Naive_Average_Holt_winter-1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "# #Importing data\n",
    "# df = pd.read_csv('train.csv')\n",
    "# #Printing head\n",
    "# df.head()\n",
    "\n",
    "# df = pd.read_csv('bitcoin_price.csv', parse_dates=['Date'])\n",
    "# df=df.iloc[::-1]\n",
    "# print(df.head())\n",
    "# df.index = df['Date']\n",
    "# del df['Date']\n",
    "# df.info()\n",
    "\n",
    "# df = df[['Close']]\n",
    "# print(df.head())\n",
    "\n",
    "# df['HL_PCT'] = (df['High'] - df['Low']) / df['Close'] * 100.0\n",
    "# df['PCT_change'] = (df['Close'] - df['Open']) / df['Open'] * 100.0\n",
    "# # df = df[['Close', 'HL_PCT', 'PCT_change']]\n",
    "# df = df[['Close']]\n",
    "# print(df.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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>Timestamp</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume_(BTC)</th>\n",
       "      <th>Volume_(Currency)</th>\n",
       "      <th>Weighted_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1325317920</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>0.455581</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1325317980</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>0.455581</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1325318040</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>0.455581</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1325318100</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>0.455581</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1325318160</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>0.455581</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1325318220</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>0.455581</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1325318280</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>0.455581</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1325318340</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>0.455581</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1325318400</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>0.455581</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1325318460</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>4.39</td>\n",
       "      <td>0.455581</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Timestamp  Open  High   Low  Close  Volume_(BTC)  Volume_(Currency)  \\\n",
       "0  1325317920  4.39  4.39  4.39   4.39      0.455581                2.0   \n",
       "1  1325317980  4.39  4.39  4.39   4.39      0.455581                2.0   \n",
       "2  1325318040  4.39  4.39  4.39   4.39      0.455581                2.0   \n",
       "3  1325318100  4.39  4.39  4.39   4.39      0.455581                2.0   \n",
       "4  1325318160  4.39  4.39  4.39   4.39      0.455581                2.0   \n",
       "5  1325318220  4.39  4.39  4.39   4.39      0.455581                2.0   \n",
       "6  1325318280  4.39  4.39  4.39   4.39      0.455581                2.0   \n",
       "7  1325318340  4.39  4.39  4.39   4.39      0.455581                2.0   \n",
       "8  1325318400  4.39  4.39  4.39   4.39      0.455581                2.0   \n",
       "9  1325318460  4.39  4.39  4.39   4.39      0.455581                2.0   \n",
       "\n",
       "   Weighted_Price  \n",
       "0            4.39  \n",
       "1            4.39  \n",
       "2            4.39  \n",
       "3            4.39  \n",
       "4            4.39  \n",
       "5            4.39  \n",
       "6            4.39  \n",
       "7            4.39  \n",
       "8            4.39  \n",
       "9            4.39  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('bitstampUSD_1-min_data_2012-01-01_to_2018-01-08.csv')\n",
    "df.isnull().values.any()\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df['Date'] = pd.to_datetime(df['Timestamp'],unit='s').dt.date\n",
    "df['Date'] = pd.to_datetime(df['Timestamp'],unit='s')\n",
    "# group = df.groupby('Date')\n",
    "# Daily_Price = group['Close'].mean()\n",
    "# Daily_Price.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3161057\n"
     ]
    }
   ],
   "source": [
    "print(len(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8e56c017b8>]"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e56c665f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# convert the date column into a time series with daily frequency\n",
    "df['Date'] = pd.to_datetime(df['Date'])\n",
    "df1={}\n",
    "df1['Date']=df['Date']\n",
    "df1['Close']=df['Close']\n",
    "indexed_df = df.set_index('Date')\n",
    "ts = indexed_df['Close']\n",
    "ts.head()\n",
    "# visualize the time series to see how Bitcoin changes over time\n",
    "plt.plot(ts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ts_df = df1.resample('D').mean()\n",
    "# plt.plot(df_day)\n",
    "# # resamply by week\n",
    "# ts_day = ts.resample('D').mean()\n",
    "# plt.plot(ts_day)\n",
    "# print(len(ts_week))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unhashable type: 'slice'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-126-9099b4594e4f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2500000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2500000\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtail\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtail\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'slice'"
     ]
    }
   ],
   "source": [
    "train=df1[0:2500000] \n",
    "test=df1[2500000:]\n",
    "print(train.tail())\n",
    "print(test.head())\n",
    "print(test.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e5152d7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train.Close.plot(figsize=(15,8), title= 'Daily Price', fontsize=14)\n",
    "test.Close.plot(figsize=(15,8), title= 'Daily Price', fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "x and y must have same first dimension, but have shapes (302,) and (1,)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-91-847c8d5cf60d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Train'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Test'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_hat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_hat\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'naive'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Naive Forecast'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'best'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Naive Forecast\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   3259\u001b[0m                       mplDeprecation)\n\u001b[1;32m   3260\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3261\u001b[0;31m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3262\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3263\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwashold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1715\u001b[0m                     warnings.warn(msg % (label_namer, func.__name__),\n\u001b[1;32m   1716\u001b[0m                                   RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1717\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1718\u001b[0m         \u001b[0mpre_doc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1719\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1370\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_alias_map\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1371\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1372\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1373\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1374\u001b[0m             \u001b[0mlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_grab_next_args\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    402\u001b[0m                 \u001b[0mthis\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    403\u001b[0m                 \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 404\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mseg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    405\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mseg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    406\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_plot_args\u001b[0;34m(self, tup, kwargs)\u001b[0m\n\u001b[1;32m    382\u001b[0m             \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindex_of\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    383\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 384\u001b[0;31m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_xy_from_xy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    385\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    386\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommand\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'plot'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_xy_from_xy\u001b[0;34m(self, x, y)\u001b[0m\n\u001b[1;32m    241\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m             raise ValueError(\"x and y must have same first dimension, but \"\n\u001b[0;32m--> 243\u001b[0;31m                              \"have shapes {} and {}\".format(x.shape, y.shape))\n\u001b[0m\u001b[1;32m    244\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    245\u001b[0m             raise ValueError(\"x and y can be no greater than 2-D, but have \"\n",
      "\u001b[0;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (302,) and (1,)"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e754b9898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dd= np.asarray(train.Close)\n",
    "y_hat = test.copy()\n",
    "y_hat['naive'] = dd[len(dd)-1]\n",
    "\n",
    "#ploting Graph between Train test NAIVE Approach for forecast\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.plot(train.index, train['Close'], label='Train')\n",
    "plt.plot(test.index,test['Close'], label='Test')\n",
    "plt.plot(y_hat.index,y_hat['naive'], label='Naive Forecast')\n",
