CompasFairML.ipynb

{
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  "metadata": {
    "colab": {
      "name": "CompasFairML.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "17kuYKYY__Wcgtn5WaRz6dbtekctEFZEk",
      "authorship_tag": "ABX9TyMG9Oo/qWuMdp0vv0aynA3o",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/bintualkassoum/fair-ml-project/blob/main/CompasFairML.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Project Objective\n",
        "The goal of this project is to identify which factors have the strongest impact on recidivism rates. Aligning with O'Neil (Weapons of Math Destruction), I argue that predictive models have the potential to generate negative feedback loops, which can then impact the individuals represented in these models. These feedback loops are perpetuated by their opacity, scale, and societal damage. \n",
        "\n",
        "I will then analyze these results against COMPAS in an attempt to re-create a better, less harmful, and more effective model. \n",
        "\n",
        "Afterwards, I will run this model against other data sets to ensure its effectiveness, as this data set is limited to Broward County, Florida. \n",
        "\n",
        "Feature Engineering Info: \n",
        "Drop age_cat, first, last, \n",
        "c_jail_in and c_jail_out (several values small time frame and similar), \n",
        "c_days_from_compas (too many similar values across rows), "
      ],
      "metadata": {
        "id": "Wzz8P0OtwFef"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Packages and Libraries "
      ],
      "metadata": {
        "id": "RIjkzA5ct-Jh"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sP1yRp7srNkL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e0a67211-52cc-4358-e74c-ed8ddfffc10a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting category_encoders\n",
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            "Installing collected packages: category-encoders\n",
            "Successfully installed category-encoders-2.4.1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ]
        }
      ],
      "source": [
        "#Import Packages\n",
        "import pandas as pd \n",
        "import os \n",
        "import scipy as sp\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import sklearn\n",
        "!pip install --upgrade category_encoders\n",
        "import category_encoders as ce\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Import Libraries \n",
        "from pandas import read_csv\n",
        "from pandas.plotting import scatter_matrix\n",
        "from pandas.core.common import random_state\n",
        "from sklearn import neighbors, metrics, utils\n",
        "from sklearn.model_selection import train_test_split, cross_val_score, StratifiedKFold, GridSearchCV, RandomizedSearchCV\n",
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
        "from sklearn.linear_model import LinearRegression, LogisticRegression, Perceptron, SGDClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.svm import SVC, LinearSVC\n",
        "from sklearn.preprocessing import LabelEncoder, OneHotEncoder, Normalizer, StandardScaler\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.pipeline import Pipeline, make_pipeline\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.impute import SimpleImputer, KNNImputer\n",
        "from sklearn import preprocessing\n",
        "from sklearn.inspection import permutation_importance\n",
        "from sklearn.datasets import make_classification\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from matplotlib import style\n",
        "from sklearn import tree\n",
        "from matplotlib import pyplot\n",
        "import time \n",
        "from IPython.display import Image  \n",
        "from six import StringIO\n",
        "from sklearn.tree import export_graphviz\n",
        "import pydotplus, graphviz\n",
        "!pip install dtreeviz\n",
        "from dtreeviz.trees import dtreeviz \n",
        "!pip install shap \n",
        "import shap\n",
        "!pip install dmba \n",
        "import dmba"
      ],
      "metadata": {
        "id": "lvYoXwvvy0oA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "be471faf-4cea-4199-99e2-ed360347ab6f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "  Building wheel for dtreeviz (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for dtreeviz: filename=dtreeviz-1.3.6-py3-none-any.whl size=67326 sha256=bce470375aa9d81056691e16c538a15ad4b087988ce3e2696c9d7c290a2dadc5\n",
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Install Dashboard Packages \n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.pipeline import Pipeline\n",
        "!pip install dash\n",
        "!pip install dash-bootstrap-components\n",
        "!pip install dash_daq\n",
        "!pip install daq\n",
        "from dash import Dash, html, dcc\n",
        "from dash.dependencies import Input, Output, State\n",
        "import plotly.express as px \n",
        "import plotly.graph_objects as go"
      ],
      "metadata": {
        "id": "xZW02fjUI2lN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c3f9624a-310e-445d-99bf-80a339d83f56"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "\u001b[?25hInstalling collected packages: brotli, flask-compress, dash-table, dash-html-components, dash-core-components, dash\n",
            "Successfully installed brotli-1.0.9 dash-2.4.1 dash-core-components-2.0.0 dash-html-components-2.0.0 dash-table-5.0.0 flask-compress-1.12\n",
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            "Building wheels for collected packages: dash-daq\n",
            "  Building wheel for dash-daq (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for dash-daq: filename=dash_daq-0.5.0-py3-none-any.whl size=669714 sha256=7a680a3a00136104da6598bfcea2e618c8a026346fcbd0f0305c2f70613f7499\n",
            "  Stored in directory: /root/.cache/pip/wheels/fe/54/53/a8d448db5592874db4313240571ca2c069e55f6a6b29bf5847\n",
            "Successfully built dash-daq\n",
            "Installing collected packages: dash-daq\n",
            "Successfully installed dash-daq-0.5.0\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting daq\n",
            "  Downloading daq-0.4.2-py2.py3-none-any.whl (7.8 kB)\n",
            "Installing collected packages: daq\n",
            "Successfully installed daq-0.4.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Import Offender Data Set\n",
        "from google.colab import drive\n",
        "drive.mount(\"/content/drive\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3nzyESn7Y5rg",
        "outputId": "7f126608-8931-43b5-95f2-39189650ba24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Import Offender Data Set\n",
        "off_data = pd.read_csv('/content/drive/MyDrive/Grad School/Misc/Fair Machine Learning (COMPAS)/Data/cox-violent-parsed.csv')\n",
        "print(off_data.head(5))\n",
        "\n",
        "print(off_data.shape)\n",
        "\n",
        "off_data.head()"
      ],
      "metadata": {
        "id": "tR_SKnIb58hG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 892
        },
        "outputId": "8fcc7ad2-bba4-494b-a81a-32b352216a58"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    id              name    first       last compas_screening_date   sex  \\\n",
            "0  1.0  miguel hernandez   miguel  hernandez            14/08/2013  Male   \n",
            "1  2.0  miguel hernandez   miguel  hernandez            14/08/2013  Male   \n",
            "2  3.0      michael ryan  michael       ryan            31/12/2014  Male   \n",
            "3  4.0       kevon dixon    kevon      dixon            27/01/2013  Male   \n",
            "4  5.0          ed philo       ed      philo            14/04/2013  Male   \n",
            "\n",
            "          dob  age          age_cat              race  ...  \\\n",
            "0  18/04/1947   69  Greater than 45             Other  ...   \n",
            "1  18/04/1947   69  Greater than 45             Other  ...   \n",
            "2  06/02/1985   31          25 - 45         Caucasian  ...   \n",
            "3  22/01/1982   34          25 - 45  African-American  ...   \n",
            "4  14/05/1991   24     Less than 25  African-American  ...   \n",
            "\n",
            "   v_type_of_assessment  v_decile_score  v_score_text  v_screening_date  \\\n",
            "0      Risk of Violence               1           Low        14/08/2013   \n",
            "1      Risk of Violence               1           Low        14/08/2013   \n",
            "2      Risk of Violence               2           Low        31/12/2014   \n",
            "3      Risk of Violence               1           Low        27/01/2013   \n",
            "4      Risk of Violence               3           Low        14/04/2013   \n",
            "\n",
            "   in_custody  out_custody priors_count.1 start  end event  \n",
            "0  07/07/2014   14/07/2014              0     0  327     0  \n",
            "1  07/07/2014   14/07/2014              0   334  961     0  \n",
            "2  30/12/2014   03/01/2015              0     3  457     0  \n",
            "3  26/01/2013   05/02/2013              0     9  159     1  \n",
            "4  16/06/2013   16/06/2013              4     0   63     0  \n",
            "\n",
            "[5 rows x 52 columns]\n",
            "(18316, 52)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    id              name    first       last compas_screening_date   sex  \\\n",
              "0  1.0  miguel hernandez   miguel  hernandez            14/08/2013  Male   \n",
              "1  2.0  miguel hernandez   miguel  hernandez            14/08/2013  Male   \n",
              "2  3.0      michael ryan  michael       ryan            31/12/2014  Male   \n",
              "3  4.0       kevon dixon    kevon      dixon            27/01/2013  Male   \n",
              "4  5.0          ed philo       ed      philo            14/04/2013  Male   \n",
              "\n",
              "          dob  age          age_cat              race  ...  \\\n",
              "0  18/04/1947   69  Greater than 45             Other  ...   \n",
              "1  18/04/1947   69  Greater than 45             Other  ...   \n",
              "2  06/02/1985   31          25 - 45         Caucasian  ...   \n",
              "3  22/01/1982   34          25 - 45  African-American  ...   \n",
              "4  14/05/1991   24     Less than 25  African-American  ...   \n",
              "\n",
              "   v_type_of_assessment  v_decile_score  v_score_text  v_screening_date  \\\n",
              "0      Risk of Violence               1           Low        14/08/2013   \n",
              "1      Risk of Violence               1           Low        14/08/2013   \n",
              "2      Risk of Violence               2           Low        31/12/2014   \n",
              "3      Risk of Violence               1           Low        27/01/2013   \n",
              "4      Risk of Violence               3           Low        14/04/2013   \n",
              "\n",
              "   in_custody  out_custody priors_count.1 start  end event  \n",
              "0  07/07/2014   14/07/2014              0     0  327     0  \n",
              "1  07/07/2014   14/07/2014              0   334  961     0  \n",
              "2  30/12/2014   03/01/2015              0     3  457     0  \n",
              "3  26/01/2013   05/02/2013              0     9  159     1  \n",
              "4  16/06/2013   16/06/2013              4     0   63     0  \n",
              "\n",
              "[5 rows x 52 columns]"
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              "      <td>14/08/2013</td>\n",
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              "      <td>Greater than 45</td>\n",
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              "      <td>31/12/2014</td>\n",
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              "      <td>06/02/1985</td>\n",
              "      <td>31</td>\n",
              "      <td>25 - 45</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>...</td>\n",
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              "      <td>2</td>\n",
              "      <td>Low</td>\n",
              "      <td>31/12/2014</td>\n",
              "      <td>30/12/2014</td>\n",
              "      <td>03/01/2015</td>\n",
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              "      <td>kevon dixon</td>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Check Null Values (Offender Data)\n",
        "total_nulls = off_data.isnull().sum()\n",
        "total_observations = off_data.shape[0]\n",
        "\n",
        "null_percentages = total_nulls[total_nulls > 0] / total_observations * 100\n",
        "total_nulls = total_nulls[total_nulls > 0]\n",
        "\n",
        "pd.concat([total_nulls, null_percentages], axis = 1, keys = [\"Total Nulls\", \"Percentage\"]).sort_values(by = \"Total Nulls\", ascending = False)\n"
      ],
      "metadata": {
        "id": "JbU36XuU4AyT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# New Section"
      ],
      "metadata": {
        "id": "t0Pu545QrDAA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "off_data.columns.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1MgxrAM_zFuY",
        "outputId": "76cd1a41-309f-4197-d065-cbfb2244f8ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                         1\n",
              "name                       1\n",
              "r_offense_date             1\n",
              "r_charge_desc              1\n",
              "r_jail_in                  1\n",
              "r_jail_out                 1\n",
              "violent_recid              1\n",
              "is_violent_recid           1\n",
              "vr_case_number             1\n",
              "vr_charge_degree           1\n",
              "vr_offense_date            1\n",
              "vr_charge_desc             1\n",
              "type_of_assessment         1\n",
              "decile_score.1             1\n",
              "score_text                 1\n",
              "screening_date             1\n",
              "v_type_of_assessment       1\n",
              "v_decile_score             1\n",
              "v_score_text               1\n",
              "v_screening_date           1\n",
              "in_custody                 1\n",
              "out_custody                1\n",
              "priors_count.1             1\n",
              "start                      1\n",
              "end                        1\n",
              "r_days_from_arrest         1\n",
              "r_charge_degree            1\n",
              "r_case_number              1\n",
              "juv_misd_count             1\n",
              "first                      1\n",
              "last                       1\n",
              "compas_screening_date      1\n",
              "sex                        1\n",
              "dob                        1\n",
              "age                        1\n",
              "age_cat                    1\n",
              "race                       1\n",
              "juv_fel_count              1\n",
              "decile_score               1\n",
              "juv_other_count            1\n",
              "is_recid                   1\n",
              "priors_count               1\n",
              "days_b_screening_arrest    1\n",
              "c_jail_in                  1\n",
              "c_jail_out                 1\n",
              "c_case_number              1\n",
              "c_offense_date             1\n",
              "c_arrest_date              1\n",
              "c_days_from_compas         1\n",
              "c_charge_degree            1\n",
              "c_charge_desc              1\n",
              "event                      1\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Drop Null Values \n",
        "off_data.dropna(axis='columns', thresh= 17000)"
      ],
      "metadata": {
        "id": "jIMER4rk-ZBV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 636
        },
        "outputId": "874515c7-1a6c-41b4-94fe-4abad568499c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        name       first        last compas_screening_date  \\\n",
              "0           miguel hernandez      miguel   hernandez            14/08/2013   \n",
              "1           miguel hernandez      miguel   hernandez            14/08/2013   \n",
              "2               michael ryan     michael        ryan            31/12/2014   \n",
              "3                kevon dixon       kevon       dixon            27/01/2013   \n",
