Module4/Module4_Trade_Call_Prediction_using_Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Module 4\n",
    "# Trade Call Prediction using Classification\n",
    "\n",
    "In this module, we'd be covering the concept of classification and utilize our skills to solve the following queries – (Stock Price = Close Price)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem Statements\n",
    "\n",
    "`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem 4.1 \n",
    "\n",
    "Import the csv file of the stock which contained the Bollinger columns as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Series</th>\n",
       "      <th>Date</th>\n",
       "      <th>Prev Close</th>\n",
       "      <th>Open Price</th>\n",
       "      <th>High Price</th>\n",
       "      <th>Low Price</th>\n",
       "      <th>Last Price</th>\n",
       "      <th>Close Price</th>\n",
       "      <th>Average Price</th>\n",
       "      <th>Total Traded Quantity</th>\n",
       "      <th>Turnover</th>\n",
       "      <th>No. of Trades</th>\n",
       "      <th>Deliverable Qty</th>\n",
       "      <th>% Dly Qt to Traded Qty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RELIANCE</td>\n",
       "      <td>EQ</td>\n",
       "      <td>15-May-2017</td>\n",
       "      <td>1350.45</td>\n",
       "      <td>1356.40</td>\n",
       "      <td>1356.40</td>\n",
       "      <td>1333.50</td>\n",
       "      <td>1343.60</td>\n",
       "      <td>1344.10</td>\n",
       "      <td>1344.22</td>\n",
       "      <td>1540213</td>\n",
       "      <td>2.070379e+09</td>\n",
       "      <td>75812</td>\n",
       "      <td>802733</td>\n",
       "      <td>52.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RELIANCE</td>\n",
       "      <td>EQ</td>\n",
       "      <td>16-May-2017</td>\n",
       "      <td>1344.10</td>\n",
       "      <td>1346.05</td>\n",
       "      <td>1376.90</td>\n",
       "      <td>1341.00</td>\n",
       "      <td>1356.20</td>\n",
       "      <td>1356.30</td>\n",
       "      <td>1360.59</td>\n",
       "      <td>2892947</td>\n",
       "      <td>3.936121e+09</td>\n",
       "      <td>79767</td>\n",
       "      <td>1238129</td>\n",
       "      <td>42.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RELIANCE</td>\n",
       "      <td>EQ</td>\n",
       "      <td>17-May-2017</td>\n",
       "      <td>1356.30</td>\n",
       "      <td>1353.00</td>\n",
       "      <td>1365.95</td>\n",
       "      <td>1347.75</td>\n",
       "      <td>1350.00</td>\n",
       "      <td>1353.10</td>\n",
       "      <td>1354.16</td>\n",
       "      <td>2250304</td>\n",
       "      <td>3.047263e+09</td>\n",
       "      <td>56277</td>\n",
       "      <td>1225837</td>\n",
       "      <td>54.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RELIANCE</td>\n",
       "      <td>EQ</td>\n",
       "      <td>18-May-2017</td>\n",
       "      <td>1353.10</td>\n",
       "      <td>1340.25</td>\n",
       "      <td>1350.00</td>\n",
       "      <td>1324.10</td>\n",
       "      <td>1327.45</td>\n",
       "      <td>1327.35</td>\n",
       "      <td>1336.14</td>\n",
       "      <td>2237916</td>\n",
       "      <td>2.990178e+09</td>\n",
       "      <td>84723</td>\n",
       "      <td>1339478</td>\n",
       "      <td>59.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RELIANCE</td>\n",
       "      <td>EQ</td>\n",
       "      <td>19-May-2017</td>\n",
       "      <td>1327.35</td>\n",
       "      <td>1333.00</td>\n",
       "      <td>1335.70</td>\n",
       "      <td>1310.00</td>\n",
       "      <td>1318.20</td>\n",
       "      <td>1318.85</td>\n",
       "      <td>1321.99</td>\n",
       "      <td>2928883</td>\n",
       "      <td>3.871940e+09</td>\n",
       "      <td>115988</td>\n",
       "      <td>1444387</td>\n",
       "      <td>49.32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Symbol Series         Date  Prev Close  Open Price  High Price  \\\n",
       "0  RELIANCE     EQ  15-May-2017     1350.45     1356.40     1356.40   \n",
       "1  RELIANCE     EQ  16-May-2017     1344.10     1346.05     1376.90   \n",
       "2  RELIANCE     EQ  17-May-2017     1356.30     1353.00     1365.95   \n",
       "3  RELIANCE     EQ  18-May-2017     1353.10     1340.25     1350.00   \n",
       "4  RELIANCE     EQ  19-May-2017     1327.35     1333.00     1335.70   \n",
       "\n",
       "   Low Price  Last Price  Close Price  Average Price  Total Traded Quantity  \\\n",
       "0    1333.50     1343.60      1344.10        1344.22                1540213   \n",
       "1    1341.00     1356.20      1356.30        1360.59                2892947   \n",
       "2    1347.75     1350.00      1353.10        1354.16                2250304   \n",
       "3    1324.10     1327.45      1327.35        1336.14                2237916   \n",
       "4    1310.00     1318.20      1318.85        1321.99                2928883   \n",
       "\n",
       "       Turnover  No. of Trades  Deliverable Qty  % Dly Qt to Traded Qty  \n",
       "0  2.070379e+09          75812           802733                   52.12  \n",
       "1  3.936121e+09          79767          1238129                   42.80  \n",
       "2  3.047263e+09          56277          1225837                   54.47  \n",
       "3  2.990178e+09          84723          1339478                   59.85  \n",
       "4  3.871940e+09         115988          1444387                   49.32  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"RELIANCE.csv\", parse_dates=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(495, 15)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 495 entries, 0 to 494\n",
      "Data columns (total 15 columns):\n",
      "Symbol                    495 non-null object\n",
      "Series                    495 non-null object\n",
      "Date                      495 non-null object\n",
      "Prev Close                495 non-null float64\n",
      "Open Price                495 non-null float64\n",
      "High Price                495 non-null float64\n",
      "Low Price                 495 non-null float64\n",
      "Last Price                495 non-null float64\n",
      "Close Price               495 non-null float64\n",
      "Average Price             495 non-null float64\n",
      "Total Traded Quantity     495 non-null int64\n",
      "Turnover                  495 non-null float64\n",
      "No. of Trades             495 non-null int64\n",
      "Deliverable Qty           495 non-null int64\n",
      "% Dly Qt to Traded Qty    495 non-null float64\n",
      "dtypes: float64(9), int64(3), object(3)\n",
      "memory usage: 58.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Symbol                    0\n",
       "Series                    0\n",
       "Date                      0\n",
       "Prev Close                0\n",
       "Open Price                0\n",
       "High Price                0\n",
       "Low Price                 0\n",
       "Last Price                0\n",
       "Close Price               0\n",
       "Average Price             0\n",
       "Total Traded Quantity     0\n",
       "Turnover                  0\n",
       "No. of Trades             0\n",
       "Deliverable Qty           0\n",
       "% Dly Qt to Traded Qty    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [Source: For Calculating Bollinger Bands](https://towardsdatascience.com/trading-technical-analysis-with-pandas-43e737a17861)\n",
    "\n",
    "[**Bollinger Bands**](https://www.bollingerbands.com/bollinger-bands) is used to define the prevailing high and low prices in a market to characterize the trading band of a financial instrument or commodity. Bollinger Bands are a volatility indicator. Bands are consists of Moving Average (MA) line, a upper band and lower band. The upper and lower bands are simply MA adding and subtracting standard deviation. Standard deviation is a measurement of volatility. That’s why it’s a volatility indictor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a new column 'Call', whose entries are -\n",
    "- 'Buy' if the stock price is below the lower Bollinger band\n",
    "- 'Hold Buy/ Liquidate Short' if the stock price is between the lower and middle Bollinger band\n",
    "- 'Hold Short/ Liquidate Buy' if the stock price is between the middle and upper Bollinger band\n",
    "- 'Short' if the stock price is above the upper Bollinger band"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculating Simple Moving Average with 20 days window\n",
    "df['sma'] = df['Close Price'].rolling(window=20).mean()\n",
    "\n",
    "# calculating the standar deviation\n",
    "df['rstd'] = df['Close Price'].rolling(window=20).std()"
   ]
  },
  {
   "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>Symbol</th>\n",
       "      <th>Series</th>\n",
       "      <th>Date</th>\n",
       "      <th>Prev Close</th>\n",
       "      <th>Open Price</th>\n",
       "      <th>High Price</th>\n",
       "      <th>Low Price</th>\n",
       "      <th>Last Price</th>\n",
       "      <th>Close Price</th>\n",
       "      <th>Average Price</th>\n",
       "      <th>Total Traded Quantity</th>\n",
       "      <th>Turnover</th>\n",
       "      <th>No. of Trades</th>\n",
       "      <th>Deliverable Qty</th>\n",
       "      <th>% Dly Qt to Traded Qty</th>\n",
       "      <th>sma</th>\n",
       "      <th>rstd</th>\n",
       "      <th>upper_band</th>\n",
       "      <th>lower_band</th>\n",
       "      <th>mid_band</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>RELIANCE</td>\n",
       "      <td>EQ</td>\n",
       "      <td>09-Jun-2017</td>\n",
       "      <td>1331.70</td>\n",
       "      <td>1345.10</td>\n",
       "      <td>1352.95</td>\n",
       "      <td>1331.0</td>\n",
       "      <td>1332.95</td>\n",
       "      <td>1335.70</td>\n",
       "      <td>1340.93</td>\n",
       "      <td>3162802</td>\n",
       "      <td>4.241089e+09</td>\n",
       "      <td>78984</td>\n",
       "      <td>1333930</td>\n",
       "      <td>42.18</td>\n",
       "      <td>1331.2675</td>\n",
       "      <td>17.996668</td>\n",
       "      <td>1367.260837</td>\n",
       "      <td>1295.274163</td>\n",
       "      <td>1331.2675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>RELIANCE</td>\n",
       "      <td>EQ</td>\n",
       "      <td>12-Jun-2017</td>\n",
       "      <td>1335.70</td>\n",
       "      <td>1326.55</td>\n",
       "      <td>1329.75</td>\n",
       "      <td>1317.0</td>\n",
       "      <td>1319.00</td>\n",
       "      <td>1319.45</td>\n",
       "      <td>1321.07</td>\n",
       "      <td>1927513</td>\n",
       "      <td>2.546372e+09</td>\n",
       "      <td>44210</td>\n",
       "      <td>1194367</td>\n",
       "      <td>61.96</td>\n",
       "      <td>1330.0350</td>\n",
       "      <td>17.915475</td>\n",
       "      <td>1365.865950</td>\n",
       "      <td>1294.204050</td>\n",
       "      <td>1330.0350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>RELIANCE</td>\n",
       "      <td>EQ</td>\n",
       "      <td>13-Jun-2017</td>\n",
       "      <td>1319.45</td>\n",
       "      <td>1320.60</td>\n",
       "      <td>1327.00</td>\n",
       "      <td>1311.0</td>\n",
       "      <td>1312.00</td>\n",
       "      <td>1314.35</td>\n",
       "      <td>1318.87</td>\n",
       "      <td>1377291</td>\n",
       "      <td>1.816467e+09</td>\n",
       "      <td>63864</td>\n",
       "      <td>679324</td>\n",
       "      <td>49.32</td>\n",
       "      <td>1327.9375</td>\n",
       "      <td>17.116473</td>\n",
       "      <td>1362.170446</td>\n",
       "      <td>1293.704554</td>\n",
       "      <td>1327.9375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>RELIANCE</td>\n",
       "      <td>EQ</td>\n",
       "      <td>14-Jun-2017</td>\n",
       "      <td>1314.35</td>\n",
       "      <td>1315.90</td>\n",
       "      <td>1360.00</td>\n",
       "      <td>1315.9</td>\n",
       "      <td>1360.00</td>\n",
       "      <td>1357.50</td>\n",
       "      <td>1348.06</td>\n",
       "      <td>12101027</td>\n",
       "      <td>1.631296e+10</td>\n",
       "      <td>139964</td>\n",
       "      <td>7232727</td>\n",
       "      <td>59.77</td>\n",
       "      <td>1328.1575</td>\n",
       "      <td>17.481300</td>\n",
       "      <td>1363.120101</td>\n",
       "      <td>1293.194899</td>\n",
       "      <td>1328.1575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>RELIANCE</td>\n",
       "      <td>EQ</td>\n",
       "      <td>15-Jun-2017</td>\n",
       "      <td>1357.50</td>\n",
       "      <td>1360.00</td>\n",
       "      <td>1395.00</td>\n",
       "      <td>1359.1</td>\n",
       "      <td>1377.35</td>\n",
       "      <td>1383.95</td>\n",
       "      <td>1379.51</td>\n",
       "      <td>6466804</td>\n",
       "      <td>8.921028e+09</td>\n",
       "      <td>122808</td>\n",
       "      <td>2913028</td>\n",
       "      <td>45.05</td>\n",
       "      <td>1330.9875</td>\n",
       "      <td>21.470046</td>\n",
       "      <td>1373.927591</td>\n",
       "      <td>1288.047409</td>\n",
       "      <td>1330.9875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Symbol Series         Date  Prev Close  Open Price  High Price  \\\n",
       "19  RELIANCE     EQ  09-Jun-2017     1331.70     1345.10     1352.95   \n",
       "20  RELIANCE     EQ  12-Jun-2017     1335.70     1326.55     1329.75   \n",
       "21  RELIANCE     EQ  13-Jun-2017     1319.45     1320.60     1327.00   \n",
       "22  RELIANCE     EQ  14-Jun-2017     1314.35     1315.90     1360.00   \n",
       "23  RELIANCE     EQ  15-Jun-2017     1357.50     1360.00     1395.00   \n",
       "\n",
       "    Low Price  Last Price  Close Price  Average Price  Total Traded Quantity  \\\n",
       "19     1331.0     1332.95      1335.70        1340.93                3162802   \n",
       "20     1317.0     1319.00      1319.45        1321.07                1927513   \n",
       "21     1311.0     1312.00      1314.35        1318.87                1377291   \n",
       "22     1315.9     1360.00      1357.50        1348.06               12101027   \n",
       "23     1359.1     1377.35      1383.95        1379.51                6466804   \n",
       "\n",
       "        Turnover  No. of Trades  Deliverable Qty  % Dly Qt to Traded Qty  \\\n",
       "19  4.241089e+09          78984          1333930                   42.18   \n",
       "20  2.546372e+09          44210          1194367                   61.96   \n",
       "21  1.816467e+09          63864           679324                   49.32   \n",
       "22  1.631296e+10         139964          7232727                   59.77   \n",
       "23  8.921028e+09         122808          2913028                   45.05   \n",
       "\n",
       "          sma       rstd   upper_band   lower_band   mid_band  \n",
       "19  1331.2675  17.996668  1367.260837  1295.274163  1331.2675  \n",
       "20  1330.0350  17.915475  1365.865950  1294.204050  1330.0350  \n",
       "21  1327.9375  17.116473  1362.170446  1293.704554  1327.9375  \n",
       "22  1328.1575  17.481300  1363.120101  1293.194899  1328.1575  \n",
       "23  1330.9875  21.470046  1373.927591  1288.047409  1330.9875  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['upper_band'] = df['sma'] + 2 * df['rstd']\n",
    "df['lower_band'] = df['sma'] - 2 * df['rstd']\n",
    "df['mid_band'] = (df['upper_band'] + df['lower_band']) / 2\n",
    "df.dropna(inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def call(df):\n",
    "    if df['Close Price'] < df['lower_band']:\n",
    "        return \"Buy\"\n",
    "    elif (df['Close Price'] > df['lower_band']) and (df['Close Price'] < df['mid_band']):\n",
    "        return \"Hold Buy/Liquidate Short\"\n",
    "    elif (df['Close Price'] > df['mid_band']) and (df['Close Price'] < df['upper_band']):\n",
    "        return \"Hold Short/Liquidate Buy\"\n",
    "    else:\n",
    "        return \"Short\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Symbol</th>\n",
       "      <th>Series</th>\n",
       "      <th>Date</th>\n",
       "      <th>Prev Close</th>\n",
       "      <th>Open Price</th>\n",
       "      <th>High Price</th>\n",
       "      <th>Low Price</th>\n",
       "      <th>Last Price</th>\n",
       "      <th>Close Price</th>\n",
       "      <th>Average Price</th>\n",
       "      <th>...</th>\n",
       "      <th>Turnover</th>\n",
       "      <th>No. of Trades</th>\n",
       "      <th>Deliverable Qty</th>\n",
       "      <th>% Dly Qt to Traded Qty</th>\n",
       "      <th>sma</th>\n",
       "      <th>rstd</th>\n",
       "      <th>upper_band</th>\n",
       "      <th>lower_band</th>\n",
       "      <th>mid_band</th>\n",
       "      <th>Call</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>RELIANCE</td>\n",
       "      <td>EQ</td>\n",
       "      <td>09-Jun-2017</td>\n",
       "      <td>1331.70</td>\n",
       "      <td>1345.10</td>\n",
       "      <td>1352.95</td>\n",
       "      <td>1331.0</td>\n",
       "      <td>1332.95</td>\n",
       "      <td>1335.70</td>\n",
       "      <td>1340.93</td>\n",
       "      <td>...</td>\n",
       "      <td>4.241089e+09</td>\n",
       "      <td>78984</td>\n",
       "      <td>1333930</td>\n",
       "      <td>42.18</td>\n",
       "      <td>1331.2675</td>\n",
       "      <td>17.996668</td>\n",
       "      <td>1367.260837</td>\n",
       "      <td>1295.274163</td>\n",
       "      <td>1331.2675</td>\n",
       "      <td>Hold Short/Liquidate Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>RELIANCE</td>\n",
       "      <td>EQ</td>\n",
       "      <td>12-Jun-2017</td>\n",
       "      <td>1335.70</td>\n",
       "      <td>1326.55</td>\n",
       "      <td>1329.75</td>\n",
       "      <td>1317.0</td>\n",
       "      <td>1319.00</td>\n",
       "      <td>1319.45</td>\n",
       "      <td>1321.07</td>\n",
       "      <td>...</td>\n",
       "      <td>2.546372e+09</td>\n",
       "      <td>44210</td>\n",
       "      <td>1194367</td>\n",
       "      <td>61.96</td>\n",
       "      <td>1330.0350</td>\n",
       "      <td>17.915475</td>\n",
       "      <td>1365.865950</td>\n",
       "      <td>1294.204050</td>\n",
       "      <td>1330.0350</td>\n",
       "      <td>Hold Buy/Liquidate Short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>RELIANCE</td>\n",
       "      <td>EQ</td>\n",
       "      <td>13-Jun-2017</td>\n",
       "      <td>1319.45</td>\n",
       "      <td>1320.60</td>\n",
       "      <td>1327.00</td>\n",
       "      <td>1311.0</td>\n",
       "      <td>1312.00</td>\n",
       "      <td>1314.35</td>\n",
       "      <td>1318.87</td>\n",
       "      <td>...</td>\n",
       "      <td>1.816467e+09</td>\n",
       "      <td>63864</td>\n",
       "      <td>679324</td>\n",
       "      <td>49.32</td>\n",
       "      <td>1327.9375</td>\n",
       "      <td>17.116473</td>\n",
       "      <td>1362.170446</td>\n",
       "      <td>1293.704554</td>\n",
       "      <td>1327.9375</td>\n",
       "      <td>Hold Buy/Liquidate Short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>RELIANCE</td>\n",
       "      <td>EQ</td>\n",
       "      <td>14-Jun-2017</td>\n",
       "      <td>1314.35</td>\n",
       "      <td>1315.90</td>\n",
       "      <td>1360.00</td>\n",
       "      <td>1315.9</td>\n",
       "      <td>1360.00</td>\n",
       "      <td>1357.50</td>\n",
       "      <td>1348.06</td>\n",
       "      <td>...</td>\n",
       "      <td>1.631296e+10</td>\n",
       "      <td>139964</td>\n",
       "      <td>7232727</td>\n",
       "      <td>59.77</td>\n",
       "      <td>1328.1575</td>\n",
       "      <td>17.481300</td>\n",
       "      <td>1363.120101</td>\n",
       "      <td>1293.194899</td>\n",
       "      <td>1328.1575</td>\n",
       "      <td>Hold Short/Liquidate Buy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>RELIANCE</td>\n",
       "      <td>EQ</td>\n",
       "      <td>15-Jun-2017</td>\n",
       "      <td>1357.50</td>\n",
       "      <td>1360.00</td>\n",
       "      <td>1395.00</td>\n",
       "      <td>1359.1</td>\n",
       "      <td>1377.35</td>\n",
       "      <td>1383.95</td>\n",
       "      <td>1379.51</td>\n",
       "      <td>...</td>\n",
       "      <td>8.921028e+09</td>\n",
       "      <td>122808</td>\n",
       "      <td>2913028</td>\n",
       "      <td>45.05</td>\n",
       "      <td>1330.9875</td>\n",
       "      <td>21.470046</td>\n",
       "      <td>1373.927591</td>\n",
       "      <td>1288.047409</td>\n",
       "      <td>1330.9875</td>\n",
       "      <td>Short</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Symbol Series         Date  Prev Close  Open Price  High Price  \\\n",
       "19  RELIANCE     EQ  09-Jun-2017     1331.70     1345.10     1352.95   \n",
       "20  RELIANCE     EQ  12-Jun-2017     1335.70     1326.55     1329.75   \n",
       "21  RELIANCE     EQ  13-Jun-2017     1319.45     1320.60     1327.00   \n",
       "22  RELIANCE     EQ  14-Jun-2017     1314.35     1315.90     1360.00   \n",
       "23  RELIANCE     EQ  15-Jun-2017     1357.50     1360.00     1395.00   \n",
       "\n",
       "    Low Price  Last Price  Close Price  Average Price  ...      Turnover  \\\n",
       "19     1331.0     1332.95      1335.70        1340.93  ...  4.241089e+09   \n",
       "20     1317.0     1319.00      1319.45        1321.07  ...  2.546372e+09   \n",
       "21     1311.0     1312.00      1314.35        1318.87  ...  1.816467e+09   \n",
       "22     1315.9     1360.00      1357.50        1348.06  ...  1.631296e+10   \n",
       "23     1359.1     1377.35      1383.95        1379.51  ...  8.921028e+09   \n",
       "\n",
       "    No. of Trades  Deliverable Qty  % Dly Qt to Traded Qty        sma  \\\n",
       "19          78984          1333930                   42.18  1331.2675   \n",
       "20          44210          1194367                   61.96  1330.0350   \n",
       "21          63864           679324                   49.32  1327.9375   \n",
       "22         139964          7232727                   59.77  1328.1575   \n",
       "23         122808          2913028                   45.05  1330.9875   \n",
       "\n",
       "         rstd   upper_band   lower_band   mid_band                      Call  \n",
       "19  17.996668  1367.260837  1295.274163  1331.2675  Hold Short/Liquidate Buy  \n",
       "20  17.915475  1365.865950  1294.204050  1330.0350  Hold Buy/Liquidate Short  \n",
       "21  17.116473  1362.170446  1293.704554  1327.9375  Hold Buy/Liquidate Short  \n",
       "22  17.481300  1363.120101  1293.194899  1328.1575  Hold Short/Liquidate Buy  \n",
       "23  21.470046  1373.927591  1288.047409  1330.9875                     Short  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Call'] = df.apply(call, axis = 1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now train a classification model with the 3 bollinger columns and the stock price as inputs and 'Calls' as output. Check the accuracy on a test set. (There are many classifier models to choose from, try each one out and compare the accuracy for each)\n",
    "\n",
    "Import another stock data and create the bollinger columns. Using the already defined model, predict the daily calls for this new stock."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler,LabelEncoder\n",
    "scr = StandardScaler()\n",
    "lbc = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df[['Close Price', 'lower_band', 'mid_band', 'upper_band']]\n",
    "x = scr.fit_transform(x)\n",
    "y = df['Call']\n",
    "y = lbc.fit_transform(y).reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(x, y, test_size = 0.3, random_state = 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_features = y.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import OrderedDict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('Nearest Neighbors',\n",
       "              KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                         metric_params=None, n_jobs=None, n_neighbors=3, p=2,\n",
       "                         weights='uniform')),\n",
       "             ('Linear SVM',\n",
       "              SVC(C=0.025, cache_size=200, class_weight=None, coef0=0.0,\n",
       "                decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "                kernel='linear', max_iter=-1, probability=False, random_state=None,\n",
       "                shrinking=True, tol=0.001, verbose=False)),\n",
       "             ('RBF SVM', SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n",
       "                decision_function_shape='ovr', degree=3, gamma=2, kernel='rbf',\n",
       "                max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "                tol=0.001, verbose=False)),\n",
       "             ('Decision Tree',\n",
       "              DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=5,\n",
       "                          max_features=None, max_leaf_nodes=None,\n",
