covid19_analysis_prediction.ipynb

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  "metadata": {
    "colab": {
      "name": "covid19_analysis",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oWel-KudctR3"
      },
      "source": [
        "# Covid 19 Analysis\n",
        "**Data by** Nexoid\n",
        "\n",
        "**Author** Vishal Sharma\n",
        "\n",
        "**Predicting Covid-19 risk infection**\n",
        "\n",
        "NB: Built from scratch, no other references required"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jpK4tamUnIcI"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from scipy.stats import chi2_contingency\n",
        "import scipy.stats as stats\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SS1Hp4UCPIIP"
      },
      "source": [
        "## Data processing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mXx2Cl7vRrh7",
        "outputId": "afac15b3-8ca6-4aac-de18-0550bbb213a3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MKYp_n9s9LV2",
        "outputId": "a8a2dbce-add7-40db-9744-f5b79cf63bc5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "data = pd.read_csv(\"/content/drive/My Drive/Colab Notebooks/master_dataset.csv\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (8,9,12,13,14,15,36) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  interactivity=interactivity, compiler=compiler, result=result)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m0mCwf7p9YO4",
        "outputId": "7cd353e1-6146-4037-9f52-45f616eb650d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        }
      },
      "source": [
        "pd.set_option('display.max_rows', None)\n",
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>survey_date</th>\n",
              "      <th>region</th>\n",
              "      <th>country</th>\n",
              "      <th>ip_latitude</th>\n",
              "      <th>ip_longitude</th>\n",
              "      <th>ip_accuracy</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>height</th>\n",
              "      <th>weight</th>\n",
              "      <th>bmi</th>\n",
              "      <th>blood_type</th>\n",
              "      <th>insurance</th>\n",
              "      <th>income</th>\n",
              "      <th>race</th>\n",
              "      <th>immigrant</th>\n",
              "      <th>smoking</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>cannabis</th>\n",
              "      <th>amphetamines</th>\n",
              "      <th>cocaine</th>\n",
              "      <th>lsd</th>\n",
              "      <th>mdma</th>\n",
              "      <th>contacts_count</th>\n",
              "      <th>house_count</th>\n",
              "      <th>public_transport_count</th>\n",
              "      <th>working</th>\n",
              "      <th>worried</th>\n",
              "      <th>rate_reducing_risk_single</th>\n",
              "      <th>rate_reducing_risk_single_social_distancing</th>\n",
              "      <th>rate_reducing_risk_single_washing_hands</th>\n",
              "      <th>rate_reducing_risk_house</th>\n",
              "      <th>rate_reducing_risk_house_social_distancing</th>\n",
              "      <th>rate_reducing_risk_house_washing_hands</th>\n",
              "      <th>rate_reducing_risk_single_sanitizer</th>\n",
              "      <th>rate_reducing_mask</th>\n",
              "      <th>rate_reducing_mask_type</th>\n",
              "      <th>rate_government_action</th>\n",
              "      <th>rate_government_control</th>\n",
              "      <th>rate_government_spend</th>\n",
              "      <th>covid19_positive</th>\n",
              "      <th>covid19_symptoms</th>\n",
              "      <th>covid19_contact</th>\n",
              "      <th>asthma</th>\n",
              "      <th>kidney_disease</th>\n",
              "      <th>liver_disease</th>\n",
              "      <th>compromised_immune</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>lung_disease</th>\n",
              "      <th>diabetes</th>\n",
              "      <th>hiv_positive</th>\n",
              "      <th>hypertension</th>\n",
              "      <th>other_chronic</th>\n",
              "      <th>nursing_home</th>\n",
              "      <th>health_worker</th>\n",
              "      <th>prescription_medication</th>\n",
              "      <th>opinion_infection</th>\n",
              "      <th>opinion_mortality</th>\n",
              "      <th>risk_infection</th>\n",
              "      <th>risk_mortality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-03-25</th>\n",
              "      <td>NaN</td>\n",
              "      <td>CA</td>\n",
              "      <td>43.7597</td>\n",
              "      <td>-79.2985</td>\n",
              "      <td>100.0</td>\n",
              "      <td>male</td>\n",
              "      <td>20_30</td>\n",
              "      <td>178</td>\n",
              "      <td>88</td>\n",
              "      <td>27.7</td>\n",
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              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-26</th>\n",
              "      <td>NaN</td>\n",
              "      <td>CA</td>\n",
              "      <td>51.1443</td>\n",
              "      <td>-114.0953</td>\n",
              "      <td>5.0</td>\n",
              "      <td>female</td>\n",
              "      <td>30_40</td>\n",
              "      <td>158</td>\n",
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              "      <td>21.6</td>\n",
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              "      <td>0.050</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-25</th>\n",
              "      <td>NaN</td>\n",
              "      <td>CA</td>\n",
              "      <td>43.2322</td>\n",
              "      <td>-79.8854</td>\n",
              "      <td>100.0</td>\n",
              "      <td>male</td>\n",
              "      <td>90_100</td>\n",
              "      <td>184</td>\n",
              "      <td>94</td>\n",
              "      <td>27.7</td>\n",
              "      <td>an</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>12.0</td>\n",
              "      <td>14.701</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-25</th>\n",
              "      <td>NaN</td>\n",
              "      <td>CA</td>\n",
              "      <td>45.6766</td>\n",
              "      <td>-73.8329</td>\n",
              "      <td>5.0</td>\n",
              "      <td>male</td>\n",
              "      <td>60_70</td>\n",
              "      <td>172</td>\n",
              "      <td>96</td>\n",
              "      <td>32.4</td>\n",
              "      <td>unknown</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>6.0</td>\n",
              "      <td>2.250</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-26</th>\n",
              "      <td>NaN</td>\n",
              "      <td>CA</td>\n",
              "      <td>49.1839</td>\n",
              "      <td>-123.1285</td>\n",
              "      <td>1.0</td>\n",
              "      <td>male</td>\n",
              "      <td>30_40</td>\n",
              "      <td>166</td>\n",
              "      <td>70</td>\n",
              "      <td>25.4</td>\n",
              "      <td>abp</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>7.0</td>\n",
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              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           survey_date region  ...  risk_infection  risk_mortality\n",
              "2020-03-25         NaN     CA  ...           0.050             NaN\n",
              "2020-03-26         NaN     CA  ...           0.050             NaN\n",
              "2020-03-25         NaN     CA  ...          14.701             NaN\n",
              "2020-03-25         NaN     CA  ...           2.250             NaN\n",
              "2020-03-26         NaN     CA  ...           0.050             NaN\n",
              "\n",
              "[5 rows x 60 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oRUPMG_CX4qw"
      },
      "source": [
        "data = data.shift(1, axis = 1) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hD5UJ_opYHdr"
      },
      "source": [
        "data['survey_date'] = data.index"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZbHZWfeHOFw-"
      },
      "source": [
        "data['ID'] = range(1, len(data) + 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6H5n41B9VAn4",
        "outputId": "2389b6e1-0c5c-4b24-974b-a3373af3189c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        }
      },
      "source": [
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>survey_date</th>\n",
              "      <th>region</th>\n",
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              "      <th>race</th>\n",
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              "      <th>alcohol</th>\n",
              "      <th>cannabis</th>\n",
              "      <th>amphetamines</th>\n",
              "      <th>cocaine</th>\n",
              "      <th>lsd</th>\n",
              "      <th>mdma</th>\n",
              "      <th>contacts_count</th>\n",
              "      <th>house_count</th>\n",
              "      <th>public_transport_count</th>\n",
              "      <th>working</th>\n",
              "      <th>worried</th>\n",
              "      <th>rate_reducing_risk_single</th>\n",
              "      <th>rate_reducing_risk_single_social_distancing</th>\n",
              "      <th>rate_reducing_risk_single_washing_hands</th>\n",
              "      <th>rate_reducing_risk_house</th>\n",
              "      <th>rate_reducing_risk_house_social_distancing</th>\n",
              "      <th>rate_reducing_risk_house_washing_hands</th>\n",
              "      <th>rate_reducing_risk_single_sanitizer</th>\n",
              "      <th>rate_reducing_mask</th>\n",
              "      <th>rate_reducing_mask_type</th>\n",
              "      <th>rate_government_action</th>\n",
              "      <th>rate_government_control</th>\n",
              "      <th>rate_government_spend</th>\n",
              "      <th>covid19_positive</th>\n",
              "      <th>covid19_symptoms</th>\n",
              "      <th>covid19_contact</th>\n",
              "      <th>asthma</th>\n",
              "      <th>kidney_disease</th>\n",
              "      <th>liver_disease</th>\n",
              "      <th>compromised_immune</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>lung_disease</th>\n",
              "      <th>diabetes</th>\n",
              "      <th>hiv_positive</th>\n",
              "      <th>hypertension</th>\n",
              "      <th>other_chronic</th>\n",
              "      <th>nursing_home</th>\n",
              "      <th>health_worker</th>\n",
              "      <th>prescription_medication</th>\n",
              "      <th>opinion_infection</th>\n",
              "      <th>opinion_mortality</th>\n",
              "      <th>risk_infection</th>\n",
              "      <th>risk_mortality</th>\n",
              "      <th>ID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2020-03-25</th>\n",
              "      <td>2020-03-25</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CA</td>\n",
              "      <td>43.7597</td>\n",
              "      <td>-79.2985</td>\n",
              "      <td>100.0</td>\n",
              "      <td>male</td>\n",
              "      <td>20_30</td>\n",
              "      <td>178</td>\n",
              "      <td>88</td>\n",
              "      <td>27.7</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2020-03-26</th>\n",
              "      <td>2020-03-26</td>\n",
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              "      <td>51.1443</td>\n",
              "      <td>-114.0953</td>\n",
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              "      <td>female</td>\n",
              "      <td>30_40</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2020-03-25</th>\n",
              "      <td>2020-03-25</td>\n",
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              "      <td>-79.8854</td>\n",
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              "      <td>male</td>\n",
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              "      <td>94</td>\n",
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              "    <tr>\n",
              "      <th>2020-03-25</th>\n",
              "      <td>2020-03-25</td>\n",
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              "      <td>45.6766</td>\n",
              "      <td>-73.8329</td>\n",
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              "      <td>male</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2.250</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-26</th>\n",
              "      <td>2020-03-26</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CA</td>\n",
              "      <td>49.1839</td>\n",
              "      <td>-123.1285</td>\n",
              "      <td>1.0</td>\n",
              "      <td>male</td>\n",
              "      <td>30_40</td>\n",
              "      <td>166</td>\n",
              "      <td>70</td>\n",
              "      <td>25.4</td>\n",
              "      <td>abp</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           survey_date region country  ...  risk_infection  risk_mortality  ID\n",
              "2020-03-25  2020-03-25    NaN      CA  ...             5.0           0.050   1\n",
              "2020-03-26  2020-03-26    NaN      CA  ...             5.0           0.050   2\n",
              "2020-03-25  2020-03-25    NaN      CA  ...            12.0          14.701   3\n",
              "2020-03-25  2020-03-25    NaN      CA  ...             6.0           2.250   4\n",
              "2020-03-26  2020-03-26    NaN      CA  ...             5.0           0.050   5\n",
              "\n",
              "[5 rows x 61 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dA5tf4bwVGNs"
      },
      "source": [
        "data.index = data['ID']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hUNLT0POVNkC",
        "outputId": "f21731ab-b610-4eca-8140-6ea40286ae63",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        }
      },
      "source": [
        "data.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>-73.8128</td>\n",
              "      <td>1.0</td>\n",
              "      <td>female</td>\n",
              "      <td>20_30</td>\n",
              "      <td>172</td>\n",
              "      <td>74</td>\n",
              "      <td>25.0</td>\n",
              "      <td>op</td>\n",
              "      <td>yes</td>\n",
              "      <td>med</td>\n",
              "      <td>asian</td>\n",
              "      <td>immigrant</td>\n",
              "      <td>never</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>never</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>5.0</td>\n",
              "      <td>clothstore</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>35.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.050</td>\n",
              "      <td>912764</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>912765</th>\n",
              "      <td>2020-09-25</td>\n",
              "      <td>NaN</td>\n",
              "      <td>US</td>\n",
              "      <td>43.5736</td>\n",
              "      <td>-70.4031</td>\n",
              "      <td>10.0</td>\n",
              "      <td>female</td>\n",
              "      <td>50_60</td>\n",
              "      <td>172</td>\n",
              "      <td>76</td>\n",
              "      <td>25.6</td>\n",
              "      <td>unknown</td>\n",
              "      <td>yes</td>\n",
              "      <td>med</td>\n",
              "      <td>white</td>\n",
              "      <td>native</td>\n",
              "      <td>yesmedium</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>never</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-2</td>\n",
              "      <td>5.0</td>\n",
              "      <td>clothhome</td>\n",
              "      <td>-2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.267</td>\n",
              "      <td>912765</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>912766</th>\n",
              "      <td>2020-09-24</td>\n",
              "      <td>AF</td>\n",
              "      <td>EG</td>\n",
              "      <td>31.2977</td>\n",
              "      <td>32.2361</td>\n",
              "      <td>100.0</td>\n",
              "      <td>male</td>\n",
              "      <td>20_30</td>\n",
              "      <td>184</td>\n",
              "      <td>80</td>\n",
              "      <td>23.6</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>low</td>\n",
              "      <td>white</td>\n",
              "      <td>native</td>\n",
              "      <td>yesmedium</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>21.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>stopped</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>surgical</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>CLOZAPINE;PAROXETINE HYDROCHLORIDE;PROPRANOLOL...</td>\n",
              "      <td>45.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0.072</td>\n",
              "      <td>912766</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>912767</th>\n",
              "      <td>2020-09-25</td>\n",
              "      <td>EU</td>\n",
              "      <td>GB</td>\n",
              "      <td>51.4449</td>\n",
              "      <td>-0.1024</td>\n",
              "      <td>500.0</td>\n",
              "      <td>male</td>\n",
              "      <td>60_70</td>\n",
              "      <td>184</td>\n",
              "      <td>86</td>\n",
              "      <td>25.4</td>\n",
              "      <td>ap</td>\n",
              "      <td>yes</td>\n",
              "      <td>med</td>\n",
              "      <td>white</td>\n",
              "      <td>native</td>\n",
              "      <td>quit10</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>travel critical</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>surgical</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13.0</td>\n",
              "      <td>1.322</td>\n",
              "      <td>912767</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>912768</th>\n",
              "      <td>2020-09-25</td>\n",
              "      <td>EU</td>\n",
              "      <td>GB</td>\n",
              "      <td>51.5641</td>\n",
              "      <td>-0.1073</td>\n",
              "      <td>200.0</td>\n",
              "      <td>male</td>\n",
              "      <td>70_80</td>\n",
              "      <td>176</td>\n",
              "      <td>56</td>\n",
              "      <td>18.0</td>\n",
              "      <td>unknown</td>\n",
              "      <td>no</td>\n",
              "      <td>high</td>\n",
              "      <td>white</td>\n",
              "      <td>native</td>\n",
              "      <td>yeslight</td>\n",
              "      <td>7.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>home</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>-2</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>surgical</td>\n",
              "      <td>0</td>\n",
              "      <td>-2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>2.399</td>\n",
              "      <td>912768</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       survey_date region country  ...  risk_infection  risk_mortality      ID\n",
              "ID                                 ...                                        \n",
              "912764  2020-09-24    NaN      US  ...             5.0           0.050  912764\n",
              "912765  2020-09-25    NaN      US  ...             5.0           0.267  912765\n",
              "912766  2020-09-24     AF      EG  ...            62.0           0.072  912766\n",
              "912767  2020-09-25     EU      GB  ...            13.0           1.322  912767\n",
              "912768  2020-09-25     EU      GB  ...            13.0           2.399  912768\n",
              "\n",
              "[5 rows x 61 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EnxrL8G_VRIX"
      },
      "source": [
        "data = data.drop(['ID'], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o8zlBTU-wYk9",
        "outputId": "4571567a-eae6-497c-886f-e047bd67344d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(912768, 60)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0hfuDNnZwjxs",
        "outputId": "6a085aed-e691-4d52-89dd-a43e635c5c3f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "data.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['survey_date', 'region', 'country', 'ip_latitude', 'ip_longitude',\n",
              "       'ip_accuracy', 'sex', 'age', 'height', 'weight', 'bmi', 'blood_type',\n",
              "       'insurance', 'income', 'race', 'immigrant', 'smoking', 'alcohol',\n",
              "       'cannabis', 'amphetamines', 'cocaine', 'lsd', 'mdma', 'contacts_count',\n",
              "       'house_count', 'public_transport_count', 'working', 'worried',\n",
              "       'rate_reducing_risk_single',\n",
              "       'rate_reducing_risk_single_social_distancing',\n",
              "       'rate_reducing_risk_single_washing_hands', 'rate_reducing_risk_house',\n",
              "       'rate_reducing_risk_house_social_distancing',\n",
              "       'rate_reducing_risk_house_washing_hands',\n",
              "       'rate_reducing_risk_single_sanitizer', 'rate_reducing_mask',\n",
              "       'rate_reducing_mask_type', 'rate_government_action',\n",
              "       'rate_government_control', 'rate_government_spend', 'covid19_positive',\n",
              "       'covid19_symptoms', 'covid19_contact', 'asthma', 'kidney_disease',\n",
              "       'liver_disease', 'compromised_immune', 'heart_disease', 'lung_disease',\n",
              "       'diabetes', 'hiv_positive', 'hypertension', 'other_chronic',\n",
              "       'nursing_home', 'health_worker', 'prescription_medication',\n",
              "       'opinion_infection', 'opinion_mortality', 'risk_infection',\n",
              "       'risk_mortality'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xe1xXjsewp2g",
        "outputId": "67b9964e-e1a0-491f-f3bb-e60888f573f5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "data.nunique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "survey_date                                       186\n",
              "region                                              5\n",
              "country                                           194\n",
              "ip_latitude                                    284435\n",
              "ip_longitude                                   412436\n",
              "ip_accuracy                                         9\n",
              "sex                                                 4\n",
              "age                                                11\n",
              "height                                            124\n",
              "weight                                            141\n",
              "bmi                                               735\n",
              "blood_type                                         10\n",
              "insurance                                           3\n",
              "income                                              5\n",
              "race                                                7\n",
              "immigrant                                           3\n",
              "smoking                                             8\n",
              "alcohol                                            16\n",
              "cannabis                                           31\n",
              "amphetamines                                       31\n",
              "cocaine                                            31\n",
              "lsd                                                31\n",
              "mdma                                               31\n",
              "contacts_count                                     22\n",
              "house_count                                        12\n",
              "public_transport_count                             16\n",
              "working                                             5\n",
              "worried                                             5\n",
              "rate_reducing_risk_single                           5\n",
              "rate_reducing_risk_single_social_distancing         5\n",
              "rate_reducing_risk_single_washing_hands             5\n",
              "rate_reducing_risk_house                            5\n",
              "rate_reducing_risk_house_social_distancing          5\n",
              "rate_reducing_risk_house_washing_hands              5\n",
              "rate_reducing_risk_single_sanitizer                 5\n",
              "rate_reducing_mask                                  5\n",
              "rate_reducing_mask_type                             7\n",
              "rate_government_action                              5\n",
              "rate_government_control                             5\n",
              "rate_government_spend                               5\n",
              "covid19_positive                                    2\n",
              "covid19_symptoms                                    2\n",
              "covid19_contact                                     2\n",
              "asthma                                              2\n",
              "kidney_disease                                      2\n",
              "liver_disease                                       2\n",
              "compromised_immune                                  2\n",
              "heart_disease                                       2\n",
              "lung_disease                                        2\n",
              "diabetes                                            2\n",
              "hiv_positive                                        2\n",
              "hypertension                                        2\n",
              "other_chronic                                       2\n",
              "nursing_home                                        2\n",
              "health_worker                                       2\n",
              "prescription_medication                         75444\n",
              "opinion_infection                                  10\n",
              "opinion_mortality                                  10\n",
              "risk_infection                                     86\n",
              "risk_mortality                                   4091\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b-N2t6S1wyMm",
        "outputId": "e3537125-dc5d-415a-987c-f8313311d3d9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "data.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "survey_date                                         0\n",
              "region                                         758423\n",
              "country                                           102\n",
              "ip_latitude                                         0\n",
              "ip_longitude                                        0\n",
              "ip_accuracy                                        12\n",
              "sex                                                 0\n",
              "age                                                 5\n",
              "height                                              0\n",
              "weight                                              0\n",
              "bmi                                                 4\n",
              "blood_type                                          0\n",
              "insurance                                      688743\n",
              "income                                         688743\n",
              "race                                           688743\n",
              "immigrant                                      688743\n",
              "smoking                                          1857\n",
              "alcohol                                          1897\n",
              "cannabis                                       185040\n",
              "amphetamines                                   204172\n",
              "cocaine                                        206311\n",
              "lsd                                            201944\n",
              "mdma                                           197553\n",
              "contacts_count                                   4247\n",
              "house_count                                        22\n",
              "public_transport_count                         688743\n",
              "working                                          4236\n",
              "worried                                        688743\n",
              "rate_reducing_risk_single                           0\n",
              "rate_reducing_risk_single_social_distancing         0\n",
              "rate_reducing_risk_single_washing_hands             0\n",
              "rate_reducing_risk_house                            0\n",
              "rate_reducing_risk_house_social_distancing          0\n",
              "rate_reducing_risk_house_washing_hands              0\n",
              "rate_reducing_risk_single_sanitizer                 0\n",
              "rate_reducing_mask                               1857\n",
              "rate_reducing_mask_type                        715283\n",
              "rate_government_action                              0\n",
              "rate_government_control                             0\n",
              "rate_government_spend                               0\n",
              "covid19_positive                                    0\n",
              "covid19_symptoms                                    0\n",
              "covid19_contact                                     0\n",
              "asthma                                              0\n",
              "kidney_disease                                      0\n",
              "liver_disease                                       0\n",
              "compromised_immune                                  0\n",
              "heart_disease                                       0\n",
              "lung_disease                                        0\n",
              "diabetes                                            0\n",
              "hiv_positive                                        0\n",
              "hypertension                                        0\n",
              "other_chronic                                       0\n",
              "nursing_home                                        0\n",
              "health_worker                                       0\n",
              "prescription_medication                        640503\n",
              "opinion_infection                              165254\n",
              "opinion_mortality                              165254\n",
              "risk_infection                                     14\n",
              "risk_mortality                                     14\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JYU1l1-N8p5f",
        "outputId": "e418f9e1-6571-4073-8097-e6052b9da522",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "data['covid19_positive'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    909414\n",
              "1      3354\n",
              "Name: covid19_positive, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8DqMS94oBbWL",
        "outputId": "72cfcbd8-3170-4745-eb60-af9e3d56df7c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "data.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "survey_date                                     object\n",
              "region                                          object\n",
              "country                                         object\n",
              "ip_latitude                                    float64\n",
              "ip_longitude                                   float64\n",
              "ip_accuracy                                    float64\n",
              "sex                                             object\n",
              "age                                             object\n",
              "height                                          object\n",
              "weight                                          object\n",
              "bmi                                            float64\n",
              "blood_type                                      object\n",
              "insurance                                       object\n",
              "income                                          object\n",
              "race                                            object\n",
              "immigrant                                       object\n",
              "smoking                                         object\n",
              "alcohol                                        float64\n",
              "cannabis                                       float64\n",
              "amphetamines                                   float64\n",
              "cocaine                                        float64\n",
              "lsd                                            float64\n",
              "mdma                                           float64\n",
              "contacts_count                                 float64\n",
              "house_count                                    float64\n",
              "public_transport_count                         float64\n",
              "working                                         object\n",
              "worried                                        float64\n",
              "rate_reducing_risk_single                        int64\n",
              "rate_reducing_risk_single_social_distancing      int64\n",
              "rate_reducing_risk_single_washing_hands          int64\n",
              "rate_reducing_risk_house                         int64\n",
              "rate_reducing_risk_house_social_distancing       int64\n",
              "rate_reducing_risk_house_washing_hands           int64\n",
              "rate_reducing_risk_single_sanitizer              int64\n",
              "rate_reducing_mask                             float64\n",
              "rate_reducing_mask_type                         object\n",
              "rate_government_action                           int64\n",
              "rate_government_control                          int64\n",
              "rate_government_spend                            int64\n",
              "covid19_positive                                 int64\n",
              "covid19_symptoms                                 int64\n",
              "covid19_contact                                  int64\n",
              "asthma                                           int64\n",
              "kidney_disease                                   int64\n",
              "liver_disease                                    int64\n",
              "compromised_immune                               int64\n",
              "heart_disease                                    int64\n",
              "lung_disease                                     int64\n",
              "diabetes                                         int64\n",
              "hiv_positive                                     int64\n",
              "hypertension                                     int64\n",
              "other_chronic                                    int64\n",
              "nursing_home                                     int64\n",
              "health_worker                                    int64\n",
              "prescription_medication                         object\n",
              "opinion_infection                              float64\n",
              "opinion_mortality                              float64\n",
              "risk_infection                                 float64\n",
              "risk_mortality                                 float64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f51qqTYZY8Pv",
        "outputId": "34c08aba-5d0d-4cb1-d40c-9761481facb9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        }
      },
      "source": [
        "data.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ip_latitude</th>\n",
              "      <th>ip_longitude</th>\n",
              "      <th>ip_accuracy</th>\n",
              "      <th>bmi</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>cannabis</th>\n",
              "      <th>amphetamines</th>\n",
              "      <th>cocaine</th>\n",
              "      <th>lsd</th>\n",
              "      <th>mdma</th>\n",
              "      <th>contacts_count</th>\n",
              "      <th>house_count</th>\n",
              "      <th>public_transport_count</th>\n",
              "      <th>worried</th>\n",
              "      <th>rate_reducing_risk_single</th>\n",
              "      <th>rate_reducing_risk_single_social_distancing</th>\n",
              "      <th>rate_reducing_risk_single_washing_hands</th>\n",
              "      <th>rate_reducing_risk_house</th>\n",
              "      <th>rate_reducing_risk_house_social_distancing</th>\n",
              "      <th>rate_reducing_risk_house_washing_hands</th>\n",
              "      <th>rate_reducing_risk_single_sanitizer</th>\n",
              "      <th>rate_reducing_mask</th>\n",
              "      <th>rate_government_action</th>\n",
              "      <th>rate_government_control</th>\n",
              "      <th>rate_government_spend</th>\n",
              "      <th>covid19_positive</th>\n",
              "      <th>covid19_symptoms</th>\n",
              "      <th>covid19_contact</th>\n",
              "      <th>asthma</th>\n",
              "      <th>kidney_disease</th>\n",
              "      <th>liver_disease</th>\n",
              "      <th>compromised_immune</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>lung_disease</th>\n",
              "      <th>diabetes</th>\n",
              "      <th>hiv_positive</th>\n",
              "      <th>hypertension</th>\n",
              "      <th>other_chronic</th>\n",
              "      <th>nursing_home</th>\n",
              "      <th>health_worker</th>\n",
              "      <th>opinion_infection</th>\n",
              "      <th>opinion_mortality</th>\n",
              "      <th>risk_infection</th>\n",
              "      <th>risk_mortality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>912768.000000</td>\n",
              "      <td>912768.000000</td>\n",
              "      <td>912756.000000</td>\n",
              "      <td>912764.000000</td>\n",
              "      <td>910871.000000</td>\n",
              "      <td>727728.000000</td>\n",
              "      <td>708596.000000</td>\n",
              "      <td>706457.000000</td>\n",
              "      <td>710824.000000</td>\n",
              "      <td>715215.000000</td>\n",
              "      <td>908521.000000</td>\n",
              "      <td>912746.000000</td>\n",
              "      <td>224025.000000</td>\n",
              "      <td>224025.000000</td>\n",
              "      <td>912768.000000</td>\n",
              "      <td>912768.000000</td>\n",
              "      <td>912768.000000</td>\n",
              "      <td>912768.000000</td>\n",
              "      <td>912768.000000</td>\n",
              "      <td>912768.000000</td>\n",
              "      <td>912768.000000</td>\n",
              "      <td>910911.000000</td>\n",
              "      <td>912768.000000</td>\n",
              "      <td>912768.000000</td>\n",
              "      <td>912768.000000</td>\n",
              "      <td>912768.000000</td>\n",
              "      <td>912768.000000</td>\n",
              "      <td>912768.000000</td>\n",
              "      <td>912768.000000</td>\n",
              "      <td>912768.000000</td>\n",
              "      <td>912768.000000</td>\n",
              "      <td>912768.000000</td>\n",
              "      <td>912768.000000</td>\n",
              "      <td>912768.000000</td>\n",
              "      <td>912768.000000</td>\n",
              "      <td>912768.000000</td>\n",
              "      <td>912768.000000</td>\n",
              "      <td>912768.000000</td>\n",
              "      <td>912768.000000</td>\n",
              "      <td>912768.000000</td>\n",
              "      <td>747514.000000</td>\n",
              "      <td>747514.000000</td>\n",
              "      <td>912754.000000</td>\n",
              "      <td>912754.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>35.166173</td>\n",
              "      <td>-75.376272</td>\n",
              "      <td>93.715393</td>\n",
              "      <td>29.443102</td>\n",
              "      <td>2.832133</td>\n",
              "      <td>2.583978</td>\n",
              "      <td>-0.718110</td>\n",
              "      <td>-0.785364</td>\n",
              "      <td>-0.850327</td>\n",
              "      <td>-0.848506</td>\n",
              "      <td>7.384985</td>\n",
              "      <td>3.124772</td>\n",
              "      <td>0.298252</td>\n",
              "      <td>3.475938</td>\n",
              "      <td>0.761646</td>\n",
              "      <td>0.227799</td>\n",
              "      <td>0.244739</td>\n",
              "      <td>0.655133</td>\n",
              "      <td>0.192854</td>\n",
              "      <td>0.208317</td>\n",
              "      <td>0.106275</td>\n",
              "      <td>2.704594</td>\n",
              "      <td>0.229800</td>\n",
              "      <td>0.121542</td>\n",
              "      <td>0.196645</td>\n",
              "      <td>0.003675</td>\n",
              "      <td>0.017556</td>\n",
              "      <td>0.038794</td>\n",
              "      <td>0.153845</td>\n",
              "      <td>0.003558</td>\n",
              "      <td>0.001816</td>\n",
              "      <td>0.057864</td>\n",
              "      <td>0.017963</td>\n",
              "      <td>0.014252</td>\n",
              "      <td>0.061337</td>\n",
              "      <td>0.003288</td>\n",
              "      <td>0.137126</td>\n",
              "      <td>0.073415</td>\n",
              "      <td>0.000559</td>\n",
              "      <td>0.015678</td>\n",
              "      <td>34.879935</td>\n",
              "      <td>20.787825</td>\n",
              "      <td>11.803091</td>\n",
              "      <td>0.371800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>17.221443</td>\n",
              "      <td>44.255528</td>\n",
              "      <td>194.699538</td>\n",
              "      <td>7.829676</td>\n",
              "      <td>4.238200</td>\n",
              "      <td>8.289530</td>\n",
              "      <td>1.965667</td>\n",
              "      <td>1.032003</td>\n",
              "      <td>0.601285</td>\n",
              "      <td>0.593170</td>\n",
              "      <td>6.631909</td>\n",
              "      <td>1.538358</td>\n",
              "      <td>1.481820</td>\n",
              "      <td>0.829280</td>\n",
              "      <td>0.653204</td>\n",
              "      <td>0.518377</td>\n",
              "      <td>0.529803</td>\n",
              "      <td>0.684354</td>\n",
              "      <td>0.498019</td>\n",
              "      <td>0.499626</td>\n",
              "      <td>0.562632</td>\n",
              "      <td>1.311244</td>\n",
              "      <td>0.901596</td>\n",
              "      <td>0.467558</td>\n",
              "      <td>0.485981</td>\n",
              "      <td>0.060507</td>\n",
              "      <td>0.131333</td>\n",
              "      <td>0.193104</td>\n",
              "      <td>0.360801</td>\n",
              "      <td>0.059546</td>\n",
              "      <td>0.042581</td>\n",
              "      <td>0.233485</td>\n",
              "      <td>0.132817</td>\n",
              "      <td>0.118529</td>\n",
              "      <td>0.239947</td>\n",
              "      <td>0.057245</td>\n",
              "      <td>0.343980</td>\n",
              "      <td>0.260817</td>\n",
              "      <td>0.023631</td>\n",
              "      <td>0.124225</td>\n",
              "      <td>22.442738</td>\n",
              "      <td>21.613566</td>\n",
              "      <td>14.970780</td>\n",
              "      <td>1.062757</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-53.862900</td>\n",
              "      <td>-175.379600</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-2.000000</td>\n",
              "      <td>-2.000000</td>\n",
              "      <td>-2.000000</td>\n",
              "      <td>-2.000000</td>\n",
              "      <td>-2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-2.000000</td>\n",
              "      <td>-2.000000</td>\n",
              "      <td>-2.000000</td>\n",
              "      <td>-2.000000</td>\n",
              "      <td>-2.000000</td>\n",
              "      <td>-2.000000</td>\n",
              "      <td>-2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-2.000000</td>\n",
              "      <td>-2.000000</td>\n",
              "      <td>-2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.050000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>33.709800</td>\n",
              "      <td>-96.075800</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>23.800000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.050000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>38.989600</td>\n",
              "      <td>-84.454200</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>27.700000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.068000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>42.411200</td>\n",
              "      <td>-75.930650</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>33.400000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>0.367000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>71.333000</td>\n",
              "      <td>178.474500</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>147.100000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>75.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         ip_latitude   ip_longitude  ...  risk_infection  risk_mortality\n",
              "count  912768.000000  912768.000000  ...   912754.000000   912754.000000\n",
              "mean       35.166173     -75.376272  ...       11.803091        0.371800\n",
              "std        17.221443      44.255528  ...       14.970780        1.062757\n",
              "min       -53.862900    -175.379600  ...        5.000000        0.050000\n",
              "25%        33.709800     -96.075800  ...        5.000000        0.050000\n",
              "50%        38.989600     -84.454200  ...        5.000000        0.068000\n",
              "75%        42.411200     -75.930650  ...       13.000000        0.367000\n",
              "max        71.333000     178.474500  ...      100.000000       75.000000\n",
              "\n",
              "[8 rows x 44 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uswJ4PRZmo0f"
      },
      "source": [
        "## Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X5-kaqhCOmBi"
      },
      "source": [
        "### BMI vs Covid risk/mortality"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "il-kdKsTy5Z2",
        "outputId": "7b0f5abe-638f-49e8-c9bb-c4ae47e15750",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        }
      },
      "source": [
        "bmi_mortality = data[['bmi','risk_mortality','sex','risk_infection','age','covid19_positive']]\n",
        "bmi_mortality = bmi_mortality.dropna()\n",
        "sns.relplot(x='bmi', y='risk_mortality', hue='sex', data=bmi_mortality)\n",
        "column_1 = bmi_mortality[\"bmi\"]\n",
        "\n",
        "column_2 = bmi_mortality[\"risk_mortality\"]\n",
        "\n",
        "correlation = column_1.corr(column_2)\n",
        "correlation"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.08363449486372952"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 430.5x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gWy8KftWt0jm",
        "outputId": "742d8729-1a4f-43eb-87f2-56d78b77a386",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        }
      },
      "source": [
        "sns.distplot(bmi_mortality['bmi'], kde=False, bins=30);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SLDWWgWfrVVU",
        "outputId": "fa862066-812e-4815-d5a6-e3963bded5fb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        }
      },
      "source": [
        "sns.relplot(x='bmi', y='risk_infection', hue='sex', data=bmi_mortality)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f40241c7cc0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 430.5x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6MeUWuD2vBbM",
        "outputId": "60fcaee4-db85-4a43-d1ac-f3c2758303f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "from numpy import median\n",
        "sns.barplot(x='covid19_positive', y='bmi', data=bmi_mortality, estimator=median)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f402873e518>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEHCAYAAACp9y31AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAOdElEQVR4nO3de6ykdX3H8fdHl8aqWEVOgVJ1W0LULcW1nKCtqHhpg8ZWbbxRo7S1rok3SEVDifGWNFXrJWitdQ1bkCqiVSulihpAwUuJZ2GVRbRYFYXusoesCoZ6Wfj2j/Nsejic3T0Hz8wc9vt+JScz85uZ5/kObN47++zMs6kqJEl93GPSA0iSxsvwS1Izhl+SmjH8ktSM4ZekZtZMeoClOPjgg2vt2rWTHkOS7lY2b958U1VNLVy/W4R/7dq1zMzMTHoMSbpbSXLdYuse6pGkZgy/JDVj+CWpGcMvSc0YfklqxvBLUjOGX5KaMfyS1Mzd4gtckvZ/r3nNa9i+fTuHHnoob33rWyc9zn7N8EtaFbZv384NN9ww6TFa8FCPJDVj+CWpmTaHeo559QcmPYJWoc1//8JJj8D33/S7kx5hVdi18yBgDbt2Xud/E+DBr7tqZNv2Hb8kNdPmHb+k1e3ge90O7BouNUqGX9KqcOrRP5r0CG14qEeSmjH8ktSM4ZekZgy/JDVj+CWpGcMvSc0YfklqxvBLUjOGX5KaMfyS1Izhl6RmDL8kNWP4JamZkYU/yYOSXJLkG0muTnLysP6GJDck2TL8PHVUM0iS7myUp2XeBbyqqq5IciCwOcnnhvveWVVvG+G+JUl7MLLwV9U2YNtw/ZYk1wCHj2p/kqSlGcsx/iRrgUcClw9LL0/y9SSbkjxgD8/ZkGQmyczs7Ow4xpSkFkYe/iT3BT4GnFJVNwPvBY4A1jP3J4K3L/a8qtpYVdNVNT01NTXqMSWpjZGGP8kBzEX/g1X1cYCqurGqbquq24H3A8eOcgZJ0h2N8lM9Ac4Erqmqd8xbP2zew54JbB3VDJKkOxvlp3oeA7wAuCrJlmHtdODEJOuBAr4HvGSEM0iSFhjlp3q+CGSRuz41qn1KkvbNb+5KUjOGX5KaMfyS1Izhl6RmDL8kNWP4JakZwy9JzRh+SWrG8EtSM4Zfkpox/JLUjOGXpGYMvyQ1Y/glqRnDL0nNGH5JasbwS1Izhl+SmjH8ktSM4ZekZgy/JDVj+CWpGcMvSc0YfklqxvBLUjOGX5KaMfyS1Izhl6RmRhb+JA9KckmSbyS5OsnJw/pBST6X5Nrh8gGjmkGSdGejfMe/C3hVVa0DHg28LMk64DTgoqo6ErhouC1JGpORhb+qtlXVFcP1W4BrgMOBpwNnDw87G3jGqGaQJN3ZWI7xJ1kLPBK4HDikqrYNd20HDtnDczYkmUkyMzs7O44xJamFkYc/yX2BjwGnVNXN8++rqgJqsedV1caqmq6q6ampqVGPKUltjDT8SQ5gLvofrKqPD8s3JjlsuP8wYMcoZ5Ak3dEoP9UT4Ezgmqp6x7y7zgdOGq6fBHxyVDNIku5szQi3/RjgBcBVSbYMa6cDbwY+kuRFwHXAc0Y4gyRpgZGFv6q+CGQPdz9pVPuVJO2d39yVpGYMvyQ1Y/glqRnDL0nNGH5JasbwS1Izhl+SmjH8ktSM4ZekZgy/JDVj+CWpGcMvSc0YfklqxvBLUjOGX5KaMfyS1Izhl6RmDL8kNWP4JakZwy9JzRh+SWrG8EtSM4Zfkpox/JLUjOGXpGYMvyQ1s2Zvdyb5SFU9J8lVQM2/C6iqOnqk00mSVtxeww+cPFw+bbkbTrJpeN6OqjpqWHsD8GJgdnjY6VX1qeVuW5J01+31UE9VbRsur6uq64AfArfM+9mbs4ATFll/Z1WtH36MviSN2b7e8QOQ5CXAG4Gf8v+HfAr47T09p6ouTbL2l5xPkrTClhR+4FTgqKq6aQX2+fIkLwRmgFdV1Q9XYJuSpCVa6qd6/hu4dQX2917gCGA9sA14+54emGRDkpkkM7Ozs3t6mCRpmZb6jv9vgC8nuRz42e7FqnrlcnZWVTfuvp7k/cAFe3nsRmAjwPT0dO3pcZKk5Vlq+N8HXAxcBdx+V3eW5LDdf2EMPBPYele3JUm6a5Ya/gOq6q+Xs+Ek5wLHAwcnuR54PXB8kvXM/cXw94CXLGebkqRf3lLD/+kkG4B/546Henbu6QlVdeIiy2cubzxJ0kpbavhPZO5d+mkL1vf4cU5J0uq01PCvA14KHMfcbwCXAf80qqEkSaOz1PCfDdwMvGu4/WfD2nNGMZQkaXSWGv6jqmrdvNuXJPnGKAaSJI3WUr/AdUWSR+++keRRzH3zVpJ0N7Ov0zLvPh3zAcx9gev7w+2HAN8c/XiSpJW2r0M9yz4dsyRpddtr+IdTMUuS9iP+04uS1Izhl6RmDL8kNWP4JakZwy9JzRh+SWrG8EtSM4Zfkpox/JLUjOGXpGYMvyQ1Y/glqRnDL0nNGH5JasbwS1Izhl+SmjH8ktSM4ZekZgy/JDVj+CWpmZGFP8mmJDuSbJ23dlCSzyW5drh8wKj2L0la3Cjf8Z8FnLBg7TTgoqo6ErhouC1JGqORhb+qLgV2Llh+OnD2cP1s4Bmj2r8kaXHjPsZ/SFVtG65vBw7Z0wOTbEgyk2RmdnZ2PNNJUgMT+8vdqiqg9nL/xqqarqrpqampMU4mSfu3cYf/xiSHAQyXO8a8f0lqb9zhPx84abh+EvDJMe9fktob5cc5zwW+Ajw0yfVJXgS8GfjDJNcCTx5uS5LGaM2oNlxVJ+7hrieNap+SpH3zm7uS1Izhl6RmDL8kNWP4JakZwy9JzRh+SWrG8EtSM4Zfkpox/JLUjOGXpGYMvyQ1Y/glqRnDL0nNGH5JasbwS1Izhl+SmjH8ktSM4ZekZgy/JDVj+CWpGcMvSc0YfklqxvBLUjOGX5KaMfyS1Izhl6RmDL8kNWP4JamZNZPYaZLvAbcAtwG7qmp6EnNIUkcTCf/gCVV10wT3L0kteahHkpqZVPgL+GySzUk2LPaAJBuSzCSZmZ2dHfN4krT/mlT4j6uq3wOeArwsyeMWPqCqNlbVdFVNT01NjX9CSdpPTST8VXXDcLkD+ARw7CTmkKSOxh7+JPdJcuDu68AfAVvHPYckdTWJT/UcAnwiye79f6iqLpzAHJLU0tjDX1XfAR4x7v1Kkub4cU5JasbwS1Izhl+SmjH8ktSM4ZekZgy/JDVj+CWpGcMvSc0YfklqxvBLUjOGX5KaMfyS1Izhl6RmDL8kNWP4JakZwy9JzRh+SWrG8EtSM4Zfkpox/JLUjOGXpGYMvyQ1Y/glqRnDL0nNGH5JasbwS1Izhl+SmjH8ktTMRMKf5IQk30ry7SSnTWIGSepq7OFPck/gPcBTgHXAiUnWjXsOSepqEu/4jwW+XVXfqaqfAx8Gnj6BOSSppTUT2OfhwA/m3b4eeNTCByXZAGwYbv4kybfGMFsXBwM3TXqI1SBvO2nSI+iO/LW52+uzElt5yGKLkwj/klTVRmDjpOfYHyWZqarpSc8hLeSvzfGYxKGeG4AHzbv9m8OaJGkMJhH+rwJHJvmtJL8CPA84fwJzSFJLYz/UU1W7krwc+AxwT2BTVV097jma8xCaVit/bY5BqmrSM0iSxshv7kpSM4Zfkpox/I14qgytVkk2JdmRZOukZ+nA8DfhqTK0yp0FnDDpIbow/H14qgytWlV1KbBz0nN0Yfj7WOxUGYdPaBZJE2T4JakZw9+Hp8qQBBj+TjxVhiTA8LdRVbuA3afKuAb4iKfK0GqR5FzgK8BDk1yf5EWTnml/5ikbJKkZ3/FLUjOGX5KaMfyS1Izhl6RmDL8kNWP4JakZw6/9WpI3JXnyIuvHJ7lguP6wJF9J8rMkpy543MlJtia5OskpY5j3N5L863B9fZKnzrvvTzydtlbC2P/NXWmcqup1S3jYTuCVwDPmLyY5Cngxc2c2/TlwYZILqurbKz7ooKr+B3jWcHM9MA18arjvfPy2tVaA7/i1qiV5YZKvJ/laknOSrE1y8bB2UZIHJ/m1JNclucfwnPsk+UGSA5KcleRZw/oJSb6Z5ArgT3fvo6p2VNVXgV8s2P3Dgcur6tbhm89fmP+8RWb9fJIzkmwZ/pRw7LB+UJJ/G2b+zyRHD+uPHx67JcmVSQ4cXt/W4bQabwKeO9z/3CR/nuQf9vF6j0hyYZLNSS5L8rCV+n+h/Yfh16qV5HeA1wJPrKpHACcD7wbOrqqjgQ8C76qqHwNbgMcPT30a8Jmq+sW8bd0LeD/wx8AxwKFLGGEr8NgkD0xyb+Cp3PFEd4u5d1WtB14KbBrW3ghcOcx8OvCBYf1U4GXD4x8L/O/ujQz/ZsLrgPOqan1VnTfvvr293o3AK6rqmGH7/7iE16lmDL9WsycCH62qmwCqaifw+8CHhvvPAY4brp8HPHe4/rzh9nwPA75bVdfW3HlK/mVfO6+qa4C3AJ8FLmQutrft42nnDs+9FLhfkvsPM54zrF8MPDDJ/YAvAe9I8krg/sOfKpbqTq83yX2BPwA+mmQL8D7gsGVsU00Yfu0vzgdOSHIQc+/oL16JjVbVmVV1TFU9Dvgh8F/7eso+bs/f9puBvwJ+FfjSMg/LLPZ67wH8aPgTwu6fhy9jm2rC8Gs1uxh4dpIHwtyxcuDLzL3DBXg+cBlAVf2EuVNPnwFcUFUL35l/E1ib5Ijh9olLGSDJrw+XD2bu+P6H9v6MuXfhSY4DfjwclrlsmJUkxwM3VdXNSY6oqquq6i3D7AvDfwtw4GI7Wez1VtXNwHeTPHvYV5I8YimvU734qR6tWlV1dZK/Bb6Q5DbgSuAVwD8neTUwC/zFvKecB3wUOH6Rbf00yQbgP5LcylyMDwRIcigwA9wPuH342Oa6IaQfG37j+QVzx+N/tI+xf5rkSuAA4C+HtTcAm5J8HbgVOGlYPyXJE4DbgauBT3PHQzOXAKcNh23+bpF9LfZ6nw+8N8lrhxk+DHxtHzOrGU/LLK2QJJ8HTq2qmUnPIu2Nh3okqRkP9UjLlOQ9wGMWLJ9RVcdPYBxp2TzUI0nNeKhHkpox/JLUjOGXpGYMvyQ183/8zJAmVJHW0AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tdRrTcp7WaZh"
      },
      "source": [
        "**Insight:** BMI and covid19_positive has very low correlation of 0.083, so we can't infer relation between them\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FXGHp9sqCmAr"
      },
      "source": [
        "### Income Vs Covid-19\n",
        "\n",
        "\n",
        "*   H0 = Income and Covid-19 are independent\n",
        "*   H1 = They are related in some way\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0z-RmxIKAX0w",
        "outputId": "48365f48-3a2f-448c-9baf-431c0d7471e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "data['income'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([nan, 'low', 'med', 'high', 'gov', 'blank'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_8SHVqLpDOSa",