              "4                   ed philo          ed       philo            14/04/2013   \n",
              "...                      ...         ...         ...                   ...   \n",
              "18311  alexsandra beauchamps  alexsandra  beauchamps            29/12/2014   \n",
              "18312        winston gregory     winston     gregory            14/01/2014   \n",
              "18313            farrah jean      farrah        jean            09/03/2014   \n",
              "18314    florencia sanmartin   florencia   sanmartin            30/06/2014   \n",
              "18315    florencia sanmartin   florencia   sanmartin            30/06/2014   \n",
              "\n",
              "          sex         dob  age          age_cat              race  \\\n",
              "0        Male  18/04/1947   69  Greater than 45             Other   \n",
              "1        Male  18/04/1947   69  Greater than 45             Other   \n",
              "2        Male  06/02/1985   31          25 - 45         Caucasian   \n",
              "3        Male  22/01/1982   34          25 - 45  African-American   \n",
              "4        Male  14/05/1991   24     Less than 25  African-American   \n",
              "...       ...         ...  ...              ...               ...   \n",
              "18311  Female  21/12/1984   31          25 - 45  African-American   \n",
              "18312    Male  01/10/1958   57  Greater than 45             Other   \n",
              "18313  Female  17/11/1982   33          25 - 45  African-American   \n",
              "18314  Female  18/12/1992   23     Less than 25          Hispanic   \n",
              "18315  Female  18/12/1992   23     Less than 25          Hispanic   \n",
              "\n",
              "       juv_fel_count  ...  v_type_of_assessment  v_decile_score  v_score_text  \\\n",
              "0                  0  ...      Risk of Violence               1           Low   \n",
              "1                  0  ...      Risk of Violence               1           Low   \n",
              "2                  0  ...      Risk of Violence               2           Low   \n",
              "3                  0  ...      Risk of Violence               1           Low   \n",
              "4                  0  ...      Risk of Violence               3           Low   \n",
              "...              ...  ...                   ...             ...           ...   \n",
              "18311              0  ...      Risk of Violence               4           Low   \n",
              "18312              0  ...      Risk of Violence               1           Low   \n",
              "18313              0  ...      Risk of Violence               2           Low   \n",
              "18314              0  ...      Risk of Violence               4           Low   \n",
              "18315              0  ...      Risk of Violence               4           Low   \n",
              "\n",
              "       v_screening_date  in_custody out_custody priors_count.1 start  end  \\\n",
              "0            14/08/2013  07/07/2014  14/07/2014              0     0  327   \n",
              "1            14/08/2013  07/07/2014  14/07/2014              0   334  961   \n",
              "2            31/12/2014  30/12/2014  03/01/2015              0     3  457   \n",
              "3            27/01/2013  26/01/2013  05/02/2013              0     9  159   \n",
              "4            14/04/2013  16/06/2013  16/06/2013              4     0   63   \n",
              "...                 ...         ...         ...            ...   ...  ...   \n",
              "18311        29/12/2014  28/12/2014  07/01/2015              5     9  459   \n",
              "18312        14/01/2014  13/01/2014  14/01/2014              0     0  808   \n",
              "18313        09/03/2014  08/03/2014  09/03/2014              3     0  754   \n",
              "18314        30/06/2014  15/03/2015  15/03/2015              2     0  258   \n",
              "18315        30/06/2014  15/03/2015  15/03/2015              2   258  641   \n",
              "\n",
              "      event  \n",
              "0         0  \n",
              "1         0  \n",
              "2         0  \n",
              "3         1  \n",
              "4         0  \n",
              "...     ...  \n",
              "18311     0  \n",
              "18312     0  \n",
              "18313     0  \n",
              "18314     0  \n",
              "18315     0  \n",
              "\n",
              "[18316 rows x 37 columns]"
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              "      <th>name</th>\n",
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              "      <td>14/08/2013</td>\n",
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              "      <th>1</th>\n",
              "      <td>miguel hernandez</td>\n",
              "      <td>miguel</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>michael ryan</td>\n",
              "      <td>michael</td>\n",
              "      <td>ryan</td>\n",
              "      <td>31/12/2014</td>\n",
              "      <td>Male</td>\n",
              "      <td>06/02/1985</td>\n",
              "      <td>31</td>\n",
              "      <td>25 - 45</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>Risk of Violence</td>\n",
              "      <td>2</td>\n",
              "      <td>Low</td>\n",
              "      <td>31/12/2014</td>\n",
              "      <td>30/12/2014</td>\n",
              "      <td>03/01/2015</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>457</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>kevon dixon</td>\n",
              "      <td>kevon</td>\n",
              "      <td>dixon</td>\n",
              "      <td>27/01/2013</td>\n",
              "      <td>Male</td>\n",
              "      <td>22/01/1982</td>\n",
              "      <td>34</td>\n",
              "      <td>25 - 45</td>\n",
              "      <td>African-American</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>Risk of Violence</td>\n",
              "      <td>1</td>\n",
              "      <td>Low</td>\n",
              "      <td>27/01/2013</td>\n",
              "      <td>26/01/2013</td>\n",
              "      <td>05/02/2013</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>159</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ed philo</td>\n",
              "      <td>ed</td>\n",
              "      <td>philo</td>\n",
              "      <td>14/04/2013</td>\n",
              "      <td>Male</td>\n",
              "      <td>14/05/1991</td>\n",
              "      <td>24</td>\n",
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              "      <th>18311</th>\n",
              "      <td>alexsandra beauchamps</td>\n",
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              "      <td>29/12/2014</td>\n",
              "      <td>28/12/2014</td>\n",
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              "      <th>18312</th>\n",
              "      <td>winston gregory</td>\n",
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              "      <th>18313</th>\n",
              "      <td>farrah jean</td>\n",
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              "      <td>15/03/2015</td>\n",
              "      <td>2</td>\n",
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              "                                                     [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",
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              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Drop Duplicate Rows \n",
        "off_data.drop_duplicates(subset=['name', 'score_text'], keep='first')\n",
        "off_data[off_data.duplicated(keep='first')]"
      ],
      "metadata": {
        "id": "uaRCvQzZEXim",
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          "height": 142
        },
        "outputId": "7025315a-9a42-431c-92e9-ee254c4a7963"
      },
      "execution_count": null,
      "outputs": [
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              "Empty DataFrame\n",
              "Columns: [id, name, first, last, compas_screening_date, sex, dob, age, age_cat, race, juv_fel_count, decile_score, juv_misd_count, juv_other_count, priors_count, days_b_screening_arrest, c_jail_in, c_jail_out, c_case_number, c_offense_date, c_arrest_date, c_days_from_compas, c_charge_degree, c_charge_desc, is_recid, r_case_number, r_charge_degree, r_days_from_arrest, r_offense_date, r_charge_desc, r_jail_in, r_jail_out, violent_recid, is_violent_recid, vr_case_number, vr_charge_degree, vr_offense_date, vr_charge_desc, type_of_assessment, decile_score.1, score_text, screening_date, v_type_of_assessment, v_decile_score, v_score_text, v_screening_date, in_custody, out_custody, priors_count.1, start, end, event]\n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "#Combine all Juvenile Counts \n",
        "off_data['juv_misd_count']. value_counts()\n",
        "\n",
        "off_data['juv_fel_count']. value_counts()\n",
        "\n",
        "off_data['juv_other_count']. value_counts()\n",
        "\n",
        "juv_count = off_data['juv_misd_count'] + off_data['juv_fel_count'] + off_data['juv_other_count']\n",
        "off_data['juv_count'] = juv_count"
      ],
      "metadata": {
        "id": "_ZCOvTcMYvgd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Combine Custody Dates \n",
        "off_data['in_custody'] = pd.to_datetime(off_data['in_custody'])\n",
        "off_data['out_custody'] = pd.to_datetime(off_data['out_custody'])\n",
        "off_data['custody_duration'] = (off_data['out_custody'] - off_data['in_custody']).dt.days\n",
        "\n",
        "#Several dates inputted incorrectly, so I will drop all three categories "
      ],
      "metadata": {
        "id": "uUDZ8g3Q_k0v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Combine Violent and Non-Violent Recidivisim (Target Variable)\n",
        "total_recid = off_data['is_recid'] + off_data['is_violent_recid']\n",
        "off_data['total_recid'] = total_recid"
      ],
      "metadata": {
        "id": "AGStVWA3HzC6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Combine Decile and Violent Decile Scores (Target Variable)\n",
        "recid_score = (off_data['decile_score'] + off_data['v_decile_score']) / 2\n",
        "off_data['recid_score'] = recid_score.astype(int)\n",
        "\n",
        "off_data.head()"
      ],
      "metadata": {
        "id": "Uk53yluhLXsb",
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          "height": 382
        },
        "outputId": "b9752323-380f-4b54-9250-15ffe4637d13"
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      "execution_count": null,
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              "    id              name    first       last compas_screening_date   sex  \\\n",
              "0  1.0  miguel hernandez   miguel  hernandez            14/08/2013  Male   \n",
              "1  2.0  miguel hernandez   miguel  hernandez            14/08/2013  Male   \n",
              "2  3.0      michael ryan  michael       ryan            31/12/2014  Male   \n",
              "3  4.0       kevon dixon    kevon      dixon            27/01/2013  Male   \n",
              "4  5.0          ed philo       ed      philo            14/04/2013  Male   \n",
              "\n",
              "          dob  age          age_cat              race  ...  in_custody  \\\n",
              "0  18/04/1947   69  Greater than 45             Other  ...  2014-07-07   \n",
              "1  18/04/1947   69  Greater than 45             Other  ...  2014-07-07   \n",
              "2  06/02/1985   31          25 - 45         Caucasian  ...  2014-12-30   \n",
              "3  22/01/1982   34          25 - 45  African-American  ...  2013-01-26   \n",
              "4  14/05/1991   24     Less than 25  African-American  ...  2013-06-16   \n",
              "\n",
              "   out_custody  priors_count.1  start  end  event juv_count custody_duration  \\\n",
              "0   2014-07-14               0      0  327      0         0              7.0   \n",
              "1   2014-07-14               0    334  961      0         0              7.0   \n",
              "2   2015-03-01               0      3  457      0         0             61.0   \n",
              "3   2013-05-02               0      9  159      1         0             96.0   \n",
              "4   2013-06-16               4      0   63      0         1              0.0   \n",
              "\n",
              "  total_recid recid_score  \n",
              "0           0           1  \n",
              "1           0           1  \n",
              "2          -1           3  \n",
              "3           2           2  \n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c9757110-7ce3-4ff6-a0f1-0c83309e18c8 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-c9757110-7ce3-4ff6-a0f1-0c83309e18c8');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Data Cleaning - Feature Engineer Recid Scores\n",
        "r = [0,4,7,10]\n",
        "g = ['Low','Medium','High']\n",
        "off_data['recid_score'] = pd.cut(off_data['recid_score'], bins = r, labels = g)\n",
        "\n",
        "off_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "ZhAFWeJJpduF",
        "outputId": "bc352ea1-39df-4389-d931-845aa573872d"
      },
      "execution_count": null,
      "outputs": [
        {
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            "text/plain": [
              "    id              name    first       last compas_screening_date   sex  \\\n",
              "0  1.0  miguel hernandez   miguel  hernandez            14/08/2013  Male   \n",
              "1  2.0  miguel hernandez   miguel  hernandez            14/08/2013  Male   \n",
              "2  3.0      michael ryan  michael       ryan            31/12/2014  Male   \n",
              "3  4.0       kevon dixon    kevon      dixon            27/01/2013  Male   \n",
              "4  5.0          ed philo       ed      philo            14/04/2013  Male   \n",
              "\n",
              "          dob  age          age_cat              race  ...  in_custody  \\\n",
              "0  18/04/1947   69  Greater than 45             Other  ...  2014-07-07   \n",
              "1  18/04/1947   69  Greater than 45             Other  ...  2014-07-07   \n",
              "2  06/02/1985   31          25 - 45         Caucasian  ...  2014-12-30   \n",
              "3  22/01/1982   34          25 - 45  African-American  ...  2013-01-26   \n",
              "4  14/05/1991   24     Less than 25  African-American  ...  2013-06-16   \n",
              "\n",
              "   out_custody  priors_count.1  start  end  event juv_count custody_duration  \\\n",
              "0   2014-07-14               0      0  327      0         0              7.0   \n",
              "1   2014-07-14               0    334  961      0         0              7.0   \n",
              "2   2015-03-01               0      3  457      0         0             61.0   \n",
              "3   2013-05-02               0      9  159      1         0             96.0   \n",
              "4   2013-06-16               4      0   63      0         1              0.0   \n",
              "\n",
              "  total_recid recid_score  \n",
              "0           0         Low  \n",
              "1           0         Low  \n",
              "2          -1         Low  \n",
              "3           2         Low  \n",
              "4           1         Low  \n",
              "\n",
              "[5 rows x 56 columns]"
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
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              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Final Data Cleaning \n",
        "\n",
        "column_names = ['sex', 'age', 'race', 'juv_count', 'priors_count',\n",
        "                'c_charge_degree', 'recid_score']\n",
        "data = pd.DataFrame(off_data, columns=column_names)\n",
        "\n",
        "data.loc[data['recid_score'] == -1]\n",
        "data = data.drop([900, 1269, 1270, 5018, 13669, 17695])\n",
        "\n",
        "data = data.dropna()\n",
        "\n",
        "data"
      ],
      "metadata": {
        "id": "ugT5dSL6Bam7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "805662eb-1e84-4538-e4e6-521346523615"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "          sex  age              race  juv_count  priors_count c_charge_degree  \\\n",
