IDAPredictive.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "The International Development Association (IDA) credits are public and publicly guaranteed debt extended by the World Bank Group. IDA provides development credits, grants and guarantees to its recipient member countries to help meet their development needs. Credits from IDA are at concessional rates. Data are in U.S. dollars calculated using historical rates. This dataset contains historical snapshots of the IDA Statement of Credits and Grants including the latest available snapshot. The World Bank complies with all sanctions applicable to World Bank transactions.\n"
      ],
      "metadata": {
        "id": "Ib-yZ4r6Re5M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install sqlalchemy"
      ],
      "metadata": {
        "id": "bhwIymTnyXpd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cb1f2b66-f8d0-48f1-cad6-33896afc05f8"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: sqlalchemy in /usr/local/lib/python3.10/dist-packages (2.0.30)\n",
            "Requirement already satisfied: typing-extensions>=4.6.0 in /usr/local/lib/python3.10/dist-packages (from sqlalchemy) (4.11.0)\n",
            "Requirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.10/dist-packages (from sqlalchemy) (3.0.3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Libraries"
      ],
      "metadata": {
        "id": "0HZT-y3p6_1d"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "w8nTZ_qvgiBi"
      },
      "outputs": [],
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('https://finances.worldbank.org/resource/tdwh-3krx.csv')"
      ],
      "metadata": {
        "id": "Eb-iHOcVhAZm"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#df.head()"
      ],
      "metadata": {
        "id": "i9YiVgxqjPeq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The main question here is which country has a probability greater than 0.1 of being in debt with IDA."
      ],
      "metadata": {
        "id": "2EAZWO9Q6YHU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Missing data"
      ],
      "metadata": {
        "id": "mte4YGAWli9u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "missing_percentages = (df.isnull().sum() / len(df)) * 100\n",
        "\n",
        "print(\"Percentage of missing data for each column:\")\n",
        "print(missing_percentages)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SOEEzgulllH8",
        "outputId": "3c106044-8ad8-4824-c2d5-00702574b65c"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of missing data for each column:\n",
            "end_of_period                    0.0\n",
            "credit_number                    0.0\n",
            "region                           0.0\n",
            "country_code                     0.0\n",
            "country                          0.0\n",
            "borrower                         0.0\n",
            "credit_status                    0.0\n",
            "service_charge_rate              0.0\n",
            "currency_of_commitment           0.0\n",
            "project_id                       0.0\n",
            "project_name                     0.0\n",
            "original_principal_amount        0.0\n",
            "cancelled_amount                 0.0\n",
            "undisbursed_amount               0.0\n",
            "disbursed_amount                 0.0\n",
            "repaid_to_ida                    0.0\n",
            "due_to_ida                       0.0\n",
            "exchange_adjustment              0.0\n",
            "borrower_s_obligation            0.0\n",
            "sold_3rd_party                   0.0\n",
            "repaid_3rd_party                 0.0\n",
            "due_3rd_party                    0.0\n",
            "credits_held                     0.0\n",
            "first_repayment_date             0.0\n",
            "last_repayment_date              0.0\n",
            "agreement_signing_date           0.0\n",
            "board_approval_date              0.0\n",
            "effective_date_most_recent_      0.0\n",
            "closed_date_most_recent_         0.0\n",
            "last_disbursement_date         100.0\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Converting date columns to datetime\n",
        "date_columns = ['end_of_period', 'first_repayment_date', 'last_repayment_date',\n",
        "                 'agreement_signing_date', 'board_approval_date', 'effective_date_most_recent_',\n",
        "                 'closed_date_most_recent_', 'last_disbursement_date']\n",
        "for column in date_columns:\n",
        "    df[column] =pd.to_datetime(df[column])"
      ],
      "metadata": {
        "id": "HVxYzrbPxw-w"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Categorical vs numerical data"
      ],
      "metadata": {
        "id": "_7WgjV9oLbzB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.dtypes)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jLAuSHmrA8KK",
        "outputId": "f04e7cdc-7b26-4fea-ed9e-a1fb0cbec763"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "end_of_period                  datetime64[ns]\n",
            "credit_number                          object\n",
            "region                                 object\n",
            "country_code                           object\n",
            "country                                object\n",
            "borrower                               object\n",
            "credit_status                          object\n",
            "service_charge_rate                   float64\n",
            "currency_of_commitment                 object\n",
            "project_id                             object\n",
            "project_name                           object\n",
            "original_principal_amount             float64\n",
            "cancelled_amount                      float64\n",
            "undisbursed_amount                    float64\n",
            "disbursed_amount                      float64\n",
            "repaid_to_ida                         float64\n",
            "due_to_ida                            float64\n",
            "exchange_adjustment                   float64\n",
            "borrower_s_obligation                 float64\n",
            "sold_3rd_party                        float64\n",
            "repaid_3rd_party                      float64\n",
            "due_3rd_party                         float64\n",
            "credits_held                          float64\n",
            "first_repayment_date           datetime64[ns]\n",
            "last_repayment_date            datetime64[ns]\n",
            "agreement_signing_date         datetime64[ns]\n",
            "board_approval_date            datetime64[ns]\n",
            "effective_date_most_recent_    datetime64[ns]\n",
            "closed_date_most_recent_       datetime64[ns]\n",
            "last_disbursement_date         datetime64[ns]\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dropping columns that provides no useful information"
      ],
      "metadata": {
        "id": "HgpVWV40u-lg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 454
        },
        "id": "xC7wmyI4yIei",
        "outputId": "f5873701-a5d8-4a18-dc40-806d1e700546"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       end_of_period  service_charge_rate  \\\n",
              "count                           1000              1000.00   \n",
              "mean   2017-07-17 10:53:45.599999744                 0.75   \n",
              "min              2011-04-30 00:00:00                 0.75   \n",
              "25%              2014-03-31 00:00:00                 0.75   \n",
              "50%              2017-04-30 00:00:00                 0.75   \n",
              "75%              2020-10-31 00:00:00                 0.75   \n",
              "max              2024-03-31 00:00:00                 0.75   \n",
              "std                              NaN                 0.00   \n",
              "\n",
              "       original_principal_amount  cancelled_amount  undisbursed_amount  \\\n",
              "count               1.000000e+03      1.000000e+03              1000.0   \n",
              "mean                2.295905e+07      3.812300e+05                 0.0   \n",
              "min                 2.200000e+06      0.000000e+00                 0.0   \n",
              "25%                 9.000000e+06      2.244580e+03                 0.0   \n",
              "50%                 1.550970e+07      5.300000e+05                 0.0   \n",
              "75%                 2.348100e+07      5.720260e+05                 0.0   \n",
              "max                 7.211271e+07      1.668669e+06                 0.0   \n",
              "std                 2.241931e+07      4.365159e+05                 0.0   \n",
              "\n",
              "       disbursed_amount  repaid_to_ida    due_to_ida  exchange_adjustment  \\\n",
              "count      1.000000e+03   1.000000e+03  1.000000e+03               1000.0   \n",
              "mean       2.260075e+07   2.172142e+07  8.793296e+05                  0.0   \n",
              "min        2.447321e+06   2.410509e+06  0.000000e+00                  0.0   \n",
              "25%        8.427974e+06   7.502114e+06  0.000000e+00                  0.0   \n",
              "50%        1.487650e+07   1.487650e+07  0.000000e+00                  0.0   \n",
              "75%        2.348100e+07   2.348100e+07  0.000000e+00                  0.0   \n",
              "max        7.158271e+07   7.158271e+07  7.374385e+06                  0.0   \n",
              "std        2.244518e+07   2.287082e+07  2.388386e+06                  0.0   \n",
              "\n",
              "       borrower_s_obligation  ...  repaid_3rd_party  due_3rd_party  \\\n",
              "count           1.000000e+03  ...            1000.0         1000.0   \n",
              "mean            8.793296e+05  ...               0.0            0.0   \n",
              "min             0.000000e+00  ...               0.0            0.0   \n",
              "25%             0.000000e+00  ...               0.0            0.0   \n",
              "50%             0.000000e+00  ...               0.0            0.0   \n",
              "75%             0.000000e+00  ...               0.0            0.0   \n",
              "max             7.374385e+06  ...               0.0            0.0   \n",
              "std             2.388386e+06  ...               0.0            0.0   \n",
              "\n",
              "       credits_held        first_repayment_date  last_repayment_date  \\\n",
              "count  1.000000e+03                        1000                 1000   \n",
              "mean   8.793296e+05  1971-11-01 08:55:40.800000  2011-05-03 01:26:24   \n",
              "min    0.000000e+00         1971-07-15 00:00:00  2011-01-15 00:00:00   \n",
              "25%    0.000000e+00         1971-08-15 00:00:00  2011-02-15 00:00:00   \n",
              "50%    0.000000e+00         1971-12-01 00:00:00  2011-06-01 00:00:00   \n",
              "75%    0.000000e+00         1971-12-01 00:00:00  2011-06-01 00:00:00   \n",
              "max    7.374385e+06         1972-03-15 00:00:00  2011-09-15 00:00:00   \n",
              "std    2.388386e+06                         NaN                  NaN   \n",
              "\n",
              "           agreement_signing_date         board_approval_date  \\\n",
              "count                        1000                        1000   \n",
              "mean   1961-07-10 18:25:55.200000  1961-07-07 21:30:14.400000   \n",
              "min           1961-05-12 00:00:00         1961-05-11 00:00:00   \n",
              "25%           1961-06-14 00:00:00         1961-06-13 00:00:00   \n",
              "50%           1961-06-28 00:00:00         1961-06-27 00:00:00   \n",
              "75%           1961-08-28 00:00:00         1961-08-15 00:00:00   \n",
              "max           1961-08-30 00:00:00         1961-08-29 00:00:00   \n",
              "std                           NaN                         NaN   \n",
              "\n",
              "      effective_date_most_recent_    closed_date_most_recent_  \\\n",
              "count                        1000                        1000   \n",
              "mean   1961-10-17 03:04:19.200000  1968-04-20 19:35:02.400000   \n",
              "min           1961-06-29 00:00:00         1965-06-30 00:00:00   \n",
              "25%           1961-08-25 00:00:00         1967-06-30 00:00:00   \n",
              "50%           1961-11-25 00:00:00         1967-07-31 00:00:00   \n",
              "75%           1961-11-27 00:00:00         1968-12-31 00:00:00   \n",
              "max           1961-12-21 00:00:00         1971-12-31 00:00:00   \n",
              "std                           NaN                         NaN   \n",
              "\n",
              "      last_disbursement_date  \n",
              "count                      0  \n",
              "mean                     NaT  \n",
              "min                      NaT  \n",
              "25%                      NaT  \n",
              "50%                      NaT  \n",
              "75%                      NaT  \n",
              "max                      NaT  \n",
              "std                      NaN  \n",
              "\n",
              "[8 rows x 21 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-66f15a04-1162-4bac-a5ff-92ebb9277cab\" class=\"colab-df-container\">\n",
              "    <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>end_of_period</th>\n",
              "      <th>service_charge_rate</th>\n",
              "      <th>original_principal_amount</th>\n",
              "      <th>cancelled_amount</th>\n",
              "      <th>undisbursed_amount</th>\n",
              "      <th>disbursed_amount</th>\n",
              "      <th>repaid_to_ida</th>\n",
              "      <th>due_to_ida</th>\n",
              "      <th>exchange_adjustment</th>\n",
              "      <th>borrower_s_obligation</th>\n",
              "      <th>...</th>\n",
              "      <th>repaid_3rd_party</th>\n",
              "      <th>due_3rd_party</th>\n",
              "      <th>credits_held</th>\n",
              "      <th>first_repayment_date</th>\n",
              "      <th>last_repayment_date</th>\n",
              "      <th>agreement_signing_date</th>\n",
              "      <th>board_approval_date</th>\n",
              "      <th>effective_date_most_recent_</th>\n",
              "      <th>closed_date_most_recent_</th>\n",
              "      <th>last_disbursement_date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1000</td>\n",
              "      <td>1000.00</td>\n",
              "      <td>1.000000e+03</td>\n",
              "      <td>1.000000e+03</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>1.000000e+03</td>\n",
              "      <td>1.000000e+03</td>\n",
              "      <td>1.000000e+03</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>1.000000e+03</td>\n",
              "      <td>...</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>1.000000e+03</td>\n",
              "      <td>1000</td>\n",
              "      <td>1000</td>\n",
              "      <td>1000</td>\n",
              "      <td>1000</td>\n",
              "      <td>1000</td>\n",
              "      <td>1000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2017-07-17 10:53:45.599999744</td>\n",
              "      <td>0.75</td>\n",
              "      <td>2.295905e+07</td>\n",
              "      <td>3.812300e+05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.260075e+07</td>\n",
              "      <td>2.172142e+07</td>\n",
              "      <td>8.793296e+05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.793296e+05</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.793296e+05</td>\n",
              "      <td>1971-11-01 08:55:40.800000</td>\n",
              "      <td>2011-05-03 01:26:24</td>\n",
              "      <td>1961-07-10 18:25:55.200000</td>\n",
              "      <td>1961-07-07 21:30:14.400000</td>\n",
              "      <td>1961-10-17 03:04:19.200000</td>\n",
              "      <td>1968-04-20 19:35:02.400000</td>\n",
              "      <td>NaT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2011-04-30 00:00:00</td>\n",
              "      <td>0.75</td>\n",
              "      <td>2.200000e+06</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.447321e+06</td>\n",
              "      <td>2.410509e+06</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1971-07-15 00:00:00</td>\n",
              "      <td>2011-01-15 00:00:00</td>\n",
              "      <td>1961-05-12 00:00:00</td>\n",
              "      <td>1961-05-11 00:00:00</td>\n",
              "      <td>1961-06-29 00:00:00</td>\n",
              "      <td>1965-06-30 00:00:00</td>\n",
              "      <td>NaT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2014-03-31 00:00:00</td>\n",
              "      <td>0.75</td>\n",
              "      <td>9.000000e+06</td>\n",
              "      <td>2.244580e+03</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.427974e+06</td>\n",
              "      <td>7.502114e+06</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1971-08-15 00:00:00</td>\n",
              "      <td>2011-02-15 00:00:00</td>\n",
              "      <td>1961-06-14 00:00:00</td>\n",
              "      <td>1961-06-13 00:00:00</td>\n",
