Prediction_of_plant_associated_bacteria.ipynb

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        "<a href=\"https://colab.research.google.com/github/ajlukasiewicz/Team_9_MacLearn/blob/main/Prediction_of_plant_associated_bacteria.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "**Predicting plant associated bacterial from genetic markers**\n",
        "\n",
        "The soil microbiome is a diverse and sensitive assembly of organisms (bacterial and fungal) whose composition changes relative to the soil type, location, and even the plant species. \n",
        "\n",
        "Based on a large dataset of 3800, well-curated, bacterial genomes (Levy et. al., 2017) we aimed to construct a classifier of bacteria, based on three major feature families: Carbohydrate Associated Enzyme families, COG counts, and the % of horizontally transmitted genes within each bacterial genome. \n",
        "\n",
        "Carbohydrate associated enzymes were identified for each genome using the DBCan4 tool, and COG percentages were calculated from the metadata provided by Levy et. al., 2017. \n",
        "\n",
        "Authors of this project are Muyoung Lee (ML), Sarah Coleman (SC), and Alexandra Lukasiewicz (AL) to fulfill the requirements of our final project in SDS 384, Scientific Machine Learning. \n",
        "\n",
        "We each have contributed equally to the review, curation, writing and interpretation of this project. This colab document represents the aggregate of all individual scripts used to modify, train, tune, and evaluate multiple models. For more information on our individual approaches, please see the jupyter notebooks in each of our git branches. \n",
        "\n",
        "We have included author contributions in the #comments for each script section."
      ],
      "metadata": {
        "id": "X1TJVOPI9kT8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#imports\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from plotnine import *\n",
        "\n",
        "#normalization\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "#splitting and feature selection\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import cross_val_score, StratifiedKFold\n",
        "from sklearn.feature_selection import VarianceThreshold\n",
        "\n",
        "#models\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn import tree\n",
        "from sklearn.tree import DecisionTreeClassifier, plot_tree\n",
        "import xgboost as xgb\n",
        "\n",
        "#evaluation\n",
        "from sklearn import metrics\n",
        "from sklearn.model_selection import KFold\n",
        "from sklearn.metrics import accuracy_score, f1_score, precision_score, roc_auc_score, recall_score, PrecisionRecallDisplay, precision_recall_curve\n",
        "\n",
        "#shhhhhh\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
        "id": "veEpXSif_sPw"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#functions\n",
        "def soilrm(df):\n",
        "    df2 = df[df[\"Classification\"] != \"soil\"]\n",
        "    soil = df[df[\"Classification\"] == \"soil\"]\n",
        "    return df2, soil"
      ],
      "metadata": {
        "id": "gsjQZUJUFxiS"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#set matplotlib theme \n",
        "custom_style = {\n",
        "    'figure.figsize': (8, 6),\n",
        "    'axes.spines.top': False,\n",
        "    'axes.spines.right': False,\n",
        "    'axes.labelsize': 14,\n",
        "    'xtick.labelsize': 12,\n",
        "    'ytick.labelsize': 12,\n",
        "    'font.family': 'serif',\n",
        "    'font.serif': ['arial'],\n",
        "    'text.color': '#333333',\n",
        "    'axes.edgecolor': '#CCCCCC',\n",
        "    'axes.linewidth': 1.5,\n",
        "    'grid.linewidth': 0.5,\n",
        "    'grid.color': '#DDDDDD',\n",
        "}\n"
      ],
      "metadata": {
        "id": "FEp6YirTAIVF"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#AL\n",
        "#import data\n",
        "#from google.colab import drive\n",
        "#drive.mount('/content/drive')\n",
        "#data_dir = \"/content/drive/MyDrive/Datasets/\"\n",
        "\n",
        "#dbcan = pd.read_csv(data_dir + \"dbcan_result.3772genomes.matrix.divided_by_gene_counts.tsv\", sep=\"\\t\")\n",
