Cookbook_Serious_Feature_Reductions.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Feature Reductions 10 Sep.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/RandomForestRanger/Cookbooks/blob/master/Feature_Reductions_10_Sep.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yfti12OXWJop",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#installations\n",
        "!pip install --upgrade -q gspread\n",
        "!pip install gspread-dataframe\n",
        "!pip install --upgrade Cython\n",
        "!pip install --upgrade git+https://github.com/statsmodels/statsmodels\n",
        "  \n",
        "#! pip install jupyter_contrib_nbextensions && jupyter contrib nbextension install # see if this works"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fx7gZboDWRSO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#import libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib as mpl\n",
        "from matplotlib.colors import LinearSegmentedColormap\n",
        "import seaborn as sns\n",
        "import math\n",
        "\n",
        "from sklearn import linear_model\n",
        "from sklearn.linear_model import LinearRegression\n",
        "import sklearn.metrics as sklm\n",
        "from sklearn import preprocessing as pp\n",
        "from sklearn.preprocessing import StandardScaler \n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.metrics import r2_score\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "\n",
        "import statsmodels.api as sm\n",
        "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
        "from statsmodels.discrete.discrete_model import NegativeBinomial    #\n",
        "from statsmodels import discrete\n",
        "from statsmodels.formula.api import ols\n",
        "\n",
        "\n",
        "from scipy import stats\n",
        "import scipy.stats as ss\n",
        "\n",
        "from patsy import dmatrices\n",
        "\n",
        "import gspread\n",
        "from gspread_dataframe import get_as_dataframe, set_with_dataframe\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JIYF4u964DVQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gcqLhmpRWXMH",
        "colab_type": "code",
        "outputId": "426e9b90-d293-4263-9061-badc8d80d0ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        }
      },
      "source": [
        "#import data (google drive csv)\n",
        "\n",
        "#authorise node to access Gdrive via KDL\n",
        "from google.colab import auth\n",
        "auth.authenticate_user()\n",
        "from oauth2client.client import GoogleCredentials\n",
        "gc = gspread.authorize(GoogleCredentials.get_application_default())\n",
        "\n",
        "#link to pre-opened csv file in my gdrive \n",
        "data1 = gc.open_by_url('https://docs.google.com/spreadsheets/d/12xOwkchQqGupMzfgOYJioRnjeDKKanC2oygUCLgJg44/edit#gid=406608549')\n",
        "\n",
        "#get the data as a worksheet  \n",
        "ws = data1.worksheet('RegAll01')\n",
        "\n",
        "#convert the data to pandas dataframe (can include argument , header = None   if this is needed)\n",
        "#Woernjaps = get_as_dataframe(ws, usecols=[1,2,3,4,5,6,8,9,10,11,12,13,14,15,16,17,18,19,21,24, 30])\n",
        "Woernjaps = get_as_dataframe(ws, usecols=[1,2,4,8,10,12,13,14,16,17,19,21,22,49]) \n",
        "\n",
        "#since g-sheets brings along a lot of empty/Nan cellumns or rows, this chops them out. Can replace \"all\" with \"any\" if I want to discard any incomplete collumns or rows\n",
        "Woernjaps.dropna(axis=1, how='all' ,inplace=True )\n",
        "Woernjaps.dropna(axis=0, how='any', thresh=None, subset=None, inplace=True)\n",
        " \n",
        "Woernjaps.tail()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Change_in_pop</th>\n",
              "      <th>WAP_not_working</th>\n",
              "      <th>HH_Income_Percent_Poor</th>\n",
              "      <th>Voter_Turnout</th>\n",
              "      <th>Mean_Age</th>\n",
              "      <th>Small_Households</th>\n",
              "      <th>Percentage_Youth</th>\n",
              "      <th>Percentage_NWA</th>\n",
              "      <th>Percentage_White</th>\n",
              "      <th>Percentage_HHWW</th>\n",
              "      <th>Inequality</th>\n",
              "      <th>Percentage_PWM</th>\n",
              "      <th>Percentage_NSL</th>\n",
              "      <th>Percentage_Tribal</th>\n",
              "      <th>Percentage_Male</th>\n",
              "      <th>Percentage_Informal</th>\n",
              "      <th>Percentage_IOT</th>\n",
              "      <th>COGTA_Score</th>\n",
              "      <th>AG_Rating</th>\n",
              "      <th>Pop</th>\n",
              "      <th>Target2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>229</th>\n",
              "      <td>0.32</td>\n",
              "      <td>0.36</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.7246</td>\n",
              "      <td>27.495502</td>\n",
              "      <td>37.044553</td>\n",
              "      <td>32.417989</td>\n",
              "      <td>48.148148</td>\n",
              "      <td>5.961538</td>\n",
              "      <td>31.135380</td>\n",
              "      <td>5.205402</td>\n",
              "      <td>86.665856</td>\n",
              "      <td>0.790241</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>51.579282</td>\n",
              "      <td>39.673050</td>\n",
              "      <td>39.673050</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>56703.0</td>\n",
              "      <td>30.686207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>230</th>\n",
              "      <td>0.34</td>\n",
              "      <td>0.34</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.8302</td>\n",
              "      <td>28.323922</td>\n",
              "      <td>40.339771</td>\n",
              "      <td>37.217716</td>\n",
              "      <td>41.022583</td>\n",
              "      <td>16.033823</td>\n",
              "      <td>16.776172</td>\n",
              "      <td>14.510426</td>\n",
              "      <td>74.061356</td>\n",
              "      <td>2.840637</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>51.445271</td>\n",
              "      <td>4.660040</td>\n",
              "      <td>4.660040</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>75453.0</td>\n",
              "      <td>11.265291</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>231</th>\n",
              "      <td>-0.07</td>\n",
              "      <td>0.67</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.7777</td>\n",
              "      <td>25.945665</td>\n",
              "      <td>25.146995</td>\n",
              "      <td>32.072400</td>\n",
              "      <td>53.870005</td>\n",
              "      <td>0.329700</td>\n",
              "      <td>83.246560</td>\n",
              "      <td>4.070981</td>\n",
              "      <td>84.535850</td>\n",
              "      <td>0.793188</td>\n",
              "      <td>90.271695</td>\n",
              "      <td>46.587342</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>97.011744</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>77403.0</td>\n",
              "      <td>11.885844</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>232</th>\n",
              "      <td>0.02</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.7242</td>\n",
              "      <td>28.155124</td>\n",
              "      <td>55.381699</td>\n",
              "      <td>41.283517</td>\n",
              "      <td>32.573745</td>\n",
              "      <td>7.077280</td>\n",
              "      <td>35.074069</td>\n",
              "      <td>9.245362</td>\n",
              "      <td>74.483008</td>\n",
              "      <td>2.882927</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>54.713335</td>\n",
              "      <td>26.959416</td>\n",
              "      <td>26.959416</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>111768.0</td>\n",
              "      <td>41.961921</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>233</th>\n",
              "      <td>0.30</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.6618</td>\n",
              "      <td>29.123616</td>\n",
              "      <td>35.633829</td>\n",
              "      <td>38.070843</td>\n",
              "      <td>37.204067</td>\n",
              "      <td>7.778822</td>\n",
              "      <td>9.157549</td>\n",
              "      <td>14.835113</td>\n",
              "      <td>81.262356</td>\n",
              "      <td>2.405668</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>51.363589</td>\n",
              "      <td>4.954978</td>\n",
              "      <td>4.954978</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>115944.0</td>\n",
              "      <td>14.834748</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Change_in_pop  WAP_not_working  ...       Pop    Target2\n",
              "229           0.32             0.36  ...   56703.0  30.686207\n",
              "230           0.34             0.34  ...   75453.0  11.265291\n",
              "231          -0.07             0.67  ...   77403.0  11.885844\n",
              "232           0.02             0.33  ...  111768.0  41.961921\n",
              "233           0.30             0.09  ...  115944.0  14.834748\n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-G7W_OMCWayw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Woernjaps.describe()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ny68lb99lYDo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Plot of target\n",
        "plt.figure(figsize=(9,9))\n",
        "\n",
        "Woernjaps['Inequality'] = np.log(Woernjaps['Inequality']+1)\n",
        "\n",
        "sns.scatterplot(x= Woernjaps['Inequality'], y=Woernjaps['NatLog7'], hue= Woernjaps['Inequality'],palette=\"nipy_spectral\", legend='brief', )\n",
        "#sns.distplot(Woernjaps['Target1'], color=\"dodgerblue\", kde = False )  #\n",
        "#plt.title('Histogram of protests per municipality')\n",
        "plt.xlabel('Ineq')\n",
        "plt.ylabel('Score')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4I2DJoBFWrns",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Scaling\n",
        "dataset = Woernjaps\n",
        "scaler = StandardScaler()\n",
        "#dataset[['Change_in_pop', 'WAP_not_working','Voter_Turnout','Percentage_Youth','Mean_Age','Percentage_White','Inequality','Percentage_PWM','Percentage_NSL','Percentage_Male','Percentage_Tribal', 'Percentage_Informal','Percentage_IOT']] = scaler.fit_transform(dataset[['Change_in_pop', 'WAP_not_working','Voter_Turnout','Percentage_Youth','Mean_Age','Percentage_White','Inequality','Percentage_PWM','Percentage_NSL','Percentage_Male','Percentage_Tribal', 'Percentage_Informal','Percentage_IOT']]) \n",
        "dataset[['Change_in_pop','WAP_not_working','Voter_Turnout','Percentage_Youth','Percentage_White','Inequality','Percentage_PWM','Percentage_NSL','Percentage_Male','Percentage_Informal']] = scaler.fit_transform(dataset[['Change_in_pop','WAP_not_working','Voter_Turnout','Percentage_Youth','Percentage_White','Inequality','Percentage_PWM','Percentage_NSL','Percentage_Male','Percentage_Informal']]) \n",
        "\n",
        "# 'Change_in_pop','WAP_not_working','HH_Income_Percent_Poor','Voter_Turnout','Mean_Age','Small_Households','Percentage_Youth','Percentage_NWA','Percentage_White','Percentage_HHWW','Inequality','Percentage_PWM','Percentage_NSL','Percentage_Tribal','Percentage_Male','Percentage_Informal','Percentage_IOT'\n",
        "# 'Change_in_pop','WAP_not_working','Voter_Turnout','Percentage_Youth', 'Percentage_White','Percentage_PWM','Percentage_NSL','Percentage_Informal'\n",
        "# 'HH_Income_Percent_Poor', 'Small_Households', 'Percentage_HHWW', 'Mean_Age',  'Percentage_NWA', 'Inequality',  'Percentage_Male', \n",
        "#dataset.tail() "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K9ddRKRhWkQQ",
        "colab_type": "code",
        "outputId": "498a1db1-ff07-4369-b733-0c68e0fce2d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "#place data into new formats, including splitting off the target from the variables (DV from IVs)\n",
        "observations = len(dataset)\n",
        "variables = dataset.columns[:-1]\n",
        "X = dataset.iloc[:, :-1] \n",
        "y = dataset['Target2'].values\n",
        "#dataset.dtypes\n",
        "\n",
        "#add constant term (for OLS)\n",
        "Xc = sm.add_constant(X)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/numpy/core/fromnumeric.py:2495: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
            "  return ptp(axis=axis, out=out, **kwargs)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0GQeytMfXKa-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Xc.describe().apply(lambda s: s.apply(lambda x: format(round(x,3), 'g')))       "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pjs3o1k3csFT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.feature_selection import RFE\n",
        "from sklearn.linear_model import LogisticRegression"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q3IdbjIQcsR2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Feature extraction\n",
        "model = LinearRegression()\n",
        "rfe = RFE(model, 1)\n",
        "fit = rfe.fit(Xc, y)\n",
        "print(\"Num Features: %s\" % (fit.n_features_))\n",
        "print(\"Selected Features: %s\" % (fit.support_))\n",
        "print(\"Feature Ranking: %s\" % (fit.ranking_))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e-gQJ6oGcsor",
        "colab_type": "code",
        "outputId": "04940a9c-e03c-46ec-a103-2b5a66c8f58a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        }
      },
      "source": [
        "Xc = pd.concat((Xc,pd.get_dummies(Xc['AG_Rating'], prefix= \"A\", drop_first=True)), axis=1)\n",
        "\n",
        "Xc.drop(['AG_Rating'], axis=1, inplace=True)\n",
        "Xc = pd.concat((Xc,pd.get_dummies(Xc['COGTA_Score'], prefix= \"C\", drop_first=True)), axis=1)\n",
        "Xc.drop(['COGTA_Score'], axis=1, inplace=True)\n",
        "#Xc = pd.concat((Xc,pd.get_dummies(Xc['Prov'], prefix=\"Prov\", drop_first=True)), axis=1)\n",
        "Xc.drop(['Prov'], axis=1, inplace=True)\n",
        "\n",
        "# dropping unneeded collumns\n",
        "Xc.drop(columns=['Percentage_PWM','Percentage_Youth','WAP_not_working','Percentage_Informal' ,'C_2.0','C_3.0','C_4.0','A_2.0','A_3.0','A_4.0','A_5.0','Percentage_White' ,'Percentage_NSL'], axis=1, inplace=True)\n",
        "# 'Voter_Turnout',      'Inequality', \n",
        "#Xc.drop(columns=['Pop' ], axis=1, inplace=True)\n",
        "#'Percentage_Male' , , 'Percentage_HHWW', 'HH_Income_Percent_Poor', ,'Percentage_White',,]\n",
        "# 'Percentage_NSL',   'Inequality', \n",
        "# 'Mean_Age',   'Percentage_Tribal', 'Small_Households','Change_in_pop',  \n",
        "\n",
        "linear_regression = sm.OLS(y,Xc)\n",
        "fitted_model = linear_regression.fit()\n",
        "\n",
        "#Look at model summary\n",
        "fitted_model.summary()\n",
        "#fitted_model.conf_int(alpha=0.05, cols=None)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>OLS Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.279</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.264</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   19.33</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>             <td>Sat, 23 Nov 2019</td> <th>  Prob (F-statistic):</th> <td>1.83e-13</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                 <td>11:58:55</td>     <th>  Log-Likelihood:    </th> <td> -277.94</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Observations:</th>      <td>   205</td>      <th>  AIC:               </th> <td>   565.9</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Residuals:</th>          <td>   200</td>      <th>  BIC:               </th> <td>   582.5</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Model:</th>              <td>     4</td>      <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "         <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>const</th>           <td>    4.6257</td> <td>    0.066</td> <td>   69.682</td> <td> 0.000</td> <td>    4.495</td> <td>    4.757</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Change_in_pop</th>   <td>    0.2906</td> <td>    0.094</td> <td>    3.075</td> <td> 0.002</td> <td>    0.104</td> <td>    0.477</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Voter_Turnout</th>   <td>   -0.0477</td> <td>    0.071</td> <td>   -0.670</td> <td> 0.503</td> <td>   -0.188</td> <td>    0.093</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Inequality</th>      <td>    0.0534</td> <td>    0.089</td> <td>    0.602</td> <td> 0.548</td> <td>   -0.122</td> <td>    0.228</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Percentage_Male</th> <td>    0.3446</td> <td>    0.081</td> <td>    4.237</td> <td> 0.000</td> <td>    0.184</td> <td>    0.505</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Omnibus:</th>       <td> 1.936</td> <th>  Durbin-Watson:     </th> <td>   1.890</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Omnibus):</th> <td> 0.380</td> <th>  Jarque-Bera (JB):  </th> <td>   1.997</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Skew:</th>          <td>-0.227</td> <th>  Prob(JB):          </th> <td>   0.368</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Kurtosis:</th>      <td> 2.832</td> <th>  Cond. No.          </th> <td>    2.62</td>\n",
              "</tr>\n",
              "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                            OLS Regression Results                            \n",
              "==============================================================================\n",
              "Dep. Variable:                      y   R-squared:                       0.279\n",
              "Model:                            OLS   Adj. R-squared:                  0.264\n",
              "Method:                 Least Squares   F-statistic:                     19.33\n",
              "Date:                Sat, 23 Nov 2019   Prob (F-statistic):           1.83e-13\n",
              "Time:                        11:58:55   Log-Likelihood:                -277.94\n",
              "No. Observations:                 205   AIC:                             565.9\n",
              "Df Residuals:                     200   BIC:                             582.5\n",
              "Df Model:                           4                                         \n",
              "Covariance Type:            nonrobust                                         \n",
              "===================================================================================\n",
              "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
              "-----------------------------------------------------------------------------------\n",
              "const               4.6257      0.066     69.682      0.000       4.495       4.757\n",
              "Change_in_pop       0.2906      0.094      3.075      0.002       0.104       0.477\n",
              "Voter_Turnout      -0.0477      0.071     -0.670      0.503      -0.188       0.093\n",
              "Inequality          0.0534      0.089      0.602      0.548      -0.122       0.228\n",
              "Percentage_Male     0.3446      0.081      4.237      0.000       0.184       0.505\n",
              "==============================================================================\n",
              "Omnibus:                        1.936   Durbin-Watson:                   1.890\n",
              "Prob(Omnibus):                  0.380   Jarque-Bera (JB):                1.997\n",
              "Skew:                          -0.227   Prob(JB):                        0.368\n",
              "Kurtosis:                       2.832   Cond. No.                         2.62\n",
              "==============================================================================\n",
              "\n",
              "Warnings:\n",
              "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
              "\"\"\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P3WhQr8uvWDQ",
        "colab_type": "code",
        "outputId": "0577ce4d-586f-4bf3-ed6c-21eb2859477e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 856
        }
      },
      "source": [
        "# The Robust model\n",
        "rlm_model = sm.RLM(y, Xc, M=sm.robust.norms.HuberT())\n",
        "fitted_model = rlm_model.fit()\n",
        "\n",
        "#Look at model summary\n",
        "fitted_model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>Robust linear Model Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>          <td>y</td>        <th>  No. Observations:  </th> <td>   234</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                 <td>RLM</td>       <th>  Df Residuals:      </th> <td>   209</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>               <td>IRLS</td>       <th>  Df Model:          </th> <td>    24</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Norm:</th>                <td>HuberT</td>      <th>                     </th>    <td> </td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Scale Est.:</th>            <td>mad</td>       <th>                     </th>    <td> </td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Cov Type:</th>              <td>H1</td>        <th>                     </th>    <td> </td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>           <td>Mon, 18 Nov 2019</td> <th>                     </th>    <td> </td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>               <td>10:03:17</td>     <th>                     </th>    <td> </td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Iterations:</th>        <td>29</td>        <th>                     </th>    <td> </td>  \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "             <td></td>               <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>const</th>                  <td>   69.7174</td> <td>   24.578</td> <td>    2.837</td> <td> 0.005</td> <td>   21.546</td> <td>  117.889</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Change_in_pop</th>          <td>    2.6313</td> <td>    0.933</td> <td>    2.822</td> <td> 0.005</td> <td>    0.804</td> <td>    4.459</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>WAP_not_working</th>        <td>    2.4315</td> <td>    1.839</td> <td>    1.322</td> <td> 0.186</td> <td>   -1.173</td> <td>    6.036</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>HH_Income_Percent_Poor</th> <td>   23.6002</td> <td>   25.338</td> <td>    0.931</td> <td> 0.352</td> <td>  -26.062</td> <td>   73.262</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Voter_Turnout</th>          <td>   -2.0721</td> <td>    0.757</td> <td>   -2.737</td> <td> 0.006</td> <td>   -3.556</td> <td>   -0.588</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Mean_Age</th>               <td>   -1.8764</td> <td>    1.869</td> <td>   -1.004</td> <td> 0.315</td> <td>   -5.540</td> <td>    1.787</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Small_Households</th>       <td>   -0.1124</td> <td>    0.157</td> <td>   -0.715</td> <td> 0.475</td> <td>   -0.421</td> <td>    0.196</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Percentage_Youth</th>       <td>   -3.4245</td> <td>    2.179</td> <td>   -1.572</td> <td> 0.116</td> <td>   -7.696</td> <td>    0.846</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Percentage_NWA</th>         <td>   -1.4260</td> <td>    0.526</td> <td>   -2.713</td> <td> 0.007</td> <td>   -2.456</td> <td>   -0.396</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Percentage_White</th>       <td>    0.4045</td> <td>    1.491</td> <td>    0.271</td> <td> 0.786</td> <td>   -2.517</td> <td>    3.326</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Percentage_HHWW</th>        <td>    0.0524</td> <td>    0.063</td> <td>    0.831</td> <td> 0.406</td> <td>   -0.071</td> <td>    0.176</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Inequality</th>             <td>   -2.4839</td> <td>    1.092</td> <td>   -2.275</td> <td> 0.023</td> <td>   -4.624</td> <td>   -0.344</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Percentage_PWM</th>         <td>   -2.6494</td> <td>    1.799</td> <td>   -1.473</td> <td> 0.141</td> <td>   -6.175</td> <td>    0.876</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Percentage_NSL</th>         <td>   -0.2488</td> <td>    0.872</td> <td>   -0.285</td> <td> 0.775</td> <td>   -1.957</td> <td>    1.460</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Percentage_Tribal</th>      <td>   -0.6670</td> <td>    3.947</td> <td>   -0.169</td> <td> 0.866</td> <td>   -8.404</td> <td>    7.069</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Percentage_Male</th>        <td>   -1.2586</td> <td>    1.300</td> <td>   -0.968</td> <td> 0.333</td> <td>   -3.806</td> <td>    1.289</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Percentage_Informal</th>    <td>    1.0683</td> <td>    0.877</td> <td>    1.218</td> <td> 0.223</td> <td>   -0.651</td> <td>    2.788</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Percentage_IOT</th>         <td>   -4.1624</td> <td>    4.489</td> <td>   -0.927</td> <td> 0.354</td> <td>  -12.960</td> <td>    4.635</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>A_2.0</th>                  <td>    7.6396</td> <td>    3.637</td> <td>    2.101</td> <td> 0.036</td> <td>    0.512</td> <td>   14.768</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>A_3.0</th>                  <td>    7.7751</td> <td>    3.684</td> <td>    2.110</td> <td> 0.035</td> <td>    0.554</td> <td>   14.996</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>A_4.0</th>                  <td>   14.2300</td> <td>    5.421</td> <td>    2.625</td> <td> 0.009</td> <td>    3.605</td> <td>   24.855</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>A_5.0</th>                  <td>   10.3942</td> <td>    3.632</td> <td>    2.862</td> <td> 0.004</td> <td>    3.275</td> <td>   17.513</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C_2.0</th>                  <td>    1.1868</td> <td>    2.081</td> <td>    0.570</td> <td> 0.569</td> <td>   -2.893</td> <td>    5.266</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C_3.0</th>                  <td>    4.4930</td> <td>    2.699</td> <td>    1.664</td> <td> 0.096</td> <td>   -0.798</td> <td>    9.784</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C_4.0</th>                  <td>    2.3618</td> <td>    3.849</td> <td>    0.614</td> <td> 0.540</td> <td>   -5.183</td> <td>    9.906</td>\n",
              "</tr>\n",
              "</table><br/><br/>If the model instance has been used for another fit with different fit<br/>parameters, then the fit options might not be the correct ones anymore ."
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                    Robust linear Model Regression Results                    \n",
              "==============================================================================\n",
              "Dep. Variable:                      y   No. Observations:                  234\n",
              "Model:                            RLM   Df Residuals:                      209\n",
              "Method:                          IRLS   Df Model:                           24\n",
              "Norm:                          HuberT                                         \n",
              "Scale Est.:                       mad                                         \n",
              "Cov Type:                          H1                                         \n",
              "Date:                Mon, 18 Nov 2019                                         \n",
              "Time:                        10:03:17                                         \n",
              "No. Iterations:                    29                                         \n",
              "==========================================================================================\n",
              "                             coef    std err          z      P>|z|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------------------\n",
              "const                     69.7174     24.578      2.837      0.005      21.546     117.889\n",
              "Change_in_pop              2.6313      0.933      2.822      0.005       0.804       4.459\n",
              "WAP_not_working            2.4315      1.839      1.322      0.186      -1.173       6.036\n",
              "HH_Income_Percent_Poor    23.6002     25.338      0.931      0.352     -26.062      73.262\n",
              "Voter_Turnout             -2.0721      0.757     -2.737      0.006      -3.556      -0.588\n",
              "Mean_Age                  -1.8764      1.869     -1.004      0.315      -5.540       1.787\n",
              "Small_Households          -0.1124      0.157     -0.715      0.475      -0.421       0.196\n",
              "Percentage_Youth          -3.4245      2.179     -1.572      0.116      -7.696       0.846\n",