        "outputId": "5dd78ee0-f2c4-430b-8c04-d13800e1e7a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "incomePosi = data[['income','covid19_positive']]\n",
        "incomePosi.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>income</th>\n",
              "      <th>covid19_positive</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   income  covid19_positive\n",
              "ID                         \n",
              "1     NaN                 0\n",
              "2     NaN                 0\n",
              "3     NaN                 0\n",
              "4     NaN                 0\n",
              "5     NaN                 0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aqutzP5HDXDW",
        "outputId": "11790794-0535-43d3-ab33-a1a8025c77b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "incomePosi.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "income              688743\n",
              "covid19_positive         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JocSIZUdDZT5",
        "outputId": "95a18388-3ada-4334-867d-a5c3b41c8029",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "incomePosi = incomePosi.dropna()\n",
        "incomePosi.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>income</th>\n",
              "      <th>covid19_positive</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>47937</th>\n",
              "      <td>low</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47938</th>\n",
              "      <td>med</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47939</th>\n",
              "      <td>med</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47940</th>\n",
              "      <td>high</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47941</th>\n",
              "      <td>high</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      income  covid19_positive\n",
              "ID                            \n",
              "47937    low                 0\n",
              "47938    med                 0\n",
              "47939    med                 0\n",
              "47940   high                 0\n",
              "47941   high                 0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b99qusR7EPBd",
        "outputId": "733d4da7-fd9d-41f6-977f-518c384762db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        }
      },
      "source": [
        "cln,clp,cmn,cpm,chn,chp =0,0,0,0,0,0\n",
        "for i in range(len(incomePosi['income'])):\n",
        "  if (incomePosi[i:i+1]['income'].str.contains(\"low\")).bool() & ((incomePosi[i:i+1]['covid19_positive'] == 0).bool()):\n",
        "    cln+=1\n",
        "  elif (incomePosi[i:i+1]['income'].str.contains(\"low\")).bool() & ((incomePosi[i:i+1]['covid19_positive'] == 1).bool()):\n",
        "    clp+=1\n",
        "  elif (incomePosi[i:i+1]['income'].str.contains(\"med\")).bool() & ((incomePosi[i:i+1]['covid19_positive'] == 0).bool()):\n",
        "    cmn+=1\n",
        "  elif (incomePosi[i:i+1]['income'].str.contains(\"med\")).bool() & ((incomePosi[i:i+1]['covid19_positive'] == 1).bool()):\n",
        "    cpm+=1\n",
        "  elif (incomePosi[i:i+1]['income'].str.contains(\"high\")).bool() & ((incomePosi[i:i+1]['covid19_positive'] == 0).bool()):\n",
        "    chn+=1\n",
        "  elif (incomePosi[i:i+1]['income'].str.contains(\"high\")).bool() & ((incomePosi[i:i+1]['covid19_positive'] == 1).bool()):\n",
        "    chp+=1\n",
        "incomeMatrix = pd.DataFrame(\n",
        "    [\n",
        "        [cln,clp],\n",
        "        [cmn,cpm],\n",
        "        [chn,chp]\n",
        "    ],\n",
        "    index=[\"Low income\",\"Med income\",\"High income\"],\n",
        "    columns=[\"Covid19_Negative\",\"Covid19_Positive\"])\n",
        "incomeMatrix"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Covid19_Negative</th>\n",
              "      <th>Covid19_Positive</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Low income</th>\n",
              "      <td>22539</td>\n",
              "      <td>232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Med income</th>\n",
              "      <td>110428</td>\n",
              "      <td>1094</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>High income</th>\n",
              "      <td>77612</td>\n",
              "      <td>693</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Covid19_Negative  Covid19_Positive\n",
              "Low income              22539               232\n",
              "Med income             110428              1094\n",
              "High income             77612               693"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9U5YgzyzGixD",
        "outputId": "2874a951-556d-4b69-fa8f-2d8dcb049e2c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "source": [
        "from scipy.stats import chi2\n",
        "chi, pval, dof, exp = chi2_contingency(incomeMatrix)\n",
        "print('p-value is: ', pval)\n",
        "significance = 0.01\n",
        "p = 1 - significance\n",
        "critical_value = chi2.ppf(p, dof)\n",
        "print('chi=%.6f, critical value=%.6f\\n' % (chi, critical_value))\n",
        "if chi > critical_value:\n",
        "    print(\"\"\"At %.2f level of significance, we reject the null hypotheses and accept H1. \n",
        "They are not independent.\"\"\" % (significance))\n",
        "else:\n",
        "    print(\"\"\"At %.2f level of significance, we accept the null hypotheses. \n",
        "They are independent.\"\"\" % (significance))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "p-value is:  0.054989533050271655\n",
            "chi=5.801225, critical value=9.210340\n",
            "\n",
            "At 0.01 level of significance, we accept the null hypotheses. \n",
            "They are independent.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wqYvV4rDLDY0",
        "outputId": "9fa7fac8-522a-4d91-ef6c-8fcee0fa60a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "def cramers_stat(confusion_matrix):\n",
        "    chi2 = chi2_contingency(confusion_matrix)[0]\n",
        "    n = confusion_matrix.sum().sum()\n",
        "    return np.sqrt(chi2 / (n*(min(confusion_matrix.shape)-1)))\n",
        "insurCramers_v = cramers_stat(incomeMatrix)\n",
        "print(\"Cramér's V is a number between 0 and 1 that indicates how strongly two categorical variables are associated. It is based on Pearson's chi-squared statistic\")\n",
        "insurCramers_v"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cramér's V is a number between 0 and 1 that indicates how strongly two categorical variables are associated. It is based on Pearson's chi-squared statistic\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.005223724470229346"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8mZEQ-aLH7hO"
      },
      "source": [
        "### Insurance Vs Covid-19\n",
        "\n",
        "\n",
        "*   H0 = Insurance and Covid-19 are independent\n",
        "*   H1 = They are related in some way\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FRrrHWHx_LsY",
        "outputId": "c47775cb-0658-482c-d4ac-f2be86559d95",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "data['insurance'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([nan, 'no', 'yes', 'blank'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uqmXnZHg_i92",
        "outputId": "9f197032-d396-43ab-9920-1ba8e277c7e2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "insurPosi = data[['insurance','covid19_positive']]\n",
        "insurPosi.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>insurance</th>\n",
              "      <th>covid19_positive</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   insurance  covid19_positive\n",
              "ID                            \n",
              "1        NaN                 0\n",
              "2        NaN                 0\n",
              "3        NaN                 0\n",
              "4        NaN                 0\n",
              "5        NaN                 0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tuu8LfPCIk9u",
        "outputId": "ab8380ed-ebb7-47a6-d14a-34ff86e40347",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "insurPosi.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "insurance           688743\n",
              "covid19_positive         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ehXUpFWRI1SI",
        "outputId": "1a8217fa-cfcc-42c3-988f-52dd17e4c5b6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "insurPosi = insurPosi.dropna()\n",
        "insurPosi.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>insurance</th>\n",
              "      <th>covid19_positive</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>47937</th>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47938</th>\n",
              "      <td>yes</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47939</th>\n",
              "      <td>no</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47940</th>\n",
              "      <td>yes</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47941</th>\n",
              "      <td>yes</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      insurance  covid19_positive\n",
              "ID                               \n",
              "47937        no                 0\n",
              "47938       yes                 0\n",
              "47939        no                 0\n",
              "47940       yes                 0\n",
              "47941       yes                 0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QtzNunlfQ20o",
        "outputId": "1cbed349-dc01-4212-de96-c331a5ae5bde",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "cnn,cnp,cpn,cpp =0,0,0,0\n",
        "for i in range(len(insurPosi['insurance'])):\n",
        "  if (insurPosi[i:i+1]['insurance'].str.contains(\"no\")).bool() & ((insurPosi[i:i+1]['covid19_positive'] == 0).bool()):\n",
        "    cnn+=1\n",
        "  elif (insurPosi[i:i+1]['insurance'].str.contains(\"no\")).bool() & ((insurPosi[i:i+1]['covid19_positive'] == 1).bool()):\n",
        "    cnp+=1\n",
        "  elif (insurPosi[i:i+1]['insurance'].str.contains(\"yes\")).bool() & ((insurPosi[i:i+1]['covid19_positive'] == 0).bool()):\n",
        "    cpn+=1\n",
        "  elif (insurPosi[i:i+1]['insurance'].str.contains(\"yes\")).bool() & ((insurPosi[i:i+1]['covid19_positive'] == 1).bool()):\n",
        "    cpp+=1\n",
        "insurMatrix = pd.DataFrame(\n",
        "    [\n",
        "        [cnn,cnp],\n",
        "        [cpn,cpp]\n",
        "    ],\n",
        "    index=[\"No insurance\",\"Has insurance\"],\n",
        "    columns=[\"Covid19_Negative\",\"Covid19_Positive\"])\n",
        "insurMatrix"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Covid19_Negative</th>\n",
              "      <th>Covid19_Positive</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>No insurance</th>\n",
              "      <td>45553</td>\n",
              "      <td>446</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Has insurance</th>\n",
              "      <td>159434</td>\n",
              "      <td>1480</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Covid19_Negative  Covid19_Positive\n",
              "No insurance              45553               446\n",
              "Has insurance            159434              1480"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pwkags3-KMm_",
        "outputId": "9c1e763d-919a-45d9-c6e9-10cd060301ea",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "chi2_contingency(insurMatrix)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.9103386540915883,\n",
              " 0.3400245797942681,\n",
              " 1,\n",
              " array([[ 45570.82934857,    428.17065143],\n",
              "        [159416.17065143,   1497.82934857]]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jZtpiw87Pu45",
        "outputId": "53c39fd2-a72b-43c5-9143-832865792521",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df=chi2_contingency(insurMatrix)[3]\n",
        "pd.DataFrame(\n",
        "    data=df[:,:], \n",
        "    index=[\"No insurance\",\"Has insurance\"],\n",
        "    columns=[\"Covid19_Negative\",\"Covid19_Positive\"]\n",
        ").round(2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Covid19_Negative</th>\n",
              "      <th>Covid19_Positive</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>No insurance</th>\n",
              "      <td>45570.83</td>\n",
              "      <td>428.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Has insurance</th>\n",
              "      <td>159416.17</td>\n",
              "      <td>1497.83</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Covid19_Negative  Covid19_Positive\n",
              "No insurance           45570.83            428.17\n",
              "Has insurance         159416.17           1497.83"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JrhfnTjvVwNk",
        "outputId": "3328e17e-5531-46bf-8f54-a841149c63f0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "chisquare=chi2_contingency(insurMatrix)[0]\n",
        "chisquare"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9103386540915883"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hucGSOTGWkL0",
        "outputId": "2f27b866-e19f-4298-c7e5-83a7c207fd42",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "p_value = chi2_contingency(insurMatrix)[1]\n",
        "p_value"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.3400245797942681"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RL9oT1K7B4Ob",
        "outputId": "495adc56-7b4e-4a60-e07f-d719eae354ee",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from scipy.stats import chi2\n",
        "chi, pval, dof, exp = chi2_contingency(insurMatrix)\n",
        "print('p-value is: ', pval)\n",
        "significance = 0.01\n",
        "p = 1 - significance\n",
        "critical_value = chi2.ppf(p, dof)\n",
        "print('chi=%.6f, critical value=%.6f\\n' % (chi, critical_value))\n",
        "if chi > critical_value:\n",
        "    print(\"\"\"At %.2f level of significance, we reject the null hypotheses and accept H1. \n",
        "They are not independent.\"\"\" % (significance))\n",
        "else:\n",
        "    print(\"\"\"At %.2f level of significance, we accept the null hypotheses. \n",
        "They are independent.\"\"\" % (significance))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "p-value is:  0.3400245797942681\n",
            "chi=0.910339, critical value=6.634897\n",
            "\n",
            "At 0.01 level of significance, we accept the null hypotheses. \n",
            "They are independent.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fhJkHmFL-i9k",
        "outputId": "b8d4ce3e-577e-4e54-8860-2b137eafd24c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "def cramers_stat(confusion_matrix):\n",
        "    chi2 = chi2_contingency(confusion_matrix)[0]\n",
        "    n = confusion_matrix.sum().sum()\n",
        "    return np.sqrt(chi2 / (n*(min(confusion_matrix.shape)-1)))\n",
        "insurCramers_v = cramers_stat(insurMatrix)\n",
        "print(\"Cramér's V is a number between 0 and 1 that indicates how strongly two categorical variables are associated. It is based on Pearson's chi-squared statistic\")\n",
        "insurCramers_v"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cramér's V is a number between 0 and 1 that indicates how strongly two categorical variables are associated. It is based on Pearson's chi-squared statistic\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.00209752720879171"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gxZJF4cd90fl"
      },
      "source": [
        "### Sex Vs Covid-19\n",
        "\n",
        "\n",
        "*   H0 = Sex and Covid-19 are independent\n",
        "*   H1 = They are related in some way\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l8TbH6KS90fv",
        "outputId": "c8c9b5fc-c8bb-487c-fb37-6d71ae656a41",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "data['sex'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['male', 'female', 'other', 'undefined'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wrTO9-D090gE",
        "outputId": "d3613a9a-fb64-4801-9c6a-1b5c43a51515",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "insurPosi = data[['sex','covid19_positive']]\n",
        "insurPosi.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sex</th>\n",
              "      <th>covid19_positive</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>male</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>female</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>male</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>male</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>male</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       sex  covid19_positive\n",
              "ID                          \n",
              "1     male                 0\n",
              "2   female                 0\n",
              "3     male                 0\n",
              "4     male                 0\n",
              "5     male                 0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GqKPIAeH90gY",
        "outputId": "d703f701-350f-4513-9277-80cd660d8eb5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "insurPosi.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sex                 0\n",
              "covid19_positive    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dxty1ioT90g0",
        "outputId": "4457caff-f16e-4837-d1d0-b431509d62c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        }
      },
      "source": [
        "cmn,cpm,cfn,cfp =0,0,0,0\n",
        "for i in range(len(insurPosi['sex'])):\n",
        "  if (insurPosi[i:i+1]['sex'].str.contains(\"female\")).bool() & ((insurPosi[i:i+1]['covid19_positive'] == 0).bool()):\n",
        "    cfn+=1\n",
        "  elif (insurPosi[i:i+1]['sex'].str.contains(\"female\")).bool() & ((insurPosi[i:i+1]['covid19_positive'] == 1).bool()):\n",
        "    cfp+=1\n",
        "  elif (insurPosi[i:i+1]['sex'].str.contains(\"male\")).bool() & ((insurPosi[i:i+1]['covid19_positive'] == 0).bool()):\n",
        "    cmn+=1\n",
        "  elif (insurPosi[i:i+1]['sex'].str.contains(\"male\")).bool() & ((insurPosi[i:i+1]['covid19_positive'] == 1).bool()):\n",
        "    cpm+=1\n",
        "  \n",
        "insurMatrix = pd.DataFrame(\n",
        "    [\n",
        "        [cmn,cpm],\n",
        "        [cfn,cfp]\n",
        "    ],\n",
        "    index=[\"Male\",\"Female\"],\n",
        "    columns=[\"Covid19_Negative\",\"Covid19_Positive\"])\n",
        "insurMatrix"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Covid19_Negative</th>\n",
              "      <th>Covid19_Positive</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Male</th>\n",
              "      <td>321238</td>\n",
              "      <td>1758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Female</th>\n",
              "      <td>585017</td>\n",
              "      <td>1587</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Covid19_Negative  Covid19_Positive\n",
              "Male              321238              1758\n",
              "Female            585017              1587"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MyAwgaqM90hE",
        "outputId": "6499f214-a8c5-4a66-a278-86f6abf8afe7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "source": [
        "chi2_contingency(insurMatrix)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(425.262613661738,\n",
              " 1.7457936909893287e-94,\n",
              " 1,\n",
              " array([[321808.20138522,   1187.79861478],\n",
              "        [584446.79861478,   2157.20138522]]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QJ6ZUKHG90hT",
        "outputId": "f73ceae2-8087-40e9-9a2d-73d92763ccf4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        }
      },
      "source": [
        "df=chi2_contingency(insurMatrix)[3]\n",
        "pd.DataFrame(\n",
        "    data=df[:,:], \n",
        "    index=[\"Male\",\"Female\"],\n",
        "    columns=[\"Covid19_Negative\",\"Covid19_Positive\"]\n",
        ").round(2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Covid19_Negative</th>\n",
              "      <th>Covid19_Positive</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Male</th>\n",
              "      <td>321808.2</td>\n",
              "      <td>1187.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Female</th>\n",
              "      <td>584446.8</td>\n",
              "      <td>2157.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Covid19_Negative  Covid19_Positive\n",
              "Male            321808.2            1187.8\n",
              "Female          584446.8            2157.2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HgG7afOY90hj",
        "outputId": "2fa2d2b5-862c-4b49-ecaf-18f26803bedd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "chisquare=chi2_contingency(insurMatrix)[0]\n",
        "chisquare"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "425.262613661738"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LveT6EnL90hw",
        "outputId": "287b9c08-307c-464b-e05c-12e7e68a8d04",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "p_value = chi2_contingency(insurMatrix)[1]\n",
        "p_value"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.7457936909893287e-94"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nfu-UcY190h8",
        "outputId": "6cab01ad-9493-48ed-9abf-d46927da21d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "def cramers_stat(confusion_matrix):\n",
        "    chi2 = chi2_contingency(confusion_matrix)[0]\n",
        "    n = confusion_matrix.sum().sum()\n",
        "    return np.sqrt(chi2 / (n*(min(confusion_matrix.shape)-1)))\n",
        "insurCramers_v = cramers_stat(insurMatrix)\n",
        "insurCramers_v"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.02162237405731346"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2qlVS3xj90iF",
        "outputId": "edf734b3-fb5a-4bfa-b32a-b822082a51a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "source": [
        "from scipy.stats import chi2\n",
        "chi, pval, dof, exp = chi2_contingency(insurMatrix)\n",
        "print('p-value is: ', pval)\n",
        "significance = 0.01\n",
        "p = 1 - significance\n",
        "critical_value = chi2.ppf(p, dof)\n",
        "print('chi=%.6f, critical value=%.6f\\n' % (chi, critical_value))\n",
        "if chi > critical_value:\n",
        "    print(\"\"\"At %.2f level of significance, we reject the null hypotheses and accept H1. \n",
        "They are not independent.\"\"\" % (significance))\n",
        "else:\n",
        "    print(\"\"\"At %.2f level of significance, we accept the null hypotheses. \n",
        "They are independent.\"\"\" % (significance))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "p-value is:  1.7457936909893287e-94\n",
            "chi=425.262614, critical value=6.634897\n",
            "\n",
            "At 0.01 level of significance, we reject the null hypotheses and accept H1. \n",
            "They are not independent.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-4IqpbaE6TOa",
        "outputId": "8deb214c-2239-42f1-bf70-b09e6a5664b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "sns.barplot(y='covid19_positive', x ='sex',data=bmi_mortality)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4018d6fd68>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jki7cVCkO2yb"
      },
      "source": [
        "### Sex vs Covid risk/mortality\n",
        "\n",
        "*   H0 = Sex and Risk mortality are independent\n",
        "*   H1 = They are related in some way"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oa5SrwRTXIvL",
        "outputId": "14dd60ec-8191-4328-cf0a-5e6c104887d2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "sns.barplot(x='sex', y='risk_mortality', data=bmi_mortality)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4018e47208>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UcQaBJHWcQSBJHWcQSFLHGQSS1HEGgSR1nEEgSR1nEEhSxxkEktRxBoEkdZxBIEkdZxBIUscZBJLUcQaBJHWcQSBJHWcQSFLHGQSS1HEGgSR1nEEgSR1nEEhSxxkEktRxBoEkdZxBIEkdZxBIUse1HgRJVifZmmRbkqumef7xST7QPP+FJMvbrkmS9JhWgyDJImAdcA5wCnBJklOmdLscuL+q/i3wB8A726xJkrS/to8IzgC2VdX2qnoE2ACcP6XP+cD7m+UPAT+VJC3XJUlqLG55+ycDO/rWdwLPn6lPVe1J8i3gh4F7+zsluQK4oln9TpKtrVQ8P5zElPc/n+X3fmnUJcwnC2rfAfAW/+7qs+D2X64caP89dbrGtoNgzlTVemD9qOsYhiQTVbVq1HVocO67ha2r+6/toaFdwLK+9aVN27R9kiwGjge+2XJdkqRG20GwCViZZEWSJcDFwPiUPuPAvrGFXwA+XVXVcl2SpEarQ0PNmP8a4CZgEXB9VW1Jcg0wUVXjwHuB/51kG3AfvbDouk4MgR2h3HcLWyf3X/zjW5K6zSuLJanjDAJJ6jiDYJ5LclaSj4+6jq5IcmWSO5Pc0NL235rkdW1sWweX5IQkv9a37v8vDAJpql8DXlRVLxt1IWrFCfT28ZxoPvK+4BkEQ5BkeZJ/TPK+JF9NckOSs5PcmuRrSc5oHp9L8qUkn03yjGm2c0yS65N8sek3dboOHYYkfwI8DfhkkjdO97NOclmSjyb5VJK7k6xJ8tqmz+eTnNj0e1WSTUm+nOTDSY6e5vv9WJK/SrI5yd8leeZw3/GRr9k3X2kevwG8A/ixJLclua7pdmySDzX/R2/YN8VNktOT/E2zf25K8iNN+y1J/jDJBPCa0byzOVZVPlp+AMuBPcCz6YXvZuB6IPTmWvoo8ERgcdP/bODDzfJZwMeb5f8GvLxZPgH4KnDMqN/fkfQA7qY3zcC0P2vgMmAbcBwwBnwL+JWm3x8Av9Es/3DfNn8beHWz/Fbgdc3yzcDKZvn59K6hGfnP4Eh5AKcDtzf77VhgC/Ac4Ct9fc5q9uHS5v/m54AXAo8DPguMNf0uovfxd4BbgP856vc3l48j4rBmgbirqm4HSLIFuLmqKsnt9ILieOD9SVYCRe8f4lQvBs7rG2N+AvAU4M62i++gmX7WAJ+pqgeBB5u5sT7WtN8OnNYsn5rkt+mFyLH0rqX5viTHAv8e+Iu+ORYf38Yb6bAXAv+3qh4CSPIR4D9M0++LVbWz6XMbvf+PDwCnAp9q9s8i4Bt9r/lAe2UPn0EwPA/3Le/tW99Lbz+8nd4vmJ9v7slwyzTbCHBBVR3JE+7NF9P+rJM8n4PvS4D3AT9XVV9Ochm9vzz7HQU8UFU/Mbdl6xD078/v0duHAbZU1QtmeM1DrVc1RJ4jmD+O57F5mC6boc9NwKv7xjCfM4S6uupwf9bHAd9I8jjgB048V9W3gbuSXNhsP0l+/DBr1v7+Dvi5JEcnOQb4eeBWevvmYLYCY0leAJDkcUme1V6po2UQzB/XAr+b5EvMfKT2dnpDRv/QDC+9fVjFddDh/qzfDHyB3i+ef5yhz8uAy5N8md74tSf/51BV/T29I7Mv0tsX76mqzcCtzcnj6w7w2kfozX32zmb/3EZvKO+I5BQTktRxHhFIUscZBJLUcQaBJHWcQSBJHWcQSFLHGQSS1HEGgSR1nEEgDaCZAfYTzayiX0ly0XSzVCY5PsnWfbPIJvk/SV416vql6TjXkDSY1cA/V9W5AEmOBz4JnF9Vk0kuAn6nql6RZA3wviR/BDypqt49urKlmXllsTSAJE8H/pre7JMfB+6nN13x9qbLIuAbVfXipv964ALgx/fNcCnNNx4RSAOoqq8meS7w0/TuM/BpZpilMslRwL8Dvgs8CTAINC95jkAaQJIfBb5bVX8OXEfvhjIzzVL5m/TuFfFS4E+bmUileccjAmkwzwauS7IXeBT4VXp3n/vj5nzBYuAPk+wBXgmcUVUPJvlb4E3AW0ZUtzQjzxFIUsc5NCRJHWcQSFLHGQSS1HEGgSR1nEEgSR1nEEhSxxkEktRx/x+8FvjawWxpWwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "svUFCAi5NJhY",
        "outputId": "96038a5c-fa71-4231-b97b-f89dac8981e5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "sns.barplot(x='sex', y='risk_infection', data=bmi_mortality)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4018e07470>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NFvw4IbQSzYM",
        "outputId": "88a281eb-6ff6-4b9d-d02c-69dfc7c00ebc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "sns.countplot(x='sex',data=data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4018afc358>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Okyh4tkSrv8",
        "outputId": "276e534c-98db-4058-bfef-65d13876aaae",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "sns.catplot(x=\"sex\", y=\"risk_mortality\", data=bmi_mortality)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f4018af07b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NMXu93w+IyAfAZKBARPoYY/aLSB/ggNNxBNrwjESGZySyPO8g324v5ECpi/zSag5V1mIMXD1pAKc3WnzhcGUtn67dT0SYcP64PiRER7CzqIK8g5VMzkxr0h2jOoHzn4DaSshdAL3HwkV/C3ZEqptwtJ63iMQDYcaYMu/rucD/AmcCxcaYx0TkASDNGPOrlq4VivW8G9fubkgEXr3lRE4ZahWpKi53ceHfFrL/cDVgFa26YGwf/r5gG8ZAj/goXrvtREb2TvJ7PaVUlxWUet4ZwEIRWQ0sBT41xswBHgNmiMhW4Czvdpey+2Albyz1n7gBjIGPVu2t335/xd76xA2wo6iCZ72JG6z6KE/P2+pYvEqp0OJot4kxJhc43s/+YqzWd5dV7nLT2h81Det41/k5uPFixYVlLjtCU0p1ATo93iGj+iRxwsCUZt8fkZHITadk1W9fOqEfPROi6rf7pcRyXD/fLpLLTuhvf6BKqZCka1g6qKy6lteW7GLXwQr6JMeSkRTDkPQEwDBhQGqTafIHSqv5YOVeIsLDuHRCPwSY+U0uecUVnDOmNxeP7xeUn0MpFVR++7w1eQfZ6t2H+PPcLRSWubj8hH7ceurgYIeklOpc/CZvnfoXROUuN9e/sITSajcAj3xaSkpcFFdMtLpH5qzbz9dbixjVJ4mrswfoyjtKqXqavINo2c6D9Yn7iC83FXDFxP68sHAHD8/eUL9/+c6DPPn9CYEOUSnVSWlTLsA8HsOCzQd4bUkeyTGRSKM/iIb2sioPNi5m9fHqfZRW1wYqTKVUJ6ct7wC7561VfLJ6HwBxUeHcfHImry/dRXWth1OG9uDWU60RKImNilnFRIYTpavMK6W8NHkHUG5heX3iBqisqeNAmYucB2dQXu2md/LRcd8/O2s4P5q1nJo6DwB3njGUmEidHq+UsmjyDiCX29NkX3Wth4ToCBIatbRPH9mLBfdNZ3FuMaP6JDGqj06LV0odpX+HB9CoPkmcmJVWvx0mcMOUQc0ev27vYV5dnMev31/L7DX7mj1OdQK1VeDWGbAqcLTlHWDnHdebnJ0l1BlDYkwkPRrMqmxoc34ZP3ltBXXeOfJ3vbGS/qlxjB/Q/KxNFQQeD8x5AJa/BGERcPLdcPqvgx2V6ga05R1AlTVunvh8c30dk8NVtTzx+Wa/x3615UB94garkNX8TV2ucm7o2/AhLP0n1NVYpWG/egzyFgU7KtUNtDl5e1fC+amIaKX5Y1Ra5aaips5n3/5D1RSWuZoMAxzWq+lixcMyEhyNTx2D/DVN9+33s08pm7Wn5X010BdYJiJvisg5Io1HKauW9E6OYXJmms8+jzFMevQLsh/+gj//92grPEwgusGMyhmjMzjvuD4Bi1W10eDpjXYIZJ0WhEBUd9Pm5G2M2WaM+S0wHHgdeBHIE5E/iEhay2erI/55/URunZrFacPTOX9sb7YeKAegps7D377cxto9h/F4DL9+f63P6JTSqlrCw/R3ZaczeDpc8GdIGwLpo+Dy5yFjdLCjUt1Aux5Yisg44GbgfOA94DVgKvAlMN726ELQ8ryDuGo9nDi4h99kmxofxYMXWv9zP/jh2ibvbykoY3B6PPsaLMwAsL2wwpmAVcdNutX6UiqA2py8RWQ5cAh4AXjAGHNkXNQSETnFieBCibvOw80vL+ObrUWANSzw7R+dRGJMZLPnnDGyF68uPrraTlREGKcM7Ul8dARTBvfgu9zi+vdmjO7l7xJKqW6qPX3eVxpjzjTGvH4kcYtIFoAx5jJHogshX246UJ+4ATbuL+WJzzdT7nL7Pb66to64qAh+c95IxvS1xn//9vxRlHkfXD51zXhOH5FOVs94fnhKFv9z4ZiA/BxKqdDQnm6Td4ET/OybaF84oetgRU2Tff/+Lo8PVuxl5g3ZTBnSo37/ur2HufHFpRRX1BARJtx/3kg+Wb2P33+8HrDGgu8oqmBTfhkAkzPdunK8UspHqy1vERkpIpcDySJyWYOvm4CYVk7vNs4anUFybNMukjKXm//32UaffX/672aKvcne7TE8PmcTa/Ycrn//P+vy6xM3wFs5u1m1+5BDkSulQlFbuk1GABcCKcD3GnydANzmXGihpWdCNO/fcTLXTh7Y5L39jR4+5jfarq1rfTWj/MNVHQtQKdWltNptYoz5CPhIRKYYY74LQEwha0h6Av/vsrEcqqrhs7X59fsvOr6vz3EXje/LpjlHx3SP6p3I5oKy+tXi4yLDqKr1cCSlp8VHMXVYutPhK6VCSKvJW0R+ZYx5HLhWRK5p/L4x5m5HIgtR8zYWsHLXIQTomRjNDScN4ifTh/gc85NpQ0iMjuDLTQcYlpHIHdOHsHbvYV5fsou4qAhuP20wew9V8tay3STHRvKjaUOaVB1USnVvbckIRzpsu97qvzYrq67l7jdW1k+BLyxzUVlbR0SjRRREhOunZHL9lMz6facOS+fUBq3rEb0TOWNkRkDiVkqFnrZ0m3zi/f6K8+GEtq0HypvULlm169geNL61bBf/+CoXYww/mjaEa/z0pSuluq+2dJt8AjT7RM0Yc5GtEYWwkb0TSYqJ8FlUeHJW2yoHHCirptJVR2bPeFbsKuH+947Ovvz1+2sZ1iuB7EytQqCUsrSl2+RPjkfRRcRFRfCDkwbx3FfbMQYSoiM497jeAFS43Hy56QBJsZGcOrQnYQ2mzj8yewMvLdpJncdw8pAeTYpXASzaXqzJWylVry3dJl8FIpCuoKqmjlnf5eEt1025y81TX2zloYvGcMnfvyW/1BoieMrQHsz64YmEhQmrdx/i+YU76q+xaHsxA9Nim1x7bL/kgPwMSqnQ0J563sNE5F0R2SAiuUe+nAwu1BSVuyhrNB1+Z3EFsxbvrE/cAN9uK2bR9uL69xsTCeOWU7KIjggjJjKMn0wfwukjtbaJUuqo9tQ2eQl4DnADpwP/Bl51IqhQVe5yE9GokuA5Y3pT4arzeyxYo0ziG019jwoXZi3Jw+X20DMhiquyBzgXtFIqJLUneccaY+YBYozJM8Y8BFzQlhNFJFxEVorIbO92logsEZFtIvKWiPhfyDHEPD5nE+4GS5dFhYdx69QsrszuT1SDhRX6p8YyfYQ1LDAtPorXbjuJs0ZlcGJWGo9dNpa3cnZT463lvaek2meRBqWUgvYVpnKJSBiwVUTuBPYCbV2X6x6s8eJJ3u0/An81xrwpIv8AbsFq1Ye0fYd8p73X1Hn4yWsruPHkTD684xRe+W4nSTER3H7aEGIij7a2xw9I4fkbswHILSynutbjc53dBysdj10pFVrak7zvAeKAu4GHsbpObmjtJBHpj9VCfxS417t02hnAtd5DXgEeogsk7wvH9WHz3DKffQu3FbFwWxGRYUKtxyACJZW1PH75OJ8RJ0cMTk9gZO9En8JU54/V5c+UUr7a022SaYwpN8bsMcbcbIy5HGjLzJEngV8BR5qTPYBDxpgjT/b2AP38nSgit4tIjojkFBYWtiPU4Pjp6UP5nwtHM7J308WDa73dKcbAu8v3MH9z8yvBv3zzZK6ZPIDJWWk8eMEobjt1sGMxK6VCU3ta3r8G3mnDvnoiciFwwBizXESmtzc4Y8xMYCZAdnZ266X3giwsTPjh1CyG9Urg+heXtnjsjqLmlzXrnRzD/102zu7wlFJdSFtmWJ6HtWZlPxF5usFbSVgjT1pyCnCRiJyPVfs7CXgKSBGRCG/ruz9W/3mXcLiylgfeb7o2ZUPhYi2BppRSx6ot3Sb7sIpSVQPLG3x9DJzT0onGmF8bY/obYzKB7wNfGmOuA+YDV3gPuxH46Jii74Rmr93H3kMt196uM/D7j9bz41nLW+w+UUqp5rRlhuVqEVkHnGNjcar7gTdF5BFgJdaixl3WuH7JrNl72GffN9us9S4/35DPa7eeyMlDegYjNKVUiGrTA0tjTB0woCPjsY0xC4wxF3pf5xpjJhtjhhpjrmywEn3Iu3BcXwY0mN7eJzmGSS0UpzIG7n93DSV+1sBUSqnmiDFtew4oIv8GRmF1l9Q/bTPG/MWZ0HxlZ2ebnJzQKCl+uKqWT1bvwxjDhAGpzPwml9lr9uFp4Z86MlwY3TeZh743mgkDUwMXrFKqs2s6ppj2Je/f+9tvjPlDB4Jqs1BK3kd4PIYz/ryAncVtn2STnhjNogfOIDK8PaM4lVJdmN/k3eahgkeStIgkeLfL7Ymr61iRV8ITn28it6iCjKRoRvZOalfiBmv1ncue/ZZpw3tx0ymZ9EyIdihaVc8Y+OZPsPpNSMiAM/8HBp4U7KiUalF7Wt7HAbOAIx24RcANxpj1DsXmo7O3vFfsKuGK5xa12DXSXoN7xvP5z0/TVrjTcl6C2T87uh2dBD9fDzFJzZ+jVOD4bXm3JyvMBO41xgwyxgwCfgH8y47IuoJ3cvbYmrgBcosqWJJ70N6Lqqa2z/PddpXCnpYnWSkVbO1J3vHGmPlHNowxC4B42yMKUUmxzqzu7tR1VQO9xvhuSzikjwxOLEq1UXuSd66I/E5EMr1fDwK6GIPXTSdnkhIX2ez7STEtJ2Gh6Ydx/tjejOuf0vHgVMtOvguGeeebRSfB+Y9Dcv/gxqRUK9rT550K/AGY6t31DfCQMabEodh8dPY+b7CWQXt/xR427Csls2ccp4/sxcOzN/LVlqNFtcb2S+LqSQN48EPfRwUCjB+YQmpcJJOzenDCwNQ2L16sbFJ5ECLjIDIm2JEo1VCHR5uUYJWDVc2IjQrnupMG+exbsqPYZ3vt3lL6phQxuk8iG/YfLftqgJW7DlmvDfx42hDH41WNxOkvSxU62rOGZbaIvC8iK0RkzZEvJ4PrCqIjwpvs+3x9AdERzf/Tz99cyOHKWifDUkqFuPY8DXsNuA9Yy9Ha3KoVZ4/O4J3le5rsX7n7sJ+jLUkxEcRGNU36Sil1RHuSd6Ex5mPHIukC5m4o4P8+20hRuYvLJ/bnt+eP4r5zR/De8j0t/raLChdiIsMprXYTJnD5RN81L5VSqrH2JO/fi8jzwDygvpCUMeZ926MKQUXlLn76+or6hYNf+nYnA9PiuPmULK47aRCzFuc1e25YmFDnHSTuMda5KbFR3HPWsIDErpQKPe1p3t0MjAfOBb7n/brQiaBC0erdh+oT9xHLdloTbG6YMoiE6OZ/T1bXeqioqfPZ9/xCHYWplGpee1rek4wxIxyLJMTV+ZleOWGAVR3wyXlbKXe1tuiQr3A/ixMrpdQR7Unei0RktDFmg2PRhLB/fLXdZzsqXCiucHHBU9+wqaC03de7Y/oQFucW8/n6fLJ6xnNV9gBiIvUhplLK0p7kfRKwSkR2YPV5C2CMMbpSLpB/uNpnu6bO8I+vWu76GNk7kWtPHMhf5m6hwuXmtGE9yc5MIzszjQOlLr4/c3H9sfM2HuCVH052JHalVOhpT/I+t6U3RSQ1ULMtO6OLxvdr0vpuSWS4MOuWE0lPjOa6Ewfh9nh8xoR/f+Z3Psd/taWQXcWVDOwRZ1vMqhXVpXB4j1XnJExH/6jOpT0zLJsfLmGZB5zQsXBC133njKBnQhQLtxUxIDWWWYt3tXj8vTNGkJ5o1eoODxPCw3y7ROKifD+aMIGYSE0gAbP6TZh9L9RWQGoWXPcu9Bwa7KiUqmdnNujWT9jCw4RbTx3MyzdP5uFLxnLphH7NHhsbGc6tp2a1eL07pg/xSdbXnTiIXklacyMgairh019aiRugZAfMC8iCUUq1mZ31Rm2uZh3a/nr1eC4Z35c56/P5cNVeqmqODiOsM4bW6oFlZ6bx1X2n89XmQjJ7xmuRqkCqOAA1Zb77DurQzWDIyc/hjU1vEB4Wzo2jb2RMzzGtn9RNaLFoB00b0YtpI3oxtFciD88+OkjnBycOatMMyoykGK6aNMDJEJU/qZnQeyzkrz26b9T3ghZOd7WlZAu3zb0Nt8caZrtg9wI+uvgj+iT0CXJknYOdybtbd5u05JapWQztlcCi7UWM7ZfMBWP1P75O75q3YP6jULgZRpwLp/w82BF1O3Pz5tYnboAqdxVf7v6S60ZdF8SoOo82J28RucUY80KjfY8ZYx7wbp5pa2QhbPfBSn79/lpW7CphUmYa/3fZWKYNT2fa8PPbVNkAACAASURBVPQWz/t8fT5//M8mDlbWcOXE/hzfP4XVew4xOasHM0ZnBCh6BUByP7jk2WBH0a31juvdpn3dVXsWY/gMeM0Y85p3++9AjDHmFgfjqxcKizEccdU/v2PpjqNrT546rCezbjkRgKU7DvLsgm1U19Zxw5RMzh/bh9lr9vHiwh2s3HWo2QcH950zgp+erqMdVPdR7a7mjnl3sCx/GQDTB0znr9P/SkRYt+vt7dhiDMDlwMci4sEa830oUIk7lJRU1PgkboAl3u19h6q4/oUluLw1UJbsOMjvLhjN/85ufdLqS9/u0OStupWYiBhePOdFNhZvJDwsnOGpw4MdUqfSavIWkYbDHG4FPgS+Bf4gImnGGF3evIH/+8/GJvvGe9eh/GpLYX3iBmvFnJe+3dGm6/pb1EGp7mBUj1HBDqFTakvLeznWMEBp8P0C75cBBjsWXYjJLSzny00Hmuz/+QyrtOsgP7Mjd5dUtenad52hrW6l1FGtJm9jTMuzSRQAzy3Yzh/nbGqyv0d8FEVlLn75zmqGpCdw9aQBvJOzGz9FCOt/O/pz3nE6QkUpdVR7RptcCcwxxpSJyINYU+EfNsasdCy6EFHucvPkF1v8vldcUcNdb66q306IjuDO04eyrbCcz9bm+xz7xJXjWLe3lFcX5+FukN2P759MclykM8ErpUJSe6bH/86buKcCZwEvAP9o6QQRiRGRpSKyWkTWi8gfvPuzRGSJiGwTkbdEJOrYf4Tgq3S5ffqyW1LucvP0l9vonRTjU7N7YFocFx3fj4cuGsPK/5nB1dkD6JcSy4zRGTxzbbctGaOUakZ7RpscWerlAmCmMeZTEXmklXNcwBnGmHIRiQQWish/gHuBvxpj3hSRfwC3AM+1N/jOoldSDNOGp/PVlsI2n/Peij289aOT+GDFXtLio7h+ytFZl4kxkfzxCq20q5RqXnta3ntF5J/A1cBnIhLd2vnGUu7djPR+GeAM4F3v/leAS9oVdSf07HUn8MuzhxMZ3raJpoer3NS6DY9eOpZfnD2CuKgIfv3+Gk557EtufSWH3QcrHY5YNVF5EN6/Hf56HLxzE5QVBDsipZrVnuR9FfA5cI4x5hCQBtzX2kkiEi4iq4ADwFxgO9YY8SPzXvcAfkvwicjtIpIjIjmFhW1v1QZDfHQEd54xjB7x0W0+Z1P+0RV2Hv10A28s3c3eQ1V8sbGAC57+hhcX7sBd17buGGWDT+6GNW/B4d2w/gP44EfBjkipZrWavEUkyfsyBlgAFHvHfruAVqc8GmPqjDHjgf7AZGBkW4Mzxsw0xmQbY7LT01ueWt5Z/HBqZpuPfebLrbjcVm/U/EZDDEur3fzv7A0+Ba2Uw7Z96buduwA8+stTdU5taXm/LiICFGEl6+UNvto8X93bWp8PTAFSRORIf3t/YG97gu7MDpS62nxscUUtWwusXqXmhgi+u3yPDVGpNul9nO92r1G6go7qtFr9L9MYc6GxCqBsMMYMNsZkNfhqcYKOiKSLSIr3dSwwA9iIlcSv8B52I/BRh36KTuTj1W3/PZQQHUF8VDiLc4ubLcnYI6Ht3TCqgy58EnpYE6pIzYSLnwlqOEq1pD2jTZaLyCRjzLJ2nNMHeEVEwrF+UbxtjJktIhuAN72jVVZiDTvsEmIjI4CaZt+PjgjD5fbQOymGqUN7cOZfvsJjIDKsafoOD4PfnN/mXibVURmj4exHYPlLkNQP4noGOyKlmtWeqoKbgKFAHlBBgFePD5Wqgq8v2cVvPljb4jH/vH4iw9ITOPMvX/l0l4hQv8JOWlwkn91zGr2TdemzgNk6F1674uh2Uj+4awVE6meggqrDVQXPsSmQLu3sMRn0SY7mm63FvLlsF5U1dU2OiQ4P462c3U36ueMjwyn3Hn+wspbXl+Rx79kjAhC1AqyRJg2V7oW8hTD0rODEo1QL7Fw9vlurrnVzyd8XsSm/DBGrkqDbXwET4OaXlzE8I6HJflejYYEvfbtTk3cgJfhZ8CJBi/+rzkkfpdvkvnfXsCnfWrTWGFi5+xA1zUyZN8DmgnIaLA5PSmwkSTG+v0vLa9w8PHuDjvUOlCl3QlqDZ/DZP2w6AuUIj8da47KiKDCxKdVIt1uSwikr8krafU6dgR+dNpjU+CiunNif/6zL58EP19W/bwy8sHAHvRKj+dG0IXaGq/xJ6gM/XQa7F0N8OqQ381fP4T0w6zIo2gxhkXDW7+HkuwIbq+r2tOVtkwGpTWt1t8ZjrKqDP542hJ3FFby40P/CDN/lFnc0PNVW4RGQObX5xA3w9RNW4gbw1MIXD+lUehVwmrxt0i819pjOKypzUecx3P3GKnKLKvwes3xniU7W6UxKdvpue9xQqp+PCixN3jZp5tlkq248ZRDF5S72Hmp+RZ0yl5v73l3Nur2HjzE61WblB6CglZIEoy/23U7NhD7jHQtJKX+0z9sm5dW1x3ReZo8E0hOjGZweT26h/5Y3WP3fi3OLOa5f8rGGqFqz4DGrS8Tjht7j4AfvQ4KfmjrZPwRPHaz/EFIGwLT7IUzXGFWBpS1vm5hmJ7i37LcfrGXrgXKeu24iPRJaXpNidJ+kFt9XHXAw10reHm+xy/w1sOip5o+ffBvc/Clc+g9I05UCVeBp8rbJdScN9Lv/SEo/MSuN/ilNZ+ot2l7MZc8uIik2gnBp+RdAabW7xfdVB5Tk0aQ8WOO+baU6EU3eNjl9RC/OP853QocAk7PS+PG0wdx/7kjuPHOY33PLXW4uf3YRFa6Wk3NhWbVd4arGBp7UdJLOqIv9H6tUJ9Dm2ibBFgq1Tcpdbn725irmbSogMiyMmkaTa6IjBJf72P69E6IjmPeLaWQkaZ0NxxRuhq8eh/ICGHcVnHCDtd/tgo2fWCvtjL4IEnXWpQoov3+Sa/J2QGlVLSc8PLfZ6fHtESbWSJaJg1J58aZJJMfqKvIB5fHAS+dZE3cAYpLh1nnQ0/9fUUo5wG/y1m4TGz3/TS7nPfUNP319BcmxrQ/kiYtqfYTCkfy/PK+Ev8/f1tEQVXvlLTyauAGqD8PSfwUvHqW8dKigTd5dvodHPt1Yvx0V0froE38VB1uyfp+O8w44j5/nEP72KRVg2vK2SeM1KGuOsW+7Jf1S2j8FX3VQ1jToPfbodmScNc5bqSDTlrdNhvRqWuLVbkPT4x2/h2okLBxungNr3rQqCBZtg+fPtFbZOft/4bjLgx2h6qa05W2T207NYupQ55bNEuCMUb0cu75qQXQCTLoVYlJg3TvgrrZqmbx/O5TuD3Z0qpvS5G2TmMhwxvVPJir82GZatiY+OoL4aP1DKah2L/Hd9rhh34rgxKK6PU3eNnl63laeXbCdmrq29XXPGN2LqPC2//OXu9y8v6LtK9OrNtq9FFa+CqX7Wj82qZ/vdlgk9JvoTFxKtUKbcjb5stEDy9bsKKxoMomnNRF+VphXHfDfB2HR36zXETHwg/esWt7+fPkIfPe3o9sJGXD+EzphRwWNtrxtMqydDyy3tVBB0J/eSTFcPrF/u85RLagohsXPHd12V1sVBf2pLoVvGxWpShvctDSsUgGkydsmvzp3pKNV/4akxxMik2FDg7u66XjtqkP+j62rgbpGJX9r2vfLVym7afK2Sd+UWD6751SuPXGAI9f/dnsxD3283pFrd0vJ/SCj0eLCZfnWdPjG4nvCmEt89026xbnYlGoD7fO20e6Dlby5dLdj11+686Bj1+6WYlJ8t8vzoWAt9Dm+6bGXzoTMU6FwEww720r8K1+FtCEwaEpg4lWqAU3eNlq1+9AxL4fWFmP76WIMtkodZNUuOSIsAhKaeQAZEXW0tZ33HTw9Adzepesm/wjOf9zZWJVqRLtNbOKu87Apv9TRe4S1sliDaqeT7oCUQdZrCYfTfwuJGS2fA/DNn48mboBl/9LV41XAacvbJn+cs4l/fbOjfjsiTBicHs+WgvJWz02OjSApJpLYqHBq3B52Flf6PW7h1iLb4u3WirbCOzdBwTpAIDYNzniw7f3YtY0+H+PxTebKdvvL9/Pm5jdx1bm4dOiljEgbEeyQgk6Tt00+WOk7ycPtMWxtQ+IGKK1yc7iq9Up1ra1xqdrok3u8iRvAQNVB+M+vYPB0qx88vkfz59ZWwdgrIe/bo/uGnmWtIK8cUVpTyjWfXkNxdTEA7255l7cvfJvBKYODHFlwafK2Se/kaIrKXT772tr93dbjMtO0MJUt9q9uus/jhmenQJ0LhpxpLTDcf5I10uSInBfhv/8DNWXQdwL0GQ+9RsMJ1wcu9m5o/q759YkbwFXn4uPtH/OziT8LYlTB52ift4gMEJH5IrJBRNaLyD3e/WkiMldEtnq/pzoZRyD85rxRrS4g3FEbHO5T7zayTvO/v877y3f7PHjj+/CX0bDufWvq/Ox7ra+aMuuYfSutWZll++DTX0LuV4GJvRtKjEps077uxukHlm7gF8aY0cBJwE9FZDTwADDPGDMMmOfdDmknD+3JTadkOnqPHvHabWKL7z0Noy6ykm9L6lww5wF4/mzIeYEmfyPlvAAL/wqrXoV/Xwzb5zsWcnd2Wv/TOKHXCfXbAxMHcvkwLcXraLeJMWY/sN/7ukxENgL9gIuB6d7DXgEWAPc7GUsg/HzGcF5fkkdVbftqlrTV5KwW+mJV2yWkw9Wz4JlJULSl5WMrCq0Hkv7U1TTYMLDqNRhyum1hKktEWAQvnfsSi/cvptpdzdR+U4kK14ZMwPq8RSQTmAAsATK8iR0gH/A7PktEbgduBxg4cKDzQXZQjdtDtUOJG2BUH/1T0TaFm/0n7ugkcDXonup7AuxttPC1hDWT0HXkrVPCJIyT+54c7DA6lYAkbxFJAN4DfmaMKZUGfcPGGCMifp/ZGWNmAjPBWj0+ELF2xHMLtrX54WN79UuJ5aLx/Vo/ULVNRaH//ec9DpVFsHMhpI+CZc83PaZNLXGlnOV4U0FEIrES92vGmPe9uwtEpI/3/T5A++qpdlKrdzdT2MgGwzISiI7Qlp1tBpxklXVtbPNncPJdcO1b0Pu4ow8o28LowsQqcJwebSLAC8BGY8xfGrz1MXCj9/WNwEdOxhEoUwY71ye9YHMhf5nbSv+sarvwCBh+btP9DcdrN56Mc0RkM0M2h5/X4bCUaiunm3KnANcDZ4jIKu/X+cBjwAwR2Qqc5d0Oeev2OTuU7+stzfypr9qvzg3r3vPdFx4Fp9xjvV7wR/j4Lv/netzWsY0tehqt26sCxenRJgux1s7150wn7x1oHo9p92o67dWOVdNUq0zTet4JGdaknNpqawhgc+pcMP3XVrJuWNe7cJP1EDRdp24r52k6sMmG/aWOPaw8orDU1fpBqm3CI61ZlA1N+an1vfqwtVhDcyQMxl8LI8733R8Rq8uiqYDR6fE2eXf5HsfvEaFNb3vNeBj6T7ZmS2adCkPOAFc5zH+UFosWTLsfUgZaVQj3rYTibVbiPu8xiEkOWPiqe9PkbYN9h6p4ZdFOx+9z3UmDHL9HtyICoy+yvsCqNvivM3zHefvTP9v6npYFP11mdZck9YXYlJbPU8pGmrxtUFTucrzLBCBcy3nbo/wAzPk15C6AqDg44QaYei/M/nnriRush51gLZm2NwdiUzVxq4DT5G2DMX2SENpeHfBYvb5sNz+ePtThu3RxHg88NxUqvIsnVAJfPgKEWS3oxiJimvZ/f/YLq4zsd38/Wlp2wvVw8TNORq6UD+1EtcG2woqAtLyrauoCcJcubtd3RxN3Q/Mf9b/4sL8Hl4f3wIc/aVATHFg5C/Ysty9OpVqhydsGPROiCAtAl8aA1Fjnb9LVRUT732/qoKrY/3ttVbq3Y+cr1Q6avG3QIyGaEb2dLxp18ylZjt+jy0sZZC00bLe4nlpRUAWUJm+b3DLV2SWZfv+90Xzv+L6O3qNbWPKPppNzOkSsYYJ9x8Pad6CqxMZrK9U8Td42uWR8X47rm+TY9Y/rp+OHbVGWb/MFjbX48LYvrNEqz02FCl0oWjlPk7dNwsOEzQXtqEDXTv1SWln1RbXN2CtovmIDEN8LBp1y7Ncv3QOr3zz285VqI03eNtl6oJzaOufGnHy21u4WYzc15HS47h0YfTH0aDTscshZcOsXsGdZx+5hdFSQcp6O87ZJekIzoxhsosXqbDRshvXlqYMNH8Hyl63FGRJ7WzVPUrOgaPOxXTs+HcZdbWu4SvmjLW+bLM51tp/zn19vx+XWFp2twsLhwAbY8ZX1fdWr8MY1kDKg0YECw872Xwa2oYk/hB9/q8WpVEBo8rZBjdvDHa+tdPQeReU1/OD5pY7eo1vaONt3e/8qPw8cDZTktb7MWfoISPS7HKtSttPkbYNF24oCMsNyxS4dhma7tEZDPKOTYcwlTY9pSzfK2rehYL19sSnVAk3eNhjVNzCrumthKgfM+IM1cQcgIs6qMFhWAL3HQXi0Nfpk+m+s143F9YTYtKPbe5db3S7+ptkrZTN9YGmDA4FaJEGTt/16DoO7V0LhZvj0F1aNkoYqDsBHd/jvMuk91nrQWXXw6L5DeVCyA3oMcTZu1e1py9sGO4srWj/IBrVuHXLiiLBwOLAedi3y/35zfd3VhyF9pO++2DRI6mdvfEr5ocnbBmeMDMxDqkAUv+qWvvkLvHdr+8/bt8IaGjhoqrWd2BcumwmROqFKOU+7TWywcFtgpkMnxujHZYtNn8Li56BkJ7jK2rYAQ3OKtsKgk6E83xofnqT1Z1RgaDawgZM1TRoqd+k47w7bvQzevNa+6+2YD9u/sF4Xb7NGm/xsjTXZRykHabeJDVLjowLyLNHj0T7vDtv8qb3X8zT6hVq2z3eRBqUcosnbBlU1dQEZ5x0RHoCbdHVpTo8CEUjq7/A9lNLkbYseCdGkxzv/Z3L/FF1Jp8PGXQ19xjt4AwPL/uXg9ZWyaPK2QXVtHYUVtY7fZ3+gxpN3ZRFRcMvc1uuUdMTWuc5dWykvfWBpg6U7DrZ+kA2qanXmni2+e6b1OiUdEd/DuWsrv4qqivjvzv+SGJXIjEEziIno+sM1NXnbYGQA1q9UNtrxtbPXz5rm7PWVj12lu7jm02sorbGGfL6+8XVmnT+LCCfWKu1EtNskhOjzShvsWgy7vnP2HtH6yzyQ3t78dn3iBlhXvI7F+xcHMaLA0ORtg+S4wIzpjY7Uj6vDPv8tuKudvce3Tzt7feWjzs/KRR7T9bsYHc0GIvKiiBwQkXUN9qWJyFwR2er9nupkDIEQHRHOqcPSWj+wg5xcZq3bOLzH+XtUFjt/D1XvyuFXEh8ZX789LHUYU/pOCWJEgeF0U+5l4NxG+x4A5hljhgHzvNshLz7awdELXpq6bXDc5c7fY8IPnL+HqhcXGced4+/khtE38OCJDzLrvFlEhnX9Ga6OJm9jzNdA46EYFwOveF+/AjSqfB+avt3qfH2TcO016bgzfgdO/4/dd4Kz11f15uXN47z3zuOPy/7IrA1WOd+GrfCuLBjpIMMYs9/7Oh9otiSfiNwuIjkiklNYWBiY6I5BUbmLMpfb+Rtp07vjcueDx+Ex+fMfdfb6qt4zq57Bbaz/9wyGp1c+TV3jkgVdVFDbcsYYQwspyRgz0xiTbYzJTk9PD2Bk7VNU5vADMC+X9nl3XGUAxuRXHXL+HgrAZ5QJQKW7sls8rITgJO8CEekD4P1+IAgx2CqzZ0JA7hOtYwU7bv8q5++RqGVhA+WKYVf4bF885GIiu0lFx2CMYv8YuBF4zPv9oyDEYKuYyMBk1RmjdWXyDlvztvP3OLTL+XsoAH58/I8ZkDSApfuXMqbHGC4fHoAH0p2Eo8lbRN4ApgM9RWQP8HuspP22iNwC5AFXORlDVzJ7bQHPBDuIkBeArqeaUmtNzPQRzt+rmxMRLhx8IRcOvjDYoQSco8nbGHNNM2+d6eR9A81d1z362LqE2NSOrZzTVjrLUjlMB5/ZYNnOwBSmUjaorXL+HhExuhxagFXWVvLANw8w6dVJXPrRpeTk5wQ7JMdp8raBtrxDSCCqzbmr4fBe5++j6j276lk+zf2U6rpqth3axr0L7qXGycqRnYAmbxvsPxyYoYLKBsPPCcx9ogMzAklZVh5Y6bNd4iphx+EdQYomMDR522Bc/5Rgh6Da6tDuANxEICY5APdRR4xLH+eznRKdQmZyZnCCCRBN3jZIiOnadYO7lLxvnb+H6ID8QLtzwp2ck3kOERJBZlImf5r2J6LDo4MdlqM069igf2pcsENQnYlxQ00FRHWPGhudQXxkPH+a9qdghxFQ2vK2wew1ASgzquzh5NqVDZXub/0Y1WYHqw/ywdYPWLh3YbeZ/t4abXnbYO76kJ/h331UBaDWdlQi9Bji/H26iW0l27hhzg2U1ZQBcObAM3ny9Cf9HlvnqeOplU/xWe5nZMRlcG/2vUzMmBjIcANGW942OHmwLjirGvDUgkiwo+gyZm2cVZ+4Aebtmsemg5v8Hvvqxld5ad1LFFQWsKZoDXfNu4vK2spAhRpQmrxtEBelf8CoBtzVWlnQRlXuphOr/O0DWLJ/ic92WW0Z64vXOxJXsGnytkFafPeoYqbaQYcK2ubK4VcSIUcbSKPSRnF8+vF+jx3VY5TPdmRYJENSumYXljYZbfDM/K3BDkF1NrWVOtrEJpN6T+LVC15lzo459IztyeXDLidM/Lc7bznuFraVbGP+7vmkxqRy36T7SItxfn3ZYNDkbYP9h13BDkF1Np4ArKzUjYzpMYYxPca0elxcZBxPnfEUlbWVRIVHERHWdVOcdpvY4Japg4IdgupstNskoA5VHyInP6f+4WRcZFyXTtygLW9bvLtcixCpRg7vheR+wY6iW5izcw4PLnwQV52LxMhEnjrjKSb1nhTssBynLW8bRITp2pKqkXUfBDuCbsFjPDy25DFcdVbXZVltGX/O+XOQowoMTd42GBygNSxVCKnT5yCBUOuppcRV4rOvoLIgSNEEliZvGwxI01EFqpHeY4MdQbcQHR7NGQPO8Nl3XtZ5QYomsLTP2wYHyrt20Xd1DOJ7BTuCbuPRqY+StTaLDQc3MLn3ZG4YfUOwQwoITd42+GpTfrBDUJ1N8VboNz7YUXQLcZFx3H3C3cEOI+A0edugzKVjelUj+Wth3JXBjqJLmLVhFh9t+4jUmFR+Ov6nxEXGsXDvQoYkD+G0/qch3bSOjCZvG4SH62gT1ci62XD2/wY7ipD3yfZPeHzZ4/XbKwtW4vIcfRg8KWMS/zz7n3y37zvCJIyT+pzUZHz3tpJt/GX5X9hVtosxPcZw78R7yYjPCNjP4BRN3jYorgh2BKrTqSkPdgRdwjd7vvHZbpi4AZYVLOOSDy5hV/kuAEb3GM3L575MbEQsn27/lH9v/DcbijfUH59Xmsee8j28dv5rzgfvME3eSjmhj//CSap9BiQOaPWYI4kbYEPxBu7+8m6iwqP4es/Xfo9fU7iGkuoSUmNSbYszGDR5K+UEl5aE7aiCigL+veHf7T5v8f7FLb6fHptOYlTisYbVaeg4b6WckL8m2BGENGMMN8y5geq6aluvKwhu4+a1jaHfbaLJWykneOxNOt3N5pLN7CvfZ/t1DYaS6hL+lPMn/rvzv7ZfP5A0eSulOp3UaOf7o3/19a98llcLNZq8lVKdztL8pY7fo87U8dyq5xy/j1M0eSulOp1l+csCcp+9ZaFbzjloyVtEzhWRzSKyTUQeCFYcSqnOZ0XBioDcJ78idEtbBCV5i0g48HfgPGA0cI2IjA5GLEqpzievLC8g98k9lBuQ+zghWC3vycA2Y0yuMaYGeBO4OEixKKW6qWoTuqOCgpW8+wG7G2zv8e7zISK3i0iOiOQUFhYGLDillOrsOvUDS2PMTGNMtjEmOz09PdjhKKW6mOFxw4MdwjELVvLeCzQsWtDfuy8k7XzsgmCHoDqbhw4HO4KQtvbGtQG5z3tXvheQ+zhBjAl8OVMRiQC2AGdiJe1lwLXGmPXNnZOdnW1ycnICFKFSSnUafguWB6UwlTHGLSJ3Ap8D4cCLLSVupZRSvoJWVdAY8xnwWbDur5RSoaxTP7BUSinlnyZvpZQKQZq8lVIqBGnyVkqpEKTJWymlQpAmb6WUCkGavJVSKgRp8lZKqRCkyVsppUJQUGqbHAsRKQQCU6E9cHoCRcEOQrVKP6fQ0FU/pyJjzLmNd4ZM8u6KRCTHGJMd7DhUy/RzCg3d7XPSbhOllApBmryVUioEafIOrpnBDkC1iX5OoaFbfU7a562UUiFIW95KKRWCNHkrpVQI0uTdSYjIdBGZHew4uiIRuVtENorIaw5d/yER+aUT11a+RCRFRO5osN1t/7/R5K26gzuAGcaY64IdiOqwFKzP0xbexdBDkiZvG4lIpohsEpGXRWSLiLwmImeJyLcislVEJnu/vhORlSKySERG+LlOvIi8KCJLvcddHIyfpysQkX8Ag4H/iMhv/f27ishNIvKhiMwVkZ0icqeI3Os9ZrGIpHmPu01ElonIahF5T0Ti/NxviIjMEZHlIvKNiIwM7E/ctXg/h3Xer58BjwFDRGSViDzhPSxBRN71/r/3moiI99yJIvKV97P4XET6ePcvEJEnRSQHuCc4P5kNjDH6ZdMXkAm4gbFYvxiXAy8CAlwMfAgkARHe488C3vO+ng7M9r7+f8APvK9TgC1AfLB/vlD9AnZiTZ32++8K3ARsAxKBdOAw8GPvcX8FfuZ93aPBNR8B7vK+fgj4pff1PGCY9/WJwJfB/vlD9QuYCKz1fkYJwHpgArCuwTHTvZ9Xf+//c98BU4FIYBGQ7j3uauBF7+sFwLPB/vk6+hWyfzJ0YjuMMWsBRGQ9MM8YY0RkLVZyTwZeEZFhgMH6j6yxs4GLGvSjxgADgY1OB9/FNffvCjDfGFMGlInIYeAT7/61wDjv6+NE5BGsxJ8AfN7w4iKSAJwMvONt/AFEO/GDA1ERfQAAApBJREFUdBNTgQ+MMRUAIvI+cKqf45YaY/Z4j1mF9f/ZIeA4YK73swgH9jc45y3nwg4MTd72czV47Wmw7cH6934YK1FcKiKZWK2AxgS43Biz2bkwuyW//64iciKtf24ALwOXGGNWi8hNWK2+hsKAQ8aY8faGrVrR8LOrw/q8BFhvjJnSzDkVjkflMO3zDrxkYK/39U3NHPM5cFeDvrsJAYirO+jov2sisF9EIoEmDz+NMaXADhG50nt9EZHjOxhzd/YNcImIxIlIPHAp8C3W59CazUC6iEwBEJFIERnjXKiBp8k78B4H/k9EVtL8Xz4PY3WnrPF2vTwcqOC6uI7+u/4OWIKVQDY1c8x1wC0ishqrj1YfNh8jY8wKrL92lmL9uz9vjFkOfOt9gPlEC+fWAFcAf/R+FquwurS6DJ0er5RSIUhb3kopFYI0eSulVAjS5K2UUiFIk7dSSoUgTd5KKRWCNHkrpVQI0uStlFIhSJO36va8VRw/9VYLXCciV/urSCciySKy+UglSBF5Q0RuC3b8qnvS2iZKwbnAPmPMBQAikgz8B7jYGFMoIlcDjxpjfigidwIvi8hTQKox5l/BC1t1ZzrDUnV7IjIc+C9WpbnZQAlWOdFc7yHhwH5jzNne42cClwPHH6lmp1SgactbdXvGmC0icgJwPlad7i9ppiKdiIQBo4BKIBXQ5K2CQvu8VbcnIn2BSmPMq8ATWIsoNFeR7udYddWvBV7yVhhUKuC05a2UtfLREyLiAWqBn2CtiPS0t/87AnhSRNzArcBkY0yZiHwNPAj8Pkhxq25M+7yVUioEabeJUkqFIE3eSikVgjR5K6VUCNLkrZRSIUiTt1JKhSBN3kopFYI0eSulVAj6/4ThGu6qxAPEAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wCDL_S2eR2l-",
        "outputId": "8396b22b-e524-4cf0-ba21-a50c5e10da7e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "sns.catplot(x=\"sex\", y=\"risk_infection\", data=bmi_mortality)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f4018817518>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 117
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fNa-Kp-ml-os",
        "outputId": "6105aec7-3025-4507-f720-f155b64f23d2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "stats.f_oneway(bmi_mortality['risk_infection'][bmi_mortality['sex'] == 'male'],\n",
        "               bmi_mortality['risk_infection'][bmi_mortality['sex'] == 'female'],\n",
        "               bmi_mortality['risk_infection'][bmi_mortality['sex'] == 'other'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "F_onewayResult(statistic=314.31011948544915, pvalue=3.4981118559002447e-137)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XVusIEg0Xdmh",
        "outputId": "50f8fc11-bd22-4fe7-f3fc-7f71006baf22",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "stats.f_oneway(bmi_mortality['risk_mortality'][bmi_mortality['sex'] == 'male'],\n",
        "               bmi_mortality['risk_mortality'][bmi_mortality['sex'] == 'female'],\n",
        "               bmi_mortality['risk_mortality'][bmi_mortality['sex'] == 'other'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "F_onewayResult(statistic=10104.56778742565, pvalue=0.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "302PDamdXExU"
      },
      "source": [
        "**Insight:** Risk Mortality and Sex has very low p-value<'0.01', so we reject null hypothesis that they are independent.\n",
        "\n",
        "Risk Mortality and Sex has very low p-value<'0.01', so we reject null hypothesis that they are independent."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JBCq7EYHbUbe"
      },
      "source": [
        "### Contacts count Vs Covid-19\n",
        "\n",
        "\n",
        "*   H0 = Contacts count and Covid-19 are independent\n",
        "*   H1 = They are related in some way\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7F_tvOjfzoY0",
        "outputId": "281d2e03-6230-41d3-d17c-b1fdb8d991c0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "sns.distplot(data['contacts_count'],bins=10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4018aa2a90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 120
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KLv0PaC20OVP",
        "outputId": "76a0785b-b5bd-46ee-c747-b521a6d20ce2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "sns.catplot(x='contacts_count',kind='box',data=data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f4014780160>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAFvCAYAAAB0NxGZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAM4ElEQVR4nO3df6zdd13H8dd7LbgW5nQjNlohE7qE1D+c2hg1mxnGmLGYAEZ+qNFN/8AYbMqQRKJ/uJiQYCLorIoZgiME/DFlE8myiIMphsTZ4X5v4M2C0ZuxrS4Zkw5w3cc/zrd6e7fb23a9531u7+ORnNzv+d57zufTb7999tvvOefbGmMEgPk7p3sCAFuVAAM0EWCAJgIM0ESAAZoIMECT7afyw1dcccW49dZbN2ouAGerer6Vp3QEfPjw4TMzFQCcggDoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAm27snsGgOHjyYpaWluY65vLycJNm9e/dcx91s9uzZk/3793dPA84YAV5laWkpd933YI7uvGBuY2478mSS5Mtf99uxlm1HnuieApxx/sQ/j6M7L8jTr75ybuPteOiWJJnrmJvNsW0EZxPngAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoMlcAnzw4MEcPHhwHkMBPK9F7ND2eQyytLQ0j2EA1rSIHXIKAqCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQZHv3BAAW2eWXX/5/y7fffvsZfW5HwABNBBhgDSuPfp/v/gs1l1MQy8vLefrpp3PgwIF5DPeCLC0t5ZxvjO5psMo5X/tKlpae2hT7EItpaWkpO3bs6J7GcdY9Aq6qt1bVoao69Pjjj89jTgBbwrpHwGOM65NcnyT79u07rUPD3bt3J0muu+6603n4XB04cCB3Pvxo9zRY5dlzvzl7XrlrU+xDLKZF/NeTc8AATQQYYA2r33bmbWgAZwkfxAA4gTN91LuSI2CAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQZPs8BtmzZ888hgFY0yJ2aC4B3r9//zyGAVjTInbIKQiAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmiyvXsCi2jbkSey46Fb5jjefyXJXMfcbLYdeSLJru5pwBklwKvs2bNn7mMuLz+TJNm9W2DWtqvl9wY2Uo0xTvqH9+3bNw4dOrSB0wE4K9XzrXQOGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMECTGmOc/A9XPZ7k309zrJclOXyaj90KbJ/12UYnZvusr2sbHR5jXLF65SkF+IWoqkNjjH1zGWwTsn3WZxudmO2zvkXbRk5BADQRYIAm8wzw9XMcazOyfdZnG52Y7bO+hdpGczsHDMDxnIIAaCLAAE02PMBVdUVVfaGqlqrqXRs93mZUVV+qqnur6q6qOtQ9n0VQVR+qqseq6r4V6y6oqk9V1b9NX7+1c46d1tg+11bV8rQf3VVVV3bOsVNVvbyqPlNVD1TV/VV1YFq/UPvQhga4qrYl+cMkr02yN8lPV9XejRxzE3vNGOOSRXqPYrMbkqx+4/q7ktw2xrg4yW3T/a3qhjx3+yTJ70770SVjjFvmPKdF8kySXx1j7E3yg0neNrVnofahjT4C/oEkS2OMh8cY30jy50let8FjchYYY/xjkidWrX5dkg9Pyx9O8vq5TmqBrLF9mIwxHhljfH5afirJg0l2Z8H2oY0O8O4k/7Hi/n9O6zjeSPJ3VXVnVb21ezILbNcY45Fp+ctJdnVOZkH9SlXdM52i2LKnaFaqqouSfG+Sf86C7UNehFsMl44xvi+zUzVvq6of6Z7Qohuz9096D+Xx3p/kVUkuSfJIkvf2TqdfVb00yV8nefsY4ysrv7cI+9BGB3g5yctX3P/OaR0rjDGWp6+PJbkps1M3PNejVfXtSTJ9fax5PgtljPHoGOPoGOPZJB/IFt+PqupFmcX3o2OMj0+rF2of2ugA/0uSi6vqu6rqxUnekuQTGzzmplJVL6mq844tJ/nxJPed+FFb1ieSXDUtX5XkbxrnsnCOhWXyhmzh/aiqKskHkzw4xnjfim8t1D604Z+Em94K83tJtiX50Bjj3Rs64CZTVa/M7Kg3SbYn+ZhtlFTVnyW5PLPLBz6a5DeT3JzkL5O8IrPLor5pjLElX4haY/tcntnph5HkS0l+acX5zi2lqi5N8tkk9yZ5dlr965mdB16YfchHkQGaeBEOoIkAAzQRYIAmAgzQRIBpUVUXVdXPvIDHX11V33Em53QmVNUlW/kiOJwaAabLRUlOO8BJrk6ycAHO7G1gAsxJEWBOS1X9/HTNgbur6iPTEe2np3W3VdUrpp+7oap+v6o+V1UPV9VPTU/xniSXTZdNvGZ6/Ger6vPT7YdXjPVr0+U6766q90zPsS/JR6fH75jWPzCN/zsnmPeuqrppeq67j41TVe+oqvum29undRetutzjO6vq2mn59qr67aq6o6q+WFWXTR82+q0kb57m9eYzutE5+4wx3NxO6Zbku5N8McnLpvsXJPnbJFdN938xyc3T8g1JbszsL/u9mV0dL5l9aOCTK55zZ5Jzp+WLkxyall+b5HNJdh4ba/p6e5J90/KFSb6Q/39f+7ecYO5/kdl1AZLZh4POT/L9mb1h/yVJXprk/swu3nJRkvtWPPadSa5dMf57p+Urk/z9tHx1kj/o/j1y2xw3R8Ccjh9NcuMY43CSjNkniX4oycem738kyaUrfv7mMcazY4wHsvbVp16U5ANVdW9mwT523egfS/KnY4wjK8Za7ckkX0vywar6ySRH1pn7+6fnOjrGeHKa601jjK+OMf47yceTXHaC5zjm2PUF7sws1nBKBJh5+PqK5VrjZ67J7CO135PZ6YUXn+yTjzGeyezCM3+V5CeS3Hp603yOZ3L8n5FzV33/2K/raGYfI4dTIsCcjk8neWNVXZjM/puXzE4TvGX6/s9m9jn8E3kqyXkr7p+f5JExu5LXz2V2eiBJPpXkF6pq54qxjnv8dMnB88fsf4C4JrOIr+W2JL88PW5bVZ0/zfX1VbVzuiDSG6Z1jyb5tqq6sKq+KbO4r2f1rwvWJMCcsjHG/UneneQfquruJO9Lsj+zUN6TWUAPrPM09yQ5Or0Qdk2SP0py1fR8r07y1WmsWzO7gtWhqrors/Owyezc8h9P685L8slp7H9K8o4TjHsgyWumUx13Jtk7Zv9zwg1J7sjsYi1/Msb41zHG/2T2otodmf1F8NBJbJ7PJNnrRThOhovxADRxBAzQxAsHnJWq6jeSvHHV6huHay2zQJyCAGjiFARAEwEGaCLAAE0EGKCJAAM0EWCAJv8L1d7gXIWqOyUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mubw7sYhFIMk",
        "outputId": "ea7f13c3-59d0-408e-8180-e677f9a100f3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df = data[[\"contacts_count\",\"covid19_positive\"]]\n",
        "df=df.dropna()\n",
        "sns.boxplot(x ='covid19_positive', y ='contacts_count', data = df) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f401872e748>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 122
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J4_tek93bmIu",
        "outputId": "b50bd280-075d-4787-d5e7-d4684a31f0bb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "confusion_matrix = pd.crosstab(df['covid19_positive'], df['contacts_count'])\n",
        "confusion_matrix"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>contacts_count</th>\n",
              "      <th>0.0</th>\n",
              "      <th>1.0</th>\n",
              "      <th>2.0</th>\n",
              "      <th>3.0</th>\n",
              "      <th>4.0</th>\n",
              "      <th>5.0</th>\n",
              "      <th>6.0</th>\n",
              "      <th>7.0</th>\n",
              "      <th>8.0</th>\n",
              "      <th>9.0</th>\n",
              "      <th>10.0</th>\n",
              "      <th>11.0</th>\n",
              "      <th>12.0</th>\n",
              "      <th>13.0</th>\n",
              "      <th>14.0</th>\n",
              "      <th>15.0</th>\n",
              "      <th>16.0</th>\n",
              "      <th>17.0</th>\n",
              "      <th>18.0</th>\n",
              "      <th>19.0</th>\n",
              "      <th>20.0</th>\n",
              "      <th>21.0</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>covid19_positive</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>33114</td>\n",
              "      <td>94550</td>\n",
              "      <td>89937</td>\n",
              "      <td>108674</td>\n",
              "      <td>95232</td>\n",
              "      <td>86631</td>\n",
              "      <td>57054</td>\n",
              "      <td>30747</td>\n",
              "      <td>32553</td>\n",
              "      <td>11100</td>\n",
              "      <td>73625</td>\n",
              "      <td>4676</td>\n",
              "      <td>14439</td>\n",
              "      <td>3212</td>\n",
              "      <td>4314</td>\n",
              "      <td>23596</td>\n",
              "      <td>2208</td>\n",
              "      <td>1287</td>\n",
              "      <td>1652</td>\n",
              "      <td>1500</td>\n",
              "      <td>21303</td>\n",
              "      <td>113809</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>133</td>\n",
              "      <td>261</td>\n",
              "      <td>271</td>\n",
              "      <td>300</td>\n",
              "      <td>325</td>\n",
              "      <td>343</td>\n",
              "      <td>184</td>\n",
              "      <td>85</td>\n",
              "      <td>123</td>\n",
              "      <td>50</td>\n",
              "      <td>313</td>\n",
              "      <td>14</td>\n",
              "      <td>57</td>\n",
              "      <td>19</td>\n",
              "      <td>18</td>\n",
              "      <td>91</td>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>20</td>\n",
              "      <td>193</td>\n",
              "      <td>480</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "contacts_count     0.0    1.0    2.0     3.0   ...  18.0  19.0   20.0    21.0\n",
              "covid19_positive                               ...                           \n",
              "0                 33114  94550  89937  108674  ...  1652  1500  21303  113809\n",
              "1                   133    261    271     300  ...     9    20    193     480\n",
              "\n",
              "[2 rows x 22 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4_1GinP2b-P1",
        "outputId": "c7c92a48-b002-44b6-d6ed-48ddea2dd25a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from scipy.stats import chi2\n",
