yogeshchappewar/Normal_Disribution_(1).ipynb

## Normal_Disribution_(1).ipynb
{
  "cells": [
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-15T04:01:32.249145Z",
          "end_time": "2023-01-15T04:01:32.877525Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "from scipy import stats\nstats.norm.cdf(680,711,29)",
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 1,
          "data": {
            "text/plain": "0.14254260383881612"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-15T04:02:37.418364Z",
          "end_time": "2023-01-15T04:02:37.432496Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "stats.norm.cdf(740,711,29)-stats.norm.cdf(697,711,29)",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/plain": "0.5267111786415019"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-15T04:03:35.571351Z",
          "end_time": "2023-01-15T04:03:36.021999Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "import pandas as pd\nimport numpy as np",
      "execution_count": 3,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-15T04:05:10.427346Z",
          "end_time": "2023-01-15T04:05:10.451690Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "beml_df = pd.read_csv(\"BEML.csv\")\nbeml_df[0:5]",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 4,
          "data": {
            "text/plain": "         Date    Open     High      Low    Last    Close  \\\n0  2010-01-04  1121.0  1151.00  1121.00  1134.0  1135.60   \n1  2010-01-05  1146.8  1149.00  1128.75  1135.0  1134.60   \n2  2010-01-06  1140.0  1164.25  1130.05  1137.0  1139.60   \n3  2010-01-07  1142.0  1159.40  1119.20  1141.0  1144.15   \n4  2010-01-08  1156.0  1172.00  1140.00  1141.2  1144.05   \n\n   Total Trade Quantity  Turnover (Lacs)  \n0              101651.0          1157.18  \n1               59504.0           676.47  \n2              128908.0          1482.84  \n3              117871.0          1352.98  \n4              170063.0          1971.42  ",
            "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>Date</th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Last</th>\n      <th>Close</th>\n      <th>Total Trade Quantity</th>\n      <th>Turnover (Lacs)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2010-01-04</td>\n      <td>1121.0</td>\n      <td>1151.00</td>\n      <td>1121.00</td>\n      <td>1134.0</td>\n      <td>1135.60</td>\n      <td>101651.0</td>\n      <td>1157.18</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2010-01-05</td>\n      <td>1146.8</td>\n      <td>1149.00</td>\n      <td>1128.75</td>\n      <td>1135.0</td>\n      <td>1134.60</td>\n      <td>59504.0</td>\n      <td>676.47</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2010-01-06</td>\n      <td>1140.0</td>\n      <td>1164.25</td>\n      <td>1130.05</td>\n      <td>1137.0</td>\n      <td>1139.60</td>\n      <td>128908.0</td>\n      <td>1482.84</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2010-01-07</td>\n      <td>1142.0</td>\n      <td>1159.40</td>\n      <td>1119.20</td>\n      <td>1141.0</td>\n      <td>1144.15</td>\n      <td>117871.0</td>\n      <td>1352.98</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2010-01-08</td>\n      <td>1156.0</td>\n      <td>1172.00</td>\n      <td>1140.00</td>\n      <td>1141.2</td>\n      <td>1144.05</td>\n      <td>170063.0</td>\n      <td>1971.42</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-15T04:06:14.485915Z",
          "end_time": "2023-01-15T04:06:14.509354Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "glaxo_df = pd.read_csv(\"GLAXO.csv\")\nglaxo_df[0:5]",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 5,
          "data": {
            "text/plain": "         Date     Open     High      Low    Last    Close  \\\n0  2010-01-04  1613.00  1629.10  1602.00  1629.0  1625.65   \n1  2010-01-05  1639.95  1639.95  1611.05  1620.0  1616.80   \n2  2010-01-06  1618.00  1644.00  1617.00  1639.0  1638.50   \n3  2010-01-07  1645.00  1654.00  1636.00  1648.0  1648.70   \n4  2010-01-08  1650.00  1650.00  1626.55  1640.0  1639.80   \n\n   Total Trade Quantity  Turnover (Lacs)  \n0                9365.0           151.74  \n1               38148.0           622.58  \n2               36519.0           595.09  \n3               12809.0           211.00  \n4               28035.0           459.11  ",
            "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>Date</th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Last</th>\n      <th>Close</th>\n      <th>Total Trade Quantity</th>\n      <th>Turnover (Lacs)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2010-01-04</td>\n      <td>1613.00</td>\n      <td>1629.10</td>\n      <td>1602.00</td>\n      <td>1629.0</td>\n      <td>1625.65</td>\n      <td>9365.0</td>\n      <td>151.74</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2010-01-05</td>\n      <td>1639.95</td>\n      <td>1639.95</td>\n      <td>1611.05</td>\n      <td>1620.0</td>\n      <td>1616.80</td>\n      <td>38148.0</td>\n      <td>622.58</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2010-01-06</td>\n      <td>1618.00</td>\n      <td>1644.00</td>\n      <td>1617.00</td>\n      <td>1639.0</td>\n      <td>1638.50</td>\n      <td>36519.0</td>\n      <td>595.09</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2010-01-07</td>\n      <td>1645.00</td>\n      <td>1654.00</td>\n      <td>1636.00</td>\n      <td>1648.0</td>\n      <td>1648.70</td>\n      <td>12809.0</td>\n      <td>211.00</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2010-01-08</td>\n      <td>1650.00</td>\n      <td>1650.00</td>\n      <td>1626.55</td>\n      <td>1640.0</td>\n      <td>1639.80</td>\n      <td>28035.0</td>\n      <td>459.11</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-15T04:08:33.282155Z",
          "end_time": "2023-01-15T04:08:33.298695Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "beml_df = beml_df[['Date','Close']]\nglaxo_df = glaxo_df[['Date','Close']]",
      "execution_count": 6,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-15T04:09:29.329177Z",
          "end_time": "2023-01-15T04:09:29.342403Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "beml_df",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 9,
          "data": {
            "text/plain": "            Date    Close\n0     2010-01-04  1135.60\n1     2010-01-05  1134.60\n2     2010-01-06  1139.60\n3     2010-01-07  1144.15\n4     2010-01-08  1144.05\n...          ...      ...\n1734  2016-12-26   950.25\n1735  2016-12-27   975.70\n1736  2016-12-28   974.40\n1737  2016-12-29   986.05\n1738  2016-12-30  1000.60\n\n[1739 rows x 2 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 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>Date</th>\n      <th>Close</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2010-01-04</td>\n      <td>1135.60</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2010-01-05</td>\n      <td>1134.60</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2010-01-06</td>\n      <td>1139.60</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2010-01-07</td>\n      <td>1144.15</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2010-01-08</td>\n      <td>1144.05</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1734</th>\n      <td>2016-12-26</td>\n      <td>950.25</td>\n    </tr>\n    <tr>\n      <th>1735</th>\n      <td>2016-12-27</td>\n      <td>975.70</td>\n    </tr>\n    <tr>\n      <th>1736</th>\n      <td>2016-12-28</td>\n      <td>974.40</td>\n    </tr>\n    <tr>\n      <th>1737</th>\n      <td>2016-12-29</td>\n      <td>986.05</td>\n    </tr>\n    <tr>\n      <th>1738</th>\n      <td>2016-12-30</td>\n      <td>1000.60</td>\n    </tr>\n  </tbody>\n</table>\n<p>1739 rows × 2 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-15T04:09:44.038411Z",
          "end_time": "2023-01-15T04:09:44.061112Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "glaxo_df",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 10,
          "data": {
            "text/plain": "            Date    Close\n0     2010-01-04  1625.65\n1     2010-01-05  1616.80\n2     2010-01-06  1638.50\n3     2010-01-07  1648.70\n4     2010-01-08  1639.80\n...          ...      ...\n1734  2016-12-26  2723.50\n1735  2016-12-27  2701.75\n1736  2016-12-28  2702.15\n1737  2016-12-29  2727.90\n1738  2016-12-30  2729.80\n\n[1739 rows x 2 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 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>Date</th>\n      <th>Close</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2010-01-04</td>\n      <td>1625.65</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2010-01-05</td>\n      <td>1616.80</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2010-01-06</td>\n      <td>1638.50</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2010-01-07</td>\n      <td>1648.70</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2010-01-08</td>\n      <td>1639.80</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1734</th>\n      <td>2016-12-26</td>\n      <td>2723.50</td>\n    </tr>\n    <tr>\n      <th>1735</th>\n      <td>2016-12-27</td>\n      <td>2701.75</td>\n    </tr>\n    <tr>\n      <th>1736</th>\n      <td>2016-12-28</td>\n      <td>2702.15</td>\n    </tr>\n    <tr>\n      <th>1737</th>\n      <td>2016-12-29</td>\n      <td>2727.90</td>\n    </tr>\n    <tr>\n      <th>1738</th>\n      <td>2016-12-30</td>\n      <td>2729.80</td>\n    </tr>\n  </tbody>\n</table>\n<p>1739 rows × 2 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-15T04:13:35.659132Z",
