BDA_StockForecastingForMU_Proj (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stock Foreasting for MU stock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.core.common.is_list_like = pd.api.types.is_list_like\n",
    "from pandas_datareader.data import DataReader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "end = dt.datetime.now()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Taking 5 years of data till date\n",
    "start = end - dt.timedelta(days=5*365)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5y\n"
     ]
    }
   ],
   "source": [
    "#importing MU data from IEX\n",
    "df = DataReader('MU','iex',start,end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-10-15</th>\n",
       "      <td>17.08</td>\n",
       "      <td>17.43</td>\n",
       "      <td>16.84</td>\n",
       "      <td>16.920</td>\n",
       "      <td>63043897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-16</th>\n",
       "      <td>17.08</td>\n",
       "      <td>17.30</td>\n",
       "      <td>16.91</td>\n",
       "      <td>16.960</td>\n",
       "      <td>49096355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-17</th>\n",
       "      <td>17.00</td>\n",
       "      <td>17.16</td>\n",
       "      <td>16.74</td>\n",
       "      <td>17.145</td>\n",
       "      <td>40714771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-18</th>\n",
       "      <td>17.17</td>\n",
       "      <td>17.25</td>\n",
       "      <td>16.96</td>\n",
       "      <td>17.029</td>\n",
       "      <td>34970601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-21</th>\n",
       "      <td>17.03</td>\n",
       "      <td>17.14</td>\n",
       "      <td>16.51</td>\n",
       "      <td>16.565</td>\n",
       "      <td>44886230</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "             open   high    low   close    volume\n",
       "date                                             \n",
       "2013-10-15  17.08  17.43  16.84  16.920  63043897\n",
       "2013-10-16  17.08  17.30  16.91  16.960  49096355\n",
       "2013-10-17  17.00  17.16  16.74  17.145  40714771\n",
       "2013-10-18  17.17  17.25  16.96  17.029  34970601\n",
       "2013-10-21  17.03  17.14  16.51  16.565  44886230"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1259.000000</td>\n",
       "      <td>1259.000000</td>\n",
       "      <td>1259.000000</td>\n",
       "      <td>1259.000000</td>\n",
       "      <td>1.259000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>28.362914</td>\n",
       "      <td>28.802636</td>\n",
       "      <td>27.870288</td>\n",
       "      <td>28.337052</td>\n",
       "      <td>3.132442e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.703094</td>\n",
       "      <td>12.884766</td>\n",
       "      <td>12.463976</td>\n",
       "      <td>12.683826</td>\n",
       "      <td>1.599719e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9.610000</td>\n",
       "      <td>9.690000</td>\n",
       "      <td>9.310000</td>\n",
       "      <td>9.560000</td>\n",
       "      <td>4.672964e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17.745000</td>\n",
       "      <td>17.990000</td>\n",
       "      <td>17.380000</td>\n",
       "      <td>17.650000</td>\n",
       "      <td>2.119935e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>27.220000</td>\n",
       "      <td>27.550000</td>\n",
       "      <td>26.860000</td>\n",
       "      <td>27.140000</td>\n",
       "      <td>2.720240e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>33.670000</td>\n",
       "      <td>34.040000</td>\n",
       "      <td>33.170000</td>\n",
       "      <td>33.675000</td>\n",
       "      <td>3.693571e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>63.700000</td>\n",
       "      <td>64.660000</td>\n",
       "      <td>61.350000</td>\n",
       "      <td>62.620000</td>\n",
       "      <td>1.490103e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              open         high          low        close        volume\n",
       "count  1259.000000  1259.000000  1259.000000  1259.000000  1.259000e+03\n",
       "mean     28.362914    28.802636    27.870288    28.337052  3.132442e+07\n",
       "std      12.703094    12.884766    12.463976    12.683826  1.599719e+07\n",
       "min       9.610000     9.690000     9.310000     9.560000  4.672964e+06\n",
       "25%      17.745000    17.990000    17.380000    17.650000  2.119935e+07\n",
       "50%      27.220000    27.550000    26.860000    27.140000  2.720240e+07\n",
       "75%      33.670000    34.040000    33.170000    33.675000  3.693571e+07\n",
       "max      63.700000    64.660000    61.350000    62.620000  1.490103e+08"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1259, 5)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implement features from table 1 and 2 and visualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating a 1 day ahead forecast column for close price called label\n",
    "df['label'] = df['close'].shift(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculating momentum\n",
    "df['momentum'] = df.close - df.close.shift(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#average return in the last 3 days\n",
    "df['ASY3'] = (df.close - df.close.shift(1)).rolling(3).sum()/3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#average return in the last 2 days\n",
    "df['ASY2'] = (df.close - df.close.shift(1)).rolling(2).sum()/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['return'] = (df.close - df.close.shift(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#average return in the last 1 day\n",
    "df['ASY1']  = df['return'].shift(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#moving average with no of observations: 5\n",
    "df['MA5'] = df['close'].rolling(5).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-10-15</th>\n",
       "      <td>17.08</td>\n",
       "      <td>17.43</td>\n",
       "      <td>16.84</td>\n",
       "      <td>16.920</td>\n",
       "      <td>63043897</td>\n",
       "      <td>16.960</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-16</th>\n",
       "      <td>17.08</td>\n",
       "      <td>17.30</td>\n",
       "      <td>16.91</td>\n",
       "      <td>16.960</td>\n",
       "      <td>49096355</td>\n",
       "      <td>17.145</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2013-10-17</th>\n",
       "      <td>17.00</td>\n",
       "      <td>17.16</td>\n",
       "      <td>16.74</td>\n",
       "      <td>17.145</td>\n",
       "      <td>40714771</td>\n",
       "      <td>17.029</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.1125</td>\n",
       "      <td>0.185</td>\n",
       "      <td>0.040</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2013-10-18</th>\n",
       "      <td>17.17</td>\n",
       "      <td>17.25</td>\n",
       "      <td>16.96</td>\n",
       "      <td>17.029</td>\n",
       "      <td>34970601</td>\n",
       "      <td>16.565</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.036333</td>\n",
       "      <td>0.0345</td>\n",
       "      <td>-0.116</td>\n",
       "      <td>0.185</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-21</th>\n",
       "      <td>17.03</td>\n",
       "      <td>17.14</td>\n",
       "      <td>16.51</td>\n",
       "      <td>16.565</td>\n",
       "      <td>44886230</td>\n",
       "      <td>16.760</td>\n",
       "      <td>-0.355</td>\n",
       "      <td>-0.131667</td>\n",
       "      <td>-0.2900</td>\n",
       "      <td>-0.464</td>\n",
       "      <td>-0.116</td>\n",
       "      <td>16.9238</td>\n",
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      "text/plain": [
       "             open   high    low   close    volume   label  momentum      ASY3  \\\n",
       "date                                                                            \n",
       "2013-10-15  17.08  17.43  16.84  16.920  63043897  16.960       NaN       NaN   \n",
       "2013-10-16  17.08  17.30  16.91  16.960  49096355  17.145       NaN       NaN   \n",
       "2013-10-17  17.00  17.16  16.74  17.145  40714771  17.029       NaN       NaN   \n",
       "2013-10-18  17.17  17.25  16.96  17.029  34970601  16.565       NaN  0.036333   \n",
       "2013-10-21  17.03  17.14  16.51  16.565  44886230  16.760    -0.355 -0.131667   \n",
       "\n",
       "              ASY2  return   ASY1      MA5  \n",
       "date                                        \n",
       "2013-10-15     NaN     NaN    NaN      NaN  \n",
       "2013-10-16     NaN   0.040    NaN      NaN  \n",
       "2013-10-17  0.1125   0.185  0.040      NaN  \n",
       "2013-10-18  0.0345  -0.116  0.185      NaN  \n",
       "2013-10-21 -0.2900  -0.464 -0.116  16.9238  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop( columns =['open' , 'high', 'low', 'volume' , 'return'], axis =1 , inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "      <th>2013-10-15</th>\n",
       "      <td>16.920</td>\n",
       "      <td>16.960</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2013-10-16</th>\n",
       "      <td>16.960</td>\n",
       "      <td>17.145</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2013-10-17</th>\n",
       "      <td>17.145</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>2013-10-18</th>\n",
       "      <td>17.029</td>\n",
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       "      <td>0.185</td>\n",
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       "    <tr>\n",
       "      <th>2013-10-21</th>\n",
       "      <td>16.565</td>\n",
       "      <td>16.760</td>\n",
       "      <td>-0.355</td>\n",
       "      <td>-0.131667</td>\n",
       "      <td>-0.2900</td>\n",
       "      <td>-0.116</td>\n",
       "      <td>16.9238</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             close   label  momentum      ASY3    ASY2   ASY1      MA5\n",
       "date                                                                  \n",
       "2013-10-15  16.920  16.960       NaN       NaN     NaN    NaN      NaN\n",
       "2013-10-16  16.960  17.145       NaN       NaN     NaN    NaN      NaN\n",
       "2013-10-17  17.145  17.029       NaN       NaN  0.1125  0.040      NaN\n",
       "2013-10-18  17.029  16.565       NaN  0.036333  0.0345  0.185      NaN\n",
       "2013-10-21  16.565  16.760    -0.355 -0.131667 -0.2900 -0.116  16.9238"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>close</th>\n",
       "      <th>label</th>\n",
       "      <th>momentum</th>\n",
       "      <th>ASY3</th>\n",
       "      <th>ASY2</th>\n",
       "      <th>ASY1</th>\n",
       "      <th>MA5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-10-21</th>\n",
       "      <td>16.565</td>\n",
       "      <td>16.760</td>\n",
       "      <td>-0.355</td>\n",
       "      <td>-0.131667</td>\n",
       "      <td>-0.2900</td>\n",
       "      <td>-0.116</td>\n",
       "      <td>16.9238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-22</th>\n",
       "      <td>16.760</td>\n",
       "      <td>16.551</td>\n",
       "      <td>-0.200</td>\n",
       "      <td>-0.128333</td>\n",
       "      <td>-0.1345</td>\n",
       "      <td>-0.464</td>\n",
       "      <td>16.8918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-23</th>\n",
       "      <td>16.551</td>\n",
       "      <td>16.830</td>\n",
       "      <td>-0.594</td>\n",
       "      <td>-0.159333</td>\n",
       "      <td>-0.0070</td>\n",
       "      <td>0.195</td>\n",
       "      <td>16.8100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-24</th>\n",
       "      <td>16.830</td>\n",
       "      <td>16.580</td>\n",
       "      <td>-0.199</td>\n",
       "      <td>0.088333</td>\n",
       "      <td>0.0350</td>\n",
       "      <td>-0.209</td>\n",
       "      <td>16.7470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-10-25</th>\n",
       "      <td>16.580</td>\n",
       "      <td>16.720</td>\n",
       "      <td>0.015</td>\n",
       "      <td>-0.060000</td>\n",
       "      <td>0.0145</td>\n",
       "      <td>0.279</td>\n",
       "      <td>16.6572</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             close   label  momentum      ASY3    ASY2   ASY1      MA5\n",
       "date                                                                  \n",
       "2013-10-21  16.565  16.760    -0.355 -0.131667 -0.2900 -0.116  16.9238\n",
       "2013-10-22  16.760  16.551    -0.200 -0.128333 -0.1345 -0.464  16.8918\n",
       "2013-10-23  16.551  16.830    -0.594 -0.159333 -0.0070  0.195  16.8100\n",
       "2013-10-24  16.830  16.580    -0.199  0.088333  0.0350 -0.209  16.7470\n",
       "2013-10-25  16.580  16.720     0.015 -0.060000  0.0145  0.279  16.6572"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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Wln7fP5440IbRXVgGePXGM1l/2ycIlU+wXi78cHO/n3uiUc9yH/j9fl577TVeeeUVLrroIm6//XZqamoYOXIkto5+o/Ly8tT4b3/723z3u9/llFNOYdasWSxcuBAgtf11PB4nGo2qZ0hERGQYC0YTeOMR7NEIjpJi7DaI2K3WiGQo3O/7W5VlKyzbfN2H5fIC67iR5aUhv5RcVZaP2pAKyz9b8zM2N6X3b0RTC6dy44Ibjzjm8ccf57zzzmPy5MkUFhby7rvv8rnPfY6FCxeyevVqlixZwpe+9CXmzp0LwHXXXceyZctYuXIlh+5IeO6557JmzRqWLl3KpZdemtbvIiIiIoNHMJogLxoAwF5QiN1mI2azg91OMhzq9/1jiSQ5Mes+9jyrYj0yz9PtWLfDxp6iMYze3HOO2lHvJ8/rpDjb3e+5DSdqw+iD5cuXc9lllwFw2WWXsXz5csrLy9myZQs//elPsdlsLFmyhJdeegkAm83G1772NZYuXUpRUVGXez333HPU1NQQiUR4+eWXj/t3ERERkeMjFE3gi1kVZHtuDnbDAMMAtwcz1P+wHE+YZEc71lnOzeXBa0/jyesXdjvW5bDRmJVPvL4e0zS7HbPkv/7J+fes7ve8hpshVVnurQI8EBobG3n55ZfZuHEjhmGQSCQwDIM777wTt9vN0qVLWbp0KWVlZTz++OMsWWLtw2Kz2VItGofyeDxcdNFFPPHEE5xzzjnH8+uIiIjIcRKKJcjqqPzasnOw26z2S9PtTlMbRpLsWAjT7sDweDhtgrfHsS67jVanDzMaxQyHMbxdxwajcQD2t0X6Pa/hRpXlXvztb3/jyiuvZPfu3VRWVrJnzx7Gjx/PqlWr2LdvH2CtjLF+/XrGjRvX4338fj81Hcu1xONxnn76aaZOnXpcvoOIiIgcf8FoHF/cCsW2nGwcHWEZjzdNbRgm2bEQyeycXt+D8jjttHRsWJJobT3sfFVTsN/zGa6GVGU5E5YvX85NN93U5dhnPvMZrr76agoLC4lErL+BLViwgOuvv77H+wQCAS666CIikQiJRIKzzjqL6667bkDnLiIiIpnTpQ0jJwd78EBl2UxDZTmRtNowkr7sXsf63A522zrCcksLzhEjupyva4vgTMTIjSo0H0phuRcrV6487NgNN9zADTfccMTrrr76aq6++urU57KyMt5+++00z05EREQGq+BBYdmWk4M9bLU6mG4PyX72LC9fU4XXabfaMApzeh2f43HQYrdaLxIth1eWGwMRbn/zfj5SvxUzeimGy9Wv+Q0nasMQERERGQChWIKsjjYMu8+XasMw3e5+tWEkkiY3P7qBb65YR3YsBNm9h2Wfy07QaVWWk8FAl3PPbKjhWyve5yP1WwEIvvvuMc9tOFJYFhERERkAVhtGCNxuDJcLW0dYTro9/WrDiCWSqZ9zoiHIye31Gp/bQchhLQmXDHQNy0+s24c9eWBX4YZf/0+PK2aciBSWRURERAZAZxuGPdvqKe6sLCdd/WvDiMQPhGVfLITRh8pytttBsIewbLcbFITbAdiRO4rg2rWE3lt3zPMbbhSWRURERAZAMBYnJxHBnmOFWXua2jCiHWHZMJNWWM7pQxtGD5Xllz7cz1Pra1iYbS1Y8GzFqdYzdu065vkNNwrLIiIiIgMgHE2QnYhgOyQsv7Srlai/H2G5ow3DG49gx8SW27c2jLDdhWkYJPz+1PGvLLN2Gp69ZwOmYeP1kTMAiNfXH/P8hhuFZREREZEBUNkYJDsWwp5jtWF0huWw3UUieOxLtHVWlrOjVuB25uf3ek222wGGgenJOqwNozDUyrx1rxA8fRFN3jzwZSssH0RhuQ8Mw+CKK65IfY7H45SUlHDBBRd0GfepT32K008/vcuxBx54gJKSEubMmcOcOXO49957j8ucRUREJHP2NAX559Z6CvxNOEaMBMDRsbNvxO7CGY9iJpNHukWPOsNyTsfugLmlhb1e43PbAYh7vIeF5Qt2vY4tESfy5a8BkCwsUlg+iNZZ7gOfz8fGjRsJhUJ4vV5eeOEFRo8e3WVMS0sL7777LtnZ2ezatYvx48enzn3+85/n17/+9fGetoiIiGRIgz+COx6lKNyGa+wYAOwdJcqI3QmAGYkctu10X6QqyzGrOp1d1MfKMhB3e0kGula1x/jraC8sI3d8BVBNJLdAYfkgqiz30dKlS3nqqacAa1e/yy+/vMv5Rx55hAsvvJDLLruMBx98MBNTFBERkUEinjQZEWwEwDmmMyxbsSvssDb8SIaPbfm4aMJa5q0s0ASAo7AvlWUrLMfcnsMqyyXBFgL5xUwZkYPPZafWqTaMgw2pynLtT35C5MPNab2ne9pURvzHf/Q67rLLLuMHP/gBF1xwAevXr+eaa65h9erVqfPLly/ntttuo6ysjEsvvZSbb745de6RRx5h1apVTJ48mbvvvpsxHf/RiIiIyPDkD8cZGbDCsmvsWODA0nERuxWWzWAQCgqO+t6dS8d9ouptGvLLmDppUq/XODvK2juDkN3YwvI1VXx2XjkApaFmGgom43LYOP2kIrZ/4GZCfT2maWIYxlHPb7hRZbmPZs2aRWVlJcuXL+eTn/xkl3P79+9n+/btLFy4kMmTJ+NwONi4cSMAF154IZWVlaxfv56zzz6bq666KhPTFxERkePIH4kzyt8AgKujSGZLhWWrDeOYK8vxJGPbapneVMkH85YcVaANOdzs3dfIzY9u4MG39+BKxCiI+AkVlADwsUklVJpezEiEZHv7Mc1vuBlSleW+VIAH0kUXXcS3v/1tVq5cSWNjY+r4ihUraG5uTvUpt7W18eCDD/KjH/2IoqKi1Lh/+Zd/4cYbbzzu8xYREZHjyx+JMzLYiJGTg71jtQrnQathACS72cXPNE3aI3FyPc4e7x2NJ5lTvw2AxgUfP6p5BR1uXB2raETiSUpCLQC4Ro8CYN64Al7xWEvRxevrsfdhWbrhTpXlo3DNNddw6623MnPmzC7Hly9fzrPPPktlZSWVlZW88847qb7lmpqa1Lgnn3ySadOmHdc5i4iIyPHXEowxMtCI46DWy9JcD//4+kIMjwcAM3T48nF/eHUXs25/nprWntdhjiaSTGitocXlw1VWdlTzCjrc+GJWSG8ORCkJNgOwZNEsAPK8Tpo6wvLfX15/VPcerhSWj0J5eTnf+MY3uhyrrKykqqqK0047LXVs/Pjx5Obm8tZbb3HPPfcwffp0Zs+ezT333MMDDzxwnGctIiIix9vayibK4gHcI7qG2Rmj86AjLHfXhvH39VaRbV9Lzy0abaE4E1r3sjNvNL4jVKC743dl4YuFMcwk2+raKe2oLHvKrf7lXI+T3bllJDHY8PeXjurew9WQasPIFP9BO910Wrx4MYsXLwZg7969h51/9913ATj11FP56U9/OqDzExERkcGlujlETiKCLTv7sHNJV8e206HDq8fJpAkc2MCkO62BCNPb9/P3CWcwznN0Ua7N5cOGSXYsxPrqVj4RbgGbDWdZKQDZHget7hxeHzmDz2xbScuat8mZPx+bwQn7sp8qyyIiIiJpFo4ncEXD2Hy+w84lOsJysD1AxU1Psez1ygPnOsPyEYJpoLUdVzJOszuHsYVZRzWvdqc1/qGnb8NRvZvZ/r24KiownFaFujOk/3Lu59ifVcCGa6/ni795lVm3P8+//eWdo3rWcKGwLCIiIpJmoagVlu3dhOWk22rDaKmz+oX//ObuA+dMKyzHjrC7X7i5FYBLF01l0eSSo5pXwOlJ/fyxve8zZe9mshctOnycy8v908+nONyG641VjKzdyXtvbDyqZw0XasMQERERSbNoOII9Ee+2shzKzifgyyPr3bVQdD4O+4HaZWdluXOXvu6EOsLy9MnlRzWnJ68/gyv+y8+64onMadjOFZufJ+FyU/jlq7uMW3bNAkpz3Hzhf6F6UzGXbX2J8W2dCxaceEvgDonKstnxtyw5PvT7FhER6R9bx0oX3YVlu8POtsnzYM0buONRXHar9cE0TRKdleVE92H5zZ2NVO6ute6Tm3NUc5oxKo9WdzY3L7wudSx05bU4S0u7jFs0uYRpI3OZMKqAv09YeFBQhmQkcth9V26p49y7VxGJJ45qPkPFoA/LHo+HxsZGBbjjxDRNGhsb8Xg8vQ8WERGRw8QSSRwRazWL7sKy027jg8nzMSJh5tVtwWG38cS6vYy/+Wm8ldv5yWu/I9Z2+IYg8USSy37/ZuqcLefowrLtoJcG7/rI5Xxn4b9ScOUVPY4fV5TFkxPO4BuLbuDxCQsBCLz22mHjfvr0Zrbsb2dtZfNRzWeoGPRtGOXl5VRXV1OvPcqPG4/HQ3n50f2vHREREbGEYwmy4lYFtruw7LLb2D5yEsmcXD66bwOv2hfxzAarWnzhBy8xt34bDatfgXkTut63ozXDF7NW0bAfZVg+2Mtj5wFQktNzcayiyAeGwaKLFuFMfJS9P9iM47bb8c6di6Njm+5AJM62mhb+ZdNTVP3wReZ+80qy5s495nkNRoM+LDudztTOeCIiIiKDXSiWwJsKy4cvHed12fnn1iYaxkxi7N5anHYbE0qsUN3ksQKwvXLHYddFYlabQ3ZHWLYd4+5655xcxgsf7Acg9whLz31u/hhWvL2Hz58yhpc+rOPnH7mcu1f9Cv/Lr5D/mUsAeGZjLZNbqrlkxyrYAXvurGfK8j8f07wGq0HfhiEiIiIylAQjCbLiPbdhNAWiAKzzG+RH/TjtNjxOOwC5kQAAns0bDrsukqosW/e2d7OGc28q7zif/7tyfurzkdZOHpHn4bWbzmJiaQ55Xidb88vB7iBaVZUas7sxwPjWfQB8WFRB2+49Rz2nwU5hWURERCSNWkOxgyrL3Swd1/EeVsDppSTUyoj6qtTLcZ076nkrtxPZtq3LdeGOyrIvFsLweDBcrgH7DofK8zpJ2uyYZSOI7TkQiHc2BBgZbCRmc7B/3FS8LY2YieH1op/CsoiIiEgatYZiR+xZ7qwQF4atF/U+8cx9RGJJME3Gt+7j1ZEzSdrttDz6WLfXLRrl7Ve/8rHI81qbliRyckm0tqaO76oPMNkWwjtqBOGcfOzJBMludj4eyhSWRURERNKopUtl+fAd9m678GQA/jT1EwAkHS4i8SQz401kx8O8XTaV1skzCbz1ZpfrOivLOeF27Pl5/Zrj1BE5XH/mxD6Pz8+yqtib/SbBJqv6bZomlY0BRkXbcI0YkdpspbttvIcyhWURERGRNGoNRsk6Ql/xxyaVcMOSSezNKWX1qFl4g21E4gnm7t8CwKaRU/B7c0kesnxcJG5Vn13bN+OeOq1fc3z2mx/n2+dO6fP4KSNyuOGsiTTiItRiVZbr2iMEI3EK6vbgGl+B6fECkAwG+zW3wUZhWURERCSNWkMxsmMhDK8Xw+nsdsyYAitYNruz8fpbiMSTzKrdjGv8eJKlI2h3eA5rZ4jEk5SGmrE1NeKdPXvAv8fB7DaDfz9rIgGnF6NjXjvrA5QFm3AE/HimzzgoLKuyLCIiIiI9aAnGyE+Esef13CoxptBqz2j25OIOB0kGgkyq3YbvjDMoynbRZnOS8Pu7bMoWjiWY1rQbAO+cOQP7JbrhdtiJe7Kwh6wVO3Y1BBjtb7DOnTQBvFZYNkOqLB/GMIxKwzA2GIaxzjCMtR3HCg3DeMEwjG0dfxak41kiIiIig1lLqCMsH+ElvM6wXOMrAiD24nO44jHcUyZT5HPThBviccxwOHVNJJ5katNucLvxTJk8sF+iJ9nZOOIxkpEIta0hRgetsOwcOw7UhtGrM03TnGOaZufifTcBL5mmOQl4qeOziIiIyLDWGoqRGw9jy+t505ARuR6cdoPXR86kzZfP19Y/DoCjqJjRBV5qE9a6y4n2A33LkViCUYEGbGMremzvGGhRr9WDnWxv55/bGpjesgd7YSGO0hKMjsqyXvDru08Byzp+XgZcPIDPEhERERkUWoMxsuMh7Lk9t2HYbQaj8r3E7A5ennk2nkQMAEdxEeUFXuqTVhj+3dPrU9eE40l8sSO3dwy0eEf1+P3N1bxf1cy0uu1kLViAYRgYWVa1PBlQZbk7JvC8YRjvGIZxbcexMtM0awADkHWKAAAgAElEQVQ6/ixN07NEREREBq2WUJSsSLDXtZAXVBQC8PrYA/3HjqIiyguyCDitZdiefOPAxiSRWIKcWBB7/rFtc50OUa+1bvT2nbWMDDZSGmoha8EpANizhmdluecNwY/OGaZp7jMMoxR4wTCMzX29sCNcXwswduzYNE1HRERETkRPrNvLR8YWpHqCM6ElGMMTCR6xDQPgrs/OZl9riE17D2zy4SgtpTzhJ+i0gmfn1tZg9SxnR0M4M1lZ9lq/13BLKzPrdwDgO/VUAGwdleVEIJCZyQ2QtFSWTdPc1/FnHfAYsADYbxjGSICOP+t6uPb3pmnON01zfklJSTqmIyIiIiegPU1BvvHgOq790zsZnUd7IIwrcuQ2jE5ZLgctoXjqs+F0MqYgi4DDqiz7YgeqtJFYguxYZsNyoqNnGX87Y/11JJ0uXBMmAODwekhiEFcbRleGYfgMw8jp/Bn4BLAReBK4qmPYVcAT/X2WiIiISHdM0+Rjd74CQHMgmrF5hGMJHGErLNpze9+S2ueyXuSr9hWTyLMWDivOdhHrqOBmHVRZjoXCuJJx7LmZa8OIZVvPNtrbGBVtwzNqJIZhAOC02wk7XMSH2WoY6WjDKAMe6/hFOYC/mqb5rGEYbwMPGYbxFaAK+GwaniUiIiJymEg8mfo5PyszK0WA1YJRFLLaKuwFhb2Oz3JbUey6Jd/hgS9bvb+GYZBfYgXnnIMqy8m2Nuu+vbR3DKR4tvUXAHtbK/mhVhwnjUyds9sMwnbXsHvBr99h2TTNncBh28iYptkILOnv/UVERER6E4kdCMvR2loi27bhnjTpuM+jti3M9MZdAHjn9L7LXnZHWE7Y7LjcrtTxwpICGjx5TG6uOjDYby0jZ8tgZdnmchFyenD42ygKNuMsm5k6Z7cZhB0uEtqURERERGRwCcUSAEzONvifx2+j8ktXZGQeF//Pa8xq2EG8uBRneXmv47M62jAAXI4DsczndrK5cCwT2mpSxzq3me5LL/RAcdlt+D3ZuNuayQu24hg5InXObjMI2d3alERERERksOkMy5ft/CcAydbWIw0fEKZpgmkys3EniZlzUr28R9JZWQZw2g/EMo/TRr03n6JQK8mkVTVvrW8E+tYLPVAcNoPq/FFM2rwGu5nEOaJrG0a7K4tkU1PG5jcQFJZFRERkyAvHEtiTCea/8zwApsvVyxXpt78twohgEwURP/bZc/t0TWmuJ/Wz23FwWLbT4MnDm4gSbW1jf1uY2r31QGbbMJwOG2tHz8BmmtbnQyrLtb5Ckvv2ZWp6A0JhWURERIa8UCzBmPb9OCNhdueUYUSjmNHjuyrGzgZ/6uU+Vx9aMADOnnZgzzbXIWG5Osc6F1i/nnd3N5Pd8bJfJnfwc9oM1o46OfXZcXBl2TCozSrCbGocVq0YCssiIiIy5IWjCSa27gVg64iJwPHfHKOyIUhhxFqxwjuibxsXZ7kclBdYG5C4HQf6l91OG+tKJhF0uGl/9nne2d1MXtxaRq63nQEHktNuo9HhO/B5RFnqZ4fdqiwDRKurj/vcBorCsoiIiAx5oViCk1r2YXo82KdNByB5nMPyrgY/hWFrxQrfQSGyN0/8+xn88OIZlOW6U8dcdhtRu5M1ZdMIv/wS63fWUeFOYMvKwnBmbmk8h91GIJrga2d9m92XXN2lym0zDGp8RQDEqvdmaoppp7AsIiIiQ157OM7IQAPGmLEks6xd5pIdq0ccL7saAhSG24gZdrJKi/p8XVG2mytOG9ftC4EvjDsFs7WFsaufYbQ9ntF+ZQCX3ZpjVe4ISr72L13OOWwG+7OsynKses9xn9tAUVgWERGRIa+qKUhBpB1vaSlJr9XWkImwfLI7hqu0BK+rf1tZNPitfut3S6cQLxvFlMZd5EfasRcUpGOqxyzXe6CqPX1U195pu82g1eXD9GYR3aM2DBEREZFBo6opSFE0gKukmITX6qk9nm0Y8USSqqYgpTE/7tKSft+vKRBJ/dxYNoax7fvJ3luJe9LEft+7PyaXHeiX9jjtXc7ZbQYYBuaIkcTUsywiIiIyeFQ1BMiLtOMoKSbpzQIg0cfKciyR5A+v7iJ60JbZR2tvS4hYwiQn2IqjpP9h+aal01gy1XpJcI89m7HtdTiaGvDO7n1XwIE0Z2w+C8YX8pNPzzzsnN1mtWiYObkk29uP99QGjMKyiIiIDHn1tQ04EnHsRUUkOyvL/r5VlpevqeKH//iA+17bdczP39VgPcvT1pyWsDy+2Mcfrj6FHI+DTeEDrQ9555/f73v3R67HyUNfO50vnDr2sHOpsOz2kAyFjvfUBozCsoiIiAxp4ViCWIO1u52jqBjTZ1WW+9qzHIpau/81+iO9jOzZroYA9mQCo7UlLWG5U7bbQZPban1wnvEx7Pn5abt3uh0Iy+5hFZb7130uIiIikmGdL/cBOIqLoMVLEoOEv2+tAJ2bgfSnDaM5GDtoDsXHfJ9D+dwOXh81k7xogO//9Ka03Xcg2DtW80i6PSTDwycsq7IsIiIiQ9odz2xmcrO1VJlz5Eicdhthhwsz2LfAlgrLiWMPy5F4gtKYVcl2pOEFv04+twO/K4uHJ59FVhrvOxAc9gNhufN33xaO8dKH+zM5rX5TWBYREZEhraGxjUu2/5OsU0/FVVGB3WYjbHeR6OOWywZWyIv0o7IcjScpjXaE5TS2YcwanbmtrY+WrbOy7DrQs/zNB9fxlWVrqWkdupVmhWUREREZsla8XUX+e29SGGmn+LqvAVaFM+zoPizvrPcz9wfPU9184Fw4ZvUsb9zbSvwYq8uReJKSWPrbMG45fxqzy/O6faFusHHYrFiZcLkxIxHMZJIttdbvJBKzfq8vb97PjNuewx+JZ2yeR0thWURERIas+1+rZGJLNTGbnaz58wHrRTOrsnx4NXPF23toDsb4x/qa1LFw3ArLW2vbuf/VY1sRIxpPMsrfAE4njqK+797XG4/TzhPXL+x2qbbBpiMrk3B7ADBDIeJJKyQHolY4vvPZLfgjcbbXHd8NY/pDYVlERESGrJrWMBPa9lFbOBrDaS2x5rBZleVkN5VlW8eKDYmkmToW7qh6/nbVf1P8h18e0zyi8SQn124la84cDJfrmO4x1B1cWQaIBILsb4tgmEkizzzNvqpattf5OX3fRlr2N2ZyqkdFYVlERESGpKrGIK3BKFPaa5hw2pzU8c7KcnfLlzm6DcsJRsbaGNdczdQ1L2ImEkc9F6OthbGNe/B99PRj+CbDg70jVcY7wnJdXQsA5+1eg/euH/C3r3yHMc17uXXNA3ju+n6mpnnUFJZFRERkSHp1ewMFkXayQ+0UzpqROu7oDMvdVZaN7sPyzJaq1Ofozp1HNY9N+1ppevV1AHwf/ehRXTuc2Dsqy399vx6AqD9ASbCZazb+g5hh55w9a7n5nb8CkLX1g4zN82gpLIuIiMiQtGZXI/OidQB4pk1NHbfbbASdHsxu1lm2d1NZDkQSTG/anfoc3b37sOuO5K7ntnBSyz7iNjue6dOP6trhpONXS0vSDlhtGIv2riM7HubO+V8AYExbLQDOUIBYXV1G5nm0FJZFRERkyEgmTZIdQbcxEGVGyApc7qkHwrLDZtDgzSNZ33BYS4Vpgi2ZgIOqzoFInCkNO2nIs5Z8izc09Hk+Na0hVm2tpzjUQoMnF8Nx4u73lp9l9WqH7VbveGdlOeTO4tXRs/nRKVcSePgZbrz4e5iGQfOf/pTJ6faZwrKIiIgMCaZp8qU/vMWZ/7WSQCROezjO+LqdOMvLsefkpMbZbQZ1WYWQiBM/pHoZjie4ZtNTLPnh14js2AFYKzWUtexn57gZmBjE6+p5a2cjuxsDvc7pb2urSZpQEmqhwTt4t6I+HvK8Tnb+5JOcMnUUACtWb6M01IJ71Chmj8nntdGzKB1VSqSsnC3TTqX5L3/ttlVmsFFYFhERkSEhGE3w+o5GdjcGeWNDFXu37GLizvfJXXpel3EOu8H+rAIAYnv3djkXjiWY2lyFJ9hO1Ve+Sry5mVAghCcWJphTQMCbTbyhgc///k0W3bWy1zk9vbGWj4/yMrmlmsTY8Wn7rkOVzWbgzPYBsG9/MyXBFoyyMv7vynncduHJjCn0kud1sr5iDslgkGh1dYZn3DuFZRERERkSYh0bhoz0N1Bw9SX88fkfYzNNCi6/vMs4h83Gfm/3Ybk9FKOirZbt+eXEa2sJrF4Nba0ARLJzacvK69KGcdV9a/jVS9tSn5NJk3tX7yTYsW5wTWuIU2P78SSiXHD9F9L/pYcgly8LAF8sTEmoBfvIkZTmePjyGeMxDIPSXDd78AIQr6/P5FT75MRtrBEREZEhJdqxHfX17z9KVjwCgN/twzlqVJdxdptBfWdled++LucqN+/CFw/z/Nj5TAw3ENq4EXtbuTU2O5c2b64V4HKt8f/cWs8/t9bz0YlFLHt9NwvGF/Kjpz6kqinILedPoyUYozy0BwDPlCkD9t2HEltpGa0uHwv3rSc3FsR1yD+fUflenktYG5ckjqI/PFMUlkVERGRIiCaSZEeDfKR+K3+dfDajA/WsmvRR/nrIOI/TRtTuxCwoJHpQZTkcS5DcXQlAZe5I4kWlxOvqMULZAMRy8mjx5hKr2wUnWdfcufp/2Fg0gSv/O0TU7uS5TdZqDo2BKDvqAtiTCca98QyeGTPSus31UObLcvGP8R/li1teAMA7vqLL+dH5XuocVqvG0bxMmSlqwxAREZEhIRpPUhhuA6Aqt4w7TrmCb974pcPG+dxWLTBeOoJo9V4qG6wX9XY1BCgIWdc3ePIIZecRqavDHbCOtTp9rPOUkdi/n1vWLOOhp77HzMZdXL71Jf729Pf46sYnU8+IxZM8vaGGM/e8i7u+luJ/+7cB/e5Diddp58EpS4jbrH8OhXNmdTm/eEoJIaeHuMtDvG7wt2EoLIuIiMiQEEuYFHWE5UZPLj+9ZCYLxhceNs7nskJatKiUuu27Wfzzldz9wlZ21PspiFhrL4c6qsjR+gZyo1aYfrY6zLPjFhDy5bJw3wY8iShN7gOrbFy063WyYtaugPGkyd6WEJdUvYF76lSyz1w8kF99SPE47cRtDn725Z8x6q67cJWP7nJ+XJGPj55URJM7m5gqyyIiIiLpEY0nKQpbL+P97ZaLuHzB2G7HZXdUliMeH/aQFYT/+6VtPLuxlvxwO4bHy4iyQursPpKNjeRFApiGgacwn4jDzeNjTwXgtzM/xTXn3MQjExexJ9tag/mq9X8HrJcNa1pDlAab8M6ZjdGxM6BYYRnAn5VH3oUXdDvmwtmjqHPlENhXezyndkwUlkVERGRIiCaSFIStyrCrrLTHcT53xw5yTjfOSBiAitZ9hFetYly8HWdZKRVFPqptXmzBAKXBZozcXB79+scB+OukJdw957O8MHYBEYebBT+7nR8tuAqAMn8jYO0AuL85QFYogKNIvcoH6wzLRzIyz8N+bwHxIbB0nF7wExERkSEhlkhSFG4j6cvGlpXV47jOnuWQw4MzHqXMmeQ3r/wCgNbcIlzzZjGuOIsdcTcAU1qqcJWPYnS+lzdvXkJrKMbE0ou4/7VdfOLkEYwtyuLa3BH8c/QcTmqpTs0lVNeAgYmjRGH5YB6nVYs9UrG9JMdNdU4ptup3SQaDR/znmWmqLIuIiMiQEI0nKQq1QmHREce5HTacdoOAw9p++aK9b6fO5bU1kjXvIxRmuWj2WP3IY9vrcI22ljcbkedhyogc7DaDr35sAmOLDoS4uqx8ykLNOJJx6tsj+AJWS4hWweiqs7Jsmj2PKcl2U5k7gsC4k0g0Nx+nmR0bhWUREREZVMweUlYskaQ43AKlZUe83jAMxhZmURu1Ys5Ht7/Ffm8+j0xcRPC6b1J4zTWcMbGYDUUTeGLCQpqyC/Gdelqv8/qwoAJnMsH16x6ltqEt9bKgvejI4f1EY7f13r9d6HPx5sgZvPCNu3COHt3r+ExSWBYREZFBIxCJM/7mp7l39c7DzkXjSYpDrdhKeu5X7jSpNIc91r4ljKir4vVRM7l3xoXMveFaDJuNGaPzOGPGGH4762J+9293U3jF4UvQHWpD8QQAzq1awxc2PZvqn3aUlBzFNxQAh92KoL9btZNdHUv7DVYKyyIiIjJoNPqjANz53BaC0TivbK4jnkjS4I8QjUQpDLdjGzGi1/uMyveyN+5Mfd417RSuW3QStoOqnmW5Vs/yiI4/e+N3HWjJmNJcxf977yEAHKosd+HqCMK5XmcvIy1jCrwDOZ1+0wt+IiIiMmi0hmKAVUU++dbnAGsli6s2P0fwS19lMiZZo0b2ep+SHDdPuQ+swfz7O76M4egaewp9Vk9zcXbvYfk/z5/Gj576kH+Ons2ive8zq/FA5dvmHdxh73ibPiqXm5dO5ZKPlB9xXLbbgT8ST1WZByuFZRERERk02sKx1M8jAw2UBFu4Yd3fGB1oYNMf/xeAgvFjer1PaY6b+qwCAPxTZx0WlAFsHcs1OPrQY/vVj01gw95W7kp+gdxokLn12wDI/eTS3r/UCcYwDL626KRex71645kkkkd4C3CQUFgWERGRQaM1FMOeTPCfa5ZxWu0HACSd1v/On77fCqiOsiO/4AdQmusmadi44tz/5HfXL+l2jLOjounuw7rAYG3jnLDZ+aCwgrn121h5yvn86y9+3qdr5XD5Wa5MT6FPFJZFRERk0GgJxji9ZmMqKAOU3XgjF71t8tnNLzD/1OlMnTix1/uU5FitFQ3efPLzs7sdc/UZFdS2hbny9HF9mlvnkmiPTFxE1O4gvviiPl0nQ9vgbhIRERGRE0pVU5AZjbsI2V3cd/IneXPEyRR++mKc5aP577mfY+p3voFh6z2+lOZ4Uj93bn99qFyPk598eiY5nr69iNZ5n5DTw0OTl1A0QusrnwjSVlk2DMMOrAX2mqZ5gWEY44EHgULgXeAK0zSj6XqeiIiIDD876v0sbd7D9vxyHp58FgBf9vmYVJpDlsuRqhj3Jv+glRh6CstHa0xh1xf5RuTpxb4TQTory98APjzo88+Au03TnAQ0A19J47NERERkGFnxdhUVNz3FpqpGTmrbx9aCri/x/fSSmdx39fw+3+/gJeKyXH3rSe5NRZGvy+dReZ4eRspwkpa/ahmGUQ6cD/wY+H+GYRjAWcAXOoYsA24HfpOO54mIiMjw8uOnrHqbt3o3rkSMSy8/hy+duzi1G1xfK8rdMYzeV7voi4rirmF5Qkn3vdAyvKSrDeOXwHeBnI7PRUCLaZrxjs/VwODey1BEREQypi0cx2YmuXjHKgAmLDoV1yHhNNNKDwnsFcVZPYyU4aTfYdkwjAuAOtM03zEMY3Hn4W6GdruQnmEY1wLXAowdO7a/0xEREZEhpr7d2pf6mk1Pcfaed0h88Wpc5Ufe0KIv7rl8Ljvq/P2+T6eDK9Qv/r+P43akp71DBrd0VJbPAC4yDOOTgAfIxao05xuG4eioLpcD+7q72DTN3wO/B5g/f/7gX5laRERE0mrlljowTc7a8w6vjpzJF777/6XlvhfNHpWW+3TnJLVgnDD6/YKfaZo3m6ZZbppmBXAZ8LJpml8EXgEu7Rh2FfBEf58lIiIiw8/KLfVMc4QoiPhZVzIRX5pWrxgI88ZZuwKmqw9aBr+B/LfxRuBBwzB+BLwH/GEAnyUiIiJD1J7mIPOdQQD+/YqzMjybI/vTVxawvy2S6WnIcZTWsGya5kpgZcfPO4EF6by/iIiIDD+BSJyyQCMAE+dMyfBsjizL5WB88eCtfEv6aQc/ERERyahgNEFO1KosOwoLMzwbka4UlkVERCSj/JE4vngYDAMjS8uxyeCisCwiIiIZY5omwWiCrFgYW3a2XpyTQUdhWURERDImEk+SSJp4OsKyyGCjsCwiIiIZ449Ym/26o2Hs2YNrxz4RUFgWERGRDGoNxQBwR4LYfKosy+CjsCwiIiIZ0xyIAuCpq8ExckSGZyNyOIVlERERyZimQJTsaBB7XS3e6dMzPR2Rwygsi4iISMY0B6NMbN0LgOfkkzM8G5HDKSyLiIhIxvgjCU5qqQbAPW1ahmcjcjiFZREREcmYWCLJxJa92EeNwlFQkOnpiBxGYVmkD17ZXMeKt6syPQ0RkWEnFk9S7q/HPXFipqci0i1HpicgMhR8+YG3AThrahklOe4Mz0ZEZPiIJZIUhttwlpVleioi3VJlWaQXNa0hAKY27eaNH/0CMx7P8IxERIa+eCLJfa/uoqbJT37Ej7O0NNNTEumWKssiPQhFE1z8P69RnONiVv12fvbabwHwLz2dnMWLMzs5EZEhzB+JM+PWZ/lk5Ru0uHOwYeIYocqyDE4KyyI9+Pv6fWzZ305NVYDfrf0zgeIR+BpqaXnuBYVlEZF+2F7nZ2LrXr7+/qOpY+6TTsrgjER6pjYMkR48v6kWgLP2vEN+xE/zd3/AqyNn0v7WmsPGLnu9km8//D6maR7vaYqIDDl7moLMbNjR5ZjCsgxWCssiPahrjwAwv24zTfml5M44meqcUthf06VvOZZIctuTm3hyzS427W3N1HRFRIaMbfvbmdm4k7bCMhKGFUXseXkZnpVI9xSWRXrQ2B7hO5seY17dVio/soiibDc1WYUYiQTRPXsAME2TVzbXMb1hJw899T2Sn72A8AcfZHjmIiKD2xs7GpjZUkX85Fl86dzv8cVP3p7pKYn0SGFZpAeuun2cte01nhl/Ou2f+SJFPhfrS6x1QNufex6Anz27hRv/7xW+/f7DOMwk8UiMPf9+PQm/P5NTFxEZtILRODUf7iA71E72R+bQ4smhyZWd6WmJ9EhhWY6LRNLkxQ/2D5me3rd2NjKpZhsAS2//Bld+7CTyvE5qfUWsL5pAy2OPkkwmeXblen6x6leUxfy89vUf8dPZnyVeU0PovXUZ/gYiIoPT0xtqmdKwC4ARH12Q4dmI9E5hWY6Le1fv5Kt/XMvzH+wfsGeEYwlufnQ9e5qC/b7XL1/cxiltu7EVFDJ1wSyyXA5sNoMbzprIy2PnEdtdxX/c/SSnbX2DEcFmxj9wH2d95mx25I4CILpHu/2JiByqpjXEdx5ex8W7XsUxejQlM6ZmekoivVJYluPig5o2AAKRgdvQ4/kP9rN8zR5++I/+9Qyvr27hjZ2NzE624J02BcMwUueyPQ525Y4EYNf6rZxv1OGeMgXvnDlMKMkmkltA3OkitlthWUTkUGt2NTG/djMTm6sp+bd/xebQCrYy+Cksy4AIxxJdPreGYgBkuewD9sxVW+sBsNXvJ9HWdsz3WV9trWiR29qAc3R5l3M5Hid13gIAyoLNlDbuwz15MgB2m8H08nwacopTLwCKiMgBlQ1BZjVsx3C5yLvookxPR6RPFJYl7V7b3sDU7z3LO7ubU8eaA1EAwrEk7+xu5qG30xsmk0lrVYrCUCvf+L9vs/aSy4+5P7otHMMTj2A2N+EcPbrLuXyvkxZ3NgFvDp/d9grJ/bVkzZuXOj+7PI9KTyGRKlWWRUQO1RSIMD5Yj6uiAsPpBGDVd85k9XfPzPDMRHqmsCxpt2mfVZn9zsPvp47taw2zcO/75C37Ddf+4hm++8j6tL7sV9UUpDEQZULbPgByq3fif+mlXq/7y1u7WfZ6ZZdj7eE4E/1Wb7V70sQu5xaML8RmM3h+1ByKw634Fi4k/3OfTZ2fWZ7PXm8hocrdhCKxfn4rEZHhpSkYY0x7Pa6TJqSOjS3KYkxhVgZnJXJkCsuSdg6b9a/Vnv0tVN7xc/zvrSNvzw5ueftPjHj2Ef783A/5yzPfZ+/qN9P2zGDUavsY12aF3KDDTfOKh454zYbqVr73yPv88Y/Pd9lkpC0U4yMtlQC4p3R9+aQo2838ikIenbiIV2csZtSdP8OwHfjPaHS+hxpfEY54jF+ueD0dX01EZNhoa/VT7G/APX5C74NFBgmFZUm71lCMrFiIH73+f4Qe+AO777qbWfXbAfivs/8NgMJIO23fuoFYbW2393hzZyMVNz3FPS9upaWt99UtInErLH/K2M9eXzFvl00jtKvyiNf861/e4dyqt/n1yrvZcuYSap58CoC2UJQlO9/AO38ervLRh133iZPLaPDm8+Dpn8dRWNjlXEGWy9rlD2jd+GGv8xYROZHEq6qwmSauCQrLMnQoLEvatYZi/OuHTzG9qZKNReMx31vLwn0b2O/N58XsCSybdh7/M+vTGAE//lde6fYedz23BUyT5J0/ZseixYQ/tILn2somPvnfq9m6v73L+Eg8CUBhw16K5s1hr6+YxL59JKPRbu//blUz1c0hZjdYIX5nxM6+//we8cZGfBvfo7StnoLLLu/22rlj8wFo8B9+7yKfmw8LK4jYHEzaurYPvy0RkRNDeziGsWc3AO4J4zM8G5G+U1iWtGsNxZhZv4Odk+by25kXYzeTTGvezfqTzwAg8LkreW7iGYTyiwi8tabbe4zI9XDxjtWcW7WGrFA7W771XRpa/Fz62zf4oKaNDR0rVnSKxJNgmtga68kdO5qtBWMwkglC/z979x1eRZU+cPw7t6fc9N5IIR1CryK9g6Ii2EWx965rWXfV1dVVse7+7L0rKqJiQekdQgtJSO+93CQ39+bW+f1xMRjppALn8zw+JjNzZt6JcfLeM+e8Z8eOw55/Z4kBgAm2KtZHDOY/wy5Babex69KrCEtfh1OhQD9l8mHbxga4VppSK6VD9ul1KqxKNSujhjMiZwu26prj+6EJgiCc5tbl1hFhdFUt0sSIZFk4dYhkWehyjRU1BLfUEjd+FPneYazoN4q9sy/j1vefpujpObxyyRAi/TwojkzCnJ5++HO0Wrg873dMg0bw7+GXoynKo+yZ59r3t1o71mu22Bx4W41INhs+UeHkB7kexJbc3EPObXM42VNmILqpAqqrmHbRdF7/x8U8OXIRirISphVtRREXj2db4ZEAACAASURBVMLN7bCx+XpouHtaAu9dfejKUwqFK4H+Kn4SStmB4Yujj5sWBEE43ZU2mChtMHHzx+lEtVSjCA074vNVEPoiUQ1c6DK/7KuioK4Vda5ryET0+FHkDB3OL5lDmZwU1KH4fD9/d7bY9STV1OC0WFBotR3OZa+vx6PNSPCcafTzGsIPhiLmfPM5A8f5UefmjfeKb3AOvpEfchoJ8NRisTuJM7gqYWjjYvGttWFXqrBVH7pi4H9+yuaXbQUs2f4xCm9vIi48D5WvHq+JE7jHzZuZRVu4/Zm7j3qvt0+JP+r+Kg9/ivUheGZkHNfPThAEoSe8tDKXxBBPZg4I7bB91f4asiqbuXli/yO0PDnlBjPjn13FlKRg0mrzGFmVhfv4sV16DUHobiJZFjrtl31VDIr04foPXUMerijPQ5YUuA0YgEKlYG5a2CFt+vl7UOrmGvtrr6xEEx3dYb9bWREA2oR4xukDuC15DtNKtrMocwWBZgNBZgPNaZHctt3VO3Hn1HgG1+WCSoX70KH47N1JrdYLdXE5wX+59qr9tcwo3kp0SzUR776Dyte1yMgdU+M5N7uG/xt0Pg+kpHT651KiDyYuL/+4jpVlmds+3cmwfr5cfZZ4PSkIQtezO5y8sDIHgKKn53TYd/W72wCYPzSCYC9dl12zuK4VWYbNuwt5b8t7GL38SHvooS47vyD0BDEMQ+iUJpON6z/cweX/W4deqySktZ6ZRVtoS0xB4X7kupn9/N2pcXclqbbKyg776owWfGrKAND278/IaD+sSjVbg5JIbSjC02YGwLBpS3ubF1fmMqg2D9XANBRubmRVtmDQ6inKPXTxE4UEqQ2FVLr74TFmTPv2tAgfHpiZxH0zEk/+B/InJfogHBXlOM3mYx67fE8l3++p5Mc3v6J0w+HHcQuCIHTGHyup/pksy6SXuBaQCm6t5/wlh69Pb3M42V7UcML18aua2wAYUZ2Fh72NYa8+d9gqQ4LQl4meZaFTMiqa0FtbefyTf9Ds7kWUwZX42m++46jtInzdqTnQs2wtK8fjT/s25dcT3VyJ08sHpb8/vpJEUoied5NmUBIcw7KgwVy/dxnjV61GOfVsHAolHlYzcYZy3EaeA4DDKdOq1hHg6FixwumUKWs0k9RQwt6AWP46he+miXGd+nkAvHXlcL7eWUZJeTCSLGMtLER3lJ7qL7aXcv9Xexhas5/HN7+DcfM71N15BwE33tjpWARBEP7QaLKhdthROw/O+VibW8eid7aS0FjCkjWvsCsoHv55sNfZandS2mji8seWcln2L5hiAjnr2X+i9PE5rmtWNrURaqzj2uI1mH0D8Rw8qMvvSxC6m+hZFjplc0E9k0vT8bG2EmWopMbNh/+lnUfUuFFHbTc1OYjktP44kUhf/jubsyva932zs5wkYyWeqclIkmvC3KgYPyo8A8kaN5dP7p3BhrA01CYjA+vy8TM3ceeuL1Ai4zbI9SB+bsEgTCotbra2DtctN5hxN9QT0NZEWWjnE+PD3ltKMP88J5V6nTcA9rq6ox7/7c5yAG6XC3AgkeMTQeP3P3RLbIIgnLmazFbuTv+MpT88QvWzz2IpKKSx1YrC6eDajO9RIjOsJgdn28Hn5qu/5zLl+TXcuPdbppSlE7juZ6pXrj7uaxYWVPDK2pcIsLWS8Ny/OyziJAinCvFbK3TK2tw6ZtbtozowklnznmXRjEdYHjsOb3f1UdtJksT45FCy/aII27qKjXf9HYAKg5nVmZVENlWhSzq4et7Qfq4hG34eGgaEe1PWP402pZp/b3yDt1c+zbiKvQB4xrsK3c9JC6VNrUPV1nFBk+yqFpIaXXU+/3bPhV3zQzgMf08tLVpXf7nDYDjqsXqdihR/LaF7t9A6cQabQ1Kx5efjaG7utvgEQTjz1ButTCzfBUDd2++y46klmKwO5uetYWB9AZtCUgGw5LjGNZutDl7+PY9pxVsZWZ2N+eJFOJAo2515XNdrszmo37EbD6uZ8CXP43nWWd1zY4LQzUSyLJw0g8nK/uJa+lXl4zlpEhzoBX58Xupxtdepldx79i2sCR/EWRW7kWWZsU//TlJjCUq7DV3SwbHDU5ODuWRkJI/PGwBAaJAP38e4ZlRn+MeyMzAeY2wimrCDkwmtWjeUbQfHC5usdnaXGkhuKAatFn1Kcqd/BkeiVEio/VwJ/rGS5UaTjeE1+3EajaRcNp+y8HgkWaZq0zY25B29V1oQBOF4NLZaWbO7BAcSHyVOY0PYQKw7tlNQa2RoTQ7qlBQqrrkDJxLG9esBePLHTOIMZdy6+2vcx4zG47obqPTwb19Y5Fj2VTThfWD+ydGGoglCXyeSZeGk/bi3Cv/WRiRZJmZIMpF+brxyyRCuHBN9XO11agWypCDDPxYvSyvWqmp0dgv3pH+GIiAQj/Hj24/10Kr49wVphHi7ZmlH+rnzTuocbpp0D4+OuYaHzrqBiqf+i6RUtrex6dxRW8zITtfqfoMf/5VXV+UxoiEft4EDkDSarvthHIbe3xcZqT1ZbjLZ2JRf32GCjNFiZ2thAynZW1D6+uI5ZjT2xBSckoL3XlvGZW9tOeEJNYIgCH/1+PeZ7Fy1FSUyeT4R7A2IJdhsYPnP24lprsA9ORmFfwC5fpEYV68BoKHVynn562hTaghfsgR3Ny1l+iBUZSXHdc2caiP9WqqQfHzbqw4JwqlIJMvCSXE4ZR76Zi/BZtcsareIcNbdP5lzBh1aJu5I1ErXr1++t2tmdOnaTQyvziastZ7wfz951IdrlJ87sqSgMSQKWXKdJ+Qv5Y4s7q6V9hz19YBrokqcoYzIxnK8Zs8+7jhPVqC3G606d+yNjfyeXc2gx3/hkjc3k15ysKc5s6IZrd1CcuFu9DNnIKlU+AV4U+wXQUJNHgqno30pb0EQhJO1u8zAJY0ZOJUqMv2j2evvmrPx0LYP8baacB86BJ1ayebgFNr27MFWU4PR4iC5oRjPEcNR+fqiUysp1QehqSpHttuPcUXYWdJInLEaXXzX1m4WhJ4mkmXhpPyxgt6g2jxQKtH0P/GHYUub6xzlIbHU67zI+PBLBtQX4tTq8Bg9+qhto/xd9ZX/XAopzKfjilD1gZEAtO3Pad82vXgrTrUG77lzTzjeExXkpaVF7YHDYODzbaUgyyQ2FNNYf3CpbpvDSUJjKRq7Ff1kV22OUC8d6T7RDKgv5K2Vz2AyWbo9VkEQTl+fbClBn7mL4RlraJg8hxaNB8VewZR5BJBgcA2T8Bg7FneNknXhg0ChoP71N9CUFBLeWkfoFNdbPp1aQalnEAq7DVt5+SHX2VfRxKu/H1w1dU9mMXENpbgPG9ozNyoI3aTTybIkSTpJkrZKkrRbkqR9kiQ9dmB7jCRJWyRJypUk6XNJkrr3nbfQo0wWB8gy8xr34TF27Em9YjvQscxZiUGsjBxOYt5O5hWsR5eWhqQ++gTBKL+DNZznprlWogry6rgKYH1wFACW/dnt2wbWF6AaPgKll9cJx3uiAjy1GNRuOBoN5FYbuaglmxfXvoLHW6+0H2N1OAkyu3qaNZGu5D4xRM8nSdP4In4SoaYGWvbs6fZYBUE4fb23sZCLSzaiCArGfLWrJOW8IRHcMPV+/jPsUjSLr0MdGoq7Rkm5ZyCNY6dQ++VS4jK34JQkvGbMAGjvWQaw5Bcccp0L/reR537JIb/WyKPLMojM2oFCdqKfMqXnblYQukFX9CxbgMmyLA8CBgMzJUkaDTwDvCDLcjzQCFzTBdcS+giT1U5SYwnauhq85pzckIb5QyN4Zv5A7p6WwEfJ09kW7Kp+4Ttt6jHbRv4pWV6ycDAb/jYZrUrZ4Ri7p55mT1/a9u93bZBlwk31+CR0T8m4v/L30NCsdsfa0EhFYyvn7vkJAF3m7vZjbHYngQeSZVVICABTkoNJSwzHb/HVOJFo27ixR+IVBOH0U2Ewk1NtJLmlAs9RI5k2pB+r753IlWOjcUoKVkUOJebeuwBXMgzwUYselbWN1OI9WMMiUQUEAK6hc5VerjVR2/IPXZ30jyFjU55fw4YVG7l5zzcQ2x9d6vFN+haEvqrTybLsYjzwrfrAPzIwGfjqwPb3gfM6ey2h7zBZHaTWFwLgOWHCSZ1DpVRw0YgofD002BUqHh91FY4nn8f30kuO2TbQ82AvskalIPwvQzDA9WCvCojAkr0fWZbxtzSjsdtQR0WdVLwnKkCvxaDVYysvZ3jJbvzqyinwCkVXVY69oQFw9SyHtDaArx8KnWvMtZ+Hhs+uH0NyYhQ5vhHYt27ukXgFQTj9rN5fi4fVjNZQjzYhHkmSiA7w6PAMVShclYzcNa5kuUTvSojjm8oJSE7ocL4mlRsNWj1ZG3ce8ZqTSnfw6uoX0Dls9HtG1FYWTn1d8hssSZJSkqRdQA3wK5APGGRZ/mMGQBkg1rc8jZisDmKaKnD4B3Z6lrO3m2vIhV2hIvWCWUiqYy8s+cdiJYMjj7yKlFqpoNw/AktBAW1mCyFG10Q/TQ8ly/4eWr6PHQttZh7e9iEA76fMAsCcng64xixHGGuQovod0t5No2RXYDxSViZOq/WQ/YIgCMeyen8NQ3HNk9DGHXyrFqjXHnKs24Ge5f2+B5+R3nPnHHLcPv8Y9Fl7Dlup59ZdS7l/x6cAhC1ZgvvAAZ27AUHoA7okWZZl2SHL8mAgAhgJHK6A7WHrX0mSdL0kSdslSdpeW1vbFeEIPaDVaie2uRLiOj/L+Y+qGHAwCT4e2U/M5Msbxxz1vPUefmC301JdS2jrgWT5wNjg7hao17hKNM24FABZo2VnYAJWhYr6da6hFTa7TISxFlW/6EPau2mUFOtDkJwObMXHV9dUEAThDw6nzMb8eia4uRZn0sbGtu/TqZWkhHrxj3MO1j8O0rvebtmUan67+V/Efr8cr5kzO5wzNsCDnUEJ6Ax1WAuLOl5QlplYtpP0wHgunfko3rNndc+NCUIP69J3I7IsG4DVwGjAR5KkP7oII4CKI7R5Q5bl4bIsDw8MDOzKcIRu8tLKXK5/axORLdUo4xOO3eA4XDmmH88vGHRCbXRqZYdE+680KolmjWtsc3lJ9cGxwWHHX96uM/w9XD03L/qP4Mv+E9E+swSbUsXWkGQM33/PO6v2s3l3Id7WVtQx0Ye093PXtL8ONWfsO6kYsiqbueSNzewuPfrCKIIgnH5KGkwYLXYSLHVIajXqiIgO+3+842yuPium/fuUMK/23mVp4CC0h6ly9NOd49kZGA9A61/mU/haWvCwt7ElJIVGXfdPohaEntIV1TACJUnyOfC1GzAVyAJWAX+sJ7wIWNbZawl9wwsrc4hsqUYlO9F30apMj88bwPxhEcc+8ASolQqa1a6xzHl5FfhZmpG8vFF082Ikf/B2U6NUSFS2WNk79wriZrlKw/0QPQZtawvr3vqczK2uZbq1MTGHtI/0c6fQO5QazwCavvnmpGL4PbuGTQX1PPDEJ+y49xGcFlGGThBOR7nVLVz5zlZa2g6W09xf1YwkOwnI2I42IeGYQ9yUConEED1w+GEa4JojYgkMwegXROumTe3bG1qtRBhdb4evvXQSv99zcnNZBKEv6oqe5VBglSRJe4BtwK+yLH8PPADcLUlSHuAPvN0F1xJ6mSzLqB12Jpa7JncEDh3YyxEdmVqpoEnl6lkuLa4izN6KJrjn3l4oFBIOp2v00cUjDo4B3B3YnxrvIK7LWM7YigwA3OJiD2mvVEj4eGj5MXIEpq1bsZ7EUIyWNjsxTRW8sPYV3L9fStmXX5/k3QiC0FMsdscJt3l1VR5rc2r5YXM+6ct/B2B/lZHxFXuQigrwW3z1cZ1nbJw/AJqjvLUL9tKRF5mCacuW9sVJ8mqM7cly8sgBxAZ6nvA9CEJf1RXVMPbIsjxEluU0WZYHyLL8+IHtBbIsj5Rlub8sywtkWRZdWqeBlVk13LXzcxbkriY9KOGwPaJ9hVqpwHCgZ7mmvJowZyuqwKAejWFktB8Aswe6akG/d/UIZEnB/5LmoHbYWZC3GgC3qMOPo75/ZhKrIwYDh77yPB4Gk5Xbd3+N2csVx4bXPmZLRukJn0cQhJ7R2Gol7dEV/H3pkatNHI6vu4bopgoC7rkOt/tuIW/zLnKrm7ky9ze08f3xmnV844dvnxLPo3NT2p9Zh5MYomeTbxxOoxHz3gyW764gs6KJ8JYa0GpRhR65rSCcikQ9F+G41BktbMqvZ+V3azm7fDdu8xcw47tPejuso9IoJRqUrmTZbmjCx9yMqofHxb+5aDjr7p+E24GSTBMTg3h+wSC2hKZyy6S7eSdlNv8efjlqzeEXYfHz0FDt7ofZzZN1P67nxZU55FS38N9VeYedif5XpsYm4htL8LvwAt5Jmc3gunxsV1+Cram5S+9TEITOK6g1MvTxn/n3qldZ+MjlbJ45j8L045uvYGi18MD2j/G2uCq5WrKzaa2qJsxQic+CBcddvk2nVrJ4XAwa1ZGPTwjWs1ntmk+xddU2bvt0J/9cnkk/Ux3a6GhRKk447YjfaOG4PPj1Xq76vzVM+eoVbHofIu69iwAfj94O66jUSgVGVMhqDV6WVrTNBlRBPZsse7upOyygAjAyxtXLW+Phx5cJk1kbMfiIVUACPDUgSWR5hiHl7OfFlblMf2Etz/68n/3VLce8vmduJkrZScC4MXyZMJlHRy/Gv6We4qXfHbXd3rKmDmMfBUHoXrIs84/v9hFurCO5sZhin3DkinIqrr6a5py8Y7Z3FBYQ3VKNc/EN2CQljvw8tKWuoVvahMQujTUxWE+duw82Scn61btQO+yoHTYSmso6lKcThNOFSJaF45JZ0cwtGcuIbK0j8eUlna6t3BPUKgU2p4zdw5Oolmoku63He5YPJ8LXDZVC4qqx0cc8NjXMm6nJweT5hNO/qZwwYy2Da3JIrS/k9z3lR2zncMq02Rzo9+/BoVTiPmQIANuCkynRB2FefvhkubLJzM0f7+CcV9fz5A9ZJ3V/giCcuOpmC+ty6zg30LUKnu+DD3Lf2bcgIVN69WKcZvMhbUobTDy6LIPSBhN+mQeGbYw5m1J9EHJhPvqqEgC0CfFdGmtiiB6npKDCM4D5Ob+z9PuH+W75g3iZmvE+b16XXksQ+oJjr/4gnPFkWcZZV8O0oq34XX01HqNH9XZIx0WtVGBzyLS56RlV6Ur83IYN6+WoXLWkc5+chSRJXDMuhrwa4xGP1amVvHrpEC5bHcfC3FW8vfKZgzvX/RfjW2/hOe6s9k1tNgd7y5t4ZkU2+fuLebo8A0diKgo3tz8uzsrI4SzO/BFbdTXq4OAO17vxwx3sLmvi/Lw1xNRrYH5al967IAiH12p1TZQbpXMlxQOHJWPabeeVQRfy6Nb3MO/Zi8eokR3aXPv+dvZXt7BhxUYezlxNU1AEuohw8nzCiczaR5SfDZveG5WfX5fG+seKqXsDYunXUo1CdrIrIA4uuZLk8eO79FqC0BeIZFk4pt1lTcRVFwDgNWvmMY7uOzRK19CGFncv9IA2Ph5dF5W666w/hl1E+rkfMkzjr3RqJTsD43kzdS52hQqTSovWaePW3V+T+/0vDPlTsvzq73m8uiqP6/cu44n8dQCEXXM/AJ9cOwqT1cF/Xy2GTGjbl3lIstzcZmdW4Sauz1iOXanCaXoIhfvR4xMEofPabK4KGNr6WiStFn1YMOl/D2Huk1bYCuZdu5CGDCXl0Z95Yl4ql43qx/7qFsZW7OXhrR+gQKZ12lR0agXrwwYxvWQ7U1t3oBrQ9RWLFAqJ/8xP40mLkS0hKewMSsAhKXltwtAuv5Yg9AViGIZwiLyaFnL+NB72qx2lJDZXgEqFNimpFyM7MboDxfXNB3psvM+bd0IrBPYlToWSr+MnsiLhbFb2G8FVz9xDtm8ktv3ZHY5bm1uLp9XE3MKNFEalELNsGd5zXMvVju0fwNSUYKJHDsak0rL1vkf5dc2eDu29ra3ctOdbarwCUTnstG7Z0mP3KAhnsjaba/iFtr4adWho+7MqOSGCcq9gTDt3UtnUBsB/V+WzLq8OgEVZKzCrtKjufZDBf7sTrUrJtpBkXhiyEACNm65b4l04IpLo/hHYh4/BTeeqyXysD/6CcKoSybLQgdXuZOqStcx6ydUr2WZz8N2uCkY469HGxvbYgh5dYUiUDwBr9K7ydl4HksZTVZSfO88tGEQ/f3figzzJ9YnErTAX2en6I7u/qoXSvFJe2P8VaqeDAY89hC7x0BUW/QN8eGTMdQSYDVS+9maHfZF5u1HLDrZdfBsWpZrWDSderk4QhBNnOdCzrKqrQf2nVUYHR/mQ7RWOKTOLeqMVSXbicDp5d0MhaYZiolpqUF9/E/HXXonK1xed2vVn/ZeoEQQ+/DAhjz3WbTEvvXEMX980FtOBDgmRLAunKzEMQ+hgc0E9AA6HKwH7NbOa5jY7EQ3laM8a3ZuhnbC0CFeyvDR+IrGLLiU5JKSXIzp5GY/NQKWQ0KmVzBscjt3hJNcngnMKN2ItKkYVHc2MF9dyTd4awgr3EXj33fiPPvwr0UC9liz/aLYGJzMkZxuyw8Gu8mZu/WQnc6qLcKg1KFJSyfSLxnP9emRZPmV75AXhVNF2YCESZW01qrSDw8ViAjxI9w5F3peOYedu/rn5HeJaqvhl1Dwuz9uIKjCQxGsvbz9eq3K9UUOSCLjicrqT6sDCJZ9cN5oVeyvx0h2+BKYgnOpEz7LQwW9Z1cwo2sI7K5/GXl/P0m3F3Je1DGV9bXtFhVOF+sCD3CkpGDXk0BXyTiWeWlX7sBJw/ZEqCugHwLIl7xH30I8kNhRzbsEGjAmpBFx/3RET3EGRrg8Rv0UNw6O1iaLPlrIhr45yg5nRjjpU8QkE+niwOnwwtqIizDt2dP8NCsIZzmJzonbYkBobOvQsxwR48FvkcMx+QUQ+dDMjq7PxNxm4ZNX7hJflEnDLLR3mFfzRs9yTRsf689i8AT1+XUHoKSJZFtptKahn6Zos7tz1JaGt9bz9xBtoVq5g8v51+C1ejM/Chb0d4gl7ZE4yAEkh+l6OpOvVB0ZQlDaWgSu/4L2fn+TFta8gA6pLrjhqu9Gx/qy442w2h6RSrA/G+ObrVOaXMqW1iPDaErzTUhka5cOaiMHYde40fv5Fz9yQIJzB2uwOUusLAdDGHqxVHKzXYfb05vYhV2FVqJAlicXT/sbPUSPJjR+Gz/wLOpznj57l2QNP3TdpgtDXiGEYAuDqUb7trXW8vOZl7AolKqeDIb99yXibGbt/IEH33XtKvoq/ZlwMi8+KQaE49WI/lgaTjXsiZ7N0z0aCzY18kjCVspkX8Pr8CcdsmxzqxTVnx/JSwwL+telNLn/+FgAkX198zjuP0DBvgoN8yYsfwoDNm7v7VgThjNdmczKzeAuSlzeekya2b1coJCQJyvRB7Pr328yO0FL5ZRkvDl3IDeNjOVfdceiDUiGx5aEp+LqfOvNLBKGvE8mygNMp8+SPWVyR9TPhrXWU/v05HttUy4PbPkTfZEY5ZNgpmSiDq0TbKRr6MWmUCkzo+N+cOwkM9OK2m88nyOv4Z74/MieZmPWFvDhkAQ9t+wi71o3ENavbJ3EmhujJ0vqTVFuLw9iK0rNvr9goCKcyW1MzYysycLv4YhRabYd9j85NpbihlctnJKFUSPBlGQB3Tz90Ai9A8Ak8BwRBODaRLJ+hyg1mNuTVsWBYBF/vLKeluIzzCtazMX40CSNHUr53K++kzuHve74gcdGlvR2ucBjf3z4OnUpJlP/JzUD/4wPQurBBvDLIxMVXze5Q7eTs+AB+/tWH8wFrcRFuqaldEbYgCH9hsTvI2b6PEbIDr7GHTqS+dFRUh+/X3jcJu9N5cDKfIAjdSiTLZ6hbPk5nV6kBrUrBE99nMk+qBeDCJ+6i9MDru/SgRAZu2dg+41noWxKCOz8O+5PrRpFfY6S4PpYx0zvW0J6cFMQ7nq7lwa1FIlkWhK5mstq5+t1tbClsYHxZHgBuUVHHaMVJf0AWBOHkiGT5DFXZZCa6qZKcdXaazCrOKdmCKjgYr9RkfIy29uNEonx6GxsXwNi4gMPui/B1Rx8XjbxKwppf0MORCcLpL6faSH5mAY/t+oqR1dnYFCo0kRG9HZYgCH8hkuUzVJvNyZKNb+C3qgVN1HA8S3bjd//9SGo1wV5KBkV4c8OEuGOfSDitJfULpNI7CH129rEPFgThmLYXNfDSb7ncPLE/T/6YyeJ9P5BWl8+KfqMoSxzCErG8vCD0OSJZPgXsKG7g480lPDg7mUC99tgNjsHucOJsbsLP4lrSenrJdmSttr00nFqpYNmt4zp9HeHUF6jXkuMVRlRmZm+HIginvJqWNi59cwtqcyt7lr9FjCaA8eW7Mcy+gJfVo5mQENjbIQqCcBjiHXsfl17SyGWvrCbq7RfIeeBhbOXlnT6nwWzD39QEwLex4zCqdEjnLxDVDoRDBHhqyfGOwF5Vha2mprfDEYRTWmmDGavDya2V6xmfvZ6b93yLQqFgxJ03cPe0BJ5dkNbbIQqCcBgiWe7DDCYrN320g6tK1zOjZCu+q36k6vEnaDLbuPrdrZQ2mE7qvPk1RnysRgA2hg1kwdx/4XvXPV0ZunCaiPB1Y0dQIg4kyl/5X2+HIwintMZWKwCjKjLYHRDHY3Pvp/8Py9FGRnD7lHiC9KLkmyD0RSJZ7qN2FDcy4eGveWD5c8zbvYLs+GH8lDAe4+bNbMgoY9X+Wh78aievvbOC73adWG/zvopmvC2uZLlJ4+pN9vMQBeyFQ01JDqYuIJwfYsZi+upLLAViop8gHE6d0YLZ6jjqMY0mKyqnHbe6KsLHjWLJIxejiY7umQAFQThpIlnugyx2B/P/byMLc1aRaCgl+KGHSHllCRsDEsFiQbE7ndT6Qm74391M+M/dtN56IzZz23GfP7OyL0oyWQAAIABJREFUmTAsABi0rvJjGpX4VRAOpVRIpP99Gp8lT8WpUND42eesyaml3mjp7dAEoc+oMJgZ/q+V3P/VbnJ+24DTZjvscfWtVlLqi8DhIO2sIYT5uPVsoIIgnBSRIfVBn24pQZKdzCzdhvv0GfhdeQWJ0UE4Bw3Bqtai3LSOO3Z+gV2hJMM/hsF1eaQ/+Tz/WZHV4TzbixrIKG865Pz7KpqJU1tBoaBF4+ZaEUoQjkCnVpKY1I91IQOo+vRzNtz1MD9fexey09nboQlCn5BV2UxqXQGzXrwXxy3X8slV9x72uFVZ1VyXvxJlQAD6qVN6OEpBEE6WSJb7oA359YzUmnG3mvE9e2z79ohAb7YHxBOy8VcijbW8kzqH3258gpWRw/D66iOUL/2HJtPBHo0LX9vE3FfWY7UfTGosdge51S2ES20ofX3Z+NBUNjwwuUfvTzj1DOvny0dJ01HabZyfv44hu1Zh+PLL3g5LEPqEvBojt+1eSrCpAYBhO36h7B+P4bR0fAPjKC6if1UeAdddi8JN9CoLwqlCJMt9UF6NkdG2agB0Awa0b5+UFMj7yTPZGxQPI8fw+hv3cd34WJYMvYhvY8cxu2gz2a++huzoOG4ut6bl4NfVRuxOmQC7CZWfL6HeboR4i0klwtFNSgyixieY/f98mV3PfUCL2o2aXft6OyxB6HXZVc28/dVG+rVU43HTLZw39yl+jhpJy+ef0bRsWYdjvWpd80vchg7tjVAFQThJos5yD9pR3EhZo4l5g8OPeIwsy1QYzCRW5iLpdGj792/fd/6QCNIeuwiN8hIi/VyF6xNDFHjqNHyYPJN4Qxmp7/yPom3riPjoIwCCWhsozCkhNWwg4BqvDKBvM6L09euuWxVOM8Oj/ch+YhZKhUReTQtFGg9aSqpJ7O3ABKGX3f7pTobW5gAQM3MyP3mHMH2JhlE1Wag2bsH7wgW0WOzotSr8G6oA0PTr15shC4JwgkSy3IPm/99GAPaUGkgOdOPC0bEs312Bu0bJlORgAFqtDtyNBvptX43XeeciqTr+J4oL9OzwvadWxcfXjeLXzGruV93M5cXruGTXcmo2bUXtsPP+r09h3+CJvH0LkkJBbYvrtaCq2YAyPKkH7lo4Xfwxtr1/kJ59Wne8jc29HJEg9K42m4PcGiMPSpWogoPRxMYSI0l8ev0YdmyOYcz27fzrhyw+2FTEqAAlNxRuoDU4HKWXV2+HLgjCCRDDMHqB9NrLJF01F1NlNbd9upNr3t+O1e7kw01FVDe3cU7BBiSHg4Drrz+u86VF+HDP9ERevHQYX0SMxqJQUfTtDyQ0lgCgMhlp+flnAFotdtRKCUdjIyrRsyycpFatOyqTsbfDEIReteTXHNysbQRm78Tj7HFIkusDZUKInoyAWNR1NXz/yw7m5qzhgTfvIdDcRFHisF6OWhCEEyV6lnvB/Py1AGQ//m80nhOJaargt8eWsKQlnM0jE5lSm4ctKQVNVNQJnffcQWGU1A8ka3M0Qbt2kxDgGrts07lj+GopXrNmYbI60KsknE1NKP1EsiycnFatB9qmkt4OQxC63OaCej7ZUkJzm40XFg7G9yg16Kub25hevBWl2YTvRRe3b9drVWQGJwDw+I4PiGwsZ3dAf7J8+zHnlpu6/R4EQehaIlnuAv+3Op/nf9nPOYPCeOK8AXhqD/9j1akVyOY2bGotapsFt1U/s4yf2/c/5+HPDxXjSG4sRnX+8fUq/9XNE/vzb99wUvI3kKzQU+vmjSllMG75+wFXz3K43dUjqPIXybJwcmq8gvAoTsdpNotZ/cJp5bZPd7qGq8kyOwrrmTog9IjHNpttLCrfhtvQobgNPDgZW5IkmoIj+DZ2HDNrM7CGhPPRtJv4+r7p6NTKnrgNQRC6kBiG0QWe+Skbn9ZGQt97hctveoVV2dWHPS4l1IurnEWobRZeP+tKXh9wLt/FjGVp/wn8GD2asNZ6rtuzjCaNOzE3LD6pWBQKibqQaDROO2dX7KEgMIZ6/zDsVVU4jK2YrA6GVmcD4D58+Enfs3Bmq/ANQ5JlsaKfcNoxmKxcNDySW3cvJfTi6ciy3L6vtMHU4VijoYXgxko8zx53yHmmJAXzetp5VL/7NUNW/cqPD80SibIgnKJEz3InlTW6Hp4X5fzG3MJNnFO4kZ+XGJj0xmOHHGuxOxmSuR51RAS5A8eyr8rI2Dh/YgI8eGtzMZ8mTEUj2zlvTH9Ge3ufdExNMYmwzvX15pSzSfR2/Wduy8+n1WpnevEuNNHRaP5UaUMQTkSVbxgAltxc3FJTezkaQegaTqeMzSET4q1jZtFmAAyff4HPggv5NbuW6z/cwXtXj2BiYhAAbuVFAGgTEg4511MXDOSSUVEMijj5Z7kgCH2DSJY7aUtBA+42M1NKdrAqYgi+bc2M27US2fYIklrd4ViHxUJkcRZe116Lv5cbVBm5ckw0MweEcN6QcLKrWrhwaESnl55WR0ayeOrfSIoPo1aho6qsmJnATz9uRnaLIa58P/prFrdPRhGEE9XgG4RDqcKSm9vboQhCl7E6XAs4aZQHn41V//wnm9ftZu2Uiwkz1lK1ah1y//OQlEp8q0qBwyfLSoXE4EifnglcEIRuJZLlTtpSWM+0ukzcHFYG3HY9S1fuZvCvr9G8eg3e06Z2ONaroQqFLKNNTCTC5oavu5pJSYEAjIj2Y0R014whDvHS8bNnAGNCAwlUSCwt9cIuKWjNzcObOpROB/rp07rkWsKZSVKpaAwIxysvr7dDEYQuYzuQLOub6gF4Ne18BtYXMuG3bzDk5HFl6V5YCZs/eIXy6+8lsq4Yu84NdfiRa+cLgnDqE2OWO6mwrpXx9Tko/f05+9wJFMSmUenuR+4j/8Te0NDh2IB6V0F6bWwM989IZNkt49Cqun4MW4i3a8KVTq1keLQfDoWSCs8A9FWlBFUX49Ro0YlX50InKCWJhoBQrAWFvR2KIHQZq92VLPvlZQCQ7xPON4mTsElKUssz2RWSxBvDF2I3mVG+/jKjqrIwJg9CUog/pYJwOhP/h3dCTnUL9bsySM5Lx+eCC5AkCaND4smRV6JpNpD99JIOS08HGg6s3hQdjY+7hih/926JK8LXlSzLMgzv5+uK1SeSqJIs5uevRRkWJh7uQqcoFRKtbnocBkNvhyIIXcbmkEGWCfv1G8xhUez3jWLsrLN48qpn+Plf7zHiiw/4vt8ovokbT1JjKQFtTTiGj+7tsAVB6GZiGEYn3PxxOhfn/IbFzQP/664FwGJzUOETQXpQAqO+W0r66lUsjxpF4lWXEGKowuQbgMK9e5LkP8xIDeHGCXFcMy6GAE8N/h4aVkUMJcFQil3jxuiLFnbr9YXTn0op0aZxx2k0Ijud4sOXcFqw2p0Mqc3FraQAv38+zm3+idwwPhaPcw6+ibvu7Fj+z+6g0iOAidRx/mUX9mLEgiD0BJEsd4K5oZFhNfuxT57evnzpPdMTuefL3bw4ZCFjKjNYlLWCizJWUP/IBvzUOkwRkd0el0al4G+zDi5l/fe5Kdz5uZUbgu/n/CHhzLlocLfHIJzelAoFJq07yDJOo1Es3yucFqwOBxPL0nF46gm8YB53aw5dkOT2KfHUGS3EBqZww4S4XohSEISeJpLlk2BzOGm12BmWvx13u4Xoay9v3zd/WARzB4Xy1A9ZqJVp3PJrClNLtnNV1gr825qpDB/T4/GeNyScdzcWsbvUwOhYsRCJ0HkqhYRJ43pD4mhuEcmycFqw2mWCTY3YwqNQHCZRBtdckP9cOKiHIxMEoTeJZPkkPPj1Xr7aUcZdDaU4vbzRDRzYYb9WpeSxea7VnFLDvXjh11CKvEK4IG8NHrPO642Q0R9YVXBkjH+vXF84vSgVEiaNDgBnSzMgqgEIpz6rw0mg2YAcmNbboQiC0IeIZPkk7CxpBGCSohH9wAFHrVd8/pAI3DUqbmgwsSU0lbdjY3oqzA6eXZDG2pxaortpUqFwZvlrz7IgnA5sdgcB5iYcQSG9HYogCH2ImJVzEmwOmavCnKhLC4+rBNuEhMD2rz21vfP5JNTbjYtGRImFSIQuoVRIGDWuqiuO5qZejkYQTs62ogaW7Spv/35/dgkapx2CgnsxKkEQ+ppOJ8uSJEVKkrRKkqQsSZL2SZJ0x4HtfpIk/SpJUu6Bf/t2Pty+wdpo4JyPn0bl64vvcVSW0KmVLL91HKNj/UgM0fdAhILQvVQKiVa1K1l2ip5l4RS14LVN3PHZLqqbzQB8+eN2ANzCw3ozLEEQ+piu6Oa0A/fIspwuSZIe2CFJ0q/AVcBvsiw/LUnS34C/AQ90wfV6jcMpU1BrZO6en3BrNhDxxefHvXLTwAhvPru+5yf3CUJ3UCoUNKkO9Cy3NPdyNIJw4mRZBkDhdJB/zjx+D45lnNEOQMwwsWiTIAgHdbpnWZblSlmW0w983QJk4ZrtMw94/8Bh7wO9M7OtC727oZBpL6wlrS6f1tTBuA0c0NshCUKvUCkkTCotSNIhPctf7Sjj7i929VJkgnB4LW025r26noe/2YvZ6sBscy0YNboqE9/qUgbvWcPcgg1oz5+PLja2l6MVBKEv6dIxy5IkRQNDgC1AsCzLleBKqIGgI7S5XpKk7ZIkba+tre3KcLrcyqxq1+pOrfVoe2miniD0BUqlhB0JhV6Po6VjsvxbVjXf7iyn7UAysim/nmve20a90dIboQoCAP9dlU9maT0tn3zC9698RKvFgZellbszvqHQK4TnRl5O5JtvEvOvx3o7VEEQ+pgum20mSZInsBS4U5bl5uOdSCbL8hvAGwDDhw+Xuyqe7mC2OQk1N+Bhb8N3QNKxGwjCaUqlkHA4ZZR6Pc7mg8MwGlqtrM+rwylDQW0rKWFeXPLmZgD2lDUxKemwn5kFoVs5nTLfbSvkf+nvElG6H/aCcexARlZl4mFqZuzHrzN9cBoqpZjzLgjCobrkySBJkhpXovyxLMtfH9hcLUlS6IH9oUBNV1yrN6zNqWXZrnI0NZX8Z9fHAPiOO6uXoxKE3uPrrqGmuc3Vs/ynZPmcV9bT0mbH39xE6abtHdpYC/JxNInKGULP21rUwOQdK4go3c87KbMBaFi/keiWKpwaDd6DB4pEWRCEI+qKahgS8DaQJcvykj/t+g5YdODrRcCyzl6rt1z7/nbu+jSdi399C+/GasJffkkMwxDOaGkR3rRaHVh07h0m+JUbzHhaTTy77r+E/+NO7A2umuTnFKwn8p5rqXj44W6PzeZwdvs1hFNHk8nGQ9/s5azaLLQjRrB59BzKPQIwbdtGv+YqnJHRSEplb4cpCEIf1hUfpc8CrgAmS5K068A/s4GngWmSJOUC0w58f8qpaW7DZrdz947PSGkopmjkZLymT+/tsAShVw2O9AGgoE1JfVV9h30X719JoNmA0m6jfulSBtfmcvOeb7GrNbT8vhpbdXW3xVVhMBP/8AqW7ijrtmsIp5anfsyitMpAv6ZKPIcM4cWLhrA1JBm/vdsZXrMfYuJ6O0RBEPq4rqiGsV6WZUmW5TRZlgcf+OdHWZbrZVmeIsty/IF/N3RFwD1tc2EDl2f/wpSydNaED+LHkad8UQ9B6LSYAA80SgU77e5oy4op+/q7g/uaq8j3DmefXzSN33zL8OpsbJKSu8beiOR0kLnkv+zYmtnlMeXVtPDK73kAbMiv6/LzC6emwvpWputakBwOdANS8XZT80HyTMwHFtVxGzyolyMUBKGvE8tdH8OuEgPTSrajOmscTwfM474EUaxeECRJwtdDzUdJ00loLGXAIw9iio4gOdSLEFM9ckIyv7bpSc1awUAfCwXeYeR7h5PvHUbcsi+x/Pg98o7NSBpNp+KQZZkHlu4h1NuNl37LReOwcXHeGvqPvKiL7lQ41TWZbCzIWQ9qNW6DB+PlpqJNpeWOs2/l3Pz13Lng/N4OURCEPk7MaDiG4sw8As0G/CdOIP3R6dw4QbyyEwSAtxeN4O5zB/HunNto1XrQ+NFHeChlgs0GolL7k+sTAUCCoYwC7zCQJO6ZdCdLhixEbzPTuHZdp2MoazTzxfYyXvotF39zE49teotFWT8R+cNnnT63cHrwqCgidc86/C67DHVQEF46NQDlnoFsO3cxWm+vXo5QEIS+TvQsH0WTyYZq3x4A3IcPQ+fRuV4wQTidDAj3ZkC4NwCbVyUwdeNmdL5no3Q68Ijpx84WD36IHsOcok00jp3Ml4vGkFXZzOPfOLky6yekTz7Hb+qUTsVQ2mAiwGzgge0fM6C+sH27V3Fup84rnB5kWSaueB+SLON/zWIAdGolV4zux6yBIQzr59vLEQqCcCoQyfIRWOwO5r+2kdm1BcjuHmgTEno7JEHok+YPjeCxgBimlu4grsj14dIrNhp5bz2vDp7P26lzeP2S8YyI9mNEtB/+HlrWZ6Qxe/MmnFYrik4MxShrNDOjaAsp9UV8kjiV4fNnkvfVd0wr2oosyxxvvXehb6pubkMhSQTqtSfVfndZE1GN5Vh9/FEFBrZvf+I8sfqqIAjHTwzDOIz0kkbmvrweW14uUw256EeNFKWFBOEI/D21tMYlAjB5z0qcCgXuAwcCMDDcm5tmpzE8+mAPns3hZG9AHBqnHfPOzi2LXdZoIsJYS4tPAL+Mmse4C6ZS4xuK2tqG/U9VN1bsrcRgsnbqWkLPG/XUb4x4ciUl9Sayq5qP3eAvNhfUE9NciT5FLCIlCMLJEz3Lf/HBpiKeWprOwpzfuSTnN1Cr8b38st4OSxD6NEVcPGa1jqCWOhpC+qH09GDPP6ejUynRqDp+Jp+cHMRDgfE0aPXonn2W6M8/O+kPo2UGM1MsjYSl9Gfzg1OQJIk6vxAArAUFqENCqDNauOnjdEbG+PHFDWM6fa9C91uxtxJJgsiWalLrC/lx4YcMr97PFncP+p0/l+D772v/nZFlmWW7Khjb358gva79HD9lVPHZulxebqnBI3lmb92KIAinAZEs/8nesiYe+2YPj295j6G1uewKiOP8L99CFxrS26EJQp8W7u/J4yMXcWf6FzSeNRWgfSLVX3np1Fw3cyBvVp/LA9s/xrhmDfrJkw857oq3tzAi2o/bp8Qf8bpljWb8LEZUgYHtQy4a/MMBaMvJwWPsWOqMFgC2FZ2S1StPe06nzPubirhgaATbChvYkF/HuxuKUDodfLD+NfwsLbSpNNRpvWhSeeL1/vvoxo6lZeBwZBn2V7dw3yfbWaio5G8zEvGcNJH82lYeX76P0blbUDvteIwZ29u3KQjCKUwky3/y2bYS5hRuZGhtLpXX343bpNkiURaE46BVK9gVGM/Kf7zOY+ceezxogF7LhtCBOFVqjNu2H5Is59UYWZdbx7rcOm6Z1B+l4vBjj8sbTHiam1H6B7Rvs3n7UKkPxLFyDf5XXUVdi2v4hSx34gaFbvNLZjWPLc+kuN7EexsKUckOLs35nfl5a3C3W1gblsY5H/+PwSF+vLQik6gHrkDx1TfMWNFCZEs1CweF8PimNxlcl0/ZMvB74SWmrrFx9b4fWJi7Ck10NB6jR/X2bQqCcAoTyTLgbG1F4eGBWiExtWQ72uRkJt99XW+HJQinjDBv1wIPw/r5HjGx/bNATw02pYoi9wAse7IJ/cv+r3aUgSxzXv46Vt+2lrE3XIZbWlqHY2wOJ4Z6Ayq7DZW/X/t2rUrJ2pCBXLhjNdbqampabAysy2dW0WbasuPQJYnxq33JrlIDw6uziH/2dT43lONlMwHQGJ2I7YrFLFo4G53a9adqREIwa8MHM+X33zg/Wc31GcvhN9d5/m/gPK7Y/yv1b3+IX/gs5hZuJDtmEOd8/D8klfhTJwjCyTvjnyD2+nryZ86iZuZ8LLoo4pvK8bljcW+HJQinlMtH9yPYS8eM1ODjOj7wwNjSKnc/QqsqOuyzO5x8nV5GWl0+N2R8BxlQWZJJzLJlSIqD45+rmtrwajMCoPT3b9/upVPxS78RXJT7Oz+cv4jXJ1/PY1s/wNvaSumNNxGz9CtUfzpe6Hk/ZVTyxfYybp3cn7U/rOP5TW9jUmlxt1swqbT8EDOG/nfdxqWTUzq0C/bS8U3/8UwqS+f6jOUYVTreHHguKVPGsnDCMH68u5mFGat4viAfheyk7KJrUPn5HSEKQRCE43PGJ8sAq7ximfDle0x398Wi1uJ97rm9HZIgnFKUComZA45/yNKQSB+eXzCI/Xv90JTmdSjzll5ioKbFwvPaMtqUGt5LmcWNe5dR+N4nxC6+vP0cZY1mfC0tAB2SX3etigrPQLJ9I0lqKOaJZf9CbzPzTspsFmf+iOHLrwi48YYuunPhRFntTm78KJ04Qxl5b/6Dp5rKsSrVLJr+MA5JwUe3TMC3sJGZo/od0jbSzw1LSDh3z36QN4Kq0U2cxmVeAYyPD0CSJJ4bOoWZRVvwkhz4/+spbjinc3W8BUEQQCTLVEpu/Gf4ZZhUOpIai9FefR1KT8/eDksQTmsKhcT8YRHcow9AabXgaGxE5edHc5uN19bkM6dwIwEZK9BOnc5KrwnMKN6C+xdf/yVZNuFjcfUs/zlZrmluA+CJkVfx4tqXCTQ3URI/hG/6j+fqunQseXmHjWnRO1vJqmxm68NTu/HOhW93loMsc9+OT/GytqKbPIWQC+bx+7BRZFe2MCw2gGGxAYdtq1UpWXHHeIDD1l6eOXkwi+2P8fbi0UTFHf4cgiAIJ+qMT5YDPLU8fn4aTTOTGTE88qSL3wuCcOLM/q5hG7ayMlR+fsx6cR36gixe2P01nhMnEvbkE3zfBh/sWs78/LUY163D8+yzASg3mPG1unqW/zwMY9HYaLYVNfLFw+cwTacnpLWBLx5fgGLJGiq9g3ErKDhsLGtyarv5bs9sP2VUUdZoYtmuCs4xZNOvpZrqa+8k6d6Dvfx/Lv12JEd7Rt84IY5rxsWgVoolBARB6DpnfLLsplFy+ehDX/cJgtD9VOGuMm/W0lIM/eIpN5i5vnw3NpWa8OefQ+HhQYwnlE47n2pDHqrbbid+7RqUXl6UNZoJl11l4VS+Bxc9mZsWxty0MAASQrypb3Uj0s+daSnB7MnwIbxwK7LT2WH88585nPJxTVIUTszfl2VgMBi5PPsXFuauQpuUxPhbF3X5dUSiLAhCVxNPFUEQeo0+xvVB1VZayhfbygBIrS+kKrw/Cg+P9uO0Af58Ovoi5LY2WjduAlzDMEJlEwpvb6QjLJn9w+3j2PSgqyzd7AGh5OgCkc1mbGVl/JpZzdfprmsaLXYG1ubx1IbXKX3uBWSns9vu+UzUarFT12zmgX3fsDB3FbkDxhD92acodMfuSRYEQehtZ3zPsiAIvcff34s6nTf6nFy2KAcToHQQ11SBadqlHY7zcVPzBQHcISkwZmTgNXMGja02/KzGo1a2UP2pl3FSUiDPB8YAYNq2jeu2uRI1P7uJpbsquXbf9yQYyjC/k0utSiLo7ru64Y7PTHk1Ru7f/glnle9ibVgaGefdzLkiURYE4RQhepYFQeg1ep2a7cGJGFevprLawMWezShlJynTzj7kWJtSRak+mMb03QC0tNnwMjUfd2kwd40Kn6QEmnR6an9bjdZuYVHmCvyuuoCbl9xIgqGMDwadS27icBo//xxZrGLSJcoaTVzy4m+cXbGH6klz+PeIK9B7iLkhgiCcOkTPsiAIvUavU/Fd5DBmFm8lKS+dWG0zSBJugwd3OO7y0f1oaLWyPSORiJ3rMBaXYDGaCKwoQDfp0iOc/VD3zEhi9Q+DmPf7Sr5UrELpdLI9OBHf/jGMmDEOvXsCv3z0OfFN27GVV6CJCO/qWz7jLNtVQWp9IUrZydDLL+AOUwCLz4rp7bAEQRCOm0iWBUHoNXqdmgz/GP6fvfMOr6JK//hnbknvPQRCL1IEFAWxi2LBisraG6u7ll3rqj+7a1372teCoKiIqBQFpUmRntADJJT03svtZX5/nDu5k5CEBBIiej7Pk+fezJ0758zcmXO+5z3v+x7i4jlj/3oG1BUQPnEixoiIJvsdlxzBW9eM4tSNp3FJ9hryXniZfs6+GN0uws48o93lnTogjgdPuYogt5Pz8zZSf+MduM64jEmn9sVoUDgvp4rHI0VwoH33LimWO4Eftxcz2ZGPYjYTdsJo7g8O7u4qSSQSSYeQbhgSiaTbCAsyoSoGHIOGMqp8Lyannfh77m5x30CTkYqQKL4ZNAFl1a/cumsR7oAggseM6VCZY/rH89YJU7j6khc4+fH7+evp/RqzX4zuFUV1Yi+8BiO29M2N36mxOnF5mgb9pedWNeZ0lrRMXqWV3cV1nFCym5CTTsIghbJEIjkGkWJZIpF0GxFBYnKrIjEVAEdCMoEDB7a6f5DZwHcDzgSgb10xltT+GFrJhNEaL04ewT8nDOT/rjrxoM9MRgOnDetJWvxAShYtBqDa4mTUv5fwzPyMxv28XpUrP1jHlR+u7VDZf2S+WJfD+gOVTbZlltYzoKaAkOJ8Qs842A9dIpFIjgWkWJZIJN3GcckRBJuNzCwSll1PfNtLZu985nyG9Ynn02GTABj8j44vWx0RZOaB8wZxfQvLKQOcPSSBbXEDCCgtwnEgm09/y6ZvbRHD3nicbZdfTcX/PqK41kawy05RRUOHy/+j8uS8DK75aD0A87YWcu4bK/m/D5fy9PppGBITibzkkm6uoUQikRwe0mdZIpF0G0FmI29MGcmD0y2sTBnFqAcebXN/k9HAuH6xfJh/NoXnXcHX55/a6XUa1y+G51OO55qsZWRNvZ1lA67ghY3TMaheclwxBLz5JvvnLeP7A9vJCU/EazvvT+9eYHd5mvy/dHcZ+0vreHPjDILdTlI/+rzNFH8SiUTye0ZaliUSSbdy4Yhk3p56Gntuf5iTxw0/5P4je0YC4FW6pvlKCA8ieXA/nh43FW9pKW+sfpeoQCNL//4cD5x2F+HqAAAgAElEQVT1Txb0HU9EcS4AfepLqZ03r0vq8Xtg7pZC9pcf2npe0SBWUrxi30pK3/+AyuJy/hJQyeCafJZNuI7gwYO7uqoSiUTSZSi/p1yiY8aMUdPS0rq7GhKJ5HdMg8PNHZ+n8dhFxzE8JbJLykjLqeKqD9dxUslubspczLkfv0nw