              "0        Male   69             Other          0             0            (F3)   \n",
              "1        Male   69             Other          0             0            (F3)   \n",
              "3        Male   34  African-American          0             0            (F3)   \n",
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              "5        Male   24  African-American          1             4            (F3)   \n",
              "...       ...  ...               ...        ...           ...             ...   \n",
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              "18312    Male   57             Other          0             0            (F2)   \n",
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              "18314  Female   23          Hispanic          0             2            (F3)   \n",
              "18315  Female   23          Hispanic          0             2            (F3)   \n",
              "\n",
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              "3             Low  \n",
              "4             Low  \n",
              "5             Low  \n",
              "...           ...  \n",
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              "18312         Low  \n",
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              "[17441 rows x 7 columns]"
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              "    <tr>\n",
              "      <th>18314</th>\n",
              "      <td>Female</td>\n",
              "      <td>23</td>\n",
              "      <td>Hispanic</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>(F3)</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18315</th>\n",
              "      <td>Female</td>\n",
              "      <td>23</td>\n",
              "      <td>Hispanic</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>(F3)</td>\n",
              "      <td>Low</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>17441 rows × 7 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-45f922a4-4133-484c-a483-dd25d5a4d2d4')\"\n",
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              "            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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Exploratory Data Analysis \n",
        "data_num = data[['age', 'juv_count', 'priors_count', 'recid_score']]\n",
        "data_cat = data[['sex', 'race', 'c_charge_degree']] \n",
        "\n",
        "#Data Distribution \n",
        "fig, ax = plt.subplots(figsize=(11, 11))\n",
        "data.hist(bins=100, ax=ax)\n",
        "\n",
        "#Skew of Data \n",
        "skew = data.skew()\n",
        "pd.DataFrame(data = skew, columns=['Skew'])"
      ],
      "metadata": {
        "id": "cxCr8-Zvyn_3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "outputId": "402da485-2e63-4bd9-e628-8df1a4a34d57"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "To output multiple subplots, the figure containing the passed axes is being cleared\n",
            "Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Skew\n",
              "age           0.968166\n",
              "juv_count     6.429407\n",
              "priors_count  2.157277"
            ],
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>0.968166</td>\n",
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              "    <tr>\n",
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              "      <td>6.429407</td>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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            ]
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Pivot Tables for Categorical Variables \n",
        "print(pd.pivot_table(data, index = 'recid_score', columns = 'sex', values = 'age', aggfunc = 'count'))\n",
        "print(pd.pivot_table(data, index = 'recid_score', columns = 'race', values = 'age', aggfunc = 'count'))\n",
        "print(pd.pivot_table(data, index = 'recid_score', columns = 'c_charge_degree', values = 'age', aggfunc = 'count'))\n",
        "print(pd.pivot_table(data, index = 'recid_score', values = ['age', 'juv_count', 'priors_count']))"
      ],
      "metadata": {
        "id": "4qqesYy0OgPS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "596b4abd-0703-4046-fede-786e58d69f7e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sex          Female  Male\n",
            "recid_score              \n",
            "Low            2043  7516\n",
            "Medium          879  4482\n",
            "High            272  2249\n",
            "race         African-American  Asian  Caucasian  Hispanic  Native American  \\\n",
            "recid_score                                                                  \n",
            "Low                      3933     48       3955       963               24   \n",
            "Medium                   3512     12       1394       271               16   \n",
            "High                     1953      4        411       113                9   \n",
            "\n",
            "race         Other  \n",
            "recid_score         \n",
            "Low            636  \n",
            "Medium         156  \n",
            "High            31  \n",
            "c_charge_degree  (CO3)  (CT)  (F1)  (F2)  (F3)  (F5)  (F6)  (F7)  (M1)  (M2)  \\\n",
            "recid_score                                                                    \n",
            "Low                  6     1   171   708  5207     7     8   111  2527   738   \n",
            "Medium               0     0    73   497  3456     6     5    44   853   375   \n",
            "High                 0     0    53   298  1629     0     2    29   296   170   \n",
            "\n",
            "c_charge_degree  (MO3)  (NI0)  (TCX)  (X)  \n",
            "recid_score                                \n",
            "Low                 63     10      1    1  \n",
            "Medium              51      1      0    0  \n",
            "High                40      4      0    0  \n",
            "                   age  juv_count  priors_count\n",
            "recid_score                                    \n",
            "Low          37.841929   0.081703      2.692855\n",
            "Medium       30.351427   0.413356      5.577877\n",
            "High         26.840539   0.982150      6.355811\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Histograph for Categorical Features \n",
        "for i in data_cat.columns:\n",
        "    sns.barplot(data_cat[i].value_counts().index,data_cat[i].value_counts()).set_title(i)\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "JpNjoudgfQjb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6a130fde-9ecc-4b20-c616-dcf0f6c197c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Data Visulaization - Heatmap Plot\n",
        "corr = data.corr()\n",
        "\n",
        "sns.heatmap(corr)"
      ],
      "metadata": {
        "id": "p0LWNmqhe16q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dc467783-080b-41c8-9fcb-76999d0dd97d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd9767442d0>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Split Original Data Set \n",
        "X = pd.get_dummies(data=data, columns=['sex', 'race', 'c_charge_degree'])\n",
        "X = X.drop(columns=['recid_score'], axis=1)\n",
        "\n",
        "y = pd.DataFrame(data=data['recid_score'], columns=['recid_score'])\n",
        "\n",
        "le = preprocessing.LabelEncoder()\n",
        "\n",
        "y = le.fit_transform(y)\n",
        "y.ravel()\n",
        "y"
      ],
      "metadata": {
        "id": "0OShgYir3csS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7ed72af2-572f-4e9f-97c9-5b509d722a88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, ..., 1, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().any()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SDLt0470yTyT",
        "outputId": "857990a7-dc7d-488d-b0a1-a17ef9a5b4a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sex                False\n",
              "age                False\n",
              "race               False\n",
              "juv_count          False\n",
              "priors_count       False\n",
              "c_charge_degree    False\n",
              "recid_score        False\n",
              "dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Split Original Data Set \n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=True, random_state=8)\n",
        "\n",
        "print(\"X_train Shape: {}\".format(X_train.shape))\n",
        "print(\"X_test Shape: {}\".format(X_test.shape))\n",
        "print(\"y_train Shape: {}\".format(y_train.shape))\n",
        "print(\"y_test Shape: {}\".format(y_test.shape))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lj1-oA2Ixfsf",
        "outputId": "b1de0161-061a-45d6-ab7e-2cf1a8abbb0a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train Shape: (13952, 25)\n",
            "X_test Shape: (3489, 25)\n",
            "y_train Shape: (13952,)\n",
            "y_test Shape: (3489,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Standardize Data \n",
        "scaler = StandardScaler()\n",
        "scaler.fit(X_train) \n",
        "X_train = scaler.transform(X_train)\n",
        "X_test = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "i1EXdHb51kjs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Building ML Models \n",
        "\n",
        "#Random Forest:\n",
        "random_forest = RandomForestClassifier(n_estimators=2000)\n",
        "random_forest.fit(X_train, y_train)\n",
        "\n",
        "y_pred = random_forest.predict(X_test)\n",
        "\n",
        "random_forest.score(X_train, y_train)\n",
        "acc_random_forest = round(random_forest.score(X_train, y_train) * 100, 2)\n",
        "\n",
        "#Logistic Regression:\n",
        "logreg = LogisticRegression(max_iter=3000)\n",
        "logreg.fit(X_train, y_train)\n",
        "\n",
        "y_pred = logreg.predict(X_test)\n",
        "\n",
        "acc_log = round(logreg.score(X_train, y_train) * 100, 2)\n",
        "\n",
        "#K Nearest Neighbor (KNN):\n",
        "knn = neighbors.KNeighborsClassifier(n_neighbors = 3)\n",
        "knn.fit(X_train, y_train)\n",
        "\n",
        "y_pred = knn.predict(X_test)\n",
        "\n",
        "acc_knn = round(knn.score(X_train, y_train) * 100, 2)\n",
        "\n",
        "#Gaussian Naive Bayes:\n",
        "gaussian = GaussianNB() \n",
        "gaussian.fit(X_train, y_train)  \n",
        "\n",
        "y_pred = gaussian.predict(X_test) \n",
        "\n",
        "acc_gaussian = round(gaussian.score(X_train, y_train) * 100, 2)\n",
        "\n",
        "#Results\n",
        "results = pd.DataFrame({\n",
        "    'Model': ['Random Forest', 'Logistic Regression', 'KNN', 'Naive Bayes'],\n",
        "    'Score': [acc_random_forest, acc_log, acc_knn, acc_gaussian]})\n",
        "result_data = results.sort_values(by='Score', ascending=False)\n",
        "result_data = result_data.set_index('Score')\n",
        "result_data.head()"
      ],
      "metadata": {
        "id": "kWSm2f4FESkp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
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        "outputId": "247e1a5b-4fb7-4e63-be1f-83c5aeac490e"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     Model\n",
              "Score                     \n",
              "86.66        Random Forest\n",
              "81.00                  KNN\n",
              "65.54  Logistic Regression\n",
              "15.52          Naive Bayes"
            ],
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Random Forest: Hyperparameter Tuning \n",
        "#Grid Search Parameters\n",
        "random_forest = RandomForestClassifier(random_state=42)\n",
        "\n",
        "n_estimators = [10, 100, 1000, 2000]\n",
        "max_depth = [None, 5, 10, 20]\n",
        "criterion = ['gini', 'entropy']\n",
        "max_features = ['auto', 'sqrt', 'log2']\n",
        "param_grid = dict(n_estimators=n_estimators, max_depth=max_depth, criterion=criterion,\n",
        "                  max_features=max_features)\n",
        "\n",
        "#grid = GridSearchCV(estimator=random_forest, param_grid=param_grid, n_jobs=-1, cv=3)\n",
        "#grid_result = grid.fit(X_train, y_train)\n",
        "\n",
        "#Results\n",
        "#print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
        "#means = grid_result.cv_results_['mean_test_score']\n",
        "#stds = grid_result.cv_results_['std_test_score']\n",
        "#params = grid_result.cv_results_['params']\n",
        "#for mean, stdev, param in zip(means, stds, params):\n",
        "    #print(\"%f (%f) with: %r\" % (mean, stdev, param))"
      ],
      "metadata": {
        "id": "z5dMLqeZYmyZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Feature Importance - Random Forest \n",
        "# define the model\n",
        "model = RandomForestClassifier(n_estimators=1000)\n",
        "# fit the model\n",
        "model.fit(X_train, y_train)\n",
        "# get importance\n",
        "importance = model.feature_importances_\n",
        "# summarize feature importance\n",
        "for i,v in enumerate(importance):\n",
        "\tprint('Feature: %0d, Score: %.5f' % (i,v))\n",
        "# plot feature importance\n",
        "pyplot.bar([x for x in range(len(importance))], importance)\n",
        "pyplot.title(\"COMPAS Feature Importance\")\n",
        "pyplot.show()"
      ],
      "metadata": {
        "id": "EkTeWPSNlOsx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bb69cfb3-cc5b-4ff4-8236-052865744ade"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Feature: 0, Score: 0.45889\n",
            "Feature: 1, Score: 0.09143\n",
            "Feature: 2, Score: 0.30694\n",
            "Feature: 3, Score: 0.00831\n",
            "Feature: 4, Score: 0.00827\n",
            "Feature: 5, Score: 0.03225\n",
            "Feature: 6, Score: 0.00108\n",
            "Feature: 7, Score: 0.01435\n",
            "Feature: 8, Score: 0.00757\n",
            "Feature: 9, Score: 0.00135\n",
            "Feature: 10, Score: 0.00584\n",
            "Feature: 11, Score: 0.00010\n",
            "Feature: 12, Score: 0.00000\n",
            "Feature: 13, Score: 0.00563\n",
            "Feature: 14, Score: 0.01023\n",
            "Feature: 15, Score: 0.01421\n",
            "Feature: 16, Score: 0.00042\n",
            "Feature: 17, Score: 0.00035\n",
            "Feature: 18, Score: 0.00369\n",
            "Feature: 19, Score: 0.01397\n",
            "Feature: 20, Score: 0.00915\n",
            "Feature: 21, Score: 0.00501\n",
            "Feature: 22, Score: 0.00093\n",
            "Feature: 23, Score: 0.00000\n",
            "Feature: 24, Score: 0.00002\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Confusion Matrix \n",
        "print(confusion_matrix(y_test, y_pred))"
      ],
      "metadata": {
        "id": "FvR4JE0NSIsT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "85bfa9a0-7229-43a7-df20-9633d0352b34"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 531    0    8]\n",
            " [1631   10  237]\n",
            " [1035    1   36]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Cross Validation \n",
        "\n",
        "print(cross_val_score(random_forest, X_train, y_train, cv=3).mean())\n",
        "\n",
        "print(cross_val_score(logreg, X_train, y_train, cv=3).mean())\n",
        "\n",
        "print(cross_val_score(knn, X_train, y_train, cv=3).mean())\n",
        "\n",
        "print(cross_val_score(gaussian, X_train, y_train, cv=3).mean())"
      ],
      "metadata": {
        "id": "ZHjYswYaB6Dm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "efeb8d08-a51f-4062-ee3a-34ab96402404"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7510032374430596\n",
            "0.6548878300346247\n",
            "0.6879300169771175\n",
            "0.15933253649540816\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "random_forest = RandomForestClassifier(max_depth=3, n_estimators=200)\n",
        "random_forest.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "QGQj9bCZxSQf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "24c2c75b-7ac2-48b6-a62b-fdfc9130e912"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_depth=3, n_estimators=200)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "explainer = shap.TreeExplainer(random_forest)\n",
        "shap_values = explainer.shap_values(X_test)\n",
        "\n",
        "shap.summary_plot(shap_values, X_test, plot_type=\"bar\")"
      ],
      "metadata": {
        "id": "2dqQwNQS3VOx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20,20))\n",
        "\n",
