testPipeline.ipynb

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        "<a href=\"https://colab.research.google.com/github/AndromathArcanitus/Sciences/blob/master/testPipeline.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## Packages Loading"
      ],
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        "collapsed": false,
        "id": "6cc4cb4e04daadb5"
      },
      "id": "6cc4cb4e04daadb5"
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          "start_time": "2023-09-18T15:58:31.681736400Z"
        },
        "id": "initial_id"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import sklearn\n",
        "import matplotlib.pyplot as plt\n",
        "import pickle"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Loading"
      ],
      "metadata": {
        "collapsed": false,
        "id": "6e40d6444e3af9d"
      },
      "id": "6e40d6444e3af9d"
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    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [],
      "source": [
        "#df = pd.read_csv('./data/primary_clinical_radiomic_features/complete_clinical_radiomic_features_20220510.csv')\n",
        "df = pd.read_csv('./complete_clinical_radiomic_features_20220510.csv')"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-09-18T20:57:53.139218100Z",
          "start_time": "2023-09-18T20:57:53.107595600Z"
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      },
      "id": "318cdd1bcddc5b2e"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Exploration"
      ],
      "metadata": {
        "collapsed": false,
        "id": "24fbb289d0b41ab3"
      },
      "id": "24fbb289d0b41ab3"
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      "cell_type": "code",
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(175, 655)"
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          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "df.shape"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-09-18T20:57:53.680992800Z",
          "start_time": "2023-09-18T20:57:53.671628900Z"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        MRN  Primary_Recurrence  hpv_total  Clinical N Stage  \\\n",
              "0  94608596                 0.0          1                 1   \n",
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              "   Clinical T Stage  Concurrent Chemo Regimen_cat_4  Treatment Laterality  \\\n",
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              "4                 3                               0                     2   \n",
              "\n",
              "   pack_year_strat  tx_duration_strat  Disease Site_cat_0  ...  \\\n",
              "0                2                  0                   0  ...   \n",
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              "   original_glszm_SmallAreaHighGrayLevelEmphasis-ct_diff  \\\n",
              "0                                           0.263881       \n",
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              "3                                           0.447048       \n",
              "4                                          -0.103750       \n",
              "\n",
              "   original_glszm_SmallAreaLowGrayLevelEmphasis-ct_diff  \\\n",
              "0                                          -0.821938      \n",
              "1                                          -1.211781      \n",
              "2                                          -1.841186      \n",
              "3                                          -0.907776      \n",
              "4                                           0.381798      \n",
              "\n",
              "   original_glszm_ZoneEntropy-ct_diff  original_glszm_ZonePercentage-ct_diff  \\\n",
              "0                            0.009144                              -0.127575   \n",
              "1                            0.015916                              -0.133305   \n",
              "2                            0.042348                              -0.494975   \n",
              "3                            0.064297                              -0.135727   \n",
              "4                           -0.003017                               0.474527   \n",
              "\n",
              "   original_glszm_ZoneVariance-ct_diff  original_ngtdm_Busyness-ct_diff  \\\n",
              "0                             0.164118                        -0.397751   \n",
              "1                             0.191036                         0.070698   \n",
              "2                             0.349844                        -0.694662   \n",
              "3                            -0.275819                        -0.488918   \n",
              "4                            -2.022937                         0.252527   \n",
              "\n",
              "   original_ngtdm_Coarseness-ct_diff  original_ngtdm_Complexity-ct_diff  \\\n",
              "0                           0.109260                           0.221093   \n",
              "1                          -0.335800                           0.121717   \n",
              "2                           0.123868                           0.336382   \n",
              "3                           0.131763                           0.447150   \n",
              "4                          -0.174755                           0.366038   \n",
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              "   original_ngtdm_Contrast-ct_diff  original_ngtdm_Strength-ct_diff  \n",
              "0                        -1.721071                         0.585230  \n",
              "1                        -4.346959                        -0.011635  \n",
              "2                       -10.307440                         0.603565  \n",
              "3                        -0.953653                         0.384193  \n",
              "4                         0.708984                        -1.060524  \n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9e49d3fc-6fd9-471b-91ba-42226bda9af2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "df.head()"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-09-18T20:57:55.699883900Z",
          "start_time": "2023-09-18T20:57:55.692810600Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "1880601150a98401",
        "outputId": "f2af1bd1-91bc-4474-d772-41d0e6227bc2"
      },
      "id": "1880601150a98401"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [],
      "source": [
        "df['Primary_Recurrence'] = df['Primary_Recurrence'].astype(int)"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-09-18T17:39:38.328439800Z",
          "start_time": "2023-09-18T17:39:38.304808300Z"
        },
        "id": "f0d77cacac32f373"
      },
      "id": "f0d77cacac32f373"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "float64    643\n",
              "int64       12\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "df.dtypes.value_counts()"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-09-18T17:39:38.511315500Z",
          "start_time": "2023-09-18T17:39:38.505226Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a3496c88c1e8f7e7",
        "outputId": "f6554288-72fb-4da1-f4bd-2e036d99df59"
      },
      "id": "a3496c88c1e8f7e7"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Check for missing values"
      ],
      "metadata": {
        "collapsed": false,
        "id": "f509c3c2934fb17"
      },
      "id": "f509c3c2934fb17"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total missing values in the data set:  0\n",
            "No further actions for missing data needed.\n"
          ]
        }
      ],
      "source": [
        "print('Total missing values in the data set: ', sum(df.isnull().sum()))\n",
        "print('No further actions for missing data needed.')"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-09-18T16:29:14.906162900Z",
          "start_time": "2023-09-18T16:29:14.889928700Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2686338b54573168",
        "outputId": "eca16eeb-3de0-4993-a913-34d085521da6"
      },
      "id": "2686338b54573168"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Plot labels to see if the data set is unbalanced"
      ],
      "metadata": {
        "collapsed": false,
        "id": "ff649afda8152ffb"
      },
      "id": "ff649afda8152ffb"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "Primary_Recurrence_count = df['Primary_Recurrence'].value_counts()\n",
        "\n",
        "plt.bar(Primary_Recurrence_count.index, Primary_Recurrence_count.values)\n",
        "plt.xlabel('Primary_Recurrence')\n",
        "plt.ylabel('Count')\n",
        "plt.title('Bar Plot of Primary_Recurrence')\n",
        "plt.xticks(list(map(int, Primary_Recurrence_count.index)))\n",
        "plt.show()"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-09-18T17:39:53.140240900Z",
          "start_time": "2023-09-18T17:39:53.085366Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "f4f29b3ec4225f85",
        "outputId": "582ede61-9a5a-4fed-f570-e4492c929f99"
      },
      "id": "f4f29b3ec4225f85"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Transformation\n",
        "#### Encode categorical variables"
      ],
      "metadata": {
        "collapsed": false,
        "id": "47935d2803725974"
      },
      "id": "47935d2803725974"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [],
      "source": [
        "category_var_as_int = ['hpv_total', 'Clinical N Stage', 'Clinical T Stage', 'Treatment Laterality', 'pack_year_strat', 'tx_duration_strat']\n",
        "\n",
        "for column in category_var_as_int:\n",
        "    df = pd.get_dummies(df, columns=[column], prefix=[column])"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-09-18T21:24:16.423033700Z",
          "start_time": "2023-09-18T21:24:16.380298Z"
        },
        "id": "b5f7247079b8871a"
      },
      "id": "b5f7247079b8871a"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Scale numerical features"
      ],
      "metadata": {
        "collapsed": false,
        "id": "626743249fc8f8c5"
      },
      "id": "626743249fc8f8c5"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "numerical_features = df.select_dtypes(include = ['float64'])\n",
        "\n",
        "scaler = MinMaxScaler()\n",
        "normalized_features = scaler.fit_transform(numerical_features)\n",
        "df[numerical_features.columns] = normalized_features"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-09-18T21:00:26.132724100Z",
          "start_time": "2023-09-18T21:00:26.101392200Z"
        },
        "id": "1bfb5bd296ee0169"
      },
      "id": "1bfb5bd296ee0169"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Distinguish predictors and outcomes"
      ],
      "metadata": {
        "collapsed": false,
        "id": "c6202012b2c1e87d"
      },
      "id": "c6202012b2c1e87d"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [],
      "source": [
        "X = df.drop(columns = ['MRN', 'Primary_Recurrence'])\n",
        "y = df['Primary_Recurrence']"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-09-18T21:00:27.184826500Z",
          "start_time": "2023-09-18T21:00:27.175265600Z"
        },
        "id": "2d8d18644f7317bc"
      },
      "id": "2d8d18644f7317bc"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Splitting"
      ],
      "metadata": {
        "collapsed": false,
        "id": "1a8a347e8d536b5e"
      },
      "id": "1a8a347e8d536b5e"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "random_state = np.random.randint(1000)\n",
        "# Split the data in a 60-20-20 fashion\n",
        "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size = 0.4, random_state = random_state)\n",
        "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size = 0.5, random_state = random_state)"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-09-18T21:36:37.731256800Z",
          "start_time": "2023-09-18T21:36:37.724840400Z"
        },
        "id": "cc58f9a841e20533"
      },
      "id": "cc58f9a841e20533"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Feature Selection"
      ],
      "metadata": {
        "collapsed": false,
        "id": "2173a92673c1de84"
      },
      "id": "2173a92673c1de84"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [],
      "source": [
        "num_features_to_select = 30"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-09-18T21:27:06.589172700Z",
          "start_time": "2023-09-18T21:27:06.583419Z"
        },
        "id": "d7d723af1a6a81c9"
      },
      "id": "d7d723af1a6a81c9"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 1. using RFE"
      ],
      "metadata": {
        "collapsed": false,
        "id": "351eaf81a40e60ef"
      },
      "id": "351eaf81a40e60ef"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [],
      "source": [
        "from sklearn.feature_selection import RFE\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "# Temporarily using the Logistic regression model\n",
        "REF_LR_model = LogisticRegression(max_iter = 1000)\n",
        "\n",
        "rfe_selector = RFE(estimator = REF_LR_model, n_features_to_select = num_features_to_select)\n",
        "\n",
        "rfe_selector.fit(X, y)\n",
        "\n",
        "selected_features1 = rfe_selector.support_"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-09-18T21:27:17.072177600Z",
          "start_time": "2023-09-18T21:27:06.966387700Z"
        },
        "id": "b1c08611dac2bfd"
      },
      "id": "b1c08611dac2bfd"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Selected Features:\n",
            "Index(['original_shape_Maximum2DDiameterRow-pet_postrt',\n",
            "       'original_shape_MinorAxisLength-pet_postrt',\n",
            "       'original_gldm_DependenceNonUniformity-pet_postrt',\n",
            "       'original_glrlm_ShortRunEmphasis-pet_postrt',\n",
            "       'original_glszm_GrayLevelNonUniformity-pet_postrt',\n",
            "       'original_shape_MajorAxisLength-pet_prert',\n",
            "       'original_shape_Maximum2DDiameterSlice-pet_prert',\n",
            "       'original_firstorder_Kurtosis-pet_prert',\n",
            "       'original_shape_Maximum2DDiameterColumn-ct_postrt',\n",
            "       'original_shape_Maximum2DDiameterRow-ct_postrt',\n",
            "       'original_shape_MinorAxisLength-ct_postrt',\n",
            "       'original_firstorder_10Percentile-ct_postrt',\n",
            "       'original_firstorder_Mean-ct_postrt',\n",
            "       'original_gldm_LowGrayLevelEmphasis-ct_postrt',\n",
            "       'original_gldm_SmallDependenceLowGrayLevelEmphasis-ct_postrt',\n",
            "       'original_glrlm_ShortRunLowGrayLevelEmphasis-ct_postrt',\n",
            "       'original_glszm_HighGrayLevelZoneEmphasis-ct_postrt',\n",
            "       'original_glszm_LargeAreaHighGrayLevelEmphasis-ct_postrt',\n",
            "       'original_glszm_ZoneEntropy-ct_postrt',\n",
            "       'original_shape_MajorAxisLength-ct_prert',\n",
            "       'original_firstorder_10Percentile-ct_prert',\n",
            "       'original_glszm_HighGrayLevelZoneEmphasis-ct_prert',\n",
            "       'original_glszm_SmallAreaHighGrayLevelEmphasis-ct_prert',\n",
            "       'original_glcm_Idmn-pet_diff', 'original_glcm_Idn-pet_diff',\n",
            "       'original_glcm_Correlation-ct_diff',\n",
            "       'original_glszm_SmallAreaLowGrayLevelEmphasis-ct_diff', 'hpv_total_1',\n",
            "       'Clinical N Stage_2', 'Clinical T Stage_3'],\n",
            "      dtype='object')\n"
          ]
        }
      ],
      "source": [
        "print(\"Selected Features:\")\n",
        "selected_features1_index = X.columns[selected_features1 == True]\n",
        "print(selected_features1_index)"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-09-18T21:27:17.072682400Z",
          "start_time": "2023-09-18T21:27:17.072682400Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4a6770e657c13f48",
        "outputId": "23f9be2d-5d00-4ef2-e7b2-4e68b7d3540d"
      },
      "id": "4a6770e657c13f48"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2. using LASSO"
      ],
      "metadata": {
        "collapsed": false,
        "id": "9e5dbb8fe7284974"
      },
      "id": "9e5dbb8fe7284974"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "i =  161\n"
          ]
        }
      ],
      "source": [
        "from sklearn.linear_model import Lasso\n",
        "\n",
        "alphas = np.logspace(4, -4, 200)\n",
        "\n",
        "# Use a loop to find a model that keeps the desired number of predictors\n",
        "num_of_predictors = 0\n",
        "i = 0\n",
        "\n",
        "while num_of_predictors <= num_features_to_select:\n",
        "    # create a Lasso model for feature selection\n",
        "    lasso_selector = Lasso(alpha = alphas[i], max_iter = 10000)\n",
        "\n",
        "    lasso_selector.fit(X, y)\n",
