfQ/eXLlyeoJSIiFRet5SIgfKUL4MwzzyQ3N9fd2HrEiBHuMGM0Q1fr1q0B2L59uxvqIl3wtaN9MSTpGmKsFIWuGHJCV7grEb3CzelyeBfGq+7Di36/n6lTp7Js2bISjzlrwzgUukSkJojV8GLbtm2DHqtfv37YNbmiucm2s47X999/715V7yymWlltW9j/bt8VnbbVFQpdMVSVOV0O73IZ1b3SNX36dK644oqgbSQcztIWp5xyCgC//PJLXNsmIhKJaA4vekNXuJXmYx26nO2B/vznPwNw7LHHBrWpMhoHRkpza91+MrGl0BVDVR1eBDjiiCPc2+GunAkNXeGWmogXZ/f6cJxJoSNHjgRU6RKRmiFWw4udOnUq8bizBJCXdx5YVYXuyThixIiIX6th4EvJiXHoKiysXRP1FbpiqCoT6R1JSUm8/fbbTJo0yd0iyMs7vPjee+/RvHlzZsyYUeK8eCjrikTn8udjjjkGUOgSkZohmpWu3Nxc97Yzv6o0PXv25B//+EeV39PLG7oGDRrEhAkTIn6tRoHQlZtf1VaV7rbni2hzmp+N22pP8IpkcVSpoKqs0+V1zjnncM4554R9rEmTJiQlJZGdnc3ZZ9vbWJ5++uns378/wlZHrrTQ5ff73dB1+OGHk5qayrZt28jIyCj3g0dEJJG2b98ORGdC+9atW93b4baG834e/vbbb1V+v1A9e/Z0b//vf/8r9XdORTQMDLzEstL171ftf5/5wM+/r6wd2w2p0hVDFQ1dTvk63Jyt8vh8vhITPMubuB8rzrwtx9q1a/nb3/7Ghg0bKCwspEmTJjRs2JBDDz0UULVLRKq/NWvWANCtW7cqv5Y3dIVz5ZVXcumll/Lpp59W+b3C6dChA3fddRcPP/xw2DllleGErlhWuhwvfgy/rS/+o35ftp9tO2tm9UuVrhiq6NWLTlXKWbelspo1a1Zis9RE8IauoqIievfuTXZ2NuvXrweKJ4QecsghLFiwgBUrVnDcccclpK0iIhWxevVqIDqh67rrruPNN9/kqquuCvt4SkoKL730UpXfpyzR2hGkUYzndO3PKQ5VWzLhsEv85H9pV7tO+5ufH36Bpf+Fg9rXrAqYQlcMVbTS5YSySENXpOusRJs3XH7xxRfu/c8++wwoLs87Je4bb7yRAQMGMGjQoDi3VESkbCtXruSdd95xl8CJRug65phjyMjIiOraW4nSMNkH+AMr0kc/+GzYFny/IHCN2MZtfmb/aN/uco6fqbfAZafUnOCl0BVDFQldBQUF5OfnU69evYj39aouocu7/+NJJ53k3g7dzsIJXXv37sUYoy2BRKTaGT58OJs2bQKgXr16VR6Oczh7INZ0sa50rQ8zEvvx937G3hL8++Kht/w1KnRpTlcMOVcv3nbbbZx11llhw4W3yhVuYmVFHH300SWOJSLIlLfptvPXXehlyyIi1Y0TuMBe3qGqm13XNrGe0xUudIUGLoBf1kH69przh7tCVww5lS6wrxQJt4CpE7q8W0JU1tVXX13iWCIWSy0vdDnr0oSGrkRN/BcRqYhwCz7XdbFeMmLzjrIfP3s4nHyUfXtO6UtEVjsKXTHUpEmToPt5eSXrsM4k+qqErq5du5YYYnT28IqncKHLe0mys9BramoqZ555pnt8w4YNMW+biEikBg4cmOgmVDuxXjJiV5ZdvaoXklIO6Qyd28Edl/jo0t4+ti3x15FVmEJXDIWuOByuolPVSfSO0PWunCsG4ykrK6vEsZNPPtm9bYxxb7/33nsMGzYMSExbRURKE7rO4YABAxLUkurLWZE+VtsA7d5n//vMjT7mPOWjZyc4rCt886SPde/Uo+/BPloFag0ZuzW8KBRvLuoIF7qiUemCkpMzfT5f3Od1hat0jRo1yr3tXZgPivtHoUtEqpPNmzcH3VfoKinWE+l3Bf6Gb94EhvT18dvr9fj5v/Vo06J47nPr5vbtFRvhX//114i5Xbp6MYZCr3aJZaXLO4yXnJxMXl4eeXl5Ud23qzzhQteQIUN46aWX6NChA/VC6sROJVChS0SqEyd0NWzYkOnTp9OlS5fENqgaivVE+t2B0NWijN2XWgcqXW/OAvAzc4FdCavOFLpiKHSLhXBLR0Sr0uV9fuPGjcnLy2P//v1hQ5ff74/4SsnSZGZmunuUebVt25ZLL7007HOc6pyz96SISHXghK4xY8YwcuTIBLemeopnpas0rUNWS/p2SWzaEk0aXoyj0ErXjh073P0Sq1rp8l4p6byW9/0mTpxI9+7d6dy5M+3bt4/q5PX//ve/tGrVKmzoKmuTWO9m3SIi1cWSJfZv7+7duye4JdVXzCtdgTldlQldNYFCV4x5J7iHhq67777bHZKraqXLW9FyXst5v8LCQp566il+//13NmzYwLZt23j55Zer9H6OvXv3llrJmjt3bpkVNeeKS4UuEalOvv/+e8CeHiHhNYxTpatFGVsSt24Rm/eOJYWuGFu4cKF7O3R40dm9HqoeusJVupyhy3CbrFb1/Ry//fZb0P1DDz2UAQMGMHfu3LCLtno5la5ErCkmIhJOQUEB8+fPB8IvPC22RnG6erF5WaErTKXrqgeLyNpffSfUK3TFWOfOnbnggguAkpUuJ3RAdIcXQytd4SaqR2seVWhgeu655/jxxx8r9GGl4UURiaXJkyfz5JNPBh0r76rupUuXkpWVRbdu3Wjfvn0sm1ejxXJ4MSfXT24eNKgPjcu4FiylUcmRlOc/ggkPKXTVaU4gckJQVlYWH3/8cdDiqVWtPJ177rmAXQ53XsupdIULXVu2bKnS+zmc8DZixAgWL17M0KFDK/xc7/Di0qVLVfESkahZtWoVt9xyC5MmTeK1114D4Mknn6RNmzYsX7681OfNnTsXUJWrPLGcSO+dz1XeRV/XnQHDQ1b0+GAOgY24qx+FrjhwQpAzvHjLLbcwduxYHnnkEfecwsLCKr3HkCFD+O2335g1a1aJifRO6HJWhIfohS4nKB144IEcfvjhlXquU+n69ddf6du3L507dy4xXCkiEolZs2a5t2+++Ways7OZNGkSO3bs4D//+U+pz3NCl+Zzla1hDLcBckJXWfO5HE/+uR5f/qceH9zr47wRcGAb2JcN31XTrYEUuuIgdLjvueeeK3FONKo8PXv2pHHjxqUOL5533nnuXIVoh67QbYgqwju8CnbF66WXXopKu0SkbvOGri1btvDOO++499u0aVPq83788UcAjjzyyNg1rhaI5TZAzhpdzcq4cjHUacf6ePPOepw3wr7/1U+qdNVZocOL4fZFjOa8ptDhxY0bNwJ2Napdu3ZA9IcXIwldTZo0KVE6/vnnavrniYjUKCtXrgTstbYA7r33XvexBg0alPq8bdu2ASW3cZNglZ1I//H3fv72XBHL1pQfhpwg57xHZRxxiP07ZdGqyj83HhS64iB0eDFcQMnNzY3a+4UOLzpXSbZt29YNXVu3bmXfvn1Vfi+n0hW6EGxF1KtXr0S1ywldRUVFXH/99bz44otVbqOI1D3OZ9MhhxwCBF9pXdofuYWFhWRmZuLz+UhLS4t9I2uwBoGl1QsKobCw7CDl9/s57W9+7n8drpxcfuhyhiwblp6NS9W/h/3vd0tgxg9+du6tXhUvha44CB3uC3floPevsGi/n/OXW9u2bWnUqBFHHXUUBQUFTJ06tcrvVZXhRSg5xLhhwwZ2796NZVk8/vjjXH755eTlxeiaZBGptZxgFbrnK5Q+nSMzMxO/30/Lli2pX18btpTF5/ORGrj+a13JVYmC7N0PRUX27dWbyz4XPKErgkrXwQfY/+7KglF/8fPXZxS66pzQ4cXQ0PXFF1/Qt2/fqL1f6PCiU+ly5jHccMMNALz33ntVfq+qDC962wZw2GGHAbBgwQI2bdrkHp83b17kDUygrVu3xn3TcRGxKytO6OrRo0eJx0sLXRkZGUDwotZSutOOsf997oOyP+e2Zgbfzskt+/zcKgwvJiX5GDGo+P7iajbMqNAVB6HDi6Ghy7t0RDR4hxfz8/PZuXNnULm8f//+AKSnp0f0+nv27GHNmjVA1YYXobhvBg0axKhRowCYPXs269atc8+ZOXNmRK+dSNOmTaN9+/bccccdiW6KSJ2zb98+CgsLSUlJ4cADD3SPO3/clja8qNBVOZefYs+f+uLH4OObdtQjY1dxsNq6M/jxjdvLXi+tKsOLAG/c4ePNO+22bdgW2WvEikJXHHiH+4qKikr8lRXt0OV9vx07dgD2h0hSUhIABxxg1183bdoUUSVmwIABdOvWjfXr11d5ePGFF17gwgsvZMaMGYwYYV928sUXXwSFrtWrV0f02ol09913A3DPPfckuCUidY/zudSsWbOgBU6dZW3Kq3SVdXWjFOseyLNbPJWsVRv9DLupDcOv97u/X7yVLoAZC+CAM/z867/hf/9UNXS1benj7GFQr57dtuq0ZpdCVxx4hxezsrIocga3A6q6Gn0o5/Uefvhhrr76aiD4Q6Rp06Y0bdqU7OzsiFamd0LQnDlzqjy8eOaZZ/Laa6+RlpbmrouzZMkS1q5d657jzEmrSTp06JDoJojUWU4lq3nz5kHzRg8++GBAw4vR0q6l/e/WTCgq8rM1088Z//CTnVuPZWtgdWCWSGil67pH/WzJhDumhg9DztWLkczpctSv7+OAVvbt9IzIXyfaFLriwDu8GC7kxKrSVVRUxAcffACU/MvNW+2qDO+k9m3btrFx40Z8Ph9t27atSpMB+wMyLS2NnJwc3n//ffd4uL0jqzvnKlGAHTt28MQTT/D7778nsEUidYe3Au9dlsa5krG04UVnjqlCV8U0TPbRsql9BWPmHrjvNT9L1xQ//uVP9r9bd5ZeacoOzO/K3ONn5Qb7tjOnK9JKl6Nz4GO4Og0xKnTFgXe4Lx6hq169kv9boxW6vFWnF198kby8PI4++uiI53SFOuigg8p8z5roqKOO4k9/+hN9+vQJWrBRRGLDO7wI8M477/D3v/+dU045JejxUM48V4WuimsfWFnjoHP8/Odd+7bpaaem6XP9bN/l5+6X7eN/OgvOPA5uv6T4+UsDs0f+8Hc/PS/0c8UDRVUeXnR0CtQCVlfu11xMKXTFQcOG9o6dubm5YUNXtIcXTzjhhBLhxVt5AejYsSNQ+cn0mzcXX++7ZMkSAE477bRImhlWly5d3NvTpk0D7L8+Q4dkq7u9e/e6t1etsi+fyc3N5Y9//GOimiRSq0yYMIEBAwaQnZ3NLbfcwnnnned+TniHFwHOPvts/v3vf5OamorP52Pfvn0UFBQEvV5eXh7vvmunBm0BVHFO6NqfU3zslnP30qA+fDgHJjxYXOU6YaCP9+6px92X1+PCE+1jP6201/n6zv51woufwP4c+zmRXL3odWRvu8r5xHv+ctcSixeFrjgIF7q8f0mFq0xVxUEHHcTatWu54oor3GNOyHJEWunyhi7HoEGDwpwZGe/csFNOOYWWLVtSVFREZmZmGc+qfryhC+D888+nUaNGbN682V3KQ0Qik5+fz5QpU1i0aBELFixg8uTJvP322+4WPqGVLod3QebQIcbZs2ezbds2DjvsMG12XQmB67OCmJ55XHgi+P3w/rf2sZZNYdTg4nMG9LAD0Q+/+IPmXPn9sCQwE6NhctmbXZdnwmn2Xow/rYT7XqvSS0VNRKu/GWPqAS8B3QKHLgUMcD2QA1xiWdYGY0xv4LnA+9xuWdYsY0wq8CrQBphuWdb9VfsSqr9woatr167upM1Y8c6zCg1dzv3Khq5w2wd17do1gtaF510lv379+rRr146dO3eybdu2GlXyDw1dPXr0oHXr1mzcuJGMjAw6d+6coJaJ1Hy//PKLe9v7Ofrbb79hjCnzquq0tDR2795NZmZm0KrzzmsOGzasxPZkUrr1YabcNqgPT1zv44Pv/OwMfBQ+faOPRg2L+/UIe3odUz+G/34WXIX6fpn9b1WHF1Ma+XjuL3DKLX6e+9DPbZck/v9rpCWW/kBDy7KGAncDE4EbgeHA7YH/AO4FLgNGBc4DuBL42LKsY4HhxpjiRVRqqXCh68gjj+Tvf/87U6ZMidn7eudxlVbpqsrwoiOae5Tdfvvt1K9fn/vvt7O4Exxr2mT60NB18MEH06qVfSmNs4yHiETmp59+cm87w/dQHJycz7XQShcUjzJkZGSwZ88edu7cSVZWljvfsnv37jFrd2308HU+fD449wT7vrMNT2qKL6iydWDIKhwDPRsFFBQGP+YsQVHV0AUwfID977ZdZa8NFi+R7nOwEcAY4wNaANuBZZZl5QFzjDEPBc7rYFnWysC5O4wxrYGjgb8EHp8JHAW8631xY8xVwFUAEydO5MQTT4ywmRWXn58f8WKh5XGGxrKzs1m/fj0ASUlJXHvttUDki5SWJ8lT961fv37Q+zgbvk6bNo2XX37Z7ePy+sEp33t5V5WvqrS0NFatWkVSUhLp6emkpqYC8Ouvv4bdziOWqvI9ETp3r1mzZjRt2hSwv5ZoXO0ZT7H8+aiJ1B+2RPXDt99+6972fibNmjWLs846i+eeew6APn36lGif9zPlwgsvZN++ffTu3Zsvv/wSgJYtW0b0NdXV74kBB8GaV6F+Epx2ZDK9Oxe4fdGuWSpgf+7VL9pKenpwuuravg1rthTHkANbF7Axo/h+9r5dpKdXbTqG3w8NG7QnN9/HqtWbSWkU++AVWuTwijR0ZQBFwC9AQ+BCwPtbxPlt763l7QbSsEPanpBjQSzLmgI4JaC4RNP09PQyO6oqnEpXfn6+O9GzU6dOMXs/R69evdzbAwcOdH/pA0GTSMePH+/+BVBaP/j9fk466ST3r0Gfz+c+J5Zfh7OatM/ni3l/harK90TovK0jjzwy6LXi/bVUVSx/Pmoi9YctUf2wYsUK97bzhyzA/PnzmThxIvv372fMmDGcc845JZ7rfKasW7fOXXPQW0kP/VmtKH1PwAWBL9/pi749/Di/wgf2aUdyg+Dhvc8e9nPrs3533tdx/evzhucC73ZtW9CxY8sqt6tV8yI2ZUDD1A50bJfYIcZIhxdHAdmWZR0CnIU9l8tbx3XirPeSsxZAJrDTc65zrFYLN7wYrSUWyuJUs4CgwAUErdIM5Zddd+/e7Qau5s2b89hjjwFw6qmnRqGlpXPmXNTUifQ333wzkyZNokOHDu7wYqzn8onUZkVFRSxatMi9/+uvvwY9PnfuXADGjRsX9vnO8OJXX30V9vFozlGt69I8qSA0cAH07OTjrbt83D/Bx6u3+bhibPA5Vb160dEq0I4d4VcKiauqbKPurDG7C2gNHGSMSQaOAAIXf7LFGNMD2AqkWZaVYYyZB5wEvACMBC6vQhtqhESFLmMMTZs25Ygjjii1TY6cnBx3PbFwnM26AZ566ikuuOACevbsyVFHHRW9BofRsqX9V87OnTvLObP6yMvLIy8vj6SkJB544AF3Uq7zYa85XSKRW716ddCcydI+G8aMGRP2eFmh67bbbivx2SiRGzUYDj8Yjh9Q+jnJDXzccqF9e8nvwX/8R2NOF0CrwPUUmXvLPi8eIg1dM4CLjTFfYw8v3gh0Ar7GvnrRWYzo78CLgfdxdv59HnjNGDMe+MiyrFo/CO5UnAoLC91fuPEIXU2bNmXLli3uNkRl2b9/f4VCV5cuXbjwQvsnZPTo0dFpaBlqYqXL+YXQtGnToKugvBN4RSQyS5cuBWDo0KFBc7uaNGniXv3cqVOnEtV8R2lXQXfq1Il//etfUW5t3ZbSyMfilyo+nNciNfh+VbYB8qrxlS7LsgqBC8I89HbIecuBoSHHsoDTI3nfmsrn89GwYUNyc3PdK23iEbqg7IVXZ82axciRIwF7qQZn+CscJ3RFeyHX8lQkdD377LP88ssvPPbYYxQWFuL3+4OGVuPNG7q8FLpEqs4ZLejSpQvff/+9Oz+1V69e7qR6Z7ufcErbzDo/Pz+6DZVKKxG6ovQx7gxz7gi/+1NcaXHUOHFK1s66WPEKXWUZMWKEO9neuz5WOE7oKqsaFgvlha4tW7ZwzTXX8PjjjzNjxgz69+/PwQcfXOreavHgDHeErhHkLK3h/KUuIpXnfFY1adLEnX4AwbtulBasIHgZiWXLlrm3c3Jywp0ucZTaGLxrhUdteDHwvzxToavuCJ0nUB1CFxTv+1jeKunVNXS9+OKL7u3Ro0ezbNkyNmzYwAsvvBCX9oXjXDYeeiXT4MGDSU1NZdmyZWzcuDERTROp8byhy7u4qbcKX9Z+tv3796dZs2aMHTuW3r17u8cVuhKvXj0fzT3/66IXuuwhzh17Er9Ol0JXnISGrnArJSeC80FVXqXLCWXxDl3OX7JLly7l8ccfL/H4e++9F/Z5zz77bMIWwtuwYQNQctHY5ORkRowYAcAHH3wQ93aJ1AbOZ1FopSslJYVzzz0XgKuvvrrU57dq1YqtW7fyv//9D4D77rsPgMmTJ8eqyVIJ3iHGaM3pat3C/nf7rui8XlUodMWJN3R169aN+vWrcuFo9Dh/EVb34UWA66+/Pmh9sdWrV5dYrPXdd9+lTZs2rFy5MmHDeE4Vy1kPyOu8884D4JFHHimx4a6IlK+04cWUlBReffVVNmzYwMCBA8t8jUaNGrnzPm+55RZWrVrFxIkTY9doqTBv6IrWkhHOptxbqsH1WApdcZKcXPzdM3/+/AS2JFh1D12hE/fnzp1LUVER69at44wzzgAIugBg5MiRnH766QB89NFHcWsnwLx58/j1119LrXSBvXZQ9+7dWb16tbsCtohUnPNZlZKSUiJ0NWjQIOwfO2Xx+XwcfPDB2m+xmgiqdEVpeLFD4FfE5mqwWo9CV5x4K13ONhTVgRNqnJJ9Tk4OS5YsKTE0l6irF30+H3fffbd7/6OPPuKFF16gS5cuLFmyhHbt2jFnzhyMMUyYMIHmzZtz2GGHAeE3546VFStWcPTRRzNw4EB3hexwH/5JSUnuoo0ff/xx3NonUluUVumK9x+EEhvOUCBEL3Q5lS6FrjqknueSjOq0+F5opevqq6+mX79+vP/++0HnJarSBfYm2E5VaNq0aTz99NPuYxMmTKBXr14sWLCAZ599FigOtVlZWXFr42uvvQbY/fT1118DpW8EPnbsWAA+/fTTmLSloKAgbHAWqQ28c7pKm0gvNddlJxdXHBtH6VdlmxaQlGRfvZibl9jPRYWuOPHO36lOZezQ0OVs9fP6668HnZeoifSOY445hhYtWrBq1SoWL17sHr/oootKnBvv0LVv374SV0v27t2b7t27hz1/wAB7eea1a9fGJBhdcskl9OvXT5U0qZXKmtMlNd/oI308fJ2Pu8b7aNE0Or8r69Xz0S7wrTI3wSv2KHTFSXVdeK+0qxdDP8ASWekCe1X/q666KujYRx99RI8ePUqcG+/Q9dxzz7F582b3fvv27Vm6dGmpC7Q2btyY1NRU8vLyor6e2Pvvv88bb7wBFFffRGoT75wu78+/QlftceO5Pu4cH93ihLMUxQk3+Jm3LHHVLoWuOKmuV6qVtk5X6Do3iQ5dAA888AD9+/d37w8aNCjsefEOXb/88gsATzzxBE899RQLFy4st5rZtm1bALZt2xa1dqxdu5YzzzzTve8sfCtSm3grXSeeeKJ7XDs9SFlyPXWPASX/Vo8bha44qa6VLu/wYm5urns8tEpTHUIXQNeuXd3bTnAJFe/QtXu3vaFXmzZtuPbaaznggAPKfU4sQtc777wTdD+ec9pE4iEzM5OFCxcC9mdXw4YNuemmm6hXrx5/+MMfEtw6qc7uucLH4QfDohd9NExO3BQfha44qa6hyzu86L3aL/QXdqKuXgzl3c8wKSkp7DlO6CpvGYxocUJXZRa8jUXomjZtGmDPfwMSuhWSSCwcddRR7m3nD8YHH3yQvXv3ulcti4Rz/kgfi1+qR7/uiZ1TrdAVJ9U1dHkrXd55SU6QcFSXSldFQp/zNcW70lWZ0OXsDbd9+/aotCE3NxfLsvD5fFx44YVA8cbbIrXFypUr3dvOZ4HP50v4H4MiFaXQFSfVNXS1bt0agK1btwaFrtAqSaKvXnQ423tccMEFpZ6TqOHFRFS6Xn31VUaNGsWHH35IXl4evXv3dtcHU+iS2qys/RVFqqvqsRdNHVBdJ9J36dIFsCdhezdhdkLXhAkTyMvLqzaVrn79+rFlyxY3LIbjrXT5/f6YL9FRldB1++23c+GFFwbNVauMCRMmkJ2dzYwZMwA4+uij3SFYhS6pzRL9WSQSCVW64qS6Vro6deqEz+dj48aN7lV4YAeJ1atXM2XKFF5++WXWrVsHVI8Punbt2pU6nwugfv36NGrUiKKiInJycmLenkhCV8+ePd3bzoa7lZWTk+OGYceZZ56p0CV1gnfBaZGaQt+1cVJdK10NGzbkgAMOoKioiGeeecY9vmfPHj7//HP3/rJly4DgiezVWbyGGPPz89m/fz/16tWr1HDHmDFjmDx5MmAvRBtJO50NvevXr88JJ5zAf/7zH8aMGaPQJbVSYWGhe3vMmDEJbIlI5BS64mT69Ok0a9aMDz/8MNFNKSHcquh79uxh9uzZQceaNWtGv3794tWsKnFC13XXXRfT93GGYZs1a1apYUyfz8fNN99Mv3792L9/vxugKuOnn34C4JxzzuGLL77gT3/6k9sWb9tEagPvoqjabUFqKoWuOBk1ahQ7d+7k1FNPTXRTSghXodm/fz/Lly8POjZ27NhqtW9kWdLT0wF77apYVrsiGVr0OvTQQwH49ddfK/1cJ3Q52wo5VOmS2sgJXampqdVqKzWRylDoiqPqOgdh6tSpQQt6tmjRAqBE6Lrkkkvi2awq8c6hcwKY97EffviBvLy8Cr9eRkaGO6/NqzqELu8q/WBXAurVq0dOTk6Fh7VXrFgRteUrRGLBuxK9SE1VPVOAxNXQoUNJT0/nq6++Yvbs2Vx55ZUlzmnTpk3QlhvVnXezaW/o+uWXX+jZsydHHnkko0ePrnDwGj58OL169WLRokVBx50hvEhD1yGHHOK2qzLeeOMN5s2bB5SsdPl8Pnd4tSLVrszMTPr160fnzp0jGuYUiQeFLqkNFLrENWzYMHr27Bl0NV1aWhpz5sxh+fLlNaqk/+mnn7q3vaFr/PjxrF27FoAvv/wy6LzSrF+/nmXLlpGbm8tFF10UdMXgzp07geLqYGU5+yOuWLGiws+ZP3++uwBqu3btaNWqVYlznCHGiszr+u2338jJySEnJ4ejjjqK5cuXM2/ePP72t7+VOjRbWFjIihUrguYDzp8/ny5dutChQwdeeeWVCn89IhWh0CW1gUKXlOBdjiEzM5MhQ4aUuS5WddS9e3duuukmAB555BEuvfRScnNz3UqVs0/b1q1bwz6/qKiIXbt2AfDdd9+5x5ctW8aTTz7p3nee365du4ja6QzrVmZo74EHHnBv33777WHPcRZIXbNmTbmv54RQsH+xvfLKK1x++eXcf//9nHDCCTz55JPuIq5z5szhmWee4bLLLqNXr16MGDGCzMxMAN577z3WrVvHli1buPzyy93jItGg0CW1gRZHlbCGDh3Kt99+S+/evRPdlIh17NgRgEWLFrFo0SJ69uxJbm4ubdu2pVu3bkDplaCnn36aSZMm0bt3b3duW48ePVi5cmXQEJyzX2X79u0jamPLli3x+XxkZmZSWFhYYv2xzZs3s3nzZgYOHAjYV5rOnTsXsKtjPXr0CPu6hx9+OPPnz2fJkiUMHz68zDY4oat///4sWrSITz/91P2aFyxYwIIFC2jXrh3/+9//OOmkk9zdCcCuFl522WVMmzYtaO/OgoIC3n//fdq2bctBBx3E4YcfXql+EQml0CW1gSpdEta7777LFVdcwfvvv5/opkTMCV2Or776CoBu3bqVe4XfpEmTgOCLCZwtiLzbJVU1dNWvX5+WLVvi9/t56qmngtYiAhg0aBCDBg1i8eLFgD3UuXXrVtLS0oLmrYXq27cvAEuWLAn7uHdY0AldF198MU2aNAl6jnPxx9atWznmmGOCAtdFF10E2MHMOQfsK3UBrrjiCk477TQGDRrEHXfcUaEh1Keeeoq0tDT3NUUcCl1SGyh0SVht27bl+eefD1o5vabp3Llz0P2ZM2cCFQtdzobUjrPPPpsTTjgBIKiiU9XQBcX7X15//fW8+uqrQY85Ae/ll19m+/btTJs2DYDBgweXOcfOqSw5Yc2xadMmbrjhBlJTU7nuuuvw+/1u6OrVqxdDhw51zx0wYACZmZmsXr066DU++eQTVq5cyfPPPw/Y+0cWFRW5fXH99dcHnV9QUMC//vUvevXqRa9evbj55ptLtCsvL4/ffvuNiRMnsnPnTm677bZSvzapmxS6pDZQ6JJaq7RKUIcOHcoMXX6/P2iD71deeYWpU6e6wSqalS4gaL7cnDlz3NvOchQAjz32GG3btuWGG24A4MgjjyzzNfv3709SUhKLFi1yh1ALCgoYNGgQ//nPf9i/fz9PP/00c+bMcXcb6NKlC8OGDXNf45BDDqF58+Z07dqVxx9/HJ/Px6WXXsqYMWPo3r07jRo1okWLFhQUFJCZmen2Rb9+/XjkkUfw+Xy8/fbb/N///R9Dhw7F5/OxYsUKHnroIYYMGeL2vd/v55xzznGv5AT4+uuv+fzzz3nqqaeYM2dOiQqg1D0KXVIbaE6X1FrhruoDGDFihHvVYbjQ9eqrr7Jv3z6aN2/Ozp073YpSkyZNqFevHhkZGaxfv57OnTtHJXR52+n9hRJuXTBHeaGrefPmHHXUUcyZM4fZs2dz+umns2jRoqAqHdgVtPT0dA488EAOPfRQ9xcbFF9sAPaejmeddVaJCmC7du3YtWsXmzZtIiMjA5/PR5s2bfjzn//MhAkTSElJAWDcuHFs2rSJn3/+mdGjR7N//37mz5/P+vXrufzyy93X69ChA507d2b+/PmMHj3aPX7xxRfrisg6TqFLagNVuqTWCh1+a968OWvWrGH06NGlVrp27drlLgIbuvJ1UlKS+7yDDjqI7Oxst+oV6dWLELynXG5urnvbe1VhqMGDB5f7us7cqlmzZgHw7bffAvayGXfccQdgL4wLcO6551KvXj2OOOIInn/+eWbNmsW5554b9HoHHHAADRo0CDrmfN1Lly6lqKiIVq1auec4gcv7/FGjRrnVuosvvjgocJ1wwgn88ssvvPTSSzRu3JiUlBROOeUUABYuXFju1yu1m7N8iUKX1GSqdEmdsXLlSrdSU1ro8s41ckKLl3fI77///S+5ubm0bt3aXYw0Et7X/Omnn5g0aRI33nhjqaGrc+fOpVbxvJxq2Lx58zj//PN56623APvK1Pr1i3/0mzZtysSJEwE7qF5xxRUVbrtT4XPWDatIxW/IkCE89thjbtXt7LPPJjs7m0ceeYTmzZvTvHlzVq1aRUpKCnv37qVz585BfSR108qVKwF7GFykplKlS2q1CRMmAPZ6Vt6hMWdT6L1795Kens7//vc//H6/u47X2LFjufPOO0u8njOZHuCaa64BcKsxkfIuEzF//nyefPJJLrroIjckPfLII+7j9erV4/vvv6/Q6zrLfSxcuNB9rbZt23LqqacGzXf7xz/+