              "Percentage_NWA            -1.4260      0.526     -2.713      0.007      -2.456      -0.396\n",
              "Percentage_White           0.4045      1.491      0.271      0.786      -2.517       3.326\n",
              "Percentage_HHWW            0.0524      0.063      0.831      0.406      -0.071       0.176\n",
              "Inequality                -2.4839      1.092     -2.275      0.023      -4.624      -0.344\n",
              "Percentage_PWM            -2.6494      1.799     -1.473      0.141      -6.175       0.876\n",
              "Percentage_NSL            -0.2488      0.872     -0.285      0.775      -1.957       1.460\n",
              "Percentage_Tribal         -0.6670      3.947     -0.169      0.866      -8.404       7.069\n",
              "Percentage_Male           -1.2586      1.300     -0.968      0.333      -3.806       1.289\n",
              "Percentage_Informal        1.0683      0.877      1.218      0.223      -0.651       2.788\n",
              "Percentage_IOT            -4.1624      4.489     -0.927      0.354     -12.960       4.635\n",
              "A_2.0                      7.6396      3.637      2.101      0.036       0.512      14.768\n",
              "A_3.0                      7.7751      3.684      2.110      0.035       0.554      14.996\n",
              "A_4.0                     14.2300      5.421      2.625      0.009       3.605      24.855\n",
              "A_5.0                     10.3942      3.632      2.862      0.004       3.275      17.513\n",
              "C_2.0                      1.1868      2.081      0.570      0.569      -2.893       5.266\n",
              "C_3.0                      4.4930      2.699      1.664      0.096      -0.798       9.784\n",
              "C_4.0                      2.3618      3.849      0.614      0.540      -5.183       9.906\n",
              "==========================================================================================\n",
              "\n",
              "If the model instance has been used for another fit with different fit\n",
              "parameters, then the fit options might not be the correct ones anymore .\n",
              "\"\"\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6I3uNgdn3vEe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Xc = pd.concat((Xc,pd.get_dummies(Xc['AG_Rating'], prefix= \"A\", drop_first=True)), axis=1)\n",
        "Xc.drop(['AG_Rating'], axis=1, inplace=True)\n",
        "Xc = pd.concat((Xc,pd.get_dummies(Xc['COGTA_Score'], prefix=\"C\", drop_first=True)), axis=1)\n",
        "Xc.drop(['COGTA_Score'], axis=1, inplace=True)\n",
        "Xc.drop(['Pop'], axis=1, inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x3xwgeZ2t2jC",
        "colab_type": "code",
        "outputId": "72877e61-88b8-471b-f6fb-3c79c1883e4e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 978
        }
      },
      "source": [
        "#Weighted Least Squares\n",
        "wls_model = sm.WLS(y,Xc, weights=dataset['Pop'] )\n",
        "fitted_model = wls_model.fit()\n",
        "\n",
        "#Look at model summary\n",
        "fitted_model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>WLS Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.422</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                   <td>WLS</td>       <th>  Adj. R-squared:    </th> <td>   0.356</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   6.370</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>             <td>Mon, 18 Nov 2019</td> <th>  Prob (F-statistic):</th> <td>8.08e-15</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                 <td>10:07:38</td>     <th>  Log-Likelihood:    </th> <td> -932.00</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Observations:</th>      <td>   234</td>      <th>  AIC:               </th> <td>   1914.</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Residuals:</th>          <td>   209</td>      <th>  BIC:               </th> <td>   2000.</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Model:</th>              <td>    24</td>      <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "             <td></td>               <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>const</th>                  <td>  159.8303</td> <td>   33.245</td> <td>    4.808</td> <td> 0.000</td> <td>   94.292</td> <td>  225.369</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Change_in_pop</th>          <td>    4.6107</td> <td>    1.454</td> <td>    3.172</td> <td> 0.002</td> <td>    1.745</td> <td>    7.476</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>WAP_not_working</th>        <td>    8.2427</td> <td>    2.564</td> <td>    3.215</td> <td> 0.002</td> <td>    3.188</td> <td>   13.297</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>HH_Income_Percent_Poor</th> <td>  -43.5244</td> <td>   34.657</td> <td>   -1.256</td> <td> 0.211</td> <td> -111.846</td> <td>   24.798</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Voter_Turnout</th>          <td>   -1.3408</td> <td>    0.978</td> <td>   -1.371</td> <td> 0.172</td> <td>   -3.269</td> <td>    0.587</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Mean_Age</th>               <td>   -5.1748</td> <td>    2.686</td> <td>   -1.927</td> <td> 0.055</td> <td>  -10.470</td> <td>    0.120</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Small_Households</th>       <td>    0.2195</td> <td>    0.224</td> <td>    0.979</td> <td> 0.329</td> <td>   -0.222</td> <td>    0.662</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Percentage_Youth</th>       <td>  -11.8399</td> <td>    3.386</td> <td>   -3.497</td> <td> 0.001</td> <td>  -18.514</td> <td>   -5.166</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Percentage_NWA</th>         <td>   -3.2221</td> <td>    0.717</td> <td>   -4.497</td> <td> 0.000</td> <td>   -4.635</td> <td>   -1.809</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Percentage_White</th>       <td>    0.5447</td> <td>    1.861</td> <td>    0.293</td> <td> 0.770</td> <td>   -3.124</td> <td>    4.214</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Percentage_HHWW</th>        <td>   -0.0303</td> <td>    0.084</td> <td>   -0.361</td> <td> 0.719</td> <td>   -0.196</td> <td>    0.135</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Inequality</th>             <td>   -3.5785</td> <td>    0.800</td> <td>   -4.471</td> <td> 0.000</td> <td>   -5.156</td> <td>   -2.001</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Percentage_PWM</th>         <td>   -1.5327</td> <td>    2.401</td> <td>   -0.638</td> <td> 0.524</td> <td>   -6.267</td> <td>    3.201</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Percentage_NSL</th>         <td>    2.4106</td> <td>    1.381</td> <td>    1.746</td> <td> 0.082</td> <td>   -0.312</td> <td>    5.133</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Percentage_Tribal</th>      <td>   -0.2219</td> <td>    5.295</td> <td>   -0.042</td> <td> 0.967</td> <td>  -10.660</td> <td>   10.216</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Percentage_Male</th>        <td>   -6.3071</td> <td>    1.847</td> <td>   -3.415</td> <td> 0.001</td> <td>   -9.948</td> <td>   -2.666</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Percentage_Informal</th>    <td>   -0.1965</td> <td>    1.256</td> <td>   -0.156</td> <td> 0.876</td> <td>   -2.673</td> <td>    2.280</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Percentage_IOT</th>         <td>    0.6515</td> <td>    6.127</td> <td>    0.106</td> <td> 0.915</td> <td>  -11.428</td> <td>   12.731</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>A_2.0</th>                  <td>   10.6019</td> <td>    6.423</td> <td>    1.651</td> <td> 0.100</td> <td>   -2.060</td> <td>   23.264</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>A_3.0</th>                  <td>    9.8132</td> <td>    6.399</td> <td>    1.534</td> <td> 0.127</td> <td>   -2.802</td> <td>   22.428</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>A_4.0</th>                  <td>   15.0415</td> <td>    7.693</td> <td>    1.955</td> <td> 0.052</td> <td>   -0.125</td> <td>   30.208</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>A_5.0</th>                  <td>   14.1127</td> <td>    6.385</td> <td>    2.210</td> <td> 0.028</td> <td>    1.525</td> <td>   26.701</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C_2.0</th>                  <td>   -2.4096</td> <td>    2.843</td> <td>   -0.848</td> <td> 0.398</td> <td>   -8.014</td> <td>    3.195</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C_3.0</th>                  <td>   -2.9943</td> <td>    4.178</td> <td>   -0.717</td> <td> 0.474</td> <td>  -11.230</td> <td>    5.241</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>C_4.0</th>                  <td>   -6.6805</td> <td>    5.615</td> <td>   -1.190</td> <td> 0.235</td> <td>  -17.749</td> <td>    4.388</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Omnibus:</th>       <td>78.838</td> <th>  Durbin-Watson:     </th> <td>   2.026</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 401.331</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Skew:</th>          <td> 1.231</td> <th>  Prob(JB):          </th> <td>7.11e-88</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Kurtosis:</th>      <td> 8.924</td> <th>  Cond. No.          </th> <td>3.98e+03</td>\n",
              "</tr>\n",
              "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 3.98e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                            WLS Regression Results                            \n",
              "==============================================================================\n",
              "Dep. Variable:                      y   R-squared:                       0.422\n",
              "Model:                            WLS   Adj. R-squared:                  0.356\n",
              "Method:                 Least Squares   F-statistic:                     6.370\n",
              "Date:                Mon, 18 Nov 2019   Prob (F-statistic):           8.08e-15\n",
              "Time:                        10:07:38   Log-Likelihood:                -932.00\n",
              "No. Observations:                 234   AIC:                             1914.\n",
              "Df Residuals:                     209   BIC:                             2000.\n",
              "Df Model:                          24                                         \n",
              "Covariance Type:            nonrobust                                         \n",
              "==========================================================================================\n",
              "                             coef    std err          t      P>|t|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------------------\n",
              "const                    159.8303     33.245      4.808      0.000      94.292     225.369\n",
              "Change_in_pop              4.6107      1.454      3.172      0.002       1.745       7.476\n",
              "WAP_not_working            8.2427      2.564      3.215      0.002       3.188      13.297\n",
              "HH_Income_Percent_Poor   -43.5244     34.657     -1.256      0.211    -111.846      24.798\n",
              "Voter_Turnout             -1.3408      0.978     -1.371      0.172      -3.269       0.587\n",
              "Mean_Age                  -5.1748      2.686     -1.927      0.055     -10.470       0.120\n",
              "Small_Households           0.2195      0.224      0.979      0.329      -0.222       0.662\n",
              "Percentage_Youth         -11.8399      3.386     -3.497      0.001     -18.514      -5.166\n",
              "Percentage_NWA            -3.2221      0.717     -4.497      0.000      -4.635      -1.809\n",
              "Percentage_White           0.5447      1.861      0.293      0.770      -3.124       4.214\n",
              "Percentage_HHWW           -0.0303      0.084     -0.361      0.719      -0.196       0.135\n",
              "Inequality                -3.5785      0.800     -4.471      0.000      -5.156      -2.001\n",
              "Percentage_PWM            -1.5327      2.401     -0.638      0.524      -6.267       3.201\n",
              "Percentage_NSL             2.4106      1.381      1.746      0.082      -0.312       5.133\n",
              "Percentage_Tribal         -0.2219      5.295     -0.042      0.967     -10.660      10.216\n",
              "Percentage_Male           -6.3071      1.847     -3.415      0.001      -9.948      -2.666\n",
              "Percentage_Informal       -0.1965      1.256     -0.156      0.876      -2.673       2.280\n",
              "Percentage_IOT             0.6515      6.127      0.106      0.915     -11.428      12.731\n",
              "A_2.0                     10.6019      6.423      1.651      0.100      -2.060      23.264\n",
              "A_3.0                      9.8132      6.399      1.534      0.127      -2.802      22.428\n",
              "A_4.0                     15.0415      7.693      1.955      0.052      -0.125      30.208\n",
              "A_5.0                     14.1127      6.385      2.210      0.028       1.525      26.701\n",
              "C_2.0                     -2.4096      2.843     -0.848      0.398      -8.014       3.195\n",
              "C_3.0                     -2.9943      4.178     -0.717      0.474     -11.230       5.241\n",
              "C_4.0                     -6.6805      5.615     -1.190      0.235     -17.749       4.388\n",
              "==============================================================================\n",
              "Omnibus:                       78.838   Durbin-Watson:                   2.026\n",
              "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              401.331\n",
              "Skew:                           1.231   Prob(JB):                     7.11e-88\n",
              "Kurtosis:                       8.924   Cond. No.                     3.98e+03\n",
              "==============================================================================\n",
              "\n",
              "Warnings:\n",
              "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
              "[2] The condition number is large, 3.98e+03. This might indicate that there are\n",
              "strong multicollinearity or other numerical problems.\n",
              "\"\"\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RGsKl4AqhcgH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Scaling\n",
        "dataset = Woernjaps\n",
        "scaler = StandardScaler()\n",
        "dataset[['Change_in_pop','WAP_not_working','HH_Income_Percent_Poor','Voter_Turnout','Mean_Age','Small_Households','Percentage_Youth','Percentage_NWA','Percentage_White','Percentage_HHWW','Inequality','Percentage_PWM','Percentage_NSL','Percentage_Tribal','Percentage_Male','Percentage_Informal','Percentage_IOT']] = scaler.fit_transform(dataset[['Change_in_pop','WAP_not_working','HH_Income_Percent_Poor','Voter_Turnout','Mean_Age','Small_Households','Percentage_Youth','Percentage_NWA','Percentage_White','Percentage_HHWW','Inequality','Percentage_PWM','Percentage_NSL','Percentage_Tribal','Percentage_Male','Percentage_Informal','Percentage_IOT']]) \n",
        "\n",
        "# The Frequency-Weighted model\n",
        "import statsmodels.formula.api as smf\n",
        "\n",
        "f = \"\"\" Q('Target2') ~ Q('Change_in_pop') + Q('WAP_not_working') + Q('HH_Income_Percent_Poor') + Q('Voter_Turnout') + Q('Mean_Age')\t+ Q('Small_Households') + Q('Percentage_Youth') + Q('Percentage_NWA') + Q('Percentage_White')+ Q('Percentage_HHWW')\t+ Q('Inequality')\t+ Q('Percentage_PWM') + Q('Percentage_NSL') + Q('Percentage_Tribal') + Q('Percentage_Male')\t+ Q('Percentage_Informal') +  Q('Percentage_IOT') + C(AG_Rating) + C(COGTA_Score) \"\"\"\n",
        "glm = smf.glm(f, data=dataset, family=sm.families.q(),  freq_weights=np.asarray(dataset['Pop']))\n",
        "res_f = glm.fit()\n",
        "print(res_f.summary())\n",
        "#family=sm.families.GLM()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S2x5pNAEcsyt",
        "colab_type": "code",
        "outputId": "65c21902-681c-4935-9108-2b6142e1867b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "source": [
        "# Xc.drop(['const'], axis=1, inplace=True)\n",
        "\n",
        "print(\"VIF Scores for each variable: \")\n",
        "pd.Series([variance_inflation_factor(Xc.values, i) \n",
        "               for i in range(Xc.shape[1])], \n",
        "              index=Xc.columns)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "VIF Scores for each variable: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Change_in_pop          2.757398\n",
              "Percentage_White       4.911568\n",
              "Inequality             4.615493\n",
              "Percentage_PWM         2.026654\n",
              "Percentage_Informal    1.610813\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zCzUtqgDlsaZ",
        "colab_type": "code",
        "outputId": "8b4472b8-860e-4bf2-a5a1-2b45037cd9fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "#Eigenvectors\n",
        "corr = np.corrcoef(X, rowvar=0)\n",
        "eigenvalues, eigenvectors = np.linalg.eig(corr)\n",
        "#check for low eigenvalues (under 0.1)\n",
        "np.set_printoptions(suppress=True) #to suppress scientific notation\n",
        "print(np.round(eigenvalues, 2))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[10.19  2.23  1.15  0.97  0.84  0.73  0.69  0.49  0.36  0.29  0.25  0.22\n",
            "  0.01  0.02  0.05  0.08  0.11  0.16  0.15]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RYNwoNj6Yqm2",
        "colab_type": "code",
        "outputId": "b6a9d0df-0185-4e54-f18e-e154379d7fb9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 712
        }
      },
      "source": [
        "# Neg Binomial\n",
        "\n",
        "f = \"\"\" Target1 ~  Q('Change_in_pop') + Q('WAP_not_working') + Q('HH_Income_Percent_Poor') + Q('Voter_Turnout') + Q('Mean_Age')\t+ Q('Small_Households') + Q('Percentage_Youth') + Q('Percentage_NWA') + Q('Percentage_White')+ Q('Percentage_HHWW')\t+ Q('Inequality')\t+ Q('Percentage_PWM') + Q('Percentage_NSL') + Q('Percentage_Tribal') + Q('Percentage_Male')\t+ Q('Percentage_Informal') +  Q('Percentage_IOT') + C(AG_Rating) + C(COGTA_Score)  \"\"\"\n",
        "#    \n",
        "data =  dataset\n",
        "\n",
        "response, predictors = dmatrices(f, data, return_type='dataframe')\n",
        "\n",
        "# Method 1 for Neg Binomial \n",
        "nb_results = sm.GLM(response, predictors, family=sm.families.NegativeBinomial()).fit()\n",
        "print(nb_results.summary())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                 Generalized Linear Model Regression Results                  \n",
            "==============================================================================\n",
            "Dep. Variable:                Target1   No. Observations:                  234\n",
            "Model:                            GLM   Df Residuals:                      209\n",
            "Model Family:        NegativeBinomial   Df Model:                           24\n",
            "Link Function:                    log   Scale:                          1.0000\n",
            "Method:                          IRLS   Log-Likelihood:                -1448.9\n",
            "Date:                Mon, 14 Oct 2019   Deviance:                       154.50\n",
            "Time:                        08:44:48   Pearson chi2:                     186.\n",
            "No. Iterations:                    15                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "===============================================================================================\n",
            "                                  coef    std err          z      P>|z|      [0.025      0.975]\n",
            "-----------------------------------------------------------------------------------------------\n",
            "Intercept                       4.5476      0.472      9.640      0.000       3.623       5.472\n",
            "C(AG_Rating)[T.2.0]             0.7148      0.425      1.682      0.093      -0.118       1.548\n",
            "C(AG_Rating)[T.3.0]             0.8864      0.430      2.059      0.039       0.043       1.730\n",
            "C(AG_Rating)[T.4.0]             0.9669      0.633      1.528      0.126      -0.273       2.207\n",
            "C(AG_Rating)[T.5.0]             1.0038      0.424      2.365      0.018       0.172       1.836\n",
            "C(COGTA_Score)[T.2.0]          -0.1897      0.243     -0.781      0.435      -0.665       0.286\n",
            "C(COGTA_Score)[T.3.0]          -0.1097      0.315     -0.348      0.728      -0.727       0.508\n",
            "C(COGTA_Score)[T.4.0]          -0.5171      0.449     -1.152      0.250      -1.397       0.363\n",
            "Q('Change_in_pop')             -0.0087      0.109     -0.080      0.936      -0.222       0.204\n",
            "Q('WAP_not_working')            0.0381      0.215      0.177      0.859      -0.383       0.459\n",
            "Q('HH_Income_Percent_Poor')     0.4077      0.124      3.288      0.001       0.165       0.651\n",
            "Q('Voter_Turnout')             -0.2806      0.088     -3.175      0.001      -0.454      -0.107\n",
            "Q('Mean_Age')                  -0.2473      0.218     -1.134      0.257      -0.675       0.180\n",
            "Q('Small_Households')          -0.3729      0.142     -2.627      0.009      -0.651      -0.095\n",
            "Q('Percentage_Youth')          -0.1472      0.254     -0.579      0.563      -0.646       0.351\n",
            "Q('Percentage_NWA')            -1.1507      0.409     -2.811      0.005      -1.953      -0.348\n",
            "Q('Percentage_White')          -0.1126      0.174     -0.648      0.517      -0.453       0.228\n",
            "Q('Percentage_HHWW')            0.5232      0.201      2.603      0.009       0.129       0.917\n",
            "Q('Inequality')                 0.0700      0.127      0.550      0.582      -0.179       0.319\n",
            "Q('Percentage_PWM')            -0.7944      0.210     -3.786      0.000      -1.206      -0.383\n",
            "Q('Percentage_NSL')             0.0340      0.102      0.335      0.738      -0.165       0.233\n",
            "Q('Percentage_Tribal')          0.4037      0.461      0.876      0.381      -0.499       1.307\n",
            "Q('Percentage_Male')           -0.3704      0.152     -2.441      0.015      -0.668      -0.073\n",
            "Q('Percentage_Informal')        0.1733      0.102      1.694      0.090      -0.027       0.374\n",
            "Q('Percentage_IOT')            -0.5532      0.524     -1.055      0.291      -1.580       0.474\n",
            "===============================================================================================\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_3OAMR-rX4TR",
        "colab_type": "code",
        "outputId": "90063d39-5c16-4d59-a7a3-6af5351866df",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 800
        }
      },
      "source": [
        "# Now doing NB regression using Statsmodels\n",
        "linear_regression = NegativeBinomial(y, Xc, loglike_method='nb2', offset=None, exposure=None, missing='none')\n",
        "\n",
        "fitted_model = linear_regression.fit()\n",
        "fitted_model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Warning: Maximum number of iterations has been exceeded.\n",
            "         Current function value: 6.137818\n",
            "         Iterations: 35\n",
            "         Function evaluations: 39\n",
            "         Gradient evaluations: 39\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/base/model.py:512: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
            "  \"Check mle_retvals\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>NegativeBinomial Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>  <td>   234</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>           <td>NegativeBinomial</td> <th>  Df Residuals:      </th>  <td>   214</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>                 <td>MLE</td>       <th>  Df Model:          </th>  <td>    19</td>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>            <td>Mon, 14 Oct 2019</td> <th>  Pseudo R-squ.:     </th>  <td>0.09906</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                <td>08:49:44</td>     <th>  Log-Likelihood:    </th> <td> -1436.2</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>converged:</th>             <td>False</td>      <th>  LL-Null:           </th> <td> -1594.2</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>  LLR p-value:       </th> <td>1.131e-55</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "             <td></td>               <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>const</th>                  <td>    5.1109</td> <td>    0.308</td> <td>   16.614</td> <td> 0.000</td> <td>    4.508</td> <td>    5.714</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Change_in_pop</th>          <td>   -0.0285</td> <td>    0.094</td> <td>   -0.304</td> <td> 0.761</td> <td>   -0.213</td> <td>    0.155</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>WAP_not_working</th>        <td>   -0.0273</td> <td>    0.168</td> <td>   -0.162</td> <td> 0.871</td> <td>   -0.357</td> <td>    0.303</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>HH_Income_Percent_Poor</th> <td>    0.4389</td> <td>    0.100</td> <td>    4.395</td> <td> 0.000</td> <td>    0.243</td> <td>    0.635</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Voter_Turnout</th>          <td>   -0.2693</td> <td>    0.066</td> <td>   -4.054</td> <td> 0.000</td> <td>   -0.399</td> <td>   -0.139</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Mean_Age</th>               <td>   -0.2208</td> <td>    0.186</td> <td>   -1.189</td> <td> 0.234</td> <td>   -0.585</td> <td>    0.143</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Small_Households</th>       <td>   -0.3755</td> <td>    0.121</td> <td>   -3.101</td> <td> 0.002</td> <td>   -0.613</td> <td>   -0.138</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Percentage_Youth</th>       <td>   -0.1179</td> <td>    0.213</td> <td>   -0.555</td> <td> 0.579</td> <td>   -0.535</td> <td>    0.299</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Percentage_NWA</th>         <td>   -1.1792</td> <td>    0.343</td> <td>   -3.439</td> <td> 0.001</td> <td>   -1.851</td> <td>   -0.507</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Percentage_White</th>       <td>   -0.1398</td> <td>    0.154</td> <td>   -0.909</td> <td> 0.363</td> <td>   -0.441</td> <td>    0.162</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Percentage_HHWW</th>        <td>    0.4317</td> <td>    0.162</td> <td>    2.671</td> <td> 0.008</td> <td>    0.115</td> <td>    0.748</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Inequality</th>             <td>    0.1003</td> <td>    0.099</td> <td>    1.017</td> <td> 0.309</td> <td>   -0.093</td> <td>    0.294</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Percentage_PWM</th>         <td>   -0.7347</td> <td>    0.175</td> <td>   -4.191</td> <td> 0.000</td> <td>   -1.078</td> <td>   -0.391</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Percentage_NSL</th>         <td>    0.0146</td> <td>    0.090</td> <td>    0.163</td> <td> 0.871</td> <td>   -0.161</td> <td>    0.190</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Percentage_Tribal</th>      <td>    0.3000</td> <td>    0.403</td> <td>    0.744</td> <td> 0.457</td> <td>   -0.490</td> <td>    1.090</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Percentage_Male</th>        <td>   -0.3926</td> <td>    0.131</td> <td>   -3.000</td> <td> 0.003</td> <td>   -0.649</td> <td>   -0.136</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Percentage_Informal</th>    <td>    0.1541</td> <td>    0.086</td> <td>    1.795</td> <td> 0.073</td> <td>   -0.014</td> <td>    0.322</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Percentage_IOT</th>         <td>   -0.3860</td> <td>    0.431</td> <td>   -0.896</td> <td> 0.370</td> <td>   -1.231</td> <td>    0.459</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>COGTA_Score</th>            <td>   -0.1214</td> <td>    0.117</td> <td>   -1.040</td> <td> 0.298</td> <td>   -0.350</td> <td>    0.107</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>AG_Rating</th>              <td>    0.1150</td> <td>    0.050</td> <td>    2.304</td> <td> 0.021</td> <td>    0.017</td> <td>    0.213</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>alpha</th>                  <td>    0.6118</td> <td>    0.053</td> <td>   11.580</td> <td> 0.000</td> <td>    0.508</td> <td>    0.715</td>\n",
              "</tr>\n",
              "</table>"
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                     NegativeBinomial Regression Results                      \n",
              "==============================================================================\n",
              "Dep. Variable:                      y   No. Observations:                  234\n",
