bmtgoncalves/DJIA.ipynb

## DJIA.ipynb
{
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "<div style=\"width: 100%; overflow: hidden;\">\n    <div style=\"width: 150px; float: left;\"> <img src=\"https://raw.githubusercontent.com/DataForScience/Networks/master/data/D4Sci_logo_ball.png\" alt=\"Data For Science, Inc\" align=\"left\" border=\"0\" width=150px> </div>\n    <div style=\"float: left; margin-left: 10px;\"> <h1>Dow-Jones Industrial Average</h1>\n<h1>Linear Regression and Feature Selection</h1>\n        <p>Bruno Gonçalves<br/>\n        <a href=\"http://www.data4sci.com/\">www.data4sci.com</a><br/>\n            @bgoncalves, @data4sci</p></div>\n</div>"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "from collections import Counter\nfrom pprint import pprint\n\nimport pandas as pd\nimport numpy as np\n\nimport matplotlib\nimport matplotlib.pyplot as plt \n\n\nimport sklearn\nfrom sklearn.preprocessing import StandardScaler\n\nimport tqdm as tq\nfrom tqdm import tqdm\n\nimport yfinance as yf\nimport statsmodels.api as sm\n\nimport watermark\n\n%load_ext watermark\n%matplotlib inline",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "We start by print out the versions of the libraries we're using for future reference"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "%watermark -n -v -m -g -iv",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Python implementation: CPython\nPython version       : 3.8.5\nIPython version      : 7.19.0\n\nCompiler    : Clang 10.0.0 \nOS          : Darwin\nRelease     : 21.4.0\nMachine     : x86_64\nProcessor   : i386\nCPU cores   : 16\nArchitecture: 64bit\n\nGit hash: HEAD\n\nwatermark  : 2.1.0\nsklearn    : 0.0\njson       : 2.0.9\nmatplotlib : 3.3.2\nyfinance   : 0.1.70\nstatsmodels: 0.12.0\ntqdm       : 4.62.0\npandas     : 1.1.3\nnumpy      : 1.19.2\n\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Load default figure style"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.style.use('./d4sci.mplstyle')\ncolors = plt.rcParams['axes.prop_cycle'].by_key()['color']",
      "execution_count": 3,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Data Preprocessing"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "We start by reading a list of tickers and company names from an Excel spreadsheet"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "tables = pd.read_excel('DJIA.xlsx')",
      "execution_count": 4,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "The list of selected tickers that we'll be using is given by the SYMBOL column"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "selected = list(tables.SYMBOL.values)",
      "execution_count": 5,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Which we can download using the Yahoo! Finance API. Naturally, we also include the DJI ticker since that's what we'll be trying to predict."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "data = yf.download(\"DJI \" + \" \".join(selected))",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": "[*********************100%***********************]  36 of 36 completed\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "By default we get all the data going as far back as 1962. For each trading day we have the __High__, __Low__, __Open__, __Close__, __Adj Close__ and trading __Volume__ values for each of the tickers."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "data",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 7,
          "data": {
            "text/plain": "             Adj Close                                                  \\\n                  AAPL        AMGN         AXP          BA         CAT   \nDate                                                                     \n1962-01-02         NaN         NaN         NaN    0.190931    0.495367   \n1962-01-03         NaN         NaN         NaN    0.194750    0.500192   \n1962-01-04         NaN         NaN         NaN    0.192840    0.513058   \n1962-01-05         NaN         NaN         NaN    0.189022    0.517883   \n1962-01-08         NaN         NaN         NaN    0.189499    0.521100   \n...                ...         ...         ...         ...         ...   \n2022-05-09  152.059998  239.240005  161.020004  133.309998  206.289993   \n2022-05-10  154.509995  240.710007  161.830002  132.949997  202.990005   \n2022-05-11  146.500000  240.639999  159.389999  129.389999  205.210007   \n2022-05-12  142.559998  244.720001  153.270004  123.139999  203.029999   \n2022-05-13  147.110001  243.399994  158.750000  127.199997  204.330002   \n\n                                                                      ...  \\\n                   CRM       CSCO         CVX         DD         DIS  ...   \nDate                                                                  ...   \n1962-01-02         NaN        NaN    0.361663        NaN    0.058398  ...   \n1962-01-03         NaN        NaN    0.360847        NaN    0.059182  ...   \n1962-01-04         NaN        NaN    0.357581        NaN    0.059182  ...   \n1962-01-05         NaN        NaN    0.348601        NaN    0.059378  ...   \n1962-01-08         NaN        NaN    0.346968        NaN    0.059182  ...   \n...                ...        ...         ...        ...         ...  ...   \n2022-05-09  163.600006  48.959999  159.250000  64.330002  106.980003  ...   \n2022-05-10  167.149994  49.549999  160.779999  63.320000  107.680000  ...   \n2022-05-11  161.270004  48.650002  163.160004  63.380001  105.209999  ...   \n2022-05-12  160.419998  48.290001  164.710007  63.250000  104.309998  ...   \n2022-05-13  166.910004  49.560001  167.869995  64.139999  107.330002  ...   \n\n                Volume                                                        \\\n                   PFE       PG        RTX        TRV        UNH           V   \nDate                                                                           \n1962-01-02         NaN   192000        NaN        NaN        NaN         NaN   \n1962-01-03         NaN   428800        NaN        NaN        NaN         NaN   \n1962-01-04         NaN   326400        NaN        NaN        NaN         NaN   \n1962-01-05         NaN   544000        NaN        NaN        NaN         NaN   \n1962-01-08         NaN  1523200        NaN        NaN        NaN         NaN   \n...                ...      ...        ...        ...        ...         ...   \n2022-05-09  25012300.0  8540800  6071900.0  2043500.0  3862700.0  10314100.0   \n2022-05-10  27335900.0  7447400  7526300.0  1468300.0  4275500.0  11016500.0   \n2022-05-11  29699500.0  7692200  6493900.0  1170000.0  3936300.0   9427300.0   \n2022-05-12  33468400.0  9600800  6356200.0  1315300.0  3997600.0   9308600.0   \n2022-05-13  22413900.0  5675200  5465000.0  1189700.0  2453400.0   6684100.0   \n\n                                                        \n                    VZ        WBA        WMT       XOM  \nDate                                                    \n1962-01-02         NaN        NaN        NaN    902400  \n1962-01-03         NaN        NaN        NaN   1200000  \n1962-01-04         NaN        NaN        NaN   1088000  \n1962-01-05         NaN        NaN        NaN   1222400  \n1962-01-08         NaN        NaN        NaN   1388800  \n...                ...        ...        ...       ...  \n2022-05-09  24391300.0  8900400.0  9402400.0  45851600  \n2022-05-10  23007300.0  8095900.0  8287100.0  34483200  \n2022-05-11  20133200.0  6218400.0  9292900.0  33020600  \n2022-05-12  21623900.0  6545700.0  8414800.0  29491500  \n2022-05-13  21078700.0  5781500.0  6648100.0  25777800  \n\n[15197 rows x 216 columns]",
            "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 tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th colspan=\"10\" halign=\"left\">Adj Close</th>\n      <th>...</th>\n      <th colspan=\"10\" halign=\"left\">Volume</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th>AAPL</th>\n      <th>AMGN</th>\n      <th>AXP</th>\n      <th>BA</th>\n      <th>CAT</th>\n      <th>CRM</th>\n      <th>CSCO</th>\n      <th>CVX</th>\n      <th>DD</th>\n      <th>DIS</th>\n      <th>...</th>\n      <th>PFE</th>\n      <th>PG</th>\n      <th>RTX</th>\n      <th>TRV</th>\n      <th>UNH</th>\n      <th>V</th>\n      <th>VZ</th>\n      <th>WBA</th>\n      <th>WMT</th>\n      <th>XOM</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      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1962-01-02</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.190931</td>\n      <td>0.495367</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.361663</td>\n      <td>NaN</td>\n      <td>0.058398</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>192000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>902400</td>\n    </tr>\n    <tr>\n      <th>1962-01-03</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.194750</td>\n      <td>0.500192</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.360847</td>\n      <td>NaN</td>\n      <td>0.059182</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>428800</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1200000</td>\n    </tr>\n    <tr>\n      <th>1962-01-04</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.192840</td>\n      <td>0.513058</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.357581</td>\n      <td>NaN</td>\n      <td>0.059182</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>326400</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1088000</td>\n    </tr>\n    <tr>\n      <th>1962-01-05</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.189022</td>\n      <td>0.517883</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.348601</td>\n      <td>NaN</td>\n      <td>0.059378</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>544000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1222400</td>\n    </tr>\n    <tr>\n      <th>1962-01-08</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.189499</td>\n      <td>0.521100</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.346968</td>\n      <td>NaN</td>\n      <td>0.059182</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>1523200</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1388800</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2022-05-09</th>\n      <td>152.059998</td>\n      <td>239.240005</td>\n      <td>161.020004</td>\n      <td>133.309998</td>\n      <td>206.289993</td>\n      <td>163.600006</td>\n      <td>48.959999</td>\n      <td>159.250000</td>\n      <td>64.330002</td>\n      <td>106.980003</td>\n      <td>...