pushkarsaini18/normal distribution (stock data).ipynb

## normal distribution (stock data).ipynb
{
  "cells": [
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom scipy import stats",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "beml=pd.read_csv(\"BEML.csv\")\nglaxo=pd.read_csv(\"GLAXO.csv\")",
      "execution_count": 5,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "beml",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 6,
          "data": {
            "text/plain": "            Date     Open     High      Low     Last    Close  \\\n0     2010-01-04  1121.00  1151.00  1121.00  1134.00  1135.60   \n1     2010-01-05  1146.80  1149.00  1128.75  1135.00  1134.60   \n2     2010-01-06  1140.00  1164.25  1130.05  1137.00  1139.60   \n3     2010-01-07  1142.00  1159.40  1119.20  1141.00  1144.15   \n4     2010-01-08  1156.00  1172.00  1140.00  1141.20  1144.05   \n...          ...      ...      ...      ...      ...      ...   \n1734  2016-12-26   965.00   965.05   935.00   950.10   950.25   \n1735  2016-12-27   960.70   989.00   952.35   974.00   975.70   \n1736  2016-12-28   980.75   985.00   970.15   977.00   974.40   \n1737  2016-12-29   977.10   997.95   974.55   985.15   986.05   \n1738  2016-12-30   986.00  1006.95   985.90  1004.00  1000.60   \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  \n...                    ...              ...  \n1734              398696.0          3783.63  \n1735              808561.0          7885.14  \n1736              367041.0          3592.49  \n1737              555233.0          5489.14  \n1738              460675.0          4606.48  \n\n[1739 rows x 8 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>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>1121.00</td>\n      <td>1151.00</td>\n      <td>1121.00</td>\n      <td>1134.00</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>1146.80</td>\n      <td>1149.00</td>\n      <td>1128.75</td>\n      <td>1135.00</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>1140.00</td>\n      <td>1164.25</td>\n      <td>1130.05</td>\n      <td>1137.00</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>1142.00</td>\n      <td>1159.40</td>\n      <td>1119.20</td>\n      <td>1141.00</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>1156.00</td>\n      <td>1172.00</td>\n      <td>1140.00</td>\n      <td>1141.20</td>\n      <td>1144.05</td>\n      <td>170063.0</td>\n      <td>1971.42</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    </tr>\n    <tr>\n      <th>1734</th>\n      <td>2016-12-26</td>\n      <td>965.00</td>\n      <td>965.05</td>\n      <td>935.00</td>\n      <td>950.10</td>\n      <td>950.25</td>\n      <td>398696.0</td>\n      <td>3783.63</td>\n    </tr>\n    <tr>\n      <th>1735</th>\n      <td>2016-12-27</td>\n      <td>960.70</td>\n      <td>989.00</td>\n      <td>952.35</td>\n      <td>974.00</td>\n      <td>975.70</td>\n      <td>808561.0</td>\n      <td>7885.14</td>\n    </tr>\n    <tr>\n      <th>1736</th>\n      <td>2016-12-28</td>\n      <td>980.75</td>\n      <td>985.00</td>\n      <td>970.15</td>\n      <td>977.00</td>\n      <td>974.40</td>\n      <td>367041.0</td>\n      <td>3592.49</td>\n    </tr>\n    <tr>\n      <th>1737</th>\n      <td>2016-12-29</td>\n      <td>977.10</td>\n      <td>997.95</td>\n      <td>974.55</td>\n      <td>985.15</td>\n      <td>986.05</td>\n      <td>555233.0</td>\n      <td>5489.14</td>\n    </tr>\n    <tr>\n      <th>1738</th>\n      <td>2016-12-30</td>\n      <td>986.00</td>\n      <td>1006.95</td>\n      <td>985.90</td>\n      <td>1004.00</td>\n      <td>1000.60</td>\n      <td>460675.0</td>\n      <td>4606.48</td>\n    </tr>\n  </tbody>\n</table>\n<p>1739 rows × 8 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "glaxo",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 7,
          "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...          ...      ...      ...      ...     ...      ...   \n1734  2016-12-26  2703.00  2740.00  2677.00  2715.0  2723.50   \n1735  2016-12-27  2722.95  2725.00  2683.00  2692.0  2701.75   \n1736  2016-12-28  2701.75  2718.00  2690.00  2698.0  2702.15   \n1737  2016-12-29  2702.05  2739.00  2691.95  2710.0  2727.90   \n1738  2016-12-30  2730.00  2740.45  2705.00  2730.0  2729.80   \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  \n...                    ...              ...  \n1734                3953.0           107.15  \n1735               10600.0           286.10  \n1736                6050.0           163.44  \n1737                7649.0           207.87  \n1738                6513.0           177.65  \n\n[1739 rows x 8 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>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    <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    </tr>\n    <tr>\n      <th>1734</th>\n      <td>2016-12-26</td>\n      <td>2703.00</td>\n      <td>2740.00</td>\n      <td>2677.00</td>\n      <td>2715.0</td>\n      <td>2723.50</td>\n      <td>3953.0</td>\n      <td>107.15</td>\n    </tr>\n    <tr>\n      <th>1735</th>\n      <td>2016-12-27</td>\n      <td>2722.95</td>\n      <td>2725.00</td>\n      <td>2683.00</td>\n      <td>2692.0</td>\n      <td>2701.75</td>\n      <td>10600.0</td>\n      <td>286.10</td>\n    </tr>\n    <tr>\n      <th>1736</th>\n      <td>2016-12-28</td>\n      <td>2701.75</td>\n      <td>2718.00</td>\n      <td>2690.00</td>\n      <td>2698.0</td>\n      <td>2702.15</td>\n      <td>6050.0</td>\n      <td>163.44</td>\n    </tr>\n    <tr>\n      <th>1737</th>\n      <td>2016-12-29</td>\n      <td>2702.05</td>\n      <td>2739.00</td>\n      <td>2691.95</td>\n      <td>2710.0</td>\n      <td>2727.90</td>\n      <td>7649.0</td>\n      <td>207.87</td>\n    </tr>\n    <tr>\n      <th>1738</th>\n      <td>2016-12-30</td>\n      <td>2730.00</td>\n      <td>2740.45</td>\n      <td>2705.00</td>\n      <td>2730.0</td>\n      <td>2729.80</td>\n      <td>6513.0</td>\n      <td>177.65</td>\n    </tr>\n  </tbody>\n</table>\n<p>1739 rows × 8 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "beml_df=beml[['Date','Close']]",
      "execution_count": 10,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "beml_df",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 11,
          "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": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "glaxo_df=glaxo[['Date','Close']]",
      "execution_count": 12,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "glaxo_df",
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 13,
          "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": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "glaxo_df=glaxo_df.set_index(pd.DatetimeIndex(glaxo_df['Date']))",
      "execution_count": 17,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "glaxo_df",
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 18,
          "data": {
            "text/plain": "                  Date    Close\nDate                           \n2010-01-04  2010-01-04  1625.65\n2010-01-05  2010-01-05  1616.80\n2010-01-06  2010-01-06  1638.50\n2010-01-07  2010-01-07  1648.70\n2010-01-08  2010-01-08  1639.80\n...                ...      ...\n2016-12-26  2016-12-26  2723.50\n2016-12-27  2016-12-27  2701.75\n2016-12-28  2016-12-28  2702.15\n2016-12-29  2016-12-29  2727.90\n2016-12-30  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    <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>1625.65</td>\n    </tr>\n    <tr>\n      <th>2010-01-05</th>\n      <td>2010-01-05</td>\n      <td>1616.80</td>\n    </tr>\n    <tr>\n      <th>2010-01-06</th>\n      <td>2010-01-06</td>\n      <td>1638.50</td>\n    </tr>\n    <tr>\n      <th>2010-01-07</th>\n      <td>2010-01-07</td>\n      <td>1648.70</td>\n    </tr>\n    <tr>\n      <th>2010-01-08</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>2016-12-26</th>\n      <td>2016-12-26</td>\n      <td>2723.50</td>\n    </tr>\n    <tr>\n      <th>2016-12-27</th>\n      <td>2016-12-27</td>\n      <td>2701.75</td>\n    </tr>\n    <tr>\n      <th>2016-12-28</th>\n      <td>2016-12-28</td>\n      <td>2702.15</td>\n    </tr>\n    <tr>\n      <th>2016-12-29</th>\n      <td>2016-12-29</td>\n      <td>2727.90</td>\n    </tr>\n    <tr>\n      <th>2016-12-30</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": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "beml_df=beml_df.set_index(pd.DatetimeIndex(beml_df['Date']))",