       "                          min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                          min_samples_leaf=1, min_samples_split=2,\n",
       "                          min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "                          splitter='best')),\n",
       "             ('Random Forest',\n",
       "              RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "                          max_depth=5, max_features=3, max_leaf_nodes=None,\n",
       "                          min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                          min_samples_leaf=1, min_samples_split=2,\n",
       "                          min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
       "                          oob_score=False, random_state=None, verbose=0,\n",
       "                          warm_start=False)),\n",
       "             ('AdaBoost',\n",
       "              AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,\n",
       "                        learning_rate=1.0, n_estimators=50, random_state=None)),\n",
       "             ('Naive Bayes', GaussianNB(priors=None, var_smoothing=1e-09))])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier_models = OrderedDict([\n",
    "    (\"Nearest Neighbors\", KNeighborsClassifier(max_features)),\n",
    "    (\"Linear SVM\",        SVC(kernel=\"linear\", C=0.025)),\n",
    "    (\"RBF SVM\",           SVC(gamma=2, C=1)),\n",
    "    (\"Decision Tree\",     DecisionTreeClassifier(max_depth=5)),\n",
    "    (\"Random Forest\",     RandomForestClassifier(max_depth=5, n_estimators=10, max_features=max_features)),\n",
    "    (\"AdaBoost\",          AdaBoostClassifier()),\n",
    "    (\"Naive Bayes\",       GaussianNB())\n",
    "])\n",
    "\n",
    "classifier_models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Jesus\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "C:\\Users\\Jesus\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Jesus\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Jesus\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "C:\\Users\\Jesus\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "C:\\Users\\Jesus\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:761: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "OrderedDict([('Naive Bayes', 0.5174825174825175),\n",
       "             ('Linear SVM', 0.5454545454545454),\n",
       "             ('AdaBoost', 0.6713286713286714),\n",
       "             ('Decision Tree', 0.7272727272727273),\n",
       "             ('RBF SVM', 0.7692307692307693),\n",
       "             ('Random Forest', 0.7832167832167832),\n",
       "             ('Nearest Neighbors', 0.8041958041958042)])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_scores = {}\n",
    "for model_name, classifier in classifier_models.items():\n",
    "    classifier.fit(X_train, Y_train)\n",
    "    y_pred = classifier.predict(X_test)\n",
    "    accuracy_scores[model_name] = classifier.score(X_test, Y_test)\n",
    "    \n",
    "accuracy_scores = OrderedDict(sorted(accuracy_scores.items(), key=lambda x: x[1]))\n",
    "accuracy_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "hero_df = pd.read_csv('HEROMOTOCO.csv', parse_dates=['Date'])\n",
    "hero_df.set_index('Date', inplace=True)\n",
    "\n",
    "hero_df[\"sma\"] = hero_df[\"Close Price\"].rolling(20).mean()\n",
    "hero_df[\"std\"] = hero_df[\"Close Price\"].rolling(20).std()\n",
    "hero_df[\"upper_band\"] = hero_df[\"sma\"] + hero_df[\"std\"] * 2\n",
    "hero_df[\"lower_band\"] = hero_df[\"sma\"] - hero_df[\"std\"] * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Symbol                     0\n",
       "Series                     0\n",
       "Prev Close                 0\n",
       "Open Price                 0\n",
       "High Price                 0\n",
       "Low Price                  0\n",
       "Last Price                 0\n",
       "Close Price                0\n",
       "Average Price              0\n",
       "Total Traded Quantity      0\n",
       "Turnover                   0\n",
       "No. of Trades              0\n",
       "Deliverable Qty            0\n",
       "% Dly Qt to Traded Qty     0\n",
       "sma                       19\n",
       "std                       19\n",
       "upper_band                19\n",
       "lower_band                19\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hero_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "hero_df   = hero_df.dropna()\n",
    "hero_X = scr.fit_transform(hero_df[['Close Price', 'std', 'upper_band', 'lower_band']])\n",
    "\n",
    "hero_df['Call'] = classifier_models[\"Nearest Neighbors\"].predict(hero_X)\n",
    "\n",
    "\n",
    "hero_df.to_csv('hero_trained.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hero_df['Call'].unique() = [2 1 3]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Series</th>\n",
       "      <th>Prev Close</th>\n",
       "      <th>Open Price</th>\n",
       "      <th>High Price</th>\n",
       "      <th>Low Price</th>\n",
       "      <th>Last Price</th>\n",
       "      <th>Close Price</th>\n",
       "      <th>Average Price</th>\n",
       "      <th>Total Traded Quantity</th>\n",
       "      <th>Turnover</th>\n",
       "      <th>No. of Trades</th>\n",
       "      <th>Deliverable Qty</th>\n",
       "      <th>% Dly Qt to Traded Qty</th>\n",
       "      <th>sma</th>\n",
       "      <th>std</th>\n",
       "      <th>upper_band</th>\n",
       "      <th>lower_band</th>\n",
       "      <th>Call</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",
       "      <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>2017-06-09</th>\n",
       "      <td>HEROMOTOCO</td>\n",
       "      <td>EQ</td>\n",
       "      <td>3784.45</td>\n",
       "      <td>3796.3</td>\n",
       "      <td>3800.50</td>\n",
       "      <td>3755.60</td>\n",
       "      <td>3786.00</td>\n",
       "      <td>3780.10</td>\n",
       "      <td>3779.46</td>\n",
       "      <td>302560</td>\n",
       "      <td>1.143514e+09</td>\n",
       "      <td>19861</td>\n",
       "      <td>198672</td>\n",
       "      <td>65.66</td>\n",
       "      <td>3684.0600</td>\n",
       "      <td>107.366537</td>\n",
       "      <td>3898.793074</td>\n",
       "      <td>3469.326926</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-12</th>\n",
       "      <td>HEROMOTOCO</td>\n",
       "      <td>EQ</td>\n",
       "      <td>3780.10</td>\n",
       "      <td>3777.0</td>\n",
       "      <td>3790.00</td>\n",
       "      <td>3760.35</td>\n",
       "      <td>3771.05</td>\n",
       "      <td>3773.25</td>\n",
       "      <td>3779.97</td>\n",
       "      <td>182605</td>\n",
       "      <td>6.902422e+08</td>\n",
       "      <td>17760</td>\n",
       "      <td>117935</td>\n",
       "      <td>64.58</td>\n",
       "      <td>3696.9500</td>\n",
       "      <td>101.366032</td>\n",
       "      <td>3899.682063</td>\n",
       "      <td>3494.217937</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-13</th>\n",
       "      <td>HEROMOTOCO</td>\n",
       "      <td>EQ</td>\n",
       "      <td>3773.25</td>\n",
       "      <td>3780.0</td>\n",
       "      <td>3785.35</td>\n",
       "      <td>3742.25</td>\n",
       "      <td>3751.00</td>\n",
       "      <td>3752.50</td>\n",
       "      <td>3765.91</td>\n",
       "      <td>212476</td>\n",
       "      <td>8.001665e+08</td>\n",
       "      <td>16843</td>\n",
       "      <td>128289</td>\n",
       "      <td>60.38</td>\n",
       "      <td>3703.6050</td>\n",
       "      <td>100.370995</td>\n",
       "      <td>3904.346989</td>\n",
       "      <td>3502.863011</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-14</th>\n",
       "      <td>HEROMOTOCO</td>\n",
       "      <td>EQ</td>\n",
       "      <td>3752.50</td>\n",
       "      <td>3764.0</td>\n",
       "      <td>3808.00</td>\n",
       "      <td>3751.05</td>\n",
       "      <td>3781.30</td>\n",
       "      <td>3789.05</td>\n",
       "      <td>3788.77</td>\n",
       "      <td>374661</td>\n",
       "      <td>1.419506e+09</td>\n",
       "      <td>39825</td>\n",
       "      <td>260012</td>\n",
       "      <td>69.40</td>\n",
       "      <td>3710.7600</td>\n",
       "      <td>101.142230</td>\n",
       "      <td>3913.044460</td>\n",
       "      <td>3508.475540</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-15</th>\n",
       "      <td>HEROMOTOCO</td>\n",
       "      <td>EQ</td>\n",
       "      <td>3789.05</td>\n",
       "      <td>3786.0</td>\n",
       "      <td>3820.00</td>\n",
       "      <td>3760.00</td>\n",
       "      <td>3774.00</td>\n",
       "      <td>3777.70</td>\n",
       "      <td>3785.08</td>\n",
       "      <td>163019</td>\n",
       "      <td>6.170397e+08</td>\n",
       "      <td>14888</td>\n",
       "      <td>94856</td>\n",
       "      <td>58.19</td>\n",
       "      <td>3720.0425</td>\n",
       "      <td>98.148884</td>\n",
       "      <td>3916.340269</td>\n",
       "      <td>3523.744731</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Symbol Series  Prev Close  Open Price  High Price  Low Price  \\\n",
       "Date                                                                           \n",
       "2017-06-09  HEROMOTOCO     EQ     3784.45      3796.3     3800.50    3755.60   \n",
       "2017-06-12  HEROMOTOCO     EQ     3780.10      3777.0     3790.00    3760.35   \n",
       "2017-06-13  HEROMOTOCO     EQ     3773.25      3780.0     3785.35    3742.25   \n",
       "2017-06-14  HEROMOTOCO     EQ     3752.50      3764.0     3808.00    3751.05   \n",
       "2017-06-15  HEROMOTOCO     EQ     3789.05      3786.0     3820.00    3760.00   \n",
       "\n",
       "            Last Price  Close Price  Average Price  Total Traded Quantity  \\\n",
       "Date                                                                        \n",
       "2017-06-09     3786.00      3780.10        3779.46                 302560   \n",
       "2017-06-12     3771.05      3773.25        3779.97                 182605   \n",
       "2017-06-13     3751.00      3752.50        3765.91                 212476   \n",
       "2017-06-14     3781.30      3789.05        3788.77                 374661   \n",
       "2017-06-15     3774.00      3777.70        3785.08                 163019   \n",
       "\n",
       "                Turnover  No. of Trades  Deliverable Qty  \\\n",
       "Date                                                       \n",
       "2017-06-09  1.143514e+09          19861           198672   \n",
       "2017-06-12  6.902422e+08          17760           117935   \n",
       "2017-06-13  8.001665e+08          16843           128289   \n",
       "2017-06-14  1.419506e+09          39825           260012   \n",
       "2017-06-15  6.170397e+08          14888            94856   \n",
       "\n",
       "            % Dly Qt to Traded Qty        sma         std   upper_band  \\\n",
       "Date                                                                     \n",
       "2017-06-09                   65.66  3684.0600  107.366537  3898.793074   \n",
       "2017-06-12                   64.58  3696.9500  101.366032  3899.682063   \n",
       "2017-06-13                   60.38  3703.6050  100.370995  3904.346989   \n",
       "2017-06-14                   69.40  3710.7600  101.142230  3913.044460   \n",
       "2017-06-15                   58.19  3720.0425   98.148884  3916.340269   \n",
       "\n",
       "             lower_band  Call  \n",
       "Date                           \n",
       "2017-06-09  3469.326926     2  \n",
       "2017-06-12  3494.217937     2  \n",
       "2017-06-13  3502.863011     2  \n",
       "2017-06-14  3508.475540     2  \n",
       "2017-06-15  3523.744731     2  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"hero_df['Call'].unique() =\", hero_df['Call'].unique())\n",
    "hero_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "hero_df.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x250d4fd53c8>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8+WfXXE9ERERERETkGqVV/CTzTp60JWxPPWWzjNzkiy++YNCgQXTu3JmgoCBOnTqFb1qbo2eUMe7pqQW2LLJWLShYEP76S1lUIiIiIiKSY2kVP8/SKn6SO8yaZYMtd93l1mEOHDgAQP/+/Tl//jzr169363iA+4JTYMsiX3gBNm60KwOKiIiIiIhIjlewYEG3XHfgwIHMnDnTLdfOCupBJZn3ww9EVapEmfbtWbR4MYGBgW4ZJiQkhNKlS9OpUyfA9qFq0aKFW8bKMvfcAzNmQHz5ooiIiIiISK7Qtu2V23r3hmHDIDwcunS5cv/AgfZx4gT06nXpviVLXD/HTDDGYIzBS5UyaaZ3SjLn5En480921KnD6TNnmDt3rtuGCgkJoUKFCpQqVYo6deq4vg+VJziO7UH1v/95eiYiIiIiIiI52v79+6lbt27i67fffpuxY8fStm1bHn/8cVq2bEndunVZu3YtAGPHjuXee++lffv2VK9enc8++yzx3LfeeoumTZtSv359XnzxxcTr16pVi2HDhtGoUaPEKqDkPPHEEzRq1IgOHTpw/PhxAD777DOaNm1KgwYNuPPOOwkPDwdsZtSjjz5Ky5YtCQgISMySMsYwYsQIateuzW233caxY8dc+4ZlMWVQSeYsXAixsfzu7w/AsmXL3DZUSEgIAQEBALRr147PP/+cqKgo8uTJ47Yxs0TCSoIrV9peXtdd59n5iIiIiIiIuFtKGU8FCqS8v0QJl2dMhYWFsXLlSpYtW8agQYPYtm0bAFu2bGH16tWEhYXRsGFDbrvtNrZt28bu3btZu3Ytxhi6devGsmXLqFSpEsHBwXzxxRdMSKGNS1hYGI0aNeKdd97h5Zdf5qWXXuKjjz7ijjvu4KGHHgLg+eefZ8qUKTzyyCMAHD58mKCgIHbu3Em3bt3o1asXs2bNIjg4mK1bt3L06FFq167NoEGDXPq+ZCVlUEnm9OkDO3bw26FDAKxcuZLo6Gi3DJWQQQXQtm1bwsPDWbdunVvGynJnzkDHjvDqq56eiYiIiIiISK7Tt29fANq0acO5c+c4c+YMAN27dyd//vyUKFGCdu3asXbtWhYsWMCCBQto2LAhjRo1YufOnezevRuAypUrp9qKxsvLiz59+gBwzz33EBQUBMC2bdto3bo19erVY9q0aWzfvj3xnB49euDl5UXt2rU5evQoYBNE+vbti7e3N+XKlaN9+/aufVOyWJoDVI7jeDuOs9FxnF/iX1d1HGeN4zi7HceZ4ThOnvjteeNf74nfXyXJNUbHbw92HOcWV38w4hmmRg22bN1KmTJlCA8Pd0vz8rCwME6fPp0YoLrpppsAckaZH0CRIraWeto0W/IXF+fpGYmIiIiIiOQoPj4+xCW514qIiEh87ly2QFbC6+S2G2MYPXo0mzZtYtOmTezZs4cHHngAAD+/9C+KlzDGwIED+eijj9i6dSsvvvjiJfPLmzdv4nNjzFXnfS1LTwbVY8COJK/fAN4zxlQHTgMPxG9/ADhtjKkGvBd/HI7j1AbuBuoAnYEJjuN4Z2764lHvvgv9+3MwOJjTp08zePBgwD1lfgcPHgRIDFCVKFGC+vXrsySbNcLLlKefhooVoWdPCAyE5cs9PSMREREREZEco3Tp0hw7doyTJ08SGRnJL7/8krhvxowZAAQFBeHv749/fBub2bNnExERwcmTJ1myZAlNmzbllltu4fPPP+f8+fOAvV9NT/+nuLi4xD5S3377LTfeeCMAoaGhlC1blujoaKZNm5bqddq0acP06dOJjY3l8OHD13wCR5oCVI7jVABuAybHv3aA9kDC+oVfAj3in3ePf038/g7xx3cHphtjIo0x+4A9QDNXfBDiAWvW2IBKVBSb9+wB4Oabb6ZmzZosXbrU5cOFxK9ylxCgAtuHasWKFURGRrp8PI+oXBl27oSvv4aYGMif39MzEhERERERyTF8fX0ZM2YMzZs3p2vXrtSsWTNxX9GiRWnZsiVDhgxhypQpidubNWvGbbfdRosWLXjhhRcoV64cnTp1ol+/ftxwww3Uq1ePXr16ERoamuZ5+Pn5sX37dho3bsyiRYsYM2YMAK+88grNmzdPvLdOTc+ePalevTr16tVj6NChiZVG1yonaWrYVQ9ynJnA60AhYBQwEFgdnyWF4zgVgXnGmLqO42wDOhtjQuL37QWaA2Pjz/kmfvuU+HNmXjYcjuM8DDwMkCdPnsY5JgCRU5w9azN8jIFNm3htwgSee+45zp49y9ixY/noo4/YuXNnYkNzV5gwYQLDhw9n9+7dVKtWDbCR7B49erB06VLatGnjsrGyBWPsCn/Hjtm+VMOGQY0anp6ViIiIiIhIhuzYsYNatWp5ehrJatu2LW+//TZNmjS5ZPvYsWMpWLAgo0aN8tDMXCe5999xnHBjTPprEt0k1Qwqx3G6AseMMUkbCyVX5GhS2ZfSOZduNGaSMaaJMaaJj48WGsxWjIHBg+HAAfjuOyhShC1btlC1alUKFy7MqFGj8PHxSVxm0xW2b9/O6NGjCQwMpGrVqonb27Rpg+M413waY7IS6ogdByZNgnfe8ex8RERERERERNwoLSV+rYBujuPsB6ZjS/veB4o4jpMQPaoAHIp/HgJUBIjf7w+cSro9mXPkWnHgACxYAC+/DDfcAMDmzZupX78+AOXKlePRRx9l2rRpLllhLy4ujp49e1KgQAHmzJmDt/fFtmVFixYlMDAwZ/WhulzJknD//fDll3DkiKdnIyIiIiIikuMsWbLkiuwpsBlUmcmeat68OYGBgZc8tm7dmpmp5mipBqiMMaONMRWMMVWwTc4XGWP6A4uBXvGHDQBmxz+fE/+a+P2LjK0jnAPcHb/KX1WgOrDWZR+JZI1KlWD7dtt/Crhw4QK7du2iQYMGiYc8/fTTlClTho4dOzJv3rxMDXf48GF2797Nc889R8WKFa/Y365dO1atWnXJ6gYXLlxg0qRJxMbGZmrsbGPkSNuT6r33PD0TERERERERSaM1a9YkrvSX8KhXr56np5VtpWcVv8s9DYx0HGcPUBxI6CI2BSgev30k8AyAMWY78D3wNzAfGG6MySERhFzg/HmYMgXi4qBsWYjPZNq+fTtxcXGJGVRgM5tWr15NQEAAXbt25d133yUtvc6Ssye+Afv111+f7P527doRGRnJqlWrErfNnDmTwYMH8+eff2ZozGynWjXo2xc+/BAOH/b0bERERERERERcLl0BKmPMEmNM1/jn/xhjmhljqhlj7jLGRMZvj4h/XS1+/z9Jzn/VGHOdMaaGMSZzqTWSdc6fhy5d4OGH4bKyvS1btgBckkEFUKlSJYKCgujZsydPPPEEgwYNytBqe3v37gVIbIx+uTZt2uDn58drr71GXFwcAJs2bQJg9erV6R4v23r5ZbjrLtsDTERERERERCSHyUwGleQWAwfCypW2KXrz5pfs2rx5M35+fsmu2Ofn58f333/PmDFjmDp1Kh06dEh3kGrPnj34+PhQqVKlZPcXLlyYd955h4ULF/Lhhx8CF4NmSbOqrnkBAbYPVblynp6JiIiIiIiIiMspQCUp27sXfvoJRo+G3r2v2L1lyxbq1auHl1fyX0peXl689NJLjB8/nhUrVrB2bfraju3du5cqVaqQ0mqODz/8MF27duWZZ57h7NmzbN68GbD1vglZVTnG5s1a0U9ERERERERyHAWoJGUTJ4KXFwwZcsUuY8wlK/ilpGXLlgCcOnUqXcPv2bPnquV9CRzH4amnniIiIoKvvvqK48eP06BBA06fPs2uXbvSNV629/33MGoUxJcxioiIiIiISNoULFjQo+NXqVKFEydOuPy6Y8eO5e2333b5dbPa1dNSRAB69YIKFaB8+St2HTx4kNOnT1/Rfyo5xYoVA9IeoDpz5gz+/v7s2bMnMbiVkhtuuIFixYrx5ptvAjBkyBCGDh3K6tWrqVmzZuJxCS2cHAciI8HX18bfrhlPPgmffAIvvABz53p6NiIiIiIiIhnTtu2V23r3hmHDIDzc9kG+3MCB9nHihL1XTWrJEtfPMYNiY2Pxjl9YTNLuWro1FxeLjo7m66+/JigoiPDw8OQPatYMHn002V0JpXRpyaBKT4Dq77//pmTJkkyaNIlz586lmkEF4OPjw6233kpISAgAvXv3pnBhf37/fRfz5sF779kksEaN4PPP7TnjxkG3bhAVlerls48iRWDkSPjlF2VRSdrFxsLZs56ehYiIiIhItmCM4cknn6Ru3brUq1ePGTNmADBs2DDmzJkDQM+ePRk0aBAAU6ZM4fnnnwfgm2++oVmzZgQGBjJ48GBiY2MBm501ZswYmjdvnmI/5LfeeotmzZrRrFmzxFXr586dS/PmzWnYsCEdO3bk6NGjgM2MGjRoEG3btiUgIIDx48cnXufVV1+lRo0adOzYkeDgYBe/Q56hDKpcbMmSJdx3330AeHt706BBA1q0aEHXrl259dZb4fXXoXt3qF072fO3b98OQN26dVMdq3Dhwnh7e6cpQPXDDz8QExPD6NGjAbjuuuvS9PF07dqVadOmUalSJYoVK0a7dh2YPn0M06fb/cWKQY0akCePfV2mDPz6KzzwgO0/fs1kUo0YAW+9ZT8/8T9IRZIVHg4TJsBHH0GpUnaxgxT6uYmIiIiIZJmUMp4KFEh5f4kSmcqY+umnn9i0aRObN2/mxIkTNG3alDZt2tCmTRuWL19Ot27dOHjwIIcPHwYgKCiIu+++mx07djBjxgxWrFiBr68vw4YNY9q0adx3332EhYVRt25dXn755RTHLly4MGvXruWrr77i8ccf55dffuHGG29k9erVOI7D5MmTefPNN3knvvfwzp07Wbx4MaGhodSoUYOhQ4eyZcsWpk+fzsaNG4mJiaFRo0Y0btw4w+9HdqE7lVwsIVj0wQcfcOzYMVavXs1XX33FpEmTOL90KXmffRb8/K4aoAoODqZ06dIUKVIk1bEcx6Fo0aJpClDNnj0bX19fTp8+DZBqBpUxcPw4VKnSBS+vhuTP/38YA5MnT+TkyYkEBf1AzZoOX331Lk2bNk08b/hwm1Ty3HP2nv2zzy69d//666/x8/PjjjvuSHXOWapIEXjmGRt8MMbWK4okFRcHX39tv7gPHoSmTWHaNAWnRERERESwAae+ffvi7e1N6dKluemmm1i3bh2tW7fm/fff5++//6Z27dqcPn2aw4cPs2rVKsaPH8+XX37J+vXrE+8rL1y4QKlSpQCb9HHnnXemOnbfvn0T//2///s/AEJCQujTpw+HDx8mKiqKqlWrJh5/2223kTdvXvLmzUupUqU4evQoy5cvp2fPnhQoUACAbt26ufT98ZRrJWdE3OD8+fMA9OjRg3HjxrFw4UImTpxITEwM4e+8Y6PWAwZc9fydO3dSo0aNNI9XrFixVANU//33Hxs3bmT06NEUKVIEx3Eu+ea83M8/Q/HiULo03HBDYeLiNrBvXz927IASJUqwfPljzJ37DKGh+2jRogWjRo3iwoULieePHg1jx8LUqRD/swGATz+N5sEH53DnnU/w0UcfpfljzDKjR8Mrr9jg1F9/2RIukQQvvGBr88uVg2XLYO1aqF4dQkPh2DFPz05ERERExKNMQnPiy5QvX57Tp08zf/582rRpQ+vWrfn+++8pWLAghQoVwhjDgAED2LRpE5s2bSI4OJixY8cCkC9fvjT1nXKSJBgkPH/kkUcYMWIEW7duZeLEiURERCQekzdv3sTn3t7exMTEXHGdnEIBqlwsNDQUgEKFCiVuu/766/EFCv32G/TtC/7+Vz0/ODjY5QGqhHrffv36MXr0aG655ZZLviHDwmwPqdWr7etateCWW2D8ePjuO5skEhzsfUnSV9euXdm+fTsPPvgg77zzDq+88kriPseBF1+ESZMgIMBui4iAoUN9iYr6AdjHI490pXv3U2zcmOYPNevs2wetWkHHjnDggKdnI55kzMWGaoMH22+I1auhdWu7LSoK6tSBl17y3BxFRERERLKBNm3aMGPGDGJjYzl+/DjLli2jWbNmgF2A6/33308MUL399tu0jv8/dYcOHZg5cybH4v/oe+rUKf799990jZ3Q72rGjBnccMMNAJw9e5akUFLHAAAgAElEQVTy8QuTffnll2ma/6xZs7hw4QKhoaHMzSELaKneIxe7WoCqOeATEQG33XbVc0+ePMnJkyfTHaA6cuRIisfMnj2bGjVqUKNGDZ566imeeuqpxH1xcdCmDWzYYPu2t2hhe0p9913qY/v7+zNx4kR27tzJn3/+ecX+hx66+DxvXnjwwbF89dUihg//gvfe28jixbdz003QsCEEB8PGjdCnTzaorqtSBSZOhEcegfr1bZ3i5atZSO7w8cc2evvHH1Cpkn0klScP1KsHixd7Zn4iIiIiItlEz549WbVqFQ0aNMBxHN58803KlCkDQOvWrVmwYAHVqlWjcuXKnDp1KjFAVbt2bcaNG0enTp2Ii4vD19eXjz/+mMqVK6d57MjISJo3b05cXBzfxd/Mjh07lrvuuovy5cvTokUL9u3bl+I1GjVqRJ8+fQgMDKRy5cqJ87vWOVdLbcsu/Pz8TFhYmKenkSM99dRTfPjhh5eUvAE8XKQIb0dGUvjQIShaNNlzV65cSatWrZg7dy5du3ZN03j33nsvQUFBV/1mO3PmDCVLlmTkyJG88cYbV+yfNQvuuAM++cQmiGQkOPTcc8/x5ptvcvbs2cR63eTUqFGDgIAAJkyYQEBAAFOmfMG99w7E19c2Vf/8c7j5Znj3XUhDj3j327sX+vWz5X7790PFip6ekWSlDRvghhvsF+XcuVf/5hg3zpb/nTp11e9tERERERF32LFjB7Vq1fL0NHKt5N5/x3HCjTF+HprSFVTil4uFhoZekj2VYFdgILc2bJjiDWzCMpbpzaBKaHyenHnz5hETE0P37t2v2GcMvPoqVKtms50ymrnUqlUrYmJiWLt27VWP+eeff9i1axe33norRePfg7NnT+Pra/d/+il88AGsWwcNGthm6ydOZGw+LnPddXalNoBt2zw7F8laR47Y1TZLlbLN1FL65mjZ0v67Zk2WTE1ERERERCStFKDKxc6fP0/BggUv3WgMNWrUYNfu3SmeGxwcjK+vb4oNzC9XrFgxzp49m9jU7XKzZ8+mVKlSNG/e/Ip9QUGwfr1dvC4NfeeuKqHGNygo6KrHzJs3D4AuXbpQuHBhHMe5JLDm62tLDPfsgaFDbYXdpEkZn5PLNGwI587Brbd6eiaSVeLibEnnqVMwZ45dbjclzZqBlxesXJk18xMRERERyYV69uxJYGDgJY/ff//d09PK9tSDKhdLNoNq/nz+99NPLD1xglOnTlGsWLFkzw0ODqZatWr4pGPZ+oRrnTlzhhKX3UhHRUUxb9487rrrrmRXPrjxRts6JyEBJKOKFi1K3bp1WbFixVWP+e2336hWrRrVqlUDoEiRIsk2dy9e3CYtDR0K6YjTuY+Pj31I7nH0KISH2y/Ehg1TP75gQdunrEkT989NJKlvv7VlqNnih6WIiIh4ijEmR64+d7lZs2Z5egqXyO6tnRIogyoXSzZA9dNPFD53jn+BXbt2XfXc9K7gBxcDVMkFe5YsWcK5c+eSLe8DW7XUtq3t85xZrVq1YtWqVcTGxl6xLyIigsWLF3NrkiykokWLpliaWKcOFChg2z+1bu3hcr9Zs6BnT1sTKTlf2bI2tXDAgLSfM2iQbagv4m7h4Refv/qqLUXu3t028tfPKBERkVwnX758nDx58poJluQUxhhOnjxJvnz5PD2VVF3b6RbR0TBlim0QXLq0rbsqWdIusbZ8ORQrZpdaS2geJJcIDQ2lePHiFzccOwZff01oz55EzJhBcHAwLVq0uOK8mJgY9uzZQ7du3dI1XsJYyQWoZs+eTYECBejYseMV+x5/HPLnh9dfT9dwV3XjjTcyceJEtm/fTv3LbtSXLl3KhQsX6NKlS+K21AJUCRIqp95+G/73P9fMNd2OHYOff7aN0pWpkLNt324DVMWKpa8pW1gY/PknNG4M8UvZirjctm3QsSPMmAE33WRLUKdOtTXRc+bYJVg//NA29hcREZFcoUKFCoSEhHD8+HFPTyXXyZcvHxUqVPD0NFJ17Qao/vsPbrkFdu60N2hnzkD//jZAtXgxPPGEPe6bb+CHHyCZZuC5XWhoKFWqVLm44eOPITISv+efx+fHHxMboV9u//79REdHuyyDyhjDnDlz6NSpE/nz579k3+nTtin5wIHpGipFrVq1AmDFihVXBKh+++038uXLx0033ZS4La0BqkaN4M477f3X88/baqos17ix/fevvxSgysmMgXvvtYGp9evTd+6hQzaL5bPP4MEH3TM/yd1iYuwP7bg4SPgdc9118Mor9ofj99/b4JR+L4uIiOQq6e1hLLnPtVniZwzcfz+EhMDs2bamKjra/kUWYNgwOHnSdq5euBD69vXsfLOp0NDQi03Sw8NtgKpbN3zr1qVmzZps3rw52fMysoIfXD1AtWHDBkJCQpIt7/v+e4iMtCv3uUqVKlUoW7Zsso3S582bR7t27S4JlKW2+mBSI0faWOnnn7tsuulTr57NGExv0EKuLXPn2kzRRx9N/7nVqkGRInYZShF3ePtt+zNowgSoXPnSfXnz2uDq2rWQzIIYIiIiIpJ7XZsBKseB4cNtak23bva1l9fFMpd8+WxW1UMPwbPPwm+/2awBucT58+cv9qDKl8+WX7z4IgCNGzdm/fr1ydYHuzpANXv2bLy8vOjatesV53z9NdSqZbOTXMVxHG688cYrGqXv2bOH3bt3X9J/CtKeQQXQooVt5P7++zaJIMvlzQt169rgheRMcXHw8ssQEGCzRtPLcWyTdAWoxB3Cw+G116BHD7vCZEqCgmD8+KyZl4iIiIhke9dGgCoy0mZJ/fOPLd8DuOOOtN2cDR4Mq1fbXi2SyBhzaZN0Ly/o2jUxEtS4cWOOHj3KoWQCe8HBwRQvXvzS/lVp4O/vj+M4yQaoWrVqdcXKfnv3wooVcN996WuxkxatWrXi33//JSQkJHHbvHnzAJINUJ06dSrNzfxefhnGjHHdXNOtTRsoWtSDExC3iYiw36fr19svsoyu2tisGWzdChcuuHZ+IsuWQWgoPPZY6sf+8guMGmX7oomIiIhIrpf9A1Th4Ta7p2RJ28OiQwfYsiXt55cvb2/GcsFSlulx4cIF4uLibIBq/nybaZbkJqFxfC+j9cmUiu3cuTPd2VMA3t7eFClS5JIA1b59+9iyZUuy5X3e3ja+mJEkkdQk7UOVYN68eVSvXp1q1apdcmzRokWJiYkhLCyMXbt2ERUVleK1O3Sw7VcyGjvItPffh+nTPTS4uFXevFCunC3Hve++jF+naVOb4rdpk+vmJgLQuTPs2WMD5am5+WZbnr90qfvnJSIiIiLp4jhORcdxFjuOs8NxnO2O41zxF0jHcZ50HGdT/GOb4zixjuMUi9+333GcrfH7/krLmNk/QOXjY5enHj8evvjCrj5Vr176rrF/v410/POPW6Z4LQoNDQWwAapXX4XvvrM3v/ECAwPx8vJKNkAVHBycoQAV2DK/pAGqOXPmACQboKpSxVZxVqyYoaFSFBgYiJ+fH7NmzSIsLIwLFy6wePHiK7KnwAaoAHbt2kXdunWZNm1aqtcPD4d337W9ykUyxRj782/nThtonzzZ9tnLTNC9Qwf4+2/1ABL3uO46m5Wbmlat7O+dP/5w/5xEREREJL1igCeMMbWAFsBwx3FqJz3AGPOWMSbQGBMIjAaWGmOSlky1i9/fJC0DZv8AVZ48NrvnkUdsWkq7dum/MfP2hilTbGaJABcDVFWOHLF9QB5//JKUnwIFClCrVi3Wr1/P2rVr2Rjf0+js2bMcPXo0UwGq//77j3Xr1hETE8Ps2bOpXbv2FVlLu3fbysw0VtWlm4+PD71792bGjBmUK1eOgQMHEhERQZcuXa44NiFAtWrVKqKjo5Mte7ycMTBuHPzvfy6feuqOHoX69SENgbRs4ehR932ic4IHHrDlUpMnu+6ahQrZ5m5pCSKIpNXzz9ulTCMi0nZ8/vz2d/rs2foZICIiIpLNGGMOG2M2xD8PBXYA5VM4pS/wXWbGzB13JxUr2pu8Tz9VFlW88+fPA9Bg4UK7otegQVcc07hxY5YsWcKNN97IiBEjgIw3SE9QsmRJli9fTrNmzejRowfLli1LNnvq3XehfXuIn6ZbTJkyhWXLltGxY0e+//578ufPz0033XTFcQkBqrVr1wI2SJcaPz8YMgRmzfLAl1zx4rBrF1xlFUaP+vdfGDvWZkGGhdlUs9atbUPl8HBPzy77CQmxmaPDh8Nbb7n22vv2QadONsNUJLNmzLDZuMWK2bL8tLr7btsL7cAB981NRERERK7Gx3Gcv5I8Hk7uIMdxqgANgTVX2V8A6Az8mGSzARY4jrP+ate9XO4IUIFdnc7HB154wdMzyRZCQ0OpDJRbs8ZGUhKapSfRuHFjwsLCiI6OZt++fcDFAFXNmjUzNO6bb77JpEmTGDNmDL/++iuxsbH06NHjkmOOHbP3Oj17Jjstl3Ech9atW/Pjjz+yevVqfv31V/Ilc2OVEKBas8Z+L547dy5N1x8xwibvZXnino8P1KhhS7iyC2Nsll7VqraLfPnycPy4zaAYNgzmzoUHH1QWxeXiS2AZPtw9ffTWrIF+/WwfIJGM2rAB7r/flux99FH6zu3b1wZiK1Vyz9xEREREJCUxxpgmSR6TLj/AcZyC2MDT48aYq90M3w6suKy8r5UxphFwK7Y8MNUmpZ5q45z1ypWDkSPtX3jvvx86dvT0jDwqNDSUAsDx22+n1AMPJHvMfffdR2hoKKdOneLdd98lMjKS4OBgvL29CQgIyNC4derUoU6dOoBd1W/p0qU0aWLLUcPCbObUW2/ZZJphwzI0RIY0T6EXT7FixYCLwbm0BqjKlbP3XpMn237WTdJUdesitWvb4EN2MWECfPCBDUI9/zxUrnxx3+OP2wyKZ5+1b9orr9jAldjSp+rVIYMB4RRVrQqTJtkMlrFj7c9GkfQ6cgS6d4cSJeCnny7pZZgmefLYf+PibIDa29v1cxQRERGRDHEcxxcbnJpmjPkphUPv5rLyPmPMofh/jzmOMwtoBixLabzck0EF8Nxz8NRTEL9CXYoOHrRLYI8cCZ98Yle8ykFCQ0PZAZx47TW4rP9TgiJFivDcc89Rv359AA4cOEBwcDABAQHkSbipyISRI0cye/ZsvOL74Jw5A6+/bmOH27bZP8ZnBwkZVAnSUuKX4JVX4KabbM9gsPGGTz+FU6dSPi/TatWypVvZpWzu77+ha1eYOPHS4FSCZ56Bhx6Cd95RlmNS/frZwJ27ViHt08cGDV9/3S5AkZy4OPeMLTnDgQM2qDRnDpQqlbFr7NhhM6iu9jUoIiIiIlnOcRwHmALsMMa8m8Jx/sBNwOwk2/wcxymU8BzoBGxLbczcFaDKnx/eeAOKFrXBp9q1bfbA66/Dr7/aMoOEEqPOneH22225wrBhNqiVnTJSMikhg6pggQKpHls5PqCwf//+TK3gdzlj4McfL1Z2lS9vm6P/9JN7EkYSHThgb7rTeONdqFChxCAapD2DCuw917x59ksOYOZMGDoUypaFO+6AvXvTNfO0a9XKBjfc2cQrPT7+2H6yr9aU23FsNs+iRTBqlN32xx82qLVjR9bNM7sZMMAuDuFOH3xgv+Fef/3Kfd98A/7+NoAYG+veeci1qWlT2/MuMDDj16hQAQ4dsitjiIiIiEh20Qq4F2jvOM6m+EcXx3GGOI4zJMlxPYEFxpiwJNtKA0GO42wG1gK/GmPmpzZg7gpQJVWwIFx/PaxdazMUuna1zdR37bL733oLFi+Gc+dsVOHUKdscKYc4f/48U4Hyyaxad7mEANW+ffvYvXu3ywJUI0dCr16wahWcPGm3lU9pTQBXMMauGtW2LbRoAb//nuopXl5eFClSJPF1egJUl/vqK9uuZdgwmyxw111uav/ToYMNLmQ0o8FV9u61HzBcLOVJSbt2UKaMfX70KCxfbhuqjxgBJ064b57Zzd69duXSyEj3j1WgAPz228V+VwnGj4d777VN9994w6YCqpG1JJg+HV56yf4Ay2xGbaFC9vfx+vWumZuIiIiIZJoxJsgY4xhj6htjAuMfvxljPjXGfJrkuKnGmLsvO/cfY0yD+EcdY0ya+onk3gBV27bw8892ibUzZyAoyGZ5JJS7de5sj8mXzy6bvWuXzagCW4a0dKmnZu4SoaGhVACccuVSPbZChQp4eXkRFBRERESESwJUv/5qm4cPHWoXmytRItOXTJvNm+3N/1132fK3Pn0uBlBSkFDmV7FixXSV+F3OcaBhQ3jvPZg61SYHrVuX4culbsMGzzbAHjPGBjZCQ9N/7j33wJ49MHiwrYusVs2uaJfTRUbCbbfBd9/ZrJKsUKWKDVSdPw9LlththQvb5uy7dsHnn9t9JUvafYsWZa8m/JK1Tp60AdT5813XM6pxYwWoRERERHK53BugSsrf35ZEDRt29f9sJzRtNsZGFu67L2M33dlEaGgoFQGvihVTPdbX15dy5cqxYMECgEwHqI4ft33q69e3TdF9srJV/w8/2M9x3742SubvD506pVpGlhCgatKkSaYyqJLq2dPGylq2dMnlrrRokb3p++UXNw2Qir/+skGW4cMzvhxjyZI2cLx1q/1YoqJcO8fs6PffITjYdtevWjVrxx42zAYGo6JsaeFHH9nsmPvvh02bbMAebHCiTh2bfSq5izE2/fXMGVuWe7Wy3fRq3Nj2fjx61DXXExEREZFrjgJU6eU4ttHzgQO2V05Cz6prTNjZs5QF2/sjDapUqcKRI0eAzAeoDh60VUPTpl28380S8+bZDJy2bW3KVuXKNojj7W2z4xLqDJNRtGhRChQoQK1atTh37hzGRZ/3cuXsl9DixW74UrrpJlu2+v77Wd/oOi7OBjFKlXJNEKNWLduTavBg+zoiIvPXzK6+/x6KFbNZVFmtXz/7DTpgAKSUKbhkiV257YMPbBm05B4TJtha5WefteW3rtKunQ1m57AFSUREREQk7RSgyoiWLe1fkCdNsj1asksj6nTwPn4cb0hzgCqhD5W/vz+lMtnXKDAQtm+HunUzdZm0+ecfuHDBPt++3UbExo69uP+662