              "Model:               NegativeBinomial   Df Residuals:                      214\n",
              "Method:                           MLE   Df Model:                           19\n",
              "Date:                Mon, 14 Oct 2019   Pseudo R-squ.:                 0.09906\n",
              "Time:                        08:49:44   Log-Likelihood:                -1436.2\n",
              "converged:                      False   LL-Null:                       -1594.2\n",
              "Covariance Type:            nonrobust   LLR p-value:                 1.131e-55\n",
              "==========================================================================================\n",
              "                             coef    std err          z      P>|z|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------------------\n",
              "const                      5.1109      0.308     16.614      0.000       4.508       5.714\n",
              "Change_in_pop             -0.0285      0.094     -0.304      0.761      -0.213       0.155\n",
              "WAP_not_working           -0.0273      0.168     -0.162      0.871      -0.357       0.303\n",
              "HH_Income_Percent_Poor     0.4389      0.100      4.395      0.000       0.243       0.635\n",
              "Voter_Turnout             -0.2693      0.066     -4.054      0.000      -0.399      -0.139\n",
              "Mean_Age                  -0.2208      0.186     -1.189      0.234      -0.585       0.143\n",
              "Small_Households          -0.3755      0.121     -3.101      0.002      -0.613      -0.138\n",
              "Percentage_Youth          -0.1179      0.213     -0.555      0.579      -0.535       0.299\n",
              "Percentage_NWA            -1.1792      0.343     -3.439      0.001      -1.851      -0.507\n",
              "Percentage_White          -0.1398      0.154     -0.909      0.363      -0.441       0.162\n",
              "Percentage_HHWW            0.4317      0.162      2.671      0.008       0.115       0.748\n",
              "Inequality                 0.1003      0.099      1.017      0.309      -0.093       0.294\n",
              "Percentage_PWM            -0.7347      0.175     -4.191      0.000      -1.078      -0.391\n",
              "Percentage_NSL             0.0146      0.090      0.163      0.871      -0.161       0.190\n",
              "Percentage_Tribal          0.3000      0.403      0.744      0.457      -0.490       1.090\n",
              "Percentage_Male           -0.3926      0.131     -3.000      0.003      -0.649      -0.136\n",
              "Percentage_Informal        0.1541      0.086      1.795      0.073      -0.014       0.322\n",
              "Percentage_IOT            -0.3860      0.431     -0.896      0.370      -1.231       0.459\n",
              "COGTA_Score               -0.1214      0.117     -1.040      0.298      -0.350       0.107\n",
              "AG_Rating                  0.1150      0.050      2.304      0.021       0.017       0.213\n",
              "alpha                      0.6118      0.053     11.580      0.000       0.508       0.715\n",
              "==========================================================================================\n",
              "\"\"\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_r2mqMDOvNcq",
        "colab_type": "code",
        "outputId": "9a204191-5d73-44b1-a790-df0d3fe352d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        }
      },
      "source": [
        "f = \"\"\" Target1 ~ Q('Change_in_pop') + Q('WAP_not_working') + Q('HH_Income_Percent_Poor') + Q('Voter_Turnout') + Mean_Age\t+ Q('Small_Households') + Q('Percentage_NWA') + Q('Percentage_White')+ Q('Percentage_HHWW')\t+ Q('Inequality')\t+ Q('Percentage_PWM') + Q('Percentage_NSL') + Q('Percentage_Tribal') + Q('Percentage_Male')\t+ Q('Percentage_Informal')  + C(AG_Rating)  \"\"\"\n",
        "data = dataset\n",
        "\n",
        "response, predictors = dmatrices(f, data, return_type='dataframe')\n",
        "\n",
        "# Method 1 for Neg Binomial \n",
        "nb_results = sm.GLM(response, predictors, family=sm.families.NegativeBinomial()).fit()\n",
        "print(nb_results.summary())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                 Generalized Linear Model Regression Results                  \n",
            "==============================================================================\n",
            "Dep. Variable:                Target1   No. Observations:                  234\n",
            "Model:                            GLM   Df Residuals:                      214\n",
            "Model Family:        NegativeBinomial   Df Model:                           19\n",
            "Link Function:                    log   Scale:                          1.0000\n",
            "Method:                          IRLS   Log-Likelihood:                -1450.5\n",
            "Date:                Tue, 03 Sep 2019   Deviance:                       157.78\n",
            "Time:                        14:28:05   Pearson chi2:                     194.\n",
            "No. Iterations:                    14                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "===============================================================================================\n",
            "                                  coef    std err          z      P>|z|      [0.025      0.975]\n",
            "-----------------------------------------------------------------------------------------------\n",
            "Intercept                       4.4024      0.399     11.031      0.000       3.620       5.185\n",
            "C(AG_Rating)[T.2.0]             0.6669      0.414      1.610      0.107      -0.145       1.479\n",
            "C(AG_Rating)[T.3.0]             0.8172      0.425      1.924      0.054      -0.015       1.650\n",
            "C(AG_Rating)[T.4.0]             0.8926      0.621      1.437      0.151      -0.325       2.110\n",
            "C(AG_Rating)[T.5.0]             0.9665      0.420      2.299      0.021       0.143       1.790\n",
            "Q('Change_in_pop')             -0.0021      0.107     -0.020      0.984      -0.212       0.208\n",
            "Q('WAP_not_working')           -0.0215      0.207     -0.103      0.918      -0.428       0.385\n",
            "Q('HH_Income_Percent_Poor')     0.4259      0.122      3.483      0.000       0.186       0.666\n",
            "Q('Voter_Turnout')             -0.2930      0.086     -3.411      0.001      -0.461      -0.125\n",
            "Mean_Age                       -0.1210      0.120     -1.007      0.314      -0.357       0.114\n",
            "Q('Small_Households')          -0.4010      0.135     -2.964      0.003      -0.666      -0.136\n",
            "Q('Percentage_NWA')            -1.0984      0.276     -3.986      0.000      -1.639      -0.558\n",
            "Q('Percentage_White')          -0.0928      0.165     -0.562      0.574      -0.416       0.231\n",
            "Q('Percentage_HHWW')            0.3995      0.176      2.268      0.023       0.054       0.745\n",
            "Q('Inequality')                 0.0797      0.125      0.639      0.523      -0.165       0.324\n",
            "Q('Percentage_PWM')            -0.7503      0.192     -3.905      0.000      -1.127      -0.374\n",
            "Q('Percentage_NSL')            -0.0059      0.100     -0.059      0.953      -0.201       0.190\n",
            "Q('Percentage_Tribal')         -0.1220      0.190     -0.641      0.521      -0.495       0.251\n",
            "Q('Percentage_Male')           -0.3790      0.148     -2.553      0.011      -0.670      -0.088\n",
            "Q('Percentage_Informal')        0.1062      0.088      1.207      0.227      -0.066       0.279\n",
            "===============================================================================================\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aeyWY1V3ySYO",
        "colab_type": "code",
        "outputId": "39051b67-6863-4f38-f015-ed724aaee601",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "source": [
        "# Drop superfluous features\n",
        "Xc.drop(['Large_Households',  'Percentage_Youth', 'Percentage_IOT', 'COGTA_Score', 'Percentage_Poor'], axis=1, inplace=True)\n",
        "print(Xc.columns[1:])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['Change_in_pop', 'WAP_not_working', 'HH_Income_Percent_Poor',\n",
            "       'Voter_Turnout', 'Mean_Age', 'Small_Households', 'Percentage_White',\n",
            "       'Percentage_HHWW', 'Inequality', 'Percentage_PWM', 'Percentage_NSL',\n",
            "       'Percentage_Male', 'Percentage_Informal', 'AG_Rating'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rd2iLr8Gy6HR",
        "colab_type": "code",
        "outputId": "df016cd9-436b-4ae2-a879-2811cfeeaf33",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 896
        }
      },
      "source": [
        "#Correlation table \n",
        "\n",
        "#Temporarily drop constant\n",
        "Xc.drop('const', axis=1, inplace=True)\n",
        "\n",
        "hurdle = 0.0\n",
        "R = np.corrcoef(Xc, rowvar=0) \n",
        "R[np.where(np.abs(R)<hurdle)] = 0.0\n",
        "\n",
        "MJB = [\"#2680FA\", \"#3498db\", \"#89E6FF\", \"#FFFFFF\", \"#FFFFFF\", \"#FFFFFF\", \"#FFFFFF\", \"#FFFFFF\", \"#FEC95F\", \"#FEB159\", \"#FF8D28\"]\n",
        "sns.set_palette(MJB)\n",
        "sns.palplot(sns.color_palette())\n",
        "\n",
        "cmap = LinearSegmentedColormap.from_list('mycmap', MJB)\n",
        "\n",
        "# Generate a mask for the upper triangle\n",
        "mask = np.zeros_like(R, dtype=np.bool)\n",
        "mask[np.triu_indices_from(mask)] = True\n",
        "\n",
        "# Set up the matplotlib figure\n",
        "f, ax = plt.subplots(figsize=(14, 11))\n",
        "\n",
        "# Generate a custom diverging colormap\n",
        "#cmap = sns.diverging_palette(220, 10, as_cmap=True, )\n",
        "#cmap=\"Spectral\"\n",
        "\n",
        "# Draw the heatmap with the mask and correct aspect ratio\n",
        "sns.heatmap(R, mask=mask, cmap=cmap, annot=True, vmax=1.0, center=0,\n",
        "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5}, xticklabels=Xc.columns[:], yticklabels=Xc.columns[:] )\n",
        "\n",
        "#Add contant back\n",
        "Xc = sm.add_constant(X)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/numpy/core/fromnumeric.py:2389: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
            "  return ptp(axis=axis, out=out, **kwargs)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnsAAABLCAYAAAAI0UETAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAAo9JREFUeJzt3b1qlEEYhuF31FVBEIUEok0awcYu\nOQ47e6v1AAQPJkckYrCxsLBQsLBIq/hTjI2NjV+WMMx+D9fVBaZ4n+4mu7Ct914AAGS6NvsAAADG\nEXsAAMHEHgBAMLEHABBM7AEABBN7AADBxB4AQDCxBwAQTOwBAAS7sfSgtbatqm1VVdvcObl58Hj4\nUbNc3/yafcJQ9+/dmn3CMEeb2RcAkb59mn3BWL/Df0Xr5/fZFwx1/uXHRe/9cOld2+Xn0m4/POnH\nL15f6bB9dvfo8+wThnr29NHsE4Z59WD2BUCi/ub57BPG+pr9T4728d3sE4ZqLz+c995Pl975GBcA\nIJjYAwAIJvYAAIKJPQCAYGIPACCY2AMACCb2AACCiT0AgGBiDwAgmNgDAAgm9gAAgok9AIBgYg8A\nIJjYAwAIJvYAAIKJPQCAYGIPACCY2AMACCb2AACCiT0AgGBiDwAgmNgDAAgm9gAAgok9AIBgYg8A\nIJjYAwAIJvYAAIKJPQCAYGIPACCY2AMACCb2AACCiT0AgGBiDwAgmNgDAAgm9gAAgok9AIBgYg8A\nIJjYAwAIJvYAAIKJPQCAYGIPACCY2AMACCb2AACCiT0AgGBiDwAgmNgDAAgm9gAAgok9AIBgYg8A\nIJjYAwAIJvYAAIKJPQCAYK33/v8HrW2ravv3zydV9X70URMdVNXF7CMGSd5WZd/a2bdeyduq7Fu7\n9H3HvffDpUeLsffP49be9t5Pr3TWHkvel7ytyr61s2+9krdV2bd26fsuy8e4AADBxB4AQLBdY+9s\nyBX7I3lf8rYq+9bOvvVK3lZl39ql77uUnb6zBwDAuvgYFwAgmNgDAAgm9gAAgok9AIBgYg8AINgf\n7rVwIwvi/BUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 792x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAz8AAALtCAYAAAAG1NrTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3Xl4VeW1x/HvSkII80xAZVDAAYiC\nQ4sTyFQVpIDItQheqSi1vThUcagVxIFRLXVGbMEBtAIqqNhCRSYVZRAIgygKQQYTZgMKCUnW/ePs\nxIQkEJJAQs7v8zx5OPt919577ZOjT1bWu3fM3RERERERESnrIko6ARERERERkRNBxY+IiIiIiIQF\nFT8iIiIiIhIWVPyIiIiIiEhYUPEjIiIiIiJhQcWPiIiIiIiEBRU/IiIiIiISFlT8iIiIiIhIWFDx\nIyIiIiIiYSGqpBOQk5KXdAIiIiIiZYSVdALhRJ0fEREREREJCyp+REREREQkLKj4ERERERGRsKDi\nR0REREREwoKKHxERERERCQsqfkREREREJCyo+BERERERkbCg4kdERERERMKCih8REREREQkLKn5E\nRERERCQsqPgREREREZGwoOJHRERERETCgoofEREREREJCyp+REREREQkLKj4ERERERGRsBBWxY+Z\n1TOzf5nZd2a2zMw+NLOBZvZBSeeWnZn9w8yal3QeIiIiIiJlSVRJJ3CimJkB7wKvuvvvgrHzgN+W\naGJ5cPdbSjoHEREREZGyJpw6P+2BQ+4+LnPA3VcCC4HKZjbNzNaZ2eSgUMLMhprZEjNbbWbjs43P\nM7PRZrbYzL4xs8uD8YpmNsXM1prZu2b2hZldGMz9xswWmdmXZjbVzCrnl2hw/Mz99pvZcDNbaWaf\nm1nsEfZ7xczGmdnSIK9rgvEYM5toZqvMbLmZtQ/G+5vZjOB8683s4SK+xyIiIiIipVY4FT8tgWX5\nzLUG7gKaA2cAlwbjz7n7Re7eEqgAXJNtnyh3/1WwX2bR8Cdgj7s3B4YAFwCYWW3gIaCTu58PLAXu\nLmDelYDP3f08YAFw61HiGwO/AroC48wsBvg/wN09DugDvBqME8T2As4FemcWXYcLlgcuNbOl48eP\nL2DqIiIiIiKlR9gsezuKxe6+BcDMVhAqID4B2pvZfUBFoCawBng/2Oed4N9lQTzAZcDTAO6+2szi\ng/E2hAqrT4PmUTSwqIC5pQKZ9yQtAzofJX6Ku2cA681sA3B2kNezQV7rzGwTcGYQ/1933xVc+ztB\n7NLDD+ru44HMqscLmLuIiIiISKkRTsXPGuC6fOZSsr1OB6KCzsgLwIXuvtnMhgExeeyTztHfRyNU\nZPQ55qxDS/Uyi42CnOvwwuRohcqxxouIiIiInJTCadnbx0B5MxuYOWBm5wKX5xOfWejsDO7Pya9w\nyu5T4H+CYzcH4oLxz4FLzaxpMFfJzM7M+xBF1tvMIsysCaElfF8Tuq+pb3DuM4GGwThAZzOraWYV\ngB7BNYiIiIiIlDlh0/lxdzeznsDfzex+4CCQAEzPJ36vmb0MrAYSgSUFOM0LhO6nWQusI9Rt+tHd\nd5hZf+BNMysfxD4EfFOES8rP98BioCpwm7sfNLMXgBfNbBWQBvR395RgCd5i4G3gNGCSu+da8iYi\nIiIiUhbYLyuqpKjMLBIoFxQcTYCPgLPcPfUEnf8V4AN3n1bA+P6ElvUNOsZT6UMjIiIiUjyspBMI\nJ2HT+TlBKgJzzawcoQ/yn05U4SMiIiIiIkemzk8JMrN3gdMPG77f3WcdZb+/Ar0PG57q7sOLM78j\n0IdGREREpHio83MCqfiRwtCHRkRERKR4qPg5gcLpaW8iIiIiIhLGVPyIiIiIiEhYUPEjIiIiIiJh\nQcWPiIiIiIiEBRU/IiIiIiISFlT8iIiIiIhIWFDxIyIiIiIiYUHFj4iIiIiIhAUVPyIiIiIiEhZU\n/IiIiIiISFiIKukE5OT02Kb0kk4hhyGNIks6BREREREp5dT5ERERERGRsKDiR0REREREwoKKHxER\nERERCQsqfkREREREJCyo+BERERERkbCg4kdERERERMKCih8REREREQkLKn5ERERERCQsqPgRERER\nEZGwoOJHRERERETCgoofEREREREJCyp+REREREQkLKj4ERERERGRsKDiR44Ld2fJ88OZ3v9KPvhD\nD3atX5tnXPqhVD4f+zAzfn81793cle8XzgYgKX4pM//Ui8lXxbFpwawTmbqIiIiIlFFRJZ2AlE3b\nlixg39ZNdJ/4H3aui2fxM49w9bNv5Ypb/eZLxFSvSfeJ/8YzMkjZ9yMAlerW55LBI1g7beKJTl1E\nREREyqiTtvNjZmPN7K5s27PM7B/Ztp8ys7uD13eZ2UEzq5Zt/goz+9HMVpjZV2b2cDHn92BxHu8o\n50ows9p5jH92onI43ObPPub0zt0xM+qccx6pP+3j5107csV99593afm7WwGwiAhiqtUAoHK9U6lx\nxlmYnbQfUREREREpZU7mnyw/BS4BsNBPyLWBFtnmLwEyf/jvAywBrj3sGAvdvRVwIdDPzM4vxvxO\nSPFjZpH5zbn7JScih7wc2LWdSnXqZW1Xqh3LgV1JOWJS9ycDsOLVZ5n5p14seOwuDuzZeULzFBER\nEZHwcTIXP58BFwevWwCrgX1mVsPMygPnAF+aWROgMvAQoSIoF3f/CVgGNM1r3sz6m9k7ZvYfM1tv\nZmOyzfUxs1VmttrMRgdjo4AKQVdpcj7HvNfM7ghejzWzj4PXHTL3yevYwfj+oLO1Mtt7gJlVMLN/\nm9mtmXHBv1eY2Twzm2Zm68xssplZMNclGFtmZs+Y2Qf55DvQzJaa2dLx48fnFXLMMtLT+XlnInWa\nt6LrC29T+5xWfDn+iWI5toiIiIjI4U7ae37cfZuZpZlZQ0JdnkXAqYSKgR+BVe6eama/A/4FLATO\nMrNYd8/RgjCzWkAb4LEjnLIV0BpIAb42s2eBdGA0cAGwB5htZj3c/QEzGxR0lfKzELgHeIZQ56m8\nmZUDLgcWmNkp+Rx7OlAJ+MLd7wnyh1CB9y/gNXd/LY/ztSZUJG4j1DW71MyWAi8Bbd19o5m9mV+y\n7j4eyKx6/LFN6blivn7vDb79cCoAtc6K46cdiVlzP+1MokKt2Bzx5atWJ7J8BRpe1hmARm2v5LtZ\nb+eXgoiIiIhIkZzMnR8IdX8u4ZfiZ1G27U+DmD7Av9w9A3gb6J1t/8vNbDkwGxjl7muOcK457v6j\nux8E1gKNgIuAee6+w93TgMlA2wLmvgy4wMyqEiqoFhEqgi4nVBgd6djpwbVkNwOYmE/hA7DY3bcE\n78MKoDFwNrDB3TcGMfkWPwVx1m9voOu4d+k67l1Ou6QjG/87A3dnx1cria5UhYq16uSINzNOa3MF\nSSsXA5C44nOqNWxSlBRERERERPJ1shc/mff9xBFa9vY5oc7PJcBnZhYHNAP+a2YJwO/IufRtobu3\ndvcL3H3cUc6Vku11OkXsmrn7IWAj0J9QEbcQaE9o6d1XR9n9oLsf3nr5FLgqczlbHoo1/6M59Vdt\nqVy/ATP6X8UXY4fyq9uHZM3NvK1n1uvWt9xN/OvP88EferDxo/e44A/3AbDz61W8c0N7Ni2YxRdP\nD+P9W7sdz3RFREREJAyctMveAp8Bgwl1L9KB3WZWndDyrluBu4Fh7j4ycwcz22hmjYrp/IuBZ4In\nre0hVFg9G8wdMrNyQZGTn4VB/jcDq4C/Acvc3c3sSMfOy9Dg63ngTwXM/2vgDDNr7O4JwPUF3O+o\nzCxHwZNd13HvZr2uHHsqv/nb67liap8Vx7VvzC2udERERERETvrOzypCT3n7/LCxH919J6FOz7uH\n7fNuMF5k7v4D8AAwF1hJqHCZEUyPB+Lze+BBYCFQH1gU3Id0MBg72rHzcyehBy2MOUpcZv4HCBVK\n/zGzZcA+QvdLiYiIiIiUOebuJZ2DlCAzq+zu+4Plcs8D69197FF2y/OBByVpSKN8n/gtIiIiUprl\nd8uCHAcne+dHiu5WM1sBrAGqEXr6m4iIiIhImXOy3/NTrMzsSkKPl85uo7v3zCu+gMesBczJY6qj\nu+8q7HGLS9DlOVqnR0RERETkpKfiJxt3nwXMKuZj7iL0N4JERERERKQEadmbiIiIiIiEBRU/IiIi\nIiISFlT8iIiIiIhIWFDxIyIiIiIiYUHFj4iIiIiIhAUVPyIiIiIiEhZU/IiIiIiISFhQ8SMiIiIi\nImHB3L2kc5CTjz40IiIiIsXDSjqBcKLOj4iIiIiIhIWokk5ApLj4/P8t6RRysHavlXQKIiIiIpKN\nOj8iIiIiIhIWVPyIiIiIiEhYUPEjIiIiIiJhQcWPiIiIiIiEBRU/IiIiIiISFlT8iIiIiIhIWFDx\nIyIiIiIiYUHFj4iIiIiIhAUVPyIiIiIiEhZU/IiIiIiISFhQ8SMiIiIiImFBxY+IiIiIiISFqJJO\nQMomd2f48OHMnz+fmJgYRo0aRYsWLXLEHDhwgDvvvJPvv/+eyMhI2rdvz+DBgwEYMWIEX3zxBQAH\nDx5k165dLF26tMh5LVy9l+FvbSIjw7nusroMvPqUXDH/XrqL597fgmGc1aAiT93SFIAnpn3P/FV7\nyXDnkubV+Ov1jTCzIuckIiIiIieGih85LhYsWEBCQgKzZ89m5cqVDBs2jKlTp+aKu/nmm2nTpg2p\nqan079+f+fPn065dOx588MGsmNdff521a9cWOaf0DOfRNxKY8Oezia0RTe8Ra+hwXnWanlIxKyYh\n6SDj/72NN+5rQbVKUexKPgTAl9/t48vv9jHj4TgAbhizlsXf7OPXZ1Utcl4iIiIicmIcddmbme0/\nbLu/mT0XvB5mZoMPm08ws9oFPV5pFFzXVjNbYWarzey3JZRHKzPrcpSY/ma2I8h1rZndeqLyO5I5\nc+bQo0cPzIxWrVqRnJzM9u3bc8RUqFCBNm3aABAdHU3z5s1JSkrKdayZM2dyzTXXFDmn+I37aVg3\nhgZ1YoiOiqDLRTWZs3JPjpipC7dzwxWxVKsU+r1ArarlADAg5VAGh9Kc1EMZpKU7tYM5ERERETk5\n6J6f/I1191ZAb2CCmRXovTKz4uymtQKOWPwE3gpyvQIYYWaxRTlpcVxDUlIS9erVy9quV69enoVN\npuTkZObOncvFF1+cY3zr1q1s2bIlq0gqUk57U6lfM/qXnKpHk7TnUI6YhKSDJCQdpM/oNVw/cjUL\nV+8FoHWTKvz6rKpcfu+XXH7vci5rXo0m9SsUOScREREROXFKrPgxsyvMbJ6ZTTOzdWY22YIbKMzs\nIjP7zMxWmtliM6tiZjFmNtHMVpnZcjNrH8T2N7PpZvbfoOs0yMzuDmI+N7OaQVwTM/uPmS0zs4Vm\ndnZB8nT3r4A0oLaZ1TGzt81sSfB1aXDsYWb2upl9CrxuZpFm9mTQNYo3s9uDuAvMbH6Qwywzqx+M\nzzOz0cG1fmNml5tZNPAocH3Q1bm+ALluB74DGplZzeB9iQ/eh3ODc+U3nuMaCv6dLLq0tDTuvvtu\nbrzxRho0aJBjbubMmVx55ZVERkaemFwynE3bD/LaPefw1K1NGfL6RpJ/TmPT9oNs+OEg80a3Zv6Y\n1nz+dTJL1yefkJxEREREpHgU5Df8FcxsRbbtmsB72bb/bGb9sm3nvoM8f62BFsA24FPgUjNbDLwF\nXO/uS8ysKnAAuBNwd48LCpfZZnZmcJyWwbFigG+B+929tZmNBf4X+DswHrjN3deb2a+BF4AOR0sw\niM0AdgCTCXWEPjGzhsAs4JwgtDlwmbsfMLM/Ao2BVu6eFhQc5YBnge7uviMoZoYDNwf7R7n7r4Jl\nbg+7eyczGwpc6O6DCvJmmtkZwBnBe/AIsNzde5hZB+A1Qp2k/MZzXEMexx4IDAR46aWXGDhwYK7z\nT548mSlTpgAQFxdHYmJi1lxiYiKxsXk3pIYMGULjxo3p379/rrkPP/yQoUOHFuTyjyq2ejQ/7E79\nJae9qcTWyLl0rV6NaM49vTLloiI4rXYMjWNj2LT9IIu/Tua8MypTKSZUhLVtWY0V3+3nwma650dE\nRETkZFGQ4udAsKQKCHVagAuzzY919yezzSccw/kXu/uWYL8VhAqGH4Ef3H0JgLsnB/OXESoecPd1\nZrYJyCx+5rr7PmCfmf0IvB+MrwLONbPKwCXA1GxP5yp/lNwyi7p9hAoxN7NOQPNsx6gaHBvgvWxF\nQydgnLunBfnuNrOWhIq0/wb7RwI/ZDvfO8G/y4L34VhcH7w/KcAfgvNdBvQKzv+xmdUKCsn8xg+/\nhhzcfTyhAhLA84rp27cvffv2BWDevHlMmjSJrl27snLlSqpUqULdunVz7TN27Fj279/P8OHDc819\n9913JCcn07p16wK/EUcS17gym7YfZMvOg9StHs2HS3bz5C1NcsR0alWDD5bsotelddiz7xAJSQc5\nrXZ5Nu8oz9SF20lLPwV3Z8k3+/jfjvXyOZOIiIiIlEYl/bS3lGyv0yl8PtmPk5FtOyM4ZgSwN3sR\nVwA5irpABNDG3Q9mHwyKmZ+OcjwD1rj7xfnMZ+ZcmPfhrYJ2h47iaNdQYO3atWP+/Pl07tyZChUq\nMGLEiKy57t27M2PGDBITExk3bhxnnHEGPXv2BKBfv3707t0bCHV9unTpUmyPk46KNIb0acyAv39N\nRobT69I6NDulIs/M2ELLRpXo0KoGl7Woxidrf6TrwyuJMOPeXg2pUbkcV15Qk8/XJfPbR+Ixg8ta\nVKfDeTWKJS8REREROTFKuvjJy9dAfTO7KFj2VoXQsreFQF/g42C5W8Mg9vyjHdDdk81so5n1dvep\nwb1F57r7ymPMbTZwO/AEhJ7G5u4r8oj7L/AHM5ubuewtyLWOmV3s7ouCZXBnuvuaI5xvH1DlGHPM\nlPl+PWZmVwA7g/chv/FCniZvZsbDDz+c59yMGTOA0EMQvv7663yPcfvttxdrTgDt4qrTLq56jrE7\nup+W9drM+Mv/NAIa5YiJjDAevfH0Ys9HRERERE6cUve0N3dPBa4HnjWzlYQKiRhC9+hEmNkqQvcE\n9Xf3lPyPlEtfYEBwzDVA90KkdwdwYfCwgLXAbfnE/QP4HogPzndDcF3XAaODsRWEluIdyVxCy+wK\n9MCDwwwDLjCzeGAUcNNRxkVEREREyjRzz/P2DZEjKZUfGp//vyWdQg7W7rWSTkFERERKv+JdfiNH\nVOo6PyIiIiIiIsfDcbnnx8xqAXPymOro7ruOxzkLw8z+SuiPmGY31d1zP3qshJnZ7wk97ju7T939\n/0oiHxERERGRk42WvUlhlMoPjZa9iYiIyElIy95OIC17ExERERGRsKDiR0REREREwoKKHxERERER\nCQsqfkREREREJCyo+BERERERkbCg4kdERERERMKCih8REREREQkLKn5ERERERCQs6I+cSmHoQyMi\nIiJSPPRHTk8gdX5ERERERCQsqPgREREREZGwoOJHRERERETCgoofEREREREJCyp+REREREQkLKj4\nERERERGRsKDiR0REREREwoKKHxERERERCQsqfkREREREJCyo+BERERERkbCg4kdERERERMKCih8R\nEREREQkLKn5ERERERCQsqPgREREREZGwoOJHRERERETCgoofOS4WLFjAlVdeSefOnRk/fnyu+YkT\nJ9KlSxe6devGTTfdxNatW7Pmtm3bxs0338zVV19Nly5d2LJlS7Hl5e48/vjjdO7cmW7durFmzZo8\n48aOHUu7du1o3bp1jvFt27Zx44030qNHD7p168b8+fOPax6rV6+mW7dudO7cmccffxx3z5p7/fXX\nueqqq+jatStjxowBYMuWLZx77rl0796d7t27M3To0ELlJyIiIlImuXvYfgFzgSsPG7sLeDGf+MbA\nDcVw3lrAiuArEdiabTu6hN6Lu4GYAsYfUVpamnfs2NG///57T0lJ8W7duvn69etzxCxatMh//vln\nd3efPHmy33nnnVlz/fr1808++cTd3ffv358VVxzmzZvnAwYM8IyMDF++fLlfd911ecYtX77ck5KS\nvFWrVjnGH3roIZ88ebK7u69fv97bt29/XPPo1auXL1++3DMyMnzAgAE+b948dw+9fzfddJOnpKS4\nu/vOnTvd3X3z5s3etWvXQuUkIiIiJaLEfyYOp69w7/y8CfzusLHfBeN5aQzccCwnMLOow8fcfZe7\nt3L3VsA4YGzmtrunFva4RXQ3EFMcB4qPj6dRo0Y0aNCA6Ohounbtypw5c3LEtGnThgoVKgDQqlUr\nEhMTAfj2229JS0vj0ksvBaBSpUpZccVhzpw59OjRAzOjVatWJCcns3379lxxrVq1om7durnGzYz9\n+/cDsG/fvjxjiiuP7du3s3//flq1aoWZ0aNHj6z38c0332TgwIFER0cDUKtWrULlISIiIhJOwr34\nmQZ0NbNoADNrDJwCLDSzJ8xstZmtMrPrg/hRwOVmtsLM/mxmkUHcEjOLN7M/BMe5wswWmtl7wNpj\nScjMmprZimzbD5jZQ8HrT8xsrJktBQaZ2SQze9rMPjOzDWbWM4iLMLO/Zcv/umC8k5lNz3bscWbW\nz8z+DNQNrvujQryPOSQlJVGvXr2s7djYWJKSkvKNnzZtGm3btgUgISGBqlWrMmjQIHr06MHo0aNJ\nT08vakr55lavXr0j5na4QYMG8f7779O2bVsGDhzIQw89dNzyOFJMQkICS5cupXfv3vTr14/4+Pis\nuC1bttCjRw/69evH0qVLC5WfiIiISFkU1sWPu+8GFgNXB0O/A6YA1wKtgPOATsATZlYfeABYGHRo\nxgIDgB/d/SLgIuBWMzs9ONb5wJ3ufmYxpx3p7he6+9+D7brApUAPYGQw1hs4J8i/MzDWzPJtUQTX\nsh243N075RVjZgPNbKmZLc3rHp7CmjFjBqtXr+aWW24BIC0tjaVLl3L//fczbdo0tmzZwjvvvFNs\n5yuqmTNn0rNnTxYsWMD48eO57777yMjIOOF5pKen8+OPPzJlyhTuu+8+7rrrLtydunXrMnfuXKZP\nn84DDzzAPffck9WpEhEREQl3xb106mSUufRtRvDvAKAf8Ka7pwNJZjafUHGTfNi+vwHOzeysANWA\nZkAqsNjdNx6HfN86bHu6uzsQb2anBmOXZcs/0cw+AS4M8ioUdx8PZFY9fqTY2NjYrGVsEOpgxMbG\n5or77LPPGDduHJMmTcpavlWvXj3OOeccGjRoAEDHjh1ZuXJlYdMGYPLkyUyZMgWAuLi4HLklJibm\nmVt+pk2bxj/+8Q8AWrduTUpKCnv27CnQsrNjzePw9zF7TGxsLJ07d8bMOPfcc4mIiGDPnj3UrFkz\n671s2bIlDRs2ZOPGjcTFxRX4GkVERETKqrDu/ARmAB3N7HygorsvO4Z9Dbg92/06p7v77GDup0Lm\nk0bO78vh9+EcftyUw/IpyrGLRVxcHAkJCWzevJnU1FRmzpxJhw4dcsSsXbuWoUOH8uKLL+YoHOLi\n4khOTmb37t0AfPHFFzRt2rRI+fTt25cZM2YwY8YMOnXqxPTp03F3VqxYQZUqVY7pvp369euzaNEi\nAL777jtSUlKoWbPmccmjbt26VK5cmRUrVuDuTJ8+nY4dOwLQqVMnvvjiCwA2btzIoUOHqFGjBrt3\n785aJrh582YSEhKyCkkRERGRcBf2nR93329mc4EJ/PKgg4XAH8zsVaAm0Ba4FzgVqJJt91nAH83s\nY3c/ZGZnEnpyW1EkAqeYWQ3gANCVUIF2LBYC/c1sElCH0LK4O4FKQIvgHqdKQAcg8x6ffYSubW8R\n8ycqKoqhQ4dyyy23kJ6eTq9evWjWrBlPP/00LVu2pGPHjowZM4aff/6ZO++8EwgVFePGjSMyMpL7\n77+fm266CYAWLVrQu3fvoqaUpV27dsyfP5/OnTtToUIFRowYkTXXvXt3ZswIvdVjxozhgw8+4MCB\nA7Rt25bevXtz++2388ADD/DQQw/xyiuvYGaMGjUKs6PVnIXP4+GHH+Yvf/kLBw8epG3btln3RvXq\n1YsHH3yQa665hnLlymXlsWTJEp555hmioqKIiIjgkUceoXr16kV5y0RERETKDAutmApvZtYDeBc4\nx93XWein2TGE7gVy4HF3f8vMyhEqeGoBrwBPA48D3Qh1XXYQuvemNTDY3a8pwLmHAfvd/clsY3cD\n/0eokNoIrHf3x4Pla4PcfUUQNwmY5u7Tg+397l7ZzCKAJwkty3PgEXefFsQ8BfwW2ECouJrm7pOC\nhx7cBmzO776fbPShERERESkex/5bVCk0FT9SGPrQiIiIiBQPFT8nkO75ERERERGRsBD29/wcb2ZW\nC5iTx1RHd991ovMREREREQlXWvYmhaEPjYiIiEjx0LK3E0jL3kREREREJCyo+BERERERkbCg4kdE\nRERERMKCih8REREREQkLKn5ERERERCQsqPgREREREZGwoOJHRERERETCgoofEREREREJCyp+RERE\nREQkLKj4ERERERGRsBBV0gmIFJdbP91b0ink8PKl1fkupaSzyKlJ+ZLOQERERE4afzvHC73v3V9Z\nMWZSbNT5ERERERGRsKDOj4iIiIiI5GZlr09S9q5IREREREQkD+r8iIiIiIhIbhFlr0+i4kdERERE\nRHIrg8veVPyIiIiIiEhu6vyIiIiIiEhYUOdHRERERETCgoofEREREREJCxGl8u+UFknZK+dERERE\nRETyoM6PiIiIiIjkpmVvIiIiIiISFvS0NxERERERCQtlsPNT9q5ISoUFCxZw5ZVX0rlzZ8aPH59r\n/s0336Rbt250796dPn368O233wLw6aefcu2119KtWzeuvfZaFi1aVKx5uTvfTn6KxQ/0YtnQvuzb\ntC7PuIy0Q3zzygiW/OU6ljz4P+xY+jEAB3clsnLMH1k27EaWDe3L7vhPiyWncaMeZ0DXzvypVze+\nXbvmiPGP3H4bf+x5TY6x9954nYG/vYrbenbln38bU+Scjvb9W7JkCT179qR58+b85z//yTE3YMAA\nLrzwQv7whz8UOQ8RERGR4qTOjxS79PR0Hn30USZOnEhsbCzXXXcdHTp0oGnTplkx3bp1o0+fPgDM\nmTOHkSNH8s9//pMaNWrw4os7cAuCAAAgAElEQVQvEhsbyzfffMOAAQNYuHBhseW2Z9VnHEjazEUj\np7Fvw2q+fW0MrYdMyBX3/QcTKVe1JheNnIZnZJD2U3Jo/P0J1LmoE6e078VPWzew+u938+snLi1S\nTks/WcDWTQn844PZfB2/kuceH8bf35iaZ+ynH80mpmKlHGMrF3/O53Pn8Py09ygXHc3eXbuKlE9B\nvn/169dn5MiRTJiQ+7275ZZbOHDgAG+99VaR8hAREZESVgaXvZW9KypBZuZmNinbdpSZ7TCzD05g\nDrXN7JCZ3Xaiznm4+Ph4GjVqRIMGDYiOjqZr167MmTMnR0zlypWzXh84cACz0KMUmzdvTmxsLADN\nmjUjJSWF1NTUYstt5/IFxF5yNWZG1SZxpP28j5S9O3PFJS58n4ZdbwLAIiIoV6V6aMKM9AM/AZB+\n4CfKV69d5Jw+nzuHjt16YGacfV4rftqXzO4d23PFHfj5J959fSJ9Bv4xx/jMKW/Se8BAykVHA1C9\nVq0i5VOQ799pp53G2WefTUQe/1O8+OKLqVSpUq5xEREROclYROG/Sil1forXT0BLM6vg7geAzsDW\nE5xDb+BzoA8w7gSfG4CkpCTq1auXtR0bG0t8fHyuuMmTJzNx4kQOHTrEq6++mmt+1qxZNG/enOjg\nh/rikLpnB+VrxmZtl69ZNzSWrYhJ+3kfAAnvvsSP674kpu6pNO07mOhqtWjU/VZWPXUHW+dMISPl\nIHGDny1yTju3J1En2/tVO7YeO7cnUbNO3Rxxrz/3NNf+782Uj4nJMb5tUwJrli3l1WfGEl2+PLfc\ncx9ntjy30PkU9PsnIiIicrIpvWXZyetDoGvwug/wZuaEmVUyswlmttjMlptZ92C8sZktNLMvg69L\ngvErzGyemU0zs3VmNtkyWyT56wPcA5xqZqdlO/cAM/smOPfLZvZcMF7HzN42syXBV55ruMxsoJkt\nNbOled0DUhh9+/blo48+YvDgwbz44os55tavX8+TTz7Jo48+WiznOhaenk7qnu1UbRrH+cNeo2qT\nODZMeQaAHV/Mpt6lXWnz1Ae0vGssX788DM/IOO45fbfuK37Y/D2XdOycay49LZ19yT8ydvIUBtx9\nHyMH34W7H/ecREREpGxziyj0V2mlzk/x+xcwNFjqdi4wAbg8mPsr8LG732xm1YHFZvYRsB3o7O4H\nzawZoYLpwmCf1kALYBvwKXAp8EleJzazBkB9d19sZlOA64GnzOwUYAhwPrAP+BhYGez2NDDW3T8x\ns4bALOCcw4/t7uOBzKrniD9Zx8bGkpiYmLWdlJSUtZQtL127dmXYsGFZ24mJiQwaNIjRo0fTsGHD\nI52qQLbNmcoPC2YAUOX05qTsTsqaS9m9negadXLER1WuRkR0DLXPbw9A7Ys6krjwvVBuC9+j5d1P\nA1C1aRwZh1I5tH8v0VVrHlNO7/9rMrPengJAsxZx7Mj2fu1MSqR23Zzv17qVy1m/djX9r+pAeloa\nP+7ezf0338joCa9TOzaWSzp2xsw4K+5cLCKC5D17qFbz2HLKdKzfPxERESmjdM+PHI27xwONCXVg\nPjxs+jfAA2a2ApgHxAANgXLAy2a2CpgKNM+2z2J33+LuGcCK4Nj5uR6YErz+V5ADwK+A+e6+290P\nBefI1Al4LsjpPaCqmVWmCOLi4khISGDz5s2kpqYyc+ZMOnTokCMmISEh6/W8efNo1KgRAMnJyQwc\nOJB77rmHCy64oChpZDmlY28ueGQSFzwyiVqt25L02b9xd5K/W0VUxcq57tsxM2q1uoy9X38JwN61\nS6h4yukAlK9Zj71rlwDw87aNZBxKpVyVGsecU7ff9eW5qTN4buoMLu7QiTnvT8fdWbdyBZWqVMm1\n5K3r9Tcwac4nvPKfj3ny1Tc4tVFjRk94HYA2HToRv+QLALYkbCTt0CGq1jj2nDIV5PsnIiIiYUD3\n/EgBvQc8CVwBZL/73IBe7v519mAzGwYkAecRKkgPZptOyfY6nSN/z/oA9cysb7B9StBJOpIIoI27\nHzxKXIFFRUUxdOhQbrnlFtLT0+nVqxfNmjXj6aefpmXLlnTs2JFJkyaxaNEioqKiqFq1KqNHjwZg\n0qRJfP/99zz//PM8//zzAEyYMIFaRbyJP1PNcy9ld/xnLHmgFxHRMZx185CsuWUP9+OCR0LPqzj9\nukGs+8cwNrw5lnJVqnNmEHfG9Xew/tWRbJ39Jphx5oAhHH0l4pFddHk7liycz4CunSkfU4E/PzYi\na25Q7+48N3XGEff/Tc9e/H3og/yx5zVElSvH3Y+PKlJOBfn+xcfHM2jQIJKTk5k7dy7PPvssM2fO\nBOCGG25gw4YN/Pzzz7Rt25bhw4dz+eWXH+WsIiIiUuqUwc6P6d6A4mNm+929cnCvzbXu/oyZXQEM\ndvdrzGwEUBW43d3dzFq7+3IzGwtscfenzOz3wITQ9C/7Bsd/Dljq7q/kce4zgffd/axsY48QKpj+\nSWjJXGtCy97mAKvcfZCZvQEsd/cngn1aufuKo1xqqfzQ3Prp3pJOIYeXL63OdylHjzuRmpQv6QxE\nRETkMEX7Lepx5G90KfTPfHbDh6XyuspeOVcKBMvUnslj6jFCS9zizWxNsA3wAnCTma0Ezib01Lhj\n1Qd497Cxt4E+7r4VGAEsJlQEJQA/BjF3ABeaWbyZrQVK7BHZIiIiIlKKlMFlb+r8hAkzq+zu+80s\nilCRNMHdDy+WCqpUfmjU+Tk6dX5ERERKnVLZIQHwN68pfOenzwel8rp0z0/4GGZmnQg9ZGE2ML2E\n8xERERGR0qwM3vOj4uckZGbvAqcfNny/u8/Kbx93H3x8sxIRERGRMqWID3UqjVT8nITcvWdJ5yAi\nIiIicrJR8SMiIiIiIrmVwWVvZe+KRERERESk6I7z097M7Coz+9rMvjWzB/KYb2hmc81sefBk4i5F\nvSR1fkREREREJLfj+MhqM4sEngc6A1uAJWb2nruvzRb2EDDF3V80s+bAh0DjopxXnR8RERERETnR\nfgV86+4b3D0V+BfQ/bAYB6oGr6sB24p6UnV+REREREQkt+N7z8+pwOZs21uAXx8WMwyYbWa3A5WA\nTkU9qTo/IiIiIiKSWxHu+TGzgWa2NNvXwEJk0Ad4xd1PA7oAr5sVbS2eOj8iIiIiIpJbEf7Oj7uP\nB8YfIWQr0CDb9mnBWHYDgKuC4y0ysxigNrC9sHmp8yMiIiIiIrlZEb6ObgnQzMxON7No4HfAe4fF\nfA90BDCzc4AYYEdRLkmdHykzXr60ekmnkEuT8iWdgYiIiEghFaHzczTunmZmg4BZQCQwwd3XmNmj\nwFJ3fw+4B3jZzP5M6OEH/d3di3JeK+L+Ep70oREREREpHsevwigin9m30D/zWdfJpfK61PmRQvFN\nk0s6hRysUV96f5hU0mnkMLVLbEmncFLxRTeVdAo52MWvlnQKIiIiUsxU/IiIiIiISG6lsndTNHrg\ngYiIiIiIhAV1fkREREREJLcy2PlR8SMiIiIiIrkdx6e9lRQVPyIiIiIiklvZq31U/IiIiIiISB7K\nYPGjBx6IiIiIiEhYUOdHRERERERyK4OdHxU/IiIiIiKSm4ofEREREREJC3ram4iIiIiIhIWyV/uo\n+BERERERkTyo+BE5dguXfMvwF2eRkZHBdVe1ZuDvLssx/87sFTzx8kfE1qoCQN/uF9H76vOPWz6/\nb16F8+tEk5LuPB+fzMbktBzzMZHGYxfXyNquGRPJwq0HeOWr/VxzekU6nlaBdHeSUzN4IT6ZnQcz\nipSPuzN8+HDmz59PTEwMo0aNokWLFjliDhw4wJ133sn3339PZGQk7du3Z/DgwQC88847jBkzhtjY\nWAD69etH7969i5TTggULGD58OBkZGfTu3ZuBAwfmmH/zzTd54403iIiIoGLFijz22GM0bdqU+Ph4\nhgwZknVdt99+O507dy5SLtktjN/L8DcSyMhwrmtbl4HXnJor5t+Ld/Hc9C0YcFbDijx1WzO+2vQT\nw17byE8H0omIMG7rdgpdfl272PISEREpk1T8iByb9PQMHn3u30wY1Y/Y2lXpffs/6HDxWTRtVCdH\n3NXtWjB00NXHPZ/WdaKpXzGS2+fvoln1ctzasioPfrY7R8zBdOfeT34ZG31pTb5ITAFg44+HuH/T\nz6RmwG8aVuDGs6swdsWPRcppwYIFJCQkMHv2bFauXMmwYcOYOnVqrribb76ZNm3akJqaSv/+/Zk/\nfz7t2rUDoEuXLgwdOrRIeWRKT0/n0UcfZeLEicTGxnLdddfRoUMHmjZtmhXTrVs3+vTpA8CcOXMY\nOXIk//znP2nWrBlvv/02UVFRbN++ne7du9O+fXuioor+v5r0DOfR1zcy4d5ziK0ZTe9HVtOhdQ2a\nnloxKyYh8QDjP9jKG39tQbVKUexKPgRATPkIRt/ahMb1KpC0J5Xrhq3ispbVqVpJ/wsUEREJJyfl\n3/kxs7+a2RozizezFWb262I45v7g38ZmtvoIcVeY2QeHjb1iZtcVNYcC5HjE3PLZJ8/c8rqO4yH+\n6600PKUGDerXILpcJF3atWDOZ18f79Pm66LY8szfehCA9XsPUSnKqF4+//8M6leKpGp0BF/tCf0Q\nvWb3IVKDRs83ew9RM6bo/wnNmTOHHj16YGa0atWK5ORktm/fniOmQoUKtGnTBoDo6GiaN29OUlJS\nkc+dl/j4eBo1akSDBg2Ijo6ma9euzJkzJ0dM5cqVs14fOHAAC26IrFChQlahk5KSkjVeLHlt2E/D\n2Bga1I0hOiqCLr+uxZzle3LETJ2/nRs61qNaUNTUqloOgNPrVaBxvQoAxNaIpmbVcuzed6jYchMR\nESmTzAr/VUqddL/2NLOLgWuA8909xcxqA9ElnJbkI2nnPurXqZa1Xa9OVVau25or7r+ffMXSVZto\nfGot/nLbb6hft1qumOJQMyaSXQcPZm3vOphOzZgI9qbkvXTt0voxfPbDwTznOp5WgeU7UoucU1JS\nEvXq1cvarlevHklJSdStWzfP+OTkZObOnctNN92UNTZ79myWLFnC6aefzl/+8hfq169fbPnExsYS\nHx+fK27y5MlMnDiRQ4cO8eqrr2aNr1y5kgcffJBt27YxZsyYYun6ACTtSaV+zV/+U69XI5qVG/bn\niElIDH2v+jy+mowMGNTjNC4/t3qOmPgN+zmUlkHDujHFkpeIiIicPE7Gzk99YKe7pwC4+05332Zm\nCWY2MugELTWz881slpl9Z2a3AZhZZTObY2ZfmtkqM+te3MmZWUczWx4cf4KZlQ/GE4JCDTO70Mzm\nBa/bBTmvCParEozfa2ZLgu7WI9lOEWlmLwedr9lmViGIb2Vmnwfx75pZDQ5jZleZ2Toz+xK4Ntt4\nnjmcKO3bnMmc1+7gvZdu45Lzz+CBJ2acyNMf0aX1Y/h0W+7i5/JTYjijWhTvbfzphOaTlpbG3Xff\nzY033kiDBg0AaN++PR9//DHvv/8+l1xyCffff/8JyaVv37589NFHDB48mBdffDFr/LzzzmPmzJlM\nmzaNl156iZSUlBOSD0BahrMp6SCvPdCcp/7YlCGvbCD5p1/u6dq+N5X7xn/LiAFNiIgovb+VEhER\nKRWsCF+l1MlY/MwGGpjZN2b2gpm1yzb3vbu3AhYCrwDXAW2AzOLhINDT3c8H2gNPWeHW5VyerVhY\nAfwWwMxigvNe7+5xhDprfzzKsQYD/xfkfTlwwMx+AzQDfgW0Ai4ws7ZBfDPgeXdvAewFegXjrwH3\nu/u5wCrg4ewnCXJ7GegGXADUyzadK4fDkzSzgUFRuXT8+PFHuaRfxNauwg87frknJnFHctaDDTLV\nqFqR6OhQd6D31a1Zs/6HAh+/IK5sVIEnLqvJE5fVZM/BdGrFRGbN1YqJZHc+DyxoVCWKiAjYcNgD\nEeJqRXNt00qMXraXtEI+62Dy5Ml0796d7t27U6dOHRITE7PmEhMTsx5ecLghQ4bQuHFj+vfvnzVW\no0YNoqNDHZHevXuzZs2awiUViI2NzZFPUlJSvvkAdO3alY8++ijXeJMmTahYsSLffPNNkfLJyqtG\nND/s/qXTlrgnldgaOZu+9WpE0751DcpFRXBanRgax8awKSlUvO4/kMZtY9dxV68GtGp6Qut7ERER\nKSVOuuLH3fcT+uF9ILADeMvM+gfT7wX/rgK+cPd97r4DSDGz6oTq0BFmFg98BJwK5P9TXf4Wunur\nzK9s