        "chi, pval, dof, exp = chi2_contingency(confusion_matrix)\n",
        "print('p-value is: ', pval)\n",
        "significance = 0.01\n",
        "p = 1 - significance\n",
        "critical_value = chi2.ppf(p, dof)\n",
        "print('chi=%.6f, critical value=%.6f\\n' % (chi, critical_value))\n",
        "if chi > critical_value:\n",
        "    print(\"\"\"At %.2f level of significance, we reject the null hypotheses and accept H1. \n",
        "They are not independent.\"\"\" % (significance))\n",
        "else:\n",
        "    print(\"\"\"At %.2f level of significance, we accept the null hypotheses. \n",
        "They are independent.\"\"\" % (significance))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "p-value is:  9.537846249159013e-55\n",
            "chi=317.281434, critical value=38.932173\n",
            "\n",
            "At 0.01 level of significance, we reject the null hypotheses and accept H1. \n",
            "They are not independent.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2w0n16UhcJd7",
        "outputId": "b85ca532-0240-418a-832b-6842fdefb2d1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "def cramers_stat(confusion_matrix):\n",
        "    chi2 = chi2_contingency(confusion_matrix)[0]\n",
        "    n = confusion_matrix.sum().sum()\n",
        "    return np.sqrt(chi2 / (n*(min(confusion_matrix.shape)-1)))\n",
        "insurCramers_v = cramers_stat(confusion_matrix)\n",
        "print(\"Cramér's V is a number between 0 and 1 that indicates how strongly two categorical variables are associated. It is based on Pearson's chi-squared statistic\")\n",
        "insurCramers_v"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cramér's V is a number between 0 and 1 that indicates how strongly two categorical variables are associated. It is based on Pearson's chi-squared statistic\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.01868765658265211"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mteYF9S0Na5V"
      },
      "source": [
        "### Age Vs Covid-19\n",
        "\n",
        "\n",
        "*   H0 = Age and Covid-19 are independent\n",
        "*   H1 = They are related in some way\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mjTtJuKjNa5b",
        "outputId": "2577e1b9-fd13-4c85-aef7-f63dd2a27c91",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df = data[[\"covid19_positive\",\"age\"]]\n",
        "df=df.dropna()\n",
        "df = df.sort_values('age')\n",
        "plt.figure(figsize=(16, 6))\n",
        "sns.countplot(x='age',data=df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f40189a79b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 126
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FSblVPgsNa5v",
        "outputId": "274f6a95-0c0d-4b04-dd3b-e9c0f9ceab26",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "plt.figure(figsize=(16, 6))\n",
        "sns.barplot(x ='age', y ='covid19_positive', data = df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f40186d5470>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 127
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7VeWtcz4Na58",
        "outputId": "d023d712-5954-4051-e6bd-e08f0b248a00",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "confusion_matrix = pd.crosstab(df['covid19_positive'], df['age'])\n",
        "confusion_matrix"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>age</th>\n",
              "      <th>0_10</th>\n",
              "      <th>100_110</th>\n",
              "      <th>10_20</th>\n",
              "      <th>20_30</th>\n",
              "      <th>30_40</th>\n",
              "      <th>40_50</th>\n",
              "      <th>50_60</th>\n",
              "      <th>60_70</th>\n",
              "      <th>70_80</th>\n",
              "      <th>80_90</th>\n",
              "      <th>90_100</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>covid19_positive</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5579</td>\n",
              "      <td>73</td>\n",
              "      <td>27561</td>\n",
              "      <td>200984</td>\n",
              "      <td>277466</td>\n",
              "      <td>193011</td>\n",
              "      <td>111512</td>\n",
              "      <td>65645</td>\n",
              "      <td>22327</td>\n",
              "      <td>4383</td>\n",
              "      <td>868</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>161</td>\n",
              "      <td>608</td>\n",
              "      <td>801</td>\n",
              "      <td>528</td>\n",
              "      <td>489</td>\n",
              "      <td>338</td>\n",
              "      <td>230</td>\n",
              "      <td>133</td>\n",
              "      <td>52</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "age               0_10  100_110  10_20   20_30  ...  60_70  70_80  80_90  90_100\n",
              "covid19_positive                                ...                             \n",
              "0                 5579       73  27561  200984  ...  65645  22327   4383     868\n",
              "1                    8        6    161     608  ...    338    230    133      52\n",
              "\n",
              "[2 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zxd1PgvSNa6G",
        "outputId": "96547c3d-d0a9-48ba-dc3a-85c9eee9ae51",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from scipy.stats import chi2\n",
        "chi, pval, dof, exp = chi2_contingency(confusion_matrix)\n",
        "print('p-value is: ', pval)\n",
        "significance = 0.01\n",
        "p = 1 - significance\n",
        "critical_value = chi2.ppf(p, dof)\n",
        "print('chi=%.6f, critical value=%.6f\\n' % (chi, critical_value))\n",
        "if chi > critical_value:\n",
        "    print(\"\"\"At %.2f level of significance, we reject the null hypotheses and accept H1. \n",
        "They are not independent.\"\"\" % (significance))\n",
        "else:\n",
        "    print(\"\"\"At %.2f level of significance, we accept the null hypotheses. \n",
        "They are independent.\"\"\" % (significance))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "p-value is:  0.0\n",
            "chi=2110.120625, critical value=23.209251\n",
            "\n",
            "At 0.01 level of significance, we reject the null hypotheses and accept H1. \n",
            "They are not independent.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oIDmLCJtNa6X",
        "outputId": "f1954bef-c665-4a12-e60d-86cb0ac05ffb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "def cramers_stat(confusion_matrix):\n",
        "    chi2 = chi2_contingency(confusion_matrix)[0]\n",
        "    n = confusion_matrix.sum().sum()\n",
        "    return np.sqrt(chi2 / (n*(min(confusion_matrix.shape)-1)))\n",
        "insurCramers_v = cramers_stat(confusion_matrix)\n",
        "print(\"Cramér's V is a number between 0 and 1 that indicates how strongly two categorical variables are associated. It is based on Pearson's chi-squared statistic\")\n",
        "insurCramers_v"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cramér's V is a number between 0 and 1 that indicates how strongly two categorical variables are associated. It is based on Pearson's chi-squared statistic\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.04808112577083457"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5txGrR6ox_JD"
      },
      "source": [
        "### Blood type Vs Covid-19\n",
        "\n",
        "\n",
        "*   H0 = Blood type and Covid-19 are independent\n",
        "*   H1 = They are related in some way\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aBy3SA8-x_Jv",
        "outputId": "a2ab9581-7925-47df-c339-11f0268df721",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "df = data[[\"covid19_positive\",\"blood_type\"]]\n",
        "df=df.dropna()\n",
        "df = df[df[\"blood_type\"] != \"unknown\"]\n",
        "df = df[df[\"blood_type\"] != \"undefined\"]\n",
        "sns.countplot(x='blood_type',data=df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f40187fdf28>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 131
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e7ysrt9jz7nM",
        "outputId": "78ff594f-b490-410d-be55-90eef9daee1b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "sns.barplot(x ='blood_type', y ='covid19_positive', data = df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4018570160>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 132
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dl7mAbaax_J7",
        "outputId": "ec68dad6-3547-437f-fb28-b442641a40cf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        }
      },
      "source": [
        "confusion_matrix = pd.crosstab(df['covid19_positive'], df['blood_type'])\n",
        "confusion_matrix"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>blood_type</th>\n",
              "      <th>abn</th>\n",
              "      <th>abp</th>\n",
              "      <th>an</th>\n",
              "      <th>ap</th>\n",
              "      <th>bn</th>\n",
              "      <th>bp</th>\n",
              "      <th>on</th>\n",
              "      <th>op</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>covid19_positive</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8902</td>\n",
              "      <td>30361</td>\n",
              "      <td>44007</td>\n",
              "      <td>176100</td>\n",
              "      <td>14701</td>\n",
              "      <td>58523</td>\n",
              "      <td>77741</td>\n",
              "      <td>200859</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>15</td>\n",
              "      <td>113</td>\n",
              "      <td>123</td>\n",
              "      <td>680</td>\n",
              "      <td>34</td>\n",
              "      <td>226</td>\n",
              "      <td>247</td>\n",
              "      <td>717</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "blood_type         abn    abp     an      ap     bn     bp     on      op\n",
              "covid19_positive                                                         \n",
              "0                 8902  30361  44007  176100  14701  58523  77741  200859\n",
              "1                   15    113    123     680     34    226    247     717"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CLJ-Dagvx_KF",
        "outputId": "78dc3a2d-6043-4c86-86e4-66a326f4171f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "source": [
        "from scipy.stats import chi2\n",
        "chi, pval, dof, exp = chi2_contingency(confusion_matrix)\n",
        "print('p-value is: ', pval)\n",
        "significance = 0.01\n",
        "p = 1 - significance\n",
        "critical_value = chi2.ppf(p, dof)\n",
        "print('chi=%.6f, critical value=%.6f\\n' % (chi, critical_value))\n",
        "if chi > critical_value:\n",
        "    print(\"\"\"At %.2f level of significance, we reject the null hypotheses and accept H1. \n",
        "They are not independent.\"\"\" % (significance))\n",
        "else:\n",
        "    print(\"\"\"At %.2f level of significance, we accept the null hypotheses. \n",
        "They are independent.\"\"\" % (significance))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "p-value is:  4.25816213574962e-05\n",
            "chi=31.889247, critical value=18.475307\n",
            "\n",
            "At 0.01 level of significance, we reject the null hypotheses and accept H1. \n",
            "They are not independent.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tMUznkB9x_KQ",
        "outputId": "79684167-1a65-43b9-864e-0f64f719756c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "def cramers_stat(confusion_matrix):\n",
        "    chi2 = chi2_contingency(confusion_matrix)[0]\n",
        "    n = confusion_matrix.sum().sum()\n",
        "    return np.sqrt(chi2 / (n*(min(confusion_matrix.shape)-1)))\n",
        "insurCramers_v = cramers_stat(confusion_matrix)\n",
        "print(\"Cramér's V is a number between 0 and 1 that indicates how strongly two categorical variables are associated. It is based on Pearson's chi-squared statistic\")\n",
        "insurCramers_v"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cramér's V is a number between 0 and 1 that indicates how strongly two categorical variables are associated. It is based on Pearson's chi-squared statistic\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.007210548491075127"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bPjxEcFPao1t"
      },
      "source": [
        "### Working Vs Covid-19\n",
        "\n",
        "\n",
        "*   H0 = Working and Covid-19 are independent\n",
        "*   H1 = They are related in some way\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h9Cm8V-6RjzS",
        "outputId": "caf43800-4288-4592-aed2-726c649288e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 438
        }
      },
      "source": [
        "df = data[[\"working\",\"covid19_positive\"]]\n",
        "df=df.dropna()\n",
        "sns.factorplot(x =\"working\", y =\"covid19_positive\", data = df); "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:3704: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
            "  warnings.warn(msg)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JEB7Ch7EJj9g",
        "outputId": "420c7c28-a7da-413a-82f0-1af98f102b93",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        }
      },
      "source": [
        "confusion_matrix = pd.crosstab(df['covid19_positive'], df['working'])\n",
        "confusion_matrix"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>working</th>\n",
              "      <th>home</th>\n",
              "      <th>never</th>\n",
              "      <th>stopped</th>\n",
              "      <th>travel critical</th>\n",
              "      <th>travel non critical</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>covid19_positive</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>66193</td>\n",
              "      <td>177287</td>\n",
              "      <td>363736</td>\n",
              "      <td>211389</td>\n",
              "      <td>86619</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>212</td>\n",
              "      <td>1024</td>\n",
              "      <td>833</td>\n",
              "      <td>909</td>\n",
              "      <td>330</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "working            home   never  stopped  travel critical  travel non critical\n",
              "covid19_positive                                                              \n",
              "0                 66193  177287   363736           211389                86619\n",
              "1                   212    1024      833              909                  330"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1udpKr5yMJJk",
        "outputId": "dd4dad1f-98e8-423b-ec6c-3967685db559",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "source": [
        "from scipy.stats import chi2\n",
        "chi, pval, dof, exp = chi2_contingency(confusion_matrix)\n",
        "print('p-value is: ', pval)\n",
        "significance = 0.01\n",
        "p = 1 - significance\n",
        "critical_value = chi2.ppf(p, dof)\n",
        "print('chi=%.6f, critical value=%.6f\\n' % (chi, critical_value))\n",
        "if chi > critical_value:\n",
        "    print(\"\"\"At %.2f level of significance, we reject the null hypotheses and accept H1. \n",
        "They are not independent.\"\"\" % (significance))\n",
        "else:\n",
        "    print(\"\"\"At %.2f level of significance, we accept the null hypotheses. \n",
        "They are independent.\"\"\" % (significance))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "p-value is:  8.227975223320316e-92\n",
            "chi=430.212116, critical value=13.276704\n",
            "\n",
            "At 0.01 level of significance, we reject the null hypotheses and accept H1. \n",
            "They are not independent.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gdzqWv3AMcd7",
        "outputId": "a8ce1d9f-fa0d-4c40-f010-85db6869d07a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        }
      },
      "source": [
        "def cramers_stat(confusion_matrix):\n",
        "    chi2 = chi2_contingency(confusion_matrix)[0]\n",
        "    n = confusion_matrix.sum().sum()\n",
        "    return np.sqrt(chi2 / (n*(min(confusion_matrix.shape)-1)))\n",
        "insurCramers_v = cramers_stat(confusion_matrix)\n",
        "print(\"Cramér's V is a number between 0 and 1 that indicates how strongly two categorical variables are associated. \\nIt is based on Pearson's chi-squared statistic\")\n",
        "insurCramers_v"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cramér's V is a number between 0 and 1 that indicates how strongly two categorical variables are associated. \n",
            "It is based on Pearson's chi-squared statistic\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.021760616953383376"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YMD3YKWZmChE"
      },
      "source": [
        "## Model building"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cEmXIzzGl_Ue",
        "outputId": "168d2603-b8d0-411c-f10b-48b80cf460b2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from pandas.plotting import scatter_matrix\n",
        "from sklearn import preprocessing\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.preprocessing import RobustScaler\n",
        "import statsmodels.api as sm"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EPjV8KQdqyUh",
        "outputId": "b945c641-5e9e-4d9a-f6dc-0b988a3711b5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "data.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 912768 entries, 1 to 912768\n",
            "Data columns (total 60 columns):\n",
            " #   Column                                       Non-Null Count   Dtype  \n",
            "---  ------                                       --------------   -----  \n",
            " 0   survey_date                                  912768 non-null  object \n",
            " 1   region                                       154345 non-null  object \n",
            " 2   country                                      912666 non-null  object \n",
            " 3   ip_latitude                                  912768 non-null  float64\n",
            " 4   ip_longitude                                 912768 non-null  float64\n",
            " 5   ip_accuracy                                  912756 non-null  float64\n",
            " 6   sex                                          912768 non-null  object \n",
            " 7   age                                          912763 non-null  object \n",
            " 8   height                                       912768 non-null  object \n",
            " 9   weight                                       912768 non-null  object \n",
            " 10  bmi                                          912764 non-null  float64\n",
            " 11  blood_type                                   912768 non-null  object \n",
            " 12  insurance                                    224025 non-null  object \n",
            " 13  income                                       224025 non-null  object \n",
            " 14  race                                         224025 non-null  object \n",
            " 15  immigrant                                    224025 non-null  object \n",
            " 16  smoking                                      910911 non-null  object \n",
            " 17  alcohol                                      910871 non-null  float64\n",
            " 18  cannabis                                     727728 non-null  float64\n",
            " 19  amphetamines                                 708596 non-null  float64\n",
            " 20  cocaine                                      706457 non-null  float64\n",
            " 21  lsd                                          710824 non-null  float64\n",
            " 22  mdma                                         715215 non-null  float64\n",
            " 23  contacts_count                               908521 non-null  float64\n",
            " 24  house_count                                  912746 non-null  float64\n",
            " 25  public_transport_count                       224025 non-null  float64\n",
            " 26  working                                      908532 non-null  object \n",
            " 27  worried                                      224025 non-null  float64\n",
            " 28  rate_reducing_risk_single                    912768 non-null  int64  \n",
            " 29  rate_reducing_risk_single_social_distancing  912768 non-null  int64  \n",
            " 30  rate_reducing_risk_single_washing_hands      912768 non-null  int64  \n",
            " 31  rate_reducing_risk_house                     912768 non-null  int64  \n",
            " 32  rate_reducing_risk_house_social_distancing   912768 non-null  int64  \n",
            " 33  rate_reducing_risk_house_washing_hands       912768 non-null  int64  \n",
            " 34  rate_reducing_risk_single_sanitizer          912768 non-null  int64  \n",
            " 35  rate_reducing_mask                           910911 non-null  float64\n",
            " 36  rate_reducing_mask_type                      197485 non-null  object \n",
            " 37  rate_government_action                       912768 non-null  int64  \n",
            " 38  rate_government_control                      912768 non-null  int64  \n",
            " 39  rate_government_spend                        912768 non-null  int64  \n",
            " 40  covid19_positive                             912768 non-null  int64  \n",
            " 41  covid19_symptoms                             912768 non-null  int64  \n",
            " 42  covid19_contact                              912768 non-null  int64  \n",
            " 43  asthma                                       912768 non-null  int64  \n",
            " 44  kidney_disease                               912768 non-null  int64  \n",
            " 45  liver_disease                                912768 non-null  int64  \n",
            " 46  compromised_immune                           912768 non-null  int64  \n",
            " 47  heart_disease                                912768 non-null  int64  \n",
            " 48  lung_disease                                 912768 non-null  int64  \n",
            " 49  diabetes                                     912768 non-null  int64  \n",
            " 50  hiv_positive                                 912768 non-null  int64  \n",
            " 51  hypertension                                 912768 non-null  int64  \n",
            " 52  other_chronic                                912768 non-null  int64  \n",
            " 53  nursing_home                                 912768 non-null  int64  \n",
            " 54  health_worker                                912768 non-null  int64  \n",
            " 55  prescription_medication                      272265 non-null  object \n",
            " 56  opinion_infection                            747514 non-null  float64\n",
            " 57  opinion_mortality                            747514 non-null  float64\n",
            " 58  risk_infection                               912754 non-null  float64\n",
            " 59  risk_mortality                               912754 non-null  float64\n",
            "dtypes: float64(19), int64(25), object(16)\n",
            "memory usage: 424.8+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PKDHiz71Ji2U"
      },
      "source": [
        "Selecting features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w_fb64kOtEkU"
      },
      "source": [
        "df1 = data[['sex', 'age', 'bmi', 'blood_type',\n",
        "       'insurance', 'income', 'race', 'immigrant', 'smoking', 'alcohol',\n",
        "       'cannabis', 'amphetamines', 'cocaine', 'lsd', 'mdma', 'contacts_count',\n",
        "       'house_count', 'public_transport_count', 'working', 'worried',\n",
        "       'rate_reducing_risk_single_social_distancing',\n",
        "       'rate_reducing_risk_single_washing_hands',\n",
        "       'rate_reducing_risk_house_social_distancing',\n",
        "       'rate_reducing_risk_house_washing_hands',\n",
        "       'rate_reducing_risk_single_sanitizer', 'rate_reducing_mask',\n",
        "       'covid19_symptoms', 'covid19_contact', 'asthma', 'kidney_disease',\n",
        "       'liver_disease', 'compromised_immune', 'heart_disease', 'lung_disease',\n",
        "       'diabetes', 'hiv_positive', 'hypertension', 'other_chronic',\n",
        "       'nursing_home', 'health_worker',\n",
        "       'opinion_infection', 'opinion_mortality', 'risk_infection',\n",
        "       'risk_mortality']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4oxdQBGeJsni"
      },
      "source": [
        "Since dataset is fairly large (~ 1 Million) we can drop all rows which has null values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8T09MTkZhNy1"
      },
      "source": [
        "df1 = df1.dropna() #dropping null values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JchdwlvRKW4N"
      },
      "source": [
        "Encoding categorical data for regression models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vZGP7qBqrgjz",
        "outputId": "38df7469-2a5c-4e8f-d9aa-c0ddcab3b2e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        }
      },
      "source": [
        "df = pd.get_dummies(df1,columns=['sex', 'age','blood_type',\n",
        "       'insurance', 'income', 'race', 'immigrant', 'smoking', 'working'],drop_first=True)\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>bmi</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>cannabis</th>\n",
              "      <th>amphetamines</th>\n",
              "      <th>cocaine</th>\n",
              "      <th>lsd</th>\n",
              "      <th>mdma</th>\n",
              "      <th>contacts_count</th>\n",
              "      <th>house_count</th>\n",
              "      <th>public_transport_count</th>\n",
              "      <th>worried</th>\n",
              "      <th>rate_reducing_risk_single_social_distancing</th>\n",
              "      <th>rate_reducing_risk_single_washing_hands</th>\n",
              "      <th>rate_reducing_risk_house_social_distancing</th>\n",
              "      <th>rate_reducing_risk_house_washing_hands</th>\n",
              "      <th>rate_reducing_risk_single_sanitizer</th>\n",
              "      <th>rate_reducing_mask</th>\n",
              "      <th>covid19_symptoms</th>\n",
              "      <th>covid19_contact</th>\n",
              "      <th>asthma</th>\n",
              "      <th>kidney_disease</th>\n",
              "      <th>liver_disease</th>\n",
              "      <th>compromised_immune</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>lung_disease</th>\n",
              "      <th>diabetes</th>\n",
              "      <th>hiv_positive</th>\n",
              "      <th>hypertension</th>\n",
              "      <th>other_chronic</th>\n",
              "      <th>nursing_home</th>\n",
              "      <th>health_worker</th>\n",
              "      <th>opinion_infection</th>\n",
              "      <th>opinion_mortality</th>\n",
              "      <th>risk_infection</th>\n",
              "      <th>risk_mortality</th>\n",
              "      <th>sex_male</th>\n",
              "      <th>sex_other</th>\n",
              "      <th>age_100_110</th>\n",
              "      <th>age_10_20</th>\n",
              "      <th>age_20_30</th>\n",
              "      <th>age_30_40</th>\n",
              "      <th>age_40_50</th>\n",
              "      <th>age_50_60</th>\n",
              "      <th>age_60_70</th>\n",
              "      <th>age_70_80</th>\n",
              "      <th>age_80_90</th>\n",
              "      <th>age_90_100</th>\n",
              "      <th>blood_type_abp</th>\n",
              "      <th>blood_type_an</th>\n",
              "      <th>blood_type_ap</th>\n",
              "      <th>blood_type_bn</th>\n",
              "      <th>blood_type_bp</th>\n",
              "      <th>blood_type_on</th>\n",
              "      <th>blood_type_op</th>\n",
              "      <th>blood_type_unknown</th>\n",
              "      <th>insurance_no</th>\n",
              "      <th>insurance_yes</th>\n",
              "      <th>income_gov</th>\n",
              "      <th>income_high</th>\n",
              "      <th>income_low</th>\n",
              "      <th>income_med</th>\n",
              "      <th>race_black</th>\n",
              "      <th>race_blank</th>\n",
              "      <th>race_hispanic</th>\n",
              "      <th>race_mixed</th>\n",
              "      <th>race_other</th>\n",
              "      <th>race_white</th>\n",
              "      <th>immigrant_immigrant</th>\n",
              "      <th>immigrant_native</th>\n",
              "      <th>smoking_quit0</th>\n",
              "      <th>smoking_quit10</th>\n",
              "      <th>smoking_quit5</th>\n",
              "      <th>smoking_vape</th>\n",
              "      <th>smoking_yesheavy</th>\n",
              "      <th>smoking_yeslight</th>\n",
              "      <th>smoking_yesmedium</th>\n",
              "      <th>working_never</th>\n",
              "      <th>working_stopped</th>\n",
              "      <th>working_travel critical</th>\n",
              "      <th>working_travel non critical</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>47938</th>\n",
              "      <td>25.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.059</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47939</th>\n",
              "      <td>23.2</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.062</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47940</th>\n",
              "      <td>32.8</td>\n",
              "      <td>11.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>-2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.129</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47941</th>\n",
              "      <td>37.8</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.118</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47945</th>\n",
              "      <td>35.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.559</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        bmi  alcohol  ...  working_travel critical  working_travel non critical\n",
              "ID                    ...                                                      \n",
              "47938  25.2      2.0  ...                        0                            0\n",
              "47939  23.2     -1.0  ...                        0                            0\n",
              "47940  32.8     11.0  ...                        0                            0\n",
              "47941  37.8      1.0  ...                        0                            0\n",
              "47945  35.5      0.0  ...                        0                            0\n",
              "\n",
              "[5 rows x 80 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZQyZHIWZvUdT",
        "outputId": "005a75c4-8b33-41ba-c9cb-ebe560ea32e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        }
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>bmi</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>cannabis</th>\n",
              "      <th>amphetamines</th>\n",
              "      <th>cocaine</th>\n",
              "      <th>lsd</th>\n",
              "      <th>mdma</th>\n",
              "      <th>contacts_count</th>\n",
              "      <th>house_count</th>\n",
              "      <th>public_transport_count</th>\n",
              "      <th>worried</th>\n",
              "      <th>rate_reducing_risk_single_social_distancing</th>\n",
              "      <th>rate_reducing_risk_single_washing_hands</th>\n",
              "      <th>rate_reducing_risk_house_social_distancing</th>\n",
              "      <th>rate_reducing_risk_house_washing_hands</th>\n",
              "      <th>rate_reducing_risk_single_sanitizer</th>\n",
              "      <th>rate_reducing_mask</th>\n",
              "      <th>covid19_symptoms</th>\n",
              "      <th>covid19_contact</th>\n",
              "      <th>asthma</th>\n",
              "      <th>kidney_disease</th>\n",
              "      <th>liver_disease</th>\n",
              "      <th>compromised_immune</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>lung_disease</th>\n",
              "      <th>diabetes</th>\n",
              "      <th>hiv_positive</th>\n",
              "      <th>hypertension</th>\n",
              "      <th>other_chronic</th>\n",
              "      <th>nursing_home</th>\n",
              "      <th>health_worker</th>\n",
              "      <th>opinion_infection</th>\n",
              "      <th>opinion_mortality</th>\n",
              "      <th>risk_infection</th>\n",
              "      <th>risk_mortality</th>\n",
              "      <th>sex_male</th>\n",
              "      <th>sex_other</th>\n",
              "      <th>age_100_110</th>\n",
              "      <th>age_10_20</th>\n",
              "      <th>age_20_30</th>\n",
              "      <th>age_30_40</th>\n",
              "      <th>age_40_50</th>\n",
              "      <th>age_50_60</th>\n",
              "      <th>age_60_70</th>\n",
              "      <th>age_70_80</th>\n",
              "      <th>age_80_90</th>\n",
              "      <th>age_90_100</th>\n",
              "      <th>blood_type_abp</th>\n",
              "      <th>blood_type_an</th>\n",
              "      <th>blood_type_ap</th>\n",
              "      <th>blood_type_bn</th>\n",
              "      <th>blood_type_bp</th>\n",
              "      <th>blood_type_on</th>\n",
              "      <th>blood_type_op</th>\n",
              "      <th>blood_type_unknown</th>\n",
              "      <th>insurance_no</th>\n",
              "      <th>insurance_yes</th>\n",
              "      <th>income_gov</th>\n",
              "      <th>income_high</th>\n",
              "      <th>income_low</th>\n",
              "      <th>income_med</th>\n",
              "      <th>race_black</th>\n",
              "      <th>race_blank</th>\n",
              "      <th>race_hispanic</th>\n",
              "      <th>race_mixed</th>\n",
              "      <th>race_other</th>\n",
              "      <th>race_white</th>\n",
              "      <th>immigrant_immigrant</th>\n",
              "      <th>immigrant_native</th>\n",
              "      <th>smoking_quit0</th>\n",
              "      <th>smoking_quit10</th>\n",
              "      <th>smoking_quit5</th>\n",
              "      <th>smoking_vape</th>\n",
              "      <th>smoking_yesheavy</th>\n",
              "      <th>smoking_yeslight</th>\n",
              "      <th>smoking_yesmedium</th>\n",
              "      <th>working_never</th>\n",
              "      <th>working_stopped</th>\n",
              "      <th>working_travel critical</th>\n",
              "      <th>working_travel non critical</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.00000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "      <td>41155.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>28.593955</td>\n",
              "      <td>3.362605</td>\n",
              "      <td>4.666019</td>\n",
              "      <td>-0.716171</td>\n",
              "      <td>-0.703948</td>\n",
              "      <td>-0.789673</td>\n",
              "      <td>-0.792881</td>\n",
              "      <td>7.171061</td>\n",
              "      <td>2.976528</td>\n",
              "      <td>0.200680</td>\n",
              "      <td>3.465144</td>\n",
              "      <td>0.998372</td>\n",
              "      <td>1.06874</td>\n",
              "      <td>0.825781</td>\n",
              "      <td>0.891556</td>\n",
              "      <td>0.457636</td>\n",
              "      <td>3.587535</td>\n",
              "      <td>0.018661</td>\n",
              "      <td>0.044320</td>\n",
              "      <td>0.146349</td>\n",
              "      <td>0.003693</td>\n",
              "      <td>0.006682</td>\n",
              "      <td>0.057660</td>\n",
              "      <td>0.014652</td>\n",
              "      <td>0.016985</td>\n",
              "      <td>0.052727</td>\n",
              "      <td>0.005370</td>\n",
              "      <td>0.133447</td>\n",
              "      <td>0.075519</td>\n",
              "      <td>0.002211</td>\n",
              "      <td>0.070295</td>\n",
              "      <td>33.385858</td>\n",
              "      <td>19.841696</td>\n",
              "      <td>13.107592</td>\n",
              "      <td>0.366113</td>\n",
              "      <td>0.443907</td>\n",
              "      <td>0.006050</td>\n",
              "      <td>0.000121</td>\n",
              "      <td>0.029571</td>\n",
              "      <td>0.198542</td>\n",
              "      <td>0.303025</td>\n",
              "      <td>0.228939</td>\n",
              "      <td>0.135439</td>\n",
              "      <td>0.077269</td>\n",
              "      <td>0.023278</td>\n",
              "      <td>0.002017</td>\n",
              "      <td>0.000316</td>\n",
              "      <td>0.032341</td>\n",
              "      <td>0.045851</td>\n",
              "      <td>0.200680</td>\n",
              "      <td>0.017179</td>\n",
              "      <td>0.065508</td>\n",
              "      <td>0.081861</td>\n",
              "      <td>0.236958</td>\n",
              "      <td>0.311092</td>\n",
              "      <td>0.192881</td>\n",
              "      <td>0.742364</td>\n",
              "      <td>0.025343</td>\n",
              "      <td>0.283635</td>\n",
              "      <td>0.140469</td>\n",
              "      <td>0.519742</td>\n",
              "      <td>0.017616</td>\n",
              "      <td>0.045001</td>\n",
              "      <td>0.059191</td>\n",
              "      <td>0.047309</td>\n",
              "      <td>0.010327</td>\n",
              "      <td>0.789868</td>\n",
              "      <td>0.079553</td>\n",
              "      <td>0.914737</td>\n",
              "      <td>0.064439</td>\n",
              "      <td>0.085943</td>\n",
              "      <td>0.080379</td>\n",
              "      <td>0.068837</td>\n",
              "      <td>0.014506</td>\n",
              "      <td>0.056445</td>\n",
              "      <td>0.065800</td>\n",
              "      <td>0.264658</td>\n",
              "      <td>0.359033</td>\n",
              "      <td>0.170915</td>\n",
              "      <td>0.101810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>7.407103</td>\n",
              "      <td>4.349344</td>\n",
              "      <td>9.956418</td>\n",
              "      <td>1.880716</td>\n",
              "      <td>1.190722</td>\n",
              "      <td>0.604197</td>\n",
              "      <td>0.595877</td>\n",
              "      <td>6.346282</td>\n",
              "      <td>1.499747</td>\n",
              "      <td>1.244003</td>\n",
              "      <td>0.853352</td>\n",
              "      <td>0.653380</td>\n",
              "      <td>0.61423</td>\n",
              "      <td>0.748374</td>\n",
              "      <td>0.697454</td>\n",
              "      <td>1.039658</td>\n",
              "      <td>1.157993</td>\n",
              "      <td>0.135327</td>\n",
              "      <td>0.205808</td>\n",
              "      <td>0.353460</td>\n",
              "      <td>0.060661</td>\n",
              "      <td>0.081471</td>\n",
              "      <td>0.233102</td>\n",
              "      <td>0.120157</td>\n",
              "      <td>0.129215</td>\n",
              "      <td>0.223492</td>\n",
              "      <td>0.073084</td>\n",
              "      <td>0.340061</td>\n",
              "      <td>0.264231</td>\n",
              "      <td>0.046971</td>\n",
              "      <td>0.255647</td>\n",
              "      <td>23.156779</td>\n",
              "      <td>21.357809</td>\n",
              "      <td>16.977553</td>\n",
              "      <td>0.880807</td>\n",
              "      <td>0.496850</td>\n",
              "      <td>0.077549</td>\n",
              "      <td>0.011022</td>\n",
              "      <td>0.169403</td>\n",
              "      <td>0.398907</td>\n",
              "      <td>0.459572</td>\n",
              "      <td>0.420155</td>\n",
              "      <td>0.342196</td>\n",
              "      <td>0.267021</td>\n",
              "      <td>0.150786</td>\n",
              "      <td>0.044864</td>\n",
              "      <td>0.017770</td>\n",
              "      <td>0.176907</td>\n",
              "      <td>0.209165</td>\n",
              "      <td>0.400514</td>\n",
              "      <td>0.129939</td>\n",
              "      <td>0.247424</td>\n",
              "      <td>0.274157</td>\n",
              "      <td>0.425221</td>\n",
              "      <td>0.462946</td>\n",
              "      <td>0.394565</td>\n",
              "      <td>0.437338</td>\n",
              "      <td>0.157167</td>\n",
              "      <td>0.450767</td>\n",
              "      <td>0.347477</td>\n",
              "      <td>0.499616</td>\n",
              "      <td>0.131554</td>\n",
              "      <td>0.207308</td>\n",
              "      <td>0.235984</td>\n",
              "      <td>0.212301</td>\n",
              "      <td>0.101096</td>\n",
              "      <td>0.407407</td>\n",
              "      <td>0.270603</td>\n",
              "      <td>0.279276</td>\n",
              "      <td>0.245537</td>\n",
              "      <td>0.280284</td>\n",
              "      <td>0.271882</td>\n",
              "      <td>0.253180</td>\n",
              "      <td>0.119566</td>\n",
              "      <td>0.230782</td>\n",
              "      <td>0.247935</td>\n",
              "      <td>0.441156</td>\n",
              "      <td>0.479723</td>\n",
              "      <td>0.376439</td>\n",
              "      <td>0.302402</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>10.500000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-2.000000</td>\n",
              "      <td>-2.000000</td>\n",
              "      <td>-2.000000</td>\n",
              "      <td>-2.000000</td>\n",
              "      <td>-2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-2.000000</td>\n",
              "      <td>-2.00000</td>\n",
              "      <td>-2.000000</td>\n",
              "      <td>-2.000000</td>\n",
              "      <td>-2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.050000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>23.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.050000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>27.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.088000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>32.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>0.447000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>91.900000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.00000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                bmi  ...  working_travel non critical\n",
              "count  41155.000000  ...                 41155.000000\n",
              "mean      28.593955  ...                     0.101810\n",
              "std        7.407103  ...                     0.302402\n",
              "min       10.500000  ...                     0.000000\n",
              "25%       23.500000  ...                     0.000000\n",
              "50%       27.000000  ...                     0.000000\n",
              "75%       32.000000  ...                     0.000000\n",
              "max       91.900000  ...                     1.000000\n",
              "\n",
              "[8 rows x 80 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "80lzUowVkVMe",
        "outputId": "12964ebd-589c-40f0-ad11-b0885ad412d5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "pd.set_option('display.max_columns', None)\n",
        "pd.set_option('display.max_rows', None)\n",
        "df.columns.tolist()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['bmi',\n",
              " 'alcohol',\n",
              " 'cannabis',\n",
              " 'amphetamines',\n",
              " 'cocaine',\n",
              " 'lsd',\n",
              " 'mdma',\n",
              " 'contacts_count',\n",
              " 'house_count',\n",
              " 'public_transport_count',\n",
              " 'worried',\n",
              " 'rate_reducing_risk_single_social_distancing',\n",
              " 'rate_reducing_risk_single_washing_hands',\n",
              " 'rate_reducing_risk_house_social_distancing',\n",
              " 'rate_reducing_risk_house_washing_hands',\n",
              " 'rate_reducing_risk_single_sanitizer',\n",
              " 'rate_reducing_mask',\n",
              " 'covid19_symptoms',\n",
              " 'covid19_contact',\n",
              " 'asthma',\n",
              " 'kidney_disease',\n",
              " 'liver_disease',\n",
              " 'compromised_immune',\n",
              " 'heart_disease',\n",
              " 'lung_disease',\n",
              " 'diabetes',\n",
              " 'hiv_positive',\n",
              " 'hypertension',\n",
              " 'other_chronic',\n",
              " 'nursing_home',\n",
              " 'health_worker',\n",
              " 'opinion_infection',\n",
              " 'opinion_mortality',\n",
              " 'risk_infection',\n",
              " 'risk_mortality',\n",
              " 'sex_male',\n",
              " 'sex_other',\n",
              " 'age_100_110',\n",
              " 'age_10_20',\n",
              " 'age_20_30',\n",
              " 'age_30_40',\n",
              " 'age_40_50',\n",
              " 'age_50_60',\n",
              " 'age_60_70',\n",
              " 'age_70_80',\n",
              " 'age_80_90',\n",
              " 'age_90_100',\n",
              " 'blood_type_abp',\n",
              " 'blood_type_an',\n",
              " 'blood_type_ap',\n",
              " 'blood_type_bn',\n",
              " 'blood_type_bp',\n",
              " 'blood_type_on',\n",
              " 'blood_type_op',\n",
              " 'blood_type_unknown',\n",
              " 'insurance_no',\n",
              " 'insurance_yes',\n",
              " 'income_gov',\n",
              " 'income_high',\n",
              " 'income_low',\n",
              " 'income_med',\n",
              " 'race_black',\n",
              " 'race_blank',\n",
              " 'race_hispanic',\n",
              " 'race_mixed',\n",
              " 'race_other',\n",
              " 'race_white',\n",
              " 'immigrant_immigrant',\n",
              " 'immigrant_native',\n",
              " 'smoking_quit0',\n",
              " 'smoking_quit10',\n",
              " 'smoking_quit5',\n",
              " 'smoking_vape',\n",
              " 'smoking_yesheavy',\n",
              " 'smoking_yeslight',\n",
              " 'smoking_yesmedium',\n",
              " 'working_never',\n",
              " 'working_stopped',\n",
              " 'working_travel critical',\n",
              " 'working_travel non critical']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hfoDPUVVjMiM",
        "outputId": "bc4fc33a-64a0-4608-95d6-9c72b05cba45",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "corr = df.corr()\n",
        "plt.figure(figsize=(20,20))\n",
        "ax = sns.heatmap(\n",
        "    corr, \n",
        "    vmin=-1, vmax=1, center=0,\n",
        "    cmap=sns.diverging_palette(20, 220, n=200),\n",
        "    square=True\n",
        ")\n",
        "ax.set_xticklabels(\n",
        "    ax.get_xticklabels(),\n",
        "    rotation=45,\n",
        "    horizontalalignment='right'\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Text(0.5, 0, 'bmi'),\n",
              " Text(1.5, 0, 'alcohol'),\n",
              " Text(2.5, 0, 'cannabis'),\n",
              " Text(3.5, 0, 'amphetamines'),\n",
              " Text(4.5, 0, 'cocaine'),\n",
              " Text(5.5, 0, 'lsd'),\n",
              " Text(6.5, 0, 'mdma'),\n",
              " Text(7.5, 0, 'contacts_count'),\n",
              " Text(8.5, 0, 'house_count'),\n",
              " Text(9.5, 0, 'public_transport_count'),\n",
              " Text(10.5, 0, 'worried'),\n",
              " Text(11.5, 0, 'rate_reducing_risk_single_social_distancing'),\n",
              " Text(12.5, 0, 'rate_reducing_risk_single_washing_hands'),\n",
              " Text(13.5, 0, 'rate_reducing_risk_house_social_distancing'),\n",
              " Text(14.5, 0, 'rate_reducing_risk_house_washing_hands'),\n",
              " Text(15.5, 0, 'rate_reducing_risk_single_sanitizer'),\n",
              " Text(16.5, 0, 'rate_reducing_mask'),\n",
              " Text(17.5, 0, 'covid19_symptoms'),\n",
              " Text(18.5, 0, 'covid19_contact'),\n",
              " Text(19.5, 0, 'asthma'),\n",
              " Text(20.5, 0, 'kidney_disease'),\n",
              " Text(21.5, 0, 'liver_disease'),\n",
              " Text(22.5, 0, 'compromised_immune'),\n",
              " Text(23.5, 0, 'heart_disease'),\n",
              " Text(24.5, 0, 'lung_disease'),\n",
              " Text(25.5, 0, 'diabetes'),\n",
              " Text(26.5, 0, 'hiv_positive'),\n",
              " Text(27.5, 0, 'hypertension'),\n",
              " Text(28.5, 0, 'other_chronic'),\n",
              " Text(29.5, 0, 'nursing_home'),\n",
              " Text(30.5, 0, 'health_worker'),\n",
              " Text(31.5, 0, 'opinion_infection'),\n",
              " Text(32.5, 0, 'opinion_mortality'),\n",
              " Text(33.5, 0, 'risk_infection'),\n",
              " Text(34.5, 0, 'risk_mortality'),\n",
              " Text(35.5, 0, 'sex_male'),\n",
              " Text(36.5, 0, 'sex_other'),\n",
              " Text(37.5, 0, 'age_100_110'),\n",
              " Text(38.5, 0, 'age_10_20'),\n",
              " Text(39.5, 0, 'age_20_30'),\n",
              " Text(40.5, 0, 'age_30_40'),\n",
              " Text(41.5, 0, 'age_40_50'),\n",
              " Text(42.5, 0, 'age_50_60'),\n",
              " Text(43.5, 0, 'age_60_70'),\n",
              " Text(44.5, 0, 'age_70_80'),\n",
              " Text(45.5, 0, 'age_80_90'),\n",
              " Text(46.5, 0, 'age_90_100'),\n",
              " Text(47.5, 0, 'blood_type_abp'),\n",
              " Text(48.5, 0, 'blood_type_an'),\n",
              " Text(49.5, 0, 'blood_type_ap'),\n",
              " Text(50.5, 0, 'blood_type_bn'),\n",
              " Text(51.5, 0, 'blood_type_bp'),\n",
              " Text(52.5, 0, 'blood_type_on'),\n",
              " Text(53.5, 0, 'blood_type_op'),\n",
              " Text(54.5, 0, 'blood_type_unknown'),\n",
              " Text(55.5, 0, 'insurance_no'),\n",
              " Text(56.5, 0, 'insurance_yes'),\n",
              " Text(57.5, 0, 'income_gov'),\n",
              " Text(58.5, 0, 'income_high'),\n",
              " Text(59.5, 0, 'income_low'),\n",
              " Text(60.5, 0, 'income_med'),\n",
              " Text(61.5, 0, 'race_black'),\n",
              " Text(62.5, 0, 'race_blank'),\n",
              " Text(63.5, 0, 'race_hispanic'),\n",
              " Text(64.5, 0, 'race_mixed'),\n",
              " Text(65.5, 0, 'race_other'),\n",
              " Text(66.5, 0, 'race_white'),\n",
              " Text(67.5, 0, 'immigrant_immigrant'),\n",
              " Text(68.5, 0, 'immigrant_native'),\n",
              " Text(69.5, 0, 'smoking_quit0'),\n",
              " Text(70.5, 0, 'smoking_quit10'),\n",
              " Text(71.5, 0, 'smoking_quit5'),\n",
              " Text(72.5, 0, 'smoking_vape'),\n",
              " Text(73.5, 0, 'smoking_yesheavy'),\n",
              " Text(74.5, 0, 'smoking_yeslight'),\n",
              " Text(75.5, 0, 'smoking_yesmedium'),\n",
              " Text(76.5, 0, 'working_never'),\n",
              " Text(77.5, 0, 'working_stopped'),\n",
              " Text(78.5, 0, 'working_travel critical'),\n",
              " Text(79.5, 0, 'working_travel non critical')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