EfEvstAK30knnVTuc0aNGsURRxyBz+fjvvvu45133mH69OlBF20ccMABtGjRwl3pX6FLfv75Z6D4ylyRmkiVLqnVHnvsMc4++2yOO+64oOPeStegQYPYunUr06dPd0PXmDFjSqyZBfYcqNtuuy1oftGZZ55Z5TY663A5nLW4OnTowCWXXMLKlSt55plneP3114P2ySxLx44dSUlJcS8KqFevHtOnTy+x0G1V9tT0boC+YMECjDHlPqdZs2b88MMP5ObmlruBujPEm5WVFXYdM6kbsrOzWbVqFUlJSe4uDiI1kUKX1GqNGjVi5MiRJY47oev33393jz333HPuEEZpf0136tSJqVOnkpeXx1tvvcUVV1zB2LFjq9RGZ2mGtLQ0wJ5Yv2PHDgAeeugh0tLSePTRR7n22ms57LDDKvy6Pp8vaOPv7OxskpOTAfuKyRdeeIGmTZtW6SKASy65hJkzZ3LXXXdVKHB5lRe4wA6KzZo1Y/fu3ezZs4eWLVtG2lSpwZYvX47f76dXr14V+r4Rqa4UuqROckKX10cffeTePuigg0p9bv369XnzzTeZOnVqicnikfLu3fjqq6/yxBNP0KBBA3cye8OGDSsVuBz//ve/+de//sWHH37oBi6HdxJ7pA477LASm4BHW/Pmzdm9eze7d+9W6KqjNLQotUXEocsYMxy4PfAajwCNgOuBHOASy7I2GGN6A88FzrndsqxZxphU4FWgDTDdsqz7q/YliFReWWHJ5/PRoUMHd7/BSF6jsnw+H0888QRbtmxh9OjRjBkzJiqve/PNN3PjjTfW6GE5Z16Xsxem1D3O1luR/OEhUp1ENJHeGNMIuAkYY1nWMOAT4EZgOHYQc3bhvRe4DBgF3B04diXwsWVZxwLDjTEHRtx6kQh5l4L4/PPPgyaEe5c9iKeJEydyzz33lLnSfCRqcuACNJleVOmSWiPSqxeHANnAR8aY94EjgGWWZeVZljUHcH4yOliWtdKyrD3ADmNMa+BoYEbg8ZnAUZE3XyRyjzzyCJMmTWLkyJHuquxQcn0vSSyFLnEqXQpdUtNFOrzYDugKHAOMAO4Clnsed/609v722g2kAS2APSHHghhjrgKuAvuv/xNPPDHCZlZcfn4+6enpMX+f6q4u9cM555wDBG/rA/YCpenp6XWqL8qTyL5wqo5r1qypNv8/9L1hi0c/bN68mU2bNpGamkqDBg2qbb/re6JYXe+Ljh07lvpYpKFrF/CdZVl5xpjZwCvARs/jzhLbRZ5jLYBMYCfQLPAaLYC1oS9uWdYUYErgrj/CNlZKenp6mR1VV6gf7D0KO3bsqL7wSGRfdOjQAbCHSavL/w99b9ji0Q9ff/01YC/q26lTp5i+V1Xoe6KY+qJ0kQ4v/gD0DtwegD1c2NsYk2yMOQZYEnhsizGmhzGmGZBmWVYGMA9wVlAcCVRspUeRGDvrrLMAOO+88xLcEvHS8GLdtHTpUubPn++GLu9m7CI1VUSVLsuydhhjPjTGfINdzboMe17X19hXL/4xcOrfgRcD73NH4NjzwGvGmPHAR5Zl1d0apFQrL7zwAieffDLjxo1LdFPEo23btgCsXr06wS2ReNm+fTvHHHMMe/fuxe+3BzuOP/74BLdKpOoiXjLCsqyngKc8h1YDb4ecsxwYGnIsCzg90vcViZUWLVpw2WWXJboZEmL48OEAzJgxA7/frwsd6oCHHnqIPXv2uPfbt2/PEUcckcAWiUSH9l4UkWqtX79+tG3blo0bN7pXsUntNn/+/KD7F110kcK21AoKXSJSrdWrV4/TTz8dgPHjx3PxxRezdu3ahLZJYmvNmjUALFq0iLfffjvs5vMiNZFCl4hUe9dccw0ACxcu5LXXXuO1115LcIskVvLz89m4cSM+n49DDz2Uc845h9TU1EQ3SyQqFLpEpNrr379/ULUjKysrga2RWNq4cSNFRUV07NixxH6hIjWdQpeI1Ah33XUXDz/8MADZ2dkJbo1U1VtvvcU777xT4rgztNilS5c4t0gk9iK+elFEJN6aNm0KwL59+xLcEqmKnTt3cv755wP2QpoHHHCA+9jKlSsBhS6pnVTpEpEao0mTJoBCV01nWZZ7+8033wx67JNPPgFgyJAhcW2TSDwodIlIjeGELs3pqtl++OEH9/b06dPd23v27GHGjBkA/OEPf4h7u0RiTaFLRGoM5yo2Vbqqr8LCQpYuXequJB/OvHnz3NvLli0jPz+f7du38+CDD5KTk8OwYcOChhxFaguFLhGpMVTpqv5uvfVW+vbtW2LYcM+ePbz++uscccQRQdWt7du3k5ycTNu2bbnnnnsA3H9FahtNpBeRGkOVruqroKCAP/zhD+6crHvvvZcLLrgAgKKiIsaMGcPcuXPd83v16sWePXvYvHlz0Ot0796dY489Nn4NF4kjVbpEpMbQRPrq6+2333YDF+AOD/r9fiZNmhQUuADuvPNOOnTo4N5v1aoVoCqX1G4KXSJSY2h4sfp66qmngu7v3r2bVatWcfrpp/P0008DMG3aNPbv309eXh7nn38+U6ZM4eSTT+a3335jy5Yt/Pzzz5x77rmJaL5IXGh4UURqDA0vVk87d+50N6lu27Yt27ZtY926dQwbNoxNmzYBcPHFF5e4InHQoEF8/PHH7v3DDjssfo0WSQBVukSkxmjcuDE+n4+cnBwKCwsT3RwJmDlzJkVFRRx33HFuyNq6dat7G2DYsGGJap5ItaHQJSI1hs/n07yuambr1q1ce+21AJx66qkkJSXRokWLEucdc8wxcW6ZSPWj0CUiNYpCV/Xx0EMP0b59e3bs2MHQoUP505/+BMAjjzzCBRdcwIIFC/jiiy949dVXOeSQQxLcWpHE05wuEalRNJm+eigsLOSxxx5z71999dUkJycDMH78eMaPH5+glolUX6p0iUiN4kymV+hKrDlz5ri3mzZtyimnnJLA1ojUDApdIlKjtG7dGrBXMpfEcTatHjNmDJZl0bx58wS3SKT6U+gSkRrFWVAzdCVzia+9e/cC9rIPPXv2THBrRGoGhS4RqVEUuqoHZ3i3adOmCW6JSM2h0CUiNUr79u0Bha5Ec0KXM8dORMqn0CUiNYpT6Xr88cd5/PHHE9yaussZXlSlS6TiFLpEpEbxbpJ8/fXXJ7AldZsqXSKVp9AlIjWKM7woieVUuhS6RCpOoUtEapROnTq5t30+n/ZgTBBNpBepPIUuEalRUlNTWbp0KQB+v18T6hNEw4silafQJSI1Tp8+fRg0aBAA3bp1Iz8/P8Etqns0vChSeQpdIlIjNWrUCID8/HxWrFiR4NbUPRpeFKk8hS4RqZFuuOEG9/aOHTsA+P777znjjDNYtGhRYhpVh2h4UaTyFLpEpEY6++yzOfXUUwHIzMwkIyODIUOGMG3aNJ5++ukEt652y83NJT8/n/r165OcnJzo5ojUGApdIlJjtWrVCrArXT///LN7/Pnnn+fJJ5/E7/cnqmm1mlPlatKkCT6fL8GtEak5FLpEpMZKS0sD7ErXtm3bgh6bNGkSDz30UCKaVes5k+ibNGmS4JaI1CwKXSJSY3krXaGhC+Ctt96q1OsVFBRQVFQUlbbF26pVqzjwwANjOrS6d+9eLr/8crp27QpAmzZtYvZeIrVR/ao82RhzPvC4ZVltjDHnAtcDOcAllmVtMMb0Bp4LvM/tlmXNMsakAq8CbYDplmXdX7UvQUTqKqfS9cADDzBy5MgSj69evbrCr1VYWMjhhx9Oq1at+Pbbb6PWxni5+uqrSU9P57rrruPaa6+t0mutWrUKv99Pjx493GObNm3i3HPP5bvvvgOgXr163HzzzVV6H5G6JuJKlzGmHnA2sMEY0wC4ERgO3B74D+Be4DJgFHB34NiVwMeWZR0LDDfGHBhpG0SkbnMqXQCzZs0q8fiuXbvYuXNnhV5ry5Yt/PLLL3z33Xds3LiR/v37M378+DKfs23bNh566CGWLVtWuYbHwG+//Val53/66adMnjyZu+66i0MOOYR+/frx7LPP8sADD7By5UrGjBnjBq4HHniABQsWMHz48Ci0XKTuqMrw4gXAu0AR0ANYZllWnmVZc4C+gXM6WJa10rKsPcAOY0xr4GhgRuDxmcBRVWiDiNRhTqXL66abbuLWW2+lV69egL20REUm1HuHJzt16sTixYt5+eWXy3zuvffey80338xhhx1GamoqL7/8cuW/iCr66quvGDhwIBs3bqzwc9LT08nIyHDvZ2RkcMYZZ3DLLbfwz3/+k8LCQrKzs7nmmmu49dZb6dmzJ0uWLAHg/PPP569//SsDBw6M+tciUttFNLxojEkCzgFOB24CWgB7PKckBf71XtayG0gLOdc5Fvr6VwFXAUycOJETTzwxkmZWSn5+Punp6TF/n+pO/VBMfVGsuvZFdnZ2iWPHHHMMgwcPZvbs2QC88sor/OEPf+DII48s87VKq1b99NNPtGvXLuiY0x8//PCDe2zfvn289dZbcfm8cqxZs4bTTz+d3bt3Bx1fu3YtDRo0CPucjIwMhg0bRn5+Ps8//zzHHnssTzzxBLm5uQAMHDiQ888/nzfeeIMmTZqwfv161q9fD8Cll17KPffc434vVNfvi0RQXxSr633RsWPHUh+LdE7XRcD/WZZVZIwB2Ak08zzu7EDrnZHaAsj0nLsrcGxt6ItbljUFmBK4G5drvtPT08vsqLpC/VBMfVGsuvZFamoqjRs3Dgpfffr0oWPHjvTo0cMNRRs3buTMM88s87VKm0CfnZ3N3Llz6dmzJ/369WPx4sX85S9/4dFHH2XNmjUATJkyhauuuordu3fHrZ8KCws59thj3cA1dOhQdy5agwYNSrRjzpw5vPfeezz66KPusQsvvDDonBkzZrih8S9/+QsA69ev59xzz6VZs2bccccdQa9bXb8vEkF9UUx9UbpIQ1dvYIAx5iLsocWrgN7GmGTgCGBJ4LwtxpgewFYgzbKsDGPMPOAk4AVgJHB5Vb4AEam7mjdvTnp6OoMHD2bVqlUAHHDAAQD885//5PXXXwdg8eLF5b7W1q1bg+6fe+65vP3227z11ls8+eSTADz99NP89a9/JSsriyOPPJL9+/eTkpLizm3asmVLtL60cm3YsIG1a9e679uuXTv69+/P4sWL6dKlCzfccAN79+5l27ZtHHzwwTzyyCNBwbJVq1buSv4Af/7zn8NW6Tp37sz3338f869HpC6IKHRZlnWLc9sYY1mW9efA1YtfY1+9+MfAw38HXgy8zx2BY88DrxljxgMfWZZVd2uQIlJlLVu25MYbb+See+7hiSeecLelOfjgg/nmm2847rjjKh265s6dy4cffgjgBi4g6KrA/fv3A9C9e3c6dOgA2OHH7/fHZcFQZ7/JYcOGucOfTtWroKCg1DXK3n77bdq3b8+QIUP48ccfycnJoWHDhgwePDjmbRap66q0ZASAZVkm8O/bwNshjy0HhoYcy8KeCyYiEhXXXHMN11xzTYnjhx9+OGDP1yovDDmh66WXXuLoo4+u8NWAZ599NqmpqTRp0oR9+/axd+9emjVrVv4Tq8gJXT179nSPtWvXzq1+3X777bRv356MjAzuvPNOACZMmMA555zjnq+gJRJfVQ5dIiLVVfPmzd1htC1btrgVqVA5OTl88sknAG7VaNy4cdx9993uvC2vzz//nIEDB9K6dWv3WLt27Vi9ejVbtmwpEbpeeeUVGjVqFBR4KsLv9/PTTz/x5Zdfkpubyy233EJSkn2dUrjQ9dxzz/H4449zzz33BH2tTugaOjTob2ARiTOFLhGp1bp168aOHTtYvXp1qaFr8uTJZGZmkpSUxCGHHALYW9y8/vrrDBkyBACfz8eECRPo3r07ffr0CQpcAO3bt2f16tV89dVXrFmzhldeeYXbb7+de++9l1dffRWAUaNG0bx58wq3/euvv+b444937//f//0f999/P0cddRQzZ84EcJfGAOjXrx9Tp04t8TpLly7liy++4Pzzz6/we4tI9Cl0iUit1q1bNxYsWMCaNWvo3LkzTZo0KbG+14wZ9tKBTzzxhLvFDcChhx7q3v7+++/dZSfCXQ7vVMgmTJjgHnvjjTeCzpk8eTLNmjWjefPmJCcn0717d4YOHVrqsOfChQuD7i9evJgxY8ZQv359CgoK6N69OyNGjCi3D/r06UOfPn3KPU9EYkuhS0RqNSdEzZs3j2uuuYauXbuyePFiN+hkZ2fzww8/4PP5SlSCWrRoQcOGDcnNzQ0axgvnggsuYN68eWzevLnUc+69994Sxy677LKw1Sko3sbo0UcfZcSIEe6SD3v37mX48OE89dRTpKSklNkuEak+tOG1iNRq3bp1A+CZZ54hKyuLn3/+mR9//JGCggIAfvzxR/Lz8zn88MNp0aJFieevW7eONWvW0LJlyzLf5+yzz2bTpk1Bx+6++262b99eYsmFU045hQsuuACA//73v+zYsQO/389dd93FsGHDWLp0KRs3bnTnk3Xr1o2+ffty1113sXnzZrZv386XX35J7969I+oTEUkMVbpEpFZzrtDzrlFljKFz5878+uuv/P777wClBpjQ1ejLM3nyZP76179y/fXXc/vt9ja0rVu3ZuHChaxZs4ZTTz2V5ORkwN56aNasWSXmh/Xt2zfovnfIMyUlRdUtkRpKlS4RqdX69etHYOeMIOvXr2f+/PnuEgtdunSJyvv95S9/YdasWdx3331BxwcOHMhZZ53lBi6As846K+xrhG7h4w1dIlJzKXSJSK334IMPMmzYMG677TYmTZrkHp8zZw7r1q0Dohe6fD4fI0aMoHHjxuWee8UVVzBp0iS6dOnCddddx6JFiygqKiInJ8dd5b5nz57ugq8iUrNpeFFEar3hw4fz1VdfAfbaV3379uWqq67i22+/JT8/H4CDDjoo7u2qX78+jz/+OI8//njQcZ/Px+eff863335b7gR+Eak5FLpEpE7x+XyceuqpNGjQgBkzZuD3+4HoVbqiJTk5uULLQYhIzaHhRRGpc9q3b8/ll1/uBq7GjRvTuXPnBLdKRGo7VbpEpE564IEH2LdvH/Pnz+fee++t0BwsEZGqUOgSkTqpWbNmvPLKK4luhojUIRpeFBEREYkDhS4RERGROFDoEhEREYkDhS4RERGROFDoEhEREYkDhS4RERGROFDoEhEREYkDhS4RERGROFDoEhEREYkDhS4RERGROFDoEhEREYkDhS4RERGROFDoEhEREYkDn9/vT3QbRERERGo9VbpERERE4kChS0RERCQOFLpERERE4kChS0RERCQOFLpERERE4kChS0RERCQOFLpERERE4qBOhi5jjC/RbZDqwRhzWODfOvmzIOXT54U49HkhVVVnFkc1xhwFtLAs67NEtyWRjDHNgJnAdZZlWYluT6IYY44A/g70Ao6zLCsjwU1KKGPMUOA84C1giWVZu40xPsuy6sYHRAh9Xtj0eWHT50UwfV5ErlandecvVGPMlcBHwKmBD9M6xdMPPiAXaAKcYIxpkch2xZunH+4BHgRmAK8AzRLZrkQzxnQB/gz8CAwD7gWoax+g+ryw6fPCps+L8PR5UTW1OnQBbQP/fgocB3wFGGNMasJalBitwf2haAOsBfoAA+rY0Inz/fC6ZVnDLct6BhiM/UulLg8ZdADqWZY11bKse4BDjTHDoc71iT4vbPq8sOnzIjx9XlRBrewgY8xwY8xs4H5jzFhgp2VZvwCbgabYH6i1nqcfJhtjxhpjUi3L2gi8D3wJjCLwAVKbhfl+WOd52AIuAbAsqygR7Ys3Y8zlxpjpxpgDASzL+h7YbowZETjlMeCvgcdqfZ/o88KmzwubPi+C6fMiumpl6ALOxi4HPwj0B64KHF8IbAIONsY0NsbUh1o9UTa0Hy4xxjQCjGVZL2P/JfeNMWZ0wloYH6H9cKXnsW+BLYF+qfUCc3RGAg2A440xycaYJOx5OyONMcmWZX2I3ScmkW2NI31e2PR5YdPnRYA+L6KvVoUuz4fhOiDFsqzl2BP9Bhpj+liWlQ18DrQC/gf8ozZO/iujH4YABwLrjTH/B3TD/h74KSENjbEy+sEYY3oHHksBDrMsKycRbYynwPf6HsuyzgcmACcA3S3LKgS+w/5euNoY0xgoAH5NXGtjT58XNn1e2PR5EUyfF7FR40OXU/IEew5CYEx5O9DCGNPKsqxV2N8gxwZOOxw4DVgA/Lu2fIBWsB++BkYD64FvLMsaDlxjWdbWRLQ5Firx/TA0cNoMoJcxpmv8Wxt7YfrDF7i9FlgGnG6MSbEsaxP2MEE/YBqQAeyLd3tjzRjjzMfx1fHPi4r0Q134vKjo90Nd+bwI7Y86/XkRC/UT3YBIGWPGANcBOcaYl4AvLcvab1lWkTFmAfAH4EjgE2APxQFzCzA28E1T41WyH7KAAsuy/s95fmB8vsaL4PvB+TDxG2NGWpZVqz4wyugPb2h4FbgL6BEYMliF/RdtE8uydse7zbEUmJtzIbDJGPMw9nwt6uDnRWX6oTZ/XlT2+6G2f16U1h918vMilmpk6Ar8D58ATMH+UDwD+CbwWHfsK0zWACOMMZ2AE4HZAJZlLUlEm2Mhgn44CZiVmNbGToTfD184z6+FH6Bl9UcP7GGCdyzL2mqMWQ98iF3VuNWyrD1ArfoANcY0wJ78/CT2OksTsH+Zzg/0R135vKhsP9TWz4tIvh9q8+dFWf3REzieOvR5EWs1ZnjRGNPQFK8T0wn4GUgHfsOe7JhmjBkM3AHstyzrTeBp7DkJH1uW9XTcGx0DVeyH6YHLnmu8KHw/1Ip+cFSiP/4B7LMsKzMwF+MY4D7Lsv5YW6o5UKI/egKbLcv6GngZ6AyMMfbVV3cDWXXk86Ky/VBbPy8i+X6oFf3gqER/3Ansre2fF/FUI0KXMeY8YDnwb3DHl2cDFwGLsMfgLwZGAOMty3o7cN7vwB2WZb0U/1ZHn/rBpn4IVsn+uNyyrNcC52UDp1uW9Wz8Wx07YfpjGdDRGHOuZVl52P3RGHuu0qWWZb0TOK9WfX+oH2zqh2CV7I/xlmW9ETivVn5exFu1D13GvjQ3hcBlu8aYkwEsy/oSu/T9hGVZf8ReRwbLsgpN4NLuwP3aMvFV/YD6IVSk/eGZIFuQkIbHSJj+GBt46FHsuSifYs/P6QikWZaVGxiOBWrP94f6waZ+CBZBf+QZY5Jq6+dFIlTLvReNMQcBtwAfA4ste4E+jDGnYO/3dElgwuNfgSTsv+onAZmWZf0pQc2OOvWDTf0QTP0RrJz+OB+7PwoDx5pb9j5x9wAvBaoZtYL6waZ+CKb+qF6qXaXLGJOMPY68AWgHPOV5eDZ26dNZvPB17AmPdwDzatMvFPWDTf0QTP0RrAL9sQ24LHCuD+hujJkB7K5Nv1DUDzb1QzD1R/VTbSpdxpizsFc8ngFMtew1YTDGTAV+sSzrocA3xUHYY9HzgRmWZf1q7FVx8xLU9KhSP9jUD8HUH8Ei7I9PsC8uSLIsKyshDY8y9YNN/RBM/VF9JbzSZYxpbYyZhr31Qi/syb5bjDHjA6f8EzjbGNMmML7eDDgKOB3wA9SGXyjqB5v6IZj6I1gV+uMM7E16s2vDLxT1g039EEz9Uf0lPHQBRcALlr3VwD1Ab+A94DBjTA/LstZjb8UxytgTogcBN1mWdYJlWb8lrNXRp36wqR+CqT+CRdofx1uWtSJhrY4+9YNN/RBM/VHNVYfFUXdil0CxLCvDGNMWewXglcBNwNVAGvYEwAKgVl2+66F+sKkfgqk/gqk/bOoHm/ohmPqjmqtOc7p8QHPgDcuyTg4cm4K9Xkgy9iWue61adglvKPWDTf0QTP0RTP1hUz/Y1A/B1B/VV3WodHnVB74zxgzC3mh1KrDCsqydiW1W3KkfbOqHYOqPYOoPm/rBpn4Ipv6ohqpNpQvAGDMK+Aj7UtbXLct6NcFNSgj1g039EEz9EUz9YVM/2NQPwdQf1VN1q3TtAP4OPF6brriKgPrBpn4Ipv4Ipv6wqR9s6odg6o9qqLqFroWWZVmJbkQ1oH6wqR+CqT+CqT9s6geb+iGY+qMaqlbDiyIiIiK1VXVYp0tERESk1lPoEhEREYkDhS4RERGROKhuE+lFRCJijOkCrAnczQW2A98At1mWtaaM56UAfwXWWpb1coybKSJ1mCpdIlLb/IS93cls4AJgbmA7lNKkAHcCl8a+aSJSl6nSJSK1zaZAxeplY0wu9pYnE4wxhwMjsbdCWQ38w7Ks9wHnsvphxhg/8E/g3sB/5wNNgJnAtZZlbY/rVyIitYoqXSJSm30a+LcfsAB7GPFvgWOvGGMaYS8gCfALdsh6N3DOTdgrej8GjAGeiU+TRaS2UqVLRGozX+BfP9AbO1Qlex7vAswI3N5mWdZbAMaYlwLHJnjOPSl2zRSRukChS0Rqs1GBf5cAdwNfYFeurgZOARphB7JQPqAAGAsUBo5pZEBEqkShS0RqmwOMMZcCw4BLgC3A+sBjKdjVrWM85+8BioDuxpgLge+whxUHBZ4/C7tK1pXiqpiISKXpLzcRqW0GAM9hT5p/AzgaeA14C+gLnAl87pxsWVY+8CDQInDeUOC+wLGhwJPYc7q+jtcXICK1k/ZeFBEREYkDVbpERERE4kChS0RERCQOFLpERERE4kChS0RERCQOFLpERERE4kChS0RERCQOFLpERERE4kChS0RERCQO/h+KoJmkr6zxbgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "train, test = series.split_before(0.90)\n",
    "train.plot()\n",
    "test.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "238729aa",
   "metadata": {},
   "source": [
    "# Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "9b3fa716",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "scaler = Scaler()\n",
    "train_scaled = scaler.fit_transform(train)\n",
    "\n",
    "train_scaled.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "fde96939",
   "metadata": {},
   "outputs": [],
   "source": [
    "#parameters = {\n",
    "#     \"input_chunk_length\" : [30,100],\n",
    "#     \"output_chunk_length\" : [10,30], \n",
    "    \n",
    "# }\n",
    "\n",
    "# best_model = model.gridsearch(parameters=parameters,series=train_scaled,forecast_horizon=30,start=0.7,stride=20\n",
    "#                               ,last_points_only=False,metric=mape,reduction=np.mean)\n",
    "\n",
    "# best_model.fit(train,epochs=50,verbose=True)\n",
    "# best_model.forecast = best_model.predict(len(test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "0ed970af",
   "metadata": {},
   "outputs": [],
   "source": [
    "# historical_fcast_theta = model.historical_forecasts(\n",
    "#     series, start=0.7, forecast_horizon=14, stride=6, verbose=True\n",
    "# )\n",
    "\n",
    "# series.plot(label=\"data\")\n",
    "# historical_fcast_theta.plot(label=\"backtest 3-months ahead forecast (Theta)\")\n",
    "# print(\"MAPE = {:.2f}%\".format(mape(historical_fcast_theta, series)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "504c906b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df.copy()\n",
    "\n",
    "df2 = df2.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "00f1ccfc",
   "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>Date</th>\n",
       "      <th>Price</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Vol.</th>\n",
       "      <th>Change %</th>\n",
       "      <th>sentiment_scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>13,444.90</td>\n",
       "      <td>13,850.50</td>\n",
       "      <td>13,921.50</td>\n",
       "      <td>12,877.70</td>\n",
       "      <td>78,430.00</td>\n",
       "      <td>-2.93</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>14,754.10</td>\n",
       "      <td>13,444.90</td>\n",
       "      <td>15,306.10</td>\n",
       "      <td>12,934.20</td>\n",
       "      <td>137,730.00</td>\n",
       "      <td>9.74</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>15,156.60</td>\n",
       "      <td>14,754.10</td>\n",
       "      <td>15,435.00</td>\n",
       "      <td>14,579.70</td>\n",
       "      <td>106,540.00</td>\n",
       "      <td>2.73</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>15,180.10</td>\n",
       "      <td>15,156.50</td>\n",
       "      <td>15,408.70</td>\n",