        "#cog = pd.read_csv(data_dir + \"cog_ratios.csv\")\n",
        "\n",
        "#metadata = pd.read_csv(data_dir + \"jgi_metadata_processed.csv\")\n",
        "metadata = pd.read_csv(\"https://raw.githubusercontent.com/ajlukasiewicz/Team_9_MacLearn/main/Data/jgi_metadata_processed.csv\")\n",
        "\n",
        "#overlapping features from all three of our feature selection approaches\n",
        "#union_features = pd.read_csv(data_dir + \"overlapping_features.csv\")\n",
        "union_features = pd.read_csv(\"https://raw.githubusercontent.com/ajlukasiewicz/Team_9_MacLearn/main/Data/overlapping_features.csv\")\n",
        "union_features = union_features['elements'].values"
      ],
      "metadata": {
        "id": "wQHWXqUUCg7Y"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#AL\n",
        "#modify and merge hgt% to dbcan dataset\n",
        "#dbcan = dbcan.rename(columns = {\"taxonID\" : \"2016_taxon_oid\"})\n",
        "\n",
        "#add hgt % from metadata to dbcan dataset\n",
        "#dbcan = pd.merge(dbcan, cog[['2016_taxon_oid', 'Horizontally_Transferred_%']], on='2016_taxon_oid', how='left')"
      ],
      "metadata": {
        "id": "z_dZ1WFZF2Le"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#AL\n",
        "#remove soil bacteria from all three datasets \n",
        "#cog, soilcog = soilrm(cog)\n",
        "#dbcan, soilcan = soilrm(dbcan)"
      ],
      "metadata": {
        "id": "n2-fk2iXGA0Q"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#AL\n",
        "#merge the dbcan and cog datasets\n",
        "#columns = ['Classification', 'Horizontally_Transferred_%', \"Gene_Count___*_assembled\", \"Unnamed: 0\"]\n",
        "#merged = pd.merge(dbcan, cog.drop(columns, axis = 1), on='2016_taxon_oid', how='left')\n",
        "\n",
        "#merged.head()"
      ],
      "metadata": {
        "id": "SLvFo-ZAIK-Q"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#merged.to_csv('merged.tsv', sep=\"\\t\", index=False)\n",
        "#soil_dataset = pd.merge(soilcan, soilcog.drop(columns, axis = 1), on='2016_taxon_oid', how='left')\n",
        "#soil_dataset.to_csv(\"soil_dataset.tsv\", sep=\"\\t\", index=False)"
      ],
      "metadata": {
        "id": "TCLEwGmXDCuv"
      },
      "execution_count": 8,
      "outputs": []
    },
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        "merged = pd.read_csv(\"https://utexas.box.com/shared/static/21oborsfx8kzhushr24vgfc69gpf4ztp.tsv\", sep=\"\\t\")\n",
        "merged.head()"
      ],
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              "   2016_taxon_oid          Order Classification Root_info       AA1  AA10  \\\n",
              "0      2511231155  Acinetobacter            NPA       NPA  0.000252   0.0   \n",
              "1      2519103115  Acinetobacter            NPA       NPA  0.000265   0.0   \n",
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              "3      2526164562  Acinetobacter            NPA       NPA  0.000495   0.0   \n",
              "4      2529293005  Acinetobacter            NPA       NPA  0.000263   0.0   \n",
              "\n",
              "       AA12       AA3       AA4  AA5  ...  COG5653  COG5654  COG5655  COG5658  \\\n",
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              "4  0.000263  0.000263  0.000263  0.0  ...      0.0      0.0      0.0      0.0   \n",
              "\n",
              "   COG5659  COG5660  COG5661  COG5662  COG5663  COG5664  \n",
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        "soil_dataset = pd.read_csv(\"https://raw.githubusercontent.com/ajlukasiewicz/Team_9_MacLearn/main/Data/soil_dataset.tsv\", sep=\"\\t\")\n",
        "soil_dataset.head()"
      ],
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      "execution_count": 10,
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              "   2016_taxon_oid           Order Classification Root_info       AA1  \\\n",
              "0      2534682017   Acinetobacter           soil      soil  0.000267   \n",
              "1      2534682020   Acinetobacter           soil      soil  0.000257   \n",
              "2      2576861110   Acinetobacter           soil      soil  0.000497   \n",
              "3       648028004   Acinetobacter           soil      soil  0.000252   \n",
