BrentCrude_LSTMmodel.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Brent crude price prediction ANN model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train = pd.read_csv('brent-daily_csv.csv', index_col=\"Date\",parse_dates=True,nrows=8000)\n",
    "data_test = pd.read_csv('brent-daily_csv.csv',index_col=\"Date\",parse_dates=True, skiprows=[i for i in range(1,8000)], nrows=250)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-11-21</th>\n",
       "      <td>61.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-22</th>\n",
       "      <td>61.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-23</th>\n",
       "      <td>57.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-26</th>\n",
       "      <td>59.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-27</th>\n",
       "      <td>59.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Price\n",
       "Date             \n",
       "2018-11-21  61.65\n",
       "2018-11-22  61.11\n",
       "2018-11-23  57.69\n",
       "2018-11-26  59.70\n",
       "2018-11-27  59.58"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 8000 entries, 1987-05-20 to 2018-11-21\n",
      "Data columns (total 1 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Price   8000 non-null   float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 125.0 KB\n"
     ]
    }
   ],
   "source": [
    "data_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 250 entries, 2018-11-21 to 2019-11-14\n",
      "Data columns (total 1 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Price   250 non-null    float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 3.9 KB\n"
     ]
    }
   ],
   "source": [
    "data_test.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking for any null data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Price    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Price    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XmzGxX3sM7FyITOLW1/r53pgvNxwEAFQ3uPV9RTkONLi8IT1nk1t5zryswNNOT5Do+O3lZbjznTX67dLZM1scc7i8XomP1uzD2cd1C5wmS5RkmGkjIiKijKMVIrH5ZdqEuqitqsGNH/1jUauPK5HueW8tvt+uTIF0WAQxWQ7faaMxu1XXQiuAJo9XfXzgcza4rR+7vDT0nnAfrPIVKj9S03xWzmjKn77GbW+swovfRd80nCjeGLQRERFRxpn8h68ABBYiEYYzI5cnc6bdSSnx6uLd+Ol/VwCwKNACc1bsUHUj9h5TpkVe+cLiZp+7ya0GbXZ7wH1t87IsH+PyhJbFA4DVhgInLQWQRrsrlKIq2s9BlMwYtBEREVHG8s+0GW/LDOrW5r8srKWg7b4P1uvbLa05c+mZtsDTzv6dCiwf8+7Kvabbk/8wH5+vP2B57AWju+vbTWEEexp3BgXnlLoYtBEREVHGsgXp0wZkVgFJ/2Iei7YfCfs56ps8WLT9cMB+LdPmtAcGgqE2xd5TUY97319ned/hmiZ9O5wMncbtDf8xRK2NQRsRERFlLP+1W9qaNiDTgjbz7cNBsmcdCrIBADOGdQm47/cfrccV/1qMbYdqTPuby7QFK0RiJdiR6/dV+l7LHf6HVpTjDPsx/qSU2HygOurnIQqGQRsRERFlrICgzZhpy6Dpkf6Ztj/86DjL4xw2gZL2eai3aK69VQ3WKmqbTPsb9Uyb77SzMMehvm7oYww2DdP4euFMj2yfr6yna5MXfdD20qJSnPH0AiwrrYj6uYisMGgjIiKijOVspjFzJmXajD/rhL7t0LNdnuVxdpuAzSZQb1HwQyu97/CbBqln2gzv9Q93TcXoXsVR91jzeKVpKue2Q6Fnu3KcSmGUWHzOa/cq2b4d5bXRPxmRBQZtRERElLH8Awxj5i2DYjZTVvFQM4VFbDalIbl/pq1k1hy9iuNbS/eY7mtye+G0C9P6wfxsB9rmZUUdtGnBkua3/1sb8mO19XTeGHRSd6p97VxcH0dxwqCNiIiIMpbDZj4VcthtyFUzMNEGFKlk4VZfAZFjda6gxzlsNthtAnVN7qDHrNx9zHTb5fFaZjRtQsAqxtEKl8SbFqvFIGaDU+1Bt/9YQ/RPRmSBQRsRERFlLKuKhlpCSMrQqxumuj98tknfntSvfdDjbEIJtqymR2oa1YbZs/63BiWz5qDJ7bUsQmIT1oFx6ZHQpxhmq88787iuAIDBXQpDfqxHfe0DVeY+bVJKHKgML/jKcSiB/j8XbA/rcUShYtBGREREGcUYiFllgIShGkkssjCpwBg7VTUEz6JlOexKps2iEIlmRI9iAMAb6jTJl7/fZfk+223CMmj7YNXegH1Wdh6uxZnPLAQAXDi6O9rlZ+GEknYhPRbwTYt8fYl5OudLi0ox4bF52How9PVx5TXKlNKLx/YM+TFE4WDQRkRERBnFWGbef00bYK4gmSk9vNyG96S2MXjQluO0wSbQbKZteLeigH1ZQaZHHq5pCshmjusTPNNndOqfvta3nXYbnHYRVp82d5CI/Ft1quiuI3UhP9cRtVfcxv1VIT+GKBwM2oiIiCijGE/WrTJAxgAjQ2I2uA3BTrBgBlCmI9psQi/jP6hz4HREq3YA2RbTI7cdqkFFbRPeWmbOdLkjaJBdlOuE024Lq+R/SwHejsM1zd5vpAX3gZcAiGKDQRsRERFlFOPJun+fNsAcdGRKpq3GkF1rLmjKcdphN6Qiu7fNDTjGvxAJYN1YWwuKvt5cbtqvBYRDugZm7IwuH+ebitipMBtOuw0uT2jzWVfuPoq6INnCeZsOAQAe/WQTBv3u05CeT8ve2i1+n4higUEbERERZRS34cTe6iTbeDLvyZBFbcZ1bNrP/JszBun7tAIfX28uN5Xu15pkGxXlBjarzlYrchpplTtr/KZjatUj//7j43H9iX1M9xmnUho/xw4F2XDaRchZugv+vkjf7tomB+f+9Vu8u6Is4LjGECtZau+ZVZaRKBYYtBEREVFGeW3Jbn3bJprPjDQ3VTCdTB/aWd/WMpG3TOmn7+vd3tds25hpy7UIxj5avQ8frd5n2pdjkWkrylUCPmO7AcBXfTLLYcP3O46Y7nN7JZ5fuAPn/vVb01TILIdNzbSFnxnNddqxpqwSt7+1GmVHg69jm7NmP06cPd8UGK4tq0TJrDlYoWYXg2XviKLFoI2IiIgyyrPf+MqytxCzZUymbXwfX9XFYd3aADBX0RSG1VrG7KTVWjUA+MXrK023rTJt7fOzLR+rZbeyHbaAwh67K+rw8JyNWFNWiY/X7Dfdp6xpC//z2nHY12Kgst7co84YlM56dw32Hqs3ZQZ/+Yb552xg0EZxwqCNiIiIMsqonsX6tmCmzeSVG8bh8YuOC9hv7EFunB6ZYxGMaQZ2LtC3l+6sCLi/S5scy8dp0yOt1sHd/tZqfds/oF615xgWbCmPqjm3/3RZLZvX4PKgWp1CanzZnYfNPeWaa4VAFA0GbURERJRRTuzfQd9uqWyEN0OCNq1f2uhebZGbFRiIGYNbY5eEYJk2wLzmzGqt1x9+pASHZw7vYtpvzLT5W73nWMC+S8b2MN1evPNIwDFG+47VB73P7Zep83glPF6Jwfd+ZjhGGd8RtTebEadHUrwwaCMiIqKMEk5J+UzJtGn1PYIFsca1f6bpkc1k2oxFPB69YETA/R0KlOmRn647oK9jMz7Oqrebv25tcgIyg1sO1uCVH3ZZHv/15kOYNHs+APOUUI1V0HXHW6tMt5s8XryzvAxjHp4bcGyT25sxU2qpdTFoIyIiooywvbwGK3YfNZWFb+n02pMhJf/dzZSsL85z6lUb7z17qCmAW7HraNDn3GvIaDW0MG2w2lC9stHtQZbD1uLUVQA4d1R3/bjnrhoDAHjo4w249/11lsdf++JSfdt//RoAy2Dv/VXmoipNbi/e9ustZ8QKkhQPDNqIiIgoI/z8tZW48O+LTL3XjCXkrWRKpk3LDvk3G3/+6rGY88vJenBbnOs0FW+pqGsK6fmrGgIDJCNjRcpGl7fZaZdGXsPn11zWz0q2025adwdYV7n099yCHc1WqaxpcAe9jyhSDNqIiIgoI2iVCI3rlqxitlMHddS3/dc4pSstOPVPtE0b2hndi3P1lKTdJkxTCItyAnuyWSnIDuznZmQMvpo8XmQ7zAHY7dMHWj6uotYXNHYqtK5Gqb+GXwDeJteJR/ymbb69PLBXm79vtx22nAJ52uBOAIDlzWQfiSLFoI2IiIgySlMLa9pevG4cfjdzCIDMKfnv9njhsImgUxK1oMpmEyiv9hXgaGPRSNvKSEPFTuvn920bM23PXDYKQ7sWoWuQSpPnjeqmbw/qXNjsa9Q0mTNguU4buhRZP29zyo7Wo0fbvID9V4zrBQA4Vh9a9pEoHAzaiIiIKKMYp68Fmx3Zv5Mybc7TwvTJdOHxSsv1bBrtbbAJX7GOf/z4eAzoVBD0MQDQt2M+Vtw7HSeUBBb9MFq3rxKAUijkfyvK9KDtvFHd8cltk02ZOKPJA3xZUVsz4weAo7XmYErKwOmgmqlq1sz8Wr6qoxBA9+JcXHR8D3z16ykonT1T/xkbXJmxDpJaF4M2IiIiyijGKXAySCkSh9qYLGMybV4ZNIABfO+TXQh9TVthjhNXTyxp9nkbXV60y88Kev+kfu0BAI9/ugmAr1CIf482uy36U9b1+8yNur1Som1+YKZwRPc2eOHaE/DAOUNN+88Y5mtNMGfNfjjtAk9cMhJ9OuQDALKdyhiNlTCJYoVBGxEREWUsrey8Py3rlClr2kLNtBmnT9psvkAlmJaqRuapPeH8C774FyK5cHT3gMc+denIZp/b3xE103a5Oo3x2kl9kO2w45Yp/ZBj+Dn0z95vTP7vT77fOj1tzH/4bHPQlgNEkWLQRkRERBnnhJK2KJ09M+DEW+NQO0hnSqbNpa5pC0Z7F4xL3uxCmLJzUwwFXDRagBTMqeo0RP/1aAerzI2rbTahFERRvXTdCTh/VGAg1xyX2v9t1ozBKJ09Eyep0x3vnDEYS++Zph+n9Yc7VmeueGn3W+937shuptvGgDZYywGiSDFoIyIiooxSnOfEwBaKVmi9yNwZ0qet5UybErYZj7DbhOkxL103LuBxd5xuXfVRoxXv6FZsLghyoKoh4FhtyuQrN4zDlEGdLIum/OSUvgFj1mifpRaQGxmDTy17OHWIeV3bhL7tMaxbkX57f2XgGInipfn6q0RERERp5lidC28s3RNQ7t1IyzplSqatxTVtltMjW25+3VKDbCEECrIdCOVt1j6TToXBKz7+39SBWL+3Ct9uOwyvBLT4bNeRWszfdAiAdfERY5ZRC9hH92qLhXeeiqJcp14l07g+T2shQdQamGkjIiKijNNSMGbPsKCtxUyb+n9Tps0iIFt456l6u4RQCYGA6pAnDwycatlNnR7ZXKyYm2XHRLW4ifGzO+WPX+OHHRUAAKdFps34sxsLifRsl2dqa3Dm8K76drCKlkTxwKCNiIiIyA/XtJlJvU+bb59VkNezXR5unNw3YH9zbEIEtF6Y2Ld9wHHPXDYKsy8cgQEhTm3dcrAa5/3tO1T6rU2zyv4JIdCznRIUasGdlcvH9dS3/QuVGBW20EycKFz8jSIiIqK05x98dSgIXoYe8E2Xa+7EPJ2EmmkzLvGztTD1MVQ2i0ybx2ItYXFeFi5robAJ4Huuc/76LaQE5qzdH9I4BnYqxJ6K+maPMQZ8fTsE71GXKb831HqYaSMiIqK0V91gzrbcNq35Ahn2DOzT5mhmTdvpQ5UeZW0Na7qaC/LCYRMi4H2OJujZdaQWgG8d3t3vrQ3pcVqGcET3Ns0ep2XRfjtjUMB9z1w2CgBQ7/IEFEIhigaDNiIiIkp7/kGAs4WAwy4yL9PW3PTIK8b3woLfnIoxvdvq+5qJ8cIihAgoRBJNsBxpBlD78f17xPnTRpbttAfcd96o7jh/lNIK4IVvd0Y0DiIrDNqIiIgo7Xn9goDmskoAYNfXtGVGyf89FXWobXQ3e0yv9nkAfA3Hm6s2+dsZg/Hva8eG9Np2m1V5/siDthyLYMr3WsEDOu0lW6qKqY012HOt26dUlXxm7tZmn4coHFzTRkRERGnP4xcUWFUQNMq0NW1bD9WEfGyVOtW0ODf4usCfTekX8vPZhAhY0+YfZIfDWJbfX1YzgaY2BquqmEbDurXBktKKoJnJ/ceUdXHVLQTBROFgpo2IiIjSnv90u+ayRIDv5L7Jnf6ZtmN1TWEdr61vy80KntEKh81iemQ0wfK5I7sFva/e5Ql6X692SiZx+tDOzT7/v64Zi7d/OjFoRu+2aQNCGCVReBi0ERERUdrzD9qaW78FAFmOzAnamjzh/YyPXTgCS+6Zqr9H0RIiMLMWzZq2kg75yHGGP7ae7fKw+r7Tcd2JJc0e1ybXiRNK2gW9/4LRPQAAXdsEbwJOFC4GbURERJT2/IOATQeqmz1eC0jeXl6Gz9aFVjI+VYW7bC/LYUOnwtgFJMbpkdrMxCmDAptrhyNYzNfc9EgAaJPntOzjFo6OhdkAgP2VDVE9D5ERgzYiIiJKe19tLjfdHmuogmjFYRMQAth2qAY//e+KeA4t4T5esw8AcMNJfRLy+kqfNqDB5YGUwI/H98KUQZ2ie9IgQdvpw5qf+kiUrFiIhIiIiNLecwu2m247W5jaJ4RAtsOGBlf6T498eM5GAMBxPZrvTxbM3NtPRpM7uoItR+uacP1LSwEAnYuiz+LJIFFbS2sZY+2rzYcwZWDHqLN3RC3+5goh/i2EOCSEWGfY104I8aUQYqv6/7aG++4SQmwTQmwWQpwRr4ETERERhepITXjFNoCWp9Klm0h/3v6dCjG0W1HEr1t6pA4Ltx7Gou1HAPj6pUUjWF/rWDUEb8n1JypZy+teXIq3lu1pldek9BbKX+dLAGb47ZsFYJ6UcgCAeeptCCGGArgMwDD1MX8XQsSmtBARERFRhCKpRpjlyKxTmB2HaxM9BACIa1aqpQI0sZJnqKy59xjXtlH0WgzapJQLAFT47T4PwMvq9ssAzjfsf0NK2Sil3AlgG4BxsRkqERERUevJjlF1xFSx9WDzxVlaSyyyYcFC9NaapWisyNnQTJsBolBF+m3UWUq5HwDU/2urRbsDMOaAy9R9REREREkjlHP3WJW0TxWdk6RE/ZYYB4+bHpqBJy8ZCQAorw5/mmwkjD/DiO6RrRUkMor1t5HVd6DlxQ4hxM1CiGVCiGXl5eVWhxARERElTKZl2m44MTHVI/0t3Ho46ueQhkVtOU47zhjWBSf2b487ZwyK+rlD4bD5fneW7PSfsEYUvki/jQ4KIboCgPr/Q+r+MgA9Dcf1ALDP6gmklM9JKcdKKcd27BhdLw4iIiKicISywi3TMm0FOclRVDwW0wn9lzDmZzvw6o0TMLBzYdTPHYrrDQ26c7Mya20kxUek30YfArhG3b4GwAeG/ZcJIbKFEH0ADACwJLohEhEREcVWKMumMqF6pJaRmnlcV+RlJUfQ9uK1JyR6CFGb1L8DSmfPRGG2A25PdO0QiIDQSv6/DuB7AIOEEGVCiBsAzAYwXQixFcB09TaklOsBvAVgA4DPANwqpeTqSyIiImpVX28+hJJZc7DpQBUAoENBNgDgb1ccj0vH9sRxPYpbfI7WKg+fSGf9+VsAwLAoSvbH2pCuyTOWaNntAh5v+vf6o/hr8ZKKlPLyIHdNDXL8IwAeiWZQRERERNH4YsNBAMDS0qMY1LkQh2sa0b9TAWYe1xUzj+sa0nM47OkdtHm9Ehv3K0FtrjM5pvDZbQL52bHL+N139tCYPVckHDaBRjeDNope+uf9iYiIKOPY1druUkqsKasEAGw7VBPec9jS+zRpW7nv/UiWoK13u7yYPt9VE3vH9PnCdbimCW8s3YP9lfUJHQelvvT+NiIiIqKMpM1s9HqlqWdWOJxpPj2yvsm3giWRxTLeu2WSvm2L8XvuTJJ1ieFeMCDylxwrTomIiIhiSKiZtjVllXD7lxIMUbqvaTMGs4lsb9DTkF2zt1b361YW6e8gkYZBGxEREaUdm3ry/+7KvSjpkB/Rc6T7mrYmw1qr8prWaTptxdhaIVaZtnd+OhE7D9fG5LliwcMKkhQlBm1ERESUdvYeq4v6OWxpmvXR1DS69W1HArOKRTlOfTtWsxnHlrTD2JJ2sXmyGKiPQe85ymzJMdGXiIiIKIY+X39Q39amps355UlhPYdI86DtJ68sBwD0bp+H80Z1S+hYJvZtDyD9pkfOvf0UAMDtb61K7EAo5TFoIyIiorQgpcStr67Awq3lpv0erxd2m8Cwbm0ifm5vGq9Jev7qsQlvrJ3jVE5JY12IJNG6tMkBALg4PZKixOmRRERElBae/HIL5qzdjzlr95v2u70youl/xke4vRJZaRZQaLISWIREk6O2HEiW1gOxkpdmPw8lTuL/SomIiIiidKCyAX+Zv83yPo8nwqDN8BC3N30bJLfLz0r0EPSgrU2us4UjU0u6ZQ4pcZhpIyIiopTX3FKo57/dGfXzp3PJ9oLsxJ8Oai0H0i1oA5SegV6pTN9N93WSFD/MtBEREVHK8wQJqgZ0Koj4OY2n1+lWsv1ora/EfzIEEvr0yAQ2+Y6XX00bCCD47yhRKBi0ERERUcpzBwmq+nTIR9u8yLI3xmAm3TJt4x+bl+ghmGSrhUgS2XogXhxqH4N0+x2i1pX4fDgRERFRlFx+a856tcvD7oo6eCUwoFMhbBFcpjYmoNItS2JsrJ0MtGDNEatGbUnEqTZpb/J49YwiUbjS7y+DiIiIMo5/pm13hdJce+7Gg1ix+2hEJ8vGrI/Lk1xBTrQ6FmYDiG76aCxp/dmc6ZhpU3+mYNlgolAwaCMiIqKUV9fkDnqf2yuR4wg/aLMbAoh0y7T1apcHALh8XK8Ej8QsHast6tMj0yzwp9bFoI2IiIhS3tZDNTF/Tlsar2kryHZgWLciXHdiSaKHAgDQ3l1bEhRFibUsNWhzpdnvELUuBm1ERESU8hpcnmbvjyToStdMm5QSG/dXwWm3JUXlSADwSuX9TY7RxJZDXdPmSrJ1hJRaWIiEiIiIUl5LQdXhmsawn9MYtKVTc+1pT36DQ9WNOFQd/nsSL9rHl87TI9NtXSS1LmbaiIiIKOVZBW3GUv/7jtWH/Zx24/TINCoisb28FgBw4fHdEzwSH5k+b28ArVLnY59uSvBIKJUxaCMiIqKUZxW0jepZrG9HOz0y3da0AcAFo5MpaFPe33Rc03ZCSVsAQH1T81N4iZrDoI2IiIhSXoMrcOrZoC5F+na0U9PSaU2bpk1uZE3H40Ff05Z+MRt6t8+H0y4wqldxoodCKYxBGxEREaW8Oldgyf/9lb4pkdEGbem0pk2THUEbhHjRpkem4ZI2AECO095isRyi5jBoIyIiopTXZFGZ74NV+/TtaNekpdOaNo3Wqy0ZaIlMkZb1I4FcBm0UJVaPJCIiopRnFbQZRbImzfiIdJoemZ9lx+XjeiE3K3kybfnZyliqG1wJHkl85DjtXNNGUWGmjYiIiFJek9uLzkXZyHX6AhHj+qiOhdlRPX86FSJxeSXs9uTKaJ06uBMAYGi3NgkeSXzkOu2oZ6aNosCgjYiIiFJek8eLHKcd3Ypz9H3GQhvv/mxSVM/vSbI1bVJKPPbJRmzcXxXyY3YfqcPN/1mGJrcXjiRbPHZ8r7ZYdd90zBjeJdFDiYucLDvqLYrlEIWKQRsRERGlPJfHiyy7DScP7Kjve/XG8fp2zyjXbyVbpu1onQv/XLADV72wJOTH3PzKMnyx4SAAwG5LvlPA4rysRA8hbnKdNjRweiRFIfn+YomIiIjC1OT2Isthw91nDdH3DTaU/I+ENHR8TrZCJI1uJQCINGPmTLJMW7rLddrR4GbQRpFj0EZEREQpr1EN2px2G359+kA8f/VYvTl2tzY5LTzamiFmS6pMm8vjxRNfbAEAZDtDP5UrPVKrbyfbmrZ0l5tlx5qySry3sizRQ6EUxeqRRERElPKa3F447UoA8/PTBuj7/3P9OAzqUhj18yfTmra3lu3BO8uVk/9dR+pCfpyxAXmyrWlLdzlqgZxfvbkaF4zukeDRUCpi0EZEREQpr8njRUF24GmNcY1bNJIp0xaL0vHJuKYtnaVTy4hUtL+yHpX1rqinTCcSgzYiIiJKeVohklhyGJ4vmU66jdM2B3YuiOg5nJwe2aqSKejPRBMfmw8AKJ09M8EjiRwvsxAREVFKc3u82H+sAVmO2J7W/OyUfrh4jDKVzZVEhUikoe13YY6zmSPNjO9POldqTEbGojZEkWDQRkRERCnt8c824UhtExrdsV131ibPifvPHQYguda0PfrJJn3b7Wl5XN9vP4KvNh9CjiFoa5/PoK01fbL2QKKHQAD2VIS+BjTZcHokERERpTSt91hVvSvmz60V7EjW6W2hZAAv/9cPAJSy85p2DNoSZt7Gg5g6pHOih5GRFu+siLpnY6Iw00ZEREQpTQus4hFWac/tSaLpkUbhVMZ0G7KFvVL0xDUdHKxqTPQQMlZ9kzvRQ4gYgzYiIiJKaVqp/1CmCobLnuSZtrYWa9P2HqtHZV1g1tGYlcsxZN0o/kb1LNa3u7TJTtxAMlz3trmJHkLEGLQRERFRSnOolRDjUSxECAG7TZiyVMnEa1Hg4sTZ8zHtqW+afRzbtLWuF64Zi+tOLAEAZNkZMCdKrjN1V4YxaCMiIqKU5lB7jsUrsHLYRNJk2vyrEAarSlhe3fwUPCEYtbWm9gXZOPu4rgCArzYfSvBoMlcyte4IF4M2IiIiSmla/OGO07ozh00kzZq2Jr8poJ4WSsmz1HzyOFLTBAB44dudCR5JZjH+DSRrxjwUDNqIiIgopWkXz11xOiGzJ1Gmrb7Jo293KMhGS8OqMxxPibV2b2Wih5CRjNOmmWkjIiIiSpDiXKXB9M9O6R+X53fYbUlzslfvUoKw+84eCptoOZPm8svM9e2Qj0n92sdtfBRcm9zQG6FT7Mzf5JuOGo91r62FQRsRERGlNLtNYHj3Ilwxvldcnr+itgkfrNobl+cOl5Zpa5efBZsQaCm56D+d8lfTB+K1mybEa3jUjOtP7AMA6FzE6pGtaXXZMX07WS6+RCJ1S6gQERERAWhye5Flj+916KqG5OjvpGXacpx22ERg9Uj/zFu81vlR+Gw2gZP6d9A/Q2odfdrn69sZu6ZNCPErIcR6IcQ6IcTrQogcIUQ7IcSXQoit6v/bxmqwREREREZ3vbsW3247jCxHZkwealBP+HOz7BBCBBQi8Z/+5R+0TR/aOb4DpGbZbSIu/QQpuPxsX45q7sbUrdwZ8TecEKI7gF8CGCulHA7ADuAyALMAzJNSDgAwT71NREREFHOvL9kNAMhypF/vq6e+3IJ731+n3/Z4pV6BMNdph90m4L+kzT+T4F+chU21E8tpFym9rioZlR2tw6Jth4Pe3+j2ZTY/Wr2vNYYUF9FOj3QAyBVCuADkAdgH4C4AU9T7XwbwNYDfRvk6REREREHFc3rk6F7FKMhu/RUlz8zbCgD4/bnDYLMJzPzzQmw6UA1ACdqspke63L7bUko90/bny0fjjGHMsiWaw5Y8RW3SxUmPfwUAKJ090/L+eX7ZtZpGd0L+nqMV8TeclHIvgD8B2A1gP4BKKeUXADpLKferx+wH0Mnq8UKIm4UQy4QQy8rLyyMdBhERERGy4zg9UgABGa3WVFGnZNe0gA0AcrNsSiESv3EZM2te6asemeOwITsNs5GpxmEX2HywGoeqGxI9lIyw83At5qzdb9rX5E7N6anRTI9sC+A8AH0AdAOQL4S4MtTHSymfk1KOlVKO7dixY6TDICIiIorrmjYhBCQSF7XVW/Ray3HaseNwLT5avU9f5waY17C5PF489ulGAIAzzoVaKDQOm9IJ/ry/fpfgkWSGqnqXvn31xN4AAttgpIpo/oKnAdgppSyXUroAvAtgEoCDQoiuAKD+P3VX/BEREVHSWLKzAiWz5uBglZKlMFZKPFzTGLfXTXSmzaraYK5hbdqaMl/TZuMJqdsr8d22IwCUDA8lnvZZ7q9kpq01GP9sh3QtApCBmTYo0yInCCHyhBACwFQAGwF8COAa9ZhrAHwQ3RCJiIiIgFd+2AUAWLRdKTrwzRbf8oqFW4MXIoiWEK0TtEkp8YvXV6Jk1hys3H1U31/f5AmoOJib5Qva8rLsWLn7KDbsq0KlIbPw4Spf0QWHjZm2ZJCiSZ6kVdXg+323WitovLCjXehI1UxbxKvwpJSLhRDvAFgBwA1gJYDnABQAeEsIcQOUwO7iWAyUiIiIkl+Dy4Pf/m8NfnPGIPRomxfT59Yqv9U0KtmKN5fuienzByMQ/+mRU5/4GtvLa/Xbj36yUd+ud3kCMjM5hvVpOU4bzv7LtwCAaUN8xUaMAZx/k22idOAyZM1cHi/sNvO6TeNfrTaFOlWrd0ZVOkVKeT+A+/12N0LJuhEREVGG+WLDQXywah/cHom//fj4uLyGti7o03UH4vL8AVoh02YM2ABg5+E6fbu+yQO7zTy90Waznu44d+NBfXvTgSp9uyZJmoNnOsFZqjFV2+ibOuzyeOHxSry1bA+unVQCIQSW7KzQ79fWdWZcpo2IiIjI3y9fXwkAMS+pbZzm5G7lkukCaPUyJMY1evUuj+n1B3cpNB0b7O34wDA9sk+H/FgOjygpvL50t77t9kiMfugLAMrv+5RBnTD7000AlGBZi5c/WrMPw7u3ae2hRo0TnImIiCjmCnJiG7Q1GqZBGRtOZ4L6Jg/Wlh3Tb190fA/T/cH6fo3uVaxvD+1WFI+hUZj81yZSdHYd8WWoD1X7LnT4/03848fHo1D9TjKu9UwlDNqIiIgo5gpjHLS1VG3vwtHdY/p6RiIRqTaDepcHP/3vCv32hcebf9ZgQdueCmWK5ZK7uWolWWgVDI3VPylyx/dqq28bq0L6t7jIzXJgXJ92AIDzRsXvuyKeGLQRERFRTBj7hcV6euTeo/Wm29JvkdkfLx4Z09czao1CJM0x9ml7+tJRaF+Qbbq/ttF6vdrhmia0z89Cp6KcuI6PQver6QMBAGN6t23hSAqF1/A98IfPN+nbWQ6b6TsiL8sOIQTysuzweFMz28mgjYiIiGJi8L2f6dsixhUXGvx6lX2/44i+PbJncUChjlhqrZL/wWh96QBge3mNvv27mUMAmKtE+jtS2xS/gVHYnHYbTihpGzQ7SqGrbnDh0U98gZqx7UeWw2aqmJqfpVxEsttEq6+JjRUWIiEiIqKYi/XVbP+S9WUVvszbuz+bFNPX8idEfGdHNroDm2cDygmmTQDPf7tT33fZuF76trZO7Vhd8KCNko/dJhi0xcCVLywJep+AebqkVu7fkcLvPTNtREREFHOxvpptPAEDgEZDEBfPLJsmngUkjGXLjYpyHAFrn7IdtoBtY/aNkp/dJuBJZOo2TazecyzofTWNbjS4fH+zvdopPSPtNlvKZtoYtBEREVHMGU+YYsE/0+ZshUBN8922I1hdVhkwRTNSjW6PKQgMtiYtL8uBtvlZpn3Gn1p7j/+5YIfpmFlnDo7JOCk+bCJ1sz2p4qoXluBvX20DAMy+cIQ505aizbUZtBEREVHYPlt3AG8t3RP0/roggUiktEzb3WeZA5InL4lfARJ/je7YBKKDfvcZTv7DV9i4X2l+XaO+Vx0KsjCmd1tMHtABAOC0i4BMWyhrBXurWQUAeOPmCTEZM8VOKk/RSyUvLSoF4JsaCaT2mjYGbURERBSWmkY3fvrf5bjzf2v0fV9tPmQ6prYpPkFbYY4TAFBRpxTYaI2m0cV5ymvGMrm3r7IBZz6zEIDvZ3v8ouPwv59NQlGu8np2mwgoXW5speAIMqAT1aAP8E0Lo+TBNW2tq50hW+2wC1aPJCIioszwl/lbA/Zd9+JS0+2aIOu0IqVNj9SClkq1+EZeVvxrqv381P4A4leMxK2eRDrsvilcgBa0+QKzZy4bZQriOgcp5V+kBrbac1BysQlhKlVP8TWoS6G+zUwbERERZYz9x5pvdA0EX6cVKS0bpfV/08rct0aTYm1KoozTBXqXusZGW6fnVm/vO9agB3IAkOP3s/Zu33IWzRbj1gsUPYc9dQOHVJRl+BtK5ampDNqIiIgoLKFkb+IRtNkEkO8XtOU4438qo/20sWiwbVWF0uUxZ9oWbVf6TdU0uk1TICNZ38ZMW/KxCQFvigYOyWLd3kp9e2DngmaPNa9pY/VIIiIiyhChZG8W76zAloPVMXm98upGHK5pREG2Qy9zr/Umy8lqjUyb8v9YzGjzL2ZysKpBz6xpUyHtNt/p2aLtvibiuRH8rIzZkg9L/kfvneVl+vYHt56ERy8YEfRYY9C2cX8VvtxwMK5jixcGbURERBQWYyCw7VDwwOyMpxdE/VqLdxzBCY/MxRtL96Ao16mfgLXq9Ej1/7E4zfZvG1BV79IzbU6/NW3+QvlZOxSYWwTYGLUlHbtN6IE6RWbDvip9OzfLjivG9wp6bJY9PcKd9PgpiIiIqNUYE223vbEKANDFoihGtMmEg1UNuPS5H/TbZUfrke1QApfKepdldcV40Ne0RfgD7amo06c8+mfaPFLq07UcaqbN+P5eeHx3fdt/TZuVQkMREgCwc01b0knldVXJwhbkz/74XsUB+6ymEafi+8+gjYiIiMJinG6kZQwa3bGtFgkAuyvqAvZp0yP3HqtvlSwb4MssRnqad8ofv8IV/1oMIDDTNuPphXhZ7SflUM9EjYHd6F5t9e2Wpkf26ZCPu88aYtrHNW3JJz/boffmo8gIBP5er77/dLz5k4khPd5lsbY02TFoIyIiorAYp3a51HL1jW4vrprQG0vunqrfZyxXHwn/Yg0nlLQ1BYytUYQEgJ76irRMu/HHsGrQvXhnBQDf+9VG7dN2fK9ilB6u1Y9rKUj96tdTMH1oZ9M+Vo9MPlsOVqOm0Y1DVS1XYSWg7GgdLnvue31KNABcPLYHAOBNQ/P4NrnOkDPvDNqIiIgo7dU1+bJFjS7l5KfJ7UWbXCc6GaZJXnR8j6hex79Yw+XjeumZNiC06YKxoIc9Uc6ocnm8lkGbRgtIteTYQ+cPR02DLyPTXND27JXHW+5npi35HKhUgrVNB2JTqCfd/XX+NvywowKfrt2v79OCM2Pj7FCcObwLAF+bjVTCoI2IiIjCcrSuSd+ua3LD7fHC7ZWmLBgAvLF0T1Sv478ea3SvtvqaNqB1ipAAhuqRUT7PmIe+DJgeadShIBsAUFmvBGrt87Pxy2kD9PuzHcFP22YM72q6/aeLR2Jkz2IGbUnotzMGAwCK85wtHEl7Kur07xGXIWWtZb3DLbRz0oAOAKxbbyQ7Bm1EREQUliM1vqDtaJ0L767cC8CX1Vk067SYvI5/P6U+HfJNUy5bL9OmFSKJ7nmqGtw4GGRKnNPuK6oyqmcbAMpJfffiXP2YcE5QfzSmBz649cQoRkvxov3eNjWTdSXF28t8F348hkBLKyRiVWhn88MzsOXhMy2fT/sbO2z4DksVDNqIiIgoZFJKbNhfZdp35ztrTLe7GQKNaFgVNzFWgis7GlioJB58hUiin1KlVdv0ZwxAn7lsND77v8mtFpRS69Iy0gzaWmacTmw3rFfTrudYZZKzHfaArL/moDo19fa3VsVukK2EQRsRERGFrMEV/ETzlIEdA/aFWya/7GgdfvXmKjS5vdh+qLbZY4/WuZq9P1a0ODGeVcKNUz3zsx0Y3KVIvz3/jlPwwjVj4/fi1Kq0gKIxBafotbZh3dvo29rfyJGaRjz+2SYA5vYYoThc0whAqT6bahyJHgARERGlDq1apBWrao5NHq9pHVpLTnr8KwBAdYMb/TsVhD/AOPBNj4xf1NZcOf++HQvQt2NyvBcUPa3ZMzNtLft60yF9Wwu4zv3rdyivVrbDXbN5Yv8OePn7XZjQt33sBtlKmGkjIiKikDV3omkVnDU0RXZiOnfjQdNrFWT7rjOffZxSdGNwl8KInjts2vTIOGbadh1pnamelHjZnB4ZMm29LADM/lTJrhmzZOE2jz9ZnQ1wvKH/Yapg0EZEREQhq1J7Jc08rmvAfVbVDZvLzLXkULWvaMc3v5mib582uBOA1gvaoq2/OLx7UcC+f13N6Y6ZimvaIvfsN9vhMGTXmivO8+PxvQKmFfumOrPkPxEREaWxbYdqACjVCf1ZZdrcUfRD+niNry9Te7UcvnG7OC+8Hk2R0hpUR9KQt7rBhXV7qwL2N1e+n9KbHrRxTVvYZn+6yVRVNq+ZacWPXDACU4eYm81rmTlvPBeoxgm/MYiIiDKI1ytx3wfrsKO8JqLH3/zKcgBAhUXJ7GyLNW3uMDNtIwyFBzQPnz/cdPvkAR3w+EUjcOeMQWE9d6SqG5Ts4kMfbwj7sZ8YGgIb+Ve3M7YyoPTm5Jq2kI3qWYzJam81KzlhrJcFfBdgPMy0ERERUTLbV1mP/3y/C6c98Q2WlVZE/DyDuxYGNLe2yh6Fm2nz7802qHMhrpzQ27RPCIFLT+iFvKzWqadWoVap/HpLediPrWm0bqbttJvfK1eEGclfntYfnYuyWz6QkganR4ZGSolVe46h0eXFpWN7Wh4TbnNt7Xhm2oiIiCipGaut/ejZ7yN+nkGdC9GrXZ5pn7GH2i1T+gEIP9Pm9psyFqzfUmvSTq6z7OGPpabBbbk/VtMjbz99EBbfPS0mz0WtQ68eGcL0yKoGV9CG7Olu88FqAMDG/VXoWpwTs+e120Rc23fES+K/CYmIiKjVRLPGzMhht+GFa4MX09CmOYabQfL4nU0lw9qvBpeSLYskgKx3WWfawi1VTulDC9q+aSFzW9XgwnEPfIHxj85rjWElHe1iya/PGBRW25CW2ASnRxIREVGS8w+KWrLrSC1W7TlmeV+Ptnn45dQBlvc51BPTcF/Pv9pkMmTatKAtkkBrYGelv1qHAnPRFAZtmUubordkZ/PTk3eWN99cPp15vRKfrD0AABjQucDy4o1Wvj9cLo/EwcrUy14m/puQiIiIWk24V5hP+ePXOP9v3+m3C7IdmDLId7J088l9LR+nleUOt+Kixy8z16Eg8eu1tPLg4QaggC9b8PhFx5n2D+hUEFCOnMgoFbNBsfLiolI8+812AMp3gH+Ro4l92+M/14+L+PmN/d9SBYM2IiKiDBJO4GG1lsZhF+htWMtmbHrtfxwQWFikJS6/4/ODPH9r0oYUSdCmTY9sk+vU9/1sSj8IIQLKkVPm6NsxX28SH4w0BG2bD1THe0hJxVjdtkNBdsD0yEa39bTjdMagjYiIKIP4r2m76oXF+vQ/f/e8ty5gn8vt1ac+ap698ng8feko0z6HTTnGP9P2i9dXomTWnKDjK69uxFS1eXayiCbTpv38BTm+4NO/ciQAPHXpyAhHR6nIYRMtri81/umc8fQC3P3e2jiPKvHqmtxYsKUcry7ere8rznUixy/TVtfEoI2IiIjSmNdvytXCrYexdm+l5bF1TYGVD10eGRB0zBjeFeeP7m7apzW9rfMref/R6n0AzFkEjVY5ct6mQ7j/nKEAkqN/mVYevDGCEu1apjHf0J4g39AQWAt2zxrRfNaF0ovDZmsxC320ztwL8TVDIJOuZn+6CVf/e4lpn80mAjJt0UybPvu4rujTIT/ixycKgzYiIqIMYnWi6AhSFMP/WCklmjxeZIUQSGnTAavUxtT+KusD99c0KkHi2cd1hRbT2UTig7ZLT+ilbwfLSgajBXy5hkCtOM83VfL80d1ROntmTKvjUfJz2EWL7TD+9tW2VhpN8gg2DdS/EEmtxQWlUBVkO1DbGPnjE4VBGxERUQaxKgyiTWVs6dh73lemS1pN7/OnZ9qCTGN69JONAfuq1Z5mpwzsqGcEkyFom9ivvb4d7rQs7S00NiIvynEGOZoyhd0mWpxum5eVeYF8sL93/6BtQKeCyF/DJgJmHKQCBm1EREQZxOoKs9NhfaK0zjBtcvmuCn16ljOEMvxaZqk+SJDz1rKygOmXWtBWmOPQT6oi6GcdV1bTOpujVQA0Bm05GXgyTmZOm63FNW3B/nbSmX/MtuHBMwAA2erfT5eiHLx8/Tg8dP7wiF/DLthcm4iIiJKcVabo+YU7LY81NsbWeiYBwJ6KuhZfJ09dw7Xb79iJfX1Zq++3HzHd16BWhMt22vWTKluS9TObt+lQWMd7vF7YhPnncAbJbFLmsNtanh65usx6rWkm0b5HtEIkbq/EKQM7RjWd2CYiKyqUaPzWICIiyiBWmbZ3lpcF7Ptqszk4eeFbX2BnzBoFozWPfuWHXag2rGszNpWu8RuLdiLltNlw2Qk9ceqgjrjxJOs+cIny+w/Xh3W8xxs4/dSRBMVVKLGUNW2pFzjEW7BiP1qQFmz9bTg4PZKIiIiSXqhrslbtPhb0vktO6BnWazYZTsSM6+SMV8tX7zmGN5fuAaAEdsV5WXjxunHoWJj45tpG4TY89koJ/8RaMlTEpMRyWKxp23qwGl+s92W02+Q6ce2kEtMx6d6fzFjox1jhUQvWYpF4FxB6gaBUwqCNiIgog4RadW3Tgaqg9/kXBWiJMbvWZCpu4jtxOu9v3+kZv2QOalqY0RbA45Ww+y3UCVb4hTKHw24zXcw4UtOI6U8twM2vLNf3uT1e098OAAz63WfYdqgG6Upb1woAX/16ir7drTgXF4zujn9cOSbq1/jvD7tQ2+SxrGCbzPitQURElEG0vmmv3zRB39elKCfguM/XHwQAjOvTLuC+rDCDNuNF7UaXF6N6FgMADlY1Wh7vf6KaTMKdVuXxyoB1eZweSR0KsnGo2vf7/9DHG/TtJrcXy3dVoLbJg60WAdrWg9Zl8VOdlDJgDazGbhN46tJRGKl+d0Tjd2cPQfv8LBQZGt6ngqiCNiFEsRDiHSHEJiHERiHERCFEOyHEl0KIrer/28ZqsERERBSd2iY3CnMcGNylUN9nFSSN7a388/34RccF3JcVZknHg1UN+vaG/VUozHHAbhOm/UbJnImKJGjzf3+T+eej1tG1TQ4qapv0bJsxgPto9T58s+UwAGDBlvKAxybzRY1o1IfZAzFSV08swfJ7p0MkQTuRcET7rfEMgM+klIMBjASwEcAsAPOklAMAzFNvExERURJ48btSVDe4kZftW0/m9nrx5YaDuPW1Ffq+7m1z0bt9nmVWLdyg48xnFgIAjtU1AQAWbj2M9vlZOFLTZHl8Mmeiwl0K45EyoHhCMv981Dq0Yj7aGjXjVL2ubXKabWAfSp/EVFTb6AvaVtw7PYEjSU4Rf+pCiCIAJwN4AQCklE1SymMAzgPwsnrYywDOj26IREREFGvZDjv+dsXx6NomBwerGnHTf5Zhzpr9eh+yRpcXOQ675foyEcHZwyX//B4NLiWrcPXE3shx2i1PWIHkLscd7sV5r1cGNAxmyX/KVkvYa38TxgI9Ly0qhV39Hcm36OmXrkG/Vtn2yUtGol1+VoJHk3yi+dboC6AcwItCiJVCiOeFEPkAOksp9wOA+v9OVg8WQtwshFgmhFhWXh6Y+iUiIqLY8q+YNvO4rqa+aYBScvvVxbvw2foDyHbakG0PPGkMpeQ/ANwxfaC+vWRnBS76xyIAwMgexchy2PSiJG7DCeugzoWmqZvJ5sLRPcI63nJ6ZJqedFPochzmTJsxsN9dUacX+3n95gl495ZJOHdkN/3+dJteu2rPMZTMmoPt5cr6Pa03G5lF86k7ABwP4B9SytEAahHGVEgp5XNSyrFSyrEdO3aMYhhEREQUCpda+vA3ZwzS9/nntBpcHtzz3joASpVI4zRKAJh15uCQp2fl+AV3e4/VAwC+3lKOLEP1PGO/qg9+fiIcSTz9q0ub8FoQHK0LnALKoI38M23G9VUer9Qbb/frWIDje7XFBaO76/en2+/Pv9UekHM3Kr0hC7IZtFmJ5luxDECZlHKxevsdKEHcQSFEVwBQ/38oyOOJiIiolczdcBCDfvcZAHODWv/pfsZiADlOe0CAFk5z2wVbrWfS7D5Si2ynTW+kqwVtF4/pERDoJZtwZm7WN3kwd+Mh7K80F1zh9EjSXPWCchpt/DMb1bMYLo/yi6YFaFMG+RIcKdgXullattGjBqr52cn9HZAoEX9rSCkPANgjhNAu100FsAHAhwCuUfddA+CDqEZIREREUZFS4sb/LNNvByu1DyiBhsaqH1tRrjPk171t6gDL/V3a5KBbcS52lNdiTdkxnDh7PgBggt9UzWTy6AUjAIR3wtwQpBpeumVKKHzaxQktoO/dztdIul1+lr7GTQvwhRD46xWjAaRfg20t26hdvGGmzVq0l3p+AeBVIcQaAKMAPApgNoDpQoitAKart4mIiChBluysMN3+ZkvwSTDGTFu2I/CKt9YKIBTtC6ynEt4ypT/6dSzA3mP1mLvhoL4/mYOZK8b3QrbDBhkwoTQ4/wIk2lrAdK3+R6Gb2M98gaJLmxzYbQJtcp2od3ng9ihrIY09/kraK4FdXVO6BW3Kz7O0VPmeymfQZimqd0VKuQrAWIu7pkbzvERERBQ7NWpVNo1xTZu/BlfwTNv2R88Kq0dUsOa1WQ4bnBbPk+wFFoQIL9Pm8Tt47h2n4Nut5Uk/BZTiryjHicFdCrGjvBaA0v8vP8sOu03AKyVcHm/AVORctZLk8wt34IxhXVp9zPGiZRX3VChrXvNZiMRScn87EhERUdQ2Hag23e5Y6MuANbq8pvuMV/Gz/YKLcJv6Bsu02W0CdjWrJv32JzMBobdECIVWTOL35w4DAHQvzsWlJ/SKy9go9ZzUv4OeXZZSmQJpEwJuj8Q/F+zQ13xq8tSgbWnp0VYfazx5/P6kCoNc7Ml0DNqIiIjS3B8/32y63akwR99es/eY6b7NhgBv04GquIynbV4W7OrUQeMUwnCKnCRCvcuDfy3cGfLxWkDM6ZBkxWG3wa1GLJ+u24/KehdsAnhj6R7L43MM05UPVTdYHpOKjBdCHjhnqGlKKPnwW4SIiCjDdC7yBW1v3DzRdF/pkVp929jwN5Y6FmbrWTXjdEx7Eq9pi8TkP3wFwLqgC5HTLvQ2HFpxoEPVwYsEGf8+Gpri87fZ2irrXVhTVqnf7mi4oERm/BYhIiJKE16vDGigbcVpOPnrXpyL0tkz8cA5QwEAx+pc+n19OhQAABbeeSo+/sVJEY2pfX6W6fbKe6cD8E2FrKz3vV5Ng3ntXbrQprURGTlsNkip9GULhd2Qlf7NO6vjNayIPfXlFvzsv8vDeszuI3Wm2/HK7qcDTholIiJKE6c98TVKj9ThtzMG42dT+gU9Tvg3ZwNwzshueOCjDfh4zX59329nKAVLerbLQ88Ix+T/Wm3VIE4L2qoNgVpFbWAj6nRw6uBOiR4CJSFtPVuoJfyNaz4X+1WETZQ5a/Zj04Eq/GX+Nn2f1ytDnuKY5ZeFHtatTUzHl06YaSMiIkoTpepV68c/22Ta36nQuiCIUa5fNuiO6QPRo21e1GO668zBsAlgw4NnYNNDM/T92lq2OWt9QeJFY3pE/XrxdOHx3dG5qOX30qh/pwJWiyRLWhBmzG4b3eJ34cV4/eOUgR2RDG59bYUpYAOAZ+ZtDfnx/lnGCX3bxWRc6YhBGxERUZrTCmH0bJeLZy4bZXlMjl9PtlG9imPy2heN6YEdj81EXpbDFLxYFR1J9qa62Q47QpzJBkDpy3bqoOQ4uabks2KXUgVy9Z5j+r6Zx3XVty89wZzfNk6PLM4Lvcl9a/t++xE0uj14a+meFtfF+mcZrXpDkoJBGxERUZpzebw4+7iumH/HFJw3qrvlMTabMBXMmNi3veVxsZKKFeIcNhHy+iNAed9ZOZKCOX+08rdo/I3KMvy++AcwxumRH6zaF9exhWLRtsOW+5eUVuBvX23Hnf9bg39+s73Z56hXCxG9cM1Y/O9nEwMy/uTDbxIiIqI05/ZKFOc5WwwgtL5Qw7oVwRHnYCPZy/tbsdsE3CFW1HR5vHB7JXI5NZKC0LJlxgqqxr8Lp181VSEETihp2zqDC8EVzy8Oet+asmMAgJrG5tfrXfEv5TlynXaM6c2pkc1h0EZERJQGPli1N+h9Lnd4GZ8tB6tbPihKyd5I20o4mTbtRJzr2SgY7W+y3hi0GQI1/yIdAPD2Tyfp2+FkfWNte3lNs/drGcOsENt4FOUm73TPZMGgjYiIKA34N9D+YccRfdvlDS9oc3nifzJobKp9zcTe2P7oWXF/zWjZ7QLuEE+UtRPxHE73oiAceq9CX/Z268Eaw/3Wf7PTh3YGEHrVyXi4463mWw7UNSljc7Xw96IVHhnenVUjW8KgjYiIKA3sO1Zvun3Zcz/oPdDcHpl00xGN4zljWJeUyLyFlWlTmx9zeiQFo11IMU6P1IL94jxn0PVdk/q1Vx+XuAbbqwzFUzR3nzVY365rUlp5uD1elMyag5JZcyyfZ1npUXQMobotMWgjIiJKC1axROnhWkgp4fbKsNaodSjIavmgKBkLkWQ7U+N0xG6zwe2VkLLlwK3BrU2PTI2fjVqfNhVSC9r+cvlo/aLAS9eNC/o4rUBJkztxQZu/ayb2xk2T+0IIpUqtlmkLlpn+21fbMHfDQbi9EuXVja051JSV3LV1iYiICG6PF3UuD4pywlv3UdfkwaOfbAQQ2tqSv1w+Gr94fSX+fe0JEY0zHF3b5OjbWfbUyEZp2UGvBJp7Ow9VNWCX2jOPmTYKRpv+WK8GOHlZdjSphW6aC/a1tW7JELSN7lWM9245Ub99/qjuWLarQu895w4y1dp/Oje1jEEbERFRkvv126vx/qp92PrImWGtTWtwefCvhTsBKFmilpwzshvOGdkt4nGGY0jXIn3bquBCMtKmcLq9Xtht1sGYy+PFuEfn6bcZtFEwWnVILStrtwnUNirTCgubuUCjB20e37TKP8/bim7FufhRKzSoN04R7l6ca7rPYRPYU+Gbqu32+gLLZaUVGFvSLqB324WjrduQkFlqfEsSERFlsPfVnkz/+Lr5nkf+jFXp9h6ri+mYouU09aNKjdMRLdPW3Lq2Afd8arqdzaCNgtCmLNer6x+ddhtuPKkvgOanKGuVGRsNmbYnv9yCX7/dfHGQaDS5lbVpd7271hSI+Qdg/tOwm9y+v5VF25XiSF9vLvd7TPKvZ00GqfEtSURElKG8hgChb8f8oMe1zQu8Mq+tKwGA4d2StzpbqmXawqmuyTVtFIzTZl7TZrcJ3HRyX5TOnhnQWNsoOwHTI3/x+goAwOtLdsO4pPOes4aajvMveHSsrsl3nxqc1TS6TMecO5KZtlDwm4SIiCiJvbp4l76dnx18VUNhjlOvKqdZufsoerZTpi+dn8RTkFLlSrsWtIVzspwV5ybllLocfn3a/JtpBxOvNW3eZjLIn68/qG9rmeY7ZwxCr/Z5puP8/5a1CraAL6Dzr+Nz0oAOEY030/CbhIiIKIkZK6t5msnw1Ls86FKUY9r36uLdcNptmDq4U1I3ec7PSo0l9jsP1wIAFm0/HPJjwlmDSJnFv3pksL5s/nxr2rwoO1oXUjXTllzy7PcY9+jckI71qq9ndUHC2IbAbhPYdKBav93cOj1qGb9JiIiIklhRru9Ex7iWxEhKiWN1TehQmI2rJvQ23bejvDbppx82l0FMJmcM6wIAYQXAziR/7ylxnDZzpi3UjLMWLK0pq8RJj3+FF78rjXosS0orcLimqeUDAWhfQ0IEjvdgVYO+7fFK1KiFVQDgkTkb9f0UPn6TEBERJbG2eb6CBMHWUm07VAOXR6JLUQ6uO7Ek4P5kD9pSRXu1OEQ4J52hTnmjzKMFaVrJ/1Czstrf85aDShbrhx1H4jA6JQO4bm9lQCZPy7RZ/Wr3bJsbuFOlBXDG77Hm1umSGb/FiYiIklhtk+9KdbBMW1WDsm6kX6cCtMkNnIL0gVp9Mtm8d8skzL/jlEQPI2Ta9LVbXl1heb9VMMc1bRSMtsZLm0LoX8QjGC1o03qgfbHBt94skqmSxnWz2u+wlBKD7/0MZ//lW9MURwDwqK9hsxhv/86FLb6eVnHyF6f1xxs3TQh7vJkqNeYjEBERZajaRl8FyGCZNq2sttMu0L4gO+D+Mb3bxmdwURrdKznHFYz/SfXR2ibkZtn16ZL+5c8Brmmj4PynF4acabP71rT5a/J4m608aeWe99bp2weqGtC9ONe0Nu3MZxaajtcygzaL6ZFXju+FmgY3JvVrj0/XHcCz3/jalIwraYdrX1yCFbuOAgB+cko/FKTI1OhkwG8SIiKiJNbo9gVt7iBBmxYsBOt3dteZg2M/sAxk9wvaRj/0JS78+yL9NoM2ikaoa9q0v3OraZFzNxwK6zW14jqav87fBsBXHMXKngql56NV0CaEwM+m9MPInsWY5fe9M6FvO3y9uRxVDcrsAU4dDg+/SYiIiJKY8Yp3sOmRWulvLUDoXmxeV5LMlSNTiVUAtmF/lb5tFVTzxJSao7XkAMKvHlnd4A64b3t5TVivX3a0znT79SW7caiqAQ3u4EEb1F/pcK9HGPtGAr5CLBQavltERERJbI/hpCpYXyYtw6OdzH036zR89n+T9fuZ7YkNY6bNuHbo2heXADBn2joXZeOh84ZZVtgj0tgNvx/h9mmz8uSXW7B6z7GQX7+2MTDwu+Pt1aaLRZqBnQsA+L6HwvndHtmjDVaXmcdltSaOguO3OBERUZKSUmLOmv367fom66vf2toWY3Bm3E6V5tXJzrimrbzG1z/v683lAMxrjO6ZORRXTSxptbFRaio94rso4wjx4kpLa9Y+XB164aGq+sCgbeHWw5bTIyf1U5pgr9h9DIA54GxJx8IcU89JCh+DNiIioiS1XF2wDyhX12uDBW3qlW9jpULj1CNWMIwNY/C7p6LedF/JrDnmqawW69uImhNq9Uj/tZX+urbJCfk1j9UH9mabNqQzGi2y+kU5StGQP8/bCgAIZ3ZjdYPLFKBS+PgtTkRElKSMzW7zsuyoawq8Kg74qkoap00ZA4xwTuIoOOOaI6vS6oeqfY2FgxWNIQomnGnMz1w2Kuh9VgFXMFYVab/dVm6ZaSvMMbcTsSpEEszinRWm2x/9/KSQH0sKBm1ERERJ6pUfSvXt/CyHqfw/AFTWuzD0vs/w9WalYpwxo9Yu39eUO9RpV9Q8YyBs1ZPNGKi5ghSNIQqmpQyaUZ8OwZtS//2rbWG/5qMXjND3Nbi8OFwTOJXRf83dstKjAcf462DRggQAOhVZ76fg+C1ORESUpL7bppT0fvbK4y0zbd9uPYy6Jo/eXNdpyLTlOO342ZR+eP7qsa034DRnXMNjEbPh5leW6duuMLIdROFqrhhJsGnUVmrUCpT+z/fp2gMBxzr9jglWzdZo3h2n4Pu7TjNVyTxzeBd0LmL2P1wM2oiIiJJMo9uD+iYPbjipDwBg+tAuyMt2mEpmuzxe3PraCv12p8Js5GeZCxT8dsZgTBvauXUGnQGM1e68FtMjjWvaxvRu1ypjovRw3YklYR3vv0719Aj+zlfsPoq/qlk5/xzfnLX7A47P9WsdEkoT7za5TnRtk4unLx2t79O+1yg8DNqIiIiSzPQnF2DIfZ8h12mH3SZgtwnk+2Xa6v3WnPRsl8fy8q3Ianqk5rUbx2NEjzatOBpKdfefMyys47MNAdS3vz0Vz109FtPDDNzmqhl6AJgxvEvLrxlCkBb8sYbCSM1kCSk4vmtERERJZneFUmWtyePV15E47DYsNawh8U/0tM0zFwmg+Lr630uC3pfNZuYUoskDOkT0OGOmrUfbPADAc1eNQf9OBSE/h9tw4SE/24FpQzo1e3ybXPN3jFUxnmCGd/ddxGDfyMjwXSMiIkpSTW6vfnK2YIvSC+xQlVKhcOHWctOxxXlZoPib0DeUaY+sHEmheeGaE7DmgdPDfpxVtkoIgfF9lN/Pg1UNAff7c/m1pfjz5aMDjnn2yjH45jdTcMuUfpjUr73pvt7tgxdDaQ4zbZHhu0ZERJREjA20G92egBOc0574Bq/8sAvfbz9i2t+3Y2QnUBSeu88a0uIxVQ3WrRmI/GU5bCjKCT9Lnh0k8NF6vY1/dF6Lz2FsKQIAeVmOgGMmD+iA3u3zceeMwaY1nQBw7aSSEEdrFk6rAPJh0EZERJRE9hz1NaCtbfTombY/XTwSAFDT6Ma9768zBXcA0L04FxR/oZxwju3dthVGQpnMvxCJxm7oJehtZt0lAHy0el/Avqsm9PZ7Puvf98cvGhEQxIWqCytHRiQwpCYiIqKEMQZjlfUu5KjrowpzzP9k1/kFbcFO4ii2Qgna/JsQE8VasIDJ2Evth51HMKlfeGvmHjxvGOw2gZcWlQLwZe78WTXlbsnq+0+HTQC5WVzzGQkGbUREREnk9x+t17d3Hq5F7/ZKkQH/aZJr91aabnOdSOuw8W2mJGbMjDVX8GPexoOW+4UQKDIUHAmn4XdL/AuZUHj41UNERJREVuw+pm8frWvSC4z4r2HZe6weAPRqcRW15vUpFB92rsehJGbMjDU10+D9hpeXBb0vz5AJC9ZGhH8GrY9BGxERUZKqbnDrTW+DFR6484xBAIA+HViIpDWwFx4lM2MZf/8p1EbNrYHNa2b64lkjlH5uIqAdN8UbgzYiIqIk4l9W+0O1WECwxrbTh3bG6vtPx9iSUErRU7Rami42IIw+WUSx1uDyZdfqmoJXMfWqPdZO7N8e3806zXSfVRVJTWG2MsWR1y5aH4M2ipvaRjeeW7AdnhaqFxERkUJKiUV+pfw1Oc7Af7LtNgEhBNeKtKKWlvh8efsprTMQIgvS0CPQv8KsUbXalqJbm9yArJuWaRvXJ/BCUHGe8l3TtQ0rQLa2qAuRCCHsAJYB2CulPFsI0Q7AmwBKAJQCuERKeTTa16HU88fPN+OlRaV49JNN2PrImc0uiCUiIuBgVWPQ+4Jl2qh1sccUJYtXbhgXUKnUOG2xpjF4pi3LYQMagWlDOwfcp1V3dHsC18TdMqU/hnVvg1MGdox02BShWJxF3wZgo+H2LADzpJQDAMxTb1MGWri1XN9e51fljIiIAtU0ugAoU5b8aaX/jVjmv/VF2puKKNYmD+iIUT2LTfuM1xSCrWmrbXRjWLci5GfZccawLgH356nfNVYTpdrkOXHuyG5c25kAUX3bCyF6AJgJ4HnD7vMAvKxuvwzg/Gheg1LX9vJafZsTJImIWqZNWRpX4gvaCrOVSTFW0yN7tmND7dbG6pGUzIy/nbUWmbZth6ox7P7PsXDrYQzv3sbyOZxq0SMpefaWTKK9RPc0gDsBGPOnnaWU+wFA/X+nKF+D0oCTjW2IiFqkTWeaaChGMv/XUwBYT4/ccrCmVcZFPky0UTI7b1R3fdtqeuS0Jxfo28GWrWjnbFzWklwi/jSEEGcDOCSlXB7h428WQiwTQiwrLy9v+QGU0hizERG1rOyo0nutXb5vnUphjpJpc9oZLSQDq2lh04Yo16ebK5VO1BpG9GiD0tkz0bNdrmWmzShYJdSh3Ypw9cTemH3RiHgMkSIUzan0iQDOFUKUAngDwGlCiP8COCiE6AoA6v8PWT1YSvmclHKslHJsx45czJiOpg3xLW5troIREVGmmrfxIO7/YB0aXMp35IItykXMvh0KsGjWadj44Ax9LRvXkCQH/xPdV24Yhz9dPBIAcNaIrokYElGA/CwHahqbP/dyBAna7DaBB88bjv6dCuMxNIpQxEGblPIuKWUPKWUJgMsAzJdSXgngQwDXqIddA+CDqEdJKcl4VfjdlXsTOBIiouSzp6ION7y8DC9/vws/eUWZtPLpugMAlGIX3Ypz9SpumueuGmMqPtK3Ixtqtzb/81ybECjOy8K8O07BIxcMT8ygiPwUZDsizrRRcorHpLXZAKYLIbYCmK7epgz32uLdiR4CEVFSqaht0re/2VIe0qL/04d1QZNahrtjYTZevXF83MZH1vyrR2oJ0H4dC9iWgZJGfrYDtc001wYAB6dcp5So+7QBgJTyawBfq9tHAEyNxfNSajM21c52cFEbEZFRk18PpEa3LxgLxdCuRejahtUjW5t/nzb2baNkVJDtwJ6jdaZ9/heGvIFt2CiJ8Uya4sZr+HL4+an9EzgSIqLk0+gynzFtL1cqQV45vndIjy+vDt6Im+LHv+Q/QzZKRl4psaO81hSo+V8ocjNqSykM2ihu3IZMGzt9EKWW6gYXSmbNwRfrDyR6KGlr4/4q0+073loNANh0oMrq8ABe9lBKCP/EGpttUzLS1sf++7tSfV9Dk3/Qxu+QVMKgjeLGOD3Swy8GopSy7ZCS9bldDSQo9h75ZKPp9rE6FwBlLUootOOpdQVOj0zQQIhC8NDHG/RzMP/MGs/NUguDNoob41VgfjEQpRbtT9aqOSvF3qDOhThQ1QAA6Nomp9ljHzpvGACWl0+ULIcNT106Ur/NVgyU7HYergUAeNTzspE9iwEAB9XvHEoNDNoobkyZNk7jIUopoVQypNgoznOiV/s8/fYvThvQ7PFXTSzB17+egrvPGhzvoVEQF4zuoW+zEAklo5E92ujbj3+2CQCwcvcxAMCWA9UAgMp6ZutTCYM2ipsfdlTo23sq6vTmsUSU/D5YtS/RQ8gYTW4vvtxwUL+dFUK13ZIO+XDY+U94MmDIRsnojZsn6tuHa5SiRa98vwsAUK+ej7k8vDiXSmJS8p+oJR+v2Y/lu47i+7vYDYIoFTB50HrqmnhBK5WxQTElo9wsX8/ALkXKlOtORUo7kasm9MbQbkUYpU6TpNTAy3TUavZXNuC6F5eglmtkiJKe1v/rxP7tEzyS1rP3WD22HKyO62ss2VmBY3VN+pVvSn05TjbUpuTWo63yfT6hr/J9fvPJfXH5uF4Y0rUokcOiMDFoo7ga3KXQdPurzeV4b+XeBI2GiEKlrXVYtze08vPp4MTZ83H6Uwvi9vwujxeX/PN7XPPvJRj78Fx9vzFR86tpA+P2+hQf2SFMZyVKhCX3TIXDJtDkVqpGav/PdvJ3NhXxU6O4cKsNHK2qm93/4frWHg4RhammUQnaKutdevn/2kY3Hvhwvf4PfzppjZ/pnL98CwBYXVap73v8ohH46Bcn6bdrmzgTIdUwaKNk1akwB8V5WXpTbT1oszM7nIr4TUNx0aB+MeRYXM3p0yG/tYdDRGGqbvAFD1oRoWH3f46XFpXir/O3JmpYcfPXr7bp2/FoUSKlxKYDgVMv+3cqwLBubfD7c5Uy/myxkHqMa4eIkk22w4ZGLdOmBm+hFDui5MNPjeJCO8mzmuv/4/G9Wns4RBSmGkPQ9sOOI1iwpVy//dn6A4kYUlz9eZ4vEHV5Yp91awySyTu+V1sAQGGOUheMa35TR9+OygXIwhxngkdCFFy2w6Zn2GZ/qpT+Z9CWmlg9kuJCD9ocgUEby1QTJb8fdhzRtx+es9F035aDNa09nLjaU1Fnuh2PTFuwDJrWmDkvi0FbqnnvZyeihtNZKck57AIHKhvwx8836ftY8TQ1MWijuGhwBV/s6uCXBVHSs8oMZRmu2KYTY4AKAN44NBbfUV4bsO9PF4/Ut7XvymAZOUo+bfKcaJPHLBslN5sQWLbrKJbtOprooVCUmPKgmNu4vwqfq9OnrKZHMmYjSm5er4TbItt06qCOCRhNfH2wai/mbTxk2ueNUdz0v+VlKJk1B5+s3Y+K2qaA+380poe+3atdHgBgfJ92sXlxIiIomTZKDwzaKObOfGYh/vj5ZgCA2xN44heHi9hE1AKPV+LTtfshg/wBPr9wB0pmzYHb49UXq/tbvy99yv8fq2vCmc8sxG1vrNLX6N139lAAscu03fH2agDALa+uwIHKegDA1MGdLI/t17EAC35zKm6Z0j8mr01EBAB2waAtXTBoo7jaXh649uWIxRVnIoqvVxfvws9eXYG3l5dZ3q+tW6tzeYIW4ig7Wq9vBwv+UsXvP9qAjft9QeiEvu3gVK9IRxK0Hatr/nvtgY82AABmnTk46DG92ufBxqkIRBRD/E5JHwzaKK60imhGWhaOiFrP4RolqDAGXlZ2Ha7DriN1zR4D+Natpiqt95zmhx0VsNuUfxKtpoY257N1BzDqwS9NFTaDYcsTImpNVgXhKDUxaKO4uuwElvcnSgbaFBmrDFllvUvfPuev3+KFb3cGfZ6ubXIAANUNvsc0uDxxqbgYT2VHAwNT7SJTleH9CMVv1GmQWtDW4PKgZNYcy2MddhtK2ufhvFHdwnoNIqJI+F88v+esIQkaCUWLQRvFzcwRXS2bjp4WZE0HEcWPNkPGaurf99vN1RO1ac2DuxQGHHv5OOVCTJWhj9vgez/DT15ZHquhtoqjdebA7IVrxqIoV6kEWNlC0Hb7W6tw9b+XYFlpBQBgfF+leEjv9koxkd9/tN7ycf+6eiwAYP4dU/DMZaMjHzwRUYj8+whePal3gkZC0WLQRnETrA+IdkW+ZNYcPPCh9ckNEcXWyj3HAAD+CbHXFu/GT/9rDri0xtoDOwcGbSO6twFgzrQBwNyNB2M00vjzX7P36W2TMXVIZxRpmbaG5oO2d1fsxYIt5fjRs9/jq82HUKSeFK0uq8QrP+zC60v2WD5u1xGl7D/XmBBRa/HPtLEwSepi0EZx439ecuNJfQAoQZtXPXN8aVFpK4+KKDPN36SUtS8yXHWVUuLu99YGHHtCiZI5euDcYab9104qCaufWE2jG9e/tBRryyqTqml0dYN5LEO6FgEAstW1H831ovOfBlpe3ai3OHlneRnufX9d0MdO6tchovESEUWqyC9oszFoS1kM2ihu/L8Yfnf2UJxQ0hYerwxaUpyI4kPLfBsDkmCB15vLlExRu/wsfd+NJ/XBA+cOQ7bDZnoedzN/y8tKKzB/0yGc89dvMWn2/ID765s82B1C0ZNo/OSVZSiZNQeHaxr1fTUN1gFklkN9jyxalQBKE+6rXlhs2tfk9qK2yRPSWDoUZLV8EBFRDPlPj2TMlroYtFHcWE0BsgkBj5T6yWKWnb+CRK2hc2E2AOCpuVv0fSt3HzMdM6BTAToXZVs+/vjebQEAWXYlG6X9Da/dWxn0NY0BotU6sSH3fYaT//iVnnmPtW2HqvH5emXa5n9/2KXvN05/HNWzWN/Wfjb/TNvWg9V45ftSXPbcD1jkt/6vwdV8wPbMZaP0bas1vkRE8eQ/PVIwaktZgfXYiWLEat704p3Kwn2tp5HWF4mI4ksLi4wXSi7/1w+mY7KdNhw81AiHTeCK8ebKr2eN6Ko83i/TdrCqEcEcqwutCmNtkzvganAsTHtygb799NytuHpiCdrlZ6FGnar52o3jMam/b8qi/8+mmf7UAgSz+UB1s2M457huKK9uxBcbDsblZyQiag6/d9IH0xwUN7ZmfrtO+ePXAACng7+CRPG291g99lc2AACGditq8Xi3V6JYraSY6zRnh/TpkR6Pemzw6ZH3fhB8fZdRVZDpirH23bbDAHxr2vxPZrSgzaodgL+Pf3ESAOjNyqcP7Wx5nM0mcOPkvnjrJxMjGzQRURS0TFvv9nlYNOu0BI+GosEzZoqbUFLwnB5JFF/l1Y040bCeTMtyW9EqQwK+DNp3s07DwjtP1ff7Z6P2VARv1q1Nofzl1AEAoGe4/PlXooyFeywKrGhFRLTX8582lJ+tBKh//3o7Fu9QpkEGm/5YkG1+rP+00gtHd8fTl44Kf+BERDFUoH7PuT0S3YpzEzwaigbPmCmm9lf6TuBCqRaXxUwbUVzd+uoKfVsIoKbRF4T06ZCPM4d3wa2n9gMA9GyXp9+3v0rJzLXLzzLt1/5mqxvcqGl04/HPNlm+7iI1qwUAvdTHD7//c8tj/as5RqvJ7cWri3cH7K+obTK9XoFf0Ga8iFR2VPkuG//oPMvXaJNrztL5V5V89MIROH909zBHTkQUW1r1yIPqdzqlLp4xU0xNfMx3RX/WmYMBAL8+fSAuCHLywkwbUfxsOlCFJWoDaAAozHag0ZA5OlbXhA4F2bhj+iAsvPNU9G6Xr9/32xmDLJ9TC9oenrMRVX7FRerVKoqv/LALVzyvVFm8ffpAfaplMI2u0KvJSinxwrc7UdPoxhNfbMaMpxcE9F37YYevWEi2w4b1vz8DAHC0TgvarDNtxtkBWQ4bDlQ2WBZQ+ez/JqNtvrkS5JUTfA1rX79pAnKcLDpCRInXLl+ZBeCOU8Enaj08Y6a4aJefha5tlDT8z08bgKfUaUJbHznTdBwzbUTxs3Rnhel2n44FaHArgZXXK1FZ70JxnhM2m0DPdnmmqYDdg0yjyTb8zdb5lbr/xzfbAcDUq+zC47vDeKogpXLrO0MmziNDP5m4+711eOjjDRh+/+f4y/xt2HSgGkdqlGCsweXBAx+uR6naxBoAStrnI1+dyvjeyr0AgOpGN7IcNr0vm5VGtxcbD1QF7J97+8kY3EVZF/jIBcP1/QM6FWLVfdOx8cEZmNivfcg/DxFRPPlfnKLUxTNmiouTB1g3kXXabVhy91T99s7DtZbHEVFk5m86iJJZc7B4xxG9+EhRjgNf/3oKThvUCS6PhMcrUd3ghleap/kN6lKobwe7oGIMdPynQP953taAaoo92uaZjvvngh0AgB8/7+t3Fk7Jf6teZ1INC99ZXoaXFpXivg/W6/edO6qbvq1NeaxucAc0nPXX6PZYZgD7d/K9R3mGEv5ZDhuK87JY1p+IkorDov0SpSaG3xQXf7x4ZND7OhXl6NvBmvsSUfi+3nwI17+0DABw6XM/YNoQpaLh1785Fe3ys5DjVAKxRrcHx+qV7FTbPF8Q1K9jgb7d3NTlM4d3wfbyGry7oizgvmv+vUTf7tFWydaN6OErcLJgSzn2VJirM/qvB2tOfnbgP1s7ymvRtU0ufve+uVrlknumomOBMjXojGGdsbT0KOqa3HjNYr2bvyM1TchRA9RpQzph7sZDuHycuQ3C+D7MqBFRcmNftvTBoI3iwhniWrWpgzvFeSSUDmob3ZAIrNhHZte+uNR0e+7GgxjQqQDt1PVXby3bAwC4463V+MkpSvGR4jxfps2YXXM08zdsEwIer8TL3ysNq1+7aTyu+JeSOTtgWOyuZbaMweC6vZUBDar3VQavQOlPKyZi9Lv31+Hxi44L2N+p0HeBSGuyfYMa1LbkyS99TcgfPn8E/nK5MyD72MVwAYqIKFndc9YQTttOA5weSQllY9qeQjDs/s+DVh4kxVGLYAYAurTxBRbby5XpyJ+uO4DN6nqtYkOmzR7i36Pb69WfCwCMi9Z6tM2F1dM8oWbfjT3ZStorVSU/X38gpNcFrIO2Yd2KcMk/vw/p8d+rRUr+/uPjLe+/aXKfgH0FOQ7kZtkD3h+bTaAox4HJQaaDExElg5tO7ovhhpYulJoYtBFRUqtvsu6Ttay0An/83LrcfCaqbbIum29sjn3p2J4AlMDqXnXdl3/z7Hd+OhG/OcO6cqRGy1ppjIVECrId8EolkNrysK/w0EVjeqBbG3Nm6oxhXQAAJ/Xv2OzraaSUeGe5b0rm/36mNKy2mmb94nUnmG6vvu900+3TgmT575k51HR7cJfCZjO8ax44A6/cML75gRMREUWJc40opjoUZOknYqGQYVSNo8w05L7P9O0mtxe/fns1PF6JOWv3AwB+cdoAllcHEOxPyVgY475zhuLNZXtQdrQe04d2xpcbDmJI10LT8WNL2mFsSbuwX/vuswbj0U82YZNaiGT9vqqA6YQ5fkU6tGDL7Qltbes3W8pNt/OylH/CvtzgCyJfu2k8TihpFzBFu02eE+3ys/RMXXaIlWuNa/6IiIgShZk2iqlGlzesMv6M2TJPg8uD8urGkI49XGM+bvmuo/hw9T49YAMQ0CssUzW6rTOSxuDEWMSjss6F43q0iWiRun81su5tc3Hzyf1aXKO6o9xcLfZU9XhXiIVIahvNP6NV4DWpX4ega2q1rG334txmf+7ZF47Qt60KnxAREbU2Bm0UU00eb1gNs72M2jLONf9eghMemdvicQu3lmPsw+bjLv/XDwHHHcvgoK3J7cUTX2zG3mP1etbqN2cMwhs3T8A9Zw0BENhLTbOktAId1MqK4XrpunH69uXjeuqFRuZtOqTvv/fsoQGP04zv0w47HzsLpwzsCIdNhJxpm7fRPC0z2y/D+tHPT2r28fVqH7ozhzc/G+AyQ5XIsqN1zRxJRETUOhi0kaWqBlfQtUTN8UoZUnERreoaQ7bMs9iv4XMw763YG9Jxpz+1AJ+t29/ygWnotcW78Jf523Di7Pl6cDa8extM6NsefTrkAwjMVhqzYfMNQVY48rIN6+RO6GV5zJRBwdepnTq4k57pctgF3CFm2hr8son+F4iGdy8K6XmcIcwGGN2rGADQLUiTcSIiotbEoI0sHffAFzj96W/CfpzHK2EPY7pVGO2Zgrry+cW4/a1VaHCFH2RS8mrpRP6yE3rq27M/zcyCJA98tEHf1qacan3Juqs90vynop41omvUr5tjaLCtVYAElAyapmfbPPi7Y/pAAMDonsX6PqfNBlcImTavV+KTtUqVyd/NHILBXQrRoSALi2adph/T0lRPrcpjXzWgbc6tU/oDgGUrASIiotbGyfoU1J6K0HsnAUpREa8Mr4x/tIVIqhtc+HbbYQDAwq2HsfSeaVE9H7WeA5UNpnL0/raX1+jbH/78RJz71+9M958xvAveWKr0HSs9knlT2PwbUr/w7U4AQMdCJWjTGluf1N9cjr6zobfYoxeMQCS0Jt2AuWXAmz+ZiAaXJ2hhmJ9N6YfxfdtjnCG4c9hFSEFbneGizI2T++LGyX0BAF2b+R3yd+kJPbFw62HT6wczbWhnlM6eGfJzExERxRMzbWnG45WY+eeF+CKMvkfNCSeo0s4hQ8m0jendFoASaEXjlldX6Nvl1Y2sRpkC8tUKgnuPBb8o4PVKrN9Xpd/uYcjaPH7RCDx43jBMGdgRC35zavwGmuS+22b+21m+6ygAoL3aSLswx4kf7pqK3/mtLWtf4AuyrhhvPbWxJc1V62zuPofdFhAwOew2uD0t/93WNVq3NAinkMrZx3XD9kfPQu/2LWfaiIiIkgmDtjRT1+TG+n1VuP2t1TF5vpV7joV8rHblP5Q6JE9cMlLfjibQ8g/69lU2RPQ8DPZax6HqBtSqa6+ay66U+63Dapvn1LcvPaEXrp5YAiEEerXPw1kjuqB/p4L4DDiJaVVaZx5nnu5ozHR3aZMTUEmxKNeJaHVtk4NTB3XEU5eObPngFpRXN+oZ02B2HanF7z9WpoJqUyyNVt03HT/cNTWk1wu1gTgREVEyYdCWZrTAKVj571AYA5gDYQRBWiXIUKZHGq/GN5dxaU51g69qoLZG5cTZ88N6jm2HatD3rjnoc9cnWL4rtAIZFLmpf/KtkwwWtLk9Xox/dJ5+u1e7PAghcONJfXDB6O4BxxdmO7HtUE3A/lDtKK8JuQVBMqlpUDJP3cMslFGUE/2seCEEXrxuHC4Y3SPq59Jo0zuNtHWqP3r2e8xZoxSbybMowV+cl9XsVFsiIqJUF3HQJoToKYT4SgixUQixXghxm7q/nRDiSyHEVvX/bWM33MSqbnBh/qaDLR+YQE3qibArhOlGweyu8K0PMk4/bImeaQuz79NJj38V0DS3OWvLKvHDjiPYcrBa3/fw+cPDek3NtCe/0ad1vvrDbstjGlyegPVDFJlqwxS3YIHSEkN1yX9eNQYL7lSmQP7u7KF46tJRAcd/t13Jtv5rwY6IxnTaE9+E1IIgmXy1+RBu/M8yAEC7/PCaP+dnJedS5oc+3mC6Xd3gwuB7P8MTX2xGZZ3vAs2lhgI0REREmSKaTJsbwB1SyiEAJgC4VQgxFMAsAPOklAMAzFNvp7wN+6ow4oEvcP1Ly5K6b091g/W6j3B8sGqf6fbtb64K6XEeqU2PDH/60eIdR0I+9py/fovLnvsBF/3jewDAQ+cPx8R+7cN+TeOJIAC8u3IvSmbNwX++LwUAPLdgOx74cD0G3/sZ+t39CadQxliwKbyvLvEFz6EEGIfU4O+RTzZGNZ5Uqj563YtL9W2tRxoAvHbT+BYfa7MJ/OTkvnjtxpaPbW0ls+bgiS82A/AF9X+Zvw1Fub7fg7xm1swRERGlq4iDNinlfinlCnW7GsBGAN0BnAfgZfWwlwGcH+UYk8Jjn/pOCGuCLIhPtHdXlGHqE+GX6ff35JdbzM+7MrR+WV41G2ULM9MGAN/vOIKTHp+PIzWN+O8Pu3CougF1Teb3+a2leyz7cZ0/qhuEELht6gAAoU8N3XmkFgBwod+Uu/s+WI+jtU149JNNeGlRqb4/FafQJZMd5YFTGI1TJKsaXPj126vRxrDmali3lvtuPXfVmKD3Ha5pROnh2pDGt3F/FV75vhSHqiNbF9la9lSYLxqdPLADHr1gBNb//gxM6tchyKPM7jprCCb1D+3Y1vaX+dsAmL9nD9c06dvhVKclIiJKFzFZ0yaEKAEwGsBiAJ2llPsBJbAD0KmZh6aMzQd8U/FqG5Pzirx/5uKL9QfCzg4dqvKdsA7t6jthfmTOBqvDTdzeyDNtK3cfQ9nRevzqrdX43fvrMO6ReRh63+f6+L1eiTv/twY//a95uuaATgUozFFO8juopc5vfXVli6/38qJSnP83pYT8dSf2Cbh/9ENfBuzbk8QZ1mTk9nhNv3+V9a6AYybNno9jdcoJ+XEPfIF3lpfhtcW7UZTjwP9+NhFtQ5j6N2WQ7ytmzENfmqZJjn14Lqb86eugjzVOe/3126tx7wfrcfN/lrf4molS1eAy/TyT+rVHtsOOK8b3Qr7FWq9UNeCeTyJu/E1ERJSOog7ahBAFAP4H4P+klFUtHW943M1CiGVCiGXl5aGvZ0qEwzWN+hQsAHh7WfOVzpLFza8sx4IWSuov31WBkllzsH5fJQDga3Vt2Rs3T8Ant03WA7B/LQwsEuBPy5r4V6sLx16/wKjBpTznFxusWxi8+ZOJ+naxmqGZu7HldYf3f7he3+5UlI3fnDHI8riV9/qq0l30j+/xwIfrLQPhb7cexiX//D6kflOpoLy6ESt3H43qOcY9Og/Xv+Sbxjdvo3IS/qShcmh5dSNGPfilXmRCU9XgxpjeLffS0mil64/UNunTJI0ZKeNndvGzi/CA+vkfqfX9XW8vVzJypUdCy8wlwnEPfGEKNI/vlfpLhtf9/gxcO6nEtM/lkXh67tbEDIiIiCgJRRW0CSGcUAK2V6WU76q7Dwohuqr3dwVgeblUSvmclHKslHJsx44doxlGXB2uacTYh81FCoxrvg5VNWDAPZ9gX4QVEGPlqyBXpY/WNlnu1/zzGyUrMfPP3+LP87biznfWAADGqn3UVt03XT/29x+tD3wCA61PlLHxbri0E2fNtS8uwZGaRqwpqww4tnT2TFMRhrMNpc9rDVOrjGvXXvpuJ0pmzTE9T6fCbPz0lH5495ZJWHinr+/Xg+cNQ9t8c1W6lxaVos9dnwSM5ep/L8aSnRVYvKMCu4/U4VB1A+55b21UVTwjUdXgijrYcnm8OOGRubjg74sss2OhaHR7UFHbhK82KxcBJv9hPv76lTLtrY1FyflbXzNnULMd4f0OXed30g+Yp/neoWahG90eLC09ipcWlcLrlVi3N/D36lidC+4kDL6b3OYx3TKlH263KH+fagqyHXpvuZb8alrq/7xERESRiKZ6pADwAoCNUsonDXd9COAadfsaAB9EPrzE2XusHj99ZbkpYJvzy5NwxrDOqHd5sPVgNZ76cgvGPToPLo/EpDBLzcfaE19u1rdH9mijb7fNz4KUEs8v3IFth6pNj5FS4osNvqyU8STXoWbLCnOcej+oL9Y3n8H6+WvKtMTORaGV3r7shJ4BV9g1t57aDwCweGcFxjw8F2VHlaD4lRvG4eaT+5qCK40QAhP6KtmZc/7yLW59dQXeWLIbIx/8Qs+OPvCRb5rnlRN64a2fTIQQAnabwPG92qJnuzyUzp6J0tkzcfVE67EZ1Ta68c9vtusVKK98YTFO/uNXGPfIPLy6eDee/TqyioaRuvk/y3DB3xdFVVTjneVl+vaHq/ZGVIDlZcNaQADYU+G7qDF1SGfccFLglFSjaUM6h/V6fTua+7S5PF44DNN03125F9f8ewkG/e4z32Pu/gSbDyjr7Hq2M5fN/93768J6/dZgrJZ62Qk98ZszBqXN+q4bJ/dF347NN7x+/aYJuG3agFYaERERUXKJJtN2IoCrAJwmhFil/ncWgNkApgshtgKYrt5OOe+v3IvP1vum5N0xfSCGdWujr5+Z/tQCPDPPPH3HuB6stZ0+tAsAYPPDM/D+rSfq+6/59xLc9J/leHjORlxnmKoGBG9EPfvCEabbf7vieABKIPv6kt0tZiEm9g2tkuPsi47D/ecMDdhvE8Avp5pPzj5cvQ9FOQ5MHtARd581BD3b5Vk+Z/sCZV3bjsO1mLN2P2a9uxYA8Bs1g2h040l9Ma5PaFPwdj52FrY+cia0GiuXPfc9Gt0eDLv/czz26aagj3tq7haUzJrTKpmbdXsr8cMOpVx+OP31jOqbPLhLfc8A4N4P1mPmn78Nq+WB2+PFo58o70lhjsNyyui9Zw/FvWcHfvbf/lYJxh8Ks4WD3SZwxjBfoDd/0yEsKa3Qp/fOPK6rZVuJxz9TxvnhrSfhesPaxqWlydezb8N+3+zz3583DCKCgj/JKjfLjj9d3HyjbuMaWyIiokwTTfXIb6WUQkp5nJRylPrfJ1LKI1LKqVLKAer/k+/sJwQ/Ht8LPx7fC1/+6mSUzp6JX6hBhFUj29/OGAwA+P3HG3DnO6txoLIBF/79O1Pz51jzeiVKZs3B03OV7FhlvQtCANkOO4QQ+joswLfGa09FPeap2++uKDM1on7q0pF6g2r/6WvTh3bWT5juenct/jzPeq3JwM4FmDGsS1hX/61OPL1S+TmMUzMB4KbJfVt8Pu1nsGLMNG548AyUNHOs1Tiddps+be+HHRW4ya9gxfSh5uxQR7UwCgA8PCe6cvShOPsv3+rbxhN8QCln/9DHG3CkpvkKmP4XIrTn6nd34JTQYN42ZOqqG9x45ftd+u2Pf3GSvt3PL7PSsTAbPdrmBUx7DdXTl47G81ePBaBMh9x1pA4er0TfjvmW0yCN2uZn4b5zhmLTQzMABE7TTQbaGr2l90xDtiP9yt4f36stLh1r7sH2yg3jAAC3TR2ANnmB02qJiIgyRUyqR6aj4rwsPHLBCAzoXGja372tOWgrnT0TI3sq0xHnrNmPt5aVYcJj87Bi97GAtXCaWGTk9qvP8fTcrdi4vwovfLsTxllswU56b3hZachrrDQ5745TcMHoHvjiVyfj2SuPx4zhXQIe98HPfdm75eqaKeO0uaoGF7YcrIl4DRQAvHz9ONPt4rws5Gf5Tk5vmNz8lDoAuGVK/6D3TXtygfL/IZ2QF2GD4X9c6Ssvv8Avc/PkJSPxxs0TsPOxs1A6eybeMhRJ2bi/qsWAKVINLg+mPvG1eZxfbzfd/nbrYbzw7U7T9FCN1ytRWeeC1yvx7DfK43KcNvzjx8ebjgu15YExUwcAD6pNkz//v5MxvLtv6u7Efu1xmaFRshZwRSo3y45TBinrY43l4gtznNh1xFeUZNaZg9G7vS9T++fLR+vbOYYeYK29HrEleyrq0L0413QxIN10KjL/bCf264DXbhyPX5wW/O+aiIgoEzBoC5Mx0/bOT5WT8mDTARvdXr13meb9lXsx7tF5AcUwwrVom68q5JnPLAy4PytIIYdpQzpjwz5zFqaHGog67DbMGN7VMvvltNvwL/Wkum1eFtaWVaLPXZ9g+S4lkbpOLRQyqEthwGNb8vtzh+HWU/vhlIEdcfXE3njx2hP0+5bfOx3ZDhvm33FKSIFWbpYdr980Qb/t34MNAJ64ZFTYY9ScOqgTnrp0JMYbplU+dP5wfP3rKSjMcWJC3/b6+9enQz5KZ88E4Fubt+lAFQ5VN6C+yQOXx4slOyvw4EcbsDeKQjYfr9kfkBnq45dFXKp+Th+t3odth5R1XB6vxJnPLETfuz/ByAe/QN+7P4FNANdM7I1ND52JM0d0xbgS38/p37+sqsGFxz/bhIOGixDzN/nWPU4dbO724R9sZDvsmH3Rcdj52Fn48lcnY2TP4jB/8kD+lUvf+elEFGT7ArHnrx6Lmyb3xZOXjELHwmysuHc6zh3ZzfQYbdxXPb8k6vHE0v7KBnQrDm29aKoyTsO9eEwP2GwCk/p30NfYEhERZar0aezTSnKcdiy5Zyra52fr62WEEPjX1WNx03+WBRx/3t++w0eGKWHvrwqtUXVLrNZorX3gdNPtD249Ebe/tQrby2tx4ejuOFrXhLkbD+rTJd+8eQK6FeeGPNVq+tDOOKGkLT5esx8fqyXaP1q9H2N6t8MxNcN2id/0plBcYyhG8uB55rVMOU47Nj98ZljPN7Ffe3x622Rc9I9F+Mkp/fDwBcMx9L7PAQCf/HKyZfXCcFwwugemDumM4x74AgDQozi32amWJ/XvgG/VIHvG074AuyjHgaoGJSP07+92Yt3vz0BBmL22PF4Z0IQcAHarU+n2V9Zj4mPzTdUYpz35DTY9NAPby2uw0W8apVfCtF7whWvH4sPV+3DPe+twwd8WYcsjvs9C+/nznHbcfEpfU5GP564ag9OHdTFdnCjKsf7ZhBABGe1Y6NYmB2NL2pl+v6epU1jH9G6LpfdMs3zcqJ7FmKeuiatqcKEoJzHT8p74YjOW7zqK19SLEJX1LvRoa72WM11orVVunz4wYF0rERFRJuPlywh0KswJaCA9fWhnlM6eiVMGmtsXrN1biR92HNFvG0+eS2bNwc9fW2EqSR8K/+wdAFw7qURvMq0Z2bMYQ7sp09F6tsszjXlYtyKM69MuaEGPYDoUmLMl2nRIbf1esqw7GdK1CBsenIFBXQqRl+XA93edhu9mnYah3WJTzMB4Im+camflP37TPjVawKZ5b0WZ5XFWlu+qwK2vrUC/uz/BfR/4WjH848fH4ycn98X6fZVweby49J8/AFCyvkZH65rwxBdbYKVrG182uTDHiZMHKL/TTR6vPmXQuK7xiS+34D+Ldpme4/RhyhTb4d2V93vygNbLlvzzqjGYNqSTfrFkhDol0zhdtTk3GtZObjlQjSa3N6IKmtGQUuIv87dh0Xblu+NgVQN2HamL+oJDstMqly7a3nx/SSIiokzDoC3G8rICs1aXPfeDvl3vMp88f7xmPy755/dhvYZWcMJYLv+uswZbHvvMpaPw4rUn4LapA/D9dl/wOOeXkyOqPud/0vjeSqUkfE2jcjJfEOFasXjr2ibXsohMNLY/eha++vWUgHLz/mw2ga9+PaXF53N5QgsM9lfW46J/fB/QkLp09kycOaIr+ncqgMsj8aNnv9czbgCQa1ivNfGx+Zhv6O333azT9G1jFUYA6GZ43yY9Nh8ls+aY2kMA0Bta92iba/pZe7dTMpD3zBwS0s8WC2cM64LnrzlBryT6i9P644tfnRxypdDcLLv+fqzacwwDf/cp/jxvW9zGa+VOQyb9682HMP7Reah3eZCfnX4FSIw++7/JAICnLh2V2IEQERElmeQ8w05hwbIJOw/X4qtNhwKKVwDA5oPV8Hgl+qlrirY/elbQgMrrlXplwF+c1h+zzhwMp90WkPnT2GwCp6prdOb8cjKm/OlrzDQ0oQ6X/3Q6APjvD7vwkFpsItciaE1XdpsIWDsWjHF925qyY2hye/GjZ5Vg3WkXcHkkHvx4A04e2BH9OzUfBB6sar4giBacrt5zzLT/w5+fiKoGNy76xyLT/m2PnAmH3YYl90xFfZMn4HfYbhP48Ocn4ty/focjfs3a/3DRcbjzf0qA0akwG9/+9jTT/bMvGoHTBnfC4C6JK9fusNswMMzpl93a5KAg24HFO5W1gG8s3d2qPcKMFThfX7Jb3z5vVOAazXQyuEuR/ndCREREPsy0xdhHq/cBAC4d2xOr7puOAeoJ+Kl/+lqvonfywI5YeOepppMTraS6V/oqPFp5Y+kefbt9QTZynPagAZu/EjVw0PquRWJ1WWDp9HsN0/OCFUAhn+N6FJvWcC2/19faYNqT32BHeY1+e9G2wyiZNQf7DIVKfmvIwjys9jN72NDXrK1f5VCtAmfPdnkY07ut6b7ND8/Qg7ROhTno3d46CD2uR7GpKMnMEV2x+eEZuOSEnpgxrAs6FGTjPzcETgMtzHHiojE9LJ8zmQkhUNfkxpdq83mtWbmU0lR4JV66tfEVHNF67wG+6aZERESUWXiGHWNan7CLxvRAcV4WPv7lSQHH3DvT1xz6jz86LuD++ZsOmarwaRpcHtz9nlJOfULf0KZ6xdojF/iChK9DmPJH1trkOjG4SyH6dshHUY4TL17nq5j51rIyPUjQpiFqfcYOVjVg80Gl39zJAzviygm9UTp7Jq6c0Ft/vNaWolAt/PHVr6dg+e+m6eXsl/9uGn5+an+sfeD0sPp9vXrTeJwxrDP+edUY/O3Hx+uPffaqMVj2u2kJzabFg3E65dE6F6SUeHXxbox/dB42HVAyztsO1TTbA05KibUWFzqa8+SXW7CvsgHt1eBbWze687Gz0rI/GxEREbVMtPYCeytjx46Vy5YFzy6lkt1H6vDOijL8atoAfYqjsYLeS9edgCmDfKXQtWmRVvynCfW/+xO41SIkOx8LPoWyNRl/tpsm98E9M4cmcDSp7avNh3Ddi0v12z8a00MvzPDny0ejY0E2Lv+Xsj6yINuBdb8/I+hzuTxeOO02/f8UvvomD4bc56uI+c1vpuDXb6/G0tKjePKSkfhs3QF8oWbi/nX12IDm6gDw3ILtePSTTTixf3u8euOEgPutBGsHwmmDRERE6U0IsVxKadm4lmdzMdarfR5unz7QFFAZ1yhN7Gfu6Wa3CVwcZPrYgUrzNCy3oWpkMgRsgJIpPHlgR8y9/RQGbFE6dZC5r9k7hnVNZUfrcP+H6/Tbo3sVN/tcWqDGgC1y/uszV+05hqWlSmP5bYdq9IANgKnIj9F6tSfid9us7/fX5Fflk4iIiAhg0NYqehnK6ltNb8p2+j6GnY+dhVfUtUEn/+EruD2+kzitCfZKwxqoRLt4bE/85/pxLRbPoOjsqajDloO+tW4/Ht+7maMpVr781cm47ASl9+Btb6zS9//96+2m4/793U7LxxsvtHgsWnX4225Yz/joBSOw6r7pOPu4rjhrRJdwhk1ERERphkFbK7h6onKC/dSlIy3v/79pA9GvYz7evHkChBAYrvZWa/J48fXmcpTMmoPP1u1HdYMbXYpyAgpNUPp4OUhPt9eX7DHd9s/YUnwM6FyI2Rf51p12NRQI8efyBGbJjD0Yq+pb7sd466sr9O2T+ndAcV4W/nrF8fj7j8eEOmQiIiJKQwzaWsGUQZ1QOnsmLhhtPQ2yQ0E25t0xBeP7KifixqDsxv8oa/1++t8VqKx34UArVK6jxJnUrz06F/kamC++e6rlceneZDlZ7febsrz8d9P07Uq/oKy8uhGbDlTrtxdua7lh9I7DtQCAT2+bjF4tNG0nIiKizMGgjSiJOO02LL57Gp65bBQeOm8YOhfl4P5zlLWC/TsV4M2bJ+DjXwRWJKX4+uY3UwL2vXfLJLQvyMbNJ/cFABypacIHq/aiZNYcLNp+GCc8MheHa3w99X75+sqQ2wUM6ZpelTiJiIgoOmyunaT+esVo/Py1lQH7j+vRJgGjodZmbKJ83Yl9cN2JfRI4GurdPh/j+7TTm20/esEIjO6l9Lw7Xi0Kc8bTC/Tjr/jXYn27MMeB6gY3AGD8o/OCVoH0qmvejm+hyAwRERFlHmbaktTZx3XDk5cEroHrWJBtcTQRxZuxoNAV43vp2/nZzV/7mnfHKZb7K+tc+npVwFeEZMXuY1GOlIiIiNINg7Yk1qOt7yTxmctG4Xczh+BPF1sXMyGi+Lr/3GGW+wuaCdoePG8YOhWai5eUquvWth5S1rs9+80OAMDeY/UAgD8YCp8QERERAQzakppWqS7Xacd5o7rjxsl9WTmSKEGCBWcd/LLft00dAAAY3KUQV08sCTh+5p8XAgBqGpUpk6v2HMM1/16Ca9XG6m3yWGSGiIiIzLimLYl1K87FhaO74+pJJYkeChGpThtsboLeuciXSSudPROHaxrxxYaDeOayUfr+L351Mr7ZXI5HPtmI2iYPbn9zFd5duVe//5st5fr2VL/nJyIiImLQlsTsNoEnLx2V6GEQkWrtA6cjx2k37ctyKBMWJg/oAEDJvH1622TTMQM7F2Jg50I88slGADAFbP4cdk6AICIiIjMGbUREISrMsZ66GKwiZCiEAKRSOBKXj+vV/MFERESUkXhJl4iolay8d7rp9rNXjsE/fjxGv/3YhSNae0hERESUAphpIyJqJW3zszCgUwG2HlLK+08Z1BFOuw3j+7TD46waSUREREEw00ZE1IrunjkEAHDWiC7Icdphtwm8+ZOJKOmQn+CRERERUbJipo2IqBWdOqgT3r1lEkb3LE70UIiIiChFMGgjImplx/dqm+ghEBERUQrh9EgiIiIiIqIkxqCNiIiIiIgoiTFoIyIiIiIiSmIM2oiIiIiIiJIYgzYiIiIiIqIkxqCNiIiIiIgoiTFoIyIiIiIiSmIM2oiIiIiIiJIYgzYiIiIiIqIkxqCNiIiIiIgoiTFoIyIiIiIiSmIM2oiIiIiIiJIYgzYiIiIiIqIkxqCNiIiIiIgoiTFoIyIiIiIiSmJCSpnoMUAIUQ5gV6LHEWMdABxO9CAyHD+DxONnkFh8/xOPn0Fi8f1PPH4GicfPILHCef97Syk7Wt2RFEFbOhJCLJNSjk30ODIZP4PE42eQWHz/E4+fQWLx/U88fgaJx88gsWL1/nN6JBERERERURJj0EZERERERJTEGLTFz3OJHgDxM0gC/AwSi+9/4vEzSCy+/4nHzyDx+BkkVkzef65pIyIiIiIiSmLMtBERERERESUxBm1ERERERERJjEEbEVEKE0KIRI+BiDIbv4co07XG3wCDNkp5/McisYQQjkSPIcM5Ez2ATCaE6KD+357osWQqIUQbwzb/PUgMnk8mkBBirBCiU6LHkeHi/m8x/8giIIQYJYS4SQjRJdFjyVRCiCFCiIkAIFlNJyGEEBOFEP8CcEKix5KJ1Pf/bQB/EkIMZdDQeoQiTwjxOoAPAEBK6UnwsDKOEGK8EOIDAM8LIa4XQmTz34PWJYQYJ4T4L4DHhBAjhBA8r2xFQohhQohFAO4HUJzg4WSk1vy3mH9cYRBCOIUQ/wTwAoBTADwihBif4GFlFCFEGzVQeAPAQ0KIR4QQ/RM9rkwjhLgJSgnbFQBWMmBoXeoV1b8C+ATAYQC3AbhevY+ZhjiTijr1ZgchxM8AgCesrUcIcRyAvwF4B8DbAE4DwH8LWokQwiaEuB/A8wA+BeAAcCuAkQkdWOa5DcB7UspzpJRbAP4b0Jpa+99i/gMTnhEA2kgpx0gpr4Ty/h1O8JgyzW+gtKoYCeAnANoDKEnoiDJTLwD3SCn/IaVsYJah1Y0EsEVK+SKAJwC8C+A8IcRAKaXkP9rxJYRwCCG6AjgI4AYAPxNCFEspvQzcWs04ANuklK8A+BJADoDd2p38G4gvKaUXwC4A10opXwXwCIDeAHgBrxUIIexCiHYAJJSgAUKIC4QQPQDkqrf5NxB/rfpvMf9xaYEQ4nghxED1pgfAJWq250IAEwBMFUKMVo/lH0gcCCH6CCFy1Zv/AnAfAEgpt0OZDjAiQUPLGOpnkK1utwMwHMASIcRpQojPhRB3q38T/DuIAyHE5UKI3wshzlV3rQQwVgjRT0pZC2ApgGVQLmRwynCMGd7/cwBASumWUu4H0AdAKYBvAMxSPw9vAoeatgyfwXnqro8AXCCEeATAWgA9APxZCPFbgH8D8SCEOMVvdtEbAFap01KPAKgG0DUxo0t/xvdfvVBaB+BkAKepU1R/AuBhAE+rx/BvIMaEEOer5zsz1V2r0Ir/FjNoC0I9SZ0DZfrFK0KI6VLK1QD+AODvAJ4F8CiAngAe1KLqxI04/QghSoQQn0KZfvFfIcQgKeUuKeU+IUSWelg9gO2JG2V68/sMXhNCDJFSVgA4AuBVAOdD+XvYD+A+IcRI/h3Ejrp26qcA7oQSHPxRCHEjgBoA/4EyFQMAjgGYCyBPzQBRDFi8/38SQlwnhMgXQvQGsFNKWQYl03MLgLeFENlCCBaHiRGLz+APQoibpZQHAQyGsvj/binlBAAvAThJqOudKTaEEIVCiHcBvAfgJ+qFOwBolFJ6pZSN6u98DwCbEzbQNGXx/rcFACllA4AXoZynfi6lnAHgHgDDhRBnJmzAaUgI0VEI8T6A2wFUAHhRCPEjKWU5gP8B+IV66DHE8d9iBm0GfhmCXwNYJaWcCOB9ADeq++8CsBHAj9RpGU8D2AngxNYbafqy+AwWSymnAvgKyhq2Yep92nS87gD2qI/l73MMNPMZzAfwsBCiD5RFzyMA7JNSfqBODfgEwHkBT0gRUwPgiQBmq+/xrQCmAJgK5f3uL4SYpmZ3jkD5e6hM0HDTTpD3fxqAyQCOAugjhPgIwB+hZNt2SSkbpZSuRI053QT5DE4RQpwppdwJZR1bmXr4cgCHADQmZLDpqwnK9/+VAPYB+BEQkEUYAuCglHKLGmSMa/1hpi3/9/9iw31/hzIdsiMASCn3AvgWADP+sdUPwHdSypOllM8CuAPAr9T7XgcwWAgxNd7/FvMk1ywH0E9aawFo//C2AbBOCDFU/ZJqBHApAKhTAroD2ND6w01L2meglZFfDwBSyr9CWcNwhRCik5TSI5QCJBVSypVCKQRwrxCiOBGDTjPBPoO/ARgD4GYA5VCybz8yPK4TgEWtN8z0JIS4Wp0Go13N3giguxDCIaWcC2AdlKnZ5QBeA/C0+rcwFYAAkGX1vBSaEN7/NQBOAjAQwF4AOwCMkVKeA6CnEGJMQgaeRkL8DKYIpQjA5wDuV//dvgzAMCgnTRQFw2dQLKVshPJ9PxfAFijTwQaqx2n/TrQDUCeEuBbKvwMjOFU+cqG+/1LKGihZnmuEUtn8Z1AuLJUmaOhpQ/0Mpggh8qBcEPqPut8O5ZxfO+9fC2Wq8DPx/reY/ZUACCGmQ5l6sVkIsUBK+ZYQ4lsAlwohVkJ5898H8LIQ4h4AnwF4TwjxJwDj4fuHmyIU5DOoADBaCLFFPWwdlIXO7aFcTe0L4AQhxFcAGgD8n5TyWOuPPj2E+Bmsh1KEpJeU8m4hxGAhxGwo2Z996v0UJvXkpguUIMwLZcpvvvoP8B4oWc3+ADZB+cfhKQDtpZT/FUL0BDALylSxm/g3EL4w3/+3oCw4fxvKd06T4ammSimZ6YxAmJ/Bm1D+BrpJKf8phDgFvgqG10spdyXgR0h5QT6Dm4UQt0kpD6vHfA/lgsUlAB6WUrrVh58B4HIoF7V/LKVc09rjT3WRvP8AoP5bLdR9wwBcJaXkNNUIBPkMbgJwm5TyoBDCriYNhkBJ6GhFeV5SLyLF9d/ijA/a1Kj4YSjr03YD+I0QopeU8k9CiM0AHpNSagUWvADOlFL+SgjxIygB23dSyvcSNf50YPEZ3CmUhrV/BPB/UKpSFavbtwOYDuXKa0cAbQH8RL36ShGK4DM4B8pJ07UAugGYL6X8opWHnRYM/wgUAtgrpbxSvXr9ZwB/gTI1eyqUCxT7pZSlQohKKFnOlVLKx4QQWX7BA4Uogvd/pxCiGsBFUsr71H/khbq2hwFbBCL8G6gCcBGUQgDXQLmIcSBBP0LKa+YzeArAP6G815BSbhVCLAcwXf13Y59U2l98BGVJyZuJ+hlSWRTv/34ATVLKN4UQb3FNeeRa+AyeA3Ch4fDTobQbgRCii5TygJTyD/H+tzgjgzahrn1So+PxAJZLKT9Q75sL4EkhxCtQFhvuEUrxhY1Q5hT/nxDCJqVcBeUfC4pACJ/BEwDellI+JIToK6Xcod73HXzrFd6QSqlhikCMPoNqKeUmKFe/KQzqPwYPArALIT4BUAR1raaU0i2E+DmUf5CHQrnqdz6Uhf6PQbkCqE9FZcAWvijffw+AxeqxEkrZbQpTDD6DH9RjXQAYsEUghM/glwD2CSFOkVJ+o+5/T800fAagQAhxqpSSU+MjEIv3H8CpADYyYItMJJ8BlGJgO4UQDwK4UAgxQ0pZFu9/izNuTZsQ4jooi5YfUnetBXC5EKJEve2EMtXxISjla9sB+KUQ4jYoVzvmAmAfpCiE8Bk4oKSkn1Jv71QfdzOUnkgrAL3kLUUghp8B/5GIgDqdazmUTPE2KJ+DC8CpQl3ArwbTDwJ4XM0kPwelMt5i9XFfJ2DoaYHvf+LxM0i8ED8DCeUzeMDwuIuhVCn8CsBx6kVtChPf/8SL5DMQypq266Fk2ooAnCqVKsLxH28mnXMJIQoA/BfKL/o1AK6QUm4SQjwNoDOUtTo7ATwOpbT/j9R90wCMBfAPKeUPCRh62gjzM5gNZX3CQSHE/wH4MYBbpJRLEzH2dMHPIPGEEJMBlEilAi2EEH+HEjjXA/iFlHKMmgntBGV62G/UKWHFAPKlUiGMIsT3P/H4GSRemJ/BnwH8Vp0ePBkApJQLEzT0tMD3P/Ei+Ax+A+Wi9i8A/EdKuaJVByylzKj/oBRQAJST0TfVbTuUjNpJ6u2eAF4GkJXo8abjf2F8Bi8ByFZv5yV63On0Hz+DhL//eQCyAdjV2z+Gsn4WUKZd/0LdHgvg9USPN93+4/uf+P/4GST+P34GfP8z/b8wP4M3Ej3ejJseKaXcrW4+DaXHzhlSmWZXKaX8Vr3vp1BK/nP6XRyE8RnUAXCrj6lr9YGmMX4GiSWlrJNKPy/tO2Y6lBL+AHAdgCFCiI+h9H9p3St5GYDvf+LxM0g8fgaJxfc/8cL8DJYDAb1sW1VGFiIBACnlASHECwDuhtJJ3qPOX70Hyrq26yXXTMUVP4PE42eQWOrceAllWuqH6u5qKJ/HcAA7JaeBxQ3f/8TjZ5B4/AwSi+9/4oXzGUg19ZYIGbWmzUgoFSC9Qoh3oFSnaoRSZGSrlHJ7YkeXGfgZJB4/g8RSr9hlQWmc+h6Uxc1HoEzJqErk2DIB3//E42eQePwMEovvf+KlymeQyZk2r1C6nHeC0hj4QSnlZ4kdVWbhZ5B4/AwSS0ophRCjocyj7wPgRSnlCwkeVsbg+594/AwSj59BYvH9T7xU+QwyNtMGAEKIX0Pp+fJbKWVjS8dT7PEzSDx+BoklhOgB4CoAT/L9b318/xOPn0Hi8TNILL7/iZcKn0GmB202qfSBoQThZ5B4/AyIiIiIkltGB21ERERERETJLuNK/hMREREREaUSBm1ERERERERJjEEbERERERFREmPQRkREaUsI4RFCrBJCrBdCrBZC3C6EaPbfPiFEiRDiitYaIxERUUsYtBERUTqrl1KOklIOAzAdwFkA7m/hMSUAGLQREVHSYPVIIiJKW0KIGillgeF2XwBLAXQA0BvAKwDy1bt/LqX8//buGKeLIAzD+PNGCTGEUOENINooBQewsrKgkJKWhMLOI1hYmUjQSKeVlQegssJQGeAClAQsKIgJBfBZ7BSGhNJ1wv/5NZudnd3MlG9m5tsfSfaAx8AR8AXYBN4y/IB+GvhQVdujTUKSNPEMbZKkO+tmaGttZ8Aj4By4rqqLJAvA16paTvIMeF1VL1r/deBhVb1JMg3sAqtVdTTmXCRJk+v+/x6AJEkjS7tOAVtJloArYPGW/s+BJ0letvs5YIFhJU6SpH/O0CZJmhhte+QVcMpwtu0EeMpwxvvitteAV1W1M8ogJUm6wUIkkqSJkGQe+ARs1XA2YA44rqprYA2417qeA7N/vboDbCSZat9ZTDKDJEkjcaVNknSXPUiyz7AV8pKh8Mi79uwj8C3JKvAd+N3aD4HLJAfAZ+A9Q0XJn0kC/AJWxhm+JEkWIpEkSZKkrrk9UpIkSZI6ZmiTJEmSpI4Z2iRJkiSpY4Y2SZIkSeqYoU2SJEmSOmZokyRJkqSOGdokSZIkqWOGNkmSJEnq2B9ujemuguI7lgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_train.plot(figsize=(15,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preparation \n",
