LSTM Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "97b58bb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas_datareader as data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "35cd5180",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</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>2022-12-23</th>\n",
       "      <td>87.110001</td>\n",
       "      <td>89.550003</td>\n",
       "      <td>87.070000</td>\n",
       "      <td>89.230003</td>\n",
       "      <td>89.230003</td>\n",
       "      <td>23003000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-27</th>\n",
       "      <td>88.800003</td>\n",
       "      <td>88.940002</td>\n",
       "      <td>87.010002</td>\n",
       "      <td>87.389999</td>\n",
       "      <td>87.389999</td>\n",
       "      <td>20097300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-28</th>\n",
       "      <td>86.980003</td>\n",
       "      <td>88.040001</td>\n",
       "      <td>85.940002</td>\n",
       "      <td>86.019997</td>\n",
       "      <td>86.019997</td>\n",
       "      <td>19523200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-29</th>\n",
       "      <td>86.620003</td>\n",
       "      <td>88.849998</td>\n",
       "      <td>86.610001</td>\n",
       "      <td>88.449997</td>\n",
       "      <td>88.449997</td>\n",
       "      <td>23333500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-12-30</th>\n",
       "      <td>86.980003</td>\n",
       "      <td>88.300003</td>\n",
       "      <td>86.570000</td>\n",
       "      <td>88.230003</td>\n",
       "      <td>88.230003</td>\n",
       "      <td>23986300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open       High        Low      Close  Adj Close    Volume\n",
       "Date                                                                       \n",
       "2022-12-23  87.110001  89.550003  87.070000  89.230003  89.230003  23003000\n",
       "2022-12-27  88.800003  88.940002  87.010002  87.389999  87.389999  20097300\n",
       "2022-12-28  86.980003  88.040001  85.940002  86.019997  86.019997  19523200\n",
       "2022-12-29  86.620003  88.849998  86.610001  88.449997  88.449997  23333500\n",
       "2022-12-30  86.980003  88.300003  86.570000  88.230003  88.230003  23986300"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas_datareader import data as pdr\n",
    "import yfinance as yf\n",
    "yf.pdr_override()\n",
    "\n",
    "df = pdr.get_data_yahoo(\"GOOGL\", start=\"2009-01-01\", end=\"2023-01-01\")\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4e730810",
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.722723</td>\n",
       "      <td>8.053554</td>\n",
       "      <td>7.645145</td>\n",
       "      <td>8.041041</td>\n",
       "      <td>144275580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.033033</td>\n",
       "      <td>8.289289</td>\n",
       "      <td>7.882883</td>\n",
       "      <td>8.209459</td>\n",
       "      <td>195364440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.332833</td>\n",
       "      <td>8.528529</td>\n",
       "      <td>8.167918</td>\n",
       "      <td>8.359860</td>\n",
       "      <td>256750992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8.216216</td>\n",
       "      <td>8.281031</td>\n",
       "      <td>7.976727</td>\n",
       "      <td>8.058308</td>\n",
       "      <td>179600220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.964965</td>\n",
       "      <td>8.137888</td>\n",
       "      <td>7.941441</td>\n",
       "      <td>8.137888</td>\n",
       "      <td>143883972</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Open      High       Low     Close     Volume\n",
       "0  7.722723  8.053554  7.645145  8.041041  144275580\n",
       "1  8.033033  8.289289  7.882883  8.209459  195364440\n",
       "2  8.332833  8.528529  8.167918  8.359860  256750992\n",
       "3  8.216216  8.281031  7.976727  8.058308  179600220\n",
       "4  7.964965  8.137888  7.941441  8.137888  143883972"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.reset_index()\n",
    "df = df.drop(['Date', 'Adj Close'], axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0cf9e238",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3524, 5)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2cd24a64",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Moving Average for 100\n",
    "ma100 = df.Close.rolling(100).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "eef5f1c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a140cb1b20>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lvoiI3AgFPyJSpPXp04d//vmH9957j59++okZM2bg7e1Nw4YNmTJlCo8++mia95YtW5aNGzfy/PPPM336dGJjY2nYsCE///wzvXr1srYbNmwYc+fO5f333ycqKooKFSowcuRIxo8f75LPULFiRdasWcPIkSN58803KV26NE899RT+/v6MHDkyT6Z0ubu788svvzBp0iTmzZvHggULCA4Otn6Zt5g5cybNmjXj008/5cUXX8TDw4PKlStz33330aZNm0y9y83NjXvvvZf33nuP3r17Ow1mcvp3kBPq1q3L33//zcSJE5k9ezYXL16kTJkyNGnShFdeeSXD+0uXLs3GjRv55JNP+P7773nppZeIj4+nUqVK9OnTJ8MNfJ259dZb+fLLL3n77bcZNWoUVapU4Z133uH48eOZDn4AvvjiC0aMGMEzzzxDfHw8r776qoIfEckVJnNOrJoVEZE8N2rUKD799FOioqIytZhdRESksNOaHxGRQuDatWsO1xcvXuSbb76hbdu2CnxERESu07Q3EZFCoFWrVnTs2JE6deoQHh7Ol19+SWRkJC+//HJed01ERCTfUPAjIlII9OzZkx9//JHPPvsMk8lE06ZN+fLLL2nfvn1ed01ERCTf0JofEREREREpErTmR0REREREigQFPyIiIiIiUiQUyDU/ycnJnDlzhmLFiuXJ5n0iIiIiIpI/mM1mrl69Svny5a0bjKelQAY/Z86coWLFinndDRERERERySdOnTpFhQoV0m1TIIMfy+7dp06dIjAwMI97IyIiIiIieSUyMpKKFStaY4T0FMjgxzLVLTAwUMGPiIiIiIhkajmMEh6IiIiIiEiRoOBHRERERESKBAU/IiIiIiJSJBTINT+ZlZSUREJCQl53QyTXeHp64u7untfdEBEREcmXCmXwYzabCQsL48qVK3ndFZFcV7x4cUJCQrQHloiIiEgKhTL4sQQ+ZcqUwc/PT18CpUgwm83ExMRw7tw5AMqVK5fHPRIRERHJXwpd8JOUlGQNfIKDg/O6OyK5ytfXF4Bz585RpkwZTYETERERsVPoEh5Y1vj4+fnlcU9E8obl777Wu4mIiIg4KnTBj4WmuklRpb/7IiIiIs4V2uBHRERERETEnoIfyZcmTJhA48aNXfY8k8nE4sWLXfY8ERERESl4FPzkE0OHDsVkMmEymfDy8qJ69eq89tprJCYmuvQ9x48ft77HZDJRrFgx6tWrx1NPPcWhQ4dc+q70LFiwgI4dOxIUFERAQAANGzbktdde49KlSznyvrNnz9KjR48cebaIiIiIFAwKfvKR7t27c/bsWQ4dOsTo0aOZMGEC77333g09KykpieTk5DTrf//9d86ePcuuXbt488032b9/P40aNWLVqlU32v1Me+mll7jnnnu4+eab+fXXX9mzZw9Tpkxh165dfPPNNznyzpCQELy9vXPk2SIiIiJSMCj4yUe8vb0JCQmhUqVKPPHEE3Tp0oUlS5YAEBcXx5gxY7jpppvw9/enZcuW/PHHH9Z7Z8+eTfHixVmyZAl169bF29ubkydPpvmu4OBgQkJCqFq1Kn379uX333+nZcuWPPzwwyQlJQFw5MgR+vbtS9myZQkICODmm2/m999/tz7jtddeo379+qme3bhxY15++WWn792yZQtvvvkmU6ZM4b333qN169ZUrlyZrl27smDBAoYMGeL0vuTkZF577TUqVKiAt7c3jRs3Zvny5db6+Ph4hg8fTrly5fDx8aFSpUq89dZb1nr7aW+W0a+FCxfSqVMn/Pz8aNSoEZs2bXJ45+eff07FihXx8/Pjjjvu4P3336d48eJp/pmKiIiISP5WNIIfsxmio3P/x2zOVrd9fX2Jj48HYPjw4WzatInvvvuOf/75h7vuuovu3bs7TFWLiYnhnXfe4YsvvmDv3r2UKVMm0+9yc3Pj6aef5sSJE2zbtg2AqKgoevbsyapVq9ixYwfdu3end+/e1qDqoYceYv/+/WzdutX6nB07dvDPP//w4IMPOn3P3LlzCQgI4Mknn3Ran1Zw8eGHHzJlyhQmT57MP//8Q7du3ejTp4/180+bNo0lS5bwww8/cODAAebOnUvlypXT/cwvvfQSY8aMYefOndSsWZN7773XOs1ww4YNPP744zz99NPs3LmTrl27MmnSpHSfJyIiIiL5W6Hb5NSpmBgICMj990ZFgb9/lm8zm82sWrWKFStWMGLECE6ePMmsWbM4efIk5cuXB2DMmDEsX76cWbNm8eabbwLGvi6ffPIJjRo1uqHu1q5dGzBGRlq0aEGjRo0cnvX666+zaNEilixZwvDhw6lQoQLdunVj1qxZ3HzzzQDMmjWLDh06ULVqVafvOHToEFWrVsXT0zNLfZs8eTLPP/88AwcOBOCdd95hzZo1TJ06lY8//piTJ09So0YN2rZti8lkolKlShk+c8yYMfTq1QuAiRMnUq9ePQ4fPkzt2rWZPn06PXr0YMyYMQDUrFmTjRs3snTp0iz1W0RERETyj6Ix8lNALF26lICAAHx8fOjRowf33HMPEyZMYPfu3SQlJVGzZk0CAgKsP3/++SdHjhyx3u/l5UXDhg1v+P3m6yNVln1ioqKiGDNmDHXq1KF48eIEBASwf/9+h+l0jz76KN9++y2xsbHEx8czb948HnrooQzfkRWRkZGcOXOGNm3aOJS3adOG/fv3A0bCiJ07d1KrVi1GjhzJb7/9luFz7f+sypUrB8C5c+cAOHDgAC1atHBon/JaRERERAqWojHy4+dnjMLkxXuzoFOnTsyYMQMvLy/Kly+Ph4fx64mKisLd3Z1t27bh7u7ucE+A3YiWr69vtja4tAQSVapUAYyRkZUrVzJ58mSqV6+Or68vAwYMsE7FA+jduzfe3t4sWrQILy8vEhISGDBgQJrvqFmzJuvXrychISHLoz/padq0KceOHePXX3/l999/5+6776ZLly78+OOPad5j/37Ln1t6SSJEREREMiU5GZ56Cho1gscfz+veiJ2iEfyYTDc0/Sy3+fv7U7169VTlTZo0ISkpiXPnztGuXbsceXdycjLTpk2jSpUqNGnSBDDWvQwdOpQ77rgDMIKw48ePO9zn4eHBkCFDmDVrFl5eXgwcOBBfX9803zNo0CCmTZvGJ598wtNPP52q/sqVK6nW/QQGBlK+fHk2bNhAhw4drOUbNmxwGI0JDAzknnvu4Z577mHAgAF0796dS5cuUbJkyaz+cVCrVi2HtUxAqmsRERERp9asgZkzjXMFP/lK0Qh+CriaNWsyePBgHnjgAaZMmUKTJk04f/48q1atomHDhtZ1K1lx8eJFwsLCiImJYc+ePUydOpUtW7awbNky6+hSjRo1WLhwIb1798ZkMvHyyy87HRl55JFHqFOnDmAEJOlp2bIlY8eOZfTo0Zw+fZo77riD8uXLc/jwYWbOnEnbtm2dBkXPPfccr776KtWqVaNx48bMmjWLnTt3MnfuXADef/99ypUrR5MmTXBzc2P+/PmEhITccHa2ESNG0L59e95//3169+7N6tWr+fXXX7M1siYiIiJFQFgY/Pyz7ToxETz0lTu/0G+igJg1axZvvPGGNWgoVaoUt9xyC7fffvsNPa9Lly4A+Pn5UalSJTp16sRnn33mMPL0/vvv89BDD9G6dWtKlSrF888/T2RkZKpn1ahRg9atW3Pp0iVatmyZ4bvfeecdmjVrxscff8zMmTNJTk6mWrVqDBgwIM1U1yNHjiQiIoLRo0dz7tw56taty5IlS6hRowYAxYoV49133+XQoUO4u7tz880388svv+DmdmPL2tq0acPMmTOZOHEi48ePp1u3bjzzzDN89NFHN/Q8ERERKSLuugvWr7ddX74MpUvnXX/Egcl8IyvQ81hkZCRBQUFEREQQGBjoUBcbG8uxY8eoUqUKPj4+edTDosVsNlOjRg2efPJJnn322bzuTo559NFH+ffff1m3bl1edyVd+t+AiIhIHomPh5Sbqh89CtfXU0vOSC82SEnZ3iRbzp8/z0cffURYWFiae/sUVJMnT2bXrl0cPnyY6dOnM2fOnDRHpkRERERwlmhpxQpj/fkPPxhT4FKOOxw7Brt3507/RNPeJHvKlClDqVKl+OyzzyhRokRed8eltmzZwrvvvsvVq1epWrUq06ZN45FHHsnrbomIiEh+NXhw6rInnjCO99wDN90EN98MixbZ6i17I4aFQdmyOd/HIk7Bj2RLAZw1mWk//PBDXndBRERECorMfCc6fdr4cebwYQU/uUDT3kREREREsstuH8QMOdtXUHsN5goFPyIiIiIi2RUbm/m2TrLnKvjJHQp+RERERESy69q1zLddsyZ1WSFeSpCfKPgREREREcmuTZsy3/bwYeNoH/AkJbm2P+KUgh8RERERkeyaPDnzbc+dM472wY+mveUKBT8iIiIiIplx9SokJDivu+uurD0HHAMeTXvLFQp+ijCTycTixYvzuhsiIiIi+d/58xAUBJ07O6/397edlyoFn3yS9rP++MM42gc/GvnJFQp+8omhQ4diMpl4/PHHU9U99dRTmEwmhg4d6tJ3nj17lh49erj0mekZNmwY7u7uzJ8/P9femVfOnj3LoEGDqFmzJm5ubowaNcppu/nz51O7dm18fHxo0KABv/zyi0O92WzmlVdeoVy5cvj6+tKlSxcOHTqUC59AREREHPz4ozE6s26d83r7NTt16kBISNrPOnDAOGraW65T8JOPVKxYke+++45rdtlCYmNjmTdvHqGhoS5/X0hICN7e3i5/rjMxMTF89913jB07lq+++irH3xeflVz7OSAuLo7SpUszfvx4GjVq5LTNxo0buffee3n44YfZsWMH/fr1o1+/fuzZs8fa5t1332XatGnMnDmTzZs34+/vT7du3YjNSjpNERERyb4tW2znrVoZwZA9++DFxyfjaWwxMRr5yQMKfvKRpk2bUrFiRRYuXGgtW7hwIaGhoTRp0sShbVxcHCNHjqRMmTL4+PjQtm1btm7dCkBycjIVKlRgxowZDvfs2LEDNzc3Tpw4AThOezt+/Dgmk4mFCxfSqVMn/Pz8aNSoEZtSZC75/PPPqVixIn5+ftxxxx28//77FC9ePMPPNn/+fOrWrcsLL7zA2rVrOXXqFACRkZH4+vry66+/OrRftGgRxYoVIyYmBoBTp05x9913U7x4cUqWLEnfvn05fvy4tf3QoUPp168fkyZNonz58tSqVQuAb775hubNm1OsWDFCQkIYNGgQ5yyLDK9bsmQJNWrUwMfHh06dOjFnzhxMJhNXrlyxtlm/fj3t2rXD19eXihUrMnLkSKKjo9P8vJUrV+bDDz/kgQceICgoyGmbDz/8kO7du/Pcc89Rp04dXn/9dZo2bcpHH30EGKM+U6dOZfz48fTt25eGDRvy9ddfc+bMGU1XFBERyW3Xvz8B8Ndfqdf42I/8eHtnHPxcuKDgJw8UieDHbDYTHR+d6z/mG1i49tBDDzFr1izr9VdffcWDDz6Yqt3YsWNZsGABc+bMYfv27VSvXp1u3bpx6dIl3NzcuPfee5k3b57DPXPnzqVNmzZUqlQpzfe/9NJLjBkzhp07d1KzZk3uvfdeEhMTAdiwYQOPP/44Tz/9NDt37qRr165MmjQpU5/ryy+/5L777iMoKIgePXowe/ZsAAIDA7n99tud9rVfv374+fmRkJBAt27dKFasGOvWrWPDhg0EBATQvXt3hxGeVatWceDAAVauXMnSpUsBSEhI4PXXX2fXrl0sXryY48ePO0wfPHbsGAMGDKBfv37s2rWLYcOG8dJLLzn05ciRI3Tv3p3+/fvzzz//8P3337N+/XqGDx+eqc+elk2bNtGlSxeHsm7dulkDzmPHjhEWFubQJigoiJYtW6YKSkVERCSH2f2jqFP2wUtmgp+kJE17ywMeed2B3BCTEEPAWwG5/t6ocVH4e/ln3NDOfffdx7hx46yjMxs2bOC7777jD8vCOCA6OpoZM2Ywe/Zs65qdzz//nJUrV/Lll1/y3HPPMXjwYKZMmcLJkycJDQ0lOTmZ7777jvHjx6f7/jFjxtCrVy8AJk6cSL169Th8+DC1a9dm+vTp9OjRgzFjxgBQs2ZNNm7caA000nLo0CH++usv64jWfffdx7PPPsv48eMxmUwMHjyY+++/n5iYGPz8/IiMjGTZsmUsWrQIgO+//57k5GS++OILTCYTALNmzaJ48eL88ccf3HbbbQD4+/vzxRdf4OXlZX33Qw89ZD2vWrUq06ZN4+abbyYqKoqAgAA+/fRTatWqxXvvvQdArVq12LNnj0NQ99ZbbzF48GDrup0aNWowbdo0OnTowIwZM/Dx8Un386clLCyMsmXLOpSVLVuWsLAwa72lLK02IiIikg/ExsLq1bbrYsUyvicpyTHgiYhwfb8klSIx8lOQlC5dml69ejF79mxmzZpFr169KFWqlEObI0eOkJCQQJs2baxlnp6etGjRgv379wPQuHFj6tSpYx1R+fPPPzl37hx3ZZCGsWHDhtbzcuXKAViniR04cIAWLVo4tE957cxXX31Ft27drJ+jZ8+eREREsPr6/0n07NkTT09PlixZAsCCBQsIDAy0jnjs2rWLw4cPU6xYMQICAggICKBkyZLExsZy5MgR63saNGjgEPgAbNu2jd69exMaGkqxYsXo0KEDACdPnrR+pptvvjndz7Rr1y5mz55tfXdAQADdunUjOTmZY8eOZfj5RUREpBBIK8U1wOOPw/XvMQBMmpTmyM+BYHi9Pdy/YTRbzmx1vMeZs2fhnXeMaXKSbUVi5MfP04+ocVF58t4b8dBDD1mnVH388cc3/P7Bgwczb948XnjhBebNm0f37t0JDg5O9x5PT0/ruWWUJTkbw7BJSUnMmTOHsLAwPDw8HMq/+uorOnfujJeXFwMGDGDevHkMHDiQefPmcc8991jbR0VF0axZM+bOnZvq+aVLl7ae+/s7jrJFR0fTrVs3unXrxty5cyldujQnT56kW7duWUqIEBUVxbBhwxg5cmSquuwkoggJCSE8PNyhLDw8nJDr2WEsx/DwcGsgarlu3LjxDb9XREREbkBcXNp1c+bYzocOhfLlnTb7pyzc8ghc8wSOL+Gn06vZUQKqXQbS+o7WrJkRAP3+O6xceaO9l+uKRPBjMpmyPP0sL1nWsphMJrp165aqvlq1anh5ebFhwwbr+p2EhAS2bt3qkFJ50KBBjB8/nm3btvHjjz8yc+bMbPWrVq1a1qQKFimvU/rll1+4evUqO3bswN3d3Vq+Z88eHnzwQa5cuULx4sUZPHgwXbt2Ze/evaxevZo33njD2rZp06Z8//33lClThsDAwEz3999//+XixYu8/fbbVKxYEYC///471WdKmV465Wdq2rQp+/bto3r16pl+d2a0atWKVatWOfzOVq5cSatWrQCoUqUKISEhrFq1yhrsREZGsnnzZp544gmX9kVEREQy4Cz42bsX6tVzLLN830kx8pNkgof7GIFP1UsQGVKCC/GXmdARvlmEkUEupXXrjMAHjOBHsk3T3vIhd3d39u/fz759+xwCBgt/f3+eeOIJnnvuOZYvX86+fft49NFHiYmJ4eGHH7a2q1y5Mq1bt+bhhx8mKSmJPn36ZKtfI0aM4JdffuH999/n0KFDfPrpp/z666/WESJnvvzyS3r16kWjRo2oX7++9ceSuc0ymtO+fXtCQkIYPHgwVapUoWXLltZnDB48mFKlStG3b1/WrVvHsWPH+OOPPxg5ciT//fdfmu8ODQ3Fy8uL6dOnc/ToUZYsWcLrr7/u0GbYsGH8+++/PP/88xw8eJAffvjBmozB8rmef/55Nm7cyPDhw9m5cyeHDh3ip59+yjDhwc6dO9m5cydRUVGcP3+enTt3sm/fPmv9008/zfLly5kyZQr//vsvEyZM4O+//7Y+12QyMWrUKN544w2WLFnC7t27eeCBByhfvjz9+vVL990iIiLiQvHxcH3KvIP69Y3NT+25Xf96nWJGypTW8PdNEBQL67+C5a2NTVD/1wh+rAvJSYmpn5/JxFKSeQp+8qnAwMB0Rznefvtt+vfvz/3330/Tpk05fPgwK1asoESJEg7tBg8ezK5du7jjjjvw9fXNVp/atGnDzJkzef/992nUqBHLly/nmWeeSXPBf3h4OMuWLaN///6p6tzc3Ljjjjv48ssvAeOL/r333suuXbsYPHiwQ1s/Pz/Wrl1LaGgod955J3Xq1OHhhx8mNjY23T+j0qVLM3v2bGua7bfffpvJkyc7tKlSpQo//vgjCxcupGHDhsyYMcOa7c2yB1LDhg35888/OXjwIO3ataNJkya88sorlE9jSNuiSZMmNGnShG3btjFv3jyaNGlCz549rfWtW7dm3rx5fPbZZzRq1Igff/yRxYsXU79+fWubsWPHMmLECB577DFroobly5ffcJIFERERuQH2yQxSSrn5uOUfrrt3txZtrAjjOhvnk3+DclHQLLAWfSobM3zuuhvK+n7MT//+5Pgsjfa4nMl8I/mY81hkZCRBQUFERESk+vIbGxvLsWPHqFKlir4g5oJHH32Uf//9l3Vp7XZcAE2aNImZM2da9yIqaPS/ARERERdbvRo6d3Zet349tG1ru37ySbCs2TaZMAMNnoS9ZWDwP/DNL96YYo0pdHtXzqP9ykFcslsmfm/9e5nQcQI1g2tCytk1Be9re65ILzZIKcsjP2vXrqV3796UL1/eYZNMZx5//HFMJhNTp051KL906RKDBw8mMDCQ4sWL8/DDDxMVlfsJCSTrJk+ebM2+Nn36dObMmcOQIUPyulvZ8sknn7B161aOHj3KN998w3vvvVfgP5OIiIi4UKKTKWkWBw44XqdYsrDlJiPw8XfzYfq932AqY9vCot7UuZydApffhie3GGXf7vmW5p81Z//5/a7qvdjJcvATHR1No0aNMsxCtmjRIv766y+nU4MGDx7M3r17rZtRrl27lsceeyyrXZE8sGXLFrp27UqDBg2YOXMm06ZN45FHHsnrbmXLoUOH6Nu3L3Xr1uX1119n9OjRTJgwIa+7JSIiIvlFTIxxdJaU4PrejFZudl+vJ09m7vVdRPrWu5MSA+5LFRx5JUHxWPj4F1jZ8mMalW3E1firPPbzo2icx/WynO2tR48e1o0103L69GlGjBjBihUrrBtmWuzfv5/ly5ezdetWmjdvDsD06dPp2bMnkydPznAdheStH374Ia+74HIffPABH3zwQV53Q0RERPKr6Gjj6O8PX30FdpuoExvr2NYuuLk28km+SXgd4iK4v+H9RqF9cOTh+FW8S0hrlrS6nTof12H9qQ183QiG7HLlBxGXJzxITk7m/vvv57nnnqNeytR/wKZNmyhevLg18AHo0qULbm5ubN682ekz4+LiiIyMdPgREREREckV9sHPgw861l2+7HhtF9z8uO9HrsRFUCmoEl2rdjUK7Ud+PFKPQ4QGhTK+3XgAHukDL3eCuNTJf+UGuTz4eeedd/Dw8HC6ISRAWFgYZcqUcSjz8PCgZMmShIWFOb3nrbfeIigoyPpj2bNFRERERCTH2Qc/KX3+ueO1XZKCz7cbdY80fQR3t+sRjH3wk3ItUUICAKNbj+Yu6pLoDm90gM5D4FqR2J0z57k0+Nm2bRsffvghs2fPTnfvl6waN24cERER1p/MZOFKTk522ftFChL93RcREXGx9IKflK7/d/js1bOsO2lkwx3aeKit3j7g+SlFauvrwY+XuxffL/Jg/g9Q/BpsCDVGgST7XBpDrlu3jnPnzhEaGmotS0pKYvTo0UydOpXjx48TEhLCuXPnHO5LTEzk0qVLhISEOH2ut7e3dc+VjHh5eeHm5saZM2coXbo0Xl5eLg3ERPIrs9lMfHw858+fx83NDS8vr7zukoiISOGQleDnuuWHlwPQvHxzKgRWsFVcu5b2TXaBkcnkxoB9UCoGOj8A8xrCmLM7aFKuSZa6Lo5cGvzcf//9dOnSxaGsW7du3H///Tx4fX5kq1atuHLlCtu2baNZs2YArF69muTkZFq2bJntPri5uVGlShXOnj3LmTNnsv08kYLGz8+P0NBQ3Ny0h7GIiIhLWIIfP7/02wF4egLwy+FfAOhZvadjfXrBz/WRH3sdj8OAffBDffjmn28U/GRTloOfqKgoDh8+bL0+duwYO3fupGTJkoSGhhIcHOzQ3tPTk5CQEGrVqgVAnTp16N69O48++igzZ84kISGB4cOHM3DgQJdlevPy8iI0NJTExESSkpJc8kyRgsDd3R0PDw+NdoqIiLjKpk0wfbpxbhn5efVVmDjReXsvLxKSEvjtyG8A9KyRIvixpM12xj74sftHzEG7jeDnh70/MPm2ybiZ9A+cNyrLwc/ff/9Np06drNfPPvssAEOGDGH27NmZesbcuXMZPnw4nTt3xs3Njf79+zNt2rSsdiVdJpMJT09PPK9H3yIiIiIiWda6te3cEvxMmAD79sH8+anbe3qy8dRGIuMiKeVXiublmzvW24/8uLlZ1wgBjpnjmjeH7dsB6H4YguLdOH31NBtObqBdpXbZ+0xFWJaDn44dO2I2Z37LpePHj6cqK1myJPPmzcvqq0VERERE8o79mh8naaot5b8cMqa8da/e3ZblLY22xMfbrgcNgr59jel1ljX0gYF4R0bS778A5lSN5Pu93yv4yQaNmYmIiIiIZEZmgp+bbkp7vU9K9oGPxf79xtGS/KBqVQAGHvEFYP6++SQmJ6a+TzJFwY+IiIiIiDP79jle2wc/7k5GdAYN4mTXFuw5twc3kxu3VbstdZuOHdN/Z0CAcbSs//HxAaDzKU+CfYM5F32OP4//mbn+SyoKfkREREREnHn1Vcdr+2xvzoKfuXP59b8/ALilwi0E+wWnbvPRR+m/05K0yBL8XH+nZ0ISd9S+A4AlB5Zk1HNJg4IfEREREZGUdu+GH390LEtv5KdOHQB+O2pkeeterbvz59arB1u2pP1eS6biY8eMo2UkKCaGW6vcCsCGUxsy7L44p+BHRERERMTe2bPQsGHq8vSCn6VLSUpOYs2xNQB0qeq496WD9DYit6z1WbzYOFqCn4gI2pRuCsDOsJ1ExUel8wEkLQp+RERERETsOclWDDgGP/Z76rVuDVWrsjNsJ5djL1PMqxg333Rz2s8vViztOsvIT/36xrFFC2uwFBrvS4XACiSZk9hyOp3RI0mTgh8REREREXve3s7Lb7rJdn49EQEAvkYmttXHVgPQoXIHPNzS2VGmVKm067ZvN/b+sWSCa9DA9q64ONpUbAPAxlMb0/0I4lyW9/kRERERESnUElOkkn79dWNNj33AY5/84Hrws+rYKgA6V+mc/vOLFTNSZad8D8DDD8P58xAba1z7+NimyV0Pfr7f+73W/dwgBT8iIiIiIvYsmdbACFTGj0/dxj748fEhPimedSfXAZkIfkwmYwpdRITz+hdegAoVjHNvb9tIVHw8bUKNkZ9NpzaRbE7GzaSJXFmhPy0REREREXtxcbZzS9a1lFIkP9hwcgMxCTGU8S9DvTL1Mn6HffDkjP3IjyX4iYujYdmG+Hv6ExEXwd5zezN+jzhQ8CMiIiIiYm/UKNt5sJO9esA61Q0Ad3eWHlwKQI/qPTI3GmN/v5uT9hcuGMcU09483Dy4pcItgFJe3wgFPyIiIiIi9nbvzriNfdKCwECWHjKCn9tr3p65d9iP/AwZArVrO2/n7+8w7Q2wJj1Q8JN1Cn5ERERERLKqd2/r6eFnh3Lw4kE83Dy4rdptmbvffuTH09MxmYKFlxeUKeMw7Q2wrvvZcFLBT1Yp+BERERERySoPDzCbwWzm10vGnjvtQtsR6B2Yufvtgx0vL+N5KZUoYUyJ8/Q0rq8nYmh5U0tMmDh25Rhnr57NzqcochT8iIiIiIhkwy+HfwGgZ42emb/Jso4HjODG3T11G0viBUtgdD01dpBPEA3KNgA09S2rFPyIiIiIiNygmIQY1hxbA2Qj+PHyso3u2IuONo4pgh+wW/ejqW9ZouBHREREROQGrTm2hrikOEKDQqlTqk7mb0w58uNszU9ysnF0Evy0rtgagM2nN2e1y0Wagh8REREREWdat86wya+HfwWgZ/WemEymzD875ciP/XVKToKfZuWaAbArfBdJyUmZf28Rp+BHRERERMSeJe30m2+m28xsNrPs0DIgi1PeIPXIj7Npb2azcXQS/NQMromfpx8xCTEcunQoa+8uwhT8iIiIiIjYS7o+kuIsA5udAxcPcPzKcbzcvbi1yq1Ze8fVq7bztNb8pAx+kmwjPO5u7jQs2xCAHWd3ZO3dRZiCHxERERERe5YRlgyCn18OGVneOlbuiL+Xf9besWSJ7TytkR8LJyM/AE1CmgCwI0zBT2Yp+BERERERsWcJMpyln7ZjnfJWPYtT3lLy8rJtZGrPMvJj6cd330FMjLW6abmmgIKfrFDwIyIiIiJiLxMjP+eiz/HH8T8A6F2rd9bf8fbbtnNPTyhbNu22164Zx40b4cknrcWWkZ/tZ7djtgRKki4FPyIiIiIi9jKx5mfh/oUkm5NpXr45VUtUzfo7atWynXt5QalSabddvtx2PmeO9bR+mfp4uHlw6dolTkWeynofiiAFPyIiIiIi9jIx8rNg/wIA7q579429w/7Z0dHpr/lxc/6V3dvDm3ql6wGw5fSWG+tHEaPgR0RERETEXgZrfq4lXGPdiXXADU55A8fgJyoq/eAnnf2DOlXuBMCvh369sX4UMQp+REREREQszp+HyEjjPI2Rnw2nNhCXFMdNxW6iVnAtp22yZPDg9DPLpbMBaq+avQAj+UKyOTn7fSnkFPyIiIiIiACEh0OZMrZrZxnYgN+P/g5Al6pdMKUzKpMuuz17CApKf+SnWLE0q9pXak+AVwDh0eFsP7v9xvpShCj4EREREREB+P13x+vgYOfN7IKfG2Yf/Hh7pz/yExiYZpWXuxe3VbsNgGUHl914f4oIBT8iIiIiIgDx8bZzX1+nIz8XYy5aR1g6V+l84++yD37c3NLP9jZ0aLqP6lXDNvVN0qfgR0REREQEjKxrFmnsm7Pm+BrMmKlbui7lipW78XfZBz8A3brBo486bztiRLqP6lnD2GR165mthEWF3XifigAFPyIiIiIiAEeO2M5jY502WXV0FQBdqmRjyhuknlLn5gaffea8bXrrgYCQgBCalWsGKOtbRhT8iIiIiIhERsLUqRk2+/2YC9b7AHTsCOPGwdy5WbuvYkWnxZr6ljkKfkREREREvvoqwyYnI05y+NJh3E3udKjcIXvvM5ngzTdh0CDH8scfd97+m2+MY/HiTqtvr3k7AL8d+Y34pHinbUTBj4iIiIiIsb9PBjb/txmARiGNCPROOwNbtnz4oe28g12AFRpqHOOdBzbNyjejXEA5rsZf5bcjv+VM3woBBT8iIiIiImkEFfa2ntkKwM3lb865fnh5walTMHEifP+9rdyy7ichweltbiY37ql3DwDzds/Luf4VcAp+RERERETi4jJskivBD0CFCvDKK1C2rK3My8s4phH8AAysPxAw1v0km5NzsocFloIfEREREZF0ggqAZHMy285sA+Dmm3I4+HHGMvKTzghVs/LN8PXwJTIukkMXD+VSxwoWBT8iIiIiIimDipYtHS4PXDjA1fir+Hr4Urd03Vzs2HUeHsYxMTHtJm4eNCnXBLCNUokjBT8iIiIiIpZpbyNGwPPPw8KFDtWWYKJpuaZ4uHnkdu/A3d04ptwcNQXLlLytpxX8OJMHvzkRERERkXzGMvJTsyYMH56q2hJM5Ph6n7RkYuQHoOVNxojVupPrcrpHBZJGfkRERERELMGPJbFACn+f/RuA5uWb51aPHGVy5OfWKrcCsCNsB+FR4TndqwJHwY+IiIiIiCX48fZOVZWQlMDOsJ1AHiU7ANvIz7VrcPJkms3KBpSlabmmACz6d1Fu9KxAUfAjIiIiIkXXhQvGz6+/GtdORn72nNtDbGIsQd5BVC9ZPZc7eJ1l5AeMPYDSMbjBYAC++eebnOxRgaTgR0RERESKpvXroXRp48fCklLajiXZQfPyzXEz5dHXZ/vg56uvYPv2NJta9vvZeGojpyNP53TPChQFPyIiIiJSNL3ySuqy8uVTFeV5sgOwTXuzaNYszabli5WnTcU2ACzcfz1r3e7d4OsLr7+eUz0sEBT8iIiIiEjRtGaN43W3bnBz6gDHMvKTZ+t9wHHkJxP61+kPwI/7fzQKRo+G2FjnAV8RouBHRERERARg+fJU095iEmLYc24PkMcjP1kNfuoawc/6k+s5H30+wxTZRUWWg5+1a9fSu3dvypcvj8lkYvHixda6hIQEnn/+eRo0aIC/vz/ly5fngQce4MyZMw7PuHTpEoMHDyYwMJDixYvz8MMPExUVle0PIyIiIiLiSrvCdpFkTqKsf1kqBFbIu46knPaWgdCgUJqENCHZnMzSg0vBTWMecAPBT3R0NI0aNeLjjz9OVRcTE8P27dt5+eWX2b59OwsXLuTAgQP06dPHod3gwYPZu3cvK1euZOnSpaxdu5bHHnvsxj+FiIiIiEhWlS2bYRP7ZAcmkymne5S2LI78APSpZXwH/+XwL47Bj9nsql4VOFkLIYEePXrQo0cPp3VBQUGsXLnSoeyjjz6iRYsWnDx5ktDQUPbv38/y5cvZunUrzZsbm0RNnz6dnj17MnnyZMo7WWQmIiIiIuJy4RlvAmpd75OXU97ghoKfnjV6MvHPifx25DcSPG7BOqEvLg58fFzavYIix8e/IiIiMJlMFC9eHIBNmzZRvHhxa+AD0KVLF9zc3Ni8ebPTZ8TFxREZGenwIyIiIiJyw3780fG6alWnzayZ3vIy2QHc0LS15uWbU8qvFJFxkawIumCrmD3bdf0qYHI0+ImNjeX555/n3nvvJTAwEICwsDDKlCnj0M7Dw4OSJUsSFhbm9DlvvfUWQUFB1p+KFSvmZLdFREREpDBZswYaNYK//rKVLVxoO7/zTli6NNVtEbERHLh4AMgHIz9ZlZCAm8mN+xveD8ArQduxTnbbuDHPupXXciz4SUhI4O6778ZsNjNjxoxsPWvcuHFERERYf06dOuWiXoqIiIhIoXfrrfDPP8bRIibGOD76KCxYAHXqpLpt29ltAFQKqkRp/9Kp6vOt//0PAgPh5595sd2L+Hr4sqMcLKl1vd7JZy0qciT4sQQ+J06cYOXKldZRH4CQkBDOnTvn0D4xMZFLly4REhLi9Hne3t4EBgY6/IiIiIiIZMm1a8bxoYfgp5+M8ypV0mz+95m/gXww5c1i6NDMtbv/fmNPn8GDKeVXiqdbjATglU4Yoz9pfOcuClwe/FgCn0OHDvH7778THBzsUN+qVSuuXLnCtm3brGWrV68mOTmZli1buro7IiIiIiKOZs2ynaezlibfJDuw8PPLWvvrAwZjW4zCOxH+CYFt5YH4eNf3rYDIcra3qKgoDh8+bL0+duwYO3fupGTJkpQrV44BAwawfft2li5dSlJSknUdT8mSJfHy8qJOnTp0796dRx99lJkzZ5KQkMDw4cMZOHCgMr2JiIiISO66dCnNKkuyg+blm6fZJlelkRzMgX0a6+t7A5XYsJ0798O3DeDLJtC8CAc/WR75+fvvv2nSpAlNmjQB4Nlnn6VJkya88sornD59miVLlvDff//RuHFjypUrZ/3ZaLewau7cudSuXZvOnTvTs2dP2rZty2effea6TyUiIiIi4sz5847XsbHOm0Wf50TECQCalWuW073KnIxSc+/f7zilLTnZOPbqxcPbjdN5DSAmLipn+lcAZHnkp2PHjpjT2RgpvTqLkiVLMm/evKy+WkREREQke95+2/E6jeDHMuWtVnAtgnyCcrpXmePh5Kv7oUPQtSuMHWusY7JfW5+UZD3tdBwqX4bjJeCXuN0MyPne5ks5vs+PiIiIiEi+8f77jtclSzptlm/297HnLPgZORJOnICnnoIjRxzrkpOtAZCbGfoYWbtZlXgohzuafyn4EREREZHCrVgx5+V9+sDzzzutynfJDgDc3VOXxcXZzlMGP0lJDskNOh8zjr8nHaaoUvAjIiIiIoWbl1fqslq1jGlixYunqjKbzbY01/k5+HnlFcdpbiklJzsEPx2Og3syHHa/wsmtv+dMH/M5BT8iIiIiUrg5GzHx9Eyz+X+R/xEeHY67yZ3GIY1zrl9ZlXLa2+uvw969abdPTobJk62XQXHQ/Ixx/sfXr+VAB/M/BT8iIiIiUrg528vH2WjQdZYpb/XL1MfX0zenepV1ztb8pCcpCd54w3b96qu0PWmcbvA97/yeQk7Bj4iIiIgUXsnJcH3fSQfpbW56Oh+u94GsBz+WVNcWXl60uR78bPRKZ7pcIabgR0REREQKrxUrnJf//Xeat2w+bWwmmq8yvcGNjfzY8/Ki9SnjdK/HJS5dS3uD18JKwY+IiIiIFF5Hj2apeUxCDBtPbQSgXWi7nOjRjXPByE/ZaKgfDmYT/HzgZ9f1rYBQ8CMiIiIihVNSEgwfnqVb1p1YR1xSHBUDK1K7VO0c6tgNcpa4IT32abDBmuSh/37jcsH+BS7oVMGi4EdERERECqcHHki77v77nRavPLoSgNuq3YbJZMqJXt24NDZkzZS5c61JHgbsM4p+O/IbkXGRLuhYwaHgR0REREQKJ/s00AEBjnWdOzu95bcjvwFG8JPvfPDBjd9bvDjcdBMA9c5BreSSxCXFseTAEtf0rYBQ8CMiIiIihc+RI7Brl+3abrNPwOkUsrNXz7L73G5MmOhcxXlwlKcqVnT8TFkRFAStWwNgAu6hHgDzds9zUecKBgU/IiIiIlL4PPOM43XKxf++qffv+fXwrwA0K9+MYL/gnOpZ9jRseGP3BQVBYKD1cnBwJwBWHFnB2atnXdGzAkHBj4iIiIgUPpcvO14nJjpeXx8Fsffjvh8B6Furb071Ku8UK2Yc774bgJqmUrSu2JpkczJzds3Jw47lLgU/IiIiIlL4pJzmVqWK7bxYMShXzqH60rVL1mQHd9W9K6d7l/uuZ3qzTvdLSuLRpo8C8PHWj0lMTkzjxsJFwY+IiIiIFD6nTjle/2y3p02DBqmaL/53MYnJiTQs25BapWrlcOeyaejQrN/jdv1rv13wM7D+QEr7lea/yP9YenCpy7qXnyn4EREREZHCx36kZ9kyqFfPdu2W+ivw/H3zAbi77t053bPs++ormD49a/dYPrPlmJyMj4cPDzZ+EIBPt33qwg7mXwp+RERERKTwuXbNOL74IvTo4ViXIvi5dO0Svx/9HYC76hWAKW8mE5QqlbV7nIz8ADzazJj6tuLwCo5fOe6iDuZfCn5EREREpPDZscM4du9uBAv2UqS5tkx5a1S2ETWDa+ZSB7Mp5b5FGXEW/MTHU71kdTpX6YwZM19s/8K1fcyHFPyIiIiISOFiv97HWZDg4+NwufzwcgDurHNnTvbKtfz9s9Y+ZfDz6afGM376iWHNhgHwxfYviE2MdWEn8x8FPyIiIiJSuBw9ajtPOeoD4O1tPTWbzaw9sRaAjpU75nDHXCirIz+WoMcSBJ06ZaT/fv11+tXuR8XAioRHhzNnZ+FOe63gR0REREQKF/vAoJaTzG12wc/hS4cJjw7Hy92LFje1yIXOuciNjvx4eTmWJyXh6e7J6FajAXh347uFOu21gh8RERERKVwsG5qWLg2+vqnrO3e2nq47uQ6Am8vfjI+HT+q2+dWNrvkpXdqx3MMDgEeaPkKwbzBHLx9l9IrRLuhg/qTgR0REREQKl4QE41i8uGP54cMwZw48/LC1yBL8tK/UPpc65yI3OvKTYnNXy+an/l7+TO9hpM+etmUae87tyW4P8yUFPyIiIiJSuFhGfq6PalhVqwYPPOCQ6nrDyQ0AtA1tm1u9c40bHflJI/gBuLfBvQyoOwCASesmZad3+ZaCHxEREREpXCwjP3Zf7J25EHOBQ5cOAXBLhVtyuleulXLtTkbSCn5SpP1+se2LACzYt4CrcVdvtHf5loIfERERESlc0hr5SWHzf5sBqBVci5K+JXO6V67lLIudvWHDIDDQdp1W8GPZDPa6JuWaUKNkDRKSE/jtyG8u6Gj+ouBHRERERAoXS/CTwcjPpv82AdCqYquc7lHOaNLEefmhQ/DRR9Cli63MEiyVKuXYtlKlVLf3rtkbgKWHlrqil/mKgh8RERERKVws094yGPmxBj8VCmjwYzY7L69e3fjsbk6+6qeY5uYwOnTd7TVvB2DZwWUkm5Oz28t8RcGPiIiIiBQumRj5SUpOYsvpLUABXO9jkZSUfn1oqPPyZs1s58mpg5u2oW0J9A7kfMx569TAwkLBj4iIiIgULpkY+dl7fi9R8VEU8ypGvdL1cqljLhYfn359yiluFvZBoZMAytPd0zr6892e7260d/mSgh8RERERKVwykfBg0yljyluLm1rg7uaeZrt8zRLkpSWtka8yZWznTkZ+AAY3GAzAt3u+JSEpg/cUIAp+RERERKRwyUSq679O/wUU4ClvkPHIT1qff/p023kaU+e6Vu1KWf+ynI85z4L9C26wg/mPgh8RERERKZj++w8mToTwcMfyLIz8FNhkB3DjwU9oKEydapynEfx4unvyRPMnAHh/0/uY00quUMAo+BERERGRgqlvX5gwAYYOdSzPYOTn0rVLHLh4ACjgIz/2aapXrDCO995rK0sv250l61sa094Anrj5Cbzdvdl6ZisbT23MRkfzDwU/IiIiIlIwbd9uHJcvdyzPYOTHksGsRskaBPsF51Tvct6CBXDbbbBwoXEMC4P//c9Wn94+R5Y02OlkjCvjX4b7G94PwPt/ve+KHuc5BT8iIiIiUvDNng3e3jBrVoaprgv85qYWFSsaIz533GFcly3ruLdPesGPZRrbgvTX84y6ZRQAi/Yv4nTk6Wx0Nn9Q8CMiIiIiBc+1a47XDz5orIF56KEMU11bpnAV6PU+mdGtm3GsXTt13ZIlzu8xm2HLFoiOBqBemXq0qdgGM+ZCkfhAwY+IiIiIFDylS6ddFxtrHJ0EP3GJcdbgp21o25zoWf5RujRcuQL//JO67uxZ5/d8/TW0bAmdO1uL7qp7FwBfbP+CZHPaa4QKAgU/IiIiIlKwmM3WkQmnXn/dODqZ9rXl9BauJV6jtF/pgru5aVYEBTmf/jZ6tO3cPpPbZ58Zx82brUUPNHqAYl7F2H1uNz8f+DmHOpo7FPyIiIiISMESF5e5dk5GftYcXwNApyqdMJlMruxVwdKnj+3cskYKnP7ZlvAtwfAWwwF4c/2bOd2zHKXgR0REREQKlpiYzLVzMuJhDX4qd3JljwoeHx/buf36qTQCy1G3jMLTzZMtp7fwT7iTaXQFhIIfERERESlYjh3LXLsUIz+xibHWzU0V/NgFP198YTu3rJdKoYx/GXrX6g3AR1s+ysme5SgFPyIiIiJSsHzwQebalSjhcLnp1CbikuIoF1COmsE1c6BjBYj9lL/Jk23n6UwpHNVyFAD/++d/xCVmcuphPqPgR0REREQKlubNM9euTBmHS633ScHy53j77baydIKftqFtCQkI4VriNWvGvIJGwY+IiIiIFCzFihnH22+HcePSbletmsOl1vuk0K+fcbTP9pZOFj2TyUSXql0AWHl0ZQ52LOco+BERERGRgiUpyTi6u0NyOvvONGtmPY2Oj2bzf0b6ZgU/1/n6Gsddu4xA8eWX008hDnSpYgQ/lkCyoMly8LN27Vp69+5N+fLlMZlMLF682KHebDbzyiuvUK5cOXx9fenSpQuHDh1yaHPp0iUGDx5MYGAgxYsX5+GHHyYqKipbH0REREREighLwOPubguEnLF8uQc2nNpAQnICFQMrUrVE1RzuYAFh+fPZuhWOHoU33ki7bUICmM20q9QOgG1ntnEt4Vra7fOpLAc/0dHRNGrUiI8//thp/bvvvsu0adOYOXMmmzdvxt/fn27duhFrlzli8ODB7N27l5UrV7J06VLWrl3LY489duOfQkRERESKDkvA4+aW9hqV0qUdLtccM0Yqbq1yq9b7WNgFh+nauxeCg+Gee6hSvArlAsqRkJzA1jNbc7Z/OSDLwU+PHj144403uOOOO1LVmc1mpk6dyvjx4+nbty8NGzbk66+/5syZM9YRov3797N8+XK++OILWrZsSdu2bZk+fTrfffcdZ86cyfYHEhEREZFCzn7kJ8W6Hvz84J13YM8eh2Kt93Eis8HPzz/D1aswfz4ms5k2oW0A2HByQw52Lme4dM3PsWPHCAsLo0uXLtayoKAgWrZsyaZNRk71TZs2Ubx4cZrbZeno0qULbm5ubN682ZXdEREREZHCyH7k59FHHevWrIGxYx0yvV2Nu8rfZ/4GjExvcp2fX9p17u628+LFbedhYbSpaAQ/60+tz5l+5SCPjJtkXlhYGABly5Z1KC9btqy1LiwsjDIp0g56eHhQsmRJa5uU4uLiiLMb0oyMjHRlt0VERESkILEf+fHzM4Khe+4xRjJuvjlV83Un15FkTqJqiaqEBoXmcmfzsfRGfgICnJdfvEjb0LYAbDy1kWRzMm6mgpNDzaXBT0556623mDhxYl53Q0RERETyA/uRH8tx/vw0m1vW+2jKWwoJCWnX2Q9W2CeVuHiRRnXbMKjBIFre1JKEpAS8Pbxzro8u5tIwLSQkBIDw8HCH8vDwcGtdSEgI586dc6hPTEzk0qVL1jYpjRs3joiICOvPqVOnXNltERERESlI7Ed+MkHrfdJwLZ1sbfajQvbBz5UreLp7MvfOuYxsObJABT7g4uCnSpUqhISEsGrVKmtZZGQkmzdvplWrVgC0atWKK1eusG3bNmub1atXk5ycTMuWLZ0+19vbm8DAQIcfERERESmiUo78pOPytcvsCNsBaL1PKt26pV2XnGz8PPEEfPKJrdwug3NBlOVpb1FRURw+fNh6fezYMXbu3EnJkiUJDQ1l1KhRvPHGG9SoUYMqVarw8ssvU758efpd30G2Tp06dO/enUcffZSZM2eSkJDA8OHDGThwIOXLl3fZBxMRERGRQioLIz9rT6wl2ZxMzeCalC+m75oO/P3TrktOhuXLYeZMx/KiFvz8/fffdOpki5qfffZZAIYMGcLs2bMZO3Ys0dHRPPbYY1y5coW2bduyfPlyfHx8rPfMnTuX4cOH07lzZ9zc3Ojfvz/Tpk1zwccRERERkUIvCyM/lilvt1a+NSd7VPgkJ8PZs6nLi1rw07FjR8xmc5r1JpOJ1157jddeey3NNiVLlmTevHlZfbWIiIiISJZGfqzrfTTlzbmQEHCWcdlsdlzrY5HWprIFRMHJSyciIiIiApke+bkQc4F/wv8BoGPljjncqQIqxRY0VsnJzgOdAj7yo+BHRERERAqWTI78/Hn8TwDqla5HGf80vuQXdS+84Lw8ORni41OXa+RHRERERCQXZXLkRymuM2HgQOflaQU/GvkREREREclFlpGfzAY/Wu+TNpPJefmJE/DXX6nLFfyIiIiIiOQiy8hPOtPewqPC2Xd+HyZMdKjUIZc6VogkJ8OSJanLNe1NRERERCQXZWLam2XUp2HZhgT7BedGrwq+hx6CLVvSb7Npk/MscAWEgh8RERERKThOnQLL/pB+fmk2W3lkJaD1PllSoQJ4ZLATzo4dMGFCrnQnJyj4EREREZGCo3Nn23mxYk6bJCYnsuSgMWWrd63eudGrgm3ZMnjwQRg7NlMbx/LGGznfpxyi4EdERERECo5Dh2zngYFOm6w/uZ4LMRco6VuS9pXa51LHCrCePeGrr8Df33nw8+STud+nHKLgR0REREQKBrPZ8TogwGmzRfsXAdCnVh883DKYxiWOPD1Tlz3+OPzvf7nflxyg4EdERERECoZTpxyvfXxSNTGbzSw+sBiAO2rfkQudKmS8vFKXeXtD2bK535ccoOBHRERERAqGTZscr52MUuw+t5uTESfx9fCla9WuudSxQiSt4Kd48VzvSk5Q8CMiIiIiBUNMjOO1k+Bn2cFlAHSu2hlfT9/c6FXh4u2duszDA0qUyP2+5AAFPyIiIiJSMCQmOl47GaVYemgpAL1q9MqNHhU+zkZ+3N018iMiIiIikqtSbq6ZYuTnYsxF/vrvLwB61uiZW70qXJyN/Li7Q1BQ7vclByj4EREREZGCIYORn+WHl5NsTqZBmQaEBoXmYscKEWfZ3tzcjKlvlgQTNWrkbp9cSMGPiIiIiBQMKYOfFF/Ulx0y1vtoyls2uLunXbZ8edptCggFPyIiIiJSMKSc9ma3709iciLLDxtfznvVVPCTLSmnuFmCHUuwmTIILUAU/IiIiIhI/hIeDvPmQUKCY3nKL92xsdbTv/77i8uxlynhU4JbKtySC50sxCZNcrx2ux4yeFzfMFbBj4iIiIiIi9xxBwweDA0aOJan/NJdpYr19PejvwNwW7Xb8HDzyOkeFm4eKf78LCM/lvKUQWkBouBHRERERPIXy2amBw44lqec9la6tPV0zfE1ANxa5dac7FnRkDLpQcrgJz4+d/vjQgp+RERERCR/CbXL1BYebju3H/nZu9d6ei3hmjXFdafKnXK6d4VfyuDHMu2tVCnjePFigZ36puBHRERERPIXk8l2fuWKcQwLg59/Ns6ffhrq1rU22fTfJuKT4ilfrDzVS1bPvX4WVimnvVmCn5AQYxQoORnOncv9frmAgh8RERERyV9iYmzn0dHGsV492LnTOE/x5fyP438AxqiPyT5wkhtjP/Lj5mYLRt3cbJug7tuX+/1yAQU/IiIiIpJ/nDsH58/bri2B0KVLtrIU+8xY1vt0rNwxhztXRKQMfuxZfh9du+Zef1xIwY+IiIiI5B+jRzte248CWdgFPzEJMWz+bzOg9T4uYx/8pLe25/TpnO+LiykPoIiIiIjkPbMZHnkE/vc/x/KHH4abbnIss0y9Ajae2khCcgIVAytStUTVXOhoEZByzU9aTp5M/bvJ5zTyIyIiIiJ5b+1a+Oqr1OX//QebNzuWeXlZTzee2ghAu0rttN7HVVJme0vLk0/mbD9ygIIfEREREclbW7ZAx46Zb28X/Gw+bQRGt9x0i4s7VYSlF/y0bGk737mzwG14quBHRERERPLWL79krf314MdsNlvX+7Ss0DK9OyQr0pv29txzjtcFbL8fBT8iIiIikreSk7PW/nrwc/TyUS5eu4iXuxeNyjbKgY4VUemN/Nx+u+3czw98fXO+Py6k4EdERERE8lZEROqyv/4y9vZx5nrwY5ny1iSkCd4e3s7bStalF/zYJZsgKSnn++JiCn5EREREJG85mzrl75/2OqDr60ysU95u0pQ3l7Kf9jZ1atrtCtiUN1DwIyIiIiJ5zfIl2t/fVmY2w4YNztvHxwPw1+m/AK33cTn7kZ/g4LTbaeRHRERERMRObCy89x7s25d2G8uX6BdfhH79oFEjqF0bJk9O3bZyZejfn7jEOHaG7QQ08uNy9sGPd+GaTqjgR0RERERyzptvwtixaa/fAdvIj4cHLFoEO3YYX8A7d4aqVaF8eejdG77+Go4dg3Ll2BG2g/ikeIJ9g7W5qavZT3tzFvzMm2ckO1i6NPf65CKZ3L5VREREROQGbNyYcRvLyI+7u3G036z04EEjG1yKRfjrT64HoG1oW21u6mr2f9Z2eypZ3Xsv3HVX+imx86mC12MRERERKTjM5ozbWIIfZ1+m3d1tQZEd++BHXMw++HHyZw8UyMAHNO1NRERERPKaZdpbWl+0U0g2Jyv4yUn2wY9b4QoXCtenEREREZGCJ72RHycOXDjAxWsX8fHwoWm5pjnYsSLK/veQyYC0oFDwIyIiIiJ5K4sjP6uOrQKMLG9e7k7WpEj2aORHREREROQGZGXNTyaDn0X/LgLg9pq332ivJD32vwcFPyIiIiIimZSZ4Mc+1XUGtp7eyupjqwG4s86d2emZpMU+e17JknnXjxxQMNM0iIiIiEjhkcmRn6TkJJ785UkA7m94v/b3yUlffglhYVC3bl73xKUU/IiIiIhIznHhyM+UTVP4+8zfBHoH8m7Xd13QOUnTQw/ldQ9yhKa9iYiIiEjOOH8e/vwz43aZGPnZHb6bF1e9CMDkrpMJCQhxRQ+liFHwIyIiIiI545VXMm4TFQWRkca5r6/TJmazmWdWPEOSOYk7at/BI00fcWEnpSjRtDcRERERyRnHjqVff+kSBAfbrkuUcNps6cGlrDq2Ci93L6bcNgWT/YJ8kSxw+chPUlISL7/8MlWqVMHX15dq1arx+uuvY7ab72k2m3nllVcoV64cvr6+dOnShUOHDrm6KyIiIiKSlzLKFLZiheN18eKpmsQnxTP6t9EAPHvLs1QpUcVFnZOiyOXBzzvvvMOMGTP46KOP2L9/P++88w7vvvsu06dPt7Z59913mTZtGjNnzmTz5s34+/vTrVs3YmNjXd0dEREREckrKTOFpUx+EB/veB0YmOoRn2z9hEOXDlHGvwzj2o1zcQelqHH5tLeNGzfSt29fevXqBUDlypX59ttv2bJlC2CM+kydOpXx48fTt29fAL7++mvKli3L4sWLGThwoKu7JCIiIiJ54do1x+vERPD0tF2nDH78/Bwuz0WfY+KfEwGYdOskAr1TB0ciWeHykZ/WrVuzatUqDh48CMCuXbtYv349PXr0AODYsWOEhYXRpUsX6z1BQUG0bNmSTZs2ubo7IiIiIpJXUgY/CQmO1ymDnxQJD8auHMuV2Cs0CWnCg40fzIEOSlHj8pGfF154gcjISGrXro27uztJSUlMmjSJwYMHAxAWFgZA2bJlHe4rW7astS6luLg44uLirNeRlowgIiIiIpJ/pfzOFh/vOLqTMvjx8bGerj+5njm75mDCxIxeM3B3S38DVJHMcPnIzw8//MDcuXOZN28e27dvZ86cOUyePJk5c+bc8DPfeustgoKCrD8VK1Z0YY9FREREJEdcvux4ndHIz/UsbmazmRd+fwGAR5o+QssKLXOqh1LEuDz4ee6553jhhRcYOHAgDRo04P777+eZZ57hrbfeAiAkxNiQKjw83OG+8PBwa11K48aNIyIiwvpz6tQpV3dbRERERFwto+AnJsbpbauOrWLDqQ34ePgwsePEHOqcFEUuD35iYmJwc3N8rLu7O8nJyQBUqVKFkJAQVq1aZa2PjIxk8+bNtGrVyukzvb29CQwMdPgRERERkXwsKQnWrHEsSznSExWV6jaz2cyEPyYAMKzZMMoVK5dDHZSiyOVrfnr37s2kSZMIDQ2lXr167Nixg/fff5+HHnoIAJPJxKhRo3jjjTeoUaMGVapU4eWXX6Z8+fL069fP1d0RERERkdwSFQX+/sb0tf37U9fbj/ycOAHvv5+qif2oz/Ntns/BzkpR5PLgZ/r06bz88ss8+eSTnDt3jvLlyzNs2DBeeeUVa5uxY8cSHR3NY489xpUrV2jbti3Lly/Hx26Rm4iIiIgUIHfeCYsWwb33wrx5cPWqra5UKbhwwXHkp3Jlp495d8O7ADzW9DGN+ojLmczmlLtN5X+RkZEEBQURERGhKXAiIiIi+cH1ZAWAsZnpu+/C888b5eXKwZkzsH07NGmSuv11e8P3UH9GfdxMbhwdeZRKxSvlUuelIMtKbODyNT8iIiIiIjx/fcqa2WxLfHD2rHG8vhY8pWmbpwHQr3Y/BT6SIxT8iIiIiEj2pMzEmzKRgWWz03vuMY4pEx8Al3zhm3++AeDplk+7uocigIIfEREREcmu0FDH6+rVoU0b47x+fVt5VJSxFshJ8PN5U7iWeI3GIY1pF9ouBzsrRZmCHxEREZGi5to1I9uaKzgJZAgPhzJljPMnn3Sse/NNiItzKEpwg49aGOdPt3wak5P1QCKuoOBHREREpChJSDAyrVWuDNu2Zf95KTcytbBsYOrr61h+/HiqgGlRHfgvCEr7lWZg/YHZ75NIGhT8iIiIiBQlTzwB584Z5x99lP3npRX8rFhhHH19oVcvW7nZbBv58fXlsg+Mv/V615o/gY+Htj6RnKPgR0RERKQo+fJL23lwcPafFx6efn1gIMycabuOjbWO/CT4eXPX3XAoGCpGmhjRckT2+yOSDgU/IiIiIkWF2Qzu7rZry9S07Fi1Kv36UqXgppts12XKQHQ0ySZ4/LZ4VlWFgEQ3lvb4mlJ+pbLfH5F0KPgRERERKSri4yEpyXbtiuDn9ddt57Nnp65v3tzY0PTll43rxESSI64wogd8VSsGN5Mb8+5bTMMu92W/LyIZUPAjIiIiUtglJ8Mff8CZM47l0dGufc+QIY7X7u5G4ANQtiwA50/so+8XXfikBZjMMKffHHrX6u3afoikQcGPiIiISGE3ciR06gRVqzqWuzL4eeON1GX2o0yBgSytCfWbbmZpLfBOhG/21eK+hhrxkdzjkdcdEBEREZEc9vHHzstjY7P/7FKl4MIF6Ns3dd3o0dbTbe7n6DsQkt2gfjj8byE0MqeRKU4kh2jkR0RERKQwS0xMuy7FZqOZtn49HDwIu3YZgQ9AsWLGcfhw41injrGh6XXPn5tLshv0/Re2fg6NwoGgoBt7v8gN0siPiIiISGGW3tS2Gwl+du+Gdu1Sl1uCn3fega5doUcP8PQEYOWRlayK2IFXIkxdDj6WeCwgIOvvF8kGBT8iIiIihVlUVNp1NxL8/PKL83JLIOPnB336OFRNWjcJgCe3QuUrdhVeXll/v0g2aNqbiIiISGF29WradTcS/Ozfn7rMyyvNQCY8Kpy1J9YC8MxfKSo7dMj6+0WyQcGPiIiISGE2fXrqshYtjGNWg59//oE5c1KX+/unecuSA0swY6Z5mcaERthVPPEETJiQtfeLZJOCHxEREZHC7JNPUpd9+KFxzGrwM2mS8/LLaWdtW/jvQgDuqHNn6n75+mbt/SLZpOBHREREpKgpUcI4ZjXV9fr1zsvTCGIiYiNYdXQVAHfWvytr7xLJAUp4ICIiIlJY2Wd6mzIFzpyBp54Ck8koy8rIz/79xv3O+Pg4LV52aBkJyQnULlWb2qVqZ/5dIjlEwY+IiIhIYXXypHEMDIRnn7WVh4UZx7g4MJttwVB6vvkm7Tpvb6fFi/5dBMCdte90Wi+S2zTtTURERKSwOnHCOFaq5FhuCVbM5vQ3QQVISoKff4a33kq7jZNpb9cSrvHroV8BuKPOHUahZbpdcHBGPRfJEQp+RERERAory548N93kWG4/UpPR1LfPP3fct2fZstRtnEx7W3l0JdEJ0VQMrEizcs2Mwt9+g27dYPXqTHRexPU07U1ERESksLKkuT50yLHcPviJibFtUOrM9987Xrdta6SpPnYMli83ypwEP5Ypb3fUvgOTZVpd8+a2e0TygIIfERERkcJu8GDHa3d323nFiumP/sTHO177+trSZ1uCmhTT3hKTE1lyYAlgN+VNJB/QtDcRERGRwqr29Qxrt96adpv4eNi6Ne36hATHa0/P1G1SjPysPbGWS9cuUcqvFG1D22aysyI5T8GPiIiISGH177/GMaPNRKdNS7su5ciPMymCn+/3GFPl+tTsg4ebJhpJ/qHgR0RERKSwSU6GTZts135+TpuF+8PzXeDF4J2ciz7n/FkpR36csQt+rsZdZd6eeQDc3+j+THdZJDcoFBcREREpTPbtg3r1HMvs1/hcd9EXOg6Ff0sD7GHhrPZsengTJXxLODa0H/mZOtX5O2vbNjCdu3suUfFR1AquRYdKHW7kE4jkGI38iIiIiBQmzvbjqVXL4TIpOYmBA4zAJyAOiiV5cODiAe784U7ik1JMczt82HZerJhj3cqVMGwYvPQSAGazmZl/zwTg8eaP27K8ieQTCn5ERERECovERPjf/1KXuzl+5Zu1cxa/VwO/eNj0Jaw/fisBXgH8cfwPHvrpIZLNyUbDoUMdn1O1quN1ly4wc6Z1Wt2fJ/5kV/gufDx8eKDRAy76UCKuo+BHREREpKCLjTWOCxZk2DQhKYE3170JwOtroP45aBjlz/y75uPh5sHc3XMZ89sYzGYzzJlju3HQIJLbt2P/+f389d9fhEeFOzw3IjaCoYuHAvBAwwco6VvSJR9NxJW05kdERESkINu5E265BZ591hj5Aahf30hvPW0avPKKQ/OvdnzFsSvHKBMFj/99vbByZbpX786Xfb5kyOIhfPDXB2w5vYU7W0HTs3DOH/4eehMLPqrJ0ctHAXAzuVG7VG3qlq5Lu9B2/Hr4V05EnKBqiaq8d9t7uff5RbLAZDabzXndiayKjIwkKCiIiIgIAgMD87o7IiIiInnnrrvgxx+N85494ZdfYMYMePxxuHbNIc11TEIM1adV52zUWab9AiO2XK944w3rup0ZW2fwzIpniEtyvvGpn6cfwb7BnIo8larOw82DP4b8QZvQNi79iCLpyUpsoJEfERERkYLMPgnBrl3GsX5945hif5/pm6dzNuoslYtX5rFtx20Vdumsn7j5CXrW6Mmi/Qv57eNnOVYcisVDsz6P06piK/rX6Y+/lz+nIk6x7/w+/j7zN1vObMHDzYNhzYYp8JF8TSM/IiIiIgVVQgLcdBOcP+9Yfvo0lC/vUHT52mWqTqvKldgrfN3va+7vMx5OnjQqx42DN990fEZSEnhc/3fyAQNg/vwc+hAi2ZOV2EAJD0REREQKqhdfTB34AAQFpSp6Z8M7XIm9Qv0y9RnUYBCsWGGrjIhI/YykJNv5xx+7oLMieU/Bj4iIiIgrnToF48fDmTM5/y7LWp+Urqeetrh07RIfbzUCmEm3TsLdzd3YmPSDD643uJT6GfbBT4rpcyIFldb8iIiISMFlNkN+20izSxc4eBDWr4c//si598yeDcePO69L8WfyydZPiIqPokGZBvSu2dtWUaKEcbx8OfUzLJnjANzds9VVkfxCIz8iIiJSMP37LwQHw1tv5XVPHB08aBzXrs25d2zfDg8+aLt+6KE0m0bHR/Ph5g8BeKHtC5jsAyNvb+Nol/DAyrJ3ENjW/ogUcAp+REREpGB6+mljxOLFF/O6J845WXfjMkeO2M5LloQvvzSClfffh/37HZp+8NcHXIi5QNUSVbm73t2Oz7EENatXO5Z/+y2UKWO71siPFBIK40VERKRgcrZOJa+FhdnOS5XKufckJ9vO69Y1jt7e8MwzDs3ORZ/jnQ3vAMZaHw+3FF/97Ed0jh6FqlWN80GDHNu56d/LpXDQ32QREREpmK5etZ03bQpffAG//gqVKsHvv+dNn15+2XZevXrOvcc++EmR3MDe878/T1R8FM3LN0896gPg6Wk7tyRoiIlJ3S6/rasSuUEa+REREZGCKTLSdr5jBzz6qO26a1cjGUJuO3rUdl6hQs68w2yG776zXdsHMHbm753P7J2zMWFiarepuJmc/Ju3fVBjGd35+28XdlYkf9HIj4iIiBRM9qmY84tatWznWQm+kpMds6ullJAAZ88a51OmwJIltroxY1I133RqE0N/GgrAc62fo01oG+fPte+jJfhJZyRJpKBT8CMiIiIFUxojHnnq9GnbeXrBjL3oaAgNhdtucx4wbd4MfftC+fKwahU895xjXceODs33n99P12+6EpMQw23VbmNS50lpv9v+fZZRoPj4zPVbpABS8CMiIiIFU3rBT26NXixeDHffDRERRiCxfr2tLrPBz5EjRtC0Zg0cO+ZY9913cMstxlomgFGjHOtTTK2LS4xj0MJBRCdE075SexbevTB1kgN79muHFPxIEaA1PyIiIlIwpZeBLLdSM99xh3GsV8/Yd8c+A11mp+XZj76cOGHLuAbwzjuObe03I/38c2M0yM64VePYGbaTUn6l+K7/d/h7+Wf+3ZZASMGPFGI5MvJz+vRp7rvvPoKDg/H19aVBgwb8bbd4zmw288orr1CuXDl8fX3p0qULhw4dyomuiIiISGFl/yW9Th3HuqtXc/dL/H//GWtx7GV25Mc+SDp1yrHuppscr+2n1dlvcgr8duQ3PvjrAwBm9Z1FuWLlMn63ffBj2eg0Ls6xTcoATKQAc3nwc/nyZdq0aYOnpye//vor+/btY8qUKZQoUcLa5t1332XatGnMnDmTzZs34+/vT7du3Yi130lYREREJC3nzhkBBxjTzuxTTFtYRmGSkpynb84u+6Dliy9g2jTj3PKdJ7MjP/btTpywnScnw6ZNzu8JCHAY3boYc5Ghi4cC8NTNT3F7zdsz9277aW+7dxvHlEHj2LGZe5ZIAeDy4Oedd96hYsWKzJo1ixYtWlClShVuu+02qlWrBhijPlOnTmX8+PH07duXhg0b8vXXX3PmzBkWL17s6u6IiIhIYTRkiO38jTfg1ltTt7l40QiSPDzA3x8OHHBtH+z3GbJn2d/nRkZ+3n/fFrRNnWo7L5diFKdJE+up2WzmsaWPcTbqLLVL1ebdru9m7r0AN99sO3/qKeO4b1/m7xcpYFwe/CxZsoTmzZtz1113UaZMGZo0acLnn39urT927BhhYWF06dLFWhYUFETLli3ZlNa/boiIiIhYhIfD8uW26xIloGxZY9Tk/HmoUcMov3gRPvjA1s4+PbQrpBUk1KtnHDM78mMfJF25AsHBMH8+jB5tK1+zxvGezp2tp3N2zWHhfiOxwdw75+LnmYVkDxUrpi5budJ2/vbbmX+WSAHg8uDn6NGjzJgxgxo1arBixQqeeOIJRo4cyZw5cwAICwsDoGzZsg73lS1b1lqXUlxcHJGRkQ4/IiIiUkQtWuR4HRxsHENDoVQp4wfgwgXH9Sp2U/CzLSoK2jjZO6dRI+jUyTj/5Ze0R4fsOQuS7r7b8bpmTcfr60kRjl4+yohfRwDwWsfXaFquacbvy8iVK8Zx9mx4/vnsP08kH3F58JOcnEzTpk158803adKkCY899hiPPvooM2fOvOFnvvXWWwQFBVl/Kjr7VwoRERHJP2Ji4IUXYO1a1z3TsiDf3iOP2FI0W5QubRz797cu6I93h00Re5i+eTqL9i8iKTmbG6Q6S9TUrh2sW2esx7EYN85Ig52ejEaIPv009WcMDiYxOZEHFj1AVHwUbUPbMraNi9bmWP6RuXZt1zxPJB9xefBTrlw56tat61BWp04dTp48CUBISAgA4eHhDm3Cw8OtdSmNGzeOiIgI68+plJlQREREJH+ZN88YdenQAfbsyf7z+vYFLy9jRCQqyigbMMBI95ySZeTnurWVIPQZaB31ISOXj+TOH+6k2WfN+Ou/v268P84CmtBQKFYMAgNtZR9/bARj6Y0AZRT8+DtJV92pE1M2TmHDqQ0U8yrGN3d8g7ubC9J7x8baki7Yfw6RQsLlwU+bNm04kGJB4cGDB6lUqRIAVapUISQkhFWrVlnrIyMj2bx5M61atXL6TG9vbwIDAx1+REREJB979FHb+YQJ2X+eZb3O/Pm24McywpNS8eLW0xXVoPt9EB4AwWZfOlbuSJB3ELvCd9H6y9Y8uexJrsReyXp/LMGPr6+t7OJF41ismGPbhATYuTPtZ2UU/KTcsPWhh9h79Siv/PEKAB92/5DKxStn2OVMOXLEdq6ZNlIIuTz4eeaZZ/jrr7948803OXz4MPPmzeOzzz7jqesZREwmE6NGjeKNN95gyZIl7N69mwceeIDy5cvTr18/V3dHREREcpt9+mSAMmUc6+bNS72fTVZY1vw4GxEBaya4n2pBn3vhmif0PAinTt/NmiFrODTiEA80egAzZmb8PYM6H9fh+z3fY7bf8yY9CQlg+c7Stq2t3JLowC74skrv2RllhbN8ztmzoX17Lrz6HIMXDiY+KZ5eNXoxtPHQzPU7MyxTC318HKfviRQSHq5+4M0338yiRYsYN24cr732GlWqVGHq1KkMHjzY2mbs2LFER0fz2GOPceXKFdq2bcvy5cvx8fFxdXdEREQkt1nSM1vYr1eZPNlYRN+sGdhtgJ4l//xjHC3BRgrmBg349MEGjKiwm0R36H+uFPO+v4BXR2NfoNL+pZnTbw5DGw3l8WWPc/DiQQYuGMiKIyv4os8XuJky+Lfh7dtt5/Hx8Oef8NNPRsptsCYjsPYH+OX4SsICDhHsZyRniIiNoEZwDRKTE9kV9hP/9Iafa4FfAlS/ZPzUuAgNw6FekxqUNZvZ2qUO88u1YPb37bgQc4FSfqX4rPdnmFKuB8oOSwrtFFMHRQoLkznT/8yRf0RGRhIUFERERISmwImIiOQ3+/eD/frfRx+Fzz4zzqtWhWPHjPPMfAXZu9dY1/Phh6nr4uKMdUApTN44medWPgfAAzvhy6dX4dGps/Fu+2ldQFxiHG+vf5vX175OkjmJ0a1GM/m2yanfdfCgkQWtRQsjzXaPHkZ548awY0fq9qtWQZcuXPGBO++BNVUy/qjpKeZVjKvxtnVDdUrVYcHdC6hTuk72HgypkylYFLyviFJEZSU2cPnIj4iIiBRx5887XsfHOy/fudMIHtLz9NNGIJFS6dJOA5/d4bt5cdWLALxWsj/jnxmJqcr1yOPkSWN9jbstMYC3hzevdnyVKiWqMGTxEKZsmsLN5W/mnvr3OD64Vi3jePo0HD5sK7++iXsqnTtzrnJp+nQ5z+YK4JsAt1TvyLWkWAB8PHw4dPEQnidP0zAcKl+BVv9B+atw9L0XOTzzTfaXgn/aVufo5aNcjb+Kr4cvt9e8nTvr3En/Ov3xdPdM/89ORFJR8CMiIiKulXLT8vh4WL/eSAVtb+3ajIMfZ4EPwLVrqYoSkhIYsngICckJ9KnVh/H3zDemhCUlgYeHsbbm7FmoUCHVvQ80eoBDFw/xxro3ePa3Z+lZoyfFvK8nLoiNtTU8csQY2bJ4/HGn3dt3fh/dB1ziVACUuAZrZkOj716Fjh1tjRYuhNH9HW88cYL2oaEQ2B7Kl4cGDYiOj+bgxYNUK1mNQG/NeBHJDpcnPBAREZEi7oUXHK/j42HixNTtoqNv/B2WjG923lr/FjvCdlDStySf3v6pbS2Mu7staYD9pqcpvNT+JaqVqMaZq2d4Zc0rtgr7EavkZNsmoFOmQJcuqZ7z+9HfaflFS04FJFHzAqz/ChqFA3PnOja0Ww9tFRpqHLt1gwYNAPD38qdJuSYKfERcQMGPiIiIuI4l3TOAZbpZfLxt6pu9lIkRMtK9e5pV285s4/W1rwPwUY+PCAlIsXegt7dx/OgjYxTKCR8PH6b3mA7A1M1T+enfn4yK3bttjTp2NLLVgdOsbhdiLnDfwvuMjUcv+LHxS6ibYrYfmzbBq686jigBTJqU5ucTEddQ8CMiIiKuY59Q4JXroydpBT9Opq45MJvB8/q6lg8/hKAgp83CosK4a/5dJCYn0r9OfwbWH5i6kf2mpM723Ll8GcxmetTowaiWowAYsngIRy4dsaW1Tqls2RTdNfPUL08RHh1O3dJ1WWm+n2D7j/jFF8Znat0aXnvNVl6pEnzzDbz4ovP3iIjLKPgRERER1xk2zDhWr27sFQPG3jF//ZW6bUbT3uLibPvODBkCwcG2Og9j2fLaE2tp8mkTjl05RuXilfm89+fOUz/HxdnOR4xw3Ito/XooWRKefRaAd7u+S+uKrYmIi2DA/AFcMyc479+ttzpcfrTlI37Y+wPuJnfm9JuDz9uTYeRI258JQHh46ud88QXcd1+afwwi4joKfkRERMQ1zp61jar4+tqysaUV5MTEOC//9FNjmpplPx8wNtx8+WVbprbVq9lxdgfd/9edsKgw6pWux4r7VlDCt0Tm+jp8ONSsaWy2Onq0UTZ1KgCe7p58P+B7SvmVYmfYTgbcDfHuKe5v3Nj4jNd9u/tbRq0YBcDbXd6mefnmRp8//NDY28ji999T96VTp8z1WUSyTcGPiIiIuIZl/x6AGTNswc+//zpvn1ZQ9PjjxjS5QYOM64AAI+gJCTEytpnNLCx1nq7fdOVa4jVuq3Ybmx/ZTM3gmpnv64wZcOiQkZzh7NlU1RUCK7AoZBS+CfBLTXiwL0R42zXo1ct6+sGmDxi0cBDJ5mQeavwQo1uNdnxYQIBtFCzlfkVTpzqk3s4TljVMIkWAgh8RERFxjdOnjWOrVtCmjW29jv16G3tbt9qmtcXEGAkQEuymmIWFAXCpdADHrxznv8j/OH7lOPctvI/+P/Tn4rWLNA5pzPcDvsffy//G+nzunDH640Tb0+7M/wHckmFeQwgZA0MG+XHoiXtg3DiSkpN4cdWLPPubMV1uRIsRfN4njWl3loQLf//tWB4SkrptbitXLq97IJJrtM+PiIiIZM+FCzBnjjGSAlC/vnF0sgkpQFKVSrxV+T9WVD5H2W96E3pTXfw/n80x82Wu9uzM1SFwyRdOBUZzxQeS3cLgwyoOz3AzufF8m+d5tcOreHt4O32PA29vx3U/FpGRztvv3AnjxtEL+Ok7eL4L7CsDX9eMYZ7bAgYsN7Hv/D7+CTem5k26dRLj2o5zHviALRBMKUXShDyR4GRNU9Wqud8PkVyg4EdERESyZ+RI+PZb23W1asbRSfCT6AYPdLnKtzclGQWnVhg/Da43+G8VVEl1G74eviQkJ5CYnEiTkCbM6DWDlhVaZr6PW7fCJ5/AzJmO5WkFP02aWE9vPwi9DsLmpTN5I+Jnlh1axnd7vgMg0DuQD7t/yNDGQ9N/f0EKfpYuhRYt8qYvIjlMwY+IiIhk3YkTMH06PP00LF7sWGfJyubmOLs+ygse6gvzb7qEZxK8/CcEPTGK//b9xaVdf1HjIpRsfSsBv66meCxUiISS18A/AYpfM5IjXI27SoBXQNojLGlp0MBY55My+Em5HqlxY1iyJNXtJuCWUo1Z2msYa0+s5fs931MhsALDmg+jpG/JjN+fVvBTxUmkl9tSpiG3W88kUtgo+BEREZGsu/deY7POP/6A2rVhxw5bXcnrwYDdPj5XfKDjUNgVAh64Mf+vCvRdexJe7gXFK8KS66mw46/CnrRfW8y7mMs/ioNdu2DLFud115MWtK/UnvaV2mftufbBz9KlsHq1kW3OkgghLzVrltc9EMk1Cn5EREQk8xYuNBIbbNpkXG/bZpvmZmEJfq6PzsR6wJ33GIFPWVMxfrjvJ9r/Og44mTrd9datjtcmk9ORmBx1/rzj9cMPG6NZDRve+DPtg5/69fPX6ErFivDYY/DZZ3ndE5Ecp+BHREREMicmBvr3T11+5IjjtSX4ad+eKz7QbyD8WRmKefiz/OG1NA5pbEsvPWkS3H576meGhhpT68xmaxCVa5580vH6iSeyPzpiP72uVKnsPSsntGql4EeKBKW6FhERkcxp1y79+tq1jS/21zOFXYi7TLsHrwc+cbDkroVG4ANw8qRx3LLF+Wano0YZR1cHPu+/n/V7ypRxbR/8/Fz7PFfo398YAerXL697IpKjNPIjIiIiGTObYfv2tOtHjoS33jLOr3+5f3bFs+wpC+Wuwq//g0Zv3ub83rffTl1Wr142O5yGm2/OWvvly42gwJVyeyQrM4oVg6NH837DVZEcppEfERERydgPP6Rf/847RtBzPfBZd2Id3/zzDSYzLP4OGoVn8X2BgTfWz4zY7/WzeXPG7bt1c81769Qxjh995Jrn5QQPj/wZmIm4kIIfERERydiGDbbzF15IXW+XtSwpOYkRv44A4NFt0OL0Dbwvp4Kf9u2hQwdjWp19prWc3tRz40bjzzDleiIRyVUKfkRERCRjsbHG8eWXjeltZnOaTT/Z+gm7wndR3Kc4k1an0eihh9J/X2jojfUzI56eRnruDz4w1iiVK2eUr1uXOqhr08Z17y1eHFq31siKSB5T8CMiIiJpS0qCoUPh88+N6wxGZPac28PY38cCMOnWSZRykssASD0CYglCAIYPh4CAG+tvVnh5wZkzRiBXvryRec6+buXKnO+DiOQqBT8iIiKF0cKFEBKS9oadmTVkCMyZY7u2nyo2YYJxfOUVACLjIrl3wb3EJsbSvXp3Hm/+uK2tl5fjc729Ha9HjoT4eGMdzgcfZK/PN8rN7mtR7drg65s3/RCRHKPgR0REpLA5dsxIXRweDi1bOtYlJhqBxscfZ/wcsxnmznUssw8IXnoJ/v4bXn6ZyLhIuv+vO3vO7aGMfxlm952Nm8kN/vwTmjQxpprZq1bN9qwXX4TRo40paS1aGAvv81pCQl73QERyQD74fxcRERFxmaSk9BfvP/oozJ5tnD/1VPrPmjEjdZn9yI+HBzRrxoWYC/Sa14stp7dQwqcEvw7+lbIBZY027ds7T5Ht6wunThl7/Lg6lbQrxMfndQ9EJAdo5EdERKQw2bPH8bpaNWjUCO6/37hevNhWl07SAgBmzUpdZhdYhUeF8/qfr9NoZiO2nN5CSd+SrLx/JU3LNc1cX4OD82fgA3DhQl73QERygEZ+RERECpOzZx2vjxwxjv/8A9OmwZUrtrpr16z78qRiNhujSBYmE3TvDrfcAsDyw8u5e/7dXI2/CkDVElVZNmgZtUvVdtEHySNPPgmffALjx+d1T0QkByj4ERERKUwswc5NN8HpFBvsfPih43VUVNrBz6efwo4dxvmKFUbQExBAZPxV3lr3Fu9tfI8kcxJNQpowutVoBtQdgLeHt/NnFSRTpxppuJs0yeueiEgOUPAjIiJSWOzcaaSJBmMjz3nzHOu//dbx+uRJKFPG+bOeeAIAM3DSL4HdYWs5ePEg72x4h3PR5wAY0mgIn/X+DC93L+fPKIg8PaFZs7zuhYjkEAU/IiIihcEDD8A339iu77gjdfBz8KDj9ciRsHGjcX71KixZwsFODflq31z23+fBVfdE9paGc6tud7itZnBN3u3yLn1q9cGkTTtFpAAxmc0ZrXbMfyIjIwkKCiIiIoLADDZbExERKfROnYLQUMeyY8eM4Oell9K/NzkZTCaSXhjLOxvf49WOkOju2MTDzYN6petR2r80fWr2YVjzYYVrtEdECrSsxAYa+RERESnoNm1KXVapkrF/zjPPOK7rCQoy0l1Pnmxcu7mxp7IvT3S6xvrORlH3Q9D7IATGQY2Ff9AwtAW+ntrwU0QKPgU/IiIiBd2+fanLLNPRfH2NNNeWKXE1a8KECTB5MtGe8HoHmNLqGonu4B8PH/0CQ3aCdTJb1fa2Z4mIFHAKfkRERAq6f/4xjm+/bWxC2q2bY33//rbgJzoa/P1Z9/uX3L/0YU4UN4rv2A8f/goVI1M8W4GPiBQiCn5EREQKOkvwc/PNcOutqetr1LCdnzjBl9u/5ImNT5BQHEKvGKM9vS25EH78EQYMMM7//DMHOy0ikvsU/IiIiBR0//1nHKtVc15/vTzJBGPf7sT7Pz8CwN2m+nz18R78E663W77cGDVatw7q1oWSJXO44yIiuUvBj4iISEGWlARxccZ5QIDzNt7eRI4ezr3JP/DLxaUATOgwgVfavoSpzx5YuNBYC2SZLte2bS50XEQk9yn4ERERKchiYmzn9lnd7Jy4coKeVVez7/w5fD18mdNvDnfVu8uobNzY+BERKQIU/IiIiBRk0dHG0WQykh2kEBYVRpdvunD40mHKFyvPTwN/onn55rncSRGR/EHBj4iISEG2f79x9PNLlZntVMQpa+BTKagS6x9aT4XACnnQSRGR/EHBj4iISEH200/GsUwZh+JTEadoN6sdJyJOEBoUyqoHVinwEZEizy2vOyAiIiKZFBtrG+mxOHPGOD78sLUoLjGOAfMHcCLiBDVK1mD9g+upVjKNTHAiIkWIgh8REZGCIDkZunc3UlD/8YetPDzcONqluR61fBRbTm+hhE8JVty3gopBFXO3ryIi+ZSCHxERkfwkKgrOnUtd/skntk1HFy+2la9daxxDQgD4cvuXzNw2ExMm5t45lyolquRsf0VEChAFPyIiIvlJ69ZQuTIcOmRch4VBrVowYoStzcKFULUqdOpkKwsJ4fejv/PkL08CMLHjRHrU6JF7/RYRKQCU8EBERCS/uHQJdu82zpctg969oXr11O1OnTKOx45Zizb7X6HfN/2IT4rn7np381L7l3KhwyIiBYtGfkRERPKLrVtt55cuGWt87KWxGenexZ/R89teRCdE07VqV77u9zVuJv0nXkQkJf0/o4iISE5Zvx6aN4cNGzLXfvNm2/mXX8Lhw7brjh0dEx1c92cl6HpwPJeuXaLlTS1ZeM9CvD28s9VtEZHCStPeREREckq7dsaxY0dISEi7XXIyBAZCdLStzJLCGiAmBnx9jfNBg2DePDZ/+jIvH5jJysDzEHOOBmUasGzQMgK8Alz+MURECoscH/l5++23MZlMjBo1yloWGxvLU089RXBwMAEBAfTv359wS6pOERGRwuDvv23niYlGgJPSv/8ae/esWOEY+Njbvdsa+Ow/v59JD1an3UdNueXs66wMPI+HmwfDmg1jzZA1BPsF58AHEREpPHJ05Gfr1q18+umnNGzY0KH8mWeeYdmyZcyfP5+goCCGDx/OnXfeyYbMTgsQERHJjx54ANasgR9/hFtucax77DF4800oU8a4XrIE+vY1zvv0cfo48x392F06mUV/TGT+vvnsPb/XWuducmdIoyG83OFlKhevnAMfRkSk8DGZzWZzTjw4KiqKpk2b8sknn/DGG2/QuHFjpk6dSkREBKVLl2bevHkMGDAAgH///Zc6deqwadMmbkn5HwsnIiMjCQoKIiIigsDAwJzovoiIiHPHj0OV63vnfPop3HmnEfS0bAkTJqR/b40acPCgcW4ypa4fO5Yjzz3C4sfas9HvAuvqB3I+7pK12tPNk67VutK7Zm961uhJaFCoSz6SiEhBlpXYIMdGfp566il69epFly5deOONN6zl27ZtIyEhgS5duljLateuTWhoaJrBT1xcHHFxcdbryMjInOq2iIhI2i5dsgU+AMOGwaZN8Ouvxk9GLHv3JCUR7w7Lq8Nv1SDGE674wI6g2Rz/+F1odL193CV8PXy5tcqt3FX3LvrW7ktxn+Ku/lQiIkVGjgQ/3333Hdu3b2erfcrO68LCwvDy8qJ48eIO5WXLliUsLMzp89566y0mTpyYE10VEZGC6quv4OhReO01cMul5KWLFqUumz077fbFihlT3ew2KN1xZjtT/3ibxc9BpE+K9gnncDe506FyB7pX606riq1ocVMLvNy9XNJ9EZGizuXBz6lTp3j66adZuXIlPj4p/1/9xowbN45nn33Weh0ZGUnFihVd8mwRESkg4uPh5Elj00+zGR5+2Chv2tSYepYVkyYZozBffZW1wMk+9fSPP8L16dsOPv7YGCGqXx/69YOdOwEIC4BXO8Jnnzcz2vlAuRh3BtQbQPnPvycgHmrMXU7riq0p5l0sa59HREQyxeXBz7Zt2zh37hxNmza1liUlJbF27Vo++ugjVqxYQXx8PFeuXHEY/QkPDyckJMTpM729vfH21p4FIiJF1tWr0Ls3/PknPPccPPigre6//4zjhQvG2puHH4b+/dN+VmwsjB9vnNerZzwvsy5fNo4TJxrv+Plno18WtWvDk0863BJZsQxvdYbpLSH6+gDOPXtgxGZo1bIfbu98BwldITQUqnfNfF9ERCTLXB78dO7cmd27dzuUPfjgg9SuXZvnn3+eihUr4unpyapVq+h//T9OBw4c4OTJk7Rq1crV3RERkcKgQwfYscM4f+8948ciIsI4Pvecbe1NWrl8rl0z1uhYjB2bteDHsua02PWRmdtvN95lNkN4OJQta21qNpv5ds+3jPltDGevb/fT8j94dyW0P3G9UdULxtEyiiUiIjnK5cFPsWLFqF+/vkOZv78/wcHB1vKHH36YZ599lpIlSxIYGMiIESNo1apVpjK9iYhIEWI2w6xZtsDHmXPnjGOKf3hL5euvYciQ7PXn6lXjmDKbkMkEdrMXdobtZMSvI1h/cj0ANUpU571PDtPnADjkeOvQIXv9ERGRLMnRfX7S8sEHH+Dm5kb//v2Ji4ujW7dufPLJJ3nRFRERya8SE8HTM+N2H30E06bBxYvO681mY/3Nvn2p65o0yXx/Nm+GpUuN85IlnTaJio/i5dUvM23LNJLNyfh5+jGu7TjGtB6Dz9O+qW+4777Mv19ERLItV4KfP/74w+Hax8eHjz/+mI8//jg3Xi8iIgXRxo2Zb3v4sJFkwJkffrAGPmYgzsNILR3jCUEJUTikFkhIMIIu3xSBysWLjpuWduxoPTWbzRy9fJQ/T/zJa3++xokIY07b3fXuZnLXyVQMcpKgZ/x4Y7SoevXMf0YREcm2PBn5ERERydCwYanL/vzTNlXs3XeNNTsATz1lW49jxxwby64vJzHjdlhdBY6VgCS75G5eSYfoO/9uHmj0AB3Kt6ZYm06wf7+Rya1PH1tD+z18Hn+cMM84ft72OT8d+In1J9cTERdhra4UVIlPb/+UbtW7OXbmiSdgxgx4+ml4/fWs/mmIiIgLmMzmtFaF5l9Z2cVVREQKoH37jExs9ho0gF27YNQoqFnTCHiKFYOoqFS3J8fFMnfOaN7a9TH7Szt/hUcSJLrbrr1MHnQ+mMgt/0GSCRJ79aBW14H0r9Mfr6nTOfbuOHa3rMzX9zfk5wM/Y8b2n09vd28alm1I31p9GXXLKPy9/FO/MD4e/vrLGEHy0r49IiKukpXYQMGPiIjkP199lToD2vHjUKmSY5nJIX0AMZ7wv4YwvXsJ9ngaaam9E+H2g/Dwqz9R/6YmBPkE4XvyDJ4167AzBL5sAstqGqNCWdHiphb0rdWX7tW706BMAzzdM7E+SUREXE7Bj4iIFGzdu8OKFcb5hQtGlrXKlVO3e+wx+PxzAHaEwJ33wPHrQUxAHLy0Dp7cCoF4G/v72LMLnMzAv6VgcW0jCPJMgmQT/NIqmJOJRiIF3wSo7VuRdk3v4Mmbn6RWqVou/tAiInIjshIbaM2PiIjkP4cOGcc//4TgYOPHmVdfJe6rz5nWEsbfCvEeUDECntkED+6E4pZ4x8eU+t5u3awBlgmocwHqrAeaNoXt2wFIrvcQl0NLw3NjKRELbgnHwN099bNERKRAUPAjIiL5T3i4cbzppjSbXEu4xvtHZjPjaTh9/R/6+v4LsxZDiRSDPKn25QG4+Wbb6JK977+Hb7+FV17B7d33sIZdo0Yp8BERKeAU/IiISP5iNkNMjHHu5+e0ydHLR+n/Q392hu2EQLgpEl5fDUN3pthE1OKll1KXudmlfatUCU6cgFatjPTTzt5bpUoWP4iIiOQ3bhk3ERERuUFz5xqjN0ePZv6eTz81AiBIFYSYzWbm7JxDs8+asTNsJ6X9SjOn3fsc8RzNgz+fwlShgq3x1q0QHQ3btsGIEanf06aN7fyPP4x3WvYWcrb/zq23Zv4ziIhIvqSEByIiknPss7ElJkKdOsZ6nmLFnO7LQ3w8eHvbruPirGmhzWYzd82/iwX7FwBwS4VbmH/XfCoE2gU88+fD3XfD8OEwfXr6fTObYelSY7SnVCnHusREqF0bjhwxrh95xJpYQURE8hclPBARkfxn6VJbIoOrVyE52XHqGRijNfY8bemjv9rxFQv2L8Db3ZvXOr3GM7c8kzq99F132UaNMmIyQe/ezus8POD5541scuB85EhERAocTXsTEZGc06CB7fzwYce6ixdTtx83zvH6+shRWFQYY1aOAWDSrZMY22Zszu+rYz8CVbduzr5LRERyhUZ+REQk59gHEGPGONYdPGhMN3v5ZWMPnwoVYN06p495evnTXIm9QrNyzXj6lqdzrr/27rrLWLNUt64xEiQiIgWe/t9cRERyTlJS2nVt28K778KkScZ1ynnaXboA8NO/P/HD3h9wN7nzee/P8XDLpf90+fo6T4UtIiIFloIfERHJOYmJ6de/8ILt3JIAoXx5WLQIatfmQswFHltqrLsZ8//27js8qmL/4/g7vZDeKQm9d0IL0gSkqigoKEjTi0rxilgQUNF7RSzXgv7sDZQmFopSLEAAMaJgKKFEQJAaWkhCS0iZ3x/HbLIkoaZBPq/nyZOzZ2Z35+xw1nydme+0eYym5ZsWUUNFRKQsUPAjIiJF50IjP2AlPTjfypW2VNOjvurPkdNHqB9cn2c7Plv47RMRkTJFCQ9ERKTo5B75mTHDysTm7X3h54SFAfBF3BfM3TIXZ0dnpt82HXdn9yJsqIiIlAUa+RERkaKTPfLzyy/Wfjpgl74aACcn+xEiLy8STiUwcvFIACa2m0hkhchiaKyIiFzvNPIjIiJFJ3vkx8kp51zuY4AOHXKO/f1JPJtIz5k9STybSNOwpkxsN7Ho2ykiImWCgh8RESk62SM6uVNFn582umdP2+EJH1e6ft6V2IRYQsqFMLvv7KLfz0dERMoMBT8iIlJ08hv5yT3trX9/GDkS6tYlyR26DXFk/aH1BHsGs3zwcmoH1S7e9oqIyHVNwY+IyNUyBl5/3Vqo37w5xMVdPMVzWZH9OeQe7QkKyjmeMwc8PNg17xM6TajI746HCPQIZNngZdQPqV+8bRURkeuegh8RkathDNSsCWPHwuHDsH49NGwI/v6QnHzlr9uiBTg4wLhxecu2bIEff4R27WDYsCt/j+KQPe0t98jPjBng5wf//S8Ac+Lm0PSbrsRmHbAFPg1DGxZ/W0VE5LqnbG8iIlfjzTdh166850+dgv/7P5h4mYv1T5+GQ4dg3Trr8csvQ5Mm0KABTJgA//43dO2aU//nn601M3feecWXUKTOnbN+5x75qVsXjh/ndMZZHl74Lz6O/RiAthFtmdVnFuG+4SXQUBERKQs08iMiciUyM+GWW2DMmJxzTz1lX2fDhst7zR07wMvLGknKbdw4mDIFvvvOPvDJ1q8frF59ee9VHM6ds4I5gIAAu6Ktx7fT8qOWfBz7MQ448HT7p1kxZIUCHxERKVIa+RERuVyxsXDrrbB/f8656dNh8GA4eRKmTrXOrVlz6a956hTUqpV/2b59MHt2vkUG2BwKy55oT9K9dxPZ/i5CyoXg6eKJl6sXlX0r4+TolO9zC2QMfPEFtGkDERGX99zctm+3fjs4WNPc/vFp7KeMWjyKsxlnCfMKY2afmXSq2unK30dEROQSORhjTEk34nKlpKTg6+tLcnIyPj4+Jd0cESlLdu2CGjXyns/Ksv7IT0y0kh7s3m2dP348z6gHYAUY06dD+/ZQrRpER8ONN15yMzIdYEEdeLEt/F6x4HqBHoHcXOtmBjQcQKeqnXB2vIT/53XTTfDTT+DmBm+/De+9BwsWQIUKVrvXroU6dewCmnx9+CHcf791bAypGak8vORhPvjjA+ttqt3EjD4zCCkXcmkXLSIiko/LiQ0U/IiIXKrYWGjWLO/5nj1h0aKcx5mZ4OyMAbYGw6oHuvFzk0AOnjqIu7M77s7uOO87gMuvv+OcBc444pJpcM4wuGSBdxoEnoXgO4cQ9M50qiZBtRNw1BN+eKAzS32P8oPjbhIzTgLgng4d90DwGdjYtREpSYc5Y9JJzjxNWmaarVkRvhG80e0NbqtzGw4ODgVfZ35l994LH39sXefNN1ufw/r1F/68/vtfeOYZuOsudr39PHd+eSexCbE44MBzHZ9jYvuJODpo9rWIiFwdBT8iIoXtxIn8R3AA3ngDHn7Y9jA9M50PhtTnXd8dbCmkQQ3HLMg6L04I8AhgZPORPLToKCGvvQ/168P8+bY1QxlPT2TNkE58sf0r5m6Zy/GzxwGoH1yf4c2Gc0+jewj0DLR/0bNnwdMz/0YYA4MGWdnawFrPc37d9HQYMQKqV4e9e+G995j/TD+GenxPcloyQZ5BzOwzk67V81m7JCIicgUU/IiIFKb4eGuaV0EyMmypnE+cPcFNn9/E+kPWqIh7OrTda/3UatGNcwPvJjUjlYyP3idjQyzpTpDhaP2kO0L6mIdIccni+NnjHD19lCOnj7AzcSdnM84CEFk+ku41utO9RndaVWyFi5ML/P47tGwJFSta0+8OHcpp22OPwSuvcCb9DJNXTWbq2qmcTreSEHi5ejG502RGtRiVsy6ooNEtsBIyfPKJlXwB4P33oXFj6NDBStQwZgykppL1wmR2+cOORhVYy0H+29EBg6FNeBu+uOMLKvlUuuKuEBEROZ+CHxGRwvTyy/b77Tz0ELz1Vs7jf75Gz2Weo9uMbkTvicbf0ZPnvjvD4I3gmz3zLHvqGECjRrB5c973yucrOctkcfDkQXzdfPF28877nIMHrcDnQhYuhFtuISk1iVmbZ/HeuvfYfMR6/2r+1Xik9SMMe3M15WbOvfDruLtDaqrdqQxH2B4E39WCxTVhSzAknjcgdH+z+/m/nv9nBWsiIiKFSMGPiEhhSE+3Ap333885N3y4lQAgMtJKZb11K9StizGGYQuGMX3jdLxdvVlz7xoafrnSen5uSUlW5rYRI6zHo0dbKaGnT4cHHsjJFHc5/lljZMfVNWePnWybN4OPD7z8MllPP8WH+xYwfsmjnMiyRoJ8U2HAZui9Hep16sexX37iYL8eHDyyi8QNv+KcBS5Z4JwFiR7wZyBsCrUCn7Tz3t49HWodhxqJ0LHf44zq86LW94iISJFQ8CMicrXS0qwgJ/c+PjEx0Lq1dWyMNcXsn+luk1dN5qkVT+Hk4MR3A76je43usHIldOxY8Hs4O1ujKE5OVopsL6/8kw1citzPu+MO+PJLaNfO2gQ1P23awPffcybAm+mN4dU2sKuAJU2XwiMd2v8Nt2+DlgegwRErUAJyrk1ERKQIKPgREblaffvCN9/Yn8tOZ32e2ZtnM+CbAQC80/MdRrQYkVM4fjy8+GL+71GzJvz5Z+G09+23rVEksNJUd+5sHScmQmBg/s/58UcrrTWQ5QA/VYOv6llT1474OhNYLogK3hWo4FWewAOJZMXEWOuSnMDH059qf52gcQLUW7aRqhUb4LhhozUFr2NHeO45a+3RE09Aw4aFc40iIiL5uJzYQJucioiANTpx5gyEhlpZys4PfN54I9/AZ/72+QyaNwiAsa3H2gc+YCUHeOEFqFoV/v7bvmzgwMJrf3h4znGjRjnHAQGweLGVjvt8n3xiO3Q00HUXdB3/kbXZart2eeufOGFNzRs02NrLaMMG6N49p7xpU+sHrHVSIiIipYxGfkREZs6Ee+6xjgMCrNGSbFOmwJNP5vu0RX8u4vYvbic9K517Gt3DtN7TcrKmne+DD6zAIVunTtbGoYU1HezECahc2QqyNm60LzMGWrWyssLlVrOmlcEt25tv5l2jJCIiUspp2puIyKUyBhwLWIjv728fCOXyw64fuHX2raRlptGvfj9m9pmJs+NFBtP/+suaAvfnn1bg4+t7lY0/z7Fj4OEB5crlLVuzBtq2vfDzr73/HIiIiGjam4jIJVuxIv/zDg6wZUu+RdF7ouk9pzdpmWncXud2Ztw+4+KBD0C1atYIUFEJCiq4rHLlnOMuXax1Qbl9+mnRtElERKQUUd5RESnb1q3LOX7jDet3x45W+ujy5fNUX7N3DTfPupnUjFR61ezFnDvmXBt71+S+lpEj7cvOnIGhQ4u1OSIiIiVBwY+IlG3BwdbvVq3g4YetqV8rVuSb3GDlnpX0mNmD0+mn6Vq9K1/1+wpXJ9dibvAVcnKCiROhTx+45RZYtMjKAvfdd9ZUORERkTJA095EpGzL3gg0n1Ge3GZsmsF9C+/jXOY5OlbpyLz+83B3di+GBhai55/POe7Z01ojJCIiUoZo5EdEyrbs4Mc1/xGck2kneeDbBxg0bxDnMs/Rp24fFg9YjKeLZzE2UkRERAqDRn5E5PpiTL5T1gqUnm79Pi/4yczKZPrG6UxYNoHDpw/jgANPtX+KSR0mFZzOWkREREo1BT8icu347Tcr/XTNmnnLTp2y1rIkJMDy5RedxmaTPfLjkpO0IHpPNGO/H0tsQiwANQJq8E7Pd7ip+k1XewUiIiJSgjTtTUSuDQkJVlKCWrXg22+tc1lZ8McfkJpqZTCLjobt26FCBRgyJCd7W27791uZzfbvtx7nmva2/uB6bpl9CzdOv5HYhFh83Xx5teurbBm5RYGPiIjIdUCbnIrItWH1amjf3jquUcPap6ZfP2s0qEcPWLIk/+dNnJiz0H/SJPjPfzBAghekLV5IcOdbOegN4wcE83XQUQCcHJx4sPmDTOowieBywUV/bSIiInLFLic2UPAjIteGTp0K3pD0YhYt4khmCkuevpulNWBlFTjknbeao4MjAxoO4Kl2T1E7qPZVNVdERESKx+XEBlrzIyKl34EDlxb4jBxpje6EhnLGBX4Jh9UR8P28XvxWEcztOVUds8A1E1L/WerTMyiKl+78gAYhDYrmGkRERKTEKfgRkdLvkUdyjvv3hy++yFunb1/Spr7G19u+5uNPOrJ6/xrSs9LtqjQ7CL0a9aVTw1tpNekDPKLXkOQOZuhQ/Cd9WsQXISIiIiWt0BMeTJkyhRYtWuDt7U1ISAi33XYb8fHxdnVSU1MZNWoUgYGBeHl50bdvXw4fPlzYTRGRa8Wzz0JUFBw6lH/5jh3W77AwmDMH5s+HOnXg888hLY0d33zIY/eFU/G1igz8ZiDL90aTnpVOpXLlGbirHB8uhAOvwvoP4D9Dp9Ox/WA8lq+GmjXxO+eI//CHiutKRUREpAQV+pqf7t27c9ddd9GiRQsyMjKYMGECcXFxbN26lXLlygEwYsQIFi1axLRp0/D19WX06NE4OjqyZs2aS3oPrfkRuY7ExUHDhtZxZCT8+is45xqUPnUKvP9ZoLNiBXTsaCtaf3A9L/z8AvO2zcNgfZVV8qnEv5r+i4GNBlLdvzoOxsDWrdZ7PPYYvPJKzmsfOQLJyfmnzhYREZFrQqlKeHD06FFCQkJYuXIl7du3Jzk5meDgYGbNmsUdd9wBwPbt26lbty4xMTG0bt36oq+p4EfkOrF9u5W2+okn8pZ99ZW1b4+bW865EyfAz4+diTuZuHwic7fMtRX1rNmTByMfpEfNHjg7akaviIhIWVGqEh4kJycDEBAQAMD69etJT0+nS5cutjp16tQhIiKiwOAnLS2NtLQ02+OUlJQibrWIFLkDB6Bu3YLL77jDmgqXzcmJVC93nl/+FC+teYmMrAwccGBgo4GMbzueesH1ir7NIiIick0r0k1Os7KyGDNmDDfccAMNGlgZlBISEnB1dcXPz8+ubmhoKAkJCfm+zpQpU/D19bX9hIeHF2WzRaSoJSZC9er25+bPh7vusj8XE2M7XPPTJzR5rwmTV08mIyuD7jW6s+HBDXx+++cKfEREROSSFGnwM2rUKOLi4pgzZ85Vvc748eNJTk62/ezbt6+QWigily0zEz74wFqbcyX+/BMCAyHXaC533AG9e8Ps2fDLL3bVtwfBPW/dSLuVQ4k/Hk95r/J83e9rlgxcQqPQRldxISIiIlLWFNm0t9GjR/Pdd9+xatUqKlWqZDsfFhbGuXPnSEpKshv9OXz4MGFhYfm+lpubG2655/2LSMlITobco7bjxsHDD0P58pf2/JQUqJ1r81AfHzh6FFxdc85FRcHs2fz22N28dAPMqwvmuLXHz7Amw3i166v4e/hf/bWIiIhImVPowY8xhoceeoh58+YRHR1N1apV7cojIyNxcXFh2bJl9O3bF4D4+Hj27t1LVO75/SJS+rRvb//4pZdg1y748ssLPy8+3hrV+f13+/OHD9sFPsYYvt/1PS+lvU/08JxqvWv35pkOz9CsfLOrvAAREREpywo9+Bk1ahSzZs1iwYIFeHt729bx+Pr64uHhga+vL/fddx9jx44lICAAHx8fHnroIaKioi4p05uIlJA9e2DTprznL5Si/vRpa31PnTp5y3btAnd30jPTiU2IZenOpXy+6XN2Ju4EwNnRmYENB/J4m8epH1K/cK5BREREyrRCD37effddADrm2osD4NNPP2Xo0KEAvP766zg6OtK3b1/S0tLo1q0b77zzTmE3RUQK0/PPW7/r1LECnsBA6/F597rNt99aa3nOnbM7neQOMZ/+hzV7PmHN6jX8duA3zqSfsZV7uXoxvNlwHmn9COG+Sm4iIiIihafI9/kpCtrnR6QYnT0LDRrAX39Zjz/5BIYNg/ffhwcftBIVzJ+fU//4cdi8GW68EYD9PrCiCkRXgVWVYWdg3rfwd/enXeV23FH3Dm6vezterl5FfVUiIiJynShV+/yIyDUsPR0qVICkpJxzAwZYv73+CVD+/BOeegoeeADCw+Hee0n8cSFzm8OMRrAmIu/L1vStRpsq7bgh/AZuiLiBOkF1cHQo0uSTIiIiIgp+REq15GTw9gbHYg4MjIGhQ+Gzz+zPv/46ZGdejPgnqtm2DSZPJn3hfH54bSTT3Ray4FE498+3iwMOtHCpTMcWd9LBsRqtnasQ0KF7sV2KiIiISDYFPyKl1Y8/Qq9eVhDywQcXr5+WZqWNzpVa/op9/HHewGfyZBg9Oudxy5aYAH/+cDvBZ41hdsMtHF0zCv7JTdAo0ZXB3cdxd9sHqeBd4erbJCIiInKVtOZHpLTq1AlWWPvbkJqaM+KSn+PHISjIOg4Kgpo1rfTTFSte/vuePGnt5ZOVlXPu4EG7vXyMMazYs4Lnlk9i1f6fbeeDT8Pdm2FYnbto8vrsy39vERERkct0ObGBJtlLjnPnrOlO14KzZy9eZ8AAqF8/Z71KYiKsXAknThRp065acjLMmJET+AC4u8OLLxb8nOx1OADHjkFMjDVSk/s1Dx68+Ht//rm18Wh24LNqlfVv4p/AxxjDsr+W0WFaBzp/1plV+3/G1cmV/pl1+W4mHHgVpm6qQJPnP7qMCxYREREpHhr5ESvoyR5V6NMHvv66ZNuTn7Q0K6NYbKy1sSbAv/8NU6fmX3/16pwNOR0dYeZMuPtu6/Gtt8KCBYXXto8/hiVL4MMPwd//8p5rDOzfbwVkLi5Qr97F64OViGDuXJg+3UpIMH06BjjmCX/7wd++8Hf7hhxuF4l/RC0qTH6T8jsTKD/rW6o17YSni2fe1z5zBsqVy3ncvTssXAguLpxNP8s3277h7d/fJmZ/DACuTq7c3+x+xrUdRyWfSrBjByxdCvfcc/mfg4iIiMgVupzYQMGPwOzZeUcOAvPJR1ySxo/Pf+Tjzz+tKV5gTQ378EPrD/affsr3ZTIdYGMYLK8KieP+TY3wxtQMqEntoNoEewbj4OBwee3avBkaNcr1BpmXnpwgNRXatoX1663HzZrBH3/Y1+nUCZYvt47d3eGxx+D55zHA9iCY0wDWVrKCnb2BTpxxzLzo2zo6OFI/uD6V/SoT6BFo/XgGEjj9SwJiNnDKFQ72aseBqAYcOHmA/Sn72X5su20vHjcnN+6PvJ9xN4yjos8VTKsTERERKUQKfuTiTpyA33+HV1+FH37IW36xNSa5X2fMGOjXz1qcX1Tc3PJslmnzxBNQrZq150wBDnjD2y3hw2ZwrFz+dfzd/KgbUo+6QXWpE1SHukF1aRjakAjffHI1gxXoOJ+XM+RCo1FgjdyMGwevvFJwHYB27ayRHS8v+OgjssY+wo6qvqwvl8zPEbCkBuwpYHClvLM/lXefICIZwk7BCQ845AWHvK3PIcnjwm9dkMq+lbm36b38q9m/lMBARERESg0FP1Kwo0fhrrtyRhNyCwy0Fs5nS0+3/+M+M9Paz8Xf3wo4AKKi4NdfreOi/KfUrh38/DP873/w6KMwYQJMmXLh5+zbx+7jO3l66m18EZ5ChqPVPu806LAHwlNgR0V3dnimstcXTAGDPk3CmtCvXj/61e9H9YDqOQU7dkCtWnmfcKHPYe5c6N//wu1OSIDQUAC2H9vOzBVTmRbzHvt97au5ZjrQJasKvZfupnrf4VR+4AnCfcJxc3C2+jI5OW/TgIPe8Ed9fxLqhnP8XDLHj/zNcU847gGJHuDVpiNhwVWo5F2Jij4Vqehdkar+VakfXP/yR8ZEREREipiCH8nftm35rylxcoI9e6xpVcHBOefXryelfg22H9vOjuM72DHvQ3ZsWsl+Hzjr7UaqoyEt8xypzuBkoE6LHrSNaEuvmr1oFNqocP9QbtbMWu+zZIm1FuWPPyAyMm+9Z5+Frl3Z53qWN48v4a3f3iItMw2A9pXbM6bVGG6p1h1nd/s1L2dcYEcAbAuGbSP7sT0Yth3dxtajW8k0OVPJIstHMrDhQO5pdA/BOw9CkyZWwYQJ8MIL1vEHH8Dw4flfx7PPwnPP2Z8bMoSzM6cTXQXiQmD7E/eyL2Ufe5L2sCNxh62aRzo0SYDmIU3oOvS/3FjlRsq5FjCMZQxs3WoFZydOQEgI7N0LlSvnXz/bTTflPxIoIiIiUkop+JEc69bBM89AdHTeDGnffmtNVcsdpKSm8mtND2Y2hJU3ViHuzN8YLv+fSJOwJoxsPpJBjQfh7ux+ddcAULu2tb5n1SprFAjg8GEIC8upk5zM2pRtvP7r63y19Stb0NK5amde6vISkRVyBUvR0XDjjfm/12uvwSOPAHDszDHmbZvH3K1zWb57OVnGyoLm7OjMzT7NGfbur0Q5VcEsXkR6w/qkO0HoKfBITLE2JwUrsCxXzgoss4MfNzeSZn7M9yfWMd8/ge/+mMMp17xNcXZ0pmv1rgw5Hs6tY9/HPYO8I3KXo0cPKylBQTZvhgYNruy1RUREREqAgh/Jkd/oy9dfQ5cuVkrjXOKPxfPET0+wMH6h3fnyJ6HmcaiZaP2ukgTl0sEtA9wdXXE/Y43+bKwfyA+D2vDDrh9soy2h5UIZ2mQofer2IbJ8JE6OTpfX/owMeOgheO896/Eff0DTpjnln31GxrAhzHv+Hl4P2WXLRAZwY5UbeTTqUXrW7Jn/KFSrVvDbb9Z0ulq1YNgwWLTIvs6ECbaU0UdOH+GrrV8xbcM0fj/4+wWb3TAjgCaJroRmeuK39S8AMtu2IePgfs4d2MvaDtVZnbXHblSpUqYXbavfSJ3KkVT1r0qYVxgtKrTA38PfGsnZuNFa23S1/+YTE62NU7/91no8ZAi89ZY18uficnWvLSIiIlLMFPyI5fPPYfBg+3PNmuVkF/vH0dNHeW7lc7y37j0yTSZOODJgYxa3b4M2+yD0dK7K991nrft59lkrMKhVy8pA9uqrtirHl3/HNLftTF07lX0p+2znPV08aVmxJTdVu4mu1bvSNKzphYOh/NbHHDlim5p3Mu0kH//xEVN/eZ09p6z3cXVyZUDDATzc6mGahDW58OdjjJVC2/2fkalHHoE33ii4fu/e8M034OhIXJsaTPPexYxGcNjLKnbJcsAp05B6GfFD3aC63FzrZvrW7UuLii1wdNDWWyIiIiKXQ8GPwF9/QfVci/MfeMAaOfjsM1tq6NSMVN5c+yaTV08mJS0FgFtq3cJL7f5D3R6DIC7O/jUdHWHDBmjYMO/7depkvynnlCmkPzaW+etn8NW+71myYwknz520e4q3qzetQprRwbEqnTv/i5aVWucEQ8nJ4Odn/x7/rPfZn7Kft9a+xfvr3yc5zVrUH+QZxMjmIxnRYgRhXmFckc8+s0ZBLubsWahSBQ4fxgCmdSscfomxRpdeeonD/32SVZVhtz8cKQdJ7uBgwNGASxY4+/pT/eHn6FWrF9X8q11ZW0VEREQEUPBTtv38szXas3t3zrm9eyE83K7a4h2LGb14NLuTrHpNw5ryv67/o1PVTlYFY2D7dmtkpEkTa3pYZqaV3S0/xuTd32bSJGt9S+PGZDk7Ef/IYKJrufJD/GKW7VnOycwzdtWDPYPpX78/wyOH0+jj72DiRKuga1d47TW2BMPLv7zMrM2zyMjKAKBWYC0ejXqUQY0G4eFyhTmcs2VmWlnsdu6EadOs9S8dOuSt5+5upQIHa+PVm2