              "4      2502894011  Actinobacteria           soil      soil  0.000128   \n",
              "\n",
              "       AA10      AA12       AA3       AA4       AA5  ...  COG5653  COG5654  \\\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "#AL\n",
        "#x and y variables\n",
        "variables = merged.columns[:4]\n",
        "classif = merged['Classification']\n",
        "\n",
        "scaler = StandardScaler()\n",
        "merged_n = pd.DataFrame(scaler.fit_transform(merged.iloc[:,4:]), columns = merged.iloc[:,4:].columns)\n",
        "merged_n.head()"
      ],
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        "id": "lWXSRR3PGZe-",
        "outputId": "eea5eaf5-c187-47ac-d43b-e893767083e4"
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      "execution_count": 11,
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              "        AA1      AA10      AA12       AA3       AA4       AA5       AA6  \\\n",
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              "      <td>-0.404687</td>\n",
              "      <td>-0.304982</td>\n",
              "      <td>-0.76716</td>\n",
              "      <td>-0.229263</td>\n",
              "      <td>-0.271738</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.123815</td>\n",
              "      <td>-0.378513</td>\n",
              "      <td>2.446855</td>\n",
              "      <td>-0.356327</td>\n",
              "      <td>1.079278</td>\n",
              "      <td>-0.170719</td>\n",
              "      <td>2.687882</td>\n",
              "      <td>-0.644416</td>\n",
              "      <td>-0.115373</td>\n",
              "      <td>-0.150815</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.406102</td>\n",
              "      <td>-0.578561</td>\n",
              "      <td>-0.105001</td>\n",
              "      <td>-0.268621</td>\n",
              "      <td>-0.087445</td>\n",
              "      <td>-0.404687</td>\n",
              "      <td>-0.304982</td>\n",
              "      <td>-0.76716</td>\n",
              "      <td>-0.229263</td>\n",
              "      <td>-0.271738</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 4560 columns</p>\n",
              "</div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The classifications appear to be roughly 65% NPA, and 35% PA. We therefore stratify our training and testing datasets by the classification\n",
        "\n"
      ],
      "metadata": {
        "id": "zqXoZvzcNKHA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Train test split dataset, scaled by classification ()\n",
        "X_train, X_test, y_train, y_test = train_test_split(merged_n,\n",
        "                                                    classif, test_size=0.33,\n",
        "                                                    random_state=42, \n",
        "                                                    stratify=classif)"
      ],
      "metadata": {
        "id": "TxIIm7XRJi_Q"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#ML, AL, SC\n",
        "#filter the main, training, and test datasets by the union features\n",
        "X = merged_n[union_features]\n",
        "X_train = X_train[union_features]\n",
        "X_test = X_test[union_features]"
      ],
      "metadata": {
        "id": "zbiugPWCNo5b"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#ML \n",
        "#Decision tree model building and evaluation \n",
        "\n",
        "'''\n",
        "twenty = [i for i in range(1, 20+1)]\n",
        "accuracies = {}\n",
        "\n",
        "for depth in twenty:\n",
        "  for _min_samples_leaf in twenty:\n",
        "    clf1 = tree.DecisionTreeClassifier(max_depth=depth, min_samples_leaf=_min_samples_leaf, random_state=42)\n",
        "    clf1.fit(X_train, y_train)\n",
        "    y_pred1 = clf1.predict(X_test)\n",
        "    accuracies[(depth, _min_samples_leaf)] = metrics.accuracy_score(y_test, y_pred1)\n",
        "\n",
        "sorted_accuracies = sorted(accuracies.items(), key=lambda x:x[1], reverse=True)\n",
        "depth, _min_samples_leaf = sorted_accuracies[0][0]\n",
        "print(depth, _min_samples_leaf)\n",
        "'''\n",
        "depth, _min_samples_leaf = 11, 5  # Based on the former result, to save time\n",