    "\n",
    "We will scale our data for optimal performance. In our case, we’ll use Scikit- Learn’s MinMaxScaler and scale our dataset to numbers between zero and one.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "sc = MinMaxScaler(feature_range = (0, 1))\n",
    "training_set = data_train.iloc[:,0].values\n",
    "training_set_scaled = sc.fit_transform(training_set.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LSTMs expect our data to be in a specific format, usually a 3D array. We start by creating data in 60 timesteps and converting it into an array using NumPy. Next, we convert the data into a 3D dimension array with X_train samples, 60 timestamps, and one feature at each step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = []\n",
    "y_train = []\n",
    "for i in range(60, 500):\n",
    "    X_train.append(training_set_scaled[i-60:i, 0])\n",
    "    y_train.append(training_set_scaled[i, 0])\n",
    "X_train, y_train = np.array(X_train), np.array(y_train)\n",
    "\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building LSTM model\n",
    "\n",
    "In order to build the LSTM, we need to import a couple of modules from Keras:\n",
    "\n",
    "- __Sequential__ for initializing the neural network\n",
    "- __Dense__ for adding a densely connected neural network layer\n",
    "- __LSTM__ for adding the Long Short-Term Memory layer\n",
    "- __Dropout__ for adding dropout layers that prevent overfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'tensorflow'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-12-1b08a8769e32>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSequential\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDense\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLSTM\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDropout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/keras/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     19\u001b[0m \"\"\"\n\u001b[1;32m     20\u001b[0m \u001b[0;31m# pylint: disable=unused-import\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtf2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdistribute\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'tensorflow'"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "from keras.layers import Dropout"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We add the LSTM layer and later add a few Dropout layers to prevent overfitting. We add the LSTM layer with the following arguments:\n",
    "\n",
    "1. 50 units which is the dimensionality of the output space\n",
    "2. return_sequences=True which determines whether to return the last output in the output sequence, or the full sequence\n",
    "3. input_shape as the shape of our training set.\n",
    "\n",
    "When defining the Dropout layers, we specify 0.2, meaning that 20% of the layers will be dropped. Thereafter, we add the Dense layer that specifies the output of 1 unit. After this, we compile our model using the popular adam optimizer and set the loss as the mean_squarred_error. This will compute the mean of the squared errors. Next, we fit the model to run on 100 epochs with a batch size of 32. Depending on the specs of the computer, this might take a few minutes to finish running."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "14/14 [==============================] - 17s 161ms/step - loss: 7.1685e-04\n",
      "Epoch 2/100\n",
      "14/14 [==============================] - 2s 161ms/step - loss: 2.4876e-04\n",
      "Epoch 3/100\n",
      "14/14 [==============================] - 2s 161ms/step - loss: 2.1043e-04\n",
      "Epoch 4/100\n",
      "14/14 [==============================] - 2s 160ms/step - loss: 1.6986e-04\n",
      "Epoch 5/100\n",
      "14/14 [==============================] - 2s 162ms/step - loss: 1.1705e-04\n",
      "Epoch 6/100\n",
      "14/14 [==============================] - 2s 161ms/step - loss: 1.1190e-04\n",
      "Epoch 7/100\n",
      "14/14 [==============================] - 2s 161ms/step - loss: 9.0528e-05\n",
      "Epoch 8/100\n",
      "14/14 [==============================] - 2s 161ms/step - loss: 9.0417e-05\n",
      "Epoch 9/100\n",
      "14/14 [==============================] - 2s 163ms/step - loss: 7.1171e-05\n",
      "Epoch 10/100\n",
      "14/14 [==============================] - 2s 164ms/step - loss: 7.3868e-05\n",
      "Epoch 11/100\n",
      "14/14 [==============================] - 2s 176ms/step - loss: 6.8370e-05\n",
      "Epoch 12/100\n",
      "14/14 [==============================] - 2s 167ms/step - loss: 6.7271e-05\n",
      "Epoch 13/100\n",
      "14/14 [==============================] - 3s 184ms/step - loss: 6.8994e-05\n",
      "Epoch 14/100\n",
      "14/14 [==============================] - 3s 179ms/step - loss: 5.4155e-05\n",
      "Epoch 15/100\n",
      "14/14 [==============================] - 3s 191ms/step - loss: 5.9755e-05\n",
      "Epoch 16/100\n",
      "14/14 [==============================] - 2s 170ms/step - loss: 5.5435e-05\n",
      "Epoch 17/100\n",
      "14/14 [==============================] - 2s 173ms/step - loss: 5.9895e-05\n",
      "Epoch 18/100\n",
      "14/14 [==============================] - 2s 171ms/step - loss: 5.2178e-05\n",
      "Epoch 19/100\n",
      "14/14 [==============================] - 2s 180ms/step - loss: 5.3749e-05\n",
      "Epoch 20/100\n",
      "14/14 [==============================] - 2s 176ms/step - loss: 5.2968e-05\n",
      "Epoch 21/100\n",
      "14/14 [==============================] - 2s 168ms/step - loss: 5.8238e-05\n",
      "Epoch 22/100\n",
      "14/14 [==============================] - 3s 183ms/step - loss: 5.0631e-05\n",
      "Epoch 23/100\n",
      "14/14 [==============================] - 2s 171ms/step - loss: 4.8411e-05\n",
      "Epoch 24/100\n",
      "14/14 [==============================] - 2s 162ms/step - loss: 5.7663e-05\n",
      "Epoch 25/100\n",
      "14/14 [==============================] - 2s 167ms/step - loss: 5.0271e-05\n",
      "Epoch 26/100\n",
      "14/14 [==============================] - 2s 171ms/step - loss: 4.9419e-05\n",
      "Epoch 27/100\n",
      "14/14 [==============================] - 2s 165ms/step - loss: 5.1380e-05\n",
      "Epoch 28/100\n",
      "14/14 [==============================] - 2s 171ms/step - loss: 5.1818e-05\n",
      "Epoch 29/100\n",
      "14/14 [==============================] - 2s 167ms/step - loss: 4.3780e-05\n",
      "Epoch 30/100\n",
      "14/14 [==============================] - 2s 169ms/step - loss: 4.9553e-05\n",
      "Epoch 31/100\n",
      "14/14 [==============================] - 3s 180ms/step - loss: 4.9717e-05\n",
      "Epoch 32/100\n",
      "14/14 [==============================] - 3s 225ms/step - loss: 4.6206e-05\n",
      "Epoch 33/100\n",
      "14/14 [==============================] - 3s 182ms/step - loss: 4.4501e-05\n",
      "Epoch 34/100\n",
      "14/14 [==============================] - 3s 189ms/step - loss: 4.5605e-05\n",
      "Epoch 35/100\n",
      "14/14 [==============================] - 3s 186ms/step - loss: 5.4089e-05\n",
      "Epoch 36/100\n",
      "14/14 [==============================] - 2s 166ms/step - loss: 5.1363e-05\n",
      "Epoch 37/100\n",
      "14/14 [==============================] - 2s 176ms/step - loss: 5.5228e-05\n",
      "Epoch 38/100\n",
      "14/14 [==============================] - 3s 184ms/step - loss: 4.5569e-05\n",
      "Epoch 39/100\n",
      "14/14 [==============================] - 3s 183ms/step - loss: 4.9735e-05\n",
      "Epoch 40/100\n",
      "14/14 [==============================] - 3s 183ms/step - loss: 4.4790e-05\n",
      "Epoch 41/100\n",
      "14/14 [==============================] - 3s 179ms/step - loss: 3.9464e-05\n",
      "Epoch 42/100\n",
      "14/14 [==============================] - 3s 181ms/step - loss: 4.5136e-05\n",
      "Epoch 43/100\n",
      "14/14 [==============================] - 2s 179ms/step - loss: 4.5912e-05\n",
      "Epoch 44/100\n",
      "14/14 [==============================] - 3s 183ms/step - loss: 3.8445e-05\n",
      "Epoch 45/100\n",
      "14/14 [==============================] - 3s 191ms/step - loss: 3.9617e-05\n",
      "Epoch 46/100\n",
      "14/14 [==============================] - 2s 176ms/step - loss: 4.3031e-05\n",
      "Epoch 47/100\n",
      "14/14 [==============================] - 2s 174ms/step - loss: 4.4345e-05\n",