8GE43V5WZZUTHWpm44Eqkv/vLoYGOhnw86I/nHVZVVX6/t9CjAaFrOcvxONVCTC1PEDZnFfNY89/xZur3gVgR/IQemAnrrqUPqtWEhQRfjSrLpFIJO1CUZR0VVUPGSUuLcsSieSYIizQxFe3j+syoQxwYu9ojAaFTUnHET59JsHDhwEQYDJw7tBETuwdQ4/oYD4ZfjGe0lKqv57VZXXpLpy+7B8er8otn21k0BOLWtyvsMbGmr0V3LJrEVWB4WT0P4ERxXuIKckl5a03pFCWSCTHPFIsSyQSSTMURWH+Padyct8YRvWKanGfPrGh7IzrjzMlFWt6+lGuYddjcfj9kFfvrWhxH1VVOfXl5fzvp62MqDiA66LL+Cx5HB4Uqq6+mfCzzjpKtZVIJJKuQwb4SSQSSQsM6xHJ7L+d0urng5PCMRkUKmJTCNu37yjW7OhgcbgP2qaqKoqisKekjmfmZ6CuWc3re5fTp64EAypjr7mYMTlGpkT2YNXDF3dDrSUSiaTzkWJZIpFIDoMgs5ERPSPZUxBLj50b8DocGAIDu7taHeJAeQP3fbOVu87sR7zi4sTh/nR6DQ43cdYaRpXv5ap9K9ge1x+r83xCA01M+y2b1J+/5dZdi6gNCGVr/EB6Tr2F40aO5InhXh6YOJjIYHP3nZhEIpF0ItINQyKRSA6TU/rFkqZGgdeLMyenu6vTYZbvKaNy917K7rmbwKsuZOu8JY2fWRxu/rFtDg9u+Ybe9aVckr2Wwr9MwbplCyUVdVybuQzrmPEMXr2S4Jdf5+xrLgTAbDRIoSyRSP5QSLEskUgkh8m4frFkhyUC4DgGXTHScqp5cPM3jCzfhxEV9Z03UD3CV/ndpZmMKt/H4tSTuHHiE3wxZCKekhL2/ucN3LsyCPI4GXTLtURHhnLd2NRWM2VIJBLJsY5s3SQSieQwGdMnmsKweDwoOPbt7+7qdIjVe8tZu3k/x1XnEjL1dl4acwNBBTnkTf0rXrsdY3EhAV43O+L6URESxVdDJuI69wKU7VsYmr0NVVEIOemk7j4NiUQi6XKkWJZIJJLDJCTAxNjBSRSHxdGwJ6u7q9NuvF6Vx37YwZneUgB6nD6O3ldeykejrsC6fj2bZs4lODMDgJyIZABMBoX5If0wez1cceA3go4bgjGy69L3SSQSye8FKZYlEonkCLh+bCp54YlYd+9udGH4vZNbZSW/ysbkoBowGAgeNowT+kQzr/cpVIXFUvfh+1yzZzH07c/4C8YzJCmcC0ck81lNBNWBYRg8bsLPmdDdpyGRSCRHBSmWJRKJ5Ag4oXc0a5JHYCwpouL9Dw7rGNUWJ49+t519ZYdeWroz2FVUR6KlksSViwgeMQJDaCiTRvTg+lP68u7QS+jRUEFUVDipTz/Bvy8fwc/3ncFtp/bBYzBy5zkPErVgEXF333VU6iqRSCTdjUwdJ5FIJEdAYkQQOSeczm5PAce9/z4RF5xP4MCBHTrG8z/t5rv0fKrXb+Sdf99AQHDQYddnW34NigLH92x5MRWAjMIant4wHSMekl96CRCrEz53+XBm9Yhgq2My1502AEVRGr8zOjWaUb2i2FdmImlA7yafSSQSyR8ZaVmWSCSSIyQ6NJAVJ1wIqop9z542982usHDXl+kU1dgat/22t4x7d3zPfQte57cHn6aiwQHAd+kFnPvGSkrr7O2qR3puFZe9t4Y7Z25udR9VVUlft4O+dcXE33cvgf36Nvn8mpNTuf70gS2K4deuHsk7142WQlkikfypkGJZIpFIjpCoYDO55ggAXIWFTT77bW8F+8v97hXvLt/Hwh0lLF+xDU9NDS6Pl4Sc3VxwYB0Akat+4dRnfqKg2sqD325jf2kdy/eUtaseW/JqCHNaGbZnPfVLl7a4T36VjcADewEIHjmyQ+c5ICGMswcndOg7EolEcqwjxbJEIpEcIZEhZopsKoaEBNKWbaDa4mT13nJ+3lnCDZ9uYMLrKxv3tbncDKvMZvjjf2PP5VdSWtXAuKIMvCYTT42bSojbwb1b5/DEPa8ztjiDOT89ScprT+KuqGBTThVpOVWt1qPG6uKZ9dO4f/0XFNzzD+y7dx+0z9r9FQyoKUA1mwnqoLuIRCKR/BmRPssSiURyhJwzJIHvNxfyU3Bfzti9lf8t38O0DYU43V4e2/g50Y566g8MJbxfX4pr7Ty0eRaBHieUFFEy90fGlWTgPv5ENiUOYVHvsVyYu4GzC7Y0Hj9k+0b2n38BT5w0lcyoVL67czwn9o7mm015VDQ42VVUx63jesKBvQyryuGnPuOYlLOeht9+I+i44xqP0+Bw88aSLJ60lxA0aBBKQEB3XC6JRCI5ppBiWSKRSI6Qi4/vQW6lleWlI7gwdwNnPHsntaknszZpGKcXbQcg77rr6bdwIZU5BSRZKlly9rUMTFtOn9efIxQInHAH7FV4e/TVmG+ZyrIlaZzbM4gfA3szJhJunP40V2cuZ0dcfxbdNZ8vBwxnbmBvArxu7t72PaHPpTPRYMRlMPL5cRdyliWXsK3bmtRz6a5Syurs9K8pIHj8Rd1wpSQSieTYo1PEsqIo04CLgTJVVYf7tsUA3wB9gBxgiqqq1Z1RnkQikfzeOH9YEq8mDOaDEZdx54553LTrZ27a9TNWUyDvHT+Zf23+mm2fzCS1uA6AzNg+zDnpRu7e/SOxzgbOmjKZu9YUEBMawC3j+xDcM4XJJ/Rk65zt/Jhbxcjex3NqxlpOLd4pCty/itt15ReGx5Mblkh1z36cemJ/NuxI5uyt21BVtTEgr6TOTrK1EqWhgaBhw47yFZJIJJJjk86yLE8H3gU+1217FFimqurLiqI86vv/kU4qTyKRSH5XxIcFgqIwv//ppCUO4b4t3xI7ajgjH7mP+oXZFOeuwrNyFSNNsSgBAdx+24Us2VvJlPduaxSzD18wpPF4fz29HwD/On8w987awnsJ43i4vJjSS67hhfJIbqnfxaDtqykNiWHyI7czZMIZZBTVERJgJDjAyIvzUzln+2ac+/cTOGAAABX1DsaXZwIQNFyKZYlEImkPnSKWVVVdpShKn2abLwPO8r2fAaxAimWJRPIHJSLY35yec+6JPBwWz1MXD2VCajLj+9ezOTyVSfvXERUVT9CwYQzpF8+ofvGHPO7gpHAW3Xs6WaWjGZDwVwwKXOv2UlQzgXNeP57bTu1LyrlDARie4l9+enWPkdyasZBd193EilETuePtx0jfuIundi4k5JRxTXyZJRKJRNI6XemznKiqajGAqqrFiqK0mG9IUZQ7gDsAUlNTu7A6EolE0nUoisKEIQmcNSSBqgYnAFanG4AzBsXzctJxTMpZR1hNOTE3P97hYw9OCm/8P8hspF98GEsfOIPUmNAWv3P62MH8y343T234jAtWfcPue8q5dF8JgW4nyc89j2KQyZAkEomkPSiqqnbOgYRl+Uedz3KNqqpRus+rVVWNbusYY8aMUdPS0jqlPhKJRNJdNDjcvPDTLh694DgiQ8yoqkrfR39iTNkePnrpFmKS4rq8Dk63l4yiWh6ZvZULlszg/NyNGFBxDxnGiLlzurx8iUQi+b2jKEq6qqpjDrVfV1qWSxVFSfZZlZOB9mXVl0gkkmOcsEATL00+vvF/RVGYc+d41u4ffFSEMojlq0enRvPT/Wcy3uFhRsUF3FG/kxsf++tRKV8ikUj+KHSlWJ4P3Ay87Hud14VlSSQSye+aMX1iGNMn5qiXazYa+P7O8VidHgYnXXPUy5dIJJJjnc5KHfc1IpgvTlGUAuBphEierSjKVCAPuLozypJIJBJJx+gVE9LdVZBIJJJjls7KhnFtKx9N6IzjSyQSiUQikUgk3YEMh5ZIJBKJRCKRSFpBimWJRCKRSCQSiaQVpFiWSCQSiUQikUhaQYpliUQikUgkkmOV8ix4/xSwVnV3Tf6wSLEskUgkEolEcqzy2xtQtguyfu7umvxhkWJZIpFIJBKJ5JhFES+dtCKz5GCkWJZIJBKJRCI5VlE0sezt3nr8gZFiWSKRSCQSieRYRRPLSMtyVyHFskQikUgkEsmxiuKTctKy3GVIsSyRSCQSiURyzCJ9lrsaKZYlEolEIpFIjlWkZbnLkWJZIpFIJBKJpLNRVXA7ur4cKZa7HCmWJRKJRCKRSDqbde/C8wlgq+7acrQAv6MhzP+kSLEskUgkEolE0tlkLhKvuWu7uCCfWPZIsdxVSLEskUgkEomk+8nfJP7+KITGiVdr5dEpz2U/OuX8CZFiWSKRSCQSSffz6bni74+C2yleD6yE/I1dV47XJV5tVV1Xxp8cKZYlEolEIpF0L3/EtGeaW8TOOfDpeeDtogA8j08s1xZ0zfElUixLJBKJRCLpZhrKursGnY9mWdboKsuvx1eOFMtdhhTLEolEIpFIupfKfd1bvr0O5t3TuZkrmgfcWTtBLKsqLPxXU99uLQtGbf6RH1/SIlIsSyQSiUQi6V40sawYu6f8jO9hyxew/PnOO2bzVG6dYVm218DGj2DmZP82zQ3DXitEv6TTkWJZIpFIJBJJ96KJ5dD47ik/NEG8VmQd+bGcFpGZwtPMDaMzsmLUl4pXc4h/m76cusIjL0NyEFIsSyQSiUQi6V6qDohX1dM95Wur31kqjvxYL/aAD08Dp7Xp9obSIz+2doyGEjiwQrz3OMFgEu9rpCtGVyDFskQikUgkku5FC/BrHhR3tNCss86Gzjle5d6Dj1W05RB1cMGPD0DJztb3sZT7339+mf97Mf3E+8PxWy5Ih/UfdPx7fyKkWP6zUpoBZXu6uxYSiUQikfiD4Y50FbotX/qt1B0qXxPL1rb3OxT6AEFXs2NV57b93V3zIO1TWPtO6/s0PyaIukf2FNblw8mI8ck58POjHf/enwgplv+sfDAe3h/b3bWQSI4MrxdWvgL1Jd1dE4lEciRoQWpux6FzLlsqW37mvV6Ydxd8dHbHy9eC8ZyWjn9XT12x7pi6FfWCY8Drbvu77cnE4bI1/b/qgLh2pmCI6CHTx3URUiz/2XHUd3cNjh6Zi2Dv0u6uhaQzKdgEv74AC+7r7ppIJJIjoTFzhAreQ/gtv3cSvD744O2aVdpe0/HyNcuy29b2foeiNcEbGu8fELSGJqZdbQj25pblzyaJ8zaaIbLXkYnlrloYpnhb97nXdBJSLP8Z0a8f/2cSy19fA19e2d21OJj6EhEMUpXd3TU59tA6xSOdupVIJN2LPqPDoZ5nLatEc2Gqt+Smz2j9+2V74LkEqM5pufyOrLRXngnr3vf/35pQD40/ODtGczQx3ZZ1u7llub5IHNcYICzLR5IN41CW78OhJg/+dwb8/EjnH/soIsXyn5HcNf73Rzrl9HvB64ElT4uHsq3giN8j276Gkh2w4X/dXZNjDy3dVEBo99ZDIukMvF5Y++6xu5qdrQYK0w/vu3oh2Tw/sR699bO+WXYJ/feWPet/X7YHXuzp9xne8oUQ5Lvmt/xdRwdyFX9zI/zyf9DgC7xrzbIcFNl+y3JbftMt+iy7hFgOjhG/weFyKDHfEoeyRttrxWv+xo4f+3eEFMtHm61fQXkn5HE8EjZ94n//RxHLVdmw5i0x3VOY1t216Rja9JTR3L31OBbZ+Z14DQjr3npIJJ1B6U5Y/DjsmNPdNTk8Zl4JH5/TMcushtshBB80tRA3Ry8Wm/dfesGbMNT/fvs34KwXr62hF4odWcXPHCxeS3eI19ZW6TOawdtesdxGRg7Nspw43L/N4wRTAARHgaP20G4srXEoMd+cmnx4NsrfDreEtsjM4dbpd4IUy0cTrwfm3imC67qL6lzhu5syRvzf0ij1WEQ/bXesuZZoqzppI3BJ+1BVMQUKnZfuSSL59HwxQ9UZ+XY7SvFW8XqsLiyhGSoOx+/X44SgKPG+uatBk/10gq75c68Xy4Hh/vfhSeK1vrjp/uisok3EcgdW2tOOrQX2FW9reT9jQPvdMNrqw1w24Zt8ps+twRTsd8PQrt/h9iUeF5Ttbr/vctlu8dqWy4vik5ndlT+7k5Bi+WiijTgPNbrsSjIXASqMu1P8/0exLOsbUEcLwqmrAhc6A63Bl1HMHaO+2H/t5BKvks4if70QPNMnHf2Bd5EmlouObrmdzeGkX3M7IDja9/02+qUmYrm5ZVlnkda7I2jWXy2DhqK0XH7jdw/DsmyvFf1M9sqW9zOawXMIn2Cvbtnq1nBZRZlDL4Whl0NEss8Nw+y/fh2pv559S+D9cbD58/btrwUitnbO4BfJXeEPfRSRYvlook8m3l1ojX/cQPF6pGLZZYO9S7pfjDYRyy10cPpGtL1ThBV7W7cSdCZax/JHEcurXhMR2l2N5q8Mh+drJ5E0R2vHEoZC+R7IXXt0y9fam862LFfuh4Kj4J6mWRE7OmPp9QhRFaxZlq3CFaUllwZvW2LZJ3hNQU0Fp7a90bLsE8tqK5ZlawfEpilIvNproWyXr5/3HV9zQQCfWG6vZbmu9X7KXue3mptDhBuf5sKibT/cQZ6Wn7q9973eZ7y1BVe0c5JuGJJ2U5Hpf99daVTcNpG4PDBC/H8kbhgeN7x3Mnx5lRCWesqzIHv14R+7IA1+e6v9++sb0JaCM/QNZ3un7N8dI6Zjuxqtwa/N7/5BR2ew/DnI/e3wvqu2I22UhnbPJQxt28dRImkvWsfez5enVz8g6ywcDTD37qaZGEC0p6W+4OTOtix/fwfMua1zj9kSmjjsaL+iiUjNjaC2AL6bCp9MaGHfttwwfO1AWELTrBSNYrlZQKDe2um0+IVvRyyz2jH2Lva7WA7w1Ts0Dh7YAw/t87th5K5tPdhNO5bqFT7WLWGv8V8nUwDYayg3qBxQPP4lr1WPyEIx41IxUGovtb5BWnsDHPX7pU1ruf/Sn9MxjBTLR5Osxf73NXldX96en+C/o0SmBQ2XXTQI5hDf/0cglnfN9Z+HlspH472TYMbFh3/sT86FpU+3HRWtp60GFGCfLr/y1q/E0p6tCVOXHXYv0B2vk/y6mzfU2rH3/uIr13r402e/F/SDkkNNObbEokfg3zHtGzRU7hf+etF923+fSCR6LBVNB2eaaAtPEkGjNe1YOnjZc7Dz+/aXufJl2DoTtjULNqsvEmIvNEFYQDvLEleeKXyJawtafyZ3ze+YqGoNzbLcVptZnQvPRMLX1/m3ac+vZlnW/MVbWomvLTcMLXYlLLGZZdknohtKhMW2MZ+xrp72WohKFe874rOs1b1os39bdB8AVoVHsclaAGHxYDCLcj+7ED49r+Vj6c+ttawWtmq/u4UxEJwN3JycyGX539P4ba9XCPLslWLA0V4jzLavxGt73dq06zjqBuG6MXPywWU1WpalG4akvehF3KGW49zyJax89cjKS58O1dlQvN2/ze0TyyYt6vgwLdxejxj5a7Q2EtUeFEsl/HBny/7EbdGezkpfTkCYP+hLz/bZEJEi3v/8iFjasyJLXOOVrzTdd9dc+OYG///lnbAsePYqeH2Qz2dcR8YPTf+vbef5/l6ZrhsgafeEy95+4bzRlz6vPQEqlXshdoDw3zuWxLKqisHb4QwmjjYbP2462P6j8Wp/MTjTpvs1sWwMAFPgoafNi7bC6tdgzq3tK8/rgS0zxfvAZhlcNOEX218IiyNJAaZn65fiVfUI9w6vt6mg8Xph9o0ii8WR0uiG0YZ737y7xWvmT/5tzS3LzY0velqaRVRVWPqs32Iblij628ZVAX1iWfUKwawFpulE/V57OSPC7WSFRrWe0aIltDLCEv3bDCKz0d1BNm775TYO1B4gX3W2fD+5HfD2aNEm6AVlRStZs2w1/kGFrx/PN4vy9ttK/eepZaiwVTcV8u2hvbOvXo/4zc99RgwQ9i8/2AVEO6eGFoxFxxBSLB9N3A6I6CnetyWW06eLJTt/fb7jqVz0aFMq+sbFbRfiQptuctsh82d4Lh5+erD1EWhBOuTo8zM3e5j0I1G9BVV7QFa8KEatO2a3XedZ14sE7yGx4v/mU5Ver7g+WrS0pRL2/+o/x75nCL8xfTS1rVqI1VHX+91PtO2/Pi9WgNPT3Lpbtqvlunpc8Ep/f+fXFlojrnVcGkGRTf9v7+Cgq1DVIxNxJbqBmSZ4X0iEj9ux/KzelcdSDjOvgl9fbH3/hlIR3GIKPLbE8q65IsVW+mfdVwdVPbS1SVVh4UNiwZw/Inqf0AO/ildNzJgC2pe94KMz/e/bI7BKtvvbl+bWO024hcaL17YEZ3tx1MPWryEkTvxfkwf/joZFD/v30dpoe42INzgSDD43DL1lOWeNcANpq12x1fB0XAwPWH0iti2xrO8T7bXi/+XPw29vwMr/iO0+y25jG6R306othByfm5juGi9yi99veWhox7J5eBwiu9REXT9iNDXZ5bK5l3FR2S8sCzRyENU5Qg8sehTV46LxKn151cH7ZvzQxA2jVgG9Qqj3+tpB1QOZC3UftLA0eGfgdQvXj7B4GP8Psa35bLXWN6veY3rmVIrljuJxwdsniGm3nx6EuXeJRkCLYm7zuw6xwo4puG0L4gFdZOmRuGtYyvx11nDZhLgwBvo+cwrHfI9T5F9uyVfO7YBPzoHpF/mnBptPfzl0lsC8df732vE0K4n5EItH7PlRJHgP8ona6mar2mV8Dwvu9fszz5wMX1zut1hrIlsvlhvKARXiBwsfMo2D0gj50AYCj5eKDrM8E9b8F374e9P9DqwAa4WYhj0UWsBO7rqmnYaWV1Sjs4P8CtKE2Mnb0L79v70FXht4+L7TQy/zv9dbh0u2i0FNW+zRWZrqCkVkttb5tYRLG/gFHls+y7m+56O7Oo7yLJEb9curxfvPL2vZitlW8O/mz+Hra7uujp3Jry/Ca4PhmShhGNDQp5ss8C2koQ26jAH+afO2CNQNdrNXHbou+gWTmlvgNOEWliBeOxp8vecn4d6gb8OXvyDaqPN9Qm7Jk+J140f+ffTBhMubtWW2amGQaC+aEUYvdn+8X+Q33rdE/K+1LSGxUJoh+riGUr4PD2OJJQeHQvvFsq1GLEa1WifyTUH+HMSNYln3W9cX+QWcTtQ7fPdDoGLomMHA4xJtkC+4zg18ZNlHteFgefVLaMjB39d8eQ1GZjsKGN03lQrtu/pz9bjg21shug87U4ZT56zjtKJ5PBMX07iLRTuv5i48HU4u0M72X/X4/dS1vr25WNZfS61/czRA1i8drFP3IsXyobDXCUum5s9VXwxV+8W0W9o0IQx/ekBYGA7l8+V2iIcqILTtPJL1xf7prOZisSNoVhF9g+92gCmYNcXr2BQcKkSGRbdalL1WTLtq4t9lbxpFnbdevDZvyPVLNetdMrSGWOsYTIHtq7tmeW9uWdamprRz0vKSasJXs9Tqr69WdmA4RPbU1a01sWwRnaU5SFgoqg7AkqfESnt6a5Q2zZUw5NDno1mRrBX+OgN4nNQYDOy++BUxiDqS37sl9i8Xv6feytAWu+YKf73DrYe+kW7uStHatKKGPpr6wIpDl+W24TUG0mAwHtoCmLfhyAJOOxNNFOmfIZcNFj/Zsl97Z6KqQryAEC9fXiWutd6nX6OtIJ/5/xD3VFvtmJ6F/4JNn/r/93qFi8fRSM22fbaYekeFzbp8sHoBpeUHbnTDCGxf9oK+p4tXcwh8e/Ohr0dphtg3LLGpgQH894NmWW5NLGfM9Vuxd8wRQVyOev/11dxmVFW0UUMvg2FXiG0tZSxoLZ+0qsJ/+sCr/eDNEW2fl4bmitBSphrNBU0f5/LBeJGiT2f53BQU1LJYttWI8/a6+DIijEyzWVhZKzIheaR/oB47UJdCzTcI1A+mtVX8oImwc/jqGYBB59NsO3SwpZaJwmfgWRYSzDt1GbwVE3XQrn1dLcwUa2JZMfCdU/TFxSafZVpfttsBqNSMupZrNzzFP5YJS+78cL87T4M2AGweTKeJ5dx17Qseba+xxOv1BxUG+AYCzf3V9TPb2mz3/Hvgqymd4yd/lJBiWY+qig5rydP+bXNuE5bMd04Qn+uXIVW9QkhpDfAhxIDVbaPSaBLWsLYa1boi6H2qeF91BOJJ80dusoyoDYc5kL8v/Tu3JcWKfRqaieUPTxPi3+2EV/oKi7LGrrniVbO+jrtbZCPI0lls9B2gJki1/duyALaUKmf3gqYjU+1B1xpDDa2xbczyoRfLotOvN5poOFM3/dja7+W0+JdPjh3Q9IGuyRGvquq3hLbH8qLv+PRR2h4H1/VIZErGu5BywsE+zB1l5StC2Gt1066DtYMLLLw9WqQE7ChuR6O/3kFi+VCr7JVn+hfL0YJhm1vem5X1qbuUU8p/odarEz5lew52y5g28cgCTjsTbVlc/SzChg9h7dvi70hx2VqPDTiwQmQqST1F/F/jEw4tZX1oj9+43nVm0aP+1UHdThE0tmOOEEcbP4JVr/oHU5kLhYvHry+165QOG0uFGPid+4wYSOvvwUaXi2BhHHALn1KroqAaTD6xfAg3OGcD9DzZL7pWHOJ8SneK9jIoyj+Artwv2hOnhTyTCavmj9qSWK4tFKL8u78K167vpoogrnzdzJHWxnrdwhCSMEwYKYa3MK0PrQd56y3OtXnta+c0kam1rS6b/x7bu0Rcz7pCv5sEQE0e9hK/AWFlSEhTl5ay3cLP+T+94ZW+NDjqeDk2hqt6Jot71GWDgHAIjUcFno0IYIXFV6avrf3SksP9CZorSi67A8yM6JtKntPXFqsqDp+osxsMfoH32UXwxnEtn6uqwq75VLjqWaE4GvuePQ5l5JUAACAASURBVIGizbK1kMu5RcHVaGBQANEHOgy+7+r7bt/7dIdoyzeXHeyHbNHEst5AFhjpHxB9dkGLi6LVJR9PuVFfu/aJZbvHwXsRwVTYKlpPGqCvizajXprh2/cwFq/pJqRY1rP+A9FZrXnL30jqBdW691pOZRPpi6A9xCjpSWMdZ3n3U2cOatsnyloFicN8lsacgz+3VbcrJUwZbpaFBDdzw7DzlklXttsuHiRtCkUv5Ow1/hs/bjAcd4noANOnC0EGMPgC6H+O6Pi10ai9jgZFwWsM1FmWfR2D0+JLD9aCMG5+TeKHiIZ29zz/Nu14vnqlBway12zGXlfIw/Gx3FS2lBXBwU2P5RPvZ6x5kMs2PQPX+yzCrS3R6bT4O9WYflC+2/+ZZrWxVfvPqb5I3Buaxa7FYzb4r7FeyHlcjcEZjt7jhf9gW1HwP94vLHIt4bIJ/+s1/4VZ14kBhFbH3LWHFj97m1kXf3uz7f1bINdjYW20bxrZXtt0oHOodES2auEqgwJlvsa0uU+3HpedmQ5xP2wyG8Q9VZgO74+F9e832a9FdsyBDf/z///DnU0DFDsDe62YOtUPSBt8Azu9lU9b3vhI83rba+GFpNbThGn+91O+EM/B/RkQltSyW1h7fHAbV1C0wIYPhGua2yGmxWffKMTcjw+IfeqLIdcX96ANel0W8btlzO38BZJUVdwPAD1PEgJ1x2z/ggvac5g6TrhkVGSB28FFPXtwfPpTXBhip9RziM7c0dA0UK+ilVRzWttYXywyLkSmiEFEXbEwxMy7h0pbOZN69WDsnvdICwps+XpYdAMt/Yxf8Xb/QhvLn/edn+++N/tcI856tOW6tTZt3ty1ULuWLR7DBh4Xyzw13JcQh1qRJdqpwnRhVBpwnmgnv5sqpu5Pux/u2gCn3ANAZaV/0LU6JBi3tZJnYmPIDDDDunebxIXU6SzytbZqcFlRTUEUGqDIZGSOu5x/7HgXu6I09mcvO3NYGhqCB6Amjzk+a+xqt6+/czZg8QlUi0FnWdYC47weMTBs0LkzZK+E2TdycVA9//AWYPe5z6wKFouUZEUlH3SZHIpCoclIkxZeZ1n2+u6TuhNvEdv0xiWPCxW4r6hZkLiOLwt/5b/RkU37/NA4cd9o96DO/cvj9UB5JucGVnNlau9Wj+s/gaYzQZ9Z9/NheBDfZn3LA1kzyTabWrifXFQZDMwLC/VrFm0QIMXyMYp+3fiMuaIRry+Gk24X4mnx4+IzgxnOeVJYVQESjhOd+iEyXGw0iAdwcaCh6U2iF09eX37FoEgx+q7cJ4Txd7f7b/bs1eJB/eFvrRfm9XBuzyTuS4zHqrt5N7lrman4b/haVwNWew3WaJ/g/+lB/zH0QXy9x4vVghpKhKU9exWZAWbW1ecIP2y3XTyEXi/F1lJO6dOLaQnJjZ2iy1HHgtAQvE6LCPZoKeBLd03Kz3uatMnviGk9vW+TZmn3PbS39Ehkcs9k9jTksSgslC22Eh5OiG0qkBz1HDCbcKseyqxllPYYwe5+pzY9P/20k7OB+oAQ1hevR43p17SOms9hfYkYeycOF1OGvzwm3HJaEbp7XDUsjPL5U+saQFVXzzqTubH8FnHZRRkLH2q63V4rOtbPL2+6vXyPP3inJk+kZWuNumIRVKoPgNT8/jrA1Wohf4swsi4o0CeWHXiAdUGBeA4h1p1OC194KnknLt6/0VLeqpU0S3FRpYpGd2+AWQiejT7Lpr5Rr87BRQu2ku+mikAn7bff9hXkNHPVUFWR4urH+zsWIQ/i99r8ufCz13zs3U4slfs4YDYJdy5LhShD60Ty1jcVSft/hVcH+gNEd353cJqyhjJ4PkkMdrQZhb2t+APWFQkLUGgcDDxXuCVF9Gh5ajbjewpMRqyKIqyu2vSq1+u3ImkDSb3IL9vVtC3ct0TcSwFh8NVfxLWs9U3FB0aIoNdvb4a0z8RUfXt8f0EMADXXmpr8pmXOuwc+PN2/LX6I3592/j984s7J7PAwXgvwtb+2apwFG6k0CR/MAoOX/R7dvWepFPeCfuDjbKBWE6PQ1Nigr+er/SF3LTXOBi6172JFRLQQ55pLwtaZlG2e3viVLyLCW7b4au5m5mDxbBhMoq+oK/QLkIos0Q41+l8HUmIpoSwo/ODjAXvq8xjRN5Ucbepfa580d7HovuL1q6sPTnen8UISTJ/EfVGBLAsNwVq5V7RT032LE516r3jdNY8qg4EL9k4nXbXAKJE+rrpOXIdTksZSaDKyyVvPdxFh3JGUcNCMVJ3L/2yXuurAZedzQwMXlC3mO51LwqLQkIN88TMDzFCTR7lR/MbBmqi011Hl8xOuMnBwO+5sEHn3Xxsg/MIL0xvbJYvve8UeC9lmE1k+y/J+d1PjgIJCTnAYF/RK4f24BP8HmjA3GPD6WqmKoFC2BAY0M6w42RnQxkwbkG0tZlZEOG63jdnhYTzYsw//F26iwVJ60Kzu5tLNjPpiFOs/PR2bolCNh9XBQXwQFdGyMatsD7zU0z+wB6p9luzZmbNZUrGZ96MiD3LDqHHWcWbvnjwRH0tJha+90AZkreWS/h3S5WJZUZQLFEXJVBRln6IorQxtfyeE60aCa/4rOk6PE3qMhr5n4gI2JQ4i75+bWNRrGJlJgwCYHxnNrYmxNFTug90/thp5muoRD8I6k9ooDLOXPsna1/uIiOVnIiF7BR9ERTA2fw7XhnuFe0P2SmER0TpyrREt2NTqKj+q04LqszRUuP0N/veKhShM/Od0EThV4Krj2iAL54faxWNaV0iFwSDe6zrtGWY3r1j2NAoOL3BjciJ3bHmViTlfiwf7tzfgxWTWlQmL2Y+BxsZO+PUAJ48lxLGu/oA4l+KtB1s6XVa+Cg9j8/CLubZoIbcunkpdWHzjdGWDtYLbAy1c2yORHFspLp0vVIHdP0WY6nI36WgqLaXcm+AXYBO/m8gUNZ/frvmUCwcO81mi/Q1Jpq2MSaF2bl98O4+Ur6baYKDWYBATZD7Lsr02j8kpSbwf3cwvrRW3mb+Ya3kkDBH04XOPse5eQOayxxv3mVmfRUaA2S8O89b7LfhATe4qMUUMOJ1W/302fRK82APy1+MEMgLM4nfaNZ/0A4u5LSmBsr6ntZ0Cb+FDOBtKybroBbb6GvtWp2eLtrbqa2pTxB1yR7Ivz6nbwfOx0dyRnEhaXRuDSVXlzTATrzTs4qPwIPJNRhZEJ7DPbG4a+Kfh9fJORAgRhgBCDQHkms2wa54/T2igXxhYK7I4oW8qn0ZGHHwcEOKiTHdt9NZwS7lIcZU2Db6+pun3Fj7cNHe6ni+niCwgi5/wnZ+v8y3YyGvhAVzWswf7zSbxDFvKqffYyRx4jpj+zVsnXME2fCSeKUuZ3wo95zYRL6FNY1qrhBB324QLRH0xLsBqNLdYLXfxVtZGJ3HfivtJK0mj1lHL9vBoEejXrC3x5K/nwl4p/DMxng9mnM7Nn40Uwr5gI26XVVju9vzks+hv5vG4GGaFi9zE1voispKHoQJWReHDnoMov/QtiOoNadPYtO1zXoqJFoPFfF8chMsmrvGMS8R5vdIPPj1fdM75m5qKVBD5amdcLMp/a7hwHQJRxy1fQOkO1L1LeTEujptXPcic8bfgPdv3e2z/Bo/LynNxMcxwFFBhNICjnprFjwFwXQ+R5aLOq5sG3zxd3Avr3m3c9KFSz2mOnaxP8Q0s64oO9vdMny4G1Nu+5q1glWyvlX/UbKLGY28i8Kt9Ll7h5jAKTaaWZ4K0QY05BEdDKb9ExXNdQjSW2nyctfnsCfD97mW7UV120oICuS73W86bcx7XLLwOm6LwXVgovwUHNdZzQbVo05ZowWe+dr++aDN7EwczfcJ9ODS3tx/uOLhOXq8YjOpcQap8YtQLfN57BF9YD+BKEn7P88NCKXRU8lb6W7yw7xt+DQmmyiIG9UPjxHXcGCiEe43BQIOtkm/CwxrFbL1OYNW7GlBdFj5yi3tjZkQ4cQGRxARGC+u87xomqKI+9yTGk1+Xz6++c210W7DXUumr80qjG7fbCR4Xr8VEMSc89CC/btuG//Fkzg/sNfufs0vnXsqlPXsAkBSaBMDA6IGNnwebglniy4axMlg3wPI4+SUkmFoFnD43jOcOzOGmHknsqz1AraNWuDl4nPwcJur9l8F/ISUspUmdHhrzEHf1u4IGg4Fphb/yXFwMi81efjTY2GAvA6cF/RP+S84vvt/DP8C4KymB96Oj2EILFl9twZxd/pneap/7W4VNXJ8QVT2o33gu3x8vY63x9Y+aQehoLyd/BJgOvcvhoyiKEXgPOA8oADYpijJfVdVWcnF1L6UBQTyQnMig5JP427ZF1H99JW+m9CIj83+ca4wgMjqSj0PsMO/Sxu+MHnUOWyrXggF21WRy8jfXCyvKnWtEY6TzW6pVVEAhzeDE6bISAFxaOBeS4ti+5El2BAZw3BdX8HGfXqh42Km6yDOZcCoKJlWlT8Ve6HsGJdX7+FtKMk9XVDHaUoE7NBqzwYyqqii+8irr/f5mFa56fHZj0o0exgXENT7EWa46DpgUwMOLsdFc3GDhhh5J3F5Ty9VV+5jY1/fN0pVQChMDA9gWGMi64CBs2ojaWcvdiQm8n/4/nk2MIcleCCHBmIxmqC6Ckh3MCxKNRIOznjeiozCh8s/yTOh1MvXOemrsNTSUZ/BSXAxY/OnHPg1Sud/3QO3OXcl6XyOzyVaCUbNOAXs8DRjVEM6JG82ukrQm4ve76u3kBJh5cexTPLbh33h90153rhe+6UtDgznLaRXWGuAzZzGuAIWTksawqGQTlQlxbAkKZIjDyZeVWSjAmtI09gUEsM+RR7/+4+gbPRDX9m8YXrYL4gawPG85A6MG0iuiF6qq4vXdBs/ExfCmy4YBGLvxMUjyRzJPq9jIvugo3qvNh8gUtn55CeuDg/j7hKfBYOSOTS+wu08vrmiw8cPXY1mRW0DsYyXUle3EbTAQ4/XycVQkH0ZHcqHbxMtpnzIvLoZNwUF8YbDwYBsN06r6A9zdNxW2vgo9kvih3sSAlqaBlz8vfE9PuccfYa8jRlWoUlQSPF6w16K6ncyJEMK12laJmrseJbKHP/m/hsvK4tDgxn8v6iU6ghMDo5led3CGEKu9mjUhwVwbPpAsk0KeNZ0tK56lMDSESRYrisclhMXGj8mrEVO8/42J4q9eLxiEy8aD8bFYDAY+3PIFuOw8HxtNttnMp5ZykZIOsJZsZ0F4GK6IZMaX7aCf9n1rFb/u/JwBmz+l1xNNfTlVVcWybzE2o4FNQUF4gfO9HsyANesX5vmsX+tDQumfvwFMQdzcI5G97n18G2CmIGcx1m0zubjBwq8R0ayIi+H/LOWEADkmE1aDwtDlz8O1X8Mrfck3GUkCzFmL2G42cH3fVM6w2njPZWu8pwFwNDCtejvvxERB3jKW5S1r/OgtdyUT9i8X1maAPT/xlTUXgqPZEBzEBqxgCkKtzuHbtLf5IDUFhzmEL/L2kfBCIorXxfw+vZgfDn+xVPC2s5Avg7yMTE5khMPBzKo03qtK4+pew7mo/gC3JQj3mhsaCnBbyvkmJppbqjJxmkwsDg3h0k/OZo3BzgUFGwjIX09jwq37dkJUL0BMuXuBlLRP+TY8jHE2O6mqilqTz4dREQSqKsbyDXwdGw1lm9lctpn58aN5JiSCfmV7KIpI8t/WISFMWf481T6/zVSTuGfrdQPyWq+Lj2Ki+Lu9hnAARz1f+zTPI+EGThp8Ejdnb2PEkidh4vON31tWlsaS+Fjuq9rLd6H+AOd3oiO5s2wnt/RM5qmKKmp9ZQ+MHkiuPa1FN7va6v08Fx/LjUaVV+vS2BZpBrxkVGfyo7GBH1KSea68ksvzN/BrSDD3JieCrYQbh97IF7u+4LlTrmVBsUibttlpxWVQCPAK0fx2TBTDnE5Gz7qWoL8u42x3Fo4QBdJfR+05iMKqLO6priWq6oBwT/ORv+MrLuqbykOV/un9V2KiOBBg5qr6Bt4w1ELaayxNHck9NVm8Hy1++63lW9lavpUf42N5pLIaCGvsl372idkor5fnG3bzU1wMz8fFsCU7jzqn32Jb73XxqdFOHeKHsBkMXNxnIgX1BeytK2t0/YpQoVpRKDeZ+FesXxxWe0Q/YbWUUWQy0Tsonlx7Oau9dZxlrWaGb4A9uWxXE8viAU8Dc8t2MLen38B2XMxx7K7a3fi+xFLCyPiR7K0W7Y/V7ReRXt08V159AQ8lxnOO10KZ6qEnJgoUMWAvs5bz0foXOFB7gBljHufHsFDOjBzEE+OeYF/1Pq6Yf0XjcXqG98TrEoO7PHvTNmmP18KYhmLO6d2Lf1bXcDNQ6dunxniwzXSOycVoYEbGDFLCUpiQOgFQyDabCHVb0TJKV+oHk0BWQIAwKI0Q/vGZVZksrvWve1DXUEL2V1cyI9SANyyGp+y1XStCO5GurufJwD5VVQ8AKIoyC7gM+F2JZavLyt9+up68ukyqggLZXr2dOamisw42mOkflsLsyp0Q5fefjAuOY3TCaJbk+oOgnggz4AhP4aHaQozpH7B609tcOuwGMiLiiAqKoVYBIwpVeDnRXEp6