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GqOA6NFmUrF+9nn+8dVRbs687C7bx4hc+Xzf//D/yK4/8z5xaQexALT+2o0WnSL3k+i2p4vx9XXF+o2fNyL8eoknJ9a3g2KwXjsIcBd/LKp35x4ULLp2VgdZ8vyj5zlmbMVOdvdvzKy84RyaDkm1RLf6+V/A5Evl11wquWSXqpSdJSd0l30UK/n6qF/wdAEx1U+vqCgAAgHFXlPgDgExFiT8AQMvQ8w/YQbbXS1ocEY9PdFsAAAAAAJhsSnrIo3Xo+QcAAAAAAAC0KZJ/gCTbi2zfZ3uF7fttX2L7ZNs32v657aNsL7T9Q9vrbH9JaSCOnHlT3FG2b7Z9p+2bbP/WhK40AAAAAABoeyT/gKe9SNLfSTow/bxN0nGSzpb0MUmflPTjiDhY0hWS9i2YV5Luk3R8RPy2pE9I+qsWrw8AAAAAAJjmSP4BT3swItZGRF3SOklXR+PxrmslLZJ0gqSvSVJEXCVpY8G8kjRf0qW275H095IOHq4RtpfZXm179cUrvzLe6wgAAAAAwC5ht+fPVMMDP4Cn9TZN15te19U4V0Z7bvxY80rSX0q6NiJeb3uRpFXDVRQRyyUtl6TNTzye/1x7AAAAAACAIej5B+S7Xo3beWX7FEkLCuefL+nhNL10/JoFAAAAAAAwPJJ/QL5zJZ1ge52kN0j6j8L5/7ekv7Z9p+h1CwAAAAAAdgESEICkiFgv6ZCm10tHeO9Vw8z+eM68EXGzpBc3zffxnWw2AAAAAADAqEj+AZNYdM3Ijt2jK7/eWV2d2bGVSn4H4b0XzM+OPfGg/bNjJek9J708O/aMz389O/b8038vO3a3Wd3ZsbVtW7Njex/7z+xYSZr5/P2yY/s3b8iO7ZiTv/+2OP8YmlfNH7qyVs+PffTJzdmxkrT/Hrtlx3bOyF+/Eu7rHTtoUD1/JGFXq0XtiIHRhjB9pgUz8r8qlNRbHejLjq115F/gbn7wkexYSdprt3nZsfvOzN8Wt//yN9mxJ+yzMDu2bFfnn09LXryPBpxf+baBWnbsQK2eH1vJr1fVgmOznl9vbfu2/DZIiv78Y7ljXv51qFJwg069YOTxoliVXVs6omCoYrfmBiR35l8v5kT+sdkbZaO7u9ptBlYAACAASURBVJ7/OTKrYDOXHJ+u5n9v6evI/87ZWS3Yd7Nmq6c///zrLhlFv0UjY1f78z+rKwWfe1urBV/WJc0q+C5S357/vXNGd/5x0d+Rf3B2FsSWfN+TpHo9/1zd/tAvsmNLrt/ffTL/2HzpPntlx0rSy+fNLYqfaqoFf0+iddgLADCFlCT+ACBXSeIPAHKVJP4AAK1D8g+Tlu1Ftt+2E/Mvtb33eLZpPNg+3PZrJ7odAAAAAACg/ZH8w2S2SOnpujtoqaRJl/yTdLgkkn8AAAAAAKDlSP6hZWyfaftu22tsfzX15LsmlV1te98Ut8L2BbZvsv2A7TelKs6TdLztu2x/OM1/g+2fpJ9jm5b1Z7bXpmWdl+pYLOmSNP/MVP7TtPy/HaXde9q+ItW1ZnA5tv/U9j3p50OpbJHte5rmPdv2OWl6le3zbd9m+37bx9vukvQpSW9J7XrLuG50AAAAAACAJjzwAy1h+2A1nmZ7bEQ8bnt3SSslrYyIlbbfJekCSaemWfaSdJykAyV9R9K3JH1U0tkR8bpU5yxJvxsRPbYPkPQNSYttnyLpDyW9PCK22d49IjbY/kCaf7XthZJeL+nAiAjbo424fYGk6yLi9barkubYPlLSOyW9XJIl3Wr7Okkbx9gUHRFxVLrN95MRcbLtT0haHBEfyN2eAAAAAAAAO4Kef2iVkyRdGhGPS1JEbJB0jKTBx7B+VY1k36ArI6IeET+VtOcIdXZKusj2WkmXSjoolZ8s6eKI2Na0rKE2SeqR9E+23yBptEelnSTpwlRXLSI2pbZeERFbI+IpSZdLOn6UOgZdnn7focZtzGOyvcz2aturV1x8cc4sAAAAAAAAw6LnHyaL3qbpkZ6j/mFJv5Z0mBqJ657cyiNiwPZRkn5H0pskfUCNJN/OGtAzk+jdQ94fXK+aMs+3iFguabkkbdqyJXa2gQAAAAAAYPqi5x9a5RpJp6XbbZVu+71J0lvT+2dIumGMOrZImtv0er6kRyOiLuntkqqp/EeS3pluCx5c1jPmtz1H0vyI+J4aScTDRlnu1ZL+OM1XtT0/tfVU27Nsz1bjFuIb1EhGPtf2QtszJL1ujHUabr0AAAAAAGg7ttvyZ6oh+YeWiIh1kj4j6TrbayR9VtIH1UjS3a1G8u5Pxqjmbkm19NCND0v6gqR3pPoOlLQ1Lev7aowTuNr2XZLOTvOvkPTFVDZX0nfTsn8s6U9HWe6fSDox3V58h6SDIuInqb7bJN0q6UsRcWdE9KvxAI/b1EhC3pexea6VdBAP/AAAAAAAAK3Gbb9omYhYqcZDPpo961bbiFg65PWc9Lt/mPhDm6b/rGme89R4OnBzPZdJuqyp6KjMdv9ajQeIDC3/rBpJzKHlF6jxkJCh5Uuaph9XGvMvjUn4spy2AAAAAAAA7AxHMKQYMFlt3Jw/5l9l+9bset2Rn/ePrhnZsZPFfY88nh37Z9+4Kjv2ij99R3ZstW97dmx9YCA7VpIqLdon7ujMjh0YcWjOZ6sO9GXH1jq6smNLVQp659cLPhpL6q3Ua/nBk0TUCtpc8J3Cnfn7ettAPTu2q6M6dtAOqvb3jh2UbK7lHxjzCv4V62rr1q+nnt/mzduzh93VHnNn7UhzxvTEU/nX2T3yL2/qf3K454aNrDJj6HC/I+uYv/vYQUlvwYWos5p/M0/J9a1UR+RfLwacfyy36vpd8lnd11H22dtV68+OrXW25nO9Q/kbIwYK2tvCz+qBWv71vqPguC/hbVuyY6sz869vJce8VHbcuy//86nk87decGtjyblXcq2YLHqj4LtvpezYXDBv7tS7h7TAx77+3bZMOv3V2143pfYbPf8wbdn+c0mnDSm+NCI+MxHtAXK0KvEHYHorSfwBAADkmoLD47Ulkn+YtlKSj0QfAAAAAABoWzzwA5OG7UW275noduwKtpfa3nui2wEAAAAAANobyT9gYiyVRPIPAAAAAAC0FMk/TDZV2xfZXmf7h7Zn2j7c9i2277Z9he0FkmR7le3FaXoP2+vT9MG2b7N9V5rngFT+/zWV/x975FF3bb/G9k9sr7F9dSrb3faVqc5bbB+ays+xfXbTvPekXoyLbN87zPq8SdJiSZektsxs0bYEAAAAAADTHMk/TDYHSPp8RBws6UlJb5T0FUl/FhGHSlor6ZNj1PFeSf8QEYerkWR7yPZLJL1F0itSeU3SGcPNbPs5ki6S9MaIOExPPxTkXEl3pnZ8LLWreH0i4luSVks6IyIOj4hnPGrO9jLbq22vXnHxxRmLAAAAAABg8qlWKm35M9XwwA9MNg9GxF1p+g5J+0vaLSKuS2UrJV06Rh03S/pz28+XdHlE/Nz270g6UtLtbjxuaKak34ww/9GSro+IByUpIjak8uPUSEYqIq6xvdD2vML1WTRGvCJiuaTlkrRx85a2fCw6AAAAAADYNUj+YbLpbZquSdptlNgBPd17tXuwMCK+bvtWSb8n6Xu2/7skS1oZEf9rnNs7tB3PaIuevT7c4gsAAAAAAHaZqddXEdPNJkkbbR+fXr9d0mAvwPVq9OaTpDcNzmD7hZIeiIgLJH1b0qGSrpb0JtvPTTG7237BCMu8RdIJtvcbjE3lNyjdKmx7iaTHI2JzascRqfwISftlrNcWSXMz4gAAAAAAAHYYPf8wFbxD0hdtz5L0gKR3pvK/lfRN28skXdUU/2ZJb7fdL+k/Jf1VRGyw/XFJP7RdkdQv6f2Sfjl0YRHxWKrz8hT7G0m/K+kcSV+2fbekbaldknSZpDNtr5N0q6T7M9ZpRVqn7ZKOGTruHwAAAAAAU11FnugmQJIjGFIMmKw2b3gi+wSNzq7sevtr9fzYgVp2bG9BbF9BrCS16lr13PlzsmNf/9mV2bHf+cCbs2Nr27dlx0pSdeas7Nj6wEB2bCPXnafS3T12UBK1gmPI+f+TqhceE10dIz7g+1kGCs6Rjmr+dutQfpsHJskXpVo9f1vMcP761Sv5+6NesKtL9p1Utv8qBbvkF7/eMHZQsuf8/I7gJcdxybpJ0taevuzYGZ3552rJ9X7ejPx6oz+/vSXc0br/jfdG/kFUMph4ybFZKbl2Rtn5VHJel2hVmwec395N23ry2yBpfv/W/HbMWZAdO6Penx27veAzdVbBris5jqWyY7lEyedT0fm0PX/fbavmf/+eVSu7ZtVnzs6O7Yiy79W5Ss6Rnr78Y7OjWnatKPnO191fdq7m2hj5ba6UXJQl7fvcPSbHl74WOedf/rUtk07nvOWUKbXfuO0XAKaQksQfAOQqSfwBQK6p+ERMAGhH3PaLaS09GGTGkOK3R8TaiWgPAAAAAADAeOJfMdOM7XNsnz1M+SLb96TpxbYv2IG6PzYebWw12x9K4wcqIl4eEYcP+Wl54s/2Utt7t3o5AAAAAABgeiP5h2eJiNURcdYOzDps8s8Nk+JYs12V9CFJE33v5FJJJP8AAAAAAEBLTYqEDHZc6rF3n+1LbN9r+1u2Z9leb3uPFLPY9qqm2Q6zfbPtn9t+zzB1LrH93TQ9x/bFttfavtv2G0dox3mSZtq+K7Vlke2f2f6KpHsk7WP7Qturba+zfW7TvOttn2v7J2k5B6byV6b67rJ9p+25qW3X274q1f/FwcSi7dPT/PfYPr+p/qds/53tNZL+XI2k27W2rx1lu74mtWeN7atT2e62r0zb4Rbbh6byZ/SmTMtflH7utX1RWucf2p5p+02SFku6JK3bzNH2MQAAAAAAwI4i+dcefkvSFyLiJZI2S3rfGPGHSjpJ0jGSPjHG7ad/IWlTRLw0Ig6VdM1wQRHxUUnb022zZ6TiA1K7Do6IX0r684hYnJb/ysHkWfJ4RBwh6UJJg4m0syW9PyIOl3S8pO2p/ChJH5R0kKT9Jb0hrcP5ab0Ol/Qy26em+NmSbo2IwyLiU5IekXRiRJw43LrYfo6kiyS9MSIOk3RaeutcSXem7fAxSV8ZZbsNOkDS5yPiYElPpjq/JWm1pDPS9trePIPtZSlJuvrilflPlwUAAAAAABiKB360h19FxI1p+muSxrpl99sp4bQ99X47StJdI8SeLOmtgy8iYmNBu34ZEbc0vX6z7WVqHHd7qZG8uzu9d3n6fYekN6TpGyV91vYlki6PiIdsS9JtEfGAJNn+hqTjJPVLWhURj6XySySdIOlKSTVJlxW0+2hJ10fEg5IUERtS+XGS3pjKrrG90Pa8Mep6MCIGt+0dkhaNtfCIWC5puSRt3vBEWz4WHQAAAADQ/hp/wmOi0fOvPQxNEIWkAT29f7sz4lth6+CE7f3U6Mn3O6nn3FVD2tWbfteUktIRcZ6kP5I0U9KNg7cDD9PesdrfExG1HVqDPM3bWhp+vaSmdQMAAAAAANgVSP61h31tH5Om3ybpx5LWSzoylQ0dp+8PbXfbXihpiaTbR6n7R5LeP/jC9oJRYvttd47w3jw1koGbbO8p6ZRR6hlc1v4RsTYizk9tHEz+HWV7vzTW31vUWN/b1LiVeI/0UI/TJV03QtVbJM0dZdG3SDohJSxle/dUfoOkM1LZEjVuVd6sxrY+IpUfIWm/sdYtow0AAAAAAAA7jeRfe/iZpPfbvlfSAjXGzTtX0j/YXq1Gj7Nmd0u6Vo0k119GxCOj1P1pSQvSQyzWSBp2nLxkuaS70y23zxARayTdKek+SV9X45besXwoLfduNW7r/ddUfrukf5R0r6QHJV0REY9K+mharzWS7oiIb4/Szu+P9MCPdOvwMkmXp3X+l/TWOZKOTO05T9I7Uvllkna3vU7SByTdn7FuKyR9kQd+AAAAAACAVnIEQ4pNZbYXSfpuRBwywU3ZJVKPu7Mj4nUT3ZZd4eHHNmSfoHM9kF3v1oK7j+d05v+PoN7Tkx1b6R56N/roor8vO7a2bevYQUl11uyiduT6g3/8Znbs+191bFHd82flb7vnzptTVHeuTdvy9/UTW7Zlx5548P7ZsR2FIxZs7Mk/huZ2zyiqO9eWnt6xg5KZnSN1pH62gVrrRjaYNSO/Hdt6+7Njt/fnX7MWzM7/H0npcbF+w+bs2Ce25F9bjtx3z6J25Op99FfZsdW9FxXV3VEwQkYM5O+/WmdrzqeifR31lrRBkuSCz8mCgY8qBd/RW1XvgMoGairZJ0VtrucfmwOuZsdWB/I/F6KzKztWkuoFh+dALf/47Ox5Kjt2a0f+tXNOd/61vr+gvZL0yMb86+w+C3fLjq3V89sxo5J/vPVvfDw7tmPOWMN/P623kr+NJamjOvH9dCbNdbZAyTWgpy//e8usWv71woX7bt4ez23rUfHO/ea/tmXS6ZNvPmVK7TfGHwOAKaQk8QcAuUoSfwCQqyTxB6A9mSd+TAok/6a4iFgvaZf2+rN9q6Sh/8Z/e0SsbfWyI2KVpFXjVd9ErgsAAAAAAECrkfxDsYh4+US3YbyM17rY/pKkz0bETzPjl2ga3b4MAAAAAAAmBsk/oJDtasTT90el1380kW0CAAAAAAAYzsSPIgrsQrY/YvusNP33tq9J0yfZvsT26bbXpqcMn98031O2/y49/feYYV6vsr04xb7K9s22f2L7UttzUvlrbN9n+yeS3rDLVx4AAAAAgF2oWnFb/kw1JP8w3dwg6fg0vVjSHNudqex+SedLOknS4ZJeZvvUFDtb0q0RcVhE/HiY15Ik23tI+rikkyPiCEmrJf2p7W5JF0n6fUlHSvpvIzXQ9jLbq22v/tpXVo7bigMAAAAAgOmH234x3dwh6Ujb8yT1SvqJGknA4yX9X0mrIuIxSbJ9iaQTJF0pqSbpsqZ6hr4edLSkgyTdmJ5q1CXpZkkHSnowIn6e6v6apGXDNTAilktaLkkPP7ahLR+LDgAAAAAAdg2Sf5hWIqLf9oOSlkq6SdLdkk6U9CJJ69XolTecnuZx/oZ5PciSfhQRpz+j0D58J5sOAAAAAABQjNt+MR3dIOlsSden6fdKulPSbZJeaXsP21VJp0u6rrDuWyS9wvaLJMn2bNsvlnSfpEW2909xp49UAQAAAAAAwHgh+Yfp6AZJe0m6OSJ+LalH0g0R8aikj0q6VtIaSXdExLdLKk63DC+V9A3bdyvd8hsRPWrc5ntVeuDHb8ZrZQAAAAAAAEbiCIYUAyarjZu3ZJ+g1f7e/Ipd8HSi+nB3N4+stn1bfjM6u1rSjp5HH8qO7X7eC7Jjo78vO1aS/u1XG7JjP//Dm7Jj33jUS7NjuzvzR3dYMGdWfuysmdmxm7b1ZMdK0skvfn52bAwMZMf2z8hv84ySJ3hFPTu0p172ZLDuan78gPJjS1avUnDuDbiaHdvT15/fCElzugpGKinYJyVtLjmW5/dvzY7tmLdbdqwk1bZuyY6NOfl1Vwfyr3H13vxt4Ur+Ni45T6uVqfc/7I5hRwzZefWCbVxcd8GfCq16+GGl4O+VkmuhJFX7tucHO/+Y2xr5sbNmdGbHluyP4uOtYP1KtvNvNj2VHbv3/NnZsa1SL/guK0mV7u7s2JLPnJL91xv5+6Pk2ll0fkiKWv7nrzvyP9db9X3vqZ78z72Sz3VJcjV/X++29z5T79GxBT5z2Q/aMun052989ZTab1PvWxOASatlib8paDIk/qaiViX+pqJWJf6molYl/qaiyZD4w/RRkmiailqV+JuSJkHibypqVeJvKmpV4m8qKkn8AbtKe591AAAAAAAAmBAuuesMLdPm/8ICAAAAAAAApq9xS/7Z/pDt/AGjdn55S2x/dwfnfa/tM8e7TUOW8SXbB43y/irbi3ei/k/ZPnkn5p+QPvi2F9u+YIyYon1re73tPdL0qPdP2v5Ybr0lxlouAAAAAADARCi67deN/pqOGHZAnQ9J+pqkshFSn72MakSLRkNOIuKLraw/rcMftXIZEfGJVtbfKhGxWtLqFtZ/7BghH5P0VxOwXAAAAAAAgF1uzJ5/thfZ/pntr0i6R9I/2V5te53tc1PMWZL2lnSt7WtT2ats32z7J7YvtT1nlGWst32+7Z9IOm2keW2/xvZ9Ke4NTfOfY/vsptf32F6Ups+0fbftNba/OjQ+9cA73/Zttu+3fXwqn2X7m7Z/avsK27eO1lPP9lO2/872GknHDPbss121vSK1aa3tDw+Zr5Le//QI9Q47fyp7U9P2Ozdtr7W2D0zlz7H9o7SvvmT7l4M95IYs4yO2b0/b6dxR1nG27avStrzH9ltS+e/YvjMt+8u2Z6Tyl9m+KcXfZntuc68+20el/XxnivutkZY9pB0Lbf9wcL2kp0cdHuzRaHsv29fbviu19Xjb50mamcouSXFX2r4j1bWsuR7bn0ltv8X2nql8z3Q8rEk/xw5Z7pK077+VjtVL7MYgB7Zfm8rusH2Bd7DnKgAAAAAAQK7c234PkPSFiDhY0v+IiMWSDpX0StuHRsQFkh6RdGJEnJgSTB+XdHJEHKFGT68/HWMZT6TYfxtuXtvdki6S9PuSjpT038ZqtO2DU10nRcRhkv5khNCOiDhKjd6Ln0xl75O0MSIOkvQXaZmjmS3p1og4LCJ+3FR+uKTnRcQhEfFSSRc3L1fSJZJ+HhEfH6He0eZv9njaXhdKGkyEflLSNWm/fUvSvkNnsv0qNfbvUWlZR9o+YYRlvEbSI2kdD5H0/bRfVkh6S2pfh6Q/tt0l6V8k/Una9idLGvpItfskHR8Rvy3pE8rvkfdJST9O63XFcOsl6W2SfhARh0s6TNJdEfFRSdsj4vCIOCPFvSsijpS0WNJZthem8tmSbkltv17Se1L5BZKuS+VHSFo3zLJ/W41j6SBJL5T0irSd/o+kU9LynjPSytle5kaCffWKi0fa3QAAAAAATG4Vuy1/pprc235/GRG3pOk3px5SHZL2UiPBcfeQ+KNT+Y2p01OXpJvHWMa/jDHvgZIejIifS5Ltr0laNkw9zU6SdGlEPC5JEbFhhLjL0+87JC1K08dJ+oc03z22h67jUDVJlw1T/oCkF9r+nKSrJP2w6b3/I+mbEfGZUeodbf6R1mGwV+Rxkl6f1uH7tjcOM9+r0s+d6fUcNZKB1w8Tu1bS39k+X9J3I+IG24epsV/uTzErJb1f0tWSHo2I29PyN0vPetLPfEkrbR8gKSR1jrBuQ50wuI4RcdUI63W7pC/b7pR0ZUTcNUJdZ9l+fZreR411f0JSn6TBnnl3SPrdNH2SpDPTsmuSNg1T520R8ZAk2b5LjWPqKUkPRMSDKeYbGuH4jYjlkpZL0sbNW2KEdgMAAAAAAIwpt+ffVkmyvZ8avcp+JyIOVSMZ1T1MvCX9KPWwOjwiDoqId+csYwfnHRiyLsO1aTS96XdNheMgNukZbqzCiNioRs+zVZLeK+lLTW/fJOnE1CtsWGPM32xH18GS/rppe78oIv5phLbcr0Zvt7WSPm17Z8cd/EtJ16ZehL+v8v02ooi4Xo0k4cOSVniYB7zYXqJGj8RjUk++O5va0B8Rg4m30m3a2zS9M8cUAAAAAADATil92u88NZJ0m9IYaKc0vbdF0tw0fYsatzq+SPqvseJenLmMkea9T9Ii2/unuNOb5lmvRlJKto+QtF8qv0aNMQQXpvd2z11RSTdKenOa7yBJLy2Y97+kW6ArEXGZGrcgH9H09j9J+p6kb9oeNkE0xvxjaV6HV0laMEzMDyS9y0+Pq/g8288doS17S9oWEV+T9DepLT9TY7+8KIW9XdJ1qXwv2y9L884dZh3nq5Gck6SlBet1vRq39cr2KcOtl+0XSPp1RFykRsJ0cLv1p96Ag8vfGBHb3Bgn8eiMZV8t6Y/TMqq252e2+Wdq9OBclF6/JXM+AAAAAACAHVbUIyki1ti+U41E3K/USC4NWq7GGHCPpHH/lkr6xuDDH9RIXN2vMUTEY8PNGxH3p9uNr7K9TdINejrZeJmkM22vk3Tr4HIiYp3tz0i6znZNjZ5dSzNX9wtq3JL607S+6zT8LZ5jeZ6ki20PJlr/V/ObEfHZlED6qu0zhnmS8qjzj+FcNbbj2/X/2LvzOLmqOv//r3d3ZyELQQLKMmIAUUxYYggosgYZBBUdlEVlwICaQRwRFMbxJwr4ENcRv+wYwITVgRBxAQeQNayBCIQkgDgDKBplDVkISXr5/P64p0nRqeq6p9OVpDvv5+ORR1ff+7nnnrvUrcqnz1J0nf4HRZK2cv+3SHoPcH/qkrsE+FfghSrl7Qj8WFIH0Ap8MSKWSToGmJaSew8BF0XEChUTgpwraQOK8f7271LejyjO8akUrUhzj2seRevJv1SJ2Rc4RVJrOqbOln+TgcdUTBpzLHCcpCcoknMPVCmnq68AkyV9jqJV3xep36WdiHhd0vEU75HXKM6TmZmZmZmZWb/V3NT3xsfrj7SyZ6NVktQMDEjJrW0pJiJ5d0SsWMtVKy0lT9sjok3S7sCFaQIMWwskDYuIJWn23/MpJnr5aXfbLF60qPQbNNpay1emY5Ue6rXLbe+aj+4mNqPcbKvkxWtTc9nhI0EDysfmnLcH/vpS6dg/zn+xfB2A6Q/OKR173P67l459YeHi+kHJxsOGlI4dMWSD0rH7bLtF6Vg1N5eOBWhT+fiWVUdx6BUdTeXr0JTzflJmQ/6M91OjLI/yXwQHZXxpXNae972mI+N7UFvG83B4c/lyo7X887tjxbLSsU0bjawfVEErltcP6gENGFg6to3G/AehhYz7ooHvj2gv/77OesZlPAM6MgYp78j8b0LWeW6QnGfAwBVd56KrLev7ArC8eqeeqgY3ZZy33Od9SY167wE880KtYddXtfVbczpqlZdzb7a9+nL5ckdU61hVo9yM7yGQWeeM65eTh1Fr+f/65nyWNQ3OG+0p2tpKx6ql/Hsv57vIgOby772s73DA8BEb9evs2I9/feva/3BogFM+vn+fum4ei6y2IcAdqYuogOP7UuIv2YqiS3ETxQQWX6gTb431BUmfpZjE5hGKCV/MsuQk/szMympU4s/M1m85iT8zM2ucNZr8k3Q9K8fj6/T1iLh5TdajjIhYDIzvulzSTGBQl8VHRcRq/4+8t8tOMyO/twf1GEkxtl1XH4yI8n8KW02pO/FXuiy+NyK+tKbq0JtSK79uW/qZmZmZmZmZmfWmNd3y7y7gyIhYuiZ2lmZzPTkiPtqDbY+jmNzi8srlEfG+Xqoeki4BzoqIx6uVLelOVk0G5pT/HWBGRNyas11K8I2VtCQihvV0/6srIqZI2ge4ISKuq1yXJh85JyIO7a39SZpabV+9UO6oVO4OvVmumZmZmZmZ2bpMGcNNWOP0evIvjWemKhNXAJwIXAmsVvJPUnNEgwZkSiLiokaWn47h843cR0R8u5Hlr00RMR/otcSfmZmZmZmZmVl/1CsjxkoaJemPki4H5gKXSpolaZ6kM1LMCcAWFOPo3ZGWHSDpfkkPS5omqWYrM0nPSvphmqX1sFrbSjpQ0pMp7hMV258u6eSK3+emFllIOlrSY5JmS7qia7ykO9O+H5T0lKS90vIhkq6V9Lik6yXNlLRKV+GKfS6R9BNJs4HdU7njJTVLmprqNEfSSV22a0rrv1uj3Krbp2WHVpy/M9L5miNp+7R8U0m/T9fqEkl/lrRJlX2cIumhdJ7O6OYYT0nXGkk/lXR7er2fpKvS6wu73h9p+Q/SuXxM0n9VFLu3pPskPV1xPKMkzU2vJ0r6paSbJP1J0o8qyvxcumYPSrpY0nm16t7NvoZJuq3i3H28og5PpHLnSbpFxczGSNol3U+zgTe6KUsak+ryaDrO7erUx8zMzMzMzMysx3pzuqjtgAsiYgzwtYgYD+wE7CNpp4g4B5gPTIiICSnBdCqwf0SMA2YBX62zj5dT7K3VtpU0GLgYOBjYBdisXqUljUll7RcRO7PqGHOdWiJiN4rWi6elZccDCyJiNPCttM/uDAVmRsTOEXFPxfKxwJYRsUNE7AhMqdwvcBXFzLCn1ii3u+0rvZTO14VA/aq6zQAAIABJREFUZyL0NOD2dN2uo5gk5E0kHUBxfXdL+9pF0t419nE3sFd6PR4YpmLSlL2AGWn5N7veHyrGGTwEGBMROwGVic7NgT2BjwI/6OYcHAHsCBwh6e0qugZ/C3g/sAewfY1tK1Xb1zLgkHTuJgA/kd5ou7wdcH46f68Cn0zLpwBfTvdUpeOAs9Osy+OBv3atgKRJKTk6a8qUWpfSzMzMzMzMzKy+3uz2++eIeCC9PlzSpFT+5sBo4LEu8e9Py+9NeZSBwP119nFNnW23B55JE10g6UpgUp0y9wOmRcRLABFRa0qqX6affwBGpdd7Amen7eZK6nqMXbUD06ssfxrYRtK5wI3ALRXrfgZcGxFndlNud9vXOobOVpF7UiTdiIibJC2ost0B6d8j6fdhFEmvGVVi/0CRHNwQWA48TJHk2gs4IcVUuz8ep0iyXSrpBuCGijJ/lbqRPy7pbTWO7baIWAgg6XHgHcAmwF2d11TSNOBdNbbvbl8CvpcSnh3AlkDnumci4tGKYx8laSNgo4joPD9XAAel1/cD35T0T8AvO+/VShExGZgMsHjRon45LbqZmZmZmZmZrRm92fLvNQBJW1O0KvtgasF1IzC4SryA30fE2PRvdER8rsw+erhtG28+3mp16s7y9LOdnidNl1UbqzAiFgA7A3dStAy7pGL1fcCE1KqxqjrbV+rpMQj4fsX5fmdEXFqjLq3AM8DEVPe7KVrLvRN4otb9ERFtFC0Lr6NodXdTlXp31qW7Y+vJ8dUqp3NfRwKbArukFnvPs/L+ydpvRFwNfAx4HfidpP16WE8zMzMzMzMzs7p6M/nXaUOKJN3C1HLqoIp1i4Hh6fUDwB6S3gkgaaikeq2yOtXa9kmKllfbprhPV2zzLDAuxY8Dtk7Lb6cYQ3BkWrdx2QMF7gUOT9uNpuhymi11gW6KiOkUXZDHVay+FPgdcK2kqomlOtvXU3kMBwBvqRJzM3CsVo6ruKWkt3ZT5t0UCb4Z6fVxwCMREdS4P1LZIyLid8BJFMnM1fUQRbfit6Rz98l6G9QwAnghIlolTaBoVVhTRLwKvCppz7ToyM51krYBnk7d4H9N0fXZzMzMzMzMzKwhen2234iYLekRikTccxTJpU6TgZskzU/j/k0EfiFpUFp/KvBUiX28WG3biHgqdSe9UdJSisRTZ7JxOnC0pHnAzM79RMQ8SWcCd0lqp+jaOrHk4V4AXJa6mT4JzAMWlty20pbAFEmdydhvVK6MiLMkjQCukHRklZmUu92+jjMozuNRFF1S/0GRpK3c/y2S3gPcn7pZLwH+FXihRpl3A98E7o+I1yQtS8u6uz+GA79OLRxF/fEf64qIv0n6HvAg8EraZ0+uz1XAbyXNoRhf8skS2xwD/FxS8OZu2IcDR0lqpTjX3+tBfczMzMzMzMzMSlHRGMt6QlIzMCAilqXWhrcC746IFWu5aqWl5Gl7RLRJ2h24MHVt7RckDYuIJanl3/XAzyPi+rVdr7IWLFpc+g3aVKtDdBVqzbhFO1bpqV5TtHfNS68drYtqDd25qoEjaw0juaqOFcvrByXPLm0rHfvQ/z1XOhZg+AblRy246NZ6Q6mutPd7tikdu9/obesHJYMGlP8705hNhtcPSnLvt6YNhpQPXuVvLP2MGtHwHzpU/kHUlPP9I+d6ZB5bZDwP2wcMqh/UWY0lr5aObR5a/r7P0dHUnBXfqM+Gjrbyz8MYUv5ctGU8A1qaG3PP52ph7d/3bTVHUFlVzncLyHxfN0jO8bWsOhpPTR3LlmXVY0nTwNKxw5vLn7fXOsof35D2jPf0kKHlYzMtay1/nge2lH9uNbeVPz415z0Py8p9zuZoynjOLi0f2rBznPO5oAHl3x+Q+Vk9cIPysR3ln7MD21tLx0bG5x7AiLdtlvm07Vv+6ze3rf0PhwY4+WMf7FPXrddb/q1nhgB3pNlsBRzflxJ/yVYUXYqbgBXAF9ZyfXrb6ZL2pxij7xbgV2u5PmarJSfxZ2ZWVlbiz8yspJzEn5n1T8r4o4w1zjqX/JN0PSvH4+v09Yi4eW3UpzsRsZhiJts3kTQT6No84KiImLO6++ztstNss+/tQT1GArdVWfXBiHi5J3VphIg4uesySd8EDuuyeFqdGZXNzMzMzMzMzPqcXkv+SToRmBwRS1ennIg4pOT+9gVOjoiP5u5D0nHA0oi4PHfbMiLifZIuAc6KiMdr1OFOivrPqleepCURMayz7F6tbA+lBF+PugdLui8iPlAn5o1jLlHeVOCGiLiuxHmfCEzp7URfvf2amZmZmZmZma0NWck/FbM9qMqEEwAnAlcCq5X8k9QckTEARw9ExEWNLD8dw+cbuY++rF7ibzXLrnfeJwJzgflreL9mZmZmZmZmZmtc3dGBJY2S9EdJl1MkTS6VNEvSPElnpJgTgC0oxr+7Iy07QNL9kh6WNE1SzVZckp6V9ENJDwOH1dpW0oGSnkxxn6jY/nRJJ1f8PlfSqPT6aEmPSZot6Yqu8ZLuTPt+UNJTkvZKy4dIulbS45KulzRT0ipdfCv2uUTSTyTNBnZP5Y6X1CxpaqrTHEknddmuKa3/bp3rcGY6hgckva3i2tyeju82SVul5VMlHVpZt/Rzc0kzJD2a6tN5rDnX6gfpnDwm6b/q1ONt6dzNTv8+0KU+w1L8w+ncfLy7c1BRB0k6L92XtwJvrVhX87ynczIeuCqdgw0kfVvSQyluckpwd3dfNEv6rxT/mKQvV+638/hqXK9t0+9zJH238zyYmZmZmZmZ9UdNUr/819eUnRpsO+CCiBgDfC0ixgM7AftI2ikizqFoSTUhIiZI2gQ4Fdg/IsYBs4Cv1tnHyyn21mrbShoMXAwcDOwCbFav0pLGpLL2i4idga/UCG2JiN0oWi+elpYdDyyIiNHAt9I+uzMUmBkRO0fEPRXLxwJbRsQOEbEjMKVyv8BVwJ8i4tQ6ZT+QjmEGKyflOBe4LCJ2SuWcU6eOnwFuTrP57gw8mnOtVIzzdwgwJu2zM2FZqx7nAHeleo8D5nUpchlwSNrvBOAnncm3Og4B3g2MBo4GqrUkXOW8R8R16fiOjIixEfE6cF5E7BoROwAbAJXdyKvdF5OAUcDYiuPtqtb1Ohs4O9Xnr7UOTtIkFQn2WVOnTKkVZmZmZmZmZmZWV9nk358j4oH0+nAVLe8eAcZQJGC6en9afq+kR4HPAu+os49r6my7PfBMRPwpIoKii3E9+1FM5PASQES8UiPul+nnHygSOwB7Av+dtpsLPFZnX+3A9CrLnwa2kXSupAOBRRXrfgbMLTH+3Arghip13B24Or2+ItW5Ow8Bx0g6HdgxTViSc60WUiTsLpX0CVZ28a5Vj/2ACwEioj0iFnYpT8D3JD1GkfTdEnhbnWMA2Bv4RSpzPnB7lZjuznulCalV55xU3zEV66rdF/sDP4uItnRc1e6p7q7XtPT6amqIiMkRMT4ixk885phaYWZmZmZmZmZmdZVN/r0GIGlr4GSKGV13Am4EBleJF/D71LpqbESMjojPldlHD7dt63Is1erUneXpZzs9nwRlWbWxCiNiAUUruzuB44BLKlbfR5F8qlff1pTwLFvHN86HpCZgYKrLDIrE2d+AqZKOJuN8p4TXbsB1FC3kbqpTj3qOBDYFdkmtEZ8n/9pVVee8A5DO+wXAoak13sVd9t/T+yL3epmZmZmZmZmZNUTZ5F+nDSmSdAvTOGYHVaxbDAxPrx8A9pD0TgBJQyW9q+Q+am37JDBK0rYp7tMV2zxL0a0USeOArdPy2ynGEByZ1m1c9kCBe4HD03ajgR0ztn1D6lbbFBHTKbrXjqtYfSnwO+BaST1JEN0HfCq9PhK4O71+lpXdlD8GDEh1eQfwfERcTJEMG0fGtUpjAY6IiN8BJ1Ek17qrx23AF9O2zZJGdClyBPBCRLRKmkD91qGdZgBHpDI3p+gy3LWutc575X3ameh7KR3bodT3e+DfOq9X5j31APDJ9PpT3QWamZmZmZmZ9XXNTeqX//qarIRTRMyW9AhFIu45igRZp8nATZLmp3H/JgK/kDQorT8VeKrEPl6stm1EPCVpEnCjpKUUCabOJM504GhJ84CZnfuJiHmSzgTuktRO0VV5YsnDvQC4TNLj6XjnUXR7zbUlMCW1wAP4RuXKiDgrJcWukHRkjZmUa/lyKvsU4EWgs4/oxcCvVUw+chMrW1XuC5wiqRVYAhxd63xT/VoNT+UOpmgx2Dk2YK16fAWYLOlzFC3gvgjcX1HeVcBvU5fbWRTnuYzrKbroPg78pUuZnWqd96nARZJep+iGezHFRDb/oOgWXc8lwLuAx9J5vBg4r2S9TwSulPRNiuvSk/vJzMzMzMzMzKw0reydaJUkNQMDImJZam14K/DuiFixlqtmfZSkIcDrERGSPgV8OiK6neF48cJXy79BVb4hb7S1lo9tLR+7roiOVXrg19Q8dHj9oKTj9aX1g5JnXiv/qPjf518uHQsw/5XyeeOXlpSv84wnni4d++8f2qN07KbDh5aOfe/mOY1p82jAwPLBWX+H6YMynhcNOxc5dciwvCPve83AjrbSse0t5e+hptdfqx+UqKUxo0PEwEH1gyrr0Vr+uaWWARkVKX8Ptam5dGzOH92bcr7v5t7zfez91JExQ2Hm24kWGvT/iozztjzKH1/O+5+M7xYAr2W0sRiq8se3orn8e29ge8b3vYznRdb7CXj2lVpDb6/qnzbu2lGotpZVR1zqFe1Lyz+/m4eU/46T83yDvOPLue8HNJd/XjRl3PeR8xmS850MiLby79WcsnO+MwxS+dhoz7s3N9x4ZN9rRpbhnBvv7JdJpxM+sm+fum4ei6y2IcAdkgZQtHI73ok/W027AOelGY1fBY5dy/WxPign8WdmVlZO4s/MrKycxJ+ZmTXOGk3+SbqelePxdfp6RNy8JutRRpoJd3zX5ZJmAl3/NHZURMxZ3X02suwe1GWtXytJO1LMHlxpeUS8b03VoTdFxN2sHCfRzMzMzMzMrF9TRotza5w13fLvLuDIiCjfD201SNoXODkiPtqDbY8DlkbE5ZXLezPxJOkS4KyIeLxa2ZLuZNVkYK2ylkTEsN6qW0Qc0ltlrUYd5kj6f8D4iPj3rusl/Q74TES82hv7S2MfVt1XL5T9bCr7pd4u28zMzMzMzMysll5P/qUujaoxccWJwJXAaiX/JDVHNGiQhyQiLmpk+ekYPt/IffR3EfHhtV0HMzMzMzMzM7N1Wa+Mti1plKQ/SrqcYubUSyXNkjRP0hkp5gRgC4px9O5Iyw6QdL+khyVNk1Sz5ZqkZyX9UNLDwGG1tpV0oKQnU9wnKrY/XdLJFb/PlTQqvT5a0mOSZku6omu8pDvTvh+U9JSkvdLyIZKulfS4pOslzZS0Slfhin0ukfSTNAvv7qnc8ZKaJU1NdZoj6aQu2zWl9d+tcx3OTMfwgKS3VVyb29Px3SZpq7R8qqRDK+uWfm4uaYakR1N9Oo+11LWStKukX6bXH5f0uqSBkgZLejot/4Kkh1Jdp6eJMJB0WNrnbEkzKordQtJNkv4k6UcV+3pW0ibpGJ+QdHG6526RtEFFfR5Lx/NjSXO7O4fd7OvCrvd0RR3OSOdljqTt0/KRqR7zVLTwVFo+VNKN6RjnSjqiTn3MzMzMzMzMzHqsN6fa2w64ICLGAF+LiPHATsA+knaKiHOA+cCEiJggaRPgVGD/iBgHzAK+WmcfL6fYW6ttK2kwcDFwMMXkCpvVq7SkMams/SJiZ+ArNUJbImI3itaLp6VlxwMLImI08K20z+4MBWZGxM4RcU/F8rHAlhGxQ0TsCEyp3C9wFfCniDi1TtkPpGOYAXwhLT8XuCwidkrlnFOnjp8Bbo6IsRTj0z2aea0eSccDsBdFMnhX4H3AzLT8lxGxa6rrE8Dn0vJvAx9Kyz9WUeZY4AhgR+AISW+vst/tgPPT/fcq8Mm0fArwb+l4yrQWrbWvb3a9pyu2eSmdlwuBzgTzacA9qT7XA1ul5QcC89M9sANwU9cKSJqUEo2zpkydWqLKZmZmZmZmZmbV9Wby788R8UB6fXhqefcIMAYYXSX+/Wn5vZIeBT4LvKPOPq6ps+32wDMR8aeICIouxvXsB0zrHIstIl6pEffL9PMPwKj0ek/gv9N2c4HH6uyrHZheZfnTwDaSzpV0IFA5LdbPgLkRcWadslcAN1Sp4+7A1en1FanO3XkIOEbS6cCOaeKT0tcqItqA/5P0HmA34Cxgb4pE4N0pbAdJd0uaAxxJcY8A3AtMlfQFoLmi2NsiYmFELAMer7HvZyLi0crjl7QRMDwi7k/Lr66yXVe19tXdPV3t3tibdP9FxI3AgrR8DvDPqSXpXhGxytStETE5IsZHxPhjJk4sUWUzMzMzMzMzs+p6c8y/1wAkbU3R+mnXiFggaSowuEq8gN9HxKdz91FrW0ljV93kDW28OdlZrU7dWZ5+ttPz87as2liF6TztDHwIOA44HDg2rb4PmCDpJykhVUtrSniWreMb50NSEzAw1WWGpL2Bj1Ak4s6iSFzlXKsZwEFAK0UrzakUybxT0vqpwL9ExGwVk2zsm/Z9nKT3pX3/QVJnS8rOc9/dsXWN2aBkXeuV01Lini59b0TEU5LGAR8Gvivptoj4Tg/ramZmZmZmZmbWrd5s+ddpQ4ok3cI07txBFesWA8PT6weAPSS9E94YC+1dJfdRa9snKVp8bZviKpNVzwLjUvw4YOu0/HaKMQRHpnUblz1QipZqh6ftRlN0Fc2WutU2RcR0iu614ypWXwr8DrhWUk+SjvcBn0qvj2Rl67tnWdlN+WPAgFSXdwDPR8TFwCWpLrnX6m6K7tH3R8SLwEjg3RRdgKG4B/4uaUCqE6ncbSNiZkR8G3gRqNa9t7Q0C/DilFCElechV3f3dC0zKLpQI+kg4C3p9RYUs0hfCfyYN19rMzMzMzMzM7Ne1euz/abWXI9QJOKeo0iQdZoM3CRpfhr3byLwC0mD0vpTgadK7OPFatumVlWTgBslLaVIQnUmG6cDR0uaRzH23FOprHmSzgTuktRO0a1zYsnDvQC4TNLj6XjnAat04yxhS2BKaoEH8I3KlRFxlqQRwBWSjqwxk3ItX05ln0KRUDsmLb8Y+LWKyUduYmWryn2BUyS1AkuAo2udb2pfq5nA2ygSYFB0h96somXit1LMi+ln5zX6saTtKFp23gbMZuX4gT31OeBiSR3AXfTg+tS5p2s5g+J8zaNIwP4lLd+R4jg7KFpGfjG3PmZmZmZmZmZmZWllPsZySWoGBkTEstTa8Fbg3RGxYi1XzRJJwyKicybj/wQ2j4hak7qscxYuXlz6DaoVy+sHdcYOGFg6NtpaS8eS8zzJymHnWdTRXD8oGTq4/LloWbXXfk0PP/dC6di29rxz8fdXF9UPSv5p4xGlY19asrR07Hk3l8mBF/79Q3uUjv3Q6K3rByXLQ6VjAZqbyjd2z7nWqBGN6Ml6j3Q0lb/nAZo61oHjy9Ch8tf6+YWv1Q+qMDzjGdDaUf6aNGect2Ed5b82dCx/vXRsy0YjS8cCDXsut6n8/dmecY4HNJc/x2rN+GrWyO/GGfdyVrEtA0rH5ryfVrTl3RODm8uXnVOPHMtayz/fBq0o/7nXvMGQrHrkfEYN7GgrHavmvOd9WTn1zfk8Bfi/518uHfv2kRtl1KN8nQdlxK54YX7p2AEbb1o6dnlmJ66WjGdcxuHRkfGIyyk35znb3lL+s7eR9SDj+9ArbeUrMSzjuwXAJhuNaMwDcR1x3v/M6JdJp38/aO8+dd16veXfemYIcEfqvirgeCf+1jkfkfQNinv9z5Rv1Wm2TspJ/JmZldbAP8iY2forJ/FnZmaNs84l/yRdz8rx+Dp9PSJuXhv16U6aCXd81+WSZgKDuiw+KiLmrO4+G1l2D+qyzl+riLiGlbNEAyDpQ8APu4Q+ExGHrLGKmZmZmZmZmZmtAetc8m9NJGAknQhMjojybf0zRMT7Kn+XtC/wfeCjuWVJOo5igojLq5XdGyRdApwVEY/XWH8ncHJEzKpcXvZaSfoOMCMibu1h/ZZExLCebFtNSk6WSlBKGk8x7uEJ6TquiIj70ro3XRszMzMzMzMzs3XNOpf86y2SRDGmYbV+LCcCVwKrlfyT1ByRMzBUvoi4qJHlp2P4fCP3kWbv7ZNSwrMz6bkvxSQo96V1q31tJLVERPmBXszMzMzMzMz6iJzxOfsbSQcCZwPNwCUR8YMu6ycCPwb+lhadFxGXpHWfpZhoFeC7EXHZ6tRl7Y/i3YskjZL0R0mXA3OBSyXNkjRP0hkp5gRgC4qx+u5Iyw6QdL+khyVNk1SzlZmkZyX9UNLDwGG1tpV0oKQnU9wnKrY/XdLJFb/PlTQqvT5a0mOSZku6omu8pDvTvh+U9JSkvdLyIZKulfS4pOslzUwt1modwxJJP0kz/e6eyh0vqVnS1FSnOZJO6rJdU1r/3RrlVt0+LTu04vydkc7XHEnbp+WbSvp9ulaXSPqzpE2q7OMUSQ+l83RGN8c4VNKN6VzOlXREWv7ttP1cSZNTkri7c7uvpBvSNToOOEnSo5L26rw2krZIyzr/tUt6Rzqm6Wl/D0nao+KaXiHpXuCKWsdgZmZmZmZmZn2PiglizwcOAkYDn5Y0ukroNRExNv3rTPxtDJwGvA/YDThN0ltWpz79KvmXbAdcEBFjgK9FxHhgJ2AfSTtFxDnAfGBCRExICaZTgf0jYhxFK6+v1tnHyyn21mrbShoMXAwcDOwCbFav0pLGpLL2i4idgVoz0rZExG4UrRdPS8uOBxZExGjgW2mf3RkKzIyInSPinorlY4EtI2KHiNgRmFK5X+Aq4E8RcSrVdbd9pZfS+boQ6EyEngbcnq7bdcBWXTeSdADF9d0t7WsXSXvX2MeBwPx0jDsAN6Xl50XErmnZBry5K3a1cwtARDwLXAT8NL0p765YN7/zzUpx3adHxJ8pMvw/jYhdgU8Cl1QUOZrivvl0leOclJLWs6ZOqXUKzczMzMzMzGwdtRvwvxHxdJoY9r+Bj5fc9kPA7yPilYhYAPyeIsfRY/2x2++fI+KB9PpwSZMojnNzioTLY13i35+W35sagQ0E7q+zj84JJGptuz3FBBJ/ApB0JTCpTpn7AdMi4iWAiHilRtwv088/AKPS6z0pEk1ExFxJXY+xq3ZgepXlTwPbSDoXuBG4pWLdz4BrI+LMbsrtbvtax9DZKnJP4JB0DDdJWlBluwPSv0fS78MokoEzqsTOAX4i6YfADRXJugmS/oNipuaNgXnAb6vUa1Ttw6wutez7QjoWgP2B0eneANhQK1uV/iYiXq9WTkRMBiYDLFy8uF9Oi25mZmZmZmbWj20JPFfx+18pWvJ19cnUqOkp4KSIeK7GtluuTmX6Y/LvNQBJW1O0Kts1IhZImgoMrhIviozqKi2w6u2j1raSxnazbRtvbnFZrU7dWZ5+ttPz67es2liF6TztTJFlPg44HDg2rb6PInH2k4hYVq3QOtv3xjEI+H5E/KxeYEQ8JWkc8GHgu5JuA34EXACMj4jnJJ3Om89/j8+tpM2BS4GPRcSStLgJeH/X85WSga9hZmZmZmZm1o81qX+O+ZcamlU28pqcGvLk+C3wi4hYLunfgMsoGob1uv7Y7bfThhQJloWS3kbRz7rTYmB4ev0AsIekd8IbY8W9q+Q+am37JDBK0rYprjI5+CwwLsWPA7ZOy2+nGENwZFq3cdkDBe6lSLSR+pDvmLHtG1IX6KaImE7RBXlcxepLgd8B10qqmhirs309lcdwAFCtP/vNwLFaOa7ilpLeWqMuW1DMxHslxQCa41iZ6HsplXFoRv3gzfdN5b4GANOAr0fEUxWrbgG+XBHXXVLYzMzMzMzMzPqAiJgcEeMr/nVN/P0NeHvF7//Eyok9Ost4OSI6GyFdwsoh3Opum6vfJv8iYjZF99AngaspkkudJgM3SbojIl4EJgK/SN1lO7vtltlH1W1TS69JwI0qJvx4oWKz6cDGkuYB/07RtJOImAecCdylYiKOszIO9wJgU0mPA9+l6Mq6MGP7TlsCd0p6lGI25G9UroyIsyjO6RWSqt073W5fxxnAAZLmAocB/6BItlXu/xaKa3m/pDkUYwOukoxLdgQeTHU5jWJ2nFcpxuSbS5FIfCijflBk5Q/pnPCjYvkHgPHAGRWTfmwBnACMT5OTPE7RGtLMzMzMzMzM+reHgO0kbS1pIPAp4DeVAakHYaePAU+k1zdT5Efekib6OCAt6zFFeEixvi7NIjMgIpal1oa3Au9Og0r2CZIGAe0R0SZpd+DCNIHGem3BovJj/jW9Xr4n8dLmgaVjhzZlPCM6VulNXpMG5vV4j9YG3c4NaoZ+6x+fqx/UQwNamkvHbjJ8SOnYFW3lr9+Li8vfb+fdfG/9oOS3Xz26dGwbedeuvaOjdOygpvJl59Qj5xwPaSn/97ncc5Ej41TQ2l7+HLfmnItBA0rHLl3eWjoW4PXWttKxbRnHt+mGQ0vHNreVf761LS7/tz29ZdPSsQAtq44I0iuWR/mbqLmp/H3fqPpS9e+bvSTK30M52lT+cyHnPb2sNe8cN+q51UL57yJLVpR/Tw9+fVHpWLWUfw4BvEL5+JHDNigd25TxXSvnvZfzube8I+//j39/dXH9oGTEkPLfDwdkPC8GDyx/Pdr+9kzp2EFv3aJ07PKmvHuopbn88eW8r3MuX065WrG8flDSPmBQ+YKB5tbGlJ313VDlT1y0lX8OAWw4cpP+2S82+dkt9/TLpNO/HbBn3esm6cPA/wOagZ9HxJmSvgPMiojfSPo+RdKvDXgF+GJEPJm2PRb4/1JRZ0bEas0G2h/H/FsfDQHuSN1PBRzflxJ/yVYUXYqbgBUUE2eYWRc5iT8zs7Ialkgzs/VaTuLPzKy/iYjfUQyua/MMAAAgAElEQVSfVrns2xWvv0GNHpMR8XPg571VFyf/apB0PSvH4+v09YhYraaWjRARiym6nb6JpJlA1z99HBURc1Z3n71ddpoZ+b09qMdI4LYqqz4YES/3pC5mZmZmZmZmZv2Fk381RMQha7sOqysiqk0jXZqkEylmrFna22XX2N++wMkR8dGy26QE31hJx1FM8HF5b9erN0laEhHD1nY9zMzMzMzMzGz90G8n/LByVKh1H5xI0aV4dffR8H6KEXHRup74MzMzMzMzMzNb05z8Ww9JGiXpj5Iup5j59lJJsyTNk3RGijkB2IJiLME70rIDJN0v6WFJ0yTVbMEm6VlJP0yzHR9Wa1tJB0p6MsV9omL70yWdXPH7XEmj0uuj0wy6syVd0TVe0p1p3w9KeqpzZl5JQyRdK+lxSddLmilple7SFftcIunH6bzcKmm3VPbTkj5WcS7vTsf1sKQPpOWbS5qRZv6d22V2YCRtks7HR8pdNTMzMzMzMzOzfE7+rb+2Ay6IiDHA1yJiPLATsI+knSLiHGA+MCEiJkjaBDgV2D8ixgGzgK/W2cfLKfbWattKGgxcDBwM7AJsVq/SksaksvaLiJ2Br9QIbYmI3ShaL56Wlh0PLIiI0cC30j67MxS4PZ2jxcB3gX8GDgG+k2JeAP45HdcRwDlp+WeAm9OMxTsDj1Ycw9uAG4FvR8SNVY5xUkrGzpo6ZbUm9DEzMzMzMzNba5qkfvmvr/GYf+uvP0fEA+n14ZImUdwPmwOjgce6xL8/Lb9XxY0+ELi/zj6uqbPt9sAzabIPJF0JTKpT5n7AtIh4CSAiXqkR98v08w/AqPR6T+DstN1cSV2PsasVwE3p9RxgeUS0SppTUeYA4DxJY4F24F1p+UPAz9MMzL+KiEcr4m8DvhQRd1XbaURMBiYDLFi0uF9Oi25mZmZmZmZma4aTf+uv1wAkbQ2cDOwaEQskTQUGV4kX8PuI+HTuPmptmxJmtbTx5pap1erUneXpZzs9v89bI6Iz+dbRWWZEdEjqLPMk4HmK1n1NwLIUM0PS3sBHgKmSzkpjErZRJCQ/BFRN/pmZmZmZmZmZ9RZ3+7UNKZJ0C1N31IMq1i0GhqfXDwB7SHongKShkt5FObW2fRIYJWnbFFeZHHwWGJfixwFbp+W3U4whODKt27jsgQL3Aoen7UYDO2ZsW8sI4O8R0QEcBTSn8t8BPB8RFwOXkI4FCOBYYHtJX++F/ZuZmZmZmZmZ1eSWf+u5iJgt6RGKRNxzFAmyTpOBmyTNT+P+TQR+IWlQWn8q8FSJfbxYbduIeCp1N75R0lLgblYmG6cDR0uaB8zs3E9EzJN0JnCXpHbgEWBiycO9ALhM0uPpeOcBC0tu212Z0yUdTdFFuLO1477AKZJagSXA0Z0bRES7pE8Dv5G0OCIuWM06mJmZmZmZma1z1AfHx+uPtLJXo1n/JqkZGBARy1Jrw1uBd0fEirVctZpyxvxrofx7eXnH2n/fNzflNTxu7+jIiC1/fANbmkvHtrWXr0NLc/nja25dXj+oUs4HaEd7Rrnl66yWjL8dZZR78FmXly8XmPz5Q0vHbjxsg9KxufdnX5PzvOjIuN+aMr5TtLFufBFcuHRZ6di3DC0/AsUV9zxSOvazH9ipdGxHU/lnVu6jPue+WNbemM+RgS3l33tNOc+3TNFevmw1l78mOc/DRr1Hcq7zuiLrOZRxX7Qp49plam4r//VyRVP5z9RBynjOZhxfoz4XYN34bGjKKDbnHsp5JkPjzkVLNOb73rpwPSDv86xR5yLn/08DMv4fADBi+PB140tRg1xy631974OnhM/v/4E+dd3c8s/WJ0OAO9IkHAKOX5cTf2ZV+S9nb8hJ/JlZ9/piQqhRchJ/Zta9nGRXf+dzYWZrk5N/tlokXc/K8fg6fT0ibl4b9elORCwGxnddLmkmMKjL4qMiYs4aqZiZmZmZmZmZWYM4+WerJSIOWdt1WF0R8b61XQczMzMzMzOz/qY5t5+3NUT/HtTI1hhJ35G0f5Xl+0q6Ib3eXtL9kpZLOrlL3FckzZU0T9KJa6re9UiaKGmLtV0PMzMzMzMzM7OecMs/6xUR8e0SYa8AJwD/UrlQ0g7AF4DdgBUUMwzfEBH/2+sVzTcRmAvMX8v1MDMzMzMzMzPL5pZ/BoCkoyU9Jmm2pCskjZJ0e1p2m6StJI2Q9GepmBZJ0lBJz0kaIGmqpEPT8gMlPSnpYeATnfuIiBci4iGgtcvu3wPMjIilEdEG3FW5XZW6niDp8VS3/5bUJOlPkjZN65sk/a+kTVO9LpT0gKSnU0vEn0t6QtLUijKXSPppanl4W9r2UIoxAq+S9KikDSR9UNIjkuakcgal7Z+V9P0UN0vSOEk3S/o/ScelmM0lzUgxcyXttdoXzszMzMzMzMysG07+GZLGAKcC+0XEzsBXgHOByyJiJ+Aq4JyIWAg8CuyTNv0ocHNEtFaUNRi4GDgY2AXYrEQV5gJ7SRopaQjwYeDt3cT/J/DeVLfjIqIDuBI4Mq3fH5gdES+m398C7A6cBPwG+CkwBthR0tgUMxSYFRFjKJKPp0XEdcAs4MiIGAsEMBU4IiJ2pGg5+8WKev0lxd2d4g4F3g+ckdZ/huJ8jQV2pjiXq5A0KSUQZ02dMqWb02BmZmZmZmZm1j0n/wxgP2BaRLwEEBGvUCTLrk7rrwD2TK+vAY5Irz+Vfq+0PfBMRPwpIoIiKdetiHgC+CFwC3ATRVKsvZtNHqNojfevQFta9nPg6PT6WKAya/bbVJc5wPMRMSclDOcBo1JMR8WxXFlxvJXenY7tqfT7ZcDeFet/k37OoWjJuDglIJdL2gh4CDhG0unAjmn24VVExOSIGB8R4ycec0w3p8HMzMzMzMxs3SWpX/7ra5z8s1y/AQ6UtDFFy77be6PQiLg0InaJiL2BBcBT3YR/BDgfGAc8JKklIp4Dnpe0H8XYgf9TEb88/eyoeN35e61xL6MHh9HtfiJiBkWy8G/AVElHY2ZmZmZmZmbWQE7+GRQJvMMkjQRIib37KFr2QdGd9m6AiFhC0YLtbOCGiOjaQu9JYJSkbdPvny5TAUlvTT+3ohjv7+oacU3A2yPiDuDrwAhgWFp9CUWrvWlV6lVPE0U3XSi6596TXi8GhqfXf6Q4tnem34+i6CJciqR3ULQ8vDjVdVxmHc3MzMzMzMzMsni2XyMi5kk6E7hLUjvwCPBlYIqkU4AXgcr+p9cA04B9q5S1TNIk4EZJSymShsMBJG1GMYbehkCHpBOB0RGxCJieko+twJci4tUa1W0GrpQ0AhDFWISdsb+h6O7bk4HyXgN2k3Qq8AIruzZPBS6S9DpFV+hjgGmSWiiSoBdl7GNf4BRJrcASVnZTNjMzMzMzMzNrCBVDoZn1fZLGAz+NiOxZdCUtiYhh9SPXrMWLFpV+g3Y0aNyB1vaO0rGNfJx0ZBT+91cXlY59xyZvKR27oq18g9IhLeUbVne8vrR0LEDT4MHly162rHSsWsr/PWhF84DSsc1N5c/Fi4teKx076ZLrSscC/Pprny0d29Gge7klY0SBRr2nAZo6yt/LHU3N5cvNeJ/mHF/O9Xhy/gvlg4ERG5R/P719o6GlY79/4z31g5JvfKTaMLPVNep6ALTRmHsu59k5sKX88bVkNPKP9owOAZnnLefZico/D3OuR1PGpct5/7ep/PXIrUdWuY36ghHlv+PkXDvIe8Y17PgyNOqZDI27L3K+ow5oLn/91LqifGxL+e9DuZ/rOec553mYI/cZUFbuPZFzLppeL/9dMjKeAU8vaa0flGy36YjSsQDDR2zU9waQyzD1jgfW/kOuASZOeH+fum5u+Wf9gqT/pJh598h6sWZ9WU7iz8ysrEYl/sxs/daoxJ+ZmeVx8s/WWZLOB/bosvjsiFilW29E/AD4QU/3tS62+jMzMzMzMzMzW12e8MPWOknfkbR/lVXTgL9GxFiKyUdepxh/7+Qu239F0lxJ89I4gg0n6URJQ3q47VhJH+7tOpmZmZmZmZmZdeWWf7bWRcS3S4S9ApwA/EvlQkk7AF8AdgNWADdJuiEi/rfXK/pmJ1LMLJw3WFthLDAe+F2v1sjMzMzMzMxsHSIPLbJOcMs/W22Sjpb0mKTZkq6QNErS7WnZbZK2kjRC0p+lYrRkSUMlPSdpgKSpkg5Nyw+U9KSkh4FPdO4jIl6IiIcoZgOu9B5gZkQsjYg24K7K7arU9Z2Sbk11fVjStir8OLUenCPpiBS7r6Q7JV2X6nRVij0B2AK4Q9IdKfZCSbNS68MzKva3q6T70v4eTLMUfwc4QtKjnfsyMzMzMzMzM2sEJ/9stUgaA5wK7BcROwNfAc4FLouInYCrgHMiYiHwKLBP2vSjwM0R0VpR1mDgYuBgYBdgsxJVmAvsJWlk6ob7YeDt3cRfBZyf6voB4O8UycKxwM7A/sCPJW2e4t9L0cpvNLANsEdEnAPMByZExIQU982IGA/sBOwjaSdJA4FrgK+k/e0PvAZ8G7gmIsZGxDVdKyhpUkokzpoyZZXhDc3MzMzMzMzMSnO3X1td+wHTIuIlgIh4RdLurGx9dwXwo/T6GuAI4A6KMfwu6FLW9sAzEfEnAElXApO623lEPCHph8AtFIm1R4Gqc91LGg5sGRHXp22XpeV7Ar+IiHbgeUl3AbsCi4AHI+KvKe5RYBRwT5XiD5c0ieI9tTlFsjCAv6cWi0TEolROd4dEREwGJgMsXrSoX06LbmZmZmZmZmZrhpN/tib9BviepI0pWvbd3huFRsSlwKUAkr4H/LU3yk2WV7xup8p7RtLWwMnArhGxQNJUYHAv1sHMzMzMzMysz2lu8ph/6wJ3+7XVdTtwmKSRACmxdx9Fyz6AI4G7ASJiCfAQcDZwQ2ppV+lJYJSkbdPvny5TAUlvTT+3omhxeHW1uIhYDPxV0r+k+EGpq/DdFGPwNUvaFNgbeLDObhcDw9PrDSlaHS6U9DbgoLT8j8DmknZN+xsuqaXLtmZmZmZmZmZmDeOWf7ZaImKepDOBuyS1A48AXwamSDoFeBE4pmKTa4BpwL5VylqWus7eKGkpRVJuOICkzYBZFIm2DkknAqNTV9rpKfnYCnwpIl7tpspHAT+T9J0UfxhwPbA7MJuiq+5/RMQ/JG3fTTmTKWYWnh8REyQ9QpG8fA64Nx3PijShx7mSNgBepxj37w7gP1M34u9XG/fPzMzMzMzMzKw3KMJDipmtq579x4ul36AjNxhQutwlrR2lYwe0NJeObW4q35i4rb18HQDqDJX4JoMympZ3ZBTckfG4XLxsef2gZMjA8tcO8s5zC415xrdR/ry1d+Rd67IGNOc1Xv/4Ty4rHXv9Vz+bW51SFi5dVjp2xJDyowcseO31rHpsuMGg0rGDVP4ealP550XOuXjL0MaNpLB0eddJ5Gt7cfFrpWPfPnKj0rE5vWE6Xn25fPCIkeVjM+XUeUVb+WdAS8b7Ouv5FuXrEG1t5csF1JLxt3Q1ptNNoz7LGvUZAnl1buqoOpzzast5ZuVqWaWDS20538s2aCv/7Hytufyzc8NB5e/j5Tk3ETCwo/x7Si3lvxM16r7PkXOdG3m/NapXZcPOWwOfLTnP++VR/sTl/P8ipw4Aw0ds1K/7xV5514P9Mun0r/vs1qeum7v9mpn1ITmJPzMzM7O1KSfxZ2ZmjeNuv9YvSTof2KPL4rMjYkpGGROBWyJifvr9WWB858zGZmZmZmZmZlZbU04XLmsYJ/+sX4qIL/VCMROBucD8XijLzMzMzMzMzGyNc/LP1juSfgW8HRhMMfPwpenfeIoJP35OMXHHeOAqSa9TTAgC8GVJBwMDgMMi4klJpwNbA9sAWwEnAe+nmPX3b8DBEdEq6dvAwcAGFDMi/1t40E0zMzMzMzMzayAPHmXro2MjYheK5N4JwFhgy4jYISJ2BKZExHUUswsfGRFjI6JzNP2XImIccCFwckWZ2wL7AR8DrgTuSGW9DnwkxZwXEbtGxA4UCcCPNvYwzczMzMzMzGx95+SfrY9OkDQbeICiBeBAYBtJ50o6EFjUzba/TD//AIyqWP4/EdEKzAGagZvS8jkVcRMkzZQ0hyJROKbaDiRNkjRL0qyrr7g8++DMzMzMzMzM1gWS+uW/vsbdfm29ImlfYH9g94hYKulOYBCwM/Ah4DjgcODYGkUsTz/befP7ZzlARHRIaq3oztsBtEgaDFxAMWHIc6mr8OBqO4iIycBkgGf/8aK7BZuZmZmZmZlZj7nln61vRgALUuJve4qx+TYBmiJiOnAqMC7FLgaG99J+OxN9L0kaBhzaS+WamZmZmZmZmdXkln+2vrkJOE7SE8AfKbr+bgncKakzGf6N9HMqcFGXCT96JCJelXQxxezB/wAeWp3yzMzMzMzMzMzKcPLP1isRsZxiFt6uzq4SOx2YXrFoVMW6WcC+6fXpXbYbVvH69IrXp1K0LDQzMzMzMzMzWyO0cmgyM1vXLFi0uPQbtIXy7+U2GjNAaXtHR+nY1rb2htQBYMigAaVjOzIegW3t5Y+vpbn8qAo51w6AKF+PdYLKn4tG3Zu5DjnrstKxlx//qdKxTRnHlzOQcM79BtCR8V7NeT8tXd5aOraluTkjtnHvp5x7btmK8seXY/GyFaVjN99wSEPqALCsvfy5G9hS/po0dTToed+gZ0vOZxlAc1NjRtHJ/mwoqaOBg5Q3Zfy/olH1yKlDzudpm8o/swBaovx9n1N2TrkdTeXLzTlvuZ/VOXXOeV/naOR9X1bWvUnj6pzz3bcpowqtGd+TB+UUnCnn/sx5zuZcj9zPveEjNlr7N2gD/eLuWf0y6fTpvcb3qevmMf/MzPqSvpb4M7M+ISfxZ2ZWVlbiz8zMGsbJPzMzMzMzMzMzs37KyT/rdZJGSZrbZdl4SefUiH9W0iZrpnZv7PONOnZXNzMzMzMzMzPrGUn98l9f4wk/bI1IE2TMWtv1qGZdrpuZmZmZmZmZ2epwyz9rKEnbSHpE0imSbkjLRkq6RdI8SZdAMSprao33hKSL07pbJG2Q1m0r6SZJf5B0t6TtJQ2X9IykASlmw8rfq9RlF0mzJc0GvlSxfN+Kuu0j6dH07xFJw9PyUyQ9JOkxSWdUbPurVKd5kialZc2SpkqaK2mOpJNqHUPvn3EzMzMzMzMzs5Wc/LOGkfRuYDowEXioYtVpwD0RMQa4HtiqYt12wPlp3avAJ9PyycCXI2IX4GTggohYDNwJfCTFfAr4ZUTUmo5xSipj526qfTLwpYgYC+wFvC7pgFSv3YCxwC6S9k7xx6Y6jQdOkDQyxWwZETtExI5pv1WPoVoFJE2SNEvSrKlTplQLMTMzMzMzMzMrxd1+rVE2BX4NfCIiHpe0b8W6vYFPAETEjZIWVKx7JiIeTa//AIySNAz4ADCtom/9oPTzEuA/gF8BxwBfqFYZSRsBG0XEjLToCuCgKqH3AmdJuooikfjXlPw7AHgkxQyjSAbOoEj4HZKWvz0t/yOwjaRzgRuBW+ocw5tExGSKRCELFi329ItmZmZmZmZm1mNO/lmjLAT+AuwJPJ6x3fKK1+3ABhQtVF9NrfHeJCLuTd2F9wWaI2Ju15gcEfEDSTcCHwbulfQhim7J34+In1XGpn3uD+weEUsl3QkMjogFknYGPgQcBxwOnFjrGMzMzMzMzMz6o+amvjc5Rn/kbr/WKCuAQ4CjJX2my7oZwGcAJB0EvKW7giJiEfCMpMPSNkrJtU6XA1ezsntttTJeBV6VtGdadGS1OEnbRsSciPghRVfl7YGbgWNT6z0kbSnprcAIYEFK/G0PvD+t3wRoiojpwKnAuBLHYGZmZmZmZmbW65z8s4aJiNeAjwInARtWrDoD2FvSPIruv38pUdyRwOfSZB3zgI9XrLuKIoH4izplHAOcL+lR0iQjVZyYJup4DGgF/icibqFILt4vaQ5wHTAcuAlokfQE8APggVTGlsCdaT9XAt8ocQxmZmZmZmZmZr1OER5SzPo2SYcCH4+Io9Z2XXrb86+8WvoNOqSlfC6/rWbuc81pa+9oWNkDM85Fjo4GPS5bor0xBTeS+tb9lmvxsuX1g5KjL/jv0rFXfalrQ+ja1pUeEoMHVp1AvaoVbeXv5Y6O8s+AnDrknrec9/XS5bXmk1o9y9vaSsduOnRw6djc9157xjUZpAY9EDOeLR0qf3yNen43UgvlK51zLpo6Mt6nTc2lY9cVTRn/t2nkPZTz2b48ytcj573XpvLXL6e+ufdFzjUhyj+HGnV/NuoeyjoPDSy7UeUuWVH+s2zYwLzRxxr1Xs36Dp7x+ZRr+IYbriPf+hpj2n0P98FP4foO+8C4PnXdPOaf9WlpUo2DKMboMzMzsx7ISfyZmZWVm/Ays/5HGclbaxwn/6xPi4gvd10m6Xxgjy6Lz46ImmMCmpmZmZmZmZn1Rx7zz9Y6SUvSzy0kXbe65UXElyJibJd/qyT+JE1NXYaRdImk0au7bzMzMzP7/9m78zC7qjLt/9/71JQ5YVIZxDSIIGEIEEBEEJTGfh0YBIxiM2mL2DaCNqjd8APE9m0RZ0Ex8EKYBBoFRVRAmUGGBBIyoGBLUBFlMHNCKqk6z++PsyKHooa1Qp3UwP25rnPVPms/e+21513r7LW2mZmZDSZ+8s8GjYh4Gjj8leYjqTki8judqM37X17pfM3MzMzMzMzMBhs/+WeDhqSJkual4fslTaobd4ekKZJGS7pY0oOSZkk6OI0/VtINkm4Dbu0hf0k6T9Jjkn4FvKab/JvSE4HzJM2V9Ok0fmtJN0l6SNLdkrZL6e+T9EAqy68kvTalv13S7PSZJWlsSj9V0gxJcyR9oTFr0szMzMzMzMysxpV/NlhdA3wAQNKmwKYRMRM4DbgtIvYA9gfOlTQ6TbMrcHhEvL2HPA8FtgW2B44G3tpNzGRg84jYISJ2BNY2F54GnBgRuwGnAN9N6fcAb4mIXYCrgc+m9FOAT0bEZGAf4AVJBwLbAHuk+ewmad+uBZB0vKSZkmZefun0XleSmZmZmZmZmVlv3OzXBqv/AW4BzqRWCbi2L8ADgYMknZK+jwC2TMO/jIiFveS5L3BVRHQCT6enBLt6AtgqvUX4Z8AtksZQqyi8tu5NRW3p7xbANamCshVYkNLvBb4u6Urguoh4KlX+HQjMSjFjqFUG3lVfgIiYRq2ykWcWLvYr0szMzMzMzMxsnbnyzwaliPizpL9J2gmYCpyQRgk4LCIeq4+XtCewoh/mu0jSzsC70jw/AJwMLE5P8XX1HeDrEXGDpP2As1I+X5b0M+DdwL2S3pXK/t8R8f1XWk4zMzMzMzMzsxxu9muD2TXUmtGOj4g5Ke1m4ESlR/Ak7VKQ313A1NSv36bUmg2/hKSNgUpE/Ag4Hdg1IpYCCyQdkWKUKggBxgN/TsPH1OWzdUTMjYhzgBnAdqnsH0lPEiJpc0l/73fQzMzMzMzMbDipoGH5GWpc+WeD2Q+BD1JrArzWF4EWYI6k+el7ruuB3wGPApcB93UTszlwh6TZwBXAf6T0DwMflfQIMB84OKWfRa058EPA83X5nJxeGjIHWAP8IiJuAX4A3Cdpblq+sQXlNzMzMzMzMzMrogh3KWY2WP3l+YXZB+hoOrLzXVHQ4r+5qakgNv/3hM5qNTu2VGvnmuxYNRf0fqD85Vu0anV27MiWlvwyAK3N+eWoVDuL8s5WsC5WduRv69bmgv2NsuvX31a25+ddyV++ur5A+/Th83+QHXvZv34wO3ZFwf4GMG7kiOzYtpb8bdJZzd8mv/nzM9mxu0zcLDu2dJ9fWRD+x+cXZce+8XUbZ8cuWvFCduyE1cuzYzvHbZQdC1CwK9OmQXD/WHAe6mjgL/SVgqwLDpGG5Vui9DxbLdiJKgX/g5Rsv+YoOKgbuA+VlONvL+Tft0wgP3ZFU/65flxr/rqoVvKvC9C4e5GScjTsGCnYzh0qW28lx18jz3GNUHpuKVm+js78+86S/12a1uTfRxb9fwGMHT9haG3AQtfdN3sQ3DT0v/fvNXlIbTc/+WdmNoSUVPyZmeUqqfgzM8vVsB8hzcysiF/4YcOOpB2By7skt0fEngNRHjMzMzMzMzOzgeLKPxsQkjYDvh0Rh7/CfI4FpkTEv61Ni4i5wOQ0/gRgZURc9krmY2ZmZmZmZmZlmkr6sbCGceWfrRNJzRGR38lcFxHxNPCKKv4y53NBo+dhZmZmZmZmZjZYufOoYUDS0ZLmSHpE0uWSJkq6LaXdKmnLFDdd0vck3S/pCUn7SbpY0m8kTa/Lb7mkb0ian6bfJKXfIembkmYCJ0l6p6RZkuamfNpS3JOS/lvSbEkzJe0q6WZJv09P4pHKOC8NT5L0YIqfI2mblP7Pdenfl2q95Eo6TtLjkh4E9u5j3Zwl6ZS68n8jlek3knaXdJ2k30n6r7py/Tatq8clXSnpAEn3prg9uuabvs9L005MeV+Y1t8tkkammK0l3STpIUl3S9quHza/mZmZmZmZmVmPXPk3xEmaBJwOvCMidgZOAr4DXBoROwFXAt+um2QDYC/g08ANwDeAScCOkianmNHAzIiYBNwJnFk3fWtETAHOB6YDUyNiR2pPkX6iLu6PETEZuDvFHQ68BfhCN4txAvCtFD8FeErSm4GpwN4pvRP4sKRNUx57A28Dts9fWwCsTuW/APgJ8ElgB+BYSWtfi/hG4GvAdulzZJrXKcB/ZsxjG+D8tP4WA4el9GnAiRGxW8rru91NLOn4VEE584rLLi1cPDMzMzMzMzOzF7nZ79D3DuDaiHgeICIWStoLeH8afznwlbr4n0ZESJoLPJP6x0PSfGAiMBuoAtek+CuA6+qmX5u+LbAgIh5P3y+lVpH2zfT9hvR3LjAmIpYByyS1S5rQZRnuA06TtAVwXUT8TtI7gd2AGaq9gnAk8CywJ3BHRDyXyn0N8Ka8VW6kw7sAACAASURBVPWycs2PiL+kfJ4AXk+tsm5Bl/Vya906m5gxjwURMTsNPwRMlDQGeCtwrV58pWJbdxNHxDRqFYX85fmFw/K16GZmZmZmZjb81f3/awPIlX+vPu3pb7VueO33nvaH+gqoFf09n4j4gaQHgPcAP5f0cUDUnl78j/pYSYdkzv+VlKtrens3MR289MnZEd3MA2pPLI5MsYvTU4xmZmZmZmZmZuuFm/0OfbcBR6xtsippQ+DXwAfT+A9Ta3pbosKLL+M4Erinm5jHqD3R9sb0/ShqTYSLSdoKeCIivk2tKe5OwK3A4ZJek2I2lPQG4AHg7ZI2ktQCHLEu8+wHTwK7prLtCvxDb8ERsRRYIOmINI0k7dzoQpqZmZmZmZnZq5uf/BviImK+pC8Bd0rqBGYBJwKXSDoVeA44rjDbFcAekk6n1tR2ajfzXSXpOGrNWJuBGdT60VsXHwCOkrQG+Cvwf1Pz5dOBWyRVgDXAJyPifklnUWsqvJhaM+WB8CPg6NQs+AHg8T7ioVYR+720XC3A1cAjjSuimZmZmZmZmb3aKcJditlLSVoeEWMGuhwGzy1akn2AjqjkH8vVSlN+bMEpolLQncOazmp+MNBUyX9QualjdXasmvLXRYcKYguWb0RTYT8YUbbuBpzyt10H+euiZH+Dsn15ZfuahpRjTTV/2x393auzY6ef8LLfaPrNRqO67Z60W8+tWJUd29ac//vjqLaW7Njm6MyOhbLz4TNLcnu+gBGt+cv37JLl2bETN9kgO7alqayBR8l+P6Zg+WJN/jm5hAr2oZLz94pVZeUt6cuos+D8XXSMNOdv62oD+16qDIL/K0qWr6S8peutJO9VnfmxJfd7Jft90bmz4LperOQep6AcJduv5H6hmcGxD7UXFLrknrpk+Uru4Rp5rV61Jj/vUfnZNtTY8ROGdad4P50xd+AvDg3wvt13HFLbzc1+zcyGkqFW8WdmQ0JJxZ+ZmZmZDS1u9msvMxSf+pN0Gi/v/+/aiPjSQJTHzMzMzMzMzGwwcOWfDQupks8VfWZmZmZmZmZmddzs19YrSRMlzWtAvpMlvbtwmiclbZyGf93fZTIzMzMzMzMzG2iu/LMhL71teDJQVPlXLyLe2n8lMjMzMzMzMzMbHFz5ZwOhSdKFkuZLukXSSElbS7pJ0kOS7pa0HYCk90l6QNIsSb+S9NqUfpakyyXdC1wOnA1MlTRbUrevvZS0UZrffEkXwYuvpJK0PP3dVNJdKZ95kvZJ6QdKuk/Sw5KulTQmpZ8haUaKnab0yj9Jn5L0qKQ5kq5OaaMlXSzpwbQ8B/dQzuMlzZQ087Lp0/tlhZuZmZmZmZnZq5Mr/2wgbAOcHxGTgMXAYcA04MSI2A04Bfhuir0HeEtE7AJcDXy2Lp/tgQMi4kPAGcA1ETE5Iq7pYb5nAvek+V4PbNlNzJHAzRExGdgZmJ2aBp+e5rUrMBP4TIo/LyJ2j4gdgJHAe1P654FdImIn4ISUdhpwW0TsAewPnCtpdNcCRMS0iJgSEVOOPvbYHhbFzMzMzMzMzKxvfuGHDYQFETE7DT8ETATeClybHpwDaEt/twCukbQp0AosqMvnhoh4oWC++wLvB4iIn0la1E3MDOBiSS3AjyNitqS3U6tovDeVrxW4L8XvL+mzwChgQ2A+8FNgDnClpB8DP06xBwIHSTolfR9BrQLyNwXLYGZmZmZmZmaWzZV/NhDa64Y7gdcCi9PTdl19B/h6RNwgaT/grLpxK/q7YBFxl6R9gfcA0yV9HVgE/DI9Yfh3kkZQe0JxSkT8SdJZ1Cr0SNPvC7wPOE3SjtSaGR8WEY/1d7nNzMzMzMzMBpuK+o6xxnOzXxsMlgILJB0BoJqd07jxwJ/T8DG95LEMGNvHfO6i1qwXSf8H2KBrgKQ3AM9ExIXARcCuwP3A3pLemGJGS3oTL1b0PZ/6ADw8ja8Ar4+I24HPpWUYA9wMnFjXL+AufZTXzMzMzMzMzOwVceWfDRYfBj4q6RFqTWfXvgzjLGrNgR8Cnu9l+tuB7Xt74QfwBWBfSfOpNf/9Yzcx+wGPSJoFTAW+FRHPAccCV0maQ63J73YRsRi4EJhHrWJvRsqjCbhC0lxgFvDtFPtFoAWYk8rwxV6Wx8zMzMzMzMzsFVNEDHQZzKwHzy9ekn2Atin/WF5VzX/2urmpMb8RdFarRfFNlfxyNEdnaXGydKgpO3bV6jXZsSNaW4rK0UzBeTvK1nOuaiV/XVQbdJkpbULwt+X5XYSObmvNjq0W7MuLV67Kjh1ZsF8ce0FP7znq3lX/dmR27Ki2/HKsbM/f79s78o/TjUa19R20jjrI35GeWrgkO/Z14/t6GP1Fi1bk75ubjh3Rd1BScs4C6OjM35dbm/PPyVqzOr8QBfelas7vvaZR5+9GGtNasHwF+3EjDYamXZVqY+4B2qNs4VqrHdmxKwruy0ZX8o+R1ZX8fajkPrL03FKi6B5O+eehqhqzc5bsbyX3TtC4+6cSJcf0ohX59zgbjM6/lgGs7si/PnV05m+TMS2D41moseMnDIKzZ+P8bObcQbA397/3TNlxSG039/lnZjaEFFX8mZllKqn4MzMzM8tV8hCHNY4r/2zYkXQccFKX5Hsj4pMDUR4zMzMzMzMzs4Hiyj8bcJKWR8SY/sovIi4BLsmY71nA8oj4qqSzgbsi4lf9VQ4zMzMzMzMzs4Hmyj8zICLOGOgymJmZmZmZmZn1Nze+tkFD0n6Sbqz7fp6kY9Pwk5K+IOlhSXMlbZfSN5H0S0nzJV0k6Q+SNu5lHqdJelzSPcC2denTJR2ehr8s6VFJcyR9tW4+P5I0I332Tul7SLpP0ixJv5a0bUqfJOnB9PbhOZK2Sen/XJf+famBPSabmZmZmZmZDSBJw/Iz1Ljyz4aS5yNiV+B7wCkp7UzgtoiYBPwQ2LKniSXtBnwQmAy8G9i9m5iNgEOBSRGxE/BfadS3gG9ExO7AYcBFKf23wD4RsQtwBvB/U/oJwLciYjIwBXhK0puBqcDeKb0T+HA3ZThe0kxJMy+bPr3vtWJmZmZmZmZm1gM3+7Wh5Lr09yHg/Wn4bdQq64iImyQt6mX6fYDrI2IlgKQbuolZAqwC/l96CnHtk4gHANvX1fCPkzQGGA9cmp7sC6Aljb8POE3SFsB1EfE7Se8EdgNmpHxGAs92LUBETAOmATy/eIlf7WpmZmZmZmZm68yVfzaYdPDSp1FHdBnfnv520qB9NyI6JO0BvBM4HPg34B2pXG+JiFX18ZLOA26PiEMlTQTuSPn8QNIDwHuAn0v6OCDg0oj4j0aU3czMzMzMzMysKzf7tcHkD9SermuTNIFaBVxf7gU+ACDpQGCDXmLvAg6RNFLSWOB9XQPWPs0XET8HPg3snEbdApxYFzc5DY4H/pyGj60bvxXwRER8G/gJsBNwK3C4pNekmA0lvSFjGc3MzMzMzMyGHGl4foYaV/7ZoBERfwL+B5iX/s7KmOwLwIGS5gFHAH8FlvWQ/8PANcAjwC+AGd2EjQVulDQHuAf4TEr/FDAlvbzjUWp9+gF8BfhvSbN46dOIHwDmSZoN7ABcFhGPAqcDt6T8fwlsmrGMZmZmZmZmZmbrRBHuUsyGLkltQGdqrrsX8L30Mo1hYc6CP2UfoFuOzH9xcNPIUdmxHQUvJG4m/3xSLfy5pFJwroo1q7Nj1ZzfgrxkXaxavSY7dlRbS99BdSrVzvxgNeg3nqhmhzZqHyopA0B75O9zazoK1nGBasHirenML0Nbc9mLwz903g+yY3/y78dkx/7+mYXZsZtvOD47trVg+Yr2IaCD/P3iT39bnB276YRx2bGP/vmZ7Nidtsz/zahS+Kv06o78Y6qtmn+Oq65u7zsoUSV/W1dHjs6OXbEq/7pQLdyHWir559kRrWXn+1yNvP42Ssl1vUjBtaFasL8VXXsL8y469qIjO7azuTU7drDcw5XkXbJNBsP9bOk+VGJVNb8czU3556zmyC9zyX1Wm8qO/5LjaWV7/vVpTMvgeBZq7PgJg+PE3CC3zH50WFY6HTh5+yG13dznnw11WwL/I6kCrAY+NsDlMWuoRt44mtmrV0nlg5lZroZV8pqZWRFX/tmQFhG/A3apT5O0EbX+9bq6GXgOGAfcFRG/6ilfSXcAp0TEzJxypD4AN0t9BZqZmZmZmZmZDQqu/LNhJyL+Brys6a+ks9L4Mxow28nAFMCVf2ZmZmZmZmY2aAyORu5mDSLpNEmPS7oH2DalTZd0eBo+Q9IMSfMkTZNe0nHHUZJmp3F7pPjRki6W9KCkWZIOltQKnA1MTfFTu4tL009KabPTy0O2Wb9rxMzMzMzMzMxeTVz5Z8OWpN2AD1J7Ku/dwO7dhJ0XEbtHxA7ASOC9deNGpZeH/CtwcUo7DbgtIvYA9gfOBVqAM4BrImJyRFzTXZyk0dTeEvytlO8U4Kl+XWgzMzMzMzMzszqu/LPhbB/g+ohYGRFLgRu6idlf0gOS5gLvACbVjbsKICLuAsZJmgAcCHxe0mzgDmAEtZeOdNVT3H3Af0r6HPCGiHih64SSjpc0U9LMH1515bost5mZmZmZmZkZ4D7/7FVM0gjgu8CUiPhT6hNwRF1I19eTBSDgsIh4rEtee3bNvrs44DeSHgDeA/xc0scj4raXzCRiGjANYM6CP/kVaWZmZmZmZjYkNcnPnA0G3go2nN0FHCJppKSxwPu6jF9b0fe8pDHA4V3GTwWQ9DZgSUQsofbG4BPX9g0oae2bhpcBY+um7TZO0lbAExHxbeAnwE6vfDHNzMzMzMzMzLrnyj8btiLiYeAa4BHgF8CMLuMXAxcC86hV1s3oksUqSbOAC4CPprQvUuvjb46k+ek7wO3A9mtf+NFL3AeAeak58A7AZf20uGZmZmZmZmZmL+NmvzasRcSXgC/1Mv504PRu0vfrIf4F4OPdpC/k5S8U6S7uy8CXey20mZmZmZmZmVk/UYS7FDMbrJYtWZx9gFYrTdn5VgsO+1Wr12THVir5DxOv6ejMLwRQfVkXjD3boC3/d43ozC/H6kp+vi1N+euiUi1bF0VK+tiIakPyrdZavw+4kvXcqOOps5q/jtsq+eutkev44K9dmh37k38/Jju2UnD/0UH+8jUXnCugbN2t6czffiXngJJ9aGV7/jl5zIiW/Iwp2yZF54sCJcdeUb4Fi1Zw6NXiC9Zbyf5WUuaS/X4onpM7lL9fNMfAn+uhbJuUnONK98/sfBu0PaCszKXruRFl0JrV2bGdza3ZsSX7JpSt55LlK7rvLLjfK9mPG6loXTTqulfYx93YceMGx8prkNvmPDYsK53esdO2Q2q7udmvmZmZ2atc0T9AZmaZGlVZaWZmZVz5Z2ZmZmZmZmZmNky58s8GLUkTJc3rJv1sSQcMUJkOkvT5NHyIpO0HQ7nMzMzMzMzMzLrjF37YkBMRZwzgvG8AbkhfDwFuBB4d6HKZmZmZmZmZDTZu/j84+Mk/G+yaJF0oab6kWySNlDRd0uGS/knStWsDJe0n6caeMpK0XNI3Ul63StokpU+WdL+kOZKul7RBSv+UpEdT+tUp7VhJ50l6K3AQcK6k2ZK2zimXpAMl3SfpYUnXShrTmNVmZmZmZmZmZubKPxv8tgHOj4hJwGLgsLpxvwL2lDQ6fZ8KXN1LXqOBmSmvO4EzU/plwOciYidgbl3654FdUvoJ9RlFxK+pPQF4akRMjojf91UuSRsDpwMHRMSuwEzgM10LKel4STMlzbxk+vReFsfMzMzMzMzMrHdu9muD3YKImJ2GHwImrh0RER2SbgLeJ+mHwHuAz/aSVxW4Jg1fAVwnaTwwISLuTOmXAmuf2psDXCnpx8CPcwvcS7neDmwP3CsJoBW4r5vppwHTAJYtWezXL5qZmZmZmZnZOnPlnw127XXDncDILuOvBv4NWEjtqb5lBXn3VbH2HmBf4H3AaZJ2LMj7ZeVSrcbvlxHxoYJ8zMzMzMzMzMzWmZv92lB3J7Ar8DF6b/ILtf398DR8JHBPRCwBFknaJ6UfBdwpqQK8PiJuBz4HjAe69s+3DBhbUK77gb0lvRFA0mhJb+p7Ec3MzMzMzMzM1o0r/2xIi4hOam/c/T/pb29WAHtImge8Azg7pR9D7cUdc4DJKb0JuELSXGAW8O2IWNwlv6uBUyXNkrR1X+WKiOeAY4Gr0rzuA7YrXmgzMzMzMzMzs0yKcJdi9uogaXlEDKm36z7w2ILsA/RNm26cnW9Tx+rs2M7m1uzY5ujMji0VnQV5N+i8pub8nhKWr6lmx45obSkqR8l6rlaasmMr1YJ1rPzfjtqr+dujraL8MhTqID/vVavXZMdWKo35HW1Fe/5xOrKlrBePvyzO7yFh69dumB178NcuzY699qSjsmObm/LXcXOfPTqsuz8tyl9vm284Ljv2988szI59zbj8y9j4EWXnlpJjpPLCivx8l3X97ayXfNtG5Oc7ZoPs2KUvrMqOXVlw/AOMKjiHjxuZv3ytzY05t1QKrpEl+wRAA0/h2QouOTStae87KCm5BwBoj/yV0dSg60jR+TDy71tK7i1KFd2LlCi4bynZ70vWcVWFx1PBuli6On/7jWrLP2eV/M+wupJ/jLSpcdfqknUxtpIfW3oOKDF2/IRBcPZsnDvmPjYsK53223HbIbXd/OSfmdkQ0sgKVjN79Sqt5DEzMzOzocMv/LABJWkicGNE7NCPeT4AtHVJPioixkiaDGwWET/vr/mleZ4N3BURv+rPfM3MzMzMzMyGKhU+9WqN4co/G3YiYs/u0iU1U+vTbwrQr5V/EXFGf+ZnZmZmZmZmZtYf3OzXBoMmSRdKmi/pFkmTJD28dqSkbdZ+l/SkpK9Imivpwbo3524i6UeSZqTP3in9LEmXS7oXuJzayzymSpotaWp64+7FKa9Zkg5O0x0r6TpJN0n6naSvpPQmSdMlzUtl+HRKny7p8DT8zpTX3JR3W13ZvyDp4TTOL/swMzMzMzMzs4Zy5Z8NBtsA50fEJGAxsAuwJDXRBTgOuKQufklE7AicB3wzpX0L+EZE7A4cBlxUF789cEBEfAg4A7gmIiZHxDXAacBtEbEHsD+1t/6OTtNNBqYCO1KrMHx9Sts8InZIZagvF5JGANOBqWl8M/CJupDnI2JX4HvAKaUryszMzMzMzMyshCv/bDBYEBGz0/BDwERqlXfHSWqiVgH3g7r4q+r+7pWGDwDOkzQbuAEYJ2ntKxFviIgXepj3gcDn03R3ACOALdO4WyNiSUSsAh4F3gA8AWwl6TuS/glY2iW/bdPyPJ6+XwrsWzf+ui7L+TKSjpc0U9LMH19zVXchZmZmZmZmZoNeU6UyLD85JP2TpMck/a+kz3cz/jOSHpU0R9Ktkt5QN64ztVicLemGV7od3OefDQbtdcOdwEjgR8CZwG3AQxHxt7qY6Ga4ArwlVdT9XepcdEUv8xZwWEQ81mW6PbspV3NELJK0M/Au4ATgA8BHel26l1qbZyc9HH8RMQ2YBvDAYwuG5WvRzczMzMzMzIar9CDT+cA/Ak8BMyTdEBGP1oXNAqZExEpJnwC+Qu3hJ4AXImIy/cRP/tmglCrxbqbWPPaSLqOn1v29Lw3fApy4NqCuyXBXy4Cxdd9vBk5UqiWUtEtv5ZK0MVCJiB8BpwO7dgl5DJi4ti9C4Cjgzt7yNDMzMzMzM7NhZQ/gfyPiiYhYDVwNHFwfEBG3R8TK9PV+YItGFcaVfzaYXQlUqVXs1dtA0hzgJODTKe1TwJT0uOyj1J7K687twPZrX/gBfBFoAeZImp++92Zz4I7UTPgK4D/qR6ZKy+OAayXNTeW/oO9FNTMzMzMzM7NhYnPgT3Xfn0ppPfko8Iu67yNSd2D3SzrklRbGzX5tQEXEk8AOdd+/Wjf6bcAlEdHZZbJzI+JzXfJ5nhefCKxPP6vL94XA7l3CPt7NdNOpvbhj7ff31o3u+rQfEXFs3fCt1F5a0jVmYt3wTGC/rjFmZmZmZmZmw0Wtjd3wI+l44Pi6pGmpC691yeufgSnA2+uS3xARf5a0FXCbpLkR8ft1La8r/2xQknQ9sDXwjoEuy0DafINx2bFauSw7dkXziOzY/EhA+Q8Td1B4FWhuyg5t6lidHavmluzYasGV64U1+WUo1VKwLtqioNvIgu1XYk1H1/r7njW15m+PSuEutGTFqr6DktFtrdmxHZ35y/fYX57Ljt3mdZtkx7YXrGOAzTccnx1bKdiHrj3pqOzYI751eXbsT/79mOxYCntKXb66Izu2o1rNjq0WlOP1G03Ijq0sejY7dnllo/xCACNKjr8R+VeH1hGvy47tUP75rbXgHLDx2FH5wYUq1fzjr5rZQXhxGQqO05JrWenxVKKkzCX3DM0lhW7O/1eoZN8EaCN/v3huRU/vpXu5DVmTHbukKf84Hd+Wv3wl+zyUrbuSI6Rayc+35JxcJAquC5TtQxQs36iC7VciWvLvh5pK1vHLnu3oXdG1oTn/fBEF/wfQwHt7G5rq++rvwZ+B19d93yKlvYSkA4DTgLdHxN/fOxARf05/n5B0B7UHjNa58s/Nfm1QiohDI2Kn9ERfffrErmlmryYlFX9mZrlKKv7MzHKVVpqamQ0jM4BtJP2DpFbgg8BL3tqb3jnwfeCgiHi2Ln0DSW1peGNgb6D+RSHF/OSfmZmZmZmZmZlZP4mIDkn/Ru0lo03AxRExX9LZwMyIuAE4FxhD7Z0BAH+MiIOANwPfl1Sl9tDel7u8JbiYK/9s0JA0ATgyIr6bvu8HnNKlv71GzXsicGNE7NBH6LrkfRCwfUR8ub/zNjMzMzMzMxusKsO1078MEfFz4Odd0s6oGz6gh+l+DezYn2Vxs18bTCYA/9pfmUnq98rtdckzIm5wxZ+ZmZmZmZmZDQRX/tmAkfQZSfPS52Tgy8DWkmZLOjeFjZH0Q0m/lXSl0rOwknaTdKekhyTdLGnTlH6HpG9Kmgmc1MN8XyvpekmPpM9b06gmSRdKmi/pFkkju8tT0jslzZI0V9LFdW3xn5T0BUkPp3HbpfRjJZ3Xx7zNzMzMzMzMzPqdK/9sQEjaDTgO2BN4C/Ax4Bzg9xExOSJOTaG7ACcD2wNbAXtLagG+AxweEbsBFwNfqsu+NSKmRMTXepj9t4E7I2JnYFdgfkrfBjg/IiYBi4HDuuYJnA9MB6ZGxI7Ums5/oi7u+YjYFfgecErBvOvXzfGSZkqaeeVll/awCGZmZmZmZmZmfXOffzZQ3gZcHxErACRdB+zTTdyDEfFUipkNTKRWMbcD8Mv0IGAT8Je6aa7pY97vAI4GiIhOYImkDYAFETE7xTyU5tU1z21T3OPp+6XAJ4Fvpu/X1U3//px5dw2of2X4U8/+LfpYFjMzMzMzMzOzHrnyzwa79rrhTmr7rID5EbFXD9Os6Kd5jVyHPNfmsbasZmZmZmZmZmYDxs1+baDcDRwiaZSk0cChwL3A2IxpHwM2kbQXgKQWSZMK5n0rqamupCZJ4wumfQyYKOmN6ftRwJ3rad5mZmZmZmZmZkVc+WcDIiIeptZ33oPAA8BFEfEQcG96Aci5vUy7GjgcOEfSI8BsoOTFGScB+0uaS6157vYF5V5Fra/Ca9P0VeCC9TFvMzMzMzMzM7NSinCXYmaD1bKlS/MP0Kg2phDK/42gWuuDMS92kJx6Vnd0Zse2Njc1pAzNNHBllOwXDdrWJRq5X1QKityocjSqDKX7UAeN2X4lStbFwV/Lf/nR9Z85Zh1K0/+eWviyLl179LrxOQ+91zQ35R+njTy3NOp83xz55+SSc1bJubBaacy5HmDVmvzlW7h8ZXbsFhPG5BeiQdeFwaK9YIdrU35se5SdN0vyLtnnKtX8fahR+3Kl8P/HlR35+1xTwcWhqZK/f5Zcc7S6ve+gtbEtrdmxjbp3gvJtMuQUnLc6lL/fN/QevMDYceMG/sasge777e8Hx4ruZ3ttt/WQ2m7uk8zMzMzMzMz6XUnFn5kNT5Uh+EPScOTKPxu2JJ0GHNEl+dqI+NJAlMfMzMzMzMzMbH1z5Z8NW6mSzxV9ZmZmZmZmZvaq5ecvbciT9HNJE/oxv2Mlnddf+ZmZmZmZmZmZDRQ/+WdDgqTmiOjoblxEvHt9l8fMzMzMzMzMelfywh1rHD/5Z+uNpImSfiPpQknzJd0iaaSkOyRNSTEbS3oyDR8r6QZJtwG3StpU0l2SZkuaJ2mfFPdkmq7b/FPM7pLmpGnPlTSvj+JuJukmSb+T9JW6ZfiQpLlp/ufUpS9P+c6X9CtJe6TlekLSQSmmKcXMSGX5eA/r6XhJMyXNvOSSS17BGjczMzMzMzOzVztX/tn6tg1wfkRMAhYDh/URvytweES8HTgSuDkiJgM7A7ML8r8E+HiatjOjnJOBqcCOwFRJr5e0GXAO8I40fndJh6T40cBtab7LgP8C/hE4FDg7xXwUWBIRuwO7Ax+T9A9dZxwR0yJiSkRMOe644zKKamZmZmZmZmbWPTf7tfVtQUSsrbR7CJjYR/wvI2JhGp4BXCypBfhxXT695p/6AxwbEfel9B8A7+1jvrdGxBIASY8CbwA2Au6IiOdS+pXAvsCPgdXATWnauUB7RKyRNLduGQ8EdpJ0ePo+nlpl5YI+ymJmZmZmZmZmtk5c+WfrW3vdcCcwEujgxadQR3SJX7F2ICLukrQv8B5guqSvR8RlGfn3Rzn7OlbWRESk4era6SOiKmnttAJOjIib17FMZmZmZmZmZkOG5E7/BgM3+7XB4ElgtzR8eE9Bkt4APBMRFwIXUWsS3KeIWAwsk7RnSvrgOpbzQeDtqX/BJuBDwJ0F098MfCI9uYikN0kavY5lMTMzMzMzMzPrk5/8s8Hgq8D/SDoe+FkvcfsBp0paAywHji6Yx0eBCyVVqVXYLSktZET8RdLngdupPcX3s4j4SUEWF1FrAvywaj9/PAccSdQWWQAAIABJREFU0usUZmZmZmZmZmavgF5sqWg2fEkaExHL0/DngU0j4qQBLlafFi1dln2ANkfOe0xqYs3q7NjO1vyW0yVlaKT7FjydHbv7Vq/Pju3orGbHjmgqeLw98vMFWFXNz7ukHO3V/OvBwuUvZMeOHdGaHTuitSU79rdPP5sdC7D95q/Jjq0UXBurBU0ZKtUGHSMa3g/yd5C/jg/9+qVFeX/vI+/Pjt1i/Kjs2On3PpIde8yek7Jjq+35x17T6LHZsUDRflRyHYnWtuzYle1rsmPHtBb8hl1wno2Ojvx8C6m5oMwF26PkGClRct0DaG3OL3PBJYdKweKV5KuVy7Jjm0bmH/8AHWrKz7uj4Hhqyb+mllxzSspbqrOavx+1FWzskv2+ZB9SwflNTfnrrXQdD5b76mwl9yKF977VSsF6fu6v+cXozL/m/PgPi7NjD5/0huxYgAmbbj6s28XO/N2Tw7LSaco2E4fUdvOTf/Zq8R5J/0Ftn/8DcOzAFsds3ZRU/JmZZRvmFchmNjBKKv7MzKxxXPlnrwoRcQ1wTX2apHcB53QJXRARh663gpmZmZmZmZmZNZB/5rX1RtJESfP6IZ9jJZ2Xhg+RtH3duDskTcnJJyJujojJXT7FFX/9tVxmZmZmZmZmZv3NlX821B0CbN9nVINIekVPz77S6c3MzMzMzMzMeuPKP1vfmiRdKGm+pFskjZS0taSbJD0k6W5J2wFIep+kByTNkvQrSa+tz0jSW4GDgHMlzZa0dRp1hKQHJT0uaZ+eCiLpZ5J2SsOzJJ2Rhs+W9DHVnCtpnqS5kqam8fulct4APNolz61SXrv3slzTJV0g6QHgK/2yVs3MzMzMzMzMuuHKP1vftgHOj4hJwGLgMGAacGJE7AacAnw3xd4DvCUidgGuBj5bn1FE/Bq4ATg1Ndn9fRrVHBF7ACcDZ/ZSlruBfSSNBzqAvVP6PsBdwPuBycDOwAHUKhk3TTG7AidFxJvWZiZpW+BHwLERMaOX5QLYAnhrRHyma6EkHS9ppqSZ0y+5pJfim5mZmZmZmQ1eFWlYfoYaNzm09W1BRMxOww8BE4G3AtfqxQOoLf3dArgmVbi1Agsy53Fdl/x7cjfwqZTvz4B/lDQK+IeIeEzSCcBVEdEJPCPpTmB3YCnwYETUl2cT4CfA+yPiUUljelkugGtTvi8TEdOoVRyyaOmyYfladDMzMzMzMzNbP1z5Z+tbe91wJ/BaYHFETO4m9jvA1yPiBkn7AWcVzqOT3vfxGcAU4Angl8DGwMeoVRr2ZUWX70uAPwJvo9YUuELPy9Xd9GZmZmZmZmZm/c7Nfm2gLQUWSDoCIPWzt3MaNx74cxo+pofplwFj12XGEbEa+BNwBHAftScBT6HW5Jf0faqkJkmbAPsCD/aQ3WrgUOBoSUdGRG/LZWZmZmZmZma2XrjyzwaDDwMflfQIMB84OKWfRa3Z7EPA8z1MezVwanrJxtY9xPTmbuDZiHghDW+R/gJcD8wBHgFuAz4bEX/tKaOIWAG8F/i0pIN6WS4zMzMzMzOzYa9S0bD8DDWKcJdiZoPVMwsXZx+go5ry811VzT9ZlfRl2tI0OH5P+OPflmTHbrHh+IaU4amFjSvD6o5uu4vs1sjoyM+4YGOvKNiHXliTX4aSznNXtq/OjgXYYPTI7NgRrS1Feecq2XYjWvIP6moDL+Ul9zaVgnuK5avz94uS7fHXxcuyYwE+cfF1fQcl53zoPdmxTQUrbptxI7JjO1etzI7VBptkxwI0d98V7Su2fE21IfmW7BeVF/J724jONWUFqeQfq00jR2XHdqjgwl6g5JguPbc005iTUbXg2lByHiLy981qwXYuVbKetWJpduwLLY257pX+z1uyfCV5r+7I336tzfn3qJVq/rkwOvKvZe2VsnuL5oL76pLzd8m+3Kj7i9JzRQcF/7ssX5wd2zQ6vwHZU0vyr7+vm1DWMG2DcWOHXk1SgdlP/HFYVjpN3mrLIbXdBsd/6mZmlqWk8sjMLFejKv7M7NWtkT9OmZlZPr/ww4Y9Se8CzumSvCAiDh2I8piZmZmZmZmZrS9+8s8aStJFkrbvI+YESUf34zynSPr22u8RcXNETO7yOVTSpyT9RtKV6zCPkyWNqvv+c0kT+msZzMzMzMzMzIa6ijQsP0ONn/yzhoqIf8mIuaCf5zkTmJkR+q/AARHx1DrM5mTgCmBlmue71yEPMzMzMzMzM7OG8pN/VkzSZyTNS5+TJU2U9FtJV6Yn6X649qk4SXdImpKGl0v6kqRHJN0v6bUp/SxJp6ThyWncHEnXS9qgLp9zJD0o6XFJ+/RSvv0k3ViX98Vp+ickfSqlXwBsBfxC0qcljU5xD6Y3Bx+c4pokfTUt6xxJJ6Y8NgNul3R7intS0sbdrZ+UNjGtmwslzZd0i6T8XpjNzMzMzMzMzNaBK/+siKTdgOOAPYG3AB8DNgC2Bb4bEW8GllJ7qq6r0cD9EbEzcFeatqvLgM9FxE7AXODMunHNEbEHtafuzuxm2p5sB7wL2AM4U1JLRJwAPA3sHxHfAE4Dbkv57w+cK2k0cDwwEZicynRlRHy7btr9+1o/knZJo7cBzo+IScBi4LDuCivpeEkzJc28/NLpBYtpZmZmZmZmZvZSrvyzUm8Dro+IFRGxHLgO2Af4U0Tcm2KuSHFdrQZuTMMPUatU+ztJ44EJEXFnSroU2Lcu5Lqepu3DzyKiPSKeB54FXttNzIHA5yXNBu4ARgBbAgcA34+IDoCIWNjHvHpaP1B7ycjsvpYhIqZFxJSImHLUMcdmLqKZmZmZmZmZ2cu5zz/rL9HHd4A1EbE2vZPy/a99HadtrxvuaVoBh0XEYy9J7N+OPLuWw81+zczMzMzMbNjq5/+pbR35yT8rdTdwiKRRqVnsoSltS0l7pZgjgXtKM46IJcCiuv78jgLu7GWS/nQzcKLSmamuqe4vgY9Lak7pG6b0ZcDYbvLpaf2YmZmZmZmZma13rvyzIhHxMDAdeBB4ALgIWAQ8BnxS0m+o9QH4vXWcxTHU+tubA0wGzn6lZc70RaAFmCNpfvoOteX7Y0p/hFrFJsA04Ka1L/xYq7v1ExGzGl98MzMzMzMzM7OX04utMM3WjaSJwI0RscMAF2XYefh//5B9gG41Or8l9Ast+S2OR7S2ZMc2d9vauwdRzY8ForMzP3bNmuxYNeevt2hty46d9eTT2bETN9mw76A6LU1N2bGj2vK3X4llq9r7DkpWre7Ijn3t+NHZsZVq/j4BsGDhsuzYTcbml6NasNs/vWhJduz4UfnHaXtH/joGKLn2b7bBuOzYZ5csz47tqOafA16/0YTs2OYo2y8e/eui7NjPXfWz7Niffubo7Ni/LF2ZHbvRqsXZsbHxptmxAG2VgmY5BefwKNg/S2IrI0dlx7YXHKgrV+dfQ0q1VPJ/dy+5/jZK0XV9kKgWNC8ruY4sai87z45tzz8fLmnJv+aMWfzX/HzHvSY7dvyoEdmxpdZ05K/nSsEx0lRwzmoqyFcrlmbHVtry19vqSuN63OosOMe1NuffR5ZcFla25587S+9Pl72wOju2WnDe2qAtf5tUV63KjlVL2fKN23CjYd0udu6TTw29i0mGHSduMaS2m5/8MzMbQkoq/szMchVV/JmZZSqp+DMzs8bxCz/sFYuIJ4H1/tSfpHcB53RJXhARh67vspiZmZmZmZmZDUZ+8s8aRtJFkrbvI+YESflto+pExM0RMbnLp18r/iQd0tcypLizJJ2Shs+WdEAaPllSfrskMzMzMzMzM7N+5Cf/rGEi4l8yYi5YH2VZF+kNv4cANwKP5k4XEWfUfT0ZuALI79DJzMzMzMzMzKyfuPLPikj6DPCR9PUi4MfATcBDwK7AfODoiFgp6Q7glIiYKWk58C3gvcALwMER8Yyks4DlEfFVSZOBC4BRwO+Bj0TEopTPA8D+wATgoxFxdw/lO5Zahd1oYBvgq0ArcBTQDrw7Ihb2Ma/ZwNuA64GDgLdLOh04DHgHcHzK83+BoyLiJRV7kqZTqzDcLH1ul/Q8cDmwU0ScnOI+BmwfEZ/OW/tmZmZmZmZmQ0dTwYuYrHHc7NeySdoNOA7YE3gL8DFgA2Bb4LsR8WZgKfCv3Uw+Grg/InYG7krTdnUZ8LmI2AmYC5xZN645Ivag9iTdmd1MW28H4P3A7sCXgJURsQtwH7C2iXFv82qNiCkR8SXgBuDU1KT498B1EbF7Wo7fAB/tqRAR8W3gaWD/iNgf+B/gfZLWvv7pOODirtNJOl7STEkzr7v6B30sqpmZmZmZmZlZz1z5ZyXeBlwfESsiYjlwHbAP8KeIuDfFXJHiulpN7Wk4qD0lOLF+pKTxwISIuDMlXQrsWxdyXU/TduP2iFgWEc8BS4CfpvS5wMSMeV3TS947SLpb0lzgw8CkPsryd2md3Qa8V9J2QEtEzO0mblqqfJzy/g8emZu9mZmZmZmZmdnLuNmv9Yfo4zvAmohYm95J+b7XXjBte91wte57NXO+K3oZNx04JCIeSU2M98vIr95FwH8CvwUuKZzWzMzMzMzMzKyIn/yzEncDh0gaJWk0cGhK21LSXinmSOCe0owjYgmwSNI+Keko4M5eJllnhfNaBoyt+z4W+EtquvvhjNm9ZPqIeAB4PbX1dFVh0c3MzMzMzMyGDEnD8jPU+Mk/yxYRD6eXWTyYki4CFgGPAZ+UdDG1t+J+bx1ncQxwgaRRwBPU+sRrlNx5XQ1cKOlTwOHA/0ft5SPPpb9je5hurWnATZKeTv3+Qa3vv8kRsegVLoOZmZmZmZmZWa/0YktMs3KSJgI3RsQOA1yUIUPSjcA3IuLWvmIXLV2WfYA2R2d2GTrUlB27avWa7NhKpXEPE1er1ezYGU88lR2768TNs2OfXbo8O3abTcZnx3YsXZwdC9A0uq865xdVV63sOyhRU0vfQcnySmt27Ki2/HyvvHdWduzTi5ZmxwKc+t63Z8eW7PfVgstoybpYtqq976BkZEt+vqVamxtzXJest6cWLsmOvePR3xeVY69t3pAdu+1rN8yOfd/XL8uOvf4zx2THNnfbs0b/aC/YKE0F5/uS46mlOf/61Kb88kZn/jUy1uSXF0BN+euis3Vkduzqjvwytxast5J9qIOyJxsqBeEl54CSfCsl/9tE/r1Fe5Sti9bO/P0oWtuyY7U6/9rQXsm/NjSX7McF92RQdr4oUVKOkjJUXuitB6AusSNHZcdWC58UKjlGmtbk7xdqzn/+p2S/7ywocMk5C6CjM39b65k/ZsdWC46nX/wt/5y8z3b/kB0LsNWmrxl6j5EV+M0fnx6WlU5v3nKzIbXd3OzXbD2RNEHS48ALORV/Zt0pqfgzM8tVUvFnZparURV/ZmZWxs1+7RWJiCeB9f7Un6R3Aed0SV4QEYeu77LkiojFwJsGuhxmZmZmZmZm9urhyj8bNCT9HDgyVZJ1N/5JYEpEPB8RNwM395LXryPirX3Mbx/gAmANsFdEvFBQ1kOAxyPi0fT9bOCuiPhVbh5mZmZmZmZmw1llCL4cYzjyc9g2KKj2upz39lTxV6qvir/kw8B/R8Tkkoq/5BBg+7r5neGKPzMzMzMzMzMbbFz5ZwNG0kRJj0m6DJgHdEraWNJoST+T9IikeZKmdplupKRfSPpYL3kvT3/3k3SHpB9K+q2kK1XzL8AHgC9KujLFnipphqQ5kr5Ql9fRKe0RSZdLeitwEHCupNmStpY0XdLhKf6dkmZJmivpYkltKf1JSV+Q9HAat13/rlEzMzMzMzMzs5dy5Z8NtG2A70bEJOAPKe2fgKcjYuf0FuGb6uLHAD8FroqICzPnsQtwMrUn9bYC9o6Ii4AbgFMj4sOSDkxl2QOYDOwmaV9Jk4DTgXdExM7ASRHx67ppJ0fE318tKWkEMB2YGhE7Umta/4m6sjwfEbsC3wNO6a6wko6XNFPSzOmXXJK5iGZmZmZmZmZmL+c+/2yg/SEi7u+SNhf4mqRzgBsj4u66cT8BvhIRVxbM48GIeApA0mxgInBPl5gD02dW+j6GWmXgzsC1EfE8QEQs7GNe21J78cjj6fulwCeBb6bv16W/DwHv7y6DiJgGTANYtHSZX79oZmZmZmZmQ5Lc59+g4Cf/bKCt6JqQKs52pVYJ+F+SzqgbfS/wTyo7g7TXDXfSfaW3eLH/v8kR8caI+H8F8ygtS0/lMDMzMzMzMzPrN678s0FH0mbAyoi4AjiXWkXgWmcAi4Dz+3m2NwMfkTQmlWFzSa8BbgOOkLRRSt8wxS8DxnaTz2PARElvTN+PAu7s57KamZmZmZmZmWVx5Z8NRjsCD6YmumcC/9Vl/EnASElf6a8ZRsQtwA+A+yTNBX4IjI2I+cCXgDslPQJ8PU1yNXBqerHH1nX5rAKOA65N+VSBC/qrnGZmZmZmZmZmJRThLsXMBqvHn/pr9gE6dkRrdr6jVc2OXVbN/42gvaMjO3bh8pXZsQDNTfnlmDiyKTu20jYiO3ZZZ35r83GtjfttpbpqVXZsZUT+8kXB9iuJVUtLfmxT/rYrVa3k5/3Mkpf1SNCjkn2zRMkxMm5k/nYG+MvipdmxO225aXbsgmf76hb1Ra/faEJ2bIm26pqi+JJ9+ZmCrDcZNzo79tCvX5od+72PdNtdbLe2GD8qOxbgRw//Njv20O0nZseuXvhsdmznyvxjb8WmW2XHbtKaf/5++C+LsmMBni84Vlsq+eeLHQuOvY3GjMyOrTbw1r+Z/MyrBT24VAr+X2kvWMDWjva+gxK15N9nAXSoMdezVavzT0RjWvL3t1izOjv2hUrZuhhVsCrK7i/yy1Gyv5UcIx2d+ffUpdcnNef3EPTUkvzz0IZj8q8NI9bk33MuL9gvxlTz9zcoXBfL84/rzVryt1+ltS07FpXdG47baONh3Sleyf+0Q8mbtnjdkNpufvLPzGwI+f/Zu/cou6oy3/vf366q3JMiQFQM2KCCIV7IkVtotOEIKjpagUYbaF9AwM7BBsLRQQ88fUQ4XjggrTQ2aL8BCeBdxMRI0wRaBXxRTIIiFwOYBsQgipBQuSdVtZ/3jzVLtkVVZc4km6pd/D5j7JG953r2s+Ze98yaa82Shj8zs1wlDX9mZrlKGv7MzKx5POCAtaz0HL4fDDDpiIh49sWuj5mZmZmZmZnZSOOefzYiSLpZ0qD3gEl6XNKujWUR8WzD6LyNrx3W8CdplqR3Z8QdLumm9P69kj6W3h8jaeaOqo+ZmZmZmZmZWQn3/LNhJ0nAX0dE/kMXXgSS2oFZwAHAzbnfi4hFwKL08RjgJuBXO7yCZmZmZmZmZiNYW62lHo03arnnnw0LSXtKeljS9cADQK+kXSVNlPTvkn4p6QFJx/f73nhJ/yHp74fI+5CkayU9Iulrko6UdJekX0s6KMXtLGmhpPsk3S3pTan8QklfkXQX8BXgk8Dxku6VdLykgyT9NI3y+xNJrxugDh+UdIWkvwTeC1yavv8aST9viNu78bOZmZmZmZmZ2Y7mxj8bTnsDX4yI1wO/SWVHAb+LiP0i4g3ALQ3xk4DvA9+IiKuGyPta4HPAjPT6O+AtwLnAP6WY/wP8IiLelMqub/j+TODIiDgR+ATwrXQ78beAh4C3RsR/S9MuGqwSEfETqh6A/5i+/19Al6RZKeRUYH7/70maI2mZpGXf+tpXhviZZmZmZmZmZmZD822/Npx+ExF39yu7H/icpEuAmyLixw3Tvgd8NiK+tpW8j0XE/QCSHgR+EBEh6X5gzxTzFuA4gIj4oaRdJE1J0xZFxMZBcncC10naGwigY+s/889cDZwq6aPA8cBB/QMiYh4wD0bvsOhmZmZmZmZm9uJwzz8bTuv7F0TEI8CbqRoBPy3pEw2T7wKOSs8IHMrmhvf1hs918hq8X1CvBp8CfpR6Jb4HGJeRr9GNwLuAvwbu8ajEZmZmZmZmZtZMbvyzEUXSK4ENEfFV4FKqhsA+nwBWA1fugFn9GPhAmufhwDMRsWaAuLXA5IbPncCT6f0HM+bzZ9+PiE3AYuBLDHDLr5mZmZmZmdloIWlUvlqNG/9spHkjsETSvcAFwKf7TT8HGC/ps9s5nwuB/SXdB1wMnDJI3I+AmX0DfgCfBf6vpF+Q14vwm8A/pgFCXpPKvkbVC/HW7fkBZmZmZmZmZmZbowg/UszsxSTpXKAzIs7fWmzXH/Kf+del/Ed4Th2bH9ujtuzYLT292bHNNCG/ykU21fP/wqM/PJEd2z5lp6J6RG/+ci7JHT092bG969fm5y04z7TtPC07tlT9ufy77Ns7d25KHbpXP5MdW+/cJTt2rMrO5SX79YbN3dmxJet6/LpV2bG1XV+RHat1z2XHAtS7C35f95bs2DEve2V27Mrn1mXHfvia72bHLjzjb7JjAWLC5K0HJbWCP3hr82CP0d0+vWPGZ8eWnJ9qTfxrfntb/t/d22nONXrJdkzhslB76aOQmyDq2aHruvNjJ4xt3m8r2UceWb0hO/Y1pQ+nyVR8jixYJxsKLiXHdTTngq9kfawveIT+hN6CfQ/oHpt/jCu9DshVcr3Q05u/nksPsx0Fx856waKobRzqaU/9TJiYHbqlJ39ZAEyb2tl63cgKPPrU06Oy0enVu72spdabB/wwexFJWgC8BnjbcNfFWlNJw5+ZWa6Shj8zs2wFDX9mZtY8bvyzliRpF+AHA0w6YiQPohERxw53HczMzMzMzMxeDM3sUW/53PhnLSk18M0a7npsjaQ9gZvS6MBmZmZmZmZmZi8qD/hhZmZmZmZmZmY2Srnxz0YNSRMl/bukX0p6QNLxkvaXdIekeyQtlrSbpE5JD0t6XfreNyT9/RB510m6VNKDkv5T0kGSbpf0qKT3ppg9Jf1Y0s/T6y8HyNOW8iyVdJ+k/9G8pWFmZmZmZmZm5sY/G12OAn4XEful22xvAf4VeF9E7A9cA3wmIrqAs4BrJZ0ATI2Iq4bIOxH4YUS8HlgLfBp4O3As8MkU8zTw9oh4M3A88IUB8pwOdEXEgcCBwN9L2qt/kKQ5kpZJWnbtV75SugzMzMzMzMzMzP7Ez/yz0eR+4HOSLgFuAlYDbwBuU/WQ0TbgKYCIuE3S+4Ergf22kncLVUNi3zw2R0S3pPuBPVN5B3CFpFlAL7DPAHneAbxJ0vvS505gb+CxxqCImAfMA+j6w+9H5bDoZmZmZmZmZvbicOOfjRoR8YikNwPvpuqd90PgwYg4pH+spBqwL7ABmAqsHCJ1d0T0NcLVgc1pfnVJffvQR4A/UDUk1oBNA+QRcHZELC79bWZmZmZmZmZm28K3/dqoIemVwIaI+CpwKXAwME3SIWl6h6TXp/CPAMuBvwPmS+rYztl3Ak9FRB04iaqXYX+LgQ/3zUvSPpImbud8zczMzMzMzMwG5Z5/Npq8EbhUUh3oBj4M9ABfkNRJtb3/i6Qe4EPAQRGxVtKdwMeBC7Zj3l8EbpR0MtUtwusHiLma6jbhn6u6D/mPwDHbMU8zMzMzMzOzEatW03BXwQA9fzejmY00q9eszd5B26M3O++67np27Lgx29spcnCbtnRnx3a0D9SZcmBj1ZzjWo/y69BMI+H8WSs4d/TQvAq305x6NCtvT2/+vgcwpj2/g369YLNv1jZUsl2s29JTlHvSmIK/V0b+ci7Zr3vr+XnHlizkgvoC9G7ckB17zL99Nzv2+x89OTu2rvzft6Un//eVbPO1ev55b8RQ/u8rObaU7tMl+2rJ9lmvNec8WVTfQiXLueQYMKaef4xbX8+vw0Tl12F9lN3kNbGWv5zV3rzrw1zNur4ouQYoVXLsLDmvl9S5WXUorcfmguQdbc05N5RcA5RuFyXLuXPy5BFwdd88Tzz9zKhsdHrVy3ZtqfXm237NbFiUNPzZ80ZCw99I0cyL85GgWQ1/rahZDX+taCQ0/Flra1bDXytqVsNfK2q1hr9WVNIY1Iqa1fDXikb7urbW5Nt+zRJJPwPG9is+KSLuH476mJmZmZmZmZltLzf+mSURcfBw18HMzMzMzMzMbEfybb9mhST9U8P7PSU9MJz1MTMzMzMzMxuJamhUvlqNG//Myv3T1kPySHLvWzMzMzMzMzNrGjf+WcuTNFHSv0v6paQHJB0vaX9Jd0i6R9JiSbtJ6pT0sKTXpe99Q9LfD5H3REn3p5yXpLKLgfGS7pX0tRTaJukqSQ9KulXS+BT7Gkm3pDr8WNKMVH6tpH9Lzxj8bFMXjpmZmZmZmZm9pLnxz0aDo4DfRcR+EfEG4BbgX4H3RcT+wDXAZyKiCzgLuFbSCcDUiLhqoISSXglcArwNmAUcKOmYiPgYsDEiZkXEB1L43sCVEfF64DnguFQ+Dzg71eFc4IsNs9gd+MuI+OgA854jaZmkZdfOn7/tS8XMzMzMzMzMXvJ8y6GNBvcDn0u9824CVgNvAG5TNcx6G/AUQETcJun9wJXAfkPkPBC4PSL+CJB6+f0VsHCA2Mci4t70/h5gT0mTgL8EbtDzQ703jiR8Q0T0DjTjiJhH1XDI6jVrY4g6mpmZmZmZmY1YDf8ftmHkxj9reRHxiKQ3A+8GPg38EHgwIg7pHyupBuwLbACmAit3QBU2N7zvBcZT9ap9LiJmDfKd9TtgvmZmZmZmZmZmQ/Jtv9by0i26GyLiq8ClwMHANEmHpOkdkl6fwj8CLAf+DpgvqWOQtEuAwyTtKqkNOBG4I03rHuJ7AETEGuCx1MsQVYbqaWhmZmZmZmZmtsO555+NBm8ELpVUB7qBDwM9wBckdVJt5/8iqQf4EHBQRKyVdCfwceCC/gkj4ilJHwN+BAj494j4Xpo8D7hP0s+B/z1EvT4AfEnSx4EO4JvAL7f/55qZmZmZmZmZ5VGEHylmNlI981xX9g7aVmtOR97V6zdmx67fvCU79slVXUX16O6tZ8e+5XV7Zsf2FOTd3N2THds5ti07NnoHfPzS3KBHAAAgAElEQVTjEF8oOG436Rkbasv/fWWJ87fjHpr3/JCS7aJZjzHp7snfLtqbtT6AcW35P7BZ66SdJl6rRP66Ltk+N9fz63zTL5Znxx69/+u3HpSULrf3fP767Njvf/Tkotw2cpTsp03d95qkXnBQrhWcT0uPb+0DP9p54NzKP4aXrJNmretNvWXbRcl5pFnrr0jBeaFk3bX15F8nA6h9yBuNRpyuTd3ZsZ3jmvfbirahev5+Wq8171qrc/LkUf1QvKeeWdV6J5MMu+26c0utN9/2a2bWSvwHGzNrgpKGPzOzXCUNf2Zm1jy+7dde8iT9jD8fiRfgpIi4fzjqY2ZmZmZmZma2o7jnn41KkmZI+qmkzZLO7TftKEkPS1oh6WMRcXAalfdYqpF7JwEflzRmiPx/Jennknokva/ftFskPSfppn7le0n6WZrvt4bKb2ZmZmZmZma2I7jxz0arVcBc4J8bC9PIvVcC7wJmAidKmpkmXwJcFhGvBVYDpw+R/wngg8DXB5h2KXDSAOUl+c3MzMzMzMzMtpsb/2xYSVoo6R5JD0qak8pOl/SIpCWSrpJ0RSqfJulGSUvT69DB8kbE0xGxlGr030YHASsi4tGI2EI1Au/RkgS8DfhOirsOOGaI/I9HxH3AC54IHBE/ANb2+51F+c3MzMzMzMzMdgQ3/tlwOy0i9gcOAOZKmg6cD8wGDgVmNMReTtVz7kDgOODqbZjfdOC3DZ9XprJdgOcioqdf+Y6SnV/SHEnLJC27/tprd2AVzMzMzMzMzOylxgN+2HCbK+nY9H4Pqttl74iIVQCSbgD2SdOPBGbq+eHbp0iaFBHrXswKN1tEzAPmATzzXJeHdjUzMzMzMzOzbebGPxs2kg6natA7JCI2SLodeAjYd5Cv1IDZEbFpO2b7JFUjY5/dU9mzwE6S2lPvvL7yHaXZ+c3MzMzMzMzMXsC3/dpw6gRWp4a/GVS3+k4EDpM0VVI71e29fW4Fzu77IGnWNsxzKbB3Gnl3DHACsCgiAvgR0Ddy7ynA97Yh/4Cand/MzMzMzMxspKnVaqPy1Wpar8Y2mtwCtEtaDlwM3E3VG+4iYAlwF/A40JXi5wIHSLpP0q+AMwZLLOkVklYCHwU+LmmlpCmp191ZwGJgOfDtiHgwfe084KOSVlA9o+/LQ+Q/MOV/P/D/SnqwYdqPgRuAI9J831ma38zMzMzMzMxsR1DVIcls5Oh7jl/q+bcAuCYiFgx3vYbD6icey99Bn38W4la1d+68LdXZunjB4MeDU+HfHgpyb+jNT7tu0+bs2Cnjx2XHjq33H2h6cPUt+XUAqI0Zmx9csNw0Jv/3Fa3rApvqBdtxW9k2VMtPXZa3XrDBlSjYR+oF+z+ACrY5tec/IaS+Kf+pDLVx+dtbvdaWHVvy26Ds95XoUX6daxvX5yeeMHEbapNZj4Jrwvd8/vrs2AWn/3V2bNQLjlkFf3VXx5j82MLzkzo68mPb8reLovNkk47J0Vt2fCv6fU1SUufnevK3+fEF6xmgY3P+fq2Jk/Nju7dkx0bBdl+y/5eec7b05G+fY9oLzn1N+m9s25aN2bElx5aS8wIUXrdsKDiPFOhS/jly6tiC64WC8zo071prXXf+tjlR+bG9HQXX6sDUKZObdJU6Mvxh1XOjstHp5Tvv1FLrzT3/bCS6UNK9wAPAY8DCYa6P2YhR1PBnZpap5D/+Zma5Shr+zMyseTzgh404EXFubqykU4Fz+hXfFRFn7oi6SPrfVLf2NrohIj6zI/KbmZmZmZmZjVa1wt7C1hzu+WctLSLmR8Ssfq9taviTNEPSTyVtlnRuyv+ZiJgFfAwYD0wChux3Lulrkh6W9ICkayR1pHJJ+oKkFem5hW/elnqamZmZmZmZmeVy45/Z81ZRDSryz42FktqAK4F3ATOBEyXNHCLP14AZwBupGgw/lMrfBeydXnOAL+3IypuZmZmZmZmZ9efGP2s5khZKukfSg5LmpLLTJT0iaYmkqyRdkcqnSbpR0tL0OnSwvBHxdEQsBfqP1HAQsCIiHo2ILcA3gaOHyHNzJFSjFu+eJh0NXJ8m3Q3sJGm3bV0OZmZmZmZmZmZb48Y/a0WnRcT+wAHAXEnTgfOB2cChVL3u+lwOXBYRBwLHAVdvw/ymA79t+LwylQ0p3e57EnBLSR5JcyQtk7Ts2q9/Yxuqa2ZmZmZmZjb8pNH5ajUe8MNa0VxJx6b3e1A1sN0REasAJN0A7JOmHwnM1PN75xRJkyJi3YtQzy8Cd0bEj0u+FBHzgHkAq594zMMvmpmZmZmZmdk2c+OftRRJh1M16B0SERsk3Q48BOw7yFdqwOyI2LQds32SqpGxz+6pbKh6XgBMA/7H9uQxMzMzMzMzM9sevu3XWk0nsDo1/M2gutV3InCYpKmS2qlu7+1zK3B23wdJs7ZhnkuBvSXtJWkMcAKwaLBgSR8C3gmcGBH1hkmLgJPTqL+zga6IeGob6mNmZmZmZmZmlsU9/6zV3AKcIWk58DBwN1XvuYuoBtdYRdUTsCvFzwWulHQf1fZ+J3DGQIklvQJYBkwB6pL+JzAzItZIOgtYDLQB10TEg0PU8d+A3wA/TbcbfzciPgncDLwbWAFsAE7dpiVgZmZmZmZm1gLaau5zNhKoGpDUrLX1Pccv9fxbQNVAt2C467W9Vv3m0ewdtDZ2XHbep3vyD8DTpkzMjt20pf9AyYOrFx56ev+sE+XQenrzY3eZND47dt2m/N83YWxHdmxt4/rsWIDa+AnZsb0b8h9v2VaQt0dt+XWo56+PklNSvfD8VSt4Mu