        "tree.plot_tree(random_forest.estimators_[0], feature_names=X.columns, filled=True)"
      ],
      "metadata": {
        "id": "-l0UJOK2wQR8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "264c52b2-7d03-4365-dd9a-a8f4fb618186"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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    },
    {
      "cell_type": "code",
      "source": [
        "viz = dtreeviz(random_forest.estimators_[0], X_train, y_train, feature_names=X.columns,\n",
        "               target_name=\"Recidivism Rate\", show_node_labels = True)\n",
        "viz"
      ],
      "metadata": {
        "id": "35vqApN8yWs-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        },
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      "execution_count": null,
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            "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n",
            "Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
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          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Random Forest Confusion Matrix\n",
        "random_forest = RandomForestClassifier(criterion='entropy', max_depth=None, n_estimators=2000)\n",
        "random_forest.fit(X_train, y_train)\n",
        "\n",
        "y_pred = random_forest.predict(X_test)\n",
        "\n",
        "random_forest.score(X_train, y_train)\n",
        "acc_random_forest = round(random_forest.score(X_train, y_train) * 100, 2)\n",
        "\n",
        "confusion_matrix(y_test, y_pred)"
      ],
      "metadata": {
        "id": "VJWDG6dexkzG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "24765c21-9faa-4b04-f81e-939d7514a759"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 341,   81,  117],\n",
              "       [  22, 1693,  163],\n",
              "       [  93,  296,  683]])"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Random Forest Heatmap\n",
        "#Get and reshape confusion matrix data\n",
        "matrix = confusion_matrix(y_test, y_pred)\n",
        "matrix = matrix.astype('float') / matrix.sum(axis=1)[:, np.newaxis]\n",
        "\n",
        "# Build the plot\n",
        "plt.figure(figsize=(16,7))\n",
        "sns.set(font_scale=1.4)\n",
        "sns.heatmap(matrix, annot=True, annot_kws={'size':10},\n",
        "            cmap=plt.cm.Reds, linewidths=0.2)\n",
        "\n",
        "# Add labels to the plot\n",
        "class_names = ['Low Risk', 'Medium Risk', 'High Risk']\n",
        "tick_marks = np.arange(len(class_names))\n",
        "tick_marks2 = tick_marks \n",
        "plt.xticks(tick_marks, class_names, rotation=0)\n",
        "plt.yticks(tick_marks2, class_names, rotation=0)\n",
        "plt.xlabel('Predicted label')\n",
        "plt.ylabel('True label')\n",
        "plt.title('Confusion Matrix for Random Forest Model')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "RJHGY7UJJ-OO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Our model is the most accurate at predciting medium risk of recidivism and is relatively as accurate at predicting low risk as it is at predicting high risk. \n",
        "\n",
        "However, our model inaccurately classified 22% of low risk offenders as high risk and 27% of high risk offenders as medium risk. \n",
        "\n",
        "**In the context of this project, the 33% misclassification of low risk offenders as high risk is significantly problematic.**"
      ],
      "metadata": {
        "id": "e4VV4EMXMAi-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Classification Report \n",
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "id": "1g25SOkqMNuN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "beccdcce-0519-43d2-8402-152dc5bfdd13"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.75      0.63      0.69       539\n",
            "           1       0.82      0.90      0.86      1878\n",
            "           2       0.71      0.64      0.67      1072\n",
            "\n",
            "    accuracy                           0.78      3489\n",
            "   macro avg       0.76      0.72      0.74      3489\n",
            "weighted avg       0.77      0.78      0.77      3489\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Precision is lowest for Class 2 (high risk offenders), recall is also low between Class 0 (low risk offenders) and Class 2 (high risk offenders), meaning that the model missed classifying some high and low risk offenders, instead classifying them as medium risk (which we see based on the high recall for Class 1)."
      ],
      "metadata": {
        "id": "EJCtbzhZNhXf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "random_forest = RandomForestClassifier(criterion='entropy', max_depth=3, n_estimators=2000)\n",
        "random_forest.fit(X_train, y_train)\n",
        "\n",
        "viz = dtreeviz(random_forest.estimators_[0], X_train, y_train, feature_names=X.columns,\n",
        "               target_name=\"Recidivism Rate\", show_node_labels = True)\n",
        "viz"
      ],
      "metadata": {
        "id": "rF56pyx9nZ1x",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 630
        },
        "outputId": "21a970d8-8e95-4209-8b2e-a71465a691e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
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            "Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n",
            "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n",
            "findfont: Font family ['Arial'] not found. Falling back to DejaVu Sans.\n"
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          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Random Forest Feature Importances\n",
        "%matplotlib inline \n",
        "random_forest = RandomForestClassifier(max_depth=20, n_estimators=200)\n",
        "random_forest.fit(X_train, y_train)\n",
        "fig = plt.figure(figsize=(10, 8))\n",
        "sorted_idx = random_forest.feature_importances_.argsort()\n",
        "plt.barh(X.columns[sorted_idx], random_forest.feature_importances_[sorted_idx],\n",
        "         height=0.5)\n",
        "plt.style.use('seaborn-muted')\n",
        "plt.xlabel(\"Random Forest Feature Importance\")\n"
      ],
      "metadata": {
        "id": "whJ3m0jvz_ru",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 529
        },
        "outputId": "113a40b2-351a-471a-a8b7-054cdaf05dce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Random Forest Feature Importance')"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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IJ3X5CQBwaPpzRzP7orv/JXf8J8AXgIOBd4BZZep6ExhExJqvRU31tSP9GPhMRzeiKWlHfc1Ytnwl7737QUc3Q0RERDqRqkwAUnbXtd19WTWuVykz60lEyPkjsDsxQD47V2wb4GF3v7dMPdn+PdZOzW2VCvtadWbW090/cPdXOrotlThy/FTmLa6dAfeUi0eggGoiIiLSHO0yASgs5wBOBc4DvgQcaWYDiCyuWwDzicHo6e6+KHf+YUTCp22ApUQUm+PdfU463ifVuy+xLOdZ4DR3f6SZTR2Rzj+PWM/+bdKgOLOcB+CrZvY/wBx371umfw9TZAlQuf6Y2cbEW4YhQB9gLnA7cLa7f5CpYxURyacncHxq79RUz+LW9DVzjUK/TgYuBoz4bQ9P7boCOABYApzn7lflzh9AJP0alL6aCpzs7m/kftNRwEDircoSYMtiS4DMbDMiQdhewHpE0rKr3P3ydPy/gWOBbYHuRBKxsdl/B2Z2DnAasDOR/GynVM84d7+lgt9NREREpK6s0Y51bwr8nBg07kVEjVmTGHTuDYwl1tXfkz3JzE4HrgeeAUYCRwIvAw3p+AbADKA/MJoYkM4FpqX1+s1xKLFs5wHgRuDzZlYYvBaW87wK3JX+fmCZ/hWNs99Uf4DexCD4VOJ3mUAMjH9dpLoTge2AI4jfbxiRRbe1fc3aBLiMmCh8C9iYyBh8I/AG8E1iYP8zM9sx088BwEPA8nStUcDWwL0pZGjWBKBHKndyscaaWW9gJjCYyBy8H3ApsFmmWF/gN8B/Av9FLL16wMy+mqtuTeB3qQ8HEP8Wf5cmJCIiIiJdSnsuAdoQGObuj2a+++jvZvYpYj39n8xsR3d/xszWB84Bfunux2bOm5T5+2hi0Gzu/laqayrwInAWcFQljUsDzL2AK9290cx+D/wfMSid6e7LgcfM7ANgvrvnl/as1r/8gLKS/rj7LGBM5pwZwALgNjPr7e4LM+fNc/dDMmW3IQbaZfvcVF9zxXsRG51fSOduAFwLzHD3s9J304nJzMHExAbgAuJtwXB3X5XKPUUMyr9FDNQLXnT3w8u1mZgQbQR8MRNO9P5sAXcfn+njGsQbpe2JSdZ3M0XXIt4M3JHKPg28TUwGLmuiHTWvoWHdjm5Cp6bfr77oftYf3dP6ovtZG9pzArAwN/gnLaM5BdgKyO6m3JoYSA4i4tZfXabeocRT7AVpElFwH7BbM9p3CPFkeCKAuy82s7uB/zSz0RUkslqtf0U02Z+0f+Bk4BgiXXOPzOGtgOwEYGru9FnA+ma2jrsvzSfnyvShOX19Mxfhp7Ch+aNru/sKM5sN/FvqQ09gV+B0oLuZFYrOBZx405OdAEwu+mN80u7A/eVyCaQJ0P8S2Y43zhyanyvaCEzLtH+hmc0DNq+gHTVPadVbTmnp64vuZ/3RPa0vup/Vs8Ya3coGLmnPJUBvZz+Y2YHEspaniQHpQGLJC3w86O2d/iwXQ76BWM++IvfficTegkodCswGXjazDdKT7kmp/qEVnP9200Uq6s9o4BJgCvFEemdizT18cjIAkF/r/69cuVfI/CaZNxLN6Wupaywp8n3hur2INfiXsPp9+TKr35dKf7uSv5uZrUtMSj5PrPH/OrEsbCar/24fpDc6pdovIiIi0mW05xuAVbnPBwPPufvRhS/MLB+LvvC0e1NivXkxi4hY9uOKHPuwkoalvQL/nj4W20B7KLHuv5x8/4qppD8HA5Pd/XuZ9v1bBXUXk0/O9Y826mtTlhC/xwTgthLHsyr97TYtc3wQ8QR/f3d/tvBlmhgsLHmWiIiISBdXzTwAPYkNolmH5j7PBP5JPAF/okQ904DDgL+4+9IWtqVw3W+y+mDxRGCEmX3G3d9vYf0FlfSnkt+lIsWSc5lZu/fV3d83s0eBbQv7BNrAH4HTzGwLd/97keM9058f/XZp8+92xGbkdnH1uEpeDlXPsuVNrVQTERER+aRqTgCmAVemsIyPAHsAw7MF3P0dMzsXOD+tZ7+dWKY0BPituz9FLDP5FvCQmV1OLG3pTYR3XO7u51bQlkOBR4uFgTSzRmKD6wFE1JgWq7A/04DRZvZd4C/EG4HtW3PdnKr0lViG84CZ3UzsNVhEPMEfAtzp7rc3s75LiYneQ2Y2nlje9Hlg6/S25DEipOpVZjaB2APwI0q/aWkTCxcupbGxkhcYIiIiIrWpPfcA5P0CuAg4jlgmsg3xVPoT3P0CIszlwFTuOmKT8Lx0fBGx/ONJIn7+NOCnRCz+pjblkkJXfpHiYTYBCrH8W/QUPq+p/hCD1huAHxLhNtciNgS3WjX7mqIk7UK0/2oivOuPiX9jf25BfQtTfY8A5xPLlE4jDfDd/W3i309vYmJ1BrHB/PlWdkVERESkrnVbtUpPM0Uq0Bd4TW8A6osiUtQX3c/6o3taX3Q/qycTBagfsVrmk8er3SAREREREek41dwDUBUprn4+82xWo7s3Vqs9IiIiIiK1pB7fAHyH1WPRZ/+7puOaJiIiIiLSseruDQCRUKt/meMLqtWQjmBmvYiMvAcQ69Y/JDbh/g64yt2XdVzrWs7MBhMZoPun6EkiIiIi0gJ1NwFI0WO6ZCKolPn3AeDTRBjNJ4jlULsCZwKNwOUd1b5WeoaI/vRSRzaiXFrt5li2fCXvvftBm9QlIiIi0hx1NwGolrTXYO0ae6I+kRj87+Tur2e+n2Zm/wdYxzSr9dz9XSL2f4c6cvxU5i1u/cB9ysUjUBwEERER6QiaAFTIzK4jko2dCpxH5B040swGAHsCWwDziQy2p6d8BdnzDyPi1G9DJLB6Ajje3eek431SvfsC6wLPAqe5+yMVtm8X4gn56NzgH/jozcijqezGRA6FIUAfYC4RS/9sd/8glelL5Ag42N1vzlznnNSudTLfbUDE/D8QaAD+AfzO3cem4/ukvm9PZPD9CzDe3Sdl6lgfuAAYBnyWWKr1GHCIu68stgTIzEYTOQy2Bv4FPA2Mcfc/Z+q9jrhv/w+4jMiL4MDJ7t5uGYNFREREalU9bgJuT5sCPweuAPYistOuCZwN7A2MBQYQSbA+YmanA9cTy1hGAkcCLxOD5cIAegaxd2E0sX5/LvHkvl+FbRuS/ryrgrK9gSXEZGZvYAKRgbhUwrCSzGxt4H5iIH4RsA9wDjGIL+gL3An8DzFJuB+4zcz2y5S5hMgMfSYxoTqVmCiV+zf6b8CVxO91OLAceDRNprI2SeUuJX7/fwG3m9m6zeqsiIiISB3QG4Dm2RAY5u7ZjMMf/d3MPgXMAv5kZju6+zPpyfY5wC/d/djMeZMyfx9NDMrN3d9KdU0FXgTOAo6qoG2bpT//3lRBd58FjMm0ewbxxP02M+ud3hZU6jBgB+Df3X1m5vvrM9e7KnOtNYDpwFbAscTEAGBnYKK7X5+p46Ym+pHtQ3dgKpEp+FvEhKKgFzDE3V9IZd8E/gTsTrz56BANDZp/1ALdh/qi+1l/dE/ri+5nbdAEoHkW5gb/mNn/EMtbtgKyO0S35uONq58Gri5T71BiecuCNIkouA/YrZltbDJNbdq/cDJwDJEhrkfm8FY0bxP17sBLucF//nqbA+OBPYglR4Wn+p4p9gwwyszeAu4FXnD3sn0xs4HAj4AdiQlUwda5om8WBv/JrPTn5uXqb2/KhtjxlJWyvuh+1h/d0/qi+1k9mUzAxY9XsS314O3sBzM7kFg28zRwCDCQWFIDHw+qCwPTf5SptwEYweo5C04k9hZUYm76s5Lyo4kn5FOI5TM7E0tosu2uVG/K9C098Z9MTGTOJSYM/YFbc9c6ifgtTwGeA143s5PL1LsF8cR/LeB4ItJRf2BOkT4szn5w93+lvza3ryIiIiKdnt4ANE/+ifTBwHPufnThCzP7Wq5M4Wn6psTylGIWEU+9xxU59mGFbbuf2Ii7D/DTJsoeDEx29+8VvjCzf8uVKUQ3Wiv3fa/c54XAV8pca0tiidCB7v7Rchsz+0S97v4OMfg/xcy2JW3aNTN390/sqUj2Jt64HOjuHw3wUx4EERERESlBE4DW6UlsPM06NPd5JvBP4gn7EyXqmUaspf+Luy9tSUPc/VEzexQ408xudfe52eNpYPzFtISpknbPIzbLbpupozuxjCfrj8AhZrazuz9epGk9058fXS9FIdo9XaNYX2alCD/Hp+sXmwD0JCZkKzL1DiciKLWbq8cNbZN6li1f2Sb1iIiIiDSXJgCtMw24MoXGfIQYHA/PFnD3d8zsXOD8NIC+nVh6NQT4bQppeQmxcfUhM7scmE0srdkJWO7u51bYnkOJDbZPm1k2EdggYjnRT4hNy9OA0Wb2XSIk58FEiM5suxvN7GbgRDN7hVj+dAzwmdw1byCe1t9pZj8CXiA2JH/d3Y9J9b8BXJD2N/QEfgC8RWYJWtqIfDux8XlF+j0aU3+KuT/9eZ2Z/ZxY9z+WCMXabhYuXEpjY5PbLERERERqlvYAtM4viNCXxwG3ETH+v5kv5O4XAEcQewRuA64jBqzz0vFFxCD9SWKQPo1YxvMlMlGGmuLus4GvpfqPAO4mBtX7pXp/nor+iBi4/5CItLMWMbjPO5l4wn8psYn5CeDa3DWXE0/zf08MwO8h1vrPyxw/kHgLclNqx/+xerjSGcB/p3puIX7LEe7+TIm+vgB8h5i4TAFGEZOGom8VRERERCR0W7VKTzNFKtAXeE1vAOqLIlLUF93P+qN7Wl90P6snEwWoH7Gy5JPHq90gERERERHpONoD0AmkuP3dyxRpdPfGarVHRERERDovvQHoHL7D6jkCsv9d03FNExEREZHOpFO+ATCz2cAd7n5iBzelXRX6SWzW7V+m6IJqtKe9mNkEYDt3H54+jyI2G68ANkmbpLPlzwfOAOa4e9/M92cDXyd+q/WB/inKUvbc7wDfB77k7pXmWBARERGpG51yAtDVuPtCPk4oVlfMbBPgu0QkobzlRIjSX2TKdyOi/RTbRXQs8AoRuWhkiUveSERBOoxcRKNKlEurXYlly1fy3rsftKoOERERkdbQBCAnxerv7u7/6ui2VJuZ9XT3ao9OjwVedffHihy7Hfg2mQkA8YR/EyJU6KBc+S1S/oLBlJgAuPtKM7uemHQ0ewJw5PipzFvc8p9oysUjis5cRERERKql3SYAZjaIiAc/EOgGzALGufu0Cs7dDJgA7AWsB8wBrnL3y3PljiOWc/Qi4sgf4+6vZ47/BBgGfB54l4ipf0quzHRgKfBb4GxgSyJJ18NmdgwR234TIgb+8cCfgdPd/aJMHXsTya22B5YRA9dT3X1JBT9VoY79gAtTW2cBo0uUGwCM5+PB71TgZHd/I1NmM+BnwJ7AkvT3NYEx7r5OKjMYeADYl9hjsA+Rh2APM1svXWMk8FnAgR+4++25trS630T8/lJ7GG4E7jKzzTP9OxS4lyJvRJqxEfoPwNlm9lV3f64ZbRURERHp9NplE7CZ7UJkcO0BHEUMJCcBW1Rwbm9gJjAYGEcksbqUyC6btT+RdOskIhHXjqz+RHcj4DxiEnASsDEww8x65sp9DTiLmLDsA7xmZsOJJ8/TgQNS+28r0t4DgDuJQfJIInnWHkTSq4qY2ZeJwfOcVMfPiURdG+bKDQAeIpbGHEoMnrcG7k1vLgpLZCYDA4gJyzHEb3l4icv/isjUOxKYYGZrEpOKg4BziN9uJnBLuq9t2e8tifj6M0oUeQp4mVjyg5mtRdzziZVeo4QXgXeICZKIiIhIl9JebwAuAP4GDMlstJxa4bmnEgP3L6bMtgD3FynXHRjm7ssAzGwj4FIz26DwBNrdjyoUTgPk6USm2H2AWzN19QYGuvucTPlbgQfdvTBwvtfMGonJSKFMt/T55kw5zOxlYKaZ7eruj1TQ5zOBucD+7r4y1bGAWOaSdQHwLDDc3Velck8BfyUGyb9JfduR+O2npzLTiMlFMXe5+2mZto8CdgJ2SNl2AaaZ2eeICdIebdjvwsbm58uUmUhMdi5MfVuTmIztUuacstx9lZk9B+zc0jpao6Fh3Y64rJSg+1FfdD/rj+5pfdH9rA1tPgEws08Ty37GtjDKyu7A/ZnBfykPFgb/yaz05+bEshfMbB/iLcJ2RFSYgvBY6L4AACAASURBVK1zdT2fG/x3B3YATs+Vu4XMBADYiniCfaqZZX/Lp4glRwOASgbCA4FJhcF/Mgn46HN6a7FralN3Myscmks8hR9ATAD6A+8UBv8A7r7czO4EDily7cm5z0OBF4CXcn2aRiz3gbbrdx+gkfIbnCcC55jZNsREYJK7/zPT/5ZakK5fdcqCWDuUlbK+6H7WH93T+qL7WT2ZTMDFj7fDNTdM9f6jhef3rvDcxbnPhU27PQDMrD8xuH2bWOM+iBgc/6tQJuPt3OcGYnI0P/f9vCLlIN4m5GPzr0cFS56SPvm60+QpG96zF/HW45Ii1/py5lp9irS7WNsLivV9+yLXuAhYz8w2oO363QNYUXibUYy7v0zsTTiOWPZ1Y4V1N2UZkF8KJiIiIlL32mMJ0BLiqe6mLTx/YSvOzTqQeBp9cOFNRNpfsFaRsvkB6Hzi6XtD7vuNcp8L8elPBB4vUm+pQXfem/m601uI3pmvlqR2TqDIXoR0vFBXvt3k68/I930RsSTnyBLll9J2/V4ErG1mPXJvc/ImAhcT/zaa3EReoQ2p09CqIiIiIuW0+QTA3d83s5nAYWZ2cQuWAf0ROM3MtnD3v7eiKT2JJ9LZyDCHVnKiu39oZn8iNv9eljmUDy35F+B1YEt3v7IVbX0cGGFmYzLLgEYQ690LbXrfzB4FtnX3s8rU9SSwvpkNzuwBWJvYTF2JaURkoDfdfW6xAmbWVv329Gc/4KUy5X5HbGSenlsm1Rr9iH9rzXL1uKGtuuiy5W3VfBEREZGWaa9NwN8nNu7eZ2ZXEst1dgQWuHupkI8FlxJJmh4ys/FEYqfPA1u7+/ea0YZpRCjNK83sFmL5z9F8vFSoKeOBSWZ2LTEA3ZaIqgNpUpE2k44GbjKzzxBZe98jlsDsBVzu7sWekOdNINbPTzazK4iIR+OINxhZpwEPmNnNxFPxRcTbkiHAnSlM593AM8BEMxtLPOUezeqToVJ+TfxOD5rZRcQkZ31imVEfdz+hDfv9BPGmZSfKTADc/S1iMlaWme1GvP3YLn31DTPrC8x39wcz5dYDjMiw3CwLFy6lsbHkiiURERGRmtcuYUBTBJjBxIDzOmKt+IGUjkSTPXchEeHlEeB84C5i4PtGufOK1HMXcAaxbnwKMTAdTgyEKzl/MpGkagixIfdgIqQpRAjJQrlbU91fINan30FMgBYCsyu81nNE2M1+xPKeE4h9C4tz5R4jfpu1gKuBe4AfE/fxz6nMKuLtwVPAVancTOIevEMTUgK03VP57xHRm34B7AY8mCnXFv1+n7i/+1ZSvgLnEjH+z0mfz0+fz82V2xv4JzFZEhEREelSuq1apaeZlTKzkcDNwE7u/nRHt6dSZrYG8VbgdXffv6Pbk2Vmw4g3LJu4+9IqXfM2YLG7H9GM0/oCr+kNQH1RRIr6ovtZf3RP64vuZ/VkogD1o8iD2XbLBNzZmVkvYonIA8RSnO2JZGEP1frgP2Uw7k6ssV8POAL4CjCmI9tVjLvfYWYvEVF+LmqqfGul5GN7A19q72uJiIiI1KKqTgDSk+hyy44+LBcSsspWEHsPvsXHEWNuJZYVVSxF8+lWpkh79PkDYjlOX+L3fhE4yN3va+PrlNTMfh/Hx0nB2tumwNHu/kqVriciIiJSU6r9BuAaYm17KYcTewY6nLu/R+wfaK37iPXzpQwhMhS3GXe/AbihLetsgYr7nd6oVOWtirs/VI3riIiIiNSqak8AzgGuKHP8tZZWbGazgTvc/cSW1tFOjgXK5b32MsdWU8P9zGtVv81sArCduw9Pn0cB15Yovqa7rzSzY4n8BVsSYWDnENmRL3T35amewtKoH7p7WyUVExEREek0qjoBcPfZVBghpl64e7MG+PWiNf02s02A7xLRiPL2JhfNKJMboBcR8el5IizpQOAHRHjSY1LZD83sfOBcM7upuXkFyqXVrsSy5St5790PWlWHiIiISGtoE3AF0lPj7ilEZpdiZj3dvdoj1mOBV1PY07yn3X1BsZPcfULuq/vNbB1gtJkdn0lK91vgcmAYcHtzGnbk+KnMW9zyn2PKxSNQ/AMRERHpSNXeBDyIiMk+kNggOgsY5+7TKjh3MyJh1l5EZJs5wFXufnmu3HHEBthewAzgGHd/PXP8J8TA7/NEdJ9HgVNyZaYDS4mB4tnEkpIhwMMpws5YYBMikdXxRAz+0939okwdexNPn7cHlhEDzVPdfUkFP1Whjv2AC1NbZxEJvYqVG0AkLhuUvpoKnOzub2TKbAb8DNgTWJL+viYwxt3XSWUGE1GP9iX2auxDZBbeIyXPGk9kQ/4ssYzmByn5WLYtre43MIrYL9IWFhD9XAP4EMDdl5rZ3UQfmzUBEBEREensqjYBMLNdiOzAjxMJtZYQGWC3qODc3kQyK4gMua8AWxFJqLL2B74InESsP7+UWDe+R6bMRsB5wD+A3sSgeoaZWe5J99eIgfe5xCDyNTMbTiTFuo6IXb8dkbgr394DgFuIrLo/JgbM/wvcRExgmmRmXyYGp38ETieyA98AbJArNwB4iMh8fCgx2D0HuNfMvpKWvHQDJhMRcI7n4+zAW5e4/K9S/0YCq8xsTWJSsXmqezbwTeAWM/u6u89ow35vSUQvmlGiSHczy/67bXT3T2Q4TsfXIv59jQZ+5u75BHCPAOeY2Rr580VERETqWTXfAFwA/A0YklmKMbXCc08lBu5fTPsIICYTed2BYe6+DMDMNgIuNbMNCk+g3b2QzbewtGc6MI942n1rpq7ewEB3n5MpfyvwoLsfnr6618waiYlGoUy39PnmTDnM7GVgppntmjIlN+VMYC6wf2GdupktIAbYWRcAzwLDC2E1zewp4K9ECNPfpL7tSPz201OZaZTOzHyXu5+WafsoYjC9g7u/kL6eZmafIyZIe7RhvwvhQJ8vcfyt3OfziTc+hettwCczKF8PnFKknmeJN0nbkLIoV0tDQ7m90VJtuh/1Rfez/uie1hfdz9pQlQmAmX2aWPYzNjP4b47dgfszg/9SHiwM/pNZ6c/NiTcOmNk+xFuE7YD1M2XzT8Ofzw3+uwM7EE/js24hMwEg3kz0BU7NPal+ilhyNIB4+tyUgcCk3CbVScBHn82sJ7BralN3Myscmkss0RlATAD6A+8UBv8A7r7czO4EDily7cm5z0OBF4CXcn2aRiz3gbbrdx+gkXhLUcwefHIT8Ju54+8R/e2Z/jyLeAuUDz9b2EfQhypPAJQFsXYoK2V90f2sP7qn9UX3s3oymYCLqtYbgA2JNdj/aOH5vYlkVk1ZnPtc2LTbA8DM+hOD2ynEk/O3iQH1jEKZjLdznxuI32t+7vt5RcrBJ98mZDW55Cnpk687LefJboDtRbz1uCT9l/dqpq58u8nXn1Gs79sTydFWk566t1W/ewAryiRHe67UJmCI34iYdEDs2ZgD3GxmP3X3pzJFCxPFnhW2S0RERKQuVGsCsIR4qrtpC89f2Ipzsw4knkYfXHgTkfYXrFWkbH4AOp+YLDTkvt8o93lR+vNEYr9DXqlBd96b+brTW4jema+WpHZOoMhehHS8UFe+3eTrz8j3fRGxJOfIEuWX0nb9XgSsbWY9cm9zWqqQYGxLPp4YQExKofSbBhEREZG6VJUJgLu/b2YzgcPM7OIWLAP6I3CamW3h7n9vRVN6Ek+xs5s+D63kxPT0/U/AAcBlmUMjc0X/ArwObOnuV7airY8DI8xsTGYZ0Ahik2+hTe+b2aPAtu5+Vpm6ngTWN7PBmT0AawP7VdiWaURkoDfdfW6xAmbWVv0u5A/oB7zUinoKdk1/vpr7vl/686/NqezqcUNb1Zhly5uVdkBERESkzVVzE/D3iY2795nZlcRynR2BBe7eVMjHS4HDgIfMbDwRBejzwNbu/r1mtGEaERXmSjO7hVgjfjQfLxVqynhgkpldS0TJ2ZaIqgNpUuHuq8xsNHCTmX0GuINYl74FEQnncncv9oQ8bwLxxHqymV1BRAEaR7zByDoNeMDMbgYmEk/QNyXClt6ZwnTeDTwDTDSzsXwcBSg/GSrl