        "clf1 = tree.DecisionTreeClassifier(max_depth=depth, min_samples_leaf=_min_samples_leaf, random_state=42)\n",
        "clf1.fit(X_train, y_train)\n",
        "y_pred1 = clf1.predict(X_test)\n",
        "\n",
        "# Accuracy scores measured with the test set and train set\n",
        "print(metrics.accuracy_score(y_test, y_pred1))\n",
        "print(metrics.accuracy_score(y_train, clf1.predict(X_train)))\n",
        "\n",
        "dt_depth = depth\n",
        "dt_min_samples_leaf = _min_samples_leaf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pNy1rjzkOzch",
        "outputId": "ac06e76a-ebaa-4a7f-f2ba-8b3146427740"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8477251624883937\n",
            "0.9624542124542125\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#ML\n",
        "#Random Forest model building and evaluation\n",
        "\n",
        "'''\n",
        "twenty = [i for i in range(1, 20+1)]\n",
        "accuracies = {}\n",
        "\n",
        "for _n_estimators in [50, 100, 150]:\n",
        "  for depth in twenty:\n",
        "    for _min_samples_leaf in twenty:\n",
        "      clf2 = RandomForestClassifier(n_estimators=_n_estimators, max_depth=depth, min_samples_leaf=_min_samples_leaf, random_state=42)\n",
        "      clf2.fit(X_train, y_train.values.ravel())\n",
        "      y_pred2 = clf2.predict(X_test)\n",
        "      accuracies[(_n_estimators, depth, _min_samples_leaf)] = metrics.accuracy_score(y_test, y_pred2)\n",
        "\n",
        "sorted_accuracies = sorted(accuracies.items(), key=lambda x:x[1], reverse=True)\n",
        "_n_estimators, depth, _min_samples_leaf = sorted_accuracies[0][0]\n",
        "print(_n_estimators, depth, _min_samples_leaf)\n",
        "'''\n",
        "_n_estimators, depth, _min_samples_leaf = 50, 18, 1 # Based on the former result, to save time\n",
        "clf2 = RandomForestClassifier(n_estimators=_n_estimators, max_depth=depth, min_samples_leaf=_min_samples_leaf, random_state=42)\n",
        "clf2.fit(X_train, y_train.values.ravel())\n",
        "y_pred2 = clf2.predict(X_test)\n",
        "\n",
        "# Accuracy scores measured with the test set and train set\n",
        "print(metrics.accuracy_score(y_test, y_pred2))\n",
        "print(metrics.accuracy_score(y_train, clf2.predict(X_train)))\n",
        "\n",
        "RF_n_estimators, RF_depth, RF_min_samples_leaf = _n_estimators, depth, _min_samples_leaf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gdofroy1QHAp",
        "outputId": "869f972b-6c0f-4e91-fd6c-5e5aa4fb3570"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8885793871866295\n",
            "0.9995421245421245\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#ML\n",
        "#XGBoost model building and evaluation\n",
        "XGB_y_test = y_test.replace(\"PA\", 1).replace(\"NPA\", 0)\n",
        "XGB_y_train = y_train.replace(\"PA\", 1).replace(\"NPA\", 0)\n",
        "\n",
        "'''\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "#XGBoost model building and evaluation\n",
        "XGB_y_test = y_test.replace(\"PA\", 1).replace(\"NPA\", 0)\n",
        "XGB_y_train = y_train.replace(\"PA\", 1).replace(\"NPA\", 0)\n",
        "\n",
        "# define the parameter grid\n",
        "param_grid = {\n",
        "    'max_depth': range(1, 11),\n",
        "    'n_estimators': [100, 150]\n",
        "}\n",
        "\n",
        "# create the XGBoost classifier\n",
        "xgb_model = xgb.XGBClassifier()\n",
        "\n",
        "# create the GridSearchCV object\n",
        "grid_search = GridSearchCV(xgb_model, param_grid=param_grid, cv=5, n_jobs=-1)\n",
        "\n",
        "# fit the GridSearchCV object to the data\n",
        "grid_search.fit(X_train, XGB_y_train)\n",
        "\n",
        "# print the best hyperparameters\n",
        "print(\"Best parameters: \", grid_search.best_params_)\n",
        "'''\n",
        "\n",
        "_n_estimators, depth = 150, 2 # Based on the former result, to save time\n",
        "\n",
        "clf3 = xgb.XGBClassifier(objective=\"binary:logistic\", n_estimators=_n_estimators, max_depth=depth, random_state=42)\n",
        "clf3.fit(X_train, XGB_y_train)\n",
        "y_pred3 = clf3.predict(X_test)\n",
        "\n",
        "# Accuracy scores measured with the test set and train set\n",