+2kkaANaXwjjty6oaEWGuI7rkn59wtt1h7EYmIiIjIVVPwU1bll81ryBDrD/l//HXiL8Z+P5YF8QsAqOBdgRc6vcCgxoOufsrVnXfCV18VXB4WZqWHrluXzAP7iQuBNRGwogr8VM1+/5nIQw603mdwqlWb3S1rWRnncmU+61C5A49GPUqvWr2Kb6rYww/Dm2/mPG7VKifNd7b166F5c+t4xw6oUcM63r4d6ta1jv/6C6pWLfr2ioiIiJQBCn7KkhMn4KWXrGlnCxbAO+/Yly9bBp068XfS37zx6xu8u+5d0jLTcHJwYkzrMUzqMAlvN+/CaUtGBowebbUjISH/Os2bWxnozpPuCMurwkeP3sj8QyvIyGcpkAMO9Knbh3E3jKNFxRaF0+bLcf7o1uuvWxu85nbunJVMomJFmDXLPuHEW29ZI0EX2+dHRERERC6Zgp/rnTHwyiswblz+5XPnWqmhDx8mtkEQr/zyCnO3zLVlFutSrQtTu0+lXnA+e/4UhthYK7FCbudvoJrt2Wfh3/+GgADbqcPl4PsasK1ZBNx9N+F+EdQJqkP94PqEeoUWTZsvVfa1+ftb+yaFlnB7RERERMq4y4kNrnCzEClS+/ZZozljxuRMmwJrPc9rr8G8eQU/t39/Tt7anW+2fcO0g9OI/iXaVtSlWhceb/M4N1W7qXA3ID1f06bWdDBXV2tT1SNHoFIl8PWFk7mSHoweba0LArtNQkNPw+CIW+CTUrgupmlTOHPGGuXyLqQRMxEREREpFhr5KW2MgW7d4McfoXx5a33Ivn3WSMM/KZ7z8PUl67be/JQcy2f3NGBe/ALOpFvJBJwcnOjfoD+PRT1G0/JN839+cbn55px9dObNg1tvzZlGlp5uBUuQbzpuEREREZH8aOTnWrZ0qRX4ABw6BB4XyGB28iQHv53Fp86b+fDAt/yd/DfEbQasLGiDGg1icOPBRPhGFEPDL8ELL1ipsps2hdtusy9zcbECPxERERGRIqLgpzR46imYPNlKKX3ggHXOw8PaTyYfWTM+54dWQXywaDALdyy0reXxc/djQIMBDG48mJYVWxbt1LYr0aiRNYpV2tolIiIiImWCgp+Ssm8fDBwIq1fnnNuwIef4jz/gww+tNT7ZT/GBz+eM58M/n2bPzD228zeE38D9kfdzZ707r36vm6KmwEdERERESoiCn6Iwb54VuHz+uZXl7HwpKRBxgalo/fpBnToc++945nUP4ae/V/DLzuXs90iH36YA1ijP4EaDGR45nAYhDYroQkRERERErh8KfopCnz7W74YN4eBB+7LExLwBUZUqsHAhfPEFplcv1lVy4L0F9zFz80zSMtOsOh7g6OBIVKWoa2eUR0RERESkFFHwU5gyMqyUzdkOHbLWuWzcaE33evddGDnS/jkJCba9Yo5VL89Nn9/Ehh822IqbhjWld+3edKzSkcgKkXi5ehXDhYiIiIiIXH8U/FytzEz46CNo1w42bbI2H81t82a46SYr09n5gc+BA3abZAZ6BJKemY6bkxt31LuDEc1H0Ca8TelLXCAiIiIicg1S8HO1XnoJJk60jitXzjkfFmaN6gAsWwatWuWUubvD0aPgZT+K4+DgwIw+M4jwjSDAI6CIGy4iIiIiUrY4lnQDrmnGWMFPtr//tn6//ro15e3VV/M+59ZbrRTWXvlPX2sS1kSBj4iIiIhIEVDwc7W+/z7vuYcesn6PHQsff5xzvkkTWLCgWJolIiIiIiL2FPxcDQcHaN0aUlPtzzs55RwPHJhz/OKLxdMuERERERHJQ8FPYXBzg2eesY4feyxv2axZMGkSdO1a/G0TEREREREAHIwxpqQbcblSUlLw9fUlOTkZHx+fkm6OJT0dfv8dmjcHV9eSbo2IiIiISJlwObGBsr0VFhcXaNOmpFshIiIiIiIF0LQ3EREREREpExT8iIiIiIhImaDgR0REREREygQFPyIiIiIiUiYo+BERERERkTJBwY+IiIiIiJQJCn5ERERERKRMUPAjIiIiIiJlgoIfEREREREpExT8iIiIiIhImaDgR0REREREygQFPyIiIiIiUiYo+BERERERkTJBwY+IiIiIiJQJJRr8vP3221SpUgV3d3datWrFb7/9VpLNERERERGR61iJBT9ffPEFY8eOZdKkSfzxxx80btyYbt26ceTIkZJqkoiIiIiIXMdKLPh57bXXGD58OMOGDaNevXq89957eHp68sknn5RUk0RERERE5DrmXBJveu7cOdavX8/48eNt5xwdHenSpQsxMTF56qelpZGWlmZ7nJycDEBKSkrRN1ZEREREREqt7JjAGHPRuiUS/Bw7dozMzExCQ0PtzoeGhrJ9+/Y89adMmcJzzz2X53x4eHiRtVFERERERK4dJ0+exNfX94J1SiT4uVzjx49n7NixtsdZWVkkJiYSGBiIg4NDCbbMijTDw8PZt28fPj4+JdoWUX+UNuqP0kX9UbqoP0oX9Ufpov4oXUp7fxhjOHnyJBUqVLho3RIJfoKCgnBycuLw4cN25w8fPkxYWFie+m5ubri5udmd8/PzK8omXjYfH59S+Y+hrFJ/lC7qj9JF/VG6qD9KF/VH6aL+KF1Kc39cbMQnW4kkPHB1dSUyMpJly5bZzmVlZbFs2TKioqJKokkiIiIiInKdK7Fpb2PHjmXIkCE0b96cli1b8sYbb3D69GmGDRtWUk0SEREREZHrWIkFP/379+fo0aM888wzJCQk0KRJE5YuXZonCUJp5+bmxqRJk/JMy5OSof4oXdQfpYv6o3RRf5Qu6o/SRf1RulxP/eFgLiUnnIiIiIiIyDWuxDY5FRERERERKU4KfkREREREpExQ8CMiIiIiImWCgh8RERERESkTFPxcpbfffpsqVarg7u5Oq1at+O2330q6SdedZ599FgcHB7ufOnXq2MpTU1MZNWoUgYGBeHl50bdv3zwb6O7du5devXrh6elJSEgIjz/+OBkZGcV9KdekVatWccstt1ChQgUcHByYP3++XbkxhmeeeYby5cvj4eFBly5d2LFjh12dxMREBg4ciI+PD35+ftx3332cOnXKrs6mTZto164d7u7uhIeH8/LLLxf1pV2TLtYfQ4cOzXO/dO/e3a6O+qPwTJkyhRYtWuDt7U1ISAi33XYb8fHxdnUK6zsqOjqaZs2a4ebmRo0aNZg2bVpRX94151L6o2PHjnnukQcffNCujvqjcLz77rs0atTItjFmVFQUS5YssZXr3iheF+uPMnNvGLlic+bMMa6uruaTTz4xW7ZsMcOHDzd+fn7m8OHDJd2068qkSZNM/fr1zaFDh2w/R48etZU/+OCDJjw83CxbtsysW7fOtG7d2rRp08ZWnpGRYRo0aGC6dOliYmNjzeLFi01QUJAZP358SVzONWfx4sVm4sSJ5ptvvjGAmTdvnl35iy++aHx9fc38+fPNxo0bza233mqqVq1qzp49a6vTvXt307hxY/Prr7+a1atXmxo1api7777bVp6cnGxCQ0PNwIEDTVxcnJk9e7bx8PAw77//fnFd5jXjYv0xZMgQ0717d7v7JTEx0a6O+qPwdOvWzXz66acmLi7ObNiwwfTs2dNERESYU6dO2eoUxnfUX3/9ZTw9Pc3YsWPN1q1bzVtvvWWcnJzM0qVLi/V6S7tL6Y8OHTqY4cOH290jycnJtnL1R+FZuHChWbRokfnzzz9NfHy8mTBhgnFxcTFxcXHGGN0bxe1i/VFW7g0FP1ehZcuWZtSoUbbHmZmZpkKFCmbKlCkl2Krrz6RJk0zjxo3zLUtKSjIuLi7myy+/tJ3btm2bAUxMTIwxxvpj0dHR0SQkJNjqvPvuu8bHx8ekpaUVaduvN+f/sZ2VlWXCwsLMK6+8YjuXlJRk3NzczOzZs40xxmzdutUA5vfff7fVWbJkiXFwcDAHDhwwxhjzzjvvGH9/f7v+GDdunKldu3YRX9G1raDgp3fv3gU+R/1RtI4cOWIAs3LlSmNM4X1HPfHEE6Z+/fp279W/f3/TrVu3or6ka9r5/WGM9Qfeww8/XOBz1B9Fy9/f33z00Ue6N0qJ7P4wpuzcG5r2doXOnTvH+vXr6dKli+2co6MjXbp0ISYmpgRbdn3asWMHFSpUoFq1agwcOJC9e/cCsH79etLT0+36oU6dOkRERNj6ISYmhoYNG9ptoNutWzdSUlLYsmVL8V7IdWb37t0kJCTYff6+vr60atXK7vP38/OjefPmtjpdunTB0dGRtWvX2uq0b98eV1dXW51u3boRHx/PiRMniulqrh/R0dGEhIRQu3ZtRowYwfHjx21l6o+ilZycDEBAQABQeN9RMTExdq+RXUf/vbmw8/sj28yZMwkKCqJBgwaMHz+eM2fO2MrUH0UjMzOTOXPmcPr0aaKionRvlLDz+yNbWbg3nEu6AdeqY8eOkZmZafcPACA0NJTt27eXUKuuT61atWLatGnUrl2bQ4cO8dxzz9GuXTvi4uJISEjA1dUVPz8/u+eEhoaSkJAAQEJCQr79lF0mVy7788vv8839+YeEhNiVOzs7ExAQYFenatWqeV4ju8zf379I2n896t69O3369KFq1ars2rWLCRMm0KNHD2JiYnByclJ/FKGsrCzGjBnDDTfcQIMGDQAK7TuqoDopKSmcPXsWDw+Porika1p+/QEwYMAAKleuTIUKFdi0aRPjxo0jPj6eb775BlB/FLbNmzcTFRVFamoqXl5ezJs3j3r16rFhwwbdGyWgoP6AsnNvKPiRUq9Hjx6240aNGtGqVSsqV67M3LlzS8VNJFKa3HXXXbbjhg0b0qhRI6pXr050dDSdO3cuwZZd/0aNGkVcXBw///xzSTdFKLg/7r//fttxw4YNKV++PJ07d2bXrl1Ur169uJt53atduzYbNmwgOTmZr776iiFDhrBy5cqSblaZVVB/1KtXr8zcG5r2doWCgoJwcnLKk5Xk8OHDhIWFlVCrygY/Pz9q1arFzp07CQsL49y5cyQlJdnVyd0PYWFh+fZTdplcuezP70L3QVhYGEeOHLErz8jIIDExUX1UDKpVq0ZQUBA7d+4E1B9FZfTo0Xz33XesWLGCSpUq2c4X1ndUQXV8fHz0P4HyUVB/5KdVq1YAdveI+qPwuLq6UqNGDSIjI5kyZQqNGzdm6tSpujdKSEH9kZ/r9d5Q8HOFXF1diYyMZNmyZbZzWVlZLFu2zG7upBS+U6dOsWvXLsqXL09kZCQuLi52/RAfH8/evXtt/RAVFcXmzZvt/uD78ccf8fHxsQ31ypWpWrUqYWFhdp9/SkoKa9eutfv8k5KSWL9+va3O8uXLycrKsn2xRkVFsWrVKtLT0211fvzxR2rXrq0pVldp//79HD9+nPLlywPqj8JmjGH06NHMmzeP5cuX55kuWFjfUVFRUXavkV1H/72xd7H+yM+GDRsA7O4R9UfRycrKIi0tTfdGKZHdH/m5bu+Nks64cC2bM2eOcXNzM9OmTTNbt241999/v/Hz87PLgiFX79FHHzXR0dFm9+7dZs2aNaZLly4mKCjIHDlyxBhjpcqMiIgwy5cvN+vWrTNRUVEmKirK9vzs1Ixdu3Y1GzZsMEuXLjXBwcFKdX2JTp48aWJjY01sbKwBzGuvvWZiY2PN33//bYyxUl37+fmZBQsWmE2bNpnevXvnm+q6adOmZu3atebnn382NWvWtEutnJSUZEJDQ82gQYNMXFycmTNnjvH09FRq5XxcqD9OnjxpHnvsMRMTE2N2795tfvrpJ9OsWTNTs2ZNk5qaansN9UfhGTFihPH19TXR0dF26WHPnDljq1MY31HZ6WMff/xxs23bNvP222+XuvSxpcHF+mPnzp3mP//5j1m3bp3ZvXu3WbBggalWrZpp37697TXUH4XnySefNCtXrjS7d+82mzZtMk8++aRxcHAwP/zwgzFG90Zxu1B/lKV7Q8HPVXrrrbdMRESEcXV1NS1btjS//vprSTfputO/f39Tvnx54+rqaipWrGj69+9vdu7caSs/e/asGTlypPH39zeenp7m9ttvN4cOHbJ7jT179pgePXoYDw8PExQUZB599FGTnp5e3JdyTVqxYoUB8vwMGTLEGGOlu3766adNaGiocXNzM507dzbx8fF2r3H8+HFz9913Gy8vL+Pj42OGDRtmTp48aVdn48aNpm3btsbNzc1UrFjRvPjii8V1ideUC/XHmTNnTNeuXU1wcLBxcXExlStXNsOHD8/zP2TUH4Unv74AzKeffmqrU1jfUStWrDBNmjQxrq6uplq1anbvIZaL9cfevXtN+/btTUBAgHFzczM1atQwjz/+uN1eJsaoPwrLvffeaypXrmxcXV1NcHCw6dy5sy3wMUb3RnG7UH+UpXvDwRhjim+cSUREREREpGRozY+IiIiIiJQJCn5ERERERKRMUPAjIiIiIiJlgoIfEREREREpExT8iIiIiIhImaDgR0REREREygQFPyIiIiIiUiYo+BERERERkTJBwY+IiIiIiJQJCn5ERERERKRMUPAjIiIiIiJlgoIfEREREREpE/4fcGcdCmJW2AIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 6))\n",
    "plt.title(\"Closing Price Vs Time Chart\")\n",
    "plt.plot(df.Close, 'r', label=\"Per Day Closing\")\n",
    "plt.plot(ma100, 'g', label=\"Moving Average 100\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cc404c2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Moving Average for 200\n",
    "ma200 = df.Close.rolling(200).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "09d2faaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a14334e670>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 6))\n",
    "plt.title(\"Closing Price Vs Time Chart\")\n",
    "plt.plot(ma100, 'g', label=\"Moving Average 100\")\n",
    "plt.plot(ma200, 'b', label=\"Moving Average 200\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "784291b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2819, 1)\n",
      "(705, 1)\n"
     ]
    }
   ],
   "source": [
    "train_df = pd.DataFrame(df['Close'][0: int(len(df)*0.80)])\n",
    "test_df = pd.DataFrame(df['Close'][int(len(df)*0.80):int(len(df))])\n",
    "print(train_df.shape)\n",
    "print(test_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "985f5eb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a063aaaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.01395472],\n",
       "       [0.01638964],\n",
       "       [0.01856408],\n",
       "       ...,\n",
       "       [0.7012188 ],\n",
       "       [0.77547317],\n",
       "       [0.67335177]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_arr = scaler.fit_transform(train_df)\n",
    "train_df_arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b234b110",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2719, 100, 1), (2719,))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train = []\n",
    "y_train = []\n",
    "for i in range(100, train_df_arr.shape[0]):\n",
    "    x_train.append(train_df_arr[i-100: i])    \n",
    "    y_train.append(train_df_arr[i, 0])\n",
    "x_train, y_train = np.array(x_train), np.array(y_train)\n",
    "x_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2c907920",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Dropout is a regularization technique where randomly selected neurons are ignored during training. \n",
    "# In order to prevent Neural Network from overfitting\n",
    "# 2. LSTM, which stands for long short-term memory network, is a special kind of RNN that can perform \n",
    "# learning from long-term dependencies, which is a problem when using simple RNN.\n",
    "# 3. A recurrent neural network (RNN) is a type of artificial neural network which uses sequential data or time series data.\n",
    "# 4. A dense layer is a fully connected layer used in the neural network's end stages \n",
    "# to change the output's dimensionality from the preceding layer.\n",
    "from keras.layers import Dense, Dropout, LSTM\n",
    "from keras.models import Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6ce19e6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(LSTM(units=50, activation='relu', return_sequences=True, input_shape=(x_train.shape[1], 1)))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(LSTM(units=60, activation='relu', return_sequences=True))\n",
    "model.add(Dropout(0.3))\n",
    "\n",
    "model.add(LSTM(units=80, activation='relu', return_sequences=True))\n",
    "model.add(Dropout(0.4))\n",
    "\n",
    "model.add(LSTM(units=120, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Dense(units=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fb6205c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm (LSTM)                 (None, 100, 50)           10400     \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 100, 50)           0         \n",
      "                                                                 \n",
      " lstm_1 (LSTM)               (None, 100, 60)           26640     \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 100, 60)           0         \n",
      "                                                                 \n",
      " lstm_2 (LSTM)               (None, 100, 80)           45120     \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 100, 80)           0         \n",
      "                                                                 \n",
      " lstm_3 (LSTM)               (None, 120)               96480     \n",
      "                                                                 \n",
      " dropout_3 (Dropout)         (None, 120)               0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 1)                 121       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 178761 (698.29 KB)\n",