rV/QzjLYeLFHEj1cblyKwtT4sXxato5JvcRIVVFVZpZvo6+zntcLl3IgwMztSQmEzbuYKo+Np095mmfXPctnw+9hzIl/Y0eZ39+swmVlcc5ikkOTKTYqXBcY3zgqTfP5bCUYQ5gVAbN8wuariHBcOf4R4d9H/p0Pt33I1KREnAb/AODjiR9z99K7sKlOnoqLJTvAzD6fYTLLayXfVspvvz1Hg09Y/1K/nyVRogG6oyaHoF4nc+fCm9hWu4+HE09vPK7JYGJQ9CCmV+7iHCeMBErr/FHMhQ1F5Ff6H8LVQQGMs9mJD4mnzmBoMrrNd9aS4HZzcf9LeGzDv5v89snmCLYEubBbSsl1VJAUFMcSs4fJIX15/PxpfLZjGm9sFr67O4IC+cVWywVAlkX8dv3DU/lXfR5UFkFKEp/kLmXogLO591cx7bjtpm3UOHzXWAlgeShsqc9hcDNXi4GRA1BcFna5C3BNO5/6R7O5sYfozG+2VhAUmsBup3AD+CFMCKCn42N5qzqXm5IT2R8QQIjXi9V3nReZ3Czu0wuPb/A0213BZW4b2blLSAxJZGjsUJweJxtLNnJqyqncHSDqExccR4WtgqvD3LxlL+FMhF/bnuo9RBhDeCprBqGJ8VxdvI4zgWk7p7EsbxnvT3ifyMBIgsV4kCqDgUXZixg8wp9KbnNDDv9acTvvlZQx5B87SAjxT0VarRWUmUyMD01lrUUMgk5IOIH0ss1sqsygn62SmKCYxsFgWvEGXIrCqeH9cIQEs7B8B08Y3eRFxfG4qnJF+Rosi1czOnczNUYj+DponA0QFEF1ZSaLw4QP+awdn7EoLITNvnu/fP8S4kbdiEf18Mm+OXwcFwM4GBobzoxlz/B1cl/Cy/fybGI8MR4PukSPFNUXcttP11HYp1eT3/el8iXMt1XySMGPeH3Pz2/h0Qws28qbZcvZ65tefSoult2FiyA+ltdioqj2WbtG1mRwga2S25MTKDGZ6GXZQsDsCbh7JgurOjDeasNTux3MCqtCgoVfa9xAMlY8x9rSjVx46mO8ExNFgGLkikFXkVObw9DYoXyW8RlfR8cwcON7pA48F5ujnntXPcS62GZBtEBF7ipeqttBoNGMxevgcp8lTU9eXTbLjC7AyLagQLYF+QXit9U7+TbZv3jDXXVbyAkwQ2Q4u8pWkpGSjNOg8F+8QCxPxQvXpWEOB7UGA4+nf8Dxpz/K/UvvZqNvQPXPrR/wdlwMSW43S6qz+WnHjINmfM7seSZby7eypXwLVyVE8VbdPmjo3/j5tMgI5noq2JEixE/v/hMh70fqVDc7yrcz68epVNqrWRMZQWrdXpLyV/LRxleoMho5N2Y4GY5KfrEUszKlBy9s/ZSankOYMvQG/rP238wMdgGhGOqzIDyUVHME/UJ7sNG5g/1FP5NrNjNVd036RvZjc9kW/p49m9EfL2bsJR8yKkkEvj5ctpK1YaFUuYvZZvLbCaeGeQEhMBdGRpGQ/QvLYsRs2rRBt3LSSQ+wv2Y/C4r8y9Df+evdbChNZ1yAf9btb0kJnG7N4ylrKQ6doecNdzFEhPNNRDgzVr/ICZd90vhZ+ob/QiC8prtfVvjE7hsxYttxMceRUbWH25ITifR4OLvvRSzMFv1Lg8FAuu8eGRA1AIAC3z1dbzCwwdsgYt+A0X1Tub7a3+7/GhrM9+FBjA2IZYNTiL9zUs9hfdF6vi5ai9tpxQQ04GWAEsRu1UZGYCAXOSAjLIIdJhvYa1levA6nQeGp427jvs2vsIoGhtT4/c//u+0DVqUkMcTpwqEoXF25F3Tj0OfCRzDpohmcMPMEAGKChBFkVPwoxiSOIcQUwj9//Wfj/qpOLGdbxGzBTsWNTVG5jkheQZxLub2KMlsZmdWZTN//A1VGI5MTThbXKnoA955wL//d/F8Ago3BKL64GMVnEApWTIQrRkqUBn7OW4rToLAwLIRL7FVsLBauXatDxIkEqCpeo5kxhgh2uStQVZXX0kRKvkHRg8iqzoKePTC796KFFpZ7HUR6YUyfCYyMH8kb6W/wSO5cJhdfxNjksawqEC5VJ9nsbAoO4p3oSA7YdlMRIQYsV9Ue4HiODbpaLKcA+siRAmCsfgdFUe4A7gBITW1mbToKVNgqOO/bc3GrHjAaGaOEsMdkoMElxMMXk75mcMxg3lt6Px8WLmW818yE8Y9yesrpJIclszhnMQOjB3Lp3EspNplQVJVZYSFs3/k+BJv48cAsf2FGAxcG92SRTVySK3/yT+u+6MuVWGQWP8lfQvsRMHIUCw8s5KZhN/Hc+ud4v2Ij7l/vZ5OrgkAUHAaFKl8AyrPrngXg1p3vcoOnhqoKf4Oy2VnOlyv9vsjHhSQTYg4hQlXYrlrAAC/3mMgis4fNeSvpFxjDEksO08vF9OiTQ6cyZdTdnJh4Ik/8+gBJhiDMUb0xG8yMTRrLC6f8m3+t+T+yA/xTUtcOuZbZe2ZxUUoiWPwBcjtdNY0N387ynYxhCttqRaP0ZslqUOCZQTdy5SkP0+Bs4IpZZ3JDYD0jfryWU4NEhxKHkU/NDrL3+n2nDgSYGe/yEh4cR73RwDP75/DkcZdiNBgpcNWR4najGANQUJo0VDeknM2rOfM4adFfAIjHiFNRuKqXWI70hqE3siRvKaW1uQR73Pwn3MMZjjpybOX0cLn5/Kx3+aUiDY/Xw7vrX+SjopVMLfeveLY4dzFJIUL03hQ6gNcadvFI0WJSFmU0uQ+/umgmG9Le5x7r5zweH0vk1g8aPyuq3kd64UqaszIkmKfT/sN+n9DShPL9Pc7lzaKleBSFHsEJTB35N15Z/wJXxATAChFsNTl2FN9XigHVIJMYvIwNTOCTKcsot5Zzzexz+cFTyZnAggMLeHLNk/QMTaYgOIgQr5eNlDHfUsKqglVsL9/O7Ytv58K+F1KkqFxvTmJnfS4PhwXCcn9u6uXYACN3JyXAtxO4c+Sd3DXqLgA2FYncwxfGHE+R0cDgmMHcM+oeLpl7CbfVboLZZ3HN4Gu4KG40s3dMY7/iwaSqnBDeh4LIKL4xKNT7snB4FYXvLGJ6+2ddDlIAl62Smfu+IyNvReO2F+JiiHV7GBrak12WAlZl/8wuey4/7PuBSJcDjAYuih7BwuodnFT0A+jce6uMRpweJ8vzlzMgcgALt35EocPv29zLFEa+u4E6xcvkHy6myujm6dhx1PU5hTfT3+Q3l7AeRmHknjEP8Hyaf9XOaqORYK8Xr6Kwy17O8mX/pMRkIt7jFUGhtjLQTQOvDfH33iZVpTx/HY+mv8zGEtEpzlkl2oCXBlzLxHF+//XIwEje2vwWk9z7SJ1zPuX2SmyBJk4N78ewPhP4aIc/oHR13nLcCrwaNYa1Cb35Nuvbxs9iDYFUeh38p3gFJSYjjyWewYulorNcdPkCPtv1BfH2Bt7LW0gftwdXSCw51HCWxcqK0BC2BAnroBFo7vmfESjE1J0FC+DrBU0+eztQWH9LTCYeWHoPv9mKCPV6G31J510+jz4RfbC5bVhdVq6dfS4v2fczolgEG16VMI45ZespNJsYE9aHMf0vYGzKaZiAfNXJu4tuxm1wg+/6fuksJHv5PY3lnzPkL7zSbxIl1hIu+v4iHkyMg03/4b0dH1Nlr+Jci5X84DAWhAsh82jsyWw1G1lRs4ccnIz3mikxKBxAWOnO73M+a/KWk+n2sMbk4N1fbuWe4/9OeFA0az3CUrrJJ5RvUKKZqfotur0jerOuLpd1tt1QuJsJFisnxYhsDm+d/RYbizeyOP095tfuZkOpCNhb7yznWouTlN5n8lrFOlaHBLO70m/PmtRvEr3De/P+NhE0e3PNBv6dOZtTep7BCyv+xYpA/yxeikclMmEYu3Tff/ecdzmz15nsr9nPO19N5Pq6egZd/xinpZzGKT1OYdLsc5gbHkagqtIvym+xTlWCyMNOBU3dg76s3sZIu4NtQYF875uluT9qFLFnPMqWsi2M7zGeKnsVTkUhz1lFP8CiqIwwBLHb11+OMYSSEDOCmc5aHJX7WVS2iUS3mzHJJxGNkTlKA3OW+2OCpjnyISCAfb62NtfZgF4+JZrDMBvNDIkZwqDoQY1ieWT8SPpE9qE5+lw9eVbhTqfNYQQrpsYAi2J7eWPM0ewCMRN0Wow/liRSl+c70BSI0Sju0Rq3mBWc3fdaHs6bR6XSwBbfb5Lg9nDdT9dR7Wia5z0tJx/vU1W8892VzHBVUmr1p7HMqvYnOnD5Kuf2uilQHdziNHDv2W9hcVnYnPkDy+v2U5X+NmMv/pJSaymRhgCerCzm0p7JbPTNDF/rCuBrs5Mt9TnHjFjuJVbr9QAAIABJREFUap/lg3OnNIuzUVX1I1VVx6iqOiY+Pr6F3bsWk9fLUJuVeLebrxIm8NnkBay7bh1LgkcyzdyfwTGDAZjcX1jIxjq9TBk8heQwYYGY2GcifSP7cmnfSQx0OknyeNgeYCQSA32cLhLdbkbZ/Y/GeTEjCFfFZclx1TDE4eRUo7CCXFVXz3PllUyubyB5zB3cNeouFlyxgCmDp3Bv6EDWqBY2lGxgam0dX/YXgRGx7oMDymbunsnC8k2cYLdjVFW2u/xBDmZVZUigSKGTZAgixyAaooSwHjx1+ovMvX4NU3qf37j/f8oqmNJfuJ2MSx7H0uvWMvOa5Xx2wWf877z/oSgKE/qdz6nupisT/XXEXxkT4bfcfJF0Ib0xU6yI+hpVlVvzf2DEDH/+TpcCL5VVcOXxYhntsIAw3i2tZKzNzo7KnXxYuIx4t5uTg4T4XF62qUmZCaHJjOstspV8V72d97a+B4iptliPmD5fevVSll+9nDtH3sltw2/jyn6XcH6D392gHA+j7A4GjxbZBMxGM59f+DmL/vIr/+oxgQqTkbGzTqXcVUeSx01EaBxXD7qaa4Zcw+3JZ7DR5GXuTn8u1w3FG/gyYwbBXi8To0U+5lKPhc01/sYnwKsSFBDKmWPu4t6qGn4ODWFW5izMPp/Cy5f/nefWi8UC7qquIcLj4a81whdvftlGUtwetiRcxLLwsfy7vJJbT7yXBZfPZ+uU1fwyZRlTBk/h2bim6YI0oQyQ5a7j4gYL76WKKb34kHjONUSwTLHx2qbXGq9jgaWYQQ4n3zgjsCkKs3fOILs2m4FRA9hdtZs30t/gZJeX+0IGMLXWP1X6TomIIC81Nb1HPtj2AaM/H83Ta59maeFvRHo8TEo8mQVXLOC1M1+jT2Qfehn8AnBW5ixuWvMIP9Zlsrt2HwYVgmIHcMXAK7gyajgmVeWDqJOJ8Yh7+kJzPA9VVvNQZTUv9r0agAfWPsUb6W/wS/lmRtvtzD/nI+6srmV2UQmzLp9Hslfho/o9zM6ajcvrosJo4MXyCl6e9P/tnXd4XMXVh9/ZqlWXrGpbbnKXbYQr7hhjYxsMGGIwHUIPkBBaCvWjBBJIKCEBAwkQOiGBEFoCKRBKKAGCMQktBjfJki3b6nXn+2Pu1d5d7apYkrWWzvs8eiTt3r07e2fvzG/OnPIQl2XPYWFtHR6tubO0jOuVsYx//9VLuPTVSzn2uWN5tvRNRjY2scz6Tt2cdzDfK76Aw2vqqWiqJiEYZNXY1ZxWdBq2zL2hfAcvz7yeo8eFFs/nthjL3Bm7Kpmp/bzcVM5bOz7mpN2VPLb0VwxpaubK7RWcuquSx7eU8IF3EjeU7+Cq7Ts43jeYZqU46KObeaf0HS6q2MmSmloa63bxrZ27WFoQHlx7+uTTubnICIONNVupa2ngxN2V3L3gp1ww9dvMrAuJoat3GZvSyEA2l0y/hKcOe5LH0w/gVyXbeHmOKRv/j5ZdFNc3cOSwpVy73wXcO+ZkhqaN4MrZVzI711hID08dz9p5P+aa8h3cUXwRTx72JNnKXJGnhx/DsOShrD14LReMPJKbPAU8tqWUx7eUcG51aCx9tSWf8wqWcWBNLXdsK2cRSayv3kiq1jxIPsePP57HDn2MUWmjcCkXSd4kshOzWeHKYDPNvFhuxOLpo45gWFMTR1ZVc//cH3Fe8Xl43V7GqAC/8zSCDpLb3MwxlVXcXVpGqUPKz0spZMbgA/C6vRSkFPCLaaGwnIp6s2iaU9fAN6ecxejmIPeVbGOeDnB4wWIyWlo4dVcla+feyIPzb2ZkYxO/rlLMHjybl9e8xu2Foe/DnR/dzY3v3MhBDUHeqkslyfIvH689PLz8ISYk5PDGiif4wxF/YIJ/UOvrZtfVt6ZfDHgCLCxYyNzUkB+tzcktAY4PGGPV8KYm/rt9PUprXh9+HDfOu5Fzi8/lo5M/4rlis8i66p/XseSpJfx9+4dktLQwRBkRtNqXx20H3tZ63odyl7CwwPh/F6YXctvEM5mx8GrS/GmsLFxJViCL43xmh6BBKbwuLzOC5ntwTO7s1vOcWd3Inw/+dev/F7hzSPeYe+SAujqKcorJS8pj+cjluJSLMemWO0fdFkqqS6hSMNEbEpYj/ZkU506lWSn+ufFvvFn5Jcura3EFMki0ahwku/0cXFPbKpImNoS+e5/5wu2Mo61r/tuVv+WGeTewfORyzppyFsNTQxkmFhUs4pARh3Ba4ii2uRXaGt8rLLeSOksa+V0efjnTxKqU1u9sdd+oaKwkvaUFn8O1alRaaHGR4EkgYAXe7rRek+NLJdMdYIeCEkv8VrvdbKl2pAW0UIfciNvlJseXSrNSvF8anv3kzF0O95fGKrZWb6UZzXBtrlCSN4mfH/xLptU3UFW9lU1Vm3ji0yfIaWoiZ8hMEtyhHabzpl/IkOYWPqzbyvrt67npnZta/Z7jld62LG8GnPuRQwmzy/Q96XW7eaTEWkEtO6m1elLeMQ+T5zguf8gMXv16M2krfhr1PDcsuAmdu4irXj6HZ1KS+U2Nj1FlX5knsyfwXsn/+GlmOlNzp/Lmwmv4/j1FPJ+cxK1l5eStvJb3X7yAqfUNeM5/jyOTc1pTZtnbzmcUn0fFC9/kobRUTtldSdqEb/Dc5GMZ9sWrvPPK93g/EGBUQwPNSrHO7+eRtBRm1jWwyetnfYvxVc1ubuZH5TtIKzbRzYelFPKzSuO0n58S6qbByaG/D6xrgJS2W63OtnldXu4aNIc3P/0dpcNnsbPocHISc/ju+JNY887VXFu+g+KJY7mqeiunV5uo+aOqqvmtte1d0BRKn7ZCByApNNiPG38E9/3rAX48ehoPt5RzYG0dJ4w7iBe+fKj1mMNb/DzrbqAwdThTB8/iLxUtXJqdwb3r7uWwwsOoDDaSai3R7K1/26JJVSm3lO/g/7ZXENCaCreLFDyhPMsYlxCAcZljzTcaeLeuhBkak97PYs2k03hm48u8WPomALnNzby2+TXKass4e3cV+cmhgihXbq9gckMDhY1Nxk0AwJfEGQfdTN3fLuOejDRWVVXzZGp4BPs586/nnD9+GwXcZ7kFnbK7Cs/wRHIWXc6qsjMgcwQjIvrq0EFT+MdXf8KtNXUuFy8nJfLrkm08kJbKSbsrOaDeqi5pcWLCMB6t28WDnxjhPyNvBqOrdnDypn8wdO4pZGx4mMqt/2Jn/U5W7ygDb5DPfT4urawnYXAiYxpDvmwH1tUx1pvOl407uWJHBUdW1XBmXg4lablsadzN7z//PcnuBPZraMSbEL59/uuic3A9fwlvJ/h5Ji0dv8vDuWWlvJOSzhDtguFz8bq8XLPgRq64Y388Uw4kt/yfVLgho7mJUyrNd7+kYAHqf0/y9+0fkh3I5qmUqaT86yG8Q2bxrW/83kTNe3yc4svndw0leL1eLio6nYOfv5K8FrPrdNKS2zjpBmtUmHsh1XkTuepf1/HyJlOZsDHYyLbGRvB5eXJrKRdV7CJ/wjCK9juTT758i2er3uOkyircgQxQirt9Y/jz9vdZmTAYNW45uNysqG2gxKU5fchijvrgN+S2tPDukDncXLWeZn8qyxt2kjv0AF7cvNVYImaeZQJCj/gFh9fvgsZq6pKzyb1/Hrf6Grlg2HJO27AWqDLZIFqawgvzWCybeDwLnr+Cp1OSWDx8Kblb/9Lql3r19gquzsrk6KpqfmDlqs1LyCTRm8i4QRPgiHth2c/An8I1746iZct7HFlVjS+zkFV5R4S9z34TV/NoQxVFk0/E5fEx/Pz/QkIqE4A/5a9k29s/Z+joNJ4/2qTHmjPEWuSVfgwuNxO3fkjaXy9hbGMTmQdfxDnTTwPSwe1jUeZIk+EEYO6R/GDWD9p8ToCThx/C/V+ZQNCbyrYzNLWA5zdbWSYyQwv8Y705XNP4NWcHRnLOF+/BRZ9ASyP3/HJ/1ib7+b/tFeRc+DIkhWaKBROOYd1TZmyZbMV7LE0aRtq081hRfJapSDnrHIa3NPLaRkuwFC4mvfQjnt1SArkh48GU0Yfy+mu3st3tanV3+UnJZvwLT+LhN27h6ZQklvnS8OcU8+SxIf/zW8edyrKPzDw1r64ulAXEYnb6WEZ82WTcXzAicOik02D6aZz90T3cm57KO6XvMKKpmbSEjNa4G6UUeVkT21zPR3Y1s35UEQ9te5NjCopJSc5nRs40pqWOonjuVeEHR0lfd8qgafyqJBSXcOeStWx76zb0xGO4pfRvuLXm2+WlMHg6t24rp8ztZlbxatbmjeeHb17JsZXVUHxi2DltC/VdjZsZV2Esqgf487i9/isAipMK2DXsQPjo59z+1R9pJsji2lrwpxKwPORHeNO5texzHjz8Bm5Zt5aTd1fxuxTNu4Hw6wmQ5RDiYPzOncF9AHccZHKoP/T7NTQqRWVjJWn+NHZaAYv1KiSW5w+Zy+T6BrY27KS2JiRsk4I6LPf8tNxp/HThT3n565cZlTaKsmojOHdaFnS/20+2J5GPPS58dSYIsjJK8O+cnKkw23xvcwJZsAve2Wp2XpZX1/BichKulDx+kVzIedXruPS1SymwdMJwHOdLG0qqL4VNDbu5+O9mJ6u5pZGkKat5a+pJ7P+QCcJNSxjEpGbN+sZd/OaT3/DChhc4d79z27QrnuhtsfwuMEYpNRLYAqwBju/l9+waWWNg+c3w4qUmvVAsfElkXtV+eVo17hB++PYULv3qVVKtoAkCmbD6AabfPZfHtm6DvP3BG+CqWri4YjPZyg8jFzDTtj5ntV31AzBiHhdVN3Pezk0kJWbBoNEMVwr2H8as5y5kVn2DCSzc9jHz6uop9bhZEUzgba2wM0O+tKUc3yl/hALj83Tq0IMpe+tNMlqC+BJDAnVw+igW1tYxu66OxMzCUJ7O9j770BnM/fARs780+QwAigrms+4JKwgvcRAzJ5/Mz545ma+9Xg6qreXl1DSy/Bk8seEDpo0chj8YxJU1LvzEK26Bxhq+88nTDJ1wEId9/Qpp+TN4+y8/YpblF/q9wGiWb/wbcwunAZCTMYr9Gst53w0/+uePLLEcbZMDk5puyXUkudyw7imytr4PNIYFZtrkpo1icn0D6yz/ui98XnCHbiF/1liuL9/BGsvvsaCpmfdqy8j0pXHa7k3gS2ZZbQMvJfpZVVXdOsTktDh2B7LHc/ruSmpS8zhz52Y+8fv42O9nVf48Jqx7FpUaHgENsKay0qTySkg1OWOjMWw2N71k/OBqlGJldQ0z6huYUe/IGxoICdWC5CH8YstblK64iev+eR3fz5zB2LesiW78YST+7yEqGnej0SQ2VHPLzlo2ezyMq6uHzFHkR+x4PFJ8MZ4nT2kdcO4vLYPSMn6VlsJtmRlUt9Rz3s7dbdJE5Y05FFq+y8qaWlbWhPzQJ+8ogynHhipCZozAc+5bMKiQSe/cxH+80OQIaMzPK+bWsu08P2omp02/kMw/XGQqfblcMHR663En5M3lhDduR1/8GXz4KMrZN96ASYG1bT0s+T+SSz7iqu0VPD58EkF/CkOSh/C3TX9jQkMjCROOIP+TZ1pzqE4sPITX//C0GResSl8z/VnM3LETFp4FLjNB/7iyyVRQHJtL3mF3QCCDmds/47evvGRyBXtNH6mj7jPfvaJQgA+JmZCYSQD4ZuZUDl//FBkb1kL2BDjxKUjKMbluU6MsfhMzSUwbxgm7voZ1v4fJx5hrAwxb8wT3P3w0ALb8TEqI8Ge2so8cPfJQ+O/fzWPp4X7brX23vyMHdEIoQ4l34WUMba4P/0w2eWbrWTVUcUKl5e+fb23gHmRllHGm9ts/XDw5ySqYzeXv3cnMrCmMqtlkFgVpBSbXtF2FDFjly2fYjo1MrS0z3xGrrcUjl3CXnaM9OTf85G6v6act7/PCpi0kBzVpU44PPXfQFaFjF18NKXmmH+0FzCxHOtBAOmnBIKnBILl4GJw2Er/eCDkTGJ09iUu3fgA5bfsyO9G0aaoviyHNG9uI5Yys8Ty9pYSNXg93ZqSzuKYWhs2ClDzmFq5gbcUbvFf+ISsaG8MDRAG/I+95wBOgrrmOwZ5ECuZdzrIHDoXiEwD49fIHYl7/SDIyRrLoi1ry3ebaJw6dwcjVj9DS1MAancI3tli7cEpxcK2VpWHUgUxMyuGZLVZGIHe4lPG7/ewfdLPO1cLOejNvZ/lTeSqYS96m9/DMziUrcwxDmlv43PIPHtviAm8CAeUGDWlagXJz0n5nM+rl65hTV8+BtXW8lJTINdmh+TIhGER5O1mZFhMHBVDXXEeaP40dTeHxK36XF9x+hjY38++GCmqDjQxpCbLF6zG5oCMKNS0dsZSlI5aac1vW9p0t9fiCGrfbx4SEbJ6p2QDNNQSCQSrdbpwb/N/fUcExJ4Z80POSzPz10sZXSApqLqvYyTaPm5WDiklPLyRn1we8seWN1uOH6/Brn+z2U63r8Njlw7WGCSvxuDw8VV5l1RxIoBAffwrWsXnDC4zJGBPmUhKP9KpY1lo3K6XOB/6EcUX7tdZ6fQcv2/vMOsv89AABFAFbKI9cAIfdBoMK4fJSk1NzkLFcJDY3kdgShGVXt0bet4vHjydnAp7N70LelJCYcw4SM86A5y4kLRjktrLtMHgqlzRUcYKngVRc+HKLYHhoa0tljuR7FZaLhmMQ9CRmcuc2S0QVz+jcBx9qHedMT+YQ4AQyYMR8ltTWAXXgT+Uf1QlQbbZ4H9paSm5zCxQfHH5etxfmfJuEdb/lhI//bCb85FwSteaXlUEy6ypJnT6ZeZ++CAVWG3Imcs6HD/PH0eMoqyujgaAZ+KKhFMy1Ai+mnWpSsMVAJWfzaMk2gkffx3kf38VxJV+FH+BPocgTuo5p1lbpivw5JH26DnxJ/KiqhSuHLMRbv86kz7tyR3gfZo8jUcP3Nxqf83tKyti47HqKtm+AqmpIzIBz3wRfErfdN5fkMYeg2NjxgiZ/v9Y/k7RmkT3pOHEWAEnMZMHuHZAylqOmXoXnd2YBRCAT8iYT0IrtlvUiMSGDUb7BjCr/r8nFPetc3GkFXPvCOYxtbISENBIGTzM5dRsqYcT81jSIs+vquQ24athKJm/4RZhFH4CUCDHiJBDuj2yXHT+xyc1vA5pJVQ7xFEhnsQ6w2D0YHj0Z0HDEnbQhaywEm1FPnGBSuAGc5qhOucQRIJoxnKOrazg6fwXMuxCAT1/+IVlv3QUFlrXRDjTNmUiaPS7YuaxtMZ/jqBBWZ7U5KSsk+Kqmwd9/bNLMDTELQqasjn1dAPImkbXuSfP3SU+Hxhhn5bRILngfbhxiMsmMWRp6fHTonnxh0xZTindKIMoJCFt4tFtQJhq+JFj+4/aPsXP+glkEOEl0fB9iGR0AfCmsqaqGqjdNNT1/Cpzzepv85i6PjxnbraDipceFnlh6g8mhveInURfVnPEK1Oyg4GZrizxzRPR2zL8o9HdyDly9K/x8maNg0eWorR/w/P9eRRWeCvzFVLk8/E64e27bSpWAz5/CM5u3MiTT+p55InLzDpuF53tfMerHI/hZmbX1nV8MQHEgn7zmZko9HsY1NobyabeePIlry3ewYfxSvrXsbsofX4PbX2Gu9yUdlLOPReoQ7ijbbhavDtxeP5ef+qZJqRpJwayw4hrRKCKBz3Qtu6zc12m+NPIqyyGojWFJKUbhY4vlVpNo3ZdfanNNNzXtguRcXG4v8y1XpCStWVJbyzWE5ra85hZQnfdo9buMsH639F1ufvfmNr7DfpcPfEmMbWrmxeYqcLmYWt/AFq+HFpSZE2Ngu2FUBRtJ0RpcbpakjOHJktc5OHE4lP+X+9LTGJ2Yzxe1Jfzzq00kzb0w7JwTM8ZR2NjIl0BRUxNZ7iQeLCmD3BRILeCZzSXMsQxVKbjIcIXLyBTlo1LXkG0tClzKZcYzYFzQDY1N4EngEJXM50qTOXYFx4+PLxtqNHo9z7LW+gWt9VitdaHWum2uqf6Gy+GXmZjVKo5xe0N/Ayz/iVmFz7QsCSf+DhZHbFlFYluE8iNc4k96Bk55DqafFjofQNpQpqgE7nMP597dQas6moN0R8WeCKHUyohQhop2sSd8O/k8GMvUmkfNBJ9TZK7NgSaPKYP3Nwn2rYp8xQ2N5Le0mEqBkeRNDln9U/Nbb7z5OzZT5ArAnAvgzL+awikAuUUkNtawIHt/Nuw2eR1TO/NV9yXBmsdg9YPRn0807+uqreCupEksCEZZa2aM4JnNW1lbWtb6jsN9lhXOn4w3IY3UHf8zE/Oko9tYRPAGINMSBCmDSdGaos//FiojHMg01R0zRrC4xcOsRsvy6elALDsn4aKj2j5/yI0hIQahiejRY/DYVavSh8Oqu0EpkrRmu2U5SFTuUI7yvEmm30cvZlV1DUWNTfCdfxsr4zFW5bRJofef2NjEnwtPZXXahNZr1IZL/xdajDmJLHluMUr5easxi1WVEXlqB42Gfz9mEuGPmA8j5rV9cZbJnd4qlIuOMt+/aCSkmQWhI1fuOO1mkMtnqsVBqNCOM1WebU2ddQ7sdxyMc5STH2p2fcIEdEpeaDHfUclwG2dBmUjrZyzcHiP0Ri6E0RGVSs96FS5cR0Fzi9kF88YQy3tQyKZLJGfD/Etg0eVhVuBWLvqP+b61hyMHN3MuML8D6W3dU5KsGBpPAkw9OfR4aj4c92hUd5bQax2GgvQR7bfHJlJ4KwULL4Ppp+NvqsH312tNtcXUwaHPEEUs4w1Q2NRMwraPzeIiWjsj7x1rzFeeBM6y4iGKGtpalvEGWFVdw0WZ00nwJFDQ1GhKTXeHNGu3rL3S9janPGfGaI8/dI/FmKMCLi91aHbVbSchGCTgSw5VxLXGumyvuZ8u9Q5tvS+3WaF25+i00GL9nNfhUOPa0rprbJHf0tylSnQJljC9/f3bW4Xy+TtDcUV+lxe8CYxNDfkj225tKcGgKYoW69wOt8CADoLLQ7Y/nWe2lHJ+cwKFTU20KPiqrowFtXUkffNlUwregSsxk7Mt/+TxjY2trqmmAfmkaM2R+eaaj8CLihTLbj/NyhLJwGztsLrbx3oSGOXL4GdNKVxxwBVhgZ3xyr6S4m7fwfnF2daOEb34OPNjM/rgMAtOVHKKTELwSItKoSNgZ9lNZqIv/chM0I3VzKrZAbu2wsQIq2maY5vUaQV2DpAjOymWXW64JkoS/fGHmh+bA79nfKPeuccUW4kke2zbx5QyeRv/er25voFMTOyoNuIhITVc6OUWATCoOVRcIrOz68LxK2I/l5hprIJfvGImm2iCIWMEhVveo7CpmYKmZsoDKSwOWNc9MctUbauw8qeOXhL9fZLzjABTLiPIvnCUoXZOch4/fPKM+TvSIhuNb71t3ALs1yh3qFjGARH+YoWL4e27jQ9n7XZz3b/9YevWfKJWfGYlpE/EHRKA9laaY9BufaxwUch6tmtjazntfNyhAjjRxGDSILOw3Bwe0BmzfLfbR3K5ZeVa8ygMn2v+HjQaNluV8BZfGf21WY7v34SVsPr+6MfZDBpjqmz+/myzk1S/y/TZxCPg1ZtCQtjh4tJqrRu10Pw4Odly3fBHCJB53zX5S53b+O3hFPiuLthE8ibDKc+2fXxwcfj/kRZHG48PDv95GythjxKr7yC6i0kkzgXZ5GPaOZcl4kYf3HUruZOcCR0f0x4jF5jfwWYzzillFqfJubDsR22Pd/bNeW+HXJU6g8fP6qoaph9yKyOfOiuKWLbObYvD+t3h88ieYF/nSMOBjc9RzdA5H3kT4PRX2hqBLBLdfoItsLV6M+nBoPksR//KVFS0DFenpU/m/fJXWao8rX3sQ9GIZnlNbciglDfZ/Dgr3AIT3ClcuqOkS8U1/C6zKChIHsq22m38eujhZGxey51WukO/tWgYmzYSqo2LyP71jVxQscv4VbezqPC6/fiCmkaXYkZ9g5lD7J2F2gpGNZqFQLNuISEYjH7Nk/NYVlPLoMErmPzmWsi3roHWrYuHDCs4cQiecM0DBNx+aAGf24dPw/k+h+ugbcH2Bky/f/U6+wpS7rqncU5StqWzp5h/MXzj/ug+fTYuFwyzXC1cHjOQbfvEDLSR/q7O7blY2/jtWU/2FH+KEYTRiOU3vt/x5trOv8Tc4Lb1O5pItM4x7oMnQw9pd9vjuorLbYR/6TpjeXWWhrZxbHMXDJ7BQ+VVZFdsNMI0clCfsDL6+9iLi3HLW7dHW3FO2nYp66KjzLEdkTMech0BOgecG7J4Rlq1xi41wjFpkKlil5gVJroSUdTZ25fKHbJs25OrU6A5/7bfx2mBbKgMlT2NZTmdfppxZbIZc0hsv1S3L7Slnl8cEqrOnR1rQdUGp5Cyd0HaI2sMfP0GfPS4KRe+40tzrXInmsWjU2QefI2xiEbburfxJbUVymAWSSc9Hb4obA+nNagnSbO+L+0JsKknh7l7xR3O6xtLoAFMOBzGH9ZqUewyk0xhhrAF2J7gHKeHTA09dslnZlEWiVPgttdPU6xsGyc6yqdbx49stCzWka5Obq+ZV5pqTZXbbR+36xbQKQIZZvyIJQIv+Qx+0LYwEWBc7xKijMNAwBKlr5f8k0kNjeY9Jn8DDvtZ6zEjBk3gj5u2kLe7pHU8fyx5f26oCuKpKm27K2P5ZE+33DKeyF/BmKamNi487eG3rldVYyXJ3mRmJORS2BQqfmOL6dyU0Nxb2NTEWbsrKWxqbv96Kxd3bivj6uSJ3FC+w8xZdhaKugpGNjWhLKEb0Dq6lTq9AIViZqUJemeQyX1NxghjsEvO48CtxhixTCeauc1BotvMBeW15UwOuvE4fZFtYe3ymvNVboGy/5gy6nFezU8syz3Nwu+bbaHscTG3ifcYtydsCzsmc79trLZFq4xV0q5CFc3qkjOxzcoQgLNfCy/729OkOYT7gsvgzZ+bkr1RAthajz/HsQpNH2YYF06OAAAaZUlEQVRKyEaz7lqCZ7HDLzebHhDLYK5hdRns2hRyl3DifCwp2+wulH1iRFXkxDUshqCYc74RHL4kU1rXaYGPJrQOOLdr3zXL9YUhU42lUkcvmU5StimpvfPrNr6ug4OhdgSUO/TZWsIrOlEYsZ1v45wYP/uzWVh6Am39K514A2YHJG8KnPBk7OOc53aWsG/PjzUazoVFLCKF0NdvhCyBkcz7btfev7skDoq+Td8djnkQXrjUjBv7KlbhBgpmtX9cSi6seWTP3+fIX5pKfu19p7tKZxZLsaz+kRy11vw4se+drR8CKrrxwu0zZdjtzLD7Hdf2mK6glBlXY4nlaO42ncAWy3UtDcyvrYt+XWxjwY7PWxfQY71pjK2pNfEDKRHxRIffCZVb+OWGV9nudqEmW1bXzrpHEfJZ/nTX52QHsk2MhON5n3UdlEOoD3XskrbrruJyM7u+AbzWYtnlCR1fuwMfoK05JDkis0YrHr/pD7u8ddEq8zNmqRHq8y9i6ouX8fpJT5P66k/CXU+BgGU4KakpYWoL4XN04iDYuQHQlvFCw3v3wztrjQtVNENBnCBiuadxe9pure5t0oaa7TeA7Y6gi2hC9Ftvme2VSBwBYb3CyIUweCpsfd9YSy/+D1Rta9/q5mT+xfDEibDp7ejPH/cEnseO5fb8Q3ho2xtku5qiH9dVknKM60L5f6L7vDot8YF0Eyy17eOQz+3Ms4wLypF3tb89bltLnIPwRf8NP8Z2o0iJYaWPxYLLjMVowuHtWylSh4AdJOYo3wtwfr1ie0oWXzdXMU75o29TX1EWfSEG4QuHbevM53AGh8Xiki86ditwWiicx9oWko44/RWoKevcsU6xPPNsM+g7XZr6kgs/JiKtffcZMhXO/EvHx8UzLhd865/ddx/oCI+/c8HbXcG2LLdHLH/yzmDfl1s/MAv/aEK1tTqqNjt+7bmudZbhc7vX7ih4HGPP3Lr66LunSY7aDvYY5vKEAm0jA4xdLghkENCaguYWKDrSGKNsK30ncOYbLq8rh6DZoftOxS5uz0wn1XZhS87hhU1baVAKVbQK1j9tHm9PLNtW3ubG0P/2Yq25PuzQ83fuir2zkj4cNpoUqPiSwzXN2GXw4mWkVW41bY8wAiVa7ddoZjQ0h/frMb+Bj54wY7FtWPnayqzR08bFHkbEcn/HOSHEstp2VqD2JEoZa+LW941lM5DRtZtl/GFmgIo1eVj+3wf5cznIOwpcG3qg0YRbWDOjBCVEDr4tjcY/d+op5rEVN5ufzmKvtFOHtp14Z5xhxFlnA7hsMobD0us6Pu7QW2DsIcYSPTM8W0yiy8NP/KOgZTuoWlhwqdmJKHZENbe3BTxmKRz7sHElef5iY31fdHnHbeqM/629kzL99PDHMwvNdyzis7ShoJMZYCDcWr3oh/Df5zovynubPbTIDQi660e8tznyLiN2OuM7bVtQowXydoTbIZadsSaxKO6mVdkmWmaabjLOmwZ1MDIxn9yWjeFxFDbOe8Q2UMTambLxOlz/PAGY/s22x7SDPzIY+zOTU/yM3ZWcUFlFYIjVpuQcCmyLsrNN7bphWHO5LURdHiIXzGtLy/io6DCSN2yMHSyYMSIkliODru3vYP1uY+SIcMMIOCz4M+rqwl0l04aEssAMGmPef9vH5jr28GKppxGx3N+xt5ncvvixeNnY6XbsILMuvVa13UJ04ra2n5pqjL+2q4fcMMYsNf7W1aXRsyREs1TAnmcJsAeaaAuJZTfCoh90LYCnKySkGR+/aLg8pt+CLebvhDRYflPnz62U8dne5Ajai2ap3xMmHA7/uAUWfi/8cW+CyazRlYC3jrADgPxpZifh/Pc6zkwiCF3FuQjtCJcbLv60rb9xZ2h1GdGxXW0WXAqvWQt+O3g2DhnnS2fdthpYfSWsPyy6ZdkpfO3x2inaou3a2eOt29e+z3sMEhzC99jKKqgIFR0JaB0Sw0mOuAOnqG1PLNvznG0wcLnA5fjcGSOZs3MDc5JGhz5DNDIcmbIis534UwFlxHKwuW2An5XrOTcxh6H1pbFFsMdn3FW3fRz3VmWQAL/+T0qe+TKn