       "      <td>14,244.70</td>\n",
       "      <td>110,970.00</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>16,954.80</td>\n",
       "      <td>15,180.10</td>\n",
       "      <td>17,126.90</td>\n",
       "      <td>14,832.40</td>\n",
       "      <td>141,960.00</td>\n",
       "      <td>11.69</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date     Price      Open      High       Low       Vol.  Change %  \\\n",
       "0 2018-01-01 13,444.90 13,850.50 13,921.50 12,877.70  78,430.00     -2.93   \n",
       "1 2018-01-02 14,754.10 13,444.90 15,306.10 12,934.20 137,730.00      9.74   \n",
       "2 2018-01-03 15,156.60 14,754.10 15,435.00 14,579.70 106,540.00      2.73   \n",
       "3 2018-01-04 15,180.10 15,156.50 15,408.70 14,244.70 110,970.00      0.15   \n",
       "4 2018-01-05 16,954.80 15,180.10 17,126.90 14,832.40 141,960.00     11.69   \n",
       "\n",
       "   sentiment_scores  \n",
       "0              0.07  \n",
       "1              0.06  \n",
       "2              0.04  \n",
       "3              0.06  \n",
       "4              0.05  "
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "5df13c7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Vol.</th>\n",
       "      <th>Change %</th>\n",
       "      <th>sentiment_scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Open</th>\n",
       "      <td>0.99</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.99</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.99</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vol.</th>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.07</td>\n",
       "      <td>-0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Change %</th>\n",
       "      <td>0.05</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.07</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sentiment_scores</th>\n",
       "      <td>0.40</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.41</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>0.06</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Price  Open  High   Low  Vol.  Change %  sentiment_scores\n",
       "Price              1.00  0.99  1.00  1.00 -0.18      0.05              0.40\n",
       "Open               0.99  1.00  1.00  0.99 -0.19     -0.08              0.39\n",
       "High               1.00  1.00  1.00  0.99 -0.18     -0.02              0.39\n",
       "Low                1.00  0.99  0.99  1.00 -0.19     -0.00              0.41\n",
       "Vol.              -0.18 -0.19 -0.18 -0.19  1.00      0.07             -0.07\n",
       "Change %           0.05 -0.08 -0.02 -0.00  0.07      1.00              0.06\n",
       "sentiment_scores   0.40  0.39  0.39  0.41 -0.07      0.06              1.00"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "6a71e75d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 692 entries, 0 to 691\n",
      "Data columns (total 8 columns):\n",
      " #   Column            Non-Null Count  Dtype         \n",
      "---  ------            --------------  -----         \n",
      " 0   Date              692 non-null    datetime64[ns]\n",
      " 1   Price             692 non-null    float64       \n",
      " 2   Open              692 non-null    float64       \n",
      " 3   High              692 non-null    float64       \n",
      " 4   Low               692 non-null    float64       \n",
      " 5   Vol.              692 non-null    float64       \n",
      " 6   Change %          692 non-null    float64       \n",
      " 7   sentiment_scores  692 non-null    float64       \n",
      "dtypes: datetime64[ns](1), float64(7)\n",
      "memory usage: 43.4 KB\n"
     ]
    }
   ],
   "source": [
    "df2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "3d0fd47d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "components: Index(['Price'], dtype='object', name='component')\n",
      "duration: 691 days 00:00:00\n",
      "frequency: <Day>\n",
      "frequency: D\n",
      "has date time index? (or else, it must have an integer index): True\n",
      "deterministic: True\n",
      "univariate: True\n"
     ]
    }
   ],
   "source": [
    "# create time series object for target variable\n",
    "ts_P = TimeSeries.from_series(df[\"Price\"]) \n",
    "\n",
    "# check attributes of the time series\n",
    "print(\"components:\", ts_P.components)\n",
    "print(\"duration:\",ts_P.duration)\n",
    "print(\"frequency:\",ts_P.freq)\n",
    "print(\"frequency:\",ts_P.freq_str)\n",
    "print(\"has date time index? (or else, it must have an integer index):\",ts_P.has_datetime_index)\n",
    "print(\"deterministic:\",ts_P.is_deterministic)\n",
    "print(\"univariate:\",ts_P.is_univariate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "b30bf596",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "components (columns) of feature time series: Index(['Price', 'Open', 'High', 'Low', 'Vol.', 'Change %', 'sentiment_scores'], dtype='object', name='component')\n",
      "duration: 691 days 00:00:00\n",
      "frequency: <Day>\n",
      "frequency: D\n",
      "has date time index? (or else, it must have an integer index): True\n",
      "deterministic: True\n",
      "univariate: False\n"
     ]
    }
   ],
   "source": [
    "#df2.drop(['Price'],axis=1,inplace=True)\n",
    "ts_covF = TimeSeries.from_dataframe(df2,'Date')\n",
    "\n",
    "# check attributes of the time series\n",
    "print(\"components (columns) of feature time series:\", ts_covF.components)\n",
    "print(\"duration:\",ts_covF.duration)\n",
    "print(\"frequency:\",ts_covF.freq)\n",
    "print(\"frequency:\",ts_covF.freq_str)\n",
    "print(\"has date time index? (or else, it must have an integer index):\",ts_covF.has_datetime_index)\n",
    "print(\"deterministic:\",ts_covF.is_deterministic)\n",
    "print(\"univariate:\",ts_covF.is_univariate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3a86fc38",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(692, 6, 1)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# example: operating with time series objects:\n",
    "# we can also create a 3-dimensional numpy array from a time series object\n",
    "# 3 dimensions: time (rows) / components (columns) / samples\n",
    "ar_covF = ts_covF.all_values()\n",
    "print(type(ar_covF))\n",
    "ar_covF.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1f34ed46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# example: operating with time series objects:\n",
    "# we can also create a pandas series or dataframe from a time series object\n",
    "df_covF = ts_covF.pd_dataframe()\n",
    "type(df_covF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2a97aaa4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training start: 2018-01-01 00:00:00\n",
      "training end: 2019-09-14 00:00:00\n",
      "training duration: 621 days 00:00:00\n",
      "test start: 2019-09-15 00:00:00\n",
      "test end: 2019-11-23 00:00:00\n",
      "test duration: 69 days 00:00:00\n",
      "first and last row of scaled price time series:\n"
     ]
    },
    {
     "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>component</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-23</th>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "component   Price\n",
       "Date             \n",
       "2018-01-01   0.73\n",
       "2019-11-23   0.29"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train/test split and scaling of target variable\n",
    "ts_train, ts_test = ts_P.split_after(0.9)\n",
    "print(\"training start:\", ts_train.start_time())\n",
    "print(\"training end:\", ts_train.end_time())\n",
    "print(\"training duration:\",ts_train.duration)\n",
    "print(\"test start:\", ts_test.start_time())\n",
    "print(\"test end:\", ts_test.end_time())\n",
    "print(\"test duration:\", ts_test.duration)\n",
    "\n",
    "\n",
    "scalerP = Scaler()\n",
    "scalerP.fit_transform(ts_train)\n",
    "ts_ttrain = scalerP.transform(ts_train)\n",
    "ts_ttest = scalerP.transform(ts_test)    \n",
    "ts_t = scalerP.transform(ts_P)\n",
    "\n",
    "# make sure data are of type float\n",
    "ts_t = ts_t.astype(np.float32)\n",
    "ts_ttrain = ts_ttrain.astype(np.float32)\n",
    "ts_ttest = ts_ttest.astype(np.float32)\n",
    "\n",
    "print(\"first and last row of scaled price time series:\")\n",
    "pd.options.display.float_format = '{:,.2f}'.format\n",
    "ts_t.pd_dataframe().iloc[[0,-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c3d6c6bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first and last row of scaled feature covariates:\n"
     ]
    },
    {
     "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>component</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Vol.</th>\n",
       "      <th>Change %</th>\n",
       "      <th>sentiment_scores</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>0.76</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-23</th>\n",
       "      <td>0.29</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "component   Open  High  Low  Vol.  Change %  sentiment_scores\n",
       "Date                                                         \n",
       "2018-01-01  0.76  0.76 0.74  0.00      0.41              0.35\n",
       "2019-11-23  0.29  0.29 0.30  0.05      0.52              0.87"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train/test split and scaling of feature covariates\n",
    "covF_train, covF_test = ts_covF.split_after(0.9)\n",
    "\n",
    "scalerF = Scaler()\n",
    "scalerF.fit_transform(covF_train)\n",
    "covF_ttrain = scalerF.transform(covF_train) \n",
    "covF_ttest = scalerF.transform(covF_test)   \n",
    "covF_t = scalerF.transform(ts_covF)  \n",
    "\n",
    "# make sure data are of type float\n",
    "covF_ttrain = covF_ttrain.astype(np.float32)\n",
    "covF_ttest = covF_ttest.astype(np.float32)\n",
    "\n",
    "pd.options.display.float_format = '{:.2f}'.format\n",
    "print(\"first and last row of scaled feature covariates:\")\n",
    "covF_t.pd_dataframe().iloc[[0,-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cd89cf9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first and last row of unscaled time covariates:\n"
     ]
    },
    {
     "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>component</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>holidays</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-23</th>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "component   day_of_week  month  year  holidays\n",
       "Date                                          \n",
       "2018-01-01            0      1  2018         1\n",
       "2019-11-23            5     11  2019         0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covT = datetime_attribute_timeseries(series.time_index, attribute=\"day_of_week\")\n",
    "covT = covT.stack(  datetime_attribute_timeseries(covT.time_index, attribute=\"month\")  )\n",
    "covT = covT.stack(  datetime_attribute_timeseries(covT.time_index, attribute=\"year\")  )\n",
    "covT = covT.add_holidays(country_code=\"US\")\n",
    "covT = covT.astype(np.float32)\n",
    "\n",
    "\n",
    "# train/test split\n",
    "covT_train, covT_test = covT.split_after(ts_train.end_time())\n",
    "\n",
    "\n",
    "# rescale the covariates: fitting on the training set\n",
    "scalerT = Scaler()\n",
    "scalerT.fit(covT_train)\n",
    "covT_ttrain = scalerT.transform(covT_train)\n",
    "covT_ttest = scalerT.transform(covT_test)\n",
    "covT_t = scalerT.transform(covT)\n",
    "\n",
    "covT_t = covT_t.astype(np.float32)\n",
    "\n",
    "\n",
    "pd.options.display.float_format = '{:.0f}'.format\n",
    "print(\"first and last row of unscaled time covariates:\")\n",
    "covT.pd_dataframe().iloc[[0,-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "03c39524",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first and last row of unscaled covariates:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>component</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Vol.</th>\n",
       "      <th>Change %</th>\n",
       "      <th>sentiment_scores</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>holidays</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>13850.50</td>\n",
       "      <td>13921.50</td>\n",
       "      <td>12877.70</td>\n",
       "      <td>78430.00</td>\n",
       "      <td>-2.93</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2018.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-23</th>\n",
       "      <td>7271.60</td>\n",
       "      <td>7342.50</td>\n",
       "      <td>7093.70</td>\n",
       "      <td>783180.00</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.14</td>\n",
       "      <td>5.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>2019.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "component      Open     High      Low      Vol.  Change %  sentiment_scores  \\\n",
       "Date                                                                          \n",
       "2018-01-01 13850.50 13921.50 12877.70  78430.00     -2.93              0.07   \n",
       "2019-11-23  7271.60  7342.50  7093.70 783180.00      0.71              0.14   \n",
       "\n",
       "component   day_of_week  month    year  holidays  \n",
       "Date                                              \n",
       "2018-01-01         0.00   1.00 2018.00      1.00  \n",
       "2019-11-23         5.00  11.00 2019.00      0.00  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# combine feature and time covariates along component dimension: axis=1\n",