        "print(metrics.accuracy_score(XGB_y_test, y_pred3))\n",
        "print(metrics.accuracy_score(XGB_y_train, clf3.predict(X_train)))\n",
        "\n",
        "XGB_n_estimators, XGB_depth = _n_estimators, depth"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IkEg172ARnLQ",
        "outputId": "72da37a2-74ed-4c11-dcde-17854fbfc72e"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8885793871866295\n",
            "0.9977106227106227\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#ML\n",
        "\n",
        "# To check whether the first column from predict_proba is about PA or not (NPA).\n",
        "#Tree\n",
        "print(clf1.classes_)\n",
        "#RF\n",
        "print(clf2.classes_)\n",
        "# XGBoost\n",
        "print(clf3.classes_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bS0xkhVHHhRb",
        "outputId": "dd2e4d2d-649b-4f49-9ee2-13ee7ae53453"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['NPA' 'PA']\n",
            "['NPA' 'PA']\n",
            "[0 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#ML\n",
        "#Random Forest and Decision Tree PR curves\n",
        "\n",
        "\n",
        "#generate curves\n",
        "precision_1, recall_1, thresholds_1 = precision_recall_curve(y_test, clf1.predict_proba(X_test)[:,0], pos_label=\"NPA\")\n",
        "precision_2, recall_2, thresholds_2 = precision_recall_curve(y_test, clf2.predict_proba(X_test)[:,0], pos_label=\"NPA\")\n",
        "precision_3, recall_3, thresholds_3 = precision_recall_curve(XGB_y_test, clf3.predict_proba(X_test)[:,0], pos_label=0)\n",
        "\n",
        "#plot\n",
        "fig, ax = plt.subplots(1,3, figsize=(18, 6))\n",
        "\n",
        "ax[0].plot(recall_1, precision_1)\n",
        "ax[1].plot(recall_2, precision_2)\n",
        "ax[2].plot(recall_3, precision_3)\n",
        "\n",
        "ax[0].set_title(\"Decision Tree\")\n",
        "ax[1].set_title(\"Random Forest\")\n",
        "ax[2].set_title(\"XGBoost\")\n",
        "\n",
        "ax[0].set_xlabel(\"Recall\")\n",
        "ax[0].set_ylabel(\"Precision\")\n",
        "ax[1].set_xlabel(\"Recall\")\n",
        "ax[1].set_ylabel(\"Precision\")\n",
        "ax[2].set_xlabel(\"Recall\")\n",
        "ax[2].set_ylabel(\"Precision\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "L_2qFpoUROXA",
        "outputId": "266bfe45-ac80-4521-b265-2872fd5d7685"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x600 with 3 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#AL\n",
        "#train and evaluate KNeighbors Classification\n",
        "#find minimum variance using k fold cross validation\n",
        "\n",
        "#list of feature k to try\n",
        "neighbors = range(1,100)\n",
        "accuracy = {\"n\": [], \"accuracy\": [], \"std\": []}\n",
        "\n",
        "skf = StratifiedKFold(n_splits=5, shuffle= True, random_state= 17)\n",
        "\n",
        "for n in neighbors:\n",
        "    #fit using KNeighbors\n",
        "    KNC = KNeighborsClassifier(n_neighbors = n)\n",
        "    \n",
        "    #calculate cross val scores\n",
        "    val_scores = cross_val_score(estimator= KNC,  X= X_train, y= y_train, cv= skf)\n",
        "\n",
        "    #log accuracy\n",
        "    accuracy['n'].append(n)\n",
        "    accuracy['accuracy'].append(val_scores.mean())\n",
        "    accuracy['std'].append(val_scores.std())\n",
        "\n",
        "accuracy_dataframe = pd.DataFrame.from_dict(accuracy)"
      ],
      "metadata": {
        "id": "mIdYIfGTQoWN"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#plot dataframe \n",
        "plt.plot(accuracy_dataframe['n'], accuracy_dataframe['accuracy'],'o-', label='accuracy score', color = 'steelblue')\n",
        "plt.errorbar(accuracy_dataframe['n'], accuracy_dataframe['accuracy'], yerr=accuracy_dataframe['std'], fmt='none', color='gray', capsize=3)\n",
        "\n",
        "# add a title and labels to the plot\n",
        "plt.xlabel('n neighbors')\n",
        "plt.ylabel('score')\n",
        "plt.title('Cross validated KNeighbor accuracy scores across n neighbors')\n",
        "\n",
        "# add a legend to the plot\n",
        "plt.legend()\n",
        "\n",
        "# display the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "nGNI1N351l8c",