      "execution_count": 20,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "beml_df",
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 21,
          "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": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "%matplotlib inline",
      "execution_count": 22,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.plot(glaxo_df.Close)\nplt.xlabel('Time')\nplt.ylabel('close Price')",
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 32,
          "data": {
            "text/plain": "Text(0, 0.5, 'close Price')"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.plot(beml.Close)\nplt.xlabel('Time')\nplt.ylabel('close Price')",
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 33,
          "data": {
            "text/plain": "Text(0, 0.5, 'close Price')"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "glaxo_df['gain']=glaxo_df.Close.pct_change(periods=1)",
      "execution_count": 39,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "glaxo_df",
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 40,
          "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": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "beml_df['gain']=beml_df.Close.pct_change(periods=1)",
      "execution_count": 44,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "beml_df",
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 45,
          "data": {
            "text/plain": "                  Date    Close      gain\nDate                                     \n2010-01-04  2010-01-04  1135.60       NaN\n2010-01-05  2010-01-05  1134.60 -0.000881\n2010-01-06  2010-01-06  1139.60  0.004407\n2010-01-07  2010-01-07  1144.15  0.003993\n2010-01-08  2010-01-08  1144.05 -0.000087\n...                ...      ...       ...\n2016-12-26  2016-12-26   950.25 -0.021924\n2016-12-27  2016-12-27   975.70  0.026782\n2016-12-28  2016-12-28   974.40 -0.001332\n2016-12-29  2016-12-29   986.05  0.011956\n2016-12-30  2016-12-30  1000.60  0.014756\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>1135.60</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2010-01-05</th>\n      <td>2010-01-05</td>\n      <td>1134.60</td>\n      <td>-0.000881</td>\n    </tr>\n    <tr>\n      <th>2010-01-06</th>\n      <td>2010-01-06</td>\n      <td>1139.60</td>\n      <td>0.004407</td>\n    </tr>\n    <tr>\n      <th>2010-01-07</th>\n      <td>2010-01-07</td>\n      <td>1144.15</td>\n      <td>0.003993</td>\n    </tr>\n    <tr>\n      <th>2010-01-08</th>\n      <td>2010-01-08</td>\n      <td>1144.05</td>\n      <td>-0.000087</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>950.25</td>\n      <td>-0.021924</td>\n    </tr>\n    <tr>\n      <th>2016-12-27</th>\n      <td>2016-12-27</td>\n      <td>975.70</td>\n      <td>0.026782</td>\n    </tr>\n    <tr>\n      <th>2016-12-28</th>\n      <td>2016-12-28</td>\n      <td>974.40</td>\n      <td>-0.001332</td>\n    </tr>\n    <tr>\n      <th>2016-12-29</th>\n      <td>2016-12-29</td>\n      <td>986.05</td>\n      <td>0.011956</td>\n    </tr>\n    <tr>\n      <th>2016-12-30</th>\n      <td>2016-12-30</td>\n      <td>1000.60</td>\n      <td>0.014756</td>\n    </tr>\n  </tbody>\n</table>\n<p>1739 rows × 3 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.plot(glaxo_df['gain'])",
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 46,
          "data": {
            "text/plain": "[<matplotlib.lines.Line2D at 0x1ad494c5490>]"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.plot(beml_df['gain'])",
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 48,
          "data": {
            "text/plain": "[<matplotlib.lines.Line2D at 0x1ad4943b760>]"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "glaxo_df['gain'].mean()3",
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "'float' object has no attribute 'round'",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[1;32m<ipython-input-96-c579b502954b>\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[1;34m'gain'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mround\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[1;31mAttributeError\u001b[0m: 'float' object has no attribute 'round'"
          ]
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "beml_df['gain'].mean()",
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 52,
          "data": {
            "text/plain": "0.00027074807905723154"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "glaxo_df=glaxo_df.dropna()\nbeml_df=beml_df.dropna()",
      "execution_count": 54,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "glaxo;\nbeml",
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 58,
          "data": {
            "text/plain": "            Date     Open     High      Low     Last    Close  \\\n0     2010-01-04  1121.00  1151.00  1121.00  1134.00  1135.60   \n1     2010-01-05  1146.80  1149.00  1128.75  1135.00  1134.60   \n2     2010-01-06  1140.00  1164.25  1130.05  1137.00  1139.60   \n3     2010-01-07  1142.00  1159.40  1119.20  1141.00  1144.15   \n4     2010-01-08  1156.00  1172.00  1140.00  1141.20  1144.05   \n...          ...      ...      ...      ...      ...      ...   \n1734  2016-12-26   965.00   965.05   935.00   950.10   950.25   \n1735  2016-12-27   960.70   989.00   952.35   974.00   975.70   \n1736  2016-12-28   980.75   985.00   970.15   977.00   974.40   \n1737  2016-12-29   977.10   997.95   974.55   985.15   986.05   \n1738  2016-12-30   986.00  1006.95   985.90  1004.00  1000.60   \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  \n...                    ...              ...  \n1734              398696.0          3783.63  \n1735              808561.0          7885.14  \n1736              367041.0          3592.49  \n1737              555233.0          5489.14  \n1738              460675.0          4606.48  \n\n[1739 rows x 8 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>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>1121.00</td>\n      <td>1151.00</td>\n      <td>1121.00</td>\n      <td>1134.00</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>1146.80</td>\n      <td>1149.00</td>\n      <td>1128.75</td>\n      <td>1135.00</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>1140.00</td>\n      <td>1164.25</td>\n      <td>1130.05</td>\n      <td>1137.00</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>1142.00</td>\n      <td>1159.40</td>\n      <td>1119.20</td>\n      <td>1141.00</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>1156.00</td>\n      <td>1172.00</td>\n      <td>1140.00</td>\n      <td>1141.20</td>\n      <td>1144.05</td>\n      <td>170063.0</td>\n      <td>1971.42</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    </tr>\n    <tr>\n      <th>1734</th>\n      <td>2016-12-26</td>\n      <td>965.00</td>\n      <td>965.05</td>\n      <td>935.00</td>\n      <td>950.10</td>\n      <td>950.25</td>\n      <td>398696.0</td>\n      <td>3783.63</td>\n    </tr>\n    <tr>\n      <th>1735</th>\n      <td>2016-12-27</td>\n      <td>960.70</td>\n      <td>989.00</td>\n      <td>952.35</td>\n      <td>974.00</td>\n      <td>975.70</td>\n      <td>808561.0</td>\n      <td>7885.14</td>\n    </tr>\n    <tr>\n      <th>1736</th>\n      <td>2016-12-28</td>\n      <td>980.75</td>\n      <td>985.00</td>\n      <td>970.15</td>\n      <td>977.00</td>\n      <td>974.40</td>\n      <td>367041.0</td>\n      <td>3592.49</td>\n    </tr>\n    <tr>\n      <th>1737</th>\n      <td>2016-12-29</td>\n      <td>977.10</td>\n      <td>997.95</td>\n      <td>974.55</td>\n      <td>985.15</td>\n      <td>986.05</td>\n      <td>555233.0</td>\n      <td>5489.14</td>\n    </tr>\n    <tr>\n      <th>1738</th>\n      <td>2016-12-30</td>\n      <td>986.00</td>\n      <td>1006.95</td>\n      <td>985.90</td>\n      <td>1004.00</td>\n      <td>1000.60</td>\n      <td>460675.0</td>\n      <td>4606.48</td>\n    </tr>\n  </tbody>\n</table>\n<p>1739 rows × 8 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.figure.figuresize==(10,6);\nplt.plot(glaxo_df.index,glaxo_df.gain);\nplt.xlabel('Time')\nplt.ylabel('gain')",
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 83,
          "data": {
            "text/plain": "Text(0, 0.5, 'gain')"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.hist(glaxo_df['gain'])",
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 85,
          "data": {