              "      <td>1961-08-25 00:00:00</td>\n",
              "      <td>1967-06-30 00:00:00</td>\n",
              "      <td>NaT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2017-04-30 00:00:00</td>\n",
              "      <td>0.75</td>\n",
              "      <td>1.550970e+07</td>\n",
              "      <td>5.300000e+05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.487650e+07</td>\n",
              "      <td>1.487650e+07</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1971-12-01 00:00:00</td>\n",
              "      <td>2011-06-01 00:00:00</td>\n",
              "      <td>1961-06-28 00:00:00</td>\n",
              "      <td>1961-06-27 00:00:00</td>\n",
              "      <td>1961-11-25 00:00:00</td>\n",
              "      <td>1967-07-31 00:00:00</td>\n",
              "      <td>NaT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2020-10-31 00:00:00</td>\n",
              "      <td>0.75</td>\n",
              "      <td>2.348100e+07</td>\n",
              "      <td>5.720260e+05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.348100e+07</td>\n",
              "      <td>2.348100e+07</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1971-12-01 00:00:00</td>\n",
              "      <td>2011-06-01 00:00:00</td>\n",
              "      <td>1961-08-28 00:00:00</td>\n",
              "      <td>1961-08-15 00:00:00</td>\n",
              "      <td>1961-11-27 00:00:00</td>\n",
              "      <td>1968-12-31 00:00:00</td>\n",
              "      <td>NaT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2024-03-31 00:00:00</td>\n",
              "      <td>0.75</td>\n",
              "      <td>7.211271e+07</td>\n",
              "      <td>1.668669e+06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.158271e+07</td>\n",
              "      <td>7.158271e+07</td>\n",
              "      <td>7.374385e+06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.374385e+06</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.374385e+06</td>\n",
              "      <td>1972-03-15 00:00:00</td>\n",
              "      <td>2011-09-15 00:00:00</td>\n",
              "      <td>1961-08-30 00:00:00</td>\n",
              "      <td>1961-08-29 00:00:00</td>\n",
              "      <td>1961-12-21 00:00:00</td>\n",
              "      <td>1971-12-31 00:00:00</td>\n",
              "      <td>NaT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.241931e+07</td>\n",
              "      <td>4.365159e+05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.244518e+07</td>\n",
              "      <td>2.287082e+07</td>\n",
              "      <td>2.388386e+06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.388386e+06</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.388386e+06</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 21 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-66f15a04-1162-4bac-a5ff-92ebb9277cab')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-66f15a04-1162-4bac-a5ff-92ebb9277cab button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-66f15a04-1162-4bac-a5ff-92ebb9277cab');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-8d907cf9-5ba1-4d49-9c71-4fd7b6d46765\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8d907cf9-5ba1-4d49-9c71-4fd7b6d46765')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8d907cf9-5ba1-4d49-9c71-4fd7b6d46765 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(df['undisbursed_amount'].isnull()),len(df['exchange_adjustment'].isnull())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SHCAGivfvDHQ",
        "outputId": "1df07729-9064-4a35-e9aa-18e7d2052f6b"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 1000)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#only one entry for this variable then, we will drop it\n",
        "df['currency_of_commitment'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DeujD-CBMlDt",
        "outputId": "25af81f4-e312-4034-80b7-ada58af0f198"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['USD'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(columns=['last_disbursement_date','undisbursed_amount','exchange_adjustment','sold_3rd_party','repaid_3rd_party','due_3rd_party','currency_of_commitment' ], inplace=True)"
      ],
      "metadata": {
        "id": "k-zLIRoHmAPG"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Some meaningfull plots"
      ],
      "metadata": {
        "id": "MdQfQ-q2Cmxy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "year = (pd.to_datetime(df['end_of_period'])).dt.year"
      ],
      "metadata": {
        "id": "OVYihi0N7os1"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 6))\n",
        "sns.lineplot(data=df, x=year, y='cancelled_amount')\n",
        "plt.title('Cancelled Amount Amount Over Time')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Original Principal Amount USD')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "q5ha0H_TJZr-",
        "outputId": "b9b15886-18b5-4e28-c2d9-09b43c99644c"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 6))\n",
        "sns.lineplot(data=df, x=year, y='original_principal_amount')\n",
        "plt.title('Original Principal Amount Over Time')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Original Principal Amount USD')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "4dSzfi38zBTS",
        "outputId": "2659e04a-874f-442a-affb-0abb3f99b72c"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 6))\n",
        "sns.lineplot(data=df, x=year, y='disbursed_amount')\n",
        "plt.title('Disbursed Amount Over Time')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Disbursed Amount USD')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "_5wUNXH7JoI-",
        "outputId": "452a4a52-9b78-42a5-d481-524566b5b676"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 6))\n",
        "sns.lineplot(data=df, x=year, y='repaid_to_ida')\n",
        "plt.title('Repaid to ida Over Time')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Repaid to ida USD')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "-UyykPGQJsz-",
        "outputId": "3a94e4f5-b14d-4741-ac29-347ba2d9c91a"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 6))\n",
        "sns.lineplot(data=df, x=year, y='due_to_ida')\n",
        "plt.title('Due to ida Over Time')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Due to ida USD')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "JiHwrhg8Ju7E",
        "outputId": "0f41a9bc-e3d4-4acb-908a-ba31fbd98413"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.pairplot(df)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "1mO1YXnu0Yn4",
        "outputId": "adb33a79-e6d0-4f90-e384-9bbc30ffae34"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x2000 with 72 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_columns = df.select_dtypes(include=['float64', 'int64']).columns\n",
        "df[num_columns].hist(figsize=(10, 10))\n",
        "plt.show()\n",
        "\n",
        "#corr_matrix = df[num_columns].corr()\n",
        "#sns.heatmap(corr_matrix, annot=True, cmap='coolwarm')\n",
        "#plt.title('Correlation Matrix of Financial Metrics')\n",
        "#plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 871
        },
        "id": "DMVVQzYe6zDV",
        "outputId": "c0a4187d-b0e6-43ef-d2e2-720c2279e544"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 9 Axes>"
            ],
            "image/png": 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x6NGjNT6rL/Oqf1lRGzdunMZnIQQ2bdqEfv36QQihsR1CQkKQnZ2tcZukPnx8fBASEqJV/nhM2dnZuH37Nrp27Yq///4b2dnZT11uYmIiOnfujBo1ami0MzAwEEVFRThw4ECp2kllV1RUhKSkJAwYMAD16tWTyj08PPDaa6/h4MGDyMnJkcpHjBgBc3PzEpe5e/duFBYWPvU7WxJ7e3u8/vrr0mcrKyu0bdtW49gyNzeXjj+VSoW7d++isLAQbdq0KfV3vTgDBgzAc889J31u27Yt2rVrh+3bt2vVffLYLckrr7yicbX8yb7j1q1bOHDgAN58803UqVNHY159ni/y9PTU6MMUCgWGDh2KkydPIj09HQAgl8ulX2eLiopw584d2Nvbo3HjxuXefqXdN4MGDYKjo6P0WX218vXXX4eFhYVGeX5+Pq5fvw7g0a/K+fn5iI6O1nhubcSIEVAoFPj555/LHIM+/TtVXSqVClu3bkW/fv2kO0weJ5PJSn0MPO07cfLkSaSlpSE6OlrrbhX1cXv37l3s2bMHgwcPxr1796S/f3fu3EFISAguXrwofb/1kZiYCEdHRwQFBWn8PfX394e9vb10/qM+VsaNG6fRh5T2jhK10pwH+Pr6IiAgQPqsPr579Oih0b+py9Xb8+bNmzh16hSGDRsGZ2dnqV7Lli0RFBSksx/WV2BgIOrXr6+xTIVCUW2Ob95WZwBxcXGIiIiAl5cX/P390adPHwwdOhT16tXDxYsXAQARERHFzp+dna3xh97Hx0erzqBBgzBx4kR8//33+PDDDyGEQGJiovScQ3EuX74MT09PjQPjSRcvXoQQAg0bNtQ5vTS3VVy+fBlhYWF61X3ypEW9DR6/z/fatWuYPn06fvzxR637f5/sQCwsLKTnONSuXr0KMzMzrW3aoEEDjc+3bt1CVlYWVq5ciZUrV+psb2Zmph5R/R9d+xEAfvvtN8yYMQMpKSnIy8vTmJadna1xkqPLxYsX8ccffxR7ebq07aSyu3XrFvLy8qRbJh/XtGlTqFQqjef2ivtOPE79bOGT31FnZ+cSb599XO3atbWSgBo1auCPP/7QKFuzZg0++eQT/PnnnygoKChVO/Whq09p1KgRNmzYoFGm69gtydP6DvUf6ObNm5eqvWoNGjTQ2n6NGjUC8Og5H3d3d+lZzqVLlyItLQ1FRUVS3Zo1a5ZpvY8rzb55cnuo+xAvLy+d5ertpP6uPfn9tbKyQr169XQ+56ovffp3qrpu3bqFnJycEo+h0h4DT/tOqH/8LWmdly5dghAC06ZNw7Rp03TWyczM1PhRpiQXL15EdnY2XF1di10W8H/HypN9mouLi9798uNKcx5g6OMbePT3aefOncjNzYWdnV2p2/9km4BH+7O6HN9Mjgxg8ODB6Ny5M7Zs2YKkpCR8/PHHmD9/vnRFBgA+/vjjYu/hf/I+68d/UVDz9PRE586dsWHDBnz44Yc4fPgwrl27hvnz55e7/SqVCjKZDL/88ovOX7Ur6j7w4n5BF0IAePRLVFBQEO7evYvJkyejSZMmsLOzw/Xr1zFs2DCtK26P/4pVWuplvf7668Umsi1btizVMnXtx8uXL6Nnz55o0qQJPv30U3h5ecHKygrbt2/HwoUL9RpQQaVSISgoCO+//77O6eqTOKp6dH0nKsLTji3g0QAsw4YNw4ABAzBp0iS4urrC3NwcsbGxGleoK0Npj1194qtoH330EaZNm4Y333wTs2fPhrOzM8zMzBAdHV3ugVFKu2+K2x6G3E7FXXErKirSuZ6qsI+oYpX2GDDEd0K93Pfee0/nnRmA9g9LT1ueq6sr1q1bp3N6RTwjU9rzAGMf3xW97qqIyZGBeHh4YMyYMRgzZgwyMzPx/PPPY+7cudKDpgqFAoGBgeVaxyuvvIIxY8bgwoUL+P7772Fra4t+/fqVOE/9+vWxc+dO3L17t9irR/Xr14cQAj4+PuU+sa5fv77WaFRldfr0afz1119Ys2YNhg4dKpUnJyfrvQxvb2+oVCqkpaVp/OLz5Ag5Li4ucHBwQFFRUbn3U0l++uknKJVK/Pjjjxq/vOi6dbG4zqp+/fq4f/9+hbaT9OPi4gJbW1ud7+L5888/YWZmBi8vL50jqRVH/c6dS5cuaVwluHPnjkF/ldu4cSPq1auHzZs3a3zXdN2CW1bqK+eP++uvvyp8VDL1LY5l7YvUv04/vl3++usvAJDavnHjRnTv3h2rVq3SmDcrK0tj4JmyqIx9A/zfd+3ChQsat4Xm5+cjLS1No4+pUaOGzveVXL16VWPe0uAQ6lWXi4sLFApFiceQoY8B9W1aZ86cKfbvm/q7ZmlpaZC/gfXr18euXbvQsWPHEn+8Uh8rFy9e1Pi+37p1q9T9cmnOA8rj8eP7SX/++Sdq1aolXTUq6fguK1M+vvnMUTkVFRVp3d7l6uoKT09PKJVK+Pv7o379+liwYAHu37+vNf+tW7f0XldYWBjMzc3x7bffIjExEX379n3q5dCwsDAIITBz5kytaeoMf+DAgTA3N8fMmTO1sn4hBO7cuVOqNv7+++/YsmVLsevTl/qXicfnE0JoDelZEvUvS0uXLtUoX7Jkida6wsLCsGnTJp1/DEqzn0qiK6bs7GysXr1aq66dnZ3Ozmrw4MFISUnBzp07taZlZWWhsLDQIG2lpzM3N0dwcDB++OEHjSFKMzIysH79enTq1KnE21516dmzJywsLLSGdf38888N0WSJru/ikSNHkJKSYrB1bN26VeP+/6NHj+LIkSPSiHkVxcXFBV26dMFXX32Fa9euaUzTpx+6ceOGRh+Wk5ODtWvXws/PD+7u7gAebb8nl5WYmFiq5x2KUxn7Bnj03ICVlRUWL16ssa5Vq1YhOztbY1TP+vXr4/Dhwxojc23btq1Mr3tQs7Oz0+s5S6p8ZmZmGDBgAH766Set134Aj76bhj4Gnn/+efj4+GDRokVaf/vU63F1dUW3bt2wYsUK3Lx5U2sZpf1bPXjwYBQVFWH27Nla0woLC6V2BAYGwtLSEkuWLNGIuSyjw5bmPKA8PDw84OfnhzVr1mhszzNnziApKQl9+vSRyurXr4/s7GyNW69v3ryp81xOX+rzU1N8CSyvHJXTvXv3ULt2bbz88sto1aoV7O3tsWvXLhw7dgyffPIJzMzM8OWXX6J3795o1qwZhg8fjueeew7Xr1/H3r17oVAo8NNPP+m1LldXV3Tv3h2ffvop7t27h1deeeWp83Tv3h1vvPEGFi9ejIsXL6JXr15QqVT49ddf0b17d4wdOxb169fHnDlzMGXKFFy5cgUDBgyAg4MD0tLSsGXLFowcORLvvfeeXm2cNGkSNm7ciEGDBuHNN9+Ev78/7t69ix9//BHLly9Hq1at9FoOADRp0gT169fHe++9h+vXr0OhUGDTpk2l+pXG398fYWFhWLRoEe7cuSMN5a3+FfjxXzbmzZuHvXv3ol27dhgxYgR8fX1x9+5dnDhxArt27dL5fpDSCg4OhpWVFfr164dRo0bh/v37+OKLL+Dq6qrV0fv7+2PZsmWYM2cOGjRoAFdXV/To0QOTJk3Cjz/+iL59+2LYsGHw9/dHbm4uTp8+jY0bN+LKlSvl/uWa9DdnzhwkJyejU6dOGDNmDCwsLLBixQoolUqtdwvpw83NDe+88w4++eQTvPjii+jVqxd+//13/PLLL6hVq5bBfo3r27cvNm/ejJdeegmhoaFIS0vD8uXL4evrq/OHnLJo0KABOnXqhNGjR0OpVGLRokWoWbNmsbeEGtLixYvRqVMnPP/88xg5ciR8fHxw5coV/Pzzzzh16lSJ8zZq1AiRkZE4duwY3Nzc8NVXXyEjI0Pj5KVv376YNWsWhg8fjg4dOuD06dNYt25dma+iPK4y9g3wKImcMmUKZs6ciV69euHFF1/EhQsXsHTpUrzwwgsag3q89dZb2LhxI3r16oXBgwfj8uXL+OabbzQeyi4tf39/fP/995g4cSJeeOEF2NvbP/VuCKo8H330EZKSktC1a1eMHDkSTZs2xc2bN5GYmIiDBw8a/BgwMzPDsmXL0K9fP/j5+WH48OHw8PDAn3/+ibNnz0o/CMbHx6NTp05o0aIFRowYgXr16iEjIwMpKSn4999/S/Wusa5du2LUqFGIjY3FqVOnEBwcDEtLS1y8eBGJiYn47LPP8PLLL8PFxQXvvfceYmNj0bdvX/Tp0wcnT56U+uXSKM15QHl9/PHH6N27NwICAhAZGSkN5e3o6Kjxnsfw8HBMnjwZL730EsaPH4+8vDwsW7YMjRo1KvMAM/7+/gAeDdsfHh4OS0tL9OvXr0zPOFW6ih4Or7pTKpVi0qRJolWrVsLBwUHY2dmJVq1aiaVLl2rUO3nypBg4cKCoWbOmkMvlwtvbWwwePFjs3r1bqqMeMvHWrVvFru+LL74QAISDg4N48OCB1nRdwzEWFhaKjz/+WDRp0kRYWVkJFxcX0bt3b5GamqpRb9OmTaJTp07Czs5O2NnZiSZNmoioqChx4cKFUm2TO3fuiLFjx4rnnntOWFlZidq1a4uIiAhpiGz18JqJiYka8+ka8vbcuXMiMDBQ2Nvbi1q1aokRI0ZIQ0Y+Xi8iIkLY2dnpbE9ubq6IiooSzs7Owt7eXgwYMEBcuHBBABDz5s3TqJuRkSGioqKEl5eXsLS0FO7u7qJnz55i5cqVpdoG3t7eIjQ0VOe0H3/8UbRs2VJYW1uLunXrivnz54uvvvpKa8jL9PR0ERoaKhwcHAQAjeFy7927J6ZMmSIaNGggrKysRK1atUSHDh3EggULRH5+fqnaSuV34sQJERISIuzt7YWtra3o3r27xtCr6iGjdQ2Jq2s46cLCQjFt2jTh7u4ubGxsRI8ePcT58+dFzZo1xdtvvy3VK24o72bNmmmt58m+QaVSiY8++kh4e3sLuVwuWrduLbZt26azD0EZh/L++OOPxSeffCK8vLyEXC4XnTt3Fr///rtWu4o7dosbyvvjjz/WqqurjWfOnBEvvfSScHJyEtbW1qJx48Zi2rRp0vTihvIODQ0VO3fuFC1bthRyuVw0adJEq796+PChePfdd4WHh4ewsbERHTt2FCkpKVpDW5dlKG99901x26O4Pra47+Hnn38umjRpIiwtLYWbm5sYPXq01lDKQgjxySefiOeee07I5XLRsWNHcfz48WKH8tanf79//7547bXXhJOTkwDAYb2roKtXr4qhQ4cKFxcXIZfLRb169URUVJRQKpV6HwOl+U4IIcTBgwdFUFCQdE7VsmVLjSGihRDi8uXLYujQocLd3V1YWlqK5557TvTt21ds3LhRa70lDeWttnLlSuHv7y9sbGyEg4ODaNGihXj//ffFjRs3pDpFRUVi5syZUrzdunUTZ86cKXaY+5Loex5Q3LkEAK1XyBTXH+zatUt07NhR2NjYCIVCIfr16yfOnTuntcykpCTRvHlzYWVlJRo3biy++eabYofy1vX6Gl3bYfbs2eK5554TZmZmJjWst0yIavL0FFEpnDp1Cq1bt8Y333yDIUOGGLs5RE+VlZWFGjVqYM6cOdKLgauqK1euwMfHBx9//LHeV52rirp166J58+bYtm2bsZtCRERGwGeOqNp78OCBVtmiRYtgZmaGLl26GKFFRCUr7jsLAN26davcxhARET1D+MwR6eXBgwdPfXDW2dlZ4wVvVUVcXBxSU1PRvXt3WFhY4JdffsEvv/yCkSNHar0n4Glu3bpV7NCWwKP3g5T0TikifXz//fdISEhAnz59YG9vj4MHD+Lbb79FcHAwOnbsaLR2FRUVPfWB54oa+r86yM/Pf+qzi46OjpU25DsRGZb6JdHFsbGxeeq7DMn4mByRXr7//nsMHz68xDp79+6tkr9qd+jQAcnJyZg9ezbu37+POnXqICYmpky3Jr3wwgslDm3ZtWtX7Nu3rxytJXr0Ti0LCwvExcUhJydHGqRhzpw5Rm3XP//889QXxM6YMQPDhg2rnAaZmEOHDqF79+4l1lm9ejW3H5GJ8vDwKHF6REQEEhISKqcxVGZ85oj0cvPmTZw9e7bEOv7+/mV6U7Qp+e2333Te8qRWo0YNaYQWourm4cOHOHjwYIl16tWrZ5AR26qj//77D6mpqSXWadas2VNPsIioatq1a1eJ0z09PeHr61tJraGyYnJEREREREQEDshAREREREQEoBo/c6RSqXDjxg04ODgY7KWJRPTord737t2Dp6cnzMyezd9X2L8QVQz2L+xfiCqKvv1LtU2Obty4UeqRyIhIf//88w9q165t7GYYBfsXoorF/oX9C1FFeVr/Um2TIwcHBwCPNoBCoSi2XkFBAZKSkhAcHAxLS8vKap5BmXoMpt5+4NmKIScnB15eXtIx9ix6vH+xsbEx+X1fkurw3S4J46ta2L/od/5iavu1PBhr9WOsOPXtX6ptcqS+FK1QKJ6aHNna2kKhUJjsF9HUYzD19gPPZgzP8u0ej/cvNjY2Jr/vS1IdvtslYXxVE/uXks9fTHW/lgVjrX6MHefT+pdn84ZeIiIiIiKiJzA5IiIiIiIiApMjIiIiIiIiAGVIjg4cOIB+/frB09MTMpkMW7du1ZguhMD06dPh4eEBGxsbBAYG4uLFixp17t69iyFDhkChUMDJyQmRkZG4f/++Rp0//vgDnTt3hrW1Nby8vBAXF1f66IiIiIiIiPRU6uQoNzcXrVq1Qnx8vM7pcXFxWLx4MZYvX44jR47Azs4OISEhePjwoVRnyJAhOHv2LJKTk7Ft2zYcOHAAI0eOlKbn5OQgODgY3t7eSE1Nxccff4yYmBisXLmyDCESERERERE9XalHq+vduzd69+6tc5oQAosWLcLUqVPRv39/AMDatWvh5uaGrVu3Ijw8HOfPn8eOHTtw7NgxtGnTBgCwZMkS9OnTBwsWLICnpyfWrVuH/Px8fPXVV7CyskKzZs1w6tQpfPrppxpJFBERERERkaEYdCjvtLQ0pKenIzAwUCpzdHREu3btkJKSgvDwcKSkpMDJyUlKjAAgMDAQZmZmOHLkCF566SWkpKSgS5cusLKykuqEhIRg/vz5+O+//1CjRg2tdSuVSiiVSulzTk4OgEfDBRYUFBTbZvW0kupUdaYeg6m3H3i2YjDlGImIiIhKYtDkKD09HQDg5uamUe7m5iZNS09Ph6urq2YjLCzg7OysUcfHx0drGeppupKj2NhYzJw5U6s8KSkJtra2T217cnLyU+tUdaYeg6m3H3g2YsjLy6uklpi+uh/8bNDlXZkXatDlEZFpax6zE8oiw7wTqqr3L89SrGRc1eYlsFOmTMHEiROlz+q34AYHBz/1JbDJyckICgoy2RdumXoM6vZPO24GpcowHd+ZmBCDLEdfpr4PAP1jUF+VJSIiIqpuDJocubu7AwAyMjLg4eEhlWdkZMDPz0+qk5mZqTFfYWEh7t69K83v7u6OjIwMjTrqz+o6T5LL5ZDL5VrllpaWep2s6luvKjP1GJQqmcF+FTLWdjD1fQA8PQZTj4+IiIioOAZ9z5GPjw/c3d2xe/duqSwnJwdHjhxBQEAAACAgIABZWVlITU2V6uzZswcqlQrt2rWT6hw4cEDj2Ybk5GQ0btxY5y11RERERERE5VXq5Oj+/fs4deoUTp06BeDRIAynTp3CtWvXIJPJEB0djTlz5uDHH3/E6dOnMXToUHh6emLAgAEAgKZNm6JXr14YMWIEjh49it9++w1jx45FeHg4PD09AQCvvfYarKysEBkZibNnz+L777/HZ599pnHbHBERERERkSGV+ra648ePo3v37tJndcISERGBhIQEvP/++8jNzcXIkSORlZWFTp06YceOHbC2tpbmWbduHcaOHYuePXvCzMwMYWFhWLx4sTTd0dERSUlJiIqKgr+/P2rVqoXp06dzGG8iIiIiIqowpU6OunXrBiFEsdNlMhlmzZqFWbNmFVvH2dkZ69evL3E9LVu2xK+//lra5hEREREREZWJQZ85IiIiIiIiMlVMjoiIiIiIiMDkiIiIiIiICACTIyIiIiIiIgBMjoiIiIiIiAAwOSIiIiIiIgLA5IiIiIiIiAgAkyMiIiIiIiIATI6IiIiIiIgAMDkiIiIiIiICwOSIiIiIiIgIAJMjIiIiIiIiAEyOiIiIiIiIADA5IiIiIiIiAsDkiIiIiIiICACTIyIiIiIiIgBMjoiIiIiIiAAwOSIiIiIiIgLA5IiIiIiIiAgAkyMiIiIiDfPmzYNMJkN0dLRU9vDhQ0RFRaFmzZqwt7dHWFgYMjIyNOa7du0aQkNDYWtrC1dXV0yaNAmFhYWV3HoiKg8mR0RERET/37Fjx7BixQq0bNlSo3zChAn46aefkJiYiP379+PGjRsYOHCgNL2oqAihoaHIz8/HoUOHsGbNGiQkJGD69OmVHQIRlQOTIyIiIiIA9+/fx5AhQ/DFF1+gRo0aUnl2djZWrVqFTz/9FD169IC/vz9Wr16NQ4cO4fDhwwCApKQknDt3Dt988w38/PzQu3dvzJ49G/Hx8cjPzzdWSERUShbGbgARERFRVRAVFYXQ0FAEBgZizpw5UnlqaioKCgoQGBgolTVp0gR16tRBSkoK2rdvj5SUFLRo0QJubm5SnZCQEIwePRpnz55F69atda5TqVRCqVRKn3NycgAABQUFKCgo0DmPulxuJsoebDHLrGqexViravsMxVhx6rs+JkdERET0zPvuu+9w4sQJHDt2TGtaeno6rKys4OTkpFHu5uaG9PR0qc7jiZF6unpacWJjYzFz5kyt8qSkJNja2pbY5tltVCVOL43t27cbbFkV4VmKNTk52dhNqBSVHWdeXp5e9ZgcERER0TPtn3/+wTvvvIPk5GRYW1tX6rqnTJmCiRMnSp9zcnLg5eWF4OBgKBQKnfMUFBQgOTkZ046bQamSGaQdZ2JCDLIcQ3sWYw0KCoKlpaWxm1NhjBWn+qrs0zA5IiIiomdaamoqMjMz8fzzz0tlRUVFOHDgAD7//HPs3LkT+fn5yMrK0rh6lJGRAXd3dwCAu7s7jh49qrFc9Wh26jq6yOVyyOVyrXJLS8unnjgqVTIoiwyTMFT1k/FnKVZ99n11UNlx6rsuDshAREREz7SePXvi9OnTOHXqlPRfmzZtMGTIEOnflpaW2L17tzTPhQsXcO3aNQQEBAAAAgICcPr0aWRmZkp1kpOToVAo4OvrW+kxEVHZ8MoRERERPdMcHBzQvHlzjTI7OzvUrFlTKo+MjMTEiRPh7OwMhUKBcePGISAgAO3btwcABAcHw9fXF2+88Qbi4uKQnp6OqVOnIioqSueVISKqmpgcERERET3FwoULYWZmhrCwMCiVSoSEhGDp0qXSdHNzc2zbtg2jR49GQEAA7OzsEBERgVmzZhmx1URUWkyOiIiIiJ6wb98+jc/W1taIj49HfHx8sfN4e3tX+ZHQiKhkBn/mqKioCNOmTYOPjw9sbGxQv359zJ49G0L83/j0QghMnz4dHh4esLGxQWBgIC5evKixnLt372LIkCFQKBRwcnJCZGQk7t+/b+jmEpEJWbZsGVq2bAmFQgGFQoGAgAD88ssv0vSHDx8iKioKNWvWhL29PcLCwqQHotWuXbuG0NBQ2NrawtXVFZMmTUJhYWFlh0JERERVkMGTo/nz52PZsmX4/PPPcf78ecyfPx9xcXFYsmSJVCcuLg6LFy/G8uXLceTIEdjZ2SEkJAQPHz6U6gwZMgRnz55FcnIytm3bhgMHDmDkyJGGbi4RmZDatWtj3rx5SE1NxfHjx9GjRw/0798fZ8+eBQBMmDABP/30ExITE7F//37cuHEDAwcOlOYvKipCaGgo8vPzcejQIaxZswYJCQmYPn26sUIiIiKiKsTgt9UdOnQI/fv3R2hoKACgbt26+Pbbb6XhLYUQWLRoEaZOnYr+/fsDANauXQs3Nzds3boV4eHhOH/+PHbs2IFjx46hTZs2AIAlS5agT58+WLBgATw9PQ3dbCIyAf369dP4PHfuXCxbtgyHDx9G7dq1sWrVKqxfvx49evQAAKxevRpNmzbF4cOH0b59eyQlJeHcuXPYtWsX3Nzc4Ofnh9mzZ2Py5MmIiYmBlZWVMcIiIiKiKsLgyVGHDh2wcuVK/PXXX2jUqBF+//13HDx4EJ9++