5z0L2OjumT/tvQq0zfMIv/gU+JuZ3QFUd/c04DfB13LgS+BsQkUPwfFXBK+XAY3NrFqw7/wj\nnPfsYN/17u7ApKPkkIO7j3f3C939wsOf/HUkcWedyqatu9nywx5SD6Xz4fw1dLj4zBwx23fty3r9\n8aJvaNKweJ/CNWvTAe79ZDf3frKbJUkptDs1tNypWfVy/Jzm+S55u+yU3F2fxlWjGNiyCqOX7iU5\n1QudU9++fZkxYwYzZsygU6dOTJ8+HXdnxYoVVKlSJc8lb2PHjmX//v08+OCDOcaz3x/08ccf06RJ\nk0LnBRAXF0dCQgKbN28mNTWVmTNn0qFDhxwxCQkJWa/nzZtHo0aNANi8eTNpaaGPz9atW9mwYQOn\nnpr7iWyFyuv0ymxKOsiWHQdJTcvgwy920aF1zgZnp/NrsnhdMgB79h0iIekgp9UtT2paBoOe+Ybu\nl9ThqotqFUs+IiIiZV4Z7PycdPf8ALh7OjAPmGdmq4DMmx8y19dkZHuduR0F9AXqABe4+yEzSwBO\n1ML/NH4pNrPO6e6jzGwm0AX41MyuJPSRGenuL2U/gJk1Jud1pQMVippYXjm4+7qiHhcgKjKCIYOu\nZsCDk8nIcHpd2YpmjevyzKtzaXnmKXS4+Cxen76YuZ9/Q2RkBNWqxDBycLGvRszy5Y5UWtctz7Pt\napGaEXrUdaYnLquZ4ylvF9cvz4gle3Psf+PZlYmJMu45P3RP0s4DGYxeljPmWLVr14758+fTuXNn\nKlSowIgRI7LmunfvzowZM0hMTGTcuHGcccYZ9OzZE/jlkdavv/46H3/8MZGRkVSrVo2RI0cWKZ+o\nqCiGDh3KLbfcQnp6Or169aJZs2Y8/fTTtGzZko4dOzJp0iQWLVpEVFQUVatWZfTo0QAsW7aMl19+\nmaioKCIiIhg2bBg1a9YsUj5ZeUUaQ/o1ZsCT60Kfpcvr0uzUijzzzmZanl6JDq1rcllcNT5Zs5eu\nD64kIgLu/Z+G1Khcjvc+28HSb/axd38a736yA4CRtzThnEaViiU3ERGRMqkUP7igsCzUBDh5mNlZ\nQIa7rw+2HweqE3oIwoXuvjPoBF3o7oOCmATgQkLFT1N3v93M2gMfA6e7e4KZ7Xf3ykGB8YG7t8zn\n/FcAg939mmxjrwAfBF/fAB3c/dtgfLm7P21mHwFPufu/zWws0NrdrzCzJu7+XXCcaYQ6Mj8DjwEd\n3X2/mZ0KHAIqZs/NzAYDld19mJmtBAa5+0IzGwZUc/c/55Fbe3f/zszeBKq4+zV55eDu04/wbXDf\nNPkI0yeeNepL7w+Pz9PPCmtql8I0FcOXL7rp6EEnkF386tGDREREiq7UVhj+yU2FLhTssldL5XWd\njJ2fysCzwTK2NOBbQkvgrjniXiGTgfeDbtFSoFi6G5nc/aCZ/R6YamZRwBJgXDD9CPBPM3uMUNcq\n011BIZYBrAH+HTzF7hxgUXBL0n6gH6FOT35uAsaZWUVgA/D7PHIbCMw0s58J3ReVeeNDrhwK9QaI\niIiISNlRKsuXojnpOj9SKqjzUwDq/BwbdX5ERCRMldoSwz8rQufnEnV+RERERETkZFEqy5eiUfGT\nj+DBA6MPG97o7j1LIh8RERERkROqDD7wQMVPPtx9FjCrpPMQERERESkRZa/2UfEjIiIiIiJ5KIPF\nz0n3R05FREREREQKQ8WPiIiIiIiEBS17ExERERGR3CKiSzqDYqfiR0REREREcosoX9IZFDsVPyIi\nIiIikps6PyIiIiIiEhZU/IiIiIiISFiILHvFj572JiIiIiIiYcHcvaRzkJOPPjQiIiIixaPU/ilR\n/2pEoX/ms3MeLJXXpWVvUihD1qeWdAo5PNYsGn/rtyWdRg52/Xvw85aSTiOniqeVdAb5unP5TyWd\nQg5Pt64EwCUTNpRwJr/47OYzSjoFEREJJ7rnR0REREREwoKKHxERERERCQsqfkREREREJCyUwT9y\nqqe9iYiIiIhIWFDxIyIiIiIiYUHL3kREREREJDfd8yMiIiIiImFBxY+IiIiIiIQDiyx7DzxQ8SMi\nIiIiIrmp8yMiIiIiImGhDBY/etqbiIiIiIiEBXV+REREREQktzLY+VHxIyIiIiIiuUXogQciBeLu\nLB8/isSlC4ksH8Ov7nqcGk2b54pLP3SI5eOGs33VUizCiLvxDk67tDPffjiF72a+iUVEElWhIhcM\nephqDZsUOa+F639i+IdJZDhcd341BratlWN+295DPPDOD+w7mEG6O/d0rkO7MyuzZc8huj67kdNr\nh34Dct5pMTzy23pFzgdgwaeLGf7E82RkZNC7RxcG3twnz7hZHy3gjnsfYdqkF4hrcRbxq9cx5LG/\nAaH3+/bbbqJzh8uKJSd3Z/jw4cyfP5+YmBhGjRpFixYtcsUNGDCAHTt2kJ6ezgUXXMDDDz9MZGQk\nzz77LFOmTKFmzZoA3H333bRr165Y8vrq1SfYufwTIsrHEPfHR6h2+jm54jLSDrF2wih2r12GRUTQ\n7Pr/o96vOwLww6LZfDvtJcyMKg3P5Lw7RhQ5rz//uhYXN6jIwbQMHl+4g292peaYrxhlvND1lKzt\nupWimPXdfp7+Yhf1KkXx4OV1qB4TQXJKBo/M386On9OLnJOIiEiRqfMjUjCJSxeyf9smrh4/k91f\nx7Pshcfp9Lc3csV9NWU85avXpMv4D/CMDFL3/QhAoyu60LTL/wCw9Yu5rPzHE7R9dFyRckrPcB79\nIIkJN51GbNVy9H5pEx3OrkzTur/8VuPF+bv+n737Do+i3B44/j0pm14h2VASOkiJgIpSRCBgAxS9\ngIpyFUUR/dmu4rWiiBfBi1zrtaACooAKlnBBBQ1VkCoQ6SKEngWSQEhI3/f3xw5JNglFNhAw5/M8\nedyZObNzZuclztn3nTdc3yqEAZdHsO1AHkM+28O8x4MBiIv05dsH63uUQ7mciooYOeYtJr73b+z2\nKPrd8SAJXTrQuJH7cbKyjzF56te0ji+50W/SqD5fTXkPHx9vDhxMo8+tQ+h2VQd8fLw9zmvRokWk\npKQwd+5c1q1bx4gRI5g+fXq5uDfffJPg4GCMMTzyyCP88MMP9OrVC4BBgwYxePBgj3Mp7dDaJRzb\nv4vObyRyZNtvbPxoNB1GTS4X98c3H2ELi+SqN77FOJ0UZLnaVfb+XWxPnEj7lybiGxxK3pF0j3Pq\nUDeAumG+3DJjNy2j/HiyY03u+98+t5hjhYZBiXuLlyfcWIeFKdkAPHR5JN9vO8r327K4tJY/D1wW\nychFBz3OSymllPLYX7D4+UtMeCAiRSKyVkTWi8h0EQmsojweO1fHFpGuImJE5IZS62ZZ6/uIyLel\n1j8jIttKLd8gIjNLLbex3uu6yspv7/L51E+4ERGhxkWtKcg+Sk56+Ru6HT9+Q/P+97ry8PLCLywC\nAN/A4OKYotwcEM9zSt6TS1ykL7GRNmw+Qs/4EJI2Z7nFiEBWnhOAo7lOokPO7vcDyes3Uy+2DrF1\na2Pz9aXXtd1IWrC0XNyb707kvrtvw89W8ksoIMC/uNDJy89HKuEzOi4pKYmbbroJEaFNmzZkZmZy\n4MCBcnHBwa7rVFhYSEFBAVKZSVTAsWoBta/qjYgQ3uRiCo4dJTejfLvaO38mDfvcA7jalS3U1a72\nzPuauGtuwTc4FAC/sEiPc+ocF8QP244CsOFgHsE2L2oEnLgAjQ31JcLfm7WOXADqh9tYvT8HgNX7\nc+kcF+RxTkoppVSl8LKd+c956i9R/AA5xpg2xphWQD4w9HR3FBHPvyYv8RhwLguvPcBzFaxfCrQv\ntdwByBSRaGu5oxVz3ADgZ+u/lSIn7QABNUuGhQXUsJOT5n7znJ+VCcD6T99h7qO3sHT04+RmHCre\n/vusacy+93rWTfwPbYc843FOjqOF1ArzLV6OCfXBkVnoFvNQt5rMXJdJl9f+4P7P9vB8L3vxtj0Z\nBdz8bgoDP97FqpRjHucD4DhwiBh7VPGy3R6F4+Aht5gNm7aSmnqQrp3bl92ddb9tolffe7ix/728\n9Nw/KqXXB8DhcBATU3L9YmJicDgcFcYOHjyYjh07EhQUxLXXXlu8fsqUKdxwww0888wzHDlypFLy\nyks/QECNkmviHxlNXpmiuiDbVYj8/uW7LH36dta8/k/yDqcBrp6fY/t3suyFu/nl+Ts5uHaJxzlF\nBXrjyC5pRwezi4gKPPF16NEwiKQdJUX3tvR8utZzFTxd6gUSZPMi1O+v8qtZKaXUBc3L78x/zlN/\nxf/DLgYaA4jIQBFZYfUKfXC80BGRLBEZJyLrgA4i0k5ElorIOis+RES8RWSsiKwUkWQRud/at6uI\nLBCRGSKyWUSmiMsjQG1gvojMt2LfE5FVIrJBRF46nqCI9LT2XS0ib4nILGt9kIhMsHJYIyJ9TnGu\n64AjInJ16ZXGmIO4ip3G1qo6wFe4ih6s/y6xjilAf2AQcLWI+Fd0IBEZYp3LqvHjx5/qGpwWU1RE\nziEHNZq34Zo3v6TGRa1ZN2Fc8fYmvQfQ66PvuXjQP9j4ReUc81RmJ2dyc9swFg5rxAcD6/LUV/tx\nOg3RId7Me6IR3zxYn6evj2bYjP1k5Z795zKcTidjxr3PU09UXM+3jm/O7K8mMOOzd/lgwlTy8vIr\njDubPv74Y37++Wfy8/NZtmwZAAMGDODHH38kMTGR6OhoxowZc87yMUWF5KY7iGjamo5jphLe9GK2\nfPZ68bbs1N1c/sJ4WhHB940AACAASURBVD8ymg3j/1VcLJ0rPRoE8+P2kuLnnRVptIkJYFKfOrSN\nCeBAdiFOc05TUkoppaqNv9QzPyLiA1wP/CAizYFbgU7GmAIReRe4A5gMBAHLjTFPiIgN2AzcaoxZ\nKSKhQA4wGDhijGknIn7AEhGZax2qLdAS2IeriOhkjHlLRB4Huhljjn91/5wxJt0qupJE5GJgK/AB\ncJUxZoeITCt1Cs8B84wx94hIOLBCRH4yxmSf5LRHAS8DP5ZZvwToaB37d2AZcK1VaLUGVlpxHYEd\nxpg/RGQB0AtXoeTGGDMeOF6BmOG/l7/J/n3WNHbMce0a0aQVOYdSi7flpDkIqBHtFm8LDcfbL4C6\nHXsAEHvltez48Zty7xt31fX8+u6/TvwJnCZ7iA/7jxQUL6dmFmIPdf8n8NWvR/jwzroAtI0LIK/Q\nkHGsiBrBPtis0Fa1/YmN9GVHWgHxdTzrabFH1yTVUdJz4XAcxB5Vs3g5O/sYW//YwZ33Pg7AwbR0\nHnhsOO+98TLxLZsVxzVqWI/AwAC2btvhtv7PmDJlCl9++SUA8fHxpKaWXL/U1FTsdvuJdsXPz4/u\n3buTlJREp06dqFmz5Bz69+/P0KGn3Rlbzs45X7BnnqtdhDVqSU6agwhrW276Afwio9zifUPC8fbz\nx355AgAxV/Rg73zXKFD/GnbCGrfCy8eXwOg6BNaK41jqLsIalZ/M4WT+1jyUG5uGALD5UB72IB8g\nD4CoIO8TTljQONKGtxdsKTUhwqGcIp6d5+pVC/ARutYPIivf+afyUUoppdTp+av0/ASIyFpgFbAL\n+BjoDlwKrLS2dQcaWvFFlNzgNwP2G2NWAhhjMo0xhcA1wJ3WvsuBGkATa58Vxpg9xhgnsBaof4K8\nbhGRX4E1uIqlFsBFwHZjzA4rpnTxcw3wtHXMBYA/EHeyEzfGLAIQkbLTfC3FVdh0BH4BVgBX4Crc\nNhtjcq24AcDn1uvP8WDoW5PeA7jm7Rlc8/YM6nRIIGXeTIwxpG1eh29gMAFlblJFhNqXd+HAb646\nzLFuGaGxrkt0dO/O4rj9KxcRXPukH8Npia/jz870AvZk5JNfaPjut6MkXBTsFlMrzJdftruGtP1x\nMI+8QieRQd6kZxdSZH0dvzs9n51pBcRG+JY7xp/OqeVFpOzay+69+8kvKGD2nPkkdO1YvD0kJJjl\n879h3ndTmffdVNrEtygufHbv3U9hoesme+8+B9t37KZO7TOfge6OO+4gMTGRxMREevTowbfffosx\nhrVr1xISEkJ0tHvxmp2dXfwcUGFhIQsWLKBhQ9f1K/180E8//USTJk04U/WuvZVOr35Op1c/J/qy\nruxbNAtjDId/T8Y3MBj/iPLtKuqSq0jfuAqAtPUrCKrjyiv6sq6kb1wNQH5mBsf27yIgus6fzunr\nTZkMStzLoMS9LNqZzXWNXYVQyyg/svOdpOVUXPxc3TCYH7e7f5cR5udV/Ejbna3DmbX13PZEKaWU\nUifkbTvzn9MgIteJyBYR2SYiT58krq/1fPplnp7SX6XnJ8cY06b0Cms41yfGmIoeFsk1xpxqzJIA\nDxtj5pR5364c/4rXpYgKPkcRaQAMA9oZYzJEZBKuYuZUx+xrjNlyiriyRgHPA6UfYFkCPAx4Ax8a\nY45aQ9q6Yj3vY/UK9QX6iMhz1vFriEiIMcajO7Bal3Vm/6pFfHdfT3z8/Gn3WEnPzdyH+3HN2zMA\nuPjuf7B83DOs/fBV/EIjaffYywBsmzUNx7pleHn74BscyuX/GOVJOgD4eAvDe0UzePIenE7oe0kY\nTaL9eCvpEK3q+JNwUTBPXRfF8MRUPlmagQiMvrkWIsLKlBzenncIH2/BS2DEDXbCT/Jcx2nn5OPN\nC089zL0PPkWR00nfPtfTpFF93nx3Iq1aNKN7qUKorNVr1vPhxGn4+Pjg5SWMePYRIiPCPM4JoEuX\nLixcuJCrr76agIAAXnmlZDroPn36kJiYSE5ODg888AD5+fkYY7jiiiu47bbbABg7diybN28GoE6d\nOowcObJS8opqeyWH1v7Mokf74O3nT/zQEcXbljx1G51eddXxzW5/hOT/DmfT5NewhUQQ/4Arrmbr\njhxKXsbiJ/oiXt40G/gYtpBwj3JauieHDrGBTO8XS26hYdTiksJvUp86brO8JTQIYtjcVLf9L6kV\nwNBLIzEY1qbmMu4X92e+lFJKqSpzFicusO5D/wtcjes59pUiMtMYs7FMXAjwKK7OCM+Pa8yFP7hc\nRLKMMcFl1rUAEnENSTsgIpFAiDFmZ+n4Coa9heAa9nYP0BPobw2bawrsBdoBw4wxva393wFWGWMm\nichvwI3WcLbWuIbYtQWigGTgKeALXEPfOhtjUkRkChBmjOktIq8AobiKLiMibY0xa05wzl3L5LEc\nqAXcaYxZYBV/B62fi61z+ADoAfzTGPOViFwDPGGMubbU+34CJBljys8fXKLCYW9V6eUmNswXN1Z1\nGm7k1plwbE9Vp+EusG5VZ3BCj6452ejOc+/Ntq5JCDpO2F7FmZRYek/DUwcppZS60JzdqVI9UZRz\n5oWCd8BJz0tEOgAjjt+HisgzAMaY0WXi3sD1eMeTuO59V51xTvx1hr2VY1WNzwNzRSQZ14dWq4K4\nfFzPBr1tTYDwI64emo+AjcCvIrIe13M6p+opG4/reaP5xph1uIa7bQamYk0wYIzJAR604lYDR4Hj\nU2G9DPgCySKywVo+XaOA2FLnZXBVyGnGmOMPuvyCa+jf8ZneBgBlH7L5ikqc9U0ppZRSSl2gPJjq\nuvRkWdbPkDLvXgfYXWp5j7WumIhcAsQaY2ZX1in9JYa9le31KbX+C1w9LSeNt573KT+PMDxr/ZS2\nwPo5vu9DpV6/DbxdannQCVKeb4y5yOqd+S+uZ5WOF0b3n2CfsudQNo+ZlPnmwBjTq8zyJGBSqeW7\nK3jfmcDMsuuVUkoppVQ148FfhCkzWdafP7SIF/AfXDMSV5q/bM/Pee4+a1KDDUAYrl4lpZRSSiml\nqou9lBq1BNS11h0XArQCFohICq6OipmeTnrwl+j5udAYY14HXj+dWBG5Fni1zOodxpibKz0xpZRS\nSimlzo2VQBNrkrC9wG3A7cc3GmOOAMV/O8P6kyweP/Ojxc95zpptbs4pA5VSSimllLpAGGMKReQh\nXPe53sAEY8wGERmJazKxs/IYhhY/SimllFJKqXPOGPMd8F2ZdS+cILZrZRxTn/lRSimllFJKVQta\n/CillFJKKaWqBS1+lFJKKaWUUtWCFj9KKaWUUkqpakGLH6WUUkoppVS1oMWPUkoppZRSqlrQ4kcp\npZRSSilVLYgxpqpzUBcebTRKKaWUUpVDqjqBk/Dknu+8PC/9I6fqjJhf7qrqFNxIh0/oMW1XVafh\n5qcBcezKr+os3MXZqjqDEzNzB1Z1Cm7kms8AMIvurOJMSshVkwHo9dXeKs7E3ey+dao6BaWUUuq0\n6LA3pZRSSimlVLWgxY9SSimllFKqWtDiRymllFJKKVUtaPGjlFJKKaWUqha0+FFKKaWUUkpVC1r8\nKKWUUkoppaoFLX6UUkoppZRS1YIWP0oppZRSSqlqQYsfpZRSSimlVLWgxY9SSimllFKqWtDiRyml\nlFJKKVUtaPGjlFJKKaWUqha0+FFKKaWUUkpVCz5VnYD6a1qcfJhRU1NwOg39ropmSO865WK+X5HG\nO9/uQYBmcYGMG9qETTuzGTF5B9k5RXh5CUNvqE3PK2pWam7/d0kEl9f2J6/I8O9laWzLKHDbHuAj\nvN7DXrwcFejNTynZvPfrYQC6xAZyZ3wYBsP2jAJe+SXNo3yMMbw7ZhQrFi/Ez9+fJ/81hiYtWp4w\nfvjDQ0nds4cPv5kFwOR33+a7r74kLCISgHseeZwrruriUU6LFi1i1KhROJ1O+vfvz5AhQ9y2T5s2\njalTp+Ll5UVgYCAvv/wyjRs3Lt6+b98+evXqxUMPPcTgwYM9yqW0xRuPMOqrXa521SGKIdfUcts+\n+qtdLP89E4CcfCfpWYWs/Pcl7E3P4+EPt+E0hsIiw8Audm67Mrpyclp/mFGf73Tl1DmaIdfXLhfz\n/co03vnfHgShWWwg4+5zfVb70vJ4fvJ2UtPzEYEPHrmIujX9KiWv+1uHcVmMq52/viqDPw6Xb+f/\n7lLyb6tGgDfzd+XwYfKR4nUda/vzXIcaPJp0gG1l9ldKKaUuRFr8qEpX5DSM/HQHE55sjj3SRv+X\n1pPQNoLGdQKLY1JScxg/ay9Tn2tJWJAPaZmuGyt/Py9eva8R9WMCcGTk02/Eb1zZKpzQoMppqpfX\n8qdOiA93zdpP8xo2Hr0skod/dLjF5BQahv6QWrz87rUx/Lw7B4A6wT4MaBnKoz+mklVgCPfzvPN0\nxeJF7N2ZwqTZc9mUvI63/jWCt6dOrzB28U9zCQgIKre+798H0X9Q5RQZRUVFjBw5kokTJ2K32+nX\nrx8JCQluxc0NN9zAgAEDAEhKSmL06NF8/PHHxdvHjBlD586dKyWf4rychpHTdzLh/5piD7fRf+xG\nEuLDaVwroDjmmb5xxa8/Xehg055jAESF+vL5482x+XqRnVfEDa+sp1t8OPYwm+c5TU1hwj8uwh5h\no/+oDSS0Dqdx7VJt3ZHL+O/3MfUp97YO8NSEPxjaqw6dWoSRnVuEl3iUTrHLYvyoHezDfXMcNIv0\n5f/ahvP4/INuMTmFhoeTSta9mRDF0n05xcsBPkKfxsFsTsuvnKSUUkqp88AFN+xNRIpEZK2IrBeR\n6SISeOq9zkoej52rY4vIGhFpY732EZEsERlYavtqEblEREaIyLATvMdS67/1ReT2s5lv8vYs4uz+\nxEb7Y/PxoucVNUhak+EWM33hAW7vHkOYVdTUCPUFoEFMAPVjXDez9ggbkaG+pB+tvG+cO9YN4MeU\nbAA2peUTbPMi0v/E/wzqhPgQ7ufFbwfzAOjZOJjErUfJKjAAHM5zepzTL/OT6HHjTYgILVq3Ieto\nJmkHD5SLyzmWzVeTJ3LH/Q94fMyTSU5Opl69esTGxmKz2ejVqxdJSUluMcHBwSV55eQgUnLX/tNP\nP1GnTh2aNGlSuXntzCauph+xNa12dWkkSb9lnDB+9uo0el3q6g2z+Xhh83Vd5/xCgzGVlNOOLOKi\n/ImNsnJqF0nS2jJtffEBbu9mL9fWt+07RpHT0KlFGABB/t4E+HlXSl7tawUwb6er8NuSXkCQrxBx\nknZeO9iHMD8vNhwqKXQGtghlxtaj5Dsr6cNSSimlzgMXXPED5Bhj2hhjWgH5wNDT3VFEKufOwuUx\n4FwVXkuAjtbr1sDW48siEgQ0Atad7A2MMcf3rw+c1eLHkZFPrciSb9RjImw4Mty/PU5JzSUlNYcB\n/1rPrSPXszj5cLn3Sd6eRUGhk7ho/0rLrWaADwezi4qXDx4rombgiXuVusUFsmDXseLluiE+1A31\n5Y0edt6+2k67Wp7nduiAg+iYmJIc7TEcOuAoFzfp7Tfpd9c9+PmXP2bitCkM+dsNvDb8GY4eOVJu\n+5/hcDiIKZWP3W7H4Sifz5QpU+jRowdjx47l+eefByA7O5sPP/yQhx56yKMcKszrcD61Ikq1q3Ab\njhMMxdqbnsfetHzaNw0tXrc/I48bR6+n2/B13NsjxuNen+Kcyrb1MjmlOHJJceQyYMwGbn1lPYvX\nHy5eHxLgw8PvbuXmkb/x7+m7KKqkQqNGgDcHc0ra+aGcImr4n/jXX5e6ASzeU9Lr0yjcl6hAb1am\n5lVKPkoppdT54kIsfkpbDDQGEJGBIrLC6hX64HihY/WSjBORdUAHEWknIktFZJ0VHyIi3iIyVkRW\nikiyiNxv7dtVRBaIyAwR2SwiU8TlEaA2MF9E5lux74nIKhHZICIvHU9QRHpa+64WkbdEZJa1PkhE\nJlg5rBGRPic5z6WUFD8dgfeBNtby5cBqY8zxO50WVs7brTyP55FlvRwDdLY+p3+c6NzLEpEh1vmt\nGj9+/Olcm5MqdBp2OnKZ/HQLxj3QmOGTtpOZXVi8/cDhfP45fhuvDG6EV2WNBToD3eoFMX9ndvGy\ntwh1gn14IsnBqKWH+Ee7SIJ8z35+2zZvYt+eXVzZ/epy2264ZQCffPcj789IJDIqmg9eG3PW8wG4\n4447+Omnnxg2bBjvvfceAO+88w533XUXQUHlh+adS9+tTueaNhF4l2o7tSL8mPlMK+a8GM+3y9M4\nlHlunmEpLLLa+rDmjLuvMcMn7yDzWCGFTsPqbUf5Z/84pj/Xit2HcvlmycFTv+FZcFVsAAutoZ0C\n3HtxGB8le1ZEK6WUUuejC/aZHxHxAa4HfhCR5sCtQCdjTIGIvAvcAUwGgoDlxpgnRMQGbAZuNcas\nFJFQIAcYDBwxxrQTET9giYjMtQ7VFmgJ7MPVA9PJGPOWiDwOdDPGHLLinjPGpFtFV5KIXIyrh+YD\n4CpjzA4RmVbqFJ4D5hlj7hGRcGCFiPxkjMmmvCXAv6zXHYGXgAEiEmItLy0VexHQDQgBtojIe8aY\n0nd5TwPDjDG9rc9xSEXnbozZUToBY8x4YHzx4i9LKkjTxR5hY396SU9PakY+9gj3b9ljImxc3CgY\nXx8v6kb5U9/uz05HLvENg8nKKWTo65t5rG8sbRqHnPA4p+vGJsH0bOQaprU1LZ+oIG+wrlpUoDeH\njhVWuF/DcF+8BX4vNSHCwWOFbE7Lp8hAanYRe44WUjfEly3pf+65iMRpU/juqy8BaNYqngOpJc8Y\nHXKkUjPa7ha/ad0atm5Yz8BrEygqLORwejpP3P13xk38lIiaJQ+t9+zbn+EPnXZnaIXsdjuppfJx\nOBzY7fYTxvfq1YsRI0YAsG7dOubMmcNrr71GZmYmXl5e+Pn5MXDgwBPuf9p5hdvYX6oHMfVwPvZw\n3wpjv/s1neH94yrcZg+z0aR2AKv+OMp1bSM9z6lsWy+TU0yEjYsblm/rMeE2LooNJDbK1ZPXo00E\n67ZncaZ6NQziugauzuitGQVEBZT09NQM8CYtt6jC/RqE+eAtUjyhQYCPUC/UhzFXudpVhL83L3Ss\nwcilaTrpgVJKqQvehdjzEyAia4FVwC7gY6A7cCmw0trWHWhoxRcBX1mvmwH7jTErAYwxmcaYQuAa\n4E5r3+VADeD4AwsrjDF7jDFOYC2uYWMVuUVEfgXW4CqWWuAqRLaXKiRKFz/XAE9bx1wA+AMV3q0Z\nY3YCNhGJsd5zC7ASuAJX8VO6EpltjMmzirIDwInvWkvyONG5n5H4BsHsdOSy52Au+YVOvlueRkLb\nCLeYHpdEsmKza1aujKMFpDhyqRvtR36hk4fe2kqfjlFc166GJ2kUm/l7FkN/SGXoD6ks2XuMq+u7\neiWa17CRXeAkPbfi53YS6gUWPzdx3NK9ObS2u2bjCrV5UTfEh/1ZFRdPJ9NnwB18MCORD2Yk0imh\nBz/N/BZjDBvXrSUoOIQaUe4zkd1w6+18Me9nPpszj9cnT6Vu/fqMm/gpgNvzQUuSfqJ+Y8+etYmP\njyclJYXdu3eTn5/P7NmzSUhIcItJSUkpfr1gwQLq1asHwNSpU5k3bx7z5s3jrrvu4v7776+Uwgcg\nPi6InQfz2HMoz9WuVqeTEB9RLm57ag5HjhXStkHJc0mpGfnk5ruu85Fjhaz+4ygN7J4PWYyvH8zO\nA6Xa+sp0ElqXaettI1ixpUxbj/IjvkEwR48VFT/TtmxzJo1qB5Q7xumavT2bh5MO8nDSQZbtyyGh\nnqsQahbpS3aBIeME7bxLbCALd5e082OFhttnpXLPDw7u+cHB5vR8LXyUUkr9ZVyIPT85xpg2pVeI\n62nrT4wxz1QQn1tqSNiJCPCwMWZOmfftCpQe9F5EBZ+ZiDQAhgHtjDEZIjIJVzFzqmP2NcZsOUXc\ncUuB/riKNyMiy4BOuIa9/VIq7pT5VpBHuXP3hI+3MHxgfQa/thmn09C3czRN6gTy1te7adUgiIS2\nkVwZH8bPGw7T69l1eHnBk7fEERHsy8ylB1m19SiHswr55mfXEKDR9zaieb3KGUa1fF8ul9cKYHLv\nWuQVGcYuTy/e9v51MW6zvHWJC+LZBe4TD6zcn8ulMf583LMWTmMYv/YwmfmeTXpweecuLF+0kLt6\nXo2ffwDD/vVK8bb7+/XhgxmJJ93/w/+M5Y/NmxEBe506PPbCSI/y8fHx4YUXXuDee++lqKiIvn37\n0qRJE958801atWpF9+7d+eyzz/jll1/w8fEhNDSUV1991aNjnlZe3sLw/nEMfncLTgN929ekSa0A\n3pq9l1ZxgcWF0Oxf0+l1SaTbJAx/OHJ49ZvdCGCAe7rH0Ky254/s+XgLw2+vz+A3tuA0hr6dolxt\nPXEPreoFkdAmgitbhvHzhiP0emEdXl7Ck/1cbR3gn/3jGDRuEwZoGRdE/86VM/32ytQ8Lovx56Nr\n7cVTXR/3dvcot1neOtcN4MUlnk3XrpRSSl0oxFTWtEfniIhkGWOCy6xrASTiGpJ2QEQigRBjzM7S\n8RUMewvBNeztHqAn0N8aNtcU2Au0w32I2DvAKmPMJBH5DbjRGs7WGtcQu7ZAFJAMPAV8gWvoW2dj\nTIqITAHCjDG9ReQVIBRX4WFEpK0xZs1Jzvsx4GFgkjHmZRGpBfwMHDPGxFsxI4AsY8xr1vJ6oLd1\n7CxjTLCIXAr8xxjTxYoZUtG5n2D43XHG/HLXSTafe9LhE3pM21XVabj5aUAcu86zWYLjPH/G/6wx\ncyunh6iyyDWfAWAW3VnFmZSQqyYD0OurvVWcibvZfcv/HS+llFKnreoebj41TwqF8/K8LsSen3KM\nMRtF5Hlgroh4AQXA/wE7y8Tli8itwNsiEoCr8OkBfIRrONuvVi/SQeCmUxx2PK7njfYZY7qJyBpc\nhdVurGFoxpgcEXnQisvGNVTtuJeBN4BkK+cdQO+THG8J8DpWL48xZr/1fNHSk+xTkWSgyJoAYhLw\nJn/+3JVSSimllLrgXHA9PxcaEQk2xmRZhcV/gd+NMa9XdV4e0p6f06A9P3+O9vycmvb8KKXUX9J5\n2UNi+cv1/FyIEx5caO6zJhPYAIThmv1NKaWUUkopdY79JYa9nc+sXp7T6ukRkWuBsk+O7zDG3Fzp\niSmllFJKKVXNaPFzHrFmXKu0WdeUUkoppZRSJXTYm1JKKaWUUqpa0OJHKaWUUkopVS1o8aOUUkop\npZSqFrT4UUoppZRSSlULWvwopZRSSimlqgUtfpRSSimllFLVghY/SimllFJKqWpBix+llFJKKaVU\ntSDGmKrOQV14tNEopZRSSlUOqeoETsKTe77z8ry050cppZRSSilVLfhUdQLqwpTlrOoM3AV7gZl1\nR1Wn4UZ6T8H8cldVp+FGOnxS1Smc0KNrsqs6BTdvtg0C4P0DVZxIKUOjXf81351nbb3nFO5bcriq\n03DzYafwqk5BKaXUeUh7fpRSSimllFLVghY/SimllFJKqWpBix+llFJKKaVUtaDFj1JKKaWUUqpa\n0OJHKaWUUkopVS1o8aOUUkoppZSqFrT4UUoppZRSSlULWvwopZRSSimlqgUtfpRSSimllFLVghY/\nSimllFJKqWpBix+llFJKKaXUOSci14nIFhHZJiJPV7DdT0S+sLYvF5H6nh5Tix+llFJKKaXUOSUi\n3sB/geuBFsAAEWlRJmwwkGGMaQy8Drzq6XF9PH0DpSpijGHsK6NYsmgh/v7+jHhlDM1btjxh/D8e\nHMre3Xv48n+zAPjxh+8Z/8477Nj+B5O/nE6LVvGVktfizUcY9e0unE7od0VNhnSv5bZ9dOIulm87\nCkBOvpP0rEJWjmoLwNj/7WbhpiM4DXRsGspzN8UiIp7nlHyYUVNTcDoN/a6KZkjvOuVivl+Rxjvf\n7kGAZnGBjBvahE07sxkxeQfZOUV4eQlDb6hNzytqepxPWcYYRo0axcKFrms5ZswYWpa5ljk5OTz6\n6KPs2rULb29vunXrxrBhw85KLps+GcuhNT/j5edP/AMvEdagebk4Z2EBGyeMIX3jasTLiya3/h8x\nV3QHYP8vc9k24wNEhJC4prR+5BWPc1rw5ih2LFuIr58/1zw7Bnuz8m19+sN/JzvtAD5+/gD87T8T\nCIyowYK3XmHPmuUAFOTmknM4jQe/X+VRTos3HWHUN7twGqud9yjTzr8p1c4LnKQfLWTlaFc735eR\nx/Of7yT1cD4i8MGQJtSN9PMon+OMMfwx9T+k/7YUb5s/TQcPJ6TeReXinIUFbPtsLEe2/AriRf2/\nDSXqsgRy01LZ8vFLFB7LAqeTBv0eJPLiTpWSm1JKqXPucmCbMWY7gIh8DvQBNpaK6QOMsF7PAN4R\nETHGmDM9qBY/6qxYsmgRu3em8O0Pc1m/bh2jR45g8hfTK4ydN3cuAYFBbusaN2nK2Lff5pUXX6y0\nnIqchpFf72LC/U2xh/nS/41NJLQMp3FMQHHMM33iil9/utjBpr3HAPh1Rxa/pmSROMx1U3v7O5tZ\n8cdRrmgc6nlOn+5gwpPNsUfa6P/SehLaRtC4TmBxTEpqDuNn7WXqcy0JC/IhLbMAAH8/L169rxH1\nYwJwZOTTb8RvXNkqnNCgyv1nvWjRIlJSUpg7dy7r1q1jxIgRTJ9e/lrec889tG/fnvz8fAYNGsTC\nhQvp0qVLpeZyaO0Sju3fRec3Ejmy7Tc2fjSaDqMml4v745uPsIVFctUb32KcTgqyjgCQvX8X2xMn\n0v6lifgGh5J3JN3jnFKWLeLwnhTunjaX1I3rmDduBAPGV9zWr3vhNWIuci/kuz7ybPHrNTM+5eDv\nG8vu9qcUOQ0jv9rFhKFNsYf70v/1TSS0KtPOby7VzheVtHOAp6bsYOjVtejULIzsvCK8PK/vi2X8\ntpQcx27ajZ7B0e3r2Tb537QdPqFc3K5ZE/ENjaTd6BkYp5PC7EzX+v9NIKpdD2p360v23u2sf+Nx\nrhirxY9SSp2PhFCJ4QAAIABJREFURGQIMKTUqvHGmPGllusAu0st7wGuKPM2xTHGmEIROQLUAA6d\naV7n7bA3ESkSkbUisl5EpotI4Kn3Oit5PHauji0iXUVkVpl1k0Skn/V6gYhcVmpbfRFZb71eIyJt\nrNc+IpIlIgNLxa4WkUtF5JCIRFjraomIEZErS8UdFJEanp7LwnlJ9OpzEyJCfJs2ZGVmcvDAgXJx\nx7Kz+eyTidw79AG39Q0aNaJ+g4aepuEmeVc2cTX8iK3hh83Hi55tI0nacPiE8bPXpNOrbSQAIpBX\naCgoMuQXOiksMtQM8fU8p+1ZxNn9iY32d+V0RQ2S1mS4xUxfeIDbu8cQZhU1NUJdx20QE0B964bW\nHmEjMtSX9KMFHudUVlJSEjfd5LqWbdq0ITMzkwNlrmVAQADt27cHwGaz0aJFCxwOR6Xn4li1gNpX\n9UZECG9yMQXHjpKbcbBc3N75M2nY5x4AxMsLW2gEAHvmfU3cNbfgG+wqWv3CIj3O6Y+fk2h+nevz\nqdWyDXlZmWQdKt/WT8eWpNk069Hbo3ySd2UTV9OP2Jql2vn6U7TzS1yfw7bUHIqc0KlZGABBft4E\n2Lw9yqe0Q2sWYe94PSJCaKN4Co8dJe9w+f9/pS7+H3G97gJc1883JNy1QYSinGwAinKy8Quv/J5O\npZRSlcMYM94Yc1mpn/Gn3uvsO2+LHyDHGNPGGNMKyAeGnu6O1hjCyvIYUCWF15+0BOhovW4NbD2+\nLCJBQCNgLbAM6GDFdQTWlIprBqQZY9I8TeaAw4E9JqZ4OTomhoMHyt8Mv/fWmwwcdA/+Af6eHvKU\nHEfyqRVuK16OCbPhOJJfYeze9Dz2pufTvonrJrlt/WCuaBRC5xHr6PxSMlc2C6WRPaDCff9UThn5\n1IoslVOEDUeGe04pqbmkpOYw4F/ruXXkehYnl7+RTd6eRUGhk7joyv8cHQ4HMaWuZUxMzEkLm8zM\nTObPn0+HDh1OGHOm8tIPEFDDXrzsHxlNXrp78VOQ7RrO9fuX77L06dtZ8/o/yTvsatLZ+3dxbP9O\nlr1wN788fycH1y7xOKesgw5Coks+n+CoGLIOVfz5zB39LJ/d3Ydlk/5L2R77zNS9HNm3h9hL2nuU\nj+Pwn2znaSXtPOVgLiEB3jw8YRs3v7aBf8/cTZHzjEcWlJOfcRC/yJLr5xcZTX6Z4rXwmOv6pXzz\nAb+OuJON7z5D/hHX9avX5z4cv/zAsid6s/6Nf9DojicqLTellFLn3F4gttRyXWtdhTEi4gOEAR7d\np57PxU9pi4HGACIyUERWWL1CHxwvdKyejnEisg7oICLtRGSpiKyz4kNExFtExorIShFJFpH7rX27\nWr0qM0Rks4hMEZdHgNrAfBGZb8W+JyKrRGSDiLx0PEER6Wntu1pE3jregyMiQSIywcphjYj0OUuf\n0VJKip+OwPtAG2v5cmC1MaaogrjXcS+GPL8bPE1bNm1iz+5dJFx99bk65Gn7bm0611wcgbc15mfn\noVy2H8hlwQsXs/CFi1m27Sirth89J7kUOg07HblMfroF4x5ozPBJ28nMLizefuBwPv8cv41XBjfC\nqzLHKJ2BwsJCHn/8cf7+978TGxt76h3OAlNUSG66g4imrek4ZirhTS9my2evF2/LTt3N5S+Mp/Uj\no9kw/l/FxdLZdv0Lr3HnJ//jlv9OYW/yajbNSXTbviVpNk27XouXd2V+d3Ny361J55rWJe28sMiw\nensW/7wxlun/aMHutDy+WXHGIwvOiCkqIj/jAKGN47lkxGRCG8Wz/cu3ADi4fC4xnXrRftwsWj32\nOls+HIFxOs9pfkoppSrNSqCJiDQQERtwGzCzTMxM4C7rdT9gnifP+8AFUPxYVd71wG8i0hy4Fehk\njGkDFAF3WKFBwHJjTGtgBfAF8Ki13APIwTVjxBFjTDugHXCfiDSw9m+Lq5enBdDQOsZbwD6gmzGm\nmxX3nDHmMuBioIuIXCwi/sAHwPXGmEuBqFKn8ByuC3U50A0Ya/XEnEhnq7BbKyJrgRvLbJ9Satt3\npdaX7vnpCCwC8kQkxFpeWkHc5cA3lFTdpePciMgQq+hbNX58xb2WX06ZwoCb+zDg5j7UjIrCkZpa\nvO1AaipR0Xa3+OS1a9i4fj29uycw+I7b2bkzhSF3/r3iT6US2MNs7D9c8g146pF87GG2CmO/KzXk\nDeCn3w7Tul4QQX7eBPl5c9VFYaxNyfI8pwgb+9NL5ZSRjz3CPaeYCBvd2kbg6+NF3Sh/6tv92enI\nBSArp5Chr2/msb6xtGkc4nE+x02ZMoU+ffrQp08foqKiSC11LVNTU7Hb7RXuN3z4cOrXr8+gQYMq\nLZedc75gyVO3seSp2/CLiCInraRXJTf9AH6RUW7xviHhePv5Y788AYCYK3qQmbIZAP8adqIvvQov\nH18Co+sQWCuOY6m7/nROa7+ewmd39+Gzu/sQVCOKowdKPp+sg6kE1yz/+QRHudbZAoO5qEdvUjcl\nu23fkvQdzXr0+tO5lGUP/5Pt/JKSdh4TbuOiOgHE1vTDx1vo0SqCjXuOVbjv6dqXNJ3VLw5k9YsD\nsYXXJC+95PrlpR/AFuF+/XyCw/Cy+VPzEtev3JrtupO1c4vrXBbPpOblPQAIbRyPsyCfgqwTD+lT\nSil1/jLGFAIPAXOATcCXxpgNIjJSRI7f/34M1BCRbcDjQLnpsP+s87n4CbBu8FcBu3CdfHfgUmCl\nta07rkIFXIXQV9brZsB+Y8xKAGNMpvUBXwPcae27HNcDU02sfVYYY/YYY5y4hofVP0Fet4jIr7iG\ni7XEVSxdBGw3xuywYqaVir8GeNo65gLAH4jjxBZbw/3aWAVe2Qr4jlLbeh5faYzZCdhEJMbKZwuu\nivoK3Ht0VgJtrQLM1xiTBWwXkcacpOen9LjNIUOGVBTCLXfcwbRvEpn2TSJdu/dgduK3GGP4be1a\ngkNCiIqOdovvP+B25iz6mVlJ8/h4ylTq1avP+MmfnuSj8Ux8bBA7D+WyJy2P/EIn361JJ6FleLm4\n7Y4cjuQU0bZ+SY1aK9zGyj+OUlhkKChysvKPozSshGFv8Q2C2enIZc/BXFdOy9NIaBvhFtPjkkhW\nbHY98J1xtIAURy51o/3IL3Ty0Ftb6dMxiuvaefyYlps77riDxMREEhMT6dGjB99+67qWa9euJSQk\nhOgy1xLg9ddfJysri2effbaCdzxz9a69lU6vfk6nVz8n+rKu7Fs0C2MMh39PxjcwGP8yN88iQtQl\nV5G+0TVjWtr6FQTVcf2aiL6sK+kbVwOQn5nBsf27CIguP7veqbT52x0MnJjIwImJNOrcg00/uD6f\n/RvWYgsOIbim++fjLCwk57BrcoWiwgK2L11AjQZNiren7/yDvKOZ1GrV9k/nUlZ8bBA7D55mOz/m\n3s7j44I4mlNEepbr2bFl2zJpFONZO6/dvT+XvvQZl770GTXaXoVj6fcYY8j84zd8AoPLPbcjItRo\ncyWHt/wKwOGNKwms7fqOyi8yhsMbVwJwbN8OnAX5+Ia4/3tRSil14TDGfGeMaWqMaWSMGWWte8EY\nM9N6nWuM6W+MaWyMufz4zHCeOJ9ne8uxbvCLiWte4U+MMc9UEJ9rDes6GQEeNsbMKfO+XYG8UquK\nqOCzsXqJhgHtjDEZIjIJVzFzqmP2NcZsOUVcZVgK9MdV+BkRWQZ0wtXD8wuAMeaYiPwO3AP8au23\nDFchFY2raPLYlV26sGTRQvpcezX+/gGMeKVkOuEBN/dh2jeJJ9kb5v34I2NHvUxGejqPDr2fphc1\n578ffexRTj7ewvC/xTF4/FacBvpeXoMmMQG89cNeWtUNIqGV6wZx9tp0erWJdJvG+trWESzblsmN\nr21ABK5sFlbhDeUZ5TSwPoNf24zTaejbOZomdQJ56+vdtGoQRELbSK6MD+PnDYfp9ew6vLzgyVvi\niAj2ZebSg6zaepTDWYV887PruYnR9zaieb2TdSz+eV26dGHhwoVcffXVBAQE8Eqpa9mnTx8SExNJ\nTU3l/fffp2HDhtx8880ADBw4kP79+1dqLlFtr+TQ2p9Z9GgfvP38iR86onjbkqduo9OrnwPQ7PZH\nSP7vcDZNfg1bSATxD7jiarbuyKHkZSx+oi/i5U2zgY9hC/HsOjbo0IWUZQuZeNvV+PgHcM0zJZ/P\nZ3f3YeDERIoK8vn6iXtxFhbgdDqJu6wD8TfcUhy3Jek7mnbvWSlTp/t4C8P7xjH4g604ndD3iho0\nqRXAW9/vpVVsqXZu9W6WPqa3l/DPG+sy6N2tGKBl3UD6t6+8SQUiL+5EevJSVj7dFy+bP83uGV68\nbfWLA7n0pc8AaNDvITZ/NILt017HNyScplZcw1sf4fdPRrN37jQQoeng4ZXymSmllKo+xMNhc2eN\niGQZY4LLrGsBJOIaknZARCKBEGPMztLx1rjBzcCtxpiV1tCvHFw3/D2B/saYAhFpiutBqnbAMGNM\nb2v/d4BVxphJIvIbcKMxZoeItAYm4xoiFwUkA0/hGmK3FehsjEkRkSlAmDGmt4i8AoTiKrqMiLQ1\nxqw5wTl3LZ2HtW4SMMsYM0NEFljbV1nb6lvbWlnLjwEPA5OMMS+LSC3gZ+CYMSa+1Hu+AdwAjDDG\nfCoiHYDPgI3GmBtO4/KYrPNsmH2wF5hZd5w68ByS3lMwv9x16sBzSDp8UtUpnNCja7KrOgU3b7Z1\nFZHvn9nEbWfFUKtDyXx3nrX1nlO4b8n5Nfzsw06efzmhlFLnyPn8LY4nhcJ5eV7n87C3cowxG4Hn\ngbkikgz8CNSqIC4f17NBb1sTIPyIq4fmI1x/OOlXcU0R/QGn7v0aD/wgIvONMetwDXfbDEzFGiJm\njMkBHrTiVgNHgSPW/i8DvkCyiGywls+WJbiGAR7v5dkPeFP+OR63OFw9QHUriFNKKaWUUuov47wd\n9la216fU+i9w9bScNN563qeiOWOftX5KW2D9HN/3oVKv3wbeLrU86AQpzzfGXGQNzfsvrmeVjhdG\n959gn7Ln4JZH2eMZY7qW2ZYCtCq1vJIyVbYxpn4Fx5leOs4YkwdUzp9wV0oppZRS6jx1QfX8nOfu\nsyY12IBrDvIPqjgfpZRSSimlVCnnbc/PhcYY8zquv5lzSiJyLfBqmdU7jDE3V3piSimllFJKKUCL\nnyphzTY355SBSimllFJKqUqjw96UUkoppZRS1YIWP0oppZRSSqlqQYsfpZRSSimlVLWgxY9SSiml\nlFKqWtDiRymllFJKKVUtaPGjlFJKKaWUqha0+FFKKaWUUkpVC1r8KKWUUkoppaoFLX6UUkoppZRS\n1YIYY6o6B3Xh0UajlFJKKVU5pKoTOAlP7vnOy/PyqeoE1IXJLL2rqlNwIx0/4eODVZ2Fu8FRVZ3B\nBebwuqrOwF14a9d/982s2jxKq30jAA+tPFrFibh7p10I5seBVZ2GG7n6MwDW51RxImW0CqjqDJRS\nqnrTYW9KKaWUUkqpakGLH6WUUkoppVS1oMWPUkoppZRSqlrQ4kcppZRSSilVLWjxo5RSSimllKoW\ntPhRSimllFJKVQta/CillFJKKaWqBS1+lFJKKaWUUtWCFj9KKaWUUkqpakGLH6WUUkoppVS1oMWP\nUkoppZRSqlrQ4kcppZRSSilVLWjxo5RSSimllKoWfKo6AfXXtPi3w4yamoLTaeh3VTRDetUpF/P9\nijTeSdyDAM1iAxk3tAl7D+Xx8NtbcBooLDIM7BHDbd3slZaXMYakN0ex/ZeF+Pr7c/2zY4hp1rJc\n3LSH/k5W2gF8/fwB6P/6BIIiapD01ivs/nU5AAW5uRw7nMajP6zyOKdRo0axcOFC/P39GTNmDC1b\nuueUk5PDo48+yq5du/D29qZbt24MGzYMgIkTJzJ9+nS8vb2JjIzklVdeoU6d8p/3n7Fo0SJGjRqF\n0+mkf//+DBkyxG37tGnTmDp1Kl5eXgQGBvLyyy/TuHFjlixZwrhx4ygoKMDX15cnn3ySDh06eJSL\nW16/rGXUfya68rqxO0PuuqnCuDnzlvHIM/9hxqTRxDdvBMDm33fy4pjxZGXn4OUlzJg4Gj8/m+c5\nrdjMqHdm4ixy0r/X5Qy5PaHinBYm88iIT5nx/iPEN4slv6CQF//zFeu37EFEeO7hPlzRppHH+YCr\nTW359DUOrV2Ct58/LYeMILTBReXinIUFbP7k32RsWg0iNO7/IPbLuwOQuuxHtn89HkQIiWtC/P+N\n8jivxRuPMGrGLtfvhY5RDLmmltv20V/tYvnWTABy8p2kZxWycuwlbNpzjBGfp5CdW4SXlzD02lr0\nvLSGx/mA67Oa8O9R/PrzQmz+/jw8cgwNm5f/nVCc46NDcezZwxtfzQJg3D8fY1/KDgCyjx4lKCSE\ncV8mVkpuSimlKp8WP2eBiGQZY4LPgzwWAMOMMatE5DvgdmvT7caYd8/WcYuchpGf7mDCsObYI230\nH7mehDYRNK4TWByTkprD+Nl7mfpsS8KCfEjLLAAgKtyXz59vhc3Xi+zcIm54fh3d2kRgj/D8JhVg\n+7JFZOxO4b7P57J/wzp+fG0Ef/9weoWxvV98jVoXxbut6/7Is8WvV8/4lANbN3qc06JFi0hJSWHu\n3LmsW7eOESNGMH16+Zzuuece2rdvT35+PoMGDWLhwoV06dKF5s2b89VXXxEQEMDUqVMZO3Ysb7zx\nxhnnU1RUxMiRI5k4cSJ2u51+/fqRkJBA48aNi2NuuOEGBgwYAEBSUhKjR4/m448/JiIigvfeew+7\n3c7WrVsZPHgwixcvPuNc3PNyMnLsx0x8+3ns0TXoN+gZEjpfRuOGdd3isrJzmPzF97Ru2aR4XWFh\nEU+OeJuxLz7ERU3rk3HkKD4+nv/6KypyMvLNb5g4dgj2qDD6DX2LhI4taVzfvWDPOpbL5K9/pnXz\nuOJ102e5iuj/TXiCtIws7nvqI2a8/wheXp53yB9at4RjqbvpNO4bjvyxnk2TRnPFS5+Ui9uROAFb\naASdXvsa43RSkO0qPLJTd5Hyv4m0e/FjfINCyT+S7nFORU7DyC93MuGhptjDbfQfu5GE+HAa1woo\njnmmb8nn8+kCB5v2HAPA39eLV+9sSP1ofxyH8+n3741c2TyM0EDPr+GvPy9i/64U3pk5l99/W8f4\nUSMY81nFvxOWJc3FPyDIbd0T/y75tzZp3BgCg6v8V79SSqmT0GFv1YQxpqcx5jAQDjx4No+VvD2L\nuGh/YqP9sfl40fPyGiStyXCLmb7oALcnxBAW5Lp5qRHqC4DNxwubr6tZ5hc6MaZyc9u2OImW192E\niFC7VRtyszLJOnTgjN5r00+zaX51b49zSkpK4qabXDm1adOGzMxMDhxwzykgIID27dsDYLPZaNGi\nBQ6HA4D27dsTEOC6gWzTpg2pqake5ZOcnEy9evWIjY3FZrPRq1cvkpKS3GKCS93g5eTkICIAtGjR\nArvddePfpEkT8vLyyM/P9yif4rw2bqNe3Rhi69ix+frQ6+qOJC1aWS7uzQ++4L6/98HPz7d43ZLl\n62jWOI6LmtYHICIsBG9vz3/9JW/eRb3aNYmtXcOVU0IbkpZsKJ/ThDncd1s3/GwlN+vbdjq4oq2r\noKwREUxIcADrt+zxOCeAg6sXUuvKnogI4Y3jKcw+Sl7GoXJxexfOpMENdwMgXl7YQsJd6+d/Q90e\nt+AbFAqALSzS45ySU7KJq+lHbE3r98IlkSQlZ5wwfvbqNHpd6jpuA7s/9aNdvbD2cBuRIT6kZxV6\nnBPAygVJdOnt+vfX9OI2ZB/NJONg+d8JOcey+d+nE+l33wMVvo8xhqVzv+fK6zz/naCUUurs0eLn\nLBKRriKyQERmiMhmEZki1l2iiFwqIgtFZLWIzBGRWqXWr7N+xorIemv9IBF5p9R7zxKRrtbr90Rk\nlYhsEJGXTpBLiojUBMYAjURkrfX+k0XkplJxU0Skjyfn7cjIp1ZkSU9NTKQNR4b7DXBKai4pjhwG\njFrPrS+vZ/Fvh4u37U/L48bhyXR7Yg339qxdab0+AEcPOQiNjileDomO4eghR4Wx37/yLJMG9WHp\npP9iylRhR1L3cmT/HuIuae9xTg6Hg5iYkpxiYmKKC5uKZGZmMn/+/AqHk82YMYOrrrqqUvOx2+0V\n5jNlyhR69OjB2LFjef7558ttnzNnDi1atMBmq5zr5ziQToy9ZKiTPboGjoPuPRIbNm8n1XGIrlde\n4rZ+x679CMLgR/6fvTuPi6re/zj++gCyCKiAMJD7Qq6YlpZa7lgumZVZ3ax+pmbLNdtMy25mdnEp\nrWy5ll3Ncmmxq1lppeG+L6m4WykqyiYoKoLAzPf3x4zAMLjEoJh+no8HD2fO+Z4z7zmzOJ/5fs93\nYrjn0WF8Or10hiUlHz1BeFilgkyhFUk+muGcaW8CSSnHad+qgdPy+nWuY/HqneRZrRxKTGfH3gQS\nU45TGs4cS8U3pOAx9A22kH3M+QN9buZJAP74dhJrX+3D1veHcSYjDYDTSQc5nXSA9W/0Y/3rfTm6\ndbXbmZIzcogo9FoOD/ImOSO32LaH089wOC2HlvUquKyLiz9Fbp6hemUftzMBpKckU7nQ8z3EEk5a\niuvz/auPJnLXo/3w8fUtdj87f9tIpZAQrqtRs1RyKaWUujS0+Ln0mgHPAQ2B2sCtIlIO+AC4zxhz\nEzAVODug/jPgGWPMDX/hNl41xjQHmgDtRKTJedq+DPxpjGlqjHkJmAL0BRCRikBrYH7RjURkoKPA\n2jh58uS/EK14eTbDgeRsvhjWkAlP1uW1z/Zx4rT9m9yIEB++f7MJv4xtynerUjmaUTo9B3/Fna+P\np98XP/CP/8wkYesmdvzs/GF596/zqdf+Djw8PS9rrry8PF544QUeeeQRqlWr5rRu3rx5bN++nQED\nBlyWLH369OHXX39lyJAhTJo0yWnd77//zvjx4xk1atRlyQJgs9kYO/ELhj37qMs6q9XKpq27eXvU\nM8yaPIpfl65nzYZtlyfTf35g2NM9XNb16taC8NCK9HpiIqM/nEezxjVLpTfqYhmblTPpyVS6vgkt\nY2ZSqW4Uv8+yD+EyViunkw7R/NXJRP0zhp1TYvKLpcthwaZ0bm8ahKeHOC1Pychh6Bf7Gf1wLTyK\nrLuU9u/eRVLCQW7p2PmcbVb+/KP2+iil1N+AnvNz6a03xiQAiMgWoCZwHGgMLHJ0BHkCiSJSCahk\njFnu2HY60PUibuN+ERmI/fGMwF5oxV1MOGPMMhH5j4iEAr2A/xljXMaTGGMmA5Pzr65edc59WoK8\nSUwvKFiS0nNcem/Cg7xpUjuAcl4eVA31pWa4LweSsomqHeC0n8gq5dm49yRdWpT85Obf/jeTuB++\nsd9ugyhOpBQMCzuZkkRgZdcJFQJD7ct8ygfQoPOdJO6Ko3HXgpPrd8UuoPMLI0qcaebMmXzzjT1T\nVFSU01C1pKSk/KFjRb322mvUrFmTvn37Oi1fvXo1H3/8MTNmzHC7p8VisTjlSU5OPmcegO7duzNy\n5Ein/IMGDWLcuHFUr179nNv95VxhwSQlpxXkSknDElowHCvzdDZ7/zzEo0/bOz9T047z1JC3mDR+\nKOFhIbRo1oDgSvaehLatm7Fj935atXA+p+svZ6pcgaRCvTXJqRlYKlcslOkMe/cn8ehzH9szpZ/k\nqVenMSmmL1H1qjH8n3flt31w0IfUrBpa4iyHFn1DwpLvAKhYuyHZaQWPYXZ6Mr5BYU7tywVUxMPH\nl7Dm9gkaLLdEc3jZ9wD4BIdRsU5jPLy88Aurgn94dU4nHaRinXNPBHAhloreJBbqAU46loOlYrli\n2y7YlM5r9zs/d05lWXly0u8816MKTWu5d17NT1/N5Nc59tdf3UZRHC30fE9LTiIkzPn5viduM3/u\n3M6TXTtiteZxIj2dEf0fYdSU6QBY8/JYF7uIt7+c41YupZRSl572/Fx6ZwpdtmIvUATY4eh9aWqM\niTLG3H6B/eTh/Hj5AohILWAI0MkY0wR7r03x4zLO7QvgYeAx7L1QbomqFcCBlGwSUrPJybOxYH0a\nHZsFObWJvjGY9bvtJ1cfO5lLfFI2VcN8SEo/Q3aODYCMzDw2/X6SWuF+LrfxV9zYqw99p82j77R5\nRLaJZsfP32GM4cj2LfgEBBJQ2flDoS0vj9PH7cOprHm5/Ll6KZVrF5w8n3bgT7JPnuC6xs1KnKlP\nnz7MmzePefPmER0dzXff2TNt2bKFwMBAwsLCXLZ59913OXXqFMOHD3davnPnTkaMGMGkSZMICXF/\nBqyoqCji4+M5dOgQOTk5zJ8/n44dnWcwi4+Pz7+8dOlSatSoAdiH5A0cOJAXX3yRm266ye0sTrka\n1CH+UCKHjqSQk5vH/EWr6di2ef76wIDyrFs4hcXffcTi7z6iaeNIJo0fSlSDOtzW8gb2/nmIrOwz\n5OVZ2bB5F3VrVT3PrV1kpvrViD98lEOJ6fZMi7fQsXXDQpn8WDfvDRZ/NZzFXw2nacPq+YVPVnYO\np7PsxcCqjXvx9PRwmSjhr6jW+X5ajZ5Fq9GzCL2pPYkrF2CM4fgf2/AqH4BPUGWn9iJCaLM29pne\ngPQdG/CvUguAsJva5y/POXmczKSD+IW5N4NgVA1/DqSeIeHoGfv7wm/pdGwS5NJuX1IWGafzaFao\nwMnJszHo09/peUsIXZq5f/5R1wf7MOGbeUz4Zh43d4hm2Y/219/euC2UDwgkKNT59dfl/of476KV\nfPzTYmI+m0VEjZr5hQ9A3LrVVKlVmxBLeNGbUkopdYXRnp+ysQcIFZFWxpg1jmFw1xtjdojIcRG5\nzRizEuhTaJt44GkR8QCqADc7llcAMoEMEbFg7ylaep7bPgkEFlk2DVgPJBlj3J6+zMtTeK1PTfpP\n2I3NZujVJozIKuV5f+4hGtf0p2OzYG5rXJGV24/T/dWteAi89EB1ggLKsWrHccZ9tQcRMAb6dYmg\nXrXyF75OdvbFAAAgAElEQVTRi1S7VTv2rVnGpw90xsvXj67DR+evm9a3J32nzSMvN4fZLwzAZs3F\nZrVRs3krbuhxf367Xb8uoEGnbvkn+burXbt2LFu2jM6dO+Pn58fo0QWZevbsybx580hKSuLjjz+m\ndu3a3HPPPQA8/PDD9O7dm7feeovTp0/z7LPPAhAREcHHH39c4jxeXl6MGDGCAQMGYLVa6dWrF5GR\nkUycOJHGjRvTqVMnZsyYwZo1a/Dy8qJChQqMGzcOgBkzZnDw4EE++ugjPvroIwCmTp1aKkWZl5cn\nI4b0Y8DgGKw2G716dCCydjUmfvI1jRvUoVOhQqioihUC6PuP7tzX9xVEhLatm7mcF1SiTJ6ejBh8\nNwOGfmrP1PVmImuFM3HqLzSuV5VOt567pyTt+Cn6D/0vHiJYKlfgrVf+4Xaesyo3vZWjW1ex6sW7\n8fT2peHA1/PXrRn+EK1GzwIg8sHBbJ80gj0zJuAdGJTfLqRJK9K2rWX10N6IhwfX/2Nw/mQIJeXl\nKbx2f3X6f2Sfyr5Xy8pERvjx/o+HaVy9fH4hNH9TOt1vCnZ6ff38Wzob/zjF8cw85q61T9ww5pHa\nNKjq/nvDjW3a8dvKZfyzR2d8fP345xsFr78X7+95UdNWr/x5Abd16e52FqWUUpeeFD2RW7nv7FTX\njgkJhhhj7nQs/xDYaIyZJiJNgfeBitiL0PeMMZ+KyNlzgAywEOhmjGnsmChhBnATsAsIAkYaY5aK\nyDTs5+ocAjKA7x23sZSCqa7jgebGmKMiMgv7+UE/Oc77QUR+Br4zxlzMp2ZjVv+f28epNEnrz5mS\nWtYpnPUv+Qima9PxrWWdwFklx2l3R74v2xyFXWcfJjdow+U7/+ZifNgiELPo4bKO4UQ6zwBge1YZ\nBymisXsd2Uqpq9PlO4nxr3OnULgi75f2/FwCZ3/jxxizlEK9MMaYQYUubwFcpuUyxmwCbgAQkZpA\nN8dyg3NPUOFt+p5jeftCl2sWuvxQ4XYiUh6IBL48971SSimllFLq703P+bnGiUg09p6kD4wxGRdq\nr5RSSiml1N+V9vxcwYwx8dhnhbuUt/ErUONS3oZSSimllFJXAu35UUoppZRSSl0TtPhRSimllFJK\nXRO0+FFKKaWUUkpdE7T4UUoppZRSSl0TtPhRSimllFJKXRO0+FFKKaWUUkpdE7T4UUoppZRSSl0T\ntPhRSimllFJKXRO0+FFKKaWUUkpdE7T4UUoppZRSSl0TxBhT1hnU348+aZRSSimlSoeUdYDzcOcz\n3xV5v7TnRymllFJKKXVN8CrrAOrv6a3Esk7gbGgEmF2jyzqGE2kwvKwjnNOe7LJO4KyeL6TmlXUK\nZ6GOd8ePU8o2R2FPhtn/NfP7lG2QIqT7TPotP1bWMZxMbRsEgIl7pYyTOJMmY2g9dV9Zx3Cyul/t\nso6glFKXjfb8KKWUUkoppa4JWvwopZRSSimlrhgiEiwii0Tkd8e/QedpW0FEEkTkw4vZtxY/Siml\nlFJKqSvJy0CsMSYSiHVcP5c3geUXu2MtfpRSSimllFJXkp7A547LnwN3F9dIRG4CLMDCi92xFj9K\nKaWUUkopFzY3/kRkoIhsLPQ38C/ctMUYc3Z6rSTsBY4TEfEAJgBD/sp90tnelFJKKaWUUi5y3fiV\nH2PMZGDyudaLyK9AeDGrXi2yHyMixSV5GlhgjEkQufifFNLiRymllFJKKeXCneLH5wL1iDEm+lzr\nRCRZRCKMMYkiEgEU98MTrYA2IvI0EAB4i8gpY8z5zg/S4kcppZRSSil1Rfke+D9grOPfeUUbGGPy\nf/RORPoCzS9U+ICe86OUUkoppZQqRp4p+Z+bxgKdReR3INpxHRFpLiL/dWfH2vOjlFJKKaWUcuHO\nsDd3GGPSgE7FLN8IDChm+TRg2sXsW4sfpZRSSimllItS6MG54mjxo5RSSimllHJRVj0/l5IWP+qS\nMMaw9oMYDq1dhpevL21fHkvl6xu5tJv/7CNkpafg6e0LQJfxU/ELCuFk0mFWvDWc7OPp+ARWov2r\nb+MfVtxsiH/Nit8OE/Pf9dhshvs6RzKwV5TT+jmxf/D25xuxBJcHoE/3+vTufD0ADe/9guurVwIg\nItSfSa+69MaWyPLly4mJicFms9G7d28GDnSeBn/Dhg2MHj2aPXv28M4779ClSxen9adOnaJbt25E\nR0czYsSIUslkjOHTcTFsXLkMH19fnntzLHUauD5+Z/178JMkJSTw4Zwf85f9OGs687+eiYeHJ83b\ntuOx54eWSq6JY2JYs3wZvn6+DI8ZS72G58417J9PciQhgenz7Lk+ff89Vi6JRcSDoJAQXo0ZQ+Uw\nl58O+MuZlk6MYf/aZZTz8eX24WOx1HPNNPuZR8hMS8HLx/5cv/edqZQPCmHp+6NJ2LwOgNzsbLKO\np/H0TxvdyrRiVwYx3x3EZoP7WlZmYKcIp/VjvjvIuj9OApCVayP9ZB4bRjdj7e8nGDvvUH67fSnZ\nvPNIbaKjgtzKUxxjDPu/eodj29bg4e1D5GOvEVCjvlObvOxMto97Mv/6meMphN7ShdoPPl9qOVZs\nTiLmszj7e0Knmgy8p57T+jlLDvD29G1Ygv0A6NO1Nr071QLgSOpp/vXxbySlZSHAJ8NbUzXMv9Sy\nPX9LCK2qlSc7z8a/V6SyNy3HaX15L+E/3a/Lvx7m78Uvf55i4ro0wv29GN4mlEq+Hpw4Y+ONZSmk\nnraWWjallPq70uJHXRIJ65ZzIiGe3jMXkrpzK6vfHcldk2YX27bdq+MJre9chKyfNI7I2+8msss9\nHPltDRs+nUD7V992K5PVamPUJ2uZ+sbtWELK0/ul+XS8uRp1q1Vyatf1tpqMGNjSZXtfb0++e+8u\ntzK4ZrIyatQoPvvsMywWC/fddx8dO3akbt26+W0iIiIYM2YMU6dOLXYf7733Hi1atCjVXJtWLufI\nwXg++WEhe7ZtZdK/RzJ+ZvGP3+pfF+Jb3vkDX9z6taxbGsv7s7+nnLc3x9PSSiXX2hXLOXQgnq9+\nWsiOuK2MHzWST78qPteyRQvxK5LroX4DeHzwcwDMnvEFn036iJdeH+VWpvi1yzmeEM9jXy4kaedW\nFk8YyT8mF5+py4jxhBd5rrcfPDz/8uZvp5P6+0638lhthlFzDjL1yeuxVCxH73d30bFRJeqG++W3\neeXu6vmXp69IZtfh0wC0jKzAd0PshdvxzDzuGL2NW+tVcCvPuRzbvoaslEPcGDObU/t28OfMt7hh\nuPNz3MvXn6avT8+/vuXN/yPkxvallsFqNYyaspWpr92GJdiP3q8soWPzCOpWc77PXVtXZcSApi7b\nD/twI0/eW49bb7CQmZWHRylOIdSqqh9VK5bj/m8P0SjUh5daV+bxH444tTmdZ+g773D+9al3VWFZ\nfCYAg24O5qc/TvLTH6e4KcKXp5oHM2p5aukFVEpdE67Gnp+rYrY3EbGKyBYR2S4is0WkfBnleO5y\n3baItBeRDMf93iUirzuWbxaRpo7LXiJySkQeLrTdJhG5UUT6iogRkehC6+52LLvP3XwHVsVS9467\nERHCGjUl59QJTqcVN0V78Y4f+JOIG+0FSESzlhxcFetuJOJ+P0r1iApUCw/Eu5wn3W6rRey6Qxfe\n8BKKi4ujRo0aVKtWDW9vb7p3705srPN9rVq1KvXr18ejmE9W27dvJy0tjVtvvbVUc61bEkuHHvbH\nr36TpmSePEF6quvjl3U6k3nTP+P+x59yWv7T7C/p1W8g5by9AagUElIquVYsjqXLXfZcjW9oyqmT\nJzhaTK7TmZl89fln/N8Tzrn8AwLyL2dnZfFXfhTtXP5cGUuDLvZMEY2acubUCU4dvfjnemF7YudT\nL/pOt/LEHcykemUfqoX44O3lQbdmwcRuP37O9vM3p9O9WbDL8l/ijtGmQUX8vD3dynMu6VuWE9ay\nGyJCYJ3G5J0+Rc7xo+dsn5V0kNyTx6gQ6VqElFTcH+lUD/enmsUf73IedLu1KrEbEy+8IfDHoRNY\nrYZbb7D3HPr7eeHnU3rfJ7ap7s/Pjt65HalnCPD2IMTv3I9FtQrlCPL1ZEtyNgA1K3mzKTELgE2J\n2bSpXno9Ukop9Xd2VRQ/QJYxpqkxpjGQAzx5oQ3OEpHS/J/9OeByFl4rjDFNgebAwyJyI7AKaO1Y\nfwOw9+x1EfEH6gBbHeu3AQ8W2t8/Cq1zy+nUZPxDC4aplQ8NJzM1ufg7MW44c/v3ZPMXH2GM/SuG\n4Dr1iV++EIADKxaRezqT7IxjbmVKTj9NROWCDwDhIeVJTs90abdozUHuevZ7Bo9bSmJqwfozOVZ6\nvfgjDwydz69rD7qVJT9TcjLh4QXHyWKxkJxc/HEqymazMW7cOIYNG1YqWQpLS0km1FKQK8QSTlqK\na66ZH03k7kf74ePr67T8yIF4dv62kSF9evNKv4f5fXtcqeQ6mpJMWKHjFWYJ52gxx+u/H0zkwb79\n8PXzdVn3ycR3ubdTOxb++AP9Bz3rdqZTqckEFhqSGRAazqmjxT+GC8cMZ8ZjPVk7reC5ftaJpMNk\nHEmg2o2uvY5/RXJGDhGVvPOvh1fyJjkjp9i2h9PPcDgth5aRrr07C85RFJWWnGOp+ASH5V/3CQrj\nzPFz90ykblhE5RbRpVKwnpWcnk1ESEGPWHiwH8lpWS7tFq07zF0v/srg8WtJPGrvJYtPPEWgfzme\neXst97wUy1tfbMNqLb2vSEPLe5KcmZd/PTXTSmj5c/93FV3bn9j9p/Kv/5GeQ/sa9ve7djXK4+/t\nQQWfq+W/fKXU5VKGU11fMlfjO+EKoC6AiDwsIusdvSOfnC10HL0hE0RkK9BKRFqIyGoR2epoHygi\nniLytohsEJE4EXnCsW17EVkqIt+KyG4RmSl2g4HrgCUissTRdpKIbBSRHSLyxtmAItLNse0mEXlf\nRH50LPcXkamODJtFpOfF3GFjTCawyXG/V1NQ/LQGPgbOflV6M7DJGHN24PcK4GYRKSciAY7tt5Tg\nmJdY+3+N597PfqD7BzNJitvEHwvtv2F181NDSdq6gbkD7iZx63rKV7YgHpfmG+jCOrSoSuzkXnw/\n8S5aN43g5fdX5q9b/Gkv/jfhTsa/0JbRU9ZzMPHEJc9zPrNmzaJt27ZOxdPltG/3LpIOHaRVp84u\n66x5Vk5mZPD2jG947PmhjHvpOZcP+5fK77t2cfjQQdpFu+YCeOLZ55kTu4zb7+zBnFkzLksmgK4j\nxvPo5z9w/0czORy3iV2/OP9e257Y+Vzf/g48PC/98/ysBZvTuf2GIDw9nAuKlBM57E3M4rb6l2bI\nW0kc3bCI0Jtvv+y326F5OLH/6cL3E6JpfUMYL3+4CYA8q2HTrqMMfTSK2WM7cCglk7lLD1z2fGdF\n1wpg0b6C4ufD9Wk0DfdjWs8qNAv3IyUzD9sV/GFEKXVlyjUl/7tSXVXn/IiIF9AV+FlEGgAPALca\nY3JF5D9AH+ALwB9YZ4x5UUS8gd3AA8aYDSJSAcgC+gMZxpgWIuIDrBKRhY6bagY0Ao5g72m51Rjz\nvoi8AHQwxpwdu/GqMSbdUXTFikgT7D0xnwBtjTH7ReTLQnfhVWCxMaafiFQC1ovIr47i5nz3OwRo\nCbwJnAL+7VjVGngD+IeIBDqury60qQF+Be4AKmL/Nd1a57iNgcBAgE8++QR6DHRps3PuTPb8+A0A\nletHkZmalL/udGoS/qGuJ5afXeZdPoA6ne4kdVcckXfcjX9lC9FvfghA7ulM4pctxCfQvQ9iluDy\nJB4tOJRJaaexBDsPBQmqUNBT0Ds6kvGfbyrYPsTetlp4IDc3Dmfn/nSqR7iZyWIhKangOCUnJ2Ox\nXNwJ+Js3b2bTpk18+eWXZGZmkpubS/ny5RkyZEiJssz/aiYL59gfv8hGUaQmF+RKS04ipMjEALvj\nNvPHzu0M6NoRa14eGenpDO//CKOnTCfEYqFVp86ICNdHNcHDw4MTx45RMfiv9yT8b9ZMfvjWnqtB\n4yhSCh2vlOQkKhc5Xtu3bmb3ju3c17kjVmsex9LSGdT3ET6cNt2pXefuPXjpqYH0HzT4L2faMmcm\n23+wZ7LUj+JkSkGmU6lJBFR2fQwDCj3X60ffSdKuOBp2uTt//Z7YBXR83v0JKywVvUk8XtDTk3Q8\nB0tF72LbLtiSzmv31nBZ/vOWY0RHVaKcZ+l+P5a45FuSl9uLvoBaDTiTXjA88MyxFHwqhRa7Xeah\n3zFWq8uECO6yBPuSWKinJyk9C0uhniCAoECf/Mu9O9Zi/PTtAISH+FG/ZiWqWezvC9EtrmPr7+lu\n5bm3QQXuuj4QgN1Hz2Dx9wLOABDq73nOCQvqBnvj6QF7Ck2IcDTLyvDF9h5IPy+hfU1/TuXY3Mqn\nlLr2XMlFTEldLcWPn4ic7bFYAUzB/kH9JmCDY5iEH3D2f1or8D/H5XpAojFmA4Ax5gSAiNwONCl0\n/ktFIBL7sLr1xpgER7stQE2goIugwP2OosELiAAaYu9t22eM2e9o86UjK8DtwF0icvbTqy9QHdh1\njvvdRkQ2AzZgrDFmhyOTt4iEA/WBPcAG4Bbsxc8HRfbxFTDYcf9eBIZTDGPMZGDy2atvFTMsvuE9\nfWh4Tx8ADq5Zyq65M6jdsTupO7dSzj+Q8iFhTu1teXnknDqBb6VgbHm5HFqzlOtuagVgn+WtQiXE\nw4OtsyZzfbde5zgEFy8qsjIHEk+QkHySsODyLFi5n/EvtHFqk5J+mjDHTG+LNxyiTtWKAGScOoOf\njxfe5Tw5diKbzbtTGHBvY/czRUURHx/PoUOHsFgszJ8/nwkTJlzUtoXbzZkzh+3bt5e48AHo/mAf\nuj9of/w2LF/K/K9m0LZLd/Zs20r5gECCQ50fv273P0S3+x8CIPlwAm8+8ySjp9gLjJYdotm2YR1N\nbm7J4fj95OXmUiGoZDOG9XqoD70esudavWwp/5s1g+hu3dkRt5WAgEAqF8l1z4MPcc+D9lyJhxMY\n+vST+YXPoQPxVKtRE4CVS2KpUat2iTI1vbcPTe+1Z9q3eilb58ygXqfuJO3cindAIAGVXZ/rZ06d\nwK9SMNa8XPatXkr15q3y16cf+JMzJ08Q0bhZifIUFlXNnwOp2SSknSGsYjkWbE5n/COu93NfchYZ\np600q+l6Lsj839J5vnsVt7MUFdHhPiI62N9S0+NWkbhkNpVv7sypfTvw8gvAu1LlYrdLXb/wkvT6\nRNUN4kDiKRKSMwkL9mPBqgTGP+s8eUjKsSzCguwF0eKNR6hT1V6cRNUJ4uTpXNIzzhBc0Ye121No\nXMe9WfHm7DrBnF32HuXWVf3o1bAii/Zl0ijUh8wcG2lZxRc/nWsHsGif83dkFX3ss7wZ4NEbKvHj\n3pNuZVNKXZuu5OFrJXW1FD9ZjnNf8om94vncGPNKMe2zCw39OhcBnjHG/FJkv+05+1WcnZVijqOI\n1AKGAC2MMcdEZBr2YuZCt9nLGLPnAu3OWmGMKe7s6NVAb+xFnRGRtcCt2Ie9rSnc0BizXkSigNPG\nmL2lNZ6+Wst2JKxbxuw+nfHy8aPNsNH56+b278k9U+Zhzc3h56EDsOXlYmw2rrupFfXuvB+AxC3r\n2fjpOyBCeJPmtH7udbczeXl68Nrjt9D/jV+xWW30io4ksnoQ78/aTOO6IXS8uTrT5+9iyfpDeHp6\nUDHAhzGDbwPgz4QMXv/PGjw8BJvN8Pi9US6zxJUok5cXI0aMYMCAAVitVnr16kVkZCQTJ06kcePG\ndOrUibi4OAYNGsSJEydYsmQJH3zwAfPnz3f7ts+neZt2bFq5jCfu7IyPrx+DRxU8fs/e35OJ38w7\nz9YQfU8v3h8xnEH33olXuXI8++bYUjlXo1XbdqxZvowHunbG19eP4f8uyNX33p5Mm3P+XB+/M4GD\n8fvx8BAsEVV46fU3ztv+YtRq1Y74tcv47MHOePn6cfsrBZlmPNaThz+zP9fnvGh/rttsNqo3b0VU\nj/vz2+2JXcD1nbqVyjHy8hReu7c6/SfvxWaDXjeHEBnux/s/HaZxNX86NrY/b89OdFD0NhPSz5B4\nPIeb6wS6neV8gqJac2zban579T48vH2p2/df+eu2vPGI0yxvRzfG0nDwO6WewcvTg9f6N6V/zCps\nNkOvDjWIrFaB97/aSeM6lejY4jqmL/iTJRsTHe8J5Rjzz+YAeHoKQx9pTN9RKzAGGtWulD8FdmlY\nnZBFq2rlmX1fNbLzDDErCnrJpvWs4jTLW8da/gxZmOS0/Y0Rfjx5UzAGw5akbCasOfdkEkopdS5X\nY8+PXK5x+JeSiJwyxgQUWdYQmId9SFqKiAQDgcaYA4XbFzPsLRD7sLd+QDegt2PY3PXAYaAFMORs\n0SEiHwIbjTHTRGQbcJdjONsN2IfYNQNCgThgGPA19qFvbYwx8SIyE6hojLlTREYDFbAXXUZEmhlj\nNp/jPrcvnKPIuueAZ4Bpxpg3RSQCe8/UaWNMlKNNX6C5MWaQiHTFXhAucRRpPxpjvj3PIS+256cs\nDY0As2v0hRteRtKg2E60K8Ke7LJO4KyeL6TmXbjd5RTq+Erj45JN3HZJPOnoUDLz+5RtkCKk+0z6\nLXdvQpLSNrWtvRfGxBX3/VfZkSZjaD11X1nHcLK6X8l6QJVSpab0ZnIpZatPUeJCoXXAlXm/rpae\nHxfGmJ0i8i9goYh4ALnAP4EDRdrliMgDwAci4oe98IkG/ot9ONtvjl6kVOBuzm8y9vONjhhjOjiG\npO0GDmE/NwhjTJaIPO1ol4l9SNpZbwLvAXGOzPuBksx7uwp4F0cvjzEm0XHe0eriGhtjfirBbSil\nlFJKqauYDnu7QhXt9Sm0/GvsPS3nbe8436e4+WWH43oOzFLH39ltBxW6/AGFzqkxxvQ9R+Qlxpj6\njqLqI2Cjo30W8MQ5til6H5xyFFm3gSLfIhhjaha5Pg2YVsy258qslFJKKaWuIVfjsLerovj5G3pc\nRP4P8AY2Y5/9TSmllFJKqSuGFj+qVBhj3sU+LO2CROQOYFyRxfuNMfeUejCllFJKKaUcdNibuuwc\ns839csGGSimllFJKlaKrseendH/BTimllFJKKaWuUFr8KKWUUkoppa4JOuxNKaWUUkop5eJqHPam\nxY9SSimllFLKhU54oJRSSimllLomaM+PUkoppZRS6pqgPT9KKaWUUkqpa4L2/CillFJKKaWuCVdj\n8SPGXIX3Sl1q+qRRSimllCodUtYBzuWD5JJ/5nvGcmXeL+35UUoppZRSSrnQc36UcjD7PyvrCE6k\n1mO8lVjWKZwNjSjrBH8zR74v6wTOrrsLALP6/8o4SAFp/TkAgzacLOMkzj5sEXhFvicA7Mku4yBF\n1PMFjq4s6xjOKt92ZR4npVSZuxqHvWnxo5RSSimllHJhtbpT/VyRo960+FFKKaWUUkq5yrPa3Nja\ns9RylCaPsg6glFJKKaWUUpeD9vwopZRSSimlXLg37O3KpD0/SimllFJKqWuC9vwopZRSSimlXFjd\nOufnyqTFj1JKKaWUUspF3lU47E2LH6WUUkoppZSLq/GcHy1+lFJKKaWUUi502JtSSimllFLqmqDD\n3pRSSimllFLXBGve1dfzo1NdK6WUUkoppa4J2vOjLokVG/cRM+lXbDYb93W5gYEPtHJaP2dhHG9P\nWYIlJBCAPj1uonfXGwAY8OrXbN19hBsbVeWTUb0vWUZjDGs/iOHQ2mV4+frS9uWxVL6+kUs7a24O\naya+SeKW9YgINw14nlrt7ijVHDExMSxbtgxfX1/Gjh1Lo0auOfr3709qaipWq5WbbrqJ119/HU9P\nz/z1U6dOZdy4caxZs4bg4GC3Mi1fvpyYmBhsNhu9e/dm4MCBTus3bNjA6NGj2bNnD++88w5dunQB\n4PDhwwwaNAibzUZeXh4PP/ww//jHP9zK4pRr/W5iPvwem9VG7+43M/ChjsW2+2VZHINHTufbjwcT\nVa8aObl5vP7O/9i+JwER4dVnenJL0zqlkmnFtuPEzIrHZjPc1zaMgd2ruLT5aX0aH85LQIB61coz\n4clIDh89wzMf7MFm7MMKHo4O58EOllLJZIxhz/TxHN2yCk8fXxoNHEmFWvVd2tnyctn9+Vsc27UJ\nRKjb+2ksN3diz4wJpO/cZG+Tk03OiXQ6TF7qdq6Svi/s+jOZkR/8QubpHDw8hCf/0Zpu7Rq4nQfs\nx+rTcTFsXLkMH19fnntzLHUauL7+zvr34CdJSkjgwzk/AvDWS89x+MB+ADJPnsQ/MJCJ38xzO9fy\ntduIee9LbDZD7x5tGPhIt2Lb/bJkI4P/NYlv//saUQ1q8v0va5ky6+f89Xv+TGDu1BE0uL6625nc\nPVYAP86azvyvZ+Lh4Unztu147PmhbudSSl06OuGBUhfBarUx6qOFTB39IJbKgfQePI2OLSOpW6Oy\nU7uubRsw4p+3u2zf/75byDqTy9cLtlzSnAnrlnMiIZ7eMxeSunMrq98dyV2TZru02zrjY3wrBdN7\nxi8Ym40zJ46Xao7ly5cTHx/PwoUL2bp1KyNHjmT2bNccEydOJCAgAGMMgwcP5ueff6Z79+4AJCYm\nsmrVKq677jq381itVkaNGsVnn32GxWLhvvvuo2PHjtStWze/TUREBGPGjGHq1KlO24aGhvL111/j\n7e1NZmYmPXr0oGPHjlgs7n+ot1ptjJo4l8/eHogltCL3Pfk+HVs3om5N532fOp3NF3NWckODgg97\ns39cB8APU18k7dgpHh/2X779eDAeHu51fltthlHT9zN1SAMswd70HrWdjk2DqFulfH6b+KQsJs8/\nzKzhjajo70XaiVwAQiuV46t/Nca7nAeZ2VZ6/GsrHZoGYQnydisTwNGtqziddIhbJ8wl48/t7Jo2\nhlve+Nyl3f55U/GuEMSt4+dgbDZyM08AUO/hF/PbHFz4FSfj97idyZ33BV+fcox76U5qVgkmOe0k\n97HN8ygAACAASURBVA2axm031aJCgK/buTatXM6Rg/F88sNC9mzbyqR/j2T8TNfXH8DqXxfiW97f\nadnQt9/Lvzxl/Fj8AwLczmS12hg1YSafvfcilrAg7hvwJh1va0rdWs6v71OZWXwx+1duaFg7f9ld\nd7TkrjtaAvbC558vf1gqhQ+4f6zi1q9l3dJY3p/9PeW8vTmellYquZRSl07eVTjhwVU77E1ErCKy\nRUS2i8hsESl/4a0uSY7nLtdti0h7ETEi0qPQsh9FpL3j8p0isllEtorIThF5wrF8pIgMKa0ccXsS\nqR4RRLWISniX86Rbu4bErvn9ordv1awm/n7ufwC8kAOrYql7x92ICGGNmpJz6gSn01Jc2u1d8D9u\n6PMEAOLhgW8l93pVioqNjeXuu+05mjZtyokTJ0hJcc0R4PhQlZeXR25uLiKSv27MmDG89NJLTstK\nKi4ujho1alCtWjW8vb3p3r07sbGxTm2qVq1K/fr1XYoHb29vvL3tj11OTg42W+m9acbtPkiN6ypT\n7boQvMt50b1jU2JX7XBpN3HqLzz+YAd8vAu+2/njQDK3NLMXbyFBAQQG+LF9T4L7mfadonqYL9XC\nfPH28qDbzSHEbj7m1Gb28hQe6hhORX97npAK5QDw9vLAu5z9+OXk2TCl+OVa6qZlRNzWDRGhUt0o\n8jJPcubYUZd2h5d9T60ejwH257Z3YCWXNklrFhLeyv2eTnfeF2pVDaZmFfvrzhISSHCl8qRnnHY7\nE8C6JbF06GF//dVv0pTMkydIT3V9/WWdzmTe9M+4//Gnit2PMYZVC3+ibdc73c4Ut2sfNaqGUa1K\nqP253ulmYldsdmk38dPvePzhrvj4lCt2P/MXraN79M1u5znL3WP10+wv6dVvIOUc7xGVQkJKLZtS\n6tKwWk2J/65UV23xA2QZY5oaYxoDOcCTF7uhiHheuNVFew64nIVXAvBq0YUiUg6YDPQwxtwANAOW\nXooAyWkniQgNzL8eXjmQ5LSTLu0WrdzDXU9OYfC/55KYeuJSRDmv06nJ+IeG518vHxpOZmqyU5sz\nJ+25Nk2dyHeP30Ps64PJSnf9EOmO5ORkwsMLcoSHh5OcnFxs2/79+9O6dWv8/f254w77B9Jff/2V\nsLAw6td3HdZUGnksFss58xQnMTGRHj160L59ex5//PFS6fUBSD56gvCwgg/nltCKJB/NcGqzY28C\nSSnHad/KeUhU/TrXsXj1TvKsVg4lprNjbwKJKe734CUfyyEiuKBQDw/2JvlYjlOb+KRs4pOz+EfM\ndh54czsrthXcbmLaGe56LY4OL25mQLfrSqXXB+DMsVR8QwoeQ99gC9nHnD+k5mbaX5N/fDuJta/2\nYev7wziT4fxNfNbRRLJSDhPcqIXbmUrrfSFuzxFy82xUjwhyOxNAWkoyoZaCYxViCSctxfX5PvOj\nidz9aD98fIvvbdrx20YqhYRwXY2abmdKTj1OeFjBlyyWsCCSU52frzv2HCApJZ32rW84534WxG6g\ne+fSK37cPVZHDsSz87eNDOnTm1f6Pczv2+NKLZtSSl2sq7n4KWwFUBdARB4WkfWOXqFPzhY6InJK\nRCaIyFaglYi0EJHVjl6S9SISKCKeIvK2iGwQkbhCPSftRWSpiHwrIrtFZKbYDQauA5aIyBJH20ki\nslFEdojIG2cDikg3x7abROR9EfnRsdxfRKY6MmwWkZ4XuK9bgQwR6VxkeSD2YY5pAMaYM8YY98ey\nlFCHlpHEfv4U33/cn9bNavLy+B8vvFEZMNY8MlOTsDRqxt2fziWsUTPWTRpXZnmmTJnCypUrycnJ\nYe3atWRlZfHJJ5/w7LPPllmmoiIiIvjhhx9YuHAhc+fO5ejR0i0Wz8VmszH2Pz8w7OkeLut6dWtB\neGhFej0xkdEfzqNZ45p4el6et788m+FAcjZfDGvIhCfr8tpn+zhxOg+AiBAfvn+zCb+Mbcp3q1I5\nmpFzgb2VHmOzciY9mUrXN6FlzEwq1Y3i91nvObVJWvMLlps7IR6l+X3QuV3ofSEl7RRD3/qR0S90\nw8PD/V7Oi7Vv9y6SDh2kVaeib6sFlv/0I226uN/rczFsNhtjP/iaYc88cM42W3fsw8/Xm+trV70s\nmc4637Gy5lk5mZHB2zO+4bHnhzLupecwpdnlqZQqdVdjz89Vf86PiHgBXYGfRaQB8ABwqzEmV0T+\nA/QBvgD8gXXGmBdFxBvYDTxgjNkgIhWALKA/kGGMaSEiPsAqEVnouKlmQCPgCLDKcRvvi8gLQAdj\nzNlPgK8aY9IdRVesiDQB9gKfAG2NMftF5MtCd+FVYLExpp+IVALWi8ivxpjM89ztGOBNYNHZBY7b\n/B44ICKxwI/Al8aYixqXJCIDgYEAn3zyCY93Ln6YBdiHpSSmFnyjm3T0ZP4JzGcFVfDLv9y7yw2M\nn7L0YmK4befcmez58RsAKtePIjM1KX/d6dQk/EOdeyl8Kgbh5etHzbb2cxBqte/C3gXfup1j5syZ\nfPONPUdUVBRJSQU5kpKSzttb4uPjQ6dOnYiNjaVy5cokJCTQs2fP/G3vvfdeZs+eTWhoaImyWSwW\npzzJyckl6r2xWCxERkaycePG/AkR3GGpXIGkQr01yakZWCpXzL+eefoMe/cn8ehzHwOQmn6Sp16d\nxqSYvkTVq8bwf96V3/bBQR9Ss2rJjo9TpiBvEtMLCpak9ByX3pvwIG+a1A6gnJcHVUN9qRnuy4Gk\nbKJqBzjtJ7JKeTbuPUmXFiUbCnRo0TckLPkOgIq1G5KdVvAYZqcn4xsU5tS+XEBFPHx8CWtunzTC\ncks0h5d979Qmee1C6v/fsBLlKcrd94VTmWd4csRsnuvblqYNXCeV+CvmfzWThXPsr7/IRlGkJhcc\nq7TkJELCnJ/vu+M288fO7Qzo2hFrXh4Z6ekM7/8Io6dMB8Cal8ea2EW8+9Uct3KdZQmtRFJKev71\n5JRjWEILej0zT2ezd99hHh30FgCp6Rk8Nex9Jo0bTFSDmvb7+Ot6ukff4naW0jxWIRYLrTp1RkS4\nPqoJHh4enDh2jIpuTtCilLp09Jyfvxc/EdkCbAQOAlOATsBNwAbHuk7A2TNFrcD/HJfrAYnGmA0A\nxpgTxpg84HbgUce264AQINKxzXpjTIKjmNgC1DxHrvtF5DdgM/ZiqSFQH9hnjNnvaFO4+LkdeNlx\nm0sBX+C8Z68aY5YDiMhtRZYPcNzn9cAQYKrr1ufc52RjTHNjTPOiM38VFVUvggNH0klIOk5OrpUF\ny3bSsWVdpzYpaafyLy9e+zt1ql+esd8N7+nDPVPmcc+UedS4LZo/fvkOYwwpO7ZQzj+Q8iHOHxBF\nhGqtOpC4xX7C/JFNa6hUw/1Zwvr06cO8efOYN28e0dHRfPedPceWLVsIDAwkLMw5R2ZmZv55QHl5\neSxdupTatWtTr1491qxZw+LFi1m8eDHh4eHMmTOnxIUP2Iux+Ph4Dh06RE5ODvPnz6djx+JnVSsq\nKSmJ7OxsADIyMvjtt9+oVatWibM45apfjfjDRzmUmE5Obh7zF2+hY+uG+esDA/xYN+8NFn81nMVf\nDadpw+r5hU9Wdg6ns+xFyqqNe/H09HCZKKFEmWoFcCAlm4TUbHLybCxYn0bHZs7DsaJvDGb9bvvw\nrWMnc4lPyqZqmA9J6WfIzrH/p5KRmcem309SK9zP5TYuVrXO99Nq9CxajZ5F6E3tSVy5AGMMx//Y\nhlf5AHyCnCcWEBFCm7Wxz/QGpO/YgH+Vgscq80g8uZknqRjZpMSZCnPnfSEn18qgN+fQM7oxXdq4\nP7yz+4N9mPjNPCZ+M49bOkSz5Af762933BbKBwQSHOr8+ut2/0NM+3Ul//1pMWOnzeK6GjXzCx+A\nLetWU7VWbSoXGhLmjqj6tYhPSObQkVT7cz12PR1va5q/PjCgPOsWTGTx/95i8f/eommjOk6Fj81m\n46fFG0rlfJ/SPFYtO0SzbYP9vfRw/H7ycnOpEFQ6wxeVUupiXc09P1nGmKaFF4j9bPDPjTGvFNM+\n2xhjvcA+BXjGGPNLkf22B84UWmSlmGMrIrWwFx0tjDHHRGQa9mLmQrfZqwRD1GKAfwF5hRcaY7YB\n20RkOrAf6PsX93tBXp4evPb07fR/9WtsNkOv25sQWTOU979YTuPICDq2imT6vI0sWfsHnp5CxUA/\nxrzYPX/7Pi/OYF9CGqezcmn38Ef8+7mutGle+zy3WDLVWrYjYd0yZvfpjJePH22Gjc5fN7d/T+6Z\nYp+utsUTQ1g2eihrPxyNb6Vg2g4bU6o52rVrx7Jly+jcuTN+fn6MHl2Qo2fPnsybN4+srCyeeuop\ncnJyMMZwyy238OCDD5ZqjrO8vLwYMWIEAwYMwGq10qtXLyIjI5k4cSKNGzemU6dOxMXFMWjQIE6c\nOMGSJUv44IMPmD9/Pn/++Sdjx45FRDDG0K9fP+rVq1c6uTw9GTH4bgYM/RSrzUavrjcTWSuciVN/\noXG9qnS69dxT7qYdP0X/of/FQwRL5Qq89UrpTL/t5Sm81qcm/Sfstj/X24QRWaU87889ROOa/nRs\nFsxtjSuycvtxur+6FQ+Blx6oTlBAOVbtOM64r/YgAsZAvy4R1KtWOqcHVm56K0e3rmLVi3fj6e1L\nw4Gv569bM/whWo2eBUDkg4PZPmkEe2ZMwDswyKld0ppfCG95e6lMogHuvS/8vHwXG7cd4viJLOYu\n2gbAmBe706CO+wVs8zbt2LRyGU/c2RkfXz8Gjyp4/T17f8+LmrZ6xc8LaNul+wXbXSwvL09GPN+H\nAS+8i9Vqo9edtxFZuwoTP/2OxvVr0qlN0/Nuv2HLXiLCgqlWxf3ezcLcPVbR9/Ti/RHDGXTvnXiV\nK8ezb44tteeXUurSuJKHr5WUXK3jbUXklDEmoMiyhsA87EPSUkQkGAg0xhwo3L6YYW+B2Ie99QO6\nAb0dw+auBw4DLYAhxpg7Hdt/CGw0xkwTkW3AXY7hbDdgH2LXDAgF4oBhwNfYh761McbEi8hMoKIx\n5k4RGQ1UwF50GRFpZoxxnfaH/CKscI51QATwKPYesObGmKWOddHAe8aYxiIyEjhljBl/kYfXmP2f\nXWTTy0NqPcZbiWWdwtnQiLJO8Ddz5PsLt7mcrrMPkzOr/6+MgxSQ1vbpqgdtcJ0ooCx92CKQK/E9\nAWBPdhkHKaKeL3B0ZVnHcFb5tivzOCl17bhivwXovSC5xIXC7G6WK/J+Xc09Py6MMTtF5F/AQhHx\nAHKBfwIHirTLEZEHgA9ExA974RMN/Bf7cLbfHL1IqcDdF7jZydjPNzpijOkgIpuxF1aHsJ8bhDEm\nS0SedrTLBDYU2v5N4D0gzpF5P3CxZ9XGYC/2wP7CGioinzjuTybOvT7/EpHnCh2Dy3uWrFJKKaWU\nuqLkXYU9P1dt8VO016fQ8q+x97Sct73jfJ+WxexiuOOvsKUUmjbaGDOo0OUPgA8KXe97jshLjDH1\nHUXVR9h7ajDGZAFPnGObovehaI7vcf42odifCDfGjARGXsxtKKWUUkqpa8PVOOztqi1+/oYeF5H/\nA7yxT4bwSRnnUUoppZRS1zDrVTjbmxY/VwhjzLvAuxfTVkTuAIr+2Mx+Y8w9pR5MKaWUUkpdk3TY\nm7oiOGab++WCDZVSSimllFL5tPhRSimllFJKudBhb0oppZRSSqlrgk54oJRSSimllLomXI3n/HiU\ndQCllFJKKaWUOktEgkVkkYj87vg36Bzt3hKRHSKyS0Ted/xkzHlp8aOUUkoppZRyYbXaSvznppeB\nWGNMJBDruO5ERFoDtwJNgMZAC6DdhXasw96UUkoppZRSLsrwnJ+eQHvH5c+BpcCwIm0M4Iv9NzIF\nKAckX2jH2vOjlFJKKaWUupJYjDGJjstJgKVoA2PMGmAJkOj4+8UYs+tCO9aeH6WUUkoppZQLmxuj\n10RkIDCw0KLJxpjJhdb/CoQXs+mrha8YY4yIuHRBiUhdoAFQ1bFokYi0McasOF8uLX6UUkoppZRS\nLoy54PwB59nWTAYmn2d99LnWiUiyiEQYYxJFJAJIKabZPcBaY8wpxzY/Aa2A8xY/YszVN4WduuT0\nSaOUUkopVTpKXmFcYs0n7S/xZ76NT9Uq8f0SkbeBNGPMWBF5GQg2xgwt0uYB4HGgC/Zj+DPwnjHm\nh/PuW4sfVQImMbesIziLKAfm+4fKOoYTuWsWZm3fso7hRFpOK+sI5/TU2hNlHcHJpJYVAHhl95ky\nTlJgTH0fAMyCPmWcxJl0m8k9PySVdQwnc3vYR1KYWd3KOIkzeWgBGzLLOoWzFv7A0ZVlHcNZ5dsA\nWH6yjHMU0TawrBOoq9QVW/yUFREJAb4BqgMHgPuNMeki0hx40hgzQEQ8gf8AbbF/Mf+zMeaFC+1b\nh70ppZRSSimlrhjGmDSgUzHLNwIDHJetwBN/dd8625tSSimllFLqmqDFj1JKKaWUUuqaoMWPUkop\npZRS6pqgxY9SSimllFLqmqDFj1JKKaWUUuqaoMWPUkoppZRS6pqgxY9SSimllFLqmqDFj1JKKaWU\nUuqaoMWPUkoppZRS6pqgxY9SSimllFLqmqDFj1JKKaWUUuqaoMWPUkoppZRS6prgVdYB1NXJGMMH\nY2JYu2IZvr6+vBwzlusbNjpn++GDnuRIQgLTvvsRgCkfvMeqxbGIhwdBwSG8HDOGymEWt3Ot2J1B\nzPeHsNngvpsrM7BjuNP6Md8fYt0fJwHIyrWRfiqPDW82BWD8/ASW7coA4KnoCLo1DXY7D8CKuGPE\nzIzHZjPc187CwDuruLT5ad1RPvwuAQHqVS/PhKeuB2DA+J1s/fMUN0YG8skLDUolD9gfv5iYGJYt\nsz9+Y8eOpVEj58cvKyuLZ599loMHD+Lp6UmHDh0YMmQIAEeOHGHYsGGcPHkSq9XKkCFDaNeuXank\n+n3mBNK2rsLD25eGj79OYM36Lu1sebns/eItju3+DfEQavd6mrAWHfl95jsc270RAOuZM+SeTKft\npCVuZ4r7dBxJm1bg6ePLTc++SVCdhq6ZcnPZMnk0R7dvBBEaPfwMVVp3Zt9P37Dvp68QD0+8fMvT\n7OkRVKhex61MK3ZlEDP3IDYD991SmYHREU7rx8w96Pw8P5nHhjHNWPv7CcZ+dyi/3b6UbN55tDbR\nUUFu5Smsf6NAbrL4cMZq+GBLBvsy8pzW+3oKo28teG2F+HmyLCGLqTtOckcNP7rWLI/NQLbV8J+t\nGSScsrqVZ8Ufp4n5+aj99XdjBQbe5nxfj2Tk8vJ3KZzMtmG1wYvRwbSL9AdgT/IZRvyYSuYZGyLC\nt49XwcerdL5TNMYw/e0Ytqxcho+vLwPfGEutBud+D53w3JOkHk5g7Gz7e+iBPbuYGvM6uTln8PT0\npO8rI6nTuIlbmZav3UbMe19isxl692jDwEe6FdvulyUbGfyvSXz739eIalCT739Zy5RZP+ev3/Nn\nAnOnjqDB9dXdylOUMYavxsewbdUyvH19eWzkWGrUdz1mbw98hIyjKZTz9QXg+Q+nUiE4pFSzKKWu\nXFr8qEti3YrlJByMZ+aCheyM28q7b45k0pezi227fNFC/Mr7Oy178LEB9H/mOQD+N+MLPp/0ES++\nPsqtTFabYdTcg0wdeD2WiuXo/f5uOjaqSF2LX36bV+6qln95+soUdh05DcDSXRnsPHyauc83JMdq\n49FJe2lbvyIBvp7uZ/piP1OHNsQS7E3vkdvo2CyIulXK57eJT8pi8o+HmfWvxlT09yLtRG7+uv5d\nq5CVY+XrJclu5Shq+fLlxMfHs3DhQrZu3crIkSOZPdv18evXrx8tW7YkJyeHvn37smzZMtq1a8ek\nSZPo2rUrDz30EH/88QcDBw5k8eL/Z+++w6Oo1geOf9/0CiSBbEKvUiQaVBSlh3YlhCJyURBFQBR/\nqIjdKyoqWEARuEpTQAQsgIoKXtCANOkCMfTeswlJKOnJ7vn9MUuSTYGQ3RA05/M8ecjOvLPz7uzZ\nybx7zhxWOZxXYswfpMWdoNUH33HxcCz7v3iPO96YWyju2I+zca8UyN0fLEFZrWSnXgSg0cDRuTEn\nf/2GlOP7Hc7JvH09KWeP03X6zyQfiGHntHfoOHFhobh9i2biWTmQrtN+QlmtZKUYhXSt9t2pf++/\nATizeTUxsyfQ5s3ppc7HYlW8teQEs5+4CVMVd/pN2ktE8yo0DMnXzvvkXXR+udbM3tNGO2/VqBI/\nvGBcLJ5PzaHb+L9o3bhSqXMp6LZgD6r7ufHkqnPcVMWdx8Mq8dL6JLuYDIti9NrE3McT2wax6WwG\nAGtPZ7DieDoALU2ePHpzJd7enFzqfCxWxVvLE5g9qDqmSm70m3WKiMa+NKzmkRszbW0y9zbz48GW\nlTmUkMXwBWdZNcqXHKvihe/i+aBPME1CPElOs+DmIqXOpaBdG9YSd+IYHy5dyeG/djH33TcZO6/o\nc+jW6JV4FTiHfjV5Avc9/n/c2ro9O9ev4avJE3ht1pelzsdisfLWhwuY8/FzmIIDuH/Y20S0Cadh\nvep2cSmp6cxb9Bu3Nqufu6xnt1b07NYKMAqf/3v5v04vfABiN6wl/uQxxn2/kiOxu1jw7pu8+kXR\nx2zYOxOp2yzM6Tlomnbj+1sOexMRi4jsFJFYEVkkIj5X36pM8hh1PfYtIt1sr3eniKSIyH7b7/OK\niK0lIt/Yfh8mIh9f475OiUgVR3PesDqabj17IyLcfGs4KZcukpgQXyguLS2Vb+fNYdDjI+yW+/r5\n5f6ekZ6OiOMXFTEnUqld1YtaQZ54uLnQPTyA6N3ni41ftjOJSFvvzmFzOnfU88fNVfDxcKVxqDfr\n9l9wPKcjKdQ2eVEr2MvI6a6qRP9pfzG3aE08AzqFUNnX+K4iqJJ77rq7b66Mr4MFWFGio6Pp3dt4\n/8LDw7l48SLx8fbvn7e3N61aGRc0Hh4eNGvWDLPZKMJEhJSUFAAuXbpEcHCwU/I69+caQlpHIiJU\nbhhGTtolMs+fKxR3dt2P1I0abOTi4oKHf+Embd60AlOrbg7ndGbLamp3jEJECGx8K9mpl0hPSigU\nd/y3H2h8/9DcnDwrGT0M7j55bd2S6XhbN9q5J7Wq2tp5i0CiY6/QznckEXlb4V7MFbuSadukMt4e\nzmtfd4Z4sfqkUbwcOJ+Nr7sLAZ7F/xmq7utKZU8X9iQZBX96jspd5+nqhHPC6UxqB7pTK8AdD1eh\n+81+RO9LtYsRhJRMKwCXMqwE+xvHY8PhNBqbPGgS4glAgI8rrk4sfrb/Hk2bHsZnsOEt4aReukhy\nEefQjLRUflkwh97D7M+hgpCeYryWtJRLBFRz7DMYs/cIdWoGU6tGNTzc3YjsdCfR63YUips86wce\ne+hePD3di3gWWPbrZiI73+lQLsXZuSaaVt2NY9YgLJy0Sxc5f67wMdM0rWL7u/b8pCulwgFEZAHw\nBPBRSTYUEVellGPjJPKMAuYDaU56viIppVYAKwBE5HfgeaXUtoJxIuKmlDoJ9C/LfEoiwWymWkje\nkLJqphASzGaCCvwBnj11Mv0fGYKnbfhBfp9NnsSKH3/A19+fj2cXqvOumfliNqFV8v4gh1T2YNeJ\n1CJjTydncjopk1YN/QFoHOrDJ7+e4dH2JjKyrWw+fIkG+XqMSp1TchahgZ55OQV6sOvwJbuYY3HG\nxeKDb8diVYqRvWvS9hbnDUMqMi+zmZB8719ISAhms7nYIubixYusXr2aRx55BICRI0cydOhQ5s+f\nT3p6OnPmzHFKXpnJCXgF5Q1/9AwMJjM5Hs8qVXOXZacax+/Ikukk79uOd3BNGg96AY/KecNa0s+d\nJSPhDAHN7nA4p4zEeLyr5h0r76omY1lgtdxlWSlGz9OeBZ+QELsVv5Ba3Pr4q3hVMXI6vOxrDv04\nD2t2Nm3f+cyhfMznswitktdzYbTzlCJjTydlcjoxi1aNCvfuLN+RxOAOjg81zS/Iy4XEjLzTb2K6\nhUAvF5JtxUVBbap7sf5Mht2ye+v60LO+D24uwusbk4rcrqTMl3IIrZT3ZzCkkhu7Ttvvb2SHAIbO\nP8v8LRdIz1bMHmT0dBxLzEYEhs4/Q3Kqhe7N/RjW2nmfy+R4M0GmvHYVGBxCcoK5UBGz+NPJ3PvQ\nEDwKnEMfev5VPhg5lIUfv4+yWnljztcO5WNOOE9IcF6RbAoOIGb3UbuY3fuPExefRId7buXzhSuK\nfJ7l0Vv59P2RDuVSnOQEM4H5zlsBphDOx5upUrXweWvu2FcRVxduj+hK5NAnnfIFm6Zpfw9/y56f\nAtYBDQFE5CER2WLrFZkhIq625Ski8qGI7ALuFpGWIvKHiOyyxfuLiKuITBCRrSISIyKP27btICK/\ni8hiEdknIgvE8DRQHVgtIqttsdNEZJuI7BaRsZcTFJHutm23i8gUEfnZttxXRGbbctghIr2u9cXb\nend+sOWwQkQaisjOfCF1RGSNiBwUkdfybfeTLZ/dIjKsBPsZbntt22bOnHmtaRbp4L69nDl5grad\nuxS5ftgzz7Ioeg1dIqP4fuF8p+yzpJbvTKbrLQG53+S2aVyJ9k0r8+B/9/HcgiOE1/HD9Tp9enIs\niuNx6cx7pRkfjmjEmDlHuJiac/UNr5OcnBxGjx7NoEGDqFXLGDa4bNky+vTpw9q1a5k5cyYvvvgi\nVmvRF7jOpqwWMpPiqdzoFu58az6VG4Zx8OvJdjHxm1cS3LIT4uL8XrPickpPNBPU5FY6TfqWwCa3\n8tecD3PXN4h8gG4zltP8kVHs+9Y5n6+SWL4jia63BhTqsYi/kMWBs+m0aeK8IW+l0aaGN+tOp9st\n++VYGiNWnWPe3kv0a+RXzJbOsyw2hT63+rNmdF1mDAjlpe/NWJUixwrbT2Qw8T4TC4bU4Nd9qWw8\nUqbfgxVyfP9ezKdO0DKi8Dk0evFXDHzuFab8soaBz73CrLf+U6a5WK1W3pv6DS89Vfx3b7t2MRwG\n4gAAIABJREFUH8Hby4Ob6tcs01yuZtg7E3nzm594adYCDu7YzsZlS8s1H03Trq+/a88PYPR0APcC\n/xORphg9Hq2VUtki8ikwEJgH+AKblVLPiYgHsA/or5TaKiKVgHRgKHBBKdVSRDyBDSKy0rarFsDN\nwBlgg20fU0RkNNBRKXV53M1/lFJJtqIrWkRuAQ4AM4B2SqmjIvJVvpfwH2CVUmqIbajZFhH5TSlV\ndHdE8VoA4UqpZBFpWGDdnUBzIAvYKiI/K6V2Ao/YcvUBtonIEqVUsYPnlVIzgctXZepsduGY779a\nwM+LvwWgSfMwEuLictclmOOoZrL/FnnPzh3s3x1L/64RWCw5nE9M4pnBg5g8135ceuceUbw0YjiP\njny6BIeieKZK7pw9n5d43IUsTJWLHpqxfGcSY/rYj0l/olMoT3Qybhx/bsER6lYt3Ft1zTkFeHA2\nKTMvp6QsTAGedjEhgZ7cUt8PdzcXalbzom6IF8fNGYTVd+6F34IFC/j2W+P9CwsLIy7f+xcXF4fJ\nVHQvwJgxY6hbty6DBw/OXbZ48WI++8zowWjRogWZmZkkJycTFHTtNxWf+u1bzqz5AQD/es3ISMy7\nvykzKR7PAPtvdd39KuPi4UW12zsCENyyE2fX2F/cmDetpPHDL15zLpcdXvY1x35dAkBAw5tJP5d3\nrNLPmfEKss/Jw78Krp5eVL+7MwA17unKsV+/L/S8Ndvey47p40qdF4Cpigdnz2flPjbauUeRsct3\nJDGmb51Cy/+3M5nOYVVwd0KFf29dH7rUNnpJD53PJsjLFTA+h0HeriRlFF0U163khqtQaEKEy9af\nzuDxMMeKM5O/G2cv5j1/3MUcTP72fxaX7LjIrIFGb0+LWl5k5iiS0yyEVHLljjpeBPgYBXT7hj7s\nOZvJ3fVLPxL6128WsPp74zNY/+YwEs157SopPo6AavafwYMxOzi6J5ZRkcY59GJSEu88NojXZn3J\nup+/Z9ALRsFzV5d7+ezt13CEqVoV4uLzetrM8cmYquUNJ01Ny+DAkdM8PPIDABKSLjDipSlMe/9p\nwprWBWDZb1uI7HyXQ3kUtPrbBaz9wThm9ZqFkZTvvJVsjqNKERPlBNiWefn6cee/enBsdwz39Ojt\n1Lw0Tbtx/V17frxtvRvbgBPA50An4HaMC/ydtseX77i0AEtsvzcGziqltgIopS4qpXKArsDDtm03\nA0FAI9s2W5RSp5RSVmAnULeYvP4tIn8COzCKpWZAE+CIUury+ID8xU9X4GXbPn8HvIDS3AW68gqF\nywqlVLKtoPoBaGNb/qytJ2wjUBNwbHopoM+DA/l8yVI+X7KUNhGdWfHjDyil2L1rJ75+/oWGvPV6\nYABLVq/nm5WrmDpvITXr1s0tfE4dP5Ybt2FVNLXr1cdRYbV8OX4ug1NJmWTlWFm+M5mIZoXvBTkS\nn8GFdAst6uTdQGyxKpJtvS37z6Rx4Gw6rW9y/FvxsHp+HDdncCohw8hp8zkiWtgPnel8WwBb9hnD\nppIvZXMsLoOawZ5FPZ1DBg4cyNKlS1m6dCmdO3fmhx+M92/nzp34+/sXOeRt0qRJpKSk8Oqrr9ot\nDw0NZePGjQAcPnyYzMxMAgNLNztezc7/5s63F3Ln2wupdlsH4jYsQynFhUN/4ertZzfkDYz7jaq2\naEvyvu0AJO/Zik+NvPaTeuYYOWmXqNSw9DNfNYh8gE4fL6LTx4sIbRXBidU/oZQiaf8u3H397Ya8\nXc4ptGUHEmK3ApAQs5lKtYycUs4cz42L27YWv1DHbgQPq+XL8YQMTiXa2vmOJCJuLqKdm9O5kGah\nRV3fQuuKuw+oNH45lsbotYmMXpvI5rhMOtYyCqGbqriTlm0tdshb2+perCswBC3UN6+n7naTJ2dT\nHRvBHFbDk+OJ2ZxKzibLoli+O4WIxvbHI7SyGxuPGj06hxOyyMxRBPq40qaBDwfNWaRnW8mxKrYe\nz6BBtaKLzJLq0n8g479eyvivl3J7h86s/9n4DB6K2YmPn3+hIW+d+w3gvyvX8/GyVbw+eyGhderm\nTmoQUDWYvdu3ALB7yyZCatV1KLewJvU4dsrMyTMJZGXnsCx6CxFtwnPX+/v5sHn5ZFYt+YBVSz4g\n/OYGdoWP1Wrll1VbnX6/T8d/D+SNhUt5Y+FSwjt0ZtNy45gd/msn3n7+hYa8WXJyuHTeKOJycrKJ\nWfc71Rs0KuqpNU37h/q79vzk3vNzmRgDdr9QSr1SRHxGCe7zEeAp2/01+Z+3A5CZb5GFIo6biNQD\nngda2npg5mIUM1fbZ1+llKNTTl2pp0gVfCwinYF2QCulVLqIrOfquV6TVu3as3ndGgbe2wVPb29e\nent87rqhfXvx+ZIrDzOYOelDThw7iosIpuo1GP362CvGl4SbqzCmd22GzjqI1aroe2dVGoV4M2XF\nGZrX9Mm9QDQmOgiwGwOeY1E89KnxNvl5ufLBg/Vwc8IN126uwphB9Rg6Ya+RU7tgGtX0Ycp3J2he\n14+I2wJpE1aF9bEXiHxlJy4u8EL/OgT4GT1WA8fFcuRsOmkZFtqP2s47QxvQNszh+Spo3749a9as\noUuXLnh7ezN+fN7716tXL5YuXUpcXBzTp0+nfv369OnTB4CHHnqIfv368fLLL/Paa68xd+5cRIT3\n3nvPKWPqg25tTWLMBja+0AdXTy+aDns9d92WMQO4821jlrUG/36KPTPe4OCCj/CoVIWmw97IjTNv\nXknwXV2cNsY/5Pa2mLetY+UTkcZU10+9nbsuelQ/On1szDbV/JFRbJ30KjGffYBn5QBuf9qIO7zs\nK+J3bcbFzQ1330rcMeodh/JxcxXG9K3N0BkHsFqh711BNAr1Zsovp2ley5eI5rZ2viOJyBaBhY7D\nqaRMzp7P4s4G/g7lUZTt8ZncHuzBtIiquVNdX/ZRuyC7Wd7uqe7FO1vsv9PpXteHW6p5YLFCSraV\nKTscm3TEzUUY070qQ+efxaoUfcMr0SjYgymrk2he3ZOIxr681LUqY35K4ItNFxDg3d7BxoQb3q4M\nvrsK/WYZU9C3a+RLh5sKF5KlFd6mPbvWr+G5Xl3w8PJm+Jt5n8FXH+jF+K+vfA4dOuZtvpwwHqsl\nB3dPT4a+5thsmW5urrz+7ECGjZ6ExWKlb482NKpfg8mzfqB5k7p0aht+xe237jxAaHAgtWpUu2Kc\nI8Jat+evDWv4T2/jmA1+I++YjR3QizcWLiUnO4uPRw7DkpON1Wql2Z13067Pv8ssJ03TbjyiVMFr\n4xufiKQopfwKLGsGLMUYkhYvIoGAv1LqeP74Ioa9+WMMexsCdAf62YbN3QScBlpiTDDQw7b9f4Ft\nSqm5IvIX0NM2nO1WjCF2LYBqQAzwEvANxtC3tkqpY7YJGiorpXqIyHigEkbRpUSkhVKq8PQ59q/z\nd/JNeGC7X6e5UmqU7XFDYLFSKty27g0gDNuwN4yhgA2Ah5RSfUTkZuBPoJNSar2InLI9X/HTQxUz\n7K08hbqD+nFAeadhR3ouRG0aXN5p2JFWc8s7hWKN2HSxvFOwM62V0bP3yr7Mq0ReP+82MXr81PKB\n5ZyJPem+gD4/xV098Dr6Psq48V0tLPr/oikvMmA5W691YHMZa+kLnFtf3mnYq2oMUlh76Spx11k7\n538noGlgfBmuXSd/156fQpRSe2w39K8UEReMQeX/BxwvEJclIv2BqSLijVH4dAY+wxjO9qetFykB\nuNog4JkY9xudUUp1FJEdGIXVSYx7g7D1rDxpi0vFKEAuexv4GIix5XwU6FHqg1C0rRhFYXWMnrGd\nIrIPGC4ie4D9GMP8NE3TNE3TNO0f7W9Z/BTs9cm3/BuMnpYrxtvu92lVxFO8avvJ73fbz+VtR+b7\nfSowNd/jwcWkvFop1cRWVH2Cca8SSql04PFitimSUqpDgcefFXh8CAjPt67QvLlKqQygyP/gRClV\nvtPwaJqmaZqmaVoZ+btOePB385htUoPdQGWM2d80TdM0TdM0TbuO/pY9P383SqlJwKSSxIpIN+D9\nAouPKqX6OD0xTdM0TdM0TatAdPFzg7HNNlf0f42taZqmaZqmaVqp6WFvmqZpmqZpmqZVCLr40TRN\n0zRN0zStQtDFj6ZpmqZpmqZpFYIufjRN0zRN0zRNqxB08aNpmqZpmqZpWoWgix9N0zRN0zRN0yoE\nXfxomqZpmqZpmlYh6OJH0zRN0zRN07QKQZRS5Z2D9vejG42maZqmaZpzSHknUJHonh9N0zRN0zRN\n0yoEt/JOQPt7UvsnlncKdqTx8/T5Ka6807DzfVQICTnlnYW9ajfwJ17tHV/eKdiRpq8CoFY+VM6Z\n5JGu8wG4Y9rRcs7E3rYR9VCbBpd3Gnak1VwA3j5uKd9EChhTxxVSDpd3Gvb8GnAiq7yTsFfbw/hX\nbR5crnkUJHfNZcja5PJOw87sdgHlnYKm/a3onh9N0zRN0zRN0yoEXfxomqZpmqZpmlYh6OJH0zRN\n0zRN07QKQRc/mqZpmqZpmqZVCLr40TRN0zRN0zStQtDFj6ZpmqZpmqZpFYIufjRN0zRN0zRNqxB0\n8aNpmqZpmqZpWoWgix9N0zRN0zRN0yoEXfxomqZpmqZpmlYh6OJH0zRN0zRN07QKQRc/mqZpmqZp\nmqZVCG7lnYD2z7Ru+0nGfbYRq0Vxf9fGDL8/3G79d9EHmDBnM6YgHwAGRt5Mv65NctenpGUR+X+L\n6XRXHV5/orVTcxt6sz+3mzzJtCim7rzAkQs5duu9XIXxrQNzHwd5u7LmVDqzd1+iWx1v7q3rg1VB\nhkXx6a4LnEqxOJSPUorJ745j49o1eHl78eq492jc7OZi41/6vyc4c+oUXy79GYBZUz5m/epoRFwI\nCAriP+PepWqwyaGc1q5dy7hx47BarfTr14/hw4fbrZ8zZw6LFi3C1dWVwMBAxo8fT40aNQAYOnQo\nu3bt4vbbb2fGjBkO5VHQuj9PM+6zLVitivu7NGJ43zC79d9FH2LCF9swBV5uV03o1+UmAJrdN4+b\nalcBILSaL9P+08k5Oe25wLglJ4yc7q7G8K6hduvfXXKCzQcvApCeZSUpJYetH9zG6aRMnpp1CKtS\n5FgUD7U38UCbYKfkBPB860Ba1/EhI0fx5qoE9p/LKhTTraEvj95WBQUkpOYwJjqBCxlWnr47gHZ1\nfMi2wqkL2YxdfY6ULKvDOa2LSWbcgmPGsWpvYniPGoViftl8jv/+cAoBGtf24cMRN7H3eCpvfnGE\n1HQLLi7CEz1r0P2uqg7nU5BSim2fjuf01rW4eXpz9/PjCWrUrFCcJTuLrf8dhzlmCyIuhD/6DLXb\ndnVqLmv/2Ma4iTOwWqz0692N4Y/+u8i4FdHrefrF8Sz+8mPCmt2Uu/zM2Xgi+z3ByOEDGfpwX6fk\npJTi0/fGsWXdGjy9vHjhnfdodIVz1ZinniDu1ClmfW+cq+Z9OpXlS76lcoBxfh3y9GjuatfeKbmB\nrX3Nz9e+ooppX9+fQgQa1/LhwydvKuKZHKeU4ujXH5H810ZcPDxp9OgY/Oo0KRRnzcnmyMKJXNj/\nJ+LiQu3ej1P19ggyE+M4MOctLGkpKKuFOn3/j8Cwe8okV02raHTxozmdxWLlrRkbmP1Wd0xBvvR7\n7gci7qxDw9oBdnH3tqlfbGEzecE27rg5xOm53RbsQXU/N55cdY6bqrjzeFglXlqfZBeTYVGMXpuY\n+3hi2yA2nc0AYO3pDFYcTwegpcmTR2+uxNubkx3KadO6tZw8foyvf1nJ7phdTHzrTWZ9vajI2DW/\nrsTbx9du2YAhw3js6VEALJo/jznTPuGFN94qdT4Wi4W33nqLOXPmYDKZuP/++4mIiKBhw4a5MU2b\nNmXJkiV4e3uzcOFCJkyYwMcffwzAsGHDSE9P55tvvil1DkXnZeWtGZuYPbYrpiAf+r2wjIg7a9Gw\nVhW7uHvb1OX14a0Kbe/l4coPH/d0bk5WxVuLjjP7/27CVMWDfhP2EBFWhYah3rkxr/Stnfv7l2vM\n7D2VBkC1Su58PbopHu4upGZaiBofS8ewKpgqezicV+va3tSq4k6fhadobvLklXZBDP7urF2Mq8Bz\nbYLo9/Upo+BpFUD/5pWYue08m09m8MmmZCwKnmoVwKO3VWbqJsfaucWqeGveUWa/2AxToAf93vyL\niBYBNKzhkxtzLC6dmT+fZuFrzans60bixWwAvDxdeH94Q+qGeGNOzuL+N2Jo07wKlXyd+yfszNa1\nXDp9nF5z/se5fTFsmTKWe6cWbsexX83Aq0ogveb8grJaybx0wal5WCwW3nrvU+Z8Og6TqSr3DxpF\nRPtWNKxf2y4uJTWNeV8t5dbmjQs9x3uTZtH2njucmteWdWs5ffwYc5etZG/MLqa88yZTFxZ9rlr3\n20q8vX0LLe87aDD9Bg91al5QRPt64y8ibiuiff10moVj7NtXWUiO3Uh6/EluG7eIlCO7ObzgA259\ndXahuFPL5uLuH8Dt4xahrFZyUo0vSk4um0PVOzoR2qEvaWeOsmfKswS+90OZ5atpFck/YtibiFhE\nZKeIxIrIIhHxufpWZZLHqOu1bxHpICJKRIblWxZuW/b8VbZ982oxjog5mEDt0ErUCqmEh7sr3ds2\nIHrz8RJvH3sogcTz6bRuUdPpud0Z4sXqk0bxcuB8Nr7uLgR4Fv8xqO7rSmVPF/YkGX8k03NU7jpP\nV3FKTutWRfOvnr0REZrfGk7KpYucS4gvFJeWmsrXX8zhkcdH2C339fPL/T0jPR0Rx/KKiYmhTp06\n1KpVCw8PDyIjI4mOjraLadWqFd7exgV+eHg4cXFxuevuvvtufH0LX/Q4KubgOVu78jfaVZt6RG8+\n6fT9XFNOx1OpXdWTWlW98HBzofvtgUT/VXyRsGx7IpG3G996e7i54OFutL2sHIVSxW52zdrX9WH5\n/hQAYs2Z+Hu6EOTjah8kIIC3m5GDr4cLCalGL+bmU+lYbPn8Zc4k2AlFRsyRFGqbvKgVbDtWd1Ul\n+k/7Y7VoTTwDOoVQ2ba/oEruANQL8aZuiNHeTAEeBFZyJ+mS8y9cT/6xinpdeiEiVGt6K1mpl0hL\nTCgUd/h/39P8gccAEBcXvCoHFIpxRMzuA9SpVZ1aNUPxcHcnsms7on/fWChu8rQveeyRfnh62hfM\nv63+gxrVQ2jUoHahbRyxcXU0nW3nqma2c1ViEeeq9LRUlsybw8AC56qyFHM4hdrB+dpXqyLa1+/x\nDOhcuH2VhaSdawlu1R0Rwb9Bc3LSUsg6f65QnHnDT9Ts/ghgtCV3f9uXOSJY0lMByElPwaNKtTLL\nVdMqmn9E8QOkK6XClVLNgSzgiZJuKCKuV48qsVHA9Sy8YoH8YyEeBHZdx/0XyZyYSmjVvAvykKq+\nmBNTC8X9uvEoPZ9awtPv/cbZBONCzWpVvD97My8+eleZ5Bbk5UJiRt4wtcR0C4FexX8M2lT3Yv2Z\nDLtl99b1YVpEVR5p5s9nsRcdzulcvJngkLxermBTCOfM5kJxn02dzAODh+Dl7VVo3YzJk7ivU3tW\n/vwTQ0c+41A+ZrOZkHz5mEwmzEXkc9nixYtp166dQ/ssUV5JaYRWzSuqQoJ8MCcV1a5O0POZH3n6\n/d85m5C3PjPLQt/nfqb/i8v4bdMJ5+R0PovQgLwLz5AqHpjPF31Rfjopk9OJWbS6qVLusrPJmfR8\nN5aOY3YxrHOIU3p9AKr5uhGXkjec05xiIdjX/lRnscJ7a8/xdf8a/O/hWtQL8GDpvkuFnqtnE3/+\nOJHmcE7m5CxCAz1zH4cEemBOzrSLORaXzjFzBg++HUv/t/5iXUzhQjLm8CWycxS1gwt/DhyVnhiP\nb7W8tu9b1UR6on3bz0oxPvM7v5jKsif7svbtUaQnF76odYQ5PpEQU96wPpOpKuaERLuY3XsPEWdO\noEPbO+2Wp6alM+uLxYwcPsCpOUHhc1VVUwjn4gufG+ZOncz9jwzB06vwe7T0qwUMvy+KiWNe4dIF\n5/WYmZOzCA0qQfuKs7WvsUW3L2fJSk7AMzBvGKtnQDCZ5+0L6Zw04/N24ocZ7Hz7YfZNf5Wsi8b7\nXDtqGAmbV7D1hSj2TBlN/QefK7NcNa2i+acUP/mtAxoCiMhDIrLF1is043KhIyIpIvKhiOwC7haR\nliLyh4jsssX7i4iriEwQka0iEiMij9u27SAiv4vIYhHZJyILxPA0UB1YLSKrbbHTRGSbiOwWkbGX\nExSR7rZtt4vIFBH52bbcV0Rm23LYISK9rvJajwNeImIS4+v+fwG/5NvPY7b8d4nIkqJ6pUSkgYj8\nz5bLOhEpPCi5DHRsWZvozx7kx6l9uSe8Bi9//DsAC5fvof3ttQjJVzyVpzY1vFl3Ot1u2S/H0hix\n6hzz9l6iX6Prk+fBvXs5ffIE7Tt3KXL94888y3fRa+jaI4rvFs6/LjkBLF26lNjYWIYNG3b14Oug\nY8uaRM/sy4+Te3JPeCgvT1mfu27VrL4s+bAHE0e3Y/znWzhx1vHC9Vos355E1/AAXF3yeuZCAzz5\n8ZXmrHgjjB82J3KuDIfhFOTqAn1vrsTARaf517yTHErM4tEW9kMIh9xWGYtV8cvBwkVmWcixKI7H\npTPvlWZ8OKIRY+Yc4WJqXhEXfz6LF2ceYvywBri4OKfn9VpZLRbSzsVRrVk4kZ8uoWrTcP6cOeH6\n5mC18t6kWbz07GOF1v13xgIeGdAbXx/vIrYse4f27eXMqRO06VT4XBX17wf5YvmvTF+8lMBqwcyY\n+N51zS3HojhutrWvJxsxZrZ9+7relMVCVnI8/g1vIXzMPPzrN+fYoqkAJGxZSfA93Wk54SeaPf0R\nBz5/E2V1/L47TdP+Yff8iIgbcC/wPxFpCvQHWiulskXkU2AgMA/wBTYrpZ4TEQ9gH9BfKbVVRCoB\n6cBQ4IJSqqWIeAIbRGSlbVctgJuBM8AG2z6miMhooKNS6vLXgP9RSiXZiq5