    "ts_cov = ts_covF.concatenate( covT.slice_intersect(ts_covF), axis=1 )                      # unscaled F+T\n",
    "cov_t = covF_t.concatenate( covT_t.slice_intersect(covF_t), axis=1 )                       # scaled F+T\n",
    "cov_ttrain = covF_ttrain.concatenate( covT_ttrain.slice_intersect(covF_ttrain), axis=1 )   # scaled F+T training set\n",
    "cov_ttest = covF_ttest.concatenate( covT_ttest.slice_intersect(covF_ttest), axis=1 )       # scaled F+T test set\n",
    "\n",
    "\n",
    "pd.options.display.float_format = '{:.2f}'.format\n",
    "print(\"first and last row of unscaled covariates:\")\n",
    "ts_cov.pd_dataframe().iloc[[0,-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e30a196d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first and last row of scaled covariates, training + test set:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>component</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Vol.</th>\n",
       "      <th>Change %</th>\n",
       "      <th>sentiment_scores</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>holidays</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>0.76</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>0.73</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>0.83</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>0.86</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>0.86</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-23</th>\n",
       "      <td>0.29</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.91</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "component   Open  High  Low  Vol.  Change %  sentiment_scores  day_of_week  \\\n",
       "Date                                                                         \n",
       "2018-01-01  0.76  0.76 0.74  0.00      0.41              0.35         0.00   \n",
       "2018-01-02  0.73  0.86 0.74  0.00      0.78              0.27         0.17   \n",
       "2018-01-03  0.83  0.87 0.87  0.00      0.58              0.20         0.33   \n",
       "2018-01-04  0.86  0.87 0.84  0.00      0.50              0.28         0.50   \n",
       "2018-01-05  0.86  0.99 0.89  0.01      0.84              0.22         0.67   \n",
       "2019-11-23  0.29  0.29 0.30  0.05      0.52              0.87         0.83   \n",
       "\n",
       "component   month  year  holidays  \n",
       "Date                               \n",
       "2018-01-01   0.00  0.00      1.00  \n",
       "2018-01-02   0.00  0.00      0.00  \n",
       "2018-01-03   0.00  0.00      0.00  \n",
       "2018-01-04   0.00  0.00      0.00  \n",
       "2018-01-05   0.00  0.00      0.00  \n",
       "2019-11-23   0.91  1.00      0.00  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.options.display.float_format = '{:.2f}'.format\n",
    "print(\"first and last row of scaled covariates, training + test set:\")\n",
    "cov_t = cov_t.astype(np.float32)\n",
    "cov_t.pd_dataframe().iloc[[0,1,2,3,4,-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "91921ccc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "first and last row of scaled covariates, training + test set:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 692 entries, 2018-01-01 to 2019-11-23\n",
      "Freq: D\n",
      "Data columns (total 10 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Open              692 non-null    float32\n",
      " 1   High              692 non-null    float32\n",
      " 2   Low               692 non-null    float32\n",
      " 3   Vol.              692 non-null    float32\n",
      " 4   Change %          692 non-null    float32\n",
      " 5   sentiment_scores  692 non-null    float32\n",
      " 6   day_of_week       692 non-null    float32\n",
      " 7   month             692 non-null    float32\n",
      " 8   year              692 non-null    float32\n",
      " 9   holidays          692 non-null    float32\n",
      "dtypes: float32(10)\n",
      "memory usage: 32.4 KB\n"
     ]
    }
   ],
   "source": [
    "pd.options.display.float_format = '{:.2f}'.format\n",
    "print(\"first and last row of scaled covariates, training + test set:\")\n",
    "#cov_t.pd_dataframe().iloc[[0,-1]]\n",
    "cov_t.pd_dataframe().info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "11182def",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>component</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Vol.</th>\n",
       "      <th>Change %</th>\n",
       "      <th>sentiment_scores</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>holidays</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>component</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Open</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.99</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>0.39</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High</th>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.99</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.39</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low</th>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.41</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vol.</th>\n",
       "      <td>-0.19</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.19</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.07</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Change %</th>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>0.07</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.07</td>\n",
       "      <td>-0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sentiment_scores</th>\n",
       "      <td>0.39</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.41</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>0.06</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.73</td>\n",
       "      <td>-0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day_of_week</th>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.03</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.10</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>holidays</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.00</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>0.02</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "component         Open  High   Low  Vol.  Change %  sentiment_scores  \\\n",
       "component                                                              \n",
       "Open              1.00  1.00  0.99 -0.19     -0.08              0.39   \n",
       "High              1.00  1.00  0.99 -0.18     -0.02              0.39   \n",
       "Low               0.99  0.99  1.00 -0.19     -0.00              0.41   \n",
       "Vol.             -0.19 -0.18 -0.19  1.00      0.07             -0.07   \n",
       "Change %         -0.08 -0.02 -0.00  0.07      1.00              0.06   \n",
       "sentiment_scores  0.39  0.39  0.41 -0.07      0.06              1.00   \n",
       "day_of_week      -0.01 -0.01 -0.00 -0.03      0.03             -0.01   \n",
       "month            -0.05 -0.06 -0.03  0.10     -0.04              0.07   \n",
       "year             -0.04 -0.04 -0.02  0.02      0.07              0.73   \n",
       "holidays          0.01  0.01  0.02 -0.04     -0.00             -0.03   \n",
       "\n",
       "component         day_of_week  month  year  holidays  \n",
       "component                                             \n",
       "Open                    -0.01  -0.05 -0.04      0.01  \n",
       "High                    -0.01  -0.06 -0.04      0.01  \n",
       "Low                     -0.00  -0.03 -0.02      0.02  \n",
       "Vol.                    -0.03   0.10  0.02     -0.04  \n",
       "Change %                 0.03  -0.04  0.07     -0.00  \n",
       "sentiment_scores        -0.01   0.07  0.73     -0.03  \n",
       "day_of_week              1.00   0.01  0.00     -0.18  \n",
       "month                    0.01   1.00 -0.09      0.02  \n",
       "year                     0.00  -0.09  1.00     -0.02  \n",
       "holidays                -0.18   0.02 -0.02      1.00  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cov_t.pd_dataframe().corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1a721b80",
   "metadata": {},
   "outputs": [],
   "source": [
    "MSEAS = 90          # max seasonality to check: days\n",
    "ALPHA = 0.05        # significance level for seasonality test\n",
    "FIGSIZE = (9, 6)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1a68238b",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import logging\n",
    "logging.disable(logging.CRITICAL)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "pd.set_option(\"display.precision\",2)\n",
    "np.set_printoptions(precision=2, suppress=True)\n",
    "pd.options.display.float_format = '{:,.2f}'.format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3768396d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seasonal? True\n",
      "There is seasonality of order 36 days\n"
     ]
    }
   ],
   "source": [
    "# check for seasonality, via ACF\n",
    "mseas=MSEAS\n",
    "for m in range(2, MSEAS):\n",
    "    is_seasonal, mseas = check_seasonality(series, m=m, alpha=ALPHA, max_lag=MSEAS)\n",
    "    if is_seasonal:\n",
    "        break\n",
    "\n",
    "print(\"seasonal? \" + str(is_seasonal))\n",
    "if is_seasonal:\n",
    "    print('There is seasonality of order {} days'.format(mseas))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "099e03eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ETS analysis: try to discern trend and seasonal components\n",
    "ts_trend, ts_seas = extract_trend_and_seasonality(ts=series, freq=mseas)\n",
    "\n",
    "plt.figure(100, figsize=(18, 5))\n",
    "ts_trend.plot()\n",
    "plt.title(\"trend component\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "403af0ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(100, figsize=(18, 5))\n",
    "ts_seas.plot()\n",
    "plt.title(\"seasonal component\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fc9b74e",
   "metadata": {},
   "source": [
    "# Naive Baseline Forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3b93a6c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x15c61f88488>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# naive seasonal forecast\n",
    "modelNs = NaiveSeasonal(K=36)\n",
    "modelNs.fit(train)\n",
    "ts_predNs = modelNs.predict(len(test))\n",
    "\n",
    "# naive drift (trend) forecast\n",
    "modelNd = NaiveDrift()\n",
    "modelNd.fit(train)\n",
    "ts_predNd = modelNd.predict(len(test))\n",
    "\n",
    "ts_predN = ts_predNd + ts_predNs - ts_train.last_value()\n",
    "\n",
    "plt.figure(100, figsize=(18, 5))\n",
    "series.plot(label=\"actual\")\n",
    "ts_predN.plot(label=\"naive forecast\")\n",
    "plt.title(\"Naive Forecast (MAPE: {:.2f}%)\".format(mape(test, ts_predN)))\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d311302",
   "metadata": {},
   "source": [
    "# Exponential Smoothing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d7b55de9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "modelX = ExponentialSmoothing(\n",
    "                seasonal_periods=36, \n",
    "                seasonal=ModelMode.MULTIPLICATIVE)\n",
    "modelX.fit(train)\n",
    "ts_predX = modelX.predict(  n=len(test), \n",
    "                            num_samples=100)\n",
    "\n",
    "plt.figure(100, figsize=(18, 5))\n",
    "series.plot(label=\"actual\")\n",
    "ts_predX.plot(label=\"Exponential Smoothing\")\n",
    "plt.title(\"Exponential Smoothing (MAPE: {:.2f}%)\".format(mape(test, ts_predX)))\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0870b4f",
   "metadata": {},
   "source": [
    "# TFT Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8cc088b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = TFTModel(   input_chunk_length=30,\n",
    "                    output_chunk_length=1,\n",
    "                    hidden_size=64,\n",
    "                    lstm_layers=1,  #LSTMLAYERS,\n",
    "                    num_attention_heads=1,\n",
    "                    dropout=0.1,\n",
    "                    batch_size=64,\n",
    "                    n_epochs=100,\n",
    "                    #likelihood=QuantileRegression(quantiles=QUANTILES), \n",
    "                    random_state=42, \n",
    "                    force_reset=True,\n",
    "                 torch_device_str =\"cuda\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2b246e77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "22c76768c7b54a229d06bc183e36f213",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<darts.models.forecasting.tft_model.TFTModel at 0x15c62054048>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# training\n",
    "model.fit(  ts_ttrain, \n",
    "            future_covariates=cov_t, \n",
    "            verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c5ae5c71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b3eb6a47e2ca44599e1770de5eab42f7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 10it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# testing: generate predictions\n",
    "ts_tpred = model.predict(   n=len(ts_ttest), \n",
    "                            num_samples=100,   \n",
    "                            n_jobs=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "cce585a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Do the predictions constitute a probabilistic time series? True\n"
     ]
    }
   ],
   "source": [