ikAIC0tDenp6QgMDJTmcXR0RLt27ZCSkoLw8HCkpKTAyclJSowAIDAwEGZmZjhy5AheeuklrfUqlUoolUrps/pFTwUFBSgoKCi2veppJdWp6kw9BnW75WbiKTVLv8zKYur7ANA/hqoSY1FRERITE5Gbm4uAgACkpqaioKBAo29p0qQJ6tSpg5SUFLRv3x4pKSlo0aKFxlvsQ0JCMHr0aJw9exatW7fWua6S+hcLCwvp30+SmxvuO13cOipadfhul4TxVS2m0k4iqr4Mnhx98MEHyMnJQZMmTWBubo6ioiLMnTsXQ4YMAQCkp6cDgMbJifqzelp6ejpcXV01G2phAWdnZ6nOk2JjYzFz5kyt8qSkJNja2j613cnJyU8Prooz9Rhmt1EZbFnGeiDW1PcB8PQY8vLyKqklup0+fRoBAQF4+PAh7O3tsWXLFvj6+uLUqVOwsrLSeEEjoN236Op71NOKo0//omu7xbUtVWhPZcwHvavDd7skjK9qMHb/QkRk8ORow4YNWLduHdavX49mzZrh1KlTiI6OhqenJyIiIgy9OsmUKVMwceJE6XNOTg68vLwQHBwMhUJR7HwFBQVITk5GUFCQyb6N2NRjULd/2nEzKFWGefv1mZgQgyxHX6a+DwD9Y1BfNTGWxo0b49SpU8jOzsbGjRsRERGB/fv3V+g6S+pfbGxsit1uzWN2GrQdlf29BqrHd7skjK9qMXb/QkRk8ORo0qRJ+OCDDxAeHg4AaNGiBa5evYrY2FhERETA3d0dAJCRkQEPDw9pvoyMDPj5+QEA3N3dNd4wDQCFhYW4e/euNP+T5HK5zpesWVpa6vUHQd96VZmpx6BUyaAsMkxyZKztYOr7AHh6DMaOz8rKCg0aNAAA+Pv749ixY/jss8/wyiuvID8/H1lZWRpXjzIyMqR+w93dXXr+8fHp6mnF0ad/0bXdDPV9fnx9xlIdvtslYXxVgym0kYiqN4OPVpeXlwczM83FmpubQ6V6dMuUj48P3N3dsXv3bml6Tk4Ojhw5goCAAABAQEAAsrKykJqaKtXZs2cPVCoV2rVrZ+gmE5EJU6lUUCqV8Pf3h6WlpUbfcuHCBVy7dk2jbzl9+rTGjy/JyclQKBTw9fWt9LYTERFR1WLwK0f9+vXD3LlzUadOHTRr1gwnT57Ep59+ijfffBMAIJPJEB0djTlz5qBhw4bw8fHBtGnT4OnpiQEDBgAAmjZtil69emHEiBFYvnw5CgoKMHbsWISHh3OkOqJn2JQpU9C7d2/UqVMH9+7dw/r167Fv3z7s3LkTjo6OiIyMxMSJE+Hs7AyFQoFx48YhICAA7du3BwAEBwfD19cXb7zxBuLi4pCeno6pU6ciKipK55UhIiIierYYPDlasmQJpk2bhjFjxiAzMxOenp4YNWqUxntE3n//feTm5mLkyJHIyspCp06dsGPHDlhbW0t11q1bh7Fjx6Jnz54wMzNDWFgYFi9ebOjmEpEJyczMxNChQ3Hz5k04OjqiZcuW2LlzJ4KCggAACxculPoLpVKJkJAQLF26VJrf3Nwc27Ztw+jRoxEQEAA7OztERERg1qxZxgqJiIiIqhCDJ0cODg5YtGgRFi1aVGwdmUyGWbNmlXhC4uzsjPXr1xu6eURkwlatWlXidGtra8THxyM+Pr7YOt7e3kYd9Y2IiIiqLoM/c0RERERERGSKmBwRERERERGByREREREREREAJkdEREREREQAmBwREREREREBYHJEREREREQEgMkRERERERERACZHREREREREAJgcERERERERAWByREREREREBIDJEREREREREQAmR0RERERERACYHBEREREREQFgckRERERERASAyREREREREREAJkdEREREREQAmBwREREREREBYHJEREREREQEgMkRERERERERACZHREREREREAJgcERERERERAWByRERERIRly5ahZcuWUCgUUCgUCAgIwC+//CJNf/jwIaKiolCzZk3Y29sjLCwMGRkZGsu4du0aQkNDYWtrC1dXV0yaNAmFhYWVHQoRlQOTIyIiInrm1a5dG/PmzUNqaiqOHz+OHj16oH///jh79iwAYMKECfjpp5+QmJiI/fv348aNGxg4cKA0f1FREUJDQ5Gfn49Dhw5hzZo1SEhIwPTp040VEhGVgYWxG0BERERkbP369dP4PHfuXCxbtgyHDx9G7dq1sWrVKqxfvx49evQAAKxevRpNmzbF4cOH0b59eyQlJeHcuXPYtWsX3Nzc4Ofnh9mzZ2Py5MmIiYmBlZWVMcIiolJickRERET0mKKiIiQmJiI3NxcBAQFITU1FQUEBAgMDpTpNmjRBnTp1kJKSgvbt2yMlJQUtWrSAm5ubVCckJASjR4/G2bNn0bp1a53rUiqVUCqV0uecnBwAQEFBAQoKCnTOoy6Xm4lyx/rkMquaZzHWqto+QzFWnPquj8kREREREYDTp08jICAADx8+hL29PbZs2QJfX1+cOnUKVlZWcHJy0qjv5uaG9PR0AEB6erpGYqSerp5WnNjYWMycOVOrPCkpCba2tiW2d3YblT5h6WX79u0GW1ZFeJZiTU5ONnYTKkVlx5mXl6dXPSZHRERERAAaN26MU6dOITs7Gxs3bkRERAT2799foeucMmUKJk6cKH3OycmBl5cXgoODoVAodM5TUFCA5ORkTDtuBqVKZpB2nIkJMchyDO1ZjDUoKAiWlpbGbk6FMVac6quyT8PkiIiIiAiAlZUVGjRoAADw9/fHsWPH8Nlnn+GVV15Bfn4+srKyNK4eZWRkwN3dHQDg7u6Oo0ePaixPPZqduo4ucrkccrlcq9zS0vKpJ45KlQzKIsMkDFX9ZPxZilWffV8dVHac+q6Lo9URERER6aBSqaBUKuHv7w9LS0vs3r1bmnbhwgVcu3YNAQEBAICAgACcPn0amZmZUp3k5GQoFAr4+vpWetuJqGx45YiIiIieeVOmTEHv3r1Rp04d3Lt3D+vXr8e+ffuwc+dOODo6IjIyEhMnToSzszMUCgXGjRuHgIAAtG/fHgAQHBwMX19fvPHGG4iLi0N6ejqmTp2KqKgonVeGiKhqqpArR9evX8frr7+OmjVrwsbGBi1atMDx48el6UIITJ8+HR4eHrCxsUFgYCAuXryosYy7d+9iyJAhUCgUcHJyQmRkJO7fv18RzSUiIqJnXGZmJoYOHYrGjRujZ8+eOHbsGHbu3ImgoCAAwMKFC9G3b1+EhYWhS5cucHd3x+bNm6X5zc3NsW3bNpibmyMgIACvv/46hg4dilmzZhkrJCIqA4NfOfrvv//QsWNHdO/eHb/88gtcXFxw8eJF1KhRQ6oTFxeHxYsXY82aNfDx8cG0adMQEhKCc+fOwdraGgAwZMgQ3Lx5E8nJySgoKMDw4cMxcuRIrF+/3tBNJiIiomfcqlWrSpxubW2N+Ph4xMfHF1vH29u7yo+ERkQlM3hyNH/+fHh5eWH16tVSmY+Pj/RvIQQWLVqEqVOnon///gCAtWvXws3NDVu3bkV4eDjOnz+PHTt24NixY2jTpg0AYMmSJejTpw8WLFgAT09PQzebiIiIiIiecQZPjn788UeEhIRg0KBB2L9/P5577jmMGTMGI0aMAACkpaUhPT1d40Vqjo6OaNeuHVJSUhAeHo6UlBQ4OTlJiREABAYGwszMDEeOHMFLL72ktd6yvERNPf3x/5siU4+hOrzgzdT3AaB/DKYcIxEREVFJDJ4c/f3331i2bBkmTpyIDz/8EMeOHcP48eNhZWWFiIgI6UVoul6U9viL1FxdXTUbamEBZ2fnYl+kVp6XqAHV44Vbph5DdXjBm6nvA+DpMej7EjUiIiIiU2Pw5EilUqFNmzb46KOPAACtW7fGmTNnsHz5ckRERBh6dZKyvEQNqB4v3DL1GKrDC95MfR8A+seg70vUiIiIiEyNwZMjDw8PrfH8mzZtik2bNgH4vxehZWRkwMPDQ6qTkZEBPz8/qc7j7wkAgMLCQty9e7fYF6mV5yVqpalXlZl6DNXhBW+mvg+Ap8dg6vERERERFcfgQ3l37NgRFy5c0Cj766+/4O3tDeDR4Azu7u4aL1LLycnBkSNHNF6klpWVhdTUVKnOnj17oFKp0K5dO0M3mYiIiIiIyPBXjiZMmIAOHTrgo48+wuDBg3H06FGsXLkSK1euBADIZDJER0djzpw5aNiwoTSUt6enJwYMGADg0ZWmXr16YcSIEVi+fDkKCgowduxYhIeHc6Q6IiIiIiKqEAZPjl544QVs2bIFU6ZMwaxZs+Dj44NFixZhyJAhUp33338fubm5GDlyJLKystCpUyfs2LFDescRAKxbtw5jx45Fz549YWZmhrCwMCxevNjQzSUiIiIiIgJQAckRAPTt2xd9+/YtdrpMJsOsWbNKfGu0s7MzX/hKRERERESVxuDPHBEREREREZkiJkdERERERERgckREJiQ2NhYvvPACHBwc4OrqigEDBmiNjvnw4UNERUWhZs2asLe3R1hYGDIyMjTqXLt2DaGhobC1tYWrqysmTZqEwsLCygyFiIiIqiAmR0RkMvbv34+oqCgcPnwYycnJKCgoQHBwMHJzc6U6EyZMwE8//YTExETs378fN27cwMCBA6XpRUVFCA0NRX5+Pg4dOoQ1a9YgISEB06dPN0ZIREREVIVUyIAMREQVYceOHRqfExIS4OrqitTUVHTp0gXZ2dlYtWoV1q9fjx49egAAVq9ejaZNm+Lw4cNo3749kpKScO7cOezatQtubm7w8/PD7NmzMXnyZMTExMDKysoYoREREVEVwCtHRGSysrOzATwa3RIAUlNTUVBQgMDAQKlOkyZNUKdOHaSkpAAAUlJS0KJFC7i5uUl1QkJCkJOTg7Nnz1Zi64mIiKiq4ZUjIjJJKpUK0dHR6NixI5o3bw4ASE9Ph5WVFZycnDTqurm5IT09XarzeGKknq6epotSqYRSqZQ+5+TkAAAKCgpgYWEh/ftJcnNRhsiKp2sdFU29TmOsuzIwvqrFVNpJRNUXkyMiMklRUVE4c+YMDh48WOHrio2NxcyZM7XKk5KSYGtrCwBITk7Wmh7X1rDt2L59u2EXWAq64qtOGF/VkJeXZ+wmENEzjskREZmcsWPHYtu2bThw4ABq164tlbu7uyM/Px9ZWVkaV48yMjLg7u4u1Tl69KjG8tSj2anrPGnKlCmYOHGi9DknJwdeXl4IDg6GjY0NkpOTERQUBEtLS435msfsLFecTzoTE2LQ5emjoKCg2PiqA8ZXtaivyhIRGQuTIyIyGUIIjBs3Dlu2bMG+ffvg4+OjMd3f3x+WlpbYvXs3wsLCAAAXLlzAtWvXEBAQAAAICAjA3LlzkZmZCVdXVwCPflVXKBTw9fXVuV65XA65XK5VbmlpKZ1wPv5vNWWRrHwB61ifseiKrzphfFWDKbSRiKo3JkdEZDKioqKwfv16/PDDD3BwcJCeEXJ0dISNjQ0cHR0RGRmJiRMnwtnZGQqFAuPGjUNAQADat28PAAgODoavry/eeOMNxMXFIT09HVOnTkVUVJTOBIiIiIieHUyOiMhkLFu2DADQrVs3jfLVq1dj2LBhAICFCxfCzMwMYWFhUCqVCAkJwdKlS6W65ubm2LZtG0aPHo2AgADY2dkhIiICs2bNqqwwiIiIqIpickREJkOIp4/+Zm1tjfj4eMTHxxdbx9vb26iDGxAREVHVxPccERERERERgckRERERERERACZHREREREREAJgcERERERERAWByREREREREBIDJEREREREREQAmR0RERERERACYHBEREREREQFgckRERERERASAyREREREREREAJkdEREREREQAmBwRERERITY2Fi+88AIcHBzg6uqKAQMG4MKFCxp1Hj58iKioKNSsWRP29vYICwtDRkaGRp1r164hNDQUtra2cHV1xaRJk1BYWFiZoRBROTA5IiIiomfe/v37ERUVhcOHDyM5ORkFBQUIDg5Gbm6uVGfChAn46aefkJiYiP379+PGjRsYOHCgNL2oqAihoaHIz8/HoUOHsGbNGiQkJGD69OnGCImIysDC2A0gIiIiMrYdO3ZofE5ISICrqytSU1PRpUsXZGdnY9WqVVi/fj169OgBAFi9ejWaNm2Kw4cPo3379khKSsK5c+ewa9cuuLm5wc/PD7Nnz8bkyZMRExMDKysrY4RGRKXA5IiIiIjoCdnZ2QAAZ2dnAEBqaioKCgoQGBgo1WnSpAnq1KmDlJQUtG/fHikpKWjRogXc3NykOiEhIRg9ejTOnj2L1q1ba61HqVRCqVRKn3NycgAABQUFKCgo0Nk2dbncTJQzSu1lVjXPYqxVtX2GYqw49V0fkyMiIiKix6hUKkRHR6Njx45o3rw5ACA9PR1WVlZwcnLSqOvm5ob09HSpzuOJkXq6epousbGxmDlzplZ5UlISbG1tS2zn7DYqveLRx/bt2w22rIrwLMWanJxs7CZUisqOMy8vT696FZ4czZs3D1OmTME777yDRYsWAXj0QOO7776L7777DkqlEiEhIVi6dKlGh3Lt2jWMHj0ae/fuhb29PSIiIhAbGwsLC+ZzREREVHGioqJw5swZHDx4sMLXNWXKFEycOFH6nJOTAy8vLwQHB0OhUOicp6CgAMnJyZh23AxKlcwg7TgTE2KQ5RjasxhrUFAQLC0tjd2cCmOsONVXZZ+mQjONY8eOYcWKFWjZsqVG+YQJE/Dzzz8jMTERjo6OGDt2LAYOHIjffvsNwP890Oju7o5Dhw7h5s2bGDp0KCwtLfHRRx9VZJOJiKqsuh/8XOnrlJsLxLUFmsfshLKo+BOTK/NCK7FVRBVn7Nix2LZtGw4cOIDatWtL5e7u7sjPz0dWVpbG1aOMjAy4u7tLdY4ePaqxPPVoduo6T5LL5ZDL5VrllpaWTz1xVKpkJR6XpVHVT8afpVj12ffVQWXHqe+6Kiw5un//PoYMGYIvvvgCc+bMkcr5QCMRUfVj6MSNyVbVYMj9WtX3qRAC48aNw5YtW7Bv3z74+PhoTPf394elpSV2796NsLAwAMCFCxdw7do1BAQEAAACAgIwd+5cZGZmwtXVFcCjW4cUCgV8fX0rNyAiKpMKG8o7KioKoaGhGg8uAk9/oBFAsQ805uTk4OzZsxXVZCIiInpGRUVF4ZtvvsH69evh4OCA9PR0pKen48GDBwAAR0dHREZGYuLEidi7dy9SU1MxfPhwBAQEoH379gCA4OBg+Pr64o033sDvv/+OnTt3YurUqYiKitJ5dYiIqp4KuXL03Xff4cSJEzh27JjWtIp6oLEso72opz/+f1Nk6jFUh5FoTH0fAPrHYMoxEhEVZ9myZQCAbt26aZSvXr0aw4YNAwAsXLgQZmZmCAsL03hmWs3c3Bzbtm3D6NGjERAQADs7O0RERGDWrFmVFQYRlZPBk6N//vkH77zzDpKTk2FtbW3oxRerPKO9ANVjZBBTj6E6jERj6vsAeHoM+o72QkRkSoR4+g901tbWiI+PR3x8fLF1vL29q/xoaERUPIMnR6mpqcjMzMTzzz8vlRUVFeHAgQP4/PPPsXPnzgp5oLEso70A1WNkEFOPoTqMRGPq+wDQPwZ9R3shIiIiMjUGT4569uyJ06dPa5QNHz4cTZo0weTJk+Hl5VUhDzSWZ7SX0tSrykw9huowEo2p7wPg6TGYenxERERExTF4cuTg4CC9ME3Nzs4ONWvWlMrVDzQ6OztDoVBg3LhxxT7QGBcXh/T0dD7QSEREREREFcoob1TlA41ERERERFTVVEpytG/fPo3PfKCRiIiIiIiqmgp7zxEREREREZEpYXJEREREREQEJkdEREREREQAmBwREREREREBYHJEREREREQEgMkRERERERERACZHREREREREAJgcERERERERAWByREREREREBIDJEREREREREQAmR0RERERERACYHBEREREREQFgckRERERERASAyREREREREREAJkdEREREREQAmBwREREREREBYHJEREREREQEgMkREZmQAwcOoF+/fvD09IRMJsPWrVs1pgshMH36dHh4eMDGxgaBgYG4ePGiRp27d+9iyJAhUCgUcHJyQmRkJO7fv1+JURAREVFVxeSIiExGbm4uWrVqhfj4eJ3T4+LisHjxYixfvhxHjhyBnZ0dQkJC8PDhQ6nOkCFDcPbsWSQnJ2Pbtm04cOAARo4cWVkhEBERURVmYewGEBHpq3fv3ujdu7fOaUIILFq0CFOnTkX//v0BAGvXroWbmxu2bt2K8PBwnD9/Hjt27MCxY8fQpk0bAMCSJUvQp08fLFiwAJ6enpUWCxEREVU9TI6IqFpIS0tDeno6AgMDpTJHR0e0a9cOKSkpCA8PR0pKCpycnKTECAACAwNhZmaGI0eO4KWXXtK5bKVSCaVSKX3OyckBABQUFMDCwkL695Pk5sIgsRmT3Exo/L+y6NqeFbmeylpfZStvfIb8DuvThuq6H4jIdDA5IqJqIT09HQDg5uamUe7m5iZNS09Ph6urq8Z0CwsLODs7S3V0iY2NxcyZM7XKk5KSYGtrCwBITk7Wmh7XtnQxVGWz26gqdX3bt2+v1PXp2n/VSVnjM+R3WJ99mpeXZ7gVEhGVAZMjIqKnmDJlCiZOnCh9zsnJgZeXF4KDg2FjY4Pk5GQEBQXB0tJSY77mMTsru6kGJzcTmN1GhWnHzaBUySptvWdiQiplPQUFBcXuv+qgvPEZ8juszz5VX5UlIjIWJkdEVC24u7sDADIyMuDh4SGVZ2RkwM/PT6qTmZmpMV9hYSHu3r0rza+LXC6HXC7XKre0tJROOB//t5qyqPKSiYqmVMkqNZ7KTlR07b/qpKzxGXKf67P+6rwPiMg0cLQ6IqoWfHx84O7ujt27d0tlOTk5OHLkCAICAgAAAQEByMrKQmpqqlRnz549UKlUaNeuXaW3mYiIiKoWXjkiIpNx//59XLp0SfqclpaGU6dOwdnZGXXq1EF0dDTmzJmDhg0bwsfHB9OmTYOnpycGDBgAAGjatCl69eqFESNGYPny5SgoKMDYsWMRHh7OkeqIiIiIyRERmY7jx4+je/fu0mf1c0ARERFISEjA+++/j9zcXIwcORJZWVno1KkTduzYAWtra2medevWYezYsejZsyfMzMwQFhaGxYsXV3osREREVPXwtjoiMhndunWDEELrv4SEBACATCbDrFmzkJ6ejocPH2LXrl1o1KiRxjKcnZ2xfv163Lt3D9nZ2fjqq69gb29vhGiIqCo5cOAA+vXrB09PT8hkMmzdulVjuhAC06dPh4eHB2xsbBAYGIiLFy9q1Ll79y6GDBkChUIBJycnREZG4v79+5UYBRGVF5MjIiIieubl5uaiVatWiI+P1zk9Li4OixcvxvLly3HkyBHY2dkhJCQEDx8+lOoMGTIEZ8+eRXJyMrZt24YDBw5g5MiRlRUCERkAb6sjIiKiZ17v3r3Ru3dvndOEEFi0aBGmTp2K/v37AwDWrl0LNzc3bN26FeHh4Th//jx27NiBY8eOSS+aXrJkCfr06YMFCxbwuUYiE2Hw5Cg2NhabN2/Gn3/+CRsbG3To0AHz589H48aNpToPHz7Eu+++i++++w5KpRIhISFYunSpxssbr127htGjR2Pv3r2wt7dHREQEYmNjpbfRExEREVWGtLQ0pKenIzAwUCpzdHREu3btkJKSgvDwcKSkpMDJyUlKjAAgMDAQZmZmOHLkCF566SWdy1YqlVAqldJn9bueCgoKUFBQoHMedbncTJQ7tieXWdU8i7FW1fYZirHi1Hd9Bs809u/fj6ioKLzwwgsoLCzEhx9+iODgYJw7dw52dnYAgAkTJuDnn39GYmIiHB0dMXbsWAwcOBC//fYbAKCoqAihoaFwd3fHoUOHcPPmTQwdOhSWlpb46KOPDN1kIiIiomKlp6cDgMaPuOrP6mnp6elwdXXVmG5hYQFnZ2epji6xsbGYOXOmVnlSUhJsbW1LbNfsNiq92q+P7du3G2xZFeFZijU5OdnYTagUlR1nXl6eXvUMnhzt2LFD43NCQgJcXV2RmpqKLl26IDs7G6tWrcL69evRo0cPAMDq1avRtGlTHD58GO3bt0dSUhLOnTuHXbt2wc3NDX5+fpg9ezYmT56MmJgYWFlZGbrZRERERJVuypQp0sibwKMrR15eXggODoZCodA5T0FBAZKTkzHtuBmUKsO8qPdMTIhBlmNoz2KsQUFB1fqFyMaKU31V9mkq/B617OxsAI9GiAKA1NRUFBQUaFyabtKkCerUqYOUlBS0b98eKSkpaNGihcYvNCEhIRg9ejTOnj2L1q1ba62nLJel1dMf/78pMvUYqsMlc1PfB4D+MZhyjEREZeHu7g4AyMjIgIeHh1SekZEBPz8/qU5mZqbGfIWFhbh79640vy5yuRxyuVyr3NLS8qknjkqVDMoiwyQMVf1k/FmKVZ99Xx1Udpz6rqtCkyOVSoXo6Gh07NgRzZs3B/DosrOVlRWcnJw06j55aVrXpWv1NF3Kc1kaqB6XME09hupwydzU9wHw9Bj0vSxNRFRd+Pj4wN3dHbt375aSoZycHBw5cgSjR48GAAQEBCArKwupqanw9/cHAOzZswcqlQrt2rUzVtOJqJQqNDmKiorCmTNncPDgwYpcDYCyXZYGqsclTFOPoTpcMjf1fQDoH4O+l6WJiEzJ/fv3cenSJelzWloaTp06BWdnZ9SpUwfR0dGYM2cOGjZsCB8fH0ybNg2enp4YMGAAAKBp06bo1asXRowYgeXLl6OgoABjx45FeHg4R6ojMiEVlhyNHTtWGuO/du3aUrm7uzvy8/ORlZWlcfUoIyNDuuzs7u6Oo0ePaiwvIyNDmqZLeS5Ll6ZeVWbqMVSHS+amvg+Ap8dg6vEREely/PhxdO/eXfqs/sE1IiICCQkJeP/995Gbm4uRI0ciKysLnTp1wo4dO2BtbS3Ns27dOowdOxY9e/aEmZkZwsLCsHjx4kqPhYjKzuDJkRAC48aNw5YtW7Bv3z74+PhoTPf394elpSV2796NsLAwAMCFCxdw7do1BAQEAHh0aXru3LnIzMyURn5JTk6GQqGAr6+voZtMREREz7hu3bpBiOKffZXJZJg1axZmzZpVbB1nZ2esX7++IppHRJXE4MlRVFQU1q9fjx9++AEODg7SM0KOjo6wsbGBo6MjIiMjMXHiRDg7O0OhUGDcuHEICAhA+/btAQDBwcHw9fXFG2+8gbi4OKSnp2Pq1KmIiorSeXWIiIiIiIiovAyeHC1btgzAo19gHrd69WoMGzYMALBw4ULpcvPjL4FVMzc3x7Zt2zB69GgEBATAzs4OERERJf5aQ0REREREVB4Vclvd01hbWyM+Ph7x8fHF1vH29q7yL+kiIiIiIqLqw8zYDSAiIiIiIqoKmBwRERERERGByREREREREREAJkdEREREREQAmBwREREREREBYHJEREREREQEoAKG8iYiIiIiIipJ85idUBbJDLKsK/NCDbIcgFeOiIiIiIiIADA5IiIiIiIiAsDkiIiIiIiICACTIyIiIiIiIgBMjoiIiIiIiAAwOSIiIiIiIgLA5IiIiIiIiAgAkyMiIiIiIiIATI6IiIiIiIgAMDkiIiIiIiICwOSIiIiIiIgIAJMjIiIiIiIiAEyOiIiIiIiIADA5IiIiIiIiAsDkiIiIiIiICACTIyIiIiIiIgBMjoiIiIiIiAAwOSIiIiIiIgLA5IiIiIiIiAgAkyMiIiIiIiIATI6IiIiIiIgAMDkiIiIiIiICwOSIiIiIiIgIQBVPjuLj41G3bl1YW1ujXbt2OHr0qLGbRETVBPsXIqoo7F+ITFeVTY6+//57TJw4ETNmzMCJEyfQqlUrhISEIDMz09hNIyITx/6FiCoK+xci01Zlk6NPP/0UI0aMwPDhw+Hr64vly5fD1tYWX331lbGbRkQmjv0LEVUU9i9Eps3C2A3QJT8/H6mpqZgyZYpUZmZmhsDAQKSkpOicR6lUQqlUSp+zs7MBAHfv3kVBQUGx6yooKEBeXh7u3LkDS0tLA0VQuUw9BnX7LQrMUKSSGWSZd+7cMchy9GXq+wDQP4Z79+4BAIQQldU0gzJ0/2JtbV3sdrMozK2ACCqXhUogL09l0ONTH5V1DFeHY/dx7WJ3a3yWmwlMba2C3/82Q1mG/WfIkwR99in7F/3OX6rD3019PYuxVpf+qDjG2qf69i9VMjm6ffs2ioqK4ObmplHu5uaGP//8U+c8sbGxmDlzpla5j49PhbSRqrZanxi7BdXfvXv34OjoaOxmlBr7l9J7zQjr5DFsOMbYf7qUZp+yf6n8/uVZOuaepVifFYbsX6pkclQWU6ZMwcSJE6XPKpUKd+/eRc2aNSGTFZ+V5uTkwMvLC//88w8UCkVlNNXgTD0GU28/8GzFIITAvXv34OnpWYmtM66S+pd79+6Z/L4vSXX4bpeE8VUt7F/0O38xtf1aHoy1+jFWnPr2L1UyOapVqxbMzc2RkZGhUZ6RkQF3d3ed88jlcsjlco0yJycnvdepUChM/oto6jGYevuBZycGU/xFV83Q/Yv65KU67PuSMD7TZkrxsX/R//zFlPZreTHW6scYcerTv1TJARmsrKzg7++P3bv/795plUqF3bt3IyAgwIgtIyJTx/6FiCoK+xci01clrxwBwMSJExEREYE2bdqgbdu2WLRoEXJzczF8+HBjN42ITBz7FyKqKOxfiExblU2OXnnlFdy6dQvTp09Heno6/Pz8sGPHDq2HHMtLLpdjxowZWpe0TYmpx2Dq7QcYg6kxZP9S3bcb4zNt1T2+qqgyzl+epf3KWKufqh6nTJjqeJlEREREREQGVCWfOSIiIiIiIqpsTI6IiIiIiIjA5IiIiIiIiAgAkyMiIiIiIiIA1TA5io+PR926dWFtbY127drh6NGjxdb94osv0LlzZ9SoUQM1atRAYGCgVv1hw4ZBJpNp/NerV68qE0NCQoJW+6ytrTXqCCEwffp0eHh4wMbGBoGBgbh48WKViaFbt25aMchkMoSGhkp1Kns/HDhwAP369YOnpydkMhm2bt361Hn27duH559/HnK5HA0aNEBCQoJWndJsl/IqbQybN29GUFAQXFxcoFAoEBAQgJ07d2rUiYmJ0doPTZo0qbAYTEFl7tPKFBsbixdeeAEODg5wdXXFgAEDcOHCBWM3q8LMmzcPMpkM0dHRxm6KwVy/fh2vv/46atasCRsbG7Ro0QLHjx83drNIT6XtWxITE9GkSRNYW1ujRYsW2L59eyW1tPwMfd5TFVXUeUVVVNpY9+3bp/M8MD09vXIa/IRqlRx9//33mDhxImbMmIETJ06gVatWCAkJQWZmps76+/btw6uvvoq9e/ciJSUFXl5eCA4OxvXr1zXq9erVCzdv3pT++/bbb6tMDMCjNww/3r6rV69qTI+Li8PixYuxfPlyHDlyBHZ2dggJCcHDhw+rRAybN2/WaP+ZM2dgbm6OQYMGadSrzP2Qm5uLVq1aIT4+Xq/6aWlpCA0NRffu3XHq1ClER0fjrbfe0kguyrJvKzOGAwcOICgoCNu3b0dqaiq6d++Ofv364eTJkxr1mjVrprEfDh48WBHNNwmVvU8r0/79+xEVFYXDhw8jOTkZBQUFCA4ORm5urrGbZnDHjh3DihUr0LJlS2M3xWD+++8/dOzYEZaWlvjll19w7tw5fPLJJ6hRo4axm0Z6KG3fcujQIbz66quIjIzEyZMnMWDAAAwYMABnzpyp5JaXXkWc91RFFXFeUVWVNla1CxcuaOxXV1fXCmrhU4hqpG3btiIqKkr6XFRUJDw9PUVsbKxe8xcWFgoHBwexZs0aqSwiIkL079/f0E0tVmljWL16tXB0dCx2eSqVSri7u4uPP/5YKsvKyhJyuVx8++23Bmv348q7HxYuXCgcHBzE/fv3pbLK3g+PAyC2bNlSYp33339fNGvWTKPslVdeESEhIdLn8m6X8tAnBl18fX3FzJkzpc8zZswQrVq1MlzDTJwx92lly8zMFADE/v37jd0Ug7p3755o2LChSE5OFl27dhXvvPOOsZtkEJMnTxadOnUydjOojErbtwwePFiEhoZqlLVr106MGjWqQttpCIY+7zEFhjqvMAX6xLp3714BQPz333+V0qanqTZXjvLz85GamorAwECpzMzMDIGBgUhJSdFrGXl5eSgoKICzs7NG+b59++Dq6orGjRtj9OjRuHPnjkHbrlbWGO7fvw9vb294eXmhf//+OHv2rDQtLS0N6enpGst0dHREu3bt9N4ulRHD41atWoXw8HDY2dlplFfWfiiLlJQUjZgBICQkRIrZENulsqlUKty7d0/reLh48SI8PT1Rr149DBkyBNeuXTNSC43LFPdpeWRnZwOA1vfB1EVFRSE0NFTr+DV1P/74I9q0aYNBgwbB1dUVrVu3xhdffGHsZpEeytK3PO1vUFVVEec91YWp7tPy8PPzg4eHB4KCgvDbb78ZrR3VJjm6ffs2ioqKtN5A7ebmpvc9i5MnT4anp6fGl7FXr15Yu3Ytdu/ejfnz52P//v3o3bs3ioqKDNp+oGwxNG7cGF999RV++OEHfPPNN1CpVOjQoQP+/fdfAJDmK892qegYHnf06FGcOXMGb731lkZ5Ze6HskhPT9cZc05ODh48eGCQ72dlW7BgAe7fv4/BgwdLZe3atUNCQgJ27NiBZcuWIS0tDZ07d8a9e/eM2FLjMMV9WlYqlQrR0dHo2LEjmjdvbuzmGMx3332HEydOIDY21thNMbi///4by5YtQ8OGDbFz506MHj0a48ePx5o1a4zdNHqKsvQtxf0Nqup9UUWc91QXTzuvqE48PDywfPlybNq0CZs2bYKXlxe6deuGEydOGKU9FkZZaxU0b948fPfdd9i3b5/Gg33h4eHSv1u0aIGWLVuifv362LdvH3r27GmMpmoICAhAQECA9LlDhw5o2rQpVqxYgdmzZxuxZWWzatUqtGjRAm3bttUor+r7obpZv349Zs6ciR9++EHjnt/evXtL/27ZsiXatWsHb29vbNiwAZGRkcZoKlWCqKgonDlzplo9X/bPP//gnXfeQXJyskk8zF1aKpUKbdq0wUcffQQAaN26Nc6cOYPly5cjIiLCyK0jKrvqdt5DjxLexo0bS587dOiAy5cvY+HChfj6668rvT3V5spRrVq1YG5ujoyMDI3yjIwMuLu7lzjvggULMG/ePCQlJT31gdx69eqhVq1auHTpUrnb/KTyxKBmaWmJ1q1bS+1Tz1eeZZZGeWLIzc3Fd999p9dJdkXuh7Jwd3fXGbNCoYCNjY1B9m1l+e677/DWW29hw4YNT73VyMnJCY0aNaoy+6EymdI+LY+xY8di27Zt2Lt3L2rXrm3s5hhMamoqMjMz8fzzz8PCwgIWFhbYv38/Fi9eDAsLiypzVbqsPDw84Ovrq1HWtGnTZ/Y2WFNSlr6luL9BVb0vqojznuriaecV1V3btm2Ntk+rTXJkZWUFf39/7N69WypTqVTYvXu3xi8MT4qLi8Ps2bOxY8cOtGnT5qnr+ffff3Hnzh14eHgYpN2PK2sMjysqKsLp06el9vn4+MDd3V1jmTk5OThy5IjeyyyN8sSQmJgIpVKJ119//anrqcj9UBYBAQEaMQNAcnKyFLMh9m1l+PbbbzF8+HB8++23GkOpF+f+/fu4fPlyldkPlclU9mlZCSEwduxYbNmyBXv27IGPj4+xm2RQPXv2xOnTp3Hq1CnpvzZt2mDIkCE4deoUzM3Njd3EcunYsaPW0Ot//fUXvL29jdQi0ldZ+pan/Q2qqirivKe6MNV9aiinTp0y3j419ogQhvTdd98JuVwuEhISxLlz58TIkSOFk5OTSE9PF0II8cYbb4gPPvhAqj9v3jxhZWUlNm7cKG7evCn9d+/ePSHEo1GM3nvvPZGSkiLS0tLErl27xPPPPy8aNmwoHj58WCVimDlzpti5c6e4fPmySE1NFeHh4cLa2lqcPXtWI04nJyfxww8/iD/++EP0799f+Pj4iAcPHlSJGNQ6deokXnnlFa1yY+yHe/fuiZMnT4qTJ08KAOLTTz8VJ0+eFFevXhVCCPHBBx+IN954Q6r/999/C1tbWzFp0iRx/vx5ER8fL8zNzcWOHTukOk/bLsaOYd26dcLCwkLEx8drHA9ZWVlSnXfffVfs27dPpKWlid9++00EBgaKWrVqiczMzAqJoaqr7H1amUaPHi0cHR3Fvn37NL4PeXl5xm5ahalOo9UdPXpUWFhYiLlz54qLFy+KdevWCVtbW/HNN98Yu2mkh9L+Hf3tt9+EhYWFWLBggTh//ryYMWOGsLS0FKdPnzZWCHqriPOeqqgiziuqqtLGunDhQrF161Zx8eJFcfr0afHOO+8IMzMzsWvXLqO0v1olR0IIsWTJElGnTh1hZWUl2rZtKw4fPixN69q1q4iIiJA+e3t7CwBa/82YMUMIIUReXp4IDg4WLi4uwtLSUnh7e4sRI0ZU+IlPaWKIjo6W6rq5uYk+ffqIEydOaCxPpVKJadOmCTc3NyGXy0XPnj3FhQsXqkwMQgjx559/CgAiKSlJa1nG2A/qYSWf/E/d7oiICNG1a1etefz8/ISVlZWoV6+eWL16tdZyS9ouxo6ha9euJdYX4tEwoh4eHsLKyko899xz4pVXXhGXLl2qsBhMQWXu08qk67sAQOf3urqoTsmREEL89NNPonnz5kIul4smTZqIlStXGrtJVAql/Tu6YcMG0ahRI2FlZSWaNWsmfv7550pucdkZ+rynKqqo84qqqLSxzp8/X9SvX19YW1sLZ2dn0a1bN7Fnzx7jNF4IIRNCiAq7LEVERERERGQiqs0zR0REREREROXB5IiIiIiIiAhMjoiIiIiIiAAwOSIiIiIiIgLA5IiIiIiIiAgAkyMiIiIiIiIATI6IiIiIiIgAMDkiIiIiPR04cAD9+vWDp6cnZDIZtm7dWqr5Y2JiIJPJtP6zs7OrmAYTkckob/8CAEIILFiwAI0aNYJcLsdzzz2HuXPnlmoZTI6IiIhIL7m5uWjVqhXi4+PLNP97772Hmzdvavzn6+uLQYMGGbilRGRqytu/AMA777yDL7/8EgsWLMCff/6JH3/8EW3bti3VMmRCCFHmFhAREdEzSSaTYcuWLRgwYIBUplQq8b///Q/ffvstsrKy0Lx5c8yfPx/dunXTuYzff/8dfn5+OHDgADp37lw5DSeiKq8s/cv58+fRsmVLnDlzBo0bNy7zunnliIiIiAxi7NixSElJwXfffYc//vgDgwYNQq9evXDx4kWd9b/88ks0atSIiRERPdXT+peffvoJ9erVw7Zt2+Dj44O6devirbfewt27d0u1HiZHREREVG7Xrl3D6tWrkZiYiM6dO6N+/fp477330KlTJ6xevVqr/sOHD7Fu3TpERkYaobVEZEr06V/+/vtvXL16FYmJiVi7di0SEhKQmpqKl19+uVTrsqiIAIiIiOjZcvr0aRQVFaFRo0Ya5UqlEjVr1tSqv2XLFty7dw8RERGV1UQiMlH69C8qlQpKpRJr166V6q1atQr+/v64cOGC3rfaMTkiIiKicrt//z7Mzc2RmpoKc3NzjWn29vZa9b/88kv07dsXbm5uldVEIjJR+vQvHh4esLCw0EigmjZtCuDRlScmR0RERFRpWrdujaKiImRmZj71GaK0tDTs3bsXP/74YyW1johMmT79S8eOHVFYWIjLly+jfv36AIC//voLAODt7a33upgcERERkV7u37+PS5cuSZ/T0tJw6tQpODs7o1GjRhgyZAiGDh2KTz75BK1bt8atW7ewe/dutGzZEqGhodJ8X331FTw8PNC7d29jhEFEVVB5+5fAwEA8//zzePPNN7Fo0SKoVCpERUUhKChI63a8knAobyIiItLLvn370L17d63yiIgIJCQkoKCgAHPmzMHatWtx/fp11KpVC+3bt8fMmTPRokULAI+eC/D29sbQoUNL/XJGIqq+DNG/3LhxA+PGjUNSUhLs7OzQu3dvfPLJJ3B2dta7HUyOiIiIiIiIwKG8iYiIiIiIADA5IiIiIiIiAsDkyGhiYmIgk8mkz3Xr1sWwYcNKtYxhw4bpHB7VlHXr1g3dunUzdjOIqIqQyWSIiYl5ar0n+9SqojTt0jdWomdVVT3ODU3fc6F9+/ZBJpNh3759Fd6mZwmTIyITcO7cOcTExODKlSvGbgpRtbF9+3YmI0RUKkuXLkVCQoKxm0EViMlRFXHhwgV88cUXxm4GVVHnzp3DzJkzmRzRM+fBgweYOnVqhSx7+/btmDlzZoUsW23q1Kl48OBBha6DiCpPZSRHSUlJSEpKqtB1UPGYHFURcrkclpaWxm6GTrm5ucZuAhEZibGPf2tra1hYmO4r+SwsLGBtbW3sZhCRCbGysoKVlZWxm/HMYnJUCQ4ePIgXXngB1tbWqF+/PlasWKFV58lnjgoKCjBz5kw0bNgQ1tbWqFmzJjp16oTk5GStef/++2+EhITAzs4Onp6emDVrFh4fob24e1KvXLkCmUym8QuI+jmmy5cvo0+fPnBwcMCQIUMAABcvXkRYWBjc3d1hbW2N2rVrIzw8HNnZ2RrL/eabb+Dv7w8bGxs4OzsjPDwc//zzj1a7V65cifr168PGxgZt27bFr7/+qs/m1PLDDz8gNDQUnp6ekMvlqF+/PmbPno2ioiKNet26dUPz5s3xxx9/oGvXrrC1tUWDBg2wceNGAMD+/fvRrl072NjYoHHjxti1a5fWuk6ePInevXtDoVDA3t4ePXv2xOHDhzXqFHdPdEJCAmQymcbVn7p166Jv3744ePAg2rZtC2tra9SrVw9r167VmG/QoEEAgO7du0Mmk/EeY6oQ6u/uuXPn8Nprr6FGjRro1KkTAP2Oa/Uxlpqaig4dOsDGxgY+Pj5Yvny5Rr38/HxMnz4d/v7+cHR0hJ2dHTp37oy9e/dqtUnXczj69KlPM2zYMMTHx0vrUP+nlpubi3fffRdeXl6Qy+Vo3LgxFixYgNK+/UJXf6BUKjFhwgS4uLjAwcEBL774Iv7991+tea9evYoxY8agcePGsLGxQc2aNTFo0CBeQaZnwtOOc13nMGq6+o3r16/jzTffhJubG+RyOZo1a4avvvqqVG2qW7cuzp49i/3790t9xuPPBv39998YNGgQnJ2dYWtri/bt2+Pnn38u1ToA3c8c/fvvvxgwYADs7Ozg6uqKCRMmQKlUas3766+/YtCgQahTpw7kcjm8vLwwYcIEXsEuBdP9Oc5EnD59GsHBwXBxcUFMTAwKCwsxY8YMuLm5lThfTEwMYmNj8dZbb6Ft27bIycnB8ePHceLECQQFBUn1ioqK0KtXL7Rv3x5xcXHYsWMHZsyYgcLCQsyaNatMbS4sLERISAg6deqEBQsWwNbWFvn5+QgJCYFSqcS4cePg7u6O69evY9u2bcjKyoKjoyMAYO7cuZg2bRoGDx6Mt956C7du3cKSJUvQpUsXnDx5Ek5OTgCAVatWYdSoUejQoQOio6Px999/48UXX4SzszO8vLxK1d6EhATY29tj4sSJsLe3x549ezB9+nTk5OTg448/1qj733//oW/fvggPD8egQYOwbNkyhIeHY926dYiOjsbbb7+N1157DR9//DFefvll/PPPP3BwcAAAnD17Fp07d4ZCocD7778PS0tLrFixAt26dZMSq7K4dOkSXn75ZURGRiIiIgJfffUVhg0bBn9/fzRr1gxdunTB+PHjsXjxYnz44Ydo2rQpAEj/JzK0QYMGoWHDhvjoo48ghND7uAYeHWN9+vTB4MGD8eqrr2LDhg0YPXo0rKys8OabbwIAcnJy8OWXX+LVV1/FiBEjcO/ePaxatQohISE4evQo/Pz8im1bWfvUJ40aNQo3btxAcnIyvv76a41pQgi8+OKL2Lt3LyIjI+Hn54edO3di0qRJuH79OhYuXFiqdT3prbfewjfffIPXXnsNHTp0wJ49exAaGqpV79ixYzh06BDCw8NRu3ZtXLlyBcuWLUO3bt1w7tw52NralqsdRFWVoY5ztYyMDLRv3x4ymQxjx46Fi4sLfvnlF0RGRiInJwfR0dF6LWfRokUYN24c7O3t8b///Q8ApDZlZGSgQ4cOyMvLw/jx41GzZk2sWbMGL774IjZu3IiXXnqpTG0HHt1e3LNnT1y7dg3jx4+Hp6cnvv76a+zZs0erbmJiIvLy8jB69GjUrFkTR48exZIlS/Dvv/8iMTGxzG14pgiqUAMGDBDW1tbi6tWrUtm5c+eEubm5eHzze3t7i4iICOlzq1atRGhoaInLjoiIEADEuHHjpDKVSiVCQ0OFlZWVuHXrlhBCiL179woAYu/evRrzp6WlCQBi9erVWsv84IMPNOqePHlSABCJiYnFtufKlSvC3NxczJ07V6P89OnTwsLCQirPz88Xrq6uws/PTyiVSqneypUrBQDRtWvXEuN+Ul5enlbZqFGjhK2trXj48KFU1rVrVwFArF+/Xir7888/BQBhZmYmDh8+LJXv3LlTa9sMGDBAWFlZicuXL0tlN27cEA4ODqJLly5S2YwZM4SuQ2v16tUCgEhLS5PKvL29BQBx4MABqSwzM1PI5XLx7rvvSmWJiYk69yGRIam/u6+++qpUpu9xLcT/HWOffPKJVKZUKoWfn59wdXUV+fn5QgghCgsLNY59IYT477//hJubm3jzzTc1ygGIGTNmSJ/17VP1ERUVpXOerVu3CgBizpw5GuUvv/yykMlk4tKlS3qv48n+4NSpUwKAGDNmjEa91157TStWXX1bSkqKACDWrl2rdxuITI0+x7mucxi1J4+lyMhI4eHhIW7fvq1RLzw8XDg6Ouo81orTrFkznecp0dHRAoD49ddfpbJ79+4JHx8fUbduXVFUVKT3Orp27aqxjkWLFgkAYsOGDVJZbm6uaNCggda5ga5YYmNjhUwm09ieVDzeVleBioqKsHPnTgwYMAB16tSRyps2bYqQkJAS53VycsLZs2dx8eLFp65n7Nix0r/Vv4rk5+frvC1MX6NHj9b4rL4ytHPnTuTl5emcZ/PmzVCpVBg8eDBu374t/efu7o6GDRtKt8wcP34cmZmZePvttzXuqR02bJi0ntKwsbGR/n3v3j3cvn0bnTt3Rl5eHv7880+Nuvb29ggPD5c+N27cGE5OTmjatKnGlR/1v//++28Aj/ZlUlISBgwYgHr16kn1PDw88Nprr+HgwYPIyckpddsBwNfXF507d5Y+u7i4oHHjxtK6iSrb22+/Lf1b3+NazcLCAqNGjZI+W1lZYdSoUcjMzERqaioAwNzcXDr2VSoV7t69i8LCQrRp0wYnTpwotl3l6VNLY/v27TA3N8f48eM1yt99910IIfDLL7+Ua9kAtJat65frx/u2goIC3LlzBw0aNICTk1OJ24nIlBn6OBdCYNOmTejXrx+EEBr9WEhICLKzsw1yPG3fvh1t27aVbkUGHp1zjBw5EleuXMG5c+fKtWwPDw+8/PLLUpmtrS1GjhypVffxfiM3Nxe3b99Ghw4dIITAyZMny9yGZwmTowp069YtPHjwAA0bNtSa1rhx4xLnnTVrFrKystCoUSO0aNECkyZNwh9//KFVz8zMTONkHQAaNWoEAGW+L93CwgK1a9fWKPPx8cHEiRPx5ZdfolatWggJCUF8fLzG80YXL16EEAINGzaEi4uLxn/nz59HZmYmgEf30QPQ2i6WlpZasejj7NmzeOmll+Do6AiFQgEXFxe8/vrrAKD1PFTt2rW17v93dHTUupVPnaT9999/AB7ty7y8PJ37rWnTplCpVDqfq9LH452/Wo0aNaR1E1U2Hx8f6d/6Htdqnp6esLOz0yjT1SetWbMGLVu2lJ6pdHFxwc8//6x1zD6uPH1qaVy9ehWenp7SLbVq6ltZ1X1YWZdtZmaG+vXra5Trav+DBw8wffp06bmnWrVqwcXFBVlZWSVuJyJTZujj/NatW8jKysLKlSu1+rDhw4cDgFY/VhZXr14t9hxBPb08y27QoIHW+Yuu9V27dg3Dhg2Ds7Mz7O3t4eLigq5duwLQPici3fjMURXVpUsXXL58GT/88AOSkpLw5ZdfYuHChVi+fDneeuutUi2ruBemPTlggZpcLoeZmXbe/Mknn2DYsGFSm8aPH4/Y2FgcPnwYtWvXhkqlgkwmwy+//AJzc3Ot+SvihbVZWVno2rUrFAoFZs2ahfr168Pa2honTpzA5MmToVKpNOrraldJ5aKUD18Dpd/ehlw3kSE8/stjRRzX33zzDYYNG4YBAwZg0qRJcHV1hbm5OWJjY3H58uVytb06GTduHFavXo3o6GgEBATA0dERMpkM4eHhWn0b0bNG37+16mPl9ddfR0REhM55WrZsadjGGUlRURGCgoJw9+5dTJ48GU2aNIGdnR2uX7+OYcOGsd/QE5OjCuTi4gIbGxudt8ZduHDhqfM7Oztj+PDhGD58OO7fv48uXbogJiZGIzlSqVT4+++/pV9mAeCvv/4C8GhUFeDRVQjgUSLxuLL8itGiRQu0aNECU6dOxaFDh9CxY0csX74cc+bMQf369SGEgI+Pj0Z7nuTt7Q3g0S/SPXr0kMoLCgqQlpaGVq1a6d2effv24c6dO9i8eTO6dOkilaelpZU6tpK4uLjA1tZW5377888/YWZmJl19enx7P/6genl+NXoW3ghOVZO+x7XajRs3kJubq3H16Mk+aePGjahXrx42b96s8d2eMWNGicsub5/6pOKOK29vb+zatQv37t3TuHqkvk1X3YeVhbe3N1QqFS5fvqzxq6+u9m/cuBERERH45JNPpLKHDx9q9eVE1Ym+x7m+5zbqUSGLiooQGBhY7vaV1G8Ud46gnl5W3t7eOHPmDIQQGut/cn2nT5/GX3/9hTVr1mDo0KFSua6Rjql4vK2uApmbmyMkJARbt27FtWvXpPLz589j586dJc57584djc/29vZo0KCBzmEbP//8c+nfQgh8/vnnsLS0RM+ePQE8OqjMzc1x4MABjfmWLl2qdyw5OTkoLCzUKGvRogXMzMykNg0cOBDm5uaYOXOm1lUPIYQUU5s2beDi4oLly5cjPz9fqpOQkFDqP/rqX7IfX19+fn6pYtN3PcHBwfjhhx80bg3KyMjA+vXr0alTJygUCgCQbpd5fHvn5uZizZo1ZV6/+kSTJ0VU2fQ9rtUKCws1htzNz8/HihUr4OLiAn9/fwC6j9sjR44gJSWlxLaUp0/Vpbjjqk+fPigqKtLoWwFg4cKFkMlk6N27d6nXpaaed/HixRrlixYt0qprbm6utc2XLFlS7FVooupA3+NcoVCgVq1aTz23MTc3R1hYGDZt2oQzZ85ore/WrVulap+dnZ3Ov8V9+vTB0aNHNfqx3NxcrFy5EnXr1oWvr2+p1vPksm/cuCG9egQA8vLysHLlSo16uvpWIQQ+++yzMq/7WcQrRxVs5syZ2LFjBzp37owxY8agsLAQS5YsQbNmzXQ+Q6Tm6+uLbt26wd/fH87Ozjh+/Dg2btyoMfgC8OgFiTt27EBERATatWuHX375BT///DM+/PBDuLi4AHj0/MygQYOwZMkSyGQy1K9fH9u2bSvVPbZ79uzB2LFjMWjQIDRq1AiFhYX4+uuvpU4HeJQUzJkzB1OmTMGVK1cwYMAAODg4IC0tDVu2bMHIkSPx3nvvwdLSEnPmzMGoUaPQo0cPvPLKK0hLS8Pq1atL/cxRhw4dUKNGDURERGD8+PGQyWT4+uuvK+SWtDlz5iA5ORmdOnXCmDFjYGFhgRUrVkCpVCIuLk6qFxwcjDp16iAyMhKTJk2Cubk5vvrqK7i4uGh09KXh5+cHc3NzzJ8/H9nZ2ZDL5ejRowdcXV0NFR6RTvoe12qenp6YP38+rly5gkaNGuH777/HqVOnsHLlSulF13379sXmzZvx0ksvITQ0FGlpaVi+fDl8fX1x//79EttT1j5VF3WyNn78eISEhMDc3Bzh4eHo168funfvjv/973+4cuUKWrVqhaSkJPzwww+Ijo7Wel6oNPz8/PDqq69i6dKlyM7ORocOHbB7925cunRJq27fvn3x9ddfw9HREb6+vkhJScGuXbtQs2bNMq+fyBToe5y/9dZbmDdvHt566y20adMGBw4ckK5UP27evHnYu3cv2rVrhxEjRsDX1xd3797FiRMnsGvXLty9e1fvtvn7+2PZsmWYM2cOGjRoAFdXV/To0QMffPABvv32W/Tu3Rvjx4+Hs7Mz1qxZg7S0NGzatEnn4wr6GjFiBD7//HMMHToUqamp8PDwwNdff601nH+TJk1Qv359vPfee7h+/ToUCgU2bdrEZ5hLqzKHxntW7d+/X/j7+wsrKytRr149sXz5cq3hXZ8cynvOnDmibdu2wsnJSdjY2IgmTZqIuXPnSkPhCvFo2G07Oztx+fJlERwcLGxtbYWbm5uYMWOG1pCRt27dEmFhYcLW1lbUqFFDjBo1Spw5c0bnUN52dnZaMfz999/izTffFPXr1xfW1tbC2dlZdO/eXezatUur7qZNm0SnTp2EnZ2dsLOzE02aNBFRUVHiwoULGvWWLl0qfHx8hFwuF23atBEHDhzQGr5SH7/99pto3769sLGxEZ6enuL999+XhuJ+fHjLrl27imbNmmnN7+3trXPYdAAiKipKo+zEiRMiJCRE2NvbC1tbW9G9e3dx6NAhrXlTU1NFu3bthJWVlahTp4749NNPix3KW9e6dW2HL774QtSrV08aypTDepOhqfsl9WsAHqfPca0+xo4fPy4CAgKEtbW18Pb2Fp9//rnGslQqlfjoo4+Et7e3kMvlonXr1mLbtm0iIiJCeHt7a9TFE0PyCqFfn6qPwsJCMW7cOOHi4iJkMpnG/Pfu3RMTJkwQnp6ewtLSUjRs2FB8/PHHQqVSlWodutr14MEDMX78eFGzZk1hZ2cn+vXrJ/755x+tWP/77z8xfPhwUatWLWFvby9CQkLEn3/+qfX3gqg60uc4z8vLE5GRkcLR0VE4ODiIwYMHi8zMTJ39RkZGhoiKihJeXl7C0tJSuLu7i549e4qVK1eWql3p6ekiNDRUODg4aL1+5PLly+Lll18WTk5OwtraWrRt21Zs27at1LHrOge4evWqePHFF4Wtra2oVauWeOedd8SOHTu0zgfOnTsnAgMDhb29vahVq5YYMWKE+P3334sd9py0yYTgU99ERFR+3bp1w+3bt3XeukJERGQK+MwRERERERER+MwRVWG3bt0q8cFjKysrODs7V2KLiMgUZGdn48GDByXWcXd3r/LrIKLKUxnnHDyvMQ1MjqjKeuGFF0oc/rpr167Yt29f5TWIiEzCO++889TRIct7R3llrIOIKk9lnHPwvMY08JkjqrJ+++23En+ZrVGjhjTaFBGR2rlz53Djxo0S65T3fSeVsQ4iqjyVcc7B8xrTwOSIiIiIiIgIHJCBiIiIiIgIQDV+5kilUuHGjRtwcHCATCYzdnOIqg0hBO7duwdPT89yvdTOlLF/IaoY7F/YvxBVFH37l2qbHN24cQNeXl7GbgZRtfXPP/+gdu3axm6GUbB/IapY7F/YvxBVlKf1L9U2OXJwcADwaAPY2NggKSkJwcHBsLS0NHLLDK+goKBaxwcwxqokJycHXl5e0jH2LGL/Ur1U9xhNKT72L5r9i0Kh0FnHlPapvqpjTED1jMtUY9K3f6m2yZH6UrRCoYCNjQ1sbW2hUChMaifqq6CgoFrHBzDGquhZvt2D/Uv1Ut1jNMX42L886l9KSo5MbZ8+TXWMCaiecZl6TE/rX57NG3qJiIiIiIiewOSIiExGTEwMZDKZxn9NmjSRpj98+BBRUVGoWbMm7O3tERYWhoyMDI1lXLt2DaGhobC1tYWrqysmTZqEwsLCyg6FiIiIqqBqe1sdEVVPzZo1w65du6TPFhb/141NmDABP//8MxITE+Ho6IixY8di4MCB+O233wAARUVFCA0Nhbu7Ow4dOoSbN29i6NChsLS0xEcffVTpsRAREVHVwuSIiEyKhYUF3N3dtcqzs7OxatUqrF+/Hj169AAArF69Gk2bNsXhw4fRvn17JCUl4dy5c9i1axfc3Nzg5+eH2bNnY/LkyYiJiYGVlVVlh0NERERVCJMjIjIpFy9ehKenJ6ytrREQEIDY2FjUqVMHqampKCgoQGBgoFS3SZMmqFOnDlJSUtC+fXukpKSgRYsWcHNzk+qEhIRg9OjROHv2LFq3bq1znUqlEkqlUvqck5MD4NFDqeorVwUFBRURrtGp46qu8QHVP0ZTis8U2khE1RuTIyIyGe3atUNCQgIaN26MmzdvYubMmejcuTPOnDmD9PR0WFlZwcnJSWMeNzc3pKenAwDS09M1EiP1dPW04sTGxmLmzJla5UlJSbC1tQUAJCcnlye0Kq+6xwdU/xhNIb68vDxjN4GInnFMjojIZPTu3Vv6d8uWLdGuXTt4e3tjw4YNsLGxqbD1TpkyBRMnTpQ+q9+VEBwcDBsbGyQnJyMoKMgkhzR9moKCgmodH1D9YzSl+NRXZYmIjIXJEYC6H/xssGVdmRdqsGURUcmcnJzQqFEjXLp0CUFBQcjPz0dWVpbG1aOMjAzpGSV3d3ccPXpUYxnq0ex0PcekJpfLIZfLtcotLS2lk83H/61myL4FMG7/oiu+6qa6x2gK8VX19lU1zWN2QllkmHdC8fyF6BEO5U1EJuv+/fu4fPkyPDw84O/vD0tLS+zevVuafuHCBVy7dg0BAQEAgICAAJw+fRqZmZlSneTkZCgUCvj6+lZ6+4mIiKhq4ZUjIjIZ7733Hvr16wdvb2/cuHEDM2bMgLm5OV599VU4OjoiMjISEydOhLOzMxQKBcaNG4eAgAC0b98eABAcHAxfX1+88cYbiIuLQ3p6OqZOnYqoqCidV4aIiIjo2cLkiIhMxr///otXX30Vd+7cgYuLCzp16oTDhw/DxcUFALBw4UKYmZkhLCwMSqUSISEhWLp0qTS/ubk5tm3bhtGjRyMgIAB2dnaIiIjArFmzjBUSERERVSFMjojIZHz33XclTre2tkZ8fDzi4+OLrePt7Y3t27cbumlERERUDfCZIyIiIiIiIjA5IiIiIiIiAsDkiIiIiIiICACTIyIiIiIiIgBMjoiIiIiIiAAwOSIiIiIiIgLA5IiIiIiIiAgAkyMiIiIiIiIATI6IiIiIiIgAMDkiIiIiIiICwOSIiIiInnFFRUWYNm0afHx8YGNjg/r162P27NkQQkh1hBCYPn06PDw8YGNjg8DAQFy8eFFjOXfv3sWQIUOgUCjg5OSEyMhI3L9/v7LDIaJyYHJEREREz7T58+dj2bJl+Pzzz3H+/HnMnz8fcXFxWLJkiVQnLi4OixcvxvLly3HkyBHY2dkhJCQEDx8+lOoMGTIEZ8+eRXJyMrZt24YDBw5g5MiRxgiJiMrIwtgNICIiIjKmQ4cOoX///ggNDQUA1K1bF99++y2OHj0K4NFVo0WLFmHq1Kno378/AGDt2rVwc3PD1q1bER4ejvPnz2PHjh04duwY2rRpAwBYsmQJ+vTpgwULFsDT09M4wRFRqTA5IiIiomdahw4dsHLlSvz1119o1KgRfv/9dxw8eBCffvopACAtLQ3p6ekIDAyU5nF0dES7du2QkpKC8PBwpKSkwMnJSUqMACAwMBBmZmY4cuQIXnrpJZ3rViqVUCqV0uecnBwAQEFBAQoKCnTOoy6Xmwmd08uiuHVVFvX6jd0OQ6uOcZlqTPq2l8kRERERPdM++OAD5OTkoEmTJjA3N0dRURHmzp2LIUOGAADS09MBAG5ubhrzubm5SdPS09Ph6uqqMd3CwgLOzs5SHV1iY2Mxc+ZMrfKkpCTY2tqW2O7ZbVRPD05P27dvN9iyyiM5OdnYTagQ1TEuU4spLy9Pr3pMjoiIiOiZtmHDBqxbtw7r169Hs2bNcOrUKURHR8PT0xMREREVuu4pU6Zg4sSJ0uecnBx4eXkhODgYCoVC5zwFBQVITk7GtONmUKpkBmnHmZgQgyynrNQxBQUFwdLS0qhtMaTqGJepxqS+Kvs0TI6IiIjomTZp0iR88MEHCA8PBwC0aNECV69eRWxsLCIiIuDu7g4AyMjIgIeHhzRfRkYG/Pz8AADu7u7IzMzUWG5hYSHu3r0rza+LXC6HXC7XKre0tHzqiadSJYOyyDDJUVU5ydUnblNUHeMytZj0bStHqyMiIqJnWl5eHszMNE+JzM3NoVI9um3Nx8cH7u7u2L17tzQ9JycHR44cQUBAAAAgICAAWVlZSE1Nlers2bMHKpUK7dq1q4QoiMgQeOWIiIiInmn9+vXD3LlzUadOHTRr1gwnT57Ep59+ijfffBMAIJPJEB0djTlz5qBhw4bw8fHBtGnT4OnpiQEDBgAAmjZtil69emHEiBFYvnw5CgoKMHbsWISHh3OkOiITwuSIiIiInmlLlizBtGnTMGbMGGRmZsLT0xOjRo3C9OnTpTrvv/8+cnNzMXLkSGRlZaFTp07YsWMHrK2tpTrr1q3D2LFj0bNnT5iZmSEsLAyLFy82RkhEVEZMjoiIiOiZ5uDggEWLFmHRokXF1pHJZJg1axZmzZpVbB1nZ2esX7++AlpIRJWFzxwRERERERGByREREREREREAJkdEREREREQAmBwREREREREBYHJEREREREQEgMkRERERERERACZHREREREREAJgcERERERERAWByREREREREBIDJERGZqHnz5kEmkyE6Oloqe/jwIaKiolCzZk3Y29sjLCwMGRkZGvNdu3YNoaGhsLW1haurKyZNmoTCwsJKbj0RERFVRUyOiMjkHDt2DCtWrEDLli01yidMmICffvoJiYmJ2L9/P27cuIGBAwdK04uKihAaGor8/HwcOnQIa9asQUJCAqZPn17ZIRAREVEVZPDkaNmyZWjZsiUUCgUUCgUCAgLwyy+/SNP5yy4Rlcf9+/cxZMgQfPHFF6hRo4ZUnp2djVWrVuHTTz9Fjx494O/vj9WrV+PQoUM4fPgwACApKQnnzp3DN998Az8/P/Tu3RuzZ89GfHw88vPzjRUSERERVREWhl5g7dq1MW/ePDRs2BBCCKxZswb9+/fHyZMn0axZM0yYMAE///wzEhMT4ejoiLFjx2LgwIH47bffAPzfL7vu7u44dOgQbt68iaFDh8LS0hIfffSRoZtLRCYmKioKoaGhCAwMxJw5c6Ty1NRUFBQUIDAwUCpr0qQJ6tSpg5SUFLRv3x4pKSlo0aIF3NzcpDohISEYPXo0zp49i9atW+tcp1KphFKplD7n5OQAAAoKCmBhYSH9+0lyc1G+YJ+gax0VTb1OY6y7slT3GE0pPlNoIxFVbwZPjvr166fxee7cuVi2bBkOHz6M2rVrY9WqVVi/fj169OgBAFi9ejWaNm2Kw4cPo3379tIvu7t27YKbmxv8/Pwwe/ZsTJ48GTExMbCysjJ0k4nIRHz33Xc4ceIEjh07pjUtPT0dVlZWcHJy0ih3c3NDenq6VOfxxEg9XT2tOLGxsZg5c6ZWeVJSEmxtbQEAycnJWtPj2pYcT2lt377dsAssBV3xVTfVPUZTiC8vL8/YTSCiZ5zBk6PHFRUVITExEbm5uQgICKjQX3aJqHr7559/8M477yA5ORnW1taVuu4pU6Zg4sSJ0uecnBx4eXkhODgYNjY2SE5ORlBQECwtLTXmax6z06DtOBMTYtDl6aOgoKDY+KqL6h6jKcWnvipLRGQsFZIcnT59GgEBAXj48CHs7e2xZcsW+Pr64tSpUxX2y25Zb3sBDHvrC297qRiMseowVvtSU1ORmZmJ559/XiorKirCgQMH8Pnnn2Pnzp3Iz89HVlaWRh+TkZEBd3d3AIC7uzuOHj2qsVz1M4/qOrrI5XLI5XKtcktLS+lk8/F/qymLZKUL8imMeWKrK77qprrHaArxVfX2EVH1VyHJUePGjXHq1ClkZ2dj48aNiIiIwP79+ytiVZKy3vYCGPbWF972UrEYo/EZ67aXnj174vTp0xplw4cPR5MmTTB58mR4eXnB0tISu3fvRlhYGADgwoULuHbtGgICAgAAAQEBmDt3LjIzM+Hq6grg0fZWKBTw9fWt3ICIiIioyqmQ5MjKygoNGjQAAPj7++PYsWP47LPP8Morr1TYL7tlve0FMOytL7ztpWIwxqrDWLe9ODg4oHnz5hpldnZ2qFmzplQeGRmJiRMnwtnZGQqFAuPGjUNAQADat28PAAgODoavry/eeOMNxMXFIT09HVOnTkVUVJTOK0NERET0bKnQZ47UVCoVlEol/P39K+yX3bLe9gIY9tYX3vZSsRij8VXlti1cuBBmZmYICwuDUqlESEgIli5dKk03NzfHtm3bMHr0aAQEBMDOzg4RERGYNWuWEVtNREREVYXBk6MpU6agd+/eqFOnDu7du4f169dj37592LlzJxwdHfnLLhEZzL59+zQ+W1tbIz4+HvHx8cXO4+3tbdTbX4mIiKjqMnhylJmZiaFDh+LmzZtwdHREy5YtsXPnTgQFBQHgL7tERERERFQ1GTw5WrVqVYnT+csuERERERFVRWbGbgAREREREVFVwOSIiIiIiIgITI6IiIiIiIgAMDkiIiIiIiICwOSIiIiIiIgIAJMjIiIiIiIiAEyOiIiIiIiIADA5IiIiIiIiAsDkiIiIiIiICACTIyIiIiIiIgBMjoiIiOj/tXfvcVHW+f//nxyHgxzEBCRPZJaSZ/FAaraKkFGbabXu1wpd0/0oWMZ2sjXzTLlWruYhq/Xw2azWWk1dU0hTM/FEuesps9WyrwpYhijmODLX7w9/zNcRUJAZhhke99utW877el/X9XoxzIt5zTXXdQEAJNEcAQAAAIAkmiMAAAAAkERzBAAAoOPHj+vRRx9VgwYNFBgYqLZt22r37t225YZhaMKECWrUqJECAwOVmJiow4cP223j9OnTGjJkiEJDQxUeHq7hw4fr3LlzNZ0KgGqgOQIAAHXaL7/8oh49esjPz0+ffvqpDhw4oNdee03169e3zZkxY4Zmz56tBQsWaMeOHQoODlZycrIuXLhgmzNkyBDt379f2dnZWrNmjbZs2aKRI0e6IiUAN8jX1QEAAAC40quvvqomTZpo0aJFtrHY2Fjbvw3D0KxZszR+/Hg98MADkqSlS5cqKipKK1eu1ODBg3Xw4EGtW7dOu3btUnx8vCRpzpw5uvfeezVz5kzFxMTUbFIAbgjNEQAAqNNWrVql5ORkPfzww9q8ebNuvvlmjR49WiNGjJAkHT16VHl5eUpMTLStExYWpm7duiknJ0eDBw9WTk6OwsPDbY2RJCUmJsrb21s7duzQgw8+WO6+zWazzGaz7XFRUZEkyWKxyGKxlLtO6bjJ26he4uVs01VK9+/qOBzNE/Ny15wqGy/NEQAAqNOOHDmi+fPnKyMjQy+++KJ27dqlJ598Uv7+/kpNTVVeXp4kKSoqym69qKgo27K8vDxFRkbaLff19VVERIRtTnkyMzM1adKkMuNZWVkKCgq6ZtxT4q2Vyq8y1q5d67BtVUd2drarQ3AKT8zL3XI6f/58pebRHAEAgDrNarUqPj5e06dPlyR17NhR+/bt04IFC5SamurUfY8bN04ZGRm2x0VFRWrSpImSkpIUGhpa7joWi0XZ2dl6abe3zFYvh8Sxb2KyQ7Zzo0pz6tevn/z8/FwaiyN5Yl7umlPpUdnroTkCAAB1WqNGjRQXF2c31rp1a3388ceSpOjoaElSfn6+GjVqZJuTn5+vDh062OYUFBTYbePSpUs6ffq0bf3ymEwmmUymMuN+fn7XfeNptnrJXOKY5qi2vMmtTN7uyBPzcrecKhsrV6sDAAB1Wo8ePXTo0CG7sW+//VbNmjWTdPniDNHR0dqwYYNteVFRkXbs2KGEhARJUkJCggoLC5Wbm2ubs3HjRlmtVnXr1q0GsgDgCBw5AgAAddrTTz+tO++8U9OnT9cjjzyinTt3auHChVq4cKEkycvLS2PHjtXUqVPVsmVLxcbG6qWXXlJMTIwGDBgg6fKRpnvuuUcjRozQggULZLFYlJ6ersGDB3OlOsCN0BwBAIA6rUuXLlqxYoXGjRunyZMnKzY2VrNmzdKQIUNsc5577jkVFxdr5MiRKiwsVM+ePbVu3ToFBATY5rz33ntKT09X37595e3trUGDBmn27NmuSAnADaI5AgAAdd59992n++67r8LlXl5emjx5siZPnlzhnIiICC1btswZ4QGoIZxzBAAAAACiOQIAAAAASTRHAAAAACCJ5ggAAAAAJNEcAXAj8+fPV7t27RQaGqrQ0FAlJCTo008/tS2/cOGC0tLS1KBBA9WrV0+DBg1Sfn6+3TaOHTumlJQUBQUFKTIyUs8++6wuXbpU06kAAIBaiOYIgNto3LixXnnlFeXm5mr37t3q06ePHnjgAe3fv1/S5XuVrF69WsuXL9fmzZt14sQJDRw40LZ+SUmJUlJSdPHiRW3btk1LlizR4sWLNWHCBFelBAAAahEu5Q3Abdx///12j6dNm6b58+dr+/btaty4sd59910tW7ZMffr0kSQtWrRIrVu31vbt29W9e3dlZWXpwIED+uyzzxQVFaUOHTpoypQpev755zVx4kT5+/u7Ii0AAFBLcOQIgFsqKSnRBx98oOLiYiUkJCg3N1cWi0WJiYm2Oa1atVLTpk2Vk5MjScrJyVHbtm0VFRVlm5OcnKyioiLb0ScAAFB3ceQIgFvZu3evEhISdOHCBdWrV08rVqxQXFyc9uzZI39/f4WHh9vNj4qKUl5eniQpLy/PrjEqXV66rCJms1lms9n2uKioSJJksVjk6+tr+/fVTD5G1RO8hvL24Wyl+3TFvmuKp+foTvm5Q4wAPBvNEQC3cvvtt2vPnj06c+aMPvroI6Wmpmrz5s1O3WdmZqYmTZpUZjwrK0tBQUGSpOzs7DLLZ3R1bBxr16517AaroLz8PI2n5+gO+Z0/f97VIQCo42iOALgVf39/3XrrrZKkzp07a9euXfrrX/+q3/3ud7p48aIKCwvtjh7l5+crOjpakhQdHa2dO3faba/0analc8ozbtw4ZWRk2B4XFRWpSZMmSkpKUmBgoLKzs9WvXz/5+fnZrddm4vpq5Xq1fROTHbq9yrBYLBXm5yk8PUd3yq/0qCwAuArNEQC3ZrVaZTab1blzZ/n5+WnDhg0aNGiQJOnQoUM6duyYEhISJEkJCQmaNm2aCgoKFBkZKenyp+mhoaGKi4urcB8mk0kmk6nMuJ+fn+3N5pX/LmUu8XJIjlfuz1XKy8/TeHqO7pBfbY8PgOejOQLgNsaNG6f+/furadOmOnv2rJYtW6ZNmzZp/fr1CgsL0/Dhw5WRkaGIiAiFhoZqzJgxSkhIUPfu3SVJSUlJiouL02OPPaYZM2YoLy9P48ePV1paWrnNDwAAqFtojgC4jYKCAj3++OM6efKkwsLC1K5dO61fv179+vWTJL3xxhvy9vbWoEGDZDablZycrHnz5tnW9/Hx0Zo1azRq1CglJCQoODhYqampmjx5sqtSAgAAtQjNEQC38e67715zeUBAgObOnau5c+dWOKdZs2YuvbABAACovbjPEQAAAACI5ggAAAAAJNEcAQAAAIAkmiMAAAAAkOSE5igzM1NdunRRSEiIIiMjNWDAAB06dMhuzoULF5SWlqYGDRqoXr16GjRokO1GjKWOHTumlJQUBQUFKTIyUs8++6wuXbrk6HABAAAAQJITmqPNmzcrLS1N27dvV3Z2tiwWi5KSklRcXGyb8/TTT2v16tVavny5Nm/erBMnTmjgwIG25SUlJUpJSdHFixe1bds2LVmyRIsXL9aECRMcHS4AAAAASHLCpbzXrVtn93jx4sWKjIxUbm6u7rrrLp05c0bvvvuuli1bpj59+kiSFi1apNatW2v79u3q3r27srKydODAAX322WeKiopShw4dNGXKFD3//POaOHGi/P39HR02AAAAgDrO6eccnTlzRpIUEREhScrNzZXFYlFiYqJtTqtWrdS0aVPl5ORIknJyctS2bVtFRUXZ5iQnJ6uoqEj79+93dsgAAAAA6iCn3gTWarVq7Nix6tGjh9q0aSNJysvLk7+/v8LDw+3mRkVFKS8vzzbnysaodHnpsvKYzWaZzWbb46KiIkmSxWKRr6+v7d/lMfkYVcysYhXtw5lK9+mKfdcUcqw9ant8AAAAN8qpzVFaWpr27dunrVu3OnM3ki5fCGLSpEllxrOyshQUFCRJys7OLnfdGV0dF8fatWsdt7Eqqig/T0KOrnf+/HlXhwAAAOAUTmuO0tPTtWbNGm3ZskWNGze2jUdHR+vixYsqLCy0O3qUn5+v6Oho25ydO3faba/0analc642btw4ZWRk2B4XFRWpSZMmSkpKUmBgoLKzs9WvXz/5+fmVWbfNxPU3nOfV9k1Mdti2KstisVwzP09AjrVH6VFZAAAAT+Pw5sgwDI0ZM0YrVqzQpk2bFBsba7e8c+fO8vPz04YNGzRo0CBJ0qFDh3Ts2DElJCRIkhISEjRt2jQVFBQoMjJS0uVP00NDQxUXF1fufk0mk0wmU5lxPz8/2xvNK/99JXOJ140nXM7+XKWi/DwJObpebY4NAACgOhzeHKWlpWnZsmX65JNPFBISYjtHKCwsTIGBgQoLC9Pw4cOVkZGhiIgIhYaGasyYMUpISFD37t0lSUlJSYqLi9Njjz2mGTNmKC8vT+PHj1daWlq5DRAAAAAAVJfDm6P58+dLku6++2678UWLFmno0KGSpDfeeEPe3t4aNGiQzGazkpOTNW/ePNtcHx8frVmzRqNGjVJCQoKCg4OVmpqqyZMnOzpcAAAAAJDkpK/VXU9AQIDmzp2ruXPnVjinWbNmLr24AQAAAIC6xen3OQIAAAAAd0BzBAAAAACiOQIAAAAASTRHAAAAACCJ5ggAAAAAJNEcAQAA2HnllVfk5eWlsWPH2sYuXLigtLQ0NWjQQPXq1dOgQYOUn59vt96xY8eUkpKioKAgRUZG6tlnn9WlS5dqOHoA1UFzBAAA8P/btWuX3nrrLbVr185u/Omnn9bq1au1fPlybd68WSdOnNDAgQNty0tKSpSSkqKLFy9q27ZtWrJkiRYvXqwJEybUdAoAqoHmCAAAQNK5c+c0ZMgQvf3226pfv75t/MyZM3r33Xf1+uuvq0+fPurcubMWLVqkbdu2afv27ZKkrKwsHThwQH//+9/VoUMH9e/fX1OmTNHcuXN18eJFV6UEoIpojgAAACSlpaUpJSVFiYmJduO5ubmyWCx2461atVLTpk2Vk5MjScrJyVHbtm0VFRVlm5OcnKyioiLt37+/ZhIAUG2+rg4AAADA1T744AN99dVX2rVrV5lleXl58vf3V3h4uN14VFSU8vLybHOubIxKl5cuq4jZbJbZbLY9LioqkiRZLBZZLJZy1ykdN3kb18mq8iraV00p3b+r43A0T8zLXXOqbLw0RwAAoE778ccf9dRTTyk7O1sBAQE1uu/MzExNmjSpzHhWVpaCgoKuue6UeKvD4li7dq3DtlUd2dnZrg7BKTwxL3fL6fz585WaR3MEAADqtNzcXBUUFKhTp062sZKSEm3ZskVvvvmm1q9fr4sXL6qwsNDu6FF+fr6io6MlSdHR0dq5c6fddkuvZlc6pzzjxo1TRkaG7XFRUZGaNGmipKQkhYaGlruOxWJRdna2XtrtLbPVq8r5lmffxGSHbOdGlebUr18/+fn5uTQWR/LEvNw1p9KjstdDcwQAAOq0vn37au/evXZjw4YNU6tWrfT888+rSZMm8vPz04YNGzRo0CBJ0qFDh3Ts2DElJCRIkhISEjRt2jQVFBQoMjJS0uVP1kNDQxUXF1fhvk0mk0wmU5lxPz+/677xNFu9ZC5xTHNUW97kViZvd+SJeblbTpWNleYIAADUaSEhIWrTpo3dWHBwsBo0aGAbHz58uDIyMhQREaHQ0FCNGTNGCQkJ6t69uyQpKSlJcXFxeuyxxzRjxgzl5eVp/PjxSktLK7f5AVA70RwBAABcxxtvvCFvb28NGjRIZrNZycnJmjdvnm25j4+P1qxZo1GjRikhIUHBwcFKTU3V5MmTXRg1gKriUt4A3EZmZqa6dOmikJAQRUZGasCAATp06JDdHO5iD8ARNm3apFmzZtkeBwQEaO7cuTp9+rSKi4v1z3/+s8y5RM2aNdPatWt1/vx5nTp1SjNnzpSvL59DA+6E5giA29i8ebPS0tK0fft2ZWdny2KxKCkpScXFxbY53MUeAADcKD7OAOA21q1bZ/d48eLFioyMVG5uru666y7bXeyXLVumPn36SJIWLVqk1q1ba/v27erevbvtLvafffaZoqKi1KFDB02ZMkXPP/+8Jk6cKH9/f1ekBgAAagGaIwdr/sK/anyfJh9DM7pKbSaud9hVayrj+1dSamxfqJgjf+fc7Tk9c+aMJCkiIkLS9e9i37179wrvYj9q1Cjt379fHTt2rNkkAABArUFzBMAtWa1WjR07Vj169LBdTcpZd7G/1h3sS88nKO/O2yYfx929vqJ9OJu73gm9Kjw9R3fKzx1iBODZaI4AuKW0tDTt27dPW7dudfq+KnMH+/LuFD6jq2PjcOUd7N3tTug3wtNzdIf8KnsHewBwFpojAG4nPT1da9as0ZYtW9S4cWPbeHR0tFPuYn+tO9gHBgZWeKfwNhPXVyvPq7niDvbueif0qvD0HN0pv8rewR4AnIXmCIDbMAxDY8aM0YoVK7Rp0ybFxsbaLe/cubNT7mJfmTvYl3encEefA+jKN7budif0G+HpObpDfrU9PgCej+YIgNtIS0vTsmXL9MknnygkJMR2jlBYWJgCAwMVFhbGXewBAMANozkC4Dbmz58vSbr77rvtxhctWqShQ4dK4i72AADgxtEcAXAbhnH9q7+V3sV+7ty5Fc4pvYs9AADAlbxdHQAAAAAA1AY0RwAAAAAgmiMAAAAAkERzBAAAAACSaI4AAAAAQBLNEQAAAABIojkCAAAAAEk0RwAAAAAgieYIAAAAACTRHAEAAACAJJojAAAAAJAk+bo6AACAazR/4V/XnWPyMTSjq9Rm4nqZS7wqnPf9KymODA0AAJegOQIAAOWqTANdWTTQANwBzREAoNoc+SZa4o00AMA1OOcIAAAAAERzBAAAAACSaI4AAAAAQBLNEQAAAABIckJztGXLFt1///2KiYmRl5eXVq5cabfcMAxNmDBBjRo1UmBgoBITE3X48GG7OadPn9aQIUMUGhqq8PBwDR8+XOfOnXN0qAAAAABg4/DmqLi4WO3bt9fcuXPLXT5jxgzNnj1bCxYs0I4dOxQcHKzk5GRduHDBNmfIkCHav3+/srOztWbNGm3ZskUjR450dKgAAAAAYOPwS3n3799f/fv3L3eZYRiaNWuWxo8frwceeECStHTpUkVFRWnlypUaPHiwDh48qHXr1mnXrl2Kj4+XJM2ZM0f33nuvZs6cqZiYGEeHDAAAAAA1e5+jo0ePKi8vT4mJibaxsLAwdevWTTk5ORo8eLBycnIUHh5ua4wkKTExUd7e3tqxY4cefPDBcrdtNptlNpttj4uKiiRJFotFvr6+tn+Xx+RjVDs3VzJ5G3b/rykV/Tydua+a3GdNu9EcHfn7W5l9e/JzAAAA6rYabY7y8vIkSVFRUXbjUVFRtmV5eXmKjIy0W+7r66uIiAjbnPJkZmZq0qRJZcazsrIUFBQkScrOzi533RldK59DbTYl3lqj+1u7dm2N7k+q+Dn0JFXN0ZG/v5V5Ts+fP++4HQIAANQiNdocOdO4ceOUkZFhe1xUVKQmTZooKSlJgYGBys7OVr9+/eTn51dm3TYT19dkqA5n8jY0Jd6ql3Z7y2z1qrH97puYXGP7slgs13wOPcGN5ujI39/KPKelR2UBAAA8TY02R9HR0ZKk/Px8NWrUyDaen5+vDh062OYUFBTYrXfp0iWdPn3atn55TCaTTCZTmXE/Pz/bG80r/30lc0nNNRTOZLZ61WgurmhSKnoOPUlVc3Tkc16Z/Xr6zx8AANRdNXqfo9jYWEVHR2vDhg22saKiIu3YsUMJCQmSpISEBBUWFio3N9c2Z+PGjbJarerWrVtNhgsAAACgDnF4c3Tu3Dnt2bNHe/bskXT5Igx79uzRsWPH5OXlpbFjx2rq1KlatWqV9u7dq8cff1wxMTEaMGCAJKl169a65557NGLECO3cuVNffvml0tPTNXjwYK5UBwAAnCIzM1NdunRRSEiIIiMjNWDAAB06dMhuzoULF5SWlqYGDRqoXr16GjRokPLz8+3mHDt2TCkpKQoKClJkZKSeffZZXbp0qSZTAVANDm+Odu/erY4dO6pjx46SpIyMDHXs2FETJkyQJD333HMaM2aMRo4cqS5duujcuXNat26dAgICbNt477331KpVK/Xt21f33nuvevbsqYULFzo6VAAAAEnS5s2blZaWpu3btys7O1sWi0VJSUkqLi62zXn66ae1evVqLV++XJs3b9aJEyc0cOBA2/KSkhKlpKTo4sWL2rZtm5YsWaLFixfb3gMBqP0cfs7R3XffLcOo+NLCXl5emjx5siZPnlzhnIiICC1btszRoQEAAJRr3bp1do8XL16syMhI5ebm6q677tKZM2f07rvvatmyZerTp48kadGiRWrdurW2b9+u7t27KysrSwcOHNBnn32mqKgodejQQVOmTNHzzz+viRMnyt/f3xWpAagCj7laHQAAgKOcOXNG0uUPbCUpNzdXFovF7l6NrVq1UtOmTZWTk6Pu3bsrJydHbdu2tbtlSXJyskaNGqX9+/fbvlVzpWvdp7Gi+8qVjjvy/oauvoedp97P0BPzctecKhsvzREAAMAVrFarxo4dqx49eqhNmzaSLt+H0d/fX+Hh4XZzr75XY3n3cixdVp7K3KexIo68v6Er7l1YHk+9n6En5uVuOVX2Po00RwDcxpYtW/SXv/xFubm5OnnypFasWGG7mIskGYahl19+WW+//bYKCwvVo0cPzZ8/Xy1btrTNOX36tMaMGaPVq1fL29tbgwYN0l//+lfVq1fPBRkBqI3S0tK0b98+bd261en7utZ9GkNDQ8tdp/S+eI68v2FN3ruwPJ56P0NPzMtdc6rsfRppjgC4jeLiYrVv315/+MMf7E6CLjVjxgzNnj1bS5YsUWxsrF566SUlJyfrwIEDtou+DBkyRCdPnrSdcD1s2DCNHDmS8xwBSJLS09O1Zs0abdmyRY0bN7aNR0dH6+LFiyosLLQ7epSfn2+7D2N0dLR27txpt73Sq9lVdK/GytynsSKOvL9hbXmT66n3M/TEvNwtp8rGWqP3OQKA6ujfv7+mTp2qBx98sMwywzA0a9YsjR8/Xg888IDatWunpUuX6sSJE1q5cqUk6eDBg1q3bp3eeecddevWTT179tScOXP0wQcf6MSJEzWcDYDaxDAMpaena8WKFdq4caNiY2Ptlnfu3Fl+fn5292o8dOiQjh07Znevxr1799rdzD47O1uhoaGKi4urmUQAVAtHjgB4hKNHjyovL8/uZOmwsDB169ZNOTk5Gjx4sHJychQeHq74+HjbnMTERHl7e2vHjh3lNl3StU+Y9vX1tf37aiYfx50sXdE+qqMy8ZWe8O3IE78royZP9HXXk4srqzr5OfJ3uDL7d+VzkJaWpmXLlumTTz5RSEiI7RyhsLAwBQYGKiwsTMOHD1dGRoYiIiIUGhqqMWPGKCEhQd27d5ckJSUlKS4uTo899phmzJihvLw8jR8/XmlpaeUeHQJQ+9AcAfAIpW9kyjsZ+sqTpSMjI+2W+/r6KiIiosKTpaXKnTBd3ompM7pWLYfrcfQJ01WJz5EnfleGK04Od7eTi6vqRvJz5O9wZZ7Typ4w7Qzz58+XdPmWJFdatGiRhg4dKkl64403bOcqms1mJScna968eba5Pj4+WrNmjUaNGqWEhAQFBwcrNTX1mrcvAVC70BwBwHVc64TpwMDACk9MbTNxvUPjcPQJ05WJz+RtaEq81aEnfldGTZ4c7q4nF1dWdfJz5O9wZZ7Typ4w7QzXukdjqYCAAM2dO1dz586tcE6zZs1qzZXfAFQdzREAj1B6snN+fr4aNWpkG8/Pz1eHDh1sc648F0CSLl26pNOnT1d4srRUuROmyzsx1VEnSl+5P0eqSnyOPPG7MlzRpLjbycVVdSP5OfI5r8y+PfnnD8A9cEEGAB4hNjZW0dHRdidLFxUVaceOHXYnSxcWFio3N9c2Z+PGjbJarerWrVuNxwwAAGoXjhwBcBvnzp3Td999Z3t89OhR7dmzRxEREWratKnGjh2rqVOnqmXLlrZLecfExNjuhdS6dWvdc889GjFihBYsWCCLxaL09HQNHjxYMTExLsoKAADUFjRHANzG7t279Zvf/Mb2uPQ8oNTUVC1evFjPPfeciouLNXLkSBUWFqpnz55at26d7R5HkvTee+8pPT1dffv2tZ1YPXv27BrPBQAA1D40RwDcxt13333Nk6a9vLw0efLka14ZKiIighu+AgCAcnHOEQAAAACI5ggAAAAAJNEcAQAAAIAkmiMAAAAAkERzBAAAAACSaI4AAAAAQBLNEQAAAABI4j5HAAAAAGpQ8xf+5dDtff9KisO2xZEjAAAAABDNEQAAAABIojkCAAAAAEk0RwAAAAAgieYIAAAAACTRHAEAAACAJJojAAAAAJBEcwQAAAAAkmiOAAAAAEASzREAAAAASKI5AgAAAABJkq+rAwAAVE7zF/7l6hAAAPBoHDkCAAAAANEcAQAAAIAkmiMAAAAAkERzBAAAAACSaI4AAAAAQBLNEQAAAABIojkCAAAAAEk0RwAAAAAgieYIAAAAACTRHAEAAACApFreHM2dO1fNmzdXQECAunXrpp07d7o6JAAegvoCwFmoL4D7qrXN0YcffqiMjAy9/PLL+uqrr9S+fXslJyeroKDA1aEBcHPUFwDOQn0B3FutbY5ef/11jRgxQsOGDVNcXJwWLFigoKAg/e1vf3N1aADcHPUFgLNQXwD35uvqAMpz8eJF5ebmaty4cbYxb29vJSYmKicnp9x1zGazzGaz7fGZM2ckSadPn1ZAQIDOnz+vn3/+WX5+fmXW9b1U7OAMapav1dD581b5WrxVYvWqsf3+/PPPNbYvi8VyzefQE9xojo78/a3Mc3r27FlJkmEYDttvTarJ+uLutUWivniC6uRHfakaR9cXi8VS7jqlz6kjX5c1+Zorj6e+Dj0xL0fk5Oi/j46sL7WyOfrpp59UUlKiqKgou/GoqCh988035a6TmZmpSZMmlRmPjY11Soy1zf9xwT5ves0FO4VTVeU5PXv2rMLCwpwXjJNQX6qO+gJHoL7U7vrCaw7uzJH1pVY2Rzdi3LhxysjIsD22Wq06ffq0GjRooLNnz6pJkyb68ccfFRoa6sIonaOoqMij85PIsTYxDENnz55VTEyMq0OpMdQXz81P8vwc3Sk/6ot9ffHyKv+okDs9p5XliTlJnpmXu+ZU2fpSK5ujm266ST4+PsrPz7cbz8/PV3R0dLnrmEwmmUwmu7Hw8HBJshWX0NBQt3oSq8rT85PIsbZwx090S1Ffboyn5yd5fo7ukh/15f/Vl+txl+e0KjwxJ8kz83LHnCpTX2rlBRn8/f3VuXNnbdiwwTZmtVq1YcMGJSQkuDAyAO6O+gLAWagvgPurlUeOJCkjI0OpqamKj49X165dNWvWLBUXF2vYsGGuDg2Am6O+AHAW6gvg3mptc/S73/1Op06d0oQJE5SXl6cOHTpo3bp1ZU5yrAyTyaSXX365zGFrT+Hp+UnkCMeivlSep+cneX6Onp5fbePI+lIRT3xOPTEnyTPz8sScruRluOv1MgEAAADAgWrlOUcAAAAAUNNojgAAAABANEcAAAAAIInmCAAAAAAk1YHmaO7cuWrevLkCAgLUrVs37dy509UhOUxmZqa6dOmikJAQRUZGasCAATp06JCrw3KaV155RV5eXho7dqyrQ3Go48eP69FHH1WDBg0UGBiotm3bavfu3a4OC5VAffEc1Be4E0+qPXWh1nhKfakr9cSjm6MPP/xQGRkZevnll/XVV1+pffv2Sk5OVkFBgatDc4jNmzcrLS1N27dvV3Z2tiwWi5KSklRcXOzq0Bxu165deuutt9SuXTtXh+JQv/zyi3r06CE/Pz99+umnOnDggF577TXVr1/f1aHhOqgvnoP6AnfiabXH02uNp9SXOlVPDA/WtWtXIy0tzfa4pKTEiImJMTIzM10YlfMUFBQYkozNmze7OhSHOnv2rNGyZUsjOzvb6N27t/HUU0+5OiSHef75542ePXu6OgzcAOqLZ6C+wN14eu3xpFrjSfWlLtUTjz1ydPHiReXm5ioxMdE25u3trcTEROXk5LgwMuc5c+aMJCkiIsLFkThWWlqaUlJS7J5LT7Fq1SrFx8fr4YcfVmRkpDp27Ki3337b1WHhOqgvnoP6AndSF2qPJ9UaT6ovdameeGxz9NNPP6mkpKTMHamjoqKUl5fnoqicx2q1auzYserRo4fatGnj6nAc5oMPPtBXX32lzMxMV4fiFEeOHNH8+fPVsmVLrV+/XqNGjdKTTz6pJUuWuDo0XAP1xTNQX+BuPL32eFKt8bT6Upfqia+rA4BjpKWlad++fdq6daurQ3GYH3/8UU899ZSys7MVEBDg6nCcwmq1Kj4+XtOnT5ckdezYUfv27dOCBQuUmprq4uiAy6gv7on6AnfjKbXGE+tLXaonHnvk6KabbpKPj4/y8/PtxvPz8xUdHe2iqJwjPT1da9as0eeff67GjRu7OhyHyc3NVUFBgTp16iRfX1/5+vpq8+bNmj17tnx9fVVSUuLqEKutUaNGiouLsxtr3bq1jh075qKIUBnUF/dHfYE78uTa40m1xhPrS12qJx7bHPn7+6tz587asGGDbcxqtWrDhg1KSEhwYWSOYxiG0tPTtWLFCm3cuFGxsbGuDsmh+vbtq71792rPnj22/+Lj4zVkyBDt2bNHPj4+rg6x2nr06FHmkqXffvutmjVr5qKIUBnUF/dHfYE78sTa44m1xhPrS52qJy6+IIRTffDBB4bJZDIWL15sHDhwwBg5cqQRHh5u5OXluTo0hxg1apQRFhZmbNq0yTh58qTtv/Pnz7s6NKdx96u9XG3nzp2Gr6+vMW3aNOPw4cPGe++9ZwQFBRl///vfXR0aroP64nmoL3AHnlZ76kqtcff6UpfqiUc3R4ZhGHPmzDGaNm1q+Pv7G127djW2b9/u6pAcRlK5/y1atMjVoTmNuxeX8qxevdpo06aNYTKZjFatWhkLFy50dUioJOqLZ6G+wF14Uu2pK7XGE+pLXaknXoZhGDV9tAoAAAAAahuPPecIAAAAAKqC5ggAAAAARHMEAAAAAJJojgAAAABAEs0RAAAAAEiiOQIAAAAASTRHAAAAACCJ5gjwSFu2bNH999+vmJgYeXl5aeXKlVVaf+LEifLy8irzX3BwsHMCBuAWqC0AnKW69UWSDMPQzJkzddttt8lkMunmm2/WtGnTqrQNmiPAAxUXF6t9+/aaO3fuDa3/zDPP6OTJk3b/xcXF6eGHH3ZwpADcCbUFgLNUt75I0lNPPaV33nlHM2fO1DfffKNVq1apa9euVduIAcCjSTJWrFhhN3bhwgXjT3/6kxETE2MEBQUZXbt2NT7//PMKt7Fnzx5DkrFlyxbnBgvAbVBbADjLjdSXAwcOGL6+vsY333xTrX1z5Aiog9LT05WTk6MPPvhA//nPf/Twww/rnnvu0eHDh8ud/8477+i2225Tr169ajhSAO6E2gLAWa5XX1avXq1bbrlFa9asUWxsrJo3b64nnnhCp0+frtJ+aI6AOubYsWNatGiRli9frl69eqlFixZ65pln1LNnTy1atKjM/AsXLui9997T8OHDXRAtAHdBbQHgLJWpL0eOHNEPP/yg5cuXa+nSpVq8eLFyc3P10EMPVWlfvs5IAEDttXfvXpWUlOi2226zGzebzWrQoEGZ+StWrNDZs2eVmppaUyECcEPUFgDOUpn6YrVaZTabtXTpUtu8d999V507d9ahQ4d0++23V2pfNEdAHXPu3Dn5+PgoNzdXPj4+dsvq1atXZv4777yj++67T1FRUTUVIgA3RG0B4CyVqS+NGjWSr6+vXQPVunVrSZePPNEcAShXx44dVVJSooKCgut+z//o0aP6/PPPtWrVqhqKDoC7orYAcJbK1JcePXro0qVL+u9//6sWLVpIkr799ltJUrNmzSq9L5ojwAOdO3dO3333ne3x0aNHtWfPHkVEROi2227TkCFD9Pjjj+u1115Tx44dderUKW3YsEHt2rVTSkqKbb2//e1vatSokfr37++KNADUMtQWAM5S3fqSmJioTp066Q9/+INmzZolq9WqtLQ09evXr8zX8a6pWte6A1Arff7554akMv+lpqYahmEYFy9eNCZMmGA0b97c8PPzMxo1amQ8+OCDxn/+8x/bNkpKSozGjRsbL774oouyAFDbUFsAOIsj6svx48eNgQMHGvXq1TOioqKMoUOHGj///HOV4vAyDMNwRLcHAAAAAO6MS3kDAAAAgGiOAAAAAEASzVGlTJw4UV5eXvrpp59cHQoqadOmTfLy8tJHH3103blDhw5V8+bN7ca8vLw0ceJE5wR3HaWxb9q0ySX7B2qT2vD6bN68ue677z6HbrOyOZT+/QEA1AyaI8BF5s2bp8WLF7s6DMDtbdu2TRMnTlRhYaGrQwEAuDku5Q2U49dff5Wvr3NfHvPmzdNNN92koUOH2o3fdddd+vXXX+Xv7+/U/QPu6urX57Zt2zRp0iQNHTpU4eHhrgsMAOD2OHJUCxiGoV9//bXcZRcuXJDVaq3hiKrm/Pnzrg7B4QICApzeHFXE29tbAQEB8vbm5Qn3VVxc7LRtu/L1CQDwbLz7qoKffvpJjzzyiEJDQ9WgQQM99dRTunDhgm35pUuXNGXKFLVo0UImk0nNmzfXiy++KLPZbLed0u+vr1+/XvHx8QoMDNRbb71lO9fkgw8+0Pjx43XzzTcrKChIRUVFkqTly5erc+fOCgwM1E033aRHH31Ux48ft2131apV8vLy0n/+8x/b2McffywvLy8NHDjQLobWrVvrd7/7nd3Y3//+d9v2IyIiNHjwYP344492c+6++261adNGubm5uuuuuxQUFKQXX3yxUj+/s2fPauzYsWrevLlMJpMiIyPVr18/ffXVV5Vav9SRI0f08MMPKyIiQkFBQerevbv+9a9/lTu3pKREL774oqKjoxUcHKzf/va3ZXIqT3nnA2zatEnx8fEKCAhQixYt9NZbb5V7PsCiRYvUp08fRUZGymQyKS4uTvPnz7eb07x5c+3fv1+bN2+Wl5eXvLy8dPfdd9v2U945R9d7/qXL52fUq1dPx48f14ABA1SvXj01bNhQzzzzjEpKSq6bN1CR48ePa/jw4YqJiZHJZFJsbKxGjRqlixcvavHixfLy8tLmzZs1evRoRUZGqnHjxrZ1P/30U/Xq1UvBwcEKCQlRSkqK9u/fX2YfK1euVJs2bRQQEKA2bdpoxYoV5cZy5etz4sSJevbZZyVJsbGxttfT999/L0nKzs5Wz549FR4ernr16un222+vdM262tatW9W1a1cFBATolltu0dKlS8vMKSws1NixY9WkSROZTCbdeuutevXVVyv1IdfWrVvVpUsXuxoDAKhZfPRWBY888oiaN2+uzMxMbd++XbNnz9Yvv/xi+wP5xBNPaMmSJXrooYf0pz/9STt27FBmZqYOHjxY5o/8oUOH9Pvf/15//OMfNWLECN1+++22ZVOmTJG/v7+eeeYZmc1m+fv7a/HixRo2bJi6dOmizMxM5efn669//au+/PJLff311woPD1fPnj3l5eWlLVu2qF27dpKkL774Qt7e3tq6datt+6dOndI333yj9PR029i0adP00ksv6ZFHHtETTzyhU6dOac6cObrrrrts2y/1888/q3///ho8eLAeffRRRUVFVern9z//8z/66KOPlJ6erri4OP3888/aunWrDh48qE6dOlVqG/n5+brzzjt1/vx5Pfnkk2rQoIGWLFmi3/72t/roo4/04IMP2s2fNm2avLy89Pzzz6ugoECzZs1SYmKi9uzZo8DAwErtU5K+/vpr3XPPPWrUqJEmTZqkkpISTZ48WQ0bNiwzd/78+brjjjv029/+Vr6+vlq9erVGjx5tu1OzJM2aNUtjxoxRvXr19Oc//1mSrvlzrMzzX6qkpETJycnq1q2bZs6cqc8++0yvvfaaWrRooVGjRlU6Z6DUiRMn1LVrVxUWFmrkyJFq1aqVjh8/ro8++sjuyPHo0aPVsGFDTZgwwXbk6H//93+Vmpqq5ORkvfrqqzp//rzmz5+vnj176uuvv7ZdbCErK0uDBg1SXFycMjMz9fPPP2vYsGF2TVZ5Bg4cqG+//Vbvv/++3njjDd10002SpIYNG2r//v2677771K5dO02ePFkmk0nfffedvvzyyyr/DL777js99NBDGj58uFJTU/W3v/1NQ4cOVefOnXXHHXdIunwUvXfv3jp+/Lj++Mc/qmnTptq2bZvGjRunkydPatasWRVuf+/evUpKSlLDhg01ceJEXbp0SS+//HKl6ysAwEEcdltbD/byyy8bkozf/va3duOjR482JBn//ve/jT179hiSjCeeeMJuzjPPPGNIMjZu3Ggba9asmSHJWLdund3c0jsD33LLLcb58+dt4xcvXjQiIyONNm3aGL/++qttfM2aNYYkY8KECbaxO+64w3jkkUdsjzt16mQ8/PDDhiTj4MGDhmEYxj//+U9b3IZhGN9//73h4+NjTJs2zS6evXv3Gr6+vnbjvXv3NiQZCxYsqNwP7wphYWFGWlpalde70tixYw1JxhdffGEbO3v2rBEbG2s0b97cKCkpMQzj//0sb775ZqOoqMg29x//+IchyfjrX/9qG0tNTTWaNWtmtx9Jxssvv2x7fP/99xtBQUHG8ePHbWOHDx82fH19jatfRlc+d6WSk5ONW265xW7sjjvuMHr37l1mbmnsn3/+uWEYVXv+U1NTDUnG5MmT7bbZsWNHo3PnzmX2BVTG448/bnh7exu7du0qs8xqtRqLFi0yJBk9e/Y0Ll26ZFt29uxZIzw83BgxYoTdOnl5eUZYWJjdeIcOHYxGjRoZhYWFtrGsrCxD0nVfn3/5y18MScbRo0ft5r3xxhuGJOPUqVM3kPX/U1qzt2zZYhsrKCgwTCaT8ac//ck2NmXKFCM4ONj49ttv7dZ/4YUXDB8fH+PYsWMV5jBgwAAjICDA+OGHH2xjBw4cMHx8fMrUGACA8/C1uioo/dS/1JgxYyRJa9eu1dq1ayVJGRkZdnP+9Kc/SVKZr33FxsYqOTm53P2kpqbaHdXYvXu3CgoKNHr0aAUEBNjGU1JS1KpVK7tt9+rVS1988YWky19j+/e//62RI0fqpptuso1/8cUXCg8PV5s2bSRJ//znP2W1WvXII4/op59+sv0XHR2tli1b6vPPP7eLz2QyadiwYdf6UZUrPDxcO3bs0IkTJ6q8bqm1a9eqa9eu6tmzp22sXr16GjlypL7//nsdOHDAbv7jjz+ukJAQ2+OHHnpIjRo1sj1flVFSUqLPPvtMAwYMUExMjG381ltvVf/+/cvMv/K5O3PmjH766Sf17t1bR44c0ZkzZyq931JVef5L/c///I/d4169eunIkSNV3jdgtVq1cuVK3X///YqPjy+z/MqvlY4YMUI+Pj62x9nZ2SosLNTvf/97u9ri4+Ojbt262WrLyZMntWfPHqWmpiosLMy2fr9+/RQXF3fDsZceUf3kk0+qfe5mXFycevXqZXvcsGFD3X777Xavq+XLl6tXr16qX7++Xb6JiYkqKSnRli1byt12SUmJ1q9frwEDBqhp06a28datW1f4dwIA4Bw0R1XQsmVLu8ctWrSQt7e3vv/+e/3www/y9vbWrbfeajcnOjpa4eHh+uGHH+zGY2NjK9zP1ctK173yq3elWrVqZbftXr166eTJk/ruu++0bds2eXl5KSEhwa5p+uKLL9SjRw/bCf+HDx+WYRhq2bKlGjZsaPffwYMHVVBQYLfPm2+++YaupDZjxgzt27dPTZo0UdeuXTVx4sQqv2H/4Ycfyv05tG7d2rb8Slc/Z15eXrr11ltt5yNURkFBgX799dcyz62kcse+/PJLJSYmKjg4WOHh4WrYsKHtHIcbaY6q8vxLl09Wv/rrfvXr19cvv/xS5X0Dp06dUlFRke3DlGu5unYdPnxYktSnT58ytSUrK8tWW0p/h69+vUrl/95X1u9+9zv16NFDTzzxhKKiojR48GD94x//uKFG6cqmpdTVr6vDhw9r3bp1ZXJNTEyUpDK1tNSpU6f066+/Ojx/AEDVcc5RNZR3Y77K3qzvWue7VOVcmKuVHlHZsmWLjhw5ok6dOik4OFi9evXS7Nmzde7cOX399deaNm2abR2r1SovLy99+umndp/6lqpXr55D4nvkkUfUq1cvrVixQllZWfrLX/6iV199Vf/85z/LPQLjjv773/+qb9++atWqlV5//XU1adJE/v7+Wrt2rd54440aufJgec8hUBOurg2lv+//+7//q+jo6DLznX3FucDAQG3ZskWff/65/vWvf2ndunX68MMP1adPH2VlZVXptVLRXMMwbP+2Wq3q16+fnnvuuXLn3nbbbVVLAABQ42iOquDw4cN2n4x+9913slqtat68uQzDkNVq1eHDh21HMaTLFxAoLCxUs2bNbni/peseOnRIffr0sVt26NAhu203bdpUTZs21RdffKEjR47YvgZy1113KSMjQ8uXL1dJSYnuuusu2zotWrSQYRiKjY11+h/vRo0aafTo0Ro9erQKCgrUqVMnTZs2rdLNUbNmzXTo0KEy4998841t+ZVKP7kuZRiGvvvuO9sFKyojMjJSAQEB+u6778osu3ps9erVMpvNWrVqld0nzVd/NVGqfCNdlecfcLSGDRsqNDRU+/btq/K6LVq0kHT5NVR69KQ8pb/DV79eJZX7er/atV5L3t7e6tu3r/r27avXX39d06dP15///Gd9/vnn14zpRrRo0ULnzp2r8nYbNmyowMDAG84fAOA4fK2uCubOnWv3eM6cOZKk/v37695775WkMlcjev311yVdPj/kRsXHxysyMlILFiywuyz4p59+qoMHD5bZdq9evbRx40bt3LnT1hx16NBBISEheuWVVxQYGKjOnTvb5g8cOFA+Pj6aNGmS3aeg0uVm4ueff77h2EuVlJSU+UpZZGSkYmJiylzq/Fruvfde7dy5Uzk5Obax4uJiLVy4UM2bNy9zfsLSpUt19uxZ2+OPPvpIJ0+erNKRKh8fHyUmJmrlypV250t99913+vTTT8vMlew/TT5z5owWLVpUZrvBwcEqLCy87v6r+vwDjuTt7a0BAwZo9erV2r17d5nlV9eMKyUnJys0NFTTp0+XxWIps/zUqVOSLn9o0qFDBy1ZssSuTmRnZ5c5j7A8wcHBklTm9XT69Okyczt06CBJVao7lfXII48oJydH69evL7OssLBQly5dKnc9Hx8fJScna+XKlTp27Jht/ODBg+VuCwDgPBw5qoKjR4/qt7/9re655x7l5OTo73//u/7P//k/at++vaTLF1JYuHChCgsL1bt3b+3cuVNLlizRgAED9Jvf/OaG9+vn56dXX31Vw4YNU+/evfX73//edinn5s2b6+mnn7ab36tXL7333nvy8vKyfc3Ox8dHd955p9avX6+7777b7pyhFi1aaOrUqRo3bpy+//57DRgwQCEhITp69KhWrFihkSNH6plnnrnh+KXLF4do3LixHnroIbVv31716tXTZ599pl27dum1116r9HZeeOEFvf/+++rfv7+efPJJRUREaMmSJTp69Kg+/vjjMjdOjYiIUM+ePTVs2DDl5+dr1qxZuvXWWzVixIgqxT9x4kRlZWWpR48eGjVqlEpKSvTmm2+qTZs22rNnj21eUlKS/P39df/99+uPf/yjzp07p7fffluRkZE6efKk3TY7d+6s+fPna+rUqbr11lsVGRlZ5siQVPXnH3C06dOnKysrS71799bIkSPVunVrnTx5UsuXL7e7TcDVQkNDNX/+fD322GPq1KmTBg8erIYNG+rYsWP617/+pR49eujNN9+UJGVmZiolJUU9e/bUH/7wB50+fVpz5szRHXfcoXPnzl0zvtIPe/785z9r8ODB8vPz0/3336/Jkydry5YtSklJUbNmzVRQUKB58+apcePGdhd1cZRnn31Wq1at0n333We7zHdxcbH27t2rjz76SN9//73tUuNXmzRpktatW6devXpp9OjRunTpki3/K+9dBwBwMpddJ8+NlF7K+8CBA8ZDDz1khISEGPXr1zfS09PtLq1ssViMSZMmGbGxsYafn5/RpEkTY9y4ccaFCxfsttesWTMjJSWlzH5KL+G8fPnycuP48MMPjY4dOxomk8mIiIgwhgwZYvzf//t/y8zbv3+/Iclo3bq13fjUqVMNScZLL71U7vY//vhjo2fPnkZwcLARHBxstGrVykhLSzMOHTpkm9O7d2/jjjvuqPiHVQGz2Ww8++yzRvv27Y2QkBAjODjYaN++vTFv3rwqb+u///2v8dBDDxnh4eFGQECA0bVrV2PNmjV2c0p/lu+//74xbtw4IzIy0ggMDDRSUlLsLpVrGJW7lLdhGMaGDRuMjh07Gv7+/kaLFi2Md955x/jTn/5kBAQE2M1btWqV0a5dOyMgIMBo3ry58eqrrxp/+9vfylxqOC8vz0hJSTFCQkIMSbbLel99Ke9SlXn+U1NTjeDg4DI/s9LfYeBG/fDDD8bjjz9uNGzY0DCZTMYtt9xipKWlGWaz2XYp7/Iu9W0Yl3+nk5OTjbCwMCMgIMBo0aKFMXToUGP37t128z7++GOjdevWhslkMuLi4ox//vOflX59Tpkyxbj55psNb29v22ttw4YNxgMPPGDExMQY/v7+RkxMjPH73/++zKW2r6eimt27d+8yl+M/e/asMW7cOOPWW281/P39jZtuusm48847jZkzZxoXL168Zg6bN282OnfubPj7+xu33HKLsWDBAl67AFDDvAzjGt+JAHBNAwYM0P79+8s9VwAAAADuhXOOgEr69ddf7R4fPnxYa9eu1d133+2agAAAAOBQHDlCtZ07d+665wQ0bNjwmpfN/fXXX697D6CIiIgbur+SozRq1EhDhw7VLbfcoh9++EHz58+X2WzW119/Xe79SQDUbqdOnVJJSUmFy/39/RUREVGDEQEAXI0LMqDaZs6cqUmTJl1zztGjR9W8efMKl3/44YcaNmzYNbfx+eefu/QozT333KP3339feXl5MplMSkhI0PTp02mMADfVpUuXMjdRvlLv3r21adOmmgsIAOByHDlCtR05ckRHjhy55pyePXsqICCgwuUnT57U/v37r7mNzp07q379+jcUIwBc7csvvyzzddkr1a9f3+62BwAAz0dzBAAAAADiggwAAAAAIMmDzzmyWq06ceKEQkJC5OXl5epwAI9hGIbOnj2rmJiYMjfdrSuoL4BzUF8AuJrHNkcnTpxQkyZNXB0G4LF+/PFHNW7c2NVhuAT1BXCuulxfALiWxzZHISEhki4X2NDQ0ArnWSwWZWVlKSkpSX5+fjUVnlORk3tw15yKiorUpEkT22usLqpMfXHX5/d6PDEvcqo9qC8AXM1jm6PSr7qEhoZetzkKCgpSaGioW/0BuRZycg/unlNd/jpZZeqLuz+/FfHEvMip9qnL9QWAa/GFXgAAAAAQzREAAAAASKI5AgAAAABJNEcAAAAAIInmCAAAAAAk0RwBAAAAgCSaIwAAAACQ5MH3OaqqNhPXy1xS/fsqfP9KigOiAeApHFVbJOoLAADOxpEjAAAAABDNEQAAAABIojkCAAAAAEk0RwAAAAAgieYIAAAAACTRHAEAAACAJJojAAAAAJBEcwQAAAAAkmiOAAAAAEASzREAAAAASKI5AgAAAABJNEcAAAAAIInmCAAAAAAk0RwBAAAAgCQnNEclJSV66aWXFBsbq8DAQLVo0UJTpkyRYRi2OYZhaMKECWrUqJECAwOVmJiow4cP223n9OnTGjJkiEJDQxUeHq7hw4fr3Llzjg4XgBuhvgAAAGdyeHP06quvav78+XrzzTd18OBBvfrqq5oxY4bmzJljmzNjxgzNnj1bCxYs0I4dOxQcHKzk5GRduHDBNmfIkCHav3+/srOztWbNGm3ZskUjR450dLgA3Aj1BQAAOJOvoze4bds2PfDAA0pJSZEkNW/eXO+//7527twp6fKnurNmzdL48eP1wAMPSJKWLl2qqKgorVy5UoMHD9bBgwe1bt067dq1S/Hx8ZKkOXPm6N5779XMmTMVExPj6LABuAHqCwAAcCaHN0d33nmnFi5cqG+//Va33Xab/v3vf2vr1q16/fXXJUlHjx5VXl6eEhMTbeuEhYWpW7duysnJ0eDBg5WTk6Pw8HDbGxdJSkxMlLe3t3bs2KEHH3ywzH7NZrPMZrPtcVFRkSTJYrHIYrFUGG/pMpO3UeGcqrjWvmpKaQy1IRZHIafaw5XxulN9cXRtuXKbruSuv7fXQk61h7vFC8DzOLw5euGFF1RUVKRWrVrJx8dHJSUlmjZtmoYMGSJJysvLkyRFRUXZrRcVFWVblpeXp8jISPtAfX0VERFhm3O1zMxMTZo0qcx4VlaWgoKCrhv3lHjr9ZOrhLVr1zpkO46QnZ3t6hAcjpxc7/z58y7btzvWF0fVFon64mzk5HqurC8AIDmhOfrHP/6h9957T8uWLdMdd9yhPXv2aOzYsYqJiVFqaqqjd2czbtw4ZWRk2B4XFRWpSZMmSkpKUmhoaIXrWSwWZWdn66Xd3jJbvaodx76JydXeRnWV5tSvXz/5+fm5OhyHIKfao/SoiSu4U31xdG2RqC/OQk61hyvrCwBITmiOnn32Wb3wwgsaPHiwJKlt27b64YcflJmZqdTUVEVHR0uS8vPz1ahRI9t6+fn56tChgyQpOjpaBQUFdtu9dOmSTp8+bVv/aiaTSSaTqcy4n59fpf4wmK1eMpdU/w1MbfojVNnc3Qk5uZ4rY3XH+uKo2lK6v9rC3X5vK4OcXM+dYgXgmRx+tbrz58/L29t+sz4+PrJaL3+1JDY2VtHR0dqwYYNteVFRkXbs2KGEhARJUkJCggoLC5Wbm2ubs3HjRlmtVnXr1s3RIQNwE9QXAADgTA4/cnT//fdr2rRpatq0qe644w59/fXXev311/WHP/xBkuTl5aWxY8dq6tSpatmypWJjY/XSSy8pJiZGAwYMkCS1bt1a99xzj0aMGKEFCxbIYrEoPT1dgwcP5kpSQB1GfQEAAM7k8OZozpw5eumllzR69GgVFBQoJiZGf/zjHzVhwgTbnOeee07FxcUaOXKkCgsL1bNnT61bt04BAQG2Oe+9957S09PVt29feXt7a9CgQZo9e7ajwwXgRqgvAADAmRzeHIWEhGjWrFmaNWtWhXO8vLw0efJkTZ48ucI5ERERWrZsmaPDA+DGqC8AAMCZHH7OEQAAAAC4I5ojAAAAABDNEQAAAABIojkCAAAAAEk0RwAAAAAgieYIAAAAACTRHAEAAACAJJojAAAAAJBEcwQAAAAAkmiOAAAAAEASzREAAAAASKI5AgAAAABJNEcAAAAAIInmCAAAAAAk0RwBAAAAgCSaIwAAAACQRHMEAAAAAJJojgAAAABAEs0RAAAAAEiiOQIAAAAASTRHAAAAACCJ5ggAAAAAJNEcAQAAAIAkmiMAAAAAkERzBAAAAACSaI4AAAAAQBLNEQAAAABIojkCAAAAAEk0RwAAAAAgieYIAAAAACTRHAEAAACAJJojAAAAAJBEcwQAAAAAkmiOAAAAAEASzREAAAAASHJSc3T8+HE9+uijatCggQIDA9W2bVvt3r3bttwwDE2YMEGNGjVSYGCgEhMTdfjwYbttnD59WkOGDFFoaKjCw8M1fPhwnTt3zhnhAnAj1BcAAOAsDm+OfvnlF/Xo0UN+fn769NNPdeDAAb322muqX7++bc6MGTM0e/ZsLViwQDt27FBwcLCSk5N14cIF25whQ4Zo//79ys7O1po1a7RlyxaNHDnS0eECcCPUFwAA4Ey+jt7gq6++qiZNmmjRokW2sdjYWNu/DcPQrFmzNH78eD3wwAOSpKVLlyoqKkorV67U4MGDdfDgQa1bt067du1SfHy8JGnOnDm69957NXPmTMXExDg6bABugPoCAACcyeHN0apVq5ScnKyHH35Ymzdv1s0336zRo0drxIgRkqSjR48qLy9PiYmJtnXCwsLUrVs35eTkaPDgwcrJyVF4eLjtjYskJSYmytvbWzt27NCDDz5YZr9ms1lms9n2uKioSJJksVhksVgqjLd0mcnbqF7iV23PlUpjqA2xOAo51R6ujNed6ouja8uV23Qld/29vRZyqj3cLV4AnsfhzdGRI0c0f/58ZWRk6MUXX9SuXbv05JNPyt/fX6mpqcrLy5MkRUVF2a0XFRVlW5aXl6fIyEj7QH19FRERYZtztczMTE2aNKnMeFZWloKCgq4b95R4a6Xyu561a9c6ZDuOkJ2d7eoQHI6cXO/8+fMu27c71hdH1RaJ+uJs5OR6rqwvACA5oTmyWq2Kj4/X9OnTJUkdO3bUvn37tGDBAqWmpjp6dzbjxo1TRkaG7XFRUZGaNGmipKQkhYaGVriexWJRdna2XtrtLbPVq9px7JuYXO1tVFdpTv369ZOfn5+rw3EIcqo9So+auII71RdH1xaJ+uIs5FR7uLK+AIDkhOaoUaNGiouLsxtr3bq1Pv74Y0lSdHS0JCk/P1+NGjWyzcnPz1eHDh1scwoKCuy2cenSJZ0+fdq2/tVMJpNMJlOZcT8/v0r9YTBbvWQuqf4bmNr0R6iyubsTcnI9V8bqjvXFUbWldH+1hbv93lYGObmeO8UKwDM5/Gp1PXr00KFDh+zGvv32WzVr1kzS5ZOno6OjtWHDBtvyoqIi7dixQwkJCZKkhIQEFRYWKjc31zZn48aNslqt6tatm6NDBuAmqC8AAMCZHH7k6Omnn9add96p6dOn65FHHtHOnTu1cOFCLVy4UJLk5eWlsWPHaurUqWrZsqViY2P10ksvKSYmRgMGDJB0+ZPge+65RyNGjNCCBQtksViUnp6uwYMHcyUpoA6jvgAAAGdyeHPUpUsXrVixQuPGjdPkyZMVGxurWbNmaciQIbY5zz33nIqLizVy5EgVFhaqZ8+eWrdunQICAmxz3nvvPaWnp6tv377y9vbWoEGDNHv2bEeHC8CNUF8AAIAzObw5kqT77rtP9913X4XLvby8NHnyZE2ePLnCOREREVq2bJkzwgPgxqgvAADAWRx+zhEAAAAAuCOaIwAAAAAQzREAAAAASKI5AgAAAABJNEcAAAAAIInmCAAAAAAk0RwBAAAAgCSaIwAAAACQRHMEAAAAAJJojgAAAABAEs0RAAAAAEiiOQIAAAAASTRHAAAAACCJ5ggAAAAAJNEcAQAAAIAkmiMAAAAAkERzBAAAAACSaI4AAAAAQBLNEQAAAABIojkCAAAAAEk0RwAAAAAgieYIAAAAACTRHAEAAACAJJojAAAAAJBEcwQAAAAAkmiOAAAAAEASzREAAAAASKI5AgAAAABJNEcAAAAAIInmCAAAAAAk0RwBAAAAgCSaIwAAAACQRHMEAAAAAJJojgAAAABAEs0RAAAAAEiqgebolVdekZeXl8aOHWsbu3DhgtLS0tSgQQPVq1dPgwYNUn5+vt16x44dU0pKioKCghQZGalnn31Wly5dcna4ANwEtQUAADiaU5ujXbt26a233lK7du3sxp9++mmtXr1ay5cv1+bNm3XixAkNHDjQtrykpEQpKSm6ePGitm3bpiVLlmjx4sWaMGGCM8MF4CaoLQAAwBmc1hydO3dOQ4YM0dtvv6369evbxs+cOaN3331Xr7/+uvr06aPOnTtr0aJF2rZtm7Zv3y5JysrK0oEDB/T3v/9dHTp0UP/+/TVlyhTNnTtXFy9edFbIANwAtQUAADiL05qjtLQ0paSkKDEx0W48NzdXFovFbrxVq1Zq2rSpcnJyJEk5OTlq27atoqKibHOSk5NVVFSk/fv3OytkAG6A2gIAAJzF1xkb/eCDD/TVV19p165dZZbl5eXJ399f4eHhduNRUVHKy8uzzbnyzUvp8tJl5TGbzTKbzbbHRUVFkiSLxSKLxVJhrKXLTN7GdbKqnGvtq6aUxlAbYnEUcqo9XBmvK2qLdGP1xdG15cptupK7/t5eCznVHu4WLwDP4/Dm6Mcff9RTTz2l7OxsBQQEOHrzFcrMzNSkSZPKjGdlZSkoKOi660+JtzokjrVr1zpkO46QnZ3t6hAcjpxc7/z58y7Zr6tqi1S9+uKo2iJRX5yNnFzPVfUFAEo5vDnKzc1VQUGBOnXqZBsrKSnRli1b9Oabb2r9+vW6ePGiCgsL7T7hzc/PV3R0tCQpOjpaO3futNtu6RWnSudcbdy4ccrIyLA9LioqUpMmTZSUlKTQ0NAK47VYLMrOztZLu71ltnpVOd+r7ZuYXO1tVFdpTv369ZOfn5+rw3EIcqo9So+a1DRX1RbpxuqLo2uLRH1xFnKqPVxVXwCglMObo759+2rv3r12Y8OGDVOrVq30/PPPq0mTJvLz89OGDRs0aNAgSdKhQ4d07NgxJSQkSJISEhI0bdo0FRQUKDIyUtLlT79CQ0MVFxdX7n5NJpNMJlOZcT8/v0r9YTBbvWQuqf4bmNr0R6iyubsTcnI9V8XqqtoiVa++OKq2lO6vtnC339vKICfXc6dYAXgmhzdHISEhatOmjd1YcHCwGjRoYBsfPny4MjIyFBERodDQUI0ZM0YJCQnq3r27JCkpKUlxcXF67LHHNGPGDOXl5Wn8+PFKS0sr9w0KAM9HbQEAAM7mlAsyXM8bb7whb29vDRo0SGazWcnJyZo3b55tuY+Pj9asWaNRo0YpISFBwcHBSk1N1eTJk10RLgA3QW0BAADVUSPN0aZNm+weBwQEaO7cuZo7d26F6zRr1qxWnXwMoPahtgAAAEdy2n2OAAAAAMCd0BwBAAAAgGiOAAAAAEASzREAAAAASKI5AgAAAABJNEcAAAAAIInmCAAAAAAk0RwBAAAAgCSaIwAAAACQRHMEAAAAAJJojgAAAABAEs0RAAAAAEiiOQIAAAAASTRHAAAAACCJ5ggAAAAAJNEcAQAAAIAkmiMAAAAAkERzBAAAAACSaI4AAAAAQBLNEQAAAABIojkCAAAAAEk0RwAAAAAgieYIAAAAACTRHAEAAACAJJojAAAAAJBEcwQAAAAAkmiOAAAAAEASzREAAAAASKI5AgAAAABJNEcAAAAAIInmCAAAAAAk0RwBAAAAgCSaIwAAAACQRHMEAAAAAJJojgAAAABAEs0RAAAAAEhyQnOUmZmpLl26KCQkRJGRkRowYIAOHTpkN+fChQtKS0tTgwYNVK9ePQ0aNEj5+fl2c44dO6aUlBQFBQUpMjJSzz77rC5duuTocAG4EeoLAABwJoc3R5s3b1ZaWpq2b9+u7OxsWSwWJSUlqbi42Dbn6aef1urVq7V8+XJt3rxZJ06c0MCBA23LS0pKlJKSoosXL2rbtm1asmSJFi9erAkTJjg6XABuhPoCAACcydfRG1y3bp3d48WLFysyMlK5ubm66667dObMGb377rtatmyZ+vTpI0latGiRWrdure3bt6t79+7KysrSgQMH9NlnnykqKkodOnTQlClT9Pzzz2vixIny9/d3dNgA3AD1BQAAOJPDm6OrnTlzRpIUEREhScrNzZXFYlFiYqJtTqtWrdS0aVPl5OSoe/fuysnJUdu2bRUVFWWbk5ycrFGjRmn//v3q2LFjmf2YzWaZzWbb46KiIkmSxWKRxWKpML7SZSZvoxpZlt2eK5XGUBticRRyqj1qU7y1ub44urZcuU1Xctff22shp9rD3eIF4Hmc2hxZrVaNHTtWPXr0UJs2bSRJeXl58vf3V3h4uN3cqKgo5eXl2eZc+caldHnpsvJkZmZq0qRJZcazsrIUFBR03VinxFuvO6cy1q5d65DtOEJ2drarQ3A4cnK98+fPuzoESe5TXxxVWyTqi7ORk+vVlvoCoO5yanOUlpamffv2aevWrc7cjSRp3LhxysjIsD0uKipSkyZNlJSUpNDQ0ArXs1gsys7O1ku7vWW2elU7jn0Tk6u9jeoqzalfv37y8/NzdTgOQU61R+lRE1er7fXF0bVFor44CznVHrWlvgCou5zWHKWnp2vNmjXasmWLGjdubBuPjo7WxYsXVVhYaPfpbn5+vqKjo21zdu7cabe90qtNlc65mslkkslkKjPu5+dXqT8MZquXzCXVfwNTm/4IVTZ3d0JOrlcbYnWn+uKo2lK6v9rC3X5vK4OcXM+dYgXgmRx+tTrDMJSenq4VK1Zo48aNio2NtVveuXNn+fn5acOGDbaxQ4cO6dixY0pISJAkJSQkaO/evSooKLDNyc7OVmhoqOLi4hwdMgA3QX0BAADO5PAjR2lpaVq2bJk++eQThYSE2L7DHxYWpsDAQIWFhWn48OHKyMhQRESEQkNDNWbMGCUkJKh79+6SpKSkJMXFxemxxx7TjBkzlJeXp/HjxystLa3cT28B1A3UFwAA4EwOb47mz58vSbr77rvtxhctWqShQ4dKkt544w15e3tr0KBBMpvNSk5O1rx582xzfXx8tGbNGo0aNUoJCQkKDg5WamqqJk+e7OhwAbgR6gsAAHAmhzdHhnH9y9YGBARo7ty5mjt3boVzmjVrVquuzATA9agvAADAmRx+zhEAAAAAuCOaIwAAAAAQzREAAAAASKI5AgAAAABJNEcAAAAAIInmCAAAAAAk0RwBAAAAgCSaIwAAAACQRHMEAAAAAJJojgAAAABAEs0RAAAAAEiiOQIAAAAASZKvqwMAUD3NX/iXw7b1/SspDtsWAPdHfQFQ13DkCAAAAABEcwQAAAAAkmiOAAAAAEASzREAAAAASKI5AgAAAABJNEcAAAAAIInmCAAAAAAk0RwBAAAAgCSaIwAAAACQRHMEAAAAAJJojgAAAABAEs0RAAAAAEiiOQIAAAAASTRHAAAAACCJ5ggAAAAAJNEcAQAAAIAkmiMAAAAAkERzBAAAAACSaI4AAAAAQBLNEQAAAABIojkCAAAAAEk0RwAAAAAgqZY3R3PnzlXz5s0VEBCgbt26aefOna4OCYCHoL4AAICr1drm6MMPP1RGRoZefvllffXVV2rfvr2Sk5NVUFDg6tAAuDnqCwAAKE+tbY5ef/11jRgxQsOGDVNcXJwWLFigoKAg/e1vf3N1aADcHPUFAACUx9fVAZTn4sWLys3N1bhx42xj3t7eSkxMVE5OTrnrmM1mmc1m2+MzZ85Ikk6fPi2LxVLhviwWi86fPy9fi7dKrF7Vjv3nn3+u9jaqqzSnn3/+WX5+fq4OxyHIqWK+l4odFlNlfn/Pnj0rSTIMw2H7rUk1VV8cXVsk6ouzkFPFqC8A6ppa2Rz99NNPKikpUVRUlN14VFSUvvnmm3LXyczM1KRJk8qMx8bGOiXGitz0Wo3uDnCoqvz+nj17VmFhYc4LxkmoL4Br1IX6AsD91crm6EaMGzdOGRkZtsdWq1WnT59WgwYN5OVV8ae2RUVFatKkiX788UeFhobWRKhOR07uwV1zMgxDZ8+eVUxMjKtDqTE3Ul/c9fm9Hk/Mi5xqj7pYXwDULrWyObrpppvk4+Oj/Px8u/H8/HxFR0eXu47JZJLJZLIbCw8Pr/Q+Q0ND3eoPSGWQk3twx5zc+RPdmq4v7vj8VoYn5kVOtYM71xcA7q9WXpDB399fnTt31oYNG2xjVqtVGzZsUEJCggsjA+DuqC8AAKAitfLIkSRlZGQoNTVV8fHx6tq1q2bNmqXi4mINGzbM1aEBcHPUFwAAUJ5a2xz97ne/06lTpzRhwgTl5eWpQ4cOWrduXZmTqKvLZDLp5ZdfLvOVGXdGTu7BE3NyFzVRXzz1+fXEvMgJAFDKy+B6mQAAAABQO885AgAAAICaRnMEAAAAAKI5AgAAAABJNEcAAAAAIKmON0dz585V8+bNFRAQoG7dumnnzp2uDumGZWZmqkuXLgoJCVFkZKQGDBigQ4cOuTosh3rllVfk5eWlsWPHujqUajl+/LgeffRRNWjQQIGBgWrbtq12797t6rDgYNQX90J9AQBIdbg5+vDDD5WRkaGXX35ZX331ldq3b6/k5GQVFBS4OrQbsnnzZqWlpWn79u3Kzs6WxWJRUlKSiouLXR2aQ+zatUtvvfWW2rVr5+pQquWXX35Rjx495Ofnp08//VQHDhzQa6+9pvr167s6NDgQ9cW9UF8AAKXq7KW8u3Xrpi5duujNN9+UJFmtVjVp0kRjxozRCy+84OLoqu/UqVOKjIzU5s2bddddd7k6nGo5d+6cOnXqpHnz5mnq1Knq0KGDZs2a5eqwbsgLL7ygL7/8Ul988YWrQ4ETUV/cB/UFAHClOnnk6OLFi8rNzVViYqJtzNvbW4mJicrJyXFhZI5z5swZSVJERISLI6m+tLQ0paSk2D1f7mrVqlWKj4/Xww8/rMjISHXs2FFvv/22q8OCA1Ff3Av1BQBwpTrZHP30008qKSlRVFSU3XhUVJTy8vJcFJXjWK1WjR07Vj169FCbNm1cHU61fPDBB/rqq6+UmZnp6lAc4siRI5o/f75atmyp9evXa9SoUXryySe1ZMkSV4cGB6G+uA/qCwDgar6uDgCOl5aWpn379mnr1q2uDqVafvzxRz311FPKzs5WQECAq8NxCKvVqvj4eE2fPl2S1LFjR+3bt08LFixQamqqi6MDro/6UntRXwCg+urkkaObbrpJPj4+ys/PtxvPz89XdHS0i6JyjPT0dK1Zs0aff/65Gjdu7OpwqiU3N1cFBQXq1KmTfH195evrq82bN2v27Nny9fVVSUmJq0OsskaNGikuLs5urHXr1jp27JiLIoKjUV/cA/UFAFCeOtkc+fv7q3PnztqwYYNtzGq1asOGDUpISHBhZDfOMAylp6drxYoV2rhxo2JjY10dUrX17dtXe/fu1Z49e2z/xcfHa8iQIdqzZ498fHxcHWKV9ejRo8wlkL/99ls1a9bMRRHB0agv7oH6AgAoT539Wl1GRoZSU1MVHx+vrl27atasWSouLtawYcNcHdoNSUtL07Jly/TJJ58oJCTEdm5DWFiYAgMDXRzdjQkJCSlzTkNwcLAaNGjgtuc6PP3007rzzjs1ffp0PfLII9q5c6cWLlyohQsXujo0OBD1pfajvgAAymXUYXPmzDGaNm1q+Pv7G127djW2b9/u6pBumKRy/1u0aJGrQ3Oo3r17G0899ZSrw6iW1atXG23atDFMJpPRqlUrY+HCha4OCU5AfXE/1BcAQJ29zxEAAAAAXKlOnnMEAAAAAFejOQIAAAAA0RwBAAAAgCSaIwAAAACQRHMEAAAAAJJojgAAAABAEs0RAAAAAEiiOQI8zpYtW3T//fcrJiZGXl5eWrlyZZW3YRiGZs6cqdtuu00mk0k333yzpk2b5vhgAbgV6gsAT+fr6gAAOFZxcbHat2+vP/zhDxo4cOANbeOpp55SVlaWZs6cqbZt2+r06dM6ffq0gyMF4G6oLwA8nZdhGIargwDgHF5eXlqxYoUGDBhgGzObzfrzn/+s999/X4WFhWrTpo1effVV3X333ZKkgwcPql27dtq3b59uv/121wQOoNajvgDwRHytDqhj0tPTlZOTow8++ED/+c9/9PDDD+uee+7R4cOHJUmrV6/WLbfcojVr1ig2NlbNmzfXE088wSe7AK6L+gLA3dEcAXXIsWPHtGjRIi1fvly9evVSixYt9Mwzz6hnz55atGiRJOnIkSP64YcftHz5ci1dulSLFy9Wbm6uHnroIRdHD6A2o74A8ASccwTUIXv37lVJSYluu+02u3Gz2awGDRpIkqxWq8xms5YuXWqb9+6776pz5846dOgQX4UBUC7qCwBPQHME1CHnzp2Tj4+PcnNz5ePjY7esXr16kqRGjRrJ19fX7g1O69atJV3+ZJg3LwDKQ30B4AlojoA6pGPHjiopKVFBQYF69epV7pwePXro0qVL+u9//6sWLVpIkr799ltJUrNmzWosVgDuhfoCwBNwtTrAw5w7d07fffedpMtvVl5//XX95je/UUREhJo2bapHH31UX375pV577TV17NhRp06d0oYNG9SuXTulpKTIarWqS5cuqlevnmbNmiWr1aq0tDSFhoYqKyvLxdkBcCXqCwBPR3MEeJhNmzbpN7/5TZnx1NRULV68WBaLRVOnTtXSpUt1/Phx3XTTTerevbsmTZqktm3bSpJOnDihMWPGKCsrS8HBwerfv79ee+01RURE1HQ6AGoR6gsAT0dzBAAAAADiUt4AAAAAIInmCAAAAAAk0RwBAAAAgCSaIwAAAACQRHMEAAAAAJJojgAAAABAEs0RAAAAAEiiOQIAAAAASTRHAAAAACCJ5ggAAAAAJNEcAQAAAIAkmiMAAAAAkCT9f5B4WKg+DjpwAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import scipy.stats as stats\n",
        "\n",
        "# ANOVA test across credit statuses for 'disbursed_amount'\n",
        "f_val, p_val = stats.f_oneway(df[df['credit_status'] == 'Fully Repaid']['disbursed_amount'],\n",
        "                              df[df['credit_status'] == 'Repaid']['disbursed_amount'],\n",
        "                              df[df['credit_status'] == 'Repaying']['disbursed_amount'],\n",
        "                              df[df['credit_status'] == 'Disbursed']['disbursed_amount'])\n",
        "\n",
        "print(f\"F-Statistic: {f_val}, P-Value: {p_val}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p3XMpltqE6Jl",
        "outputId": "54a6c767-eded-4ddd-e3d5-61dd3a04466f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "F-Statistic: 10.457070938484215, P-Value: 8.910830261508064e-07\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ANOVA test across credit statuses for 'original_principal_amount'\n",
        "f_val, p_val = stats.f_oneway(df[df['credit_status'] == 'Fully Repaid']['original_principal_amount'],\n",
        "                              df[df['credit_status'] == 'Repaid']['original_principal_amount'],\n",
        "                              df[df['credit_status'] == 'Repaying']['original_principal_amount'],\n",
        "                              df[df['credit_status'] == 'Disbursed']['original_principal_amount'])\n",
        "\n",
        "print(f\"F-Statistic: {f_val}, P-Value: {p_val}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TdxqZEEEFOns",
        "outputId": "f3496487-da30-446d-c76b-408f6706e90c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "F-Statistic: 9.751829239401049, P-Value: 2.409741981231476e-06\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Predictive modeling - Debt servicing ratio"
      ],
      "metadata": {
        "id": "oTSX5mhFQ0rU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['debt_servicing_ratio'] = df['repaid_to_ida'] / df['disbursed_amount']\n"
      ],
      "metadata": {
        "id": "idaBY2gPYHh2"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['past_due'] = (df['due_to_ida'] > 0).astype(int)\n"
      ],
      "metadata": {
        "id": "b_nVvxeuYJ8z"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['non_repayment'] = (df['due_to_ida'] / df['disbursed_amount'] > 0.1).astype(int)\n"
      ],
      "metadata": {
        "id": "p8yO62QvYYCn"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Histograms for continuous features\n",
        "df[['debt_servicing_ratio', 'non_repayment']].hist(bins=20, figsize=(10, 5))\n",
        "plt.show()\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "ne_DZskaZQHt",
        "outputId": "9507d268-8e05-4fa5-80f8-9cb3080e5df9"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Bar chart for 'past_due' status\n",
        "sns.countplot(x='past_due', data=df)\n",
        "plt.title('Distribution of Past Due Status')\n",
        "plt.show()\n",
        "\n",
        "# Bar chart for 'non_repayment'\n",
        "sns.countplot(x='non_repayment', data=df)\n",
        "plt.title('Distribution of Non-Repayment')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "gnhxneTYZ1jq",
        "outputId": "801a3003-5109-46b4-c60e-98cf57fed5bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "correlation_matrix = df[['debt_servicing_ratio', 'past_due', 'non_repayment', 'original_principal_amount']].corr()\n",
        "\n",
        "# Plot the heatmap\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')\n",
        "plt.title('Correlation Matrix')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 618
        },
        "id": "Hxtuf6WjaPgR",
        "outputId": "c39a721d-5bed-49b5-efa3-d276dc46ead2"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from sklearn.preprocessing import StandardScaler\n"
      ],
      "metadata": {
        "id": "To4R-v-99IQj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['non_repayment'].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2NwCEWkI9rCz",
        "outputId": "79702714-f279-4c1f-e274-594657e4cb5e"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000,)"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()\n",
        "X = scaler.fit(df[['debt_servicing_ratio', 'past_due', 'non_repayment', 'original_principal_amount', 'disbursed_amount', 'due_to_ida']])\n",
        "y = scaler.fit_transform(df[['non_repayment']])"
      ],
      "metadata": {
        "id": "LiO0dGHX9Fda"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "\n",
        "X = df[['debt_servicing_ratio', 'past_due', 'non_repayment', 'original_principal_amount', 'disbursed_amount', 'due_to_ida']]\n",
        "y = df['non_repayment']\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "Wjtm6sUobDXm"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = LogisticRegression()\n",
        "\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "y_pred = model.predict(X_test)\n",
        "\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "print(classification_report(y_test, y_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SeQa8GhrfPM9",
        "outputId": "a02a12a2-1cde-4ff4-b2e3-17c3905f85f9"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[177   0]\n",
            " [  0  23]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00       177\n",
            "           1       1.00      1.00      1.00        23\n",
            "\n",
            "    accuracy                           1.00       200\n",
            "   macro avg       1.00      1.00      1.00       200\n",
            "weighted avg       1.00      1.00      1.00       200\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "importance = model.coef_[0]\n",
        "for i, v in enumerate(importance):\n",
        "    print('Feature: %0d, Score: %.5f' % (i, v))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eUmAgAYTpEiy",
        "outputId": "2332c817-1830-4d88-eb5f-7c25fc7560bf"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Feature: 0, Score: -0.00000\n",
            "Feature: 1, Score: 0.00000\n",
            "Feature: 2, Score: 0.00000\n",
            "Feature: 3, Score: -0.00001\n",
            "Feature: 4, Score: -0.00000\n",
            "Feature: 5, Score: 0.00003\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_curve, auc\n",
        "\n",
        "fpr, tpr, _ = roc_curve(y_test, model.predict_proba(X_test)[:, 1])\n",
        "roc_auc = auc(fpr, tpr)\n",
        "\n",
        "plt.figure()\n",
        "plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc)\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Receiver Operating Characteristic')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "eUOwxXm8pMd-",
        "outputId": "0009c265-f9a2-43ed-9a7d-4ed379f56c99"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['default_probability'] = model.predict_proba(X)[:, 1]\n",
        "high_risk_countries = df[df['default_probability'] > 0.1]\n",
        "high_risk_countries.reset_index(inplace=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Um2IVI_krDq",
        "outputId": "f602e1f5-4d6b-4154-8a50-6e6c1aa4a9a1"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    end_of_period country  default_probability\n",
            "0      2011-04-30   Sudan                  1.0\n",
            "1      2011-05-31   Sudan                  1.0\n",
            "2      2011-06-30   Sudan                  1.0\n",
            "3      2011-07-31   Sudan                  1.0\n",
            "4      2011-08-31   Sudan                  1.0\n",
            "..            ...     ...                  ...\n",
            "114    2020-10-31   Sudan                  1.0\n",
            "115    2020-11-30   Sudan                  1.0\n",
            "116    2020-12-31   Sudan                  1.0\n",
            "117    2021-01-31   Sudan                  1.0\n",
            "118    2021-02-28   Sudan                  1.0\n",