ypZ/HiF1e8a93aZofUrm0bAPfqRYdq1Wjfvj3+/v7ExsYS7sLV8X77Ddq3twE7l/L2tjeRy5a5Jki0du3FJsYnTtiv/0GD7PtVt+6lq0E+/rg99n//s6VirpCQqfH77xAQYMv/cpoLF+xSj3fcAb6+WT/+LbdA6dK2v9Brr139uJIl7ddUeDh8+23WzU886+RJePppG9B/8UXXXrthQ5ux5/ZGhCIiIiKSXSlAlVFvvAEvv2yDGzNneno26XbmwgU+Ll0abrghTccnBKhq1KiBk8El7y9csG/bhQuuqwpJ0ciRNgCV8Pl55BEb1LjxxkuPu+EGG0xJKHesVs0Gp+rUsTfiCxbwdOHCzJ07l0phYXiTuUbpl7v1VntvNm6cGxKdBg+2jb7eeAM++yzj1zGGM3ffzYYBA+zrt96y1541y9ZoXn89tGlz8QMIDIQXXrClsK5Wp47NoLrrLjh92vXX96TwcFtO5473LS0cx36tgM2iSknTptCggQ1USu5QvLjNZPzmG/f8EI+JUSN+ERERkVzMcVWpkrv4+fmZsLCw1A/0lMhIyJvX07NItw4dOhAZGUlQUFCajp80aRKDBw/m3nvv5auvvsrQmM8+a1eyX7LEVp+51Q8/QO/e9mb/lVcy91f53bttoAsIL1mSUadO8eiOHdSsWdNFk4Wvv7YxiQUL4OabXXZZKyYG+ve3A2S0bGzpUmjbloeAd8+do5Cvr22SXa+eZzJ95s+Hbt1sgGThQttLTFzn3Lm0Zb4tXGh7VLVubYNbkjOdP28XErnlFvd+nu+91/6CUJBKREREJEs4jhNujPHz9DwSKIMqsxKCU2fOeHYe6eRz6hSV8uRJc+OjhAyqjAZlFi60STcDBmRBcGrDBpvd06yZ7ReW2ZKR6tVttoCXF3lDQ+mEazOowMbSSpa0FS4u5+MDM2ZcDE4llDymw7mXX+YE8A2wefNmWyrZqJFnglNgV9n86SfYuNEudX+tM8Zm+GWX1UHTWpbZsaPNnFNwKmebONGmem7a5N5xmjSBkBAFqERERERyKQWoXCEh6pLNs9GS6vPff3yxbFmaj69bty558+alZTqXnDMGxo+38YTrr4e3307vTFMREWEDFE8+efH1rbfawOE337g8gHK8UyfaAOdcHJDMmxceftiusu7WhMGpU22J3OHDaT9n8mQKLlrERCAC2LBhg5sml05du9oeWJ4KkrnSc8/Z6GQaMxqzlWPHbEB4yxZPz0TcITLSLrfavr2tRXanhPLrFSvcO46IiIiIZEsKULlCy5b25qx5c/jwQ9i8GWJjPT2rqzLG4H/uHGcKFUpz5kP58uU5e/Ysbdu2TddYp07Bq6/a5J3Vq+3ieS5z9qyNfI0da/uigM3s+eknW35WvboLB7OiWrakGLjlZvzpp21Pdz8/G2dzS7yzfn0bUOjWLc2RMHPsGEH58/NX27aULl2ajRs3umFiGTRp0sWeSdeqjz+2ta+DB7ummX1W8/WF776zjbPr1LG9xzKQpSfZ1JdfwqFDMHq0+8dq0MD+AFSASkRERCRXUoDKFR580GY/nD8Pjz5qG0SvWWP37dwJO3Z4dn6XCQkJoUxMDNGlS6frvLzp6LV18qTtl128OKxaZXtpFyqU3pmm4MgRm7W2YoVdRSxp6UmrVlC0qAsHu8iJD9AVWr/e5dcuVMi284mLsyV/99zjhmyqRo3sCm0bNti+VNHRyR/3yy+2zxOwqnVr2l64QPcBA2jYsGH2yaBKYIzNPLoGV9Nk1ixb2tetm/0Zci2WyhUtaoOE5ctDmTK22/8dd3h6VuIKZ8/CmDF2IYkOHdw/no+P/UOPAlQiIiIiuVKODlDt27cvawby9obhw2H7dti3D776Cho3tvsmT7YrwnXoYMskQkKyZk4p2L5lCzUAn/jG3652/rxtJfLKK/Z1QICLF3wyBu680zYv/+UX6NvXhRdPWcFatRgN7C1Xzm1jOI69H/zuO/vv3r0uHqBrV3j/fZg92w6QEKQ6fBhCQ2HIEJsN8847AHzx1VcU8PPjzjvvpFGjRvz9999ERES4eFKZsHWr7YOUUOZ5LZkzB1q0sJ9sHx9Pzybjhg6FlSvhzz/t47nn7PawMDh40LNzk4zbs8dmyI0fn3XB0+eegzffzJqxRERERCRbybEBqunTpxMQEGAbOmcVx4EqVexKRAnZRk8+aTMKQkLgiSdsCUM6ej+5w+n58ykB+PXo4ZbrT54M+/fbhb3cwnFstsmiRXZVqSxUqFAh/gfsceOqcY5jq2nmz7f39jfcYBdVc6lHHrHZO3372hvQrVuhalWoVs2WzY0aBb/8QlhYGNOnT6d3794UKlSIhg0bEhMTw7Zt21w8oUyoXx/+7//g008vlnpeK6ZMsZ/oAgU8PRPXad/+Yi+hV16BWrVsgCMmxrPzkvRr3NhGyJs0ybox27e3zfezUxBcRERERLJEjg1QvffeewAsX77csxMpXdr+RTg42PZFKlXKZk2A7dPigf+Erz5zhscLFaLQXXe5/NrR0fDeezY41b69iy8eFwczZ0J4uG3W27y5iwdInY+PDwUKFKDYjh3w669uHatTJ5g3D44fh08+ccMAPXrYoClA5cq2S3uVKrZc7q23IG9eZs6cyfnz5xk0aBAAjRo1AshefajABoFr1IAHHnBDNM/FoqOhSxcbxfXySvuKedeihx6yPfoee8wGk6OiPD0jSQtj7MqfUVG27jirHTgAxYrZTM7PPkvfog4iIiIics3KkQGqtWvXsnbt2sTn2UatWrZTeEJT52nTbEPYGjWgZ08byNqzx+3TWLtnD1uaNIEiRVx+7e+/h//+g6eecvmlbebZXXfZzB8P8i9cmM6LFtmypshIt47VrJntn92li1uHsUGS8eNt77QkKzV+/vnnVK9enVatWgFQtWpV8uTJw54s+DpNl/z5bTPngwdh5EhPzyZlmzbZyGMyfXaMMcTkpEyj666zH+vEiTbj8dFHPT0jSYs5c+Duu225uqc8/DBs22b/LVfO/kEiaa/Bo0dh7dqr99ETERERkWtOjgxQffzxxxQsWJC2bdtmrwAVgL+/7VkFtnzi+eehXj2bYfXGGzYS4cYsA3PgAC03baJJQIBbrv/xx3YhL7cEVL780nYS79nTDRdPu8L+/syoWdP+lX/qVLeP98wz9kskq+3evZtly5YxaNAgnPj+M47jULx4cU6dOpX1E0pN8+bw0ku2JjI7S8jqbNfuks1HjhyhefPmdHF7NDKLOY4NMjzzDPzwg13gQLKviAhbMlu7NgwY4Jk5VKxo++T9849dMXXcOLu9TBn775QpULas/Z4vWdL2oBs69OL569erpFRERETkGnQNd+VN3vHjx5k+fToPP/wwZcqU4fnnn+fs2bP4u7FnUIY1bGgfCRYvhhMnbE8gNzk3aRLvREQwrUIFt1z/119tuy2XNkUH22x55ky7vJ2H+/UULlyYoPz5bSDk1Vfh/vszVAYTGxuLd0KwMhW7d9ue5e+/D/nypXuoDJk6dSpeXl7cd999l2wvVqxY9gxQgQ34ZnfLl9uVA5I02g8ODqZz587s378fb29vwsLC8PPz8+Ak3WDcOBg82AYZjLk2VyzMDd55xy72sXChW38XpYnj2Oh8vXoXG++DXV3wxRehZk07z+Bg+wcDsF9bQ4faoNXbb3tm3iIiIiKSITkug+qzzz4jKiqK4cOH07RpUwD++usvD88qjdq1syVsjmOXwnO16GjyTJrEcqDCZdkbLrg0cXF2xXm3ZPv88ot9Tzz1F/0k/P39OXvuHIwda2+Kvvgi3dfYs2cP5cuXZ/z48Wk6PiTEVklNnJjuoTIkNjaWqVOn0lhdPGcAACAASURBVLlzZ8pdtmJhtg5QJfj8c9voPZt56803OfPrr5iEJuLAqlWraNmyJeHh4YwdO5bY2FjWr1/vwVm6ibe37XEGdvGIQYPg9GmPTkkuExICr71mV0nt0CHdp0dHR/Pff/+5YWKXGTnSBqj69LE9qpYts78jwP7+rFnTRvM3bbI/o8+edf+cRERERCTTclSAKiYmhk8++YSOHTtSs2ZNmsSvPJTtyvxS8+23ti/V1q3pP/f0aRvFSK43x/ffk//YMd4EatWq5ZKpgh2mTx9o1MiN95tbt9ospST9kTylcOHCnDt3Dm6+Gbp2Tff5ERER9O7dm6NHj/Liiy9yOg1vWrt2tun8a69lzb3WggULOHToUGJz9KSuiQDV/Pm2nCyb2fjHH4RER7O1WDEAfv75Z9q3b0/x4sVZtWoVw4YNA2DNmjWenKb75ctn+xv16WMzXiR7OHjQ/rB5/fVLNsfFxaXp9A8++IBatWpx3h1/YEmPDz6A4sVtCWClSrYPGsDSpVCiBFSvbvd17gz9+2fsd62IiIiIuFyOClDNmTOHkJAQRowYAdgb6erVq197AaoGDey/rVrZruPHj0NaAwJDh8KQIfY/30WLwj332O3GwBNPcKx4cX739qZkyZIumWp0tO2lO2uWXUCtaFGXXPZKr7xib558PF+V6u/vbwNUjgNz59qypXQYNWoUGzdu5NVXX+XMmTO8ncYylP/9z34Z9OsHsbEZmXnaff7555QoUYLbb7/9in3ZtgdVUnXr2m794eGenskldp48ST3g8U2bmDBhAnfeeScNGjRgxYoVBAQEULJkSQICAli9erWnp+pe48bZpvx//JGhDERxk+bNbSZS9eqAzaS89957adasWZpOX7hwIeHh4fz999/unGXqiha1AdBevezXWevWdnvJkjYo2rSpPeb0aVi50v5uERERERHPM8Zk60eBAgVMWrVt29ZUrlzZxMTEJG7r37+/KVeuXJqvkW38958xzZsbY0NLxowbZ7efOGHMqFHGfP21MatWGbNrlzGRkXbfjh3GeHkZ8+STxsyYYcyIEcaMHn3xmoGBZnynTqZMmTIumWJUlDF33mmn9/77LrnkNeGxxx4zhQsXvrghJsaYmTMvfh5S8MMPPxjAjBw50hhjTJ8+fYyfn58JDw9P09iffuq69zsuLi7Z7cePHze+vr7m8ccfT3b/qFGjTHq+Lz1ixgz7Rm3a5OmZXKJUqVKmQIECBjCAuf32201YWNglx/Tr18+ULVv2qp+fHCM21pg2bYzx9zdm0SJPz0aMMSbJ12JcXJwZMmSIAYyPj0+qX48xMTGmcOHCBjCTJ09290xFRERExAWAMJMN4j4Jj2s2gyo2NpZvv/2WqPgV77Zt28aSJUsYNmzYJY2nmzVrxqFDhzh4rf2FtGJF21fjk09sucLdd9vtu3fDhx/CvffaJt3XX2+bhv/5p+278ddfNtuod2973GuvXbzmhg0sKlDAZdlTw4fDjz/Cu+/CY4+55JLJi462vaeWLHHjIGlXpEgRQkNDOZtQa7d0qf1L/dSpRKew5Pk///zDAw88QLNmzXg9voTmjjvuICwsjF27dqVp7MGDYdIkePDBqx+zZs0a/p+98w6L4urC+Dt0kSIdC4goYAEx9qioUWPvNYqxo8beUkyxxTQb8inGXmOPBU3svRdCsIGgQkABFVSadPZ8f5zdpS2wuywtmd/z8AA7d+69s2V27jvnvOfRo0dF9nPjxg1Uq1YNz549K7Bt9+7dyMzMVJjeB3BkYkpKCtLS0pSac7ng4sK/Hz8u33nkIiMjA4dev8bhxo1Rp04dTJkyBYcPH4ZhPtP/1q1bIyYmBi9evCinmZYRWlpcBbNOHcDEpLxnI5KeDpibA8uWAQAWLlyI9evXw8HBAVlZWUhKSipy90ePHnFkKfj7uFLx7l2OyXpxiCmpIiIiIiIiIiKlRqUVqM6fPw9PT0+sXr0aAODr6wsDAwOMHz8+TztZakJlSvOTixx6epyuN2MGULcuP9a6NZCUxJ4ZJ07wAu+rr3hhAXBVQH19xR0LAmJjY0skUF27BkRE8N8zZnCG2+zZanenHGFhnK4hG7ic6du3L4gox+D8o4+AFi2Q6e2NatWq4fz58wX2SU9Px7Bhw6ClpYX9+/dDT1r1z9nZGQCUFqgAwMsLqFoVeP0aaNcOWL4cePqUtyUmJqJ79+6YWYxiuHz5ciQmJuLmzZsFtm3fvh3NmzeHWyFu9+bS91qFTvNzcuJqcamp5T0TOa9CQ9EGgIWNDZ49ewZfX1/oKEhZbd26NYAcH6pbt24hOjparTHT09MxYMAAXLlyRe15lyp16gABAUCzZvz/7NmAr2/p57CKFOTRIxapHBzg4+OD77//HuPHj8e30sqYb968KXL3GzduAABsbGzwoDJ5OkkkfHOnsAqgEglw/z6nCg4YAFhbA/HxvO36deCff8psqiIiIiIiIiIi/3YqrUAlu0P7888/4/Lly9i5cyeGDx8OCwuLPO2aNGkCHR0d3L17tzymqTJ3796FsbFx0SbJurrssdOjB0cWLV3KwpQSxMbGwtLSUuV5RUUB/fuzlYf0BjtcXdXyCFcdWRRM/fplMFjxNG3aFH379sWqVasQHx/PXlRDhkD38WOYpaTg3r17Bfb58ssv4e/vj23btsFBVskMgJPU60UVgUpGdDRbLH3xBesxrq7A0KGBiI9PLDKCKiIiAseOHQOAAu3ev3+Pe/fuKfSeklEpBCpDQyAmBhgzprxnIifx4kVoAZA0awZBEAptV1/6Pg8PDwcA9OnTByNGjFBrzN9//x1Hjx7FkSNH1Nq/TJA9FxkZLJJMm8YnG5Ey5YavLwDg97AwzJo1CwMHDsT69evl3xfFfd6vX78OGxsb9OjRo3JFUGlp8Xfp0aPA1avA7ds51T5OnABsbNgXcuZMFqr69gXev+fIXlkksyhSiYiIiIiIiIhohAovUKWlpWHnzp1IyWd2HBwcDAMDA7x79w4dO3aEhYUFFi5cWGB/AwMDuLu7V5oIqn379iE9PR3LZCqQhomLi1M5gur4ccDNjf2Mly7NEajKjJAQ/i1L26oALFq0CPHx8fDx8eEHevQAAHQDEBMTk6ft0aNH4ePjgxkzZqB/voV31apVUatWLYTIjlEFmjTh4JPwcK6obmaWjdOnm0NLqylevnxZ6IJy3bp1AABbW9sCZsaPHz8GEaFRo0aFjlspBKoKiERqfG7YoUOR7YyMjGBgYIDXr18jPT0dcXFxuHz5slpRUGvXrgXA58sKj54ecPo0MH8+G3XLwgJFyoSHu3YhEcDQ+fPRqVMn7N69Gzo6OvKbPsVFUF2/fh1t27aFm5sbXr16hdjY2DKYtYaYMwcwNgbat+co5S1b+PG6dYFevThS+Z9/gGfPeFvNmnyj6Ngx4NUrYNeu8px9+SKmPIqIiIiIiIhokAovUBERRo8ejerVq2Py5MkIDAwEAAQFBaFVq1YYPXo0HB0dcenSJdSuXVthHy1atMDdu3cVlsqWSACZlRCPV/R8kpOT0bhxY5w9e1btYyoMIsKxY8egpaWFI0eOKPQHKglZWVl4+/atShFU168DgwYBjo5AYCDwzTecXlamhITwXexq1cp44ML54IMP0L9/f3h7e3MUVaNGiDMwwIfIK1A9f/4cY8eORbNmzQoVHV1cXNSKoJLh4ACMHPkG5uaDADhj7tyPACgWJVJTU7F582b0798fHh4eBQQq2f8NGzYsdLxKI1Bt28aLzQqygKry8CGeALAt4rkFAEEQYGVlhdjY2DyL/O+//16l8fz9/XHr1i1UqVKlcghUAEdTTZzIfx89Wr5z+Q8hkUjgmpmJ5+bm+OLLL3H06FEYGBgAgFICVUxMDMLDw+UCFVDJfKgaNwZCQwFvb04xlVVmdXFhcWr0aEDR9YWrK0cvK0jr/k+wcmWOIWJGBkec3bkD3LrFkWi3b/PjIiIiIiIiIiJKUuEFqipVquDSpUvo168fdu7cidatWyMqKgrBwcFo0KABtm7ditDQUDg6OhbaR8uWLZGYmIjQ0FAcO8aWQXXqcJVpHR3WPWS2T199xRH8e/cCL18W7OvGjRt48OBB0Sl4ahISEoKnT5/i66+/ho6OTo7HkYaQCQqqRFC5uACenhw9Jc1GK3vS03kBUcFYtGgREhIS4O3tDQgCelpawgt5BaqDBw8iPj4eu3fvhn4h3mDOzs4ICQkBqSmkXLp0Ce7u7jh58gS8vefhs8+mAOiPwMCCUVl79uzB27dvMX36dDRs2BBhYWFITU3Fs2fPkJ2djaCgIOjq6qJevXqFjldpBKq0NF4gqenfpGlCzcywU1u7QBqyIqytrfH69Wu8fv0aANCuXTucO3dO7vOjDL6+vqhatSqmTp2KyMhIvH//Xu25lykODv/tRX85kJycjK0Awnr2xM8//wxjY2P5NmUEKtn7sk2bNnB1dQVQyQQqgNOCZ80CpkzhaCpl6dQJuHmT863/S9y9y/nlcXH8//ffc7RZq1ac9ti6Nf/IvicqyI0CERERERERkYpNhReoAKBDhw7YuXMnbt68ifT0dGzfvh3v3r1DgwYNoKWlladqnyJatGgBgP2dXr0CYmPZS+nTT9kXdcUKjqQCACsrXtOOGAFUrw40agQsXpzT19Wr1wCgVNIXjh8/DgDw8vJCnz595D5BmkI2Z2UEqqws9im2tORAFDMzjU5FNX77jVN/Khju7u4YOHAgVq9ejfDwcNyVVl3LbWj98OFD2NjYwKWI9ERnZ2fEx8cjTnahrySZmZn45ptv0KlTJ1StWhW3bt3CrFmz8OaNPYAj2LmzVp72RIS1a9fC1dUVHTp0QMOGDSGRSHDkyBE4Oztjw4YNCAoKgrOzM3R1dQsdt9IIVBWskt+eWrXwm51dkf5TMvILVAsWLIClpaXSUVRxcXHYu3cvRo0ahVatWgGAWmmk5cbRo5xbLFImJCYmYguAGA+PAtvMpCf/ogSq69evw8DAAE2bNoWtrS3Mzc1x//790ppuxaJTJ666W0GKeJQJqakcVVajBhcwAYB584AjR/hze+IE//z5J188nDjBN5lyh6uLiIiIiIiIiCigUghUMho3boxatWrJfVWKSkPKjbm5C7S0RuHx48cYPx54+JCvqf73P2DJEmDu3JzCd/PmccCFvz97LdnZscUEwDcAV6yYBOAKgoNNNXZciYmJ2L59O3799Ve4u7vD3t4erq6uiIyMRHp6usbGkQlUyqT4LVwIdO7MQSgVAiUW9eXBwoULkZiYiLFjx0IbwGFjY3TItVB59OiRPKKgMGTilSppfu/fv0eHDh3w448/Yty4cfjrr7/QtGlTAEDz5lowN/8Td+9+hNy1Aa5fv47AwEBMnz4dgiDIfaa++OILSCQSHDx4EI8ePSryc/XuHRAZaQQdnaqVR6CqCMLMP//gzfPnqFGjhlLNZSl+MoHK0dERc+fOxalTp5Ty09uyZQvS09MxdepUNGjQAAD7i1Ua7O05vFWkTEgJD0c9AMYK8rd1dXVhYmJS5Of9+vXraNGiBfT09CAIApo0aaKwWERxZGVlIbUCVd5Uiu7d2S9N+jn710PEpWSDg9mPy1R6LWRqysUNevdmT8YePYCePfniytaWL7xWrCjfuYuIiIiIiIhUeCqVQCUIAnr06IGX0ty7BkpeEC5dqguijXjw4Dm0lDhibW2uev7558CpU4DUUxrJyRnIzDwCoDYuXvwcRRRKK5bMzEz4+flh6NChsLGxwdixYyEIAn766ScAXN1NIpEgLCxM/UHyIYvQKS6C6tIl4KefgHr1AKkNSfkRGspKWQU1uW/cuDEGDx6My5cvIxtAByJ4pKYiNTUVEolEKYHK2dkZgGoRLlu2bMHNmzexc+dObN68GUZGRnm2d+p0AlpaMejWLeepW7NmDapVqwZPT08A/B7T1tZGVFQU9PX1ceXKFYSFhSkUqIjYi8zcHHB1FSCRPMaNGy7IylJ6ymVPjRqAkVHpR1BlZQF//110Gy8vrLx9GzVr1lSqS1kE1SupOm5tbY2pU6fC3NwcS5cuLXLf7Oxs/Prrr+jYsSMaNWqEevXqQUtLq/L4UMlYuxYYPry8Z/GfwOD33/EEgEUhX5AWFhaFRlClpqYiICAAbdu2lT/WpEkTPHjwAFkqniAWLVokj3iuNMhunrx/z2HH/3bCwjhKaulSoGtX5fZp2hQYMoSreSQlle78RERERERERCo1xco1giAYCIJwRxCEe4IgPBIEYbH08auCIARKf6IFQTgqfbyjIAgJubYtyNVXd0EQQgRBeCoIwlfqTLiHtFqaiYmJUtEIDx4AGzYADg5nEBYWoM6Qch4+/AvZ2dOhp/cxgDR07cpeoKqSmZmJ3r17o3///rh06RImTJiAmzdv4unTp/Ljc5IaPj158qREc86NMhFUb99y6mO9enwtWe48fQpcuFChL/wXLlwoN7ZOrlcPTcA+VBEREXj//n2RFfEAoHbt2tDV1VU6giozMxMrV65Eu3bt8Omnnyps06yZHbKz26NqVQm6dgWeP4/CoUOH0Lnzz7h/vyrS0wE9PT35+2zVqlWQSCQgIjRs2BCZmcC1a5zempjIa7AePfj/LVsAPb23uHx5NGbN4vFevQJ+/RXYsQM4eBDYvZuzOxTUJSg7BAEYMIArbmma4GA2Uk5PB65cAZo3Zz8WRT5PsbGgCxfwh0SikkCVlpaG8PBwGBgYwMjICMbGxpg9ezaOHz+Ov3MJYnfu3MkT3fLnn38iIiIC06ZNAwDo6+ujbt26lSuCCuCF7L59vCAWKVV0QkPxCoBBIe/PogSqu3fvIisrq4BAlZaWpnJaqb+/Px49eoS0ChO6qySXLrF3Wq9eOZ5L/1bq1uVoqK+/Vm2/2bOB5GRg//7SmZeIiIiIiIjIvwMiKvIHgADASPq3LoDbAFrna3MIwCjp3x0B/KGgH20AzwA4AtADcA9Aw+LGNzQ0pNwkJCSQjo4OtWrViopDIiHq3JnIzIxo2rSFpK+vT1lZWcXuVxjLli0jANS7d2+ytu5EdeoQjRqlej9Tp04lAOTj40MZGRkK27x584YA0IoVK9Seb36WLFlCAAod8++/iRwdiXR0iO7e1diwJWP9eiKA6Pnz8p5JkcyYMYNmzpxJT4YPp0yAbpw/T8eOHSMAdOPGjWL3d3FxoUGDBik11u7duwkAHTt2rNA2fn5+0jZ36fBhou+++44EQSAXlzQCiPT0iD78kKh5cz9q2nQcSSQSsrOzI6ApffxxPFWtyk+7IBBdvFiw/zZt2pKr6wJ69Ij/v3KF2+f/2b9fqUOqXKSnEzk5EdnYEEVGEsXHE3l58QHb2BD5+BClpua0//VXIoDcAFq2bJlSQ2zbto0AkIeHB9nb28sfj4+PJ1NTUxowYAARET1+/Ji0tLRowoQJ8jYff/wx1apVizIzM+WP9enThxo1alTCAy9jIiL4Dbh4cXnP5F9PnIsLXQAoICBA4fZu3bpRixYtFG778ccfCQDFxcXJH3vw4AEBoN9++02leTg5OREACg0NVWm/CsGGDUS6uvwlGhhY3rPRPPfuEXl784WVOkgkRA0bEilx7SYiIiIiIiJSdgB4T8VoMmX5U2wElXTeydJ/daU/8nIsgiAYA+gEoLia4C0BPCWiMCLKALAPQL/ixs+PiYkJZs6ciVGjRhXb9o8/uBDU4sVAkyZ2SE9PR0QJjEyvXr0KJycnNGrUCPHx1+DvT/D15W1FWUXFx8djy5YtICIEBgbC19cXs2fPxowZMwo1ozY3N4eFhYVKvkTFERsbi2rVqhUYU1Zcp1Yttoo4d44DQioEkZGcc1m9ennPpEh8fHywevVq6DRvDh0AKXfvyqtYFRdBBQD29vZ4/vy5UmPt2LEDTk5O6NWrV6FtWrVqBS0tLfj7H0fPnunYsGEDevXqhYsX9XH4MDBzJgel/fVXH9SvvwmCIKB//+EAbsHf3wSjRgGHDnGBpo4dC/ZvYWEOHZ1jkGUDtm4NxMRwwNv9+2z79PvvnBYIAFIrpfJBppdpii1bgCdP+LedHXuvbNwIXL/OPjQzZwJdunDbx4+BVauQ7uiIB4BKEVQAm+zL/gYAU1NTzJo1C0eOHMGVK1ewYMECSCQSHDhwAKmpqXj8+DHOnj2LyZMnQyeXh1ODBg0QGhqqUU+7Usfenkuu7twpVgArTYhg/Pw5goE81ftyY25unieCat68efj1118BsP9U/fr181SndHFxgb6+fp5Iv+LIzs7GP//8AwDy35WKiRM5mjItjavY7d1b3jPSHLGxXN54+XI2IlQHQQB++QVYsED8PIuIiIiIiIgUilIeVIIgaAuCEAjgNYCzRHQ71+YBAM4TUWKuxz6UpgSeFARBtjqvCSD3CvyF9DFF400UBMFfEAR/RR4WK1aswJQpU4qdd3o60L49MHlyjhG1upWsJBIJrl27Bg8PD1haWiIjIwM6OkkwMuJFfPPmwM8/K77uWrFiBSZMmIDAwEDclbpWy9JvisLJyUnjKX650/uIeF3dvXtOxb7r14EOHTQ2ZMmJjGTlrJhKjRUFYw8PPASQ8OIFHj58CHt7e5iYmBS7n729PSIjI5UaIzIyEu7u7tAqwlDNxsYGHTp0wP79+3Hw4EG8fv0a06dPR/XqnPW2bBlXq4yJEfD999zPrFkL8d13bxEeLmDdOmDgQPacUoS5uXmetDJdXRY369YF3NwAZ2cWp7S1gRcvgIYNgV27lDo8zXL+PGBhwaqZJkhJ4XLq7dqxAXBu2rQBLl7kMb+SZjD7+wNv3+LBpEkAgFq1akEZZKLUu3fv8ghUADBz5kzUq1cP3bp1w4EDB9ChQwckJibi+PHj+N///gc9PT14eXnl2adr167IzMzETlnFrcrCqFHAs2fAzZvlPZN/LzEx0EtJQRAKF6hyp/idPn0aK1euxOrVqyGRSHDz5s086X0AG6u7ubkhMDBQ6WlERUUhMzMTAEp0I6lcad0a+OsvNrE8f7585iCR5ORWh4cDZ89y1T11IWL/qFevAD+/wr8UlKF3bz5vlmbRk7i4nONNT+c7J6Gh/LqI/lciIiIiIiIVHqUEKiLKJqImAGoBaCkIQm7X5+EAct8qDABQm4jcAaxBTmSVoisShbfRiGgjETUnouY6alRyev2ar6kGD2ZrCF1doH79+gDUF6iCgoLw7t07eHh4yE3GZZ5OxsZcQXn+fI42mT6d/ZvevOEUyj179gAA/vrrL9y7dw/GxsZwcHAodkxNC1RxcXF5DNIPHmQbncxMID5eY8NoFmtrIN/ipyJj1qwZPtDRgb+xMR4+fFisQboMOzs7vHz5UqkIl5iYGNja2hbbbtiwYQgJCcH8+fPh4uKCLrKonlzY2ACOjvy3o6MhliyxkRdlKor8AlVRWFuzaOXlVQ5e91ZWfMe/JP5LEkmOB9rq1bzg+fHHwhdZnTrxQgwAPD2BJ08QUK0aACj1uedp53xO8wtUZmZmuH79Oho3bgwrKyscOnQINWrUwNy5c/Hrr79izJgxBfbp1KkTmjVrhmXLliG7Avu5FWDgQGDkSDa7FykdTE1xaORI/ImiBaqEhASkpqZiltR4LjQ0FJcuXcLbt28LCFQA+1D9/fffICLExsaiZs2a2FWESp27IEilFagAVuovXIA8vLqsCA8HGjXiqnkyYfDsWTYyt7AAVq5Ur98rV4DLl4FVqzQTWh0eziJ/aVTYOHmSo61lFSQ3buRiGS4uPHdXVw7zFREREREREamwqFTFj4jiAVwC0B0ABEGwAKfu/ZmrTaIsJZCITgDQFQTBEhwxZZeru1oAoksyeUUcOwbUr8/XJTxH/m1paQlzc3O1jYKvXbsGAAoFKn19jg758Uf2AN25k/1AU1OBmzdvIjy8B4Ar2LzZAjdvJsDNrXGR0S8ynJyc8OLFC6SkpKg15/zkj6BavZrN0M+d4+vXCsnKley4XUnQ0tKCra0tAgICEBQUBHd3d6X2s7e3B8BRBEWRmpqKhIQEVFci5XHQoEHQ1tbGixcvMHXqVKXec8pibm6O5ORkZGRkFNtWT4/FUFtbjmicPbsMU/6cnPgkoI4wHRsLjBvHEz99mh/r1w/YtAnw8FCuD0EAzMwQEREBbW1tpQo7AEULVLLHbty4gdDQUFhYWMDT0xMvXrzAgAEDsGbNGgXTEDB//nw8ffoUhw4dKnTc2Fg+j02aVEGycIyN+eTauLHm+jxzhiNCnJw4zO/wYc31XRmpWhUB9vaI1NJClSpVFDaRpe+tXbsWjx8/xuLFiwEAP/zwAwAoFKg++OADvH37FpGRkfDz80N0dDQmT55c6E0imUClq6ubR6BKSuKP35IlwNSpnF0LcEDMkiWlo3OUGF1d/uyfPs2CTFnw448cbfj55zlf6IMGsWjz0Uf8uDpRXRs2cBrz6NGamefff3Oan6YjzJ49A0aM4IhraWVcfPQRV+/YvRv47TcuYvHJJxXk5CYiIiIiIiKikOJMqgBYAagm/bsKgKsAekv/nwxgR772tgAE6d8tAUSCo6d0AIQBqIMck/RGxY2f3yS9MNLTiWbNYrOZDz4gUuSx+uGHH1LHjh2V6i8/I0aMIFtbW5JIJHTnzh0CQMePH1fYViIhio0lys4mmjJlCunqjidDwyC5cbSJSWwec/Xjx4l27CA6fJgoPDzn8b179xIAun//vlpzzk/NmjVp3LhxRER05w7PxcdHI12L5GK5vT09BUhXEOjp06dK7XP27FkCQJcuXSqyXVhYGAGgrVu3KtVvt27dyMjIiBISEpRqryzr1q0jABQVFaX0Ps+fE40dS6SlRfTLLxqdTtE4OBANH67aPrGxRG5uRAYGRCNG8AemBHh6epKDg4NK+xgZGSldKCE+Pp62bNlSaAEEIqKsrCyqXbs29e/fP8/j798T7d1LwCbm1QAAIABJREFU1Ls3F0gAiM6cUWmqavHXX3/R69evlWscHMwmzZogIIDI2ZmoXz8iOzs+4F9/1UzflQ2JhOinn2jRqFFUrVq1QpvJCjM0aNCA7O3tKSsri0xNTQkAWVpakkSBcfajR48IAC1fvpx69epFNWrUIAsLC2rSpAmlpaUVaP/NN9+QtrY2ffjhh+Th4UGBgUTNmxNpa8sKNkjIwiKnaMOePfz4iBFEJah9Unq8esUm/wsXlv5YL16wQftnnynenpxM1KABka0tUS4ze6W4dIlo+/aSz1FGWhpRtWpEI0dqrk8ioh49uCLOs2eFt3nwgOSVPURERERERESIqBKapAOoDuCiIAj3AdwFe1D9Id32CfKm9wHAYAAPBUG4B+B/AD6RHnsWgGkATgMIBnCAiB4pMX6xpKVxZMbq1Zxed/Mm3xzPT/369dVO8bt69So8PDwgCII8CkkWQZUfQWA/p+zsTBw4cAADByZjypStYMutSXB0TEJCQk77Zcv45uTAgUCdOmxj4ePDEVQAik3ze/uWqz4X5V1K0jQLWWTGrl2cNTNmjLLPQDnw8iW/kH5+5T0TlSArK9QF8Hnnzqhbt65S+9jZcXBhcT5UMTExAKBUih8AbNq0CZcvX1bKB0sVZBFfyvpmAXxje+tWICiIIyEANmIv9QC5+vVVi6D680+gRQsO1fjjD55gixYlmsI///yD2rVrq7SP7LOqKIIqP6amphg3blyhRRcAQFtbG61bty5gXP3RR8Dw4RzYMGsW8OgR8PHHHJmyeXNOdqMmkUgk6NixI5YtW6ZMYzad/+yzHG8dVblyBfj2W/77gw/4/XD0KKf79OrFb0SJhNM3Bwxg76svvuA87X8zly8D8+fDMSSk0PQ+ICeCKjg4GN27d4e2tjbat28PAGjTpg0EBemuDRs2hIeHB9auXYtz585h8ODB2LZtGwIDAzF//vwC7cPCwmBvb4+6desiIiIC1taAoSHw9dfAmDG7QWSE48dvyos2DB/OQUN79rAn+YYNFSxV3dqav8yPHy/9sby9+f37+eeKt1etyuexXr34gkkZiLhthw6ai54COOR86FCOXExOLr69MiQkcCi4l1dOzroiXF3ZEJGobF4XEREREREREZVRporffSL6gIgaE5ErES3Jta0jEZ3K134tETUiInciak1EN3JtO0FEzkRUl4h+0NRBXL8OBARwBPf//sfXP4qoXbs2YmJioMh4vSgiIyPx/PlzeEjTevKn+MkM1PNz5swZxMXFwdPTE82bNwdnNG7E+vWvcTRXzcOjRzk63d8/x2j94UOgXr16AIANG6xw/jyvodauBb75ho8X4Oh9S0v2+DE3Z8NzRYX/kpKSkJGRIRfXVq0Crl0DNKxbaJaICF5AVhKDdBmxUt+pqVLfM2WQCVTFVfJ7+fIlACiV4ifrt2nTpkrPQ1lkXkrqVNtyceH1EsDv/U8/5bTYUqNrVx5EWXR0+EN15gzQubNGphAREaGyQCUTpmxsbDQyB4DTriIiIvDmzTt5lsuiReztHhnJRbpklRlPnuT13pIlhXanNlFRUUhKSkJcXFzxjbW02LPmxg1WzFQhIIBPih06ANu2FRSc9PS43OSxYzxOSgqfc65e5UV/164VTPXQMD/8ANjY4GT16kUKVOa5jLG7desGAOgoVYoUpffJmDp1KiIiIpCeno7+/fujT58+mDp1Kry9vXHy5Mk8bVmgcsWNGwvw4oUtrKyycPkyYGHhg+3bRwJIwZ18Jnbz53M6//v3XAxFJnZnZFSQLK4+ffg9+OJF6Y4zciSnstWpU3ibDz7gz48ylUQzM1mkHTSodBTqkSP5s3a0uOLPSvL4Md9x69tXufY7dnDbkngTioiIiIiIiJQO5R3CVdyPsil+ymQa+fr6EgCKiYlRqk8Zv/32GwGgv//+m4iIJBIJGRgY0Lx584iIaMOGDQSAAgMD8+w3fPhwMjc3p/T0dHry5AkBIEEQKDk5udgxZRkTxsbNSUsrQ54eCHDKgyzD69UroiVLOEVn0SIiExNO03nzhrdnZ/PvZ8+eEWBAH3/8iCIiVDr88uPgQT5gTaX2lBH379+nuJo1iTp3Vmk/CwsLmjx5cpFt1q5dSwDo5cuXJZliiUlKSiIA9NNPP5Won/fv+WkSBKJduzQ0uZIikeR8ADVAeno6CYJACxYsUGm/Pn365DnvaIJTp04RAJo9O4Q8PIjevSu8rURCNHo0vzaXL2tsCkREdP78eQJAAwcOVG4HiYToo4+ITE05/7C41yc5mWjYMD5/mJsTLV9OlJKi2iT//JPTprp2VW2/ysLvv/Pzs3w5de3alVq1alVoU/7+AGlra1N8fDwRET158oRq1qxJDx8+LHS/9PR0srGxITMzM8rMzCQiopSUFHJ1dSUrK6s838VWVlbk6nqGBCGbgNYUHh5OW7Zskb9PLCwsaMKECQrHkUiI/P2J3r7l/zdsIJozR9UnpBQICeEv5F69Kk4e4u3b/GQVRnY2500CRN9/r9FzYZ4xGjQgKuH3Rx4yM3MueIrj1SsiPT2iqVM1N76IiIiIiEglBZUwxa9C80iaJKiM97AsIqGw1LzCuHr1KkxMTODm5gaADYetrKzk/WzZsgUA8hiwJycnw8/PD0OGDIGenh7q1q0LU1NTODk5oaosfKQIZBkTjo6Z6NRpDE6f5hScly+5cvLYsbJjAubPz0KXLnFYuJAzV9aty6kE3b8/BxDMn28I4G+cPdsQZ86odPjlhyx9zM6u6HYVDDc3N1gMGcKhfcqmU4DT5pRJ8dPS0spjdl8eGBkZwcrKCuElNAA2NOTglY4dOYukiCJfJSMtjVP3imLjRs55TU/XaBn0Fy9egIhKNcVPWZo0aQLAAFu21EB2Noqs2CgIfC6pVQuYO1f97DpFPJVW0kpMTFRuB0Hg18fUlKOabt8uuv3Ll1wy8ttvgbAwYN48oBAD8ELp2ZPDcqdMqSDhOBrkn3+A8eOBli2BGTOQlJSkVIpfmzZtYCp909SrVw8vXrxAo0aNCt1PT08PW7duxYYNGyCryFulShXs27cPSUlJ8PT0xN27d/HkyRPExibj6VMPdOz4CsAtrFy5El5eXujWrRv27NkDNzc3PHz4UOE4ggA0awaYmeUc3qpVOcVSyg1nZw7rDgsDlIkWVIfFi/niQBmysthLwMuLKyLk5+VLjpzaswf46Sf+/GjwXChHSwt48AD46quS9yW7d6ejw/0qg7U1G6pv3448fgsiIiIiIiIi5U6lFqiuX+fUth07lGsvW/C9VqKE2Llz59CiRQskJibi6tWraNOmDbRzpZpZWVkhLi4OQUFB8rSDZ8+eybf7+fkhJSUFnp6eAFjUGjt2rPx/ZXFwcEBMzD107Qo0aQLY2OTNeCMi9O3bFw0aNEBSUhJsbfnak7cB7u4sWvn5WQF4g5UrQzFhgkpTKD8iIzlsv1q18p6J6gwaBMycyaUclcTOzk6pFD8bG5s878XywsHBQa0Uv/wYGrIdSIcObBVUKmzcCPTuDdy/r3h7RgYvyEJDC88RVhNZRTJZWqSyVK9eXeNipI2NDUxNpyIx0QhLlxa/9jQ0BJYu5fTjgwc1Ng25r15SUpLyO9Wrx6/PgQNAq1b82KZNwKlTQHQ0n/DevuWyb3XrAsHBnBpYlApXHEOHcuXG0liklxVEnC69Zw+/mACnsH7yCbBvH6Cnh8TExCIFKhMTE7i4uGDkyJEqD9+zZ08MGTIkz2ONGjXCmjVrcOHCBbRs2RLOzs4ABiMtzQDjxnEK/tq1a9G2bVscPnwY+vr6coFKooRSumQJ0K0bV6Ps3ZuzNsuNzz7jkoMaTNWV8/ffnKN79apy7XV0gDVr+LPRtCm/t3v2zNk+axawfz+wcCHw5Zean29uZN9hJRWIrl5lIbAQ8bJQZszg3NA9e0o2voiIiIiIiIhGqbQCVXo6CzF2dqwFKEN+76iiOHToEPz9/eHr64ugoCC5/5QMS0tLxMbGYtu2bdDR0YGJiUkegWr37t2wt7fP48/h7e2NBQsWKDdZKQ4ODoiIiAAVcgff19cXJ0+eRFxcHHblCz8RBF6fhYUB69btAtAOAwYUbqJc4ahXDxg8uHIuDtu1Y0MxMzOloy+UjaBS1n+qtHFwcChxBJWMqlXZ82jdOv4/IkKl4LPi8fQEDAw4pHD/fl6Y5Gb1ag67WLBA4+83mYinagTV1KlTcezYMejp6WlsLhxsMBkGBqFys+ni8PTkiE0Vp18kKkdQydDXB4YM4dcoM5NFxR492FenalWe5MKFOW01wcuX3GdFi7RITGSBaetWNif85Rd+LmQ+TadPs8BWqxbg4MAv5PLlLJobGQHr18s9i5KSkoospCAIAh4/foyJEydqbPoTJkzA8+fPcfDgQWzcuBH163vDyYkwaJAV9PT00KxZMxw/fhyGhoYAAFdXVyQnJytVmEFHBzhyhE/B166xV7mqbzWNUqUKP+/jxvGdNU2xYwf7qKnisTdgAM/Bzo5PtMnJOd9RQ4dyFYtFi8rme3fSJMDKit3uMzPV68PfnxVIVQXADz5goTYwUL1xRUREREREREoFnfKegDoQsTgVHAycOMHX2sogS5VRJoJKFhW1ROoQnF+gsrKywt27dxESEoLevXvj9evXCAsLk/d/5swZfP7559BSNuS8EGrXro3k5GS8fftWnmYhIyoqCp9//jl69OiB2NhYrFmzBp999lmBikqCALx5w6JceaeGqcS0aeU9g5Jz8CAvBEeN4nQaF5dC0xDs7OyQkJBQZLpNRRKo6tSpAz8/P0gkkhK/z4EcPSE9nb3Jq1blan9Dh2ogiM7CghWwadM4ckRHh8t9Nm/OC5Svv2bT3O7dS3wc+YmIiIAgCHIjfGWpXr06evXqpdG5+PsDiYn1oKU1Henpy2FgYFDsPtrarIEA7Jc8eDB7HA8cqP4aVhZBpbJAlRtdXY6auHqVq0w8e8YikiYrjgFscL1kCWBry9EwFYVXr7iUXW5q1wZk329Pn3IqZIcOQNu2QJs2HHKsU/Brv7gUv9KiVq1aGDx4sDxLy9gYqFLFADdu3ICTk1Me0cxVWnziwYMHSkUjVqnCQUCffMIf9XIvCJKczCGie/YAvr6cYqZq2mlusrNZoOzdOye3UVmaNuWiA/kZOFD9+ajD4sVcfvibbzgycvNmPierwoMHLE5Jb0CqRGBg6US2iYiIiIiIiKhNpYygWrqUvWqWLOEbxspibm4OLS2tYiOoUlNTcf/+fVSvXh1paWnQ09NDi3xl5q2srPDu3TsYGhpi5cqVqFu3rjyC6sCBA8jOzlY5nU8RRVVL27NnD9LS0uDj44Pp06fj8ePHOHfunMJ+4uLioK+vDyNl1byKwL/B9yUlhVWBMWO4PJqZGadUKDg2e3t7ADkpYYp4+fJlhRGoHBwckJGRIa8sqCn09TnQIyuLb7BXr87r8L/+KmHHHTvyguTUKeCLLwCZd87mzbxo9/Yu6dQV8s8//6BGjRoajYRSFxcXYPz4AEgkO3Dv3j2V94+K4iy7wYM5KkWd10QikcjPlSUSqADOQezWjX2iVq7k19LdvWR95qdZM37iDh3SbL/q8vvvnMZYuzbfpYmIYFEqKQkID2fBAuCydrLUvqlTOWJEgThFROUmUMkQBGDixBy9rVmzZgUiumQCVWE+VIVRuzaLVADf0NqwofSL6inEygq4dYvTUydMYNW9Q4eC0ZzKcvEii5QjRmh2nmWJrS0LU0ePsidWq1Ys3qnCw4csvKpDzZoKPxMiIiIiIiIi5UelFKhcXXnh+u23qu0n83MpTqD6+++/kZWVhR9++AH6+vpo2bJlgUgDd3d3WFtb4/Tp03B0dISjoyNevHiB9PR07N69G25ubvIL6pIgSwtSJFrs2bMHLVu2hJOTE4YNGwZ9fX2cPn1aYT+xsbGwtLQsEF1VYUlL49C4tWvLeyYlY/Ro9sV59IjL3I8YwQsVQWCRatUq4M0bALwo09bWho+Pj8KusrOz8erVqwolUAHQWJpfbgYM4HXH3bvs5Xz6NFCMPZdy6OiwoPHDDznRC+PGAZcvA46OGhigIBERESqn95UWJibAL7/UhrZ2Co4dO6by/vb2bOO1dSu/Hn36APHxqvURFRWFtLQ0WFtbIykpSSlPoXJFEDiy5NIl+We1pOzfvx/dunVDVlYWR7I0acIpV/b2LGS3asURYQALGFlZbLI9aRKnOP78M6d21a/P+1hZ8fky9/ldSZ+69PR0ZGVllZtAdf8+Z9gWZ9dnYmICe3t7lQUqGdnZnME7eTI/1XZ2/FTu3KlWd+phaQmcOwf88QcwfTrfsJAVTdm1S7X318uX7LWW20OqstKvH6cWTpzI6fHKkp3N363qClR37rC4nZys3v4iIiIiIiIiGqdSCVQhIfx7wADOmlJHa7Gysio0xS81NRXR0dHy9L7u3btjx44d+PHHHwu0HTNmDKKjo+WV/erWrQsiwoULF3Dr1i2NRE8BhUdQBQUFITAwECOkd0/19fVhbW1dqPgWFxcn9+CqFLx4wdFH5XhXX2Noa/Oic8wY4Ndfc1z9Hz/m1LL27YGUFDg5OWHOnDnYvHkzLl68mKeL6OhoREREQCKRwNbWtuyPQQF1pP41mjBKV4QgcLbH2rVATExOYMiSJRquztW0aY7pdinw9u3bCvHZO3iQo0fMzCzQsWNHHDp0qFBvu6LQ1mZPqmPHOIDj++9V21/mP9W0aVMAwHt1I0jKkkGDeDF8/HiJuzp37hxGjhyJM2fOIDo6mkVTbW2uTti5M0f2mZvn5LzOn89laq2tgS1bgDlzgO++K/E8ZMiM6stLoFqyhC2P0tOLb+vm5oYHDx6oNY62Nqf63b4N+PgAHh4cAXjpklrdqY+uLtCrF7BiBUcOAex/5+XFQk1KSsF9FIm4I0ciyM8PoyZNws8//4xbt25VfLG3KExN+ftRlQjI9+857K5TJ/XGjIzkMaUpxyIiIiIiIiLlT6WIbU5LYy+JNWv4YrJ9e/X7srKyUijiSCQS9OjRA/7+/mjQoAFq1aqF6tWrY9iwYYX2lbuSWt26dQEAS6VVkobn9wZREzMzMxgbG+eJoCIibNmyBVpaWnnmJ6ssqIjY2NgKsUhWGlm4jDTt7V9Jgwa8QOnRg1dM8+dj0aJFOHz4MLy8vPDgwQNUqVIFGRkZaNKkiXy3ihJBJYsKKi2BKjeytToRF646epTfIgsW8HqvIpOamooqJfGa0QBELAIYGXEQzsCBAzF16lQEBwejYcOGavXZvDlb4HTurNp+Mv+ppk2b4tSpU8VWkKsQNG3KaX4lzA1LT0/H0KFDoa+nB4+sLMRER8O+dWtOAy7sjstHH3GVwsaNOZJLA5G5uSlPgSomhj/Lc+Yo5zPn6uqKM2fOIDMzE7pqfPB1ddkKsGVLLuIG5Hhzv3unupWTxnBw4AiqYcP4fTZ2LEfFTZ/O4lS9enyTo2dP9sm7fRsZAwZghKcngoKCkCk9CHt7e/zxxx/yG2eVkuhoNrmbOZMFu6IwMWHRVl2cnfn3kyecAisiIiIiIiKSB0EQ7ADsBGALQAJgIxH55GvTEYAfAFlazWEiWiLd1h2ADwBtAJuJ6OfixqzwEVQSCXud/O9/fEFZ0kAHa2trhRFUa9asweXLl6GtrQ1/f3+0bNlSpX4dpelBN27cQPv27eV+QiVFEATUrl1bLgI8fvwYPXr0wKpVqzBgwIA80TRFpS/KUvwqDbJKTSoaS1c6undnc+6ffwbi4mBoaIiNGzfi2bNnWLRoEQDg8uXLiI2NxRtp+kdFEaiqVKkCGxsb7N27F1OmTMH06dMxa9YsfPXVV6UaVXXgAAejLV3Ka4qgoFIZSmNUBIHq6lV+niZP5v8HDBgAQRBwqIS+SkOGcLBPWhrbHRXHkSNHsHjxYlSrVk0ujJXYh6osEAROJVI1rzwfoaGhePfuHTZ27IgLAEgWkVVUOPCAAew7tWCBxsUpIOf5L6qKX2mxYwcHpk2YoFx7V1dXZGZmykVOTaCry9Z0jo7lEE2VmyFD2FfK1pbDEvft48dTUjh8NDiYvcTq1gVGjMDBiRNx7949HDx4EHFxcfjtt9+QlpaGcePGcepoZcXamo9VGeEpKalkXpX16vHv0FD1+xAREREREfl3kwVgLhE1ANAawFRBEBTd3b5KRE2kPzJxShuAL4AeABoCGF7Ivnmo8AJVWhob8x4/zj4VJa0criiCKjQ0FPPnz0fv3r1x8+ZNWFtbo7uK1bxsbGxQVeoloan0PhkODg4ICQnBnDlz4Obmhlu3bsHb2xt79+7N066w6DCgEqb4yQSqWrXKdx5lwU8/sQfGL78AADp16oTx48dj5cqVCAgIwJEjR2BoaIjz589jyJAhaNy4cTlPOIdBgwYhLi4OBw8exO7du7Ft2zasWLEC3bt3R0JCQqmMqavLdl5+fmzL07MnnycqKhVBoPr5ZxaSZAGX1atXR5s2bXD48GGN9D9sGNClC9utFUZUVBQGDx4MS0tLnD59GmbScJVKIVABOZ5ON2+qvSgOkqqpHVNSEAXgXs2aGpqc+pRXBBURaxDt2+cEshSHLDJI3TS/wnBx4c/HpEnlfC7p0IF9kZKSgOvX+TEjI75D9/Qpp4WvXAnJqlXwOngQgwYNQr9+/WBhYQFPT0+sXbsW/v7+hfoYVgp0dNi78cSJnIqUhTFuHEc3qouhId8EEwUqERERERERhRBRDBEFSP9OAhAMQNkL2JYAnhJRGBFlANgHoJjw6EogUGlpAffu5fjPlBRZ9T1ZSHx2djbGjBkDAwMDbNiwAQ0bNkR0dDS8vLxU6lcQBDg6OkJXVxeDBw/WzGSl1K5dGyEhIVi9ejXGjh2L0NBQzJo1q0CKQ2EpfhkZGUhISKhcAlXjxsC0aUA+c/p/JQ0bsvqaK51h+fLlsLKywvjx4+Hn54fu3bujY8eOOHDggFwIrQj4+vri5cuXiI2Nxdu3b5GQkIALFy7g2bNnGD16dKmO3bcvm6evXs1vk6ysiln4sbwFqlu3gJMngc8/5/WYjH79+iEwMBDPNeA+/+WXrCkPHZqTMpWfkJAQSCQSeHt7o2XLlvKInUojUAHA+fNAmzaskKpBUFAQtLS0YBsWhusAYopbgJcB5SVQvXnD9x9U+aqtX78+tLW11TZKL4wqVdiKKDSUxdxyRRBYlFL0uIsLMGcOEsaMQWpaGtrlMxQfPHgw+vXrh++++05eKbNS8sknHFrn51d4G1m+t9SnU21cXIBSupkiIiIiIiJSSdARBME/189ERY0EQXAA8AGA2wo2fygIwj1BEE4KgiAtlY6aAHIvNF5ACXGrwgtUBgbsD6sprK2tAUAu5Hh7e+PmzZtYt3IlagQEAGfO5PGWUoVPPvkEs2fPhrm5ucbmCwDDhg3D0KFD4e/vj40bN8qPIT+WlpZITk5GWr5bwLLUsEqV4tevH5uO/VeYPj1P9SIzMzP4+voiMDAQ0dHRGDBggObHfPaMy+Rdvsy/AXYqbtWK82onTeKS31evqnQB3759e3z55Zfw8/PD26JCatQgKioKXbt2RaQ0wi4s7Ajat+cxfH25GNr+/by2qQgQUbkLVNnZHN00bVrex/v27QsAOK4B4+82bdiA/fx5Pl+PGVNQqJJVe5SZ68sEKplAUin46CMO+Zk7l6uoqUhQUBDa1K4NrchIPDAyQkxMTClMUjXKWqBKTWWbIUtLzmhTJeBYX18fzs7OGo+gAtijfsQIDmh9/Fjj3WsU2fVL/u90QRDg6+sLXV1dTJw4Ua0iCBUCd3fOuSwqwvPaNf4O69GjZGOdOlW0ECYiIiIiIvLvJ4uImuf6KVCOShAEIwCHAMwiovx3lwMA1CYidwBrAByV7aZgrGIvTiq8QKVpZFFEr1+/RnBwML799lt817Ythk2bxjXTe/bk0j5q8PXXX+MXaZqWJvHw8MD+/fvlVa8KQ3Zs+dP8ZP9Xqgiq+PiKGQ5TmoSEAN7e8n8HDhyIgQMHQl9fH7169dLcOAkJvCqsV4/dgjt2ZENagHNoW7ZkZfjgQVY12rfnEBkVkKUhRkdHa27eAPbt24ezZ89i27ZtCAgIwMCBA7F69WoA7KefkcE33xs0AI4dI+zfvx/J5VhCPCMjA0RULgLVmzdsX9O2LXD2bMGgDBcXFzg5OWlEoAJYlDp+HOjWjQ3sZQGeS5cC69YBDx7EQVtbG3ZSX7lKGUGlpQVs2sQqy/TpKu8eFBSE3lJRIczWFi/VELk0TVkKVKGhwIcfcvSjrOCcqtV4XV1dNR5BJcPbG6haFThypFS61xiFCVQAULNmTSxbtgwXLlzANjUj/codQeCQz6LEJx8fzsscObJkY8luSFbmCogiIiIiIiKliCAIumBxajcRFbh7RESJRJQs/fsEAF1BECzBEVO5DaVrASh2cfifE6isra3hCcB+xAgc794dRkZGmLZxI4TRo/kumo0Nr7QyMsp7qiojE6Dyp/nJBKpKE0FFxL4Q8+aV90zKluPHuZzVhQvyh3bt2oWAgAC5X49GWLKEw4y+/Rb44w8Oe1m/Pme7rFzmmzesNPz5JzBRGul59Sq3f/++yCFkRu6qRIhMmDABU6ZMKbLNn3/+CQDYv38/duzYAYBN5AH2kn70iP2k9fSAwYMl+OST7+UCVnmQIi0Zb5g7t64MuHWLDeTnzCm6Xd++fXHhwgWNRTH17g389hu/RQD+KP/+O3s7r1kzB1Wq7MDz51w8ViaIVCqBCmDDpIUL+cCOHi2+vZTMzEyEhoYi8aOPgIAAxNep85+KoNq3D2jWjAshLlnCWp86uLq6IiwsDO+LOQepg7U1n0Pmz9d41xqlKIEKALy8vNC+fXvMnTu3QrzH1GLy5JxSi/l5/pxVxIkT8+YtqwMRMHx44WOJiIiIiIj8hxEEQQCwBUAwEa15j6WPAAAgAElEQVQqpI2ttB0EQWgJ1pjeALgLwEkQhDqCIOgB+ATAseLG/M8JVDUTE/EbgHdhYbgfGYl169bBumFDvsXfty+wcSObkWo48qPUIUKdV68AFIygkl3MVpoIqvh4Ng2vAAbCZYqXF4f+DBjAxmtgYUNW7axEZGfnpCQtXMgGvN9/D/TqBXTqxJ5f+REENonp2TPHiPb77zlfrHp1jroqxHCohjQvV9kIqtevX2P79u3YuHFjoQuqhIQEXL16FTVr1kRwcDA2b94MALh9+7Y8rVVLCxg0CPjzz1QIwkMAJti/f79ScygNUlNTAaDMIqiIgJUrAQ8P9hourjpanz59kJGRgTNnzpTKfASBbWKCggArq+N4/34InJxY86y0AhXA4nn79hxJpSRPnz5FVlYW6ru6Ah98AKsaNf4zEVSBgawBuLvz3z17qt+Xm5sbiEhuOK9pZEVS790DSiGTUCMUJ1BpaWlh06ZNSE1NxXQ1Iv0qDMnJbDSYn1q1OCxUE8cmCBw2t3UrV90QERERERERyU1bAJ8C6CQIQqD0p6cgCJMFQZDWB8dgAA8FQbgH4H8APiEmC8A0AKfB5uoHiOhRcQP+9wSqffuQCqBVWhoyhw7F0KFD8zbo1Qu4fTvHePP+ff4dH89ePevWVZzUs5AQ4MYN/jssDE0++wzf4F+Q4rd2Lf92cirfeZQ1pqZ8MW5iwnlSYWGa6Tcykr1zunXjyEATE/aZUoft29lxu29friz1ww8Km8kiqJQVqPbv34/s7GxkZ2cXmpZy9uxZZGVlwcfHB1paWkhJSYGXlxfS09Nx584debvMzEz4+HyDjIwmGDKkFh4+fIhHj4o9F5YKZS1QjR3L2kmfPkBAANC8edHt27ZtCzMzM42l+SlCEFh31dKahqFDv8LMmaw96+npQU+vNSIidIvvpKKhq8tRhsOHK71LUFAQagPodvw4EBgIW2mKX3n7BN2/fx92dnZqey8qw6ZNHOjyxx8lL8zaxMwMOwAkLV9eapHOWVnAwIGcYXbxYqkMUSKKE6gAwNnZGd9++y0OHToEf3//spqaZvnpJ1Yzo6LyPi4IfGNFUwalc+aw2Jw7klhEREREREQERHSNiAQiakxETaQ/J4hoPRGtl7ZZS0SNiMidiFoT0Y1c+58gImciqktEiheOCgat0D+GhoakMaKiSKKnR76CQNbW1hQbG1t0+wMHiAAie3v+Lft58YK3BwQQZWcTSSRE8fGam2dWFtH+/URTpxKNGEF0/XrOthcviFasIGralOdiZUWUnEyUlEQZ7dpRBECrV63K093ChQtJEATKysrS3BxLi++/5+MaOZKfh/8iQUFE5uZEgweXrJ+kJKK1a4mqVSMyNibatUsz85Px6adEurpEz5/z/xJJns2mpqY0ffp0pbpq1aoVubu7U6dOncjBwYGys7MLtBkzZgyZmZlRZmYmffzxx2RtbU2vXr0iQRBoyZIlJJFI6NChQ+Tk5EQAaNSoURQREUPAjzR69OYSH646PHjwgADQgQMHSn2sFy/4pf7qqwIvRZF4enqSpaVlqZ4fUlJSCAAtWbJE/lhWFpG2dihVqZJAX3xBdO1aJfzIZ2cTbdxIdOVKsU2XLFpE5wGSGBsT/fMPrV69mgBQXFxcGUxUMVlZWWRmZkZjx44t1XHevye6erUEHWRnE4WEEBGRJCmJEnR0iABKdXSk2V5etHLlSsrMzMyzi0QiKdF7OjCQyNmZSBCIPv+cKC2tBPPXMF988QXp6+uTpJgPekJCAhkbG9OIESPKaGYaJjSUrwd++CHnsS1biGbNIkpP1+xYPXoQ2dgQpaZqtl8REREREZEKDoD3VAF0H9lPuU+guB+NClR//03UogX9Om8eXb58ufj2KSlEc+cSDR1K9NNPRCdPEsXE8LawMCJ9faKuXYk6dSLS0iI6frzkc7x/n6+KASJTUyIzM6IuXXjF+fPPfLUMELVoQeTtTRQdLd81e9cuIoA2jRyZp8vJkyeThYVFyedW2mRnE33yCQsflW6lqmHu3SNKSFB///BwIgsLfq+0a0f07JnGpiYnPp7o0iX+++1bfr+2aEE0bBhRu3Z0x9CQhg0YUGw3T548IQC0bNky2rdvHwGgc+fO5WkTFhZGhoaGNGrUKCIiio6OpuDgYCIicnd3Jzc3N2rTpg0BoIYNG9Iff/xBEomEkpKIqlR5QTo60RQUpNnDV4Y7d+4QADquiXODErx6pfq6bf/+/QSArl27VjqTIqLg4GACQLvyiaR2dt3J1vY+SfUGsrQk2r691Kahed6/J6pTh8/ZxSxst8puKmzaREQkf68/fPiwLGaqkLt37xIA2rNnT6mNoYpYqpB374h69iRycuLvCCJ6+uQJeerrEwG0hKvBUIsWLfI8l3PnziUjIyP66quv6N27d2oNnZxMNHkyv2zu7pq9D1USxo0bRzVr1lSq7axZs0hHR4d2795NM2bMoKSkpFKenYbp2JHI0ZFf++xsfh+0aqX5cc6e5Rd6yxbN9y0iIiIiIlKBEQWq8hSoNIlEQrR+PYtUlpZ80dS/v3p9ZWXxQoeIaOVKoho1iH7/nS/GYmN58U/EkVSLFsnvJBcgOZmSAbpav778oWfPnpGRkRH16tVLvbmVFbKL5sxMUZzKTXIy0YIF8oWZ0sTEEH33HdHp0xpYISrBq1cc8delCy/Y3dwoSUeHPm3cuNhdt27dSgDo8ePHlJSURNra2vTtt9/Kt0skEurSpQsZGxtTZGRkgf1nzJhBAMjW1pY2btxYIJLiyy/3EhBDJiZZdPp0yQ9VFS5fvqxQcNM0jx+r/haRER8fTzo6OvTFF19odlK5OHnypEIRrGnTptS7d2+Kjyfat48DRs+eLbVplA5nzvBX6ddfF97myRNKEQS6Y2Ul/zzK3htny/GAf/rpJwJAL1++LJX+k5KIWrYkOnVKzQ4ePeLvVh0dIl/fPOeyvXv30llLS8rS1ye/tWvJ0tKS9PT0aOnSpXTlyhUSBIHq169PgiBQp06dFEZlKsvx40RKBoNqjCNHjlBQIap63759yd3dXal+wsLCSEtLiyAV8nbs2KHJaZY+v/3Gn68TJ4j++IP/3rtX8+NIJEQ+PkSl9FkQERERERGpqIgCVXkJVCkppXP7MyqKr8JfvmRxhYgjgKpU4ein6tWJHBz4Kj0jg7ePHcspejVrEhkZ8cvQo0dOn2/eqD0dP1NTem5sTCRNb2jbti2ZmppSRERECQ6ylFm8mCMQyjHVpcKydy+/P44cKe+ZqEZmJo0ZPpwcHByKbbps2TICIL+z37RpU/roo4/k27dt20YAaN26dQr3j4qKoo0bNxYaGRAUFERAbapR403uAJYy4dSpUwSArudO09Uw8fF8qinJArpLly7UoEEDzU0qH+vWrSMAFBUVlefxDh06UPv27Qu0f/u2kglVo0eziBIYqHCzZNAgegvQkkmT5I+FhIQojCorK7Kzs6lz587UWAkRWV0WL+bT182baux85Ah/P1pbF55CGR5ONGcO0du39Pr1axo6dCgBIC0tLbKzs6PExETatGkTAaCVK1cW2D07O5vmzZtHzs7OVKdOHUpVIr0rOjrnflJp8erVK9LW1qauXbsq3N6mTRvq3Lmz0v2tX7+efH19ydbWloYNG6apaZYN6elsszBzJt8AqVkz51pKREREREREpMRUNIHqv2OSfuwYYGEBPHyo2X5r1ACMjAAbGy6bBbCz6tSpwMiRXHfdwwNo146NdQFAW5vbd+3KZbYWLgRGj87p09xc7elsatgQn7q7A4KAfZMno/7161jv7Q17e/sSHGQpsngxH3+bNkC1auU9m4rH4MFA3bpcPY+UNFN+8oRLcJeSgbBS6OjA2s4O0VFRoOzsIpvGxcVBX18fVatWBcDG3bdv30ZWVhZevnyJ2bNnw8PDA5MmTVK4f40aNeDl5QUjIyOF2+vXr4/q1TPQps1sfPYZF2wCuLJcz57AsmVAaKj6h1oUZWGS7uMDvHsHjBmjfh99+vRBcHAwnj59qtb+q1atKvT1AYDw8HDo6+vD1tY2z+MmJiYKq/jNmcPFLN+8UWs6Zc/KlXzenjhR4ec0fP589AVQK1dxAlkhgcKqVpYmkydPRtWqVXHx4kV06dKlVMZ4/RpYvpzNxlu3VnFnIsDbG6hfH/jrL/4OVYSDAz/3Zmawev0a+2vWxNHffoObmxu2bt0KY2NjjB8/Hv3798f8+fPxSlrpVsb27duxYsUKSCQShIeHF/taPH/Op2NfXxWPR0X27duH7OxsnDt3TuGc4uLiijRIz8+kSZMwZcoUdO/eHWfOnEFWVpYmp1u66OkBd+9yldtz5/jaSrcUCyucPg188UXp9S8iIiIiIiJSNOWtkBX3o7EIqnHj2EH4X54+NmjQIGrQoAEFBATQRqlflcTSku88WloSTZxY3lPMYcsWvr0+duy//nUpEbLn6c8/lWs/bx6bl5cgEk8TbPn2WwoGKLGYlJL8fip79+4lAOTv70+DBw8mfX19CiksrVVJPD09ydramiQSiTxL6OJFovr1+amtWrXwzNmSsHv3bnn6Ymnw7h1bf/XrV7J+wsLCCACtyldgQRnS0tLI3NycrK2tC20zcOBAcnZ2LvC4p6cnOTo6Fnj8wQN+XebOVXk65YcsBSl3Sm1MDFFGBvn5+REAunXrlnyTRCIhQ0NDateuHc2aNYvel3ZYjpTMzEwyNTUld3d36t+/f6l5YE2bRqStzemnahEXp5ph9erVJC9qcuJEnk03b94kAHT48GH5Y+/evSMrKytq27YtHTt2jADQ7du3ix2mRw+uYVGaflTNmzenWrVqFRr5ZW5uTlOnTlW53wMHDpS631yp8fQpX8eVdqT1jz/y++j+/dIdR0REREREpIIAMYKqHCDiu2JdunD00r8YKysrxMTEYOTIkVhoY4PEo0chtGkDxMYCnTsDzs7lPcUcNm4E3N2BzZv/9a9Lifj0U6B2beWiqNLTgV27OIqvBJF4msC4YUNYAMg+cKDIdvmjAdq2bQsAmD9/Pn7//ff/s3fe8TWe7x9/P1lkiEQGEWIkthAr1KitaFGrqsavVqutVttvq0Vbo3SXtlqlSlGjtEqt0Jg1q6g9agUhQQgSIjKe3x9XToasc05OchLu9+uV10meeZ+Ts+7P87k+F+PGjaNqHp+3bdu25erVqxw9ehRNk2WtWsHx42I4K1YM+vSRh8+S5LeD6quv4NYtGD8+b8epVKkStWvXZuXKlSbvu2rVKm7cuMH169dJTk7OtD4+Pp4NGzbQtGnTTOtcXV2JiYnJsOzYsWPcurWDoUPFRLNvn8lDsg6dOsGTT4KmyT9lwgQIDIQuXTh27BgANWrUSN1c0zQCAgLYvn07X331Fbt37y6QYe7atYtbt27x/vvvs3z5cmrVqmXxc5w/DzNmiOmlWjUTd75xQxwzTk5QvLjx+40cCdu3i0Wyc2cYMSLVRRoUFIS9vT179uxJ3XzcuHFERUUxbdo0vL29AXkvyo1Jk2SIU6aYdreM5cSJE+zdu5c333yTRo0a8fPPP2dYn5iYSHR0tEkOKgPt27fH1taWV18NwcUlHC+vBAIC5Gm6cKFsk5wsT19zmTFjBqGhoeYfIDv8/WH2bHHC5ycvvijPvfz6BysUCoVCociRR0OgOnsWLl0Sgeohx9PTk5s3b3Ls2DHmzpuHa7du8McfcOAA/PIL/O9/suGhQ5BL6VW+kpAA5cpJXZLNo/E0NBt7exgzBnx84M6dnLf95Re4ckUmZ1bGp3x51gOOO3bIrCcbHhSoypcvT7ly5QgNDSUoKIi33norz2Np06YNABs3bsy0LiAA5s6Vl8iWLXk+VQbyU6DSdVi1SkqogoLyfryuXbuybds2oqOjTdpvzpw5ACQlJXHz5s1M60NDQ7l9+zbPPPNMpnXpS/x0Xefrr7+mXr169OzZk88/l0roIUOsW61qElFR0LGjlCtPmAANG8KECRw7doxy5crh6uqaYfM///yTVatWAWT52OUHa9euxc7OLt9K+wD8/GDJEqneNpnNmyE4GE6cMH3fZs3g33+lRvS77+R/ABQvXpy6devy999/A3D48GG+++47hg8fTr169VLff65du5brKerXh969Rb8wYnOTWbp0KZqm8eyzzzJgwAAOHDhASEhI6vro6Gh0XTdLoHJzc6NJkw78++/r3LkTjZvbDpo0gSpVoGRJ2WbLFqmc/PRTKWk0lQkTJtCrVy8uXLhg+s6FgVKlpL54/Xprj0ShUCgUikeSR0MZSLl6Td261h1HAeDl5QXAq6++SocOHbLe6NAh+ZY9bVoBjuwB7O3ht9/g9detN4aixAsvwO+/S95Zdi4qQ25L7dqFQoz18fEhFCh286Y857IhqzyV5s2bY2try+zZs7G3QN5IhQoVqFChArt27cpyfZcusH8/PPGEPIznzuX5lED+ClSaBrt3w8yZljlely5dSEpKyjAZzo3Lly+zfv16KleuDGTtQFm6dCnu7u60bds20zpXV1fi4+O5dOkSXbp04fXXX8fR0ZGoqChKltSZMUNi/u7eNf9+FShJSTKxdXSEbdsgJAQaN+bYsWPUrFkz0+alS5cmMDAQKDiBKiQkhKZNm1LSoEhYGF2X52aPHvBA5JhxGHLQ/P3NG0CxYpJLtWMHTJyYujg4OJh//vmHpKQkXn31VUqWLMmHH34IpH1uGuOgAjnsvXvw55/mDTEntmzZQlBQED4+PgwZMoQ6derQv39/wsLCMozRHIEKYPHimcybF8GAAdO4cqUrs2bF8fvvEpcJ4OsrkZDvvitCY/PmuV8XSU9sbCy3b9/m+eefz9JRWSSoXRsiIiCLfDyFQqFQKBT5y6MhUNWpA99/D/lQylDY6NKlC2+++SaffPJJ9hsFBopA9euvBTewB7FCMPBDwd690KiRXN6eOVMSvt9+W75IX7kiM4nXXye1js2KGAQqIMeZXFYC1ccff8yff/5J/fr1LTaeBg0asC+HerF69eR2716oXBmefVbCx/NCfglU69bBzZui85o5T81EcHAw7u7ubN261eh9Nm/eTHJycmpA+oMOlHv37vHHH3/QvXt3HBwcMu1fokQJQEqwQkND+frrrxkzZgxJSUncuXOHrl1hzZoi1D+hdGk4eRIuXxY3D5CcnMzx48ezFKhAXC1QMALV5cuXOXjwIJ07d86X4+s6PP20BPebzZkz4OUFD7jNTKZpUykdj4iArVtp3LgxsbGxTJgwga1btzJ58mQ8UsrFSpQogb29vVEOKpDs9vPnoV8/04d19OhRhgwZwrksVPD79++ze/duWqSEwjs5ObFs2TKSkpLo1asX9+7dM1ugOnhQQuvLlSvPwIGBDBz4DDExMaxZsybDdtWqyWvu0CH4+GPYuRNee824c+i6zp07d6hatSqbN2/m6zw9EaxIUBA89pjUcioUCoVCoShQHg2BqkIFGD4cUiZDDzOVKlXiyy+/xMnJKfuNNE0CePbutXzojjFcviyXaS1l/XiUuHkTYmLk8vbw4fDOO/Dtt9Iyq0wZKYtJ3xHSijg5OXG3ZElC69VLU38eILs8lYoVK6aW5VmKBg0acPr0aW7lErBSoQK8/z4sWyZa7gsvSLWQsU0U0xMXF4e9vT22FshY03Upv+nTR9wO77yT50NmwMbGhmrVqpnUyW/37t04OzvTunVrILMD5c8//8y2vA/A3d0dgFKlSrF7925ee+211GUGwUbTIDxc4p3OnjX5bhU8VatmUNQuXLjA3bt3sxWoSpQogaZpBSJQrVu3DoBOnTpZ/NixsSJorFyZx6rt06el7tZSDB0KvXvTtEoVAD788EPq1avHsGHDUjfRNA0vLy+jHVQgzj4w/VrL8uXLmTNnDkFBQSxatCjDuv379xMXF5cqUAEEBAQwf/589u3bx2uvvWaWQBUfL1GGU6ak5Uu1bt2a0qVLs3jx4iz3CQyUj5nvvzfe6BwXF4eu6wwePJiuXbsyevRojh49avQ4Cw0dO4oyV7GitUeiUCgUCsUjx6MhUG3ebLmanYeFpk0l2GX//oI/94oVMtvOrnW4InvatRMRKiZGctViYqT+yTChs7UFOzvrjjEd1atXZ6KzM7Rvn+X6vOSpmEqDBg0AmQTmhLe3lPDs3Ak1a4pQ9fnnaaa0yZMhxRiVK3FxcRZxT/30kxhAW7eG0FBxNHz2WZ4Pmwl/f3/iT5wQBWzZsly33717N8HBwZQuXRrI7KBaunQppUqVylZs7NGjBz/88AP79u2jXoqImZWjKCFB/h/PPAO7dhn/+BcGDAHp2QlUNjY2lCxZskAEqrVr1+Lr65taVmgJoqPhvfekHGzsWHmppxjqzOPMGfPL+7Li00/h5k38v/oqtazx22+/zSQae3p6miRQAcyZIxrGmTPG73P9+nUcHR0JDAykX79+DBgwIDWHbdu2bQAZBCogVeyZNWtWqivJlPfMcePg8GHpR2LQTm1tbenTpw9r1qzJUbR/8UURq0BEri++yP66VmxsLAAuLi7MmjULV1dX+vfvz/0iEyKnUCgUCoXC2jz8ApWuS+DlF19YeySFi8cek9udOwv+3MuXi8sgXUcrhQlommRRlS0rt4WgnC87WrRowZ6//yb+8GGpiXkAg6BRkAJVTmV+6WnUSCoTo6LSdNwrVyS67auvjDvn3bt3c3YzGsnx41Lx9NNPoktOmZIWamxJapUrx6bLl6XGJ5fE+Li4OA4cOECTJk1S/3/pJ/j37t1j5cqV9OjRI9scMScnJ4YNG4aLi0vqsqwEqkqVJMj+4EHR1l1dYeDAtOPs3SuPS2GMvMmqg9+DuLm55btAlZCQQGhoKJ06dUKz4HtGUpK8Hlq1ko+TP/+ELKo5jWfuXMtmE9auDR98gLZ0KZM6deLtt9/OsqOkp6en0SV+Bjp2lGsCo0cbv8+NGzfw9vZmy5YtjB8/nkWLFhEUFMT27dv566+/qFKlSqrgm56JEyfSpk2b1BJcDyO72YWEiJg9dKi4ENPTt29f4uPjWb58ea7HiYuD69elorxGDUkIeNBVeiclrMrZ2Rlvb29mzZrFgQMHmJASVl+k6NWr0LiRFQqFQqF4lHj4BarISPG0V69u7ZEULkqXlklo+lleQXDjhjjaevQo1MKKwjI0b94c24QE7Bs0yDKUP6+Bv6bg6emJn5+f0QKVAU2Txk4gL5smTaSU6erV3PfNi4MqIQEM1TGTJ0sg+vPPS/52ftHi5k0cAN3ODnJ5nPbv309iYiJNmjTByckJJyenDALV+vXriYmJoXfv3iaNIbtMpm7dpKvYihUySW7UCNasWUPr1m15/HGdcuXA3V3EAhONMPnKsWPHKFOmDKUMT6IsKAiBateuXdy+fTvP5X3JybB2LfTvL+KUpydcuCA9HAzXPfJE69aQIiZbjDfegFKlGBEfz2fZWA9NLfEDuUYwapSINdu3Z16fkJDAkiVLuJsu5f/69euUKlUKOzs7xo0bx7Zt29B1nRYtWhASEpLJPWXAzs6OxYsX4+vri4uLi1HvK3fuyHtGYGDWuWCNGzemUqVK2Zb5pcfRUf7vf/4p10WeeUZKn9OT3kEF0K1bNwYPHswnn3xi8vuu1UlMzPU9UKFQKBQKheV5+AWq48flVrl1MtO5s4TRFiSrV8uspnv3gj2vwio0b96cOOBC+fLooaGZujoVpEAFuQelG8Onn0pV5eTJuW9rrkC1aRM8/jg0bCjGMws0MjSKmseOcREI69QJDhyQSVo27N69G5BJLmR2oCxduhQPD4/UfCpjySk0vEwZEao++ghefRVmz57Nli1bqFdvIt99p9Oxo/x/qlSRUsjCQHYd/NJTEALV2rVrsbOzo52ZHT4jI0WYrVpVnDh79sgySBNw88zp06J0Wbpto7Oz1MW6uMjnTxaY46ACEUvLloVBg+CffzKumzZtGs8++yxvvvlm6rIbN25kcD81bdqUw4cPM378eNzd3enVq1e25/L29mbDhg3Mnz8/xzEdPChleM7OsGSJVOtmZeTUNI2+ffuyYcMGrly5YtT9bd8e/v1Xvj58+WXGcj+Dgyq9I3LKlCkArFy50qjjFxqqVYNTp3J8D1QoFAqFQmF5Hn6B6sQJuVUOqsxERMg3zEOHCu6cnTvDvHky81Y89Hh4eFCzZk022NigHTpE9yZNMuSRGAQqrwISShs0aMCpU6dSr+r//vvvHDlyxKSMlGrVxHg4axbkNp81R6D6+mto21ZK1mbOlND2AiE8HPfdu/kVOFWypNT0GN4/s2D79u1UrFgxtRwpvQMlLi4u1/K+7DC2q11iYiKbNm2iTBlvdu4cT1LStyxZIlk7zZtLfhiIaPDPP+aF3OcVXdcLjUAVEhJCs2bNcDWjO96xY/K8HzMGypWDBQvkY8PX18KDXL0aevYU64+lGTcO5s+XmryNGyWDMR1eXl5ER0eTaKIg4ewsVYm2tlC8uCzbvh0mTLjDhAkzcXZ2ZubMmWzatAkQB9WD5XkuLi6MGzeOa9eu5epwq169Ot2zucDzzz/Qtas0oZs3T5a1apVz5nzfvn1JTk7mVxO6+trayvH//huKFUtbbnBQOTs7py4rWbIktWvXZs+ePUYfv1BQvbo8R8LCrD0ShUKhUCgeKR5+ger4cblqavFv0g8ByckSTtGhg8xACgJPT5nd56nNk6Io0aJFC6andIZz/ecfPvjgg9R1BkHD2DyVvNK7d29at27NunXrGD16ND179iQwMJDmzZubdJy33wYPD/jvv5y3M1Wgeucdid/p2VOOXaAVuFOngq7zo5MTe0BUniycLHFxcbzwwgusWLGCJ9OF2qQPmV63bh2xsbHZdu/LCUOQdW6Czd69e7l16xZTp07lySef5K233mLfvn3UqgWrVqW95U+cCMHBMt/8+GMpnSwoLl26RExMTJTDyRoAACAASURBVK4CVX6HpF+6dIlDhw7RuXNns/avXFlK+o4elWiyfv3SxBiLcuaMdNvNT0fliRPSbKJ0aQlmWr0axo/HK6V75I0bN0w+ZPv2ctjateXv9eth/Hhnbt/ewIIFO6hSpUqqi+rGjRs5lnuay88/y/N8+3Z5zhv70qtduzaBgYFGlfmlx9NTGkokJkrZLWTtoAIIDg5mz5496NZQic3FcFHz5EnrjkOhUCgUikeMh18lGDVKspZU3lFmfH1ltqFpcpn18OH8Pd/27RJace9e/p5HUaho2bIlB4BYR0deCQjgs88+Sw36jYqKwsXFheL5MtvNTNWqVdm0aROXL1/m1q1b7N27l+7du5vcCr1GDbmw3qxZztvlJFDdvSv9AoYPT6s6srOT0rVffsknASAn3nsP7bffKFa1KjuvX4dt22TGm44TJ04QHBzMrFmzGD16NFOnTk1dl75EaunSpXh6etKqVSuTh+Hg4ICTk1Ougk1oaCiaptGuXTvmzZuHt7c3ffr0yZD3A+L2mT1byrDGjIHevU17C7pz5w69e/fmnwfrt4wgtw5+BvLbQRUSEgJgUv5UfLwIeufOyXPxu+/SXGn5xqVLUL58/n5e+/tLmNJTT0n9W5cuMGkS1VI66ZlT5mfAMOx33omlePEWODi488YbdWnU6DVOnDhBcnJyphI/S7BlCwwZAm3aSEnw+++ndeszhr59+7Jz507CzHALzZ4tFfuLF2ftoAIRqG7cuMHZs2dNPr7VqF5dgtLzQUxUKBQKhUKRPQ+/QFW+vIS5KLKmRg3YulXaLrVuLbkz+cXMmfDBBzILVzwy9OrVi9+WLaP4n39Se8MG/P39GThwIDdv3iQqKqrA8qcexNXVlQYNGtCwYUPu3r3LPROFU1tbqQDZvDn7bbISqOLipDmUp6f0Cvj1VzDM2yZPhm++sdJLxN0dunXD39+fM2fOAHA9PJymTZvy8ccf89NPP9GgQQOuXLnCunXr+OijjzKU7xlK/OLi4li1ahU9e/bEzsw7YoxgExoaSr169fD09MTDw4MpU6Zw5swZ/v333wzblSwJgwfL/+m77+CPP+QxN5Zp06bx22+/8f3335t8P0wRqGJiYkjKJh8pr4SEhODr60ttg8UnF9atk2DtMWPgt9/yZUhZc/ky+Pjk7zns7aFTJ7EcXbkibe7OnkV77DGqgclB6VmxbNky7t3bzvTpp7h3DxYtGkF8fBPOnj1LcnKyxQUqNze5xrRsmRjQTOXZZ58F4JdffjF53yFDRKh/8UUIC5OvlFk5qICiVebn7i5vFBZJ/lcoFAqFQmEsD7dAdeaMlK3k4YroI0HVqiJSeXlBdHT+nOP+fam76dpVCVSPGPb29vTo0QO75s1xqVCBRTNnEh0ezogRIwpOoEofzv5AULthsmhOac/EiVIhm50B60GBKilJyqN+/lnK9zZskKDpKlVMPrXliI6Gli1h1y4AAgICOHfuHMnz5uFWuTJndu1izJgxDB48mODgYA4cOMATTzyR6TCenp7ExMSwYsWKVNeRueQmUMXExLBr1y7at2+fuiwgJWgnJwfMyy9LTk///saNIzo6mk8//RSQjoEPhvznRFRUFAcOHMDT0zPXjDVD7tbtFBePJUlISCA0NJTOnTuj5eJMio4WN0ynTuIGWr9eylkLjIgIsboVFE5O0LEj+PlRY9UqjgC3c6vbNYKffvqJKlWqMHhwEKdPw7Bh+4G/OHToEFDaYiV+MTGSrRYUJN31THFNpadSpUo89thjLFq0yOR97exg4UIR7KdM6QwsTHVQTZwoD+977wXi4NC6aAlUBvLrO5FCoVAoFIosebgFqvXr4c038ydw9WHD319K/AwdtwxXke/dk8cvfQ5DZKTpicOffw63bqnufY86cXE0eukltgQFsXDhQrZs2ZL/AlVIiNgL7t2TOpTHH4effpLaJUidLF6/ft3kQ7/+ujgWBg3K+m3m7t27OKVrn5WYKPFrU6fCjBkShl5QHfqyZdo0+OsvSXsG/P39SUhI4JKbG7YJCTxlb8+qVauYNm0aGzZsoGw2AoJBhJk+fTpeXl60bNnS7CHlJlBt3bqVxMTEDAKVt7c3AFevXs3x2A0biviybx+MGJFJr8zA1KlTuXXrFqNGjeLq1atGl/ktX74cLy8v5s2bl6t7CowPhjeHHTt2EBMTY1R539Spch3h448lBL1DB4sPJ2dCQyXM3ArY9uiBHeCxfn2ejnP27Fm2bt3K888/j6ZpODvD88/fA3R27jwLRPLuuz1p00bE6v/9TzoimkpUFDRtKqZkS9C3b18OHz7McUPnYxOoUEGqJZ2cYgDPVIEqJkb0nX37bEhICGHt2ixaCRZmPvtMrK6W7iqpUCgUCoUiWx5ugWrXLulLXmBtsIo4BmfTokUiWDVsCI6OEjL/+ONpXY9atpT8qkGDpF4mN7Hqq6/gvfegTx/pT654dHF0hJ49qb9/P+9UrUpcXFz+C1TffSduyvh4USZOnZKar8qVoWJFmv30E2CeQOXpCXPmiNjRqxc8aIBJ76BKSJCOV0uXwsiReb5XliEmRl6fXbtCnToANEsJ1lodHs4Ne3v6urnx1FNPMWLECGxtbbM9lOH/uH379jyV90FmgWrYsGF8+eWXqX+HhoZSvHjx1LFCmkCWm0BlwBCJl2KQymab7TRu3Jh33nkHGxsbVq9ebdSxt23bRvHixfn8888z5HRlR34KVCEhIdjZ2dG2bdtct33/fdi0Cd59N2N3tgKjalX57LECbk2asA8on0eXz7x589A0jQEDBqQu801J7D958gjwP6pXv0d8vHxFmT49rVnmP/+Is3LMGHmP6NFDKswM6w8elO6hn34qy0+fTrumlFe6desGYPRz/EE6dIDnnptGsWJdU1/7n38unf4OH4YyZS7z33/j+ftv412IVqdqVVGwjxyx9kgUCoVCoXhkePgFqsceUwHpptKyJdStK4LVBx/AJ59IfpSNjXxZGz1aBKs//oCnn5ZZTU4iVe/eso2hDkDxaDN+PDRsyOSrV6nq7EylSpXy71wJCVK+2q2bhBE9+6w4AI8cgW+/hUaNcE6xPpkjUIG8BH74QXJ70peOJSSIQFWsmDNjx0KDBlLBVKgaWM6YIRaHsWNTF9WoUYOAgACW/vYba5KSeCwmJi3FPQfSC43mdO9LT3qB6syZM/z4448sXbo0dX1oaCgtW7bMEK7v4OCAm5ub0QLVa69B375y1//8M+ttwsLCqFy5MqVKlaJZs2b88ccfRnUiO3jwIIGBgbz11lvUr18/1+3zW6Bq0aIFrq6uOW6XkCBuPqtFNkZEwNdfw8WLVjm9g4MDG4sVwzc8HMwo9wVITk5m3rx5tG/fnvLly6cu90nJ1frvv13AFGbMuM6OHZI9d/euOKlAIrg2bxZhZ+5cEaacndNypdatgxdeEAHRzU36v7Rpk4c7nQ4/Pz/q1KnDmjVrzD5GbGxspvwpkI6nb765EZiCp6dxr89CQVCQ3D6Qa6dQKBQKhSL/KExTJcty9aq4JlTApen4+krJz+7dMGECvPOOzMLt7GR2/fzz0mbs2jX5tjx5slz6TU98vDgzkpLkeBMnKnFKITg4wMKF2N6/z+H69Rk7enT+nWvPHoiNlbbyBjQNatWCV16BX3/l9oIFgHkZVAaGDJFTvfee/H3pEnh56dy9u4zZs8fw0UfQqJHk7hYa4uLgyy+hffsM3fo0TaNr165s2bKF1cnJON+7JzaIXDA4mLy9vXk8jypHeoFq/vz5gASO67pOeHg4x48fz1DeZ8Db29togUrTxI1Su7bollu2pK2LiID4+CQuXrxIxYoVAXjuuec4fPgw/fr1Iz4+Ptvj6rrOwYMHqZPiSDOG/BKoLl68yOHDh3Mt7zt0CCpVkrd8q3H4sNTMnj9vtSH84+mJja5LOJwZbN68mfPnzzNo0KAMyx0cHPD29ubUqVMAGULSNS3to7FbN9HnEhKkIv7YMRlKigGLN9+Uh+fSJfnItZQ4ZeCpp55i+/btRJuZu3Tnzp1MHfwM1KxZFniXq1fP5VhWW6ioUEEubORn8xiFQqFQKBQZeHgFqvPnpUYhtz7wCvOxtYXvv5eZnWGCq+tS59SlC7zxBmzcaNUhKgopVavC11/joOsUM7F7nkls2CAzwBzqYDxcXJgLlDZzUmqgUaO0l0FCAjz9dDJQjvLlI1m6VNqxpzP8WB9bW5g0ScTjBzCU+6wHYt99V9SLXDAIVD179syxFNAYDAJVcnIy8+fPx9bWltjYWMLDw9mQ8n/Kq0AF4k5Zvlw0U4NxJDZWcrpLlrQhMXE6zs6BALz44ot89NFHLF68mPbt22fruIuIiOD69evUrVvXpPsLlheo1q1bB5CrQDV2rGSoVatm0dObxuXLcpvfXfxywKlVK/qWKEFSx45m7T937lxKliyZ+vpJT9myZdF1HU3TUv/fpmJvD35++Zcj/9RTT5GUlMR6M3O4snNQAalC786dUdSpA9u2mTvKAkTTxEWlBCqFQqFQKAqMh1egatQIjh9XDqr8xsZGSgJBAm6bN5efTZsknKfAU3YVRYYhQ0TczE9bUUAADB+e4zmc3N1pDfgdPGix01asCF99FQMEMmRICHloaJd/ODjA0KHQpEmmVU2bNqVUqVKUqlQJl48/Nko08PT0ZNasWbxnsJHlATc3N5KSkli7di1hYWEMHToUEBdVaGgopUuXJjAwMNN+pgpUIJFH589LFTLIW9r330OHDpHAAD78sBeLF4uzbPTo0SxevJi///6bxx57jDNnzmQ63sGU51FeBKrERMn0B3HmPf449OwJH34orhpjCQkJoXz58tSqVSvbbbZuhdWrxShrVYdfRITcWlGgeqJzZ36JiWF/+qYgRnLr1i2WLVtG3759M3TuNGDIoXJzc8uzgJtfBAcH4+npaXaZX04OKoNAFRNzgtu3xcCamGjuSAuQ116DV1+19igUCoVCoXhkePgEquTkNNdOpUoqf6oguXNHyiovXJCwjAfKHBSKDBhqW65eldmKicKCUfTrJynEubDP0ZEKFy+a3p0yB+Li4gCynKwWCpYvl9quLLCzs+OLL75g/Pjx0hzh118loTkXhg4dmm2XP1MwCDaLFi3CwcGBMWPGAHDkyBE2bNhAu3bt0LJ4bzdHoAIx2xoimpycRNPs3XsDEECtWvcZMiS16SPPPvssGzdu5Pr16zRp0oRdu3YBkpW1bt26VIHK2BK/69fh9ddLAn/zwQdDcHYWp8yHH8r6KlXEkXf8uDS4q1UrrRQMxLB644YIWumfvvfv3yc0NJROnTpl+VgB7Nwp1dsVKsg83KpcvizBSk7W6/TWoUMHvIE777zD/QMHTCp1W7p0KXFxcZnK+wwYBCpD11CrExYmtYLp6u1sbW1p1aoVO3bsMOuQOTmonJyc8Pb25vLlU0yZIhWdixaZdZqCpUcPeO45a49CoVAoFIpHhodPoJoxQ/Jm8liuozCDp58WgerMmYyZPwpFThw7Jm67oCCZMVuKy5clyMUIjrq74373rkzaLMTdlNbkTlaccGeLrsOAAfK4Z8OgQYMYOHCg2BwGDZJQ+QLCIFCtXbuW4OBg/Pz88PLyYsmSJVy9ejXL8j4QgSoqKookI0LdcyMsLAy4xObNNuzeLdc77t4VHbV58+bs3r0bNzc3WrduzUcffURwcDCdOnVi1qxZVKhQIccyrsOHJaJP7iscPKhhZ3cHP7+jvPSSVF0a7qK7u/T7OHZM9ITJk6VrpKHL3ujREkLt6AiNG8s6gB07dhAbG5upvC85WSICAUJCpBPl1q1W1YWEiAiruqdAXIB16tWj2ZYtLGjbljYmhDzNnTuXmjVr0qhRoyzXGwSq9PlTVuPYMXGXlisnbUVBxOo336Rp3bqcO3eOqKgokw+bk4MKxEUVFhZGz57ydj9pUhFwUem6NNUwtFJUKBQKhUKRrzxcAtW5czBqFDzxBBjRUluRDzg7y2xJoTCWVq0khNvZWWblZl69z8T48VC5slEd6M4ZXD/bt1vm3BRyB9WtW+J4TNdpLFucnKS+bN48+PjjAplRGsSdW7du0aJFCwBq1qzJPynNGNplI4B7e3uj67rZHRnTExYWho+PDyVKFMdghvrlF8n/6doVDh2qwpYtu2jQoAFjx47Fzc2NBg0acPbs2RzL+27ckIi+uXPlb1tb2L8ffH0H0bDhDL74QsoNW7XKvK+PD4wZI93fvL1l2TPPiNg1fryYVxs1EgdYSEgIdnau/Pzzk7RuLWHwpUuLO+vTT2XfiRMlbLtChTw/XHln3jwpE7cyTbt0YbWu0+nGDQ4fOMBFI7oKnjx5kp07d/L8889n61YzOAsLhUA1ZYqU+H77bVoMQlgYTJ1K361bAdhnUDpNICcHFYhAde7cOTRNGgSfOgW//27OHShgWreGzz6z9igUCoVCoXgkeHgEquRkGDxYvu3PmqVK+xSKokSdOiIOlSsHnTubFrSTFbou9pBWrYzqHhnj58ehdAnmISEhZk3Q0lOoBarwcLktV8647adOFYfkmDHQogWcPp1/Y4MM7iNDR8CaNWum3hrcKA/inaLamFPm9yDnz5+nwgPKzeOPw1tviUupVy+oU8eTwMCtTJs2gx07drBixQrKlSuXpfMmOVl02GeeEXPfDz+krdO0jJ0Lc8PBIe33li1h5Egp/ztxQib+DRuK+6xFi0YcP25PYqIEoHfvLv9CQ2ygnNfkhyZ/cHZOa1dnRfr06UOIpyc+QDtg06ZNue7zyy+/YGNjQ//+/bPdptCU+EVGws8/SzfeV15JUye7doVJkyizYQMvQ6oYbAq5OagqVarE+fPnSU5Opls3qRzu3t28u1FgaJrk9Fm1xaVCoVAoFI8OD49AZegmN2WKca4AhUJRuChdWkpzmzbNu8B85IiIMJ07G7W5m4cHHUqWlLI34IUXXmD06NF5GkKhFqgMrhBj3ytLlYIlSyQ0JixMat3yEYNAZWNjQ9OmTYE0gSq78j6wrEAVFhaWGuxsICAAPvkkLWavQwc4etSOESNepEyZMtjaluP8+fOMHDkSyBDvQ7duMs/dulWMK4aOjwZMEaiyw81NnFRPPHGRo0eP0rlzR44dk45py5ZJBfyHH6YJVIUGXZeU9kLQ2q1mzZr8EB6OXqoULzo4GCVQnT59Gj8/P3xyKFEsNCV+v/wioWavv5553ejR0L49k21sOGKGIGOMgyohIYGIiAhsbETktbeXJnmtWsF//5l8yoKhSRMJgbNwl02FQqFQKBSZeXgEKm9vCbIcPNjaI1EoFOZSvrw4n2rUgJMnRQkwJ7h85Uq5fSB/Jzs8PDy4fv06elIS92JjuXTpEgcOHEDPQ2h6oRaoTHVQgYiGfftKKbWh5m3SpHxpwW4QqIKCgnBNSS+vX78+AJ1zEB0tJVAlJSVx4cKFTAKVAVtbqSRfvFgEJxBjStWq8PbbNrz/vjxE7u4QEyPrBw8W48rVq/DCC1mP/eTJk6nPm7wQEhIC5PxYFSqio6WEKo+uRYtRrBjaoEGU8vFh04YNub4PREZGUqZMmRy3KTQOqpEjYe9eebI+iI0NTJyIW3IyASaKhbquG5VBBYZ8tzQuXZIeDEFB8M03GYXdQoGh0+mePdYdh0KhUCgUjwAPj0DVuzcsXKhK+xSKh4W33pLyEw8PyQB54w3444/c99N1cfo0aiSBQUbg4eFB1cREdC8von7+GV3XuXbtGhEREWYPv1ALVL17S8mKOR33DKWQ167B119D/fpiyVm92mLDK1myJHZ2drRMZ/Vp2rQphw4dokOHDtnuZymBKiIigoSEhGwFqvTYpHyKurhA//5i4p08Gby8JAvKIFB17y7r3d2zPs4rr7zClStX+OKLL/I0doDt27fj4+NDjRo18nysAuHSJbm1ckh6Bj77jBOjRxN++TL/5WLtMUag8vDwYNq0adJ4wJpomrxms6NJE9Y8/zxTbt/m8uXLRh82Pj6epKSkHB1UlSpVAuCcoSVmCk8+CUePiotq5Ej43/+MPm3B0KiRPG4pHTsVCoVCoVDkHw+HQLV/v/TqVigUDw/jx8P06SKmxMXBzJmQfvL+zz9w/778HhMjtVexsTKRmDNHapqMpFSpUpwFtNhY7qcr6TmQB3dQoRao3Nyk5ZudnfnH8PKSmpwJE6RksE8fuH3bIsNzcHBg/fr1jB07NsPywMDAHPdzd3fH1tY2zwLVyZMnATJlUOWEi4tUmh85Isa/jRsljNxYDbBly5b06tWLTz75hHCDw81Mrl69iq+vb7aB3YWOM2fk1t/fuuNIj40Nbdq0oTZwetq0HDc1RqACGDFiRKpIYxUOHxbV9Pz5HDdzGzqUOGCvCQ0r7ty5A5Cjg8rPzw/ILFCBvE7WrIGXX5bQ/7/+MvrU+Y+rq9T0FjrlTKFQKBSKh4+HQ6Dq0wf+7/+sPQqFQmFJGjSAl14SYWr3buk8t3ixrLtxQ7KqSpeWROhKlSQ/Ze1aWd+4sUlZdB4eHtwD7tSogeP+/anL8yJQ/fvvv2iaZv2SnqxYuDDtscoL7u7Scm7xYsmlWrIk78dMoU2bNibn9djY2ODl5ZVngWr+/Pm4uLjQrFkzk/etVcu0ysn0fP755yQlJfHuu++ad4AUrl+/jqenZ56OUaAYHEpVqlh3HA8QEBDArGLFaP7jj1KGmAUJCQlERUUZJVBZna1b5f3UJuevfg0bNqRJsWI8PmgQ7Nxp1KFjY2MBcnRQOTo64uvryxmDIPkAmgZffikZbSnRc4WHDh1EhVYoFAqFQpGvFH2B6tw56SiVQ9mHQqF4CLC3T5v5lygBy5dLym7p0lICuGePtEgzA4MQElW1Kl5hYZR3cKBixYpmC1QXL15k2rRp9O/fP7XsrFDx4Yfw00+WO15wsJT7PfFE7tsmJUngzPTpYjPKQ85XVnh7e+dJoLp+/TpLlixhwIABlChRwoIjy52KFSvy1ltvsXDhQnbloZwoKirK+mHcphARITmSJUtaeyQZ0DSNtR064BIfj/7ee1luY3iuFQmBau9eeb/MRUEtVqwYPs2acfv+fen2ZwTGOKgAqlevzokTJ7JdX7y4NBe0s5OnRXy8UafPf5KSxKX766/WHolCoVAoFA81RV+gCg2V2xw6OykUiocMe3t46imYNUvqQn79VXJCzMQwmT8WHEwyMNvenvr165stUH3wwQfous6HH35o9pjyDV2XkHRLdjvVNHjtNREZZsyA776DadOkVmfKlLQW7QcPiggRFCSz0HbtJGcspaSO+fPB11fez80s2y5durRJ2TkPMnfuXOLj43nppZfMPkZeePfdd/Hx8WHkyJEkm5kWXeQcVFOnysWmQkiV3r2ZDvK8/vffTOsjIyOBIiJQ/fOPOE6NKP1s1rkzUxMS4NQp6QCQC8Y4qEAEquPHj+caPB8dLVFZb7+d66kLBltbWLBA3tsUCoVCoVDkG0VfoFq9Wq4GVq9u7ZEoFIoiiqEM75yTE6MqVmRhgwYEBQVx+vRpYgwp10Zy6NAh5s2bx2uvvWZShlGBcfMm3Lljfh1aTty7J2WZI0aIYPXGG5LbYriQ4O8PgwbBvHnifJ07V8ox7e1lffnyIk5t2yauuAsXTB6CYQJsjriTnJzMjBkzaNasWa55V/mFi4sLn376Kf/88w8LFiwwef/79+8TExNTtBxUAE5O1h5BlrRp04b3gThHRxFVH3heFRmBKiYGjh83Wshv3749qT0V05U9Z4cpDqrbt2+nPm7Z4e4u8YPffgsmRGHlLz16yHtTFkKlQqFQKBQKy1C0BaobN6Qlfb9+qnufQqEwGw8PD4oVK8aRI0eYHx2NU61a1KtXD13XadCgASNGjGDFihXcvHkz12ONGjUKNzc3Ro8eXQAjN4OLF+XWkg4qA66uUpdz5QpERcl79K1bMGqUrHdxEWfVwIEiVv3f/4n7rXJlWd+6tYhWq1eLgOXvn5aWrOtG9Z8PDAwkNjY2Uyt7Y9i4cSOnT5+2mnvKQL9+/QgODubdd99NdaYYy/UU51mREahiYkSJ2LrV2iPJEl9fX8pUq8a3/v7y/ExMzLC+yAhUly6JW9FIgSowMJBLXl7yx759OW+MaQ4qIMcyPwMffSRvU0OHFpJSv6FDJc29eXMpMVcoFAqFQmFxirZAVaoUHDsmV+oVCoXCTOzs7OjVqxc///wz0dHR1PD2pvOXX7LuuecICAhg7ty5dO/eHQ8PD5566qlsy1NCQ0NZv34977//Pu7u7gV8L4zE0CEuPxxUNjZQpoyU+nl4iA3C1RWKFTPtOO3ayXv7iBFSXpSUBIMHw1tviYNh2TIpuTJMnO/fFyErKoo6deoA4mQzle+//x5PT0969epl8r6WxMbGhq+//pqIiAiaNGnCe++9R+IDwkh2GASqIlPid+oU/PZboe7E27ZtWyaeOcP9cePAwSHDOoNAVbp0aWsMzXiqV5cyyk6djNpc0zSatG/P+66u0LFjrtub4qAC4wQqQ2fMEycsG5lnNn5+knUYGCjfO+/etfaIFAqFQqF46CjaAhVI1x9j+3grFApFNgwbNix1kuVTqxY2ERE8cfYsa9eu5caNG/z111906dKFNWvWcPv27Uz7JycnM2rUKCpWrMjLL79c0MM3nieeEDdF/frWHknOVKwo2UTPPCPCl6Oj/P344xKO/9JL4qAFcVu1bAm+vgTevImmaRw+fNik04WHh7Ny5UqGDBlCMVMFtXygSZMmzJ49G0dHRyZPnsy/RpYVRUVFAUXIQVVIO/ilp23btty5c4f9W7eKuy+dnScyMhI3NzeKFy9uxRGagAlu8zp16jDp9m1ijIhQMNZB5evri4uLi1ECFYieFhwMeWioall8fGDLFmnw4OQk4nlcnLVHpVAoFArFQ0OuApWmacU1TdujadpBTdOOapo2G8/KYQAAIABJREFUIWX5XE3TzmmadiDlJyhluaZp2jeapp3WNO2Qpmn10x3r/zRNO5Xy8395GvnUqdCzZ5obQKFQKPLA448/TtWqVQGo7O8Pw4dLuPeUKTjY2tKiRQu6d+8OwLVr1zLtv3DhQg4cOMBHH31UKASObLG1FVG/MI/xQTRNSgPXr5c8qwMHRGQzlA76+cG6dVC2LE6vv06NypVNdlDNmjWL5ORkXnzxxXy4A+YxePBgZs2aBcAFI/O4ilyJn0GgCgiw7jhyoFWrVmiaxvk5c6BLF3HRpBAZGVn4y/uSk6FWLZg506Td/P39cQauLFoEWYjy6THWQaVpWq6d/Axs27aN48ePsXmzGCYLDcWLQ8pnBe+8A127Wnc8CoVCoVA8RBjjoIoH2ui6XhcIAjpqmtYkZd3buq4HpfwYrm91Aqqk/LwAfA+gaVopYBzQGAgGxmmaZl4NzGefwZtvyu+F3VavUCiKBJqm8eqrr+Ls7ExAQAAMGwZPPikh348/Dtev45WSyWJwqRiIj49n7NixNGjQgD59+lhj+MYzb54kDxc1bG2hQwcp/6tbV0Q2Q7mVi4s4w6ZPhxMneM/BwSSBKiEhgVmzZtGxY0cqVaqUT3fAPPz8/ADTBaoiVeLn5ycOuUJKqVKlqFevHj9fuCBi6ebNqeuKhEB19KiUzJr4GAcEBNAICBg+HHbtynHb6OhoNE2jRIkSuR7X0MggJ+7fv0/Xrl0ZPXp0an7+3r3Sh6FQ4egIGzZI8wmFQqFQKBR5JleBShcMKa32KT859QfuBsxP2W834KZpmg/wBBCq6/oNXdejgVAg92CDB9myBd59F/r0gSVL0ro/KRQKRR555ZVXuHjxIq6uruDsDKtWwfz5IoS7u6cKVA86qA4fPszFixcZNWoUNjaFvHL6559h4UJrjyJ/6NQJunfnyfBwzvz3H3eNzIhZuXIlERERVg9Hzwp3d3ecnZ2NFqiKXImfjY3Rwd3WpG3btoTu3UtMQABHv/uO6OhooIgIVNu3y23z5ibt5u/vT2r/vlxa6Z0/f56yZctib8R3surVq3Px4sUcGwBs2LCBmzdvcjGlqcPFizL8YcOkX0KhoWlTuTWxpFihUCgUCkXWGDWT0jTNVtO0A8BVRGT6O2XV5JQyvqmaphnqRXyBi+l2D09Zlt3yrM73gqZpezVN25shGDY6GgYMkKyK2bPBzs6Y4SsUCoVRaJqWMdxc0+Q95/ffwcaG0klJLAWuXb2aYb9Tp04BUKtWrQIcrZlcvJg/AemFhW++4a/p00kCjh07ZtQu33//PX5+fnTu3Dl/x2YGmqZRoUIFkxxUTk5OOBZiR1IG5s2TkPRCTps2bbh//z6zz5zB/+pVlsybB4hA5ePjY+XR5cKOHZKdZKI7sESJEhT39uaory/MmQMJCdluGxYWRsWKFY06brVq1YC0982sWLp0KQCXLl0CpJvfe+/BggXw8cdG3oGCIKUpA2Y0ZVAoFAqFQpEZowQqXdeTdF0PAsoBwZqm1QZGA9WBRkAp4J2UzbNK4NRzWJ7V+X7Qdb2hrusN7dKLUJGRUKKEXP3PJedAoVAoLE3pTZvoDdw/fTrD8tMpf1euXNkKozIBXZfcvvLlrT2S/KNcOWqnOEWWZSN86LrOli1bSExM5NixY2zcuJEXX3wRW1vbghyp0fj5+ZkkUBUZ91QRokWLFtjZ2bGzWDGKA0d+/JHY2FhiY2MLt4MqOVlKEps3Nykg3YC/vz8LS5WSzLfly7PdzhSByt/fH4AzZ85kuT4+Pp4VK1ZgY2PD1atXiU8JpR87Fvr1k9tCo2mWLSvdSpWDSqFQKBQKi2BSLYqu6zeBLUBHXdcjUsr44oGfkFwpEGdU+tlPOeByDsuNp0YN+RLQsKFJuykUCoUlcGjZEoBiDwT8nj59mnLlyhV+10p0tLRGf5gdVEDF+HjOlSrFv198kaWLauXKlbRu3ZpJkybxzTffULx4cYYNG2aFkRqHn58f58+fN2rbqKiooiNQhYfDY49JR7RCjrOzM7Nnz+atNWuY9eqrTD96lDlz5gDSma7QcveudMLs39+s3QMCAlgYHQ3+/rByZZbbJCYmcvHiRYsJVKGhody6dYsePXoAEBERAYi+9uOPUlU3cCCcPGninckPNA3eeANatLD2SBQKhUKheCgwpoufl6Zpbim/OwLtgBMpuVJomqYBTwNHUnZZCQxM6ebXBLil63oEsB7ooGmae0o4eoeUZbkTFgZvvSWtfAvpFW6FQvHwo6WUc7iGhWVYfvr0aapUqWKFEZlIZKS8hz7MDiqAcuUoX6IEc5OTGTtoEMnJyRlWT5kyBYCPP/6YefPm0b9//9R8scKIn58f165dI86IdvbXr18vOgHpYWHSKTOH0rHCxMCBAwlu3Zp2b7yBDowcOZKgoCCefvppaw8te1xcpOuxmZ3m/P39uXjpEvfWrZM8viwIDw8nKSnJaIHK1dUVLy+vbAUqQ4OD5557LvX4BooXFyPXuHGS9lAoeP99sXYpFAqFQvGQoWlaeU3TNmuadlzTtKOapo3MYpt+KbFPhzRN26lpWt1068I0TTusadoBTdP2GnNOYxxUPsBmTdMOAf8gGVSrgYWaph0GDgOewKSU7dcCZ4HTwCzgZQBd128AH6Yc4x9gYsqy3Bk4EH74Aa5cMWpzhUKhyBdKlOCigwOlIyMzLD516pR0/ivs1KwJ8fFQmCfUlsDZGdtVq/Cwt2fUnj3MnzUrddW+ffv466+/ePvtt3F0dOTevXuMHJnps7ZQYejkZwiMzopXXnmFCRMmFC0HleH+FDHBtNKtW/zh44N/mTKsWrUK58IaOZCYCFu3SpmfmQQEBKDrOucSEiTQPn0uaAphKYK9KR0w/f39sxWoIiMjcXV1pWrVqkBaDpUBb2945x0ZTqFA1yEiAu7csfZIFAqFQqGwNInA/3RdrwE0AV7RNK3mA9ucA1rqul4H0Xt+eGB9a13Xg3RdN6oMLteUcV3XDwH1sljeJpvtdeCVbNbNAeYYM7BUEhJg2za5cmfk1TmFQqHIL8Lc3CierjvcrVu3uHbtWtEQqEAcVI+CEzUwELsFC3jsmWc4NXIk17p3x8vbmylTplCiRAnGjh1L06ZNOXjwILVr17b2aHPEIFBduHAhddKenrt37/Ljjz9SvHhxdF0vOg6qIipQERlJ14gI2v7+O86FuVx2zRoRo//4I08OKhCXaI2TJ2H4cDh4UDqbpmAQqIx1UBmOu93QXfABDMHzhtLJ9A6q9Pz4o2S379hhVryW5di7F4KDYcUK6NbNigNRKBQKhcKypFTCRaT8HqNp2nGk0d2xdNvsTLfLbiTKyWwKy/Wn7ElIyFN+gkKhUFiSH9q0oU86h4rBBVAkBKqff4aXXy5kfdrzD613b6698AJV7t9nzBtvcPLkSX755RdefPFFSpYsydNPP824ceOsPcxcSS9QZcWOHTu4f/8+t2/fJiYmpmg5qFxd5aco0bw52NnhvGePtUeSPevWSdlZ5crQoYPZhzG8r50+fRqqVxcn+w8ZL4yeO3cOTdMob4LQ6O/vz4ULF1ID0NMTGRlJmTJlKFmyJM7OzpkcVAY0DXbtAiObdeYfNWrIYA4csPJAFAqFQqEwCztN0/am+3khq400TauIGJf+zuFYQ4CQdH/rwJ+apu3L7rgPUvgFKk2DGTOsfHlMoVAoBI/SpYmKikr929DBr0gIVBs3wqpVj9T7qdf06YS8/TY/LlpE3759KV68OG+//ba1h2US5cqVQ9O0bAWqjRs3Ymdnl+qcKjIClY8PtG9v7VGYjosLNG4Mf/5puWMmJ0se1/Xr8ndYGEyfDgsWwDffSOu6YcNkeW789BM89RRUrQrbt0twk5l4eHhQrlw5tm3bJgLVE0/A99/D/fup24SFheHr64uDg4PRx/X390fX9VT3VXoiIiIoU6YMmqbh6+ubrYPK8NTZsMGku2R5XFygbl3plqhQKBQKRdEjUdf1hul+HizRQ9M0F2AZ8Lqu67ezOoimaa0RgeqddIub6bpeH+iElAc+nttgCr9AVayYtPBVKBSKQoBviRIsiIkhYcECIE2gMpTCFGrCwx/6Dn6ZsLVl9Pjx1KtQgTf//Zf/DRmCt7e3tUdlEvb29pQtWzZbgWrTpk00adKEPn36ABSdEr8xY+C336w9CvN4+mnYvx+yyVECxCHWsSOULQu1a8P69RATI+uioqT72+DB0LMn+PlJR8N162T93r3wyiswYACMHAmffgqrV6dlcS5ZIts8yKVLsl/btpI/5eOTp7upaRpdu3Zl/fr13Lt3D157TfKWli1L3SYsLMyk8j5IE/SzyqEyOKhAOiRm56Dy85OgdKsLVCAutZ07ITbW2iNRKBQKhcKiaJpmj4hTC3Vd/z2bbeoAPwLddF2/bliu6/rllNurwHIgOLfzFX6BqtCkYCoUCgW4+vrSEogPDQUkIL1s2bKFNyg5PRcvFr28Hwvg6OjInFGj6A+8bUKQc2GiQoUKHDp0CP2B8sybN2+yb98+2rZty/PPP4+trW2WOVUKC9O7t7yWcnI0ffaZiBYdO4rjqGNH+PJLWXf/voQo/fknnDwJjRrBwoXQqZOsf/ppEYJOnIBr12T7iAhxbiUkwAcfQLNm8Pnnco5Nm0S88vUVYWr1aihRwiJ3tWvXrty9e5eNGzfKfahSBb7+OnV9WFiYSQHpkCboPyhQxcbGEhsbmypQlStXLlsHFUC7drBlSyFoBNm+vQzir7+sPBCFQqFQKCyHpmkaMBs4ruv6lGy28QN+Bwbouv5fuuXOmqaVMPwOdACO5HbOXEPSFQqFQpGGl7c3h4B6Ka3QT58+XTTK+xIT4dw56N7d2iOxCkEvvQRff02JFSvEuVLEeO655xgxYgShoaF0SJcptGXLFpKTk2nbti0NGzYkOjqaEhYSJvKVe/dE6JgwQVxERY0KFeD8eSmX1XW4elWEpv/+k/s2YgRMmQJDhkBQkCxbsEDuM4iryuCmygo7OyhTRn4exN5eRKkBA2DUqLTlixZB374idlmQVq1a4eLiwsqVK3nyySfh229Tne337t0jPDzcZAeVt7c3zs7OmQSqKykOMZ8U55evry+XL18mOTkZmywuWD79tGh3t2+DVStbmzeHuXMlLF2hUCgUioeHZsAA4LCmaYawxTGAH4Cu6zOADwAPYLroWVIyCJQGlqcsswMW6bq+LrcTKoFKoVAoTMDLy4uDwGP//QfJyZw+fZrOnTtbe1i5ExUlk+Jq1aw9EuugaTKhf/99ERYqVMi/c8XFwalTMon39bWIE3jYsGF8/vnnjB07lvbt25PyYc/GjRtxcnKicePGAEVDnAIpNw0PL9ouaU2DI0dEJUkvtJQrJwKVvb2IUyA5UEOHWu7cHh7Spe/IEXkcixVLO5eFKVasGB07dmTVqlV8//332KQTSGfOnElycjKtWrUy6ZiaphEQEMD+/fszLI+MjATI4KBKTEzk6tWrqcvS06FDnjLgLUfx4vB//2ftUSgUCoVCYVF0Xd8O5Bheq+v6UCDTlxxd188CdU09ZxH+ZqhQKBQFj6enJ4cAu7t3uXP0KJGRkVQxuCIKM2XKSDnSoEHWHon16NdPbhctyp/jJyXJ7dmzEprs5ydulhxKlIzFwcGB8ePHs3fvXjakC93ZtGkTLVq0MCmgulBw8aLcFuWS07g4CS6vXFlK90JC5H9vTJC5JdA0CAyUssA2baBUqXw7VdeuXYmIiGDfvn2yYOtW4mbMYNKkSbRp04Y2bdqYfMz+/fuzbds21qxZk7rsQYHK19cXIMcyP13POQqswIiKkmD7lPugUCgUCoXCdJRApVAoFCbg5eXFPuBypUqEHz8OFJEOfgqoVAmefNLyrp3Ll+Gdd0QkiIsTYeq332DqVHFSBQdnHWhtIj169ABgb8qxIiIiOHbsGG3bts3zsQuc3bvltohmggHg6Ai7dkmO1JtvSj5TpUpga2vtkVmczp07Y2try8qVK2XBvHno//sf16Oi+Pjjj8065muvvUa1atUYOXKkBLAjz2lIE6gMpX6G0r+smDRJjKG3s+wpVIBcviwB9etyrV5QKBQKhUKRDUqgUigUChNwd3fngI0N3/frx+EUoaNICFSffALPPmvtUVifVatETLIUP/8sDpovvhA3UHKyhFP37Amvvy5ZQQ4OMHy4WD3ygKurKz4+Ppw8eRIQ9xRQ9ASq27fl8XriCXnsFIUeDw8PmjdvniZQNW+O0927PBMYSLCZuUsODg588803nDlzhilTJHc1MjISW1vb1E6Uhttr165le5zmzcW8uHWrWcOwHIGBULq0CJYKhaLwoeswfz7cuWPtkSgUihxQApVCoVCYgK2tLV5eXoSHh3P61CkgrSNVoeavvyTE+VHHEGq9a1feBKOkJHj3XRg4EJo2FafUggXwYDfH2rVhzx749Vc5d0JCns5bvXr1VIFq48aNlCpViqB8yh7KN2xtJah+8mRrj0RhAl27duXQoUOEhYWhN28OQJeUsHRz6dChAz169GDy5MlcvHiRyMhIvL29sU1xoXl5eQEQFRWV7TEee0zMbOkqX62Dpkk3v9BQEaoVCkXhYuFCeOklaRqjUCgKLUqgUigUChNp0qQJTZYvZ8Bnn1GmTJmiEUx96lRaB7FHnSVLRFTKS0v4+/dlRjx8OKxfn7MTyNtbSr90XQStL780/jwREdC2LcyaBfHxVKtWjRMnTqDrOhs3bqR169ZZdjcr1Dg7w3vvQYMG1h6JwgS6dOkCwKpVqzhvb88VoGF8fJ6P++WXX5KcnMxbb71FZGRkhjD0EiVKYG9vn6NAVbw4PP64RIDl0aSYdzp0kCyqgwetPBCFQpGB8+elBDcoCFxcYNkyuaikUCgKHUXsW61CoVBYn3bt2nHx1i18b96kjont1a1CQgKcO6cEKgPduoGXF3z+uen7hoVJiZqjI2zZIqHI9vbG7x8TAxMnQg6ZOhnw8YE6deCFF6BlS2pWrszNmzfZvXs3Fy5cMCuc2qp8843kcymKHFWqVKFGjRqsXLmSQ4cPsw3wvXkzz8etWLEio0ePZunSpWzbti2DQKVpGl5eXjmW+AF07Soa/IkTeR5O3mjXTjLu/v3XygNRKBSpXLsGXbqIs3HBAnHxzpol5f4JCdYenUKheAAlUCkUCoWJtGvXjpReVjzh4mLVsRhFWJiUpCmBSnB0hBEjYM0aOHbM+P3++ku68r38svzt4iJlPcaiaeKeiouDsWPTuv6l5+pVEb6++w7WrpVlU6ZI1tXff/N0Su7U9OnTgSKWPxURIWWRf/xh7ZEozKRr165s2bKFv/76i+Eg5asW4O2336ZSpUrcvn07g0AFkkOVk4MKJPJtxQqw+vUCHx9xUA0ebOWBKBRFhGvX0j4Lw8MtH0WQnCzOxtOnYfnytMYcr74qjQ2WLbPs+RQKRZ5RApVCoVCYSLVq1Tjl68ttoM2NG9YeTu7cuydJwrVqWXskhYeXX5baIGPL7X77TdwRHh7wwQfmn7daNSkzmD1bXFwGp8WECfJ36dLQurUIaAMHwqFDImz17w+jR1N+3TqeApYuXYqvry9Vq1Y1fywFzeTJcrV6/Hhrj0RhJl27diUxMZGZM2fiHhCAi6urRY7r6OjIV199BaR17jPg6emZq4OqdGkxRjo6WmQ4eSOPuVwKxUPPrVtie/T0lBJ4gyg1bRpUrw4NG8JXX8nFHHM5fVpubWygc2dYvVo+ww106pT2ebxrl/nnUSgUFkcJVAqFQmEimqbRon17VgI1/vuv8FvEAwNh2zZx/ygET08YNkzcSrl9Cd61SwSiRo1g927Iqyj0xReweDF07552rIAA+cI+ZYpkWoWHy5XlOnXS9ps4Eb1TJ2zt7Lh//z5t27ZFM8XBZU3CwuCHH8RZUhSaCiiypHHjxnh5eREbG0vdunXlufzccxY5dpcuXZgzZw7Dhg3LsNzLyytXBxWIGWLiRLm1Kps2wVNPSTmvQqHIzPz50lHX8JlXqpQsHzlS/gZppBEUJJ1wTeHoUbFUVqkCO3bIssmT4cFyeBsbcSmXKgUdO0JRuNioUDwiKIFKoVAozKBTp07MBKKHDy/8ApUFgowfSiZNEgeTo2P26cq6LldYy5eHlSvBzS3v57Wzg2efFReVoetfv37y9xtvSDmCr2/m8kE7O7S1azlbowZQxMr7Jk6UCcH771t7JIo8YGtrmxqWXrduXXFCLFlikcmdpmkMGjSIig/U6RlT4gcylHHj4Jdf8jyUvHH7tpQPWz0QS6EopMybB/XqwZw58plnKOstW1b+3rtXmpDcuycXNkAE34sXs/8+c+cOjBoFdetKJ80PPsjdNV65MmzfLiX0BpFMoVBYHSVQKRQKhRn07t2baf/+S5nPPwcnJ2sPJ3vi4+XLX0r5jCIdrq7yk5wswlONGuJ8GDlSnCG7dolINHeuXGn18LD2iAEIqlyZYChaAent2olIVa6ctUeiyCPdunUDoH79+vDkk/L6WbUq387n5eVFdHQ0CblcCKhRA1q0kCqhrOLdCoyaNeXWlHw7heJRIS5OLsAMHZrzdm3bwpEj8PXX8ndICPj5SWm+m5sIUTNmyHccXYfGjaXxyfPPS1OYCROMu6BUurQ4uQB+/920LrsKhSJfsLP2ABQKhaIoomkaQUFBMjmbPh0eewwaNLD2sDKzfTvcvCklZIqsiY+H4cMls+LMGdi69f/Zu+/oqMqtj+PfkwZJgPQACUGQEiLSOygWWmgqcBWwoaiogCKgUrwqoqKA2LjoRS6CBbCgiNIEKb703qWIgNQIgdBCMoHkvH/smRQgZCaZyWTC/qyVFZg5c84zMAnML3vvBy5cgGHD5O81e5tdEfDOiRNEg/x0uEIF+ahdO3+7EhYWJ7WBKffr3LkzCxYsoE2bNnJDrVpSGdetm2wc4GTh4eEAnD59mrJly1732BdekGX8/LN00LrFzTeDn58GVEpdi7+//RtllC6d9euGDaWa6sQJ2QV37VoZdB4fL7sjvPaaBF8tWuR/bT/9JNVUMTHwwAP5P49SqkAMM7e2hiIiMDDQTE5OdvcylFLq2s6elZ+Yh4ZKlU2FCo7t7OZqL70EH38Mp0655M1jsWSa0qZTqpRsR13UJCbClCnS7nDkiHxEREhbUVG0bRsEBcFNN7l7JcoVVq2SN4UvvQRjxsD+/XD0aNb96eny/adbt3yd/ttvv6VHjx7s2LGDmnm07KSnSxYfFATr14Ovb74uWXC1a8vr3YWVZUp5pLNn5Qu0oEwTdu3Kqlh0hvR0+bpt1gy+/95551WqiDMM46JpmoHuXoeNVlAppVRBBAXJT/U6dZLy8+BgqaRatEiCqvR094YcCxZI34uGU/YzDOf8B9pVwsMlDLiWEydkV6Si5LnnpEptzRp3r0S5QvPmEoLHx8vvP/1UWmSzMwxp7SlRQtqNw8Nl7podYX5ERARAnjv5gXyrfesteW9pC6dM0w0/M2jeXN6IK6WyJCfLD/PGjIHBgwt2LsNwbjgF8g0kPl527b18WeZFKqUKnX7lKaVUQXXsCJs2ycyibdvkP2G2d0SdOslP00ePLvx1ffedzHDo06fwr60K38qVcNdd0vrXooW8EQgOhs6dZUejM2ekhTEkROaSFcbstMuXZeBtXvNGlGd77rmsX/fpkxVWgXwvLF9eEqOMDJg1C/7v/2To8bPP5nlqW4ufPYPSQXKvnj3l1/v3S+HWu+9Cu3Z2P5uC++9/C/FiSnmILVvke0BBd8J1pfbtZcOS1avlh3tKqUKnAZVSSjlDvXrykZ1pShgwZgy0bClBVmG67z4ZMNq3b+FeV7lHo0bw5pvSVjRtmlRwmKb0PFWrJq1Yttegl5cEWY0awdChcr8r7NgBFy9C06auOb8qeqpVu/7rackSGUr8/PMy2fzOO697OkcqqGy8rFsAnTwp4+Ti4+Hpp6W4q9Cqqc6ehaQkmY+jlJIf5AHUr+/edVxP69ayy25gkel2UuqGozOolFLKlSwWCQFOnoSdO12/lXFGBowaJUO/rZUH6gaVkSHvzkuUkI9//pE2O1sl1fr18rFyJcTGumYNEyfKa/Gvv2R4tFIg4U2TJrLb1ocfXreSKi0tjRIlSjBy5EheffVVhy9lsUgG++GHElA980xBFu6ARx+FOXMkLG7fvpAuWkycPw/Dh0vVna+vVITadlpTnuvxx2VWZ0JC0ZrVqdQNTmdQKaXUjaRECZg6VeZSTZoEQ4a47lqXL8sWy9OmSRCmlVM3Ni8vKFMm6/dly8K99+Y8xtU/pFqzRga4V67s2usozxIUJDuMPvGEbBt/HX5+fpQpU8buFr8rlSghO8fv2QMDBsj84zp18nUqx4wYIS3fHTvKDmOvvSZfk5s3y/dn3TTgaocPS/txcrL8O3bpkiSMY8bAjBlS2aI818aN8n+hoh5OmaaE52XLaiWVUm7g5e4FKKVUsVe/vswysHdr5fx68kn5T/2oURpOKfsYhryJfuwxSE11/vnHjoUvvyz6b0hU4QsPl23dH3tMfj9tmvweZKD6f/4Dv/4KSJufIy1+V/Lykt3jX3nFdcWCV7n5ZmmrffRReOMNCapOnYK9e6FWLZg/v5AW4kG6dJE/p3Ll4PRpqaQ6d07+/ezfX36vPNegQXbNnXO7lSuhShVYtszdK1HqhqQBlVJKFYavv4alS113/nXr4IsvpJdl2DDXXUcVPydPymvn22+df+7w8JwDs5XKzjDkwzRlN9QuXWSAcrVqMnjduotWeHh4viuobMLCpIipZEnXFw5mCgiAKVNkaPrOnRICN20qc6k6dZLdDov4qI1Cc+aMVJc1b57z9pIl5Yc7ixdD6dLuWZtyjscek007irr69eV7z6pV7l6JUjckDaiUUqowVKwovSauejPy/vsSBgwf7przq+Lr7rs1aGBiAAAgAElEQVShbl14+WWZDeIsQ4bIdt1K5cUwYNEi6cWrXVsqjJYuhVatICODWwMDC1RBld2qVbKfxfHjTjld3gxDJrTv3QvR0dLat3o1dO0KL70kVSVKdnbMyJCdSK8UElJIfZnKZXbvhj/+8IxANiBAvkloQKWUW2hApZRSheXXX2XXqoMHnX/uyZNl+Kj+hFk5ystL2qvOn4dHHpHB6gV19KhUh2zdWvBzqRuDn5+ENTNnSvubbXe/Z59l3KpVnDlxwimXKVdONpccO9Ypp7Nf9llbgYHw3XfQqxd88okMjb/RLVkif0a57fg5cSK88ELhrkk5z7vvXjt8LKqaN5fK9EuX3L0SpW44GlAppVRhqVkT/v5b+kycLTBQdgtUKj9uuQXGj4fffoOPPir4+f77X/lJuW2+kFL5dd99BKWm0uyff3DGztM33wwPPSQvUSdlXvljGPDWW1JVEhTkxoUUEUuXym59JUpc+/7t26Vd0hMqcNTV1q6VnTs9ZR5h8+Zw8aLMaFRKFSoNqJRSqrBUqCDbSH39tczTcIbz56Fx48xhwkrl2xNPSOtRv37y+wUL4MMP5V38zp3yn3V7XLwIn34q28JXqeK69aobQ7t2nAkN5Yn0dM6dO+eUUw4fLuOgPvvMKafLvwoV9GvE5uOPr//Dm2rVZGC6k1o9VSE6c0Za/Jo0cfdK7Hf33VLlqF+fShU6DaiUUqowDRkic0hat4Zu3Qr+0+Aff4T167W1TzlH06YQHCy//uUXGDhQttq+9VbpjbJnXs5XX8luZTpbRzmDlxdHmzShBXDy6FGnnDI2Fpo1g1mznHK6gtmzR/4t2L/f3StxrzvugJYtc7+/WjX5/OefhbMe5Tzr18vnJk2YP38+i531AzpXCg+H++/P+vdQKVVofNy9AKWUuqGEhMCuXdJfkpGRtYPV8uWylbaj5e9ffik/4WvWzDXrVTeuCROge3d5cxEZKTNibExTWkqbN5dKqZYtZYYQSJD18MPyelbKCdLq1aPk/PlcXL1a2lGdYMgQKcgxTTd3Hfn7Sxi8fz/MmSM/wHCWDRukhTA2Nqt65bffZL6XTxF6C/B//yeVl9fb8TN7QNWiReGsSznH2rXyRdaoEUNbtsTPz4/1ttCqKNu2Tf6/1r27u1ei1A3FcEY/vysFBgaaycnJ7l6GUkq5zq+/yn/MGzaUQaKtWtn3uK1bZfe1kSPh1Vddu0alsktJkaqPpUulV6pMGdk+/OOPITTU3atTxcyWJUsY3KoVA7/5hk7F8c3iggVZ1Rrz5skuhgU1eTL06SM/COnTR4aMZ2SAt7cEyF9+WXTmAd1zD+zbJ2Fabi5dgsqV5d+6p58uvLWpgktMlP+vtGpFeHg4FouFc+fOYRSV119uBg2SHyZeuCCbiShVTBmGcdE0zUB3r8NGv9qUUsrd7roLJk2Stqj4eHmDYo/XXpM3NM8959r1KXUlf395nZ46BbNnwwMPSGXGxInuXpkqhkKrVmUJkHD+vFPPe+AA/PCDU0+ZP/HxsGKFlHO1aCGz4PJr4ECIioInn4Q2bWT2z8iRcp9hQP/+Mgfxww+ds/aCMk2psGnc+PrH+frCkSMaTnmi8HBo1YrU1FROnTrFhQsXOOqkdl2XuuUW+WGMK3ZeVkrlSgMqpZRyNz8/eTOxZQvUqQP33SezpfLy5JPw/vs6I0G5T0CAVD9MmgTHjmlYqlwiIiKC+sBNTh4aNWEC9OwJK1c69bT5U6cOrFkjYVWNGvk/zxtvQJcuMGoU/PyztPeVLSv3GYbs0tmli1SHfPCBc9ZeEIcOyUYMnjRAW9kvMRHGjoW//+bYsWOZN+/atcuNi7JTzZryeedO965DqRuMBlRKKVVUlCkjrR7t22fNBzlwAP79b/mpeO/e8K9/SbWKaUpL1eOPu3fNStl4eUGpUu5ehSqG/P396eznR5t58+D0aaedd+BA6Rpr1cq+nwm4XIUKsnNYSIi0zj75pAxRt8fMmbKra5kykrwNG5Y1Fy47Ly+YPl1adAcNyhrOvnOnVEUuWCBt5wsXZr0xT0iATZuc8xyvtHatfM6rggrg889lI4ciPp5EZbN+Pbz8Mhw6lKNqavfu3W5clJ1s8+6u13qqlHK6IjQhUSmlFOHh0jJl8+9/y5uJUqUgKEg+QkPh+HFp41BKqRvAvrAw+b63bt31h2k7IDpaqqfuuUey/48/lg64ImHrVvj+e/jiC3j2WQlwwsKkJbxkSRkqnpoqx37zDfTrJ6HUqFF5n7tkSfj2W6mmqlRJbnv/fQmAsgsLk3V06QJnz8KOHdJq50yXL0OJElC7dt7HWiwSaB0+DBUrOncdyjW2bJHPtWtzdP78zJs9ooIqKEhCY62gUqpQaUCllFJF2Zdfyoe3t7tXopRSbnMsKoqM48fxcmJABfIzgcWL4cEHYcoUmSd+rcKjQtekiexY9+qrUhE1frzcnpQkAdOwYZKo2XTqJO199vL2lgoqmxEj4KmnpDrJ9uHlJSHVq69KivfJJzBgQP6f05kzsGgRzJ0rpWuvvy47pPn52Rd82UKs7ds1oPIUW7bI33VQUGYFVVxcnGcEVCCvV/1hoFKFSgMqpZQqyjSYUkopSkVFsX/nTqraWsKcyN9fOuTOni0i4ZRNZKRsPDBqlLQ2JiZmzRzs2hVuvlnmSpUqJcO0ClLdFBMjH9fSqRO0bQvDh0O7do7PyPr0U5gxA1atgvR0aWHs3Vvu8/aW8jV73HqrfN62DTp2dGwNyj22bpX5asDRo0cJCAigSZMmLFiwwM0Ls1NB5sEppfJFZ1AppZRSSqkiLSIigg2G4bJ2G29v6Z7etw969IC//3bJZfInLAyqVYNmzbJuu+MOqWZ6/nkJe/z9r3rYfffdx4gRIwp+fcOQ8jJ/fwnCLBbHHr9mDVy4AEOHSk/liRPw3nuOryMoCG66SSqoVNGXmio74NWtC0hAFR0dTVxcHAkJCZw5c8a967PHX3/BK6/IJiBKqUKhAZVSSimllCrSIiMjee7SJcw//3TpdXx9ZfTTZ5+59DKFYu3atUyYMIFLly4V/GRRUTB1quxs5uj5Jk+WIetvvQXNm2dtApIfXbpI5Zgq+kqWlLLEgQMBCaiioqKIi4sDPGQO1cGDUsG4d6+7V6LUDUMDKqWUUkopVaRFRESQePkyZ5OTXXqdm26Sjrb//S9noVBGRgarV68mPT3dpdd3ppSUFBITE1m8eLFzTtipE3z9tbQU2hNS7dsHH3wA58455/og53vrLeedT7lWiRKysyRZFVTVqlUD4K+//nLnyuxjmz91/Lh716HUDUQDKqWUUkopVaRFRkYCkDF4sOw450J9+0oX2nffZd02e/ZsmjdvTvPmzdm2bVu+zz1lyhSWLl3qhFXmLSUlBYAZM2Y498R79siugvHx8PPPMlfqWn74QQax23YbdBbTzP2aqtB16NCBO++8k+1Xtl527w6TJgFgmibHjh0jOjqaGOuss8OHDxf2Uh1Xvrx81oBKqUKjAZVSSimllCrSIiIiAPDeuDFncuQCbdrIPO5Ro7JykK1bt2IYBgcOHKB+/foMHTqUixcvOnTegwcP8tRTT9G/f39M03TByrOkp6eTlpaGYRjMmjUrM6xyirAwabXbvh3uvVd2aRs5Emx/Hn/8AfPmSXhVr55zd0E7eFCu/803zjunyjfTNFmyZAm///479evXZ82aNXLH4sXydWp9TSQmJpKWlkZ0dDSBgYGEhoZy6NAhN67cTkFB0qqoM6iUKjQaUCmllFJKqSLNVkF1olIl2LwZ0tJcdi0vL+ki69o16zK7d++mcuXK7N69m169ejF69Ghq1arFsmXL7D7vuHHjSE9P548//mDTpk2uWbyVLZBq06YN58+fZ968ec47eXg4vPaaTJL/8UeIjYXXX4cdO+T+xYtll71Vq6BDB+ddFyA6GpKTZXc45XZJSUlYLBaGDx9OuXLl6NOnD5fS0mSweEwMPP00AMesAU90dDQAMTExnlFBZRgSsP7zj7tXotQNQwMqpZRSSilVpNkqqA6WLSupkYsDinvvhbffztocb/fu3dSoUYPQ0FAmT57M0qVLMQyDrl272lUNlZiYyOTJk+nSpQt+fn58+eWXLl2/LaDq0KEDkZGRzm/zAxl23qULLFoEBw5IUAXwxBNSQfXii/Dss869pq8vNGwIS5Y497wqX45bW99q1arF+PHj2b59O7/06QNr10poWbIkIPOnwAMDKoAtW8DFX69KqSwaUCmllFJKqSLNFlDtsg5cZu1al1/TNGHOHPjppwz27t1LrC2AAe68804GDRpEUlISCQkJeZ5rypQppKSk8Pbbb3PPPfcwffp00lxYBWYLqEqVKsUDDzzAnDlzOOfMYeVXqlRJ2qEAAgKgfXsYO1YqnpytY0fYuFHnAhUBtoAqKiqK++67j3s7d6b6l19yqXJl6NULgNTUVN58800CAgIyv4Y8KqAqXVoqqZRShUIDKqWUUkopVaSVKFGCMmXK8JfFIgO6XTTD6fDhw0yaNIl///vfxMfH8/DDe3j22QxSUkxq1KiR49iqVasCsG/fvjzPO3fuXOrWrUtcXBy9evUiMTGRBQsWuOQ5QFZA5e/vT8+ePbFYLPz0008uu16h6thRPs+f7951qMzWvfLWYeIf/+c/vFiiBCPKlsX09gbg2WefZc2aNXz11VeEhoYCElAlJSWR7OJdOZ1izhx45hl3r0KpG4YGVEoppZRSqsiLiIjgZGKiVE8995xDj01LS+OVV14hMTHxusc99NBD9OnTh3feeYc9e3Zz9mwfEhJ8gKG5BlR//vnndc959uxZVqxYQfv27QFo164dkZGRfPHFFw49B0fYBrj7+/vTrFkzbrrpJte0+blD7drw6qvQoIG7V3LDs1VQ2QKqihUr0uattxi1Zg2zZs1i3bp1TJ06lWHDhtG1a9fMx1WsWBHwkJ38duyAiRNl9plSyuU0oFJKKaWUUkVeZGQkJ06cyLrBgSqqjRs3MmrUKMaNG5frMXv37mX58uWMGDGC1NRUdu7cib//enx8pgOvs3t3nRzHV6xYEV9f3zwrqBYtWkR6ejodrAPDfX19efDBB/nll184ffq03c/BEbYKqoCAAAzDoEePHixatIiTJ0+65HqFyjBk18A6dfI+VrnUsWPHKF26NKVKlYLPP4fnn2fAM89Qp04dnn/+eYYNG0ZoaCjDhg3L8biYmBgAz9jJzxq+aUupUoVDAyqllFJKKVXkRURESMCyYwdUrAi//mr3Y21B0KRJk0hNTb3mMVOnTsXb25s+ffrg6+tLYGAgnTp14vLl3vj4rKZ//zKcPy/Hmib4+PhQuXLlPAOq+fPnExwcTNOmTTNv69WrF5cuXeKbb76x+zk4InuLH0DPnj1JT09n5syZLrmeW6xfD8OHu6zdU+Xt+PHjUj2VkiJVbRs34hMQwMSJEzl27BhLlixh8ODBlC5dOsfjbAGVR1RQRUXJZw2olCoUGlAppZRSSqkiL7OCqmJFOHLEoUHptoDq1KlTfPvtt1fdn56ezhdffEF8fHxmuxLAAw88AFioW3cE48YZ2N5n33uvbGAXFHR/joBq5cqVbN++PfP3pmkyf/582rZti4+PT+btdevWpVatWoUWUNWuXZu4uLji0+YH8Msv8M478MYb7l7JDev48eNERUXBwoVw7Jjs3GcYNGnShEGDBhETE0P//v2velx0dDSGYXhGQGX7fmCdt6WUci0NqJRSSimlVJEXERFBYmIiGaVKSUhlx3ByG1tAFRMTw4QJE666f+HChRw7dozevXvnuL1Dhw4EBgZSt27FzLFXpgnVqkk+tmHDG+zda2KaJjt37qR169YMHDgw8/Fbt27l+PHjmfOnsqtfvz4HDx60+zk44sqAyjAMevbsyfLlyz0jFLDHG29A797yefx4d6/mhnTs2DEJdPfvlxsaNsy877333mPfvn2Use28mY2fnx9ly5b1jNdi+fLg7w8XLrh7JUoVOsMwYgzDWGoYxi7DMHYahjHgGscYhmF8bBjGPsMwthmGUT/bfb0Mw/jT+tHLnmtqQKWUUkoppYq8yMhILl++zJkzZyA8HE6dsvuxp0+fxjAMBgwYwPr169m9e3eO+6dMmUJ4eDidOnXKcXtAQADLly/nzTffzLzNMGDcONi8GXx900lOHsKhQ4fo0aMHqamp7NmzJ/PYefPmARAfH3/Vmmwti2YuLWrp6bBhA/zvf453F2Ufkm7TrVs3QGZiFQuGIcOr77sPnn8epk1z94puKKZpZrX4HTwIpUtDSEiOY/z8/HJ9fExMjGcEVKGhMiD9iSfcvRKl3OEyMNg0zTigKdDPMIxbrjimPVDN+tEH+BTAMIxQ4HWgCdAYeN0wjBDyoAGVUkoppZQq8iIiIgBkDlVYmMMBVUhICD179sQwDKZPn55536lTp5g9ezYPPfTQNd9Q16tXj3Llyl11e9my0LXrIaAH99zzCjt27ODuu+/myJEjmQHR/PnzadCgwTUfHxERQWpqKsnW3cEyMuDSJayPk6fYqBE89RTExsKIEXD5stxvsVx/9FL2Iek2tp3TTjnw51bk+fjAjBlw550wa5bOoypEZ8+eJSUlRVr8qlSBbt0kNLRTxYoVPSOgMgyHnpdSxYlpmsdN09xk/fV5YBcQfcVh9wJfmmINEGwYRnmgHbDINM3TpmkmAYuAq39acwUNqJRSSimlVJEXGRkJIHOo2reHtm3tfuzp06cJDQ0lKiqKu+++m+nTp2dWLk2fPp20tDQef/xxh9f08stewBS2bfuOQYMG8fTTTwOwb98+kpKSWLVq1TXb+wDCw8MBg4kTU+jeHcqVyyoCql4dHngApk+XVsLbb5dKKtsYq65d5dehoRJeffRRVrgFV7f4AQQGBuLt7S0VaMVJyZIyj+qbbzRIKETHrWV95cuXlwq2KVMcerytgiq3CsIi5a23YNAgd69CKbcyDKMSUA+4cgBkNJA9bT5ivS2326/LJ68DlFJKKaWUcrccFVQvvODQY20VVAAPPfQQvXv3Zt26dTRp0oTPP/+c+vXrU6dOHYfXdOutMfj5PcOtt9Zi4MBRrFx5EIC9e/eye/duMjIy6NChwzUfGxRUFpjBiy9GEB0N8fFSiALy+bPPso6dOzfnCJyHH4Z69eDsWdi6Vf445syRWdWGce2AyjAMgoODi19ABVCqlJSXbdkC0dFgfa0o18kRUJmmw+FgTEwMycnJJCUlERoa6oolOk9SEvznP7JTYUieHUpKeRofwzA2ZPv9Z6Zpfpb9AMMwSgE/AC+Ypnnuisdf64vfvM7t119MXgcopZRSSinlbjkqqEB64uxsv7FVUAF07dqVfv36MWnSJPz8/NiyZQv/+c9/8rUmX19fFixYQFxcHH37lmDx4upAM/bu3cvevXsJDQ2lcePG13zslCmNgEgef3wXkyfH5fk0SpXK+nXPnlm/Nk346SeoWTPrjyIlJQXDMK5qWSy2ARXAgQOS2n32mfRFKpc6Zt3VrkKpUvLi/Phjh+Y0xcTEAHD48OGiH1B17w7vvw+zZ8Njj7l7NUo522XTNBvmdqdhGL5IODXNNM0fr3HIESAm2+8rAMest995xe3L8lpMni1+hmGUNAxjnWEYW62T29+w3j7NMIw9hmHsMAzjc+vCMQzjTsMwzhqGscX68Vq2c8VbH7PPMIyheV1bKaWUUkopsLXEWSuoJk0CX19ITLTrsdkDqqCgIHr16sVXX33F6NGj8fPzo2f2xMdBd911F+XKleOjjyAy0sDb+3t27jzAggULaNeuHd7e3jmOT0+XzwMGpAJdaNlybYE60wwDunSRtkDTlJbAlJQU/P39Ma44cbEOqKpWlZ7HtVd2nyhXSEhIAKCcxQIXL8I1duu7nuwBVZHXqBFUrgzffuvulShVqAz5R2QysMs0zfdzOexn4FHrbn5NgbOmaR4HfgXaGoYRYh2O3tZ623XZM4PKAtxtmmYdoC4Qb73wNKAGUAvwB57M9pjlpmnWtX6MtD45b2ACMuX9FqDnNSbAK6WUUkopdRU/Pz+Cg4MloAoMlAoqOwd+Zw+oAAYOHMilS5f49ttv6dKli1MqOGJiZE5Ueno0P/5YiX/++eeq+VNDh8Ijj0iQ1KRJMPATiXaGbACvv/46U6ZMyXVuz8SJ0KwZrF7dBHiFQYPg88+z7j93rjcnThTTEbSGAY0ba0BVSC5Ye04Dba/fSpUcerxHBVSGIUPhfvvNoc0ZlCoGWgCPAHdnK0DqYBjGM4ZhPGM9Zh6wH9gHTAL6ApimeRp4E1hv/Rhpve268vwXyjqN3db17mv9ME3TnGe9zwTWISVb19MY2Gea5n7TNNOAb5CJ70oppZRSSuUpIiJCWvzCwuQGO94sZmRkXDXnpnr16nTu3BkgX8PRc3PHHVCp0iZSU18CgmnXrl3mxnL/+x+MHg2lS0u2Vrp0afz8/CRws0NCQgIjR46kd+/etGrVin379l11TK9eElCtWnU/Fy8OZ9IkWLAg6/4DBx5n+/YxOeZZFStNm8LOnXD+vLtXUuylpaXh4+OD16FDcoODAVW5cuXw8fHxjIAKpK/2/vv1taVuKKZprjBN0zBNs3a2AqR5pmn+1zTN/1qPMU3T7GeaZhXTNGuZprkh2+M/N02zqvXDrp0U7PoRimEY3oZhbAFOIFsFrs12ny+SqmX7549m1pbA+YZh1LTeZvcUd8Mw+hiGscEwjA2XbfvpKqWUUkqpG1pkZKQEOg4EVOfOncM0zauqpEaPHs2wYcNo3bq1U9fYvfsGYD2NGlUnMjKSJ56AWrWgXz/ZePCTT8DbW4aWR0REXDegSklJYciQISQkJLB582YA+vbty6ZNm6hVqxajR4/mUrbt+/z9Yf58aNnyXapUacL58/Ddd1nnu/vuiVgs1Xn44axWw2KlSRMpT9uwIe9jVYFYLBZKlCgBf/8tLzxrC669vL29iY6O9pyAqk4d2VbTwSBOKeUYuwIq0zTTTdOsi1RJNTYM49Zsd38C/J9pmsutv98E3GRtCRwP/GS93e4p7qZpfmaaZkPTNBv6+Ogcd6WUUkoplb8KqtOnpaPgyoCqRo0ajBo16qoZUQXVrFlZ4DY6dJD2voYNoWJFqWyaPl3CKZu8AqovvviCMWPGMHXqVDZu3AjAu+++yx9//EGHDh0YOnQojRs3zjFXqkwZCA5eTalSlqvOV7v2UXx9X2L2bBg2zDnPt0hp0UISuoa5zvtVTpIZUDVsCP37O7yLH0ib3yFbBZan2L1bdvVTSrmEQ03opmmeQSavxwMYhvE6EAEMynbMOVtLoGma8wBfwzDCyX26u1JKKaWUUnnKrKCKjJQ3xbGxeT4mt4DKVZo3b85tt93Gww8/DEDfvjB3LixblpWr2YSHh+caUGVkZPDhhx8C8Ntvv7Fp0yaqV69O6dKliYqK4ocffmDy5Mls2bKF3377LcdjbUPSrxQcHMylSx/w9NPpjBtXDMfplC4N8fHyWblUZkDVsyeMGZOvc8TExHhOBRXAnj0QFwczZrh7JUoVW/bs4hdhGEaw9df+QGtgt2EYTwLtgJ6maWZkO76cddo7hmE0tl7jFDIYq5phGJUNw/ADeiAT35VSSimllMpTREQEiYmJZPj7w/jx0Lx5no8p7IAqIiKC5cuXU7VqVbuOzS2gmj9/Pnv27KFKlSqsWLGCNWvWUL9+/RzH2OZoHT9+PMftFy9ezDWgAnjttVN8+GFWYPboozBkCHhaMcs1JSbCK6/Ajh3uXkmxZrFY8PPzgzNnIJeh/XmJiYnhyJEjZGRk5H1wUVC9ugRUupufUi5jTwVVeWCpYRjbkJBpkWmac4D/AmWB1dZp7q9Zj/8XsMMwjK3Ax0AP6+Csy0B/ZGvBXcB3pmnudPLzUUoppZRSxVRkZCTp6ekkJSVBWhqcO5fnYwo7oHJEbgFVRkYGb731FtHR0YwbNw6LxcLx48evCqjCwsLw9fXl2LGcTQkpKSkEBARcdV5bQHXhwhmee852Lbh4EcaNg5tvljnQK1fmO3NwPy8veO89mDzZ3Ssp1iwWC6V9fSEkBN55J1/niImJ4dKlS9K26wkMA7p3h+XL4Zg2AinlCvbs4rfNNM161sntt5qmOdJ6u491Urttmrvt9v+YplnTNM06pmk2NU1zVbZzzTNNs7r1cW+77mkppZRSSqniJiIiAkDe0NavD3bswGcLqEJCQly6tvyIiIjg/PnzWCw550VNmDCBNWvW8Pbbb9OqVStsM1kbNGiQ4zgvLy/Kly9/zYDqehVU2WdWeXnBzJmwfz8MHgyLF8Ntt8GkSU55ioUvNBTuuQe+/hrs3CFROc5isVDBNlCtfPl8nSMmRqa/eFSbX/fukt5+/727V6JUseTQDCqllFJKKaXcxRZQnTx5UoIIB4akF9WACiAxMTHztrVr1zJ06FDat2/Po48+SqlSpWjSpAkA9erVu+ocUVFRBQqobCpWhNGj4fBh2Wmwa9f8Py+3GzBAqutuvRV+1okirmCxWLKGC1eokK9zVKxYEfCwgKpGDahdG3780d0rUapY0oBKKaWUUkp5hMjISICsnfzsDKhKlSol83KKmOyBW3JyMoMGDaJ58+aEhoYyceJErGNd6du3L48++ug1QzZnBVQ2gYHw7LMQHg5HjkD79pCQkO+n6B633QYbNkhlz+jR0seonCotLY0oWx9odHS+zmGroPK4nfymTdPgUykX0YBKKaWUUkp5hBwVVA4EVEVx/hRkPZ/vv/+eWrVq8cEHH/D000+zc+fOzDfvAA8++CBffPHFNc9xrRa/vIakXy+gyi4xEX7/HR54AC5dsushRUetWrBunVS6eHl58FCtoslisVA+PV1+k88KqrCwMEqWLOlZFVQglXlBQe5ehVLFkgZUSimllFLKI4SHhwNXVFDlETx4QkI4vVkAACAASURBVEA1atQofHx8+P333/nkk08oU6aMHJCWBp99BlfMqMouKiqKM2fOcPHixczb8hqSbm9AVbcu/O9/MhO6Z0/45x97n1kR4ecHZcvKLKq774a5c929omLDYrHwZ3g4vPYa2F6vDjIMg5iYGM8LqACmToWXX3b3KpQqdjSgUkoppZRSHsHX15eQkBCpoGrfHkaMyLV9a+/evbz11lusWrWqyAZUN910Ey1atGDo0KFs3bqVli1bZt1pmtC3LyxdKhVAuYiKigLg+PHjAFy+fJnLly9fs4IqICAAHx8fuwMqgAcfhHfflY6m2FhYu9buhxYdZcrA6dPQuzd4yo5xRZzFYuGv8uXhjTcKdB6PDai2bYOPP4aUFHevRKliRQMqpZRSSinlMSIjI6WC6s47YdgwsO0khlRLvfvuu9SrV4/Y2FheffVV4uLiGDp0qPsWfB3+/v6sWLGCd9555+pA6cMPYfJkqFIFfH1h4kSYMQN274Zs1VJXBlQp1jfM1wqoDMMgJCTEoYAKYMgQ2L5dBqfXri23HTgAtg6vIq9ECdnV7+xZePJJbfdzAovFQkxamvyZFoDHBlRt2khl4/Ll7l6JUsWKBlRKKaWUUspjRERESAXVpUtw8CAkJ2fe9+677zJs2DBKlizJBx98wOHDh1m+fDlt2rRx34LzY/JkGDRIEqGRIyVQ+eADKWeKi5NJ5pGRMGBAZkBlm0N1vYAKpM0vKSnJ4SXFxsLnn4O/v/zR33WXjOKZNg0uX87n8yxMtWpJKdgvv8CkSe5ejcezWCy8vmyZ7JhYADExMRw/fpxLnjbkrGVLaSFdtMjdK1GqWNGASimllFJKeYzIyEgJqDZtgsqVZYq31dGjR7n55ptZvXo1L7zwAhXyObzZrebMgaeegvh4mD5d2vsMA3buhPXrpRLo7behSxeoWjVfAZWjFVRX8vaGsWOlsOvhh+GWWzxkvNPzz0OrVjB+vAeVfxVNl1JTCU5JyfeAdJuKFSuSkZFx1aD/Ii8wEFq00IBKKSfTgEoppZRSSnmMiIgIafErV05usLa2Afzzzz+ULVvWTStzknr14NlnZfe5EiWybvf2hoYN4aGHYPhwafl77jlCjh/nGy8vTh04AJA5LN2VAZWXF9x/P2zZkrXMzp09YD6Vl5eUfK1cmaM1VDkuKDUVb9OE6OgCnce2W6VHtvndey+Ehl53EwOllGM0oFJKKaWUUh4jMjKSU6dOkW7d0Y+EhMz7Tpw4QWRkpJtWVkDp6TLwPToaJkyQXjo7GCkpdM/IoMaKFUBWBdW1dvED5wRUNl5eUsi1di188gk0buyU07pW2bIyNN00tYqqAMJTU+UXBayg8uiAasAAWLIkZ5CslCoQDaiUUkoppZTHiIiIICMjg9MXL0JwcI6AyqMrqEaMkLY+2xt/ezVsyOYyZbhz1y4gfy1+U6dOZfr06Q4v2SYgAJ55RjoR9+6Fzz7L96kKx7ZtElItWODulXisSNvMqBs5oLLJZSdRpZTjNKBSSimllFIew1YhdfLkSShfPjOgSk9PJzEx0TMrqJYskblSFSpAyZIOP3xTlSpEp6TAgQN5BlQhISGcPn2ay9bJ5ocPH+bpp59m8ODBZDjhjfYHH0hY9fPPBT6V61SoABcugDXUU47JyMhgY0YGczp2lF0mC6BMmTKUKVPGcwOqRx+VYFkp5RQaUCmllFJKKY8REREBSDsfr78uA8WBU6dOkZGR4XkVVCdOyFyp6tVleHc+nK1fH4D0337LM6Bq2LAhaWlprLUOjHr77bdJS0sjISGBjRs35uv62b3/PjRoIE9p794Cn841QkNlF8Tdu929Eo9ksVg4COy47TapRCugmJgYhwKqixfhhRegTRt46SWZU+62TQCDgmDVKg/ZylKpok8DKqWUUkop5TFyVFB17w5t2wLWwCrb/R7BNOGxxyApCb77TnYGy4eQ5s1ZBpxMSspzSHqbNm3w9vZm3rx57N+/n8mTJ9OjRw+8vLz45Zdf8rzW0qVLmTZtWq73+/vDrFnyfv3DD/PzbApJXJxWUOWTxWKhNlAuKckp56tYsWJmQPXHH3/wxhtvkGptdTVNOHYMNmwg8/e9esHHH0u2O368fAsYM0buT0uT4rhC06wZJCfLLptKqQLTgEoppZRSSnmMHBVUJ0/KjmzI/CnAsyqojh2TeUhvvAG1a+f7NLE1anAXsOGWW+wakt6iRQvmzZvHyJEj8fHxYdy4cbRo0YKf8+jLO3/+PD179qRPnz5YrrNzWYUK0LUrzJjh+EitQlOjhgRUpunulXgci8XCR0DbH35wyvliYmI4cOAAzz33HLVr12bEiBEsWrSI1atlz4DoaGjRQmbaGwbUrSvjw7ZuhdOn4aef4MEH5Vzz50N4uOwq+fnn8i3CpZo2lc+rV7v4QkrdGDSgUkoppZRSHiMsLAzDMKSCaupUuO02OH/eMyuooqPhyBHpUyqA2NhYAPb+8Qdp584BuVdQAXTs2JEtW7bw1Vdf0a9fP6KioujcuTNbt27l0KFDuT5uzJgx/PPPP1y8eJHVebwh798fhg8vwp1P99wD/foV4QUWXRaLhWggJTTUKeeLiYkhKSmJTz75hIcffhiAOXMyaNVKBvCPHy8zzWxZ4iuvZBZOEhAA994LlSvL76tWlRlo27bBE0/ImLoXXwRrbut8lStDRIQGVEo5iQZUSimllFLKY/j4+BAaGioVU+XKyY0JCZ5ZQWXjVbD/koeFhVEjNJRn//1vqi1eDFw/oOrQoUPmMUOGDAGgc+fOAMyZM+eajzly5Ajjxo2jU6dOeHt7s3DhwuuuqVkzyd0CA4tokVKHDvDmm+Dr6+6VeBxLaioVgNTwcKec7/777+epp55iy5YtTJkyhYCASL766m7i4mS8U//+0K4d+Pjkfa6aNaW19OBB2LQJHn9cZlR5eztlqVczDBg8GFq3dtEFlLqxaECllFJKKaU8Svny5UlISJDyCIDjxzlx4gQ+Pj6EhIS4d3GOiI/PGp5TQGFxcRwoWZLYJUuILl+eMtcZXl2zZk3uuOMORowYkdkyGRsbS9WqVXOdQ/Xqq6+Snp7O+PHjadasWZ4Blc3EiZIFWTdbLFrOnCmEHrDi5/KJE/gDadbXTkHFxsby2WefUatWLQzDoHLlCJo0eZ1ff5VZ9vlhGFCvHkyaBGvWgJ+fCzduHDIEHnnEBSdW6sajAZVSSimllPIoUVFRHD169KoKqsjISAzDcO/i7HXmDPz6q0x1doLY2Fg+NE2iLlxgaN261/1zMAyDZcuW8eKLL+a47Z577mHJkiVcuGLK9JYtW/jiiy8YMGAAlSpVom3btmzatInExMQ81+XtDcuWQa1a0qZVZJimDMt6+213r8TzHD0KwGUnVysuWiS7QFauXJmkpCU4qUALWzFh794wcqRzznmVhAQduq+UE2hApZRSSimlPEp0dDTHjh3LqqBKSODEiROe1d63bp18tg1ZLqDY2FimXLjAUaDnkSP5mk7euXNn0tLSrqqOevXVVwkNDWX48OEAtG3bFtM0WWxtJ7yep56CjRslC7r3XpkPlJzs8NKczzCkH2zLFnevxOOcDw2lI3C+bl2nnfO776BjR/jyS6hQoSoHDx502rltwsIkJHXJLn933QWDBrngxErdWDSgUkoppZRSHiUqKoqEhATSg4Jkq7iOHTMrqDzG6tUSkjRu7JTTxcbGchn42NeXsO3bYd8+uWP8eHnzfNdd0lK4alWu52jRogXBwcFXtfmtXr2abt26ERwcDEDDhg0JDg62u83vlltg7Vp4+WWYMqUIVVI1bQrr1+ugdAdd9PVlHmDYAuJ8Sk6G996D9u2hRw/561i2DKpVi+Hs2bMkJSU5Zb02PXvCxYsuev21bQu//16Et61UyjNoQKWUUkoppTxKdHQ06enpnDh5Ut7ZVqnieRVU8+ZBnTpwnVlRjsjcya9DBwm/br1V7jBNyMiQj+3bJaSyVW9dwdfXlw4dOjB37lzS09MBOHPmDKdOnaJq1aqZx3l7e9O6dWsWLlyIaecEdD8/GD1aOqF69izAE3WmZs0ksdi+3d0r8SglduwgHihRooTDj01JgVmz5Nd+fvDGGzLQfPBg6XgNDpYWP4ADBw44b9HIhp8VKkim7XTt2smTmz3bBSdX6sahAZVSSimllPIoUVFRANLmt2kT5rJlnlVBdemSvFt+/nmnnbJKlSq0atWKvv365WwbfP55qez4/XcJpsqVg99+y/U8nTt35uTJk6yzhlh//fUXQI6ACqTN78iRI+zevduhddpm2P/yC2ze7NBDnc/257RmjXvX4WGiZs/mc8DPz8/hx/78s1TS/fOPbKB46JCMbho7NmtWVKVKlQCc3ubn5QXdu8OCBTICzqnatpXAefBgOHfOySdX6sZhx2adSimllFJKFR22gOro0aM0+PxzMvbuJTU11XMqqHx9Ydw4J5/Sl9+uEzwBEB0Nf/wBPrm/BYiPj8fHx4eff/6ZZs2aZQZUVapUyXFcmzZtAFi4cCFxcXEOrfXiRZlFFRws86lKlnTo4c5z003w2Wdw991uWoBnKnHyJIeBkvmooJo3D06fJnMA+rU23XRVBRXA009Lt6stDHMaHx95Ld1+u4TBnTvDhg1QujRUrOiCCypVPGkFlVJKKaWU8ijR0dGAtYKqcmUMa6VFOduufkVZQoJsV2Zna5zT2cKppUvh00+lmiub4OBgWrZsmTmHap91ltWVAVWlSpWoVq2a3XOosgsIgM8/l6zMOnfdPQxDprhf8dzU9ZU4dYqjON7il5EB8+dLl6m3d+7HBQcHExQU5JJB6dWqyTD2fGRreWvcGP7+W8IpgPvvhxo15AX/xhsuuKBSxY8GVEoppZRSyqNERkbi5eXF0aNHoXJlvFJSiAAaNGjg7qXlbcIEmVfz99/uW0NGBowcCX37QtWqsqaUlMy7O3fuzM6dOzlw4AD79u2jfPnyBAYGXnWatm3bsmzZMiwWi8NLaNcOHnsMPvkE8vFw5zl1SoYSOb3nqxhJSoKPPpLSN6DUyZMcwfGAasMGOHkSOnTI+9jKlSu7pIIKYPduGDPGRRlx9pD866/hq6+gdm2YOdMFF1Oq+NGASimllFJKeRQfHx/KlSsnFVQ33wxA7VKlMgeFF1mpqTBxolRYWOfsuIWXFyxZAnPnytTo/v2hQQOZVo0EVACzZ8/mr7/+uqp6yqZt27ZcvHiR1atX52sZ990n4VQuM9sLx7Zt8OCDss2gymnDBujdG6Ki4IUXYOFCuHwZX4slXwHVvHlStBYfn/ex1apVY/Xq1Zktplf66KOPGDNmjEPXt1m5EoYMgZ078/Vw+7VoAQ8/DN26ycU0BFUqTxpQKaWUUkopjxMVFZXZ4gfQukoVvLyK+H9tZ8yQEpIBA9y9EkkKOnSAFSskOThxAnbsAKSdr27dusyYMYN9+/ZdNSDd5s4778THxydfbX4g43ruuEMKutymVi357PK0wsNMnAiNGsF330GvXrBliySKhsGCBx/kMxwPqB5/HL75BsLC8j729ddfxzAMWrVqxaFDh3LcZ5omY8aM4euvv3bo+jbt2snn+fPz9XDHde4Mb77p5he6Up6hiP8rrpRSSiml1NWio6M5evQo5yIjaQkY7du7e0nXd/myDEa/9VaZ0lxUGAa0bw/790OnTnLbkSM8/PDDrFu3jmPHjuVaQVWmTBmaNWuW74AqNBSWLZOQym3Cw6UtyxrOKas5c+DOO+HoUfjvf2WHOgBvb7bWrk0SjgdUN90EDzxg37E1a9Zk4cKFnDlzhlatWkkYbfXnn39y7NgxTp8+7dD1bSpUkOztvffgyJF8ncIx9erBK6/IC14pdV0aUCmllFJKKY9jq6Bat3Ury4F6RSn0uZb9+2X7shEjJBQqasqUkc//939QpQpPpqRgWNeZWwUVSJvfpk2bOHnyZL4vfeGC5Hduc+utGlBd6eefYdYsCAq66i7bzDFHAqoNG2DqVOlytVf9+vVZsGABCQkJtG7dOvM1tnTpUgBOnTpl/8mu8MUXMlKrWzfH1pRvSUnaRqqUHTSgUkoppZRSHicqKopTp06xbNkyWgO3uXPoeF5SUqB6dfjzT+ja1d2rub4GDaB9e4JefZWxNWoAV+/gl13btm0xTZN+/frRqVMn/vnnH4cut3QphIS4eQ7VrbdKi9+N3oK1axe0aiWvU8OA4OBrHmaxWPDy8sL7elvxXWHqVOjXT8afOaJp06bMmTOHgwcP0qZNG06fPp0ZUKWmpnLROrjdUXFxElK1bJm1saVL/fvf0Lq1m5NYpYo+DaiUUkoppZTHiY6OBmDs2LEMDA4m4J133LyiK5w6JVvUNW2atW1ZYGDRrJ7KLjAQfvgB6tal9+XL3HTTTcTFxeV6eIMGDQgPD+f7779n7ty5jB071qHL1aol79mXLSvgugti8GAJZ4r6340rpaTIjKmtWyGP3fMsFovD7X2LF0sY5Ofn+NLuuOMOfvrpJ3bt2kV8fDxLly7F19cXIN9tfiBZ8dixElBdupTv09inRQspFdywwcUXUsqzaUCllFJKKaU8zu23307Dhg154oknaNy9Oxw6VDSqEy5cgB49oHx5KRlJTpbZTi7Z095FvL2hZ09C/vyTg0uWUKpUqesc6s3vv//O7t27eeSRR/j0009JTEy0+