      "Trainable params: 178761 (698.29 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2d94769d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "85/85 [==============================] - 38s 358ms/step - loss: 0.0280\n",
      "Epoch 2/50\n",
      "85/85 [==============================] - 29s 337ms/step - loss: 0.0063\n",
      "Epoch 3/50\n",
      "85/85 [==============================] - 24s 280ms/step - loss: 0.0054\n",
      "Epoch 4/50\n",
      "85/85 [==============================] - 25s 296ms/step - loss: 0.0047\n",
      "Epoch 5/50\n",
      "85/85 [==============================] - 24s 280ms/step - loss: 0.0048\n",
      "Epoch 6/50\n",
      "85/85 [==============================] - 25s 289ms/step - loss: 0.0046\n",
      "Epoch 7/50\n",
      "85/85 [==============================] - 24s 277ms/step - loss: 0.0043\n",
      "Epoch 8/50\n",
      "85/85 [==============================] - 25s 289ms/step - loss: 0.0038\n",
      "Epoch 9/50\n",
      "85/85 [==============================] - 26s 306ms/step - loss: 0.0039\n",
      "Epoch 10/50\n",
      "85/85 [==============================] - 25s 298ms/step - loss: 0.0035\n",
      "Epoch 11/50\n",
      "85/85 [==============================] - 28s 330ms/step - loss: 0.0038\n",
      "Epoch 12/50\n",
      "85/85 [==============================] - 27s 321ms/step - loss: 0.0031\n",
      "Epoch 13/50\n",
      "85/85 [==============================] - 24s 283ms/step - loss: 0.0031\n",
      "Epoch 14/50\n",
      "85/85 [==============================] - 27s 323ms/step - loss: 0.0030\n",
      "Epoch 15/50\n",
      "85/85 [==============================] - 28s 335ms/step - loss: 0.0030\n",
      "Epoch 16/50\n",
      "85/85 [==============================] - 29s 344ms/step - loss: 0.0032\n",
      "Epoch 17/50\n",
      "85/85 [==============================] - 26s 306ms/step - loss: 0.0028\n",
      "Epoch 18/50\n",
      "85/85 [==============================] - 27s 318ms/step - loss: 0.0028\n",
      "Epoch 19/50\n",
      "85/85 [==============================] - 28s 335ms/step - loss: 0.0029\n",
      "Epoch 20/50\n",
      "85/85 [==============================] - 25s 291ms/step - loss: 0.0025\n",
      "Epoch 21/50\n",
      "85/85 [==============================] - 26s 303ms/step - loss: 0.0025\n",
      "Epoch 22/50\n",
      "85/85 [==============================] - 30s 352ms/step - loss: 0.0024\n",
      "Epoch 23/50\n",
      "85/85 [==============================] - 29s 337ms/step - loss: 0.0024\n",
      "Epoch 24/50\n",
      "85/85 [==============================] - 23s 272ms/step - loss: 0.0024\n",
      "Epoch 25/50\n",
      "85/85 [==============================] - 21s 253ms/step - loss: 0.0023\n",
      "Epoch 26/50\n",
      "85/85 [==============================] - 22s 261ms/step - loss: 0.0023\n",
      "Epoch 27/50\n",
      "85/85 [==============================] - 24s 288ms/step - loss: 0.0024\n",
      "Epoch 28/50\n",
      "85/85 [==============================] - 22s 253ms/step - loss: 0.0022\n",
      "Epoch 29/50\n",
      "85/85 [==============================] - 22s 253ms/step - loss: 0.0019\n",
      "Epoch 30/50\n",
      "85/85 [==============================] - 22s 256ms/step - loss: 0.0022\n",
      "Epoch 31/50\n",
      "85/85 [==============================] - 22s 254ms/step - loss: 0.0022\n",
      "Epoch 32/50\n",
      "85/85 [==============================] - 22s 255ms/step - loss: 0.0023\n",
      "Epoch 33/50\n",
      "85/85 [==============================] - 22s 256ms/step - loss: 0.0020\n",
      "Epoch 34/50\n",
      "85/85 [==============================] - 24s 286ms/step - loss: 0.0024\n",
      "Epoch 35/50\n",
      "85/85 [==============================] - 22s 262ms/step - loss: 0.0022\n",
      "Epoch 36/50\n",
      "85/85 [==============================] - 22s 259ms/step - loss: 0.0019\n",
      "Epoch 37/50\n",
      "85/85 [==============================] - 22s 260ms/step - loss: 0.0018\n",
      "Epoch 38/50\n",
      "85/85 [==============================] - 22s 257ms/step - loss: 0.0019\n",
      "Epoch 39/50\n",
      "85/85 [==============================] - 22s 263ms/step - loss: 0.0020\n",
      "Epoch 40/50\n",
      "85/85 [==============================] - 22s 260ms/step - loss: 0.0022\n",
      "Epoch 41/50\n",
      "85/85 [==============================] - 22s 258ms/step - loss: 0.0019\n",
      "Epoch 42/50\n",
      "85/85 [==============================] - 22s 257ms/step - loss: 0.0019\n",
      "Epoch 43/50\n",
      "85/85 [==============================] - 22s 261ms/step - loss: 0.0019\n",
      "Epoch 44/50\n",
      "85/85 [==============================] - 22s 258ms/step - loss: 0.0021\n",
      "Epoch 45/50\n",
      "85/85 [==============================] - 23s 269ms/step - loss: 0.0021\n",
      "Epoch 46/50\n",
      "85/85 [==============================] - 22s 258ms/step - loss: 0.0021\n",
      "Epoch 47/50\n",
      "85/85 [==============================] - 22s 259ms/step - loss: 0.0020\n",
      "Epoch 48/50\n",
      "85/85 [==============================] - 22s 259ms/step - loss: 0.0019\n",
      "Epoch 49/50\n",
      "85/85 [==============================] - 23s 265ms/step - loss: 0.0020\n",
      "Epoch 50/50\n",
      "85/85 [==============================] - 22s 260ms/step - loss: 0.0020\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x1a153059b20>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "model.fit(x_train, y_train, epochs = 50)"
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  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "19253600",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\abhis\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\keras\\src\\engine\\training.py:3000: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    }
   ],
   "source": [
    "model.save('keras_model.h5')"
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  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3dba80de",
   "metadata": {},
   "outputs": [],
   "source": [
    "past_100_days = train_df.tail(100)"
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  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "67629bae",
   "metadata": {},
   "outputs": [
    {
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    "final_df = past_100_days._append(test_df, ignore_index=True)\n",
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  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8b8e789b",
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       "       [0.36572399]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_data = scaler.fit_transform(final_df)\n",
    "input_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "13fcaff1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(805, 1)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "42f50a9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((705, 100, 1), (705,))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test = []\n",
    "y_test = []\n",
    "for i in range(100, input_data.shape[0]):\n",
    "    x_test.append(input_data[i-100: i])    \n",
    "    y_test.append(input_data[i, 0])\n",
    "x_test, y_test = np.array(x_test), np.array(y_test)\n",
    "x_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "eabff110",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23/23 [==============================] - 3s 92ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(705, 1)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.predict(x_test)\n",
    "y_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "27143ed6",
   "metadata": {},
   "outputs": [
    {
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       "       [0.4284467 ]], dtype=float32)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ab68eb67",
   "metadata": {},
   "outputs": [
    {
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       "       0.57152638, 0.58717515, 0.5677171 , 0.55701001, 0.58418951,\n",
       "       0.57317358, 0.59654388, 0.59870588, 0.53137482, 0.5383756 ,\n",
       "       0.51685853, 0.51572604, 0.51850573, 0.49863586, 0.47948666,\n",
       "       0.48834059, 0.4739272 , 0.4680589 , 0.46116109, 0.48741406,\n",
       "       0.46033745, 0.44211486, 0.47289769, 0.50378349, 0.5016215 ,\n",
       "       0.50151852, 0.47330951, 0.46486739, 0.45786661, 0.46177878,\n",
       "       0.47722168, 0.45148351, 0.48659042, 0.49482658, 0.48308998,\n",
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       "       0.42450996, 0.44993929, 0.44262958, 0.47083867, 0.47505969,\n",
       "       0.47001503, 0.46044042, 0.44160006, 0.45652825, 0.47104454,\n",
       "       0.46074927, 0.44623298, 0.43737905, 0.49709155, 0.49709155,\n",
       "       0.4914292 , 0.48154576, 0.45580759, 0.43480523, 0.42214201,\n",
       "       0.41308221, 0.41802389, 0.44190891, 0.43614359, 0.39280051,\n",
       "       0.38662337, 0.36788598, 0.37385718, 0.37962258, 0.3608852 ,\n",
       "       0.37601926, 0.35707592, 0.34297138, 0.36798888, 0.36572399])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "94d13e27",
   "metadata": {},
   "outputs": [],
   "source": [
    "scale = scaler.scale_\n",
    "scale_factor = 1/scale\n",
    "y_pred = y_pred*scale_factor\n",
    "y_test = y_test*scale_factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "843154b8",
   "metadata": {},
   "outputs": [
    {
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       "       [43.17371496],\n",
       "       [43.3945764 ],\n",
       "       [43.62080183],\n",
       "       [43.81975863],\n",
       "       [43.96478317],\n",
       "       [44.03927979],\n",
       "       [44.04278824],\n",
       "       [43.99099521],\n",
       "       [43.90065557],\n",
       "       [43.77477407],\n",
       "       [43.61464757],\n",
       "       [43.42491926],\n",
       "       [43.21577291],\n",
       "       [42.99771939],\n",
       "       [42.77822428],\n",
       "       [42.55165437],\n",
       "       [42.29229842],\n",
       "       [41.97800863],\n",
       "       [41.61588583]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6ac3b574",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3.19649887,  1.85300064,  2.8769989 ,  0.70399857,  0.        ,\n",
       "        3.79399872,  2.37450027,  5.4394989 ,  2.80649948,  4.60899734,\n",
       "        5.39099884,  2.39849854,  3.14500046,  1.92849731,  6.45299911,\n",
       "        6.42149734,  7.64349747,  7.62199783,  7.81399918, 10.55500031,\n",
       "       10.15850067, 10.1649971 , 11.24349976, 10.35099792,  7.9015007 ,\n",
       "       10.2140007 , 10.85199738, 11.12350082, 10.83649826,  8.92300034,\n",
       "       14.40250015, 14.62849808, 13.15949631, 13.43849564, 14.74449539,\n",
       "       14.56499863, 15.75749588, 16.51050186, 17.47299576, 16.05250168,\n",
       "       14.70999527, 15.13650131, 15.94649887, 16.55250168, 16.01350021,\n",
       "       17.75149918, 17.63100052, 17.95550156, 18.3619957 , 18.30749893,\n",
       "       18.20550156, 18.9695015 , 19.03699875, 19.4090004 , 19.25600052,\n",
       "       18.00849533, 19.29449844, 19.69549942, 19.89749527, 20.5284996 ,\n",