    "plt.legend(loc='best')\n",
    "plt.title(\"Naive Forecast\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16238.243634443847\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt\n",
    "rms = sqrt(mean_squared_error(test.Close, y_hat.naive))\n",
    "print(rms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8e7549b550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_hat_avg = test.copy()\n",
    "y_hat_avg['avg_forecast'] = train['Close'].mean()\n",
    "\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.plot(train['Close'], label='Train')\n",
    "plt.plot(test['Close'], label='Test')\n",
    "plt.plot(y_hat_avg['avg_forecast'], label='Average Forecast')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15743.82530393843\n"
     ]
    }
   ],
   "source": [
    "rms = sqrt(mean_squared_error(test.Close, y_hat_avg.avg_forecast))\n",
    "print(rms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+/Hiuv/566urq9jmvGCPf//73Oe+883Ka++737k1lZWWb677whS/ssQT5pptuIoQ9V6d/5jOf4ZprruEDH/gADz/8MNdffz0A1157Le973/v44x//yKmnnsoDDzzQ4TlLkiRJBddLg2tOS4VjjDtjjCNijNta9W2KMZ4dY5yYtJuT/hhjvDrGeHiM8dgY4+JW98yJMR6R/Plx/n9O97riiiv44he/yLHHHtum/2/+5m+ag+PDDz/MyJEjGTx4MCEELrroIq655homTZrEiBEjcv6u6dOnM3/+fACWL1/Os88+u8c1L774Ijt27OD1119nzZo1rFmzhi984QvceeedQKaSe9dddzFv3rzmau/ZZ5/Nr3/9azZsyKz03rx5M6+++uoeY2eX6Y4cOZLa2trmKuqwYcMYNGgQTzzxBEDzdwGcd9553HrrrTQ2NgKwcuVKduzYkdPvbf2f4cqVK3nttdc48sgj97juvPPOY86cOdTW1gLw+uuvs2HDBs4++2zmz5/f/A8Kmzdn/o1k27ZtzRtpzZ07t3mcl156iWOPPZbPf/7zTJs2jRdeeIFBgwZRU1OT03wlSZKkopCu75Wvw+m6LVn7gHHjxjUv323t+uuvZ9asWUyZMoWBAwe2CUgzZszgpJNO4o477ujQd336059m5syZTJkyheOPP54pU6YwZMiQNtfMmzev+RnRrIsvvpjLLruMf//3f2fYsGFMnjyZ5cuXc/LJJwMwefJkvvKVr3DuuefS1NREeXk5t9xyC29/+9vbjDN06FCuvPJKjj32WCZMmNC8tBgyz7JeeeWVVFZW8q53vat5Xp/4xCdYs2YNJ5xwAjFGRo0axV133ZXz7/3kJz/JscceS1lZGXfccUdzJbq1c889lxUrVnDaaacBUFVVxc9+9jOOPvpo/u3f/o13vvOdlJaWcvzxx3PHHXdw/fXXc+mllzJ27FhOPfXU5mduv/vd7/LQQw9RWlrK5MmTOf/88ykpKaGsrIzjjjuOj3/84z7nKkmSpOLXSyuuIdclqIUwbdq0uHjx4jZ9K1asYNKkSQWaUeGk02kaGxsZMGAAL730EmeffTYrV66kX7/CP3hdW1tLVVUVADfeeCNvvPFG82ZIvVlf/bsoSZKkInbDcJj+T3D2Fws9k5yEEJbEGPd8VcpurLj2EDt37uSss86isbGRGCO33nprUYRWgHvvvZevfe1rpFIp3v72t3e4mixJkiQpD9IpiGmXCqtwBg0axO7V52IxY8aMve6QLEmSJKmbpOszbS9cKtyR97gWjWJe3qy+wb+DkiRJKjopg2vRGDBgAJs2bTI4qGBijGzatIkBAwYUeiqSJElSi3RDpi0tjkcK86nHLRUeN24c69ato7q6utBTUR82YMAAxo0bV+hpSJIkSS1SmVdYUtb7Ciw9LriWl5dz6KGHFnoakiRJklRcUknF1aXCkiRJkqSi1FxxNbhKkiRJkopR8zOuBldJkiRJUjFq3lW4923OZHCVJEmSpN6gF2/OZHCVJEmSpN6gF78Ox+AqSZIkSb2BmzNJkiRJkopa8+twXCosSZIkSSpG6WRzJpcKS5IkSZKKUvOuwlZcJUmSJEnFyNfhSJIkSZKKWvNSYTdnkiRJkiQVo+aKq8FVkiRJklSMUvWZjZlCKPRM8s7gKkmSJEm9Qaq+V27MBAZXSZIkSeod0vW98lU4YHCVJEmSpN4h1dArn28Fg6skSZIk9Q6pOoOrJEmSJKmIpet75atwwOAqSZIkSb1DqgHKfMZVkiRJklSsUnXuKixJkiRJKmLpBncVliRJkiQVMSuukiRJkqSi5utwJEmSJElFLV3vUmFJkiRJUhFL1btUWJIkSZJUxFL1vg5HkiRJklTErLhKkiRJkoqaz7hKkiRJkopWjEnF1V2FJUmSJEnFKN0IRIOrJEmSJKlIpeszbanBVZIkSZJUjNY/nWmtuEqSJEmSitLcv820DTsKO48uYnCVJEmSpJ5u8LhMO+jgws6ji5QVegKSJEmSpE4aezw07oApMwo9ky5hxVWSJEmSerp0Iwx9G5T0zojXO3+VJEmSJPUl6QYo7VfoWXSZnIJrCGFoCOHXIYQXQggrQginhRCGhxAWhBBWJe2w5NoQQrgphLA6hLAshHBCq3FmJtevCiHM7KofJUmSJEl9SrrR4Ap8D/ifGONRwHHACuBa4M8xxonAn5NzgPOBicmfq4BbAUIIw4HrgFOAk4HrsmFXkiRJktQBi/4bfnwBNKUz5+lGKC0v7Jy60H6DawhhMPA3wO0AMcaGGONW4EJgbnLZXOCDyfGFwE9ixhPA0BDCwcB5wIIY4+YY4xZgAfDevP4aSZIkSertmtJw77/Aq4/Dyw9l+tINUNKHgytwGFAN/DiE8HQI4b9DCJXAmBjjGwBJOzq5fiywttX965K+vfVLkiRJknJVX9NyvHZRpnWpMGXACcCtMcbjgR20LAtuT2inL+6jv+3NIVwVQlgcQlhcXV2dw/QkSZIkqQ9JN7Qcb1/X0teXlwqTqYyuizE+mZz/mkyQfStZAkzSbmh1/fhW948D1u+jv40Y420xxmkxxmmjRo3qyG+RJEmSpN4vVddyvK11cO3DFdcY45vA2hDCkUnX2cBy4PdAdmfgmcDdyfHvgY8luwufCmxLlhLfD5wbQhiWbMp0btInSZIkScpVqr7luDm49u6lwmU5XvcZ4OchhH7Ay8AsMqF3fghhNvAacGly7R+BC4DVwM7kWmKMm0MIXwaSRdh8Kca4OS+/QpIkSZL6imxwHXQI1FZDqiGpuOYa73qenH5ZjHEpMK2dj85u59oIXL2XceYAczoyQUmSJElSK9ng2n8Q1KyHrySPWPbiimuu73GVJEmSJBWD7DOu/Qe17Te4SpIkSZKKwl6Da9/eVViSJEmSVGh12yCdankdjhVXSZIkSVLRiBFufBvc9am9V1xLrLhKkiRJkgoluyHTs/NbjvtVtb3GpcKSJEmSpIJp2NFy7OZMkiRJkqSi01DbcpzKPuO6W8W1qbH75tPNDK6SJEmSVOzaBNek4rr7UuEdG7tvPt2srNATkCRJkiTtR+ulwtvWZtr+g1v63vctmHxR986pGxlcJUmSJKnYta64LrwNQgmUV7T0nfSJ7p9TN3KpsCRJkiQVu9YVV4DygVDWvzBzKQCDqyRJkiQVu92D68DhUNJ3FtAaXCVJkiSp2LVeKgwwcETL62/6QIA1uEqSJElSsctWXA99Z6ZtE1zLCzOnbmRwlSRJkqRilw2uQ8Zn2oEjoDQJrFZcJUmSJEkF17Aj897WypGZ85KyluBaanCVJEmSJBVaQy30q4SjP5g5H3RQn1oq3PujuSRJkiT1dPVJcD3keLh6YWbJcO2bmc9KDa6SJEmSpEJr2JEJrgCjjkw6Q6YpKS3IlLqTS4UlSZIkqdg11GaecW2tKZ1ps0uGezErrpIkSZJU7Bp2wMDhbfuGHwYnXQknX1mYOXUjg6skSZIkFbuGHTB0fNu+khJ43zcLM59u5lJhSZIkSSp22dfh9FEGV0mSJEkqdu0949qHGFwlSZIkqdg17oLyAYWeRcEYXCVJkiSp2DWloKT3v691bwyukiRJklTMYoSYhpK+u7euwVWSJEmSiln2fa0lpYWdRwEZXCVJkiSpmEWDq8FVkiRJkopZUyrTulRYkiRJklSUDK4GV0mSJEkqatlnXINLhSVJkiRJxcjNmQyukiRJklTUXCpscJUkSZKkomZwNbhKkiRJUlEzuBpcJUmSJKmoxaZM6zOukiRJkqSi1FxxNbhKkiRJkoqRS4UNrpIkSZJU1AyuBldJkiRJKmrZ97gGlwpLkiRJkopRNrhacZUkSZIkFSU3ZzK4SpIkSVJR8xlXg6skSZIkFbWYXSpsxXWfQghrQgjPhhCWhhAWJ33DQwgLQgirknZY0h9CCDeFEFaHEJaFEE5oNc7M5PpVIYSZXfOTJEmSJKkXseLaoYrrWTHGqTHGacn5tcCfY4wTgT8n5wDnAxOTP1cBt0Im6ALXAacAJwPXZcOuJEmSJGkvmqy4dmap8IXA3OR4LvDBVv0/iRlPAENDCAcD5wELYoybY4xbgAXAezvx/ZIkSZLU+1lxzTm4RuBPIYQlIYSrkr4xMcY3AJJ2dNI/Fljb6t51Sd/e+tsIIVwVQlgcQlhcXV2d+y+RJEmSpN7I1+GQ6y8/I8a4PoQwGlgQQnhhH9eGdvriPvrbdsR4G3AbwLRp0/b4XJIkSZL6lGzFNbhUeJ9ijOuTdgPwOzLPqL6VLAEmaTckl68Dxre6fRywfh/9kiRJkqS9seK6/+AaQqgMIQzKHgPnAs8BvweyOwPPBO5Ojn8PfCzZXfhUYFuylPh+4NwQwrBkU6Zzkz5JkiRJ0t40P+PadyuuuUT2McDvQgjZ638RY/yfEMIiYH4IYTbwGnBpcv0fgQuA1cBOYBZAjHFzCOHLwKLkui/FGDfn7ZdIkiRJUm8Urbju95fHGF8GjmunfxNwdjv9Ebh6L2PNAeZ0fJqSJEmS1IMsuQOOej9Ujuz8WFZcO/U6HEmSJEnS7qpXwj2fhd9emZ/xfB2OwVWSJEmS8qpxR6bdsTE/47k5k8FVkiRJkvIqnVRIS8vzM15zcHWpsCRJkiQpH1J1mbYkT8E1XZ+MZ8VVkiRJkpQPDclS4XxVXDe8AFUHQb/K/IzXAxlcJUmSJCmfGmozbT4qpE1NsPYJOOT4zo/VgxlcJUmSJCmf8llxffZXsGUNHHl+58fqwQyukiRJkpRPzRXXPATXra9m2qmXd36sHszgKkmSJEn51FxxzcNS4foaKBuQv+dleyiDqyRJkiTlU31Nps2+xqazY/Wr6vw4PZzBVZIkSZLyKbtUOPtanM6O1X9Q58fp4QyukiRJkpRPu7Zm2lR958eqr4H+VlwNrpIkSZKUT3X5DK610H9w58fp4QyukiRJkpRP2YprOh/BdbvPuGJwlSRJkqT8qtuWafNRcfUZV8DgKkmSJEn51Xqp8KoFMPdvoXrlgY3lM64A5OHFQpIkSZIkAF79K+zclDne8gr84sMQm+D1xTDqHR0fr96KK1hxlSRJkqT8eeC6TDtgaKaNTZn2QJYNp1OQ2gX9DK4GV0mSJEnKl2GHZtrZf2rbn27s+FgNNZnWiqvBVZIkSZLypnEnjJoEQ9/Wtj/d0PGx6mszrc+4GlwlSZIkKW8adkC/Sijt17b/QF6NU2/FNcvgKkmSJEn5kg2uJaUQSlv6D2ipcFJx9RlXg6skSZIk5U02uAKU9W/pP6ClwtszrRVXg6skSZIk5U1DbUtwbb1c2GdcO8XgKkmSJEn50rri2jq4pg4kuPqMa5bBVZIkSZLypWEH9EsqpCG09OdacW1Kw1M/gTefhR0bMn0Vw/M7xx6orNATkCRJkqReoSkNja0qrk2pls9y3Zxp+d3w+8/AYWdB1RgYPNalwlhxlSRJkqSOu//fYOX9LeebX4HvTc0cDxiaadsE1xxfh7Nheaatr4HqF2DUkZ2fay9gcJUkSZKkjvrrzfCLD7ec//ZK2PZa5vj4v8+0rZ9rzXWp8OaXM23Nm0lwndT5ufYCLhWWJEmSpI5Ip/bsywbOi26DAYMzx6ldre7Jcalwdpzt6zLt2BMObI69jBVXSZIkSeqIL49oOa7bBql62LkJzvo3OG5G+/ekclwqXFvd9tzgChhcJUmSJOnA1W1ved/qgCHtX1M5KvelwvU1meuzBo/r3Px6CYOrJEmSJOUqxrbnDbWZP9DyGpzdVY7ObalwjNBQAyMmtvSV9dv79X2IwVWSJEmScrV7AK2vaRVcK9u/p3JkbrsKN+6C2AQjJ+7/2j7G4CpJkiRJuWq94RJkgmt2qfDe3rfaryq3imt9TaY1uO7B4CpJkiRJuWqsa3teX5NZ3gvQb1Dbz0YdlWnL+sFbz8HWtfseO1u5bf2MqwCDqyRJkiTlbveKa0MtNOzIHO++VPgTf4Z/WQmHn505f/nhvY/blIZXH0/GqYILfwCzH8jLlHsDg6skSZIk5apxt+B699WZV+LAnkuF+1fBoDFwzMWZ8x0b9j7usvnw+88k9w2C4z8C40/Kz5x7AYOrJEmSJOVq9+AKsPnlTLv7UuGsfgMaTaQCAAAgAElEQVQzVdS3nt/HuDtbjvf2rGwfZnCVJEmSpFylkmdcL/lxS9+mlzLt3nYVhsyS4ud+A0//rP3PS8pajqsO6twceyGDqyRJkiTlKltxHTwWPnBz5vj1p2DAECjrv//7l/6i/f50Q6b95GMwZGzn59nLGFwlSZIkKVfZimv5ADjsnZnj7etg9NEQwt7v6z8k0+7asu9xh03IyzR7G4OrJEmSJOUqW3Etq4CqMS39Yybv+76rn4CDp7b/jCxAqj7TluZQte2DDK6SJEmSlKts8CwfkFkaHJJItb9K6eBD4OAp+wmuAUrL8zXTXsXgKkmSJEm5ag6uA9v2V47e/71lFS3vgb3j/fDf57R8lq5PgvA+lhv3YTkH1xBCaQjh6RDCH5LzQ0MIT4YQVoUQfhlC6Jf090/OVyefT2g1xheS/hdDCOfl+8dIkiRJUpdqqM20/bKvrEmCZlUOwbW8oiX4rnkU1i1q+SxVn9vmTn1URyqunwVWtDr/OvCdGONEYAswO+mfDWyJMR4BfCe5jhDCZOAy4GjgvcAPQgilnZu+JEmSJHWjhh0QSltCZrZC2vp5170pr8jsHtyU3vOzVJ3Pt+5DTsE1hDAOeB/w38l5AN4N/Dq5ZC7wweT4wuSc5POzk+svBO6MMdbHGF8BVgMn5+NHSJIkSVK3aKjNVFt3X9KbS8W1bECmze4g3FqqoeVz7SHXiut3gf8DNCXnI4CtMcZUcr4OyL5saCywFiD5fFtyfXN/O/c0CyFcFUJYHEJYXF1d3YGfIkmSJEldrKEW+le1nF86F8ZOg4rh+783+1xs6w2astXXVB2U9cvfPHuZ/QbXEML7gQ0xxiWtu9u5NO7ns33d09IR420xxmkxxmmjRo3a3/QkSZIkqfvU10K/ypbzSe+HK/8MJTnUBMuTimrr4Fq3LdOmrbjuS1kO15wBfCCEcAEwABhMpgI7NIRQllRVxwHrk+vXAeOBdSGEMmAIsLlVf1breyRJkiSp+DXsaBtcO6K9imvdNhg4PHnG1Yrr3uz3nwVijF+IMY6LMU4gs7nSgzHGjwAPAZckl80E7k6Of5+ck3z+YIwxJv2XJbsOHwpMBBbm7ZdIkiRJUldr2NFqR+EOan7GtXVw3ZoJsqsf8B2u+5BLxXVvPg/cGUL4CvA0cHvSfzvw0xDCajKV1ssAYozPhxDmA8uBFHB1jLGd7bQkSZIkqUg11MDgcQd2b3lFpm1dcX31r/DaE5njtU92bm69WIeCa4zxYeDh5Phl2tkVOMZYB1y6l/u/Cny1o5OUJEmSpKLQqaXCrYLrwJGwcyOsvA+OuWTf96lD73GVJEmSpL6tM8G19etwmpIXtDTugpi8vOXs6zo/v17K4CpJkiRJuWrc1bLJUkeVJAtem1Ktgmtdy3tdT/x4p6fXWxlcJUmSJClXjbtaXmvTUSWlmbYpDenGzHGqDhp3Zo4PNBD3AQZXSZIkScpFOgVNjVBWcWD3hyS4xnRmHEiCax0QoKx/XqbZGxlcJUmSJCkX2dfYHHDFNVkqnG5sea61cRe89tfMxk0hdH6OvVRnXocjSZIkSX1HY/Is6gE/45rUDVP1LX07N8KaRzs3rz7AiqskSZIk5SJbcS07wIprdqlwNrhmz7VfBldJkiRJykVjdqnwAT7jmt2cKZ0E1/6DOj+nPsLgKkmSJEm5aMxXxTVZcmxwzZnBVZIkSZJykQ2cB1xxTbYYyj4r26+q83PqIwyukiRJkpSLfC0VtuLaYQZXSZIkScpFp5cK77arcP9WFde3nXbg8+oDDK6SJEmSlItUniuu2aXCoQSu+J/Oza2XM7hKkiRJUi4aO/mMa3Zzpqd/mmmzS4UPtILbh5QVegKSJEmS1CM0v8e1kxXXrIOmwKglcMwlnZtXH2BwlSRJkqRcNG/OdIAV0pLd4tegg+DqJzs3pz7CpcKSJEmSlIvsUuEDrbiG3eJXdpMm7ZfBVZIkSZJykdqVeU61tPzA7g+h5fjUq+GYi/Mzrz7A4CpJkiRJuWjcldmYqXUAPVBTPgylPrmZK4OrJEmSJOWicVf+dgA+0KptH2VwlSRJkqRcpOoO/FU4uysxuHaEwVWSJEmScpFdKpwPu78aR/tkcJUkSZKkXLhUuGAMrpIkSZKUi1Q+K64G144wuEqSJEnS/tS8BbXVVlwLxP2XJUmSJGl/vvWOTDtsQn7GKzGKdYQVV0mSJEnal22vtxyXW3EtBIOrJEmSJO3L+qdajvtV5mdMn3HtEIOrJEmSJO3LjuqW49FH52dMlwp3iMFVkiRJkvalvqbleOwJ+RmzxCjWEf6nJUmSJEn7kg2uH/kNvO3Uws6ljzK4SpIkSdK+1NdA/yEw8ZxCz6TPMrhKkiRJ0r7U10D/QYWeRZ9mcJUkSZKkvWlqgvVLDa4F5lZWkiRJkrQ3z/wCNjxf6Fn0eVZcJUmSJGlvat4o9AyEwVWSJEmS9q5ydKFnIAyukiRJkrR36YZM+6m/FHYefZzBVZIkSZL2Jt2YaQcfUth59HEGV0mSJEnam3R9pi3tX9h59HHuKixJkiRJe5OtuJb2y894//AoNO7Kz1h9iMFVkiRJkvYmVQ+hBErzFJ0OnpKfcfoYlwpLkiRJ0t6kG1wmXAQMrpIkSZK0N+mG/C0T1gEzuEqSJEnS3qTqoczgWmj7Da4hhAEhhIUhhGdCCM+HEG5I+g8NITwZQlgVQvhlCKFf0t8/OV+dfD6h1VhfSPpfDCGc11U/SpIkSZLyIt1oxbUI5FJxrQfeHWM8DpgKvDeEcCrwdeA7McaJwBZgdnL9bGBLjPEI4DvJdYQQJgOXAUcD7wV+EEIozeePkSRJkqS8StcbXIvAfoNrzKhNTsuTPxF4N/DrpH8u8MHk+MLknOTzs0MIIem/M8ZYH2N8BVgNnJyXXyFJkiRJXcFnXItCTs+4hhBKQwhLgQ3AAuAlYGuMMZVcsg4YmxyPBdYCJJ9vA0a07m/nntbfdVUIYXEIYXF1dXXHf5EkSZIk5UuqwWdci0BOwTXGmI4xTgXGkamSTmrvsqQNe/lsb/27f9dtMcZpMcZpo0aNymV6kiRJkvqSxjpY/QDEPeJE/llxLQod2lU4xrgVeBg4FRgaQsi+hXccsD45XgeMB0g+HwJsbt3fzj2SJEmSlJsfnw8/uxg2rur67/I9rkUhl12FR4UQhibHFcA5wArgIeCS5LKZwN3J8e+Tc5LPH4wxxqT/smTX4UOBicDCfP0QSZIkSX3E+qcybX1N139Xqh5Ky7v+e7RPZfu/hIOBuckOwCXA/BjjH0IIy4E7QwhfAZ4Gbk+uvx34aQhhNZlK62UAMcbnQwjzgeVACrg6xpjO78+RJEmS1Gek67vhOxqgYmjXf4/2ab/BNca4DDi+nf6XaWdX4BhjHXDpXsb6KvDVjk9TkiRJkoB0quU4Vdf135fydTjFIJeKqyRJkiQV3utPwc5NLeepLqy4/uZKGDgCdm6E8b7Fs9AMrpIkSZJ6hj/+K9S+1XLelRXXZ+cnBwEGHdR136OcdGhXYUmSJEkqmIphsG1ty3lXVlybRaga0w3fo30xuEqSJEnqGSqGtT3PR8W1vhZ+ehEs/C9oSmfeDfvqX9peY8W14FwqLEmSJKlnyAbXgSMzz57mo+L64n3w0oOZP/U1MGQc/PbKttdUGVwLzYqrJEmSpJ4h+1qaw96ZafMRXPtVthy/9RxsfXXPa6y4FpzBVZIkSVLPkH0tTbYCmpfgOrDl+LnfQGn/Pa+pGt3571GnGFwlSZIk9QwNtZm2YiiEkj2fcV31ANx+HqQbcx+zZLenJ5/91Z7XlJZ3bJ7KO4OrJEmSpJ5h1KRMe8gJmcC58v6WzxbPgZ9fDGufaPvKnP1pSrU937W18/NU3hlcJUmSJPUMUz4Mn/orTDwH0g3w1rOwfmnmsz/8c8t1ddtyHzO9W3Dd/nqmPeeGTHvwcQc+X+WNuwpLkiRJKnrPrtvG9fc8z5cuPJqjW3+QbtwzfHakarp7xTWmYcREmP5PMO6kPV/Bo4IwuEqSJEkqehtq6ljy6hZS6dj2g1Qd7NrStm/3833ZPbhCy+7FE87o2CTVZVwqLEmSJKno1dZnAmbVgN1qb4v+C755ROb4rH/LtHUdqbgmGzmdOAvKKjLHA4Z0YqbqCgZXSZIkSUWvObj2T4LrJ/6caZff3XLRsEMzbYeWCqcz7amfhv5VmeMBQzsxU3UFg6skSZKkorcjCa6V2eBaOWrPi4aOB8KeS4W3rYMnb4PaDXvek10qXFoG/ZLgWmFwLTYGV0mSJElFr7YuEzAHlpdmOvpV7nnRwBEw4gh4+aGWvrrtcNPxcN+/wpK5e96TDa4lrYKrS4WLjsFVkiRJUtGrrU9T1b+MkpKQ6SgfuOdFVaNh6uXw+hK462p49S+w9snMq3MAHvnGnlXXdPKMa0kZJEO7VLj4GFwlSZIkFb0d9Skq+5e2dJRX0JI0EwOGwJBxmeOlP4NfzYLXnoCQ3JdugN/9Q9t7miuu5dB/cObYpcJFx+AqSZIkqejV1qdanm8FCKFt1XXsiZl24IiWvvrtsGkVDJvQ0rdpdduBs5szlZTC0LdnjrMVWhUNg6skSZKkolbXmObeZ9+grGS3Cmu/JLgedhbMui9zXDmy5fPGnbDmcRh+WEtf3O09sE2tlgpPvjBzPPzw/E1eeWFwlSRJklTU7nr6dQAOHlLR9oNsxXXUkVDWP3M8cGTba3ZuzATXj/w6c777q3Jab8505HvhX16Ew8/K4+yVDwZXSZIkSUVtwfK3GDWoP7d97MS2H2R3Fh49qaWvcrfgCpngOvE98K4vQENNy/JgaPU6nPJMO+ig/E1ceWNwlSRJklTUXt+6i6njh9K/rLTtB4PHZtphh7b0lfWHke+A934dBicbNWWXCmdfc1O3reX6bIgNu42tomJwlSRJklQ0UukmvnbfCjbV1jf3baxtYGRVvz0vfv934OSr4G2nte3/x0Vw6idhzOTM+b6Ca7oRQgmUGI2Kmf/tSJIkSSoaj6yq5kf/+zI33LMcgKamyOYd9Yyo7L/nxUPHwwX/CWXthFqAg6dmnoMd+rbMebsV11TmVTgqagZXSZIkSUWjIdUEwK7GzBLeLTsbaIq0X3HdnzM+C1c+1BJss8H1jaXQlPmeTHAta/9+FQ2DqyRJkqSikU7yZPbFN5t2ZN6pOqKqnYrr/vSvgtFHtZxng+s9n4WHvpo5Nrj2CAZXSZIkSUWjpq6xzfnG5FnXEQdScd1dNrgCLPtlpm1KQanBtdj535AkSZKkorF1Vya4hqTkurE2U3EdVdWfry/8Oi9sfuHAB48RDhqdOS5Jw//Mgq2rYVhF5rgXOmr4UXz+5M8XehqdZsVVkiRJUtHYlgTXxnQEaN5d+ICWCu8um4Zbi7H9fhUVK66SJEmSikY2uG5P2k21DZSWBIZWlOencrhhBdz1KVj/NPzd1+BbR2b6P/Fc58dWl7HiKkmSJKlobNuZCazZJcObdtQzvLIfJSV5qoqOngTHXZ45XrUgP2OqyxlcJUmSJBWNbc2V1swS4eqaBkZU5mFjptYGDk++bG1+x1WXMbhKkiRJKhrZ4LplZyNLXt3MAyveYmQ+nm9trWJYpn3r+Ux70LH5HV95Z3CVJEmSVDS27mpoPr741r8CMDIfr8JpLRtcN6yAsgr4h0fzO77yzuAqSZIkqWhs29nIQYMHtOnLy47CrWWD6+aXoGq0uwr3AAZXSZIkSUUh3RTZXpfiiNFVbfr7leU5tvSrbDnuPyi/Y6tLGFwlSZIkFYWauszzrRNGDmzT35Bqyu8XlVe0HJfluZqrLmFwlSRJklQUshszjR3aNrgefcjg/H5RWevgOmDv16loGFwlSZIkFYWtyTtcxw5rCZafefcRXHT82Px+UWkZlCYbPllx7REMrpIkSZKKQrbievCQliroyYcOJ3TF5knZ5cJWXHsEg6skSZKkorA1Ca5DK8qb+4ZX5vlVOFnlyXLk0i4aX3llcJUkSZJUFB5bVQ3AkIEtwXVkvl+Fk2XFtUcxuEqSJEkquNc27WT+4nUADGlVcR02sIsrrj7j2iMYXCVJkiQVXHVtXfNx/7LS5uO8v8M1y4prj7LfvwUhhPEhhIdCCCtCCM+HED6b9A8PISwIIaxK2mFJfwgh3BRCWB1CWBZCOKHVWDOT61eFEGZ23c+SJEmS1JNU1zR07xc2B1efce0JynK4JgX8S4zxqRDCIGBJCGEB8HHgzzHGG0MI1wLXAp8HzgcmJn9OAW4FTgkhDAeuA6YBMRnn9zHGLfn+UZIkSZJ6luraegC+/eHjAPjFJ05p3qypS5RZce1J9htcY4xvAG8kxzUhhBXAWOBC4F3JZXOBh8kE1wuBn8QYI/BECGFoCOHg5NoFMcbNAEn4fS8wL4+/R5IkSVIPVF1TTwjwgeMOAeD0I0Z27ReWJs/R+oxrj9ChBeMhhAnA8cCTwJgk1GbD7ejksrHA2la3rUv69ta/+3dcFUJYHEJYXF1d3ZHpSZIkSeqhNtbWM6KyH2Wl3bQNT7/KTGvFtUfI+W9FCKEK+A3wTzHG7fu6tJ2+uI/+th0x3hZjnBZjnDZq1KhcpydJkiSpB6uuqe+6V9+0Z9BBmTZVt+/rVBRyCq4hhHIyofXnMcbfJt1vJUuASdoNSf86YHyr28cB6/fRL0mSJKmPq66pZ9SgbgyuVUlwrXmr+75TByyXXYUDcDuwIsb47VYf/R7I7gw8E7i7Vf/Hkt2FTwW2JUuJ7wfODSEMS3YgPjfpkyRJktTHVdfUM6o7K64jjsi0PuPaI+Syq/AZwEeBZ0MIS5O+/x+4EZgfQpgNvAZcmnz2R+ACYDWwE5gFEGPcHEL4MrAoue5L2Y2aJEmSJPVdMUaqa7u54jrxPfDBW2HSB7rvO3XActlV+DHafz4V4Ox2ro/A1XsZaw4wpyMTlCRJktS7ba9L0ZBq6t5nXEOAqZd33/epU7ppyy5JkiRJat8rG3cAMH74wALPRMXK4CpJkiSp2618q4Z5C18DYMUbmZeWTD54cCGnpCKWyzOukiRJkpRX537nEQBmTBvPi2/WUNmvlHHDKgo8KxUrK66SJEmSCmbO46+w8q0aDh5aQUnJ3rbWUV9nxVWSJElSwXzl3hUAnHLo8ALPRMXMiqskSZKkbrWptn6PvpHd+Soc9TgGV0mSJEnd6pFV1Xv0jazsV4CZqKcwuEqSJEnqVg+/uGdwHdGd73BVj2NwlSRJktRtYow8srKadx05qrmvoryUY8cNKeCsVOzcnEmSJElSt6mtT7FlZyNnHD6ScyaN4d1HjeaQob4GR/tmcJUkSZLUbbbubARg6MByLp02vsCzUU/hUmFJkiRJ3aYluLoZk3JncJUkSZLUbbbsbAAyFVcpVwZXSZIkSd1m665MxXWYwVUdYHCVJEmS1G22JhXXIRUuFVbuDK6SJEmSus36rXWAS4XVMQZXSZIkSd2iIdXEb59ax5kTR1JeahRR7vzbIkmSJKlb3PPMejbU1DN7+qGFnop6GIOrJEmSpC4XY+SWh1dz1EGDeOc7RhV6OuphDK6SJEmSutxb2+t5uXoHM04aTwih0NNRD2NwlSRJktTlnl+/DYBjxg4p8EzUExlcJUmSJHW5Z9ZupSTApIMHF3oq6oEMrpIkSZK63OMvbeLYcUOp6l9W6KmoBzK4SpIkSepSNXWNLF27lelHjCj0VNRDGVwlSZIkdaknX95MuilyxhEjCz0V9VAGV0mSJEld6rlkY6YT3jaswDNRT2VwlSRJktSl1m3ZxZjB/RlQXlroqaiHMrhKkiRJ6lKvb9nFuGEDCz0N9WAGV0mSJEldat3WnYwdWlHoaagHM7hKkiRJ6jJrN+9k7eZdHHXwoEJPRT2YwVWSJElSl3hg+Vuc+Y2HKAnwt1MOKfR01IMZXCVJkiR1if95/k0A5l5xMuOH+4yrDlxZoScgSZIkqfu8VF3LrQ+/xGGjKnnXO0Yz+ZDBXfI9dY1pHllZzXlHj+HMiaO65DvUdxhcJUmSpD7khnuW88jKagCWvraV2z42DYBtuxpZs3EHx40fmpfvue2Rl9lQU8/M0ybkZTz1bQZXSZIkqY9oSDXxzNqtzecvVdcCsHVnA1O/tACAF7783k69b3VTbT2vbNzBtxes5JxJYzj9iJGdm7SEwVWSJEnqMx5fvZFtuxqZ8/FpLF27jZsfXEVdY5or7ljUfM3azTuZOObAdgBevaGW8777COmmCMC7jxqdl3lLbs4kSZIk9RFPvrKZ8tLA6YeP5PBRlTRFWLRmM0+9tpX3TTkYgJc37sh5vOXrt3PWNx9m/uK17GpIc8kP/9IcWgGmjBuS99+gvsmKqyRJktTLVdfUU1PXyA//9yWOGzeEAeWljB1aAcBHb18IwKUnjuPeZW+wJofgevfS17nnmfU8sGIDADfe9wJPvLSJrTsb+fKFR3PJieN5ZFU1x4w1uCo/DK6SJElSAW3Z0UC/shIq+3fN/zVf+VYN537nkebzC6eOBWDcsLavpznl0BEM6l/GG9vq9jvmf97/Iuu27OKQIQM45bAR/O7p1/nt069zzXvewUeTzZjOO/qg/P0I9XkGV/UZx15/P++fcghf+9CxhZ6KJElSs+O/vIAJIwby8L+e1SXjP/zihubjq886nCumHwrA6EH921xX0a+UMUMG8Nb2vQfX+lSaHzz0Euu27OKSE8fx9Yun0Jhu4ojRVRw8ZAAXHT+2S36D5DOu6tWefm0Lx15/Pz/835eoqUsxb+FrhZ6SJEnSHtZs2tllYy98ZTMApx8+gtnTD2vuLykJfOWDx7S5dszg/ry5vY6NtfUsW7eV3f1l9Sa+9+dVABw3fiilJYEB5aVcfdYRfOiEcYQQuux3qG8zuKpXe+71bdTUpfjx468UeiqSJEl7qE+lu3T8RWs288CKDZx91Gh+ceWpDK/s1+bzy09+GwAnTRgGwJjBA9iwvZ6Lb/0LH7j5cWKMba5/+rUtABw5ZhBnHD6iS+cuteZSYfVq1bUNAGyoqS/wTCRJkva0ZUdjl47/x2ffAODLu1VWs0pKAg/+yzsZPXgAkATXmjoa05nAurG2gVHJkuIYIw+s2MCUcUP4/T9O79J5S7uz4qperToJrLv9Y6EkSVJR2Fibn39cX72hhte37tqj/9FVGzlz4kgOSXYQbs9ho6qoSjaGGjawvDm0Aqzb0rKE+WdPvsbyN7Yz46TxeZmz1BEGV/Vq7f2PQVcvyZEkScrV5h0NzcepdFOH729INfGXlzZyzrcfYfYdi5r7G9NNzL5jEas31HLmxJE5jzd0YNulxGs27eDNbXXMX7SWOY+9wlEHDWLGNIOrut9+g2sIYU4IYUMI4blWfcNDCAtCCKuSdljSH0IIN4UQVocQloUQTmh1z8zk+lUhhJld83OktqrbWSLc1UtyJEmScvVSdW3z8fa6VIfvnzlnIZf/15MAvPBmTfMzqXcuWsufX8jsJnzu5NxfSzO0orzN+e2PvcJ/3v8i/+c3y3hl4w5O+H/t3Xl4lNXZ+PHvmS2ZTPZ9IQFCgBC2sMgmoGwCKq61atVqa11+rbZ9rVZ9W1urdWnft2rt29Z9qda9Vq1aLKKyCUjYwh4CBLKQfd8ms5zfHzMZEkggQJZJcn+ua67MPPPM85w5HGbmfs459xkagckofV+i93Wl1b0CLDlu233ASq31SGCl9zHAUmCk93Yr8FfwBLrAr4HpwDTg163BrhA9pdnhIqekzvc4Icwzd6Ox5fS/FIQQQgghupvWmrc35fseVzW2nGTvE20+XMX6gxUAJHmHAh8sb6Cu2cGfv8hlUko4hx67kGHRti4fM6JN8qbb5qay52gd+ZXHhgvPPY3eWyG60ykDV631aqDyuM2XAq96778KXNZm+9+0xwYgXCmVACwGVmitK7XWVcAKTgyGhehWWXlVNLa4fB/kQ6M8i2w3trgoqGqkwS4BrBBCCCH6TnZBDXuL61iUEQdAdePpjQp76vMcIm0WtjywiN9e7km+tOAPqxj/4H8orm3mzvlpp708Tdse1/iwQFxuze6jtVyamciWBxaxeGzXe2+F6E5n2s8fp7U+CuD9G+vdngTkt9mvwLuts+0nUErdqpTKUkpllZWVnWHxhDg29CY1xnOVMd6bLa/J4WL2777kppe/6bOyCSGEEGLwqm128Pi/9/LU5zkYFFw/YygA1afR47r5cCVr9pdz29xUIm0WJg4JJzzoWND5+ysnMD897rTLFtbmGNHBnmzC9XYnCWFWIm0WWadV9JnuXg6no5asT7L9xI1aPwc8BzB16lTJBSvOWEFVIwEmAwEmz/WZ1lTuFd6ETZvyqk7rePmVjdidLtJiQ7q3oEIIIYQYVP694yjPrDoAwMQhYQzzjgo7nR7XNzbmE2Y1c8NMT9AbabOw9t75uNwaq9mIxXRm/VMR3uRMD1861he4wrGRa0L0lTPtcS3xDgHG+7fUu70AaJtmbAhQdJLtQvSYwuomkiKsfP/c4QDMHRUDwOGKxpO9rENut2bO779k4ROru7WMQgghhBh8tuXXAHDF5CQeunQc4VZPsFjd1PXAdVdRDZnJ4QRZjvVDBQeYCLOazzhoBTAbDeQ9fhE3zBxGVPCx+a5XTO5wsKQQveZMe1w/Am4EHvf+/bDN9juUUm/hScRUo7U+qpT6DHi0TUKmC4D7z7zYQpxaYVUTSeFWZqVFk/f4ReSVNwBwpPL0A9fdR2vbPT5QVs+ImOBuKacQQgghBocPtxXicGk2HKxg7qgYnvh2JuC5QG5QUFrbjNb6hOG42QXVPPSv3cxKi2ZoZBCrcsrYW1zH+aNjOzpNt0mOCCI+NJAHLxlLgMnYo+cS4lROGbgqpd4EzgeilVIFeLIDPw68o5S6GTgCXOXd/VPgQiAXaAS+B6C1rlRKPQy0Li71kNb6+IRPQpO6VewAACAASURBVHSrioaWdsGl1eL5wG0buDa1uHzbT6ZtNr0Vu0u45W9ZPHP9ZJaMS+jGEgshhBBiIPvJW9t892+bm+q7bzAo3BqeXX2QZ1cf5H++NYGrpiazYncJ9/4j27fWa9ZhzzSnSJuF+NBAFozp2cDVajGy4b8X9Og5hOiqUwauWutrO3nqhFasPQtH/aiT47wEvHRapRPiLNQ0OtolGGgNUNsOFR7zq+Us/+kc0uNDT3qsgqom3/2sw55rLtsLajoMXJ9ddYDR8SE9fhVUCCGEEP2H0+X23U+NsbFsYmK752+ePZx9xXWszS3nq5wyrpqazJ+/zPUFrZdMTOSj7UWEBprY+N8LMMtaqmKQ6e7kTEL4BYfLTZ3d6ZszAhBkPrHHFWDJU2vIfWTpSRfTLqw+FrjuLvIMGy6samLF7hJiQwK4+dUsZo2I4oGLM3js33sByHv8om57P0IIIYTo3/K9F8HvW5rOddNTsAW0/xn+wMUZACz701oavUv2tSaW/N2V47lsUhLfnz2c4dE2CVrFoCSBqxiQar3JDdqmhTcZDViMBlpcbqKDA/jdleP53fK95JTU02B3ERbU+ZfAQe/8WIA1+8sB+Gh7ER9tLyLKZqGioYWPthdR0eDJWHw2SRGEEEIIMfBs9g7znZ0WTUigudP9woPMviRNRdVNzBsdw9XnpACQmRze8wUVwk/Jr2sxIFV3ELjCseHCcaEBLBgTx82zPRmH61ucnR7L7dZsO1JFWJsFuR++bJzvfkVDC0vGxjMxOZx1uRUABBgN/OStrSz94xrufnc7T32eg9stqzsJIYQQg9VX+0qJCQkgI+Hk05PCrGbyK5t4Nyuf3NJ6EsOtvVRCIfyb9LiKAal1HbS2wSZ40sTXNDl8Q2+CAzzP1zd3HrhuPlJFbbOTC8fH8+mOYjKTw7lhxlCibRZeXZ/HhoOVLB0fT2OLi+351QDU2Z18uM2z4tMeb0biYVE2LpskqeSFEEKIwcbpcrNmfzmLMuIwGNRJ9w0PMlNeb+ee97IxGRQXT0g86f5CDBbS4yoGnB0FNVz516+BY4tot2oNZGO9gastwNMDW2/vfN2051cfJDrYwvXTPQt8t65jtnR8As99dyr3LB7N4rHxXDShfaKm80bFsPmXC9n90GKGRgXx/tbCbnh3QgghhOgPmh0uSmqbeWdTPt9/NYuaJgfz00+duNFk8Pw8T4sNZvlP5zBzRFRPF1WIfkF6XMWA88yqA777Y44bjhNq9TT51h7XkEDP43q7q91+brfGYFA0O1ys2V/OVVOHMCstmi9+dh7Do23Hjhdo5kfz0gAINBt56aap/OKfOzla08xVU4cQFew5z4L0OF7fcJgWp1vmvwohhPALOSV1JIZbcWtN6EnmXIqTa7A7ue6FjZiNiqvPSeFQeT2rc8rZUVjj2yc6OICbZw9n8dj4Lh0P4MFlY0mLDemxcgvR30jgKgac3d6huY9fMb7TIDEmuPOhwrXNDiY8+B9+smAk720uoMnhYql32ZvUNuvCdmR+ehx/vs7Cve9lM3dUjG/7mIQQWlxuiqqbGNYm8BVCCCH6gtPl5oInVwOe0Ujr7ptPcID8LOxMs8PF8p3F1DQ5iLRZSIkMYqI3UdLrGw6zzTtVaFNeFUaDIjrYwui4EL41ZQixoQEsm5B4yiHCre5ZPJrxQ8I4N016WoVoSz6hxIBSb3eSV9HAXYtGcc20lBOeb3F61lCLCQkEINjX43psqPAR7zqvf1y5H/AMDZ6RGtnlMkxOiWDFXee125YSGeQ5dmWjBK5CCCH6XHFts+9+TZODlXtKuDRz8OZhcLrc3PnmVlKigrh/6Zh2z2mtWfTkKvIrm9ptTwq3kpEYyordJSxIj+XOBSP5+kA535mWQvhxU5VOR2xoIN+dOeyMXy/EQCWBqxhQ9hXXofWJQ4RbtXgX/44O9nyhBFs8/wXq2vS4FrVZs/WuRaP48YKRZ12ulKhjgasQQgjR11qDsOe/O5Ufv7mVHQU1gzZw3ZRXyQdbC/n3zmIArpg0hK8PlPPsqoMsHR/PheMTyK9sYni0jRanm+RIKzNSo/jPrhI2HKhg4Zg4nrx6IiGBZlmuRogeJIGrGDDuf38HH27zJEBKDA/scB+H07MkTWtPqy3AiFKQX9lIXnkDw6JtHK3xXIV+57aZTB0a0S1liwsJxGo2klta3y3HE0IIIc7U8p1Huf31LQCMigtmSIT1hAurlQ0tfOf5DTxy+XimdNN3oT+yO11c9cz6dtsWP7Xad//ldXm8vC4Pi9HAW7fOIMpmwWT0TEP66cJRvVpWIQY7yRIjBow3vzlCY4snyVJnQ3Qeu3I8s0ZEkRbrmatqMhqYMTyKV9cf5vz//YraZgfrcsuxGA1MHRrR5fkop2IwKEbFBfPK13nc949smlpcp36REEII0QN+v3yf735CmJXkyCDyq9oPg3121QH2Ftdx5V+/5qdvbaXxuPXOP84u4sW1hyita6a/ampxcen/rQMgyGJk3mhPboqEsEBumjWMrF8u5NkbpnD9jBTevm0GcaGBvqBVCNH7pMdVDEjh1o6zI05OieCNW2a02zZrRBTrD1YA8LN3trNidwnnpkV1W9DaatnERLYX1PDWpnz2l9YzIzWSuxaNxtjBeYqqm1j6xzUEB5i4bkYKPzw/rVvL0hM2H67kx29uY0RsMJdPSiQmOJCMxFAibaee59PidGMyqG6vcyGEEO1prSmtsxNmNfPObTOxmAwkR1j5+kA53352PQEmA9+dOYzn1hz0veaDbUV8sK2IP16TyTtZ+Ticmm/yKgH43fK9zEyNYni0jaXj4jla00xiuJVpw7ueG6K3ud2a97cWsiqnjL3FdTx82Tiun56CW8P6AxXMGnHsN8DisfFdygQshOh5EriKAcdkUARZjF3ev3VJHIAVu0sAeOzyCd1eru+fO5zvTE/hnnez+WTHUTYfriI5IqjDJFI5JXXUNDmwmAw8t/og04dHYgswkVtaT1xoIFNSuq83uLv83xe5FFY3UVjdxOqcMsBz1fqre84nwNT5v8eqnDL++/0dBFmMPHrFeEbFhhAWZKa0tpnINkOyhBBCnL2yejv1dicPLstgdLxnqZXkyCCaHW6+OeQJRtfsLwfgiklJbDlSRZ43aeFP3trmO056fAg/mJPK3zceZlVOGatyynjl6zwAlIIV/zXXL5Zy2Vtcy+qcMlxuqKi3kxhuJcJm5u53twOei9c3zPCs025UMHtkdF8WVwhxEhK4igHHYjKgVNeDupDj1q779tQhvmRK3clgUARZTJw3OoZPdhwF4L73d/DF3lIev3JCu57JivoWAK49J5mnv8jlyr+2n39zbloUf71+it+su7fxYAXrDlRgNRsZnxTGPUtG87f1h/nX9iJW7inlwvEJHb5Oa83d726nrM4OwFXPrCciyExVoyfL84gYG3NHxbAprxKtYc7IGM4fHcOMVFkiQAghzkRr5vyhbTLcD4k48Ttvzshonrg6E4AnVuTwxd4SFo2JJyE8kIVj4nzfWRdPSOCLvaWkx4eQU1JHoNnITS9v4rNdJd0SuD69cj/7iutYNjGRJeNO3vOptcbudBNoNnK0poltR6p55NM9FHiHQSsF2pPqguAAE2/cMp30+I6TOQoh/I8ErmLAcbd+K3VRcGD7/wadZSTuLhnHHf8/u0todm4jOtjCzy4YTVK4lYoGTyB37fQUXttwmKpGB3fOT6OxxUV5vZ1Pso+y9Kk1fHTHuUR516TtK40tTr73yiaGRFh57ebpJIVbAchMDuezXcW8si6PMQmhfP+VTcwbHcv5o2MIMBmYNjySz3YVU1Zn59HLxxMfFsAHW4vYW1xLVaOD+NBAyursvLwuj8SwQKKCA3hx7UGeWXWAx64Yz1VThmAyGnC43JilV1YIIbqktfd0aOSxYDU50uq7/5tLxrJ4bDzxYceSHN61aBR3Leo4EVGg2ei7ONm61vk5wyL4+4bD3Do39Yw+n7XW/OmLXD7YVsjBsgYAVu4tIfqTAKK8AXO93cmCMXFEBFnYWVSDw+kmr6KBnJJ6TAaF033st8Bt56Vy8+zhRARZyC6oYfPhSmamRjN+SNhpl00I0XckcBUDgm4TrLpPL25tN1R42cREru1g6G53Gh0fwoL0WG6cNYwxCaEsfGKVb2htaKCZBWNiqWhowWI0EB8ayNf3LUCjCbIcK+dVU5K54aWNvLTuEFOHRnKovIHLJyUR0YX5pN1p5Z4SqhsdNLa4+O+lY3xBK4DZaKDF6eabvErm/e9XABwqP8RL6w4BcNH4BD7ZcZRAs4HzRseQFG5lfnocAMU1zYQHmTEaFKV1dqJsFgLNRsrr7Vz89Fruf38Hf1q5nzkjY/hweyEZCaEMiQiisLqJ4ppm5o6K5tHLx59Wz7sQQgxk72blU1pnZ+3+ckICTSS3CVxb1xqfmRrFjbOGnfW5rp2Wwl3vbGdTXiXvZRWw/mAFJqMiNiSQC8cncNXUIQSZjazKKWPDwQqGRduYOCScjIRQqhpb2FlUyxMrckgKt/LgsgzmjIrhVx/uJCLIQmmtHYfbc8HyudWeebgG5ek1jgq2cPPs4WgNSRFWMpPDCQ4w+YZEA0wZGjGgsyQLMZBJ4CoGBLvT7bv/yGXjTuu1rcNtgyxG/nTtpG4tV0fMRgMv3nSO73GA6djV6Fe+zvPNETIZFEoprB3M1509Mpql4+L585cHgAMA5Fc18utlY3u07G2t2F3CLX/L8j0em3RiT/Ujl4/jF//cCcAfr8kkNNDMgbJ6Xl6Xxyc7jpIYFsjbt81sF/AC7a70t30uOjiAr+45nxW7S/jVhzt5OysfgAa7i68PlBMaaEZrzZvf5JMUbuWO+We/Bq8QQvRnR2uauPONrWQdrvJtuywzsV1PaEigmQ9+dC6j47pnTuoo73G+8/xGAKYNi8RkVJTUNvPwx7t5+OPdpzxGpM3CF3ef58uR8PcftE+s6HC5Wbu/nLTYYKKDAzr8rhRCDCwSuIoBoXV5mV8vy+Cqqcmn9drgAM9/g9McYdxtWhNJRQdbKPfObQV8S/Z05mcXjGbDwUqqGlvQGkpr7T1azuOt2O1ZqH1kbDBjE0OJDz1x7dzrpg/l/NGxNNidvh8y89JjmToskvv+kc2TV2e2u+rfFYFmI8smJjI7LZobX/6Gny9Ob5dMw+lyc9trm/nTF7ncMHMYYZ1kmBZCiMHgtfWH2ZpfzQ/PH0GY1YzJaOCKSUkn7JeZHN5t5xzeZv7sfUvTuW1uKkopHC43d72znay8Si7IiCMzJZz5o+Moqmni7U35VDS0MCLGRkV9C5dkJp40sZ/ZaGBeemy3lVkI4f8kcBUDQqPDE7ieTjbhVq1DhTV9E7m+cONUjlQ2snJPKX/feIRhUUGkxgTz6OXjT/q6ETHBrL9/PqW1du56Z5tvXuzJHCpvoLS2melnmdzocEUDK/eUMmdkNK/dPP2k+x7fmwqeH0jLfzr3rMoQYbPw0R2zT9huMhq4Y34aK/eWsiqnjEsmJp7VeYQQor+ptzt59NM97CuuY/PhKqYPj+TnS9J77fy2ABOz06KZnBLO7eeN8G03Gw0djmwKCzLz4CW9N2JICNE/SeAqBoQm78LoVsvpN+nWrMJzR8Z0a5m6Ki02hLTYEOaMjGF2WjQLxsRhMXUtmUWAyUhyZBCRNosvgUVnmh0u31zTnN8u7fI5OjrO4qdW0+xw+2123/FJYVjNRn785la+2FPC7JExvLj2EFOGhhNgMmIyKkbFhnDh+AQZXiaEGHA+21nMGxuPAJ4lbW6eM7zXy/D6D05+UVMIIU6XBK5iQGj0DhUOMp9+EGIxGfj8rvM67BnsTWajgaWdLBtzKlHBAXy2q4RX1h3ipnM7/oGy52it7/6oX/6bjIRQzEbFyLgQ/veqiV0+1+d7Smh2uLnmnGRunZt6RuXtaSajgYzEUDYfruKDbZ4leersznZ1ALDxUAW/uCiD9QfKmZwSQWwHw52FEKK/2V9aD8Deh5cQeAbfi0II4Y8kcBUDQus6oBG2M5vPeKr5pP6udX7rg//azcfZR/nJwpHMOa4HOdf7Q6ZVTZODwuomthfU8Mjl4046l6iVy6158KPdRAcH8OtlY/16GZqnrs7k9tc3s6uoljq7k6euzmTrkSomDAln8bh47n0vm3eyClixu8S3buzYxFAa7E4CzUaSwq3cuWBkt877EkJ0jSxzBXXNDmwWEwbD6WdHzy2tZ2RssAStQogBRQJXMSDsLa4D6JbFzvujpePi+XxPCQBZh6u47bXNjIwNxunW3DEvjaXjE8gtq8diNPDcd6dQUtvMFZOH8MHWQu55L5snV+znkomJvPHNYW6dM4KUqCBcbs3H2UWkRgczLimUI5WNbC+oobzezv98a4LfD7FNjgzioztmM+2RzzEZFcsmJnJZm4Qkd10wioKqRkxGA6PiggkJNLN8ZzGj4jz1ll1Yw3XPb+Cd22cyNlHW+hOip2mtWb2/HLNRcfMrWVgtRqxmI2/dOoNdRTXsK67njvlpGNsEclpr/vCfHBLDrUwbHsmIGFu/Xgarxekmp6SOZ1Yd4NMdR7ksM4nkyCAmD43AbFS8+nUe5fUtRNksjEkIpabJgdmoiLBZuHHmMGwBJvYW1/L1gXKWjjuzETxCCOGvlO6rVKpdMHXqVJ2VlXXqHcWgd+ebW9lyuIp1983v66L0GZdb8+SKHP7vy9wTnnv1+9N4ce0hyuvsfPqTOb7tdqeL7zy/kc1tlklIjrRyycRE1u4vZ3tBDQAJYYEcrWn27bPm5/NOOxtwX/lsVzGWM8g+WVzTzMInVrFsYiKPXj6OtzflszAjjujggB4qqRCD2+e7S/hBmyW2QgJM1NmdJIVbKaxuAuBv35+GBhrsTpodLo7WNPM/n+3zvebiCQncuyT9rD+fSmqbeerzHGKCA7hlbirrcsvZXVRLcmQQ35oypFuDY7dbs7e4DrvTxS8/2MmuolqMBsWQCCuHKxpP2D8tNtg3giYk0ITLrX3TZcKsZmqaHFhMBj744blkJJ64TJkQQvgbpdRmrfXUU+4ngavo797Nyuee97K5YlIST1yd2dfF6XMr95Twl68OcNeiUUQFW1jy1Brfc9+eOoTff6v9fNYDZfXc+NI3zBsdy+Sh4Tz26V5K6+xYzUYWZcRRVN1EVLCFuaNiCDAZCbIYufAM5+L2N7e/tpntBdX85brJXP6Xr4kOtrDh/gWYemAI48fZRby+4TC/vWx8vx+6LgYXt1tT1dhC1Flc1DlYVs+FT6+h2eFZk/vJqydy+aQhvLe5gGdXHSA8yMymvKpOX//k1RNZta+MD7YVERFkZssDi1h/oIItR6r44flppzXcduWeEm59bTMud8e/j5aOi+eXF2ew6VAl+0vrKKpu5pxhkRwqr8dkNDBtWCRbjlTR2OJi8+EqjtY0cf30oVQ1OsiraKCu2YHTrYmyWdhbXEdVQwsNbQLP66ancPGEREICTazLLWdEbDDfHKrEZjFyzvBIxiaGUVFvJ9BsxOZdzu1PK/ez+UgVWsPcUTFcOD6ehLC+zdsghBBdJYGrGBQKq5uY+/svPcNa75zNuCQZ0nm8Yfd94rv/9LWTTrk8jNaa7IIaIoIspET1j17VnvLOpnx+/o/sdtumDI3g2RumdNrz+k5WPuDJ5Ol06y7PMbv1b1n8Z3cJF01I4M/fmXx2BReiF/3o71v4ZMdRvrr7fIa1Wb+zq5wuN4ueXM2h8gbmjY7hz9dNJqiDDPE/eHUTxbXNjIwN4bJJSYRZzTQ7XJiNBqYMjQDg+dUHeeTTPWz6xUKufX4DuaX1LBwTy7AoG4sy4pg6LLLdUOO21uwv47svfYPWEGWzcO+SdLbmV9HscLN0XDznDIvkL1/l8vyaQxgUdBLXAqCUJ1lggNmIQSnK6+0Emg0Mjw4m3GpGKSitszM6PoRwq5n0+BCUUsxPjyWxjxMFCiFEb5PAVQwKH24r5CdvbeOTH8+WeYidaA1c3751BtOGR/br+V+9rbS2mWmPrgTg+hkpTBkawd3vZuNya6YPj+ShS8cxOv7YvOqPs4u4442tvsdKQXp8KP99YfoJybKON/8PX/mWNFIK5o+O5YlvZ2J3ujhQ1kBKVBC7CmvIKakjPsxKYlggsaEBg3Zet/APjS1OMn71me/xuWlRPHb5hFNe9CqsbqK+2YktwMjiJ1fT0OLij9dksmxC4hklI2r15d5SvvfKpk6fv2RiIjfMHEpRdRO//OdOzk+P5bLMRL4+UMEHWwupaGjh21OH8NCl4zq96PS39Xl8vP0o9y5NJy0mmJK6ZkwGRUpkEF/uKyPA5Mlq3npxy+3WVDR45qWezXsTQoiBSgJXMSg8+ukeXlmXx66HFg/6DJSdaQ1c9zy0xO8TKvmjktpmYoIDfD8473p7G+9vLQQgMSyQlT87n5omB79bvpePthcxPimM0XEh5JbVE241s3JvKUEWIyt/dh7ldS0Miw7yrR0Mnh7ux/+9l2dXH+Si8Ql8suOo77lLMxNZl1tOeX1Lp+V77IrxXDstpYfevRAn9/WBcr7z/Ebf3ErwDKX96/VTOtxfa837Wwr52bvbMRkUc0ZG8+W+Mh66dCw3zBh61hfWDpTVs+APqwAIshiZnx5Lvd3JheMSeCcrn6zDnQ83zkgI5elrM+VikBBC9DIJXMWAVtvsYGdhDfe8m01ieCDv3j6rr4vkt77cW8ra3HIeuDijr4syIDhcbvYV11HT5OC6FzbyP9+awAfbClmXW0FsSACv3Ty9XS9sR8ONX/neOZw/OpY3Nh7h6ZX7Ka5tZtrwSB69fDyRNgtmo+L6FzayvaCGAJOBW+akYne6OG9ULOOHhLFyTwk1TQ4+2l7E1iPVhASacLjczBsdyx+vmYTF5LmI43LrTodFCtEdnl65nyc/z2HzLxdRWNXE9S9uJCTQxDPXT2FkXDBag0EpX5v8w3/28acvjiWQUwqunprM41dO6JbyOF1ubn99C7fMGc701KgTni+ta+aVdXnEhAQwKi6EpHArB8rqmTI0gvAgS7eUQQghxOmRwFUMaD9+cysfbS8CPFkm5446+TBMIbqb1poZj62kpHUN3WUZ3HTu8A73+2xXCR9uK2TF7hKc3olx04ZF8k1eJeBJpvLSjVPbJX2qaXLw+obDTE6JYOaIE3+AA9Q0Onjo491UNbZQXm8nu6CGe5eks6uoho+zj2KzGJk9MporJg/h3LRoimuapDdJdJsX1x7i4Y93kx4fwvKfzgXgz1/mtsvyC575ovO8czefX32QzORwnrl+ClaLkRaXm+AAWZlPCCEGMwlcxYClteacRz6nvL6F66an8NvLxsm8TdEnnlt9gEc/3ctNs4bxwMUZp+zdbGpxsS63nBfXHuJoTRPnjYrhFxdl+HqjzobWmu+9somv9pWddL/0+BA+/fEcmWvXi55YkUN8aCDfmT5whnS35hcA+NG8EdyzOB2AnJI6LnhyNQAWk4GMhFBqmxwcLPfM3w4wGXjjlhm+ZEpCCCFEVwNXucwp+p1D5Q2U17fI3D7R526dO4JLM5OICw3s0v5Wi5GFGXEszIjr9rIopXj2hik89fl+hkfbWDQmDqVgV1Etu4tq2ZZfzb93HmVvcR2f7ynhjyv3Y3e6ef67Uxl+BplgT0dhdRMJoYHYnW4e/mQ3n2QfZerQCMYkhGIxGfjRvLQBO6R5dU4ZT6/cD4Bbay6blITNO9f83awCPtlxlMMVDUxOiWBhRhwhgSbsDjeTUsKJtFn87qJcRb2dT3cc5cF/7WZUXDDfnprMd2cO8z0/Ki6EO+encW5aNKkxNiKDPO/B6XZT2+TEoDirZXOEEEIMXtLjKvqdt745wn3v72Dlz85jRIysdylEVxXXNDPjsZXttqVEBvH7b01gRgfzAV9edwiHy80tc1LPOIB6Yc1BfvvJHowG5VsXc9rwSLLyKn3LifzmkrGkRAVR1+xk2YSEE87ldLkxGpTfBXHHa3a4sDvcNDlcPLkih0CzgVfXHybAZCDUaqaszjOs3GYx+tbtjLJZyEwOZ+OhSurtznbHWzI2ntHxnnmYqTE2pgyN6PU6KKuz8+rXeUQFW1izv5yvD5TT7A2sX7t5ugzzFUIIcdakx1UMWJ/vKSE6OIDUHu4lEmKgiQ8L5OIJCXycfZQ3b5lBblk9D3ywk9te28y2Xy1qFxRtPVLFb/61G4DM5AimDY/s8Jhf7SvlL18d4P++M4kwq5kjFY2kxgTj1pqH/rWbN785AsCM1EgCTEZmpEZy69wR5Fc2ojUsfmo1v/5ol+94B8vqAbA73aTFBPPlvlLW5ZYTGxLI7eensmxCYru5wF1hd7qoa3ZiNRsJshjPKvjbWVjD53tKGBZlIzY0gACTgSERQfx94xHe2HiE6sYWrBYjdc2eIDTKZuGNW2YwKi6YL/aWsiqnjOKaZmJDA0gKD+KWOcMxGQ04XW625VdTb3did7q57x/ZLN9VzPJdxb5zhweZefV705iYHH7SMmqtqbM7CW2TvborqhpaKK5txmhQ5JU3cKi8gb+tP0xhdZNvnysnD+H6GSmMTwo77X8HIYQQ4mxIj6voVzYerODq5zbws0WjuHPByL4ujhD9TovTTXVTC7EhnuHNf/kql98v38e6++aTFG5Fa827mwu49x/ZBFtM1NmdJEdauXdJOq9vOMwd80aSFGEl0GwgPjSQUb/8Nw7Xid8jSeFWCqubmDsqhqeuziTS1nHG1k+yj5JTUkdCWCB/WJHj65VsFWmzEBsSQL3dSUFVE0nhVmaNiOLHC0b61sZ8fs1B8isbWTw2ntjQALILasguqGFHYQ2hgSYKqpqwO92+csWEBGAxGRgVF8zGg5XkVzUSGxJISKCJ4AATS8fFM3WYJ1B3a01uaT2ldXbK6uy+tT47MiLGxtxR/yTbZgAAD5lJREFUMVTUt/D92cNJjbGddvDYqrimmeyCasYlhVHV2MKWw1U88OEuksKtzBkZzblp0cwZGc263ApKapsJshgxGQ1sPVLFh9uKqLc7CQkwkZkSzuy0aFxa43ZrTEYDLU43OSV1VDc6KKhqJMxqpqKhhbI6u6+eWo1LCuWWOalEBFmYNjyy07VNhRBCiDMlyZlEv1ZU3cTGQxVclpnk6x3Jr2zke69soqnFxed3nSdrkgrRDbblV3PZn9dx06xhXDA2jv/9bB9bjlQTEWTmtZun88HWQl5Ye+iE17XOVWwNNEMCTcxOi2ZkbDAHyhpwuTWJ4VYeuHhMl3s4y+rsFFY3ERxgZM/ROiJtFqYOiyDAZMTudPH+lkLeycpn65HqE14bEmjy9XK2mjUiiqLqJpxuzfUzhtLi9PRqOlxuKhtaOFLRyMi4YEbHh1BR34JbawqqmthbXNdh+axmI8OjbTx82ThCAk0UVDVS1eCgsqGFWWlRZCSE9uhQ3tY1mU/GYjSwZFw8wYEmX0bryg4C7cSwQAwGRXJEECajIjo4gDCrmRExNkICzSRHBhETHEBKVFBPvBUhhBDCRwJX0a9d9PQadhXVctt5qWw5XEV2QQ12pxuDghdvOod5o2P7uohCDAjNDhfpDyxvt21SSjjv3T7LlzBpy5EqXlmXx61zU/n7xsNEBFkwGRSHKxsZmxh6VnNgz8TyncVsPVJFpM1CkMVIRmIYk5LDeX9rITsLa5ifHsvMEVGYjQbcbo1La8xdHNaqtebLfaU0trgwGw3UNDlIjw9hREwwtj6ez3n/+9ks31nM+vsXcOtrmymqbuLuC0aRmRxBi9NNo8NJXEggEW16t50uNy0uNwalMChFcU0ztgCjXyZ+EkIIMThJ4Cr6pWaHi798mcvTbRaoB5g3OoapwyKZMzKaCUNOPr9LCHF6WnvyFo6J457FoxkRY5P5i35Ia43T7QnCHS43CuTfSQghRL8nyZlEv/TrD3fxdlY+S8fFc//SMfzXO9u4ZU4qS8bF93XRhBiwHrp0LAdK6/nNpeP6uijiJJRSmI2eXtKu9iALIYQQA4X0uJ6FjQcr+NMXudy9eDSZp8jy2KqgqpG6ZidJEVaAM07c0VcOlTewZn8ZQRYTC8fEEh7kGZK2s7CG2mYHYxPCaHK4CA8y+5J4aK355lAl+VVNzBwRRVK41Xe82mYHeeUN/HNrIWv3l7O/tJ7vzhzKby4ZK8PYhBBCCCGEGOCkx7WH2Z0u/uvtbRTVNFPZ0MLbt80gr7yR2NAADpTW0+x04XZDSV0zWXlVKOVJPLJmf3m74wSYDMxPjyUuNJBLMhOZnBLRR+/Io9nhwqAUB8vrWb6zmIKqJoZEWMktraewuolt+dW0XuuwWYyMjAvxJVRpSykIDjBhNRtpcbmpbnS0e741e+fRmmYAzEZFenwot8wZzs+XpEvQKoQQQgghhPCRHtezsKuohs92Fp8wH/N4MSEBWIwGwoPMTBseSUZCKIXVTbjcnmUWthyporbJSZPDRUiAiXCbmdFxIZwzLJLtBdXsLKylscVJUriVkXEhpMbYmDUimvT4kBOWJiirs7OrqIYGu4uhUUGMSwpDa01lQwtF1c1szfcslRBls6CBBrsTt9aMiAlmf2k9W49U+Za2UArCrWaqGh3YLEYsJgNjEkL5zSVj2V9azwtrDrKjsIZR3rKOTwqjuLaZMKuZktpmqhpbqGt2EmQxMTklnOTIIK55bgMA189IobbJybCoIIZG2ViYEUeYtX/1PgshhBBCCCHOjiRn6iUut+a217L4cl8Zd85PI9xqJiYkkKhgC/mVjaTG2JicEnHKHsQGu5M/fZHLi2sPMjstmuyCGioaWrBZjMxLjyUk0Mz2/GpK6+yU13uWnzAouHhCIr9elkGLy80T/8nho+1F7dbhCwk04XRpmhwu37aYkABCA02YjQZsASYa7E5KaptJirBy7ohoDAZFXEgAC8bEMSTCSpPDRZCl4855l1v7Mo92RWF1ExajgZiQgC6/RgghhBBCCDEwSeDai9xuTUVDS7cEYy1ONxaTJ2Ok3enGZjGeEPRuOVLF1iPV/PWrXMrr26/Pd9WUIcweGU1yZBBr95dTUNVISKCZpHArUcEWksKtTBl66kBaCCGEEEIIIXqa3wauSqklwB8BI/CC1vrxzvbtL4FrX2l2uHh21UE25VUyc0QUM0dE9fkcWSGEEEIIIYToKr9MzqSUMgJ/BhYBBcAmpdRHWuvdvVmOgSLQbOQnC0f2dTGEEEIIIYQQokf19kJw04BcrfVBrXUL8BZwaS+XQQghhBBCCCFEP9LbgWsSkN/mcYF3mxBCCCGEEEII0aHeDlw7ygjUbpKtUupWpVSWUiqrrKysl4olhBBCCCGEEMJf9XbgWgAkt3k8BChqu4PW+jmt9VSt9dSYmJheLZwQQgghhBBCCP/T24HrJmCkUmq4UsoCXAN81MtlEEIIIYQQQgjRj/RqVmGttVMpdQfwGZ7lcF7SWu/qzTIIIYQQQgghhOhfejVwBdBafwp82tvnFUIIIYQQQgjRP/X2UGEhhBBCCCGEEOK0SOAqhBBCCCGEEMKvSeAqhBBCCCGEEMKvSeAqhBBCCCGEEMKvSeAqhBBCCCGEEMKvSeAqhBBCCCGEEMKvSeAqhBBCCCGEEMKvSeAqhBBCCCGEEMKvSeAqhBBCCCGEEMKvSeAqhBBCCCGEEMKvSeAqhBBCCCGEEMKvKa11X5ehU0qpMuBwX5djAIsGyvu6EIOA1HPPkzruHVLPPU/quHdIPfc8qePeI3Xds6R+e95QrXXMqXby68BV9CylVJbWempfl2Ogk3rueVLHvUPquedJHfcOqeeeJ3Xce6Sue5bUr/+QocJCCCGEEEIIIfyaBK5CCCGEEEIIIfyaBK6D23N9XYBBQuq550kd9w6p554nddw7pJ57ntRx75G67llSv35C5rgKIYQQQgghhPBr0uMqhBBCCCGEEMKvSeAqhBBCCCGEEMKvSeDajyilkpVSXyql9iildimlfuLdHqmUWqGU2u/9G+Hdnq6UWq+Usiul7m5znECl1DdKqe3e4/zmJOe80Xvc/UqpG9tsv1YptUMpla2UWq6Uiu7J996buque2xzPqJTaqpT6+CTnPKGelVJBSqlPlFJ7veV4vKfec2/zlzr2bpe23IV6Vkrleetpm1Iq6yTnXKKU2qeUylVK3ddm+9+923cqpV5SSpl76n33Jj+r4wVKqS3e169VSqX11Pvubd1cz+FKqfe8n617lFIzOzmntGX6rI4HbFuGbv09N9pbR623WqXUTzs556Bpz35WvwO6Lfc6rbXc+skNSAAme++HADlABvB74D7v9vuA33nvxwLnAI8Ad7c5jgKCvffNwEZgRgfniwQOev9GeO9HACagFIj27vd74MG+rh9/q+c2x7sLeAP4uJPzdVbPQcA87z4WYA2wtK/rZ4DVsbTlLtYzkNdaTyc5nxE4AKR62+x2IMP73IXezx4FvAn8v76unwFYxznAGO/9HwKv9HX9+Gk9vwr8wHvfAoRLW/a7Oh6wbbm76/q4+iwGhg729uxn9Tug23Jv36THtR/RWh/VWm/x3q8D9gBJwKV4viTw/r3Mu0+p1noT4DjuOFprXe99aPbeOsrStRhYobWu1FpXASuAJRz7gLMppRQQChR12xvtY91VzwBKqSHARcALJzllh/WstW7UWn/pPUcLsAUY0g1vsc/5Sx0jbRm6WM9dNA3I1Vof9LbZt7znQmv9qfezRwPfIG252+sYz+d4qPd+GNKWT6hnpVQoMBd40btfi9a6uoNTSlvuwzpmALdl6LHPjQXAAa314Q6eG1Tt2Z/qlwHelnubBK79lFJqGDAJT29pnNb6KHj+s+K5cnSq1xuVUtvw9Dat0Fpv7GC3JCC/zeMCIElr7QD+H7ADz3/ADLxfUAPN2dYz8BTwc8B9kn06rOfjyhEOLANWdrHo/UZf1rG05dOqZw38Rym1WSl1ayf7dKUtm4EbgOWnU/7+wA/q+AfAp0qpAjx1PGCmF7R1lvWcCpQBLyvP9IIXlFK2DvaTtty3dTwo2jJ0y+dGq2vw9Jh2ZNC2Zz+o30HTlnuDBK79kFIqGPgH8FOtde2ZHENr7dJaZ+K5sjZNKTWuo1N19FLvh9v/w/NBkAhkA/efSTn82dnWs1LqYqBUa735VLt2sM3XA66UMuH5sHxaa33wdMvhz/q6jqUtn5ZztdaTgaXAj5RSczs6VQfbjh/N8RdgtdZ6zRmWwy/5SR3/F3Ch1noI8DLwxBmWw291Qz2bgMnAX7XWk4AGPEMGTzhVB9ukLXdNd9TxgG/L0G2fGyilLMAlwLud7dLBtgHfnv2kfgdFW+4tErj2M94f2v8A/q61ft+7uUQpleB9PgFPL2qXeIfvfAUsUUpNbzMB/RI8V4yS2+w+BE+vVKb3tQe8Q0veAWad3TvzL91Uz+cClyil8vAMG5mvlHr9NOq51XPAfq31U2f9xvyIn9SxtOUufmZorYu8f0uBf+K54JXcpp5v5xRtWSn1ayAGz5zkAcMf6lgpFQNMbDN65m2kLXekAChoU0/vAZOlLXv4Qx0PhrYM3f57bimwRWtd4n3toG/P/lC/g6Ut9yYJXPsR7xy8F4E9Wuu2V2w+AlqzpN4IfHiK48R4h56ilLICC4G9WuuNWutM7+0j4DPgAqVUhPJkXrvAu60QyPD+hwRYhGf+wIDQXfWstb5faz1Eaz0MzxCTL7TW159GPaOU+i2eOREdZrHrr/yojqUtd+0zw6aUCmm9j6f+dmqt89vU8zPAJmCkUmq49wr1Nd5zoZT6AZ65xtdqrU82rLtf8aM6rgLClFKjvIeUttwBrXUxkK+UGu3dtADYLW3Zr+p4QLdl6L66buNa2gxjHezt2Y/qd8C35V6n/SBDlNy6dgNm4xl6kA1s894uBKLwzH3c7/0b6d0/Hs9VoFqg2ns/FJgAbPUeZyfwq5Oc8/tArvf2vTbbb8fzny8b+BcQ1df142/1fNwxz6eTjLed1TOeK3baW8+t5fhBX9fPQKpjactd/sxIxZMlcTuwC/jFSc55IZ4sigfa7gc4vdtay9Hp505/uvlZHV+OZ772djwjaVL7un78rZ69z2UCWd5jfQBESFv2uzoesG25B+o6CKgAwk5xzkHTnv2sfgd0W+7tm/JWqhBCCCGEEEII4ZdkqLAQQgghhBBCCL8mgasQQgghhBBCCL8mgasQQgghhBBCCL8mgasQQgghhBBCCL8mgasQQgghhBBCCL8mgasQQgghhBBCCL8mgasQQgghhBBCCL/2/wHHiSQnyxipEAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b86645128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_hat_avg = test.copy()\n",
    "y_hat_avg['moving_avg_forecast'] = train['Close'].rolling(10).mean().iloc[-1]\n",
    "\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.plot(train['Close'], label='Train')\n",
    "plt.plot(test['Close'], label='Test')\n",
    "plt.plot(y_hat_avg['moving_avg_forecast'], label='Moving Average Forecast')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2026.4186117619163\n"
     ]
    }
   ],
   "source": [
    "rms = sqrt(mean_squared_error(test.Close, y_hat_avg.moving_avg_forecast))\n",
    "print(rms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.api import ExponentialSmoothing, SimpleExpSmoothing, Holt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dI0nrVGGqgBOAb8QYjwd2kZ8W3JfQR18coL/3l0O4PISwMoSwsrGxsYDhSZIkSdI4ku3IH+9cn+8bz1OFSSqj62OMD6XnPyIJspvSKcCk7eYe1y/s8f0FwIYB+nuJMd4YY1waY1w6a9aswTyLJEmSJI19mbb88Y6ewXUcV1xjjK8D60IIh6VdpwOrgduA3MrAFwO3pse3AR9LVxd+C7AjnUp8J3BmCGFauijTmWmfJEmSJKlQmfb8cXdwHdtThasKvO7TwPdCCDXAi8AlJKH3lhDCZcCrwIXptb8AzgXWArvTa4kxbgshXAOkk7D5UoxxW1GeQpIkSZLGi1xwnXwAtDRCpiOtuBYa70afgp4sxrgKWNrHR6f3cW0ErujnPsuAZYMZoCRJkiSph1xwrZ0MzRvgb9NXLMdwxbXQfVwlSZIkSeUg945r7eTe/QZXSZIkSVJZ6De4ju9VhSVJkiRJpda2A7KZ/HY4VlwlSZIkSWUjRrj2QPh/f9x/xbXCiqskSZIkqVRyCzI9dUv+uGZS72ucKixJkiRJKpmOXfljF2eSJEmSJJWdjpb8cSb3juseFdeuzpEbzwgzuEqSJElSuesVXNOK655ThXdtGbnxjLCqUg9AkiRJkrQPPacK71iXtLVT8n3v+Sc48v0jO6YRZHCVJEmSpHLXs+L68I0QKqC6Lt/3po+P/JhGkFOFJUmSJKnc9ay4AlRPhKra0oylBAyukiRJklTu9gyuE6dDxfiZQGtwlSRJkqRy13OqMMDEGfntb8ZBgDW4SpIkSVK5y1VcD3pn0vYKrtWlGdMIMrhKkiRJUrnLBdepC5N24gyoTAOrFVdJkiRJUsl17Er2ba2fmZxXVOWDa6XBVZIkSZJUah0tUFMPR52fnE+eO66mCo/9aC5JkiRJo117GlwPOB6ueDiZMtzyevJZpcFVkiRJklRqHbuS4Aow67C0MyRNRWVJhjSSnCosSZIkSeWuoyV5x7WnrmzS5qYMj2FWXCVJkiSp3HXsgonTe/dNPxje9Ak46ROlGdMIMrhKkiRJUrnr2AUNC3v3VVTAe/6xNOMZYU4VliRJkqRyl9sOZ5wyuEqSJElSuevrHddxxOAqSZIkSeWusxWqJ5R6FCVjcJUkSZKkcteVgYqxv19rfwyukiRJklTOYoSYhYrxu7auwVWSJEmSylluv9aKytKOo4QMrpIkSZJUzqLB1eAqSZIkSeWsK5O0ThWWJEmSJJUlg6vBVZIkSZLKWu4d1+BUYUmSJElSOXJxJoOrJEmSJJU1pwobXCVJkiSprBlcDa6SJEmSVNYMrgZXSZIkSSprsStpfcdVkiRJklSWuiuuBldJkiRJUjlyqrDBVZIkSZLKmsHV4CpJkiRJZS23j2twqrAkSZIkqRzlgqsVV0mSJElSWXJxJoOrJEmSJJU133E1uEqSJElSWYu5qcJWXAcUQng5hPBUCGFVCGFl2jc9hLAihLAmbael/SGE8PUQwtoQwpMhhBN63Ofi9Po1IYSLh+eRJEmSJGkMseI6qIrru2KMS2KMS9PzLwB3xxgXA3en5wDnAIvTP5cD34Ak6AJXAW8GTgKuyoVdSZIkSVI/uqy4DmWq8HnA8vR4OXB+j/7vxsSDQEMIYR5wFrAixrgtxrgdWAGcPYTflyRJkqSxz4prwcE1Av8bQng0hHB52jcnxrgRIG1np/3zgXU9vrs+7euvv5cQwuUhhJUhhJWNjY2FP4kkSZIkjUVuh0OhT35yjHFDCGE2sCKE8OwA14Y++uIA/b07YrwRuBFg6dKle30uSZIkSeNKruIanCo8oBjjhrTdDPyU5B3VTekUYNJ2c3r5emBhj68vADYM0C9JkiRJ6o8V130H1xBCfQhhcu4YOBN4GrgNyK0MfDFwa3p8G/CxdHXhtwA70qnEdwJnhhCmpYsynZn2SZIkSZL60/2O6/ituBYS2ecAPw0h5K7/fozxjhDCI8AtIYTLgFeBC9PrfwGcC6wFdgOXAMQYt4UQrgEeSa/7UoxxW9GeRJIkSZLGomjFdZ9PHmN8ETiuj/6twOl99Efgin7utQxYNvhhSpIkSdIo8uh34PD3Qv3Mod/LiuuQtsORJEmSJO2p8Xn42ZXwk08U535uh2NwlSRJkqSi6tyVtLu2FOd+Ls5kcJUkSZKkosqmFdLK6uLcrzu4OlVYkiRJklQMmbakrShScM22p/ez4ipJkiRJKoaOdKpwsSqum5+FSXOhpr449xuFDK6SJEmSVEwdLUlbjAppVxesexAOOH7o9xrFDK6SJEmSVEzFrLg+9UPY/jIcds7Q7zWKGVwlSZIkqZi6K65FCK5NryTtkj8Y+r1GMYOrJEmSJBVTd8W1CFOF25uhakLx3pcdpQyukiRJklRM7c1Jm9vGZqj3qpk09PuMcgZXSZIkSSqm3FTh3LY4Q71X7eSh32eUM7hKkiRJUjG1NiVtpn3o92pvhlorrgZXSZIkSSqmtmIG1xaonTL0+4xyBldJkiRJKqZcxTVbjOC603dcMbhKkiRJUnG17UjaYlRcfccVMLhKkiRJUnH1nCq8ZgUs/z1ofH7/7uU7rgAUYWMhSZIkSRIAr/wWdm9Njre/BN//IMQueG0lzHrj4O/XbsUVrLhKkiRJUvHcdVXSTmhI2tiVtPszbTibgUwr1BhcDa6SJEmSVCzTDkray/63d3+2c/D36mhOWiuuBldJkiRJKprO3TDrCGg4sHd/tmPw92pvSVrfcTW4SpIkSVLRdOyCmnqorOndvz9b47Rbcc0xuEqSJElSseSCa0UlhMp8/35NFU4rrr7janCVJEmSpKLJBVeAqtp8/35NFd6ZtFZcDa6SJEmSVDQdLfng2nO6sO+4DonBVZIkSZKKpWfFtWdwzexPcPUd1xyDqyRJkiQVS8cuqEkrpCHk+wutuHZl4bHvwutPwa7NSV/d9OKOcRSqKvUAJEmSJGlM6MpCZ4+Ka1cm/1mhizOtvhVu+zQc/C6YNAemzHeqMFZcJUmSJGnw7vwreP7O/Pm2l+BrS5LjCQ1J2yu4FrgdzubVSdveDI3PwqzDhj7WMcDgKkmSJEmD9dsb4PsfzJ//5BOw49Xk+PiPJG3P91oLnSq87cWkbX49Da5HDH2sY4BThSVJkiRpMLKZvftygfP9N8KEKclxprXHdwqcKpy7z871STv/hP0b4xhjxVWSJEmSBuOaGfnjth2QaYfdW+FdfwXHXdT3dzIFThVuaex9bnAFDK6SJEmStP/adub3W50wte9r6mcVPlW4vTm5PmfKgqGNb4wwuEqSJElSoWLsfd7RkvyB/DY4e6qfXdhU4RihoxlmLM73VdX0f/04YnCVJEmSpELtGUDbm3sE1/q+v1M/s7BVhTtbIXbBzMX7vnacMbhKkiRJUqF6LrgESXDNTRXub7/VmkmFVVzbm5PW4LoXg6skSZIkFaqzrfd5e3MyvRegZnLvz2YdnrRVNbDpaWhaN/C9c5Xbnu+4CjC4SpIkSVLh9qy4drRAx67keM+pwh+/G/78eTjk9OT8xXv7v29XFl75TXqfSXDev8FldxVlyGOBwVWSJEmSCtW5R3C99YpkSxzYe6pw7SSYPAeO/kByvmtz//d98ha47dPp9ybD8R+GhW8qzpjHAIOrJEmSJBVqz+AKsO3FpN1zqnBOzcSkirrpdwPcd3f+uL93Zccxg6skSZIkFSqTvuN6wbfzfVtfSNr+VhWGZErx0z+Gx/+r788rqvLHk+YObYxjkMFVkiRJkgqVq7hOmQ/vuyE5fu0xmDAVqmr3/f1V3++7P9uRtH/0a5g6f+jjHGMMrpIkSZJUqFzFtXoCHPzO5Hjneph9FITQ//dqpyZt6/aB7zttUVGGOdYYXCVJkiSpULmKa1UdTJqT759z5MDfu+JBmLek73dkATLtSVtZQNV2HDK4SpIkSVKhcsGzekIyNTikkWpfldIpB8C8Y/cRXANUVhdrpGOKwVWSJEmSCtUdXCf27q+fve/vVtXl94H9znvhP87If5ZtT4PwANONx7GCg2sIoTKE8HgI4efp+UEhhIdCCGtCCP8dQqhJ+2vT87Xp54t63OMv0v7nQghnFfthJEmSJGlYdbQkbU1uy5o0aE4qILhW1+WD78v3w/pH8p9l2gtb3GmcGkzF9UrgmR7n1wFfjTEuBrYDl6X9lwHbY4yHAl9NryOEcCTwIeAo4Gzg30IIlUMbviRJkiSNoI5dECrzITNXIe35vmt/quuS1YO7snt/lmnz/dYBFBRcQwgLgPcA/5GeB+A04EfpJcuB89Pj89Jz0s9PT68/D7g5xtgeY3wJWAucVIyHkCRJkqQR0dGSVFv3nNJbSMW1akLS5lYQ7inTkf9ceym04vrPwP8FutLzGUBTjDGTnq8HcpsNzQfWAaSf70iv7+7v4zvdQgiXhxBWhhBWNjY2DuJRJEmSJGmYdbRA7aT8+YXLYf5SqJu+7+/m3ovtuUBTrvqaaYOqmuKNc4zZZ3ANIbwX2BxjfLRndx+Xxn18NtB38h0x3hhjXBpjXDpr1qx9DU+SJEmSRk57C9TU58+PeC984m6oKKAmWJ1WVHsG17YdSZu14jqQqgKuORl4XwjhXGACMIWkAtsQQqhKq6oLgA3p9euBhcD6EEIVMBXY1qM/p+d3JEmSJKn8dezqHVwHo6+Ka9sOmDg9fcfVimt/9vnXAjHGv4gxLogxLiJZXOmXMcYPA/cAF6SXXQzcmh7flp6Tfv7LGGNM+z+Urjp8ELAYeLhoTyJJkiRJw61jV48VhQep+x3XnsG1KQmya+9yD9cBFFJx7c/ngZtDCH8LPA7clPbfBPxnCGEtSaX1QwAxxt+FEG4BVgMZ4IoYYx/LaUmSJElSmepohikL9u+71XVJ27Pi+spv4dUHk+N1Dw1tbGPYoIJrjPFe4N70+EX6WBU4xtgGXNjP978MfHmwg5QkSZKksjCkqcI9guvEmbB7Czx/Oxx9wcDf06D2cZUkSZKk8W0owbXndjhd6QYtna0Q081bTr9q6OMbowyukiRJklSoztb8IkuDVZFOeO3K9Aiubfl9XU/8wyEPb6wyuEqSJElSoTpb89vaDFZFZdJ2ZSHbmRxn2qBzd3K8v4F4HDC4SpIkSVIhshno6oSquv37fkiDa8wm94E0uLYBAapqizLMscjgKkmSJEmFyG1js98V13SqcLYz/15rZyu8+ttk4aYQhj7GMWoo2+FIkiRJ0vjRmb6Lut/vuKZ1w0x7vm/3Fnj5/qGNaxyw4ipJkiRJhchVXKv2s+KamyqcC665c+2TwVWSJEmSCtGZmyq8n++45hZnyqbBtXby0Mc0ThhcJUmSJKkQncWquKZTjg2uBTO4SpIkSVIhcoFzvyuu6RJDuXdlayYNfUzjhMFVkiRJkgpRrKnCVlwHzeAqSZIkSYUY8lThPVYVru1RcT3wrfs/rnHA4CpJkiRJhcgUueKamyocKuDSO4Y2tjHO4CpJkiRJhegc4juuucWZHv/PpM1NFd7fCu44UlXqAUiSJEnSqNC9j+sQK645c4+FWY/C0RcMbVzjgMFVkiRJkgrRvTjTflZIK/aIX5PnwhUPDW1M44RThSVJkiSpELmpwvtbcQ17xK/cIk3aJ4OrJEmSJBUi05q8p1pZvX/fDyF//JYr4OgPFGdc44DBVZIkSZIK0dmaLMzUM4Dur2M/CJW+uVkog6skSZIkFaKztXgrAO9v1XacMrhKkiRJUiEybfu/Fc6eKgyug2FwlSRJkqRC5KYKF8OeW+NoQAZXSZIkSSqEU4VLxuAqSZIkSYXIFLPianAdDIOrJEmSJO1L8yZoabTiWiKuvyxJkiRJ+/JPb0zaaYuKc78Ko9hgWHGVJEmSpIHseC1/XG3FtRQMrpIkSZI0kA2P5Y9r6otzT99xHRSDqyRJkiQNZFdj/nj2UcW5p1OFB8XgKkmSJEkDaW/OH88/oTj3rDCKDYb/aUmSJEnSQHLB9cM/hgPfUtqxjFMGV0mSJEkaSHsz1E6FxWeUeiTjlsFVkiRJkgbS3gy1k0s9inHN4CpJkiRJ/enqgg2rDK4l5lJWkiRJktSfJ74Pm39X6lGMe1ZcJUmSJKk/zRtLPQJhcJUkSZKk/tXPLvUIhMFVkiTqjxMCAAAgAElEQVRJkvqX7UjaP36gtOMY5wyukiRJktSfbGfSTjmgtOMY5wyukiRJktSfbHvSVtaWdhzjnKsKS5IkSVJ/chXXypri3O+T90Nna3HuNY4YXCVJkiSpP5l2CBVQWaToNO/Y4txnnHGqsCRJkiT1J9vhNOEyYHCVJEmSpP5kO4o3TVj7zeAqSZIkSf3JtEOVwbXU9hlcQwgTQggPhxCeCCH8LoRwddp/UAjhoRDCmhDCf4cQatL+2vR8bfr5oh73+ou0/7kQwlnD9VCSJEmSVBTZTiuuZaCQims7cFqM8ThgCXB2COEtwHXAV2OMi4HtwGXp9ZcB22OMhwJfTa8jhHAk8CHgKOBs4N9CCJXFfBhJkiRJKqpsu8G1DOwzuMZES3panf6JwGnAj9L+5cD56fF56Tnp56eHEELaf3OMsT3G+BKwFjipKE8hSZIkScPBd1zLQkHvuIYQKkMIq4DNwArgBaApxphJL1kPzE+P5wPrANLPdwAzevb38Z2ev3V5CGFlCGFlY2Pj4J9IkiRJkool0+E7rmWgoOAaY8zGGJcAC0iqpEf0dVnahn4+669/z9+6Mca4NMa4dNasWYUMT5IkSdJ40tkGa++CuFecKD4rrmVhUKsKxxibgHuBtwANIYTcLrwLgA3p8XpgIUD6+VRgW8/+Pr4jSZIkSYX59jnwXx+ALWuG/7fcx7UsFLKq8KwQQkN6XAecATwD3ANckF52MXBrenxbek76+S9jjDHt/1C66vBBwGLg4WI9iCRJkqRxYsNjSdvePPy/lWmHyurh/x0NqGrflzAPWJ6uAFwB3BJj/HkIYTVwcwjhb4HHgZvS628C/jOEsJak0vohgBjj70IItwCrgQxwRYwxW9zHkSRJkjRuZNtH4Dc6oK5h+H9HA9pncI0xPgkc30f/i/SxKnCMsQ24sJ97fRn48uCHKUmSJElANpM/zrQN/+9l3A6nHBRScZUkSZKk0nvtMdi9NX+eGcaK648/ARNnwO4tsNBdPEvN4CpJkiRpdPjF56BlU/58OCuuT92SHgSYPHf4fkcFGdSqwpIkSZJUMnXTYMe6/PlwVly7RZg0ZwR+RwMxuEqSJEkaHeqm9T4vRsW1vQX+8/3w8L9DVzbZG/aVB3pfY8W15JwqLEmSJGl0yAXXiTOTd0+LUXF97nZ44ZfJn/ZmmLoAfvKJ3tdMMriWmhVXSZIkSaNDbluag9+ZtMUIrjX1+eNNT0PTK3tfY8W15AyukiRJkkaH3LY0uQpoUYLrxPzx0z+Gytq9r5k0e+i/oyExuEqSJEkaHTpakrauAULF3u+4rrkLbjoLsp2F37Nij7cnn/rh3tdUVg9unCo6g6skSZKk0WHWEUl7wAlJ4Hz+zvxnK5fB9z4A6x7svWXOvnRlep+3Ng19nCo6g6skSZKk0eHYD8If/xYWnwHZDtj0FGxYlXz288/kr2vbUfg9s3sE152vJe0ZVyftvOP2f7wqGlcVliRJklT2nlq/gy/+7Hd86byjOKrnB9nOvcPnYKqme1ZcYxZmLIZT/gwWvGnvLXhUEgZXSZIkSWVvc3Mbj76ynUw29v4g0wat23v37Xk+kD2DK+RXL1508uAGqWHjVGFJkiRJZa+lPQmYkybsUXt75N/hHw9Njt/1V0nbNpiKa7qQ04mXQFVdcjxh6hBGquFgcJUkSZJU9rqDa20aXD9+d9KuvjV/0bSDknZQU4WzSfuWT0HtpOR4QsMQRqrh4FRhSZIkSWVvV3uG2jk/43O//iGVFSGZIjx3j/1VX7o56XvlJ7Dz0Xx/ph12b4P6mXtvbdOyOfnOQ1fB9DqYPBva18Adlwz/Q42Aw6cfzudP+nyphzFkBldJkiRJZa+lLam4VlaEpCNU7n1RRTVU16VThd+Q9HVl4bVHIXYl77M2LNzjS7l3ZkP+nnvu7aqS878RSZIkSWWvpT1LddP7+fbZZyUdHbvh7+b1vugPvwWP3AR3Xw3T3grHfxg6dsHD6XTixib4zPdhUo9K7cpvw9N/Bn/wj/D9D8LrL8Exn4aT/3RkHkwF8R1XSZIkSWVvV3uG+toeVdbqOiD0vmjCVJi6IDle9V/ww0vg1QfzldRsB/z0k72/k1tVuKIaaqckx3W+41puDK6SJEmSyl5Le4b62h4TRkOA6on58/knJu3EGfm+9p2wdQ1MW5Tv27q2941zizNVVEJDOr0421G0cas4DK6SJEmSylpbZ5b/eWojVRV7VFhr0uB68LvgktuT4/qZ+c87d8PLv4HpB+f74h77wOa2w6mogiPPS46nH1K8wasoDK6SJEmSytr/e/w1AOZNrev9Qa7iOuswqKpNjifO7H3N7i1JcP3wj5LzPbfK6Z4qXAWHnQ1//hwc8q4ijl7FYHCVJEmSVNZWrN7ErMm13PixE3t/UFOftLOPyPfV7xFcIQmui98Np/4FdDTnpwdDPrjmtsmZPLd4A1fRGFwlSZIklbXXmlpZsrCB2qo9tsCZMj9ppx2U76uqhZlvhLOvgynpQk25qcITpiZt24789bkQ29f2OiobBldJkiRJZSOT7eLvb3+GrS3t3X1bWjqYOalm74vf+1U46XI48K29+//kEXjLH8GcI5PzgYJrthNCBVQYjcqZ/+1IkiRJKhv3rWnkW796kat/thqArq7Itl3tzKiv3fvihoVw7legqo9QCzBvSfIebMOByXmfFddMshWOyprBVZIkSVLZ6Mh0AdDamUzh3b67g65I3xXXfTn5SvjEPflgmwuuG1dBV/I7SXCt6vv7KhsGV0mSJEllI5vmydzGN1t3JXuqzpjUR8V1X2onwezD8+e54PqzK+GeLyfHBtdRweAqSZIkqWw0t3X2Ot+Svus6Y38qrnvKBVeAJ/87absyUGlwLXcGV0mSJEllo6k1Ca4hLbluaUkqrrP2p+K6p8nz8se591ytuI4KBldJkiRJZWNHGlw7sxGge3Xh/ZoqvKfKPhZhyhpcRwODqyRJkqSykQuuO9N2a0sHlRWBhroirfz7qQfhgOOhfSc0vw6r/gt2vlace2vYGFwlSZIklY0du5PAmpsyvHVXO9Pra6ioCAN9rXCzj4Dj/iA5XrOiOPfUsDO4SpIkSSobO7orrckU4cbmDmbUF2Fhpp4mTk9/bF1x76thY3CVJEmSVDZywXX77k4efWUbdz2ziZnFeL+1p7ppSbvpd0k795ji3l9FZ3CVJEmSVDaaWju6jz/wjd8CMLMYW+H0lAuum5+Bqjr45P3Fvb+KzuAqSZIkqWzs2N3J3CkTevUVZUXhnnLBddsLMGl2fu8dlS2DqyRJkqSykO2K7GzLcOjsSb36a6qKHFtq6vPHtZOLe28NC4OrJEmSpLLQ3Ja837po5sRe/R2ZruL+UHVd/riqyNVcDQuDqyRJkqSykFuYaX5D7+B61AFTivtDVT2D64T+r1PZMLhKkiRJKgtN6R6u86flg+WnTzuU9x8/v7g/VFkFlemCT1ZcRwWDqyRJkqSykKu4zpuar4KedNB0wnAsnpSbLmzFdVQwuEqSJEkqC01pcG2oq+7um15f5K1wcqrT6ciVw3R/FZXBVZIkSVJZ+PWaRgCmTswH15nF3gonx4rrqGJwlSRJklRyr27dzS0r1wMwtUfFddrEYa64+o7rqGBwlSRJklRyjS1t3ce1VZXdx0XfwzXHiuuoss9/CkIIC0MI94QQngkh/C6EcGXaPz2EsCKEsCZtp6X9IYTw9RDC2hDCkyGEE3rc6+L0+jUhhIuH77EkSZIkjSaNzR0j+4PdwdV3XEeDqgKuyQB/HmN8LIQwGXg0hLAC+EPg7hjjtSGELwBfAD4PnAMsTv+8GfgG8OYQwnTgKmApENP73BZj3F7sh5IkSZI0ujS2tANw/QePA+D7H39z92JNw6LKiutoss/gGmPcCGxMj5tDCM8A84HzgFPTy5YD95IE1/OA78YYI/BgCKEhhDAvvXZFjHEbQBp+zwZ+UMTnkSRJkjQKNTa3EwK877gDAHjboTOH9wcr0/dofcd1VBjUhPEQwiLgeOAhYE4aanPhdnZ62XxgXY+vrU/7+uvf8zcuDyGsDCGsbGxsHMzwJEmSJI1SW1ramVFfQ1XlCC3DU1OftFZcR4WC/6kIIUwCfgz8WYxx50CX9tEXB+jv3RHjjTHGpTHGpbNmzSp0eJIkSZJGscbm9uHb+qYvk+cmbaZt4OtUFgoKriGEapLQ+r0Y40/S7k3pFGDSdnPavx5Y2OPrC4ANA/RLkiRJGucam9uZNXkEg+ukNLg2bxq539R+K2RV4QDcBDwTY7y+x0e3AbmVgS8Gbu3R/7F0deG3ADvSqcR3AmeGEKalKxCfmfZJkiRJGucam9uZNZIV1xmHJq3vuI4KhawqfDLwUeCpEMKqtO8vgWuBW0IIlwGvAhemn/0COBdYC+wGLgGIMW4LIVwDPJJe96XcQk2SJEmSxq8YI40tI1xxXfxuOP8bcMT7Ru43td8KWVX41/T9firA6X1cH4Er+rnXMmDZYAYoSZIkaWzb2ZahI9M1su+4hgBL/mDkfk9DMkJLdkmSJElS317asguAhdMnlngkKlcGV0mSJEkj7vlNzfzg4VcBeGZjsmnJkfOmlHJIKmOFvOMqSZIkSUV15lfvA+CipQt57vVm6msqWTCtrsSjUrmy4ipJkiSpZJb95iWe39TMvIY6Kir6W1pH450VV0mSJEkl87f/8wwAbz5oeolHonJmxVWSJEnSiNra0r5X38yR3ApHo47BVZIkSdKIum9N4159M+trSjASjRYGV0mSJEkj6t7n9g6uM0ZyD1eNOgZXSZIkSSMmxsh9zzdy6mGzuvvqqis5ZsHUEo5K5c7FmSRJkiSNmJb2DNt3d3LyITM544g5nHb4bA5ocBscDczgKkmSJGnENO3uBKBhYjUXLl1Y4tFotHCqsCRJkqQRkw+uLsakwhlcJUmSJI2Y7bs7gKTiKhXK4CpJkiRpxDS1JhXXaQZXDYLBVZIkSdKIaUorrlPrnCqswhlcJUmSJI2YDU1tgFOFNTgGV0mSJEkjoiPTxU8eW8/bF8+kutIoosL5T4skSZKkEfGzJzawubmdy045qNRD0ShjcJUkSZI07GKM/Ou9azl87mTe+cZZpR6ORhmDqyRJkqRht2lnOy827uKiNy0khFDq4WiUMbhKkiRJGna/27ADgKPnTy3xSDQaGVwlSZIkDbsn1jVREeCIeVNKPRSNQgZXSZIkScPuNy9s5ZgFDUyqrSr1UDQKGVwlSZIkDavmtk5WrWvilENnlHooGqUMrpIkSZKG1UMvbiPbFTn50JmlHopGKYOrJEmSpGH1dLow0wkHTivxSDRaGVwlSZIkDav121uZM6WWCdWVpR6KRimDqyRJkqRh9dr2VhZMm1jqYWgUM7hKkiRJGlbrm3Yzv6Gu1MPQKGZwlSRJkjRs1m3bzbptrRw+b3Kph6JRzOAqSZIkaVjctXoTb/+He6gI8HvHHlDq4WgUM7hKkiRJGhZ3/O51AJZfehILp/uOq/ZfVakHIEmSJGnkvNDYwjfufYGDZ9Vz6htnc+QBU4bld9o6s9z3fCNnHTWHty+eNSy/ofHD4CpJkiSNI1f/bDX3Pd8IwKpXm7jxY0sB2NHayctbdnHcwoai/M6N973I5uZ2Ln7roqLcT+ObwVWSJEkaJzoyXTyxrqn7/IXGFgCadnew5EsrAHj2mrOHtN/q1pZ2Xtqyi+tXPM8ZR8zhbYfOHNqgJQyukiRJ0rjxm7Vb2NHaybI/XMqqdTu44ZdraOvMcul3Hum+Zt223Syes38rAK/d3MJZ/3wf2a4IwGmHzy7KuCUXZ5IkSZLGiYde2kZ1ZeBth8zkkFn1dEV45OVtPPZqE+85dh4AL27ZVfD9Vm/Yybv+8V5uWbmO1o4sF3zzge7QCnDsgqlFfwaNT1ZcJUmSpDGusbmd5rZOvvmrFzhuwVQmVFcyv6EOgI/e9DAAF564gP95ciMvFxBcb131Gj97YgN3PbMZgGtvf5YHX9hK0+5OrjnvKC44cSH3rWnk6PkGVxWHwVWSJEkqoe27OqipqqC+dnj+r/nzm5o586v3dZ+ft2Q+AAum9d6e5s0HzWBybRUbd7Tt855fufM51m9v5YCpE3jzwTP46eOv8ZPHX+P/e/cb+Wi6GNNZR80t3kNo3DO4atw45ot38t5jD+Dvf/+YUg9FkiSp2/HXrGDRjInc+7l3Dcv9731uc/fxFe86hEtPOQiA2ZNre11XV1PJnKkT2LSz/+Dansnyb/e8wPrtrVxw4gKu+8CxdGa7OHT2JOZNncD7j58/LM8g+Y6rxrTHX93OMV+8k2/+6gWa2zL84OFXSz0kSZKkvby8dfew3fvhl7YB8LZDZnDZKQd391dUBP72/KN7XTtnSi2v72xjS0s7T65vYk8PrN3K1+5eA8BxCxuorAhMqK7kincdyu+fsIAQwrA9h8Y3g6vGtKdf20FzW4Zv/+alUg9FkiRpL+2Z7LDe/5GXt3HXM5s5/fDZfP8Tb2F6fU2vz//gpAMBeNOiaQDMmTKBzTvb+cA3HuB9N/yGGGOv6x9/dTsAh82ZzMmHzBjWsUs9OVVYY1pjSwcAm5vbSzwSSZKkvW3f1Tms9//FUxsBuGaPympORUXgl3/+TmZPmQCkwbW5jc5sEli3tHQwK51SHGPkrmc2c+yCqdz2J6cM67ilPVlx1ZjWmAbWPf6yUJIkqSxsaSnOX66v3dzMa02te/Xfv2YLb188kwPSFYT7cvCsSUxKF4aaNrG6O7QCrN+en8L8Xw+9yuqNO7noTQuLMmZpMAyuGtP6+pfBcE/JkSRJKtS2XR3dx5ls16C/35Hp4oEXtnDG9fdx2Xce6e7vzHZx2XceYe3mFt6+eGbB92uY2Hsq8ctbd/H6jjZueWQdy379EofPncxFSw2uGnn7DK4hhGUhhM0hhKd79E0PIawIIaxJ22lpfwghfD2EsDaE8GQI4YQe37k4vX5NCOHi4XkcqbfGPqYID/eUHEmSpEK90NjSfbyzLTPo71+87GH+4N8fAuDZ15u730m9+ZF13P1ssprwmUcWvi1NQ111r/Obfv0SX7nzOf7vj5/kpS27OOEN06iqtPalkVfIP3XfAc7eo+8LwN0xxsXA3ek5wDnA4vTP5cA3IAm6wFXAm4GTgKtyYVcaLm2dWZ7f1Nx9Pm9q8u7G7o7B/0tBkiSp2GKM/Pcj67rPt+/uGODqvT36ynZ+++JWAOanU4Ff3LKL5rZO/vWXazn+wAZe+vtzWTSzvuB7TuuxeNMn33Ewz2xsZt22/HThdwyieisV0z6Da4zxPmDbHt3nAcvT4+XA+T36vxsTDwINIYR5wFnAihjjthjjdmAFe4dhqahWvryd3R3Z7v8hf8OMZJPt3R1Z1m/fza52A6wkSSqdJ9fv4NnXm3n3kXMAaNo9uFlh/3zX80yvr+Gxv343f/v+ZPGl0//pVxzzxf/l9Z1tfPq0Qwe9PU3PiuvcqRPIdkVWb9zJeUsO4LG/fjdnHVV49VYqpv2t88+JMW4ESNvZaf98YF2P69anff317yWEcHkIYWUIYWVjY+N+Dk/KT705eFbyt4xz09XyWjuznHLdPfzhtx8u2dgkSdL4tbOtk2tvf5Z/vut5KgJ85C1vAKBpEBXXR1/Zxv1rtvDJdxzM9PoajlvQQMPEfOj8hw8cy2mHzxn02Kb2uMfMSclqwi3tGeZNrWN6fY37tKpkir0dTl//JMcB+vfujPFG4EaApUuXuhas9tv67bupraqgtir5+5ncUu5b0wWbHnl5+6Dut27bbtozWQ6dPbm4A5UkSePK7U9t5Ju/egGA4xZMZVE6K2wwFdfvP7SOqXXVfPStSeidXl/Drz9/GtmuSF11JTVV+1efmpYuznTNeUd1B1fIz1yTSmV/K66b0inApO3mtH890HOZsQXAhgH6pWHzWlMr86fVcenJBwHwjjfOAuCVrbsH+lqfuroib/+Hezjj+vuKOkZJkjT+rFq3A4DfP2E+XzrvaBrqkrDY1Fp4cP3dhh0sWdjAxJp8HWpSbRVT66r3O7QCVFdW8PK17+Gjb13EjEn5911//4Q+J0tKI2Z/K663ARcD16btrT36/ySEcDPJQkw7YowbQwh3An/XY0GmM4G/2P9hS/v22vZW5jfU8bZDZ/Lyte/h5S27AHh12+CD6+qNO3udv9DYwiGzJhVlnJIkaXy4ddVrdGYjD764lXe8cRbXf3AJkPwFeUWAzTvbiDHuNR33yfVNfOlnq3nboTN5w/SJ/Or5Rp59vZlTD5vd188UzcJpE5k7ZQJffN9R1FZVDutvSfuyz+AaQvgBcCowM4SwnmR14GuBW0IIlwGvAheml/8COBdYC+wGLgGIMW4LIVwD5DaX+lKMcc8Fn6Si2rqro1e4rKtJ/ge3Z3Bt7ch29w+k52p6K1Zv4hPfXck3P3ICZx89r4gjliRJY9mVN6/qPv7kOw7uPq6oCHRF+NZ9L/Kt+17kKxccy4VLF7Ji9SY+/+Mnu/d6XflK8prT9Poa5k6ZwOlHDG9wraup5MG/PH1Yf0Mq1D6Da4zx//Tz0V7/FMdk46gr+rnPMmDZoEYnDcGO3Z29FhjIBdSeU4WP+Js7uOPP3s7hc6cMeK/121u7j1e+kvydyxPrd/QZXL/1qxc4bO7kYf9bUEmSNHpksl3dxwfPquf3jjug1+eXnXIQz73ezK/XbuHe5xu5cOlC/vWetd2h9X3HHcBtT2xgyoQqHvrL06l2L1WNM8VenEkqC53ZLprbM93vjABMrN674gpw9j/fz9ovnzPgZtqvNeWD6+oNybTh17a3smL1JmZPruWy5St52yEz+Ov3Hsnf3/4sAC9f+56iPY8kSRrd1qV/Cf6Fcw7nw28+kPra3v83/K/feyQAv/cvv2Z3umVfbmHJ6z5wDOcfP59LTzmIg2bWG1o1LhlcNSbtTBc36LksfFVlBTWVFXRku5g5qZbrPnAM193xLM9vamFXe5apE/v/l8CL6fuxAPev2QLAbU9s4LYnNjCjvoatuzq47YkNbN2VrFg8lEURJEnS2PNoOs33lENnMnlCdb/XNUys7l6kaUNTK+86bBYXvelAAJYsbBj+gUplyv93rTGpqY/gCvnpwnOm1HL6EXO47JRkxeGWjky/9+rqiqx6dTtTe2zIfc35R3cfb93VwdlHzeW4hQ38Zu1WAGorK7jy5sc552v389kfPsE/3/U8XV3u7iRJ0nh173ObmTW5liPnDfx60tS6atZta+WHK9exdnMLBzTUjdAIpfJmxVVjUm4ftJ5hE5Jl4ne0dnZPvZlUm3ze0tZ/cH301e3sbMtw7jFz+cVTr7NkYQMffcsbmFlfw/LfvsyDL27jnGPmsrsjyxPrmgBobs9w66pkx6dn0hWJF82o5/zjXUpekqTxJpPt4v41W3j3kXOoqAgDXtswsZotLe187kdPUlUReO+xBwx4vTReWHHVmPPU+h184BsPAPlNtHNyQXZ2Glzra5MKbEt7//um/ft9LzJzUg0feXOywXduH7NzjpnHjR9b+v+3d9/xbVbnAsd/R8uS9952bMcrznIGGZAEskhCCaul7ELLvC20vYwWbhelhfb23gKlt4MNZY9SoEBDQ4AkhBCynZ3YiROPeO8la5z7h2RhJ07iJB6y83w/H30ivXr1vkdPjiU971ncsyiHRWPj+dqEnhM1nZsdw8afLmDnA4sYFRXIW5vL+uHdCSGEEGI46HC4qGzq4PX1JXzn+Q00tjuYl3viiRtNBs/P88zYYJb9cDYzR0cNdFGFGBakxVWMOH9dWeS7P+aI7jihNk+V72pxDbF6HrfYXT32c7s1BoOiw+Fi9b4aLp+azNmZ0Xx817mkRwd9dTyrme/NzQTAajbyzA1T+ck/tnO4sYPLpyYTFew5z/zcOF784iCdTreMfxVCCOEX9lY2kxhuw601occZcymOr9Xu5Jqn1mE2Kq44K5UDNS2s2lvDtrJG3z7RwQHcOCudRWPj+3Q8gPuXjiUzNmTAyi3EcCOJqxhxdnq75v72svHHTBJjgo/dVbipw8GE+//ND+Zn8ebGUtodLpZ4l73J6LYubG/m5cbxp2ss/PjNAuZkx/i2j0kIodPlpryhnbRuia8QQggxFJwuN+c/sgrw9EZac+88ggPkZ+GxdDhcLNteQWO7g8ggC6mRgUz0TpT04hcH2eIdKrS+uB6jQREdbCEnLoRvTEkmNjSApRMST9hFuMs9i3IYnxzGOZnS0ipEd/IJJUaUFruT4tpW7lyYzZXTUo96vtPpWUMtJsQKQLCvxfWrrsKHvOu8/mHFPsDTNXhGRmSfyzA5NYLld57bY1tqZKDn2HVtkrgKIYQYchVNHb77je0OVuyq5OL8M3ceBqfLzR2vbCY1KpD7lozp8ZzWmoWPrKSkrr3H9qRwG3mJoSzfWcn83FjumJ/F50U1XD0tlfAjhiqdjNhQK9+amXbKrxdipJLEVYwoeyqa0froLsJdOr2Lf0cHe75Qgi2eP4Hmbi2u5d3WbL1zYTbfn5912uVKjfoqcRVCCCGGWlcS9uS3pvL9VzazrbTxjE1c1xfX8fbmMv61vQKAyyYl83lRDY+v3M+S8fFcMD6Bkrp20qOD6HS6SYm0MSMjin/vqOSLoloWjInjkSsmEmI1y3I1QgwgSVzFiHHfW9t4Z4tnAqTEcGuv+zicniVpulpagwKMKAUldW0U17SSFh3E4UbPVejXb53J1FER/VK2uBArNrORwqqWfjmeEEIIcaqWbT/MbS9uAiA7LpjkCNtRF1brWju5+skvePDS8Uzpp+9Cf2R3urj8r2t7bFv06Crf/WfXFPPsmmIsRgOv3jKDqCALJqNnGNIPF2QPalmFONPJLDFixHjly0O0dXomWTpWF53ffH08Z4+OIjPWM1bVZDQwIz2K59ce5Lz//XAf+q4AACAASURBVJSmDgdrCmuwGA1MHRXR5/EoJ2IwKLLjgnnu82Lu/XsB7Z2uE79ICCGEGAC/W7bHdz8hzEZKZCAl9T27wT6+sojdFc18/S+f88NXN9N2xHrn7xWU8/RnB6hq7mC4au90cfH/rQEg0GJkbo5nboqEMCs3nJ3Ghp8u4PHrpnDtjFReu3UGcaFWX9IqhBh80uIqRqRwW++zI05OjeDlm2f02Hb26CjW7q8F4K7Xt7J8ZyXnZEb1W9LaZenERLaWNvLq+hL2VbUwIyOSOxfmYOzlPOUN7Sz5w2qCA0xcMyOV756X2a9lGQgbD9bx/Ve2MDo2mEsnJRITbCUvMZTIoBOP8+l0ujEZVL/HXAghRE9aa6qa7YTZzLx+60wsJgMpETY+L6rhm4+vJcBk4Fsz03hi9X7fa97eUs7bW8r5w5X5vL6hBIdT82VxHQD/vWw3MzOiSI8OYsm4eA43dpAYbmNaet/nhhhsbrfmrc1lrNxbze6KZn51yTiunZ6KW8PaolrOHv3Vb4BFY+P7NBOwEGLgSeIqRhyTQRFoMfZ5/64lcQCW76wE4DeXTuj3cn3nnHSunp7KPW8U8P62w2w8WE9KRGCvk0jtrWymsd2BxWTgiVX7mZ4eSVCAicKqFuJCrUxJ7b/W4P7yfx8XUtbQTllDO6v2VgOeq9af3nMeAaZj/3+s3FvNf721jUCLkYcuG092bAhhgWaqmjqI7NYlSwghxOmrbrHTYndy/9I8cuI9S62kRAbS4XDz5QFPMrp6Xw0Al01KYtOheoq9kxb+4NUtvuPkxodw0+wMXlp3kJV7q1m5t5rnPi8GQClY/p9z/GIpl90VTazaW43LDbUtdhLDbUQEmbn7ja2A5+L1dTM867QbFczKih7K4gohjkMSVzHiWEwGlOp7UhdyxNp135ya7JtMqT8ZDIpAi4lzc2J4f9thAO59axsf767it1+f0KNlsralE4CrzkrhsY8L+fpfeo6/OSczir9cO8Vv1t1bt7+WNUW12MxGxieFcc/iHP629iD/3FrOil1VXDA+odfXaa25+42tVDfbAbj8r2uJCDRT3+aZ5Xl0TBBzsmNYX1yH1jA7K4bzcmKYkSFLBAghxKnomjl/VLcZ7pMjjv7Om50VzcNX5APw8PK9fLy7koVj4kkIt7JgTJzvO+vCCQl8vLuK3PgQ9lY2YzUbueHZ9Xy4o7JfEtfHVuxjT0UzSycmsnjc8Vs+tdbYnW6sZiOHG9vZcqiBBz/YRam3G7RSoD1TXRAcYOLlm6eTG9/7ZI5CCP8jiasYcdxd30p9FGzt+WdwrBmJ+0veEcf/985KOpxbiA62cNf5OSSF26ht9SRyV01P5YUvDlLf5uCOeZm0dbqoabHzfsFhljy6mndvP4co75q0Q6Wt08m3n1tPcoSNF26cTlK4DYD8lHA+3FHBc2uKGZMQyneeW8/cnFjOy4khwGRgWnokH+6ooLrZzkOXjic+LIC3N5ezu6KJ+jYH8aFWqpvtPLummMQwK1HBATz92X7+urKI31w2nsunJGMyGnC43JilVVYIIfqkq/V0VORXyWpKpM13/5cXjWXR2Hjiw76a5PDOhdncubD3iYisZqPv4mTXWudnpUXw0hcHuWVOxil9Pmut+ePHhby9pYz91a0ArNhdSfT7AUR5E+YWu5P5Y+KICLSwvbwRh9NNcW0reytbMBkUTvdXvwVuPTeDG2elExFooaC0kY0H65iZEc345LCTLpsQYuhI4ipGBN0tWXWfXN7ao6vw0omJXNVL193+lBMfwvzcWK4/O40xCaEseHilr2ttqNXM/DGx1LZ2YjEaiA+18vm989FoAi1flfPyKSlc98w6nllzgKmjIjlQ08qlk5KI6MN40v60YlclDW0O2jpd/NeSMb6kFcBsNNDpdPNlcR1z//dTAA7UHOCZNQcA+Nr4BN7fdhir2cC5OTEkhduYlxsHQEVjB+GBZowGRVWznaggC1azkZoWOxc+9hn3vbWNP67Yx+ysGN7ZWkZeQijJEYGUNbRT0djBnOxoHrp0/Em1vAshxEj2xoYSqprtfLavhhCriZRuiWvXWuMzM6K4/uy00z7XVdNSufP1rawvruPNDaWs3V+LyaiIDbFywfgELp+aTKDZyMq91Xyxv5a06CAmJoeTlxBKfVsn28ubeHj5XpLCbdy/NI/Z2TH8/J3tRARaqGqy43B7Llg+scozDtegPK3GUcEWbpyVjtaQFGEjPyWc4ACTr0s0wJRRESN6lmQhRjJJXMWIYHe6ffcfvGTcSb22q7ttoMXIH6+a1K/l6o3ZaODpG87yPQ4wfXU1+rnPi31jhEwGhVIKWy/jdWdlRbNkXDx/+qQIKAKgpL6NXywdO6Bl7275zkpu/tsG3+OxSUe3VD946Th+8o/tAPzhynxCrWaKqlt4dk0x7287TGKYlddundkj4QV6XOnv/lx0cACf3nMey3dW8vN3tvPahhIAWu0uPi+qIdRqRmvNK1+WkBRu4/Z5p78GrxBCDGeHG9u54+XNbDhY79t2SX5ij5bQEKuZt793Djlx/TMmNdt7nKufXAfAtLRITEZFZVMHv3pvJ796b+cJjxEZZOHju8/1zZHw0k09J1Z0uNx8tq+GzNhgooMDev2uFEKMLJK4ihGha3mZXyzN4/KpKSf12uAAz5/BSfYw7jddE0lFB1uo8Y5tBXxL9hzLXefn8MX+OurbOtEaqprsA1rOIy3f6VmoPSs2mLGJocSHHr127jXTR3FeTiytdqfvh8zc3FimpkVy798LeOSK/B5X/fvCajaydGIiszKjuf7ZL/nRotwek2k4XW5ufWEjf/y4kOtmphF2jBmmhRDiTPDC2oNsLmngu+eNJsxmxmQ0cNmkpKP2y08J77dzpncbP3vvklxunZOBUgqHy82dr29lQ3Ed5+fFkZ8azrycOMob23ltfQm1rZ2MjgmitqWTi/ITjzuxn9loYG5ubL+VWQjh/yRxFSNCm8OTuJ7MbMJduroKa4Ymc33q+qkcqmtjxa4qXlp3iLSoQDJignno0vHHfd3omGDW3jePqiY7d76+xTcu9ngO1LRS1dTB9NOc3OhgbSsrdlUxOyuaF26cftx9j2xNBc8PpGU/nHNaZYgIsvDu7bOO2m4yGrh9XiYrdlexcm81F01MPK3zCCHEcNNid/LQB7vYU9HMxoP1TE+P5EeLcwft/EEBJmZlRjM5NZzbzh3t2242Gnrt2RQWaOb+iwavx5AQYniSxFWMCO3ehdFtlpOv0l2zCs/JiunXMvVVZmwImbEhzM6KYVZmNPPHxGEx9W0yiwCTkZTIQCKDLL4JLI6lw+HyjTXd++slfT5Hb8dZ9OgqOhxuv53dd3xSGDazke+/spmPd1UyKyuGpz87wJRR4QSYjJiMiuzYEC4YnyDdy4QQI86H2yt4ed0hwLOkzY2z0we9DC/edPyLmkIIcbIkcRUjQpu3q3Cg+eSTEIvJwEd3nttry+BgMhsNLDnGsjEnEhUcwIc7KnluzQFuOKf3Hyi7Djf57mf/9F/kJYRiNiqy4kL438sn9vlcH+2qpMPh5sqzUrhlTsYplXegmYwG8hJD2Xiwnre3eJbkabY7e8QAYN2BWn7ytTzWFtUwOTWC2F66OwshxHCzr6oFgN2/Woz1FL4XhRDCH0niKkaErnVAI4JObTzjicaT+ruu8a33/3Mn7xUc5gcLsph9RAtyofeHTJfGdgdlDe1sLW3kwUvHHXcsUReXW3P/uzuJDg7gF0vH+vUyNI9ekc9tL25kR3kTzXYnj16Rz+ZD9UxIDmfRuHh+/GYBr28oZfnOSt+6sWMTQ2m1O7GajSSF27hjfla/jvsSQvSNLHMFzR0OgiwmDIaTnx29sKqFrNhgSVqFECOKJK5iRNhd0QzQL4udD0dLxsXz0a5KADYcrOfWFzaSFRuM0625fW4mS8YnUFjdgsVo4IlvTaGyqYPLJifz9uYy7nmzgEeW7+OiiYm8/OVBbpk9mtSoQFxuzXsF5WREBzMuKZRDdW1sLW2kpsXO/3xjgt93sU2JDOTd22cx7cGPMBkVSycmckm3CUnuPD+b0vo2TEYD2XHBhFjNLNteQXacJ24FZY1c8+QXvH7bTMYmylp/Qgw0rTWr9tVgNipufG4DNosRm9nIq7fMYEd5I3sqWrh9XibGbomc1prf/3svieE2pqVHMjomaFgvg9XpdLO3spm/rizig22HuSQ/iZTIQCaPisBsVDz/eTE1LZ1EBVkYkxBKY7sDs1EREWTh+plpBAWY2F3RxOdFNSwZd2o9eIQQwl8pPVRTqfbB1KlT9YYNG068ozjj3fHKZjYdrGfNvfOGuihDxuXWPLJ8L//3SeFRzz3/nWk8/dkBaprtfPCD2b7tdqeLq59cx8ZuyySkRNq4aGIin+2rYWtpIwAJYVYON3b49ln9o7knPRvwUPlwRwWWU5h9sqKxgwUPr2TpxEQeunQcr60vYUFeHNHBAQNUUiHObB/trOSmbktshQSYaLY7SQq3UdbQDsDfvjMNDbTanXQ4XBxu7OB/Ptzje82FExL48eLc0/58qmzq4NGP9hITHMDNczJYU1jDzvImUiID+caU5H5Njt1uze6KZuxOFz99ezs7ypswGhTJETYO1rYdtX9mbLCvB02I1YTLrX3DZcJsZhrbHVhMBt7+7jnkJR69TJkQQvgbpdRGrfXUE+4niasY7t7YUMI9bxZw2aQkHr4if6iLM+RW7Krkz58WcefCbKKCLSx+dLXvuW9OTeZ33+g5nrWouoXrn/mSuTmxTB4Vzm8+2E1Vsx2b2cjCvDjKG9qJCrYwJzuGAJORQIuRC05xLO5wc9sLG9la2sCfr5nMpX/+nOhgC1/cNx/TAHRhfK+gnBe/OMivLxk/7LuuizOL262pb+sk6jQu6uyvbuGCx1bT4fCsyf3IFRO5dFIyb24s5fGVRYQHmllfXH/M1z9yxURW7qnm7S3lRASa2fSzhawtqmXToXq+e17mSXW3XbGrklte2IjL3fvvoyXj4vnphXmsP1DHvqpmyhs6OCstkgM1LZiMBqalRbLpUD1tnS42HqzncGM7104fRX2bg+LaVpo7HDjdmqggC7srmqlv7aS1W+J5zfRULpyQSIjVxJrCGkbHBvPlgTqCLEbOSo9kbGIYtS12rGYjQd7l3P64Yh8bD9WjNczJjuGC8fEkhA3tvA1CCNFXkriKM0JZQztzfveJp1vrHbMYlyRdOo+Udu/7vvuPXTXphMvDaK0pKG0kItBCatTwaFUdKK+vL+FHfy/osW3KqAgev27KMVteX99QAnhm8nS6dZ/HmN3ytw38e2clX5uQwJ+unnx6BRdiEH3vpU28v+0wn959Hmnd1u/sK6fLzcJHVnGgppW5OTH86ZrJBPYyQ/xNz6+noqmDrNgQLpmURJjNTIfDhdloYMqoCACeXLWfBz/YxfqfLOCqJ7+gsKqFBWNiSYsKYmFeHFPTInt0Ne5u9b5qvvXMl2gNUUEWfrw4l80l9XQ43CwZF89ZaZH8+dNCnlx9AIOCY+S1ACjlmSwwwGzEoBQ1LXasZgPp0cGE28woBVXNdnLiQwi3mcmND0EpxbzcWBKHeKJAIYQYbJK4ijPCO1vK+MGrW3j/+7NkHOIxdCWur90yg2npkcN6/Ndgq2rqYNpDKwC4dkYqU0ZFcPcbBbjcmunpkTxw8Thy4r8aV/1eQTm3v7zZ91gpyI0P5b8uyD1qsqwjzfv9p74ljZSCeTmxPPzNfOxOF0XVraRGBbKjrJG9lc3Eh9lIDLMSGxpwxo7rFv6hrdNJ3s8/9D0+JzOK31w64YQXvcoa2mnpcBIUYGTRI6to7XTxhyvzWToh8ZQmI+ryye4qvv3c+mM+f9HERK6bOYryhnZ++o/tnJcbyyX5iXxeVMvbm8uobe3km1OTeeDicce86PS3tcW8t/UwP16SS2ZMMJXNHZgMitTIQD7ZU02AyTOredfFLbdbU9vqGZd6Ou9NCCFGKklcxRnhoQ928dyaYnY8sOiMn4HyWLoS110PLPb7CZX8UWVTBzHBAb4fnHe+toW3NpcBkBhmZcVd59HY7uC/l+3m3a3ljE8KIycuhMLqFsJtZlbsriLQYmTFXedS09xJWnSgb+1g8LRw//Zfu3l81X6+Nj6B97cd9j13cX4iawprqGnpPGb5fnPZeK6aljpA716I4/u8qIarn1znG1sJnq60f7l2Sq/7a615a1MZd72xFZNBMTsrmk/2VPPAxWO5bsao076wVlTdwvzfrwQg0GJkXm4sLXYnF4xL4PUNJWw4eOzuxnkJoTx2Vb5cDBJCiEEmiasY0Zo6HGwva+SeNwpIDLfyxm1nD3WR/NYnu6v4rLCGn12YN9RFGREcLjd7KpppbHdwzVPr+J9vTODtLWWsKawlNiSAF26c3qMVtrfuxs99+yzOy4nl5XWHeGzFPiqaOpiWHslDl44nMsiC2ai49ql1bC1tJMBk4ObZGdidLs7NjmV8chgrdlXS2O7g3a3lbD7UQIjVhMPlZm5OLH+4chIWk+cijsutj9ktUoj+8NiKfTzy0V42/nQhZfXtXPv0OkKsJv567RSy4oLRGgxK+erk7/+9hz9+/NUEckrBFVNT+O3XJ/RLeZwuN7e9uImbZ6czPSPqqOermjt4bk0xMSEBZMeFkBRuo6i6hSmjIggPtPRLGYQQQpwcSVzFiPb9Vzbz7tZywDPL5Jzs43fDFKK/aa2Z8ZsVVHatobs0jxvOSe91vw93VPLOljKW76zE6R0YNy0tki+L6wDPZCrPXD+1x6RPje0OXvziIJNTI5g5+ugf4ACNbQ4eeG8n9W2d1LTYKSht5MeLc9lR3sh7BYcJshiZlRXNZZOTOSczmorGdmlNEv3m6c8O8Kv3dpIbH8KyH84B4E+fFPaY5Rc840XnesduPrlqP/kp4fz12inYLEY6XW6CA2RlPiGEOJNJ4ipGLK01Zz34ETUtnVwzPZVfXzJOxm2KIfHEqiIe+mA3N5ydxs8uzDth62Z7p4s1hTU8/dkBDje2c252DD/5Wp6vNep0aK359nPr+XRP9XH3y40P4YPvz5axdoPo4eV7iQ+1cvX0kdOlu2t+AYDvzR3NPYtyAdhb2cz5j6wCwGIykJcQSlO7g/01nvHbASYDL988wzeZkhBCCNHXxFUuc4ph50BNKzUtnTK2Twy5W+aM5uL8JOJCrX3a32YxsiAvjgV5cf1eFqUUj183hUc/2kd6dBALx8ShFOwob2JneRNbShr41/bD7K5o5qNdlfxhxT7sTjdPfmsq6acwE+zJKGtoJyHUit3p5lfv7+T9gsNMHRXBmIRQLCYD35ubOWK7NK/aW81jK/YB4NaaSyYlEeQda/7GhlLe33aYg7WtTE6NYEFeHCFWE3aHm0mp4UQGWfzuolxti50Pth3m/n/uJDsumG9OTeFbM9N8z2fHhXDHvEzOyYwmIyaIyEDPe3C63TS1OzEoTmvZHCGEEGcuaXEVw86rXx7i3re2seKucxkdI+tdCtFXFY0dzPjNih7bUiMD+d03JjCjl/GAz645gMPl5ubZGaecQD21ej+/fn8XRoPyrYs5LT2SDcV1vuVEfnnRWFKjAmnucLJ0QsJR53K63BgNyu+SuCN1OFzYHW7aHS4eWb4Xq9nA82sPEmAyEGozU93s6VYeZDH61u2MCrKQnxLOugN1tNidPY63eGw8OfGecZgZMUFMGRUx6DGobrbz/OfFRAVbWL2vhs+LaujwJtYv3DhduvkKIYQ4bdLiKkasj3ZVEh0cQMYAtxIJMdLEh1m5cEIC7xUc5pWbZ1BY3cLP3t7OrS9sZMvPF/ZIijYfqueX/9wJQH5KBNPSI3s95qd7qvjzp0X839WTCLOZOVTbRkZMMG6teeCfO3nly0MAzMiIJMBkZEZGJLfMGU1JXRtaw6JHV/GLd3f4jre/ugUAu9NNZkwwn+ypYk1hDbEhVm47L4OlExJ7jAXuC7vTRXOHE5vZSKDFeFrJ3/ayRj7aVUlaVBCxoQEEmAwkRwTy0rpDvLzuEA1tndgsRpo7PEloVJCFl2+eQXZcMB/vrmLl3moqGjuIDQ0gKTyQm2enYzIacLrcbClpoMXuxO50c+/fC1i2o4JlOyp85w4PNPP8t6cxMSX8uGXUWtNsdxLabfbqvqhv7aSiqQOjQVFc08qBmlb+tvYgZQ3tvn2+PjmZa2ekMj4p7KT/H4QQQojTIS2uYlhZt7+WK574grsWZnPH/KyhLo4Qw06n001DeyexIZ7uzX/+tJDfLdvDmnvnkRRuQ2vNGxtL+fHfCwi2mGi2O0mJtPHjxbm8+MVBbp+bRVKEDavZQHyoleyf/guH6+jvkaRwG2UN7czJjuHRK/KJDOp9xtb3Cw6zt7KZhDArv1++19cq2SUyyEJsSAAtdiel9e0khds4e3QU35+f5Vsb88nV+ympa2PR2HhiQwMoKG2koLSRbWWNhFpNlNa3Y3e6feWKCQnAYjKQHRfMuv11lNS3ERtiJcRqIjjAxJJx8UxN8yTqbq0prGqhqtlOdbPdt9Znb0bHBDEnO4balk6+MyudjJigk04eu1Q0dlBQ2sC4pDDq2zrZdLCen72zg6RwG7OzojknM5rZWdGsKaylsqmDQIsRk9HA5kP1vLOlnBa7k5AAE/mp4czKjMalNW63xmQ00Ol0s7eymYY2B6X1bYTZzNS2dlLdbPfFqcu4pFBunp1BRKCFaemRx1zbVAghhDhVMjmTGNbKG9pZd6CWS/KTfK0jJXVtfPu59bR3uvjoznNlTVIh+sGWkgYu+dMabjg7jfPHxvG/H+5h06EGIgLNvHDjdN7eXMZTnx046nVdYxW7Es0Qq4lZmdFkxQZTVN2Ky61JDLfxswvH9LmFs7rZTllDO8EBRnYdbiYyyMLUtAgCTEbsThdvbSrj9Q0lbD7UcNRrQ6wmXytnl7NHR1He0I7Trbl2xig6nZ5WTYfLTV1rJ4dq28iKCyYnPoTalk7cWlNa387uiuZey2czG0mPDuJXl4wjxGqitL6N+lYHda2dnJ0ZRV5C6IB25e1ak/l4LEYDi8fFE2w1+Wa0rusl0U4Ms2IwKFIiAjEZFdHBAYTZzIyOCSLEaiYlMpCY4ABSowIH4q0IIYQQPpK4imHta4+tZkd5E7eem8Gmg/UUlDZid7oxKHj6hrOYmxM71EUUYkTocLjI/dmyHtsmpYbz5m1n+yZM2nSonufWFHPLnAxeWneQiEALJoPiYF0bYxNDT2sM7KlYtr2CzYfqiQyyEGgxkpcYxqSUcN7aXMb2skbm5cYyc3QUZqMBt1vj0hpzH7u1aq35ZE8VbZ0uzEYDje0OcuNDGB0TTNAQj+e8760Clm2vYO1987nlhY2UN7Rz9/nZ5KdE0Ol00+ZwEhdiJaJb67bT5abT5cagFAalqGjsICjA6JcTPwkhhDgzSeIqhqUOh4s/f1LIY90WqAeYmxPD1LRIZmdFMyH5+OO7hBAnp6slb8GYOO5ZlMPomCAZv+iHtNY43Z4k3OFyo0D+n4QQQgx7MjmTGJZ+8c4OXttQwpJx8dy3ZAz/+foWbp6dweJx8UNdNCFGrAcuHktRVQu/vHjcUBdFHIdSCrPR00ra1xZkIYQQYqSQFtfTsG5/LX/8uJC7F+WQf4JZHruU1rfR3OEkKcIGcMoTdwyVAzWtrN5XTaDFxIIxsYQHerqkbS9rpKnDwdiEMNodLsIDzb5JPLTWfHmgjpL6dmaOjiIp3OY7XlOHg+KaVv6xuYzP9tWwr6qFb80cxS8vGivd2IQQQgghhBjhpMV1gNmdLv7ztS2UN3ZQ19rJa7fOoLimjdjQAIqqWuhwunC7obK5gw3F9SjlmXhk9b6aHscJMBmYlxtLXKiVi/ITmZwaMUTvyKPD4cKgFPtrWli2vYLS+naSI2wUVrVQ1tDOlpIGuq51BFmMZMWF+CZU6U4pCA4wYTMb6XS5aWhz9Hi+a/bOw40dAJiNitz4UG6enc6PFudK0iqEEEIIIYTwkRbX07CjvJEPt1ccNR7zSDEhAViMBsIDzUxLjyQvIZSyhnZcbs8yC5sO1dPU7qTd4SIkwER4kJmcuBDOSotka2kD28uaaOt0khRuIysuhIyYIM4eHU1ufMhRSxNUN9vZUd5Iq93FqKhAxiWFobWmrrWT8oYONpd4lkqICrKggVa7E7fWjI4JZl9VC5sP1fuWtlAKwm1m6tscBFmMWEwGxiSE8suLxrKvqoWnVu9nW1kj2d6yjk8Ko6KpgzCbmcqmDurbOmnucBJoMTE5NZyUyECufOILAK6dkUpTu5O0qEBGRQWxIC+OMNvwan0WQgghhBBCnB6ZnGmQuNyaW1/YwCd7qrljXibhNjMxIVaigi2U1LWRERPE5NSIE7Ygttqd/PHjQp7+bD+zMqMpKG2ktrWTIIuRubmxhFjNbC1poKrZTk2LZ/kJg4ILJyTyi6V5dLrcPPzvvby7tbzHOnwhVhNOl6bd4fJtiwkJINRqwmw0EBRgotXupLKpg6QIG+eMjsZgUMSFBDB/TBzJETbaHS4CLb03zrvc2jfzaF+UNbRjMRqICQno82uEEEIIIYQQI5MkroPI7dbUtnb2SzLW6XRjMXlmjLQ73QRZjEclvZsO1bP5UAN/+bSQmpae6/NdPiWZWVnRpEQG8tm+Gkrr2wixmkkKtxEVbCEp3MaUUSdOpIUQQgghhBBioPlt4qqUWgz8ATACT2mtf3usfYdL4jpUOhwuHl+5n/XFdcwcHcXM0VFDPkZWCCGEEEIIIfrKLydnUkoZgT8BC4FSYL1S6l2t9c7BLMdIYTUb+cGCrKEuhhBCCCGEEEIMqMFeCG4aUKi13q+17gReBS4e5DIIIYQQQgghhBhGBjtxTQJKuj0u9W4TQgghhBBCCCF6NdiJa28zAvUYZKuUvToCYgAACKNJREFUukUptUEptaG6unqQiiWEEEIIIYQQwl8NduJaCqR0e5wMlHffQWv9hNZ6qtZ6akxMzKAWTgghhBBCCCGE/xnsxHU9kKWUSldKWYArgXcHuQxCCCGEEEIIIYaRQZ1VWGvtVErdDnyIZzmcZ7TWOwazDEIIIYQQQgghhpdBTVwBtNYfAB8M9nmFEEIIIYQQQgxPg91VWAghhBBCCCGEOCmSuAohhBBCCCGE8GuSuAohhBBCCCGE8GuSuAohhBBCCCGE8GuSuAohhBBCCCGE8GuSuAohhBBCCCGE8GuSuAohhBBCCCGE8GuSuAohhBBCCCGE8GuSuAohhBBCCCGE8GuSuAohhBBCCCGE8GuSuAohhBBCCCGE8GtKaz3UZTgmpVQ1cHCoyzGCRQM1Q12IM4DEeeBJjAeHxHngSYwHh8R54EmMB4/EemBJfAfeKK11zIl28uvEVQwspdQGrfXUoS7HSCdxHngS48EhcR54EuPBIXEeeBLjwSOxHlgSX/8hXYWFEEIIIYQQQvg1SVyFEEIIIYQQQvg1SVzPbE8MdQHOEBLngScxHhwS54EnMR4cEueBJzEePBLrgSXx9RMyxlUIIYQQQgghhF+TFlchhBBCCCGEEH5NElchhBBCCCGEEH5NEtdhRCmVopT6RCm1Sym1Qyn1A+/2SKXUcqXUPu+/Ed7tuUqptUopu1Lq7m7HsSqlvlRKbfUe55fHOef13uPuU0pd3237VUqpbUqpAqXUMqVU9EC+98HUX3HudjyjUmqzUuq945zzqDgrpQKVUu8rpXZ7y/HbgXrPg81fYuzdLnW5D3FWShV747RFKbXhOOdcrJTao5QqVErd2237S97t25VSzyilzAP1vgeTn8V4vlJqk/f1nymlMgfqfQ+2fo5zuFLqTe9n6y6l1MxjnFPqMkMW4xFbl6Fff8/leGPUdWtSSv3wGOc8Y+qzn8V3RNflQae1ltswuQEJwGTv/RBgL5AH/A6417v9XuC/vfdjgbOAB4G7ux1HAcHe+2ZgHTCjl/NFAvu9/0Z470cAJqAKiPbu9zvg/qGOj7/Fudvx7gReBt47xvmOFedAYK53HwuwGlgy1PEZYTGWutzHOAPFXXE6zvmMQBGQ4a2zW4E873MXeD97FPAK8B9DHZ8RGOO9wBjv/e8Czw11fPw0zs8DN3nvW4Bwqct+F+MRW5f7O9ZHxLMCGHWm12c/i++IrsuDfZMW12FEa31Ya73Je78Z2AUkARfj+ZLA++8l3n2qtNbrAccRx9Fa6xbvQ7P31tssXYuA5VrrOq11PbAcWMxXH3BBSikFhALl/fZGh1h/xRlAKZUMfA146jin7DXOWus2rfUn3nN0ApuA5H54i0POX2KM1GXoY5z7aBpQqLXe762zr3rPhdb6A+9njwa+ROpyv8cYz+d4qPd+GFKXj4qzUioUmAM87d2vU2vd0MsppS4PYYwZwXUZBuxzYz5QpLU+2MtzZ1R99qf4MsLr8mCTxHWYUkqlAZPwtJbGaa0Pg+ePFc+VoxO93qiU2oKntWm51npdL7slASXdHpcCSVprB/AfwDY8f4B5eL+gRprTjTPwKPAjwH2cfXqN8xHlCAeWAiv6WPRhYyhjLHX5pOKsgX8rpTYqpW45xj59qctm4Dpg2cmUfzjwgxjfBHyglCrFE+MRM7ygu9OMcwZQDTyrPMMLnlJKBfWyn9TloY3xGVGXoV8+N7pciafFtDdnbH32g/ieMXV5MEjiOgwppYKBvwM/1Fo3ncoxtNYurXU+nitr05RS43o7VW8v9X64/QeeD4JEoAC471TK4c9ON85KqQuBKq31xhPt2ss2Xwu4UsqE58PyMa31/pMthz8b6hhLXT4p52itJwNLgO8ppeb0dqpeth3Zm+PPwCqt9epTLIdf8pMY/ydwgdY6GXgWePgUy+G3+iHOJmAy8Bet9SSgFU+XwaNO1cs2qct90x8xHvF1GfrtcwOllAW4CHjjWLv0sm3E12c/ie8ZUZcHiySuw4z3h/bfgZe01m95N1cqpRK8zyfgaUXtE2/3nU+BxUqp6d0GoF+E54pRSrfdk/G0SuV7X1vk7VryOnD26b0z/9JPcT4HuEgpVYyn28g8pdSLJxHnLk8A+7TWj572G/MjfhJjqct9/MzQWpd7/60C/oHngldKtzjfxgnqslLqF0AMnjHJI4Y/xFgpFQNM7NZ75jWkLvemFCjtFqc3gclSlz38IcZnQl2Gfv89twTYpLWu9L72jK/P/hDfM6UuDyZJXIcR7xi8p4FdWuvuV2zeBbpmSb0eeOcEx4nxdj1FKWUDFgC7tdbrtNb53tu7wIfA+UqpCOWZee1877YyIM/7BwmwEM/4gRGhv+Kstb5Pa52stU7D08XkY631tScRZ5RSv8YzJqLXWeyGKz+KsdTlvn1mBCmlQrru44nfdq11Sbc4/xVYD2QppdK9V6iv9J4LpdRNeMYaX6W1Pl637mHFj2JcD4QppbK9h5S63AutdQVQopTK8W6aD+yUuuxXMR7RdRn6L9bdXEW3bqxnen32o/iO+Lo86LQfzBAlt77dgFl4uh4UAFu8twuAKDxjH/d5/4307h+P5ypQE9DgvR8KTAA2e4+zHfj5cc75HaDQe/t2t+234fnjKwD+CUQNdXz8Lc5HHPM8jjHj7bHijOeKnfbGuascNw11fEZSjKUu9/kzIwPPLIlbgR3AT45zzgvwzKJY1H0/wOnd1lWOY37uDKebn8X4Ujzjtbfi6UmTMdTx8bc4e5/LBzZ4j/U2ECF12e9iPGLr8gDEOhCoBcJOcM4zpj77WXxHdF0e7JvyBlUIIYQQQgghhPBL0lVYCCGEEEIIIYRfk8RVCCGEEEIIIYRfk8RVCCGEEEIIIYRfk8RVCCGEEEIIIYRfk8RVCCGEEEIIIYRfk8RVCCGEEEIIIYRfk8RVCCGEEEIIIYRf+3+SLCy9DSkGrQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b867eb710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_hat_avg = test.copy()\n",
    "fit2 = SimpleExpSmoothing(np.asarray(train['Close'])).fit(smoothing_level=0.6,optimized=False)\n",
    "y_hat_avg['SES'] = fit2.forecast(len(test))\n",
    "\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.plot(train['Close'], label='Train')\n",
    "plt.plot(test['Close'], label='Test')\n",
    "plt.plot(y_hat_avg['SES'], label='SES')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1837.0992146655035\n"
     ]
    }
   ],
   "source": [
    "rms = sqrt(mean_squared_error(test.Close, y_hat_avg.SES))\n",
    "print(rms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b86638e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "sm.tsa.seasonal_decompose(train.Close).plot()\n",
    "result = sm.tsa.stattools.adfuller(train.Close)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b8683d390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_hat_avg = test.copy()\n",
    "\n",
    "fit1 = Holt(np.asarray(train['Close'])).fit(smoothing_level = 0.3,smoothing_slope = 0.1)\n",
    "y_hat_avg['Holt_linear'] = fit1.forecast(len(test))\n",
    "\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.plot(train['Close'], label='Train')\n",
    "plt.plot(test['Close'], label='Test')\n",
    "plt.plot(y_hat_avg['Holt_linear'], label='Holt_linear')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1754.2470748369246\n"
     ]
    }
   ],
   "source": [
    "rms = sqrt(mean_squared_error(test.Close, y_hat_avg.Holt_linear))\n",
    "print(rms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b86993b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# y_hat_avg = test.copy()\n",
    "# fit1 = ExponentialSmoothing(np.asarray(train['Close']) ,seasonal_periods=7 ,trend='add', seasonal='add',).fit()\n",
    "# y_hat_avg['Holt_Winter'] = fit1.forecast(len(test))\n",
    "\n",
    "y_hat_avg = test.copy()\n",
    "fit1 = ExponentialSmoothing(np.asarray(train['Close']) ,seasonal_periods=60 ,trend='add', seasonal='add',).fit()\n",
    "y_hat_avg['Holt_Winter'] = fit1.forecast(len(test))\n",
    "\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.plot( train['Close'], label='Train')\n",
    "plt.plot(test['Close'], label='Test')\n",
    "plt.plot(y_hat_avg['Holt_Winter'], label='Holt_Winter')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "808.3535921056981\n"
     ]
    }
   ],
   "source": [
    "rms = sqrt(mean_squared_error(test.Close, y_hat_avg.Holt_Winter))\n",
    "print(rms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # y_hat_avg = test.copy()\n",
    "# # fit1 = sm.tsa.statespace.SARIMAX(train.Close, order=(2, 1, 2),seasonal_order=(0,1,1,7)).fit()\n",
    "# # y_hat_avg['SARIMA'] = fit1.predict(start=\"2017-06-06\", end=\"2017-11-03\", dynamic=True)\n",
    "\n",
    "# y_hat_avg = test.copy()\n",
    "# fit1 = sm.tsa.statespace.SARIMAX(train.Close, order=(2, 1, 4),seasonal_order=(0,1,1,7)).fit()\n",
    "# y_hat_avg['SARIMA'] = fit1.predict(start=\"2017-06-04\", end=\"2017-11-03\", dynamic=True)\n",
    "\n",
    "# # y_hat_avg = test.copy()\n",
    "# # fit1 = sm.tsa.statespace.SARIMAX(train.Close, order=(2, 1, 1)).fit()\n",
    "# # y_hat_avg['SARIMA'] = fit1.predict(start=\"2017-06-06\", end=\"2017-11-03\", dynamic=True)\n",
    "\n",
    "# # y_hat_avg = test.copy()\n",
    "# # fit1 = sm.tsa.statespace.SARIMAX(train.Close, order=(2, 1, 2),seasonal_order=(-3,0,1,3)).fit()\n",
    "# # y_hat_avg['SARIMA'] = fit1.predict(start=\"2017-06-06\", end=\"2017-11-03\", dynamic=True)\n",
    "\n",
    "# # y_hat_avg = test.copy()\n",
    "# # fit1 = sm.tsa.statespace.SARIMAX(train.Close, order=(2, 1, 4),seasonal_order=(0,1,1,7)).fit(disp=-1)\n",
    "# # y_hat_avg['SARIMA'] = fit1.predict(start=\"2017-06-06\", end=\"2017-11-03\", dynamic=True)\n",
    "\n",
    "# plt.figure(figsize=(16,8))\n",
    "# plt.plot( train['Close'], label='Train')\n",
    "# plt.plot(test['Close'], label='Test')\n",
    "# plt.plot(y_hat_avg['SARIMA'], label='SARIMA',linewidth=3)\n",
    "# plt.legend(loc='best')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rms = sqrt(mean_squared_error(test.Close, y_hat_avg.SARIMA))\n",
    "# print(rms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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