            "text/plain": "(array([5.00e+00, 5.40e+01, 1.17e+03, 4.75e+02, 2.80e+01, 4.00e+00,\n        1.00e+00, 0.00e+00, 0.00e+00, 1.00e+00]),\n array([-0.07471894, -0.04832747, -0.021936  ,  0.00445548,  0.03084695,\n         0.05723842,  0.08362989,  0.11002136,  0.13641284,  0.16280431,\n         0.18919578]),\n <BarContainer object of 10 artists>)"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "sns.pairplot(beml_df)",
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 88,
          "data": {
            "text/plain": "<seaborn.axisgrid.PairGrid at 0x1ad4d37bf40>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 360x360 with 6 Axes>",
            "image/png": 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886JpWbqfssVk4Kbzsnnhy33YnR5uXbmJnJTYoNcTKtrvK7XLqlNPoeiBHI8ohZrMod1Dq5gwGiDCbOSB/3g32R6fOzbk84xJjcMaLnh8zlj2VDWRlhjJwTobf5g+ggZbK9vK7QFCe+eMkTS0OHnl22LypwylocXJnz7cQbzVzPxJGQgBBgEeKfV8cntvMMc6lupUQtlvKhQ9kM4YDtrWjF5r/rjljY26Gf1DH2zn/ksDmz3unDGSZz7bzf4aO0ajwOH2sKuikSaHG7eUJEZbgqaILF61jbQEKzPHpjC0X6RuWlReb+fptYU8taaQpasLda8M7blCvcH4N7G8ln8m7y+c3Cc29EBFyApFj6QzhoNq90iJtTD3ua9DVkw8vbYQS5iBl649gy/2HMLtgafWFFJeb2fLgS0s/3keZqMBh9vD8IExPPtZIVOGJYVMc5iMBqYNT8IjJRMzEvVBqRoWkwFt+dobTFq8Vfe98K8k6atz+JQgKxQ9lM4QJYNBUG1ztNv8YTEZiI80c6i5laWrC/XUhvAJ59ayetISrViMRnZUNDJlWBIDo80snJaJth+3cn0ptTYHESYji1dtZerwgawoKGHh1KyAOuYlc8YyMjmas4cmkhRtIS3eysfbK/j7F3u45uwMSmpsVDTYmTAoHnMfNbtXgqxQnKJodcxOt9eQ/vWC0oDa4UizkYVTs9hR3kiD3Ul6YgRz89ICRPTui0dR1+zgwQ926Mfuu2Q072zYS3F1i172lhhpornVxbU/ymBXRSMOl+Tlr4uZPykDowGmDU9iVHIsJbU23feitM7G37/Yw+Xj0/j9vzcGTLW+bGxKyJK4Ux0lyArFKUioOuZF07J46StvNcSiaVkMT45mf7WN1Hgr1c0OHrg0h+teKgjo2jtY30Kk2RiQ7rjr3S16ukMzsr/qzMH8xu+5Fk7N4uWvi/U65ilZ/fh4e0XAeh68LIdfTslk0Ws/BE21zh4QxZhB8d32++su+t5bkELRBwhVx/zE6t3c8T8jeGT2GMxGAwJvpLyhtI7iGhtNrS5uv3A4C6ZmsvyLIlauL0UC/WMs/O4n2STHWvR7aV4Xt1yQzf+enUFJdXOQQ5w2udpiMmAyGoLWc/tbmzEZRMh0yv7aFoqqmkLWKZ/KqAhZoTgFaa8GucXh5o53tugjmW48J1PffNMqLJat20N2UhTXn5PJjoMN7Klq5r2NZfzv2YPxSEmL082E9DhGJcew/WADOyoadRP7l7/2tkn756iXzBmLzeEOuR6H2xOy7G57eSO3vLGxz7RMa6gIWaE4BdHqmP2xmAyU+NzdclNiuG/maP76WSHzJ2WwYGom103OYNm6PVx5eho//9Fgdvi8LYwCfnH2ED7ZVs7Q/lEMGxBNk91NcXUzbxSU8vznRczNS2NFQUlAVDw5s59estbeeob2j2LxzGCP5Te/Lz1qd+KpiIqQFYpTkFDNFYtnjmbJJ7vITYnh2h8NodXpIX/KUJat28OM3BRiLUbun5lDU6sTo8HAOxvK9I27+y4ZxS8nZ7Cnqilk551WRqdFxQ9elsPpgxMwGAQej0RKeGz2GHZXNvJ6gbcq4+HLcxmSGMmQxEiyB0Sxv7aF7eWNepQNfW/ckxJkhaIHcqL2k3oN8i8n8n1JLUOToqlstDMnL5WxaXHsKG/E6LudJspz89L45csFQRtz5fV27np3K4/NHhPUEPL4p7tYcG4mj328C6MBspOiefrq8Qzt712vy+XhP1vKdU8N/66+JZ/sxGQ0MH3UQMYMiifaYuKWNzb2yZZpDSXIIaitrWVAytH9bpOTk9lQ8M1JWJGiL9FZ9pMej8TudhNlMZHvJ7SPzR5DeJi3/thkNPDoRzt097f2mkfsTg/NDlfIPPDAGAvpiRHkpsZRVNlIbmqc/vxfFlUHGRwtXrWN+ZMyKK5uCXBx68st0xpKkEPg8UjOvXvFUc9be+/ck7AaRV+jM+wnPR7Jf7aUs6eqKaBjLt5qZn+tLSDtsHBqFoZ2Ri8Jv866SHNYyA24klobd84YxdJPd7GprIE7/2cECHB7YEd5vZ7KAG8TSXm9nfAwA786NxMhoKqxlbR4KyW1NuKtJlbkn4XT7SYhsu+Ne1KCrFD0MI7V6S0U+6qbuXXlJq6bnBFwr1njU3li9e6AWuNWl5uRp8WGFFvNbvPm87MxhYl23dvCDAaqmhwsnJZJej8rRiEormkiPjKcJZ9uCRD/FQUlZCZF6W867230GuL7TxlZMmcs49MS+pQYgxJkxQnQkdSOSuscO51hP+kv6v73EsIbJbcdz3TXjJHcNn04f/pwR0Cut9HuJH9KBv2jw3ntmxLOGprIMz8dz6EmBxUNdl74ch+1NgcGAdeclc5r35VgCTPq6Y7lXxQFpUGeuno8i1cd9mCekZuii7F2Xl8xpG9LlwmyEOLveEc1VUopR/uOJeAdbDoY2AfMkVLW+h67DZgPuIGFUsqPfMcnAC8AEcD7wCJt9JOie+lIakeldY6dzsilJkV7RX3dzkrunDGSxau2eX2OBVyRFzxp+r5V2/jbvAk8f00e28obGDEwGrPJQPEhG2nxYdQ0t/I/uadR1+KgxubgzncOR7P3XzqaGIuJha/9wPxJGfq9teki/nhroV0UV7fox9o7ry9VV2h0ZYT8At4JIS/5HfsDsFpK+ZAQ4g++n28VQowErgRGAacBnwohsqWUbuAZvIMlv8YryNNRU0MUpzCd4fRmNMDtFw6n2eFm2bo9zJ+UwZB+VmqaWkmICg8pgOuLawFv7ve74lpOT4+ntK4FjwQp4c3vizCHCf4wfQSPzh6DEJAYaaKqyYHd5Q4pwqEifYEIebwvV1dodFljiJRyHVDT5vBM4EXf9y8Cl/odf01K2Sql3It3XNMZQohkIEZK+ZUvKn7J7xqF4pRFc3qbmNGPjP5RIcXY45EUVTXx1Z5DFFU14XJ5Dv98qJkGu4snVu+muLqFp9cW4nZLHvxgJ/U2Z8gmDbcHwgwGwsMMeCTYfBUUz39exNNrC0mJC+fXU7O46fUN/PrVH7jljY3sq27hzx/vJMocxqJpmUSFG/V7r1xfysKpWQFNHw9fnktCVBiLph0+/t7GsiBP5r5WXaFxsnPIA6SU5QBSynIhRJLveAreCFij1HfM6fu+7XHFCTA270zKy8uPeE5tXd3JWYziuGhbGpeeGBGwMXb7hcPI7B/FvRePwhoeRlmdjaTYcG6/cBhxkSbumjGS+3xpDP/Ntt9dMJzoiDDcHg/ldXZirWaem5dHcXUTA+OsLHjl+4BUx73vbeWR2WOosTkAiAoP03PR5fV2VhSU8PicseyubMLl8VBvcxAZbuSinIGMT4vH5nCRlhBJeoKV8Wnxx/2J4FShp2zqhfrNyyMcD30TIfLxpjdISzt6HXFvoiMi2tENtPLy8qPmfv+98PxjWl9foCf9fbUtjfPfGEuOtWAwGALc126/cDiHGh00OdwseWsL2UlRLJkzlh0HG3B7YEVBCXPz0pBIiqubsTs9LPlkl379Mz+bwA8ltSFTHYWVjSxdXag7yuWmxvKP/83D5YYD9S0UHWri1W9LKK+3YzEZyJ+SQUa/KM7MSAy4V180pG/LyRbkCiFEsi86TgYqfcdLgUF+56UCB3zHU0McD4mUchmwDCAvL++U2vjriIiqDbSupSf9fbUtjfPP3c4an8pjH+8MiGQPNTtY9sEOvQxuU1kDi1dt00vfbrlgOI99vIMrT08jJd6q+xNr1/9QUotHhs71un0/ao5yj88Zy77q5qBaZ63rzyPptA27E+1o7GmcbHOhd4Gf+77/OfCO3/ErhRDhQoghQBbwrS+90SiEmCiEEMA1ftcoFH2Ctrlij0e2a9YDwVULybEWUmIjAsrg/CeDGAWU19n0SdH7DjUHRcIe6c31ts0J3zljJG9+fzirqF3XtsVas+PUxjh1xoadlra5aOnnXPXcN1y09HM+3HqwV1t2dmXZ26vAOUA/IUQpcDfwEPC6EGI+UAJcASCl3CqEeB3YBriAX/kqLABu4HDZ2weoCgtFH6K9NuoLRgwIKI3TNsbueHsLEDhtet7EdA7Ue02CVq4v5bbpw7E53QER7IOX5WBrdfL0Z0VcN2lIUCT83sYy3fNCmwIyblAcT6/drRsBac/r9siQqY0IX0oja0CUvmF3IhFuZ3Q09jQ6JMi+6PSnQIaU8j4hRBowUEr5bXvXSCmvauehae2c/wDwQIjjBcDojqxToTjVaE90VuRPDGozTov3bozVNLeSnRTF71duYtZ4b81xvNWsz7hrbHXxlM+fQrvn7W9t5u8/P52bzsvEHGYM6sibm5fGim9LmJGbgtEAZw9NpKS6mcvGD2LLgcYARzlTmCFkauOMwQkkxYSTlhCpu8CdiGdHZ3Q09jQ6GiH/FfAAU4H7gEZgJXB6F61LoVDQvuis3lGpb6T5txlrG2Mej2TUabHsrGjA7vRQXm/XZ9wNjLWEvGd1swOLKYwoixGbI4xHZ4+h1eUmOtzEgx9so7i6hV2VTdxywTB2HWzkwQ92EG8165t0sdYw7nl3Kw6XDBJ0zS9jcL8oPQWzr7qZnQcbiLeadVP7Y4lwO6OjsafRUUE+U0o5XgjxA4CUslYIYe7CdZ0ydGZ1hKJ30RkbTu2Jjv9GWigRMxgEQ5OidH9iTZQ/31XJ2UOHh7xnaa0Nt0cyMNZCRUMrT67ZQ63NwZ8uy+HK09NodrgxCBjcz8qCV37Q76m9MeRPydA78F76qpj8KRmkJVg5LS6CneUNmIxGXC5P0Gw9/w2/Y4lwT0V3uI4KslMIYcRXciaE6I83YlYcBVUd0TfpLAvNUKKjCZjGkURscGIkD1+eq1tg3nxBNrU2Z1AEe/P52YQbDQFeFtrz3PbWZt2GE2DhtMyQEbb/Xpom1H+ZO5ZfvPCdfs9l8/KCUjD+Np/HEuF2RkdjT6OjgrwUeAtIEkI8AMwG7uiyVSkUvZzO2nBqKzoRJiMLX/shaCOtPREzGAQXjhxIUnQ4lY12pIRb3thIvNXMgnMzGRhjwWIysr/Wxp8+2dGuUAo/jWuv/K2tDlpMBooONQXcs6C4pl2bz+OJcP3TNKcCHSp7k1L+C/g98CegHLhUSvlGVy5MoejNHGnD6Wi0LXMD9DbqnJQ4bp0+4ohtxv7X76loZP3+Wr4qqsYgDGw9cDin3OL0cMc7W9hR0UiLM/QQUk0o/e283ttYxp0zRgas4abzskm0mgOO3X9pDlazkQVTM/WJ1ZqY+9N2/t7RItxQZYCnCh2tshgK7JVSPi2EOAc4XwhRLqWs68K1KRS9luPdcDpaquNoH9NDXb9oWhZvFJRy7dnp5KQe9j3W6pVXri/lLp/Ahop675s5mqfX7taP5U8Zitvt4fc/GUZiVDh7qpp44ct9mMMES+aMpbTWRtaAaO55d4s+k09Lf7y3sSwghaK9Pm3+3tHorFRQT6WjKYuVQJ4QIhN4HngPeAW4qKsWplD0Zo53w6kjqY5QH9O1DcSqxtag659YvZtF07KItJi4650tevkbeAW2vN7OM58VBth0ah7J9S1OXv+umLsvHsWWsgZaXR6eWlOop0zunjGC4QNjuCIvFbcHHv5wO/dcMpob/rk+KP2RPyWD4QNjGHVaNC9ee4buYzGkX9+uPfano4LskVK6hBCzgCeklE9qFRcKhSKY491wOp7aWv+ose2EEO361Hgrv/O1Q2vlbzHhRhbPHM2d72xhU1kD9ev2sGTOWAo1I6AWJw9/uJPkWAsb9tcxtH8kJqOBIf2yCQ8zUl5nY1CilcWrtun1ybdOH0FdsyPkGkYlx2I0wk/+8nnAm9SQfh3PGZ+Ktcf+HEuVxVV4W5cv9h0zdc2SFIpTg+PZcDqeVEfbqDHU9bbWwwNKy+vtesXE/TNHsmzeBL7dV4uUsHjVNj36ffjyHH73k2yizGH8/cu9zM1LC5gysmhaFlUNdr0kTouQb7kgdFldQqSJzwsPcd3kDH223rFGt6di7bE/HfWyuBY4C3hASrnX5zfxz65bVt9CG4V0pC9lh9k30FIdx+INrEWNybEWLGHebrlF07wbaZpwHmpuDdpMS0+MICLcxPriWt3zWBNji8lASU0LT64pxOZ0c8WEQUFTRp5YvZvyhlYGxVsBCDPAb6Zl8eKXRUGeF/dfmsPDH25n6epCnv+8iHkT00n2Nah0ZKPzRH4/vYkORchSym1CiFuAbCHEaGCnlPKhrl1a36Ejo5CUHWbf4HhSHQNiLKQnRgRFsIsvHU1SlJmdBxs5LS4iKEd898WjuPFf3we0VbetQdaE996LR7Vbe7yrspGn1nhriG+5YBgX56bw9y/36p4X49PieeWbvRQU1+vX+c/cO5bo9lSsPfano1UW5+Cd8LEPr0fxICHEz31TQRQKRSdyrKmOwYmR3HvJaK5vs5F259tbyJ+SwdLVhSyYmsl7G8uYPykDIbwjmWwOd1BbdVpCBCU1LXrnnHYva3hYu1UY/l2Dj328kwXnZjJzbArZA6JJiDRR3eTg422HAtZsd3owGjiu6PZUqz32p6Mpiz8DF0gpfyylnAL8BHi865alUCg6isEgcLhC1xFrJbor15fyi7OHYPT9Hx9mgKTocP2jv5ZXPlDXwvIvioIaT8rrbEFpiEXTski0moPsN5Oiw8lLTyA3NYaaJictDreeQkmOtfCrczNZOC2TH2f1Z8TAaL7ZW33K1RMfLx3d1DNJKXdqP0gpdwkh1KaeQtFDGBATcdTuObvLw7J1RXpaYki/SG46L5vHPz08GSTBauaBy3L441ubA9qqh/aPxOWRLP95Ho12F5HhYbQ43Dzw/rYg8S6uaeHpzwpZNC2b29vcx789e9m6IhZNy+Klr4qptTlOqXri46WjEXKBEGK5EOIc39dzwPquXJhCoeg4IwZEc9/MwEGhN52XTb9Ib/fcrPGpuvCCN5L9w5ubEUjmT8pgwdRM5k/K4O9f7iU13kL+lMPH/vHfffzqlR/YeqABt0cSG2GiqrGVu9/dyty8tIDnvO+SUbz5fSkzclN0Mdaeb8knu6i2OYI2BmeNT9XrifdVN3fDb6/n0NEI+QbgV8BCvDnkdXgtOY8ZIcQwwH8HKwO4C4gDfglU+Y7fLqV833fNbcB8wA0slFJ+dDzPrVCcqpTWt/D0Wu/4pEa7kwizd7Dpv9eX6vaYoVIaSTEWlnx6OIq9c8ZIfiiuZ+nqwqDn8Ej4dl8tBgF56fHU2hx67lkIMAiI9VlpGg0cMYXif0zzyTiV6omPl45WWbQCS3xfJ4Qv9TEWwOcgV4bXuOha4HEp5WP+5wshRgJXAqOA04BPhRDZfhNFFIo+T0WDHYdLUmNzBFRSaNUSt180oh3LzRavTWa8lYMNdhrtTlqc7iOmP9weKKlu5r5LRnPXu1t0l7a7Lx7F02t2YzEZOD09ocMGRFJ6x0xdkZeKzeGmqKrplKqcOBaOKMhCiM0cYcqzlDL3BJ9/GrBHSlksRLu//JnAa743hb1CiELgDOCrE3xuheKUYUCMhWvOStfFGAJblktrbe2WtmnToOdPyuDNb0q5fkoGN5+fHTB1etG0LCLNRqSEv3+5l/tn5vDW9/tZNm8CDXYXA2MsmAyCm87PJi0hkvQEa1DruJZD1oRau+8Hm8u55qz0ADvQhy/P5X9GJxMWdrLHfnYvR4uQZwEDgP1tjqdzhOnPx8CVwKt+Py8QQlwDFAC/lVLWAinA137nlPqOKRQKH2nxXiP4UGmCtAQrf/54Fylx4Sybl0etzcGuiqag0jYhvNUWf/9yL4/MzuW5a/JotDuJDjfR4nSzp6qJ174rYW5eGne8s5n5PxpCc6ub5lYXJqOBnJTYAJMjc5ggf0oGHgmRZiPJsRZe/HIvS+aMZcfBBsxGA0YBt/xkGPkvB5bs3bpyE/FWM5My+/WpSPlogvw43lxusf9Bn0H94xxuoz5mfBNHLgFu8x16BliMNyJfjLfU7hd4c9ZtCRm1CyHygXyAtLS0412aQhGSnvz3VVJro6iqKWSaINIcxs/PTifaYiL/5QKum+xtyGh7nvRZYy44N4s6m4t6m4MBMeHsr20mOTaCxEgz8yam43RLLh8/iP4xFh76YLvu6OZfJbGvulmfKqKRnhjBn68Yy8+WfxNwvD3D+02ldaTGR/SpnPLRPg8MllJuanvQN3h08Ak+94XA91LKCt89K6SUbimlB3gOb1oCvBHxIL/rUmknOpdSLpNS5kkp8/r373+Cy1MoAunJf18VDXZeLygNqhVePHM0VY12spOi9XTGyvXB5905YyTRFiP5UzKIjTDx9JrdVDc7qLW5qGho5fa3tvD0Z4X0jw6nxenG4fawo7yBX5w9RG+Bvvn1Dew91My+Q02U1bVw3eSMAC/k4uoWDjW1Bolvex7JaYmRx9RWfSpwtAj5SD2NESf43Ffhl64QQiRLKbXhc5cBW3zfvwu8IoRYgndTLwtod9q1QtEXGRBjCap6iDQbiQoP46m1u7nxx4ejUP/OvOwBUeypauKpNYXU2hzcOWMkn247wKLzstlT2YjRIIg0h/HY7FxcHg9hRgP7a1qwuzy8vaGMK09P45qz0nn4w53YnR6KqpsorGgKGnD68tfeWuPk2OB6ac3wvu1m5ME6G6NPi+muX2m3cDRB/k4I8Usp5XP+B4UQ8zmBOmQhhBU4H/h/focfEUKMxZuO2Kc9JqXcKoR4HdgGuIBfqQoLhSIQf/9lreph2bw87nxnM3Pz0qhvcbBwWmZA597yL4q45YJsclPjAG/1xLJ1e7jxnEye+Ww3U4cP1C07tSqKZ/+vUE9R3H3xKBpbHKT386YULCYDVpNRF2M4vLH4+JyxGA0Co4AHL8tp0zAyjJe/2hvQ1r2ioITFM3NCtlV3xvDYnsrRBPk3wFtCiJ9yWIDzADPeKPa4kFLagMQ2x+Yd4fwHgAeO9/kUip7A8QpJR64zGAQXjBjAivyJlNfbSY6NoLnVxYzcFFYUlHD1GekBXXretmcT0RFmNpXW4ZHw5vdeS8y7393KI7PH8HufGINXWO99b6s+Y0/7edG0LOptDt3c6IeSupD54D1VTRiEYOFrPxBvNZM/JYPsAdGMGBhDeoIVk9EQUJHx8OW5nJ2RGPQ6+/TEEF9+92whxLnAaN/h/0gp13T5yhSKU4jjFZKOXufxSD7eXhFw3nPz8jAaYEZuSlCX3mvflfD/pgzlphDTrMvr7bT4+Sdr+DdxaD8nWM3c9e5WllwxhsX/2c7lE1JDbizmpMby/3yVFNpEaovJwPsLJxMWZuiwg9upPjGko0NO10opn/R9KTFWKI6R9oTkaK3CHb2upKaZHQcbuG5yBr/7STaLpmWxvbye8WnxIbvmZuSmcF+ImuVZ472Cqrm7+dN22Kl2nt3podnhprzeHnIAqnctDSEFvri6maLKRr4qOsSuikas5jDS4q3tvkmdyPDY4+VkDlXtaOu0QqE4AY539FBHrvN4JN+X1AWlJFZtKmeO2ci4QfFBUWt7rc1GA9x7ySjdZN6/kUTLIcNhoS2ttWExGUj0eWbkTxnKim9LmD8pg/SECErrWnjpq+J2I2e3R/LRtoqATcD7Lx3NpWNSQjaFnOyJISc7RdK32mAUim5CExJ/OiIkHbluX3VzkJHPE6t3c/05mdz//g4e/2Qn97cxHhoxMCbkfTOTovnX18Wcndmf1HgLj8wewx8uHM7SueNIiDQxc2wKC6Zmkj8lA6vJyGvflXD3xaMwGb0Tp1d8W8KmsgaeXlvIwYZWlq72TiFpr9SuqKopaBPwjre3sLW8PuTv42RPDDneTzbHi4qQFYqTwPFOoe7Ide1F0c2tLh6dPYayOhspPgc3j/RWMTzzWSGLpmUFRKaLpmXxp/e3U2tzMGPMadQ2O3WrTIvJwG3ThxMXYWJgrIWo8DDqbA7uuWQUVQ12LGYjT6zexaayBn0NGf0jsZgMxFvNzBqfisEAj80egxCwrbyRRruTptbQPs4H6+2MGUQQJ3tiyMkeqqoEWaE4CRyvkLR3HUBRVRMVDXas5tDTPPZV21j+RRF3zhhJWW0LyTER3Ltqqy6w18ZYeOqqcTS3utlfawPwphbCDIQbDfoYpgiTgdzUOPZUNjIo3kphVRMtTje5qXGU1dgwhxmobnTwi0kZ/CizkdcLSqm1OfBIyW3Th2NzugOE/+6LvRadl09IxShCD2UdGNv+J4eTOTHkZKdIlCArFCeJ4xWStte1zWumJ0YE1fb6z8RbvGob//jf0/nDm5sCan3/snoXi2fmsLOikad8pWwa2j2/2VtDi9PDne9s5srT02h1e0iNjyAmwkRhRRMOtweDw8Nd7x5u6lg8czSD4iNodXkoq20JSknc+95W8qdk8EZBKb86d2hQpH7/paMZlRzbeb/4E+B4P9kcL0qQFYpeRtu8ZnF1C7ZWJ0vmjKXJ7qQ4xEy8sroWiqtbeHptoM/xtvIGXi/Yz/2X5nDH24cFfW5eGo9/spP/PTuDHRWNzMhN0Sd7aHP6kmMt3HPJKLYeqOe6yRmAt+Hkzne26IL72wuyQ37kHzcojrOHJjIwxoLHI8lNjaXJ7iY5NpyRybE9xuXtZKdIlCArFJ1AV3aPtb13dXOwH8TBRgdvbyhj0bRs7np3a9BH7PAwY9BH7/TECEYmR/P7nwwnzmpiwbneTr7B/SI5UGdj4tD+lNbZeGqNV3xnjU9FCBg3KI689Fguzk3hUFNrQHWHFpl7pLdF2+4I7a2clhDJ0KTDnxQykqI75XfVFZzMFIkSZIXiBOnK0qhQ93748lzSEyMorm4BvObuCVYT884awtYDDTxw2Wj++NaWAJF88csi7r90NHe8vUVPc/zqnCzd9tI7fmkk0RYz2w824JFej4n8KUPJTYlh+ujkgBK4+2eOpqqplSd8m34Q6L/s9njXNTDOEnLz0NgzAuAehxJkheIE6crusVD3vnXlJpbNyyP/5QLirWauOSudRz7aqQvevZeM4m/zJrC+uBa3x+sL8eupWXy89QALzs1kYKwFS5iRW/xao+OtZpwegjr3lq3bw+8uGM4t/96oV0sIAftrbWT0izyi//Ks8ansKG/kX9+UBOSuX/qqmLGD4hjcr/d31vnTGZ+SlCArFCdIV5ZGtXdvk1Hw/sLJVDW28vN/fIvd6dHTCmV1LcREmMhNjaXe5uK3FwwnxmLksnFpRJiNXP/P9Vw3OSPgvrPGpwZNG1lRUMJvLxiOBH5zXhYCETChevHM0QGROnjTEf2iwqm1ORAC0hMjqbU5AnLXFpMBq9l4Qr+XnkZnfUpSHxwUihPkeJs+TuTeA2IsZPSPwiOlLsbzJqaz/Isilq4u5KYVG9hd0USUJYzf/3sjLQ6vwY+/wPvfV4jAzr3kWAtz89L4/b838utXf8DmcAf5Ydz5zhb+MH1EQJPG/ZeO5pVvvOVywwdGU15nC2oIWTg1C6c78E2mt9NZDSRKkBVdSm1tLQNS0o74NTbvzO5e5gnRld1jR7u3JtizxqfqOV443K3ndnu46bxsHvpwOwNiLMRGmLCYDN5qCD/PCa0eWKPt/TwydKt1s8PFY7PH8ORV41hwbiavfVvMrPFpZPSzkhrnnfaxosCbslgwNZP5kzJYUVBCXIT5pPlDnAw6y2NDpSwUXYrHIzn37hVHPGftvXNP0mq6hq4sjTravTXB3nEwtHmPR8ILX+6jvN7OwJhwqppa9U22RruTR2aPobCyEYvJyN0Xj+Le97bqnhZt7xeqWqKkxkZaQiRx1jBGJscQYwmjot7Os+uKePKqsURHhHHl6WlBm3rVtlZ+/vfvThkLzc5qIOkWQRZC7AMaATfgklLmCSESgBV4R0PtA+b4hpwihLgNmO87f6GU8qNuWLZC0S5dWRp1pHtrgp0SF6GXn2lYTAbCTQYun5DKexvLMIcZuP2tLcRbzcyflMGgeCs7KxpZutqb3/3DhcP0zbespOgAgVm5vjSoWuLOGSMZFB/B0tW7KCiu14+t+LaEWpuDhMhwiqubeemr4qBNvcRI8yllodlZDSTdGSGfK6U85PfzH4DVUsqHhBB/8P18qxBiJN7p1KPwjnD6VAiRraaGKBReDAZBbERYyPKywsomnv+8iLtnjGLbgQbdj/jN70sZeM5QRiTH6MLb1OrWh58mx1oC3N5qbQ6SYsJZNC2LZocbKdHHPs2flEFBcb3eFZg/JYP//dEQBidGUtXYGnJTL8IcKD1d6Q9xMuisT0k9KWUxEzjH9/2LwGfArb7jr0kpW4G9QohCvANQv+qGNSoUPZLyenvISPTyCaneduVVW3l09hhdfGeNT+WB97eTnRSlz7Pzj4LL6+2sKChhyZyxFFU1kZMay/byBh7+cGfQc7c1rc8eEE1OSgwGgyApOjzkG0V5nS3gHl3pD3Gy6IxPSd0lyBL4WAghgb9JKZcBA7Qhp1LKciFEku/cFOBrv2tLfccUilOaY6lr1Yacto1ENUN5u9PD3kPNetSrVVVsKmugak2hLuSZSVE8eeU4Nh+ox+2Bhz/czpWnp/mia3eAe5sQ2magMeA5d1U0MmJgDB6PpKS2maSYcN1pziAgKjwMj0fqbw5d7Q/Rm+guQf6RlPKAT3Q/EULsOMK5of4CQ27JCiHygXyAtLS0E1+lQuHHyfz7Ota61lA5TK2NGbxCaTJ6r1syZyxxVhPvbSyjuLqF8nq7PhhV86C4Ii+VIYlWrjw9jbQEKxFmA5HmMLIGRLO/xhYQ8d58fjbJsd43hLtmjOTJNYWMT4uj1uagqtHBocZWIkxGWpxuhg+M4dnPCqlq8npijBsUR3pi5Ck1qPRE6BZBllIe8P23UgjxFt4URIUQItkXHScDlb7TSwF/Z9RU4EA7910GLAPIy8vr3XU0ih7Hyfz7OtbuP23I6bJ5eWwqrWN4cgyLV22lvN6uexm3uj0s+eRwY8d9M0fz9NrdOFySK/JSyUqK4rTYCM7KSMDlgSa7i7REKzaHC4MQ2F1uKhvsuhhrjShNrS7+PGcMhRWN1Lc4MYcJDjU5uP6f3we8Obzhs+WcPymD5V8UMXxgDD/OTlJC7MdJF2QhRCRgkFI2+r6/ALgPeBf4OfCQ77/v+C55F3hFCLEE76ZeFvDtyV53b0Cr+T3qeXV1Xb8YxQlxtO6/UOmM0jobBcU1eCSU1jTzwKU5bNhfR0a/SCItYfqQUe1eT6/dzYOX5XCoyUF5XQstrS62Hqin2RHsX7x09W4AfjMtK6ARRdv0W7auiHsvGcW7G8u4e8Yobnzl+yCPC21idXZSFPlTMhiZHH1UMe5K06aeSHdEyAOAt4R3JyAMeEVK+aEQ4jvgdSHEfKAEuAJASrlVCPE6sA1wAb9SFRah6UjNL8C/F55/ElajOBGOVNcaKp3x1NXjaGhx6aVv6YkR3Pk/I0mNt+IBdrYZMqp14s1/sSCgjM3uZyYPgf7FS1cXsr+upd1GlLvf3cojs8ewrZ2BpsLXfLK/1sbS1YWcPTTxiH4WJ3ueXU/gpAuylLIIGBPieDUwrZ1rHgAe6OKlKRQ9hiPVtYZKZ2wqrdfFWBPbBa/+ECC2/r4ToQR18aptPD5nrH7M33JzQlo86YkReiVGizP06KVdFY1A6CYSg4CbzsvmhS/3YTEZ6B8VXFXhHxFbzcYuM23qqfSksjeFQuHjSHWtodIZ/q3NocR22bo9LJ6Zo6c0LGGGkILa6nKTnhhBrMXE9edkssNnxXnXu1v49dQsappacUsC6pc1tKqOVZvKAqw+NSOiFoeLZ/6viFqbg9svHE5zq5Ov9hzSUxFAQES8cFpml5k29VSUICsUPZT26lpDpTP8Z9O1ZxSU//Lh9MQzPx0fUlD7R4fz0KwcnG7J0tW7mDZiIEMSIxmVHOMti+tvxeaQNLW6gsZG3TljJM12J3fNGEW/yDD+Of9MDjW1kmA188xnhYxMiePyCalEmo20OD3MWfZ1QPQ/Mjk6ICL2yNCRdm+vVz4SylzoBOiIcY7aQFN0Fh6P1AebPjcvj/TECMArUjmpsUEmRBr+EXNyrIX5kzKorG/hwctyglzYbn9rMwfqW3G5Pfz2guEMTYrCYBB4pMTjkTQ7PHxfUsumsnpe+WYff716PH+bN57lP8+j2e6kodXNfau2UlbvYGxqHNkDomlyuMgdFMeb35fy1JpCmlqDneNufn0DFQ2Bk1BWri8Ncorrznpl7ffflYZIKkI+ATqyiaY20BSdQbuTQxIiEEJgc7gZkmjhw0WTqbM5mJAezw8ltXgkxIQbAyojVhSUMDcvjTU7SnluXh7fFdfg9qDP4fvjW5tZMmcs967axA0/zsThcrP8v3uZm5fG0jcOV18snJrFXz/bzdVnDmbRa4H1zw9/uJ2o8LCAqHzRtCxe+qo4pHGR3enB5nAFRMRat+CK/Im0ON1dPs/uWH//XbHBqCJkhaIX0N7kkAa7m7nLvuaq577hf578nMKqJg7Ut3LDP9ezdHUhz39eRHxkOOmJEXqkPCM3hRUFJUwdPpAD9S0sXV3I02sLA4ai7jjYwIzcFO55byuHmh3MyE0JyksvXbOba87O0NMW/sdn5KZQUFwTZAf6l7ljmTZ8QEiP57SEYKvRW6ePICcljokZ/cjoH9Vt1RWd5Xd8NFSErFD0AtqrS24rev7VFtqxO9/ZwuNzxrKnqknPMWsC6+9voWExGXB7DpvWe2Swgb12b7vDFfK40QBtPejtTg8SSU5KbMgKkiH9IhnSL/KkTXg+FrpyKow/SpAVil5Ae3XJbUWvPSN5i8nAWRmJhw3pfWmDsjobd80YyX2+8U1aymFFQQkzclP0crX2NthS4iNCHh+fFs/d724JWIe2IXc0Z7STNeH5WOgsv+OjoVIWCkUvINTkkIcvz2XVprKA89pO/tDOTU+MxIPkpvOyeW9jGSMGesvWXvyymDABj88Zy8Jphyd6zM1LY9WmMu65eBT9Is28t7EsaINt8czRrCzYH3T8T5flcPaQRG5tM97Jf0NOqyDp7lRER+nKqTD+qAhZoegFhIoq0+KtmIyGgI/+WrWFdiw9MYLFM3N8jRZhRJoNzBybQlWTncUzR3vTGasLufbsdManxWNzuDgrI4eGFicPXpaDw+0hIszI4pmjqWtxsuSKMbQ43QxOjOTmNzZQXN1Ccmwt8ydlYDTAWRmJTBgUT2l9C/FWEyvyz8LpdpMQGR6UfjietujuaqXuyqkw/ihBVih6CaHqkjWRqGluxWQ0BFRbHGpqpazOHlDp8MjsXOpaXNhaPWQmWXj2ZxNocbgpq7Nx25ubAbh+SgbVNgce6Y24E6xm3ttUxo3nZGF3uUlLsOKWkvtn5nDHO5sprm5h+RdFPHx5LhMGxfPpzsqg/PD4tIQgMT7WqoXubqXuyqkwGkqQFYpejMEgGJwYyY6DjUFCNTI5mp8t/1bPe8ZbzRyst/PU2kL9vHsvGUVaQgQuTwSPzx2D3enGLSVuKYk0hxEfZSbKbOSXk4dic7gpOuSNTkeeFkOtzcEjs8fgcruICg9nVHIMJbW2DrU7H6ubXUev6e1mRCqHrFD0ctoTKq3RIjnWwq/OzeSuGSN1+03tvLvf3YpBCFpdbraXN3i/d3pIirZQa3Pi9kiWfLKTPVXN3PLvjbz6bQm1Nif7a1rYX2vj9//eSEl1K4tXbeHj7RXtViNUNHhL6rTmil0VjVw3OYPkWEvAeUea0ny0yc5aBH3R0s+56rlvuGjp53y49WCvmmitImSFopfTVqg0U6Dq5lZuv3AYEsHjn+7iuskZIQXtYIOdeKuJSHMYQgjirSZanG6+KTqEzRnHvLOGUG9zcNeMEYQZDNz17lY9wr7pvGyeXVfIby8Yzs2vb2BF/lkhqxGcbonL5eHj7RUhTfQ132b/qoW20W5S9JErHY4n6u5pqAhZoejlaCVZgN6Nt/yLIm781w8ANDtcXDc5g2EDokNWYERbTPzqlR+44V/f88uXCtiwv54DdXYum5DKsnVFPPTBDuwuDwcbWnUxBq/gPf7pLu6+eBQxljCum5yBy+Ph4ctzg1qy73xnM1vL64MEc+ma3cwanxpUtRAq2t1b3XTESoejRdC9ARUhKxS9HH+rzra+FZEWE0s+3e1XceGtrNAi1Acuy+He9wJF9onVu8mfkkFlozflMWt8Kks+aT/C1tqeI0zeio8/XjQyYNiqFgGX14cWzNyUGN5fODkg3xsq2l3wyg98uGgy77dT6XCyaoW7ku6YGDIIeAkYCHiAZVLKJ4QQ9wC/BKp8p94upXzfd81twHzADSyUUn50stetUPRU/EuydlU0BthwLvY1fAAUV7dQ29xK/pQMwgwGMpOiKK1p1j2SNbTuPA3/Lr1QgrfjYANLVxfqKYyiqiaWf1EUdF5ybGjBHDYwGo+Eb/ZW6xtx7UW7Bxvseu1yW47kId1b6I6UhQv4rZRyBDAR+JUQYqTvscellGN9X5oYjwSuBEYB04G/CiGMoW6sUPRVtJKsbL+0RCgbTodbMqRfJBn9I3n4w+04fdOf/dG68/yLEywmQ0j3tTtnjOSNglLgcApjSP+oICe5JXPGMio52JHuqavHsa28MWgjTssXt13XkaJd7Y3p/YWTeS3/TN5fOLnXTRfpjokh5UC57/tGIcR2IOUIl8wEXpNStgJ7hRCFeIeiftXli1WcFDo6CzA5OZkNBd+chBX1XvyjRDgc0eamxOiG8412JwOiI/j9T4YzMNbCgBhLgJn8omlZRFvCiDSH6UJ88/nZLPlkFy9/XUz+lAzSEqwkRpl5/ONduikReEW53uYg1mpixS8nYvO5tBkN8F1xDcMGRPPhoskcbPCmHKSE/3ny86CNuA8XTT6uaPdk1Ap3Jd2aQxZCDAbGAd8APwIWCCGuAQrwRtG1eMX6a7/LSjmygCt6GR2dBbj23rknYTW9Gy1KHLloMlWNrWQnRXGgroX+MeHc/PoGspOiuOrMdBatODze6W8/G89f5oylyeEiwWrG6StNe/uHUh6ZPYbCykbSEyP5/U+GkRRjwQAkRJnZtL+OXZVNAc9vMRkorWvh9ys36yLa7HCx4JXDz/fw5bmcFueNdCsb209NHE9nnKpDPk6EEFHASuA3UsoG4BlgKDAWbwT9Z+3UEJeHLCwUQuQLIQqEEAVVVVWhTlEojpve8Pfl8UhKapr5vqSOeX//loWvbeAvq3fTbHdxywXZXH9OJgfrW4i3mgGv+O082IjN6aKqsZUbX/me//fyep5YvZvzRybz5493kBLnNcKPNIdRWmOjuMbGrSs3kRgdzqJpgSmMm8/PDkhh3Pz6BjaV1gfZhn628xAXLf0clzt0ykQzIToWvwtVh3ycCCFMeMX4X1LKNwGklBV+jz8HrPL9WAoM8rs8FTgQ6r5SymXAMoC8vLze86+g6BX09L8vj0eyZmcFdoeHfdXN/Oa8LFxuiUdC/xgLD32wneJq79To26YPp7HVhd3lISEqnBf+u4+F52Xx2GyvV0V0eBgIuP/SHCJMBpweNx6PiXCTgdPiIhjSL5L+UWYGxUfw15+Op9XpwWQU3PnO1qAURls91CxA7U4Pd7yzmT9dlsNtfqOgjncjTqvMiLea9eGsOw82MDI5+ojTrXsS3VFlIYDlwHYp5RK/48m+/DLAZYDm3fcu8IoQYglwGpAFfHsSl6xQ9Fj8P6JHhodxsN7O/f/ZTrzVzDVnpfPU2kLirWauyEtlwblZVDbaKdhbQ2pCBC0OD26Ph2iLiV2VTdz59lZdyAwCosxGHvxgJxaTgfsuGc3rBcUUFNfrtcV/+mA7V56exktfFVNrc7D853nU2hwB69M2CNsekz6RLq5uoarRzrM/m4DD5WFATDijkmOPK81Q0WAn3mpm3sR0vfRPc7pLS+gdqYvuSFn8CJgHTBVCbPB9XQQ8IoTYLITYBJwL3AQgpdwKvA5sAz4EfiWldHfDuhWKHoUWEb+9oYz/7qlm9fYKPB6pR4hPrN6tC9SydUX87t+beHJNIZeOT9FboXdVNnPvqq3cOWMktTYHT6/1ThmJMBn5x5fFgDeSvevdLVxzdob+szYV5InV3sYOu9PD1rL6oCqMm87LDmhI0cT8ze9L9Z/r7W6u/+d6NpbWM3fZ13zsex0aLpeHjftr+XBLORv31+FytTGB9jEgxsIVecETt29/a3OnT/boKrqjyuILQueF3z/CNQ8AD3TZohSKXkhJTTO7K5r0CSFahcQ1Z6XT7HDrTR1tBWpPVbN+jRDeKLXR7tSbOcYPiuOPb28JSj20OFwBP2tpB+H7v7mh1c17G8sCmkJe+baYF689g/cXTqaiwY7TLbnznc16q7TWOu1/P/92Z5fLw9sbywKqQB64LIeLRydjNgdWvw5OjCQ7KbrTJnt0xwah6tRTKHoJbQWiqrGVJ1YHiu0Tq71jmXZWNGIxGQgPMwQJVNupIhaTgaZWt97M8eRV40KmHiLMYQE/SxmYfnhvYxn5U4bqzSiaif3AKAsHm+wYhEAID3++YizrdlcFDFb1v4+/GdGhxlZdjLXH/vjWZhIjzUzJ6h8gkAaDYERyTKd063WX1afyslAoegGhKghqmh0ho8EWh4uV60tZNC2LjP6RQVUM/lNFtGYP/4kgz63bw90XjwpIM9x7yShe+rJI/3nh1CxWbSpj0TRv+sFiMjA3L40V35aQPyWDR2fnkj8lg4x+kawrOsTH2yr4+T++Zd7y7/jtGxtIS7Cy/IuigEjZP43hdEuvf8Wh5pCv8fuS2pBpiCH9Omeyx8kaatoWFSErFL2AUBUEQoRuZY6xmLh8QqouvAunZgVsciVYzdx98Sjufc9bEbGioIQ7LhqJ3eXmsdljKDrUjNvt5h//ezpVja0YhGDl+v2cndmfn545mP7R4eyuaOSOi0Z6KzYuHE5hVRMvf+3d3Js+Opmn1u7mxnMy2XuoEadH8K9vigNauJ9cs5sV+ROxOdxBaYw/zcqlpLrJa88Z1/4swbZpCO0TRP9os37v4001nKyhpm1RgqxQ9AJCVRCEMgu6/9IcoiPCGJJoJSo8jKjwMFYUlATkdf/+5V7+eNFIHp09hhaHiwEx4VjDw6hpksREmHBLDwNiLICHgXEWWp0e5p6RhsloINoSxsH6FvpFW9h7qIn+MRbSEiIQQpA/eQjDkmNoaHFy38zR/OvrvWQNiGP5F0XMn5TB02sL9ddTXN1Ci9PNWUP74XJ5WDwzh4LiGtwe+MunO7ny9DRWri/lvY1l3DdzNHf5vUZtCOvl4w/3h7WXYjhzSOJxpRi6y6hICbJC0QsIVUFQXN3CU2t3s2xeni5mT67ZxfVThmI0wKIV3s68G8/J5G4/D+N7LxlFeJjALSExKpy73t1KrMXE/MlDKNrfrFdnXHNWup6jtpgM3DdzNM98tpszM/pjNMCIgTEIIWmwu3js4x3kTxnKL174Lsjr2O70YGyTHPUXt5Jamz5mSuOJ1bt1EX967W6e/3ke3+71vsYVBSXcOn1EQBqis72Qu8uoSAmy4pRibN6ZlJeXH/Gc3uiJ0V4FQXF1CwXFNSxdfTj6vHfVNh6fM5Z4q5lNZQ0kbi7n+WvyqG52EGc1sb+6mRqbk399vY95E9P5w/QRbD/YQL3NycjkaJbNy6PZ4SIpysxTV42juNpG9oBoKhps/Oa8YTTYnUSFhyGlpLnVzdNrdvOrczL559fFPDp7DLsrG4M27MakxukRZ0d9jLXqjeLqFupbnMwc4x3Oevn4lKA0RGenGE7WUNO2KEFWnFKUl5cf1RejN3piHKmCwN2mLNfu9LD9YAOzxqfy9NpC1u46xI6KJv40K4fv9tVy+uB4/vzRTiZnJ1He0MqIgWby0uOxOdyEGQ1YTUbufGczDpfkoctzqG1xsuVAPSajwGhoZUCshaZWF5YwA4eaWzk7sz+xPvF/8P3tISLrUUSYBX++YgzNrS6GD4xmdErcUX2MtaoLi8nA8AExDE2KYmhSaHHtihRDdxgVKUFWKHoJWgWB/8foP12Ww19W7wo4TxNp4RfM1docGAQ8/3kR8RHD2FXZxKayhoBr/nr1eB7+YAeJkWY9zVFY0cTznwd7G//+J8O4b9V2PQXy3LpC/XmsJiMLzs3E4fYwaWg/dlU08NvXN1Nrc7BkztgAMYbQ6YFF07J46atiPZoe0u/IqYJTwQsZlCArFL2GUB+j0+KtGAyCW1duCtr0mjnWu+mlHfvHF3u5++JRPPt/hdxywTAe+3in32bgaFzSw67KJuxOD42tTpbNy6OhxRHkNfHAZTkMTrDy15+OwygEK78v4a4ZoyipbWFXRSPPrivSxXd8Wjz9osPJHBDd7sf+tq+rf5TXrnNcWlyHUwXdlWLobJQgK3oNHfFNrq2rOzmL6SZCfYz+n9HJxFvN+sbeioISfnv+MEafFkP2gGh2VTTqJWn/O2kwS+eOo8bWyj/nn4nT7aF/VDh2l5s6WyvP/mwC35fU4vbAne9s5tdTs3jt22LmT8ogwmRgQno8DXYn/7e7ijcKSgOi3tEpcYxMjuHsoYkBgtiRj/2hzjtWQ6De7oUMSpAVvYiO+Cb/e+H5J2k1PYewMAOTMvuRGh9BZWPgptfgflEhRbI9PB5JWoJVv09avJXxafEBUSdAVlJ0yHv2dkHsbpQgKxSnAO1Fh8caNYY6P9T1Sni7BtU6rVAoFD0EJcgKhULRQxBS9rjBB52CEKIKKO7udRyBfsCh7l7EMXKqr/mQlHJ6R07s5L+vnvx7VWs7PkKt7ah/X6esIPd0hBAFUsq87l7HsaDW3DX05DWqtR0fx7s2lbJQKBSKHoISZIVCoeghKEHuPpZ19wKOA7XmrqEnr1Gt7fg4rrWpHLJCoVD0EFSErFAoFD0EJcgKhULRQzhlBXn69OkSUF/q61i+Ooz6+1Jfx/F1VE5ZQT50qKfWiytOBdTfl6IrOGUFWaFQKHob3S7IQojpQoidQohCIcQfQjz+UyHEJt/Xl0KIMd2xToVCoehqutV+UwhhBJ4GzgdKge+EEO9KKbf5nbYX+LGUslYIcSHe+r4zT/5quw6PR7KvupmKBjsDYnrnpAOFQnHidLcf8hlAoZSyCEAI8RowE9AFWUr5pd/5XwOpJ3WFXYzHI/lw68GgWWDTRw1UoqxQ9DG6O2WRAuz3+7nUd6w95gMftPegECJfCFEghCioqqrqpCV2Lfuqm3UxBu/E4Jtf38C+6uZuXpmiLb3x70vRu+huQQ4VAoYsDxFCnItXkG9t72ZSymVSyjwpZV7//v07aYldS0WDPWCiL3hFubLR3k0rUrRHb/z7UvQuuluQS4FBfj+nAgfaniSEyAWeB2ZKKatP0tpOCgNiLFhMgf8MFpOBpGhLN61IoVB0F90tyN8BWUKIIUIIM3Al8K7/CUKINOBNYJ6Uclc3rLFLGZwYyZI5Y3VR1nLI2jBJhULRd+jWTT0ppUsIsQD4CDACf5dSbhVCXO97/FngLiAR+KsQAsDVU02pjweDQTB91ECGL5wcMNlXbegpFH2P7q6yQEr5PvB+m2PP+n1/HXDdyV7XyeRYJwMrFIpTk+5OWSgUCoXCR7dHyKcKqrlDoVCcKEqQOwHV3KFQKDoDlbLoBLq6ucPjkRRVNfHVnkMUVTXh8XTIyU+hUPQyVITcCRypueNEN+pU9K1Q9B36bITcmVFnVzZ3qNZqhaLv0CcFWYs6L1r6OVc99w0XLf2cD7cePG5R7srmDtVarVD0HfpkyqK9qHP4wsnHlWLoyuYOLfr2F2XVWq1QnJr0yQi5K6JOrbljYkY/MvpHdVp+V7VWKxR9hz4ZIfemqFO1VisUfYc+GSH3tqizq6JvhULRs+iTEbKKOhUKRU+kTwoyKEMfhULR8+iTKQuFQqHoiShBVigUih6CEmSFQqHoIShBVigUih6CEmSFQqHoIShBVigUih6CEmSFQqHoIShBVigUih6CEmSFQqHoIShBVigUih6CEmSFQqHoIShBVigUih5CnzUX8sfjkeyrbqaiwev8ZjRAeb2dATGBLnD+57V9TKFQKE6UPifIbUU1Ld7Kx9srAqY6L5qWxUtfFVNrc+gTngE1/VmhUHQpfSplEWq46ZdF1UHz9Z5YvZtZ41MDJjyr6c8KhaKr6VOCHEpUC4prQs7XE+Lw95WNdjX9WaFQdDl9KmURSlQ9kpDz9aQ8/L02a6+3zOFTKBS9kz4VIWvDTf15b2MZD1+eGzBfb9G0LN78vjRg1l5vm8N3Ing8kqKqJr7ac4iiqiY8HtndS1Io+gR9KkLWRNV/Y+7W6SO4YMQAclJiqWy00z/KW2UxLi0uaNZeX5jDp+XZ1ealQnHyEVJ2b/QjhJgOPAEYgeellA+1eXw48A9gPPBHKeVjHblvXl6eLCgoCDquVVmcyqJ6IhRVNXHR0s+DUjPvL5zcF+YPdvgPob2/L4XiCBz176tbI2QhhBF4GjgfKAW+E0K8K6Xc5ndaDbAQuLQznrO3DzftjFroI93jSJuXvfV3plD0Fro7ZXEGUCilLAIQQrwGzAR0QZZSVgKVQoj/6Z4lBtNdDSKdkU442j20PLvavFQoTj7dvamXAuz3+7nUd6zHEqqW+cOtB0/Kxldn1EIf7R59afNSoehpdHeEHCqsO25lE0LkA/kAaWlpRz3/eCLd9gRt+EnIsXZGOuFo9zAYRJ/YvDwejvXvS6E4VrpbkEuBQX4/pwIHjvdmUsplwDLwbroc6VyPR7JmZwWbSuvxSDAKyEmNZeqwAUcUn+7MsXZGOqEj9+jtefau4lj+vhSK46G7UxbfAVlCiCFCCDNwJfDuyXji4upmdlc0sWxdEU+tKeRv64rYXdFE8VE+/oeqZW5PFDu7nrcz0gkqJaE4GlJKDh06RHdXYPVFujVCllK6hBALgI/wlr39XUq5VQhxve/xZ4UQA4ECIAbwCCF+A4yUUjacyHMfqG/hidW7gzwsclNjGdImMvRPbSTHWoJqmUMJWlfU83ZGOkGlJBRHo7q6misffZPXfjeLfv36dfdy+hTdnbJASvk+8H6bY8/6fX8Qbyqj0/B4JI12V8jUQ5PdHXRuW2F96upx/OfXk6lqal/QuirX3BnphO5MSSgL096BOTK6u5fQJ+l2QT6ZeDySvYea2V7eQKzVFDKXmhwbHnBNKGFd8MoPvL9wMhMz2o8eVD1vMKoLUKE4Mt2dQz5paGLwP09+zoJXf+COtzdz7yWjAnKp9186mpHJsQHXHY/Lm8cjsZrDOpxrPhXoSL5cWZgqFEemz0TIbcWguLqFv35WyN/mTeBgvZ0hiZGMT4snLCxQRI+1skET/oc/3M7CqVksXbP7iLnmU4GORr7qU4NCcWT6jCBrYpAca2HW+FTd73h7eQN/+XQ37y+cHCTGENqQ6EjC6i/8L39dzPxJGRgNMG14EjkpcZ3+0VxLwxyotxFuNNLscJGWEMmQficvN9vRfLnqAlQojkyfEeQBMRbSEyOYm5cWELU+cNlonv95HhUN3hRE202mY61K8I8Cy+vtPL22EICzhyYet0C2txHmH423fV1dlZsNtZa2ka/2prerohE4/Ds91jc3haKv0WcEeXBiJItn5pD/coEuHvFWM5UNrfzxrS1BQgYECU9HPlZ3dhToL7ozclMwGuD09ATOykikpNbGza9vYP6kDF2Moeu6B9tLTYxMjtZfc3KshXkT09t9c1AldwpF+/SZTT2DQWAyigChnDU+NagW+ebXN1BS03zcfhWd3Xixr7qZv3+xh/wpQ1n+RRFLVxfyy5cL+M+Wcj0yFYJOGS91tI259lITbg/6a541PjXkm8PeQ96NO63kbmJGP71VW6FQeOkzETIER6/tCVlFQ2u7OdHBiZFHrKPt7CiwurmVX0waGrSeW1duYkX+WQHC3zYqjzAZ+WrPoQ7V+3ZkY669TbmqJrv+mnccbAh5TklNM0OT1MadQnEk+pQgt81hGoVXuOKtZn2jzyjA5gjdNFLRYGfHwcYjilZHGx86ep7ZaGhX5JxuN0vmjA1Z0XH/paNZ+NoPFFe3HDWn7PFINpfVseNgA9dNzmDl+lLK6+1BaY8jpWO0yPdgQ0vIc8whNkwVPR8pJdXV1QAkJiYihPpE05X0KUFuG70OjLGQkxrL7oomPXVhMRl4bl5eSFGxmo1c+8J37eZqO1r+dSwNEjaHu91BrAmR4YxPS2DEwGgqm+w8d00erU43/aLCWbTCK8ah1ulPqLUsnJrFy18XU15vDyhJS4u3cv+lo7nj7S0Bwp8Wb9XvF240smhaVsDvc9G0LCxhxk76V1ScDDQhrq6u5qYVPwDw0o3nqVbqLqZPCTIEtw27PbDglR8CSuK2l9fzp8tyue2tTQGC6XJLrpucAaBHkf51tB0t/zqWtuqkaAvvbSwLioAfvjxXz0tvbxO1PzZ7DA6X5FfnZurlfSvXl+qVJP5Reai1LF2zm/mTMlj+RVHAZmRxjY0nfY8JAVLCk2t2MyjeSv/ocAYnRpIYZSbSbCR/SgYeCQYBkWYjiVHmzv6nVHQhTlsjv/7Xd3habVgSBmIymbp7SX2CPifIbalsPFyf7F8dkJ4YwbJ5eZiMgqRoC3urm/jp8m+Cosham0MXrY42PhxLg4TRAFeensZr35Uwf1IGsRYjI5JjcXk87KtuRkqCBPVAnY1rzkoPilIBfV6e9iYTbzWFXIvRQNBmZHFNM8XVLXopn8bnhYd4/vMilswZy3nDkshIiiK8tgWrOYzyOhunxUeQGmelqKpJeVj0IsyRMXjC+rxEnFT65G/bP3+rtTi3rQ4orm4h/+UC3l84GTgcRcPhKDJ/SgbDB8bootVejtVqMrKnsonKRq9bnMstO1waV15v56Wvipk1PpVoi5FIi4lf+kr3tGi4raA63JKn1hYGOdktuWJMUI5Y2xhsu5ZQjSyRvt9V23Ol9D7Hwx9ux+n2bjj6R/JThvbn4+0VAVH8w5fn8j+jk0M24ygUfZU+J8htc6bpiREsnjma/bW2dqNWTXDaPjZuUBw/zk7SRStU48NN52WzYX8dD36wA7vTw8JpmbyzwZuCWLPjINecnUGLw0VG/0jcHsl3+6oxGw3YHG6SYy1Emo1ckZeKR4LLLVm8aluA0O6ubAwSSYfbE3K92w428vznRQE5Ym1jsG0+OyclDiAgqh0YGx6QH05PjODW6SMorGxiwdRMIs1GXYy157x15SYGJ1pDVonEW81MyuwXlF/vqBucco5TnGr0OUHeeyjY0+Kptbt55PIxLFtXFFRxMTDG0u6mWno7JW8p+RNZvaMStwc8UvLgBzuIt5r56ZlppMZbuXhMCoUVDVyRl8bv/72ReKuZa85K57XvSvSOO+2Yf9rhoVm5QUL7ekEpD16Ww+1vbdbPGzEw5oiRrH+OWNsYbFumB4RsSJkwOJbHZo/BgyTSHMZ9q7bqlRx3zhhJvNVMef3h+me706Pn2v2xOz0UFNeQGh+hp2mOZbNTOccpTkU6LMhCiB8B9wDpvusEIKWUGV2ztM7H45FsLw8uISuubmHj/lr+Mncsew81B4jgsIExXDBiwFFbfv2jNYMQvFHgTQssmJpJvNXM/549mMc/3aVfv2xent41qDWo+HfchWpa2XuoKUhoa20OspOiWJE/kfJ6O/2iwnnog23cNn041TaHPp4qwWrm2XVF+r20HHFavDVklFlU1RTUkp2eGMGCc7O4853DVRb+0fbiVdvIn5LB0tWHc8wWk4HTYi0snJaJ1meybmcl5wxPIiU2gqqmVv05j2WzsztnGyoUXcWxRMjLgZuA9YD7KOf2SPZVN1NaawsZPdbb3SRGmvnNiuD/yd9fOJkLRgzQRS85NoJRyTEBtcdto7VF07J46atiAK45K10XY+2+5fUtQQ0q/o0qoZpWXi8o5c4ZI/W0hRaVljfa+bUvx52eGMEtFwyjvN7OsnVFAakT/9c7bXgSo5Jjg3K7S+aM5YIRA6hqbOXGH2dyoL5Fj3pn5KboYqy9Di3aftqXs84ecLiNWrtfZVOrvpb0xAiu/3Em9763NSCffFqcBY+HkBF2qM1O5RynOBU5FkGul1J+0GUrOQlUN7cSYTJy98WjAgThpvOyeeXbYiakx4X8n7ymuTWgIcSbd87BZBQMiLFgEMGVDk+s9m76fbajkhvOydRLxcBbgmZts0EWquMuVDTc0OIMKDtrtjtxWc26kBVXt1BaGzye6vFPd+lpiievGgcSPt1Rwc6DDfq12sac3ekOSIFoUXB4mCHk70d7XVq65D+/nkxJTTNWcxiR4UZmP/uVft2M3BT9d69df+vKTfratDcyTZTb2+xUznGKU5FjEeS1QohHgTeBVu2glPL7Tl9VF2E2GnhvUxk3nJPJvZeM0suyrOFG7ps5iriI8JCde2ajQRfc5FgLc/PS9HSDxWTgwctyQkZ24wbF8eOs/uysaGT5F0UBAvfexv3ce8ko7n53KyvXl7JoWhavfVei1xtrx9qWrvmLlfbGsKm0jtsvGsGD72+nvN5Os8MdUjjTEyJYNm88lY0Ofv3qDyHTDjNyU3Qx1q5bumY3C87NZGj/qHZz01qkm55gDYi6F07LDDi/beSv1X6nxUdw3eQMXvuuhCvyUlm6uvCIPiDKOa7zaNuNp+g+jkWQz/T9N8/vmASmdt5yuhaH28PsCWn8yu/j/eKZozEIqGhwcNc7W7lt+nBsTneAEGYlResiEso85/a3NofMnaYnRlLV2Mp9bSojlq7ZzeNzxhJrNbLilxOpaGwlzuotvBcCHpk9hn2HmjEKWP7zPL7ZW4PZaCDSbKTW5gC8YnzjOZkBbwyasGot4UHld+YwLKYw7nj7+3bTDkZD6IqS/lHhPBSqRXvmaGqaW1k2L4+z/RzotHu0tyGqvbm1dYZbODWL3NQYXss/84g+IKE8Q9rLhyuOTHV1Ndf89VPA242n6D46LMhSynO7ciEng/AwA/f4Pi4nx1r4xdlD2HGwkTCD4JGPdmJ3emhsdQXV8PqXlrVnSBQqdzo4MZJ9h5pDnm8yCiYO6a8Lhsvl4WC9nd/71fAunJrFwx/s4JzhSQzpF4nJaOBv8yZQUW8nKjyMm9/YGCSs+VMyyEmNDYoeH7wsh9EpMew42BhyPdEWIwunZTJuUFxIAe0fE05xdYtuuq+lTKIsYUxIj9crTtrmdleuLw0Q8fc2lnH3jFHcu2pryDe3pWt2syJ/ImMGxR/139O/61JVXZwY4VGxRz9J0eUcVZCFED+TUv5TCHFzqMellEs6f1mdj7cK4nCt8azxqdicbl77roTfnJd9WBRcwTW8/qVlEDriG5Ucw+v5EzlQbycmIozkmAg8Hkl8ZOhhqkkx4eyrbqa6uRWz0UBzq5v0RCuPzR7DjopGIs1G3B7JlGFJhIcZcHs8/NYnwJqHRChh1WqjAYb55XIHxISTGmdlf02w+U96YgT9oy388a3NxFvNQamSO2eMBLzNLP6m+1oaJd5qRuJNIyS3qahYub6UFQUl+uuSEl79ttjXVBMd8jXYHMe+Z3y0qgtVs6zoDXQkQtaScr16Lvi+6mb2VDYFRLoJVjMzclOoszmOupk2OMHKivyJONwespKiArrRnrp6HJvL6gOi20XTskiNj0AIggTunotH0Wh3cd97W5k6fCBL1+wmOymKaycNYU9VM+9tLGNuXpoeqfsLn7b5tr8mdLWIFqm6XB62HKgPWOeyeXnsO9QUtJ4/TB/BTT4x0zoDbz4vi8ykaKqaWomJMPHXtYVBFR5aO/YP+2uxmoxUNtgprWsJqO5YNC2LqPAwappbef5zb533tWenkzUgmjCjIeRrMAhv2d2xiOaRqi4GJ0aq6FnRKziqIEsp/+b7771dv5yuo6LBztodlfrHZYBISxixFiMxESZdpPw30+KtZq7IS2VQvJXKplb+9MF2bj5/GGkJEazIn4jN4SYp2kJ5Q0tQa7VWZQHwRkEpC87NZGCMhZJaG698U8w5w5P437Mz2F3ZSHZSFNefk8nOgw3sOljHg5fl8M3emoA2Z61OWYtO1+6o5P5Lc7jj7cPVEJrhkMcj+bKomiWf7GT+pAyiLUZS4qxUNtppbHUxbGA0j88ZS7PDRUmNjeLq4C5FlwdufOX7gCjZZCQgXfHSV8X89oJsosPDaHV7ONTkoKTGFrTuWy7IZvkX+1h65TgaW51UNrRyw7++JzspKmQZ3w/7a3n12xJunT6iw6J5pKoLVbOs6C0cS2OIBZgPjAL02iIp5S+6YF2dzoAYC+cMT+LZdYXMn5RBVLiRGEsYsafFcsfbm7ny9DQenT0Gm8NFq9PN01ePo6bZGdAEcdN52Sz5ZCczclNY/oXXTCc51sKGkrqQ0ZlHQlS4kVnjU+kfFc6hplb6RZq58ow0faNPE6GK+hbW7qjkmrMHM//F4I268no7QnirEvInDyEyPIwn1+xiwbmZJEWHkxRjIT0+AvB+GthRXs/VZ6TzyrfFzM1L43f/3hgQof97fQlzT0/njYJS7poxMkDMQuV2F6/axlNXj+fxTwoDStJKamxEmLzWmv5Rt/+646xmam0O7C4PxdU2vSNy+uhklq3b432zirVQUmPjqTWF1Noc3vz5h9sZPjC6Q6LpX3WhvZFmJ0UjJdTYWlXNsqJXcCxVFi8DO4CfAPcBPwW2d8WiuoK0eCuZSVG6W1lyrIUbfpzBwDjv4NO2YiIlQU0Qj3/qFUAtz3zz6xt46RdntFsOFhPuNQP6y6eH7333xaN49v8Kg8TuuWvyuDAnWfca1h7zb3NOjgnnrhkjabA7KatrIdZiwiAEd727NeCjeP9oM4MSIrmpnXl797y3lUdmj2FPZSNX5KXy0IfbAzr70uIjQgrYptK6gJI0rQzvnktGsfVAfYA1qf+6T4uLYMmcsRgEWM1G7E5PgOi3OD0BrxvQry+sbKLR7sTmcB8x96tVXYxcNJnvS+p4YvUuZuSmsKuykQlp8Vwwsh9ZA+L0mun3NpapmmVFj+NYBDlTSnmFEGKmlPJFIcQrwEddtbDOxOORfLy9ggO1LeSlx/LLKZnU2xzc9e5Wnpg7LuRO/1/mjg0pSgNjLQyIsRB14TBe/LKY+hYnD324nZvOyw5ojV40LYthyTH8v5fXB9z73ve28ujsMXrNsHbc5nAxODEy5HMaDXDvJaOICg/jIT9vid/9ZDh3vBNYM3zz6xv45/wzafZNPWmvKsTucPF6QSk3n5+NwyWxuzx67nfRtMyQbzAT0uMxAEuvGoslzMiuikauOiMNk0HwzoYy3dNCL7/zrfu2Nzfpj901YyTpiREMiDHzyOwxtLS6SI6zsK0sgZEpcQHNM0aDtz9/7rKvO5T7NRgEHglPrN4V1PL9q3OzuOud9o31FYqewLEIstP33zohxGjgIDC401fUBWg5xItGDWDu6elsPVDPl4VVPPOz8bjdMqARBLxiEB4WuOGUHGvhirxUrOYwWp1urCYjN/w4g8RIM8XVLbzw5T5uuSCb5NgI4qwmWl1umu2hR0Htrmxk3sR0/SN9emIEjXYXFpMxpBDmpcfjcHt49rPCoLbjxTNHYzYKDAYDz63bw6ayBoqrm+kfFR7Q/de22SW9n5Vam4PKRjtX5KUGtHa/XlAa5Or2h+kj2Li/HrfHw8jTYmiwO3lqbaGeHlhwbhaVjXb++XWJXn43YmAMVY12HC6pv/b7Vm3jH/+bR0lNC7/3pVG0muq7392q3++3F2STHGuhrNYW0OV4tDRGRYO3ucX/TXZGboouxto67nh7C+PT4lXKQtGjOBZBXiaEiAfuAN4FooA7u2RVnUxFg514q5nL8wYx/8UCbr9oOL/4UQZhBggThpBm7lGWw6OIspOimHtGWlCFgVFAi9NNemIEsRYT6QmR1Nud7NrbyOsFpczJSw0psG6P9yP5o7PHUFjZyPi0ONaX1JGWYOXxuWN56IPtekS5aFoWLU4PxYeauP7HmSx49Qeyk6K4bspQWlpdRJiN1DS18uAHO7j74lGY15dgNYfhlh69+++26cMRAg41O/RytH1VzTw6O5fHPt7JwqnZAWvUKi2WXjmOKIuR0poWvQpDexN4au3ukKZJN52XzQtf7iMtwcp9q7ZRa3MEbEZ6o3bB3e9uDRBMTYzbNorcd8loVm0KjL5rmlvbFdKkaEtQc8uRpnIrQVb0JI5FkGOBa33fP+37r0sIMVZKuaFTV9XJWM1hXHNWOnU2J3anh8GJkUgpCTMY2V7eEOT78MTq3fxr/hkkxYRz83lZZA2M5oZ/fh90zuNzxlJW28JDs3IpOtTMr18LbEf+cEt5UImZ9nHe7vSws6KR9zaWkRJvDSgVu3PGSD1vajUZKa2xMSghkupmr7PbVWek69GlxWTg3ktGkZ0Uxb3vbeX5a/Ioqmpi7Y5KLpuQysyxKQzuZ2VbeWNQOZqUkgcuzcFgCC71M4cJGltdbDlQr1+nvfY739nC/EkZRJgMQaZJj3+6i0XTsqizOfSUjP9cTIvJQFWjI+CTh5azDrWZeNe7WwLMi7TGkfYwGmBEcmj70VAVGApFT+JYxjVMAK4HUoDTgF8C5wDPCSF+3/lL6zwcbjeD4q0kRpq5a8YI7C5vlFZncxLnM+b51bmZLJiaqdtlHqi38+o3xeSkxlHd5AgZYdldbhKjzBiECDKOX7pmN5Ozk3jpq2KWzZvAwmlegyEtTaF5QIT6OL141TZGJsdwdkYif/9yL0kxFpodLqzmMPKnDOXeVYHmPHe/u5XrpgzF7vRQ3exg+X/3cu2kDA7WtTDqtFiEECHfdAbERnDdSwUUVjRxp6/SArxidcdFI/njW5vxtGPObzRA/6jwkI8NSrBi9Kmwt64Y/ftF07JIijazcFomN5+fzV0zRlLf4q0Dby+SjbaEsfSqcTw8K4dHZ4/BI2W7/9bl9Xae8dVMW0wGkmMtxIQbWTxzdMDrU74XwfgPNlV0D8cSIScC46WUTQBCiLuBfwNT8FpyPtL5y+scEiPDabQ7Ka+388hHO/nH/55ObbOD/jHh2FpdIVMWSdHhTB0+kNpmR7tNGHsPNTNxSAL1LaEFOzzMQK3NwdYDDUSawwI+2muR8uUTUkNeW1Bcy2mxEdw6fTgGIYiJCKf4UDNxVnPI81udbm+Lc7Q5wPxo4bTMAC8O/2tqmh3EW80YjQaWrdvD/EkZGA2QmxpLve1w/jvUax8+MAate6/tYwLoH2PhlguyGXVaDGW1NhZMzWRsaizVjXZK64KtQW+bPpwW32toe7+spCh+5VcTvXjmaEYNjKW0viWo825AjIVdlU2s+LaEp64eR0VDK4tXbSPeaubm87IYnhyDyy1JV2IchKO5QR9s6nL1SofdXs+xCHIa4PD72QmkSylbhBCt7VzTI0iLt1LRYOfGt7wphfJ6OwmRJmytLhwuGTJ6fPpqb/XFY7PH8HpBaZCpzn2XjOLxT3eTPSCa/lHmkEKS0S+SxTNH8/6mA+yoaGL+pAxGJEezq6JRj5S1c0Plme95b6teOvanWTmkJkTotp1tN+lS4y0snjkau8OjTxyZNT6VlNgILGGhO+Ks5jB+eubh3LiW5104LRPhO6etF4WWUnF5XCRGWkI60pmMBvYdauKxj3fpbz5bS+sYPjCaGKvXpMh/o+6Vb4uZNzGd/jHhQfd78LIc7mvzieDOd7bQPzqc6/+5Pqj6QqtH3nGwgU2lgekWlwfyXw6+RnXrHUYbbOqqrwk4rkXPiYmJCKF+X13FsaQsXgG+FkLc7YuO/wu8KoSIBLYd7wKEENOFEDuFEIVCiD+EeFwIIZb6Ht8khBh/rM9RUmtjfXGt/j9meX0LVrMBg8HA5gP1IaPHQ740RZzVRK3NoZvqLJiaSf6UDFweD7U2B/FWE/UtLhZOzQr4SLxwahbhJgNPrd3NnNPTAHh6bSHNLQ4GxVt117av91Rx/6Wjg6598/vSgLK1f329D4OARruLP12WwzVnpbP8iyKeWlPI39YVUVZnR0oPNTYHZw1J4OHLc0lLiCA5zkJYmOTBy3KCnuOhD7czIMYS9Po9Ev1NSHvt+VMyeOLKsTx55Tje+n4/YYYwXvyyiIz+kTw2ewwPz8rhsSvGMCghggfe34ZEkBxr0dM3107KYFdFI/trmpmbl6av/fnPi5ibl8aAGAvF1TaEgEdnj+Hm87PJn5JB/2hvFUvbf5/vS2oDRPrm1zewr7pZr0ceOyguIN0SKj+tXaM4Oo7mBvL/9im7du3i0KFDyCOkjRTHz7G4vS0WQrwPTMJbHnq9lLLA9/BPj+fJhRBGvBuE5wOlwHdCiHellP4CfyGQ5fs6E3iGw1agHaKiwY7d5dGjxI+2HGRcWjzNrU48EvLSY73DRltdWMO9QhMf4Y16bQ43N5+fzZJPdvH0Wm9DxM3nZ9Nkd3H/paN5/JOdLJqWzYqCkoC24hUFJdxz8SgcLsmeqia9oSI+ykJ8ZBh/mTMWh9tNq0vypK8JwmiA4QNjePazwoA8c25KDJePT6PJ7i2Zi40wBUX1d7y9hVsuyCYnJYbpOckBtpz3XTIah8sdsD4tQu8XZSY9MYIZuSl6xBplNmIOE7o42hwuapodJEWbufaFAuZPyqCmyc6Fo1Morrax5JPDqZj7Lx1NrMWkN9E89rH3sYP1LXgkJMdZ9a5Bbe1L1+xm+c/zAtIYC6dm8eq3pQwfGB0yujcbA2MJ/6oJg0EwODGSjfvrArxLVKXFiSGE4Nf/+o6wsDBeuvE8+vXr191LOuU4piGnUsr1ePPFncUZQKGUsghACPEaMJPAiHsm8JL0viV/LYSIE0IkSynLO/okA2IsVNY3c/+lo7nj7S1Mzk6ipslBnNXEroN1+rBRfwHrFx3GY1fkUt/i5B//3RcgZv/47z7umjECIQTR4V4f4ytPTwv66L67wtsJl5YYidkoePan44kwGWlocVHb4qTO5tCv8XdQmz8pg12VTXqe+baLRvD7f2/kxnMyeb2glFunDw8pLvFWMy4PASVldqeHpz/bzZ9m5VLZ1IpHwpvfl+qCX1ZrC2qaWHLFGG74caZuVaqlaDQrUSFgUGIk