Copy_of_Heart_disease_detection.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Copy of Heart disease detection.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/prabhkaur301/Heart-Disease-prediction/blob/main/Copy_of_Heart_disease_detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JoRq_X1y825u"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iAanAYjVTRU9",
        "outputId": "5c632089-3ae2-4d6f-e0be-ea8995035ed0"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3VDNCkV8Xops"
      },
      "source": [
        "Important Reference Links:-\n",
        "\n",
        "\n",
        "* [Guide to statistical description (data.decsribe() method)](https://towardsdatascience.com/how-do-we-describe-data-9ce688943c8e)\n",
        "*  [Linear Regression](https://machinelearningmastery.com/linear-regression-for-machine-learning/)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cUGKUkZz_NwF"
      },
      "source": [
        "Importing the dependencies \n",
        "\n",
        "\n",
        "*   numpy is used to handle the multidimensional arrays\n",
        "*   pandas is used to handle the data in the form of dataframes(structured data)\n",
        "*   train_test.split is used to split the data in two different datasets , one is for training the model and other for testing .\n",
        "*   Logistic Regression is the model in which we fetch the data for training. We used this model because it is apt for binary classification . In this case, We have two option either heart diseased or not.\n",
        "*   accuracy_score is used to test how well our model is working.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OT2Wnnmi_Si0"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from operator import add\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8t2UJ48iz1_Z"
      },
      "source": [
        "class color:\n",
        "   PURPLE = '\\033[95m'\n",
        "   CYAN = '\\033[96m'\n",
        "   DARKCYAN = '\\033[36m'\n",
        "   BLUE = '\\033[94m'\n",
        "   GREEN = '\\033[92m'\n",
        "   YELLOW = '\\033[93m'\n",
        "   RED = '\\033[91m'\n",
        "   BOLD = '\\033[1m'\n",
        "   UNDERLINE = '\\033[4m'\n",
        "   END = '\\033[0m'\n"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LhjI-T33RV8I"
      },
      "source": [
        "Data collection & Data Pre-processing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4E6GkrDTRT4N"
      },
      "source": [
        "## loading csv data into pandas dataframe\n",
        "raw_data=pd.read_csv('/content/drive/MyDrive/Colab Notebooks/Heart disease prediction/heart.csv')\n"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-RlLF62JSHz1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        },
        "outputId": "cd372963-cb39-479c-a777-e7efcfbadd0a"
      },
      "source": [
        "## printing the data\n",
        "raw_data.head(20)"
      ],
      "execution_count": 5,
      "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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>63</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>145</td>\n",
              "      <td>233</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>0</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>37</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>130</td>\n",
              "      <td>250</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>187</td>\n",
              "      <td>0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>41</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>130</td>\n",
              "      <td>204</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>172</td>\n",
              "      <td>0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>56</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>120</td>\n",
              "      <td>236</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>178</td>\n",
              "      <td>0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "      <td>354</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>163</td>\n",
              "      <td>1</td>\n",
              "      <td>0.6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>57</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>140</td>\n",
              "      <td>192</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>148</td>\n",
              "      <td>0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>56</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>140</td>\n",
              "      <td>294</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>153</td>\n",
              "      <td>0</td>\n",
              "      <td>1.3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>44</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>120</td>\n",
              "      <td>263</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>173</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>52</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>172</td>\n",
              "      <td>199</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>162</td>\n",
              "      <td>0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>57</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>150</td>\n",
              "      <td>168</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>174</td>\n",
              "      <td>0</td>\n",
              "      <td>1.6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>54</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>140</td>\n",
              "      <td>239</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>160</td>\n",
              "      <td>0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>48</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>130</td>\n",
              "      <td>275</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>139</td>\n",
              "      <td>0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>49</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>130</td>\n",
              "      <td>266</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>171</td>\n",
              "      <td>0</td>\n",
              "      <td>0.6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>64</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>110</td>\n",
              "      <td>211</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>144</td>\n",
              "      <td>1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>58</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>150</td>\n",
              "      <td>283</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>162</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>50</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>120</td>\n",
              "      <td>219</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>158</td>\n",
              "      <td>0</td>\n",
              "      <td>1.6</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>58</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>120</td>\n",
              "      <td>340</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>172</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>66</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>150</td>\n",
              "      <td>226</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>114</td>\n",
              "      <td>0</td>\n",
              "      <td>2.6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>43</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>247</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>171</td>\n",
              "      <td>0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>69</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>140</td>\n",
              "      <td>239</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>151</td>\n",
              "      <td>0</td>\n",
              "      <td>1.8</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    age  sex  cp  trestbps  chol  fbs  ...  exang  oldpeak  slope  ca  thal  target\n",
              "0    63    1   3       145   233    1  ...      0      2.3      0   0     1       1\n",
              "1    37    1   2       130   250    0  ...      0      3.5      0   0     2       1\n",
              "2    41    0   1       130   204    0  ...      0      1.4      2   0     2       1\n",
              "3    56    1   1       120   236    0  ...      0      0.8      2   0     2       1\n",
              "4    57    0   0       120   354    0  ...      1      0.6      2   0     2       1\n",
              "5    57    1   0       140   192    0  ...      0      0.4      1   0     1       1\n",
              "6    56    0   1       140   294    0  ...      0      1.3      1   0     2       1\n",
              "7    44    1   1       120   263    0  ...      0      0.0      2   0     3       1\n",
              "8    52    1   2       172   199    1  ...      0      0.5      2   0     3       1\n",
              "9    57    1   2       150   168    0  ...      0      1.6      2   0     2       1\n",
              "10   54    1   0       140   239    0  ...      0      1.2      2   0     2       1\n",
              "11   48    0   2       130   275    0  ...      0      0.2      2   0     2       1\n",
              "12   49    1   1       130   266    0  ...      0      0.6      2   0     2       1\n",
              "13   64    1   3       110   211    0  ...      1      1.8      1   0     2       1\n",
              "14   58    0   3       150   283    1  ...      0      1.0      2   0     2       1\n",
              "15   50    0   2       120   219    0  ...      0      1.6      1   0     2       1\n",
              "16   58    0   2       120   340    0  ...      0      0.0      2   0     2       1\n",
              "17   66    0   3       150   226    0  ...      0      2.6      0   0     2       1\n",
              "18   43    1   0       150   247    0  ...      0      1.5      2   0     2       1\n",
              "19   69    0   3       140   239    0  ...      0      1.8      2   2     2       1\n",
              "\n",
              "[20 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vhx4qVwCUi07",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e1d97c18-6d6b-4a8d-d1e3-c4483326a8c4"
      },
      "source": [
        "## getting the number of rows and columns\n",
        "raw_data.shape"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(303, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4PrRaDfTU5CJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "033fee0c-7a38-48d9-ef7c-cb5542b0f8a2"
      },
      "source": [
        "## getting the detailed information about dataset\n",
        "raw_data.info()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 303 entries, 0 to 302\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       303 non-null    int64  \n",
            " 1   sex       303 non-null    int64  \n",
            " 2   cp        303 non-null    int64  \n",
            " 3   trestbps  303 non-null    int64  \n",
            " 4   chol      303 non-null    int64  \n",
            " 5   fbs       303 non-null    int64  \n",
            " 6   restecg   303 non-null    int64  \n",
            " 7   thalach   303 non-null    int64  \n",
            " 8   exang     303 non-null    int64  \n",
            " 9   oldpeak   303 non-null    float64\n",
            " 10  slope     303 non-null    int64  \n",
            " 11  ca        303 non-null    int64  \n",
            " 12  thal      303 non-null    int64  \n",
            " 13  target    303 non-null    int64  \n",
            "dtypes: float64(1), int64(13)\n",
            "memory usage: 33.3 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "exWXaS8nVb63",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0f7cd3f2-2048-4d72-8426-41f9cd0f572f"
      },
      "source": [
        "## checking for null values\n",
        "raw_data.isnull().sum()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age         0\n",
              "sex         0\n",
              "cp          0\n",
              "trestbps    0\n",
              "chol        0\n",
              "fbs         0\n",
              "restecg     0\n",
              "thalach     0\n",
              "exang       0\n",
              "oldpeak     0\n",
              "slope       0\n",
              "ca          0\n",
              "thal        0\n",
              "target      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "RotQkWLK4hZU",
        "outputId": "1f41e45b-21cf-4ed7-be2c-f4a9fae4b3d4"
      },
      "source": [
        "# plot histogram to see the distribution of the data\n",
        "fig = plt.figure(figsize = (15,20))\n",
        "ax = fig.gca()\n",
        "raw_data.hist(ax = ax)\n",
        "plt.show()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:4: UserWarning: To output multiple subplots, the figure containing the passed axes is being cleared\n",
            "  after removing the cwd from sys.path.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SJHOTNrZlD7CQiyYrd+ouER6WJHjSMCXmS2GctmectkVaTkzepNHyAuC6qto4WZDkkcAdVXV/kscC+wI3DSpAaVwleTuwDThzIest5KLJB868gJOuWvxP84ajZn7tQRjmixmLMU7bM07bIi0n9nmThlCSs2iaXj0xycYkx7azjuBnByp5HnBle+uA84DXVtW0g51IWpwkxwAvBY6qqmqLNwF7dyy2V1smSVJPWPMmDaGqOnKG8mOmKfsk8MlexyQtV0nWAm8Bfrmqvt8x60LgE0neB/w8Ta33lwcQojS22tFdPwo8BSiapsvfpGm+vArYABxeVXcOKESpr6x5kySpNUOt9weBnYGLklyR5MMAVfUN4FzgGuBzwOuq6v4BhS6Nq1OAz1XVk4CnAtcCxwMXV9W+wMXttLQsWPMmSVJrhlrv02ZZ/j3Ae3oXkbR8JXk4TdeAYwCq6ofAD5McCqxpFzsDmADe2v8Ipf7rOnlLsgG4B7gf2FZVq5PshtXZkiRJWrx9gO8Af53kqcDlwBuBlVW1uV3mVmDldCsvZKTXufRqdM5uRpcdttFph3EE03GMaalq3n6lqr7bMT1Znb0+yfHttFdEJEmSNF/bA08H3lBVlyU5hSlNJKuqktR0Ky9kpNe59Gp0zm5us7Ju/21DNTrtMI5gOo4x9arP26E01di0f1/Ro/eRJEnSeNoIbKyqy9rp82iSuduS7AHQ/t0yoPikvluK5K2Azye5vK2ehnlWZ0uSJEnTqapbgVuSPLEtOohmgKALgaPbsqOBCwYQnjQQS9Fs8rlVtSnJo2hG4rquc+ZM1dlL2Q6534ax/ex0umkHPZv5tLEehf0jSZKG3huAM5PsCNwEvJqm8uHcdjTYm4HDBxif1FddJ29Vtan9uyXJ+cCBtNXZVbV5purspWyH3G/D2H52Ot20o57NfNpYL3U7akmStPxU1RXA6mlmHdTvWKRh0FWzySQrkuw8+Rx4EXA1VmdLkiRJ0pLqtuZtJXB+ksnX+kRVfS7Jv2J1tiRJkiQtma6St6q6ieZu91PLb8fqbEmSJElaMkt1nzdJkiRJI2JVl2MjbFh/yBJFooXo1X3eJEmSJElLyORNkiRJkkaAyZskSZIkjQCTN0mSJEkaASZvkiRJkjQCTN6kIZXkY0m2JLm6o+zEJJuSXNE+Du6Yd0KSG5J8M8mLBxO1JEmSesXkTRpepwNrpyk/uaoOaB+fBUiyH3AE8OR2nf+VZLu+RSpJkqSeM3mThlRVfRG4Y56LHwqcXVX3VdW3gBuAA3sWnCRJkvrOm3RLo+f1SV4FfAVYV1V3AnsCl3Yss7Ete4AkxwHHAaxcuZKJiYkZ32Tr1q2zzh8kY1ucXsa2bv9ti153mPeZJEnDxORNGi0fAv4YqPbvScBr5rtyVZ0KnAqwevXqWrNmzYzLTkxMMNv8QTK2xellbMcc/5lFr3v62hVDu88kSRomNpuURkhV3VZV91fVj4GP8NOmkZuAvTsW3astk7QAMwwUtFuSi5Jc3/7dtS1Pkve3AwVdmeTpg4tckrQcmLxJIyTJHh2TvwpMnmBeCByR5MFJ9gH2Bb7c7/ikMXA6PztQ0PHAxVW1L3BxOw3wEppjbV+a5sgf6lOMkqRlymaT0pBKchawBtg9yUbgncCaJAfQNJvcAPwOQFV9I8m5wDXANuB1VXX/IOKWRllVfTHJqinFh9IciwBnABPAW9vyj1dVAZcm2SXJHlW1uT/RSpKWG5M3aUhV1ZHTFJ82y/LvAd7Tu4ikZWtlR0J2K7Cyfb4ncEvHcpMDBZm8SZJ6wuRNkqR5qqpKUgtZZyGjvK7cqbuRO4dt1M5xG0l0nLZnnLZFWk5M3iRJmt1tk80h236nW9ryeQ0UtJBRXj9w5gWcdNXif5o3HDXzaw/CMI++uhjjtD2jsi1JNgD3APcD26pqdZLdgHOAVTRdCA5vb5sjjT0HLJEkaXYXAke3z48GLugof1U76uSzgO/Z303qiV+pqgOqanU7PdMgQtLYs+ZNPbGqi3s+bVh/yBJGIknzN8NAQeuBc5McC9wMHN4u/lngYOAG4PvAq/sesLQ8zTSIkDT2TN4kSWrNMFAQwEHTLFvA63obkbTsFfD5tq/pX7XNkGcaREgaeyZvkiRJGlbPrapNSR4FXJTkus6Zsw0itJDBgubSqwFeuhmgqNsBjro1dX8M4yA44xiTyZskSZKGUlVtav9uSXI+cCAzDyI0dd15DxY0l14N8HJMF91M1u2/rasBjro1dYCkYRwEZxxjcsASSZIkDZ0kK5LsPPkceBFwNTMPIiSNPWveJEmSNIxWAucngeac9RNV9bkk/8r0gwhJY8/kTZIkSUOnqm4CnjpN+e1MM4iQtBx03WwyyXZJvpbk0+30PkkuS3JDknOS7Nh9mJIkSZK0vC1Fn7c3Atd2TL8XOLmqHg/cCRy7BO8hSZIkSctaV8lbkr2AQ4CPttMBng+c1y5yBvCKbt5DkiRJktR9n7e/AN4C7NxOPwK4q6ombzqxEdhzuhWX8t4b/TaM94yYTq/u/dHr+4qMwr6VJEmS+m3RyVuSlwJbquryJGsWuv5S3nuj34bxnhHT6ebeIbPp9X1Fpt43RJIkSVJ3NW/PAV6e5GDgIcDDgFOAXZJs39a+7QVs6j5MSZIkSVreFt3nrapOqKq9qmoVcATwhao6CrgEOKxdzBsnSouQ5GNJtiS5uqPsz5Jcl+TKJOcn2aUtX5XkB0muaB8fHlzkkiRJ6pWlGG1yqrcCb05yA00fuNN68B7SuDsdWDul7CLgKVX1C8C/ASd0zLuxqg5oH6/tU4ySJEnqoyXpuFRVE8BE+/wm4MCleF1puaqqLyZZNaXs8x2Tl/LTGm5JkiQtA70bdUJSL70GOKdjep8kXwPuBv6wqv7PYMKSpNGyagGDe63bf9vPDAa2Yf0hSx2SJM3I5K3HFvKjMJU/CJpOkrcD24Az26LNwKOr6vYkzwD+PsmTq+ruadad9y06hvmWGMa2OL2MrZvbhwzzPpMkaZiYvEkjJMkxwEuBg6qqAKrqPuC+9vnlSW4EngB8Zer6C7lFxzDfEsPYFqeXsXVza5LT164Y2n0mSdIw6cWAJZJ6IMla4C3Ay6vq+x3lj0yyXfv8scC+wE2DiVKSJEm9Ys3bEOumyaVGW5KzgDXA7kk2Au+kGV3ywcBFSQAubUeWfB7wR0l+BPwYeG1V3TGQwCVJktQzJm/SEKqqI6cpnva2G1X1SeCTvY1IkiRJg2azSUmSJEkaASZvkiTNQ5LfS/KNJFcnOSvJQ5Lsk+SyJDckOSfJjoOOU5I0vkzeJEmaQ5I9gf8BrK6qpwDbAUcA7wVOrqrHA3cCxw4uSknSuLPPmyRJ87M9sFM7ONDP0dxj8fnAb7bzzwBOBD40kOgkDYQDzKmfTN4kSZpDVW1K8ufAt4EfAJ8HLgfuqqrJO5RvBPacum6S44DjAFauXDnrDclX7tTdDc+H7Wbno3AD9oXs7+k+n2HfvpmMwmcj6WeZvEmSNIckuwKHAvsAdwF/B6ydz7pVdSpwKsDq1atrthuSf+DMCzjpqsX/NG84aubXHoRhvmn9pIXcYH7d/tt+5vMZtn0+X6Pw2QC09zH9CrCpql6aZB/gbOARNBdQXllVPxxkjFI/2edNkqS5vQD4VlV9p6p+BHwKeA6wS5LJs/m9gE2DClAaU28Eru2Ytp+pljWTN0mS5vZt4FlJfi5JgIOAa4BLgMPaZY4GLhhQfNLYSbIXcAjw0XY6NP1Mz2sXOQN4xWCikwbDZpOSJM2hqi5Lch7wVWAb8DWappCfAc5O8u627LTBRSmNnb8A3gLs3E4/gnn0M520kP6mc5mtj2A3/VS70W0f2W5N3R/D2I9yHGMyeZMkaR6q6p3AO6cU3wQcOIBwpLGW5KXAlqq6PMmaxbzGQvqbzmW2PoIL6Te5lKbrg9lPU/t7DmM/ynGMyeRNkiRJw+Y5wMuTHAw8BHgYcAptP9O29s1+plp27PMmSZKkoVJVJ1TVXlW1CjgC+EJVHYX9TLXMWfM2h+luvLhu/20DqyKXJElaxt6K/Uy1jJm8SZIkaWhV1QQw0T63n6mWNZtNSpIkSdIIMHmTJEmSpBFg8iZJkiRJI8DkTRpSST6WZEuSqzvKdktyUZLr27+7tuVJ8v4kNyS5MsnTBxe5JEmSeqGr5C3JQ5J8OcnXk3wjybva8n2SXNaeSJ6TZMelCVdaVk4H1k4pOx64uKr2BS5upwFeAuzbPo4DPtSnGCVJktQn3da83Qc8v6qeChwArE3yLOC9wMlV9XjgTuDYLt9HWnaq6ovAHVOKDwXOaJ+fAbyio/zj1biU5iame/QnUkmSJPVDV7cKqKoCtraTO7SPAp4P/GZbfgZwItYESEthZVVtbp/fCqxsn+8J3NKx3Ma2bHNHGUmOo6mZY+XKlUxMTMz4Rlu3bp11/iAZ2+L0MrZ1+29b9LrDvM8kSRomXd/nLcl2wOXA44G/BG4E7qqqyV/yyZNISUuoqipJLXCdU4FTAVavXl1r1qyZcdmJiQlmmz9IxrY4vYztmOM/s+h1T1+7Ymj3mSRJw6Tr5K2q7gcOSLILcD7wpPmst5AagEGa7mryyp26u8o86nq9/cP6XRgStyXZo6o2t80it7Tlm4C9O5bbqy2TJEnSmOg6eZtUVXcluQR4Nk1/m+3b2rdpTyIXUgMwSNNdTV63/zZOumrJdt3I6fX2bzhqTc9eewxcCBwNrG//XtBR/vokZwPPBL7X0bxSkiRJY6CrM/AkjwR+1CZuOwEvpBms5BLgMOBsHniCKWmekpwFrAF2T7IReCdN0nZukmOBm4HD28U/CxwM3AB8H3h1t+9/1abvddUUbsP6Q7oNQZIkSR26rT7ZAzij7ff2IODcqvp0kmuAs5O8G/gacFqX7yMtO1V15AyzDppm2QJe19uIJEmSNEjdjjZ5JfC0acpvAg7s5rUlSZIkST/V7X3eJEmSJEl9YPImSZIkSSPA5E2SJEmSRoDJmyRJ85BklyTnJbkuybVJnp1ktyQXJbm+/bvroOOUJI0vkzdJkubnFOBzVfUk4KnAtcDxwMVVtS9wcTstSVJPmLxJkjSHJA8Hnkd765uq+mFV3QUcCpzRLnYG8IrBRChJWg5M3iRJmts+wHeAv07ytSQfTbICWFlVm9tlbgVWDixCacwkeUiSLyf5epJvJHlXW75PksuS3JDknCQ7DjpWqV+6vUm3JEnLwfbA04E3VNVlSU5hShPJqqokNXXFJMcBxwGsXLmSiYmJGd9k5U6wbv9tiw5yttcehK1btw5dTFMtZH9P9/kM+/bNZBQ+G+A+4PlVtTXJDsCXkvxv4M3AyVV1dpIPA8cCHxpkoFK/mLxJkjS3jcDGqrqsnT6PJnm7LckeVbU5yR7AlqkrVtWpwKkAq1evrjVr1sz4Jh848wJOumrxP80bjpr5tQdhYmKC2bZ3GBxz/Gfmvey6/bf9zOczbPt8vkbhs6mqAra2kzu0jwKeD/xmW34GcCImb1ombDYpSdIcqupW4JYkT2yLDgKuAS4Ejm7LjgYuGEB40thKsl2SK2gujFwE3AjcVVWTVaAbgT0HFZ/Ub9a8SZI0P28Azmz719wEvJrmIui5SY4FbgYOH2B80tipqvuBA5LsApwPPGm+6y6kyfJcZmtm2k1T525028y6W1P3xzA2xR3HmEzeJEmah6q6Alg9zayD+h2LtNxU1V1JLgGeDeySZPu29m0vYNMM68y7yfJcZmtmupCmt0tpuma8/TS1yfAwNsUdx5hsNilJkqShk+SRbY0bSXYCXkhzf8VLgMPaxWyurGXFmjdJkiQNoz2AM5JsR9tEuao+neQa4Owk7wa+Rnv/RWk5MHmTJEnS0KmqK4GnTVN+E3Bg/yOSBs9mk5IkSZI0Aqx5k0ZIO0z5OR1FjwXeAewC/Dbwnbb8bVX12T6HJ0mSpB4yeZNGSFV9EzgAmnvf0IywdT7NkOUnV9WfDzA8SZIk9ZDJmzS6DgJurKqbkww6FkmStIysmnKLhHX7b5v3bRM2rD+kFyEtCyZv0ug6AjirY/r1SV4FfAVYV1V3Tl1hITcs7fbmn728KeYw3nRz0nKNrZvvyrBjnpIAACAASURBVDDvM0mShonJmzSCkuwIvBw4oS36EPDHQLV/TwJeM3W9hdyw9ANnXtDVzT+n3rxzKQ3jTTcnLdfYurlJ7elrVwztPpMkaZg42qQ0ml4CfLWqbgOoqtuq6v6q+jHwERxCWZIkaexY8yaNpiPpaDKZZI+q2txO/ipw9UCikrRsTe3/AvPvA2P/F0maH5M3acQkWQG8EPidjuI/TXIATbPJDVPmSZIkaQyYvEkjpqruBR4xpeyVAwpHkiRJfbLoPm9J9k5ySZJrknwjyRvb8t2SXJTk+vbvrksXriRJkiQtT90MWLKNZjjy/YBnAa9Lsh9wPHBxVe0LXNxOS5IkSZK6sOjkrao2V9VX2+f3ANcCewKHAme0i50BvKLbICVJkiRpuVuSPm9JVgFPAy4DVnaMencrsHKGdeZ9s+BBmu7Gs93evHjU9Xr7h/W7IEmSJA1S18lbkocCnwTeVFV3J/nJvKqqJDXdegu5WfAgTTfE8br9t3V18+JR1+vt7+XNnSVJkqRR1dVNupPsQJO4nVlVn2qLb0uyRzt/D2BLdyFKkiRJkroZbTLAacC1VfW+jlkXAke3z48GLlh8eJIkSZIk6K7Z5HOAVwJXJbmiLXsbsB44N8mxwM3A4d2FKEmSJEladPJWVV8CMsPsgxb7upIkDaMk2wFfATZV1UuT7AOcDTwCuBx4ZVX9cJAxSpLGW1d93iRJWkbeSHNbnEnvBU6uqscDdwLHDiQqSdKyYfImSdIckuwFHAJ8tJ0O8HzgvHYR72sqLbEkeye5JMk1Sb6R5I1t+W5JLkpyfft310HHKvXL8h3vXpKk+fsL4C3Azu30I4C7qmryppcbgT2nW3Eh9zXt9j6ag7xPZjf3RR22uGcy3faM6r1Jt27dOgqxbwPWVdVXk+wMXJ7kIuAY4OKqWp/keOB44K0DjFPqG5M3SZJmkeSlwJaqujzJmoWuv5D7mn7gzAu6uo/mIO+T2c19UYct7plMtz2jem/SiYkJhvUeu5OqajOwuX1+T5JraS6SHAqsaRc7A5jA5E3LhMmbJEmzew7w8iQHAw8BHgacAuySZPu29m0vYNMAY5TGWpJVwNOAy4CVbWIHcCuwcoZ15l3rPZfZaiq7qS3vRrc19UttIfH0q9Z3GGuYu43J5E2SpFlU1QnACQBtzdvvV9VRSf4OOIxmxEnvayr1SJKHAp8E3lRVdzddThtVVUlquvUWUus9l9lqKhdSe7uU5luz3S8LiadfNdbDWMPcbUwOWCJJ0uK8FXhzkhto+sCdNuB4pLGTZAeaxO3MqvpUW3xbkj3a+XsAWwYVn9Rvw5OuS5q3JBuAe4D7gW1VtTrJbsA5wCpgA3B4Vd05qBilcVRVEzT9a6iqm4ADBxmPNM7aUV1PA66tqvd1zLqQprZ7PdZ6a5mx5k0aXb9SVQdU1ep2+nia0bf2BS5upyVJGlXPAV4JPD/JFe3jYJqk7YVJrgde0E5Ly4I1b9L4cPQtSdLYqKovAZlh9kH9jEUaFiZv0mgq4PNtJ+2/ajtlzzn61rjcb2oYR4+atFxj6+a7Msz7TJKkYWLyJo2m51bVpiSPAi5Kcl3nzJlG3xqX+00N4+hRk5ZrbN2Mtnb62hVDu88kSRom9nmTRlBVbWr/bgHOpxk0wdG3JEmSxpjJmzRikqxIsvPkc+BFwNX8dPQtcPQtSZKksWOzSWn0rATOb29Suj3wiar6XJJ/Bc5NcixwM3D4AGOUJEnSEjN509BZ1UXfGYAN6w9ZokiGU3tvqadOU347jr4lSZI0tmw2KUmSJEkjwORNkiRJkkaAyZskSZIkjQCTN0mSJEkaASZvkiRJkjQCTN4kSZIkaQSYvEmSJEnSCDB5kyRJkqQR4E26JUmSJPXNquM/09X6G9YfskSRjJ6ukrckHwNeCmypqqe0ZbsB5wCrgA3A4VV1Z3dhdqfbL4gkSZIkDVq3zSZPB9ZOKTseuLiq9gUubqclSZIkSV3oquatqr6YZNWU4kOBNe3zM4AJ4K3dvI8kSZIeqJuWRaevXbGEkUjql170eVtZVZvb57cCK6dbKMlxwHEAK1euZGJiogehNNbtv21JX2/lTkv/mqNk2Le/l98lSZIkaVB6OmBJVVWSmmHeqcCpAKtXr641a9b0LI5jlrjP27r9t3HSVct3rJdh3/4NR60ZdAiSxkySvYGP01yQLODUqjplGPt5S5LGVy9uFXBbkj0A2r9bevAekiT10zZgXVXtBzwLeF2S/bCft9QzST6WZEuSqzvKdktyUZLr27+7DjJGqd96kbxdCBzdPj8auKAH7yFJUt9U1eaq+mr7/B7gWmBPmn7eZ7SLnQG8YjARSmPpdBwYT3qAbm8VcBbN4CS7J9kIvBNYD5yb5FjgZuDwboOUJGlYtAN1PQ24jHn0815IH+9u+xQPss/vdHHPd3uGLe6ZTLc9oxL7VFu3bh36PuIOjCf9rG5HmzxyhlkHdfO6kqY3S7+bE4HfBr7TLvq2qvrsYKKUxleShwKfBN5UVXcn+cm8mfp5L6SP9wfOvKCrPsWD7PM7Xf/y+faRHra4ZzLd9oxK7FOdvnYFvRxvoIfmNTAezP/CyVWbvjf3m+7UHJ/TWbf/nKv3xLANINfPeOZ74WEYL1J0G9PwjjohaTqT/W6+mmRn4PIkF7XzTq6qPx9gbNJYS7IDTeJ2ZlV9qi2+LckeVbXZft5Sf802MF47f14XTuaTBA/jYG3DFlM/45nvRZOJiYmhu0jRbUy96PMmqUdm6XcjqYfSVLGdBlxbVe/rmGU/b6m/HBhPy9rwpOuSFmRKv5vnAK9P8irgKzS1cz8zXPm49L0ZxmYQk5ZrbOPe94bmGHslcFWSK9qyt2E/b6nfJi+YrMcLJlqGTN6kETRNv5sPAX9M0w/uj4GTgNdMXW9c+t4MYzOIScs1tnHve1NVXwIyw2z7eUs94MB40s8yeZNGzHT9bqrqto75HwE+PaDwJElaEg6MJ/0skzdphMzU72ZywIR28