oEbkFOADMANoppY6K\nyFf5XsJ/gFVKqSEiUgXYIiK/KaWudOWxGOgH7AD+BPJ/1fWdUmqW7di8Y3tNUwtsPxN4Qil1UETu\nAj4FIoo4tsOB4QAzZszgsSvco2oK8uXsuZTcx3HnUjEF2Q+DCqiU941gvy6NmTh3MwA795vZvjuO\nhb/sIS09m+wcK77e7jz3iP23m9fi3ro+dKltXAgcOp9NkJcrYFxkBnm7kpRR9B+UupXccBUKTYhw\n2frTGTweVqnIdVezZOECflr8LQBNm4cRn2/YWLw5jqom+wkLYnftYN/uWO7vEoHFkkNyYhIjBw/i\nv3O/tIvrEhnFCyOGM3Tk06XKC4yenvzD2MxmMyZT4QkU/vjjD6ZPn878+fPx8HBOj8UV8wr04ey5\nvI9CXGIapsArtKvOjZj4xfa87W1tsFaIP3c2D2HP0SRqh5bu/ct9zioenE3Om0gg7nwWpipFD6VZ\n/mcSY/oVPQzJVNmDRtW92Xb4Ev9qEVhkzNX0u9mf3s38AdgTn0WInxu7bKcDk58r8an2E3M0DjLe\ns9MXjfb96+FUBreonLu+R2M/2tTxYcRPcTiDKcCDs0l5p6e4pCxMAZ52MSGBntxS3w93NxdqVvOi\nbogXx80ZhNX3IyU9hyc+2seo+2sT3tDfKTkB7P9xIYeWG/etBDUOIzUh7/WmnjPjHWTf9j0rVcHV\n05vabYyL+zrtunF4xRKn5QNgCg4izpzXm2Q2n8NULSgvr9R0Dhw6zsPDXwIgITGZEc++xbRJr7Mr\ndj8rotczccpsLl5KxcVF8PT04KH+UaXKZelXC1i+xDhXNS5wrjpnjis0ucreXTs4sDuWh7pFYMnJ\n4XxSEs89OogP53xJQNW83qzuffsxZmSJB2lclSnAg7OJJWhfDYpuX85wdvVizGuXAuBXrymZSXlD\nAjOT4/EsMHTNza8yLh5eBLXoAEDVOzphXv8TAOb1P3HzKOM+ykoNwrBmZ5Gdch6PSqU7P2ialuef\nUvx4i8hO2+/rgM8xLtRvB7ba7oHwBi6fiSzA5b9WjYGzSqmtAEqpiwAi0hW4RUTut8VVBhphDKvb\nopQ6ZYvbCdQF8r5izvNvW9HgBoQCzTB6244opY7aYr6y5QrQFeiZ734cL6A2sPcKr/1b4Bugie25\n8k8H09xW9FQB/IAV+TcUET9b/KJ894nY/7WwUUrNxCiUjIf7JxabUFijahw/c5FTcRcJDvJl+brD\nTHy+o11MfFIawbYZuVZtOU6DmsaY+YnP5dVd30UfIPZggkOFDxg9Nb8cM4bt3B7sSfd6Pqw/k8FN\nVdxJy7aSnFl08dO2uhfrTtsPeQv1deWs7SLydpNn7u/Xqu+AgfQdMBCAP9b8zpKF8+ncPZLdMbvw\n8/OnajX7Wb/6PDCAPg8Yw1jOnj7Fi08+kVv4nDx+jFp16gKwfnU0derVL1VOl4WFhXHs2DFOnjyJ\nyWRi2bJlfPjhh3Yxe/bs4fXXX+ezzz4jKCiomGdyrrBGVTl+9iKnzJcIDvRh+fqjTBzd1i7Grl1t\nPUmDmsbF/IWUTLw93fBwdyX5YgY79sUz7L7mjudU25fjCZmcOpdJcBV3lm9PYuLgBoXijsSlcyEt\nhxb18i6y4pKzqOLrhpeHCxfScth++BKPdCz9LH2Ldl9i0W5jGE3r2t78O6wSKw6l0tzkSUqmIjHN\nvq3Gp1qoH+BOFS8XzmdYuaumF0eTjS8F7q7lzcPhlRm+9CyZOc65GSmsnh/HzRmcSsggOMCD5ZvP\nMfGJRnYxnW8L4OdNifRtF0zypWyOxWVQM9iTrBwrI6fsp1fravyrpXPbW+OeA2jc0/hsndq8hgNL\nF1C3Q3fO7YvBw9cfnyD7C1YRoWarDph3bSGkRSvidm6icu3C77kjwprdxLGTZzh5Og5TcBDLVq7l\nw3Ev5q739/dl86qvcx8PGv4SL44aSlizm1j4eV4v1NQZ8/Hx9i514QPQ68GB9HrQOFchF6eLAAAg\nAElEQVRtXvs7SxfOp+O9keyN2YWvnz9BBc5VUf0HENXfOJ5xp08xZuQTfDjHOFclJsTnxm+I/o26\nDe3ff0eE1S/QvjadY+KIAu3r9gB+3ligfVUr8k9eqYR2vJ/QjsYlQ1LMBs6uXkTVO7uQcmQ3bt5+\neFSxn6FQRAi8tQ0X9v9JlaZ3cH7vVnyq1wPAM8jE+b1bMbXuQdrZo1izs3D3d+69ZZpWUf1Tip90\npZTdXMq2YWBfKKVeKSI+Qyl1tatWAZ5SShUsGDpg37tioYjjKCL1gOeBlkqpZBGZi1HMXG2ffZVS\n+68Sl0spFSci2UAX4Bnsi5+5QG+l1C4RGQx0KLC5C3C+4LFzlJurC2Mev4ehb/6C1aro27kxjWoH\nMmXBNpo3rEbEXXX48qdYVm85jqurC5X9PXl3lPOmO72S7fGZ3B7swbSIqrlTXV/2Ubsgu1ne7qnu\nxTtb7MeEd6/rwy3VPLBYISXbypQdjo9Zv7tdezauXUP/e7vg5eXNq++Mz103+L5ezP1u6RW3n/7R\nh5w4dhQXF8EUWoMX3hh7xfircXNz4/XXX2fYsGFYLBb69u1Lo0aNmDx5Ms2bN6dTp0588MEHpKWl\n8cwzxv1FoaGhTJ8+HYABAwZw5MgR0tLSaNeuHePGjaNt27ZX2mXJ8nJ1YcxjdzF07G9YLVb6dm5E\no9oBTFm4g+YNg4i4szZfLtvL6i0njXbl58m7T7cB4PCpC7zx6UZcXASrVfHYfWGFZokrXU7CmH61\nGfrpfqwK+raqSqNQb6YsO03z2j5EhBkXK8v+TCLytkC7ySgOm9N5//uTCKCAIZ1CaFzdObcMbjiR\nTus6PvwwoCYZOYqxq/PuNVjQrzoDF53hXJqFWdvOM6t3KDlWOHsph7GrjLgX2wbh7ip8EmXc3xFr\nzuTdtYlF7quk3FyFMYPqMXTCXuO80C6YRjV9mPLdCZrX9SPitkDahFVhfewFIl/ZiYsLvNC/DgF+\n7vy4IYFt+y9xPiWH79cb32G9O6whTes4d2KNGne248yWtSwd/C/cPL24+/lxueuWPdGHyOnfA9Bi\n2Gj+eP9ltk1/D6/KAXZxzuDm5srrL45g2MjXsFis9O3VlUYN6jB52pc0b9aITu0Lz2Z4PdzZtj2b\n167hke5d8PTy5vl856rH7+/FjMVXPlfN+mgCh/ftQwRMNWow6vXSz0pZkJurMObhegz9YC9Wla99\nLTlB83r52tdfF4h82da+HqhDgH/ZTHoQEHYPyX/9wZ//uR8XDy8aDn4td93OsYMIf8MoCOv0/T8O\nfj6Wo99Mwt0/gEa2uHr9nuHQvPGc+e1rBKHRo2McnsxG0zSDKGdOMVRORCRFKeVXYFkzYCnGkLR4\nEQkE/JVSx/PHFzHszR9j2NsQoDvQzzZs7ibgNNASeF4p1cO2/X+BbUqpuSLyF9DTNpztVowhdi2A\nakAM8BJGL80BoK1S6piILAAqK6V6iMh4oBJG0aVEpIVSakcxr7nD5TxE5B4gWCn1g4i8CaQopSaK\nyDmM3qZkYDlwWik1uEDMH8AkpdQiW8F4i1LqapMmXLHnpzxI4+fp46ThOc7yfVQICeU3nLxI1W7g\nrzvU3vFXD7qOpOmrAKiVD5VzJnmkq3E/1x3Tjl4l8vraNqIeatPg8k7DjrSaC8Dbxx37f7icbUwd\nV0g5XN5p2PNrwInC/w1UuaptG0mrNg8u1zwKkrvmMmRt2U2UUBqz2+keoX8AXdleRzfwpZBjlFJ7\nROQ1YKWIuGDc5PF/GJME5I/LEpH+wFQR8cYofDoDn2EMZ/vTVhQkAL2vstuZGPcbnVFKdRSRHRiF\n1UmMe4NQSqWLyJO2uFRga77t3wY+BmJsOR8FepTgtf5RzKoxwGZb7puBogbKDwSm2Y6VO/A1N8CM\ncZqmaZqmaZrmbP+I4qdgr0++5d9g9LRcMd52v09R4whetf3k97vt5/K2I/P9PpV8EwoopQYXk/Jq\npVQTW1H1CbDNFp8OPF7MNgVfg10e+Za/me/3acC0q8QcxZglTtM0TdM0TdP+0f6JU13/HTxmmyhh\nN8ZECjPKOR9N0zRN0zRN+8f7R/T8/N0opSYBk0oSKyLdgPcLLD6qlOrj9MQ0TdM0TdM07R9MFz83\nONtscyuuGqhpmqZpmqZp2hXpYW+apmmapmmaplUIuvjRNE3TNE3TNK1C0MWPpmmapmmapmkVgi5+\nNE3TNE3TNE2rEHTxo2mapmmapmlahaCLH03TNE3TNE3TKgRd/GiapmmapmmaViHo4kfTNE3TNE3T\ntApBFz+apmmapmmaplUIopQq7xy0vx/daDRN0zRN05xDyjuBisStvBPQNE3Tro21vBMoQA8h0CqS\nG/bzl32+PNOw516lvDPQtGLpv1mapmmapmmaplUIuvjRNE3TNE3TNK1C0MWPpmmapmmapmkVgi5+\nNE3TNE3TNE2rEHTxo2mapmmapmlahaCLH03TNE3TNE3TKgRd/GiapmmapmmaViHo4kfTNE3TNE3T\ntApBFz+apmmapmmaplUIuvjRNE3TNE3TNK1C0MWPpmmapmmapmkVgi5+NE3TNE3TNE2rEHTxo2ma\npmmapmlahaCLH61MrF27lm7dutGlSxdmzpxZaP1XX31FVFQUvXr14sEHH+TQoUMAbNiwgfvuu4+o\nqCjuu+8+Nm7c6NS8lFK88847dOnShaioKHbv3l0oJj09neHDh/Ovf/2LyMhIJk6cmLtu/Pjx9OrV\ni169etGtWzfuuOOOUuVxteOTlZXFqFGj6NKlC/369ePUqVMAxMTE5O6/Z8+e/Prrr7nbzJ07l8jI\nSHr06MHo0aPJzMwsVW4lyW/OnDl0796dqKgoHnnkEU6fPg3A3r176d+/P5GRkURFRbF8+fJS51BW\nOV7vPIpr6z/++GPue9mrVy+aNGnC3r17yyRHpRTj3nmHbl260KuYdp/fk088QVSPHmWSy2WlPW7O\n5ug5oSzamSNte8KECfTo0YMePXo4/fNXkmMFMHToUHr27ElkZCSvv/46FosFgI8//jj3PR0yZAhm\ns9nhnK52rLZu3UqfPn1o1qwZ//vf/wqtT0lJoV27drz11lsO51KcG+Xzt3b9Rrr16EeXe/sy87Mv\nio1b8esqGje/i79ijfNR8vkLDHp0BC1aduCtcROcnpemXXdKKf2jf67154pycnJUp06d1IkTJ1Rm\nZqaKiopSBw8etIu5dOlS7u+//fabGjJkiFJKqd27d6u4uDillFL79+9Xbdq0udrursnvv/+uhg4d\nqqxWq9qxY4e6//77C8WkpaWpjRs3KqWUyszMVA8++KD6/fffC8XNmzdPvfzyy9ecQ0mOz/z589WY\nMWOUUkr9/PPP6plnnsnNLTs7WymllNlsVq1atVLZ2dkqLi5OdezYUaWnpyullHr66afVkiVLrjm3\nkua3ceNGlZaWppRSasGCBbn5HTlyRB09elQppVRcXJxq3bq1unDhQqnyKKscr3cexbX1/Pbt26c6\ndepU4v1arvFnla3d51itarut3RcX+8uKFerZ0aNV98jIEj//tXLWcXMGR88Jzm5njrTt1atXq8GD\nB6vs7GyVmpqq7rvvPrvj6KiSHCul8t47q9WqRo4cqX7++We75Uop9cUXX+Se40qrJMfq5MmTau/e\nveqFF15Qv/zyS6HnePvtt9Xo0aPV2LFjS7zfG/bzl5Vc7E9O+jnVKaKjOnE4VmWmxquoHt3Vwb3b\nC8VdSj6lBjz4b9Xv/j4q5s8/lMpKVqnnz6itm1arhV9+rsa+8eoV95P7o12r8r6uq1A/5d7zIyIW\nEdkpIrEiskhEfMopj1HXa98i0kFEfi5B3NMisldEFlyPvGz7fFNEnnfkOWJiYqhTpw61atXCw8OD\nyMhIoqOj7WL8/Pxyf09PT0dEAGjWrBkmkwmARo0akZmZSVZWliPp2ImOjqZ3796ICOHh4Vy8eJH4\n+Hi7GG9vb1q1agWAh4cHzZo1K/IbymXLltGjFN/OleT4rFq1ij59+gDQrVs3Nm7ciFIKb29v3Nzc\nAMjMzMw9bgAWi4WMjAxycnLIyMggODj4mnMraX6tWrXC29sbgPDwcOLi4gCoV68edevWBcBkMhEY\nGEhSUlKp8iirHK93HsW19fyWLVtGZGSk0/O7bFV0NL2u0u4BUlNT+WLOHJ4YMaLMcgHnHTdncPSc\n4Ox25kjbPnToEHfccQdubm74+PjQuHFj1q5d61A++ZXkWEHee5eTk0N2dnbue+fs97Qkx6pmzZo0\nadIEF5fClzuxsbEkJibSunVrh/K4mhvh8xfz1x7q1K5JrVo18HB3J/LeLkSvKtw2Jk+dwWNDBuHp\n4Zm7zMfHmztuC8fT08PpeWlaeSj34gdIV0qFK6WaA1nAEyXdUERcnZjHKKBcCq8reBLoopQaWJJg\nEXEr43xKxGw2ExISkvvYZDIVWTwsWLCAzp07M2HCBF577bVC61esWEGzZs3w8HDeCbdgbiEhIVcc\nenHx4kVWr17N3Xffbbf89OnTnDp1KveCyJEcijo+ZrOZ0NBQANzc3PD39yc5ORmAXbt2ERkZSc+e\nPRk7dixubm6YTCaGDBlCx44dadOmDX5+frRp0+aacytpfvktXryYdu3aFVoeExNDdnY2tWvXLlUe\n1yPH65XH1dr68uXLy7T4KardxxeR55TJkxk8ZAjeXl5llktR+ZT2uJVFLqU9J4Bz2pkjbbtJkyas\nW7eO9PR0kpKS2Lx5s1OL/ms5VkOHDuWee+7B19eXbt265S6fNGkS7du356effuKZZ55xaj5XO1b5\nWa1W3n//fV566SWHciiJG+HzZ46PJyTElPvYZArGHJ9gF7N7zz7i4sx0aF+6vx2a9ndxIxQ/+a0D\nGgKIyEMissXWKzTjcqEjIiki8qGI7ALuFpGWIvKHiOyyxfuLiKuITBCRrSISIyKP27btICK/i8hi\nEdknIgvE8DRQHVgtIqttsdNEZJuI7BaRsZcTFJHutm23i8iUyz04IuIrIrNtOewQkV4lecG2npbZ\ntryO2HJBRKYD9YFfRORZEQkUkR9sr2eTiNySb/svRWQD8KWIDLbF/Soix0RkpIiMtuW0SUQCbds9\nZjs+u0RkydV6vURkuO14bCtqXHVpDBw4kN9++43nn3+eadOm2a07ePAgEydOLNNx2FeTk5PD6NGj\nGTRoELVq1bJbt2zZMrp164arqzPr75K59dZbWbZsGYsXL2bGjBlkZmZy4cIFoqOjiY6Ozr34Wbp0\naZnnsnTpUmJjYxk2bJjd8vj4eF544QXefffdIr9xvZ6Ky/F6ulJb37VrF97e3tx0003llJ1h7969\nnDxxgi5dupRrHvld6biVhyudE8qjnRXcZ5s2bWjfvj0PPPAAzz33HOHh4eX2+fv8889Zv349WVlZ\nbNq0KXf5s88+y5o1a4iKimL+/PnlkhvAwoULadeunV1RUp7K+/NntVp574PJvPSCYwWppv0d3DDF\nj63X4l7gLxFpCvQHWiulwgELcLn3wxfYrJS6FdgCfAM8Y3vcGUgHhgIXlFItgZbAYyJSz7Z9C4xe\nnmYYxUVrpdQU4AzQUSnV0Rb3H6XUHcAtQHsRuUVEvIAZwL1KqduBavlewn+AVUqpO4GOwAQR8S3h\ny28CdAPuBN4QEXel1BP5cpoEjAV2KKVuAV4F5uXbvhnQWSn1oO1xc+A+22sfB6QppVoAG4GHbTHf\nKaVa2o7bXtsxK5ZSaqZS6g6l1B3Dhw+/4osxmUx23zaazebcoWxFiYyM5Lfffst9HBcXx8iRI3n/\n/fed0muwYMGC3JvKq1WrZpdbXFxcsbmNGTOGunXrMnjw4ELrHPmmviTHx2QycfbsWcC44Lp06RIB\nAQF2MQ0aNMDHx4cDBw7wxx9/ULNmTQIDA3F3d6dr167s2LGjzPID+OOPP5g+fTrTpk2z651LSUnh\n8ccf59lnnyU8PLxUOZR1jtc7j8sKtnUouyFvCxYsoE+vXvQppt0HF8hz544dxMbG0ikigoEDBnD8\n2DEeHjTI6XmBc46bI5x9TnBmO3O0bY8YMYKlS5cyZ84cwBiK6ojSHisAT09POnXqVGgoGkBUVBQr\nV650KLdrbUf57dixgwULFhAREcH777/PDz/8YDeRhaNutM+fKTiYuLi83iazOR5TcN4lTGpqGgcO\nHebhR58komtvdsbEMuKp53MnPdC0f5TyvukIo7DZafuZCngAIzEu/C8v3w+8aYvPAVxtv4cBG4p4\nzsXAgXzbHwW6Ah2AX/PFTQMesv1+DKiab90TwJ9ADJAAPACEA2vyxfQEfrb9vg2IzbfPE0DTYl5z\nh3zbvYlRaF1etxeoWTAnYAdQP1/cSaCSbfs38i0fDMzK9/gEUMP2+xDgY9vv7TF62v6yHZ/p+fJ5\n/irv2xVlZ2eriIgIu5tQDxw4YBdz+aZ4pZSKjo5Wffr0UUopdeHCBRUVFaVWrFhxtd2UyurVq+1u\n2O3bt2+RcR999JEaOXKkslgK38596NAh1bFjR2W1WkuVQ0mOT8EJD55++mmllFInTpzInfDg1KlT\nqnXr1ioxMVHt3LlTde/eXaWlpSmr1apefPFFNW/evDLLb/fu3apTp05276NSxs3gDz/8sJozZ06p\n9n09crzeeRTX1pVSymKxqDZt2qgTJ05c036v9YbraFu7v3zDdd++fa8Yf+LkyTKd8MDR4+ZMjp4T\nnN3OHGnbOTk5KikpSSml1N69e1VkZGTu+cIZSnKsUlJSlNlszn0tzzzzjPryyy+VUvbv6bx589RT\nTz3lUD4lOVaXvfTSS0VOeKCUUkuWLCnTCQ+u2+fvChMQZKclqIiIDurEkd25Ex4c2FN4woPLPw8N\neCB3woPLP0u+/VJPeFB2yv16vCL93Aj3iKQro3cnlxh3QX6hlHqliPgMpZTlKs8pwFNKqRUFnrcD\nkH/+XwsUPga2XqLngZZKqWQRmQtcbRCuAH2VUvuvEleUq+Z0FalXeD5rvsfWfM89F+itlNolIoMx\nCjKncHNz4/XXX2fYsGFYLBb69u1Lo0aNmDx5Ms2bN6dTp07Mnz+fjRs34ubmRqVKlXj//fcBmD9/\nPidOnOCTTz7hk08+AWD27NkEBQU5Jbf27duzZs0aunTpgre3N+PHj89d16tXL5YuXUpcXBzTp0+n\nfv36uZMOPPTQQ/Tr1w8wen26d+9e6pt1S3J87r//fl544QW6dOlC5cqVmTRpEgDbt29n1qxZuLm5\n4eLiwptvvklgYCCBgYF069aNPn364ObmRtOmTenfv3+Z5ffBBx+QlpaWO2Y/NDSU6dOn88svv7Bt\n2zbOnz/P999/D8B7771H06ZNS5VLWeR4vfMorq2DMQ1vaGhooSFUzta+fXvWrllDty5d8CrQ7vv0\n6sX312GIZH6OHjdncvSc4Ox25kjbzsnJYeBAY5CEn58fEyZMyJ0gxRlKcqzS09MZMWIEWVlZKKW4\n6667eOCBBwD48MMPOXr0KCJCjRo1GDt2bHG7KpGSHKuYmBhGjhyZe6/W1KlTWbZsmUP7vVY3wufP\nzc2N1199nmGPP43FYqVvnygaNazP5P/OoPnNTenU8cr3qkV07U1KSirZ2dn8tmoNs2dOoWGD+mWe\nt6aVBVHGt/3ll4BIilLKr8CyZsBSjCFp8bb7VPyVUsfzx4uIB7AP6K+U2ioi/hjD3oYA3YF+Sqls\nEbkJOI0xDOx5pVQP2/b/BbYppeaKyF9AT6XUURG5FWNYWQuMoW0xwEsYQ+wOAG2VUsdss7BVVkr1\nEJHxGD0xTymllIi0UEoVOe7IVoQ9b9vuTSBFKTXRti4W6GF7/mPAHUqpcyIyBUhQSr1t236SUqpF\nEdsPtm0z0vY4/3PkrhORcxjD5ZKB5cBppdTggs9XjPJtNJpWwVnLO4ECbpjx05p2Hdywn7/s8+WZ\nhj33KuWdwd9N2UxnqRXpRuj5KUQptUdEXgNWiogLkA38H3C8QFyWiPQHpoqIN0bh0xn4DKgL/Gnr\nRUoAel9ltzOB/4nIGaVURxHZgVFYnQQ22PaXLiJP2uJSga35tn8b+BiIseV8FHDm/1L2JjBbRGKA\nNOARB59vDLAZ49hsBvwdfD5N0zRN0zRNu6GVe8/P342I+CmlUmxF1SfAQWVMSFCR6EajaeXohv3m\n+f/ZO+8wu6qyfd8PASQIiCgQVLq0CCZU6R8QEkoqxR8gIE1BAamigopgQUFU1E+aCIpgoSPFBiSE\nEikhIRB6kyKQjxoCMSHJ8/tj7TM5M8xMZiYzZ61h3vu65srsdc6Z/eScdfZe71rv+6wg6AMU+/2L\nlZ/eTKz8NJC4Z3WeL0qaDEwFPkRyfwuCIAiCIAiCoHBi5acHkbQT0LJK92nbu+XQ041EpwmCjBQ7\n8xwEfYBiv3+x8tObiZWfBhLBT9AVotMEQUaKHXwFQR+g2O9fBD+9mQh+Gkjcs4IgCIIgCIIg6BNE\n8BMEQRAEQRAEQZ8ggp8gCIIgCIIgCPoEEfwEQRAEQRAEQdAniOAnCIIgCIIgCII+QQQ/QRAEQRAE\nQRD0CSL4CYIgCIIgCIKgTxDBTxAEQRAEQRAEfYLY5DToCtFpgiAIgiAIuofY5LSBxMpPEARBEARB\nEAR9ggh+giAIgiAIgiDoE0TwEwRBEARBEARBnyCCnyAIgiAIgiAI+gQR/ARBEARBEARB0CeI4CcI\ngiAIgiAIgj5BBD9BEARBEARBEPQJIvgJgiAIgiAIgqBPEMFPEARBEARBEAR9ggh+giAIgiAIgiDo\nE0TwEwRBEARBEARBnyCCnyAIgiAIgiAI+gQR/ARBEARBEARB0CeI4CcIgiAIgiAIgj5BBD9Bj2Cb\n73//+wwdOpSRI0cyderUVp/3s5/9jP/5n/9hww03bNZ+0UUXseuuuzJy5EgOOOAAXnjhhW4/94MP\nPsjIkSMZOnQo3//+97ENwBtvvMFBBx3EsGHDOOigg3jzzTebXnPXXXcxevRohg8fzn777dfUPn36\ndI466ih23nlndtllFyZNmtSjGk8//XR23nlnRo4cyRFHHMH06dMBmDJlCqNHj2b06NGMGjWKf/7z\nnx1+3zqrb+bMmRx66KHsvPPODB8+nDPPPLPpsdNOO61Jx0477cQmm2zSJR0tGT9+PDvttBNDhw7l\n/PPPf8/j99xzD7vtthsDBw7kb3/7W1P7ww8/zF577cXw4cMZOXIkN954Y4/qmD17NscccwxDhw7l\ns5/9LM8//zyw4M9n7ty5jBkzhsMOO6xhml5//XX2339/NtxwQ7773e82PX/GjBlNWkePHs1nPvMZ\nfvCDH3Ra18L29RoXXngh66yzDq+99hoAF1xwQZO2ESNGsN566/HGG2/0mL62rlX/+c9/2H///Rkz\nZgwjR47k1ltv7bSGntDVE9/Brn7/IL1PBx98MLvssgu77rprU//rCgvbp8466yxGjhzJ6NGjOfjg\ng3n55ZebXtPWNb6nNPbEPbCerl4XZs+ezYknnsjIkSMZNWoUd911F9D+db+nNd1xxx3svvvujBw5\nkt13350JEyY0vWb//fdnp512aurzr776aqd1BX0I2/ETP539WSDjxo3zIYcc4nnz5nnSpEnec889\nW33epEmT/PLLL3vw4MHN2idMmOB33nnHtn3ppZf66KOP7shpO3XuPfbYw5MmTfK8efN8yCGHeNy4\ncbbt008/3eedd55t+7zzzvMZZ5xh237zzTe9yy67+IUXXrBtv/LKK01/62tf+5ovu+wy2/asWbP8\n5ptv9qjG2267ze+++65t+4wzzmjS+M477zS1v/zyy958882bjjtDR/S98847njBhQtP/eZ999mnS\nV8/FF1/sb3zjG53W0JI5c+Z4yJAhfvbZZz1r1iyPHDnSjz/+eLPnPPfcc3744Yd9wgkn+K9//WtT\n+1NPPeWnn37atv3SSy95q622WuBntDA6LrnkEn/729+2bV9//fVN/XdBn8+FF17o4447zoceemjD\nNL399tu+5557/Ic//MGnnnpqm+fYbbfdfPfdd3dKl73wfd22//Of//jggw/2dttt51dfffU9r735\n5pu9//77d1pbZ/S1da361re+5UsvvdS2/fjjj3v77bfvko7u1lVPd3wHF+b7Z9v77befb7/9dtv2\njBkzmq7vXWFh+9Rbb73V9Jzf/e53Td+L9q7xPaWxJ+6BNRbmunDJJZc09ZlXXnnFu+22m+fOndvh\n635PaJo6dapfeukl2/ajjz7qrbfeuuk1++23n6dMmdJhHQWSe1zXp35i5aebkTRGkiWtW9e2lqTr\nJT0paaKksZK2bedvHCjp/yRNlvSIpGM7cN7tJG1Zd/wlSZ9f+P9R17j55psZM2YMkhg8eDDTp09n\n2rRp73ne4MGDWWGFFd7Tvvnmm9O/f/+m57z00kvdeu5p06YxY8YMBg8ejCTGjBnDzTff3Oz1AGPG\njOGmm24C4LrrrmPo0KF87GMfA+AjH/kIAG+99Rb33HMPe+65JwCLL744yyyzTI9q3HrrrVl00UXf\n8/7079+/qX3WrFlI6vD71ll9/fv3Z/PNN2/6Pw8cOLDZDGqNG264gREjRnRJRz1Tpkxh1VVXZeWV\nV2bxxRdn+PDhTe9HjU984hOsu+66LLJI80vb6quvzmqrrQbAiiuuyHLLLde0gtATOm655RZ22203\nAHbaaScmTJiA7XY/n5deeolx48Y19aNGaVpyySXZZJNN+MAHPtDm33/66ad59dVXu7R6sLB9HeCH\nP/whJ5xwQpv9eWH62MJeqyQxY8YMIF0LWntODl31dMd3cGG+f0888QRz5sxhq622AuCDH/xg0/W9\nKyxsn1pqqaWanjdz5symftXWNb6nNELP3ANrLMx14YknnuAzn/kMkN6HpZdemgcffLDD1/2e0DRw\n4EBWXHFFANZaay1mzZrF7NmzO/2+BEEEP93PPsDt1b9IWgK4ATjf9pq2Nwa+AkztJQUAACAASURB\nVKyxgL/zZ9uDga2Ab0paeQHP3w5oCn5sn2v74q79Fxael19+mQEDBjQdDxgwoFMXyHquuOIKtt22\nzVixS+du7zmvvvpq081o+eWXb1o+f+aZZ5g+fTr7778/u+++O9dccw0Azz//PMsttxwnnngiY8aM\n4Zvf/CbvvPNOj2qs58orr2z2/tx///0MHz6cUaNGceqppzYNtjtDZz+/6dOnM3bsWLbYYotm7S+8\n8ALPP/98081yYWipacUVV+xSn5oyZQrvvvsuq6yySo/pePnll1lppZUAWHTRRVl66aV5/fXXgbY/\nn9NOO40TTjjhPQPHRmhaEDfccAO77rprl4Lphe3rN910EyussALrrrsurTFz5kxuu+02hg0b1mlt\nHdXXHkceeSTXXXcd2267LYceeijf+ta3uqSju3XV6K7v4MJ8/5555hmWWWYZjjzySMaMGcPpp5/O\n3Llzu01LV66ftXSz6667jqOPPrpJZ2vX+J7S2FE6ew9sS0Nnrgvrrrsut9xyC3PmzOG5555j6tSp\nvPjii81e29Z1v6c01fP3v/+dgQMHsvjiize1nXTSSYwePZpf/epX2M3TZoOgngh+uhFJSwFbA4cA\ne1fN+wITbP+l9jzbD9r+bUf+pu1XgSeAlapzjJR0l6RJkm6StKKk1YAvAcdWq0XbSDpF0ler14yT\ndLqkuyU9Jmmbqn1JSZdJekjS1dXfbXVqV9Khku6VdG9rObo9xbXXXsuDDz7IF77whYadsx5JTQO+\nuXPnMnXqVM477zwuuOACzj77bJ5++mnmzJnDQw89xD777MM111xD//79W81j7gnOOecc+vXrx6hR\no5raBg0axA033MAVV1zBeeedx6xZs3pUw5w5czjuuOPYf//9WXnl5jH6DTfcwE477US/fv16VENH\nmTZtGieccAI//OEPuxRkdAetfT5jx45lueWWY/3118+iaUHceOONDB8+vOHnnTlzJuedd17T4LQ1\nxo4dy0YbbcSyyy7bQGXzueGGG9htt90YP348559/Pl/72teYN29eFi2tUcJ3cM6cOdx77718/etf\n54orruD555/nqquuyqYH4Nhjj+XWW29l5MiRXHLJJUDb1/ic5LoH7rHHHgwYMIA99tiD0047jQ03\n3LBZH2rvut/TPP7445x55pnNahTPPPNMrrvuOi699FImTpzItdde21BNQe+i81PCQXuMBv5m+zFJ\nr0raGPgUcF9X/6CkVYAlgClV0+3A5rYt6QvA12wfL+lcYIbtM6vXDWnxpxa1vZmkXYHvADsChwOv\n2x4oaX1gcls6bJ8P1Eb0rU6pXHrppVx22WUAbLDBBs2W6V966aWm5eqOcuedd3LuuedyySWXNJvd\n6Y5zr7jiim0+5yMf+QjTpk1jhRVWYNq0aSy33HJAmrlbdtllWXLJJZtShR555BE22WQTBgwYwKBB\ngwDYeeedWw1+ulMjwFVXXcW4ceP47W9/2+qM/JprrsmSSy7JY489xgYbbNDe29clfTW+/e1vs9pq\nq3HggQe+57Ebb7yRk08+eYHn7ggt34+XX365U31qxowZHHbYYRx77LEMHjy4R3WsuOKKvPjiiwwY\nMIA5c+bw1ltv8eEPf7jZc+o/n/vuu49bbrmF8ePHM2vWLGbMmMFXv/rVDhcTd5em1njkkUeYO3du\npwKz7urrzz77LM8//zyjR49uat999925/PLLWX755YE0uO9sYNad16orrriCCy64AIANN9yQWbNm\n8frrr3cpZaq7r6HQfd/Bhfn+DRgwgPXWW69pkDxkyBDuv//+Tp2/u6+fNUaOHMmhhx7KUUcd1eY1\nfvXVV+8RjQuiM/fA1liY64IkTjrppKbn7b333k2pw9D+db+nNEF6H4888khOP/30Zqv3tb+x1FJL\nMWLECKZMmdKUvh4ELYmVn+5lH+BP1e9/qo6bUa2wPChpQdNee0maQlr1Odv2f6v2TwB/l/QAcAIp\nuOoItfNNBFarft+6ptf2g8wPsLrEvvvuy7XXXsu1117LjjvuyDXXXINtJk+ezNJLL92pXPiHHnqI\nk08+mXPOOadDg4jOnnuFFVZgqaWWYvLkydjmmmuuYciQFC/usMMOTekO9e1Dhgxh4sSJzJkzh5kz\nZzJlyhTWXHNNll9+eQYMGMBTTz0FwIQJE1hzzTV7VOP48eO54IILOOecc5rlzj/33HPMmTMHSOku\nTz31FB//+Mc79J535fP72c9+xowZM5rdJGs8+eSTTJ8+/T0uRl1lgw024JlnnuG5555j9uzZ3HDD\nDeywww4deu3s2bM54ogjGD16NDvvvHOP69hhhx24+uqrgZSesfnmmyOpzc/n+OOPZ/z48dxyyy38\n9Kc/ZfPNN++Ui9LCaFoQ119/faeDi+7q6+ussw4TJkzglltu4ZZbbmHAgAFcddVVTYFPrd6u9r3o\nKX3tsdJKKzW5Tj355JPMmjWracKks3Snrpqe7voOLsz3b4MNNmD69OlNdXZ33XUXn/zkJzt1/u68\nfj7zzDNNz7v55ptZY42Uhd7WNb6nNLZHZ++BrbEw14WZM2c2pW/fcccd9OvXr+kza++635Oapk+f\nzqGHHsrxxx/Pxhtv3PT8OXPmNPWtd999l3HjxrHWWmt1WlvQh8jtuPB++QGWA94B/g08AzwHPEtK\ngftdi+duAoxr528dCPxv3XNfAwZUx+OAUdXv29X+DnAK8NW6v9F0XL1mk+r3jwLPVL9fA2xf95r7\nas9bwM8CmTdvnk855RQPGTLEI0aMaObCMmrUqKbfTz/9dG+zzTZeZ511vM022/gXv/iFbfuAAw7w\nFlts4VGjRnnUqFE+7LDDOnLaTp17ypQpHj58uIcMGeJTTz3V8+bNs22/9tpr/vznP++hQ4f6gAMO\n8Ouvv970ml//+tfeZZddPHz4cF900UVN7Q899JB32203jxgxwl/+8pf9xhtv9KjGHXfc0dtuu23T\n+1Nzxrn66qu96667etSoUR4zZoz/+c9/dvh966y+F1980WuvvbZ33nnnJh01xzvb/sUvfuEf//jH\nXTp/W4wbN87Dhg3zkCFDfPbZZ9u2zzrrLN9000227fvvv9/bbLONBw0a5M0228y77rqrbfuaa67x\nwIEDm3SOGjXKDz30UI/p+O9//+uvfOUr3nHHHb3HHnv42Weftd2xz+df//pXp93eFkaTbW+//fbe\ndNNNPXjwYG+zzTbN3Jd22GEHP/HEE53WU2Nh+3o922+/fTO3tyuvvNLHHHNMl7V1Rl9b16rHH3/c\ne+21l0eOHOlRo0b5tttuWyg93aXL7v7vYFe/f7Z9++23e8SIER4xYoS//vWve9asWV3WsbB96sgj\nj/Tw4cM9YsQIH3bYYU0OYnbb1/ie0tgT98B6unpdeO655zxs2DDvvPPOPuCAA/z888/bXvB1vyc1\n/epXv/KgQYOaXcdfeeUVv/32203331133dXf+973PGfOnC69XxnJPo7tSz+yoyisO5B0KLCx7cPq\n2m4FTgZ+Axznqu6ncnr7ru3t2vhbB5KCkCOr458D79g+UdIk4Au2J0q6CFjd9naSjgeWsf2d6jWn\nUKXBSRpHCoTulfRR4F7bq0k6AVjD9pclDQTuB7awfe8C/rvRaYIgCIIgCLqHrlmzBl0i0t66j32A\nq1u0XUkyPhgBfEnSU5ImAN8Cvt+Jv306cJCkpUkrOpdLmgi8Uvec64DdaoYHHfy7ZwPLS3qo0jMV\neLP9lwRBEARBEARB7yRWfvowkvoBi9n+r6Q1gZuAdWwvyDg/Ok0QBEEQBEH3ECs/DSTc3vo2SwJj\nJS1G+uId3oHAJwiCIAiCIAh6JbHykxFJBwEtN7C4w/YROfR0gug0QRAEQRAE3UOs/DSQCH6CrhCd\nJgiCIAiCoHuI4KeBhOFBEARBEARBEAR9ggh+giAIgiAIgiDoE0TwEwRBEARBEARBnyCCnyAIgiAI\ngiAI+gQR/ARBEARBEARB0CeI4CcIgiAIgiAIgj5BBD9BEARBEARBEPQJIvgJgiAIgiAIgqBPEMFP\nEARBEARBEAR9ggh+gq6g7vqRdFh3/r3QFLpCU+/WVaKmUnWFpt6tKzT1bl3drCloIBH8BLk5NLeA\nVghNHadEXaGp45Soq0RNUKau0NRxStQVmjpOibpK1BR0gAh+giAIgiAIgiDoE0TwEwRBEARBEARB\nnyCCnyA35+cW0AqhqeOUqCs0dZwSdZWoCcrUFZo6Tom6QlPHKVFXiZqCDiDbuTUEQRAEQRAEQRD0\nOLHyEwRBEARBEARBnyCCnyAIgiAIgiAI+gQR/ARBEARBEARB0CeI4CcICkVSP0mX5tbR25C0ZG4N\nQe9G0odza2gLSSMlxb07CIKgiyyaW0DQN5G0EbA1YOAO2/dl1nNdpaWeN4F7gfNs/7fRmmzPlbSq\npMVtz270+VtD0j9sD1tQWw4kbQlcACwFrCJpEHCY7cMzavogMNP2PElrA+sCf7X9bi5N9VT6dgf2\ntj28AD2rAmvZvklSf2BR229lkPKopFeAO4A7SdeoxzLoaI29gLMkXQlcaPuRnGIk7d7e47avapSW\njiDpWdurZDr3Mu09bnt6o7S0pLo+nQOsaHt9SZ8GRtn+fgYtx7X3uO2fNkpLS6qxS0veBP5te06j\n9QRdI9zegoYj6WTgs0DtpjgGuDzHRbZO08+B5YE/Vk17AdNJAdEytvfPpOtiYD3gL8DbtfZGX/wl\nLQ4sAdxGClpVPbQMcJPtdRuppzUk3QXsCfzF9oZV24O218+oaSKwDfBh0kD6HmC27X0zalocGA58\nDtgJuBK4yvZ1uTRVur5I2jF9OdtrSloLONf2kEx61ga2rPtZHvgXKRA6I4emOm3LAPsAB5GuURcB\nf8wRKEqaB0yufmD+tQHAtg9utKb2kPSc7ZVznZv0eamVh50rKAOQdCtwAmmyL+v1U9J32nvc9qmN\n0tISSf8CNgKmkD7H9YGpwIeAL9v+Ry5tQceJlZ8gB/sCg2qrKZJ+RLpxZgt+gC1tb1p3fJ2ke2xv\nKmlqNlXwZPWzCLB0Rh1HAMcBKwAP1bVPB87NoqgVbD8nNRtXzM2lpUK235F0CHC27TMkTV7gq3pC\niDSMNGAeBowFLgY2tX1QDj2tcASwGXAXgO3HJa2QS0y10vMY8FtJawK7AkeT3r+swY/t6ZKuAPoD\nxwC7ASdI+oXtXzZYzu7A3sCngWtJQdgTDdbQGbLN+OYKujrIkrbvbnH9zLKSkTO46QD/AQ6xPRVA\n0kDgu8DXSBO6Efz0AiL4CXLwH9IqQi2V7APAC/nkALCUpFVsPwsgaRVS+hRAtpSz2k1A0lLV8YxM\nOn4G/EzSMbbPyqGhAzxXpb5Z0mKkgerDmTVJ0hakgP+Qqq1fJi1/o1q5s/10Je7nmbS0xizbs2uD\nL0mLkmmgWvWjLYEtgJWBp0irPvsBuVN0RwMHAp8kBbCb2Z5W1bo9BDQ0+LF9DXBNlUI5GviJpI8A\n37R9ayO11GgnbUrMv65nRdKHgDVJ90IAbN+ZTxGvVEG+ASTtCbyYUQ+SliBdNz9F8/cp52ri2rXA\np9LykKR1bT/VInAMCiaCnyAHbwJTJf2TdKEdCtwt6RcAto/KoOl44HZJT5JukKsDh1c39N9l0AOA\npPWB3wPLVcevAJ+vv/g2mGmSPtey0fYfcohpwZeAnwMfJwXT/yCtJuTkGOBE4GrbUyWtQVp1ycFG\npBn6myQ9BfyJfIFYa9wq6SSgv6ShwOFArlS820lBzs9In907mXS0xu7Az2yPr2+sW2HMxX9J1/bp\nwKrUDVYz0N4qefaAv/qcjiNdqx4ANiUF19tllHUEcD6wrqQXgKdJwX5Ofg88QkrP/S5pEin3hNZU\nSeeQrp+QUuQfkvQBoIhazmDBRM1P0HAkHdDe47azBBvVxatWu/JoDpODlki6kzSDOrY63g44zfaW\nmfScU3e4BLADMNF2u0XPQVlUKxv7AHsA95MG+Odn1rQIaZZ3GGkC4u+2f51JywDm1/psRpoovA+Y\nAEyw/VQOXZW2021/fUFtDdSzAymo3gy4CfiT7XtzaOktSHqA9H5NsD1Y0qeA79reI7O0mgnKIpmM\nRlpqmWR7Q0lTbH+6WtG/zfbmGTX1J03MbF013QGcTQr+l8yVnRF0jgh+gixURddrV4ePluB+VQ0I\nV6NuRdT2xdkEAZLutz1oQW25qCyB/2B7lwK0/KKV5jeBe21f22AtrbkHNmF7VAPltEkVcOxIcnvL\nWpgu6WjbP19QWw6qlLKDSSt5q9vOtmIm6T7bG7Vom2L705n0zCMVf99O6vPN+n2OlfzKPGNcVTcm\n4DekQP/fwAG2JzVaUwt9tXrSyaS0xdkZzQVKdla72/ZmksaTAo6XgLttr5FLU/D+INLegoZTrV78\nDniGNMO7sqQDWqZxNFjT70n515OZXyRvUk59Tp6S9G3S8j+kNIRss86t8BZQyo1oCdLK3eXV8R6k\n1I1Bkra3fUwDtZxZ/bs7MAC4pDreB3i5gTqakHQtaZbyDuAe27NtzyOlB5ZQpHsA701JOrCVth6n\nqsfYgvmrPxsCj5PS8O5otJ5K05dJA8A1JU2pe2jpXJoqSjHMqOdo4LfV7/sAg0jXqQ2BX5AcGHPy\noqRlSf3p75JeA57PpCWnkc6COL+aYPs2yfF0KeDknIIkbQWcQkrtrJ8oLeU+GHSAWPkJGk5l//s5\n249Wx2uTHII2zqjpYWCgC/tCVBf+U5m/xH4bcIrt1zPpuZr5M7uLkApRr7F9Qg499VQWpFvZnlsd\nL8p8a+4HbA/MoOle25ssqK1BWkYwfzA/iJQ7fyfVXja2cwVl+5Bst7cmfV41lgbm5bC6lvR/VClu\nzA8WZzZaRwtNHyJZpv8Q+EbdQ2/Zfi2PKpA0GLi/pGunpMm2B1e//wG4q7aC2NrKWU4kDSHZJN9g\ne1ZuPUH7SHoEOBaYSJ2bqO1Xs4kKOk2s/AQ5WKwW+ECylK1yeXPyIGmGPqu7TUuqIOcoSUunw+z5\nxP9b9/sc0sZuz2TS0pIPk2YG36yOP0jaM2aupFyDig9KWqNWIyJp9UpXw7F9PXB9paMfaRZ8O+DH\nJIOPXKlcd5K+dx8FflLX/hYpnSoHOwGTShrQk77/z0h6j4mHpOUyBkAXAGtUk1q1YHpC5pqReZJW\nAl4HhgA/qHusfx5JzVHaRLR+o++sgU+JzmrV6tjneW86eg5TpBpv2v5rxvMH3UAEP0EO7pV0AfNT\ngfYFchfIfpTk2HI30HQTyl2bIWkDUupdvdvbAbYfzKHH9s2SPgpsAiwG5A7G6jkDmCxpHCmdclvg\ntKqA96ZMmo4FxlXuaiKlShyWSQvVZ1db/dmcNMi5ibTCkQXb/ybVYmyRS0MrnE95A/o/ACNIM84t\nN8o0mdJPbW9S1URtRupXRwG/l/QSaVB/eAZZJ5PuKf1Imx7X9mT5HwpIG5b0TdJq5zVV0x8kXWr7\nhxllleisdiPJBe8BYF5mLTXGSvoxaU+f+rFCVgv8oHNE2lvQcCpXtSNonsp1ds6Zr+qm+B5y7VNR\no0C3tz1I9r+3kQZfWwLH2r46h56WVLO9m1WH99j+T0498B4XwUdy9XNJj5NWxa4kDSjuKWAlEUm3\n295a0ls0L5YXabVjmUy66gf0W5LsiHMO6IunmmjYHNiKNGO/SK5aiCrtden6FOFKn3L3e0mPAhvW\nLNSrvjbJ9joZNZXorFZUiiKApNa2KrDtHRouJugyEfwEWajc3tYjzeY8ajvbRqIlU5rbm6T7gWG1\n+hBJKwL/KMV9robSZn2fI7mYfSrD+du1/rZ9VaO01JB0Imlg+nHgMebXtEyq1UkF76WUAb2kdgeB\nuWaelfb92hIYTJoJvwe4i7RS9lImTdu293hOcx2AanV6lO3p1fEypBWq7TJqKs5ZTdKxpOyC62m+\nypKtxi14fxBpb0HDkTQcOBdo2lBU0mE58mhLnXWuozS3t0VaFMZPIxkfZEfSx0gbzn0O2IBUGL53\nJjkj23nMpJSJhlKfUlOZjGwJfBHYWtIrtltd/WwkVS3SijTP7382g462BvRb5xrQ07weqiUm7bmV\ng/OAR0nX9PG2H8uko57WDFgMfBpYmUz1bZJ+Vul4jbRZ5t+r42GkPpaT4pzVgNmkmsRvMv/+nCXF\nU9J+ti9pyxo8pyV40Hli5SdoOJVbygjbT1THa5KcbtZt/5V9jxZubyalm52a0e3tJ6QUrj9WTXuT\nUrm+mkNPpelQkp3tx4HLqp9rba+eS1PJSFqDNLDfqvr3YyQ3rBGZdX0F+A7JCryW329n2L+mmgwp\nbUBfJFXAOoj56YHrkAwsahvC3pJRHtBkT/wtkinKD2xfl0nHIe09bvs3jdLSG6hqJTez/UoBWg6z\nfZ6k77TysG1/t+Gigi4TwU/QcFRt8FZ3LNLy+qbtvKynNR3S8sYj6Ue2v9HWaxqgaXlSgfwTtt/I\npaOe6rP6f6SBM6Rg7IqcrliSZpMGWse72lle0lM50zVqVPbE3yGZLwDcStrJ/c22X9VjWq4mpW+9\nSSriv5NUv5K7qBkASU8AnynBMrb0Ab2k9YGBNHflyr0nGdCUCvtZytgQdghpJcOkWsl/5tJSKm2t\nZNTIuaIh6R/AmFptVAlI2sr2HQtqC8omgp+g4Ug6hzSov4x0U/os8CyVI1emeogbgUttX1od/wro\nn8vmU9IXgNNIqYGrA4fa/ksOLaUj6SOkPrQPya78MuBA2ytnFQZIupJko/67qml/YJDtdmuCekjL\nKNJ+PtlnUVujKiQeantObi0tKWxA/x2SRflAkhvWLsDttvfMpOfTzA8StwQWJwXWE0jBdcOdPKvU\n6m+SAv0f2L690RpaQ9Ifbe8jaRLN06wByFHcL2keaXPvv5JSPOtdBLF9aqM11agmbD4FjKV5zU82\nq+vWTBhKNGYI2ieCn6DhSLqonYedI+CQ1J+U53whsDPwhu2jG62jTs+DwPa2/69KU7rUdnYrYEmj\ngR+RUqVEObVRAEj6BKnuZx/SfjpX2z4po56mzRbba2uQliKLwOtmnj9FWmG5geYDnYbPPJc4oK/T\n9gBpVWqS7UFVYHaJ7aGZ9NwH3M7896bhNVotqQb0zwP303qQkWULA0mfsP18ler9Hmw/mUHTINL1\ncmeSjfofgZtzrubXkHRAa+22f9daew9r2YJ0LTiG5HhaYxlgt9JMf4L2CcODoOHYPqi9xyWd6Abt\ndyBpubrDL5D2XbgDOFV5Nw6cbfv/AGw/Vdkll8BPSBf6B3ILaQ3bz5M0/qQq6m8yPJA0NEPay0xJ\nW9dmnqvag5kN1lCjyCJwYOnq32ern8Wrn5z8lnQd+CvwrRIG9HXMtD1P0pzKJWwa6fPLgu2NJA0G\nPkmmDXxbYfvcAlqjCnz6AefmClZbYvt+UpD4DUlbkgKhX0r6es5sg+p9GmZ731waWrA4yQRiUeZf\nswCmA1lWXYOuEys/QXE0cglZ0tO81+WthnPVjUiaBvyprmnv+uNcy/6S7rC91YKfWR45UhOqWdWL\ngQ+R+tZrpJS8+xupozUKKgIfDNxfwkxzb0DS2cBJpGvC8SQr4MkLmlTqQT3fJrlQ3gd8Bvih7V/n\n0NJZJF1pe48M572FVMsyvdHnbouqxvT/kdI73wW+bftfmTXdDuzggrbCkLSq08bMQS8mgp+gOFRt\nttbA8y0CbFFSwWJby/01ciz7A0g6C1ietEJWn5pUfD1So/tVi3MvA1DCYKe0InBJ95KsaydSmTCQ\nTAXeyqjpAVpJl2J+mmfDHehaQ9JqwDK2p2TUMBXY1PY7Vf3d33Ka13SGXNeEqpZlMPAP4O1au+12\nzQd6SMvBpKBnCeAK4DLb0xqtozUkXUzaD/AvNH+fcpowLA98jZSmW284Epuc9iIi7S0okYZG5FUK\nyf8CWQbGrdHR4EbSL21/paf11PERkg1xfc68STen0mn4TE+VrrgHsBqwaDLLgxy2qC2KwL9VShG4\n7U2UdrjfjJRTfxTwe0kvkWpIDs8gq2b7LVIN0q4ZNLRKa7VbkrbNVbMFzKq5cdl+tZpM6i3kmv29\nvvopgQtIpiz/BnYChtWuU5CvPqriyepnEZqnmuXkUuDPpGvEl4ADgP/LqijoNLHyExRHjtk4SWeS\nCnav6k3pNw1OEewHHGH7F404X3eTKe3tb6RgYyIwt9Zuu70NK3tKS5FF4PVI+iDJjnsr4POkTXWz\nWpaX5uQkqT49cQlS0Dgx18yzpDeAWuAlYJu64yL6VVs0+rOV9FvbBzbqfB1BUrubG9u+tVFa2kLS\nUpWWGQVomWh7Y0lTaivAarF9R1A+sfITlMjlGc55GHAcMFfSTApzMSsB23Ml7Qf0yuAHeCbDOT9h\ne+cM522NIovAJX2OtOIzmJRKeQ9wF7C17ZdyaisR2yPrjyWtDJyVSQ7A6BbHZ2ZR0TW04Kd0K0Wk\nS9bT0eAmR31UtZ/V74HlquNXgM/bntpIHS14t/r3xWo1/T9U+oLeQwQ/QcOpcma/SJUKVGuvWVzb\nPq3RmmyXsqReOrdXdT9/pnkOdraag3oqt6LVaN6vLq7+bfjeOsCdkjYowR2v4EHOecCjwLnAeNuP\nNfDcrSKpfjWgv6QNqRso276v8ara5HlSXUQWCu5XtfP2B1ax/WgrD3+9wXKWbNmX6imsX7Ukxwrs\n+cBxtscCSNoO+DVpsiQX31favPp44Jckq+tjM+oJukCkvQUNR9KdwG28NxXoymyiaNoEspZPP852\nKTnZbZLBHOK2Vpptu909ZBqBpN8Da5I27Kv1K+dwxqsrmF8UWAt4ivkbCBZTMN8aGfpUP9K+NbU9\nddYBXiSloU6wfUujtNRpGtvOw85Z3Czpl8xPW1yEtGL2jO39cmnqCJnSmUeSVqIWt7165Sz43Vyp\neJLeIq1sthb8ZO1XCyJT2vD9brF/TmttuZH0QdtvL/iZQSlE8BM0HGXa8wl/oQAAIABJREFU5LE9\nJP0I2JRUzAhpr4N7bZ+YTxVI+qzty9tqk3Sg7d9mEVcYkh4GBpZQsyVp1fYeL9kqNXeNi9KmnZ8l\nbSa4uu1c+w8VSQsnyDmkwKcYp8q2yDR4ngjsQJrM2rBqe8D2Bo3UUacnm+PkwpLp87uaZKH++6pp\nP2Bj27s1Ukedno8DKwFTbM+WtALpOnWg7Y/l0BR0jUh7C3JwvaRdbd+YW0gduwKDbc8DkPQ7YBKQ\nNfipzt+yBqqprdGBj6STWmvPkarYCg8CA0irBlmpD24kbQ2sZfuiKuVzqXzKykPSp5m/6rMlaTPB\nO0kpJVkG9ZLaTZG0fVWjtLRy7iw2972Ud22/We9eRj6Ht95Oo+ujAA4GTgWuIn1ut1VtDUfSMSS3\nzCeAD1T7bZ1O2sdt4xyagq4TwU+Qg6OBkyTNIhUPlmIusCxpE0pIm1JmQ9IupIDs45LqDQaWIc32\n5mJu3e9LAMOBnMWn9XwUeEjS3TTfgyib25Sk7wCbkFK5LgIWAy4huZmVSqMHOb8Fbgf+SrLgfrbB\n52+NkS1+r3dYM2kwloXesgdRK+QYPE+tDDX6SVqLZKN+ZwYdNTpUY9TX66Mk/d72/iRzgywberfC\nocA6tl+TtArwGLCV7YmZdQVdINLeggCQtA/wI2As6Sa9LfAN23/OpGcQKZf/u8DJdQ+9BYy1/XoO\nXS2RtARpU8PtCtDSqmVrTqtWSZNJ+0fdV5d2MyX3ALW9QY6kYbb/kUFWu2QcEBaVqiTpjOrXWirQ\nvtW/50DelMrS+lW1f9Q3gWGk6/rfge/Z/m8jdXSWvl4fJekhYEfShMh2tAicbb/Wyst6WlOztL8S\na4+CjhPBT9AwJK1r+5EWTkpN5Ha6kbQSqe4H4O4SbHYlLWb73QU/Mw+V681E25/MraVEJN1te7Pa\njbPax2ZCzuCnpEFOZ8gVhOSugWpJa+9DCRp7a78qkb5eHyXpKODLJIe5F2ge/NgZ9v6SNA34U13T\n3vXHBa1QBR0g0t6CRnIcaem4tQ0eTbrwZkHSJcCtwG22H8mloxU2k3QKsCrp+1pLbcmy8aOkScxP\nuelHKv4sod4HSZuT6kTWI9WN9APezpxOeZmk84BlJX2RlK/+64x6AE4hbYw5DsD2ZEmr5xTUQWKm\nLiFJW9VMDip790Uya4IC+5XShrAt+82bwL3AeaWvADWYYuqjnDbS/oWkc2x/OYeGVjihxXGku/Vi\nIvgJGobtQ6t/291sUdJQ2/9sjKomfkPamfyXktYkmR2Mt/3zButoyW9Iewg0swVvNJIWtT0H2LOu\neQ7wku1Zbbys0fwvaTbuclKdzeeBtXMKsn2mpKHAdFLdz8kZ+nZLihnklEqLQfMakv5S/3jm1YxD\ngAurVVeAN8hUBN6CEvvVU8DywB+r471IqcNrkyYh9s+ka0FEfRRg+8uVFf6KNN+7reF1gR01GpH0\nS9tf6Wk9wcIRaW9BceRK4aguspsC2wNfAmbaXrfROlpousv2Z3JqqHRkT6tZEJLutb1JfU1N7nqN\nKs3tv7bnSlqHFAD9NWcqo6TfADcD3wD2IA1yFrP9pVyaOkIjP8u26sdq5Kwjq1ELfmy/mVsLlNmv\nJN1je9PW2iRNtf2pjNqiPmrBmo4krSi+DMyrmks29egV98ogVn6CMmn4rJekm4EPkjZWvA3Y1Pa0\nRutohbGSfkxyl6p3MGt0fVSOmcjO8o6kxYHJVVH4i+RPBxoPbCPpw8DfSOk2ezG/SD0HXyENcmaR\nZsT/Dnwvo54mSnGb6mhwk8OEQWkfpNOAj9neRdJAYAvbv2mkjlYosV8tJWmV2kpB5dJVs5qfnUtU\nfX0U8J76qByGI7bfIX1+32z0udvhGJLD2qu5hQTvL2LlJyiOTMWePyN59c8i7S0ynlSYPrOROlrR\n1dpO83aDdwKX9Dzw07Yet93mY41CaWPRl0kDimNJduVn234io6aa0cFXgP62z1CBm/yWQG8smM/k\nyvVXkm36N20PkrQoMClHYXrpSNoVOBd4kjSBszpwOKku6Yu2z8qkqxhzgTpNxdVHVfe/oVXKda8g\nVn56B7HyEwSA7WMBJC0NHEgaXAwAPpBR1gLroxpIP9KMabErQLb/Xa0crGT71Nx6KiRpC9JKzyFV\nW7+Meooc5FScQmEF8x0gx+zhR21fJulEANtzJGWrB6xRYr+yfWNVv1JLX360TkeWwKci6qM6rmmc\npBtonvmQfbKtHYq9RwbzieAnKJFnGn3CKrd4G9LqzzPAhaT0t6wUlOLyou3vNvicnWJBqSSZOAY4\nEbja9lRJa5D2kspJiYMcKHNAWCJvS/oI1XtTuRyWUPdTar9ai1RrtwQwSBK2L86kpUZx5gLAli3q\no66rr4/KpOnZ6mfx6qc3kNskKegAkfYWNJyqsPJ4Um7/F6uL/zq2r8+o6aukYGdia0vskj7sDBuL\nlpLi0tH0nlzvU3Xu4lJJSqTUIvASC+YXRKa0t41Ilu7rAw+SAo49bU9ppI5WdBXXryR9h7RJ5kDg\nRmAX4Hbbe7b3ugboKtFc4GFgpxb1UX+3vV5u45gSkLQ1sEYtcJZ0BbBc9fD3bd+STVzQaWLlJ8jB\nRSTr5i2q4xdI9sTZgh/bZy7gKTcDOfJ4S0lxGdLB5+V6n6DAlYMqZ/09Ghpds9WCIovAKbNgvhgT\nhhq276vc6NYhDZwfzekeWEeJ/WpPYBBpwuigaiX9kkxamijUXOB44HZJzeqjKsfKDtk8dxdtpFA2\nkWk1/1TSNarGOqQU+Q8CJwER/PQiIvgJcrCm7b0k7QPpRqAWI9YCyaWviBQX26918Kk5P8cSU0m+\nWvf7EqQVjdzFu8UMcuopcUBYoitXxWbAaqR7+EaFpHKV2K9m2p4naY6kZYBpwMqZtDQR9VELZEGT\nkTlYxvZDdceP254IIOmHmTQFXSSCnyAHs6vZ1NqAfk3qihkLJdcKwnHAX4A1Jd1BleKSSUtHyLnS\nUtzKQe3mWMcdku7OIqaisEFOEyUOCCnQhEHS74E1gcnM3/jYQNbgp9B+da+kZUk1RxOBGaTtDHIT\n9VHtUKjV/LL1B7Z3rztcsUEagm4igp8gB98h7XmysqRLga1Iy8dBCwpOcSmOQlcOlqs7XIRkqPGh\nTHLqKWKQ04ISB4TFpVICmwADXWbBblH9yvbh1a/nSvobafY+a21URXHmAm3VR5E5qF4AazTwXI9I\nGm77hvpGSSOA1lJig4KJ4CdoOLb/Kek+YHPSgP5o269klrUgsqRzSToCuNT21Or4w5L2sX12Dj0d\nIMcGtX9p7/HMbm8TSYNlkdLdnma+5XUWCh7kFDcgpMxUygdJNvwvZtbRjBL7laSbbQ8BsP1My7aM\nRH1U99DICYBjgRsk7QnUNhnfGNgSGNFAHUE3EMFP0HAqtyKYf/NeRdKHgH/n2sxM0u9t799OW66b\n5Rdt/6p2YPt1SV8EGh78SOoHTLW9bjtPy/E+bQE8R1oxuIuC9lmwXeI+NaUOckocEBaXSgl8FHio\nSp+s3/sk92awxfQrSUsASwIflfRh5l8TlgE+nkNTC6I+qpdh+wlJnybt2fYpUuB1K2lbjKOBIzLK\nCzpJBD9BDs4mOYJNIV341wemAh+S9OVMRcTNbFirgf7GteNOFPx3N/0kqZbiUunKst+B7bmSHq0f\noLbynBzv0wBgKLAP8DngBuCPtdWynEhaDPgysG3VNI5Uv5IzdbHUQU5xA8ISUylJdUglUlK/Ooy0\nx9bHSKuvteBnOvC/mTQ1EfVR3UZDJ7pszwIurCZw9yGl8D8NXNlIHcHCE/v8BA1H0lXAt+tSuQYC\n3wW+Blxle3ADtZxIsqnsD7zD/IvpbOB82yc2SktrSDoTWAU4r2o6DHjO9vGZ9IwHNgTuBt6utRcw\n6wyApA+Qbko/Bk61nXWgI+kCYDHmD973B+ba/kJGTWeT+vzepIBjBjDZ9kG5NNWoPr/WBoS59JRo\nwlAkJfYrSV+x/ctc528PSeuTUgSXqLUVUHcHgKTVKKQ+qj2reUnDGjVZKmlt0r1lH+AV4M/AV22v\n2ojzB91LBD9Bw5H0oO31W2uTNLmRwU/d+X+YO9BpDUmLAIcCO1ZN/wQusJ1jrx8q84X30FF3np6i\nGjQPJ92YViM55F1o+4XMuu63PWhBbbkoaZAD5Q0IJf2c95owTCcFRMu0TJXtYS1v0XqNgwDbXqZR\nWhZESf1K0pbMtwUH8gcZbdVHOePmq63VQuWuj6q3mrf9Hqv5BmuZR9oI/RDbT1RtT9lupOlC0E1E\n2luQg6mSzgH+VB3vRcph/wCQJR3I9omSRlGXnmQ726ar0JTidrHtfYFzc2qpYftWSasCa9m+SWmn\n8n45NUm6mJQ6eSNptefBnHpaMFfSmrafBJC0BvPtibNQahF4iQXzFGTCYHvpRp6vs5TYr1SoLThR\nH9VRTqEcq/ndSauaYyvnwD9RUH1p0Dki+AlycCBwOCknG+AO0maQ7wLb5xCktEnZZsClVdPRkra0\nfVIOPdBUY7OqpMVt5yr4bkZltnAosBxpUPFxUmCWc+C8HykF72jgKM23JS5hRvwE0s3yqUrPqkCW\nNKDCBzlQ0ICwjhJNGIqi8H5Vqi141Ed1jGKs5m1fA1xT1SCOJr1nK1QTuVdnqlUOukgEP0HDsT0T\n+En105IZDZZTYzgw2PY8AEm/AyaRcthz8hRpY8y/0LzG5qeZ9BxBChLvqnQ8LmmFTFqoNCyS8/zt\nYfvmqrB5narp0apoNgclD3KgrAFhjeJMGAqk5H5VpC04BZkL2P458PNC66OKs5q3/TbwB+APVbD/\nWeDrQAQ/vYio+QkajqStSMvZq9I8Dztb7qykKcB2Nbcypc0