1Lh4TLe6LffnLHwfKpQASpWvLEDqjfegL17ZYp527bXPdTRgOroUdi9WwqI8qtt27bMnDmTzZs3c+LECdq0aQMUrM3PZts2iI11cQdex47SRvvBBy68iFKeTwMqpZRSSinlcapWrcr69ev55JNPCG/UCNLT4fBhdy8LfvwRvv0WnnkGNm+Wd7+DB3te+PHAAxLc5FFNA3DLLbcQGxvLsGHDSElJ4aOPPnLoUq1bw8qVMhPILUxTgoM5c9y0ADf76y8pJXrySbtSJEcDql9/lc+tWuV3gaJz587MmDGD+vXr88gjjwDOCagqVJDP3bqBgx2q9gsKgr594fvvJQhUSl2TBlRKKaWUUsqzVa4sn+0IU1zukUckmPr4Y6hb1/OCKZtKlaQqzYFUIS4ujm7dujF+/HjOnj1r9+Nat4a0NFixIh/rdAbDkL+vadPctAA3mztX5m+9/rpdhzsaUG3eLF+ito0AC+Jf//oXGzdupGbNmkDBWvxsQkNlHvzx4zBhQoFPl7sXXoASJeCnn1x4EaU8mwZUSimllFLKs9l2mfvzT/euAyTsqFvX3atwDsOQlsXXX4f162HKFBg4UNrBzp+/5kOGDx/O2bNnmeDAO/2WLaWA5zqjrlyvZs0bdye/556TGVy2UqI8pKWlORRQjR8vIZUzs9qwsDDAORVUIOHZ7bdLUOUyZcvCnj3w8ssuvIhSnk0DKqWUUkop5dliYqRtpk8f967jxRelSqI4WbAARo6Exo2hd2+YOFESh7S0ax5er149OnbsyPvvv09ycrJdlwgIkD+6mBhnLtxBsbGwb9+NuZOfYYB1x0Z7OFJBZRu9FhSUn4XlLjQ0FHBeQAXQpYtklPv2Oe2UV6tYUT67fCq7Up5JAyqllFJKKeXZDAOqVXNvO93BgxLc2BnKeIx//QtWr4aZMyUEPH9eKtXCwiTMefRRWLo0x0OGDBnCqVOn+Pnnnx261Lhx8NFHbponX706pKbCkSNuuLgbTZgATzzh0AYDjgRUnTq5pmCoZMmSBAQEOKXFz6ZnT1i1Cm6+2WmnvLYPPpBAUEMqpa6iAZVSSimllPJ827ZJhY/Lphzn4ZVXZPc7O+f4eJSmTWWCdLVq8hxDQuT2Q4fg//4P7r4b3nwz8/DY2FgAkpKS7L6EaUo48MILsgliLh2ErlOtGnh5yXMq7v7+W9LApk2hf39pO/PxsfvhFosFPz+/PI87fBjmz4fAwIIsNndhYWFOraCKjIRmzeRl4FLVq8P+/eBggKvUjUADKqWUUkop5fkuXJAZSatXF/61N2yA6dNlPpOdc3yKhUqVZHZRt27w9tsSfAABAQEAXHRgWz7DkCKt0aPhhx+gUSPYudMVi87F7bfLNoK33VaIF3WDTZvk7+3FF6VNc9Qo+QN3gL0VVF99JcHjo4/mc615cHZABVIkOGAAnDzp1NPmFB8vFYgaUCl1FQ2olFJKKaWU56tfH3x9pQynsA0fDhERMGRI4V/b3fz9pWUJ4NVXrTf5A44FVCAh1csvw+LFcPYsNG8OTs4fcufrKzusFQPdu3fno48+uvaddevC++/LoKVNm2DYMBne7QB7AirThKlT4Y47sjbZdLbQ0FCnB1RnzsiGjr/95tTT5uTtDW3bwq+/3pgzz5S6Dg2olFJKKaWU5ytZEho0cE8F1aefSrlImTKFf+2iICZGevM2boTkZLy9vfHz83M4oLK54w7JTiZOlEKTQvPeezBiRCFe0PkuX77MrFmzWLFiRc47PvwQpk2T/rWBA6FKlRx3O/J3ZU9AtWaNjCrr1cvu0zosLCzMqTOoQL6FhIZKduRS7dtLO/KWLS6+kFIFYxjG54ZhnDAM45rbnBqG8ZJhGFusHzsMw0g3DCPUet9BwzC2W+/bYM/1NKBSSimllFLFwx13SEB1xdBul7FN865SBdq1K5xrFlUjRsgcMOvAoYCAAFJSUvJ9uvLlZRYVQAFO45i1a2HGjEK6mGscPHiQS5cuceHChawbd+6EoUNh7txrPubAgQOEhIQwb968zNsSExOZMmUKGdeo8LEnoKpQQQoL//Wv/D0Pe7iixc/bG9q0gYULXTysv107mVcXEeHCiyjlFFOB+NzuNE1zrGmadU3TrAsMA343TTN7cnyX9f6G9lxMAyqllFJKKVU8DB8ufWGFtZvfxImyTVmhT/QugkqWlHf3Fy7AiRMEBATku4Iqu9degzp1ID3dCWvMS7VqMrzagR3tipq9e/cCcN72mkxNle3pgoKyWjGvsHz5ctLS0pg0aVLmbVOnTqV379689NJLVx1/vYDqzz/h2DEpqnv7bShduoBP6DpCQ0M5ffo0ppOTpLZt4fhx2L7dqafNKTJSQt2YGBdeRKmCM03z/wB7SxV7AgVK+TWgUkoppZRSxUOZMvD773DnnfJ7V853OXdOKiDOn4dSpVx3HU+SlgZxcTBkiNMCqlq1JPTIVtzjOtWrSzh18GAhXMw19u7dizdw/tw5ef337StJyxdf5DprauPGjQDMmzcvc+fFPXv2APD+++8zfvz4HMenpaVdM6DauBFatHDdUPQrhYWFkZ6ezrlz55x63rZtJT8qlA0dp0+Hzz4rhAsplSsfwzA2ZPvok5+TGIYRgFRaZd91wQQWGoax0d7zakCllFJKKaWKD1v11Lvvwn33Ob/05ptvpGqqZk04cULmFhVWxVZR5+cHXbvC119TxdvbKQFVly4QEgI//uiE9eWlWjX5bK1C8kQJW7awHSh95oxM+p4yRcrQ4uM5e/Ysa9euveoxGzZsIDIykrS0NGbOnAnAn3/+SdOmTbn33nsZMGAAs2fPzjz+WhVUixdLLhwYKCPZCkOYdUCZs9v8KlSAhAT5Mncp04Rvv4Wnn4bJk118MaVyddk0zYbZPvKbmHYGVl7R3tfCNM36QHugn2EYLfM6iQZUSimllFKq+ClTBn75RXYpK4jkZHj+eQmjAI4eldKK226Dr7+GRo0Kvtbi5KWXwDB4KinJKQGVj4+M65k/vxA2PKtRQyZke2qL38WLPPbjjwQBZ5KTpRRo+fLMwe/vvPMOTZs2ZdiwYZmzpS5fvszmzZvp2bMnsbGxTJs2DZBKrLi4OKZPn06jRo3o2bMn69atA64OqLZuhQ4doFIlWLkyK+dztdDQUMD5ARVI5myaLn7NGYYEVPHx8NRTsu2hUp6rB1e095mmecz6+QQwC2ic10k0oFJKKaWUUsVP377w7LMwdqzssJcfR4/KTKsJE2DJErlt8GAZBj5jBjz0kPPWW1xUqACPPUbnxEQCzpxxyik7dJANzzZvdsrpchcWJhe65x4XX8gFMjLg4Yepfv48zwD7kpPl9ttuy6zw2759Oz4+Prz77rs8+OCDpKamsmvXLlJSUmjUqBHdunVjxYoVJCQkcPz4capXr05AQAC//PIL5cqVo1OnTuzfvx+LxYKfn1/mpadPl8svWQJRUYX3lG0VVM7eyQ9g1y6oXLkQdvMrWRJmzYLWraF37/x/r1LKjQzDCALuAGZnuy3QMIzStl8DbYFr7gSYnQZUSimllFKqeProI7j9dujfX3p2HLF3LzRrBgcOyAAk25ZyKm8DB+KXkUEjR//McxEfD++/D9HRTjnd9fn4SOmME6q/CtWQITBrFgOB3/z9sVgsXLp0Kcche/bsoWvXrowePZpvv/2WVq1asWDBAgAaNGjAbbfdRnp6emYVVTVrKVRkZCTz588nPT2djh07cunSpRwVVEOGSIVbYW9IZwuoTtiqG52oUiXJKhcudPqpr1ayJMyeDXfdJQPXlCpCDMOYAawGYg3DOGIYxhOGYTxjGMYz2Q7rAiw0TTM5221lgRWGYWwF1gFzTdNckNf1fJy5eKWUUkoppYoMX1/43/+kDW/lSujWzb7HnTsHnTvLDmi//w716rl2ncVNbCxPd+rEyn37GO6E00VEwMCBTjiRPdLToX59aNkSrhgOXmSdOwc//URijx58/M03NK9Xj1WrVnHhwgVCQkIAacs7cOAADz30EC+//DKVK1fmkUceYdWqVZQqVYrq1atTrlw5DMNgypQpAFSvXj3zErGxscycOZPWrVsD5AioQkOlAKiwVa5cGV9fX/744w+nn9vfX14CLq+gyn7B+fPle5ZSRYhpmj3tOGYqMPWK2/YDdRy9nlZQKaWUUkqp4qt6dZkZZW84BbIzX0gIzJyp4VR+eHmRGhrKhZQUp53y3DlpJXPBuKGcvL1lFtU338AVFUhFVpky0Slf1AAAIABJREFUsHYtS62tiQ0aNADg/PnzmYfs27ePjIwMYmNjAbj//vtZunQpERERtGzZEi8vL4KDg6lZsyY7d+4EoGrVqjkuc9ddd/H2228D4O/vD8jw+vffL4T5YNfg5+dHXFwcW7dudcn527WTVr/Dh11y+qv5+clr7scfwQWhm1KeQAMqpZRSSilVvAUFyeelS+3b1S86GlavlhIKlS93HT5MLyemSXv2yMivBXk2iDjBI49AYmIhls/k0/bt0K8fpKVBaCh7/voLgHrWUDV7QLVnzx4AatSokXlbs2bN+Ouvv5gxI2uucYsWLQCoWLFiZgiV3csvv8ykSZPoYW15/c9/4PPPwctN7yrr1q3Lli1bXHLudu3kc6G+DFJT5YX+3/8W4kWVKjo0oFJKKaWUUsXf0qVw991gbV+6ptOnZbj6iROZg6VV/tQ5doxeycl5H2inBg0gMhLmznXaKXP3/+zdeVTU1f/H8eeHRWBYBPedRdEU3Ndc0jTNbNHUzHJJy8xfZVlpZdli2qJllraYfss0NbVFU9PKFk1TU3HfQEFExQUUV3b4/P64MywywMwww0C+H+d4gM9yPxccPGdevu/73nknVKlSNhtWZ2TA229D//5ql76VK3PKyqKioqhTpw7Vq1cH8gdUR44cAfIv2wPw9fXFz88v5+uOHTsCuf2nbuTi4sKoUaOoWbMmycmweTPcdZf9vj1rNW/enDNnzpCQkGD3sZs0gXHj1MdS4+endgX47rv8Ybquq80Zli6Ft96Cf/8txUkJUXokoBJCCCGEEP993bpBp04waZJawpeXrqtuyPfcA3PnQny8U6b4X3KtUiVq6zq6JRVrFnBxUUHIL79YVgRXIu7uqin+qlVw+bKDH2aF8+dVVd+kSXDkCNSvrxK7mjUBFVA1atQIX19foGAFVe3atfHx8SnyEaYKqhuDLHO2blV5Wffutn5DJdeiRQsAhyzz0zSYOVNt5FmqBg9WmzqYygV1HcaOhebN4aGH4M03Yds2uHYNxo+H9etLeYJCOI4EVEIIIYQQ4r/P9G7z3Dl4993c48uWqT5Td94JsbGqqbrxTa+wXXLlyngA6adP223MPn0gKamUikeefFJVUJlZ5uY0BgOMHKmqaA4dUuVLxteqrutERkbSsGHDnIDq2rVrObceOXIk3/K+woSEhDB27FiGDBlS7LUbNqiWXZ072/bt2EPz5qoHs6OW+ZkKl06dcsjw5t17L4SGqt1Hr11T/259+qn6eu9euH4dnn1W9az6/nt45RU1USH+AySgEkIIIYQQN4e2bWHoUBVQmaqkVq1SPXy++gqOH4cRI5w6xf+KtGrVAEg39kWyh549VSCyc6fdhixc48YwcKAKAcoKHx8YPRoefLDAqcTERC5dupQvoDJVUJnCK1OD9KJomsasWbNyKqmKcvEi3HorGB/nFJUrV6ZOnToOa5SelKQywP/9zyHDm+fpqZYie3jA6dNqfeuoUTBrFjRrlhuaVqgAb7yhfiFWrCjFCQrhOBJQCSGEEEKIm8e776p31KalW599BgcOqMoUDw/nzu0/JKNmTTKwbwVVQIBqlv7MM3YbsmgJCTBlSilu41aE339XAUVamtnTUVFRAGYDqlOnTnH58mWLKqis8emnsHGjXYe0SfPmzR1WQVWpErRvD4sXgx03pSxep05w8CA0agRdu6qlx+b64g0bBuHhqqLqxqXLQpRDElAJIYQQQoibR506EBcHDRqorytWdN4WZP9hKQ0b4gkkdehg13Hr11cfS2VF09Wr8PrrKp1wtvffhw8+ADc3s6dNAVWjRo1y+kyZAqqVK1cCcMcdd9h9WmXhV6dly5YcPnyY63Zsyp/XW2/BsWPqpVCqXF1zPy9s0wY3N5g3T1ValfoEhbC/Yv9J0TTNU9O07Zqm7dU07aCmaZONx4M1TftX07SjmqYt0zStgvG4h/HrY8bzQXnGmmg8Hqlp2p2O+qaEEEIIIYQolL+/aoQtHMbg40M2kJycbPexX3oJ7r/f7sMWFBKiKlm++ca5PX6WLlVN/EePzh9a5BEZGYm7uzuBgYF4eXnh4uKSE1AtX76cpk2b0rhxY7tN6bXXoEcPyM6225A269ChA1lZWURERDhk/J491Y9+xgzVGL7M6dBBVYI+9ZSzZyJEiVmSeacB3XVdbw60AHprmtYBmAbM1HU9FEgCHjNe/xiQpOt6A2Cm8To0TWsCDAbCgN7AZ5qmmf8XVgghhBBCCFFueXl58QbgP2eO3cc2GOCnn+CHH+w+dEFDh6qG5Lt2lcLDzNizBx59VHUif/HFQi+Lioqifv36uLm5oWkavr6+XL16ldOnT7N582YeeOABu01J19XeAq6uZaOCqn379gBsdWB69MEHaiVdqTZLt8aYMblVoUKUY8X+k6Irpi0g3I1/dKA78L3x+AKgn/HzvsavMZ7voWmaZjy+VNf1NF3XjwPHgHZ2+S6EEEIIIYQQZYbBYKAzUNEBTYrGjoV27VQP81dfhawsuz8i1+DBqil1qXbJNsrMVA3RK1VSu7UV0bA9KiqKhg0b5nzt6+vLtWvX+P579XbNngHVkSNw9Cj061f8taWhSpUqhIaGsm3bNoc9w9cXdu8GO/4Y7S8qCoYMgcREZ89ECJtZlHlrmuaqadoe4DywHogGLum6nmm85BRQ2/h5beAkgPH8ZaBy3uNm7rnxeaM1TdupadrOzMxMc5cIIYQQQgghyiiDwcBJwOPsWbuPXakS/P232tjsnXccvPGivz889BA4qL9RkdzcYMECWLkSqlcv9LKsrCyOHTuWb5c+UwXVr7/+SuPGje3aIP2nn9TH++6z25Al1qFDB7Zt24buwKWYrq6qeuzLL2HLFoc9xnaZmbBkCXz+ubNnIoTNLAqodF3P0nW9BVAHVfVkbgGz6V8Dcx3c9CKOm3veXF3X2+i63satkEaAQgghhBBCiLLJYDCwC/C8eBHWr7f7+B4eqjf0F184OKACVT21cKGDH3KD3bvVxw4doE2bIi+Ni4sjLS0tXwWVj48PV69eJTIykmbNmtl1aitXQtu2ar+BsuLWW2/l7NmznDhxwqHPSU6GqVPVa84B7dVKpkkT6N1bBVQZGc6ejRA2sWrVsK7rl4ANQAfAX9M0U3pUB4g3fn4KqAtgPF8RuJj3uJl7hBBCCCGEEP8RXl5ezAOuVKsGzz7rsDfMo0erZt0ZGWoVnENCA9MOateuFX2dvSxaBK1awerVFl1u2sHvxiV+Fy9eJDY2ltDQULtNTdfVqsdnn7XbkHbRwbhbpCOX+QF4e6sKqqNH4cMPHfoo2zz1FJw5o1JEIcohS3bxq6ppmr/xcy/gDuAw8Bcw0HjZI4Cx2JNVxq8xnv9TV7WWq4DBxl3+goFQYLu9vhEhhBBCCCFE2WAwGEgDNg8cCHfcAenpDn3etm2qP9CyZQ56wNSpUKNG6VSmTJ2qSpR697bo8sICqoMHD5KdnW3XgErTYNw41eqoLGnatCl+fn58++23Dn9W9+7qz8KFzt3c0ay77oLAQPj0U2fPRAibWFJBVRP4S9O0fcAOYL2u62uAl4DnNU07huox9aXx+i+BysbjzwMvA+i6fhBYDhwCfgGe0nXdkS0NhRBCCCGEEE5gMBgAOFK/PsyapUpPHKhzZ2jcGBywaaASGqr6UO3b56AHGJ06BZGRqkzJ3T3n8O7du7leSB+sqKgo/Pz8qJ6nT5Wvry/JxnKykgZU2dmwd6/aRPDTTx3clN5Gbm5uvPLKK6xatYpffvnF4c8bPFhVUZlWYpYZrq7w8stw663qL2rbNrUL5OTJsGZNGUzUhMjPkl389um63lLX9Wa6rofruv6W8XiMruvtdF1voOv6A7qupxmPpxq/bmA8H5NnrLd1Xa+v63ojXdfXOe7bEkIIIYQQQjiLl5cXQE5IwqZNqqO5g2gajBkD27fDrl0OeMCtt6qPW7c6YPA8/vhDfezRI+fQ/PnzadWqFa+88orZWyIjI2nYsCGaltvy19fXN+dzWwOq776D/v2halVo0QLefx/Wrctd8VjWjBs3jtDQUJ555hnS0tIc+qz+/aFnzzLa6mnMGHj3XRVWnToFv/wCb74J994LvXrBxo0SVIkyy6oeVEIIIYQQQghRHHd3d9zd3XMDqtWr4dVXOb9mDWcdsLMfwLBh4OmpmqfbXd26UKuW47dv++svlQg1bQrAd999x6hRo3BxcWH16tVmd6mLiorKt7wPVJN0AH9/fypXrmzTVLZtU2HfffepzQTj4lQRjksZfQfp4eHBrFmzOHr0KB999JFDn1W5Mvz2G7Rv79DHlNzAgRAfDykpqvxt+3b1i2KSkqJ2/xOijCij/7wIIYQQQgghyjMvL6/cgOq119Br1ODMwIEMHzrUIc8LCID771ehit0LRDRNVVHZUkF1/Dj07QtdusBzzxW9THDOHNiwAVxcWLt2LQ8//DAdO3Zk+vTpHD9+PKfflElKSgpxcXEFAipTBVVoaGi+yipLmPKKd9+F2FiYPx+GD1cZXVnXu3dv+vbty5QpUzh9+jQA27dvd1hvqvPn4dw5hwxtX56e8OSTcPIk/PSTej2npECjRmopaePGamvCzz6DY8ecPVtxE5OASgghhBBCCGF3BoOBlJQU9YWvLxGDBtE8LY36//xDdna2Q575xReq8schy9AefxwmTlRNmW6UlaUaNX37rVpWBSol++oraNZMVUZlZakJxsWp83v2wNChsHmz+jo5WQUJTZqwYcMGBgwYQLNmzVizZg0DBgwAYO3atfkeGx0dja7rNGrUKN/xvAGVNRISVFaxahVUqGDVrWXGzJkzyczMZMKECWRlZTF8+HBGjRpl99fctWsqtPv4Y7sO61h+ftCypfr87Fm169+kSarH2rp16mvTDoBXrqjXrRClyM3ZExBCCCGEEEL89xgMhtwKKuD5iAhmAE+mpnL06NECoYo9mFovZWWpFjx2deedBY/t3KlCq23bVGIBKh3bvh1at4bly6FNG/j6a7W7WmZmbnlXVJRaM7d4MTzxhFoz9vrrbG/ShHvvvZeQkBB+/fVXKlasSMWKFWncuDHr1q3jueeey3l8ZGQkQJEVVJbSddW+KC4O6te3/MdS1gQHB/Pyyy8zefJkqlSpkvMziomJoUGDBnZ7jo+Pas6/cqVD26s5TnAwvPRS7te6rv7yjRscsHYtPPSQKp8bPBg6doSKFZ0zV3HTkAoqIYQQQgghhN3lDai2b9/Opn/+4d9u3ZgPROzY4bDnrlih2kWdPOmAwc+ehWXLcr/29ITERPUmftEiFUy9846qUtE0de0ff6hwCsDNLXd3vkGD4MwZeOYZVVl1/To0acKECRPw9/dn/fr1VKlSJedRffr0YePGjflCP9OSvxuDKFsCqmXL4McfYcoUCAuz5odS9rz00ksEBgYye/bsnH5c+xywA2O/fnD4sNp4sdzTNPU6rVpVfd23rwpflyyBPn2gYUOIiCh4X1YWJCVBenrpzlf8J0lAJYQQQgghhLC7vD2oZs6ciZ+fHw//8AOfeXgQsXu3w57bqpVanTRhggMG/+QTePhhmD4dDh6E8HDYvVs1oB4yBNq2hZdfzi3fqlgRXFx49NFH+djcWjAvL7VGbMsWNU67dhw6dIi77rqLWrVq5bu0ffv2pKencyxPj6CoqChq1aqVb9c+gKCgIFxcXGhpWs5VjKtX4fnn1fRfeMG6H0lZ5OXlldMo/YMPPkDTNIcEVH37qo8//WT3oZ3Py0uFrRcuqOo+Ly947TV1bu9eqFNHlSy6uUGlSqqcLCvLuXMW5Z4EVEIIIYQQQgi7M/WgiouL47vvvmP06NFUqlSJzmFhaOvXO+y5gYGq8GPZMge00HnsMdWD6qWX4P33LbolIyODxYsXs2LFisIvuvVWqFWLixcvkpiYWGDJHqjQCSA2NjbnmLkd/ADatWvH+fPnadKkiUVzXL1aFXPNnu2ApZFO0q9fP+Lj43niiScIDQ11SEBVr54KRH/4we5Dlx1+ftCzp9ogYPFidaxyZbXk9fHH4c03VRVgQkJufzUhbCQ9qIQQQgghhBB2ZzAYSExMZNasWQCMHTsWgPHAHfv3k52YiEueJWz2NGGC2n1u7FhVmGRaVVdiwcHQqxesX68qpSxw9OhR0tPTc3ohFXctFOwpBRBoXCZ44sSJnGNRUVH079/f7FiVK1e2aH6gisJatYJbbrH4Fuf6/XfVjH7YsCITtZo1awLQvHlzdu3a5ZCpzJkDNWo4ZOiyxfizBFT11Jdf5n6t6zBtmlryKkQJSAWVEEIIIYQQwu4MBgPnzp1j3rx5PPDAA9SrVw+A9N69cQPOf/21w57t5QUffaRW4f3xh50Hnz0bvvvO4jTnwIEDAJw9e5YrV64Uea2pp5S5BvJVq1bFy8srp4KqqGorS2VmgnF65SecunIFHnwQRo6E9u3h+PFib2nWrBnR0dFcMzWyt6O2bdVufjc1TVPhVHIy5FmCKoS1JKASQgghhBBC2J2XlxdxcXFcuXIl385z9QYMIB5I//57hz7/vvtg/37o3dvOAzdsCAMGWHz5/v37cz43BVCFiYqKwtXVleDg4ALnNE0jMDAwp4KqqDDLEtnZMGmS6ud++LBNQzjH7Nlw8SK89RbExKi+YMVo1qwZkBsW2tvvv8OTT+Zu0GhP586d4+DBgyQkJNh/cHu7804YOtTZsxDlmARUQgghhBBCCLszGLer79y5M+3atcs53iQ8nLUuLlTbtQvS0hz2fE1TPcwBNm2ClBSHPapIBw4cwMvLC6DYZX5RUVEEBwdToUIFs+cDAwNzKqhMAZUtFVSnT6vgbto0tUqucWOrh3Ce6tVh1CjVsHv7dtWwvhimgGrPnj0OmdLRo/D55yoQtafs7GwaN25MeHg4derU4aRDtqa0o7vvhn//lV5UwmYSUAkhhBBCCCHszhRQvXDDtnAVKlTgQEgInhkZavc6BztxArp3h0cfdUyFS3H2799Pz549cXFxsSigKipwCgoKyldBVVi1VVG+/x6aNoV//oEvvsjfSqhMunQJ5s2DESPUX+CoUeprgAYNVA+quDi1G2IhAgMDqV27Nr/++qtDptivn/r4yy/2HTcxMZGkpCQGDRpEeno6S5Ysse8D7G3gQPXRwdWR4r9LAiohhBBCCCGE3d1+++3079+fe++9t8C5zG7daO3jg96tm8PnERgIU6fC0qVqVVhpun79OjExMbRu3ZqgoKAil/jpul5sQBUYGEhiYiLXr18nMjKSkJAQ3K3sAH/8uFqluGcPjB6tKs3KpMxMtWtijRpqotu3q6V95nzxBYwbB4sWmT2taRoDBgxg3bp1XL161e5TrVkTmjSxf7+zM2fOADBo0CA6dOjAYtMuemVVgwbQooXq0SaEDSSgEkIIIYQQQtjdfffdxw8//ICrmV3Wmrdrx65r14iOji6Vubz4IjzyCLz5pgqqSsuhQ4fQdZ3w8HAaNWpUZAVVfHw8ycnJxVZQgdrJLyoqyqr+U5mZ6uPzz8PmzRAaavGtzrFmDXz1FTz0EOzYoTreF7Yz4ZtvQteuqkzuzz/NXjJw4EDS0tJYu3atQ6bbo4daSmrPVavx8fEA1KpViyFDhrB///58Pc3KpHvvVcv8kpOdPRNRDklAJYQQQgghhChVrVu3pjegjRlj0bo7XdfJysqy+XmapopsunRRK8WOHrV5KKuYmnI3bdqURo0aERUVRXZ2ttlrLekpFRgYCMDx48c5evSoxf2nrl6FevVg4UK1Is7NzZrvwknWr4c6ddRyvjZtii71cneHFStUadj998O+fQUu6dixIzVq1OA7B1X39OypHm/MlOwib0A1aNAgXF1dy34V1WOPqWq3QvqoCVEUCaiEEEIIIYQQpSo8PJwWrq7U/+MPuH692OvnzZtHnTp1yMjIsPmZHh7w448wY4ZaiVQaTA3SQ0JCaNiwIcnJyTmhw43ijI2lTVVS5pjObd68mZSUFIsDqiVL4MwZsHHDP+f45BNVOWVpmhYQAOvWga8vTJxY4LSrqyv9+/dn7dq1JDuguufee9WySStbghXJtMSvRo0aVKtWjV69erFkyZJCQ84yITBQBYrlIgUVZY0EVEIIIYQQQohSVaFCBTzr1FFfJCYWe/0vv/zC2bNnOX36dImeW6UKPPVU6fVd2r9/P02aNMHV1TVnOV5hy/wSjT+HqlWrFjpejRo1qFChAh9++CGaptG+ffti56DrMGcONG8OeTZTLNuys9VfUo0a1t1Xt65qBPXtt2ZP9+vXj5SUFP4sZBmgPcTF2a8Zf3x8PJUrV8bDwwOAoUOHcvLkSTZv3myfBzjKt9+Cg5ZSiv82CaiEEEIIIYQQpc7fVMaUkFDstdu3bwfI2cGuJHQdxo+H2bNLPFSxDhw4QHh4OIBFAZW7uzu+vr6Fjufi4kK9evXIzMxk4cKFtGjRotg5bN6sKnvGjCnDDdHzyspSHcdt/Qtq1Aj8/FQPpNdfz9cU6rbbbsPb27vQPlSHDh1ixowZtj0X2LsXbrlFtc6yh/j4eGrVqpXzdd++ffH29i77y/zeeQc++8zZsxDlkARUQgghhBBCiFKnmxpeF1NBdebMmZzKKdMyuJLQNNXD+ZtvSjxUkS5cuMCZM2do2rQpoPoIeXt7F7qTX0JCAlWrVkUrJkWaMWMG69atY+jQoRbN4513oFo11SS+XPj7b4iMhOrVSzbOhg0wZYpqPmbk4eFBz549+fnnn9HNlDnNnTuX8ePHc+7cOZse2bQpdOgAzz4Lx47ZOvFc8fHx1KxZM+drb29v+vXrx/Lly0mzZzd2ewsPV03thbCSBFRCCCGEEEKIUqdXrkwKoF+7VuR1O3bsyPncHhVUoHZci4iAS5fsMpxZpgbppgoqTdNo2LBhkRVUVapUKXbc++67j169elk8jw8+gC+/BC8vi29xrqVLwdsb7rmnZOP06QMdO8KsWaoqK+dwH+Li4jhoJkCJiYkBYM+ePSQkJDB+/HhOnTpl8SNdXGDBAtWzfejQ3J0TbXXmzJl8FVQAQ4YM4dKlS6xbt65kgztSWBjExkIxv9tC3EgCKiGEEEIIIUSpS6ldGwOQ0bdvkddt374dV1dX/P397RpQZWerIhtH2b9/P5AbUIFa5lfSgMpaYWElz3pKTUYGfP899O0LBkPJxxs3DqKjYc2anEN9+vQB4Oeffy5wuSmg2r17NwsWLGDGjBm0bds2X0hanLp14fPPVZXeO+/YPvXs7GyzAVXPnj2pWrVq2V7mFxamPh4+7Nx5iHJHAiohhBBCCCFEqTN4ewMUu6Pajh07CA8PJzQ0NCegMrc8C+Cff/6xaHlW+/Yq//j9dysnncfYsWP57bffCj1/4MABAgIC8gUMDRs2JDY21uzyLHsHVEeOwIMPqqbd5cbq1XDxIgwebJ/x7r9fJUYff5xzqHbt2rRv3545c+bk+3vQdT1fQLVx40bq1KmDm5sbY8eOteqxgwfDkCFw5YrtU09ISCArKyvfEj8ANzc3HnzwQVavXs3ly5dtf4AjmULZI0ecOw9R7khAJYQQQgghhCh1BoOBjwHtk08KvUbXdXbs2EHbtm0JDAwkLi6OLVu24O3tzVtvvUVmnjVUp06domvXrnz44YfFPrtCBXjgAfDxsW3uWVlZfPLJJ7z55puFXmNqkJ63p1SjRo3QdZ1jZhoUmXpQ2cu0aSrv8fS025CO16IFvPyyWp5nD25u8MwzULlyvvV2U6ZMITY2lk8//TTn2NmzZ0lJSQFg586d/P3339x9990MGDCA/fv3k52dbdWjFy5UyyttdebMGYACFVSglvmlpaXx448/2v4AR6pfH86cUeschbCCBFRCCCGEEEKIUmcwGOgBuG7aVOg1+/btIykpiVtvvTUnoPrhhx9ISUnhjTfe4Pbbb8+pqvr888/Jysri/PnzFj3/66/hvfdsm/vVq1cB2Lp1q9mwSdd1Dhw4kNMg3aSwnfwyMzNJSkqyWwVVXBwsWgSPP64apJcbISHw7rvg6mq/McePh+++U2GVUc+ePbnzzjuZOnUqSUlJQO7yvo4dOxITE8OVK1fo2rUr4eHhJCcnExsba9VjXYzvtLdsUW2wrBUfHw+YD6jat29P/fr1WbJkifUDlwYXF6hRQ+1IkJ7u7NmIckQCKiGEEEIIIUSpMxgMJABcuFDoNStWrMDFxYV77rmHevXqkZKSwnfffcftt9/ON998w969e2nevDkLFy5k3rx5ADmBgyV0HYxZk1Wu5Fm7tWjRogLnT548yeXLl/P1nwIIDQ0FKLCTX1JSErqu2y2gMlXujB9vl+FKx7RpqnGTo9yw9HP69OlcunSJt99+G4Do6GgABgwYkHNN165dCTP2UzLXVN0SkyeD8aVpFVNAdeMSP1AN97t27WrznErNqFFqmaUQFpKASgghhBBCCFHqDAYDiYDLxYuFXrNixQo6depEtWrVCAwMBFT4c/vttzN06FD27NlD48aNeeSRR0hISMDX19eqgOruu217/2zq/ePu7s6iRYsK9MTau3cvAM2bN8933M/Pj5o1a+ZUUKWlpXHq1CkSEhIA7LLE7/x5FYgMH67aL5ULkZEwcSKYaVxuFx9+CHXqQJ7XRrNmzXjkkUeYPXs2sbGxxMTEoGka/fr1A1S/sFq1auUEVKZdGa3VtSscOFBkDmuWaYlfjRo1zJ6vVKmSVa91p7jlFli7Fv74w9kzEeWEBFRCCCGEEEKIUmcKqNwKeZMdExPDvn37uN+YIJkCKoDu3bsDEBISwqZNm5g8eTLDhg2je/fuXCwi8LpReDj8/bf1VVSmCqoHHniA6Ohotm7dmu/8vn37AAos8YP8O/nNmDGDsLCwnGoZe1RQubqqtksvvljioUrPhx+Chwc8/bRjxu/YUfWguiEAmzJlCq6urrz66qvExMRQt27T4SNuAAAgAElEQVRdgoODCQwM5K677gJUqFi3bl2bq5W6dFEf//nHuvuio6OpXr06Hh4eZs8HBASQmppKamqqTfMqFU8/DYGBqpTPyh5e4uYkAZUQQgghhBCi1BkMBuKAdG9vtdbuBitWrAAoEFB5e3vTtm3bnOvc3Nx4/fXXWbhwodVVJX36QEaG9bv5mSqoRo4ciZeXF998802+83v37iUkJARfX98C9zZs2DBnid+ePXu4cuUKm4x9uEoSUOk6nDyp+oFPmwbGdldl3+nTsGABPPKI4xpmtWsHfn6qIVQederU4bnnnmPJkiWsX7+e+vXro2kaERERvJenQVl4eLjNFVRt26qm/EW0WjNry5YttG/fvtDzAQEBgHVLWkudp6fqKbZnj2qKVtpOnlTPNVajsWKFeo35+UFQEBiXdYqyQwIqIYQQQgghRKkzGAy8C/w8Y4ZqpnyDjRs30rhxY4KCggDw9/fHz8+PLl26UKFCBbNjWhtQdeqk3quuXWvd3E0VVLVr16Zfv34sW7aMtLS0nPP79u0rsLzPpFGjRly4cIELFy7kBFW/GxMyWwOq5GS1YVrr1gVaLZVtqakQFqaqa55/3nHPcXGBZs3AWNmW14svvkiVKlU4e/YsISEhAFSuXBnPPNsfhoWFceTIkXy7RlrK01PlYzf0xS/S+fPnOXr0KJ06dSr0mnIRUAE8+CC0aQPvv282iHaICxfU2t2gIBg2DLZtU8cDA6FfP3jsMTh1Cr76qnTmIywmAZUQQgghhBCi1BkMBgCSk5PNno+Ojs7Z9Q5UY+gvvviCqVOnFjpmQEAAycnJ+cKiori7Q69eKqCy5r2zqYKqYsWKDBs2jKSkJNYaU67k5GSOHj1Ks2bNzN5r+p6OHDnC0aNHAfjX2BzcloDqxAno3Bm+/RbGjSvju/ZlZqrlXnffrb729FTL+3bsgIYNHfvs5s1VQHXDUrOKFSvyxhtvAFC/fn2zt4aFhZGWlpbTSN1aa9fCTz9Zfv0WY6VX586dC72m3ARULi7wzTfw119mg2i7S02Fvn1h3Tp4+WXYv1+VSgK0agVz58LMmXDHHbBkiSw9LGPcir9ECCGEEEIIIezL29ubZsAd06ap0p88gY6u68TExOT0ATIZPHhwkWPmfdNeWHPpGz3zDAwapN6nurpaNndTBZWfnx89e/akevXqfPPNN9x///0cPHiQ7OzsQiuoGhqDmA0bNuSEc1lZWfj6+hbab6gwGzfCwIGQng6rV+fmPmXW77/DjBkqQEhPV2vfHn20dJ49dKgqZcrKUqFJHk888QSnT59m0KBBZm817cZ48ODBfKGppcys9CzS5s2b8fDwoHXr1oVeU24CKlDN0kH9kqWlgZeX4541fbpq+LV0qareKsyLL6qlf9nZBV4PIpemaV8B9wDndV0PN3O+G/ATcNx46Edd198ynusNfAy4Av/Tdf29G++/kfxNCCGEEEIIIUqdwWDAHagbGQlxcfnOnT17ltTU1JwlV5ay5U17ly7wwAOWh1OgKqhcXFzw9vbGzc2Nhx56iDVr1nDx4sWcHfwKq6AKDg7G3d2dNWvWAOQsYbSleuqzz6BKFdi+vRyEU6AaMbm5weLFKpwqTR06qK0N3d0LnHJ3d+fdd98ttIKqcePGgO07+em6WlX2zjuWXf/PP//Qpk2bIgNLf39/oJwEVKAqm9q1g//9z7HPGTdO9Z0qKpwC6N4dhgxRr0dRlK+B3sVcs0nX9RbGP6ZwyhX4FLgLaAI8pGlak+IeJgGVEEIIIYQQotR5eXlxwfRFQkK+c6alVNYGVJUqVQKsf9N+9KhahWSpK1eu4Ofnh2ZcsjRs2DAyMjJYvnw5mzdvxsfHh+DgYLP3urm5Ub9+/ZxlfQ888AAAVatWtWrOoN7r//tvOWqI/vffapmVt7dznr9nDxgDRGt4e3sTEhJi805+mgbHj8OyZcVfm5KSQkRERJH9p6CcVVCBWs555Ypaemdvqanw6qtw/bpqKjdkiGX3JSWpJu7GikhRkK7rfwOWb42aqx1wTNf1GF3X04GlQN/ibpKASgghhBBCCFHqXFxcuGaqELkhoIqJiQEK7wlUGFvftC9aBCNGqN7Klrh8+TIVK1bM+bply5Y0adKEt99+mwULFjBixAhcilg21LBhQ3Rdx9PTk7uNpU+WVlBlZMDIkSpn8fVV78dLzYkT8MorBSreLJKaqkq9brvN/vOy1KBBMHmyTbeGhYXZXEEF0LOnaoFVXBP7qKgoMjIyaNOmTZHXlbsKKoC77lK9qFJS7DPetWuqj1S3bipo+vNP6+6PjlavZ0dXdZVtbpqm7czzZ7QNY9yqadpeTdPWaZoWZjxWGziZ55pTxmNFkoBKCCGEEEII4RTZBgMZrq6QmJjveExMDJqmERgYaNV4poDq4kXr/sO/Tx/Viua33yy73lRBZaJpGsOGDePUqVM0btyYadOmFXm/qY9RgwYNaNGiBWB5QDVvHnz9NZw8Weyl9vf77yoIKKSxfZHc3VU48fjj9p+XpUyN0m0QHh5OVFQU6enpNt3fq5f6aNywsVAJxrC2evXqRV7n7u6Oj48Ply5dsmk+TtGnjwoqN2wo2Ti6rnbnq1ZNVUudPq1S5nvvtW6cNm1UuPXRRyr5vTll6rreJs+fuVbevwsI1HW9OTAbWGk8bq4jfrFbUUhAJYQQQgghhHAKg7c3R6tVA2OwZBIdHU3dunWpYGWfIluX+LVpo3o5GTfiK9aNFVQAI0eOpFevXixbtixnh8LCmAKq0NBQKlasyPDhwws0hDcnMRFefx26dnVSzylTsDBunPVLtVxdoWNHx+/WV5TmzVXVjA1LusLCwsjMzMzZedFaLVtC5crFh6CJxrDWkiWfAQEB5auCqmtX1SDd9It25Ij6+yhKXBxMmQL33JOb8mmaCjxHjlTLRk+cgIcftm1O48ertHfBAtvuv8npun5F1/Vrxs/XAu6aplVBVUzVzXNpHSC+uPEkoBJCCCGEEEI4hcFgYErXrjBxYr7jMTExVvefAtuXPbm6qtVH69apTd6Kc2MFFaiKl19//ZWmTZsWe79pJz/TxwULFhS7QyHACy+obOXTT9V79FKl6yqg6tcPdu6EhQutu//zz1UFlTOZls3t2mX1raad/Gxd5ufiojYsDA0t+jpTBZUlFXXlLqDy9IRJk1TVEqigMzQUpk5Vr6+8Ll5U1XbBwfDGGyqEql4997qvvlK/CF26lGwXvrvuUmOMH68qsYRVNE2roRmb8Wma1g6VMV0AdgChmqYFa5pWARgMrCpuPAmohBBCCCGEEE5hMBi4fv16geMxMTFW958CcHV1xc/Pz6Y37X36wOXLqqijOOYqqKzRtGlTKlasWGwj7Lw2blSZ0IsvQlhY8dfb3fHjcOqUaqb04IOwcqU6ZomTJ+Hpp4tf3+Zobduqj9u3W31ro0aNcHFxsblROsD06SqfKUpiYiKapuVUAxal3AVUoHo+DRigPp8yRVU+vfaaaqbm55e7W8GqVTB/PjzzDMTEwP796py9k1kXFxV2depkWTp9k9E07VtgK9BI07RTmqY9pmnaGE3TxhgvGQgc0DRtLzALGKwrmcDTwK/AYWC5ruvF/vLInopCCCGEEEIIpzAYDNx36JBa+rNxIwDJycmcPXvWpgoqUG/are1BBdC3r1pCZ0nuZK6Cyhr+/v4kJSXl7AJoidat4cMPYcyY4q91iGPH1A+nWze13GrRInjkEVUV5epa9L1ffaUqX0aNKpWpFqpyZTXf5s2tvtXT05PQ0NASNUoHyMyEs2ehTh3z5xMTE6lUqRKuxf1MUa/1Y8eOlWg+TtW2rQqd7rhD7bCoablLQIcPhw4d4JZbHD+PBg3g55/V52fPwqFD0L27459bDui6/lAx5z8BPink3FrAwoXTilRQCSGEEEIIIZzCYDDgkZICW7fmLN0x7eBna0BVqVIlm6pKvLxU/qLraqO3qKjCry1pBRVgVTgF4OMDzz2n5ulwR46o5tNdu0KjRqqJdK9eapvDxo2hXj2YPRs2bVId24uSlaV2SevVSy3XcrZu3Qr0PLNUWFhYiSqoQGV7/foVfj4hIcHihvnlsoLqRpqmttD86COYORPat1fHXVxKJ5y60TvvQI8e8MQTqqRSlCoJqIQQQgghhBBOYTAYOAdqBy3jbmSmgMqWJX5Q8jft8fGqtc1tt6lVRTdKS0sjLS2tRBVU1jh/XlV3WdrAvcQuXVIpiqmipEULqGvsdezqmrvEatgwVf0ybFjR4y1apJYGjrZl93oHiI1VPY+MvZ6sER4ezrFjx0hNTSUpKYl58+ah39g7qRi33QYREXDmjPnziYmJN1dAVdZMmwYTJqhQNSxMNWEXpUYCKiGEEEIIIYRTeHt7c8bU9+X8eaDkFVS2LvEzqV1bvSd1dVXFNjt35j9/xbgDXEkrqCzx999q97dff3X4o3LNnasaUq9dq5ZdLluW2zMoL02DoUOhQgUV+syeDYsXq/u2bIG8u9117w733Vdq30KRTp9WPY+2brX61rCwMLKzszly5Ahffvklo0ePZt++fVaNcc896qOp1dKNEhMTLdrBD9RS0eTkZNLT062agyiCl5dqFrZtm9op8OmnnT2jm4oEVEIIIYQQQginMBgMxGdmqi/OnQMgOjoaPz8/i5pEm2PrEr+8brlFrV7z81PZSt5VXaaAypEVVNnZqpCje3cwGNR75T59HPa4/CZMUA/s2NGy669cgd69VTProUPh7rtVw2nThB95BNavB7cy0v64ZUuVPv77r9W35t3Jb5dxJ8AdO3ZYNUbTpiqkevNNiI4ueN7aCiqwftdKYYG2bVU4FRSkfiFFqZCASgghhBBCCOEUBoOBYxkZqkmyhweQu4OftT2aTOy17CkkRFUwVamSvx/VZWNfGkdWUC1ZAi+/rAqXIiLUKrtSkZamKqNat7b8Hj8/tRby/HmIjFTh1rp18EmevskuZehtp8EAnTurxu3GZaWWCg0Nxd3dnYMHD+YEVDtvLLErhqbBnDmqOGf+/PzndF3/zwZUly5dYvDgwWzbts3ZU7HcCy+o3QTL0uv3P66MxNhCCCGEEEKIm43BYGB/WpqqsDGKiYkhLCzM5jEDAgJIS0sjJSUFrxJ2FK9bV/ULr1Ah95gpoHJkBdVDD6kc5f77c1s+OdzZs6rnzpw58MAD1t3r7g5Vq6o/5cGMGdCuHbz6qmo4ZiF3d3caNWrEtm3biDKmltYGVKCWke7apUJQUO2+brkFqlS5QkZGhsVL/MpTQPXLL7+wbNkyfv31VzZs2EBzG3ZSdJrsbAmpSon8lIUQQgghhBBOYTAYSEtLI8vYhyo7O5vjx4/b3H8Kct+0l6QPVV4VKqj3pwsXwt695ntQpaWpPlGTJsGsWarnO8CPP8LYsWoDu1691Gq3qVNzxz5yRLVEArV74JtvqiIkV1fo378UwylQwdTFi1CeggNbtW4N48apFNDKJudhYWFs3LgRXdcJCwtj3759pKWlWT2FuLi/GDlyBMnJ6TzxhNoscdQoDahndQXVJSsrwZxh06ZNeHt74+PjQ48ePfjrr7+cPSXLdO0Kjz3m7FncNCSgEkIIIYQQQjiFwWBQn3ToAM8/T3x8PGlpaTbv4AdQq1YtAE6bkh87uHoVnn9e/bl0KbeCauNGtQyvShXVhumdd1RRjqnd0qpVsGABJCWpHev/+gu+/z533CeegDp1oHFjCAyEyZPhu+/sNm3LJSXBRx+p7QIbNnTCBJzggw/g/fetTgHDw8Nzdu57/PHHycjIsLpROsCvv/7KggULmDFjGjt2qHZHq1b5ADu5ciXYojHKUwXVpk2b6NSpExs2bKBatWr07NmT9XkqJ8ssX1+wss+YsJ0EVEIIIYQQQginMAVU+tWrcOJEiXfwAwgKCgIgNja2pNPLUbGiqm7680/45Zc6xmMVOXlS9doeOlQt07p+XW2AZ8o8Pv9cBVM7dqjr4uJg9+7ccd99V602Cw5WPcnnzoWJE+02bct98IFqdj5lihMe7iSmv6QNG9TugxYyLT+tWrUq/fr1A2xb5peamgrAlClTuHDhAB99BLNnbwRSeeONDqSnq8q9XbvUa69bN1XcFhiY296rYsUAoHqZD6iSkpI4cOAAXbp0oX79+mzbtg0PDw/Wrl3r7KkVr00bOHxY/XILh5MeVEIIIYQQQginMAVUGZUq4XbuHNHGbc1KElAFBgYC9g2oQFU7rV4NS5d2B1bi6+vH4MEwZEj+Ipy8ba/MtcDKe23HjurP88/bdarWyc6GjRvhwQfVFnM3mw8+UN9/166qnK0Ypp38WrZsSb169ahatSq///47Y8aMsaqxf2pqKj4+Pnh6evLoo4+yZcsWKlQ4ATzKyJERVKhQiYwMaN8esrLUpnLBweDvrz4CzJhRFdjJgQPOKLvLtXLlSq5evcqwYcPMnv/nn3/QdZ0uXboAqvqwXr16nDx5sjSnaZs2bdTvyJ49andK4VBSQSWEEEIIIYRwClNAle7vD+fPExMTg6urK/Xq1bN5TD8/PypVqsSJEyfsNU1A9QFfsQLq1TsGdOXCBQ/c3Eq5T5S9pKfDwIGwZo1q/ty3ryrnuhnNng2ZmaonlQVCQkKoWrUqXbp0QdM0Hn30UX788UcmTJiQs/TPEqmpqQQEBPDJJ5+wY8cOPvroIxITE4FY3njDHVCvuZUrVZ+yf/9Vn3/9Ndx9txpj+HAXXFw8WLhwGM7MeiZPnswrr7xS6PlNmzbh7u5Ou3btco7VrVu3fARUrVqpj3lLH4XDSEAlhBBCCCGEcApTQJXq5wfnzhETE0O9evVwd3cv0bhBQUF2r6AC1VO7VauXCQ6+ndq17T586cjKguHD4YcfID5eHZswAYxLI286wcHw+uvq5/Hbb8Ve7urqyuHDh3nppZcAePfdd3nyySeZMWMGmzdvtvixaWlpeHp6MmjQIPr27ctrr71mrKKqgI+PT851d98NNWuaH6NZM2jRYiJpaZ706gWJiRY/3m5SU1M5cOAAp06d4vz582av2bRpE23bts23q2bdunWJi4srrWnarmZNeOop1ShOOJwEVEIIIYQQQginMAVUF265Bfr353h0dImW95k4KqAC2L17J+3aNXLI2A6n6zBmDCxbBtOnw+jRzp5R2fDCC1C9utrJ0AKVK1fOCVE1TePJJ58E4MyZMxY/MjU1FU9PTzRN47PPPsPT05MVK1ZQtWpVq5YKhoWlU6nSCGJjVSGclZsSltiBAwfIzMwEICIiosD5lJQUdu7cmbO8z6Ru3bqcO3eO9PT0UpmnzTRNNf3q0cPZM7kpSEAlhBBCCCGEcApvb28ATrRvD/PncywmpkQ7+JkEBgYSGxtr1ZIrS1y4cIETJ07QunVru45bKnRdVUr9739qq8EJE5w9o7KjQgXVnb57d5tu9/X1BeDq1asW32MKqEDtPPnhhx8CUKVKFaueHRQUxIULK/j220zefLP0l5zmDaV27dpV4Py///5LRkaG2YBK13W77rbpMNnZEBNT+unfTUgCKiGEEEIIIYRTmCqokpOTuXrlCnckJNDeXGdxKwUFBZGcnGzs6WM/pjfj5TKgAkhOhqefvrl267PUs8+qn40NTEvybA2oAEaMGMGgQYPo3LmzVc8ODAwkOzub5s1P4u29hZiYGEqzKGnXrl0EBATQoEEDsxVUmzZtQtM0Ot3QYNzUZ65c9KH68kuoX19t0SkcSnbxE0IIIYQQQjhF3oDqxP79zAQqrFoF77+vOkTbKMjYTyk2NpaqVauWeJ7Tp0/n2rVrOfNt2bJliccsdZoGn36qqkDKZWf3UnDtmtrRz9SF3EKmCqpr165ZfI9pFz8TTdNYtmyZVc+F3Nd6TEwMAwcOJCjof2haCKtWWbQpYYlFRETQqlUrqlatypYtWwqc37RpE02bNsXf3z/f8bp16wLlJKAy7tzIvn03b6+2UiIVVEIIIYQQQginyBtQHT1/nqeAgOPH4YMPSjRu3oCqpHRdZ+bMmUydOpWlS5cSEhJCQEBAicctVZcuwe+/Q0aG2rVPmPfpp3DPPRAdbdVt7u7ueHh4WFVBZWqSXlKBgYEArFmzhkuXLgEH2LMH6taFtm3h7bfhyJESP8as9PR09u/fT6tWrWjdujVxcXH5qhYzMzPZunVrgeV9UE4Dqv37nTuPm4D86ySEEEIIIYRwirwBVUxMDD8A6X37wptvwqFDNo9retNuj4Dq6NGjnD17Fl3X2bt3L61M286XJ2vWQM+esHevs2dStj38sKouW7Qo99ju3apJ9vz5EBlZ6K0+Pj5WV1DZI6CqW7cumqbx7bffAuDmtobDh+G998DNDSZNgs8/L/FjzDp06BDp6em0bt065/ci7zK/PXv2cO3aNbMBlbe3NwEBAeVjJz9fX1U5JQGVw0lAJYQQQgghhHAKU0B16dIlYmJiCAgIoMIXX6g3hI8+qpoT26BixYoEBATYJaDauHEjAA8++CBQTvtPrVoFNWtCeZx7aapbF7p1g2++UUshMzOhXz8YO1a9Hv/4Q133888wY0a+W319fUvUg8pWHh4e1KxZk3PnzgGqkX+jRvDSS7B1K5w+ndsP3969qUxhVKtWrWjRogUA+/OEOJs2bQIwG1CBCtfKRQUVQNOmElCVgmJ7UGmaVhdYCNQAsoG5uq5/rGnaMsC0v6o/cEnX9RaapgUBhwFTvLxN1/UxxrFaA18DXsBa4Fnd3ltrCCGEEEIIIcoFd3d3wsLC2LZtG5qmERISAtWrw7x5KiAowXK0oKAguwVU1atXZ86cObi4uDBw4MASj1mq0tPhl19g8GBZ3meJ4cNh5EjYtg1OnoS4OFVR1bEjmJZ2rlkDX30FDz0EtWoBqoLKGQEVqIrB+Ph4QAVUeRmnxz//wKBB6qXQtKldHsuuXbvw8/Ojfv36uLi4UKNGDQ4ePJhzftOmTYSEhFDLNIkblKuA6rnn4OJFZ8/iP8+Sf6EygRd0XW8MdACe0jStia7rD+q63kLX9RbAD8CPee6JNp0zhVNGnwOjgVDjn972+TaEEEIIIYQQ5VH37t3ZtGkTR44coX79+urg/fdD//4lGtceAZWu62zcuJGuXbvi7+/PkiVLaNCgQYnGLHVr18LVq+pnKoo3YAAYDGqJ6SefQIMGKtwLDgZTo+8JE1R11cyZObf5+vo6ZYkf5PZcq1WrFleuXCEjI6PANbfcorLKkSNVKzJLJCfDwoXw+uuwdGnB8xEREbRs2RIXY/DZpEkTDhmX5uq6zubNmwutnoJyFlDdfrt6bQiHKjag0nX9jK7ru4yfX0VVR9U2ndc0TQMGAd8WNY6maTUBP13XtxqrphYC/UowdyGEEEIIIUQ51717d1JSUjhx4oSqoDJJSFDvjG1cVmMKqEqyYOP48eOcOnWKrl272jyG061aBdWqqR5Uoni+vvDXX2pJ37ffqoTG1TX/NSEhKrSaMweMTcFtWeLn4eFhlymbeq717dsXgItmKn0qV1bTjYhQjdMtMWsWPPIITJmiisW++CL3XGZmJnv37qV169bouip4DAsL49ChQ+i6TmRkJAkJCUUGVPXq1ePixYtcuXLF8m/WWdLSYNMmVVFX3qSl5X6+di3Mng1ffgn//uu8ORXCqhpP4/K9lkDe76QLcE7X9aN5jgVrmrZb07SNmqaZXpG1gVN5rjlFnqDrhueM1jRtp6ZpOzMzM62ZohBCCCGEEKIc6datW04FRr6AStNg6lT48cdC7ixaUFAQycnJBZY8WWP16tWACtHKrXnzYPNm1TFbWKZdO/X6q10bbr3V/DWvvqpKjN57D7CuSbqu63bbxQ9g6NChTJw4kc6dOwMFl/mZDBgAQ4eqXytje6g8c1JZ8OTJqsUWwNNPq6wuORn69IExY9RmkABffnmS1NR4Zs+ehouLam9WqVJHrl27xsmTJ4vtPwXQqVMnAFatWlWC776UXL0Kt91m879Hper8eXj5ZahUSSWTdeuq+QMsWADPPAOjRsHixc6dpxkWB1SapvmglvKN03U9b8T5EPmrp84A9XRdbwk8DyzRNM0P0MwMa/a/M3Rdn6vrehtd19u4yT+kQgghhBBC/Gf5+/vn7ACWs8QPoEoVFRSsW2fTuKZlT7Yu89N1nblz59KuXTtuueUWm8YoE1xdITTU2bP472nSRC31Cw8HrKugyszMJDs7224BVVhYGO+88w5VqlQBCg+oQK1aDAnJzVl27lQN1Rs2hGbN8gdUPj6qZ7yXFyxfDk8+Cd7eGJ+xF1jI8OGXmDhRteuaPn0gEMTBgwfZtGkT1apVI7SI117nzp0JDg7m66+/LvHPwOEqV1Y9yKKiLLpc13XOnTtHur0705uTmgqmKrS//lI7Dk6fDt27q0q/YcMgJUWdX7BAVaeeOAGvveb4uVnJovRH0zR3VDi1WNf1H/McdwP6AznbQei6ngakGT+P0DQtGmiIqpiqk2fYOkB8Sb8BIYQQQgghRPnWo0cPdu7cmb+CCuCuu9Q75oQEqFrVqjFNy55iY2Np06aN1XPasmULhw4d4n//+5/V95YZgwZBp07w7LPOnsl/k7F6CqzrQZWamgpgt4DKpHLlykDRAVXFimp3v0qV1Nfjx6sG6j16qLytb1+1T8GNvL3h009zvz537i+8vb/kiy+extUVnnoKNmxIZujQWPbv38+GDRvo0qULqiOQeS4uLjzyyCNMnjyZkydPUrduXZu+71KhaSrFKyKg0nWdiRMnsnHjRg4fPszly5d5+OGHWWyuUikyUu0WeeyYar5+yy1qTSXA/PmqCioxMfdPjx4wbpwqdQsPhwoV1HLUU6cgNlaFTZMnq1B/5EhVJdWoUcHnenqqP2VUsRVUxh5TXwKHdV3/8IbTdwBHdF0/lef6qpqmuRo/D0E1Q4/Rdf0McFXTtA7GMYcDP9np+xBCCMO+F08AACAASURBVCGEEEKUU+PGjeOTTz7JCZVy3HWXekNmQ8CSN6Cyxdy5c/H19eXBBx+06X6nS0iAH36QncccLSUFPvuM2tnZFldQOTOgUtepvAVgxgyVhfzyC4webT6cMiciIoIWLVrgauzPVbs2DBniR7Vq1XjvvZWcPBnPQw89VOw4w4cPR9d1Fi5caNmDnamYgCo+Pp5p06aRnJzMww8/zL333svSpUuJiYnJf+HIkSqQeu892L1bLb/L2+ds/Hi1RO+TT+CPP1QIZaqA0jRo3Fgt23NxgfbtVa++Xr3UeVOSaC6cKgcsqaDqBAwD9muatsd47BVd19cCgynYHP024C1N0zKBLGCMruumfxX/D/ga8ALWGf8IIYQQQgghbmI1atTgqaeeKniiTRuYNEm9owa1/ZiLS8Gm1Wb4+/vj7+9fIKA6ceIEv/32G4899lhO76sbJSUlsXz5ckaMGIGPj4+1307ZsGYNZGerkhjhOOfPw7PP0r1dOyZmZJCWllZs83NnB1R5tW5d/DU3ysrKYs+ePTz22GMFztWrdw87d35Bgwaz6W/BTpzBwcHceuutrFq1ildffdX6yZSmhg1V1VNystrp8QaRkZEAzJw5k+7duxMfH09QYCA/TJzIhBo1VCDl5QVdu6qQafhwqFGj4HP271c7Rpp5BgDff2/P76pMKTag0nV9M+b7R6Hr+ggzx35ALQc0d/1OINy6KQohhBBCCCFuSi4uagsxUBUEvXurpjiTJ1t0u2knP5PTp0/TrVs3YmNjOXfuHJMmTTJ736JFi0hNTWX06NEl/Aac6KefVJVFy5bOnsl/W2AgDB1Ky8WLqQJcu3bNaQGVj48P7u7uJdoYwBJRUVFcv349p3dcXh06eLNz5wbOnRtLYqJm0crcXr16MWXKFJKSkggICHDAjO1k6FC11M7d3expU0DVsGFDAGpt2cIxX1/qLV+O7uWFZlpyO2JE0c+pVcuesy5XrNrFTwghhBBCCCGcwstLhQFvv606O1sgb0B14cIFevXqRWJiIr169eL111/nt99+K3CPqTl6mzZtaFlew53kZPjtN1U9VUQPIGEnTz+Ne0YGPcGiZX5paWkAxQZZ1tI0jcqVKzs8oNq1axcArc2UX73yykTmz/clJcWNxo3Vzn/R0UWP17NnT7Kzs/nrr78cMV37CQpSuzoWEVB5e3tTu3Zt1TX+4YepVqkSY4AZL7ygwilRJAmohBBCCCGEEOXDxx+rJjkjRsDp08Vebgqorl69yl133UV0dDSrV69mxYoVhISE8O677xa4Z9u2bRw4cKB8V09dvKh277KgB5Cwg/Bwsl1caAwWNUp3VAUVUCoBVUREBF5eXmZ3t6xZsyYjRrTnzz/hzjvViriMDHXuwAHVGu1G7dq1w9fXl/Xr1zt03iWm6/Dtt/D332ZPR0ZG0rBhQ9UYfv58ADz//JMTvXvzwbx5OX/vonASUAkhhBBCCCHKh4AA+PJLtQNW/frw889FXh4UFMT169fp2bMnu3btYvny5XTr1g2DwUC/fv3YsmULycnJ+e6ZO3cuPj4+DB482JHfiWPVqQNffQUdOzp7JjcHDw+u16uHH5ZVUJX3gGrXrl00b94cN7fCOwZ16QKLF6tAypRjvf02hIXBBx+o/t8ffQRLl4K7uzvdunXj999/d+i8S0zTVEPymTPNno6MjKSRqTn5pElqu8R69ZgwYQLnzp1j0aJFpTjZ8kkCKiGEEEIIIUT50bu32klrxAjo0EEdO3AAzp0rcGlQUBAA//77L/Pnz+e+++7LOdejRw/S09P5559/co5dunSJZcuW8fDDD+Pr6+vI78Kxzp5V1R6i1Oz75hvGUf4DqtTUVLZs2VLo+ezsbHbv3m22/5Q5eft8T5qk2qJNmABjx8Jzz4FpVd/tt/fi2LEldO2awtCh8PzzYFxJWLZ07KiCpxt+v1JTU4mNjVUBVWam6p9nXAJ5++2307JlS2bMmEF2drYzZl1uSEAlhBBCCCGEKF+Cg2HOnNzd/UaNUvvcd+2qtm03atasGR4eHsyaNYthw4blG6JLly64ubnxxx9/5BxbvHgxKSkp5Xt5n65DeDg8/bSzZ3JT8TEGmuV9id+LL75Ip06diImJMXs+OjqaK1euWBxQ5RUWBjt2qNW5586plagff6zOdep0B5BAXNxVtm6Fzz9Xm3h+8406f/myui893cZvzF46dlSTP3483+Fjx46h6zrNq1VTvfLWrMk5p2kaEyZM4MiRI6xdu7a0Z1yuSEAlhBBCCCGEKN8WLICXXoKDB1V5hlFwcDBXr15l7NixBW7x8fGhQ4cOOQGVqTl6q1atzDZ/LjdiY+HCBWja1NkzualUPn6cPwHNuJNbUUojoNJtqKCLjo5mzpw5AIU2LC+qQbolXFzUJnXVqqkVu6YfQdu2jahV63Hat3+G6Gg4cwbGj4devdT5ZcvUylUPD5VF9+kDEydCYqJN07DdrbeqjzdUmZl28Ou4ebMKsBo3znd+4MCB1KtXj/fff79UplleSUAlhBBCCCGEKN8aNVINbp5+Gv78U+2gZeReyI5boJb5RUREkJSUxOrVq9m3bx9jxowpjRk7zvbt6mO7ds6dx03GULEitwOGY8eKvdZRu/iBCqgyMzMtWmp4o1dffRV3d3cqVapUaEAVERFBhQoVaNKkSUmnmo+madxxxx388ccfZGdn4+8P06erPREAbrtNFU1Ongw9eqhqqhkzwNQG68ABtXmlw4WFga8v7NmT73BkZCS+QLWVK2HIENUjLw93d3fGjRvH33//zXbT76goQAIqIYQQQgghxP+3d9/hUVfZH8ffJwk1dASkNxFQpEsRMXRcsCwqVhTsZcVVYS1rV1T82XZZ1kXQtaIrNqRZqIoUpQgCYhQpSpEmigESSLi/P+43IWBInclMyOf1PPMk821zBw7DzJlzzz02XHGF//D49de5Ovyss87COcf111/PsGHDaN68OUOGDAnvGMNt0SJfZqIKqkJVpnVr0oD49etzPDbcFVRAnqf5LVq0iLfeeovbb7+d3r17M2fOnCyrsJYuXUrLli0pWbJkSMabWe/evdmxYwfLly//w75mzeD6632P8ldfheXLISkJKlXyP3v29Lc9e0I+rMPFxvoeeEdUQq1YsYKLq1TB9u2Dq6/O8tRrrrmGihUr8vTTT4d5kEWXElQiIiIiInJsaNQItm2D/v1zdXj79u158sknefvtt1mzZg3PPvtsthVXRcI330CbNlDUn0cRU7pSJX4AKm7enOOx4UxQ1QhKjl5++eVcN+R2znHnnXdy3HHH8be//Y3u3buzadMm1hxRDeacY+nSpfnqP5UbPXv2BGD69Om5Oj49R1auHDz3HHzxBQwbFpahHe744yEtDRYuBCAtLY3p06czqHJlnzE7yuqZ5cuX54YbbuCdd945ao+v4k4JKhEREREROXaUKuUbhQdJgJwMHz6cxx57jGHDhtG3b98wDy5Mvv760LTGu+7ylWRSqMyM7+PiqPHzzzkeG84EVe/evbngggt4+OGH6du3L1u2bMnxnI8++ojZs2fzwAMPUKFCBbp16wbAnDlzDjtu/fr17Nq1K2w92mrWrMnJJ5/MjBkz8nzu+ef79nPPPw8ffBCGwR1p7Fjfj+qGG1gyaxY7d+4k5Yor4F//OjTvMAu33HILsbGx/OMf/yiEQRY9SlCJiIiIiMix4+BB339p+PBcn3L33Xfz1FNPhXFQYeAcfPQR9O4NrVpB+vjPOANuvDGyYyum5pYrx8YKFXJshhTOBFXJkiWZMGECY8eOZd68ebRq1YoPP/zwqMenpaVx55130rhx44zVK0888URq1qzJB0dketIbpIerggp8gm3u3LkZf0Z58cgj0Latn2GXi0K2ghk8GG6/HcaNo+n553N2TAzthw6FQYOyPa1WrVpccskl/Pe//811hVtxogSViIiIiIgcO2Ji4IQT4M03IWhGfczZudNP4/vTn/yUvpEj4cEHIz2qYm9SzZqM7NgRypbN9rjk5GTMLGzTSc2Ma6+9lsWLF3P88cfTr18/HnjggSyPff3111mxYgWPPfZYRl8pM+PGG29k6tSpzJ07N+PYJUuWEBcXxylh7G/Wq1cvkpOTmTdvXp7PLVkSxo+HSy/1M+3CKj7ed2lfsIBtBw4w6eBBKr//fq5O7dixI3v27GHr1q1hHmTRowSViIiIiIgcWwYPhl9+galTIz2S8HjvPd8leswYWLcO7rwTKleO9KiKvXLlyvnV89av9z2KjiIlJYVSpUphZmEdz0knncSXX37JgAEDeOSRR/5QlZScnMx9993HqaeeysCBAw/bN2zYMGrXrs1tt92WUemzdOlSWrRoEZbVB9MlJCQQFxeX6z5UR2rWDEaN8jnC337zhYbhtLlOHU5OTmZO3765LtuqV68eAD/++GM4h1YkKUElIiIiIiLHll69oGZNeOWVSI8kPC6/HGbOhOuuO9QpWiKufPnyNPnpJ2jY0P/9HEVycnJYpvdlpXTp0gwYMADn3B8SIosXL+ann37irrvu+kOyrGzZsowYMYIlS5Ywd+5cnHMsWbIkrNP7wCf5OnfunK8+VJlt2+an+918M0yfDjt2hGiAR/jwww85AFR98km4995cnVO3bl0AfkrvGycZlKASEREREZFjS1wcXHYZTJsG27dHejShV7o09OgBYa7AkbwpX748C2Ji/PyyV1896nGFmaACaNCgAeCbnGeWmJgIQJs2bbI8LyEhAYAffviBbdu2sWPHDlq1ahW2cabr3bs3S5cuZefOnfm+xnHH+X8izz0HffpAtWpQv75/SQilqVOnUrduXVq0aJHrc1RBdXRKUImIiIiIyLHnuut8BVX58pEeSWh9/TXcc48vEZGoUr58ebbv3g0XXeSnYf7+e5bHRSpBtW7dusO2f/vtt5QqVSojYXKk2rVrY2b8+OOPGcmtxo0bh3OogO9D5Zxj1qxZ+b5GTAyMG+fbtc2cCU8+CV26QK1afv+ePQUfZ0pKCtOnT6dfv355mq5ZqVIl4uPjVUGVBSWoRERERETk2NOkie+WXIiJgEIxcSI8/jjExkZ6JHKEk08+mQ0bNrDr7LNh3z6fpMpCYSeoatWqRYkSJbKsoGrSpAmxR4mlkiVLUrNmzcMSVPXr1w/zaA+tEvjtt98W+FpVqvhKquHD4Y03oHVr35uqY8eCt6j7/PPPSUpKon///nk6z8yoW7euElRZUIJKRERERESOTb/+Ck88AQsWRHokoTNtGrRrB1WrRnokcoT0KXGz9u2Dxo3h9dezPK6wE1SxsbHUq1cvywRV06ZNsz23Xr16hZ6gKlWqFNWqVWPTpk1huX6ZMnDgAPztb5Camv/rTJ06lVKlStGjR488n5v+5yqHU4JKRERERESOTQcPwn/+A2eeCfPnR3o0BbdiBXzxBVx8caRHIllo3749ZcuWZc6nn/pynTffzPK49FX8ClPDhg0Pm+J34MAB1q5dm6cEVdWqVSlfSFNma9euzcaNG8Ny7ZIlYeRIWL0aXnop/9eZNm0a3bp1Iz4+Ps/nFpUKKjP7r5ltM7OVR9l/mZl9Hdzmm1mrTPvWm9kKM1tmZotz83hKUImIiIiIyLGpShWYOxdq1PCdkgvQ0yYq/Oc/UKoUDBkS6ZFIFkqUKEGXLl349NNPoUMH36k7C4VdQQW+D1XmCqoffviB1NTUXCeo1q1bl9HLqjDUrl07bBVUAH/+M5x+uq+iWr487+f/8MMPJCYm5nl6X7p69erx888/k5KSkq/zC9HLwJnZ7F8HJDjnWgKPAGOP2N/dOdfaOdc+Nw+mBJWIiIiIiBy76taFzz6Dhg2hX7+CN56JJDMYPFjT+6JYQkICK1asYMeOHX465uDB4Nxhx0QiQdWwYUO2bt3K3r17gUMr+DVr1izb8+rVq0dKSgqLFy8u1ARVnTp1wpqgMoPx4/0aCk88kffzpwavI/369cvX49etWxcgrM8xFJxznwG/ZLN/vnNuV3B3IVCnII+nBJWIiIiIiBzbjj8e5syBtm39J9NQS0ryc4XOPffQnKH9++Gaa+CTTyAtLevznIOff4bPP/c/c/Lvf8OYMaEbt4Rct27dAJg7dy5s2gSvvgpLlhx2TKQqqAA2bNgAHEpQ5aaCCmDnzp2FXkG1ffv2sFYY1avnc9f5meY3bdo0mjZtmu9VDdP/XKNgml+cmS3OdLuuANe6Gvgw030HfGJmS3J7XSWoRERERETk2Fe1qk8EpVc87NtX8Gt++qmfbnf88XDVVb5H1P79ft/q1X4Vt759ffXWWWf5aYYrg1YuH3wAlSpBzZrQtauv9LroIr/E2JFmzvQJNghPgk1C5tRTT6VMmTLMmTMHBg70UzKPaJYeyQRV+jS/xMREatSoQcWKFbM9Lz2RkvkahaF27doAbN68OayP07Ch/yv67TdISICbb4Ygd3dUe/bsYc6cOfme3geHKqiioFF6qnOufabbkVP0csXMuuMTVHdm2tzFOdcW+BPwFzM7I6frKEElIiIiIiLFQ0zw8efll6F5c9i2LW/n790LM2Ycuj98uE9CXXopzJsHP/wA11/v97VqBZs3w1tv+d+3bvWVVgcO+P1VqsDll8OoUTBlCvz1r/Djjz5xlpYGX34Jjz4K3bvDn/4Et93mm75LVCtZsiSnnXaa70NVqRJ06wbTpx92TKSm+AEZjdK/+uormjdvnuN5kU5QFcYUuH379vH77z5ZNW4cNGvm/8l99FHW/+Rmz55NSkpKvqf3gZ/CCFFRQVVgZtYSeAE41zm3M327c25z8HMb8D7QIadrKUElIiIiIiLFS5s2PmF06625P2fBAmjUyFdgbd3qt40f76fmjR0Lp532x+qm0qXhwgth8mRYtMivJNimjd/XtSuMHg1Dh0L//vDUU37/8cfD/fdDx45w773w669wyy3+GjH6+FYUJCQk8PXXX/PLL7/4BOM33+DSY4bIrOJXo0YNSpcuzapVq1i7di1fffVVriqAqlSpQtmyZYHCTVClJ3DCnaDavXs31atXZ/bs13j5ZZ8jfughWLbMJ6mymlG7dOlSADp37pzvxy1btiw1atTgu+++y/c1ooGZ1QPeAy53zn2XaXu8mZVP/x3oA2S5EmBmeoUTEREREZHipVUruOkmePttX+V0NNu2+Sqnm2+GXr18R+VZs/yqgAAnngjBh/eQSE9wXXwxvPmmT4R99ZVPXtUpUO9hKUTdunXDOef7UPXsSVKbNrSvX59Vq1YBkamgiomJ4ZxzzmH8+PG8+OKLAAwcODDH88wso4qqfv36YR1jZukVVBs3bgzr46xbt46kpCReD6Zh1qjh88MbNsDSpf5l4kiJiYnUq1cvI3GXXx06dGDhwoUFuka4mdmbwAKgqZltNLOrzewGM7shOOR+oCrwnJktM7PFwfYawOdmthz4EpjqnPsop8dTgkpERERERIqfm27yU+mef/7ox1Sr5qugXnwRTjnF97A6/fTwj+2UU3ySqnr18D+WhFyHDh0oXbq0n+bXvj0vXXklS1NSmDRpEhCZBBXA8OHD+e233xg5ciSdOnXKdcKpXr16VKlShfLly4d5hIdUrFiRsmXLhr2CKv36s2bN4tdff83YXrLkoWLH5cth165D5yQmJubYXD43OnfuTGJiIjt37sz54Ahxzl3inKvpnCvhnKvjnHvROTfGOTcm2H+Nc66yc651cGsfbF/rnGsV3E52zj2am8dTgkpERERERIqfxo39dL1x4w5fZc85P/Vu5Upf0TRuHOzZ46f4pVdOiWSjVKlSdO7c2TdKB1auXElpYMaMGTjnSElJiUiC6tRTT6Vr164cPHiQCy+8MNfnXX755fzlL38J48j+yMyoU6dOoSWoUlNTmTZt2h/2b9niaN8+jXvv9b3jnHMhS1CddtppAFFfRVWYlKASEREREZHi6Ykn/DrzsbH+fmqq7wk1dOjhzWdiYrR6nuRJQkICy5Yt49dff6XW7Nn8Bqz9/POMKp1IJKgAHnjgAerXr89FF12U63MGDRrEww8/HMZRZa127dphT1ClrxJYvXp1Jk6c+If9W7Z8RWrqC4wZE0NiImzZsoWkpKSQJKhOPfVUYmNjmT9/PuAXAW3Vys8cbtEC2rb1rejef98f/8MPcPbZcP758Oc/Q8+ecMEFvrATfJXXF18U7bUUlKASEREREZHi6eST4YQTfNXUU09B797w73/71flGjYr06KQIS0hIyOhDNXfzZkoCrfbvZ+bMmUDkElQ9e/Zk/fr11KpVKyKPnxe1a9cOew+qTZs2Ub16dQYMGMC0adPYsWPHYft937D7iY3dz/DhfnofEJIE1cGDZWnQ4HYmTiwBQJMmUK8etGsHTZv6tnOVK/vphgApKbBpE3z3HaxdC8nJ8M038Pvvfv8XX0CnTj65lZ7UKmriIj0AERERERGRiHr6afjb33zPpzFj4PrrIz0iKeI6depEqVKlGD9+PPP37OGgGe3NePrpp4HIJaiKkvQpft988w0nnXRSWB5j8+bN1K5dm1tuuYUXXniBhx9+mFGZktOrV68GtlG16vNMmXIrjRolAflPUH36Kcyb528zZ0JKyv8RE/MFY8eO5fTTT2fy5KM/z5NO8o3bj6ZDB3j5Zf9ydsEFfuHPfv3yNcyIMedcpMeQrfj4eLdnz55ID0NERERERI5VzsFvv0GlSpEeiRxDEhISmD9/PqmpqSTVr8+ypCRO37mTjh078sEHH1BDPc2y9e2339K9e3f27NnDv/71Ly677DLi4kJbY9OmTRvq1KnD5MmTuemmmxg7diwrV66kWbNmAAwYMICJEycSE1OWE0/8nZo132XhwsEkJSURE3P0CWkHDsDrr8OcObBxI8yY4WcJX3qpX6CzSRM46yyoWnUe996bAKRRqVIlli9fnrFqYn4lJcEZZ8D338Mnn0Dnzkc/1sz2OufiC/SAIaQpfiIiIiIiUryZKTklIdetWzdSU1MBiGvXjg4lSzJnzhwWLFig5FQuNGvWjC+//JLmzZszZMgQTjrpJF555ZWMP9NQ2LRpU8Z0xwcffJD4+HjuuOOOjP2rV6+mXLlyHDy4l3HjFlG69Es0bdr0qMkp5+Cdd/zs4auugunTIS4O9u3z+59+Gnbv9tP0nnkG/v7301i79nsWLVpEamoqgwYNIi3zog35UK4cTJniZy9XqVKgSxU6JahEREREREREQiwhIQGA448/nlJXXkmJW28loWtXTA33c61u3bosWLCA9957j/j4eIYMGULfvn05GIJO4Pv372f79u3Url0b8I3S77nnHiZPnszMmTPZv38/a9as4bzzzgNg2bJFJCYmUqbMEO65xy/suX697w2Vbvp0GDjQ942aPNn3jPr4Yyhb1u+vWRPKlz90vJnRsGFD2rdvz+jRo5k7dy7vvvtugZ9brVp+OmDTpj5pNny4r9zavbvAlw4rTfETERERERERCbG9e/dSuXJlunbtyowZMyI9nCLPOceoUaO49dZbee6557jxxhsLdL0NGzbQoEEDXnjhBa6++moAkpOTad68ORUrVuT111/nlFNO4bXXXmP48OGUKVOG9evX06HD13z55SmHXatHD99TyjmYOBHOOefQ4qC5lZaWRtWqVRk4cCDjxo0r0HPLbPt23zx97VqfOOvVCwYMgHPPherVo2uKn5qki4iIiIiIiIRY2bJleeSRRzjxxBP9hs2bfblNw4aRHVgRZWbccsstTJ06lTvvvJOzzz6bOnXq5Pt6mzZtAjhsRcPSpUvzxBNPcNFFF3HXXXcB0Lx5c9q3b8/UqVPp27cvkyY1IzHR95bavBm2bIHjjksfo0/+5EdsbCzdunVj1qxZ+X5OWalWzU8pXLgQ3nvPr/A3bZpPpkUbVVCJiIiIiIiIhFvdupCQ4LtnF1fz5/sVM0uWhDJl4OBB2LAB+vSBf/7THzN5MjRq5Bs5ZWHdunW0aNGCHj16MGnSpHxPmXznnXcYOHAgy5cvp2XLlhnbnXN06dKFBQsWAJCUlMSkSZN44403eOONNyifeY5eiI0aNYq//vWvrF+/nvr164flMZyD5cuhfn2oUiW6KqjUg0pEREREREQk3Nq0gSVLIj2KyDrtNLj8ckhLgx074Jdf4KST4JRgyty+fX7uWYsWvqN4Fho2bMgjjzzClClTmDBhQr6Hkl5Bld6DKp2Z8cwzzwBQv3594uPjueSSS5g8eXJYk1MA3bt3B2D27NlhewwzaN0aKlcO20PkmyqoRERERERERMLtscfgnntg586it7xaKKSkQKlS2R+TlgYrV8Kdd/pqqzVroHr1PxyWmppK586d2bBhA6tXr6Zq1ap5Hs4dd9zBqFGj2LdvX5ZVWMOGDaNUqVI89thjeb52fh08eJDjjz+eM888k1dffTXsj2emCioRERERERGR4uW00/zPL76I7DgiITUVWrWCRx/N/rjYWH/cP/8Je/fCgw9meVhcXBwvvPACu3btYtiwYfka0tq1a6lVq9ZRpwg+/fTThZqcAoiJiaF79+7Mnj2baC8mCgclqERERERERETC7dRTfQJm/vycj/3hB9+Lafv28I8r3JyDv/wFEhMhU6+nbDVtCjfeCJMm+URVFlq1asUdd9zBK6+8wj333MP83Py5Br755hsmTpxI//79c31OYenevTsbN25kzZo1kR5KodMUPxEREREREZHC8MEHvgHQ0Rpg79kDt98OL77op7sBTJ8OvXr5BM+GDX6aXMmSvsH43r3QuTOUK1d4zyGvHnwQHnoI7r7bT3PMrX37/FJ5TZr45fI+/NAvkZdpemRycjL9+vXL6Nm0du1aGuZilcT+/fszb948vv/+e6pVq5bXZxRW3333HU2bNmXMmDFcf/31YX0sTfETERERERERKY7OPffoyan9++FPf4IXXoCbboJZ1C+nFAAAIABJREFUs+Dxx+GMM/z+F1+Evn2hWzc/XfD00+GCCyApye9//XVfeRVNxozxyakrr8x5et+RypTxySnw1WTXXAM1avgm67t2AVC6dGlmzZqVUT21ePHiHC87c+ZMpk2bxr333ht1ySmAJk2aULt27bA2So9WqqASERERERERKQy7dsF998GcOT6ZdOCAr5T66SeoU8dXGzVvDhdd9Mdzf/zRVxSlpEBysl+OrU4dv+IdQMOG8PvvMHv2oVXxIm3CBHjrLX+Li8v/dZyDpUt9Em70aKhZE157DRISAF9JVb58ee644w4ezSYR5pyjc+fObNq0ie+//57SpUvnf0xhdMUVV/DRRx+xdevWLHtkJScnc/7553PrrbfSu3fvfD9OtFVQKUElIiIiIiIiUhh++gnq1fO/Dx0K5cv7vlTDhkHFigW79pIlvkJr/3548km47LKCJYUKIinp0LRD53wyLVQWLfLPrWVLeOedjM0tW7akTp06TJs27ainTpkyhbPPPpuxY8dy7bXXhm5MIfbSSy9x1VVXsWLFClqkJyAzefnll7nyyitp2rQpK1euJC6ff89KUOWRElQiIiIiIiJyzEhMhLp1oWzZ0F/7u+/g4ovhq698r6slSyCmkDv7JCfDCSf4Sqc//zk8j5GU5KvPKleGdesgNpbB993H9OnT2bx582GH7t27lxkzZjB58mTee+89KleuzOrVqylRokR4xhYCGzZsoEGDBowaNYqhQ4cets85R7t27VizZg2///47L730EkOGDMnX40Rbgko9qEREREREREQKS9Om4UlOAZx4ok9KTZwI113nk1PO+X5WheW992DTpvA2bi9XzienwPe3uvNOWrduzZYtW9i6dSsAq1at4uyzz6Zq1aqce+65vPXWW/Ts2ZO33347qpNTAPXr16dRo0bMyuLv7fPPP+err77iqaeeol27djz00EPs378/AqMMPSWoRERERERERI4VZn6q3403+vvTpkHv3r5/VWF48UXfD6tHj8J5vBYtYOJE2gcN1ZctWwbA3XffzWeffca1117L9OnT2bFjBxMmTKBNmzaFM64C6t69O3PmzCEtfTXHwNixY6lUqRKDBg1ixIgRrF+/nhdeeCFCowwtJahEREREREREjlXNmsHBg/Dqq3k7zzkYPx7atYPOnWHfvpzP+eEHX6111VWFN7XwiisgOZm2a9cC8NVXX+GcY+HChQwYMIBRo0bRq1cvSpYsWTjjCZEePXrw66+/ZiTcAFJTU5k6dSrnnnsuZcuWpW/fvpx++umMGDGCvXv3RnC0oaEElYiIiIiIiMixqnFj6NYN/vtfn6jKrVdfhUGD/MqAiYmwalXO54wc6RNT+eyJlC+nngrNmhH/zjs0aNCAJUuWsH79erZv307Hjh0Lbxwh1r17dwBmz56dsW3BggXs2rWLs846CwAz49FHH2XLli0899xzERlnKClBJSIiIiIiInIsu+oqX9302We5O37PHvj736FjR/j2W9+IvH17mDsXataEGjWgenU47jioUgUWLvTn3XAD/O9/UKdO+J7Lkcx8FdXcuZx36ql88sknfBY8z06dOhXeOEKsZs2aNGvW7LA+VFOmTCEuLo4+ffpkbDvjjDPo27cvI0eOZPfu3YddY/v27Xz++eeFNuaCUoJKRERERERE5Fh2/vlQoULup/lt3w4NGsAzz/iKqIoV/fZq1eCcc2DAALjgAr9i4GWXHTqvXTsYODDkw8/RlVfCokX0GTKE3bt389hjj1GmTBlOOeWUwh9LCPXo0YPPPvuMAwcOAD5BlZCQQIUKFQ47bsSIEezcuZNnn332sO0jR46kR48eRWb6nznnIj2GbMXHx7s9e/ZEehgiIiIiIiIiRde8edCyJZQvn7vjnfPVSUXIgQMHqFmzJjt37uT0009n7ty5kR5Sgbz77rtccMEFzJ8/nxo1atC4cWOeffZZbr311j8ce9555zFjxgzmzZuXkZg788wz+fjjj1m4cGGW0x3NbK9zLj7sTySXVEElIiIiIiIicqzr0iXn5NTmzX464K5dRS45xcKFlLj/fs4bMACgSPefStetWzcAZs2axZQpUwAy+k8d6fHHH6d06dK0a9eOcePGAZCYmAj4xvFFgRJUIiIiIiIiIsXB//4HgwdnvW/FCr9a34QJsHFj4Y4rFJYtg5EjuSohAYCuXbtGeEAFV7VqVVq1apWRoGratCknnHBClsc2bdqUVatW0b59ex544AH27t3Lhg0bAFi6dGlhDjvflKASERERERERKQ62bvV9qBYtOnz7Rx/5CqvUVN9IvSj2bgpWveu0dy9LlizhnHPOifCAQqNHjx7MmzePOXPmHLV6Kl21atW45JJL2LJlC7Nnz8Y5R2xsrBJUIiIiIiIiIhJFrrwSqlaF++47tO2NN6B/f2jcGL74Atq2jdz4CuLEE6FWLZg1i7Zt22JFbYriUfTo0YOUlBQOHDiQY4IKoEOHDgC88sorAPTu3ZsVK1ZkNFqPZkpQiYiIiIiIiBQHFSrAXXfBxx/7xBRAx44waBDMnQt16kR2fAVhBj16wKxZvsF7YUhLg59/DutDdO3alZiYGCpWrEiXLl1yPL5169aULFmSSZMmYWZceOGF7N+/n2+++SbjmNTUVFatWhXOYedLjgkqM6trZrPNbLWZrTKzvwbbHzSzTWa2LLj1y3TO3Wa2xswSzaxvpu1nBtvWmNld4XlKIiIiIiIiIpKlm26C44+He+7x9xs3hldegXLlIjuuUOjRA+LifLP3cNqzB557Dpo1g/PPh+Rk+Mc/4NNPQ/5QFStW5KyzzmLQoEGUKFEix+NLlSpF69atSUlJoX79+hlJrWeffZabbrqJTp06UaFCBVq0aBHysRaUuRwyi2ZWE6jpnFtqZuWBJcCfgQuBJOfcU0ccfxLwJtABqAXMAE4Mdn8H9AY2AouAS5xz35CN+Ph4t2fPnrw+LxERERERERHJytdfw/r1cIz0acqQmgqxseFdgXDxYujbF375xVefDRvm/xwbNfIJq5kzC/4YaWmQkgJly+br9KFDhzJ69GjOPPNMpk6dSo0aNdixYwcVKlSgTZs2tG3bljZt2nDFFVfsdc7FF3zAoRGX0wHOuS3AluD3381sNVA7m1POBf7nnEsB1pnZGnyyCmCNc24tgJn9Lzg22wSViIiIiIiIiIRQy5b+dqyJyzHFUXAjR/oE2Oefw2mnHUqG3Xor3HEHfPkldOiQ/TWy8vjjsH07PPkkfPsttGgBNWv6CrfGjeG44+CKK/zf27x58NBDvl/Y4MHQvPlhl+rYsSOjR4+mWbNmxMTEsGjRIlJTU2nUqBExMYcm0l1xxRUF+ZMIuTz1oDKzBkAb4Itg081m9rWZ/dfMKgfbagM/ZTptY7DtaNuzepzrzGyxmS1OTU3NyxBFREREREREpLh66SWfxElLC/21nYO6deGWW/yqh5krta67DqpXh3PPhZUr83bduXN94/otWyAmBipVghEj4Mwz/f0ZM+A//4G1a/3xJUvCtm3w1FNw0kk+Ifbvf/uph0CXLl0wM1q1agVAgwYNOOGEEw5LTkWjHKf4ZRxoVg74FHjUOfeemdUAdgAOeAQ/DfAqM/s3sMA593pw3ovANHwyrK9z7ppg++VAB+fc0OweV1P8RERERERERCRX3ngDLrsMFi2C9u0L97FXr4ZevaBMGV8FlVNF1y+/+ETU6NFQrx4sXeob2efW1q0wfrzvIbZ2rW/YHu9n7K1cuZJmzZoRl80YzKxoTfEDMLMSwLvAeOfcewDOua2Z9o8DpgR3NwJ1M51eB0jvUHa07SIiIiIiIiIiBdOnD5Qo4RM3oUxQOQdffOH7Th2tx1Xz5n7q3/btPjm1eLE/p0IFqFjR/6xQAU45xe/v08cnpa680ieq8pKcAqhRA26/3d82bfLJKecgJSUqm6DnJDdN0g14BfjFOXdrpu01g/5UmNltQEfn3MVmdjLwBoeapM8EmgCGb5LeE9iEb5J+qXMu27UNVUElIiIiIiIiIrl24YW+WfmmTVC6dGiuuXKlTyy99hoMGpS7cx55BO6//4/bd+6EKlVg9myoWjV0/cCcg/PO89d+8cUcD4+2CqrcTEDsAlwO9DCzZcGtH/B/ZrbCzL4GugO3AQQJpwn45ucfAX9xzqU551KBm4GPgdXAhJySUyIiIiIiIiIieXLddX763Pvvh+6akyb5nz175v6cO+/00/C+/95XU82aBRMnHqqU6t49tM3qzaB2bXj9ddi1K3TXLSS57kEVKaqgEhEREREREZFcO3gQ/vY3v8JdqBJAnTtDaqrvbRXNlizxUxvHjoVrr8320KJYQSUiIiIiIiIiUjTExMDTT/vk1E8/wV/+ApsL0AJ72zbfS+rss0M3xnBp2xaaNfNVVEWMElQiIiIiIiIicmyaO9dXE51wAtx9N/z2W96vMXWq7+90zjmhH1+omfkeWZ99Bhs2RHo0eZKrVfxERERERERERIqcSy/1K+898AA88YSfojd9+tFX4svKJZdArVrQqlX4xhlKl18O5cv7lQOLEPWgEhEREREREZFj37/+BbfcAlOmQP/+kR5N4di7F8qWzXKXelCJiIiIiIiIiBS2G2+E55+HXr1yf87AgTBmTPjGFE5LlvipjYmJkR5JrihBJSIiIiIiIiLHvrg4uO46KFUKVqyAb76B9ev9qn9ZmT4d3nkHUlIKdZghU6cOJCX5FQ2LAE3xExEREREREZHiIy0NTjoJvvvO32/YEC67zE/769TJbztwwPec2r8fVq3ySa2iaORI3xz+vfdgwIDDdmmKn4iIiIiIiIhIpMTGwqRJMG4cPPecT1A9+ii8+abff+AA9OkDq1fDM88U3eQUwK23+mTceefBVVf51Qhzycz+a2bbzGzlUfabmY0yszVm9rWZtc20b7CZfR/cBufq8VRBJSIiIiIiIiLF2u7dkJwM1avDunVwxhm+mmrChLyt+BeN9u6Fxx7zv48Y4X+mpWFxcdlWUJnZGUAS8KpzrkUW+/sBQ4F+QEfgn865jmZWBVgMtAccsARo55zbld0w4/L8xEREREREREREjiUVKvgb+Iqqn36K7HhCqWzZQ4kpgBkz4NlnczzNOfeZmTXI5pBz8ckrByw0s0pmVhPoBkx3zv0CYGbTgTOBN7N7PE3xExEREREREREpLsxg6FCAODNbnOl2XR6vVBvInMnbGGw72vZsqYJKRERERERERKS46Nkz/bdU51z7Alwpq7mPLpvt2VIFlYiIiIiIiIiI5NVGoG6m+3WAzdlsz5YSVCIiIiIiIiIikleTgCuC1fw6Ab8557YAHwN9zKyymVUG+gTbsqUpfiIiIiIiIiIichgzexPf8Pw4M9sIPACUAHDOjQGm4VfwWwPsBa4M9v1iZo8Ai4JLPZzeMD3bx/PN1qNXfHy827NnT6SHISIiIiIiIiJyzDCzvc65+EiPI52m+ImIiIiIiIiISEQpQSUiIiIiIiIiIhGlBJWIiIiIiIiIiESUElQiIiIiIiIiIhJRSlCJiIiIiIiIiEhEKUElIiIiIiIiIiIRpQSViIiIiIiIiIhElBJUIiIiIiIiIiISUUpQiYiIiIiIiIhIRClBJSIiIiIiIiIiEaUElYiIiIiIiIiIRJQSVCIiIiIiIiIiElFKUImIiIiIiIiISEQpQSUiIiIiIiIiIhGlBJWIiIiIiIiIiESUElQiIiIiIiIiIhJR5pyL9BiyZWYHgX2RHgcQB6RGehByTFAsSSgojiQ/FDdSUIohCQXFkYSC4kjyS7FzSBnnXNQULkV9gipamNli51z7SI9Dij7FkoSC4kjyQ3EjBaUYklBQHEkoKI4kvxQ70StqMmUiIiIiIiIiIlI8KUElIiIiIiIiIiIRpQRV7o2N9ADkmKFYklBQHEl+KG6koBRDEgqKIwkFxZHkl2InSqkHlYiIiIiIiIiIRJQqqEREREREREREJKKUoBIRERERERERkYhSgkokxMzMIj0GESm+9BokItFAr0UiEkl6DSqalKDKxMwuNbNWwe8KaMmv0um/KI6kIMxMr9GSH+UAzCw20gORosnMzjGzxpEehxR5Ga9Bej8kIhFQCcDM4iI9EMk9ffgBzKyXmc0F/gG0AXDqHi95ZGZ9zGw+MNrMLgPFkeRd8MHw9kiPQ4oW86qb2RzgBQDnXFpkRyVFTfB+aAHwIlAz0uORosnM+pvZdOAZMzsD9H5I8s7M/mxmj0R6HFL0mFlFM/sE+AjAOZca4SFJHhTbBFXwZr6MmU0A7gVGAO8AZYP9+uZZcs3MqgEPA/8HvAFcZGZ3B/uK7b8zyT0zizOzO4FRwFNm1to5d1CvRZIbwYe/5ODW0sz+BHr9kZwF74fKmdlk/Puhe4GFQP1gv2JIcs3MGgCPAv8CVgPXmdk1wT7FkmQreD2KDWLmKeAuM+sa6XFJkZMM7AJamNlA0Gf7oqTY/kfhvH3AeOdcN+fcx8B84PJgv755llwJytZrAMudcxOdczOBu4DhZnZckGRQabtkK/h2JxFoBtwOPB9s12uR5Cj44FcHWIZ//bkfwDl3MJLjkugXvB9KAl4P3g/NxH/rfG6wXzEkedEY+Nw5Nwl4CV/ROdTMKuv9kOQkeD1KA9bgZ7XcBKiKSnItSERVwn/RchE+WY5zLk2vP0VDsUtQmdktZjbOzK4FcM59EGyPBdYBq8ysbiTHKNHPzAabWW/IqFxIAk4zsyrBtm+AtwleFEWyErwejTSzC4NNU51zyc65fwDVzezS4LgSkRulRKNMsXM+ZCQRNgMnAvOALWZ2g5k1ieQ4JXpliqGBAM65t4LtscCvwE9mViqSY5ToZ2YXmFnHTJs2AuebWang/7M5+C+A74vIAKVIyPT57Jpg06fOud+dc+OAeDO7Ojiu2H12lexlip2rzMyCBOduoL9zbgrwtZndb2YtnHNOSaroV6z+kZvZEOBS4F1gkJn93cwaQUaVwm6gFf6NmcgfmFllM3sHGAk8nV4u6pxbD3wF/DPT4XcDjcysoXovSGZBCftt+G92FgMPBa9PlTMddjvwJIBz7kChD1KiUhaxM8LMhgTJ8RPwlZw78B8InwaeCc5Tg1ABsoyhh4MYqgYZ74fW4d/cp0RwqBLFgp53n+Knpd+dnjhwziUC04EnguMMGAPUM7Maej8kRzri89nlQYuMRpkOuR+4Pb0KLwJDlCh1ROwMxr8WNQbK4yuoAP6Hj6GXg/t6PxTlilWCCugJPOGc+wgYBpQEBqXvdM6tAPYBF0dmeBLtnHO7gE+A5sASgmk0gZuBM83s1OD+HmA5sL9QBylRL3iD3h241zn3DnAbPjneN9Mx7wPfmdlw8M2LIzFWiS5HiZ3WQG/gZ+AMM5sGXImvpFobnKqpogJk+/pzZqZj5gMbzeycyIxSop1zbhvwAT5utgDXZ9r9MHCWmZ2cqT/e7/hqc5EjHfn5rDRwWfpO59yHHOpnVj696lOErGNnIP7z/J/MN0q/BZgFbAjOUcP0KFcsElSZykG/As4CcM4txmdWa5lZl+A4wycfSqv8T46UKSZedc79CjwHnGdm9QGcc7uBh4D7zGwwvtFsC/SGrFg78rUk0+vRYqArQPAf63fAyWbWNNPhNwL/Z2Y/A7ULYbgSRfIQO4lAS3yi6idgkXPuZPyXLd3MrLaqFoqnfLz+NAuOqwB8C6h6U7KLo38B3+DfO/c3s5oAzrk1+JUgnzOz0/FfBlcHVP0iGbL5fLaATJ/PAncCjwPfA8cX5jgl+mQTO/OBhsDp+ErOL51zrZ1zffDvhzSrpQg4JhNUZtYlKO8DDmvwOQ+IsWDJW2Al/lufWsFxDv8f6B4Fr2QRRy74mRz8XAR8iF+tJv2Y0cA/gHb4FZAucM79VpjjlqhTJvOdTK9Ha4DyZnZKcP9ToCK+LBkzaw2Mw5ctt3XOvVI4w5UoktvY+QyoAGwHbnDOPRAc/wvQxTm3qZDGK9Enr68/5YLjduOb7tcopHFKdMsyjpxzB4IFPubjE5p/zXTM4/gk1dVAU+DqYHEiKabS22KkJzxz+/nMzE7Afyk8Ef9+SP1di5k8xM4qYBP+vfT9zrl7M12mnnNuXSENWQrgmEpQmVnboJRvFv6NVvr29Of5PT5wLzKzWOfcRnwWvkGmywx3zv23kIYsUSibOLIsmjOOBk4ws5PNrIaZneCcmwXc5pwb7JzbXIhDlyhiZp3M7F3g32bWJ9N/rulz37/ET7vqbWZxQWP92kD7YP9O4Cbn3EDFUfGSj9hZhU+It3HOJZtfojv9TZwqOIuhELz+AFzsnHu5MMct0SWbOLIjqqp2AJOAE82sTtCfqrJz7lXgeufchc65nyPwFCQKmFlnMxsH3GZmFdK/8M30epTT57PfgJudc+fp/VDxko/Y+Qmf2KzvnNsfvB9K74+3JxLPQfLumEhQmVkJM3seGItv1vgx0C3YF5spy/o7MBffe+op8ytjVcZ/EATAOad+QcVULuLIOb9EchkzS/+W+UfgfWAF/hvoCsF29XspxsysG/7bvvfw068GAZXNLCb4tjl9CsQifGPru4JTUwjmyDvnfgr64kkxUsDYWR/sT1MVcPEVihgKjkkuvFFLtMkhjpxzzplZKfOr9aU55z7Df1BciX8/dBzofXVxF1S2jMZ/6VsL38S6D0D66xE5fD5zzm13zn1f2GOXyCpA7FTiUOykOTXWL3KOiQQVUAo/xaGr88tJvgc0D74VTAMws4eAN/BZ+PvxL3xzg/uaOiOQuzh6ABhPsLqImV0C3AQ8BZzinFsakZFLtGmJ7wM0HngdKAEkpf8naWYjzOxFfKP9UUAHM1sC/IJPjErxVZDY+SRCY5boohiSUMgpjh4GXgBqBvdvwDdKfx5oqYSCBNoD85xzbwIj8NOGLzGzGuBfj9DnM8maYqeYKrLLLJpZJ+AX59x3+J5R4zPtjgXSnHOpQQnyKUAT4C7n3A/B+VcB8c653wt77BI98hFHTYG/pccRfinubprTXLwdEUfgE50PmtlmfAJzNb5Z7Mf4JtaN8HPj1wfnXwrEOd98X4oRxY4UlGJIQiEfcXQCmeII39fstKA6T4qpLOIoEWhlZrWcc5vNLAlfXXeumc3Bvx7p85kodiRDkaugMrNKZjYV35n/QjOLD8qMzQ71B/oUGBDMf3fACufcpc65HzLNQz2oAC6+QhBHsQDOuYVKThVfWcRR+tTPZfilt+vj+0h1wzdy7IVPhF7qnFuT6fUoSR8OixfFjhSUYkhCIQRxlP5+aIaSU8XX0eII3yNoN/Cy+X5mdfErr1Vwzn2nz2ei2JEjFbkEFRCPnwIzNPj9DPArrDnfHygG30PhYyAhfR/4ZumahyqBgsaRekwJ/DGOuqbvcM59CVQj6CmFn0NfCdgFej0SxY4UmGJIQqGgcaT3QwJHf1/9HTAMeBx42zk3AN+nrHv6iXo9KvYUO3KYIpGgMrMrzCzBfPf+Tfgm1hOAZKCjmaUvQ2pBkJYOTk1O3w6HLUkpxZDiSEIhD3FUCr/09k3BqT2BKsFxiqNiSLEjBaUYklBQHEko5BBHHdLjyDm33zk32zn3v+DUdsCH6ddRHBU/ih3JTtQmqIKpVjXNbDYwGLgM+I+ZHeecS3bO7QVm4Bui9QBf4WJ+tbUkwIBO6dsj8ywk0hRHEgp5jKOeAM65FPyy2+XM7DPgEvwyydsi8ywkEhQ7UlCKIQkFxZGEQn7eV2c693TzCzJ0BaYU9tglshQ7kltRmaAKkgMOKA9scs71xH978ws+wwqAc24efhpWMzOraGZlM5UaX+Wce7BwRy7RRHEkoZCPOGpqfj59GefcKvx/wkOccz2dc6sL/xlIpCh2pKAUQxIKiiMJhQK8r44Pdq0F7nPO9XWHmutLMaDYkbywaCoKMbM44GH86mnTgArABc65wcF+AzYDFzvnPg22lcMvPXkavpFjG+fc5ggMX6KE4khCoYBx1AWoB7QNSpelGFHsSEEphiQUFEcSCiF6X93OObcxAsOXCFLsSH5ETQWVmSUAS/BlfWuAR4ADQHcz6wAZU6weBh7MdGp/fAZ2OXCKkgrFm+JIQiEEcbQMH0d6U1/MKHakoBRDEgqKIwmFEL6vVoKhmFHsSH7FRXoAmRwEnnLOvQZgZm2AhsD9wH+AduZXVnsfH9gNghK/ZKCXc+6zyAxbooziSEJBcST5pdiRglIMSSgojiQUFEeSX4odyZeoqaDCZ1gnmFlscH8eUM859zIQa2ZDne/UXwdIS59/6pz7QAEsmSiOJBQUR5Jfih0pKMWQhILiSEJBcST5pdiRfImaBJVzbq9zLsUdak7dG9ge/H4l0NzMpgBvAkshY96qSAbFkYSC4kjyS7EjBaUYklBQHEkoKI4kvxQ7kl/RNMUP8F3+AQfUwC9tC/A78HegBbAufT58MG9V5A8URxIKiiPJL8WOFJRiSEJBcSShoDiS/FLsSF5FTQVVJgeBEsAOoGWQWb0POOic+1zNGiWXFEcSCoojyS/FjhSUYkhCQXEkoaA4kvxS7EieWDQmKs2sEzA/uL3knHsxwkOSIkhxJKGgOJL8UuxIQSmGJBQURxIKiiPJL8WO5EW0JqjqAJcDzzjnUiI9HimaFEcSCoojyS/FjhSUYkhCQXEkoaA4kvxS7EheRGWCSkREREREREREio9o7EElIiIiIiIiIiLFiBJUIiIiIiIiIiISUUpQiYiIiIiIiIhIRClBJSIiIiIiIiIiEaUElYiIiIiIiIiIRJQSVCIiIiLZMLM0M1tmZqvMbLmZ3W5m2b6HMrMGZnZpYY1RREREpKhTgkpEREQke/ucc62dcycDvYF+wAM5nNMAUIJKREREJJfMORfpMYiIiIhELTNLcs6Vy3S/EbAIOA6oD7wGxAe7b3bOzTezhUBzYB3wCjAKGAl0A0rLyDPvAAABfElEQVQB/3bOPV9oT0JEREQkyilBJSIiIpKNIxNUwbZdQDPgd+Cgcy7ZzJoAbzrn2ptZN2C4c+6s4PjrgOrOuRFmVgqYBwx0zq0r1CcjIiIiEqXiIj0AERERkSLIgp8lgNFm1hpIA048yvF9gJZmdkFwvyLQBF9hJSIiIlLsKUElIiIikgfBFL80YBu+F9VWoBW+t2fy0U4DhjrnPi6UQYqIiIgUMWqSLiIiIpJLZlYNGAOMdr5PQkVgi3PuIHA5EBsc+jtQPtOpHwM3mlmJ4Donmlk8IiIiIgKogkpEREQkJ2XMbBl+Ol8qvin6M8G+54B3zWwgMBvYE2z/Gkg1s+XAy8A/8Sv7LTUzA7YDfy6sJyAiIiIS7dQkXUREREREREREIkpT/EREREREREREJKKUoBIRERERERERkYhSgkpERERERERERCJKCSoREREREREREYkoJahERERERERERCSilKASEREREREREZGIUoJKREREREREREQi6v8Bw/OnAYcJKGcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,10))\n",
    "ax1 = plt.gca()\n",
    "ax2 = ax1.twinx()\n",
    "\n",
    "\n",
    "hero_df.plot(kind='line',x='Date', y='Close Price', ax=ax1, color='black')\n",
    "hero_df.plot(kind='line',x='Date', y='sma', ax=ax1, color='blue', linestyle='--')\n",
    "hero_df.plot(kind='line',x='Date', y='upper_band', ax=ax1, color='red',  linestyle='--')\n",
    "hero_df.plot(kind='line',x='Date', y='lower_band', ax=ax1, color='red',  linestyle='--')\n",
    "ax2.plot(hero_df['Call'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem 4.2 \n",
    "\n",
    "Now, we'll again utilize classification to make a trade call, and measure the efficiency of our trading algorithm over the past two years. For this assignment , we will use RandomForest classifier.\n",
    "\n",
    "\n",
    "Import the stock data file of your choice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Symbol</th>\n",
       "      <th>Series</th>\n",
       "      <th>Prev Close</th>\n",
       "      <th>Open Price</th>\n",
       "      <th>High Price</th>\n",
       "      <th>Low Price</th>\n",
       "      <th>Last Price</th>\n",
       "      <th>Close Price</th>\n",
       "      <th>Average Price</th>\n",
       "      <th>Total Traded Quantity</th>\n",
       "      <th>Turnover</th>\n",
       "      <th>No. of Trades</th>\n",
       "      <th>Deliverable Qty</th>\n",
       "      <th>% Dly Qt to Traded Qty</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15-May-2017</th>\n",
       "      <td>MARUTI</td>\n",
       "      <td>EQ</td>\n",
       "      <td>6730.20</td>\n",
       "      <td>6759.4</td>\n",
       "      <td>6839.10</td>\n",
       "      <td>6733.45</td>\n",
       "      <td>6824.0</td>\n",
       "      <td>6823.90</td>\n",
       "      <td>6796.84</td>\n",
       "      <td>336356</td>\n",
       "      <td>2.286158e+09</td>\n",
       "      <td>31377</td>\n",
       "      <td>154743</td>\n",
       "      <td>46.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16-May-2017</th>\n",
       "      <td>MARUTI</td>\n",
       "      <td>EQ</td>\n",
       "      <td>6823.90</td>\n",
       "      <td>6850.0</td>\n",
       "      <td>6977.55</td>\n",