        "\n",
        "    selected_features2 = lasso_selector.coef_ != 0\n",
        "\n",
        "    num_of_predictors = sum(selected_features2)\n",
        "    i += 1\n",
        "\n",
        "print('i = ', i)"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-09-18T21:27:17.342773100Z",
          "start_time": "2023-09-18T21:27:17.072682400Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d1dc1d2e85d73943",
        "outputId": "89cd6a0c-4aab-4424-f62c-92bb8f926880"
      },
      "id": "d1dc1d2e85d73943"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Selected Features:\n",
            "Index(['Age at Diagnosis', 'original_shape_MinorAxisLength-pet_postrt',\n",
            "       'original_firstorder_Minimum-pet_postrt',\n",
            "       'original_gldm_DependenceNonUniformity-pet_postrt',\n",
            "       'original_glszm_GrayLevelNonUniformity-pet_postrt',\n",
            "       'original_shape_Maximum2DDiameterSlice-pet_prert',\n",
            "       'original_shape_SurfaceArea-pet_prert',\n",
            "       'original_firstorder_Kurtosis-pet_prert',\n",
            "       'original_shape_Maximum2DDiameterRow-ct_postrt',\n",
            "       'original_shape_MinorAxisLength-ct_postrt',\n",
            "       'original_firstorder_10Percentile-ct_postrt',\n",
            "       'original_firstorder_90Percentile-ct_postrt',\n",
            "       'original_gldm_LargeDependenceLowGrayLevelEmphasis-ct_postrt',\n",
            "       'original_gldm_SmallDependenceLowGrayLevelEmphasis-ct_postrt',\n",
            "       'original_glszm_HighGrayLevelZoneEmphasis-ct_postrt',\n",
            "       'original_glszm_LargeAreaHighGrayLevelEmphasis-ct_postrt',\n",
            "       'original_glszm_SizeZoneNonUniformityNormalized-ct_postrt',\n",
            "       'original_shape_SurfaceArea-ct_prert',\n",
            "       'original_firstorder_10Percentile-ct_prert',\n",
            "       'original_gldm_GrayLevelNonUniformity-ct_prert',\n",
            "       'original_glszm_HighGrayLevelZoneEmphasis-ct_prert',\n",
            "       'original_firstorder_Kurtosis-pet_diff', 'original_glcm_Idn-pet_diff',\n",
            "       'original_glcm_Correlation-ct_diff',\n",
            "       'original_gldm_DependenceNonUniformityNormalized-ct_diff',\n",
            "       'original_glszm_SmallAreaLowGrayLevelEmphasis-ct_diff', 'hpv_total_1',\n",
            "       'Clinical N Stage_0', 'Clinical N Stage_1', 'Clinical N Stage_2',\n",
            "       'Clinical T Stage_2', 'Clinical T Stage_3', 'Clinical T Stage_4',\n",
            "       'pack_year_strat_0'],\n",
            "      dtype='object')\n"
          ]
        }
      ],
      "source": [
        "print(\"Selected Features:\")\n",
        "selected_features2_index = X.columns[selected_features2 == True]\n",
        "print(selected_features2_index)"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-09-18T21:27:17.343348100Z",
          "start_time": "2023-09-18T21:27:17.339248200Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c255d0be72655165",
        "outputId": "0f6b6821-1d02-467b-ea58-2662991e9e67"
      },
      "id": "c255d0be72655165"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 3. using univariate LR + Pearson coefficient"
      ],
      "metadata": {
        "collapsed": false,
        "id": "fb2bae2cdce5f277"
      },
      "id": "fb2bae2cdce5f277"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [],
      "source": [
        "# Calculate Pearson correlation coefficient\n",
        "correlation_coefficient = X.corrwith(y)\n",
        "\n",
        "# Perform univariate logistic regression\n",
        "logistic_regression_p_values = []\n",
        "for feature_name in X.columns:\n",
        "    X_feature = X[feature_name].values.reshape(-1, 1)\n",
        "\n",
        "    # Fit logistic regression model\n",
        "    lr_model = LogisticRegression()\n",
        "    lr_model.fit(X_feature, y)\n",
        "\n",
        "    # Get the p-value from logistic regression\n",
        "    p_value = lr_model.coef_[0][0]\n",
        "    logistic_regression_p_values.append(p_value)\n",
        "\n",
        "# Combine the results into a DataFrame\n",
        "selected_features3 = pd.DataFrame({\n",
        "    'Pearson_Coefficient': correlation_coefficient,\n",
        "    'Logistic_Regression_P_Value': logistic_regression_p_values\n",
        "})\n",
        "\n",
        "# Select features based on p-value and Pearson_Coefficient\n",
        "selected_features3 = selected_features3[selected_features3['Logistic_Regression_P_Value'] <= 0.05]\n",
        "\n",
        "# Use a loop to find a list of features of the desired length\n",
        "# Starting from absolute correlation of 0.1\n",
        "num_features_kept = 0\n",
        "corr = 0.51\n",
        "while num_features_kept < num_features_to_select:\n",
        "    corr -= 0.01\n",
        "    num_features_kept = (abs(selected_features3['Pearson_Coefficient']) >= corr).sum()"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-09-18T21:27:17.904546500Z",
          "start_time": "2023-09-18T21:27:17.343348100Z"
        },
        "id": "836c5543af0e95d6"
      },
      "id": "836c5543af0e95d6"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Selected Features:\n",
            "Index(['original_shape_Sphericity-pet_postrt',\n",
            "       'original_shape_SurfaceVolumeRatio-pet_postrt',\n",
            "       'original_firstorder_Minimum-pet_postrt',\n",
            "       'original_shape_Sphericity-pet_prert',\n",
            "       'original_shape_SurfaceVolumeRatio-pet_prert',\n",
            "       'original_shape_Sphericity-ct_postrt',\n",
            "       'original_shape_SurfaceVolumeRatio-ct_postrt',\n",
            "       'original_firstorder_RobustMeanAbsoluteDeviation-ct_postrt',\n",
            "       'original_glcm_Contrast-ct_postrt',\n",
            "       'original_glcm_DifferenceVariance-ct_postrt',\n",
            "       'original_gldm_LowGrayLevelEmphasis-ct_postrt',\n",
            "       'original_gldm_SmallDependenceLowGrayLevelEmphasis-ct_postrt',\n",
            "       'original_glrlm_LowGrayLevelRunEmphasis-ct_postrt',\n",
            "       'original_glrlm_ShortRunLowGrayLevelEmphasis-ct_postrt',\n",
            "       'original_glszm_LowGrayLevelZoneEmphasis-ct_postrt',\n",
            "       'original_glszm_SmallAreaLowGrayLevelEmphasis-ct_postrt',\n",
            "       'original_ngtdm_Coarseness-ct_postrt',\n",
            "       'original_ngtdm_Contrast-ct_postrt',\n",
            "       'original_shape_Sphericity-ct_prert',\n",
            "       'original_shape_SurfaceVolumeRatio-ct_prert',\n",
            "       'original_gldm_SmallDependenceLowGrayLevelEmphasis-ct_prert',\n",
            "       'original_glrlm_RunPercentage-ct_prert',\n",
            "       'original_glszm_SmallAreaLowGrayLevelEmphasis-ct_prert',\n",
            "       'original_ngtdm_Coarseness-ct_prert',\n",
            "       'original_glrlm_LongRunHighGrayLevelEmphasis-pet_diff',\n",
            "       'original_glszm_GrayLevelNonUniformity-pet_diff',\n",
            "       'original_glszm_SmallAreaEmphasis-pet_diff',\n",
            "       'original_glszm_SmallAreaHighGrayLevelEmphasis-pet_diff',\n",
            "       'original_glszm_SmallAreaLowGrayLevelEmphasis-pet_diff',\n",
            "       'original_glszm_ZoneEntropy-pet_diff',\n",
            "       'original_glszm_ZoneVariance-pet_diff',\n",
            "       'original_firstorder_Skewness-ct_diff', 'hpv_total_1',\n",
            "       'Clinical T Stage_1'],\n",
            "      dtype='object')\n"
          ]
        }
      ],
      "source": [
        "selected_features3 = selected_features3[abs(selected_features3['Pearson_Coefficient']) >= corr]\n",
        "# Print selected features\n",
        "print(\"Selected Features:\")\n",
        "print(selected_features3.index)"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-09-18T21:27:17.907905900Z",
          "start_time": "2023-09-18T21:27:17.904546500Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8607e60b5feeefbe",
        "outputId": "601d34d2-6947-4fe7-b37c-4956415ff24d"
      },
      "id": "8607e60b5feeefbe"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Combining the Results\n",
        "\n",
        "#### Find the intersection"
      ],
      "metadata": {
        "collapsed": false,
        "id": "883ca28968c48dc3"
      },
      "id": "883ca28968c48dc3"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [],
      "source": [
        "features_to_consider = selected_features1_index.intersection(selected_features2_index.intersection(selected_features3.index))"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-09-18T21:27:17.914294500Z",
          "start_time": "2023-09-18T21:27:17.907905900Z"
        },
        "id": "291727692bfd490c"
      },
      "id": "291727692bfd490c"
    },
    {
      "cell_type": "code",
      "source": [
        "features_to_consider"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aVLJEpfxquiw",
        "outputId": "1fe22414-4331-4f1f-eb01-61999797cd83"
      },
      "id": "aVLJEpfxquiw",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['original_gldm_SmallDependenceLowGrayLevelEmphasis-ct_postrt', 'hpv_total_1'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Building"
      ],
      "metadata": {
        "collapsed": false,
        "id": "1bfe891da799716"
      },
      "id": "1bfe891da799716"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [],
      "source": [
        "from sklearn.metrics import precision_recall_curve, auc, roc_curve, roc_auc_score"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-09-18T21:28:00.911136200Z",
          "start_time": "2023-09-18T21:28:00.910632800Z"
        },
        "id": "defbf942cf32fae3"
      },
      "id": "defbf942cf32fae3"
    },
    {
      "cell_type": "code",
      "source": [
        "features_to_consider = [selected_features1_index, selected_features2_index, selected_features3.index]"
      ],
      "metadata": {
        "id": "vDCvo4gPuRpG"
      },
      "id": "vDCvo4gPuRpG",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define a list to store the split data\n",
        "split_data = []\n",
        "random_state = np.random.randint(1000)\n",
        "\n",
        "# Perform the splits for different random states\n",
        "for i in range(len(features_to_consider)):\n",
        "    X_new = X[features_to_consider[i]]\n",
        "\n",
        "    # Split the data in a 60-20-20 fashion\n",
        "    X_train, X_temp, y_train, y_temp = train_test_split(X_new, y, test_size = 0.4, random_state = random_state)\n",
        "    X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size = 0.5, random_state = random_state)\n",
        "\n",
        "    # Store the split data in a tuple or a dictionary with a label (e.g., 'train' and 'test')\n",
        "    split = {\n",
        "        'X_train': X_train,\n",
        "        'y_train': y_train,\n",
        "        'X_validation': X_val,\n",
        "        'y_validation': y_val,\n",
        "        'X_test': X_test,\n",
        "        'y_test': y_test\n",
        "    }\n",
        "\n",
        "    # Append the split data to the list\n",
        "    split_data.append(split)"
      ],
      "metadata": {
        "id": "fDRy6pE7s9so"
      },
      "id": "fDRy6pE7s9so",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random State 1:\n",
            "Train Set:\n",
            "(105, 30)\n",
            "Random State 2:\n",
            "Train Set:\n",
            "(105, 34)\n",
            "Random State 3:\n",
            "Train Set:\n",
            "(105, 34)\n"
          ]
        }
      ],
      "source": [
        "# Access the split data for each random state\n",
        "for i, split in enumerate(split_data):\n",
        "    print(f\"Random State {i + 1}:\")\n",
        "    print(\"Train Set:\")\n",
        "    print(split['X_train'].shape)"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-09-18T21:39:05.402811100Z",
          "start_time": "2023-09-18T21:39:05.386677700Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "45406df4ae620d56",
        "outputId": "f5964f05-bed8-49ce-973b-adcd03c51f6a"
      },
      "id": "45406df4ae620d56"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Logistic Regression"
      ],
      "metadata": {
        "collapsed": false,
        "id": "bc1d6beea0afd958"
      },
      "id": "bc1d6beea0afd958"
    },
    {
      "cell_type": "code",
      "source": [
        "# Define a dictionary to store the scores\n",
        "dict_scores = []"
      ],
      "metadata": {
        "id": "gt9eUN-s2xlq"
      },
      "id": "gt9eUN-s2xlq",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Use subplot to show the Precision-Recall curve and ROC-AUC in three cases\n",
        "fig, axs = plt.subplots(2, 3, figsize=(10, 7))\n",
        "\n",
        "for i, split in enumerate(split_data):\n",
        "    lr_model = LogisticRegression(max_iter = 1000)\n",
        "    lr_model.fit(split['X_train'], split['y_train'])\n",
        "\n",
        "    y_pred_prob = lr_model.predict_proba(split['X_test'])[:, 1]\n",
        "\n",
        "    # Calculate and display the Precision-Recall curve and PRAUC\n",
        "    precision, recall, _ = precision_recall_curve(split['y_test'], y_pred_prob)\n",
        "    prauc = auc(recall, precision)\n",
        "\n",
        "    axs[0, i].plot(recall, precision, marker='.')\n",
        "    axs[0, i].set_xlabel('Recall')\n",
        "    axs[0, i].set_ylabel('Precision')\n",
        "    axs[0, i].set_title('Precision-Recall Curve (PRAUC = {:.2f})'.format(prauc))\n",
        "    axs[0, i].grid(True)\n",
        "\n",
        "    # Calculate and display the ROC curve and AUC\n",
        "    fpr, tpr, _ = roc_curve(split['y_test'], y_pred_prob)\n",
        "    roc_auc = roc_auc_score(split['y_test'], y_pred_prob)\n",
        "\n",
        "    axs[1, i].plot(fpr, tpr, marker='.')\n",
        "    axs[1, i].set_xlabel('False Positive Rate')\n",
        "    axs[1, i].set_ylabel('True Positive Rate')\n",
        "    axs[1, i].set_title('ROC Curve (AUC = {:.2f})'.format(roc_auc))\n",
        "    axs[1, i].grid(True)\n",
        "\n",
        "    dict_scores.append({\"PRAUC\" : prauc,\"AUC\" : roc_auc})\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-09-18T21:39:06.250612900Z",
          "start_time": "2023-09-18T21:39:06.107546100Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 707
        },
        "id": "b25a4a44a3e7009f",
        "outputId": "c3d4e90e-9674-44ae-97c2-46351f1183b5"
      },
      "id": "b25a4a44a3e7009f"
    },
    {
      "cell_type": "code",
      "source": [
        "# Show scores in the three cases\n",
        "dict_scores1 = pd.DataFrame(dict_scores)\n",
        "row_indices = ['From RFE', 'From LASSO', 'From Uni_LR + Corr']\n",
        "dict_scores1.index = row_indices\n",
        "dict_scores1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "NXGzvuyE6qCd",
        "outputId": "c0af7cb8-fc8f-4948-9cab-7acdbe98b033"
      },
      "id": "NXGzvuyE6qCd",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       PRAUC       AUC\n",
              "From RFE            0.642262  0.943548\n",
              "From LASSO          0.395238  0.919355\n",
              "From Uni_LR + Corr  0.230426  0.814516"
            ],
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              "      <th>PRAUC</th>\n",
              "      <th>AUC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>From RFE</th>\n",
              "      <td>0.642262</td>\n",
              "      <td>0.943548</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>From LASSO</th>\n",
              "      <td>0.395238</td>\n",
              "      <td>0.919355</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>From Uni_LR + Corr</th>\n",
              "      <td>0.230426</td>\n",
              "      <td>0.814516</td>\n",
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              "        const element = document.querySelector('#df-826f0c03-c37a-4afd-be41-f54cc4e408f1');\n",
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              "                                                    [key], {});\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    }\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "        document.querySelector('#df-995b9353-3c55-4761-8613-5b11c70d0cf4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### SVM"
      ],
      "metadata": {
        "collapsed": false,
        "id": "d62407498f51c692"
      },
      "id": "d62407498f51c692"
    },
    {
      "cell_type": "code",
      "source": [
        "dict_scores = []"
      ],
      "metadata": {
        "id": "PtL2nlJjAONS"
      },
      "id": "PtL2nlJjAONS",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 6 Axes>"
            ],