            "\n",
            "[119 rows x 3 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "sns.histplot(high_risk_countries['default_probability'], bins=20, kde=True)\n",
        "plt.title('Distribution of Default Probabilities for High Risk Countries')\n",
        "plt.xlabel('Default Probability')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "9Bdw-1HKm-vo",
        "outputId": "cd0c867d-f21f-433d-975d-36e7cd133f7c"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "scores = cross_val_score(model, X, y, cv=5, scoring='roc_auc')\n",
        "print(\"Cross-validated AUC scores:\", scores)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N4ntzMbroI66",
        "outputId": "869cfd79-93c7-4153-e485-422f36b1aa27"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cross-validated AUC scores: [1. 1. 1. 1. 1.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "forest_model = RandomForestClassifier(random_state=42)\n",
        "forest_model.fit(X_train, y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "W4i5NMDdqxBm",
        "outputId": "3e854c83-e79e-46a9-f386-e7ea80a60635"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = forest_model.predict(X_test)\n",
        "\n",
        "df['default_probability'] = forest_model.predict_proba(X)[:, 1]\n",
        "high_risk_countries = df[df['default_probability'] > 0.1]\n"
      ],
      "metadata": {
        "id": "k_nXuvENr9u0"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(confusion_matrix(y_test, y_pred))\n",
        "print(classification_report(y_test, y_pred))\n",
        "print(high_risk_countries[['country', 'default_probability']])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BmPdVNYssTEu",
        "outputId": "98fb6475-9232-4d61-f2c8-a1bdf53ddeec"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[177   0]\n",
            " [  0  23]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00       177\n",
            "           1       1.00      1.00      1.00        23\n",
            "\n",
            "    accuracy                           1.00       200\n",
            "   macro avg       1.00      1.00      1.00       200\n",
            "weighted avg       1.00      1.00      1.00       200\n",
            "\n",
            "    country  default_probability\n",
            "156   Sudan                  1.0\n",
            "157   Sudan                  1.0\n",
            "158   Sudan                  1.0\n",
            "159   Sudan                  1.0\n",
            "160   Sudan                  1.0\n",
            "..      ...                  ...\n",
            "270   Sudan                  1.0\n",
            "271   Sudan                  1.0\n",
            "272   Sudan                  1.0\n",
            "273   Sudan                  1.0\n",
            "274   Sudan                  1.0\n",
            "\n",
            "[119 rows x 2 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "importances = forest_model.feature_importances_\n",
        "features = X.columns\n",
        "indices = np.argsort(importances)\n",
        "\n",
        "plt.title('Feature Importances')\n",
        "plt.barh(range(len(indices)), importances[indices], color='b', align='center')\n",
        "plt.yticks(range(len(indices)), [features[i] for i in indices])\n",
        "plt.xlabel('Relative Importance')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "2GRNFuOEru0e",
        "outputId": "53ffff60-7f60-41dc-f1fb-e1c7486776a5"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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JIAwAAABbIggDAADAlgjCAAAAsCWCMAAAAGyJIAwAAABbIggDAADAlgjCAAAAsCWCMAAAAGyJIAwAAABbIggDAADAltxLugDgWhcQUNIVAABQuhhT0hWcxx1hAAAA2BJBGAAAALZEEAYAAIAtEYQBAABgSwRhAAAA2BJBGAAAALZEEAYAAIAtEYQBAABgSwRhAAAA2BJBGAAAALZEEAYAAIAtEYQBAABgSwRhAAAA2BJBGAAAALZEEAYAAIAtEYQBAABgSwRhAAAA2FKpD8IdOnTQiBEjCtQ2NjZWgYGBxVrP1VLYawkLC9O0adOKrZ7iFhMTo0aNGpV0GQAA4DpS6oNwUbteAtd9992nvXv3Frj9pk2bNHTo0GKsqOg4HA4tWbLEZdvIkSO1evXqkikIAABcl9xLugAUTlZWlhwOh8qUufj/h/H29pa3t3eB+w0KCrrS0q5IQa8rP06nU06ns4irAgAApVmpuiN88uRJDRgwQE6nU5UrV9aUKVNc9p85c0YjR45USEiIfH191bJlS8XFxeXqZ8mSJQoPD1fZsmUVGRmpw4cPSzo/3WD8+PHasWOHHA6HHA6HYmNjL1qTMUYxMTGqVq2avLy8VKVKFQ0fPrzANeVMcVi2bJnq1q0rLy8vvfvuuypbtqyOHz/ucq7o6Gh17NjR5bi/+t///qfmzZurbNmyqlixou666y5r34VTIxwOh959913ddddd8vHxUXh4uJYtW+bS37Jly6xxuu222zRnzhw5HI5cdeUlr+tKTk7Wpk2bdPvtt6tixYoKCAhQ+/bttXXrVpc6Jemuu+6Sw+Gw1i+8U5+dna3nnntON954o7y8vNSoUSN9/vnnl6wLAADYiClFHn74YVOtWjWzatUq8/3335vu3bsbPz8/Ex0dbYwx5sEHHzStW7c2X3/9tdm/f7+ZPHmy8fLyMnv37jXGGDN79mzj4eFhmjVrZtatW2c2b95sWrRoYVq3bm2MMebUqVPmiSeeMPXq1TMpKSkmJSXFnDp16qI1LVy40Pj7+5vly5ebQ4cOmY0bN5q3337b2l/Qmlq3bm3i4+PNnj17TEZGhqlUqZJ59913rX7OnTvnsm327NkmICDA2v/pp58aNzc38+yzz5rdu3eb7du3mxdffNHaHxoaaqZOnWqtSzI33nij+eCDD8y+ffvM8OHDjdPpNKmpqcYYY3788Ufj4eFhRo4cafbs2WPmz59vQkJCjCRz7NixS75WeV3XyZMnzerVq83cuXNNQkKC2b17txk8eLCpVKmSSU9PN8YYc+TIESPJzJ4926SkpJgjR44YY4wZN26cadiwodX/q6++avz9/c38+fPNnj17zJNPPmk8PDyscc3L6dOnTVpamrUcPnzYSDJSmpEMCwsLCwsLSxEtxSktLc1IMmlpaZdsW8ylXD0nTpwwnp6e5qOPPrK2paamGm9vbxMdHW0OHTpk3NzczM8//+xyXKdOncyYMWOMMefDmSSzYcMGa39CQoKRZDZu3GiMyR24LmXKlCmmdu3aJjMzM9e+wtS0fft2lzbR0dGmY8eO1voXX3xhvLy8rBB6YRBu1aqV6devX7515hWEn3nmGWs9IyPDSDIrVqwwxhgzevRoc/PNN7v08fTTT5vCBOG8rutCWVlZxs/Pz/zvf/9zqW3x4sUu7S58XapUqWJeeOEFlzbNmzc3jzzySL7nGjdunDkffC9cCMIsLCwsLCxFuRSnwgThUjM1IikpSZmZmWrZsqW1rXz58qpTp44kaefOncrKylLt2rWt+aROp1Nr165VUlKSdYy7u7uaN29urd90000KDAxUQkLCZdV177336s8//1SNGjU0ZMgQLV68WOfOnStUTZ6enmrQoIFLv/369VNcXJx++eUXSdK8efPUrVu3fJ8UsX37dnXq1KlQtf/1nL6+vvL399eRI0ckSYmJiS7jJEktWrQoVP95Xddvv/2mIUOGKDw8XAEBAfL391dGRoaSk5ML3G96erp++eUXtWnTxmV7mzZtLvo6jhkzRmlpadaSMyUGAACUTrb5slxGRobc3Ny0ZcsWubm5uewrzi9ZVa1aVYmJiVq1apW+/PJLPfLII5o8ebLWrl1b4Jq8vb3lcDhc9jdv3lw1a9bUggUL9PDDD2vx4sUXna9cmC/O5fDw8HBZdzgcys7OLnQ/+cnrugYOHKjU1FRNnz5doaGh8vLyUqtWrZSZmVlk582Pl5eXvLy8iv08AADg2lBq7gjXrFlTHh4e2rhxo7Xt2LFj1iPEGjdurKysLB05ckS1atVyWYKDg61jzp07p82bN1vriYmJOn78uCIiIiSdv4uZlZVVqNq8vb3Vo0cPzZgxQ3FxcVq/fr127txZ4Jry069fP82bN0//+9//VKZMGXXr1i3ftg0aNCjSx4vVqVPHZZyk849gu1Lx8fEaPny47rjjDtWrV09eXl46evSoSxsPD4+Lvgb+/v6qUqWK4uPjc/Vdt27dK64RAACUDqXmjrDT6dTgwYM1atQoVahQQTfccIOefvpp63FctWvXVr9+/TRgwABNmTJFjRs31u+//67Vq1erQYMGVoj08PDQv/71L82YMUPu7u4aNmyYbrnlFuuf/cPCwnTgwAFt375dN954o/z8/C56FzE2NlZZWVlq2bKlfHx89N///lfe3t4KDQ1VhQoVClRTfvr166eYmBi98MILuueeey5ax7hx49SpUyfVrFlTffr00blz57R8+XKNHj26sEMtSXrooYf06quvavTo0Ro8eLC2b99u3ZG+8C5vYYSHh2vu3Llq1qyZ0tPTNWrUqFx3s8PCwrR69Wq1adNGXl5eKleuXK5+Ro0apXHjxqlmzZpq1KiRZs+ere3bt2vevHmXXRsAAChline68tV14sQJ88ADDxgfHx9TqVIlM2nSJNO+fXvrqRGZmZnm2WefNWFhYcbDw8NUrlzZ3HXXXeb77783xvzfF8wWLVpkatSoYby8vEznzp3NoUOHrHOcPn3a/P3vfzeBgYFGOv/0gotZvHixadmypfH39ze+vr7mlltuMatWrbL2F7Sm/LRo0cJIMl999ZXL9ryOW7RokWnUqJHx9PQ0FStWNHfffbe1L68vy134hbSAgACX6126dKmpVauW8fLyMh06dDCzZs0yksyff/550TG52HVt3brVNGvWzJQtW9aEh4ebhQsX5qpt2bJlplatWsbd3d2EhoYaY3J/WS4rK8vExMSYkJAQ4+HhYRo2bGh90a+gcibb82U5FhYWFhaWol2KU2G+LOcwxpiSDOIoPV544QW9+eabpeZLZunp6QoICJCUJsm/pMsBAKDUKM70mfP7Oy0tTf7+F//9XWqmRuDqe+ONN9S8eXNVqFBB8fHxmjx5soYNG1bSZQEAABRIqfmyXEmZN2+ey6PP/rrUq1evpMsrVvv27VPPnj1Vt25dTZgwQU888YRiYmIkSV27ds13XF588cWSLRwAAEASUyOu0IkTJ/Tbb7/luc/Dw0OhoaFXuaJrw88//6w///wzz33ly5dX+fLlr3JFhcfUCAAAigdTI0oJPz8/+fn5lXQZ15yQkJCSLgEAAOCimBoBAAAAWyIIAwAAwJYIwgAAALAlgjAAAABsiSAMAAAAWyIIAwAAwJYIwgAAALAlgjAAAABsiSAMAAAAWyIIAwAAwJYIwgAAALAlgjAAAABsyb2kCwCudWlpkr9/SVcBAACKGneEAQAAYEsEYQAAANgSQRgAAAC2RBAGAACALRGEAQAAYEsEYQAAANgSQRgAAAC2RBAGAACALRGEAQAAYEsEYQAAANgSQRgAAAC25F7SBQDXuoCAkq4AAFDSjCnpClAcuCMMAAAAWyIIAwAAwJYIwgAAALAlgjAAAABsiSAMAAAAWyIIAwAAwJYIwgAAALAlgjAAAABsiSAMAAAAWyIIAwAAwJYIwgAAALAlgjAAAABsiSAMAAAAWyIIAwAAwJYIwgAAALAlgjAAAABsiSAMAAAAWyII20CHDh00YsSIki7jskVFRalXr14XbXO9XyMAALj63Eu6AJQuUVFROn78uJYsWVJkfU6fPl3GmCLrDwAAQCII4zoQEBBQ0iUAAIBSiKkRpczJkyc1YMAAOZ1OVa5cWVOmTHHZ73A4ct2tDQwMVGxsrLV++PBh9e7dW4GBgSpfvrx69uypgwcPXvLcMTExmjNnjpYuXSqHwyGHw6G4uDhJ0s6dO9WxY0d5e3urQoUKGjp0qDIyMgp0TRdOjbjUNUrS3Llz1axZM/n5+Sk4OFh9+/bVkSNHCnQ+AABgDwThUmbUqFFau3atli5dqpUrVyouLk5bt24t8PFnz55VZGSk/Pz89M033yg+Pl5Op1NdunRRZmbmRY8dOXKkevfurS5duiglJUUpKSlq3bq1Tp48qcjISJUrV06bNm3SwoULtWrVKg0bNqzYrvHs2bOaMGGCduzYoSVLlujgwYOKioq6aL9nzpxRenq6ywIAAEoxg1LjxIkTxtPT03z00UfWttTUVOPt7W2io6ONMcZIMosXL3Y5LiAgwMyePdsYY8zcuXNNnTp1THZ2trX/zJkzxtvb23zxxReXrGHgwIGmZ8+eLtvefvttU65cOZORkWFt++yzz0yZMmXMr7/+Wqg+C3KNedm0aZORZE6cOJFvm3HjxhlJeSxpRjIsLCwsLDZecP1IS0szkkxaWtol23JHuBRJSkpSZmamWrZsaW0rX7686tSpU+A+duzYof3798vPz09Op1NOp1Ply5fX6dOnlZSUdFl1JSQkqGHDhvL19bW2tWnTRtnZ2UpMTCxUXwW9xi1btqhHjx6qVq2a/Pz81L59e0lScnJyvn2PGTNGaWlp1nL48OFC1QYAAK4vfFnOZhwOh4wxLtvOnj1r/ZyRkaGmTZtq3rx5uY4NCgoq9vqKQs5UjMjISM2bN09BQUFKTk5WZGTkRad3eHl5ycvL6ypWCgAAShJ3hEuRmjVrysPDQxs3brS2HTt2THv37rXWg4KClJKSYq3v27dPp06dstabNGmiffv26YYbblCtWrVcloI8vcHT01NZWVku2yIiIrRjxw6dPHnS2hYfH68yZcoU6m51Qa9xz549Sk1N1cSJE9W2bVvddNNNfFEOAADkQhAuRZxOpwYPHqxRo0bpq6++0g8//KCoqCiVKfN/L3PHjh312muvadu2bdq8ebP++c9/ysPDw9rfr18/VaxYUT179tQ333yjAwcOKC4uTsOHD9dPP/10yRrCwsL0/fffKzExUUePHtXZs2fVr18/lS1bVgMHDtQPP/ygNWvW6F//+pf69++vSpUqFfk1VqtWTZ6enpo5c6Z+/PFHLVu2TBMmTCjUeQAAQOlHEC5lJk+erLZt26pHjx7q3Lmzbr31VjVt2tTaP2XKFFWtWlVt27ZV3759NXLkSPn4+Fj7fXx89PXXX6tatWq6++67FRERocGDB+v06dPy9/e/5PmHDBmiOnXqqFmzZgoKClJ8fLx8fHz0xRdf6I8//lDz5s11zz33qFOnTnrttdeK5RqDgoIUGxurhQsXqm7dupo4caJeeeWVyzoXAAAovRzmwgmjACRJ6enp/386SJqkS/+fAABA6UVaun7k/P5OS0u75E087ggDAADAlgjCKJScR6rltXzzzTfXTJ8AAACXwuPTUCjbt2/Pd19ISMg10ycAAMClEIRRKLVq1bou+gQAALgUpkYAAADAlgjCAAAAsCWCMAAAAGyJIAwAAABbIggDAADAlgjCAAAAsCWCMAAAAGyJIAwAAABbIggDAADAlgjCAAAAsCWCMAAAAGyJIAwAAABbci/pAoBrXVqa5O9f0lUAAICixh1hAAAA2BJBGAAAALZEEAYAAIAtEYQBAABgSwRhAAAA2BJBGAAAALZEEAYAAIAtEYQBAABgSwRhAAAA2BJBGAAAALZEEAYAAIAtuZd0AcC1LiCgpCsAgNLLmJKuAHbGHWEAAADYEkEYAAAAtkQQBgAAgC0RhAEAAGBLBGEAAADYEkEYAAAAtkQQBgAAgC0RhAEAAGBLBGEAAADYEkEYAAAAtkQQBgAAgC0RhAEAAGBLBGEAAADYEkEYAAAAtkQQBgAAgC0RhAEAAGBLBGEAAADYEkEYAAAAtkQQRqkWFxcnh8Oh48ePl3QpAADgGkMQLqUyMzNLugQAAIBrGkG4iHTo0EHDhw/Xk08+qfLlyys4OFgxMTHW/uTkZPXs2VNOp1P+/v7q3bu3fvvtN2t/TEyMGjVqpLlz5yosLEwBAQHq06ePTpw4UeDzDxs2TCNGjFDFihUVGRkpSfrhhx/UtWtXOZ1OVapUSf3799fRo0dzHTds2DAFBASoYsWKGjt2rIwxVpu5c+eqWbNm8vPzU3BwsPr27asjR45IkowxqlWrll555RWXerZv3y6Hw6H9+/dLkhwOh9566y11795dPj4+ioiI0Pr167V//3516NBBvr6+at26tZKSklz6Wbp0qZo0aaKyZcuqRo0aGj9+vM6dO2ftdzgcevfdd3XXXXfJx8dH4eHhWrZsmSTp4MGDuu222yRJ5cqVk8PhUFRUVIHGEwAA2IBBkWjfvr3x9/c3MTExZu/evWbOnDnG4XCYlStXmqysLNOoUSNz6623ms2bN5sNGzaYpk2bmvbt21vHjxs3zjidTnP33XebnTt3mq+//toEBwebf//73wU+v9PpNKNGjTJ79uwxe/bsMceOHTNBQUFmzJgxJiEhwWzdutXcfvvt5rbbbst1XHR0tNmzZ4/573//a3x8fMzbb79ttXnvvffM8uXLTVJSklm/fr1p1aqV6dq1q7X/hRdeMHXr1nWpZ/jw4aZdu3bWuiQTEhJiPvzwQ5OYmGh69eplwsLCTMeOHc3nn39udu/ebW655RbTpUsX65ivv/7a+Pv7m9jYWJOUlGRWrlxpwsLCTExMjEu/N954o/nggw/Mvn37zPDhw43T6TSpqanm3LlzZtGiRUaSSUxMNCkpKeb48eP5juHp06dNWlqatRw+fNhIMlKakQwLCwsLSzEsQFFLS0szkkxaWtol2/IWLCLt27c3t956q8u25s2bm9GjR5uVK1caNzc3k5ycbO3btWuXkWS+++47Y8z5IOzj42PS09OtNqNGjTItW7Ys8PkbN27ssm3ChAnmb3/7m8u2nHCXmJhoHRcREWGys7OtNqNHjzYRERH5nmvTpk1Gkjlx4oQxxpiff/7ZuLm5mY0bNxpjjMnMzDQVK1Y0sbGx1jGSzDPPPGOtr1+/3kgy7733nrVt/vz5pmzZstZ6p06dzIsvvuhy7rlz55rKlSvn229GRoaRZFasWGGMMWbNmjVGkjl27Fi+15Nj3Lhx5nzwvXAhCLOwsLAU1wIUtcIEYaZGFKEGDRq4rFeuXFlHjhxRQkKCqlatqqpVq1r76tatq8DAQCUkJFjbwsLC5Ofnl+v4gmratKnL+o4dO7RmzRo5nU5ruemmmyTJZQrCLbfcIofDYa23atVK+/btU1ZWliRpy5Yt6tGjh6pVqyY/Pz+1b99e0vnpHpJUpUoVdevWTe+//74k6X//+5/OnDmje++9N9/xqVSpkiSpfv36LttOnz6t9PR0q/7nnnvOpf4hQ4YoJSVFp06dyrNfX19f+fv7F2rccowZM0ZpaWnWcvjw4UL3AQAArh/uJV1AaeLh4eGy7nA4lJ2dfdWO9/X1dVnPyMhQjx499PLLL+dqW7ly5QL1efLkSUVGRioyMlLz5s1TUFCQkpOTFRkZ6fKFvAcffFD9+/fX1KlTNXv2bN13333y8fFx6euv15cTvPPalnPNGRkZGj9+vO6+++5cdZUtWzbPfnP6Kcy45fDy8pKXl1ehjwMAANcngvBVEBERocOHD+vw4cPWXeHdu3fr+PHjqlu3brGdt0mTJlq0aJHCwsLk7p7/S71x40aX9Q0bNig8PFxubm7as2ePUlNTNXHiRKv2zZs35+rjjjvukK+vr2bNmqXPP/9cX3/9dZHUn5iYqFq1al12H56enpJk3d0GAADIwdSIq6Bz586qX7+++vXrp61bt+q7777TgAED1L59ezVr1qzYzvvoo4/qjz/+0P33369NmzYpKSlJX3zxhQYNGuQSDJOTk/X4448rMTFR8+fP18yZMxUdHS1Jqlatmjw9PTVz5kz9+OOPWrZsmSZMmJDrXG5uboqKitKYMWMUHh6uVq1aXXH9zz77rP7zn/9o/Pjx2rVrlxISErRgwQI988wzBe4jNDRUDodDn376qX7//XdlZGRccV0AAKB0IAhfBQ6HQ0uXLlW5cuXUrl07de7cWTVq1NCHH35YrOetUqWK4uPjlZWVpb/97W+qX7++RowYocDAQJUp838v/YABA/Tnn3+qRYsWevTRRxUdHa2hQ4dKkoKCghQbG6uFCxeqbt26mjhxYq5HpeUYPHiwMjMzNWjQoCKpPzIyUp9++qlWrlyp5s2b65ZbbtHUqVMVGhpa4D5CQkI0fvx4PfXUU6pUqZKGDRtWJLUBAIDrn8MYY0q6CJScDh06qFGjRpo2bdoV9/XNN9+oU6dOOnz4sPVluOtZenq6AgICJKVJ8i/pcgCgVCKFoKjl/P5OS0uTv//Ff38zRxhX7MyZM/r9998VExOje++9t1SEYAAAUPoxNeI6kJyc7PIIsQuXnMeYlZT58+crNDRUx48f16RJk0q0FgAAgIJiasR14Ny5czp48GC++y/1VAhcHqZGAEDxI4WgqDE1opRxd3e/okeIAQAAIDemRgAAAMCWCMIAAACwJYIwAAAAbIkgDAAAAFsiCAMAAMCWCMIAAACwJYIwAAAAbIkgDAAAAFsiCAMAAMCWCMIAAACwJYIwAAAAbIkgDAAAAFtyL+kCgGtdWprk71/SVQAAgKLGHWEAAADYEkEYAAAAtkQQBgAAgC0RhAEAAGBLBGEAAADYEkEYAAAAtkQQBgAAgC0RhAEAAGBLBGEAAADYEkEYAAAAtkQQBgAAgC25l3QBwLUuIKCkK8D1yJiSrgAAcCncEQYAAIAtEYQBAABgSwRhAAAA2BJBGAAAALZEEAYAAIAtEYQBAABgSwRhAAAA2BJBGAAAALZEEAYAAIAtEYQBAABgSwRhAAAA2BJBGAAAALZEEAYAAIAtEYQBAABgSwRhAAAA2BJBGAAAALZEEAYAAIAtEYRxzYuKilKvXr1KugwAAFDKEIRRbA4ePCiHw6Ht27eXdCkAAAC5EIQBAABgSwRhm+vQoYOGDRumYcOGKSAgQBUrVtTYsWNljJEkzZ07V82aNZOfn5+Cg4PVt29fHTlyxDr+2LFj6tevn4KCguTt7a3w8HDNnj1bklS9enVJUuPGjeVwONShQ4dL1pOVlaXHH39cgYGBqlChgp588kmrlhxhYWGaNm2ay7ZGjRopJibGWj9+/LgefPBBBQUFyd/fXx07dtSOHTsuY4QAAEBpRRCG5syZI3d3d3333XeaPn26Xn31Vb377ruSpLNnz2rChAnasWOHlixZooMHDyoqKso6duzYsdq9e7dWrFihhIQEzZo1SxUrVpQkfffdd5KkVatWKSUlRZ988skla5kyZYpiY2P1/vvv69tvv9Uff/yhxYsXF/qa7r33Xh05ckQrVqzQli1b1KRJE3Xq1El//PFHvsecOXNG6enpLgsAACi93Eu6AJS8qlWraurUqXI4HKpTp4527typqVOnasiQIfrHP/5htatRo4ZmzJih5s2bKyMjQ06nU8nJyWrcuLGaNWsm6fzd2hxBQUGSpAoVKig4OLhAtUybNk1jxozR3XffLUl688039cUXXxTqer799lt99913OnLkiLy8vCRJr7zyipYsWaKPP/5YQ4cOzfO4l156SePHjy/UuQAAwPWLO8LQLbfcIofDYa23atVK+/btU1ZWlrZs2aIePXqoWrVq8vPzU/v27SVJycnJkqSHH35YCxYsUKNGjfTkk09q3bp1l11HWlqaUlJS1LJlS2ubu7u7FbILaseOHcrIyFCFChXkdDqt5cCBA0pKSsr3uDFjxigtLc1aDh8+fNnXAgAArn3cEUa+Tp8+rcjISEVGRmrevHkKCgpScnKyIiMjlZmZKUnq2rWrDh06pOXLl+vLL79Up06d9Oijj+qVV14ptrrKlCmTa97w2bNnrZ8zMjJUuXJlxcXF5To2MDAw3369vLysO8gAAKD0444wtHHjRpf1DRs2KDw8XHv27FFqaqomTpyotm3b6qabbnL5olyOoKAgDRw4UP/97381bdo0vf3225IkT09PSee/AFcQAQEBqly5sks9586d05YtW3KdLyUlxVpPT0/XgQMHrPUmTZro119/lbu7u2rVquWy5MxfBgAAIAhDycnJevzxx5WYmKj58+dr5syZio6OVrVq1eTp6amZM2fqxx9/1LJlyzRhwgSXY5999lktXbpU+/fv165du/Tpp58qIiJCknTDDTfI29tbn3/+uX777TelpaVdspbo6GhNnDhRS5Ys0Z49e/TII4/o+PHjLm06duyouXPn6ptvvtHOnTs1cOBAubm5Wfs7d+6sVq1aqVevXlq5cqUOHjyodevW6emnn9bmzZuvfMAAAECpQBCGBgwYoD///FMtWrTQo48+qujoaA0dOlRBQUGKjY3VwoULVbduXU2cODHXlAdPT0+NGTNGDRo0ULt27eTm5qYFCxZIOj+/d8aMGXrrrbdUpUoV9ezZ85K1PPHEE+rfv78GDhyoVq1ayc/PT3fddZdLmzFjxqh9+/bq3r27unXrpl69eqlmzZrWfofDoeXLl6tdu3YaNGiQateurT59+ujQoUOqVKlSEYwYAAAoDRzmwsmWsJUOHTqoUaNGuZ7Li/NTLgICAiSlSfIv6XJwneGTFQBKRs7v77S0NPn7X/z3N3eEAQAAYEsEYVxVf32c2YXLN998U9LlAQAAG+HxaTaX1yPGitP27dvz3RcSEnL1CgEAALZHEMZVVatWrZIuAQAAQBJTIwAAAGBTBGEAAADYEkEYAAAAtkQQBgAAgC0RhAEAAGBLBGEAAADYEkEYAAAAtkQQBgAAgC0RhAEAAGBLBGEAAADYEkEYAAAAtkQQBgAAgC25l3QBwLUuLU3y9y/pKgAAQFHjjjAAAABsiSAMAAAAWyIIAwAAwJYIwgAAALAlgjAAAABsiSAMAAAAWyIIAwAAwJYIwgAAALAlgjAAAABsiSAMAAAAWyIIAwAAwJbcS7oA4FoXEFA0/RhTNP0AAICiwR1hAAAA2BJBGAAAALZEEAYAAIAtEYQBAABgSwRhAAAA2BJBGAAAALZEEAYAAIAtEYQBAABgSwRhAAAA2BJBGAAAALZEEAYAAIAtEYQBAABgSwRhAAAA2BJBGAAAALZEEAYAAIAtEYQBAABgSwRhAAAA2BJB+AIdOnTQiBEjJElhYWGaNm1agY4rTNtrwfVWLwAAQFEjCF/Epk2bNHTo0JIuA1eI0A8AAPLiXtIFXMuCgoJK9PzGGGVlZcndnZcJAACgqNn6jvDJkyc1YMAAOZ1OVa5cWVOmTHHZ/9c7icYYxcTEqFq1avLy8lKVKlU0fPhwl/YnTpzQ/fffL19fX4WEhOj111+39h08eFAOh0Pbt2+3th0/flwOh0NxcXGSpLi4ODkcDq1YsUJNmzaVl5eXvv32W+3YsUO33Xab/Pz85O/vr6ZNm2rz5s1WP99++63atm0rb29vVa1aVcOHD9fJkyet/UeOHFGPHj3k7e2t6tWra968eYUap1dffVX169eXr6+vqlatqkceeUQZGRnW/tjYWAUGBurTTz9VnTp15OPjo3vuuUenTp3SnDlzFBYWpnLlymn48OHKysqyjjt27JgGDBigcuXKycfHR127dtW+ffus/TExMWrUqJFLLdOmTVNYWJi1HhUVpV69eumVV15R5cqVVaFCBT366KM6e/aspPNTXQ4dOqTHHntMDodDDoejUNcOAABKL1sH4VGjRmnt2rVaunSpVq5cqbi4OG3dujXPtosWLdLUqVP11ltvad++fVqyZInq16/v0mby5Mlq2LChtm3bpqeeekrR0dH68ssvC13XU089pYkTJyohIUENGjRQv379dOONN2rTpk3asmWLnnrqKXl4eEiSkpKS1KVLF/3973/X999/rw8//FDffvuthg0bZvUXFRWlw4cPa82aNfr444/1xhtv6MiRIwWup0yZMpoxY4Z27dqlOXPm6KuvvtKTTz7p0ubUqVOaMWOGFixYoM8//1xxcXG66667tHz5ci1fvlxz587VW2+9pY8//tilrs2bN2vZsmVav369jDG64447rBBbUGvWrFFSUpLWrFmjOXPmKDY2VrGxsZKkTz75RDfeeKOee+45paSkKCUlJd9+zpw5o/T0dJcFAACUYsamTpw4YTw9Pc1HH31kbUtNTTXe3t4mOjraGGNMaGiomTp1qjHGmClTppjatWubzMzMPPsLDQ01Xbp0cdl23333ma5duxpjjDlw4ICRZLZt22btP3bsmJFk1qxZY4wxZs2aNUaSWbJkiUs/fn5+JjY2Ns/zDh482AwdOtRl2zfffGPKlClj/vzzT5OYmGgkme+++87an5CQYCRZ11ZYCxcuNBUqVLDWZ8+ebSSZ/fv3W9seeugh4+PjY06cOGFti4yMNA899JAxxpi9e/caSSY+Pt7af/ToUePt7W29JuPGjTMNGzZ0OffUqVNNaGiotT5w4EATGhpqzp07Z2279957zX333Wet//V1vJhx48YZSXksaUYyV7wAAIDil5aWZiSZtLS0S7a17R3hpKQkZWZmqmXLlta28uXLq06dOnm2v/fee/Xnn3+qRo0aGjJkiBYvXqxz5865tGnVqlWu9YSEhELX1qxZM5f1xx9/XA8++KA6d+6siRMnKikpydq3Y8cOxcbGyul0WktkZKSys7N14MABJSQkyN3dXU2bNrWOuemmmxQYGFjgelatWqVOnTopJCREfn5+6t+/v1JTU3Xq1CmrjY+Pj2rWrGmtV6pUSWFhYXI6nS7bcu5E59T11/GvUKGC6tSpU+gxq1evntzc3Kz1ypUrF+qOd44xY8YoLS3NWg4fPlzoPgAAwPXDtkG4sKpWrarExES98cYb8vb21iOPPKJ27doV+J/xy5Q5P9TGGGtbfsf6+vq6rMfExGjXrl3q1q2bvvrqK9WtW1eLFy+WJGVkZOihhx7S9u3brWXHjh3at2+fSzC9XAcPHlT37t3VoEEDLVq0SFu2bLHmPmdmZlrtcqZq5HA4HHluy87OLvC5y5Qp4zJeUt5jdqXnyeHl5SV/f3+XBQAAlF62DcI1a9aUh4eHNm7caG07duyY9u7dm+8x3t7e6tGjh2bMmKG4uDitX79eO3futPZv2LDBpf2GDRsUEREh6f+eQPHXOap//eLcpdSuXVuPPfaYVq5cqbvvvluzZ8+WJDVp0kS7d+9WrVq1ci2enp666aabdO7cOW3ZssXqKzExUcePHy/Qebds2aLs7GxNmTJFt9xyi2rXrq1ffvmlwHXnJyIiQufOnXMZ/9TUVCUmJqpu3bqSzo/Zr7/+6hKGCzNmOTw9PV2+pAcAACDZOAg7nU4NHjxYo0aN0ldffaUffvhBUVFR1p3bC8XGxuq9997TDz/8oB9//FH//e9/5e3trdDQUKtNfHy8Jk2apL179+r111/XwoULFR0dLel8iL7lllusL8GtXbtWzzzzzCXr/PPPPzVs2DDFxcXp0KFDio+P16ZNm6yAPXr0aK1bt07Dhg3T9u3btW/fPi1dutT6slydOnXUpUsXPfTQQ9q4caO2bNmiBx98UN7e3gUap1q1auns2bOaOXOmfvzxR82dO1dvvvlmgY69mPDwcPXs2VNDhgyxnozxwAMPKCQkRD179pR0/okPv//+uyZNmqSkpCS9/vrrWrFiRaHPFRYWpq+//lo///yzjh49esW1AwCA0sG2QVg6/5SHtm3bqkePHurcubNuvfVWl7m0fxUYGKh33nlHbdq0UYMGDbRq1Sr973//U4UKFaw2TzzxhDZv3qzGjRvr+eef16uvvqrIyEhr//vvv69z586padOmGjFihJ5//vlL1ujm5qbU1FQNGDBAtWvXVu/evdW1a1eNHz9ektSgQQOtXbtWe/fuVdu2bdW4cWM9++yzqlKlitXH7NmzVaVKFbVv31533323hg4dqhtuuKFAY9SwYUO9+uqrevnll3XzzTdr3rx5eumllwp07KXMnj1bTZs2Vffu3dWqVSsZY7R8+XJrqkNERITeeOMNvf7662rYsKG+++47jRw5stDnee6553Tw4EHVrFmzxJ8NDQAArh0Oc+EkTACSpPT0dAUEBEhKk3Tl84X5Lw0AgOKX8/s7LS3tkt/3sfUdYQAAANgXQdjm5s2b5/Lotb8u9erVK+nyAAAAio17SReAknXnnXe6PMv3ry58LBkAAEBpQhC2OT8/P/n5+ZV0GQAAAFcdUyMAAABgSwRhAAAA2BJBGAAAALZEEAYAAIAtEYQBAABgSwRhAAAA2BJBGAAAALZEEAYAAIAtEYQBAABgSwRhAAAA2BJBGAAAALZEEAYAAIAtuZd0AcC1Li1N8vcv6SoAAEBR444wAAAAbIkgDAAAAFsiCAMAAMCWCMIAAACwJYIwAAAAbIkgDAAAAFsiCAMAAMCWCMIAAACwJYIwAAAAbIkgDAAAAFsiCAMAAMCW3Eu6AOBaFxCQe5sxV78OAABQtLgjDAAAAFsiCAMAAMCWCMIAAACwJYIwAAAAbIkgDAAAAFsiCAMAAMCWCMIAAACwJYIwAAAAbIkgDAAAAFsiCAMAAMCWCMIAAACwJYIwAAAAbIkgDAAAAFsiCAMAAMCWCMIAAACwJYIwAAAAbIkgDAAAAFu6KkE4JiZGjRo1KtQxHTp00IgRI0q8jsvhcDi0ZMmSIuuvOMYiNjZWgYGBRdonAADA9cT9apxk5MiR+te//lWoYz755BN5eHgUU0XFKyUlReXKlSuy/q7nsbgWxMbGasSIETp+/HhJlwIAAK4hxRqEjTHKysqS0+mU0+ks1LHly5cvpqqKT2Zmpjw9PRUcHFyk/V6PYwEAAHCtK/TUiDNnzmj48OG64YYbVLZsWd16663atGmTJCkuLk4Oh0MrVqxQ06ZN5eXlpW+//TbXlIRz585p+PDhCgwMVIUKFTR69GgNHDhQvXr1stpcOB0gLCxML774ov7xj3/Iz89P1apV09tvv+1S2+jRo1W7dm35+PioRo0aGjt2rM6ePVvYS5QkRUVFqVevXho/fryCgoLk7++vf/7zn8rMzHSpcdiwYRoxYoQqVqyoyMhISa5TIw4ePCiHw6FPPvlEt912m3x8fNSwYUOtX7/e5Xzx8fHq0KGDfHx8VK5cOUVGRurYsWP5jsWECRN0//33y9fXVyEhIXr99ddd+nv11VdVv359+fr6qmrVqnrkkUeUkZFxWWORlJSknj17qlKlSnI6nWrevLlWrVrl0iYsLEzPP/+8BgwYIKfTqdDQUC1btky///67evbsKafTqQYNGmjz5s0uxy1atEj16tWTl5eXwsLCNGXKFJf9eU0zCQwMVGxsrKRLj29cXJwGDRqktLQ0ORwOORwOxcTEXNY4AACA0qXQQfjJJ5/UokWLNGfOHG3dulW1atVSZGSk/vjjD6vNU089pYkTJyohIUENGjTI1cfLL7+sefPmafbs2YqPj1d6enqB5tROmTJFzZo107Zt2/TII4/o4YcfVmJiorXfz89PsbGx2r17t6ZPn6533nlHU6dOLewlWlavXq2EhATFxcVp/vz5+uSTTzR+/HiXNnPmzJGnp6fi4+P15ptv5tvX008/rZEjR2r79u2qXbu27r//fp07d06StH37dnXq1El169bV+vXr9e2336pHjx7KysrKt7/JkyerYcOG2rZtm5566ilFR0fryy+/tPaXKVNGM2bM0K5duzRnzhx99dVXevLJJy9rHDIyMnTHHXdo9erV2rZtm7p06aIePXooOTnZpd3UqVPVpk0bbdu2Td26dVP//v01YMAAPfDAA9q6datq1qypAQMGyBgjSdqyZYt69+6tPn36aOfOnYqJidHYsWOtkFsY+Y1v69atNW3aNPn7+yslJUUpKSkaOXJknn2cOXNG6enpLgsAACjFTCFkZGQYDw8PM2/ePGtbZmamqVKlipk0aZJZs2aNkWSWLFnicty4ceNMw4YNrfVKlSqZyZMnW+vnzp0z1apVMz179rS2tW/f3kRHR1vroaGh5oEHHrDWs7OzzQ033GBmzZqVb72TJ082TZs2zbeOixk4cKApX768OXnypLVt1qxZxul0mqysLKvGxo0b5zpWklm8eLExxpgDBw4YSebdd9+19u/atctIMgkJCcYYY+6//37Tpk2bfGvJayy6dOni0ua+++4zXbt2zbePhQsXmgoVKljrs2fPNgEBAfm2v5R69eqZmTNnutT019cnJSXFSDJjx461tq1fv95IMikpKcYYY/r27Wtuv/12l35HjRpl6tata63/dSxzBAQEmNmzZxtjCja+Bb3WcePGGUl5LGlGMi4LAAC4NqWlpRlJJi0t7ZJtC3VHOCkpSWfPnlWbNm2sbR4eHmrRooUSEhKsbc2aNcu3j7S0NP32229q0aKFtc3NzU1Nmza95Pn/enfZ4XAoODhYR44csbZ9+OGHatOmjYKDg+V0OvXMM8/kumtZGA0bNpSPj4+13qpVK2VkZOjw4cPWtoLUfWHtlStXliSr9pw7woXRqlWrXOt/fQ1WrVqlTp06KSQkRH5+furfv79SU1N16tSpQp1HOn9HeOTIkYqIiFBgYKCcTqcSEhJyje1fr7FSpUqSpPr16+falnPdCQkJLu8lSWrTpo327dt30bvhebnY+BbUmDFjlJaWZi1/fZ0BAEDpUyyPT/P19S2ObnM9OcHhcCg7O1uStH79evXr10933HGHPv30U23btk1PP/20y5ze4lDQa/1r7Q6HQ5Ks2r29vYu0poMHD6p79+5q0KCBFi1apC1btlhziC9nPEaOHKnFixfrxRdf1DfffKPt27erfv36ufrK6xovdt0F4XA4rKkUOfKa932l55EkLy8v+fv7uywAAKD0KlQQrlmzpjUfNsfZs2e1adMm1a1bt0B9BAQEqFKlStYX7CQpKytLW7duLUwpuaxbt06hoaF6+umn1axZM4WHh+vQoUNX1OeOHTv0559/WusbNmyQ0+lU1apVr6jfCzVo0ECrV68u1DEbNmzItR4RESHp/Nzb7OxsTZkyRbfccotq166tX3755bLri4+PV1RUlO666y7Vr19fwcHBOnjw4GX3lyMiIsLlvZRzrtq1a8vNzU2SFBQUpJSUFGv/vn37Cn1X29PTs9B3mAEAQOlXqCDs6+urhx9+WKNGjdLnn3+u3bt3a8iQITp16pQGDx5c4H7+9a9/6aWXXtLSpUuVmJio6OhoHTt2zLqTdznCw8OVnJysBQsWKCkpSTNmzNDixYsvuz/p/N3TwYMHa/fu3Vq+fLnGjRunYcOGqUyZor2RPmbMGG3atEmPPPKIvv/+e+3Zs0ezZs3S0aNH8z0mPj5ekyZN0t69e/X6669r4cKFio6OliTVqlVLZ8+e1cyZM/Xjjz9q7ty5F/0i36WEh4frk08+0fbt27Vjxw717du30Hdb8/LEE09o9erVmjBhgvbu3as5c+botddec/kyW8eOHfXaa69p27Zt2rx5s/75z38W+pnKYWFhysjI0OrVq3X06NHLmh4CAABKn0InuokTJ+rvf/+7+vfvryZNmmj//v364osvCvUHJEaPHq37779fAwYMUKtWreR0OhUZGamyZcsWthzLnXfeqccee0zDhg1To0aNtG7dOo0dO/ay+5OkTp06KTw8XO3atdN9992nO++8s1gevVW7dm2tXLlSO3bsUIsWLdSqVSstXbpU7u75P+b5iSee0ObNm9W4cWM9//zzevXVV63HtzVs2FCvvvqqXn75Zd18882aN2+eXnrppcuu79VXX1W5cuXUunVr9ejRQ5GRkWrSpMll95ejSZMm+uijj7RgwQLdfPPNevbZZ/Xcc88pKirKajNlyhRVrVpVbdu2Vd++fTVy5EiXedsF0bp1a/3zn//Ufffdp6CgIE2aNOmKawcAANc/h7lwAmYJyM7OVkREhHr37q0JEyaUdDmSzj9H+Pjx40X6p5KLSlhYmEaMGFHkf3YZrtLT0xUQECApTZLrfOGS/68GAADkJef3d1pa2iW/73NV/sTyhQ4dOqSVK1eqffv2OnPmjF577TUdOHBAffv2LYlyAAAAYEPF8tSIS560TBnFxsaqefPmatOmjXbu3KlVq1ZZX/a6GnL+7HNeyzfffHPV6rgW1KtXL9+xmDdvXkmXBwAAUCyuiakRJWH//v357gsJCSnyR5pdyw4dOpTvn6KuVKmS/Pz8rnJF1wamRgAAcP255qdGXAtq1apV0iVcM0JDQ0u6BAAAgKuuRKZGAAAAACWNIAwAAABbIggDAADAlgjCAAAAsCWCMAAAAGyJIAwAAABbIggDAADAlgjCAAAAsCWCMAAAAGyJIAwAAABbIggDAADAlgjCAAAAsCWCMHAJaWmSMa4LAAC4/hGEAQAAYEsEYQAAANgSQRgAAAC2RBAGAACALRGEAQAAYEsEYQAAANgSQRgAAAC2RBAGAACALRGEAQAAYEsEYQAAANgSQRgAAAC2RBAGAACALRGEAQAAYEsEYQAAANgSQRgAAAC25F7SBQDXKmOMJCk9Pb2EKwEAAAWV83s75/f4xRCEgXykpqZKkqpWrVrClQAAgMI6ceKEAgICLtqGIAzko3z58pKk5OTkS/6HZAfp6emqWrWqDh8+LH9//5Iu55rAmOTGmOTGmOTGmLhiPHK7kjExxujEiROqUqXKJdsShIF8lClzfgp9QEAAH0x/4e/vz3hcgDHJjTHJjTHJjTFxxXjkdrljUtAbWHxZDgAAALZEEAYAAIAtEYSBfHh5eWncuHHy8vIq6VKuCYxHboxJboxJboxJboyJK8Yjt6s1Jg5TkGdLAAAAAKUMd4QBAABgSwRhAAAA2BJBGAAAALZEEAYAAIAtEYRhG6+//rrCwsJUtmxZtWzZUt99991F2y9cuFA33XSTypYtq/r162v58uUu+40xevbZZ1W5cmV5e3urc+fO2rdvX3FeQpEr6jGJioqSw+FwWbp06VKcl1DkCjMmu3bt0t///neFhYXJ4XBo2rRpV9zntaioxyQmJibX++Smm24qxisoWoUZj3feeUdt27ZVuXLlVK5cOXXu3DlXe7t9lhRkTOz2WfLJJ5+oWbNmCgwMlK+vrxo1aqS5c+e6tLHb+6QgY1Ik7xMD2MCCBQuMp6enef/9982uXbvMkCFDTGBgoPntt9/ybB8fH2/c3NzMpEmTzO7du80zzzxjPDw8zM6dO602EydONAEBAWbJkiVmx44d5s477zTVq1c3f/7559W6rCtSHGMycOBA06VLF5OSkmItf/zxx9W6pCtW2DH57rvvzMiRI838+fNNcHCwmTp16hX3ea0pjjEZN26cqVevnsv75Pfffy/mKykahR2Pvn37mtdff91s27bNJCQkmKioKBMQEGB++uknq43dPksKMiZ2+yxZs2aN+eSTT8zu3bvN/v37zbRp04ybm5v5/PPPrTZ2e58UZEyK4n1CEIYttGjRwjz66KPWelZWlqlSpYp56aWX8mzfu3dv061bN5dtLVu2NA899JAxxpjs7GwTHBxsJk+ebO0/fvy48fLyMvPnzy+GKyh6RT0mxpz/UOrZs2ex1Hs1FHZM/io0NDTP0HclfV4LimNMxo0bZxo2bFiEVV49V/p6njt3zvj5+Zk5c+YYY+z5WXKhC8fEGHt/luRo3LixeeaZZ4wxvE9y/HVMjCma9wlTI1DqZWZmasuWLercubO1rUyZMurcubPWr1+f5zHr1693aS9JkZGRVvsDBw7o119/dWkTEBCgli1b5tvntaQ4xiRHXFycbrjhBtWpU0cPP/ywUlNTi/4CisHljElJ9Hk1FWf9+/btU5UqVVSjRg3169dPycnJV1pusSuK8Th16pTOnj2r8uXLS7LnZ8mFLhyTHHb9LDHGaPXq1UpMTFS7du0k8T7Ja0xyXOn7hCCMUu/o0aPKyspSpUqVXLZXqlRJv/76a57H/Prrrxdtn/O/henzWlIcYyJJXbp00X/+8x+tXr1aL7/8stauXauuXbsqKyur6C+iiF3OmJREn1dTcdXfsmVLxcbG6vPPP9esWbN04MABtW3bVidOnLjSkotVUYzH6NGjVaVKFSsQ2PGz5EIXjolkz8+StLQ0OZ1OeXp6qlu3bpo5c6Zuv/12SfZ9n1xsTKSieZ+4F/5yACBvffr0sX6uX7++GjRooJo1ayouLk6dOnUqwcpwLenatav1c4MGDdSyZUuFhobqo48+0uDBg0uwsuI1ceJELViwQHFxcSpbtmxJl3NNyG9M7PhZ4ufnp+3btysjI0OrV6/W448/rho1aqhDhw4lXVqJudSYFMX7hDvCKPUqVqwoNzc3/fbbby7bf/vtNwUHB+d5THBw8EXb5/xvYfq8lhTHmOSlRo0aqlixovbv33/lRRezyxmTkujzarpa9QcGBqp27drX/PvkSsbjlVde0cSJE7Vy5Uo1aNDA2m7Hz5Ic+Y1JXuzwWVKmTBnVqlVLjRo10hNPPKF77rlHL730kiT7vk8uNiZ5uZz3CUEYpZ6np6eaNm2q1atXW9uys7O1evVqtWrVKs9jWrVq5dJekr788kurffXq1RUcHOzSJj09XRs3bsy3z2tJcYxJXn766SelpqaqcuXKRVN4MbqcMSmJPq+mq1V/RkaGkpKSrvn3yeWOx6RJkzRhwgR9/vnnatasmcs+O36WSBcfk7zY8bMkOztbZ86ckWTf98mF/jomebms98kVfdUOuE4sWLDAeHl5mdjYWLN7924zdOhQExgYaH799VdjjDH9+/c3Tz31lNU+Pj7euLu7m1deecUkJCSYcePG5fn4tMDAQLN06VLz/fffm549e153j7IpyjE5ceKEGTlypFm/fr05cOCAWbVqlWnSpIkJDw83p0+fLpFrLKzCjsmZM2fMtm3bzLZt20zlypXNyJEjzbZt28y+ffsK3Oe1rjjG5IknnjBxcXHmwIEDJj4+3nTu3NlUrFjRHDly5KpfX2EVdjwmTpxoPD09zccff+zyiKcTJ064tLHTZ8mlxsSOnyUvvviiWblypUlKSjK7d+82r7zyinF3dzfvvPOO1cZu75NLjUlRvU8IwrCNmTNnmmrVqhlPT0/TokULs2HDBmtf+/btzcCBA13af/TRR6Z27drG09PT1KtXz3z22Wcu+7Ozs83YsWNNpUqVjJeXl+nUqZNJTEy8GpdSZIpyTE6dOmX+9re/maCgIOPh4WFCQ0PNkCFDrpvAl6MwY3LgwAEjKdfSvn37Avd5PSjqMbnvvvtM5cqVjaenpwkJCTH33Xef2b9//1W8oitTmPEIDQ3NczzGjRtntbHbZ8mlxsSOnyVPP/20qVWrlilbtqwpV66cadWqlVmwYIFLf3Z7n1xqTIrqfeIwxpiC3z8GAAAASgfmCAMAAMCWCMIAAACwJYIwAAAAbIkgDAAAAFsiCAMAAMCWCMIAAACwJYIwAAAAbIkgDAAokLi4ODkcDh0/fvya6AcArhRBGABsICoqSg6HQw6HQx4eHqpevbqefPJJnT59uljP26FDB40YMcJlW+vWrZWSkqKAgIBiO+/BgwflcDi0ffv2YjvHlYqKilKvXr1KugzA1txLugAAwNXRpUsXzZ49W2fPntWWLVs0cOBAORwOvfzyy1e1Dk9PTwUHB1/Vc15LsrKy5HA4SroMAOKOMADYhpeXl4KDg1W1alX16tVLnTt31pdffmntz87O1ksvvaTq1avL29tbDRs21Mcff5xvf6mpqbr//vsVEhIiHx8f1a9fX/Pnz7f2R0VFae3atZo+fbp1N/rgwYMuUyPS09Pl7e2tFStWuPS9ePFi+fn56dSpU5Kkw4cPq3fv3goMDFT58uXVs2dPHTx4sMDXnnPOL774Qo0bN5a3t7c6duyoI0eOaMWKFYqIiJC/v7/69u1rnVM6f0d72LBhGjZsmAICAlSxYkWNHTtWxhirzbFjxzRgwACVK1dOPj4+6tq1q/bt22ftj42NVWBgoJYtW6a6devKy8tL//jHPzRnzhwtXbrUGpu4uDhJ0ujRo1W7dm35+PioRo0aGjt2rM6ePWv1FxMTo0aNGmnu3LkKCwtTQECA+vTpoxMnTri8lpMmTVKtWrXk5eWlatWq6YUXXrD2X+l4AqUFQRgAbOiHH37QunXr5OnpaW176aWX9J///Edvvvmmdu3apccee0wPPPCA1q5dm2cfp0+fVtOmTfXZZ5/phx9+0NChQ9W/f3999913kqTp06erVatWGjJkiFJSUpSSkqKqVau69OHv76/u3bvrgw8+cNk+b9489erVSz4+Pjp79qwiIyPl5+enb775RvHx8XI6nerSpYsyMzMLdd0xMTF67bXXtG7dOisMTps2TR988IE+++wzrVy5UjNnznQ5Zs6cOXJ3d9d3332n6dOn69VXX9W7775r7Y+KitLmzZu1bNkyrV+/XsYY3XHHHS7h9dSpU3r55Zf17rvvateuXZoxY4Z69+6tLl26WGPTunVrSZKfn59iY2O1e/duTZ8+Xe+8846mTp3qUlNSUpKWLFmiTz/9VJ9++qnWrl2riRMnWvvHjBmjiRMnauzYsdq9e7c++OADVapUSZKKdDyB654BAJR6AwcONG5ubsbX19d4eXkZSaZMmTLm448/NsYYc/r0aePj42PWrVvnctzgwYPN/fffb4wxZs2aNUaSOXbsWL7n6datm3niiSes9fbt25vo6GiXNhf2s3jxYuN0Os3JkyeNMcakpaWZsmXLmhUrVhhjjJk7d66pU6eOyc7Otvo4c+aM8fb2Nl988UWedRw4cMBIMtu2bXM556pVq6w2L730kpFkkpKSrG0PPfSQiYyMdKk/IiLC5dyjR482ERERxhhj9u7daySZ+Ph4a//Ro0eNt7e3+eijj4wxxsyePdtIMtu3b3epceDAgaZnz5551v9XkydPNk2bNrXWx40bZ3x8fEx6erq1bdSoUaZly5bGGGPS09ONl5eXeeedd/Ls73LGEyitmCMMADZx2223adasWTp58qSmTp0qd3d3/f3vf5ck7d+/X6dOndLtt9/uckxmZqYaN26cZ39ZWVl68cUX9dFHH+nnn39WZmamzpw5Ix8fn0LVdccdd8jDw0PLli1Tnz59tGjRIvn7+6tz586SpB07dmj//v3y8/NzOe706dNKSkoq1LkaNGhg/VypUiVr+sFft+Xc0c5xyy23uMzpbdWqlaZMmaKsrCwlJCTI3d1dLVu2tPZXqFBBderUUUJCgrXN09PT5dwX8+GHH2rGjBlKSkpSRkaGzp07J39/f5c2YWFhLuNRuXJlHTlyRJKUkJCgM2fOqFOnTnn2X5TjCVzvCMIAYBO+vr6qVauWJOn9999Xw4YN9d5772nw4MHKyMiQJH322WcKCQlxOc7LyyvP/iZPnqzp06dr2rRpql+/vnx9fTVixIhC//O6p6en7rnnHn3wwQfq06ePPvjgA913331ydz//KyojI0NNmzbVvHnzch0bFBRUqHN5eHhYP+c8QeOvHA6HsrOzC9VnQXh7exfoC3Lr169Xv379NH78eEVGRiogIEALFizQlClTXNpdrG5vb++LnqMoxxO43hGEAcCGypQpo3//+996/PHH1bdvX+tLXMnJyWrfvn2B+oiPj1fPnj31wAMPSDr/Ba29e/eqbt26VhtPT09lZWVdsq9+/frp9ttv165du/TVV1/p+eeft/Y1adJEH374oW644YZcd0avho0bN7qsb9iwQeHh4XJzc1NERITOnTunjRs3WnN8U1NTlZiY6DIOeclrbNatW6fQ0FA9/fTT1rZDhw4Vqt7w8HB5e3tr9erVevDBB3PtL+nxBK4lfFkOAGzq3nvvlZubm15//XX5+flp5MiReuyxxzRnzhwlJSVp69atmjlzpubMmZPn8eHh4fryyy+1bt06JSQk6KGHHtJvv/3m0iYsLEwbN27UwYMHdfTo0XzvtrZr107BwcHq16+fqlev7jLVoF+/fqpYsaJ69uypb775RgcOHFBcXJyGDx+un376qegGJB/Jycl6/PHHlZiYqPnz52vmzJmKjo6WdH4MevbsqSFDhujbb7/Vjh079MADDygkJEQ9e/a8aL9hYWH6/vvvlZiYqKNHj+rs2bMKDw9XcnKyFixYoKSkJM2YMUOLFy8uVL1ly5bV6NGj9eSTT+o///mPkpKStGHDBr333nuSSn48gWsJQRgAbMrd3V3Dhg3TpEmTdPLkSU2YMEFjx47VSy+9pIiICHXp0kWfffaZqlevnufxzzzzjJo0aaLIyEh16NBBwcHBuf5AxMiRI+Xm5qa6desqKChIycnJefblcDh0//33a8eOHerXr5/LPh8fH3399deqVq2a7r77bkVERGjw4ME6ffr0VbmjOWDAAP35559q0aKFHn30UUVHR2vo0KHW/tmzZ6tp06bq3r27WrVqJWOMli9fnmv6woWGDBmiOnXqqFmzZgoKClJ8fLzuvPNOPfbYYxo2bJgaNWqkdevWaezYsYWueezYsXriiSf07LPPKiIiQvfdd581h7ikxxO4ljiM+cvDEAEAgKVDhw5q1KiRpk2bVtKlACgG3BEGAACALRGEAQAAYEtMjQAAAIAtcUcYAAAAtkQQBgAAgC0RhAEAAGBLBGEAAADYEkEYAAAAtkQQBgAAgC0RhAEAAGBLBGEAAADYEkEYAAAAtvT/AKPybZ4EPsbCAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creating a Database"
      ],
      "metadata": {
        "id": "5XHwpgziyqKR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sqlalchemy import create_engine, Column, Integer, String, Float, DateTime, Boolean\n",
        "from sqlalchemy.ext.declarative import declarative_base\n",
        "from sqlalchemy.orm import sessionmaker\n",
        "\n",
        "Base = declarative_base()\n",
        "\n",
        "class HighRiskCountry(Base):\n",
        "    __tablename__ = 'high_risk_countries'\n",
        "    id = Column(Integer, primary_key=True)\n",
        "    end_of_period = Column(DateTime)\n",
        "    credit_number = Column(String)\n",
        "    region = Column(String)\n",
        "    country_code = Column(String)\n",
        "    country = Column(String)\n",
        "    borrower = Column(String)\n",
        "    credit_status = Column(String)\n",
        "    service_charge_rate = Column(Float)\n",
        "    project_id = Column(String)\n",
        "    project_name = Column(String)\n",
        "    original_principal_amount = Column(Float)\n",
        "    cancelled_amount = Column(Float)\n",
        "    disbursed_amount = Column(Float)\n",
        "    repaid_to_ida = Column(Float)\n",
        "    due_to_ida = Column(Float)\n",
        "    borrower_s_obligation = Column(Float)\n",
        "    credits_held = Column(Float)\n",
        "    first_repayment_date = Column(DateTime)\n",
        "    last_repayment_date = Column(DateTime)\n",
        "    agreement_signing_date = Column(DateTime)\n",
        "    board_approval_date = Column(DateTime)\n",
        "    effective_date_most_recent_ = Column(DateTime, name='effective_date_most_recent_')\n",
        "    closed_date_most_recent = Column(DateTime,name='closed_date_most_recent_')\n",
        "    debt_servicing_ratio = Column(Float)\n",
        "    past_due = Column(Integer)\n",
        "    non_repayment = Column(Integer)\n",
        "    prediction = Column(Integer)\n",
        "    default_probability = Column(Float)\n",
        "    high_risk = Column(Integer)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oPJ69-VSuIUT",
        "outputId": "a301975f-1cee-4de4-ed8b-380158d5f926"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-70-f225a2880f5c>:5: MovedIn20Warning: The ``declarative_base()`` function is now available as sqlalchemy.orm.declarative_base(). (deprecated since: 2.0) (Background on SQLAlchemy 2.0 at: https://sqlalche.me/e/b8d9)\n",
            "  Base = declarative_base()\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "engine = create_engine('sqlite:///high_risk_countries.db')\n",
        "Base.metadata.drop_all(engine)\n",
        "Base.metadata.create_all(engine)"
      ],
      "metadata": {
        "id": "wnhWtGjCywwI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Session = sessionmaker(bind=engine)\n",
        "session = Session()\n",
        "\n",
        "high_risk_countries.to_sql('high_risk_countries', con=engine, if_exists='append', index=False, method='multi')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2A87YC-t3PAd",
        "outputId": "f02b63f9-b588-4f96-c64d-37c18b9f2a49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1000"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sqlalchemy import create_engine, MetaData\n",
        "\n",
        "engine = create_engine('sqlite:///high_risk_countries.db')\n",
        "metadata = MetaData()\n",
        "metadata.reflect(bind=engine)\n",
        "\n",
        "for table in metadata.tables.values():\n",
        "    print(table.name)\n",
        "    for column in table.c:\n",
        "        print(\"  \", column.name, column.type)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-28Dsu2495Qf",
        "outputId": "d2bcfd51-0614-4f91-e555-387ad278054c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "high_risk_countries\n",
            "   id INTEGER\n",
            "   end_of_period DATETIME\n",
            "   credit_number VARCHAR\n",
            "   region VARCHAR\n",
            "   country_code VARCHAR\n",
            "   country VARCHAR\n",
            "   borrower VARCHAR\n",
            "   credit_status VARCHAR\n",
            "   service_charge_rate FLOAT\n",
            "   project_id VARCHAR\n",
            "   project_name VARCHAR\n",
            "   original_principal_amount FLOAT\n",
            "   cancelled_amount FLOAT\n",
            "   disbursed_amount FLOAT\n",
            "   repaid_to_ida FLOAT\n",
            "   due_to_ida FLOAT\n",
            "   borrower_s_obligation FLOAT\n",
            "   credits_held FLOAT\n",
            "   first_repayment_date DATETIME\n",
            "   last_repayment_date DATETIME\n",
            "   agreement_signing_date DATETIME\n",
            "   board_approval_date DATETIME\n",
            "   effective_date_most_recent_ DATETIME\n",
            "   closed_date_most_recent_ DATETIME\n",
            "   debt_servicing_ratio FLOAT\n",
            "   past_due INTEGER\n",
            "   non_repayment INTEGER\n",
            "   prediction INTEGER\n",
            "   default_probability FLOAT\n",
            "   high_risk INTEGER\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Session = sessionmaker(bind=engine)\n",
        "session = Session()\n",
        "\n",
        "result = session.query(HighRiskCountry).limit(10).all()\n",
        "for item in result:\n",
        "    print(item.country, item.default_probability)\n",
        "\n",
        "session.close()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yymFKl_N75Bt",
        "outputId": "efe09c3d-53b0-4e0f-8e63-15b64025458d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Honduras 0.0004616436114940771\n",
            "Honduras 0.0004616436114940771\n",
            "Honduras 0.0004616436114940771\n",
            "Honduras 0.0004616436114940771\n",
            "Honduras 0.0004616436114940771\n",
            "Honduras 0.0004616436114940771\n",
            "Honduras 0.0004616436114940771\n",
            "Honduras 0.0004616436114940771\n",
            "Honduras 0.0004616436114940771\n",
            "Honduras 0.0004616436114940771\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Then we conclude that Sudan has a probability greater than 0.1 of being in debt with IDA in the next years."
      ],
      "metadata": {
        "id": "El6XD8elAIVo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "high_risk_countries['country'].unique()"
      ],
      "metadata": {
        "id": "s0eRBtNv-TD1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a9f3a365-e50a-4e2c-d1d1-9d0bb38cb2e6"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Sudan'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5KSt_8S9k97z"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}