      "Epoch 48/100\n",
      "14/14 [==============================] - 2s 169ms/step - loss: 4.3707e-05\n",
      "Epoch 49/100\n",
      "14/14 [==============================] - 2s 165ms/step - loss: 3.7403e-05\n",
      "Epoch 50/100\n",
      "14/14 [==============================] - 2s 163ms/step - loss: 4.0212e-05\n",
      "Epoch 51/100\n",
      "14/14 [==============================] - 3s 183ms/step - loss: 4.2372e-05\n",
      "Epoch 52/100\n",
      "14/14 [==============================] - 2s 176ms/step - loss: 4.2292e-05\n",
      "Epoch 53/100\n",
      "14/14 [==============================] - 2s 167ms/step - loss: 4.1268e-05\n",
      "Epoch 54/100\n",
      "14/14 [==============================] - 2s 178ms/step - loss: 3.6920e-05 0s - loss: 3.5412\n",
      "Epoch 55/100\n",
      "14/14 [==============================] - 3s 179ms/step - loss: 3.8994e-05\n",
      "Epoch 56/100\n",
      "14/14 [==============================] - 2s 169ms/step - loss: 3.6487e-05\n",
      "Epoch 57/100\n",
      "14/14 [==============================] - 2s 166ms/step - loss: 3.7429e-05\n",
      "Epoch 58/100\n",
      "14/14 [==============================] - 3s 189ms/step - loss: 4.0977e-05\n",
      "Epoch 59/100\n",
      "14/14 [==============================] - 3s 191ms/step - loss: 4.1877e-05\n",
      "Epoch 60/100\n",
      "14/14 [==============================] - 3s 190ms/step - loss: 3.5971e-05\n",
      "Epoch 61/100\n",
      "14/14 [==============================] - 2s 173ms/step - loss: 3.5495e-05\n",
      "Epoch 62/100\n",
      "14/14 [==============================] - 2s 166ms/step - loss: 3.5246e-05\n",
      "Epoch 63/100\n",
      "14/14 [==============================] - 2s 174ms/step - loss: 3.7343e-05\n",
      "Epoch 64/100\n",
      "14/14 [==============================] - 2s 170ms/step - loss: 3.7318e-05\n",
      "Epoch 65/100\n",
      "14/14 [==============================] - 2s 176ms/step - loss: 3.5722e-05\n",
      "Epoch 66/100\n",
      "14/14 [==============================] - 2s 169ms/step - loss: 3.3446e-05\n",
      "Epoch 67/100\n",
      "14/14 [==============================] - 2s 176ms/step - loss: 3.7337e-05\n",
      "Epoch 68/100\n",
      "14/14 [==============================] - 2s 166ms/step - loss: 3.7744e-05\n",
      "Epoch 69/100\n",
      "14/14 [==============================] - 2s 169ms/step - loss: 3.2307e-05\n",
      "Epoch 70/100\n",
      "14/14 [==============================] - 2s 163ms/step - loss: 3.3588e-05\n",
      "Epoch 71/100\n",
      "14/14 [==============================] - 3s 191ms/step - loss: 3.4362e-05\n",
      "Epoch 72/100\n",
      "14/14 [==============================] - 3s 180ms/step - loss: 3.3503e-05\n",
      "Epoch 73/100\n",
      "14/14 [==============================] - 2s 178ms/step - loss: 3.7080e-05\n",
      "Epoch 74/100\n",
      "14/14 [==============================] - 3s 184ms/step - loss: 3.9328e-05\n",
      "Epoch 75/100\n",
      "14/14 [==============================] - 2s 172ms/step - loss: 3.3622e-05\n",
      "Epoch 76/100\n",
      "14/14 [==============================] - 2s 172ms/step - loss: 3.7811e-05\n",
      "Epoch 77/100\n",
      "14/14 [==============================] - 2s 170ms/step - loss: 3.7402e-05\n",
      "Epoch 78/100\n",
      "14/14 [==============================] - 2s 176ms/step - loss: 3.4788e-05\n",
      "Epoch 79/100\n",
      "14/14 [==============================] - 2s 168ms/step - loss: 3.1972e-05\n",
      "Epoch 80/100\n",
      "14/14 [==============================] - 2s 167ms/step - loss: 3.4637e-05\n",
      "Epoch 81/100\n",
      "14/14 [==============================] - 2s 166ms/step - loss: 3.3517e-05\n",
      "Epoch 82/100\n",
      "14/14 [==============================] - 2s 167ms/step - loss: 2.9724e-05\n",
      "Epoch 83/100\n",
      "14/14 [==============================] - 2s 166ms/step - loss: 3.1760e-05\n",
      "Epoch 84/100\n",
      "14/14 [==============================] - 2s 168ms/step - loss: 3.3026e-05\n",
      "Epoch 85/100\n",
      "14/14 [==============================] - 3s 182ms/step - loss: 3.0279e-05 0s - loss: 3.345\n",
      "Epoch 86/100\n",
      "14/14 [==============================] - 2s 174ms/step - loss: 3.8093e-05 0s - loss: 3.5318\n",
      "Epoch 87/100\n",
      "14/14 [==============================] - 2s 164ms/step - loss: 3.4948e-05\n",
      "Epoch 88/100\n",
      "14/14 [==============================] - 2s 172ms/step - loss: 3.3065e-05\n",
      "Epoch 89/100\n",
      "14/14 [==============================] - 3s 184ms/step - loss: 3.7153e-05\n",
      "Epoch 90/100\n",
      "14/14 [==============================] - 2s 169ms/step - loss: 3.8714e-05\n",
      "Epoch 91/100\n",
      "14/14 [==============================] - 3s 207ms/step - loss: 3.3137e-05\n",
      "Epoch 92/100\n",
      "14/14 [==============================] - 2s 172ms/step - loss: 2.9941e-05\n",
      "Epoch 93/100\n",
      "14/14 [==============================] - 2s 170ms/step - loss: 3.2167e-05\n",
      "Epoch 94/100\n",
      "14/14 [==============================] - 2s 162ms/step - loss: 2.9929e-05\n",
      "Epoch 95/100\n",
      "14/14 [==============================] - 2s 169ms/step - loss: 2.9065e-05\n",
      "Epoch 96/100\n",
      "14/14 [==============================] - 3s 180ms/step - loss: 3.0193e-05\n",
      "Epoch 97/100\n",
      "14/14 [==============================] - 2s 164ms/step - loss: 2.8255e-05\n",
      "Epoch 98/100\n",
      "14/14 [==============================] - 2s 166ms/step - loss: 3.0739e-05\n",
      "Epoch 99/100\n",
      "14/14 [==============================] - 2s 165ms/step - loss: 2.7277e-05\n",
      "Epoch 100/100\n",
      "14/14 [==============================] - 2s 164ms/step - loss: 2.8635e-05\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x26352bb8880>"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor = Sequential()\n",
    "\n",
    "regressor.add(LSTM(units = 50, return_sequences = True, input_shape = (X_train.shape[1], 1)))\n",
    "regressor.add(Dropout(0.2))\n",
    "\n",
    "regressor.add(LSTM(units = 50, return_sequences = True))\n",
    "regressor.add(Dropout(0.2))\n",
    "\n",
    "regressor.add(LSTM(units = 50, return_sequences = True))\n",
    "regressor.add(Dropout(0.2))\n",
    "\n",
    "regressor.add(LSTM(units = 50))\n",
    "regressor.add(Dropout(0.2))\n",
    "\n",
    "regressor.add(Dense(units = 1))\n",
    "\n",
    "regressor.compile(optimizer = 'adam', loss = 'mean_squared_error')\n",
    "\n",
    "regressor.fit(X_train, y_train, epochs = 100, batch_size = 32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predicting the price using test set\n",
    "\n",
    "In order to predict future prices we need to do a couple of things after loading in the test set:\n",
    "\n",
    "- Merge the training set and the test set on the 0 axis.\n",
    "- Set the time step as 60 (as seen previously)\n",
    "- Use MinMaxScaler to transform the new dataset\n",
    "- Reshape the dataset as done previously\n",
    "\n",
    "After making the predictions we use inverse_transform to get back the stock prices in normal readable format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_total = pd.concat((data_train[\"Price\"], data_test[\"Price\"]), axis = 0)\n",
    "inputs = dataset_total[len(dataset_total) - len(data_test) - 60:].values\n",
    "inputs = inputs.reshape(-1,1)\n",
    "inputs = sc.transform(inputs)\n",
    "X_test = []\n",
    "for i in range(60, 110):\n",
    "    X_test.append(inputs[i-60:i, 0])\n",
    "X_test = np.array(X_test)\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
    "predicted_price = regressor.predict(X_test)\n",
    "predicted_price = sc.inverse_transform(predicted_price)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the results \n",
    "\n",
    "We use Matplotlib to visualize the result of the predicted stock price and the real stock price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "real_price = data_test.iloc[:, 0].values\n",
    "real_price_index = data_test.index\n",
    "plt.figure(figsize=(18, 10))\n",
    "plt.plot(real_price_index,real_price, color = 'black', label = 'Brent crude Price')\n",
    "plt.plot(real_price_index,predicted_price, color = 'green', label = 'Predicted Price')\n",
    "plt.title('Brent Crude Oil Price Prediction')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Oil Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}