+42vCfG6OnJz+48KnD9c35T0Jom5B/DNjyx99nx46Z9ors2JLtrVb4AOYNm/P364nK35bXR/5xtv2PK7NjxxYst81tY7NjAca15S+8nq5V2bHHfvmm7NivvHXP7NiSbbN9Umd+3oJjIUDbxMn5sQV1LlGvFewj9d7s2KLjEKD24e9XEN1bsmNX9eRv8yXnMoCp3fnn9u7JO2fHtj37u+zYpzqmZMe+apf8faS74DoLmndurxesk3Fj8q/L6Ho2O7R9yk7ZsZuj7AS1uTt/Xx235o/ZsSXHw9WRf2zZZXz+Ml6zpWwbmjQuP3esfS47duOYgvNIW/6yKL1GffnOO7Xg8BH5Vq9ZO/wX1k0wdcrkllpvboK10eJCSfcCDwCPAQuHuT5mTVHS8Gdmlquk4c/MLJf7mZiZjQzD/+c5sx0gIs7NjZV0KnBOv+K7IuLMknlKWgDs1a/4vIhYXJLHzMzMzMzMzKxZ3PhnLzkRMR+Y379c0geA8wABa4EPR8Qv07SjgMupnvl3dURcHBHHDpRf0peBA1KeR4APpluSxwLXA/sDzwLHR8TjO/jnmZmZmZmZmZn9iW/7NXveY8BhEfFG4FPAPABJbcCVwLuAmcCJkmYOkecjEbFfRLwJeAI4K5WfTjVS8WuBy4BLmvMzzMzMzMzMzMwqbvyzliNpoaR7JD0oaU4qO13SI5KWSLpK0hWpfJqkGyUtTa9DB8sbET+JiNXp493A7un9QcCKiHg0IrYA3wSOHiLPmjRvAeOBvqedHA1cl95/BzgixZiZmZmZmZmZNYUb/6wVnRYR+1PdWjtX0nTgfGA2cCgwoyH2cuCyiDgQOA64OnMepwP/kd5PB37bMG1lKhuUpPnA71Nd/rV/nojoAbqAXQb47hxJyyQtu+7r38isrpmZmZmZmZnZC/mZf9aK5krqe97eHsBJwB0RsQpA0g3APmn6kcDMhg52UyRNioh1gyWX9N+pGv/esq0VjIhT0+3C/woczwDPGBziu/NItxyv+s2jHiPNzMzMzMzMzLaZe/5ZS5F0OFWD3iERsR/wC+ChIb5SA2ZHxKz0mr6Vhr83UfUOPDoink3FT1I1MvbZPZUNKSJ6qW4RPq5/HkntQCfVwB9mZmZmZmZmo05No/OVQ9JRkh6WtELSxwaYPlbSt9L0n0nas2Ha/0rlD0t653avh+1NYPYi66QaNGODpBlUt/pOBA6TNDU1qh3XEH8rcHbfB0mzBkss6VXAd4GTIuKRhklLgb0l7SVpDHACsGiQHJL02r73wHt5vnFyEXBKev8+4IcR4Z59ZmZmZmZmZqNI5sChAw4KmuJOAF4PHAV8MeXbZm78s1ZzC9AuaTlwMdXAHE8CFwFLgLuAx6mepwcwFzhA0n2SfgWcMUTuT1A9g++Lku6VtAz+9Hy+s4DFwHLg2xHx4CA5BFwn6X7gfmA34JNp2peBXSStAD4KvKDl38zMzMzMzMxaXs7AoYMNCno08M2I2BwRjwErUr5t5mf+WUuJiM1ULed/RtKyiJiXev4tABam+GeonrmXk/tDwIcGmXYzcHNGjjrVoCMDTdsEvD+nLn16p7xgPJBBdbTlDxw8tbc5HQ472vP/GNFWK/vbQ09vPT+21luUO9eUsfmHzJ6CvN1jx+cH16Ns2U2YnB1aUufeev76KKlvbhd6gHoz+81GwTakgvUR+cutSGEnYtXy99V6QWxt7Ljs2J6CP162F6yPdVuatIyB6MnfSzoK9uueDeu3pTpbNaY9f9usA7WC7SgKjgFfeeue2bEn/fjx/LyH7rH1oEQl+2mh2pix2bHRm7+PqG27/sA/qKJ9uqNsudWVfxAv2d6K8o7JX8a7jMkOLVbXhOzYojU9Zefs0D3H59dh3ebu7NhxYzqyYwHayV/XPUV9Uwq25YLrC03Mv3YquQboKKlEobaCOtfHT8yOnVpS5YJ9esLYsuNbyTVfyfG+5P8u3T351yITxpbtIzZqDTRw6MGDxUREj6S+QUGnU3V0avzukIOObo0b/2y0uFDSkcA4qlt9Fw5zfcyaorTR1MwsR0lDjJlZrpKGPzMbnUbrNYakOcCchqJ5afDOEcmNfzYqRMS5ubGSTgXO6Vd8V0ScWTJPSQuAvfoVnxcRi0vymJmZmZmZmVnrSA19QzX25Qwc2hezst+goNs06OhQ3IXEXnIiYn7D6L99rzMlHZ2eDXivpGWS3tL3HUmnSPp1ep2S8hw7QJ7FDd9ZJOmBhs87S7ot5bhN0tQX95ebmZmZmZmZ2YsgZ+DQwQYFXQSckEYD3gvYm2qMg23mxj+z5/0A2C8iZgGnAVdD1WgHXEB1f/5BwAVba7iT9DfAun7FHwN+EBF7p3l5wA8zMzMzMzOzUWawgUMlfVLSe1PYgIOCpgFGvw38imrQ0zMjSh5K/kK+7ddajqSFVF1gxwGXp4E+TgfOA54DfglsjoizJE0D/g14Vfr6/4yIuwbKGxGNjXUT4U8PKXkncFtErErzv41quO1vDFK/SVQ77hyqHbbP0cDh6f11wO2pzmZmZmZmZmajT7MGvGsBAw0cGhGfaHg/6KCgEfEZ4DM7qi7u+Wet6LSI2B84AJgraTpwPjCbaqTdGQ2xlwOXRcSBwHGk3nyDkXSspIeAf6fq/QcDj9Iz1Eg7nwI+B2zoV/7yiHgqvf898PJB6jAn3Xa87Pprrx2qumZmZmZmZmZmQ3LPP2tFcyUdm97vAZwE3NHQM+8GYJ80/UhgpvSnceqnSJrUr5ffn0TEAmCBpL+iasQ7sqRikmYBr4mIj0jac7C4iAhJAw571Pjg0D+u7hqdQyOZmZmZmZmZ2YvCjX/WUiQdTtUgd0hEbJB0O/AQsO8gX6kBs1N32mwRcaekV0valWpUncMbJu9OdcvuQA4BDpD0ONX+9TJJt0fE4cAfJO0WEU9J2g14uqROZmZmZmZmZmalfNuvtZpOYHVq+JtBdavvROAwSVPT8NjHNcTfCpzd9yH1zBuQpNcqdRGU9GZgLNUw24uBd6T8U4F3pLIXiIgvRcQrI2JP4C3AI6nhD/58JJ9TgO8V/XIzMzMzMzMzs0Lu+Wet5hbgDEnLgYeBu6l65l1ENfT1KqqegF0pfi5wpaT7qLb3O4EzBsl9HHCypG5gI3B8GmZ7laRPUQ3VDfDJvluMC10MfDsNTvIb4G+3IYeZmZmZmZmZWTZVbRtmra3vOX6p598C4Jr0/L6W9sTTz2TvoBPGdGTn7di8MTu2Nm5cduyIofxOzX9cn39H+K5j8vOqY0x2bA/aetA26unNH11rTHtzOoPX6ts1Kv2OU7BdlKgrf/3VSs65TRwZrUdt2bG1gs1zS09ztrei5VaoZP9rpznr7+n1m7Njd540ITu2PQr3vYJ9pKcr/29gG1c+lh3bu25NduxJd/1260HJt449ODu2bWzZea99yk7ZsbVx+etPbfn7adG6K9jme+tlx6GOtiadRwqOASW/r607f9+rd2/JjgVY35a/HU0puL54dmN3duxOkV/ntgkTs2OLNen826zzZMk5smQfGVPv2ZbqZFlfLziXFRxbxvbm7yMxdnx2rAr3p972/Ovqkv26WdfrRdcLwOQpU5r3n4ERYG3Xc6Oy0Wly504ttd5826+NFhdKuhd4AHgMWDjM9TEzM2sdzfrPuZm9tPnYYmY2Ivi2XxsVIuLc3FhJpwLn9Cu+KyLOLJmnpJ9RPRew0UkRcX9JHjMzMzMzMzOzZnHjn73kRMR8YP5g0yUdCPwUOCEivpPKTgE+nkI+HRHXRcSA9xClEYh3o3puIMA7IuJpSWOB64H9qQYSOT4iHt/+X2RmZmZmZmY28kTvCHkE0EucG//MGkhqAy6hGiW4r2xn4ALgACCAeyQtiojVQ6T6QEQs61d2OtVIxa+VdEKaz/E79AeYmZmZmZmZmTXwQxis5UhaKOkeSQ9KmpPKTpf0iKQlkq6SdEUqnybpRklL0+vQraQ/G7gReLqh7J3AbRGxKjX43QYctQ1VPxq4Lr3/DnCEVDBSgJmZmZmZmZlZITf+WSs6LSL2p+qJN1fSdOB8YDZwKDCjIfZy4LKIOBA4Drh6sKQpz7HAl/pNmg40Dim4MpUNZb6keyWd39DA96c8EdEDdAG7DFCPOZKWSVr29euv38pszMzMzMzMzMwG59t+rRXNlXRser8HcBJwR0SsApB0A7BPmn4kMLOhg90USZMiYt0Aef8FOC8i6tvZIe8DEfGkpMlUvQhPonrWX5aImAfMA3ji6WdG5bDoZmZmZmZm9hIQ/i/tSODGP2spkg6natA7JCI2pME1HgL2HeQrNWB2RGzKSH8A8M3U8Lcr8G5JPcCTwOENcbsDtw+WJCKeTP+ulfR14CCqxr8nqRorV0pqBzqpBv4wMzMzMzMzM2sK3/ZrraaTatCMDZJmUN3qOxE4TNLU1Kh2XEP8rVTP8QNA0qzBEkfEXhGxZ0TsSfVMvn+IiIXAYuAdKf9U4B2p7AUktUvaNb3vAP4aeCBNXgSckt6/D/hhhP8MYmZmZmZmZmbN455/1mpuAc6QtBx4GLibqkfdRcASYBVVT8CuFD8XuFLSfVTb+53AGSUzjIhVkj4FLE1Fn+y7xXgAY4HFqeGvDfhP4Ko07cvAVyStSPU8oaQeZmZmZmZmZmal5I5HNhr0Pccv9fxbAFwTEQuGu17b66lnVmXvoO1tbdl5OzauyY5tmzg5Oza6t+TH9tazY0tFvTc7ttbRkZ9Y+Z2l1Z7/t5Ue5a87gHbyj9s95D+/siRv9HRnx6pg2ywVvfnruqQepeuk1azflL+vThw3Jjt21boN2bG7Tp6QHVsr2KdL1TflPBWi0j12fHbsmHpPQSXyf9/GWv76mFC6GRcc43o3DPTo3IFt+ePv8/OuX5sdWxufvw0dv+Bn2bEA3z313dmxbeMKtuVx4/IrUbA+iPxz6mg/vvXW85dFx+aN2bFqK7than1BH4uJtfzz76ot+b9vqgrOkQXXLWzfs7GH1qT/m5b8vq7N+cut5BwJ0B75uUuuq9dG/u+bMjY/tr4x/7xeHz8xO7ate3N2LDTvurpofZRcc7YX/P8CmDxlShN3quG35pmnR2Wj05RdX9ZS6823/dpocaGke6lusX0MWDjM9TFripIGutGu5CLMzLaipKFplCtp+DMzy1XS0GRmtqP5tl8bFSLi3NxYSacC5/QrvisiziyZp6SfUd3m2+ikiLi/JI+ZmZmZmZmZWbO48c9eciJiPjB/B+Q5eAdUx8zMzMzMzMysaXyPh1ki6XBJXZLuTa9PNEw7StLDklZI+thW8kjSZyQ9Imm5pLkN5V9IOe6T9OZm/yYzMzMzMzMze2lzzz+zP/fjiPjrxgJJbcCVwNuBlcBSSYsi4leD5PggsAcwIyLqkl6Wyt8F7J1eBwNfSv+amZmZmZmZjTrNHOjR8rnnn7UcSQsl3SPpQUlzUtnpqafdEklXSboilU+TdKOkpel16DbM8iBgRUQ8GhFbgG8CRw8R/2HgkxHV0HsR8XQqPxq4Pip3AztJ2m0b6mNmZmZmZmZmlsWNf9aKTouI/YEDgLmSpgPnA7OBQ4EZDbGXA5dFxIHAccDVW8l9iKRfSvoPSa9PZdOB3zbErExlg3kNcLykZSnP3iV5JM1J31321euv20p1zczMzMzMzMwG59t+rRXNlXRser8HcBJwR0SsApB0A7BPmn4kMFNS33enSJoUEesGyPtz4C8iYp2kdwMLqW7RLTUW2BQRB0j6G+Aa4K25X46IecA8gKeeWRXbMH8zMzMzMzMzM8A9/6zFSDqcqkHvkIjYD/gF8NAQX6kBsyNiVnpNH6Thj4hY0zctIm4GOiTtCjxJ1cjYZ/dUNpiVwHfT+wXAm9L70jxmZmZmZmZmZtvFjX/WajqB1RGxQdIMqlt9JwKHSZoqqZ3q9t4+twJn932QNGuwxJJeodRFUNJBVPvHs8BSYG9Je0kaA5wALBqijguB/57eHwY8kt4vAk5Oo/7OBroi4qncH25mZmZmZmZmVsq3/VqruQU4Q9Jy4GHgbqrecxcBS4BVVD0Bu1L8XOBKSfdRbe93AmcMkvt9wIcl9QAbgRMiIoAeSWcBi4E24JqIeHCIOl4MfE3SR4B1wIdS+c3Au4EVwAbg1MLfbmZmZmZmZmZWRFXbhllr63uOX+r5t4CqgW7BcNdre61dsyZ7B123pSc774TeLdmxtXHjsmOjJ78Operd+XXu3bC+IHFvduiYaa/Iz1ugR21F8e2RX+eS3CV5S9a1OsYU5O3OjqWJ568tbR3ZsW215nSiX78pf5tvq2nrQQ266/Xs2M7xY7Njn3i2a+tByat26cyOLaEtm4vi65s3ZseuaR+fHdsZ+fvIL57OX26v3z3/ODSuVriPKH9brm/ckB3bs/a57Njegry1jvz9tDZ+Ynbs38y/OTsWYNFZf5sdW3I8LFofKjsGZOct3IQKD0VN0d2bf3wb01NwvKiVnau31PL7WIwtWHAl13sTlb8s1J5f3+jNv14AUFv+souC672iOhTse12b839f59j831Yv3IZKzmd/LKjzrpMn5Fei4Jq6XnCcbespW88l29B/PbsmO/YvOvL3kdqY/P8Tqa3s2nDKri8bAUfP5un6w+9HZaNT58tf0VLrzT3/bLS4UNKRwDiqW30XDnN9zJqipIHOzCxbQUOTmVmukkYbMxudIvIbWa153Phno0JEnJsbK+lU4Jx+xXdFxJkl85S0ANirX/F5EbG4JI+ZmZmZmZmZWbO48c9eciJiPjB/oGlpNOF/ATqAZyLisFR+FHA51TP/ro6IiyPi2EFy/BiYnD6+DFgSEcekwUQup3ru3wbggxHx8x32w8zMzMzMzMzM+nHjn1kiaSfgi8BREfGEpJel8jbgSuDtwEpgqaRFEfGrgfJExFsbct4IfC99fBewd3odDHwp/WtmZmZmZmZm1hR+wIu1HEkLJd0j6UFJc1LZ6ZIekbRE0lWSrkjl0yTdKGlpeh06ROq/A74bEU8ARMTTqfwgYEVEPBoRW4BvAkdn1HMK8Daef/7g0cD1Ubkb2EnSbtuwCMzMzMzMzMxGvojR+WoxbvyzVnRaROwPHADMlTQdOB+YDRwKzGiIvRy4LCIOBI4Drh4i7z7AVEm3p8bFk1P5dOC3DXErU9nWHAP8ICL6hpzKyiNpjqRlkpbNnz/g3clmZmZmZmZmZll826+1ormS+p63twdwEnBHRKwCkHQDVUMewJHAzOpxewBMkTQpItYNkLcd2B84AhgP/FTS3dtRzxMZurFxQBExD5gHsHbNmtb7k4KZmZmZmZmZjRhu/LOWkgbkOBI4JCI2SLodeAjYd5Cv1IDZEbEpI/1K4NmIWA+sl3QnsF8q36Mhbnfgya3Uc1eq24UbBwV5sjSPmZmZmZmZmdn28G2/1mo6gdWp4W8G1a2+E4HDJE2V1E51e2+fW4Gz+z5ImjVE7u8Bb5HULmkC1WAcy4GlwN6S9pI0BjgBWLSVer4PuKlfo+Mi4GRVZgNdEfFUzo82MzMzMzMzM9sW7vlnreYW4AxJy4GHgbupes9dBCwBVlH1BOxK8XOBKyXdR7W93wmcMVDiiFgu6RbgPqAOXB0RDwBIOgtYDLQB10TEg1up5wnAxf3KbgbeDawANgCnZv5mMzMzMzMzs9YT9eGugQGKFhylxKy/vuf4pZ5/C6ga6BYMd72219qu57J30E11bT0oGRs92bFqa8uObabo7c2O7V2/til1aJ+yU36w8jtW95C/7gDaI39Z9Ch//bWTfz6I7i3ZseoYkx07Ui4OSpZbiVrBql63qbspeUtNGpP/d8Kn123Mjn3ZxLHZsfVa/vrQls3ZsQD1gm15TS1/W96plr8t3/rr/CdAHDbj1dmx42qF13gFx63oyd8+65tznryR8pYcW9rzt021dRTElt0Y894rvp0d+/2Pnrz1oG1QV/5BoFZw7V98fio4jzSrzpvr+bFjevO345LtDZp3/t3Umx9bfAwYAUqu94quUQuObxt68o/fY9qbs56Bomuip9fnn/t2njQhO7atJ/+YHAXXeyo41pd6vGtDduyr8i9Fys4jhceLKbvs2sQrueH33O9+23oHoww7vXKPllpvvu3XRosLJd0LPAA8Biwc5vqYNd3+0l8AACAASURBVEVJw5+ZWbaC/xibmZmZWWvxbb82KkTEubmxkk4FzulXfFdEnFkyT0kLgL36FZ8XEYtL8piZmZmZmZmZNYsb/+wlJyLmA/P7l0v6R+AD6WM71QjC0yJilaSjgMupnvl3dURcHBHH9s+R8hwBXErVs3Yd8MGIWCFpLHA9sD/wLHB8RDy+Q3+cmZmZmZmZ2QhRcju/NY/v8TBLIuLSiJgVEbOA/wXckRr+2oArgXcBM4ETJc0cItWXgA+kPF8HPp7KT6caqfi1wGXAJc36LWZmZmZmZmZm4MY/a0GSFkq6R9KDkuakstMlPSJpiaSrJF2RyqdJulHS0vQ6NHM2JwLfSO8PAlZExKMRsQX4JnD0EN8NYEp63wn8Lr0/Grguvf8OcIRU8LRrMzMzMzMzM7NCbvyzVnRaROwPHADMlTQdOB+YDRwKzGiIvRy4LCIOBI4Drt5ackkTgKOAG1PRdOC3DSErU9lgPgTcLGklcBJwcf88EdEDdAG7DDD/OZKWSVo2/9prt1ZdMzMzMzMzM7NB+Zl/1ormSup73t4eVA1sd0TEKgBJNwD7pOlHAjMbOthNkTQpItYNkf89VAOArNrG+n0EeHdE/Cw9R/DzVA2CWSJiHjAPYG3Xc6NyWHQzMzMzMzMze3G48c9aiqTDqRr0DomIDZJuBx6iGpxjIDVgdkRsKpjNCTx/yy/Ak1SNjH12T2UD1W8asF9E/CwVfQu4pV+elZLaqW4JfragXmZmZmZmZmZmRXzbr7WaTqpBMzZImkF1q+9E4DBJU1Oj2nEN8bcCZ/d9kDRrqOSSOoHDgO81FC8F9pa0l6QxVI2DiwZJsRrolNTX8/DtwPL0fhFwSnr/PuCHEeGefWZmZmZmZmbWNO75Z63mFuAMScuBh4G7qXrUXQQsAVZR9QTsSvFzgSsl3Ue1vd8JnDFE/mOBWyNifV9BRPRIOgtYDLQB10TEgwN9OcX+PXCjpDpVY+BpafKXga9IWpHqeULpjzczMzMzMzMzKyF3PLLRoO85fqnn3wKqBroFw12v7VXyzL8etWXnbY/e7Nh6LT9vM9VKjlVRz49VfgfozfX8OnS05eet1fPXB0D05servaMgcf5yK6pDW8E2VLA+mqmH4R+Ie+2mzdmxHbWy5TZuTP52UStYFFt68rehMe35dd7Unb+9TWjiIWtdd/7vK1nGdy5/NDv2r/Z9dXZsO028xis5zpakLTi2lCg6DhUnz9+W3/P567Njv//Rk/PrULA+Ss7rRedeoK78A0ZJ7pK8zVK6LEqMiN9XcC1Sem1YcPlUdI3arGuGkvXRtN8GRb9vU29+RUrOvyXbRbP+L1Kq5Fw9sZa/3Jp5HpncudPwHwSaaPUTj43KRqepr9qrpdbbyPhfltn2u1DSvcADwGPAwmGuj1lTNOs/xmZmZmY7WknjmJmZNY9v+7VRISLOzY2VdCpwTr/iuyLizJJ5SloA7NWv+LyIWFySx8zMzMzMzMysWdz4Zy85ETEfmN+/PA328VXgVVT7xj+nWCSdAnw8hX46Iq6LiGMHyi/peOB/Uz0f8KaIOC+VjwWuB/anGuX3+Ih4fAf+NDMzMzMzMzOzP+Pbfs2edybwq4jYDzgc+JykMZJ2Bi4ADgYOAi6QNHWgBJJ2AS4FjoiI1wOvkHREmnw61UjFrwUuAy5p6q8xMzMzMzMzs5c8N/5Zy5G0UNI9kh6UNCeVnS7pEUlLJF0l6YpUPk3SjZKWptehQ6QOYLIkAZOoRuTtAd4J3BYRqyJiNXAbcNQgOV4N/Doi/pg+/ydwXHp/NHBdev8d4Ig0LzMzMzMzM7NRJyJG5avVuPHPWtFpEbE/cAAwV9J04HxgNnAoMKMh9nLgsog4kKoR7uoh8l4B7Av8DrgfOCci6sB04LcNcStT2UBWAK+TtGcaefgYYI807U95IqIH6AJ26Z9A0hxJyyQtm3/ttUNU18zMzMzMzMxsaH7mn7WiuZL6nre3B3AScEdErAKQdAOwT5p+JDCzoYPdFEmTImLdAHnfCdwLvA14DXCbpB+XVCwiVkv6MPAtoA78JOUqyTEPmAewtuu51vuTgpmZmZmZmZmNGO75Zy1F0uFUDXqHpGfz/QJ4aIiv1IDZETErvaYP0vAHcCrw3aisAB6j6kX4JM/33gPYPZUNKCK+HxEHR8QhwMPAI2nSn/KkXoGdVAN/mJmZmZmZmZk1hRv/rNV0Ug2asUHSDKpbfScCh0mamhrVjmuIvxU4u++DpFlD5H4COCLFvRx4HfAosBh4R8o/FXhHKhuQpJelf6cC/8DztxovAk5J798H/DBa8WEBZmZmZmZmZhmit3dUvlqNb/u1VnMLcIak5VS96u6m6lF3EbCEapCOh6iepwcwF7hS0n1U2/udwBmD5P4UcK2k+wEB50XEMwCSPgUsTXGf7LvFeBCXS9qvIbav59+Xga9IWpHqeUL+zzYzMzMzMzMzKyd3PLLRoO85fqnn3wLgmohYMNz12l5r16zJ30Gjnp9Y+Z1+6wUDEtcLDiebtnTnBwMTxnZkx9YKjmubCyrdVstfbu0ULIySdTdClPy1S21t2bE9yo8tVbROmqRkf9rUnb+MJ7SXdeTvoTkDjdeaNH7571avzY7dfadJRbmbtSy29OSvv/Wbt2THTps4Lju29Lc1bR8ZCce4gvPeSPGez1+fHfv9j57clDqMmG2oSUqOyaVKrkVGgpJlMVJ+W7Pq3KztolYv7CnUgsetZmnW/0fao0m9twrX3eQpU5p3MBoBnn301yPjoLGD7fLqvVtqvfmIYqPFhZLuBR6gelbfwmGuj5mZWctotUYbMzMzM8vn235tVIiIc3NjJZ0KnNOv+K6IOLNknpJ+BoztV3xSRNxfksfMzMzMzMzMrFnc889eihZS9Q6sAVuA/6ev4U/SUZIelrRC0se2kucrwCRgP+DINJrw/ap8IeW4T9Kb+74g6RRJv06vUwbNbGZmZmZmZma2A7jxz16K/gm4NyLeBJwMXA4gqQ24EngXMBM4UdLMIfLcBRwJ/KZf+buAvdNrDvCllH9n4ALgYOAg4II0IrCZmZmZmZmZWVO48c9GLEkLJd0j6UFJc1LZ6ZIekbRE0lWSrkjl0yTdKGlpeh06ROqZwA8BIuIhYE9JL6dqkFsREY9GxBbgm8DRgyWJiF9ExOMDTDoauD4qdwM7SdoNeCdwW0SsiojVwG3AUYWLxczMzMzMzMwsmxv/bCQ7LSL2Bw4A5kqaDpwPzAYOBWY0xF4OXBYRBwLHAVcPkfeXwN8ASDoI+Atgd2A68NuGuJWprNRgebLyS5ojaZmkZfPnz9+G2ZuZmZmZmZmNAFEfna8W4wE/bCSbK+nY9H4P4CTgjohYBSDpBmCfNP1IYKaeHwZ+iqRJEbFugLwXA5en0YHvB34BNGmc93IRMQ+YB7B2zRoPv2hmZmZmZmZm28yNfzYiSTqcqkHvkIjYIOl24CFg30G+UgNmR8SmreWOiDXAqWk+ohr841FgPFUjY5/dgSe3ofpPDpLnSeDwfuW3b0N+MzMzMzMzM7Msvu3XRqpOYHVq+JtBdavvROAwSVMltVPd3tvnVuDsvg+SZg2WWNJOksakjx8C7kwNgkuBvSXtlaafACzahrovAk5Oo/7OBroi4ilgMfCOVP+pwDtSmZmZmZmZmZlZU7jxz0aqW4B2ScupbtO9m6rn3EXAEqqRdh8HulL8XOAASfdJ+hVwxhC59wUekPQw1ci85wBERA9wFlWD3HLg2xHx4GBJJM2VtJKqB999kvqeM3gzVU/CFcBVwD+k/KuAT1E1Mi4FPtl3C7OZmZmZmZmZWTMowo8Us9bR9xy/1PNvAXBNRCwY7no1S9fatcO+g9YKjhH155+5aE1Usk5Ggh6as13Umri5NWu7rxesunbyg0uXcUnuEiXLolnbcbO2t2Yq2ZabeUwuWicFD7qu19qK6pGr1eoLUKsXPGJY+X+jf8/nr8+OXXTW32bH9m7ckB0L0DZxcnZsdHcX5c5V37wxP7anJzu2fdoriurRatdPJfUtPc4WHeMK9pGmHVsK6tCj/Dq0R/MeMd7M41arGQnXyaX7dOfkycN/EGiiZ1c8NPwrpQl2ee2Mllpv7vlnrebCNFDHA1TP6ls4zPUxe1GNhAsaMxt9fGwxs2Zo5h/qzMwsnwf8sJYSEefmxko6lXRLb4O7IuLMknlKWgDs1a/4vIjw8/rMzMzMzMzMbERz45/tUJL2BG6KiDf0K78dODcilm1n/g8CB0TEWYNMPwZ4JCJ+FRHzgfnbMz+AiDh2R9bRzMzMzMzMzOzF4sY/G22OAW4CfjXcFTEzMzMzMzN7KYuC5+5a8/iZf9YM7ZK+Jmm5pO9ImtA4UdKJku6X9ICkSzLKT5X0iKQlwKGDzVTSXwLvBS6VdK+k10j6ecP0vfs+S3pc0mfT/JZIem0qnybpRklL02uo+R0k6aeSfiHpJ5Je1zB5D0m3S/q1pAtS/J6SHhpq2ZiZmZmZmZmZ7Uhu/LNmeB3wxYjYF1gD/EPfBEmvBC4B3gbMAg6UdMwQ5bsB/4eq0e8twMzBZhoRPwEWAf8YEbMi4r+ALkmzUsip/PltwF0R8UbgCuBfUtnlwGURcSBwHHD1EL/zIeCtEfHfgE8AFzVMOyh9/03A+yUdsLVl07CM5khaJmnZtfO3+65lMzMzMzMzM3sJ822/1gy/jYi70vuvAnMbph0I3B4RfwSQ9DXgr4AYpJx+5d8C9imoy9XAqZI+ChxP1SjX5xsN/16W3h8JzNTzw7NPkTQpItYNkLsTuE7S3qn+HQ3TbouIZ1Odv0vVcLmQgZfNPzcmjYh5wDyArrVrPfyimZmZmZmZmW0zN/5ZM/RvsBrOBqwbgQuAHwL39DXIJTHA+xowOyI2ZeT+FPCjiDg2DXRy+yC5Gz+PpGVjZmZmZmZm1jy9vcNdA8O3/VpzvErSIen93wH/X8O0JcBhknaV1AacCNwxRPnPUvkukjqA929l3muByX0fUiPeYuBLvHDk3+Mb/v1pen8rcHZfQMMtwwPpBJ5M7z/Yb9rbJe0saTzVICR9vf2GWjZmZmZmZmZmZjuUG/+sGR4GzpS0HJhK1fAGQEQ8BXwM+BHwS6reeN/bSvmFVI1zdwHLtzLvbwL/mAbheE0q+xpQp2rYazRV0n3AOcBHUtlc4ABJ90n6FXDGEPP6LPB/Jf2CF/aiXULV6/A+4MaIWJbKB102ZmZmZmZmZmY7miJ816GNbpLOBToj4vyGsseBAyLimRexHnsCN0XEG3K/s3pN/jP/atp6zJ9i6wVdr9V6fyOoK39h1AsOge1Nuku7pL7/P3v3Hmd3Vd/7//XeM5MbuRgCRfQUUyOoqUCQhMpFQEtPe1oPBpUiUjWpPzlWbsoPfrWHWvFX0wp6fmjxp21Km2BJgQLlIrYKFYKYEEKQXIAA/pT4A6XKNReSzG1/zh97jWwmM7PXSrJn9kzez8djHvPd6/vZ67v2d39v85nvd63iuguaXLINNUul5JwU1aK6e9SW344m7U/NakPJ91xad1G9Bd9fyXbfrHUMzVvPJceLHvIb0dbdmR2rjnHZsaXtaI/mPMJTreR/f6Xbfa7S/aNp231BvdGV01tJzalf+5fs2H9d+PvZsQDtU6Zlx0Z3d35swfG+d/vL2bEUHFvGHfja/Hpp3vG+RNE5tUTh+bdkv1Z3V35sW8HxvuB6dmdv/nqb0FZwzVl4vVdy7itZx63QhqK/RQo1qx1F9Rbue1OmTm2Bq/Dmee6xDWMy6XTAWw4fVd+b+/yzMU3SzcAsaqMIm5mZ2QBKEn9mZrmalZQyM7MyTv7ZqCTpEnbt/++GiFhUXxARpw30/oiYWbCshdQeDa63IiLOya0jLXMTkH3Xn5mZmZmZmZnZnnLyz0allORb1DBw7yxrCbsOFmJmZmZmZmZm1vJGX2deNipJminp4QHKl0uauxfqXyDpa0PMny9p9p4ux8zMzMzMzMxsNHHyz/YV8wEn/8zMzMzMzMxsn+Lknw2ndknLJG2UdKOkSfUzJZ0paYOkhyVdllG+UNITklYDxw+2UEnHAacCX5K0VtIsST+sm39o32tJmyRdnpa3WtKbUvmBkm6S9ED6GWp5x0i6T9JDklZKenMqXyDpXyV9R9KPJF0+yPvPlrRG0pqlS/y0sZmZmZmZmY1OETEmf0Yb9/lnw+nNwMciYoWkfwQ+2TdD0uuAy4CjgReBOyTNB1YPUn4/8PlUvhm4G3hooIVGxEpJtwG3R8SNaXmbJc2JiLXAQl7dp9/miDhc0keArwDvAb4KXBERP5B0CPBd4K2DfM7HgHdGRI+kU4C/At6f5s0BjgI6gcclXRkRT/Vr72JgMcCLW7aOvqOKmZmZmZmZmbUMJ/9sOD0VESvS9DXA+XXz5gHLI+JZAEnLgBOBGKScfuXXA4cVtOUqYKGkC4EzgGPq5l1b9/uKNH0KMFtSX8xUSZMjYtsAdU8DrpZ0aGp/R92870XE5tTmR4E3AE/tWoWZmZmZmZmZ2Z5z8s+GU/+72EbyrrabgM8BdwEPRsTzdfNigOkK8I6I2JlR918Cd0fEaZJmAsvr5nXWTffifdDMzMzMzMzMmsh9/tlwOkTSsWn6Q8AP6uatBk6SdICkNuBM4J4hyu9P5TMkdQCnN1j2VmBK34uUxPsu8A1e/cgv1O4E7Pt9X5q+AzivL0DSnCGWNQ34WZpe0KBdZmZmZmZmZmNTxNj8GWWc/LPh9DhwjqSNwHRqiTcAIuIZ4DPU+u5bR+1uvFsblF9KLTm3AtjYYNnXARenQThmpbJlQJVaYq/edEnrgQuAT6ey84G5ktanx3U/McSyLgf+WtJD+M4+MzMzMzMzMxtBGo2jlJjtDZIuAqZFxGfryjYBcyPiuRFrWJ2SAT/aoze73urOnKeXayoTJzUO6hPV/Ngm6lFbU+otWcfNakOpihrH9KkWnA56evO/62rBeWZSe/7/pHoo+HBAe0FPA1WV1Z2rUrAuStpQ8t1B2XZRorQduUq+u529ZY1ob8vf5kraUbp95mrr6cqOVVvhcUj56yJ6uvPrLdju1TEuO7azYINrq7TG/7ubtg1tfr5xTBLV/OP3+5b8W34bgFs+8b7s2N7tL2fHRm/+9tb13C/y2/Dy1uzY18w7sXFQnc7I//7Gq2C7KLi+KDleRMG+p+78egG29uavi8kTOhoHJSXnnJJruOjNj1V7QXsLry0q1eZcd5ZcA5S0oeQc0sy/GUrWRbOu7UvqBZgy7TVNujJrDc8+um5MJp0OnH3kqPrefFeS7ZMk3QzMAt490m0xMzMbcSV/tJmZZSpJ/JmZWfM4+WdjiqRL2LX/vxsiYlF9QUScNtD7I2JmwbIWUns0uN6KiDgntw4zMzMzMzOzsarkblprHif/bFikUW9vj4i39StfDlwUEWv2sP4F1B7XPRdYNMD8+cATEfHoniynXkQsYdfBQszMzMzMzMzMWoaf8bB9xXxg9kg3wszMzMzMzMxsODn5Z8OpXdIySRsl3SjpVSNJSDpT0gZJD0u6LKN8oaQnJK0Gjh9soZKOA04FviRpraRZkn5YN//QvteSNkm6PC1vtaQ3pfIDJd0k6YH0M9TyjpF0XxpZeKWkN6fyBZJulbRc0o8kfa54DZqZmZmZmZmZFXDyz4bTm4GvR8RbgS3AJ/tmSHodcBm1ATjmAPMkzR+i/GDg89SSficwxF19EbESuA24OCLmRMSPgc2S5qSQhbz68d3NEXE48DXgK6nsq8AVETEPeD9w1RCf8zHgnRFxFPAXwF/VzTsmvf8I4HRJc/u/WdLZktZIWrN0iZ8qNjMzMzMzM7Pd5z7/bDg9FREr0vQ1wPl18+YByyPiWQBJy4ATgRiknH7l1wOHFbTlKmChpAuBM6gl5fpcW/f7ijR9CjBb+tWIZVMlTY6IbQPUPQ24WtKhqf0ddfPujIjnU5v/lVri8lX9HUbEYmAxwItbto7JYdHNzMzMzMzMbHg4+WfDqX8iayQTWzcBnwPuAh7sS8glMcB0BXhHROzMqPsvgbsj4rQ00MnyQeoe6LWZmZmZmZmZ2V7jx35tOB0i6dg0/SHgB3XzVgMnSTpAUhtwJnDPEOX3p/IZkjqA0xsseyswpe9FSuJ9F/gGu47Ye0bd7/vS9B3AeX0BdY8MD2Qa8LM0vaDfvN+RtL+kidQGIVmBmZmZmZmZmVmTOPlnw+lx4BxJG4Hp1BJvAETEM8BngLuBddTuxru1Qfml1JJzK4CNDZZ9HXBxGoRjVipbBlSpJfbqTZe0HrgA+HQqOx+YK2m9pEeBTwyxrMuBv5b0ELveXbua2l2H64GbImJN/zebmZmZmZmZme0tivBTh7ZvknQRMC0iPltXtgmYGxHPNWF5C1Ld5+a+Z/PW5vT5VynY76uv9HM4YvWWKmkHUc0OrVbamtKG0nVR9PnGsFZZb83alltlf2oFo3F/ataxs1ltKG5HwbGzRMlxtqjego9WKdydWuGcGj3d2bElql2dRfHz//Zfs2NvO++D+RWXnKu789dFyXprn7Z/diy0xvVTyXbfHr35wWri/SMlx5YmtaOHgu+u4Hjh67fd1wr7UzO/vylTp47pC7lfrl8zJjf+Xzti7qj63tznn+2TJN0MzKI2irDZqOELRzNrBh9bzMzMzMYuJ/9sTJF0Cbv2/3dDRCyqL4iI0wZ6f0TMLFjWQmqPBtdbERHnDFL3UmBpbv1mZmZmZmZmZnvKyT8bFmnU29sj4m39ypcDF+1p33f9HqldNMD8+cATEfHoniynXkQsYdfBQpB0KbAtIr68t5ZlZmZmZmZmZrY7nPyzfcV84HZgryX/zMzMzMzMzGxwHmeiNXi0XxtO7ZKWSdoo6UZJk+pnSjpT0gZJD0u6LKN8oaQnJK0Gjh9soZKOA04FviRpraRZkn5YN//QvteSNkm6PC1vtaQ3pfIDJd0k6YH0M+jykiMl3SfpR5I+nuo4WdLy9NkfS+tiVHUSamZmZmZmZmaji5N/NpzeDHw9It4KbAE+2TdD0uuAy6gNwDEHmCdp/hDlBwOfp5b0OwGYPdhCI2IlcBtwcUTMiYgfA5slzUkhC3n147ubI+Jw4GvAV1LZV4ErImIe8H7gqgaf9YjU5mOBv0ifA+Ao4FOpvW9kgKSlpLMlrZG0ZumSXZ4qNjMzMzMzMzPL5sd+bTg9FREr0vQ1wPl18+YByyPiWQBJy4ATgRiknH7l1wOHFbTlKmChpAuBM4Bj6uZdW/f7ijR9CjC77ka9qZImR8S2Qeq/NSJ2ADsk3Z3qfwlYHRFPpzavBWYCP6h/Y0QsBhYDbN661fdIm5mZmZmZmdluc/LPhlP/RNZIJrZuAj4H3AU8GBHP182LAaYrwDsiYmdm/YN91s66sl68D5qZmZmZmdlYFdWRboHhx35teB0i6dg0/SFefcfbauAkSQdIagPOBO4Zovz+VD5DUgdweoNlbwWm9L1ISbzvAt9g1xF7z6j7fV+avgM4ry+g7pHhwbxX0gRJM4CTgQcaxJuZmZmZmZmZ7XVO/tlwehw4R9JGYDq1xBsAEfEM8BngbmAdtbvxbm1Qfim15NwKYGODZV8HXCzpIUmzUtkyoEotsVdvuqT1wAXAp1PZ+cBcSeslPQp8osHy1qc2rwL+MiJ+3iDezMzMzMzMzGyvk4ddtn2VpIuAaRHx2bqyTcDciHhuxBpWZ+uWLfk7aLNup1bB/wha5Zbugjb3kD/gcqVFxmautMBxu1owUHW1oLntBb0BlLShVTTruytdF0XtKNivq5W25rShQMk+DWXbXLPaUXJsKVlvrbJdFCk4fjfrONTMY33TjgE7tjel3p6XtxbFd0ybnh176pXXZcfecvZ7s2N7t7+cHVvt7sqOnfC6Q7JjAXqUfzxsj94Rr7fk+F2qFa4Dio4BO/K3oe7xE7Njx1V78hsBqL0jO7ZkXTTtWN+k4zdApTq295FpU6aMvovaAr9Ye//I//HSBAfN+a1R9b25vzHbJ0m6GZhFbURes1GjFRJ/Zjb2+NhiZs1QknQzs7EpelvkBpF9nJN/NqZIuoRd+/+7ISIW1RdExGkDvT8iZhYsayG1R4PrrYiIc3LrMDMzMzMzMzNrJif/bFhImgncHhFv61e+HLgoItbsYf0LqD2uey6waID584EnIuLRPVlOvYhYwq6DhSDpUmBbRHx5by3LzMzMzMzMzGx3eMAP21fMB2aPdCPMzMzMzMzMzIaTk382nNolLZO0UdKNkibVz5R0pqQNkh6WdFlG+UJJT0haDRw/2EIlHQecCnxJ0lpJsyT9sG7+oX2vJW2SdHla3mpJb0rlB0q6SdID6WfQ5SVHSrpP0o8kfTzVcbKk70v6tqTHJf2tVDKahpmZmZmZmZlZGScebDi9Gfh6RLwV2AJ8sm+GpNcBl1EbgGMOME/S/CHKDwY+Ty3pdwJD3NUXESuB24CLI2JORPwY2CxpTgpZyKsf390cEYcDXwO+ksq+ClwREfOA9wNXNfisR6Q2Hwv8RfocAMcA56X2zgLe1/+Nks6WtEbSmiVLdnmq2MzMzMzMzMwsm/v8s+H0VESsSNPXAOfXzZsHLI+IZwEkLQNOBGKQcvqVXw8cVtCWq4CFki4EzqCWlOtzbd3vK9L0KcBsvTIs/VRJkyNi2yD13xoRO4Adku5O9b8ErI6In6Q2X0stcXlj/RsjYjGwGGDrli0eI83MzMzMzMzMdpuTfzac+ieyRjKxdRPwOeAu4MGIeL5uXgwwXQHeERE7M+sf7LO20jowMzMzMzMzszHOj/3acDpE0rFp+kPAD+rmrQZOknSApDbgTOCeIcrvT+UzJHUApzdY9lZgSt+LlMT7LvANdh2x94y63/el6TuoPa4LQN0jw4N5r6QJkmYAJwMPpPJjJP1G6uvvjH7rwMzMzMzMzMxsr3Lyz4bT48A591utawAAIABJREFUkjYC06kl3gCIiGeAzwB3A+uo3Y13a4PyS6kl51YAGxss+zrgYkkPSZqVypYBVWqJvXrTJa0HLgA+ncrOB+ZKWi/pUeATDZa3PrV5FfCXEfHzVP4Atb4ENwJPAjc3qMfMzMzMzMxsdIrq2PzZA5L2l3RnGiD0TknTB4iZkwYRfSTlIc6om7dU0pNpQNO1GTcnoQg/dWj7JkkXAdMi4rN1ZZuAuRHxXBOWdzJwUUS8J/c9L27Zmr2DVtQ4pk+1Sbt9e8FTzFUVNLhFVEqOlyUnhFYZ9LmgzdVKW3ZsyXor2S6Kvg9o2ucrUbLvNXN/Kll3PRR8J006DrXKsaVltuVWsIcXvYNp1rGlWdtxaTuatV1Ud2zPjo2C765n60vZsQDjph+QHVvt6syOnb/41uzYGz94Un4buruzYyce8sbsWBh958mWOWY16dhScq3VWXCC6mhr3jVc0XkyevPrbdI1TrOOycXtKKi6Us1fbz3KX28l1y0AU6ZOHX1/GBX4zzUrRuGFTmOvnXv8bn9vki4HXoiIL0r6DDA9Iv60X8xhQETEj9IAog8Cb42IlyQtBW6PiBt3qXwQ7vPP9kmSbqY22u67R7otZmZmZmZmZrbPeC+17sEArgaWA69K/kXEE3XTP5f0S+BAagOJFnPyz8YUSZewa/9/N0TEovqCiDhtoPdHxMyCZS2k9mhwvRURcc4gdS+ntlObmZmZmZmZ2b7poNSVGcB/AgcNFSzpGGAc8OO64kWS/gL4HvCZiBjylnon/2xYSJpJ7bbUt/UrX07tUdg1e1j/AmqP654LLBpg/nzgiYh4dE+WUy8ilrDrYCFmZmZmZmZmBozVruYknQ2cXVe0OCIW183/D+C1A7z1kvoXERGSBl1Jkg4G/gn4aLzSd8efUUsajgMWU7tr8P8eqr1O/tm+Yj5wO7DXkn9mZmZmZmZmtu9Jib7FQ8w/ZbB5kn4h6eCIeCYl9345SNxU4NvAJRGxqq7uvrsGOyUtAS5q1N4W6WXe9hHtkpZJ2ijpRkmT6mdKOlPSBkkPS7oso3yhpCckrQaOH2yhko4DTgW+lEbCmSXph3XzD+17LWmTpMvT8lZLelMqP1DSTZIeSD9DLW9/SbekEXlWSToilV8q6R8lLZf0E0nnF69BMzMzMzMzMxvNbgM+mqY/CuwyspWkccDNwDf7D+yREoZIErUbnR5utEAn/2w4vRn4ekS8FdgCfLJvRhq95jJqA3DMAeZJmj9E+cHA56kl/U4AZg+20IhYSW3nujgi5kTEj4HNdcNhL+TVj+9ujojDga8BX0llXwWuiIh5wPuBq4b4nJ8HHoqII4D/CXyzbt5bgN8FjgE+J6mj/5slnS1pjaQ1S5f4qWIzMzMzMzOzMeSLwO9I+hFwSnqNpLmS+nINfwicCCxINzGtrcthLJO0AdgAHAB8odEC/divDaenImJFmr4GqL/zbR6wPCKeBZC0jNqGHoOU06/8euCwgrZcBSyUdCFwBrVkXJ9r635fkaZPAWbXEusATJU0OSK2DVD3CdQShETEXZJmpNt1Ab6dOuLsTKP1HAQ8Xf/m+tuHX9yydWx2kGBmZmZmZmZjX2/vSLeg5UTE88BvD1C+Bvg/0vQ11PImA73/3aXLdPLPhlP/RNZIJrZuAj4H3AU8mHa+PjHAdAV4R0Ts3MPl1o/A04v3QTMzMzMzMzNrIj/2a8PpEEnHpukPAT+om7caOEnSAZLagDOBe4Yovz+Vz0iPzp7eYNlbgSl9L1IS77vAN9h1xN4z6n7fl6bvAM7rC6i73XYg9wJnpbiTgeciYkuD9pmZmZmZmZmZ7XVO/tlwehw4R9JGYDq1xBvwq9FqPgPcDayjdjferQ3KL6WWnFsBbGyw7OuAiyU9JGlWKlsGVKkl9upNl7QeuAD4dCo7H5ibBvF4FPjEEMu6FDg61fFFXunI08zMzMzMzMxsWCnCXYrZvknSRcC0iPhsXdkmYG5EPDdiDauzdcuW/B00qtmh1Z35Ty9XJkzIb0Iz+3MoOFapY1x2bA9qHJS0R/7n61Fbdmypnt7877q9Lf9/PL3V/Hp7q/nfR7Wg3skdBf+TUuH/rwr2keK6m9CGaiV/G6pUW6MvlZLtvr2g54fOgu2trVL23ZW0o6r840Wl4JhV8vnGq+C6rXA7Lvl86u4qqDh/+9S4/HNOyfG75PjWTB0Fx+SSbajnpecbByXVnp7s2M5fPN04qM6kmfndL/e+vDU7Nnq6s2M/cN092bFX/9bB2bH7H7dL10xDatbxsGS7b+vJ30+j4Nqp1M7u/GNAZ8H2Ob49v/ecCR3530e1YH9i2ozs0JLrSCi7DuguuDYcX8nfhqLgWK+C76NUyd8Yncpvx/jI397Uvss4jYMrueYEpkx7Tf6XMgo9s+qeMZl0OvgdJ42q7839jdk+SdLNwCxqowibjRoliT8zs1wliT8zs1wliT8zG5uiMBlqzeHkn40pki5h1/7/boiIRfUFEXHaQO+PiJkFy1pI7dHgeisi4pzcOszMzMzMzMzMmsnJPxsWkmYCt0fE2/qVLwcuSkNa70n9C6g9rnsusGiA+fOBJyLi0T1ZTr2IWMKug4WYmZmZmZmZmbUMD/hh+4r5wOyRboSZmZmZmZmZ2XBy8s+GU7ukZZI2SrpR0qT6mZLOlLRB0sOSLssoXyjpCUmrgeMHW6ik44BTgS9JWitplqQf1s0/tO+1pE2SLk/LWy3pTan8QEk3SXog/Qy1vP0l3ZJGBl4l6YhUfqmkf5J0n6QfSfp48Ro0MzMzMzMzMyvg5J8NpzcDX4+ItwJbgE/2zZD0OuAyagNwzAHmSZo/RPnBwOepJf1OYIi7+iJiJXAbcHFEzImIHwObJc1JIQt59eO7myPicOBrwFdS2VeBKyJiHvB+4KohPufngYci4gjgfwLfrJt3RPosxwJ/kT7fq0g6W9IaSWuWLPFTxWZmZmZmZma2+9znnw2npyJiRZq+Bji/bt48YHlEPAsgaRlwIhCDlNOv/HrgsIK2XAUslHQhcAZwTN28a+t+X5GmTwFm65XREKdKmhwR2wao+wRqCUIi4i5JMyRNTfNujYgdwA5Jd6fl3lL/5ohYDCwG2Lply5gcFt3MzMzMzMzMhoeTfzac+ieyRjKxdRPwOeAu4MGIeL5uXgwwXQHeERE793C5rbQOzMzMzMzMzGyM82O/NpwOkXRsmv4Q8IO6eauBkyQdIKkNOBO4Z4jy+1P5DEkdwOkNlr0VmNL3IiXxvgt8g11H7D2j7vd9afoO4Ly+gLpHhgdyL3BWijsZeC4itqR575U0QdIM4GTggQbtNjMzMzMzMxudIsbmzyjj5J8Np8eBcyRtBKZTS7wBEBHPAJ8B7gbWUbsb79YG5ZdSS86tADY2WPZ1wMWSHpI0K5UtA6rUEnv1pktaD1wAfDqVnQ/MTYN4PAp8YohlXQocner4IvDRunnr02dZBfxlRPy8QbvNzMzMzMzMzHabYhRmLM32BkkXAdMi4rN1ZZuAuRHxXBOWdymwLSK+nPuerZtfyt5BO0ONg5LN2/OfXt5/8qTGQcnOru7s2Eql7H8P1Wo1O3ZyR37d0d2VHds7bmJ2bFtPfr1Ue/NjgejNXxeVCRMK6s1vR7WrM7/egnXMtBnZoe1Rtt561NaUuquV/HorzTrnRv42AWVtLqGC7ULtzel5pHfH9qL4ton5x7hmKdlHXi7osWXCuI6idrQX9ETx4s78Nler+fXOmDQ+O7ZkvUVPT3as2gr/N66Cc864/M9XoufZ/8wPLjjnbN/0RFE7Jr/5iOzYnm1bGgclJd/fjqefzI796P3PZMdef/4fZccCTOjOv9aqTtwvO7ata0d2bMnxYvK4/NjOgn0aoG3LC/mxk/LXRYkYn38N9+LLBdfJ4/L3/972cdmxpUquO9Wef27Y1pW/75Wec0r0Fvwd0NGZv49sb8v/Tkr+vij5uwzggNdMK3vDKPPzlXeNyaTT645796j63tznn+2TJN0MzKI28q7ZqFGS+DMzy1WS+DMzy1WS+DMzs+Zx8s/GFEmXsGv/fzdExKL6gog4baD3R8TMgmUtpPZocL0VEXHOIHVfmlu3mZmZmZmZ2ahX+ISKNYeTfzampCTfooaBe2dZS9h1sBAzMzMzMzMzs5bhAT/2AZJmSnp4gPLlkubuhfoXSPraEPPnS5q9p8tplr21HszMzMzMzMzMWo2TfzYc5gMtm/wzMzMzMzMzMxurnPzbd7RLWiZpo6QbJb1qeENJZ0raIOlhSZdllC+U9ISk1cDxgy1U0nHAqcCXJK2VNEvSD+vmH9r3WtImSZen5a2W9KZUfqCkmyQ9kH6GWt6laRTfvtcPpzsfZ6bP/veSHpF0h6SJ/d5bkbRU0hfS622SFklaJ2mVpINS+UxJd0laL+l7kg6R1CbpSdW8RlKvpBNT/PfT57xU0j+mOw1/Iun8QT7D2ZLWSFqzZOnSwT6qmZmZmZmZmVlDTv7tO94MfD0i3gpsAT7ZN0PS64DLqI18OweYlx7VHaz8YODz1JJ+JzDEXX0RsRK4Dbg4IuZExI+BzZLmpJCFvLrfvM0RcTjwNeArqeyrwBURMQ94P3DVbq6DQ4H/NyJ+E3gp1dWnHVgG/Cgi/jyV7Qesiogjge8DH0/lVwJXR8QR6T1/ExG9wOPU1sUJwA+Bd0oaD/x6RPwovfctwO8CxwCfk9TRv5ERsTgi5kbE3IULFuzmRzUzMzMzMzMbWdFbHZM/o42Tf/uOpyJiRZq+hlqCqs88YHlEPBsRPdQSWicOUf5bdeVdwPWFbbkKWCipDTgD+Oe6edfW/T42TZ8CfE3SWmqJxKmSJhcuE+DJiFibph8EZtbN+zvg4X6jAncBtw8Qf2xdm/+JV9blvdTWz4nAX6fyecADdXV+OyI6I+I54JfAQbvxOczMzMzMzMzMsjj5t++IBq+H003AfwPeAzwYEc/XzYsBpivAO9Kdg3Mi4vURsW2Qunt49XY9oW66s266l1ePdr0SeJek+vjuiIhB4gfyfeCd1O7q+zfgNcDJ1JKCOW0wMzMzMzMzM9urnPzbdxwiqe9Oug8BP6ibtxo4SdIB6W68M4F7hii/P5XPSI+tnt5g2VuBKX0vImIn8F3gG7z6kV+o3QnY9/u+NH0HcF5fQN0jwwPZBLw9xb0d+I0GbevzD9QSdv8iqVFCbiXwwTR9Fq8k91YDxwHV9BnXAv+DWlLQzMzMzMzMzGzYOfm373gcOEfSRmA6tcQbABHxDPAZ4G5gHbW78W5tUH4pteTcCmBjg2VfB1ws6SFJs1LZMqBKLbFXb7qk9cAFwKdT2fnA3DTAxqPAJ4ZY1k3A/pIeAc4FnmjQtl+JiP8HeAj4J0lD7RvnUXtseT3w4dRWIqITeApYleLupZb03JDbBjMzMzMzMzOzvUmvPNVoNnzSiLzTIuKzdWWbgLmpPzwDtm5+KXsHre7cmV3vtsq47NjxHW3ZsTu7erJj2yrKjgWoFhyqStpcYtvOruzY6ftNaByUqKuzcdCr3pC/7qK7O7/atvz/B6kjfxvq3f5ydmzsNzU7tr2w94Ieyra5kVayi3QXdjo8Xvnrrkf5+1N79Dal3t5q/ucr+WwA1Up+OzbvyN9XJ43bZTynQZW0uaS9pdtFWyX/GNBTUPeWHfnnp1+bPDE7tnf7YD2A7KrSXtDLRsE6BlBbQfyQ/1t8tWrBsb5EpZq/nxYr+HzEyHeWvr1gVZzxN9cU1f2lF+9tHJRM+PT/yo597fbnGwcl/18lv4vs2dP3y46tTsyPBXi54Pqp5PqwUnDMGtdesJ9uzl/HTJuRHVpyjgTY1p2/j+xXyT+PlByzOiP/++juyf98Jdf1AL0Fx4uJHQXn32rBdXLBtW9n4Qc84DXTRtdFaqGfff+OMZl0ev2J/3VUfW/ub8yGnaSbgVnURhE2sxJN+mPQzPZtJYk/M7NcJYk/MzNrHif/bK+RdAm79v93Q78RdImI0wZ6f0TMLFjWQtLjtnVWRMQ5uXWYmZmZmZmZmY11Tv7ZXpOSfIsaBtaRtDIijtuNZS1h18FCzMzMzMzMzKxFRAt082Ae8MNG2O4k/nJljNprZmZmZmZmZjamOflnI0rStvT7ZEnLJd0o6TFJy6Ra52aSvijp0TTa75dT2VJJHxiknnsl3QY8mspukfSgpEcknV3/HkmLJK2TtErSQan8IEk3p/J1ko5L5X8kabWktZL+Thq8t/oh6p4p6a70Wb4n6ZC9vErNzMzMzMzMzH7FyT9rJUcBnwJmA28Ejpc0AzgN+M2IOAL4QkY9bwcuiIjD0us/joijgbnA+alOgP2AVRFxJPB94OOp/G+Ae1L524FHJL0VOAM4PiLmAL3AWUO0YbC6rwSuTp9lWVrWq0g6W9IaSWuWLF2a8XHNzMzMzMzMzAbmxyKtlayOiKcBJK0FZgKrgJ3AP0i6Hbg9s54n616fL6lvkJFfBw4Fnge66up7EPidNP1u4CMAEdELbJb0YeBo4IF0Q+JE4JdDtGGwuo8F3pem/wm4vP8bI2IxsBhg6+aXxuSw6GZmZmZmZrYPCP9J2wqc/LNW0lk33Qu0R0SPpGOA3wY+AJxLLTnXQ7pzVVIFGFf33pf7JiSdDJwCHBsR2yUtByak2d0RvzoS9TL0/iBqd+z9WeZnKanbzMzMzMzMzKwp/NivtTRJk4FpEfFvwKeBI9OsTdTuxAM4FegYpIppwIsp8fcW4B0Zi/0e8Cdp+W2SpqWyD0j6tVS+v6Q37MZHWgl8ME2fBdy7G3WYmZmZmZmZmWVx8s9a3RTgdknrgR8AF6byvwdOkrSO2qO0Lw/y/u8A7ZI2Al+k9hhxIxcA75K0gdoju7Mj4lHgz4E7UlvuBA7ejc9zHrAw1fHhtCwzMzMzMzMzs6ZQ+Plrs5b13Eubs3fQnV092fVOie7sWHUMdlPlrqpdnY2Dkkp72ZPQ0VvNji1pc0kfFL3bt+XXO21G45hkZ1f+9wHQ3jboQNO76OrpzY6tKL8NHe35bdiyI3+7OGB8fr3RMa5xUJ1KNX9dtATl/38uesq2oZJ1VynYRzqr+bHj1Zzrj57BB2IfUMl2X33p+ezYtkmTs2N7O8Znx5a0V91d+cGACo4tvTu2Z8dWO3dmx7btf2B27Lad+dt9s45vAG2V/H21nZG/7u4hf2X0VvPPvQAdbfnrouBwUbafltS7Y7D/He/qib8q+3/txdPfmR174wdPyo59spJ/bDlkx3PZsdXXz8qOnTyu7Bqu5Pqp6Pqw4DyJ8jeiF7ryt/sZE/OvObcXXoZM6M4/dnaNm5gdO649f72VnEeiu+xapES1q+A8MnFSduyOSv710ISO/HND6fl36v4zCo5yo89T37t95E9+TfDrv/2eUfW9+c4/M7NRpCTxZ2aWqyTxZ2aWq+gfp2Zm1jQehMBsD0i6H+h/y8aHI2LDSLTHzMzMzMzMzKyek382bCStjIjjRrode1NE/NZIt8HMzMzMzMzMbDB+7NeGTTMTf5KcyDYzMzMzMzMz68fJPxs2kral3ydLWi7pRkmPSVom1XrhlfRFSY9KWi/py6lsqaQPDFLPvZJuAx5NZbdIelDSI5LOrn+PpEWS1klaJemgVH6QpJtT+TpJx6XyP5K0WtJaSX8nDdyDvKQ/lvSVutcfl3TFYHWkn6WSHpa0QdKn9+pKNjMzMzMzM2sVUR2bP6OMk382Uo4CPgXMBt4IHC9pBnAa8JsRcQTwhYx63g5cEBGHpdd/HBFHA3OB81OdAPsBqyLiSOD7wMdT+d8A96TytwOPSHorcAZwfETMAXqBswZZ/r8A/11S31BfC4F/HKKOOcDrI+JtEXE4sKR/hZLOlrRG0ppvLl2asQrMzMzMzMzMzAbmRyVtpKyOiKcBJK0FZgKrgJ3AP0i6Hbg9s54n616fL+m0NP3rwKHA80BXXX0PAr+Tpt8NfAQgInqBzZI+DBwNPJBuSJwI/HKghUfENkl3Ae+RtBHoiIgNks4dpI5vAW+UdCXwbeCOAepcDCwGeO6lzWNyWHQzMzMzMzMzGx5O/tlI6ayb7gXaI6JH0jHAbwMfAM6llpzrId2lKqkCjKt778t9E5JOBk4Bjo2I7ZKWAxPS7O6I6Euk9TL0ti/g6oj4s8zPchXwP4HHeOVOvkHrkHQk8LvAJ4A/BP44czlmZmZmZmZmZkX82K+1DEmTgWkR8W/Ap4Ej06xN1O6iAzgV6Nj13QBMA15Mib+3AO/IWOz3gD9Jy2+TNC2VfUDSr6Xy/SW9YbAKIuJ+ancZfgi4tq7eXeqQdABQiYibgD+n9qixmZmZmZmZmVlT+M4/ayVTgFslTaB259yFqfzvU/k64DvU3e3Xz3eAT6THbx+n9hhxIxcAiyV9jNodgX8SEfdJ+nPgjnSnYTdwDvDTIer5F2BORLwIEBGPDlLHDmBJKgPIvbvQzMzMzMzMzKyYXnkS0sx2V+qj8IqI+N7erPeZ517I3kH3oye73peq+Tf9jm/P/x9Bpda/YZae3t7sWIBKJb/NkwYcm3mQdgw8kPPAsb3NGdVpXHvZTdiVav66i5L1XHI+KPiuo2Nc46Cku2Adj6/ktwFgW1f+PjJh3GA3GO+qt5rf5m07u7Jj9xufv95K96f2tvztvr2tOQ8JvPjyjuzYGZMnZseqO38dA/S256/nzdt3ZsdO329C46CkWrDrtXXlr7eXC//HW7Ldl+x+O7vzt88JHfnbZrOOhWrPXw+lqgXHzkrBMbmk3hIl6xgAFRwvSkZKLKm3QA/56+3pFzYX1X3wzhezYz9w3T3ZsTd/7D3ZsSo4//a89Hx2bMfrZ2bHAnT15H/XEyoF230l/3hRcpxtj8LtPlPJNSeUXV+MV8k1XP7+1Fmw4rp7Co71BecbgJ1d3dmxvQVtnjYhvx3VHduzY1Xw9xPA1BkHNOcg3iKe+o/bxmTS6ddPOXVUfW9+7NdsD0h6jaQngB17O/FnNpDiP8TMzDKU/iFmZpajJPFnZmbN48d+zTJJuh8Y36/4wxFx2Ei0x8zMzMzMzKyl+WnTluDkn7UkSSsj4riRbke9iPitkW6DmZmZmZmZmVkJP/ZrLanVEn9mZmZmZmZmZqORk3/WkiRtS79PlrRc0o2SHpO0TKr1ai1pnqSVktZJWi1piqQJkpZI2iDpIUnvSrELJN0i6U5JmySdK+nCFLNK0v4pbpak70h6UNK9kt4yRBtnpfdukPSFujZL0pckPZzmnZHKr5P0B3XvXyrpA81bi2ZmZmZmZma2r3Pyz0aDo4BPAbOBNwLHSxoHXA9cEBFHAqcAO4BzgIiIw4Ezgasl9Q25+DbgfcA8YBGwPSKOAu4DPpJiFgPnRcTRwEXA14do11eBr6ZlPV1X/j5gDtDXri9JOji19w8BUvt/G/h2/0olnS1pjaQ113zz6sxVZGZmZmZmZtZaImJM/ow27vPPRoPVEfE0gKS1wExgM/BMRDwAEBFb0vwTgCtT2WOSfgr0Dchxd0RsBbZK2gx8K5VvAI6QNBk4Drgh3VwIuw7wUe9YYH6a/mfgy2n6BODaiOgFfiHpHmoJx38HvippPPB7wPcjYkf/SiNiMbUkJM8898LoO6qYmZmZmZmZWctw8s9Gg8666V52f7utr6da97qa6qwAL0XEnN2sf0gRsVPScuB3gTOA65qxHDMzMzMzMzOzPn7s10arx4GDJc0DSP39tQP3AmelssOAQ1JsQ+nuwSclnZ7eL0lHDvGWVcD70/QH68rvBc6Q1CbpQOBEYHWadz2wEHgn8J2cdpmZmZmZmZmZ7S4n/2xUioguanfPXSlpHXAnMIFaH30VSRuoJdoWRETn4DXt4izgY6nOR4D3DhH7KeBCSeuBN1F7FBngZmA9sA64C/i/IuI/07w7gJOA/0ifwczMzMzMzMysaTQaOyo0awWSJgE7IiIkfRA4MyKGShYWe/GpTdk76LO/GteksQOru3Q1OKj2adOzY0tEb29RvNrasmN7d2zPb8ekKdmxbd35eeRqV35s7Dc1Oxagnfzj9raunuzYjvb8dTxeLXDuUNn/r3q3b8uObZs4qbQ1eW0o2DZL2lC6P5WIjnHZsdWCzUIvb8mObZu0X34bKvnbMUBsy29Hz+YXsmPHHXhwduzLkb8t/+zF/PYetv/k7FgAFXzX0dOdHdv1/C+yYzum7p8d+2KUfde5xhccCwHaC85P49rzv+tKwTV69ZW+iveqkn0aoFLQjJK6m1VvW1f+9VDnL5/JrxjY1J5/bn/TxPwPeNo/3J4de+0f5Pdk8/zy/HoP+djF2bEAVPPPUWXHofxrHLXn9xq0rbuaHVuyvU2ulv3fX235x4vecROzY9sj//uI7oI2F16XlSi5ru6ZmH/ua6vkt7nkeBG9+dsQwLSDXtucg3iL+Om/39QCfzjsfW/4b+8fVd+b+/wz231HA19TbXSQl4A/HuH22D6gJPFnZpar5A9uM7NsBYk/MzNrHif/zBqQdAlwer/iGyJiETBUn4BmZmZmZmZmZiPKff5Zy5C0cqTbMJCIWBQRc9IowGuBL6TE369Iep2kGxvVJSn/uUMzMzMzMzMzsz3kO/+sZUTEcSPdht0VET8HPjDS7TAzMzMzMzNrGVHWB6I1h+/8s5bRd1ecpJMlLZd0o6THJC1L/eohaZ6klZLWSVotaYqkCZKWSNog6SFJ70qxCyTdIulOSZsknSvpwhSzStL+KW6WpO9IelDSvZLe0qCpJ6Y2/ETSB1IdMyU9nKYnSfoXSY9KulnS/ZLm1n3ORan9qyQd1IRVaWZmZmZmZmYGOPlnreso4FPAbOCNwPGSxgHXAxdExJHAKcAO4BwgIuJw4EzgaulXQ9++DXgfMA9YBGyPiKOA+4CPpJjFwHkRcTRwEfD1Bm07GDgBeA/wxQHmfxJ4MSJmA5+lNjBIn/2AVan93wc+3v9VvtyDAAAgAElEQVTNks6WtEbSmqXL/rlBU8zMzMzMzMzMBufHfq1VrY6IpwEkrQVmApuBZyLiAYCI2JLmnwBcmcoek/RT4LBUz90RsRXYKmkz8K1UvgE4QtJk4DjghnRzIcD4Bm27JSKqwKOD3Ll3AvDV1J6HJa2vm9cF3J6mHwR+p/+bI2IxtYQkLz61yUO7mpmZmZmZmdluc/LPWlVn3XQvu7+t1tdTrXtdTXVWgJfSYB67U6cGjRpYd0T0JfT25HOZmZmZmZmZmTXkx35tNHkcOFjSPIDU3187cC9wVio7DDgkxTaU7h58UtLp6f2SdOQetnMF8IepvtnA4XtYn5mZmZmZmZnZbnHyz0aNiOgCzgCulLQOuBOYQK2PvoqkDdT6BFwQEZ2D17SLs4CPpTofAd67h039OnCgpEeBL6Q6N+9hnWZmZmZmZmZmxfTKE4hmtjdIagM6ImKnpFnAfwBvTsnLIs8890L2DjqpN7/6n+3szY6dMWW/7Nie3vxh3HsLh3yvFDxhXdd/Y0NTx+c/eb29J7/NJeti6rjC/8MoP766Y3t+te3566IktjPyv4+2Sv5na6fs/LWlsyc7dvKEjuzYakEztnd2Z8dOGp/fht5q2f5Usp4rBZ0bVAquKTbvzF8XUyaOy29DNf/4BrCtO3/dbe/Kb/PUiRMaByUTKvnrrWfLS9mxOydOzY4FmDAuf5sr2S7+/+fz//81c//8Nve+vDU7VpW2/NiC41tpfLWgHSX7U7XgvFdSb/Tkb/MAve35+2p7FOyrBee9kja/XM1fb5ueezE7FuCNnS9kx44/6PXZsTue+kl27JnfXpsdu/So/bNjJ5/0B9mxAB1tBdctLfCnaVtP8SV7lq5K2bGl5M/08ZF/jRMd+ftpyXVLe1v+8a1amINo6nV1pu0Fh6xKwTEZ4MDp00q7khpVfvpvN7TAnr33veH3Tx9V35v7GzPb+yYBd0vqoNYn4Cd3J/FnNqCCP4DMzHKVJP7MzHKVJP7MbIzyDWctwck/swFIugQ4vV/xDRGxqNF70+jCc5vSMDMzMzMzMzOzAk7+WUuTtDIijhvu5aYkX8NEH4CkS4FtEfHlpjbKzMzMzMzMzKyQ78O2ljYSiT8zMzMzMzMzs7HCyT9raZK2pd8nS1ou6UZJj0lapjSqg6R5klZKWidptaQpkiZIWiJpg6SHJL0rxS6QdIukOyVtknSupAtTzCpJ+6e4WZK+I+lBSfdKektme+eketZLulnSdEm/JunBNP9ISSHpkPT6x5ImNWPdmZmZmZmZmZk5+WejyVHAp4DZwBuB4yWNA64HLoiII4FTgB3AOUBExOHAmcDVkvqGXnwb8D5gHrVHe7dHxFHAfcBHUsxi4LyIOBq4CPh6Zhu/CfxpRBwBbAA+FxG/BCZImgq8E1gDvFPSG4BfRsSrhmOVdLakNZLWXPPNq0vWj5mZmZmZmVnLiN7eMfkz2rjPPxtNVkfE0wCS1gIzgc3AMxHxAEBEbEnzTwCuTGWPSfopcFiq5+40KMdWSZuBb6XyDcARkiYDxwE36JVh2sc3apykacBrIuKeVHQ1cEOaXgkcD5wI/BXwe9RGAr63fz0RsZha8pFnnnvBQyOZmZmZmZmZ2W5z8s9Gk8666V52f/utr6da97qa6qwAL0XEnN2sfyDfp3bX3xuAW4E/BQL49l5chpmZmZmZmZnZq/ixXxvtHgcOljQPIPX3107tjrqzUtlhwCEptqF09+CTkk5P75ekIzPetxl4UdI7U9GHgb67AO8F/gj4UURUgReA3wd+kPUpzczMzMzMzMx2g+/8s1EtIroknQFcKWkitf7+TqHWR983JG0AeoAFEdFZ9xhvI2el9/850AFcB6zLeN9Hgb9Ng3j8BFiY2rkpDVDy/RT3A+C/RMSLuQ0yMzMzMzMzG1XCPVm1AoW/CLOW9eyLm7N30HFdO7LrVUfHbrWnoZLjSVSb0wagq71hF42/Mq7akx2rtrbs2GolP7ZSeBzuITuJTSU/tEh3b/73N75JjajmJ/OB8vWcrYnbctOo4Mb/Fvh8JftTtYmXNb3V/HXRVslfx20Fx+/eHdsbB/XVu9+U7FgAtTfn3FCyr27v7M6OndyRv46jp+BY394a/xtv5nkkuw2lx9lqfgfoJZ+vWUrWW7Vg3wPY3jYuO7bjuZ9lx/7nt5Zlx04+7Ijs2AUPvZAde+v/+dHsWAB1d2XHRkf+eis53rdH/rbZo/xts60n/7N1quzYMq69OQ/pFa03mnRtX3IdQtmxSJ3559QYPzE7tquneddDB06f1qQr9taw6dZ/HpNJp5nv/dCo+t782K+Z2ShSkvgzM8vVrMSfme3bShJ/ZmbWPK3xr02zUUDSJcDp/YpviIhFI9EeMzMzMzMzM7NGnPyzliBpZUQcN9LtGEpK8u2VRJ+kk4GLIuI9e6M+MzMzMzMzM7OB+LFfawmtnvgzMzMzMzMzMxuNnPyzliBpW/p9sqTlkm6U9JikZWmUXCTNk7RS0jpJqyVNkTRB0hJJGyQ9JOldKXaBpFsk3Slpk6RzJV2YYlZJ2j/FzZL0HUkPSrpX0luGaONSSd9I7/9Jaus/StooaWld3H+VdJ+kH0q6QdLkVP576TP9EHhf89ammZmZmZmZmVmNk3/Wio4CPgXMBt4IHC9pHHA9cEFEHAmcAuwAzgEiIg4HzgSuljQh1fM2akm2edQe190eEUcB9wEfSTGLgfMi4mjgIuDrDdo2HTgW+DRwG3AF8JvA4ZLmSDoA+HPglIh4O7AGuDC16e+B/w4cDbx2sAVIOlvSGklrvrl0acOVZWZmZmZmZmY2GPf5Z61odUQ8DSBpLTAT2Aw8ExEPAETEljT/BODKVPaYpJ8Ch6V67o6IrcBWSZuBb6XyDcAR6Y6844Ab9Mrw8eMbtO1bERGSNgC/iIgNqR2PpHb+F2pJyxWpznHUko1vAZ6MiB+l+GuAswdaQEQsppaU5NkXN4/JYdHNzMzMzMxs7IuojnQTDCf/rDV11k33svvbaX091brX1VRnBXgpIubsRp319dXX2QvcGRFn1r9JUskyzMzMzMzMzMz2Cj/2a6PF48DBkuYBpP7+2oF7gbNS2WHAISm2oXT34JOSTk/vl6Qj97Cdq6g9pvymVOd+qV2PATMlzUpxZw5WgZmZmZmZmZnZ3uLkn40KEdEFnAFcKWkdcCcwgVoffZX0GO71wIKI6By8pl2cBXws1fkI8N49bOezwALgWknrSY/8RsROao/5fjsN+PHLPVmOmZmZmZmZmVkORbhLMbNWVdLn34RK/r7co7bdas9I6q3m9xXR0Zb/f41qwSGwpA2l2irN+V9MRY1jfhVbcD7oIb/i0vXWCt9fyfdRso5LNes7KWlzyTou2t6qvfnBAMr/Tpq1Lrp7m3MM6C1ZycCk9uYcL3b25rdjXEEbStZbyb7XHmXbUPQWbnOZ1JZ/Tq1W8mObtf9D2borWW9q78iOrapgPy1YFyX1QhO3T5rTZnV3Zcd2Vcp6zik5/773f12dHXvb+QUPvJT8bVqy3gr2020b1+W3AZj0m2/Pji3ZlktET3d2bMm6KDn3Qtm2XHLqKz3eZ7eh4JhceKouur6YNmVKE68mR96Tt1wzJpNOvzH/j0bV9+Y+/8zMRlizEn+jUbMSf6NRM//wH3WalPgbjVoh8TcaNSvxNxo1K/E3GjUr8TcatUTibxRqhcRfq2hW4m80auY/hkelMb7tjxZO/pn1I+kS4PR+xTdExKKRaI+ZmZmZmZmZ2e5y8s+sn5Tkc6LPzMzMzMzMzEa9sX3/utkekHSypNsHmbdJ0gG7Ueelki7a89aZmZmZmZmZmTXmO/9sVJMkagPXNG8kBjMzMzMzMzMrNtb7jx0tfOefjTqSZkp6XNI3gYeBf5C0RtIjkj5fFzdP0kpJ6yStljRFUpukL0l6QNJ6Sf+jweKmSvp2Wt7fSrv2Oi/pFkkPpuWfXVf+e5J+mJb/vQHe93FJ/y5p4h6sDjMzMzMzMzOzQfnOPxutDgU+GhGrJO0fES9IagO+p//N3r3H2VXV9/9/veeWCUkIF2kM6tdQhC93AjLcobFGigoiEEnRqhFrhFawtKj0UfxWvuoPbL4WFUQaUQLSAgUKREAipYAxgCQGQgAB5aLI/RJyz2Rmzuf3x1mjh2Eua4U5yczk/Xw85jHn7P3Za629z76c+czaa0t7AY8AVwHTI2KhpC2BtcCngeUR0SZpFLBA0k8j4sk+6tkf2A34LXALcBxwTY+Yk1L9o4GFkq6lmlj/PnB4RDwpaZvaBSR9Dngf8OGIaO8xbyYwE+D//eu3+MSMGRuyfczMzMzMzMzMnPyzYeu3EXFPen1CSpg1AROpJusCeC4iFgJExAoASUcAe0malpYdTzWR2Ffy796IeCItewVwKG9M/p0m6dj0+h2pvO2An3UnFSPi1Zr4TwBPU038dfSsMCJmA7MBXlq23M9FNzMzMzMzM7MN5uSfDVerASTtAJwBtEXEMklzgNZ+lhNwakTMy6ynZ/Ltde8lTQGmAgdFxBpJdwxQP8BSYDLwdvpOOpqZmZmZmZkNbx6ef0jwmH823G1JNRG4XNIE4P1p+qPAREltAGm8vyZgHnCKpOY0fWdJY/opf39JO6Sx/qYDP+8xfzywLCX+dgEOTNPvAQ5PyUl63PZ7H/BZYK6k7Tdstc3MzMzMzMzMBuaefzasRcQSSfdRHePvaWBBmr5e0nTg/DQW31qqPfQuBiYBi9OTgl8CPtxPFQuBC4B3AbcD1/WYfwtwsqRfUU043pPqfyndivxfKXH4ItUx/rrb/XNJZwA3SXpfRLz8JjaDmZmZmZmZmVmvFOEhxcyGqhUvv5h9gEZXfnfqyvp12bGNY8Zlx1JwPonOzvxyC61rHujO6z9qaWqsSxuefuW17NhRTWX/h1ndvj479m3bjM+OfXHFquzY9o78z29Uc/76Tdpmy+zYipQdC6CO/O2mxoL94o0PAe9byW0PBeV2UrYtmt4wokHfSrdzdrkFXz/Wd3ZlxzY2lLW3sSF/O69b/4ZhWvvU2tKcHdsU+etXpGTfLLSuqz7fH1sb67O/RWf+Z1d0/BfqVH7ZQ+E4XbUuf7sBbDkq/3xfct4q2RZrOvPPsy2rlmXHrm7Nvz4BjK20DxyUdIzq7yaU1xvVlV9ujBqdHVtyjSz1oe9ckR17wynTBg7aAA2t+d8NX7nj5uzYrQ/88+zYku+nAFs01ef7Rb2+g6u5JT+4jreBVtbl/52zqiG/zfU6vwFsveW4+pzEh4gnrv7hiEw6/elHThpWn5tv+zUzG0ZKEn9mZmZmm1JJ4s/MzOrHt/3aZk/SnsCPekxuj4gDNkV7zMzMzMzMzMwGi5N/ttmLiO6n7/6BpCmSboyIo3rGS3oK2G9DxulLTyO+MSKu2cDmmpmZmZmZmZll822/NmypyvuwmZmZmZmZmVkfnDixYUXSJEmPSroMeBD4gaRFkh6SdHZNXJukuyQtkXSvpHGSGiXNkrRQ0gOSPjtAdVtKuinVd1FviUZJ10v6Zap/Zs30VZK+nuq/R9KEXpb9qqQ5UsHo32ZmZmZmZmZmBZz8s+FoJ+DCiNgd+IeI2A/YC/gzSXtJagGuAj4fEXsDU4G1wKeB5RHRBrQBn5G0Qz/17A+cCuwG7Agc10vMSRHxbmA/4DRJ26bpY4B7Uv0/Az5Tu5CkWcB2wKciXv+YR0kzU0Jz0SWXXZa7TczMzMzMzMyGlIgYkT/Djcf8s+HotxFxT3p9Qupx1wRMpJqoC+C5iFgIEBErACQdAewlaVpadjzVROKTfdRzb0Q8kZa9AjgU6DlW32mSjk2v35HKewVYD9yYpv8SeF/NMl8GfhERM+lFRMwGZgOsePnF4XdWMTMzMzMzM7Mhw8k/G45WA6Ree2cAbRGxLD1Mo7Wf5QScGhHzMuvpmXh73XtJU6j2KjwoItZIuqOm/o74478Dunj9sbYQeLekbSLi1cy2mJmZmZmZmZkV822/NpxtSTURuDyNqff+NP1RYKKkNoA03l8TMA84RVJzmr6zpDH9lL+/pB3SWH/TgZ/3mD8eWJYSf7sAB2a2+xbgXOAmSeMylzEzMzMzMzMzK+aefzZsRcQSSfcBjwBPAwvS9PWSpgPnSxpNdby/qcDFwCRgsSQBLwEf7qeKhcAFwLuA24Hresy/BThZ0q+oJhzvIVNEXJ0Sf3MlfSAi1uYua2ZmZmZmZjYsdHUNHGN1p+E4UKHZ5mLlihXZB2hl7ZrschtGb5HfiKhkh3YWPLi4KQovAm982HKfKlJ27PrO/PVraqxPZ+mmN9xh3r9O8tevXtZ35n9+7Z2d2bHjWkdlxzYUboaGel3vCo6RoaLSkH+sVgo2W/Fxnak98j/sUYU7Rsn5Yl1H/vq1NOVv48aO9uzYtcr/v+0WTWXnrJJtUaJe263k3Bkd67Nji4/pguNJjfmxJde9kjZHwR9h0dyS3wagoVJwDqjT+i1rz7/mbNWQX+7qwj4TY8hvhwq3c66S72Ulp06tzz9nAUTB94BjvtdzeO2+3XDKtIGDEjU3Z8e++JP/zI5tfd8J2bFjO/O/qwO0t+bfILRFwaml5DtA0XenguO05LoOZdf2NQXf7UeTf84qOX+XbGOA8ePGbfov93X0+BWzR2TSaccTZw6rz823/ZqZDSNDIfFnZiNPvRJ/ZrZ5K0n8mZlZ/fi2X9usSdoT+FGPye0RccCmaI+ZmZmZmZmZ2WBy8s9GrPQ03jMi4qhe5l0M/GtELAUm17kdNwMfjYjX6lmPmZmZmZmZ2VDioeaGBif/bJNKD95QxMYdNCsi/noj1vWBjVWXmZmZmZmZmVktj/lnG52kSZIelXQZ8CDwA0mLJD0k6eyauDZJd0laIuleSeMkNUqaJWmhpAckfXaA6sZKukbSI5L+PSUbkXSHpP1SeXMkPShpqaTTa+Z/W9L9ad7+afr+ku6WdF9q2/9O02dI+i9Jt0j6taR/qVmPpyS9Jb3+RGr3Ekk9bzc2MzMzMzMzMxtU7vlnm8pOwCcj4h5J20TEq5Iagdsk7QU8AlwFTI+IhZK2BNYCnwaWR0SbpFHAAkk/jYgn+6hnH2B34FlgAXAI8POa+ZOBt0XEHgCStqqZt0VETJZ0OPBDYI/UrsMiolPSVOD/A46vKWsfoB14VNL5EfF0d2GSdgfOAg6OiJclbdNbgyXNBGYCfPtb3+JTn/rUAJvSzMzMzMzMzKx3Tv7ZpvLbiLgnvT4hJbyagInAbkAAz0XEQoCIWAEg6QhgL0nT0rLjqSYS+0r+3RsRv0/L3g9M4vXJvyeAP5V0PnAT8NOaeVekun8macuUGBwHXCppp9TG5pr42yJiearrYeCdwNM18/8cuDoiXk7lvtpbgyNiNjAbYOWKFR4gwczMzMzMzIanjTvCl/XByT/bVFYDSNoBOANoi4hlkuYArf0sJ+DUiJiXWU97zesueuzzqc69gb8ATgZOAE7qnt2jrAC+CtweEcdKmgTckVuXmZmZmZmZmdnG5jH/bFPbkmoicLmkCcD70/RHgYmS2gDSeH9NwDzgFEnNafrOksZsaOVpLL6GiLiW6i25+9bMnp5iDqV6q/Fyqj0Nn0nzZxRW9z/ARyRtm8rt9bZfMzMzMzMzM7PB4p5JtklFxBJJ91EdS+9pquPyERHrJU0Hzpc0mup4f1OBi6neurs4PbzjJeDDb6IJbwMukdSdCP/HmnnrUtua+WNvwH+hetvvWVRvE84WEQ9J+jpwp6Qu4D7KE4hmZmZmZmZmZtkU4SHFzHqSdAdwRkQs2pTtWPHqK/kHaMGxHC2jsmMbCsqtVB+mnBc7DE89XZX6jFcxqiF/uwF0kh/f2Lk+O1aNjdmx7ZHfho7OruzY1pbmgYOS0s+juTG/s3u99vsSJW0oVjD2Sqfy94umyP+sKw355dbzfNHZlb8t6vRRF50D6nmebXrDaBd9KzkPrVvfkR1bcg4o2d+iszM7tnRsIjW3FATnn4fqdm6p5G+3kuMfyvahkvUrafOa/FBGV/KvkWsbCj5nYHTk73Ml38u0vn3goKSrOb/cEiXfLQCiI/8cUOKY712THfvj0/8qO/bl//lxduw2U/P7IJRe10vOsyXnw5LzUJGCc2fJ90go+w5Xcu2r13YrPX+PHzeuTt8whobf/Pv3huFffgN718dOGVafm2/7NTMbRkq+CJqZ5SpJ2piZ5apX4s/MbDiTtI2kWyX9Ov3euo+4Lkn3p5+5NdN3kPQLSb+RdJWkAf9D5eSfDXuS9qw5ILp/fvFmyoyIKZu615+ZmZmZmZmZjThnArdFxE7Abel9b9ZGxOT086Ga6d8AzouIdwHLgE8PVKGTfzbsRcTSmgOi++eAetQl6WRJnxiEcmZIumAw2mRmZmZmZmZmw8YxwKXp9aUUPMcgPfvgz4HusQ+ylnfyz4YNVW3SfTYiLoqIyzZlG8zMzMzMzMxs05E0U9Kimp+ZBYtPiIjn0uvngQl9xLWmsu+R1J3g2xZ4LeIPg8v+nuqDTPvlp/3akCZpEjAP+AXwbuBeSXsCo4FrIuKfU1wb8G1gDNAOvBdYA5wLTAFGAd+NiH/ro54pwNnAa8CewH8CS4HPp7o+HBGPS/oKsAr4FnA38IWIuEPSOUAlIv5J0l8BpwEtqd1/ExFdkj5F9WnCrwFLUjvNzMzMzMzMRqSo00MTN7WImA3M7mu+pP8G3trLrH/qUU5I6mvw5XdGxDOS/hT4H0lLgeUb0l73/LPhYCfgwojYHfiHiNgP2Av4M0l7pcEtrwI+HxF7A1OBtVTve18eEW1AG/AZSTv0U8/ewMnArsDHgZ0jYn/gYuDU2sCUZZ8BfE/SVOBI4GxJuwLTgUMiYjLQBXxM0kSqycVDgEOB3fpqRO1/EC659NK+wszMzMzMzMxsCIqIqRGxRy8/NwAvpBwB6feLfZTxTPr9BHAHsA/wCrCVpO7OfG8HnhmoPe75Z8PBbyPinvT6hNSdtgmYSDWJFsBzEbEQICJWAEg6AthL0rS07HiqicQn+6hnYXfXW0mPAz9N05cC7+kZHBEPSfoRcCNwUESsl/Reqj0UF1ZvxWc01QP5AOCOiHgplX8VsHNvjaj9D8KKV1/x4xfNzMzMzMzMRo65wCep3qn4SeCGngHpCcBrIqJd0luodiT6l9RT8HZgGnBlX8v35OSfDQerofo4a+AMoC0ilkmaA7T2s5yAUyNiXmY9tbfhVmreV+j7WNmT6m28f1JT56UR8Y+va8gf7883MzMzMzMzs83XucB/Svo08FvgBABJ+wEnR8RfU70j8d8kVajetXtuRDyclv8ScKWkrwH3AT8YqEIn/2w42ZJqInC5pAnA+6l2fX0UmCipLSIWShpH9bbfecApkv4nIjok7Qw8ExGrB6Mxko4DtgEOB26UtD/Vx3TfIOm8iHhR0jbAOKpj/31b0rbACuAjVMf9MzMzMzMzMxuZKl2bugVDTkS8QvU5BT2nLwL+Or2+i2pno96WfwLYv6ROJ/9s2IiIJZLuAx4BngYWpOnrJU0Hzpc0mmribyrVsfomAYvT47BfouAR2v1J3W7PBd4bEU9LugD4dkR8UtJZwE/Tk4k7gL+NiHvSw0LuptpT8P7BaIeZmZmZmZmZWX8U4SHFzIaqZStWZh+gjR35Dw+OllEb1J7BVBnhp54mhsgKRp2erqX850V1ouzYhvzQYiX7XL3aMRTaUM92NBR8pxgq+0WJep23hsL+BmXnrZLPr0TR/jZUehIUnA/rpaKC46nku3/pNaRO14aSfbNkW5QoPZ4aO9dnx3Y1tWTHNkX+ft+pxrqUW0/Rld8ONeav39HnXZ4d+5/HH5wd2zVxUnbsFl35+wRAZfSY7Niiz69O56ySY6+jq+zc0tiQ3+Z6fW+p17kFYPy4cUPkm059/PrS7wyRP4wG106fPG1YfW6b/tuKmZmZmW1SQ+YfFmZmZmY26Hzbr21WJO0J/KjH5PaIOGBTtMfMzMzMzMxspPLdpkODk3+2WYmIpcDkN1OGpEnAwRHxH+n9DGC/iPjcm22fmZmZmZmZmdlg8m2/NqypamPvx5OAjw5WYVLBgCxmZmZmZmZmZgWc/LNhR9IkSY9Kugx4EPiBpEWSHpJ0dk1cm6S7JC2RdK+kcZIaJc2StFDSA5I+2089SrEPSlqanigM1af8Hibpfkmnp2nbS7pF0q8l/UtNGUdIulvSYklXSxqbpj8l6RuSFgMfGextZGZmZmZmZmYGTv7Z8LUTcGFE7A78Q0TsB+wF/JmkvSS1AFcBn4+IvYGpwFrg08DyiGgD2oDPSNqhjzqOo3qLcPfysyRNBM4E5kfE5Ig4L8VOBqYDewLTJb1D0luAs4CpEbEvsAj4+5ryX4mIfSPiytpKJc1MycxFcy655M1sIzMzMzMzMzPbzHnMPxuufhsR96TXJ0iaSXV/ngjsBgTwXEQsBIiIFVDtiQfsJWlaWnY81UTik73UcShwRUR0AS9IupNqwnBFL7G3RcTyVMfDwDuBrVJbFqj6aPgW4O6aZa7qbcUiYjYwG2DZipUeHdXMzMzMzMzMNpiTfzZcrQZIvfbOANoiYpmkOUBrP8sJODUi5g1ye9prXndRPbYE3BoRJ/axzOpBboOZmZmZmZmZ2ev4tl8b7rakmkRbLmkC8P40/VFgoqQ2gDTeXxMwDzhFUnOavrOkMX2UPZ/qLbyNkrYDDgfuBVYC4zLadg9wiKR3pbrGSNp5g9bSzMzMzMzMzGwDuOefDWsRsUTSfcAjwNPAgjR9fXpAx/mSRlMd728qcDHVp/UuVvVe3JeAD/dR/HXAQcASqrcRfzEinpf0CtAlaQkwB1jWR9tekjQDuELSqDT5LOCxN7XSZmZmZmZmZmaZFOEhxcyGqudefjX7AF25bn12ueNaW7JjW1uas2MblB1KpY6nnsaO9oGDuvXFK2YAACAASURBVNvRkb/dpPzO0l3r1mTHNrZukR0LUGlfmx3bNH7r7NiuNfl3onetXpkdW7KNW7f/X9mx0dWVHQvQ1ZS/35fsyw0F19FOCgouUNJeKGtzdHZkx5Zs4xIlxzQq2xhqbMyOLTlG1JBfbmV0Xx3Q36hxff7xr5b+RsHoRVTyQwuO6661BefDMTkd61O5Bfvb6oJrZKnmpvzPurU5P7ZEQyX/fLiukn+MjIrOonYUnWfX5h9PHaNGZ8c2ruz1f7K9Wt6cf+yN7ygbraWk7G1H53/XWtWRf5yOacg/15d8duseXpwdC9D+wjPZsZ2rlmfHNm4xNjt2zLt2z4494dq7smOvO+mD2bEvNeTvxwBvHZ3fT2d15H9HLTlnNTbU50bBkmsZQFdL/rZrouCPjILrXqfyt1tTlH1HHTd+q/p8QRwiHvvheSMy6bTzSacPq8/Nt/2amQ0jJYk/M7NsBX8AmZnlKkn8mZlZ/fi2X9vsSdoT+FGPye0RccCmaI+ZmZmZmZmZ2WBxzz/b7EXE0oiY3OOn38SfpEmSHuxj3v+VNDW9/jtJZfd0mpmZmZmZmZkNEvf8s2ErPbBDEUPrXqWI+D81b/8OuBzIH/DIzMzMzMzMbASIgnFprX7c88+GldTj7lFJlwEPAj+QtEjSQ5LOrolrk3SXpCWS7pU0TlKjpFmSFkp6QNJn+6nnu5I+lF5fJ+mH6fVJkr6ewholfT/V/dP0VGEkzZE0TdJpwPbA7ZJuT/OOkHS3pMWSrpaUP1KxmZmZmZmZmVkhJ/9sONoJuDAidgf+ISL2A/YC/kzSXpJagKuAz0fE3sBUYC3waWB5RLQBbcBnJO3QRx3zgcPS67cBu6XXhwE/q2nHd1M7XgOOry0gIr4DPAu8JyLeI+ktwFnA1IjYF1gE/H3PiiXNTAnNRZdfdmnZljEzMzMzMzMzq+Hbfm04+m1E3JNenyBpJtV9eSLVJF0Az0XEQoCIWAHVXnfAXpKmpWXHU03gPdlLHfOBv5O0G/AwsLWkicBBwGnAtsCTEXF/iv8lMGmAdh+Y2regescyLcDdPYMiYjYwG+C5l18dkY9FNzMzMzMzM7ONw8k/G45WA6Ree2cAbRGxTNIcoLWf5QScGhHzBqogIp6RtBVwJNWeftsAJwCrImKlpG2B9ppFuoDRAxQr4NaIOHGg+s3MzMzMzMyGvXB/lqHAt/3acLYl1UTgckkTgPen6Y8CEyW1AaTx/pqAecApkprT9J0ljemn/HuoPrDjZ1R7Ap6RfpdYCYyrKe8QSe9K9Y+RtHNheWZmZmZmZmZm2dzzz4atiFgi6T7gEeBpYEGavl7SdOD89BCOtVTH/buY6q25i9OTgl8CPtxPFfOBIyLiN5J+S7X3X2nybzZwi6Rn07h/M4ArJI1K888CHiss08zMzMzMzMwsi8JdMM2GrOUvPJ99gFZG99eJ8fUa16/NjlVLf3dSv14nyo4t1VWpZMc2t+evX0Nr/vqt6shvwxjlx6qpfv+HqTQ0Zsc2VLqyY6MrP7akq3+0jBo4qEZDyTUs8j+Tku1WomQbo4LO+QXrBmXrVynYxE3U5/Oo57aIzs6CduSf49TUnB27rit/u3UWHHtjW8rOLSXn8KYo2JdLFHzW7QU7Z2ND2c0uJevXqTqdLwouqXU7TgtVCo6Rjq6C63pj/udXsi0aO9dnx3Y1teQXTP2OkRXr87fbuMb6fNbRXLYtWLM6O3Sl8s+d40bnt2NNe0d2bOua5dmxx/7wpuxYgK8/dnV27G4X3VhUdj2U7Mf1+m5R2o6Sc3LJeVbr2wcO6o4t/G4/bvxW9fsjagh49PuzRmTS6X9/5gvD6nPzbb9mZjYsFSX+zKxf9fznzXBTt8Sm2eaoIPE30pUk/szMBptv+7XNmqQ9gR/1mNweEQdsivaYmZmZmZmZjRRR2tXT6sLJP9usRcRSYPKmboeZmZmZmZmZWT34tt8RSNJddSr3Zklb1aPsmjq2kvQ3A8RsL+maOtVfl23Xo45Jkj5a73rMzMzMzMzMzJz8G4Ei4uA6lfuBiHgtN17ShvQs3QroN/kXEc9GxLQNKHtApdtO2qBRvicBTv6ZmZmZmZmZWd05+TcCSVqVfk+RdKekGyQ9IelcSR+TdK+kpZJ2THFzJH1P0j0pboqkH0r6laQ5NeU+Jekt6fWXJT0q6eeSrpB0Rpp+h6RvSVoEfF7S0ZJ+Iek+Sf8taUKK+0qq445U52mpmnOBHSXdL2lWH+s3SdKD6fUMSddLujW173OS/j7Vd4+kbWradZ6kRWm92iT9l6RfS/paL9uuQdKFkh5JZd8saVrNdviGpMXARyR9RtJCSUskXStpi5rt+h1Jd6V17E5Yngscltbx9Df9gZuZmZmZmZmZ9cHJv5Fvb+BkYFfg48DOEbE/cDFwak3c1sBBwOnAXOA8YHdgT0mvGxNPUhtwfCr7/cB+PepsiYj9IuKbwM+BAyNiH+BK4Is1cbsAfwHsD/yzpGbgTODxiJgcEV/IXMc9gOOANuDrwJpU393AJ2ri1kfEfsBFwA3A36ZlZ0jatkeZx1Htobcb1e12UI/5r0TEvhFxJfBfEdEWEXsDvwI+XRM3ETgUOIpq0o+0jvPTOp7Xc2UkzUxJykVzftTzWSRmZmZmZmZmZvn8wI+Rb2FEPAcg6XHgp2n6UuA9NXE/joiQtBR4IT0IA0kPUU2C3V8TewhwQ0SsA9ZJ+nGPOq+qef124CpJE4EW4MmaeTdFRDvQLulFYMIGruPtEbESWClpOdDdnqXAXjVxc2umP1SzXZ4A3gG8UhN7KHB1RFSA5yXd3s867pF6D24FjAXm1cy7PpXxcHevx4FExGxgNsDyF573o5HMzMzMzMzMbIO559/I117zulLzvsLrk7/tvcT0Fpdjdc3r84ELImJP4LNAax9t69qAenorZ1Os4xzgc2kdz6bvdVRhHWZmZmZmZmZmb4qTf7YhFgBHS2qVNJbqLa19GQ88k15/MqPslcC4N9m+wbAAOD6N/TcBmNJP7DjguXTb8scyyh4q62hmZmZmZmZWP1EZmT/DjJN/ViwiFlK9hfYB4CdUb6Nd3kf4V4CrJf0SeDmj7FeABZIe7OuBHxvJtcDvgYeBy4HF9L2OXwZ+QTVh+EhG2Q8AXekBIX7gh5mZmZmZmZnVjSI8pJiVkzQ2IlalJ9v+DJgZEYs3dbsGU806bgvcCxwSEc9vzDY89/Kr2Qfo2Ob8XP6arvw2tDY3Zsc2VAoKLhRd+WWvruTfYd3a0pwd27B29cBByfqW0dmxTY31+z9MQ8HN5pWCy0FnV33+29XamN/gisrupC9Zvybqc23sLLj7v+SzK1WvY7XSkH++KPk86rUfQ9m+XLLLNRcc11rfPnBQEp2d+Y3YYkx+LKCO9dmxXU0t+bGV/G1ctN0K2qvGgn2zYD8GaCj5Lj0EegqUrl+Jkm0RnR35sc35+1vJ+a1T+duiKcrOmyXftVqaCr5rFXwX6RiV/12kZDcuuVYDrOnM3+9HrVuZHds4dnx2bGXtmuzYF/J3TV764vTs2H/a+SP5BQM/Pv2viuLroegYKfnuVHgurNuxqvxrzrqu/PUr/W6/9ZbjRvTwUI9cdO6ITDrtcvKZw+pz8wM/bEPNlrQb1fHtLh1pib/kRklbUX1QyVc3duLPzMxsYylJpJmZ5SpJ/JmZWf04+WcbJCI+Wu86JO0J/KjH5PaIOKDedQNExJSNUY+ZmZmZmZnZSBR1vDvM8nnMP8si6a46lXtz6l33BhGxNCIm9/gpTvxJ2krS37z51r6h3BmStq95f3HqDWlmZmZmZmZmNiQ4+WdZIuLgOpX7gYh4LTde0ob0Vt0KGPTkHzAD+EPyLyL+OiIerkM9ZmZmZmZmZmYbxMk/yyJpVfo9RdKdkm6Q9ISkcyV9TNK9kpZK2jHFzZH0PUn3pLgpkn4o6VeS5tSU+5Skt6TXX5b0qKSfS7pC0hlp+h2SviVpEfB5SUdL+oWk+yT9t6QJKe4rqY47Up2npWrOBXaUdH9fTxBO7btD0jWSHpH071J1aHdJ/0fSwvQE4tmqmgbsB/x7Knd0Wn4/SSfX1pN6CF6QXv9V2lb3S/o3qWD0WjMzMzMzMzOzQk7+2YbYGzgZ2BX4OLBzROwPXAycWhO3NXAQcDowFzgP2B3YU9Lk2gIltQHHp7LfTzWxVqslIvaLiG8CPwcOjIh9gCuBL9bE7QL8BbA/8M+SmoEzgcfTbcNf6Ge99gH+DtgN+FPgkDT9gohoi4g9gNHAURFxDbAI+Fgqd21NOdcCx9a8nw5cKWnX9PqQiJgMdAEf69kISTMlLZK06PLLLu2nuWZmZmZmZmZm/fMDP2xDLIyI5wAkPQ78NE1fCrynJu7HERGSlgIvRMTStMxDwCTg/prYQ4AbImIdsE7Sj3vUeVXN67cDV0maSPVJvE/WzLspItqBdkkvAhMK1uveiPh9auP9qY0/B94j6YvAFsA2wENAz/b9QUS8lHoeHgj8mmpCcgHwt8C7gYWpU+Fo4MVelp8NzAZ47uVXR+Rj0c3MzMzMzGwzEP6Tdihw8s82RHvN60rN+wqv36fae4npLS7H6prX5wP/GhFzJU0BvtJH27oK63nDspJagQuB/SLiaUlfAVozyroSOAF4BLguJUEFXBoR/1jQJjMzMzMzMzOzDebbfm2oWAAcLalV0ljgqH5ixwPPpNefzCh7JTBuA9vVneh7ObVrWma51wHHACdSTQQC3AZMk/QnAJK2kfTODWyXmZmZmZmZmdmAnPyzISEiFlIdF/AB4CdUbyFe3kf4V4CrJf0SeDmj7FeABemBHb0+8KOfZV8Dvg88CMwDFtbMngNc1P3Ajx7LLQN+BbwzIu5N0x4GzgJ+KukB4FZgYkl7zMzMzMzMzMxKKHz/tQ0RksZGxCpJWwA/A2ZGxOJN3a5NafnKldkHaKXgUF7T3pEdu8Wo5uzY9Z1d2bGdXZXs2FIlbW5Qfrkl27hEU+RvNwBU8H+bqM927hzhD6ou2S+Kyq3TNbeTsgaX7HMln3VJuZWG+uxDDZWy46lk/boq+cdTY0P+cVqy3doj/7MuaQNAE/n7Z0X57Vi1Lv+aM6o5//NoqXRmx1KyX5ScYwE1FYwwUlh2tjqd64vVaf1KznH1uq6vW5+/HwNs0bU+O7Zj1OiBg5KWzvaBg5L2xlH55Tblf3b1upYBrOnM35dbmupzHWnsyN/GXc3527j0+97R512eHXv9zGPyC67TcdrQkr8tKh35x0epxi3GZseWnFvqdY0EGD9uXJ2+eQ4Nv/ru10Zk0mnXvz1rWH1uHvPPhpLZknajeqvtpZt74s+sV/X6w9HMNmslf9SYmZmZ2fDi5J8NGRHx0XrXIWlP4Ec9JrdHxAH1rtvMzMzMzMzMbGNzFxIbdiRNkXRjL9M/JOnM/paNiKURMbnHz6Am/iTdlX5PkvTRHvP+UdJvJD0q6S8Gs14zMzMzMzMzs57c889GjIiYS/WhIZu6HQenl5OAjwL/AZBuaf5LYHdge+C/Je0cUTrgm5mZmZmZmdnQFwVjJlv9uOef1Z2kMZJukrQkPXF3uqSnJJ2TnpS7SNK+kuZJelzSyWk5SZqVllkqaXovZbdJuk/SjpJmSLogTZ8j6TuS7pL0hKRpaXqDpAslPSLpVkk3d8/ro+1HptjFqbwb0/SvSDqjJu5BSZPS61Vp8rnAYWkdTweOAa6MiPaIeBL4DbD/m9/CZmZmZmZmZma9c/LPNoYjgWcjYu+I2AO4JU3/XURMBuYDc4BpwIHA2Wn+ccBkYG9gKjBL0sTuQiUdDFwEHBMRj/dS70TgUOAoqom47jInAbsBHwcO6qvRklqB7wNHA+8G3lqy0sCZwPx0a/F5wNuAp2vm/z5N61nvzJQQXTTnkksKqzQzMzMzMzMz+yPf9msbw1Lgm5K+AdwYEfNVffz53Jr5YyNiJbBSUrukragm7q5It8W+IOlOoA1YAewKzAaOiIhn+6j3+oioAA9LmpCmHQpcnaY/L+n2ftq9C/BkRPwaQNLlwMwN2gIFImI21XVj+cqVfvyimZmZmZmZmW0wJ/+s7iLiMUn7Ah8AvibptjSrPf2u1Lzufj/Qvvkc0ArsA/SV/KstU0WNHlgnr+8525qxzDPAO2revz1NMzMzMzMzMxt5Kh7ifijwbb9Wd5K2B9ZExOXALGDfzEXnA9MlNUraDjgcuDfNew34IHCOpCkFzVkAHJ/G/psA9LfsI8AkSTum9yfWzHuKtB4psblDL8uvBMbVvJ8L/KWkUZJ2AHaqWR8zMzMzMzMzs0Hnnn+2MexJdby+CtABnAJck7HcdVTH5FsCBPDFiHhe0i4AEfGCpKOAn0g6KbMt1wLvBR6mOv7eYmB5b4ERsU7STOAmSWuoJiPH1ZTzCUkPAb8AHuuliAeALklLgDkRcZ6k/0x1dwJ/6yf9mpmZmZmZmVk9KcJDitnmRdLYiFglaVuqPe8OiYjnM5abApwREUfVu43dXlq2PPsAXd+Zn0fcsmXTd/rtVGNRfFNBnrSybl12rJry/wdSEluyfl2VSnYsQGND/udXsl80NuTfHd/cWJ99qKGOtwVUGsr2uVwNBdfRzoIRCEr2i5J9AqCzK7/slqb8slet68iO3WJUc3ZsEwXfVaLseCqxpmD3HB2d2bHPrF6fHfv28VvkN0L1O9dX1q7Jju1c1ev/2HrVOGbcwEHJ2qacETeqKgW70LqO/P241Jaj89vc2ph/vqhosEc3SeUW/plQcqyWtLnk2tAe+eW2VPKP01Ltyv/O0NpQcB0p+H7R2Jl/bonmluxYdeSXC6DG+lx/62VVR/51pLWl4FpW+D//rtUrs2M/PPuG7NjrPp3/p0zJZ9cwanR2bGV9+8BBtQqu7WtbxmTHjik49ij4vlfyNwPAuPFb1eckPkQ8/O1/HpFJp90+f/aw+tzc8882RzemB4q0AF/NSfyZDRWlSR4zMzOzTWW4Jf7MzEYqJ/9ssxMRU3pOk3Qdbxy370sRMa9muTuAO+rZNjMzMzMzM7ORwnebDg3uQmJDkqQpkm7sZfqHJJ052PVFxLERMbnHz7y+4iXdlX5PkvTRmunbSrpd0ipJF/RY5t2Slkr6jaTvSHW6R8fMzMzMzMzMLHHyz4aViJgbEecOgXYcnF5OAj5aM2sd8GXgjF4W+x7wGapP+d0JOLKOTTQzMzMzMzMzc/LPBoekMZJukrRE0oOSpkt6StI5ku6XtEjSvpLmSXpc0slpOUmalZZZKml6L2W3SbpP0o6SZnT3qJM0J/Wgu0vSE5KmpekNki6U9IikWyXd3D2vj7YfmWIXp/JuTNO/IumMmrgHJU1Kr1elyecCh6V1PD0iVkfEz6kmAWvrmAhsGRH3RLXf82XAhzd0e5uZmZmZmZmZ5XDyzwbLkcCzEbF3ROwB3JKm/y4iJgPzgTnANOBA4Ow0/zhgMrA3MBWYlRJlAEg6GLgIOCYiHu+l3onAocBRVBNx3WVOAnYDPg4c1FejJbUC3weOBt4NvLVkpYEzgfnpNuHz+ol7G/D7mve/T9N6a9PMlCxddNmcOYXNMTMzMzMzMxsiKjEyf4YZP/DDBstS4JuSvgHcGBHz05B2c2vmj42IlcBKSe3pibuHAldERBfwgqQ7gTZgBbArMBs4IiKe7aPe6yOiAjwsaUKadihwdZr+vKTb+2n3LsCTEfFrAEmXAzM3aAsMkoiYTXW9eWnZ8uF3VjEzMzMzMzOzIcPJPxsUEfGYpH2BDwBfk3RbmtWefldqXne/H2j/ew5oBfYB+kr+1ZY52A/Q6OT1vWNb30RZzwBvr3n/9jTNzMzMzMzMzKxufNuvDQpJ2wNrIuJyYBawb+ai84HpkholbQccDtyb5r0GfBA4R9KUguYsAI5PY/9NAPpb9hFgkqQd0/sTa+Y9RVqPlNjcoZflVwLjBmpQRDwHrJB0YHrK7yeAGwZazszMzMzMzMzszXDyzwbLnsC9ku4H/hn4WuZy1wEPAEuA/wG+GBHPd8+MiBeojuf3XUkHZJZ5LdUx9R4GLgcWA8t7C4yIdVRv871J0mLgxR7lbCPpIeBzwGO9FPEA0JUedHI6gKSngH8FZkj6vaTdUuzfABcDvwEeB36SuT5mZmZmZmZmZhtE1QePmo0sksZGxCpJ21LtSXhIbVKxn+WmAGdExFH1bmOOkjH/KnU6llubG7NjS8Y97eyqFLVDBTd1j1JBQ1TwP5DIb3On8rdbqYaCbVHymdSr3BJNFBRc8HkAVBrqsy8Phe1WT03RlR1br/1+OO4XJdttTX4oWxRs4pJ1A2gouI5U1q4pKju73NFjsmNLriOtjfkHaqXkgkP9zhdFn0dhm+ulpM0lStavXm2o57mlqM0F7RgK5zeA6OzMj20ZlR1br8+6vWBjNDbkf48s3W5da1Znx0ZnR3bssT+4MTv2+pnHZMc2jBqdHVtZ3z5wUK2C/V7jtsqOHQrnLIDx48YNjZN4nTz0r18eht+AB7b73391WH1uHvPPRqob0wNFWoCv5iT+zIaDkj8czcxy1S1hYmabtZLEn5mZ1Y+TfzYiRcSUntMkXccbx+37UkTMq1nuDuCOerbNzMzMzMzMzGxjcfLPhp2+bs2V9CFgt4g4t7flIuLYjdA8JN0VEQdLmgQcHBH/kaZPAn4FPJpC74mIkzdGm8zMzMzMzMw2usJhE6w+nPyzESMi5gJzh0A7Dk4vJwEfBf6jZvbjETF5ozfKzMzMzMzMzDZLftqv1Z2kMZJuSk/EfVDSdElPSTpH0v2SFknaV9I8SY9LOjktJ0mz0jJLJU3vpew2SfdJ2lHSDEkXpOlzJH1H0l2SnpA0LU1vkHShpEck3Srp5u55fbT9yBS7OJV3Y5r+FUln1MQ9mHr2IWlVmnwucFhax9MHZ2uamZmZmZmZmeVz8s82hiOBZyNi74jYA7glTf9d6gU3H5gDTAMOBM5O848DJgN7A1OBWZImdhcq6WDgIuCYiHi8l3onAocCR1FNxHWXOQnYDfg4cFBfjZbUCnwfOBp4N/DWkpUGzgTmR8TkiDgvTdshJSvvlHRYH/XOTAnRRZfNmVNYpZmZmZmZmZnZH/m2X9sYlgLflPQN4MaImK/q48/n1swfGxErgZWS2tOTeg8FroiILuAFSXcCbcAKYFdgNnBERDzbR73XR0QFeFjShDTtUODqNP15Sbf30+5dgCcj4tcAki4HZm7QFqh6DvhfEfGKpHcD10vaPSJW1AZFxGyq68ZLy5b78YtmZmZmZmZmtsGc/LO6i4jHJO0LfAD4mqTb0qz29LtS87r7/UD75nNAK7AP0Ffyr7ZMFTV6YJ28vuds60ALRER7d5si4peSHgd2BhYNctvMzMzMzMzMNrmodG3qJhi+7dc2AknbA2si4nJgFrBv5qLzgemSGiVtBxwO3JvmvQZ8EDgnPf031wLg+DT23wSgv2UfASZJ2jG9P7Fm3lOk9UiJzR16WX4lMK77jaTtJDWm138K7AQ8UdB2MzMzMzMzM7Mi7vlnG8OeVMfrqwAdwCnANRnLXUd1TL4lQABfjIjnJe0CEBEvSDoK+ImkkzLbci3wXuBh4GlgMbC8t8CIWCdpJnCTpDVUk5Hjasr5hKSHgF8Aj/VSxANAl6QlVMc0/B3wfyV1UO3deHJEvJrZbjMzMzMzMzOzYorwkGK2eZE0NiJWSdqWak/CQyLi+YzlpgBnRMRR9W5jt5XLX8s+QDurnQqzPP7CK9mxb9t6y+zYEu2d9ev+Paopf1tsMao5O7ZScLpsIj841q/LLxigIX/9Sqgxv9xKndpQso3rqaFgoICGgutoZ8EIBCX7UEVlIxs0lNx+ofybBKKzIz+2uSU7tmQbE5X8WMrOnSX7RUdXfjua29dmx1ZGj8lvRKGSfW7ZuvXZsZWCA3vrMQOOovEHKthuUfB5lFJz/nWk5DxbcuyVnAOKjv9SBW0uOVZLrjla3z5wULI68tvbXPDdAqCl0pkdu74hvz9GS2fB+hX08xjbUr8+IdGRf75QwbWhXoqu1ZF/PJVcbwC0esXAQd0Kr325Pjz7huzYuaf+ZXZspWCfAKDge0DDqILrSFP++btkv+gsvOZst/X4wR6iakh5cNaZQ+Qb/uDa4wvnDqvPzT3/bHN0Y3qgSAvw1ZzEn9mQUaekm5lt3koSf2ZmuUoSf2Y2MrnD2dDg5J9tdiJiSs9pkq7jjeP2fSki5tUsdwdwRz3bZmZmZmZmZmY2mJz8s2Gpr1twJX0I2C0izi0pLyKOHcTmmZmZmZmZmZkNCU7+2YgSEXOBuZu6HWZmZmZmZmZmQ0HBqLxmG07SGEk3SVoi6UFJ0yU9JekcSfdLWiRpX0nzJD0u6eS0nCTNSssslTS9l7LbJN0naUdJMyRdkKbPkfQdSXdJekLStDS9QdKFkh6RdKukm7vn9VL2kZKurnk/RdKN6fX3UrsfknR2TcxTkv4ltfdeSe9K07eTdK2khennkMHcxmZmZmZmZmZDSqUyMn+GGSf/bGM5Eng2IvaOiD2AW9L030XEZGA+MAeYBhwIdCfTjgMmA3sDU4FZkiZ2FyrpYOAi4JiIeLyXeicChwJHAd23Ah8HTAJ2Az4OHNRPu/8bOEBS9+MVpwNXptf/FBH7AXsBfyZpr5rllkfEnsAFwLfStG8D50VEG3A8cHFvFUqamZKKiy6ZM6efppmZmZmZmZmZ9c+3/drGshT4pqRvADdGxHxJ8MdbdJcCYyNiJbBSUnt6Iu+hwBUR0QW8IOlOoA1YAewKzAaOiIhn+6j3+oioAA9LmpCmHQpcnaY/L+n2vhodEZ2SbgGOlnQN8EHgi2n2CZJmUj2OJlJNJj6Q5l1R8/u89HoqsFtab4AtJY2NiFU96pyd1ouVy1/zo5HMzMzMzMzMbIM5+WcbRUQ8Jmlf4APA1yTdlma1p9+VmtfdGDrDtgAAIABJREFU7wfaP58DWoF9gL6Sf7Vlqo+YgVwJfA54FVgUESsl7QCcAbRFxDJJc1JbukUvrxuAAyNi3Qa2w8zMzMzMzMysiG/7tY1C0vbAmoi4HJgF7Ju56HxguqRGSdsBhwP3pnmvUe2Jd056+m+uBcDxaey/CcBAy96Z2vsZ/njL75bAamB5KuP9PZaZXvP77vT6p8Cp3QGSJhe02czMzMzMzMysmHv+2cayJ9Xx+ipAB3AKcE3GctdRHZNvCdUedF+MiOcl7QIQES9IOgr4iaSTMttyLfBe4GHgaWAxsLyv4IjoSg/5mAF8Mk1bIuk+4JFUxoIei20t6QGqPQ9PTNNOA76bpjcBPwNOzmyzmZmZmZmZmVkxRXhIMdv8dI+1J2lbqj0JD4mI5wep7KeA/SLi5Tdb1rIVK7MP0M6u/CcOre3oyI5tbsjvINxRx6ceVSr556qxrS3ZsY0F67duff52G9ucX25lXf3uBFdjnTp4NzTmxxZcZ7qaR21AYwZfV8G+3NHZlR3b2tK8Ic0Z0PI1ZftQvY6Rku1WUm4T9fuu0lkwIkTJOaChYP1Kzt8tTfnHXmtj2WgXy9flr9/4UQXngBIqOHcqf/0KLiHFGgo2c0k7Svb7km1RomE4/p0Q+cdTp/L345LjH2CLrvXZsWsa88/JY5S/fu0N+dec1oaC/a3kOwCF+1HB51eipM0lx2nJ8V96PEVH/j40FHzo/CsHDkrmnnbiwEG16vRdsrEzfxt3NeUfpyX7BcD4cePqcxIfIpae+4VheDEZ2J5nzhpWn5t7/tnm6sb0QJEW4KuDlfgzMzMbjkoSf2ZmuYZlAtnMbARy8s82SxExpec0SdcBO/SY/KWImFdY9qQNb5mZmZmZmZmZ2eBx8s8siYhjN3UbzMzMzMzMzEaKqOPQUJbPT/u1upE0JT0oo+f0D0k6c1O0qT+S5kiatqnbYWZmZmZmZmY2WNzzzza6iJgLzN3U7TAzMzMzMzMzG+nc88/+QNIYSTdJWiLpQUnTJT0l6RxJ90taJGlfSfMkPS7p5LScJM1KyyyVNL2Xstsk3SdpR0kzJF2Qps+R9B1Jd0l6orvnnaQGSRdKekTSrZJu7qtXnqQ/l3R9zfv3pfH7kHSEpLslLZZ0taSxafq5kh6W9ICk/1dT3OE925LivyBpYYo/u2b69ZJ+KekhSTPTtJMlzaqJmSHpAkn/V9Lf1Uz/uqTP97I+M9O2XjTnkksG/NzMzMzMzMzMzPrinn9W60jg2Yj4IICk8cA3gN9FxGRJ5wFzgEOAVuBB4CLgOGAysDfwFmChpJ91FyrpYOB84JiI+J2kw3rUOxE4FNiFao/Aa1KZk4DdgD8BfgX8sI923w5cKGm7iHgJ+BTwQ0lvAc4CpkbEaklfAv5e0neBY4FdIiLSU3/7bIukI4CdgP0BAXMlHR4RPwNOiohXJY1O630tcC1wN/CFVOZ04OvA74H/Ar4lqQH4y1Tm60TEbGA2wLIVK/2INDMzMzMzMxuewmP+DQXu+We1lgLvk/QNSYdFxPI0fW7N/F9ExMqUZGtPibNDgSsioisiXgDuBNrSMrtSTWQdHRG/66Pe6yOiEhEPAxPStEOBq9P056km+HoVEQH8CPir1J6DgJ8AB1JNHi6QdD/wSeCdwHJgHfADSccBawZoyxHp5z5gMdXE4E5p3mmSlgD3AO8Adkrb5glJB0raNsUviIingFck7dNdXkS80td6mZmZmZmZmZm9We75Z38QEY9J2hf4APA1SbelWe3pd6Xmdff7gfah56j2EtwHeLaPmNoyVdToP7oE+DHVpN7VEdEpScCtEXFiz2BJ+wPvBaYBnwP+vJ+2CDgnIv6tRxlTgKnAQRGxRtIdVNcV4ErgBOAR4LqUoAS4GJgBvJW+ezKamZmZmZmZmQ0K9/yzP5C0PbAmIi4HZgH7Zi46H5guqVHSdsDhwL1p3mvAB4FzUrIs1wLg+DT23wSg32Uj4lmqycWzqCYCodob7xBJ74I/jGm4cxr3b3xE3AycTvV25f7MA06qGS/wbZL+BBgPLEuJv12o9jTsdh1wDHAi1URg7fQjqfaMnDdAvWZmZmZmZmZm/z975x1mV1X14fc36QVCFYMgoYcESAKhEwih9xbBKGhAmjRpAioiTSEg5UNABCFRjICAdMFGCwGJCRBCE6SoSEdII21m1vfH2jc53Nx755yQIXeG9T7PPHPvueuss84+u6699j6fioj8C7JsAFwkqRmYB3wb33+vJW7Hl9pOBgw41czeTg4xzOwdSXsA90k6NKctt+GRec8D/8GX206teQaMBVY0sxfSdd+TNBK4UVKXJHMGMB24U1JXPKrvpFpKzexPktYDHvdgQmYABwH3A0dJegH4B+5sLJ3zYTrez8wmZI7PlfQg8JGZNbWcDEEQBEEQBEEQBEHQRmmObezrAS1YjRgE9YWknmY2I+2bNwHYKu3/V03+Cnwfves+MyMLkl708STwFTN7uSX5af/7IHcBndmcf8X0hzNn5ZZdcemeuWVnzZuXW7a5FRuBbp075Zbt3iG/3tkF0rhrQ/77s8bG/EYA6rjk520aVSDhCtCwqAv/8+hua+1dgc2RmxuKPY+G5vxzD0WedcfWmtNQgYUKRTeVLqC7WfkzaJEqrmHWzNyy87p0y6+4IF0KFMAiaTFjdv62oXuX/PV3h8a5uWUpUv4L3FtR1KFAWW2lvFmk/BemDspTkXqoSP328Zz8+RigZ3P+/NncrUdu2Q7z5rQslJil/P2F7h3zP7vGgrv0FHkmc6wVOwI56dyYP42bOuevk4u2kU2zPm5ZqERr9XGK1C2du7QslNjr8hsLmXHHUfvllu3QrXt+xa1UZ81tLNYXWXHZXks+47ciz5x7QhvrhOdjwx9e1qae25IfQQZBde5JL/DoDJzbguNvEjATOPmzMq4okvoB9+B7ALbo+AuCStSD4y8IgvZHEcdfEARBXlptUigIgiAoRIwig7rFzIaWH5N0O7B62eHTzGzjz8SoT0F6g/AaS9qOIAiCIAiCIAiCIAg+P8QLP4IlhqShku6pcHwvSadXOsfM9jWzgWV/n8mLMySdJemU9PkcSTvklS87voyko1vLziAIgiAIgiAIgiAIghIR+RfUHWZ2F3DXkrajFmZ25qc4fRngaOCqxWROEARBEARBEARBEARBRSLyL8iNpB6S7pU0WdKzkg6U9Lqk8yU9LWmipI0k/VHSK5KOSudJ0kXpnCmSDqygexNJT0laU9LI9PIOJI2RdLmkxyS9Kml4Ot4g6SpJL0r6s6Q/lH6roHuYpDsy33dMy4eRtJOkxyU9KekWST3T8QskPS/pGUk/raBzTMaW3ZIdk5Kt2WjGfpIeSrYfn45dAKyZ0uyiRXgUQRAEQRAEQRAEQRAEuQjnX1CEXYA3zWyAma0P3J+O/9vMBgLjgDHAcGBz4Oz0+37AQGAAsANwkaTeJaWStgSuBvY2s1cqXLc3sDWwB+44K+nsA/QDDga2qGH3g0BfSSum74cA10taATgD2MHMNgImAieltwvvC/Q3sw2B86opltQV+AWwa9p3cMUykb7AzsCmwI8kdQJOB15JS5a/W0HnEcmROnH0r35V47aCIAiCIAiCIAiCIAhqE8t+gyJMAS6WNAq4x8zGyV95flfm955mNh2YLmlOelvv1sCNZtYEvCPpYWATYBqwHnANsJOZvVnluneYWTPwvKSV0rGtgVvS8bclPVjNaDMzSTcAB0kajTsKv4E7M/sB49N9dAYeB6YCs4HrUhTfQvsSZugLvGpmr6XvNwJHZH6/18zmAHMkvQusVK6ggr3X4GnCtP990C5fix4EQRAEQRAEQRAEwWdDOP+C3JjZS5I2AnYDzpP01/TTnPS/OfO59L2lPPYW0BUYBFRz/mV1qpDRCxgN3I079W4xs0a5x+/PZjaiXFjSpsD2eBTjscCwRbxu1vYmoswFQRAEQRAEQRAEnxM8XidY0sSy3yA3klYGPjaz3wAXARvlPHUccKCkDmnp7TbAhPTbR8DuwPmShhYwZzywf9r7byWg5rkpqvBNfJnv6HT4b8BWktaC+XsarpP2/etlZn8ATsSXK1fjH8Aakvqk7wvtZ1iB6cBSOeSCIAiCIAiCIAiCIAg+FRGFFBRhA3y/vmZgHvBt4NYc592OL7WdDBhwqpm9LakvgJm9I2kP4D5Jh+a05TY8Mu954D/Ak/hy3VqMBVY0sxfSdd+TNBK4UVKXJHMG7py7M+3nJ+CkagrNbJako4H7Jc0E/t6S4Wb2gaTxkp4F7qu0718QBEEQBEEQBEEQBMHiQGaxpVjQNpHU08xmpBd0TAC2MrO3a8hfATxlZte1kh0CrgReNrNLF4fuqdOn5y6gzQWK8lsfTsst26t7t9yyM+fMzS3bvXOn3LIAzeS/wU4N+YOae3bKL/txU25RunfIL9s8e3Z+YUAdC8zbFAmzV/60sM5dWhYqqZ07p2WhRFOn/HqL0rFAHipCs/LvRlCknDYU2ORgbmOx5RSdO+Z/1kVs7mj5C0mj8heSInqbGwoUPord38zZ+eu4Lp3yl9MuzfNyyxYpe0VpKNInLFC3FHkmRZ5HEYrkoaJYU37d6lAgLQrm5bw0NLdeWhSxuYgdReqLDo35y+nchvzl9OO5+cspwFJzZuSWnd1t6dyy3ebNyi07s2P+PlzPrvn7Za2Zh4r0RVqr/S1Ca7V7APp4en7h1nomBZ5HQ4H2qXle/nIKsM/Vv88te/dJ38gta3Pz98HndGi99nfFZXst6tZWbYLJ5xzfLp1OA868vE09t4j8C9oy96QXinQGzm3B8TcJmAmc3Ap2HC7pm8mOp/C3/wZBq1DI8RcEQZCTQo6/IAiCIAiCnFhrThgEuYlRZNBmMbOh5cck3Q6sXnb4NDPbuBXtuBRYLJF+QRAEQRAEQRAEQRAEi5N44UdQCElDJd1T4fhekk5fEjZlMbN9zWxg2d8fF4fu7L3Xy/0GQRAEQRAEQRAEQRDUIiL/gsWCmd0F3LWk7fis+LzdbxAEQRAEQRAEQRAEbZOI/GvnSOoh6V5JkyU9K+lASa9LOl/S05ImStpI0h8lvSLpqHSeJF2Uzpki6cAKujeR9JSkNSWNTC/UQNIYSZdLekzSq5KGp+MNkq6S9KKkP0v6Q+m3CrqHSboj833HtKQXSTtJelzSk5JukdQzHb9A0vOSnpH004wtP5f0t2TLUEnXS3pB0piM/mo6d0n2Pgnsl5Evv9/hmd9mpP9DJT0s6c507QskfV3ShJSmay7aUw2CIAiCIAiCIAiCNoBZ+/xrY4Tzr/2zC/CmmQ0ws/WB+9Pxf5vZQGAcMAYYDmwOnJ1+3w8YCAwAdgAuktS7pFTSlsDVwN5m9kqF6/YGtgb2AC7I6OwD9AMOBraoYfeDQF9JK6bvhwDXS1oBOAPYwcw2AiYCJ6U3/u4L9DezDYHzMrqWTdc6EY/WuxToD2wgaWANnV2Ba4E9gY2BL9awtxoDgKOA9dI9r2NmmwK/BI6rdIKkI5JTduKY0aMX4ZJBEARBEARBEARBEAROLPtt/0wBLpY0CrjHzMZJggVLVqcAPc1sOjBd0pz0Bt2tgRvNrAl4R9LDwCbANNyRdQ2wk5m9WeW6d5hZM/C8pJXSsa2BW9LxtyU9WM1oMzNJNwAHSRqNO+++gTsz+wHj0310Bh4HpgKzgevSvnzZfQnvTvqmAO+Y2RQASc/hzshVqujsC7xmZi8n+d8AR1SzuQp/N7O30vmvAH9Kx6cA21W592vw9GXq9Oltb0ohCIIgCIIgCIIgCIK6IZx/7Rwze0nSRsBuwHmS/pp+mpP+N2c+l763lC/eAroCg4Bqzr+sThUyegGjgbtxp94tZtYo98792cxGlAtL2hTYHo9iPBYYVmZLtXttqqRT0sCcdjaSomglNeDOwxLl18vaEuUvCIIgCIIgCIIgCIJWJZb9tnMkrQx8bGa/AS4CNsp56jjgQEkd0tLbbYAJ6bePgN2B8yUNLWDOeGD/tPffSkDNc1NU4Zv4ktzS+te/AVtJWgvm72m4Ttqjr5eZ/QFf3juggF0VdQIvAn0ye/Mt5HBMvI4vCwbYC+hU4NpBEARBEARBEARB0D5ptvb518aIyKP2zwb4fn3NwDzg28CtOc67HV9qOxkw4FQze1tSXwAze0fSHsB9kg7NactteGTe88B/gCfx5bq1GAusaGYvpOu+J2kkcKOkLknmDGA6cGfap0/ASTltqqozRU0eAdwr6WPcIbpUBRXXpmtPxvdUnJn32kEQBEEQBEEQBEEQBK2JrA2+pSRou0jqaWYz0gs6JgBbmdnbNeSvAJ4ys+s+MyPriOnTpuUvoNbcOkaoQIBwa9kAWGNjbtlZb7yWW7Z7n7VzyzbPnp1ftluP3LJNzcXSrYvyZ4tGdcgt25EC2a1xXm7Zpo6dWxYq2WBNuWWLPA+Ahq5dc8sWSbciNDblf9bNBdrn5oJ5qAjdu+QPZp4xO3++6NyxQN7skL8ean6/apNSkQ4F8sW0jt1yy3b/31u5Zd/ttlxu2d7LLp1btkh5AmhuyP9MGprz626aOT2/EQXanIbOXVoWKtnQKb/sv97/MLcsQIPy726y4lL524aunfOXvSL1d3MBe4sGNhSxo7HArjANBTaQmduYvz7sPHdWfr2d85d/gC5Nc1oWSkxrzl/2unbOH7tRpL9g8+bmllWn/O06FGuvZ3fKXycXaUeK5KGGAu1vkXxctE4uUh/OmJu/n1ykXVeBfNFa/T03JH9a7HnJr3PLXt+/e27Zrqusnlu2Y89euWUBvrDBRou6TVab4OkffrtdOp0GnvvzNvXcIvIv+Ky5J71QpDNwbguOv0l4FN3Jn5VxQVDvFOnIB0EQ5KWI4y8IgiAvRSfqgiAIgtYhnH/BZ4qZDS0/Jul2oHwq5TQz27hcNgiCIAiCIAiCIAiCoK0iaTngZqAP/g6BA8zswzKZ7YBLM4f6Al81szskjQG2ZcE2aiPN7Ola1wznX7DEMbN9W5KR9Dow2MzeLzv+mJlt2Vq2BUEQBEEQBEEQBEEQLEZOB/5qZhdIOj19Py0rYGYPAgNhvrPwn8CfMiLfNbM873MA4m2/QRtAqr75Vlt3/NW6tyAIgiAIgiAIgiAI2h17A79Kn38F7NOC/HDgPjP7eFEvGM6/oFWR9F1Jx6fPl0p6IH0eJmmspBGSpkh6VtKozHkzJF2c3qC7ReZ4N0n3STq8JJf+D5X0kKRbJb2YdCv9tls6NknS5ZLuqWHvWZKuT7peLdmefjtI0gRJT0v6haQOko6SdFFGZmR6SUlF+Vr3FgRBEARBEARBEARBu2clMyu9He5tYKUW5L8K3Fh27MeSnkl+lhbfbBbOv6C1GQcMSZ8HAz0ldUrHXgJGAcPwcNZNJJU83j2AJ8xsgJk9mo71BO4GbjSzaytcaxBwAtAPWAPYSlJX4BfArmkPwRVz2NwX2BnYFPiRpE6S1gMOxN9OPBBoAr4O3AZkly0fCNxUQ77avc1H0hGSJkqaOHr06BzmBkEQBEEQBEEQBEHwWZEdt6e/I8p+/0sKcir/2zsrZ2YGVH2ro6TewAbAHzOHv4f7LTYBlqNsyXAlYs+/oLWZBGwsaWlgDvAk7gQcgjvyHjKz9wAkjQW2Ae7AnWW3lem6E7jQzMZWudYEM3sj6Xoa3zxzBvCqmb2WZG4Ejqh8+nzuNbM5wBxJ7+Je+O2BjYG/p4DCbsC7ZvZeihDcHHgZL4DjgWMqySf9le5tPmZ2DXANwPRp0+LVrkEQBEEQBEEQBEGbxJqblrQJrUJ23F7l9x2q/SbpHUm9zeyt5Nx7t5oscABwu5nNy+guRQ3OkTQaOKUle8P5F7QqZjZP0mvASOAx4BlgO2At/K021d7oO9vMymuJ8cAukn6bvOPlzMl8bmLR83clPQJ+ZWbfqyB/E14gX8QLpaUlx9XkK91bEARBEARBEARBEATtn7uAbwIXpP931pAdgUf6zSfjOBS+X+CzLV0wlv0GnwXjcE/0I+nzUcBTwARgW0krpP3wRgAP19BzJvAhcGWBa/8DWENSn/T9wEKWL+CvwHBJXwB/246k1dJvt+Mbdo7AHYEtyQdBEARBEARBEARB8PnkAmBHSS8DO6TvSBos6ZcloeTHWJWF/SRjJU0BpgArAOe1dMGI/As+C8YBPwAeN7OZkmYD45Kn+nTgQTyy7l4zq+XxBvgOcL2kC83s1JYubGazJB0N3C9pJvD3RbkBM3te0hnAnyQ1APPwpb3/MrMPJb0A9DOzCS3JL8r1gyAIgiAIgiAIgiBo+5jZB/jWYuXHJwKHZb6/DnypgtywotdU5dWTQdB+kNTTzGakkNgrgZfN7NIlbVcepk/9KHcBbfSXCeeiQfltaCiyR4PyBxM3q4ARQHOBqmrOS8/klu2xdv/8NsyenVv24w6dc8vOaSy2Crxn1/y6W4vOjXNaFkrM7djiy6fm06VA5mz6eEZuWYDGrj1axQ6sOb8NBcppx3rZHaBAubZ5c/PLds6fLxoK9FXmvPNGblmALl9YuZB8Xmb965+5ZTuv0NIL3hagDp1yyzZ3y5/noViemzEvf74vQqeO+ctIq5XTaR/l1wuoQ4H2t2v3VtFbpJwWSYuiNDcUSItW2gPKmvLrndmcPw/1ULF0m6X8MRbdO+Z/fo3kt7lImS7y7IoyY/a8loUSS3XIX9/XQxkplOcLjruLPOsOjfnbX3XM344UwRrzP+eiNtjc/H3w9x+8J7fsoc99nFv2hq2/nFu2Y6/lcssC9N5k62IDozbGU98/rF06nQb95Jdt6rnFst/g88Dh6QUgzwG98Lf/BkGbpB4cf0EQtD/qxtkcBEG7oojjLwiCIGg9Ytlv0O5JUX6fiPSTdAi+hDjLeDM75jMzLAiCIAiCIAiCIAiCoJUJ51+wxJD0OjDYzN4vO/6YmW3Zypd/EJhjZr9t5etUJG3ceY+Zrb8krh8EQRAEQRAEQRAEweeDcP4FS4T0dt+KfAaOP4A+wNeAJeL8C4IgCIIgCIIgCIL2Trxnoj6IPf+Cwkj6rqTj0+dLJT2QPg+TNFbSCElTJD0raVTmvBmSLpY0Gdgic7ybpPskHV6SS/+HSnpI0q2SXky6lX7bLR2bJOlySVV3dpW0raSn099TkpbCX6U9JB07UVJXSaOT3U9J2i6dO1LSncmOlyX9KB3vk7HphWRj9/TbxpIeTrb9UVLvzPHJ6f5jeXEQBEEQBEEQBEEQBK1OOP+CRWEcMCR9Hgz0lNQpHXsJGAUMAwYCm0jaJ8n2AJ4wswFm9mg61hO4G7jRzK6tcK1BwAlAP2ANYCtJXfGXduxqZhsDK7Zg7ynAMWY2MNk4CzgdGGdmA9OegMcAZmYbACOAX6XrAGwK7A9sCHxF0uB0fF3gKjNbD5gGHJ3S4WfA8GTb9cCPk/xo4DgzG1DLWElHSJooaeLoMWNauLUgCIIgCIIgCIIgCILqhPMvWBQmARtLWhqYAzyOOwGHAB8BD5nZe2bWCIwFtknnNQG3lem6ExhtZr+ucq0JZvaGmTUDT+PLdfsCr5rZa0nmxhbsHQ9ckqIVl0l2lbM18BsAM3sR+BewTvrtz2b2gZnNAn6fZAH+Y2bj0+ffpOPrAusDf05vGD4DWEXSMunajyT5G6oZa2bXmNlgMxt8yMiRLdxaEARBEARBEARBEARBdcL5FxTGzOYBrwEjgcfwSMDtgLWA12ucOtvMmsqOjQd2KS3nrcCczOcmFmGfSjO7ADgM6AaMl9S3qIoq3ysdF/BciigcaGYbmNlORW0OgiAIgiAIgiAIgiBYHITzL1hUxuHLaR9Jn48CngImANtKWiG91GME8HANPWcCHwJXFrj2P4A10htzAQ6sJSxpTTObYmajgL/jkYPTgaXK7ufrSX4d4MvpOgA7SlpOUjdgH9xhCfBlSaW9C78GPJrOWbF0XFInSf3N7CPgI0mlqMGvF7jfIAiCIAiCIAiCIGh7NDe3z782Rjj/gkVlHNAbeNzM3gFm43vovYXvp/cgMBmYZGZ3tqDrO0A3SRfmuXBafns0cL+kSbgjb2qNU05ILx95BpgH3Ac8AzSlF3CcCFwFNEiaAtwMjDSzUtThBHy58jPAbWY2MR3/B3CMpBeAZYGfm9lcYDgwKr3Y42mg9PbiQ4Ar03LgapGOQRAEQRAEQRAEQRAEiw3Fa5eDtoiknmY2Iy0XvhJ4Ob24Y3FfZyQw2MyOLTveB7jHzNZf3NfM8v5HU3MX0C7KX5abGzrkl22lKqKhoPuziB23TZiSW3afwf1zy86YPTe37LIqX+Fenea5s3PLAjQss3xuWc2d07JQSbZj/lX1H8yal1t2+W6dcsvOsfwZ49/vf5RbFmDtLyybW7a56k4EC9NQoB21efnzUJHn0RaxpvxlRB3z56GbJzxbyI6t1umTW3blLvnnTO94/l+5ZffdYPXcstalW27ZInkTiuX7InXLnIb8z6+xQL7o2jm/3g6N+cuezctfvxWloWvXloUWBeXPm0Wec9E+QMeFdkT57O0oYgNWIHKjQBpDsfvj45n5zeiQ345ZDZ1zyzYUsLdjARugPp5Ja+W3DvNapy4E6Nohv82F+hed8ueLxlaKVyg6DpjbmD9fzHvxydyyTTOn55Y9+NF/55a99evDcssCrLB2v3YdGPLkaYe0S6fTRqNGt6nnFpF/QVvl8BRB9xzQC3/7bxC0e4o4/oIgCPJSyFERBEGQk0KOvyAIgqDVaN9hBUG7JUX5fSLST9Ih+BLiLOPN7JhPcZ0xwJgKx1/H3+obBEEQBEEQBEEQBEFQt0TkX1ARSa9LWqHC8ccW83WGStqyZcmWMbPRmbfslv6OyVznnk9h5zmSdkifT5DUPfPbHyQtswg6R0q6YlFtCoIgCIIgCIIgCIIgaIlw/gULkd7SWxEzWyyOugxDWfBCxqG9AAAgAElEQVRCjHI76iIyVVIHMzvTzP6SDp0AzHf+mdlu6W2+QRAEQRAEQRAEQRAEdUU4/9oRkr4r6fj0+VJJD6TPwySNTZ9HSJqS3n47KnPuDEkXpzfUbpE53k3SfZIOL8ml/0MlPSTpVkkvShqbXr6BpN3SsUmSLq8WcZdemnEUcKKkpyUNkTRG0tWSngAulLSppMclPSXpMUnrpnP/Jql/RtdDkgZL6iHpekkT0jl7t5BmHST9tPQ2YEnHpeOvSxol6UngK8mu4Sl9VwYelPRgRnaF9PkbSc9kSTekY3tKeiLZ8xdJK+V6oEEQBEEQBEEQBEEQBJ+ScP61L8YBQ9LnwUBPSZ3SsUckrQyMAoYBA4FNJO2T5HsAT5jZADN7NB3rCdwN3Ghm11a43iA8Cq4fsAawlaSu+Ms3djWzjYEVqxmb9s27Grg0LdEdl35aBdjSzE4CXgSGmNkg4EzgJ0nmZuAAAEm9gd5mNhH4AfCAmW0KbAdcJKlHjTQ7AugDDDSzDYGxmd8+MLONzOymjM2XA28C25nZdllFyRl5BjDMzAawYP/BR4HN0z3cBJxawx4kHSFpoqSJvx4zppZoEARBEARBEARBENQt1tzULv/aGnWxrDJYbEwCNpa0NDAHeBJ3Ag4Bjgc2AR4ys/cAUjTgNsAdQBNwW5m+O4ELzWwslZlgZm8kXU/jTrQZwKtm9lqSuRF3sBXhFjMrlaZewK8krQ0Y0Ckd/x3wJ+BHuBPw1nR8J2AvSaek712BL9e41g7A1WbWCGBm/8v8dnNBu4cl298v07UKcHNyUnYGXqtyPum8a4BrAN7/aGq8Ii0IgiAIgiAIgiAIgkUmIv/aEWY2D3csjQQewyMBtwPWAl5o4fTZGYdbifHALqXlvBWYk/ncxOJzJs/MfD4XeNDM1gf2xJ15mNl/gQ8kbQgcyAJHnYD9My/8+LKZtXTveez4NPwMuMLMNgCOJN1DEARBEARBEARBEARBaxPOv/bHOOAU4JH0+SjgKTMzYAKwraQV0ks9RgAP19B1JvAhcGWB6/8DWCPt5wfumKvFdGCpGr/3Av6bPo8s++1mfAltLzN7Jh37I3BcZv/BQS1c/8/AkaWXi0hargX5WjY/gO8PuHyZruw9fDOH/iAIgiAIgiAIgiAIgsVCOP/aH+OA3sDjZvYOMDsdw8zeAk4HHgQmA5PM7M4W9H0H6CbpwjwXN7NZwNHA/ZIm4Y6yqTVOuRvYt/TCjwq/XwicL+kpFo4svBX4Kr4EuMS5+NLgZyQ9l77X4pfAv5P8ZOBrLciDL8m9v/TCjxJm9hzwY+DhpOuS9NNZwC0pPd7PoT8IgiAIgiAIgiAI2j5m7fOvjSFrg0YH9Y2knmY2I0XfXQm8bGaXLmm72iJTp0/PXUAbCpTlOc35ZTs0tL05gpfeyu9jXaf3Crllp8+e07JQYpmG5tyyzXPz6wXo0KNWsGyZ7tmzc8uqU6eWhRJzG/Kv8u/cNC+3rHXuklu2oV422lWBMmL580Xd0Er3Z035n5865s+bTTOn55YFsB5L55bt0Dg3t+z7c/Lf3wqd86fxnIb8adG5Y+vV3wWaEeY25k+Lrp065JYt0u4VwebmrzeBQmVEHQvskFJAb3PVHVoWplC6Fa2z2pjNcyy/DUXp3NyYX7jA/Vljfr1zO3fLb0MBitYtReqLhtZ7JPltaKX+RaPy129QLC2K2NzckN+OuY35y1ORfFFEb1HsjVdyyzbN/ji3bKdl8iwQc4aPfSC3LMBDZx1bBzm/9Zh0ysHt0um08U9vaFPPre2N6oO2wOHpBSDP4Utef7GE7QmCdkMRx18QBEEQBMGSpIjjLwiCIGg94m2/wWInRfl9ItJP0iH4EuIs483smM/MsCAIgiAIgiAIgiAIgs8Z4fwLPhPMbDQweknbEQRBEARBEARBEARB8Hkilv22cSS9LmmhTcskPbaYrzNU0paLU2eN69zT2tcpiqRfSuqXPn+/7LdFSmtJZ0k6ZXHYFwRBEARBEARBEAT1hjVbu/xra4Tzrw0jVd811swWt6NuKFBRp6R2HUEqqYOZHWZmz6dDn3D+tUJaB0EQBEEQBEEQBEEQLBbC+bcEkPRdScenz5dKeiB9HiZpbPo8QtIUSc9KGpU5d4akiyVNBrbIHO8m6T5Jh5fk0v+hkh6SdKukFyWNTW/hRdJu6dgkSZdXi7iT1Ac4CjhR0tOShkgaI+lqSU8AF0raVNLjkp6S9JikddO5f5PUP6PrIUmDJfWQdL2kCemcvVtIs5GSfi/pfkkvS7ow81uttPqxpMnJjpUq6O0paXQ6/xlJ+1dK54zdFwDdUjqUntWMjL7Tkq7JSRZJh0v6ezp2m6TuLdzrEZImSpo4ZnSslA6CIAiCIAiCIAiCYNEJ59+SYRwwJH0eDPSU1Ckde0TSysAoYBgwENhE0j5JvgfwhJkNMLNH07GewN3AjWZ2bYXrDQJOAPoBawBbSeqKv4V3VzPbGFixmrFm9jpwNXCpmQ00s3Hpp1WALc3sJOBFYIiZDQLOBH6SZG4GDgCQ1BvobWYTgR8AD5jZpsB2wEWSerSQbgOBA4ENgAMlrZojrf5mZgOAR4DDK+j8ITDVzDYwsw2BBzLnlqczZnY6MCulw9eziiTtCuwNbJauWXJQ/t7MNknHXgC+VesmzewaMxtsZoNHHnJIC0kSBEEQBEEQBEEQBEFQnXD+LRkmARtLWhqYAzyOOwGH4I7BTYCHzOw9M2sExgLbpHObgNvK9N0JjDazX1e53gQze8PMmoGngT5AX+BVM3stydy4CPdxi5k1pc+9gFskPYu/6bcU7fc7YHj6fABwa/q8E3C6pKeBh4CuwJdbuN5fzWyqmc0GngdWo3ZazQVK0YyT8PsuZwfgytIXM/swfayUzi2xA/4cPk66/peOry9pnKQpwNdZkDZBEARBEARBEARBEAStSjj/lgBmNg94DRgJPIY7/LYD1sIjw2oxO+NwKzEe2KW0nLcCczKfm1h8b3memfl8LvCgma0P7Ik78zCz/wIfSNoQj9q7OckL2D9F0A00sy+bWUv3XvQ+5plZaSfOovddKZ0XlTHAsWa2AXA2KW2CIAiCIAiCIAiCIAham3D+LTnGAafgy1HH4XvqPZWcVROAbSWtkF7qMQJ4uIauM4EPyUSw5eAfwBppPz9wx1wtpgNL1fi9F/Df9Hlk2W83A6cCvczsmXTsj8Bxmf0HB+WyemGKplU5fwaOKX2RtGyOc+alZdqVdB1S2tNP0nLp+FLAW+mcr1c4LwiCIAiCIAiCIAiCoFUI59+SYxzQG3jczN4BZqdjmNlbwOnAg8BkYJKZ3dmCvu/gL6K4sAU50jVmAUcD90uahDv3ptY45W5g39ILPyr8fiFwvqSnWDjC7lbgq/gS4BLnAp2AZyQ9l74XZhHTKst5wLLpZSGT8QjMlrgGt3tsmS33A3cBE9Ny5lPSTz8EnsAjNF8sYFsQBEEQBEEQBEEQBMGnQgtWRQafNyT1NLMZKfruSuBlM7t0SdsVtIykI8zsms+7bL3YUQ+y9WJHPcjWix1tTbZe7KgH2Xqxox5k68WOepCtFzvammy92FEPsvViRz3I1osd9SBbL3a0Ndl6saM176+9MPGEEe3S6TT4shurbbtWl0Tk3+ebw1OE2nP4st1fLGF7gvwcEbJ1ZUc9yNaLHfUgWy92tDXZerGjHmTrxY56kK0XO+pBtl7saGuy9WJHPcjWix31IFsvdtSDbL3Y0dZk68WO1ry/IFhsLK4XPwRtkBTl94lIP0mH4EuIs4w3s2MIgiAIgiAIgiAIgiAI2hTh/As+gZmNBkYvaTuCIAiCIAiCIAiCIAiCT084/4KgbVJkr4j2LFsvdtSDbL3YUQ+y9WJHW5OtFzvqQbZe7KgH2Xqxox5k68WOtiZbL3bUg2y92FEPsvViRz3I1osdbU22XuxozftrF5g1L2kTAuKFH0EQBEEQBEEQBEEQBEEr8PfvHNgunU6b/N/N8cKPIAiCIAiCIAiCIAiCIAiWPOH8C4I6Q1LhGYRFOScIgiAIgiAIgiAIgvZPOP+CoP5YK6+gpLUBzMzaigNQ0uqSOi1mnXVx75I2kvSVJW1HEARBEARBEASVkZTLDyJpBUkrLm69QbAkiMwZBHWEpB7A9yQtm0O2G3CdpMugtgOwqHMsr3wluVrnpsbzZOAHLTkA8+qWtDSwRfq8v6RVc5heOjd3HdhSmqT7WRv4tqR98uptTRa3U7SkT1Kfxam3NSi/93pxEC8Jsve+uB3vnydSnbu4darS58WlV9Jyi0tnmf5C/ceCde3nVlbS0pK+lFdf5rzPbf0WVCZv3SJpZUn9W9uGxax36dbQm9HfWnYX7Yu3VF90+DT6lwSSOmY+d11CNqwiqauZNedI467A0cCPJH0hh+4VgcMl9V5M5rYfzNrnXxsjnH9BUEeY2UzgSGADSRe1IDsLOAxYX9IF6dhCDkBJsvRmH0nrtmRDSV7SbpLObUkufV5P0qqZc6t1QD4E7gKWAb6T7QTU0L1RihbsnnQ3ZOWA1YH9JT0O7Gdm/2nh/rpkBvG9WpDtKqln+rpSDTmZ2Twzuxm4FThG0i61dGfOzfXW9SKduoxsp/S9Q0uymfusSkr/7XGnc80BakbvppIG5JSt6VxJz2P59Lm3pC7V9GXyz2BJKwGLNFj4tJ3pSufX6my2hsM2kxYnAictzg73IgxmSs96+dZwREraWtKQPDYU1Lsm3vnv2sJAupDuVKa2kbRT6TktDpLenYBRkpapJCNpbUk/lrSPpM1r6ZPUX9L5ks6VtJ618No+SX0lfU/SoZK+nAZZ1San1pJ0lKSdJH2x1oCsoN7cNheULWJDEdkNgQeBGyRdI2mYpM7V7EjnCPx515JrLyyu+rFcz+LWK2lDSRsUOK+vpK2r2bIo9qU6YIikrcr7Thm9HYB9gCvz2lvSI5+sriYzP19K2lLSbi3oLKXbZmrBESlpKbwuPiaPvZnz1pO0ch47gJYme7srrbyRtI6kL+bUm8fOFSX1kdS5hXpoXeAySd+RtB20GASwtqSTq9WtiwNJ69Z6fvL+7oHyfv0APNihYj/uU9iwgbzPV+13AScAD0nq1pID0MxmA48A04CT1bIDcGNgE2B4LTuCYEkRzr8gqAOyjbWZzcOdZMMk/agF2ZeAo4AtJI1Kx0yJ0vd03vHAbyWtUMuWdP5ewE+Ax2vJJb3HAr8Avgv8VVrgbKhwTiOwKvAF4ADgeFVwAJTp/jnuEP2tpKWzDXWSexlYF1gNeLjWvaU02RXYT9II4E+SetVo+LcHRko6ON3bCpVkM/aeAGwLdAROkTS8hh1IWh+4VlUG51n59Fx2lfTVWgPCjOwmwAuSeptZU5WO/3xHL3BqS52a1Nk8FjjLzP5bRefakvbL5IEfUcPJmrFhTzwidPkqcg3AQOCbko4GLqKKQy/zPE4CLgFGASe00CktPZNtJe0p6cCsriqyG8oH6L0r5ePMve0s6RRJP5TUo1qHvuw5/1DuiKjolC0bLA1PnemFBmNlZWl/4GYzm11rMJLRPVA+YFqvmmxmouBiSce1lIcy9csvgd41BiolGzaX9HW5E7clR8gg4KfABzls2EnSFelZr1ZLPrEKsAYwt9YAK/22RcpHG+XQC/BlfAKjU7Vymv5vJekQuYOzpgM3ldUjgZ+b2Ufl9kpaA7gPr6u2AM6SdFQVXV8Cfge8D8wDfidpD1Vx1ssjg3+Pl/t1gfGSBleZnFoLuBMv23sA90tat9KArKDe3DYXlC1iQxHZLnhdeQmwA/DflB5fq5Xvk64dU14+QlUc35k81E/JyVSNjOw6kjZRCw7vJDs4pdmyBeqWHeVOjqqTgOl/H6W6u1rfIiO7qqR1atla0pPK0dGSNsuhd1CqYzcv/62C3r2B64GqzrEK1zgUGFnSUUlvkl1X3g+p2mcos6sPMFrSmpXaHTNrAkYDfwTOldS3ht4e6ZxmuePmekkdK5TTLwK7a0FbPgJorKa3dH+S9gCuAFpaXtkATAL6SRpZSzD7/IAxQM2J0FJ5Aq6WVGsSd1XgWHm/+wqgan2c0bs9cKmkgyRtWUW2PzAR77NMSGVkoeht+QqXvwKv4e3HcEkXQ9UggHWB3wDvZvNBtbKaSbdlJNWcJM+c0wMfj6yVvlfqKzcCLwH3AvcAvzGzOTns6JWVqXB/JbkNgF8DVSeSzTkFeA64UTUiAEt6zewh4A943qvpADSz+/EghzWBEQoHYFBnhPMvCJYwpY5B+rympNXNbApwMLCTpLOryK4iqY+Z/RP4FrCxpAthfkexV+a8EUnfHmb2vqQvqUo0WBpo7At8Axgnafs0sFi9ZENGdqckuzs+K9ZYpqu8gR4BnIQ7bu7GHXYnVer8y5fOjsAHQh2A/iRnHTB/FtvMPgaOSX8byp0cJR2fmI1N6fIIcCrwf8C5Zja1xuzq/cmGy4Efmtn7NRw3/fFIzMPxjvwvgEMk7VoumzpnO+BLCTYFLlCVpd6SGjIOup/inbe5Vewt6d4Z2Btowmc3v1Spc5Pp6F4E3G9m71axQSlf7IMvbd48nV8piuWLwK2SvpY59nHSMz/PZTpVpQil85INn3DcZOSagTeBIcBZwJ/N7L1KHbZ03r7Arma2DZ5/dsMduf0qyWfS+HKgC/AzSUfWkN0b+BWwH97Z3K6K3K7Aj/EO/X7AxZV0ZuSHAefjDpHDgHMqldWMDVcBG+DPcP8qadEJ2Aw4HWiWdBhwlaT9KnW6Mw66a4Cv4gOW7cp0ljrb6+HP7n+4c+wXqrHcRdIW+PO70Mz+XW3QnWzYBR+wLYuXxeE1Bgn98dn8x8zs+WrXT7IbJRvm4PXXoaoy6FUaZJvZw4CRnl+53Zn02Aq4HTgIOE/S1yvoLL+H5/HI4h7lZUpSh0yZvh7Py3cBX60yWGlIdeSZwOpAz0r24uX4r2Z2GnAucDZwpKRvV0iG9YBXzOxiMzsHz89HAzuWrlkmvxnwtJmdnvSfjzsKNk33kpXfERhnZkeZ2fHADcDDkvpWqLOK6O1fwOb1C8gWsaGIbAPQHSi1RxcAU4ABeBtYEUmDgXPwunF1fBJn33K5TB76PR7ldY3c8boQmXrrTrw+/DnwdZVNcGTy/Ha4Y+E04EJg+0p1Vqn/IneC3ASckmw/XRWif5Ls7njZv1nSldX6LUl2T9yhfaOka+XO8mrtw7Z4/b0m8AdJFevOTD10A96uXyvpB6XfKuhdD/ghMNzM/ibvp1V09GTTJKVDf0n7lf2+ulK/LqXzA/gzOUtVVnIkm7eTR7DegLcNX5VHzH3i2unjtrjDZmW8L7JQpH7qn3xP0lfToe7A+8mZU54OOwKHANum9G8mOd2UcRaW1XMrAd8Hjk6OlorI+0NT8QmeLsBBZX2NSmmxDZ7XrjSz/1RrQzJ9kZ/ieX8n4Cvl+T7xMjAb+DbwqJm9Xn5PZXp3xPucD+EOsv1U1u+V97G+A5xtZgfi7e/BwJ5aeFJ0aeBuM7sEz2/XAEtLuqR0zYzeVYG/AZeZ2Q3ySaaecqdXrfZ3T+AO4JeSLq0kV3bOTLwtO0PSijX61f8C/o23v6VIzPIVPeV2PARclPouCzk40/et8L7YKDN7vTx9s7rl/az/4ZMyN6lCBGCmvvpiusajwFh8wmwhB2CZPXfh9VbJAVgzMjQIPkvC+RcES5hS4yvpVLxD/BdJR6UB7OHAUEk/LZP9Lt7Zvk++NLcLHuUxSNLP5JEsZ2U61J1xJ8GWkr4HPIovB1sp6ct2WEoOvHOAG/GBx9pJX3lU30e4k+sw3Im1R6YDVYkvAdeb2VN4hMN4YGfgu9kOVrLnNWA47nwbgA9Ap+LLopZKDfXFcufoNmZ2Ox75N0DSkZJ+jDvAsjoxs//hDfhLwBryJRbVOh5NuPPhiZS2q5QGH5lOREm+Iz5wm2Zmr+FOxg+An6hsyYvc+fAr4GZ8oPkxvnxjmYxMH3mUSHPqtJ8AnGBmD0gaKunbkjYtT+A0IPgl3vHYAu+8/U0eAdgsdzDvmmQ74p3LS5PMAZIulS8Pzd7bMkCTmY3Cn/eqpXvKdsLkjopxwDB85nwH4C/AbHmkUkPqdHYoy0f7Az8Dnpbv2/h/8oiMkuOz9Hz+A0zAoxTWk7R+qYNZYUDxMb7vyrG4Y+UEfCnGKZIGlqVZg3zW+jA8z8wD/gncXakzL186dCiwDZ4fVwAmVRlU7Ig70HoBM/HnvZDDRAsGtbsCxwPL4Z3kS1I+LLehF3Ag7nR8Oum/P93LJwbI5tHEb+D7bf4S73DPAQZXGcD2TrI749FQPYFnsnozne3zgQvM7Md4tMLzuHNh5aTri5IOyKhfA/iTmT2uFFGU+ET5lw82Dwb2BP6OOzf+Wm2wku6nAVhLHlFbEfnSyl8CZ5rZyXj57oh30Ptln2GqR8+VdFo6dC4wTRWiblJ6bIk/7/3M7HC8zj1UyQGYqYNMHkl0saROZvZkSreLMr9/IX1ukkd+fA3Pm8/g5eB+qxzBURoc/wB4Fo8KX71CUszFnUWY2Qwzexx3eA2XNLRMdgowQx6FKTO7Ca+/zpG0QYVB3vN4me+d5K/CHUi3yyessvKvJVtKg62LcQfS75UmLTKyLwAza+nVgojIp/FnVdVmLYhomQRMz3l/zxW4txeAWXlkzbfxGIsP9PuZ2Zz0/W180mAh5BMZvwGuMLOfpHS7HthL0kplebk/HrW9m5kNwPP8MargAEzl5yRgbzPbAu8vDEx/80n5dCBeV+xuZkPwvLk7sF2FesjkEVjfA/Y1s53xwfoyuBO+3I518T7Q3sBgoB/uAKgUZd0XnwD8Cj459Q4+WbXQSgd5ZOBhwDGpDjgEX0a6UKS+fG+504GTzOzYdG/VnOTgjq638ed4DnA17jA8tILujYB9JQ0wsxl4vVSaZC21Dx/g7div8Lp+X9wx/C7u6F23TGfpmZ8J3Cvp9JQG8/A2ZX5bk55HX9xxdE26z8fwfF8e7d2EO7s2kztkl2VBhPUn2r3kcPwDnv7b43mtFInVDCyV2oerMnmkW9LzUrKxtGXJJyZFU7ndHq9bH8bbyKGSvlWevhk64o68DSv9WNZur4/XtdPwdvJHZjZPyXmTqcObgXF432mg0oveUppWigLcLOl9K93rpWbWqIxTz3xS1/CoQlJ9cRfen9osXT/bb9hJ0ubmE+DP4X2oLnJnWZZ5eLqunb7/Bs+XE+SO0UoT9ZvjztiD8D7XrqqyzFveV90v2Xwt8Ce8r/WJCd/0vZeZvWtmm+FO0J9J2j+1c2tL+kKZ43I5vA9wDp43d5avYqoU4fgiPvk8Iv3eqMoT3oNSGtyL14nv4HXyJyIAk+yu6bfzJH3fzJ7G+wwCvq/MGCrJby3pG5I2w/upt+MOwAMUewBizc3t8q+tEc6/IFhClHXMO+OD4iF45/VkSd9ODsBj8X39VkyyW+AdwB3xaKbuwAFm9gpwHN557IR3/galRuhpvCP2DbyTMBxfDvKlTKM1TD5I3QePJLwN+L6ZfS/pXQkon32cR4oKMLOdzGyufBnGYamRzy5VEd44f0XShmY208x+l3SsTFrCmRrTHmY22czewgcco8xnmB/HBxfLpU7thvhA82RJp5rZLXhjuzXeWbquZGhpoJIGO1fhnfgd8Znm5jTw2yRj82B5VMXvzWwXfGb8FKCDfGnVzkl1H7mTajLwqtIMqZm9k+73T/jgOfvMlwXuMI8mugXv9H8ROF8LNrLeBu90bG5m04HJ+MD8btwpshtQaUnKTDwq7lHgQ/OIkyeBB+QOhXXwKIo9U5rel/LFg/gA6794BMJyKc1Ks7+/kztUx+COpKFKLzYpdcJSB+7L5jP3++BRJhfjjrffA7/FB9W9U3qUOoYT0v38GRiU7mFtoFvS2yyPRLsp6TsXH4gcKV8CtRqwY0rfPsmmP5rPxg8CDk3OjX/iS+r/W5ZmDeaz1v/FHXUnAoeY2Zspzbcu62TOSrpOwwe++5tHLA7NduZTJ7IjXhaPSzr/kzrKn3AI4eUY4HW8/J2Ll6s35PuFHV9mw1x8gHJesuEA88jN7Vmw7GakpJPkS5ivxJ0DB5tHN40HNtfC0SBd8EHdS8BeuIPxkHR/2+iTeyb9Dy+DO6c0fxuPbngNH/B2xiOwJmfSZQ7uyMfMZqfythk+eUA6bmb2Ie7oOgUf1OxlZu/IHdT9S2mhtMQWd34ejg+K91b15WsfpbQuDSAewctAT+Cb+GCHNCg6Cc+zO8n3VT0FH4h8YmllZoBxCF5fl5Zp/gWf5DhOHrGwXpLfAH92ffGoyovwyIaZ8hc+dACukA/4SxMWz+JbK1yBO2Xelm8BsHGmDt8d3y/uarycn4U7TPaWO/2zztsHcefYvZlbeTrZ0Uc+KdI71QPv4GV+V1Kkhvn+pjfjWzcoya4hX0L7D7w+H1mqT83s53jd8VVJq2mB0+JZPELo5IzsJemZbCNfelpapvpv3JHxzSp6++JOnNVS+r6D15GVbF4buEM+UTU9ye5cRbZPqgM2wdubWve2rBYs0/tnkv1WFdnSPoPnyZ0tDyU79pNHbc01n3DpX0qvsjrgHbw+K+XlD3FH+XLA/AmWVMb3xSfRShEoJwJL4W3n/GWyVWRvooKDTj55tDkwFC/n4Pn9A7yPsUMF+c3wtq0UgT0en2DYrEx2BbwsrQrIfN+t/fH65nJ9crKgJLtauu95+ETEALxOyOrtgUe6rY1vrdLNPErne7hj8UA+ySy8Tv4XgJn9G3da9E/6SvXQmvI+2jS8jTsQdyp/C6+jKy3d7gdshU+UjUjpNkILlr13NLNp6b4G4JMKE8zsGdwp9Aqe37N1XcnBcxq+LHQK3iYcR5p4Su10KR91Bf5uZk+Y2QN4np+DT0aun+6tQ7LjErxu3xzPC1vLI6gOkE9Izt/mwMyuw/PzESm9f5zqswTiBeMAACAASURBVEfw5Z7d8Xp9NXlf8XW8j3OQfHuXeakO/r9SnyjVMw14m36ZuZPxdNw5vZekb5Q9k37yqLdH8Ly4n6QDM+Wip6Q1Ut25Rcr78/D8fhGwZ2p/d036OyXZLeVR9/8wsx/hfZtvypforom/9K1LxoZlcIfw9eme9zDfNmUPvG3JjsXHAp1TXYOZ3Zru7zxJy1iaMDCz53An8Ply53Fjyg/vprySdVS+nZ7XRpIa8Xr0m/hy78vlUXrlk2oNeBnaEp982NXMZqpsX0h5lOi3UvrcI58AW54FbeRy6T5J6XiXpAmStjSzv+BbHVwon8Qfw4I6p+SA/BYww3xy/17cabm1fBuVUru3vnwSYhre7g3SggjISst5G/D+90O4A/lUPDL190oOwHT9bfA+0+F4OTlI0hXmK7NuxB21pZUBpb7y5fgy7B/i7fUj+D7gG+Jlu+bWJUHwWRDOvyBYApQarfT5WHz2cDVgafMokIPwF2KchA+O9jKz90qnAzPwTu5r+GB7uHypz0v4jPo/8U7rtrgTYZaZ7Y0vRbkLH0B9GXgvNVq74R27abhj7Bgzu8HMnpE7Km7F9456X74x+2/l++C9gjuOVpbvY/IDfCByjnn0CekersAdPg/izrkTJe0gdx51BH5sZh/Iox/vw5cZZGfWh0k6A5/5PhzvWK+DDwJvxh2mB0k6zcz+YGYHAzuaz/yVIvW2w5dyXI0vcfsSHr21maQr8ZnqFZLsNumeL8BnwbfAOyG9U/rchnfQjsFnzC+QO8YuBlaQ9Bd5dObIJP9GWRZ4E98TZzfzF4U8jztll8Vn+RvM7NfAZfjAYO2k54mUVt/CnTlDJC2VOsWlKM+Z+BLwb9mCyJKb8E7hr/EO+el4p3oPM7sRd2ocYWan4rPZA4FOcsfx93FH1WO4g2k67qycmp7LiplO2F74Eor10kBie9zZ8k98/6rSfoFvyGfvT5W0VxoonIcPqM/AnY3bAsskvdvh+fj6NCB+KeWj99I9jU/3fTQe7XCVpL1S57cBd3Yehg8aLjdfLlwaIGyDd4RJtv4EOMrM/iGPrDwTmJ3s2D4NcD7Gy9eeeETIq3IH1FX4Pnb9Ja2S0n8s3vH+rZm9nDrHPwH+lUm3nYGfywe0/8ad15eY2SupU3siPtAo7e+1i3mk0OSUxudlbLgC6C7p8JRnH8c7qkPMHaAfprQ4GzguPU9SWqyJD1w7pb9R6Zm8nJ7BhUn3OilPvpRs3TyVXcwdkKOAE80jGR7Eox3Ol3R0GswsJ+nXcmfRDriDrVROV9GCPfhm44PjE8yX8WyERwAsm6m3rsEnPB7AHYij8LrtYPl+haXnvKqkddLgfUfciX9VsvlRvN65HlhF0mspHcemNNsbHxQ/hztMDpVHNJYG0EsnPYfjEw5nS1oqOSz+gkdH/hff6Pz/8IiAmWa2e7L/3ZQnjgQON4/0PAVYXtLP0zWm4Q6Sk1NaDMIHTj1LeRN39l2A16lnmdmr+GBzK9xh8QtlIr3MbDgwVdJd6fssvAzsgg9or8MH7Zvig+F+eP00KKmYgkeK98XL43npPlbFB1T7S8ru4/kvfL/X3+PldJCZvYFHapwm6WQWMBXYKOldI9n3UUqX4RX0roZHO79qZv9KaX8p7nA9qszmHvgef72A1c2j7C7Do37K768nXt8fgpeZDdO97SfpjDIb1kp6fy7p+3h+PArYo4LsKnxyn8Gb8CVoj+KOoqNSWd8Az19NKQ1MPpm2aXpWewL/kXSrPOJomfQ85u+Hmsr4aHyi6SC5s2AqPmnQg8w+aEn2OrxMHpye0Uy8ffyCFjg1SlFAv8DryCMlbWMewXYZ7pic//KtJK8k//10f9umPPcMHrHbs1SmzOz9ZMcU3LGwhrkT/ID0nLL5+H3gWnxrhb0lrZ3u49f4BFIpKrwv3s8ZjS/t7I7n0S5mdi++5PLNJLuK3Pk2D29n5k8k4istVtICZ9BeuOPiTDzidjww1MzuTHb+AHglUxcNki8DfRzPz8fjjs11cKfi8Ul3ozxCdBpez/aQO/Yxs2dxJ9qLeFvdIHcsXCbperxO6ZVs/QruCNxUCxyFpcmmF/FI/hOS3n/hfc538Siy0qTernheuw6f9FkNLxvb407zEXjfZ0dJ35c71cfgdeo/cafI2bgjaYSZ/TP1e84F/iyPwr0TL+u/lk8iX4e3m9OSbZba1PfxfNw79YvvwftuO6d2t7R/4Gh8We7t6bkegUdyfyM9i+Xx/tXZeJ9mNbyMvIpvRfC2PLr9UuB1c4fkdklmJ+DvKc//Bm/nz8LboefM97HrgbfB26Y88QFwi5m9lalTpyVdpYjUfnhk4K6SNk73fS3eho6VT/CU+G1Ks5/KJ65n4mWp5HDcOentj28NchzwAzP7rpk1mdmleHTyGpm2rEQHvN06DtjFzF5LbczZWhCIsDSet54ws5F4ft4Kr7ePTs9gf7z8HoE7w07A+xhXStre3Kl3aEr7c8wd28gnfH6FO7O/ImlYqqcfSXlqCO443js9u0PxNro33s/bPdN2lsZapXrufWCf1I9qTHXheLxtz64aWB4fa/ROz/AoYB1JPzNfvXSmmf0j6V4NjybeA5/86oPX5yXn9G+BP1qNLXuC4LNC9vl4QVgQ1CXywfpP8M7EEHwwfXnqHGyNd5i2N7MPJXXHHQ5L4Q6/3wGPm2/mfi7wjHnkWyl6pxFfYnEM3gk438wekw/8j8SjEZ5JHcbReAd1AD6wOQB4M3WiRgEPm9kf0mD7BHyg3BdvRK/AOyy74o3sGPNZSeSRhMfinYepafCzHj7wOTLdz/fMbLI8WuI8fLC6Et4x+j88GuzbeCf6oiR7Au5YOxu4J3XK1sE7Bj9MnaVsKP6WeOfgQnxW+2vpGr/AB+Wb4VFyE5PsUXin9B2887YG3rmbiEfHzcIdDKfgndlr8A7xEbjT5NiU/n/NpMUOeEfoftwhsFO6TmkJ8mW402t5M/tuSuvdcUdcd9wp8P/svXX0XdW1vv/MhECKJAGCuxdpcYoUCZKEQCA4wYsTNBAsuBYLVty9BHdrcacEKBS34u5umb8/nrlzDiHIvd+O3t77++wxMpKcs87ea68111rvfKc9WPdausZp55qXFVGBuB09Ar6usTgOQfxmCLxWQzJmFQSYKwLrZebFpSAtXb/ZOTOvDknPyVAZ3RFB+4sRMT1asqdICegGrB2PnmWPRsSEJbfzoOfhRimh2Xg+XYugencMGz2ywFmv6sNOpZARFiL4JjNPDy2zX9bnk6Iy/hWC4/3rz4olY42Xwb71/8NTqy31+3417ptn5RmKiD+iknMNkiB7Z+ZVBbqPBLbJzL/U+26IRMsbNaY7I2F1Ws3pfQhUZ67P/orAeO/MvKae1xflfKuUMCXMP7UJytkUNT5XVX+PQnLy1tBbaG0M734S1/lOSPAeh8rosqiYLV9KXBckSW9oA67NOpkR1/9BqExticnXr6n5H4prfE9UiD9GUDsC5fqSzDyg/Z7173FrTJdFJXQ45tD6FkH/4SXHKyGB/iySnYMj4mRUYLvi3rFXjUUPJEy2xDUyFA0fr4VhrnsjUf5caGTYDcmHB3H/erTG6J165y1xz5kA85F2y8wfhA/X/rAVcHBmPhkSt9uWDDyemUdHREMkrY5rZ2BmHhMaC44CjsjMoaPdd0bcW9akFYo0JSrQD2fmLhFxLCr04+OetE9qzCE0xjyJnhNDgXWKJOyKCtkF6Nn3EXp0P1e/64znTw9UUjbG/WOnGquBwDiZeUDoRbNrPb8rVjUcVr85GpXsY5E0vS0Mczqp3uUrlM8dkciYCtfuNpl5Z8ny8JKPD3B/Tzy3Tivl9FeZ+XnofXpSjfnXdd9bMO3CrrWX/Rrl8108YyfFtbowyt0Q9Oi6GVg2Mx+OiKlLTpr3a/b6wzPznLAA1z21/ifGNf1OvVvf6steNW7LUjk70RvvTCQgvqn+ng38LjNXqnlYh1Z+2RdR2dwCCb7hSAydgOfOKfX5M0jK3VHzOzOu/dNr/JdHXPEpnnVdUM4nwnP6odCr67sfaTsI1+2lKMuXZeb+tQ8NQoLxMDRYLFhtj6m9aVJgvsy84Ufa/w735QtxTzyq1nXfGruuSORPgcanV9Bb57mQlPt2DG0nR6/4GZFgH4TnyHXUFXq6PpmZQ8JQ0bmQYDy/yIXGQ+m4Gst/ZOZREXFWje+t6NW+a+1Zs2AIYR/cR5rIjK9rrs5B40yzTvtitMSNuC5PLDnqXv3fC41cN4TGtxOAW6u/E+Be+9fMHFT3Gz8zPw295z6q/f1QlP0uiMsG1vhNlBre+uAe9jc0TD6De8bTuI8fhef4I/WMFfBM2KtwwQS0jKF3Y+65rHP9AjQgdkHssS8aT9bAfebSajtekVWEZOXkiMu64x4YiBturva/Lzl5rO67MpIzzZ59PLB97cnTIj5esfq5Ip4N7xYOOxvYJTPPj4ghSHLtmZmHhkV+Fi3Z6Yk46YB67zkQT19SMr5Ozd+KtYdNhwbLv7fJ215oYNg4TH+xIhqruiLGvBpx44b1Povg/jMY9573kKC9ssb1ceD6NmzUreZ3N9wn/oD78XVt950Iw/Kfb9Z7/XZhJFhXr3FbHvfmxglgk5KT1ZA83BPlvsEunXFvHT8zt277bF7c+56usV4PdYtxM3NgtWvuvVdmXh+S3d/UPt+Eog+tcd0e5fjQGvcuuJd0RRzRv/q6ds3zW7U/P4zr8qn6e13cW2+q356KOOGderdNM/OpOuM/R4wwdrU7KTPvjogmNH9b3Eey5qCJVhm/2q+B++nOuG/uScfFA9us/n+SdFrouEvGmEf0P/XqIP86ro7r33iFHl2NS/nqqAgdWCByCSR7RiKQ6YKH5eNhDrYF0YtpPTyMl0blqAn3HZSZ14Sedr9H0mYnBH0r4eF3ACpMXVILbxOKcwwqXIvR8npas+5/SR1wSyNIXbONJGuskKeWotkTFZ/5MvP9UkinRUvqzHiAP4EA9xPMI/dFkYQLopK/Q2o1XQQVoTMy88RS6AYCU2bm4aFn4IqoXI0oZWAaJC1H5UgrMDEMFamZUmvurxHATw+cm5l3t7Xfo8ZpniKxpkUw3wsVvyb59ipoHe2GoKN/9XuOrIIDbUrKwqgc/xUV5htRiZgEQcfH9feUCC53Q3JsXSTZGqJwYwQ0hyMovi4MBzuo7t0dQdrRqPRuht6EJ6Lye0T1ecfM/Gu0qq5ulZmX1ny9VTL4FSqqp5ds9MvMjws8NyGs77eN2woIdm5BELRWzfsQVPLHysybiuSYEJg5M4eHVulLEDAdhWD1lcy8ue3e+wKLZmbvts8WA17NzJfCUK2ewJyZOSiskLs+gsi/ZeZ5Dbhs+/3E6BG3ayn+S6EMnoUW27HRO+v+0NJ9bY3TiDDUZRxUDMZDIuNxXC8b1fiPgzI2Xr3XyGo/TmY+VX0YG2XtBiQK+9ccH0TLQ6lrgfYeSMLtkZm3l0xNWrIT1ecHkdh4DeV9LgSjqxSw3gcVx7sb2ax+TNTMZUScil7FG4eE/PYog8+gp+G1qFS8hoB+OyThG++g5VKPM8Lw+B64JkeEVvoBmPPvz9WmZyllY9EKVfs7hoLfmpnbheFn09d9HgrzL31Sz30B94VN2/at+4C3aj1OgUrR5ki+bICE46ko42fjmvuwxvG39exdqZCnUiZmSInvhiy7B9fcZXXvidHTa5zM3CYizq3nbIiKxvu0kur3Ri+J61NDzzjV17HRGHAg8FwaZjUVKjj3ZuYeoZFjunq/RyNGEbfb1py/A6xdfe6HIZan4V5yRr3/fGh0eYa6aj/rgsRJd1S8sm3v6p96zE6A+9YCSJ5NBPRMvV+IiCYn4RP1jlcj0TMnErq3hV5xT+N62IHKpYgeR33Qc3D8kpW+uCcdj0r+G7hfPEQrD9xT1Y+5M3PfiLi5fjM7elvsVor5QrgmXgLOrPc7EImaw9Iq2OPj2TlvjeU2SCq8HhEPoTL+La7ZY0tGfoNrpMmp93VIQB+DZ/ZhSLzOU316qt7/CCSZ7q++DERlcf0697uXTE5R8zYBkoDblqxvinvcuUioHYMpM1YLDUL7oXFlcMng2rgP7VRzvAuu235jaDsQFeCDaj4+xPXyEO5x29b7LIxr4RrEJsuhrK1bYz0CCYnR21+Gyn9f4JzUsLNkjekf8ZzsVzIwLq7R5+v7rxDj/GkMbTvXu3yFZOFloZfddugp+1q993VoLBuE59UhqeFgXiShrsczoB/wXmbuF3qzTYBFt+4sbDEDns331H3Xr/16vpqTrinhFvXb07HwxG2hJ9W6wLWZeSmMCrFtyNjBeN5vgHkdD6r19zS1D5fc9MFz9nncI/ev83EhxD7DgN3qvkvgWtq8xrQLEjVTIFH3Ma6Zy6s/XfBMPDkz72gjaXrgfjNdjfccKLsH1fmyGBLGn9V9V8P1/0i924ZIPO5Re9+ZuO4GZkWNNFfJ8oHVj9VKdu5Gw2sflOmDMvOyaj9TvdtdNS4bpKRxryKPVsPz7JWSoylq7oak3prNc8dDfPc2nu0H4d59GuLSr0IC8Lzq13X12UyIU++s+/wVODur2EaNWQ+U/03rnpcA92VmU2RoBtwr1i25eRFx3NK4N12RRQBW+wVx3U6IxGj7fe9tsFOdX4nr8DzcS64tHHYhGilmRKw+L67d2XG/OCszbywM163mcgLEs8enZGpzHs2KxPf6aeTAFjVGp2bLSWFL3BeWxDXWGAsXqX7ck5kb12fbIqbcJzNvDo1aXXAPfQT3ng1Hm+cGfy+HHvE71rj0yMz+hVk3Rq/qC2uv6I/yunOaX7srEtqX4bmxb83bs/WevZGs3jWNXloRWCwzdw+J/lWAP6Weuv+/vzrIv/+Mq4P867g6rv+BK7SK90AAd3kabtl4Tw1E74yp0QPmBTxstkUgMyse3LMj0J4SwfnLqDxtjoBhDSSsjkIL9s6orG/Qppy8Vv/eBJXhRYrs+B0tT4U1UyJvSgTEL2dm/+rvwqhQvIcKztdIyB2BCt8UqBDNgV5TH1S/Ts4WQTYAlY57EKwehkr/xwVU90eQ9wGC4PmBJzLzpAIEfdDSfXe2iNUGQP8GFdQvUEHqiVbar0Ir7qpoiX6ywPobpYwfTVnwS+mbDj0jz0cA+2aN0ZUYDrJwPXer5l2z5RE3C4LWQ1Lr8ZIIYp5HMPVeER9LoLV1jZr/w9OKbw0APwFzK21WMvEKKn73oPX0uJD8XKb+nJeZN7bJ1ZkIRAajAvWPGqMhNeb9EMQtWfP8cGYuGhab6Vd9nouy/qLC0oC1QGJjOwTTRyNgXhwJjgYEL4eA8HkEoBum4aqzI5FyBObyyZqfbmnFxHFRYfm4+r9IjekmKPv71H33xzx5fy7QNgg9Cg7MCh1qvyJiGMrp0wgkQUJinfw+UdgT5fexmt+JEMAflZlnV5uxULHrC0xVoHN+JPQmrrkeMYY+7FDjOQJJp5EolyukOS+bdmMjgO1c7xSomNyKhNlKuBdMVr/fDhWg5UoZXQ2t3I1C1gtJsclRHu5Ivda6onJzIZI3W2DI/wfRIiD7FDE1Qc3HZ5k5rIiTz9pIo0uR/JkGlZVTC1yvjYTHsSGp3xvlawL09BpR4zkCiZLt0rxzTWjUmbj3HYvgffLMfDsMpTod18aqReZMi2Td2il5NxF6FdxXbXdFJeOqkGSeEb2XRoSedvPg2jsIFYepUHHdvuZ1jZSg7FLveQDuR28hMbFl7bGHIZk7GNfIpjU2UXOxPa6h61DJeBWrSP6p9t6zUAHcIQ2VbAjwyTAdwOvoOdypFJveSAYNTr2IGqV9SlTo5keC76nQ26XJv0hETFn7Xpeak/PwHPg0DGceFSreftX4dcf1P3vJ4M5Z3s9t7RZBkmyv+s2u6FkxeLR22+PeMz4q8leiQjofkhnvtLVdGvem84HJMvPg0CPjfvTcueBH+rw6klmr5hhCsiKiCZsOPF83Qxk5HYmLW9ranoYK+Pa47nuht07PzDxktPuOi4TGR8BpmflafT4UCaUt8vsFXX5b99wCFerbi5w4Fsmo3UODxkUoP5/R8sofjHvX/LhPBO6tz9YaPvhH2q6M+/TGSIq/gmdoj8xcrfrVVDNfG2Vw4tQg0xPP6znH0P7gav8Grvv1EZ8sjsW89qm2gxHPzFffvZKZT9Z3Q36k7QK4py+PhOU5uE6vwrMyUSYi9ejrikaUV1DWX0Es0a/23AVRuf8GQ+k/ref9usbyVDQizQL0ysyXS+kfgoTEW826qt+dhUaaE9J8ZBvgOfaHbBlNuuGedVJmXl5zf0L1a9ewCMY8RXDMX8/fHsnaTdB40+SU3QwrWTde5WsiXvscydNVq88Tp3ldm342JE5X9JQ6LjMvaiNUJsczfpqa7wVrXA9LyfbOiOc2pDxy676/w7N0WM3X8+jR9XJEnI/G4qWAkfX8cXAf24dWVfvl0lBvaj/Lwm2/atsbr6f2itrLlkaScr3q74S4T92emWeEhtCjUdZB2d0ciaFx67zrgnI6ZY35/TUWG+O52njrb444/hLctxet3xw7mizMDbyf5gGeB9fbr3HPfDXKKFfr/Bta+bFXrne7qjBlt3Z88yP3nQ3JzdfDyI15kHC7s86RoeihdnrdYy88l5ep86sxUHVHEm2VkskmBLpHaqTvjBhmQcQQrwKnpAbYbXCd3JEtsnvqNPUDYUj14il5vRASso9l5n71/WAkfd9Bo9H+eMYug3lw/xES6ofjedV4t29asjkZ7lVrlLyNXfObtRZ7ImYZkpl/a1sDK+O+25xtlxV2XhwNapum+bup8R6BetfauK7/QscFdJB//ynXD8pgd1wdV8f1r79CL7lpM/PCkLDaHD0HHgE2iohXMvNPmXlXRHyHgGgqVN5nQe+7h4F1I+I8VMoXr8OuMwKCXZF4uy/16jsiIt7HEJ4F8aDuVAd5HyRTHo2IW9H7pTtwXlhQYum637VYxKB/GoK3HHB2RJyWmZumxMx3SIB9Va97dX12H7BgZm4dEeOm4Vor4UF9ZI1LPzyMVyqCYhCCioiIW1NLc18EcPdExDkIWheLiC1KMZ4AD/4724a8UZoOxMN9fcp6jkl9V8/MJyLipbofNdYzRcTamblDmMPvwohYt8Ditkj4XBMRB2M4clPprS+StZvVcw6JiP1SgrPJrTooIq4tpe27koHNQoIhUaFcKzMfK5A5U0QcnJlDS2n/GxLGGyC5EClpeUPN1XEF9m5Cj4WNIuLvCPTHrjF+MiK+QJC6Y/Xr70i8fY7KzD5Imi0MkIYbfYneDRPW726q+VuxZGUsJCC3jZYn11yorF5dbeevtqvVs9aoMTm++tUbleQmh9J+wFMFGE9GgvJAXDcTopL6HSpm62XmAxFxX81vp9QKfTyGCn7cBuRmppVb8+Cas1tT0nuBum+Tm2kGVN7ejYgrUJm5MLV+b4hr43xco88gIL0YCYi1UgKpU43rp219WBRB/n0p4fYQejG+EHqqLdcIcujZ+QmS3xcgSXxprY3VSrbPRuV8CbRAfwMMCy30h9UeMVmN07M1Nw8jeThlje0h0SqU8Bh6LrwXERdgrqn5U2/Rf2CVxs0y85OIeAvXQGdauRF/jyTo2qVcLImV8TIN4eyMeaCocdiFVsGZ5SPiq9rb5sfQmzMj4uPMXDszn4mIt9MKh5uGXpkX1T62KpL0TwLTRsTI1BPgNszRNryUqitRGRoLPYQWj4gPS+Z2w3xkp6Wk3pAa1+1Kbh/CtR8lfwMi4uoC+C+ECdVnr/t2qXnYAY0P65cM71+/XwkVw0Nqf5yMlpFjQ8zL2SMNud245OrOUma+QsXyWfTAbjyFz42IS2peD87MG2qcvwUo5e+sev7uEXF79WFIRHyUXq9X228i4iP0Ym2UnfUiYq80t+Po17HZ8iB9vd638xjafQX0CD1kVqq+942I81Ov8k6ZOTINlf4Wz86GPHi+3nfc9humXhrLIVGwX332cRhSOCZSL+pdLwmNX8fiHjf699vWulgdCyl9CTwREfdSBoOmbT13H5zrwH22K8rvLZTnSVpd+fPQ+HA0ngNX1hn/KBIqI9vuS+rl+Ra1jms9PByGsa4QKufvhVVHx0WiYDxU6tdEouZh3D9/l0WmY5jvgT/S9nokAW9C+WlSIQyIiDUy8+I0ZG814DepJ2mzz71bcrbBj7SfK/VevALP69fRO3aaGtNOKTk3D54LN402hT/WdqKShXGRWFw4TWtxOHpnz4lyuUBEvJ7mDH657vlxaJy4PiI2SEO978N9YjXMB/p0zcmswKypUeQCXEOb1Tl9EBLDb1b/lkaSbRs01MxJywPyESRBN4uICzLzsZLbJ7Eqbtea+/2ASyPibdx3rouIKBm8usa1ExKLZ4b5hK9DYrkpyJW4Fs7GyIClU3KpD7BomOLli2rXPSK+TQn/k3F9vpxivsUQT/bHnKHdU7JkEeCvEfFCZp4SEffXOviwxmE6xCo3pV5WNyEW3CUiDs/MdcPq2k1Y6jSFaT5B7DYlMKBkq1/JzUvZClldr/aHI3BdrQHsXWtvL8QoTeGWDyLiRqBfvec5NZ77I146Iluh5VtFxF249x+KRrQ1MdfiXSl5eBWep73QQ+9iKq8unh8TIyl0VxSBmqawuTEsuDFfiEf3wDP7CjyH1qUVKXEV8Fo9KxB/LFB/rwJ8/DP3PSIiLsb9c/80/22zlmavvp9e6+mA0NDWIyLeAb4Oib9PMnOfiLgGidDp0Zg1bkTciakvGuPt87hvbBoa5I8LMX6f+v9FVI7Nul5EfP1d6uV6OLBDRBySmbulOQoJicGeiJvPxr1uaIgBBmNI93Nt9/0Vknpvowf72zWvi1X/Ny5ZatKLNEbXLsDXqTfolaFX8Cu09IvfI+6+PTRWZuoNOQ8aINbNIgU7ro7rP+nqKPjR+1rYxAAAIABJREFUcXVc/55rQkx4fwB6LQ1ABfIW9GwZHBEHFUC7NzPfLkVgGB4084XeUWTmengA3VH3zsx8BS1gr2NC2iZR8BkIOCbPzI8K8PwOgezaaBWfFhXb4zDc5SLMB3g1Hq7PoXJxQCl8G2Hi7+H1jL9lmwdGfXYdKjqPhLnfPg9Dmg5CknHVavpyjU1TLOAEJA3XR4U8kFi5NCI2LFB6GZITG0bEDpl5MIKQ9mu8ut/KCJSbvGFbIxl2dd37C1rJ+jdBD7DTI2LyzNwDc+JcGlqfN0Gi4nhUEhdHQHFN/X9h9Kr8R+qx901EnJvmVWvCd48p0HMXegxck5mfp9bqN+r7vdEDYyVgmYg4NQwt2R492DZGMuicMOH1QOCOiHg09O55HZXpIaV8NETvjhGxHRJU06OCcBwSCscgoTw/5kg6EytMTlbjuW+a02WjtIpuokfOviVDcwP31Li9Gyrhf0bA+UC0PBdXQC+ER5AU/ALYOUzm/niBqPlQQeiNyucyqMR1RzC/BnpvXlOy0R0LAEyUVo8bgATIWpn5VVokoL0a26UIFIcD3TPzkJT464seYRenBHm/krU9QxLx1gKgN4bk1hD0nEvMP3ZiWm1zdaBTSNKTmX9DL86mYMeK1X4GrLS3LXqyvlBA/yb0PHuj+nBpydfl6Kl7ZEr8LVHjf3LN84lIRv8mJACoOTuo5nxVJC06pyEoTZhgb1RqVqpnTILKzdGhJ9HLuC9sEpJ4R6DieFeYlH0IkpHfUUUJap63pVUd/N7qx0ZhFfMrUnJvXtwHT0tzBR6D63GlqAqGmTkH5Y0REWeHXhCjFIZa4+cgWbF1KUgv1fvcVArnlUianBjm3TsAPT7ewbXxbs3b4uhd+RLwh4j4fWYegXvZdeiZ+UqN87q4ZoegDK9XitjcqMQ8g+Ryl2r/BHosdsJ18XBmbo+5GC8t5aEbegvNjgpjLyyEsUfqHdEbCeDTkLBZKTP74Z7ZC700Vqn5mw3X8Kw1Tll7Hpn5WmYeiArkSRjm92Gt69GvsdHT4g/oKXRljpn4a6oSN9dsuI/94J4p8d4JDQ8XpoVHjkcl7XsVGjPzeJTz5poU5arx1KWt7e54jq0XVucdgLLzvfOpbTwaDLwLKq8zjOn7Igy+w/U6QcntsrgP04xbZr6SmZui189qtRdPgmTnyfWb80qGSb39dkBFdvuIuBBl5S+FBTIilo+Ig0OPwG9Rpt7Aatr70PLY+aqZg8x8NSX3RuK6eAu95y7B3LGjvJrT68fabl7fP4TE7lMow9egAW7PkAhahraiVo2slTz8ZPtag2cWhrkZSagdkLxfBA0uvxp9/n6ibTO216C3314hcT45yvCp9feJSLSPumpvvAPPtj8V5vgOvU73qfdv+nITMFsYungBEr4To9K/C4UxQkNPbzR0HZxWqP0Si3oMR6/ai9Br9cOQ9AFJwSWpYjcYrnwPno290Eg6PZK360TEQilh/hlVrKNtLhZDEmV6DHW+qO7fEPrDEOt8Ue0H1PsMrzPoISQVzwijIs5AuZuv2l0QEStn5v3VtwMjYtvqzz1ZUR41N68i+bNg6kW5Axo19wiJzsdqLqaq+4yF3l7TI9HyUr3P0TVPu4TejHvXfC+OkSRv4l7+BRWSnJlXhEW7dg2NWRcgTlk+ItZLSZ5VcO1eEnqRDaMVybIH4ub9ag4HUtXBcS+dBEPq10gjP7ZC3HN7yU2XmpNvQ3KMzOyDxb/uLPywH2LuPfDMuLT+jAobrfV9fM3xDhhy+/4vvO++6IX/cljUZrLUm3Y5LBS3KzB5ye0SaChN9B48H0mwFQvXbF6y8Dg6K0xa8jNJ9WsInpOHofFtbTxvHq93a/b6SQu//ROJ8W0iYt+Sp+OAWSJiljDygMx8oOb1+DRlz1Eln7/CHLLXhNWb1yo82ThM/DMl/nqV/NyFuGT60Nv9tZK1NUIj9tcRsWREDA89RRfDc60xaH2ARoSJCqs06ykz87TsIP46rv/QqyPst+PquP5NV0iIHAX8PbVwjoPAbiMEYoPQOvxOaIn9Ai1yr9bvnsUQ4SdLiZsi9eLog8rNGxjqczBad59EYHgEFhg4vZSOa9Eb6nfVrz6olHyJRELjgj8dhl68EoZWXIc5TYaG3knHYC6kUaGJY3jn5avvC+PB3OTIuxRDPc4qsHI65rDbs95tYyR+3igw0eTsODZbYZY3IgA/sk3ZaHKNDMSQiIer7ZX17LVQgZolJR9mQUXgupSQIyJORwCySkrAzIqkX3+0lr4SknGbYIhq49nWKNYZejYFgq2bM3OTetYu9fnm+f2QrnkRvF6Jiv84SI6NQKXgOwz5uSoMW2y8wD5DD4O3Qg+XXvVuTS63WRGMXomK6g4Y/nA4hkXNgIrCnQj+Lk8t/T3qs4WREGws3R8WWBsHyeYzgZkwtLupojovKvZTplbQsQtETYhk2iNZ4X0F4lfBBPVPhmTjZ+hV0QNB44Y1BtPU+N8UEuGvpp5nyyPZ9ChwUVoAZwn02Hu6bYx/h0Cy8VY8Ccmuw3CdDcbwzytDy/Jp1bf1EPA/h6Ef39R3p9bcflHvdhEC2h2QkLwA+DQz12yTzRnR02HrGqejUYl9BonH3ug9d22B1rORzFwOFZpPSg7fqt9ehaFqE6CV/UEE2/NgyM1YGIJ8ZhjishjuI/PUsHyE8v8kVlVsQg/7l1wAjJ167w2tObgMQfNmuJaexTyCWYC5CccagqRh/zSXVpd6/ucF3pt5uRNJ2N/W/5dEgvftGqvP0WDS7INToMHgOCT0Xi+Z6YQh/iAJuEboobsMEntNSoLJa55vjVYobHdUZJoQ4TvRA2QKlL2Zao7mw31yPFwP96IBZ2Zc228Bw7PyZdX7NOv+C5SxWWr+v8XQ1a9qbL6tMexR835cWljiNFwDs9AyliyKxPN6qQdN93rHRatPs9V7vo9K8CWZ+Wz1p5HF+XFP3zQNHRvlZdbW9yaU5RZckxvlz4Qw1W/WRnndAw0cPwCaJWOdM/OK9me1tx29T6FH8IGYnP+q0e7Xnsh+S9w/FsDcXNfxE1cYwngu5s199ifanYmhh42H0FU/09++uNeegiF2F4QEX3dMst8UPOiGBOCCwIupF3PgvjamXHyfISkxOZ6jd//I/E2C3qGvIvm/QWZe/yPvNsa28f08xY3sTIT74qa4Lk7NSjEx2j1/sn17n5vnhJ7Of0RvsTkwZPva0e77k21Dz5vE9dh4/vfE/ftSNHZ+naN5V45279+jgXXT1IjarOUdcN+/LCRIlgf2aztz299pUSrlAO4ZDXk+JPR6nRsJiYdqvsdHMuqdzPxDSO7+GuVtXpSHgXgOTIuGzKPxTDgIjRQfU2dMGi2xGBLP/8Bz4lTED0vV/T7Eveaq6sOyda8V8WxcHPfAq3Gfm7h+M1Fbu0PrfgcWzlwYowlmR9JyFjxrPqt32QH37jNS7/hxcY99vE3WeqJhaDU8c9epvr1LqzDdK3hmLgw8kBbsmKje/200KLyF+9yFuI9dhljhO8RXJ+D5ugXigfNSEmfymrcb8Nw7FDFDY2y6GHHquDUeQzPz14VV96g27UVkpig82anG4C8Yxr5hfX91zc/6SNDtithr9Jx9Tdj1jHj2rlJz95P3zcylmnFNDbQrl7x8gGmBTsDolStrnu4BbkmJtOXa5vrwkol98az7qvDPHqh7fIB773VA78JoPdCjvheu13trHE8vOTgMscxfU0/UaRBLHZ6GAHdHYnUI4r4Da888sObrgXq3Zr9ZrO79EBo1vihZ2Bn3gQkwR+S1Ibm8F57nvXC9rYgpbq7CfXe7Zt8s+XoK12Pn+u3NSBo2KUW2ScnRjmu064FBq/6fJJ0WOuGy/1Vhvx3kX8fVcf0brzpwz8I8UMPrs6vxkLuj/j8IFeubUfE/AA+hw5HgOzcNfWtA1OqojE+LhNzjGHrUFy2ONyJgejJb1VcvQWDS5Mvph0ryiWmY3BAEWt3wcD2hiJlrEIhs165s/cw7D0BgsFAdzL2RLFoBvdOOCgnAE4GH2oihoWhVvb/ebyo8WE9FpXwqzH30PQBfJM+GNVZXpSEQ4yNQvBiLNjRK4hw11p9hDrt76/OX0BK+To3rY5jfZalo5RNcCwHaXgUiGuDRD5g3DVsYC70pHiwwP2uNxWHZqgI8LYLSUwu8ToOk08yoOI8Kd6p7r1Hz/gkCHJBEeCsMQT0JgciU6BF0Q2ZuWgTDbAja/okhge+H3l0DMWz0DiQKvoyIo1CWNkbZu2I0JalRWC9AS/crEfEyAq05C6Aug2D2xZrDeymglJlD6j7rIfB/AMFcbwSQO6OCdlRE7I9K1jolN9sjQfNamutqABJ6z2Fi7e8lDK/nTI3K8sS4ptZCAmn8ut/TSOyA4RwfIjA9FIneXWl5ZjVW/gHodfNsAdSr0bPxDwWIZ0i9Zpo+/AoViR4o7wMQaG6PROphJUONx+vYtAq1LFfzsGi964e0wtkPQ8WlN4LmFdHzbS70/HwjDSXbGhXF2dC795ZS1LaiwvyyVQG4f737oGxVQh5Sv70QuC2/X1inLyoTb9U8/BGJ5wGYf+eltjUyO8rOQ2kupeuxUMbSda9e1eenqn9n1Xy9QUvZ3aDGqCvK+xuosHyKxMp9da8/omK6fu1tjXLfG+XpXlwnT+O+NDXut7eiXL6OZN0SuEbWS8nQ3jX+d2M+xy+jRSb2Kvn4tkiCGdFT5hOU61mQ53qqzoRVUPaG1pr8E3rFvYVEzHNIgt6JRNBGSKRsip5c95T8rVX9yTStwVw1B8+gnLaT4YsiYdLsIcD3ybe2tgdhntG/4V7Y7t01pvbbYQjaWW1zPjrJ0nzeA8nfl0b/bgz3bcbwuDH1Ib5PVE2Ca+3hn+pvrbWeaIh74Be821woqyN+7N3a2q6C+c5uie8T4w0BuFaty1Hftf3253LxgcU9XhjTuLX1bTLcxzplVW/9F7XtjoTJVVkFjP477cc0f3W2fEurOu0Pxvkn2vbF/fIu9Jw7IiWkpqJVLbl/toycY5KJ5t5L1lw3aS7mQvJ3MGKhqfGcHpxtFV7b7rMc5kdtzrruuJbPwLXe4JCpkQh/KTSsnY+Go43CNBVz4Fk8PZIYK6Bsr4yG35Nxf9oQibJzU0KoG+7/76bGjq3R8HMd5gcdp+65NRIcGYbAP4Hnzi5Iig9Bg8RUmbll9Xn0dmfjGXlsGq7frWS7H55PlyFJt371cRDuryeg4apLimF6oaHhC/TkPD1bRtzpEfdNmYZyNxXSF8Qze8sU7/WoMXkLSadFERN/ix6AD0bEqjVmL1Qf+iKx9Ejt7fPXPHyGnmNb1vzchvhkGzwb7kRj8WaZeUn1c9l67kXAn7MtR2pUXsJa3xdj2PIW9d1f0bizMRpaX4ox5+ybJHUSWChbhoKuP3ffzJw/NEwvimukX/Vz1Zq/w2o+r8YzcO/q9mZIHk9Sc30enitHIG5+Jcxj3hWYI80xewxFuKcG5YG4HsdC/PAayu77JRNLIfF4d71bgx9mRezeDcnk/fGsfqHG/b7MPKVtfBeuNkNrnmdBw95bmXlEnQtr417/K2DjzNyg+jsv4msQt4yNsvly6iywEBLGh9acLIgGwdUQGwQwLNuMfx3X968O8u8/4+og/zqujuvffIVhf8eiZfARJCJWwcNsSgQMTXWyWZCQ+UN9vz168b0X5pHZNTP7hKEYC6LV/ti6797omfZGaFl9Ar3kjg0LYZyMxND+1a/GIrgx5qpYJiSTmrwWh4cegBdiWMQPQql+4p3HbwMAeyA5sUD19dACi79F8NFYPqdEQmNpBApnIGnSWLe3SxXtBqjPidbc92klXf8Aic6RCEzPShX3xmr3EVrPd0ZgeB2SKjsh4TBHPfMvSAbsl5V3pN5rVaw0/FL9vxcqa4OylQB4LAQaz2bmWlH5D0cbn2MRQCyWVk2eAkmm6dCa/mYY1nQWgpW763edkezrhl6YTY6hRlnaAsHaummunk6ovBxXY/spWtH3RbLot0jInB16uWyIYYk3FsBcGi2j76TJv5uk2YfVOK2I+YfuCb3vzkQgNlnN93XoefEAEk07Vn8fRSVkxWyR4AOQzD4eFYXuCFTnqP53RaXk/TQf5Zq4Bg5Kvf+aMZgGScSmYMT+CKyPDMNst6OVC68b5rRpFMxj0Cv0xgKjs6N3yYgC+GsisXJFrdcZEJgOQ7KnIRIWKHn7PM3p2B/zVG5a47QtkshPhWTst1kEZvX3pVJgt0Hi6wC0+h+Fcr5CyUufNoVy8nrmJ/WOw2oeLkUPsl3rvl8VqB1S83ICgvY/4z70fCkgM6TJ53eucdg5K0F8mPz++pKXbjUP02fmhhFxJFrUF65nDcC96R00StyRmSeHeYwmycxFqKuUvQvRK/HYksHJSubeS/OJtiemPxTX0db5/aqNhyBYnx+Vyt6ouAxFxSCpoie4F0yF+9RHtbfMgcryiBq3V+vzZZE4vRVDl7+suT0Qidqd0NNn33qXg3B/2ab63BM9sQ9DxagfKhPNv5dEz97LI+I99G5cLFvFkjbDkPAd05D5xjDRToL9BvfMZ3E9zYYEeOMd8yYSJRdk5kftZEjb2P4GSf6fbF+/+S0SBu9SxirgzjT1w/9L2/9KH/7lbWvvnOuX9ndM12jzMrzm8wxcn5uhMWPU2KMiPh56ebbn4vsU19PXtWdljqEIS9v8NX93Q9w/JuPIf6ftOLi3NiTudz/Tj6ZowL+0H23/nxCJi33SiqBzoGFt12xFDMyeepnPiGd/ZKWGGO3eP/B4bPtuWjyb16w/N+Ke88lo9+iDRpAlslUoZH80Jt2Oe3ZTmOcN4JnM3LzG9WwsNLFS/W4WJNDWyVZo7FJ4br+NxUE+jJZn2Eq4r3yK2GNg/WZLNPQORwwQuO8/lIasNyTlGehp//eQrJ4Uz4UHfqbdJHievhFj9l7/CA15XyPpeA3ucxenoagronFkHiSEvkOP9ueRBLoGz8uGsF8XSZy169mnpTkSe2Cu0EdDb61p8Iw6O00P0Bjj+9W4D6vP5sEz4fzUo3ZylKmBGLZ7JGKtZ6r9JmiAfwgx9RspLl0WcdXa2SKaf41Y/7I03+V49T5vZubAECvOXfjiRjwP54uWZ92USOytWX14+5fct9rMn61CWguUiPZEz7VDESc+UH2eGI1Ih7aNS1fEBHsXhrm4fv8YetJfGIbgL1rj8BIap5ZDjDoUPUh3wjP6JcQab9UzB+JauLtkZAU8Q6es7+5HvWYEkpWzII74BPHIu3Umr1ZjtG1mHh9GPPRHEnxnyjiN3o4fogGmMRQei3h43bY5a5weHqhx2yQlAg9FonDBVB+bHnHbKGMFHdcPrg7y7z/j6sj513F1XP/mK81FsyMeun9AEqBJTvsmApyPq+2zqGwuisrbjkhugQrIFgU+foteH1/igTk/eoO9UQfR5wg0B0XElgUeNwNWC/MQki3Pg9eBzesgH5+qbBkRB6U5jHrlLyT+opUz6dP6qBOSQ/9MraSrYKGBwZn5KALZb1H5/kNmnoPhvt0whGAEkl9bFcDqXAd+HwTGOyI4mBoBx7io1N+EIQp3VtvhSDLegcDzWMzLtBcSVJcgsJyj+vswhjrsUePSzOVl2eaxUu9zbJYi3gBxtGDPVcpuU41ullI8yczt0ANneJj/7g20Oh+RRejVPS4tQNop9DL6Dr0pPkVShJBoWjusonZyjd85oUfkFEgQD0YwtA9aTt9BRfZxzC+5fo3f77IVzjVntRkfOCjMX/QByuqQGv87MvOeaj89rTDt45F86otES69q31xnomV5nZDQJA0H3BHJiq1QfvfEqnLPYujFAcCEEXFqmjx6v/x+jr8BqHwcGREHhqTqo0D/MLfNNkicPBsRK1SfTihgB4aHLBV6kyyLAPyrMGTrTgzjmhZYJVpk25+RMMzqw0ro5dcHOCr0BnkI88qcUe9+Wkr8rYgW91PC8HNQ+Zk7Iv6A+8UOmXlXSmY+gyTWmkjSfhcRQwvov5mG8SaGKfdEcN0XlaltS6aaHDp/woIGn6My+QrK+1G4LnaMiAMz83AksZrCDtOjYnhTStxeX88jIhZLCd5125T+plppn5qbuSJimVJyvwuJyOZqwoq3KHnOWg+DgRkiYp4a42Xrsz1xDzmqxovQKLA30LferRuu5dVwzU9Tc7k1eqYcgXtoE+K+FBJy/WpMtqaUpzTH5KlIAH1ZMjAIlZNvcD9eISKGpfmM9kIlPes9f49r+tLaA65H8un2zNwKld8mHO9CVIo2bAYnM09Fb+gTS0Fscs+1kxaPIVk6Ga7D22usjsnMZZB8nAFzC3ZvV1qqn11o5YRd86fal6I1lFZagRdR3tYNjR4/1rYvhp7/WNtOSHIfU21u+Ik+tLfth0reT7Xd4Ze0RY+Vpr/90Bg3xv7Wvceqv7vUGDb5rZr9bS2UvSuxGuaro499/nTevq9Dovly4KzQOPG9q+avc/29aj3rjH9R27HSkMaupXBf+jP9GKv2gNX+xf3oXOPcFfeL5+sPKUm+DeYya8b9ydqTz0KSfr+QWBv9irb7TjjqQ9fUy5l5Z2Zui+txJKMVoKln3YhkxgMRMV89d06cwwWRHNkYPZBXxDzHp9W4boRnTZO/+VlgyZR4aULkb6vxmQ7zpI2He+hs9fvNcL+dLvTeJTNPQjl/tv7f4JOZI+Ls+uwj3N/Xq/1vAlo5/n6u3R7ZSgXzKRI309Gq2jwCQ4LHRkPdg3gGfltn8oOpgXVj3HPuRZzyMvC3tEjFO0i4PVbr5lncl97GXIrzpjlMH609eR/EXesAK0bEVtX/K5G4bUI6uyLGWQjz5UWdN9cgNrgIPRGfiYi5Qq/M4ZnZpFAZAowVeox+gGdyQyJ1LhmZAVg5ImZNw/43QPxwVM3FF9W3n8rZd362iL+fvW/db0T19xzEWiOQYBucmZeh9/s06EH7bM33whGxT+ho8HWN+7r1fr+qPr0BLBQWmDkaMfXa6BG7H+LaCZAAHqv6uVWdURdiqOxu9e/L6nc71797ooHzHDx7NsnMbzJzeJqzdk00GL8NTBw6GVxa87xjRKyQVnD/GI3eeyFW+TuttCjjU1edwY8B14b4fNnag0+iCPs0XzJpAaqLgOcLm/yzmevRz4KOq+P6T7s6yL+Oq+P6H7hSYmNptN69EBJ4u9bhPxKV4uaaBEkb8ACercDfP0qhnBG9hl5Fa9ptGHqYdWgfVcr1XQjEhoSVOh9HQqUBPquGSfZvwEN+cSz+cBtaz2cPK0/+bKhv23s2SuiAMJRiDkysPm5oWb8fD/k/FDEwSQGdjxDMdk1DhG4GlgjzxzU5VAJoQpX2xLCMzVGxPR9DyYaiZ8zKWIWuKS6ybZpsf1W05C6AhMYGwAo1BhvQliy+lOglkEja+kdeud2jr0tqgZ/Pn+ecmflozcsKCNp3iIimUMbeaGG+OiJmyszXa36b63UEON3TRNrfhHmJ5snMTVLL+wooO/MBl4SVi09F74MrUCmeFIHfbRjG1y0iZsvMr9Pwiecw/OF1JIkIvSTGQ++GJr/PhhExpBSgg5CUuj4ilouIuZGI2CL0KP0MrbZdgdlSYuquiJgn9MI8NjMXRWDYeGj0RgJxU1r5zgYBS0ZEnzRp/fP1biPDxNUNyUzo9bELkkGvoTx/jCD3VCTID0zJ1MVoVeZ7A5V6MOxuWlSgDsV1eDuCyKuQrD8FrdPnoIfoOSnJHKFn1zZIHL6JgPmxlJT7bc3DRqln4YJILq6FBNAS1YfTUInqVePcuZQ7kLz6PRLi35RivSat6oqRmZ+UArNU/Vmo3uUjTMK9R1hZ8kWs1DlnWtH0cATHV+M+sTeVRB54oeR4KQTnI4Hlwoqe36XJ+79EL1GQ2KHu15OWMn0XpSTV/xdPw5hmqXXzISoCF2HV3KkBSrkckIZo9ar5vLve9SFUfPcuhfdWXMtv1HqZCUmwT2l5IZ+Gis8OwPiZ+WK9X3/KQy410AzDdbBSROwSVmn+otbejLhXb4FeqkPrvYagMeUozOf2WBiadBHK2yphaBy1lh4A/hESRu/iGpgcz4fpkbg+seb3N7iGe2XmB9mWR7Tu13hQ/b3mYE1UjN6jVTBjOCq3Y+E+PKo6b/17ZL3PNqhk/mh7JL/HQ8X+8hrXEbjvL1v3bCzkY7W1vQyVxJ9qO0714zJcZz/Wh7Fxn2na3vQTbbuMdt+fatv0d5yf629IXn0brTyno8ijbKtkimthjdSz88c8Bz5DY9hRda87S5FfBGVsw+rHgNDDn6Yv0fIEXQ7nYs9/YdtvQ++qG3Ad/Vw/vq17n/ov7kdDdg3Hs+1T9DxqvyZu+92sdc+V8byeC3g7WsVfaLtvDzwnJ2q+axT7aJGJN6CxcVSF9vq+MXxui55I2+BesD/uex+h3IyH+8uneCYtHFa9/hLTJYxo69sHTR8aeUkjDC5CI8JnNQZN9eanswymWBzlyPrN8Zhf77uIGK9w57JY6OG8etbZuC6OQ/LjsV/SLo0wmLDu+0yRJUsBJ6Se4iPwLJ+BKhCVFob5LZ7z60TE9LVOrsdw6CcxH3WTR+0m9CLsEaaiaIoI3YEGsa9rDpp8fAHMV1h2W2DjKCNuasB9otZjJ/QSC/R6b4wp+yF5tXxaCGQlPO/3wHNpnhrjSWocLimZaLw956pxeg693aZCeZ4S95PhwOUlU8dFi1xdBYvA3FZn297otXltyf8vue9lbXvLSJS7CQs/fQvsGxrEF8K0I8+FRSsGI9G4PJ7/zf161LNOrfE8GnFYv5AAPA7JxdXQsH5yZh6JBOBWiCPmjojt6vd/xrN3Zyw8tSuupatxbW5Z49gN1w5tOOCKzLw3WoXcbgyJ3UfQcDw8rDi+JRrBD0Jcuk7N3apYOGmtuu9sJR+rVp8+CMOoX6TI9IjYvMaSzNwNsVSDczo7ezgvAAAgAElEQVSujut/xdVB/nVcHdf/0JWZt6chntvg4XR1fT4QyYybwmqs6wFHFRBpwiTORgDTFS2FFxQIWgu9CF4LlecjUQnfKyIGpR5sqwMHhFU3H0tDNLfEg7WpXvgxEia7hdVIe6KH1A/CY8Z0tSsyYYWvE5GkWBYP0X2B3vXuTWLz45AsGh/B0/gIFsBKa++nVrxRYThFgjUeUO8VaL8EFaUdQu+LFzPz79X2A7R4fxqSj/ci6NgIybp3qUqimbksEj2jPNRST4I5qGqM1Ze5ImKaMJ/bTVihbv7UE2gBtCQ3ZE3jmXcwEkzXYHjL8IiYJrVmXoLKPhGxcEQsG3ov3YbgbqWImLfm9yRKiS/gsgN6mN1cbVeOiHXT6r17o9L8IVo9z0ZybDYkgbqFeVseRE+YV0vJ6F33OwGtvjMWebUQEiyHIZmwRCkEJ2G40rlIOJ5YYG2W6lND3i6DSsvJWFn21wg0JwtDSk5FoHs3eg18khZX2a3u2bfWxDMYRvkW37+61/v2o0WQfYbkzgXAwKxcjUjK7YZEej9aObU+o1Xw4yb0nuqfmRshiXgFKm6DURFfIzNvLllJVG5erXusj0TfmzWmX2Tm8Zl5Z1ufj8CiGOuilx7oIfIUKtfTIMg9KiKuw3V1GyoMl9Z8rp+G6jZhc0uERMHlKG+rIuG1L4L0yerdf4OE9KURsQeGim2Qmbeg5/GRVIXxuu/86Ol6bmpY2AnB8fb1vIWpin5YHXy8UvLOQU+R36SeeH9DUntcrJK8YvVzv/q76fvj6JU5dd3zm1KWVsZQ7DsiYqxa13egEvc6WuynRCLhwur/G0hkdUqNJlOiQrpbtkKZJ0Dieau0yEyXkrEDUN5/Xff8XUScXPd+L/VM6YYpFT5EpelPWEyn8VLcGEnp5XHfXzL0LiUNqetbytlKGJL6Z6wEvgIaKvqGIXYXYzL3JqT9BwRSPXM83EM3qjV0Cu4NS2armul9SD40Hmtj1XcToKL+BTDjT7TvklYtvwjPksYr+oIa2yaXUtfQqPN5jdn+GBr95hja9gzzW31d3+2OxRbeHL0PETFD7U1fovFnd/RGfWkMbWeNiCVSQ9KFP9N27NCj5PN6t/1+4t16hsaMkSFxNBxTbYypOnKT8+/K0c7L7/079creGAnr5bJVsKMLniOLoKz3S8OP52h+npI73VHxPQzl/F/Z9jKUwX3+B/uxOnpGBkYHbA18EhG3hAUz9kFPqcZo+Sv0PPs9Glg2Tz3Y5omIrrV/fBcasC7HkNYmMmPUVW3GrnmeCMMSR81ffr9i9T7ohdcbCaIDEHOciwTMkqEB8HM8V38XFRUQemtNFhpe2ytQtxOAt6YEGbUXXIiESZ+ImDg1oG4DLBIRi9c6+TYslnVKROyL8t4X9+KT0LP1kOrztz/R7u7U6No7LRy0MuKFSyLihBqurzCiYJT3ev1uZETMHOYHvAGxwPToFT8VYtd5a/+KOss2RwPOOei9uWBEbFn4YXHMUf14nU/nIF47Az08F0oJp10RP08bRlEsjLhl39TgugaeUTu3zfcLaUjnPEji9kaSaWnEqXPiGX8yVl2/suavyXG9AJ6hL9EqdHEWYpRz8RwciefZZHWmkJn90StyRHXlg8IWy/2S+xa+mLB5BzTQHFL32geNQrsChxQeBonYPZFA7oQRFV8gYbxlzfWVodF0/hrf57Bqcr/MPAbTnwykZfj4uMa4PxKQa4ZV7O/BHIKzYqRBZ8wX2xCVJ9WYrpDmFuyPBGeDe+dDom9QvdcUqAfdjHrVgli45EpaIcSrACulzgebAIeWbN+J+9gLhXteRUw0WWZeiOf1oRGxZoj7T6px++uYzt6O64dX5sj/k3/+t10dOf86ro7r33wV8Hqh/j0ekioHpBa3UfngQsv3OFio4/lo5Sc5GC2pc6OH3/Fh7qepMW/KP0LL35zo/XZJgbx1sFjHCXVgdsvM28IQxrPR0vlCtKzeiyDIWwI95f7xC9+vPU/StHiYPljv0OTD+hIVp5XRG20tYNbMXLN+1xUJigFotf8G8wx+09y/lOU5keg7Ez22hhagXKHuvUVb2yXSSsXHIqjZJk2QvCwq+esiEFgOQ1muKTB7BSZWH2XZb+vD0ghc7kIvnX0QgO6PysA8CJyuavvtxEji9ERL5NII/uZAUPVytVuSVijEUuhR9B6G4E2F5Oix2aqW2QUB1Pi0ElhvQllbs5XzaBokaiZCED4NKpdNzr5+BYoocNcfCckJEfy8gkTGPwugz5YmtO9dc3p5Zm5WSkpPBJaLoAJxdFoAYT4kI3dAz8p9EGSeimTesvX5uDW3dyC465SGjq2NoHDNbCVjb+ZkrloDUyPpMguG1j9fcrETAsC3MRzp3ZqD45EsWibNT7l0ycQQJLK2QlLnmKyEzgUYV637v9jWh0Uw/8vfSt7WRMX9sdBb7mjMBfRUKbNvopX7BATJvdv68Ef0ZjoXPTKHpInRz6w5WRVDZXpgzrDX2mRtQI3tbql3Yfeaz6WQRPlzzdOc9Zxt6qfDMDT5vLb/n1uAv8mx+UdUkg6reWsSiQ9FOX0vLQzUG4nGTvXdh/X8DZHI3Bi9dm8IPSUvQC+3hyNiU1RuTqq5GYwVnR9ue8ehSEAdmi2v4AWr/es1H2shUToccxU2HhmX4r45cX3eXqW3G6YA2LfAfZNLq8mN2vz/0HqHczJzp/rtMijfd6HSv2rq6Rq4fo/CcKXrwrDpgaiY3JAS0p1wfV5Rv38LZbQ3KnqvodFiZlRO3shWrsxRucra3mVKYOaUIA0k9NZDeTq37bd/RXLr7ZqfsXE/G4J72W/H0P4sJCyaRO77oSL6TyTJHq22d9V3h9Tff6v2L/5I20PR2LUXKpaXoLLd7Uf6sBuuq0uQtHm43vO8bOVf/SsqyEciWbQMrRy5Y7rv2WiY6l7tdvmJdxuG+/7beH7MiQazG9rPxNHmpT2vXeYvyNtXn3VGr5rjcJ9cOc331lQt3bJktDuGQ16EivD/c1tc292q7W4oI//2flTb3+OaHobeR32weu5hoXfWBGjEuj1MufEBkmLD0dDTqwiFvri3rJ+Zb4e5iU9AAuE1zHn8vSrQNQcja24WRxn9ovo5pgrJnZCwXQ14OIusq71pBQw/vSWrImztscvjerkcz5DFs4wTbf1o9qFxcY0/Oob73ljjNQEa0nojobJT/f2bGqvba2zuxfX87i9o92xmrlf7yiy4bofhum7a7I9rZxos+HJZ9bE3rrEbal52x/xt69a8n4QY9oU6C8/AdbgeLQ/gX9W950Ii94a690rV/lU8u1fBvX7bNEfbxPX38ugF9iDim9NxH5kaDWsnZuaokPOQ3A88Mw7BPWoQlYs226ppl8xdj8bDSZGImqZ+81n1uXO9+y/K2fdfuW9a8GKaGrPb0tzaXfG8Hp6mICIiJkoLTbUXrduzxnaFtMjIGnXf/dB7bmXEFkPS/Jrjo4f9HGhIuSr04PxnPaNb9Xl73JsXQbxxX2YeXWv5/RqPKQuvTIIY7unM3CdMlXEGntXXh1E/BwG/yczf1XMWqs/2qzN3tXr//Wjh+yXwzL0lzaXc5Nl9GEN7G51hQtTB1keniuGF7c5EnHZwg787rl923b/VgP+TpNPvTrzifxX5O9b/dAc6ro7r/y9XgaNx0GX/kszcK6102RNJriOyRfz9Hg/rb+r/U6DCeF9aAew8POQXD62Tx6WWOergPh093CbE6q3XR8RIDD/rklrmmj59iArNW/X/ZhN7ow7cHxSo+Kl3bFNQtkMQNwGGyr6Wkmmd8UC/Hzi+gOsGCEiIVpW46xD0ToogqMkF9F2940FIao2MiMFIJJwaEe8g8bVX/aYB0HuAeT0i4nLgzIh4Ew/9fZBY3bH6vAywTERMkZkDIuKBiLgiMwfUPTIkxpZEwuxjBG/DUCFaut57nDSkJJCc6VTg/b0w8fdVJQMXopI7IfByAczl0TvttpD4PZfKd1gK0ngFymZqngM8XorL39OKaXdjuOgDYTGKr0vZ2bvG4zgEjI0HyRnZIv4CPQAnTcMbCL0bl8TQnD+jovVamL/roRrDoXX/P6UeLzvVnE9QitE4CMCWBiZOScRjMNxiWyRR/lJzTLU7AkmmI5DgujAi3kavm/YwreXRO2wAWrTvReVtlYh4tmRm95Q8WwkVySEFVBevueweEsIHIXE5E4L7C2qOF4yIFzPzkbSQQ2dgyoh4ua0PxyMQBtfiF+i5dRkqIrunxN9KJXM7psTMcCSSZg89GXarPnxd8vEtlVsqrSh8HYZi/4nRrgLGO1GVLUPv0Wkz87yQKO4dEXfgPrI+erJ9U/O5B3qnjIVAd1P0pmmUnnfSKssflpzeBTxV73APEoxrhMTcEqhw/h7B/pmo8P0drfTrpqHXPRD8f45E2MOZeVr1e6caz31T8ncuXF9PYg7HtYDfRsQ/UAE9BhXB10rBXKTGtTewdURcnZlPZOZqoSfHp5i7J+rZX5QydD6Gy71a87UYelFvUvdetOTjTyU3gzLzhFKGutVU3Jx6RjbeoI0Cdm5ELJLmj7qw5qDJVTay1lRX9PR4P6wyuRQq9JehMedCJOQ2DL1ajsgfVj//NXrPXhd63N0EfBwmlA9MAzEzelZOg0TX0bj3LlGfvYTkxsjR2s+EivcpaHQYXnPxYf29WUTcjERATzT6TI5k+na0iMa1gE0j4pZqOyUaiQ5NCfRzcD0+ShHybX2YFombIzPzjIg4HPfzp5FoGVxt/1H93b3uPSkS3vshUdxntPvOiATroTUmZ6PX/PuodLf3t2e9wzEls3dWX5eIiHtztGIV0coFO3aY125TDO97JqsYQXONdu71Rw/OD5CcOrfGY9KS832AXYro6YfGlUfwbB3vX9S2M55zz1Rf/qf60R3TkdydFm/6FcrdmhFxIHrsNZECyyMBsDYSWpcgmb1RGLp/COaqezsMC/4jkt+LofHh7fg+odfMx4QRcS+SIcPr2d8j6Eabv0Bj7pPNGi1CYWTJT6c6A76tfuyLxp1Fce9vz1cZiCea8OuLUNbHdN/OEfHnzPwkIi5CQ8xOmM/4nPr9EohDL8a9enmMCvi5dvNXl6YoOXgT8ccnIWH3CHB/Zu5VsjOy+t4NDdhroOF2C8Qj2+C+tgGuuxfDCI7VEdOdGxGXoEGiT2buHxH349n2dGhMm7zO9I3Qw3I5JJVWQQ/+s1Lirxue7QemJP2l9fy9M3PP6v9UNd5TpqlYmsrDQ4HD0zDnBdEA8iLfv8ZH4vXOkPx9DM+mo0veHq4xmZtWzr4r6kzYAHg2zAU4OIv4+6X3bZORz9HYcWBojP8U98Kp6hz6qs6XBdFz+Qk0tr6BGL1T6EW5F+KWkaHRd0da2GIuYKbUsWA79AC8N7+ftqYz4okvay08gmTblmEhoENrjZ8CPFdreBXcd/tExAN4fgzNFsH6IRoKZo2InTJzWJo25Ak0bN2VmZeGUTGrYW7lj4FrSqZWDbHQeWnYODVuC6GMX1CY5hNg21paFxUOGLewdEdxj47rf93V4fnXcXVc/6YrWtbcmRE4XZWSayujcnpv6lmzNgKhgWmIYPO7/mgJ3TMN4ySsVjoXgpD3S9HbE0HUE2itHTszV6v2yyOp90hoVfu8/tyE3nlNFbT1qXCGNIzqv/quAxC0HoiKzTioTN5VYHVtzG31IgLRU5DU2bvtHlsD12bLctiMQ+d6rwtSj7OuaYhtN1TgJkKA3Xi5HIkeNTdGxK+yRZL2RULulXrk/Eh+nBha0ZdGBXv9AivTpcRrYx29G4mG6VIPwtmQ9FgISaqXgDky89ZSavZG8HJOKanrItn0GJJmOyDh8V0YTrBEzeWV9dlKSCTthoD0YFROjkdgdR4qYhNhPrr7UK62Q++Mk5AM65SZa4ReALujpXabrGTFNTbtFVRHoGxsWN+tiiTEUam324poUX0SLeUPohJwGyoCg5Cw+ACt0d+Glu29kHDaLSVWpkIl/rRqvwaGKT4VVus7Hom8L0q2FkDSs8lv91tcVwMz86FSDrvRUuDGxgrD14fFT06ptk+EhGQg+bgwgtSTkKC6E63kQws874qg87I0H9PU2UrqPS2GcW5c382JHgSvlnyNTYWUVH/PR4/WZyJiglKYNkSlenwEpTeVHC+MBOgp9R7vRsR+SMQdx2hX6O11HZKWM9JKur0DhkNNjHtBVj93Rm+Lo1JSeQ5UgocBY1WfV0AS5h4sanJN9WH2+vyxWqPNWlwOmDoz+1WfNqw+XIg5qr6tzxuvhZNRQf8W1/4TYTXkprphQ64OQxlfBwm91UqWJsKwukNSD9O50Xvo0tR7b/6a47+jN/HyqGR+WPcegMrfm0gAvYAeXL1KDtbEKuPX1ZidCGxY62AgEuP34ZpeEr1Gvmibk3YCYbfqS+80TK3r6HttRBxR/zwsJSY2RrLvKpTdzWotzVJycXFakGXU82peZ0Jj0JJoKGq8YybC/KC7YIjVlfWuD6QVmJvQ0jtLmZ4Q5XgXTBHRHdfGqWHKgJNQWXsU1+nS9Y4f1OdNiNtzaOg4Hs+p+ZCMG1z96Ip5qeapfg7CPe6WGqs+SEp8iOtt9cxcudoeiaTRTfXOE6HH6WdIUJyUmWdFxO7ojbv0GMbiQ/S2HC8zN67vr6v+34dnZpNm4UNcTysiEXViSEQtWJ+9joaQJtffKOIIvXu6ohfNBkgAz58tI2BD8DSe+DfiHj8xEi/z4f4+Y43nGUVizIVk1AUYRvn/3BbJhqz2F9dns/67+1Ft58PzdUncQwdm5hUhYb44EkVHp2TQDBg2PzgrrLHGfm7cPz5EQ+t1Nd5N8bR7S27WS73G50NZ/br2uO64v06I5/6iuNaWStMbjD5/E9b7DU7zLY9uLB0IPJHm56TOw1Wp9CtY5fe5kv0722SkR83HgdnycP3R+9Znjff/AFwDzxYZchOSR/f+knZojM42bLQphvXujXvIJ6Fx+u2syIO6b+8as5lxzT+NXvhb1dxvgljho7bf7IZ7xP6pEXEmJDz7p4Xomnbblrx0R4NwV8SZr6DhqQsa/JqKySfiPnFcWkRnAO5L+6cF0xqD+gFobDwNPULXRZy3A2KCLfL7KTyIiOkw5+yumXlxfXZYves36LG8EWKkGWvsXkMsNy5ipNP+G/fdPc272/6bidFzc/V6TqDR6gI0XpyL8rwSYqZx0TtuMdzvTshWioKJEM8/iPvyJIgJj68xmQJ4qWRjJpz/T0JM3xfx5kuhTtO7xuGTGuOD0xy6x6NhZ+u0CvSUiM/fCaMnZkLdodl7G4L3AjyXt0wN511Qzv6COtegtjFZA9fYzm0YbhyUh3fTSssNFl4F94MjMnM4Hdd/6+rw/PvPuDrIv46r4/o3XO1grP4/FR605yOYGIiK5Qd4UK2dhi6ujaDgltTCuCwersdkiwCcABWbCfDwnREV5b/Vd8ej91bftucPQiX7TbTgH4BKzRPVh0Ww2u6j/413nQqB818yc5MikPZAou0q4NZG6W/7zcxIKBxT47IOkmi98ochbGOhte/K1NLYkIKjwqlHa38xkoHtxOKcbQC8G1r+u+PYb5at0Jmb0NL4YBsI6NymyI3AnIKr1/9/jeDpRlrFFa5GL4L9kfy5gpZ1eyP0Mvxz6hU5XpqXrlH8J0Hr/z/r2Ssj8TcXKtezonIEysUIVMp6Vj/upRUueS5aWk/Dirn96t33rOePCqVsxjkl6rqiV9+9mblJfTdpkRFzICF1GRI2DYFzNXrUTIH5li4JScL+1bfDUWZ7I6m1T7ZIuLnRS2MRBHeLIBC9Ga3901LWYZTbYakX1lyoPN+DIHcNVCoOQ0C9BZJCX0YrJOoQBKNLoxK3QikWo4iY0ItxW8zDNqLk+wAkyg5Eon1Ym8wMq3lOJFbfR6L7zOrDCfWMXkg0DK2+LFH9XjC1pnfBNTpLzeNNNRYHoeL7BBIGaxQh0sjnbAjW30YCYvOa3+tLiVm2xnGFeu8uNUbT1PwkeuS+WsRSH1QS9kVl+4J6r2nRo+P8iDgEybf1shVSO0H9dhskpY6vzzdBBWSj1ANzHDQOnI4KxY01vj3qHVZGT4Q/oEfYmagczowgf/40RHoyXC9fowddIuG0AJLcV5cSPh8qs/NiuNmV2fJeOA2ViANwjfVFeZ0dPdZerH14ihqP+YClS7HpgfLcD0nFjbItBKy54vsE4N64NqemPCJGazs/Kie9UEHaHcOAv0TZ650t76Z5ah6HZVXoDg0lE9dYXIPr+FP0Rryh7Tld0atvJMrEfZhjM0upHCctkNS0/1Xdc0pa3i7Xodz/Bfe7zdKwq/FxHX9av9kZldul0LOjCde7s63t9DjfXevfr6FyuHKN6x0lN1kK+2nopbIYknZP4b56T50RXVHOp6FVfAZUoK9Iq1Q27zZ2/XNhXDuPVj9fw71lZTx/LysZj9RTfSkkaQ4ppbMTytkqwKapx2qzpzaeWtfU+HVFT5p1UiJ5jtoPm7OtSalwSlrEiog4GI0EfZp9uva2xXBPuTgzz/pXta3vfo8y+C+998+1RdJnEtzzxqqxOCQz769z5c+YtuTymr/xs0XATYLrYotmimsuumfmR2375n8lLHhC9B48DtfuGAk69CDO0JP/aCTxr40xhwU3f49T/euJHrsTYs6xJp3Efkg0vVhr/iQMcbx+TPflR64al62RzN8HSdBrMA/lY7+0XWiUHIQFv54KPb/61/s/gYaaTTPzL3W/hRHnnYZnw0WYvuSTsGjSNuiZ9z3sWWO6Iu4vN+FaPhVTKrw/WttJcZ3uivvBiLSSa/P9zOgpvFIYFroYngP/H3vnHa5XVbzte5LQQuhdepciRaSj9A6hdwJSQwkgXaSHjvRepQjSFZSiUgRCFWkqCNKRIkUEkU6Y7497dt6dk5PCJ4I/Peu6uMJ53/WuvfaqM888M3NH3Z9nouyyKa63/dAQtiTeBXfWvbc9yng/y8wbuvSh2btr1/hcj/LZyXjv74D3fSBzfu96py3r89nxDL2lPZdj0O6qCM5t35JTu+ofq2Mcxk1QPhkfZYQhEbEbyoRLpeD55ACpsXEBBBffwfv5QEz8cUvIgl8rTdjRfs4BaAieAb2XVka591yUqwagfNQk3zoyC1wLPUIWRnD/hfpsOZT/jsM9/SuUHebE9fk33Ge31ZpZOjXcLoB79odZoG61NxWCk1nye8M8/y2e84e36q6PYQTuo6f8f5X7B671Xwk6LXr2dT3gX0/pKT2lU7pc3Lug4ty4JP4YwYBTSqCcHnitlOIBKBBci5fc9qnlbTlUgPdHYKV9qc+NAs8f0d33uVJQzsVL78GQOXMMWrc/QBDqfrTwroVxPu7NzGf+hXdeFwXjPdO4Yn1QAP8MXSreb9VtmBBzILDxGgJxW5WC0F0Mq9URQPhJ6iK6OIJOG6bBrdt1GyXulyllfzEUPDdDS+LjBUjMjIDOAqhM9EWhby2ckyZY/zeRbdWArw9g7MWN6+923MZ9EGQYmpmb12dL4ZzumZkXtYT+VVAoegCV80ci4lQUzI4Cnm6toyVRUFoDAYBXa+4H49yfiMryJMjmeR8F8BcjInDdTZ+ZS0cLzOxmHhtldRwEie/MzAHVxlwIEhzbCEghG+oUdLF8HRlj74YuJRfTiTc0NQZdH4IC7DdQ0P6w+nw2CnEf4p6YARWEi2otjZO6S0+G7L5VUAA8E8GOK9Al7ABkcJ2NYNFHdECOY2quT0VBbxNks90Run3Mi4DRG7VvtwG2qT00fY3fPfXesyHTZI0SOFdDoPuOWmeLZObOofW6d621e+jEUTq95mlv4JHMvKqeuSHu9Z1QyP0hKqU/rvE7LlsW/ugwFJ5EZfn47LC8VkZFbK8a1+Oq/YYlugMCZZsigLY/7o/PSkEajPFwdgpBmoZ9+VDKpJo9R4yL1ZdOjMFHM/Os+nza7LjNDkQ208sI8G6KjLv5ca99F4GRu1FR2h7BknVRKXimzpubgX/WXuqXAoJj1TuPW/1/uVGwkSVxD2YNvisMsL8usvb2QjDh6egAMV0VqFVqzG7Bc/jv1W5vZDv+OYzn91bqatQel7aSPuuoztqQYbQxKnRvZOZ59fnpuEZXSGOhjosslLcy85wYPmPpVfX+zyKoNgkCo30xNuXvWs8b7jwohWeB1A1ukWp/uAQIISC+YHZiSO2K4PKaWeBkq+7huN8erH9frX4/lMMz3hrm6V/ThAKECaJWQyWzHf91E1TCp0VQoonHehiekW93mbux6vcD8Hzes/mudcb2w/0xOYJVy9fnGyEQtHN9/z6eEw+FmSYXxPXQjjN4aCnWQYeRdAgCkqOLlzcXAr8z4vm0des9LkHQ5c1qe1o0wtyPd8gXUrfGc7p/R9tjUrfGegmKsRURP8az7ZGqtyaenxuh3JOtOZ4M9+hh2Yk119yfg1PWftst+HEEJBbAtTnMLbjmOcMwI48iMDw6gG493GtXIqA8qrh966L8djWC6Z+hgeJkBCV3QkPZddXns/FeWHI07Q4XD7D1/dfwztkB76qTs3uDRbvenVRsu+iGQZ8Cl2vj3rsDY7Q2XhjTo7x5VWaeWGv7xGrz9BSMHYEB3erHjjUvDbPvmMy8pvV91zN6aTzvVkfD2XPZAcT+jHLuliWnzY9s+znwjNkO53hj9J5ZvX63NYLS96LxPmuMRxbXc2yc9/1RprkKjVO/puMVM9KYfd2Nw8jaTcHv2YB30kRBXX/T3AnNvxPV75dAmfjH9fmueG8ulp04g/1RnvplPXef1v5bGeWJ/Vp3wBw412sgo3MJBGrfqfXRD+/j31T9hvnYB8+5IfX56egt04SjOQvvinNqXZ9LJ77wusj8fRbv8fPRC6M5ixs59Kys0EetsVmTzr59ICJmx719QmYeTU/5QkoP+PefUXp91R3oKT3lv720lL9BHCwAACAASURBVImdELA6Ai1U66OlbtuIOCIz/56Zv0+BvyWQMbBJyljbGTP09k+zUG2BrIYMs8EeXwrQ39BN6xsYz2JWFBo3S0GLWSj3rsz8U2Y+n5mLoVV3kcy8JjMvGZUyOobv/FMUnvarfg2tdz0mu8QPbAkjf65+DkLF+9P6vLtUSg9RF3NEnIMAyX7ZBfir8mcEBvYOY2tdgFb6vsBNETEQhc/vICh1PQq6AxBwbeINNgrCH4AfRsRxIdC3MDB/GEeQNCNhk5HvWARWJomIlUNQ4i6c+zNCEKlXdBidpyBYcmhErJ6Zu6ACcDBmyWyy+92NbLuHMc7PNGnMv0MQMJkizW78NwQwx8IYhn1rPQ7AOEYLV/sNo7LrPH5aCsRHaFk9tz7Pet7PMRZK06/HEMSZIjM/yE4A+3nQVWlIZh6IluDdEIw7BZXvrRCI2w1BvLHreeuhkjN+tTUu8Mswi/Hfql8bIYizTWaunZmXIQi4DAJU30iBqUMQ9OuTmQOAJTPzHARs1sK4dssg+LsK8Ovai6dVH66oMfsrcGyUW2YKhswWEZdm5q8yc7c06++3ECiYLCLWSt2TtkQAa+GUSblk6hK1HAKQL4bsg28iS2/8mqMJcQ89XfN3Qg4P/E2FbLBBCB4cAxwVEauGLo2H4B75JSob+yMg+i66jH2aWrR/hEJz4942DTJtj8Jsz6uk7L6rUPldNIyHNBzwV+PyPiqxtwGLhW4/UBm1kdGzCgrki9KJlfhJmpXxiqo7U62FWaved4F1UuBvYVQ0Z699ulLN06nATinjI3DPT1/9+ggV9h9jrL5j0BCyKIJkm6fA3xqYiXryanvFiNgnIg6odzoP198GETFJZn5U7/xUgSXHoXLXdVw+C7NM9q53mCk6MQK71n0HQcpNgU0i4oowltU+CKD9OgQTP0QWx2LRyVg6fs3T4Rhy4WUEIp7B9TAEGS7t53U1BHwCfBwyJi9D4LprH19ulL4qT6GL3DCGd3NGIMC7Ep4dx1d/tkLmetNeIggzCAGPpjyP58unrXrgmf0Wro/t6rOXcG337qqUZ8XSRQV+DWTPZuuujsz8Z2b+CBlbD5dSDQLr7yLo8Sy6E14egoK3Y4yzPSNi6wJHpsd7uenvuTU2i9AxAE6Gce02rOddkIJj/RDcOQvPh29HxK4RMWUIYC2F5wJotPspzueqX1TdWvd9/x1tj2ndzDwU7/xDWjLNsPsqzWC9NGYJzdA4eE1E7EWxoDAe8AEhs+ksdFn9IHQLPhhljgdTN/2rEKybvtrdNytpV7W9BspMUyG78wO8h7fHu+v0FPibA42eA5BlOkLcvjoDPg0NZ2fheTkOgp6T4B0wI56Be6fAXxMPcHmUXUbV7sRobBzBwFf3UZOA58AU0BtBiW3VOwHX/G/qq5kQICMi9gB+GoZD+Q2u41mBoSVDZPXxEWCbiJgzTXqyW839btXnhmU6rB8teepMZJvtgSzMa+pdG/kj279NQZ+9gG/WHT1zyKYmM+cA5o6Ii0pO2xvPojXrvdbH++0iYIZQfqfOhIdQXpwiO14sI5zzVf/jNInQ6hQ7FYGvG+vM/BneuZPWmE2ZmQ9neZuERp0xarfW0EqMRLevO6G5G5ZCA8uhKKPOQ8VvzMxTajwmq/GcBmW0lekkYnspIsaLiKlrjPdPw4A08zYOAvLfQDb9wBT4W7ze/ZIs4K+e+Syd7NcrR8SyIcP8gpThO1doTHsSmCI6Gdh3wkzSE+BZ+QCd+Ts2Neb2qbX1AO7F74UhWqj3mxPloW1T4K9PyTOLY4zfYV5DPaWn/DeUHuZfT+kpX0Ipxe4EFFw2wAv7bwh+/BwBmlXR7aQXKj0bo1Lzw9Saui4CJQOzXAxCpk8Tl2sTDGY9GIWsozAmxykl5O6IFs1rUBFeOjNfq3bORtr/7V/we6+KStIeKZtpT2SPPTCS+l1ZJ6NzXZkNBfB/pIyAkdYPGQAzIFPjOQQerkQw6VBUNFZG9s8zaO1tsof2q7r71PNORFbACxi/5OOIWDIF5Zrntdk9+6EA/1NU5N/HLHZ/Dy24B6Mr09eQ3XU1CmJnpbEK227KbSbpUqiIJAJnr6Jg9q16n7tRwV8E18MFyIx4r8vYfKued3G2YpS1vm8C1DfW09mrnY9DFsaiuF4Dhdn1SoiarUCUxVDIPzo7MY2uQKbBPSVYb4QAxyPoFnhuyiYYgMLlJingSAjKHEYFfw4Bn12Q3Xl6GCfuWATslqn5vBgV9TNRcTwUWaYL1ne7I1NhWWQ33RUyApYBjkiTUuyCzLwhtZ5+g+zTo6tf9yNTaa0SVI9BF+gZUeG9EZW1JpHIpSisLlt92Bpd/P9R7c+DTI/lQ3DhcFRGjswR3cYmQ9Bu3dZ3e+D6vhWVtkkR6GwyeU+FDNtnQkBppsw8pDXv/Wtctqt53Kz6vk8ae2s8YNIcPsPwCHuwzr/VgD+m4QwWBEgDnq+EzJXJEQzYGDPh3la/PQmNH3tk5qkhQ+9CZE6MhcrOAWlMukVQgdgHQasdUFnYF4HEN5F19FEIiF6NLItBKSv4BwhEPIlnxDEImP4idNM+FRW01dDtaV1c+xsiEHd2l/PrIlzLt9QctUMG9EZQdy4EEddOE+QMV0rxPQfBivMiYlMEht/Dc/9oZMP8BrNgDswO23NBBFL/2KXNzXBvbJS6IY7q3FwD19UrDB9svdtSe/0MBBNu6Ob7KXDufpIVnzBGw36sOksgOHxAu93W+h8fQcSv4zk6GzLurhlJe83vtsYzdIssl/Uu9cap536GLo8r436aMDO3rTqHImi9Q73bSnRiEl6NrJwmXt4VuH5HGS+v9fz90NXsx3X2/hhZ1Q8hU+4Xrbp7ogvbF1r339n256x7bI3X1Cg/PY8M56EIBr1divwPa44mqnE+Au/IDfB+vCVNyhN47oyJW3Cgi++paAyZCs/1CxAQGYR78jepN0KTPGg9BKJH5Ra8BJ09vm0BE6vhnXxpV7ksxiAeYD2/YZkOzmI/dS1Vr3Hj/7DevzuDK7XHZkDwO2rcuzLoN0Xj8t21dhfCvdWweqfAfTI/nid/Dt0tJ0yTNQyLy9y1ny25p5mT8TBhRbf97fL7NfCsDIwnu099fg8mVWqYfXPjvTyg7qo+CF413iNNHMDp6DDJ50GQ+Josmbqb57flwTGK2fd52g0NyPuiYe8IlOdf6lK3YfxthODxLqnL7qSog/SqZ93T+s04uDZOQ3lyO5QZnqo78Y90XIFBr4wmBvctyOydL02wsgLKE9tk5l9H8j5zICMyUSYaUG3MivfsQnjPnYbG7xnw/lwz9dLoizLOdnhHH5EdOX5R9AShfdaHesThaJgd6tfD7umZgVlSY25P+RdLD/PvP6P0ZPvtKT3lSyilzO+Misk6mblsCV1vI0C3XFbcqrrkT4qIv+FFvl5EXJXGGBpKxSwKY3GsigzBOfCCfBCBpAMwhk7fFPjrj9T7NVLXz5mB+0L3lRkRHDrm3/DeN5Vy9UxE/AQv9LPbdVrCS7Qu3HkQsPloZG3X+L2E7mZ3lyCyIIKsI9TNTpbdxVFAvRKFiJNQ6XoKFbZN67P5gLuq/l8RXJ0GBasFUAF5FvhrRAzOFvBX7/5Z826ZeVQIJG2OYMzb1fZqyGI4EgXwE3AtvBURj2L2wIezgL9qd1h8oBSgajL67YFzvgzO5TnV19uRgbQPKrF9IuKCliC6LAqNK2G2t5OyCzhIsQqiw358EegfZqIbEBE/QuvxGbi+G/fy40MXj5dQQVq1BK1nUYl7u4TEbyLoswEqfZMhM/JcFMg2y+FdL1+oeidHRLPOArNZf4YMim1LeH8LrcOz0Ym1cw4qTccjqLEBsjweQCH2j8iUOrbaOzIiDkjBp+aSn7rqDQ5jIO6RmYtGxF0RcWMaU3HnlMGzOoIDe6GycnQpRutXOwujMPsacHBEXJ6CpxOjCy4olN6Lcb/aGV0nw+DUf4uIjxBE3rh+8yGu1fdQ6P4GKhMPIrPkNOCVUrr3rPGh5vpbeJbsnOXmmcY5+gQ4MyIGFQjzcsjSmSMzL2ivz2GLx/Pvp9lxAV0RM/S9hkr8I+j21wcV1t6ttfcQ7sedIuLR1IVsc1QC+mG8pbtrXvohKN0EJr8PFYh5ah3NWYp91B57Cs+YyUJ2w6l4pi6HZ/U+2UkEsBoCM+cB54WB4q/OzBVKeXq0FKvlaj39A/fNdM1YtM63xh1vIlRc3kH28AggISpk4wAThgagK3E/9Uelf6dSbHthnNXbW+fDcHE869m9EPDcNgv461qnS3kOz6h900zBIy2lXB2IWZlHABXrPHwjzHI8zLUvRw/8NQlpRmi3td7eqznpTZ0taQyyboHN1mf3IIu+O+CvAYL2RWbwpLiPeuO93MSZfRbX6SnIIvxVRPymnvNxWJq4dj/M4ePaXc+IMfAWwOQ5QxFQOSsi7qszYQNcA2+koPQCwO/rPH/kC6wbCND8O9r+PHW/jWf37Zm5T2iA2QL36lN4ZryXAn/zIcv/B5l5fijnrFTjfm5mHtash+i4BX+GZ+Qa2cUtOCIG4xkK7pkFMfnVudEB6LZCgG63VtuN++tHaLjo6ha8J/DnlB24JjIN78Gs9AenMtr1uO+3iIg/ISDaGCQ/QrCx23Zxz06AYM2zwEbVl4fr98Mtc2QQroV3zcWhzDFcvTqT3qsz84L63Q/q/p+wzviF8Fy6qtb+USEjepi3R+3/H+F5fHJEfC/LY6PGon/dMRe3+9vs19bZuRaC9p+E2cBH1uehoaFlcbxv/4qZ1o9EttoSEfG7iFgoZX4+HhErZuab9dxPw0zrCexRzz89Owki2qDb+GFW5ZGBbuNjVtzfh4a5nereeRDvnCZm3yGhcXTvMWx3SZSr18Q12h/4MCKuyDIoterORSdW4i2hQWLGzNw7jLG3QUQ8lgLfsyJIuB3KrcdgLMCnQ7nxWDTKPl3PWBk9mYbUGji21tUOoTx7BCaDGc77p11SMPj8Gu+nUCYahHvsJWQczlR96otGk0NrXfXKDsj8HIKF34yIx5ANeAqtmN6t0geNs7OkbNTG2LQ0eis9N7J7pKd8vpKfdRthqKd8yaUH/OspPeVLKiUMvo/gyzcQdPslcH0K/O0MrFWX5B/T+GZjIbNk3Ii4EmPLPVtC1tto4R4LL+jV8QK7Ai2cW7eErq8Bl5dQ2TvNMvwqCgrTo5vbCMky/pXSXJapFXwVBCLXjojpw8xZ/TLzzGyBZPW7AWgtvxD4TkT8rt77/i7jmSUkzhURz6KCum13fam6jUL9EjLALkKw6gZkDjbg6u0ItBwWEe+ihb+J3zMjuvg0wsLNGAeoawKTBvRrA4DHRsT+9ezNUWCcA5Xw90KgZ0ZgmjD+4xvo2vl6q93emTm03qcZ33tKuH0dgaAmi/AcyGqYGIXKI5AdR2usF6kx2ACFox8A70fEmdlxv2mEximRNfeDzPxJyHbZJgTfto6IdzD+4PdKoDwKBa0/Vzun4xrdGF1Z98oOk687YPw1BKs2bgniWQLm6cj8exvdb8dOAZ8+KChelwJ/qyAQfikqaXui0rg9MrL2Q+VsXAR3d6253Soi9svMozLzuGr3w1YfFqs2N8V1elZEfJqZ+2TmUhHxUEQsmDLbVsR9ejyyNTYLlfyjSrlcAYGD6dCVaFIEST8Gbqp1eCvu02HssOrH6sDhEfEwstoGIjB3M8bb2QnBikVQwft5jeWbYfbE7ZF1+DU6LMpGyJ0EWST3xfAMi2sQaPpHzd23as2tFxFDM/Pi9vpsraGPQ6v80NoL8yC4cAAC39fV2lm/fj8fJqa5uNp4Hjg/IrZMmQl/aj+jfjMU2DIifpQCSm9FxMsIwP0hIh7BMkNE/DUzB9aebhKMXJiZVwJX1pr6uHlGnZdt19QdI+LqUPG9jE55AUHHqZHZtUmN0QyojJ9a+31N3E+3o6vTqEDCS9Hl9sD6+y7c2xtExLlZ7lbtsajfjxAvtc6kJmlEV3e5EerX+6ydXZjVI6n7KLpav9Bd3aZ+62zpLp5rd+0+jSyVl0bSbnvchkbE/WMyFlWebOp07UPNe6/UcPSj1veTIHhxaJ29s2TmYqGb6QroLvhJq91p0Zg1L55rF1bffhDGtZsiIj4A3glZ5psD3wrBzLvwPl8+Ip5GVur2wLV15s70RdSt+R2AbuSv43r7qvqxCXBrjcU69fvvRMStqQFmwurbAdnKYpuCKr/H++78urNvwhASO0bEE3j2rQYMjIg70TVxewwlMTey2rattj+o+V4eAa/7qp8X5EgAujqT94iIXyLTe22U83aPiHbcvudCxtvhGF7k4og4Bd2V16u1/lM0brz2edqt4dgb77KdUSacEOPWDiu1dhdD2aDJst1dveYsnAaZmRvXbw6NiEOA10Lw8WLMZvxAdOStt+lS0nivP0bZdfx6xpLotbLWqPpb59/K1Y8l0ZA3srpDay0dh/vvhtQgMAiBoBNCmWzh2uszZuYLjbzR6u8HEXEH3nuvtZ6xULWzPqMH3Sau+WrCEtyIoQr2b+YgTVLxFMpup36Odn8EPJvKU4/X/j0D9YYLMSZeU/dMBIdXqztoCmDZkGF/AMohTYzaPgg0j42G6Q9Q3rqeMjZnB/ibHb0pzkE5px/edyehDDIRypfPYJiEg3LkMQ3/XPMxOcrn7wNLRMQPMvPIzDw5IuZHo+ZgYJmIuLbutj5pCJOfhWGOdsS7cjIE8kZIYlhrYgiGBvgJguD7YuLE5s7qAf56yn9N6XH77Sk95UssIYX+e6ggfA1ZAo+HLnfboRJ9LMaRurSU5J0RFLoYQZomk+tKmfmn0Ip+eJpsYEFkFw3OzCdbz10V3S53az4PWSQf5/Cxmr6o95wQA8m/H1rQXkNh5yE6jJj+qNC1AzZvgkLFOdXfg1FwuButtO0YUo1ivADGtHk3M79R37Xd63rhWTe0lLaDUMiZDQGqCTB2y9eb+iVErEgF3s7MTeq7Keu3c6Lr6w5ZbJhSHL6dHbeQNqDZtNn8+w10j30oMzdsvcveCFJ9hor+z0KAbmB2Mu22361h7QSCqf8IwYy+CG6ujiyYs5BNcUB24l01rjCbtd7vWzVPJyJg1Vi+N0XA7XV0DT6iFIdmHM9K2Vc/R/BqYPV51zBmzdCUZdEPBbbJshVMvp49OwqxO6FyuDnGOHqxy9raE5keZ1Uf+qMAuG/K0poc2WRj1Tq6PU1IMWPNfS8UZOdFpfJldL1/oZ6ZKIzvhELowXQpobV/k9acTIexIC/IzD1a9XpT2Xkz88wwPs42yDQ5EZl8s6CFecOax18hU+0VBChfQFDwifZY1LrfCcG4v6ECOzECkrujoP7n+ncnZBBNji46J6aW/TnqOROVQtaejzUxKc0yrWcuCUySnaDeK9YYN0D2erUWmj0QQBNjaGJ0e985y7reeo9D0a1nndSdaKVq95nq8+qlDA9EQ8e6mXlv/X5VVBhvQxbVgHr/bWquT0G2529LoVgVlcEGhLmy5mBPdCO8vPoQdDJNv4wKwc/q+bfV+55XfftL6II/AYKkjcvyBrgXBiCj44ECN/rV3F+HYEN/dNU/D0HC36OL2ZCQHXgdskMmxKDsd1b7t+DaH1LvtjwCTeOmsSxHKLUmPxuT+nV25hjWHQYkjmE/mrPwi263UYq/6LrNGd30eyI0TkyALL2XQjZP7yx35vpdXwR4B6IifR0q9pfjHXIpnksfoIK6WpqFdHNUzLfDtfoJuuqvgIr0fMDiKfP0X6qbmSvVXK9Q79QXAZEvvR+tcdsMwyDsUjLSFlScxJSBfCga3n4XMoEmyMpSGxE34Dm1RP09I675v8TndwuekwrZkjLaT6l+rFdn5vQoX7xY/z+mbsFj4X2zHzKKm9ARJyJbcaWscAqfs91mfQ6q719HOWejFJSfIE3C1TAf98Lz6QIE9YbVw7XdN2V6bYAywLUol72GbLOheJ5+ggajR2uvduu+2y4lGzSxZXdGA9bI+tuHTuzbe/Hcvn1kdWuvLoL36YU1r1PXPP6+1sxZyFCbs9bDlnjnvTqS/rbDnzRxgjfLzPXqsxURdDunnvk3BM7Grecc1pzd7TZBOav12bqja7fGbJIah/MQDD4dXcc/rXU6C3Bmypb+ds3TMSgXjDRTb0RMlpU8JgTD3srMQaHL9kBcV0/jPQiGrpgCmD9NXjg73qevYHiHBiBcDeWcefFOPSq7ZM5tncnr45r/Foaz+Q7KMfei0X1TNNotgLLPq9nJhD52lpdB7Z1xULZqPBjabPT2nA5A2fNr6P58Cz3lCy33bbfGfyXotNi51/+fcvvtSfjRU3rKl1hSt4QTUKhdqYTab6EC2GRvnRAZSOtGxF6ZeTpaBf+IF+ZWqKD+qdp8BJksv0LF4opsAX9V7kaXxu9GxBolWO+H4Me/o6yLrpKXI2j3TL3To5jtdwe03LYv4U3RYr81CmkbIUtvPIxb+GnoXte2AK+Acdr2BO6IiCGh6/TQqIC+KYOjyWx2FSoM76WMge+iUPV2GHQ/SqiaFRW1NYDFI6IBwl5HgXEfBGpvrv58p/rw3VJShrn9tv6/T/27BAosqwOfRsQJdILuH4duu/1T4G9ptP6uGmY5G5YgpTXWvdD96PiI+HZmvoAKwt/TjG+voWvVBbQC8Fd5DsiI+HroqvQ7BOA2BtZvCdCnoaB1KDKZtkgByMMQMHmv+tYfhbtx0IVpocz8sJSMZTD2T2aLydfqy4sI3pxABSHPLsBflX41dg3T5y4UDk+OiCky8816xsfAE8A3QtejFxBY749g1R+q72+gAvE1YMUUHL0HBe15ay10LR8jW2eKWjMvoSK0WUTM3AIshtZ7rRwRU6dxbs5HBXtzBEoXQ0bm91Eonwj35eQo1M6amb9uxiIsk1Ufp8nMX2HsmyY5zPKZ+cPMPK2ePRgVnhNrrCZGFsxKFFuiUTLb85HG2no7Im6LiIVCMPBiOm5wIPB7XMqWOwqB+93rfIEO6N7Enjo4hwf+os6v3TFmz1OlnG+DykgT0P6iiJghBRWPoIKrh+Dl/jWHS6L7/C+oWH01pgdn5v2lkC6M58sGuO+Xqr+fxXU3J53A+IsgKNwfgfRp0d1wIColZ+LZ9pcam1MQuDsxIhoGchMD8lUEhv8QgoTLYbzLAzLzGGRHDEEW0L3I1tk+DNfQZK0+A/fxXjF8Mok3691WRkD5FeDUMKTDcCU6hoMJI+K80dWv+cuQqX0JniUjq9trTOtWPz77N7U79N9Ut3HT3quUy3cy867MvCkF/pbH/fpg+7cp+/4KYN6UmbMpKrFX4zrcBfflrggsX1O/u6T27IYIuEyBQM/TVXc7vEP/5boRsWtqqHoWQYSdv6p+NOONYOiyCMCDd/f9wHJ1/xycAn/90ahweERcGCbnWR14NfSiIGVz/SVkE9+JLOOTqy9DkJE0ZWYelpn7ZSe+V596/mwYqoA0gdC9yJCcFuMxvhga9Jal3ILxrHwQ5bVpM3M3PC9uDsF/MvMC3POztOSG3TEe3Ew1FqNtNwU9GqBi6vr3eTyvjqEDjm2CLqV9arwnQTnmPVyX7XqH1nenl2x2FoJME6EBey48C/vV+L2WmY/UXl0TOCUizoiIxUKj93AlOkmKpq2PXhhVf1OAZs5aBxcgKDTSuqGh6gYMzbA7Aux3Y1iNBTPzeWQ0n19zfBUCZa9GN8nPak0ODZNc9Kpz9BFMhrRUPfdmZOsvjffHXLiXfoEG+Tu7tot3dttQzejarbNzPVwHj2fmFZggqT+wXRh6YoH6foc6487HeHc7YMKzjVLgb2WUXZrsutMBF0bEHmE86u+hnDpLZr6RmYdn5tmZeWvJC80dvTtwRETMnSbLOAYNeltHRL8QEDwD785d8F7bPyoBSz27T53JK+Kdu1/JJuOj8fIUvLOvR4PXB/X56cCMoSt3E2phrPr/v2Tm05n5VPV32YjYLiKWq3n8tFX3x5l5GBooe4C/nvJfW3rAv57SU77kkpmf1IX0cpiEY38ExfqghXzzNDjzK0hnnzTN+AgKMxth9tgdQuslKAgdhSyUnzfgQ+uZ/8CL9wVUPNbAoLsjZOj8V0oL9LgQ3ZWXQto9aRbUo1MXltMxlkmTQW9qBEG2TK337yAIdSqCAi+HLLUVWgJCf1TajkizFA9C4eaKiNgY+E0Y34SQdXQVAkqNtZLMfD112fs2srMawfVatHYOAZYA1o+Iw0N232BkhN1b9ZdFa+wtyGxcMiJ2qPY/C0ujPG6KQtzHKUA7CIG/3cLYiA2g+2zoWnoZBpDfGbNBNopTAwD2QrDwaHQPaSztQzCL6a9RiTsvM5+o/q4aEQeHMdseQ4VvDwTr1kSh8QIE+WZCQPLRzDwhM3+CIOeuEbFdCqwemJmPtATmVzPzIUqRjYiNQivuaejy3G3JboDxkVQ9DOgbERfU37MgyLVWCna2y8MIFH27FJCPUSA+HvfCYfXuPwdOxiydq5Zgfye6ZI8Qj6yE+Fdwja5YwvWcwKqZ+VwJmU25DUG9jUJW4ngIap+RnWzFP0lBsD0x6c8SVWcsZEK0n531u02AlcKs0ENr/F5Dtx1CgHAQJrZplK9bkNk5Jcb9GZLdxzrrXc9aG/fU9qg0DOoiFI+DSmMDdD6Ae2C36tdntfduRLbIPa3fNmyuiZDl+04pG1tiFsKvV51dkAl3ZcjgOT2NazcTniHnpxmkL0YXs70xlMIaCNJ/rfZgo7TOnJmPZ+bVeJ7OX+/2DLJVnw0zJ0+MAPiuqAifjslNVqaTgOYXIbNra2Q4vYvhB5okBe8BXw/dj3pHByScBrNkDwcS1vx9imyOB3Bvn5uCTM/geXAKgvN7IXvjyVrbA3BNgMrjlV3mtDk3J8X1Pt3o6te/U6Livxcqqf9zdbPDXL0OY4F+3KXOlGjwOiA7rM8FuijzB0XE7Gmy/57HHgAAIABJREFUqw0QGHsjBdmfwzl5GF315mzazsw/pkDV/sD4aXiOL7xuffb0V92P0H10iTT+3pZ4Jq+WnSy8QyiANTSkHYJJWK5FF+GjQiPQesBfqk7zrN/jWbJP/f0c3se/RYPIlM2cxRgCdHWGLY9nyRpo/Jg2NdZcj3Fht6gzhZJbrkVj5V4IJN6Nbo2NMWZQyqQfo3YjhoUmWA24oZ71HLKdLsdQMwujQeQ2ZBbegGfJKXhudq13a312K96Xl6cg497V7hbIUN8CmeSNK3/jvnsYyiSD6FKiA/5vADxW62Gk/S3ZafWa+7Oq76Oq2wtdt/8ATBUR06SxtC9EWXtwyGz8pOo+iC7dA6MAoS797VP320T1+xMiYrda1yMD3Q5AmeLQ+s1zdCnNOKA8swOCYaNrd4eSDU9G2W7FiDglBa1+j3fi9xGMuxXBxe+hPP4wo8/U+y4a2JakEzt6DvQg6dr/efAuWrtkhSMwZuQ8tbcORdnmnyj3PJrGVby3+v5PBGznh2FeJhOjPHshxgrfFeWGo5H8sDEaaW+q/k+WGq2PB6aLDgDYzO0wvST0MjgTwd5NMZNv76rbBnz/K9lpPaWnNKXH7ben9JSvqJQQeDiCWy+E8VQuRaFhDhT4dshOwoBVUUE5Fd2DdkFgqA9e1Lt1p8h389yxQevYF/w+bZfB5apfGyEgdUp2Yr/NjUDAZjm8G9X6CHochO5QR2K2v+NC9tlFwK6p5XwcFOxmQ7eg01v92B9ZXDem7g5jI+hxYHaT8S4i+mYnSPDCCOhsksaMmxZdUfqiQp/A8Zl5bfPOKFhlZp5UivWqaDG/DBWeD9N4jwsgS3H7zLyiBN5JMOHLQejKc112go4vD6yQmfuFYMyUqHw+kJk7t/p/Sj1zEDL+lsdAySciI/B5ZA1k9eEytOB/B3gnM7eIiO3RJWseFAYnQWVsC2QpbYjg8W9SV+7VkUW3EoJ9I8sOuAmu44+BK3P4mHKfu0SHrTMBKlDvoAvJPjV2I7QbZhKeH9fUVJjEocmWvSGuuacR3Fm32hqc3WQqrd+03bkPRPbeAqgA/Xwkv1kTrfZLU5mGW2tobWSg7p+djM4PoIvp2QX0dduH0Pp/Oa6fexDQ2zGLuRLGXtwbmYSXZStwdURMlxVDDef9rcx8petYt/4eP41V1wTgH5K6g11MJ/PtIghgPoOC+tP194t4Pr3TTbsT4f7qi4DIH1F5GQtBvIaRcDq6nN8fxnI8DYHRPpm5SNVZEJWDfsju7Fd9fTIr0QSy7H6Z5c5dY7gGMgRfLAX6wOrPlcBJ9d6r1TMPwpAMWWdZb2Q1PYWM1O3SoOX9ce+9lCYX6Yvn+0A08uyM7suvhUDtr3CffYDuYlPWupgIz8F2Bthx0Y3x4+jE4zocFeIlEcR8MnStfCszb6zfTYyMs0MRMBld/SkwgcwrCEb/T9UFbqp5boxHg4G7uzvvqg54Jo2PoNS3UNm8C8HhsXBPN2fxrcD3U0ZXczfui4D72cj+GifNgvo9BGuXQfbtXP+Ouq11/VX14woE47cqAGxjPJMPzUrkU32cvsb663hfDUY23Jl432xFJxvrTIyhW3D93R/3dF9kFV6H98TieP+e35r3rzPmbsFNpvLt0U32SAQRz0IwYvFq5+XP0271YxGUXQZlZQcOPQfWQ9liHLzD/9rUAx5M3WRHqNfcZfWcfZD52LCzp8Uz+hP05Phn604arftuyX3L1bgemJknjay/2cni/iMMA3LTaOouhffRE3jvnI8GtSMz868hMDp+auSZreb92WrzUnT9Xy9k/M9RMksvdIG+GZmVR6H8fUsa2mRzlJOmQxByQQSjlkTZoz+eAcPF7EN5ckIENfuhTNVdu7MgmPoJsiyfrbk7P2StPYrA56Bqe3yUzQ5Etu1ARpGpN02W0zU5Uz8E7HZEOTJqLp+v76P6u2yt08tr/vdG4HW9VIYeL42ZOFa957XZcXHfGw19b6C8OS4Cu5fgnTwentFP4H15SXYy2fevNfgmen4cgOfJTnjO70mrlDy9HcrvD9T6WxfX6RFtmaSn/PtKj9vvf0bpAf96Sk/5ikpo6Zs0M4+MTvyVPVFhmAGp54+E7nmH46V2AAonq5QyujwKjCekTJavvIQZWt/LzO/V3xeiC+1g7P+wWBotMGcVFIR3z8w7wgQDG6PA/XUUAg5NmTZTYtzAKdAaeSVmS7y91Ycma2KjKDRZlNv9DBTorkc327dDF+xNkakxFQoHL6HV8TEUKl+D4QLJb4mCyQYl5I6PVs2Xq48DUaG/CVk9dyJgMBcKrudm5uktIanp81Ktd2uUlqNQoPxVGndvQmQtvIMswF+gcP8tFBITMwS+WgL01qi8/jgERa8E/pmZm1f7fVHIH4zgap/M3DgidkNX1WsQwJwILcx3AB/kKIDkGufhshSPrO4o2mgDbm2QeUpgrFKGJs7Mt0fymykREP4kMx/r0sY6KGD/CRXHDREQeKEUthkwnuTfW213BbD6lQLUNYFCV4F69urD8615nhgBOpCVMR5a7bfITsynEcat9fu1EdD5BZ2MvqtWO4MRRNqo3ulnWUzG1u8XRXbnFtkJFt/1GY1itxgqr08gW+Q2XBNnopvyTPWcd3Gtz4bK19yoEK2WmX9v7fvm30kQoP8YWYBN5u0mg+1vWn2aB5WOvdKYp3eh69P29f036/nPVJ/7VJ/OT4H0RZHt+VhmDm7WR2a+Xt/tgsDEd3B/XgPcWXtzdUwONCQEdAdjhsWtUUFeqs7sJvviZpn5aHwOkLDL+E+LIMYkaPzoW+P6YM3L1xEgPASB+jOAb9S4LIhsyB1R8RsHjUUvVX9HWj/NIj4fAtA71rz+r9ZdBRkwN2fmXnRTSpFfHs/F0cXtWyM78ajmA97PiolVn30T98A/kM25fuqetxlmNH38C6q7cWb+sru6X3I/mro7AnekoVB2QgBht9prA/A++w4C499GIH0hVP6PBp7OzLNDQ9Y26EHxVO2vQ5B1/nc8N96MiGuA2TJz/i7v/XkAujGO21efL17Pb2K5zYhn6C64R/v9/7Rb362I8RQHh8aBT+ps7UvFyqszbkzrte/tKVGOmR9dMZ8K3UP7UQlzIuJrmflK6KGxI677RibaBGWSfVPgrznvH87MbcegHyuMYZ8XRWPYA7W+rsUz7xw8Nw9IQ28Qys8no2z2ezxHx0Ovh+nwrD2w5uVTBOpfwKQhowPdjgOey8wVWnPTjtnXCw35n6Jc+OrI2q333an6uS3etZsjcHd0asAbG70L7s1O/OaDasw3rOeuxCgy9Y6qhCDpTghe3h+yaRNlxAPxbrs8K4FHROxHB9DcCQ2TP0GZem/cw9fVOJ1IGV7QkPtxyWhT4tn4z3r+lUiG+G1oODkFmb4b1TMWrjt6EbzfD87h456vWc/cMzNPDHWMxWosX0m9B3rKv7n0gH//GaUn229P6SlfXXkBWDsirmldUk/ixXhFdoIlfx2F2ZlRoGriad2cJjiYNGWWfOWp6EPr/eSZuV3r421QYPkhClW7tL5rGBSr1/ePh8GOv4OMuL1RYHivhJw10WKdaEk9GwG6qyNi4yxQMcsFBa2VmV2Avyp90gQZGwHThrFH7kQr7UAUDAchkDJLZj6IwBoRsWgpbr9A4GV6dMs5CZXscevf31AxC+kISbshe/NoFHrmqz5/UELi6qG77s2o8FxTCk0vtPxfgUAW9fe2lDttCdoL1RhPUX1vEqo08Yvei4ifpmym9YFfhBkEV01ZfROgQPQqusdMnWZX26M+nwlBjN+i4PWHiLgyR54tuomP87kypkUrkU22mDalaDTA3hv198rAviEQ9m5aPivFYeKq93p03PAizFKcaWzFz9DSvEvNzfwYv29sdJcZLpNgKR4NMNa7BNT5gRdTcKvJxJxdfjeCm30KOjesw/1RIdi9i3I3GSq57d81YOq1pXBdivOxLs7TuRhXrn8pJRsCG0bEiWkcxixBeStk1I3glgSMExGNorU0st7WK+F8AALAQ1P26Hi47jdE1sMtCGyfUQN+EnB9RKxZ51XDAJkI99BBuNcPRmba8bj+V4uIR2pcx0ew7Ru4B/6ESs2NEXFJZm6eupy3x6mJW3lnRLyfmYdFxPHo8jM4Mw8C3qh1fzKunyHAkIjYt8Zz7Ii4JTuM0QWQibFxnUuXI1B4Wpj0ZgtUkh+N4UHCv9R7LpUCf02cws1yROAvCgi4qH57SP27bs3divWchdB96kg8hy4KY519E9mkd1V76+HdsgCye0dav9reEc/nRf6H6zZxFI8EzouIl9M4de15agDsJgHCeMhuIzMvqTo3Izi4Da7VfdAFrrvMkw+FWcDnwbPrrfr80i+o7gzISF8xIl7vru6X2I9LCkyYCb0d3kQg/4w6q6+IiM1TY9UtKXt3Htx7Wzb3ekQ8Bmxe5/WGeH4+FcO7Ba+BrN9Pwqyh60XE9RGxRJo9vCn9gOfrvidkst2GZ82V6FHwcsiM+igzLwizjC8VxiK8ODN3j4jT8K58OTSaPIPg44chwPxE3ddnIVP0Hykzeozb7TK042LM4QuzwwZcAY3F19OJ1TpG9dp3V92dP0I37FNC19Q/N9+HQNoRIVA+Mvfd/bLjknsMMiq3CWO5jrIfIQDWbZ8RNHs9NKItgEasISHwvE51cSCCepMBfw29LrZDIP7JMF72aciaXD9MwPc8glGT4N79McbDXDA6rPkFgD+HCTI2QYBuGjQGbxkacO9LvVWamH1PoRx8N95xl+EdM9J2I+J8PLN+j/LfnxDwWrb2xauhYXGxRvaoejMwmky9UYZyuilNW1VvWryLp8E7eu+Se5r4upuEhrbfZeZRdefujSy+VXCN/xqNeoNRzh5Qnw8AxsvMh9trrvqwOsoDgzPzt/X1x9XuWngHrpnKz/Ol4OD2aAAkdA3+Z0ocWBm4LiKeSt2c70W5+q/dvX9P6Sn/raWH+ddTespXVELW1t4IytyNQvNudLHGha6Fq+DlvVUauLo/CtrH5xfsvvuvlIg4AjORnVCC+CeNEBm6W7yRgjKNwjRnCV+7IstvJowPNhWCIHtlZacNrX+/QNegqfD950AhaHO0zE8HvJ0jcUNt9XNyBMW2r+dvgRbZxUsZaZhc8yEL5HvI/vkstJqeiQLY1Cj4fISMufVQqXwS5+tDBGVOQSDmolYflkag6fspiLsQup7ej2vh2XrOCigc9UGhbXZk7qwNnSxxJXgtXb/Zo4SbyRCE3Kr6MDdafM9G94v363fzlVK2LIIq96ZuMeeiVbRxkdwXleOfpxkXV0Ew45F6v+ESyLTmuS8wZ1u4G8XcNCyzQGvxCambRlcWXdP2Aijc7p26R7VZff0QdH0fBc4me2l3LMJ1USF7PASyTkUB9ZjMPL5r/S59WLzm+X3M+Pz3buo2YNdY2cq43OXdx8d7+Z+tz6ZGl6c9s5Uso/V9A0JuiPO6Piqw+yD75bkay0VRwWyzcNZHRfoXaCn/oPXdJMja2xJB61tw7ndMGTbj4npfFpWS65ApMwgZAVuhgrp7FiMzIs5GwHDRlOk8CQJ/h5TCNnH1vz+6Vj2BmbjbiuasKPBPBVyYmffV+roN2XR/6DI+zbjPgHGdTk9Zs4uhkv1wq+53cB5PyAJ5QgbFzOgu/kZ91rgv3ocK5bLIqJsC99m7mXlPrc0LESR8IkxQsi/GeWyDhE2MwG5LKbJno9vVDSFD+Ro8FxZEt7B3MvOgeuZYePY+UnO/0Oeo/3na/q+sS7Gecf0fhQr9EQi8tplczdqaGFlGf8Qg99dilvGu5+FABOj/CJyTJvEa2ZzPhUyziZC1+ugXUbdL/YnRDfkr6Ufrd1Mi024iZKY3IRF+jeDLCllxj+uOuggTd+1cn82C9+Sa9T7Xxxi4BRfQ0pyfDUA3Gd4Zx+F98EHonvh8Zl5Vz/s8bsGr4L2+eWbeG2YpnhGNZ68jEPbdOv/GqN1WnxdGeeDuFEjcG8+iQ6qN01DGfHNM6mXHpbZbQ3KN6dY19g/VZ2Pkbpy65Db9faTu2ZH2Y0zfrequhUbQ2TFMw1kl+zSg7xUos2R9fhDKSltl5sN131+N4OyWWcbK0DA1HhpLDkVD7U7AL9E199WQqbcYglgL4x17cii3T4Bg6JPV79/gHfpdNHivjC7uI2t3yTS+7QToKTKATky+xfH+uBMB6Veqz5Oh22vGKDL1ptms56lxuCa7N5AThtj5hE4ircOBTWvvzAvMWHfSBdW3A/Fc+Um1e0LIcN0ZZformnMhTAJ3Bt6PIxhCqs5c6Kb9u1BGnhbjIZ6PwOZ6KeN0hRqXdVpj0R8NukknLMocNdfbpIbTr5w08b9Ueph//xmlJ+FHT+kpX1HJkSfheDoiFomI74QA4ckoQD2dAn8NZf93+RUCf6VUdi1/wMDYM2bmxyWAHBgRK2Xma1kZcAswGQe4PCKOzMxT0Aq6VeqKdw6y4r7Wes4k6ErxSJrh9EYUMpbLzIuB2TPzrRwN8AeQZpu9B7ggImat328B3BwRy6XA3zIYG+/ozLy9+v51tFxumpnroBCxA1otByMLaRkUTtZFJthdKIBsFWbpnDgEMpssrDeGIOPZCATshsrNuGjtvSkzV0VhcXJkDH4/Mz/N4QNTj4XC0HbILglUfqZFUHVHOm4a22ACir7I3HooBMreQ+DloFJSzgOWL8CFNDPpB8iUIY2/8hgqU/3D0gRZboCxiakkJ6Oblyrj1b+9kSm1bD2rO9Bt6hqreVt9agsXnyKwPinGh/kVMHF0sjBns77SWIsNuDZ/tXcZMH3oet/UH6tLH9ar9zsbBetfhdmFs8tYNCDBrVHB37uWNAv1MPfhKkNReZio2hp2b1e9PvXbK/Gs2BsBuAEp8LcBKh73N+8XEXPV2F2Pa2FRTKYzVuu5/0BFdM4atz1xj+wWsvc+xPV/J55hm1RbG6NL5R41hluWEkVmDkQluAmwfTiyO4fU929XmzdVnz/LFvBXdZrEF88h22fJNGbnEtkF+Kv6n9b4v4hMr+9HxKGZeV92AaPTRC4bIutz1/psMHBYDp9M5i/ITN6ixrTJAHppmtioYRJ9hMD4MmEs0jNwXb+Drl3bp4yEkQqONd/zYvKZG6rudMj8vhPBxp9jUpuD8cx5IE0g0+yHMa7fUzcfAXqnCXSeRzDrAMoNPyI2D5lO7QD1V+FZPgj3yRLAWhExUUTMXwAJeLZNh3G+BtV90t2c906B/kcRVNjxi6pb//s0Jubp+1X2IyLGKuX7ddwbr2PCrM1DMO4VjPP7Tpid83tpCIABwAxhwgwy89nMPAeBgOtD8OleHO8HERj8UWr4uBTHf7z6bYYA3aXoBvxEjc9ueGevj3d8w/r/BjKf9q3Pl8E78+p61pIhS4qQJXUEgnv31vMOxjtpGjR07JQCf2PcbvV5DTRMzofJkDbDc/dm9KLYC++83mNSLwXRho1Jd/OWxkQ8JodnV0+E8Xxvj4hxax3cUc/eCPfNz7v094LR9GOM3q3qfhOBpcNQbtwtIr6Zxsr9Fd4lzzXvVJ9fgK7Bq4YG6A9qLqYC5m7dg58he24cNPI+Ur9tPDSmTQ15H+Ld+yYmDRlZAo7b0UD4KRq23hpJu3OliQFvD40SP6m2Lq35PJCOK+2KyLQkKlMveqGMMlNvCJadhKzmLeq3w5Way49SmfoglPUA9oiIk5GZf15oFH4VuCcF6V9DGXibMMnRC3jeTgAMiE581Dmr7thdn92UzPxTljsx3oPHorxxVY3jYmGinJPwnm6Av1lQVt4TDTh/p5PxeXvgshAQ/T8F2vyfL/nZf+d//8dKD/jXU3rKV1gy89XMPAutqZNiFq750GXiQLy8vokW7fki4gq8yPbIzJu/om53ZUztFxE7ljBxI4JWO0TEdqF73RIYLwWAEiSa7K7rovvRkaV4/akUqyuQDfQCHTDosXr0TvX7J5BZNHd93wjno7zMo5PV61wUOm6OiDlSF62dgasiYpnUir09KnHT1G+WQIFl5erD8cj+2Sd0J3sTBcV5UfjZISK2ycy70aVzU8z0+A5aSR+u/r6L89+8221oBZ4Usx32RcvvQsCG2Q1TowTYWyiGIgqFx6TZJS9DRt9uKPQejkDNxC3l5ypU3LZFgWkWtFB/C90sGyDiYmDOMPvxjhjX7U6MmzdWDs/snBhdpQ7MSmYxinnpFbp/PRrGypoTGX0bhiyApl4b+LsEBetTgTVDN8ph7aHF+kNkc+yDjJu/Vx/bAGCzJsat9ffDmouDEGRaPyIWCl1rNgjjMzbvdzK6zR6TmcuiUH97RExUzxm3y1gclKO2sjd9mq7+/w0MXH5atdWWNHoBi0TE+hHxh5rD6ZEl8mzIZBuMQnlWu6uhErEdsuUeR8V7F1zrY0UnpuE/8CyaDePvXIKg3KEh6DkRunVfh+vrAOC01K34jzXmq+E+mLjep2Ee9sJ1MSyeX33fZJ7cJjtZzulS5wlkV72B4GKjTDSM6q71m/iCLyFj+M7u2q269+Lc7xjGYCW7uLRn5j8z8zRgmRQ0Hr/Gr+u8jhQkBH6dBRK2ztIRZLKa7x9k5uUhCD97tTF/RKxY03orrtMZgJWjU2YN2YajrF/P7qlr3a9juINxcd/ti2zXJ0N3zX1RgafqXIYB4xsA+3E8E+bBc/YOBJtAQ8xn6H49C2Z8nq89363zbUNks3/8BddtshYfjnE7v8p+nIpZ7CN1ez8fY5ethEadq2s/gnv9h2GW+jvQWLhIRBzQtJ0mvhnOLbjOscfw3N4FgcPds0IwxOcA6Ooxw9yCU7bSznheLE3ddQhagmfcy9lJWtS3Pr8ojTG2VWrIHKN2sxMDdkn0dlgJ2WTTonfIEpl5YmYuXf3++5jUSwHTNdGl94yIWKy5i7rMXa80lnI7ZFTjkjtDnftDSxZcrv5+fUz7W/0YZV1kol9fdadEQ1efzPxtZp6ABsuLI2LRFOi7LDN/HxErRsQh0XHpPRXj4a4TEXOn8tOGmfmH1Dg1KtDtBjTGUuv7u8CxmXkmGg5XiIjTMvOSzNwaGaczIJv0GpRbRgbmrQIcHDIHwXX/Zo3lfbh3QdBuCK7Nho08Rpl6QwbdKSgP7owy3jo1nk2d3sBnobH6D9X/8XBNTovsz02R4Tlpmgn6joj4EzLttkf26lEhAPhS9em8NNTJashenABDcCxGlxIdI8GEEbFh6jlzA4bJOA33xQIIsO6enYzFIDHgL5n5+5IxbqxnLZ+Z1wAzp4Do/z3kpqf0lH+x9MT86yk95T+gpFnIHkUmzjMYq+0vqKA3GT2XxMt3kuwS7PnLLi1l9VxknP0KWRSrI+NnA2TZvIvxOIZGx51zIVSW702ZSesCP4uIjzPzEHSl3SkzbwtBqW0j4hkE2c7FGDhn4MW/Jgpe5BjGlUvZGqugkHQ4xlC8KSJWzU4ijBvDANaPhGDj5CV8/CiMw/OdiNgsMy9Ns/z2RveKoaWgz4dKxkrAKhHxaWZeFGbkPAwFkQ0QqNknM48Js5FdFBEnZOYeaeKTXqhIrISMrheAWSPizOziJlFK1ScRMX4IRB6Fwh01loFAzD4Ibq2Zuqcui4Lw1mlW4l6Z+S5aaCdDRfXkiLgrDZT+KIJqmyF4umWaGGNjdI98ssZhQrTWH5wybbotpfg1VvnnI2JbdKMZgMr6YwgEPhAdN7tNUMF+CoXnK5E5u1mNQ+Oi/R1UBt7CeDNjo9X6hOwAgJEdBs81qDD9PTuJMX6BCvvByJDbNHUFm7Dm8RdUzMUq2yGgdkmN8YdhFugrMG7NELopIRtwozC+Tz/gyFpXg9IA1ZMiiHROzWWv6s+VaAH/BBW+bwPXRsQlKLDv1SiYETEzgndrodt4bwTvroiIxPUyJM1Au1TV2xxB50siYkBmXhkyIw6v/1aqvy9DUGS6kCl5b8ooPQiF/iuAt0s5B5XityKGT55S5Z0cPsHKsOQtTSkw5krg0+y4FS+OIPBBOTwrdhgAiArBizV/73Z3XqRxg7Zm9AbSoXWenYYA3a3tL1P37dMi4pwCJhZGkHC3zMza89NinKtzulNEWmDJmjjez6Px4BpU2KbGPTI3xkFdJDMv+zz1I+KfPXVZJCLeZPRxFI/C2Ff/wLNxUHNWtOa9u7h246ARornbFsIza/uIOD01fDVzPQWeb4fX/fJF1Z0IwYN/Art8xf24lpYhpO6BtyLigsw8NyKmTz0dJke37T+ELK/bw9AJJ9Z+3jUiZs5OzNIpkem+Lh2j45149q+JBrF7WvfOCABdyiS+qPZsk4jr88TtGx/jFL9c9/FhmXlgGmZjBWRabQt8NCbtVpv/qP4tgQDr9nh2HImA03eBw0Ng7pJaq2NS76MQcBuM5/0VCFYPF+c2WmAQ8OtQ9vt1ylz+OnBWRByCbNKTqCRWn6O/H41h3XtQFpgsBRZ/jiDz9zLzpMw8vvbapbVe3q2z8BA0Jm6CQP9uFFMQWDeM2fnDiHguZe0NA92A+0pW6I932XMI+L8cGhXmATK6idmHhv3DMCTEljXPq3bT7pFoIH4E5cdtImJ3lKvfbeahWbv1m+lTQ1jz3TvV5pYMn6l374j4XVamXnTH/lNqqHg8Ihrm7bhhgr6/ZQegvx2xggWrvX0yc6Oa25UR0NwvZEG+HBF/RLltMTx3v0+XOJGhEfUM1G3GRl1h/4g4JDuxNhtZbyV01T+u+n4HxnK8MDNPrbq96cQPH5dOEpeMiF0z85Q0BuiLeN7fVPM7Uhf3ntJT/ptLD/Ovp/SU/5CSmXujYL4OMFEJoFejJXwrYO367JWRt/LllYhYDt2jvotg5U0IAi6Xmedn5sDM3Ksu8N5oDQTdOI9Euv5YqSvJkQjK7JeZPyqlZDF0ibgCgY3F0Mp3IQqYa2MctHaw7jEt3wGuy8yfZeb69YxbIuLraVyd2RpAoUBZ8eWeAAAgAElEQVSIWVEwXD+1Pt4FLB0RW1Wd49N4c70wxtADKZPxCATA9o3KZoiA0MuppXls4HsRsVcpL1sC80bEmdXPO9Dt83vI5Hu4xuDFEgCbuWgrVXeh0rVvZv46BDMpYOJ6dG2ZJgX+eqHVdDDwSAmNN5cQDzIPd8O7YkBEjJ2Zf8vMIZm5A7opvxgCRfMhCNWUNdE1Z7TAXxjU+cJ670izG25Kx03u6IiYsNbSkigIbo1C+J5pbK0mw/IWobU6kC35OgLS26IyuDwqjJGZn7WE3KtQObgHGC9092pYX5cgG3L11GVmIVRQDsd4TltExOohE+gb6J7UL4o5hvGCjs6RAH9VPkK2Zz+01u+FrMWjalzGZnjX5oYh8Vr9Ox8qNpsgC3MgxtK5obVWhiJguQCCt5uVwr186ja8fgr8rYrMm5tS99jTkWlzYRgg/1Lcxz9D8HF+jBv5/ZqDDYB5qp2ZMA7P8yXI34LsqRtLWWpAuWZN9E6B2UUjYmCYxfmzdp1WeQOVzpnr70kxW+CnXeuX4tiwKpfGs6W7Nql696aM3ZGyiVPQ8oka55+PrB7dgITRSSbxCnBqKXrDlda+Xh6VpVVxvFfDcb0LFbJDcT3ejKESmuQsK45B/cbd8H+97jzIPDsXXfinQPb5gVXnHAREHsa9+l1gguwC/DUlM5/JzN9k5v310VC8txqX4QeRab0s3gETdjmLHsOz74usex0CDO9+hf2YHu+hA9p3Q0sBb4x4fwndXE8Elqvf/wHv7iNLqb8V2XPPxRi6BQPNedi3vnsZGD8iDqu/G4DuvLo7P4oxdAuus2VVTEB2aGgI3B2YPSKujIhNKfZXGrJlpTFptxmfMJvtYQjc3ouM7D+kDLd7kV18DwI1Y1Lv0xr3BdDLZFZ0g9w/NQROAMOAmKHI9L4NZYhZGb1L7hj1t8ZikTGs+wQy+G8KjTzXIottrpDZSWYeCayYhtaZAu/8DTCsyVR4Z55Z/56GDLV30cg+b53F49IFdEND3/sIJr8UET9EZvf+GJZm2YiYpt6nAbiGVrvv4zk0snY/QjDvr8h4PBNl3hOBRUu+2DaMY5coTwwD/tolZaa/kYas2BxZrFNFh1n+CPBxRCxVc3szyu9Lo/GoAXmvr/pL4F3bt957vDBpyl717k8DZ0fE+Jm5AQJvv2v6iW7r/Vr343iY8OjBmutr0CBxSJg4rR1S4UD0Vlk0jGH4e2Rv79p65c9qf6yCxtFD6MSFnCMizqs9vR6SCMaYLNBTesp/Y+kB/3pKT/kKS3MZRrmqpTHVTgHODK3ZL6Ng9RjG/frKLquImCQqhk+YVfXPwM4hsyczcz20+P40ZJM1v4u6aL8WMniOQ8HmIBRuQIDmTOpiDl0ej0dh7yq0tt6Objgvp4DjXjmaeFmjKH9BIQzs/A9QIPtxGPemYSOMH1r+f4lgysERsUFmXoDCzdIR8bVWO5+hwtM7tDqTmT9BgGBFBN3ejojpQmbhHzG2zzYRsU8KAB4MbBwGOk4UlO5HMGwdjJn2NvDNaLlohsDfNeha+B4CBZQg2rh63IeC+TOt/j6O83Y1CsbHI3tqLmSW/RTvinnRBXdYfJZ67pIIyO7TVoRTVmSj+HZbSmBbAa3yJyJ74tAa87+hErJ89W3l+lmTZGUcdC3buj5/E1kDR2bm27VPXqh6f83Md1N3jz4IeM4Ulglxbf0M3a5OQKH322Eg8QYAfjoNOr00Amjz0slcPRABxnNRCT4DAeX36/s9swsrrF1CEPzt1DL+AxRSJ8zMJpbNY8hI2j8iZg+ZDfMgoPmrFIx9HwG9hWto38N9RY0BqEiuivO1Yhpf9NvAAWGcztdCtsIxmHjotohYOAqcrTm6pNbaa/XMm+u/RUIX9+PxrNoKAZOPU6B5nnr2epm5BYKAwwGArbW8DipYa3Wt0xqz3mnszmeAX4ZBxfvRiYE4tFV3cmR+zlAKxflAv+wGJGzarn/HK+VopGduGqfxufr/kcXKGgYSIvgwDgIUm1SVRxCcavehTwtYORzPhXnRELAorv3Ncd1tiPviRGQ0P4PuWQuMQf2jEQj7X697AzKZRhkPMI1r9xCeVaOMl1f/zh2yucbBfTVWyPqi5ukx4MQ08/zcuGeORObUv1wXmU9joZHixwhefun9qLprI5DzGeU22U1p4qTOiczKh9EV8tthLNXH8Jw9vuSEhiE8Rm7BtUdXYcwBuqkYvVvwoDRu3yLV1sW4zrZFg8QgZJ5Oj0a56+LzuxuD4NsyCJRRY7lomGzhPOC4AoTGtN7U9f0LeL8cg0mFXgjZ9YdEh4E1CZ6hbwFLMXr33fgc/fg879aEjNkOQ8bsk4ad+EXNd2NEeSGM27wG7uexMSzFxrhe50YD1xOZ+XjN9ehAt154pjyJrN5x6T5m39fSmH1D0HA9xmBe3b8TpmFnzkTwcDZcO4sg42+8ulOHybBdSyMXp8kDp0VZZf+Qgfcsyn39ge1CQ/4CCNLtUOfFciiL/LbqbYRA+5toeLwE447/HJNdvUmx70o+f7jGKdFl/SE6MvefEIz8ftV/Cc/UN9F7Y6zQOHYX8JPM3Lja/Afupd41zg2A3wDjB6NstiyeRS/hufcpyh67Zxn0espXUzLzv/K//2ulx+23p/SUr7DUpbUGBgn+FBXs0/ESvDQitkgV9DNK4PlKSgkSMwM/KCHwsexk5HsPL26w34dgLBJg2Duughfz06H1cVcUan4QEc+ikr9RZt5divE/UAjeNCKuKsHhpojYA9kct2e59Y1KMR9FGYLj+xQCPTPX864D5klBnjVQ0PgsIgZn5i+r74eXQHxORAzLstYqLyLoslopME+j9fmBNCvZ8hi0eOyIOC51B+6PoOk4mXlYRDyIAcoXj4jHUdBcHdggM58JGVSHI5Dyl+i4Z/wFgcKP0IV04zRW2EIISA3MTvD7Zn6uL+HvtdT9vAn8/HFmXhIRQ1BoXh2BpYiIq7OTGfY+ZD79LuL/y4ViBgTPpkUhd5PUzeprrbHthVb1BxFQ2xGF+c2yk9RiA1Rg5g3ZCI/WeJyPSvp5CIqODRxVv+uFCs/46Op0YkScnWbrG4QCZq+UIdpkhFwaWaf3I4hwOrI1VkOl7VNkHA5EMG4YANu1RMQEKSj5SQiYT4UW+HUwmcXPa+0/UfPwaf33axRwJ0WXpfNS4HWqGsOZI+LD1AVpNXSdvwsZCIOQ6XlQRDyC1vtDSuFbDoHvnwFbh8zWmWrM58vMoyLi5tQNbbECON5NldnEmIFDM/O4UmKOBZ4vIf14ZEf8uMbkoPrNnRGxdMoApsb4JOB7mXlm6D55Y0SsVu/TgHVR63423LuXVL/HiogBdJSRT1IQ808IIr9ffRir+jGcy3EMn8H1NgTohsva+v9TUjD2uZA9+1FEPI+A/pK0kklg+IAbc/hkEvuk4MIR6Eb6TOjWPaje8yNU3tbJTobZlz5P/f/husshYPMI7rkVUybMrXWONPEAL6t7cDW8427Bu2iXOjOGhWGIDoi9GSr7T6LC+xLePQPrzJ8XXcCfCIGx/ZHtu/4XVbf6szqCHdMgAPql9yN0yd8X75HVkUl+WGpAaGSMJhnYRhiKYuM0W+hu6MY7eUS8g6yeheo8mDwiRusWjO7+n0YHoLug5nJb9FgYhCExpq9+/rrpEyNxC6618U5mvhEyGo9HY8xlEfEblHNWw3ip+7Tek1G1my13Y1olZfKvCxwbES+k4RrWx/vooqxsu2NSr+6FI0LZ7Dlk/F0O9AnDE+xX/w2te/JCTDi2G6Nx360+ZI3haPv7ed6tVf/R0Jvi3Lpzjq8+NAbI2XCfbp/Gv53v/7F33lFWVFkX/x0QFDGACXNkzHGMiAEVJZgRnZFvFMWMYs5hVDBjzjmLigkT6mBEB8x5MOEYx5wxoAL7+2Ofsp9tN/1aaUCts5ZL+r16t25V3br33H322Qd4OueANXDA6iIyWyHXK/LekNdZgG6r5XHX4pTZAdjfuAav1UfgNXUaHPS6K+InX2iWibT71xwH18pg3mY4w6BVRNyNfdPTsT8cknYu2o2s1BsRdVbqzWOmw/qb8+R//8bz1SKS9sz1Zt28hn2wH7Q4ydbHAOuO2EdaQ96LrIOlDw4FTo2IL/C7NJ6KzA9JvcPyPKtJGp7jrG9EFNqG/XA68nnY7+6N58qOeH4YiN+xXfPdOEXSruFgyyickl3MHQvi9OIh+e4Nw1kcf8eVpneNGtmhMs23tD+9leBfaaVNQUsn62DsdK6OF75FcGRyVuD6dFSmGPAHPzkSL+HN++J4kS7YDWOwDt992JFZLxfZYhO9KHZgdsLAwuaYVdEFO2pzATfIwF+h4bctjrB+glN8TsbR6XmxblODFk5B+Cb/XVmgJCS9GBG7YKetE773BTNkjnTk98JpYYtjZ667XJl3GswweCA3Hz9zJmRdvDNx2utmGJD5F9bqOwan5PwdMwP2CBdxuCAMYN0eEddI6hwRN0fEY5JWjYgbsI7JthHxMt6cHFgAJjgV6gDMphyInbNC5HxHrIN0hGoBf0XfJT2Xf/8Ng7SvYXBoB7noCuEI/PYYAPwoNzFzyJWSn8xrb9CpqsP5agtcihkFW0h6P5w6tXpEnIijvNNjZsCR6VTugAHW6cL6hkWVxHXxuDkj/74DM3qaYRDwG8xCKYqPLIw3qOtgBsNs+UxaYVBvb34O/OyD39Hbc4zfiCPbF2Xfhoa1+3bGjI5R1GNhQOzOHCvP5flexgyRD/Amo1tuAv+NU6AmhMHxF3Gq2LGRjIyIGIrfpTMwc3DtdL73w852FyyCfRcGJg/GoPfBku4Jg24n4Q3ro3md90l6IAxiFGzet8PA+LF4M7RuRFySm7XxGLCfCThL0qe5Ef82DCSeDWwU1lX6XNKR+T4tGBHv5Vi4G6fCPgZ1g4Q59mbGG7irZbC2f/bpbsy2WAszAc/Kfh+OAZ4v8JzVLuoACXPOmhmDbnupBkz71VaxYVsci+MfhYME5wLL6OfFJHbL3/yimASu1r1LmJnRAzMZRufxJ+uXmoGNOf5Pd2yuN90wWD9RPcB8Fi3xu7WPGtbLmx+P9z0xqNIV63F+m238FfhY0n/CIG8/vAF+bRIeG/id74cDEZOy7cb2oyUGjBbFa2P3nBeWzHky8r6tjdeD3fMeT5Pv9yZ4fu+E59rnw2nBx2PN4PtkAHAt4MkwIHRmghOf5HvdIEBX8a4W/kO9un14TWkX1op8DwdB/xERt8vg1GkYQNs6IkbLRU2mp0o9QOowOdAyDhelaSEXYfpFMa2JHRcGQE/CjMUPgQ9zrd0CgybT4rTsOyuOnxnrAo8Mg0Yv5Pw+EqcvF+m7v6q/9R0bEaNq+W6VvtaLea+uzeNPyOMWyjE0WjXV4j/APsXFePxtr6wOHxMH3Vri9eEtWa5hHurWAiw0+56QsxYaajck7VxcS0S0x2Nle+yn7IPX7PMwk7RfGNR6PcfJQdgXax0R18rsuaKtOfL+fBQGPd8DLpN0SV7/c+GCJHtgJn/rfIaH40yKZnj8tczx8B0GZe/AjOlDsV90KH6flsUg56lh7cRp8bq6p2oA9wMwONwVg4z/ynvTP+/fNvn5Dhgc7YL98e9xGvyMko6SAxovhwvEFeOhDd5bbBQOyL8QEXtj+Yb9IuJgJZBejY9aWml/dIvyPSittClj6UScjCuk9szPuuN0rT1kjay/KKvSTaE+/szZSsd6ARzlv0rSjfl5Z+wwFJvnaZTMvHA63uGSdoqa6Ns5uCDA1RVtd8XAzb7p9DejZuOyD04/ODKdz4lG78JaNRdiUPHm2tdS0Y/ZcLRyVjki3AaDIvMBX0jaPY/fCTt3m2c/XgYuknR8HW0Xm4dChHhWDDZ0wgzH5SR1zGM3wIyDYZLOCgNCC8ipZUTEEFxJba2IWAlvNJsB/5YBm4KR2VJmV7TCUdSXsGM2HoM4s2PQ57u6HPSK+9YJp2aMxs7atzi9enx+3wpvlHoDnXGxjxvqa28i5+mAgbbHcWrXnZhB1jMM5p2FN9h35fHTYWbCYBzN/y8GOcHA5vlYz28ABtHmx6k+XVUjKN8WM0s+zfsGBsvaYMf1bAzWboJBvwFyejdh0KaVpGci4hrs3G5ZbBLxpulVuRofYUB3bBX3YXM83j7DINxzYaCtPX7Oi+GxdhoWfn8vf3cisLCsr0O4MmAf/DyGhyPhW+b1DJJ0Tji9rS925K9VRfXlPP4MYJSkQ6KiwEY+q3PwOzw0NymXYtZa12zzC1zx+PJwqvR/0wHvjDczb2FA8SHMlngUOK94FnkfW+BU5llwBcLnMID4efajAPYewRvopzC410upHRZO+foHTmn+LDeEP+a8tTF+BzrhsX033oS2y3ZOlwsztMXFCA6T9EhDz7Bay3FdFJMYhOeTo/EmpygmcbScRlXMm9OoIpU+DKpujsfopRWb8zrnw8Yc/2c7Ngx2n4VZJuvj8X8jTjffA89Lh+E5og8OSo3FIOC7kvbPtjfDoPMjwAFyemsbPKfNiueJF8KA8uHA15KOrtW/djhdbbtJeWxTtt3QsXX4DuviOfZjYFNZ/mJ9HIjYFc+BM+I14b84gLIVBmfb4tTS7yNilny3F0wfaV88T96GAajPwwGV3fDauAAO0IzEc+qp+J37W863s2PQJfCc/1k4+LRn9uVKDEocmr8fgoO1R8pg1Yz4/V0xz30Yrp56pAxQzo7X8Fca027t51jHc90UA5+dMWu/dtGkeo/L+95BUlHV+sf8fHrss8wkA0fFOrk+njsPkXRSriV34Hl8VRwkGTYp+ltx7AlYq+9DYG7VBDrrOn4ZrL85IiLayfIVh2BfZS/gedkvbYeDuWNkRn0Bul3Dz0G3zzHotgx+XrfgcfgoHmd7Sdqz4vwd8dxymWrYtg212w+z7l/P9W8pPB9tKmlMWE5mCHC2pCsrxv2KmLneEweSN8Fzz/X5zKbDgfa7MAA3Is//GE4tfjcs3fIq9sG3zv72xTp8hc7tIMxC3yPH8C743R0NvI9Zeevh9Xow9omOxL7XfMBtMlt9Abze3SSzeBfA6/u47HMReO6EwfST8dzZDqd7r4nX7EH53dly6vFPDOv892I4qD4rcKEMDM+CdRSfq2/slDZ5beQO3f6QoFOHS+76NfJTU8xK8K+00iajVTrE4TSDbfCm4nhJ1+fnNwKDZQH+qcIiogCS7pEr4PbBzsfZGKj4Kjf+M+II+vhwesCieON/Kd5MF2BKf5wuc0rFOQ7GDskzmGmxHXYwRmLQrBNmu42ub7Nb0daMWKNkYwzS3ZGf17dJLgCIGXCaxBHUiGA/mtezO46Wz4MZY0OxM3V6XW0XjnO2uwAuhLAWjpyOwBo2P4QB3z3xZqctdnxGqgbYuRUXgOmUf8+e/ToOb5LOwE7loNxgtMSbkZdxVHUQ3mxMgxkwg2XNl7ruWwGKroeBozUwqHawasDcrtgp7SGDQVWlUVTc4474uY7Gabm3YVDoOuykLghcIun8PLYVjrq/luNwbDqR0+azmhmPJYX1J9fEYM5WMktsI5zC/K+KPiyOBabHykzN3njzcHZY82mV7M/jmHk4AIOE5+aGbgiOSPfSzytZNzqlJDdig7FW4cAwE25LHE0Hgw1vYJbb7XnvX8WA3A3FpisiTsApqmvicTQLfoadcaGSN8Mg34EYjD4+N+DTYcB3ANAB2E3SE9nmUjg6f2tudIOJA6bHS7oof7sWNVpgzfDm4RKsIXkLTps8Pjdl1YCEc8ms0MhrOg2nfT+XQM58GPg+Er/3HfIZFWByHzUAEuZ3OwBvSLq/Mc9xYhYG7m/C8/0KeP74UmY1Lo9TkH/MubXBMRQ1rOpq372qj/+zHIvn8bnwu3Is3jiehzel/fEcuiZm6hyEU1pfxWvadpj5dHECD0fgyrUvh7XcjsTz7oqYyX96rltb4Q1zP8xOnQ0HmX7MMTlJjs05bvamaLuRx3bBKZNjMMt1b8xA7IHX1yPxmn5nGKDdBbNzD8XA2GUYZBoAXCNpWD67DTGo0zfXhb3wfDkcMzg3As5UpmJHdQDd7JJGhVlKx1CTFvwuTgt+Ec+dnwLPKoNvea8LsGo1zOoqWOgHYImUcb+mXRqwiJhd0seNPS7MeD0D6CTp7fysM646fkc9bWyK/Z9/yizvpfDc/2/VSsn9rf3NY7fCz+g+HGzcT9L79RxbPIdl8Bi7XdKQcFBsBRwwHKVaFeDztw2Cbnj8dsSAcisMTPXH88dHGGR7ST+vUF8tmNcaBxWE18s7gYfyu74Aks6taLcHXve2yL/Xx+/WhTg1+1MMohcZJ7tgX6cvDnjdm+toC5ySW6Sdz4vnvwfwut4Gv4dnVvqLuV4NxVW9z83PFspru1AONB4F3C3p0fQvzsLZNZvl+zovfke+z3a+CBfw2B7Lvrwc9os7SDoi/90J+0Cb4zlurzx3JdFgObz2L5D9fqH28y5tytrI7bv+IUGnDpfdXYJ/pZVWWv0WFtifF0f8Hg+nn66OncBhGAj4h6THp2A3f7JwRbMVsP7VQcAGMkOmFwatvsLObEvsXBRpjENwasAo7Ej0wA7nazg9de9KpzEidsUgwswYIJgZbyz2wFH5vjgNqw/wfRUbvZmzvb9jAOEXAGDUsPOmxc76DpiF8H32sTl+Ho8luDMf8F4CPsvlPRmgOgDAqEn/2gxvDDbDgNcG1Dj/p8oA4GwYcNoTO7v9gEck9cq27gHaSVo++9AXOzh7YLDs/7ADemtuYLbDLIzHgTslXRMG7f6JN1dnqCKdMcyyrEy7ugEDFe2xrs8PONo9PsGRryTdkBuxiaZS1HLOivTqI2SgclecdnunXB2weT6D53CFt+4YxJ0BA0avYsbE1li/p9DMXAYzOlajZqN4QYJCV2CtwwezD92whs1g7Gx2xADRAdiBPhA71w9HxAySvg6n0exJbkhzwzgMs1w2r+/aq7WoYQ8dI6ejzYyd2Meo0UL8DrMsfsAb50/xuDw125gXO/vPYiH3vhgU3h0zYw6Udf0Wwpu8l/Lfd+HN8rt57XNiwPyZHBezyABpQ4BpJzy+T8535e846j4w+7cEnhu2wfqKbXL+qwokxBuT03J8B043mhuP+/nxOzs6zzFzbiDmwfPQjmoAJKxrYzipLJ/vRpJ2zL6vi9/PBzG78bNGttcokLkxx//Zjg0zSj+SU0u3xXNqLxxY2R+P+e0xADAavyej8Dj9jhpdu9sTVNkdrxv34lTDGfGcdjWe13eWAxHFseMxaPWbj8UMcjVF27/i2KVx0aPLMWC6Bma37onB77aYhXl31DDinsZzz5uRGnjhtMXrsP7f82Eg/Soc3HkhagDd2mnBt1Y+84kAdGdisGVMrq2DcJDzmHD69544cHbjxHyyBCjOwIDVV3g9ujnXkV/d7m+1iutfGVeg/XeuAwdgKYejqKmMeoCSbV9PWxti0Os0VWRtNGHfH8b+x4Z5H3/yJSqOKfysLfD4a43XvfMl3RwGetfC699zFfejKtAtarQAPw2z0/bK9g6jQrMPZ71U3W72fVMchGqBAc5uWFN4LA6CH42DVg9ETYBxYewrnIMD0+PCTNeFMfh2W/oCt+Bg2qt5fAfMPB8O/KRVXfhweE1eAvtBb2PWbEv8jt5Y657fj4vXLVHx2ZJ4Xd0Qz5PtsH+8PPZZ+mf7h6gGAJwh1+mlMTPyU9VoY26MfcWHMZOxJ/ZnV8Fz3ftyob7aPuYK2NcerIrshtKmDivBv6nDSvCvtNImo6UDNhRrhG2PUxuvS6dgX6yBd5ykh6KC0j6lLEGkzlgf5ccw4+9krNfzaIJWn6VT0gwz9GqnMf4DOyZz4vTTl4DH5UITXTE7YrQcTV4cb8TfSCdnMHb6R4ej8+MntlGuZ3O3HWZS/QwApEZcvA0GCObPcz2ex0yDHby5MeA0AoOQ0+Vvvw5HQe+jFgBY0fZaGJDpnddXMAs7YWfmbeyYTZvXeoOkq/Lcj2GAeKtsd5WKvnXCUeix2EGaB4OiLbDj1T37vgeuZldo4VyE2XX/y+cY+VyKcdkaO1uPS9ohHLVdAkf8X8Ip2YWTVQ3wNzveuO0ki27vgkGcjWTW4Kw4ZXZVDH5fijdORdT67wn2rJr9vQ0735dj8GYDDAwVqeJtsDO9JnZm58GM09uzP+3xJvX/8pwHA2vKBSy2wWmnT0q6MSL+ggGmIxMImx2z4BbI5z0qIlaU9FR9198Yy83jAJzCvA2/BNH/i5/bBdn/Ltih766a1Jk98Hi6TNLe+dlCeGOxEtBPWZm24rxH4Wj6ZnjT1Bdv1s9RDQOwGsC0qJD6MR7jG2GAa3nVpOZcju//DDgtqGqQML9bO69lJxyQ6JX3ZyROQS7S4k9T6i1GxKnUARLK6c2Lq6ba5CSzig3mIhgI+hIzmA5VDVPzUrxBvFcGfEsh8ilgOf52wZv0HhgAKVLf2uJU4H78XNfuQZxqtyIGDv8T1ra9DQcm5sGBq0Xw+7wEZsq+IqflT8pj2wKvyRVumcL9eDWP7YhZQzdIujz7dRyec7skYNFa0jfxS0bchxhEGJnHX4Xn/DtzvtgG+ySHM/G04Lp8gdoA3eF4Tn2ZRqQFU4+lP3MZTi1dmkamG9fX7m+1cIBsIL6v6+D59TFqdInHYLCsTtZfrbaqTt/9jX0u7tmSmF23rSp0PNM3GZdjaTEsA9FDBp73xGPyzvQz++P15smKa2gQdMMB6P0w26/Q7Js2v3tU0nm1+twYMK9ttncIDlAvg8fzgniOWQAXsBiW42plHHQ9I8xonBFnBLyC359hpHQEZgIemOfdHI/D/hi03B77S/+r577PkNe7G34fAwdgW2GJladloPxOYHpJ61T8tgDs55E1LW/Ac8Rq2c7B+e8DgAnpQ82fz3g7siJzxdq9RT6Dd6kJbDTHa/8+OCOkLgBwRklj6rq+0ql+/icAACAASURBVKasleDf1GHNpnQHSivtj24FSBKm/bfArL5D8UJ/Qbgi67nYORuNF/VfVKGcnH3Nf0+HNzftcdXTlpIuxWyIERGxvKRPCuBP0gRJt2CHejUMzIDBn9FYF2wEBlPuCKdonIqjzt3DQsT/TeCvB3ZUByhF3CV9XA3wFxHdI+KsiDgmrJl4OWay7Rxm4EANOFewDF/GwNN/ImKXMNPuLOwwjcEAp7CzfCUwOCLWlQtorAMcHBEHFW3jCsEz443GYCxQfADeMF6LHbabgFtkG4vBtbF5reOwE9k9XOwEmSUV4YjoqRg4bIfBtA9w5P5lnHZ1Vjq6VwKL5b3YLZ/lcMx4aJHO9GJ4Yzt33oszgQ0iYjVJYyU9k+daOI8l+6OGwAo5xedZ4PKImE/SBXhTeGICZ5/izfWTGCB5E7O2PsAg0ZXZj8cwSPhOHjMyT/EPzFx4NMxK6Is3c7tj8LMHrhRbjOvPcTrpihg07ZHA3/r5PO4FLk4QcTR22A+PiOXyWgZiB3uXiJhpUgF/ea+GYsd9HwxuHoCfRS9JPTArZkEMeF0t64SejZ1r0olujZ321cNAPQn23YCfe9s89i+5+UTSUXhM3oHnngvwpuK7PLY9Blo3xwDkN1g78qq8H62BXSWdjIG/YzBz8noc+X8gIuZMAHc5vDEeBcwcZkkIi9w3z/68hJ/h3LJYewFAdsBAQxdyfEvaV07l7ZD37g5JOwGzR8Sl2eYgLHg+GI+hK8kKjEyCKr51Wc5DG+P3aWD+VxST2CZclXRJPB5XKX7TFH0prUEbit+3Dri4SgH8FUUq2gDN5bTw4XgTuzJ+V1bDYx4MVI+W9EyCKPdi/ak5cPr6bHh+nmTHSroJM3dXjxT5n1L9kHRxAn/z4iDm0hhsACB9nveBFSLlQaKmAMdwSdfiOe97DDqshefgLeXgzfTZVBGouwlr0m6P5SyKc32e5/vF+5RzbL+8ppkkDUif5oXs80xyOuFVwFERsWzO+8djcGyiAJ2ku3F11BVyHE2Sdn+LJRD7D+yPPYCB264YLD1N0tpAz/TJGtzAyozKtSW9N6l91ApfuT2woKS9JW2A37kb8rtFwsXRBuFCGjPjYPpb1LyLF2Owav+I6CzpnxgcLAD9A7F/9x5e9x7C68L7GHDeGfsah+DAwBY4YNUF+2lXAl0ion1Fn6tqN4G/NfB4fVDScEl98To3GGeWnISJAcPChIEzcPBql4g4U9IA4Hk8Dx2a1z0Pfh8+xKnmD8oaxIV+5HE4sNu3PuAPQNLX6Wv3x+Pm33ivck3ejwsiYjdJGwJfRMSjee0A34UD+BckuL9l9vNJvM6fkM9hcaBzmLE4LPt1BN4jbJxtkPPKpXkMYcbsrOnbngLMm0EFlFrj+e8S+CuttIlYCf6VVloTWgUg1Q07XmcCPSNiNllrY0NgUFjk/zq8Ge2czvEU6Wv++y+4iEQ/zIhYG1g5zEa8FINgLxa/VUVEVma2bI+rWm6dQNb1efw9eT/WwiDiQZIOw+DCj9hpaIGZQ3uqRmesQct2N8TsqZtx5Pf6dLQvx3ppu+UmaUICPLfjVNhzscN3N1lVFm/OF8g+vpTR1yMwaPQpcF1E9JR1hboBh4ZFyMfjTdNd2faC2On5NO/bj8DSku4BxkfEvAm0Pgnsk/cezDAcjJ2hnvlZM8xi3F/SAXgj8zpmxX2LHc5LgaIYyHP592w4jaO3XABlTP6NpAeww/h4Hj8o+90/IrqGBab/ghkxVadRFI4YTk+ZERiW9+cE7JxfEBGr5sbnohxXWwNPRUTbdICvyONWk/QR3gzui8G/CTilZQu8ge+EmXury4LQr0kanc+jUzjFeEUMrp0MdJQZpathB3ouSffiKPwTeMN2JHZWjwyzC6bP+32ZpK+qvRfVmlzsYe28F7VB9MHYEV8Zj1VwCmuXcOGQW/PYJTFrct+I6BU1zL9LJD2d4+KU/L5dnvcE/Pzvx2De8ZKK97shwPQk1Wg/fp7tLBdOCz4Ij+tLqGFRFqL/DYGEE7Jvys+Ox0zQebL9SwDCepp9gP5y6mVVIGHR9q98VBO1HFP/xOzIwRjMWBCD/7tlH3bGm5q5I6JVtfNcaZPWJH0l6QrMbhmaAZbZcbGVD3EQYpeIaC/pSzw3zI0Znd1xdfA2GBRoHS4ORc6VH+M5aUITHUsTtt2ofuR9mx6vvXPhNXHNiNgzIuZIEGoN/B4sk32vrJC7nBz0OR2zqzbGWQBPpf90Q0QciTf/62C9uvNwQGMVvIY1+E5XAnQVn43B68KIBJOOw8y4k7NfH6vKqt+S7pT0YM6Hk6zdxlgFILU6ngvPwODQcXhufRY4JiL6hDWCv8++VzUfqkrdvsZazvXdse80JCKuTj9iV0AR8ShOZ/0I+z6LYRbuoTiA1S0iFpP0LV6bxgBbhQPXqhJ02zt92GlxoOsdSW/h7IcNcRGx+zCDb3Rj28217BoMrG0cNUG6fTFQNjQMoI8Lp7xvh9fY8/Cz6xyu1Hu1pEJ7+xkc6HwbByx+Ar8kjcB+7veYYV9UIp6uvudQjB858L4AThfeXlLBbF0iItaXJU9+AIZHxJE5ft7FQGWxfm+X/XsFA4BHyGnEc+E18GZJ38iFDc/AAeq/hTMAfhoXeD64GQdnL8Dg7FnAXBFxSh7XZNIdpZX2R7IS/CuttCa0io1rZ+xwnoEdih7h9JRHMGjxSW4srgOO0hSIXFUAf+djNtn1URNl/BKDTmuFWX53qiLSVkdbQ3AUf/+I6C1pvKRBSjo/ZkB2x5txsNNyPJ6TLpR0R4JSDaWVzpMgA+FKtGvjlMiZcYrxHRhcXUouRNAHb1iaYTDiZaxb9jwGN7ZIR+1TzH44VDXR7UUxs2xFDLr0x8BUL5kdN4+sVdQFO6iPY5baOsDGCei0wqDkR+FKvw9ih/xS7PBeDZwfTgm8BYNUF5OMQBwBb4OjyGAw7FGcxnQZBlj74I1sC0mfSnpY0q5Ya+XtdFSXxcL2hKsw7oABz1nxcx6e7R2R/TtTWYG4Wsvx0RU7wMdgoO7uBAALRuQlYaH3wlG8G6cqP5kA4Fk4rfSaBACfxBVaH8IMuJMwWLm3DIaeBawRLmxxZ0T0TDD1bAyMrY2ZI9PjTW0/PNZPV1ZVzT7slX2YWdJx2JEfgkHG82XGZ5NYsbFS3SD6jXjMFMUohJkFuwDbSFoPg5NfYrbZ/nhcPagazaItMBj312x7zmzrX5jZt4CsQ7lOlYCpst3eEbFSAin3Y+CyZ25qNsKp3rc1AiSsvMcz41So7vn3esDCEXEl3gTsJQu8NwYkbEqw7V08VyyPAdNVMXP2Hxi83gqn25+G2WbfNRUQWVrVNj6fwUaYoTUkzOb+DK8TN0TEfnjOuVUulLA5nkv3wsygM4EVI+L0cICrG640+n4THPt0ghpN0Xaj+gE/McG/xYG+pXOt74XfgxvxutJPrg7fECPuBBwQ+iKcFrwPnufa43l7TeCHBBSH4ADd89U+6EqAruKzW3GK55OY5XgiZiTV6eNUcQ41RbvVnjvnwgH4no/E9+6FBH9GYjB3hKQfppa5Jxxg2wmvFctiH+5szDTviYHM3eQU9/fxurEA1uW8DQcZTw2nxQ7E93oeoH0VoNtt6UNOG2aMv4TXw87pq7+HGcIzZHeLKvTVtjs+x+3WOIV5y+zjKoUPi8fJzViTdwL2x5bCbNl5Zfbx8sBGEXFtnucLDLbthxmxpwGrRsSGEbFjWAuzYN29kn1eCugTGfyrbTl+pg3r+K2Yvy8ICY/gObFbruVrYQCyZ86X02NN0HEV7fXGfshqFae5DPuW4yNih3AmRRGsXpgEpLO/y2NfZmMcoO+I9ZafwP7bPDl2SiuttCqs1PwrrbQmtHBF0teADyStkp9tgzfeb+DCBEUkbopoPlWeN6wZtjHeAM2IwbNncRrlubii2ZmNaHsT7HR0xqmpswPfyHp5a2Cn/1A55afYoM+kKqt0hdPnfsD395MwY3I2HAXeVtKrEfF0XssKkr7O33XFQOyxmKFzLU6lfCW/Ows7G+9hTae38ndz4E3IgbL4+B2YQdcJA4adMXhxII5WtwSukjQozF44CTO63saR2rvx+OiLmQjbYnBvvjz3gpgJ8U+c1vIRTlk8Mfs7KJ3PPng8tcXg2Ipk5FnSDxX3q6i0u6/MMiMidsbOWzcMrmyFdQyvxGBjS0nfVTM+w2lc7QvgNpyy/LmkY/PvU3ERlvXl9O4FJb1ZRzvdsNO/Ym4A98JaM0Vluu4YUDxNTj8lQdfTMSvyzjAbrrb+5DZ4szAXjuy/iFNkhtW+vqjRh1o5+7AkZqK8Pjnf1ajRAjwzgbXK76bBTL2h+D16KMycPRdvrs8G5kzAblXscL+CgVhhFuEjGFzeCjvtJ/BzrcGXMGi9LX5fxuHxdpTMzP0rBnJHYBD3GVmXaHOsAToCO/TjMfi6IPCipCfDmj6dsQj91bkZnznvd+3nUVSb7C8XsJkWg8A7KlmKYUD9LqwbdHyY3XAXBgi3w0D/l5Pr+UXDxSRuVRMwf0qr3iIlK/LfDenazYkBlOEJeM+A59xdMABwH5luiIN8QxPwnpTHfoGLQV2ffZ6S/RiWxy6PAcOiUvz5WIv0tbC8x2C8Xh1bAbgVvkFjC3D0w8G7YXitm1dmB06SdzoqdPvkgOwksaZqdyLnK+bCQySdFNYfvQMHC1fFAZNhTd2Pai2s//tPvEZsL+vstsLA8eeYvT4hgak5JX2Q698OOMCyFQ66rIsBs1tx9sT52e56WGblofQDOgIjizU1nGq6GFVq9uVv1sTBtIm2Kxe1WBgXeemNg5VXhBnGXfK/ByVdEmaItsJgXn8MvtdbqTeqLEgisxUJV3U+CK/9x+Z371Y8h0rN6mUx8P5l3ssL8rlsiMH8vnlv5sWsxFOw3zkLDkx/iP2sT+SA7y/2ORGxI2YCj8jzzJ/P7qOKfcmSmDU8DrMFt07/cdn0w2dSE2RilDbprdT8mzqsySJPpZX2Z7VicQvrlnyBF+LHIuIQScfLBR1aYrCnNQaNpojmUy3gb0UMON2VEcbP0pG/A2+E9seppVVbbgxGSvo4N++7Y52QIQlcbYvZXy0lXVGAbI2wZ7FTck1EPC1pQDpGLwNfpnN2H954FMDfLNiB2DivazyONPYOFyR4B2uY3ZeO0BMR8YmkrpI+ioh3MbtsNuyUDsjra4HZST0xwDQ/jhRvmd81x4DgEzj1ZzwGGT/O/++B2X47SnoknK7ZH+vZHIoBno557DnA6eHUyy5YS+aOsIbajzj6urIvN26U9F3er0cxK+rJimd/C9ZnWkpmRLXADvA0wJXFb6sA/prhlNMPKpyxNzDQRraxb1gL7aqIWK8u4C+PuysidgdeSef5jIgodOyektNMf8DpU83ltO7l8YbmX9nGLRHxNd54boABrWvxJmEGLE5/RsU5VasPQyNiAvBq9mFUfcc2pWU/pgFOCFcY/qAAK2Q24JcRcROwdkR8KunFiLgFpyh9m8DfKnjjUlTM/Aceo49ih7o11k5sRg1g2qsWYHo93lS9gsHvYbmR2AaL2D8VBmB7RsSeks6MZFDILNC/YhbrCJyGXYCEE4BNwymDl+INQF3P49aI+BEYkPPFZRHRofI4Wey9B3BSRLytmirXDwFLFiDhZHx+L+CU0RYY7N9HqWEaESerQi6htMlv4WrQ20bEJbKswE+6dsAzYYbMKpgFfhfegH+QQMF5WBLgfTyXb483updgsOxuLHUwyY6VtE1YkH9oRIzD4Ndk7wcGXGaQC2vMgNeLlSLiPBxMOAFYLyJGyxV5twbui4iPZAY+QPE+P4mZ6ydi0GUOYJmIGMnP04Jvz/nodKw9tjEOCD0Fk+6dlqsPF2nBD06KNpuy3Ymcr3IufEsuONYTF/i4QlKT96Ehq/Q/E7y6DPsu3SLiWzkY2xMD8kvmWOqOWaIf4sryF0SE8Nq+s6yDeVP6rsdiEGwDDLo9g9eCu3Hwq0u4UMQlGKi6hJoCHHdgX+Et7NcVWoAPZt8XxsBfQ+2+Eg6CH4XX5JexRvRTuVbfg33Dgrk6AfuO02VfjsWg8baYlVhU6n04rGG9H9Y+LAqSnI7B9JBUyIMU93tFUnoIj8NNgLERcX3Of2AfYKWwfMbWOPg3DV63r0jfojdmYH6bwNwVGFzdAPtb02Lt4Pmxr/VE0YeK/Ubx7C/DgbkOJKAoyy0U/Z0HawT/Hw6m95D0Xvoex0bE5nk/SiuttCqtZP6VVloTWAIcx2DH4RXsGFwODJR0Yh4zjyYivDs5LZ32ZhgMOAxHFT/J727BbJtn8u9GR9jDVTpPxs7PCdiRukjS6eG00yuAVatdxIs+hFMh3s02dsSso4ERcRV2oroCO6imyu+yGNwbj3WbLqdGK+5fuJDGaRVOzX8ljY2If2PQZYtwdcj1cIR57wTM1sLaLqPDhV2uxg7Sx9hZHIOdv9cwgDojZlbdLevKkWBiP1y0oChysAB2SHtixlAvYJPcdLXDWnw7Y4dzB9VUO5sWb9aWxJqNgYXZr8zv18xrOFlmYR4KrCVrupCbhteVVWQbY3n9l2D22bPYIT0l+7EwTuu5Qims30BbG2I2yg+YAXBdXtfWuZlaB4OjRyjZo3W0sRl2oI+Rq6o2x2nRz6kKDcPsw7fKaP+UsoiYXfVoLSWIsStm0YzEgNzussB9wQIZitPjTkkwcSvMDHgHa+NNiIjATLzBuOr4wIpjl8XA3NyYWfdlRGyFn8mOki5NIKADHvsvYK0iVYCEZ1aChJhFdWZu8F6q8nk0WG0yatiSZydIOKVY1TPh1MlNgEsl3Zmfl5V9pwKLiLnwWH8Az/0/klIRBUiVYNNrks6JGjbxN/gdGBEO8m2Igw/t8Sb5MAxOTfJjE0if0v14AK9hs2KG3ybhgMLseH7/EPhRLtZQ3OtlyWBErWfwC0ZcmMH/HGZ+fx4Rh2GA8MgMXswBtFUTs2ab6j2dnO9/rl/9sV9z9eQ4ZzVW4cOtj4Oab1JTzKUPzhS5Ta4IWxzbBvtsx2JG3tKYPX1LRPTFUjMryMzxBfEa0ReP300xK27LBN1mx5kOT2NZiVmBVWQN6iJLYWVcoO+tiGghB8WLjJajGmo3wcrls89bK6VTIuIaHFDri9ffryX9mMcOwCDY8kykUm++l9fkZ99ght7nGLRfBvuS+2M/rgDcemCd3S3y7/Wxn3Zh9vFTDESehwG54zA4fxqeH97Ac8AbOTcsj/2KY+SihYSD1kOwfM85dTz3SqZ18VwDzydt814Xn/fGAOgyODjxt+xL27zeg1VFherSph4rmX9Th5XMv9JKm8QWPxd7Xx8vrN/hxfTGMFPpuKkI+NsXV9bcNP9eCDMVB2CnbFpqopJVR9grFvAZMfurL2ZRLIXBoN3yu2OBZWUdsKos2+2GmV/d8IZkHLBXRGwvMyTmx+DDf9K5AAMO32GWQRus8zQ2zBB7EjuS3yZ4cCoGLJ6Q1DEinoyIS2WR5esiYoHCUcFptkeGGQ4F8+4TDHZ9iJ3SNzCz6b84fbEHcFdEjJfUX05bPqbCwVwq2/kvBl964XSYzxLMeQEDPR9k+wMjYn9ZX/H7iLgCO6G7Yye40JUBszlWA46PiE8xILt0RGwo6yHdXO2ziIgZVMOq7JB9GYI3gcfh6PHJOCpcpBtVA/yFnL47B9Zb3F7SPRHxBHBOROwhMwR3o0YT8Rcma8H9QA1j7AqcSlaVTS2ATX3AX373v4g4CQNv7bGDP6Li+4IFcnxEvJcg6PX59bOqYRIKM3+2x1H1d2sd+xxmFcwSrvY9OMxiviAiXpUZqyPwZqoHZlJ9idOAtsFBkKdw4ZIJmG27n6RTGnEfbo2IEQ3cj6Fhtt3xYWbFhxjwn6wms1+viIhrErQJpU3uvpT2c8u54P0E0i7GG/jzMTOmS86/N+PUv+vhJzbxGFxMYF287ryF5/Y5saRDOzkI01THTvF+4IyGK3Dw6aDs09V5X4fhwNJ2UcEAVj2afKqDESen/BaFMlbD68hBeL0/QA5KfVRXe5PSmuo9nZzvf66h0+C58H7qCZhMbksfbmMMop2K/YQu+D28MP/fIyLewlVk18Vj7zM5OPpEAn6bJjB3bpgd+kX6ZG1wNsXWsn7l+eGg5/n5uxewH/BXDKI9DiwWEa9LulTOUjgHa/atRmrQJeDVv752E5weVPhx+btnsV72lphF+H727wacBfIaBv0+ximyY4FHw9kUm+Dxfxj2RT/NdisLkozJ9X8IBuaujIhnsOSKKgC3Z3EmyhrAozJ7/y4MpI7Cfn5LHDR+DUsePJ392gEXjHus4hk+GxEvY6Dx3PzsjfSDb825YDRZqETSQDnI2ExSkcJd7BPGUFOopBn2za8IBw3+T9LZ+VyXx+zgohpyGUj7HZkmTPGppzRK8K+00prCKsXe98IL3wVYG24b7DhPTTYrZmWRTtQ/0+FqiR3s3WT9j+bVOo0VC3pnrHPyN7yg74FBnFHhtIxlcQWyRqX7hlMIB+IUgbfzs5E4SnpomCV1UsVPFiULTmRf2kp6NCI+iIgHMTi5jxy5/Qt2SHtLeiLvyY+SVoqIURFxm6RNgLfD1fSexZHSFrgC4eCIGIXTn2bAkcvm2Fk7Ne/BnlhPrhtO32gh6QhqinAsjx3gjTFo0T/bfi2s63IC6XyG2YE/YAbjiRFxsKRxsk7fNxh065GASAfsOH0uqWtucPcF7sEATRdyLFT5HKbHKWiXJKj2FfCynIrzNS4icgyOkLcFZpP0ajVt5/jpjlOcP8j7PTzBqPEYVOmjGlZnvU6gJpI6W61N7Q6mXCToX/lfXd/fFk4VbBAErQswjYjrVBOxPwDYIjfiV+cmZUhEbCnpgYi4GW9qGgIJmwONDoJMDPirOKZBkHAy2niY+sfQn8EiYkZJY+SiNp3wHH0TZpZ8hGUiCk3GLXGV85HF7yXdGxHb4WDL6zkffYnnzhMlPd3Ux04N/YiI2zBAOF24wuor+fsXgRcj4jXs+9CQ36A6AiyqSfOvnRZc7lsaaVPZXAj8pJ+8Hn7HlsXA3mjsS+2M9WrHS/omwbdLMAi/YThIengCfi2BzSLiIUnvVJxiYqDbYMzqWx6n7m6rCs2+cED2Ckm7hyU/vqu23YhYGRcgKewdPIZ74yDojVi/7nIMrC8C7BAGu2+jVqXeBLw2wX7yy2Fpl7H8vCDJQ3Iq7OVkQRJJn+V97gqsHBFfyTIbo7K9ZSLilbwHD+A9y3M4QHslBv63yfMMwQDf9NnmEtiPfFrSuhFxZ0Q8IFfhJv375eRA+hrY514vIr6XdGYdAGDz3GNMHwZ5l8vx+gAOGG4OXC4XgKNyL1KuqaWV1ngr035LK62JLH4p9r4XsJmkd6amaFU4dW9TLOD/Wn52GPCwpOH5d9XAX0W7HbEQ8zlKfZlwusMPOFXzBLyxeqzeRupvew1gQ0mHpPM3Pp2HFsDqwJiKTcp8wO1Yd+QwrJn3paSdwwUBuuKU3ccrjj9aUp9w+ux4JeMiv189nbJW2ebXmMm4EjBONWkjy2EwbFnMWthb0uNhHZM9sVN1FgbFFlJNMYs18/u7ZQHoVbCmUnsshLwnTne4PZ3im3CaTHu82foBs+vGR8QOwFeSbkgncCDWrloDeFNSrzxnVwwejlAWAmnEs9gMM11PxBpUu0jql2Db5jgd9UKlQH0j2l0Gp3LfijXp1scg4KkyW7MX8L4akY4bE0md/bNY1CrCMzEQtPaxOHX8g/yuN9Yh6p8bpx3weJ4X+CId/APw5uoAWZx8WwyAFyBhs4mdv7TSJqVlsOJuHHj5D9Y7LXTtVsFC+4Owvt6Yik3pL9brMGvpCly5dSzWlb29nvM2ybFTuh9haYyD8L28ABfzmTbXub0xm6sTMPbX+jsxmQtllNb0lqD7wpjp2RLLpPwNB0hvwsWqNpdTYZfF2QvDZQ3XJXFg9D7VSKbMrwwCV5xjBpxt0wuDbi9jv2d0/jcN1RXgqF2gYqLt1u5Hxe9aZsBhZfxe9ZN1pVvgDJvd8PzTDgd758LBiE+wJMbnYcmLBguS4II5E/JcV2MdwH64YMie4RT9dfFafQj2r3bHvtqKmMV3C/ZNT5P0UnGPw0zpI7Jvn+F9wnlheaC5JK2W1xvZtytxgHlJnKb9jKTj85hmGIcYH07pHoSZ151x4b6WWO/6Zgz+nVm0PbXsn0prnI3ovf4f8rmtfsWw31Xabwn+lVZaE1lYG24XzKTqgTfADaY7Tm4LF8DYFzO/HsaboPYYqKwa8IuIaSUVqRGz4IrBlwPrSHooP/8LBooWwSyCW6tsu7YD1gFHb7srKwOHU2FnUEXKam72fsQbk/WxU9Ucg4D7yyLYP+mO5P/nAO7HrMIC/OyIHZIB6VTNnZHWXliI+EysvdINuFjSfvm75fDGZUu5SuwcWH+vWf7mOaytNqHi/KtgwOvGChCtDXb2hIWgB2KWYHdgbUkHJpC5BE4pfglrwxRMwmZYEPv64v5ExCNYc2234pjKflTzXCru84Y4ffs57JhelV/NkP//r1KYvYq2muN00Wew09str20d7JSPAY6VU2NKR/BXWGNA0OLYMAuzP2bQvoF19zbH4/8kSfdH6phGVmPM39cHEs6DQfgS/Cttslk0Qi+vMuhTT1s98Duxk6SRE5uLmurYydCPAVjTs85jwyz8vXCgaxugp8we/D+80R/1i4Ybabm+fKOpoEhFab/NwhW1L8TFOV5NcG8PORi7BpY4uajCr9sKZ0s8CRyfa9ES2J8ZKunQBs73C9CNmgJXDWr2NaZdZUXdeo5vjueXc7HPd2s0olJvRLTFeqRFQZJl8l4tiNmHC2Aw9QFZQmZZvP94OkHMFtg/u1/SHtmn1jgz5HBcxfi5/HxRDEb2At6StEr2vzUO3B8nF99ZH2en3C6n4D6Ei4E9IRoeswAAIABJREFUlu30AWaStb1nwsDi0cBgSWfnMYH9vfsxs/NhJdM6Is7HwfUi8Lu5pEYVHSxt6rIS/Js6rKTPl1Za09lQHMncBIMVUx3wB04PiIgzMXC1Htbo2jyjcVUxcxJc2jYivsXaIQfg1I1FgbMioqekV2Vm4TYRMYdcObdB4KYCFFsTRytHY8bdQcBpEXEyBvhOwZu64nftcYT2sezLhRjgfBY7G1tFxF2SvkrgcOuI+AKnZeyG0xiPxky6fsAhCY7NApwaEW9gwGtbHKU8CEdr146aSPR0OA18mbAOyuo44v0PvKkKVeit5f1+PMy4ui4inpUrxn0CnBIunPIxBtUCa5ltHRE3S3oUV6h8O8+xGPCfdCpnx5HaSsdpJ2C/cFW6cfq57lujTNYVGo8BzVkxiLwWFnE+shrgr3jOCTh/HhF/oya199KIuBcDi11wxHr0r+3vn92qBf6KYyNiVTzO98XR+I4YUO+Nx/5hYY2h9wqQMCIqQcIJwCFhJtUlEXG3GqHxWVppk8rUOL28htq6OSIeVKbYTWwuaqpjm7jtB3Ea9Pf1HSvp6Yjojws1XF1s/CVdM7E+N8Y0leiulvbbLKwnfSlmyBXyHx8Aq0fExRhI2l4ulLE48LEsGfEBZqWtn2vHSwkuz1TFaceHsy3OxsDZp9hXrFazr6p2Jwb8gVPfw/p4f5d18aqu1Jug6KyYjTgcGB4uSDIYFyQ5KWoYiW2wvzgr1teuLIy3PPBqRMwqaWs5pXouLHHzYkVfXw1n/wzCbESwL7879i1nzM8ewb5AN2AYlqapfD/H4Wr310n6ICzN8z9g3Yh4X67MPDOeg6fNfp8fEVdKOkXSrjkORmEtxBL4+71bGeudKqxk/pVWWhNbAa78HhzXyj5GI1N9M7L3AWZUrCHplQQFDwA2w9VofxULIJz6cxp2dtrgaOflGIjYBTt0V0i6owIsXBDrumyPUzMexWmLZ6czFXIq4lrYgSs0hU7G6Uof48jq9MCdGdksWHuz4WjvI9hZWxczoGYBWkt6OfvdGqc+b5HtP4oZkZ+p/uq0BQOvI9a5OUPSeRXft8TMuuF5D7bK/p6afT4Np96+FE7d6o8d55Xws+gos7M64wjzpjhN+jePzbBey4nYGb63Eb8rntna+P68hjedrTALcoCkyzN63aYx4FVpv83CRXnuxal7a+c7PTceV1fLjL+FJf03QcJ9MLuhAAkXxSBhP/xse+K0YP0e5sTS/pgWZgAOJCuF59xzGrCupKlNl3eKWJhh1Qtv0C+sBAga+N001YCnpf15LCLaSfowIg7BzLq9gOfTN24HLE7KtSSj7Gqcoj8WOBBr0W2DGWK3qabwRTXnbo19vzdyTB+Ei6W1o0az76+YELMyrr7b4LpUq92q17JoRKVenIkzmCxIApwu6dJs5xycclwUJJkR+0z74fV5WazldzdO+X0/fajVlDIzDfSzshrvMtjPbIWD2hdIeibMyt0yzzMWB7hXwmDmm/heF+zgWXFmygvAuzIjcD88x2yJn/E+2Oe+UdJRdfWn4Ttc2tRqI7Zd7w/5/Fa/8r7fFfOvBP9KK62J7c+wYEUWxYiIy/AGf4Bc5a9wHopUhdWBbxt7PyLiKOC5ZG3MhlMVOsmpIq2AH+sDWNPZuRizOlbDabLP5neBgYkFC0cjnMZxFdBFdRQiqbimZpghOA9mHO4h6dx6+t9KLsCxKk4D3k1OgaxzbFQAgGtlXzpm5HZdzPabDVdfu5+aVO09cIrERZJuzEjwVcBAZeXXiDgSO1n3YAd8/4JVMaksI9rHYYbe+9VuAtPhPxc/q9lwCvMxuDrzMOAgSZdNyr6WVrdVjPFF8IZieiz6fXDFxuN8nJZ9Rr5HM1AlSDhFLqq00mpZNFJf789oCZJuhNn1pxeBrVrHFNqILapgTJX2J7GKdWQZDP7cIReTOgJXdz4KGFXpu0XEzFi37jHgbRzcXQT7Wqtjdts+kt77lX36VZp9k9LChdbOAjaVtUXnxuvr2XKl3lnkjJw1ceD4FlUUJAFGygW7SGbc6+l/z4j9pm1IeRSgA2btD8dp0u/l7wIaZhWnz7q8XMRtIAb/Hsfg5C3Yf+4rF1VbAwOYr2Ag9XpcLfjveA6J7MuqeR2XYl/vWQzEXozB2E6YdXhybQCwtN+3leDf1GHNpnQHSivtj25/dOAPoHD4JW2PnY9DI+KfFY7fEFyg45sqI6qR/2+bH82CBaGR9AnWLpk9ItpI+q4AmOoA/ppn37bDjLQHcAosEbFQHj8eR3uLa3kij5u+nmst0nMnSDoHO1j9ceW1em9RWBvpYhwJHV5XfyvOUVRDG44dr3fzq/aYnfIIcANO014TO1lrYZ3GG/P+Ka91hrzekHQ0ZrtchtNF7izu9aQySUMwwPpOQ8BfxXOePq/tn5JOxPf0EmBXWYdmaxxFLm0yWI7xzXBq5FmYuXsRcEREnBCucr0GjuCDU4EWwVH7JcKp2hNy3P6AWQPg1MrSSpsqLIG+HfHYPV0uovS7cuKb0pLVcxTwBX7H+4W1xCqPKYC/Nljio93k72lpU6PlOrIxXkOWA/pGRA9JA3ChnYE4NbU4tiv20zbEjLj/YXmJ0TjLYQSw+68F/vI8X8t6c51k/eMiOyOaCvir8HNah7XzKiv1zpLXczkwQ/ppn0XEwhj4640zXcAMvkfydzukf/xKAn/LY8DsecwqbI4Lc4zIz9fH4FtxH1QF8FcUFhkY1rd+GKf4/oBlY94BeiXwtzTW89tGUk+sdb4msEL6neti3eZ2OP36bpyt8mre99kxo/MrnC58LpYdKK200iaxleBfaaWVNsksI/+fYGBgn2QHDQZmrQCwGmqjiABvBByTm4qjgWYRcUwe1hw7C20m1pZqdAsl6X4sRj4s2z4nIhaRdBV2uoZExJzJtlsTpy3W7luzbHdCxTm+yejkuxExbz39GEtWsMPRb0VE54g4IyJ2zghv7d9MCKdPfR4RzdOBvAVHqpfKjestWFOyD9Bc0nf5W0n6BoOCq0fEEnnODjgK+7FSh68pwGlVmZabfdoAa8ktgdNgkNPungHahour3CdXhy035pPBwkLkuwL/J2lNPOY+w5uJ3njTtpXM5utBlSDhnyEQUtrvyxIAWEspMl+OUVu40v0WmGW1H96wfwTsHE6drAT+ZsYBvkGSPpxinS5tilu4OFfx77lwtdadJa2MU0HXj4iNJB2L2X3T5bGr4mJwI7CWX08s8fEuXlNeBhZK/3JSWKM0+36LpZ+zKQb4bsJs+ntxBssREbEtlmR5KY/dBGtPn4jfu4MjYun0q+7J3z6HtQsPz9N8jHXxxsr6z0UxveMwaNdX0v+q7XNEzIf965Mxk29DjBl8jLNOPsYsvtfzJwviar6b5zVfhIu0bBSuLvwdZg2ujVmD3XAF54JJ3Bz7qkfh532LpAdLn+8PZhP0x/zvd2Yl+FdaaaVNMssIZHNJo3Gk9w2sP/dII9pQRKyHnZYrEgj6ArPBlg+LI1+LAYi3ACJikYhYOBlktdubkMeEnF6yCnZoDpH0eh6zNvBetnkiTm98LiJWiYg18zc/A/0iYo6ImDsiWiRYchw14sh12dd5XwoNw5NxZHNToFe4qu9PlvdxXER0w87eHOn8fYB1U4qN69XAE5J+qOOcN+N5/sKIOD6PPXtq2aBFxGK4GMsNkvYEPoiI0/PrGYA5qGBglhvzyWbj8AZltvz7KgzOzovTdwA65LjfmepAwuaTsf+llVa1KYtflFZjkr7Hc2/3/PspDDisA+wdETNVMP5uxZWTh0+xDpc2xS2ZaIPC6afg7IPA7Dpw5kMrYP8EvAZKeiyBplNwYPRKrGO8PA4gzyLpHeAopVzLpDBZz7oowHFrU4BMFYy/tli38CzsZ94BPARcifUGV8UA6QPJ4OuP19T3JZ2PU2fPj4hl0wcsGH7XAUtHxD4YRB1TcX0jgNuxFuB8apxGYiucbn0ylu7YIfv9Egb5l8R+5abAbBGxtaQ7sC+wUkTsnH24NK9zFLCopI+wD/oDHhPtI2LJPPZ2zPZ7Gxd9KYMxpZXWRFZW+y2ttNIaZRXMvPZY3+tnjL7cEDSX9HZEDKwFvlW7kHfDgMPLEdEbs4fek7RRpkOMl/RW2DbDUdM3ga8i4jxJz9fqc8FQaI3FiB8EPkynaWN3W+sls28WSZ9k9PUIvLFZJ9u9saLZ/tiRPQQ7Xa2AH9PhC5xGMr44PzAhN0rdcQpVTwymzIEB0nERMVueu5JRcVS2d3hEfAqcgB2+DSXdmQBgnSYX9jgRawO2A4YoqzFOScv7PCvWfBlLVpPEGn+nRMQwzOw8qgBMS5t8JunLiLgJV67+VNKLEXEzfi+fwWPycAxK1wYJz8OFczbC1Z87YF2nqosHlVZaaZPXKtb1RfHG/FUcCNsuInaUdDFm+fwHOE3SV+G0wOOwXEPVAb7S/piWWQr9gLkjoq2kRyPiDqBbRHwrF4G7CYNEWwBvRsTDGFh6CtgmIoZKejYi+mI9zmnCEjLf13fe39Dfb0gpiibKgFBUV6m3Uivze6yBt1ZEbElNQZI2wOCwJvXX2fYDGEjbjUypDafqz4VZup8AJ0j6vJH9/i6s77cy9tFuwUV/mkk6ISKuTf+7RfbrmogYJ+mGiJgA9ImIlpLOlnRRWONw/YjYFWtB/xXPJYcAG0fEBEkvy9V/gT+HVnpppU0pK5l/pZVWWqMsnY6NcfRxlgRyah8zPpyyOqGIfjZyIb8X6Iwrvi4APIEdyjkl/Tcdj2ZYq2lvrGfyLI4Wv10Zxa0FpD2CI6YrYofmR6z59ElEdEqg8tOM1O6afXgPA3sP1WIv7Q9MwIzEh3GxjQmyTcBRb/K8c+JUloewIzcOsxePw6LP7ybDr1s6TePDBTCGA1dKWgU4H5gPp30sg52oBk1OS35Q0vVTGvirGAsTMoJ9MAaK1koH+FVJG+Nn0l0WBy/TPqaMXY+fzWkR0R84B7grn90woKtcEKcACZfODczNuAhOARJuGxFty+dYWmlTr+W6vhnWCzsFa8tujNe2v0XEbTgQdpUyVS/f96NKxl9pUSOJ8j8c4BySLLbbcIDo1HChjyK7YhrgWwwCzSRpL8wKOzIilku233bAzfqdVY6uYPytit+nf2CQqw+ApH2xH3pbsuwqA2Pv4HTZ3sCLuFDKI8CeOE24AP5mxfftQRxwG491k+fDBeA2AVo1FvgrTNKXku7FQbyWmOV3RX79bkR0BA6V9G/sf18SEVslg+9KoHtEzJcg3n+wD7wzfp7fSHoNOCP7/LdIKYGK85fAX2mlNZGV1X5LK620Bq0A0PLfS2AnYFdJT0/s+ATR9sLRx7GNON/0ZGGOBPpWxHopOwLHSFo/j1sGO1avYd27bSS9nsc/j8G4Avi7CTha0sMJ4s2UkeqlcRR2K0kvZrszYdbSC1jHrI+k1xKQewOLNTfDUc8XcfrIjDg9+dX8N3ntc2Fh44Www9sVR2svAB6V9M+MDl+CxazvrQBXb8daKm9K6pV964qroY2QdFu193Rqsez/Ztjpvx2nlp2Gwc3bMhpf2lRg4fStDthBf1bSiNoR+YiYBwPlqwEjcaXB3SUNze9bKbUoSyuttKnTkpV+E9APr6ddMevoQQw+/BVrxf6nYLerQgajtD+vVbBG55T0QX62Cw7MbgW8iws+LIUB5Onwet8Fry8n4PXjK1wQbV3gQE3CNN/JbVFlpV7VUT07v2sp6Ydk+l0B9FPqEiZIvx8OSt+N04inxT7wo5LOm8TXMj2eCw7HwerPIuL/gAUkHZfHrIN1CveUdE1EzCHpo1qM4r9jIPENLPXyVViHelfsm0+saF5pfwD7d6+1/5CgU8dBD/2ugtsl86+00kqbqEXEHMCApPiDF+/XgWcjYpqCDZebh9pMu5uwqG/VwB+ApG+TVfS/sP7ftVij7zFcOXdkHjoKmB87mTsk8LcBjoTOqRpNoqGYofBwuADIeJwivB7WMDlITm1cIyI6yhXH3sfsvr0T+OuEQbyWeGN0OWY5/R9OhRqLAb3rcAR7oKyz8gZm6i0EnCzpFSyWfDqwcjjF9UxgvwT+mmFAcz9JvSStDswfEeflvbkbR1xv+72wqSoi4UviVOrR2Fm9Aa9D++OiH5v+Xq7pz2CSxkj6l6RzZQ2hX0Tkk+lxEmZ0fIC1ioZWsDxL4K+00qZ+mxYHs5ono284lptYWdJXyR7/D/xUUKoE/koDfmKNdsdMttsjYm1JF+Cg3rXAEpJuktQfS0ScAWwn6SNJt2Ig60mcHXEiZpv+7jRiK/ycBiv15mevTKS5OguShOV2DsGafFtgbeQuWI/vSqBLRLSflH5U+uP3YJ/4+vSpZ6h1zAMY3LswXOjl4/xc+f9X8/m/CSyLs1y6Yt/4wBL4K620yWcl86+00kqbqIV18ubF1brG4XTWyzHz7408ZnWspXexpG/TObgB6C/p4d9w7laY5fYt8G+s9aew/tg8klYNVxvdCPgQM/AOws7EHdnGJsCXwMO1NyzJWppP1qbpilMbt0uQcElgaxyFvhmLHh+E9f3Ow9HqlyQ9ngDomXiz1L+Ifuc5IttZCIN6u2WqBuEiHzMBrSW9kwBjO6xxeGcCfQXbcr+857+rFJjCwkVTrsf357L8bDOgL04T2hD4UK5UV1pppZVWWhNZBSNnduALuVjXPlhK43RJoyNiK2A9zAb8sUzFK63SKsZQG+wTHouruS4N3CrplrB23wHACpK+iIgF8d7zjVptdQUuAxaX9OVkvIxJaulvHoX9mU1x9seWGVyeHfs6T0t6oYq2WuNCb29U3OulcOGQTSWNiYi5cQD7bElXhgukTNICRmEJn3GZCXAB1gS9F2sZPov9a2H/+xtJY+poo+h/c5zOvQywJa5CfGvt40v7Y1rJ/Js6rAT/SiuttDoto3f9Je2Ufw8E/gL0wjT9TbDA/4+YzdVP0t0RMS3WtjtQ9WgBVTgCi2Cn4QNJ39Zz7E9iyBGxpKRR+e8huDjHWhGxEgYAm2GQb1iCTdMC30t6fCLXGbii2R248MbjEbEc1k55G6elLI6LZTwUEcdifb/TMajXFbP7Dsn7cQLwYl7fahg4fUJOX94Op070xALKPTCzsNBR7J/3diXsMHeUi3Z0Bg7DzuSY3+MmLO/zC9g5XDU/aw1chMfKuxP7fWmllVZaab/dKtbfjYHdsV7YcMzcnREHuq7GoN/Okv41xTpb2lRtmT3RDugsqU9+1hfrzg2VNDgi5svg5kSLOISLVXxTBEd/bxbWOLwc2FrSS/nZNVi3uq+k5+PnxT2qabN4V1vj7BIBFwJ3Ag/JKbh9ASSdO2mv6GfnXwP7+RthRucuWDf7fxgEnAE4UakrXfG7ZqpV9C99wRmBtukXl8U9/iRWgn9Th5XgX2mllVanhXXvBgFfSeoVFhg+CFen3RkDUStgId/rKjcIhbPXQPs/q9IL1FmlFxfVmB5YDmvzjZS0ZX5/KzCzpE7F8XKq788q9WbbN9ZuO4+dERcK2QSz8CZglt6XwHNYsLol8BbwObAqcC52xIbmMb2xM/Rp4cRExEY4FfI2zJ44XdLVCQDugPVajpd0U0TMgIHDgcr0yog4EkdG78HR4v0l3Tmxezo1WYWjtyzQpgCCI+I5rJnYB7NMbsLFPf4z5XpbWmmllfbHtlob8UXx2rQ1LtCzSP53PrAE0BZ4tb4AXmmlZXDzOszo3w64SNLh+d3eGADcR9KH+VlVIM/vFQzKDI2DsPZtO2oq9f4V6z3/VKm3ke1uirNGWmBfsxtmWI7FhbWOxrrUD0yaK/nF+TsD22M///b87GRcOK+rpO8jYvoigB8RK2AweGD+XScAWOEr/6QpXtof20rwb+qwUvOvtNJKq9Nk3bteQPOIuE7WrzsO+ART/x+QdChmy/0LfgLrmBjwFxHNwrolVVXpxYDc81hX7Axg1YgYlOfZFPg+IgphaEUVlXorgL9OOHL5LS4+8QUwWFJXvDFaSC4C8g5O/e2Hwb4NcSrHAAxetgdmrHBmOmJnbQPgfuwIbhgRvSVdjkG9Hgn8BY7mzk7qqKRjdDQGDy8D/iHpzsr7M7VbOnjr443BqRFxWkTMK2k5rNM4Cvgb0LsE/korrbTSms7CEhcHhTV8wevqaEnPyBIZRRrfHLI+28Ul8FdafZZBve2BwyQdhIOsnSLiaABJpwMHF8BfflbVxv/3CPylTbRSr6yhW9W1Fb5e+rMH4lTf93CGykNY3+99HIzeuamAv7R2OEgwd/GBpP2Bj4D7I6IlLmJHMgT7ANtHxJ557ISoqQat9L8VEdOHi8SUwF9ppU1GK8G/0kor7WdWLNK5QH+FF/LxETFY0hfAMaTuXURMh5lyANS3iEfE3OHCFmQEsBXwGAYXN8fg1hfAXyOiRfy8aMjN2Mm5ICOJCwOLRcTgbK8LZiIWbY/HTsmO2DntLeljYN2wEHIz1RQCORxXojtS0nBJB0l6IFyBbD/glojogh2fYzDotwNmABbpuDdmO2/mta6O2RNnYEbFcRjcfBIXTukDfCLp7eyz5Aq31wOrR8QS6Rh1wALKH0t6qji26gc5hazCaZ0GV/DbCkfAWwB7JQC4Ii6SsoRq9A9/N8BmaaWVVtrvzCZgBvkeufa9BLSOiJ0AMgDzMa7IWs7HpTVki2OG6IoRMbssx7ITLtp1HEDh4/xZTNLXks4GOkm6GWvj7Y2z7Bp1L9IHXANYC3gw/dO+wBM4A+Y9SSdh2Zhhk/I6Kny4OcJVh6/BftwZGdgu+vg3rEP9g6wJ2AlnCz2EGaFrRMQheeyEDPwXvn0b7DvPMin7XlpppTVsJfhXWmmlARARs8T/t3fncVbX5d/HXxcg4gqGS7mlpj/NLDEUSlCxlNxwRUNzi5SyNCytzA33fddExT03vHPBfZdbIVzQnyjumrmkZiq3OyLwvv+4PgePNMycAzPAzLyfj0ePPGfOueZ7vuLMl+v7+VzviJXLL+nNgGMj4mjyAmYo8FlEXFWadIcDh0qaXMtdO0lvMvspvcPIsI/JpdZUclXdFhFxSlkl90jUmNRbPt+3gduBIyWtCSwcEZVtqcuSIRRHkisBNydn+N1NbiXeDtiNXCXxOXnXtbIVojdwNDn/cBy5IvCpsmpyHPkXrnFqOLTjevJn8gURcTw5c+mc6jvnrUG5aN2WvDO9FRmo8hk5C7ELufpkZUmbAGtFxMWV982zgzYza6PKX+DfIn93fY/8fb4oGVLVMyLOiIgNyd91j4N/HttXVTWE1oiI7pKuJW96fh3YNDJo4lngZ+QuivaswaTeWlSd597AleS17oBy0xhJvyd3wtwUGYjX7KvmyjXcAPIablREHEzuYBlUHm9U9drq4JJVgNOUI3bOIK/nN4+Ifctrp1fd1L8OOKE0jc1sLvLMPzOrpOoeTF5ITCBT244hLzxeIJN7nyIbUp+rzNyrsXanSrMrakjpJVNuFRH7kCsRbgEGAL8jV/G9GDlTZD+gDxmE8TG1JfXeCnQjL6qeB87Ul4nFT5Iz+zYujccPgEfIrQ5rqCSYRQaJnA1cRq5GnHHxVRqYt5MXfCdFBprcAjxEbs8Y2thd2sihzuuR2yz+qTI8uTWJTKMbTl74rkOulvy5pMciYgXy39c5yu3UlEbgK7MsaGZmdYuIxap+b/UjG35LkPNprwTuJVfhH0je6Lpb0k3z5mhtflfGeFwB3EHejP0jeV2zG9kcuqnc6Gz3ooGk3jreuwGwA3CDMmRuN/Jad5yky8pr1pD0XAsde/U80GXJFZ7fkrRPRPyCvGmwHPBB9eeKiN3J4LuNJb1ddgZdQgbvXakcc9ONvA7/k6QxLXH8Nv/yzL/5g5t/ZgZARGxONsuWIVe6nVR+eR8ErCJp98gQkG9J+t86a39bX6afzTKll/zLiMhZgJcDd5GDkocAu5MXRK+W49yCbCz9m2wMNpTUu1p5/5ulKVj5Ab0esC/Z3Lu9rDzcg5wruFnl80UmFz8OjJe0R9Xn+SEwvaHmXORw5pOAwyWNLM2wbYGxaqUpdrUqF41HAe+XLSpEzn3ZDdhX0sMRsaByQHRdqXdmZlabiFiYbNKcSY5YuAF4jFwV34ts+l0FXCTpo6rteK0ybMFaRuXPQ1mtNZgc1/Ia2UD+FrAPsD4Z+PG7ssvD6lR1nlchZwTuQe5cuSwilgJ+Uv43WtJFLfnfadkZc4qkLaseDyNvdt8fVYF+ZWvyuuRN7n+SN9m/T64u7k5eCz8FvCHpjIjYC3i67IyxdmbMzhu0yd8tfa9+0M0/M2s94qtJXGuSK7OWIi88ninPP0Ku3qo5mKHqYmZt8k5fYym9nSVVBgavA2xP/sXlRXIL7jpk868b2dh7E1iJ3FowkJw1819JvZKOqzqO/qXuB+TMlEnk1t6XyVUPPwEOqPrMncocky5kA3CspL1r/Oxbkk2w0yVdUes5a42qL0IjU4sPAb5Lzjp8qGyz/gN5Mbs+8EktW8XNzGz2RcR25M27T8gxHX+PDNvakpxDuyrwdzK0oaFRFGZEjoHZlvzzcgC57XQ58qbsd8nG3wKS3p1Xx9gWRMTWwBHkf5/bkA20HSVNLA3ALYDHZ9pq25zff01ytt8pwCgy3XdE+doZZEDQOVXXxn3JXR7PkzfZR5LXyoPIm/pBXrf3BvpI2isiFipjYKwdcvNv/uCZf2btXGnOrBYRt5ENsWHkNtwdIuL7EbEauV3o0zrrKiK2AE6kkZTeyBl7gyKTvzoDF5Ozh14kt/2eTV5Q3AB0LFsFRM7w+1zSCzSc1LtS1XH0Iy9oHibTfe8mm4iHlPdtCAyvnj9SLm46SZpMNh/7R8RlNX72W8nm38GRYScdm3pPa1TVWP1hWfHYS9KfyRUmg4Be5TUnA1tJ+tCNPzOzlifpBvJ3XE9ytTzkyvlXgLfJn9GQTmkjAAAgAElEQVQj3fizmUUGdlVmzx1ONokXJ2+2dpP0BjCCvFZc2Y2/ORMRPchrxp9JekvSeeR173kR8T1laN1Vzd34q9oNA9CRnOG4Ajm2pXdEnB5fzgN9FmZcG69F3jzfTdJA8gb/BsA6ko4kf95sTO4k+jNwenmvG39m85ibf2YG2fR7kryL9yFwPrmC7mpyvsef65nLFmlB4FfkrI/GUnoXJ5NwFwU6k3c3pwG/UXqPnOf3ILmNgHIsGwLTI+ImNZzUe2PVIa1B3sW8RNJZ5F96hpfvcyawI3DbTBdC1Q3Az4HVgYtqPQeSRgEbSXqzrTa8SuNvM+ACcibNMRFxoaRhwHvkNqEflNf+c54dqJlZOyTpHnJl1p4RsXMZtfABudL9E0mPz8vjs/lLRKxaZkVOjZzReypwi6TLyVVhPYDfRQZ8vA4cIemJeXnMbcTnwBPAhhFxeETcTV6fdgOujYjFgGZv0pdruN4R8cvSWPwY2KTcODiLbAYOJG8iDIuIZcpbVwLWIkOEKCsExwNbRcSuwGfkaIGNgG3r2TVkZi3L237N2rGIWFXSS+Wfu5Nz8NYl/7LQnUzjPUfSM7MzYyQiTiTn5f2f8vjbwKPAeZIOrGq2dSJX/P2DnBHSjQzO+Juko8p7F5D0Rbkg7aQvgzruIVN8NyyrCE8s7xtVtTJtb3KW3w5Vx3YBmTb2j8jZgz2Byxu6MxlfDS3xTKQiIjqQDeKRkq4vz40jh38fSSb8jlCZ92hmZnNfZHrnZeRNtMnAFSop9WYVEdGH3FkxjlwgchrQH9hZ0hPl+usyMsTscK8abR5lZMqewC7kLpXngL7AS+R229da6PsuQN4A35Wc4zi5PN5XJfynapvvEmQAyPckXV12ewwBRkm6oLx2b3LETyXQbcFy89yMMYP6tsm/O/W9Zoy3/ZrZ/KvScCuNmxsjYhhAWWF3Ljkf6ArgfTKR65ny9UZ/aFfVXSMili+z8saTd4lXKy/rQs7bGxARA0vNNcg0sNOA5clhx5PIbQZ7RsTR5b3TIuJrZENp+4hYuRzXJkC3iLi/HPN+pfG3EfDTsp3iQuAbEXFJRHQts0rWBxaKiI3JZOPhwO8jU9pmVj3TbpUmT3IbVvXvuR8ZwPIOX90SPhhYVjnD8Q9u/JmZzVul0bcXGdJwhqSbZ17pbiZpLBnQ8DKwuKSh5PXgsIhYu6z22xO43o2/5iPpY0nnAP3KjdRFyJvv0YKNvxXIG+2nkHP7tiJXG74F7Fvmg0LO0oZcFdgNuDIidiy7W84DNo+IfcvnGKGcUdihPHbjz2w+4+afWTtTVsL1JRN1zyQbbL8pX/sP2bD7DPimpI9qqRkZGlIJ1RhNhj1cTK7eu4KcW3IxObfvFLIZN7ncObwIWFOZsHsu2Qz8LV/O4rurHNt0Se+XY14b2DIivlUO4VTgO+ScwOUj4pvAJeTg5APItNm+5Bbjc8j5IweRF1jnAgcCW5fXDymNy8pnq6QgdiufbdFazklbVf49DyDP4WvkHerzImK58pLlgJUi0wHb5F0+M7PWpjQVNlRJ2vQKdmtIue77HfD38nv8OHJe8skR0UPSa5LGz9ODbLumRURP8jr1z5LubYlvEhELkSv9TiFX8/2CDNJ7nrxOX7PyWuVc8D7AwaU5vClwUUTsVG4qXA5sERErVG4oqIQImtn8p9O8PgAzmzuqtsCuTw5qfhx4nbzLd2hZ1fY28FMy2XdiDTU7SZpaLg7WIQf+7sCXKb1Xkmlft5JDhI8hZ4XsBhxKhovsKOnliFgaeINsyp1FXnweJ+mN+O+k3iPIbaVLRkQlqbcfuVrwfnLe346SHosMHdkR6CBph3Jx8nVJb0XEVsAT5bNOjIi3gduALhFxMsyY+9cV+BvwO0kT6jz1bUr5czKYnMn4MPBwRCwJ3BkRd5IzGw+U9MG8PE4zM/uqcgPNrFFl98QX5M3gdclxKguQoRDWQsqN5ueAQZJeaakxM5I+K9e465E36m8AupLXySdExF3AKRExuPzMWAn4orz33nLj/m9lHM+VETFW0jvNfZxm1vzc/DNrJ0rjrxdwLNnce6gs638D6A2sSK6eO67Gxt+ywI8j4jpyEPHF5JaBs/kypXdf8qJiL0ljylbdo8j5IguV7/3diNiZ3Ia7Svna0eR2h+nxZVLv6cBiZFLvduQA4u35alLvMxHxXjmW0WTq7Bhy28IeEbGMpBNLkw8y9VARsQbwiqTxZYXioPL4mjLn5GbgIGXScHsnYCnKCshycXpkRPyTPN9XlaarZyOamZm1QpJui4jp5Or+NSQd3dR7bM5J+oS8Nm3R1bnlBu095Sb4PsA2ZKBLL2ACMAUYGRE7MtOOF2W43iBydNB95MIBs8ZN918J5gcO/DBrRyJiU+AO4DBJx5Vhv1uQDbQDYUaTsMnGTWmYdQDeJWe+LUauuLtDmfZKWRG2H5kW92h5bnFJH5bZevuTKwVPJAdIbwW8L+nqqu/zK+Brko4rj/sDZwCbSvpX1bbcGcccEb8mZwNuXBpRi5GrEl8n5wr2At6TdE5EHAMsTYZUfAL8ppyjjcmUsw2AqZIeqP+Mt00RsR8ZCDNS0rORCcuHA3tK+ve8PTozMzNrDhGxJZkMPXpeH4u1jIhYmLz5vxDw97LjZTHgfHI8zj3kNd8T5A4ckU3hT2odD2Q2Zqc+bbLp1Pfasa1qfq5n/pm1I5LuJlfLDY6InSV9wZez9Zaqel2T4R6SniO3954K/AH4sNTeLiIOL3XeBY6pNP7Kcx+W//9E0rFAH0kjga+Tjbc3Z/p208gk3sr77yJX8y1YHk+rHHPVvJFzye3Dt0dEb3JQ8Z3klpXTyuNNI+JySYeSW1v6Ar8nVxROLN83JN3nxt9/uZ78/XFBRBxPznU8x40/MzOztkPSrZJGOyCm7ZL0ablOf5CcO9iXHHWzB3nT/DRyDuBGwM/Judwz5oL7z4ZZ6+GVf2btUAlsuJIM05gOXCHpphrfW5kd+G3yomA1cnvvS2QYxxLkBcSVkg6roV4X8qLicnLA8c2RSb3fIO8sTgDGkoOI9we+SyaM/VTS07M6vvLPBwGHkQON1yAvWsZK+mtEdCbnB34sadfy+oXJYcZHAbtJerKWc9IelZWb6wHLAP8s8//MzMzMrBWKiE3Ia+VrSqAHEXEKeRN+M0mfR8TCkj6dl8dprY9X/s0fvPLPrB0qv9B3BVYFHpV0UxQ1vFf1pPQCRMRaEbH8LOpNJmcELga8UmtSr6SnG6pbWQFYGnlTgE3KbJOOwA+BHhGxiKQp5Lbe7hFxR3lvZfvyrm78Na6s3BwtaaQbf2ZmZmat3jLAzuRNcwAkHQi8A9xXbpxPmUfHZtamRMSOEfF0REyPiHUbed1mEfF8RLxUFrZUnl85Ih4uz48s/302ys0/s3aqrPT7IzA0IrZX0dT7ImJtMqV3N0mPxFdTenuSKb1fAC9HRMeI6A4cR84SmZU3yYTf+8kU4B0l7QZcTc7e213SDmQzcG9yO+8s65bP8RnwV+DViDgEeAoYQibXbVPuXE4FBgAHV733CklPNXUezMzMzMxaq8pN/4hYOiI6S7oS2Ak4MyL6VF4n6afAryRNKdfOZjbnJpIjs2Y5XioiOgJ/ATYnd8rtHBFrli+fCJwuaVVgEvCLpr6h037N2jFJd0XEz4GX63hbF5pI6QWOLK87WNKbEfExMKVcZAQ5cmAa5A+1EthxEbmFeAizSOoFVq6lbkR0KI8nkYEdPyYTic8mtwEPAxaMiJFltd/jdZ88MzMzM7NWquyWGUDO3FZEPEiO1hkEjIqIHST93/Ja3xg3a0aSngVoYuNdL+AlSf8or72GXMTyLPAjYJfyusuAI4DhjRXzyj+zdk7S3ZUfKDWaCDxMpru+COxDNtVWk/SkpAlkcIaAYRGxCpkGXFlcOL18jYjoVJp1XYFbgavI+YHDI6JnCQcZS67gu62WuqUR2IH8gXg6cAxwEzlDcCjwaHnuF0C32ThlZmZmZmatWkT8D3Ay8GdytM4U4FhJNwJ/Am6LiG4O9TCbZ5YjZ+xXvFGe6w78v6qVuJXnG+XADzObLRGxkKTPSpruJWQT8IFyF7ETufLuMjKVdyVyFuAL5Ew9gKGS3iuNv+uAIySNKbV/Td69GCDp4arVgTXVJbcfnw18AvxW0piI+DGwBZlKfDKwoKRJLXN2zMzMzMzmXyW87xRJW1Y9HgacL+n+iFhB0uuNFjFrxyJiCLlrreICSRdUff0e4OsNvPUQSaPKa0YDB0oa30D9gWTYzl7l8W5Ab/LvyQ+VLb9ExArA7ZLWaux4ve3XzGaXIuL7wIXk3cFK428LYEeyCTcYOJX8oXc+8G9yRt9bpfG3CHAfsH9p0EVZxXduafTdGxHLAR/VWpfc6tufDPgYCjwCIOneiJheanxDUj1bnc3MzMzMWr0yM2wn4BSgS0TsLWmEpGcj4m3gO+Qc7jfL66OWueBm7U1p9F3QyNc3mcNv8S9gharHy5fn3gO6lV10U6ueb5S3/ZrZLNWQ0vsysJ2km0vjrx/ZlBsu6cPymt8CTwD9gOcl3SFpQkSsBSwB7CHpwVKzsm0XcuXemiWpd8Na6gJPlu2/CwCPSXqwpPoSET2Bh8ikYDf+zMzMzKxdmGnrbkfyBvoK5Hbf3hFxekRsSAYLPAZQmc/txp/ZPPMosFpJ9u1MzuO8qfw3eT8wsLxuD2BUU8Xc/DOzGUra17J1pPR+DVinJBEBbApcDjwVEYMj4tpSYz9gaWDNmWtLmlhdsDQAv0nekfxXDXW7AkuV5t6IiOgCXAm8ERGDyufqCZwLrF7mCJqZmZmZtQvl+rp3RPyyhHd8DGwi6QbgLLIZOBD4g6Rx8/JYzdqDiNguIt4AfgjcGhF3lueXjYjbAMqqvn2BO4FngWslPV1K/An4fUS8RM4AvKjJ7+lGvplVRMR5fDVN9ypyCPBrNJDSC3ybbACuAVwMrEc22SaTAR0TyDsRvyRDNmqt/T1yLMEk8gfiq+SdyYbqvlcuaLoAd5PJvceT8/22JJuDSwOHVWYrmJmZmZm1FxGxAHAmsCt5/Ty5PN5X0k3lNZ0kTfU2X7O2yc0/M5shIhYG/kKmfZ0IHAwcJem18vUOkqaXeXwCFgXuIsM3LgFGkAm6HSS9UZp4fwW2Jefy1VK7s6QpJc33f8lwj4NmqtujPL8t2TjsLOm5iFiIXPL8bKk/DVgd+EjSP3wxY2ZmZmbtSQkDmEyG440kg/JGAocAHwC/lvTSvDtCM5sb3PwzsxmDfOtI030fWJycNfAAcCvwB+BmMgBkMvBjchXgAZJurjMBeDvgBGA4uYKvuu62wKVkKtKIiDiLXCF4laTnyzyECcBzwC6SPmvWk2VmZmZm1gqUG+OHkPP9RgAfAruR19XbkVsK+7n5Z9b2ufln1s5VNf6q03SnkAEbPcgtAdUpvRNKSu9jwJPAeZLui4je5Hbba4DryS3BHSWNrrP2j4CrgeMkndlA3Q2BHchG4r5kE/FnwLvAKEnPRMSewKHAFpJeaKFTZ2ZmZmY2X4uIrnw5mucGMnDv9JLuu2JlF46ZtW1u/pm1U9VbYEtK73AyefeR8lxHcgBwkNtz365+L/AUefGwhqSPyvO9yJTey4DzJU2rp3ap+wiwIrDqrOoCGwD7kCsB9weWAwaTKb8vkasFD5E0vnnPmpmZmZlZ6xMR/0NeP+8CvCqpV0R0rMzcNrO2zWm/Zu1QSdMdWGNKb1dgqar3dixNw57ktt1zKl8rzb0Tynuotzb5M6kvuYpv5rr7k6sNtwBOI4NCliEbi2+X1z9Hbjc+240/MzMzM7NUdsMcAmwFHFiec+PPrJ3wyj+zdigi1gI6kym6k4DezDql972ZQzKq0sC6kOm6YyXtPae1G6tbvt6RDBa5tGw1Xg3Yk1z5d5Ckt6sCQxzuYWZmZmbWAF8rm7UvXvln1s6UlXsTgdfJGXr7kQ25LYEdJR0N/BNYFVisoYuC0qDrJGkysA7QPyIum9Pas6pbffhk6m/v8vgl4CFgXeDsMtR4WqnlixkzMzMzswb4Wtmsfek0rw/AzOaecodvWkT8BFgdOIZM6Z1OSdONiE0oKb1ko27GirzqWlWNus8jYnWgVz21Jb1Sa92I+D4g4B1yu8KJEfGqpKsi4h1gLLnV18m+ZmZmZmZmZlXc/DNrR0qq7/rA5sBNZevsh2Sa7ufkar3PgSElpTciYl2gZ0RcPnNzbaaVeg/UWbumuhHRDTiF3DLchwz++AtwRkRsSs4UHFJWHJqZmZmZmZlZFc/8M2tHqtJ0l6WJlN7y/MbAn4D+wGHAGZI+malmx7Lib1Hg70D3pmrXWpfc4vs34HfA+mQ62daS3o+IZYCVgcmSnmjO82RmZmZmZmbWVnjmn1k7UmZ79KXhlN79gceqGn+9yC26BwJbk3P7hpQwDsprKo2/bsBoYHBTtWutC6wBfA9YC9iebPz9vDT++gMdJD3kxp+ZmZmZmZnZrHnbr1k7UjVLryfweESMqKTpSho308uXBp4o22knRsTb5NbbLhFxcnnP1IjoSq7OO1DS+BpqN1mXbPhdBHxCzvMbAvST9GJE9ANOAAYBbzXn+TEzMzMzMzNra9z8M2tHZpqltw7wQkRcJmmPEgZSPQfgFUARsQbwSmnsXUw23V6RdE1ELAHcDBwkaUxDtYE966kL/IsMG1kcGApMAXYFTouIUcBvy/d7oeXOlJmZmZmZmVnb4Jl/Zm1QpZHXUJpu+Xqn0gjsQqb0PlCe3xzoBbwn6ZyIOIZcqXcfuQrvN8AdwMbAT8k5fFMr759V7TrrngDcAoyWtENEdCJn/+0BTCIbhvc30Kw0MzMzMzMzs5m4+WfWRlXSdIH/StMtX6806aI8tTZwNXAhsCHwgaTdI2IIOXvvO+ScviWAg8mtvpfMqjYwrTQge9RR99fkCsC1gWuA4yRd1DxnxMzMzMzMzKz9cfPPrA2qMU13KXLr/0fApsBPgLGS/hoRnYFrgY8l7Vpev3B53anA2+Sqv/+qHRHdgS6S/hURGwG/BG6voe5RwG7AREnTI6IPOffvTEnDm/8smZmZmZmZmbV9Tvs1a2NqTNNdlGy0LUmm9C4G/BDoERGLSJoCDAS6R8QdAJI+JVfkdQB+1VDt0sjbE+hUEoDPI1cfNli3bN39tHz/XSU9WRp/HSRVgj4OiojlW+p8mZmZmZmZmbVlDvwwa3tqSen9OCKuBy4HHgduAJ4HTgK2iYgbJX0aEQPIrbkVjwPfbqT2FOBSsql4L7mN94NZ1a3M7JN0RfUHqGoAPhARPSRNav7TZGZmZmZmZtb2eeWfWStXNbOvYkaabkQsKGk8UEnTHQhMLym9V5DNvwD2AZ4it/H+AvhpWcU3TdLjNdbesYSLBDASeAbo0VDd8rqvRcSZETEkIjaY+XOVBmAnSZMiomMDn9PMzMzMzMzMmuCZf2ZtQJ1pugOB/YGekgZFxI/ILbz/Bs4AfgAcDewk6c06aw8HBgPnkCuLG6xLriY8ATgS2At4DdivOpk4IjpKmla2Dw8FTpX0cbOfPDMzMzMzM7M2zCv/zFqpykq4kqZ7GvAxsGlEXC7pUGA80Bf4PXAIMBGYRoZrDCZX7CHpPuBmcqvuH4GHgQGl8VdP7e7A6cCFkh5orC45B3AgMIlsJB5bkoeXLJ+p0vjrCtwI/F83/szMzMzMzMzq55V/Zq1MSdPtLOmtEu4xmNpSeo8Cdie36I6TdFNEdC4hHJWE4F2BEZIemo3ajwI3NVB38/I9jwK+BjwBHADsAHwBDKxaYbgkMFLSlLLi7zpgmKQxLXdGzczMzMzMzNour/wza0VKs20PoHN5qhO1pfRW0nQnAAsDm5evVRp0PYEJwEvAW7NTm9wC3FDdd4BvANuTYSCrkDMBJwH3lMZfX3Jr8Ful8bcgcA1whBt/ZmZmZmZmZrPPzT+zVqQ02y4DvoiIQ8gwjSHAumSabiVMYwBwcNVbnwWGltV7VwKfRsQgmNGgOxdYEbigntrlvf3IpOBZ1Z1Grgo8DjhX0vPAf8hm33oRcTdwFnCApHvK5/wcGCLpweY8f2ZmZmZmZmbtjbf9mrUSEdGhkoALrEM25l4kwzXWBYaRCb4jS5Ow+r1dgLuBx4HjgS2ALYGu5My9wyXdWG/tGuoeJmlURPwA+DG5QnAfSaPL+zsBiwOLSHq9zDHsIGlas504MzMzMzMzs3bMzT+zViAiQpIiYlNgM0kHNJLSO0jSG+V9q5DzAZ+LiIWAUeQqwIPJFXmrAx9J+keNtSsJwDXVJWf4rQg8KunViNgTOJTcPtyV3Aq8v/yDyMzMzMzMzKxFeNuvWStQGn/9ye2xd5bnGkrT3R04MiK+Vd66P7BzRKwu6TNgK6A/uYovJE0ojb+mar8IvAIsVGtdYE1yi/J6wHURsaukS4FjgLOBU4EH3PgzMzMzMzMzazlu/pm1AhHRAdgY+JOku8rsPiTdC9wCLEOGakwhAzaOjoivkQEbiwLbR8SaJYjjROC7wAp11P4m8EatdSOiD7nFtz9wX6mxZUTsURqAOwLbS7qubPU1MzMzMzMzsxbgbb9m87mqLb9nkltt96n6Wk/gGWABSR+W5/oB+wCTyRV6ywGDgQXINN8tgEMkja+ndq11ySTi88m5gQL+QjYB9wSGAkcAl5fwEDMzMzMzMzNrQV75ZzYfKw24EU2k9K5e1fgbAJwGvEauthsOvE0GdzxHhm6cXRp/NdeutS7QkZwNuJ+kccCqwFOS3gPGkXMBx7nxZ2ZmZmZmZjZ3eOWf2Xys1jTd8tqOwCXApZLui4jVyNV2ywEHSXo7IjpLmlK22i5YS+066/YHbgf+LOmkMnvwFuAhoDcwVNLdLXrSzMzMzMzMzGwGN//M5hMR0VHStPLP9aT0VrbudgKuJ1fWHV8afFuRjb1nyTCQKeT8vpprk6v5mqxbdezbACcBh0saGRHfAbYFxkoa3YKn0MzMzMzMzMxm0mleH4CZQUR0B06KiOMkvUzO1JsUEVdJej4itgImACsCu0iaUBWUsU5EiAz6OAQ4MSJelXRVRLwDjAcWB5aV9HJENFobeLI0E79PzuybVd2x5Bbiz6o/S1ktOBU4KiIWkHQF8HTLnT0zMzMzMzMzmxXP/DObPyxEnSm9pUG3NXAesE15z8pkwMaREXEJcB1wL/B8rbXrqDtK0sSGPoykW4GjgIMjYtmyddjMzMzMzMzM5jJv+zWbT9ST0lte3x0YCQwkV+ztAmwt6f2IWIZs2E2W9ESdCcA1163hMy0l6T9zem7MzMzMzMzMbPZ45Z/ZfKCelN7y+u8AqwD/AHYiG3Q/Lw26/kAHSQ+Vxl89CcA1163lc7nxZ2ZmZmZmZjZveeWf2TxWa5ou8EV5y9rABcAA4AhgO6BfCfDoRzb6Bkl6oc6k3h611m3ZM2JmZmZmZmZmzcUr/8zmvQC6Ab3L45eAh4B1gbNLIu80Zae+Lxm+cb6kf5ONvWuB0yLil+RcvmFVDbqaakfEBnXWNTMzMzMzM7NWwGm/ZnNZRMQcpOl+TjYA3y6PHye36O5BrgzcV9L99daOiFrrhrxc2MzMzMzMzKzV8LZfs3mgpOkeCtwG9AHOBqYBZwBjgE2BIZJuq3pPB0nTI2I94BrgOEkXzWntWuuamZmZmZmZWevj5p/ZXDYnabpVjbo+wEXAmZKGz2ntpuqamZmZmZmZWevk5p/ZXFTSdBcG9gbGA7uTabovljTdpyS9VV7b4BbbqkbdhsBfgT6S3qi1dr11W+I8mJmZmZmZmdnc4cAPsxYWEVH+vwcZpPEauQ33KGCv0pzrB5wALFZ5/axm65UGXQdJDwA9SuOvptr11m2eM2BmZmZmZmZm84oDP8xaWAn32AD4LSVNNyIuIYM0TouIUeVrBwEvltdvAgwAngaelfTgTDWnR0QnSZMiYiNg36ZqS3qhzrodgekO+DAzMzMzMzNrvbzyz2zuqKTp9iiPHydX593Ll2m6NwNExGbAKcBoYBtgl4j4SqM+IjpKmhoR3YCfARs0VXs26h4KLNJsZ8DMzMzMzMzM5jqv/DNrYWUr7SMlhfeaiHiipOm+C5xaXjNjq29E9CQDO5YHlgZ+WRpyS0p6tzTopkVEV+BG4AhgREO1Z5rvV3ddSR/PlZNkZmZmZmZmZi3CzT+zFlY1S+/RiNgduCgiOksaHhGLAl0l/Ssi1gOeIGf2XU2u2ttG0psRsTmwZESMlDSlrMy7jlyd9/fyPWbUJgM75qiupDFz8zyZmZmZmZmZWfPztl+zZlZZxVetqgE4FhgCHBQRywMrASMi4g/ApcAqwEhgEnBPadD1Bc4A3gK+iIgFgWvIlXljGqoN9K6nbmn8faVuy5wdMzMzMzMzM5ubwrP8zZrHTFtsZ/WaDqVZt4SkSeW5Y4E/AvtL+ktZDbghsB+5Orc7cLikW6rqrCjptcZqN1ddMzMzMzMzM2u93PwzawaVxl9TabrltZ3KrL2OwHRyld6PgV2BfSSNrrwOWBxYRNLrpfbWwMQaa69XY90AOkia1pznxMzMzMzMzMzmPW/7NWsGpfFXU5ouMK3M1htOJvW+JelY4ETgwojoEREbkWEgk0qDrlL7/sZql8bfj4CrgA+aqls5djf+zMzMzMzMzNomB36YzYE5SNN9kFx9NwnYPyLOkHRpGRd4NrAQcPxs1N4JuAQYBfy1hrpmZmZmZmZm1oa5+Wc2G+Ywpfce4N/AFsCawC7AlqWBd2lEPAB0L+nA9dT+CXAWsBvwCXDhTHXvADpLeq2W+YRmZmZmZmZm1vp526/Z7FmJ2UvpvQNYAjgMWA44DugBjAeOjojBwGLAkXUm9W4E/A04AnhzFnXfrYR5uPFnZmZmZmZm1j64+Wc2GyRNBP6XbLKdK+l54D9kQ269iLibXIV3gKR7SrOtB2Z+J8MAAAJVSURBVDAV+IWkccCqwFOS3gPGAc8C4yRNqKd2RPQGDi91z2uk7tS5cGrMzMzMzMzMbD7ibb9ms+9m4FNg34h4uqTp3hYRd9FAmi7QFfgh0IsMBRkHHBIRl5CJv0MlPTsbtZcA+gF3lvc2VtfMzMzMzMzM2pHw7j+z2lTm5EVEL2BF4FFJr0bEnsChZCBHV2B7YP+GttZGxDbAScDhkkZGxHeAbYGxkkbPbu2m6jb7yTAzMzMzMzOzVsEr/8xqVBp/WwEnAzcBB9WbpitpVERMBY6KiAUkXQE8DTAntRura2ZmZmZmZmbtl5t/ZjWKiD7ArkB/Gk7prSlNV9KtEdEJOD4i7iOTf38wp7UbqitpWrOfCDMzMzMzMzNrNbzt16wRVVt91wfOB4YAAv5CNur2BIaSKbuX1xOqERFLSfpPc9eu1K31OMzMzMzMzMys7XLar1kjSuOvN3A0sF8TKb11pemWxl+z13bjz8zMzMzMzMwq3Pwza1pXMk23V3k8Duhd0nQvBE6ZgzTdlqxtZmZmZmZmZu2ct/2a1aAl03Sd1GtmZmZmZmZmLcWBH2Y1aMk0XSf1mpmZmZmZmVlL8co/szqUVXrHA5vQzGm6LVnbzMzMzMzMzNonN//M6tSSabpO6jUzMzMzMzOz5uTmn5mZmZmZmZmZWRvltF8zMzMzMzMzM7M2ys0/MzMzMzMzMzOzNsrNPzMzMzMzMzMzszbKzT8zMzMzMzMzM7M2ys0/MzMzMzMzMzOzNsrNPzMzMzMzMzMzszbKzT8zMzMzMzMzM7M26v8D6ZA/zpWJ+poAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1440x1440 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8V_h4rGbIcET",