64trgyow7nh7C0vmjGXxqm2kxEeQmxLDrsomoixhGAUUV4e2Iz1Ybw/q9MufkkG81awb+fv/bgfEhJObEsOmsgb99+ZfNTE4MZKc1Fg9BaKdoyotTgxzZAwmk6m7l3HK0t1Tp1OA/X4/lxIc/YY6JwUIEmQhRD6QD5CWlqYfT4u3MnPcIG5/azO3XJDNoIRIEiNNCATzzsrgly8VBJVavfrLM71VC02t1NocumCC939kiymM/TXN/OyswdQ02RmUEMFjs8fQ3Ooi0hKGze7k8dWF/H76MA7W2WhyeCs9ws1eEb9v1TYWnJsVUpyGD4zmuXl5PLF6J+X1dlp8XXcOtzdN0q+dnPXAWAu1zc6A49qEk1+88F1A9LmiwDvhuq7FxVPv7wh4/TsqGoOE9q53t/LY7DF62qOyobXdCowdBxu4Ii+VvYeaueGcTKoa7XikJDMpql2Ht2Kfj4X/hmdavJWqRjt4PORPycAjD5sa1docPD5nrF7rrFVN+NtsDkmMIispivFp8YCHvPR4vi+pxSO9rdO3Th9xTJUW7f19KRSdRXcLcqjdgbbJqY6c4z0o5TJgGUBeXp5+TkmtjQN13nlz9/9nB09eNY4WhwuXR9LU6gkSsGvOSqfW5uS1b4u56sx0br9wuN5UYRQwbEA097y3xdu9VlpHbkos1c2OgCjulguGYQ4TRIQZSU2IZLFPOP569XhcHjczclPaHbgKUFBcwzVnDeaXU8KIshgDNthqmluDNtoWTs2i2lc1cTSjoKVrdvO3eRP4w8rNXHt2etC8v/aE1oPUm03un5lDbXNrkPdxrc2B2wNDEq38+ZNdXJGXyoiBMWw/2MCEtHiiI4zcP3M0d/hF5P4DYbX15U/JoKqplRan18NCm+Ltz/aDDfxl7lj6R4frwhrKZvOCEQOChrk+fHkuF4wYcEwbeu39fSkUnUV3C3IpMMjv51TgwHGcc0S8pVHhulDtPdRMZlIkCVYT5rDD7craSKEWp5vCikbmTxrKQx9u5+oz0gPym3+6LIdYi4kWpxu3B8JNRv76WWGAsL389T7umzkaKT0s+Xi3LiYbSus4e2giRgMhqzcWTcviwfe3U2tzsGhaFtHhRlocJl0IPVISG2Hmpa/2eb0gfPXJL35ZxDnDBhBtCQv4iN+2a02bYVfR0Mq1Z6eTEBXOkk+3BAi7oZ0RUEnR4SRYTYw8bTQGg8d77duBwhobEcbf1hVxxYRBujhvP9jA0tWFpCdGcN/M0USGh7Fs3gSMQtDqcrOzoonLJ6QC6Lad6QlWmuwuPRoONSLL7QGJ1HPARVVNIW02V+RPDDp+68pN5KTEqvyxokdxLFUWXcF3QJYQYogQwgxcibct2593gWt81RYT8U6/7nD+GLw55JLqZu6+eJTXhc1iJN5qpqnVzd6qJv24Fk16JAxKiGT7wQZm5KbwyreHKyyum5zBX1bv4oZzMjktzsI3RVU43W7m5qWxalMZUoJBwB+mjwAktlZPQJ7T7YH9NS2MGBgTUL3x5FXjyJ+SoQ8x1crvBiVG8oc3N/PB5nLypwzlqbWFvPDfvczx5b1vXbmZ3/17I1fkpbF+Xw37qppJignnsdljeHR2LmdlJOpRt/aGs/yLIn7/7000Odwh3eVGnRbDH6YPD6jKWDQti10HG/nZ8u/4fy+vx9Yqg67V8uFz89J4Y/1+7pwxklWbynB7vM999Rnp/L+X13PDv74n/+X1bCqrp8nhndqiVVzMm5hOemIE/aLDWenLddudHkYMjGHhNG/jTnpiBAunZrFqU6BjW3sm9do92h6vbAyMuBWK7qZbBVlK6QIW4HWN2w68LqXcKoS4Xghxve+094EioBB4DrjxWJ9ncGIkaYlRrFxfwpIrxjDytFga7C5aXR5qbC5e/aaYR2aPISspSv8ft9nhwiMh1mIMWaZlNAqKq5q44ZwsrKYwPSe7/Isilq4u5KbXN9BkdxMdYeLm87NZNC2T26YPZ9WmMiLNRp75rJC7Lx6l56dbHC6Wri4Mmlyt5YQnZyfp9cKXTRikW25q5/31s0JunJpJjNXMPe9u9XoLr91NyaEm7vN1qbVNX4QZDCGFamNpPTkpsfzxwhHe7sVzMwGIjTDx5FXjuPGczJCjruxOD6fFRRBlMTJzbArNdidz89J48/tSZo1PDWqzfmL1bgorm5iRm6IfW7pmN7dOH8Fd73g3X+FwGueNglKe/7yI/ClDWbPjYFAOWDOp98fbrRcR8rja0FP0NLo7QkZK+b6UMltKOVRK+YDv2LNSymd930sp5a98j+f4ldp1GINB0D/axFVnplNcY6O6qRUDYDIayOgf6a1oePUHCqua9FxtYqSZ9zaWMSI5NmQONtZiIi4ynLK6Fg41OYIcxuKtZkpqbPzypQKWfLKLv60rotXt4TfnZWMNNzJlWBJJ0Wbyp3gj7+gIU0jRMIeJgLbi5FgLUWYj1032Xpcca9E37j7ffYg/vrWZ4uoWPtzijajLGlp5eq23rM7f5zg51kJWUlTI52xxeiita6HJ4WLl+lKiw8OINBuJjjDR4nBhNHg9I0JdGx5mAAmZ/SMZmRLLmh0HdXP9kHlpGeh1YXd6rUgdLokQgTXTPz0zDbvT21p+54xRAU0dHo/EIAiqt37q6nFEmo08NnsMi6Z5f1+qdVrRU+l2QT5ZlNfbaXW6aXG6SYgKp6KhBbdHIqXUmzq08U21NgfF1c1ceXoa28sbQgpJVVMrbinpF2kGQVCudtb41KBa4SWf7KK6qZXvS+p4ak0hh5ocLF1dyFNrCnlmbSH3+FIn4BWTey4exds/7Ofui0d5LTMTI5g3MZ0bX/k+4CP+NWd5HdKsZqOeWvnllKEsXrUNj0Q35d9f16Lff9Z4rzF9Ww+L26YPJyrciEEIRp8Www0/9ubFmx1u7n1vK8U1LbS6vA0zt18YnNbYXdHIoWYHlrAwrnuxgP83JTPgHH80nwv/HgOLyTuF5Jqz0slJidX9P4qrWxgYY9GbTVqc7gAx/nDrQaY/8TmPfrSTm8/L4sVrT+epq8ZhNhqZ/9J3LHj1B/62rojf/WQYHy6arDr0fGgdeIqeQXdv6p00Eqzh7DzYxLJ1RYw6LZZwUxh1tlb6R1sCmjrAO5T0tHgrdqcbgxAhN7iMQhAfaaKioZXlnxex6LzsgPPaiwgb7C7cvsNaG7SWknjm/woD6p2f+b9CHpqVS3VzK40tRu6eMUqfc6fdT2vvjreaifabTrJwWqZ+nvYc/m3QQniFesW3JXqrcYTJSKvbE9S6HBUexgPvbwswfV+2zsB9M0dz83lZNLS6MQi8ueuPdnH5hFQaWr2plroWJ4/NHkOYAR64LIc/vnV4BuCiaVkMjLXwl0936evUSt5qbQ4WnJsZUJ9dUmtj1vhUln9RRITJ6I2KDSJoZp7LA//Pr63af5zU7W9t5v2Fk5UY+6iuruaXT/2HmNTM7l6Kgj4UIdtdh4UmymykuqmVFqeHxf/ZFpAjfmL1bvpHh3OwroU/f7xTN7PRorv0xAie+el4PFIigPAwA7sqm/jnV/sCzjOK0BHh8IExvPl9KQBldTYWTcvSUxJaJPvUmkKeXltIcXULtTYHt7yxiX98WYzN6Q4p8kaD4Iq81ADHOY8M9KKwmAyU19tZUVDC01ePZ/RpXovKTWUNLF61DbcHBsVb9a477d63v7UZh8sTlJKxOz3c9c4WxgyKJyclFrPRQFWjt2bbICDS92bTL8pMrNWE0yOJt4bx5JXjePKqcfz16vHERoTR2OLk3ktGs2BqoBueVnet/d4WTs3ijQLvFJGFU7NY+NoPfLj1IB6PDNjMa6/Mb9b4VP1ntZkXiNmqUjc9hT4TIdtaD7uX7TnUxMBYC2EGA8XVLXqlgxaZ1jS1khxnpbjaO036pfln8OcrxlBWayMx2sIN/zrsPPbAZaO5b+Zo7npnCzsqmsifkkFavJVGu5PFM0cHDEl94LIc/v7FHn3j7sUvi7nhxxnkT8kgKyl0i7DHg27cvruiMeQ5UkqG9j+8IZkca8ES5n0jKa21+fLJ3nU12J1EmA1Ih9TXV15vZ/kXRV5zohCC3+zLG4d67L97DnFabARvrN/PzLEp3DljJNHhRmxOF7dfOBy3lNzwUkFAVKxVkmjrf3T2GJ7/vCjodWUnRbNgaqbe6l1rc5CZFM2ffOOvtMnR/hOn2/tkon36UZt5ip5Mn4mQ46yHN80qGx388+t9xEaE6ZGjFpku/6KIersbu9NrP2h3eiitaWFnRSOpCZH6R27tsT++tYXUeAv5UzK4fEIqbg/8+ZNdPPzRTmqbW1n+8zwenzOGv82bgNkoyJ/iLdsCqLU5iLSEMWJgDGV1tqB87v2XjiYhysRvL8gmc0AUuamx3HdJYJ757hmjePijHeyubPJVFHhL255aW8jv/r2Jv60r4qoz0zkrI4GEKBPRFhO/eKGA615cT21zq55znj8pg8pGe8iovqqxlREDY0I+prnS3TljFFFmI/2jwgk3hfHHt7aQOSCKkmqbvgEZbzXzxOrdXJGXql+/cGoWL35ZFPTabz4/m+qmVp7/vIin1x6eRP2nNrMIKxvtusPbkXLVUiofZEXPp89EyHUtDm6dPpyHP9zByvWl3Haht9rhwctyuN0vr3nTedm88m0xd1w0kuRYC7U2BwNjLDTZnRRWNoWMvrYfaGRAjCVgxNPCqVn848ti/nDhcBrtTm7ze44HL8shMjyMTaX1es71+c+LiLea9Ug90mxECPjFCwXEW826Z3O81cyiaVmkJViJtoSxo7wBh0vqqQm7yx30kf1e35TpwsrAluiGVjfLvygKiKxD2Wm+9FUx/aPM3DljZNBr1KafFFU14ZbQ3OriH//dS7zVzL5DNh75aGfQ+dkDolk4LRO3x2vClD9lKCu+LfF9UoiitLaFf/x3H4BuujQlqz+/fWNDQFmgFu1qDm/Dfj2ZPYeagtapTep+8dozOH1wgsofK3osfUaQYy1mXvhyG7dckM3QpCia7G62lzfi8UjdJ8EgIDLcyHWTMrj//W1ckZdKcmwEpbXNeut0qJRBvd3J4H7WAL8F7SP2nqomgACBvP2tzSybN0EXQ//NNs1R7vE5Y/U5dm0rNgxC8Ns3NgYJ3ctfF/ObaaH9MaRHBrVEt/U6rrU5SEuw8vw1eXy9twZLmAGryUitbxxT/bo9PDcvj++Ka3B7CJh+kpUUDULy+Me72FTWwMJpmdwXYopK/pQM9h1qxu3xbnzOHJvCmNRYhvYfQVK0hermVn796gZ9jdqm3pSsftw6fURQW7QW7RoMgqFJUQzpF8m28nrdV6SqqZUn13gjbLWZp+jp9BlBNoUJfbKxFi0CAREj4POhyKa4uoWRyTH8dW0hU4YlMWxANB7p4e6LRwVMfX7wshxsrU6e+HQ3F+Ykh5yfp7UFa9idHjbsr9ejUW2zbcmcsRRVNTEqJZbNpYd9mv3zoj89My2owWKpz77z6bWFHKhvCfmmYTAIfaNRe0x73r/Nm0BFvZ3+MeEIoMXh1nO6ybEWPUodMTCGRz/aETTwdeHULO55bwszx6awqawBi8nAoHhryDcGLQLWjPVzUmMZmRwbIJSh1p8QGc74tASGL5xMZaOdpOjDU0L8MRgEI5NjKalp4W6/fyeVqlD0BvqEIHs8kr2HbKzeXs7f5k2gocWpG+KEEo24CK9HsNsj2VXZxDnDk9hxsJHlXxTxwGWjueWCbOpaXJwxOJ6lq3cx76wh3HXxSCwmI+Ct2fWPkocPiCY51hKwkdXq8vDqt6XkT8lg2IBoYiwmbE43wwZG4/FIoi3GAGHSvm9vjp3WRJGeaOWei0cFWGcunJrFc+v2cPn4VN3bWXssf8pQGlqcVDa28vinu/nNtCz+9U2xHjlrG343n5+NW0qumzyESEtYSG/lYQOieeqqcTS3uogKN5KeGBFgLm8xGYg0hwW8aS2ZMzbgtWj54FCRsMEgyOgfdVT/CS2FcTTxVih6Gn1CkPdVN7Pkk53MzUvj/728nkdnj8Hot+veNhorqbVx14xRlPvK0qwmI8/6Iuk/vrVFH6sUF5FNQXE9Ww5s4j+/noyUktPiIiiqauJ1n/PZwqlZPPrxDq45K103ytGmSl8+IZXPdlQyLi2e74pr8Ehv7viHkmpmjR+k50K1hpUnVu/GGh4Wcs3jB8Xx6OwxmAxwqNHmHQbqe0PRBLOqqYh7LhnFomlZJFjNWMPDKKuzsWzdHmbkplBrc1BS28KmsgaqmryGPimxEZTVtxBuNPDoRzuYkZtCmIGA3LO2BovJGDD77u6LR/Hs/3nL97RPE20HtGqVEprIdpaYdlS8FYqeRJ8Q5IoGe0Ad7XPr9nDtj4bQaHcGbWLddF42L3y5D7PRwGlxEXgkPPN/RQE7+0YD3HReNsu/2Kcf236wgVv88rp3zhhJQ4tTL/F6YvVuls2bQHOrm4c+3K6L1N0Xj+Ked7dQXN1CXnos100eSkb/KIqqmnjtO2/DSoTJwLhBcTx/TR5SypAbb1oKJj4ynCW+5pBF0zIDhLO83k7xoSakJODj/MKpWYQZ4L6Zo3l6rdfMvdbmwBJm5C++lIrFZNCj4tcLSoM2zh4IMfvu3ve28tIvzqC51UVaQiQ1ttaQ45jaTu1QYqroq/QJQR4QYwmoo91U1sA//ruXX03NIiHSFLAB9MKX+/R5d7e/tZn8KRlBO/sjBsZw36ptASmIXRWNAWK0eNU25k/KCBDyioZWvS5ZO/bs/xXy2wuGc6C2hawBUeypbCQzKTrkJJG/Xj2eP769hRvPGRq4EWn2pkrSE62sL6nTJ2+s3VGp23bOyE3xTpQeFM+tK4NHKP1z/pnknhbLsAFR7K9tYXt54CBW7Y3I7fGKdUOLUxdog4A4a1hIsfVIybnDBwAgqtTUDoXiSPQJQR6cGMmZQxKCzNQffH8bN52XTWVja0BeVUtTxFvNDEmM1EVEe0wbfAroH8Uf/WhnwHP6NyNo50WYjPpG2azxqURbjERbTAHjo+6/dDQS2U6DhpvyejvPf1HEbdNHYDQIrOFGLGFGwsMMbD/YGDSX7tuiahZOy9brp5etKwpoJdbu7XR7MJuNjBkUT7TFpEf7/usfPjCGhz/czt0zRvHsusOpiIcvz2VwfORRxTZUfvjBy3IwCPQ2aIWiL9MnBBmgqtERIFaLpmWR2T+KX7/2A4umZYUcEZQ/JYPwMEPABtZLXxUzpF8kK/LPwul2kxAZjkGgC7SGZpyjfX/z+dn6QFHNE2L+pAzdewIOz6R7/pq8AHFLjrVwRV4qDpebRdMySbCauXfVNv74PyM4d3ASH2+vYMfBhqCxS5rPxS0hhopqVRna+gbEHFk4F88cTaOtlYdm5WIxGVh65ThsDu80D616ob3NOA3/euHtBxvYVdHIox/t1EcwKcOfk4dmKnQ8xkL+hkSJiYkIof7NOos+Icj7qpv5w5ubiLeamTU+1VdG5qbR107d7HDz1JrCoOvS4q2U1tqC5ultLmvgNys2sGTOWManJQDBYvSny3KoarTrrb//+O8+zGGCu2aM0je+2mvzLa+38/TV47lv1VYcLqk3hfjnuc1hgl0VjQyKj+Dm1zdw3eSMkPdyy9DRtja0+UjCmZI/kdU7KnF7YMknu/Rc8vt+m3D+dGQzzmDwTrNuG4G33dw7Ev5z87Q3BCXkx0Z1dTXX/PVTHM0NuFzuY7rW0dygpk93EX1CkCsa7LofhP+4pGXzJnDr9GGkxlt56upxlNbaePHLw80OVU2tjDotNiBl4d+dponI4MRIRiZH8+K1Z2BzHN7Auun1jUFrqbU5gj7Wt/15X7WNO9/Zwp0zRpIWH8EvX14fEOE+/ukufbLzyOSYoNI4/3uZjKHd6jJ9PhEGASOTo0MKp83hDhibpD1/2004/2s6shnX3mSP9u7rj2a12TYSV9H1sRMeFQuAq77mmK9V06e7hj7hZTEgxvuRv62B/O7KJp5YvZtfv/oDt7yxESnh+ikZpCdGsGhaFlkDojg7I5H3F07m2Z+ND3AjA22jzq578c5d9jU3/Ot7dlY00j8qPKSngpaTBgKc2LTHF07N4s3vS/WNQac7dIRbWNlErc2hG663d6+/fxHsE6F5Qjy1ppClqws52BDa/ay9CRwnugl3Ivdta7WpvTHuq24+oTUpFD2BPiHIgxMjyU6KDhC2WeNTedTnswCHRwpV2xw8evkYLhw9kKnDBhAWZiCjfxTZA6JZ/kVRUMWF1WwMKRBuD0GGN0vmjGVMapx+XOuUe+an43l0dm5IwW9xukOKl8vjYcmcsYxKjmXJnLH6fL78KRk8dkUuC87NZEVBCb+YNFQ3P3r48hzypwQ+x5GEMC3eysOX5wa9hhPteAtlBtTR+x4pulYoejt9ImVhMAhGJMcEfHQ/0kghD5LB/QI/OrfXQeZwh54tV9loxxwmAsrTzGEiZONDWryVreUNASVx4BvhZDQE2Xg+eFkO49PiSEuIDLpf/yhvid/BBjsX5STrIjcoPpKa5lYsPm8K7f6aELbNy6bFW/l4ewVLPtmpt07npSdwdkbiCacGTqT5w99q0//3pErnFKcCfUKQAYb0iwxwdmvr6wCHKyNC/c/dnojsq24OeR+r2ci1L3wXdFzbEGuba81JiQ0S/IVTs7jr3a0A5E/JYNygONITI4PEK1Tutu0bivb4eI8kJyU24DUAQXnZhy/PZcknO3XT/LbrP1GOt/njSK3VCkVvp88IssEgmJAex2Ozx2B3uUlPtDIowaqPstdK4bIGRLX7P3dI4TuOyLm9DTFN8HdVNLK5rCEgtbB0dSGv5Z95wmIY6jUUVTUFpV1uXbkpoDTuaOs/WSifCsWpTJ8RZI9HsvVAo16TazF5B3r+/ifDGBhrITEynAEx4XoaoKP4C0RNcysmowGbw43ZGNpc50gfrTWxBPjNig0n7WN5e3lZY5sdhp6SGlCt1YpTlT6xqQehd+f/9OEOqpud3Pz6RvpHhzO4X9RxRVoGg2BwYiSVjQ7mLvuaq577hrnLvuLXU7P06SDH8tH6RDa9jof2qh7y0hNO2hoUJwcpJYcOHVKTpnsofSZCbi8K1Db3TvSjeCjBv+PtLazIn0iL092hj9b+G2vDBkTz4aLJHGwIzPUWVTV1ekOEf9ol3mrmirxUspOiSYm1BK1BpQZ6N/4NIZaEgd29HEUb+owgt7c7r81aO9GP4u0JfovTzcSMo3cyHa3hoSsbIrS0y8hFk/m+pC5gpFVnNl2oDruegdYQouh59JmURag0wMKpWazaVNYpH8WP1Ozg8UiKqpr4as8hiqqa8GgOR34creGhqxsiDAaBR6KLcWc/h/aGctHSz7nquW+4aOnnfLj1YMjfhULRV+kzEbL/5ltFgx2r2YjT7WH66IGdEqm1V22RFm/tUGR7tHbiE2k3bkt7keqxPMexRrvtvaF01L9CoegL9BlBhq7dnT9SnXJHhOhoDQ+d1RBxpNRHR5/jeNInnfmGolCcqvSZlMXJQBP8iRn9yOgfddSo05+jVVZ0VuXFkVIfHX2O40mfdJUvhkJxKtGnIuTuoKNR59EaHjqrIeJokWpHnuN4ol3VYadQHB0lyF3MsQjR0VIqnZFyOdobREee43jSJ6rD7tRDq2mWUiKEUGb1nYAS5C6mpwlRZ0Sqx3sP1WF3auFobmD+0neJ7HeaMqvvJLpNkIUQCcAKYDCwD5gjpawNcd7fgRlApZRy9MlcY2fRk4SoM94getqbjKJj+I9e6ixMEVHKrL4T6c5NvT8Aq6WUWcBq38+heAGYfrIW1RcItfnYHfdQnFyqq6v55VP/wel0dvdSFO3QnYI8E3jR9/2LwKWhTpJSrgOOfcaMQqEIwmxVm6g9me4U5AFSynIA33+TunEtCoVC0e10aQ5ZCPEpEMrB5I9d9Hz5QD5AWlpaVzyFog+j/r4UXU2XCrKU8rz2HhNCVAghkqWU5UKIZKCyE55vGbAMIC8vT5kkKDqV3vr3pW3mKcvNnk93lr29C/wceMj333e6cS29AuWWpjge/C03XS53lz6XJv6qJvn46M4c8kPA+UKI3cD5vp8RQpwmhHhfO0kI8SrwFTBMCFEqhJjfLavtZpRbmuJECI+KxRwZ0+XPU11dzZWPvqmi8eOk2yJkKWU1MC3E8QPARX4/X3Uy19VTUW5pit6COTK6u5fQa1HmQr2EjpoUKRSK3otqne4ldJb9ZltUXlrRGXRFF2BfRAlyL6Er3NK6ciyUom/haG7g1//6Dk+rDYM5oruX02tRgtxL6Ar/CJWXPnXRnNgAampOTqOrOTIGT1gYTodDj5ZVtcWxoQS5F9HZJkVqisepS3V1NZff9zKW+CQ8rbaTOmHa2dLEr//1nXKAOw7Upl4fRk3xOLXw9ycGr2+FOTLmpJS7tcUcGaNPt267LkX7KEHuQXRkOnVn0lljoRQ9g55WA6xt9O3atYu5j6xk165dSpiPgkpZ9BC6Y4NN+RqfevSkGmD/jT63263SGB1ARcg9hOMZHNoZKF9jRVfinzLxT2MoQqMEuYegGj8UnYEyEurdqJRFD6GrGj8UfQunrVFPE3S1kdDxoAajHhkVIfcQ1Aab4njRRE6LirursqIjaINRf/rkR1zz10+prq5WVRh+qAi5h6A22BTHy8m01+wM2g5Gra6uZu4jK/nr/HNITEzs01GzEuQeRE+aTq3oOYTyGG7biadtlrnqe8/4SX//CyFotwrD/7yEhAS987A94e7NnsxKkBWKTuJowuH/+JHEQjtP+whfU1PDjcv/f3vnH1tVecbxz1fuaMeQSSEgk7lhAlHmEBUI29jUTDdlWxQ1kcUNCQ4lwz+2hCUYNCPR/eGWZZkDt6HpVpZM+GMQmXNxqBGyiAjMUsDxozjMOojMwnAM29L22R/nvd1pd2/vvW3vPeeW55OcnLfPfc+53/c573l6z/vrvMpT999IXV1dj23pmj8kMhNvqIgPi+vs7GJ03RgymUxP2bPtzKdOneK7G98E4Cf3XNuTXv/tmxk3blxP8AV6OjSX129jw/fuHNTwuvg/vWx7d/Y7+l6/Yq9tITwgO84QkZ2YAfD4ghk8srkJgLVLbugJHMvrt/Wy5TvP8vptdJw7y0Uja+nuaKOrq5MH171Md0dbL1uWjv+8T3f7Oc5/cDZn+qLOztTYen1eMwqgx9bWfo4H1x2ju6ONtrNnGFU3ke6ONmrHTiCTyXD69OlefmptbWXpmud5+qGvAvTy22BHmrS2trL4Rxup+eh4MpkMa5fcANDzffHrF7+2g/lHoOHakC7pn8A7Sevoh/HAe0mLKJHhrvk9M7u1mIxDXL/S7FfXNjByaStYv4ZtQE47knab2aykdZSCay4Padbo2gbGQLX5sDfHcZyU4AHZcRwnJXhATo51SQsYAK65PKRZo2sbGAPS5m3IjuM4KcF/ITuO46QED8hlQtIxSfskNUraHWx1krZKOhL2Y2P5H5bULOmQpC9XSGO9pJOS9sdsJWuUdH0oa7OkJ1XG6VF5NK+W9I/g60ZJ89OkOUcZ8vq4T77/K2sZNd0afNQsaWWOzxX81CypSdJ15dZUgrYrJe2Q1C5pRYp03Rt81STpNUnXFDxpdkaMb0O7AceA8X1sPwRWhvRK4ImQng7sBWqAKcBRYEQFNH4BuA7YPxiNwBvAZwABfwRuq7Dm1cCKHHlToTmHrpw+LqasZdIzIvjmCmBk8Nn0PnnmBz8JmAvsrJCvitE2AZgN/CBXPUhQ12eBsSF9WzE+81/IleV2oCGkG4A7YvYNZtZuZn8DmoE55RZjZtuBvosflKRR0iRgjJntsKjmrY8dUynN+UiF5jy6cvm4FyWWdTDMAZrN7G0z6wA2BI1xbgfWW8TrwCXBj4lrM7OTZrYLOF8BPaXoes3MslMLXwcmFzqpB+TyYcCfJO2R9ECwTTSzEwBhPyHYLwP+Hju2JdiSoFSNl4V0X3uleSg8GtbHmgDSqjmfj5OimPqXVB1N070Rp1Rd9xM9YfSLr2VRPj5nZsclTQC2SjrYT95c7ZdpG/6ST2MatP8ceCx872PAj4ElJKhZ0ktArhV/Vg3l9wwRxfgjqeuchvqVi6J1SbqJKCDPK3RSD8hlwsyOh/1JSZuJHnHelTTJzE6Ex72TIXsL8PHY4ZOB4xUV/D9K1dhC70exims3s3ezaUlPA8+HPxPTbGY35/tMUj4fJ0Ux9S+pOpqmeyNOUbokzQCeIeqjKLjakTdZlAFJH5F0cTYNfAnYD2wB7gvZ7gOeC+ktwEJJNZKmAFOJOp2SoCSN4ZH735LmhpEKi2LHVIQ+bZkLiHydZs35fJwUu4CpkqZIGgksJNIYZwuwKIy2mAucyTa7pEBbEhTUJelyYBPwTTM7XNRZK9EjeaFtRD2ve8N2AFgV7OOAl4EjYV8XO2YVUa/tISrU4w88C5wg6gxpIXqsKlkjMIsoCB4F1hAmHFVQ82+AfUBTuCkmpUlzjjLk9DHwMeCF/spaRk3zgcPBH9n6ugxYFtIC1obP9wGzKuivQtouDf55H/hXSI9Jga5ngNNAY9h2Fzqnz9RzHMdJCd5k4TiOkxI8IDuO46QED8iO4zgpwQOy4zhOSvCA7DiOkxI8IA8DJF0qaYOko5LekvSCpGmVWCXMcXIhaZmkRUnrqDZ8pl6VEyY2bAYazGxhsM0EJiapy7mwMbNfJK2hGvFfyNXPTcD5+A1gZo3EFj6RVCvpV2H93zfD3HokfUrSG2EN4SZJU4P9GzH7LyWNqHCZnBQi6VFJB8Mazs9KWiFpqaRdkvZK+p2kUSHv6uzaxJJelfREqFOHJX0+2ZKkFw/I1c/VwJ4CeZYDmNmnga8DDZJqiWYV/dTMZhLNXGuRdBVwD9HiSDOBLuDe8kh3qgVJs4C7gGuBO4nqC8AmM5ttZtcAfyWaOZmLjJnNAb4DfL/McqsWb7K4MJgH/AzAzA5KegeYBuwAVkmaTHRjHZH0ReB6YFfUGsKHSX7xGyd55gHPmdkHAJJ+H+xXS3ocuAQYDbyY5/hNYb8H+GT5ZFY3HpCrnwPA3QXy5Hw9kZn9VtJO4CvAi5K+FfI2mNnDQyvTqXLyveLq18AdZrZX0mLgxjz52sO+C487efEmi+rnFaBG0tKsQdJs4BOxPNsJzQ6SpgGXA4ckXQG8bWZPEi3KM4NosZu7wzrO2fe/xc/lXJj8Gfha6I8YTfRPHOBi4ISkD+FNW4PGA3KVY9HqUAuAW8KwtwNE75iLr836FDBC0j5gI7DYzNqJ2or3S2oEriR6Rc9bwCNEbztpArYClXhVj5NiLHpF0haiFQw3AbuBM8CjwE6ietLfSxicIvDV3hzHKQpJo83sbBhJsR14wMz+krSu4YS35TiOUyzrJE0Haon6GTwYDzH+C9lxHCcleBuy4zhOSvCA7DiOkxI8IDuO46QED8iO4zgpwQOy4zhOSvCA7DiOkxL+C6/mtJk/aDdqAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.scatter(glaxo_df.gain,glaxo_df.index,)",
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 92,
          "data": {
            "text/plain": "<matplotlib.collections.PathCollection at 0x1ad4df39940>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "sns.displot(glaxo_df['gain'])",
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 93,
          "data": {
            "text/plain": "<seaborn.axisgrid.FacetGrid at 0x1ad4df638e0>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 360x360 with 1 Axes>",
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "sns.displot(beml_df['gain'])",
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 94,
          "data": {
            "text/plain": "<seaborn.axisgrid.FacetGrid at 0x1ad4df825e0>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 360x360 with 1 Axes>",
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "from scipy import stats\nstats.norm.cdf(-0.02,loc=glaxo_df.gain.mean(), scale=glaxo_df.gain.std())",
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 102,
          "data": {
            "text/plain": "0.06352488667177397"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "1-stats.norm.cdf(0.02,loc=beml_df.gain.mean(), scale=beml_df.gain.std())",
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 100,
          "data": {
            "text/plain": "0.22769829484075343"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "stats.t.ppf(0.95, df=139)",
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 116,
          "data": {
            "text/plain": "1.6558898677725957"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "glaxo_df.count",
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 111,
          "data": {
            "text/plain": "<bound method DataFrame.count of                   Date    Close      gain\nDate                                     \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\n2010-01-11  2010-01-11  1629.45 -0.006312\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[1738 rows x 3 columns]>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "glaxo_df\n",
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 108,
          "data": {
            "text/plain": "                  Date    Close      gain\nDate                                     \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\n2010-01-11  2010-01-11  1629.45 -0.006312\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[1738 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>2010-01-05</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>2010-01-11</th>\n      <td>2010-01-11</td>\n      <td>1629.45</td>\n      <td>-0.006312</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>1738 rows × 3 columns</p>\n</div>"
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      "cell_type": "code",
      "source": "glaxo_df_ci=stats.norm.interval(0.95, loc=glaxo_df.gain.mean(), scale=glaxo_df.gain.std());\nprint('gain at 90% confidence interval is:' , np.round(glaxo_df_ci,5))",
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "text": "gain at 90% confidence interval is: [-0.0258   0.02657]\n",
          "name": "stdout"
        }
      ]
    },
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      "source": "",
      "execution_count": null,
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