leBq6dbX5IkadStmmdrj3X7b5u2ZciG9YcsdUh9Y/ImjZaZ+t0cmeQAmmaTG4DfGUx4kiRJ6hWTN2mEzNLvxnu6SZIkjTlvFSBJkiRJI8DkTZIkSZJGgMmbJEmSJI0AkzdJkiRJGgEmb5IkSZI0AkzeJEmSJGkEmLxJkiRJ0ggweZMkSZKkEWDyJkmSJEkjYPtBByAttVXHf2bR625Yf8gSRiJJkiQtHWveJEmSJGkEWPMmSZIkadkY5VZa1rxJkiRJ0ggweZMkSZKkEWCzSUmSJEmah26aXAKcvnZFV+tb8yZJkiRJI6BnNW9J1gKnANsBH62q9Yt9rW4zXGm5WMrjTtL8eNxJ/edxp+WqJzVvSbYD/hJ4CbAfcGSS/XrxXpIaHndS/3ncSf3ncaflrFfNJg8Ebqiqm6rqh8DZwKE9ei9JDY87qf887qT+87jTspWqWvoXTQ4D1lbVb7XTrwSeWVWv71jmOOC4dvKJwDeXPJDe2R347qCDGKDlvP2PqapHDjqI6fTguBvmz9nYFmdYY5srLo+70eX2DK+xPu7a8qU81xzGz37YYhq2eGD0YprzuBvYaJNVdSpw6qDevxtJvlJVqwcdx6As9+0fZQs57ob5cza2xRnW2IY1rqUyLsfdYrg9w2uctmUmS3muOYz7a9hiGrZ4YDxj6lWzyU3A3h3Te7VlknrH407qP487qf887rRs9Sp5+1dg3yT7JNkROAK4sEfvJanhcSf1n8ed1H8ed1q2etJssqq2JXk98I80Q7h+rKq+0Yv3GpCRbO65hJb79g+lHhx3w/w5G9viDGtswxrXnJbZcbcYbs/wGtltGdB55jDur2GLadjigTGMqScDlkiSJEmSllavmk1KkiRJkpaQyZskSZIkjQCTt3lIsl2SryX5dDu9T5LLktyQ5Jy2s+zYSrJLkvOSXJfk2iTPTrJbkouSXN/+3XXQcWr+kqxN8s32O3z8NPMf3H63b2i/66s65p3Qln8zyYv7HNebk1yT5MokFyd5TMe8+5Nc0T6WvOP6PGI7Jsl3OmL4rY55R7fHyvVJjh5AbCd3xPVvSe7qmNfr/faxJFuSXD3D/CR5fxv7lUme3jGvp/tt2Mz1OY6SuT73UZJk7ySXtP97vpHkjYOOqRtJHpLky0m+3m7PuwYd0zCY7js707nObP+3ehzPn7XnYlcmOT/JLm35qiQ/6Phf/uGljmeWmE5MsqnjvQ/umNez84VZ4jmnI5YNSa5oy/u1j6b9f7Gk36Wq8jHHA3gz8Ang0+30ucAR7fMPA7876Bh7vP1nAL/VPt8R2AX4U+D4tux44L2DjtPHvD/P7YAbgce2n+fXgf2mLPPfgQ+3z48Azmmf79cu/2Bgn/Z1tutjXL8C/Fz7/Hcn42qntw54nx0DfHCadXcDbmr/7to+37WfsU1Z/g00nft7vt/a138e8HTg6hnmHwz8byDAs4DL+rHfhu2x0M9x2B9zfe6j9AD2AJ7ePt8Z+LcR/2wCPLR9vgNwGfCsQcc16Md039mZznVm+r/Vh3heBGzfPn9vRzyr+nGszRDTicDvT7Nsz84XZotnyvyTgHf0eR9N+/9iKb9L1rzNIclewCHAR9vpAM8HzmsXOQN4xWCi670kD6c5OE4DqKofVtVdwKE02w5jvg/G0IHADVV1U1X9EDib5vPs1Pn5ngcc1H73DwXOrqr7qupbwA3t6/Ulrqq6pKq+305eSnNvn36Yzz6byYuBi6rqjqq6E7gIWDvA2I4EzlrC959VVX0RuGOWRQ4FPl6NS4FdkuxB7/fbsOnmOzZ05vG5j4yq2lxVX22f3wNcC+w52KgWrz3WtraTO7SPZT963Qzf2ZnOdWb6v9XTeKrq81W1rZ3s52/gjDHNopfnC3PG056zHE4ff+/amGb6f7Fk3yWTt7n9BfAW4Mft9COAuzoOno2M8D/xedgH+A7w12majn40yQpgZVVtbpe5FVg5sAi1UHsCt3RMT/cd/sky7Xf9ezTf/fms28u4Oh1Lc7Vq0kOSfCXJpUmW+mLCfGP7tbbZw3lJJm8g28t9tqDXT9PMdB/gCx3Fvdxv8zFT/L3eb8NmuW3vSErThPxpNLVVIytNd5ArgC00F0lGent6aKZznWE4Xl/DA38D92nP0/45yS/1OZbXt799H8tPu9EMeh/9EnBbVV3fUdbXfTTl/8WSfZdM3maR5KXAlqq6fNCxDND2NFXSH6qqpwH30lT3/kQ19b7L/qqd+ifJfwNWA3/WUfyYqloN/CbwF0ke1+ew/gFYVVW/QFNLdMYcyw/CEcB5VXV/R9mg95s0EpI8FPgk8KaqunvQ8XSjqu6vqgNoam4OTPKUQcc07IbpXCfJ24FtwJlt0Wbg0e152puBTyR5WJ/C+RDwOOCANo6T+vS+c5nayqSv+2i2/xfdfpdM3mb3HODlSTbQNGF5PnAKTZXm5A3O9wI2DSa8vtgIbOy4KnceTTJ322S1bvt3y4Di08JtAvbumJ7uO/yTZdrv+sOB2+e5bi/jIskLgLcDL6+q+ybLq2pT+/cmYILmatdSmTO2qrq9I56PAs+Y77q9jq3DEUxpQtLj/TYfM8Xf6/02bJbb9o6UJDvQnIidWVWfGnQ8S6XtBnEJ490kuRsznesM7HhNcgzwUuCoNgmgbZp4e/v8cpr+ZU/oRzxVdVt7MeDHwEf4adPIQe6j7YH/CpzTEWff9tEM/y+W7Ltk8jaLqjqhqvaqqlU0Jz1fqKqjaP7RHdYudjRwwYBC7LmquhW4JckT26KDgGuAC2m2HcZ8H4yhfwX2TTNq6o403+2powx2fr6H0Xz3qy0/Is1olPsA+wJf7ldcSZ4G/BVN4ralo3zXJA9un+9Oc+HlmiWKa76xdbZRfzlNO3eAfwRe1Ma4K02H83/sZ2xtfE+iGfjjXzrKer3f5uNC4FXtiFvPAr7XNi3p9X4bNvP6HNV/bd+Z04Brq+p9g46nW0kemZ+OUrgT8ELgusFGNbRmOteZ6f9WTyVZS9OV5+Ud/b8nP9Pt2uePpfltvqnX8bTv1/nb96vA5MiPvTxfmMsLgOuqamNHnH3ZR7P8v1i679Jso5n4eMDoMWv46WiTj6X5At4A/B3w4EHH1+NtPwD4CnAl8Pc0J4CPAC4Grgf+Cdht0HH6WNBnejDNCEg3Am9vy/6I5gcB4CHtd/uG9rv+2I51396u903gJX2O65+A24Ar2seFbfl/Aa6iGdnqKuDYAeyzPwG+0cZwCfCkjnVf0+7LG4BX9zu2dvpEYP2U9fqx386iaa7yI5qa/GOB1wKvbecH+Ms29quA1f3ab8P2mO5zHNXHdJ/7oGPqYlueS9PE6cqO/z0HDzquLrbnF4CvtdtzNe1ofMv9McP/qmnPdWb7v9XjeG6g6R81+T2cHBX619rfnyuArwIv6+M++pt2H1xJk4js0bF8z84XZoqnLT998jemY9l+7aNp/18s5Xcp7YqSJEmSpCFms0lJkiRJGgEmb5IkSZI0AkzeJEmSJGkEmLxJkiRJ0ggweZMkSZKkEWDyJkmSJEkjwORNkiRJkkaAyZskSZIkjQCTN0mSJEkaASZvkiRJkjQCTN4kSZIkaQSYvEmSJEnSCDB5kyRJkqQRYPImSZIkSSPA5E2SJEmSRoDJmyRJkiSNAJM3SZIkSRoBJm+SJEmSNAJM3iRJkiRpBJi8SZIkSdIIMHmTJEmSpBFg8iZJkiRJI8DkTZIkSZJGgMmbJEmSJI0AkzdJkiRJGgEmb5IkSZI0AkzeJEmSJGkEmLxJkiRJ0ggweZMkSZKkEWDyJkmSJEkjwORNkiRJkkaAyZskSZIkjQCTN0mSJEkaASZvkiRJkjQCTN4kSZIkaQSYvI2BJMck+dIi1z0xyd8udUzSuEryxCRXJLknyR1J3j3omCRJmq8kq5JUku0XuX4leXyXMZzu7+fimLxJ0sK8BbikqnYGLhx0MNJy1M1FS2k5SrIhyQsGHYe6Z/ImSQvzGOAbgw5CGgeLvfIvScuVyduISbJ3kk8l+U6S25N8sGPenye5M8m3kryko/znk1zYNvG6IclvDyZ6abQl+QLwK8AHk2wFdgR2T3JR24zyn5M8pl02SU5OsiXJ3UmuSvKUQcYvDYO2BuCtSa4E7k3y3CT/L8ldSb6eZE3Hssckuak9vr6V5Kgk/xn4MPDsJFuT3NUu++D2d/DbSW5L8uEkO3W81qFtk+e7k9yYZG1bvk+SL7bv8U9J/tLuBBonSf4GeDTwD+1v1+HtrKPa4+W7Sd7esfyBSf6lPSY3J/lgkh1neO1DknytPa5uSXLilPmdx/ctSY7pmL1rks+0x95lSR63pBs+pkzeRkiS7YBPAzcDq4A9gbPb2c8EvgnsDvwpcFqStPPOBjYCPw8cBvzPJM/vX+TSeKiq5wP/B3h9VT0U+CFwFPDHNMfeFcCZ7eIvAp4HPAF4OM2P5e39jlkaUkcChwCPBS4A3g3sBvw+8Mkkj0yyAng/8JK2mfJ/Aa6oqmuB1wL/UlUPrapd2tdcT3O8HQA8nuY38h3QnIwCHwf+ANiF5tjc0K73CeDLwCOAE4FX9myrpQGoqlcC3wZe1v52ndvOei7wROAg4B3thRGA+4Hfo/lde3Y7/7/P8PL3Aq+iOa4OAX43ySsA2ouZ/xv4APBImmPzio51jwDeBewK3AC8p9ttXQ5M3kbLgTQJ2B9U1b1V9R9VNdnm/+aq+khV3Q+cAewBrEyyN/Ac4K3t8lcAH6U50CR17zNV9cWqug94O01twN7Aj4CdgScBqaprq2rzIAOVhsj7q+oW4L8Bn62qz1bVj6vqIuArwMHtcj8GnpJkp6raXFXTNlluL1YeB/xeVd1RVfcA/5Pm5BDgWOBjVXVR+z6bquq6JI8GfhF4R1X9sP1NtS+rlot3VdUPqurrwNeBpwJU1eVVdWlVbauqDcBfAb883QtU1URVXdUeV1cCZ3Us+5vAP1XVWVX1o6q6vT0PnXR+VX25qrbRXPg8oDebOV5M3kbL3jRJ2rZp5t06+aSqvt8+fShNsjf5QzbpZporkpK6d8vkk6raCtwB/HxVfQH4IPCXwJYkpyZ52IBilIbN5HHzGODX2yZVd7VNIJ8L7FFV9wK/QVPLtrltXvWkGV7vkcDPAZd3vM7n2nJofj9vnGa9yd/I73eU3TLNctI4urXj+fdpzhtJ8oQkn05ya5K7aS6E7D7dCyR5ZpJL2u4836M5XieXnem4m/X9NTuTt9FyC/DoBXbw/ndgtyQ7d5Q9Gti0pJFJy9fek0+SPJSm6de/A1TV+6vqGcB+NM25/mAgEUrDp9q/twB/U1W7dDxWVNV6gKr6x6p6IU1rkuuAj0xZf9J3gR8AT+54nYe3TcQm32e6/jSbaX4jf66jbO9plpNG3dRjZjYfojne9q2qhwFvAzLDsp+gqa3eu6oeTtMfdXLZmY47dcHkbbR8meaHZn2SFUkekuQ5s63QNkv5f8CftMv/Ak3zETtjS0vj4LZD9o40fd8urapbkvxie0VyB5o+Af9B0wRM0k/9LfCyJC9Osl37O7UmyV5JVraDjKwA7gO28tNj6DZgr8lBFKrqxzSJ3clJHgWQZM8kL26XPw14dZKDkjyonfekqrqZppnmiUl2TPJs4GV923qpf26j6WM6HzsDdwNb29ru351j2Tuq6j/avqW/2THvTOAFSQ5Psn2SRySxaWSXTN5GSNuf7WU0HbG/TTMIyW/MY9UjaQY4+XfgfOCdVfVPPQpTWm4+AbyTprnkM2j68AA8jOZk8k6apsq3A382iAClYdVeYDyU5sr+d2iu1P8BzfnJg4A30/x23UHTj2byJPILNLfsuDXJd9uyt9IMenBp29Trn2gGY6Cqvgy8GjgZ+B7wzzRNNqEZdOjZNMfou4FzaJJFaZz8CfCHbZPiw+ZY9vdpkrB7aH7Hzpll2f8O/FGSe2gGCJocDIWq+jZN/9V1NMfwFbT96rR4qVpILaokSdL4SnIOcF1VvXPQsUjSVNa8SZKkZatt4vy4tjnlWpqawL8fdFySNJ2FDHwhSZI0bv4T8Cma+7xtBH63qr422JAkaXo2m5QkSZKkEWCzSUmSJA2lJL+X5BtJrk7y/7N3/2GSlfWd99+fACqiCSCmF4E4RIlZlIg6QV2y2V7xB6IR91kfgmEVDHlmTdTVdRIZTK5ofriLT0RDMNGMQsBnRxBRF1ZMIkF6Xa8NGEF0ECSOOMpMgFH55airGfw+f5zTWDRdM91dXVWnut+v66qrq+7zo751qu4+53vu+9znonZE0sOTXJtkS5IPzY46Kq0GnWh5O+igg2rNmjXjDoPvfve77LfffuMO4wFdiqdLscD44rnuuuu+VVWP3fOc3benete177yXsS1NV2PbU1zWu24wtqXpamyTUO+SHAJ8Bjiyqr6f5BLgEzQjGH60qi5O8l7gC1X1nt2tqyvHmnvS1d/LMPhZH2pB9a6qxv54xjOeUV1w9dVXjzuEB+lSPF2KpWp88QCfqw7UmeV47Knede0772VsS9PV2PYUl/WuG4xtaboa2yTUO+AQmttHHEgzTsPHgRfQ3JR973aeZwN/u6d1deVYc0+6+nsZBj/rQy2k3jlgiSRJkjqnqrYneQfNvW2/D3wSuA64p6p2tbNto0nyHiLJOmAdwNTUFDMzM0OPeVA7d+6ciDiXg591aUzeJEmS1DlJDqC5dcPhwD3Ah4HjF7p8VW0ENgKsXbu2pqenhxDl8pqZmWES4lwOftalccASSZIkddFzga9V1Ter6p9pbulwLLB/ktkGiEOB7eMKUBo1kzdJkiR10TeAZyV5ZJIAxwE3AVcDL2vnORW4bEzxSSNn8iZJkqTOqaprgUuB64HNNMetG4EzgDcm2UJzc/XzxhakNGJe8yZJkqROqqq3AG+ZU3wrcMwYwpHGzpY3SZIkSZoAJm+SJEmSNAFM3iRJkiRpAnjN2x6s2XDFQMtvPetFyxSJNFqbt9/LaQP8/v3tSxqVQf5f+b9KGr01G65g/VG7rLdLYMubJEmSJE0AkzdJkiRJmgAmb1IHJTksydVJbkrypSSvb8vfmmR7khvaxwk9y5yZZEuSW5K8YHzRS5IkaRi85k3qpl3A+qq6PsmjgeuSXNlOe1dVvaN35iRHAicDTwYeB/xdkp+rqvtHGrUkSZKGxpY3qYOq6vaqur59/h3gZuCQ3SxyInBxVf2gqr4GbMEbmEqSJK0otrxJHZdkDfA04FrgWOC1SV4JfI6mde5umsTump7FtjFPspdkHbAOYGpqipmZmb7vO7UvrD9q15Lj3t26B7Vz586hrn8QxrZ4XY1LkqSuMXmTOizJo4CPAG+oqvuSvAf4I6Dav2cDv77Q9VXVRmAjwNq1a2t6errvvOduuoyzNy/9X8TWU/qve1AzMzPsLvZxMrbF62pc4+AtOiRJu7OgbpNJzk+yI8mNPWUHJrkyyVfavwe05UnyZ+3ACV9M8vRhBS+tZEn2oUncNlXVRwGq6s6qur+qfgS8jx93jdwOHNaz+KFtmSRJklaIhV7zdgFw/JyyDcBVVXUEcFX7GuCFwBHtYx3wnsHDlFaXJAHOA26uqnf2lB/cM9u/A2ZPqFwOnJzk4UkOp6l/nx1VvJIkSRq+BfWJqqpPt9fd9DoRmG6fXwjMAGe05R+oqgKuSbJ/koOr6vblCFhaJY4FXgFsTnJDW/Zm4OVJjqbpNrkV+I8AVfWlJJcAN9GMVPkaR5qUJElaWQa55m2qJyG7A5hqnx8C3NYz3+zACSZv0gJV1WeAzDPpE7tZ5m3A24YWlCRJksZqWQYsqapKUotZZjGj3o3KfCOeDTLaHgw24l6XRmDrUizQvXgkSZKkYRskebtztjtkex3OjrZ8QQMnLGbUu1GZb8SzQUb9gsFG3OvSCGxdigW6F4+klSHJ+cCLgR1V9ZQ5lz+fkQAAIABJREFU09YD7wAeW1Xfaq9NPQc4AfgecNrs/RklSRqGQW7SfTlwavv8VOCynvJXtqNOPgu41+vdJEkT4gIeOkAXSQ4Dng98o6fYAbokSSO10FsFXAT8PfCkJNuSnA6cBTwvyVeA57avobkm51ZgC81Q5r+17FFLkjQEVfVp4K55Jr0LeBPNYEGzHhigq6quAfafMyKspAEkeVKSG3oe9yV5Q7/bVUmrwUJHm3x5n0nHzTNvAa8ZJChJkroiyYnA9qr6QtNT8gELGqBrMdd4T+072LXWw7wWuMvXGg+y3Yb9mbq63boaV6+qugU4GiDJXjSX4XyMH9+u6qwkG9rXZ4wtUGmElmXAEkmSVqIkj6S5Tcfzl7qOxVzjfe6myzh789J3zYNcZ70nXb7WeJDtNsxtBt3dbl2NazeOA75aVV9vT6hMt+W9t6uSVjyTN0mS+nsCcDgw2+p2KHB9kmNY4ABdkpbFycBF7fN+t6uSVjyTN0mS+qiqzcBPz75OshVY2442eTnw2iQXA8/EAbqkoUjyMOAlwJlzp+3udlVdvC3VnkxCd9blsP6oXZ3u7rzclvN7NXmTJKnVDtA1DRyUZBvwlqo6r8/sn6C5TcAWmlsFvGokQUqrzwuB66vqzvZ1v9tVPUgXb0u1JxPYnXVJTttwBeuP2tXZ7s7LbTm/V5M3SZJauxmga3b6mp7nDtAljcbL+XGXSfjx7arO4sG3q5JWvEHu8yZJkiQNTZL9gOcBH+0p7ne7KmnFs+VNkiRJnVRV3wUeM6fs28xzuyppNbDlTZIkSZImgMmbJEmSJE0AkzdJkiRJmgAmb5IkSZI0AUzeJEmSJGkCmLxJkiRJ0gQweZMkSZKkCWDyJkmSJEkTwORNkiRJkiaAyZskSZIkTQCTN0mSJEmaACZvkiRJkjQBTN4kSZIkaQKYvEmSJEnSBNh73AFIkiRJ0kKt2XDFQMtvPetFyxTJ6NnyJkmSJEkTwORNkiRJkiaAyZskSa0k5yfZkeTGnrI/SfLlJF9M8rEk+/dMOzPJliS3JHnBeKKWJK0WJm+SJP3YBcDxc8quBJ5SVb8A/CNwJkCSI4GTgSe3y/xFkr1GF6okabUxeZMkqVVVnwbumlP2yara1b68Bji0fX4icHFV/aCqvgZsAY4ZWbDSKpBk/ySXtq3fNyd5dpIDk1yZ5Cvt3wPGHac0Ko42KXVQksOADwBTQAEbq+qcJAcCHwLWAFuBk6rq7iQBzgFOAL4HnFZV148jdmmF+3WaOghwCE0yN2tbW/YgSdYB6wCmpqaYmZnpu/KpfWH9Ubv6Tt+T3a17UDt37hzq+gcxyHYb9mfq6nbralzzOAf4m6p6WZKHAY8E3gxcVVVnJdkAbADOGGeQ0qiYvEndtAtYX1XXJ3k0cF2SK4HTmH+H9ULgiPbxTOA97V9JyyTJ79LUzU2LWa6qNgIbAdauXVvT09N95z1302WcvXnpu+atp/Rf96BmZmbYXezjNMh2G+Y2g+5ut67G1SvJTwG/TLPvo6p+CPwwyYnAdDvbhcAMJm9aJUzepA6qqtuB29vn30lyM80Z/X47rBOBD1RVAde03UwObtcjaUBJTgNeDBzX1jOA7cBhPbMd2pZJWh6HA98E/irJU4HrgNcDUz37tztoeqk8xGJavbtiglpEB7L+qF0D9zQYxKi38XJ+ryZvUsclWQM8DbiW/jusQ4Dbehab7b71oOTN7lvDZ2yL19W4ZiU5HngT8G+q6ns9ky4HPpjkncDjaFq+PzuGEKWVam/g6cDrquraJOfQ9Dh5QFVVkppv4cW0enfFJLSILofTNlzB+qN2DdTTYBDDbnGfazm/14G2WJL/DPwGzTU5m4FXAQcDFwOPoTlD8oq2mVvSIiV5FPAR4A1VdV9zaVtjdzusfuy+NXzGtnhdiivJRTSt2wcl2Qa8hWZ0yYcDV7Z18JqqenVVfSnJJcBNNN0pX1NV948ncmlF2gZsq6pr29eX0iRvd872LklyMLBjbBFKI7bk0SaTHAL8J2BtVT0F2ItmyOS3A++qqicCdwOnL0eg0mqTZB+axG1TVX20Lb6z3VExZ4dl9y1pGVTVy6vq4Krap6oOrarzquqJVXVYVR3dPl7dM//bquoJVfWkqvrrccYurTRVdQdwW5IntUXH0ZwsuRw4tS07FbhsDOFJYzHorQL2BvZNsjfN6D+3A8+hOTMCzTU5Lx3wPaRVpx098jzg5qp6Z8+kfjusy4FXpvEs4F6vd5MkrQCvAzYl+SJwNPBfgLOA5yX5CvDc9rW0Kiy5T1RVbU/yDuAbwPeBT9J0k7yn53448w6bDN28iHS+6y4GvZBykM/VpetAuhQLdC+eITgWeAWwOckNbdmbaXZQlyQ5Hfg6cFI77RM0twnYQnOrgFeNNlxJkpZfVd0ArJ1n0nGjjkXqgiUnb+0NEU+kGQnoHuDDwPELXb6LF5HOd93FaRuuGGidg1z306XrQLoUC3QvnuVWVZ8B0mfyQ3ZY7eh3rxlqUJIkSRqrQbpNPhf4WlV9s6r+GfgoTWvB/m03SvC6G0mSJElaFoMkb98AnpXkke31ObMXkV4NvKydx4tIJUmSJGkZLDl5a4dtvRS4nuY2AT9B0w3yDOCNSbbQ3C7gvGWIU5IkSZJWtYHu81ZVb6G5B06vW4FjBlmvJEmSJOnBBr1VgCRJkiRpBEzeJEmSJGkCmLxJkiRJ0gQweZMkSZKkCWDyJkmSJEkTwORNkiRJkiaAyZskSZIkTQCTN0mSJEmaAAPdpHtSrNlwxYLmW3/ULk5b4LySJEnSarXQ42stL1veJEmSJGkCmLxJkiRJ0gQweZMkqZXk/CQ7ktzYU3ZgkiuTfKX9e0BbniR/lmRLki8mefr4IpckrQYmb5Ik/dgFwPFzyjYAV1XVEcBV7WuAFwJHtI91wHtGFKMkaZUyeZMkqVVVnwbumlN8InBh+/xC4KU95R+oxjXA/kkOHk2k0uqQZGuSzUluSPK5tmze1nBpNTB5kyRp96aq6vb2+R3AVPv8EOC2nvm2tWWSlte/raqjq2pt+7pfa7i04q2KWwVIkrQcqqqS1GKWSbKOplslU1NTzMzM9J13at/mtjVLtbt1D2rnzp1DXf8gBtluw/5MXd1uXY1rgU4EptvnFwIzwBnjCkYaJZO3IRvkHhgXHL/fMkYiSVqiO5McXFW3t90id7Tl24HDeuY7tC17