18Tv9KCZXURMctYHvgz0cfcT2rDfTxBvWnaimROAtPn5ej4e1A8ExgD3uPsTueL9gZfKLScqZuHCpTQ2llqdJCIiIlL71qjWhVIEmMHEgPM6Yq34gZSORJM9dyGwC7GJ9HzgLmLg+0a584rUcxdwBhEudAoxMB1OibXtRc6fTCSpGkJsyD2YCGkKmY2p7n5rqvsLwI3EYPj7xMB7doXXeg44iHhSfRtwArGRdXGu3GPEb7MWcDVwDxGCcw3S5ta0nn4EMaG4KpWbSdyDT2TVLdGWfxEbsW8FvkdEb/oFsBvwYBv3+33i/u5bSfmcZ4glSLcSoUdHEJuUDyhSdl/g5hZcQ0RERKRT67ZqlZ5mtoaZjSQGkju5+9NNla8VZrYGMWB+3d337+j2ZJnZMOINyybuvrQd6v8qsTdgK3d/rcLT+gKv6Q1AfVFEivqi+1l/dE/ri+5n9WSiAPWjyEPYqmYC7uzMrBfwQyJb7rtEZJyzgIdqffCfMhh3J9bYrwccAXyFWCJTU9z9DjN7CTgOuKip8i0wBvh1Mwb/IiIiInWjwycA6Ul0uaVIH5YJCVltK4i9B98iosgsJJabnNGcSlI0n25lirRHnz8gluP0JX7vF4GD3P2+Nr5OSc3s93F8nBSsrdvgxF4BERERkS6nwycAwDWsnqQp63Biz0CHc/f3iP0DrXUfsX6+lCFEhuI24+43ADe0ZZ0tUHG/0xuVNn+rkiJQ/W9b1ysiIiLSWdTCBOAc4IoyxytapmFms4E73P3E1jep3R0LlMuF7aUOdLJ+5lXcbzObAGzn7sPT51FERt9i1nT3lWZ2LLFx+ivAOkS0ogvd/feZegtvAH7o7je2oi8iIiIinVKHTwDcfTYVRoipF+5ecoBfzyrtt5ltAnyXiDyUtze5yEWZPAnjiAhFPydCkA4nwpI2FHITpHwO5wPnmtlNmXMrUi6tdt6y5St5790PmlO9iIiISLvr8AlAZ5CeGndP4TC7FDPr6e7VHsUeC7yaQpzmPV0mdv+O7j4/8/mPZrY5ETI2m5zst8DlwDDg9uY07MjxU5m3uLKfY8rFI1CsAxEREak1VZ0AmNkg4FwiQVM3YBYwzt2nVXDuZkRyrL2IKDZzgKvc/fJcueOIza69gBnAMe7+eub4T4iB3+eJSD6PAqfkykwHlhIDxbOBLYn16Q+naDpjgU2IpFXHE/H2T3f3izJ17E3EoN8eWEYMNE919yUV/FSFOvYDLkxtnUUk7ypWbgCRpGxQ+moqcLK7v5EpsxnwM2BPYEn6+5rAGHdfJ5UZTEQ42pfYl7EPkUV4DzNbL11jJPBZYhnND1KisWxbWt1vYBSxN6RZcoP/gj+Ry3js7kvN7G6ij82aAIiIiIh0dlVLBGZmuxAbPHsQybNGEsm0tqjg3N5E4qrBxDKP/YjswJvliu4PfBM4iYgisyOrrxvfCDiPmAScBGwMzDCznrlyXyNCfJ5LDIRfM7PhRAKs6URyqUlEkq58ew8A7iQGySOBk4E9iORUFTGzLxOD0zmpjp8Tm3g3zJUbADwELAcOJQbPWwP3pjcXmFk3YDIwgJiwHEP8loeXuPyviCRrI4EJZrYmMak4iNizMYy4H7ek+9qW/d6SiFQ0o0SR7mb2qcx/Tf0b/g+KZxR+BPhGBeeLiIiI1JVqvgG4APgbMCRFYoEYVFbiVGLg/sW0ZwDg/iLlugPD3H0ZgJltBFxqZhsUnkC7eyFzb2Fpz3RgHjHIvzVTV29goLvPyZS/FXjQ3QsD53vNrJGYjBTKdEufb86Uw8xeBmaa2a4pK3JTzgTmAvsX1qmb2QLglly5C4BngeGFEJpm9hTwVyJc6W9S33Ykfvvpqcw0SmdhvsvdT8u0fRSwE7CDu7+Qvp5mZp8jJkh7tGG/C6E/ny9x/K3c5/OJNz6rSROSPYH/KXL4WeJN0jakjMntoaGh3J5nqQW6R/VF97P+6J7WF93P2lCVCYCZfZpY9jM2M/hvjt2B+zOD/1IeLAz+k1npz82JZS+Y2T7EW4TtgPUzZbfO1fV8bvDfHdgBOD1X7hYyEwBgK+IJ9qlmlv19nyKWHA0gnj43ZSAwKbdJdRLw0ef01mLX1KbuZlY4NJd4Cj+AmAD0B94pDP4B3H25md0JHFLk2pNzn4cCLwAv5fo0jVjuA23X7z5AI5FjoZg9+OQm4DeLFTKzbYm3P39w998UKVLYR9CHdpwAKONhbVNWyvqi+1l/dE/ri+5n9WQyARdVrTcAGxLLjf7RwvN7E4mrmrI497mwabcHgJn1Jwa3U4gn528TA+oZhTIZb+c+NxC/V36d+bwi5eCTbxOymlzylPTJ150i2GQ3wPYi3npckv7LezVTV7H18fm2FxTr+/ZEIrTVmNkGtF2/ewAryiRCe67MJuBCezYH7iEmLYeVKFaYKOaXfomIiIjUtWpNAJYQT3U3beH5C1txbtaBxNPogwtvItL+grWKlM0PQOcTk4WG3Pcb5T4vSn+eCDxepN5Sg+68N/N1p7cQvTNfLUntnECRvQjpeKGufLvJ15+R7/siYknOkSXKL6Xt+r0IWNvMeuTe5lQk3c+pxH0eXqaOwl6KUm8aREREROpSVSYA7v6+mc0EDjOzi1uwDOiPwGlmtoW7/70VTelJPMVuzHx3aCUnpqfvfyI2/16WOTQyV/QvwOvAloXY8y30ODDCzMZklgGNICL3FNr0vpk9Cmzr7meVqetJYH0zG5zZA7A2ueg4ZUwjIgO96e5zixUws7bqdyFXQD+Kb94tyczWAe4GPgPs0kTkoX7pz7825xpXjxtacdlly5uVYkBERESkKqq5Cfj7xMbd+8zsSmK5zo7AAndvKuTjpcRSjofMbDzwChEac2t3/14z2jCNCKV5pZndQqyNP5qPlwo1ZTwwycyuBX4HbEtE1YE0qXD3VWY2mkhA9RngDiIp1RZECNPL3b3YE/K8CcT6+clmdgUR8Wgc8WQ76zTgATO7GZhIPEHflAhbemcK03k38Aww0czGEk+9R7P6ZKiUXxO/04NmdhExyVkf+DLQx91PaMN+P0G8admJZk4AiP0YO6S2bp6WAhX8yd2XZz73B15qajlR3sKFS2lsLLU6SURERKT2VS0EYooAM5gYcF5HrBU/kNKRaLLnLgR2ITaRng/cRQx83yh3XpF67gLOIMKFTiEGpsMpsba9yPmTiSRVQ4gNuQcTIU0hszHV3W9NdX8BuJEYDH+fGHjPrvBazxFhN/sRy3tOIOLWL86Ve4z4bdYCribWvv+YuLd/TmVWEW8PngKuSuVmEvfgE1l1S7TlX8RG7FuB7xFLbH4B7AY82Mb9fp+4v/tWUj5nKDGpvZboX/a/Prmy+wI3t+AaIiIiIp1at1Wr9DSzNcxsJDGQ3Mndn+7o9lQqxb9/Bnjd3ffv6PZkmdkw4g3LJu6+tB3q/yrwNLCVu79W4Wl9gdf0BqC+KCJFfdH9rD+6p/VF97N6MlGA+lHkIWxVMwF3dmbWC/ghkS33XSIyzlnAQ7U++E8ZjLsTa+zXA44AvgKM6ch2FePud5jZS0Qyt4uaKt8CY4BfN2PwLyIiIlI3OnwCkJ5El1uK9GGZkJDVtoLYe/AtIorMQmJZzBnNqSRF8+lWpkh79PkDYjlOX+L3fhE4yN3va+PrlNTMfh/Hx0nB2roNDlzf1nWLiIiIdAYdPgEAriHWtpdyOLFnoMO5+3vE/oHWuo9YP1/KECJDcZtx9xuAG9qyzhaouN/pjUqbv1VJEaj+t63rFREREeksamECcA5wRZnjqy3TMLPZwB3ufmL7NKndHQuUy4XtUBf9zCvV7xOINytPA5jZKGIj7wpiH8CibGEzO5946zLH3fum7ww4CfgG8Dki0+8fgbPc/a3Mud8h3oR8qYVZqUVEREQ6tQ6fALj7bCqMEFMv3N2bLlV/ivXbzDYB/hPYPb1hyVpORFr6RaZ8N2IJVr7snsDXgV8CzwKbE5PLmWb25cxm4huBHxFhZa9tbh/KpdXOWrZ8Je+9+0FzqxcRERFpdx0+Aag1aY149xT6sksxs57uXu1R67HAqymcad7twLfJTACIQf4mRMz/QZnvfwdcmd07YWbPA88RydquB3D3lWZ2PfBdWjABOHL8VOYtbvonmnLxiNVmKCIiIiK1oN0mAGY2CDgXGEhs/JwFjHP3aRWcuxmRCGsvImLNHOAqd788V+44YjlHL2AGcIy7v545/hNgGLG85F3gUeCUXJnpwFLgt8DZwJbEWvSHU+ScscSA8wki6defgdPd/aJMHXsDPyCiAi0jBq6nNpGJNt/n/YALU1tnEYm6ipUbQCQkKwx+pwInu/sbmTKbAT8jnoovSX9fExjj7uukMoOJaEb7Ensw9iEyBu9hZuula4wEPkssSfpBSiqWbUur+w2MIvaBFHMjcJeZbZ7p36HAvcQG7I+USOj1AvAhkRgt6w/A2Wb21ZRvQURERKTLaJdEYGa2C7GZsweRKGskkThriwrO7U0kbhpMZL7dj8gEvFmu6P7AN4l138cRWYXzT3Q3As4jJgEnARsDM8ysZ67c14hwnucSA+HXzGw48eR5OnBAav9tRdp7AHAnMUgeCZwM7AHc1FRfM3V8mRg8z0l1/JzYsLthrtwA4CFiacyhxOB5a+De9OaisERmMjCAmLAcQ/yWh5e4/K+IhGojgQlmtiYxqTiIWEIzjLgft6T72pb93pKISjSjRJGngJeJJT+Y2VrEPZ9Y4SUGEaFP8xmFXyQSoO1ZaVtFRERE6kV7vQG4APgbMCSz0XJqheeeSgzcv5j2BwDcX6Rcd2CYuy8DMLONgEvNbIPCE2h3L2TpLSztmQ7MIwb5t2bq6g0MdPc5mfK3Ag+6e2HgfK+ZNRKTkUKZbunzzZlymNnLxNrzXVMG5KacCcwF9nf3lamOBcQyl6wLiPXtwwtLXczsKeCvxCD5N6lvOxK//fRUZhqlMy7f5e6nZdo+CtgJ2MHdX0hfTzOzzxETpD3asN+FMJ/PlykzkZjsXJj6tiYxGdulzDmkicxlxATljuwxd19lZs8BO1fQxhZraCi3z1tqhe5TfdH9rD+6p/VF97M2tPkEwMw+TSz7GdvCKCu7A/dnBv+lPFgY/Cez0p+bE8teMLN9iLcI2wHrZ8punavr+dzgvzuwA3B6rtwtZCYAwFbEE+xTzSz7Wz5FLDkaAFQyEB4ITCoM/pNJwEef01uLXVObukfQGyAmDp6u9RtiUP1OYfAP4O7LzexO4JAi156c+zyUWDrzUq5P04jlPtB2/e4DNJJbzpMzETjHzLYhJgKT3P2fmf6XcgXwJeDrud+1YEG6frtRtsPap6yU9UX3s/7ontYX3c/qyWQCLn68Ha65Yar3Hy08v3eF5y7OfS5s2u0BYGb9icHt28Qa90HE4PhfhTIZb+c+NxCTo/m57+cVKQfxNmFF7r/1qGDJU9InX3eaPGXXtfci3npcUuRaX85cq0+Rdhdre0Gxvm9f5BoXAeuZ2Qa0Xb97ACvKJT1z95eJvQnHEcu+bmyqUjP7IXAk8F/u/lSJYsuA/FIwERERkbrXHkuAlhBPdfMbLyu1sBXnZh1IPI0+uPAmIu0vWKtI2fwAdD7x9L0h9/1Guc+F+PQnAo8XqbfUoDvvzXzd6S1E78xXS1I7J1BkL0I6Xqgr327y9Wfk+76IWJJzZInyS2m7fi8C1jazHrm3OXkTgYuJfxtlN5Gb2f8j9i4c4+75txtZhUzOIiIiIl1Km08A3P19M5sJHGZmF7dgGdAfgdPMbAt3/3srmtKTeCLdmPnu0EpOdPcPzexPxObfyzKHRuaK/gV4HdjS3a9sRVsfB0aY2ZjMcpURxHr3QpveN7NHgW3d/awydT0JrG9mgzN7ANYmNlNXYhoRGehNd59brICZtVW/C3kB+rH6Rt2s3xEbmaeXWM5TaNd/AT8lIhb9qolr9yP+rTXL1eOGVlRu2fKSzRQRERHpUO21Cfj7xMbd+8zsSmK5zo7AAncvFfKx4FIiSdNDZjYeeIUIjbm1u3+vGW2YRoTSvNLMbiGW/xzNx0uFmjIemGRm1xID0G2JqDqQJhVpM+lo4CYz+wyx2fQ9YgnMXsDl7l7sCXneBGL9/GQzu4KIeDSOeIORdRrwgJndTDwVX0S8LRkC3JnCdN4NPANMNLOxxFPu0aw+GSrl18Tv9KCZXURMctYnlhn1cfcT2rDfTxBvWnaizAQgZfI9oFxFZrZbavvDxKblgZnD8939lUzZ9QADflhBGz9h4cKlNDaWXLEkIiIiUvPaJQxoigAzmBhwXkesFT+Q0pFosucuJCK8PAKcD9xFDHzfKHdekXruAs4g1o1PIQamw4mBcCXnTyaSVA0hNuQeTIQ0hQghWSh3a6r7C8T69DuICdBCKsxwnGLRH0Q8lb4NOIHYt7A4V+4x4rdZC7gauAf4MXEf/5zKrCLeHjwFXJXKzSTuwTs0ISVA2z2V/x4RvekXwG7Ag23c7/eJ+7tvJeWbMIR4Y7Ib0d/sf2fnyu4N/JOYLImIiIh0Kd1WrdLTzEqZ2UjgZmAnd3+6o9tTKTNbg3gr8Lq779/R7ckys2HEG5ZN3H1pla55G7DY3Y9oxml9gdf0BqC+KCJFfdH9rD+6p/VF97N6MlGA+lHkwWy7ZQLu7MysF7FE5AFiKc72RLKwh2p98J8yGHcn1tivBxwBfAUY05HtKsbd7zCzl4goPxc1Vb61UvKxvYkQoSIiIiJdTlUnAOlJdLllRx+WCwlZZSuIvQff4uOIMbcSy4oqlqL5dCtTpD36/AGxHKcv8Xu/CBzk7ve18XVKama/j+PjpGDtbVPg6OyeABEREZGupNpvAK4h1raXcjixZ6DDuft7xP6B1rqPWJdeyhAiQ3Gb5SmLRQAAIABJREFUcfcbgBvass4WqLjf6Y1KVd6quPtD1biOiIiISK2q9gTgHCJDaymvVakd1XQsUC7vtZc51pk11e+dzOwB4k3LJu6+KHvQzM4n3rbMcfe+zbmwmU0Hlrr7sGa1WERERKQLqOoEwN1nU2GEmHrh7vU6wC+rqX6bWWEN/nIiwtIvMse6EUuvam6nULm02gXLlq/kvXc/qEJrRERERJpPm4Clo90OfJvMBAD4OrAJcAswqCMaVcqR46cyb3H5wf2Ui0fU3sxFREREJNEEoAsys22IiDsDiYzJc4HfuPu56fgAIhFaYfA9FTjZ3d9Ix28jErt9xd3fSd/tC9wJ/Je739SM5twI3GVmmxfqJzI230tsvM62uydwAbAnkXRsPpHN9/T8EqIifTYi4doQYG1gBjDa3f/cjLaKiIiIdHrtkghMat4UoBdwJJGE6zxiIlAY/D9ELM05FBgFbA3cmyL7ABxDDKKvSOd8lkg4NrGZg3+IhGUvE0t+MLO1gG8SmY7zehLJvs4mQnmOBQYQCdFKMrO+wKPEW4WjgEOIZGoPmNn6zWyviIiISKemNwBdTBqsfwE4xd2npK+nZ4pcADwLDC+E6TSzp4C/EoP037j7fDM7CphiZpPT9yuJDMYtMZGYbFwI7EMM8icRWY8/kp7yH5fpy6eAWcCfzGxHd3+mRP3nELkcdnf3D9K504FXgZOItx1tqqGh3P5nqSW6V/VF97P+6J7WF93P2qAJQNezEJgDTDCz3sB97v46fLTEZlfgdKB7rJoBYomQE0/bfwMfJfD6FRFudC1gqLsvaWGbJgLnpKVJhwKT3P2fmet/xMz+BzgF2ArI7sjdmsh2XMxQ4PfAijRpgMiVMDP1qc0p02HnoKyU9UX3s/7ontYX3c/qyWQCLn68im2RGpCe6g8lnpz/FP5/9s49uqrqWtxfRASsFTBGRa2KD2bVa68icKF6f0Kl+OIhIrVeLcXi86oVEVQqKlgqiq9aS9VaH1WrtRdQQFGIKCCIVnw/p7YFVESRAGKsoJD8/phrw2Zzzsk5yclJcpjfGBnJ3nuutdfaC8aYc6354EMReU1EjsbcgpoBN2PpOeM/h2B+93H+grkCvYvl/a/tmD4AXsJ29/uEfrdARPoD92M1A07BYhiODY9bZnhFGXARW86pD1vOyXEcx3Ecp6jxE4CtEFV9H/hJ2A3vClwDTMV20auxYNlHUzTduMMfTgvuBN4ADsZcaX5Xh2E9BNyEnVCUp5EZCLyuqmfFxnF4Fn2vxAKU/5DimefrdBzHcRxnq8INgK0YVV0PzBORsdgO/q5YsOxBqnpFDc1vwIJqD8F27q8TkRl1qHvwV6A7MDuMKxWtsODkOKdl0Xd5GOerqrqhluMD4O5RvWqUWbsu3fAdx3Ecx3EaHjcAtjJE5AeYi88jwD8xP/pLgU8wt6DhWHacidiu/Epgdyx95hOq+piI9AL+Fxikqh+JyGjMFecBEflhBgU+Lar6KXBiDWLlwITwvnlAT6BvFt1fhbkYlYvIH4FlmLHzQ3u13p7tOCsqKqmqqs5W3HEcx3Ecp9HhMQBbH59iyv7lmFvMn7B8+j1Vda2qvoBl39kOS+35FPBr7N/K2yLSFrgXmKyqUUDwt8Dp2C57TScHdeFOrH7BuZiL0oFYytCMqOq/sGDfTzE3pZlsOsF4qb4G6ziO4ziO0xgpqa723UzHyYJ9gEV+AlBceEaK4sLXs/jwNS0ufD0LRywLUHtg8RbPCz0gx3Ecx3Ecx3EaDo8BcPJOqBhckkFkQ1RkzHEcx3EcxyksfgLg1Aez2DLnfvznqIYbmuM4juM4ztaNnwA49cE5QKZa37VNFeo4juM4juPUETcAnKwRkcFYBqBUNI/Sf9ahFkBeEJH7gE6q+h8NOQ7HcRzHcZzGiBsATm04FvgifqM2uf+bIiGiPiNr163nyzVeYNhxHMdxnMaJGwBObXhZVVc09CAagiFjZ7J8VWblftpN/fAkZ47jOI7jNFbcAGgiiMiBWBGsrkArYCnwoKqOCc+7AGOBbqHJTOAiVf04PH8U6Aj8QFW/CPeOx4qB/VRVH8nTOGsaxz7AImBQkDkVqAJ+q6q/FpG+wDhgb2A+MFhVl8X6vxboDewLrAGeBy5W1Y9qGFc74DrgeCw+4TVguKrOy8O0HcdxHMdxmgyeBajpMA3YCRiCKbHXYYZApHTPBdYBpwGDgQ7AjJCSE+BsoAXw+9BmZ6zS70O1UP6bici2sZ9tchhHxG+wjEA/Ae4DrhGR8cBo4ErgDOAHWPXfOLuEufcGLgR2BeaLSKt0gxWRNpgx0RkYCpyIGVDlItI+x7k7juM4juM0afwEoAkQlPX9sJ3uaeH27JjIeGxHu2+UX19EFgLvYzvsD6rq5yJyJjBNRKaG++uB82sxpE8T19cDl2czjlibF1X1ovB3uYicBFwE7Bc7LdgbGC8irVT1awBVPTP2XZqF77AcOA6YnGa8Q4FSQFT109B2JvAWcAVwZpp2taasLFMSJKcx4WtVXPh6Fh++psWFr2fjwA2ApkEFsAQYJyKlwKzI5SXsfB8JjMB25qM2S7F0m10IireqPi4idwEPANsBvVR1dS3G05PNg4CX5TKOwMxEnx8AX0TKf+B9rKDYHsA/wnyPA0YBBwOtY7IdMoy3F/AssEJE4v/mZ1FPNQm81HnTwMvSFxe+nsWHr2lx4etZOLbZpiRj4hI3AJoAqlotIr0w3/rbgB1E5HXgEuA9oBlwc/hJ8q/E9V+As4B3MAW4NryeDAIWkT1yHMeqxPU3QNIY+Sb8bhne0RmYirlDjQc+w04x5kcyaSjD4g2+TfHsixT3HMdxHMdxihY3AJoIqvo+8JOwg90VuAZThjsA1Vjg7KMpmm5UqsMu/Z3AG9gO+oXA7/I0xNXZjqMO9McCfweq6gaAcCKyXQ3tVgIzsJODJBvyMC7HcRzHcZwmgxsATYyQb3+eiIzFdvB3xTLhHKSqV9TQ/AZgN+AQ4FzgOhGZkY/CXar6lYhkO47a0grbxa+K3Tsti3blWNah91S1si4DuHtUrxpl1q7bKkoiOI7jOI7TRHEDoAkgIj/A3GoeAf4J7ABcCnyCufIMB54VkYnAQ9iO9+5AD+AJVX0suBD9LzBIVT8SkdFYQa8HROSHeSrkVeM46th/ORbQO0FEJmFZfc5ik6tQOm7GgpDnisitwGIsKLgTsC5KpZoNFRWVVFVV12LojuM4juM4jQNPA9o0+BRT9i/H8vb/Cfgc6Kmqa1X1BeAIzBXmbuAp4NfY+r4tIm2Be4HJqhoFBH8LnI6dBuRlx76mceSh/+mY4dMHiwM4BuhLat/+eLuVWAzAS8C1mCFxG/Af2OmJ4ziO4zjOVkNJdbXvZjpOFuwDLPITgOLCM1IUF76exYevaXHh61k4YlmA2mOeD5s/L/SAHMdxHMdxHMdpODwGwAE2FtUqySCyISru5TiO4ziO4zRd/ATAiZiF+dKn+6mXglmO4ziO4zhOYXEDwEFEqoEXsaw6yZ+vgbuAl4PsaBGpUyrN+kZEuotItYh0auixOI7jOI7jNDbcBciJ+FxVFyZvikgV8ImqRlE7f8IyETVmXsGy/ryb744zldUGqwHw5Zqv8/1ax3Ecx3GcvOEGQIERkRKghaqubeix1AZV/Rj4uKHHkQlVXQO8UB99Dxk7k+Wr0iv4027qh+c3cBzHcRynMeMGQD0jIvdhBaeGAddhueeHiEgX4MfAXlhO/6eBESFnfbz9IOBi4ECgEvg7cJ6qLgnP24V+jwe+C7wGDFfVefU0n9Gh/x3CdXNgLPBTrMrwKmwHfrCqLheR7sCzYXxDsOJjlcDvVPXaWL8CjMbqCJRhKavuB26IipSJyD7AIqx+QRfgZ1h8wiRgWGRUxd7ZOTrVEJFtsCJiZwH7hnE+B5ypql/k8xs5juM4juM0ZtwAKAy7A3dgivIiYB2m6F4JfAbsCYzECmd1iRqJyAhgPHAPMApbrx9hCvISEWkDzAfWYsptBabglovIQaq6KIcxbiMitfn3cDlWYfgyrNhXKXA00Coh90eskvEAoBfwGxFZqap3hOftgA+Ah4E1WIGyMcBOwIhEX78BpmNGx2Hh+hPs+6bjNuAc4BbM2NoBOCH8dgPAcRzHcZytBjcACkNboLeqxqvObvw7KN7vAK+KSEdVfUVEWmM74n9U1XNi7abE/h6KKdyiqp+GvmYCb2HVfc/MYYzXh59c+S9gpqr+IXZvcgq5Z1V1ePh7Rji5GCUif1TVKlWdDcyGjW5S87Ag9TEicmkiBelCVf3f8PdMEekGnEwaA0BEOgDnAVeo6rjYo0m5TDRbysq+Wx/dOvWEr1dx4etZfPiaFhe+no0DNwAKQ0VC+UdEfoa59hyA7UJHdGBTEOv2wN0Z+u2FubqsSOzezyL3tJ23Ag+muD+3hnavACNEZAwWHPyyqm5IIfdo4noicCp2+vGhiLTETkFOA74HbBeT3RX4NHY9I9HXO8CRGcb4I6zGQaZvmTe8ymHTwatSFhe+nsWHr2lx4etZOGKVgFPiBkBh+Cx+ISL9Mf/2P2GuPRVAG8wFqGUQKw2/P8nQbxlmKHyb4lmubi0fZ8gClInfABuAQcBVQIWI3AFcnTAElifaRd+kHfAhdvpwNnANsBBYjcVI/IZN3yRiVeL6mxQycUqB9aqaHIPjOI7jOM5WhxsAhSFZQXcg8LqqnhXdEJHDEzIV4ffupM+6sxLbDR+V4lmqXfi8o6rrMF/9MSLSHgvMHY2N+Y6Y6C6JpruG38vC74HAnXEXHRH5f3kaZgWwrYjs4kaA4ziO4zhbO24ANAytsEDgOKclrhcA/wbOwDL/pKIc23l/T1UbvDhXCDq+RkTOBA5KPO7P5m5AJ2OnG5Fxs9k3CXEA/5OnoT2DGWFnULs4h43cPapXxudr162vS/eO4ziO4zj1jhsADUM5MCGk1JwH9AT6xgVU9YvgV3+9iDQDHsOCYnsADwd3nZsxP/q5InIrljqzFEs7uk5Vx9T3RETkMSwO4FXgSyzN5/ewTDtxeojIDdjcjwnjPl9VIxejcuAcEXkPcw86CwuerjOq+n5wSxorIjthMRLbY1mARqvq0mz7qqiopKoqeaDjOI7jOI7TdNimoQewlXIncCNwLrYrfiC2I74Zqjoe+AXQNcjdhwUJLw/PV2IxAC8B12JK9G1YrYHnk/3VE/OA3sADwOOYcv9zVZ2akDsH2B+bx+nAlYnMQRdgO/W/xea5GEuTmi8uAH6FnUQ8DtyO1U3waCTHcRzHcbYqSqqrfTfTqT9SFeVqouwDLPITgOLCM1IUF76exYevaXHh61k4YlmA2mObqps/L/SAHMdxHMdxHMdpODwGoIgJgbTNMohUxXzwHcdxHMdxnK0ANwCKm58D92Z4/mdgcH0OIFT4LanPdziO4ziO4zjZ4wZAcTMN6Jzh+Yr6HoCItMPqFPQGdsNqF5QD16jqPxKyo4GZKaomVwMjVPXG+h6v4ziO4zhOseMGQBGjqhVsKihWcEREgNnAeixL0TvA3sBw4GUROUZVX4g1uRqopHAZjHImXVnttevW8+Warws8GsdxHMdxnNxxA6BICP7+LVR1bUOPJcaDwHZAp3iufRGZDCwEHhGRDqGacIMQaiw0U9VvspEfMnYmy1dtqehPu6mf5xN1HMdxHKdJ4AZAE0VE7sMKfg0DrsNy/w8RkS7Aj4G9gM+xglwjQs2AePtBwMVYDYJKrNrweaq6JDxvF/o9HsuX/xowXFXnZTm+/w7juzpZaEtVK0XkN8D9WP2DvwQ3H4AbQsEwgB4hhgCgRESuAs4DWgAzw3hXxd65IzAWGADsDChwlao+FpOZHeb7MFZnYH+suNpz2czLcRzHcRynqeNpQJs2uwN3AL/HCnD9E2iOKbbHAiOBLsBT8UYiMgILAH4FU5aHAB8AZeF5G2A+Fj8wFDgRWAqUi0j7LMfWPfx+LM3zKQm5buH3beHvbmF8ERcAB2OF0UZiMQWRoYCINMeMgpOA0eH5AmCSiByRePfhwBXAGOA4YFGWc3Icx3Ecx2ny+AlA06Yt0DsRNLvxbxHZFvO7f1VEOqrqKyLSGlOQ/6iq58TaTYn9PRQoBURVPw19zQTewhTnM7MY2x7h95JUD1V1jYisBvYM1y9YyAAfJuICIpar6imxuR2IZTCKxnIaduJwmKq+Ge6Vi8jemKLfM9ZXKdA1Ou3IF2Vl381nd06B8HUrLnw9iw9f0+LC17Nx4AZA06YiRcacn2GuPQcA8YjVDtiOejdge+DuDP32wqr3rghGRMQs4Kg8jLs2zExcvwO0FpEdVLUSG/ObwLuJMZcDVyXavpFv5R/w6oZNEK9KWVz4ehYfvqbFha9n4YhVAk6JGwBNm8/iFyLSH/Or/xOWerMCaIO5ALUMYqXh9ycZ+i3DDIVvUzz7IsuxRX7/ewNvJB8Gf/02wMdZ9rcqcR0F7bbEfPrLgENJPWZEpI2qrg6Xn6WScRzHcRzH2RpwA6BpU524Hgi8rqpnRTdE5PCETJQWdHfSK98rgRmYEZFkQ5Zjmx1+9yWFAQD0ScjVlZXhPUPSPK+M/Z38bo7jOI7jOFsNbgAUF62AZErN0xLXC4B/A2dgmX9SUQ4MAt4L7jU5o6rPichCYKiI3K2qy6JnIvIdLJbgQ2BirNm3bDqpyJVyLGPRsmTWoXxy96heKe+vXbe+vl7pOI7jOI6TV9wAKC7KgQmhou48LPC1b1xAVb8QkTHA9SEH/mNYNqgewMOquhC4GTgVmCsitwKLMdehTsA6VR2T5XhOx3b4XxCRcZjf/l7ACCz495hEDYB3gf4i8hzwlQ1Xs3UWvB84C5gjIjcC7wGtgUOAdqp6fpb9ZKSiopKqKj9AcBzHcRyn6eJpQIuLO4EbgXOBR7Ec/ycnhVR1PJZOs2uQuw8LEl4enq/EYgBewir4lmPpOf+DHKr0qqoCHYEnsNSdT4fxvQEcrqoLEk0iJf3J8O6k+1Kmd30DHA1MBi7DgobvxIKW52Tbj+M4juM4TrFTUl3tu5mOkwX7AIv8BKC48IwUxYWvZ/Hha1pc+HoWjlgWoPaYJ8fmzws9IMdxHMdxHMdxGg6PAXByRkRKgGYZRKpUtapQ43Ecx3Ecx3Gyx08AnNrwcyxjT7qfexpuaI7jOI7jOE4miuYEQEQWA4+r6gUNPJR6pZHMcxrQOcPzFXV9QcgadLCq9g3Xg4F704g3V9X1ItIJCyTuCggwXVV7J/ptBihwtar+pa7jdBzHcRzHaWoUjQHgFA5VrWBTQbG8IyK7Ab/EsvokOZZENWJVjZLwHwH8N/AiVhNhC1R1g4hcD4wRkUdibbMiXVnttevW8+War3PpynEcx3Ecp0FwAyALwq5xs5BqcqtCRFqpaqE123OAf6nqCymevayq6U4YblPVWwFEZHaG/h8GbgV6Y3UQsmbI2JksX7Xl55h2Uz88r4HjOI7jOE2BghoAItINGIO5aJRghaFGqWp5Fm33AMYBxwA7AkuA2yOFLyZ3LnA5sBMwHzhbVT+KPb8WU/z2BdZgee0vTsjMBioxRfFKYH+sUNZzInI2ltN+N6yS7nnA28AIVb0x1sexwFXAocBaTNEcpqqrs/hUUR8nADeEsb4DDE0j1wUYi+XuB8uBf5GqfhyT2QP4A/BjYHX4uzlwiaruEGS6A89iFXV/DhyH5ePvKSI7hncMAHbG3GiuUtXNFOh8zBsYTC3iCLINPFbVShF5EptjTgaA4ziO4zhOU6dgQcAicgRWFbYlcCamSE7BKsPW1LYUWAB0B0YBJwC3AHskRPtgha8uxIphdWRLv/FdgOswI+BCYFdgvogkXUYOB67ADJbjgEUi0hcrLjUbODGM/9EU4z0RK36lYZ4XYVV5H6lprrE+DsGU0yWhjzuAB4C2CbkuwFxgHXAapjx3AGaEk4soa89UoAtmsJyNfcsz0rz+LuDj8N5xItIcMypOAkZj324BMCmsaz7nvT+Wc39+GpFmIrJt7Ke2/4bnAT+qQ3vHcRzHcZwmSSFPAMYD/wB6qOqGcG9mlm2HYYr791V1cbj3TAq5ZkBvVV0LICK7ALeISJtoB1pVz4yEg4I8G6uAexxWRTaiFOiqqkti8pOBOaoaKc4zRKQKM0YimZJwPTEmh4h8ACwQkSNVdV4Wc/4VsBToE/mpi8gKYFJCbjzwGtBXVauD3ELgfeBU4MEwt47Yt58dZMox4yIV01V1eGzsg4FOwGGq+ma4XS4ie2MGUs88zjsKLn4jzfNPE9fXYyc+ufIadpJ0IHaCU2fKyr6bj26cAuPrVlz4ehYfvqbFha9n46AgBoCIbI+5/YyMKf+5cDTwTEz5T8ecSPkPvBN+74m5vSAix2GnCAcDrWOyHRJ9vZFQ/psBhwEjEnKTiBkAwAHYDvYwEYl/34WYy1EXbPe5JroCUxJBqlOAjdfh1OLIMKZmIhI9WortwnfBDIDOwBeR8g+gqutE5AnglBTvnpq47gW8CbybmFM55u4D+Zt3O6CK9EHGPdk8CHhZFn2mIoojaEeeDACvbtj08KqUxYWvZ/Hha1pc+HoWjlgl4JQU6gSgLeZu9Ekt25cCb2UhtypxHQXttgQQkc6YcjsN2zn/DFOo50cyMT5LXJdh3+vzxP3lKeRg89OEODW6PAXaJfsOGWziAbA7YaceN4efJP+K9ZUcN8n+Y6Sa+6FYjv8tEJE25G/eLYFvo9OMFLyeIQg4FyJDMWW2IMdxHMdxnGKlUAbAamxXd/datq+oQ9s4/bHd6IHRSUSIL9guhWxSAf0cMxbKEvd3SVyvDL8vwNJRJkmndCdZluw7nEKUxm6tDuMcR4pYhPA86is5bpL9x0jOfSXmkjMkjXwl+Zv3SqCFiLRMnObkmyiWot7SmTqO4ziO4zRGCmIAqOpXIrIAGCQiN9XCDehpYLiI7KWqH9ZhKK2wXex4tpjTsmkYdt9fxYJ/fxt7NCAh+h7wEbC/qk6ow1hfBPqJyCUxN6B+WOaeaExficjzwEGqekWGvl4CWotI91gMQAssmDobyrHMQMtUdWkqARHJ17w1/G4PvFuHfmqiffj9fi6N7h7VK+X9tetyKifgOI7jOI7TYBQyCPhyLHB3lohMwNx1OgIrVLWmlI+3AIOAuSIyFvgnlhqzg6pelsMYyrFUmhNEZBLmG38Wm1yFamIsMEVE7gX+ChyEZdWBYFSoarWIDAUeEZHvAI8DX2IuMMcAt6pqqh3yJOMw//mpIvJ7LOPRKOwEI85w4FkRmQg8hO2g746lLX0ipOl8EngFeEhERmK73kPZ0hhKx/3Yd5ojIjdiRk5r4BCgnaqen8d5/x07aelEjgaAiJQBR4XLMmAHETk5XM9R1bgbVGfg3VzdiSoqKqmqSued5DiO4ziO0/gpWArEkAGmO6Zw3of5ivcnfSaaeNsKrMrrPCzry3RM8f04U7sU/UwHLsXShU7DFNO+pPFtT9F+KlakqgcWkDsQS2kKscBUVZ0c+t4P+AumDF+OKd6Ls3zX61jazfaYe8/5WN76VQm5F7Bvsx1wN/AU8Gtsbd8OMtXY6cFC4PYgtwBbg82q6qYZyzdYIPZk4DIse9OdmLI9J8/z/gpb3+OzkU9wMPB/4ecgLJVrdH1wQvZ4YGIt3uE4juM4jtOkKamu9t3MuiAiAzBFspOqvtzQ48mWkP/+FeAjVe3T0OOJIyK9sROW3VS1sh76/0/gZeAAVV2UZbN9gEV+AlBceEaK4sLXs/jwNS0ufD0LRywLUHtSbMIWtBJwU0dEdgKuxqrlrsEy41wBzG3syn+oYNwM87HfEfgF8APgkoYcVypU9XEReRcr5nZjTfK14BLg/hyUf8dxHMdxnKKhwQ2AsBOdyRVpQ4aUkIXmWyz24FQsi0wF5hZzaS6dhGw+JRlE6mPOX2PuOPtg3/st