            "image/png": 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dQ3Z2NgICAsy+bnp6OoAbl6eUXR5TVZMnT8bSpUsxdOhQBAcHY/DgwRg9evRtg/rChQsAyv/iAECrVq3KjU5Zdn/SzXx8fEzu/8rIyDC5n6pevXom93k8+eSTePDBB1FUVITNmzfj66+/Lnf/1blz53D06NFy71Xm5u9Vw4YN4evrW+E2AsDOnTsxY8YM7N69GwUFBSaP1UU4e3p6mgRfXWvSpEm5ZXfddRe8vLywZMkSYzgvWbIEkZGRaNGiBQAgLi4OQgi89dZbeOutt8y+dnp6eoXhXEZUcL+Ni4sL/v77bwCGkGnSpEmF9x+tWLECnp6eyMjIwNdff42EhIRy9z1eu3YNa9euxdSpUxEXF2dc3qtXL6xYsQJnz541blt1eXp6AkCtf07mRhatibJtN3cZVlFRUaX3hH755ZfIzMws9+GpLpT9vG/3YbAizEfm462Yj8zH6lJqPgLMSGZkecxIZmR11TYjb5afn4+7774bubm52LFjR5XGSxg7dixefPFFbNy4sVyDX5OMtIkGv2vXruXuebiVs7NzucDW6/UICAiocLCHioKoqgICAhAbG4v169fjv//+w3///YcFCxZg/PjxZqenqAlzUyLcqkuXLsbQBwxHW28eDKR58+bGHfzuu++Gg4MDXnvtNfTv39/4fdXr9Rg0aFC5o0hlqvMLGh8fjzvvvBOtWrXCF198gZCQEDg5OWHt2rX48ssvqz1NjDmtWrVCbGwsSkpKajV9TEUDzZj7RXV2djbeAzVv3jykpaVh586d+Oijj4zrlG3bSy+9hCFDhph97bKjnOaU3QNlbgAfwLA/VDWs+vTpY7yvZ8SIEYiIiMC4ceNw8OBB4+/KsmXLUFxcjFmzZmHWrFnlXmPRokXGD2xlZwvKjmLeqqCgwOSMQtnANceOHUNkZGSVajanqvdxeXl53TZgg4KCAAApKSkICQkxeSwlJQVdu3at8LnZ2dn44IMPMHnyZOTk5BiP1Obl5UEIgcTERLi5uVX4QbAyZT/viu7Duh3m4+0xH5mP5jAfTSk1HwFmZGWYkcxIc5iRpmqTkTcrKSnBqFGjcPToUaxfv75aBwZDQkJw5cqVcstrkpE20eDXVLNmzbBx40b06tXrtj+0snkDjx8/fttfHHOcnJwwYsQIjBgxAnq9HpMnT8Z3332Ht956y+xrNW7cGABw5swZ40iuZc6cOWN8vDoWLVpk8gvTtGnT267/xhtv4IcffsCbb75pHACmWbNmyMvLq/QXv1mzZli/fj2uXLlS4RHYv//+G8XFxVizZg1CQ0ONy7ds2VLVTarUiBEjsHv3bpOBPW7Hx8en3OAzJSUlSElJqdb7jhkzBr/88gs2bdqEU6dOQQhhvLQKuPG912g0NTpqGBoaCldXV+PovnWlXr16mDFjBiZOnIilS5fioYceAmDYd9q1a2f2crnvvvsOf/zxhzGcb953zbl1/x06dCgcHBzw+++/12qQlLJQrcyCBQtMRrO9VdkfiAMHDpgE8eXLl3Hp0iU8+eSTFT736tWryMvLw6effopPP/203ONNmjTBvffei9WrV1ep1lslJCRArVbX+Eh3TTAfzWM+GjAfmY+A/eYjwIysCDPSgBnJjASqlpFl9Ho9xo8fj02bNmHp0qXo27dvlWoDYDxY2rFjx3KP1SQjbXIU/aoaPXo0dDod3n///XKPlZaWGn9ZBw8eDA8PD8ycORNFRUUm61V0mQtw416XMmq1Gu3btwdg/hIOwDD6YkBAAObPn2+yzn///YdTp05h+PDhVdq2m/Xq1QsDBw40/qssnL29vfHUU09h/fr1iI2NBWD4Xu3evRvr168vt/61a9dQWloKwHAfiBDC7GV4Zd+rsiPGN3/vsrOzsWDBgmpvW0WefvppBAUF4cUXX8TZs2fLPZ6eno4PPvjA+HWzZs2wbds2k3W+//77ak8VM3DgQPj6+mLJkiVYsmQJunbtanIpVkBAAPr164fvvvvObPBnZGTc9vU1Gg06d+5sMl1MXRk3bhwaNWqETz75BABw8eJFbNu2DaNHj8YDDzxQ7t/EiRMRFxeHvXv3AjCEZGRkJH7//fdyf+gOHjyIPXv2YOjQocZlISEhmDRpEjZs2IA5c+aUq0ev12PWrFm4dOnSbeuOjo6u0r+KjnaXadu2LVq1alXu5/7tt99CpVKZ3E+YnZ2N06dPIzs7G4Dh57pq1apy//r37w8XFxesWrUK06dPv+37387BgwfRtm3bWl92WB3MR/OYjwbMR+YjYL/5CDAjK8KMNGBGMiOBqmVkmWeffRZLlizBvHnzMGrUqArfy9zP+dtvv0VGRobZW3hqkpGyPoPft29fPPXUU5g5cyZiY2MxePBgaDQanDt3DsuWLcNXX32FBx54AJ6envjyyy/xxBNPoEuXLhg7dix8fHxw5MgRFBQUVHip1BNPPIErV65gwIABaNSoES5cuIA5c+YgMjISrVu3NvscjUaDTz75BBMnTkTfvn3x8MMPIy0tDV999RXCwsLwwgsvWPJbYvT8889j9uzZ+Pjjj7F48WK8/PLLWLNmDe6++2489thjiIqKQn5+Po4dO4bly5cjMTERfn5+6N+/Px599FF8/fXXOHfuHO666y7o9Xps374d/fv3x9SpUzF48GDjUemnnnoKeXl5+OGHHxAQEFDto50V8fHxwapVqzBs2DBERkbikUceQVRUFADg0KFD+PPPP9GjRw/j+k888QSefvpp3H///Rg0aBCOHDmC9evXV/uSP41Gg1GjRmHx4sXIz8/H559/Xm6duXPn4o477kBERAQmTZqEpk2bIi0tDbt378alS5dw5MiR277HvffeizfeeAM5OTnGe5DqgkajwfPPP4+XX34Z69atw5EjRyCEwD333GN2/WHDhsHR0RGLFi1Ct27dAABffPEFhgwZgsjISDz22GNo2LAhTp06he+//x5BQUHlPsTNmjUL8fHxeO6557By5Urcfffd8PHxQVJSEpYtW4bTp08bjwRXpK7unwKAzz77DPfccw8GDx6Mhx56CMePH8c333yDJ554wuR3dtWqVZg4caLxiK6bmxtGjhxZ7vVWr16Nffv2lXvswoULxkFryv7Qln1YaNy4scnRaK1Wa5wz15qYjxVjPjIfmY/2nY8AM/J2mJHMSGZk1TMSAGbPno158+ahR48ecHNzw++//27y2vfddx/c3d0BGHJwzJgxiIiIgIuLC3bs2IHFixcjMjISTz31lMnzapyRVR5v3wLKppvYv3//bderbCqW77//XkRFRQlXV1fh4eEhIiIixCuvvCIuX75sst6aNWtEz549haurq/D09BRdu3YVf/75p8n73DzFyfLly8XgwYNFQECAcHJyEqGhoeKpp54SKSkpxnVuneKkzJIlS0THjh2Fs7Oz8PX1FePGjTNO2VLZdpVNYVGZsulCPvvsM7OPP/bYY8LBwUHExcUJIYTIzc0V06dPF+Hh4cLJyUn4+fmJnj17is8//1yUlJQYn1daWio+++wz0apVK+Hk5CT8/f3F0KFDxcGDB02+l+3btxcuLi4iLCxMfPLJJ+Lnn38WAERCQoJxvZpOcVLm8uXL4oUXXhAtWrQQLi4uws3NTURFRYkPP/zQZCoJnU4nXn31VeHn5yfc3NzEkCFDRFxcXIVTnNxun4uOjhYAhEqlEhcvXjS7Tnx8vBg/frxo0KCB0Gg0Ijg4WNx9991i+fLllW5TWlqacHR0FL/99pvJ8qpMOSTEjf0jIyOj3GPZ2dnCy8tL9O3bV0RERIjQ0NDbvla/fv1EQECA0Gq1xmV79uwRd999t/Dx8RGOjo4iODhYPPHEE+X23zKlpaXixx9/FL179xZeXl5Co9GIxo0bi4kTJ9Z6+pOaWLVqlYiMjBTOzs6iUaNG4s033zTZv4W4sR9Uth9W9DMp+7039+/m/V0IIf777z+TKWaqivnIfKwM87E85uPtKSUfb66TGWmKGXkDM7I8ZuTt1TQjy6atrOjfzfv1E088Idq0aSM8PDyERqMR4eHh4tVXXxU5OTnl6qlpRqqEuM31RURkEY8//jjOnj2L7du3S10KWdjIkSOhUqmwatUqqUshkgXmo/1gPhJVHzPSftQ0I9ngE0kgKSkJLVq0wKZNm9CrVy+pyyELOXXqFCIiIhAbG1vtKZaI7BXz0T4wH4lqhhlpH2qTkWzwiYiIiIiIiBRA1qPoExEREREREZEBG3wiIiIiIiIiBWCDT0RERERERKQAbPCJiIiIiIiIFMBR6gKsTa/X4/Lly/Dw8IBKpZK6HCKyMiEEcnNz0bBhQ6jVPMZ5K2YkkX1jRlaM+Uhk3+SSj3bX4F++fBkhISFSl0FEErt48SIaNWokdRk2hxlJRAAz0hzmIxEBtp+Pdtfge3h4ADD8YDw9PStdX6vVYsOGDRg8eDA0Go2ly6tTcq1drnUDrF0q1ak9JycHISEhxiwgU9XJSHvZZ2yNXGuXa92AfdXOjKyYvXyGlGvdAGuXilxrV2o+2l2DX3ZJlaenZ5XD2c3NDZ6enrLaYQH51i7XugHWLpWa1M7LK82rTkba2z5jK+Rau1zrBuyzdmZkefbyGVKudQOsXSpyrV2p+Wi7Nw8QERERERERUZWxwSciIiIiIiJSADb4RERERERERArABp+IiIiIiIhIAdjgExERERERESmApA3+tm3bMGLECDRs2BAqlQqrV6+u9DkxMTHo1KkTnJ2dER4ejoULF1q8TiIia2M+EhFVjBlJRGSepA1+fn4+OnTogLlz51Zp/YSEBAwfPhz9+/dHbGws/ve//+GJJ57A+vXrLVZjSnYRzmWrkJJdZLH3ICK6FfORiKhizEgiIvMcpXzzoUOHYujQoVVef/78+WjSpAlmzZoFAGjdujV27NiBL7/8EkOGDKnz+pbsT8L0lcegFw6Yd2obZo6KwJguoXX+PkREt2I+EhFVjBlJRGSepA1+de3evRsDBw40WTZkyBD873//q/A5xcXFKC4uNn6dk5MDANBqtdBqtRU+LyW76HowG77WC2D6ymPo0cQHQV4uNd8IKyrbvtttpy2Sa90Aa5dKdWqX4/ZVRU3yEahZRjIfpSXX2uVaN2BftctxG6vC2p8hX1txDNcjUnYZaU/7uy1h7dan1HyUVYOfmpqKwMBAk2WBgYHIyclBYWEhXF1dyz1n5syZePfdd8st37BhA9zc3Cp8r3PZKuiFg8kyvQCWrt2C5l6igmfZpujoaKlLqBG51g2wdqlUpfaCggIrVGJ9NclHoGYZyXy0DXKtXa51A/ZROzPyhtp8hhSQf0baw/5ui1i79SktH2XV4NfE9OnTMW3aNOPXOTk5CAkJweDBg+Hp6Vnh81KyizDv1DbjGSoAUKuA0cP6y+LoK2A4yhQdHY1BgwZBo9FIXU6VybVugLVLpTq1l52BIYOaZCTzUVpyrV2udQP2VTsz8obafIace3Ibbm7l5ZSR9rS/2xLWbn1KzUdZNfgNGjRAWlqaybK0tDR4enpWeHbK2dkZzs7O5ZZrNJrb/iBD/TR4Y1hrvP/vKQCGYJ45KgKhfh612AJpVLattkqudQOsXSpVqV2u21aZmuQjULOMDPXTYOaoCLy64hgAQMV8lIRca5dr3YB91C7X7auMtT9D3hvZEKtjLwOQ72dIe9jfbRFrtz6l5aOko+hXV48ePbBp0yaTZdHR0ejRo4dF3m9kx2Dj/2Om9ebgKERks6ydj2O6hCIyxAsA8M7wVsxHIrJp1s7IqMY+AIBmHnrEvNiHGUlEViNpg5+Xl4fY2FjExsYCMExhEhsbi6SkJACGS6PGjx9vXP/pp5/G+fPn8corr+D06dOYN28eli5dihdeeMHitTaQwSVVRKQccshHZ0fDnxBvNyeLvQcRkTlyyEgA8NBAFpflE5FySNrgHzhwAB07dkTHjh0BANOmTUPHjh3x9ttvAwBSUlKMQQ0ATZo0wb///ovo6Gh06NABs2bNwo8//miR6U2IiKTEfCQiqhgzkojIPEnvwe/Xrx+EqHg00YULF5p9zuHDhy1YFRGR9JiPREQVY0YSEZknq3vwiYiIiIiIiMg8NvhERERERERECsAGn4iIiIiIiEgB2OATERERERERKQAbfCIiIiIiIiIFYINPREREREREpABs8ImIiIiIiIgUgA0+ERERERERkQKwwSciIiIiIiJSADb4RERERERERArABp+IiIiIiIhIAdjgExERERERESkAG3wiIiIiIiIiBWCDT0RERERERKQAbPCJiIiIiIiIFIANPhEREREREZECsMEnIiIiIiIiUgA2+EREREREREQKwAafiIiIiIiISAHY4BMREREREREpABt8IiIiIiIiIgVgg09ERERERESkAGzwiYiIiIiIiBSADT4RERERERGRArDBJyIiIiIiIlIANvhERERERERECsAGn4iIiIiIiEgB2OATERERERERKQAbfCIiIiIiIiIFYINPREREREREpABs8ImIiIiIiIgUgA0+ERERERERkQKwwSciIiIiIiJSADb4RERERERERArABp+IiIiIiIhIAdjgExERERERESmA5A3+3LlzERYWBhcXF3Tr1g379u277fqzZ89Gy5Yt4erqipCQELzwwgsoKiqyUrVERNbFjCQiMo/5SERUnqQN/pIlSzBt2jTMmDEDhw4dQocOHTBkyBCkp6ebXf+PP/7Aa6+9hhkzZuDUqVP46aefsGTJErz++utWrpyIyPKYkURE5jEfiYjMk7TB/+KLLzBp0iRMnDgRbdq0wfz58+Hm5oaff/7Z7Pq7du1Cr169MHbsWISFhWHw4MF4+OGHKz1iS0QkR8xIIiLzmI9EROZJ1uCXlJTg4MGDGDhw4I1i1GoMHDgQu3fvNvucnj174uDBg8YwPn/+PNauXYthw4ZZpWYiImthRhIRmcd8JCKqmKNUb5yZmQmdTofAwECT5YGBgTh9+rTZ54wdOxaZmZm44447IIRAaWkpnn766dteXlVcXIzi4mLj1zk5OQAArVYLrVZ72xq1paU3/q/VQqVSVbpdtqRs+yrbTlsj17oB1i6V6tQul+2TQ0YKvQAAlOp0svm+lrGX/d2WyLVuwL5ql8M2yiEfdTqd8f9y+J7ezJ72d1vC2q1PifkISNjg10RMTAw++ugjzJs3D926dUNcXByef/55vP/++3jrrbfMPmfmzJl49913yy3fsGED3Nzcbvt+eVqg7FsUHb0RMuvvjaKjo6UuoUbkWjfA2qVSldoLCgqsUIk0rJ2RV66qAahx7OhROF4+UhebYHVK399tkVzrBuyjdqVmpLXz8XiqCoADAPnuN3KtG2DtUpFr7UrLR5UQQkjxxiUlJXBzc8Py5csxcuRI4/IJEybg2rVr+Ouvv8o9p3fv3ujevTs+++wz47Lff/8dTz75JPLy8qBWl7/jwNzR15CQEGRmZsLT0/O2NWbll6D7xzEAgBNv9YOTk1M1t1JaWq0W0dHRGDRoEDQajdTlVJlc6wZYu1SqU3tOTg78/PyQnZ1daQZISQ4ZOe7Hfdh34Rpm3d8W90QG12ArpWMv+7stkWvdgH3VLoeMlEM+LtqbhHf+OY1IXz3+mHqnrPYbe9rfbQlrtz4l5iMg4Rl8JycnREVFYdOmTcZw1uv12LRpE6ZOnWr2OQUFBeUC2MHBcHS0ouMUzs7OcHZ2Lrdco9FU+oPUOOqrtb6tkmvtcq0bYO1SqdLvtUy2TQ4ZqVIbLmtydHCQzff1Vkrf322RXOsG7KN2OWyfHPKx7LWrur4tkmvdAGuXilxrV1I+AhJfoj9t2jRMmDABnTt3RteuXTF79mzk5+dj4sSJAIDx48cjODgYM2fOBACMGDECX3zxBTp27Gi8vOqtt97CiBEjTIKUiEgJmJFEROYxH4mIzJO0wR8zZgwyMjLw9ttvIzU1FZGRkVi3bp1x0JSkpCSTo61vvvkmVCoV3nzzTSQnJ8Pf3x8jRozAhx9+KNUmEBFZDDOSiMg85iMRkXmSD7I3derUCi+niomJMfna0dERM2bMwIwZM6xQGRGR9JiRRETmMR+JiMorP6IIEREREREREckOG3wiIiIiIiIiBWCDT0RERERERKQAbPCJiIiIiIiIFIANPhEREREREZECsMEnIiIiIiIiUgA2+EREREREREQKwAafiIiIiIiISAHY4BMREREREREpABt8IiIiIiIiIgVgg09ERERERESkAGzwiYiIiIiIiBSADT4RERERERGRArDBJyIiIiIiIlIANvhERERERERECsAGn4iIiIiIiEgB2OATERERERERKQAbfCIiIiIiIiIFYINPREREREREpABs8ImIiIiIiIgUgA0+ERERERERkQKwwSciIiIiIiJSADb4RERERERERArABp+IiIgAACnZhdgVn4mU7EKpSyEiIqIacJS6ACIiIpLekv1JeG3lMQgBqFXAzFERGNMlVOqyiIiIqBp4Bp+IiMjOJWbm47UVhuYeAPQCeH3lcZ7JJyIikhk2+ERERHbscNJVPPzDHohbluuEQGJmgSQ1ERERUc3wEn0iIiI7VFyqw9ebzuHbmHjob+3uATioVAjzc7N+YURERFRjbPCJiIjszMnLOZi2NBanU3MBACMjG6JDiDfe/fskAMM9+B+NaocgL1cpyyQiIqJqYoNPRERkJ0p1eszfGo+vNp2DVifg6+6ED0e2w9CIIADA15vO4WqBFr/+Xzfc0dxP4mqJiIioutjgExER2YG49Fy8uPQIjlzKBgAMaRuID++LgF89Z+M6DmoVAMDfw9nsaxAREZFtY4NPRESkYDq9wIKdCfh0/RmUlOrh6eKI9+5th3sjG0KlUkldHhEREdUhNvhEREQKlZRVgJeWHcG+xCsAgL4t/PHJ/e3RwMtF4sqIiIjIEtjgExERKYwQAov2JuGjtadQUKKDu5MD3ry7DR7qEsKz9kRERArGBp+IiEhBLl8rxKsrjmL7uUwAQLcmvvj8wQ4I8eWUd0RERErHBp+IiEgBhBBYeSgZ7/x9ArlFpXB2VOPVu1rhsZ5hUKt51p6IiMgesMEnIiKSuYzcYry+6hiiT6YBACJDvDFrdAc0868ncWVERERkTWqpC5g7dy7CwsLg4uKCbt26Yd++fbdd/9q1a5gyZQqCgoLg7OyMFi1aYO3atVaqlojIupiRVJl/j6Zg8JdbEX0yDRoHFV65qyWWP92DzT0pHvORiKi8Gp3B1+l0WLhwITZt2oT09HTo9XqTxzdv3lyl11myZAmmTZuG+fPno1u3bpg9ezaGDBmCM2fOICAgoNz6JSUlGDRoEAICArB8+XIEBwfjwoUL8Pb2rslmEBFZBDOSrOFqQQneX3scfx+5DABoE+SJWaM7oHWQp8SVEVWM+UhEZFk1avCff/55LFy4EMOHD0e7du1qPCLvF198gUmTJmHixIkAgPnz5+Pff//Fzz//jNdee63c+j///DOuXLmCXbt2QaPRAADCwsJq9N5ERJbCjCRLO3FVhQ/m7EJGXgkc1CpM6dcMUwc0h5Oj5BfmEd0W85GIyLJq1OAvXrwYS5cuxbBhw2r8xiUlJTh48CCmT59uXKZWqzFw4EDs3r3b7HPWrFmDHj16YMqUKfjrr7/g7++PsWPH4tVXX4WDg4PZ5xQXF6O4uNj4dU5ODgBAq9VCq9XetkZtaemN/2u1sptaqGz7KttOWyPXugHWLpXq1G6N7bOXjBR6AQAo1elkt9/IdX/PLSrFB/+ewsrTDgBK0MzfHZ+Oaof2jbwAoYNWq6vV6wvDjxSlpZX//KtLrt9zwL5qt/Q22ks+6nQ3fhfltt/Y0/5uS1i79dlaPtaVGjX4Tk5OCA8Pr9UbZ2ZmQqfTITAw0GR5YGAgTp8+bfY558+fx+bNmzFu3DisXbsWcXFxmDx5MrRaLWbMmGH2OTNnzsS7775bbvmGDRvg5nb7KYPytEDZtyg6eiNk1t8bRUdHS11Cjci1boC1S6UqtRcUFFi8DnvJyCtX1QDUOHb0KBwvH6nahtkYOe3vZ7JV+DNOjaslKqgg0D9IYFhoNi4d3YlLR+vmPYpLHACosG37dsRZaFY9OX3Pb2UPtVs6I+0lH4+nqgAYDhzIdb+Ra90Aa5eKXGu3lXysKzVq8F988UV89dVX+Oabb6x6Vluv1yMgIADff/89HBwcEBUVheTkZHz22WcVhvP06dMxbdo049c5OTkICQnB4MGD4el5+/sUs/JL8MaBGADAoEED4eTkVGfbYg1arRbR0dEYNGiQ8XI0OZBr3QBrl0p1ai87A2NJ9pKRiy7vA3KuIaJ9ewyLDK7TbbE0Oe3vBSWl+HzDOfx28iIAIMTHBfc1zMPT99d97e8djUGetgR9evdGi0CPOn1tOX3Pb2VPtVs6I+0lH6/uTcLyBMPBBrntN/a0v9sS1m59tpaPdaVGDf6OHTuwZcsW/Pfff2jbtm25b8jKlSsrfQ0/Pz84ODggLS3NZHlaWhoaNGhg9jlBQUHQaDQml1K1bt0aqampKCkpMduAOzs7w9nZudxyjUZT6Q9S43hj4JeqrG+r5Fq7XOsGWLtUqvR7bYVts5eMVF2fW93RwUHR+4yUDl64gheXHkFiluGswSPdQ/HSwHBs3bTBIrWX9VuOjpb7vtj69/x27KF2S2+fveTjze8j1/1GrnUDrF0qcq3dVvKxrtRoNB5vb2/cd9996Nu3L/z8/ODl5WXyryqcnJwQFRWFTZs2GZfp9Xps2rQJPXr0MPucXr16IS4uzmTE1bNnzyIoKEh2Z9eJSLmYkVRbRVodZq49hQfm70ZiVgGCvFzw2+Nd8cHICLg71+jYPJFNYD4SEVlWjT4lLFiwoE7efNq0aZgwYQI6d+6Mrl27Yvbs2cjPzzeOiDp+/HgEBwdj5syZAIBnnnkG33zzDZ5//nk8++yzOHfuHD766CM899xzdVIPEclTSnYRzmWrkJJdhFA/6Y+uMiOpNo5dysa0pbE4l54HAHggqhHeursNvFyl37eJaov5SERkWbU6DZCRkYEzZ84AAFq2bAl/f/9qPX/MmDHIyMjA22+/jdTUVERGRmLdunXGQVOSkpKgVt+4yCAkJATr16/HCy+8gPbt2yM4OBjPP/88Xn311dpsBhHJ2JL9SZi+8hj0wgHzTm3DzFERGNMlVOqyADAjqXq0Oj2+2RyHb7bEQacX8KvnjJmjIjCoTWDlTyaSGeYjEZFl1KjBz8/Px7PPPotff/3VeKmTg4MDxo8fjzlz5lQ6sujNpk6diqlTp5p9LCYmptyyHj16YM+ePTUpm4gURAiBrWcz8NqKY7g+sxf0Anh95XH0aeGPIC9XyWpjRlJ1nUnNxYvLYnE82TCAz/D2QXj/3nbwdeelw6QszEciIsuq0T3406ZNw9atW/H333/j2rVruHbtGv766y9s3boVL774Yl3XSEQEAMgrLsW64yl4ZfkRdPtoEx5bsN/Y3JfRCYHETGmnMWFGUlXp9ALzt8ZjxJwdOJ6cA283DeY83BFzx3Zic0+KxHwkIrKsGp3BX7FiBZYvX45+/foZlw0bNgyurq4YPXo0vv3227qqj4jsmBAC8Rn5iDmTjs2n07E/8Qq0uhstvYtGjSKt3uQ5DioVwvwsNIF3FTEjqSoSMvPx4tJYHEq6BgC4s1UAZt4fgQAPF2kLI7Ig5iMRkWXVqMEvKCgw3uN0s4CAABQUSHvmjIjkrUirw57zWdhyOh1bzmQg6YpppjTxc0f/lgHo38ofXZv44tXlR7E69jIAQK0CPhrVTtLL8wFmJN2eXi/w6+5EfLzuNIq0etRzdsTbI9rgwahGVp0XnEgKzEciIsuqUYPfo0cPzJgxA7/++itcXAxnGgoLC/Huu+9WOD0JEVFFLl0twJYzGYg5nY6d8ZkmZ+WdHNTo1tT3elMfgCZ+7ibP7Rzmi9Wxl9HUQ4+FT/VDqJ+HtcsvhxlJFbl0tQAvLzuK3eezAAC9wuvj0wc6INhb2oNSRNbCfCQisqwaNfhfffUVhgwZgkaNGqFDhw4AgCNHjsDFxQXr16+v0wKJSHm0Oj0OXriKLWfSseV0Os6m5Zk8HuTlgn4tAzCgVQB6NqtfpXm/62kMz7MFzEi6lRACSw9cxPv/nEJecSlcNQ54fVgrjOvWGGo1z9qT/WA+EhFZVo0a/Hbt2uHcuXNYtGgRTp8+DQB4+OGHMW7cOLi68iwEEZWXkVuMrWczsOV0Orady0BuUanxMbUKiGrsg/6tAtC/ZQBaNfCQ9aXKzEi6WVpOEV5bcRRbzmQAADo39sHnD3ZA2C1XoxDZA+YjEZFl1ajBBwA3NzdMmjSpLmshIgXR6wWOJWdj8+l0xJxJx5FL2SaP+7o7oV8Lf/RrFYA+zf3g7aasEcOZkSSEwJojl/H2XyeQXaiFk6MaLw1ugcfvaAoHnrUnO8Z8JCKynCo3+GvWrMHQoUOh0WiwZs2a2657zz331LowIpKf7EIttp/LwJbTGdh6Nh2ZeSUmj7cL9sSA6/fSt2/kragmhxlJN8vKK8Zbfx3H2mOpAICIYC98MboDmgdKP0YEkbUxH4mIrKfKDf7IkSORmpqKgIAAjBw5ssL1VCoVdDpdXdRGRDZOCIGzaXnYcn0au4MXrkKnvzGNXT1nR/Ru7of+rQLQr4U/Ajxt4x55S2BGUpn1J1LxxqpjyMwrgaNahefubI5n+jWDxkEtdWlEkmA+EhFZT5UbfL1eb/b/RGRfCkt02BWfef3S+wwkXys0eTw8oB76t/RH/1YB6NzYF06O9tHUMCMpu1CLd9ecwMrDyQCAloEemDW6A9oFe0lcGZG0mI9ERNZT43vwb3Xt2jV4e3vX1csRkQ1JyirA5tNp2HImA7vPZ6Gk9MYHNGdHNXo0q48B1wfIC/F1k7BS28WMVLatZzPw6vKjSM0pgloFPNW3Gf43sDmcHR2kLo3I5jEfiYjqTo0a/E8++QRhYWEYM2YMAODBBx/EihUrEBQUhLVr1xqnPSEieSop1eNA4hVsPp2OLWfSEZ+Rb/J4sLeroaFv5Y8eTf3g6sQm5mbMSPuRV1yKj9aewh97kwAATfzc8fmDHRDV2EfiyohsE/ORiMiyatTgz58/H4sWLQIAREdHY+PGjVi3bh2WLl2Kl19+GRs2bKjTIonI8tJzi7E7TYV//4zFrvgryCu+MY2do1qFzmE+6H99bvrwgHqynsbO0piR9mHv+Sy8tPwILl4x3KbyWM8wvHpXKx7wIroN5iMRkWXVqMFPTU1FSEgIAOCff/7B6NGjMXjwYISFhaFbt251WiARWYZOLxB78Rpirg+Qd+JyDgAHAOkAAL96zujX0h8DWgXgjuZ+8HTRSFqvnDAjla1Iq8Nn68/g550JEMJwRctnD7ZHz2Z+UpdGZPOYj0REllWjBt/HxwcXL15ESEgI1q1bhw8++ACAYURtjn4qTynZhUjIzEcTP3cEeblKXY5F2cu2mtvOawUl2Ho2A1tOp2Pr2QxcLdAa11epgFB3gXu7hmNgmwZo19ALahlNY5enBVKyixDqJ/2BCGak8pT9PhWU6PDR2lM4f/22lYe7huD1Ya3hoZADYGWzYGTkFqNlA2VP6WcvfwtsDfORiMiyatTgjxo1CmPHjkXz5s2RlZWFoUOHAgAOHz6M8PDwOi2QLG/J/iRMX3kMegGoVcDMUREY0yVU6rIswl629ebtVKmAwW0CkZVXgkNJV3HTLHbwdHFEnxb+6N8yAL2aemPvtk0Y1r8ZNBr5NCsHEq8AAM7nqtFv1jab+JkyI5Xl5t+nMgEezvjkgfbo3zJAusLq2JL9ScaDfuN/3msTv0uWYi9/C2wR85GIyLJq1OB/+eWXCAsLw8WLF/Hpp5+iXr16AICUlBRMnjy5Tgsky0rJLjT54KoXwOsrj6NPC3/FndGwl229dTuFANafSDM+3qqBB/pdv5e+U6g3HK/Pza3Vas29nE1LyS7EX7GXjV/bys+UGakct/4+AYAKwG+Pd0XLBp6S1VXXyrazjK38LlnC2bRcvLbiGMp+pEreVlvEfCQisqwaNfgajQYvvfRSueUvvPBCrQsi60rIzDf54AoAOiGQmFmgqA86er3Ad1vPK3pbS0r12HAyFfO2xJfbTgB4/I4w/N8dTRHsLf9tLZOQmY9bN9UWfqbMSOUwl5ECwJV8+R0Qux17+Ftw7FI2ft9zAasOJ9tkbtgL5iMRkWVVucFfs2YNhg4dCo1GgzVr1tx23XvuuafWhZF1NPFzhwow+bDjoFIhzE85c5lfulqAV5Yfxa74rHKPKWFbL14pwJ/7krD0wEVk5pWYXcdBpcITvZsq7sOrLe2/zEhlMje4pBJy41ZN/NyhVsGkyVfCdhaUlOLvI5exaG8Sjl7KrnA9JWyrLWM+EhFZT5Ub/JEjRyI1NRUBAQEYOXJkheupVCoOkiIj/vWc4ePuhCv5hsZQpQI+GtVOEY2gEALLDlzCe/+cRF5xKVw1Dmgd5IlDSVcBGD7QyXVbdXqBmDPp+H3PBcSczYC4/qE8wMMZD3UJgbuzIz5ddwY6IWS9nZUJ8nLFvZENsfr6ZfpqCfdfZqQy/X3kssnXSv19CvJyxcxREXh1heEyfSl/l+rC2bRc/LE3CSsOXUJukWHKTycHNYZGNMC4bo3x/bZ4bDxlmDFEqT9TW8J8JCKynio3+Hq93uz/Sd7WHLlsbO4B4IWBLRQx0FBaThFeW3EUW85kAACiGvvg8wc7ID2nCGO+34OGXi5YMbmn7D7QpecUYcn+i/hzXxIuZxcZl98R7odHuofiztaB0Fy/p/6eyIZIzCxAmJ+b7LazOjqH+WJ17GU09dBj4VP9EOonzcjfzEjlycorxq+7LwAAPn+wPYK93RT9+zSmSyg+/u80rhZo8ev/dcMdzeU17V9xqQ7rjqdi0d4k7Eu4Ylwe6uuGcd1C8UBUI9Sv5wwA2B2fhY2n0nFn6wB8MJLNvaUxH4mIrKdG9+CTMuj0At9sjgMA46WZXq7yGT3dHCEE/j6agrdWH0d2oRZODmq8OLgFnujdFA5qFdJzDE2xq5ODbD7QCSGwKz4Li/ZewIYTaSi9fg2tj5sGD3YOwcNdQ9HEz73c84K8XGWzjXWhngYI8nKRugxSkJ92JKBQq0O7YE/c36kRVCr5TBtZUw7Xp8b093CWuJKqS8oqwB/7krDswEVkXT9g7aBW4c5WAXike2PcEe5X4ZSfgZ4udpWTRESkfDVq8J977jmEh4fjueeeM1n+zTffIC4uDrNnz66L2sjC/j2WgvOZ+fB20yAyxBsx1892y9WV/BK8tfo4/j2WAgBoF+yJL0ZHokWgPOdyvppfghWHLuGPvUk4n5lvXN65sQ/GdQ/F0HZBcNE4SFghVYQZKX/XCkqMZ++fG9DcLpp7OSnV6bH5dDp+35uEbWdv/O1q4OmCh7qGYEyXEDbuNor5SERkWTVq8FesWGF2kJSePXvi448/ZjjLgF4v8M3mcwCA/+vVBGfSciWuqHY2nEjF66uOITOvBI5qFaYOCMeU/uHGy9XlQgiBQ0nXsGjvBfxzNAUlpYZLGes5O+K+jsEY2y0UrYOUMzWXUjEj5e/nnYnIKy5FqwYeGNQmUOpy6LrU7CIs3p+ExfsuIjXnxm1KfVr4Y1y3UNzZKsA49SfZJuYjEZFl1ajBz8rKgpeXV7nlnp6eyMzMrHVRZHnrT6TibFoePFwcMaFnGF5fdazyJ9mg7EIt3v37BFYeSgYAtAishy9GR6JdcPn905blFZdi1eFkLNpzAadTbxxsaRPkiUe6N8a9kQ3h7sw7auSCGSlvOUVaLNiZAAB47k6evZeaXi+wIy4Ti/ZewMZT6dBdv03J190JD3ZuhLFdQ9G4fvnblMg2MR+JiCyrRh1DeHg41q1bh6lTp5os/++//9C0adM6KYwsR68X+GqT4ez9xJ5hsr3vftvZDLyy/ChSc4qgVgFP9mmGFwY1h7OjfC5bP3E5G4v2JuGvw8nILzGMHOzsqMaIDg3xSPfG6NDIi82FDDEj5e2XnYnILSpF84B6uKttA6nLsVtZecVYfvAS/tiXhAtZBcblXcN8Ma57KO5q10BWeU8GzEciIsuqUYM/bdo0TJ06FRkZGRgwYAAAYNOmTZg1axYvrZKBjafScDo1F+5ODvi/O5pIXU615ReX4qO1p7BobxIAIKy+G2aN7oCoxr4SV1Y1RVod/jmagkV7L+Bw0jXj8mb+7hjXrTHu79QIXm7yPOhCBsxI+corLsVP18/eTx0QXuHgbGQZQggcuHAVv++5gP+OpaJEZ7hNycPZEfdHNcLYbqGyHVeFDJiPRESWVaMG///+7/9QXFyMDz/8EO+//z4AICwsDN9++y3Gjx9fpwVS3RJCYM71kfMn9AyDt5uTxBVVz97zWXhp+RFcvFIIAHisZxheuasl3Jxs//L1+Iw8/LE3CcsPXkJ2oRYAoHFQYUhbw7zM3Zv68my9QjAj5eu33RdwrUCLpn7uuLt9Q6nLsRs5RVqsPpyMRXuSTMaEiQj2wiPdQzGiQ0NZ5DxVjvlIRGRZNf5r+cwzz+CZZ55BRkYGXF1dUa9evbqsiywk5kwGjiVnw1XjgMdldPa+SKvDZ+vP4OedCRACCPZ2xWcPtEfPcNuep7mkVI/ok2lYtPcCdsVnGZcHe7tibLdQjO4cIqvpqGxZnhZIyS5CqJ9tXP3AjJSfgpJS/LD9PABgSv9w45RxZDnHLmVj0d4L+Cv2Mgq1htuUXDRq3NshGOO6h6J9I29pCySLYD4SEVlOjRv80tJSxMTEID4+HmPHjgUAXL58GZ6engxqGyXEjXvvH+3RGPXryaOxjL14DdOWxuJ8hmGquIe6hOCN4a3h4WIbjZw5ydcKsexQPJbsv4TMvGIAgFoFDGgVgHHdGqNPC382D3XkQOIVAMD5XDX6zdqGmaMiMKZLqMRVMSPlaNGeJFzJL0GorxvujeTZe0spLNFhVWwqFu29gCOXso3LmwfUw7huobivUyPZjg1DVcN8JCKynBo1+BcuXMBdd92FpKQkFBcXY9CgQfDw8MAnn3yC4uJizJ8/v67rpDqwIy4TsRevwdlRjUm9bX8gm5JSPb7afAbzYuKgF0CAhzM+ub89+rcKkLo0s3R6gc1nMvDdKTVO7dkOYRjoGf4eznioSwge6hqKYG/Oy1yXUrIL8VfsZePXegG8vvI4+rTwl3QObGak/BRpdfhum+Hs/dT+4ZxqzQLOpedhRYIab362FblFpQAMtykNbReER7o3RpcwH96mZAeYj0REllWjBv/5559H586dceTIEdSvX9+4/L777sOkSZPqrDiqO0IIfLXRcPZ+bLdQm78sPDkfuH/+HpxOywMA3BvZEO/e09YmxwxIzy3C0v0X8ee+i0i+VgjA0Bj0Cq+PR7o1xsA2gdCwWbCIhMx8iFuW6YRAYmaBpA0+M1J+/tyXhMy8YgR7u+K+TsFSl6MYxaU6rD+Rht/3XMC+hCsw5GMpQn3dMLZbKB6IagQ/Ca8mS8suQkp2oaR5YW+Yj0REllWjBn/79u3YtWsXnJxMm62wsDAkJyfXSWFUt3afz8KBC1fh5KjG032bSV1OhUp1esyLOY+vjzlAJ/Lg6+6ED0a2w7CIIKlLMyGEwO74LCzam4T1J1JRen1eZm9XDTp6F2P66N5oEeQtbZF2oImfO1SASZPvoFIhzM9NqpIAMCPlpkirw/yt8QCAyf2b8YBcHbh4pQCL9iZh2YGLyMovAWC4Tamttx4vjOiMfq0aSDpDwfFkw60Bm06nY8vHm23m1h57wHwkIrKsGjX4er0eOp2u3PJLly7Bw4PT19iiOZsMI+c/1CUEgZ4uEldjXlx6Hl5cdgRHLl4DoMKg1gH4aFR7m7ra4FpBiWFe5r1JOJ+Zb1we1dgH47qFYlArP2yOXo8mfu4SVmk/grxccW9kQ6y+fpm+WgV8NKqd5GfjmJHysuzgJaTlFCPIywUPRDWSuhzZKtXpsfl0OhbtTcK2cxnG25QCPZ3xUJdQ3N8xCId3bkbv5n6SNvcp2YXYeCrN+LWt3NpjL5iPRESWVaMGf/DgwZg9eza+//57AIBKpUJeXh5mzJiBYcOG1WmBVHsHLlzF7vNZ0DiobPLsvV4v8PPOBHy2/gyKS/XwcHHEvY2K8fbDHcod4ZeCEAKHL17D73su4N+jKSguNczL7O7kgPs6BWNs18Zo09ATAKDVaqUs1S51DvPF6tjLaOqhx8Kn+iHUT/oPiMxI+Sgp1ePbLYYDoE/3bQZnRweJK5Kf1OwiLNl/EYv3JyElu8i4vHdzP4zr1hgDWwfA0UENrVaLwxLWWcZWb+2xF8xHIiLLqlGD//nnn+Ouu+5CmzZtUFRUhLFjx+LcuXPw8/PDn3/+Wdc1Ui3NjTEMHPVAVAga2tggb0lZBXhp+ZHr92YCfVr444N7WuPwzs2SD7aUV1xqmJd5bxJOpeQYl7cO8sQj3UNxb2Qw6jlzXmZbUU8DBHnZxtUpzEj5WB17GZezixDg4YwxXUKkLkc29HqBnfGZ+H3PBWw8lQ7d9duUfNw0GN05BA93DUWYjV7JZKu39tgL5iMRkWXVqDsJCQnBkSNHsGTJEhw5cgR5eXl4/PHHMW7cOLi62lYDae8Sc4EdcVlwUKswuZ/tnL0XQuCPfUn48N9TKCjRwc3JAW8Ob4OHu4agtLRU0rM8Jy/nYNHeC1h9OBn5JYbLCJ0d1bi7fUOM6x6KjiHekh98INvGjJQHnR74dlsCAODJPk3houHZ+8pcyS/B8oMX8cfeJCRmFRiXdwnzwSPdG+Oudg1s/iqIIC9XDGwdiOjrl+k7qFQ2cWuPvWA+EhFZVrUbfK1Wi1atWuGff/7BuHHjMG7cuFoXMXfuXHz22WdITU1Fhw4dMGfOHHTt2rXS5y1evBgPP/ww7r33XqxevbrWdSjR+kuGwaJGdQxGiK9tnJ1IyS7EK8uPYvu5TABA1ya++PyBDgitL119RVod/j2agkV7L+BQ0jXj8qZ+7saRnm1xBH+yPXWdkcxHyzmQqcKlq4Xwq+eEcd0aS12OzRJC4OCFq/h9zwWsPZaKEp3hNiUPZ0eM6hSMsd0ao2UD6W+NqY52wV6IPpWGO1sH4IORbO6thflIRGR51W7wNRoNioqKKl+xipYsWYJp06Zh/vz56NatG2bPno0hQ4bgzJkzCAioeL7zxMREvPTSS+jdu3ed1aI0x5NzcPKaGmoVMKV/uNTlQAiBlYeS8c7fJ5BbVApnRzVeuasVJvYMs/qAS4UlOqRkF6KwRIc/9iZh+aFLuFZguH/eUa3CkLYNMK57KHo0rc+z9TKRpwVSsosQ6qeRtI66zEjmo+WU6vSITjYcAJ3UuylcnWz7rLO1lF1qn5FbjIbeLlh1OBmL9iThTFqucZ12wZ54pFtjjOjQEO4yv00p0NNF8c19SnYRzmWrmI/XMR+JSOlqNBfQlClT8Mknn6C0tLTWBXzxxReYNGkSJk6ciDZt2mD+/Plwc3PDzz//XOFzdDodxo0bh3fffRdNmzatdQ1KNTfGMO3TiPZBkt8LmZFbjCd/O4gXlx1BblEpIkO8sfb53nj8jiZWbe43n04HAFzOLkKPmZsxYNZW/LgjAdcKtAj2dsXLQ1pi1/QBmDuuE3o282NzLwMHEg3jN5zPVaPfrG1Ysj9J4orqLiOZj5bz7/E0ZBSp4OOmwSPdefYeAJbsT8LV6wc6H/lpL6Lej8bbf53AmbRcuGjUGN25Ef6a0gv/PNsbD3UNlX1zbw+W7E9Cv1nb8M1JB+YjmI9EZB9q9Nd5//792LRpEzZs2ICIiAi4u5s2jytXrqzS65SUlODgwYOYPn26cZlarcbAgQOxe/fuCp/33nvvISAgAI8//ji2b99+2/coLi5GcXGx8eucHMNgaVqtttIRz7U3/fHRarWyavZOpeRi4+kMqCAwqVdIpdsq9IZLLnU6XZ2PBP/f8VTM+PsUrhZooXFQ4bn+zfDEHWHGUZVvVbasrutIyS7C99vPl1veo6kPJvYMQ5/mfnC4frChpu9tqdqtQY61p2QX4a/rU+QBhumupq88hh5NfCoccM8a21cXGWmNfARql5Hi+tneUgvkhiXp9ALzrh8AHd+tEZzUQlb1W+J3NSW7CNNXHjNZVqITaOzrike7h+K+yIbwdNXU6n1tKWN0esP4Knq9vkr12FLtVZFdqMXa46l4e80p47Kq5CNg+W20l3y8eSpAuew3ZeS2v9+MtUtDrrVXt265bF+NGnxvb2/cf//9tX7zzMxM6HQ6BAYGmiwPDAzE6dOnzT5nx44d+OmnnxAbG1ul95g5cybefffdcss3bNgAN7fb3/OdpwXKvkXR0Rsho/4eC86oAajRsb5A/OFdiK9k1LqUFMP6J06cwNorx+ukhnwtsDxBjUNZhgtFgt0ExoWXIjj/NDasN//zvVl0dHSd1FHmXLYKQpS/DDfKJROF8RlYH19371XXtVuTnGo/l62CgOnPVC+ApWu3oLnXrRNhGRQUFJhdXpfqIiOtkY9A7TLyylVDbhw7ehSOl49U+T2ldjhThfOZDnB1EGiYdw5r156TuqQaqcvf1XPZKujN5OPdgXnwv3oCO7acqLP3soWMOXdRBcABSUlJWLs2scrPs4XazdHpgcQ84PQ1Nc5kq5CUBwiU/9BSWT4Cls9Ie8nH46mGfQyw3f2mMnKtG2DtUpFr7VWt2xqfIetCtRp8vV6Pzz77DGfPnkVJSQkGDBiAd955x2qjnubm5uLRRx/FDz/8AD8/vyo9Z/r06Zg2bZrx65ycHISEhGDw4MHw9PS87XOz8kvwxoEYAMCgQQNtYk72qjiXlofY3bsAAIMa6TFo0CBoNLe/72597hEczkpD27ZtMax7aK1r2HImAx+sPoGMvBI4qFV4qncTTOnXFE6Old8VotVqER0dXaW6qyMluwjzTm2D/qbPNWoVMHpY/zqbXs1StVuDHGtPyS7C3JPbTKa7quxnWnYGxhKkzMia5CNQu4xcdHkfkHMNEe3bY1hkcI1rtya9XmDu3N0A8tA3SI8RQ+Wzv5exxO9qRfk4Zrgy8zF+Szz+uxSP0NBQDBvWptL1bal2wDCmTUJmAXbGZ2FHXBb2JlwxzvpSprGvKy5cKTRZVpW/eZbKSHvLx6t7k7A8wXCwwVb2m6qytf29Oli7NORae3XrtuRnyLpUrQb/ww8/xDvvvIOBAwfC1dUVX3/9NTIyMm57v9Pt+Pn5wcHBAWlpaSbL09LS0KBBg3Lrx8fHIzExESNGjDAu01+/tNzR0RFnzpxBs2amU8E5OzvD2dm53GtpNJpKf5AaR3211rcV87cnAgCGtAlAQ7fLVapdpTY03g4ODrXaztwiLd7/5ySWHrgEAGjm745ZoyMRGeJd7deq6+95qJ8GM0dF4PWVx6ETwjg1Uqhf3Y/+LKf95VZyqj3UT4N7Ixti9fXL9NUqYOaoiNv+TC25bXWZkdbIR6B2Gam6fkuLYy1zw5rWHU/F2fQ81HN2RN+gUlnt77eqy9rtLR8d1IYzq2q1ulq1SFn71fwS7IzPxPazmdgRl4nka6bNu6+7E+4I98Mdzf3Qu7kfgrxc8fOOBLz3z0kAVctHwHIZaW/56OBw44oYW9jna0KudQOsXSpyrb2qdctl26rV4P/666+YN28ennrqKQDAxo0bMXz4cPz4449Qq6s/Xp+TkxOioqKwadMmjBw5EoAhcDdt2oSpU6eWW79Vq1Y4dsz0HsE333wTubm5+OqrrxASElLtGpQmLj0Pfx81NDuT+zVF4uHLlTyj7uyKy8TLy48i+VohVCrg8V5N8NKQljY1t/SYLqHo08IfiZkFCPNzU/zoyfagc5gvVsdeRlMPPRY+1c8iDUlV1WVGMh/rnhACX28yXI7/aPcQuJXI89J8S7HHfEzLLkJKdqFNbmtJqR4HL1zF9nMZ2BGXiWPJ2RA3XWHh5KBG5zAf9G7uj97N/dAmyLPcoLX3dQw2NvibX+iNsIDbn3W2JOYjkXykZBciITMfTfzcbTIf6faq1eAnJSVh2LBhxq8HDhwIlUqFy5cvo1GjRjUqYNq0aZgwYQI6d+6Mrl27Yvbs2cjPz8fEiRMBAOPHj0dwcDBmzpwJFxcXtGvXzuT53t7eAFBuub2atyUOQgADWweiTZAnEiu5974uFJSU4pP/TuOX3RcAAKG+bvj8wQ7o2sTX8m9eA0FergwrBaqnQZ1dSlxTdZ2RzMe6telUOk6m5MDNyQETezbG7hg2+Leyl3w8npwNANh0Oh1bPt6MmaMiMKZL7W9Pqw0hBOLS87DtXCZ2nMvAnvNXUKg1vey+ZaAHejc3nKXv1qR+taZ3bMB8NHk+85HIvCX7kzB95THoxY0rf6TOR6qeajX4paWlcHEx/QOh0WhqNaLgmDFjkJGRgbfffhupqamIjIzEunXrjAOnJCUl1ejqAHuUmJmPv44Yztg/d6d15r0/eOEKXlx6BIlZhkEnHukeiulDW3P6JLJLdZ2RzMe6I4TAnM2Ghn58jzD4uMljTBWqeynZhdh46sal3XoBvL7yOPq08Lf6wY2svGLsiMvE9nOZ2HEuE6k5pnPE+9UzXHbfu7k/7mjuh0BPaZv02mA+Etk2IQQ2n07HayuOGcc2kjIfqeaq1YUJIfDYY4+Z3I9UVFSEp59+2mSak6pOk1dm6tSpZi+pAoCYmJjbPnfhwoXVei8lmxcTB51eoF9Lf7Rv5G3RqRyKtDp8ufEsfth2HnphOHP6yf3t0aeFv8Xek+h28rSGgcJC/aS7P8oSGcl8rBtbz2bgyKVsuGjUeKJ3E6nLIQklZObj1jHkdUIgMbPA4h9gi7Q6HLxwFdvOZWDHuUycuGw6YJOzoxpdm/gaztKH+6NVA49yl93XVGp2EcICpDuwxXwksk2XrhZg9eFkrDycjPMZ+eUet1Y+SiEluwjnslWSf36sa9Vq8CdMmFBu2SOPPFJnxVDNXbxSgJWHkgEAzw5obtH3OnYpGy8ui8XZtDwAwP2dGuHtEW3g5aqcXwySjwOJVwAA53PV6Ddrm6SXkjEjbdPN994/0q0x/Oo5y2YuW6p7TfzcoQJMmnwHlQphfref9qwmhBA4k5aLHecyse1cJvYlZKFIqzdZp3WQJ3pfHxivS5hvnY5bs+pwsvH/A77cznwkIgBATpEW/x1LwYpDydiXcMW43MlBhRKd6SFQS+Wj1G7ciuCAeaek/fxY16rV4C9YsMBSdVAtfbs1HqV6gd7N/RDV2Mci76HV6fHN5jjM3RKHUr2AXz0nfHRfBAa3LT9iLZE1pGQX4q/YGwNJSn0pGTPSNu2Kz8KhpGtwdlTjyT5NpS6HJBbk5YqBrQMRff0y/bIZA+oqM9Jzi7AzzjDa/fa4TGTkFps8HuDhjDua+6FPc3/0CveDv0f5UdrrQkp2IT7496Txa+YjkX3T6vTYdjYDKw8nY+PJNBSXGg42qlRA9yb1cV+nYAxt1wALdyZiVvRZAHWfj1JLzynC7vNZ2HgyDX8fTTEulzof6xpvlFaAy9cKsezARQCWO3t/JjUXLy6LxfFkw+WEwyOC8P7IdvB1532sJB0pL7Ul+fjq+tn7h7uGIkDG9zBT3WkX7IXoU2m4s3UAPhhZuw+vRVod9iVcwfZzGdh+LhOnU3NNHnfRqNGtSf3rZ+n90SKwHlSqurns/nYSMvOhvyUgmY9E9kUIgaOXsrHqcDL+PnIZWfklxsfCA+phVKdgjIwMRkPvG5kwNCIIs6LPwt3ZARun9ZV1XmQXaLEnIQu74jKxKz4L59LzKlxXSfnIBl8B5m+Nh1Yn0L2pb52PXK/TC/yw/Ty+2HAWJTo9vN00eP/edhjRoWGdvg9RTVjzUluSpz3ns7Av4QqcHNR4qi/P3pOpQE+Xan+Y0+sFTqXmYMc5w+B4+xKvoKTU9LL7dsGeuCPcH32a+6FTYx9Jpott4ucOtQomTT7zkcg+XLpagL9iL2PloUuIv+m+er96ThjRoSFGdWyEdsGetz3YqHFQy67ZLSgpxf7Eq9gVn4ldcVk4ftl0elGVCmgT5In2jbyweP9Fk8eUlI9s8GUuLacIi/cbzt4/V8dn7xMy8/Hi0lgcSroGALizVQBmjorgGTCyGUFerrg3siFWX79MX62Coi4lo9orGzl/dJdG3C+oxtJyirAvXYWNy45i9/kryMwrMXk8yMvFMNp9C3/0alYf9etZ5rL76gjycsWbw9vgvX8Ml+kzH4mUrey++pWHkrH3pvvqnR3VGNy2AUZ1DMYdzf2gcVDO7BIlpXrEXryGnXGZ2B2fhcMXr0J7yxgCzfzd0bOZH3o2q4/uTevD5/rVx5Eh3nh1xTEAhsZfSfnIBl/mvtt6HiWlenRu7IMezerXyWvq9QK/7k7Ex+tOo0irRz1nR7w9og0ejGpklcsKiaqjc5gvVsdeRlMPPRY+1Q+hfh5Sl0Q24uCFK9gZlwVHtQpP920mdTkkIwUlpdibcAXbz2ZiR1zG9UFlHQCkAgDcnBzQvWl93BHuhz4t/NDM3zqX3VfXfR2DjQ3+5hd6IyzAU+KKiKguaXV6bD+XgZWHkhF90331ANC9qS9GdWqEoe0awMNFGQNh6/QCJy5nY1d8FnbFZ2F/whUUanUm6zT0ckHPcD/0Cq+PHk390MDL/InJMV1CsWT/RRxKuoa3h7VSzAB7ABt8WcvILcYf+y4AAJ67s3mtPlwUlhh+Oc6k5eKRn/ZiV3wWAKBns/r47MEOCPZWxhEtIrIfX2+KAwA8ENUIjXyUcdkd1a207CKkZBci0MMFJy7nGKevO3jhKkp0Nz4oq1RAiJvA8Kim6NsqEJ1CfeDkqJyzYEQkH0IIHEvOxspD5u+rv69jMEZ2DK71Z3etTo+U7EJJz2oLIRCXnodd8VnYGZeJPeezkFNUarJOfXcn9GhWHz2bGZr6UF83mzzgak1s8GXsx+3nUaTVo0OIN3o396vx6yzZn4TNp9MBAH/sTQIAuGocMH1YKzzSrXGdzcFLZAm2NE0e2Y7Yi9ew9WwGHNQqTO4XLnU5ZGOOJ2cDADadTsemmZvh5uSAghLTs0DB3q6G+eib+6FrYy/sjtmIYYOaQ6ORz5kwW5omj4hqJ/laoWG++lrcV18V/x0zjC6fX6xDr483Wz03Ll4pMNxDf/0s/a0zkXg4O6JbU1/DZffh9dEiwKNGvcqS/UnG25Df+/c03Fw0islHNvgydSW/BL/tMZy9f/7O8Br/MqdkF2L6ymPllv/yf13QtUndXPJPZCm2Nk0e2Y4510fOHxkZjND6PHtPN6RkF2Lj9SnyyhSU6OCqUaNXuL9xTvomfu7Gv61arVaKUmvF1qbJI6Lqyy3S4r9jqVh5+BL2nDe9r35Qm0CM6hSM3s396+y++pTsQny58azxa2vkRnpuEXbHZ2FXXBZ2nc/ExSuFJo87O6rRJcwXPZrVR69wP7Rr6AnHWm7vrf2PgLLykQ2+TP204zwKSnRoF+yJ/i0Davw65qbRAQCdvvwyIlvDafLInOPJ2dh0Oh1qFTClP++9J1PmcgMAfhjfGXc097d6PZbCafKI5KlUp8eO+HSsOHTJ/H31HRvhrogG8LTAffXWyI3sQi32nM8yNPXxmdfHOLnBUa1ChxBv9GpWHz2a+aFjqHedz0Si9Hxkgy9D1wpK8Msuw9n7ZwfU7t57TqNDcsZp8sicspHzR3RoiKb+9SSuhmxNRX/3mgUoa1/h33ci+RBC4HhyDlYmqPHeZ9tM7qtv5u+OUZ0a1cl99ZWxRG4UlJTiQOLV65fcZ+J4crbJ65dNXdfz+n30XZr4op6zZVtUpecjG3wZ+nlnIvKKS9GqgQcGtQ6s1WsFebli5qgIvL7yOHRCwEGlUtQ0EaRsnCaPbnUqJQfrT6RBpQKm9ue991Sevfzd4zR5RLav7L76VYeTEZeeB0ANoAT13a/fV98pGBHBXlYbNC7IyxUvDGyBWdGGy/Rrko8lpXocuWSYum5XfBYOJ5Wfuq6pvzt6NquPXs38TKaus5ayvwOcJo9sQk6RFgt2JgAwjJxfFwPgjekSij4t/JGYWYAwPzfF7NxkH8qmyQty1WPO+B7o3KTmA06S/H2zxTBy/rCIIDQP5JSJZJ69/N27eZq8xU90RddmyrkFgUiucou0+O94KlYdSsaehCyI672vs6MabbxK8czQKPRv3UCy+eqHRgRhVvRZuDs7YOO0vpXmo05vuPpgU7IKy385iAMXrlU4dV3ZWfqKpq6zpjFdQvHb7gs4fjkH/+vfTDED7AFs8GXnl52JyC0qRfOAerirbYM6e90gL1fFfsAhZSsbRT+lUI3R3+/lKNF2LC49F2uvj/777ACevafbs4e/ezePov/Qj/uYj0QSKdXpsf1cJlYeTsaGE6km99V3a+KLUZ2CMaiVH7Zvjkb/lnU3aF5taBzUZjNSCIH4jDzsjDNccr/n/BVkF2oBOAAwTLPte33qul7NDE194/q2N3Xdkv1JOH45BwAwe3M8Gvi4KSYf2eDLSF5xKX66fvZ+6oBwTl9Hdo+j6NPNvtkcByGAIW0D0aqBp9TlEEmKo+gTSUsIgROXc7DyUDLWHElGZl75++rvjWyIRj6G+75tbbYOrU6PlOxCBHm54uKVAuyOz8LO69PX3Tp1nbuzA5q4aXFP99bo3SIALQNrNnWdtXAUfbIZv+2+gGsFWjT1c8fd7RtKXQ6R5DiKPpU5n5GHNUcMB3ueHdBc4mqIpKf0UaKJbNXla4VYHZuMVYeScS79xgjxUt1XX13/Xb8SLr9Yhx4zN8PX3QlXbhr0DzDcTtA5zMcwF32z+mgV4IYN69dhWM/G0GjqfnT/uqb0fGSDLxMFJaX4Yft5AMCU/uFwsOGjYkTWwlH0qczcLfHQC+DOVgFoF+wldTlEklP6KNFEtqSi++qdyuar7xiMPi1s49L720nJLsSXG8+aLLuSXwK1CugY6oOezeqjR7P66BTqYzJ1na1dfVAZpecjG3yZ+GNvEq7klyDU1w33RvLsPRHAUfTJICmrAKtjDfcaP3snz94TARxFn6iupWQXIiEzH0383BHk5Wq4rz4uE6sOJWPDyVQUacvfVz80Isgi89Vbirkz2wDw44QuGNAqwPoFWQhH0SfJFWl1mL+17Ox9Mzja+NE/IqkIM3+USPnmxcRBpxfo08IfkSHeUpdDZJOYj0Q1t2R/EqavPAa9MDSDvZr54XRqLjLzbtyLbu6+ermp6Mx26yBlz0qjtHxkpygDf+5LQmZeMYK9XXFfx0ZSl0NkM24dZK9skJSU7ELpiiKrunS1AMsPXgIAPH8nR84nKnPrIHvMR6KaKRuQrazpFQLYEZeJzLxi+Lo74bGeYfhrSi9snNYXU/qHy7a5B26c2Xa4Pj6Ag0qlqDPbZW4dZA9QVj7yDL6NM5y9jwcATO7fDE6OPCZDVIaD7NH8rfEo1Qv0Cq+PqMa+UpdDZDOUPogUkTUUaXWYHxNv9rL1l4e0wJN9mtn8ffXVNaZLKPq08EdiZgHC/NwUmRdKz0c2+DZu2cFLSMspRpCXCx6I4tl7optxkD37lpJdiKX7DWfvOXI+kSmlDyJFZEn5xaVYtPcCvt+WYHIZfhkHlQqjOjVSXHNfJsjLVRGNbkWUno/K3CsVoqRUj2+3xAEAnu7bDM6ODpU8g8i+lA2yV4aDSNmX77aeR4lOj65NfNG9aX2pyyGyKWWD7JVhPhJVLrtQizmbzqHXJ5vx0drTyMwrRiMfV9zXsSHKJrBS6mXr9qTsVoQyHGSPrGbloUu4nF0Efw9njOkSInU5RDZPaYOkUMXSc4rw574kAMDzHDmfqFLMR6KKXckvwYKdCVi4MxG5xaUAgKZ+7pjcPxz3RjaExkGNV+5qpejL1u2Z0vKRDb6N0ur0mBtjOHv/VJ+mJnNNEpFBRYPs9Wnhzz++Cvf9tvMoLtWjU6g3ejbj2XuiW1U0yB7zkeiG9Nwi/Lg9Ab/vuYCCEh0AoGWgB6YMCMfwiCA4lJ22h/IvW7cnFQ2yp5R8ZINvo/6KvYyLVwrhV88J47o1lrocIpvEQfbsU2ZeMRbtNZy9f+7O5lCpVJU8g8j+KH0QKaLauHytEN9tjcef+y+ipNQwf31EsBemDgjHoNaBUKv5d0XJlJ6PbPBtUKlOj7nX771/ondTuDrx7D2RORxkzz79uD0BhVod2jfyQt8W/lKXQ2STlD6IFFFNJGUV4NutcVh+8BK0OsMvR6dQbzx7Z3P0a+HPA8Z2Qun5yAbfBv1zNAUJmfnwcdPg0e48e09UkSAvV3QI8ULsxWzjspEdGyri6CuZdzW/BL/uTgQAPDeAZ++JKhLk5YrhEUH4+2iKcRnzkexVXHou5m2Jx19HLkN3vavr2aw+pg4IR4+m9fm3xM4Eebnivo7BWHEo2bhMSfnIUfRtjE4v8M1NZ+/dnXkMhqgiKdmFOHJTcw8Aqw9fRkp2oUQVkaX9vDMBBSU6tAnyxJ2tA6Quh8hmpWQX4t9jKSbLmI9kb06l5GDKokMY9OU2rDycDJ1eoH9Lf6x4pgf+mNQdPZv5sbm3QynZhVh1ONlkmZLykd2jjfnveAri0vPg6eKI8T149p7odngPvn3JLtBi4c5EAMBzd4bzQxnRbSj9HlOi2zlyKRvztyVi46k047IhbQMxtX9zRDTykrAysgVKz0c2+DZErxeYs8lw9v7/7mgCDxeNxBUR2bYmfu7llqlUUMw9VGRqwa4E5BaXomWgBwa3aSB1OUQ2zdwYJcxHUrr9iVfx7Uk1Tu/eCwBQq4C72zfElP7haNnAQ+LqyFYoPR/Z4NuQDSfTcCYtFx7OjpjYs4nU5RDJk8LmMiWD3CItft6RAAB49s5wjnBMVBPMR1IgIQR2xmXh683nsC/hCgA1HNQq3NcxGJP7NUNT/3pSl0hyoKB8ZINvI4QQmLP5HABgQs8weLnx7D1RZRIy88stE4BiLrGiG37dfQE5RaVo5u+Ooe2CpC6HyOaZu4WJ+UhKIoTA5tPpmLM5DrEXrwEANA4qdPXT4YNxfdEkwFPaAslmKT0f2eDbiM2n03Hicg7cnBzw+B08e09UFbxE3z7kF5fix+3nAQDPDmgOB569J6qU0i9BJful1wusP5GKOZvjcDIlBwDgolHj4a6h+L+eoTi0YzMa+ci/SSPLUXo+ssG3AUIIfL3JcPb+0R6N4ePuJHFFRDKmoEusyOD3PRdwtUCLsPpuuLs9z94T1RjzkWSsVKfHP0dT8M2WOMSl5wEA3J0c8GiPMDx+RxP4ezhDq9VKXCXJloLykQ2+Ddh6NgNHLmXDRaPGpN5NpS6HSDZ4ib7yFZbo8MP1s/dT+ofD0YGzuxJVhdIvQSX7UVKqx+rDyZgXE4fErAIAgKeLIyb2aoKJvcLg7cYTY1Q9Ss9Hm/ikNHfuXISFhcHFxQXdunXDvn37Klz3hx9+QO/eveHj4wMfHx8MHDjwtuvbupvP3j/SrTH86jlLXBGRfNjDJfr2nI8A8Me+JGTmlSDE1xUjOwZLXQ6RbJRdgnoz5qOy8lHpirQ6/LY7Ef0/j8ErK44iMasAvu5OeHlIS+x4bQBeGNSCzT3ViNLzUfIGf8mSJZg2bRpmzJiBQ4cOoUOHDhgyZAjS09PNrh8TE4OHH34YW7Zswe7duxESEoLBgwcjOTnZypXXjV3xWTiUdA1Ojmo82Ydn74lqTUGXWNl7PhZpdZi/NR4AMLlfODQ8e09UO8xHxeSjkhWUGMZd6fPpFrz11wkkXytEgIcz3hzeGjte7Y8p/cPhyamkqa4pKB8l/7T0xRdfYNKkSZg4cSLatGmD+fPnw83NDT///LPZ9RctWoTJkycjMjISrVq1wo8//gi9Xo9NmzZZufK68dX1s/dju4YiwNNF4mqI5OV2l+grgb3n45L9F5GRW4yGXi64v1MjqcshkpXbXYKqBPaej7YkJbsQu+IzkZJdWKvXyS3SYu6WONzxyRZ88O8ppOcWI9jbFe/f2xbbXumPJ3o3hZsT7y6m2lN6Pkr6W1JSUoKDBw9i+vTpxmVqtRoDBw7E7t27q/QaBQUF0Gq18PX1tVSZFrPnfBb2JVyBk4MaT/Xl2Xui6lLyJfr2no/FpTfO3j/TPxxOjpIfjyaSFSWPEm3v+WhLluxPwvSVx6AXgFoFzBwVgTFdQqv1GtcKSvDzzkQs3JmAnKJSAEDj+m6Y0i8cIzsGM/+pzik5HwGJG/zMzEzodDoEBgaaLA8MDMTp06er9BqvvvoqGjZsiIEDB5p9vLi4GMXFxcavc3IM02lotdpKR9rUlpbe+L9WC5Wqbqdm+nrjWQDA/Z0aws/Nsc5H/ix7PbmNKCrXugHWbm1abWn5hcKwvKLtkMv2WSMfgdplpNAb/jSW6nR1/n1dsv8iUrKLEOjhjPvaBzIfbyLX2uVaNyDP2muSj4bn2f42yiEfdTqd8f9y+J7erKr7e0p2EV5bcczYJOkFMH3lMfRo4oMgr8qvSs3KK8bPuy5g0d6LyC8xfL+a+bvjmb5NMbxdoGFQVaGDVqur5JWqX7stYu3Wo+R8BGQ+iv7HH3+MxYsXIyYmBi4u5oNk5syZePfdd8st37BhA9zcbn+UJk8LlH2LoqM3oi77+4RcYNd5R6hVAi10iVi7NrHuXvwW0dHRFnttS5Jr3QBrt5Zz2SoADibLBICla7eguZf5m6kKCpRx+VVlqpKPQO0y8spVNQA1jh09CsfLR2pbspFOD8yOdQCgQq/6BdgUvb7OXvtWctrfbyXX2uVaNyCv2s9lqyCqmY+AfWSkNfLxeOqNv09y2m9udru6dXpgwVk1xC13++pF5fvYtWJg82U1dqWroNUbPlwHuwkMbqRHe99sqJMPY0Mth0aQ6/ccYO3WoPR8lLTB9/Pzg4ODA9LS0kyWp6WloUGDBrd97ueff46PP/4YGzduRPv27Stcb/r06Zg2bZrx65ycHOPAKp6enrd9j6z8ErxxIAYAMGjQQDg51d1InY//ehBAFu7v1AiPjGxbZ697M61Wi+joaAwaNAgajXwGI5Fr3QBrt7Yjl7Lxzcm95Zb3690THRp5mX1O2RkYW2eNfARql5GLLu8Dcq4hon17DIusuxHulx1MxpW9J+BXzwnvju8NF41D5U+qJjnu72XkWrtc6wbkWXtN8hGQR0bKIR+v7k3C8gTD1QRy2m+Ayvf3qwUleG7xERy7erXcY2oVMHpYf7Nn8C9dLcR32xOw4kgytDpDE9WhkRem9GuKfi386uRKWTn+rpZh7daj5HwEJG7wnZycEBUVhU2bNmHkyJEAYBzwZOrUqRU+79NPP8WHH36I9evXo3Pnzrd9D2dnZzg7l596TqPRVLoDahz11Vq/qmIvXsO2c1lwUKswdUBzi/8i1GXt1iTXugHWbi0levPLtXpVhdsgl22zRj4CtctIldrwYczRwaHOvq+lOj3mb0sAADzVpxk83Cw7+Kic9vdbybV2udYNyKv2muQjII+MlEM+OjjcODApp/3mZubqPp2ag0m/HsDFK4Vwd3LA/Z0a4dc9FwDcuAc/1M/D5DnnM/IwLyYeqw4nQ3f91q5uTXzx7IDm6BVev85vga2odrlg7Zan5HwEbOAS/WnTpmHChAno3LkzunbtitmzZyM/Px8TJ04EAIwfPx7BwcGYOXMmAOCTTz7B22+/jT/++ANhYWFITU0FANSrVw/16tWTbDuq45vNhpHzR0YGo3H98oOEEVHVuDuZP7Pr5qSMAXnsMR/XHLmMpCuGuY7Hda/eQE1EdAPzUXn5KLV1x1MxbWksCkp0CPV1ww/jO6NlAw9jg79m6h1oF3zj7Ofp1BzM3RKPf49exvW+Hn1a+GNq/3B0bcLBDUk6Ss9HyRv8MWPGICMjA2+//TZSU1MRGRmJdevWGQdOSUpKglp945v97bffoqSkBA888IDJ68yYMQPvvPOONUuvkePJ2dh4Kh1qFTClfzOpyyGStbJBeW5VUNGhWZmxt3zU6QW+2RwHAHiidxNOh0RUC8xHZeWjlPR6ga83n8PsjYYTVL3C6+ObhzvBx9301tXA69M9H7uUjTmbz2HDyRu3UAxsHYhnB4SjQ4i31eomqojS89EmPj1NnTq1wkuqYmJiTL5OTEy0fEEWNOf62fsRHRqiqT+PGBPVhtKPwAL2lY//HL2M85n58HbTYHyPMKnLIZI15mOMyddyz0ep5BeX4sWlR7DuhOGKh4m9wvDGsNaGEe5vsfFkGtafTEXMmQwAhmnHhkUEYUq/cLRpePsxC4isSen5aBMNvr04nZqD9SfSoFIBU/uHS10Okewp/QisPdHfdPb+8V5NUM+Zf56IaoP5SLV18WoBJv9xBKdTc+HkoMYH97XD6M4hJuss2Z9k/P/0VccAAA5qFe7t0BCT+4cjPIAns8j2KD0f+QnKiuZc//A6rF0Qmgd6VLI2EVVG6Udg7cm6E6k4l54HDxdHTOgVJnU5RLLHfKTaOJetwjvz9+JqgRZ+9Zzx3aNRiGrsY7JOSnYhpq88ZrJMBWDxk93QJay+Faslqh6l56MytkIG4tJzsfZYCgBg6gCevSeqCxevFppdfqmC5WSb9HqBrzcZbl+a2KsJPF3kMUotkS1jPlJNCCHw254kzDupxtUCLdo38sLfz/Yq19wDQEJmvnHwPOPzAZSaPzlKZDOUno88g28l32yOgxDAkLaBaB3E+5CI6oIQooLlVi6EamXjqTScTs1FPWdH/B/P3hPVCeYjVVdJqR5v/3Uci/dfBKDCPe2D8OmDHeCiMX+2s4mfO9QqmDT5DioVwvzcrFMwUQ0pPR95Bt8KEjLzsebIZQDAswOaS1wNkXKE+pr/EBHi62rlSqimhDCMzgwA43s0hrebUyXPIKKqYD5SdWTkFmPsD3uweP9FqFXAvY11+PyBdhU29wAQ5OWKmaMi4HB9HnsHlQofjWqHIC/uY2TblJ6PPINvBXO3xEEvgDtbBZjMD0pEtXO7S6w6hJS/nJBsT8yZDBxPzoGbkwOe6N1U6nKIFIP5SFV17FI2nvztAFKyi+Dh4ojZo9sj79w+qK437rczpkso+rTwR2JmAcL83NjckywoPR/Z4FtYUlYBVh1OBgA8eyfP3hPVpasFJdVaTrZFCIGvrt97/0j3xvB159l7orrCfKSq+Cs2Ga8sP4riUj2a+rvjh/GdEertjLXnqv4aQV6ubOxJVpSej2zwLWxeTBx0eoE+LfwRGeItdTlERDZj+7lMxF68BmdHNSbx7D0RkdXo9AKfrT+D+VvjAQD9W/rjq4c7wtNFA61WK3F1RFQbbPAt6NLVAqw4dAkA8PydHDmfqK55u5ofbd3blWeCbZ0QN0bOH9etMfw9nCWuiEhZmI9UkZwiLZ7/8zC2nMkAADzTrxleGtwSDurKL8knUgKl5yMbfAuavzUeWp1Az2b1EdXYV+pyiBRH6YOkKNnu81k4cOEqnBzVeKovz94T1TXmI5lzPiMPT/x6AOcz8uHsqManD7THvZHBUpdFZFVKz0eOom8hqdlFWLrfcPb+Od57T2QRSp/HVMnKzt4/1CUEgZ4uEldDpDzMR7pVzJl03Dt3J85n5CPIywXLn+7J5p7sktLzkWfwLWT+1niU6PTo2sQX3ZvWl7ocIkVKzMw3vzzL/HKyDfsSrmDP+SvQOKjwdN9mUpdDpEjMRyojhMD3287jk3WnoRdA58Y++PaRKN4aRXZL6fnIBt8C0nOL8Oe+JADAc5z3nshisvLNj3aalaeMUVCVas71ee8fiApBQ29lXA5HZGuYjwQARVodpq88ZpzR6aEuIXj33rZwdqx4fnsipVN6PrLBt4Aftp1HcakenUK90SucZ++JLKV+PfODoVS0nKR3KOkqtp/LhKNahcn9ePaeyFKYj5SaXYQnfzuAo5ey4aBWYcaINni0e+MqzW9PpGRKz0c2+HUsM68Yv+8xnL1/9s7mDFEiC2pcwSApjX3drVwJVdWc6/fe39cxGCEV/PyIqPaYj/bt4IWrePr3g8jILYaPmwZzx3VCz2Z+UpdFZBOUno9s8OvYj9sTUKjVoX0jL/Rr4S91OUSK1jnMFyoA4qZlKgBRYT4SVUS3c/TSNWw5kwG1CpjSn1OHElkS89F+LT1wEW+uOo4SnR6tGnjgh/GdeUCV6CZKz0eOol+HruaX4LfdiQAM997z7D2RZQV5ueLj+yNQ9pumAvDx/REI8uJ93bZozuY4AMDIyGCE+SnjKDmRrWI+2p9SnR7v/n0Cryw/ihKdHkPaBmLFMz3Z3BPdQun5yDP4dejnnQnIL9GhTZAn7mwdIHU5RHZhTJdQ9Gjig6Vrt2D0sP4I9fOQuiQy48TlbESfTINKBUzm2Xsiq2A+2o+r+SWY8sch7IrPAgD8b2BzPDegOdRqnmwiMkfJ+cgGv45kF2qxcGciAOC5O8N59p7IioK8XNDcSyDIi/Op26pvrp+9v7t9Q4QH1JO4GiL7wXxUvjOpuZj06wEkXSmAm5MDvhjdAXe1C5K6LCKbp9R8ZINfRxbuTERucSlaBnpgcJsGUpdDRGQzzqTm4r/jqQCAZwfw7D0RUV1ZfyIV05bEIr9EhxBfV/wwvjNaNfCUuiwikhAb/DqQW6TFTzvOAwCmDgjn5VBERDf5Zovh7P3Qdg3QIlA5l8AREUlFrxeYszkOX248CwDo2aw+5o7tBB93ZUzzRUQ1xwa/Dvy6+wJyikrRzN8dwyJ4SRQRUZm49Dz8c/QyAMMBUCIiqp384lK8tOyI8cqox3qG4Y3hraFx4NjZRMQGv9byi0vx4/YbZ+8dePaeiMho3pY4CAEMbB2Itg29pC6HiEjWLl4pwKRfD+B0ai40Dip8MLIdxnQJlbosIrIhbPBr6fc9F3C1QIuw+m4Y0b6h1OUQEdmMxMx8rI5NBmAYfJSIiGpud3wWJi86iKsFWvjVc8Z3j3ZCVGNfqcsiIhvDBr8WCkt0+OH62fsp/cPhyEujiIiM5sXEQS+A/i390b6Rt9TlEBHJkhACv++5gHf/PolSvUBEsBe+ezQKDb2VMWc3EdUtNvi18Me+JGTmlaCRjytGdgyWuhwiIptx8UoBVh4ynL1/9s7mEldDRCRPJaV6zFhzAn/uSwIA3BvZEJ/c3x4uGgeJKyMiW8UGv4aKtDp8tzUegOHsPQc2ISK6YV5MPEr1Ar2b+6FTqI/U5RARyU5mXjGe+f0g9idehUoFvHpXKzzVpylUKo73REQVY4NfQ0sPXER6bjEaerng/k6NpC6HiMhmXL5WiOUHLwIAnuPZeyKiajuenI0nfz2Ay9lF8HB2xNcPd0T/VgFSl0VEMsAGvwaKS3X4NsZw9v6Zfs3g5Miz90REZeZvjYdWJ9C9qS+6hHEAKCKi6vj7yGW8vPwIirR6NPVzx/fjOyM8oJ7UZRGRTLDBr4HlBy8hJbsIgZ7OeLBziNTlEBHZjLScIizez7P3RETVpdMLzNpwBvOun0Tq28IfXz/cEV6uGokrIyI5YYNfTVqdHvO2GIL36b7NOMgJEdFNvtt6HiWlenQJ80GPpvWlLoeISBZyirT43+JYbD6dDgB4qk9TvHJXKzioeb89EVUPG/xqWnUoGcnXCuFXzxkPdw2VuhwiIpuRkVuMRXsvAACeHdCcA0EREVVBQmY+nvhlP+Iz8uHsqMYn97fn7ExEVGNs8KuhVKfH3Jg4AIYjqzx7T0R0w4/bz6O4VI/IEG/0bu4ndTlERDZv69kMPPvHIeQUlaKBpwu+Hx+F9o28pS6LiGSMDX41rDlyGReyCuDr7oRx3Xn2noioTFZeMX7dbTh7/9yd4Tx7T0R0G0II/Lg9ATP/OwW9ADqFemP+o1EI8HCRujQikjk2+FWk0wt8s9lw9v6J3k3g5sRvHRFRmZ92JKBQq0O7YE/0b8mpnIiIKlKk1eH1lcew8nAyAGB050Z4f2Q7ODvyylAiqj12qVX034k0nM/Mh7ebBuN7hEldDhGRzbhWqL1x9p733hMRVSg1uwhP/XYARy5lw0GtwlvDW2NCzzDmJhHVGTb4VTQv5jwA4P96NUE9Z37biIjK/LYnCXnFpWjVwAOD2gRKXQ4RkU06lHQVT/92EOm5xfB202Du2E7oFc7xSoiobqmlLgAA5s6di7CwMLi4uKBbt27Yt2/fbddftmwZWrVqBRcXF0RERGDt2rUWrzEuIx8eLo6Y0DPM4u9FRFRGLvkIGOa951koIrIWOeRjmeUHL+Gh7/YgPbcYLQLrYc2UO9jcE5FFSN7gL1myBNOmTcOMGTNw6NAhdOjQAUOGDEF6errZ9Xft2oWHH34Yjz/+OA4fPoyRI0di5MiROH78uMVrndirCbxcNRZ/HyIiwPbzsbhUb/x/84B6uKttA4u8DxHRrWw9H8vEZQMTFuzHS8uOoESnx+A2gVg5uRdC67tZ9H2JyH5J3uB/8cUXmDRpEiZOnIg2bdpg/vz5cHNzw88//2x2/a+++gp33XUXXn75ZbRu3Rrvv/8+OnXqhG+++abOa1t9ffCTMj5ubO6JyHpsOR+X7E9C7MVs49dRjX2gVvPsPRFZhy3nIwB8EX0WAJCnU2PX+asADFc5zX8kird6EpFFSZowJSUlOHjwIKZPn25cplarMXDgQOzevdvsc3bv3o1p06aZLBsyZAhWr15tdv3i4mIUFxcbv87JyQEAaLVaaLXaCmtLyS7Ch2tPmSx7/5+TuLOlH4K85DGFSdn23W47bZFc6wZYu1SqU7tcts8a+QjULCNTsoswfeUxk2VLD1zE5L5NmI9WINfa5Vo3YF+1y2EbbTkfAWDFoUu4WlD+8YaeGuh0pdDpKnyqTbCn/d2WsHbrU2I+AhI3+JmZmdDpdAgMNB2UKTAwEKdPnzb7nNTUVLPrp6amml1/5syZePfdd8st37BhA9zcKr486ly2CnphOl2JXgBL125Bcy9R4fNsUXR0tNQl1Ihc6wZYu1SqUntBQYEVKqk9a+QjULOMZD7aBrnWLte6AfuoXQ4Zacv5CAC/nVYBKD/l3e8xx+CaerTC59kae9jfbRFrtz4l5SNgB6PoT58+3eSIbU5ODkJCQjB48GB4enpW+LyU7CLMO7UN+ps+q6pVwOhh/WV1hio6OhqDBg2CRiOf2wvkWjfA2qVSndrLzsCQQU0ykvkoLbnWLte6AfuqnRl5Q00/QxY2uITXVp0st/yRfhEY1qmRRWqtS/a0v9sS1m59Ss1HSRt8Pz8/ODg4IC0tzWR5WloaGjQwP1hTgwYNqrW+s7MznJ2dyy3XaDS3/UGG+mkwc1QEpq88Br0wfHidOSoCoX4elW2WzalsW22VXOsGWLtUqlK7XLbNGvkI1CwjmY+2Qa61y7VuwD5ql8P22XI+AsBD3Zpg3tYEJF0pNC4L9XXFQ92aVPgcW2QP+7stYu3Wp6R8BCQeZM/JyQlRUVHYtGmTcZler8emTZvQo0cPs8/p0aOHyfqA4bKKitavjTFdQhHzYh9MbaNDzIt9MKZLaJ2/BxGROcxHIiLzbD0fAWDbKwPw8X1t0M5Hh4/va4NtrwywyPsQEd1K8kv0p02bhgkTJqBz587o2rUrZs+ejfz8fEycOBEAMH78eAQHB2PmzJkAgOeffx59+/bFrFmzMHz4cCxevBgHDhzA999/b5H6grxc0NxLyOayUyJSDuYjEZF5tp6PAHB/p0ZwTT0qi8vyiUg5JG/wx4wZg4yMDLz99ttITU1FZGQk1q1bZxwIJSkpCWr1jQsNevbsiT/++ANvvvkmXn/9dTRv3hyrV69Gu3btpNoEIiKLYD4SEZnHfCQiMk/yBh8Apk6diqlTp5p9LCYmptyyBx98EA8++KCFqyIikh7zkYjIPOYjEVF5kt6DT0RERERERER1gw0+ERERERERkQKwwSciIiIiIiJSADb4RERERERERApgE4PsWZMQAgCQk5NTpfW1Wi0KCgqQk5MDjUZjydLqnFxrl2vdAGuXSnVqL/vdL8sCMlWdjLSXfcbWyLV2udYN2FftzMiK2ctnSLnWDbB2qci1dqXmo901+Lm5uQCAkJAQiSshIinl5ubCy8tL6jJsDjOSiABmpDnMRyICbD8fVcLWD0HUMb1ej8uXL8PDwwMqlarS9XNychASEoKLFy/C09PTChXWHbnWLte6AdYulerULoRAbm4uGjZsaDJHMhlUJyPtZZ+xNXKtXa51A/ZVOzOyYvbyGVKudQOsXSpyrV2p+Wh3Z/DVajUaNWpU7ed5enrKaoe9mVxrl2vdAGuXSlVrt+WjrlKrSUbawz5ji+Rau1zrBuyndmakefb2GVKudQOsXSpyrV1p+Wi7hx6IiIiIiIiIqMrY4BMREREREREpABv8Sjg7O2PGjBlwdnaWupRqk2vtcq0bYO1SkXPtcibn7ztrtz651g2wdqoZuX7v5Vo3wNqlItfa5Vp3ZexukD0iIiIiIiIiJeIZfCIiIiIiIiIFYINPREREREREpABs8ImIiIiIiIgUgA0+ERERERERkQKwwQcwd+5chIWFwcXFBd26dcO+fftuu/6yZcvQqlUruLi4ICIiAmvXrrVSpeVVp/YffvgBvXv3ho+PD3x8fDBw4MBKt9VSqvs9L7N48WKoVCqMHDnSsgXeRnVrv3btGqZMmYKgoCA4OzujRYsWku0z1a199uzZaNmyJVxdXRESEoIXXngBRUVFVqrWYNu2bRgxYgQaNmwIlUqF1atXV/qcmJgYdOrUCc7OzggPD8fChQstXqdSMR+lIdeMZD5aNx8BZqTU5JqRzEdpyDUjmY8yI+zc4sWLhZOTk/j555/FiRMnxKRJk4S3t7dIS0szu/7OnTuFg4OD+PTTT8XJkyfFm2++KTQajTh27JiVK69+7WPHjhVz584Vhw8fFqdOnRKPPfaY8PLyEpcuXbLpusskJCSI4OBg0bt3b3Hvvfdap9hbVLf24uJi0blzZzFs2DCxY8cOkZCQIGJiYkRsbKyVK69+7YsWLRLOzs5i0aJFIiEhQaxfv14EBQWJF154wap1r127Vrzxxhti5cqVAoBYtWrVbdc/f/68cHNzE9OmTRMnT54Uc+bMEQ4ODmLdunXWKVhBmI/Wz0ch5JuRzEfr56MQzEgpyTUjmY/3WqfYW8g1I5mP8stHu2/wu3btKqZMmWL8WqfTiYYNG4qZM2eaXX/06NFi+PDhJsu6desmnnrqKYvWaU51a79VaWmp8PDwEL/88oulSjSrJnWXlpaKnj17ih9//FFMmDBBsnCubu3ffvutaNq0qSgpKbFWiRWqbu1TpkwRAwYMMFk2bdo00atXL4vWeTtVCedXXnlFtG3b1mTZmDFjxJAhQyxYmTIxH62fj0LINyOZj9LmoxDMSGuTa0YyH++1QqXlyTUjmY/yy0e7vkS/pKQEBw8exMCBA43L1Go1Bg4ciN27d5t9zu7du03WB4AhQ4ZUuL6l1KT2WxUUFECr1cLX19dSZZZT07rfe+89BAQE4PHHH7dGmWbVpPY1a9agR48emDJlCgIDA9GuXTt89NFH0Ol01iobQM1q79mzJw4ePGi8DOv8+fNYu3Ythg0bZpWaa8pWfkfljvlo/XwE5JuRzEd55CNgO7+ncifXjGQ+SkOuGcl8lGc+OkpdgJQyMzOh0+kQGBhosjwwMBCnT582+5zU1FSz66emplqsTnNqUvutXn31VTRs2LDcjmxJNal7x44d+OmnnxAbG2uFCitWk9rPnz+PzZs3Y9y4cVi7di3i4uIwefJkaLVazJgxwxplA6hZ7WPHjkVmZibuuOMOCCFQWlqKp59+Gq+//ro1Sq6xin5Hc3JyUFhYCFdXV4kqkxfmo/XzEZBvRjIf5ZGPADOyrsg1I5mP0pBrRjIf5ZmPdn0G3559/PHHWLx4MVatWgUXFxepy6lQbm4uHn30Ufzwww/w8/OTupxq0+v1CAgIwPfff4+oqCiMGTMGb7zxBubPny91aZWKiYnBRx99hHnz5uHQoUNYuXIl/v33X7z//vtSl0ZkUXLJR0DeGcl8JJIf5qP1yDUjmY/Ss+sz+H5+fnBwcEBaWprJ8rS0NDRo0MDscxo0aFCt9S2lJrWX+fzzz/Hxxx9j48aNaN++vSXLLKe6dcfHxyMxMREjRowwLtPr9QAAR0dHnDlzBs2aNbNs0dfV5HseFBQEjUYDBwcH47LWrVsjNTUVJSUlcHJysmjNZWpS+1tvvYVHH30UTzzxBAAgIiIC+fn5ePLJJ/HGG29ArbbN44MV/Y56enrK5sirLWA+Wj8fAflmJPNRHvkIMCPrilwzkvnIz5DVwXyUZz7a7nfYCpycnBAVFYVNmzYZl+n1emzatAk9evQw+5wePXqYrA8A0dHRFa5vKTWpHQA+/fRTvP/++1i3bh06d+5sjVJNVLfuVq1a4dixY4iNjTX+u+eee9C/f3/ExsYiJCTEZmsHgF69eiEuLs74BwUAzp49i6CgIKt9eAVqVntBQUG5EC77IyOEsFyxtWQrv6Nyx3y0fj4C8s1I5qM88hGwnd9TuZNrRjIf+RmyOpiPMs1HKUf4swWLFy8Wzs7OYuHCheLkyZPiySefFN7e3iI1NVUIIcSjjz4qXnvtNeP6O3fuFI6OjuLzzz8Xp06dEjNmzJB0Gqjq1P7xxx8LJycnsXz5cpGSkmL8l5uba9N130rKEVCrW3tSUpLw8PAQU6dOFWfOnBH//POPCAgIEB988IHN1z5jxgzh4eEh/vzzT3H+/HmxYcMG0axZMzF69Gir1p2bmysOHz4sDh8+LACIL774Qhw+fFhcuHBBCCHEa6+9Jh599FHj+mVTnLz88svi1KlTYu7cubKc4sQWMB+tn481qf1WUmUk89H6+SgEM1JKcs1I5uO9VqrWlFwzkvkov3y0+wZfCCHmzJkjQkNDhZOTk+jatavYs2eP8bG+ffuKCRMmmKy/dOlS0aJFC+Hk5CTatm0r/v33XytXfEN1am/cuLEAUO7fjBkzbLruW0kZzkJUv/Zdu3aJbt26CWdnZ9G0aVPx4YcfitLSUitXbVCd2rVarXjnnXdEs2bNhIuLiwgJCRGTJ08WV69etWrNW7ZsMbvfltU6YcIE0bdv33LPiYyMFE5OTqJp06ZiwYIFVq1ZSZiPM6xfuJBvRjIfr1q9bmaktOSakcxHacg1I5mP8qISwsavlSAiIiIiIiKiStn1PfhERERERERESsEGn4iIiIiIiEgB2OATERERERERKQAbfCIiIiIiIiIFYINPREREREREpABs8ImIiIiIiIgUgA0+ERERERERkQKwwSeqIpVKhdWrVwMAEhMToVKpEBsbK2lNRES2ghlJRGQe85GsiQ0+ycJjjz0GlUoFlUoFjUaDJk2a4JVXXkFRUZHUpRERSY4ZSURkHvOR7I2j1AUQVdVdd92FBQsWQKvV4uDBg5gwYQJUKhU++eQTqUsjIpIcM5KIyDzmI9kTnsEn2XB2dkaDBg0QEhKCkSNHYuDAgYiOjgYA6PV6zJw5E02aNIGrqys6dOiA5cuXmzz/xIkTuPvuu+Hp6QkPDw/07t0b8fHxAID9+/dj0KBB8PPzg5eXF/r27YtDhw5ZfRuJiGqKGUlEZB7zkewJG3ySpePHj2PXrl1wcnICAMycORO//vor5s+fjxMnTuCFF17AI488gq1btwIAkpOT0adPHzg7O2Pz5s04ePAg/u///g+lpaUAgNzcXEyYMAE7duzAnj170Lx5cwwbNgy5ubmSbSMRUU0xI4mIzGM+ktLxEn2SjX/++Qf16tVDaWkpiouLoVar8c0336C4uBgfffQRNm7ciB49egAAmjZtih07duC7775D3759MXfuXHh5eWHx4sXQaDQAgBYtWhhfe8CAASbv9f3338Pb2xtbt27F3Xffbb2NJCKqIWYkEZF5zEeyJ2zwSTb69++Pb7/9Fvn5+fjyyy/h6OiI+++/HydOnEBBQQEGDRpksn5JSQk6duwIAIiNjUXv3r2NwXyrtLQ0vPnmm4iJiUF6ejp0Oh0KCgqQlJRk8e0iIqoLzEgiIvOYj2RP2OCTbLi7uyM8PBwA8PPPP6NDhw746aef0K5dOwDAv//+i+DgYJPnODs7AwBcXV1v+9oTJkxAVlYWvvrqKzRu3BjOzs7o0aMHSkpKLLAlRER1jxlJRGQe85HsCRt8kiW1Wo3XX38d06ZNw9mzZ+Hs7IykpCT07dvX7Prt27fHL7/8Aq1Wa/YI7M6dOzFv3jwMGzYMAHDx4kVkZmZadBuIiCyFGUlEZB7zkZSOg+yRbD344INwcHDAd999h5deegkvvPACfvnlF8THx+PQoUOYM2cOfvnlFwDA1KlTkZOTg4ceeggHDhzAuXPn8Ntvv+HMmTMAgObNm+O3337DqVOnsHfvXowbN67SI7ZERLaMGUlEZB7zkZSMZ/BJthwdHTF16lR8+umnSEhIgL+/P2bOnInz58/D29sbnTp1wuuvvw4AqF+/PjZv3oyXX34Zffv2hYODAyIjI9GrVy8AwE8//YQnn3wSnTp1QkhICD766CO89NJLUm4eEVGtMCOJiMxjPpKSqYQQQuoiiIiIiIiIiKh2eIk+ERERERERkQKwwSciIiIiIiJSADb4RERERERERArABp+IiIiIiIhIAdjgExERERERESkAG3wiIiIiIiIiBWCDT0RERERERKQAbPCJiIiIiIiIFIANPhEREREREZECsMEnIiIiIiIiUgA2+EREREREREQKwAafiIiIiIiISAHY4BMREREREREpABt8IiIiIiIiIgVgg09ERERERESkAGzwiYiIiIiIiBSADT4RERERERGRArDBJ8XT6/Vo164dPvzwQ6lLoeuysrLg7u6OtWvXSl0Kkd1jRtoeZiSRbWA+2h7mY+XY4FvY/7d35+FNVgn7x++0dIW2gKULUCygqCzKzguojFooLgiDo1UcQQZxo+pQN1ChIgqMAuIoioKAIg6Lr46+LuyigigK1I1NNhkV0I7SAoW2NOf3B79GQpO2Kdme9Pu5Li7IyXme3EnTm+dknTt3rmw2m+NPnTp11KRJE91888366aefXG5jjNG8efN08cUXq379+oqNjVW7du302GOP6ciRI24v66233tLll1+uxMRERUZGqnHjxrruuuu0atWqamU9duyYnn76aXXr1k0JCQmKjo5Wq1atlJ2dre3bt9fo+geDf/3rX/rPf/6j7Oxsl+c///zzstls6tatm8vz9+zZI5vNpsmTJ7s8f/LkybLZbNqzZ0+F8073Z+JNn376qS688ELFxsYqJSVFd999tw4fPlytbQ8cOKChQ4cqKSlJMTEx6tixoxYvXlxh3ltvvaXMzEw1btxYUVFRatq0qf7yl7/o22+/dZp3xhln6JZbbtGYMWO8ct1gXXRk4NGRJ/ijI7dt26aRI0eqR48eio6Odnu70JGQ6MdgQD+ecDr9WFBQoAceeEBnn322YmJidOaZZ2rYsGHau3ev0zyOIb3IwKfmzJljJJnHHnvMzJs3z8ycOdMMGzbMhIeHm5YtW5qjR486zT9+/Li57rrrjCRz0UUXmaefftq8+OKL5q9//asJCwszbdu2Nfv373faxm63m5tvvtlIMh06dDBPPPGEefnll83jjz9uOnXqZCSZtWvXVprz119/dcy96qqrzLRp08ysWbPM/fffb9LS0kxERITXbxt/ueCCC8ytt97q9vwePXqY9PR0I8l8//33Fc7fvXu3kWSeeuopl9s/9dRTRpLZvXu3Y8wbPxNv2rRpk4mOjjYdOnQwL7zwgnn44YdNVFSU6du3b5XbFhQUmLPOOsvExcWZRx55xDz33HPm4osvNpLM/PnzneaOGzfOZGVlmUmTJplZs2aZxx9/3LRo0cLExMSYvLw8p7mbN282kszKlSu9el1hLXRk4NGR/uvIOXPmOO6n7du3r3C7nIyOBP0YePTj6fVjWVmZ6dKli6lbt665//77zcyZM82DDz5o4uLiTJMmTUxhYaFjLseQ3sMC38fKy/mLL75wGn/wwQeNJLNw4UKn8QkTJhhJ5r777quwr3feeceEhYVV+IUqL4e///3vxm63V9ju1VdfNZ9//nmlOa+88koTFhZm3njjjQrnHTt2zNx7772Vbl9dpaWlpri42Cv7qo6NGzcaSWbFihUuz9+1a5eRZN58803TqFEj8+ijj1aYU5Ny9sbPxJsuv/xyk5qaagoKChxjM2fONJLM0qVLK932ySefrFCi5YWdkpJS5c9z//79pk6dOua2226rcF7btm3NTTfd5OG1QSihI53RkSeEakf+97//dRzQurpdTkVH1m70ozP68QQr9ePatWuNJPPcc885jc+ePdtx21WGY8iaYYHvY+7K+d133zWSzIQJExxjRUVFpkGDBqZVq1amtLTU5f6GDh1qJJl169Y5tmnYsKE599xzzfHjx2uU8bPPPjOSzPDhw6s1v1evXqZXr14VxocMGWLOPPNMx+mTS+3pp582LVq0MGFhYeazzz4z4eHhLotw69atRpJ59tlnHWO///67ueeee0zTpk1NZGSkadmypZk0aZIpKyurMuvYsWNNZGSkKSkpcXn++PHjTYMGDUxxcbG54447zNlnn11hjqfl7I2fiTcVFBSYOnXqmPvvv99pvLi42NSrV88MGzas0u379etnGjVqVGG8/HovW7as0u3tdruJj483WVlZFc4bOXKkqV+/vsv/wFA70JF0ZKAFqiOrs8CnI2s3+pF+DLTT7ccPPvjASDKLFy92Of7BBx9Uuj3HkDXDe/ADpPy9Ng0aNHCMrVmzRr///rsGDRqkOnXquNxu8ODBkqR3333Xsc1vv/2mQYMGKTw8vEZZ3nnnHUnSTTfdVKPtqzJnzhw9++yzuvXWWzVlyhSlpqaqV69eWrRoUYW5CxcuVHh4uK699lpJUlFRkXr16qXXXntNgwcP1j//+U/17NlTo0ePVk5OTpWX/emnn6pt27aKiIhwef78+fM1cOBARUZG6oYbbtD333+vL7744rSurzd+JpL0+++/Kz8/v8o/RUVFle7nm2++0fHjx9W5c2en8cjISLVv316bNm2qdPvi4mLFxMRUGI+NjZUkbdiwocJ5Bw8e1K+//qpvvvlGt9xyiwoLC3XZZZdVmNepUycdPHhQ3333XaUZUPvQkXRkVazckdVFR8IV+pF+rEqw9GPnzp1Vt25djRkzRqtWrdJPP/2kjz76SA888IC6dOmijIyMCttwDHn6XDcAvK6goED5+fk6duyYPv/8c40bN05RUVG66qqrHHM2b94sSbrgggvc7qf8vC1btjj93a5duxpn88Y+KvPjjz9qx44datSokWMsKytLt912m7799lu1bdvWMb5w4UL16tVLycnJkqSpU6dq586d2rRpk84++2xJ0m233abGjRvrqaee0r333qu0tDS3l71161a3H3yyYcMGbd26Vc8++6wk6cILL1TTpk01f/58denSpcbX11u3Z4cOHfTDDz9UOS83N1ePPvqo2/P37dsnSUpNTa1wXmpqqj755JNK93/OOedoxYoV+uGHH3TmmWc6xsu3c/VBP//zP/+jbdu2SZLq1aunRx55RMOGDaswr0WLFpJO3PdPvh+g9qEj6UhPWbkjq4uOhEQ/0o+eC5Z+TExM1MKFCzV8+HCnRXpmZqbeeOMNlw9GcQx5+ljg+8mpj1Clp6frtddeU9OmTR1jhw4dkiTFxcW53U/5eYWFhU5/V7ZNVbyxj8pcc801TsUsSQMHDtSIESO0cOFCxy/lt99+q82bN+uee+5xzFu8eLEuuugiNWjQQPn5+Y7xjIwMTZo0SR9//LFuvPFGt5f93//+1+kR7pPNnz9fycnJuuSSSyRJNptNWVlZeu211zRlypQaP3Lqrdtz/vz5Onr0aJXzygvOnfJ9REVFVTgvOjq6ysu45ZZbNGPGDF133XV6+umnlZycrEWLFumtt95y2v/J5syZo8LCQu3atUtz5szR0aNHVVZWprAw5xcNlf9sTv7ZonaiI+lIT1m5I6uLjoREP9KPnguWfpSkRo0aqUOHDsrOzlabNm2Ul5enJ598UkOHDnX5bSMcQ54+Fvh+Mn36dLVq1UoFBQWaPXu2Pv744wq/LOW/zOUl7cqpBR4fH1/lNlU5eR/169ev8X7cad68eYWxxMREXXbZZVq0aJHGjx8v6cQjr3Xq1NHAgQMd877//nt9/fXXFcq93C+//FLl5RtjKoyVlZVpwYIFuuSSS7R7927HeLdu3TRlyhStXLlSffr0qXLfJ7PZbJK88zORpJ49e57W9uXKXzpaXFxc4bxjx465fGnpyc4//3y9/vrruv322x2ZUlJSNG3aNN1xxx2qV69ehW26d+/u+Pf111+v8847T5IqfE1M+c+m/LZD7UVHOqMjq2bljqwuOhIS/Xgq+rFqwdKPu3bt0iWXXKJXX31V11xzjSSpf//+Sk9P180336wPPvhAl19+udM2HEOePhb4ftK1a1fH+1cGDBigCy+8UIMGDdK2bdsc//mX34G//vprDRgwwOV+vv76a0lS69atJUnnnnuupBPvkXG3TVVO3sdFF11U5Xybzea28Fxx98t//fXXa+jQocrLy1P79u21aNEiXXbZZUpMTHTMsdvt6t27tx544AGX+2jVqlWlWc844wz9/vvvFcZXrVqlffv2acGCBVqwYEGF8+fPn+8o5+joaEnun4Upf/9S+Txv/Ewk6ddff3V7m56sXr16lR5Alr+sqvxlVifbt2+fGjduXOVl/OUvf9HVV1+tr776SmVlZerYsaNWr14tqeqfQYMGDXTppZdq/vz5Fcq5/Gdz8s8ctRMdWREdWblQ6cjK0JGQ6EdX6MfKBUs/zp07V8eOHXN6O4kkXX311ZKktWvXVljgn4xjyJrhQ/YCIDw8XBMnTtTPP/+s5557zjF+4YUXqn79+nr99dfd/lK++uqrkuT4RbnwwgvVoEED/etf/6rWL7Ir/fr1kyS99tpr1ZrfoEEDHTx4sMJ4dd7rc7IBAwYoMjJSCxcuVF5enrZv367rr7/eaU7Lli11+PBhZWRkuPzTrFmzSi/j3HPPdXp0tdz8+fOVlJSkxYsXV/hzww036K233nKUcaNGjRQbG+t4P9Cptm3bptjYWEfBeONnIkldunRRampqlX9OLbxTtW3bVnXq1NGXX37pNF5SUuL4j7E6IiMj1aVLF/3P//yPIiMjtWLFCkkVXzroytGjR1VQUFBhvPxnU35gAkh0ZDk6snKh1JHu0JE4Ff14Av1YuWDpxwMHDsgYU+G6lJaWSpKOHz9e5XXhGLIGAvXx/bWFu684McaYrl27muTkZHP06FHH2OOPP24kmQcffLDC/HfffdeEhYWZzMxMp/FJkyYZSebee+91+VUR8+bNq/L7Mvv27WvCwsLMW2+9VeG84uJip+8wve+++0xUVJT55ZdfHGN5eXkmLCzM7VecuNOvXz/TokUL8+CDD5rIyEjz+++/O53/6KOPGklmyZIlFbb9/fff3X4VTLkxY8aYiIgIc+zYMcdYUVGRiYuLM3/7299cblP+nZ0LFixwjA0YMMDEx8ebH374wWnuDz/8YOLi4syAAQOcxr3xM1mzZo1Zvnx5lX927txZ6X6MOfHzTU1NdXz/sjHGzJo1q8JXlBw5csRs2bLF/Prrr5Xub/v27SYuLs5cddVVTuMHDhyoMHf37t0mLi7OXHTRRRXOGzlypElISOArTmoxOpKOrE0debLqfk0eHVl70Y/0o9X7cfLkyUaSmTNnjtM+p02bVuF24hjSe1jg+1hl5bx48WIjybzwwguOsePHj5trrrnGSDIXX3yxeeaZZ8xLL71kBg8ebMLCwkybNm3M/v37nfZTVlZmbrrpJiPJdOzY0UyYMMHMnj3bTJgwwXTt2tVIMp9++mmlOX/55RfTvn17Y7PZzNVXX22eeeYZM2vWLPPggw+aM88800RGRjrmbt682YSFhZkOHTqY5557zowdO9YkJSWZdu3aeVzOr732mpFk4uLiTL9+/Sqcf+TIEdOxY0dTp04dc8stt5gXXnjBTJ482QwZMsTUrVu3yoOsL7/80kgyS5cudYwtWLDASDL//ve/XW5TVlZmGjVq5JRn8+bNJj4+3pxxxhlm9OjR5sUXXzSjR482Z5xxhomPjzebN2+usI/T/Zl404YNG0xUVJTp0KGDeeGFF8zDDz9soqOjTZ8+fZzmffjhh0aSyc3NdRo/77zzzNixY82sWbPMww8/bBo2bGjOPPNM8+OPPzrNS0pKMjfccIP5xz/+YV566SVz//33m4YNG5ro6Gizdu3aCrnatm1r/vrXv3r9+sI66Eg6sjZ15MGDB8348ePN+PHjTd++fR0H8ePHj3f67u5ydGTtRj/Sj1bvx/z8fJOSkmIiIyPN3XffbV588UVz2223mfDwcNOmTRtTXFzsmMsxpPewwPexysq5rKzMtGzZ0rRs2dIcP37caXzOnDmmZ8+eJj4+3kRHR5s2bdqYcePGmcOHD7u9rDfeeMP06dPHNGzY0NSpU8ekpqaarKwss3r16mplLSoqMpMnTzZdunQx9erVM5GRkebss882d911l9mxY4fT3Ndee820aNHCREZGmvbt25ulS5eaIUOGeFzOhYWFJiYmxkgyr732mss5hw4dMqNHjzZnnXWWiYyMNImJiaZHjx5m8uTJpqSkpMrrdf7555thw4Y5Tvfr189ER0ebI0eOuN3m5ptvNhERESY/P98xtmXLFpOVlWWSkpJMnTp1TFJSkrn++uvNli1b3O7ndH8m3vTJJ5+YHj16mOjoaNOoUSMzYsQIp0djjXF/8Hr99debtLQ0ExkZaRo3bmxuv/12l4+05ubmms6dO5sGDRqYOnXqmMaNG5vrr7/efP311xXmbtmyxUgyK1as8Or1hLXQkXRkberI8p+5qz8n3zeMoSNBP9KPodGPP/74o/nb3/5mmjdvbiIjI01qaqoZPnx4hQdYOIb0HpsxLj7pAggh8+bN04gRI7R3716ffMIraubvf/+7Pv74Y23YsIFPQAUCiI4MTnQkEHj0Y3CiHyvHAh8hz2636/zzz9cNN9yghx9+ONBxoBPfLXvmmWdq0aJFuuKKKwIdB6jV6MjgQ0cCwYF+DD70Y9VY4AMAAAAAEAL4mjwAAAAAAEIAC3wAAAAAAEIAC3wAAAAAAEIAC3wAAAAAAEIAC3wAAAAAAEJAnUAH8De73a6ff/5ZcXFxfG8iUAsZY3To0CE1btxYYWE8xnkqOhKo3ehI9+hHoHazSj/WugX+zz//rLS0tEDHABBg//nPf9S0adNAxwg6dCQAiY50hX4EIAV/P9a6BX5cXJykEz+Y+Pj4KueXlpZq2bJl6tOnjyIiInwdz6usmt2quSWyB4on2QsLC5WWluboAjjzpCNry30m2Fg1u1VzS7UrOx3pXm05hrRqbonsgWLV7KHaj7VugV/+kqr4+Phql3NsbKzi4+MtdYeVrJvdqrklsgdKTbLz8krXPOnI2nafCRZWzW7V3FLtzE5HVlRbjiGtmlsie6BYNXuo9mPwvnkAAAAAAABUGwt8AAAAAABCAAt8AAAAAABCAAt8AAAAAABCQEAX+B9//LH69eunxo0by2az6d///neV26xevVodO3ZUVFSUzjrrLM2dO9fnOQHA3+hHAHCPjgQA1wK6wD9y5IguuOACTZ8+vVrzd+/erSuvvFKXXHKJ8vLy9Pe//1233HKLli5d6uOkAOBf9CMAuEdHAoBrAf2avMsvv1yXX355tefPmDFDzZs315QpUyRJ5513ntasWaOnn35amZmZvorpM/sKjmp3/hE1T6yr1ISYQMcBLGtfwTF9X2DTvoJjapZona9nqUxt70df2ldwVDv2F+pgcaCTAL4Xiv0o0ZGBwHErQk2o9mNAF/ieWrdunTIyMpzGMjMz9fe//z0wgU7Dwi/2avSb38hupDCbNO7qNrqmU1OvXkZp6XEVl0lFJccVYYL7+xpPZtXcEtkD4X83/Kjcd76T3YTr+S0fa+LAdsrq0izQsfwulPrRl07uXpvCZW+8V1ldzwx0LI9Y9XfVqrkl62anH/9AR54efxy3eoNVf1clsvtbKPejpRb4+/fvV3JystNYcnKyCgsLdfToUcXEVHw0sbi4WMXFfzxNU1hYKEkqLS1VaWlplZdZPqc6c6trX8ExR0lKkt1IY97+TmPe/s5rl/GHOnpg/Sof7NfXrJpbInvg2I00+s1v1L15A6UmRLuc483f5WBSk36UTq8jfdGPvnRq9xrZNO7drRr37tbABqsRq/6uWjW3ZO3s1etHyTq/z54KlWNIfzg1t3+PW73Byr+rZA+EUOtHSy3wa2LixIkaN25chfFly5YpNja22vtZvny51zJ9X2CT3YR7bX8A/mA30qL3P9TZCcbl+UVFRX5OFNy80ZHe7EdfontR21XVjxIdebJgPIb0p/LcdCdqg1DqR0st8FNSUnTgwAGnsQMHDig+Pt7ts1OjR49WTk6O43RhYaHS0tLUp08fxcfHV3mZpaWlWr58uXr37q2ICO+8N2NfwTE9v+VjxyOh0omXOy25u4eS490/auSp0tLjWrVqlS699FJFRFjnR23V3BLZ/e1A4TH1/eenFX6XrrviErePwJY/AxNqatKP0ul1pC/60Zf81b2+ZsXfVcm6uSVrZq9JP0p05MmC8RjSH07NbaXutOLvajmy+0+o92Pw/wRO0r17d73//vtOY8uXL1f37t3dbhMVFaWoqKgK4xERER6VrafzK9MsMULjrm7jeGlTmE2aOLCdWqU28Mr+y5WWlioqXEqoG225/1ismFsiu78l1I3RxIHtnN4XOHFgOzVLjHO7jVWum6dq0o+SdzrSm/3oS6d2r01Gj/dv4/Xu9TUr/q5K1s0tWTN7TfpRoiNPFozHkP5Unttfx63eYMXf1XJk959Q78eAfk3e4cOHlZeXp7y8PEknvsIkLy9Pe/fulXTikdPBgwc75t9+++3atWuXHnjgAW3dulXPP/+8Fi1apJEjRwYi/mk5+YNJVuT0CpkPdQD8LatLM62+92Jlty7T6nsvDpnfpdrcj750cveObl+ma4PwQ6IAbwnVfpToSH/juBWhJpT7MaAL/C+//FIdOnRQhw4dJEk5OTnq0KGDxo4dK0nat2+fo6glqXnz5nrvvfe0fPlyXXDBBZoyZYpmzZpl+a83SankpSAAqpaaEK2zE0ylL6uyGvrR9+pHBjoB4Huh2I8SHRlIHLciVIRqPwb0Jfp/+tOfZIz7DzKYO3euy202bdrkw1QAEHj0IwC4R0cCgGsBfQYfAAAAAAB4Bwt8AAAAAABCAAt8AAAAAABCAAt8AAAAAABCAAt8AAAAAABCAAt8AAAAAABCAAt8AAAAAABCAAt8AAAAAABCAAt8AAAAAABCAAt8AAAAAABCAAt8AAAAAABCAAt8AAAAAABCAAt8AAAAAABCAAt8AAAAAABCAAt8AAAAAABCAAt8AAAAAABCAAt8AAAAAABCAAt8AAAAAABCAAt8AAAAAABCAAt8AAAAAABCAAt8AAAAAABCAAt8AAAAAABCAAt8AAAAAABCAAt8AAAAAABCAAt8AAAAAABCAAt8AAAAAABCAAt8AAAAAABCAAt8AAAAAABCAAt8AAAAAABCAAt8AAAAAABCAAt8AAAAAABCAAt8AAAAAABCAAt8AAAAAABCAAt8AAAAAABCAAt8AAAAAABCAAt8AAAAAABCAAt8AAAAAABCQMAX+NOnT1d6erqio6PVrVs3rV+/vtL506ZN0znnnKOYmBilpaVp5MiROnbsmJ/SAoB/0ZEA4Br9CAAVBXSBv3DhQuXk5Cg3N1cbN27UBRdcoMzMTP3yyy8u57/++usaNWqUcnNztWXLFr388stauHChHnroIT8nBwDfoyMBwDX6EQBcC+gCf+rUqRo+fLiGDh2q1q1ba8aMGYqNjdXs2bNdzv/000/Vs2dPDRo0SOnp6erTp49uuOGGKh+xBQAroiMBwDX6EQBcqxOoCy4pKdGGDRs0evRox1hYWJgyMjK0bt06l9v06NFDr732mtavX6+uXbtq165dev/993XTTTe5vZzi4mIVFxc7ThcWFkqSSktLVVpaWmXO8jnVmeuJ0tLjTpdRajNe3X/5fk/+2yqsmlsie6B4kt0q188KHWnF+8zJ3XvitHWyl7Pi7S5ZN7dUu7Jb4TpaoR/L5538t1W4yu2P41ZvsOptLpE9EEKxH6UALvDz8/NVVlam5ORkp/Hk5GRt3brV5TaDBg1Sfn6+LrzwQhljdPz4cd1+++2Vvrxq4sSJGjduXIXxZcuWKTY2ttp5ly9fXu251VFcJpXf/EuXLlNUuFd378Tb2f3FqrklsgdKdbIXFRX5Icnps1JHWuk+c3L3StbKfiqrZrdqbql2ZLdCR1qpHyXr3m9Ozu3P41ZvsOptLpE9EEKpH6UALvBrYvXq1ZowYYKef/55devWTTt27NA999yj8ePHa8yYMS63GT16tHJychynCwsLlZaWpj59+ig+Pr7KyywtLdXy5cvVu3dvRUREeO26FJUc1wPrV0mSMjP7KDbS+z8KX2X3NavmlsgeKJ5kL38GJhT5uyOteJ85uXslWSp7OSve7pJ1c0u1K3uodmQoHUP6mqvc/jhu9Qar3uYS2QMhVPsxYL+diYmJCg8P14EDB5zGDxw4oJSUFJfbjBkzRjfddJNuueUWSVK7du105MgR3XrrrXr44YcVFlbxIwWioqIUFRVVYTwiIsKjO6Cn86vcn7Gdsm/f/Si8nd1frJpbInugVCe7Va6blTrSSveZk7tXslb2U1k1u1VzS7UjuxWun5X6sSbzg8XJuf153OoNVr3NJbIHQij1oxTAD9mLjIxUp06dtHLlSseY3W7XypUr1b17d5fbFBUVVSjg8PATrxEyJjjfCwQANUFHAoBr9CMAuBfQh99ycnI0ZMgQde7cWV27dtW0adN05MgRDR06VJI0ePBgNWnSRBMnTpQk9evXT1OnTlWHDh0cL68aM2aM+vXr5yhpAAgVdCQAuEY/AoBrAV3gZ2Vl6ddff9XYsWO1f/9+tW/fXkuWLHF8aMrevXudHm195JFHZLPZ9Mgjj+inn35So0aN1K9fPz3xxBOBugoA4DN0JAC4Rj8CgGsBfwNNdna2srOzXZ63evVqp9N16tRRbm6ucnNz/ZAMAAKPjgQA1+hHAKgoYO/BBwAAAAAA3sMCHwAAAACAEMACHwAQdPYVHNWnO/O1r+Cozy7jYInPdg0AQckb3bq/4JgXEwHwtoC/Bx8AgJMt/GKvRr/5jexGCrNJ465uo2s6NfXKvl//fK/j3xPzwlUv/UcN+p/mXtk3AASz6nZraelxFZdJRSXHFWFskpy7M2PqR5o4sJ2yujTzW3YA1XdaC/xjx44pOjraW1kAIGTQjzWzr+Co4wBUkuxGGvP2dxrz9ndevywjmx55e7MuOS9FqQkxXt8/APfoSP/yvFvr6IH1q1yeYzfSQ29+q4tbNaI7gSDk8Uv07Xa7xo8fryZNmqhevXratWuXJGnMmDF6+eWXvR4QAKyCfjx9u/OPOA5A/cFupD35Rf67QKAWoyMDx9vdWmYM3QkEKY+fwX/88cf1yiuv6Mknn9Tw4cMd423bttW0adM0bNgwrwYEAKugH09f88S6CrPJ6UA0zCatyOmllITTe7Zvf8ExZUz9qMK+0xNjT2u/AKqHjgwcT7q1tLRUS5cuU2ZmH0VERLjsznCbje4EgpTHz+C/+uqreumll3TjjTcqPDzcMX7BBRdo69atXg0HAFZCP56+1IQYjbu6jeN0mE2aOLCdWjSqp9jIOqf1p0Wjepo4sJ3CbSfeU2qT0eP9W/MSU8BP6MjA8bRbo8LltjvDbTZNGNiW7gSClMfP4P/0008666yzKozb7XaVlpZ6JRQAWBH96B3XdGrqeF/oipxeatGontf2ndWlmS5u1Ug7DxRqZ95nutZLH94HoGp0ZGCdTreWd+ee/CKlJ8ayuAeCmMfP4Ldu3VqffPJJhfE33nhDHTp08EooALAi+tH7Tvdl+a6kJsSoW/OGqh/l9V0DqAQdGTxq0q2pCTHq3vIMFvdAkPP4GfyxY8dqyJAh+umnn2S32/Xmm29q27ZtevXVV/Xuu+/6IiMAWAL9CADu0ZEA4HseP4Pfv39//d///Z9WrFihunXrauzYsdqyZYv+7//+T7179/ZFRgCwBPoRANyjIwHA9zx+Bl+SLrroIi1fvtzbWQDA8uhHAHCPjgQA3/L4GfwWLVrov//9b4XxgwcPqkWLFl4JBQBWRD8CgHt0JAD4nscL/D179qisrKzCeHFxsX766SevhAIAK6IfAcA9OhIAfK/aL9F/5513HP9eunSpEhISHKfLysq0cuVKpaenezUcAFgB/QgA7tGRAOA/1V7gDxgwQJJks9k0ZMgQp/MiIiKUnp6uKVOmeDUcAFgB/QgA7tGRAOA/1V7g2+12SVLz5s31xRdfKDEx0WehAMBK6EcAcI+OBAD/8fhT9Hfv3u2LHABgefQjALhHRwKA79Xoa/KOHDmijz76SHv37lVJSYnTeXfffbdXggGAFdGPAOAeHQkAvuXxAn/Tpk264oorVFRUpCNHjqhhw4bKz89XbGyskpKSKGcAtRb9CADu0ZEA4Hsef03eyJEj1a9fP/3++++KiYnRZ599ph9++EGdOnXS5MmTfZERACyBfgQA9+hIAPA9jxf4eXl5uvfeexUWFqbw8HAVFxcrLS1NTz75pB566CFfZAQAS6AfAcA9OhIAfM/jBX5ERITCwk5slpSUpL1790qSEhIS9J///Me76QDAQuhHAHCPjgQA3/P4PfgdOnTQF198obPPPlu9evXS2LFjlZ+fr3nz5qlt27a+yAgAlkA/AoB7dCQA+J7Hz+BPmDBBqampkqQnnnhCDRo00B133KFff/1VL774otcDAoBV0I8A4B4dCQC+5/Ez+J07d3b8OykpSUuWLPFqIACwKvoRANyjIwHA9zx+Bt+djRs36qqrrvLW7gAgZNCPAOAeHQkA3uPRAn/p0qW677779NBDD2nXrl2SpK1bt2rAgAHq0qWL7Ha7T0ICQLCjHwHAPToSAPyj2i/Rf/nllzV8+HA1bNhQv//+u2bNmqWpU6fqrrvuUlZWlr799ludd955vswKAEGJfgQA9+hIAPCfaj+D/8wzz+gf//iH8vPztWjRIuXn5+v555/XN998oxkzZlDMAGot+hEA3KMjAcB/qr3A37lzp6699lpJ0sCBA1WnTh099dRTatq0qc/CAYAV0I8A4B4dCQD+U+0F/tGjRxUbGytJstlsioqKcnzVCQDUZvQjALhHRwKA/3j0NXmzZs1SvXr1JEnHjx/X3LlzlZiY6DTn7rvv9l46ALAI+hEA3KMjAcA/qr3Ab9asmWbOnOk4nZKSonnz5jnNsdlslDOAWod+BAD36EgA8J9qL/D37NnjwxgAYF30IwC4R0cCgP9U+z34vjJ9+nSlp6crOjpa3bp10/r16yudf/DgQY0YMUKpqamKiopSq1at9P777/spLQD4Fx0JAK7RjwBQkUfvwfe2hQsXKicnRzNmzFC3bt00bdo0ZWZmatu2bUpKSqowv6SkRL1791ZSUpLeeOMNNWnSRD/88IPq16/v//AA4GN0JAC4Rj8CgGsBXeBPnTpVw4cP19ChQyVJM2bM0HvvvafZs2dr1KhRFebPnj1bv/32mz799FNFRERIktLT0/0ZGQD8ho4EANfoRwBwLWAv0S8pKdGGDRuUkZHxR5iwMGVkZGjdunUut3nnnXfUvXt3jRgxQsnJyWrbtq0mTJigsrIyf8UGAL+gIwHANfoRANwL2DP4+fn5KisrU3JystN4cnKytm7d6nKbXbt2adWqVbrxxhv1/vvva8eOHbrzzjtVWlqq3Nxcl9sUFxeruLjYcbqwsFCSVFpaqtLS0ipzls+pzlxPlJYed7qMUpvx6v7L93vy31Zh1dwS2QPFk+xWuX5W6Ej6MTCsmt2quaXald0K19EK/Vg+7+S/A6263RpsuT1B9sCwavZQ7Eephgv8nTt3as6cOdq5c6eeeeYZJSUl6YMPPlCzZs3Upk0bb2d0sNvtSkpK0ksvvaTw8HB16tRJP/30k5566im35Txx4kSNGzeuwviyZcsUGxtb7ctevnx5jXO7Ulwmld/8S5cuU1S4V3fvxNvZ/cWquSWyB0p1shcVFfk0Q6D6UQpcR9KPgWHV7FbNLdWO7KHakaF0DFlTnnZrsOSuCbIHhlWzB0s/eovHC/yPPvpIl19+uXr27KmPP/5YTzzxhJKSkvTVV1/p5Zdf1htvvFGt/SQmJio8PFwHDhxwGj9w4IBSUlJcbpOamqqIiAiFh//RSOedd57279+vkpISRUZGVthm9OjRysnJcZwuLCxUWlqa+vTpo/j4+CpzlpaWavny5erdu7fjPVveUFRyXA+sXyVJyszso9hI77+YwlfZfc2quSWyB4on2cufgfEFb/WjZI2OpB8Dw6rZrZpbql3ZrdCRVuhHKfjuN9Xt1mDL7QmyB4ZVswdTP3qTx0dNo0aN0uOPP66cnBzFxcU5xi+99FI999xz1d5PZGSkOnXqpJUrV2rAgAGSTjy6unLlSmVnZ7vcpmfPnnr99ddlt9sVFnbi4wO2b9+u1NRUl8UsSVFRUYqKiqowHhER4dEd0NP5Ve7P2E7Zt+/eLeHt7P5i1dwS2QOlOtl9ed281Y+StTqSfgwMq2a3am6pdmS3QkdaqR9rMt9XPO3WYMldE2QPDKtmD4Z+9CaPP2Tvm2++0Z///OcK40lJScrPz/doXzk5OZo5c6ZeeeUVbdmyRXfccYeOHDni+ETUwYMHa/To0Y75d9xxh3777Tfdc8892r59u9577z1NmDBBI0aM8PRqAIDXebMfJToSQGjhGBIAfM/jp0Xq16+vffv2qXnz5k7jmzZtUpMmTTzaV1ZWln799VeNHTtW+/fvV/v27bVkyRLHh6bs3bvX8SirJKWlpWnp0qUaOXKkzj//fDVp0kT33HOPHnzwQU+vBgB4nTf7UaIjAYQWjiEBwPc8XuBff/31evDBB7V48WLZbDbZ7XatXbtW9913nwYPHuxxgOzsbLcvp1q9enWFse7du+uzzz7z+HIAwNe83Y8SHQkgdHAMCQC+5/FL9CdMmKBzzz1XaWlpOnz4sFq3bq2LL75YPXr00COPPOKLjABgCfQjALhHRwKA73n8DH5kZKRmzpypMWPG6Ntvv9Xhw4fVoUMHnX322b7IBwCWQT8CgHt0JAD4nscL/DVr1ujCCy9Us2bN1KxZM19kAgBLoh8BwD06EgB8z+OX6F966aVq3ry5HnroIW3evNkXmQDAkuhHAHCPjgQA3/N4gf/zzz/r3nvv1UcffaS2bduqffv2euqpp/Tjjz/6Ih8AWAb9CADu0ZEA4HseL/ATExOVnZ2ttWvXaufOnbr22mv1yiuvKD09XZdeeqkvMgKAJdCPAOAeHQkAvufxAv9kzZs316hRozRp0iS1a9dOH330kbdyAYCl0Y8A4B4dCQC+UeMF/tq1a3XnnXcqNTVVgwYNUtu2bfXee+95MxsAWBL9CADu0ZEA4Dsef4r+6NGjtWDBAv3888/q3bu3nnnmGfXv31+xsbG+yAcAlkE/AoB7dCQA+J7HC/yPP/5Y999/v6677jolJib6IhMAWBL9CADu0ZEA4HseL/DXrl3rixwAYHn0IwC4R0cCgO9Va4H/zjvv6PLLL1dERITeeeedSudeffXVXgkGAFZAPwKAe3QkAPhXtRb4AwYM0P79+5WUlKQBAwa4nWez2VRWVuatbAAQ9OhHAHCPjgQA/6rWAt9ut7v8NwDUdvQjALhHRwKAf3n8NXmvvvqqiouLK4yXlJTo1Vdf9UooALAi+hEA3KMjAcD3PF7gDx06VAUFBRXGDx06pKFDh3olVKjYV3BUn+7M176Co5XO219wzE+JAPgS/eisuh1YGfoRCB10JAD4nsefom+Mkc1mqzD+448/KiEhwSuhQsHCL/Zq9JvfyG6kMJs07uo2uqZTU8f5r3++1/HvjKkfaeLAdsrq0iwQUQF4Cf34h6o6sDL0IxCa6EgA8L1qL/A7dOggm80mm82myy67THXq/LFpWVmZdu/erb59+/okpNXsKzjqOLCVJLuRxrz9nca8/Z3L+XYjPfTmt7q4VSOlJsT4MSkAb6AfnXnagZWhHwHroyMBwH+qvcAv/+TTvLw8ZWZmql69eo7zIiMjlZ6ermuuucbrAa1od/4Rx4FtdZUZoz35RRzAAhZEPzqrSQdWhn4ErI2OBAD/qfYCPzc3V5KUnp6urKwsRUdH+yyU1TVPrKswm5wOcMNs0oqcXkpJiNb+gmPKmPqR0/nhNpvSE2P9HxbAaaMfnVXVgZWhH4HQQ0cCgP94/CF7Q4YMoZirkJoQo3FXt3GcDrNJEwe2U4tG9RQbWUctGtXTxIHtFP7/34cWbrNpwsC2PDsFWBz9eEJVHVjZH/oRCF10JAD4XrWewW/YsKG2b9+uxMRENWjQwOUHpJT77bffvBbOyq7p1NTxftMVOb3UolE9p/OzujTTxa0aaU9+kdITYzl4BSyKfnStqg6sDP0IhA46EgD8q1oL/KefflpxcXGOf1dWzqjI3UtSUxNiOHAFLI5+rFpVL8t3hX4EQgMdCQD+Va0F/pAhQxz/vvnmm32VBQAsh34EAPfoSADwL4/fg79x40Z98803jtNvv/22BgwYoIceekglJSVeDQcAVkI/AoB7dCQA+J7HC/zbbrtN27dvlyTt2rVLWVlZio2N1eLFi/XAAw94PSAAWAX9CADu0ZEA4HseL/C3b9+u9u3bS5IWL16sXr166fXXX9fcuXP1v//7v97OBwCWQT8CgHt0JAD4nscLfGOM7Ha7JGnFihW64oorJElpaWnKz8/3bjoAsBD6EQDcoyMBwPc8XuB37txZjz/+uObNm6ePPvpIV155pSRp9+7dSk5O9npAALAK+hEA3KMjAcD3PF7gT5s2TRs3blR2drYefvhhnXXWWZKkN954Qz169PB6QACwCvoRANyjIwHA96r1NXknO//8850+AbXcU089pfDwcK+EAgAroh8BwD06EgB8z+MFfrkNGzZoy5YtkqTWrVurY8eOXgsFAFZGPwKAe3QkAPiOxwv8X375RVlZWfroo49Uv359SdLBgwd1ySWXaMGCBWrUqJG3MwKAJdCPAOAeHQkAvufxe/DvuusuHT58WN99951+++03/fbbb/r2229VWFiou+++2xcZAcAS6EcAcI+OBADf8/gZ/CVLlmjFihU677zzHGOtW7fW9OnT1adPH6+GAwAroR8BwD06EgB8z+Nn8O12uyIiIiqMR0REOL7bFABqI/oRANyjIwHA9zxe4F966aW655579PPPPzvGfvrpJ40cOVKXXXaZV8MBgJXQjwDgHh0JAL7n8QL/ueeeU2FhodLT09WyZUu1bNlSzZs3V2FhoZ599tkahZg+fbrS09MVHR2tbt26af369dXabsGCBbLZbBowYECNLhcAvIl+BAD3vN2R9CMAVOTxe/DT0tK0ceNGrVy50vEVJ+edd54yMjJqFGDhwoXKycnRjBkz1K1bN02bNk2ZmZnatm2bkpKS3G63Z88e3XfffbroootqdLkA4G30IwC4582OpB8BwDWPFvgLFy7UO++8o5KSEl122WW66667TjvA1KlTNXz4cA0dOlSSNGPGDL333nuaPXu2Ro0a5XKbsrIy3XjjjRo3bpw++eQTHTx48LRzAMDpoB8BwD1vdyT9CACuVfsl+i+88IJuuOEGffnll/r+++81YsQI3X///ad14SUlJdqwYYPTI7dhYWHKyMjQunXr3G732GOPKSkpScOGDTutywcAb6AfAcA9b3ck/QgA7lX7GfznnntOubm5ys3NlSS99tpruu222/TUU0/V+MLz8/NVVlam5ORkp/Hk5GRt3brV5TZr1qzRyy+/rLy8vGpdRnFxsYqLix2nCwsLJUmlpaUqLS2tcvvyOdWZ67zdcad9lNqMR9t7Q02zB5pVc0tkDxRPsvvi+lm1H6XT68jKbvdg6MDK1Jb7ezCxam6pdmW3QkdaoR/L5538d6BVt5eDLbcnyB4YVs0eDP3oC9Ve4O/atUtDhgxxnB40aJCGDRumffv2KTU11SfhTnXo0CHddNNNmjlzphITE6u1zcSJEzVu3LgK48uWLVNsbGy1L3v58uXVnitJxWVS+c27dOkyRYV7tLlXeZo9WFg1t0T2QKlO9qKiIq9frlX7UfJOR7q63YOpAysT6vf3YGTV3FLtyB6KHRnIfpSC537jaS8HS+6aIHtgWDV7IPvRF6q9wC8uLlbdunUdp8PCwhQZGamjR4/W+MITExMVHh6uAwcOOI0fOHBAKSkpFebv3LlTe/bsUb9+/Rxj5d+bWqdOHW3btk0tW7Z02mb06NHKyclxnC4sLFRaWpr69Omj+Pj4KjOWlpZq+fLl6t27t8vvbnWnqOS4Hli/SpKUmdlHsZEef57haatp9kCzam6J7IHiSfbyZ2C8yar9KJ1eR1Z2uwdDB1amttzfg4lVc0u1K7sVOtIK/SgF3/2mur0cbLk9QfbAsGr2YOhHX/DoiGvMmDFOj1iWlJToiSeeUEJCgmNs6tSp1d5fZGSkOnXqpJUrVzq+qsRut2vlypXKzs6uMP/cc8/VN9984zT2yCOP6NChQ3rmmWeUlpZWYZuoqChFRUVVGI+IiPDoDujxfGM7ZdvAHdx6mj1YWDW3RPZAqU52X103K/aj5J2OdDU3mDqwMqF+fw9GVs0t1Y7sVuhIK/VjTeb7iqe9HCy5a4LsgWHV7IHuR2+r9hHXxRdfrG3btjmN9ejRQ7t27XKcttlsp25WpZycHA0ZMkSdO3dW165dNW3aNB05csTxqaiDBw9WkyZNNHHiREVHR6tt27ZO29evX1+SKowDgL/QjwDgni86kn4EANeqvcBfvXq1TwJkZWXp119/1dixY7V//361b99eS5YscXxwyt69exUWVu0P+wcAv6MfAcA9X3Qk/QgArgXFayazs7NdvqRKqvo/hblz53o/EAAECfoRAFyjHwGgIh7aBAAAAAAgBLDABwAAAAAgBLDABwAAAAAgBLDABwAAAAAgBNRogf/JJ5/or3/9q7p3766ffvpJkjRv3jytWbPGq+EAwGroRwBwj44EAN/yeIH/v//7v8rMzFRMTIw2bdqk4uJiSVJBQYEmTJjg9YAAYBX0IwC4R0cCgO95vMB//PHHNWPGDM2cOVMRERGO8Z49e2rjxo1eDQcAVkI/AoB7dCQA+J7HC/xt27bp4osvrjCekJCggwcPeiMTAFgS/QgA7tGRAOB7Hi/wU1JStGPHjgrja9asUYsWLbwSCgCsiH4EAPfoSADwPY8X+MOHD9c999yjzz//XDabTT///LPmz5+v++67T3fccYcvMgKAJdCPAOAeHQkAvlfH0w1GjRolu92uyy67TEVFRbr44osVFRWl++67T3fddZcvMgKAJdCPAOAeHQkAvufxAt9ms+nhhx/W/fffrx07dujw4cNq3bq16tWr54t8AGAZ9CMAuEdHAoDvebzALxcZGanWrVt7MwsAhAT6EQDcoyMBwHc8XuBfcsklstlsbs9ftWrVaQUCAKuiHwHAPToSAHzP4wV++/btnU6XlpYqLy9P3377rYYMGeKtXABgOfQjALhHRwKA73m8wH/66addjj/66KM6fPjwaQcCAKuiHwHAPToSAHzP46/Jc+evf/2rZs+e7a3dAUDIoB8BwD06EgC8x2sL/HXr1ik6OtpbuwOAkEE/AoB7dCQAeI/HL9EfOHCg02ljjPbt26cvv/xSY8aM8VowALAa+hEA3KMjAcD3PF7gJyQkOJ0OCwvTOeeco8cee0x9+vTxWjAAsBr6EQDcoyMBwPc8WuCXlZVp6NChateunRo0aOCrTABgOfQjALhHRwKAf3j0Hvzw8HD16dNHBw8e9FEcALAm+hEA3KMjAcA/PP6QvbZt22rXrl2+yAIAlkY/AoB7dCQA+J7HC/zHH39c9913n959913t27dPhYWFTn8AoLaiHwHAPToSAHyv2u/Bf+yxx3TvvffqiiuukCRdffXVstlsjvONMbLZbCorK/N+SgAIYvQjALhHRwKA/1R7gT9u3Djdfvvt+vDDD32ZBwAsh34EAPfoSADwn2ov8I0xkqRevXr5LIzV7Cs4qt35R9Q8sa5SE2LczttfcEwtGtXzYzIA/kQ/AoB7dKR/Vef4lGNTIHR59DV5J7+cqrZb+MVejX7zG9mNFGaTxl3dRtd0auo4//XP9zr+nTH1I00c2E5ZXZoFIioAP6AfAcA9OtI/Kjs+5dgUqB08WuC3atWqyoL+7bffTiuQFewrOOooT0myG2nM299pzNvfuZxvN9JDb36ri1s1qvSZfgDWRT8CgHt0pO95cnzKsSkQujxa4I8bN04JCQm+ymIZu/OPOMqzusqM0Z78IkoUCFH0IwC4R0f6nqfHpxybAqHJowX+9ddfr6SkJF9lsYzmiXUVZpNTiYbZpBU5vZSSEK39BceUMfUjp/PDbTalJ8b6PywAv6AfAcA9OtL3Kjs+lcSxKVBLhFV3Iu+d+kNqQozGXd3GcTrMJk0c2E4tGtVTbGQdtWhUTxMHtlP4/7/Nwm02TRjYlkdIgRBFPwKAe3Skf1R2fMqxKVB7ePwp+jjhmk5NHe9pWpHTq8InkWZ1aaaLWzXSnvwipSfGUqBACKMfAcA9OtJ/Kjs+5dgUqB2qvcC32+2+zGFpKQnRLsdTE2IoT6AWoB8BwD06MjBcHZ9ybAqEvmq/RB8AAAAAAAQvFvgAAAAAAIQAFvgAAAAAAISAoFjgT58+Xenp6YqOjla3bt20fv16t3Nnzpypiy66SA0aNFCDBg2UkZFR6XwAsDL6EQBcox8BoKKAL/AXLlyonJwc5ebmauPGjbrggguUmZmpX375xeX81atX64YbbtCHH36odevWKS0tTX369NFPP/3k5+QA4Fv0IwC4Rj8CgGsBX+BPnTpVw4cP19ChQ9W6dWvNmDFDsbGxmj17tsv58+fP15133qn27dvr3HPP1axZs2S327Vy5Uo/JwcA36IfAcA1+hEAXKv21+T5QklJiTZs2KDRo0c7xsLCwpSRkaF169ZVax9FRUUqLS1Vw4YNXZ5fXFys4uJix+nCwkJJUmlpqUpLS6vcf/mcU+eWlh53mlNqC77veHWXPdhZNbdE9kDxJLtVrp8/+lE6vY6s7HYP9o6sLff3YGLV3FLtym6F62iFfiyfd/Lf/uCN7q1N9/dgQnb/C8V+lAK8wM/Pz1dZWZmSk5OdxpOTk7V169Zq7ePBBx9U48aNlZGR4fL8iRMnaty4cRXGly1bptjY2GpnXb58udPp4jKp/OZbunSZosKrvSu/OzW7VVg1t0T2QKlO9qKiIj8kOX3+6EfJOx3p6na3SkeG+v09GFk1t1Q7sluhI63Uj5J/7zfe7N7acH8PRmT3v1DqRynAC/zTNWnSJC1YsECrV69WdHS0yzmjR49WTk6O43RhYaHjfVfx8fFVXkZpaamWL1+u3r17KyIiwjFeVHJcD6xfJUnKzOyj2MjguyndZQ92Vs0tkT1QPMle/gxMqKtOP0qn15GV3e7B3pG15f4eTKyaW6pd2WtDR/qjH6XA3G+80b216f4eTMjuf6HajwE94kpMTFR4eLgOHDjgNH7gwAGlpKRUuu3kyZM1adIkrVixQueff77beVFRUYqKiqowHhER4dEd8NT5EcZ2ynnBdfB6Mk+va7Cwam6J7IFSnexWuW7+6EfJOx3paq5VOjLU7+/ByKq5pdqR3QrXz0r9WJP5p8Ob3Vsb7u/BiOz+F0r9KAX4Q/YiIyPVqVMnpw84Kf/Ak+7du7vd7sknn9T48eO1ZMkSde7c2R9RAcCv6EcAcI1+BAD3Av6USk5OjoYMGaLOnTura9eumjZtmo4cOaKhQ4dKkgYPHqwmTZpo4sSJkqR//OMfGjt2rF5//XWlp6dr//79kqR69eqpXr16AbseAOBt9CMAuEY/AoBrAV/gZ2Vl6ddff9XYsWO1f/9+tW/fXkuWLHF8cMrevXsVFvbHCw1eeOEFlZSU6C9/+YvTfnJzc/Xoo4/6MzoA+BT9CACu0Y8A4FrAF/iSlJ2drezsbJfnrV692un0nj17fB8IAIIE/QgArtGPAFBRQN+DDwAAAAAAvIMFPgAAAAAAIYAFPgAAAAAAIYAFPgAAAAAAIYAFPgAAAAAAIYAFPgAAAAAAIYAFPgAAAAAAIYAFPgAAAAAAIYAFPgAAAAAAIYAFPgAAAAAAIYAFPgAAAAAAIYAFPgAAAAAAIYAFPgAAAAAAIYAFPgAAAAAAIYAFPgAAAAAAIYAFPgAAAAAAIYAFPgAAAAAAIYAFPgAAAAAAIYAFPgAAAAAAIYAFPgAAAAAAIYAFPgAAAAAAIYAFPgAAAAAAIYAFPgAAAAAAIYAFPgAAAAAAIYAFPgAAAAAAIYAFPgAAAAAAIYAFPgAAAAAAIYAFPgAAAAAAIYAFPgAAAAAAIYAFPgAAAAAAIYAFPgAAAAAAIYAFPgAAAAAAIYAFPgAAAAAAIYAFPgAAAAAAIYAFPgAAAAAAIYAFPgAAAAAAISAoFvjTp09Xenq6oqOj1a1bN61fv77S+YsXL9a5556r6OhotWvXTu+//76fkgKAf9GPAOAa/QgAFQV8gb9w4ULl5OQoNzdXGzdu1AUXXKDMzEz98ssvLud/+umnuuGGGzRs2DBt2rRJAwYM0IABA/Ttt9/6OTkA+Bb9CACu0Y8A4FrAF/hTp07V8OHDNXToULVu3VozZsxQbGysZs+e7XL+M888o759++r+++/Xeeedp/Hjx6tjx4567rnnfJJvX8ExfV9g076CY27n7K/kPACoqVDoR4mOBOB9wd6PUuUdua/gqD7dma99BUd9dvl0L1A7BXSBX1JSog0bNigjI8MxFhYWpoyMDK1bt87lNuvWrXOaL0mZmZlu55+OBev36k9TPtZzm8P1pykfa966PSoqOa6ikuN6/fO9jnkZUz/Swi/2VrInAPCMlfuRjgTgS8Hej1LlHTlv3R71nLRKg2Z+rp6TVlXoz9P5Q/cCqBPIC8/Pz1dZWZmSk5OdxpOTk7V161aX2+zfv9/l/P3797ucX1xcrOLiYsfpwsJCSVJpaalKS0vdZttXcEwPvfWN7ObEabuRxrz9nca8/V2FuXYjjX7zG3Vv3kCpCdFu9+lv5devsusZjKyaWyJ7oHiS3SrXzx/9KNWsIz3px/Lzg60ja8v9PZhYNbdUu7Jb4ToGcz9Knh9DVtafp6Om3Vub7u/BhOz+F4r9KAV4ge8PEydO1Lhx4yqML1u2TLGxsW63+77AJrsJr/bl2I206P0PdXaCqVFOX1q+fHmgI9SIVXNLZA+U6mQvKiryQxLrqElHetqPUvB2ZKjf34ORVXNLtSM7HfkHfx1D+tLpdG9tuL8HI7L7X6j1Y0AX+ImJiQoPD9eBAwecxg8cOKCUlBSX26SkpHg0f/To0crJyXGcLiwsVFpamvr06aP4+Hi32fYVHNPzWz52PPoqSWE2acndPSRJff/5aYXzrrvikqB5dko68SjT8uXL1bt3b0VERAQ6TrVZNbdE9kDxJHv5MzDBzh/9KNWsIyvrx+T4aB0oPBb0HVlb7u/BxKq5pdqV3QodGcz9KNXsGLK8P0+Ht7q3Nt3fgwnZ/S8U+1EK8AI/MjJSnTp10sqVKzVgwABJkt1u18qVK5Wdne1ym+7du2vlypX6+9//7hhbvny5unfv7nJ+VFSUoqKiKoxHRERU+oNslhihiQPbafSbJ15iFWaTJg5sp1apDSSd+PdDb36rMmMUbrNpwsC2apYYV81r7l9VXddgZdXcEtkDpTrZrXLd/NGPUs06sqp+TKgbY5mODPX7ezCyam6pdmS3wvUL5n6UanYMWX7e6fB299aG+3swIrv/hVI/SkHwEv2cnBwNGTJEnTt3VteuXTVt2jQdOXJEQ4cOlSQNHjxYTZo00cSJEyVJ99xzj3r16qUpU6boyiuv1IIFC/Tll1/qpZde8nq2rC7N1L15Ay16/0Ndd8UlTgWZ1aWZLm7VSHvyi5SeGKvUhBivXz6A2s2q/Vh+Ph0JwFeCuR+lwB1D0r0AAr7Az8rK0q+//qqxY8dq//79at++vZYsWeL4IJS9e/cqLOyPD/vv0aOHXn/9dT3yyCN66KGHdPbZZ+vf//632rZt65N8qQnROjvBuHxpU2pCDMUJwGes3I8nzqcjAfhGsPejFLhjSLoXqN0CvsCXpOzsbLcvqVq9enWFsWuvvVbXXnutj1MBQODRjwDgGv0IABWFVT0FAAAAAAAEOxb4AAAAAACEABb4AAAAAACEABb4AAAAAACEABb4AAAAAACEgKD4FH1/MsZIkgoLC6s1v7S0VEVFRSosLFRERIQvo3mdVbNbNbdE9kDxJHv57355F8CZJx1ZW+4zwcaq2a2aW6pd2elI92rLMaRVc0tkDxSrZg/Vfqx1C/xDhw5JktLS0gKcBEAgHTp0SAkJCYGOEXToSAASHekK/QhACv5+tJlgfwjCy+x2u37++WfFxcXJZrNVOb+wsFBpaWn6z3/+o/j4eD8k9B6rZrdqbonsgeJJdmOMDh06pMaNGyssjHcpncqTjqwt95lgY9XsVs0t1a7sdKR7teUY0qq5JbIHilWzh2o/1rpn8MPCwtS0aVOPt4uPj7fUHfZkVs1u1dwS2QOlutmD+VHXQKtJR9aG+0wwsmp2q+aWak92OtK12nYMadXcEtkDxarZQ60fg/ehBwAAAAAAUG0s8AEAAAAACAEs8KsQFRWl3NxcRUVFBTqKx6ya3aq5JbIHipWzW5mVb3ey+59Vc0tkR81Y9ba3am6J7IFi1exWzV2VWvchewAAAAAAhCKewQcAAAAAIASwwAcAAAAAIASwwAcAAAAAIASwwJc0ffp0paenKzo6Wt26ddP69esrnb948WKde+65io6OVrt27fT+++/7KWlFnmSfOXOmLrroIjVo0EANGjRQRkZGldfVVzy9zcstWLBANptNAwYM8G3ASnia/eDBgxoxYoRSU1MVFRWlVq1aBew+42n2adOm6ZxzzlFMTIzS0tI0cuRIHTt2zE9pT/j444/Vr18/NW7cWDabTf/+97+r3Gb16tXq2LGjoqKidNZZZ2nu3Lk+zxmq6MfAsGpH0o/+7UeJjgw0q3Yk/RgYVu1I+tFiTC23YMECExkZaWbPnm2+++47M3z4cFO/fn1z4MABl/PXrl1rwsPDzZNPPmk2b95sHnnkERMREWG++eYbPyf3PPugQYPM9OnTzaZNm8yWLVvMzTffbBISEsyPP/4Y1LnL7d692zRp0sRcdNFFpn///v4JewpPsxcXF5vOnTubK664wqxZs8bs3r3brF692uTl5fk5uefZ58+fb6Kiosz8+fPN7t27zdKlS01qaqoZOXKkX3O///775uGHHzZvvvmmkWTeeuutSufv2rXLxMbGmpycHLN582bz7LPPmvDwcLNkyRL/BA4h9KP/+9EY63Yk/ej/fjSGjgwkq3Yk/djfP2FPYdWOpB+t14+1foHftWtXM2LECMfpsrIy07hxYzNx4kSX86+77jpz5ZVXOo1169bN3HbbbT7N6Yqn2U91/PhxExcXZ1555RVfRXSpJrmPHz9uevToYWbNmmWGDBkSsHL2NPsLL7xgWrRoYUpKSvwV0S1Ps48YMcJceumlTmM5OTmmZ8+ePs1ZmeqU8wMPPGDatGnjNJaVlWUyMzN9mCw00Y/+70djrNuR9GNg+9EYOtLfrNqR9GN/PyStyKodST9arx9r9Uv0S0pKtGHDBmVkZDjGwsLClJGRoXXr1rncZt26dU7zJSkzM9PtfF+pSfZTFRUVqbS0VA0bNvRVzApqmvuxxx5TUlKShg0b5o+YLtUk+zvvvKPu3btrxIgRSk5OVtu2bTVhwgSVlZX5K7akmmXv0aOHNmzY4HgZ1q5du/T+++/riiuu8EvmmgqW31Grox/934+SdTuSfrRGP0rB83tqdVbtSPoxMKzakfSjNfuxTqADBFJ+fr7KysqUnJzsNJ6cnKytW7e63Gb//v0u5+/fv99nOV2pSfZTPfjgg2rcuHGFO7Iv1ST3mjVr9PLLLysvL88PCd2rSfZdu3Zp1apVuvHGG/X+++9rx44duvPOO1VaWqrc3Fx/xJZUs+yDBg1Sfn6+LrzwQhljdPz4cd1+++166KGH/BG5xtz9jhYWFuro0aOKiYkJUDJroR/934+SdTuSfrRGP0p0pLdYtSPpx8CwakfSj9bsx1r9DH5tNmnSJC1YsEBvvfWWoqOjAx3HrUOHDummm27SzJkzlZiYGOg