        "outputId": "0ce7c33c-f410-45b5-a4a8-ec9b6b6db177",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "corr['risk_infection'].sort_values(ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "risk_infection                                 1.000000\n",
              "covid19_contact                                0.662996\n",
              "health_worker                                  0.530407\n",
              "covid19_symptoms                               0.510146\n",
              "working_travel critical                        0.439393\n",
              "contacts_count                                 0.357054\n",
              "opinion_infection                              0.206951\n",
              "public_transport_count                         0.188528\n",
              "nursing_home                                   0.157233\n",
              "working_travel non critical                    0.106558\n",
              "heart_disease                                  0.082024\n",
              "house_count                                    0.071523\n",
              "liver_disease                                  0.068450\n",
              "rate_reducing_risk_single_sanitizer            0.063733\n",
              "age_20_30                                      0.061934\n",
              "kidney_disease                                 0.061831\n",
              "mdma                                           0.055119\n",
              "amphetamines                                   0.048572\n",
              "age_90_100                                     0.043863\n",
              "smoking_yesheavy                               0.042959\n",
              "lsd                                            0.041947\n",
              "cocaine                                        0.041834\n",
              "smoking_yesmedium                              0.034582\n",
              "diabetes                                       0.031979\n",
              "lung_disease                                   0.031588\n",
              "income_low                                     0.031407\n",
              "race_mixed                                     0.030967\n",
              "race_black                                     0.030876\n",
              "insurance_no                                   0.029882\n",
              "age_30_40                                      0.027565\n",
              "smoking_vape                                   0.026152\n",
              "age_100_110                                    0.024862\n",
              "asthma                                         0.021064\n",
              "rate_reducing_mask                             0.019144\n",
              "race_blank                                     0.018114\n",
              "other_chronic                                  0.015581\n",
              "race_hispanic                                  0.015313\n",
              "smoking_yeslight                               0.015126\n",
              "smoking_quit0                                  0.014815\n",
              "blood_type_bp                                  0.014131\n",
              "blood_type_ap                                  0.013863\n",
              "age_10_20                                      0.013223\n",
              "race_other                                     0.012547\n",
              "risk_mortality                                 0.012495\n",
              "age_80_90                                      0.012253\n",
              "blood_type_abp                                 0.012118\n",
              "cannabis                                       0.011998\n",
              "bmi                                            0.010962\n",
              "compromised_immune                             0.008471\n",
              "income_med                                     0.008309\n",
              "immigrant_native                               0.006829\n",
              "hypertension                                   0.005851\n",
              "hiv_positive                                   0.005507\n",
              "blood_type_bn                                  0.002940\n",
              "blood_type_on                                  0.002482\n",
              "income_gov                                     0.001309\n",
              "sex_other                                     -0.000421\n",
              "blood_type_op                                 -0.001654\n",
              "opinion_mortality                             -0.003766\n",
              "immigrant_immigrant                           -0.006417\n",
              "blood_type_an                                 -0.007199\n",
              "sex_male                                      -0.007532\n",
              "smoking_quit5                                 -0.011697\n",
              "worried                                       -0.012136\n",
              "alcohol                                       -0.019386\n",
              "blood_type_unknown                            -0.021303\n",
              "age_40_50                                     -0.023180\n",
              "rate_reducing_risk_single_washing_hands       -0.025539\n",
              "age_50_60                                     -0.031150\n",
              "insurance_yes                                 -0.031791\n",
              "smoking_quit10                                -0.033715\n",
              "income_high                                   -0.034272\n",
              "age_70_80                                     -0.041072\n",
              "rate_reducing_risk_house_washing_hands        -0.049369\n",
              "race_white                                    -0.053365\n",
              "age_60_70                                     -0.054502\n",
              "rate_reducing_risk_house_social_distancing    -0.083279\n",
              "rate_reducing_risk_single_social_distancing   -0.098487\n",
              "working_never                                 -0.159317\n",
              "working_stopped                               -0.204335\n",
              "Name: risk_infection, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JNl3ClDPLI5E"
      },
      "source": [
        "From the correlation values, we can see that there is varying extent to which the independent variables are correlated with the target. Lower correlation means weak linear relationship but there may be a strong non-linear relationship so, we can’t pass any judgement at this level."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "67FTT7sSyvyw"
      },
      "source": [
        "df['ID'] = range(1, len(df) + 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MEXWoqnZLfaE"
      },
      "source": [
        "After dropping rows with null values we have to do reindexing of rows"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Avv04txlyxbU"
      },
      "source": [
        "df.index = df['ID']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h2r9JZYezg7F"
      },
      "source": [
        "df = df.drop(['ID'], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2O96gO-tWP3u",
        "outputId": "7fc3e9a1-e91f-48b5-d2c7-8452d47486b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        }
      },
      "source": [
        "df.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>bmi</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>cannabis</th>\n",
              "      <th>amphetamines</th>\n",
              "      <th>cocaine</th>\n",
              "      <th>lsd</th>\n",
              "      <th>mdma</th>\n",
              "      <th>contacts_count</th>\n",
              "      <th>house_count</th>\n",
              "      <th>public_transport_count</th>\n",
              "      <th>worried</th>\n",
              "      <th>rate_reducing_risk_single_social_distancing</th>\n",
              "      <th>rate_reducing_risk_single_washing_hands</th>\n",
              "      <th>rate_reducing_risk_house_social_distancing</th>\n",
              "      <th>rate_reducing_risk_house_washing_hands</th>\n",
              "      <th>rate_reducing_risk_single_sanitizer</th>\n",
              "      <th>rate_reducing_mask</th>\n",
              "      <th>covid19_symptoms</th>\n",
              "      <th>covid19_contact</th>\n",
              "      <th>asthma</th>\n",
              "      <th>kidney_disease</th>\n",
              "      <th>liver_disease</th>\n",
              "      <th>compromised_immune</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>lung_disease</th>\n",
              "      <th>diabetes</th>\n",
              "      <th>hiv_positive</th>\n",
              "      <th>hypertension</th>\n",
              "      <th>other_chronic</th>\n",
              "      <th>nursing_home</th>\n",
              "      <th>health_worker</th>\n",
              "      <th>opinion_infection</th>\n",
              "      <th>opinion_mortality</th>\n",
              "      <th>risk_infection</th>\n",
              "      <th>risk_mortality</th>\n",
              "      <th>sex_male</th>\n",
              "      <th>sex_other</th>\n",
              "      <th>age_100_110</th>\n",
              "      <th>age_10_20</th>\n",
              "      <th>age_20_30</th>\n",
              "      <th>age_30_40</th>\n",
              "      <th>age_40_50</th>\n",
              "      <th>age_50_60</th>\n",
              "      <th>age_60_70</th>\n",
              "      <th>age_70_80</th>\n",
              "      <th>age_80_90</th>\n",
              "      <th>age_90_100</th>\n",
              "      <th>blood_type_abp</th>\n",
              "      <th>blood_type_an</th>\n",
              "      <th>blood_type_ap</th>\n",
              "      <th>blood_type_bn</th>\n",
              "      <th>blood_type_bp</th>\n",
              "      <th>blood_type_on</th>\n",
              "      <th>blood_type_op</th>\n",
              "      <th>blood_type_unknown</th>\n",
              "      <th>insurance_no</th>\n",
              "      <th>insurance_yes</th>\n",
              "      <th>income_gov</th>\n",
              "      <th>income_high</th>\n",
              "      <th>income_low</th>\n",
              "      <th>income_med</th>\n",
              "      <th>race_black</th>\n",
              "      <th>race_blank</th>\n",
              "      <th>race_hispanic</th>\n",
              "      <th>race_mixed</th>\n",
              "      <th>race_other</th>\n",
              "      <th>race_white</th>\n",
              "      <th>immigrant_immigrant</th>\n",
              "      <th>immigrant_native</th>\n",
              "      <th>smoking_quit0</th>\n",
              "      <th>smoking_quit10</th>\n",
              "      <th>smoking_quit5</th>\n",
              "      <th>smoking_vape</th>\n",
              "      <th>smoking_yesheavy</th>\n",
              "      <th>smoking_yeslight</th>\n",
              "      <th>smoking_yesmedium</th>\n",
              "      <th>working_never</th>\n",
              "      <th>working_stopped</th>\n",
              "      <th>working_travel critical</th>\n",
              "      <th>working_travel non critical</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>41151</th>\n",
              "      <td>42.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.116</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41152</th>\n",
              "      <td>27.7</td>\n",
              "      <td>1.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.050</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41153</th>\n",
              "      <td>42.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>25.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.056</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41154</th>\n",
              "      <td>23.9</td>\n",
              "      <td>12.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.964</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41155</th>\n",
              "      <td>27.5</td>\n",
              "      <td>8.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.050</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        bmi  alcohol  cannabis  amphetamines  cocaine  lsd  mdma  \\\n",
              "ID                                                                 \n",
              "41151  42.2      2.0       2.0          -1.0     -1.0 -1.0  -1.0   \n",
              "41152  27.7      1.0      27.0          -1.0     -1.0 -1.0  -1.0   \n",
              "41153  42.2      0.0      28.0          -1.0      0.0  0.0   0.0   \n",
              "41154  23.9     12.0      12.0          -1.0     -1.0 -1.0  -1.0   \n",
              "41155  27.5      8.0      13.0          -1.0     -1.0 -1.0  -1.0   \n",
              "\n",
              "       contacts_count  house_count  public_transport_count  worried  \\\n",
              "ID                                                                    \n",
              "41151            10.0          3.0                     0.0      4.0   \n",
              "41152             0.0          1.0                     0.0      4.0   \n",
              "41153             2.0          3.0                     0.0      4.0   \n",
              "41154            18.0          3.0                     0.0      2.0   \n",
              "41155            12.0          2.0                     0.0      4.0   \n",
              "\n",
              "       rate_reducing_risk_single_social_distancing  \\\n",
              "ID                                                   \n",
              "41151                                           -1   \n",
              "41152                                            1   \n",
              "41153                                            2   \n",
              "41154                                            0   \n",
              "41155                                            0   \n",
              "\n",
              "       rate_reducing_risk_single_washing_hands  \\\n",
              "ID                                               \n",
              "41151                                        1   \n",
              "41152                                        1   \n",
              "41153                                        2   \n",
              "41154                                        1   \n",
              "41155                                        1   \n",
              "\n",
              "       rate_reducing_risk_house_social_distancing  \\\n",
              "ID                                                  \n",
              "41151                                           1   \n",
              "41152                                           1   \n",
              "41153                                           2   \n",
              "41154                                           0   \n",
              "41155                                           1   \n",
              "\n",
              "       rate_reducing_risk_house_washing_hands  \\\n",
              "ID                                              \n",
              "41151                                       1   \n",
              "41152                                       1   \n",
              "41153                                       2   \n",
              "41154                                       1   \n",
              "41155                                       1   \n",
              "\n",
              "       rate_reducing_risk_single_sanitizer  rate_reducing_mask  \\\n",
              "ID                                                               \n",
              "41151                                    0                 4.0   \n",
              "41152                                    1                 4.0   \n",
              "41153                                    2                 5.0   \n",
              "41154                                    0                 3.0   \n",
              "41155                                    2                 4.0   \n",
              "\n",
              "       covid19_symptoms  covid19_contact  asthma  kidney_disease  \\\n",
              "ID                                                                 \n",
              "41151                 0                0       1               0   \n",
              "41152                 0                0       0               0   \n",
              "41153                 0                0       1               0   \n",
              "41154                 0                0       0               0   \n",
              "41155                 0                0       0               0   \n",
              "\n",
              "       liver_disease  compromised_immune  heart_disease  lung_disease  \\\n",
              "ID                                                                      \n",
              "41151              0                   0              0             0   \n",
              "41152              0                   0              0             0   \n",
              "41153              0                   0              0             0   \n",
              "41154              0                   0              0             0   \n",
              "41155              0                   0              0             0   \n",
              "\n",
              "       diabetes  hiv_positive  hypertension  other_chronic  nursing_home  \\\n",
              "ID                                                                         \n",
              "41151         0             0             0              0             0   \n",
              "41152         0             0             0              0             0   \n",
              "41153         0             0             0              0             0   \n",
              "41154         0             0             0              0             0   \n",
              "41155         0             0             0              0             0   \n",
              "\n",
              "       health_worker  opinion_infection  opinion_mortality  risk_infection  \\\n",
              "ID                                                                           \n",
              "41151              0               35.0               55.0             5.0   \n",
              "41152              0               45.0               25.0            13.0   \n",
              "41153              0               65.0               25.0             5.0   \n",
              "41154              0                5.0                5.0            13.0   \n",
              "41155              0               55.0                5.0            13.0   \n",
              "\n",
              "       risk_mortality  sex_male  sex_other  age_100_110  age_10_20  age_20_30  \\\n",
              "ID                                                                              \n",
              "41151           0.116         1          0            0          0          1   \n",
              "41152           0.050         0          0            0          0          1   \n",
              "41153           0.056         0          0            0          0          1   \n",
              "41154           0.964         1          0            0          0          0   \n",
              "41155           0.050         0          0            0          0          1   \n",
              "\n",
              "       age_30_40  age_40_50  age_50_60  age_60_70  age_70_80  age_80_90  \\\n",
              "ID                                                                        \n",
              "41151          0          0          0          0          0          0   \n",
              "41152          0          0          0          0          0          0   \n",
              "41153          0          0          0          0          0          0   \n",
              "41154          0          0          0          1          0          0   \n",
              "41155          0          0          0          0          0          0   \n",
              "\n",
              "       age_90_100  blood_type_abp  blood_type_an  blood_type_ap  \\\n",
              "ID                                                                \n",
              "41151           0               0              0              0   \n",
              "41152           0               0              0              0   \n",
              "41153           0               0              0              0   \n",
              "41154           0               0              0              0   \n",
              "41155           0               0              0              0   \n",
              "\n",
              "       blood_type_bn  blood_type_bp  blood_type_on  blood_type_op  \\\n",
              "ID                                                                  \n",
              "41151              0              0              0              0   \n",
              "41152              0              0              0              0   \n",
              "41153              0              0              0              1   \n",
              "41154              0              0              0              1   \n",
              "41155              0              0              0              0   \n",
              "\n",
              "       blood_type_unknown  insurance_no  insurance_yes  income_gov  \\\n",
              "ID                                                                   \n",
              "41151                   1             0              1           0   \n",
              "41152                   1             0              1           0   \n",
              "41153                   0             1              0           0   \n",
              "41154                   0             0              1           0   \n",
              "41155                   1             0              1           0   \n",
              "\n",
              "       income_high  income_low  income_med  race_black  race_blank  \\\n",
              "ID                                                                   \n",
              "41151            0           0           1           0           0   \n",
              "41152            0           1           0           0           0   \n",
              "41153            1           0           0           0           0   \n",
              "41154            1           0           0           0           0   \n",
              "41155            0           1           0           0           0   \n",
              "\n",
              "       race_hispanic  race_mixed  race_other  race_white  immigrant_immigrant  \\\n",
              "ID                                                                              \n",
              "41151              1           0           0           0                    0   \n",
              "41152              0           0           0           1                    0   \n",
              "41153              0           0           0           1                    0   \n",
              "41154              0           0           0           1                    0   \n",
              "41155              0           0           0           1                    0   \n",
              "\n",
              "       immigrant_native  smoking_quit0  smoking_quit10  smoking_quit5  \\\n",
              "ID                                                                      \n",
              "41151                 1              0               0              0   \n",
              "41152                 1              0               0              1   \n",
              "41153                 1              0               0              0   \n",
              "41154                 1              0               0              0   \n",
              "41155                 1              0               0              0   \n",
              "\n",
              "       smoking_vape  smoking_yesheavy  smoking_yeslight  smoking_yesmedium  \\\n",
              "ID                                                                           \n",
              "41151             0                 0                 0                  0   \n",
              "41152             0                 0                 0                  0   \n",
              "41153             0                 0                 1                  0   \n",
              "41154             0                 0                 1                  0   \n",
              "41155             0                 0                 0                  0   \n",
              "\n",
              "       working_never  working_stopped  working_travel critical  \\\n",
              "ID                                                               \n",
              "41151              0                0                        0   \n",
              "41152              0                0                        0   \n",
              "41153              1                0                        0   \n",
              "41154              0                0                        0   \n",
              "41155              0                0                        0   \n",
              "\n",
              "       working_travel non critical  \n",
              "ID                                  \n",
              "41151                            0  \n",
              "41152                            1  \n",
              "41153                            0  \n",
              "41154                            1  \n",
              "41155                            1  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TGUmDaclLt8r"
      },
      "source": [
        "Setting risk_infection as target variable y"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kiLnJ-sAjQ8E"
      },
      "source": [
        "X = df.drop(['risk_infection'], axis = 1)\n",
        "y = df['risk_infection']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0d0O9E3hL71y"
      },
      "source": [
        "Train-test split with 30% test data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Omc78yPOjfkx"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y,random_state=0, test_size=0.3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d5vzj5Wxjvgo"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "import xgboost as xgb\n",
        "from sklearn.ensemble import AdaBoostRegressor\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "from sklearn.svm import SVR\n",
        "from sklearn import model_selection\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pguqBjBUozIB"
      },
      "source": [
        "**Selecting the features for linear regression**\n",
        "\n",
        "With backward elimination, deselect features that have p_values > 0.05 which is interpreted as a failure to explain the change in the target other than chance. The following code returns only the selected features with p_values < 0.05."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4OgZTVs3jyGp",
        "outputId": "a38d5a87-30ef-480b-d99e-35d24ea3fd8a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Linear Regression\n",
        "X1 = sm.add_constant(X_train)\n",
        "ols = sm.OLS(y_train,X1)\n",
        "lr = ols.fit()\n",
        "\n",
        "selected_features = list(X.columns)\n",
        "pmax = 1\n",
        "while (len(selected_features)>0):\n",
        "    p= []\n",
        "    X_1 = X[selected_features]\n",
        "    X_1 = sm.add_constant(X_1)\n",
        "    model = sm.OLS(y,X_1).fit()\n",
        "    p = pd.Series(model.pvalues.values[1:],index = selected_features)      \n",
        "    pmax = max(p)\n",
        "    feature_with_p_max = p.idxmax()\n",
        "    if(pmax>0.05):\n",
        "        selected_features.remove(feature_with_p_max)\n",
        "    else:\n",
        "        break  \n",
        "        \n",
        "print('The selected features are :', selected_features)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The selected features are : ['bmi', 'cannabis', 'amphetamines', 'lsd', 'house_count', 'public_transport_count', 'rate_reducing_risk_house_social_distancing', 'rate_reducing_risk_house_washing_hands', 'rate_reducing_mask', 'covid19_symptoms', 'covid19_contact', 'kidney_disease', 'liver_disease', 'heart_disease', 'lung_disease', 'diabetes', 'nursing_home', 'health_worker', 'opinion_infection', 'opinion_mortality', 'risk_mortality', 'sex_male', 'age_10_20', 'age_20_30', 'age_30_40', 'age_50_60', 'age_60_70', 'age_80_90', 'age_90_100', 'blood_type_ap', 'blood_type_bp', 'insurance_yes', 'income_low', 'race_blank', 'race_hispanic', 'race_mixed', 'race_other', 'race_white', 'smoking_yesheavy', 'smoking_yeslight', 'working_travel critical', 'working_travel non critical']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aOQNEUyTnpKt",
        "outputId": "7045cc4b-317b-41d5-9fbf-2ec2dfa90dad",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(model.pvalues)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "const                                         3.133229e-105\n",
            "bmi                                            1.291895e-02\n",
            "cannabis                                       1.386698e-02\n",
            "amphetamines                                   3.397303e-05\n",
            "lsd                                            5.280891e-07\n",
            "house_count                                    2.140660e-68\n",
            "public_transport_count                         0.000000e+00\n",
            "rate_reducing_risk_house_social_distancing     1.141459e-03\n",
            "rate_reducing_risk_house_washing_hands         2.324026e-02\n",
            "rate_reducing_mask                             2.804496e-02\n",
            "covid19_symptoms                               0.000000e+00\n",
            "covid19_contact                                0.000000e+00\n",
            "kidney_disease                                 2.866702e-85\n",
            "liver_disease                                 2.080111e-122\n",
            "heart_disease                                  0.000000e+00\n",
            "lung_disease                                   3.833513e-20\n",
            "diabetes                                       3.614481e-71\n",
            "nursing_home                                   0.000000e+00\n",
            "health_worker                                  0.000000e+00\n",
            "opinion_infection                              7.239192e-15\n",
            "opinion_mortality                              2.128809e-04\n",
            "risk_mortality                                 7.466884e-03\n",
            "sex_male                                       3.112162e-03\n",
            "age_10_20                                      4.477629e-03\n",
            "age_20_30                                      3.773242e-04\n",
            "age_30_40                                      1.069186e-02\n",
            "age_50_60                                      6.812476e-03\n",
            "age_60_70                                      2.676173e-02\n",
            "age_80_90                                      1.668344e-02\n",
            "age_90_100                                     3.009100e-05\n",
            "blood_type_ap                                  1.577195e-02\n",
            "blood_type_bp                                  1.885200e-02\n",
            "insurance_yes                                  1.056837e-05\n",
            "income_low                                     3.348521e-02\n",
            "race_blank                                     2.454391e-05\n",
            "race_hispanic                                  8.789541e-04\n",
            "race_mixed                                     2.457050e-05\n",
            "race_other                                     3.789064e-02\n",
            "race_white                                     1.086832e-13\n",
            "smoking_yesheavy                               2.212792e-04\n",
            "smoking_yeslight                               1.326153e-02\n",
            "working_travel critical                        0.000000e+00\n",
            "working_travel non critical                    0.000000e+00\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E9xzWueRN2Hf"
      },
      "source": [
        "### Hyperparameter Tuning using GridsearchCV(Cross validations)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SgzZ2crmj1Uy",
        "outputId": "7bfa80df-ee41-46c6-b889-3689818b1eae",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Decision Tree\n",
        "tuned_parameters = [{'max_depth': [1,2,3,4,5,10, 15, 20, 25, 50, 100,200]}]\n",
        "MSE_dt = ['mean_squared_error(y_test,y_pred4)']\n",
        "for value in MSE_dt:\n",
        "    regressor_dt = GridSearchCV(DecisionTreeRegressor(), tuned_parameters, cv=4)\n",
        "    regressor_dt.fit(X_train, y_train)\n",
        "    y_true, y_pred4 = y_test, regressor_dt.predict(X_test)\n",
        "    \n",
        "print('The optimum max_depth for Decision Tree is: ', regressor_dt.best_params_ )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The optimum max_depth for Decision Tree is:  {'max_depth': 10}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rNrT0lOony14",
        "outputId": "7197a9cb-7492-40c2-de34-a3c275dd294b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Random Forests \n",
        "tuned_parameters = [{'max_depth': [5,10, 15, 20, 50, 70], 'n_estimators': [10, 25, 50, 100,150, 200, 250]}]\n",
        "\n",
        "MSE_rf = ['mean_squared_error(y_test, y_pred5)']\n",
        "for value in MSE_rf:\n",
        "    regr_rf = GridSearchCV(RandomForestRegressor(), tuned_parameters, cv=4)\n",
        "    regr_rf.fit(X_train, y_train)\n",
        "    y_true, y_pred5 = y_test, regr_rf.predict(X_test)\n",
        "    \n",
        "print('The best hyper-parameters for Random Forests are: ',regr_rf.best_params_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The best hyper-parameters for Random Forests are:  {'max_depth': 10, 'n_estimators': 200}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dCyVJsm9yLm3"
      },
      "source": [
        "When we have features with values at different scales, it is very important to standardize them to the same range so that the algorithm does not weigh greater values, higher and consider smaller values as the lower values, regardless of the unit of the values. This is crucial for distance-based algorithms such as KNN, SVM, Linear Regression, etc. \n",
        "\n",
        "We choose RobustScaler for scaling as it is based on percentiles and hence not easily influenced by outliers."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mvflGI85ScKr"
      },
      "source": [
        "scaler = preprocessing.RobustScaler() # Features Scaling is required for distance-based algorithms\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.fit_transform(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eaJmdlZcn3ck",
        "outputId": "fefe55ed-d65a-49a8-851c-c9225265dfa1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# KNN\n",
        "tuned_parameters = [{'n_neighbors': [1,2,3,4,5,10,15,20], 'p': [1,2]}]\n",
        "\n",
        "MSE_knn = ['mean_squared_error(y_test,y_pred)']\n",
        "for i in MSE_knn:\n",
        "    model = GridSearchCV(KNeighborsRegressor(), tuned_parameters, cv=4)\n",
        "    model.fit(X_train_scaled, y_train)\n",
        "    y_true, y_pred6 = y_test, model.predict(X_test_scaled)\n",
        "    \n",
        "print('The best hyper-parameters for KNN are: ', model.best_params_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The best hyper-parameters for KNN are:  {'n_neighbors': 10, 'p': 1}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hh6PFiERXGqQ"
      },
      "source": [
        "I tried to do hyperparameter tuning for SVM model but since SVM has more hyperparameters than others and also more computationally intensive so I couldn't do hyperparameter tuning and performance of model was also poor."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1FokRsFtn5wG"
      },
      "source": [
        "# SVM\n",
        "# tuned_parameters = [{'kernel': ['linear', 'rbf', 'poly'], 'C':[1, 2, 3, 5, 6, 7, 10], 'gamma': [0.0001, 0.001, 0.01, 0.1, 1]}]\n",
        "\n",
        "# MSE_svm = ['mean_squared_error(y_test,y_pred7)']\n",
        "# for value in MSE_svm:\n",
        "#     svr_regr = GridSearchCV(SVR(), tuned_parameters, cv=4)\n",
        "#     svr_regr.fit(X_train_scaled, y_train)\n",
        "#     y_true, y_pred7 = y_test, svr_regr.predict(X_test_scaled)\n",
        "    \n",
        "# print('The best hyper-parameters for SVR are: ', svr_regr.best_params_)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uCXZtgi6kPhT",
        "outputId": "fc68d32e-ff2d-407f-cde5-c07d556b3948",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# XGboost\n",
        "tuned_parameters = [{'max_depth': [5,10, 15, 20, 25, 30],'learning_rate':[0.001, 0.01, 0.1, 0.5], 'n_estimators': [100,150,200, 250, 300]}]\n",
        "MSE_xgb = ['mean_squared_error(y_test,y_pred2)']\n",
        "for value in MSE_xgb:\n",
        "    regr = GridSearchCV(xgb.XGBRegressor(silent = True), tuned_parameters, cv=4)\n",
        "    regr.fit(X_train, y_train)\n",
        "    y_true, y_pred2 = y_test, regr.predict(X_test)\n",
        "    \n",
        "print('The best hyper-parameters for XGBBoost are: ',regr.best_params_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The best hyper-parameters for XGBBoost are:  {'learning_rate': 0.1, 'max_depth': 5, 'n_estimators': 100}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "am7spDJZntvR",
        "outputId": "a91ee8d3-2db3-40be-9ada-0f8b62d38616",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# AdaBoost\n",
        "tuned_parameters = [{'learning_rate': [0.1,1,2,3,4,5], 'n_estimators': [100,200,300, 400, 500]}]\n",
        "MSE_ada = ['mean_squared_error(y_test,y_pred3)']\n",
        "for value in MSE_ada:\n",
        "    adaregr = GridSearchCV(AdaBoostRegressor(), tuned_parameters, cv=4)\n",
        "    adaregr.fit(X_train, y_train)\n",
        "    y_true, y_pred3 = y_test, adaregr.predict(X_test)\n",
        "    \n",
        "print('The best hyper-parameters for AdaBoost are: ', adaregr.best_params_ )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The best hyper-parameters for AdaBoost are:  {'learning_rate': 0.1, 'n_estimators': 500}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O1oSIuY0SL0j"
      },
      "source": [
        "## Training and evaluating the models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BFaHUHikn8Hv",
        "outputId": "3eb68d4d-7f8f-4401-94a2-3e04e5a7cf2c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Linear Regression\n",
        "regressor = LinearRegression()\n",
        "regressor.fit(X_train_scaled, y_train)\n",
        "\n",
        "# Decision Tree\n",
        "regressor_dt = DecisionTreeRegressor(random_state=0, max_depth = 10)\n",
        "regressor_dt.fit(X_train,y_train)\n",
        "\n",
        "# Random Forests\n",
        "regr_rf = RandomForestRegressor(max_depth=10, random_state=0,\n",
        "                             n_estimators=200)\n",
        "regr_rf.fit(X_train, y_train)\n",
        "\n",
        "# KNN\n",
        "neigh = KNeighborsRegressor(n_neighbors = 10, metric = 'minkowski', p = 1)\n",
        "neigh.fit(X_train_scaled, y_train)\n",
        "\n",
        "# XGBoost\n",
        "regr = xgb.XGBRegressor(learning_rate=0.1, max_depth=5, n_estimators=100, random_state = 0, silent = True)\n",
        "regr.fit(X_train, y_train)\n",
        "\n",
        "# AdaBoost\n",
        "adaregr = AdaBoostRegressor(random_state=0, learning_rate = 0.1, n_estimators=500)\n",
        "adaregr.fit(X_train, y_train)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AdaBoostRegressor(base_estimator=None, learning_rate=0.1, loss='linear',\n",
              "                  n_estimators=500, random_state=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-roE_X86n_iv"
      },
      "source": [
        "y_pred1 = regressor.predict(X_test_scaled)    # Linear regression\n",
        "y_pred2 = regressor_dt.predict(X_test)        # Decision Tree\n",
        "y_pred3 = regr_rf.predict(X_test)             # Random Forests\n",
        "y_pred4 = neigh.predict(X_test_scaled)        # KNN\n",
        "y_pred5 = regr.predict(X_test)                # XGBoost\n",
        "y_pred6 = adaregr.predict(X_test)             # AdaBoost"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q-SjYCbRoCe0",
        "outputId": "566f940a-f4e4-42d6-8c2e-b1d0603bd376",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print('The R^2 value for Linear Regression is        :', round((r2_score(y_test,y_pred1)), 3))\n",
        "print('The R^2 value for Decision Tree Regressor is  :', round((r2_score(y_test,y_pred2)), 3))\n",
        "print('The R^2 value for Random Forests Regressor is :', round((r2_score(y_test,y_pred3)), 3))\n",
        "print('The R^2 value for KNN Regressor is            :', round((r2_score(y_test,y_pred4)), 3))\n",
        "print('The R^2 value for XGBoost Regressor is        :', round((r2_score(y_test,y_pred5)), 3))\n",
        "print('The R^2 value for AdaBoost Regressor is       :', round((r2_score(y_test,y_pred6)), 3))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The R^2 value for Linear Regression is        : 0.885\n",
            "The R^2 value for Decision Tree Regressor is  : 0.92\n",
            "The R^2 value for Random Forests Regressor is : 0.93\n",
            "The R^2 value for KNN Regressor is            : 0.35\n",
            "The R^2 value for XGBoost Regressor is        : 0.933\n",
            "The R^2 value for AdaBoost Regressor is       : 0.663\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7x1IyHyDoH30",
        "outputId": "b2a77a59-7125-4a0e-afbd-6c71704c91b3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print('The Adj R2 value for Linear Regression is        :', round((1 - (1-r2_score(y_test,y_pred1))*(len(y)-1)/(len(y)-X.shape[1]-1)),3))\n",
        "print('The Adj R2 value for Decision Tree Regressor is  :', round((1 - (1-r2_score(y_test,y_pred2))*(len(y)-1)/(len(y)-X.shape[1]-1)),3))\n",
        "print('The Adj R2 value for Random Forests Regressor is :', round((1 - (1-r2_score(y_test,y_pred3))*(len(y)-1)/(len(y)-X.shape[1]-1)),3))\n",
        "print('The Adj R2 value for KNN Regressor is            :', round((1 - (1-r2_score(y_test,y_pred4))*(len(y)-1)/(len(y)-X.shape[1]-1)),3))\n",
        "print('The Adj R2 value for XGBoost Regressor is        :', round((1 - (1-r2_score(y_test,y_pred5))*(len(y)-1)/(len(y)-X.shape[1]-1)),3))\n",
        "print('The Adj R2 value for AdaBoost Regressor is       :', round((1 - (1-r2_score(y_test,y_pred6))*(len(y)-1)/(len(y)-X.shape[1]-1)),3))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The Adj R2 value for Linear Regression is        : 0.885\n",
            "The Adj R2 value for Decision Tree Regressor is  : 0.92\n",
            "The Adj R2 value for Random Forests Regressor is : 0.93\n",
            "The Adj R2 value for KNN Regressor is            : 0.349\n",
            "The Adj R2 value for XGBoost Regressor is        : 0.932\n",
            "The Adj R2 value for AdaBoost Regressor is       : 0.663\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mk-FAZhboKXl"
      },
      "source": [
        "MSE_lr  = mean_squared_error(y_test,y_pred1)\n",
        "MSE_dt  = mean_squared_error(y_test,y_pred2)\n",
        "MSE_rf  = mean_squared_error(y_test,y_pred3)\n",
        "MSE_knn = mean_squared_error(y_test,y_pred4)\n",
        "MSE_xgb = mean_squared_error(y_test,y_pred5)\n",
        "MSE_ada = mean_squared_error(y_test,y_pred6)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-GkR962NoM8o",
        "outputId": "ff7e0637-b858-47b6-9561-b15d125483d6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print('The RMSE value for Linear Regression is        :', round(np.sqrt(MSE_lr)));\n",
        "print('The RMSE value for Decision Tree Regressor is  :', round(np.sqrt(MSE_dt)));\n",
        "print('The RMSE value for Random Forests Regressor is :', round(np.sqrt(MSE_rf)));\n",
        "print('The RMSE value for KNN Regressor is            :', round(np.sqrt(MSE_knn)));\n",
        "print('The RMSE value for XGBoost Regressor is        :', round(np.sqrt(MSE_xgb)));\n",
        "print('The RMSE value for AdaBoost Regressor is       :', round(np.sqrt(MSE_ada)));"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The RMSE value for Linear Regression is        : 6.0\n",
            "The RMSE value for Decision Tree Regressor is  : 5.0\n",
            "The RMSE value for Random Forests Regressor is : 5.0\n",
            "The RMSE value for KNN Regressor is            : 14.0\n",
            "The RMSE value for XGBoost Regressor is        : 4.0\n",
            "The RMSE value for AdaBoost Regressor is       : 10.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S4t59ZF-HChN",
        "outputId": "32ca046a-9496-4ed1-bb40-4e743e2dff60",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 398
        }
      },
      "source": [
        "# prepare configuration for cross validation test harness\n",
        "seed = 7\n",
        "# prepare models\n",
        "models = []\n",
        "models.append(('DT', regressor_dt))\n",
        "models.append(('XGB', regr))\n",
        "models.append(('ADA', adaregr))\n",
        "models.append(('RF', regr_rf))\n",
        "\n",
        "scaled_models = []\n",
        "scaled_models.append(('LR', regressor))\n",
        "scaled_models.append(('KNN', neigh))\n",
        "\n",
        "# evaluate each model in turn\n",
        "results = []\n",
        "names = []\n",
        "for name, model in models:\n",
        "\tkfold = model_selection.KFold(n_splits=10, random_state=seed)\n",
        "\tcv_results = model_selection.cross_val_score(model, X_test, y_test, cv=kfold)\n",
        "\tresults.append(cv_results)\n",
        "\tnames.append(name)\n",
        "\tmsg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n",
        "\tprint(msg)\n",
        "\n",
        "for name, model in scaled_models:\n",
        "\tkfold = model_selection.KFold(n_splits=10, random_state=seed)\n",
        "\tcv_results = model_selection.cross_val_score(model, X_test_scaled, y_test, cv=kfold)\n",
        "\tresults.append(cv_results)\n",
        "\tnames.append(name)\n",
        "\tmsg1 = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n",
        "\tprint(msg1)\n",
        "# boxplot algorithm comparison\n",
        "fig = plt.figure()\n",
        "fig.suptitle('Model Comparison')\n",
        "ax = fig.add_subplot(111)\n",
        "plt.boxplot(results)\n",
        "ax.set_xticklabels(names)\n",
        "plt.show()"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "DT: 0.918228 (0.040159)\n",
            "XGB: 0.930013 (0.040738)\n",
            "ADA: 0.342305 (0.165354)\n",
            "RF: 0.927163 (0.040646)\n",
            "LR: 0.883194 (0.032461)\n",
            "KNN: 0.329658 (0.040847)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "74NTPpQxqhjx"
      },
      "source": [
        "Calculating statistical significance XGBoost and Random Forest using 5 by 2 CV using MLxtend package"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "daR5mD87olJE",
        "outputId": "8818bcf4-5d94-4918-96e8-67c0798b29c1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from mlxtend.evaluate import paired_ttest_5x2cv\n",
        "\n",
        "t, p = paired_ttest_5x2cv(estimator1=regr,\n",
        "                              estimator2=regr_rf,\n",
        "                              X=X_test, y=y_test,scoring='r2', \n",
        "                              random_seed=1)\n",
        "\n",
        "# summarize\n",
        "print(f'The P-value is = {p:.3f}')\n",
        "print(f'The t-statistics is = {t:.3f}')\n",
        "# interpret the result\n",
        "if p <= 0.05:\n",
        "    print('Since p<0.05, We can reject the null-hypothesis that both models perform equally well on this dataset. We may conclude that the two algorithms are significantly different.')\n",
        "else:\n",
        "    print('Since p>0.05, we cannot reject the null hypothesis and may conclude that the performance of the two algorithms is not significantly different.')\n"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The P-value is = 0.587\n",
            "The t-statistics is = 0.581\n",
            "Since p>0.05, we cannot reject the null hypothesis and may conclude that the performance of the two algorithms is not significantly different.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hu91a4BXl6Gh"
      },
      "source": [
        "Therefore, performance of XGBoost and Random forest is not statistical significant and we can use any of both models\n",
        "\n",
        "**So, on the basis of Adj-R^2, RMSE and k-fold test we can conclude that we can select XGBoost Model for predicting Covid-19 risk infection.**"
      ]
    }
  ]
}