          "end_time": "2023-01-15T04:13:35.675349Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "glaxo_df = glaxo_df.set_index(pd.DataframeIndex(glaxo_df['Date']))\nbeml_df = beml_df.set_index(pd.DataframeIndex(beml_df['Date']))",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "module 'pandas' has no attribute 'DataframeIndex'",
          "traceback": [
            "\u001b[1;31m------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mAttributeError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_3988\\2395316565.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mglaxo_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mglaxo_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataframeIndex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mglaxo_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Date'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mbeml_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbeml_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataframeIndex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbeml_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Date'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
            "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\__init__.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(name)\u001b[0m\n\u001b[0;32m    259\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0m_SparseArray\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    260\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 261\u001b[1;33m     \u001b[1;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"module 'pandas' has no attribute '{name}'\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    262\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    263\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
            "\u001b[1;31mAttributeError\u001b[0m: module 'pandas' has no attribute 'DataframeIndex'"
          ]
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-15T04:12:46.254466Z",
          "end_time": "2023-01-15T04:12:46.275531Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "beml_df",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 12,
          "data": {
            "text/plain": "            Date    Close\n0     2010-01-04  1135.60\n1     2010-01-05  1134.60\n2     2010-01-06  1139.60\n3     2010-01-07  1144.15\n4     2010-01-08  1144.05\n...          ...      ...\n1734  2016-12-26   950.25\n1735  2016-12-27   975.70\n1736  2016-12-28   974.40\n1737  2016-12-29   986.05\n1738  2016-12-30  1000.60\n\n[1739 rows x 2 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 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>Date</th>\n      <th>Close</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2010-01-04</td>\n      <td>1135.60</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2010-01-05</td>\n      <td>1134.60</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2010-01-06</td>\n      <td>1139.60</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2010-01-07</td>\n      <td>1144.15</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2010-01-08</td>\n      <td>1144.05</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1734</th>\n      <td>2016-12-26</td>\n      <td>950.25</td>\n    </tr>\n    <tr>\n      <th>1735</th>\n      <td>2016-12-27</td>\n      <td>975.70</td>\n    </tr>\n    <tr>\n      <th>1736</th>\n      <td>2016-12-28</td>\n      <td>974.40</td>\n    </tr>\n    <tr>\n      <th>1737</th>\n      <td>2016-12-29</td>\n      <td>986.05</td>\n    </tr>\n    <tr>\n      <th>1738</th>\n      <td>2016-12-30</td>\n      <td>1000.60</td>\n    </tr>\n  </tbody>\n</table>\n<p>1739 rows × 2 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-15T04:18:43.933964Z",
          "end_time": "2023-01-15T04:18:43.957990Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "'''The DataFrames have a date column, so we can\ncreate a DatetimeIndex index from this column Date. It will ensure that the rows are sorted by time in\nascending order.'''\nglaxo_df = glaxo_df.set_index(pd.DatetimeIndex(glaxo_df['Date']))\nbeml_df = beml_df.set_index(pd.DatetimeIndex(beml_df['Date']))",
      "execution_count": 15,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-15T04:19:18.497476Z",
          "end_time": "2023-01-15T04:19:18.522479Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "beml_df",
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 16,
          "data": {
            "text/plain": "                  Date    Close\nDate                           \n2010-01-04  2010-01-04  1135.60\n2010-01-05  2010-01-05  1134.60\n2010-01-06  2010-01-06  1139.60\n2010-01-07  2010-01-07  1144.15\n2010-01-08  2010-01-08  1144.05\n...                ...      ...\n2016-12-26  2016-12-26   950.25\n2016-12-27  2016-12-27   975.70\n2016-12-28  2016-12-28   974.40\n2016-12-29  2016-12-29   986.05\n2016-12-30  2016-12-30  1000.60\n\n[1739 rows x 2 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 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>Date</th>\n      <th>Close</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2010-01-04</th>\n      <td>2010-01-04</td>\n      <td>1135.60</td>\n    </tr>\n    <tr>\n      <th>2010-01-05</th>\n      <td>2010-01-05</td>\n      <td>1134.60</td>\n    </tr>\n    <tr>\n      <th>2010-01-06</th>\n      <td>2010-01-06</td>\n      <td>1139.60</td>\n    </tr>\n    <tr>\n      <th>2010-01-07</th>\n      <td>2010-01-07</td>\n      <td>1144.15</td>\n    </tr>\n    <tr>\n      <th>2010-01-08</th>\n      <td>2010-01-08</td>\n      <td>1144.05</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2016-12-26</th>\n      <td>2016-12-26</td>\n      <td>950.25</td>\n    </tr>\n    <tr>\n      <th>2016-12-27</th>\n      <td>2016-12-27</td>\n      <td>975.70</td>\n    </tr>\n    <tr>\n      <th>2016-12-28</th>\n      <td>2016-12-28</td>\n      <td>974.40</td>\n    </tr>\n    <tr>\n      <th>2016-12-29</th>\n      <td>2016-12-29</td>\n      <td>986.05</td>\n    </tr>\n    <tr>\n      <th>2016-12-30</th>\n      <td>2016-12-30</td>\n      <td>1000.60</td>\n    </tr>\n  </tbody>\n</table>\n<p>1739 rows × 2 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-15T04:23:39.297404Z",
          "end_time": "2023-01-15T04:23:39.478638Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "import matplotlib.pyplot as plt\nimport seaborn as sn\n%matplotlib inline\nplt.plot(glaxo_df.Close)\nplt.xlabel('Time')\nplt.ylabel('Close Price')",
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 20,
          "data": {
            "text/plain": "Text(0, 0.5, 'Close Price')"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-15T04:48:34.313393Z",
          "end_time": "2023-01-15T04:48:34.485362Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.plot(beml_df.Close)\nplt.xlabel('Time')\nplt.ylabel('Close')",
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 24,
          "data": {
            "text/plain": "Text(0, 0.5, 'Close')"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-15T04:49:06.103800Z",
          "end_time": "2023-01-15T04:49:06.112273Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "glaxo_df['gain'] = glaxo_df.Close.pct_change(periods = 1)\nbeml_df['gain'] = beml_df.Close.pct_change(periods = 1)",
      "execution_count": 25,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-15T04:49:24.067598Z",
          "end_time": "2023-01-15T04:49:24.091152Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "glaxo_df",
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 26,
          "data": {