       "       17.38850021, 17.93950272, 18.33050156, 19.61700058, 19.92049789,\n",
       "       18.99950027, 18.52550125, 19.82649612, 20.49249649, 18.92850113,\n",
       "       19.3484993 , 15.42049789, 17.15199661, 18.19599533, 19.39349747,\n",
       "       20.7899971 , 22.27599716, 21.78950119, 22.4734993 , 23.22649765,\n",
       "       24.24399948, 22.90500259, 23.33649826, 23.13750076, 23.03950119,\n",
       "       23.13599777, 25.48550034, 25.08949661, 25.5359993 , 23.13100052,\n",
       "       22.70399857, 23.76499557, 22.47600174, 23.46899796, 24.2120018 ,\n",
       "       21.69099808, 21.43149948, 20.95849991, 21.24799728, 22.54099655,\n",
       "       22.2120018 , 22.13450241, 21.32049942, 22.6554985 , 23.12599564,\n",
       "       22.52499771, 23.10549545, 25.08250046, 24.52399826, 26.10599899,\n",
       "       26.07200241, 26.55099869, 27.58600235, 29.49999619, 28.7195015 ,\n",
       "       29.26499557, 28.77000046, 30.0474968 , 33.1629982 , 28.76900101,\n",
       "       26.35400009, 23.4734993 , 24.65500259, 23.59599686, 23.08150101,\n",
       "       22.7349968 , 24.0494957 , 22.89799881, 21.64549637, 19.84799576,\n",
       "       18.80050278, 20.28449631, 17.76299667, 18.43649673, 19.24650192,\n",
       "       20.22649765, 20.5945015 , 20.57349777, 21.68849564, 20.07349777,\n",
       "       21.43500137, 19.8445015 , 20.25049973, 21.46500015, 22.81599808,\n",
       "       25.52299881, 25.64699936, 25.46549606, 25.06699753, 25.67850113,\n",
       "       23.79099655, 24.84749985, 26.5929985 , 27.62649918, 28.94250107,\n",
       "       26.50799942, 27.23749924, 22.83349991, 25.13750076, 28.09899521,\n",
       "       28.50950241, 29.57649612, 34.58600235, 35.41849899, 35.28000259,\n",
       "       35.36449814, 34.17950058, 34.65500259, 34.43449783, 35.90649796,\n",
       "       35.99499893, 35.37649918, 34.32549667, 35.22199631, 34.11249924,\n",
       "       33.67149734, 35.48849869, 35.49999619, 36.64449692, 35.01350021,\n",
       "       37.06149673, 38.541996  , 38.38550186, 38.48150253, 38.14500046,\n",
       "       37.8599968 , 36.18649673, 35.67599869, 36.03349686, 34.90649796,\n",
       "       35.34749985, 35.15299606, 34.31899643, 33.604496  , 34.02149582,\n",
       "       33.30450058, 33.70499802, 34.00149918, 35.99149704, 35.18149948,\n",
       "       34.10599899, 34.92550278, 33.60000229, 34.29600143, 33.43749619,\n",
       "       36.01050186, 37.18500137, 35.10799789, 34.1649971 , 34.65600204,\n",
       "       33.83949661, 33.6744957 , 36.51700211, 41.29700089, 41.50099564,\n",
       "       41.92149734, 42.00749588, 42.69099808, 38.24049759, 39.95350266,\n",
       "       38.66149521, 41.94700241, 43.24950027, 50.23749924, 49.97500229,\n",
       "       51.7349968 , 51.51949692, 51.06299973, 51.61749649, 51.73099899,\n",
       "       52.04500198, 52.82850266, 53.22449875, 52.58399582, 51.73399734,\n",
       "       50.00649643, 50.2994957 , 51.48399734, 48.0909996 , 48.38899612,\n",
       "       50.7765007 , 50.51749802, 47.86400223, 48.99000168, 52.14699936,\n",
       "       47.66849899, 49.31149673, 49.10300064, 52.32049942, 49.79349899,\n",
       "       50.01549911, 51.48800278, 51.40449905, 48.36050034, 48.64149857,\n",
       "       48.82799911, 49.3599968 , 48.92000198, 48.91650009, 48.53000259,\n",
       "       49.58299637, 49.61700058, 50.41949844, 53.78249741, 58.24149704,\n",
       "       57.75649643, 59.24499893, 59.81499863, 60.82699966, 59.5245018 ,\n",
       "       60.01499557, 59.38899612, 61.55599594, 61.43099594, 61.78149796,\n",
       "       61.24399948, 61.21100235, 59.91949844, 62.2899971 , 62.7899971 ,\n",
       "       61.84250259, 65.24550247, 66.93149948, 64.96850204, 64.44749832,\n",
       "       62.63499832, 63.0320015 , 64.1609993 , 64.88999557, 61.88100052,\n",
       "       60.79649734, 57.30599594, 58.74550247, 61.21249771, 61.73949814,\n",
       "       60.416996  , 60.86849594, 62.64100266, 61.99999619, 65.34550095,\n",
       "       65.43700027, 66.30900192, 65.42750168, 65.13599777, 66.3524971 ,\n",
       "       65.82300186, 64.6724968 , 66.97199631, 67.40849686, 67.21549606,\n",
       "       67.69050217, 69.04999924, 68.80350113, 69.73900223, 68.71300125,\n",
       "       68.06599808, 69.03699875, 67.40449905, 69.10599899, 69.62399673,\n",
       "       69.42049789, 69.79349899, 69.80199814, 69.82950211, 69.56599808,\n",
       "       69.38299942, 69.73800278, 72.55099869, 73.48350143, 73.76749802,\n",
       "       72.33749771, 72.81200027, 74.26900101, 74.63499832, 75.5304985 ,\n",
       "       74.29849625, 74.26350021, 71.87150192, 73.50300217, 74.84250259,\n",
       "       75.71499252, 80.30849838, 81.32850266, 79.19349289, 83.38749313,\n",
       "       83.07099533, 82.02000046, 82.14800644, 82.92350388, 82.41900253,\n",
       "       83.54499435, 83.03199387, 84.20649338, 84.10050583, 83.57250595,\n",
       "       84.48749924, 85.02099228, 85.60299301, 83.95499802, 82.74250412,\n",
       "       82.97349167, 84.72300339, 87.3350029 , 88.55500412, 89.37249374,\n",
       "       88.73400497, 91.2974968 , 91.88399887, 91.9909935 , 92.50899887,\n",
       "       90.58050156, 91.03300095, 91.57400131, 90.98449326, 90.83449936,\n",
       "       88.16950607, 89.62600327, 89.83799362, 91.72300339, 90.9034996 ,\n",
       "       88.09350204, 86.01299667, 86.32650375, 87.57699203, 88.50950241,\n",
       "       89.50849533, 88.36550522, 83.12350082, 81.64699936, 80.96949387,\n",
       "       83.83649826, 80.95299911, 83.31649399, 84.85850143, 86.5185051 ,\n",
       "       87.0790062 , 86.20750046, 83.74250412, 84.87549973, 88.44449997,\n",
       "       88.66149521, 90.07149887, 90.5304985 , 89.06249619, 89.17950058,\n",
       "       84.8599968 , 84.74050522, 86.60200119, 93.51099777, 93.14249802,\n",
       "       95.33950424, 90.79049301, 92.72599411, 93.8915062 , 95.56100082,\n",
       "       96.145504  , 96.32450485, 96.2120018 , 93.18699265, 93.05999374,\n",
       "       95.97149277, 95.74549484, 95.16950607, 95.34049606, 97.13199997,\n",
       "       96.21999741, 93.59550095, 93.07549667, 93.41349411, 89.47649765,\n",
       "       92.82400131, 89.19100571, 88.34499741, 90.25950241, 89.29499435,\n",
       "       90.44849777, 94.56299973, 95.47999191, 94.93200302, 95.29499435,\n",
       "       93.12000656, 91.20049667, 93.73449326, 91.73850632, 89.0185051 ,\n",
       "       88.90049362, 90.76600266, 93.70849228, 94.2100029 , 95.19999313,\n",
       "       93.98049545, 93.94849777, 93.49399948, 92.145504  , 92.28499985,\n",
       "       91.69300461, 85.06849289, 85.04099655, 84.31049728, 85.96299362,\n",
       "       87.02949142, 88.72399521, 85.88050461, 86.77399826, 83.29150009,\n",
       "       82.40999985, 80.60099411, 77.64500046, 78.09749985, 74.22849655,\n",
       "       76.5265007 , 76.29850388, 80.64449692, 82.59699631, 84.93749619,\n",
       "       95.29349899, 90.38349533, 90.58649826, 86.49450302, 86.69250107,\n",
       "       88.88549423, 85.91349411, 81.57600021, 82.81949997, 83.90200424,\n",
       "       85.03150558, 79.83250046, 77.69649887, 77.10700607, 74.88149643,\n",
       "       79.98449326, 81.75300217, 82.35050583, 81.35499191, 81.86500168,\n",
       "       81.19300461, 79.19999313, 73.67200089, 74.39799881, 80.71349716,\n",
       "       79.72300339, 77.16399765, 73.24449539, 76.49149704, 80.57400131,\n",
       "       81.13250351, 83.41900253, 83.39500046, 87.16149521, 85.56899643,\n",
       "       88.86550522, 88.96650314, 88.74900436, 89.79899216, 89.23200607,\n",
       "       86.36100388, 87.44399643, 90.26499557, 87.88450241, 83.84150314,\n",
       "       83.18200302, 80.5810051 , 76.11700058, 75.00799942, 77.18749619,\n",
       "       74.02350235, 74.96999741, 77.30250168, 75.33349228, 72.10799789,\n",
       "       66.92899704, 70.36749649, 65.94350052, 61.58800125, 65.81599808,\n",
       "       61.40299606, 63.87649918, 64.62749863, 69.55450058, 63.79899979,\n",
       "       63.0399971 , 59.80450058, 61.68849564, 60.89599991, 60.13750076,\n",
       "       63.34399796, 61.73849869, 63.76649857, 59.19299698, 57.67750168,\n",
       "       56.20149612, 58.78149796, 53.26350021, 53.0984993 , 55.08600235,\n",
       "       59.6099968 , 61.05550003, 61.18549728, 64.91599655, 61.83449936,\n",
       "       64.11400223, 64.44299698, 64.48749924, 62.12900162, 58.45499802,\n",
       "       53.68600082, 54.00899887, 57.05800247, 53.32699966, 54.43700027,\n",
       "       58.83749771, 58.78100204, 59.53549576, 65.26849747, 63.12700272,\n",
       "       59.30099869, 58.99499893, 56.25649643, 56.03100204, 60.55649948,\n",
       "       61.86549759, 66.07649612, 66.64699936, 62.96999741, 61.31399918,\n",
       "       58.64699936, 57.6609993 , 59.07099533, 56.32349777, 61.10349655,\n",
       "       61.19350052, 61.63349533, 55.19350052, 54.80350113, 52.31349564,\n",
       "       60.35349655, 61.51350021, 63.61349869, 62.1534996 , 62.42349625,\n",
       "       65.37350082, 65.48350143, 64.76350021, 64.59350204, 63.92349625,\n",
       "       66.99349594, 66.13349533, 68.9734993 , 69.37350082, 68.99349594,\n",
       "       66.84350204, 67.46349716, 64.50349808, 61.53349686, 61.1534996 ,\n",
       "       60.98350143, 63.94350052, 57.63349533, 56.71349716, 56.23350143,\n",
       "       55.51350021, 57.03349686, 55.14349747, 54.10349655, 56.74349594,\n",
       "       55.67349625, 57.94350052, 58.1534996 , 51.61349869, 52.29349899,\n",
       "       50.20350266, 50.09350204, 50.36349869, 48.43349838, 46.57349777,\n",
       "       47.43349838, 46.03349686, 45.46349716, 44.79349899, 47.34350204,\n",
       "       44.71349716, 42.94350052, 45.93349838, 48.93349838, 48.7234993 ,\n",
       "       48.71349716, 45.9734993 , 45.1534996 , 44.4734993 , 44.85349655,\n",
       "       46.35349655, 43.85349655, 47.26350021, 48.06349564, 46.92349625,\n",
       "       47.26350021, 48.42349625, 49.81349564, 51.77350235, 42.2234993 ,\n",
       "       39.51350021, 43.58349991, 41.80350113, 37.76350021, 34.26350021,\n",
       "       30.7234993 , 33.87350082, 35.78349686, 36.19350052, 34.61349869,\n",
       "       41.23350143, 43.70350266, 42.99349594, 45.73350143, 46.14349747,\n",
       "       45.6534996 , 44.7234993 , 42.89349747, 44.34350204, 45.75349808,\n",
       "       44.75349808, 43.34350204, 42.48350143, 48.28349686, 48.28349686,\n",
       "       47.73350143, 46.77350235, 44.27350235, 42.23350143, 41.00349808,\n",
       "       40.12350082, 40.60349655, 42.92349625, 42.36349869, 38.1534996 ,\n",
       "       37.55350113, 35.73350143, 36.31349564, 36.87350082, 35.05350113,\n",
       "       36.52350235, 34.68349838, 33.31349564, 35.74349594, 35.52350235])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "96228692",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(y_test, 'g', label=\"Original Price\")\n",
    "plt.plot(y_pred, 'r', label=\"Predicted Price\")\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Price\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "71010635",
   "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": "3.9.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}