    "print(\"Do the predictions constitute a probabilistic time series?\", ts_tpred.is_probabilistic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f074e976",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# testing: call helper function: plot predictions\n",
    "ts_pred = scaler.inverse_transform(ts_tpred)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "    \n",
    "test.plot(label=\"actual\")  \n",
    "ts_pred.plot(label=\"pred\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d432bf82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAPE: 1.273494561294498\n",
      "rmse: 128.98397657013996\n",
      "mae: 109.14675502232136\n"
     ]
    }
   ],
   "source": [
    "from darts.metrics import mape,mae,rmse\n",
    "\n",
    "mape_error = mape(test,ts_pred)\n",
    "rmse_error = rmse(test,ts_pred)\n",
    "mae_error = mae(test,ts_pred)\n",
    "print(\"MAPE:\",mape_error)\n",
    "print(\"rmse:\",rmse_error)\n",
    "print(\"mae:\", mae_error)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fac3133",
   "metadata": {},
   "source": [
    "# Temporal Convolutional Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e2120c76",
   "metadata": {},
   "outputs": [],
   "source": [
    "from darts.models import TCNModel\n",
    "#30 day window\n",
    "EPOCH = 100    \n",
    "FC_N = 30               # forecast periods\n",
    "FC_STRIDE = 10\n",
    "DROP = 0.1\n",
    "LEARN = 0.001\n",
    "\n",
    "model_TCN = TCNModel(\n",
    "        input_chunk_length=FC_N,\n",
    "        output_chunk_length=1, \n",
    "        n_epochs=EPOCH,\n",
    "        dropout=DROP,\n",
    "        dilation_base=2,\n",
    "        weight_norm=True,\n",
    "        batch_size=16,\n",
    "        kernel_size=5,\n",
    "        num_filters=3,\n",
    "        optimizer_kwargs={'lr': LEARN},\n",
    "        log_tensorboard=True,\n",
    "        force_reset=True,\n",
    "        random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c1529d66",
   "metadata": {},
   "outputs": [
    {
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       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation sanity check: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "model_id": "d711137e2d8f459087594174339df162",
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      },
      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "Validating: 0it [00:00, ?it/s]"
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       "Validating: 0it [00:00, ?it/s]"
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    {
     "data": {
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       "Validating: 0it [00:00, ?it/s]"
      ]
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    {
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      "text/plain": [
       "<darts.models.forecasting.tcn_model.TCNModel at 0x15c11243648>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# training: load a saved model or (re)train\n",
    "model_TCN.fit(  series=ts_ttrain, \n",
    "            past_covariates=cov_t,\n",
    "                val_series=ts_ttest,  \n",
    "          val_past_covariates=cov_t,\n",
    "                verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c7ddb6d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8ba0a1ec534542fdba244323166ab9aa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 37it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# testing: generate predictions\n",
    "TCN_ts_tpred = model_TCN.predict(   n=len(ts_ttest),  \n",
    "                            past_covariates=cov_t, \n",
    "                            verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "69bc3ae0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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Xnbxh2XV0XR8MDAYYPnw4nTt3dqOYrklOTiYqKp2scy9zzz338PHHH5OUlESXLl147rnnqFOnDkBq+StVqsTRo0dZsmRJ6robje0fSJ+3ivPOTBsd652jcmlrrr2G3JJf6tQXSd34Hqlz7yV141mZ3ZTgTtAVC4Sn+dvxaW1LsywCiEmz7QX7siOYQVr4DdtdxzCMKcAU+59y92IakZGRbNiwAU3TqFu3brrbtG3blpkzZ7J//37uuOOOdLe5LxIeXm/ju4Ua78wuxYL3NTStYOV35Zc69UVSN75H6tx7Sd3kHnfuXjwA1NZ1PVDX9VbANvvy07quV9d1PRwoZhhGNLAO6GJf3wlYe8OyO4DVbpfeR9WrVy/DgAugVatWAKxenfm/dtwwjSJh8Oc6+G2lR4sohBBCiBs4FXTpuv4nZqA0FXgA+BhYDrxt/wEYBUwD/gHG2JdNBbrrur4aWGYYRpRhGJuBU7qurwRqA7946LUIO2eDrjLFNd5+3GzdeuYzxaVESaoXQgghcooMjmpXkJpXbTYbxYsX58KFCxw7dowKFSpkuK3VqmgyWLF5P7z0ILw3tOAM3VaQ6rSgkbrxPVLn3kvqxuNkcFRfYrFYaNGiBZB1a5efn8ak5zU0DcbPgV1HvD4IF0IIIfIlCboKKGe7GAGa1dZ4ojukWM1uRiGEEEJ4ngRdBZQrQRfAu4M1/PxgsQFXkyTwEkIIITxNgq4CqmnTpvj7+7N161YSEhKy3L54EY2y9hHTTkbncOGEEEIIHyRBVwEVEhJCw4YNsdls/Pfff07tU76U+RglQZcQQgjhcRJ0FWAtW5ozLDnbxVi+pPl44lzm2wkhhBDCdRJ0FWCu5nWlBl1nc6pEQgghhO+SoKsAcwRda9euxWrNem7FyBLm0CInzkkivRBCCOFpEnQVYOXKlaNy5cokJCSwY8eOLLd35HRJ96IQQgjheRJ0FXCudDE6uhclkV4IIYTwPAm6Cjh3gi7J6RJCCCE8T4KuAs6VoKtcCfPxVAykpEhelxBCCOFJEnQVcLfddhvh4eEcPXqUqKioTLcNDNAoVRSsVjgTm0sFFEIIIXyEBF0FnJ+fn9OTX4OM1SWEEELkFAm6fIBbyfQSdAkhhBAeJUGXD3ArmV6CLiGEEMKjJOjyAc2aNcPPz48tW7Zw6dKlTLctX0oGSBVCCCFyggRdPiA0NJQGDRpgtVqznPw60n4Ho7R0CSGEEJ4lQZePcLaLUcbqEkIIIXKGBF0+omXLloATQZd9KiAZlV4IIYTwLAm6fETaya9tNluG26XtXlRK8rqEEEIIT5Ggy0eUL1+eihUrEhcXx86dOzPcLjRYo2hhSEqG6LhcLKAQQghRwEnQ5UOczetKbe2SvC4hhBDCYyTo8iGOvC7DMDLdzpHXJXcwCiGEEJ4jQZcPiYyMBCA6OvMs+dRR6SWZXgghhPAYCbp8SEREBAAXLlzIdLvyJe0DpJ6VRHohhBDCUyTo8iFFixYFnAm6zEfpXhRCCCE8R4IuH+Jo6YqNjc10u0gJuoQQQgiPk6DLhzjfvWg+StAlhBBCeI4EXT4kPDwcTdOIj4/HarVmuF3aoEsGSBVCCCE8Q4IuH2KxWChSpAgAcXEZj3xaJAxCg+FSIsRfyq3SCSGEEAWbBF0+xpkuRk3TpItRCCGE8DAJunyMs3cwpp2DUQghhBDZJ0GXj3H2DsbUli6ZCkgIIYTwCAm6fIzTdzDKVEBCCCGER0nQ5WOcHyDVHJU+KlruXhRCCCE8QYIuHyPdi0IIIUTekKDLxzjbvSiJ9EIIIYRnSdDlY5zuXpScLiGEEMKjJOjyMc52L5YoAoEBEJsAl69IXpcQ+UVsbCxPPfUUe/bsyeuiCCFu4O/OTrqu+wEzgEjgCDAI2AVE2Tf5n2EYi3Rdrw18aX+e0YZhLNZ1PQyYCZQEFhiG8V72XoJwhbPdixaLRmQJxeFTEHUOqlfI+bIJIbLvs88+Y+LEiURHRzNnzpy8Lo4QIg13W7p6A4cMw2iPGWz1BuIMw2hv/1lk3+4d4DHgDuBN+7JBwB+GYbQG2uu6Xt7t0guXOdu9CDLxtRD50dKlSwFYtWpVunOn7j+uMPYoUlKkBVuI3OZWSxdQFdhi/30T0AsI03V9OWZr13DDMGKAsoZh7AfQdf28ruslgBbASPu+i4DmwM9ulkO4yNnuRYBICbqEyFcSExNZu3YtACdPnuTIkSNUqVIldX1sgqLxIEXCZQgPhXb1Fbc30ri9MdSpYrZwCyFyjrtB127M1qtfgE5ABNDKMIzzuq4/ArwBjADSnsFxQDH7tvE3LLuOruuDgcEAw4cPp3Pnzm4W03nJyclERUVlvWE+l5iYCEBMTEyWr7dIcGEgjF0H4oiqk/9mvvaVOs2PpG5yxurVq0lKSkr9e/78+dx3332pf/+xvhAJl4vi76eIv6Tx+xr4fY3Z4lWssJWWtZNoVD2Z0EI2ggKhUIAi0F8RFKgI9IfwEBu1K6WguRGbSZ17L6kbz4qMjMxwnbtB1wKgg67r/wI7gNOGYZy3r/sJeML+uy3NPhFADBALhAMX7MuO3HhwwzCmAFPsf+ZKG3hUVFSm/6iCwtG9GB8fn+XrrVVFAYr4q+FERkbkfOE8zFfqND+SuskZ27dvB6Bw4cIkJCSwc+dOnnnmmdT1mw6Zl+Qxj1p4pCss3QT/blIs2QRR5/xYsD6YBeuDM32OAV1h+ijXM1Okzr2X1E3ucSvoMgzDBjwLoOv6G8C/uq4HGYZxFWgLHLBvelrX9erAGaCYYRjRuq6vA7oAX2G2kj2evZcgXBEcHExAQACJiYlcvXqVoKCgDLd15HRFRedS4YQQ2eLI53rqqad47733WLVq1XXrFxnmY+cmULG0xoBuMKCbhlKKAyfg302w/ZDiShJcSYKryVz7PQmMvfDt33Bnc0Xf26UrUghXuXv3YhlgNpACLAb2Amt0Xb8EXMVMngcYBUyzP8/r9mVTge90XR8I/G4YhrRp5iJN04iIiODcuXNcuHCB0qVLZ7itjNUlRP5x6dIl/vvvPywWC88++yyffPIJu3fvJjo6mhIlSnDopOLQSYgIA73m9ftqmkb1Co67lDMOpibPUwwbr3jyY0Xb+lCmuAReQrjC3Zau00D7GxY3Tme7XUCbG5ZdxEy8F3mkaNGiTgVdMiq9EPnH6tWrSU5OpnHjxpQqVYpmzZqxfPly1qxZQ48ePVi0wdzu9kbg5+desDSkB8xdAQs3wOBxinnvmgGbEMI5MjiqD3L2DsYyxcBigTMxkJQst5cL4c2WLVsGQIcOHQBo3bo1QGoX4yLDPIc7N3E/SNI0ja9f0igSBr+vMbsahRDOk6DLBzk7QKq/v0bZ4ubvJyWvSwiv5sjnujHoWrlyJVar4t9N5nad9ew9T/lSGhOeMQO3Zz5THDsjX8iEcJYEXT7InQFSJZleCO+VkJDAhg0b8PPzo00bM6OjRYsWaJrGxo0bWb3tCrEJUKUsVIvMuKUrKSmJkydPZvl8D3eBXm0g/hI8/r7CZitYgZdSCqu1YL0m4R3cHTJC5GOuDJBaviSsB06czdkyCSHct2rVKqxWK82aNaNw4cIAFClShHr16rF161ZmzD8FVM6ylWvYsGF8++23rFu3Dl3PeGNN05j8PKzaplhswKTf4Knenns9OeFqkmL/Cdh91PzZdURx6jxcvgqXr8ClK+aj4+8Af3i6t+LNxzVCCknemvAMaenyQc52L4Ik0wuRH9zYtejgaPVasskMGjLL54qJieG7777DarXyww8/ZPmcpYtpTH7ePN6LkxX7j+d+y9CqVau455572L17903rkpIV789S9HzFRo0HbYTeoaj7qKLvGMWYaYo5/8KKrWDsgV1H4OhpOHcBLiWCUpCUDOPnQP2BihVbpNVLeIa0dPkgl7oXS2mA4sQ5RWa3kgsh8k5GQVfr1q35fOI0jsaWQ9PMOxczMnv27NTR7BcsWMD48eOzfN5722s81FkxaxE8+q5ixQT374x0x4svvsjatWvZuHEj69ato1y5coAZcN03WvH7mmvbWixwSyTUqmT/qaxRqTSEBUNIIQgJsj/af9+0Dwa+p9h5GNqNUAzrpXh/qEbhELkOCvdJ0OWDXO1eBGnpEsJbxcXFsWnTJgICAmjVqtV161q1agVF2qIIQK+pKBaecefG9OnTU3/ft28f+/bto0aNGlk+/4T/01i6WbFmB7wyRfFkL6hUJueHkti9e3fqPJPHjx+nW7durFy5kkLBhek7xgy4ioXDpyM06leD6uWhUJDzZWpSCzZOhXe+U7wz0+xC/WOtYsoLcEdTCbyEe6R70Qe50r2YmkgvQZcQXmnFihXYbDaaNm1KaGjodevKly9PeCUz2ap+xYwTM3fv3s2GDRsoXLgw99xzD2C2djmjaGGNr140g5BxP0CVfoqSPRR3PG/j1ak2fl1u3uGoPNxD98033wBw3333UaNGDbZt20ave/rS53Ur81ZB0cKw+CONh7to1K2muRRwOQQFaox9zMLGrzQa14RjZ6DrSMWj79iITZAuR+E6Cbp8kDt3L0pLlxDeKaOuRQdLsS4AhCatzfAY3377LQB9+/alb9++APz+++9Ol6Fbc42Zr2l0bQYlisD5OHMA1Xdmwr2jFZX6KPQnS/HpT8ojY/4lJyczY8YMAJ5//nn++usvSpYqx9JTg5i/2kJEmBlwNazhmRapetU01k3SeH+oRlCgOT7ZQ29K0CVcJ0GXD3Kle7GcPZH+5HnkFmohvJAj6Grfvv1N605FKy4kVwDrJU7u/TXd/a1WKzNnzgRgwIABdO3aFX9/f1auXOnUNcLh4S4af42zcHa+xtGfNH55S2NUf+jSBIoXgTMX/Pi/CYrbHlH8ulyhstH09ddff3HmzBlq1apFs2bNqFCxCnXu2wYl7oHkWHrdMpFGNTMPuC5cuEB0tPNj4fj7a7z4oMaWrzX8/GChAXEX5ZooXCNBlw9ypXsxKFCjZARYrXDG+euvECIXxMTEsHXrVgIDA2nZsuVN6xdvtP8St4I1q5amG+gsWbKEkydPUrVqVVq3bk1ERARt2rTBarXyzz//uFwmTdOoWFqjdzuN/w2y8M94C+fma3wzMoaaFeFAlNn61fZpxfpd7gUt06ZNA+Cxxx4jxQoPjFUs3V6U0KBkLLu6MX3C00yaNOmm/ZKTk5k/fz733nsvpUqVolq1auzfv9+l5761kkbz2uY1cdlmt4ovfJgEXT7Ile5FSNPFKGN1CeFVVqxYgVKK5s2bExwcfNN6x9Q/IUmrOXnyJEeOHLlpG0fX4oABA1KT37t37w641sWYGU3T6Nz4Ktuna3zxrEaJIrBqGzQfqnhgrI3DJ83BSA9GKf5Yq/hojmLwOBtth9so1cNGkW42bnvExh3P23jojUvM39IArezjVKj3GA++qfhlORQJg6WfBTL146EADB8+nHnz5qGUYvPmzfzf//0fkZGR9OzZk19//ZXk5GTi4+N54oknsNlsLr2ezrr5f3L8f4Vwlty96IOKFCkCmN2LSqks7zIqXxI275dR6YXwNpnlcyllDlwK0KR6PMv3m+NaValSJXWb+Ph45s6dC8AjjzySuvzuu+9OzZVKSUnB398zHxUB/hpP3gMPdYb3v1d8/CPMXgK/LFdomjk2VkZ2XTLH04JgqPg6APe/ba4LD4WFH2o0qaXRpNZjHD9+nDfeeIP777+f6tWrs3379tTj1K5dmwEDBtCtWzc6d+7MihUrmDx5Mk8++aTTr6OzDm98A4sM1/8HwrdJS5cPCgwMJCQkBKvVyqVLl7Lcvnwp81GS6YXwLpkFXTsPw6nzULY4dGtbEbg2+bXDTz/9RGJiIu3ataNy5cqpy6tXr06NGjWIjY1lzZo1eFqRMI13BlvYO0vjoU42klPMgKt8Seikw1P3wIRnNBZ9pHH8Z43o3zW2TNP4/T0oc/FNOPYOHWofp0ND6NAQFo3XaFr72pfH119/nccff5wrV66wfft2ihcvzvDhw9mwYQM7duzgxRdfpG7dukycOBEwx/tKrxUwI01rQeEQ2HccmXtSuERaunxU0aJFuXz5MhcuXCAsLCzTbSNL2AdIPSsDpArhLc6dO8f27dspVKgQzZs3v2m9oxWmkw5t25oj098YdKXtWrzR3Xffzfjx41mwYAFt27b1cOlNJQoncn7tvbB2AxUrlGHr/FWpOac3Kl4EEqPXc3rzWEqXLs0/E14iICD9dgNN05g8eTKNGjWibNmy3HXXXQQGBt60Xe/evenTpw8//fQTgwcP5p9//nFqfDF/f40ODRXzV8NiAx67y6WXLXyYtHT5KBkgVYj8bfny5QC0bNmSoKCgm9Yv2mC2wHTWNRo1akShQoXYtWsX58+fB+DgwYOsXLmSkJAQ7rvvvpv2v/vuuwHP5XXd6MKFC3Tp0oW///4bUs5z7PBOnnjiiUzvanQk0D/yyCMEBARkenx/f3+efPJJ7rnnnnQDLofPP/+c4sWLs2jRotTjO0PyuoQ7JOjyUS4NkCrdi0J4ncy6Fq8mKZZvNX/v1BiCgoJo2rQpAKtXrwZIHeeqd+/eqZNkp9WyZUsiIiLYs2cPBw4c8GjZT58+Tfv27Vm9ejUVKlTgjz/+oHDhwvzyyy/p3nUIcOnSJWbPng3AwIEDPVaWUqVKMWHCBACee+45oqKinNqvcxPzcbEBNpsEXsI5EnT5KLdGpZdEeiG8RmZB19qdcPkK1KkCZUuYLTKtW7cGzC5Gm82WGnSl17UIEBAQQLdu3QDnR6d3xpEjR2jTpg1bt26lRo0arFq1ijvvvJOvvvoKgGeffZYtW7bctN8vv/xCQkICLVq0oFatWh4rD8D9999Pjx49iI+PZ+jQoU6NIVajAlQoBdFxsNWzMakowCTo8lGOYSOc6V6MtA+QeuIc2RrQUAjhGadPn2b37t2EhITQpEmTm9Y7urw6p1mVNuhauXIlR44coXz58hmOZA+eHzpi165dtGrVigMHDtCwYUNWrlxJxYpmkn/fvn0ZPHgwSUlJ9OvXj4SEhOv2TTs2l6dpmsakSZMoUqQICxYs4Pvvv3dqn866+bvcxSicJUGXj3KlpSssRKNIGFxNMqf3EELkrWXLlgFmIJVevtKiDeajI+8IzO5CTdMwDIPJkycDZm6Un59fhs/TrVs3/Pz8WLFiBXFx2Tv5N2/eTJs2bTh58iRt27Zl6dKllCpV6rptPvnkE+rUqcO+ffsYNmxY6pe8AwcOsHz5ckJCQlKnKfK0cuXK8fHHHwMwYsQIzpw5k+U+nZtIXpdwjQRdPsqVoAskmV4Ib5JZ12JMvMLYC4EB0Lb+teVFihShXr16JCcnp+ZGpR2bKz1FixaldevWpKSkuDU6vcOyZcu4//77iYmJoXv37vz999+p4wWmFRwczI8//khISAizZs1i+vTpAKmPffr0ITw83O1yZOXRRx/ljjvuICYmhqeeeirL7Ts2Nh9XboPEqxJ4iaxJ0OWjXOlehGtB13EZlV6IPGcYZn+Wo8swraWbQCloWQdCg68f/iDt9s2bN6dmzZpZPpeji9HdvK4VK1Zw1113cenSJR566CF+/fXXdEfPd6hVqxZffPEFAE899RTbt29PDbpyomsxLU3TmDJlCmFhYfzyyy/8+eefmW5fMkKjYXWzF2DVthwtmiggJOjyUa62dFUtZz7uP5Ez5RFCOO/QoUMA6QZNjnNUTyeeSht0ZZRAfyPH0BF//vknVqvVpXKuXr2aO++8k8uXL9O3b19mzJiR5VAPjrL179+fxMREOnToQFRUFLfccgtt2rRx6fndUbFiRZ577jkAFi1alOX21/K6pKVLZE2CLh/latBVq5L5jXn3UbmwCJGXYmNjUwc1LlGixE3rT503z9FyJW4e5LN169ZomkZQUBD9+vVz6vlq1KjBLbfcwvnz51m7dq3T5fzvv//o1q0bly5d4uGHH2bcuHFYLM595GiaxsSJE6lRo0bquGIDBw50auBST9B1M5LasWNHlttey+vK0SKJAkKCLh/lavdirUrm4+6jOVUiIYQzDh8+DECVKlXSDUJOx5iPZYrdvG/58uWZM2cO8+bNS70GZEXTtNTWLme7GDdu3EiXLl1ISEigX79+fPPNN5km7KcnLCyMH3/8kaCgIPz9/bPMP/Ok2267DYCdO3dmuW3rulAoELbsh7Ox8qVUZE6CLh/laktX7crm4+6jMmyEEHnJ0bVYtWrVdNefMhuGKFs8/f379OnDHXfc4dJzujI6/datW+ncuTNxcXHce++9zJw50+0Js+vXr8/q1atZtmwZ5cuXd+sY7qhcuTIhISGcOnWKmJiYTLctFKTRpp75+5KNuVA4ka9J0OWjXA26yhaH8FCIiYdzzu0ihMgBWQVdmbV0uat169YUKVKEXbt2pT5/enbs2EGnTp2IjY2lR48efP/9907lcGWmcePGtGrVKlvHcJXFYqF27dqAc61dMnSEcJZMeO2jXO1e1DSNWpUU63fBriNQyrmeCSGEh2W3pcsdAQEBdO3alTlz5jBs2DB0XadYsWLX/aSkpHD//fcTHR3NnXfeyY8//pjpnIferk6dOhiGwY4dO7JM4E9Npt9g9gTkVu6ZyH8k6PJR4eHhaJpGfHw8VqvVqXyLWpVg/S6zi7F9w1wopBDiJpkFXRcvKy4mmjlG4aGefd7evXszZ84cFi5cyMKFCzPcrkuXLvzyyy/pTsKdn9SpUwdwLpm+XjUoGWGOY7jvONSsmMOFE/mWBF0+ymKxEB4eTlxcHPHx8U4l1Zp3MCr7HYzyTU6IvJBZ0OXoWixbHI+3ttx33338/vvvHDt2jJiYmHR/mjVrxsSJEylUqJBHnzsvuJJMb7FodNIVPyw2W7sk6BIZkaDLhxUtWpS4uDhiY2OdCrrSJtMLIXJfSkoKR4+aJ2DlypVvWu/oWvRkPpeDxWJJHSjVF6Rt6XKmy7BTY40fFisWGYrh98qXUpE+SaT3Ya6P1WU+7jqSI8URQmThxIkTpKSkEBkZmW5rUtqWLpE9kZGRhIeHc/78ec6ezXoqDkde19LNkJwiCfUifRJ0+TBXg67KZSAoEE5GQ9xFuagIkducTaLPiZYuX6Npmkt5XRVKa9SsCAmX4b/dOV06kV9J0OXDXL2D0c9Po2YF8/c9x3KqVEKIjGQ9XIT5Zahscene8gRXgi5IexejfCkV6ZOgy4e52tIFktclRF5yjEafm8NF+DJXkukBOuvZmxJoxyHFqWgJ2AoyCbp8mDtBV+ocjEfkwiBEbnN6YFQJujzC1Zau9g3Bzw/W74aYeNeukVv2Kxo8rmj5pCLxqlxfCyoJunyYq92LkCaZXlq6hMh1jqCrSpUq6a5PbemSnC6PSNvS5cz0Z+GhGh0bgdUKH3zvWuD0yhSF1QpHTsPHP7pVXJEPSNDlw9xr6TIfpXtRiNwnLV25q1SpUpQoUYL4+HhOnDjh1D5vP2H2BnzyMxw97VzgtWyz4u/15o1KAO9+pzh9Xlq7CiIJunyYO0FX9fJgscDhU3BFmsCFyDXx8fFER0dTqFAhypQpc9P6lBTF2VjQNCgVkfvlK4g0TXM5r6tJLY0HOsHVJHjtq6yvkUopXppsbvdaf40ereBiIoz+Wq6vBZEEXT7Mne7FoECNWyLBZoN9zn3xE0J4QNok+vQG6jx7AZQyp6Px95e7Fz3F1bwugHcGaQQGwHcLYdPea8HThg0baNSoEWvXrk1dNneFOcRE6WLwbF/4YJiGvx98/QdsPSCBV0EjQZcPc6elC9J0MR7xaHGEEJnIsmtR