</td>\n      <td>25012300.0</td>\n      <td>8540800</td>\n      <td>6071900.0</td>\n      <td>2043500.0</td>\n      <td>3862700.0</td>\n      <td>10314100.0</td>\n      <td>24391300.0</td>\n      <td>8900400.0</td>\n      <td>9402400.0</td>\n      <td>45851600</td>\n    </tr>\n    <tr>\n      <th>2022-05-10</th>\n      <td>154.509995</td>\n      <td>240.710007</td>\n      <td>161.830002</td>\n      <td>132.949997</td>\n      <td>202.990005</td>\n      <td>167.149994</td>\n      <td>49.549999</td>\n      <td>160.779999</td>\n      <td>63.320000</td>\n      <td>107.680000</td>\n      <td>...</td>\n      <td>27335900.0</td>\n      <td>7447400</td>\n      <td>7526300.0</td>\n      <td>1468300.0</td>\n      <td>4275500.0</td>\n      <td>11016500.0</td>\n      <td>23007300.0</td>\n      <td>8095900.0</td>\n      <td>8287100.0</td>\n      <td>34483200</td>\n    </tr>\n    <tr>\n      <th>2022-05-11</th>\n      <td>146.500000</td>\n      <td>240.639999</td>\n      <td>159.389999</td>\n      <td>129.389999</td>\n      <td>205.210007</td>\n      <td>161.270004</td>\n      <td>48.650002</td>\n      <td>163.160004</td>\n      <td>63.380001</td>\n      <td>105.209999</td>\n      <td>...</td>\n      <td>29699500.0</td>\n      <td>7692200</td>\n      <td>6493900.0</td>\n      <td>1170000.0</td>\n      <td>3936300.0</td>\n      <td>9427300.0</td>\n      <td>20133200.0</td>\n      <td>6218400.0</td>\n      <td>9292900.0</td>\n      <td>33020600</td>\n    </tr>\n    <tr>\n      <th>2022-05-12</th>\n      <td>142.559998</td>\n      <td>244.720001</td>\n      <td>153.270004</td>\n      <td>123.139999</td>\n      <td>203.029999</td>\n      <td>160.419998</td>\n      <td>48.290001</td>\n      <td>164.710007</td>\n      <td>63.250000</td>\n      <td>104.309998</td>\n      <td>...</td>\n      <td>33468400.0</td>\n      <td>9600800</td>\n      <td>6356200.0</td>\n      <td>1315300.0</td>\n      <td>3997600.0</td>\n      <td>9308600.0</td>\n      <td>21623900.0</td>\n      <td>6545700.0</td>\n      <td>8414800.0</td>\n      <td>29491500</td>\n    </tr>\n    <tr>\n      <th>2022-05-13</th>\n      <td>147.110001</td>\n      <td>243.399994</td>\n      <td>158.750000</td>\n      <td>127.199997</td>\n      <td>204.330002</td>\n      <td>166.910004</td>\n      <td>49.560001</td>\n      <td>167.869995</td>\n      <td>64.139999</td>\n      <td>107.330002</td>\n      <td>...</td>\n      <td>22413900.0</td>\n      <td>5675200</td>\n      <td>5465000.0</td>\n      <td>1189700.0</td>\n      <td>2453400.0</td>\n      <td>6684100.0</td>\n      <td>21078700.0</td>\n      <td>5781500.0</td>\n      <td>6648100.0</td>\n      <td>25777800</td>\n    </tr>\n  </tbody>\n</table>\n<p>15197 rows × 216 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "For simplicity, we'll focus on just the data for 2020, the year  when we had the CoVID crash. We're also focusing on just the closing price, but this analysis would work with any of the other values."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "closing = data['2020']['Close'].dropna().copy()\nDJI = closing['DJI'].copy()\nclosing.drop(columns='DJI', inplace=True)",
      "execution_count": 8,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "And we add an intercept column."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "closing['intercept']=1",
      "execution_count": 9,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Utility arrays"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\nmonth_dates = ['2020-%02u-01' % i for i in range(1, 13)]",
      "execution_count": 10,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "The curve we're tying to predict looks fairly interesting"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "ax = DJI.plot()\nax.get_yaxis().set_major_formatter(\n    matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\nax.set_ylabel('DJIA')\nax.set_xlabel('2020')\nax.set_xticks(month_dates)\nax.set_xticklabels(months, rotation=0, fontdict={'horizontalalignment': 'center'})\nax.set_xlim('2020-01-01', '2020-12-31')",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 11,
          "data": {
            "text/plain": "(18262.0, 18627.0)"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 3840x2640 with 1 Axes>",
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Linear Regression"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "As usual, we'll split out dataset into a training and a testing periods. "
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "ax = DJI.plot()\nax.get_yaxis().set_major_formatter(\n    matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))\nax.set_ylabel('DJIA')\nax.set_xlabel('2020')\nax.set_xticks(['2020-%02u-01' % i for i in range(1, 13)])\nax.set_xticklabels(months, rotation=0, fontdict={'horizontalalignment': 'center'})\nax.set_xlim('2020-01-01', '2020-12-31')\n\nax.axvspan('2020-01-01', '2020-08-01', alpha=0.3, color=colors[3])\nax.text(x='2020-04-01', y=30000, s='Training')\nax.axvspan('2020-08-01', '2020-12-31', alpha=0.3, color=colors[2])\nax.text(x='2020-09-27', y=30000, s='Testing')",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 12,
          "data": {
            "text/plain": "Text(2020-09-27, 30000, 'Testing')"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 3840x2640 with 1 Axes>",
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JWrytXLomaWtgnUToVcCHmmw7Im4G3pEMf4ekuZtsv/K+ZNw3I+JPTTYcEYcDmcL9eYG3N9k2gKQlgN0ToQ8Be0bE/Q2n8GbgrkTctpLWarhtMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzM7PGucjZxlJE3B0Rn4+IE6riz+lgo2Tc4RFxTauZwGFApo05gKe3nEuX3pmM+2BEZApSa6kKfX+XCF0J2L7JtiWtDLw8EXoz8PEm257FO4EHE3F7SVqg4bbfCGTOOTMizm24bSLiSuDLiVCR/z01MzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMxsaFzmbjY81k3HHt5oFEBEB/DoZns17pElaCdgsEXol8OMWU/l8Mm7Xhtt9DTBnIu47EXFrw20DEBH/An6RCF0U2K7h5jPfzwC+0HC7s/oGZaf2XraXNF+LeZiZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZDcxFzmbjY4Vk3KmtZvGIk5NxK7WaRXd2puyS28s3I+KBFvM4BvhnIm4zScs22O4uiZgHgW822ObsfDUZl8k3RdIzgNUTocdFxIVNtftYEXEjcHAidBFg27byMDMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzNrgouczcbHwomYAK5pO5HK1cm4hVrNojs7JWIeAn7UZhLVLtqZQtc5gR2aaFPSesCTEqEnRsS/m2hzIhFxOnBxIvSFkpZsqNnMzx7goIbaa6KNnVvNwszMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMxsQC5yNhsfmb/XW1reRXhWNyTj5mo1iw5IWh54aiL0tIjIFn8P4sfJuK0aai97np801F4T7cwFvKCh9jLXfxdwZEPtTeY04MpE3Askzd12MmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZm/XKRs9n4uDkRc0/rWdRvK5P3qMsWyx7dahaViLgI+Fci9LmS5m2gyS0SMQEc00BbGdl2MnlPStLjgTUToadGxJ2DttdLtZP3cYnQhYBntpyOmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmVnfXORsNj5uSsQs0XoWj1gqGfefVrPoRrbI+YRWs6jf1vzAswdpRNL8wLMSoedFxHWDtFXDWcBtibiBi5wZ3589NHP9ZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmatcJGz2fi4PBEzj6RFW8+kyBY5X9lqFt3IFArfCvy57URmcUoyLlOgPJkNgcxu0KcM2E5aRDwA/D4RupKkFQdsLlskfsqA7dSRbWvQn72ZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZWWtc5Gw2PjJFnQDrt5rFIzZMxmXzHklV0fiqidA/R0S0nc8s/pSMG/T3IXv82QO2U9coXf99wHkDtpMWEdeTWzzgaW3nYmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmbWLxc5m42P3yXjXtRqFvXauSIirmg9k3atDygRd07bicwqIi4Bbk6EDlromi0S7vT6yRdV9339kuYB1k6EnhcR9/XbTp8y17+4pJVbz8TMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMrA8ucjYbH78HrknEvULSnG0mImkNcsWvv2gzj46sl4zrbCffWfw9EbOKpMUGaGO9RMyDwAUDtNGP85Nx6w3QxtrA3Im4Yfzsu7h+MzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzM7PWuMjZbExExP3AvonQ1YEZ7WbDZ+i9u/FDwD4t59GF1ZNxF7Waxez9Kxm32gBtZI69vPr97NJlwAOJuOzPb5BjR/lnP8j1m5mZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZm1xkXOZuNlP+D2RNzHJS3dRgKSXgi8MhF6RERc3EYOHVs1GZctOm1Sts3sNTxK9Tu0YIN5NCYiHgAuTYT2de01j51yP3szMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzs7a5yNlsjETEDcC7EqErAL+StECT7UtaD/hZIvQ24O1Ntj1EmSLR+4Hr2k5kNq5KxvVb6Jo97so+zz+ozPUvKGmpPs8/ytff9s/ezMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzKxVLnI2GzMR8V3guEToRsCvJT2+iXYlbQX8Glg4Ef72iBhW4WvTVk7EXBcR0Xom/+vaZFzmGgY5LptH06bz9bd97WZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmrXKRs9l42hE4OxH3bOBcSXtImqefhiQtJ+mbwNFAZkfcL0fED/tpa9RIWhCYPxE66kW+/e5knD3O19+xiLgPuDkR2u+1m5mZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZm1ykXOZmMoIm4FXgj8JRH+OOB7wBWSPiFpE0nzTXaApKUlbSfpYOBy4M2AEm3tGxHvTsSNiyWScTe2msXEbkjGZa+j3+Om8/XfExF39nn+QWWuf/HWszAzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMz68Ncw07AzPoTETdJejbwdWCPxCHLAB+pXvdJugD4D2U32LuBRSkF0csDq9ZM5x7gvRGxT83jRl22QPT2VrMYvN1+C119/c3l0IZM23NJWiQibuuzjdXrBD805wLcvchafTZlZlZEBKs+Y5VH/dv9Cy/MA8qst2JmU53vEWbWFd9vzKwJvpeYWRd8rzGzJvheYmZdiQhWfebKPKi7WXOOOYAFWXjehZHvN2bWoJgjeN46yzzq3xbyvcbMavK9xMy64vuNmQ3K9xEz69c8k++hOju1aozqcJGz2RirdpB9naRjgC8CT0geOg+wbkNpnAW8PiL+2tD5Rkm2OLbfAtKBRMQdkh4C5ugR2naR81Cuv0a7ta9f0pzAIg3m0IY6199vngvVip5jLh6aI/NtMzOb3IJLLPqo/x/Vy8wMfI8ws+74fmNmTfC9xMy64HuNmTXB9xIz68pCj7nfmJm1Yf5F6015MTObHd9LzKwrvt+Y2aB8HzGzjrR2s+lVGGdmYyAifg48EdgN+EdHzZ4FbBMRz5iiBc4ACyTj7mg1i8Hbzl5Hv8cN6/qzuyj3c/3zA5mli4b5s2/z+s3MzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMWuUiZ7MpIiIeiIiZEfFkYB3g4zS/y+w/gQ8Da1XFzUc3fP5RM3cy7v5Wsxi87Xn6PPeoX3+23X6uf9SvvU7b/f78zczMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMxaM9ewEzCz5kiaF9gGeAnwYmCRhpt4AvAC4C5J90XEJQ2ff9Rki0OHWej6QCImW7D7WKN+/Zlrh/6uf9SvHdq9fjMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzOzVnknZ7MpQNKCkt4JXAL8DHgtsEQLTc0FPB/4CnCRpF9IenoL7YyKbHFotti0DaOwk/Owrn8UdnIe9Z89eCdnMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMxtBLnI2G3OSdgYuB74MLN9l08B2wFmSjpG0UodtdyV7j4xWs5jcQ4mYfu/1o379mWuH/q5/1K8d2r1+MzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzM7NWufDJbExJWlTSIcCPaGfX5jq2Av4qacch59G07E65c7WaxeQyOw5nr6Pf44Z1/dndlvu5/lG/dmj3+s3MzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMWjXM4iwz65OkZYBTgCfVPPQC4HfV6x/ATdXrTmAxSrH0EsAGwCbVa5nkuRcDfixpg4h4T828RtU4FLpm2r6vz3OP+vVn2+3n+kf92uu03e/P38zMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMysNS5yNhszkpYCTiJf4Pwg8HPgCxHxp0nibqheAL8HviFpDuBlwHuAZyTbe7ekhyLifcn4UZYtDh31Qtd+i1xH/frbLPId9Wuv07aLnM3MzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMxs5Mwx7ATMLE/S3MCxwJOTh5wPPDUitu9R4DxbEfFQRBweEc8EXg7cnDz0vZKmQpHzvcm4BVrNYnILJmL6LXId9evPXDv0d/2jfu3Q7vWbmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmbXKRc5m4+WjwAbJ2AOBjSLi/CYajohfAOsD2WLpT0p6WhNtD9GtybiFW81iApLmAeZJhN7SZxMjff012r2l7okj4l5yhc7DuvY6bd/SZhJmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZv2Ya9gJmFmOpA2A9yfDfwrMiIhoMoeIuEzSi4AzgDV7hM8NHChpw6pgdBz9Jxm3SKtZDN5u9jr6PW4qX//yDeXQhmzbNw3QxqXAqjXi7wAuGqA9MzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzM2vX6sBCNeIvbSsRFzmbjY+Pk/ub/QOwa9MFzg+LiJskvbhqZ/Ee4U8BXgt8t41cOpAtDl2szSQaaLffIldff+8i54UkzRURD/TZxiAWS8TcNkhuiy666Ljvxm5mZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmI2qOYSdgZr1JWhPYOhEawJ4RcU+b+UTERcAnkuHvkKQ282nRzcBDibhl205kwHbb3sl5Ol+/gKX7PH/fqr+pTLv9XruZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZWatc5Gw2Ht5MKabs5acR8Ze2k6l8B7gyEbcW8MKWc2lFRDwEXJMIXabtXCaQLfK9qs/zZ4/z9XdvCWDuRFy/125mZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZm1ioXOZuNhy2TcV9qNYtZRMS9wDeS4S9uM5eWXZqIWUTSgq1n8r+WS8ZlrmGQ45bv8/yDylz/g+SK8WdnlK+/7Z+9mZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmVmrXORsNuIkLQ2smQi9ATi75XQe6/hk3HNbzaJdlyXjVmsziQmsnoy7rM/zXwE8lIgbxrVn2706Iu7v8/yXNZhH09r+2ZuZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZtcpFzmaj7znJuBMiIlrN5DEi4m/ANYnQdSQt2nY+LbkkGbdGq1nMXrbQta/dfKvi4KsSoZ1fu6SlgMzv1CA7GU/bn72ZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZmZWdtc5Gw2+h6fjDu3zSQm8ddEzBzkr2PUnJeMe2KrWfTf5u3A5QO0kbn+hSQtP0Ab/ch+v/82QBvj/rOHwa7fzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzKw1LnI2G31LJONubDWLid2QjMtex6g5Jxm3fqtZPEa1M/YTEqHnDrjD90hef432/txvAxFxA7mdrLu+9myb95Mv1DYzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMzMz65SLnM1GX7Y4OFts3LQpXeQcERcDtyRCuy503QBQIi5bpDzo8V1f/4bJuC6ufwlJne1ULmle4CmJ0L9HxL1t52NmNtVJmiEperxmDjtPMzMzMzMzMzMzs+lC0szEuO2MYedpZmZmZmZmZmZmZmZmvc017ATMrKe5k3HDKma8Kxk3X6tZtOtsYPMeMatKWjYiru0iIWDjZNwfB2wne/yzBmynrsz13w2cP2A7fwRekoh7FnDFgG1lbUjuvjDoz97MzMzMzMzMzMzMzMzMzMzMxoik5YC1gBWAZYElgfkpc7fmAO6c5XUHcCtwOXAZcGVEPNB91mY2VUiaH1gVWK3638WAhYAFgXkpc/oevgfdCFwCXAxcHhH3DyFlMzMzMzMzGwEucjYbfXcn45ZsNYuJLZCMy17HKDqR3kXOAC8ADm45l4dtkYw7aZBGIuIqSf8E1uwRuomkeSLivkHay5C0MrB6IvS3DQx8ngh8MhH3AuAnA7aVlf3Zn9hqFmZmZmZmZmZmZmZmZmZmZmY2VJI2pMxb2ZSycP7iA5zuQUlXUYoOz6UssP8n4KKIiAFTNbMpSNKywGbAJtXryYD6ONUDks4Bfle9ToqI2xtL1MzMzMzMzEaai5zNRt+dybhhFTkvn4y7o9Us2nUC8JlE3AvpoMhZ0kLkdjI+LyKuaaDJE+hd5LwA8BwGLKpOemEy7jcNtHUWZcXaRXvE7SFpj+q/T42I5zfQ9kQyRc6Bi5zNRoakVYBLh51HQtv3L7Npb8D7wbkR8bQG0xmYpC2AX/d5+AERMaPBdMxsCCTtC7wxGX4bsFxE3NViSmaW4D7Jo7hPYjbmatzTPO5hZq3y/cjMevE4ipk1SdKKlHvKTpSdUpsyJ7By9dp0ln+/RdLZlMLD44A/RsRDDbZrZmNE0iLAy4FdKPeKORs47VzARtXrXcDdkn4FHAIc28XmK2aWM8BzpvdHxOcbTidN0t7AxxKhq0bEZe1mY2aSlgS+1PBpHwDufczrTuAG4PpZXv+OiAcabtvMzAbkImez0Xd1Mm6VNpOYxBrJuOx1jKKzgZvovdLpSyTNGxH3tpzPdsA8ibgminyhTBJ9UyLuVXRT5LxDMm7g64+IByWdTPmeD52klcgVuJ8bETe0nY+ZmZl1aj1JT42Ivww7kVnMGHYCZjY8kuajTKDLWoTyufGAdjIys464T2JmZmZmZlaTx1HMrCmSVgU+DuwIzN1h04sBm1evvYH/SPoNpeD56Ii4scNczGxIJC0FvAN4C7Bwy83ND2xfva6S9AXguxFxT8vtmll73idpv4i4ZdiJmNlIWAjYdUht3y/pYuCC6nU+cHpEXDKkfMzMDJhj2AmYWU/Z1a5e0GoWsyFpQWDdZPhlLabSqmrl0V8kQhcFtm45Hcg//PxZQ+39Grg9EfdKSa0+QJG0LPD8ROg/I+KvDTV7eEPnacKOgBJxTf3szczMbLTMGHYCD6tW537ZsPMws6F6OWViWx27t5CHmXVvxrATeJj7JGZmZmZmNiY8jmJmA5E0j6RPAH8HXkO3Bc6zswRlDstMSrGjmU1hkuaX9BnKHNAP0H6B82OtCHwDuFTSnpIy8+fMbPQ8DnjvsJMwM6N8nnoSZRO09wMHARdLulrSoZLeKGm5YSZoZjYducjZbPSdn4xbpyoA7dJW5HaEvyIi7mw7mZYdnIzL7HjcN0lPALZMhF4SEac30WZE3A38PBG6JGXlxDbtBcyZiPtRg23+Ahj676+kOSjX30vQ7PWbmZnZ6Nil7UVlatiBsnq2mU1f/Uy0fa6k1RvPxMy65j6JmZmZmZlZPR5HMbO+SVoNOB34CDDfkNMxs2lG0hbAeZTi5gWGnM6ywHeA30paa8i5TCmSotdr2DnalPF/Q5jrbmaWtTylFmJf4CpJv5X0FklLDTkvM7NpwUXOZiMuIi4Drk2G79piKrOTfRB3RqtZdONU4IpE3AskPbXFPN5O7t7ddJHrQcm4dzbc7n9Jmg94YzK8seuvCvQzO3m3bTvgCYm40yLi8pZzMTMzs+FYCth62ElUdht2AmY2PJJWATbr83DfP8zGn/skZmZmZmZmSR5HMbNBSNoY+COwwbBzMbPpRdLckvYBfk1uzlqXngOcK2nPYSdiZrUtQFm4xcxs1M0BbALsA1wh6bteZMXMrF0ucjYbD6ck494raZE2E3mYpA3I7SgMpUB4rEVEAN9Ohn+2jRyqXZzfkAi9H9i/4eZPAv6RiFtfUlu7Ob8LWDoRd2xEXNxw29+qEZvZaboWSXMBn0qGf7Pp9s3MzGykzBh2ApLWBDYedh5mNlS7Aerz2F0lNf65ycw6N2PYCbhPYmZmZmZmY8LjKGbWF0nPB04AHjfcTMxsupG0NOX+85Zh5zKJeYDvSPqOpLmHnYyZ1fL6aj60mdm4mA94HXC+pF9KWmPYCZmZTUUucjYbDz9Lxi0OfKjNROC/BZf7knsQ9xCjsQtuE74N3J6I20rSti20/1Vg3kTcIRFxVZMNV0XeX0qGf1HSQk22L2kl4APJ8C802TZARJwJnJUMX6Xp9oG3AZnVny4BDm+hfTMzMxsdL5a05JBzmDHk9s1siCQJ2HWAU6wAvLChdMxseNwnMTMzMzMz68HjKGbWL0lPpsy3WqCPw+8GfgN8hbLQwjOBJwLLAQsBcwELA8sA6wJbUQoZ9wPOBO4bMH0zG2NV4eFZwHP7PMU9lALpjwCvotxnlqPcd+YCFqX0cTYEdgE+DZwBPNhne3sCx0vq535pZsMxN/DxYSdhZtYHAS8BzpP0WUkLDjshM7OpxEXOZuPhGODmZOx7JO3UZjLAl4GNkrEnRsS1TTYuKZKv5zfZbkTcCnwnGf59Scs11bakvSid4l4eAr7YVLuPcTCQKZ5+PPldr3uqVqb+MZD5IPCHiDilqbYfI3tNK0raoqlGJT0V+Ewy/IsR0e+Ar5mZmY2HuSkPe4dC0hzAa4bVvpmNhBcAKw94jt2bSMTMhsp9EjMzMzMzs948jmJmtUmaj7LA/WI1DgvgKMp4zdIR8cKIeFdEzIyIP0TEPyPi2oi4MyIejIg7IuL6iPhbRBwXEd+KiL0iYmNKAeLzgc8Cf2v26sxslFUFzqdQv/8SwEnADsDiEbFFRHwqIn5W3Weure47D0bEbRHx74g4OyIOiYgPR8SzgCWAvYBz+kh9U+AoFzqbjZWdJa0z7CTMzPo0D/B+SrHzBsNOxsxsqnCRs9kYiIi7yRdYCvhhk0WW/z1x8UnKrrJZX246jyH7LHBDIm4p4AhJCw/aoKTNga8lw78fEecP2ubsRMR95HdTfrWk9w3aZrWy9beBZycPedegbU7ilBqxh0hae9AGJa0IHEFuB+9/AN8btE0zG6pTI0JDfD1/2N8AM0ubMcS2twBWHGL7ZjZ8ezRwjpdIWqKB85jZcM0YYtvuk5iZmZmZ2TjwOIqZ9ePTwJNqxJ8BbBwR21YFg3cM0nhE3BMRp0bEByNiXWBV4INAK/ORzGw0SFqZMj9upZqHngCsHxGbR8RPq7mutUXErRGxX0RsQBn//UvNUzxc6JyZZ2dmwzcHpc9jZtZL7Xm1wHyURaOWAZ5AqUN4BfAW4JuUPk+mHqSXVYDfS3pDA+cyM5v2XORsNj6+DtyWjJ0XOFbSR6pdTQZWPTQ7GvhwjcPOiojjm2h/VETEzZSB+4yNgOMlLd1ve5JeDBxJrsi1Tm59iYiDgdOS4Z+T9OGqULk2SfMA+wGvTx5ycET8vp+2WrAkcKKkDfs9gaQ1KStcrpI85K0R8UC/7ZmZmdlIObPH19eTtG4nmfyvGT2+fkYXSZjZcEh6HLBdA6eaB3h1A+cxs3a5T2JmZmZmZtYnj6OYWT8krQq8NRn+APD2iHhWRPyhrZwi4rKI+GxEPIUyF+qHwF1ttWdm3at2QD6CegXONwGvrHZtPrfJfCLiBGB9yoYn99U4dFPgW03mYmat2lbSs4adhJlNPRFxb7WAyvURcWlEnB4RP4+Ib0XEWyNi04hYGlgTeCNwOP1/xpkX2E/SVNsY0Myscy5yNhsTEXE98KEah8wJfAL4raQt+21X0gKS3kJZGW+rGoc+SOn0TUXfB05Nxm4M/FnSNnUakLSwpC8AvwIWSB72roi4sU47fdoLyK64+EngyGqlxzRJ6wC/JV/gfD3wnjptdGAZ4DRJ75I0d/YgSXNI2g34I7BG8rADI+LEfpI0MzOzkfQb4OoeMbt1kcisJC1G70l5M1tPxMyGaRd6L8J1L+U+1svug6djZi1zn8TMzMzMzKx/Hkcxs358GMjMMXkQ2Dkivt5yPo8SEX+MiN2BFSkbMVzbZftm1pofAOvViD8TeGpEHN5OOhARD0XEV4BnApfXOHQPSW9qKS0za95nh52AmU1fEfGviPhORLwSWBbYg94LgU/knVXth5mZ9clFzjbWJM2UFP2+gI8lmzp5kHYkzWjokvcF6hYyPpuyq/NfJb1P0sa9Ci4lLSZpm2pFmSuAfYAVarb7mYg4p+YxYyEiAtiJUlibsTzwK0m/lbSLpIUmCpS0hqS9gYsoRbvZXZAPiogfJmMHEhHnA2+rccg2wAWSvi3pmRPtLi5pbklbSDoEOBd4RvL8DwGvjohRfHAyL/Al4B+S3ilp+YkCJS0paS/Ktf8AWCTZxgWAB2bNzMymloeAg3rE7Cxpri6SmcWOwHyTfP1u4NCOcjGz4chMqD0SyEysW1fShgPmY2btcp/EzMzMzMysfx5HMbNaJC1CmY+U8dGIOKzNfCYTETdHxGeBVeg9fmRmI0zSW4EdahxyLLB5RFzVUkqPEhF/psyB/XuNw77mvpPZ2HjuIBt5mZk1JSJuj4gfRMTGwAuB0/s4zXuqOhAzM+tD15OPzGwAEfGQpB2BPwG1dsYF1gE+V/33XZIuAW6qXncCiwGLA0sCqzHYIgjHAHsPcPzIi4hrJO1CudbsLr2bVK+HJF0AXArcCsxD+d4/BVi6j3T+Rse7ZkfE9yQ9G5iRPGQ+yg7QewF3SPoLpUj8Tkox73LAuvReyXp29o6IzOrWw7Qa8GXgy5KuBP4B3AwE5W9vTeAJfZz3NuBVEXFnQ3mamZnZ6JgJvH+Sry8NbE2ZBNeVGT2+fkRE3Cpl1+kxs3EiaT3gaYnQA4HjgeuAZXrE7k4Z4zCz0TUT90nMzMzMzMxq8TiKmfXplcD8ibjzgc+3nEtKRNwLXDzsPMysP5JWod4uqscCL42I+9vJaPYi4mpJz6UUG62ZOGRu4PuSNuw6VzN7lGMoz5B6+Yyk46sNqMzMhq6qS/iNpD0o8/8XrXH4RyWdFRHHtJOdmdnU5Z2czcZMRNwIbEbZYblfC1AKap8LbAfsArwY2BhYg8HuDSdRii4fGuAcYyEiTgBeDTxY89A5gCdTvuc7Ux5SbEZ/Bc7/BLYYUpHr64Ff9HHcQpTVFV9G+f69BHg6/RU4fz0iPtnHccO0EmWFpx0oOw9tSX8FzncBL46I8xrMzczMzEZERFwInNkjbEYHqQAg6UnAM3qEzewgFTMbnj0SMdcDx0XEA8AhifidJE22G6uZDZn7JGZmZmZmZn3xOIqZ9eOFybjPRUTduUpmZrOzH7BgMvZcYPthFQ1HxH8oxZI3Jg9ZF3hvexmZWcIfgZ8n4p4GbN9yLmZmtUXE94G1KfezLAEHSXp8O1mZmU1dLnI2G0MRcQmlQPmvw87lMX4GbBMRdw07ka5ExE+BXYH7htD8BcDmEXHdENqmeti6I3DEMNoHvgm8Y0htD9ttwLYRcdqwEzEzM7NWzezx9W0kLdlFIsBuPb5+FXBCF4mYWfckzUtZpKuXQ6rPigAHJOIXA17eb15m1pmZPb7uPomZmZmZmVnF4yhmNoDnJWLuAQ5vOxEzm/okvZL84gq3UnZwvqPFlHqKiIuBnYDsbq8fkbRSiymZWW8fIreR1CclzdV2MmZmdUXE1cDzqVcvsTjwgzbyMTObylzkbDamIuJy4JnAD4edC3Av8K6IeFVE3N1mQ5IWqhF+bWuJzCIifkTZibmT9iq/Ap4REVd12Ob/iIj7KA9SP0l+8HBQ9wNvjIi3RkRXbY6Sf1J+9icNOxEzMzNr3U8ok1UmMje5yXIDkTQn8OoeYQdFxENt52JmQ7Md5SFML/+dkBsRfwH+kjhm9z5zMrPuuE9iZmZmZmaWtx0eRzGzmiQ9Dlg2EfqHtudmmdnUJ2kO4OM1DnlTRFzRVj51RMQJlM1RMualFFia2ZBExAXAgYnQNfDnHTMbUdUGgK8Cjqlx2OaStmsnIzOzqckr3piNsWrQendJBwP7AE8eQhrHAm+LiIs6am+TZNwvqg/HnYiI30taH/gG8MoWm7oN+CjwjVEp8K3y+Kik3wPfAlZrsbk/A3tGxB9bbGNUPQTsD7wvIm6bXYCk+YE1gScCSwOLAAtX/zsvZULyPcAtwNWU3Y3+Xq0yZWbWCUkrUwamVwVWBBasXnMDdwF3AtcBlwIXAf9wkYJNVxFxq6QjgB0nCZtB6YO26YXA8j1iZracQ2OqiUJPotyLHkfpKz3cb5oTuLt63UjpM10B/C0ibh1KwmajYY9EzN8i4tzH/NsBwFd6HLeZpJWrxdzMbAS5T9IO90nMzMzMzKYsj6OYWT9WT8Zd2GoWY07SgpTxllWr1+LAApTn0fdTnkXfDlxOeR79t4i4aTjZmg3VjuTnmv4qIg5pM5k+vA94KfD4ROzukj47Kv0nSctRxoRXpuS/COU+NT9ls6HbKXNEbwcuA/4BXOJ5Mzbm9qYsljtvj7iPSjowIiZbeHdaqPo0awNPoPRpFqP0Z+anzAG+E7iV0p+5hDIPeLbzis2sGRHxgKTtgVOBDZKHfUnSMdWmckM3HefuShKltuUJlP7XCsBClP7XPJTrfrjvdQvluv8eEdcMI1+z6c5FzmZTQEScJGkdyo667wE2arnJB4GjgM9GxB9abuuxNkvEBPCxthP5n0ZLZ+ZVkrYAPgNs2ODp7wN+BHxoVDtNEXG8pKdQfgf/D1iiwdNfCXwe+E5EPNjgedtwI+UhyRwNnvNE4P0R8aeH/0HSPMAzgOcDGwNrUQY+a7cr6XrgD5RFC44elZU3myDp8cCWwFOBp/DI4PDClMLxOyk/s8sog8KnA6dExPXDyNdsKpK0NPAK4AXAs4Flap7iVklnUPoeP4uI6xpOsVOSluGR+9LDg9EP35fmogyW3Eo1SAT8HvhNRNw8lIRtFPyQyQuKniZp3Yj4a4s57Nbj66dHxD9bbL9vkhYFnkvpM21ImdiydJ/nugQ4DTgaOH6qFBhVg7nrAc+jfH+eCKxEuS8tRHnQeBePLJTz54h40zByteGQtBKweSL0gNn82yHAF5h8DFKU+8zetZMbMdUDoRcB61ImB63MI+/zorzP3wz8E/g78FvgpIi4cygJN0zSKpTfladQFuB6ArAojzwgenhyzr8pn/P3iIgbh5Ks9cN9kgG4T2JmNnsevzUzs6nG4yj2MElPArYA1qeMuT6e0sdZkLKg1cPjRBcC5wN/BE6odoey6Sn7DPU/rWYxZqpnHJsA2wHPAZ5GvTmxIekfwCnAT4HfjfMk/mp33mdRvidrU+YSLUX5nLUQpSjqduAaymesvwK/oTz7GYnNNqwzH0zGPUCZCzhSIuJuSR8CDkqEzw28F3hzu1nNXlXUvB1l3uszKQVFdd0r6ULK/JFfU54tuZjRxkZEXCHp28Dbe4SuALwV+GLrSY0gSRtRdop9HvX7NA9J+iul+PJnwO/93m7WvIi4U9JOwLmUORC9rEbZpf47beY1kek6d1fS2sDLKJ+LNqIsFFH3HLdSxmtOpvS/zoiI+xtM08xmQ+6/2DiTNBPYddh5JOwWETO7aqx6WLIzsBXlg86cDZz2bsogyZHAocOaQCLpT/Re/eanEbFDF/lMRtJzgDcCW9NH56hyAXAo8O1x6RjCf3cUfg3wWsrgXD+/g/dRPnB/Hzg8Ih5oLsP6qsnalyZCT6V8IHkz5YPByn02eR3wS+CbEfG3KofVKYMYm1MeSszf57knE5QHGPsCR3Y90CHpMnp/z1aNiMsmOccClJ/B7pR7YF0PUh5g7Uf53RvbB1g2GurcPyLi+e1m0x1JW1MGpzejmb4IDGGhFUkzKAUdkzkgImZMco55Ke+Lr6MMmqhmGg8CJ1DuzUePwYIfNoEa94OPR8Te1TFzUFaTn+xh51cj4p0DJzgb1Q6D1zD5irpviIjvznJMpv8w6d9Nv6rJLBtTBio3pfQFmlx85mF3U/rp34yIs1s4/4SSv0eXR8QqPc7zNEqfdVvqFVn1PLdNLZI+Cny8R9iDwIoRce1sjj8KeHGP4y+n9PPHbqBS0sLA6ynjU+v2cYr7KGMt+0bEyU3m1ktDn79WAPYCtqcUNtcx6bmtPe6TPIr7JD1Uf+evT4T+JCIuaCOHOiTtQlmBfDLXRcS3u8jH2tf1uEsT759NaurzQY32LsPjt2az5ftR5/ejmfSeJ9DpM3ozmLrjKF3/jWc08fymadVCt7tRnget1scp7qE8q98nIn7To6196T2u+8aIuKGPPGwIJO0A/CQR+vmIeH/b+Yy6arL+/wEzgOUbPPVVwNeA/SLijgbP2ypJTwbeQSmkXLKPU1xDmZ+1X0Rc1WBqNoIkPYsyDzTjOxHxxjbz6Vc1BnsOZTHnXm4FlouIu1tNqiJpLuCVwJsoBUVNjw0/AJwJHAj8OHu/GqH51v999mDjqc/nTEsBF1MW/pnMTcAT2ljgVdLe5DbQ6nI8ZX7KM5i3Aqs3eOorKPPM9o2I2xs8LwCS3kDvPti/IuJHTbddVzXv+tWJ0O9HxJVt52OPGOd5tZLeCnwjGX5+RDylzXwea6rM3a2jWnT8DZQxmbpzVzJup8zh/QFwrOfwmrXDOznbWKseRswYchojp5pM9lHgo9Ub9kbAOpRdhB4PLEd52DE/MB/lXnAf5YHJHZTiymuASygrkPwN+FNE3NftlTyapMXoPeHlIUZk1eKIOA04TdKclILUZ/HIKpnLUlbIXIgy6HQHcBuls/53yvf8N+M62bcaENwf2F/S4pQVgNan/A6uQSn6Xpjy+/fwKsU3UVYp/jtlleITx3U3q4i4BHgX8K5qNaBNKbtQPJlHdsV7eCevOyjXfzVlldTzKYXS50RESFpR0ruAnehd4N8EAS+sXmdLendEnNJBuwOrBojfDryfwXYSn5NSSL458A9Jb4mIkwbP0Gx6kPRi4NOUHXiaNifwUuCl1USft47ye2X1UG1PygD5sgOcak7KrpAvAs6X9K6IOL6BFG0MRMRDkg4CPjBJ2C6S3tvSojA7MXkx0cOFNUNVFezuWL0e30GT81M+i86QdBjw/qoPOPKq1X+/TNnRwGxS1XvZjETob2Y3MbdyAL0n565M6X+fkM9ucMkH2bOdaCFpbuB9lM9+iw2QxjyUiS6vlHQ68I6IOGuA83VC0vLA5yn3XY8xTwPuk+RM0T7JtcAe9N5h5PGUgsWhkbQIpehxwR6hn+4gHTN7DI/fmpnZVDfVx1FsYtUieO8D3slgC5XPR1mUcltJfwbeNcmieFvTe7GLdwMuch4f8yTj+ilgnTKqOUh7U4qB5muhiRWBLwEfkPQB4HttLyoxyOI1klalFGVvS/2Ftme1HPBh4H2SvgV8IiJuHuB8Ntr2SMY9RHmmOJKqeXVfpfSfelmU8iwms/Nz36rFQl8PfIgyR7Atc1Ge9T4H+LKkHwNfjoh/ttim2UAi4gZJX6H3s9nFKTvIf7j9rIan+vz4BsoiWXV3Vs14PPA54L2SPg18veGCvMdT7nWTuUPSESMwD/wtlMVxJvMf4DMd5GJTx76UxUyelIhdW9JmXTzHmI5zd6vnwx+mbEy4UItNLUxZ4PxlwJWSfgB8LSJuabFNs2nHE9DMprhqNavfVK9x9zx6r2p3SET8o4tksqoPhr+rXtNORNwE/LR6TTsRcT6lcLk2SdsBP2ewBxGD2AA4WdI+wHsi4t4h5dGTpKdSVuruZ+ePyawFnCjpm5Rig6HuKG42yqrdvb4BvLyjJrcBNpf0noj4VkdtplUPlGcCz2341GsDx0n6CbBnRNzW8PltNM1k8oKipSkTmo5soe3denz9F8P+PawehL1jiCm8Ctha0tsj4ntDzGNSkhakTER4Pe3sJGlT02bAqom4ySaQHAncQu9C4N0Zk8m5VRHjAZQF5Zr0LOBMSd8A3jvsxeYmUq1K/Gl6r7RuU89M3CeZ0FTtk0TEg5L2Bz7RI3QHSe9oY3eFGnahd4HzQ8DI9tnMpiqP35qZ2TThcZRpSNKmlF0Uey0MVdfTKP2c/SnFzsMuDLD23ZOM62JRtZEk6TWU5xxLddDcEpTNHXaVtOMo7m5cjdN+lt5jIXXMTVmcahdJMyLimAbPbSOgel64fTL86Ii4qM18GvAT4AvkCgR3p8UiZ0kbUwqe1murjQksTCmUvIYR2RzIbBJfBt5M70Vb3i5pn4i4roOcOlftLPxDulmYfnHK933n6r39vIbOO5PeRc4LURaYyCxG0Ypq4fKdE6E/HtVn8zaaquene1P6Ihl7Aa0VOU/XubuSdqX0BZfuuOmVKIt2nFK9zKwhntRqZuNksx5ff5Dek+3MxslCDK/AeVZvBX4raZDdNVoj6ZXA6TQ/QW5WbwGOlTTIyttmU5akzYA/090gycPmB74p6QBJk+3q1ilJTwf+QPMFzrPaEThH0pNbbMNGRLXi8hk9wmY03W71+7Vhj7CZTbfbh0WGnQBlAsl3Je1frdA9UiStBJxG2V1+5PKzkZbZkfM24JcTfbFaLCmzu+rLJD0um9iwVCvfnkbzBc7/bYKykvTp1W7JI0PSfJIOpjwcc4HzNOQ+SU9TuU/yPaBX4eACwKsbbLMfr0/EHD8Kq4qbTScevzUzs2nE4yjTjKQPUYrNmy5w/m8TlDHdUyR1PWHXupddNOzZo/RctAuS5pd0EGVBgS4KnGf1bOBsSW0+961F0lySvkcZp22ywHlWSwFHSfpstdOkTR2bk9/h7vttJtKEqiDt4GT4Jm30n1R8gLIBznpNn99sKomI28ntlrsgvQtox5KkFwJn0U2B86w2oCy23cjcvmoRjNMSobs20d4AtibXf/xh24nYlPRT4JJk7JaS5mkjiek4d1fSIpIOpcwR8HiJ2RTiia1mNk56FTkfGBH/6iQTs+lnI+B3krp+YDQpSW+nfFBcoIPmXgAcXq3uZmYVSW8Bfk33D5Rn9VrgCEnzDTEHACQ9j7I6Wxffj9Uoi1A8vYO2bPhm9vj6i1tYkKTXjolXAic23Oa4ez3w41EqdK5WAT4LP1S3miQtBrwsEfrTiLi7R0xmdeb5yK2iPDSSdqRMRO7i88cGwGmSVuugrZ6qgqFfU3ZJteltZo+vu08yGhrtk0TENcARidA3NNFePyRtRK6Acr+2czGzR3j81szMpguPo0wvkuaQtC/wKbqZe7chcMaoLYhnjbsyGbcA8OI2ExklkpalLLo3zIXVlgaOl7TlEHMAQNKcwOHAHl00B7wf+H7Vrk0NWyXjbgeOazORBh2WjJsT2KLJhqtnJ7+kFG3678QsZ19y/Z49Ja3Sci6dqnYcPQYY1oJVCwI/k/R/DZ1vZiLm+ZJWbqi9fsxIxPwtIs5pOxGbeiIiyC/EvTDw/KZzmI5zdyWtCvwJ2L6L9sysWyMz6dbMbDJVYeXak4TcD3yyo3TMpqu1gF+0tZpUXZJ2B75Kt7tdb0V5WG1mgKR3A/swGg9rtqTco+YaVgKSngj8gm4m7j5sCeAESW3tJmmj4yfAZJPf5qHBSW3VZIVeE0YOioiHmmpzCtke+PywkwCQtAxwPLDssHOxsbQzZeXVXg7sFRARZwCZRckyOx4NhaTnUB5QddnvWZWyU88KHbb5P6r3hJ8AmwwzDxsZ7pOMj6b7JPsmYtaV9IwG26wjU2D9b+CothMxs8Ljt2ZmNs14HGV6+Srwxo7bfALwK0ldPoOybl0KPJCM/cQoLbbalqqw/1TgqcPOhbK4xBGSNh9yHt8AXtJxm7sB3+u4TWtPtlj/VxFxb6uZNCQi/kB+oYhskXdPkhYGjgW2beqcZtNBdW/5eCJ0nmTcWJC0M/ADhj/HTsDXJL25gXP9FLgr0d5QdnOWtCS5xYG8i7MNYiYQydhG+/HTce6upLWA3wFrtNmOmQ3P0AoAzMxq2pTJJ8L8MCIu7SoZsxHzL+APwD+AC4CrgGuBW4F7gAeBRYHFqtdawNOr10bU+4DzbOBLwNsaybxPkrYC9k+GXwtcDFxPGVRZiLLS7nLAKn00/x5JR0fEb/s41mzKqFaB+2LNwwI4BzgBOBu4iHLPugO4l1IcvBSwOrAx5QHTM2ucf0vgy0BTK06mSVqIMlk+s9rmfZR796WUFZDnABah7My8GvUHnhYBjpH0jIj4d81jbUxExG2SjgB2miRsBmXwsglb0bswdmZDbXXhfuAvlHvPBcCFlD7CtZR70D2UzxuPo/SXFqfsfPx0yn1orZrtvVvSGRHx8wZy70tVFPYLyuS3XoLy8P8S4CZKn2k+yv1ldUqhZZeFCTYaMhNlLwFOS57vQHovTra+pKdGxF+S5+xEVWR8BDBvIvxuyj3mCsr7/NyU+8qawMrU/1taETha0iYRcXvNY5vyWfIP3G6k9PGuBe6kjD8vDDye8j0YiUWzrH/ukwxsbPskEXGypH8kctiTMk7VGUmLADsmQr8fEQ+2nY+ZefzWzMymJY+jTBOS3kn/z8pvA/4JXE35DDg3jzwfegK9nw+tT5mAv0Of7dsIi4h7JJ1HGQfoZW3KvI13tprUEElaHDiF+pPXb6bsZHYGZQzmCuAGyrjtnJSxypWBp1B2UnsJZfwlY17gp5I2ioiLa+Y1MEl7Am9Khl9HGXv6D+Vz1oLAMpRxnX52j5wh6dKI+EQfx9qIqHZEXSUZfmJ7mbTiZMougr1s2kRj1QYhRwHP7ePwO4GTgN8Df6XMG7m++vcHKAvnLAYsT+kfPIUyPrwx5R5mNhXMBN4NPKlH3KslfSEizm8/pfZI2gQ4gPqbE14I/Ar4LWVu8MP3ivmBJSnfv2dTFluouyjMPpKuiIhf1TzuvyLidkk/p/dixa8FhtGH2JnymWsyDwA/6iAXm6Ii4kpJ5wJPS4Q/r6l2p+Pc3WpX+JMpn2vqupbSvz2N8jnpMspnxzsp9+b5KNe+PPBESv9rY8rPtdd9xMwa5CJnMxsXkw0w3YdX5rfp5Sbg6Op1QkT8J3HMf6oXlA8nBwNIWgl4XfVaPtn+myUdGhG/r5V1c1agPNif6CFvUB5a/Rg4MSKumuhEklYFtqFMwJ1st/hHHQZ8XdIG3inKpitJWwBfq3HILcC3gP0j4opJ4u6oXpcCv6GsQr4m8H7Kqo6Zwd63STo9Ig6tkV8TPkcZ4JnIfZQVLH8EnBIR98wuqJqc/wJKYciLyQ9wrwj8RNLzfW+a0n7I5AVF60taJyL+1kBbM3p8/fcRkdlNZJgupjzwOZqS72S7Tj7suuoFZQLMtwEkbUjpL+xEmQiS8S1JJ0fEzbWybs77KAOuE7kPOJKyI+epEXHjRIHVSuRbUh4CjcKOCdYySesAGyRCD4qI7Kq0B1EeXvYq8t2dISxY0sN3gCUm+fodlM8oP6Xcb2a740q1WvSWwB6UCXRZT61y2KXGMU15DvCuHjG/o3zGPCEiLpkoqFq1dxPgFZTdP2x8uU9Sz1Tqk3wH+HqPmB0kvSMibm2gvaxd6P39eAjvOmTWFY/fmpnZtOJxlOlD0sbA52sedjVlx7ZfAudM9DtQjcFuSikQeCkTLxS3vaTDauZg4+NEckXOAO+QdB/wwanW763GEQ+jXoHzyZRdjo+aaHyWUsRyL2WhxrOBAyTNTRlX+DBlwYFeFgd+LmnDiLi/Rn6DWoVS2D6ZC4H9gCMnK8Ku3re2A95AecactbekP0TE8TWOsdGyfo3YcVs47LfkipxXlrR4RNw0YHvfo36B8zmUwp8jImKynU8fnjdzFXDWw/9YLXD9HMrf7470Xhz0f0TEDCYYc5fUs58aEV4U2xoREQ9K+gjl/X4ycwCfpvzejyVJy1GeIdep2fk98JGIOHmCrz98n7gMOA74iKRnUj5DbpFNDTio6tNcVCO3x/ohvYucV6sW9P7dAO30Y0Yi5piIuL7tRGzK+w25Iue1JC3Qox/Q03Scu1vNqT2aegXOQRmL2YcyX3eyz833URamu5gy/+XhdhcCXgS8rHotUC9zM6ur7oowZmbDstkkX9s/Iq7sLBOz4XiIMiH2ZcByEfHaiDg0WeA8oYi4MiI+RinM+1rVTi9z0HtCa5t+QFmJbnZ+CTwlIraMiAMmmyAHEBGXRsQ+lAeF/0fZaS1jPeCVyVizKUXSisCh5HYbDsoAyRMi4sM9Bklmf4KIf0bE7pRV4bLFC/tURURd2YjJV80+FFgtIl4TEcdNVOAMZWe8iPh5RLwEWIeygm/WJsAHa8Tb+DmRstvuZGYM2oikJSgrvU5m5qDttOROSvHNsyJi9Yh4R0SckCwmmlBE/CkiXk9Z4f7XycOWpUyKGYYlgY9N8LUAvku5L70qIg6frMAZygq8EXFYRLyMhlY4t5G3RzLuwOwJI+Jyys4bvexSrYA/Kl5JKayZnYeAfYGVI+LNEXHqJBPoiIgbI+LgiNgUeBbw5xp57Cyp1wPiNnyDiceQTwE2iojnRsT+kxU4A0TEAxFxckS8hTIpzw+Mx5f7JL1N1T7JAZTdfyazAL0ntDTt9YmYY/v5TGpmffH4rZmZTTceR5kGqiLkQ8gXJ9wGvIPyjOyjEXH2ZEXu1RjskRGxPWXXoMkKPr4JLJTMw8bLz2vGvw/4g6TGdgQbEZ9l8nlis7oEeFFEbBYRR0w2Pjs7EXF/RMyk7NT1BcozlF7WpftnspP93V9LKe5cKyK+OlmBM0BE/C0iPgmsStkNPPs5S8DMjp/DW7MyxTcA1w5Y8DYMdTbqWG+QhiS9GXhNjUOuBl4RERtExCH9FjZFxIPVc6h3UBYo2Jbx23Hb7L8i4mfAnxKhL60KeMfV/uQXJbgH2DMinjNJgfNsRcSZEfFCyuItdyQPWxQ4UNIgCxicDFyeiJsxQBu1VYu6ZN73fth2LjYtZN+P52TAjSWm8dzd75FfCBfgTOBpEfGyiDip34XBIuKOal7dqyn38j0pO0GbWUtc5GxmI0/S8sCaE3z5HsrgttlUdR9lt541I+Il1UOZ+5puJCLurgZBn88jOz5PZgNJL246j6TZ3Q/uAHaKiO0i4u91T1hNtv8GZSW725KHvb1uO2ZTxP7A4xJxtwBbRsRbooEdwyLij8DTyU3qWYqys3JX1mL2uyncA+waETv2mrQ7O9X97AWUh+TZ3R0+KqnOquY2RqoBt4N6hO1SrXA/iJ2ZeJcIgLspK82OkhsoK0euGBFvjIgz2mikWiDmRcBbyC0Os5ekpdrIpYcFmf3P8AZg84h4Qz/3JfjvBEubwqqJsZnitNN6FbXORmYy7xKU3WpGxUQPSm4Ctq6Km2uv+F/dp55BmZyW9XVJi9dta0Cz6/c9QCky2qzqo9UWETf0O5HHhs99kklN6T5JlN2Zf5wIfcOgbWVJejq5iSr7tZ2Lmf2Xx2/NzGza8DjKtPJhyqJtGecC60fE1/p5th8Rl1XFzq+hPGt6rGUoP3ubYiLidOC8modtCJwi6UxJe0harPnMulPtmP7OZPhhwLoRkV0IbkIRcU9EvA/YnjJPp5cPSnrCoO3WMNE49SnAUyPioMkWUpid6nPWV4ENyP/eLQt8sU47NlKyRc5170Oj4F+Undozst+H/yFpdcqCCFm/Ap4cEXUXsZhUVfB8VES8gLJD91Hk55SYjZLsoiFjOUdc0iuYeDHtx7oJ2CQi9h+kzYg4BNiYsghKxsbkFpKdqL0g99n1VZK63AF1RiLmRsrOsGaDOqdG7AYDtjXt5u5K2hl4VTI8gI8Dz46IvzSVA/x3cbr9gSdTNqz7W5PnN7PCRc5mNg4mW53z2xHx784yMevWn4A1IuJNvVZabUpE/I5S6JyZKPb2VpPJuxF4TkT8ZNATRcQfgC0pk/Z72VjSkwZt02ycSNoF2CoRegtl4HXgB8qzqibUbw38IRG+a/WAaVjuBraJiPTODLMTxWeB3ckVL8wNfHmQNm3kzezx9WXI/Z1OZkaPr/88IrKTyrvwA8qqk5+PiFu6aDAivgXslghdgA6LfHq4HHhG3RV/bVp6KblJkgf0ce6fUXY37WX3Ps7dpRuBTSPi+EFOUu0S8lbgo8lDFgf2HqTNBtwLvCQivlF30pxNOTN7fN19kg4MqU+ybyJm3Q53VtgzEXMVcEzbiZjZhDx+a2ZmU5nHUaYBSauRfzZ+OvC8Jp7vR8TBlM/Wmd8Dmzo+3+dxz6DsbnW9pN9IerekDSVldvgaCdWCed8nN6d1f2CHiGj076PaVXKnROg85Md123IUpXDh+kFOEhH/Ap4LnJ08ZFdJgxZn2HBMtMnMY9VenGzYIuJB4J/J8CcO0NR+lLHWjH2Bl7Y9hh0Rf46IbYEvtdmOWRsi4jfASYnQ50t6Ydv5NElSnflbd1AWl87sbN1TRJwHbApkCxs/K2nhAZqcSe+FFhYGXj5AG2lVn3KXROiPIuL+tvOxqS8ibiS/sEDfm9dMx7m71SJe+yTDHwReHRF797tzc0Y1n/cIyq7cme+FmdXgImczGwcTFTnfRbe7NJp1KiIuiIgrhtDuecBrE6GbSVqp7Xx6uBPYoskVl6pdlr6RDM+uDmU29qrdCD6dCL0PeFl1L2lcRNwN7EDvgdi5yK/42bQAXhMRJzZ2woiZ5K9nW0nPbqptGy3VRIPTe4TN6Pf8ktahrPY8mZn9nr8NEXF6RNwxhHYPBL6WCN215VQybgZeGBGXDjsRGwuZibH3UHbJqKX6W82sVv9CSSvWPX9H7qMU+f61qRNGxCeB7yTD3yhplaba7sNrIuLYIbZvI8J9kv81XfokEXEOcFYitPWFXqpJNzsmQr9XTXQ0s+55/NbMzKY6j6NMDx+gFDT28i9g2yaLmSLiFMpu4a1N0LWR8yMGmyg9N/ACym67fwRulfQ7SV+RtLOkNSWpiURbsDuwViLuGOBNbS3CWO24+tVE6KslrdpGDgl/BF4VEdmdaydV7e62FZCZoyQ8X29cZfsLF7aaRXsuSMat0M/JJW3J5Jv1zGom8JYuF4ttetEHsw5l50J9ZoT7MLPzWmDlZOyrW9hx9ALK/LrM54jFgTcN0NYlwO8SoTP6baOmrSiLMfcys+U8bHrJ7urb15z7aTx39/2Ue1TG66rd7DtRFTvf3VV7ZtOFi5zNbBxsOsG/f3PQ1SjNbPYi4pfAL3qEzQFs30E6k3ldRJzbwnk/Rm5lra1baNumr+dJio5fM2vk9zpyg69vrCZctCYiLq/y6WUHSYu2mcsEvhYRhzd90oj4PPCrZPh7mm7fRsrMHl/fRlJm95DZ6bUT4JXkVtKdLj4EXN0jZo0RWNF+j4jIrl5u01g1ITazCvYR1Sqt/TgwETMHo7FAwOy8pyqsadrbgHMScXOR3zmoad+IiNqTsm1Km9nj6+6TdKfrPklmN+cuPo/tAizYI+ZByi5QZjYcHr81M7Mpy+Mo04OkZSlFxr3cT9lV9qamc6h2CPpa0+e10VQVxO1Kczt4Lwg8B3gHpYD6QuBmSSdK+pykV4zCQgmS5gU+nAj9B+Vvre3FzN5H752N5yT3zLpptwGviIh7mjxpRNxAWSgq8719gaT1mmzf2iVpcWD+ZHivccZR9e9kXL/3vExhEZRFCPbqssDZbJxFxB+AIxKhGwCvbDebZlTF2O9Lhn+3mqvbuGqn7OwOqO+siij7NTMRs6mkxw/QRtaMRMy5LY1b2/SV3dCs337ItJu7K2kZ4K3J8G9UmwiZ2ZhzkbOZjbyIWDUiNJtX9kOgmfXn44mYF7SexcSOiIiftHHiamX0gxKh60vKPgQwG1vV4Os7E6FnRsQP2s4H/ruCdq/dwxYAduognVldSu4BfL/eSHlw3cu2klZrMQ8brkOByVYCnAfYue5JJc1FKRSZzIER4R0jKhFxF2U3hF6G2Wf6eUT0WrzG7GEzyI0XZibYTuQkSnFiL7uN4GrcZwDfbOPEEXE/sAe5CWR7SFqkjTwmcRVl1yKzWblPMiKG0Cc5FOg1cX8BcoUAg8jsFn10RFzVch5mNnsevzUzs6luBh5HmQ5eB8ybiPtSRPy5xTw+BFzW4vlthETEhZTP1G0V8i5K2Y30fcDPgCslXSXpR5JeWxX3d20HcruavbP6PNCqarz2I4nQGZLmbDufx3hfRGTeG2qLiLOAryfD39FGDtaaOgU1mUXFRtF1ybjaOzlLeg6wfiL0XsqOrI3ssm42jXyI3I7DnxzC+24/NgHWSMTdRPubWHyI3H19aWDbAdo5jN6L9MwBvGaANnqqFvXYJhE6s808bFq6JhlXu8h5Gs/dfX11jl4uxBsCmU0ZLnI2MzOz2YqIvwB/6RG2iaS5u8jnMR4E3ttyGz9KxMwDrNdyHmajYDMgUzCbGUxp0hcSMS9rPYtH+0hVYNCKiLga+GoidA7aLyiwIYmI24BeRasz+jj11pQHF5OZ2cd5p7ofAQ/0iNm8i0Rm4yHyKwTbNFc9GJmRCL0W+HW/7VRFiQcnQlcDntdvOy15T5tFldVq0ZnvzUJ038f5aJt9HBtP7pOMnM76JNVOQTMToZki5L5IejrwtETofm3lYGaT8vitmZlNaR5HmVYyi3fdBHy+zSSqz2EfbbMNGy3VDt6vAe7rqMkVKL/vBwBXSzpV0pskPa6j9l+fiDk+Io5rPZNKRBwL/K1H2PLARh2k87ALge+23MYngVsSca+QlCk6sNGweI3YbLHwqMkWZ9f5Xjxsz2TclyLin32c32xai4i/k1vQ8InAbi2n04TXJuO+EBG3tplIRNxJeW/PyOY9u3buoCye08uu/baRtDNlTHgy95MbWzar49/JuKX6OPe0m7sraQ5yu0UDvCUiuvrcbGYtc5GzmZmZTabXQ/cFgSd1kchjHB4R/2qzgarI+5JE6Fpt5mE2InZPxBwfEWe0nsmj/YLeOxg8t8Mdey4CftxBO18Bbk/Edb2LtXXrhz2+vr6kp9Q8Z6+HQadFxEU1zznlRcSNQK8dOjboIpfZOMI/M6vheeQejPwoIgbdQeSAZFymD9KVkyLi9x2080kgEnFdvs9fix/02sTcJxkRQ+iTfJve96t1JT2zwTZnlSmgvgLobPKzmT2Kx2/NzGyq8zjKNCBpHXJ9ie+0XZxQOQS4qoN2bERExI+BF9L9rqpzAM8FvkUpeP6+pNbmhUhaA3hOInTvtnKYxDcSMVu2nsUjPt3A+8qkIuIWcte9IPCSNnOxRtWZM5GZizCKsru8S9K82ZNWxfyvSITeDnw5e14z+x8fI7e4y8ckzdd2Mv2qCvMyRX13At9pOZ2H/ZCyMFMvW0lacIB2ZiZi1pD07AHa6GVGIuao6pmaWZNuTsbNJWmumueejnN3nwesnIg7LSJO6OP8ZjaiXORsZmZmk/lTImad1rP4Xz/oqJ1zEzHDKPI260w1qLJ1IvTQtnN5rGoHg5N7hM0HPKuDdAD2a3N3x4dVO+Ydkgh9YpsTDmzoTqL3QOGM7MkkLQm8uEfYzOz5pqFefabFJS3fSSaP9v0htGnjKzsRNjuxdkIRcSFwViL0FZIWGbS9hny7i0Yi4mJyOzxtLmmhtvOpHOSVb20S7pOMls76JFWheeahceO7OUtamNxiD9/r4jOamc2Wx2/NzGyq8zjK9PCiREwA+7edCEBV2Pi9Ltqy0RERp1LmZPx0SCnMT7nnnS/pYEkrtdDGSxMxl0fEmS203cuJiZjNWs+i+A/d/R7sD2SKqbdrOQ9rTp3ikntby6Jd99SIrfP9eEEy/vsRkS1wMrPHiIjLyRX9rgi8ueV0BrE+uR3jD+1ooSQi4m7gwETo3JTCwn6dClyaiGtlN2dJa5Nb7LfXAs5m/ajTf0ov1DCN5+5um4z7Uh/nNrMR5iJnMzMzm0xm0KHrnTBup0yi7sLfEjHDKFgy69LGwGI9Yu4Hjmg9k9k7NRHztNazKJNYutzhMDP4DLBpq1nY0FQDhb1+D15dY/XHXSgPLCZyF8ObQDMORrHPdCu5wiMzqgmwmVXoz42ITB85IzPJdwFgx4baG8RtwJEdtpd5n58L2KTtRCo/66gdG0Puk4ycrvsk+yZidpC0aINtQvk96bWbwAN4wRezYfH4rZmZTWkeR5lWMoWLf6wKMrpyWIdt2YiIiBsjYgdK0UnXu3M9bA7K5/F/SHqnJDV47q0SMUP53Y+IS+m9wN9TG/5+TOSwiOik+DQirib3ue75Ladizamz62mdYuFRUufvo06Rc69FOR+WncNhZhP7NLld2T8wwgs8vSAZd3irWfyvbF9q834biIgg99l1+z53du1lRiLmOuDYFto2a2uxlek6dzfT//oPcEwf5zazEeYiZzMzM5vMbYmYrieJnRoR93fU1vmJmGVaz8JsuDKDr78d4oq0pydiuihy/mNEXNNBOw87E7g+Eeci56ltZo+vLwNsmTzXbj2+/vOIuD15ruloFPtMv/POq1bDTpSJsL0MvPvQLH4CZH5Hszsjtem4jv+ejqEU5/XSxfv8rfTeGdZsZo+vu0/Sna77JL8CruoRswDw6gbbhNzu0EdFxL8bbtfMcjx+a2ZmU53HUaaPjRMxR7WexSwi4u/AJV22aaMjIn4bEc+iFOD/ktwYYtMWBL4MHCtpyUFPJmle4DmJ0F8O2tYAej2PXhhYvYM8ulyIE3Lf82UkPbn1TKwJc9aIfai1LNpV556YXZQTcgvOXhwRf65xTjObjYi4HvhqInQJ4F0tp9OvjRIxdwMntp3IY5wB3JCIe8aA7RxA2ShkMosCLxuwnUeRNCdlQZ5efhQRw+hD29TXyk7OTMO5u5KWAtZMhB7R4bMoM+uIi5zNzMxsMpkPXsu2nsWjdTkonPngt3jrWZgN14aJmKZ2IuhHprC4i4fKnQ4+VzvmnZwI3aDtXGx4IuIi4Pc9wmb0Oo+kpwJP7RE2M5fVtDWKfabTOm7PxltmAuwDwCFNNRgRN5GbBPqMEZgk1fX7/C3A2YnQLt7nz6j6HWYTcp9kpHTaJ4mIB4H9E6GZouQUSU8n9zB8v6baNLPaPH5rZmZTncdRpgFJK9F7tyQYzjhsZhKxTWERcXJEbAc8nlLgcya9i0ma9iLg95JWHvA86wLzJOKm+/PoB8jtktak7Li4n0ePhzq7C87bWhbtqlMwdHcmSNKCwBMToSfUaNvMJvclyu6cvbyzKoQbNesmYv4YEXUKIgdW7bLc61kewDoDtnMZuT7LroO0MxsvApZLxM1suF2zh81dI7bOAvvTce5u9vOF+19mU5CLnM3MzKYASQtKeq6kN0n6qqRfSjpT0kWS/iPpDkn3SYo6L+DSRPMDr85b0z86bCuzA9K4Du7b6Dk1ItTxa0Yir8zk8S7/Lh8lIm6l94q8K3aQyhkdtNFPm6tWD95s6prZ4+vbSuo1obvXjolXACelMxphkpaW9GJJ75b0HUnHSTpb0uWSbpZ0l6QH+ugz/TDRfNd9pvM6bs/GlKS1ya0ofVy1enaTsjsaDXsXolF9n39K61n4XmJ5M3t83X2SWUyxPsn36P2ZbF1Jz2yovUzB9GXArxtqz8zq8/itmZlNWR5HmVbWTsb9qdUsZu+sIbRpIygiromIr0TExsDywGuBAymfi7uwJvBbScsPcI7Ms+hrqmfCw5IptGr7efRfI+Kultt4rAvJLSzVxTi1Dc5Fzo+WKnKmLMqZmWf/uxptm9kkIuI24HOJ0IWAD7WcTi2SFgCekAj9Q9u5TODMRMwiDSxik3lW9QJJKwzYzqxmJGLOjohhFoPa1NZGPwSm59zd9ZNx7n+ZTUEucjYzMxtTkjaS9ClJ5wC3UlZA+xbwduAlwDOA1Sg7VSxIvZWi6pi/pfNO5KoO27o9EVPnw6nZWKmKEDKrHP697Vx6uKnH15eR1PZnn7+0fP7Z+WsiRsBabSdiQ3UoMNmkhnmAnSf6oqS5gV16tHFgtarr2JE0j6StJX1b0iXAdZQdT74I7ElZzXV9ym4Hi1H6NXO2lE7XfaYLO27PxtceybgDW2j7WOCGRNxrqvvVMNzPcPo6mff5pSW1vYCC7yWW5T7JJKZynyQirgGOSIQOvJuzpIWBnRKh3/Uu9GZD5fFbMzObyjyOMn2sloi5KiIy/ZGmXTCENm3ERcS1EXFQROwaEasCywIvBT4JHANc21LTjweOltRvvztTIDvsZ9GZIudBCr0zOn8WXY3DZQqBsotC2HDVKabp+plmU9ooLlojGeeiObNmfZPcGONeDRTkNmllyjytXobVn88WQq4yYDuHA3f0iJmDskDPwCQ9jjJfupeZTbRnNoE6/ZDU4jPTeO5upv91c0RcXeOcZjYmXORsZmY2RiQtIOltkv5OWdHtQ5SVmtqa+JrR9SSxXh+ImpQZ1J6r9SzMhmelZNzv6u4w1uQLWLpHfnMCCwz2rZjU3cCVLZ5/Itmioy52srYhqSZQ/aJH2IxJvrYNvXfzm1kjpZEgaQVJn6f8bR4N7AWsOtysOu8zXdNxezaGqgmvr06E3gIc2XT7EXE/8ONE6NLAi5tuP+mSiHhwCO2Oyvu87yWW4j7J7E2jPsm+iZgdJC06YDu7UBbym8wDwA8GbMfMBuPxWzMzm5I8jjLtZCYSX9R6FqPVro2RiLguIo6MiI9GxIsjYjlgBUoByMeBXwO3NdTcepSF3PqReR69+ZCfRWfGPRbu8/qz/tny+SeSGaf2s+jx0KvYbFZtL67alqWScXfXWCAx8/sdeAESs0ZFxD3AJxKh8wJ7t5tNLdlFT/7VahYTy/YnBlq8JSLuBA5LhO46SDuz2JHyuzCZ+4BDGmrPbHYWSsY9FBH3JWOn69zdTP9r2IXdZtYSFzmbmZmNARV7ARcDX2e0duXseiXw1CpWZtaIqfRAss3Vhq8Yxo5yEfFvyiBsL5mJODbeZvb4+gaSJloJf0aPY0+LiItrZzQkkhaW9CXKJK/30nsgtUtd9pnuqh4cmfWyLblJH4dGxL0t5XBAMm73ltrv5bIRb7ft9/nrWz6/TS0ze3zdfZLR0HifJCJOpvcOAAuQKwiZzOsTMb+MiLZ2pzKzHI/fmpnZVOVxlOklM+ZyXetZjFa7NuYi4t8R8auI2DsiXgQsDjwL+BSDF+i9WdJGfRw3VZ5Ht73z7WUtn3+Qdv0sejzUWdB02dayaNcyybh/1zhn5h51Q41iJTPL+yG5otzXSBqVubTZ98RhLTKdbbeJ9/aZiZgnSnpmA23NSMQcGRFdLs5p00/27+aWGuecKp+VoN7npcx1exdnsynKRc5mZmYjTtLjgZOBbzOaA8nquL1h7KJmNl2N4j2nX20+WL6hxXP3cmMiZir9HG32TgSu6BEz47H/IGlpYOsex/2wz5w6J+m5wN+Ad9H9rskZXfaZ6qzGbtPbHsm47ATa2iLiHOC8ROjWkoYxWWpY7/PZdtt+n/f9xOpwn4Rp3Sf5TiJmz35PLunpwPqJ0P36bcPMGuPxWzMzm6o8jjK9LJaIyTyjaVxE3AXcNYy2bWqJiAcj4oyI+EhErAVsTNnh7oE+TifgS30cN1WeY7Zd5DzK49RLSZqz9UxsUFcD2d2Lx/XvMlvkXKco5nGJGC+4aNaCiHgA+EgidE7g0y2nk7VoMm4oi0xHxK1AZkGu7HVM1tZvKRs69TJjkHaqAvfMQjszB2nHLCE7BnNVjXOOa59sdup8XnL/y2wac5GzmZnZCKsmcJ4FPG/YuZjZtLTgsBNoUJufff7T4rl7yUygWaj1LGyoqp3ED+wRtoukuR7zb68GHvtvs7oLOGyQ3LoiaXfgBGDlYecyItraKcamEEnLAy9KhP4rIs5oOZ3M5N85gde2nMfsDOV9PiLuB25LhLb9Pu/7iaW5TzLt+yQH0HuS/ToDrMr/hkTMJZTvv5mZmZlZozyOMi1lFq26s/UsJuYiZ2tcRJwZEbsATwGO7eMUm1RzXOqYKs+j256HO6zn0Zln0QIWaDsRG0xVLJgtqlutzVxatHoyrk5xUaYg5/Ya5zOzeg4DzknEvUxSptC1bdmFb4e5yHSm7aYWb8l8dt1B0iALBu+aiLkGOG6ANswyVkjG1emHTJXPSlDv85L7X2bTmIuczczMRpSkp1F2QcquNGlm1rRR3HVsFN0z4m375zg9zOzx9WX530l4vQb7D4+IkR8UlPQ64PvA6wOHvAAAX8pJREFU3MPOZYRkV2K36W1XyoTXXnoVLDbhR+R2/Nut7URmY7q/z/t+YnXN7PF190mmqGoHgB8nQmvv5ixpYWDHROj+VbG9mZmZmVnTPI4y/cybiLmv9Swm5oXprDURcWFEbA28Ebi/5uF1P/f7OWbOsMaps+365zgeLkvGPbnNJFqUzfuyGufMFNn4PdmsJdV4/weT4Z9tM5ekzPvhgxExzOevmXtWU+/rBwC9ntksBry0n5NLmoOykHIvB0dE5jO02SCy/ZAra5xzuvax3f8ym8Ym2x3BzMzMhkTSMpSVcRce8FQPUFZWvQ64hbIS252UB9/303sQYSHgFQPmYGbjKzOBw0Z/Est0HfCaViLiYkmnAc+ZJGwGcDSApA2AdXucdmYjybVI0ubAdxo41T2U1cuvp+ycegdwN6XP9AC9+0yrA89uIA+zLmUmugZwUNuJRMQ1kn4DbNkj9ImSnh0Rv287p1n4fd6sBvdJBjbufZJ9gT16xGwv6e1VUXTWLvTeuf5+4Ic1zmlmZmZmVofHUWx2hrnIkhd4stZFxHckXQf8jPxmOi+VtGeNQhI/j84Z1jh1tnjA49Tj4S/AMxNxY1fkLGlJYMlk+F9qnDqzyI0L58xaFBHHSzoFeH6P0M0kvSAiTmg/qwll+kvDXmA6c89qZBPFiLhC0knA5j1CZwCH9tHEC8ntnjuzj3ObpUmaH1gtGX5pjVNP189K7n+ZTWMucjYzMxtNP6D+Ds63AKcAZwJ/Bv4FXDHIKmSSVsFFzmbTWd2VuaerRgZ3++RBHZvVTCYvKNpW0uIRcRPlIcFkLgdObiivVkh6HGXl18zfwayuBE4E/kh5iH1xRFw7YC4zcJGzjRFJzwXWSITeCXxcUssZAbBUMm53oMvJuX6fN6tvJu6TZEy5PklEnCPpLGCjScIWAF4DfLPGqV+fiDkiIq6vcU4zMzMzsxSPo0xbmcK+YU44dkGhdSIifiHp/cAXkocsCTwN+FMy/n6m7+T9OoY1Tp0d7/I49Xj4czLuSZIWiYjbWs2mWc+oEZv9PkBZgLIXvyebte8DwBmJuM8CwyxyznyGmFuSql2qhyHT77qnwfZm0rvIeQtJy0XENTXPvWsi5qyI+HvN85rV9RTy/fU6/ZDpOnf3bmDBHjHuf5lNUS5yNjMzGzGSXgxsXeOQ44BvAcdFxAPtZGVm01Tmgc2NEZGdTDNVzTPEtrsefLbR9lPgG5TCkdmZF9hJ0neBnXqc68AhPlTJ+iC5VVmhrHA/E/heRPyxtYzMxsfuybiFyD0c7NL2kt4WEXd21J7f583qc59kYtOhT7Ivkxc5A7yBZJGzpA2B9ROh+2XOZ2ZmZmbWB4+jTE+ZMZeJPvd2YZht2/TzZeC1lMn7Gc8gX+R8N73HQd8SEd9Knm+qGtY4dbYA3ePU4yFbVDMnZSHFY1vMpWmbJOPuAC6qcd7MnJn5a5zPzPoQEWdKOhJ4SY/QDSW9IiIO7yKv2ci+H85bI7ZpmcLAJnP7OWVu8yKTxMxJWRw3u6gOkhYFtkuEzsye02wAz68Re06N2Ok6dzdT5Oz+l9kUNcydSMzMzGz2Pp6M+zewRURsFRFHtVTg7A8CZtPbXYmYJSRN98WTeg2qDLttP1SeJiLidsoDgsnMALYFlugRd0ATObVF0lLAm5PhZwBrR8SeLRYTuc9kY0PSwsArh53HABYCtu+wPb/Pm9XkPsmEpkuf5FDgph4x60h6ZvJ8eyZiLgJOSp7PzMzMzCzN4yjT2q2JmKFMJJY0Py5ytg5FxEPAx2ocki2Ghtzz6GVrnG+qGtY4dbZdj1OPh7+Q+5uDesU6o+C5ybgzay6qeXsiZqoVFpmNqg8BDyXiPiVpzraTmUB2caklW81iApLmYfJi44c1tkhWRNwFHJYIrbtg2I70Lti+B/hxzfOa9eMFybgrIuLGGuedrnN33f8ym8Zc5GxmZjZCJK0PbJAIvQx4ZkSc0G5GfjhqNs1dl4gR+V3LpqqlR7ztXgUGNrXM7PH1Dem9oMrvIuLiZtJpzWvIFfEcA2waEXVW5O6H+0w2TnZguIW7TcjuoNSEobzPV5OoMytp+33eRtXMHl93n6QdQ++TRMQ95FbG71m8XN0Ld0yca/8x2PHbrC1TbfKKmY0v349sqvI4yuhq+75zTSJmmZZzmIgLPm0YjiJfHLlyjfNmnkevWON8U9Wwnkdn2r23KmCyERcR9wKnJsNf2mYuTZK0LGUH+Yzjap7+34kYvy+bdSAizgN+lAh9EvULZptybTJuWO/r2c8v2evI+mEi5smSNqpxzhmJmF9GxC01zmlWm6QFgE2S4afXPP10nbub6X8t13oWZjYULnI2MzMbLZmJmw8AL4+IK9tOBg/Emk13VyTj1mo1i9E3lEks1Qqbj0uEZibi2NRxEnB5j5i1e3w984Bh2DJ9psuBHasH9m1zn8nGyR7DTqABz5G0RkdtDWuyarZdv8/bqHKf5BHTsU/ybaBX0fH2khbtEbMzZee5ydxHrqjabKoahR3czczA9yObujyOMrravu9kxlxWbzmHUWvXprGIuA84JRleZ0w18zx6uj+LhtEep/YY9Xg5Nhn3REnrtJpJc15Bfi583SLnzNy8eSWtVPO8Ztafj1GeCfSyt6R5205mNrLviau0mcQkVk3GNfreHhG/B/6VCE0Vp0taE3hmInRm5nxmA3ol+fGJo2qee7rO3c30vzwuYjZFucjZzMxstGyaiPlBRPy59UwKr4hrNr31Kkp42FNazWL0rSAps9Ni01ajrMbXix8sTyPVDnYHDnCKO4HDGkqnFZIWAdZPhH4gIm5vO5+K+0w2FiQ9idwDv3HQ1S5Ew3o4kp187Pd5G0nukzzKtOuTVDtWn9AjbAHKTtiTeUOiuZ9HxA2pxMya8WAipssxgsziZ2Y2Nfl+ZNYyj6M8yqjdc6D9+87FiZgVJPVamKkNTxpCm2YA5yfjFqhxzszz6CdLmu7zXEd5nNpj1OPlmBqxu7SWRbN2TsZdERHZ+9jDLkvGjUtBuNlYi4hLgf0ToSsBb2w5ndnJblr0xFazGLzdq1po+4BEzE7J4vQZiZirgd8k4swGtVsy7kHyi808bLrO3b0sEbOqpAXbTsTMujfdB3/MzMxGRvUB/WmJ0P3azmUWHoQ1m8Yi4mbKoF8vz2k7lxE3B/lCpCatmYy7pNUsbBTNpPfOeRM5PCLuaDCXNjwdmLNHzE3AzzrI5WHuM9m4mAq7Dz1sV0m97gVNWK4qZOxa5n3+QfIPtsyGYSbuk0znPsm+iZgJi5glbUiuiLzLcTIzgMyu7Au3nsUjRmJxAzMbCt+PzNrncZRHjNo9B9q/72SKoET5bNi1jYbQphlAdpGxuWuc82+JmIWBdWuccyoaVjFUZpzaz6LHSERcDJyZDH+9pOzOhEMhaQPgWcnwg/po4txk3DD6A2bT1acoC+X28kFJnX5GqRZkzfSXMnOE25B55nI/8M8W2j4QeKhHzOOAl0wWUC1802sBXYCDIiKzWJdZ3yStAzwvGf77iLipzvmn8dzdvyRiBGzYdiJm1j0XOZuZmY2OJ9B7cuwNQFe7OANs0GFbZjaazkrEbCJprtYzGW2jOonlLvxgedqJiEuA0/o8fGaDqbQlM6HipIi4v/VMgGplSO+cYSOveq/OPPAbF8sBW3XU1jAejmTe5y+KiMwkY7OhcJ8EmN59kl/Re7X/dSRtPMHXMrs4XxgRp9TKymxw9yRiupw89+QO2zKz0eL7kVmLPI7yPzL3nAU6WpDuYa3edyLiCuDWROgwJhNni7nMmnZzMu6uGufMPIsG2LTGOaeizp9FV8WtmV3hzms7F2vc95NxiwOvbTORBvxfMi6AH9Q9eURcB/w7Ebp53XObWX+qv8uvJ0KXAt7Zcjqzk1nA5dmtZzF7mc8RF0bEfU03HBFXAicmQnft8fXNyS14NTMRYzaoj1OKbTNm9tnGdJy7e3Yyzv0vsynIRc5mZmaj4/GJmPMiot9dkGqRtBheadLM4A+JmMXxg+VhDEBPVAwwq79HRK+VMG1qmtnHMZcBpzSaRTsyfabMg6OmbEbvhWrMRsE2wDKJuEMiQsN8Aasnr2n3/r8dtYzq+3yX9zqzfs3s45jLcJ+kHyPVJ6lWyP9uIvR/ipmr3RV2Shy7f928zBqQWWl/8dazeMSwdv0ws+Hz/cisXR5HebTbgQcScY8boI20qpi6i11dz0jEbNN6FrOQ9GTK4ulmw5Dd0TWzu+LDzgfuSMS9ssY5p6KVJK3UcZtPBzLFEh6nHj8/Ifd3B/ChUd3NWdJawM7J8JOrRTn7kdn5emNJS/Z5fjOr7wvkxkXeNYS/zcwGRstX/frOSFoOWCcR2uYGTDMTMVtKWnaSr89InOOMiLgwlZFZnyQ9A9guGf4f4Md9NjUd5+5eRPme9bJt24mYWfdc5GxmZjY6MivqX996Fo/YhhGaHGtmQ3NsMm4q7WjQjy0lZVfmG5ikRcjtDnB627nYyPop9SaRABzY1WIqAxq1PtNLO2zLbBDZiaw/ajWLhIi4mNykkW0kLdV2PnS3YzTw34k5qyZC/T5v48B9ku6MYp/ku/QuhNhe0qKP+bedgYV6HHcvcEC/iZkN4LpEzNqtZwFImgd4bhdtmdlI8v3IrF0eR3l0G0Hu800n9x3gGXSzW/1JiZinS1q59Uwe8aoO2zJ7rOWScddkT1gtkvabROjGklbLnneK6nScGnhxIibIvQfZCImIO4DvJcNXAt7RYjqD+CL5eW1fHaCdzJyZuYAdBmhjFPRcPF+Saw5sJETErcDnE6ELAx9sOZ3HOjkZ1/Uzne3I7Tib+QzUr18At/aImRN49ey+UM2Ve1minZn10jKrR9K8wA/J7+L83Yi4p8/mpt3c3WoM6rhE6HqSuhqHMrOO+AOHmZnZ6MisfNnlbpxd7YpmZiMsIv4KZFa03UFSZleDqWp5YKMO29sGmDsR1+bgs42w6uH0z+scwvgUiYxMn0nSgsD2XbRlNohqtePMBKjrgV+3nE7WwYmYuenmYc0zqtWtu5J5OAt+n7cx4D7J9O6TRMQ1wBE9whbgf+/l/7O782wcHhGZVbzNmnZ1IqaLXQUBNqf3ggBmNnX5fmTWEo+jTGiU7jtdFQQcn4gRuc8wA6t2sH5dF22ZTeApybiLa573iESMgLfVPO9Ukx03bsp2iZi/eHxmbH0OuCsZ+4FRW2RA0kvJFeIDnBURRw3Q3NGUcete3tjlwvgtuC8RM0/rWZjl7QP8OxH3JkkrtZ3MLE4F7k/E7dp2Io+RnQt8QlsJRMTdwKGJ0Im+N9vT+9lctg2zQXwOWCsZeyflftWXaTx3N9t3e3OrWZhZ51zkbGZmNjoygxtd7FKGpA2ATbtoy8zGwk8TMfMAH2g7kRG3R4dtZSaxPEAZPLfpa2aN2N9FRGZQdBSMTJ8J2ItudgwxG9RrKavI93JoRPTacbMrh5L7e+9icaY5gN06aIdqAkzmmm4A/tpyOmZNmVkj1n2S/oxyn2TfRMx/CwIkbQisnzhmv74zMhvMhYmYp7WeRdHlOISZjR7fj8za43GU2RuJ+46kuSk/o9ZVk4kvSITuKWnRtvMBdgZW7KAds/8haS7gucnwf9Q8/ZGUgpRe9pC0Qs1zTyUv7KpIS9JzgTUToa0VQlm7IuI64NvJ8IWAg6rFNoZO0tLAd2sc8rFB2qsWcvxtInRtcosDjKpM0fsirWdhllQVzH4iETovsHe72TyiWvw3s5vzEyW9oO18ACQ9A9gwEfqXiLiq5XRmJmKeUs1ffqwZiWN/Ue30bdYKSXsBb69xyGcjIrMgw2Sm49zdo4E7EnG7dbxhgZm1zEXOZmZmo+P2RMzjW8+i+HRH7ZjZePgO8GAi7k2SsqvUTUU7S1qy7UYkrUtuIYpfR8QtLadjo+1k4F/AvYnXD4aUYz9Gos9UTVp7b9vtmDUkO4E1s+tPJyLiRnK75awtaaO28wH2ktTFCvXbAJkdEQ6LiMzOAWajwH2SFo16nyQiTqZ3UcA6kjau/juzA9oFEZGZWGjWhr8nYlaVtF6bSUh6Et3v4mVmo8X3I7P2eBxl9jL3nW2rQsg27QYs23IbszokEbME8L42k5A0H7kiDrO2vBTIFvP/rs6Jq+eZmb+1BYHP1zn3FDMH8JaO2vq/ZFym4MJG12cpC6pmbAx8psVcUqp+xkHkF5f8dUQc10DT+yfjvli9Z4+jGxMxU2mHSJsavg9clIjbtRq/6Er2s+LHW83iEdnPEQe2mgUQEWeQW0Brxqz/R9LqwLMTx82sn5VZjqQdgW/WOORy4MsNND3t5u5GxO3AjxOh8wFfbDkdM+uQi5zNzMxGx5WJmDUlrdJmEtUHsRe12YaZjZeIuJyygnYvcwMHd1QANIoWBN7fQTvZweeftJqFjbwo1oyI+RKvA4adbw2ZPtMLW8+iTKRZuoN2zAYi6dnAExOh/4yIs9rOp6bsA+AudnNeiVzhXd+qXZz9Pm9TjvskrRuHPsl3EjFvkLQwsFMi1rs42zD9GbgvEbdzy3l8AT/nNZvufD8ya4HHUSb1h0TMkrT4OUjSIsBH2zr/BL5P7n77bklt7mT9aWCVFs9vQyZp/pZ/h/pWjVt+MBl+dUT8q49m9knG7SLpFX2cf6p4i6RWiwyr38PMIjYXR8Qf28zF2hUR/yFf0A7wXkmvbyufpG+S72vcCezZULuHA9cn4laju6LFpmWub9XWszCrISIeAD6SCJ0T+FTL6czq5+R2IH2WpO3bTETSi8ndNx8gt+hME2YmYnZ6zPzDXRPHXAmc2FdGZj1Iei/lb2TO5CEB7BUR9wza9jSeu/vtZNwukrZuNRMz64wfNpmZmY2Oi4GHEnE7tpWApNXIfzAws+llb3L3qPXJTWCfqt4qae22Ti5pK8pK6b3cRHnQZjYV/TMRs4qkZ7aVQDWBpqmH4mZty05c/VGrWfTnSOC2RNyOkuZvOxng4y1PIHsjsF4i7h/AaS3mYWY57pPkzQTu6hGzA+U+uFCPuHvoYDcBs4lUE1IyBU2vkdTr97kvkl4DbNvGuc1sfPh+ZNYaj6NM7Azg/kTcG/s4d9ZXgRVaPP//iIh/k/t5zw0cKmnxpnOQ9FLg7U2f10bOgsDZkg6p5m2MkrdTngFn9LU4Y0T8hfyzze9LWqefdqaABYCvtHVySXMA+wJKhH+3rTysOxHxY+DoGofsK2mXtvKZjKTPUm8s9MMRcVkTbUfEvZSdrzPeU713j5tLEjEjuRiHTXuHAucm4l4BbNhuKkVE3El+sdZvSmplIVtJj6uRx48i4to28piNA+m9K+0SwDbw3wV3XpM5b0Rk5jaapUlaRtLhlEWnM33kh302Io5rMJW9mWZzdyPiz8Avk+EHSfJiLGZTgIuczczMRkQ1uHFeIvR9kpZoun1JSwG/AhZr+txmNv4i4q/AD5Phu0n6SjXIOBSSlpT0hSGsTDcPMFPSvE2fWNKSwP7J8G9HRK8iArNxdRZlxctevtBG45KeQW5lWbOhk7QgkF39eeQm50bE3ZSVrntZlPJgum2LA99to48jaQ3gc8nwr0RE5j5oZu1ynyQpIm4FftwjbH7KzmS9HBYRNw2eldlAjknELAt8rOmGJW2AdzM3s0f4fmTWII+jpM5/ciJ0m2q3sEZJ2ov+d6Ee1GfJFXivARxZ7TjdCEnPo/y+eY7f9CBgJ+Afkn4g6UlDT0h6EWVCf9bMAZp7L7md0xcFjpe01gBtDUzSVkMq9ty5xd2s3wtkFuy7A/cFp5LXAVcnY+eiFJG8vb10Hk3SnJL2B95f47BjgW80nMq3gSsScQJ+LOn5DbfftgsSMdldtM06Uz0z/WAyvPHPKZP4EmXR1l6WAn4mae4mG5c0J6UAPLNI1EPkF3IYWLWQ1AmJ0BnV/24GrJyIn9lnSmb/Q9Lckl4PnA+8vObhpwIfbTKfaTx390PkirsXB34jafkB2zOzIfMAqJmZ2WjJfHhfDPhBNRDRCEkrVG0P9SGQmY28DwLXJ2PfARxcTQrqjKQVJH0OuBR4D8P5zLMhsH+TA0VV0fRhwIqJ8DuAfZpq22zUVEUtf06EbiLpPU22LWkT4Hh67zBoNip2IPf7ekZEXNx2Mn06OBnX1STXbYGPN3lCSYtRVqBdOBF+FXBQk+2bWX/cJ6lt30TMXIkYT6C1UXBYMu7tTe7mLulplL/9fnZeNLOpyfcjs2Z5HKW37H1nn2rh1kZI2g34VlPnqysi/kW+UOrZwCmSnjBou1Xx5HGUHX5tepkb2A04X9LPh1UoJ2l74BdVPhlHRkRmYf/ZiohLgM8kw5cDTpO0Wb/t9UPSHJJeKukMyoIzw9pReqakdZs8oaStgE8lw78dEbc02b4NT7Vz58uBe5OHCPiqpJ9IWrS9zEDSysBvgdfXOOxfwM5N7+ZZ7eb81mT4/MDRkl7ZZA4TkfTyBhZdOCsR8yxJ6w3YjlnjIuJY4HfDzmNW1b31a8nwTYCfNFXoXM0rPhDYInnIDyLiwibariFTrLlVtcv1ronY0yLiogFzMkPSQpLeAPyTshlN3Q3J/gRsFxG9divvx7SbuxsR5wNfT4avBvxO0lMGaTND0vyS3lWNk5tZg1zkbGZmNlp+mox7CeWhxcA7lFYPff4ENPoAxMymnoi4nvJAPWtn4M/VKvOtUbGZpEOAy4D3MfzJ/q8Fvi1p4M9ckuajTCB4fvKQz0XEdYO2azbisn2mL0h606CNVfeZtwInUnYIMBsX2Qmr2Qmww3Ay8O9E3PObmDya9BFJH2riRJIWp96CU++vJtKY2WhwnyQpIs4hN1FuMudHxO+byMdsENVkqVMToXMBx0l6+qBtStqZMlGv7oQaM5vCfD8ya5zHUXr7KXB7Im5V4MRBC52rnZO+APyA4c9x+wS53RsBnkZ5PvZ//RQqSFpF0qGU37X5ZhNyHfCfuue1sTQH8DLgZEkXSHqHpGXbblTSipJ+RNn9L7uoyYPARxpo/lPA6cnYh3fr+rKkzAKSfZO0jKR3UoodjiC323GbFqLcZ9dr4mTVjt0/BzKbLdxIvhjdxkREnAXsWfOwHYC/Stqp6Z0CJc0j6R3AX4Bn1Tj0Vkph0S1N5vOwiDiSfF9wAeAwSftIanz+iqS5JO0s6c/A4cAaA57yD8D9ibgDJWUW5zfr2geGncBsfJIyjy3j5cCxVVFv36pnz7+izNnLuJEyz65rRwC39IiZi/LelNlFd+Zg6dh0VhU2byPpIMrn7f2AVfo41TnAFi32Q6br3N0PUT6HZTwBOEPSG5vun0LZwEDS+4CLgS8xZs/rzcbBsAeAzczMbBYR8Qfg7GT4q4E/9vvQQtKqkn5ImVA/0YOwc/o5t5lNXRFxDPD5GoesQVmx/ldNDphImlPSJpK+SBkcORHYidzOX13ZkzJ5sO8BaEmrA2cAWyUPuRT4cr/tmY2RHwD3JGO/Jemwfv8WJT2HMnn3G0y8W4H7TDZyJK1J2Tmml/vJF+l1rlpp/8eJUFHvgc6gPiXpoEEmplQFBn8CNkgecjpwSL/tmVkr3CepJ7Ob82S8i7ONkq8k4xYFTpD0lmoHjVokrS3pSOBHzH4Hv3/VPaeZTTm+H5k1wOMo6fPfBnwvGb4ucHpVOFebpC2BP1N2/pmdTu871bXvTCnkzFiEsnPbpZI+Lulpk02wnWVS9U+AC4HtJzn3W4E7knnY1PFEyvv+1ZJOkfRmSas12YCkNSV9hvI7mC2MedjnIuKvg+ZQ7Xi2M6W4IGMO4J3AxZLeUxX2NELSspL2kHQ0cBXlGWyj3/MBLUnZzfrV/Z5AZWfq9wBHMftFFWbnQ97FeWqKiAOAt9U87PGU5xZ/lLSLpHkHyUHSopLeAvyDcs+rUzhyG/CiiPj7IDkkvJV8oQ3AW4ALJL1VUnbhiAlJWlfS54ErKZ/P1hv0nAARcQdlDmEv6wB/kbS3pCc20bZZE6oFUo8adh6zioi7KDvRZ3eW35zy97VrP8V5knagLA6RnWMG8OaIuKluW4OqFvX+SSL0I8x+HGpWdzHCn9NttEhaoPp8/hpJn5d0GnAzZXGAV1MWKenHccAL2u4nT8e5uxFxN+Uz4t3JQxaiPJM+S9JLBi12rhaW2VzSAcDVwOeA5QY5p5lNbJQKAMzMzKz4JGWlsox1gXMknQDsD5waETdMFCxpGWBTyoeJrZm8L3A98EbKSo1mZrP6ALAisEuNY7YBtpH0T+CXwK+BsyPi5szB1Uqwa1GKgJ5dvR5XJ+kWHQVsBMyuWGEL4J+SPg3sV02C6akqfHg75aF89kHgg8BrIiJbZGE2tiLiBknfofydZLyScg/6CXAg8IfqgdJsVZMaXwi8hvL3PZmTKCt2/yCZi1lX9kjGHR8RN7aayeAOBt6ViNtV0seqCb1NOYSyW8rsJp68Gthc0seAg7LvwZJWAd5PeaidXYTyDmDXiIhkvJl1wH2S2g6lTE7sZ8Lx3cBBzaZjNpBfAWeS271rEWAfYE9J+wFHRcRlEwVXYyCbUyZtbEEpQpqd/1B2nPxdPm0zm4J8PzJrhsdR8j5H+X4tkohdg7IY7DFVXsdN9FyomnS6FvBiymegdSY57+nAD4Hv1sh7YBHxe0kfpN6E4hWAj1avWyVdSNlt+w7KAlaLUHYaWo3cPL7DIuKwaiKxTU9zAM+rXki6jDImcDZlYYDzIiKz4zqSlqf8rW0IvIKyC3k/zgQ+3uex/yMiLpe0NXAq+R3AlgK+AHxC0vGU57enAxdk7nNV4eETgacAG1OeRa/LxP2frv2A0t96rAWBgyTtCnwkIs7MnKy6576QsiPz+jXyODYi9q8Rb2MmIvap6kC+UfPQDSjv9V+vFkf6DfDbiLh6soOq38UnUOayvZDSD+inuOh2YMtqc5FWRcQtkrah3Puy45wrUL6nn5T0c8rnuN9XOzJOSNIclJ0kNwKeC2wJrNpn6hkHkiuOXBz4GPAxSbcBf6d8LryD3KKgR0TEEf0maTaJD1HmxI7MJoARcYKkvYFPJA9ZlrIr8YersZtfRcSFEwVLWpUyJ28v4Mk10/t6RAyzOHgmJe/JTLTo76wOz/Z/bWw9SdLMmsfMTVnEZ17KZ4plq1fTO+8+RJnz/4mG56lMZtrN3Y2IsyW9lrKgQfYz2oaUa728ejb/a8qz+TsnO0jSPMCTKJ8Lnwe8iP6ebZtZH1zkbGZmNmIi4peSjiW/opook0u2AJB0MXAFcAtwJ2Uy/hLA6pQPNhkPATMohc5mZo8SESFpN8pD0+1qHr4mZdX99wBI+jflnnUdZdL6fZTBpfkoAyHLAcvTe1XGYfoPZfXdiQZ+F6U8VP+IpKMoAyZ/paxidzvlPr4o5eHd0ygDI1sD89TM45PVyqRm08XewI6UQeiM+Sj9mxnAA5L+Trn33EzZgWUByv1mDfKDkzcAuwIvSMabdaLales1yfCD28ylCRFxrqTzKBPMJrMS5XPR8Q02/y/KRI0vTPD15SgLTn2mmrhzAnAeZQX9Oyjjr4tR7i0bUN7jN6f+w/W3RMRFdZM3s07sjfskKRFxTzUJ4Z19HH6odwmyUVKNjbwNOAPI7oj6FEpx4T6SLgWuoYy/3kH521+aUtySWQH+IeC1lN3EzGwa8/3IbHAeR6k3jhIR10v6OGVH06ytq9cD1YTa66vXA5TnPytSnmVnJhvfQPkMtnmdvJsSEV+QtDLwpj4OX5TeC1hN5hzq7cDd1QTr/2/vvsPlO8ty8d9PCCEJHUJvoYYiSC+igpCAtNARJKIUaT894AEb53goB0VFEQuHJhyRKk2kQyABDqBA6CH0ABIIECCkkpDy/P5YExJiku+svWfNzN7787muueQy77vWPfNde/bsNeteL6u1b4by608LsFX1w5z9/evJs8duSS4+e1wiw+qrl13A/r+U5F7dfdoCtvVT3f2JqjowyZszf9E5Gc673Hv2SJKTZ0XwozKs8npKhu9m98zwuecKGT7zXDHrU2g+L8/KUEa+6fn89/2T7F9Vn8rwmn04yReSfD/Dcz7rM94NMxQl753h3NMY38249yC2qFnR+bgkL8z8K3yf5bIZjpOHJ0lVnZDhfeKYnF2A3Ttnl42um/O+yewYRyW5T3d/fJPbmVt3f7mq7pXkHZnvxi9nuWR+9vX5bpIjM3wuOjnDje33zPC90pUzvMdvdEXJjXh9hpsfjClSXyLz3XTrnL6e+ReAgbl192eq6tUZV/xbhmdm+PvsQSPmXCfJs5M8u6q+n2GF+2MyvFfsmeH99gaZ/7upc3t3ZtfurUp3f2T2HdnYcva5/dMC4rDerpDhe9B184Ukj+/uQ5e505167W53v76qnpDxN+O5RpI/nD3OqKpvZvgscmyG99Sz/j7cJ8ONaa4RPUtYGT98ALBYi1pV6xFJPpXhj7Oxrj17bMbju/sdsxXGAP6L7j6tqh6Q5HlJHrOJTV159tjSZnfNf0kueLWHiyd5yOyxaK/N/Hf9hG2hu4+rqoMyfPkytiy4e4Y78G/GiUnu3t1Hze5oDuvk7pnvYvQTMlzwtBW8MsOFXLvyiCy25JwMq47un2EVgfOzT851MeEC/VV3v2yC7QIL4DPJaM9P8nsZf+GwVYLYrEWdtz17g90fmxV8NvL3+DWzuZV3Htvdb3f+FrYk70ewfpxHGe+5GVYSPGDkvN0zXMC+0YvYj8+wSuM3V/z3z+9mWBXqt5e4zyOTHLirFYfO5dSpwrD2LpPlrDT16ST3mGqF++4+tKrukKFEePkNbmbvbO59Z12cnuSgDOXlCypU3jTnX4TejJMzlNm/O8G2WUPd/c9V9fkkb8z8C2qcl4tnuAHsVD6Y5P67WhF5Ct394araP8NnqY2uZHiFbOzawEl09+mz8tBW+cwL5+V/ZSgTz7MC8FLMCokHZSjRHbiBTeyT5JcWGOnQDDeHWOhNajboZUn+YhPzv5Hh+cAynZhh9ea/WdXP0U69dnd2M55Tkrwg47+XT4abhO47ewBraCM/2ACwE837O/P0Reysu7+T4YTGiYvY3khP6e4XrmC/wBbT3Wd092MzXMxxyqrzrIHHJTlkBft9X5Lf7O6FXyAJ666735vhPWjZTkpy3+4+bAX7hnnMW7R9Q3f/eNIki/PKzFcGuHdVLfQCvu4+I8kDk3xukdud06uT/MEK9guM4DPJ/Gar0r9n5LTPdve/T5GHbWGp523Pw58med1E2z4/T+7uFy95n8CueT+Crct5lJG6+8wkv57ki2PnbsKJGQp2n1jiPs9Td5/Z3Y9O8rRMcPOK8/CJJL/Q3d86x/9vjznm+e6OKb0pyS+d67hcuNnP/K0zlHt3tO7+XIbz1FN9njw/P0ny4O7+2JL3y4rN/s1vkWF133VzWoa/ge60ioLzWWav0W2THL6qDIvW3W/JcEMb2JK6+8gk/7jqHOc2K0I+MKtfdfgNSe65Rn/bvjzDKvYb9TLXy7FE389wk8lrdfdfrvpGATv12t3Z+egDM6zEDGwzSs4AMJ957+z2k0XtsLs/muEO2D9c1DZ34ZQkB3X3PHf1Bvip7v6HJLfKsAL9OjkxyZnL2tnsxNX9knxgWftM8tYMq7btmBNVcG7d/X+S/E6W9/P+zSS/2N1jyzmwFFV1+ST3mHP4K6bMskjd/c3M9zv2IkkeOsH+j8/w99kRi972BXhRhr/RfDELW4DPJKP8n5Hj3YyPC7L087bnNCv4PDTDBf5TOyXJQ7r7r5ewL2A870ewBTmPsvHzKLOVW/fPcorO/5nk9t29zO9fdqm7n57kLkm+PeFuXpLkDuexeuqec8z13dHW8uMMRd51Pxf4oySP6O77dvcJy9hhd38jyS9nuLHAJJ+lNuG4Ze6su9+dYUXnZb0OJ2dYRf4tS9ofa6a7v9fdD0xyrwy/j9fBfyS5eXf/z1WXi5Kku7+U5DZJXrrqLAv0pCg6s7U9I8PvsLXS3T/p7ocn+b0s/zPNGUmemuSB3b02r013H53kXRudnmElaJhSJ/lIkscmuXp3P7W7j1lxpp+xE6/d7e63ZbgZj5tkwzaj5AwA85nnTshJcuoid9rdH8pwx8ep70j9qQwXxr5y4v0A21R3H57hxMFvJ/nOCqOcmWFVsIcluWJ3L/WkcHcfl+GClqlXTekkf51h1bZ1ubsmrEx3Py/JPZNMfafuNyS5VXd/auL9wGY8LPNdXP+tJIdOnGXR5r2YeN4VmEbp7qOS/GKmv6HJaUl+v7sfMysqAFuEzyRze0uSo+Yce3K2UJmElVjJedtzml1Qe/8kz8p0Nzo4PMMqaa+ZaPvA5nk/gq3JeZRNnEeZnSu5bZJ3bHQbc/i3JLfu7s9MuI8Nm9146oZJ/jxDSXVRPpvkgO5+VHefeM7/UFW7Jbn4Luaf1N2T/c5h8br7pO6+fZKrZyi+vD/LX7H3gvw4yV9lWLXs/y5757NVyp6e5AYZzous0ncyfE97k1UsItDd/5Lkbpm+YP3VDJ/7Nlo8Yhvp7rcmuX6SJ2T+83qL9skMf+/8wuz6lLXR3Sd39yOT3CGrWdX5G0kWttp6d5/Z3b+X4Vz3Vxe1XViW7v5Okr9bdY7z093PTXLTDDe4WYZPZvib6hlrenPtf9rgvA/MVu6GRTs9w8/nk5Jco7tv290vXOdrNHfitbvd/bUkt8/wnH8w1X4uwEezPjcBgm1DyRkA5nPJOceduOsh43T3lzPc8fEPM9wVd5G+k+ELslt298cXvG1gh5l90fGPSa6d5DEZLsBYhrNOLP1RhhNLB3T3y7v7pCXt/2d096nd/aAkj8o0Xy5/I8k9uvvJ3b1OFzfASnX3O5LcKMmLM9yFdpE+k2HV9Aecx4oZsG4ePue4V2/BAu3rMl8h4KZVdbMpAnT3sUnulOSPM81qOIdnuAHVX02wbWAJfCbZte4+I8PrM4/XzG4mBednZedtz2l2TuQpGW6I8skFbvq4JH+SYYWiwxa4XWDxvB/B1uQ8yibPo3T3j7r77kl+K4u9kPbIJA/q7vus+98/3X1cd/9xkmsl+Z9JvrbBTZ2a5O0ZvgO6yaxAfV4ul2T3XWxrrV8zzl93H9Xdz+3uOybZJ8mvZVgddKPH1WZ9KcnvJ7lqd//+7PzoynT3kd39gAzFoJdksTcXuCBHJ3l5krtneC2e3N3L+i78v+juQ5LcONPcZKIzHHM36+6pF2RgC+nuH3f332W4HuSRST6Y6Vef/0mGG57co7tv3t1vXNOCXpKkuz+Q5OeTPCgLLB2fj9M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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "And train our model using just the training data"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "model = sm.OLS(DJI[:'2020-07-31'], \n               closing[:'2020-07-31']).fit()",
      "execution_count": 13,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "statsmodels provides us with a detailed statistical analysis of each of the components of the model"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "model.summary()",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 14,
          "data": {
            "text/plain": "<class 'statsmodels.iolib.summary.Summary'>\n\"\"\"\n                            OLS Regression Results                            \n==============================================================================\nDep. Variable:                    DJI   R-squared:                       1.000\nModel:                            OLS   Adj. R-squared:                  1.000\nMethod:                 Least Squares   F-statistic:                 2.107e+06\nDate:                Sat, 14 May 2022   Prob (F-statistic):          1.99e-297\nTime:                        23:52:15   Log-Likelihood:                -364.32\nNo. Observations:                 143   AIC:                             800.6\nDf Residuals:                     107   BIC:                             907.3\nDf Model:                          35                                         \nCovariance Type:            nonrobust                                         \n==============================================================================\n                 coef    std err          t      P>|t|      [0.025      0.975]\n------------------------------------------------------------------------------\nAAPL          28.1315      0.238    118.405      0.000      27.660      28.602\nAMGN           0.0701      0.098      0.714      0.477      -0.124       0.265\nAXP            6.2508      0.261     23.940      0.000       5.733       6.768\nBA             7.0296      0.047    149.349      0.000       6.936       7.123\nCAT            6.8530      0.160     42.953      0.000       6.537       7.169\nCRM            0.0452      0.123      0.367      0.714      -0.199       0.289\nCSCO           6.9258      0.564     12.274      0.000       5.807       8.044\nCVX            7.6117      0.260     29.233      0.000       7.096       8.128\nDD             0.4722      0.318      1.484      0.141      -0.159       1.103\nDIS            6.9450      0.178     39.057      0.000       6.593       7.298\nDOW            6.3940      0.387     16.541      0.000       5.628       7.160\nGE             0.2437      0.216      1.130      0.261      -0.184       0.671\nGS             6.9331      0.122     56.636      0.000       6.690       7.176\nHD             6.6309      0.096     69.009      0.000       6.440       6.821\nHON           -0.3708      0.193     -1.918      0.058      -0.754       0.012\nIBM            7.3397      0.202     36.317      0.000       6.939       7.740\nINTC           6.5288      0.199     32.789      0.000       6.134       6.924\nJNJ            6.7114      0.240     27.962      0.000       6.236       7.187\nJPM            6.7418      0.236     28.578      0.000       6.274       7.209\nKO             6.9641      0.572     12.173      0.000       5.830       8.098\nMCD            6.8349      0.150     45.446      0.000       6.537       7.133\nMMM            6.4935      0.146     44.401      0.000       6.204       6.783\nMRK            7.5556      0.318     23.792      0.000       6.926       8.185\nMSFT           6.4336      0.155     41.509      0.000       6.126       6.741\nNKE            6.7713      0.231     29.369      0.000       6.314       7.228\nPFE            8.0045      0.466     17.163      0.000       7.080       8.929\nPG             7.0308      0.294     23.894      0.000       6.447       7.614\nRTX            8.4790      0.301     28.136      0.000       7.882       9.076\nTRV            6.7275      0.194     34.767      0.000       6.344       7.111\nUNH            6.7560      0.073     93.183      0.000       6.612       6.900\nV              7.2610      0.174     41.824      0.000       6.917       7.605\nVZ             6.6514      0.631     10.546      0.000       5.401       7.902\nWBA            6.2044      0.331     18.750      0.000       5.548       6.860\nWMT            6.7940      0.211     32.258      0.000       6.376       7.212\nXOM            6.0549      0.487     12.435      0.000       5.090       7.020\nintercept     -4.1082     21.263     -0.193      0.847     -46.259      38.043\n==============================================================================\nOmnibus:                       19.720   Durbin-Watson:                   2.012\nProb(Omnibus):                  0.000   Jarque-Bera (JB):               62.557\nSkew:                          -0.398   Prob(JB):                     2.61e-14\nKurtosis:                       6.141   Cond. No.                     5.70e+04\n==============================================================================\n\nNotes:\n[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n[2] The condition number is large, 5.7e+04. This might indicate that there are\nstrong multicollinearity or other numerical problems.\n\"\"\"",