            "text/plain": "                  Date    Close      gain\nDate                                     \n2010-01-04  2010-01-04  1625.65       NaN\n2010-01-05  2010-01-05  1616.80 -0.005444\n2010-01-06  2010-01-06  1638.50  0.013422\n2010-01-07  2010-01-07  1648.70  0.006225\n2010-01-08  2010-01-08  1639.80 -0.005398\n...                ...      ...       ...\n2016-12-26  2016-12-26  2723.50 -0.001283\n2016-12-27  2016-12-27  2701.75 -0.007986\n2016-12-28  2016-12-28  2702.15  0.000148\n2016-12-29  2016-12-29  2727.90  0.009529\n2016-12-30  2016-12-30  2729.80  0.000697\n\n[1739 rows x 3 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 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>Date</th>\n      <th>Close</th>\n      <th>gain</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2010-01-04</th>\n      <td>2010-01-04</td>\n      <td>1625.65</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2010-01-05</th>\n      <td>2010-01-05</td>\n      <td>1616.80</td>\n      <td>-0.005444</td>\n    </tr>\n    <tr>\n      <th>2010-01-06</th>\n      <td>2010-01-06</td>\n      <td>1638.50</td>\n      <td>0.013422</td>\n    </tr>\n    <tr>\n      <th>2010-01-07</th>\n      <td>2010-01-07</td>\n      <td>1648.70</td>\n      <td>0.006225</td>\n    </tr>\n    <tr>\n      <th>2010-01-08</th>\n      <td>2010-01-08</td>\n      <td>1639.80</td>\n      <td>-0.005398</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2016-12-26</th>\n      <td>2016-12-26</td>\n      <td>2723.50</td>\n      <td>-0.001283</td>\n    </tr>\n    <tr>\n      <th>2016-12-27</th>\n      <td>2016-12-27</td>\n      <td>2701.75</td>\n      <td>-0.007986</td>\n    </tr>\n    <tr>\n      <th>2016-12-28</th>\n      <td>2016-12-28</td>\n      <td>2702.15</td>\n      <td>0.000148</td>\n    </tr>\n    <tr>\n      <th>2016-12-29</th>\n      <td>2016-12-29</td>\n      <td>2727.90</td>\n      <td>0.009529</td>\n    </tr>\n    <tr>\n      <th>2016-12-30</th>\n      <td>2016-12-30</td>\n      <td>2729.80</td>\n      <td>0.000697</td>\n    </tr>\n  </tbody>\n</table>\n<p>1739 rows × 3 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-15T04:50:23.394770Z",
          "end_time": "2023-01-15T04:50:23.419292Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "#drop first row since it is NaN\nglaxo_df = glaxo_df.dropna()\nbeml_df = beml_df.dropna()  \nglaxo_df.gain ",
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 27,
          "data": {
            "text/plain": "Date\n2010-01-05   -0.005444\n2010-01-06    0.013422\n2010-01-07    0.006225\n2010-01-08   -0.005398\n2010-01-11   -0.006312\n                ...   \n2016-12-26   -0.001283\n2016-12-27   -0.007986\n2016-12-28    0.000148\n2016-12-29    0.009529\n2016-12-30    0.000697\nName: gain, Length: 1738, dtype: float64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-15T04:53:00.429979Z",
          "end_time": "2023-01-15T04:53:00.618575Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Plot the gains\nplt.figure(figsize = (8, 6));\nplt.plot(glaxo_df.index, glaxo_df.gain);\nplt.xlabel('Time');\nplt.ylabel('gain');",
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 800x600 with 1 Axes>",
            "image/png": 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Nk4iI3MGsCiJym2dajgFg9OjRGDFiBHr16oV+/fph0qRJ2LZtG0aOHAmgKt1h586deOedd4KfWblyJQDgyJEj2Lt3L1auXIn09HR06tQJAHDvvfdi4MCBePrpp3HJJZfgiy++wOzZs7Fw4cKorx8RERERxZanguPhw4dj//79GDduHAoLC9G5c2dMnz4dLVu2BFA16Ie65nGPHj2Cfy9fvhwffPABWrZsiS1btgAA+vfvjw8//BCPPPII/vGPf6Bt27aYNm0a+vTpE7X1IiIik9gjj4hc5qk6x/GKdY6JiNwVqHPcrXkdfHH3gBgvDRF5UcLVOSYiIiIichuDYyIi8gwmVRCR2xgcExERERH5MTgmIiLPYH88InIbg2MiIvIMxsZE5DYGx0REREREfgyOiYiIiIj8GBwTEREREfkxOCYiSkL7j5TizQWbsP9IaawXxRKJPfKIyGUMjomIktBd76/AP7/+FSPfWx7rRbGEoTERuY3BMRFRElq6+QAA4KctB2O8JERE8YXBMRERERGRH4NjIiIiIiI/BsdEROQZ7I9HRG5jcExERJ4hsUseEbmMwTERURJK9THIJCLSwuCYiCgJpTA4JiLSxOCYiCgJseWYiEgbg2MioiSUmuLR0z9jeiJymUfPjkREFAmvthx7c6mJyEsYHBMRJaHUFIaZRERaGBwTESWhVB9P/0REWnh2JCJKQqxWQUSkjcExEVES8mzOsTcXm4g8hMExEVES8mrOMUfIIyK3MTgmIkpCzDkmItLGsyMRURLyassxEZHbGBwTESUhdsgjItLG4JiIKAmxQx4RkTYGx0REScirOccMjonIbd48OxIRUUSYc0xEpI3BMRFREmLOMRGRNgbHRERJyKs5x0REbmNwTESUhMScY1mWY7gkxsTl4yAgROQ2BsdERElIzDmuqIzv4FjEDnlE5DYGx0RESUhMqzgZ58FxnDdsE1GCYXBMRJSEUoS0ingPjomIoonBMRFRElK0HFdUxnBJjDF0J6JoYnBMRJSEUoSc4/KK+A4/473DIBElFgbHRERJ7mRlfLcciyT2yCMilzE4JiJKQmJj7Ml4bzmO9QIQUVJhcExElOTYIY+IqBqDYyKiJBf3HfIYuxNRFDE4JiJKStURZ9x3yIM4Qh4RkbsYHBMRJTlvdciL9RJ4y+iPVuLu91ew4geRBQyOiYiSkBgrxXvKMeM6e46XVeCzFTvx9ZpCFBafiPXiEHkGg2MioiTHVsXEJKajVPI3JjKNwTERURISYyWGTURE1RgcExEluXhvVBSXjynHROQ2BsdERElIVrQXx3l0LOAIeUTkNlvB8e7duzFixAg0adIEqampSElJUfwjIiLviPuWYw8F70Tkfal2PnTjjTdi27Zt+Mc//oHGjRvzTp6IyMPiPfRkWgURRZOt4HjhwoVYsGABunfv7vDiEBFRNCg65MV7dExEFEW20iqaN2/O0j9ERAlCfT4vPl6OhRv2oSJOCiDHx1IQUbKwFRxPmDABf//737FlyxaHF4eIiKIhXHe8K15fhD+9tRTvLt4SxSUyh1l85rENi8geW8Hx8OHDMXfuXLRt2xZZWVmoV6+e4h8REXmHeoCIDXuOAAC+XLUrFosTgk8q7bG61YqKT+Df367DrkPHXVkeIq+wlXM8YcIEhxeDiIiiSTZRyS1eOlsrFy8+likR3f7uMqzeUYwZPxfhu/sHx3pxiGLGVnB8ww03OL0cREQUI3otjAxDvc1qi/vqHcUAgI17j7qxOESeYTo4LikpQXZ2dvDvcALTERFRfBJrB+vFUHHScMzcWZu42YjsMR0c161bF4WFhWjYsCHq1Kmj+bhNlmVIkoSKigpHF5KIiNyjN8iGFIdtx/ESsBNR4jIdHM+ZMyfY2e777793bYGIiCi64r3lmE2g9rDFncge08HxoEGDNP+m5PP7niOY/etu3NCvFWqkc7hwIk+SNf9UWLr5QFQWxYjYsh0v8bonWAyOU3xS3NS2JoolWx3yAo4dO4Zt27ahrKxM8XrXrl0jWiiKb+c8Pw8AcPBYGcac3zHGS0NEkWKpNAIYHBMF2AqO9+7di5tuugnffPON5vvMOU4OBdsOxXoRiMgmE5Xc4gZjd3v0csn1pPkklBlPRpTwbA0CMmrUKBw8eBBLlixBjRo1MGPGDPz3v/9Fu3bt8OWXXzq9jBSn+HiTKEF4KPiMmzxoD7B6U5Hi48YlAmy2HM+ZMwdffPEFevfuDZ/Ph5YtW+Lcc89FdnY2xo8fjwsvvNDp5aQ45ONVisizxFQKqy2M0RbfS5c40lJstZcRJRxbR8LRo0fRsGFDAEC9evWwd+9eAECXLl2wYsUK55aO4hpjY6LEEO9pC2IgH4/l5eKV1Z+VLcdEVWwFx+3bt8f69esBAN27d8fEiROxc+dOvPHGG2jcuLGjC0jxiy3HRN6lyDmO8+CY7LHa0TKVwTERAJtpFaNGjUJhYSEA4LHHHsOwYcPw3nvvIT09Hf/9738dXUCKX4yNiRJDvMfG8b58iSIlhSd1IsBmy/F1112HG2+8EQDQo0cPbNmyBcuWLcOOHTswfPhwJ5cvxGuvvYbWrVsjMzMT+fn5WLBgQdjp582bh/z8fGRmZqJNmzZ44403FO9PmTIFkiSF/Dtx4oSbq5EQtEZJJCLvYSm3xGT16UCqjznHRIDNluPRo0drvi5JEjIzM3HKKafgkksuCY6o55Rp06Zh1KhReO211zBgwABMnDgR559/PtauXYsWLVqETL9582ZccMEFuO222/Dee+/hhx9+wF133YUGDRrg8ssvD06XnZ0dTBMJyMzMdHTZExGfwBF5lxgsxXtpW8bu9ljdbkyrIKpiKzguKCjAihUrUFFRgfbt20OWZWzYsAEpKSno0KEDXnvtNdx///1YuHAhOnXq5NjCPv/887jllltw6623AgAmTJiAb7/9Fq+//jrGjx8fMv0bb7yBFi1aYMKECQCAjh07YtmyZXj22WcVwbEkSWjUqJHp5SgtLUVpaWnw/0tKSmyukbfxNEqUKOI7+hSracR7ZQ0vY4c8oiq2nqFccsklOOecc7Br1y4sX74cK1aswM6dO3Huuefimmuuwc6dOzFw4EDcd999ji1oWVkZli9fjqFDhypeHzp0KBYtWqT5mcWLF4dMP2zYMCxbtgzl5eXB144cOYKWLVuiWbNmuOiii1BQUBB2WcaPH4+cnJzgv+bNm9tcK29jhzwi74pGh7zdJScw77e9TNuIwMmKSoyf/ivm/7bX8mcVNxUmfgKWciOqYutI+Pe//40nnngC2dnZwdeys7MxduxYPPPMM6hZsyYeffRRLF++3LEF3bdvHyoqKpCXl6d4PS8vD0VFRZqfKSoq0pz+5MmT2LdvHwCgQ4cOmDJlCr788ktMnToVmZmZGDBgADZs2KC7LGPGjEFxcXHw3/bt2yNcO29izjFRYnArdO3zr+9ww9s/Yuba3ZHNKIlj6w9/2o6J8zfh+rd/tP5hDgJCZIuttIri4mLs2bMnJGVi7969wRSDOnXqoKzM+YEo1QGZLMthgzSt6cXX+/bti759+wbfHzBgAHr27ImXX34ZL730kuY8MzIykJGRYWv5EwnPo0TepRgExOXgc9Hv+zDsNPOpa1Rt+8FjjszHTDoKc46JqthOq7j55pvx+eefY8eOHdi5cyc+//xz3HLLLbj00ksBAD/++CNOPfVUxxY0NzcXKSkpIa3Ee/bsCWkdDmjUqJHm9Kmpqahfv77mZ3w+H3r37h225ZiqsOGYKDG4nccb6dyTuOE4IlZTZ9hyTFTFVnA8ceJEnH322bj66qvRsmVLtGjRAldffTXOPvvsYKm0Dh064M0333RsQdPT05Gfn49Zs2YpXp81axb69++v+Zl+/fqFTD9z5kz06tULaWlpmp+RZRkrV67kYCYmMOeYyLuiOQhIpPNnyrI9lqtVsM4xEQCbaRW1a9fGf/7zH7zwwgvYtGkTZFlG27ZtUbt27eA03bt3d2oZg0aPHo0RI0agV69e6NevHyZNmoRt27Zh5MiRAKpygXfu3Il33nkHADBy5Ei88sorGD16NG677TYsXrwYb731FqZOnRqc5+OPP46+ffuiXbt2KCkpwUsvvYSVK1fi1VdfdXz5Ew1jY6LE4KXYM+kCZYfW18xsWOeYqIqt4Digdu3a6Nq1q1PLYmj48OHYv38/xo0bh8LCQnTu3BnTp09Hy5YtAQCFhYXYtm1bcPrWrVtj+vTpuO+++/Dqq6+iSZMmeOmllxRl3A4dOoTbb78dRUVFyMnJQY8ePTB//nycfvrpUVsvr2KHPKLE4HY1iUjTNli+zR5ltQrmHBOZFVFwHAt33XUX7rrrLs33pkyZEvLaoEGDsGLFCt35vfDCC3jhhRecWry4MnraShwtO4k3/pTvSiDLtAoiD4tivJl0rb1xgmkVRPbwGUqCKj1Zgc8KduLbX3Zj2wFnejur8TRKlBhczzm28ZkT5RV4esY6LN96kMG1A6ymVVTE+7CJCWTtrhI89Pka7Ck5EetFIT8GxwlKEkLX8gp3TnJ8AkfkXfE+6tzEeZvw+tyNuPz1RXG4dN4QSbWK8opK5xeINF3w0gJ8sHQbRn+0KtaLQn4MjhOUeLGrdKnZhTnHRIkhHqtV/L73iPa8IlyWZGI1l1zMObbaclx2shKbdH4zMueXXcWxXgTyY3CcBNx6PMbYmMi7xLjJ/bQF618gpr9y+GknGG9DseX4pM4Tx4JtB3HG03Mw4+dCxevX/mcJznpuHmb+oj1iLRljKkv8YHCcoMRriVsHHDvkkRO+X7cHr8zZwAAohtx6uhQJnl/8ItgMVm+AxA555ZXaaRW3v7scOw4ex8j3lB3dl209CACY+uM2rY+RCXF4GCYtz1WrIOvcuvAx55iccNOUnwAApzXJwZAODWO8NMlDETjFbjF0+YQTTFIHDVFdd+OWY+Yiuyceb1KTFVuOE1Q0Wo4l1qsgBxUWs6d2zMRhzjFvvp1l7ieonkovCGYtZPdUMDiOG2w5TgKutRzz1oocFI8VE5KF09t+0e/78N26PdXztxUcawdhjB/Ms7qtxOlP6jSqpBgEx+yobZ9OJgvFAIPjBCVe7PQej0WOJ0FyDvuiRJdy9DRn533tm0sjngfTKiIXyW9coROpcYhp9zCtIn5wL09Q4jHmVtDBp2vkqAguDMyDDK+yUsbR0pO677t9SbbTMs3zi7PM/AbiIahXH9+w5djSUpEolmkVnxfswOKN+2P2/fGGwXGCEg8x9zrkuX8aHPe/tbj1v8tQyWbFhGf3J/5q9S60e/gbfLFyp7MLlECuf/tHnPbYt9h56HjwtWiWcrMz/xTh/MKUG3si+V31njhyiGn3xCo2/rWwBPdNW4Vr/rMkNgsQhxgcJwEv1zl++4fNmP3rbhRsP+T+lzmosPg4Jsz+DXsPl8Z6URLePR8UAADu/XBlbBckji38fR8A4P8KtG8g3A4+7cxdzF3l02Z7rI6QJ+4HeqXc2CEv8ew8eNx4oiTD4DhBiTVj3QqOF2zYhzveXYZ9R9wPAL1WHP1Pby7FhNkbcOd7y2O9KJ7BOsfuE29orQZO0ab/+D4OF9ZFTq2t1d9Yr+U4xSDnmP3xKBEwOE5Q4mkt0sDy0+U7MPjf3+P3PYcVr2/edxTf/rIb//xqbUTz1/Llql14Y95Gx+cbLRv3HgVQXRifjMXT/U+iBup65RddzzmOsJRbYv4a7lPvx5v2HsEtU37Cim3a5yVFtQqWcqMkxuA4CUSa5H//x6uwZf8x/P3TNZrvF5U4X5/2L1ML8NQ364L/H4+tEb/sKsaG3YeNJyRT4iUAmvbTNvR4YhZWeSyVxwxFy3EETccnKyqxfOsBlJ001xHSXoe8ODzoPUbxE0PGre8sw3fr9uCy1xYZTl+uc7dqnHPM3428j8FxglJUq3CoSU7vZJmMio+X48KXFuLcF+azs6BD4qW19m+frsGhY+X4y4cFsV6UqLG65Z+c/isuf30xHv5c+4bZCcqc4/jYN7xMloEdB8znluqXcmPwm2h4HxqKwXGiEkfIc+jCEq2Tot7jvHiy93B1a3k0LtuyLON/q3bhtwRuqWb84z7dLF6L237yD1sAAB8v3xHR8oSTIlyduGvYE8kgIHZLuRElAgbHCUp8jOlUZ7ZUnxSVFpwyjeA4nk/H0dgm837biz9PLcDQF+a7/l2UuJQtRNX77ctzNmDEW0tNp0lYFmEpN8WsGClboNpYFk6kuqXcbAwCUlh83BONHkQBDI6TgFN1jlNTpKh0mnLtAu0osQar+37eWRyFb4mteKtlG883ZHbpdcjbd6QMCzbsw/Q1ha7c7LGUW+xZLeV2Ui+twiDnWH1Ps3TTfvQbP4c1dMlTGBwnKPFE6NQNe6rPF5XhLbVajuNZNC7cyRAcxFvqtpSAiXi6HfL8TpRXxM3vwA55kVP/xla2qF5ahdX0ug9+3AYA+GkLK/eQdzA4TlDKUm7OBJtpKVJ0gmNPtBxXi7cWz3hSerLC9LTxdgOQrKGZG8e4ndboFMXVKc52Do9QV6swvN8wUcrNas5xsh5HXsL70FAMjhOUchAQZ+aZ4pOiEsBoBcfxfPDGW1AXL6b8sBntH5mB2Wt3m5o+GjdeyU6RqqAzjRs/g51Z+oQgLF5as73MXFpFNd1SbkaDgFhYJqJ4xeA4CUQSdOw6VF36JzWFaRVk3tj/VQ0Oc69XS6Il6VXenZZj658R0yrEzmGMk80LTauo3qZGT+gq9AYBMaxzTOR9DI4TlHhOtHux+2T5DvR/ak7w/1N9UlSGcfZcWgWv1mF5NXfXm0sdnrhOeqkO6pd/33MYh0+Uu7dQOsSn93yqUGVPyQnc+t+fMO+3vaamF1O+1Fvwpik/hk4vPnHU2eRiFRGt64H6cPfq8W/X2l0l2LLvaKwXgyLE4DhBKYcBNX9h2Xu4FK/N/R17Dp/AczPXK95LkWJZrSK+TrCKjk1sy3JEvA2mkogXdaNVkhEaiJ7z/HwM/vdc15ZJj6LlOM72jVh56POfMfvXPbjh7dDA1ogsK3OOf/h9v+H0WsSc43ITT/kS7yjSt/9IKS54aQEGPzs31otCEUqN9QKQO+zWOb7j3WVYse0Qvv25SLO3eFTqHLPlOClxM7rPTKCi1Uq7/2hZRN9rK+dY0ULp/DlhwYa9qFszHZ2b5jg+b7fsPGR+hDvAxiAgJqYR0ypM3bS4FB0v+n0fftt9GDf0bxU3N7JWfx+KX2w5TgJWWl1WbDsEAFi1oxha/S6i0YBT6rGc46iMkBeF7zAye+1u/HlqgeEj9qLiE7hmUnVNU7OXrXi7yYiPy62zTHXIszQ/jc9r/JD2qlVUz/xt/4h8Ttlx8BhGvPUjLnp5oaPzdYO47YwG0thx8Bhuf2cZftx8wP9ZYT4OLY/YIa88hg0Z1765FGP/txaLN4ZvAY8mcdtEIwWR3MPgOFEp6hzbO4FpjVCl1arkdFDjvWoVyXESvPWdZfjfql14ec7vYad77MufsXiT9QtWvOWVxvM+5ybZwulCq+atUykQ4vb/enVh8G8njrddh6qHf4+3dJ5wjNIYRn+0CjPX7sZVExdrvm9YyU3W/lsk/uTlJq4tegPPOGXHwfhprRVb1c2knMQLt38jL2JwnKDE85rdi5VP48IXjQtJvKdVnKyoxLrCw8H/986l1Rl7Sk6Eff+AzUfw8bYdE/GCYTQICGDtJkUr9Uqrj0O8/baZadWXvqNlJ2O4JNboDcwRsFMVKCo65Fks5abXl0JxbdFYnmgfN/F0Eys+7bBy3RXXwUpteHIPg+MEJZ4I7Qa0IRc+KTppFfF+x33vtJW4+4MVwf+PswZP1xnl94VsD4/mVcTTRdcpdnOO9WgNCKFZitHGT+vm7iCe246WeicY0RvSWY9yG1rboHrb325nb7fE00iK4pMUoxQYkbgOpz/5XfSfRsbPJowbDI6TgN2WY7NpFU7TTKtw/VvNEx/xAohKs1icxY1hWdlHCrZVDynroVX0LoOcY1m2dgOsDkzKTlZaCgpiRTwnHimNfpk6uyLNYzW+sTWev9iirJVWEVrKzdyyWbHvSKmr87fLboUV8VpbfLw8+ud7nnxDMDhOUHarVYi0TjqxGgQkno/dRCvlNmn+Roz5bI3uhdLqtSjc9G8u2Bz8O95uAOKlB7yTzKyRlf1ZnN+ijftw6iPf4NXvN0Y0z+BnXNwhxAD+iIdajo3SKsJxqnKFOJ9YlXLr9c/Zwb/jqeVYdLJCxuodh7B86wHDadWd3yvi7WSYhBgcJyjFoy+7Lccaj0xjNXx0PEu089i/pq/D1B+3YfnWg9oTmKiVa5YYNMVbh7xIxXtHTbODgJj1109WAwDe/mFzyHt25unm1hNvwI+Weinn2H5ahQwTHfJ0Pqs3jWbOscuxqnq/jdPYGGUnK3HxKz/g8tcXo/h4+KcT6gA/6pUu4nQbxhKD4wQlHlp2DzSt4DgaB22pxbSKWD/Gje8QyL6jZdotakYdbkKGrA1z9Yrn+DGS68Uny3cg/5+zFWkj8cBMIKF3k6J57AvzczpIMROc2SUGdYdPeCc4juZgKLod8qy2HDu8X6j3w3hqORa3zQmhY51RJ2V1CmMsz4vxflMfLQyOk4DVThwB6qBGgmSrde++aSvxpzeXmu4YqNVyrBUwA8A/v1qLbo/PxPYDxywvl1MS9WSi91ubGWXNjnjbipFccx/4eBUOHC3Dn6cWaL7/8ncbcPOUn6J+Y2d4YwNZN+fYKBAKN+94azkWz4leajk2eqqm3metVqswt9GrJ4rFyIXq74yj2FhB/K2MrhHqdWBaRewxOE5Q4sFo9/qbonGitXMu/LxgJxb+vg/rig4bTwztnOOrJy3BgaNlkGUZx4UWzTcXbsbRsgq8Pi80zzFavHwamzhvI179Xrtuse2cYwsndkVVlTi7IDhx0dVbpedm/YY56/bgu3V7Iv8SC0y1HOsc5FqBkDi7eA1StIi5u1ZKuZVXVOLQschGC3RTSHAsplXIsomUKONgWtFybCIFzunSburzRFy1HIudFYXrmNF1U/22eh1X7ziEjXuPRLp4psTZaThmGBwnKHEHtzsIiNPDR5tNydBrHZm1tggPfrIaHR+dgfWqQDuWIzV59WRyvKwC479Zh39/ux77hd7fAXrrZbXlONz0ike3Ht2O4Rhtq2iXwjLVIU+v5TiCYyz+OuSJ1SrMB8cXvLgA3cfNSuphghXBcQxajtXXEVkGdsXJ7yFuGystx+q3xRvU3SUncPErP+Ds5+Y5soxGEvA0bAuD4yTg6CAgVns8C0e9+m74aOlJ/L4ntDVZLzhukJWBT5bvAABMmr9J8V4sayNHo1qF3e84UV6h2+IgPlrWrBCiFxwbhFhWWoDdGN7WKU60eBm1aqXonIFPlFeg+JgzJcbEY9DMICB6+5rWMSamXoVbV6NdItqpSeK+f8RCzvGGPVXH0ne/7nZ8mdwgq/62NkKeTs6xmFahtU+ovsXphl11W8/oj1ai/1NzQktsxliZlZZj1bYWbwC27nc/ZVD8iRI1TdAqBscJqKJSVgTEtjvkaeQcW5nXifIKHBZaZdRB09AX5uOc5+dj2RZlqRu9QDcrM6162VR7bixaMILi+FxyxRuLcPZz8zD/t70h74mBjdb50G6ag9mPrSsqwcy11UFGvJ2Unbioa9xfKqSoazj59X5yNrqNm2nYy93Iht2H0Xf8d8H/X7urBH//dLXuKIfh6hwbHWN2N9eCDXvR84lZ+PaXopBl0eLEbiLegHsp59gq8Zhy6vBSdsgznqnjHfJUKxLoj/LynA3OfpEN4pIpWo4NLhKhaRXVf4fbfodPlOPdJVux93Dokz+KTGqsF4CcJcsyzn9xPn7bXd1aaL/lOPQ1KwFT7ydnK3qCqxcj8Ghy5trd6NWqXvB1veUVv1pdScOJtIp5v+1F6/q10KJ+TUufi6+QTunnnSUAgI+X78DAUxso3lO0Fmh8Vm+3MUyrUH1Ob/LzJiwI+7lYc+KabpSalKpO7PcLHDc/7yzGgFNybX//Q5+vwe6S6gvnfxdvBVB17OldsPWOca1jTLF6YdNn9I1460cAwB3vLseWpy4UPuNiWoViEBDrdY7jbV8NMHraYWV0SzPVQjQbMlxOAdbrYB4PfRbEY1uRc2xweVI3OonrEm5z/v2zNfh6dSHeX7IVM0YNtLSsemK/FeMDW44TTKUMRWAMABUO5jVaOf+oSyTpnbzShABh6ab9wdSJ0O+u/rxPkhQd88ymVazecQj/mv5rSGmdn7YcwA1v/4iB//7e1HyUyxX+/RUulPPacfCYpY5BRhcO7VZbnQ55ErBl31Hd9JfQnGNzV8vA9aG8ohKb9x019Zl4p7Xq4oUw1ahpOYyPl23HP/7v57BVYPRuNH8t1O8cq9eCb1T1Jtya2KpW4eJVWjkISHyPkBfJdlCmVciWWnH1vlZZQ99ao4SZATGM6H1lrB4elldU4vZ3luHNBcpUP7HCktH5V32cKoJj4UdTH+szfq562mK2s7sZbh13Szbtx+tzN5quWhVrDI4TjNaFbYbqcWUk9A7yJZv2h6RHhHxW56BIE3Ikrn1zaZjvrv47xSfhyelrg/+vPrk89sXPGPvlLyivqMSIt5biuZnrAQAXv/IDJs3fhI+WbVdMv3LbobDLHo5RC9dlry3C4RORXYDFzb6n5ATOePp7dB83y8LnQ5fRKP9U7yS58Pd9GPzsXFz7nyWmv8uMwHa8493lGPLs3LA5hJWVMlZsO4jjZRWmL/jPfrseT32zzvwCOfA8WKvlWNxXtWqJm/XgJ6vx7pKtmBOm4oVeHrAsy7q/r961q+ykNy5qZiiqVXhohDyrZGV0bDy9mWoViooMGueVMK9c/vpiw5q/ASu3H8LdH6zAjoPKnFu9MmfRaDk+VnYSa3YUK85x09cUYuba3fjn178qNvG9H64M/h1u0Sor5ZCWY/H/xUNYvY5OpKIdKzuJT1fsrJ6nS23HV09agqdnrMNsj+TrM60iwYS7Kft5ZzGa1a2BOjXTbc1bkrRPQIdPnMTVk6oCpQ1Pnq8Ids0smzh9uJxm8aD1SRKmr6kOnsRWzANHy4KPjzs1zsaCDfuwYMM+3D+0fXCaVdsPKeat93jbDDPnp+Lj5Yqc6Uis2Vls+TNarS1Gy633U2w/UJUOs0xvBD2bAssTCPb+u3gLLuzaWDHNifIKZKT68N7SrXj0i1/Qu1VdSDC+7m8/cAyv+EvW/fmsU1Arw/jU51ZahdhRR+9YseJwmJZPvdhbL5CQob9faLUSKku5hW07DvOeU58wT9EhL4Fzjq0ycy5TtBzbyDnec/gE6tUyvgZd+uoPAIDCQ8fx2V0Dgq/rNbJEo0XyyjcW45ddJXj5mh74Q7cmAIBjwhNM/RtO/WUr1ziuxJfEzVchy4qgzYk1HvvlL/jfql3V83RwM54or8DCDfuQXaP62rfvSPyWQhSx5TjB6B2EV7y+CBe9vBADnzGfNmC2k5b4eD9scKuzbBmpJndD4ePqoENMqxAfmZqtYRpJkGLmXKJ1AbbSudFMlYFwtH43RaOSyd/aDLM5x6GfU36wVnqK4v+37DuKDv+YgT9PLcAHS7cBAH7aYi5AF9M0zObgO9GRSGseYkDhRI3WFJ8P+4+U4pYpP2Gm6imRXsAabhPo5hwbpC6FawR3Mq3CiWu3suXYenAc6ZMgt4g/98WvLERpuRC4wdoNn/4Iedp5tWZZrQKzRajW8Nvuw3hr4WbN6aLxtP6XXVV9OD5bUZ36pxrbTvNz4Qb10LrB0EursHIcvTDrt5BUDy1Wq3wcPlGOzwt2mDoG/vn1Wtz6zjJcNXFx8LV6tZxpJHIbg+MEo3fwBFr5SiIcKlXrBGQ22BAnE0+wZgNT8VvUH9ldUhUczFqrfGRj9oSZFlHLsfGX3P/RKsX/j/lsDXqMm2m6l3GkJc+Mi9CHD56tfZczVyl16+6URVsAAF+pTubixUOv/qy4TNGsiqGZViEEFE4E4Kk+Cf+avg7frduD299drnhPb/aVYdMq9IJjrdSc6m9werAHNzvkiUGdndHInp35m5OLY5mZbJzVO4rxP+FYMdUqLP5t4ubE1PDRxl9r+vNDX5gfPA+oRbNDnnhcm7nBFVu1Kypl3PX+cjzw8SrIsqy5DStkGf/4v59x53vK49lsg0ph8XG8+N0G/PPrXw0/Y7ZPSMC9H67EfdNW4W+frjac9n1/I4aodgaDY4qyzfuOYsxnxjusWYs27g95TevRldmTUqWixcF63qX4NeoazDsPHcd36/bgtneWKb/T5MkkVaekltXl0hNocQiY+uM2lJw4iTGfrcbUH7dZCtjsBHdanzEq82Q/d9ge9edqm0h9AJQXzwFPzcHSTaH7rcjsBcaRtAqN3UpMq3Diep7qk7DnsHZpNv2cY/356b1nOHy0zWoVVpfDCWJrXRwUObAsVadBQf0TqDvNWgmEXvxOuzSaskOe9Y1n9YZw/9EyjP3yF8NzeTQ7eonXLDNP9cTt9NGy7Zi+pgifLN+Bb34u0nyqKMsy3l2yFd/8XITfhM52oTnHOt8n7N+lJ8Pn1Kt/DqPjIZD2Nn2NcV8mrfNPPFQVMYPBcQK59j9L8H8rdxlPGAGjluPVO6ryYbUCK7GFxs4BIn4m3N36x0K1C7OtQmlCakc0WxZn/7oHYz5bY+pEE2Cv5dh6y7DdzWB3+6mXUf1Ewex8p6k6W6qZ3Sestqho0W45VrTP2ZqvouJFiqT7W+nd81XKsmbL7OQfNuN/q7XPIVqPf+Nn4F5rxDxPr1ysRWkmGxQUT0xM7GtWW5c1BwGxeNy89N0GnPXs3LAd9aYs2mJY9SeaRRCUwbGQ9qAzvRi4bxCqSa3ZWRxS1QkAxM16QghuzRYHEc+dpeUGH1IPOe7gExut3dQrxxuD4wRSWKzdeuQkzaBXOPADuUVGLZF20gQUaRVhTsD//na95rKJdh46rmhVES82VltDdpecwJsLNilKy1k17zf9igNq9nKOw89Ha5a2c45V/2/2Wmm3g6B6/lqP9xXrGsVzs1agIHYGs9tiL7bipvp8ur+VXqqD3vSb9h7FxHnaeYpaoyiaFW8DvIiBvtF+vnjjfjzyf2tiNliI1tLptRyriec/p34C8bcsc2AQkOdn/YZN+47iPwb5sUanZaeDrtEfrcQ9H6zQ3HeVaRXG8xKvKeLxX1kpo0CjUpKyD431RiVxmUrDjAHw3Mz1msG5U4zOxfGM1SrIEq1WN60AVDPYqhTfV0Yrs9fuxrYD4YfJVNQ59kkw026l9yh49Y5iXDlxMb64u6oXtHixKTtZaamD3hVvVHc2uPXMNgCqcpjVOZqfrdiBvm3qo0mdGiHz2HFQO1c2QA7zf2bYqXNs+yQW8jlVlxVZxstzfjf8mNmgWjJRr0Jcf7NpFRFUWQs7D7EkmtaSmNnuZYrgWNIPjnWrVVj/fTVbjiXx7zDDR1v7qqrP6FXUcODqKu4DRq1x1/hLFtZK175cFhWfwCfLt+Pq01sgt3ZGxMsGVK3j8fIK1NT5Tr0+EurfQH2+Nt6lTbQuC39rDx+t/n/1SKvaKmUZew+XYvIPm3F17xah32vwuzsZHJ8or8Bn/vJm9w9tj9a5tRTviy3HPhMd5sTfQQyUJ87XviGo0AmmzT71EqcKl1aheR52MHjVOiWw5Zhc82thCV79/necKLfXUjn5h824ZtISWy0hWnGFOtg4UV6heSJTdoqqfl0GcOs7yzDuq7UhnxGFthwbH2ThAiGxnJt4sbHTAxsA/vn1r8Ftmq1Rtm30R6swbMJ8zc9uPxj+xkDkWM9/4TWt1vI35m3EnHXWa1IaLd78Dfvw/KzQDk1ay/j7niN49IufUVR8wvTjPu0Tsvi3yfk4kDSglVZxTKigYrdKiDhanc8n6baqhatzbFUk1SrscPMaaqeD5pb9yoFpAp+7/u2leHbmb+j1z9lYLPTTmPlLER794mdb55N7PihAp0e/xdb9RzX3QrN9JMTH+WbW0tSmMDhvqIU82dHZT1IkCX+eugKvzd2oqG6g8bWanEyrENfrwNHQTtN6Oce7irUbOcSBuMwMyiUGxGU6g4m8rJMTDih/x3Atx5qftTKtwQ6jdf6xUqUplhgce9D5Ly7Av79djzfmbQy+ZuVi9/j/1mLxpv2YpHPXGo7WhVu9s5ecKDd8TK8XKIf9bsXgCeY+Y/ZAPCHkZUXy+Pj1uVW/SVamdouP3iMso5bj7UKrup1Ti9YFSQw2b3j7x5D3N+w5gpunLAt53Yh6X1R/9z7dCh2hrVyXvvoD3lm8FXd/sEJ/PwlJqwhl1FIYWGZx2Z2oJKEVMIpPGjRb7E3MV/1UQu/41wtYZdn6fhTuuCg5UW5QxtHil8F+x05T81bcLNmbR+A3EEckvUYYGOf2d5fjncVb8eGPoT32jXztr+H+gc5nzdZlV/8m6n1aq+XXiHIQEIMhxWG+w7VPkrBkU9VAUkUloSmCRvuQk0GXGMAeOBpaskzviclNk3/Snp9Oy7Ee8SmNIjgWNvdzGg0MAQeF8qqGOccq1jqHh39fO+fY0uLEDINjDwt0fgPs9RpWpzEcM6gJLMG4ox0AlBw/qXnQVCjSKqqZbVkRp1NXq9Bj9oQ5UiiZo1WyyqzASd3qgB9GJ5nPC3aGn8CAUSeZQL66E4+sjTZ5DVX94uDnNHaDQE/u1TsO6QZLusNYyzJ+LSzBsbKTivVS3+B9snwHuo+bhWVbDih+B2fqHFfN5GRFJUo06oJqbStTLceqihdWW47tPNrUHvBBwu6SE+g6dmbYIWyXbTmAayYtwbqikpD3nNjOVokBntltoZ7M7E10JH1B9FI5zKZ9zRRKW2od20NfmB9R67KZc3dGqvp4l/DtL0X4STWiqtE53ejJkZOP68UOm1otx6mKtArj+emlSegRr+elFdotx2pL/FV6/v3tOpz/4oLg6ycMqlVEQivNo7JSxt3vr8CLszdoXnfirf+BHgbHHhY4gFZsO4iLXlpo+fPqGrtGrZeAuZHWft9zWPNEJh5IsjCfYyY7spWHDJ5gJufY+oGoF2yZEThpmi1DZoedc4vWlnLrHGV0EctM0z7tqD8XGOXQqsD5+Pv1e3D+iwswfOISRfCoPqE/8PEqFB8vx53vr1C8Fwgsp68pxEUvL8DqHYcsL0vgwnnxKz+g69iZ2K1qEdOsL23id1GWg5NDLpoF2w5i/5HSsDnHVukFQura4lqOllVg8ab9uP2d5SHv6R3F0bqG2m3JMnuesFNHOaCmzo1kqo0cFq2l2LTvqOUOWeLqmBkhL101yNOWfUdxx7vLceUbytSJcJ2sq77Y/HJFSgxmtWrR6+Uc69l+4BgueHEBPl623VRDlnh8i/tZuMaeez8sAAC8+v1GxeuWW44tTCued1bvOIT/zN+En7YcwNdrCvHC7N8MU9ziGYNjDwvc8V/5xmKs363faqNn3xF1cGyc92rmRD/yPe1H4BViL33hENTKP9UiXpwNT6Qa32mW3ZxjoPqkabds8uZ9R/Hi7A0oPq4/+pCdUjvi5jpRXoH9R0ptdwYLp7JSDg4vrSczpCXJ+Lsl6Jcr05oWAL70lzVcs7PYdI6pcmSqqv++9N0G/LyzBBe/8oPlWqqBC+fawqoW09m/qgJJjdmZWU9Fy7HqM0s27ccfX1uEfk/NCf/EwOJvrfcI3coof1r1mPWW0c1BQMTtte9IqalAd6bqJqC8ohJbVXnIAWKtbavHlLh/1kxPNV2tYtmWA4qRIENnDGjdihwXR9EzsbDKtArt6Y+XVQQH5FF/46Z9R0I/AONUOaNDz8m0ipMGwbG4z5q5Txn/zTqsLSzBg5+sNpXKUqGXVhHm99F7mmBU51jNyv4qTnvxKz/gyem/4kthKGqtCk7skEeuC5wM7J4UrLYcf7x8B779xXw9XrVIC+8rht012XJSbmLbqIfPdKLl2O7xf96E+Xhh9m94/Mtfgq/tV93E6I00GI4YwAz+91zk/3N2aCumbD0cKa+oxAMfr8LnBVW1pV+bG9r7OeSX0suDDfM9VUtmbenEygHKahXa0/sk5ZORwDarVys9+JqYy2eGOnBUd/LT6ixj5ldQl3cS94O56/cCqNqPnewkpxcIWUmLSNe4gMei5Vg96z+9udTyPErLKzHo33M13xs+qTr32OoNlRis1kxP0Ty/a1WruOKN0E5sIr2gRGsQioB5v+3FgKfmYNHv+4KvKVqOdRofznpuLgY8NQe/7zkSsq3FJ4VPfl3dCdso9cvouDBquJFlGfN+2xty3gOqbpC+XLULZScrcfhEOQ4cqT7O92i1HCuW1dpBZuZ6rd8hT/8zWp3AAesd8kqOl+OK1xdhyg+bDafVWpftQhyh1UrO4JhcF8njOqBq5CHRdoNSagCC5W2MaLccW8/zE9npKFehCMi1v/PuD1aEfI8sy3hn8RZFRYvyikp8syb8OPS+CIPjwInsp63V+XgDnp6jO70sA1+s3InRH60MG9SLp+9AXvQPwgUPAM585nvs18ivC3cy/2jZdnyyfAfum7YKgHZpIDW9xnyjbabMB9a/IAXeEh9JixdkvQu6T5JULcdVMxKHsbZ6zK3ecQjTfqruVKVe7Jum/IRnhbrcgPICqH66EyAeCw98vAqrhP4HYhARruKG1VshrRYoCdaqVKRrPDXQ+yndvISqf8YfVfmvZlz62g+mprO6zxwR0hwyUn2a+6uYVnHoWBlGvGUc3FfK2ts60Ndk/5HSkCdWN7z9I3YeOo5rhZsHcW0CN2nLhfOVhOo86znrdods7IPCdec/C6oDMKNUEePzQ+gEYkWnmWt344a3f8SAp0LPp5e/vgh/mVqAF7/7DV3GzsQfXqlOU9R6ipdiMedYZCbVTwwqxWM93Lk4u4Z2Kt8zM9bhitcXma5uNWXRFizbehBj/xe+ehSgfR032hziZ06UV2DOut22K2+5icGxhzkxXKYsyxjz2Wo88dVazTtkJylyjm18vlyVZ2mGOFqa2U6L5Scr8fWaQjz6xS+45NXqC+B/FmzCne+vCPNJoeXYxho+IZSy237gOG6c/CPKTlYqKmkAynWXUTXW/WcrduL9pfo5uprVKlSLuOPgcUzSGACi3H+zoGXXIeM8dfV3690YyZA1W3WC75tsMQ98nzjFmM/WBP++8KWFim0d4JMkVc5x6PdazdIpOXESf/u0+ru1LhyvfK+8oRDX7d4PV2rOVyzltrtEddwKyxvBqOghSo5rtzBaGREtI1Wr5TgGPfI0js8NFlPTwo3opvgmi6cCdUuuVkAkbvPX5m7Egg37QqZR0zvu7nh3Oe6bthL5/5yNFRoDUqiJsymrqMTR0pO4/HXtVmutqih6fQnGf7Mu/PcaLJd6Mz359Vp0+McMFPhH1pv3W9UTFa3rwNb9VQ1DX60ObfxYtHF/SDUW8YmQ1REBTbUci8M/l5u77um1HG/ZfwzLth5Eh3/MwNz1xoNNiWkf6n1R3alYa1WMbhbEc+jfPl2Nm6csw98/XW24XNHG4NjDnHg88dXqQkz9cTveWrgZR0udu3vT7JAnHElmgiq1UpO5V3rMdB4Bqu7s12v0vP9qVfhWYwBI8Ucjdn6atxYqH2PNXb8Xn67YoTM1/N9T/UXqmxvliTT0jDVDI0VG6xFreUWlbmuHOnDXog5+9FrSZBk4I1wrubBPhauMEFjXcL/BWws3453FWxSt7ZKk7CgqlPYP/hXp05o3Vb+xFjP3cOFanxS/eriBOSyuilYLmtWcY63gWLRHvDly8fGr1qzPfUG7BnmkwgVD09cU4o53lymCDvEYrJSNz1tmO9TpBVaFxScsVcNRjt5WGUzjCVDvc049Rjeaj3o7B1qlr5q4GF+t3hXR7vTG3I2K9S4sPo6Plm3Hmh3FIWlvRrSeBJzSsDYaZFWngYnfJVabCHf+ya5hXCHpRp1Sc6JM4Ylb58e+DT49PVFega5jZyqm1WqgM7pZEH/HL/z9Qv5vpfaQ9bHE4NjDIqg4FvTnqQXBv60m7oejdQyLJ3nxe816SmhZsJWzbLLZr6yiQrMty8zn69WqOkE5dVkXWzwDlPWiq18P3PGXnqzAFyt3KloUte7ml289GPLacY3HW1v2HUPnx77VXD47j8PCjXwWSRk9qx794hdMFGqFh7YchwbZx8u0B7gRFeoMBABUDWpiSKMFTC1cp9EpP2wJ/h3uMmUmH1+k10nUymPlTfuOhlb9ED5/l/BkRm/pZLlqX5nyw2ZF2pMV0Ux73Hu4FAXbDmoGyXe9vwLf/rIbrwrpSGJwfPcHK/Dx8tAb5OVbD2LGz1U36+kmax5XylazY7WJy1dWURmSliaS4eC2tjmf8goZ93xQgI0mjj29ZS0sPqE45r75uQh//WQ1/vDKQvxd4xwdjtZ+UK9muiKtRJxGPMeGuwRlZaY60ilR3WE6kCqn1WE/0rSKeMbg2MOcSKsQOZX30z4vS/M8VlEpY82OYmzedzT4GMsuvfy5cMyeOMpOas/cTMvzgg37cNazc/HjZus5jHZo9Rx/dc7vuPfDleg7/rvge2a3lVZZvedmrdfN9zbTcgxUXcyf+Gotjpae1O0QZ7R1TVersLBfLNlcXVXAJ2mfuMVXznl+Hvr86zuM/mil7mN1rXJl4agHjFEvg5iXGRAu/158L1yrbrnFjjrlFZX451dr8bGQqgRYPw4vfuUHxblG/Pgy4YZN7/eulGV8XrATY/+3VpH2pDltZdVTIPW50s1KGGozfinCH19bhLYPTddtgBD7fxwx2RI88r0VKK8wP9S9U0GJuHxax7/4e2qlVdhl5TfTqghhJq9c7zua1a1h+smjEa20jhSfpDhWyxXBsbknpukpPlP9hoyoS+8FOn+G1qvWbsk2Oh94pZSbe8VYyXVOD8O4anux8UQmyJA1W9cKi08oOjpEoqqHvrXPaHVu0lrO8opKzbvfchMtx2Zy/yKlzL2t/jvQsq0uOQVUpzYYtXpq3SCF6+hnpsC8JFV1eAGqRvbq2aKu5nSGHW4Mv8n/fcHpjT8hHkOSJCn+P/CXepvtOVyKz1bshE+S8OyV3ULmuWanteNIrIYhfq/Wsh4tO4nszDTT5QbDtepaLVm4eOP+YN5mgATJUlpFQFlFJTLTqi62er3p9X6/k5UyftpSHUQfOlaGJ7/+FVf1bo7ereoppv33zPV4fe5G3D6wDR66oKPl5bRDa7CXgJ82H8QZ7XJDXpdQlWr21DfrQvaHcMorKjXLuulN68SAK2LLsZkGFaeCcjOzmf/bXvzlwwIcOqb/G4Sjd4qvXys9ohKfIq0gOzVFUnTyqxDTKsSWY4MylE5sa/X5Ltz+NW/9Xhw8VoY/9W0ZfM1KWkU8Y3DsYU4Hx5EMmyzSay3YolMTNFpe0aikoPUY/3hZBb5aHZoDVX7Sme190GRHHj3iuUV85BquZSPQMUusQanFanBcavFpw8R5m/D6dT0133Oq5fjDn7bj9z1H0KxuTcNpxYuhJGl3+tykUzt2m8bTj0nzN2pMGZ46uNS6eDz1zTp8vWYXth84jsVjzrIQHIdpObZ4vGudH4pKTuh2GgwnsIqb9uo/6tZtOa6UUXy8+hh67Mtf8MXKXfh4+Q5seepCxbSB4dwnzd+kCI715l12sjKk5cwqdV6m6LOCHaiZkYKeLeoqbtZ9koRRH660XDWjvEI2nVYRqCgTKTH1SuvGRlFTHM41HZu53F3/9o+m5hW40dy45wi6N68TfL1EJ3WoUraehqRHr+VYDI4VI+RpDAIS6GSoXkYnFlE9jzT/cmnFGw9+UtWRrk7N6hs6ww553oiNGRx7WbzegenlmTnZJ91OSskujWFcL9V4LPv2D5uxcW91QPSv6b/ioQs6ms5ZNnLjZHMncD3imv/j/34O/v3hT9tx8xmtNT8jQcKxspOGgYzYIhcQLogSH/lt23/MVE1NvU4lCzfs1XwdCNxwmfvNKyplLN18AEtNpLaIy7Jp71FFi5OMqnxRvRQgrUoQ/5oevse9ls37juK8CfPxzBVd0bVZHc1j5w0hN7rf+Dm4a3BbczMPc9BFUs87UoHjd/UO60+rVu8sRtnW6mW3Mw+9Pel4eUXEwXE4n63Yic9W7MSWpy5Er3/ODr7+7doiW62d7y3ZajqtwilikHRMqwOv+PTFybQKB693Zz03FxWVMnYcPI6OjbODrx/Wqfk87qu16NGijiPffUijVnqqT1IElWKjjTiQRmDT/vG1RSHzqIoHIt9G6rgiNUXCjJ+L8JEqnUp0NEytbDVZlnGivELRhygeMefYw+IzNPYfXC4vnJ2cY60hnQMjl4nUlRAmzd+En3cWw6nwfpWNi7ko3EXizveWaz/WkoBPTdaoVgsX8C4UaiUP/Pf3puand19z0Cg4cGGfUreGXPRyddqPLGvvHwEpDo6wsa7oMG6e8pP/e41X9LW55lqow120yizmUGoN4GGXmaofelOog3q91r6w89aZudUnIU6xmwbw72/XK+pwR4PYMKHV4KDOZXeqb4yTh//W/ceCg179GuYYFw2fuMR4IhMKNbZZrYxUZVqF0BAjpq6FaxCTTbYcj3hrqe7IjkDo75Wa4sPI95Zjzjr9MnCKAboMLswVlTL+M38TpizaYrywMcTg2MOcvJN20qa9RzVzi51cWjsdagLF7u0oKj7h6GhjkQi35hv3HtUM4TftPWr7wh9puo16eexeLN3Y28MdQzKAw2HyR7fsO4aJ8zY61pH10LFy/LyzGBe/Ym5wCTPC3XDoDTCix4l81QAzT73Mnt7MljJTzFtnbzLbwTRSTqfERUPgWDG6sVGnDTjXcmw8jdFAIpFwKu1QS5emOYqg8tmZvwX/VlarCJ9zbGYbLdiwDzeEST/RS6sIR/zNjYLjShnYHOMUSzMYHHuY06fXszo0xDOXd3VkXts0es061aEBABZu2Ge5Q1742rjh+XzW6rm6ymC9tRbz18IS/PPrX219XaSP352qe+rGzWC4C70syyG1p0U7Dx3H+G/W4dXvjUcFNOu2d5YFRzB0QqT57SKzg+iYYSZDyewNsNmg5cbJP1bXpNWZ9d0frHC8CpCWP5ocXc+MT/z9Dmqlh1YTcNKijVWVXYwCe2UdZOsdp/UZzyjQydNrruvTEqk6uePiDVu485WVDnlb9h/TLTmpnofeoC0ixbXd4DIp2+hMHwvMOfYyh3cwnwRnE4NVrI7xHs6yrQeRWzvDeEKB3UeXQFVgHD+xsfmLkxMCjx/tUufm2mk1kyR3CsUbbSqtwWDUVvpr7W7R6bhnliRFto9qOaiR32iXk62dYS/ylf49PMKvm6+qrDF3/V7k/3M27j/3VN2Aes3OYmzYcwTtG2VF9uUG7ORJ6wmk/vRrm4vZv4ZWqnHKxr1HcKT0pOF5XAxQX/zuN8c6YJmZj9YgRl5QIz0FKSYuMBWVcrCTqVqlbK2m9FCdQW/sDHRkZYwEp6pquI3BsYfJAJZs2m84nVmS5O5Ark7X/nWqg5wZlbIcNy3H4nDEWpwOsCK1/YAyuH56hvWOGG49hg7XSijLQK9W9UKCLLUFG/bhy1W78BcbA9uIyitk1MrwaQ7EYpdhHneMbNl3FE3r1NB878npv+K9JVtxZrsGlud7+EQ5sjLT8PPOYnyvM1Tuc7N+03w9IE4Oc8vcTvs6UV6BO941ruE9Sygl6eRh+4nGYCiJxGfiB/y/gl26o6Z+sHQbvlljPIprgF460rSf9Dve6REHlDLOOfZGxQrPpVW89tpraN26NTIzM5Gfn48FCxaEnX7evHnIz89HZmYm2rRpgzfeeCNkmk8//RSdOnVCRkYGOnXqhM8//9ytxXfU5n1HcfUkZzoJAECKJFkeJz6W7AaBZ2rUGTVyotyZGqHRcMhGB6Vo2nfEemumWyfTcDdYMmTT+cSRBsYBTnZ6A+I3t/W6N5fqvvfWws0oPVlpqxW0y9iZ6Df+O1z08kJMFkYKtCKwzbQGkohnbt+8RysfW0+4DmGJwEzLsVHnQSduhu1cV5dsqm74Morxn56xDis0RmeNN54KjqdNm4ZRo0bh4YcfRkFBAc4880ycf/752LZtm+b0mzdvxgUXXIAzzzwTBQUFeOihh/CXv/wFn376aXCaxYsXY/jw4RgxYgRWrVqFESNG4KqrrsLSpfon70Tl87maVRE3nruqm+XPiMPaxrtYlujyGq0RAQNkOfrVCzLSnD8lu52Latcny3dEPFJdmkaeplY1ACvKTlZi1IcF6DFuFvYettZpMZa0Sgs6yamOp6TNTPWbSDqVR4uZGGLnochS9aLBU2kVzz//PG655RbceuutAIAJEybg22+/xeuvv47x48eHTP/GG2+gRYsWmDBhAgCgY8eOWLZsGZ599llcfvnlwXmce+65GDNmDABgzJgxmDdvHiZMmICpU6dGZ8XihBRHebVuamAxVzmgOE4fUZN94QIpWY5+a5nTLccAULdWOo6Wxd/F6IGPIx+UQmsQn0iJQ1K/t8S4M1K82HXIuY6cWmLdcpzozATHW3RqrpPzPNNyXFZWhuXLl2Po0KGK14cOHYpFi0ILYgNVrcLq6YcNG4Zly5ahvLw87DR68wSA0tJSlJSUKP4lgpQkCY7tpo44mQtK8U+GbGp4bCdlpDrfymu14ypV0+vcmpcdf9s00DHULdE+FpKNk3XTY8lOJpde/4NY8kxwvG/fPlRUVCAvL0/xel5eHoqKijQ/U1RUpDn9yZMnsW/fvrDT6M0TAMaPH4+cnJzgv+bNm9tZpZh7YbgyvcAnVY2klsjq1kyz/VknS1lR/JNl5ehU0RBu0JFwwl1YOzZ2t/JCLPRuVTcq36M32EqtdE89dHXEB0u10xetapgVfzcW8SBeOnyb9caf8tGsbmhQ++Uq61WF/nlpZycWyVGeCY4D1K1+siyHbQnUml79utV5jhkzBsXFxcF/27db790ZD85qr7wpiKdyZW5584ZesV6EpFevVjruGtwWrerXjPWiaLq+X8vg317Js5w0Il/3vR7NoxNIGslv6dxyNMox39J0bZ8Wwb+v7u1MQ0ainyfd5LUgMFqi3XKcEeEw6ed1buRY2dB4bDX3THCcm5uLlJSUkBbdPXv2hLT8BjRq1Ehz+tTUVNSvXz/sNHrzBICMjAxkZ2cr/nlRiqozi5lSMm4TByGpXyvd0Xkvfehs5Les5+g8w7m0e5Pg39Fq6fKCi7o2xl/P62CrnmY09GlddW6QAZzQ6Nzo9H7540NnR/T5rMxUpIe50F0s7Iex9LyqI2xubfvbsU4N80+Aagh1d3MsfC4crwd4dw5ui67NcmLy3bG4zLRrWDv6X2pRtPcpJ06/T1ziTIuvmyMb2uWZ4Dg9PR35+fmYNWuW4vVZs2ahf//+mp/p169fyPQzZ85Er169kJaWFnYavXkmEvUO6ZPs5+M6JSuz+nHl88O7Ozbf7+4fhLzsTMfmZ8bzV3UP/h3r7RpPUv3d6k+60JkqUk9d1qW6VVDWrvxxZrtcvHZdT8e+s3ZmZI/o01N8uulQf+zRNG5GDVMvY2MLrb9qPVvW0Xx99LmnKoJhAMgUK4A4dBjGe3B8eqvwjQB1a6Zh0KnW60g7IRbnwivym9n63K1ntHZ4SfS50Bc3LCfGCRh6WiMHloQtxxEbPXo03nzzTbz99tv49ddfcd9992Hbtm0YOXIkgKp0h+uvvz44/ciRI7F161aMHj0av/76K95++2289dZbeOCBB4LT3HvvvZg5cyaefvpprFu3Dk8//TRmz56NUaNGRXv1oi40OI59xrF43nTyeGnbILotB49c2FHREh9Px36/NvVj+v2B8ltOj+TnhEY5mcFj4Mct2oPW+HwSLujSGL88Pgy/P3l+8PWsDHtBbqQXhvRUn+5j/to2l8kN6mVsUsf6zWr9WumYMLw7LunWFH1aKwPAMed3wF2D2+LXJ87DV38+I/j6MP8FPLd2hmN9KuI8NjYskSdBilmA73bJOS2n2Gw57t26Ht74k3M3wuFEO0CMpy40ekNnx5KnguPhw4djwoQJGDduHLp374758+dj+vTpaNmyKkewsLBQUfO4devWmD59OubOnYvu3bvjiSeewEsvvRQs4wYA/fv3x4cffojJkyeja9eumDJlCqZNm4Y+ffpEff2i6ZYzWoccjD6fhO0HY10qRsgF17iQ3X/uqdFcGNv6qgLQeGppqhHjurdp/iYSN8pwRap2Rqph4NPI/wSiVkYqUlN8wdy9vm3t3XSkRhgtpIVpcoq0VdpNDbOsB8edmmTj0h5N4fNJ+PD2vrhByA+/Y1BbpPq3hXi8nZqXhYV/G4J5Dw52LKh1u/WzXoSpO2YemccsOHboe+8Zcorpae1WbKlqQIrOdkqJxV2Di8KleqnF47rH3xIZuOuuu7BlyxaUlpZi+fLlGDhwYPC9KVOmYO7cuYrpBw0ahBUrVqC0tBSbN28OtjKLrrjiCqxbtw5lZWX49ddfcdlll7m9GjGnlULhk4Dyk7ENWMRF0jqH/vnsdnhwWPvoLZBN6oAlnoLjSDtivHptdUuK1iAMRgKtBPEyAln35nWCf1cFk+HX6ZxOyv4Ii/5+FuY9OBiNc6wHeyk+KeKnCnVrpesusVHLcc0o3iipD4Fw633X4LY4v3MjZKuCe3VH6n5ttUe7rCNUpUlP8aFZ3ZqolZHqSJhTOyPV1SdBc+4fFDKy4QNDrTUKVBpEx5IU/cf4we92aD41M8zvu+FuIMNJjeJGisPG04hkWrjOMOeY4oZWsOaTpJjX8pV0/had39mZPCc3qR8TRSs21mohyUzz4b1bqp+ERDKk8BOXdsaFXRvb/jwgtBxH8bneTQNaoUvT0A5IQ9o3CD52B6pKdBn9Vt2b1VH8f/3aGWhZv1bYi/7kG3vjyvxm6NemPu4Y1Cb4eqqvash29cUhK0yLrzqgff6qbroHS7j5TLmpd1TTqMRzjrgNtORlZ+L1P+Vj4ghldRn18g47LQ+vXtsTcx8YrHi9SZ0aePKPnfHi1d0V6U1OHIc5NdJcvdlt06B2yI3jBV2sHXNmjqxYdcB2attZmU96qr3vjGbQZvf3uNJmPrVTWufW0nzdyg1JPDUeBTA4TlKBFphzhVYwnyTFfHhKcVz3ohLtEZ+80LktTfWYKFonWa3v8UkSThfyMyPJ9Y201RkALu5WVT0hWi3HedkZuOWM1jhSGrpv3zigtaKVLSvTuHVR7yIWbr/s0aIO/n1lN0y9vS/GnN8x+HrgAqJOcRr7h9N059VI6Fh6zenN0bZBbd1c2nAtxyn+wDxaJKm62P/5ncMHe9UlN1Wvh8xTwoVdG6OVxgX6uj4tcUn3psrpHbgdqFsrzVTLcbigpUlOJi7r2TTk9Sf/WNX7/6iqvrbV4MFMWkWKME87Tz3sciooNzuX167rqQjUxGPt7RvDl/YMd97ubzONSk+KzWMx26EKLFrM9KP49E7t4gVWmj7i8ZLO4NiD9HKtAkGHGYFjXqyP6pNi10kj4KgQnOt1oojDJzAh0lQtFWbzr645vTmG9zJfi1XREx/6HRvE9IeyCILSSIP8yTf1RvN6VfWNo9Fw3Dq3Fhb89Sw0q1sTh0+EDv/dtWmO4oawVkaq7YAx3EVKb56B36tUqIqRWzsdf+wRGjgBVcfEa0IHIaMgqFa44DjKHXAlSJg1eiDm3D8I3ZvXMbWd1XWmyzWqh1haBgdWuG7NdMNlf/maHnjmiq6a73Vumo2FfztLUc0GAFY9OhTX9anKoVbnHFvtrCUbplVIinnqLasbwq3KX89rj0cv6mRyPtozUg/0dEGXxorgWDwXtqin3eoZkJqiX/u/Z4u6GHBK5AHyIxdW3SzbLbnYpkH4dfj0zn625guYC7z18uPTLbQcx2NVTwbHHvTAsPaaNSrHXNBB8f8PDmuvaGUSBU4s4kneJwF3DWnr4JJad0xoMemqenwdEM0A/uOR/WwNFavuZGX2EVOKTzLMFxTVr6VcNq3gVYLyd46kgo86B09rUSeNyNd91NahUXRHa/NJ1Tcmh08oW45rZ6Sibq10HC2t3ufSUny2A0Z19QSR3i6r1RnvjFNydVvXZo8ehA6NquuqV2q0sIqBTriR3CRJilZfI//3ATXTU9HGQuUY9QiFpREOYezE6p7XuVFIgPfYH6oDujsGtsEfujUJCaB/evgc/Pfm0/H+rX01f98cIaibfGNvRS681VOeqbQKYab1aqWbHpSncU4mPrjVfof1cOfvuwafgpvPaI3pfzkTbQ2CPr1jpGZ6KtrnKc8ziuBYOOYyUn04rYn+OAXqjmJPX94l+PfpreuhTo3Ia54H6qqf2a4B7hqsvP6aCb7P7pCHa/u0QJvcWnjiktAnTl2a1rG9bOHSsoyoG4jCMaquEgsMjj1KKyhRty7cPeQU3btKrfNKik9Cw6xMxwrl23FU49F3QN82VcFHNBu3e7eqh6UPnWP5c+qOamZbjlMkyVKLaq5qKFatDiTqi3Qkg2+kaVQ4ETsLXZnfDENPa4TvRg9SlIybdd9AfDKyX0S1bc2Yed9AdBOCCvFCXKpqdQy8pd7n7O5ffdvUx7DT8nBWh4bo16a+YphcvYAgsJ+Io8dZufk7rUnVTbL4CfHGOVynpWiXjrLzber0gl2HtFOtzC+E8VIYVTa4pneLkN9IrKPbrJ52kJme4sOgUxuYOr92a14Hk2/sHfx/q08zxBtsrXOPBOXvn5GaYvo7zuvcCPkRDGpk5ns6NcnGQFUdZvXuqteBLT3Vhxev6Y6W9Wvi8YurgkVFK6bwuYw0HyZd30t3kJAKVUvC8N4tMPeBwZg0Ih8DT22guKGxS9wc6gahV64xLiPn8wH/+mMXzHlgMEb0axXyfiTHeXam/fWzch5jyzE5Rqt1UStn6anLuuKCLo0w+abeite1TlCBO/FR57QLfS9K19EzTqnqfa4u5F8zPSXYqczqhWL26EHOLJwF6iBVzNUNl5rg80mGj0RFDVSjjGnNWf1aJB3y1CfajFQf7jmrHX56+BzMum8gnvKPcOjzSYogvF1eFnoZDEzghNoZqfji7gHB/w9X3zTQknxMFYDZDY5TfBImjuiFt2/sjam398WIvtVlxvRmGdiegYt42IkF0/9yJh65sCOu8w+NLB4T4nkgXM6xL7oNx7a+TN0HQq8fghP+9ccueP26nhgZprNg56bZ8PlC08/E7a+XpylZvNqK3xFu0wVGbBRb+YyeDkmSsuU1M838E5OUCNPvzD5tf3BYe/z9/OqnoervTNGZkU8COjTKxrwHh+CG/q0AKFsxxcGHMlJT0LRODTx0YUf1bAAAWRrBYavcWsHBL6yM1GhGhsk0OZFRclQk1+5I8pmtBLx1HR511AkMjj1Ka8fTekTbon5NvHZdfkgPe+1qFVX/vaFfK/zvnjPw48Nnh53eDf1PycWnd/bDwr8NUb7eNleoYWptnnYLwEdCHQCLj/XCtSKn+iQ0tjA4gviIetKIfGzdb1yn2krahpo6PSQQ9DfIykC7vCxF8FxpEIT/81Jnhh4VBb7/0zv74fKezfCEie9wqxOqODKdfstx1fYT9wkzx1qnJtm49cw2Ok8Kqv8OV6ot2tUKwl3En7m8K567slvI66eHSVWxtwz6cmun4/wujcPeUATS9cVtfMfANopzkt6jaMvnUGFySQIu76ndwW/q7X0xtFMePrqjOrdUceTpHIbi0mSkppi+efH5lL+kVme+cBWFMlKr98lwlW9qpqdi5KDqNAP1/qrXgU1rdcXzlpiaEzh/ac3rxau749S88GlgdWtGHtSJX63u8GymBrrRbqXVmGT26XB2DftpFWZSJSbf1BsvXt092FE3njA49iitAyLFQqFEzbQK/0x9PgldmuUoHkVFM883v2U91Fc92lSOnBfdizoAw/w3NXUQqbwQ6R92Pp+EuwabL24/sF31o8fTmuZod7ZTba7KShlX9zbf6U+kbskI1+mihc7j5YDr+rTA+Mu6hJ3GqsC+kd+yHp67qpviEbm682KA+tG9U93UxFYg/ZzjqjfSFMea9rThLiDi/MWgPFyKgE+KfrUKPVf1bo5LNDokifnVbi9DQLhBcg4dKwOgOgdJyn1GL7iO5F5EgoRnr+yKiUIH6oBT87Iw6fpe6Ni4eluJT5+0ghQJynXIsNByrO64rRVA3jZQv/VdPP9ZqVijDmB1n8BpNhwJN+3C+4Fl0ZqXutKJFifSKsRtqR7q3UzLsZnrobqMpZh6Fk4kaRVm2mCGtG9oajvHAoNjj9JuObYQHGt13FI/Koz5YNLaYlFQ47M7B+CDW/ugjU5HMzV1+oG4bcWWE7VUnxS2woDojoFtFEOxil8pDoIQklYhy3jq8q6mT5Ai9XqFawV/5KJOuLxnM0y7va/m+5IkGbbMWBXuGPhkZH9cqFEvNuRmxaH9S5yvbnBsouU4KzMVL17dHf8npIuoiZ9IT/Vh7gODMfeBwSEX25DPRfFYMvoqcb3dSkE0c04Ld3weDATHiuNOWdFA61G8nhv9j/1vGtAq9E1hI0hS1fFi9jG+qVJuwjpkpqbgMp2Waa3Piet7RX4zjDm/A3oJefPhSoCJ++RJC6NkpviqS1Lmt6yr++RDa456N4GS0CBkhxNpFbJGsB5g5ppuZsm/uHsA7hBuWMwO0GF2kKc590c/ddFtDI4TSFXdUu33QuuDan9eOVH46SNlt+OfE0H7gr8OMZ5IkFMzDf1PybU0JKZIbMn5o0Z90wArtS4lSVJML24Xsbd2SIc8f2ONnZrFgRbOof762LcP1K9uUq9WOp67qhv6tHG2Hmg44S5ynZvm4NXreiJXlac97pLO6NQ4Gy9f0wOAc3m4YpCl3mfP6RjYfq0BKFvgAz/XLWdUvff4xafhku5N0SBLvxVY2ZApoVVuLc3av7Fk1EodjUA93HcEjlCxxT/FJyke7Z8orzp4xEC+ZlqK4tzZUKe6jVbA+o+LOuHLewbgkQvNlS8zS2wt1vpeSdX6m5Yi4Y6BbTD5xt6G5+UU1ROHFJ+EOwa11RhpUpt43rFSVlKSqmoWP3xBR0wcka8bONpJGwvXaS3cblnHgbQKcXnVN2aSJOGtG3qFLW+n3qf/dl6HkGl8PknxdCrD4KZZ/P7m9UKfWKn7BLVpUDsk7SceO9lZYT+hhOJOoKOEVjUCdSevcDnHAZJDwfGKf5yLnk/MCnndSi9acUonUiWbGzzy1xPJIBgL/joE+4+WoUvTHNTOSMWhY2VYV3QYpzXJwRvzNgKwNsa8T1JuQ0mqqpn59sLNeOaKrhj077man6uRZi93G6huyXjl2p7YuPdI1EuzGTHT0pJTIw37jpQF/791bi1Mv/fM4P87lWogpnGoF+uNP/XE1gPH0NafMy4Gx4EOk49c2BF3DGqDhllmctCV+4EZsixHuc6xwftRiI7DfUPgFCke48sfOQd1aqYHj08ttTJSkZbiw8vX9EBFpaybyqKV/53ik3RLVtYSKo0EclvNbiPxdK/dkqp8ZC9JElJTJAzp0BA5NdJQfDy0Jrg4rRYx0A339CuSluPc2hnBlA29G2Gng+P+p+QiI9WHThol37QCR6vEDtJa15ezO+ah5EQ5xn21VvPz6t/jzsFt8d6Srdh56Lhquuq/zV7HJADN6tTE9gPKeWnl1Vspx3brGa1NP6mIFQbHCUTdUSLstBoThqZViNPbv3CJRcJza2dg35HSiOYZy4FK7LYcV92B1wwG5XcLA7m8uWBT8G8LddOR4pOUQ+MCuPXMNrj1TGW+X2BzvTC8G1767vfqihI2tmOgg0h6qk+R42iX0z+lmRuugac2wMa9R3VvDlxpOVbnS6b4goExoOpNXykHP2MuMAbEEMjp4yM91Ycyg8E3mtWtgR0Hj4edJoaHraVlEAOHwP4kScqAU9zGgRzjPxgMwmQ1+E9N8WH12KGQ5erzjtlZmKlPq279CzD6Dr1jTBx5MzNMasrw3s3xecHOkM8YMZtzbKfFMtwTu9oZqVg9dmjIqKcAHClNKTZm6aVBhdueZvcJZWqfyQuNpLzZePzi0/D0jHV49TqNEnOq7R6u6tIjJgd6iSWmVSQYvQNFXZJF6wLaRFUlQTyYjI4/vZ7UamIOk93R1mJ5kXUirSIcKy3H6rQKvR8p8PIfezTD9w8MDub52gqOLXT6jAUz6/TXYR3wwNBT8e2ogZrvO7V/ZaTqtxyriQFHpI8jrSy+mYAtzScZDpU7YXh34++Kgz4MZtY3PSW0yki4jqdm+wjYkZ2ZZin9bOKIfHRrloNnhcofWuceCcBZHfJweqt6uO3M1paWKaQ/hf+/Yh1xrVzVxy8+DfMfHIK+QppVuYWykuqWYr1j3VZwbHCAZqSm6LZUm+2HojVwF6Cs6qMu5RYQLve3QqP1Xatcp7j4Zq9jPklSxLzX92uJNWOHobdGWU6PZ1GEYHDsUXo7ot7JPy3Fhx4t6mi+N2lEPm47szUu7qbMhRXnZHRRee6qbvjORFK+3fQIZYpHDFuOrTTtmqTM3zP/uZC0Cp3gQ78zivnvCjDbQSNWzNxw1UhPwT1ntUM7nc6A7lSrCD9P8SYnklJ7Vd9lbjqz35Lik/Cf63vpvt+tWY65ADG+d50gdc4xELpf7T9aGvxbXQnATUabcNhpjfDFPWcoSjzq/c7pqT58NLIfHlblOxunv2i/LgbHWvt7RqoPLfyj8HVuWvXU6Y8mhkwef1kX1EhLwSv+PgEB4m9y6xnVAb6VOvEBEQ2IY/Kjp+iMDHlSJ63iyT9Wl6EMd/7QGuhHK5dbPK+F63Sq/AxUHUMl09tK71cYpBrcJV4xOE4w4fbbKTeeHvxbPHiGntYID1/YyXanhAAznckUw1VbOCGJrSdRLs+qEElahRlWWnPVJZWsbhc7LcdWWrbNcPqndKJ2r1P3XlY660Tacix+xukW2rQUH2plpOoGyJIkmVrm+E+rqFoJrUfO6nrSYgpJC5PDLseVMBtCfa6qVysdP/z9rOD/6/3WRqk34myn3tYXH9zWR3NEN7VrTm+Bnx8fFtKxVzxmxDKmdm4tIwmOn7xUuxylehM/+gftVAKxlVdsfDHTqL70obM1A12t30JcxdphRs5UfkYyfbOuvinR+tjM+waGvdGOJwyOE0y4C6PYgzjQ6zrsvCTtv9Wu79dS/80wzJyQJgzvjj6t6+Gv5+mPlBTpd1iRHuaOe0j7qjviOwfrV3DQIi6hpXJ8krkWdb05Gn2V1gAadlNhnHB9v5Z45oqurn+PU2vYtkFt3DPkFDyiM/qW4juF3y6S4b2rZmZuMlk2N2kglebcTnnB0mMis8eY3VxocRjlSJm5cWhapwZa1KuJDo2ygoGy+onJoWP6HdbcZGcTalarsPD5we0boFF2dcpdYGRJ9TIZB8fV35qVmYb+bXNN7zta0ymCYwtPXs7q0BDndMzDi1d3Dzt/s/rppBypc5T1bpbF5ZUkCUPaVw0xHriehJOXrd0nQeu3EPcds7nSkmT+ZkM9nVYHvVPzsmw3MEUbO+QlmHAnT/EEUFpeoT9hYF7CKVRskbuqVzN8tGwHAGB4r+YYd0lVEGXmIBKXz0xL86U9muLSHqp0DwvnMbP5YGaFSyuYfNPpuu+Fo9gm/u0sdlzUox7GVm/J9LaXUbAyom9L/OP/fla8ph7cJFJWBnkYd0lnfL9+j+77gaGUI6azWfJb1tUcxS2cB4a1t/z1dh4Li8weH2Y75RiN0mW2/KDd8GNIh4bIrZ2uqDBil5lFTU3xYc79gyAJJcvMjFSWKMT9b8Ap9fHYH05TXDv0KlkYBsfOLF6QuEwthdZ7o8OnRnoKXr1W2aHMSglNs1J8EmBwmW2QlYE+rZXB9dv+m8FI0ge10irE7dLE5Ih0Esyfj7xeuk0teY74JGG2deaEmeBYbJUUXhcf44gdtOqZeIysFQhaZXYdz+mYh0kWH+Fc0EV/2FMgNKDo53/Uly8UwLdKXJtA2sKX9wzAuEtOC3sxr0qrEOajO635XORww7lWLZ+zF5Ea6Sm475xTTU+v9e2XdG+CHi3qYJSF+YT/Du11vOb0FlGpHWwrrUL42+j4uO+cU3F17+Y4rUl2yD5zZrvckOmNOmH6fObKOEUSf0wc0Qv1a6Xj+aus3ZyELEOY98TtnpriU7ZM6uz33W0MpBMJpwIQs7/F38/rGNIhsEQvOFYFZOEGQnKCuC3yW1Z3EDNKR9DqwOb0eQ0w95Rt3oODQ1pSxZsyJx0Unnbk6dTiBqBIoYEkmUrvAIwbx4yuLfGGwXGCMXtIHTcRHOvRG/krp2Yapt7WN2zlCjHw0OsgaMTseePNG3qhtcVg5sWre4R9X90h77XreuLRizphksawrnYEZt+kTg1c368VaqdXP9xRP56vSqsQW471guDw3yU6XaMXssiNDnk10o1PQ5/d1R+A9gn46t4t8PldA8IOkmFFrHNj7XTIU+Ych3fvOe3w1OVd/fuOcmqtC7pR4GD2ZjWSXOj8lnWx7JFzYlYbVb3fv3vL6Rh4agO8cm3484XTotE4Jwb8p2nU9i05oQ6Oq7aNuuVY3ZDgdPwploETnxDqtXQGRg0d0iE0XcGV4NjgXPnKtT1QM938w/sx53dQtJCHE7jJDXR8BJSdSNVDpIs12cVh6nu2qGM+rSJMzvGFXRqHtNbHOwbHHiXuiF2a5gSHhjR7Ybeac6w3/rv6wtivbX08d1U33TxLSQJm3TcQ4y/rYrr8W8g8hIusVg5kJIzSBtR3+XVrpePmM1qjvk7hfzOU1SrUrQjVf996ZhvFhUs9bK1RKbfQ10PfMdp/1B2TnNCinvIG5oxTqk7sj1zYEX1a18Ppreqhh3+9NctSOXxd05tdpOkOZlmobqXJyvZQT6v1+2rVdxWZDSwi/Z2caE0LN4twHetGD61Kj7kyv+qcdWa7Bnjn5tPRrG74YKVvm6qbzaYmH2MbqbS5c9SvpXyqF+5G5fGLO+PuIW0x676Bmh1cm6jyVfVyjtXBsdPH6RntcnF17+Z4/qpuiuXU20Kz7huEt27ohSvzm4e850ZwPPrcqidZHRplhQyvnJHqw0VdjSt1iO4Y1BbzHhxiatoXr+6Bhy7oEEzRAID9QlqS+jqnbvT5/oHBmDgiH4PbN7T9uEKR8hfjBgc7mHOcAP735zOCf5vtrW8qrUIx6pYYHAstxzqf1TvZSADa5WWhXV4Wftx8wNSyqomzbttQuzyOW0oN8ursUKaaKN9Tb8euzXKwcvsh/+cirFahEfMYBSBudMgbdloezu2Uh1lrdwMAzunYEG/e0AuZaSnBIZQDy6V1nnZ60ItYlgoEHMg5jqCFVuv3NUyrMFmtIh6E2zanNdEvyXZxtybo1bKuomOaGS9f0xPvLN6Cq3qFBmR22L1xmnLT6fjDKwuD/x9uF8+pmYYHh4UOQfzpnf3xyfLtwfeu6tUMq3cUY7C/45g6rSIkXcCFKiqBAY1EesdPg6wMnO0ftl3NjeD4T31b4sx2DdCiXs2Q67Lbh0u9Wum4faCyY7jYh0UdHKv/v3VureBTV7vLmmLihiWeseU4wZg9xE9YDPLEYzvcyF8BekGUOL368bHZ1AQzndDs+N89ZxhOczLSZj0D6kBP/f+K/GQJqpxja2kVZoYQV3MjOJYkCc8IF7kTJyuDTyfU+Xda173aDg/CEOtWjqi2HKv+XytIMPrNze4Ssd6u4ZYhU2fwBVGTOjUslwpskJWB+4e2tz1cvZqVIXpFXZrl4M9nnWI8YRj5Leti/GVdgyOePnNFN8wYNTB4PVB38laXGIvW72/n+HGjQ54kSWiVW8uR8pJOuKxnVcf201vVU6QJdWycHfaJqelSbqr/FzdptJ66OYnBcYIxavW6qlfVY8F7hhifKPVKuSkHN9D+7MXdmiKnRhou7KJMwhcnFw+6/JZ1MfS08J3htL7TyvCjRszkvrpxnhNnqe4V36NFXQDVOY/qOtGmqlXovKMVHBu17rjRwgJUpacEbN1/THc68RQ75vwOuH1gG3TSyIuMhG5ahaPfos9ezrG9pQtJq9BsObaXVnFFfjPcPKB19XfF8SggXrl2R7KcV59eXc3FjV+iTNXRLTStIjq/v51jIV4CWDfdPKA13r+1D96+qbciGO7bpl7Y8mqmN6dqOqef6EUb0yoSjNHu+PTlXfHwBZ2QU9N4SFJlOmv1/yk65Ol8Y07NNCx/5JzwwZRwMFm5cxdPsnplhexweoAL0xQ5x8q3nrmiKybO24gre4XmZ5u92Oi3HBtPO+qcdpgwewNuO7M1LuneNCoXuN0lJ3TfEy98t5zR2pUc6Fif092uVhGO1vY0bjnWfv/ZK7vh+3V78PYPmwHEfrtWLYP2QngkNo5o9ES3N3/ZSVXLcZo6rSI67GyiWNZvt8tsKcaA1BQfBvj7c4jn0YpKOWxHa7PbU/1UQ9ykXrn5FLHlOMEYBS+SJJkKjNXzEmfbqn4tzdfVUlN8IcvTRRhfXnz8ZTcudaoY/+U9m6GVv0POS9f0QM30FEy5KXTwAbdbv9QBer1a6RhzQUec0rBqqGNlJ0mY6oiix0xaxahzTsWasUPx8IWd0NnlYXL/el57ZKb58Jez2+lOI+4zbrViR+8yrs3N4aPVQYB6f7aTcxz2d5A0/zTk1sXUeyGQUiTbxe0OUur+GLFLq4jy8NGoeooVLVNu6o02DWrhg9v62p6HeF0+WSk7klahJu5vXgyO2XKcYJyMFySdv8VhPM1+34xRZ+KzFTtxlzB6nHinaTXoHNy+Aeau34vhvZvj9Nb1MHH+Jqzyd1Qz660beuH+j1fhhau6Y0iHhsHXL+7WBBd1aRy1R22KPGKDmwRxO6Wo6hzrPU7UTbfQSqvQeC0r09zNVKTuGnwKbjuzjUHFEOVoUm5Qz7Z9XhY27zuKoZ20O/M4zc0B8oyCAO2c4/A7pdlyVLHu6Fi1DDpveOTiHVHLseJGxfnfIqRDnuo4jtZjdjtbKNJlu2NQW/yxZ1PcPOUn/LyzRHe6UxrWxu97juBs4Xpj1eD2DauqSDikokJ2ZNQ69a6pyDn2ygEmYHCcYO4a3BZj/7cWFzlQcFtRRUGn9cjsBa9Do2w8dIEyN1RsBbR68Lx9Q28UHy9H3Vrp6Ng4Gxd0aYxWf//a0jzO7piHgn+cq7kOeoGxG+d3ZbWK8CcpdXm9BrUzUKdmGlJ9PtTSCVL0fiPNtArDpXWXUSm9aLRAqLfBu7ecjpyaaSEtYW6psNGjSFHnOMxOqt6+6km1tr9RbeusTJPBsamp3KW3DJG21keLuJR/ObsdLuluvhyY27HphV0a46vVhejpr1+fbGkVDbMyDc8RH9zaB1+tLsTl+bGp163FqOXYdFpFmJxjjxxeCgyOE8wN/Vuh/ym5jgybLEkShvdqjoPHylB6shLbDxwPmaanv8OYHZH0YPX5JEUnLgBomJWBPYfDD7msFhetWarW4PDTCn9LVakrPz50DgDrnUq0WgnjvRNFNM6x6n1CkqSoBcYAkBZhK04kLcd2BgHJrqH/ZEG9v8ZaPBzvkRDPmYE6umZJdnNcTBp/WRec2S4XQztVdazO9FBahc8nYeKIfPz72/X4fc8R29+tbi1Xa5idiZvPaB12mmg7WVmJAafkYvnWg5oNJuarVahzju2n/MUD5hwnGEmScGpelmMdlZ6+oismXd8r5MT2/QOD8cq1PTDsNPuPmhsLxeSduLP86i/GpdjinVHutdbALOmpPluPxbTTKizPJqqi0QKhvsBFa5s8d2U31KmZpkg9skNrea/wt1Tdq8rnVk+q9YTIqDU/22zLcRzsXHqL4JWLd6WN4jyBc4PbWWJZmWkY3rtFsNFCfU4y2o+cYve3HHZaI1xqoSVeixPpCdF2slLG3UPa4olLTsP3DwwOed/8CHnK/1eWcrO9eDHjvV+S4kLr3Fq4qGuTiC547RtlObhEVY+1xOEy3TCib0vH5yluQqP8TmUpt/DzffSiTvBJVVUDjL43OM84CGDCicbj75rp0WslFl2e3wwF/zgXfYWcfrNkg1zspy/viu/uH4SbBrRSvK6eVms0POPgOM0zFz/dlGOPrICVpXzvlj5o17A2pt7WB4Cqg7XDy6XlD92UgaYblWW0RPJbRnoDZ7WCRDyorJSRkZqCEf1aoWX90CfOZrdn+Km8cXyJvPdLUky4fe3wyqHTLi8L3YSKG06w1iGvmlEge/MZrbH+n+ejX1vtYMtMKbd4E439RB0cR3OT2L44G2yYFJ+Etg1qG85fK4XCKK0iKzMVdXQq4MRDa3EisXJzeEa7XMwaPQj5LauGsI52tbJzOjZEP+FGL81gAZzaVWJ5n3Ohv69Pw6yM2C2ERUYDW9ndnkfLTkY8j1hicEwJJRoDDdRyeEQ2kWGtZY20inDCtfpp5TfHezATjRa+GurgOM63iZO0OngZd8hLQ/N6NfGvP3ax9F0NwgQQ3ZrXsTQv0zxe5ziS3V88N0Zjn5YkCT38nfMA/Zbjl67pgayMVLxz8+mOfG8sO1de3K0JPritD2aMGhizZbDqpMFAWnbTKpwqsxor7JDnUdE+/l0/l3rl6gR71QTCUVSrMOyQJ6RVRPijaF0g+7apamWqa7IWdiKqkRa7luNYa6XxWFVM9dG6OcmuUXUZubZPCzz0+RrT33XzgNb4bfdhDBNGxlzw1yHYuv8Y8lva7+gbjn5ahStf57iIbg5jsCOLpxi9myynS2c6fHq2RJIk9G+bG7sFsMG45djeEHm3ndkGz8/6TeMdb2BwTGSR0y0TimoVFh49Rnot0QquG2ZlYvkj57jaOh6Jtg1qu/4d6hauJGo41lxX47SK6huppy/vgr99ugbP6eS5i2qkp+DFq3soXmteryaa16tpbmFt8PpvGUng53Pw3GGWeG4L9xQrGYZvjldG1zM7Lcef39UfXZvVCQbHXimVKIrPKyAlHaeKhEfj4ud4y4SizrH5FYj0gqL38fq14zdfrnPTHLzxp55oVte9AAqoHjYbiE6qTjw7v3OjsO+LdY6H926BS7o3RWZabDo1GlH/li3q1cS2A8fw1/Pax2iJrInkPKk34mm0GI20SLFxsiL8PmUnsO0RQYnXeMHgmExxvUOeh24snU6rEDnZIc+IV1trzusc+QA3RhQlmTywmezukY1yMrHzUHX9cjGAuqxnU9x6Rht0apKt+X6AehCQeA2MgdCg8LE/dEKbBrWDQ8fHu8iGj3ZuOcyyUomHYsOpDnnhJvPS9T2AeyuRRc6nVVSzNkJehN/rgaAvVlJi3Mpmld1dcsLw7rrvDe3USBEYV31P6BeFGyAl3jadenl8Pgmtc2t5ptNl07o1jCfSoeiQF6VfRvwWo46dFBuOBcdhJvRgbMzg2Ksu7dEUANDB4VrBejxy7YjKKd/pWpbihTmaHfKMviuZiektibyVtPJ7F485C5Nv7B3RAD/xSr3Le+237dmiLsZf1gUf+GsXWxGTw1340mgNAhKJBnGcUua0i/11qI0GHXKiQpBX6oiLmFbhUbed2RodG2d5PrdHkqruTPv4qyR4wfjLuuK2d5bhz2ed4vi8tUYoE4kXuEgvdoyN9fkULceJvaFSfZKi9ahxTg3F6JWRiLdNp24xjfdBb7Rcc3oLW59z8txh+juFv72Qc3xZz6Yo2H7QcxUn7JgwvDvGXNDB8Fi3V6vC+xgce1Rqig+D2zeM9WJEbN4DQ/D9+j0Y3rt5rBfFtFMa1tYcZtMuRVqFYctxtUhbYrwYGESL11qOI+molZoiGT5atau5yx0nLVO3HHvhx3VILI53scVQa/TFeJOa4sP4y7rGejGiwueTTN0Em0+r0H+vbs10k0sVP+J/b6WE1qJ+TdzQv5VznXg8eLVT1Dk2SiQWJs6pEVkt4st7Novo84lM7KzohV2qY2P7w6bb7Sj13JXdMOf+QWGnaZVbC2/d0Av/d/cAW9/hNPVPmUyVSGKxpuXCTZcXWo4plNk+NqPOaQcAuK5P9ZONN/6UjzPb5eKRizq6smxuYssxUYyJ5x7DOsfC33pD9prVKrcWVj02FPlPzHKt5dCrFB3yPBBA5dbOwMK/DUFtG/WprQQtPVvWxX8XbwUAXJ5v7ubq7I7xk7usTpHxaMEWW2LRcixW9vFCzjGFMntp6NGiLtaOG4aa6dXnoPM6N8J5BqUg4xWDY0ooXrzWVQjRsVFwfOJkRfDvOg48qsqpkVbVSsrgWEG8jnuh5RiA7drPqRYixEAnnq7N6tj6rrjjkd/WCcqc4+isuFhDl8GxV5m/NoiBsdclzpoQeZRsITguOV49Xn2tdGdSUZIoPjAtmfKxraRVSJKES7o3dXFp3BVSyi2JfmfFICBR+s6TlZXBv60McETxw4OFJhzBWzmiGKuovn4YdsgrFoJjp1p/HhxWNTrYn/ra6wWfiFI8lnMciWQKWrxeys1rmK7lfV4c+tkJbDmmhOLFQMZKWoUYHDvlljNa4+yOeWipUfM2WSmrVXhwp7IgmQZnCAmOvXjCcEC0VrvCYGhiin/J+gsyOCaKMStpFW4Ex5JUNUoYVUumx+1J1XIcUuc4RguSJNhy7H1J2nDMtAqiaGiXV1v3PbFHt9HF+tAx54NjCpVMaRV2S7l5UWjLcWyWI9ai9TREzDkmb2JaBRE57v/uHoDFG/fj6t76+bxicGz0mLdNg9rYcfC4Y8tH2nwx6LwUK8nUchwqOdc9WjcFJ5lW4X1J+hMyOCZyUffmddC9eZ2w01i5M3/68i546bsNuL5fq8gWjMJSthwndgCVXDnHTKsAYlOtgrwpSWNjBsdEsWYlLa9xTo2kGd40lhR1jmO3GFGR6MG/KGSEvCRad1HUOuQx59jzkjWtInmSzSgpePFSxwtI/FGkVXhxp7IgmVpP1b9lMq17LJQzrcLzkjQ2ZnBMFGuVDI7jTjKlVSRTZQ51R7REL9OnLzrrzRt/75OTNLGCwTFRjPH6EX+MBmNJJEm0qqxWEWXMOfa+ZL0+MTimhOLFVr6KZH1uFcd8SfS83YvHjFOSddVZrYJMS9KfkMExUYwxrSL+JFN5syRa1dAOeUmaVpFTIy0q31POc5vnJWtaBatVEMUYW47jTzLl4SbTuiZ7WsXTl3fB73uOoE/relH5vgqmVXhesl6eGBxTQvHitS5ZS+XEs1oZKbFehKhJrgBRXec4qVYew8MMRuQGplV4X7JenxgckynJdQmJLqZVxJ/2eVm4vl9LNKidEetFcV0yBYjJ3nIcbaxW4X3J+gsyOCZTkvUAiYYKPnmMO5IkYdwlnWO9GFGRTB3y1GuaTPnWsdAgKwMb9hyJ9WJQBJK04Zgd8iixePE6n6yPrSg+JFOAGHojkEQrHwNPX94VA09tgHdvOT3Wi0JkCVuOiWKMwTHFUjKFh6HDR8dkMZJG83o18c7NDIzJe9hyTBRjzMujWErmnONkWnciMo/BMZnilUuIF+uWntkuFwCQmkzPtyluJFXOsbpDXmwWg4jiHNMqyBS2bbpn2GmN8N+bT0fHRlmxXhRKQsl0T6a+eU6i+4KE55OSd6hjch6DY6IYkyQJg05tEOvFoCSVVAEi0yoSlk+S2H+DHMO0CjLFM5cQzywoUXzwYiqSXcmzpsmH9znkJAbHRERJ7E99WwIATm8VnSGF44kvmXJKElwy5c6T+5hWQUSUxM5ol4sFfx2CRjmZsV4U16kDKIZTiYO/pTtqpafgaFlFrBcj6jzTcnzw4EGMGDECOTk5yMnJwYgRI3Do0KGwn5FlGWPHjkWTJk1Qo0YNDB48GL/88otimsGDB0OSJMW/q6++2sU18SavZHLxBElkXfN6NZGW4pnLgW2sc5y4+Fu6Y+rtfdGjRR18MrJfrBclqjxzNrz22muxcuVKzJgxAzNmzMDKlSsxYsSIsJ955pln8Pzzz+OVV17BTz/9hEaNGuHcc8/F4cOHFdPddtttKCwsDP6bOHGim6tCREREDmLnSnd0bVYHn981AL2SLO3KE2kVv/76K2bMmIElS5agT58+AID//Oc/6NevH9avX4/27duHfEaWZUyYMAEPP/wwLrvsMgDAf//7X+Tl5eGDDz7AHXfcEZy2Zs2aaNSokenlKS0tRWlpafD/S0pK7K6aZ/C0Q0Rep46fWNwgcTA4Jid5ouV48eLFyMnJCQbGANC3b1/k5ORg0aJFmp/ZvHkzioqKMHTo0OBrGRkZGDRoUMhn3n//feTm5uK0007DAw88ENKyrDZ+/PhgekdOTg6aN28ewdqRk87pmAcAqFMzLcZLQkTxjqW/EsfYi08DAIwc1DbGS0KJwBMtx0VFRWjYsGHI6w0bNkRRUZHuZwAgLy9P8XpeXh62bt0a/P/rrrsOrVu3RqNGjfDzzz9jzJgxWLVqFWbNmqW7PGPGjMHo0aOD/19SUpLwAfJZHRri+/V7UTsjvneZmwa0QpM6NdC7Vd1YLwoRxTnGxonjivxmGNK+AerVSo/1olACiGmkM3bsWDz++ONhp/npp58AaJdpkWXZsHyL+n31Z2677bbg3507d0a7du3Qq1cvrFixAj179tScZ0ZGBjIyMsJ+b6K5tk9LNMjKQM8W8R10pqb4cGHXxrFeDCKKQ8lU0zkZ1a+dXNdlck9Mg+N77rnHsDJEq1atsHr1auzevTvkvb1794a0DAcEcoiLiorQuHF1sLRnzx7dzwBAz549kZaWhg0bNugGx8koxSfhvM4MOokocbDlmIi0xDQ4zs3NRW5uruF0/fr1Q3FxMX788UecfvrpAIClS5eiuLgY/fv31/xMIFVi1qxZ6NGjBwCgrKwM8+bNw9NPP637Xb/88gvKy8sVATURESWezDRPdLshoijzxJmhY8eOOO+883DbbbdhyZIlWLJkCW677TZcdNFFikoVHTp0wOeffw6gKp1i1KhR+Ne//oXPP/8cP//8M2688UbUrFkT1157LQBg48aNGDduHJYtW4YtW7Zg+vTpuPLKK9GjRw8MGDAgJutKRETuu3NwWzTMTvyBT4jIuvjuXSV4//338Ze//CVYfeLiiy/GK6+8ophm/fr1KC4uDv7/X//6Vxw/fhx33XUXDh48iD59+mDmzJnIysoCAKSnp+O7777Diy++iCNHjqB58+a48MIL8dhjjyElJSV6K0dERFF152BWNSAibZIsM+sqUiUlJcjJyUFxcTGys7NjvThERKRh4YZ9+NNbSwEAa8YORVYmSz4SJROz8Zon0iqIiIicZFTpiIiSF4NjIiJKCoyHicgMBsdERJR0GCcTkR4Gx0RElHTYikxEehgcExFR0uFoeUSkh8ExERElBYbDRGQGg2MiIko6TKsgIj0MjomIiIiI/BgcExFRcmBrMRGZwOCYiIiSDtMqiEgPg2MiIkoOcvWfrFZBRHoYHBMRUdJhyzER6WFwTEREyYEBMRGZwOCYiIiSDuNkItLD4JiIiJKOxLwKItLB4JiIiJICO+ERkRkMjomIKOkwTCYiPQyOiYgo6TCrgoj0MDgmIqKkw5xjItLD4JiIiJIC42EiMoPBMRERERGRH4NjIiIiIiI/BsdERERERH4MjomIiIiI/BgcExERERH5MTgmIiIiIvJjcExEREmBldyIyAwGx0REREREfgyOiYiIiIj8GBwTEREREfkxOCYioqQgcfxoIjKBwTERESWFWhkpsV4EIvKA1FgvABERUTR0apyNW85ojcY5mbFeFCKKYwyOiYgoKUiShH9c1CnWi0FEcY5pFUREREREfgyOiYiIiIj8GBwTEREREfkxOCYiIiIi8mNwTERERETkx+CYiIiIiMiPwTERERERkR+DYyIiIiIiPwbHRERERER+DI6JiIiIiPwYHBMRERER+TE4JiIiIiLyY3BMREREROTH4JiIiIiIyI/BMRERERGRH4NjIiIiIiI/BsdERERERH4MjomIiIiI/FJjvQCJQJZlAEBJSUmMl4SIiIiItATitEDcpofBsQMOHz4MAGjevHmMl4SIiIiIwjl8+DBycnJ035dko/CZDFVWVmLXrl3IysqCJEmufldJSQmaN2+O7du3Izs729XvSlTchpHh9osct2HkuA0jx20YGW6/yEV7G8qyjMOHD6NJkybw+fQzi9ly7ACfz4dmzZpF9Tuzs7N5MEaI2zAy3H6R4zaMHLdh5LgNI8PtF7lobsNwLcYB7JBHREREROTH4JiIiIiIyI/BscdkZGTgscceQ0ZGRqwXxbO4DSPD7Rc5bsPIcRtGjtswMtx+kYvXbcgOeUREREREfmw5JiIiIiLyY3BMREREROTH4JiIiIiIyI/BMRERERGRH4PjKBs/fjx69+6NrKwsNGzYEJdeeinWr1+vmEaWZYwdOxZNmjRBjRo1MHjwYPzyyy+KaSZNmoTBgwcjOzsbkiTh0KFDId918OBBjBgxAjk5OcjJycGIESM0p/OaaG7DJ598Ev3790fNmjVRp04dF9cquqK1Dbds2YJbbrkFrVu3Ro0aNdC2bVs89thjKCsrc3sVXRfN/fDiiy9GixYtkJmZicaNG2PEiBHYtWuXm6vnumhuv4DS0lJ0794dkiRh5cqVLqxVdEVzG7Zq1QqSJCn+/f3vf3dz9aIi2vvh119/jT59+qBGjRrIzc3FZZdd5taqRUW0tt/cuXND9r/Av59++snx9WJwHGXz5s3D3XffjSVLlmDWrFk4efIkhg4diqNHjwaneeaZZ/D888/jlVdewU8//YRGjRrh3HPPxeHDh4PTHDt2DOeddx4eeugh3e+69tprsXLlSsyYMQMzZszAypUrMWLECFfXLxqiuQ3Lyspw5ZVX4s4773R1naItWttw3bp1qKysxMSJE/HLL7/ghRdewBtvvBF2m3tFNPfDIUOG4KOPPsL69evx6aefYuPGjbjiiitcXT+3RXP7Bfz1r39FkyZNXFmfWIj2Nhw3bhwKCwuD/x555BHX1i1aorkNP/30U4wYMQI33XQTVq1ahR9++AHXXnutq+vntmhtv/79+yv2vcLCQtx6661o1aoVevXq5fyKyRRTe/bskQHI8+bNk2VZlisrK+VGjRrJTz31VHCaEydOyDk5OfIbb7wR8vnvv/9eBiAfPHhQ8fratWtlAPKSJUuCry1evFgGIK9bt86dlYkRt7ahaPLkyXJOTo7Tix43orENA5555hm5devWji17vIjmNvziiy9kSZLksrIyx5Y/1tzeftOnT5c7dOgg//LLLzIAuaCgwI3ViCk3t2HLli3lF154wa1FjxtubcPy8nK5adOm8ptvvunq8sdatM6DZWVlcsOGDeVx48Y5uvwBbDmOseLiYgBAvXr1AACbN29GUVERhg4dGpwmIyMDgwYNwqJFi0zPd/HixcjJyUGfPn2Cr/Xt2xc5OTmW5uMFbm3DZBLNbVhcXBz8nkQSrW144MABvP/+++jfvz/S0tIiW+g44ub22717N2677Ta8++67qFmzpnMLHWfc3geffvpp1K9fH927d8eTTz6ZEOlRam5twxUrVmDnzp3w+Xzo0aMHGjdujPPPPz8kvcDronUe/PLLL7Fv3z7ceOONES2vHgbHMSTLMkaPHo0zzjgDnTt3BgAUFRUBAPLy8hTT5uXlBd8zo6ioCA0bNgx5vWHDhpbmE+/c3IbJIprbcOPGjXj55ZcxcuRI+wsch6KxDf/2t7+hVq1aqF+/PrZt24Yvvvgi8gWPE25uP1mWceONN2LkyJHuPH6NE27vg/feey8+/PBDfP/997jnnnswYcIE3HXXXc4sfJxwcxtu2rQJADB27Fg88sgj+Oqrr1C3bl0MGjQIBw4ccGgNYiua15K33noLw4YNQ/Pmze0vcBiprsyVTLnnnnuwevVqLFy4MOQ9SZIU/y/LcshrRrSmtzOfeOb2NkwG0dqGu3btwnnnnYcrr7wSt956q615xKtobMMHH3wQt9xyC7Zu3YrHH38c119/Pb766quE2Kfd3H4vv/wySkpKMGbMmIiXM565vQ/ed999wb+7du2KunXr4oorrgi2JicCN7dhZWUlAODhhx/G5ZdfDgCYPHkymjVrho8//hh33HFHBEseH6J1LdmxYwe+/fZbfPTRR7Y+bwZbjmPkz3/+M7788kt8//33aNasWfD1Ro0aAUDIHdWePXtC7rzCadSoEXbv3h3y+t69ey3NJ565vQ2TQbS24a5duzBkyBD069cPkyZNimyh40y0tmFubi5OPfVUnHvuufjwww8xffp0LFmyJLKFjwNub785c+ZgyZIlyMjIQGpqKk455RQAQK9evXDDDTc4sAaxF4tzYd++fQEAv//+e0TziRdub8PGjRsDADp16hR8LSMjA23atMG2bdsiWfS4EM19cPLkyahfvz4uvvhi+wtsgMFxlMmyjHvuuQefffYZ5syZg9atWyveb926NRo1aoRZs2YFXysrK8O8efPQv39/09/Tr18/FBcX48cffwy+tnTpUhQXF1uaTzyK1jZMZNHchjt37sTgwYPRs2dPTJ48GT5fYpx2YrkfyrIMoKo0mVdFa/u99NJLWLVqFVauXImVK1di+vTpAIBp06bhySefdGZlYiSW+2BBQQGA6qDPq6K1DfPz85GRkaEoc1ZeXo4tW7agZcuWka9IjER7H5RlGZMnT8b111/vbp8LV7r5ka4777xTzsnJkefOnSsXFhYG/x07diw4zVNPPSXn5OTIn332mbxmzRr5mmuukRs3biyXlJQEpyksLJQLCgrk//znPzIAef78+XJBQYG8f//+4DTnnXee3LVrV3nx4sXy4sWL5S5dusgXXXRRVNfXDdHchlu3bpULCgrkxx9/XK5du7ZcUFAgFxQUyIcPH47qOjstWttw586d8imnnCKfddZZ8o4dOxTf5XXR2oZLly6VX375ZbmgoEDesmWLPGfOHPmMM86Q27ZtK584cSLq6+2UaB7Hos2bNydMtYpobcNFixbJzz//vFxQUCBv2rRJnjZtmtykSRP54osvjvo6Oy2a++G9994rN23aVP7222/ldevWybfccovcsGFD+cCBA1FdZydF+ziePXu2DEBeu3atq+vF4DjKAGj+mzx5cnCayspK+bHHHpMbNWokZ2RkyAMHDpTXrFmjmM9jjz1mOJ/9+/fL1113nZyVlSVnZWXJ1113nakyUfEumtvwhhtu0Jzm+++/j87KuiRa23Dy5Mm63+V10dqGq1evlocMGSLXq1dPzsjIkFu1aiWPHDlS3rFjRxTX1nnRPI5FiRQcR2sbLl++XO7Tp4+ck5MjZ2Zmyu3bt5cfe+wx+ejRo1FcW3dEcz8sKyuT77//frlhw4ZyVlaWfM4558g///xzlNbUHdE+jq+55hq5f//+rq+X5F85IiIiIqKklxjJf0REREREDmBwTERERETkx+CYiIiIiMiPwTERERERkR+DYyIiIiIiPwbHRERERER+DI6JiIiIiPwYHBMRERER+TE4JiJKUmPHjkX37t1jvRhERHGFI+QRESUgSZLCvn/DDTfglVdeQWlpKerXrx+lpSIiin8MjomIElBRUVHw72nTpuHRRx/F+vXrg6/VqFEDOTk5sVg0IqK4xrQKIqIE1KhRo+C/nJwcSJIU8po6reLGG2/EpZdein/961/Iy8tDnTp18Pjjj+PkyZN48MEHUa9ePTRr1gxvv/224rt27tyJ4cOHo27duqhfvz4uueQSbNmyJborTETkEAbHREQUNGfOHOzatQvz58/H888/j7Fjx+Kiiy5C3bp1sXTpUowcORIjR47E9u3bAQDHjh3DkCFDULt2bcyfPx8LFy5E7dq1cd5556GsrCzGa0NEZB2DYyIiCqpXrx5eeukltG/fHjfffDPat2+PY8eO4aGHHkK7du0wZswYpKen44cffgAAfPjhh/D5fHjzzTfRpUsXdOzYEZMnT8a2bdswd+7c2K4MEZENqbFeACIiih+nnXYafL7qdpO8vDx07tw5+P8pKSmoX78+9uzZAwBYvnw5fv/9d2RlZSnmc+LECWzcuDE6C01E5CAGx0REFJSWlqb4f0mSNF+rrKwEAFRWViI/Px/vv/9+yLwaNGjg3oISEbmEwTEREdnWs2dPTJs2DQ0bNkR2dnasF4eIKGLMOSYiItuuu+465Obm4pJLLsGCBQuwefNmzJs3D/feey927NgR68UjIrKMwTEREdlWs2ZNzJ8/Hy1atMBll12Gjh074uabb8bx48fZkkxEnsRBQIiIiIiI/NhyTERERETkx+CYiIiIiMiPwTERERERkR+DYyIiIiIiPwbHRERERER+DI6JiIiIiPwYHBMRERER+TE4JiIiIiLyY3BMREREROTH4JiIiIiIyI/BMRERERGR3/8DG/Evfo94OzIAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-15T05:03:17.359554Z",
          "end_time": "2023-01-15T05:03:17.612960Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "import warnings\nwarnings.filterwarnings('ignore')\nsn.distplot(glaxo_df.gain, label = 'Glaxo')\nplt.xlabel('gain')\nplt.ylabel('Density')\nplt.legend()",
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 35,
          "data": {
            "text/plain": "<matplotlib.legend.Legend at 0x1eb7df01c40>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-15T05:03:50.875945Z",
          "end_time": "2023-01-15T05:03:51.120973Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "sn.distplot(beml_df.gain, label = 'BEML',color=\"r\")\nplt.xlabel('gain')\nplt.ylabel('Density')\nplt.legend()",
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 36,
          "data": {
            "text/plain": "<matplotlib.legend.Legend at 0x1eb7ddf1eb0>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-15T05:07:40.169584Z",
          "end_time": "2023-01-15T05:07:40.532484Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "X = pd.DataFrame(columns = [\"beml_df.gain\",\"glaxo_df.gain\"]) \nX[\"beml_df.gain\"] = pd.Series(beml_df.gain) \nX[\"glaxo_df.gain\"] = pd.Series(glaxo_df.gain) \nsn.distplot(X['glaxo_df.gain'],label = 'Glaxo')\nsn.distplot(X['beml_df.gain'],label = 'BEML')\nplt.xlabel('gain')\nplt.ylabel('Density')\nplt.legend()",
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 37,
          "data": {
            "text/plain": "<matplotlib.legend.Legend at 0x1eb7c286d60>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-15T05:09:43.813585Z",
          "end_time": "2023-01-15T05:09:43.827900Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "print('Mean:', round(glaxo_df.gain.mean(), 4))\nprint('Standard Deviation: ', round(glaxo_df.gain.std(), 4)) ",
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Mean: 0.0004\nStandard Deviation:  0.0134\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-15T05:09:54.220781Z",
          "end_time": "2023-01-15T05:09:54.229334Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "print('Mean: ', round(beml_df.gain.mean(), 4))\nprint('Standard Deviation: ', round(beml_df.gain.std(), 4))",
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Mean:  0.0003\nStandard Deviation:  0.0264\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# compute 2% loss or gain for BEML"
    },
    {
      "metadata": {
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          "start_time": "2023-01-15T05:12:50.843980Z",
          "end_time": "2023-01-15T05:12:50.852392Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "from scipy import stats\n#Probability of making 2% loss or higher in Glaxo\nstats.norm.cdf( -0.02,\nglaxo_df.gain.mean(),glaxo_df.gain.std()) ",
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 40,
          "data": {
            "text/plain": "0.06352488667177397"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-15T05:14:11.144324Z",
          "end_time": "2023-01-15T05:14:11.158893Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Probability of making 2% loss or higher in Beml\nstats.norm.cdf( -0.02,beml_df.gain.mean(),\nbeml_df.gain.std())",
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 41,
          "data": {
            "text/plain": "0.22155987503755292"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2023-01-15T05:14:43.513558Z",
          "end_time": "2023-01-15T05:14:43.533469Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "#Probability of making 2% gain or higher in BEML\n1 - stats.norm.cdf(0.02,\nbeml_df.gain.mean(),\nbeml_df.gain.std())",
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 42,
          "data": {
            "text/plain": "0.22769829484075343"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3 (ipykernel)",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.9.13",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
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      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "gist": {
      "id": "",
      "data": {
        "description": "Normal_Disribution_(1)",
        "public": true
      }
    }
  },
  "nbformat": 4,
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}