4HrPb7UpKStJLL72kTp06KSsrSw8//LBmzJgR6GhVWr16tSZMmKDnn39eGzdu1Jtvvqn33ntP48ePD3Q0wKes0o+StTuSfgSsh370H6t2JP0YeLX6GfzExESFh4frwIEDTuMHDhxQSkqKy21SUlI8mu8rNclebvLkyZo0aZJWrFih888/35cxK/A0986dO7Vnzx7169fPMWa32yVJderU0bZt29SyZUvfhv7/anKbp6amKiIiQuHh4Y6x8847T/v371dJSYkiIyN9mrlcTbKPGTNGN910k2655RZJUrt27XTkyBHdeuutevjhhxUWFpyPD7r7HY2Pj7fMI6/BgH70fz9K1u1I+tEa/SjRkd5i1Y6kHzmG9AT9aM1+DN5b2A8iIyPVqVMnrVy50jFmt9u1cuVKde/e3eU23bt3d5ovScuXL3c731dqkl2SnnzySY0fP15LlixR586d/RHViae5zz33XH3zzTfKy8tz/Ln66qt1ySWXKC8vT2lpaUGbXZJ69uypHTt2OP5DkaTt27crNTXVbwevUs2yFxUVVSjh8v9kjDG+C3uaguV31OroR//3o2TdjqQfrdGPUvD8nlqdVTuSfuQY0hP0o0X7MZCf8BcMFixYYKKioszcuXPN5s2bza233mrq169v9u/fb4wx5qabbjKjRo1yzF+7dq2pU6eOmTx5stmyZYvJzc0N6NdAeZJ90qRJJjIy0rzxxhtm3759jj+HDh0K6tynCuQnoHqafe/evSYuLs5kZ2ebbdu2mXfffdckJSWZxx9/POiz5+bmmri4OPOvf/3L7Nq1yyxbtsy0bNnSXHfddX7NfejQIbNp0yazadMmI8lMnTrVbNq0yfzwww/GGGNGjRplbrrpJsf88q84uf/++82WLVvM9OnTLfkVJ8GAfvR/P9Yk+6kC1ZH0o//70Rg6MpCs2pH0Y38/pXVm1Y6kH63Xj7V+gW+MMc8++6xp1qyZiYyMNF27djWfffaZ47xevXqZIUOGOM1ftGiRadWqlYmMjDRt2rQx7733np8T/8GT7GeeeaaRVOFPbm5uUOc+VSDL2RjPs3/66aemW7duJioqyrRo0cI88cQT5vjx435OfYIn2UtLS82jjz5qWrZsaaKjo01aWpq58847ze+//+7XzB9++KHL+2151iFDhphevXpV2KZ9+/YmMjLStGjRwsyZM8evmUMJ/Zjr/+DGuh1JP/7u99x0ZGBZtSPpx8CwakfSj9ZiMybIXysBAAAAAACqVKvfgw8AAAAAQKhggQ8AAAAAQAhggQ8AAAAAQAhggQ8AAAAAQAhggQ8AAAAAQAhggQ8AAAAAQAhggQ8AAAAAQAhggQ8AAAAAQAhggQ+PzZ07V/Xr1w90jBqz2Wz697//Xemcm2++WQMGDPBLHgChhY4EANfoR8D3WODXUjfffLNsNluFPzt27Ah0NM2dO9eRJywsTE2bNtXQoUP1yy+/eGX/+/bt0+WXXy5J2rNnj2w2m/Ly8pzmPPPMM5o7d65XLs+dRx991HE9w8PDlZaWpltvvVW//fabR/vhPxLA++hIOhKAa/Qj/YjgVifQARA4ffv21Zw5c5zGGjVqFKA0zuLj47Vt2zbZ7XZ99dVXGjp0qH7++WctXbr0tPedkpJS5ZyEhITTvpzqaNOmjVasWKGysjJt2bJFf/vb31RQUKCFCxf65fIBuEdHukdHArUb/ege/YhA4xn8WiwqKkopKSlOf8LDwzV16lS1a9dOdevWVVpamu68804dPnzY7X6++uorXXLJJYqLi1N8fLw6deqkL7/80nH+mjVrdNFFFykmJkZpaWm6++67deTIkUqz2Ww2paSkqHHjxrr88st19913a8WKFTp69Kjsdrsee+wxNW3aVFFRUWrfvr2WLFni2LakpETZ2dlKTU1VdHS0zjzzTE2cONFp3+Uvr2revLkkqUOHDrLZbPrTn/4kyfkRzZdeekmNGzeW3W53yti/f3/97W9/c5x+++231bFjR0VHR6tFixYaN26cjh8/Xun1rFOnjlJSUtSkSRNlZGTo2muv1fLlyx3nl5WVadiwYWrevLliYmJ0zjnn6JlnnnGc/+ijj+qVV17R22+/7Xgkd/Xq1ZKk//znP7ruuutUv359NWzYUP3799eePXsqzQPgD3QkHQnANfqRfkTwYoGPCsLCwvTPf/5T3333nV555RWtWrVKDzzwgNv5N954o5o2baovvvhCGzZs0KhRoxQRESFJ2rlzp/r27atrrrlGX3/9tRYuXKg1a9YoOzvbo0wxMTGy2+06fvy4nnnmGU2ZMkWTJ0/W119/rczMTF199dX6/vvvJUn//Oc/9c4772jRokXatm2b5s+fr/T0dJf7Xb9+vSRpxYoV2rdvn958880Kc6699lr997//1YcffugY++2337RkyRLdeOONkqRPPvlEgwcP1j333KPNmzfrxRdf1Ny5c/XEE09U+zru2bNHS5cuVWRkpGPMbreradOmWrx4sTZv3qyxY8fqoYce0qJFiyRJ9913n6677jr17dtX+/bt0759+9SjRw+VlpYqMzNTcXFx+uSTT7R27VrVq1dPffv2VUlJSbUzAaiIjnRGRwIoRz86ox8REAa10pAhQ0x4eLipW7eu489f/vIXl3MXL15szjjjDMfpOXPmmISEBMfpuLg4M3fuXJfbDhs2zNx6661OY5988okJCwszR48edbnNqfvfvn27adWqlencubMxxpjGjRubJ554wmmbLl26mDvvvNMYY8xdd91lLr30UmO3213uX5J56623jDHG7N6920gymzZtcpozZMgQ079/f8fp/v37m7/97W+O0y+++KJp3LixKSsrM8YYc9lll5kJEyY47WPevHkmNTXVZQZjjMnNzTVhYWGmbt26Jjo62kgykszUqVPdbmOMMSNGjDDXXHON26zll33OOec43QbFxcUmJibGLF26tNL9A6Aj6UgA7tCPbxlj6EcEL96DX4tdcskleuGFFxyn69atK+nEI5ETJ07U1q1bVVhYqOPHj+vYsWMqKipSbGxshf3k5OTolltu0bx58xwvEWrZsqWkEy+9+vrrrzV//nzHfGOM7Ha7du/erfPOO89ltoKCAtWrV092u13Hjh3ThRdeqFmzZqmwsFA///yzevbs6TS/Z8+e+uqrrySdeGlU7969dc4556hv37666qqr1KdPn9O6rW688UYNHz5czz//vKKiojR//nxdf/31CgsLc1zPtWvXOj3aWlZWVuntJknnnHOO3nnnHR07dkyvvfaa8vLydNdddznNmT59umbPnq29e/fq6NGjKikpUfv27SvN+9VXX2nHjh2Ki4tzGj927Jh27txZg1sAqH3oyOqjI4HahX6sPvoR/sYCvxarW7euzjrrLKexPXv26KqrrtIdd9yhJ554Qg0bNtSaNWs0bNgwlZSUuCyZRx99VIMGDdJ7772nDz74QLm5uVqwYIH+/Oc/6/Dhw7rtttt09913V9iuWbNmbrPFxcVp48aNCgsLU2pqqmJiYiRJhYWFVV6vjh07avfu3frggw+0YsUKXXfddcrIyNAbb7xR5bbu9OvXT8YYvffee+rSpYs++eQTPf30047zDx8+rHHjxmngwIEVto2Ojna738jISMfPYNKkSbryyis1btw4jR8/XpK0YMEC3XfffZoyZYq6d++uuLg4PfXUU/r8888rzXv48GF16tTJ6T/FcsHyIThAsKMjq4+OBGoX+rH66Ef4Gwt8ONmwYYPsdrumTJnieGSx/L06lWnVqpVatWqlkSNH6oYbbtCcOXP05z//WR07dtTmzZsr/CdQlbCwMJfbxMfHq3Hjxlq7dq169erlGF+7dq26du3qNC8rK0tZWVn6y1/+or59++q3335Tw4YNnfZX/l6lsrKySvNER0dr4MCBmj9/vnbs2KFzzjlHHTt2dJzfsWNHbdu2zePreapHHnlEl156qe644w7H9ezRo4fuvPNOx5xTHz2NjIyskL9jx45auHChkpKSFB8ff1qZAPyBjnSNjgRAP7pGP8Lf+JA9ODnrrLNUWlqqZ599Vrt27dK8efM0Y8YMt/OPHj2q7OxsrV69Wj/88IPWrl2rL774wvGyqQcffFCffvqpsrOzlZeXp++//15vv/22xx+QcrL7779f//jHP7Rw4UJt27ZNo0aNUl5enu655x5J0tSpU/Wvf/1LW7du1fbt27V48WKlpKSofv36FfaVlJSkmJgYLVmyRAcOHFBBQYHby73xxhv13nvvafbs2Y4PRik3duxYvfrqqxo3bpy+++47bdmyRQsWLNAjjzzi0XXr3r27zj//fE2YMEGSdPbZZ+vLL7/U0qVLtX37do0ZM0ZffPGF0zbp6en6+uuvtW3bNuXn56u0tFQ33nijEhMT1b9/f33yySfavXu3Vq9erbvvvls//vijR5kA/IGOpCMBuEY/0o8IEoH8AAAEjqsP1Sg3depUk5qaamJiYkxmZqZ59dVXjSTz+++/G2OcP8CkuLjYXH/99SYtLc1ERkaaxo0bm+zsbKcPP1m/fr3p3bu3qVevnqlbt645//zzK3zAyclO/YCUU5WVlZlHH33UNGnSxERERJgLLrjAfPDBB47zX3rpJdO+fXtTt25dEx8fby677DKzceNGx/k66QNSjDFm5syZJi0tzYSFhZlevXq5vX3KyspMamqqkWR27txZIdeSJUtMjx49TExMjImPjzddu3Y1L730ktvrkZubay644IIK4//6179MVFSU2bt3rzl27Ji5+eabTUJCgqlfv7654447zKhRo5y2++WXXxy3ryTz4YcfGmOM2bdvnxk8eLBJTEw0UVFRpkWLFmb48OGmoKDAbSYAJ9CRbzlO05EATkY/vuU4TT8iGNmMMSYwDy0AAAAAAABv4SX6AAAAAACEABb4AAAAAACEABb4AAAAAACEABb4AAAAAACEABb4AAAAAACEABb4AAAAAACEABb4AAAAAACEABb4AAAAAACEABb4AAAAAACEABb4AAAAAACEABb4AAAAAACEABb4AAAAAACEgP8HMdef6RlMGwQAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from sklearn.svm import SVC\n",
        "fig, axs = plt.subplots(2, 3, figsize=(10, 7))\n",
        "\n",
        "for i, split in enumerate(split_data):\n",
        "    svm_model = SVC(probability=True)\n",
        "    svm_model.fit(split['X_train'], split['y_train'])\n",
        "\n",
        "    y_pred_prob = svm_model.predict_proba(split['X_test'])[:, 1]\n",
        "\n",
        "    # Calculate and display the Precision-Recall curve and PRAUC\n",
        "    precision, recall, _ = precision_recall_curve(split['y_test'], y_pred_prob)\n",
        "    prauc = auc(recall, precision)\n",
        "\n",
        "    axs[0, i].plot(recall, precision, marker='.')\n",
        "    axs[0, i].set_xlabel('Recall')\n",
        "    axs[0, i].set_ylabel('Precision')\n",
        "    axs[0, i].set_title('Precision-Recall Curve (PRAUC = {:.2f})'.format(prauc))\n",
        "    axs[0, i].grid(True)\n",
        "\n",
        "    # Calculate and display the ROC curve and AUC\n",
        "    fpr, tpr, _ = roc_curve(split['y_test'], y_pred_prob)\n",
        "    roc_auc = roc_auc_score(split['y_test'], y_pred_prob)\n",
        "\n",
        "    axs[1, i].plot(fpr, tpr, marker='.')\n",
        "    axs[1, i].set_xlabel('False Positive Rate')\n",
        "    axs[1, i].set_ylabel('True Positive Rate')\n",
        "    axs[1, i].set_title('ROC Curve (AUC = {:.2f})'.format(roc_auc))\n",
        "    axs[1, i].grid(True)\n",
        "\n",
        "    dict_scores.append({\"PRAUC\" : prauc,\"AUC\" : roc_auc})\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-09-18T21:32:35.271519500Z",
          "start_time": "2023-09-18T21:32:35.128454400Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 707
        },
        "id": "3e121cc98bfafaeb",
        "outputId": "790453f3-560d-4daa-b96a-f4322b413c7a"
      },
      "id": "3e121cc98bfafaeb"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       PRAUC       AUC\n",
              "From RFE            0.413095  0.927419\n",
              "From LASSO          0.412500  0.911290\n",
              "From Uni_LR + Corr  0.247091  0.830645"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7f5c3161-99d4-4004-8685-9361fb182927\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PRAUC</th>\n",
              "      <th>AUC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>From RFE</th>\n",
              "      <td>0.413095</td>\n",
              "      <td>0.927419</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>From LASSO</th>\n",
              "      <td>0.412500</td>\n",
              "      <td>0.911290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>From Uni_LR + Corr</th>\n",
              "      <td>0.247091</td>\n",
              "      <td>0.830645</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7f5c3161-99d4-4004-8685-9361fb182927 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-7f5c3161-99d4-4004-8685-9361fb182927');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-c024b4d3-23d9-4bdb-a096-d0530d182c2f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c024b4d3-23d9-4bdb-a096-d0530d182c2f')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c024b4d3-23d9-4bdb-a096-d0530d182c2f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ],
      "source": [
        "# Show scores in the three cases\n",
        "dict_scores2 = pd.DataFrame(dict_scores)\n",
        "row_indices = ['From RFE', 'From LASSO', 'From Uni_LR + Corr']\n",
        "dict_scores2.index = row_indices\n",
        "dict_scores2"
      ],
      "metadata": {
        "ExecuteTime": {
          "end_time": "2023-09-18T21:34:47.659612Z",
          "start_time": "2023-09-18T21:34:47.632684Z"
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "388e62c8b0fbf80d",
        "outputId": "9e8fed3f-9984-4aa5-db96-b3f87bbd25e5"
      },
      "id": "388e62c8b0fbf80d"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### EBM"
      ],
      "metadata": {
        "id": "eHEFOpQRBgiR"
      },
      "id": "eHEFOpQRBgiR"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Building wheels for collected packages: lime\n",
            "  Building wheel for lime (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for lime: filename=lime-0.2.0.1-py3-none-any.whl size=283834 sha256=b5a937b1100413af6ac7355c6597d3af73db0522aef329c4e2fba843c72fb2a0\n",
            "  Stored in directory: /root/.cache/pip/wheels/fd/a2/af/9ac0a1a85a27f314a06b39e1f492bee1547d52549a4606ed89\n",
            "Successfully built lime\n",
            "Installing collected packages: treeinterpreter, interpret-core, dash-table, dash-html-components, dash-core-components, zope.interface, zope.event, Werkzeug, slicer, retrying, jedi, dill, ansi2html, multiprocess, gevent, skope-rules, shap, SALib, lime, dash, dash-cytoscape, interpret\n",
            "  Attempting uninstall: Werkzeug\n",
            "    Found existing installation: Werkzeug 2.3.7\n",
            "    Uninstalling Werkzeug-2.3.7:\n",
            "      Successfully uninstalled Werkzeug-2.3.7\n",
            "Successfully installed SALib-1.4.7 Werkzeug-2.2.3 ansi2html-1.8.0 dash-2.13.0 dash-core-components-2.0.0 dash-cytoscape-0.3.0 dash-html-components-2.0.0 dash-table-5.0.0 dill-0.3.7 gevent-23.9.1 interpret-0.4.4 interpret-core-0.4.4 jedi-0.19.0 lime-0.2.0.1 multiprocess-0.70.15 retrying-1.3.4 shap-0.42.1 skope-rules-1.0.1 slicer-0.0.7 treeinterpreter-0.2.3 zope.event-5.0 zope.interface-6.0\n"
          ]
        }
      ],
      "source": [
        "!pip install interpret\n",
        "from interpret.glassbox import ExplainableBoostingClassifier"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f9e8cc0fcd335697",
        "outputId": "4350eba7-f638-424b-9e67-acd3fac62082"
      },
      "id": "f9e8cc0fcd335697"
    },
    {
      "cell_type": "code",
      "source": [
        "dict_scores = []\n",
        "\n",
        "\n",
        "ebm_model = ExplainableBoostingClassifier()\n",
        "ebm_model.fit(X_train, y_train)\n",
        "\n",
        "y_pred = ebm_model.predict(X_test)\n",
        "\n",
        "# Calculate and display the Precision-Recall curve and PRAUC\n",
        "precision, recall, _ = precision_recall_curve(y_test, y_pred_prob)\n",
        "prauc = auc(recall, precision)\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot(recall, precision, marker='.')\n",
        "plt.xlabel('Recall')\n",
        "plt.ylabel('Precision')\n",
        "plt.title('Precision-Recall Curve (PRAUC = {:.2f})'.format(prauc))\n",
        "plt.grid(True)\n",
        "plt.show()\n",
        "\n",
        "# Calculate and display the ROC curve and AUC\n",
        "fpr, tpr, _ = roc_curve(y_test, y_pred_prob)\n",
        "roc_auc = roc_auc_score(y_test, y_pred_prob)\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot(fpr, tpr, marker='.')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('ROC Curve (AUC = {:.2f})'.format(roc_auc))\n",
        "plt.grid(True)\n",
        "plt.show()\n",
        "\n",
        "dict_scores.append({\"PRAUC\" : prauc,\"AUC\" : roc_auc})\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "fDEpbdplBWxC",
        "outputId": "28f73cef-2093-4ef7-fb89-9804d4ff7e0d"
      },
      "id": "fDEpbdplBWxC",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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bXFxsmjZtapo2beoSCIqLi80bb7xhunfvbsLDw01ISIi58MILTVpamjl27Fi5a7333numb9++pm7duiYgIMDExsaapKQks3LlykrVmp+fbyZPnmw6depkatasaYKCgkyzZs3MnXfeaXbv3u0y9+233zZNmjQxQUFBpm3btmbp0qUVvmlCefLy8kxoaKiRZN5+++0y5xw9etSMGzfOnHfeeSYoKMhERkaabt26mcmTJ1fq5v0XXXSRyxsD9O/f34SEhJjjx4+Xu88NN9xgAgMDXd7xavv27SYpKclERUWZgIAAExUVZa677jqzffv2co9zuj3xpFWrVplu3bqZkJAQU69ePTNq1CiXM7XGlB9mjTHm3XffNW3atDHBwcEmOjraJCcnl9o/NTXVdOzY0dSpU8cEBASY+vXrm+uuu8589913pY5X8mYM5b07GwDPsxlTxiseAAB/a7Nnz9aoUaN04MABr9ytAVVz991364svvtCGDRu4mwFQTQizAGBBDodDF110kQYPHqyHHnrI1+VAf9z/99xzz9X8+fN1+eWX+7oc4KxBmAUAAIBlcWsuAAAAWBZhFgAAAJZFmAUAAIBlEWYBAABgWQG+LqC6ORwOHTx4ULVq1eK2KQAAAH9DxhgdPXpU9evXl59fxedez7owe/DgQcXFxfm6DAAAAJzCjz/+qIYNG1Y456wLs7Vq1ZL0xxcnPDzc6+vZ7XYtW7ZMffv2VWBgoNfXg+fRQ+ujh9ZHD62N/llfdfcwLy9PcXFxztxWkbMuzJZcWhAeHl5tYTYsLEzh4eH8AFsUPbQ+emh99NDa6J/1+aqHlbkklBeAAQAAwLIIswAAALAswiwAAAAsizALAAAAyyLMAgAAwLIIswAAALAswiwAAAAsizALAAAAyyLMAgAAwLIIswAAALAswiwAAAAsizALAAAAyyLMAgAAwLIIswAAALAsn4bZL774Qv3791f9+vVls9n0wQcfnHKflStXqn379goODtZ5552nWbNmeb1OAAAA/D35NMweP35cbdq00bRp0yo1f9++fbriiivUq1cvZWZm6u6779bNN9+spUuXerlSAADgTVm5J7R2T46yck/4uhSUISu3QN/n2pSVW+DrUkoJ8OXil112mS677LJKz58+fboaN26sKVOmSJJatGih1atX65lnnlFiYqK3ygQAAF407+sDGrdwsxxG8rNJaVddqGs6NPR1Wfj//rPhJ6Uu2iqH8ddL279Q+sDWSurUyNdlOfk0zLpr3bp1SkhIcBlLTEzU3XffXe4+hYWFKiwsdD7Oy8uTJNntdtntdq/U+Wcla1THWvAOemh99ND66KG1VdS/rNwCZ5CVJIeRxn+4VeM/3FqdJaKSHEYat3Czujauo9iIEK+t487PuqXCbHZ2tqKjo13GoqOjlZeXpxMnTig0NLTUPunp6UpLSys1vmzZMoWFhXmt1r/KyMiotrXgHfTQ+uih9dFDayurf9/n2uQw/j6oBlXlMNL8xZ+pWYTx2hr5+fmVnmupMFsV48aNU0pKivNxXl6e4uLi1LdvX4WHh3t9fbvdroyMDPXp00eBgYFeXw+eRw+tjx5aHz20tor6l5VboJe2f+E8Myv9canBkru6KTrce2f+UDmH8grU7/m1pfoz6PJeXj0zW/KX9MqwVJiNiYnRoUOHXMYOHTqk8PDwMs/KSlJwcLCCg4NLjQcGBlbr/xCrez14Hj20PnpoffTQ2srqX6PIQKVddaHzsgI/m5Q+sLWax9bxRYn4i4gaoUof2Nrlmub0ga3VKLKWV9d15+fcUmG2a9euWrx4sctYRkaGunbt6qOKAADA6bqmQ0NnmF2e0lNN6tX0cUX4s6ROjdS1cR3NX/yZBl3ey+tB1l0+vTXXsWPHlJmZqczMTEl/3HorMzNTBw4ckPTHJQLDhg1zzr/tttu0d+9e3X///dqxY4deeuklzZ8/X2PGjPFF+QAAwMNivPina1RdbESImkUYr15aUFU+DbPffPON2rVrp3bt2kmSUlJS1K5dO02YMEGSlJWV5Qy2ktS4cWN9/PHHysjIUJs2bTRlyhTNmDGD23IBAACcpXx6mcE//vEPGVP+K+HKenevf/zjH9q0aZMXqwIAAIBV+PTMLAAAAHA6CLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAOC0ZOWe0No9OcrKPeHrUnAGyM4t8HUJsJgAXxcAALCueV8f0LiFm+Uwkp9NSrvqQl3ToaHH17HbT6qwWMovOqlAY/P48eFdp+rfO18dcH6eMPVzpQ9sraROjaqzRFgYYRYAUCVZuSecQVaSHEYa/+FWjf9wq5dWDND96z/10rHhfZXrn8NIDy7cokua11NsRGg11AWr4zIDAECV7Ms57gyygCcVG6P9Ofm+LgMWwZlZAECVNI6sIT+bXAKtn01antJTMREhHl3Lbrdr6dJlSkzsq8DAQI8eG95XUf+ycwuUMPVzl+8jf5tN8ZFh1VwlrIowCwCoktiIUKVddaHzsgI/m5Q+sLWa1Kvp8bXsNqNgfyksKECBgfzqspqK+tekXk2lD2ytBxduUbEx8rfZNHFgKy4xQKXxfwQAQJVd06GhM8wuT+nplSCLM19Sp0a6pHk97c/JV3xkGEEWbiHMAgA8wtOXFuDsEhsRSohFlfACMAAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZRFmAQAAYFmEWQAAAFgWYRYAAACWRZgFAACAZfk8zE6bNk3x8fEKCQlRly5dtH79+grnP/vsszr//PMVGhqquLg4jRkzRgUFBdVULQAAAP5OfBpm582bp5SUFKWmpmrjxo1q06aNEhMTdfjw4TLnv/POO3rggQeUmpqq7du36/XXX9e8efP04IMPVnPlAAAA+DvwaZidOnWqRo4cqREjRqhly5aaPn26wsLCNHPmzDLnr127Vt27d9eQIUMUHx+vvn37avDgwac8mwsAAIAzU4CvFi4qKtKGDRs0btw455ifn58SEhK0bt26Mvfp1q2b3n77ba1fv16dO3fW3r17tXjxYg0dOrTcdQoLC1VYWOh8nJeXJ0my2+2y2+0eejblK1mjOtaCd9BD66OH3mO3n/zT53bZbcZL69BDK6N/1lfdPXRnHZ+F2ZycHBUXFys6OtplPDo6Wjt27ChznyFDhignJ0cXX3yxjDE6efKkbrvttgovM0hPT1daWlqp8WXLliksLOz0noQbMjIyqm0teAc9tD566HmFxVLJr5KlS5cp2N+769FDa6N/1lddPczPz6/0XJ+F2apYuXKlJk6cqJdeekldunTR7t27NXr0aD322GMaP358mfuMGzdOKSkpzsd5eXmKi4tT3759FR4e7vWa7Xa7MjIy1KdPHwUGBnp9PXgePbQ+eug9+UUndf/6TyVJiYl9FRbknV8r9NDa6J/1VXcPS/6SXhk+C7ORkZHy9/fXoUOHXMYPHTqkmJiYMvcZP368hg4dqptvvlmS1Lp1ax0/fly33HKLHnroIfn5lb4EODg4WMHBwaXGAwMDq/UHqrrXg+fRQ+ujh54XaGz/+zwwUIGB3v21Qg+tjf5ZX3X10J01fPYCsKCgIHXo0EErVqxwjjkcDq1YsUJdu3Ytc5/8/PxSgdXf/4+/aRnjneu0AAAA8Pfl08sMUlJSNHz4cHXs2FGdO3fWs88+q+PHj2vEiBGSpGHDhqlBgwZKT0+XJPXv319Tp05Vu3btnJcZjB8/Xv3793eGWgAAAJw9fBpmk5KS9Msvv2jChAnKzs5W27ZttWTJEueLwg4cOOByJvbhhx+WzWbTww8/rJ9//ln16tVT//799cQTT/jqKQAAAMCHfP4CsOTkZCUnJ5e5beXKlS6PAwIClJqaqtTU1GqoDAAAAH93Pn87WwAAAKCqCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwrNMKswUFBZ6qAwAAAHCb22HW4XDoscceU4MGDVSzZk3t3btXkjR+/Hi9/vrrHi8QAAAAKI/bYfbxxx/XrFmz9PTTTysoKMg53qpVK82YMcOjxQEAAAAVcTvMvvXWW3r11Vd1/fXXy9/f3znepk0b7dixw6PFAQAAABVxO8z+/PPPOu+880qNOxwO2e12jxQFAAAAVIbbYbZly5ZatWpVqfH33ntP7dq180hRAFAiK/eE1u7JUVbuCV+XglPIzuVFwQCqX4C7O0yYMEHDhw/Xzz//LIfDoYULF2rnzp1666239NFHH3mjRgBnqXlfH9C4hZvlMJKfTUq76kJd06Gh28ex20+qsFjKLzqpQGPzQqVnr3e+OuD8PGHq50of2FpJnRr5sCIAZxu3w+zVV1+t//73v3r00UdVo0YNTZgwQe3bt9d///tf9enTxxs1AjgLZeWecAZZSXIYafyHWzX+w61VPGKA7l//qcfqQ2kOIz24cIsuaV5PsRGhvi4HwFnC7TArST169FBGRoanawEAp305x51BFtZRbIz25+QTZgFUG7fDbJMmTfT111/rnHPOcRn//fff1b59e+d9ZwHgdDSOrCE/m1wCrZ9NWp7SUzERIW4dy263a+nSZUpM7KvAwEAPV3r2ys4tUMLUz1165G+zKT4yzHdFATjruB1m9+/fr+Li4lLjhYWF+vnnnz1SFADERoQq7aoLnZcV+Nmk9IGt1aReTbePZbcZBftLYUEBCgys0h+kUIYm9WoqfWBrPbhwi4qNkb/NpokDW3FWFkC1qvT/1RctWuT8fOnSpYqIiHA+Li4u1ooVKxQfH+/R4gCc3a7p0NAZZpen9KxSkIV3JXVqpEua19P+nHzFR4YRZAFUu0qH2QEDBkiSbDabhg8f7rItMDBQ8fHxmjJlikeLA4AS7l5agOoTGxFKiAXgM5UOsw6HQ5LUuHFjff3114qMjPRaUQAAAEBluH3x2L59+7xRBwAAAOC2Kr0S4vjx4/r888914MABFRUVuWy766673DrWtGnTNGnSJGVnZ6tNmzZ64YUX1Llz53Ln//7773rooYe0cOFCHTlyROeee66effZZXX755VV5KgAAALAwt8Pspk2bdPnllys/P1/Hjx9X3bp1lZOTo7CwMEVFRbkVZufNm6eUlBRNnz5dXbp00bPPPqvExETt3LlTUVFRpeYXFRWpT58+ioqK0nvvvacGDRrohx9+UO3atd19GgAAADgD+Lm7w5gxY9S/f3/99ttvCg0N1ZdffqkffvhBHTp00OTJk9061tSpUzVy5EiNGDFCLVu21PTp0xUWFqaZM2eWOX/mzJk6cuSIPvjgA3Xv3l3x8fHq2bOn2rRp4+7TAAAAwBnA7TOzmZmZeuWVV+Tn5yd/f38VFhaqSZMmevrppzV8+HANHDiwUscpKirShg0bNG7cOOeYn5+fEhIStG7dujL3WbRokbp27apRo0bpww8/VL169TRkyBCNHTtW/v7+Ze5TWFiowsJC5+O8vDxJf9xE3W63V/ZpV1nJGtWxFryDHvqO3X7yT5/bZbdV7S3B6KH10UNro3/WV909dGcdt8NsYGCg/Pz+OKEbFRWlAwcOqEWLFoqIiNCPP/5Y6ePk5OSouLhY0dHRLuPR0dHasWNHmfvs3btXn376qa6//notXrxYu3fv1h133CG73a7U1NQy90lPT1daWlqp8WXLliksrPrepYa3/7U+elj9Coulkv9NLV26TMFl/5u10uih9dFDa6N/1lddPczPz6/0XLfDbLt27fT111+rWbNm6tmzpyZMmKCcnBzNnj1brVq1cvdwbnE4HIqKitKrr74qf39/dejQQT///LMmTZpUbpgdN26cUlJSnI/z8vIUFxenvn37Kjw83Kv1Sn/8yyIjI0N9+vThbTQtih76Tn7RSd2//lNJUmJiX4UFVe3du+ih9dFDa6N/1lfdPSz5S3pluP2bYeLEiTp69Kgk6YknntCwYcN0++23q1mzZnr99dcrfZzIyEj5+/vr0KFDLuOHDh1STExMmfvExsYqMDDQ5ZKCFi1aKDs7W0VFRQoKCiq1T3BwsIKDg0uNBwYGVusPVHWvB8+jh9Uv0Nj+93lg4Gm/FS09tD56aG30z/qqq4furOH2C8A6duyoXr16SfrjMoMlS5YoLy9PGzZsUNu2bSt9nKCgIHXo0EErVqxwjjkcDq1YsUJdu3Ytc5/u3btr9+7dzjdwkKRdu3YpNja2zCALAACAM5vbYbY8Gzdu1JVXXunWPikpKXrttdf05ptvavv27br99tt1/PhxjRgxQpI0bNgwlxeI3X777Tpy5IhGjx6tXbt26eOPP9bEiRM1atQoTz0NAAAAWIhbf7NbunSpMjIyFBQUpJtvvllNmjTRjh079MADD+i///2vEhMT3Vo8KSlJv/zyiyZMmKDs7Gy1bdtWS5Yscb4o7MCBA84Xm0lSXFycli5dqjFjxuiiiy5SgwYNNHr0aI0dO9atdQEAAHBmqHSYff311zVy5EjVrVtXv/32m2bMmKGpU6fqzjvvVFJSkrZs2aIWLVq4XUBycrKSk5PL3LZy5cpSY127dtWXX37p9joAAAA481T6MoPnnntOTz31lHJycjR//nzl5OTopZde0ubNmzV9+vQqBVkAAADgdFQ6zO7Zs0fXXnutJGngwIEKCAjQpEmT1LBhQ68VBwAAAFSk0mH2xIkTzjcZsNlsCg4OVmxsrNcKAwAAAE7FrReAzZgxQzVr1pQknTx5UrNmzVJkZKTLnLvuustz1QEAAAAVqHSYbdSokV577TXn45iYGM2ePdtljs1mI8wCAACg2lQ6zO7fv9+LZQAAAADu89ibJgAAAADVjTALAAAAyyLMAgAAwLIIswAAALAswiwAAAAsq0phds+ePXr44Yc1ePBgHT58WJL0ySefaOvWrR4tDgAAAKiI22H2888/V+vWrfXVV19p4cKFOnbsmCTp22+/VWpqqscLBAAAAMrjdph94IEH9PjjjysjI0NBQUHO8UsvvVRffvmlR4sDAAAAKuJ2mN28ebP++c9/lhqPiopSTk6OR4oCAAAAKsPtMFu7dm1lZWWVGt+0aZMaNGjgkaIAAACAynA7zF533XUaO3assrOzZbPZ5HA4tGbNGt17770aNmyYN2oEAAAAyuR2mJ04caIuuOACxcXF6dixY2rZsqUuueQSdevWTQ8//LA3agQAAADKFODuDkFBQXrttdc0fvx4bdmyRceOHVO7du3UrFkzb9QHAAAAlMvtMLt69WpdfPHFatSokRo1auSNmgAAAIBKcfsyg0svvVSNGzfWgw8+qG3btnmjJgAAAKBS3A6zBw8e1D333KPPP/9crVq1Utu2bTVp0iT99NNP3qgPAAAAKJfbYTYyMlLJyclas2aN9uzZo2uvvVZvvvmm4uPjdemll3qjRgAAAKBMbofZP2vcuLEeeOABPfnkk2rdurU+//xzT9UFAAAAnFKVw+yaNWt0xx13KDY2VkOGDFGrVq308ccfe7I2AAAAoEJu381g3Lhxmjt3rg4ePKg+ffroueee09VXX62wsDBv1AcAAACUy+0w+8UXX+i+++7ToEGDFBkZ6Y2aAAAAgEpxO8yuWbPGG3UAAAAAbqtUmF20aJEuu+wyBQYGatGiRRXOveqqqzxSGAAAAHAqlQqzAwYMUHZ2tqKiojRgwIBy59lsNhUXF3uqNgAAAKBClQqzDoejzM8BAAAAX3L71lxvvfWWCgsLS40XFRXprbfe8khRAAAAQGW4HWZHjBih3NzcUuNHjx7ViBEjPFIUAO/Lyj2htXtylJV7wtelVEp2boGvSwAA/A25fTcDY4xsNlup8Z9++kkREREeKQqAd837+oDGLdwsh5H8bFLaVRfqmg4NfV1WKe98dcD5ecLUz5U+sLWSOjXyYUUAgL+bSofZdu3ayWazyWazqXfv3goI+N+uxcXF2rdvn/r16+eVIgF4TlbuCWeQlSSHkcZ/uFXjP9zq28JOwWGkBxdu0SXN6yk2ItTX5QAA/iYqHWZL7mKQmZmpxMRE1axZ07ktKChI8fHxuuaaazxeIADP2pdz3BlkrabYGO3PySfMAgCcKh1mU1NTJUnx8fFKSkpSSEiI14oC4D2NI2vIzyaXQOtnk5an9FRMxN/n5zo7t0AJUz93qdPfZlN8JG+dDQD4H7evmR0+fLg36gBQTWIjQpV21YXOywr8bFL6wNZqUq/mKfasXk3q1VT6wNZ6cOEWFRsjf5tNEwe24qwsAMBFpcJs3bp1tWvXLkVGRqpOnTplvgCsxJEjRzxWHADvuKZDQ2eYXZ7S828XZEskdWqkS5rX0/6cfMVHhhFkAQClVCrMPvPMM6pVq5bz84rCLABr+TtdWlCW2IhQQiwAoFyVCrN/vrTghhtu8FYtAAAAgFvcftOEjRs3avPmzc7HH374oQYMGKAHH3xQRUVFHi0OAAAAqIjbYfbWW2/Vrl27JEl79+5VUlKSwsLCtGDBAt1///0eLxAAAAAoj9thdteuXWrbtq0kacGCBerZs6feeecdzZo1S//5z388XR8AAABQLrfDrDFGDodDkrR8+XJdfvnlkqS4uDjl5OR4tjoAAACgAm6H2Y4dO+rxxx/X7Nmz9fnnn+uKK66QJO3bt0/R0dEeLxAAAAAoj9th9tlnn9XGjRuVnJyshx56SOedd54k6b333lO3bt08XiAAAABQHrffAeyiiy5yuZtBiUmTJsnf398jRQEAAACV4XaYLbFhwwZt375dktSyZUu1b9/eY0UBAAAAleF2mD18+LCSkpL0+eefq3bt2pKk33//Xb169dLcuXNVr149T9cIAAAAlMnta2bvvPNOHTt2TFu3btWRI0d05MgRbdmyRXl5ebrrrru8USMAAABQJrfPzC5ZskTLly9XixYtnGMtW7bUtGnT1LdvX48WBwAAAFTE7TOzDodDgYGBpcYDAwOd958FAAAAqoPbYfbSSy/V6NGjdfDgQefYzz//rDFjxqh3794eLQ4AAACoiNth9sUXX1ReXp7i4+PVtGlTNW3aVI0bN1ZeXp5eeOEFb9QIAAAAlMnta2bj4uK0ceNGrVixwnlrrhYtWighIcHjxQEAAAAVcSvMzps3T4sWLVJRUZF69+6tO++801t1AQAAAKdU6TD78ssva9SoUWrWrJlCQ0O1cOFC7dmzR5MmTfJmfQAAAEC5Kn3N7IsvvqjU1FTt3LlTmZmZevPNN/XSSy95szYAAACgQpUOs3v37tXw4cOdj4cMGaKTJ08qKyvLK4UBAAAAp1LpMFtYWKgaNWr8b0c/PwUFBenEiRNeKQwAAAA4FbdeADZ+/HiFhYU5HxcVFemJJ55QRESEc2zq1Kmeqw4AAACoQKXD7CWXXKKdO3e6jHXr1k179+51PrbZbJ6rDAAAADiFSofZlStXerEMAAAAwH1uvwMYAAAA8HdBmAUAAIBlEWYBAABgWYRZAAAAWBZhFgAAAJZVpTC7atUq/fvf/1bXrl31888/S5Jmz56t1atXe7Q4AAAAoCJuh9n//Oc/SkxMVGhoqDZt2qTCwkJJUm5uriZOnOjxAgEAAIDyuB1mH3/8cU2fPl2vvfaaAgMDnePdu3fXxo0bPVocAAAAUBG3w+zOnTt1ySWXlBqPiIjQ77//7omaAAAAgEpxO8zGxMRo9+7dpcZXr16tJk2aVKmIadOmKT4+XiEhIerSpYvWr19fqf3mzp0rm82mAQMGVGldAAAAWJvbYXbkyJEaPXq0vvrqK9lsNh08eFBz5szRvffeq9tvv93tAubNm6eUlBSlpqZq48aNatOmjRITE3X48OEK99u/f7/uvfde9ejRw+01AQAAcGZwO8w+8MADGjJkiHr37q1jx47pkksu0c0336xbb71Vd955p9sFTJ06VSNHjtSIESPUsmVLTZ8+XWFhYZo5c2a5+xQXF+v6669XWlpalc8GAwAAwPoC3N3BZrPpoYce0n333afdu3fr2LFjatmypWrWrOn24kVFRdqwYYPGjRvnHPPz81NCQoLWrVtX7n6PPvqooqKidNNNN2nVqlUVrlFYWOi844Ik5eXlSZLsdrvsdrvbNburZI3qWAvecSb20G4/+afP7bLbjA+r8b4zsYdnG3pobfTP+qq7h+6s43aYLREUFKSWLVtWdXdJUk5OjoqLixUdHe0yHh0drR07dpS5z+rVq/X6668rMzOzUmukp6crLS2t1PiyZcsUFhbmds1VlZGRUW1rwTvOpB4WFkslP/5Lly5TsL9Py6k2Z1IPz1b00Nron/VVVw/z8/MrPdftMNurVy/ZbLZyt3/66afuHrLSjh49qqFDh+q1115TZGRkpfYZN26cUlJSnI/z8vIUFxenvn37Kjw83FulOtntdmVkZKhPnz4utzKDdZyJPcwvOqn71//xs5qY2FdhQVX+d60lnIk9PNvQQ2ujf9ZX3T0s+Ut6Zbj9G6xt27Yuj+12uzIzM7VlyxYNHz7crWNFRkbK399fhw4dchk/dOiQYmJiSs3fs2eP9u/fr/79+zvHHA6HJCkgIEA7d+5U06ZNXfYJDg5WcHBwqWMFBgZW6w9Uda8HzzuTehho/vcP0j+e15kdZkucST08W9FDa6N/1lddPXRnDbd/gz3zzDNljj/yyCM6duyYW8cKCgpShw4dtGLFCufttRwOh1asWKHk5ORS8y+44AJt3rzZZezhhx/W0aNH9dxzzykuLs6t9QEAAGBtHjsd8+9//1udO3fW5MmT3dovJSVFw4cPV8eOHdW5c2c9++yzOn78uEaMGCFJGjZsmBo0aKD09HSFhISoVatWLvvXrl1bkkqNAwAA4MznsTC7bt06hYSEuL1fUlKSfvnlF02YMEHZ2dlq27atlixZ4nxR2IEDB+Tn5/YdxAAAAHAWcDvMDhw40OWxMUZZWVn65ptvNH78+CoVkZycXOZlBZK0cuXKCvedNWtWldYEAACA9bkdZiMiIlwe+/n56fzzz9ejjz6qvn37eqwwAAAA4FTcCrPFxcUaMWKEWrdurTp16nirJgAAAKBS3LoY1d/fX3379tXvv//upXIAAACAynP7lVWtWrXS3r17vVELAAAA4Ba3w+zjjz+ue++9Vx999JGysrKUl5fn8gEAAABUl0pfM/voo4/qnnvu0eWXXy5Juuqqq1ze1tYYI5vNpuLiYs9XCQAAAJSh0mE2LS1Nt912mz777DNv1gMAAABUWqXDrDFGktSzZ0+vFQMAAAC4w61rZv98WQEAAADga27dZ7Z58+anDLRHjhw5rYIAAACAynIrzKalpZV6BzAAAADAV9wKs9ddd52ioqK8VQsAAADglkpfM8v1sgAAAPi7qXSYLbmbAQAAAPB3UenLDBwOhzfrAAAAANzm9tvZAgAAAH8XhFkAAABYFmEWAAAAlkWYBQAAgGURZgEAAGBZhFkAAABYFmEWAAAAlkWYBQAAgGURZgEAAGBZhFkAAABYFmEWAAAAlkWYBQAAgGURZgEAAGBZhFkAAABYFmEWAAAAlkWYBQAAgGURZgEAAGBZhFkAAABYFmEWAAAAlkWYBQAAgGURZgEAAGBZhFkAAABYFmEWAAAAlkWYBQAAgGURZgEAAGBZhFkAAABYFmEWAAAAlkWYBQAAgGURZgEAAGBZhFkAAABYFmEWAAAAlkWYBQAAgGURZgEAAGBZhFkAAABYFmEWAAAAlkWYBQAAgGURZgEAAGBZhFkAAABYFmHWgrJyT2jtnhxl5Z7wdSk4A2TnFvi6BAAAqizA1wXAPfO+PqBxCzfLYSQ/m5R21YW6pkNDX5d1RrPbT6qwWMovOqlAY/N1OR7xzlcHnJ8nTP1c6QNbK6lTIx9WBABA1RBmLSQr94QzyEqSw0jjP9yq8R9u9W1hZ4UA3b/+U18X4RUOIz24cIsuaV5PsRGhvi4HAAC3cJmBhezLOe4MsoAnFRuj/Tn5vi4DAAC3cWbWQhpH1pCfTS6B1s8mLU/pqZiIEN8Vdoaz2+1aunSZEhP7KjAw0NflnLbs3AIlTP3c5fvI32ZTfGSY74oCAKCKCLMWEhsRqrSrLnReVuBnk9IHtlaTejV9XNmZzW4zCvaXwoICFBho/R+ZJvVqKn1gaz24cIuKjZG/zaaJA1txiQEAwJKs/5v5LHNNh4bOMLs8pSdBFlWS1KmRLmleT/tz8hUfGUaQBQBYFmHWwri0AKcjNiKUEAsAsDxeAAYAAADLIswCAADAsgizAAAAsCzCLAAAACyLMAsAAADLIswCAADAsgizAAAAsCzCLAAAACyLMAsAAADLIswCAADAsgizAAAAsCzCLAAAACzrbxFmp02bpvj4eIWEhKhLly5av359uXNfe+019ejRQ3Xq1FGdOnWUkJBQ4XwAAACcuXweZufNm6eUlBSlpqZq48aNatOmjRITE3X48OEy569cuVKDBw/WZ599pnXr1ikuLk59+/bVzz//XM2VAwAAwNd8HmanTp2qkSNHasSIEWrZsqWmT5+usLAwzZw5s8z5c+bM0R133KG2bdvqggsu0IwZM+RwOLRixYpqrhwAAAC+FuDLxYuKirRhwwaNGzfOOebn56eEhAStW7euUsfIz8+X3W5X3bp1y9xeWFiowsJC5+O8vDxJkt1ul91uP43qK6dkDU+tZbefdDm23WY8clyUz9M9RPWjh9ZHD62N/llfdffQnXV8GmZzcnJUXFys6Ohol/Ho6Gjt2LGjUscYO3as6tevr4SEhDK3p6enKy0trdT4smXLFBYW5n7RVZSRkeGR4xQWSyVtW7p0mYL9PXJYVIKnegjfoYfWRw+tjf5ZX3X1MD8/v9JzfRpmT9eTTz6puXPnauXKlQoJCSlzzrhx45SSkuJ8nJeX57zONjw83Os12u12ZWRkqE+fPgoMDDzt4+UXndT96z+VJCUm9lVYkKVbaAme7iGqHz20PnpobfTP+qq7hyV/Sa8MnyahyMhI+fv769ChQy7jhw4dUkxMTIX7Tp48WU8++aSWL1+uiy66qNx5wcHBCg4OLjUeGBhYrT9Qnlov0Nj+ckzCbHWp7u8ZeB49tD56aG30z/qqq4furOHTF4AFBQWpQ4cOLi/eKnkxV9euXcvd7+mnn9Zjjz2mJUuWqGPHjtVRKgAAAP6GfH5aLyUlRcOHD1fHjh3VuXNnPfvsszp+/LhGjBghSRo2bJgaNGig9PR0SdJTTz2lCRMm6J133lF8fLyys7MlSTVr1lTNmjV99jwAAABQ/XweZpOSkvTLL79owoQJys7OVtu2bbVkyRLni8IOHDggP7//nUB++eWXVVRUpH/9618ux0lNTdUjjzxSnaUDAADAx3weZiUpOTlZycnJZW5buXKly+P9+/d7vyAAAABYgs/fNAEAAACoKsIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwrL9FmJ02bZri4+MVEhKiLl26aP369RXOX7BggS644AKFhISodevWWrx4cTVVCgAAgL8Tn4fZefPmKSUlRampqdq4caPatGmjxMREHT58uMz5a9eu1eDBg3XTTTdp06ZNGjBggAYMGKAtW7ZUc+WVk5VboO9zbcrKLfD4sbO9cEwAAAAr8XmYnTp1qkaOHKkRI0aoZcuWmj59usLCwjRz5swy5z/33HPq16+f7rvvPrVo0UKPPfaY2rdvrxdffLGaKz+1uesP6B9TvtCL2/z1jylfaPa6/covOnlaH+98dcB5/ISpn2ve1wcqqAAAAODMFuDLxYuKirRhwwaNGzfOOebn56eEhAStW7euzH3WrVunlJQUl7HExER98MEHZc4vLCxUYWGh83FeXp4kyW63y263n+YzKF9WboEefH+zHOaPxw4jjf9wq8Z/uNVjaziMNG7hZnVtXEexESEeOy5clXyfePP7Bd5FD62PHlob/bO+6u6hO+v4NMzm5OSouLhY0dHRLuPR0dHasWNHmftkZ2eXOT87O7vM+enp6UpLSys1vmzZMoWFhVWx8lP7Ptcmh/H32vFLOIw0f/FnahZhvL7W2S4jI8PXJeA00UPro4fWRv+sr7p6mJ+fX+m5Pg2z1WHcuHEuZ3Lz8vIUFxenvn37Kjw83GvrZuUW6KXtXzjPzEqSn01aclc3RYdX7SzqobwC9Xt+baljDrq8F2dmvchutysjI0N9+vRRYGCgr8tBFdBD66OH1kb/rK+6e1jyl/TK8GmYjYyMlL+/vw4dOuQyfujQIcXExJS5T0xMjFvzg4ODFRwcXGo8MDDQq81oFBmo9IGtNW7hH5ca+Nmk9IGt1Ty2TpWPGVEjVOkDW+vBhVtUbIz8bTZNHNhKjSJrebBylMfb3zPwPnpoffTQ2uif9VVXD91Zw6dhNigoSB06dNCKFSs0YMAASZLD4dCKFSuUnJxc5j5du3bVihUrdPfddzvHMjIy1LVr12qo2D1JnRqpa+M6mr/4Mw26vJdHQmdSp0a6pHk97c/JV3xkmGIjQj1QKQAAgDX5/DKDlJQUDR8+XB07dlTnzp317LPP6vjx4xoxYoQkadiwYWrQoIHS09MlSaNHj1bPnj01ZcoUXXHFFZo7d66++eYbvfrqq758GuWKjQhRswjj0csAYiNCCbEAAAD6G4TZpKQk/fLLL5owYYKys7PVtm1bLVmyxPkirwMHDsjP7393EOvWrZveeecdPfzww3rwwQfVrFkzffDBB2rVqpWvngIAAAB8xOdhVpKSk5PLvaxg5cqVpcauvfZaXXvttV6uCgAAAH93Pn/TBAAAAKCqCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyAnxdQHUzxkiS8vLyqmU9u92u/Px85eXlKTAwsFrWhGfRQ+ujh9ZHD62N/llfdfewJKeV5LaKnHVh9ujRo5KkuLg4H1cCAACAihw9elQREREVzrGZykTeM4jD4dDBgwdVq1Yt2Ww2r6+Xl5enuLg4/fjjjwoPD/f6evA8emh99ND66KG10T/rq+4eGmN09OhR1a9fX35+FV8Ve9admfXz81PDhg2rfd3w8HB+gC2OHlofPbQ+emht9M/6qrOHpzojW4IXgAEAAMCyCLMAAACwLMKslwUHBys1NVXBwcG+LgVVRA+tjx5aHz20NvpnfX/nHp51LwADAADAmYMzswAAALAswiwAAAAsizALAAAAyyLMAgAAwLIIsx4wbdo0xcfHKyQkRF26dNH69esrnL9gwQJdcMEFCgkJUevWrbV48eJqqhTlcaeHr732mnr06KE6deqoTp06SkhIOGXP4X3u/hyWmDt3rmw2mwYMGODdAnFK7vbw999/16hRoxQbG6vg4GA1b96c/5/6kLv9e/bZZ3X++ecrNDRUcXFxGjNmjAoKCqqpWvzVF198of79+6t+/fqy2Wz64IMPTrnPypUr1b59ewUHB+u8887TrFmzvF5nmQxOy9y5c01QUJCZOXOm2bp1qxk5cqSpXbu2OXToUJnz16xZY/z9/c3TTz9ttm3bZh5++GETGBhoNm/eXM2Vo4S7PRwyZIiZNm2a2bRpk9m+fbu54YYbTEREhPnpp5+quXKUcLeHJfbt22caNGhgevToYa6++urqKRZlcreHhYWFpmPHjubyyy83q1evNvv27TMrV640mZmZ1Vw5jHG/f3PmzDHBwcFmzpw5Zt++fWbp0qUmNjbWjBkzpporR4nFixebhx56yCxcuNBIMu+//36F8/fu3WvCwsJMSkqK2bZtm3nhhReMv7+/WbJkSfUU/CeE2dPUuXNnM2rUKOfj4uJiU79+fZOenl7m/EGDBpkrrrjCZaxLly7m1ltv9WqdKJ+7PfyrkydPmlq1apk333zTWyXiFKrSw5MnT5pu3bqZGTNmmOHDhxNmfczdHr788sumSZMmpqioqLpKRAXc7d+oUaPMpZde6jKWkpJiunfv7tU6UTmVCbP333+/ufDCC13GkpKSTGJiohcrKxuXGZyGoqIibdiwQQkJCc4xPz8/JSQkaN26dWXus27dOpf5kpSYmFjufHhXVXr4V/n5+bLb7apbt663ykQFqtrDRx99VFFRUbrpppuqo0xUoCo9XLRokbp27apRo0YpOjparVq10sSJE1VcXFxdZeP/q0r/unXrpg0bNjgvRdi7d68WL16syy+/vFpqxun7O+WZgGpf8QySk5Oj4uJiRUdHu4xHR0drx44dZe6TnZ1d5vzs7Gyv1YnyVaWHfzV27FjVr1+/1A81qkdVerh69Wq9/vrryszMrIYKcSpV6eHevXv16aef6vrrr9fixYu1e/du3XHHHbLb7UpNTa2OsvH/VaV/Q4YMUU5Oji6++GIZY3Ty5EnddtttevDBB6ujZHhAeXkmLy9PJ06cUGhoaLXVwplZ4DQ8+eSTmjt3rt5//32FhIT4uhxUwtGjRzV06FC99tprioyM9HU5qCKHw6GoqCi9+uqr6tChg5KSkvTQQw9p+vTpvi4NlbBy5UpNnDhRL730kjZu3KiFCxfq448/1mOPPebr0mBBnJk9DZGRkfL399ehQ4dcxg8dOqSYmJgy94mJiXFrPryrKj0sMXnyZD355JNavny5LrroIm+WiQq428M9e/Zo//796t+/v3PM4XBIkgICArRz5041bdrUu0XDRVV+DmNjYxUYGCh/f3/nWIsWLZSdna2ioiIFBQV5tWb8T1X6N378eA0dOlQ333yzJKl169Y6fvy4brnlFj300EPy8+Nc299deXkmPDy8Ws/KSpyZPS1BQUHq0KGDVqxY4RxzOBxasWKFunbtWuY+Xbt2dZkvSRkZGeXOh3dVpYeS9PTTT+uxxx7TkiVL1LFjx+ooFeVwt4cXXHCBNm/erMzMTOfHVVddpV69eikzM1NxcXHVWT5UtZ/D7t27a/fu3c5/iEjSrl27FBsbS5CtZlXpX35+fqnAWvIPE2OM94qFx/yt8ky1v+TsDDN37lwTHBxsZs2aZbZt22ZuueUWU7t2bZOdnW2MMWbo0KHmgQcecM5fs2aNCQgIMJMnTzbbt283qamp3JrLx9zt4ZNPPmmCgoLMe++9Z7KyspwfR48e9dVTOOu528O/4m4GvuduDw8cOGBq1aplkpOTzc6dO81HH31koqKizOOPP+6rp3BWc7d/qampplatWubdd981e/fuNcuWLTNNmzY1gwYN8tVTOOsdPXrUbNq0yWzatMlIMlOnTjWbNm0yP/zwgzHGmAceeMAMHTrUOb/k1lz33Xef2b59u5k2bRq35rKyF154wTRq1MgEBQWZzp07my+//NK5rWfPnmb48OEu8+fPn2+aN29ugoKCzIUXXmg+/vjjaq4Yf+VOD88991wjqdRHampq9RcOJ3d/Dv+MMPv34G4P165da7p06WKCg4NNkyZNzBNPPGFOnjxZzVWjhDv9s9vt5pFHHjFNmzY1ISEhJi4uztxxxx3mt99+q/7CYYwx5rPPPivzd1tJ34YPH2569uxZap+2bduaoKAg06RJE/PGG29Ue93GGGMzhvP5AAAAsCaumQUAAIBlEWYBAABgWYRZAAAAWBZhFgAAAJZFmAUAAIBlEWYBAABgWYRZAAAAWBZhFgAAAJZFmAUASbNmzVLt2rV9XUaV2Ww2ffDBBxXOueGGGzRgwIBqqQcAqgthFsAZ44YbbpDNZiv1sXv3bl+XplmzZjnr8fPzU8OGDTVixAgdPnzYI8fPysrSZZddJknav3+/bDabMjMzXeY899xzmjVrlkfWK88jjzzifJ7+/v6Ki4vTLbfcoiNHjrh1HII3gMoK8HUBAOBJ/fr10xtvvOEyVq9ePR9V4yo8PFw7d+6Uw+HQt99+qxEjRujgwYNaunTpaR87JibmlHMiIiJOe53KuPDCC7V8+XIVFxdr+/btuvHGG5Wbm6t58+ZVy/oAzi6cmQVwRgkODlZMTIzLh7+/v6ZOnarWrVurRo0aiouL0x133KFjx46Ve5xvv/1WvXr1Uq1atRQeHq4OHTrom2++cW5fvXq1evToodDQUMXFxemuu+7S8ePHK6zNZrMpJiZG9evX12WXXaa77rpLy5cv14kTJ+RwOPToo4+qYcOGCg4OVtu2bbVkyRLnvkVFRUpOTlZsbKxCQkJ07rnnKj093eXYJZcZNG7cWJLUrl072Ww2/eMf/5Dkerbz1VdfVf369eVwOFxqvPrqq3XjjTc6H3/44Ydq3769QkJC1KRJE6WlpenkyZMVPs+AgADFxMSoQYMGSkhI0LXXXquMjAzn9uLiYt10001q3LixQkNDdf755+u5555zbn/kkUf05ptv6sMPP3Se5V25cqUk6ccff9SgQYNUu3Zt1a1bV1dffbX2799fYT0AzmyEWQBnBT8/Pz3//PPaunWr3nzzTX366ae6//77y51//fXXq2HDhvr666+1YcMGPfDAAwoMDJQk7dmzR/369dM111yj7777TvPmzdPq1auVnJzsVk2hoaFyOBw6efKknnvuOU2ZMkWTJ0/Wd999p8TERF111VX6/vvvJUnPP/+8Fi1apPnz52vnzp2aM2eO4uPjyzzu+vXrJUnLly9XVlaWFi5cWGrOtddeq19//VWfffaZc+zIkSNasmSJrr/+eknSqlWrNGzYMI0ePVrbtm3TK6+8olmzZumJJ56o9HPcv3+/li5dqqCgIOeYw+FQw4YNtWDBAm3btk0TJkzQgw8+qPnz50uS7r33Xg0aNEj9+vVTVlaWsrKy1K1bN9ntdiUmJqpWrVpatWqV1qxZo5o1a6pfv34qKiqqdE0AzjAGAM4Qw4cPN/7+/qZGjRrOj3/9619lzl2wYIE555xznI/feOMNExER4Xxcq1YtM2vWrDL3vemmm8wtt9ziMrZq1Srj5+dnTpw4UeY+fz3+rl27TPPmzU3Hjh2NMcbUr1/fPPHEEy77dOrUydxxxx3GGGPuvPNOc+mllxqHw1Hm8SWZ999/3xhjzL59+4wks2nTJpc5w4cPN1dffbXz8dVXX21uvPFG5+NXXnnF1K9f3xQXFxtjjOndu7eZOHGiyzFmz55tYmNjy6zBGGNSU1ONn5+fqVGjhgkJCTGSjCQzderUcvcxxphRo0aZa665ptxaS9Y+//zzXb4GhYWFJjQ01CxdurTC4wM4c3HNLIAzSq9evfTyyy87H9eoUUPSH2cp09PTtWPHDuXl5enkyZMqKChQfn6+wsLCSh0nJSVFN998s2bPnu38U3nTpk0l/XEJwnfffac5c+Y45xtj5HA4tG/fPrVo0aLM2nJzc1WzZk05HA4VFBTo4osv1owZM5SXl6eDBw+qe/fuLvO7d++ub7/9VtIflwj06dNH559/vvr166crr7xSffv2Pa2v1fXXX6+RI0fqpZdeUnBwsObMmaPrrrtOfn5+zue5Zs0alzOxxcXFFX7dJOn888/XokWLVFBQoLfffluZmZm68847XeZMmzZNM2fO1IEDB3TixAkVFRWpbdu2Fdb77bffavfu3apVq5bLeEFBgfbs2VOFrwCAMwFhFsAZpUaNGjrvvPNcxvbv368rr7xSt99+u5544gnVrVtXq1ev1k033aSioqIyQ9kjjzyiIUOG6OOPP9Ynn3yi1NRUzZ07V//85z917Ngx3XrrrbrrrrtK7deoUaNya6tVq5Y2btwoPz8/xcbGKjQ0VJKUl5d3yufVvn177du3T5988omWL1+uQYMGKSEhQe+9994p9y1P//79ZYzRxx9/rE6dOmnVqlV65plnnNuPHTumtLQ0DRw4sNS+ISEh5R43KCjI2YMnn3xSV1xxhdLS0vTYY49JkubOnat7771XU6ZMUdeuXVWrVi1NmjRJX331VYX1Hjt2TB06dHD5R0SJv8uL/ABUP8IsgDPehg0b5HA4NGXKFOdZx5LrMyvSvHlzNW/eXGPGjNHgwYP1xhtv6J///Kfat2+vbdu2lQrNp+Ln51fmPuHh4apfv77WrFmjnj17OsfXrFmjzp07u8xLSkpSUlKS/vWvf6lfv346cuSI6tat63K8kutTi4uLK6wnJCREAwcO1Jw5c7R7926df/75at++vXN7+/bttXPnTref5189/PDDuvTSS3X77bc7n2e3bt10xx13OOf89cxqUFBQqfrbt2+vefPmKSoqSuHh4adVE4AzBy8AA3DGO++882S32/XCCy9o7969mj17tqZPn17u/BMnTig5OVkrV67UDz/8oDVr1ujrr792Xj4wduxYrV27VsnJycrMzNT333+vDz/80O0XgP3Zfffdp6eeekrz5s3Tzp079cADDygzM1OjR4+WJE2dOlXvvvuuduzYoV27dmnBggWKiYkp840eoqKiFBoaqiVLlujQoUPKzc0td93rr79eH3/8sWbOnOl84VeJCRMm6K233lJaWpq2bt2q7du3a+7cuXr44Yfdem5du3bVRRddpIkTJ0qSmjVrpm+++UZLly7Vrl27NH78eH399dcu+8THx+u7777Tzp07lZOTI7vdruuvv16RkZG6+uqrtWrVKu3bt08rV67UXXfdpZ9++smtmgCcOQizAM54bdq00dSpU/XUU0+pVatWmjNnjsttrf7K399fv/76q4YNG6bmzZtr0KBBuuyyy5SWliZJuuiii/T5559r165d6tGjh9q1a6cJEyaofv36Va7xrrvuUkpKiu655x61bt1aS5Ys0aJFi9SsWTNJf1yi8PTTT6tjx47q1KmT9u/fr8WLFzvPNP9ZQECAnn/+eb3yyiuqX7++rr766nLXvfTSS1W3bl3t3LlTQ4YMcdmWmJiojz76SMuWLVOnTp30f//3f3rmmWd07rnnuv38xowZoxkzZujHH3/UrbfeqoEDByopKUldunTRr7/+6nKWVpJGjhyp888/Xx07dlS9evW0Zs0ahYWF6YsvvlCjRo00cOBAtWjRQjfddJMKCgo4UwucxWzGGOPrIgAAAICq4MwsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCyCLMAAACwLMIsAAAALIswCwAAAMsizAIAAMCy/h9MmkgvwPqioAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the PRAUC and AUC scores\n",
        "print('PRAUC:', prauc)\n",
        "print('AUC:', roc_auc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KJid5Hb0DllH",
        "outputId": "b1c53db9-8455-461b-90ff-dd61ff768c45"
      },
      "id": "KJid5Hb0DllH",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PRAUC: 0.31325990819525296\n",
            "AUC: 0.6296296296296297\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zWJMssuIFoYb"
      },
      "id": "zWJMssuIFoYb",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 2
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython2",
      "version": "2.7.6"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}