px9n+dC5NlY7vtNZu+9RGawGQdJft\nz0iaZHtDSYsC9+V+n0pD0u7tPW77qkZpaUmhg5wiC+YrXUWZMJRKif1K0lhgMMmgpSRb8CaiPqpd\nPUuSnBaHkYLqvwPfi+9gsLBE8BM0HEmPkDYMm0hd/YPtVzNq2gf4ETCWdJHdFviG7T/n0lSPpCUr\n293cOs4A3gA+T9oD5XDgIdslWQFnR9JFdYcjgevqjm374AZLakZpg5yWFDYgLMqEoWRK61cFG7SU\naC7Qan2U7aNyaQqCniKCn6Dh1FYPcutoiaSVgFpx8922X8qpB0DSFsBvgKVsryJpEHCY7cMz6VkE\nOIS6mTjbv86hpbdQWyXLraNGqYOcQgeExblylUrB/eo9Bi2238qkpVYfNZbUr+rro/7m9jeQ7lEk\nPUxh9VEKq/mgh4ianyAHYyX9GLiK5qkI9+WTBMAiJP/+RYG1Ja1te3xmTWcBO5Gsm7F9v6Rt239J\nj/KVKke8KeCRdHTVFrROMYOJiqKKwAsvmC/RhKFUiupXUKRBS9RHdY6neK/V/FvA2qR7UMOs5oP3\nFxH8BDmorfpsUtdmYIcMWgCQdDrpwjoVmFenKXfwg+3nWrjd5LRKPgBoGegc2EpbUC6lDXJKHhCW\naMJQKqX1KyjMoKVwc4GPkracKKk+qhir+eD9RQQ/QcOxncXOegGMAdbJ6MTVFs9VefSWtBjJzvnh\nRouoaqI+B6xeOc/VWAZ4rdF6SqdFusYaLd6z3AOKogY5hQ8Ii3Hl6gUU1a8qZtmeXZs8qgxasq9M\n2f5lafVRJBOi0gir+aBHiOAnyIKk4cCnaF5E/N18ingKWIy6m3YhfIm0qvJx4AWSneYRGXTcSZrR\n/SjNLcrfArIXpRfImXW/t2bpnpNTcgtojRIHhHW1dedWGxsWYcJQKKfkFtAKt0o6CegvaSjJoOW6\nBbymxylx89USN7AmrOaDHiIMD4KGI+lcUo7/9sAFpLz6u20fklHTlaTc/ptpPmuZu1j3o7Zfyamh\nJVXdQ70xxLScenozkq60vUeG8xZTBF6nqbiC+RJNGEqmtH7VmkELcEHuuqRCzQWa6qNsr1ntq3Nu\n7r4eVvNBTxArP0EOtrT9aUlTbJ8q6SfAXzNr+kv1UwSSRgIXAnMkzQX+n+2sm7sBSPosaVVjHGkw\n8UtJJ9i+Iquw3kvD97YqsAi8RjEF84WbMBRJof2qP3BhzZFSUr+qLfe2AVEf1XFqm0QvAQySlDs9\nMHgfEMFPkIOZ1b/vSPoY8CqwUkY92G53CT3DDP0PgG1sPyLpM8AZQKt7VjSYbwGb1lZ7JC0P3ARE\n8NM1cgz0Sx3klDQgLNmEoVRK7Fc3AzuSarUgBT7/ALbMpigR9VEdoC2reTKmBwbvDyL4CXJwfVVE\n/GPgPtIF9oK8khZIo2fo59h+BMD2XZKWbvD522KRFmlur5IswoPeQ3GDnIpiBoSFmzCUSon9agnb\ntcAH2zOqdLzcnJJbQCuUWB8VVvNBjxDBT9BwbH+v+vVKSdeTblBv5tTUARp9E19B0nFtHdv+aYP1\n1PibpL/TfN+FGzNpeT+gBT+l2ylxkAMFDghLNGEomBL71duSNqrtISdpY+ZnHmSjUHOBb5Dqox4g\nrXzeSP5JybCaD3qEMDwIstDbBhSS7rO9UQPP9532Hrd9aqO0AEj6FfAH23dI2h3YunroNttXN1LL\n+wlJw2z/o8HnLLIIHIosmC/OhKFUSuxXkjYB/gz8p9I0ANjL9sRcmipdxZkLVA5q/7U9tzruB3zA\ndrb6KElnAycBe5Oc32YAk20flEtT8P4ggp+g4fTGAYWkSbY3zK2jJZJOtP3DBpznaNINaCXgMuCP\ntif19Hl7K5IeoPXVQpH6+qcbLGm+gAIHOZWOEgeExblylUpp/aoKxjYH7iEVzENyC3s3h556JE2m\nqo+q3VckPWB7g4ya/gXsWEsTlLQU8A/bueujAJC0GmE1H3QTkfYW5KAYVycASYOBTwJTbbe1gejX\nGyipM3wW6PHgp64GYlVSEHShpP6k9Lc/2n6spzX0MkbkFtAOpRaBl1gwX5IJQ+kU1a+qdKlfVcHF\ngzk0tEPUR3WAelt528+0bAuCrhLBT5CDYgYUkk4G9iM5Op0h6Yc1W9R6Gp2a1AkaWjNi+9/A6cDp\nkjYk2XGfTP589aKo3qdSKW6QU1HigLAYE4ZeQIn96mZJewBXlTLZVhH1Ue0QVvNBTxPBT9AwJF1H\nGswsTTkDir2AwbbfkfQR4G/Ae4KfgmnoDb0akO5CWv0ZQtrv55RGaugNSHqL9tPelmmwpHqKGeS0\noMQB4SmZz9+bKLFfHQYcB8yVNJMyvn9QprnA0cDlkprVR2XSElbzQY8SNT9Bw6hy+lcEbmvx0DbA\ni7Z/k0FTMyMDSRNtb9xoHV2lUbVI1WB0H2BX4G7gT8C1tt/u6XMH3UvBReDFFcxXuooyYSiVUvtV\niUR9VMcIq/mgp4jgJ2gYla31ibYfaNG+AXCa7ZEZNL0BjK8dkgKx2nHx6S2STrJ9WgPOcwvwB+BK\n26/39Pneb1S1K0vUjm0/m0lHkYMcKG9AWGkozoShRErtV0o5lPsCq9v+nqSVgZVs351ZV3HmAgWb\n+vQqZ9igdxDBT9AwJN1je9M2HsvidCPpf9p73PatjdLSGpLWBs4BVrS9vqRPA6Nsfz+nrqBjSBoF\n/ISUvjENWBV42PanMmoqdZBT4oCwOFeuUimxX0k6B5gH7GB7vap+5B9t3YcaqGuy7cELamuwpjOB\nCRRUH9UbnWGD3kHU/ASNZNl2HuvfMBV1tBXcVDOEewNZgx9S/dEJwHkAtqdI+gMQwU/v4HukGfGb\nbG8oaXuSwUZOSi0CL7FgvkQThlIpsV99xvZGkiYB2H5d0uK5RRH1UR2lKGfY4P3DIrkFBH2Ke6s0\nkmZI+gKpqDErkpaXdLik20iF/CtmlgSwZCspGnOyKAm6wru2XwUWkbSI7bGkG3pODgMuB2ZLmi7p\nLUnTM2uCakBYOyhkQNjShOFy8pswlEqJ/erdKn3SkK7xpJWg3NTMBW6TdDupVurInIJsL217EduL\n2V6mOs5tDFFzhg2CbiVWfoJGcgxwtaR9mR/sbAIsDuyWQ5CkpYHdgc8BawNXkfLDP5FDTyu8ImlN\n5t+896QAi/Cgw7xRpW+NBy6VNA3IahJhe+mc52+HktymapToylUkhfarXwBXAytI+gGwJ/CtnIKq\n+qjFgXWJ+qgFEVbzQY8QNT9Bw6lSf9avDqfaviWjlpkk97JvAbfbtqSnbK+RS1M9ktYAzidtFPg6\n8DSwX23Dt6BsakX8pMH8vsCHgEur1aBcmoob5BRcMF+cCUOplNivKl3rkmz5BdzstjeybhhRH9Vh\nTa3W5OauxQ16PxH8BH0aSceQans+CPyRlH7wz1KCnxrVIGyRsNjtnUhahuZuRa9l1FLcIKfSVeKA\nsDgThlIpqV8pbZL5JeCTpFW739guJl24UHOB+2r1UXXmHvfbHpRZV1jNB91OpL0FfRrbZwFnVSss\newPXAB+T9HXgatuP5dQnaVng81RWn7XC63C76R1IOgw4lbT6M4+qiBjIGVyXWgReYsF8iSYMpVJS\nv/od8C5pT7ldgPVIadelUKK5QHH1UfVW8yTXt48D55JW8oKgy0TwE/RpqpWfO4BJ1X45p0lan7Sh\n542kmcOc3Aj8izR7WUKhbtA5vgqsb/uV3ELqKG6QU1HigLBEV65SKalfDazZkUv6DSm1uRiiPqrD\nHEFlNQ9g+3GlPdOCYKGI4Cfo63wC+DmwrqQHSIHQncBPbH8zq7LEEraPyy0i6DJPAqXVh5Q4yCl1\nQFiiCUOplNSvmmrFbM+prZiXQon1UbYvlTSR+fVRYwqojwqr+aBHiJqfIACq9IxNSMYCW1Q/b9ge\nmFnXscAM4Hqau91kqxkJOo6kDYGLSDOX9Z9f1rTFQovAixoQlmrCUDKl9CtJc5nvqijSPnLvUMZq\nYtRHdRBJZwBvkFK/vwIcDjxUyMRk0IuJ4CcIAEkfIgU8W1X/Lgs8YPugzLqOAH5AugHUvqwuzZAh\naJ3KovV2WqQt2v5dBi3FDnKgrAFhnabiTBhKo/R+VSIlmQtI+jPN66OesV1EfVQ1AXEIMIwUuP4d\nuKCgmsCglxJpb0GfRtL5wKeAt0iz83cCP7X9elZh8zke+GRhNSNBx1msoLTF0ovASyqYr1GiCUNp\nlN6vSiTqozpGf+BC27+GJqv52ipeEHSZCH6Cvs4qwAeAx4EXgOdJqyyl8ARxoe/N/FXSocB15E9b\nLHmQA2UNCGuUaMJQGqX3qxKJ+qiOcTOwIyn1G1Lg8w9SenoQdJkIfoI+je2dq1qDT5EuqMcD60t6\nDZhg+ztZBaa89cmSxlJQzUjQYfap/v1Gi/YcaYslD3KgrAEhUKwJQ2mU3q+KozBzgUGSple/C+hf\nHZcQ6IfVfNAjRM1PEFRI+gSp5mdLYATwEdvLZtZ0QGvtOWpGgo4jaVPgOdsvVccHAHsAzwCn5Fj5\nKb0IHMopmK/TU5QJQ4n0hn5VClEf1Tkk3QF8pYXV/P/a3iKvsqC3E8FP0KeRdBQp2NmSNIN5Z93P\nA7Zzp93UnOjWrg7DbaoXIOk+YEfbr0naFvgTya1oMLCe7T2zCiyIkgeEJZowBL2Xks0FSkTSJsCf\ngWZW87YnZhUW9Hoi7S3o66wGXA4ca/vFzFreg6TtSAXFz5Au/itLOsD2+Jy6ggXSr251Zy/gfNtX\nAldKmpxRV4mUXDBfoglD0HuJ+qgOUjm9LQ6sS1jNB91MBD9Bn6YgJ662+AkwzPajAJLWBv4IbJxV\nVbAg+klatFrBGAIcWvdYXHebU/KAsEQThqD3EvVRHcT2PEm/qqzAH8ytJ3h/ETfhICibxWqBD4Dt\nxyQtllNQ0CH+CNwq6RVgJmlVA0mfBN7MKaxASh4QFmfCEPRqSjYXKJGwmg96hKj5CYKCkXQhaab5\nkqppX1JK1cH5VAUdQdLmwEqkGpG3q7a1gaVqBbxB+QXzpZkwBEFfQdJbwAeBuaRJpCKuCUHvJ4Kf\nICgYSR8AjgC2rppuA862PavtVwVBsDCUbMIQBEEQLBwR/ARBwUj6IPBf23Or437AB2zHxqdB0EOE\nK1cQ5Ces5oOeIoKfICgYSf8iWSbPqI6XIqVRxQ7XQdBDSHqgzoRhUeBu2xtllhUEfYqwmg96ijA8\nCIKyiR2ug6DxlGzCEAR9hbCaD3qECH6CoGzelrRRix2uZ2bWFATvd8KVKwjyE1bzQY8QwU8QlM0x\nwOWSmu1wnVdSELy/sd0vt4YgCMJqPugZouYnCAqn2tcndrgOgiAI+hRhNR/0BBH8BEHhSNoSWI26\nlVrbF2cTFARBEAQ9RFjNBz1NpL0FQcFI+j2wJjCZtNEbpPznCH6CIAiC9yO/o7nV/HqkFPAg6BZi\n5ScICkbSw8BAxxc1CIIg6AOE1XzQ0yySW0AQBO3yIMnkIAiCIAj6As2s5nMKCd6fxMpPEBSMpLHA\nYOBuYFat3faobKKCIAiCoIeQNBd4u3YI9AfeIazmg24ian6CoGxOyS0gCIIgCBpFWM0HPU2s/ARB\nEARBEARB0CeIlZ8gKBBJb1Htat3yIWLZPwiCIAj+f3t3jBJRDEUB9D2wEhS0EGzdgRuzcAPiBixs\nXYtLEJdgZ6mFYPssZJhhhGEYBpNMzqkS8ovb3p/8fNiJnR8YWGaeVdVn6xwAACNw2xuM7bl1AACA\nUSg/MLZsHQAAYBTKD4zNuVUAgC0pPwAAwBSUHxibY28AAFty2xt0KDPPN61X1cfiucUYAIDNlB/o\nUGa+xe/3PBkRlxHxHstdnqqqq1bZAABGpfxA5zLztaquW+cAABidb36gf95QAADsgfIDAABM4ah1\nAOCvzLxdmV6szaOqHv45EgDA8JQf6NPJyvhpbQ4AwA5ceAAAAEzBzg90KDMfN61X1c1/ZQEAOBTK\nD/TpZWV8HxF3rYIAABwKx96gc/7zAwCwH666hv55QwEAsAfKDwAAMAXH3qBDmfkVyx2f44j4XixF\nRFXVaZNgAAADU34AAIApOPYGAABMQfkBAACmoPwAAABTUH4AAIAeKxMfAAAADUlEQVQpKD8AAMAU\nfgATOewiRRFVCgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1008x792 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vuoNbZ6RLQc-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Xc.drop(['Percentage_Tribal',  'Percentage_NWA' ], axis=1, inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L9prsmgZMC0O",
        "colab_type": "code",
        "outputId": "847b4471-6216-4e34-d7d0-34cc19867ef8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#Correlation table \n",
        "\n",
        "#Temporarily drop constant\n",
        "#Xc.drop('const', axis=1, inplace=True)\n",
        "\n",
        "hurdle = 0.0\n",
        "R = np.corrcoef(X, rowvar=0) \n",
        "R[np.where(np.abs(R)<hurdle)] = 0.0\n",
        "\n",
        "MJB = [\"#2680FA\", \"#3498db\", \"#89E6FF\", \"#FFFFFF\", \"#FFFFFF\", \"#FFFFFF\", \"#FFFFFF\", \"#FFFFFF\", \"#FEC95F\", \"#FEB159\", \"#FF8D28\"]\n",
        "sns.set_palette(MJB)\n",
        "sns.palplot(sns.color_palette())\n",
        "\n",
        "cmap = LinearSegmentedColormap.from_list('mycmap', MJB)\n",
        "\n",
        "# Generate a mask for the upper triangle\n",
        "mask = np.zeros_like(R, dtype=np.bool)\n",
        "mask[np.triu_indices_from(mask)] = True\n",
        "\n",
        "# Set up the matplotlib figure\n",
        "f, ax = plt.subplots(figsize=(24, 21))\n",
        "\n",
        "# Draw the heatmap with the mask and correct aspect ratio\n",
        "sns.heatmap(R, mask=mask, cmap=cmap, annot=True, vmax=1.0, center=0,\n",
        "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5}, xticklabels=Xc.columns[:], yticklabels=Xc.columns[:] )\n",
        "\n",
        "#Add contant back\n",
        "#Xc = sm.add_constant(X)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc22ca668d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAncAAABICAYAAACUedMzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 792x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAABUIAAASRCAYAAADy5uY4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIiIiIiK6l0RoREREREREREREdO//A+GPBO+BQeGDAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1728x1512 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tb2z8flzMuII",
        "colab_type": "code",
        "outputId": "7a5745e3-19b9-4a36-ce16-999f76d47239",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 590
        }
      },
      "source": [
        "f = \"\"\" Target1 ~ Q('Change_in_pop') + Q('WAP_not_working') + Q('HH_Income_Percent_Poor') + Q('Voter_Turnout') + Mean_Age\t+ Q('Small_Households') + Q('Percentage_White')+ Q('Percentage_HHWW')\t+ Q('Inequality')\t+ Q('Percentage_PWM') + Q('Percentage_NSL')  + Q('Percentage_Male')\t+ Q('Percentage_Informal')  + C(AG_Rating)  \"\"\"\n",
        "data = dataset\n",
        "\n",
        "response, predictors = dmatrices(f, data, return_type='dataframe')\n",
        "\n",
        "# Method 1 for Neg Binomial \n",
        "nb_results = sm.GLM(response, predictors, family=sm.families.NegativeBinomial()).fit()\n",
        "print(nb_results.summary())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                 Generalized Linear Model Regression Results                  \n",
            "==============================================================================\n",
            "Dep. Variable:                Target1   No. Observations:                  234\n",
            "Model:                            GLM   Df Residuals:                      216\n",
            "Model Family:        NegativeBinomial   Df Model:                           17\n",
            "Link Function:                    log   Scale:                          1.0000\n",
            "Method:                          IRLS   Log-Likelihood:                -1458.0\n",
            "Date:                Tue, 03 Sep 2019   Deviance:                       172.80\n",
            "Time:                        14:30:12   Pearson chi2:                     253.\n",
            "No. Iterations:                    15                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "===============================================================================================\n",
            "                                  coef    std err          z      P>|z|      [0.025      0.975]\n",
            "-----------------------------------------------------------------------------------------------\n",
            "Intercept                       4.5501      0.395     11.532      0.000       3.777       5.323\n",
            "C(AG_Rating)[T.2.0]             0.5466      0.409      1.337      0.181      -0.255       1.348\n",
            "C(AG_Rating)[T.3.0]             0.7778      0.422      1.845      0.065      -0.049       1.604\n",
            "C(AG_Rating)[T.4.0]             0.7424      0.613      1.212      0.226      -0.458       1.943\n",
            "C(AG_Rating)[T.5.0]             0.8118      0.416      1.951      0.051      -0.004       1.627\n",
            "Q('Change_in_pop')             -0.0194      0.105     -0.183      0.854      -0.226       0.187\n",
            "Q('WAP_not_working')           -0.4048      0.180     -2.254      0.024      -0.757      -0.053\n",
            "Q('HH_Income_Percent_Poor')     0.4931      0.119      4.136      0.000       0.259       0.727\n",
            "Q('Voter_Turnout')             -0.2228      0.083     -2.695      0.007      -0.385      -0.061\n",
            "Mean_Age                        0.0306      0.116      0.264      0.792      -0.196       0.258\n",
            "Q('Small_Households')          -0.4012      0.135     -2.973      0.003      -0.666      -0.137\n",
            "Q('Percentage_White')          -0.2815      0.147     -1.916      0.055      -0.569       0.007\n",
            "Q('Percentage_HHWW')            0.1578      0.132      1.198      0.231      -0.100       0.416\n",
            "Q('Inequality')                 0.0742      0.123      0.604      0.546      -0.167       0.315\n",
            "Q('Percentage_PWM')            -1.1623      0.139     -8.376      0.000      -1.434      -0.890\n",
            "Q('Percentage_NSL')             0.0720      0.098      0.739      0.460      -0.119       0.263\n",
            "Q('Percentage_Male')           -0.0277      0.118     -0.234      0.815      -0.260       0.204\n",
            "Q('Percentage_Informal')        0.1629      0.079      2.059      0.039       0.008       0.318\n",
            "===============================================================================================\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bI1ZpbBjevfy",
        "colab_type": "code",
        "outputId": "9904946b-3878-46d2-aad9-ce5b422cb705",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        }
      },
      "source": [
        "# Exploratory final negative binomial - do not run cell\n",
        "data1 = gc.open_by_url('https://docs.google.com/spreadsheets/d/12xOwkchQqGupMzfgOYJioRnjeDKKanC2oygUCLgJg44/edit#gid=406608549')\n",
        "ws = data1.worksheet('RegAll01')\n",
        "Woernjaps = get_as_dataframe(ws, usecols=[1, 2, 3, 4, 6, 8, 10, 11, 12, 13, 14, 16, 17, 21, 22]) # \n",
        "Woernjaps.dropna(axis=1, how='all' ,inplace=True )\n",
        "Woernjaps.dropna(axis=0, how='all', thresh=None, subset=None, inplace=True)\n",
        "\n",
        "dataset = Woernjaps\n",
        "f = \"\"\" Target1 ~ Q('Change_in_pop') + Q('WAP_not_working') + Q('HH_Income_Percent_Poor') + Q('Voter_Turnout')\t+ Q('Small_Households') + Q('Percentage_Youth') + Q('Percentage_White')+ Q('Percentage_HHWW')\t+ Q('Inequality')\t+ Q('Percentage_PWM') + Q('Percentage_NSL')  + Q('Percentage_Male')\t+ Q('Percentage_Informal')  + C(AG_Rating)  \"\"\"\n",
        "data = dataset\n",
        "\n",
        "response, predictors = dmatrices(f, data, return_type='dataframe')\n",
        "\n",
        "nb_results = sm.GLM(response, predictors, family=sm.families.NegativeBinomial()).fit()\n",
        "print(nb_results.summary())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Change_in_pop</th>\n",
              "      <th>WAP_not_working</th>\n",
              "      <th>HH_Income_Percent_Poor</th>\n",
              "      <th>Voter_Turnout</th>\n",
              "      <th>Small_Households</th>\n",
              "      <th>Percentage_Youth</th>\n",
              "      <th>Percentage_White</th>\n",
              "      <th>Percentage_HHWW</th>\n",
              "      <th>Inequality</th>\n",
              "      <th>Percentage_PWM</th>\n",
              "      <th>Percentage_NSL</th>\n",
              "      <th>Percentage_Male</th>\n",
              "      <th>Percentage_Informal</th>\n",
              "      <th>AG_Rating</th>\n",
              "      <th>Target1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>229</th>\n",
              "      <td>0.32</td>\n",
              "      <td>0.36</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.7246</td>\n",
              "      <td>37.044553</td>\n",
              "      <td>32.417989</td>\n",
              "      <td>5.961538</td>\n",
              "      <td>31.135380</td>\n",
              "      <td>5.205402</td>\n",
              "      <td>86.665856</td>\n",
              "      <td>0.790241</td>\n",
              "      <td>51.579282</td>\n",
              "      <td>39.673050</td>\n",
              "      <td>5.0</td>\n",
              "      <td>174.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>230</th>\n",
              "      <td>0.34</td>\n",
              "      <td>0.34</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.8302</td>\n",
              "      <td>40.339771</td>\n",
              "      <td>37.217716</td>\n",
              "      <td>16.033823</td>\n",
              "      <td>16.776172</td>\n",
              "      <td>14.510426</td>\n",
              "      <td>74.061356</td>\n",
              "      <td>2.840637</td>\n",
              "      <td>51.445271</td>\n",
              "      <td>4.660040</td>\n",
              "      <td>1.0</td>\n",
              "      <td>85.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>231</th>\n",
              "      <td>-0.07</td>\n",
              "      <td>0.67</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.7777</td>\n",
              "      <td>25.146995</td>\n",
              "      <td>32.072400</td>\n",
              "      <td>0.329700</td>\n",
              "      <td>83.246560</td>\n",
              "      <td>4.070981</td>\n",
              "      <td>84.535850</td>\n",
              "      <td>0.793188</td>\n",
              "      <td>46.587342</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>92.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>232</th>\n",
              "      <td>0.02</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.7242</td>\n",
              "      <td>55.381699</td>\n",
              "      <td>41.283517</td>\n",
              "      <td>7.077280</td>\n",
              "      <td>35.074069</td>\n",
              "      <td>9.245362</td>\n",
              "      <td>74.483008</td>\n",
              "      <td>2.882927</td>\n",
              "      <td>54.713335</td>\n",
              "      <td>26.959416</td>\n",
              "      <td>2.0</td>\n",
              "      <td>469.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>233</th>\n",
              "      <td>0.30</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.6618</td>\n",
              "      <td>35.633829</td>\n",
              "      <td>38.070843</td>\n",
              "      <td>7.778822</td>\n",
              "      <td>9.157549</td>\n",
              "      <td>14.835113</td>\n",
              "      <td>81.262356</td>\n",
              "      <td>2.405668</td>\n",
              "      <td>51.363589</td>\n",
              "      <td>4.954978</td>\n",
              "      <td>2.0</td>\n",
              "      <td>172.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Change_in_pop  WAP_not_working  ...  AG_Rating  Target1\n",
              "229           0.32             0.36  ...        5.0    174.0\n",
              "230           0.34             0.34  ...        1.0     85.0\n",
              "231          -0.07             0.67  ...        2.0     92.0\n",
              "232           0.02             0.33  ...        2.0    469.0\n",
              "233           0.30             0.09  ...        2.0    172.0\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RiMc0lD8hhpB",
        "colab_type": "code",
        "outputId": "d69185e7-84b0-49f0-ad98-b0eea1ed1baa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 729
        }
      },
      "source": [
        "#What time is it? It's OLS time. \n",
        "\n",
        "logy = np.log(y)\n",
        "\n",
        "linear_regression = sm.OLS(logy,Xc)\n",
        "fitted_model = linear_regression.fit()\n",
        "\n",
        "#Look at model summary\n",
        "fitted_model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>OLS Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.584</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.557</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   21.93</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>             <td>Tue, 03 Sep 2019</td> <th>  Prob (F-statistic):</th> <td>2.55e-34</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                 <td>14:32:27</td>     <th>  Log-Likelihood:    </th> <td> -301.86</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Observations:</th>      <td>   234</td>      <th>  AIC:               </th> <td>   633.7</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Residuals:</th>          <td>   219</td>      <th>  BIC:               </th> <td>   685.6</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Model:</th>              <td>    14</td>      <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "             <td></td>               <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>const</th>                  <td>    4.4202</td> <td>    0.174</td> <td>   25.369</td> <td> 0.000</td> <td>    4.077</td> <td>    4.764</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Change_in_pop</th>          <td>   -0.0441</td> <td>    0.093</td> <td>   -0.472</td> <td> 0.637</td> <td>   -0.228</td> <td>    0.140</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>WAP_not_working</th>        <td>   -0.4568</td> <td>    0.161</td> <td>   -2.844</td> <td> 0.005</td> <td>   -0.773</td> <td>   -0.140</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>HH_Income_Percent_Poor</th> <td>    0.4593</td> <td>    0.107</td> <td>    4.273</td> <td> 0.000</td> <td>    0.247</td> <td>    0.671</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Voter_Turnout</th>          <td>   -0.2922</td> <td>    0.075</td> <td>   -3.907</td> <td> 0.000</td> <td>   -0.440</td> <td>   -0.145</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Mean_Age</th>               <td>    0.0145</td> <td>    0.104</td> <td>    0.139</td> <td> 0.889</td> <td>   -0.191</td> <td>    0.220</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Small_Households</th>       <td>   -0.3742</td> <td>    0.122</td> <td>   -3.076</td> <td> 0.002</td> <td>   -0.614</td> <td>   -0.134</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Percentage_White</th>       <td>   -0.2810</td> <td>    0.133</td> <td>   -2.116</td> <td> 0.035</td> <td>   -0.543</td> <td>   -0.019</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Percentage_HHWW</th>        <td>    0.1280</td> <td>    0.119</td> <td>    1.076</td> <td> 0.283</td> <td>   -0.106</td> <td>    0.362</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Inequality</th>             <td>   -0.0179</td> <td>    0.111</td> <td>   -0.161</td> <td> 0.872</td> <td>   -0.236</td> <td>    0.200</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Percentage_PWM</th>         <td>   -1.2439</td> <td>    0.125</td> <td>   -9.919</td> <td> 0.000</td> <td>   -1.491</td> <td>   -0.997</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Percentage_NSL</th>         <td>    0.0934</td> <td>    0.087</td> <td>    1.072</td> <td> 0.285</td> <td>   -0.078</td> <td>    0.265</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Percentage_Male</th>        <td>   -0.0535</td> <td>    0.105</td> <td>   -0.508</td> <td> 0.612</td> <td>   -0.261</td> <td>    0.154</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Percentage_Informal</th>    <td>    0.1586</td> <td>    0.071</td> <td>    2.240</td> <td> 0.026</td> <td>    0.019</td> <td>    0.298</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>AG_Rating</th>              <td>    0.1301</td> <td>    0.049</td> <td>    2.640</td> <td> 0.009</td> <td>    0.033</td> <td>    0.227</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Omnibus:</th>       <td> 5.316</td> <th>  Durbin-Watson:     </th> <td>   2.184</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Omnibus):</th> <td> 0.070</td> <th>  Jarque-Bera (JB):  </th> <td>   7.001</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Skew:</th>          <td>-0.119</td> <th>  Prob(JB):          </th> <td>  0.0302</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Kurtosis:</th>      <td> 3.813</td> <th>  Cond. No.          </th> <td>    12.6</td>\n",
              "</tr>\n",
              "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                            OLS Regression Results                            \n",
              "==============================================================================\n",
              "Dep. Variable:                      y   R-squared:                       0.584\n",
              "Model:                            OLS   Adj. R-squared:                  0.557\n",
              "Method:                 Least Squares   F-statistic:                     21.93\n",
              "Date:                Tue, 03 Sep 2019   Prob (F-statistic):           2.55e-34\n",
              "Time:                        14:32:27   Log-Likelihood:                -301.86\n",
              "No. Observations:                 234   AIC:                             633.7\n",
              "Df Residuals:                     219   BIC:                             685.6\n",
              "Df Model:                          14                                         \n",
              "Covariance Type:            nonrobust                                         \n",
              "==========================================================================================\n",
              "                             coef    std err          t      P>|t|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------------------\n",
              "const                      4.4202      0.174     25.369      0.000       4.077       4.764\n",
              "Change_in_pop             -0.0441      0.093     -0.472      0.637      -0.228       0.140\n",
              "WAP_not_working           -0.4568      0.161     -2.844      0.005      -0.773      -0.140\n",
              "HH_Income_Percent_Poor     0.4593      0.107      4.273      0.000       0.247       0.671\n",
              "Voter_Turnout             -0.2922      0.075     -3.907      0.000      -0.440      -0.145\n",
              "Mean_Age                   0.0145      0.104      0.139      0.889      -0.191       0.220\n",
              "Small_Households          -0.3742      0.122     -3.076      0.002      -0.614      -0.134\n",
              "Percentage_White          -0.2810      0.133     -2.116      0.035      -0.543      -0.019\n",
              "Percentage_HHWW            0.1280      0.119      1.076      0.283      -0.106       0.362\n",
              "Inequality                -0.0179      0.111     -0.161      0.872      -0.236       0.200\n",
              "Percentage_PWM            -1.2439      0.125     -9.919      0.000      -1.491      -0.997\n",
              "Percentage_NSL             0.0934      0.087      1.072      0.285      -0.078       0.265\n",
              "Percentage_Male           -0.0535      0.105     -0.508      0.612      -0.261       0.154\n",
              "Percentage_Informal        0.1586      0.071      2.240      0.026       0.019       0.298\n",
              "AG_Rating                  0.1301      0.049      2.640      0.009       0.033       0.227\n",
              "==============================================================================\n",
              "Omnibus:                        5.316   Durbin-Watson:                   2.184\n",
              "Prob(Omnibus):                  0.070   Jarque-Bera (JB):                7.001\n",
              "Skew:                          -0.119   Prob(JB):                       0.0302\n",
              "Kurtosis:                       3.813   Cond. No.                         12.6\n",
              "==============================================================================\n",
              "\n",
              "Warnings:\n",
              "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
              "\"\"\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O7yq0flSnsMX",
        "colab_type": "code",
        "outputId": "573b0e2a-e006-4547-f4e5-0870d95a812f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        }
      },
      "source": [
        "# \n",
        "data1 = gc.open_by_url('https://docs.google.com/spreadsheets/d/12xOwkchQqGupMzfgOYJioRnjeDKKanC2oygUCLgJg44/edit#gid=406608549')\n",
        "ws = data1.worksheet('RegAll01')\n",
        "Woernjaps = get_as_dataframe(ws, usecols=[1, 2, 3, 4, 5, 6,  10, 11, 12, 13, 14, 16, 17, 21, 27]) # \n",
        "Woernjaps.dropna(axis=1, how='all' ,inplace=True )\n",
        "Woernjaps.dropna(axis=0, how='all', thresh=None, subset=None, inplace=True)\n",
        "Woernjaps.tail()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Change_in_pop</th>\n",
              "      <th>WAP_not_working</th>\n",
              "      <th>HH_Income_Percent_Poor</th>\n",
              "      <th>Voter_Turnout</th>\n",
              "      <th>Mean_Age</th>\n",
              "      <th>Small_Households</th>\n",
              "      <th>Percentage_White</th>\n",
              "      <th>Percentage_HHWW</th>\n",
              "      <th>Inequality</th>\n",
              "      <th>Percentage_PWM</th>\n",
              "      <th>Percentage_NSL</th>\n",
              "      <th>Percentage_Male</th>\n",
              "      <th>Percentage_Informal</th>\n",
              "      <th>AG_Rating</th>\n",
              "      <th>Target2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>229</th>\n",
              "      <td>0.32</td>\n",
              "      <td>0.36</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.7246</td>\n",
              "      <td>27.495502</td>\n",
              "      <td>37.044553</td>\n",
              "      <td>5.961538</td>\n",
              "      <td>31.135380</td>\n",
              "      <td>5.205402</td>\n",
              "      <td>86.665856</td>\n",
              "      <td>0.790241</td>\n",
              "      <td>51.579282</td>\n",