kKraCGwEWLt2bU1PT/d9o3M3XcbZm5e+a956Sv91D2pmZobdxT5Og2y3YW4z6O5262pc8yjgk+1Jk79s61O/1nBpxTN5kyRp9y4HTgXOav9e1lP+2iQXA88E7u05oJS0PH6pqrYn+WngyiRf7p24u9bwxbR6d8UktYgO0ksABu9pMIhRb+Pl/F5N3iRJaiW5iKY71kFJtgFvoUnaLklyOvB14KR29k8AJwBbgO8Brxp5wNIKV1Xb2787knwMOIb+reFzl11wq3dXTFCLKKcN0LsMmsRtkJ4Ggxh2i/tcy/m9mrxJktSqqpf3mXTcPPMW8JrhRiStXkn2A36iqr7TPn8+8If0bw2XVjyTN0mSJHXRFPCxJNAcs36wqv4myT8wf2u4tOKZvEmSJKlzqupW4KnzlH+beVrDpdXA+7xJkiRJ0gQweZMkSZKkCWDyJkmSJEkTwORNkiRJkiaAyZskSZIkTQCTN0mSJEmaACZvUkclOT/JjiQ39pS9Ncn2JDe0jxN6pp2ZZEuSW5K8YDxRS5IkaVhM3qTuugA4fp7yd1XV0e3jEwBJjgROBp7cLvMXSfYaWaSSJEkaOpM3qaOq6tPAXQuc/UTg4qr6QVV9DdgCHDO04CRJkjRyew+ycJL9gfcDTwEK+HXgFuBDwBpgK3BSVd09UJSSer02ySuBzwHr2/p1CHBNzzzb2rIHSbIOWAcwNTXFzMxM3zeZ2hfWH7VryUHubt2D2rlz51DXPwhjW7yuxiVJUtcMlLwB5wB/U1UvS/Iw4JHAm4GrquqsJBuADcAZA76PpMZ7gD+iOVnyR8DZNCdNFqSqNgIbAdauXVvT09N95z1302WcvXnp/yK2ntJ/3YOamZlhd7GPk7EtXlfjkiSpa5bcbTLJTwG/DJwHUFU/rKp7aLpvXdjOdiHw0kGDlNSoqjur6v6q+hHwPn7cNXI7cFjPrIe2ZZIkSVohBml5Oxz4JvBXSZ4KXAe8Hpiqqtvbee4ApuZbeDHdtwa10K5fg3YTW25d6krUpVige/GMSpKDe+rXvwNmR6K8HPhgkncCjwOOAD47hhAlSZI0JIMkb3sDTwdeV1XXJjmHpovkA6qqktR8Cy+m+9agTttwxYLmW3/UroG6iS23C47frzNdibrWralr8QxDkouAaeCgJNuAtwDTSY6m6Ta5FfiPAFX1pSSXADcBu4DXVNX944hbkiRJwzFIprIN2FZV17avL6VJ3u6cbR1IcjCwY9AgpdWoql4+T/F5u5n/bcDbhheRJEmSxmnJ17xV1R3AbUme1BYdR3PW/3Lg1LbsVOCygSKUJEmSJA082uTrgE3tSJO3Aq+iSQgvSXI68HXgpAHfQ5IkSZJWvYGSt6q6AVg7z6TjBlmvJEldk+Q/A79Bc83pZpoTlgcDFwOPoRm46xVV9cOxBSlJWtGW3G1SkqTVIskhwH8C1lbVU4C9gJOBtwPvqqonAncDp48vSknSSmfyJknSwuwN7Jtkb+CRwO3Ac2gG7ALvbSotuyR7Jfl8ko+3rw9Pcm2SLUk+1F66I60aJm+SJO1BVW0H3gF8gyZpu5emm+Q9VTV7g9BtwCHjiVBasV4P3Nzz2tZurWrduamZJEkdleQA4ETgcOAe4MPA8Qtcdh2wDmBqaoqZmZm+807t29xzdKl2t+5B7dy5c6jrH8Qg223Yn6mr262rcfVKcijwIprb4LwxSWhau3+tneVC4K3Ae8YSoDQGJm+SJO3Zc4GvVdU3AZJ8FDgW2D/J3m3r26HA9rkLVtVGYCPA2rVra3p6uu+bnLvpMs7evPRd89ZT+q97UDMzM+wu9nEaZLsNc5tBd7dbV+Oa40+BNwGPbl8/hkW0di/mxElXTEJSPWuQE00w+MmqQYx6Gy/n92ryJknSnn0DeFaSRwLfpxlV+XPA1cDLaEac9N6m0jJJ8mJgR1Vdl2R6KetYzImTrpiQpBqA0zZcMdDy64/aNdDJqkEM+6TNXMv5vZq8SZK0B1V1bZJLgeuBXcDnaQ4KrwAuTvLHbdl544tSWlGOBV6S5ATgEcBPAuewgNZuaSUzeZMkaQGq6i3AW+YU3wocM4ZwpBWtqs4EzgRoW95+u6pOSfJhbO3WKuZok5IkSZoUZ9AMXrKF5ho4W7u1qtjyJkmSpM6qqhlgpn1ua7dWNVveJEmSJGkCmLxJkiRJ0gQweZMkSZKkCWDyJkmSJEkTwORNkiRJkiaAyZskSZIkTQCTN0mSJEmaACZvkiRJkjQBvEm3JEmStMqs2XDFuEPQEtjyJkmSJEkTwORNkiRJkiaAyZskSZIkTQCTN0mSJEmaACZvkiRJkjQBTN4kSZIkaQKYvEmSJEnSBDB5kyRJkqQJYPImSdICJNk/yaVJvpzk5iTPTnJgkiuTfKX9e8C445QkrVwmb5IkLcw5wN9U1c8DTwVuBjYAV1XVEcBV7WtJyyDJI5J8NskXknwpyR+05YcnuTbJliQfSvKwcccqjYrJm9RRSc5PsiPJjT1l857lT+PP2h3ZF5M8fXyRSytPkp8Cfhk4D6CqflhV9wAnAhe2s10IvHQ8EUor0g+A51TVU4GjgeOTPAt4O/CuqnoicDdw+hhjlEbK5E3qrguA4+eU9TvL/0LgiPaxDnjPiGKUVovDgW8Cf5Xk80nen2Q/YKqqbm/nuQOYGluE0gpTjZ3ty33aRwHPAS5tyz1polVl70FXkGQv4HPA9qp6cZLDgYuBxwDXAa+oqh8O+j7SalNVn06yZk7xicB0+/xCYAY4oy3/QFUVcE17bc7BPQeVkgazN/B04HVVdW2Sc5jTRbKqKknNXTDJOpqTKkxNTTEzM9P3Tab2hfVH7VpykLtb96B27tw51PUPYpDtNuzP1NXt1tW45mqPM68Dngj8OfBV4J6qmv3CtwGHjCk8aeQGTt6A19P0+//J9vVsU/bFSd5L05RtK4C0PPqd5T8EuK1nvtmd2YOSNw8ih8/YFq+rcc2xDdhWVde2ry+lSd7unD1RkuRgYMfcBatqI7ARYO3atTU9Pd33Tc7ddBlnb176rnnrKf3XPaiZmRl2F/s4DbLdhrnNoLvbratxzVVV9wNHJ9kf+Bjw8wtddjH7vK4Y5f/DQfbxy2HQ44xBjPq3sJzf60DJW5JDgRcBbwPemCQ0Tdm/1s5yIfBWTN6kZdfvLP8elvEgcsiMbfG6GlevqrojyW1JnlRVtwDHATe1j1OBs9q/l40xTGnFqqp7klwNPBvYP8nebevbocD2PssseJ/XFaP8f3jahitG8j79rD9q10DHGYMY9kmbuZbzex10i/0p8Cbg0e3rx7DApuxRng1ZaFY/zjMA8+nS2eguxQLdi2eE+p3l3w4c1jNf352ZpCV7HbCpHdnuVuBVNNeOX5LkdODrwEljjE9aUZI8FvjnNnHbF3geTQ+vq4GX0Vym40kTrSpLTt6SvBjYUVXXJZle7PKjPBuy0DML4zwDMJ8Ljt+vM2eju3ZmvGvxjNDlzH+W/3LgtUkuBp4J3Ov1btLyqqobgLXzTDpu1LFIq8TBwIXtdW8/AVxSVR9PchNwcZI/Bj5POwqstBoMkqkcC7wkyQnAI2iueTuHBTZlS9q9JBfRDE5yUJJtwFtokrb5zvJ/AjgB2AJ8j6ZFQJKkiVVVXwSeNk/5rcAxo49IGr8lJ29VdSZwJkDb8vbbVXVKkg9jU7Y0sKp6eZ9JDznL344y+ZrhRiRJkqRxGsZ93s6gGbxkC801cDZlS5IkSdKAluUCr6qaobnflE3ZkiRJkjQEw2h5kyRJkiQtM5M3SZIkSZoAJm+SJEmSNAG6c1MzSZIkSQu2ZoH3MtaDDbLdtp71omWMZPFM3jps8/Z7F3yD8fmM+8clSZIkafnYbVKSJEmSJoDJmyRJkiRNAJM3SZIkSZoAJm+SJEmSNAFM3iRJkiRpApi8SZIkSdIEMHmTJEmSpAlg8iZJkiRJE8DkTZIkSZImgMmbJEmSJE0AkzdJkhYgyV5JPp/k4+3rw5Ncm2RLkg8ledi4Y5QkrWwmb5IkLczrgZt7Xr8deFdVPRG4Gzh9LFFJK1SSw5JcneSmJF9K8vq2/MAkVyb5Svv3gHHHKo2KyZskSXuQ5FDgRcD729cBngNc2s5yIfDS8UQnrVi7gPVVdSTwLOA1SY4ENgBXVdURwFXta2lV2HvcAUiSNAH+FHgT8Oj29WOAe6pqV/t6G3DIfAsmWQesA5iammJmZqbvm0ztC+uP2tV3+p7sbt2D2rlz51DXP4hBttuwP1NXt1tX4+pVVbcDt7fPv5PkZpp6diIw3c52ITADnDGGEKWRM3mTJGk3krwY2FFV1yWZXuzyVbUR2Aiwdu3amp7uv4pzN13G2ZuXvmveekr/dQ9qZmaG3cU+ToNst2FuM+judutqXP0kWQM8DbgWmGoTO4A7gKk+yyz4xElXLDapHuRkz7gNerJqXJbyO1rOkyUmb5Ik7d6xwEuSnAA8AvhJ4Bxg/yR7t61vhwLbxxijtGIleRTwEeANVXVf02u5UVWVpOZbbjEnTrpisUn1aRuuGF4wQ7b+qF0Dnawal6Wc8FnOkyVe8yZJ0m5U1ZlVdWhVrQFOBj5VVacAVwMva2c7FbhsTCFKK1aSfWgSt01V9dG2+M4kB7fTDwZ2jCs+adRM3iRJWpozgDcm2UJzDdx5Y45HWlHagYHOA26uqnf2TLqc5oQJeOJEq8zktVVKkjQmVTVDMzgCVXUrcMw445FWuGOBVwCbk9zQlr0ZOAu4JMnpwNeBk8YUnzRyJm+SJEnqnKr6DJA+k48bZSxSV9htUpIkSZImgMmbJEmSJE0AkzdJkiRJmgAmb5IkSZI0AUzeJEmSJGkCONqkNIGSbAW+A9wP7KqqtUkOBD4ErAG2AidV1d3jilGSJEnLa8ktb0kOS3J1kpuSfCnJ69vyA5NcmeQr7d8Dli9cST3+bVUdXVVr29cbgKuq6gjgqva1JEmSVohBuk3uAtZX1ZHAs4DXJDkSDyClcTkRuLB9fiHw0jHGIkmSpGW25G6TVXU7cHv7/DtJbgYOoTmAnG5nuxCYAc4YKEpJcxXwySQF/GVVbQSm2noJcAcwNXehJOuAdQBTU1PMzMz0fYOpfWH9UbuWHODu1j2onTt3DnX9gzC2xetqXJIkdc2yXPOWZA3wNOBaFnAA2S6z4IPIQS30AHTQg9Xl1qWD564dXHUtnjH4paranuSngSuTfLl3YlVVm9gxp3wjsBFg7dq1NT093fcNzt10GWdvXvq/iK2n9F/3oGZmZthd7ONkbIvX1bgkSeqagZO3JI8CPgK8oaruS/LAtH4HkO20BR9EDuq0DVcsaL71R+0a6GB1uQ0az3IePHft4Kpr8YxaVW1v/+5I8jHgGODOJAdX1e1JDgZ2jDVISZIkLauBbhWQZB+axG1TVX20Lb6zPXDEA0hp+SXZL8mjZ58DzwduBC4HTm1nOxW4bDwRSpIkaRiW3KyTpontPODmqnpnz6TZA8iz8ABSGoYp4GNtK/fewAer6m+S/ANwSZLTga8DJ40xRq0yaxbYw2E+Fxy/3zJGIknSyjVIH8FjgVcAm5Pc0Ja9mSZp8wBSGpKquhV46jzl3waOG31EkiRJGoVBRpv8DJA+kz2AlCRJkqRlNNA1b5IkSZKk0TB5kyRJkqQJYPImSZIkSRPA5E2SpD1IcliSq5PclORLSV7flh+Y5MokX2n/HjDuWKWVIsn5SXYkubGnzDqnVa07d6SWJKm7dgHrq+r69j6L1yW5EjgNuKqqzkqyAdgAnDHGOKWV5ALg3cAHeso2sILq3NzbrKw/ahenDXDrFa18trxJkrQHVXV7VV3fPv8OcDNwCHAicGE724XAS8cTobTyVNWngbvmFFvntKrZ8iZJ0iIkWQM8DbgWmKqq29tJdwBT88y/DlgHMDU1xczMTN91T+3bnHlfqt2te1A7d+4c6voHMch2G/Zn6up262pcC7DHOjdrMXVvXOb+bgf9HzBJJvWzLuV3tJz1zeRNkqQFSvIo4CPAG6rqvuTHtzutqkpSc5epqo3ARoC1a9fW9PR03/Wfu+kyzt689F3z1lP6r3tQMzMz7C72cRpkuw1zm0F3t1tX41qMfnWuZ/qC6964zO0iuf6oXQP9D5gkk/pZl/I/Yznrm90mJUlagCT70CRum6rqo23xnUkObqcfDOwYV3zSKmGd06pm8iZJ0h6kaWI7D7i5qt7ZM+ly4NT2+anAZaOOTVplrHNa1SairXLuSDySJI3YscArgM1JbmjL3gycBVyS5HTg68BJY4pPWnGSXARMAwcl2Qa8BeucVrmJSN4kSRqnqvoMkD6TjxtlLNJqUVUv7zPJOqdVy26TkiRJkjQBTN4kSZIkaQKYvEmSJEnSBDB5kyRJkqQJYPImSZIkSRPA0SYlSZKkJfKWVholW94kSZIkaQKYvEmSJEnSBLDbpCRJkiQtwFK6ya4/ahentcttPetFA72/LW+SJEmSNAFM3iRJkiRpApi8SZIkSdIEMHmTJEmSpAlg8iZJkiRJE8DRJjWvuSPp9I6SsxCDjqQjSZIk6cFseZMkSZKkCWDL2wq2lPtQSJKkyTDIfv6C4/dbxkgkjYotb5IkSZI0AUzeJEmSJGkCDK3bZJLjgXOAvYD3V9VZw3ovSQ3rnTR61jtp9Jaz3nmZiSbJUJK3JHsBfw48D9gG/EOSy6vqpmG8n9RrkH/CkzxKpvVOGj3rnTR61jutZsPqNnkMsKWqbq2qHwIXAycO6b0kNax30uhZ76TRs95p1UpVLf9Kk5cBx1fVb7SvXwE8s6pe2zPPOmBd+/JJwC3LHsjiHQR8a9xB9OhSPF2KBcYXz+Or6rFjeN89GkK969p33svYlqarse0pLutdNxjb0nQ1thVd79ryLh5r7klXfy/D4Gd9qD3Wu7HdKqCqNgIbx/X+80nyuapaO+44ZnUpni7FAt2LZ1Ispt51eRsb29J0NbauxrVcrHfDZ2yL19W4llMXjzX3ZDV8L7P8rEszrG6T24HDel4f2pZJGh7rnTR61jtp9Kx3WrWGlbz9A3BEksOTPAw4Gbh8SO8lqWG9k0bPeieNnvVOq9ZQuk1W1a4krwX+lmYI1/Or6kvDeK9l1rWm9S7F06VYoHvxjN0Q6l2Xt7GxLU1XY+tqXHtkvesMY1u8rsa1RxN8nLkQE/u9LIGfdQmGMmCJJEmSJGl5DavbpCRJkiRpGZm8SZIkSdIEWFXJW5LDklyd5KYkX0ry+rb8wCRXJvlK+/eAtjxJ/izJliRfTPL0ZYzlEUk+m+QLbSx/0JYfnuTa9j0/1F6IS5KHt6+3tNPXLFcsPTHtleTzST7egVi2Jtmc5IYkn2vLRv49rVRJjk9yS7vNNswzve93nOTMtvyWJC8YcVxvbOvvF5NcleTxPdPub38vNyRZ9gvXFxDbaUm+2RPDb/RMO7X93X4lyaljiO1dPXH9Y5J7eqYNe7udn2RHkhv7TO9bf4e93cZpkDrYgdj6/taHHNeSf0sdiG06yb092+z3RxTXvMc9c+ZxHzoi8/1OsoKPbfp83rcm2d5TF07omTa044th61fXhvL9VtWqeQAHA09vnz8a+EfgSOD/BTa05RuAt7fPTwD+GgjwLODaZYwlwKPa5/sA17bvcQlwclv+XuA32+e/Bby3fX4y8KEhbJ83Ah8EPt6+HmcsW4GD5pSN/HtaiQ+ai7u/Cvws8DDgC8CRc+aZ9ztu68sXgIcDh7cS+BhhAAAgAElEQVTr2WuEcf1b4JHt89/s/e0BO8e8zU4D3j3PsgcCt7Z/D2ifHzDK2ObM/zqai/uHvt3a9f8y8HTgxj7T562/w95u43wMUgc7Etu8v/URxLak31JHYpum3beOeJvNe9zTle222h7z/U5Ywcc2fT7vW4HfnmfeoR1fjOizjizHWFUtb1V1e1Vd3z7/DnAzcAhwInBhO9uFwEvb5ycCH6jGNcD+SQ5epliqqna2L/dpHwU8B7i0TyyzMV4KHJckyxELQJJDgRcB729fZ1yx7MbIv6cV6hhgS1XdWlU/BC6m2Ya9+n3HJwIXV9UPquprwJZ2fSOJq6qurqrvtS+vobm3zygsZJv18wLgyqq6q6ruBq4Ejh9jbC8HLlrG99+tqvo0cNduZulXf4e93cZpkDrYhdjGYoDfUhdiG4vdHPf0ch86In1+Jyv22GaR9WKYxxdDN8ocY1Ulb73aLihPo2nxmqqq29tJdwBT7fNDgNt6FtvGQ//pDRLDXkluAHbQHJh8FbinqnbN834PxNJOvxd4zHLFAvwp8CbgR+3rx4wxFmgS2U8muS7JurZsLN/TCrSQ7dXvOx7mtl7suk+nOWs16xFJPpfkmiQv7bfQkGP79233h0uTzN5Adti/zwWvP00308OBT/UUD3O7LUS/+FdyvR6kDnYhNpj/tz5uXf/NPDvNpRJ/neTJo37zOcc9vbq+3Va61Xhs89r2/8f5s90IWUGfd9g5xqpM3pI8CvgI8Iaquq93WjVtmSO5f0JV3V9VR9O0HhwD/Pwo3neuJC8GdlTVdeN4/z5+qaqeDrwQeE2SX+6dOMrvSd2T5D8Aa4E/6Sl+fFWtBX4N+NMkTxhxWP8DWFNVv0BzMubCPcw/DicDl1bV/T1l495umjyT8Fvvmutp6tpTgXOB/z7KN9/dcY+6Y5Uc27wHeAJwNHA7cPZ4w1leo8gxVl3ylmQfmo26qao+2hbfOdtU2f7d0ZZvB3rPKB7ali2rqroHuBp4Nk2z6ezN03vf74FY2uk/BXx7mUI4FnhJkq00XWSeA5wzplgAqKrt7d8dwMdoktuxfk8ryEK2V7/veJjbekHrTvJc4HeBl1TVD2bLe34ztwIzNGe9lsseY6uqb/fE837gGQtddtix9TiZOV0mh7zdFqJf/Cu5Xg9SB8ce225+6+PW2d9MVd03e6lEVX0C2CfJQaN47z7HPb06u91WiVV1bFNVd7aNFz8C3sePu0ZO/OcdVY6xqpK39nqB84Cbq+qdPZMuB2ZHMjsVuKyn/JXtiDDPAu7tafocNJbHJtm/fb4v8Dya/rFXAy/rE8tsjC8DPtVm8AOrqjOr6tCqWkNzcPepqjplHLEAJNkvyaNnnwPPB25kDN/TCvUPwBFpRhN9GM13PneUwX7f8eXAyWlGwjscOAL47KjiSvI04C9pErcdPeUHJHl4+/wgmhMSNy1TXAuNrbev+kto6jPA3wLPb2M8gOb3/LejjK2N7+dpBv74+56yYW+3hehXf4e93cZpkDo49th281sft87uC5L8i9lrFpMcQ3P8NfRkfDfHPb06u91WiVV1bDPn/8e/ozm+g+EeXwzdSHOM6sAILaN6AL9E01z5ReCG9nECzXUEVwFfAf4OOLCdP8Cf01yLthlYu4yx/ALw+TaWG4Hfb8t/lubHugX4MPDwtvwR7est7fSfHdI2mubHo02OJZb2fb/QPr4E/G5bPvLvaaU+2t/9P7bbbHb7/iFNUrTb75im1eurwC3AC0cc198Bd/bU38vb8n/VfvdfaP+ePoZt9l/b3+sXaE58/HzPsr/ebsstwKtGHVv7+q3AWXOWG8V2u4ima8w/0/TpPx14NfDqdnrf+jvs7TbOxyB1sAOx9f2tDzmuJf+WOhDba3u22TXAvxpRXP2Oezqx3Vbbo8/vZMUe2/T5vP9f+3m+SJPAHNwz/9COL0bwWUeWY6RdgSRJkiSpw1ZVt0lJkiRJmlQmb5IkSZI0AUzeJEmSJGkCmLxJkiRJ0gQweZMkSZKkCWDyJkmSJEkTwORNkiRJkiaAyZskSZIkTQCTN0mSJEmaACZvkiRJkjQBTN4kSZIkaQKYvEmSJEnSBDB5kyRJkqQJYPImSZIkSRPA5E2SJEmSJoDJmyRJkiRNAJM3SZIkSZoAJm+SJEmSNAFM3iRJkiRpApi8SZIkSdIEMHmTJEmSpAlg8iZJkiRJE8DkTZIkSZImgMmbJEmSJE0AkzdJkiRJmgAmb5IkSZI0AUzeJEmSJGkCmLxJkiRJ0gQweZMkSZKkCWDyJkmSJEkTwORNkiRJkiaAyZskSZIkTQCTN0mSJEmaACZvkiRJkjQBTN4kqUeSC5L88W6mV5InDjmGNe377D3M95EmQZK3Jvlv445DkrrA5E2SJEmSJoDJmyRJkiRNAJO3jkryuCQfSfLNJF9L8p+SHJhkW5Jfaed5VJItSV7Zvn5Rks8nuS/JbUne2rO+2W5Ypyb5RpJvJfndnun7Jrkwyd1Jbk7ypiTbRv7BpRFJ8i+TzCS5J8mXkrykz3y/k+T2JP+U5NfnTLsgyXuTXJnkO0n+Z5LH90z/+XbaXUluSXJSz7S+9XWeGP59kq1JnrIMH13qrCRnJNne1qdbkhw3zzwvaevsPW0d/pc907YmOTPJTe3+7K+SPKJn+ouT3NAu+7+T/MKoPps0yZIcluSj7XHpt5O8O8kTknyqff2tJJuS7D/uWFc6k7cOSvITwP8AvgAcAhwHvAH4ReDXgfcl+WngXcANVfWBdtHvAq8E9gdeBPxmkpfOWf0vAU9q1/n7PTu9twBrgJ8Fngf8h6F8OKkDkuxDU8c+Cfw08DpgU5InzZnveOC3aerEEcBz51ndKcAfAQcBNwCb2mX3A64EPti+x8nAXyQ5sl1uIfWVJK8C3g48t6puXPqnlrqtrX+vBX6xqh4NvADYOmeenwMuotknPhb4BPA/kjysZ7ZT2mWfAPwc8Hvtsk8Dzgf+I/AY4C+By5M8fHifSpp8SfYCPg58neZY8RDgYiDAfwUeB/xL4DDgrWMJchUxeeumXwQeW1V/WFU/rKpbgfcBJ1fVJ4EPA1cBJ9DshACoqpmq2lxVP6qqL9Ls4P7NnHX/QVV9v6q+QJMcPrUtPwn4L1V1d1VtA/5sqJ9QGq9nAY8Czmrr2KdodkwvnzPfScBfVdWNVfVd5t8pXVFVn66qHwC/Czw7yWHAi4GtVfVXVbWrqj4PfAT4v2HB9fUNwO8A01W1ZTk+uNRh9wMPB45Msk9Vba2qr86Z51dp6tyVVfXPwDuAfYF/1TPPu6vqtqq6C3gbP67X64C/rKprq+r+qroQ+AHN/wNJ/R1Dk6D9TlV9t6r+T1V9pqq2tHXxB1X1TeCdPHQ/pmVm8tZNjwce13bruCfJPcCbgal2+kbgKcAFVfXt2YWSPDPJ1W2T9r3Aq2laA3rd0fP8ezQHsNBUytt6pvU+l1aaxwG3VdWPesq+TnM28SHzzZlnrgemV9VO4K52uccDz5xTj08B/gUsuL7+DvDn7QkVaUVrT1C8geYkyY4kFyd53JzZHkdPPWzr8G08uO7OrbOz63g8sH5OnTysZ7qk+R0GfL2qdvUWJplq6+n2JPcB/42H7se0zEzeuuk24GtVtX/P49FVdULbdL0R+ADwW3nwkOUfBC4HDquqnwLeS9OkvRC3A4f2vD5s8I8hddY/AYe1XZRn/Qywfc58t/PguvAz86zrgelJHgUc2K7/NuB/zqnHj6qq32xnX0h9fT7we0n+/eI+njSZquqDVfVLNIlW0XQZ7vVP7TQAkoSmDvbW3bl19p/a57cBb5tTJx9ZVRct9+eQVpjbgJ/JQ29f819o6ulRVfWTNJfcLPS4U0tk8tZNnwW+0164vW+SvZI8Jckv0rTAFc21b38CfKBN6AAeDdxVVf8nyTHAry3iPS8BzkxyQJJDaK47kFaqa2lant+UZJ8k08Cv0PTh73UJcFqSI5M8kuba0LlOSPJL7TU3fwRcU1W30XTD/Lkkr2jfY58kv9hznelC6uuXgOOBP+83oIq0UiR5UpLntNeg/R/g+8CP5sx2CfCiJMe1166up+n6+L975nlNkkOTHEjTlflDbfn7gFe3rd5Jsl87cNCjh/rBpMn3WZqTmWe19eYRSY6l2Y/tBO5tjx1/Z5xBrhYmbx1UVffTXC9zNPA14FvA+4HnAG8EXtnO83aaRG5Du+hvAX+Y5DvA79Ps5BbqD4Ft7fv9HXApzQ5RWnGq6oc0ydoLaerXX9DUqy/Pme+vgT8FPgVsaf/O9UGapO4u4Bm0g/1U1XdoWs5OpjnzfwdNnZ0dHGFB9bW9PvXFNAMVvXBpn1iaCA8HzqKpk3fQDPRzZu8MVXULTR07t53vV4Bfaev0rA/SDEZ0K/BV4I/bZT8H/D/Au4G7aer0aUP7NNIK0R5z/grwROAbNMeLvwr8AfB04F7gCuCj44pxNUlVjTsGdVCS36QZIMULT6U+klwAbKuq3xt3LJKaWwUAv1FVfzfuWCRpGGx5EwBJDk5ybJKfaIdrXg98bNxxSZIkSWrMvfBQq9fDaO55czhwD821P38x1ogkSZIkPcBuk5IkSZI0Aew2KUmSJEkToBPdJg866KBas2ZN3+nf/e532W+//UYX0CIY29J0NbY9xXXdddd9q6oeO8KQhmaS691i+Dm6ZSmfw3rXDca2NF2NbTXt72D3da+r3xEY21JNamwLqndVNfbHM57xjNqdq6++erfTx8nYlqarse0pLuBz1YE6sxyPSa53i+Hn6JalfA7rXTcY29J0NbbVtL+rPdS9rn5HVca2VJMa20Lqnd0mJUmSJGkCmLxJkiRJ0gQweZMkSZKkCWDyJkmSJEkTwORNkiRJkiaAyZskSZIkTQCTN0mSJI1NkvOT7EhyY0/Zh5Lc0D62JrmhLV+T5Ps90947vsil0evETbolSZK0al0AvBv4wGxBVf3q7PMkZwP39sz/1ao6emTRSR1i8iZJkqSxqapPJ1kz37QkAU4CnjPKmKSumojkbfP2ezltwxVLXn7rWS9axmik1cF6p9UoyfnAi4EdVfWUOdPWA+8AHltV32oPKs8BTgC+B5xWVdePOmatXmsG+B99wfH7LWMkQ/WvgTur6is9ZYcn+TxwH/B7VfW/5lswyTpgHcDU1BQzMzPzvsGOu+7l3E2XLTnAow75qSUvuyc7d+7sG/e4GdvSDBrbHpO3JIfRNGNPAQVsrKpzkhwIfAhYA2wFTqqqu92ZSZIm2AXM6b4FD+wLnw98o6f4hcAR7eOZwHvav5KWz8uBi3pe3w78TFV9O8kzgP+e5MlVdd/cBatqI7ARYO3atTU9PT3vG5y76TLO3rz09oytp8y/3uUwMzNDv7jHzdiWZtDYFjJgyS5gfVUdCTwLeE2SI4ENwFVVdQRwVfsaHrwzW0ezM5MkqfOq6tPAXfNMehfwJpqTmLNOBD5QjWuA/ZMcPIIwpVUhyd7A/0XTWABAVf2gqr7dPr8O+Crwc+OJUBq9PZ5mqKrbac5yUFXfSXIzcAjNTmu6ne1CYAY4g56dGXBNkv2THNyuR5KkiZLkRGB7VX2h6VzygEOA23peb2vLHrS/W2jXLVjZXX2GaZBub8Ps8gbD3W7rj9q15GW7/H32eC7w5araNluQ5LHAXVV1f5KfpWksuHVcAUqjtqg24vZi0qcB1wJTPQnZHTTdKmEIO7OpfQf7BzXMf05d/udnbIvX1bgkjUeSRwJvpukyuSQL7boFK7urzzAN0u1tmF3eYLjbbZDrki84fr/OfJ9JLqJpEDgoyTbgLVV1HnAyD+4yCfDLwB8m+WfgR8Crq2q+1nJpRVrwf7okjwI+Aryhqu7rPftYVZWk+i48j8XszOyLvDTGtnhdjUvS2DwBOByYbXU7FLg+yTHAduCwnnkPbcskLUJVvbxP+WnzlH2E5nhUWpUWdJPuJPvQVJRNVfXRtvjO2b797d8dbbk7M0nSilBVm6vqp6tqTVWtoelN8vSqugO4HHhlGs8C7vUSAUnSMO0xeWtHjzwPuLmq3tkz6XLg1Pb5qcBlPeXuzCRJE6ftvvX3wJOSbEty+m5m/wTNtTZbgPcBvzWCECVJq9hC+iIeC7wC2JzkhrbszcBZwCXtju3rNDdQhGZndgLNzux7wKuWNWJJkoakX/etnulrep4X8JphxyRJ0qyFjDb5GSB9Jh83z/zuzKRl4M2CJUmS1GtB17xJGosLgOPnFi7gZsHeX1GSJGkFMnmTOsqbBUuSJKmXyZs0QXpvFjxnUr/7K0qSJGmFWPrN0ySN1HLcLDjJOppulUxNTe32huRT+8L6o3Yt9a06c7PzlXLjdT+HJEkyeZMmx8A3C66qjcBGgLVr19bubkh+7qbLOHvz0v9FbD2l/7pHaaXceN3PIUmS7DYpTQhvFixJkrS6mbxJHeXNgiVJktTLbpNSR3mzYEmSJPWy5U2SJEmSJoDJmyRJkiRNAJM3SZIkSZoAJm+SJEmSNAFM3iRJkiRpApi8SZIkSdIEMHmTJEmSpAlg8iZJkiRJE8DkTZIkSZImwB6TtyTnJ9mR5Maesg8luaF9bE1yQ1u+Jsn3e6a9d5jBS5IkabL1OdZ8a5LtPceUJ/RMOzPJliS3JHnBeKKWxmPvBcxzAfBu4AOzBVX1q7PPk5wN3Nsz/1er6ujlClCSJEkr2gXMOdZsvauq3tFbkORI4GTgycDjgL9L8nNVdf8oApXGbY8tb1X1aeCu+aYlCXAScNEyxyVJ0sj1aQH4kyRfTvLFJB9Lsn/PNFsApAHt7lhzHicCF1fVD6rqa8AW4JihBSd1zEJa3nbnXwN3VtVXesoOT/J54D7g96rqf823YJJ1wDqAqakpZmZm+r7J1L6w/qhdSw5yd+se1M6dO4e6/kEY2+J1NS5JI3MBD20BuBI4s6p2JXk7cCZwhi0A0tC9Nskrgc8B66vqbuAQ4Jqeeba1ZQ+x0GNNjzOXxtiWZtDYBk3eXs6DW91uB36mqr6d5BnAf0/y5Kq6b+6CVbUR2Aiwdu3amp6e7vsm5266jLM3Lz3Uraf0X/egZmZm2F3s42Rsi9fVuCSNRlV9OsmaOWWf7Hl5DfCy9vkDLQDA15LMtgD8/QhC1f/P3r1HW3aWdb7//kwJhgJJQnR3OolWaAM2UBBxd8BBt24ManFpgn0YjMQICURLFNDWckAFewgtck68RIR4DBYkVugTczGASZNokxPZnXMhwQSQSgJIEQpSNSopJRcs8AAVnvPHmgUru/Z13ede388Ye6w133lZz5xrv2u9z5rvfKfWu0uAtwHVPF4EvGYtG1htW9N2Zm+MrTf9xtbzaJNJNgD/Cbj6cFlzCvvLzfM7gM8DT+k5OkmSJstrgL9unp8I3Ns1b8kzAJLWpqrur6pHqupbwHv4TtfIfcDJXYue1JRJU6GfM28vAD5TVXsPFyT5PuCBqnokyZOBU4F7+oxRkqSxS/JbwCHgijWut+rLBNZzV59h6qfb27D3aZjHrZ+ufpP8fgIkOaGq9jeTPwscvg71euAvkvwRne7KpwIfG0OI0lismLwluRKYA45Pshd4S1VdSqef/8KBSn4c+J0k3wS+Bby2qlZ7AaokSRMpyXnAS4Azqqqa4lWdAVjLZQLruavPMPXT7W2YXd5guMftvO039Lzuzi0bJ+b9XKytCcwlOY1Ot8k9wC8BVNVdSa4B7qbzY8rrvM5U02TFT7qqOnuJ8vMWKXs/8P7+w5IkaTIk2QK8EfiJqvpa1yzPAEgDsERb89Jlln878PbhRSRNrn4HLJEkad1Y4gzABcBjgZs6d8jh1qp6rWcAJEmjZvImSVLDMwCSpEnW82iTkobLmwVLkiSpm8mbNLl2AlsWlN0EPKOqngn8A53uXCy4WfAW4E+THDW6UCVJkjRsJm/ShKqqW4AHFpR9uKoOjw19K53R7aDrZsFV9QXg8M2CJUmStE54zZvUXq8Brm6en0gnmTts0ZsFr+V+U/3cNwmGf++k1Zr0exmtlvshSZJM3qQW6vVmwWu531Q/902C4d87abUm+d5Ua+F+SJIkkzepZfq5WbAkSZLay2vepBbpulnwSxe5WfBZSR6b5BS8WbAkSdK645k3aUJ5s2BJkiR1M3mTJpQ3C5YkSVI3u01KkiRJUguYvEmSJElSC5i8SZIkSVILmLxJkiRJUguYvEmSJElSC5i8SZIkSVILrJi8JbksyYEkd3aVvTXJviSfbP5e1DXvgiS7k3w2yc8MK3BJkiRJmiarOfO2E9iySPk7quq05u9GgCRPA84Cnt6s86dJjhpUsJIkSZI0rVZM3qrqFuCBVW7vTOCqqvp6VX0B2A2c3kd8kiRJkiRgQx/rvj7Jq4DbgW1V9SBwInBr1zJ7m7IjJNkKbAWYmZlhfn5+yReaORq2bT7Uc6DLbbtfBw8eHOr2+2FsazepcUmSJEm9Jm+XAG8Dqnm8CHjNWjZQVTuAHQCzs7M1Nze35LIXX3EdF+3qPc/cc87S2+7X/Pw8y8U+Tsa2dpMalyRJ61WSy4CXAAeq6hlN2R8A/xH4BvB54NVV9VCSTcCngc82q99aVa8dedDSmPQ02mRV3V9Vj1TVt4D38J2ukfuAk7sWPakpkyRJkhazkyPHV7gJeEZVPRP4B+CCrnmf7xp3wcRNU6Wn5C3JCV2TPwscHonyeuCsJI9NcgpwKvCx/kKUJGk0lhhh+bgkNyX5XPN4bFOeJO9qRlj+VJJnjy9yqb0WG1+hqj5cVYevmbmVzgkBaeqt2BcxyZXAHHB8kr3AW4C5JKfR6Ta5B/glgKq6K8k1wN3AIeB1VfXIcEKXJGngdgJ/Aryvq2w7cHNVXZhkezP9JuCFdH6kPBV4Dp1LCp4z0mil6fAa4Oqu6VOSfAL4CvBfqur/Wmyl1Y6v4NgKvTG23vQb24rJW1WdvUjxpcss/3bg7T1HJEnSmFTVLc01Nd3OpPMjJsDlwDyd5O1M4H1VVcCtSY5JckJV7R9NtNL6l+S36JwQuKIp2g/8QFV9OcmPAn+V5OlV9ZWF6652fAXHVuiNsfWm39h66jYpSdIUmelKyO4DZprnJwL3di235AjLktYuyXl0BjI5p/mRhOZ2VF9unt9BZzCTp4wtSGnE+rlVgCRJU6WqKkmtZZ213BpnPXf1GaZ+ur0Ne5+Gedz66eo3ye8nQJItwBuBn6iqr3WVfx/wQFU9kuTJdLot3zOmMKWRM3mTJGl59x/uDtkM2HWgKV/VCMtruTXOeu7qM0z9dHsbZpc3GO5xO2/7DT2vu3PLxol5P5cYX+EC4LHATUngO7cE+HHgd5J8E/gW8NqqemDRDUvrkMmbJEnLux44F7iwebyuq/z1Sa6iM1DJw17vJq3dWsZXqKr3A+8fbkTS5PKaN2lCOWS5NHrNGYCPAk9NsjfJ+XSStp9K8jngBc00wI10umvtpnPP018ZQ8iSpCnimTdpcu3EIculkVriDADAGYssW8DrhhuRJEnf4Zk3aUItdtNSOkOTX948vxx4WVf5+6rjVuCY5tocSZIkrRMmb1K7OGS5JEnSlLLbpNRSwx6yvJ+ht2H4w2+v1qQPh71a7ockSTJ5k9plZEOW9zP0Ngx/+O3VmuThzdfC/ZAkSXablNrl8JDlcOSQ5a9qRp18Lg5ZLkmStO545k2aUEvctPRC4Jpm+PIvAq9oFr8ReBGdIcu/Brx65AFLkiRpqEzepAnlkOWSJEnqZrdJSZIkSWoBkzdJkiRJagGTN0mSJElqgRWTtySXJTmQ5M6usj9I8pkkn0rywSTHNOWbkvxLkk82f+8eZvCSJEmSNC1Wc+ZtJ7BlQdlNwDOq6pnAPwAXdM37fFWd1vy9djBhSpIkSdJ0WzF5q6pbgAcWlH24qg41k7fSuSGwJEmSJGlIBnHN22uAv+6aPiXJJ5L8zyT/YQDblyRJkqSp19d93pL8FnAIuKIp2g/8QFV9OcmPAn+V5OlV9ZVF1t0KbAWYmZlhfn5+ydeZORq2bT605PyVLLftfh08eHCo2++Hsa3dpMYlSZIk9Zy8JTkPeAlwRnODYKrq68DXm+d3JPk88BTg9oXrV9UOYAfA7Oxszc3NLflaF19xHRft6j3P3HPO0tvu1/z8PMvFPk7GtnaTGpckSZLUU7fJJFuANwIvraqvdZV/X5KjmudPBk4F7hlEoJIkSZI0zVY8nZXkSmAOOD7JXuAtdEaXfCxwUxKAW5uRJX8c+J0k3wS+Bby2qh5YdMOSJEmSpFVbMXmrqrMXKb50iWXfD7y/36AkSZIkSY82iNEmJUmSpJ4kuSzJgSR3dpUdl+SmJJ9rHo9typPkXUl2J/lUkmePL3Jp9EzeJEmSNE47gS0LyrYDN1fVqcDNzTTAC+mMqXAqnVHLLxlRjNJEMHmTJGkVkvx6kruS3JnkyiTfk+SUJLc1ZwGuTvKYcccptU1V3QIsHCPhTODy5vnlwMu6yt9XHbcCxyQ5YTSRSuNn8iZJ0gqSnAj8KjBbVc8AjgLOAn4PeEdV/RDwIHD++KKU1pWZqtrfPL8PmGmenwjc27Xc3qZMmgp93aRbkqQpsgE4uhlR+XHAfuAngZ9r5l8OvBW7cUkDVVWVpNa6XpKtdLpWMjMzw/z8/KLLzRwN2zYf6jm+pbY7CAcPHhzq9vthbL3pNzaTN0mSVlBV+5L8IfAl4F+ADwN3AA9V1eFW36JnAFbbgIT13eAYpn4a38Pep2Eet34Sjkl+Pxv3JzmhqvY33SIPNOX7gJO7ljupKTtCVe0AdgDMzs7W3Nzcoi908RXXcdGu3pvEe85ZfLuDMD8/z1Jxj5ux9abf2EzeJElaQTPS3ZnAKcBDwF9y5AALi1ptAxLWd4NjmPppfA+z4Q3DPW7nbb+h53V3btk4se9n43rgXODC5vG6rvLXJ7kKeA7wcFf3SmndM3mTJGllLwC+UA4+dBQAACAASURBVFX/CJDkA8Dz6AyWsKE5+7bkGQBJS0tyJTAHHJ9kL/AWOknbNUnOB74IvKJZ/EbgRcBu4GvAq0cesDRGJm9SCyX5deAXgAJ20fnyOgG4CngSne5cr6yqb4wtSGl9+RLw3CSPo9Nt8gzgduAjwMvp1L3uswOSVqmqzl5i1hmLLFvA64YbkTS5HG1SahlHvZNGr6puA64FPk7nB5PvotMV8k3AbyTZTeeHk0vHFqQkad3zzJvUTo56J41YVb2FTneubvcAp48hHEnSFDJ5k1pmVKPeTfLQyWvRghHVVsX9kCRJJm9Sy4xq1LtJHjp5LSZ5hLy1cD8kSZLXvEnt8+1R76rqm8CjRr1rlnHUO0mSpHXG5E1qn2+PepckdEbjupvvjHoHjnonSZK07pi8SS3jqHeSJEnTyWvepBZy1DtJkqTps6ozb0kuS3IgyZ1dZccluSnJ55rHY5vyJHlXkt1JPpXk2cMKXpIkSZKmxWq7Te7kyNHstgM3V9WpwM3NNMALgVObv614nylJkiRJ6tuqkrequgV4YEHxmXRuBEzz+LKu8vdVx610RsA7YRDBSpIkSdK06mfAkpmq2t88vw+YaZ6fCNzbtdyiNwuWJEmSJK3eQAYsqapKUmtZJ8lWOt0qmZmZYX5+fsllZ46GbZsP9Rzfctvu18GDB4e6/X4Y29pNalySJElSP8nb/UlOqKr9TbfIA035PuDkruUWvVlwVe2gM7w5s7OzNTc3t+QLXXzFdVy0q/dQ95yz9Lb7NT8/z3Kxj9O0xrZp+w09r7tzy+Mn9phJkiRpuvXTbfJ6OjcChkffEPh64FXNqJPPBR7u6l4pSZIkSerBqk5nJbkSmAOOT7KXzv2lLgSuSXI+8EXgFc3iNwIvAnYDXwNePeCYJUmSJGnqrCp5q6qzl5h1xiLLFvC6foKSJEmSJD1aP90mJUmSJEkjYvImSZIkSS1g8iZJkiRJLWDyJkmSJEktMJCbdEuSJEmDlOSpwNVdRU8Gfhs4BvhF4B+b8jdX1Y0jDk8aC5M3SZIkTZyq+ixwGkCSo4B9wAfp3IbqHVX1h2MMTxoLu01KkrQKSY5Jcm2SzyT5dJIfS3JckpuSfK55PHbccUrr1BnA56vqi+MORBonkzdJklbnncDfVNUPA88CPg1sB26uqlOBm5tpSYN3FnBl1/Trk3wqyWX+aKJpYrdJSZJWkOSJwI8D5wFU1TeAbyQ5E5hrFrscmAfeNPoIpfUryWOAlwIXNEWXAG8Dqnm8CHjNIuttBbYCzMzMMD8/v+j2Z46GbZsP9RzfUtsdhIMHDw51+/0wtt70G5vJmyRJKzuFzuAIf57kWcAdwK8BM1W1v1nmPmBm4YqrbUDC+m5wDFM/je9h79Mwj1s/Ccckv5+LeCHw8aq6H+DwI0CS9wAfWmylqtoB7ACYnZ2tubm5RTd+8RXXcdGu3pvEe85ZfLuDMD8/z1Jxj5ux9abf2EzepBZKcgzwXuAZdH55fA3wWTqjcm0C9gCvqKoHxxSitN5sAJ4NvKGqbkvyThZ0kayqSlILV1xtAxLWd4NjmPppfA+z4Q3DPW7nbb+h53V3btk4se/nIs6mq8tkkhO6fjT5WeDOsUQljYHXvEnt5LU30mjtBfZW1W3N9LV0krn7k5wAnQYlcGBM8UnrUpKNwE8BH+gq/v0ku5J8Cng+8OtjCU4aA8+8SS3jtTfS6FXVfUnuTfLUZvjyM4C7m79zgQubx+vGGKa07lTVV4EnLSh75ZjCkcbO5E1qn5FcezPJF3CvRcuu61iS+zER3gBc0QyecA+de019F3BNkvOBLwKvGGN8kqR1zuRNap+RXHszyRdwr8UkX6ezFu7H+FXVJ4HZRWadMepYJEnTyWvepPbx2htJkqQpZPImtUxV3Qfcm+SpTdHha2+up3PNDXjtjSRJ0rrTc5+opuF4dVfRk4HfBo4BfpHONTkAb66qG3uOUNJivPZGkiRpyvScvDWjbZ0GkOQoYB/wQTqNyHdU1R8OJEJJR/DaG0mSpOkzqG6TZwCfr6ovDmh7kiRJkqQugxpt8iy67nwPvD7Jq4DbgW1V9eCAXkeSJElaFzZtv2HZ+ds2H+K8ZZbZc+GLBx2SJlzfyVtzzc1LgQuaokuAtwHVPF4EvGaR9dbF/aYm+Z5F0xpbP/8rk3zMJEmSNN0GcebthcDHq+p+gMOPAEneA3xosZXWy/2mJvmeRdMa23K/UK1k55aNE3vMJEmSNN0Gcc3b2XR1mTx8n6nGzwJ3DuA1JEmSJGmq9XXmLclG4KeAX+oq/v0kp9HpNrlnwTxJkiRJUg/6St6q6qvAkxaUvbKviCRJkiRJRxjUrQIkSZIkSUM0qFsFSNKjrDT88XIc+liSJOlInnmTJEmSpBYweZMkSZKkFjB5kyRJkqQWMHmTJEmSpBYweZMkSZKkFjB5kyRJkqQWMHmTJEmSpBYweZMkaRWSHJXkE0k+1EyfkuS2JLuTXJ3kMeOOUVpvkuxJsivJJ5Pc3pQdl+SmJJ9rHo8dd5zSqJi8SZK0Or8GfLpr+veAd1TVDwEPAuePJSpp/Xt+VZ1WVbPN9Hbg5qo6Fbi5mZamgsmbJEkrSHIS8GLgvc10gJ8Erm0WuRx42Xiik6bOmXTqHFj3NGU2jDsASWuX5CjgdmBfVb0kySnAVcCTgDuAV1bVN8YZo7TO/DHwRuAJzfSTgIeq6lAzvRc4cRyBSetcAR9OUsCfVdUOYKaq9jfz7wNmFlsxyVZgK8DMzAzz8/OLvsDM0bBt86FF563GUttdjZVed6XY+nntfh08eHCsr7+c9RybyZvUToe7b31vM324+9ZVSd5Np/vWJeMKTlpPkrwEOFBVdySZ62H9VTUgYX03OIapn8b3sPdpmMetn4Rjkt/PBf59Ve1L8v3ATUk+0z2zqqpJ7I7QJHo7AGZnZ2tubm7RF7j4iuu4aFfvTeI95yy+3dU4b/sNy87ftvnQsrH189r9mp+fZ6ljOm7rOTaTN6llurpvvR34ja7uWz/XLHI58FZM3qRBeR7w0iQvAr6Hzo8m7wSOSbKhOft2ErBvsZVX24CE9d3gGKZ+Gt/DbvwO87it1PBfzs4tGyf2/exWVfuaxwNJPgicDtyf5ISq2p/kBODAWIOURshr3qT2Odx961vNtN23pCGqqguq6qSq2gScBfxtVZ0DfAR4ebPYucB1YwpRWpeSbEzyhMPPgZ8G7gSup1PnwLqnKeOZN6lFRtl9q99rAPpx8RX9fQ9vPvGJ337eoq5By3I/JtKbgKuS/C7wCeDSMccjrTczwAc7HUzYAPxFVf1Nkr8DrklyPvBF4BVjjFEaqb6TtyR7gH8GHgEOVdVskuOAq4FNwB7gFVX1YL+vJWl03bf6vQZgnLq7QU1yV6+1cD8mQ1XNA/PN83vodOGSNARNHXvWIuVfBs4YfUTS+A2q26T335BGwO5bkiRJ02tY17x5/w1ptN5EZ/CS3XSugbP7liRJ0joziD5RPd9/Q1Lv7L4lSZI0XQaRvPV0/41RDpwwzIvjJ/ni+2mNbUrueyNJkqQp03fy1uv9N0Y5cMIw7+EyyRffT2ts03DfG0mSJE2fvq558/4bkiRJkjQa/Z558/4bkiRJkjQCfSVv3n9DkiRJkkajnXfglSRJkjQ2u/Y93PM4A3sufPGAo5kew7rPmyRJkiRpgEzeJEmSJKkFTN4kSZIkqQVM3iRJkiSpBUzeJEmSJKkFTN4kSZIkqQVM3iRJkiSpBUzeJEmSJKkFTN4kSZIkqQVM3iRJkiSpBUzeJEmSJKkFNow7AEkatE3bb/j2822bD3Fe1/RK9lz44mGEJEmS1DfPvEmStIIkJyf5SJK7k9yV5Nea8uOS3JTkc83jseOOVVovlql3b02yL8knm78XjTtWaVRM3iRJWtkhYFtVPQ14LvC6JE8DtgM3V9WpwM3NtKTBWKreAbyjqk5r/m4cX4jSaNltUmqZJCcD7wNmgAJ2VNU7kxwHXA1sAvYAr6iqB8cVp7SeVNV+YH/z/J+TfBo4ETgTmGsWuxyYB97U6+vs2vfwmrr5LmS3X60ny9Q7aWqZvEntc/iXyI8neQJwR5KbgPPonAG4MMl2OmcAem5ESlpckk3AjwC3ATNNAxPgPjo/qkgasAX17nnA65O8CridznfiET9WJtkKbAWYmZlhfn5+0W3PHN25PrpXS213NVZ63ZVi6+e1+9XPcRt23AcPHhzrsVlOv7H1nLwt8+v/W4FfBP6xWfTNns6WBmdUZwAkHSnJ44H3A/+5qr6S5NvzqqqS1CLrrKoBCeNtRK5kkhtD09qI7Od/ZZLfz4UWqXeXAG+j0/58G3AR8JqF61XVDmAHwOzsbM3NzS26/YuvuI6LdvV+PmPPOYtvdzVWOtO+bfOhZWPr57X71c9xG3bc8/PzLPV+j1u/sfVz5m2pX/+h0w/5D/vYtqRVWOsZgFE2IifFWvfj4iuu6+v1Np/4xL7WX0qbGlrLafN+JPluOg3IK6rqA03x/UlOqKr9SU4ADixcb7UNSBhvI3Ilk9wYmtZGZD9dbHdu2Tix72e3xepdVd3fNf89wIfGFJ40cj1/Q9gPWRqvXs4AjLIROSlW+tVy0IbVEJzkhvNatHU/0qlglwKfrqo/6pp1PXAucGHz2F/2L+nblqp3h38waSZ/FrhzHPFJ4zCQFs0w+yGD3Uh6Na2xTUM3kl7PAEjq2fOAVwK7knyyKXsznaTtmiTnA18EXjGm+KT1aKl6d3aS0+h0m9wD/NJ4wpNGr+/kbdj9kMFuJL2a1tjWezcSzwBIo1dV/zeQJWafMcpYpGmxTL1zLAVNrb6SN/shS2PhGQBJkjS1Nq1ioJflfsxv821V+hlt0n7I0hh4BkCSJGk69XPmzX7IkiRJkjQi/Yw2aT9kSZIkSRqR7xp3AJIkSZKklZm8SZIkSVILmLxJkiRJUguYvEmSJElSC5i8SZIkSVIL9HWTbknSo61049DltPmmoZIkafg88yZJkiRJLWDyJkmSJEktYPImSZIkSS1g8iZJkiRJLWDyJkmSJEktYPImSZIkSS3grQIkaUIsd5uBbZsPcd4KtyHwVgOSJK1vnnmTJEmSpBbwzJskSZIkrcJyvWRWY+eWjX2t75k3SZIkSWoBkzdJkiRJaoGhJW9JtiT5bJLdSbYP63UkfYf1Tho96500etY7TauhXPOW5Cjgfwd+CtgL/F2S66vq7mG8niTrncZrtdcALDZqZptHybTeSaNnvdM0G9aAJacDu6vqHoAkVwFnAlYqaXisd1Oun4uo25xAjZn1Tho9652mVqpq8BtNXg5sqapfaKZfCTynql7ftcxWYGsz+VTgs8ts8njgnwYe6GAYW28mNbaV4vrBqvq+UQWzFlNW79bC/ZgsveyH9W4yGFtvJjW2df1915Svtu5N6nsExtartsa2Yr0b260CqmoHsGM1yya5vapmhxxST4ytN5Ma26TGNSjrpd6thfsxWdbLfqzFeql3xtabSY1tUuMapNXWvUk+FsbWm/Uc27AGLNkHnNw1fVJTJml4rHfS6FnvpNGz3mlqDSt5+zvg1CSnJHkMcBZw/ZBeS1KH9U4aPeudNHrWO02toXSbrKpDSV4P/A/gKOCyqrqrj02uqrvJmBhbbyY1tkmNa0VTVu/Wwv2YLOtlP4Cpq3fG1ptJjW1S41qR9W5iGFtv+optKAOWSJIkSZIGa2g36ZYkSZIkDY7JmyRJkiS1wNiTtyRbknw2ye4k2xeZ/9gkVzfzb0uyqWveBU35Z5P8zIjj+o0kdyf5VJKbk/xg17xHknyy+Rv4BbSriO28JP/YFcMvdM07N8nnmr9zxxDbO7ri+ockD3XNG9pxS3JZkgNJ7lxifpK8q4n7U0me3TVvqMdsEq30PrZBkpOTfKSpp3cl+bVxx9SPJEcl+USSD407ll4lOSbJtUk+k+TTSX5s3DGNSz/ffRMQ25LfMUOOq+fP8QmIbS7Jw13H7LdHFNeKn4PjPG6TYFK/71b6nxqXSf5uTfI9ST6W5O+b2P7ruGNaaCDf5VU1tj86F5l+Hngy8Bjg74GnLVjmV4B3N8/PAq5unj+tWf6xwCnNdo4aYVzPBx7XPP/lw3E10wfHfMzOA/5kkXWPA+5pHo9tnh87ytgWLP8GOhcZj+K4/TjwbODOJea/CPhrIMBzgdtGccwm8W+t7+Ok/gEnAM9unj8B+Ic27kfX/vwG8BfAh8YdSx/7cDnwC83zxwDHjDumMR2Hnr/7JiS2Rb9jRhBbT5/jExLb3Djq7mo+B8d53Mb9N8nfdyv9T40xron9bm3+hx/fPP9u4DbgueOOa0GMfX+Xj/vM2+nA7qq6p6q+AVwFnLlgmTPpfOEDXAuckSRN+VVV9fWq+gKwu9neSOKqqo9U1deayVvp3GNkFFZzzJbyM8BNVfVAVT0I3ARsGWNsZwNXDvD1l1RVtwAPLLPImcD7quNW4JgkJzD8YzaJ+vkfmxhVtb+qPt48/2fg08CJ442qN0lOAl4MvHfcsfQqyRPpNEYuBaiqb1TVQ8uvtW718903CbGNRR+f45MQ21is8nNwbMdtArT5/30sJvm7tfkfPthMfnfzNzEjMw7qu3zcyduJwL1d03s58h/g28tU1SHgYeBJq1x3mHF1O5/Or1aHfU+S25PcmuRlA4pprbH9L033h2uTHL6R5TCP2Zq2n04301OAv+0qHuZxW8lSsQ/7mE2idbfPTZezH6HzK1wb/THwRuBb4w6kD6cA/wj8edNl5L1JNo47qDHp57tvEmKDxb9jxm3SP7t+rOnO9ddJnj7qF1/mc3DSj9swTfO+920Sv1ubbomfBA7Q+fF9YmJjQN/l407eWi/JzwOzwB90Ff9gVc0CPwf8cZJ/M+Kw/juwqaqeSedM0eUrLD8OZwHXVtUjXWXjPm5ah5I8Hng/8J+r6ivjjmetkrwEOFBVd4w7lj5toNMF6JKq+hHgq8DEXF+iNWnDd8yk+Tid77hnARcDfzXKF2/756Amz6T+T1XVI1V1Gp0ecacneca4Y4LBfpePO3nbB3T/YndSU7boMkk2AE8EvrzKdYcZF0leAPwW8NKq+vrh8qra1zzeA8zT+VViUFaMraq+3BXPe4EfXe26w46ty1ks6DI55OO2kqViH/Yxm0TrZp+TfDedL5crquoD446nR88DXppkD50uPT+Z5P8Yb0g92Qvs7foV9Fo6ydw06ue7b+yxLfMdM24T+9lVVV853J2rqm4EvjvJ8aN47VV8Dk7scRuBad73nrXhu7Xplv8RJudSl4F9l487efs74NQkpyR5DJ0G/cJRBq8HDo/w93Lgb6tzxd/1wFnNiFynAKcCHxtVXEl+BPgzOonbga7yY5M8tnl+PJ036+4BxbXa2Lr7qr+UTn9kgP8B/HQT47HATzdlI4utie+H6Qz+8dGusmEft5VcD7yqGXXrucDDVbWf4R+zSbSq93HSNdcHXQp8uqr+aNzx9KqqLqiqk6pqE5334m+r6ufHHNaaVdV9wL1JntoUncFo6/gk6ee7b+yxLfMdM25LfY6PXZJ/dfiaxSSn02l/DT0ZX+Xn4MQetxFYF993ozTJ361Jvi/JMc3zo4GfAj4z3qg6BvldvmGgka1RVR1K8no6jeGj6Iw8eFeS3wFur6rr6fyD/Lcku+lcuHlWs+5dSa6h8+V/CHjdgi54w47rD4DHA3/ZfB5/qapeCvxb4M+SfIvOh/OFVTWwBsoqY/vVJC+lc1weoDMyGFX1QJK30fmwAvidqhrYxbCrjA067+FVCxoiQz1uSa6kM9rX8Un2Am+hcyErVfVu4EY6I27tBr4GvLqZN9RjNomWeh/HHFYvnge8EtjV9H8HeHPzq7fG4w3AFU0j6R6aejZt+vnum5DYFv2OGbZeP8cnJLaXA7+c5BDwL8BZI0rGF/0cBH6gK7axHbdxm+Tvu8X+p6rq0vFGBUz2d+sJwOVJjqLTlrymqlp7e52lZDSfHZIkSZKkfoy726QkSZIkaRVM3iRJkiSpBUzeJEmSJKkFTN4kSZIkqQVM3iRJkiSpBUzeJEmSJKkFTN4kSZIkqQVM3iRJkiSpBUzeJEmSJKkFTN4kSZIkqQVM3iRJkiSpBUzeJEmSJKkFTN4kSZIkqQVM3iRJkiSpBUzeJEmSJKkFTN4kSZIkqQVM3iRJkiSpBUzeJEmSJKkFTN4kSZIkqQVM3iRJkiSpBUzeJEmSJKkFTN4kSZIkqQVM3iRJkiSpBUzeJEmSJKkFTN4kSZIkqQVM3iRJkiSpBUzeJEmSJKkFTN4kSZIkqQVM3iRJkiSpBUzeJEmSJKkFTN4kSZIkqQVM3iRJkiSpBUzeJEmSJKkFTN4kSZIkqQVM3iRJkiSpBUzeWizJpiSVZEOP61eSHxp0XJIkSZIGz+StZZLsSfKCccchrUfjrF/WbUmStBKTN0kagCRHjTsGSZK0vpm8tUiS/wb8APDfkxwEXtHMOifJl5L8U5Lf6lr+9CQfTfJQkv1J/iTJY8YRuzTpFtavJG9M8pdJ7kvycJJbkjy9a/mdSS5JcmOSrwLPT/LsJJ9I8s/Nulcn+d2udV6S5JNNnfx/kzxzqdce8e5LkqQWMHlrkap6JfAl4D9W1eOBa5pZ/x54KnAG8NtJ/m1T/gjw68DxwI81839lpEFLLbGwflXV7wN/DZwKfD/wceCKBav9HPB24AnAx4APAjuB44ArgZ89vGCSHwEuA34JeBLwZ8D1SR67xGtLkiQ9isnb+vBfq+pfqurvgb8HngVQVXdU1a1Vdaiq9tBpLP7EGOOUWqWqLquqf66qrwNvBZ6V5Ildi1xXVf9PVX0LOA3YALyrqr5ZVR+gk9AdthX4s6q6raoeqarLga8Dzx3N3kiSpLYzeVsf7ut6/jXg8QBJnpLkQ023r68A/yuds3CSVpDkqCQXJvl8U3/2NLO669C9Xc//NbCvqmqJ+T8IbGu6TD6U5CHg5GY9SZKkFZm8tU+tvMi3XQJ8Bji1qr4XeDOQoUQlrQ/d9evngDOBFwBPBDY15Vli+f3AiUm655/c9fxe4O1VdUzX3+Oq6spFtiVJknQEk7f2uR948iqXfQLwFeBgkh8GfnloUUnrQ3f9egKdbo1fBh5H58z1cj5K5zrT1yfZkORM4PSu+e8BXpvkOenYmOTFSZ6wyGtLkiQdweStff434L80Xa5evsKyv0nn7ME/02k4Xj3k2KS2665fxwFfBPYBdwO3LrdiVX0D+E/A+cBDwM8DH6KTAFJVtwO/CPwJ8CCwGzhvsddO8puD2yVJkrRe5NGXZ0iSBiXJbcC7q+rPxx2LJElqP8+8SdKAJPmJJP+q6TZ5LvBM4G/GHZckSVofNow7AElaR55K5/6LG4F7gJdX1f7xhiRJktYLu01KkiRJUgvYbVKSJEmSWmAiuk0ef/zxtWnTpiXnf/WrX2Xjxo2jC2jI3J/JtdK+3HHHHf9UVd83wpCGps31zth6M6mxTVO9kySpHxORvG3atInbb799yfnz8/PMzc2NLqAhc38m10r7kuSLo4tmuNpc74ytN5Ma2zTVO0mS+mG3SUmSJElqAZM3SZIkSWoBkzdJkiRJagGTN0mSJElqAZM3SZIkSWoBkzdJkiRJagGTN0mSJElqAZM3SZIkSWqBibhJtybPpu03rHrZbZsPcd6C5fdc+OJBhyRpgq3lM2OhnVs2DjASSZLWL8+8SZIkSVILmLxJkiRJUguYvEkTKsllSQ4kuXNB+RuSfCbJXUl+v6v8giS7k3w2yc+MPmJJkiQNk9e8SZNrJ/AnwPsOFyR5PnAm8Kyq+nqS72/KnwacBTwd+NfA/5nkKVX1yMijliRJ0lCseObNX/+l8aiqW4AHFhT/MnBhVX29WeZAU34mcFVVfb2qvgDsBk4fWbCSJEkautV0m9wJbOkuWPDr/9OBP2zKu3/93wL8aZKjBhmwNOWeAvyHJLcl+Z9J/l1TfiJwb9dye5sySZIkrRMrdpusqluSbFpQvOKv/8AXkhz+9f+jA4tYmm4bgOOA5wL/DrgmyZNXu3KSrcBWgJmZGebn55dc9uDBg8vOHydj680wY9u2+VDP607yMZMkaZL0es3b4V//3w78f8BvVtXf0fml/9au5Zb89X+9NCJ70Yb9WUtDbOboI5ef9P1bSgvem73AB6qqgI8l+RZwPLAPOLlruZOaskepqh3ADoDZ2dmam5tb8oXm5+dZbv44GVtvhhnbwns9rsXOLRsn9phJkjRJek3e+vr1H9ZPI7IXbdiftTTEtm0+xEW7Hv2vtOecuQFHNBoteG/+Cng+8JEkTwEeA/wTcD3wF0n+iM6AJacCHxtblJIkSRq4XpO3vn79l7SyJFcCc8DxSfYCbwEuAy5rBhD6BnBuUw/vSnINcDdwCHidI01KkiStL70mb/76Lw1ZVZ29xKyfX2L5twNvH15EkiRJGqcVkzd//ZckSZKk8VvNaJP++i9JkiRJY7aa+7xJkiRJksbM5E2SJEmSWsDkTZIkSZJawORNkiRJklrA5E2SJEmSWsDkTZIkSZJawORNkiRJklrA5E2SJEmSWsDkTZIkSZJawORNkiRJklrA5E2SJEmSWsDkTZIkSZJawORNkiRJklrA5E2SJEmSWsDkTZIkSZJawORNmlBJLktyIMmdi8zblqSSHN9MJ8m7kuxO8qkkzx59xJIkSRqmVSVvNiKlsdgJbFlYmORk4KeBL3UVvxA4tfnbClwygvgkSZI0Qqs987YTG5HSSFXVLcADi8x6B/BGoLrKzgTeVx23AsckOWEEYUqSJGlEVpW82YiUJkOSM4F9VfX3C2adCNzbNb23KZMkSdI6saHXFbsbkUm6Zy3ViNzf62tJgiSPA95M52x3r9vYSueMODMzM8zPzy+57MGDB5edP07G1pthxrZt86Ge153kYyZJ0iTpKXmzVATDVwAAF0RJREFUEdmfNuzPWhpiM0cfufyk799SJvy9+TfAKcDhH0xOAj6e5HRgH3By17InNWWPUlU7gB0As7OzNTc3t+SLzc/Ps9z8cTK23gwztvO239Dzuju3bJzYYyZJ0iTp9cybjcg+tGF/1tIQ27b5EBftevS/0p5z5gYc0WhM8ntTVbuA7z88nWQPMFtV/5TkeuD1Sa4CngM8XFV9ne3ete/hvhrkey58cT8vL0mSpAV6ulVAVe2qqu+vqk1VtYlO18hnV9V9wPXAq5pRJ5/LABqR0jRKciXwUeCpSfYmOX+ZxW8E7gF2A+8BfmUEIUqSJGmEVnXmrWlEzgHHJ9kLvKWqLl1i8RuBF9FpRH4NePUA4pSmTlWdvcL8TV3PC3jdsGOSJEnS+KwqebMRKUmSJEnj1VO3SUmSJEnSaJm8SZIkSVILmLxJkiRJUguYvEmSJElSC5i8SZIkSVILmLxJkiRJUguYvEmSJElSC5i8SZIkSVILmLxJkiRJUguYvEmSJElSC5i8SZIkSVILmLxJkiRJUguYvEmSJElSC5i8SZIkSVILmLxJkiRJUguYvEmSJElSC5i8SRMqyWVJDiS5s6vsD5J8JsmnknwwyTFd8y5IsjvJZ5P8zHiiliRJ0rCsmLzZgJTGZiewZUHZTcAzquqZwD8AFwAkeRpwFvD0Zp0/TXLU6EKVJEnSsK3mzNtObEBKI1dVtwAPLCj7cFUdaiZvBU5qnp8JXFVVX6+qLwC7gdNHFqwkSZKGbsNKC1TVLUk2LSj7cNfkrcDLm+ffbkACX0hyuAH50YFEK6nba4Crm+cn0qmLh+1tyh4lyVZgK8DMzAzz8/NLbnzmaNi2+dCS81ey3Lb7dfDgwaFuvx/TGls//yuTfMwkSZokKyZvq7DmBiSsrRG53r7Y27A/a2mILdbIn/T9W0ob3huAJL8FHAKuWMt6VbUD2AEwOztbc3NzSy578RXXcdGu3j8i9pyz9Lb7NT8/z3Kxj9O0xnbe9ht6Xnfnlo0Te8wkSZokfSVvvTYgYW2NyEluDPWiDfuzlobYts2HjmjkD7PhPkxteG+SnAe8BDijqqop3gec3LXYSU2ZJEmS1omeR5vsakCeYwNSGo0kW4A3Ai+tqq91zboeOCvJY5OcApwKfGwcMUqSJGk4ekrebEBKw5fkSjrXiz41yd4k5wN/AjwBuCnJJ5O8G6Cq7gKuAe4G/gZ4XVU9MqbQJUmSNAQrdptsGpBzwPFJ9gJvoTO65GPpNCABbq2q11bVXUkONyAPYQNSY7Cpz2tvJkVVnb1I8aXLLP924O3Di0iSJEnjtJrRJm1ASpIkSdKY9XzNmyRJkiRpdEzeJEmSJKkFTN4kSZIkqQVM3iRJkiSpBUzeJEmSJKkFTN4kSZIkqQVM3iRJkiSpBUzeJEmSJKkFTN4kSZIkqQVM3iRJkiSpBUzeJEmSJKkFTN4kSZIkqQVM3iRJkiSpBUzeJEmSJKkFTN4kSZIkqQVM3qQJleSyJAeS3NlVdlySm5J8rnk8tilPkncl2Z3kU0mePb7IJUmSNAyrSt5sREpjsRPYsqBsO3BzVZ0K3NxMA7wQOLX52wpcMqIYJUmSNCKrPfO2ExuR0khV1S3AAwuKzwQub55fDrysq/x91XErcEySE0YTqSRJkkZhVcmbjUhpYsxU1f7m+X3ATPP8RODeruX2NmWSJElaJzb0se5aG5H7kTQwVVVJai3rJNlK54w4MzMzzM/PL7nszNGwbfOhnuNbbtv9Onjw4FC3349pja2f/5VJPmaSJE2SfpK3bxt2I3K9fbG3YX/W0hBbrJE/zv1b543I+5OcUFX7mzPaB5ryfcDJXcud1JQ9SlXtAHYAzM7O1tzc3JIvdPEV13HRrt4/Ivacs/S2+zU/P89ysY/TtMZ23vYbel5355aNE3vMJEmaJP0kbyNrRE5yY6gXbdiftTTEtm0+dEQjf5gN95Ws80bk9cC5wIXN43Vd5a9PchXwHODhrjPjkiRJWgf6uVXA4UYkHNmIfFUz6uRzsREp9STJlcBHgacm2ZvkfDpJ208l+RzwgmYa4EbgHmA38B7gV8YQsiRJkoZoVWfemkbkHHB8kr3AW+g0Gq9pGpRfBF7RLH4j8CI6jcivAa8ecMzSVKiqs5eYdcYiyxbwuuFGJEmSpHFaVfJmI1KSJEmSxqufbpOSJEmSpBExeZMkSZKkFjB5kyRJkqQWMHmTJEmSpBYweZMkSZKkFjB5kyRJkqQWMHmTJEmSpBYweZMkSZKkFjB5kyRJkqQWMHmTJEmSpBYweZMkSZKkFjB5kyRJkqQWMHmTJEmSpBYweZMkSZKkFjB5kyRJkqQWMHmTJEmSpBYweZNaKMmvJ7kryZ1JrkzyPUlOSXJbkt1Jrk7ymHHHKUmSpMHpK3mzASmNXpITgV8FZqvqGcBRwFnA7wHvqKofAh4Ezh9flJIkSRq0npM3G5DSWG0Ajk6yAXgcsB/4SeDaZv7lwMvGFJskSZKGYMMA1j86yTd5dAPy55r5lwNvBS7p83UkNapqX5I/BL4E/AvwYeAO4KGqOtQsthc4ceG6SbYCWwFmZmaYn59f8nVmjoZtmw8tOX8ly227XwcPHhzq9vsxrbH1878yycdMkqRJ0nPy1k8DEtbWiFxvX+xt2J+1NMQWa+SPc//WeyMyybHAmcApwEPAXwJbVrNuVe0AdgDMzs7W3NzckstefMV1XLSr99939pyz9Lb7NT8/z3Kxj9O0xnbe9ht6Xnfnlo0Te8wkSZokPbfM+mlAwtoakZPcGOpFG/ZnLQ2xbZsPHdHIH2bDfSVT0Ih8AfCFqvpHgCQfAJ4HHJNkQ/PjyUnAvjHGKEmSpAHrZ8CSbzcgq+qbwKMakM0yNiClwfsS8Nwkj0sS4AzgbuAjwMubZc4FrhtTfJIkSRqCfpI3G5DSGFTVbXQGJvk4sItOPd4BvAn4jSS7gScBl44tSEmSJA1cP9e83ZbkcAPyEPAJOg3IG4CrkvxuU2YDUhqwqnoL8JYFxfcAp48hHEmSJI1AX6NN2oCUJEmSpNHo6ybdkiRJkqTRMHmTJEmSpBYweZMkSZKkFjB5kyRJkqQWMHmTJEmSpBYweZMkSZKkFjB5kyRJkqQWMHmTJEmSpBYweZMkSZKkFjB5kyRJkqQWMHmTJEmSpBYweZMkSZKkFjB5kyRJkqQWMHmTJEmSpBYweZMkSZKkFjB5kyRJkqQW6Ct5S3JMkmuTfCbJp5P8WJLjktyU5HPN47GDClZSh3VPkiRp+vR75u2dwN9U1Q8DzwI+DWwHbq6qU4Gbm2lJg2XdkyRJmjI9J29Jngj8OHApQFV9o6oeAs4ELm8Wuxx4Wb9BSvoO654kSdJ0SlX1tmJyGrADuJvOL/93AL8G7KuqY5plAjx4eHrB+luBrQAzMzM/etVVVy35WgcPHuTxj398T3FOojbsz659D6962Zmj4f5/eXTZ5hOfOOCIVm8tsS90yhOPWva9ef7zn39HVc32/AID0E/dW0u9O/DAw0e8r2sxzP+BSa5D0xrbeq93kiRNgn6St1ngVuB5VXVbkncCXwHe0N1gTPJgVS177c3s7GzdfvvtS86fn59nbm6upzgnURv2Z9P2G1a97LbNh7ho14ZHle258MWDDmnV1hL7Qju3bFz2vUky9kbkoOreSvXu4iuuO+J9XYth/g9Mch2a1tjWe72TJGkS9HPN215gb1Xd1kxfCzwbuD/JCQDN44H+QpS0gHVPkiRpCvWcvFXVfcC9SZ7aFJ1BpxvX9cC5Tdm5wHV9RSjpUax7kiRJ06n3PlEdbwCuSPIY4B7g1XQSwmuSnA98EXhFn68h6UjWPUmSpCnTV/JWVZ8EFrsO4Yx+titpedY9SZKk6dPvfd4kSZIkSSNg8iZJkiRJLWDyJkmSJEktYPImSZIkSS1g8iZJkiRJLWDyJkmSJEktYPImSZIkSS1g8iZJkiRJLWDyJkmSJEktYPImSZIkSS1g8iZJkiRJLWDyJkmSJEktYPImSZIkSS1g8iZJkiRJLWDyJkmSJEktYPImSZIkSS3Qd/KW5Kgkn0jyoWb6lCS3Jdmd5Ookj+k/TEndrHeSJEnTZxBn3n4N+HTX9O8B76iqHwIeBM4fwGtIejTrnSRJ0pTpK3lLchLwYuC9zXSAnwSubRa5HHhZP68h6dGsd5IkSdOp3zNvfwy8EfhWM/0k4KGqOtRM7wVO7PM1JD2a9U6SJGkKbeh1xSQvAQ5U1R1J5npYfyuwFWBmZob5+fkllz148OCy89umDfuzbfOhlRdqzBx95PLj3L+1xL7QpL83o6x3i72vazHM4zjJ79O0xrae650kSZOi5+QNeB7w0iQvAr4H+F7gncAxSTY0ZwFOAvYttnJV7QB2AMzOztbc3NySLzQ/P89y89umDftz3vYbVr3sts2HuGjXo/+V9pwzN+CIVm8tsS+0c8vGSX9vRlbvLr7iuiPe17UY5v/AJNehaY1tndc7SZImQs/dJqvqgqo6qao2AWcBf1tV5wAfAV7eLHYucF3fUUoCrHeSJEnTbBj3eXsT8BtJdtO5FufSIbyGpEez3kmSJK1z/XSb/Laqmgfmm+f3AKcPYruSlma9kyRJmi7DOPMmSZIkSRowkzdJkiRJagGTN0mSJElqAZM3SZIkSWoBkzdJkiRJagGTN0mSJElqAZM3SZIkSWoBkzdJkvT/t3e3sXKUZRjH/5cFlFADKIbUSqAmGCVgRBowIdHWVyIJxWi0JipVDL7xYuRL1Q9GjQnGqFFjokaIaAgVAUMVjEFoJSQCLQSpLaIVEWgIVQuVBoOp3n7YAZcjbffs7pzdOef/S07OzszO7DUz55mz987Ms5KkDrB4kyRJkqQOsHiTJEmSpA6weJMkSZKkDrB4kyRJkqQOsHiTJEmSpA6weJMkSZKkDrB4kyRJkqQOsHiTJEmSpA6weJMkSZKkDhi6eEtyTJINSbYl2Zrkomb8i5LcmOSPze8jxxdXkm1PkiRpYRrlzNte4OKqOgF4HfCJJCcAa4Gbqup44KZmWNL42PYkSZIWoKGLt6p6pKruah4/AdwLLAVWAZc3T7scOHvUkJL+x7YnSZK0MKWqRl9IchxwC3Ai8GBVHdGMD/DY08Mz5jkPOA/g6KOPPmXdunX7XP6ePXtYvHjxyDmnRRfWZ8uO3QM/9+hD4dF/PnvcSUsPH3Oiwc0m+0zLDl+0332zcuXKO6tq+dAvMGazbXuzaXc7d+3+v/06G23+DUxzG1qo2RZSu5MkaVJGLt6SLAZ+DXypqq5N8nj/G8Ykj1XVfu+9Wb58eW3evHmf0zdu3MiKFStGyjlNurA+x629fuDnXnzSXr665aBnjXvgkjPHHWlgs8k+0w/OOGy/+ybJ1LyJHLXtHajdfeuK6/5vv85Gm38D09yGFmq2hdLuJEmapJF6m0xyMHANcEVVXduMfjTJkmb6EmDnaBElzWTbkyRJWniG/li9uSzrUuDeqvpa36T1wDnAJc3v60ZK2HHP9Wn0xSftZc0An1JP8uyVppdtT5IkaWEa/pooOB14P7Alyd3NuM/Qe+N4VZJzgb8A7x4toqQZbHuSJEkL0NDFW1XdCmQfk9807HIl7Z9tT5IkaWEa6Z43SZIkSdLcsHiTJEmSpA6weJMkSZKkDrB4kyRJkqQOsHiTJEmSpA6weJMkSZKkDrB4kyRJkqQOsHiTJEmSpA6weJMkSZKkDrB4kyRJkqQOsHiTJEmSpA6weJMkSZKkDrB4kyRJkqQOsHiTJEmSpA6weJMkSZKkDrB4kyRJkqQOaK14S3JGkvuSbE+ytq3XkfQ/tjtJkqT566A2FppkEfBt4C3Aw8CmJOuratswy9uyYzdr1l4/dJ4HLjlz6Hmlrhh3u5MkSdJ0aevM26nA9qq6v6r+BawDVrX0WpJ6bHeSJEnzWFvF21Lgob7hh5txktpju5MkSZrHWrlschBJzgPOawb3JLlvP08/Cvjb0K/15WHnbMeFA67PtOXel+dan65kn2nllw+4b46dqyxtmEftbqRsLTPbLM33didJ0ri0VbztAI7pG35ZM+4ZVfU94HuDLCzJ5qpaPr54k+X6TK+Or8uCaXdmG860ZpvWXJIkTZu2LpvcBByfZFmSQ4DVwPqWXktSj+1OkiRpHmvlzFtV7U1yPvBLYBFwWVVtbeO1JPXY7iRJkua31u55q6obgBvGtLiBLvPqENdnenV6XRZQuzPbcKY127TmkiRpqqSqJp1BkiRJknQAbd3zJkmSJEkao6kv3pKckeS+JNuTrJ10nlEkuSzJziS/m3SWUSU5JsmGJNuSbE1y0aQzjSLJC5LckeS3zfp8ftKZ2nSgdpXk+Ul+3Ey/PclxfdM+3Yy/L8nb5jjXp5q/uXuS3JTk2L5p/05yd/Mz9o5aBsi2Jslf+zJ8uG/aOUn+2PycM4FsX+/L9Yckj/dNa3u77fe4l55vNtnvSfLavmmtbjdJkrpmqi+bTLII+APwFnpfOLwJeG9VbZtosCEleT2wB/hhVZ046TyjSLIEWFJVdyV5IXAncHaH902Aw6pqT5KDgVuBi6rqtglHG7tB2lWSjwOvrqqPJlkNvKOq3pPkBOBK4FTgpcCvgFdU1b/nKNdK4PaqejLJx4AVVfWeZtqeqlo8ao4Rsq0BllfV+TPmfRGwGVgOFL22ckpVPTZX2WY8/wLg5Kr6UDPc2nZrlr/f416StwMXAG8HTgO+UVWntb3dJEnqomk/83YqsL2q7q+qfwHrgFUTzjS0qroF2DXpHONQVY9U1V3N4yeAe4Glk001vOrZ0wwe3PxM7ycboxmkXa0CLm8eXw28qSlwVwHrquqpqvozsL1Z3pzkqqoNVfVkM3gbve+ymwujHIveBtxYVbuawuNG4IwJZnsvvQJ8Tgxw3FtFr7Cr5sOSI5oPh9rebpIkdc60F29LgYf6hh+mwwXCfNVcUncycPtkk4wmyaIkdwM76b1p7PT67Mcg7eqZ51TVXmA38OIB520zV79zgV/0Db8gyeYktyU5e0yZZpvtnc2lf1cnefoL09s+jg28/OYy02XAzX2j29xug9hXfo//kiTN0NpXBWhhSLIYuAb4ZFX9Y9J5RtFc+veaJEcAP01yYlV1/v7E+SjJ++hdTveGvtHHVtWOJC8Hbk6ypar+NIexfgZcWVVPJfkIvTOXb5zD1x/EauDqGZe5Tnq7SZKkAU37mbcdwDF9wy9rxmkKNPeGXQNcUVXXTjrPuFTV48AG5u8lWoO0q2eek+Qg4HDg7wPO22YukrwZ+CxwVlU99fT4qtrR/L4f2EjvbPC4HDBbVf29L8/3gVMGnbftbH1WM+OSyZa32yD2ld/jvyRJM0x78bYJOD7JsiSH0HvjMfbe0DR7zf1PlwL3VtXXJp1nVEle0pxxI8mh9Dp/+P1kU7VmkHa1Hni6d793ATdXr3ej9cDqpjfKZcDxwB1zlSvJycB36RVuO/vGH5nk+c3jo4DTgXF2njNItiV9g2fRuw8U4JfAW5uMRwJvbcbNWbYm3yuBI4Hf9I1re7sNYj3wgabXydcBu6vqEdrfbpIkdc5UXzZZVXuTnE/vH/Yi4LKq2jrhWENLciWwAjgqycPA56rq0smmGtrpwPuBLc19YgCfqaobJphpFEuAy5ue+54HXFVVP59wplbsq10l+QKwuarW0yvMf5RkO73OJlY3825NchW9N/h7gU+Mo6fJWeT6CrAY+Env8wMerKqzgFcB303yH3r775Jx9nw6YLYLk5xFb7vsAtY08+5K8kV6RRbAF6pqbB0XDZgNevtwXT27i+FWtxs893GPXodAVNV3gBvo9TS5HXgS+GAzrdXtJklSF031VwVIkiRJknqm/bJJSZIkSRIWb5IkSZLUCRZvkiRJktQBFm+SJEmS1AEWb5IkSZLUARZvkiRJktQBFm+SJEmS1AEWb5IkSZLUAf8FggNPmrJrFKQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x1440 with 16 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "jt28IlvJWYcr",
        "outputId": "cc1561ef-5306-4f69-8ce8-6e3799c481be"
      },
      "source": [
        "## getting detailed statistical information about the dataset\n",
        "raw_data.describe()"
      ],
      "execution_count": 9,
      "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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>54.366337</td>\n",
              "      <td>0.683168</td>\n",
              "      <td>0.966997</td>\n",
              "      <td>131.623762</td>\n",
              "      <td>246.264026</td>\n",
              "      <td>0.148515</td>\n",
              "      <td>0.528053</td>\n",
              "      <td>149.646865</td>\n",
              "      <td>0.326733</td>\n",
              "      <td>1.039604</td>\n",
              "      <td>1.399340</td>\n",
              "      <td>0.729373</td>\n",
              "      <td>2.313531</td>\n",
              "      <td>0.544554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.082101</td>\n",
              "      <td>0.466011</td>\n",
              "      <td>1.032052</td>\n",
              "      <td>17.538143</td>\n",
              "      <td>51.830751</td>\n",
              "      <td>0.356198</td>\n",
              "      <td>0.525860</td>\n",
              "      <td>22.905161</td>\n",
              "      <td>0.469794</td>\n",
              "      <td>1.161075</td>\n",
              "      <td>0.616226</td>\n",
              "      <td>1.022606</td>\n",
              "      <td>0.612277</td>\n",
              "      <td>0.498835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>126.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>71.000000</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>47.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>211.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>133.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>55.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>240.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>153.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>61.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>274.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>166.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>77.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>564.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              age         sex          cp  ...          ca        thal      target\n",
              "count  303.000000  303.000000  303.000000  ...  303.000000  303.000000  303.000000\n",
              "mean    54.366337    0.683168    0.966997  ...    0.729373    2.313531    0.544554\n",
              "std      9.082101    0.466011    1.032052  ...    1.022606    0.612277    0.498835\n",
              "min     29.000000    0.000000    0.000000  ...    0.000000    0.000000    0.000000\n",
              "25%     47.500000    0.000000    0.000000  ...    0.000000    2.000000    0.000000\n",
              "50%     55.000000    1.000000    1.000000  ...    0.000000    2.000000    1.000000\n",
              "75%     61.000000    1.000000    2.000000  ...    1.000000    3.000000    1.000000\n",
              "max     77.000000    1.000000    3.000000  ...    4.000000    3.000000    1.000000\n",
              "\n",
              "[8 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OLJuetYmbD2B",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "77a0172e-b3f0-439e-8445-98310368dadc"
      },
      "source": [
        "## checking the distribution of target column ( How many are suffereing from heart disease and how many are not)\n",
        "raw_data['target'].value_counts()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    165\n",
              "0    138\n",
              "Name: target, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bkbBRGzc_L_R"
      },
      "source": [
        "0 --> Represents non defective heart \n",
        "\n",
        "---\n",
        "1 --> Represents defective heart"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "Bx2jBkWt5DMH",
        "outputId": "bf37daf1-ed58-4ba6-bc91-000a44893a57"
      },
      "source": [
        "plt.figure(figsize=(15,6))\n",
        "sns.countplot(x='age',data = raw_data, hue = 'target',palette='husl')\n",
        "plt.show()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yMLcBesufp_f"
      },
      "source": [
        "Checking how many values in Target column are of the type Int"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qkuz--JzeOkD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0c874b29-a71c-4f7b-9d21-f0fb5ebdd8aa"
      },
      "source": [
        "raw_data['target'].map(type) == int"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      True\n",
              "1      True\n",
              "2      True\n",
              "3      True\n",
              "4      True\n",
              "       ... \n",
              "298    True\n",
              "299    True\n",
              "300    True\n",
              "301    True\n",
              "302    True\n",
              "Name: target, Length: 303, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WOh5x-xA6B2U"
      },
      "source": [
        "\n",
        "def stacked_barchart(data, title = None, ylabel = None, xlabel = None):\n",
        "    default_colors = ['#008080', '#5f3c41', '#219AD8']\n",
        "    # From raw value to percentage\n",
        "    totals = data.sum(axis=1)\n",
        "    bars = ((data.T / totals) * 100).T\n",
        "    r = list(range(data.index.size))\n",
        "\n",
        "    # Plot\n",
        "    barWidth = 0.95\n",
        "    names = data.index.tolist()\n",
        "    bottom = [0] * bars.shape[0]\n",
        "\n",
        "    # Create bars\n",
        "    color_index = 0\n",
        "    plots = []\n",
        "    for bar in bars.columns:\n",
        "        plots.append(plt.bar(r, bars[bar], bottom=bottom, color=default_colors[color_index], edgecolor='white', width=barWidth))\n",
        "        bottom = list(map(add, bottom, bars[bar]))\n",
        "        color_index = 0 if color_index >= len(default_colors) else color_index + 1\n",
        "\n",
        "    # Custom x axis\n",
        "    plt.title(title)\n",
        "    plt.xticks(r, names)\n",
        "    plt.xlabel(data.index.name if xlabel is None else xlabel)\n",
        "    plt.ylabel(data.columns.name if ylabel is None else ylabel)\n",
        "    ax = plt.gca()\n",
        "        \n",
        "    y_labels = ax.get_yticks()\n",
        "    ax.set_yticklabels([str(y) + '%' for y in y_labels])\n",
        "\n",
        "    flat_list = [item for sublist in data.T.values for item in sublist]\n",
        "    for i, d in zip(ax.patches, flat_list):\n",
        "        data_label = str(d) + \" (\" + str(round(i.get_height(), 2)) + \"%)\"\n",