5PesSOgJ7Bs7ls9sd/EGTNR2S1P3g1BsXC445x9w0JXRfY6dE5TTkhEroKwj7IHZmU2htS3uvhtkXB4zUAZburcQsYPuPIHOB691xKIfwnc/iZ6juafvAbfHpLI6y1Txw5cYf/eZMPBIydszX2nnQi+Wz/5BVxg3veITe84I0Ru4PGa3y87c0ZFl2Q5Ecz4WLRDdnksWdSIAOAp5/gzO2rmblw64iSt27GxrLLKFsTNI1ynKO+OGUbrOOpcnfO6w5yw7k1CsedG5gxppWXa5jGCu3w3BYBSfZXOdp2fth2qJkqvbiT0v7gNiWSzL/R2X27hf9EZWi+5iW5aDvSWWE01KsmBTu7kpu0xyDiy4JPpzzkHNafHd0+vfaPkHdid1VW/gchuhHGzuXgBTYqbJHpE3qecs5C53eABpb9MTPpuCEA9YOexJGRw7OBdMAHi8Yy/obZeEaOOUN4obhtPabF8PJ/MuMm5+Y2Kk/uwA5fZywa7d4RZbT8BYvut2huaQQGYoLsUpkGPFgUBoTrXzSasIg8qwWfDvR0NiOda5nHnhowUvJqSZc9TvBl+4S9MgbzJerVmQbUpbd0os92QQbC8hYnkgMO+78Se2IHRDdjVArbN4Ey3LcgfpdnoLW/y5vHvuFz76YJOCylk5Lh5otSy33YbrMqvW9t53QBCEzjNkmsnHnjUmvq3GncHlMYYS23KronwepxC007/ZlmV/avRrkJILKXsmlFvbBaEgPPuxxCwjlm3Djstl3PqqS8ONPe3NJZHtdXnCxXKqZbSxqhrGtFInOKzR0cSyPxnqdpl4loiy9uneJP68cQuDllnxRO1Vlx11oPndXmXROEHE8kBg+ml93YLo7H+y8cvqTI7gPcGbaCK3u2v97CqnvwwVjoDC7ohJXyJ841fdb1NP4/IYy7Ju6b5YHtNBMR5BEPYeOTFy3e9rOF3FILrwdc4LdsYi27LcWxmhWsWyIzuFyx3d5S7ZEsudNfZELghc7vB6ALb/cEduGE5XlGhuZZ4EkwJOt7TNKqNcZAWDoc8Xzf3FJn8/UzG3s5WC+xARy0Lf4XL17ha2zxLLzfXhJbx7m4KZ5me9VSmvu2IyHlHu0EQUzWIjCILQl7i9Jh4m2I5l2SYhzSGOrewRzmDtniSWWLZx+knbbei0WI4QtgnpVgXcY6DwoJAIrt8NqNi7Bx3l6vYGoMQqKx8plu1zNtd1ru1FR7b/fJzQD2dxQbDwBozPb1Nd36SlaXXD6IcBX7ZluSczjQiCIPQU9rjU0hD+fyRn/CU8pqRup/ndW0FntlhuiXDDsEuTJDret9Ulo5NiOXKusYtkHX2v+V1u1V2o391+oGBHYtnjD7m3pEdalm2xbC0G+sIFshfoh7O4IFjYPsvN9e1vBfXa+1vv2cN1IeICl8Oy3B8t54Ig7NvYAtMWbZFWV5uh08NjJsatMGlWZ53dS+2yLcsN4Y/Ntyp8huXvtyaPPRGch/607QLBaVlu75wdimXHfBoZvG6/Z5MtluM/eK8zyCwn9F9sn+W+sizbCe0bdu/99+5tXB4I9nAOa0EQhJ4iMpCus+NUegFc9r/eaZOzHWGWZTdMOtr8RKOrYtntM0WrIrFFcENleBBfmzZ2YEe1g/YSB4UXHYG2luWezETVh4hYFvov3kDfiuXUwXv/PfcWkUVJBEEQ4olI3+B4ia1wR1i8IXbbtGVZ7orgPOq+2JVtncK2OxZfOx93tJSorv7phiGznNB/8SUZn+Xmur6pqmaXqh7XibKx+xq2G4buZlESQRCE3sAd4RscL+NULDeMqOyBG8aU1bGfcwrk7hQDs7NpZI1t+5zt7tLUyQC/fQQRy0L/xRsIuUD0Vd35733dsf/XvoizKIlYlgVBiDfi1bLcJbFs4fbCJV+EghX3FGeO5trt7R/7zT9HL8oCsONL83vKmrbPtbEsi8+yIMQ3TpHaV2I5Wpnq/oCzKEm8TEKCIAg2rQF+HWTD2NtEZulwPhaJ0w0juZdS2cVi2KzYz1WVmN92BT4nkg1DEPYxcieF/m5p6rt29EdaU8eJz7IgCHFIpAU3bsRyNMtyrLZ1IxtGb7LyNsgaB8m5bZ+LzIYhAX6CEOeMXwEXfwp/vwkmrOzr1vQvEgdB5VbjDx4vk5AgCIJNpAU3XnbAIlPaQefcMOKJolXmJxptLMv9ww1DLMtC/yYlz6yCBxX2dUv6F8PnhCo0CYIgxBvueHXDiEhpBx1nw9iXBGerZdkO8OsfNlkRy4IgdJ0R80N/zz6v79ohCIIQDduCa4u2uLEsW+346HHz2+1vx3K8B6nj+ho7iNBepOxLQr8d+ofkFwRh75KYGfo7dUjftUMQBCEaduGMplrzO94syzZLroW8ye2/xpnFIt7pp24YIpYFQdgzVt4OZf/ZtwZyQRAGBnZJ5toK8ztWueu9TaRYHjEP8iZFP9Z2w9iXxtjI1HH9JAC8f3wKQRD2PtNO7esWCIIgRMe2LJd8aIRyvAjOlDwoPgE+fMT83ykxGSdt7wy2Zbmh0vzuJ3UG4mSpJQiCIAiC0EN4HLn148VfGYzl9bDbwv+Pie759z/3rZ4/pxP789TtNP7Y3j6ontsLiFgWBEEQBKF/4RRpwTjLs+8M6GvPPaQ33DCyx/XcuaJhL0xqd0JCWu++115ExLIgCIIgCP0LTx9Vbe0MTvHbqcDDHhTLvR3o6LJkZV2FiGVBEARBEIS4ZV/Z/m/PZznJKnHtjWPhH4ltWW6q7VdiWQL8BEEQBEHoX8SzZdlJe/7Uh90KI+bC0Bl7rz3dxWm5ThzUd+3oYUQsC4IgCILQv4i3EtGxaM8tIpAOM87o+fccNAYmr+7580K4+B+9uHfeow8QsSwIgiAIQv9CKTjuCXjs2L5uSfv0RR7iC97rvXM7xf/4Q3vvffYy4rMsCIIgCEL/Y9yyvm5Bx/iS+7oFPYvTspw2tO/a0cOIWBYEQRAEQegLPP2jHHQrdjaMflKMxEbEsiAIgiAI/ZOsXs4rLITTYuW0zi/u23b0MOKzLAiCIAhC/+TsV0MCTuh9ssbCkmtNSe9+hIhlQRAEQRD6J97AvpWneF9HKZj7nb5uRY8jbhiCIAiCIAiCEAOxLAuCIAiCIOxNLng/vOy1ENeIWBYEQRAEQdibDCrs6xYIXUDcMARBEARBEAQhBt0Sy0qp1Uqp9UqpoFJqesRzP1BKfaGU+lQpdUj3mikIgiAIgiAIe5/uumF8DBwFrHU+qJSaCKwBioDBwCtKqbFa65Zuvp8gCIIgCIIg7DW6ZVnWWv9Ha/1plKeOAB7XWjdorTcAXwAzu/NegiAIgiAIgrC36S2f5SHAJsf/m63H2qCUOksp9Z5S6r3y8vJeao4gCIIgCIIgdJ0O3TCUUq8AeVGeulxr/YdYL4vymI52oNb6HuAegOnTp0c9RhAEQRAEQRD6gg7Fstb64D0472agwPH/UGDrHpxHEARBEARBEPqM3nLDeBZYo5TyK6VGAmOAd3rpvQRBEARBEAShV+hu6rhVSqnNwGzgeaXUnwC01uuBJ4FPgJeA8yQThiAIgiAIgrCv0a3UcVrrp4GnYzx3A3BDd84vCIIgCIIgCH2JVPATBEEQBEEQhBiIWBYEQRAEQRCEGIhYFgRBEARBEIQYiFgWBEEQBEEQhBiIWBYEQRAEQRCEGCit46donlKqHPi6r9vRS2QB2/u6EUKvI/08MJB+HjhIXw8cpK8HBs5+Hq61zu7oBXEllvszSqn3tNbT+7odQu8i/TwwkH4eOEhfDxykrwcGe9LP4oYhCIIgCIIgCDEQsSwIgiAIgiAIMRCxvPe4p68bIOwVpJ8HBtLPAwfp64GD9PXAoMv9LD7LgiAIgiAIghADsSwLgiAIgiAIQgxELAuCIAiC0O9RSqm+boOwbyJiuQdRSqVZv+W69mOUUnnWbxl4+zFKqSKlVEJft0PofZRSc5VShX3dDqHXCfR1A4S9g1LKbf3ukXlaRF03UUq5lFKpSqnngDsAtNbBPm6W0AsopfZXSv0FuA5Ai8N/v0QpNUUp9TpwPTCor9sj9B5KqalKqT8DfwXS+ro9Qu+glDpAKfU74BdKqaW2kBL6H0qp2Uqpe4HvKqVSe2qeFrHcTSxhXAV4gSFKqWNBrMv9CWW4FfgN8KDW+sy+bpPQq1wBPKW1XqW13gKyi9DfUEp5lVJrMVHxdwB/Ag60npOxux+hlDoQ+CXwe+BT4EQgoy/bJPQOSqkFwJ2Yxe9g4AdKqUN64twyKPQM4zGlE28DTlBKpWitgzLB9g+slWky8IHW+jcASqlCmVT7F9YuUSFQrbW+zXpsiVIqHejRLT2hz/EDrwLztdbPAb8DJiilPLIz2O+YDLyrtX4EeAhj2Kru2yYJvcQ04A2t9WOYHeBcYI3tOtkdZLLvItZ2zljrb3vi/AJoBDZYP6copYbJNv2+i7OfLS4GZimlrlRKvQHcDDyglJrWNy0UegJnP1siqQyYr5Q6VCn1DHAJxvJ4qXWM3NP7KBH3dI3W+lGtdZ31vwdo0Vo3yyJ43ybK2P0PYLVS6irgfSAf+KVSanWfNFDoMaL09WdAmlIqX2u9E7Mo8gNHdPe9ZFDoJEqpdKXU88DLwDFKqSTHxDkdqNRarwfWA1cDd1lbfXKN9yGi9TOA1roS+AVwNPAD4DigBDhaKZXdV+0V9ox2+rkKuB9jlfi11voQ4D7gAKXUAX3WYGGPiTV2W+5V9vj8KrBKKZUhluV9kyj9nAygtf4QWAaMAL6ltT4QeANYppSa0EfNFbpBrL7GiOVK4EHLR70A+ABIsV63xzuDIuQ6TxLGr+0C6+8Fjuc2AilKqSeAy4B/AZ9prZtk4N3niNnPWus7gEVa69e01g3AM5iFUm1fNFToFu3dz89hJlbbr/E9YBvQsBfbJ/QcUftaG4KWYP7KOmZhXzVS6DaR/TzffkJr/Q6QjelnMD6tKUDN3m2i0EPEuqc/x+wC34gVdwJ8jBWP0J2dQRHL7aCUOlkptdCKqNyCCQZ5EqjHbMkPtg7NwNyIpcD+wLnAOFm17ht0oZ+xtnZspgGbgZa92mBhj+hEPw8B0Fp/hHG7OF8plYUJCJoE7OijpgtdpLP3tFJKWQYNO0Vgvf14X7Rb6Bpd6Gc/8CZwnvXSxZhMN/V90GxhD+igr2fafa21btRa/83yWwYzT7/U7fcXF7xwrEEyD3gUCAJfYlYu39Fab7eOmQscA7yntX7IeizL8Xwy4NNaV/TBRxA6QRf7+V2t9cPWY35gNnALxg3jYq31Z3v/EwidYU/vZ+vxi4BRwBjgu1rrT/Zy84Uu0I172q21blFKPQR8qbW+pi/aL3SObszRRRgXyTygCThfa/2fvf8JhM6yp/e09fg84HZM8oWztdZfdactYll2YA2aGrM9s0VrvRj4FlCBWcUAoLV+A7OdM04plWb5wG1XSrmVUi6tdbUI5fhlD/p5vNXPAcv9ohG4Xmu9UoRy/NKN+znFevxnGJF8iAjl+KYb93Si1treGfqmCOX4Zg/v6XRr7F4PnAKcqrVeLEI5vunGPZ1kPfU/4Epr/P6q2+0RyzIopTzAtZj0UC8AqcA3tNanWM8rYCuwRmv9qvVYMqZowRxgOLC/1nprHzRf6CTd7Oe5wDCkn+MeuZ8HDtLXA4MeGrunWtv3QhzTQ/f0NK315p5s14C3LCulFmIC8jIwKeCuw2zRLFJKzYRWp/BrgWscLz0Us8r5NzBZBtv4pgf6+UOkn+MeuZ8HDtLXA4MeHLtFKMc5PXhP96hQBpNbcqATBG5x+DXtD4wErgLuAqZZ0dJPYzpshGXSrwcO1lq/1jfNFrqI9PPAQPp54CB9PTCQfh44xG1fD3jLMmYV86QK1Yp/AximtX4AcCulLrCipYdiktZ/BaC1/oPchPsU0s8DA+nngYP09cBA+nngELd9PeDFsta6Vmvd4AjyWAKUW3+fhimB+hzwGKb6j6QV2geRfh4YSD8PHKSvBwbSzwOHeO5rccOwsFYyGlNL/Fnr4Srgh5gcqxtsnyctUZH7LNLPAwPp54GD9PXAQPp54BCPfT3gLcsOgoAXk5NvirV6uRIIaq1fl+CAfoP088BA+nngIH09MJB+HjjEXV9L6jgHSqkDMFV+3gTu11r/qo+bJPQC0s8DA+nngYP09cBA+nngEG99LWLZgVJqKHAS8DNtik8I/RDp54GB9PPAQfp6YCD9PHCIt74WsSwIgiAIgiAIMRCfZUEQBEEQBEGIgYhlQRAEQRAEQYiBiGVBEARBEARBiIGIZUEQBEEQBEGIgYhlQRAEQRAEQYiBiGVBEIR9AKXUNUqpS9p5/kil1MS92SZBEISBgIhlQRCE/sGRgIhlQRCEHkbyLAuCIMQpSqnLgZOBTUA58C9gN3AW4AO+wCTuLwaes57bDRxtneIXQDZQC5yptf7v3my/IAhCf0DEsiAIQhyilJoGPADMAjzA+8DdmNKvO6xjrge2aa1/rpR6AHhOa/2U9dxfgHO01p8rpWYBN2qtD9r7n0QQBGHfxtPXDRAEQRCiMh94WmtdC6CUetZ6fJIlktOBZOBPkS9USiUDc4DfKqXsh/293mJBEIR+iIhlQRCE+CXa1t8DwJFa638rpU4FDoxyjAvYpbUu7r2mCYIgDAwkwE8QBCE+eQ1YpZQKKKVSgJXW4ylAiVLKC5zgOL7Keg6tdSWwQSm1GkAZ9tt7TRcEQeg/iM+yIAhCnOII8Psa2Ax8AtQAl1mPrQNStNanKqXmAvcCDcA3gCBwF5APeIHHtdbX7vUPIQiCsI8jYlkQBEEQBEEQYiBuGIIgCIIgCIIQAxHLgiAIgiAIghADEcuCIAiCIAiCEAMRy4IgCIIgCIIQAxHLgiAIgiAIghADEcuCIAiCIAiCEAMRy4IgCIIgCIIQAxHLgiAIgiAIghCD/wcVc01xQV/TbgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "df[['close', 'momentum','ASY3', 'MA5']].plot(figsize=(12,8), title='MU')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1254, 7)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Do a regression based on the features, forecast 1-day ahead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#x is the input features\n",
    "x = np.array(df.drop(['label'],1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#y is the target \n",
    "y = np.array(df['label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import model_selection\n",
    "from sklearn import preprocessing\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data pre processing scaling input features x\n",
    "scaler = preprocessing.StandardScaler().fit(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = scaler.transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import TimeSeriesSplit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#multiple train and test splits. Here 5 splits are created.\n",
    "#Train split = i * n_samples // (n_splits + 1) + n_samples % (n_splits + 1)\n",
    "#Test split = n_samples//(n_splits + 1)\n",
    "# for 5th split train set will have 1052 records..nearly 80% of data.\n",
    "tscv = TimeSeriesSplit(n_splits=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "for train_index,test_index in tscv.split(x):\n",
    "    x_train,x_test = x[train_index],x[test_index]\n",
    "    y_train,y_test = y[train_index],y[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Linear least squares with l2 regularization.\n",
    "linear = Ridge(alpha=1.0,tol=0.001, solver='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,\n",
       "   normalize=False, random_state=None, solver='auto', tol=0.001)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#predict the values for the new set of test inputs using the model created.\n",
    "forecast = linear.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.07936446, -0.71393828, -0.81967447, -0.82870095, -1.39780361,\n",
       "         1.13347545],\n",
       "       [ 1.09434676, -0.60949648, -0.55755761,  0.28242642,  0.203771  ,\n",
       "         1.1183034 ],\n",
       "       [ 1.10696344, -0.40061288,  0.32288621,  0.25755043,  0.203771  ,\n",
       "         1.10550198],\n",
       "       [ 1.21026248,  1.02675841,  1.07563207,  1.1862539 ,  0.16791486,\n",
       "         1.11656493],\n",
       "       [ 1.23155312,  1.07317699,  1.12939963,  1.27746584,  1.54240053,\n",
       "         1.15006988]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This the first 5 input features used for testing the model\n",
    "x_test[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([42.24, 42.4 , 43.71, 43.98, 45.75])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is the first 5 observed values \n",
    "y_test[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([42.03318485, 42.26632487, 42.39503538, 43.78103229, 43.99183606])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is the first 5 values predicted using the model\n",
    "forecast[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#scatter plot of observed and predicted values. There is a increasing linear regression between them.\n",
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(y_test,forecast)\n",
    "plt.xlabel(\"observed values\")\n",
    "plt.ylabel(\"predicted values\")\n",
    "plt.title(\"Linear regression between oberved and predicted close price\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Harini Pravin\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26b2ece4f60>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram plot of the residuals show that they are normally distributed.\n",
    "import seaborn as sns\n",
    "sns.distplot((y_test-forecast),axlabel = 'Residual')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing plotly\n",
    "import plotly\n",
    "import plotly.plotly as py\n",
    "plotly.tools.set_credentials_file(username ='harinipraveen',api_key ='vL1Ykz5yY0MUcNl17psV')\n",
    "import plotly.graph_objs as go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#formatting the date and setting the index \n",
    "df.reset_index(inplace=True)\n",
    "df.date = pd.to_datetime(df['date'] ,format='%Y-%m-%d')\n",
    "df.set_index(['date'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>close</th>\n",
       "      <th>label</th>\n",
       "      <th>momentum</th>\n",
       "      <th>ASY3</th>\n",
       "      <th>ASY2</th>\n",
       "      <th>ASY1</th>\n",
       "      <th>MA5</th>\n",
       "      <th>forecast</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-10-05</th>\n",
       "      <td>43.58</td>\n",
       "      <td>43.07</td>\n",
       "      <td>-1.57</td>\n",
       "      <td>-0.726667</td>\n",
       "      <td>-0.785</td>\n",
       "      <td>-0.99</td>\n",
       "      <td>44.760</td>\n",
       "      <td>43.513121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-08</th>\n",
       "      <td>43.07</td>\n",
       "      <td>42.27</td>\n",
       "      <td>-2.69</td>\n",
       "      <td>-0.693333</td>\n",
       "      <td>-0.545</td>\n",
       "      <td>-0.58</td>\n",
       "      <td>44.344</td>\n",
       "      <td>43.029243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-09</th>\n",
       "      <td>42.27</td>\n",
       "      <td>41.61</td>\n",
       "      <td>-2.88</td>\n",
       "      <td>-0.630000</td>\n",
       "      <td>-0.655</td>\n",
       "      <td>-0.51</td>\n",
       "      <td>43.646</td>\n",
       "      <td>42.202368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-10</th>\n",
       "      <td>41.61</td>\n",
       "      <td>41.97</td>\n",
       "      <td>-2.55</td>\n",
       "      <td>-0.656667</td>\n",
       "      <td>-0.730</td>\n",
       "      <td>-0.80</td>\n",
       "      <td>42.938</td>\n",
       "      <td>41.546639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-11</th>\n",
       "      <td>41.97</td>\n",
       "      <td>42.47</td>\n",
       "      <td>-1.61</td>\n",
       "      <td>-0.366667</td>\n",
       "      <td>-0.150</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>42.500</td>\n",
       "      <td>41.989532</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            close  label  momentum      ASY3   ASY2  ASY1     MA5   forecast\n",
       "date                                                                        \n",
       "2018-10-05  43.58  43.07     -1.57 -0.726667 -0.785 -0.99  44.760  43.513121\n",
       "2018-10-08  43.07  42.27     -2.69 -0.693333 -0.545 -0.58  44.344  43.029243\n",
       "2018-10-09  42.27  41.61     -2.88 -0.630000 -0.655 -0.51  43.646  42.202368\n",
       "2018-10-10  41.61  41.97     -2.55 -0.656667 -0.730 -0.80  42.938  41.546639\n",
       "2018-10-11  41.97  42.47     -1.61 -0.366667 -0.150 -0.66  42.500  41.989532"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#adding forecast values predicted for the test set to the dataframe df\n",
    "end = len(df)\n",
    "start = len(df) - len(y_test)\n",
    "df.loc[start:end , 'forecast'] = forecast\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~harinipraveen/12.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Visualizing prediction and actual price for each date\n",
    "trace1 = go.Scatter(x = df.index.to_pydatetime(),y = df.label,name = 'observed close price')\n",
    "trace2 = go.Scatter(x = df.index.to_pydatetime(),y = df.forecast,name = 'predicted price')\n",
    "data = [trace1,trace2]\n",
    "layout = go.Layout(dict(title='MU Predicted and real close price', yaxis = {'title': 'MU Close price'},\n",
    "    xaxis=dict(type='date')))\n",
    "figure = go.Figure(data=data,layout=layout)\n",
    "py.iplot(figure,filename =\"Actual Vs predicted close price\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculating metrics for forecasted values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 1.123\n"
     ]
    }
   ],
   "source": [
    "#Calculating mean absolute error\n",
    "print('MAE: %.3f' %metrics.mean_absolute_error(y_test,forecast))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE: 2.215\n"
     ]
    }
   ],
   "source": [
    "#Calculating mean squared error\n",
    "print('MSE: %.3f' %metrics.mean_squared_error(y_test,forecast))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE : 1.488\n"
     ]
    }
   ],
   "source": [
    "#Calculating root mean square error\n",
    "print('RMSE : %.3f'  %np.sqrt(metrics.mean_squared_error(y_test,forecast)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAPE : 2.258\n"
     ]
    }
   ],
   "source": [
    "#Calculating Mean absolute percentage error\n",
    "MAPE = np.mean(np.abs((y_test - forecast) / y_test)) * 100\n",
    "print('MAPE : %.3f' %MAPE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score of linear model: 0.938\n"
     ]
    }
   ],
   "source": [
    "#coefficient of determination\n",
    "#Regression predictions perfectly fit the data since it is nearly equal to 1\n",
    "print('Score of linear model: %.3f' %linear.score(x_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('close', 6.32422187150784), ('momentum', 0.16555626699314716), ('ASY3', 0.11822636816826729), ('ASY2', 0.35834147412240835), ('ASY1', -0.13252238416195736), ('MA5', 6.314903521247495)]\n"
     ]
    }
   ],
   "source": [
    "feature_cols = df.drop(['label'],1)\n",
    "print(list(zip(feature_cols, linear.coef_)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2Score:0.938\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "print('R2Score:%.3f'%r2_score(y_test,forecast))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Residual sum of squares: 2.22\n"
     ]
    }
   ],
   "source": [
    "#Calculating residual sum of squares\n",
    "print(\"Residual sum of squares: %.2f\" %np.mean(( y_test - linear.predict(x_test))**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>close</th>\n",
       "      <th>label</th>\n",
       "      <th>momentum</th>\n",
       "      <th>ASY3</th>\n",
       "      <th>ASY2</th>\n",
       "      <th>ASY1</th>\n",
       "      <th>MA5</th>\n",
       "      <th>forecast</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-12-13</th>\n",
       "      <td>42.05</td>\n",
       "      <td>42.24</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>-0.386667</td>\n",