4CRVnZXn96Qlx3mfy6aiYPkegwJ/znffjuM4juM4TYEGNwCAezDf9nScgcUMNDiq+iUWP1BXZrEpWDUVPbAKxXlDVR8AHshnn7Ug63mHE5W8n6qEDFS/yXe/juM4juM4TYXGYACMxirNPgfckOJ50bhpiMhiLDD2HCBTLWzN8KzRIyLjgINVtW+4Hgzci52gHM2WmYwuxLI8/RnYO9HXAcCVWAXgUqxI2ePAr1V1WUxuW6zqcScsC9JarHrxWFUtj8kdhVVv3ldVk+NwHMdxHMcpehrcAFDVxSLyDfC5qi5s6PEUAlVt0gp+JkRkN+CXmKKfZB0WeHtnTL4E2/n/kkQBMhE5Akth+hHwK8wY/H74u5+IHKWq/wjikSvZDVia2JbA2cCTItJDVZ8DUNU5IvI+Fgdwda7zS1VWe+269Xy55utcu3Icx3Ecx2kQGtwAqG+Cz3ezkMpyq0JEWqlqoTXTc4B/hfSkSR4D/gdLIRrx/4DdsF35btFNEWmJZQL6FOgW262fIyJTscrEDwJdYWOq0p/EXyYiT2JGw8+wE6aIPwHXichYVc0qBWzEkLEzWb5q80867aZ+eE4Dx3Ecx3GaClkbACLSDRiDKVwlwDvAqLh7RYa2e2CFrY7BMtAsAW5X1VsTcudigao7AfOBs1X1o9jza4HeWCDuGuB54OKEzGygEngYcx3ZH9thfi5kwhmJKZx/x4p4vQ2MUNUbY30cC1yFZflZiymuw1R1dRafKurjBGw3el/sWw1NI9cFKzAWKb8zgYtU9eOYzB7AH4AfA6vD382BS1R1hyDTHctOdDwWU3EcVgG4p4jsGN4xANgZczG6KhQJi4+lzvMGBmNxHan4CzBdRPaMze80YAYWUB1nILAn8POkq46qLhOR3wHXiMgRqjo/1ctC9ebVWI2EOI8Cf8S+1ZTspuU4juM4jlMcZFUILLhizMbcKs7EFMkpWJXXmtqWYkWnumOVbE/AKvvukRDtA5yM+YOfi1VcD1vuAAAgAElEQVQJvjchswtwHWYEXAjsCswXkVYJucOx9JxjMEV4kYj0xXaeZwMnhvE/mmK8JwJPYEryAOAizP/8kZrmGuvjEEx5XhL6uAMLwG2bkOsCzMVcY07DlOcOwIxwchG5yEwFumAGy9nYtzwjzevvwgqkDQDGiUhzzKg4CYu36I2tx6Swrvmc9/5YlqGUCjlWiOwDLIsSIrIdtuYPpZDtHn4/luIZbFLcu8dvikiJiGwrIjuLyAjgADY/cUBVV2GG34/Tz8ZxHMdxHKc4yfYEYDzwD6BHyKICplRmwzBMcf++qi4O955JIdcM6K2qawFEZBfgFhFpE+1Aq2pUdTdy7ZkNLMeU/MmxvkqBrqq6JCY/GZijqpHiPENEqjBjJJIpCdcTY3KIyAfAAhE5MlQ0rolfAUuBPqq6PvSxAnNziTMeeA3oG6W/FJGFwPuYkvxgmFtH7NvPDjLlpK+gPF1Vh8fGPhgLjD1MVd8Mt8tFZG/MQOqZx3lHaTvfyCDzEGbs3BDm1hxT5o9IyO0BrM4QqBvNf8/E/YvYtKaVwCmquiBF+9eA/8owzpwoK8sU0+00Znztigtfz+LD17S48PVsHNRoAIjI9pjbz8iY8p8LRwPPxJT/dMyJlP/AO+H3npjbCyJyHHaKcDDQOibbIdHXGwnlvxlwGDAiITeJmAGA7RbvAwwLWWUiFmIuR12AbBThrsCUSPkPTAE2XodTiyPDmJqJSPRoKbYL3wUzADoDX0TKP4CqrhORJ4BTUrx7auK6F5YN593EnMoxdx/I37zbAVVs6c4T5yFgtIgciBkCU1T137H515WHsLGWYTEBfxOR/qr6ZEJuRRhvXvDKhk0Tr0pZXPh6Fh++psWFr2fhiFUCTkk2JwBtMVehT2o5hlKs6FRNrEpcR0G7LQFEpDOm3E7Dds4/wxTq+ZFMjM8S12XYXD9P3F+eQg42P02IU6PLU6Bdsu/gj74idmsn7NTj5vCT5F+xvpLjJtl/jFRzPxRLwbkFItKG/M27JfBtpiJmqvqBiLyEuXn1wVyTUrEUaCMiO6Y5BYjShX4cv6mqy9n0bZ4UkZ2x04akAbAWSLqOOY7jOI7jFD3ZGACrsV3d3Wv5joo6tI3TH9uNHhidRIT4gmSAJyTSSWIK9Ho2KboRuySuV4bfFwAvpug3ndKdZFmy73AKURq7tTqMcxwpYhHC86iv5LhJ9h8jOfeVmEvOkDTyleRv3iuBFiLSMnGak+Qh4Cbs30a6IPLZwC+AvthJSJI+MblMvExqX/+okrPjOI7jOM5WRY0GgKp+JSILgEEiclMt3ICeBoaLyF6q+mGtRmm0wnaxq2L3TsumYdh9fxUL/v1t7NGAhOh7WM75/VV1Qh3G+iKWp/6SmBtQP8zfPRrTVyLyPHCQql6Roa+XgNYi0j0WA9ACC6bOhnIs280yVV2aSkBE8jXvqL5Be+DdDHJ/xYJ3ZyfcpOL8H3AtcKWIPKaqlbHx7ob5+r+YLgNQjCPYdJoSpz21KLh296heW9xbuy7dFBzHcRzHcRof2QYBX44F7s4SkQmYu05HYIWqpkv5GHELVuV1roiMxYo07Qt0UNXLchhrOZZKc4KITMJ8489ik6tQTYwFpojIvZgCehCWVQeCUaGq1SIyFHhERL6DVZz9EnOBOQa4VVVT7ZAnGYf5z08Vkd9jAa2j2LIC7nDgWRGZiO2Kr8ROS3oAT4Q0nU8CrwAPichIbNd6KFsaQ+m4H/tOc0TkRszIaQ0cArRT1fPzOO+/YyctnchgAKjqp5gxlhZVXSsiPwWeAl4QkfHAYqwQ2Ehs/j+L5EXkVMzQeQpzHyrFDMSehKxDCTqRuvJ0RioqKqmqSuvh5DiO4ziO0+jJKg1oyADTHVM478N8xfuTPhNNvG0Ftgs7D7gemI4pvh9napein+nApZjrxzRMMe1LGt/2FO2nYkWqemABuQOxlKYAX8TkJoe+98Py1j+OGUAVmAKazbtex3zb22PuPedjuflXJeRewL7NdsDdmPL6a2xd3g4y1djpwULg9iC3AFuDL6iBUCDr6CB/GZa96U7gKGBOnuf9Fba+x2cjn0V/8zFD8xXs387TmCH1JNBJVT+Iib+HufXciNUVuBWLSThKVf8a7zekXy1ly6xMjuM4juM4RU9JdfXWu5spIgOAiZgy+XJDjydbRGQbTCn+SFX71CRfSESkN3bCslvcbacxISK3AP+pqj/Kodk+wCI/ASguPCNFceHrWXz4mhYXvp6FI5YFqD0pNnKzrgTc1BGRnYCrsWq5a7DMOFcAcxu78h8qGDfDfNZ3xIJjfwBc0pDjSoWqPi4i72JZfm6sSb7QhKxHv2BTELHjOI7jOM5WRZ0MgLATncmNaEOmlJAF5lss9uBUNmWAmYy5FWVNyOZTkkGkPub8NeaOsw/2vd8CTlLVWXl+T1pynPe5bCoK1tjYC7hCVec29EAcx3Ecx3EagrqeANyD+ban4wwsZqDBUdUvyc+u7yzMfz4dPag5NWVOqOoDwAP57LMWZD3vcKLSKE9VVPUNMlcqdhzHcRzHKWrqagCMBn6f4fmiOvafNSKyGHhcVS+o51edA2SqY51zaslcKOA8kxR03iIyDjhYVfuG68HAvWnEm6vq+vBt9k4jc2oUDCwizwJPq+pv8jlmx3Ecx3GcpkCdDABVXUyWGWKKBVWtVwW/sVLIeYc8/7/EshclOZZE9qNYLYH+QIuE/KVAbzYvOHYt8DcR+YOqJitQZyReVnvtuvV8uebrXJo7juM4juM0OFtNEHBdCP7vzUJKza0KEWmlqoXWcs8B/hXSpCZ5WVVXpGqkqq/Gr8O6dQWeCuloI57GAsF/zuaF4WpkyNiZLF9ln2PaTf3wXAaO4ziO4zQ1CmoAiEg3YAymlJUA7wCjVLU8Y0NruwdWYOsYLBPOEuB2Vb01IXcuFjC7EzAfOFtVP4o9vxbbEd4XUwKfBy5OyMwGKoGHgSuB/TEf9+dCRp6RwG5Y4avzsJz9I1T1xlgfxwJXYdmG1gKPAcNUdXUWnyrq4wSsWNW+2LcamkauC1borFu4NRO4SFU/jsnsAfwB+DGwOvzdHLhEVXcIMt2xLEnHY8rxcVgl4p4ismN4xwBgZ8zl56pQrCw+ljrPGxiMxZfUlR8B7YAH4zdD4bOJ1MIAcBzHcRzHaepkVQgsH4jIEViQaEusANcArCDXXlm0LcWKX3XHCkGdgFUY3iMh2gc4GbgQy0TTkS39xncBrsOMgAuBXYH5ItIqIXc4liZ0DKYILxKRvlgRrdlYJdspWKGv5HhPBJ7AlOQBwEVYRdpHapprrI9DMOV5SejjDiwQuG1CrgswF1iHVb4dDHQAZoQdcESkBJgKdMEMlrOxb3lGmtffhRVqGwCME5HmmFFxEhb30Rtbj0lhXfM57/2xbEfz04g0E5FtYz+Z/g2fjhl5U1M8mwccKiJl2Y7NcRzHcRynGCjkCcB44B9AD1XdEO7NzLLtMExx/36IOwB4JoVcM6C3qq4FEJFdgFtEpE20A62qUfXfyEVkNrAcU/Inx/oqBbqq6pKY/GRgjqpGivMMEanCjJFIpiRcT4zJISIfAAtE5MhQWbkmfgUsBfpEPu4isoItq9eOB14D+kZpOEVkIfA+lvL0wTC3jti3nx1kyklfyXm6qg6PjX0w0Ak4TFXfDLfLRWRvzEDqmcd5R+lD02Xq+TRxfT124rMZwaDrH8azNkU/r4XfXTCjpVaUlWWKi3aaAr6GxYWvZ/Hha1pc+Ho2DgpiAIjI9pjbz8iY8p8LRwPPxJT/dMxJKHvvhN97Ym4viMhx2CnCwUDrmGyHRF9vJJT/ZsBhwIiE3CRiBgBwALaDPUxE4t93IbYb3QXbfa6JrsCUWIAr2InDxuug5B4ZxtRMRKJHS7Fd+C6YAdAZ+CJS/gFUdZ2IPAGckuLdyR3zXsCbwLuJOZVj7j6Qv3m3A6qwOg2p6MnmQcDL0sj1w7IWPZjmeRRH0C6LMaXFKxo2bbwqZXHh61l8+JoWF76ehSNWCTglhToBaIu5G31Sy/alWPGrmkhmdImCdlsCiEhnTLmdhu2cf4Yp1PMjmRifJa7LsO/1eeL+8hRysPlpQpwaXZ4C7ZJ9q+qGcAoQsRN26nFz+Enyr1hfyXGT7D9GqrkfihVT24JQXTdf824JfJuhmNrr6YKAE5yOuTHNTvM8MhSTrl+O4ziO4zhFTaEMgNXYru7utWxfUYe2cfpju9EDo5OIEF+wXQrZpAL6OWYsJH3Gd0lcrwy/LwBeTNFvOqU7ybJk3+EUojR2a3UY5zhSxCKE51FfqXzdk2OPSM59JeaSMySNfCX5m/dKoIWItEzjulMjIrIzFix+i6pWpRGLYinSnTQ4juM4juMUJQUxAFT1KxFZAAwSkZtq4Qb0NDBcRPZS1Q/rMJRW2C52XCk8LZuGYff9VSz4N545ZkBC9D3gI2B/VZ1Qh7G+CPQTkUtibkD9sMw90Zi+EpHngYNU9YoMfb0EtBaR7rEYgBZYMHU2lGOZgZap6tJUAiKSr3lH9QbaA+/Wso9TsH/b6dx/ov7j78uKu0f12vj32nXrM0g6juM4juM0TgoZBHw5Frg7S0QmYO46HYEVqlpTysdbgEHAXBEZC/wTS43ZQVUvy2EM5VgqzQkiMgnzjT+LTa5CNTEWmCIi9wJ/BQ7CsupAMCpCismhwCMi8h3gceBLzAXmGOBWVU21Q55kHOY/P1VEfo9lPBqFnWDEGQ48G9JaPoTtoO+OpS19IqTpfBJ4BXhIREZiu95D2dIYSsf92HeaIyI3YkZOa+AQoJ2qnp/Hef8dO2npRO0NgNOBN1U1XSAx2NpXsikYOCsqKiqpqkrnneQ4juM4jtP4KVga0JABpjumcN6H+Yr3J30mmnjbCuAILIj0emA6pvh+nKldin6mY5Vh+2BxAMcAfUnj256i/VSsSFUPLCB3IJbSFGKBqao6OfS9H/AXTBm+HFO8F2f5rtextJvtMfee87G89asSci9g32Y74G7gKeDX2Nq+HWSqsdODhcDtQW4BtgabVdVNM5ZvsEDsycBlWPamO4GjgDl5nvdX2Poen418EhHZDwugzrT7T+j/0VoGpTuO4ziO4zRZSqqrfTezLojIAGAi0ElVX27o8WRLyJ//CvCRqvZp6PHEEZHe2AnLbqpaWQ/974wFpPdU1blZNtsHWOQnAMWFZ6QoLnw9iw9f0+LC17NwxLIAtSfFJmxBKwE3dURkJ+BqrFruGiwzzhXA3Mau/IcKxs0wn/cdgV8APwAuachxpUJVHxeRd7FibjfWJF8LfgnMy0H5dxzHcRzHKRoa3AAIO9GZXJE2ZEgJWWi+xWIPTsWyyFRgbjGX5tJJyOZTkkGkPub8NeaOsw/2vd8CTlLVWXl+T1pynPe5bCoKlm9WYEaA4ziO4zjOVkeDGwDAPZhvezrOwGIGGhxV/RKLH6grszD/+XT0IH3++lqhqg8AD+Szz1qQ9bzDiUq9nKqo6u/qo1/HcRzHcZymQGMwAEYDv8/wfFGBxoGIRLvPx6rqjBTPRqhq1i4pIrIPMBj4o6rGi6BdgynDl2GZkZLklJoyF0TkWmAkcLOqFtr95xysOm86JonINFW9oFADchzHcRzH2dpocANAVReTZYaYAnIVMKNGqZrZB4sZeJzNqyDPB7oB76vqyhTt6gURKQH+J1z+VERGZCiUlXdUNaNhIyInsmU150ZFaekOrF23ni/XfN3QQ3Ecx3Ecx6kVBUsDGkdESkSkZUO8OwueAX4oIj3r6wWquk5VXyik8h84Atgbq4cQ1QpocESkFYCqvhoMwkbLkLEzadmiwe1mx3Ecx3GcWlMQTUZE7sMKOw0DrgP+AxgSileNB36MFYz6HKv6OyKpHIvIIOBi4ECsgNPfgfNUdUl43i70fTzmZvIaMDzUH8iFJ4EdsJ37pzPM6b+AX2GBqq2x4mS3qepd4Xl3LFsQwEsiAoCqlgTXoEXAQFWdGAqLdVPV7yfe0QMzSLpGRbRE5FjshOJQYC3wGDBMVVdnMbfTsWDgnwP/wKogbxYELCKLsROL97EMQaXAE1jWoO9hdQS6hPmep6rzE+1Px2o0fB9YjRUnG6mq68LzwcC9WK7+0cD/w1J+DoneHXcBEpFuwJggXwK8A4xS1fLw/FqgNxacvQZ4HrhYVT+K9TEb+zdzD/Ab7N/aq8C5qvpWFt/NcRzHcRynaCjkCcDuwB2Yv/8xwEtAK6A5cCVwLOab3gUrZrURERkB/BnLWz8AGAJ8AJSF520wt5rOWIXbE4GlQLmItK/FWMcAR4rIjzLI7I0V0zoLU0AfBm4TkfPD81ew4l1ggczdwk8qHrZpyGGJ+6cC/4wp/ydiyrhi3+EioCfwSE0TEpHmWOGyaaq6DJgKDEhzEtMPOAGrcjwcK5b2B+BvmLI+AKvyOzneXkR+iQVsPxPajMZiIFIF3f4VW7M+WGGyVGM+AgsKbokVXBuAFWDbKya2C2b49QYuBHYF5kenCjEOxQy2KzHDZ1cs5qBBTsEcx3Ecx3EaikL6MrQFeqvq84n750Z/iMi22A7vqyLSUVVfEZHWmCL5R1U9J9ZuSuzvodhOtajqp6GvmViqyyvYVK03K1R1uogsxE4BUgXpoqp/i427BHgOM0jOASao6hoReSeIvKWqCzO8chbwGabwvxr6bI4pvH+IveMWYKKqnhF79wfAAhE5sobTjuOAnbAdebBKuT/FFOeJCdkSoG9s174rdmpwSjRvEVmBGXFHATNE5LvAWOAmVb0sNr7VwIMiMi7h3nOXql6bYbxgp0P/AHrEKvbOjAuo6sa1DWlGZwPLw3wnx0TbAoer6mdBFqzC8g+w06KcKCvLFMvsNCV8LYsLX8/iw9e0uPD1bBwU0gCoSKH8IyI/w1x7DsBcbyI6YLvo3YDtSbNLHOiFudusCEZERE1pJzMxBpgmIkep6pwU426LGSb9gD2xIlsA63J9kapuEJG/YYG5l4Vc+MewucJ+ABZUPCwxx4WY60sXIJMBcDoWYPtkuJ6B5cM/jS0NgDmR8h94P/yemeLe98Lvbpjr1SMp1qAZ0JHNg72nZhgrIrI95vYzMqb8p5I7DhgFHIy5YkV0SIi+Fin/gcg425NaGABeybA48KqUxYWvZ/Hha1pc+HoWjlgl4NTPCziWz5I3RKQ/cD+W7/0UTOE7NjyOXEtKw+9PSE8Zpoh/m/i5gM3dRbJGVR8P47o6jch9mPJ8M2aAdMbcm1rU5n2Yov89LFAX7DTgNVV9N1yXhd+T2XKeO5JhniKyI+ZqMx3YPrhM7YAp4ccHYyZOMhPPNwCJOINvwu9onaLxvZwY2/JwPzm+Lf49JGiL/ftMu+4i0hmbw2fYCUU3bB2+iY0rIuWcUsg5juM4juMUNYU8AUhV2XYg8LqqnhXdEJHDEzIV4ffuwMdp+l6J7WiPSvEs7e5xFowBporIf8dvBr/33sAl8aJSIpKpoFlGVPUFEVkEnCoir2AGzTUxkSgo+gLgxRRdLE9xL+IkTNE9LfwkORm4K+dBb040vgHAhymef5S4rqnS8WqgClv3dPTHTj8GRqcEIlIKbFfjaB3HcRzHcbZSGjqfYSu2dJlJKqgLgH9jgbR/T9NPOTAIeE9VK/M1OFWdFpTx5ClAC2x3euPYg1EwICGX6y7zw1hQ8fOY29PDsWfvYUr0/qo6Icv+Ik4PbQelePYn7JvX1QB4HvgK+J6qTq5JuCZU9SsRWQAMEpGb0rgBtcJOGeK1DFIZOHnj7lG9WLtufX2+wnEcx3Ecp15paAOgHJggIqMx//WeWPaYjajqFyIyBrg+BHk+hinfPYCHQ3DtzZjLzFwRuRXzNS/FUo+uU9UxdRjjNeGdyTG9BIwUkQrMELgES8sZ533sBOIsEdkAfFtDMPBDWKaa8cC8eCpLVa0WkaGYj/13sFSdX2KuNccAt0bZguKISJTv/zpVnZ3i+f3AaBH5Xvx9uRK+yShsnfbEYjK+AdpjpyXnq2q6E5x0XI4FYc8SkQmYG09HYIWq3oP9+xmK/RuahLn/nMUmwyvvVFRUUlVV0+GF4ziO4zhO46WhUyDeCdyIZQJ6FMvxf3JSSFXHY3nouwa5+7Agz+Xh+UrM//sl4FpMMbwNqzewReBxjkwldZDo/2AK/j3ABCxA9vbEuFdgqUCPBOaG8aVFVd8G3sDcXh5K8XwypuzvB/wFMwIux9ykFqfp9qfYOt+f5nl0/9RMY8sGVf0tdtpwJBZYPBlLzfketajwG7Iadcd2+O8L/fUHloTn04FLsfiGadi36YudCjiO4ziO4zgpKKmu9t1Mx8mCfYBFfgJQXHhGiuLC17P48DUtLnw9C0csC1B7UmwSN/QJgOM4juM4juM4BaShYwAKQiii1SyDSJWqVmV47jiO4ziO4zhFwdZyAvBztsydH/+5p+GG5jiO4ziO4ziFY6s4AcACRDtneL6iUANxHMdxHMdxnIZkqzAAVLWCTQXFnAZARHphxdpeUdVksbds2o8Ghqtq+rrWjuM4juM4To1sLS5ATsMTFejqKCLfr0X7P2H1DBqUHXbItqab4ziO4zhO48QNgCJCREpCReJGhYi0wvL3Pw1UU4tqvar6sapmrKNQCFq02CoOzRzHcRzHKWJcm2nCiMh9WLXjYcB1WOGzISLSBfgxViX4c0zxHhEKpsXbDwIuxgqwVQJ/B85T1SXhebvQ7/HAd7GCaMNDga5c6BfaXwe0wIqoXZkYy4FYUbiuQCtgKfBgVMU56QIUjIrxNc1TRBZjBdPewoqm7QTMB86uS+Vjx3Ecx3GcpoqfADR9dgfuAH6PVcL9J9AcU7CPBUYCXYCn4o1EZATwZ+AVYAAwBPgAKAvP22CKcmdgKHAippSXi0j7HMd4GrAMeBarYLyviHRLyEzDlPMhmMFxHWYIpKNVNvMM9MEqTF+IVZ3uCNyb4xwcx3Ecx3GKAj8BaPq0BXqr6vOxexv/FpFtgXeAV0Wko6q+IiKtgdHAH1X1nFi7KbG/hwKlgKjqp6GvmdhO+hXAmdkMTkRKMcNkgqpWicjfgN9hRsGCILMzsB9wsapOC01nZ+o37PKfm2meMfFm2DdaG2R3AW4RkTaqujqbecQpK/turk2cRoyvZ3Hh61l8+JoWF76ejQM3AJo+FQnlHxH5GebacwAQz5rTAdvx7wZsD9ydod9e2I79iqBcR8wCjsphfKdgO/UPAajqKhF5EviJiAxV1fVYhqYlwLhgMMzKxj0ni3lGzImU/8A74feeQM4GgJcxLx68LH1x4etZfPiaFhe+noVjm21KKC1NnzjRXYCaPp/FL0SkP3A/8DKmfHfFXGQAogDh0vD7kwz9lmG++8miaRdgPvfZchqwGPhARNoE16Ipof9eAKpaHf5+B7gN+FBEXhORo9N1muU8I1Ylrr9JI+c4juM4jlP0+AlA06c6cT0QeF1Vz4puiEgy735UE2F34OM0/a7E8vaPSvFsQzYDC7ECPwyXSSUczDiYDqCq72OnAttiyvw1wFQR2SvUcUiSzTwdx3Ecx3GcBG4AFB+tgHWJe8m0mwuAfwNnYJl/UlEODALeU9XKWo4leu/JbFmI7QKgn4h8R1W/im4Gl6B5IjIWczfaO0VbyG6eeWfduvX1/QrHcRzHcZx6xQ2A4qMcmBDSZs4DegJ94wKq+oWIjAGuF5FmwGOYO1gP4GFVXQjcDJwKzBWRWzE3nlIs7ei6KD1nDZwGPK+qk5IPRKQKyz50ooi8Gd73CJbFaAfgUsxF6Z1k22znWR9UVq6tWchxHMdxHKcR4zEAxcedWD79c4FHsRz/JyeFVHU88AvM3eZR4D4seHZ5eL4SCxZ+CbgWU7hvw2oNPJ/sL4mIdAS+j/npp+I5YBFmJHyKKfuXA09gVX8/B3omgndznqfjOI7jOI6zOSXV1UkXcsdxUrAPsKiiopKqKv8/Uyx4Roriwtez+PA1LS58PQtHLAtQe8yLY/PnhR6Q4ziO4ziO4zgNh8cAOLVCREqwAlvpqFLVqkKNx3Ecx3Ecx8kOPwFwasvP2bJGQPznnoYbmuM4juM4jpOOJnMCICKLgcdV9YIGHkq90oTmOQ3onOH5ikyNRWQccLCq9g3Xg4F7gQ+B/VX125hs9KxMVVeEe4tJfCcR2Q24FSsIVg08DgyN2gSZe4H18foBjuM4juM4WxNNxgBwGhehOFeq/Pw1EhT1XwKpKv3uBQwG7sqxz22Bp4DtsPoFzYHxWDGxI0K1YYDrgDdEZLyqfpDr2HfYoSVr1nydazPHcRzHcZxGg7sAASLSTES2a+hxNAQi0qoBXnsO8C9VfSHFs2eAX4lI8xz7HAD8JzBQVaeo6kTgdCyV6XGRkKoq8CJwfm0G3qKF28yO4ziO4zRt8qrNiEg3YAyWW74EK+I0SlXLs2i7BzAOOAbYEVgC3K6qtybkzsXyxe8EzAfOVtWPYs+vBXoD+wJrsJz1FydkZgOVwMPAlcD+WBGs50TkbGAksBtWJfc84G1ghKreGOvjWOAq4FBgLVZMa5iqrs7iU0V9nADcEMb6DjA0jVwXYCymzALMBC5S1Y9jMnsAfwB+DKwOfzcHLlHVHYJMd+BZ4HjMh/84LM9/TxHZMbxjALAzoMBVqvpYYix1nje2w58uRuCaMMZBwN059Hk88Kaqvh3dUNXnRWRJeDY9Jvt/wFUiMjxUHnYcx3Ecx9lqyNsJgIgcAcwGWgJnYorkFMylo6a2pcACoDswCjgBuAXYIyHaByv2dCFWAKoj5hseZxfMzaN3kNsVmJ9ip/tw4ArMYDkOWCQifbECU7OBE8P4H00x3hOxglUa5nkRVon2kZrmGuvjEEx5XhL6uAN4AGibkOsCzAXWYUWzBmMFu2aEKr5RRp6pQBfMYDkb+5ZnpHn9XcDH4b3jwm77TOAkYDT27RYAk8K65nPe+2M59WJvYQsAACAASURBVOenEXkbmIidAuRioB5I6qrBb2MFyeLMw4ycQ3Po33Ecx3EcpyjI5wnAeOAfQA9V3RDuzcyy7TBMcf++qi4O955JIdcM6B1VhxWRXYBbRKRNtAOtqmdGwkFBno1Vtz0OmBzrqxToqqpLYvKTgTmqGinOM0SkCjNGIpmScD0xJoeIfAAsEJEjVXVeFnP+FbAU6BPtQovICmBSQm488BrQN/JjF5GFwPvAqcCDYW4dsW8/O8iUY8ZFKqar6vDY2AcDnYDDVPXNcLtcRPbGDKSeeZx3FDj8RgaZXwOvAz9jSwMvHW2xk48kq4CDEvfeBKqA/wIWZtn/RsrKvptrE6cR4+tZXPh6Fh++psWFr2fjIC8GgIhsj7n9jIwp/7lwNPBMTPlPx5xI+Q9EO757EpQ/ETkOO0U4GGgdk+2Q6OuNhPLfDDgMGJGQm0TMAAAOwHawhyV2qBdiLkddsB3mmugKTEm4oEwBNl6HU4sjw5iaiUj0aCm2C98FMwA6A19Eyj+Aqq4TkSeAU1K8e2riuhemFL+bmFM55u4D+Zt3O0z5ThtArKpvBmPsVyJyfxZ95oSqrheR1WEsOeNVDIsHr0pZXPh6Fh++psWFr2fhiFUCTv08T+9pG/r6pJbtS7Nsuypx/U343RJARDpjyu1nmI97N0w5/iaSifFZ4roMM4g+T9xfnkIO7DQhmft+R7JweQq0S/YdjKd4+sydsFOPm1O865DYu9qlGHeqsUekmvuhKd5xI7CjiLQhf/NuCXwby8qTjmuA/TC3p2xYBbRJcb8tsDLF/bVAQwRAO47jOI7jNCj5cgFaje3q7l7L9hV1aBunP7YbPTA6iQjxBaky/CQV0M+x3feyxP1dEteRMnkBlk0mSTqlO8myZN/hFKI0dmt1GOc4UsQisMnlZRlbjptk/zGSc1+JueQMSSNfSf7mvRJoISItE6c5m6Gqb4jIo9hpzvVZ9PsuqX36D8LiFpK0pZZpTB3HcRzHcZoyeTEAVPUrEVkADBKRm2rhBvQ0MFxE9lLVD+swlFbYjnRV7F5WO8iqukFEXsWCf38bezQgIfoe8BFWrGpCHcb6ItBPRC6JuQH1wzL3RGP6SkSeBw5S1Ssy9PUS0FpEusdiAFpgwdTZUI5lylmmqktTCYhIvuat4Xd7TGnPxDXAq1isQ01MB34mIgeq6rsAItIVc1uKZwBCRHbF/q1ospOaWLfOkwY5juM4jtO0yWcQ8OVY4O4sEZmAuWR0BFaoarqUjxG3YGkf54rIWOCfWGrMDqp6WQ5jKMdSaU4QkUmY+89ZbHIVqomxwJRQLfav2O7xeeFZFYCqVovIUOAREfkOVm32S8wF5hjgVlVNtUOeZBzmPz9VRH6PZTwahZ1gxBkOPCsiE4GHsB303bG0pU+ENJ1PAq8AD4nISGxneyhbGkPpuB/7TnNE5EbMyGmNuRm1U9Xz8zjvv2MnLZ2owQBQ1ddFZApmlNXEJOwUY2L4BttiKVYXYN8nThSInE3MwmZUVqY9tHAcx3Ecx2kS5C0NaMgA0x1TOO/DfMX7kz4TTbxtBXAEppBdj+3YDsdSVeYyhunApVi60GmYYtoXU4SzaT8VK1LVAwvIHYilNAX4IiY3OfS9H/AXTBm+HFO8F2f5rtextJvtMfee87G4hVUJuRewb7Mdlhf/KSxLzjZYikuCP30/zKC4PcgtwNbgC2pAVb/BArEnA5dh2ZvuBI4C5uR53l9h63t8NvLYKUA2/a4HjgXewgKj7wVeIJY9KcbxwHOqmoyFcBzHcRzHKXpKqqtrisXcuhGRAVhe+k6q+nJDjydbRGQb7FTgI1Xt09DjiSMivbETlt1UtbLA726OGZYjVDWXDEP7AIsqKiqpqvL/M8WCZ6QoLnw9iw9f0+LC17NwxLIAtSfFJm1eKwE3dURkJ+D/s3fu4VZVVf//IKmovSAgJFIqpn69l4a8mlaQqImAF/Q1881QfyrmNcC7KZSJGmq+iZfMNE3LFAxUvCAKGpqF9wsNKwGVTAVEhbwl/P4Yc8lyufc5+5yzz4V9xud5zrPPXnvMueZccx8YY85xOQevRPs2HlR6JvBgW1f+UwXjjrhfe2fgcGA7YFRrjqsUZnaHpDl4Mbfx9clXme/ipyI3tfB9gyAIgiAI2gTNbgCknei6XI0+qiAlZEvxIR57cDArs8RMwt2KKiZl8+lQh0hzzPld3B1nY/x5Pwvsb2bTq3yfsjRw3iNY6YvfkvwHOLxQfyEIgiAIgqDd0BInAL/CfdvLcRgeM9DqmNk7ePxAU5mO+8+XYwBeobhqmNkNwA3V7LMRVDzvdKLS4qcqDXT7CYIgCIIgqDlawgAYA1xWx+dzq3ETSfOAO8zsuGr010SOBuqqdd3g9JMZbWyeRao2b0njgK3NbGh6PxwP7C3F6qm67xjchavIyWY2PvWTuUmdY2Y3VjqeIAiCIAiCWqHZDQAzm0eFGWJqBTNrtIK/KlOteUtaHzgBz0xU5FsUMhsV3HneBb5ZaDM/J/uRpAuAsZJuDlegIAiCIAjaGxEEXAFp17hjSpfZrpC0lpm928K3PRp4MaVALfKYmS2so+3yMu3y/Ba4FBgM/KGRYwyCIAiCIFglaVEDQNLOwFhgJzxY9HngLDObVkHb3njxrD3xLDfzgSvM7NKC3Ag8GLYbMAs4ysxezn1+Hq74bYJn+nkY+EFBZgawFFcUfwhsivuvP5Sy7ZwOrI8XtToGz8f/sZtJ6uNbwNl4JqH3cEVzpJktqeBRZX3sjRez2gR/VieVkeuHFzHbOV26FzjRzF7JyfQGLgd2B5ak31cHRpnZZ5NMfzwD0iA8bmMvvMrwQEmd0z2GAevhbjRnp0Jk+bE0ed7AcDx2pFkws6WS7sLnGAZAEARBEATtihYzACTtglcKfhQvrrUErwa7YQVtu+OFrcCr5f4D2AwvSJVnCLAFcDzui34J7jc+MCfTEzgf+CfQHVeqZ0lSYaf7K7jiPRZYCMyVNBQvkHUdnsd+a7yIV3G8++KVaa/Hi3atB/wEuBk3YOpF0ra4cnofcDJeKfgGYN2CXD/gQbwK8iG4Uj8GuEfSdsnlpQMwBa8gfAwrKwVvXub2V6f5DQNWpNz59wKfT33PAw4AJkr6upnNquK8N8UzGc0qI9JRUv57u9zM8tWO15L0Om4A/h34uZlNKNHPH4ExklYrtA+CIAiCIKhpWvIE4EJcIRtgZh+la/dW2HYkrrhvkWIKwI2JIh2BwWb2HoCknsAlktbNdqDNLKvsm7n2zABex3e7J+X66g7sZGbzc/KTgJlmdli6dI+k5bihkcl0SO9vzckh6W/AI5J2TVWT6+MMYAEwJPNTl7QQV7DzXAg8Sa7iraTZwAt4OtPfpLntgD/7GUlmGuWrNE81s9G5sQ/HjbXtzeyZdHmapI1wA2lgFeedpQZ9uszn/yq8vwA/8QH/fp0KPAGsiVdyvkxSDzMbU2j3JH6StCWponIlpKIaQQ3Ro0ddcevBqkasZ+0Ra1pbxHq2DVrEAJC0Nu72c3pO+W8IuwH355T/cszMlP/E8+n18/iJA5L2wk8Rtga65GSLu+FPF5T/jsD2+G58nonkDAD8ZGJjYGRhp3o27nLUD999ro+dgMmFINXJeB77bExrAbumMXWUlH20AHfR6YcbADsCb2XKP4CZvS/pTuCgEveeUni/B/AMMKcwp2m4uw9Ub969gOX4KUUpBvLJIOBXs1/M7DcF2anpmZwq6admtiz3WRZH0IsGGABRCbi2iKqUtUWsZ+0Ra1pbxHq2HLlKwCVpqROArnhxqn82sn13vLBVfbxZeJ8F7XYCkLQjrtzeju+cv4Yr1LMymRyvFd73wJ/XG4Xrr5eQg0+eJuSp1+Up0avYd3LnyQfAdsNPPS5OP0VezPVVHDfF/nOUmvuX8UJpn0LSulRv3p2AD+solPZUPUHARX6PxxRshcczZGSG4loN6CsIgiAIgmCVp6UMgCX4ru4GjWy/qAlt8+yH70YfmJ1EpPiCNUrIFhXQN3BjoUfhes/C+8Xp9Tg83qFIOaW7yKvFvtMpRPfcpSVpnOMoEYuQPs/6Ko6bYv85inNfjLvkHFFGfinVm/diYE1JnQqnOdWma3otd9IQBEEQBEFQk7SIAWBmyyQ9Ahwq6aJGuAHdB4yWtKGZvdSEoayF72Lngz4PqaRh2n1/AtgX+Fnuo2EF0b8CLwOblgk+rZRHgX0kjcq5Ae2DB/lmY1om6WFgKzM7s46+/gJ0kdQ/FwOwJrB3hWOZhmcGetXMFpQSkFSteWe1BPoAc5rQT8a38doARTefPun1hSrcIwiCIAiCYJWhJYOAT8MDd6dLmoC76+wALDSz+lI+XgIcCjwo6Vw8C9AmwOZmdmoDxjANz34zQdJE3Df+SFa6CtXHucBkSdfiWXK2wrPqQDIqzGyFpJOAmyWtA9wBvIO7wOwJXGpmpXbIi4zD/eenSLoMzwJ0Fn6CkWc08ICkW4Gb8B30DfC0pXemNJ13AY8DN0k6nZVZgIrGUDmux5/TTEnjcSOnC7At0MvMjq3ivP+Mn7T0pYEGgKTHgF/jRsQaeHzDIXiq2X8XxHcE5jTQnSgIgiAIgmCVZ7WWulHKANMfVzivw33F96N8Jpp820XALngQ6QXAVFzxfaWudiX6mQqcgqcLvR1XTIdSxre9RPspeJGqAXhA7oF4SlPIBaaa2aTU9xeBG3Fl+DRc8Z5X4b2eAvbHd6pvA47F89a/WZD7E/5s1gCuAe7GU3CuRtr1Tv70++AGxRVJ7hF8DT5RVbfMWD7AA7En4Vl27sXToX4DmFnleS/D13dQJfIF/o4bNn/Aff+3AA43s5+UkB0E3NqIewRBEARBEKzSdFixIjKaNAVJw3BFsq+ZPdba46kUSavhpwIvm9mQ1h5PHkmD8ROW9c1saTP0/yXgMWAzM5tbYbONgbmRBai2iIwUtUWsZ+0Ra1pbxHq2HLksQH0osQnbopWAV3UkdQPOwavlvo1nxjkTeLCtK/+pgnFH3D2mM3A4sB0wqjXHVQozu0PSHGAEML4++UYwCri+Acp/EARBEARBzdDqBkDaia7LFemjOlJCtjQf4rEHB+NZZBbhbjGnNKSTlM2nQx0izTHnd3F3nI3x5/0ssL+ZTa/yfcrSwHmPYGVRsGqPwfBYgSAIgiAIgnZHqxsAwK9w3/ZyHIbHDLQ6ZvYOHj/QVKbj/vPlGIBXKK4aZnYDcEM1+2wEFc87nahU/VQlZaAqFRMQBEEQBEHQLmgLBsAY4LI6Pq/ITUPSPOAOMzuu6UNqdo4G6qqFbeU+WMXmWaTieUsaB2xtZkPT++HAtWXarZ6lSk0nSsene22Cu2o9CuxjZstzJwDnmNmNTZtOEARBEATBqkerGwBmNo8KM8TUCmZWVsGvZSqdt6T1gRPwzENFvkUhc1GuTgJ4dqJ98JStT+HFzvYguR6leg4XAGMl3VxoGwRBEARBUPO0ugGwKpB2jTumdJjtCklrmdm7LXzbo4EXU4rTIo+Vy90vaTfcZayvmT2Z++iWguhvgUuBwXjK0CAIgiAIgnZDixoAknYGxgI74Tuyz+NFmqZV0LY3XhxrTzyLzXzgCjO7tCA3Ag927QbMAo4ys5dzn5+HK36Ze8jDwA8KMjOApbii+ENgU9w//aGUTed0YH28aNUxeL79k81sfK6PbwFn45mC3sMVzZFmtqSCR5X1sTfw0zTW5/Ec96Xk+uE73junS/cCJ5rZKzmZ3sDlwO7AkvT76sAoM/tskumPZzgahMdl7IVXER4oqXO6xzBgPdyN5uxUaCw/libPGxiOx4Y0lCOBGQXl/1OY2VJJd+FzDAMgCIIgCIJ2RYsZAJJ2wSsBP4oXz1qCV3vdsIK23fHCVeDVcP8BbIYXnMozBC/+dDzua34J7jc+MCfTEzgf+CfQHVeqZ0lSYaf7K7jiPRZYCMyVNBR3MbkOz1O/NV6kqzjefYGJeAXdH+MK80+Am3EDpl4kbYsrp/cBJ+OVgG8A1i3I9QMexKscH4Ir9WOAeyRtl1xeOgBT8ArBx7CyEvDmZW5/dZrfMGCFpNVxo+Lzqe95wAHARElfN7NZVZz3pnimolllRDpKyn9vl5tZVs14J+B2SZfgRsTaePG4k8zsmUI/fwTGSFot1z4IgiAIgqDmackTgAvxSq0DUiYWcKWyEkbiivsWKWYA3Jgo0hEYbGbvAUjqCVwiad1sB9rMssq9mWvPDOB1fLd7Uq6v7sBOZjY/Jz8JmGlmh6VL90hajhsamUyH9P7WnByS/gY8ImnXVBW5Ps4AFgBDcgGuC3EFO8+FwJPA0CyFpqTZwAt4utLfpLntgD/7GUlmGuWrME81s9G5sQ/HjbXtc4r0NEkb4QbSwCrOO0v9+XSZz/9VeH8BfuIDfiozHD+dOBQ/ZRoL3CdpczPLxw48iZ8kbUmqmFwJqahGUEP06FFXXHqwqhHrWXvEmtYWsZ5tgxYxACStje/Onp5T/hvCbsD9OeW/HDMz5T/xfHr9PH7igKS98FOErYEuOdnibvjTBeW/I7A9vhufZyI5AwA/mdgYGFnYqZ6Nuxz1w3ef62MnYHIhSHUy8PF7SWsBu6YxdZSUfbQAV4L74QbAjsBbmfIPYGbvS7oTOKjEvacU3u8BPAPMKcxpGu7uA9Wbdy9gOX5KUYqBfDII+NXc76vh3+nBZvYvAEmPAS8CR+HuVBlZHEEvGmAARCXg2iKqUtYWsZ61R6xpbRHr2XLkKgGXpKVOALriytk/G9m+O164qj7eLLzPgnY7AUjaEVdub8d3zl/DFepZmUyO1wrve+DP643C9ddLyMEnTxPy1OvylOhV7Du58+QDYLvhpx4Xp58iL+b6Ko6bYv85Ss39y3ghtE8haV2qN+9OwId1FEJ7qlwQML7+L2fKP4CZLZD0V2CbgmxmKK5V4biCIAiCIAhqgpYyAJbgu7obNLL9oia0zbMfvht9YHYSkeIL1ighW1RA38CNhR6F6z0L7xen1+PweIci5ZTuIq8W+06nEN1zl5akcY6jRCxC+jzrqzhuiv3nKM59Me6Sc0QZ+aVUb96LgTUldSqc5lTCc7hbTymKBl7X9FrupCEIgiAIgqAmaREDwMyWSXoEOFTSRY1wA7oPGC1pQzN7qQlDWQvfxc4HfR5SScO0+/4EsC/ws9xHwwqifwVeBjY1swlNGOujwD6SRuXcgPbBg3yzMS2T9DCwlZmdWUdffwG6SOqfiwFYE9i7wrFMwzMDvWpmC0oJpF32asw7qxXQB5jTwLZ3AOdJ2sDM/pnG9QU8MLxY9KtPen2hsQMNgiAIgiBYFWnJIODT8MDd6ZIm4O4aOwALzay+lI+X4EGdD0o6F88CtAmwuZmd2oAxTMOz30yQNBH3jT+Sla5C9XEuMFnStXiWnK3wrDqQjAozWyHpJOBmSevgSuk7uAvMnsClZlZqh7zIONx/foqky/AsQGfhJxh5RgMPSLoVuAnfQd8AT1t6Z0rTeRfwOHCTpNNZmQWoaAyV43r8Oc2UNB43croA2wK9zOzYKs77z/hJS18abgBcjWeAulPSj/Ag4HPw+f6yILsjMKcOd6IgCIIgCIKaZLWWulHKANMfVzivw33F96N8Jpp820XALngQ6QXAVFzxfaWudiX6mQqcgqcLvR1XTIdSxre9RPspeJGqAXhA7oF4SlPIBaaa2aTU9xfxnec7cANoERVWPTazp4D98Z3q24Bj8bz1bxbk/oQ/mzWAa4C78RScq5GCW5M//T64QXFFknsEX4NPVNUtM5YP8EDsScCpePamq4BvADOrPO9l+PoOqkS+0PYdfG3m40bL9en3r5vZ4oL4IODWht4jCIIgCIJgVafDihWR0aQpSBqGK5J9zeyx1h5PpUhaDT8VeNnMhrT2ePJIGoyfsKxvZkubof8vAY8Bm5nZ3AqbbQzMjSxAtUVkpKgtYj1rj1jT2iLWs+XIZQHqQ4lN2BatBLyqI6kb7lLyAO6K82XgTODBtq78pwrGHXEf+87A4cB2wKjWHFcpzOwOSXOAEcD4+uQbwSjg+gYo/0EQBEEQBDVDqxsAaSe6Llekj+pICdnSfIjHHhyMZ5FZhLvFnNKQTlI2nw51iDTHnN/F3XE2xp/3s8D+Zja9yvcpSwPnPYKVRcGqPQYDfl3tvoMgCIIgCFYFWt0AAH6F+7aX4zA8ZqDVST7m1XCXmY77z5djAF6huGqY2Q3ADdXssxFUPO90olL1U5WUgeon1e43CIIgCIJgVaEtGABjgMty76cAD7GyamstumkcjbsR5eeZx0pcWyWRNA7Y2syG4vPOaoBvjWd2+hKwLl6z4AeSOuZPJdKO/ZF4DYKtgI+Ap4Cfm9nvC/faHxiJp/38LF4R+Tbgx2b2Vk7uAeA+MwtDIAiCIAiCdkerGwBmNo9ccIKkD4A3zGx2a42puTEzaw/zlLQ+cAKeQQgzs3T9COAXuAF0EvAS8Dm8psI0SV3N7K3kHnYzXnvhcuAM/Dt7EJ5utK+Z5d2vugEP4lWRF+MxDmPS6x45ufOA30u63MyK1aODIAiCIAhqmlY3AFqStJvcMaW1bFdIWsvM3m3h2x4NvJhSlWbj2AZPRfo74H8LsQ63SLqalWlZv48bBceY2ZU5ubskLQDOkPSAmd0FYGbFXP8zJL0HXJUvDoYXlnsbdz37GUEQBEEQBO2IRhkAknYGxgI74UGdzwNnmdm0Ctr2xotc7Ylno5kPXGFmlxbkRuBBq92AWcBRZvZy7vPzgMF4UO7bwMPADwoyM4ClwG+BHwKb4n7mD6WsOKcD6+PFp47B8+afbGbjc318Czgbz/jzHvAHYKSZLangUWV97I27+myCP6uTysj1w4uN7Zwu3QucaGav5GR647vhu+NuM5fj1YFHmdlnk0x/3MVoEK7k7oVXAx4oqXO6xzBgPdzd6OxUMCw/libPGxiOx3jkORFYAZxUKtC5EJT8A+DveIGvIufjBsJIvNBZObJCX2vk7rEiFU4LAyAIgiAIgnZHgwuBSdoFD9TshBfBGoYXxdqwgrbd8QJU/fGqtnvjVX57F0SHAAfgVV1H4BWDry3I9MSVwMFJ7nPALElrFeS+gqfqHIsrwnMlDcULWc3A3Usm477ixfHuC9yJK8nDcOV1IO6WUhGStsWV5/mpjyvxYNyuBbl+uPvK+8AhuPK8OXBPOrlAUgc8RqIfbrAchT/Lw8rc/mq8WNowYJyk1XGjYn/cNWYwvh4T07pWc96b4hmHZhU+GgDMNrM36mn/Bdxguj0F7n6CFJD9ALCrpM8U2naU1EnSV3AjZkpyNcvzR+DLknpUOqcgCIIgCIJaoDEnABfiu7IDcorZvRW2HYkr7lvkFLL7S8h1BAab2XsAknoCl0haN9uBNrOsAm/m2jMDeB1X8ifl+uoO7GRm83Pyk4CZZpYpzvdIWo4bI5lMh/T+1pwckv4GPCJp11TduD7OwINRh5jZf1IfC4GJBbkLgSeBodnOuKTZwAt42tHfpLntgD/7GUlmGuWrKU81s9G5sQ8H+gLbm9kz6fI0SRvhBtLAKs47S+H5dOF6b/w0oj4yo7CuStHzcUO0O/Ba7voioEv6/W7gOyXaPple++HGTkWkohpBDdGjx3/VLxSsMsR61h6xprVFrGfboEEGgKS1cbef00vtylbAbsD9JXZji8zMlP/E8+n187jbC5L2wk8Rtmalsge+a57n6YLy3xHYHji5IDeRnAEAbIbvYI8s7DDPxl2O+uG7yPWxEzA5U/4Tk4GP36dTi13TmDpKyj5agO/C98MNgB2BtzLlH8DM3pd0Jx4YW2RK4f0ewDPAnMKcpuE75VC9efcCluPKeJHmruvQH1gb2Ab/jtwuaffCdzZzDerVkI6jEnBtEVUpa4tYz9oj1rS2iPVsOXKVgEvS0BOArrjb0D/rEyxDd7wAVX0UM7NkQbudACTtiCu3t+M756/hCvWsTCbHa4X3PfB5F11QXi8hB588TchTr8tTolexbzP7KJ0CZHTDTz0uTj9FXsz1Vcp1pjj2jFJz/zIrg2w/gaR1qd68OwEflvDzX1BhHwvS60Z1yGyExyd8wsgws2x3/2FJj+HGy37ArTmxzMAsuowFQRAEQRDUNA01AJbgu7obNPJ+i5rQNs9++G70gdmuboovWKOEbFEBfQM3Foq+3z0L7xen1+OAR0v0W07pLvJqse90CtE9d2lJGuc4SsQipM+zvkr5rBfHnlGc+2LcJeeIMvJLqd68FwNrSupUOM25H/iepO5mVup0AAAze1nSi8Dekkab2fL855I+i+/0/7FwulLkSfw7u2nhehaDUXYMQRAEQRAEtUiDDAAzWybpEeBQSRc1wg3oPmC0pA3N7KUGts2zFr6LnVcKD6mkYdp9fwIP/s1ngBlWEP0r8DKwqZlNaMJYHwX2kTQqp6jug2fuyca0TNLDwFZmdmYdff0F6CKpfy4GYE08mLoSpuGZgV41swWlBCRVa95ZMbM+wJzc9UvxAOdLJH2veEIgaQDwqJn9G3fJ+jkebP6LQv+n4Ep8qROTPDvjp1YvFq73KYwzCIIgCIKgXdCYIODT8F3c6ZIm4O46OwALzayY8rHIJXj11wclnQv8A8/0srmZndqAMUzDU2lOkDQR940/kpWuQvVxLjBZ0rV4Pvqt8Kw6kIyKlCryJLzg1DrAHcA7uPvKnsClZlZqh7zIONwFZYqky/Dg1rPwE4w8o4EHUnrKm/Ad9A3wrDl3pjSddwGPAzdJOh3fvT6JTxtD5bgef04zJY3HjZwuwLZALzM7torz/jN+0tKXnAFgZs9JOgZX6L8g6Zd4IbCe+MnOwbhLFHiK0/74Om+VxvIZ4H/wzEc/zWoAAEi6B5iOp3N9D3d3Ohk/9fhEmlP8O7OUlcHAQRAEQRAE7YIGpwFNGWD64wrndbiv+H7Una0la7sI2AUP+dvBYwAAIABJREFUIr0AmIorvq/U1a5EP1PxHeAheBzAnsBQyvi2l2g/BS9SNQAPyD0Q32UGeCsnNyn1/UXgRlwBPQ1XvOdVeK+n8LSbfXD3nmPx/PNvFuT+hD+bNYBr8Ow1P8bX6LkkswI/PZiNF9O6Bk/jOSk/7jrG8gEeiD0JOBXP3nQV8A1gZpXnvQxf30ElPrsmzXUxvoN/PzABjxvYzczeSnLL8eDm4/Eg6cnALXig8rcLVYDBjY7/xY26P+AnDVcCXytR/G0QcFsjg9mDIAiCIAhWWTqsWBEZTQAkDcODRPua2WOtPZ5KkbQafirwspkNae3x5JE0GFfG1zezpa09ngxJ6+GB7APN7MEKm20MzI0sQLVFZKSoLWI9a49Y09oi1rPlyGUB6kOJzdtGVQJe1ZHUDTgHLyT1Nu4qcibwYFtX/lMF446473pn4HBgO2BUa46rFGZ2h6Q5eDG38fXJtyAn4MHDlSr/QRAEQRAENUPVDIC0E12XS9FHJVJCthYf4rEHB+OBpItwt5iiS0mdpGw+HeoQaY45v4u742yMP+9ngf3NbHqV71OWBs57BCuLgrUVFuJGQBAEQRAEQbujmicAv8J928txGB4z0OqY2Tt4/EBTmY77z5djAF6huGqY2Q3ADdXssxFUPO90otKmTlXM7P9aewxBEARBEAStRTUNgDHAZXV8PreK92owkuYBd5jZcVXs9migrprWLZ5ispnmWaTV5y1pHLC1mQ1N76/EA4a3NLN/5eTWw7Md3W5mh6XTCwPOMbMbm3ucQRAEQRAEbY2qGQBmNo8KM8TUCmbWLnPIt/a8Ja2Pu/Dslrt8GitrO3w7d/0ivCDaaPi4DsQFwFhJN9dTRCwIgiAIgqDmaHAa0ODTSOooqVQV4ppH0lqtcNujgRdT6lQAzGwJMBI4SNJeaWzfxOtOjC5UHf4tXmNhcMsNOQiCIAiCoG3QprIASdoZGAvshAeZPg+cZWbTKmjbGy+6tSeeHWc+cIWZXVqQG4HvFncDZgFHmdnLuc/PwxXDTfAMQQ8DPyjIzMCLSP0W+CGwKe73/lDK0nM6sD6el/4YPI//yWY2PtfHt4Cz8QxE7+F560cmRbYiJO0N/DSN9Xm8KFgpuX548bOd06V7gRPN7JWcTG+88NbuwJL0++rAKDP7bJLpj2dOGoTHe+yFVyceKKlzuscwYD3czebsVMAsP5YmzxvP7/+ponNmdpOk4cDlkvriNQDuN7NfF+SWSrorzaFYICwIgiAIgqCmaTMnAJJ2wQNHO+FFuYbhhZ82rKBtd7wgVn+8yu7eeNXh3gXRIcABeGGpEXgF42sLMj2B83Ej4Hjgc8CsEjvdX8FTh47FFeG5kobihbVm4O4ok/HiX8Xx7gvciSvJw4ATgYHAzfXNNdfHtrjyOj/1cSUeHNy1INcPeBB4HzgEV543B+5J/vBI6gBMAfrhBstR+LM8rMztr8aLtw0DxklaHTcq9sdjQQbj6zExrWs1570pngFpVhmRY3Djazb+3RlRRu6PwDdT9qogCIIgCIJ2Q1s6AbgQ+DswIFed9d4K247EFfctUiwCeHXZIh2BwWb2HoCknsAlktbNdqDNLKsInKW7nAG8jiv5k3J9dQd2MrP5OflJwEwzyxTneyQtx42RTKZDen9rTg5JfwMekbRrqrZcH2cAC4AhmR+7pIXAxILchcCTwNAsNaek2cALeBrU36S57YA/+xlJZhrlqztPNbPRubEPB/oC25vZM+nyNEkb4QbSwCrOO0sp+nSpD83sH5J+BXwfGGdmfyvTz5P4SdGWpErLlZCKagQ1RI8edcWzB6sasZ61R6xpbRHr2TZoEwaApLVxt5/Tc8p/Q9gNd/WYV4/czEz5TzyfXj+Pu72Q/MfPArYGuuRkNy/09XRB+e8IbA+cXJCbSM4AADbDd7BHSso//9m4y1E/fHe6PnYCJheCWCcDH79Ppxa7pjF1lJR9tADfhe+HGwA7Am9lyj+Amb0v6U48s06RKYX3ewDPAHMKc5qGu/tA9ebdC1iO1274FKnI24F44O83JHUoU4thYa6/ig2AqARcW0RVytoi1rP2iDWtLWI9W45cJeDSn7fgWOqiKz6WfzayffcK275ZeP9Beu0EIGlHXLl9DfcP3xlXjj/IZHK8VnjfAzeo3ihcf72EHPhpwoeFn85U4PKU6FXsOxlPC3OXuuGnHheXuNe2uXv1KjHuUmPPKDX3L5e4x3igs6R1qd68OwEf1lFg7af4nA/A1+//lZHLDMHWCGIOgiAIgiBoNdrECQC++74cz8zSGBY1oW2e/fDd6AOzk4gUX1Aqw09RAX0D333vUbjes/B+cXo9Dni0RL/llO4irxb7TqcQ3XOXlqRxjqNELEL6POurOG6K/ecozn0x7pJzRBn5pVRv3ouBNSV1KpzmIOlreNzCkWY2SdJ1wPmS/mBmRQMni5UoeZIQBEEQBEFQq7QJA8DMlkl6BDhU0kWNcAO6DxgtaUMze6kJQ1kL35Fenrt2SCUNU375J1iZiz5jWEH0r8DLwKZmNqEJY30U2EfSqJwb0D545p5sTMskPQxsZWZn1tHXX4AukvrnYgDWxIOpK2EanhnoVTNbUEpAUrXmndUg6APMyfW/Bh6APYuVGYJOBobiJxHFKtV90usLTRhLEARBEATBKkebMAASp+GBu9MlTcDddXYAFprZp1I+FrgEz/f+oKRzgX/gqTE3N7NTGzCGaXgqzQmSJuLuP0ey0lWoPs4FJku6FvgdsBWelQaSUWFmKySdBNwsaR3gDuAd3AVmT+BSMyu1Q15kHO4/P0XSZXjGo7PwE4w8o4EHJN0K3ITvoG+Apy29M6XpvAt4HLhJ0un4rvhJfNoYKsf1+HOaKWk8buR0wd2MepnZsVWc95/xk5a+5AwA4FQ8HeuBmXuQmS2SdApwjaRfmdnMnPyOwBwzy7tMBUEQBEEQ1DxtJQaAlAGmP65wXof7iu9H+Uw0+baLgF3wINILgKm44vtKXe1K9DMVOAVPF3o7rpgOxRXhStpPwYtUDcADcg9kpQ/6Wzm5SanvLwI34srwabjiPa/Cez2Fp93sg7v3HIvvcr9ZkPsT/mzWAK4B7gZ+jK/9c0lmBX56MBu4Isk9gq/BW9SDmX2AB2JPwhXxe/Hd+G8AM3Ny1Zj3Mnx9B2XXUmrQM4CLzKwY0Hst8BBwRaFY2yDg1kruGQRBEARBUEt0WLEiMpo0J5KG4YpmXzN7rLXHUykpP/7jwMtmNqS1x5NH0mD8hGV9M1vaiPZfAh4DNjOzuRU22xiYG1mAaovISFFbxHrWHrGmtUWsZ8uRywLUhxKbrG3JBWiVJ6WgPAevlvs2nhnnTODBtq78pwrGHXEf+87A4cB2wKjWHFcpzOwOSXPwIl/j65MvwSjg+gYo/0EQBEEQBDVDmzcA0k50Xa5KH9WRErKl+RCPPTgYzzKzCHeLOaUhnaRsPh3qEGmOOb+Lu+NsjD/vZ4H9zWx6le9TlgbOewQri4I19B4G/LrhIwyCIAiCIFj1afMGAJ7RpZjBJc9heMxAq2Nm7+DxA01lOu4/X44BeIXiqmFmNwA3VLPPRlDxvNOJSoNPVVKGqZ80ZnBBEARBEAS1QJs1ACTNw4NExwCX1SG6SrtxZPM0s+Nyl48G6qqVbXV81iaRNA7Y2syGpjlvVE+TO4CxuFvSfnhK0ikpkHcucCce9PuqpK/iAeDfN7MrC/e9LrXd0swWSjoH2NXMdq/a5IIgCIIgCFYh2qwBkGFm86gwQ0ytYGarnIJfF5LWB07AMwWBK/Rr5kRuwxX4i3LX3sDrBtyOZ4e6Cs9g9G9gG9wF6IvAfmb2sKSrgXGp6Ne/0n2/iZ8eDc+l+/w5XjNigJk9UO25BkEQBEEQtHXavAHQHCQ/8I4pfWW7QtJaZvZuC9/2aODFlJIUM3uiMKb3gdeyz3PXzwN2BwaZ2d25j2ZIugJPKZpxGp7K9FLgIEmdgCuBB8zsY39/M1ss6TbgRDxYOwiCIAiCoF3RJANA0s64m8ZOePDm88BZZjatgra98WJWe+JZZ+YDV5jZpQW5Ebhy1w2v8nqUmb2c+/w8YDAefPs28DDwg4LMDGAp8Fvgh3jBqAHAQyn7zenA+niRqWPw/Pgnm9n4XB/fAs7GM/u8B/wBGGlmSyp4VFkfewM/TWN9Hi+2VUquH15UbOd06V7gRDN7JSfTG7gcV5CXpN9XB0aZ2WeTTH9cyR2E74TvhVf9HSipc7rHMGA93K3o7FQYLD+WJs8bGM7K6rwVkRT444DJBeUf+NiXf2ru/ZuSRgI3Svo1/uw2xL8bRW4BJklaLwqBBUEQBEHQ3mh0ITBJu+ABmZ3wYlfD8OJXG1bQtjteaKo/Xr12b7yab++C6BDgAOB43OVjB7ywU56ewPm4onc88DlglqS1CnJfwVNyjsUV4bmShuKuJTOAfdP4bysx3n1xn3NL8zwRGAjcXN9cc31siyvP81MfV+JBt10Lcv2AB4H3gUNw5Xlz4J50coGkDsAUoB9usByFP8vDytz+arwo2jDcTWZ13KjYH4+xGIyvx8S0rtWc96Z4ZqFZlbZJ9MXjIKbWJ5hhZjfh1ZyvxjMvnWdmL5QQnYUbv/0bOKYgCIIgCIJVnqacAFwI/B0YkHZjwZXKShiJK+5bJB9/gPtLyHUEBpvZewCSegKXSFo324E2s6zSbubaMwN4HVfyJ+X66g7sZGbzc/KTgJlmlinO90hajhsjmUyH9P7WnByS/gY8ImnXVMW4Ps4AFgBDzOw/qY+FwMSC3IXAk8DQLOWlpNnAC3h60d+kue2AP/sZSWYa5asmTzWz0bmxD8cV7O3N7Jl0eZqkjXADaWAV552l6ny6Atk8mTH4UgPbnYmf5MzHDcNPkdyAXgL+mwZWA05FNYIaokePuuLtg1WNWM/aI9a0toj1bBs0ygCQtDbu9nN6TvlvCLsB9+eU/3LMzJT/xPPp9fO42wuS9sJPEbYGuuRkNy/09XRB+e8IbA+cXJCbSM4AADbDd7BHSso/r9m4y1E/PIC1PnbC3Vn+k7s2Gfj4fTq12DWNqaOk7KMF+C58P9wA2BF4K1P+AczsfUl3AgeVuPeUwvs9gGeAOYU5TcPdfaB68+4FLMdrIjSGhtY7GJHabIB/B54tI7cwja1BRCXg2iKqUtYWsZ61R6xpbRHr2XLkKgGX/ryR/XZNbf/ZyPbdK2z7ZuF9FrTbCUDSjrhy+xru474zrhx/kMnkeK3wvgduAL1RuP56CTnw04QPCz+dqcDlKdGr2HcynvI+6N3wU4+LS9xr29y9epUYd6mxZ5Sa+5dL3GM80FnSulRv3p2ADxtRuGxBeq30Pkj6Gu4G9X38dOqKdJJRiveAoptYEARBEARBzdNYF6Al+K7uBo1sv6gJbfPsh+9GH5idRKT4gjVKyBYV0Dfw3fcehes9C+8Xp9fjgEdL9FtO6S7yarHvdArRPXdpSRrnOErEIqTPs76K46bYf47i3BfjLjlHlJFfSvXmvRhYU1KnwmlOfcwG3sHdnX5Zn3CqD3AVfipxFTAHdwc7HLimRJOueLB3EARBEARBu6JRBoCZLZP0CHCopIsa4QZ0H56LfUMza6iPd5618B3p5blrh1TS0Mw+kvQEHvz7s9xHwwqif8Xz0W9qZhOaMNZHgX0kjcq5Ae2DZ+7JxrRM0sPAVmZ2Zh19/QXoIql/LgZgTTyYuhKm4ZmBXjWzBaUEJFVr3llNgz64Ul5ZI7P3JF0GnCpp92JmKUmrAXua2V3p0ql4dqcD0mnDzJQN6AJJk/PZfpLhtSGrYEG1IAiCIAiCptKUIODT8MDd6ZIm4O46OwALzay+lI+XAIcCD0o6F/gHnhpzczM7tQFjmIan0pwgaSLu/nMkK12F6uNcYLKka4HfAVvhWXUgGRVmtkLSScDNktbBK9S+gyuQewKXmlmpHfIi4/Bd7SlJse2Nxy68XZAbDTwg6VbgJnwHfQM8bemdKU3nXcDjwE2STsdPVE7i08ZQOa7Hn9NMSeNxI6cL7mbUy8yOreK8/4yftPSlAQZAYkxqd7ukK4F78EJgW+G+/i8Cd0naDA+y/qmZPZ9rPxrPJPVTPpkhaUtgHeChBo4nCIIgCIJglafRaUBTBpj+uMJ5He4rvh/lM9Hk2y4CdsHdNS7AUz2OxlNVNmQMU/F0j0PwirF7AkNxRbiS9lPwIlUD8IDcA/GUpgBv5eQmpb6/CNyIK8On4Yr3vArv9RSedrMP7t5zLB638GZB7k/4s1kDd125G/gxvlbPJZkV+OnBbOCKJPcIvgZvUQ+pANpuSf5UPHvTVcA3gJlVnvcyfH0HVSJfYpyDgFF4fMctaawn4MZAZqxdjseUnFtovxD/fnwvxQdkDMK/p39p6JiCIAiCIAhWdTqsWBEZTfJIGoanhuxrZo+19ngqJbnEPA68bGZDWns8eSQNxk9Y1jezpW1gPE8At5nZjxrQbGNgbmQBqi0iI0VtEetZe8Sa1haxni1HLgtQH0ps2japEvCqjqRuwDl4tdy38cw4ZwIPtnXlP1Uw7oj7sXfGg123w3fL2xRmdoekObjbzvj65JsTSd8ENgL+rzXHEQRBEARB0FpU3QBIO9F1uRZ91IiUkM3Fh3jswcF4VphFuFvMKQ3pJAWVlks3Cc0z53dxd5yN8ef9LLC/mU2v8n3K0sB5j2BlUbDW5LPAoVkhuSAIgiAIgvZGc5wA/Ar3bS/HYXjMQKtjZu/g8QNNZTruP1+OAXhKyqphZjcAN1Szz0ZQ8bzTiUqrn6qkuI8gCIIgCIJ2S3MYAGOAy+r4fG41byZpHnCHmR1XzX4byNFAXbWtm5xuso3Ms0jV5y1pHLC1mQ0tXP9vvELyrnjthEXALDwA+B9U9r06DM/88wyeanVeQ8cXBEEQBEGwqlN1AyApVfOq3W9bxszaZT75as9b0vp4hp/dCtePAH6BK++jgZeAz+E1G6bh7ls755r0wl25zsDjOzL+YWZvSLoNGEvdJ1VBEARBEAQ1SbsOAq6P5OPeMaWjbFdIWsvM3m3h2x4NvJhSoWbj2AZPdfo74H8LsRS3SLoa+LDQZuP069/y13NcA9wtabSZvVHtSQRBEARBELRlWsQAkLQzvuO6Ex40+jxwVrG6a5m2vfEiWnvi2W7mA1eY2aUFuRF4UGw33DXkKDN7Off5ecBgPOj3beBh4AcFmRnAUuC3wA/xyrIDgIdS1p3TgfXx4lbH4Hn5Tzaz8bk+vgWcjWcUeg/4AzCyIUGnkvbGi1dtgj+rk8rI9cNz32e73/cCJ5rZKzmZ3ribzO7AkvT76sAoM/tskumP75QPwnfF98Jz5A+U1DndYxiwHu7Wc3YqSJYfS5PnDQzHY0jynAisAE4qFUjdyKDnmfiz+A5waT2yQRAEQRAENUWjC4FViqRd8EDQTniRrWF40a0NK2jbHS9w1R+vmrs3XkW4d0F0CHAAcDyebWYH4NqCTE/gfNwIOB53IZklaa2C3FfwVKBjcUV4rqSheKGsGcC+afy3lRjvvsCduJI8DFdeBwI31zfXXB/b4srz/NTHlXiwb9eCXD/gQeB94BBced4cuCedXCCpAzAF6IcbLEfhzzJfFTfP1XgxtmHAOEmr40bF/nhsx2B8PSamda3mvDfFMxrNKnw0AJhdzZ16M/sIn8fu1eozCIIgCIJgVaElTgAuBP4ODEiKF7hSWQkjccV9i1zA5v0l5DoCg83sPQBJPYFLJK2b7UCbWVbhN3PtmQG8jiv5k3J9dQd2MrP5OflJwEwzyxTneyQtx42RTKZDen9rTg5JfwMekbRrqp5cH2cAC4AhZvaf1MdCYGJB7kLgSWBotjMuaTbwAp7W9Ddpbjvgz35GkplG+WrNU81sdG7sw4G+wPZm9ky6PE3SRriBNLCK885ShD5duN6b5qnY+yTw/YY2SkU1ghqiR4+64tiDVY1Yz9oj1rS2iPVsGzSrASBpbdzt5/Sc8t8QdgPuryBby8xM+U88n14/j7t6IGkv/BRha6BLTnbzQl9PF5T/jsD2eAaaPBPJGQDAZvgO9khJ+ec6G3c56gdUogjvBEzOlP/EZODj9+nUYtc0po6Sso8W4Lvw/XADYEfgrUz5BzCz9yXdCRxU4t7FFJl74Blz5hTmNA1394HqzbsXsBzP7lOkOepGLATWk7S6mX1YaaOoBFxbRFXK2iLWs/aINa0tYj1bjlwl4NKfN/P9u6Z7/LOR7btX2PbNwvssaLcTgKQdceX2NdzHfWdcOf4gk8nxWuF9D9xQKrqgvF5CDvw04cPCT2cqcHlK9Cr2nYynhblL3fBTj4tL3Gvb3L16lRh3qbFnlJr7l0vcYzzQWdK6VG/enfBg3qJ2vaABfTSEzGAsrn8QBEEQBEFN09wuQEvwXd0NGtl+URPa5tkP340+MDuJSPEFa5SQLSqgb+C77z0K13sW3i9Or8cBj5bot5zSXeTVYt/pFKJ77tKSNM5xlIhFSJ9nfRXHTbH/HMW5L8Zdco4oI7+U6s17MbCmpE6F05z7ge9J6m5mpU4HGktX4INUDC4IgiAIgqDd0KwGgJktk/QIcKikixrhBnQfMFrShmb2UhOGsha+I708d+2QShqa2UeSnsCDf3+W+2hYQfSvwMvApmY2oQljfRTYR9KonBvQPnjmnmxMyyQ9jBezOrOOvv4CdJHUPxcDsCYeTF0J0/DMQK+a2YJSApKqNe+spkAfYE7u+qV4gPMlkr5XPCGQNAB41Mz+3cD79cHjJYIgCIIgCNoVLREEfBq+iztd0gTcXWcHYKGZFVM+FrkEOBR4UNK5eMXXTYDNzezUBoxhGp5Kc4Kkibj7z5GsdBWqj3OByZKuxfPRb4Vn1YFkVJjZCkknATdLWge4A3gHd1/ZE7jUzErtkBcZh/vPT5F0GR4EexZ+gpFnNPCApFuBm/Ad9A3wrDl3pjSddwGPAzdJOh0/UTmJTxtD5bgef04zJY3HjZwuuJtRLzM7torz/jN+0tKXnAFgZs9JOgYvBPYFSb/EC4H1xE92DsZdohrKjnhhsSAIgiAIgnZFs6cBTRlg+uMK53W4r/h+lM9Ek2+7CNgFDyK9AJiKK76v1NWuRD9TgVPwdKG344rpUFwRrqT9FLxI1QA8IPdAPKUpwFs5uUmp7y8CN+LK8Gm44j2vwns9hafd7IO79xyLxy28WZD7E/5s1iAVtgJ+jK/pc0lmBX56MBsvpnUNnv5yUn7cdYzlAzwQexJwKp696SrgG3gu/WrOexm+voNKfHZNmutiPO7hfmAC7r+/m5nVO5c8knrhgd23NqRdEARBEARBLdBhxYrIaNIYJA3DFci+ZvZYa4+nUiSthp8KvGxmQ1p7PHkkDcZPWNY3s6XNeJ8T8VoQm5UqLlaGjYG5kQWotoiMFLVFrGftEWtaW8R6thy5LEB9KLEZ2yKVgFd1JHUDzsGr5b6NZ8Y5E3iwrSv/qYJxR9zHvjNwOLAdMKo1x1UKM7tD0hy8mNv4+uQbQwqoPhEY0wDlPwiCIAiCoGZoNQMg7UTX5YL0URtS0D7EYw8OxrPHLMLdYk5pSCdJ+exQh0hzzPld3B1nY/x5Pwvsb2bTq3yfsjRw3iNYWRSsOegN/BKvkxAEQRAEQdDuaM0TgF/hvu3lOAyPGWh1UqrIarjLTMf958sxAK9QXDXM7Abghmr22Qgqnnc6UWm2U5WUTeq85uo/CIIgCIKgrdOaBsAY4LI6Pp/bQuNoSY4G6qqBbXV8tirTXucdBEEQBEHQ5mg1A8DM5lFhhphawczapaLbXucdBEEQBEHQFmn2NKBBEARBEARBELQdwgAIgiAIgiAIgnZEGABBEARBEARB0I4IAyAIgiAIgiAI2hFhAARBEARBEARBOyIMgCAIgiAIgiBoR4QBEARBEARBEATtiDAAgiAIgiAIgqAdEQZAEARBEARBELQjwgAIgiAIgiAIgnbEZ1p7AEGwitARYLXVOrT2OIIqE2taW8R61h6xprVFrGfLkHvOHUt93mHFihUtN5ogWHXZFXiotQcRBEEQBEHQAL4G/LF4MQyAIKiMNYEdgVeBj1p5LEEQBEEQBHXREegF/AV4v/hhGABBEARBEARB0I6IIOAgCIIgCIIgaEeEARAEQRAEQRAE7YgwAIIgCIIgCIKgHREGQBAEQRAEQRC0I8IACIIgCIIgCIJ2RBgAQRAEQRAEQdCOCAMgCIIgCIIgCNoRYQAEQRAEQRAEQTviM609gCBobSRtBvwc2BV4F/gdcKqZ/buCtocCZwAbA/8AfmRmNzffaIP6aOx6SjoI+B/gv4HewMlmNr6ZhxtUQGPWVFJnYCSwFyDgQ+Ax4Awze7zZBx2UpQl/oxOAbwKfB1YAfwUuNrPfNe+Ig/poyv+juT72AyYBz5nZNs0y0OBj4gQgaNdIWhd4APgv4ABgFHAw8KsK2h4A/Bq4DVcy7gN+K2mvZhtwUCdNWc8kvwlwR7MNMGgwTVjTDYGj8b/Lg4DDgI7Aw5J2aLYBB3XSxL/RtYErUrv/AZ7B/839TvOMNqiEJq5p1sfawM+A15pjjMGniROAoL1zNNAV+LKZLQSQ9B/gRkk/NrPn6mj7Y+AWMzs9vX9A0pbAWOCu5hx0UJamrOdBZrY8tTm6+YcaVEhj13Qu8MX8DqSk+4AXgeNxgyBoeRr9N2pmxTW7O/2bOxy4qZnGG9RPU/7dzfgh/rc5H+jbbCMNPiZOAIL2ziBgevaPVmIi8D6