        "        ax.text(i.get_x() + 0.45, i.get_y() + 5, data_label, horizontalalignment='center', verticalalignment='center', fontdict = dict(color = 'white', size = 20))\n",
        "\n",
        "    for item in ([ax.title]):\n",
        "        item.set_fontsize(27)\n",
        "        \n",
        "    for item in ([ax.xaxis.label, ax.yaxis.label] + ax.get_xticklabels() + ax.get_yticklabels()):\n",
        "        item.set_fontsize(24)\n",
        "    \n",
        "    legend = ax.legend(plots, bars.columns.tolist(), fancybox=True)\n",
        "    plt.setp(legend.get_texts(), fontsize='20')"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 675
        },
        "id": "8_-0pjRh6IeD",
        "outputId": "550bc704-c647-4fd0-8c17-af4704c2cec2"
      },
      "source": [
        "fig = plt.gcf()\n",
        "fig.set_size_inches(25, 35)\n",
        "grid_rows = 3\n",
        "grid_cols = 2\n",
        "\n",
        "#draw sex vs disease outcome\n",
        "plt.subplot(grid_rows, grid_cols, 1)\n",
        "temp = raw_data[['sex','target']].groupby(['sex','target']).size().unstack('target')\n",
        "temp.rename(index={0:'Female', 1:'Male'}, columns={0:'No Disease', 1:'Has Disease'}, inplace = True)\n",
        "stacked_barchart(temp, title = 'Disease Risk vs Sex', ylabel = 'Population')\n"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1800x2520 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iR2rL-viiW2r"
      },
      "source": [
        "Splitting the Target & Features as two seperate variables X and Y"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qVRqp0ZQhaLE"
      },
      "source": [
        "## splitting the columns in two diffrent variables\n",
        "X=raw_data.drop(columns='target', axis=1)\n",
        "Y=raw_data['target']"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E_C9C2LGizjM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2743252e-3029-49f4-9dd4-c5adb432ca3c"
      },
      "source": [
        "print(X)\n",
        "print(Y)\n"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     age  sex  cp  trestbps  chol  ...  exang  oldpeak  slope  ca  thal\n",
            "0     63    1   3       145   233  ...      0      2.3      0   0     1\n",
            "1     37    1   2       130   250  ...      0      3.5      0   0     2\n",
            "2     41    0   1       130   204  ...      0      1.4      2   0     2\n",
            "3     56    1   1       120   236  ...      0      0.8      2   0     2\n",
            "4     57    0   0       120   354  ...      1      0.6      2   0     2\n",
            "..   ...  ...  ..       ...   ...  ...    ...      ...    ...  ..   ...\n",
            "298   57    0   0       140   241  ...      1      0.2      1   0     3\n",
            "299   45    1   3       110   264  ...      0      1.2      1   0     3\n",
            "300   68    1   0       144   193  ...      0      3.4      1   2     3\n",
            "301   57    1   0       130   131  ...      1      1.2      1   1     3\n",
            "302   57    0   1       130   236  ...      0      0.0      1   1     2\n",
            "\n",
            "[303 rows x 13 columns]\n",
            "0      1\n",
            "1      1\n",
            "2      1\n",
            "3      1\n",
            "4      1\n",
            "      ..\n",
            "298    0\n",
            "299    0\n",
            "300    0\n",
            "301    0\n",
            "302    0\n",
            "Name: target, Length: 303, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N2ZSPqEZkpuo"
      },
      "source": [
        "Splitting the dataframe into two groups ( Training & Testing)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LS2aptNGk5Iv"
      },
      "source": [
        "X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.2,stratify=Y,random_state=2)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W3Z5T86YlciC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "addb2624-ccdc-47dc-dd91-e57cd52a77b8"
      },
      "source": [
        "print(X.shape,X_train.shape,X_test.shape)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(303, 13) (242, 13) (61, 13)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8dfYVLvUss1U",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "59ef10fd-0351-4e2e-aedc-742f4982daa5"
      },
      "source": [
        "print(Y.shape,Y_train.shape,Y_test.shape)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(303,) (242,) (61,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e-klcQeDx8cz"
      },
      "source": [
        "Training the Model \n",
        "We are choosing Logistic Regression model for training the dataset as it is good for binary classification.\n",
        "\n",
        "[Detailed Explanation](https://towardsdatascience.com/logistic-regression-detailed-overview-46c4da4303bc)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wLj9YkuNz0Uo"
      },
      "source": [
        "model=LogisticRegression()\n"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "upnCadL_0Dw5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "10b7e5e9-74b6-459a-ed78-ecc94a1dcaf8"
      },
      "source": [
        "model.fit(X_train,Y_train)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fHpwFKAg17Hb"
      },
      "source": [
        "Evaluation of Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DgH5q-9d1_h-"
      },
      "source": [
        "# Evaluation of Model using accuracy_score function from sklearn library\n",
        "\n",
        "## accuracy score on training data\n",
        "training_prediction=model.predict(X_train)\n",
        "training_accuracy_score=accuracy_score(training_prediction,Y_train)\n"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YWy4AZdn2zuN",
        "outputId": "e26f4c6b-26eb-4a9d-faf8-b80c0af7e37d"
      },
      "source": [
        "print(training_accuracy_score)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8512396694214877\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V0JhWI9923WI"
      },
      "source": [
        "## accuracy score on testing data\n",
        "testing_prediction=model.predict(X_test)\n",
        "testing_accuracy_score=accuracy_score(testing_prediction,Y_test)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L8jVvdWr3Iux",
        "outputId": "be515d5f-a1dc-4299-ed33-2a99edf484bf"
      },
      "source": [
        "print(testing_accuracy_score)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.819672131147541\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SyLDqg8o3OXV"
      },
      "source": [
        "Here we have little difference in the accuracy score of training and testing data . So we can say that model is trained well . It is not over-fitted"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Wpo3iLa3gZu"
      },
      "source": [
        "### Predictive System\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YhQOFqVf5fyK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "77d1821d-a6f3-42be-8b97-3bf5da0f2f3f"
      },
      "source": [
        "# Taking the input from user 64,1,3,110,211,0,0,144,1,1.8,1,0,2,1\n",
        "print(\"Please enter the following detail\")\n",
        "input_data=(int(input(\"Age:-\\n\")),\n",
        "            int(input(\"Gender:-\\n0 for Female \\n1 for male\")),\n",
        "            int(input(\"Chest Pain measurefrom 0-4\\n0-->No Pain 4--> Severe pain:-\\n\")),\n",
        "            int(input(\"Resting Blood Pressure value:-\\n\")),\n",
        "            int(input(\"Cholestrol Value:-\\n\")),\n",
        "            int(input(\"Fasting Blood Sugar:-\\n\")),\n",
        "            int(input(\"Rest ECG:-\\n\")),\n",
        "            int(input(\"Max Heart Rate:-\\n\")),\n",
        "            int(input(\"Exercised induced Agina:-\\n\")),\n",
        "            float(input(\"ST Depression:-\\n\")),\n",
        "            int(input(\"The slope of the peak exercise ST segment:-\\n\")),\n",
        "            int(input(\"Number of major vessels (0-3) colored by flourosopy:-\\n\")),\n",
        "            int(input(\"Thalessemia Value:- \\n thal: 3 = normal; 6 = fixed defect; 7 = reversable defect\\n\"))\n",
        "            )"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Please enter the following detail\n",
            "Age:-\n",
            "57\n",
            "Gender:-\n",
            "0 for Female \n",
            "1 for male0\n",
            "Chest Pain measurefrom 0-4\n",
            "0-->No Pain 4--> Severe pain:-\n",
            "0\n",
            "Resting Blood Pressure value:-\n",
            "176\n",
            "Cholestrol Value:-\n",
            "200\n",
            "Fasting Blood Sugar:-\n",
            "120\n",
            "Rest ECG:-\n",
            "1\n",
            "Max Heart Rate:-\n",
            "200\n",
            "Exercised induced Agina:-\n",
            "0\n",
            "ST Depression:-\n",
            "0\n",
            "The slope of the peak exercise ST segment:-\n",
            "1\n",
            "Number of major vessels (0-3) colored by flourosopy:-\n",
            "1\n",
            "Thalessemia Value:- \n",
            " thal: 3 = normal; 6 = fixed defect; 7 = reversable defect\n",
            "2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XQxWqgXGXcEn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "048252a6-f0dc-4351-bcbb-e8974c6695b3"
      },
      "source": [
        "print(input_data)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(57, 0, 0, 176, 200, 120, 1, 200, 0, 0.0, 1, 1, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4bJXJ1tuKYAg"
      },
      "source": [
        " input_data_as_numpy= np.asarray(input_data)"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JfcP3uU7LI7U",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dfa16bfb-ea8e-44d2-984f-c04bf8f442e9"
      },
      "source": [
        "input_data_reshaped=input_data_as_numpy.reshape(1,-1)\n",
        "prediction=model.predict(input_data_reshaped)\n",
        "print(prediction)\n",
        "if(prediction[0]==0):\n",
        "    print(color.BOLD+color.GREEN+\"\\n\\n*********************************************\\nThe person's heart is healthy :)\\n*********************************************\\n\\n\"+color.END)\n",
        "else:\n",
        "    print(color.BOLD+color.RED+\"\\n\\n*********************************************\\nThe person is suffering from heart disease :(\\n*********************************************\\n\\n\"+color.END)"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\n",
            "\u001b[1m\u001b[92m\n",
            "\n",
            "*********************************************\n",
            "The person's heart is healthy :)\n",
            "*********************************************\n",
            "\n",
            "\u001b[0m\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:446: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
            "  \"X does not have valid feature names, but\"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_J1_nGr1hAHo"
      },
      "source": [
        ""
      ]
    }
  ]
}