       "      <td>6830.00</td>\n",
       "      <td>6968.4</td>\n",
       "      <td>6953.95</td>\n",
       "      <td>6902.22</td>\n",
       "      <td>707624</td>\n",
       "      <td>4.884174e+09</td>\n",
       "      <td>49047</td>\n",
       "      <td>360641</td>\n",
       "      <td>50.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17-May-2017</th>\n",
       "      <td>MARUTI</td>\n",
       "      <td>EQ</td>\n",
       "      <td>6953.95</td>\n",
       "      <td>6950.0</td>\n",
       "      <td>6979.00</td>\n",
       "      <td>6885.85</td>\n",
       "      <td>6945.0</td>\n",
       "      <td>6958.20</td>\n",
       "      <td>6931.60</td>\n",
       "      <td>445461</td>\n",
       "      <td>3.087758e+09</td>\n",
       "      <td>65280</td>\n",
       "      <td>260033</td>\n",
       "      <td>58.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18-May-2017</th>\n",
       "      <td>MARUTI</td>\n",
       "      <td>EQ</td>\n",
       "      <td>6958.20</td>\n",
       "      <td>6918.3</td>\n",
       "      <td>6948.00</td>\n",
       "      <td>6814.45</td>\n",
       "      <td>6822.0</td>\n",
       "      <td>6831.05</td>\n",
       "      <td>6869.68</td>\n",
       "      <td>406814</td>\n",
       "      <td>2.794684e+09</td>\n",
       "      <td>37063</td>\n",
       "      <td>188907</td>\n",
       "      <td>46.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19-May-2017</th>\n",
       "      <td>MARUTI</td>\n",
       "      <td>EQ</td>\n",
       "      <td>6831.05</td>\n",
       "      <td>6854.8</td>\n",
       "      <td>6893.90</td>\n",
       "      <td>6691.55</td>\n",
       "      <td>6756.3</td>\n",
       "      <td>6790.55</td>\n",
       "      <td>6791.60</td>\n",
       "      <td>552223</td>\n",
       "      <td>3.750475e+09</td>\n",
       "      <td>64293</td>\n",
       "      <td>234662</td>\n",
       "      <td>42.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Symbol Series  Prev Close  Open Price  High Price  Low Price  \\\n",
       "Date                                                                        \n",
       "15-May-2017  MARUTI     EQ     6730.20      6759.4     6839.10    6733.45   \n",
       "16-May-2017  MARUTI     EQ     6823.90      6850.0     6977.55    6830.00   \n",
       "17-May-2017  MARUTI     EQ     6953.95      6950.0     6979.00    6885.85   \n",
       "18-May-2017  MARUTI     EQ     6958.20      6918.3     6948.00    6814.45   \n",
       "19-May-2017  MARUTI     EQ     6831.05      6854.8     6893.90    6691.55   \n",
       "\n",
       "             Last Price  Close Price  Average Price  Total Traded Quantity  \\\n",
       "Date                                                                         \n",
       "15-May-2017      6824.0      6823.90        6796.84                 336356   \n",
       "16-May-2017      6968.4      6953.95        6902.22                 707624   \n",
       "17-May-2017      6945.0      6958.20        6931.60                 445461   \n",
       "18-May-2017      6822.0      6831.05        6869.68                 406814   \n",
       "19-May-2017      6756.3      6790.55        6791.60                 552223   \n",
       "\n",
       "                 Turnover  No. of Trades  Deliverable Qty  \\\n",
       "Date                                                        \n",
       "15-May-2017  2.286158e+09          31377           154743   \n",
       "16-May-2017  4.884174e+09          49047           360641   \n",
       "17-May-2017  3.087758e+09          65280           260033   \n",
       "18-May-2017  2.794684e+09          37063           188907   \n",
       "19-May-2017  3.750475e+09          64293           234662   \n",
       "\n",
       "             % Dly Qt to Traded Qty  \n",
       "Date                                 \n",
       "15-May-2017                   46.01  \n",
       "16-May-2017                   50.97  \n",
       "17-May-2017                   58.37  \n",
       "18-May-2017                   46.44  \n",
       "19-May-2017                   42.49  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maruthi_df = pd.read_csv('MARUTI.csv').set_index('Date')\n",
    "\n",
    "maruthi_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(496, 14)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maruthi_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Symbol                    0\n",
       "Series                    0\n",
       "Prev Close                0\n",
       "Open Price                0\n",
       "High Price                0\n",
       "Low Price                 0\n",
       "Last Price                0\n",
       "Close Price               0\n",
       "Average Price             0\n",
       "Total Traded Quantity     0\n",
       "Turnover                  0\n",
       "No. of Trades             0\n",
       "Deliverable Qty           0\n",
       "% Dly Qt to Traded Qty    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maruthi_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define 4 new columns , whose values are:\n",
    "* % change between Open and Close price for the day\n",
    "* % change between Low and High price for the day\n",
    "* 5 day rolling mean of the day to day % change in Close Price\n",
    "* 5 day rolling std of the day to day % change in Close Price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Series</th>\n",
       "      <th>Prev Close</th>\n",
       "      <th>Open Price</th>\n",
       "      <th>High Price</th>\n",
       "      <th>Low Price</th>\n",
       "      <th>Last Price</th>\n",
       "      <th>Close Price</th>\n",
       "      <th>Average Price</th>\n",
       "      <th>Total Traded Quantity</th>\n",
       "      <th>Turnover</th>\n",
       "      <th>No. of Trades</th>\n",
       "      <th>Deliverable Qty</th>\n",
       "      <th>% Dly Qt to Traded Qty</th>\n",
       "      <th>price_Open_Close</th>\n",
       "      <th>price_High_Low</th>\n",
       "      <th>Day_Perc_Change</th>\n",
       "      <th>5day_mean</th>\n",
       "      <th>5day_std</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",
       "      <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>19-May-2017</th>\n",
       "      <td>MARUTI</td>\n",
       "      <td>EQ</td>\n",
       "      <td>6831.05</td>\n",
       "      <td>6854.80</td>\n",
       "      <td>6893.90</td>\n",
       "      <td>6691.55</td>\n",
       "      <td>6756.30</td>\n",
       "      <td>6790.55</td>\n",
       "      <td>6791.60</td>\n",
       "      <td>552223</td>\n",
       "      <td>3.750475e+09</td>\n",
       "      <td>64293</td>\n",
       "      <td>234662</td>\n",
       "      <td>42.49</td>\n",
       "      <td>-0.009373</td>\n",
       "      <td>0.030240</td>\n",
       "      <td>-0.005929</td>\n",
       "      <td>-0.000907</td>\n",
       "      <td>0.013496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22-May-2017</th>\n",
       "      <td>MARUTI</td>\n",
       "      <td>EQ</td>\n",
       "      <td>6790.55</td>\n",
       "      <td>6803.95</td>\n",
       "      <td>6843.95</td>\n",
       "      <td>6689.25</td>\n",
       "      <td>6694.30</td>\n",
       "      <td>6701.70</td>\n",
       "      <td>6732.22</td>\n",
       "      <td>327493</td>\n",
       "      <td>2.204756e+09</td>\n",
       "      <td>36146</td>\n",
       "      <td>158645</td>\n",
       "      <td>48.44</td>\n",
       "      <td>-0.015028</td>\n",
       "      <td>0.023127</td>\n",
       "      <td>-0.013084</td>\n",
       "      <td>-0.003523</td>\n",
       "      <td>0.014507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23-May-2017</th>\n",
       "      <td>MARUTI</td>\n",
       "      <td>EQ</td>\n",
       "      <td>6701.70</td>\n",
       "      <td>6765.00</td>\n",
       "      <td>6910.00</td>\n",
       "      <td>6743.65</td>\n",
       "      <td>6867.95</td>\n",
       "      <td>6878.85</td>\n",
       "      <td>6855.36</td>\n",
       "      <td>956457</td>\n",
       "      <td>6.556860e+09</td>\n",
       "      <td>98981</td>\n",
       "      <td>414258</td>\n",
       "      <td>43.31</td>\n",
       "      <td>0.016829</td>\n",
       "      <td>0.024668</td>\n",
       "      <td>0.026434</td>\n",
       "      <td>-0.002048</td>\n",
       "      <td>0.017453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24-May-2017</th>\n",
       "      <td>MARUTI</td>\n",
       "      <td>EQ</td>\n",
       "      <td>6878.85</td>\n",
       "      <td>6903.00</td>\n",
       "      <td>6912.55</td>\n",
       "      <td>6813.00</td>\n",
       "      <td>6865.00</td>\n",
       "      <td>6869.65</td>\n",
       "      <td>6863.75</td>\n",
       "      <td>433647</td>\n",
       "      <td>2.976444e+09</td>\n",
       "      <td>48950</td>\n",
       "      <td>191595</td>\n",
       "      <td>44.18</td>\n",
       "      <td>-0.004831</td>\n",
       "      <td>0.014612</td>\n",
       "      <td>-0.001337</td>\n",
       "      <td>-0.002438</td>\n",
       "      <td>0.017400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25-May-2017</th>\n",
       "      <td>MARUTI</td>\n",
       "      <td>EQ</td>\n",
       "      <td>6869.65</td>\n",
       "      <td>6879.95</td>\n",
       "      <td>7018.00</td>\n",
       "      <td>6835.00</td>\n",
       "      <td>6970.00</td>\n",
       "      <td>6985.70</td>\n",
       "      <td>6916.65</td>\n",
       "      <td>435721</td>\n",
       "      <td>3.013730e+09</td>\n",
       "      <td>44648</td>\n",
       "      <td>177404</td>\n",
       "      <td>40.72</td>\n",
       "      <td>0.015371</td>\n",
       "      <td>0.026774</td>\n",
       "      <td>0.016893</td>\n",
       "      <td>0.004595</td>\n",
       "      <td>0.016482</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Symbol Series  Prev Close  Open Price  High Price  Low Price  \\\n",
       "Date                                                                        \n",
       "19-May-2017  MARUTI     EQ     6831.05     6854.80     6893.90    6691.55   \n",
       "22-May-2017  MARUTI     EQ     6790.55     6803.95     6843.95    6689.25   \n",
       "23-May-2017  MARUTI     EQ     6701.70     6765.00     6910.00    6743.65   \n",
       "24-May-2017  MARUTI     EQ     6878.85     6903.00     6912.55    6813.00   \n",
       "25-May-2017  MARUTI     EQ     6869.65     6879.95     7018.00    6835.00   \n",
       "\n",
       "             Last Price  Close Price  Average Price  Total Traded Quantity  \\\n",
       "Date                                                                         \n",
       "19-May-2017     6756.30      6790.55        6791.60                 552223   \n",
       "22-May-2017     6694.30      6701.70        6732.22                 327493   \n",
       "23-May-2017     6867.95      6878.85        6855.36                 956457   \n",
       "24-May-2017     6865.00      6869.65        6863.75                 433647   \n",
       "25-May-2017     6970.00      6985.70        6916.65                 435721   \n",
       "\n",
       "                 Turnover  No. of Trades  Deliverable Qty  \\\n",
       "Date                                                        \n",
       "19-May-2017  3.750475e+09          64293           234662   \n",
       "22-May-2017  2.204756e+09          36146           158645   \n",
       "23-May-2017  6.556860e+09          98981           414258   \n",
       "24-May-2017  2.976444e+09          48950           191595   \n",
       "25-May-2017  3.013730e+09          44648           177404   \n",
       "\n",
       "             % Dly Qt to Traded Qty  price_Open_Close  price_High_Low  \\\n",
       "Date                                                                    \n",
       "19-May-2017                   42.49         -0.009373        0.030240   \n",
       "22-May-2017                   48.44         -0.015028        0.023127   \n",
       "23-May-2017                   43.31          0.016829        0.024668   \n",
       "24-May-2017                   44.18         -0.004831        0.014612   \n",
       "25-May-2017                   40.72          0.015371        0.026774   \n",
       "\n",
       "             Day_Perc_Change  5day_mean  5day_std  \n",
       "Date                                               \n",
       "19-May-2017        -0.005929  -0.000907  0.013496  \n",
       "22-May-2017        -0.013084  -0.003523  0.014507  \n",
       "23-May-2017         0.026434  -0.002048  0.017453  \n",
       "24-May-2017        -0.001337  -0.002438  0.017400  \n",
       "25-May-2017         0.016893   0.004595  0.016482  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maruthi_df['price_Open_Close'] = (maruthi_df['Close Price'] - maruthi_df['Open Price']) / maruthi_df['Open Price']\n",
    "maruthi_df['price_High_Low'] = (maruthi_df['High Price']  - maruthi_df['Low Price']) / maruthi_df['Low Price']\n",
    "\n",
    "maruthi_df['Day_Perc_Change'] = maruthi_df['Close Price'].pct_change().fillna(0)\n",
    "\n",
    "maruthi_df['5day_mean'] = maruthi_df['Day_Perc_Change'].rolling(5).mean()\n",
    "maruthi_df['5day_std'] = maruthi_df['Day_Perc_Change'].rolling(5).std()\n",
    "\n",
    "maruthi_df.dropna(inplace=True)\n",
    "\n",
    "maruthi_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a new column 'Action' whose values are:\n",
    "\n",
    "* 1 if next day's price(Close) is greater than present day's.\n",
    "* (-1) if next day's price(Close) is less than present day's.\n",
    "* i.e. Action [ i ] = 1 if Close[ i+1 ] > Close[ i ]\n",
    "* i.e. Action [ i ] = (-1) if Close[ i+1 ] < Close[ i ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Series</th>\n",
       "      <th>Prev Close</th>\n",
       "      <th>Open Price</th>\n",
       "      <th>High Price</th>\n",
       "      <th>Low Price</th>\n",
       "      <th>Last Price</th>\n",
       "      <th>Close Price</th>\n",
       "      <th>Average Price</th>\n",
       "      <th>Total Traded Quantity</th>\n",
       "      <th>Turnover</th>\n",
       "      <th>No. of Trades</th>\n",
       "      <th>Deliverable Qty</th>\n",
       "      <th>% Dly Qt to Traded Qty</th>\n",
       "      <th>price_Open_Close</th>\n",
       "      <th>price_High_Low</th>\n",
       "      <th>Day_Perc_Change</th>\n",
       "      <th>5day_mean</th>\n",
       "      <th>5day_std</th>\n",
       "      <th>Action</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",
       "      <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>19-May-2017</th>\n",
       "      <td>MARUTI</td>\n",
       "      <td>EQ</td>\n",
       "      <td>6831.05</td>\n",
       "      <td>6854.80</td>\n",
       "      <td>6893.90</td>\n",
       "      <td>6691.55</td>\n",
       "      <td>6756.30</td>\n",
       "      <td>6790.55</td>\n",
       "      <td>6791.60</td>\n",
       "      <td>552223</td>\n",
       "      <td>3.750475e+09</td>\n",
       "      <td>64293</td>\n",
       "      <td>234662</td>\n",
       "      <td>42.49</td>\n",
       "      <td>-0.009373</td>\n",
       "      <td>0.030240</td>\n",
       "      <td>-0.005929</td>\n",
       "      <td>-0.000907</td>\n",
       "      <td>0.013496</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22-May-2017</th>\n",
       "      <td>MARUTI</td>\n",
       "      <td>EQ</td>\n",
       "      <td>6790.55</td>\n",
       "      <td>6803.95</td>\n",
       "      <td>6843.95</td>\n",
       "      <td>6689.25</td>\n",
       "      <td>6694.30</td>\n",
       "      <td>6701.70</td>\n",
       "      <td>6732.22</td>\n",
       "      <td>327493</td>\n",
       "      <td>2.204756e+09</td>\n",
       "      <td>36146</td>\n",
       "      <td>158645</td>\n",
       "      <td>48.44</td>\n",
       "      <td>-0.015028</td>\n",
       "      <td>0.023127</td>\n",
       "      <td>-0.013084</td>\n",
       "      <td>-0.003523</td>\n",
       "      <td>0.014507</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23-May-2017</th>\n",
       "      <td>MARUTI</td>\n",
       "      <td>EQ</td>\n",
       "      <td>6701.70</td>\n",
       "      <td>6765.00</td>\n",
       "      <td>6910.00</td>\n",
       "      <td>6743.65</td>\n",
       "      <td>6867.95</td>\n",
       "      <td>6878.85</td>\n",
       "      <td>6855.36</td>\n",
       "      <td>956457</td>\n",
       "      <td>6.556860e+09</td>\n",
       "      <td>98981</td>\n",
       "      <td>414258</td>\n",
       "      <td>43.31</td>\n",
       "      <td>0.016829</td>\n",
       "      <td>0.024668</td>\n",
       "      <td>0.026434</td>\n",
       "      <td>-0.002048</td>\n",
       "      <td>0.017453</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24-May-2017</th>\n",
       "      <td>MARUTI</td>\n",
       "      <td>EQ</td>\n",
       "      <td>6878.85</td>\n",
       "      <td>6903.00</td>\n",
       "      <td>6912.55</td>\n",
       "      <td>6813.00</td>\n",
       "      <td>6865.00</td>\n",
       "      <td>6869.65</td>\n",
       "      <td>6863.75</td>\n",
       "      <td>433647</td>\n",
       "      <td>2.976444e+09</td>\n",
       "      <td>48950</td>\n",
       "      <td>191595</td>\n",
       "      <td>44.18</td>\n",
       "      <td>-0.004831</td>\n",
       "      <td>0.014612</td>\n",
       "      <td>-0.001337</td>\n",
       "      <td>-0.002438</td>\n",
       "      <td>0.017400</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25-May-2017</th>\n",
       "      <td>MARUTI</td>\n",
       "      <td>EQ</td>\n",
       "      <td>6869.65</td>\n",
       "      <td>6879.95</td>\n",
       "      <td>7018.00</td>\n",
       "      <td>6835.00</td>\n",
       "      <td>6970.00</td>\n",
       "      <td>6985.70</td>\n",
       "      <td>6916.65</td>\n",
       "      <td>435721</td>\n",
       "      <td>3.013730e+09</td>\n",
       "      <td>44648</td>\n",
       "      <td>177404</td>\n",
       "      <td>40.72</td>\n",
       "      <td>0.015371</td>\n",
       "      <td>0.026774</td>\n",
       "      <td>0.016893</td>\n",
       "      <td>0.004595</td>\n",
       "      <td>0.016482</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Symbol Series  Prev Close  Open Price  High Price  Low Price  \\\n",
       "Date                                                                        \n",
       "19-May-2017  MARUTI     EQ     6831.05     6854.80     6893.90    6691.55   \n",
       "22-May-2017  MARUTI     EQ     6790.55     6803.95     6843.95    6689.25   \n",
       "23-May-2017  MARUTI     EQ     6701.70     6765.00     6910.00    6743.65   \n",
       "24-May-2017  MARUTI     EQ     6878.85     6903.00     6912.55    6813.00   \n",
       "25-May-2017  MARUTI     EQ     6869.65     6879.95     7018.00    6835.00   \n",
       "\n",
       "             Last Price  Close Price  Average Price  Total Traded Quantity  \\\n",
       "Date                                                                         \n",
       "19-May-2017     6756.30      6790.55        6791.60                 552223   \n",
       "22-May-2017     6694.30      6701.70        6732.22                 327493   \n",
       "23-May-2017     6867.95      6878.85        6855.36                 956457   \n",
       "24-May-2017     6865.00      6869.65        6863.75                 433647   \n",
       "25-May-2017     6970.00      6985.70        6916.65                 435721   \n",
       "\n",
       "                 Turnover  No. of Trades  Deliverable Qty  \\\n",
       "Date                                                        \n",
       "19-May-2017  3.750475e+09          64293           234662   \n",
       "22-May-2017  2.204756e+09          36146           158645   \n",
       "23-May-2017  6.556860e+09          98981           414258   \n",
       "24-May-2017  2.976444e+09          48950           191595   \n",
       "25-May-2017  3.013730e+09          44648           177404   \n",
       "\n",
       "             % Dly Qt to Traded Qty  price_Open_Close  price_High_Low  \\\n",
       "Date                                                                    \n",
       "19-May-2017                   42.49         -0.009373        0.030240   \n",
       "22-May-2017                   48.44         -0.015028        0.023127   \n",
       "23-May-2017                   43.31          0.016829        0.024668   \n",
       "24-May-2017                   44.18         -0.004831        0.014612   \n",
       "25-May-2017                   40.72          0.015371        0.026774   \n",
       "\n",
       "             Day_Perc_Change  5day_mean  5day_std  Action  \n",
       "Date                                                       \n",
       "19-May-2017        -0.005929  -0.000907  0.013496      -1  \n",
       "22-May-2017        -0.013084  -0.003523  0.014507       1  \n",
       "23-May-2017         0.026434  -0.002048  0.017453      -1  \n",
       "24-May-2017        -0.001337  -0.002438  0.017400       1  \n",
       "25-May-2017         0.016893   0.004595  0.016482       1  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maruthi_df['Action'] = np.where(maruthi_df['Close Price'].shift(-1) > maruthi_df['Close Price'], 1, -1 )\n",
    "\n",
    "maruthi_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "maruthi_df.to_csv('maruthi_trained.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Construct a classification model with the 4 new inputs and 'Action' as target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 492 entries, 19-May-2017 to 13-May-2019\n",
      "Data columns (total 20 columns):\n",
      "Symbol                    492 non-null object\n",
      "Series                    492 non-null object\n",
      "Prev Close                492 non-null float64\n",
      "Open Price                492 non-null float64\n",
      "High Price                492 non-null float64\n",
      "Low Price                 492 non-null float64\n",
      "Last Price                492 non-null float64\n",
      "Close Price               492 non-null float64\n",
      "Average Price             492 non-null float64\n",
      "Total Traded Quantity     492 non-null int64\n",
      "Turnover                  492 non-null float64\n",
      "No. of Trades             492 non-null int64\n",
      "Deliverable Qty           492 non-null int64\n",
      "% Dly Qt to Traded Qty    492 non-null float64\n",
      "price_Open_Close          492 non-null float64\n",
      "price_High_Low            492 non-null float64\n",
      "Day_Perc_Change           492 non-null float64\n",
      "5day_mean                 492 non-null float64\n",
      "5day_std                  492 non-null float64\n",
      "Action                    492 non-null int32\n",
      "dtypes: float64(14), int32(1), int64(3), object(2)\n",
      "memory usage: 78.8+ KB\n"
     ]
    }
   ],
   "source": [
    "maruthi_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Symbol                    0\n",
       "Series                    0\n",
       "Prev Close                0\n",
       "Open Price                0\n",
       "High Price                0\n",
       "Low Price                 0\n",
       "Last Price                0\n",
       "Close Price               0\n",
       "Average Price             0\n",
       "Total Traded Quantity     0\n",
       "Turnover                  0\n",
       "No. of Trades             0\n",
       "Deliverable Qty           0\n",
       "% Dly Qt to Traded Qty    0\n",
       "price_Open_Close          0\n",
       "price_High_Low            0\n",
       "Day_Perc_Change           0\n",
       "5day_mean                 0\n",
       "5day_std                  0\n",
       "Action                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maruthi_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features=2, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=None,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maruthi_df.dropna(inplace=True)\n",
    "X = maruthi_df[['price_Open_Close', 'price_High_Low', '5day_mean', '5day_std']]\n",
    "Y = maruthi_df['Action']\n",
    "\n",
    "\n",
    "X = StandardScaler().fit_transform(X)\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.33, random_state=42)\n",
    "\n",
    "\n",
    "rfc = RandomForestClassifier(n_estimators=100, max_features=2)\n",
    "rfc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5153374233128835"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.fit(X_train,  Y_train)\n",
    "rfc.score(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the accuracy of this model , also , plot the net cumulative returns (in %) if we were to follow this algorithmic model\n",
    "\n",
    "![image](img.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x250d57c0a20>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1800x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cumulative Product of PCT change in Close_Price with predicted actions\n",
    "plt.figure(figsize=(25, 7))\n",
    "\n",
    "cumulative_returns = ( 1 + (maruthi_df['Close Price'].pct_change() * maruthi_df['Action']) ).dropna().cumprod()\n",
    "cumulative_returns.plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}