       "      <td>-0.480</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>42.666</td>\n",
       "      <td>42.033185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-14</th>\n",
       "      <td>42.24</td>\n",
       "      <td>42.40</td>\n",
       "      <td>-0.97</td>\n",
       "      <td>-0.256667</td>\n",
       "      <td>0.190</td>\n",
       "      <td>0.19</td>\n",
       "      <td>42.474</td>\n",
       "      <td>42.266325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-15</th>\n",
       "      <td>42.40</td>\n",
       "      <td>43.71</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>0.180000</td>\n",
       "      <td>0.175</td>\n",
       "      <td>0.19</td>\n",
       "      <td>42.312</td>\n",
       "      <td>42.395035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-18</th>\n",
       "      <td>43.71</td>\n",
       "      <td>43.98</td>\n",
       "      <td>1.85</td>\n",
       "      <td>0.553333</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.16</td>\n",
       "      <td>42.452</td>\n",
       "      <td>43.781032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-19</th>\n",
       "      <td>43.98</td>\n",
       "      <td>45.75</td>\n",
       "      <td>1.93</td>\n",
       "      <td>0.580000</td>\n",
       "      <td>0.790</td>\n",
       "      <td>1.31</td>\n",
       "      <td>42.876</td>\n",
       "      <td>43.991836</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            close  label  momentum      ASY3   ASY2  ASY1     MA5   forecast\n",
       "date                                                                        \n",
       "2017-12-13  42.05  42.24     -1.15 -0.386667 -0.480 -1.15  42.666  42.033185\n",
       "2017-12-14  42.24  42.40     -0.97 -0.256667  0.190  0.19  42.474  42.266325\n",
       "2017-12-15  42.40  43.71     -0.61  0.180000  0.175  0.19  42.312  42.395035\n",
       "2017-12-18  43.71  43.98      1.85  0.553333  0.735  0.16  42.452  43.781032\n",
       "2017-12-19  43.98  45.75      1.93  0.580000  0.790  1.31  42.876  43.991836"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#taking the test set alone as a dataframe df1 to compute confusion matrix and trading decisions.\n",
    "#end = len(df)\n",
    "#start = len(df) - len(y_test)\n",
    "df.reset_index(inplace=True)\n",
    "df1 = pd.DataFrame()\n",
    "df1 = df.loc[start:end]\n",
    "\n",
    "df.set_index(['date'], inplace=True)\n",
    "df1.set_index(['date'], inplace=True)\n",
    "df1.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confusion matrix \n",
      " Predicted  False  True  __all__\n",
      "Actual                         \n",
      "False         66    34      100\n",
      "True          70    39      109\n",
      "__all__      136    73      209\n",
      "\n",
      "Accuracy: 50.239\n"
     ]
    }
   ],
   "source": [
    "# '! pip install --user pandas_ml' ---> to install pandas_ml\n",
    "#confusion matrix using pandas ml\n",
    "from pandas_ml import ConfusionMatrix\n",
    "\n",
    "#defining input arrays\n",
    "close_val = np.array(df1['close'])\n",
    "actual_val = np.array(df1['label'])\n",
    "pred_val = np.array(df1['forecast'])\n",
    "\n",
    "#defining actual and predicted binary arrays\n",
    "act = []\n",
    "pred = []\n",
    "\n",
    "for d in range (len(close_val)):\n",
    "    act.append(True) if (actual_val[d] > close_val[d]) else act.append(False)\n",
    "    pred.append(True) if (pred_val[d] > close_val[d]) else pred.append(False)\n",
    "\n",
    "print('confusion matrix' ,'\\n', ConfusionMatrix(act,pred))\n",
    "correct =  (np.array(pred) == np.array(act)).sum()\n",
    "accuracy = (correct/len(pred))*100\n",
    "print('\\nAccuracy: %.3f' %accuracy)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "hitratio :50.239\n",
      "confusion Matrix manually created\n",
      "[[66 34]\n",
      " [70 39]]\n"
     ]
    }
   ],
   "source": [
    "#creating confusion matrix manually\n",
    "act = np.array(act)\n",
    "pred = np.array(pred)\n",
    "TP = sum(np.bitwise_and(act==True, pred==True))\n",
    "FN = sum(np.bitwise_and(act==True, pred==False))\n",
    "TN = sum(np.bitwise_and(act==False, pred==False))\n",
    "FP = sum(np.bitwise_and(act==False, pred==True))\n",
    "correct = TP + TN\n",
    "total = TP + FN + TN + FP\n",
    "hitratio = (correct/total)*100\n",
    "print('\\nhitratio :%.3f'  %hitratio)\n",
    "conf_matrix = np.array([[TN,FP],[FN,TP]])\n",
    "print('confusion Matrix manually created')\n",
    "print(conf_matrix)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Harini Pravin\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "\n",
      "C:\\Users\\Harini Pravin\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "\n",
      "C:\\Users\\Harini Pravin\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "\n",
      "C:\\Users\\Harini Pravin\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "\n",
      "C:\\Users\\Harini Pravin\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:13: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "\n",
      "C:\\Users\\Harini Pravin\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:10: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "\n",
      "C:\\Users\\Harini Pravin\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:19: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "\n",
      "C:\\Users\\Harini Pravin\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:16: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#creating confusion matrix manually using df1 columns --> execution time is more!!\n",
    "df1['TP'] = np.nan\n",
    "df1['FN'] = np.nan\n",
    "df1['TN'] = np.nan\n",
    "df1['FP'] = np.nan\n",
    "\n",
    "for i in range(len(df1)):\n",
    "    if (df1['label'].iloc[i] > df1['close'].iloc[i]): \n",
    "         if (df1['forecast'].iloc[i] > df1['close'].iloc[i]):\n",
    "            df1['TP'].iloc[i] = 1\n",
    "    if (df1['label'].iloc[i] > df1['close'].iloc[i]): \n",
    "         if (df1['forecast'].iloc[i] < df1['close'].iloc[i]):\n",
    "            df1['FN'].iloc[i] = 1            \n",
    "    if (df1['label'].iloc[i] < df1['close'].iloc[i]): \n",
    "         if (df1['forecast'].iloc[i] < df1['close'].iloc[i]):\n",
    "            df1['TN'].iloc[i] = 1\n",
    "    if (df1['label'].iloc[i] < df1['close'].iloc[i]): \n",
    "         if (df1['forecast'].iloc[i] > df1['close'].iloc[i]):\n",
    "            df1['FP'].iloc[i] = 1\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "hitratio: 50.725\n",
      "Confusion matrix\n",
      "[[66. 32.]\n",
      " [70. 39.]]\n"
     ]
    }
   ],
   "source": [
    "#creating values for confusion matrix  and calculating hit ratio\n",
    "tp = df1['TP'].sum()\n",
    "fn = df1['FN'].sum()\n",
    "tn = df1['TN'].sum()\n",
    "fp = df1['FP'].sum()\n",
    "correct = tp + tn\n",
    "total = tp + fn + tn + fp\n",
    "hitratio = (correct/total)*100\n",
    "print('\\nhitratio: %.3f' %hitratio)\n",
    "conf_matri = np.array([[tn,fp],[fn,tp]])\n",
    "print('Confusion matrix')\n",
    "print(conf_matri)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Harini Pravin\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:3694: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#dropping unwanted columns\n",
    "df1.drop(columns = ['TP', 'FN', 'TN', 'FP'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Design a decision for when to invest and when to sell based on your regression. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Harini Pravin\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#'Pi' is the prediction result for the ith trading day --> whether forecast is lagging or leading\n",
    "#formula taken from the paper https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4873195/\n",
    "df1['direction'] = ((df1['label'].shift(-1)-df1['label'])*(df1['forecast'].shift(-1)-df1['forecast']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Harini Pravin\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#creating trading decision based on direction. If forecast is predicted to go up , buy the stocks.\n",
    "df1['buy'] = np.where(df1['direction']>0, '1', '0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Harini Pravin\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#creating trading decision based on direction. If forecast is predicted to go down, sell the stocks.\n",
    "df1['sell'] = np.where(df1['direction']<0, '1', '0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction performance Hit ratio 2.05\n"
     ]
    }
   ],
   "source": [
    "#Determining the quality of output is to measure the correct state, e.g. the hit rate:\n",
    "#formula taken from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4873195/\n",
    "hit_ratio = df1['direction'].mean()*100\n",
    "\n",
    "print('prediction performance Hit ratio' ,'{0:.2f}'.format(hit_ratio))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>close</th>\n",
       "      <th>label</th>\n",
       "      <th>momentum</th>\n",
       "      <th>ASY3</th>\n",
       "      <th>ASY2</th>\n",
       "      <th>ASY1</th>\n",
       "      <th>MA5</th>\n",
       "      <th>forecast</th>\n",
       "      <th>direction</th>\n",
       "      <th>buy</th>\n",
       "      <th>sell</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-12-13</th>\n",
       "      <td>42.05</td>\n",
       "      <td>42.24</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>-0.386667</td>\n",
       "      <td>-0.480</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>42.666</td>\n",
       "      <td>42.033185</td>\n",
       "      <td>0.037302</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-14</th>\n",
       "      <td>42.24</td>\n",
       "      <td>42.40</td>\n",
       "      <td>-0.97</td>\n",
       "      <td>-0.256667</td>\n",
       "      <td>0.190</td>\n",
       "      <td>0.19</td>\n",
       "      <td>42.474</td>\n",
       "      <td>42.266325</td>\n",
       "      <td>0.168611</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-15</th>\n",
       "      <td>42.40</td>\n",
       "      <td>43.71</td>\n",
       "      <td>-0.61</td>\n",
       "      <td>0.180000</td>\n",
       "      <td>0.175</td>\n",
       "      <td>0.19</td>\n",
       "      <td>42.312</td>\n",
       "      <td>42.395035</td>\n",
       "      <td>0.374219</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-18</th>\n",
       "      <td>43.71</td>\n",
       "      <td>43.98</td>\n",
       "      <td>1.85</td>\n",
       "      <td>0.553333</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.16</td>\n",
       "      <td>42.452</td>\n",
       "      <td>43.781032</td>\n",
       "      <td>0.373123</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-19</th>\n",
       "      <td>43.98</td>\n",
       "      <td>45.75</td>\n",
       "      <td>1.93</td>\n",
       "      <td>0.580000</td>\n",
       "      <td>0.790</td>\n",
       "      <td>1.31</td>\n",
       "      <td>42.876</td>\n",
       "      <td>43.991836</td>\n",
       "      <td>-2.437980</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-20</th>\n",
       "      <td>45.75</td>\n",
       "      <td>44.42</td>\n",
       "      <td>3.51</td>\n",
       "      <td>1.116667</td>\n",
       "      <td>1.020</td>\n",
       "      <td>0.27</td>\n",
       "      <td>43.616</td>\n",
       "      <td>45.824904</td>\n",
       "      <td>0.453480</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-21</th>\n",
       "      <td>44.42</td>\n",
       "      <td>44.12</td>\n",
       "      <td>2.02</td>\n",
       "      <td>0.236667</td>\n",
       "      <td>0.220</td>\n",
       "      <td>1.77</td>\n",
       "      <td>44.052</td>\n",
       "      <td>44.313304</td>\n",
       "      <td>0.564660</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-22</th>\n",
       "      <td>44.12</td>\n",
       "      <td>42.25</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.046667</td>\n",
       "      <td>-0.815</td>\n",
       "      <td>-1.33</td>\n",
       "      <td>44.396</td>\n",
       "      <td>44.011346</td>\n",
       "      <td>-0.436233</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-26</th>\n",
       "      <td>42.25</td>\n",
       "      <td>42.48</td>\n",
       "      <td>-1.73</td>\n",
       "      <td>-1.166667</td>\n",
       "      <td>-1.085</td>\n",
       "      <td>-0.30</td>\n",
       "      <td>44.104</td>\n",
       "      <td>42.114682</td>\n",
       "      <td>-0.232607</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-27</th>\n",
       "      <td>42.48</td>\n",
       "      <td>41.81</td>\n",
       "      <td>-3.27</td>\n",
       "      <td>-0.646667</td>\n",
       "      <td>-0.820</td>\n",
       "      <td>-1.87</td>\n",
       "      <td>43.804</td>\n",
       "      <td>42.461858</td>\n",
       "      <td>0.461887</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-28</th>\n",
       "      <td>41.81</td>\n",
       "      <td>41.12</td>\n",
       "      <td>-2.61</td>\n",
       "      <td>-0.770000</td>\n",
       "      <td>-0.220</td>\n",
       "      <td>0.23</td>\n",
       "      <td>43.016</td>\n",
       "      <td>41.792456</td>\n",
       "      <td>-1.907297</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12-29</th>\n",
       "      <td>41.12</td>\n",
       "      <td>43.67</td>\n",
       "      <td>-3.00</td>\n",
       "      <td>-0.376667</td>\n",
       "      <td>-0.680</td>\n",
       "      <td>-0.67</td>\n",
       "      <td>42.356</td>\n",
       "      <td>41.044497</td>\n",
       "      <td>3.662283</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>43.67</td>\n",
       "      <td>44.98</td>\n",
       "      <td>1.42</td>\n",
       "      <td>0.396667</td>\n",
       "      <td>0.930</td>\n",
       "      <td>-0.69</td>\n",
       "      <td>42.266</td>\n",
       "      <td>43.840132</td>\n",
       "      <td>2.409022</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>44.98</td>\n",
       "      <td>46.88</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1.056667</td>\n",
       "      <td>1.930</td>\n",
       "      <td>2.55</td>\n",
       "      <td>42.812</td>\n",
       "      <td>45.108039</td>\n",
       "      <td>-1.989993</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>46.88</td>\n",
       "      <td>45.80</td>\n",
       "      <td>5.07</td>\n",
       "      <td>1.920000</td>\n",
       "      <td>1.605</td>\n",
       "      <td>1.31</td>\n",
       "      <td>43.692</td>\n",
       "      <td>46.950625</td>\n",
       "      <td>0.317471</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>45.80</td>\n",
       "      <td>45.55</td>\n",
       "      <td>4.68</td>\n",
       "      <td>0.710000</td>\n",
       "      <td>0.410</td>\n",
       "      <td>1.90</td>\n",
       "      <td>44.490</td>\n",
       "      <td>45.680740</td>\n",
       "      <td>0.625171</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-08</th>\n",
       "      <td>45.55</td>\n",
       "      <td>42.97</td>\n",
       "      <td>1.88</td>\n",
       "      <td>0.190000</td>\n",
       "      <td>-0.665</td>\n",
       "      <td>-1.08</td>\n",
       "      <td>45.376</td>\n",
       "      <td>45.438426</td>\n",
       "      <td>-0.905526</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-09</th>\n",
       "      <td>42.97</td>\n",
       "      <td>43.31</td>\n",
       "      <td>-2.01</td>\n",
       "      <td>-1.303333</td>\n",
       "      <td>-1.415</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>45.236</td>\n",
       "      <td>42.775115</td>\n",
       "      <td>-0.250010</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-10</th>\n",
       "      <td>43.31</td>\n",
       "      <td>42.82</td>\n",
       "      <td>-3.57</td>\n",
       "      <td>-0.830000</td>\n",
       "      <td>-1.120</td>\n",
       "      <td>-2.58</td>\n",
       "      <td>44.902</td>\n",
       "      <td>43.285340</td>\n",
       "      <td>0.004534</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-11</th>\n",
       "      <td>42.82</td>\n",
       "      <td>42.81</td>\n",
       "      <td>-2.98</td>\n",
       "      <td>-0.910000</td>\n",
       "      <td>-0.075</td>\n",
       "      <td>0.34</td>\n",
       "      <td>44.090</td>\n",
       "      <td>42.831903</td>\n",
       "      <td>-0.005353</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-12</th>\n",
       "      <td>42.81</td>\n",
       "      <td>42.92</td>\n",
       "      <td>-2.74</td>\n",
       "      <td>-0.053333</td>\n",
       "      <td>-0.250</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>43.492</td>\n",
       "      <td>42.783240</td>\n",
       "      <td>0.180577</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-16</th>\n",
       "      <td>42.92</td>\n",
       "      <td>44.26</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.130000</td>\n",
       "      <td>0.050</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>42.966</td>\n",
       "      <td>42.917999</td>\n",
       "      <td>-0.383559</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-17</th>\n",
       "      <td>44.26</td>\n",
       "      <td>43.99</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.725</td>\n",
       "      <td>0.11</td>\n",
       "      <td>43.224</td>\n",
       "      <td>44.338590</td>\n",
       "      <td>0.463810</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-18</th>\n",
       "      <td>43.99</td>\n",
       "      <td>42.75</td>\n",
       "      <td>1.17</td>\n",
       "      <td>0.393333</td>\n",
       "      <td>0.535</td>\n",
       "      <td>1.34</td>\n",
       "      <td>43.360</td>\n",
       "      <td>43.964549</td>\n",
       "      <td>-0.177111</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-19</th>\n",
       "      <td>42.75</td>\n",
       "      <td>42.88</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.056667</td>\n",
       "      <td>-0.755</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>43.346</td>\n",
       "      <td>42.602158</td>\n",
       "      <td>0.267592</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-22</th>\n",
       "      <td>42.88</td>\n",
       "      <td>43.95</td>\n",
       "      <td>-0.04</td>\n",
       "      <td>-0.460000</td>\n",
       "      <td>-0.555</td>\n",
       "      <td>-1.24</td>\n",
       "      <td>43.360</td>\n",
       "      <td>42.852244</td>\n",
       "      <td>-1.032102</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-23</th>\n",
       "      <td>43.95</td>\n",
       "      <td>43.08</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-0.013333</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.13</td>\n",
       "      <td>43.566</td>\n",
       "      <td>44.038568</td>\n",
       "      <td>0.071812</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-24</th>\n",
       "      <td>43.08</td>\n",
       "      <td>43.01</td>\n",
       "      <td>-0.91</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>0.100</td>\n",
       "      <td>1.07</td>\n",
       "      <td>43.330</td>\n",
       "      <td>43.012679</td>\n",
       "      <td>-0.044663</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-25</th>\n",
       "      <td>43.01</td>\n",
       "      <td>43.67</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.043333</td>\n",
       "      <td>-0.470</td>\n",
       "      <td>-0.87</td>\n",
       "      <td>43.134</td>\n",
       "      <td>42.945008</td>\n",
       "      <td>-0.291534</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-26</th>\n",
       "      <td>43.67</td>\n",
       "      <td>43.29</td>\n",
       "      <td>0.79</td>\n",
       "      <td>-0.093333</td>\n",
       "      <td>0.295</td>\n",
       "      <td>-0.07</td>\n",
       "      <td>43.318</td>\n",
       "      <td>43.712202</td>\n",
       "      <td>0.739814</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-29</th>\n",
       "      <td>43.29</td>\n",
       "      <td>41.67</td>\n",
       "      <td>-0.66</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>0.140</td>\n",
       "      <td>0.66</td>\n",
       "      <td>43.400</td>\n",
       "      <td>43.255526</td>\n",
       "      <td>-3.573932</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-30</th>\n",
       "      <td>41.67</td>\n",
       "      <td>43.72</td>\n",
       "      <td>-1.41</td>\n",
       "      <td>-0.446667</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>42.944</td>\n",
       "      <td>41.512145</td>\n",
       "      <td>-2.852046</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-31</th>\n",
       "      <td>43.72</td>\n",
       "      <td>42.49</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>0.215</td>\n",
       "      <td>-1.62</td>\n",
       "      <td>43.072</td>\n",
       "      <td>43.830881</td>\n",
       "      <td>2.304261</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-01</th>\n",
       "      <td>42.49</td>\n",
       "      <td>40.82</td>\n",
       "      <td>-1.18</td>\n",
       "      <td>-0.266667</td>\n",
       "      <td>0.410</td>\n",
       "      <td>2.05</td>\n",
       "      <td>42.968</td>\n",
       "      <td>42.451084</td>\n",
       "      <td>2.599973</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-02</th>\n",
       "      <td>40.82</td>\n",
       "      <td>39.40</td>\n",
       "      <td>-2.47</td>\n",
       "      <td>-0.283333</td>\n",
       "      <td>-1.450</td>\n",
       "      <td>-1.23</td>\n",
       "      <td>42.398</td>\n",
       "      <td>40.620117</td>\n",
       "      <td>-6.001621</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-05</th>\n",
       "      <td>39.40</td>\n",
       "      <td>43.88</td>\n",
       "      <td>-2.27</td>\n",
       "      <td>-1.440000</td>\n",
       "      <td>-1.545</td>\n",
       "      <td>-1.67</td>\n",
       "      <td>41.620</td>\n",
       "      <td>39.280470</td>\n",
       "      <td>-9.218416</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-06</th>\n",
       "      <td>43.88</td>\n",
       "      <td>42.01</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.463333</td>\n",
       "      <td>1.530</td>\n",
       "      <td>-1.42</td>\n",
       "      <td>42.062</td>\n",
       "      <td>44.210104</td>\n",
       "      <td>4.520101</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-07</th>\n",
       "      <td>42.01</td>\n",
       "      <td>40.00</td>\n",
       "      <td>-0.48</td>\n",
       "      <td>0.396667</td>\n",
       "      <td>1.305</td>\n",
       "      <td>4.48</td>\n",
       "      <td>41.720</td>\n",
       "      <td>41.961297</td>\n",
       "      <td>-0.923751</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-08</th>\n",
       "      <td>40.00</td>\n",
       "      <td>40.41</td>\n",
       "      <td>-0.82</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>-1.940</td>\n",
       "      <td>-1.87</td>\n",
       "      <td>41.222</td>\n",
       "      <td>39.708247</td>\n",
       "      <td>1.273738</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-09</th>\n",
       "      <td>40.41</td>\n",
       "      <td>42.19</td>\n",
       "      <td>1.01</td>\n",
       "      <td>-1.156667</td>\n",
       "      <td>-0.800</td>\n",
       "      <td>-2.01</td>\n",
       "      <td>41.140</td>\n",
       "      <td>40.423831</td>\n",
       "      <td>0.853433</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-12</th>\n",
       "      <td>42.19</td>\n",
       "      <td>42.63</td>\n",
       "      <td>-1.69</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>1.095</td>\n",
       "      <td>0.41</td>\n",
       "      <td>41.698</td>\n",
       "      <td>42.363452</td>\n",
       "      <td>0.248596</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-13</th>\n",
       "      <td>42.63</td>\n",
       "      <td>43.45</td>\n",
       "      <td>0.62</td>\n",
       "      <td>0.876667</td>\n",
       "      <td>1.110</td>\n",
       "      <td>1.78</td>\n",
       "      <td>41.448</td>\n",
       "      <td>42.666617</td>\n",
       "      <td>0.039203</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-14</th>\n",
       "      <td>43.45</td>\n",
       "      <td>43.50</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1.013333</td>\n",
       "      <td>0.630</td>\n",
       "      <td>0.44</td>\n",
       "      <td>41.736</td>\n",
       "      <td>43.450684</td>\n",
       "      <td>0.018548</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-15</th>\n",
       "      <td>43.50</td>\n",
       "      <td>44.21</td>\n",
       "      <td>3.09</td>\n",
       "      <td>0.436667</td>\n",
       "      <td>0.435</td>\n",
       "      <td>0.82</td>\n",
       "      <td>42.436</td>\n",
       "      <td>43.476808</td>\n",
       "      <td>0.511472</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-16</th>\n",
       "      <td>44.21</td>\n",
       "      <td>44.90</td>\n",
       "      <td>2.02</td>\n",
       "      <td>0.526667</td>\n",
       "      <td>0.380</td>\n",
       "      <td>0.05</td>\n",
       "      <td>43.196</td>\n",
       "      <td>44.218072</td>\n",
       "      <td>-0.256995</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-20</th>\n",
       "      <td>44.90</td>\n",
       "      <td>44.54</td>\n",
       "      <td>2.27</td>\n",
       "      <td>0.483333</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.71</td>\n",
       "      <td>43.738</td>\n",
       "      <td>44.931947</td>\n",
       "      <td>0.130386</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-21</th>\n",
       "      <td>44.54</td>\n",
       "      <td>44.25</td>\n",
       "      <td>1.09</td>\n",
       "      <td>0.346667</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0.69</td>\n",
       "      <td>44.120</td>\n",
       "      <td>44.482342</td>\n",
       "      <td>-0.688063</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-22</th>\n",
       "      <td>44.25</td>\n",
       "      <td>46.53</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.013333</td>\n",
       "      <td>-0.325</td>\n",
       "      <td>-0.36</td>\n",
       "      <td>44.280</td>\n",
       "      <td>44.180560</td>\n",
       "      <td>3.610394</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-23</th>\n",
       "      <td>46.53</td>\n",
       "      <td>47.98</td>\n",
       "      <td>2.32</td>\n",
       "      <td>0.543333</td>\n",
       "      <td>0.995</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>44.886</td>\n",
       "      <td>46.670487</td>\n",
       "      <td>0.855682</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-26</th>\n",
       "      <td>47.98</td>\n",
       "      <td>48.58</td>\n",
       "      <td>3.08</td>\n",
       "      <td>1.146667</td>\n",
       "      <td>1.865</td>\n",
       "      <td>2.28</td>\n",
       "      <td>45.640</td>\n",
       "      <td>48.096623</td>\n",
       "      <td>0.106191</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            close  label  momentum      ASY3   ASY2  ASY1     MA5   forecast  \\\n",
       "date                                                                           \n",
       "2017-12-13  42.05  42.24     -1.15 -0.386667 -0.480 -1.15  42.666  42.033185   \n",
       "2017-12-14  42.24  42.40     -0.97 -0.256667  0.190  0.19  42.474  42.266325   \n",
       "2017-12-15  42.40  43.71     -0.61  0.180000  0.175  0.19  42.312  42.395035   \n",
       "2017-12-18  43.71  43.98      1.85  0.553333  0.735  0.16  42.452  43.781032   \n",
       "2017-12-19  43.98  45.75      1.93  0.580000  0.790  1.31  42.876  43.991836   \n",
       "2017-12-20  45.75  44.42      3.51  1.116667  1.020  0.27  43.616  45.824904   \n",
       "2017-12-21  44.42  44.12      2.02  0.236667  0.220  1.77  44.052  44.313304   \n",
       "2017-12-22  44.12  42.25      0.41  0.046667 -0.815 -1.33  44.396  44.011346   \n",
       "2017-12-26  42.25  42.48     -1.73 -1.166667 -1.085 -0.30  44.104  42.114682   \n",
       "2017-12-27  42.48  41.81     -3.27 -0.646667 -0.820 -1.87  43.804  42.461858   \n",
       "2017-12-28  41.81  41.12     -2.61 -0.770000 -0.220  0.23  43.016  41.792456   \n",
       "2017-12-29  41.12  43.67     -3.00 -0.376667 -0.680 -0.67  42.356  41.044497   \n",
       "2018-01-02  43.67  44.98      1.42  0.396667  0.930 -0.69  42.266  43.840132   \n",
       "2018-01-03  44.98  46.88      2.50  1.056667  1.930  2.55  42.812  45.108039   \n",
       "2018-01-04  46.88  45.80      5.07  1.920000  1.605  1.31  43.692  46.950625   \n",
       "2018-01-05  45.80  45.55      4.68  0.710000  0.410  1.90  44.490  45.680740   \n",
       "2018-01-08  45.55  42.97      1.88  0.190000 -0.665 -1.08  45.376  45.438426   \n",
       "2018-01-09  42.97  43.31     -2.01 -1.303333 -1.415 -0.25  45.236  42.775115   \n",
       "2018-01-10  43.31  42.82     -3.57 -0.830000 -1.120 -2.58  44.902  43.285340   \n",
       "2018-01-11  42.82  42.81     -2.98 -0.910000 -0.075  0.34  44.090  42.831903   \n",
       "2018-01-12  42.81  42.92     -2.74 -0.053333 -0.250 -0.49  43.492  42.783240   \n",
       "2018-01-16  42.92  44.26     -0.05 -0.130000  0.050 -0.01  42.966  42.917999   \n",
       "2018-01-17  44.26  43.99      0.95  0.480000  0.725  0.11  43.224  44.338590   \n",
       "2018-01-18  43.99  42.75      1.17  0.393333  0.535  1.34  43.360  43.964549   \n",
       "2018-01-19  42.75  42.88     -0.06 -0.056667 -0.755 -0.27  43.346  42.602158   \n",
       "2018-01-22  42.88  43.95     -0.04 -0.460000 -0.555 -1.24  43.360  42.852244   \n",
       "2018-01-23  43.95  43.08     -0.31 -0.013333  0.600  0.13  43.566  44.038568   \n",
       "2018-01-24  43.08  43.01     -0.91  0.110000  0.100  1.07  43.330  43.012679   \n",
       "2018-01-25  43.01  43.67      0.26  0.043333 -0.470 -0.87  43.134  42.945008   \n",
       "2018-01-26  43.67  43.29      0.79 -0.093333  0.295 -0.07  43.318  43.712202   \n",
       "2018-01-29  43.29  41.67     -0.66  0.070000  0.140  0.66  43.400  43.255526   \n",
       "2018-01-30  41.67  43.72     -1.41 -0.446667 -1.000 -0.38  42.944  41.512145   \n",
       "2018-01-31  43.72  42.49      0.71  0.016667  0.215 -1.62  43.072  43.830881   \n",
       "2018-02-01  42.49  40.82     -1.18 -0.266667  0.410  2.05  42.968  42.451084   \n",
       "2018-02-02  40.82  39.40     -2.47 -0.283333 -1.450 -1.23  42.398  40.620117   \n",
       "2018-02-05  39.40  43.88     -2.27 -1.440000 -1.545 -1.67  41.620  39.280470   \n",
       "2018-02-06  43.88  42.01      0.16  0.463333  1.530 -1.42  42.062  44.210104   \n",
       "2018-02-07  42.01  40.00     -0.48  0.396667  1.305  4.48  41.720  41.961297   \n",
       "2018-02-08  40.00  40.41     -0.82  0.200000 -1.940 -1.87  41.222  39.708247   \n",
       "2018-02-09  40.41  42.19      1.01 -1.156667 -0.800 -2.01  41.140  40.423831   \n",
       "2018-02-12  42.19  42.63     -1.69  0.060000  1.095  0.41  41.698  42.363452   \n",
       "2018-02-13  42.63  43.45      0.62  0.876667  1.110  1.78  41.448  42.666617   \n",
       "2018-02-14  43.45  43.50      3.45  1.013333  0.630  0.44  41.736  43.450684   \n",
       "2018-02-15  43.50  44.21      3.09  0.436667  0.435  0.82  42.436  43.476808   \n",
       "2018-02-16  44.21  44.90      2.02  0.526667  0.380  0.05  43.196  44.218072   \n",
       "2018-02-20  44.90  44.54      2.27  0.483333  0.700  0.71  43.738  44.931947   \n",
       "2018-02-21  44.54  44.25      1.09  0.346667  0.165  0.69  44.120  44.482342   \n",
       "2018-02-22  44.25  46.53      0.75  0.013333 -0.325 -0.36  44.280  44.180560   \n",
       "2018-02-23  46.53  47.98      2.32  0.543333  0.995 -0.29  44.886  46.670487   \n",
       "2018-02-26  47.98  48.58      3.08  1.146667  1.865  2.28  45.640  48.096623   \n",
       "\n",
       "            direction buy sell  \n",
       "date                            \n",
       "2017-12-13   0.037302   1    0  \n",
       "2017-12-14   0.168611   1    0  \n",
       "2017-12-15   0.374219   1    0  \n",
       "2017-12-18   0.373123   1    0  \n",
       "2017-12-19  -2.437980   0    1  \n",
       "2017-12-20   0.453480   1    0  \n",
       "2017-12-21   0.564660   1    0  \n",
       "2017-12-22  -0.436233   0    1  \n",
       "2017-12-26  -0.232607   0    1  \n",
       "2017-12-27   0.461887   1    0  \n",
       "2017-12-28  -1.907297   0    1  \n",
       "2017-12-29   3.662283   1    0  \n",
       "2018-01-02   2.409022   1    0  \n",
       "2018-01-03  -1.989993   0    1  \n",
       "2018-01-04   0.317471   1    0  \n",
       "2018-01-05   0.625171   1    0  \n",
       "2018-01-08  -0.905526   0    1  \n",
       "2018-01-09  -0.250010   0    1  \n",
       "2018-01-10   0.004534   1    0  \n",
       "2018-01-11  -0.005353   0    1  \n",
       "2018-01-12   0.180577   1    0  \n",
       "2018-01-16  -0.383559   0    1  \n",
       "2018-01-17   0.463810   1    0  \n",
       "2018-01-18  -0.177111   0    1  \n",
       "2018-01-19   0.267592   1    0  \n",
       "2018-01-22  -1.032102   0    1  \n",
       "2018-01-23   0.071812   1    0  \n",
       "2018-01-24  -0.044663   0    1  \n",
       "2018-01-25  -0.291534   0    1  \n",
       "2018-01-26   0.739814   1    0  \n",
       "2018-01-29  -3.573932   0    1  \n",
       "2018-01-30  -2.852046   0    1  \n",
       "2018-01-31   2.304261   1    0  \n",
       "2018-02-01   2.599973   1    0  \n",
       "2018-02-02  -6.001621   0    1  \n",
       "2018-02-05  -9.218416   0    1  \n",
       "2018-02-06   4.520101   1    0  \n",
       "2018-02-07  -0.923751   0    1  \n",
       "2018-02-08   1.273738   1    0  \n",
       "2018-02-09   0.853433   1    0  \n",
       "2018-02-12   0.248596   1    0  \n",
       "2018-02-13   0.039203   1    0  \n",
       "2018-02-14   0.018548   1    0  \n",
       "2018-02-15   0.511472   1    0  \n",
       "2018-02-16  -0.256995   0    1  \n",
       "2018-02-20   0.130386   1    0  \n",
       "2018-02-21  -0.688063   0    1  \n",
       "2018-02-22   3.610394   1    0  \n",
       "2018-02-23   0.855682   1    0  \n",
       "2018-02-26   0.106191   1    0  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#printing the first 50 rows to show the sell buy decision. \n",
    "df1.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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