              "      <td>39.673050</td>\n",
              "      <td>5.0</td>\n",
              "      <td>30.686207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>230</th>\n",
              "      <td>0.34</td>\n",
              "      <td>0.34</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.8302</td>\n",
              "      <td>28.323922</td>\n",
              "      <td>40.339771</td>\n",
              "      <td>16.033823</td>\n",
              "      <td>16.776172</td>\n",
              "      <td>14.510426</td>\n",
              "      <td>74.061356</td>\n",
              "      <td>2.840637</td>\n",
              "      <td>51.445271</td>\n",
              "      <td>4.660040</td>\n",
              "      <td>1.0</td>\n",
              "      <td>11.265291</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>231</th>\n",
              "      <td>-0.07</td>\n",
              "      <td>0.67</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.7777</td>\n",
              "      <td>25.945665</td>\n",
              "      <td>25.146995</td>\n",
              "      <td>0.329700</td>\n",
              "      <td>83.246560</td>\n",
              "      <td>4.070981</td>\n",
              "      <td>84.535850</td>\n",
              "      <td>0.793188</td>\n",
              "      <td>46.587342</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>11.885844</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>232</th>\n",
              "      <td>0.02</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.7242</td>\n",
              "      <td>28.155124</td>\n",
              "      <td>55.381699</td>\n",
              "      <td>7.077280</td>\n",
              "      <td>35.074069</td>\n",
              "      <td>9.245362</td>\n",
              "      <td>74.483008</td>\n",
              "      <td>2.882927</td>\n",
              "      <td>54.713335</td>\n",
              "      <td>26.959416</td>\n",
              "      <td>2.0</td>\n",
              "      <td>41.961921</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>233</th>\n",
              "      <td>0.30</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.6618</td>\n",
              "      <td>29.123616</td>\n",
              "      <td>35.633829</td>\n",
              "      <td>7.778822</td>\n",
              "      <td>9.157549</td>\n",
              "      <td>14.835113</td>\n",
              "      <td>81.262356</td>\n",
              "      <td>2.405668</td>\n",
              "      <td>51.363589</td>\n",
              "      <td>4.954978</td>\n",
              "      <td>2.0</td>\n",
              "      <td>14.834748</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Change_in_pop  WAP_not_working  ...  AG_Rating    Target2\n",
              "229           0.32             0.36  ...        5.0  30.686207\n",
              "230           0.34             0.34  ...        1.0  11.265291\n",
              "231          -0.07             0.67  ...        2.0  11.885844\n",
              "232           0.02             0.33  ...        2.0  41.961921\n",
              "233           0.30             0.09  ...        2.0  14.834748\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dI8U1bdZc0md",
        "colab_type": "code",
        "outputId": "f4a24d9a-be9f-4103-b41a-d900ea4086c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        }
      },
      "source": [
        "#Scaling\n",
        "dataset = Woernjaps\n",
        "scaler = StandardScaler()\n",
        "dataset[['Change_in_pop', 'WAP_not_working', 'HH_Income_Percent_Poor', 'Voter_Turnout', 'Mean_Age' , 'Small_Households',  'Percentage_White', 'Percentage_HHWW','Inequality','Percentage_PWM', 'Percentage_NSL', \t'Percentage_Male', 'Percentage_Informal']] = scaler.fit_transform(dataset[['Change_in_pop', 'WAP_not_working', 'HH_Income_Percent_Poor', 'Voter_Turnout', 'Mean_Age' , 'Small_Households', 'Percentage_White', 'Percentage_HHWW','Inequality','Percentage_PWM', 'Percentage_NSL',\t'Percentage_Male', 'Percentage_Informal']]) \n",
        "# removed 'Log(PopSize)' from above\n",
        "dataset.tail()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Change_in_pop</th>\n",
              "      <th>WAP_not_working</th>\n",
              "      <th>HH_Income_Percent_Poor</th>\n",
              "      <th>Voter_Turnout</th>\n",
              "      <th>Mean_Age</th>\n",
              "      <th>Small_Households</th>\n",
              "      <th>Percentage_White</th>\n",
              "      <th>Percentage_HHWW</th>\n",
              "      <th>Inequality</th>\n",
              "      <th>Percentage_PWM</th>\n",
              "      <th>Percentage_NSL</th>\n",
              "      <th>Percentage_Male</th>\n",
              "      <th>Percentage_Informal</th>\n",
              "      <th>AG_Rating</th>\n",
              "      <th>Target2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>229</th>\n",
              "      <td>1.258081</td>\n",
              "      <td>-0.357200</td>\n",
              "      <td>0.206926</td>\n",
              "      <td>-0.766396</td>\n",
              "      <td>-0.112416</td>\n",
              "      <td>-0.023175</td>\n",
              "      <td>-0.166506</td>\n",
              "      <td>-0.066856</td>\n",
              "      <td>-0.606806</td>\n",
              "      <td>1.099063</td>\n",
              "      <td>-0.943279</td>\n",
              "      <td>1.366845</td>\n",
              "      <td>7.160743</td>\n",
              "      <td>5.0</td>\n",
              "      <td>30.686207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>230</th>\n",
              "      <td>1.379430</td>\n",
              "      <td>-0.495700</td>\n",
              "      <td>0.206926</td>\n",
              "      <td>1.558935</td>\n",
              "      <td>0.236041</td>\n",
              "      <td>0.402763</td>\n",
              "      <td>1.365827</td>\n",
              "      <td>-0.592777</td>\n",
              "      <td>0.119152</td>\n",
              "      <td>-0.583656</td>\n",
              "      <td>0.072208</td>\n",
              "      <td>1.307731</td>\n",
              "      <td>0.271921</td>\n",
              "      <td>1.0</td>\n",
              "      <td>11.265291</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>231</th>\n",
              "      <td>-1.108211</td>\n",
              "      <td>1.789549</td>\n",
              "      <td>0.445451</td>\n",
              "      <td>0.402875</td>\n",
              "      <td>-0.764321</td>\n",
              "      <td>-1.561044</td>\n",
              "      <td>-1.023298</td>\n",
              "      <td>1.841769</td>\n",
              "      <td>-0.695311</td>\n",
              "      <td>0.814704</td>\n",
              "      <td>-0.941820</td>\n",
              "      <td>-0.835153</td>\n",
              "      <td>-0.644943</td>\n",
              "      <td>2.0</td>\n",
              "      <td>11.885844</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>232</th>\n",
              "      <td>-0.562144</td>\n",
              "      <td>-0.564950</td>\n",
              "      <td>2.115127</td>\n",
              "      <td>-0.775204</td>\n",
              "      <td>0.165040</td>\n",
              "      <td>2.347071</td>\n",
              "      <td>0.003235</td>\n",
              "      <td>0.077402</td>\n",
              "      <td>-0.291617</td>\n",
              "      <td>-0.527365</td>\n",
              "      <td>0.093152</td>\n",
              "      <td>2.749309</td>\n",
              "      <td>4.659331</td>\n",
              "      <td>2.0</td>\n",
              "      <td>41.961921</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>233</th>\n",
              "      <td>1.136733</td>\n",
              "      <td>-2.226949</td>\n",
              "      <td>-2.416851</td>\n",
              "      <td>-2.149264</td>\n",
              "      <td>0.572415</td>\n",
              "      <td>-0.205524</td>\n",
              "      <td>0.109963</td>\n",
              "      <td>-0.871817</td>\n",
              "      <td>0.144483</td>\n",
              "      <td>0.377688</td>\n",
              "      <td>-0.143217</td>\n",
              "      <td>1.271700</td>\n",
              "      <td>0.329951</td>\n",
              "      <td>2.0</td>\n",
              "      <td>14.834748</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Change_in_pop  WAP_not_working  ...  AG_Rating    Target2\n",
              "229       1.258081        -0.357200  ...        5.0  30.686207\n",
              "230       1.379430        -0.495700  ...        1.0  11.265291\n",
              "231      -1.108211         1.789549  ...        2.0  11.885844\n",
              "232      -0.562144        -0.564950  ...        2.0  41.961921\n",
              "233       1.136733        -2.226949  ...        2.0  14.834748\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UEptXSs1c0xX",
        "colab_type": "code",
        "outputId": "a26b47f3-d397-40fb-cd0e-83a5e3c9ba15",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "#place data into new formats, including splitting off the target from the variables (DV from IVs)\n",
        "\n",
        "observations = len(dataset)\n",
        "variables = dataset.columns[:-1]\n",
        "X = dataset.iloc[:, :-1] #replaced with .ix with .iloc\n",
        "y = dataset['Target2'].values\n",
        "#dataset.dtypes\n",
        "\n",
        "#add constant term (for OLS)\n",
        "Xc = sm.add_constant(X)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/numpy/core/fromnumeric.py:2389: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
            "  return ptp(axis=axis, out=out, **kwargs)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7C60ftTLgY0r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Xc = pd.concat((Xc,pd.get_dummies(Xc['AG_Rating'], drop_first=True)), axis=1)\n",
        "Xc.drop(['AG_Rating'], axis=1, inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O9hxzMK-c1X-",
        "colab_type": "code",
        "outputId": "6d517e4b-e93b-4e6f-b60a-7557739fa762",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 794
        }
      },
      "source": [
        "#Fit model for a quick look (using OLS for now)\n",
        "linear_regression = sm.OLS(y,Xc)\n",
        "fitted_model = linear_regression.fit()\n",
        "\n",
        "#Look at model summary\n",
        "fitted_model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>OLS Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.314</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.260</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   5.804</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>             <td>Tue, 03 Sep 2019</td> <th>  Prob (F-statistic):</th> <td>7.48e-11</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                 <td>19:09:09</td>     <th>  Log-Likelihood:    </th> <td> -885.43</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Observations:</th>      <td>   234</td>      <th>  AIC:               </th> <td>   1807.</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Residuals:</th>          <td>   216</td>      <th>  BIC:               </th> <td>   1869.</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Model:</th>              <td>    17</td>      <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "             <td></td>               <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>const</th>                  <td>   10.6053</td> <td>    4.344</td> <td>    2.441</td> <td> 0.015</td> <td>    2.043</td> <td>   19.167</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Change_in_pop</th>          <td>    2.5472</td> <td>    1.164</td> <td>    2.188</td> <td> 0.030</td> <td>    0.253</td> <td>    4.842</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>WAP_not_working</th>        <td>   -0.0751</td> <td>    1.980</td> <td>   -0.038</td> <td> 0.970</td> <td>   -3.978</td> <td>    3.828</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>HH_Income_Percent_Poor</th> <td>    1.6814</td> <td>    1.315</td> <td>    1.279</td> <td> 0.202</td> <td>   -0.910</td> <td>    4.273</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Voter_Turnout</th>          <td>   -1.0980</td> <td>    0.912</td> <td>   -1.204</td> <td> 0.230</td> <td>   -2.896</td> <td>    0.700</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Mean_Age</th>               <td>    1.8685</td> <td>    1.277</td> <td>    1.463</td> <td> 0.145</td> <td>   -0.649</td> <td>    4.386</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Small_Households</th>       <td>   -1.2256</td> <td>    1.488</td> <td>   -0.823</td> <td> 0.411</td> <td>   -4.159</td> <td>    1.708</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Percentage_White</th>       <td>    0.7327</td> <td>    1.623</td> <td>    0.451</td> <td> 0.652</td> <td>   -2.466</td> <td>    3.931</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Percentage_HHWW</th>        <td>   -3.7857</td> <td>    1.453</td> <td>   -2.605</td> <td> 0.010</td> <td>   -6.650</td> <td>   -0.922</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Inequality</th>             <td>   -2.5105</td> <td>    1.358</td> <td>   -1.848</td> <td> 0.066</td> <td>   -5.187</td> <td>    0.166</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Percentage_PWM</th>         <td>   -2.0830</td> <td>    1.532</td> <td>   -1.360</td> <td> 0.175</td> <td>   -5.103</td> <td>    0.937</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Percentage_NSL</th>         <td>    0.0848</td> <td>    1.076</td> <td>    0.079</td> <td> 0.937</td> <td>   -2.036</td> <td>    2.206</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Percentage_Male</th>        <td>    1.0935</td> <td>    1.307</td> <td>    0.837</td> <td> 0.404</td> <td>   -1.482</td> <td>    3.669</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Percentage_Informal</th>    <td>    1.3493</td> <td>    0.874</td> <td>    1.544</td> <td> 0.124</td> <td>   -0.373</td> <td>    3.072</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>2.0</th>                    <td>    5.5819</td> <td>    4.501</td> <td>    1.240</td> <td> 0.216</td> <td>   -3.289</td> <td>   14.452</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>3.0</th>                    <td>    5.8049</td> <td>    4.643</td> <td>    1.250</td> <td> 0.213</td> <td>   -3.347</td> <td>   14.957</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>4.0</th>                    <td>    8.3474</td> <td>    6.760</td> <td>    1.235</td> <td> 0.218</td> <td>   -4.977</td> <td>   21.671</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>5.0</th>                    <td>    8.0879</td> <td>    4.582</td> <td>    1.765</td> <td> 0.079</td> <td>   -0.943</td> <td>   17.119</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Omnibus:</th>       <td>70.247</td> <th>  Durbin-Watson:     </th> <td>   2.066</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 157.380</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Skew:</th>          <td> 1.423</td> <th>  Prob(JB):          </th> <td>6.69e-35</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Kurtosis:</th>      <td> 5.835</td> <th>  Cond. No.          </th> <td>    36.6</td>\n",
              "</tr>\n",
              "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                            OLS Regression Results                            \n",
              "==============================================================================\n",
              "Dep. Variable:                      y   R-squared:                       0.314\n",
              "Model:                            OLS   Adj. R-squared:                  0.260\n",
              "Method:                 Least Squares   F-statistic:                     5.804\n",
              "Date:                Tue, 03 Sep 2019   Prob (F-statistic):           7.48e-11\n",
              "Time:                        19:09:09   Log-Likelihood:                -885.43\n",
              "No. Observations:                 234   AIC:                             1807.\n",
              "Df Residuals:                     216   BIC:                             1869.\n",
              "Df Model:                          17                                         \n",
              "Covariance Type:            nonrobust                                         \n",
              "==========================================================================================\n",
              "                             coef    std err          t      P>|t|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------------------\n",
              "const                     10.6053      4.344      2.441      0.015       2.043      19.167\n",
              "Change_in_pop              2.5472      1.164      2.188      0.030       0.253       4.842\n",
              "WAP_not_working           -0.0751      1.980     -0.038      0.970      -3.978       3.828\n",
              "HH_Income_Percent_Poor     1.6814      1.315      1.279      0.202      -0.910       4.273\n",
              "Voter_Turnout             -1.0980      0.912     -1.204      0.230      -2.896       0.700\n",
              "Mean_Age                   1.8685      1.277      1.463      0.145      -0.649       4.386\n",
              "Small_Households          -1.2256      1.488     -0.823      0.411      -4.159       1.708\n",
              "Percentage_White           0.7327      1.623      0.451      0.652      -2.466       3.931\n",
              "Percentage_HHWW           -3.7857      1.453     -2.605      0.010      -6.650      -0.922\n",
              "Inequality                -2.5105      1.358     -1.848      0.066      -5.187       0.166\n",
              "Percentage_PWM            -2.0830      1.532     -1.360      0.175      -5.103       0.937\n",
              "Percentage_NSL             0.0848      1.076      0.079      0.937      -2.036       2.206\n",
              "Percentage_Male            1.0935      1.307      0.837      0.404      -1.482       3.669\n",
              "Percentage_Informal        1.3493      0.874      1.544      0.124      -0.373       3.072\n",
              "2.0                        5.5819      4.501      1.240      0.216      -3.289      14.452\n",
              "3.0                        5.8049      4.643      1.250      0.213      -3.347      14.957\n",
              "4.0                        8.3474      6.760      1.235      0.218      -4.977      21.671\n",
              "5.0                        8.0879      4.582      1.765      0.079      -0.943      17.119\n",
              "==============================================================================\n",
              "Omnibus:                       70.247   Durbin-Watson:                   2.066\n",
              "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              157.380\n",
              "Skew:                           1.423   Prob(JB):                     6.69e-35\n",
              "Kurtosis:                       5.835   Cond. No.                         36.6\n",
              "==============================================================================\n",
              "\n",
              "Warnings:\n",
              "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
              "\"\"\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6vtI5CglknWw",
        "colab_type": "code",
        "outputId": "1b74c3b2-9e23-41d5-b557-0c88e0ad5d6e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        }
      },
      "source": [
        "#Labour / Cap\n",
        "data1 = gc.open_by_url('https://docs.google.com/spreadsheets/d/12xOwkchQqGupMzfgOYJioRnjeDKKanC2oygUCLgJg44/edit#gid=406608549')\n",
        "ws = data1.worksheet('RegAll01')\n",
        "Woernjaps = get_as_dataframe(ws, usecols=[1, 2, 3, 4, 5, 6,  10, 11, 12, 13, 14, 16, 17, 21, 40]) # \n",
        "Woernjaps.dropna(axis=1, how='all' ,inplace=True )\n",
        "Woernjaps.dropna(axis=0, how='all', thresh=None, subset=None, inplace=True)\n",
        "Woernjaps.tail()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Change_in_pop</th>\n",
              "      <th>WAP_not_working</th>\n",
              "      <th>HH_Income_Percent_Poor</th>\n",
              "      <th>Voter_Turnout</th>\n",
              "      <th>Mean_Age</th>\n",
              "      <th>Small_Households</th>\n",
              "      <th>Percentage_White</th>\n",
              "      <th>Percentage_HHWW</th>\n",
              "      <th>Inequality</th>\n",
              "      <th>Percentage_PWM</th>\n",
              "      <th>Percentage_NSL</th>\n",
              "      <th>Percentage_Male</th>\n",
              "      <th>Percentage_Informal</th>\n",
              "      <th>AG_Rating</th>\n",
              "      <th>Target15</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>229</th>\n",
              "      <td>0.32</td>\n",
              "      <td>0.36</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.7246</td>\n",
              "      <td>27.495502</td>\n",
              "      <td>37.044553</td>\n",
              "      <td>5.961538</td>\n",
              "      <td>31.135380</td>\n",
              "      <td>5.205402</td>\n",
              "      <td>86.665856</td>\n",
              "      <td>0.790241</td>\n",
              "      <td>51.579282</td>\n",
              "      <td>39.673050</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.292648</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>230</th>\n",
              "      <td>0.34</td>\n",
              "      <td>0.34</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.8302</td>\n",
              "      <td>28.323922</td>\n",
              "      <td>40.339771</td>\n",
              "      <td>16.033823</td>\n",
              "      <td>16.776172</td>\n",
              "      <td>14.510426</td>\n",
              "      <td>74.061356</td>\n",
              "      <td>2.840637</td>\n",
              "      <td>51.445271</td>\n",
              "      <td>4.660040</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.060263</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>231</th>\n",
              "      <td>-0.07</td>\n",
              "      <td>0.67</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.7777</td>\n",
              "      <td>25.945665</td>\n",
              "      <td>25.146995</td>\n",
              "      <td>0.329700</td>\n",
              "      <td>83.246560</td>\n",
              "      <td>4.070981</td>\n",
              "      <td>84.535850</td>\n",
              "      <td>0.793188</td>\n",
              "      <td>46.587342</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.129194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>232</th>\n",
              "      <td>0.02</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.7242</td>\n",
              "      <td>28.155124</td>\n",
              "      <td>55.381699</td>\n",
              "      <td>7.077280</td>\n",
              "      <td>35.074069</td>\n",
              "      <td>9.245362</td>\n",
              "      <td>74.483008</td>\n",
              "      <td>2.882927</td>\n",
              "      <td>54.713335</td>\n",
              "      <td>26.959416</td>\n",
              "      <td>2.0</td>\n",
              "      <td>13.957483</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>233</th>\n",
              "      <td>0.30</td>\n",
              "      <td>0.09</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.6618</td>\n",
              "      <td>29.123616</td>\n",
              "      <td>35.633829</td>\n",
              "      <td>7.778822</td>\n",
              "      <td>9.157549</td>\n",
              "      <td>14.835113</td>\n",
              "      <td>81.262356</td>\n",
              "      <td>2.405668</td>\n",
              "      <td>51.363589</td>\n",
              "      <td>4.954978</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.034982</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Change_in_pop  WAP_not_working  ...  AG_Rating   Target15\n",
              "229           0.32             0.36  ...        5.0   2.292648\n",
              "230           0.34             0.34  ...        1.0   1.060263\n",
              "231          -0.07             0.67  ...        2.0   0.129194\n",
              "232           0.02             0.33  ...        2.0  13.957483\n",
              "233           0.30             0.09  ...        2.0   1.034982\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7c6SvUsabzQ4",
        "colab_type": "code",
        "outputId": "4d2f3cfb-a4a9-47a8-dd14-3e3b1147982c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "from sklearn import linear_model\n",
        "lm = LinearRegression()\n",
        "lm.fit(Xc,y)\n",
        "y_score = lm.predict(Xc)\n",
        "\n",
        "def hist_resids(y, y_score):\n",
        "    ## first compute vector of residuals. \n",
        "    resids = np.subtract(y.reshape(-1,1), y_score.reshape(-1,1))\n",
        "    ## now make the residual plots\n",
        "    sns.distplot(resids)\n",
        "    plt.title('Histogram of residuals')\n",
        "    plt.xlabel('Residual value')\n",
        "    plt.ylabel('count')\n",
        "    \n",
        "hist_resids(y, y_score)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEWCAYAAAB8LwAVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j8Ohc1tecGRu",
        "colab_type": "code",
        "outputId": "9b6fd72d-e52a-4001-97ac-5e5e759397f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "def resid_qq(y, y_score):\n",
        "    ## first compute vector of residuals. \n",
        "    resids = np.subtract(y.reshape(-1,1), y_score.reshape(-1,1))\n",
        "    ## now make the residual plots\n",
        "    ss.probplot(resids.flatten(), plot = plt)\n",
        "    plt.title('Residuals vs. predicted values')\n",
        "    plt.xlabel('Predicted values')\n",
        "    plt.ylabel('Residual')\n",
        "    \n",
        "resid_qq(y, y_score)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D8lDLE6RcHYZ",
        "colab_type": "code",
        "outputId": "2e5f8690-66c8-49dc-8c4c-042f22dafc9e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "def resid_plot(y, y_score):\n",
        "    ## first compute vector of residuals. \n",
        "    resids = np.subtract(y.reshape(-1,1), y_score.reshape(-1,1))\n",
        "    ## now make the residual plots\n",
        "    sns.regplot(y_score, resids, fit_reg=False )\n",
        "    plt.title('Residuals vs. predicted values')\n",
        "    plt.xlabel('Predicted values')\n",
        "    plt.ylabel('Residuals')\n",
        "\n",
        "resid_plot(y, y_score)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAEWCAYAAABv+EDhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAMMwjB7FFIBhGEaPYgrAMAyjR/l/ZwWevWqOVb4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j7tVVjl_8xwl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Towards the two heterokedastic tests\n",
        "#Step1\n",
        "# copied for article at  https://medium.com/@remycanario17/tests-for-heteroskedasticity-in-python-208a0fdb04ab\n",
        "from statsmodels.stats.diagnostic import het_breuschpagan\n",
        "from statsmodels.stats.diagnostic import het_white\n",
        "import pandas as pd\n",
        "import statsmodels.api as sm\n",
        "from statsmodels.formula.api import ols"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dC7lQW1q9BHH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Step2\n",
        "f = \"\"\"Q('2NatLog') ~ Q('Change_in_pop')+Q('WAP_not_working')+Q('Percentage_White')+Q('Voter_Turnout')+Q('Percentage_Youth')+ Q('Inequality')+Q('Percentage_PWM')+Q('Percentage_NSL')+Q('Percentage_Informal')+C(COGTA_Score)+C(AG_Rating)\"\"\"\n",
        "\n",
        "model = ols(formula=f, data=dataset).fit()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P2mDHky99GHC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Step3\n",
        "white_test = het_white(model.resid,  model.model.exog)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "InzK5I5r9LYN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Step4\n",
        "bp_test = het_breuschpagan(model.resid, dataset[['Change_in_pop','WAP_not_working','Voter_Turnout','Percentage_Youth','Percentage_White','Percentage_PWM','Percentage_NSL','Percentage_Informal'] ])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BiYaQG2L9Lbd",
        "colab_type": "code",
        "outputId": "013c92e2-5eee-4b08-e93c-5e84eb4d6f13",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "#Step5\n",
        "labels = [\"LM_Statistic\", \"LM-Test_p-value\", \"F-Statistic\", \"F-Test_p-value\"]\n",
        "print(dict(zip(labels, bp_test)))\n",
        "print(dict(zip(labels, white_test)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'LM_Statistic': 12.087567182517427, 'LM-Test_p-value': 0.09771569975393998, 'F-Statistic': 1.538777113885147, 'F-Test_p-value': 0.14487307562499113}\n",
            "{'LM_Statistic': 83.89584195489125, 'LM-Test_p-value': 0.9966247717977315, 'F-Statistic': 0.5085233064582946, 'F-Test_p-value': 0.9998558969245661}\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}