            "text/html": "<table class=\"simpletable\">\n<caption>OLS Regression Results</caption>\n<tr>\n  <th>Dep. Variable:</th>           <td>DJI</td>       <th>  R-squared:         </th> <td>   1.000</td> \n</tr>\n<tr>\n  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   1.000</td> \n</tr>\n<tr>\n  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>2.107e+06</td>\n</tr>\n<tr>\n  <th>Date:</th>             <td>Sat, 14 May 2022</td> <th>  Prob (F-statistic):</th> <td>1.99e-297</td>\n</tr>\n<tr>\n  <th>Time:</th>                 <td>23:52:15</td>     <th>  Log-Likelihood:    </th> <td> -364.32</td> \n</tr>\n<tr>\n  <th>No. Observations:</th>      <td>   143</td>      <th>  AIC:               </th> <td>   800.6</td> \n</tr>\n<tr>\n  <th>Df Residuals:</th>          <td>   107</td>      <th>  BIC:               </th> <td>   907.3</td> \n</tr>\n<tr>\n  <th>Df Model:</th>              <td>    35</td>      <th>                     </th>     <td> </td>    \n</tr>\n<tr>\n  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n</tr>\n</table>\n<table class=\"simpletable\">\n<tr>\n      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n</tr>\n<tr>\n  <th>AAPL</th>      <td>   28.1315</td> <td>    0.238</td> <td>  118.405</td> <td> 0.000</td> <td>   27.660</td> <td>   28.602</td>\n</tr>\n<tr>\n  <th>AMGN</th>      <td>    0.0701</td> <td>    0.098</td> <td>    0.714</td> <td> 0.477</td> <td>   -0.124</td> <td>    0.265</td>\n</tr>\n<tr>\n  <th>AXP</th>       <td>    6.2508</td> <td>    0.261</td> <td>   23.940</td> <td> 0.000</td> <td>    5.733</td> <td>    6.768</td>\n</tr>\n<tr>\n  <th>BA</th>        <td>    7.0296</td> <td>    0.047</td> <td>  149.349</td> <td> 0.000</td> <td>    6.936</td> <td>    7.123</td>\n</tr>\n<tr>\n  <th>CAT</th>       <td>    6.8530</td> <td>    0.160</td> <td>   42.953</td> <td> 0.000</td> <td>    6.537</td> <td>    7.169</td>\n</tr>\n<tr>\n  <th>CRM</th>       <td>    0.0452</td> <td>    0.123</td> <td>    0.367</td> <td> 0.714</td> <td>   -0.199</td> <td>    0.289</td>\n</tr>\n<tr>\n  <th>CSCO</th>      <td>    6.9258</td> <td>    0.564</td> <td>   12.274</td> <td> 0.000</td> <td>    5.807</td> <td>    8.044</td>\n</tr>\n<tr>\n  <th>CVX</th>       <td>    7.6117</td> <td>    0.260</td> <td>   29.233</td> <td> 0.000</td> <td>    7.096</td> <td>    8.128</td>\n</tr>\n<tr>\n  <th>DD</th>        <td>    0.4722</td> <td>    0.318</td> <td>    1.484</td> <td> 0.141</td> <td>   -0.159</td> <td>    1.103</td>\n</tr>\n<tr>\n  <th>DIS</th>       <td>    6.9450</td> <td>    0.178</td> <td>   39.057</td> <td> 0.000</td> <td>    6.593</td> <td>    7.298</td>\n</tr>\n<tr>\n  <th>DOW</th>       <td>    6.3940</td> <td>    0.387</td> <td>   16.541</td> <td> 0.000</td> <td>    5.628</td> <td>    7.160</td>\n</tr>\n<tr>\n  <th>GE</th>        <td>    0.2437</td> <td>    0.216</td> <td>    1.130</td> <td> 0.261</td> <td>   -0.184</td> <td>    0.671</td>\n</tr>\n<tr>\n  <th>GS</th>        <td>    6.9331</td> <td>    0.122</td> <td>   56.636</td> <td> 0.000</td> <td>    6.690</td> <td>    7.176</td>\n</tr>\n<tr>\n  <th>HD</th>        <td>    6.6309</td> <td>    0.096</td> <td>   69.009</td> <td> 0.000</td> <td>    6.440</td> <td>    6.821</td>\n</tr>\n<tr>\n  <th>HON</th>       <td>   -0.3708</td> <td>    0.193</td> <td>   -1.918</td> <td> 0.058</td> <td>   -0.754</td> <td>    0.012</td>\n</tr>\n<tr>\n  <th>IBM</th>       <td>    7.3397</td> <td>    0.202</td> <td>   36.317</td> <td> 0.000</td> <td>    6.939</td> <td>    7.740</td>\n</tr>\n<tr>\n  <th>INTC</th>      <td>    6.5288</td> <td>    0.199</td> <td>   32.789</td> <td> 0.000</td> <td>    6.134</td> <td>    6.924</td>\n</tr>\n<tr>\n  <th>JNJ</th>       <td>    6.7114</td> <td>    0.240</td> <td>   27.962</td> <td> 0.000</td> <td>    6.236</td> <td>    7.187</td>\n</tr>\n<tr>\n  <th>JPM</th>       <td>    6.7418</td> <td>    0.236</td> <td>   28.578</td> <td> 0.000</td> <td>    6.274</td> <td>    7.209</td>\n</tr>\n<tr>\n  <th>KO</th>        <td>    6.9641</td> <td>    0.572</td> <td>   12.173</td> <td> 0.000</td> <td>    5.830</td> <td>    8.098</td>\n</tr>\n<tr>\n  <th>MCD</th>       <td>    6.8349</td> <td>    0.150</td> <td>   45.446</td> <td> 0.000</td> <td>    6.537</td> <td>    7.133</td>\n</tr>\n<tr>\n  <th>MMM</th>       <td>    6.4935</td> <td>    0.146</td> <td>   44.401</td> <td> 0.000</td> <td>    6.204</td> <td>    6.783</td>\n</tr>\n<tr>\n  <th>MRK</th>       <td>    7.5556</td> <td>    0.318</td> <td>   23.792</td> <td> 0.000</td> <td>    6.926</td> <td>    8.185</td>\n</tr>\n<tr>\n  <th>MSFT</th>      <td>    6.4336</td> <td>    0.155</td> <td>   41.509</td> <td> 0.000</td> <td>    6.126</td> <td>    6.741</td>\n</tr>\n<tr>\n  <th>NKE</th>       <td>    6.7713</td> <td>    0.231</td> <td>   29.369</td> <td> 0.000</td> <td>    6.314</td> <td>    7.228</td>\n</tr>\n<tr>\n  <th>PFE</th>       <td>    8.0045</td> <td>    0.466</td> <td>   17.163</td> <td> 0.000</td> <td>    7.080</td> <td>    8.929</td>\n</tr>\n<tr>\n  <th>PG</th>        <td>    7.0308</td> <td>    0.294</td> <td>   23.894</td> <td> 0.000</td> <td>    6.447</td> <td>    7.614</td>\n</tr>\n<tr>\n  <th>RTX</th>       <td>    8.4790</td> <td>    0.301</td> <td>   28.136</td> <td> 0.000</td> <td>    7.882</td> <td>    9.076</td>\n</tr>\n<tr>\n  <th>TRV</th>       <td>    6.7275</td> <td>    0.194</td> <td>   34.767</td> <td> 0.000</td> <td>    6.344</td> <td>    7.111</td>\n</tr>\n<tr>\n  <th>UNH</th>       <td>    6.7560</td> <td>    0.073</td> <td>   93.183</td> <td> 0.000</td> <td>    6.612</td> <td>    6.900</td>\n</tr>\n<tr>\n  <th>V</th>         <td>    7.2610</td> <td>    0.174</td> <td>   41.824</td> <td> 0.000</td> <td>    6.917</td> <td>    7.605</td>\n</tr>\n<tr>\n  <th>VZ</th>        <td>    6.6514</td> <td>    0.631</td> <td>   10.546</td> <td> 0.000</td> <td>    5.401</td> <td>    7.902</td>\n</tr>\n<tr>\n  <th>WBA</th>       <td>    6.2044</td> <td>    0.331</td> <td>   18.750</td> <td> 0.000</td> <td>    5.548</td> <td>    6.860</td>\n</tr>\n<tr>\n  <th>WMT</th>       <td>    6.7940</td> <td>    0.211</td> <td>   32.258</td> <td> 0.000</td> <td>    6.376</td> <td>    7.212</td>\n</tr>\n<tr>\n  <th>XOM</th>       <td>    6.0549</td> <td>    0.487</td> <td>   12.435</td> <td> 0.000</td> <td>    5.090</td> <td>    7.020</td>\n</tr>\n<tr>\n  <th>intercept</th> <td>   -4.1082</td> <td>   21.263</td> <td>   -0.193</td> <td> 0.847</td> <td>  -46.259</td> <td>   38.043</td>\n</tr>\n</table>\n<table class=\"simpletable\">\n<tr>\n  <th>Omnibus:</th>       <td>19.720</td> <th>  Durbin-Watson:     </th> <td>   2.012</td>\n</tr>\n<tr>\n  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  62.557</td>\n</tr>\n<tr>\n  <th>Skew:</th>          <td>-0.398</td> <th>  Prob(JB):          </th> <td>2.61e-14</td>\n</tr>\n<tr>\n  <th>Kurtosis:</th>      <td> 6.141</td> <th>  Cond. No.          </th> <td>5.70e+04</td>\n</tr>\n</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 5.7e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "For ease of analysis, we write a simple function to plot the p-values for each component"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "def plot_pvalues(pvalues, threshold=0.05):\n    fig, ax = plt.subplots(1, figsize=(8, 16))\n    \n    # get a copy of the series containing the values\n    # remove the intercept if \n    if 'intercept' in pvalues.index:\n        values = pvalues.drop('intercept').copy()\n    else:\n        values = pvalues.copy()\n        \n    values.sort_index(inplace=True)\n    \n    # sequential y-coordinates to use\n    coordinates = np.arange(values.shape[0])[::-1]\n    \n    # Flag to identify values below the threshold\n    selected = (values <= threshold)\n    \n    # Selected values get one color\n    ax.scatter(x = values[selected].values, \n               y = coordinates[selected], color=colors[0], s=200)\n    \n    # And the non selected get another\n    ax.scatter(x = values[~selected].values, \n               y = coordinates[~selected], color=colors[1], s=200)\n    \n    # Add feature names to y axis\n    ax.set_yticks(coordinates)\n    ax.set_yticklabels(values.index, color=colors[0])\n\n    # Change label color for the features that didn't make the cut\n    [t.set_color(colors[1]) \n         for i, t in enumerate(ax.yaxis.get_ticklabels()) if not selected[i]]\n    \n    # Adjust x-axis limits\n    ax.set_xlim(-0.05, 1)\n    \n    # Vertical line to highlight the threshold\n    ax.axvline(x=threshold, lw=3, linestyle=':', color=colors[1])\n    ax.set_xlabel('p-value')",
      "execution_count": 15,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "The resulting plot is much easier to analyze, with 5 of the components (highlighted in purple) being clearly non-significant to determine the output variable at the threshold we set."
    },
    {
      "metadata": {
        "trusted": true,
        "scrolled": false
      },
      "cell_type": "code",
      "source": "plot_pvalues(model.pvalues)",
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 2400x4800 with 1 Axes>",
            "image/png": 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