7lzMEe4EXZXLajzd2/x95ESVmg/2/vvvs3nzZiZMmACYrZOjpprrXh+gERqsUbOixpO9zAD6+S+UU7lkIv9waxogXdf9gBlAJHAEGAT0Bp4BrgADDMM4rut6beBL+/OMNgxjsa7rYcBMoCSwwDCM97L9KoRbshN0zVsFu2QORiFyTZYDozryuSSJ3qNc7V50ePURjWl/KpZuhj/XQft6l/jzzz8BWLZsGUoppv8Ne49BtUgYdPe1fccM1Ji5ULFko7nvXS089nJEHnO3pas3cMgwjPbALvvfzwHtgdH2H4B3gMeAO4A37csGAX8YhtEaaK/renk3yyCyyZ3uRUgzbIQk0wuRa6SlK284gq5du3Zhs9mc3q9oYY3XHjGvlS9OUiz4428SExMBOHXqFNt27OeNb8xWrLef0AhI0yVcLFzj9QHm3yO/UDKtUAHibtBVFdhi/30TMATYaRhGkmEYq4G69nVlDcPYbxhGPHBe1/USQAtgoX39IqC5m2UQ2eRuS1fqAKlHPFkaIURmZAqgvFGsWDHKli3L5cuXOXLkiEv7PnUPVClrzlc7/juzggICAgAYO/U8UeegYXXo2+HmfZ+8B26JNGf/+HJeNl+E8BpudS8CuzFbr34BOtmXxadZ72d/TNv3FAcUAyLSbOtYdh1d1wcDgwGGDx9O586d3Sym85KTk4mKisrx5/EmSin8/f1JTEzk0KFDBAUFObVfeIAGlGHfccXRY1H4+2W5S57wxTrNL6RuXLdv3z4AQkND0/3fHTgeDoQS7B9HVNTlXC5d1vJznVevXp1Tp06xfPlyp6+TDi/0KcSTnxXFONcdLKE8/nh/Jn81mwVbzRa0kfed59SppHT3fblfEE98VIzXv7bSoc5ZIsJypsUrP9eNN4qMjMxwnbtB1wKgg67r/wI7gFggPM16q/0xbVtsBBCTZtsL9mVHbjy4YRhTgCn2P3OlXTUqKirTf1RBVbRoUc6dO0doaCilS5d2er8KpWwcP6txVStHpUjvzOvy1TrND6RuXGOz2VLHiWrevDkhISE3bZNwxbzc1qoaQWRk0VwtnzPyc503btyYFStWcPr0aZdfw9B7FR//GMP+02Uo22QczzzTgcl/VyRZhXF7I3iga4l070YFeKycYua/iuVbLHyzuAwfPpUz977l57rJb9yqQcMwbIZhPGsYxu2YgdQnQG1d1wN1XW8FbLNvelrX9eq6rocDxQzDiAbWAV3s6zsBaxF5Ru5gFML7nTx5kqSkJMqUKZNuwAVpBkaV7kWPc+R1uXIHo4OmaVRhEgDRwQO5SA2IHAHA4C5HMgy4HPt+9JSGpsFnv8DBKMntyu/cCrp0XS+j6/oyXdcXA1cMw1gJfAwsB962/wCMAqYB/wBj7MumAt11XV8NLDMMQ9o081C2gy5Jphcix2U1/Q/IZNc5yZ1hIxySkpJYv/BDiJ5HsjWQDs8AlmA49zPRh//Kcv9GNTUGdIXkFDMhX+RvbnUvGoZxGvNOxbTL5gBzbli2C2hzw7KLQC93nld4nrt3MNaurAGK3TJshBA5LqskeqWUtHTloNq1awOwZ88eUlJS8Pd3/qNzyZIlxMXFUaPyNA769eRiIlg0G7ajr7FsWX2eeuqpLI/x9hMaPy5V/LoCvpynGNJTrrn5lQyO6uOkpUsI75dV0BV/CRKvQlgwhIXIB7KnFS5cmEqVKpGUlMSBAwdc2vfnn38G4MF7dIbYx+Lq2zYBEvezbNkyp4ahiCxpdjMCDB2vmPaHtHjlVxJ0+ThPBF0yYrIQOUuGi8h77gySmpyczG+//QbAfffdx0fDNX4cqzHt1SJERkYSHR3t9PGG9NQYbw+8nvhA8d1Cue7mRxJ0+Th3uxdLRGiUKGJOzHribE6UTAjhkOXAqPauRcnnyjnu5HUtX76cmJgYatasSe3atQkK1OjTQSO4kIX27dsD5uj0znqun8a7gzWUggHvKOYskcArv5Ggy8e529IF0sUoRG5xuqVLgq4c407Q9csvvwBmK9eNdyl26GCOiLp06VKXyvHywxpjH9Ow2eChtxW/LpfAKz+RoMvHZSfoSh2ZXoIuIXLMpUuXOHPmDIGBgZQrVy7dbVJbuqR7Mce42r1otVr59ddfAbj33ntvWu8IupYvX+7S9EIAowfAq/3BaoV+byh+Xy2BV34hQZePc7d7EdLOwSgnvBA55fDhw4A5XITFkv4l+9R58xwsU1yS6HNKrVq10DSNffv2cfXq1Sy3X716NWfPnqVq1ao0aNDgpvVVqlShQoUKxMTEsG3btpsPkAlN03jrCY2R90OKFe57XfHXOrkO5wcSdPk4T3Qv7jriseIIIW6QVdciSE5XbggODqZatWpYrdbUKZky47hr8d577013AFRN01Jbu1zJ60q7/wfDNEbcC0nJcM9rkuOVH0jQ5eMkp0sI7+Zo6cos6JKBUXOHs3ldNpsttWvxvvvuy3A7d/O6HDRN45MRGsN6wdUkuH+sos/rNs7GSvDlrSTo8nHZ6V4sX8ocFyg6DqIvyEkuRE5wqqVLhozIFY6gK6u8rvXr1xMVFUWFChVo0qRJhts57mBcvnw5Vqs1w+0yo2kaXzyrMel5jbBg+HkZ3PaI4qelck32RhJ0+bjstHRpmiatXULkMGeCrlPSvZgrnJ2DMauuRYfKlStTuXJl4uLi2Lp1q9vl0jSNoT01tk/XuL2R+UW47xhp9fJGEnT5uLRBlzuDnEpelxA5K6t5F5OSFefjwM8PShTJzZL5Hme6F5VSqUNFpHfX4o2y28WYVuWyGos/llYvbyZBl48LDAwkJCSElJQULl265PL+cgejEDlHKZVl0HXG3spVuihYLHL3Yk6qUaMG/v7+HDp0iMuXL6e7zcaNGzl69Chly5alZcuWWR7T2aArOTmZc+fOZXm8jFq97njexryViuQUuVbnJQm6hCTTC+GlTp8+zZUrVyhRogTh4eHpbiNTAOWewMBAatSogVKK3bt337ReKcWsWbMA6N27d4ZDfKTlyOtauXIlKSkp6W4TGxtLq1atKFWqFB07duSHH37gypUrmR73xlavhRug16uKivcpXvnSxoETEnzlBQm6hAyQKoSXkuEivE96yfQ7duzg1Vdf5ZZbbuGTTz4BnOtaBKhQoQLVqlUjPj6ezZs337Q+NjaWTp06sWHDBgD+/fdfHnzwQcqVK8eIESMyzQVztHodmqPx4ZMat1Y03y/vzYLqDyo6PGNj1kJFYpKzr15klwRdIlt3MFYpC4EBcPwsJFyWb05CeJJTSfTS0pWrHMn0ixcv5p133qFu3brUrVuXd955h0OHDlGmTBn+97//pbZgOSOjLsaYmBg6derEpk2bqFatGtu3b2fixIk0atSI2NhYJkyYQIMGDWjSpAlffvklycnJ6R6/ZITG8/dr7JqpseoLjUe7QUghWLYZHn5b0fqZUhw6Kdfv3CBBl8hWS5e/v0aN8ubve6S1SwiPkpYu7+No6Zo5cyavvvoqO3bsoGjRogwaNIh///2XEydOMGrUqEzvWrxReoOkxsTE0Llz59SAa9myZdSpU4dhw4axceNGNm3axFNPPUVERASGYTB06FCGDh2a6fNomkaruhrfvGLh5K8ak5/XuK0KnLngx5hpEnTlBgm6RLaCLoBalc1H6WIUwrOca+myTwFUTJLoc0PLli0pXLgwoaGhPPjgg/z++++cPn2aKVOm0KFDB/z8/Fw+Ztq8ruTk5OtauG655RaWLVtG+fLlr9unYcOGfP7555w8eZIZM2YQFBTEtGnTnB7dvkiYxpCeGgve0/D3U3y/GPbIDVE5ToIukRp0udO9CFA7NZleTlghPElaurxPmTJliIqK4ty5c8yaNYvu3bsTGBiYrWOWK1eOGjVqcPHiRRYtWkSnTp3YvHkzt9xyC0uXLr0p4EorODiY/v37M2rUKACGDh3q1NyQDpXLavRrfxmbDd78Vq7hOU2CLpGa0+VuS1ftyuY37B2HPVUiIQTIFEDeqnDhwgQHB3v0mI4uxt69e6cGXOm1cGXkpZdeombNmuzdu5cPPvjApece0esiAf4wewnsPCyBV06SoEtku3uxQXXzcVPWc8AKIZx05coVoqKi8Pf3z/SD19HSJYn0+Zuji/Hq1aupAVdkZKTT+wcFBTF58mQA/ve//7F//36n940sYeOJ7qAUvDldgq6cJEGXyHb34i2RUDgETkbD6fNywgrhCUeOHAHMqWIyyhNSSl0LuqSlK1/r1KkT4eHh1KhRw+WAy6F9+/YMGDCAq1ev8uSTT7o0y8iohzUCA+DHpbD9oFzHc4oEXSLb3YsWi0ajGubvG6W1SwiPyGokeoDYBEhKhiJhEBwkifT5WYkSJThy5Ajbtm1zK+By+PDDDylWrBiLFy/mhx9+cHq/8qU0hvQwfx8rrV05RoIuke3uRQC9pvlo7Ml+eYQQro3RVVa6FguEokWLEhQUlK1jlChRgnHjxgHw7LPPutSD8fJDGoUC4ZflsGW/BF45QYIuke3uRYDGNc1v2Rv3yokqhCe4NDCqdC2KNAYOHEibNm04e/YsL7/8stP7lSuhMbSn+fsb38i1PCdI0CWy3b0I0Nje0iXdi0J4hgwXIdylaRqTJ08mICCAKVOmsGbNGqf3felBjeAgmLdKvkTnBAm6hEe6F2+JhPBQM5n+VLScqEJkl0wBJLKjdu3avPjiiwAMGTIkwymCblSmuMaTvczfpbXL8yToEoSHhwMQFxeH1Wp16xiSTC+E5yilnGvpst8tXLa4JNGLm7366qtUq1aNHTt28NFHHzm934sPaoQUggVrYMNuCbw8SYIugZ+fH0WKFAEgPj7e7eM0dgRdez1RKiF8V3R0NJcuXSIiIiK1JTo9p2SMLpGJ4OBgJk6cCJhjdyUkJDi1X6miGk/3Nn+XORk9S4IuAXjoDsZbJZleCE+Ijo4GzClnMnNaRqMXWejSpQutW7cmISGB77//3un9Rt6vERYMf62HZZvlmu4pEnQJwEN3MNpbugxp6RIiWxxffjJr5QKZAkg4Z9iwYQBMmjTJ6QFTS0RojLjP/L3js4rH3rNx9LQEX9klQZcAPHMHYzV7Mv2p85JML0R2OM5DR7d/RmQKIOGMe++9l5IlS7J161bWrVvn9H6vPWIOmKpp8M2fUP1BxdOf2LK8videVSzfopi7Qrk0Kr4vkKBLAJ7pXrRYtGt5XZJML4Tb4uLigMxbuhKvKi5chAB/KBaeSwUT+VJQUBCPPfYYYLZ2OSs4SGPySAt7Zmo81BlSrPD5r1DtAcVLk2ycjzMDqvhLir/XK0ZNsdFmuI2IOxXtRyh6v6aY9keOvKR8S4IuAXimexGujddl7JFvN97OalW88qWN/82Qb6PexpnuxTNpWrk0Te5eFJkbMmQImqbx448/cv78eZf2vaW8xnejLWz7RuOeNpB4FT74Aarer2j8hI2idym6vaB49ztYtQ2SU6BqOXPf92YprFa5vjhI0CUAz3QvQpqR6aWly+u9+x28Nwte+0rx+tdyUfQmznQvSj6XcEWVKlXo2rUrV69e5ZtvvnHrGHWqavz6Pwsbpmjc0RTiL8GmfWDRoPlt8MID8Pt7GucXaOz9TqNqOTgQBT8v8+xryc8k6BKAZ7oXQYaNyC+Wb1GM+UahaeDnB2/PgK8WSODlLZzpXpSBUYWrHAn1kydPxmazuX0c/VaNvz+0sHGqxr+faFz4U2PtJAsfDLPQvaVG0cIa/v4aLz5gfgl/d5a0pjtI0CUAz3UvVouEImHmB8JJSabPFbGxsfz5558sW7aMzZs3c/DgQaKjozMcgfpsrOLBNxU2G7zyMEx81rwwDh1v5mWIvOdM96JMASRcdeedd1KxYkUOHjzI4sWLs328RjU1OjTSCA1Ov3t7QFfzS8HWA/D3+mw/XYEgQZcAPNe9aLFoNKpu/i6tXbnj8ccf56677qJDhw40atSIW265hZIlSxIYGEhISAgVK1bkt99+A8BmU/R/W3EyGtrUg7EDNQb30BjVH6xW6PO6YvM+CbzymnPdi2Y9SUuXcJafnx+DBw8GXEuod1ehII3n+9lbu76T6wpI0CXsPNW9CGkmv5ZBUnOcUoply5YB0LJlS+rXr0/lypUpWrQoFouFxMREjh8/zmeffQbA+9/Dwg1Qogh8/7rZBQDw9hMaD3aCi4lw10uKY2ek7vKSay1dkkQvnPf444/j7+/P/PnzOXHiRI4/35CeULQwrNwGq7bJdUWCLgF4rnsRQJdk+lxz5MgRYmNjKVGiBKtWrWLLli0cPnyYmJgYUlJSOH78OABr1qxhyYYrjLYnzM98TaN8qWsf1pqmMe1ljXYNzK7hO19UXEiQC2RekZwukVPKlClD7969sdlsTJ06Ncefr3CIxnD7lELS2iVBl7DzVPcipB02ItuHElnYtGkTAI0bN75p2ABN0yhfvjz169fnqjWM+99QWK3w0oPQtdnNrSNBgRpz/6dRqxLsPAz3jlYkJctFMi9ITpfISY6E+qlTp2aY++lJI+41J9D+cx1s2e/b1xR/d3bSdd0CfANUtS96FPgbiLL//T/DMBbpul4b+NL+PKMNw1is63oYMBMoCSwwDOO9bJRfeIgnuxcdyfSnY8xk+nIlpPsjp2zcuBGARo0aZbhNhw4d2ZrSgeiEIFrWgbeeyLg+ihbW+GscNB+q+HcTPPGB4puXwc9P6jA3uTJkRBkJuoSL2rVrR61atdi9ezfz58+nefPmOfp8JSI0BnVXfPqzOW7X7Dd893ribktXAyDIMIw2wJvAcCDOMIz29p9F9u3eAR4D7rBvBzAI+MMwjNZAe13Xy7tdeuExjpauc+fOsXdv9jLgNU2ToSNySdqWrozEhT0Gxe7EX8Uxe4xGgH/mF7xKZTQWvG9+M535DzQeZE7pIXJPVt2LNpvijD0TQLoXhas0TWPo0KGAawn10dHRrFixgsmTJzNixAg6d+7M66+/7tS+z/fTCPCHn5bBgRO+ez1xN+g6AaDrugZEAOeAMF3Xl+u6/r2u647LQFnDMPYbhhEPnNd1vQTQAlhoX78IyNkQWzglNDSUXr16kZycTOfOnVNzgdyV2sUoyfQ5RimVZUvX5n2KmatuBcC251EiQi46dezGNTUWvKdRsbR5u3f7EYo+r9s4ckrqM6clJSVx+fJl/P39CQkJSXeb6DjzbtPiRSAwwHdbDYT7HnnkEUJCQliyZAmHDh26aX1cXBxz587l6aefpkOHDpQqVYqSJUvSrl07hg0bxoQJE1i8eDFvvfUWZ8+ezfL5KpTW6N8FbDb44AffvY641b0IRAM2YDcQBLQCvjQM47yu648AbwAjgLRXgzigGGaQFn/Dsuvouj4YGAwwfPhwOnfu7GYxnZecnExUVFTWGxZgH3zwASdOnMAwDDp06MCvv/5K8eLu9V1UKVkIKMrqrVeIisp+cr47Cnqdnjx5kujoaIoUKUJAQEC6r/XdGUVIsYZQIulHoqPn89tvv3H77bc7dfwapWHJB/DlgjA+nxfGz8s0fl9tY2j3izzV4xIhhdy/cBb0uskOxxQthQsX5uTJk+lus+uoP1CSEoWTiYqKzsXSuU/q3Pv06NGD2bNn8+2331KpUiW2bdvG8uXLWb58OZs2bcJqtV63fWhoKNWrV6dGjRrUqFGD+fPns23bNubOnUv37t2zfL4BHf345q+SfPsXDO56irLF3B+g1ZtFRkZmuM7doOsOINEwjFt1XW8EfGQYxv32dT8BT9h/T/sfjQBigFggHLhgX3bkxoMbhjEFmGL/M1dC4qioqEz/Ub5i4cKFtG/fnm3btjFw4ECWLl1KeLjrs+l2aaHgM8XOo4UoV65cnswNV9DrdMOGDQA0adKE8uVv7qWPv6T44z/z9Lm3xQm+XA/btm2jf//+Lj3PuKdhRF/Fy18qvl+s8encwvy8qjDvDTGHmbBYXK/bgl432XH58mUAihUrluH/aEeUAhQVygTkm/+j1Ln3GTlyJLNnz+b7779n7ty5xMTEpK7z8/OjdevWdOnShSZNmnDbbbdRvnz5667lhQoVYtu2bWzdupUhQ4Zk+XyRkXBvOxs/L4Mflpdm/HDfu5cvO6/Y0XxxASil63qQ/e+2wAH776d1Xa+u63o4UMwwjGhgHdDFvr4TsDYbZRAeVrRoUf755x+qVavGpk2b6NGjB4mJiS4fp2o5iEhNps+BgoosuxZnL4HLV6BdA7jvrgYA/Pvvv249V4XSGrNet7DqC43GNSHqHPR/W9H2acW2g659LzpySrFgXSG5MzIDMlyEyC2NGzemWbNmJCYmEhMTQ+XKlRkyZAi//vor58+fZ+XKlYwePZquXbtSoUKFm748d+jQASB1rEBnvPKQeYwvf4fzcb53DXA36FoIlNN1fTnwPTAWWKPr+gpgJDDGvt0oYBrwT5plU4Huuq6vBpYZhiHtzV6mTJkyLF68mHLlyrF8+XL69u3r8m3FmqbRSJLpc1RWSfRf/2Fe0B6/S6Nly5YEBASwefPm677NuqpVXY3/vjTH9CpdDFZvh0ZPKJ7/3EbC5cwvoFHnFE9+ZKPGQ4qhnxalz+uKq0m+d9HNijN3Lp6Wya6Fh/z44498/PHH7N27l0OHDjF58mTuueeeTN9/DrquExISwu7duzlz5oxTz9eopjlZ9qVE+PzX7JY+H1JKeftPrjhx4kRuPVW+sXPnTlWsWDEFqIceekhZrVaX9n9holXRxqpe/9q1/TylINepzWZTpUuXVoDav3//Teu3H7Qp2lhVeFerupRoU0op1bZtWwWoX3/91SNliI23qeEfW5WlnVnP5e6xqh//tSmbzXbddmdibOrZCVYV1NHcTmtrVSGdkhVtrKrbSKtKvGLL4Bl8008//aQA1bt37wy3GfGJ+b8cPzv//O8K8vmY32Wnbjp37qwANXv2bKf3WbbZvD4Vu8uqLl7OP+9hF2QY0/heh6pwWu3atfnrr78ICwtj1qxZPPPMMy7NFJ86Mr20dHncqVOnOHPmDEWKFKFatWo3rZ/2p1lPD3aCkEJmPTgS6N3tYrxRRGGNCf9n4b8vNZrWMruR+45RdB2p2H9cEROvGDXFRtX7FR//CFeToE8H2PGtxm9jz1OiCPy1Hnq8orh8RVq8HJzpXpSBUYW3cKeLsW19aFYbYuLh6z9yqGBeSoIukammTZsyb948goKC+Pzzz1m0aFHWO9ldGzYCl4I1kTVHPlfDhg1vyrNISlbM/Mf8/fG7rq3zdNDl0LimxtpJGpOf1yha2Jzbsc6jiir9FO9+Z3Yj3N0SNn+t8eNYC7Ura9SulMLSTzVKFYVFhjnf48Usuid9hTOj0UtOl/AW7du3B2Dp0qVO76NpGi89aF6bxs9RJKf4zrkvQZfI0u23386IESMAWLFihdP7OZLpz+STZPrEq4ovflVU6WsjtIuN//vMxnEvnfg5s3yu+avNcZzqVbsW+AI0a9aM4OBgdu3a5XT+hbMsFo0hPTX2fKfxaDdISob4S9BZh3WTNea/Z6FB9euDwzpVNZZ/plG2OCzbDN1eVFnmhfkCV0ajL1ciFwokRCZ0XSc0NJS9e/dy6tQpp/fr2RpqVoRjZ2COZ78HejUJuoRTHNNEOIYpcIamaakf+t7cxRh3UfHed4rKfRXDP1EcOW3e9ffpz1D1fsXAd23sPuJdwYCjpSu9oCttAn3aVrDAwEDatGkDuPat1BWlimp884qFjVM11k3WWPiRhWa1Mx5S4tZKZuBVviSs2gZdnlfEXfSu/3Vuy6qlSynFSUmkF14iICCA1q1bA7B8+XKn97NYNF6437w2fPC98pneEAm6hFN0XQfAMAyXTg7HdEDeODL92Vgz56hiH8UrUxRnY82WoZ/f1Nj0lcYDncCmYPpfcNsARe9Xbfy3yzteR0bDRRw/o/jnPwgMgIfSGVM4p7oYb9SoppZpsJVW9QoayyeYo9+v2wmdn1PEJnjH/zkvZJXTlXDZ/FIQGgyF0x+wXohc5cjrcvXL3MNdzNba7Yfgr3U5UTLvI0GXcEqFChUoVaoUMTExHD582On9GnthMr3Vqnhxko1Kfcyco/hL0L4h/POhxoYpGve212hYQ+P71y3sm6UxtKcZxMxdCc2GKm5/xsbeY3kXFJw+fZqTJ08SFhZG9erVr1s3/W9QCnq1huJFbg56civoclXVchorJmhUKQsb9sAT7/tu0JVV96Kjq75sMfJk0GEhbuTI63IlmR4gKFDj//qY7+H3v/eNc16CLuEUTdNo0qQJ4FoXo25O++dVyfRf/wHjfoArSWaC95qJGks/tdClqXbTh1i1SI1Jz1s4Mkfj5YcgPBSWboY2wxVbD+TN63HkczVs2BCL5dopbLMpvvnzWtdieho2bEiRIkU4ePAgR48ezfnCuqBSGY2ln2qpAe7BKO94v+S2rLoXJZ9LeJtGjRoRFhbGvn37Mpy6KiNDekCRMFixFdbtLPjnvARdwmmOLkZXgq4qZc0Ph7OxMNf5HPwcc/Gy4vWvzfnEXuy5nfnvWWhRJ+vWgjLFNd4dYuHojxpdmsC5C+Yk0OvzoLsxoyT6ZZvh8CmoWBo66env6+/vT7t27YCcy+vKjkplNB7oaLbWfTG34F+A05NV92JqS5fkcwkvERAQkJov6mprV3ioxrCe5u++0NolQZdwmqOlyzAMp/fRNI1X+5tBzctf5v2twS9/cZYzsRaIX8/EsW04e/asS/tHFNaY/65GrzZw4SJ0elaxbHPuvqaM8rkcCfQDu2U+H2JOdzEqpfjyyy/5+uuv3dp/xL1m2b/+A5+8mzGrlq6T0tIlvJC7XYwAz9ynERQI81bBnqMF+5yXoEs4zdHStXHjxptmn8/MoLuhennYfwKm/p5TpcvajB/+5ovfggGwHHuFiwkJjB071uXjBAVq/DjWnOz5YiJ0e0Hx17rcu1Ck19IVm6D4ZQVoGgy8M/OWu7RBl6e7fG02G8888wxDhw7liSeeYO9e15P5GtXUaF3PzLWb8bdHi5cvZJXTdeq8WWfliks+l/Ae7ibTg9mTMOAOs4V73A8SdAkBQOnSpalQoQIXL15k3759Tu8X4K/x3hDzA2Ls9Nwfi8lms/HWW28xYPRB8AujXJDBun/GYbFY+PLLL9mzZ4/Lxwzw15jxqsagu83csJ6jFL8sy/nXFR0dzbFjxwgJCaFmzWuDcH2/yBzxvWNjs4suM7fddhslS5YkKiqK/fv3e6xsVquVxx9/nAkTJqQumzJlilvHeuY+8zV89ovCZivYF+G0rFYr8fHxaJpGeHh4uttI96LwRg0bNqRw4cIcOHCAEydOuLz/yPs1NA1mLjTnaS2oJOgSLnEnmR7gnrbQ4jYzt+vD2bl3Ql28eJG+ffvy+rvfQdlBaNhYNEWnSZMmPPHEE1itVl5++WW3ju3np/HlSI1n+0JyCvR9QzHzn5x9bY6uxQYNGuDn55e6PO3YXFmxWCyp30o91cWYlJTEAw88wPTp0wkODua9994DYPr06SQmJrp8vF6toUIp2HfcHOHeVyQkJAAQHh5+3U0SaUkivfBG/v7+bud1gTl0zL3tzGvpJz9J0CUE4F4yPZi5XeOeNAOCD2fDqeicP6mOHj1Ky5Yt+eWXX/Cv/gFo/jxxt4XaVcy3/dixYwkNDWXevHkujbSflqZpjH9KY/QAsNngkf+ZA61eTcqZ15de1+LmfYrN+6FoYTNYcYaji9ETyfSJiYncc889/PTTT4SHh7Nw4UJeeuklGjduTExMDD/99JPLx/T313jqHvP98unP3nsBvnJV8cY0G/NXeaaMzoxGLy1dwlu5Mw9jWo6pgb6cDxcK6Fh9EnQJl7iTTO/Qqq6ZgH75CrzxTc6cUAkJCfzzzz+8+uqr3HXXXWzfvp0Kde4npcjdhBSCNwZeawkqU6YML774IgAjR47EZrO59ZyapvHm4xbeH2oe+5UpilsfVny3UGG1evZ13phEfylRMXa6+RwPd4FCQc7l+aTNv0jvdR88eJDBgwfTqFEjRowYwaJFi0hKSrppu4SEBO68807+/PNPihcvztKlS1NHpx42bBgAkydPdvFVmgbdDcFB8Pd670yuvZqkuO91xdjpcM9rioX/Zb+MzoxGf8o+2bW0dAlv4848jGnpt2p0bGwOADxpngcL5k2UUt7+kytOnDiRW0+Vr8XExChAFSpUSCUlJbm8/+4jNuXX3qos7axq12FbptumpNjU76tt6vT5jLeLjo5Wv/32m3ruueeUruvKYrEoIPXnzjvvUk0HJSnaWNXor6w37X/x4kVVtmxZBagffvjB5ddzowVrbKp2f6uijflTd4BVLVhjUzZb5q/VWVWqVFGA2rZtm1q6yaaq9jOfJ/D2rP+fadlsNhUZGZl6LIedO3eqhx566Kb/I6AKFy6s+vTpo2bMmKGio6PV+fPnVdOmTRWgypUrp3bu3Hndc1y8eFGFh4crQG3ZsuW6dc6eb4M+MF/fUx/dXHd5KSnZpnq+YpYtoIP5WKSbVe05mr16XrZsmQJU27Zt010fd9GmaGNVIZ2tHntP5Ra5xnovT9VNSkpK6jl/9OhRt46x8D/zPR7U0ap6jbKqaX/Y1JmY/PVeV5nENHkdUEnQlQ/dcsstClCbNm1ya/+hH5ofUj1fyfiD9GCUTbV60tzutkesKjn55pNu4MCBNwUGfn5+qlmzZuqFF15QM2bMUD8vM49R8m6rir+U/ok7depUBajKlSurK1euuPWa0kpJsalv/rSpCvdeC75aP2VVq7Zl78Jx/vx5BaigkBJqyLiU1GPXH2hVm/e5fuz+/fsrQH3yySdq48aNqnfv3qn/R39/fzVw4ED1119/qVGjRqk6depc93+2WCyqRIkSqf+3gwcPpvscw4cPV4AaOnTodcudPd+2HzQvwKFdrCo23jsuvEnJNnXva+b/vuidVrVpr03dM8r8u/oDVhWTjXL+9ttvClB33313uuv3HDX/H7fc711BqDPkGuu9PFk33bt3V4CaMWOGW/vbbDb10JvXrp20sSqtrVW1GGpV78y0qR2HPPclNgdJ0JUVuSA474EHHlCAmjJlilv7n4q2qdAu5sm0Ysv1J4/NZlNf/W5TYV2uP+m++PX67Y4fP54aZLVr106NHj1aLVq0SCUkJKRuc/jICVXjwfT3TyslJSU1qPjwww/dek3pSbxiUx/Psani3a+9jvtGW9WVq+5dMBYvXqyI6KgCW59IbWEZ+41NJaUTkDrjm2++UUDqN1NABQUFqSeffFIdOXLkpu0PHTqkPv30U9WpUycVEBCgAHXrrbdmeu7s2LFDASosLEzFx8enLnflfLv9GfN/99GcvL/QJifbVL8x11q2jD1mmRIu2VT9gebyTs+m/yXBGdOnT1eA6t+/f7rr/91oBl1tnpKgS3iOJ+vmww8/VIAaOHBgto5z7LRNTZxrU91Gmi35aT8PajxoVfuO5f31IBMSdGVFLgjO++ijjxSgBg0a5PYx3phmfng0G3Ktm+T0eZu6+6VrJ1af163qq9/N7Yp3v74F4bPPPlOAuueeezJ8jne+iUltfcgqMPnzzz8VoCIiItT58+fdfl3pibtoU69/bU0NNCf/5vrFIu6iTTV9aFPq/6bR41a19UD2LjpHjx5NDbZCQkLU888/r06ePOnUvhcuXFB//fWXunDhQpbbtmnTRgFq8uTJqctcOd/mrTTfA1X6WlVKSt5daFNSbOrBseb/v/AdVrVu5/VlOXrapkr1MNcP/9i9oOiTTz5RgHr66afTXf/dP+b/ot8YCbqE53iybjZu3KgAVaVKFY8dM+GSTf263KYefcea+iX21SlefQ5I0JUVuSA4b8WKFQpQDRo0cPsYCZdsqnRP8+T5aalNzVtpUyXvvtaC8N0/ZhOyzWZT7UeYy5/59NpJ1q5dOwWo7777Lt3jx1+yqRJ3mblcPy/N+oPaZrOpTp06KUA9++yzbr+uzMxebEsNAl0JHuIv2dQt99uD0VaX1T0j/nO7detGkydPVm+//bY6d+6cR46XnlmzZilA1a9fPzXAduV8S0m5lrs2b2XeBF1Wq00N+J9ZhrAuVrU6g67i1dtsqd/KJ7kRXI8dO1YBavTo0emuH/e9+R76v8+8+gMnXXKN9V6erJuUlBQVERGhgHRbzLPrjzXmOdB0sFefAxnGNHL3onBZo0aNsFgs7Nixw60xmADCQrTUOwkHvqvoOUpx7gLc3gi2f6PxUBdz8mlN0/jkaXPQvC/mmnexnT17lpUrVxIQEMBdd92V7vFfmqyIjvej+W3Qu13W5dE0jXHjxqFpGp9//jkHDx5063Vl5t52ULmMOTL/vFXO7/fZz3AgCgJSDsJmndcHBhDg75nRyIcMGcKrr75KiRI5dyvcvffeS4kSJdi6dSvr1693eX8/P43hvfNu+AibTTF4nOLbvyGkEPz5gUbLuun//1vW1Zgy0lz39CeKpZtcK29WQ0acdIxGX0JGoxfeyc/Pj7Zt2wLuDx2RmXYNIMAfNuyBmHjvu6s5KxJ0CZeFhoZSu3ZtUlJS2Lp1q9vHefwuqFnRnEonKBA+Hq6x6CONCqWv/0Cpf4vGE90hxQrPf6GYN28eNpuNTp06pXtr/Wc/Kyb9BoH+ik+fNgM3ZzRo0IBHHnmE5ORkRo0a5fbryoi/v8bz/cyyfPCDvak5CxcSVOpgssm7hhCYcoDatWt7vGw5KSgoiMceewxwf/iIx+6E0GD4dxNsP5gzF9qM6uOjOeY8kMFBsOA9jTb1M38/Deim8cID5vv1vtcVB6OcL29WQ0akDowqY3QJL5adeRizEhqs0aquOWXQv5s8fvgcJ0GXcIu7g6SmFeCvMecNjaE9YeNUjf/rq2U4UfPbT2iEh8Kf6+DLH48C0Lt375u2m79K8X8TzA+58UMv0LS2ay0Cb7/9NoUKFeLHH39k0qRJLr6irA28E4qFw/pdsGpb1tt/9KPiwkVoWOUCxC2lXr16BAYGerxcOW3w4MEAzJkzh5iYGJf3LxKm8WhX8/cJv3o+6Orfvz9hYWG0bNmSp59+munTp7N9+3YuJKTw3vfm880ardGhkXPvp3cHa3RvCTHxcPfLzg+WGxcXB2Qy2bUMjCrygeyO15WVzrp5Hi7aIC1dwkdkZ5BUh9jYWLavm0WZi29RqeSlTLctVVRj9CPmibYx9gE0v0B69ux53TYb9yoeeFOhFLz5uMY9ra64XKby5cvz6aefAvDkk08yffp0l4+RmdBgjeH2WDGriV2jLyg+/tH8vWXkP8C1QVHzm2rVqnHHHXdw5coVvv32W7eO8fS9Zv1P+xOGfmjjpIdmNYiOjmbWrFlcvnyZtWvX8vnnnzNw4EDq1atHyfqvcz4OyoYeodWt0U4f089PY9ZojarlYPdRWOlEgA3OTHZtPsrAqMKb1a9fn6JFi3L06NEcSdXoYn78sHBDxi3U3kqCLuEWd1u6jh07xoQJE+jUqRMlS5akf//+vPHGG7z99ttZ7vv0vVAqPAFCalGt1QeULFkydd3xM4q7X1ZcvgKP3AGvPeLa60lr8ODBjB8/HoDHH3+c2bNnu3+wdAzvrVEoEH5fA7uOmBeM8+fPM3LkSMaPH8+KFSu4ePEi435QXEyErs0g9ugC4Prpf/KboUOHAmYXozsXypoVzemWlDKnCbnlAcVLk2zZzutYuHAhSinatm3LokWLeO+997jvvvuoVLU2KaVGAHBq/ZO8/vpol44bHqpxVwvz9w1OzqmeVffiSXvQJS1dwptZLBY6duwIwF133cW+ffs8evyG1c0egyOn4WCURw+d8zLLsveSn1whd9a45sqVKyogIEBpmnbd+EvpOXr0qHrzzTdVo0aNbhrItFWrVqmjncfGxmb5vM3u/J85InfHS+p8nHl3WNxFm6o7wLxjrN3TVnU1yfU75NLz1ltvpZZz7ty52TrWjYaNN8s78F3zDpwxY8ZcP9BrYBmltb6oaGNVL7w5R1WuXFkByjAMj5YjNyUnJ6eOgj9nzhy3j7Pr8LXBSR13u/5vhk1dvOzenY0PP/ywAtT48eOvW/7hD/aBSO87lzqsRkxMjEvHnvG3eYxeo5y706pq1aoKUPv3779pXfyl/DsavVJyjfVmOVE3x44dU3Xr1k0dimfhwoUePX7f17MegzEPyZARWZELgusaN26sALVs2bIMtzl9+rQqXrx4ajARGhqq7r33XjVz5szU8bA6duyoAPX2229n+nwXL15UQYUKKer8o2hjVU9/Yg5C2XXktQHzHIGYUtmvU5vNpl555RUFqMDAQPXXX39l63hp7T9uU1pbc4DTqHM21b59ewWorl27qoYNGyrtlk/NoKLWL6n/u4CAAI+MmJ+X3njjjUxHXHfFf7tsqtOz14Kv0j2t6vNfbC4Nx2G1WlXJkiUVoHbt2pW6/FLitTG3/lhjS32P3hiYZcUxgnxkb+eCLse5cvbs2QyPVS0fjkavlFxjvVlO1U18fLzq2bNn6pfXCRMmeOwLw9TfXftCk8sk6MqKXBBcN2TIEAWocePGZbjNgw8+qADVrFkz9fvvv6vLly/ftM2SJUsUoEqUKKEuXryY4bF++uknBah6zR9UlnZW5dfeqnq/an4wluhuVQdOXH8ye6JObTabeuaZZ1Lnm/z333+zfUyH+0abZX/+82RVqFAhBahz586pY6dtKvB2c5qfl9/6Xj3yyCOqbt266sUXX/TYc+eVEydOKD8/P+Xv769OnTrlkWMuMWyq6eBrwdfIL5y/CP/3338KUJUqVbruw+CjOeYFXR9ktirNmzcvdcDHlJQUp49vtdpUkW5muU6czfzDxmazKT8/PwWoq1ev3rQ+P49Gr5RcY71ZTtaN1WpVo0aNSv3yOGTIELfm7b3RkVPm+RDe1f0ZIHKQBF1ZkQuC67766isFqH79+qW7/p9//lGACg4OVocOHcrwODabTTVr1ix1HsCMOKYf+uCDD1Lnb3RMjJrevIaeqlObzZYaYIaGhqrVq1d75Ljrd9rnFex0VeFXWNWqVUsppdSQcdZ8O+q4M3r16qUANWbMGI8d02azqdmLzcnUaWNV81c5dxF2DEaadm7Iy1dsqkzP64+TkpKSOtn4vHnzXCqbozVu7orMy5SQkJDajZmeWQvN90vf1/Pn+0Kusd4rN+pm1qxZKigoSAGqXbt2HhmQ2THNW0aDFeehDGMaSaQXbnPcwZheMv3ly5cZNmwYAGPGjKFKlSoZHkfTtNRxscaNG0dSUtJN21y9epUFC8xk8t69e/Pm4xpFwsx101/RaJXBYJWeoGkaEydOpH///ly6dIlu3brx5ptvsn79eqxWq9vHbVpbo10DuHTVH8oMom3bthw6qfj6D7BYSB08tqB59tlnAfjiiy+4fPmyR46paRr9Omq8O9j8nw14R3H0dNYJ9n/99RcA3bp1S1029Xc4HWMm63ZvaS7z8/Nj+PDhAHz22Wcula3Jrebjf7szL4+zw0XInYsiP3rwwQdZsWIFZcuWZfny5TRt2pQtW7Zk65idzfu5WJifho7ILCLzkp9cId/CXJecnKyCg4MVoKKjo69b58iFqlOnjlNNyVarNXXS6a+++uqm9QsWLEidSsZh+0GbWrY54284nq7T5ORk1adPn+sS3osWLar69Omjpk6dqo4ePeryMRfYp7Sg6VE1/dvv1aPvmN/cHnk7f7ZmOMNms6n69esrQE2aNMmjx7Zabaq7ff7OZkOu3VSRnujoaKVpmgoMDEydKD3xik2Vuyf9lqnY2FgVEhKiALVjxw6nyzR3hS11IuzMOCYHd7R43ui5CWa53p/ldd/qnSLXWO+Vm3Vz4sQJpet6ap7Xs88+m+XNWBlxzMvacpjXXS+lpUt4nr+/Pw0bNgRg48aNqct37NiROqXOlClTCAgIyPJYFouFl19+GYD333//phakX375BTCnlHGoU1WjXYPcaw3y9/fnhx9+YN68eQwbNoxq1aoRGxvLTz/9xKBBg6hUqRK33nqrS2N7ddFtWK7shqDybDjTjRn/gL8fvP5owWzlArNVytEKOn78+Gy1Ft7IYtGY/opGhVLmALSjpmT8DdgxVESbNm0ICzObTb/+w2xRqn8L9Gx9/fYRERE88og5Fsnnn3/udJma1jIfN+wxpxTKiLPDRUhLl8jPIiMjWbFiBU8//TRKKT7++GNq1arFzz//bOY8uaB9Q/Dzg/W7Ie5i/mjtkqBLZMuNXYw2m40hQ4aQkpLC0KFDadGihdPH6tevH1WqVGH//v2pQRZAcnIy8+bNA64PuvKCn58fPXr0YOLEiRw4cIADBw4wadIkevXqRXh4OHv37uWZZ55Jt4s0Pbt27cB2dBwAX/wejs1mTnlTLbLgBl0AXbt2pUqVKhw4cID58+d79NjFi5gzHfj7wfg55iwF6XF0LXbtag51fzVJ8d4sc9vRj6Q/fdTTTz8NwIwZM4iNjXWqPOVKaESWhLiL5rybGZEpgISvCA4O5rPPPmPDhg00bdqUqKgo+vTpQ7du3Thw4IDTxwkP1WhxG1itsHRzDhbYgyToEtly4yCpU6dOZc2aNZQpU4Z33nnHpWP5+/vz0ksvAfDOO++kfutZsWIFMTEx1KxZk1q1anmw9NlXrVo1hg4dyty5c4mOjqZ27drEx8ezfPlyp/ZfuXIlnPueYD/zAzwwAF57pGAHXGDWtSO3a9y4cR4/fos61+d3HTl1feBls9n4+++/gWv5XN/8BSfOQZ0qcE/b9I9bu3ZtOnXqxOXLl5k2bZrT5XHkdWU2SKojpyvDya5lCiBRwDRq1Ig1a9YwadIkIiIi+Oeff6hTpw5jx47lyhXnZhTJb1MCSdAlsiXtdECnT59ODZo+/fTTDL+xZ2bAgAGULVuWrVu3prZEpO1adHby6rwQEBBAr169AFJb5rKycuVKUMn0aGh+Gj91DzdN+F1QPfbYYxQtWpS1a9eyZs0ajx//uX5mIvyFi3D/WEVS8rWL8qZNmzh37hwVKlSgdu3aJCUr3v3OXP/6oxnPAQowYoQ5Sv3nn3/udNdo01rm8TJLpne6pUu6F0UB4ufnx9ChQ9m7dy+PPPIIV69e5Y033qBTp07YbLYs9087JVB+IEGXyJbq1asTHh5OVFQUDz/8MHFxcXTr1o0+ffq4dbxChQrx3HPPAfDuu+9is9mYO3cukP4E197GMR/k/Pnzs8xPUEqxYsUKAMYMKs76yRrjhvlGwAUQGhrKk08+CcCHH37o8eNbLBrfjtKoWNrM73rly2v14QjoO9/Rg8OnYNwPcOwM1K4M97bL/Lh33nknVapU4ciRI/zxxx9OlcWR1/Xf7oy3ySzoSrhsTgkVHAThoU49pRD5SqlSpfj2229ZtmwZ4eHhrF69mmPHjmW5n14TioTBgSg4fNL7W7sk6BLZYrFYUucDXLJkCcHBwUycODFbLVJDhgyhaNGirFq1ig8++IDTp09TqVKlfDHZs67rlC1bluPHj7N5c+ZJBgcPHuT06dOULFmSW2+tSdPaGn5+vhN0AQwfPpzAwEB+++03p+Zn27BhA0uXLnX6+MXCNWaPMfO7PvoR+o6x0eU5G+8ufACan2Pa3s+odr/ita/suVwDMm/lAveGj2hcw3zcvJ/rWtzSymzIiLTDRXhza68Q2dWuXbvUa/3u3Zl8S7Hz99foaP9oWGTkZMk8Q4IukW2OLkaAsWPHUrly5Wwdr3DhwqldOI7xu3r37p0vPmwsFgs9evQAsu5iXLlyJQCtW7fOF68tJ5QpU4b+/fun3sWUmZkzZ9K8eXNuv/12Vq9e7fRztKij8d4Q8//701LzwpyoVYWAYgT4KyqVgRa3wbN9oU9754752GOPERISwpIlS9i5c2eW20cU1qhZEZKSYfuh9LdxtHSll9N1Sia6Fj6kdu3agHNBF0DnJva8LkNauoQPaN3avLe+Xr16/N///Z9Hjvn0008TGhqa2kWX13ctusLRxZhV0OXoWmzbNoOsbR/x/PPPAzB9+nTOnTuX7jZTpkxhwIABqTkeTz75JCkpKU4/x3P94PvXNb56UeOl7ithU0Na+fXmymILR360sGaShY+GW5xuaYyIiGDAgAEATJgwwal9supizKx7MbWlS4Iu4QMcN0w5G3Q58rqWbASr1bsDLwm6RLZ1796d2bNns3DhQqfG5HJG8eLFGTJkCGC2hrgy9EReu/322wkLC2Pr1q0cOXIkw+0cLV1t2rTJpZJ5p1q1atG9e3euXLnCF198cdP6Tz/9lCFDhqCU4q233qJy5cps27bNpbGyNE3jgU4aj3fXOL3rG7i0jZ53tsqyKzEzji7GmTNnOjV8RNNbM0+mzyzokpYu4UtcDbqqltOoWg5iE2Dj3pwsWfZJ0CWyTdM0+vXrR+nSpT163JdeeomOHTvy1ltvYbHkn7dqUFBQ6jAEGY1BderUKQ4ePEhYWBj169fPzeJ5pZEjRwI3Tw307rvvpraeTpgwgddeey21Zen111/n5MmTLj1PekNFuMvV4SOaZNHSldmQESejzUCtXAnf7IYWvsURdO3atcvpAVOvTQmUU6XyDLc+yXRdt+i6/q2u6yvtP9V0Xe+n6/oaXdf/1XW9gn272vb1a3Vd72RfFqbr+lxd11fpuv6yJ1+MKFhKlSrF4sWLeeKJJ/K6KC5zdDH+9ttv6a53tHK1bNkSf3//3CqW12rbti1NmjQhOjqaGTNmoJRi9OjRjBo1Ck3TmDp1amrLUvfu3enRowcJCQmpXZPO2rJlC2fOnKF8+fLcdttt2S63Y7DUmTNnZrlt/WoQ4A+7j5p3I94o05auGPNRWrqELyhbtizh4eHExsZy9uxZp/bpkk/yutxtPmgABBmG0QZ4ExgOPAe0B0bbfwDeAR4D7rBvBzAI+MMwjNZAe13Xy7tZBiG81p133omfn1/qwK43knyu62maltraNX78eEaOHMnbb7+Nn58fM2fOvCnw/vTTTwkODmb27NksWbLE6edJO8G1J25ecNTfvn37svxGXihIo/4toFT6XSBO5XTJGF3CB2ia5nIX4+2NwGKBNTvS/1LjLdwNuk4A6LquARHAOWCnYRhJhmGsBuratytrGMZ+wzDigfO6rpcAWgAL7esXAc3dLbwQ3qpo0aK0a9cOq9XKn3/+edN6yee6We/evalcuTIHDhzgo48+IiAggDlz5vDQQw/dtG3lypV57bXXAHjqqae4evWqU8+RNujyhIiICIoVK0ZiYiKnTp3Kcvum9pHp0+tizKx7UaYAEr7G1TsYIwprNK0FKVZYviUHC5ZN7gZd0YAN2A18ACwD4tOs97M/pv0qGQcUwwzS4m9YJkSBk9FdjLGxsWzfvp3AwECaNm2aF0XzSv7+/qkD4wYFBTF37txM71p9/vnnqVmzJnv37mX8+PFZHj82Npa1a9fi7+9Px44dPVbuatWqAea4a1lpYh+ZfsOe67+JX7lyhStXrhAQEEBwcPBN+8kUQMLXuNrSBdfyurx5SiB3k0nuABINw7hV1/VGwEvApTTrHXNjpB3DPwKIAWKBcOCCfdmRGw+u6/pgYDCYdwh17tzZzWI6Lzk5maioqBx/HpF78rpOmzVrBpitK4cOHSIoKAiAxYsXo5SiQYMGnD9/Ps/Kl5cyqpvu3btz9OhR2rZtS4MGDbKsv7Fjx3L//ffz1ltvcfvtt1OhQoUMt/3999+x2Ww0a9aMhIQEEhISsv06AMqVKweYU2FVrVo1020rFfMHSrJ2RwpRUdeGx3AMlREeHn7TzQEXEzUuJpahUKDiYtwpLsWTL+X1+Sgy5o11U6pUKcDMw3S2bPUqBgLFWbopmaio6BwsXeYiIyMzXJedDF7HPdIXgBJAJV3XA4EmwDb7utO6rlcHzgDFDMOI1nV9HdAF+AroBDx+44ENw5gCTLH/mSsha1RUVKb/KJH/5HWdRkZGUr9+fbZu3crevXtTu7R27doFQMeOHX32PZdZ3bgyJVC/fv347bffmD17Nu+++26mY6OtX78egF69enn0/163bl3mzZtHTExMlsctU0ZROEQRFe2PX6FylClutnxdvHgRgGLFit10jH3HFaAoV0KjfPn8+37J6/NRZMwb68Yx/uPBgwedLlvnIua5si8qgNKly+Hv7313+7rbvbgQKKfr+nLge8zE+Y+B5cDb9h+AUcA04B9gjH3ZVKC7ruurgWWGYXhXeC2EB6XXxSj5XJ41fvx4ChcuzPz58/n9999vWq+U4uzZsx4bKuJGrnQv+vlpNK5p/r5hz7XlmQ8XYT5KEr3wJZUrVyYoKIiTJ0+mnh9ZKRKmUaUsXE2CvcdzuIBucqulyzAMK/BgOqvm3LDdLqDNDcsuAr3ceV4h8puePXvy5ptvMn/+fCZOnMiVK1fYsGEDFouFli1b5nXxCoRy5crx5ptv8uyzz/L0009jGAbHjh3j2LFjHD9+nOPHj3PlyhXAbH2sU6eOR5/flaALzGT6ZZvNvK67W5nfxGVgVCGu5+fnR82aNdm2bRt79uxJTdfISv1b4PAp2LIfbquSw4V0gwwQJEQOatiwIRUqVOD48eMYhsGlS5dISUmhYcOG6bZqCPcMHz6cb775hm3btvHmm2/etL5o0aJUrFiRkSNHenyeS5eDrloaoK67g1GmABLiZrVr12bbtm3s3r3b6aCrwS0av61UbD2oeAjv616UoEuIHKRpGj179uTzzz9n3rx5qcn00rXoWf7+/syePZsJEyZQokQJKlasSIUKFVIfw8LCcuy5y5YtS3BwMOfPnycuLi7LYNoxB+OGPWbXp6ZpWQwXYaa1li3ufR8gQuQkd+5grH+L+bhlf06UKPsk6BIih6UNusqUKQPIoKg5oVatWkycODHXn1fTNKpWrcrOnTs5ePAgjRo1ynT78qWgdDE4EwOHTkK1yCxauhxjdElOl/AxaacDclYDR9B14NqXGm+Sfya0EyKfateuHUWKFGHnzp2pI9E77swRBYMrXYyapt00SKrkdAlxM3dauiqVgSJhcO4CnPbCEXkk6BIihwUEBHDnnXcC5ng4NWrU8Pjk4CJvuZfXBf/tNrsOHUGX3L0oxDXVq1fHYrFw+PDh1JthsqJpGvXN05Gtzp2OuUqCLiFygWPoCJCuxYLI9aDLfHS0dDlyuiSRXohrgoKCqFatGjabjX379jm9nzfndUnQJUQu6NatGwEBAYAk0RdErgZdur17cfN+SE5RGXYvJlxWXEyEQoFml4kQvsadLsYGt5gtyVsPet90QBJ0CZELwsPDeeSRRyhVqhRdu3bN6+IID3M16CoWrnFLJCRehZ2HM87pOpUmid7bEoKFyA2uTnwN11q6th7IiRJlj9y9KEQumTp1qnxwFlCVKlXCYrFw/Phxrl69mjo0SGaa1oIDUbB+V8Yj0ksSvfB17tzBeFtl8PMzR6VPvKoIDvKe6660dAmRSyTgKrgCAwOpWLEiSimOHDni1D6t6prvh99WZdy9KEn0wte5071YKEjj1opgs8GOQzlVMvdI0CWEEB7gahfj/R0hKBD++Q9iLkcAGXcvli3mqVIKkb/cequZALlv3z5SUlKc3i/teF3eRIIuIYTwAHfyuvp1AKXgcviDaJp208j5J6PNROByJaSVVPimwoULU758eZKSkjh8+LDT+9V3JNMf8K5kegm6hBDCA1wNugCG9rQHU6UHEh5REovl+kuy5HQJ4e4djOajtHQJIUQB5E7Q1fw2uLVCEgSWIqBM35vWyxRAQmTvDsZtB8Fm857WLgm6hBDCA9wJujRNo1ezMwAkRvS/ab0jkV5auoQvc+cOxlJFNcoWh4TLcPhUTpXMdRJ0CSGEBziCrkOHDmGz2Zzer1X1o2C9yCV/nd1Hrv9GfkpauoRwq3sRvHO8Lgm6hBDCAwoXLkzJkiW5evUqJ0+edHq/5Cvn4ewsAL6cfy3ounhZkXDZHI0+QkajFz7MEXTt2bMHpZzvKnTMwbjFi5LpJegSQggPcaeL8cKFC3BqCgDf/g2Xr5gfEGmT6GWMN+HLSpYsSfHixUlISCAqKsrp/RpUd9zBmFMlc50EXUII4SFuB12XtlA65AgXLsKcf83lkkQvxDXudDFea+nKiRK5R4IuIYTwEHeCLscUQM0qbgFg8rybW7qE8HXu3MFYowIEB8GxMxCb4B1djBJ0CSGEh7jd0gW0rB5FRBj8txs27VXXpgCSoEsIt+5g9PPTqFPF/H2b86dkjpKgSwghPCQ7QVfJ4qEM6Gou+3K+4tR585t52eKSzyWEu3cwNqhuPm7Z7+kSuUeCLiGE8JDsdC9GREQwpIcZYM1aDHuOmeslp0uIbAwbUc2eTH9QuheFEKJAKV26NKGhocTGxhIbG+vUPo6WroiICGpV1mjXAC4lwh9rzfWS0yUEVKhQgdDQUM6dO8f58+ed3k9auoQQooDSNI2qVasCzrd2pQ26AIbZ52N0DEckLV1CmOfWrbfeCrjW2lXPfgfjziOQnJL3rV0SdAkhhAe52sXoCLqKFCkCwD1toWTEtfXS0iWEyZ07GAuHaFSLhKRk2HM0p0rmPAm6hBDCg1wNutLmdAEEBmg8fpe5LigQihb2eBGFyJfcuYMRro3XtdUL7mCUoEsIITzIlaDLZrOlBl2Oli6AwXdrBAeZHxYyGr0QJvfvYDTPoS3787570T+vCyCEEAWJK0HXxYsXsdlshIaG4u9/7XJcpZzG9ulQOCSnSilE/uP+HYzmoze0dEnQJYQQHuRK0HVj1+J1x4mUFi4h0qpWrRoBAQEcO3aMixcvEhbm3Ezwae9gVErlaeuxdC8KIYQHVaxYET8/P6Kiorhy5Uqm295456IQImP+/v5Ur25GUHv37nV6vwqlICIMouOuTa+VVyToEkIIDwoICKBSpUoopTh8+HCm20rQJYRrHHcw7ty50+l9NE3zmvG6JOgSQggPc7aL8cbhIoQQmWvYsCEAhmG4tJ+35HVJ0CWEEB7mbNCVWU6XEOJmTZs2BWD9+vUu7ee4g3Hrgby9g1GCLiGE8DBXW7ok6BLCOU2aNEHTNLZs2cLVq1ed3s/R0rXlQA4VzEkSdAkhhIdJ96IQOaNIkSLceuutJCUlsXXrVqf3q10Z/P1g33G4lJh3rV0SdAkhhIdJ96IQOcedLsagQI161eC2ynA6JocK5gQJuoQQwsMck14fPnwYq9Wa4XbSvSiE65o1awa4nte1YYrG9m8teToGngRdQgjhYWFhYZQuXZqkpCSioqIy3E66F4VwnSPo+u+//1zaz2LJ+wGHJegSQogc4EwXo7R0CeG6unXrUqhQIfbv309MTB72FbpBgi4hhMgBzgRdktMlhOsCAgJo1KgRABs2bMjj0rjGrbkXdV1vCnxg/7Ms8CfQHXC0o//PMIxFuq7XBr60P89owzAW67oeBswESgILDMN4LzsvQAghvJG0dAmRc5o2bcqaNWtYv349d9xxR14Xx2luBV2GYfwHtAfQdf0r4DegjWEY7W/Y9B3gMeAM8DewGBgE/GEYxle6rv+t6/p3hmGccKv0QgjhpVwJuiSnSwjXuJtMn9ey1b2o67o/0BxYCYTpur5c1/XvdV0vZt+krGEY+w3DiAfO67peAmgBLLSvX2TfXwghCpSsgi6llHQvCuGmtMn0SuXtKPOucKulK43bgeWGYdh0XW9lGMZ5XdcfAd4ARgBpbxWIA4oBEUD8Dcuuo+v6YGAwwPDhw+ncuXM2i5m15OTkTO8yEvmP1Kn38oW6CQ0NBeDAgQOcOHECTbv+zqnExESSkpIICgri/PnzeVHEXOULdZ5f5ce6CQgIoFixYkRHR7N27VoqVaqU10VKFRkZmeG67AZdfYBZAIZhOK4aPwFP2H+3pdk2AogBYoFw4IJ92ZEbD2oYxhRgiv3PXAlho6KiMv1HifxH6tR7+ULdlCtXjsKFCxMfH4/FYqFcuXLXrT916hRgdi0W9P8F+Ead51f5tW5atGjBH3/8wbFjx2jZsmVeF8cpbncv2rsWWwArdF0P1HU9yL6qLeCY3ei0ruvVdV0PB4oZhhENrAO62Nd3Ata6WwYhhPBWmqal3mHVsmVLVq1add16SaIXInvyY15XdnK6OgArDMOwAUWBNbqurwBGAmPs24wCpgH/pFk2Feiu6/pqYJlhGPmrTVMIIZw0bdo0dF3n6NGjtGvXjjFjxpCSkgLIcBFCZJc70wHlNS0fJKBJ96Jwi9Sp9/KluklKSmLMmDG8//77KKVo0aIFs2bNYu/evXTr1o3OnTuzcOHCrA+Uz/lSnec3+bVuYmJiKF68OEFBQSQkJBAQEJDXRXLIcOh7GRxVCCFyUGBgIO+++y5LliwhMjKStWvX0qBBA7799ltAWrqEcFexYsWoXr06V69eZdu2bXldHKdI0CWEELmgQ4cObN26ld69exMfH8/s2bMBCbqEyA5352HMKxJ0CSFELilevDg///wzU6dOJSQkBIASJUrkcamEyL/yWzJ9doeMEEII4QJN03jiiSdo06YN06dPZ+jQoXldJCHyrfyWTC9BlxBC5IGaNWvy7rvv5nUxhMjX6tevT2BgIHv27CEuLs7rp9SS7kUhhBBC5EtBQUE0bNgQgA0bNuRxabImQZcQQggh8i1HF2N+SKaXoEsIIYQQ+VZ+SqaXoEsIIYQQ+VbaoMvbB3yXoEsIIYQQ+Va1atUoVqwYZ86c4fjx43ldnExJ0CWEEEKIfEvTNKeHjkhISMiNImVIgi4hhBBC5GtZBV2xsbH079+fNm3akJSUlJtFu44EXUIIIYTI1zKbDuiff/6hbt26fPfdd+zbt4+NGzfmdvFSSdAlhBBCiHzN0dK1ceNGUlJSALMrcejQoXTt2pWoqChatGjB1q1badGiRZ6VU0akF0IIIUS+VqJECapWrcqhQ4fYuXMncXFxPProoxw+fJjAwEDefPNNRo4ciZ+fX56WU4IuIYQQQuR7zZo149ChQwwaNAjDMFBK0aBBA2bMmEHdunXzuniAdC8KIYQQogBw5HVt2LABi8XC6NGjWb9+vdcEXCAtXUIIIYQoADp37ozFYqFmzZp8++23NGnSJK+LdBMJuoQQQgiR79WuXZtTp05RrFgx/P29M7zxzlIJIYQQQrioVKlSeV2ETElOlxBCCCFELpCgSwghhBAiF0jQJYQQQgiRCyToEkIIIYTIBRJ0CSGEEELkAgm6hBBCCCFygQRdQgghhBC5QIIuIYQQQohcIEGXEEIIIUQukKBLCCGEECIXSNAlhBBCCJELJOgSQgghhMgFmlIqr8sghBBCCFHgSUuXEEIIIUQukKBLCCGEECIXSNAlhBBCCJELJOgSQgghhMgFEnQJIYQQQuQCCbqEEEIIIXKBTwZduq5reV0GIXyFnG9CeA85H/OWzwRduq7X0nV9nK7r4YZhyOBkBYCu69XS/C4XEi8i55vvkfPRe8n56D0KfNCl67qfruuvAzOBxYZhxOd1mUT26Lqu6bo+Ctiv6/oY+2K5yHsBOd98j5yP3kvOR+9T4IMuoAwQDHwB+Om6/rCu67XzuEwie/yA/4D6QCdd18sZhmHTdd0X3s/eTs433yPno/eS89HLFMiTQtf1O3RdfxHAMIwoYCVwLzAYKAW8r+t64zwsonCRrutddV2fqev6k0AhwzAWG4axHfgLGGvfTJrN84Ccb75HzkfvJeejdytwQZeu63dinvTtdF1/2L54DfCqYRi9DMP4CFgE3G7fXprBvZyu68HAQGAWUBr4n6PeDMN4B6il63pjwzCUruv+eVhUnyPnm++R89F7yfno/Qpc0AVsAjoCzwJ367pexDCMC8CONG+w1UBNAEkqzBeqA4mGYfwNvA2EA13T1OdozAv/k0C9PCqjr5LzzffI+ei95Hz0cgUu6DIM47RhGJeAw8Au4En7KgX467reH5gMrJYo37ul+fa8DSij6/rdhmEkA3OBPmkuGP5AW6AOsCdPCutj0tSNnG8+IG3dyfnoXW6oGzkfvZymVP4MdHVdbwP0AX4AdhmGEafreqBhGElptqmJ+a3rGcMwzuu6fgvmm/A7wzA25UnBRYbsdXo/MBvYbhjGBV3XQwzDuKzr+u2YTeQd7dv+AXxoGMZSXdd7AUcMw9iSV2Uv6DKoGznfCjBd15sDEfYWLceHe6BhGFflfMxbGdSNxTAMa5pt5Hz0Qvkq6NJ1XbPnCTQFxgA/AmWBqoZhDLZvUxYINgzjkP3vl4Cngb8Nw3gij4ousqDremXgI+APzDotaxjGU/Z15YCLwDhgP/AtZrfGh4Zh7M+TAvuQLOpGzrcCSNf1QcA7mNfYmYZhrEuzTs7HPJRF3ZQBQuR89F75rXuxpP2xMHDBMIxvDcN4D2iv63onXdcbABuARgC6rjfB7N/+Qt5wXq8s5je1rw3DeBszGbeDruv1gY1AU+BNIAXzIn9aLvC5JrO6kfOtYFoItAaWAbqu62GYv3QB1iPnY17KrG7WIeejV8sXLV26rrcHXgeOAT8Du4HHgSWGYSzRdf07zD7rYZi3L0fb94sELtkTCYUX0XX9ceAeYJhhGMfty6YAc+x12gMYBPTD/OYWnWbfQoZhXMmLcvsCd+tGzrf8K706ty9vAbQHthmG8Yeu6yUwA/CzabaR8zEHuVs3cj56p/wSdH2O2bVxDOgBWDHvwHgLiAG2AUWByYZh7NV13d8wjJS8Kq/InK7r4cCXQDHgO2AOZp3eCzQExhiGkaTr+jfARMMwNui67gfY5G6bnOVm3cj5lo+lV+eOXD1d14Mwc/nCga+BJMMwUnRdDwBS5HzMWW7WzXW5lsK7eHX3Ypq7K45ifqPeCfyEOfLxccw33EvA55jN23sB5APAe9nz8uINw3gAGII5Xswt9gTQVZjvyaH2sYCSMVs1MQzDKhf4nJWNupHzLZ/KqM4d6w3DuAr8CRQHfgFete+TLOdjzspG3UjA5cW8LujSdT3U/uhImrcA54AIXdeLG4ZxAHOE3Tvszaj1MRMKHdG/3AbrZdKpU8dwA0eAnUAv+12KJ4FPMOv0NyAauJQXZfYVUje+x4k67+nIE7JrCPTEzN/7nwRbOUfqpuDzmu5FXde7Aw8BJ4HxwCnHG0jX9dsw31hbDMP4U9f1BzD7rmfput4WOGZ/UwovklmdptmmNPAG5tgxfsAB4DIQahhGXK4W2IdI3fgeN+pcw6zzcpi5QVG5WmAfInXjO7xiigZ7fsAAzG7CmphNqX8C63Vdr455p8xhoKOu6xWAzsBSAMMwVuRJoUWmsqjTGkAH4CfDMM7oun4MmA8sB142DCMekA/1HCJ143vcrPMVwIuGYezLo2L7BKkb35Jn3Yu6rgfpuh5h/7MGZmS/HJgOVAS66breEfO25IuGYfwATATKA38YhvFF7pdaZMaFOh0DJBiGEWPPD2oFvGsYxiP2bizhYVI3vscDdd7fMIxTeVD0Ak/qxnflSdCl6/r9mFMU/A/AniAfqet6P3sS4DkgGPNuxUcNw/jJvt1B4HXDML7Ji3KLjLlYpwMNw/jevl0i0MswjMl5U/KCT+rG90idey+pG9+W60GXruuFgBDMcX4cfdkAHwPVdV3/C7O/OhIoZphTTvg59pdEQe/jRp0m6brulyZJVO5+yyFSN75H6tx7Sd2IXEmk13W9EubQDn8AWw3DOGFffhfwADDAfls6ujkrepyu628D39hbt4SXkTr1XlI3vkfq3HtJ3Yi0crylS9f1QMx+6eNAaSBtLta/wFngMfu2GnCLrusLgTh5w3knqVPvJXXje6TOvZfUjbhRjrV06bp+L1AKc56orw3DaG9f/jWw2zCMD+1vskqYfdvrMe/YiAL8DMO4mCMFE26TOvVeUje+R+rce0ndiIx4POjSzfmfvgISgTOYCYO3A/8YhvGNrusVMQczvdswjHO6rtcD5mImDQ6RW2C9j9Sp95K68T1S595L6kZkJSe6F23AV4Y5dcHbQG3MKQrq6Lpe3TCMY8A/wB26rvsDjYHnDcPoIG84ryV16r2kbnyP1Ln3kroRmcqJwVFjMZtUMQwjWtf1UkA8sB94HhiKOXnnVvudGDL8g/eTOvVeUje+R+rce0ndiEzlZE6XBhQBvjcM4077simY448EYt4ymyBDQOQfUqfeS+rG90idey+pG5GRnJ4GyB9Ypet6Y6Ar8DWwzzCM2Bx+XpFzpE69l9SN75E6915SN+ImOTpOl67rdwC/Y94aO8swjJk59mQiV0idei+pG98jde69pG5EenK6pes8MAr4zD69gcj/pE69l9SN75E6915SN+ImOR10bTQMw8jh5xC5S+rUe0nd+B6pc+8ldSNukivTAAkhhBBC+Lpcn/BaCCGEEMIXSdAlhBBCCJELJOgSQgghhMgFEnQJIYQQQuSCnL57UQghcoWu65WBw/Y/rwLngBXAa4ZhHM5kvxDgReCIYRjTc7iYQggfJi1dQoiCZjPmHHf/Ag8Ca+xz4GUkBBgDPJrzRRNC+DJp6RJCFDQn7S1W03Vdv4o5z90QXdfrAZ0w5787BLxqGMZcwDGWUjtd1xUwFnjH/vMAEAosAp40DONcrr4SIUSBIi1dQoiC7C/7Y31gA2Y34iv2ZTN0XS+EOWo4wG7MIOtn+zbPY07j8gnQDZiUO0UWQhRU0tIlhCjINPujAmpjBlWBadZXBhbafz9rGMZsAF3Xv7EvG5Jm2y45V0whhC+QoEsIUZDdYX/cBrwJLMFsuRoK3AUUwgzIbqQBKUB3wGpfJj0DQohskaBLCFHQlNN1/VGgHTAAOA0cs68LwWzdapVm+3jABtyi6/pDwCrMbsXG9v0XY7aSVeFaq5gQQrhMvrkJIQqahsCXmEnz3wMtgO+A2UBdoDfwj2NjwzCSgXFAhH27NsC79mVtgM8xc7qW59YLEEIUTDLhtRBCCCFELpCWLiGEEEKIXCBBlxBCCCFELpCgSwghhBAiF0jQJYQQQgiRCyToEkIIIYTIBRJ0CSGEEELkAgm6hBBCCCFygQRdQgghhBC54P8BRclQnwlorLIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred = scaler.inverse_transform(TCN_ts_tpred)\n",
    "actual = scaler.inverse_transform(ts_ttest)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "actual.plot(label=\"actual\")\n",
    "pred.plot(label=\"pred\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f20a6b57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAPE: 3.060733526945114\n",
      "rmse: 361.75\n",
      "mae: 256.37766\n"
     ]
    }
   ],
   "source": [
    "mape_error = mape(actual,pred)\n",
    "rmse_error = rmse(actual,pred)\n",
    "mae_error = mae(actual,pred)\n",
    "print(\"MAPE:\",mape_error)\n",
    "print(\"rmse:\",rmse_error)\n",
    "print(\"mae:\", mae_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f16fafaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "#TCN model with window size 7\n",
    "model_TCN_2 = TCNModel(\n",
    "        input_chunk_length=7,\n",
    "        output_chunk_length=1, \n",
    "        n_epochs=EPOCH,\n",
    "        dropout=DROP,\n",
    "        dilation_base=2,\n",
    "        weight_norm=True,\n",
    "        batch_size=16,\n",
    "        kernel_size=5,\n",
    "        num_filters=3,\n",
    "        optimizer_kwargs={'lr': LEARN},\n",
    "        log_tensorboard=True,\n",
    "        force_reset=True,\n",
    "        random_state=42)"
   ]
  },
  {
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   "id": "6ab0e12c",
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       "<darts.models.forecasting.tcn_model.TCNModel at 0x15c61cad888>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# training: load a saved model or (re)train\n",
    "model_TCN_2.fit(  series=ts_ttrain, \n",
    "            past_covariates=cov_t,\n",
    "                val_series=ts_ttest,  \n",
    "          val_past_covariates=cov_t,\n",
    "                verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "9b894e0c",
   "metadata": {},
   "outputs": [
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       "model_id": "04ef79ad5fc7422ea2e6bd7ddef9ba20",
       "version_major": 2,
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      "text/plain": [
       "Predicting: 39it [00:00, ?it/s]"
      ]
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   ],
   "source": [
    "# testing: generate predictions\n",
    "TCN_2_ts_tpred = model_TCN_2.predict(   n=len(ts_ttest),  \n",
    "                            past_covariates=cov_t, \n",
    "                            verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "9722e7ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred = scaler.inverse_transform(TCN_2_ts_tpred)\n",
    "actual = scaler.inverse_transform(ts_ttest)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "actual.plot(label=\"actual\")\n",
    "pred.plot(label=\"pred\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c77de5db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAPE: 3.4834295511245728\n",
      "rmse: 406.7398\n",
      "mae: 289.645\n"
     ]
    }
   ],
   "source": [
    "mape_error = mape(actual,pred)\n",
    "rmse_error = rmse(actual,pred)\n",
    "mae_error = mae(actual,pred)\n",
    "print(\"MAPE:\",mape_error)\n",
    "print(\"rmse:\",rmse_error)\n",
    "print(\"mae:\", mae_error)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "333b3696",
   "metadata": {},
   "source": [
    "# NBEATS Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b5ba985d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#NBEATSModel - 7 day window\n",
    "NBEATS_model = NBEATSModel(    input_chunk_length=7,\n",
    "                        output_chunk_length=1, \n",
    "                        num_stacks=32,\n",
    "                        layer_widths=16,\n",
    "                        batch_size=64,\n",
    "                        n_epochs=100,\n",
    "                        nr_epochs_val_period=1,  \n",
    "                        log_tensorboard=True,\n",
    "                        generic_architecture=True, \n",
    "                        random_state=42,\n",
    "                        force_reset=True,\n",
    "                        save_checkpoints=True,\n",
    "                        torch_device_str=\"cuda\"\n",
    "                    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "6b6058c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation sanity check: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "33599f2f84e5441984b92d81b1e97e48",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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       "<darts.models.forecasting.nbeats.NBEATSModel at 0x15c624aa988>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# training: load a saved model or (re)train\n",
    "NBEATS_model.fit(  series=ts_ttrain, \n",
    "            past_covariates=cov_t,\n",
    "                val_series=ts_ttest,  \n",
    "          val_past_covariates=cov_t,\n",
    "                verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "84c486fa",
   "metadata": {},
   "outputs": [
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       "model_id": "d76faa4714bf475b9910a35a76468b25",
       "version_major": 2,
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      "text/plain": [
       "Predicting: 10it [00:00, ?it/s]"
      ]
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   ],
   "source": [
    "# testing: generate predictions\n",
    "ts_tpred = NBEATS_model.predict(   n=len(ts_ttest),  \n",
    "                            past_covariates=cov_t, \n",
    "                            verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8ba12d3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred = scaler.inverse_transform(ts_tpred)\n",
    "actual = scaler.inverse_transform(ts_ttest)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "actual.plot(label=\"actual\")\n",
    "pred.plot(label=\"pred\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "9a3269ba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAPE: 3.351040929555893\n",
      "rmse: 370.6418\n",
      "mae: 282.1408\n"
     ]
    }
   ],
   "source": [
    "mape_error = mape(actual,pred)\n",
    "rmse_error = rmse(actual,pred)\n",
    "mae_error = mae(actual,pred)\n",
    "print(\"MAPE:\",mape_error)\n",
    "print(\"rmse:\",rmse_error)\n",
    "print(\"mae:\", mae_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "306a437f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#NBEATSModel - 30 day window\n",
    "NBEATS_model_30 = NBEATSModel(    input_chunk_length=30,\n",
    "                        output_chunk_length=1, \n",
    "                        num_stacks=32,\n",
    "                        layer_widths=16,\n",
    "                        batch_size=64,\n",
    "                        n_epochs=100,\n",
    "                        nr_epochs_val_period=1,  \n",
    "                        log_tensorboard=True,\n",
    "                        generic_architecture=True, \n",
    "                        random_state=42,\n",
    "                        force_reset=True,\n",
    "                        save_checkpoints=True,\n",
    "                        torch_device_str=\"cuda\"\n",
    "                    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "9df07b37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation sanity check: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1ccd033ddcba4f78be62f53366e0aeb2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
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       "Validating: 0it [00:00, ?it/s]"
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      "text/plain": [
       "<darts.models.forecasting.nbeats.NBEATSModel at 0x15c8be60d88>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# training: load a saved model or (re)train\n",
    "NBEATS_model_30.fit(  series=ts_ttrain, \n",
    "            past_covariates=cov_t,\n",
    "                val_series=ts_ttest,  \n",
    "          val_past_covariates=cov_t,\n",
    "                verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "3e4f76a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "699b648e67c848bda7ac6ba5252596a7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: 10it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# testing: generate predictions\n",
    "ts_tpred_30 = NBEATS_model_30.predict(   n=len(ts_ttest),  \n",
    "                            past_covariates=cov_t, \n",
    "                            verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "87eaf1b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred = scaler.inverse_transform(ts_tpred_30)\n",
    "actual = scaler.inverse_transform(ts_ttest)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "actual.plot(label=\"actual\")\n",
    "pred.plot(label=\"pred\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "fd2b8607",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAPE: 5.4858531802892685\n",
      "rmse: 544.2699\n",
      "mae: 466.6752\n"
     ]
    }
   ],
   "source": [
    "mape_error = mape(actual,pred)\n",
    "rmse_error = rmse(actual,pred)\n",
    "mae_error = mae(actual,pred)\n",
    "print(\"MAPE:\",mape_error)\n",
    "print(\"rmse:\",rmse_error)\n",
    "print(\"mae:\", mae_error)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "14c43950",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv(\"timeseries_April24.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c7ad6b03",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.drop(['new_senti'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bae59e47",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.to_csv(\"Price_dataset.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29be6605",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}