+q18SSX2ALfBjzjw3ATtK6lHtgQYV0aj1BMiU/6DN0ag1NbNlRfcDM3sPmANs0BwDDSqi0X+jZVgIrFGNgQWNpklrKmkL4ATcMA9aiDAAgvbOlsDz+Qtm9j7uz79FPe0otgWynQ5VZXRBQ2nsegZtl6qtqaR1gO1xIyBoHZq0npI6SPqMpK4pBmsP4LJmGWlQKU2Xoi62AAAPxUlEQVT9G50A/NLMnm2GsQVlCBegoL3TFVhS4vqbQLd62lGi7Zvpta62QfPR2PUM2i7VXNNzcT/yUBhbj6au5z543BXAf4DjzOzWKo0taByNXlNJ3wa2BYY1w7iCOogTgCAIgqDmSYGiJwEjzezvrT2eoNHMAHYEdgf+D7hM0hGtOqKgUUj6L+AiPDNXKQMiaEbiBCBo77wJrFvielc8xVxd7Uht/1VoB7C46UMLGkFj1zNouzR5TSXtDlwL/NTMLq/i2IKG06T1TIri7PT2PklrABdLus7MPqreMIMG0Ng1PRP/v3JSyiQEHs+xWnr/bnIlCpqBOAEI2jtzWOnPD4CkNYEvUvc/XJkP8ZaF61ulV6vK6IKG0tj1DNouTVpTSf3w3OK/B05tjgEGDaLaf6OPAZ2BSLzQejR2TbcAtgEW4UbEm3j60C3T78c0x2ADJwyAoL0zFdhNUvfctf2ANdNnJTGzufg/bAcVPjoY+IuZvVHtgQYV0aj1DNo0jV7TlCJyKjALONzMVjTbKINKqfbf6K7A23g2oKB1aOyangUMKPzcA8xLv0dsRzPSYcWK+PcwaL+kY8Zn8X9wfgz0BC7GU5p9Oyd3DfA9M/tM7tqBwM3AOGAaHpx2IrC3mUUdgFagieu5FStPcG4BrgduB4ggw9ajsWsqqSfuKvIZ4LvAsly375vZEy0ygeATNGE9vwaMxgOA5+O7/kOAI4DTzOyCFpxGkKMp/+6W6Os6oG9UAm5+4gQgaNckf9JvAktxN4FLcKX+8IJox/STb3sLXkzoAHzXYk/gO6H8tx5NWU+8sugt6Qfg0ML7oBVowppuBXwB6IVXA34k93MbQavQhPV8GfgAVzDvBq7CXUz2DeW/dWniv7tBKxEnAEEQBEEQBEHQjogTgCAIgiAIgiBoR4QBEARBEARBEATtiDAAgiAIgiAIgqAdEQZAEARBEARBELQjwgAIgiAIgiAIgnZEGABBEARBEARB0I4IAyAIgqCGkDRGUuR3DoIgCMpSthpbEARBUB5Jw4Frc5c+Al7Dq0KfaWYLWmNcbQ1JGwNzy3z8XFus+ClpENDPzMZUKD8GOKfMxyeb2fgqDS1/z68CewA/S4WY2hTJCL3KzEa09lgag6QNgKOAP5jZk609niCoNmEABEEQNI0xwD+ATsBOwHBgV0nbmNl7rTiutsbNwB2Fa21OcU0MAo7F17YhHAe8Vbj2WDUGVIKv4kbHdbTd57gqswH+fOcBYQAENUcYAEEQBE3jHjP7U/r9l5IWAqcCQ4Hft96w2hxPmtlvqt2ppDWA5Wb2n2r33Qgmmtm/WnsQTUHSOma2rLXH0Vpk36fWHkcQNDdhAARBEFSXh3AD4IvZhaRUnInvLG+KnxY8A5xnZn/IN85cJ4C7gXOBzYC/A6PM7O6C7K7AJcC2wALgwlIDktQROA04DPgC7qr0O+AcM3s3JzcP+CtwPjAe2Bo/3TjBzO6XtC/wI2Bz4HngSDOryg53chW6ABgIrA08C/wk/3wk9QceAP4Xfy6HA73xZz1P0ub4M9sNWAeYk/q4NdfHZ9KzODQ9i3eBvwEXmNkkSdcB30uy+ViKPmY2r4lz/A7wA2Ab4D3gPuAUM5ubk/kafpKwE7A+sBg/OTnVzBYnmTGsdDmaKylrPsDMZqRxjy26MKX1nWFmw9P74bgb227AvsBBQE+gQ/p8R2AssAuwBn6a8UMze6ARc++Pr90h+NodBXQB7gWOAJbh37tD8LWbCIwofD+zv40H0vw3ASw9m+LfxsY0/vt0BCvd+66VlP0+1szGSNoOX8evJ/mlrFzLl3L9D0/99Af2Ab6bxnIvcJSZvVEY8+7A6UBffA1eAK4ws1/mZKq2JkH7JgyAIAiC6rJxen0zd60zcDSudF+LGwDfAW6TNMjM7ir0sTMwBLgCeAc4AZgoaUMzWwQgaVtckXgDd1XpiCtFb/BprsKVmknAxbiCcTKwjaS9zSyv6G4C/Bb4BXBDkpsi6ShcQbsc3yE9A7hF0mZm9lEFz2VtSesVrr1lZh9K6gk8DPwX8H9pDv8LTJJ0iJn9ttDujDSGS3FFaamkLVMf/8INoaXAfmmM382dPpyDG2PXAH/Glc3tgX7p+VyFu3/sjitsGaWea5FukvInEctzSvtpwHnArfh3oCuu6M+S9KWcMnggrhj/Angd2A74f/hafTWt1STcCDsYV0QXprZzKhhjKX6Of19/ku6NpG8A9+DuLz8CPsSfx72SdjezGY281ym48XM+bgwfj8fP/Bv4HK7c/jduoM0Hzi603wX4H/x78g5uSNwuaYCZ/TGNvanfp7vSfX+Er8NDSe7p9Lo7IOB64J+4AToC6Jdc//5d6P9nuCE3Fv/34STgMtzgIo35u8Cv8TW8EFiEr/3ewC+TTHOtSdAOCQMgCIKgaXRJim0nXHE5B3ifT/q7vwlsZGbvZxckXQY8DozCFY48WwJbmdnfk+wDwFO4wndZkvkRnsnta9muo6RbgOfyHaXdyiOA68zssNz1l9JY9y6MdTPg62b2UJKbgysdvwK2zHarJS3BleUB+O5nffww/eQZku59GtCLtIOd+r8K3928WNKtZvZhrt1/pbF87Koi6SZcGeub2zWeIOle4HxJNybleTAw1cyOLDVIM3tE0gvA7o1wWXqu8H4RsJ6kDYEfA2PM7Ee5Mf8utfkBroQCnFZUICU9AtyIK79/NLOnJT2Ofx/+0NSTCdxY6p+5UUnqgK/tH/HnsCJdvxJ4AjdkvtrIe62BB1h/kPrsAXwbD57/VrrX5ZKyHfmiAbAN8FUzeyS1vw4/wTkf2DXJVOP7dBf+N/ZIie/BFWZ2Uf6CpCnALGB/oCi/iE8+x9WAEyR1MbO3JHXG/64fx/+e86ceHXKvzbUmQTsk0oAGQRA0jbvxHcaX8d3dpcBQM3slEzCzjzLlX9IakrrhpwIPAl8p0ecDmfKf2j8NvI3vzmcuPXsCU/IuB2b2Aq6s59k7vV5cuH4JvvO6d+H6C5nyn3g0vc7Iu6rkrm9SYvyluAbfOc3/ZLETewOP53cwkxJ0Oe4Gs0Ohr+sLylo33NXj98A6ktbLfvD16Y3vmIMH6W6d3IWqzf/wyfntn67vj2+43VwY21u4K9iArINM+ZfUQVLnJPdw+rjUd6UaXF2IofgSvsN9E9A9N97OuKL+35LWbuS9bsiU/8Sj+K77tYWTqEeBDSStWWg/O1P+AdKJ2E3ALpK6pstN+j7VR95Ak/RZSd1xd50llF6jawpzewg/sdsovd8Df7bn55X/dK+sXXOuSdAOiROAIAiCpnECfmzfBc8A9HXcr/wTSPp/+E7vliQf60SpnP0vlbj2Ju42AtADWAvf+SzyAp9U6jdK93ghL5R2Hl9lpctSyXsnOXADJ0+W7aYrlfF3Myt3UvD/27uXEK3KOI7j3yEXtjAjQQgNu/oPqYxoEy2kuy6iyCLKTSldlECLMhdhDQRqOiVlFJhRLZouWOYlwoWU0gW0TQzms1CaaGWlkYZlmi1+zxlfj+e8jjOvzgzv77OZ4X3P5ZlzHuV5zvP//88kFNZSVoS0XMzxCQcoL6HR5eiavkB95Z7xKF58MbAOSBGxE02Y3k8p7Wje/H7ZVpMEXEw2dtXst6f4JSIuApajfJExpe3GDrqF1crXs2jvmib7jENhO6er3LeLflTVvzqA81HOSqGuz4P60X4G35+ayhONpcC9wAWlr6vuUflvLsIDi387Rb5QT5PTnsl7Ym3IEwAzs8HZXlQBioh1wFdAd0RE8VQxImYBq4ENKDFxL3AEJeU+WHHMupj6jprPW6nu3EPZprLyBKtYzX4F+Lxmnx6AlNLWiLgMhR/djmLNF0TEopRSZRJ1CxTtm4Hue9kh6FvZ2YwmeEvQgPWvvP8XDH7V/pyaz+uu5yLqy5j2JyeiynDsXydN2E/hIxSO1YXCbw6gSfYHVN+jVvxtZ/KeWBvyBMDMrEVSSkdzsuc2lNy4NH91H3rKe1djKEBEPHzyUfrlVzRouaLiu3JoSy8aaExG4SbFuc9DcdLl2vxDoReFN5RdmX/+dIr9iyfoR5qsMvRJKe1HCZzvRcS5aNLQGRFdOaG51W9SLp4w/5xS2tlku6vR3/xQSund4sMcD1/WrI370ZPzPqFKVBf2r7l97T3Qn+t5ljXr870NPwfTn6Dm+uan/7eifI7Ohs9H0//VsLLiel9F/SrRcL4nNgI5B8DMrIVyJZJv0VPl0fnj4glg3xO/iLgUVakZyDmOotCVO3OCaXHMySg3oNGm/HNB6fP56InwcJgAbASuyyUwgb4B1VxU1adpqdGU0l5UzvGRiJhQ/j4nmha/jyvtewgNukajsCrQU/disNcKa1EfWFwkdZbaV1RHOqmfZE9XHLOIWa9q424UitboUepXAMq+R6Vnn4qIchjSCddzCFwfETc0tGUcWkX7Jk/sYJD9Kau7vnX36EkGPqbajHJ8FuUJaZ+G/jKc74mNQF4BMDNrvRVo0DcbJR6uR4mg63O1kAnAPBSTfu0Az/E8MB3YFhFvoMHHE6g+/zXFRrlizBpgTkSMBbagJMjZKKykLmTmbFqGKtpsiojGso1TgFn9fMnXXFSF5YeIWI0GweNRZaYpKE8A4MeI2ApsR+Uzp6IymxtTSgfzNkU+wKpcDeYIsGGgL8hKKe3JK0PLgUk5VOwP4BJUH/5DlLuwC8W4d0XERFQ6cgYwseKwRRuX5ApIh4EteTL0FvBmRKxFCaJT0cTwt4rjVLX3v4iYg/rHzoh4G/gFlUedhga/NzU5xJnUA2yMiNc4XgZ0DKqfX2hFf9qNVlLmRsTBfK6elFJPRHwJLMyrKr2o+tA0VO3ntKWU/oyI+ajS1o58P39H7+GYANwzzO+JjUBeATAza7116GndMxExKodzLEQDkFeBmeiJ4WcDPUGuDHQHGtx0olKfncCnFZs/BjyH6t2vzPutQAOLVoe7nLY8aL0RDW7moQFcBzCzomZ73TESer/BehTX/3o+1ihOLD+6Eg2on0WlF6ejUK0HGrb5JG93CwoV6kZx+QOWUlqBXrZ1GN2Ll9EK0JfAx3mbf1Fuwnb0/oUX0cBzesXxdqBB7xT0XoHu/Dso32QZWgXoQhON2zj+VLs/7d2KXkb2HbqOq9CkcV8+9lD5OrfnflT68h/g7txeoGX9qaix/zfqS90o6Re04rAR/btajlYJbkYVwAYkpfQOKlG7D5WEfQm9D2RDwzbD9Z7YCNRx7NiQ/99vZmZm1lTkNwGnlB4f6raYjXReATAzMzMzayOeAJiZmZmZtRFPAMzMzMzM2ohzAMzMzMzM2ohXAMzMzMzM2ognAGZmZmZmbcQTADMzMzOzNuIJgJmZmZlZG/EEwMzMzMysjfwPl0vcu7AATN8AAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Build Dashboard - Theme \n",
        "import flask\n",
        "import dash\n",
        "import plotly.io as pio\n",
        "import dash_daq as daq\n",
        "from dash.dependencies import Input, Output, State\n",
        "app = dash.Dash()\n",
        "app.layout = html.Div([\n",
        "    dcc.Graph(figure=fig)\n",
        "])\n",
        "if __name__ == \"__main__\":\n",
        "    app.run_server(host=os.getenv('HOST', '127.0.0.1'),port=os.getenv('PORT', '8050'),\n",
        "                   debug=True,  use_reloader=False)\n"
      ],
      "metadata": {
        "id": "33vdTi0iihyN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2ac2489c-655f-46f2-ac3a-cd8bb3eb8c22"
      },
      "execution_count": null,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dash is running on http://127.0.0.1:8050/\n",
            "\n",
            " * Serving Flask app \"__main__\" (lazy loading)\n",
            " * Environment: production\n",
            "\u001b[31m   WARNING: This is a development server. Do not use it in a production deployment.\u001b[0m\n",
            "\u001b[2m   Use a production WSGI server instead.\u001b[0m\n",
            " * Debug mode: on\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Build Dashboard - Theme \n",
        "pio.templates.default = \"plotly_dark\""
      ],
      "metadata": {
        "id": "FkHOu6KFStn0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Build Dashboard - Pipeline\n",
        "pipe = Pipeline([('scaler', StandardScaler()),\n",
        "                 ('random_forest', RandomForestClassifier(random_state=42))])\n",
        "pipe.fit(X_train, y_train)\n",
        "\n",
        "Pipeline(steps=[('scaler', StandardScaler()),\n",
        "                ('random_forest', RandomForestClassifier(random_state=42))])\n",
        "pipe.score(X_test, y_test)\n"
      ],
      "metadata": {
        "id": "YLxojbPA65Nx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Build Dashboard - Add Predicted Probabilities\n",
        "#Convert Arrays into Dataframes \n",
        "X_train = pd.DataFrame(X_train)\n",
        "X_test = pd.DataFrame(X_test)\n",
        "y_train = pd.DataFrame(y_train)\n",
        "y_test = pd.DataFrame(y_test)\n",
        "\n",
        "train = pd.concat((X_train, y_train), axis=1)\n",
        "test = pd.concat((X_test, y_test), axis=1)"
      ],
      "metadata": {
        "id": "02Obk6Q18tIc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Build Dashboard - Add Predicted Probabilities\n",
        "target = pd.Series('y_test')\n",
        "\n",
        "test['Probability'] = pipe.predict_proba(X_test)[:,1]\n",
        "test['Target'] = target\n",
        "#test[target] = target.map({0: 'Low', 1: 'Medium', 2: 'High'})"
      ],
      "metadata": {
        "id": "qeP-khK-X3Gt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i, x in enumerate(np.arange(0, 101, 10)):\n",
        "    if i>0:\n",
        "        g.append(f\"{previous_x}% to <{x}%\")\n",
        "    previous_x = x\n",
        "test['Binned Probability'] = pd.cut(test['Probability'], len(g), labels=g,\n",
        "                                    ordered=False)"
      ],
      "metadata": {
        "id": "78DCnPcS_35H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Helper functions for dropdowns and slider\n",
        "def create_dropdown_options(series):\n",
        "    options = [{'label': i, 'value': i} for i in series.sort_values().unique()]\n",
        "    return options\n",
        "def create_dropdown_value(series):\n",
        "    value = series.sort_values().unique().tolist()\n",
        "    return value\n",
        "def create_slider_marks(values):\n",
        "    marks = {i: {'label': str(i)} for i in values}\n",
        "    return marks"
      ],
      "metadata": {
        "id": "ieyUxUaUDf7R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "app = dash.Dash(__name__)\n",
        "app.layout = html.Div([\n",
        "    html.Div(),\n",
        "    html.Div()\n",
        "])\n",
        "if __name__ == '__main__':\n",
        "    app.run_server(debug=True)"
      ],
      "metadata": {
        "id": "XGVVlSh4ENI1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "app = dash.Dash(__name__)\n",
        "app.layout = html.Div([\n",
        "    html.Div([\n",
        "        html.H1(),\n",
        "        html.P(),\n",
        "        html.Img(),\n",
        "        html.Label(), \n",
        "        dcc.Dropdown(),\n",
        "        html.Br(),\n",
        "        html.Label(), \n",
        "        dcc.Dropdown(),\n",
        "        html.Button()\n",
        "    ]),\n",
        "    html.Div()\n",
        "])\n",
        "if __name__ == '__main__':\n",
        "    app.run_server(debug=True)"
      ],
      "metadata": {
        "id": "J1Eh32J5Glyi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "app = dash.Dash(__name__)\n",
        "app.layout = html.Div([\n",
        "    html.Div([\n",
        "        html.H1(),\n",
        "        html.P(),\n",
        "        html.Img(),\n",
        "        html.Label(), \n",
        "        dcc.Dropdown(),\n",
        "        html.Label(), \n",
        "        dcc.Dropdown(),\n",
        "        html.Button()\n",
        "    ]),\n",
        "    html.Div([\n",
        "        html.Div([\n",
        "            dcc.Graph(),\n",
        "            dcc.Graph()\n",
        "        ]),\n",
        "        html.Div([\n",
        "            dcc.Graph(),\n",
        "            html.Div([\n",
        "                html.Label(), \n",
        "                daq.BooleanSwitch(),\n",
        "                html.Label(),\n",
        "                daq.BooleanSwitch(),\n",
        "                html.Label(), \n",
        "                dcc.Slider(),\n",
        "            ]),\n",
        "        ])\n",
        "    ])\n",
        "])\n",
        "if __name__ == '__main__':\n",
        "    app.run_server(debug=True)"
      ],
      "metadata": {
        "id": "194kr945Gux4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if __name__ == '__main__': \n",
        "    app.run_server()"
      ],
      "metadata": {
        "id": "cNxmxXMOHTU3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Build Dashboard\n",
        "#Create Race/Ethnicity Button\n",
        "app = Dash(__name__)\n",
        "\n",
        "app.layout = html.Div([\n",
        "    dcc.Dropdown(['race_African-American', 'race_Asian', 'race_Caucasian',\n",
        "       'race_Hispanic', 'race_Native American', 'race_Other'], 'Race?')\n",
        "])\n",
        "\n",
        "if __name__ == '__main__':\n",
        "    app.run_server(debug=True)"
      ],
      "metadata": {
        "id": "P0HpoJLoUAri"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Build Dashboard\n",
        "#Create Prior Crimes Button\n",
        "app = Dash(__name__)\n",
        "\n",
        "app.layout = html.Div([\n",
        "    dcc.RadioItems(['Yes', 'No'], 'Have You Committed a Crime Before?')\n",
        "])\n",
        "\n",
        "if __name__ == '__main__':\n",
        "    app.run_server(debug=True)"
      ],
      "metadata": {
        "id": "qfHg9YDIVSM0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Build Dashboard\n",
        "#Create Prior Crimes Count Button\n",
        "app = Dash(__name__)\n",
        "\n",
        "app.layout = html.Div([\n",
        "    dcc.Input(\n",
        "        placeholder='Number of Crimes Committed...',\n",
        "        type='text',\n",
        "        value=''\n",
        "    )\n",
        "])\n",
        "\n",
        "if __name__ == '__main__':\n",
        "    app.run_server(debug=True)"
      ],
      "metadata": {
        "id": "RByyWstqVG8o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.columns"
      ],
      "metadata": {
        "id": "CLXf2dn_UKez"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Model Deployment - Tabpy \n",
        "!pip install tabpy\n",
        "import tabpy_client\n",
        "from tabpy.tabpy_tools.client import Client\n",
        "client = tabpy_client.Client('http://localhost:9004/')"
      ],
      "metadata": {
        "id": "BC7VUlWuPQD9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def LoanDefaultClassifier(Loan_Amount, Loan_Tenure, Monthly_Income, Age):\n",
        "    import pandas as pd\n",
        "    data=pd.concat([Loan_Amount,Loan_Tenure,Monthly_Income,Age],axis=1)\n",
        "    return gbm.predict(data)\n",
        "\n",
        "client.deploy('WillItDefault',\n",
        "              LoanDefaultClassifier,\n",
        "              'Returns whether a loan application is likely to default.')"
      ],
      "metadata": {
        "id": "-Xk0spctoNHT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Model Deployment - Tabpy \n",
        "def compas_recidivism_predictor( _arg1, _arg2, _arg3, _arg4,_arg5, _arg6, _arg7,\n",
        "                                _arg8, _arg9, _arg10, _arg11, _arg12, _arg13, _arg14,\n",
        "                                _arg15, _arg16, _arg17, _arg18, _arg19, _arg20, _arg21,\n",
        "                                _arg22, _arg23, _arg24, _arg25):\n",
        "    import pandas as pd\n",
        "\n",
        "    #Get the new app's data in a dictionary\n",
        "    row = {'age': _arg1, 'juv_count': _arg2,\n",
        "           'priors_count': _arg3, 'sex_Female': _arg4,\n",
        "           'sex_Male': _arg5, 'race_African-American': _arg6,\n",
        "           'race_Asian\t': _arg7, 'race_Caucasian': _arg8,\n",
        "           'race_Hispanic': _arg9, 'race_Native American': _arg10,\n",
        "           'race_Other': _arg11,\n",
        "           'c_charge_degree_(CO3)': _arg12, 'c_charge_degree_(CT)': _arg13,\n",
        "           'c_charge_degree_(F1)': _arg14, 'c_charge_degree_(F2)': _arg15,\n",
        "           'c_charge_degree_(F3)': _arg16, 'c_charge_degree_(F5)': _arg17,\n",
        "           'c_charge_degree_(F6)': _arg18, 'c_charge_degree_(F7)': _arg19,\n",
        "           'c_charge_degree_(M1)': _arg20, 'c_charge_degree_(M2)': _arg21,\n",
        "           'c_charge_degree_(MO3)': _arg22, 'c_charge_degree_(N10)': _arg23,\n",
        "           'c_charge_degree_(TCX)': _arg24, 'c_charge_degree_(X)': _arg25}\n",
        "    \n",
        "    #Convert it into a dataframe\n",
        "    test_data = pd.DataFrame(data = row,index=[0])\n",
        "    #Predict the survival and death probabilities\n",
        "    predprob_recid = random_forest.predict_proba(test_data)\n",
        "    #Return only the survival probability\n",
        "    return random_forest.predict(test_data)\n"
      ],
      "metadata": {
        "id": "dcq9zPgBiSfR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "s = requests.session()\n",
        "s.keep_alive = False\n",
        "\n",
        "client.deploy('compas_recidivism_predictor', compas_recidivism_predictor,\n",
        "              'Predicts Recidivism Probability', override = True)"
      ],
      "metadata": {
        "id": "zWEVXokOoli-"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}