        "outputId": "9296986b-44a3-43b7-8204-3faad7838d5b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        }
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#AL top accuracy with minimum variability\n",
        "accuracy_dataframe.sort_values(by = [\"accuracy\",'std'], ascending = [False,True])[:10]"
      ],
      "metadata": {
        "id": "x9a4O4BB1vun",
        "outputId": "5c2c9b06-9a8d-4299-fbe5-b29695789888",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        }
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    n  accuracy       std\n",
              "0   1  0.898352  0.008001\n",
              "4   5  0.893774  0.011160\n",
              "2   3  0.889194  0.010291\n",
              "5   6  0.889193  0.011366\n",
              "3   4  0.885532  0.014172\n",
              "1   2  0.881874  0.015249\n",
              "6   7  0.881415  0.013490\n",
              "7   8  0.880494  0.016971\n",
              "9  10  0.878203  0.020139\n",
              "8   9  0.875460  0.025081"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e26e8320-f648-4ef2-b8c7-6b3bc8f9a132\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>n</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>std</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0.898352</td>\n",
              "      <td>0.008001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0.893774</td>\n",
              "      <td>0.011160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0.889194</td>\n",
              "      <td>0.010291</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>0.889193</td>\n",
              "      <td>0.011366</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>0.885532</td>\n",
              "      <td>0.014172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0.881874</td>\n",
              "      <td>0.015249</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>0.881415</td>\n",
              "      <td>0.013490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>0.880494</td>\n",
              "      <td>0.016971</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>0.878203</td>\n",
              "      <td>0.020139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>0.875460</td>\n",
              "      <td>0.025081</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e26e8320-f648-4ef2-b8c7-6b3bc8f9a132')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [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-e26e8320-f648-4ef2-b8c7-6b3bc8f9a132 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-e26e8320-f648-4ef2-b8c7-6b3bc8f9a132');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#AL \n",
        "#fit KNeighbors at n = 3\n",
        "KNC = KNeighborsClassifier(n_neighbors = 3)\n",
        "KNC.fit(X_train, y_train)\n",
        "\n",
        "#KNeighbors PRcurves\n",
        "display = PrecisionRecallDisplay.from_estimator(\n",
        "    KNC, X_test, y_test, name=\"KNC\"\n",
        ")\n",
        "_ = display.ax_.set_title(\"KNeighbors Precision-Recall curve\")"
      ],
      "metadata": {
        "id": "3GdPLSp9RxuS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "f645d327-4bbd-4db5-a678-8d8f47ebf2b1"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#SC\n",
        "# Train Logistic Regression Model - 3 Versions\n",
        "\n",
        "# https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html\n",
        "\n",
        "clf = LogisticRegression(random_state=42, max_iter = 500, penalty = 'l2').fit(X_train, y_train)\n",
        "clf2 = LogisticRegression(random_state=42, max_iter = 5000, penalty = 'elasticnet', solver = 'saga', l1_ratio = 0.5).fit(X_train, y_train)\n",
        "clf3 = LogisticRegression(random_state=42, max_iter = 5000, penalty = 'l1', solver = 'saga').fit(X_train, y_train)\n",
        "\n",
        "y_pred_1 = clf.predict_proba(X_test)\n",
        "y_pred_2 = clf2.predict_proba(X_test)\n",
        "y_pred_3 = clf3.predict_proba(X_test)\n",
        "\n",
        "print(\"accuracy score on training data for model 1 \" + str(clf.score(X_train, y_train)))\n",
        "print(\"accuracy score on training data for model 2 \" + str(clf2.score(X_train, y_train)))\n",
        "print(\"accuracy score on training data for model 3 \" + str(clf3.score(X_train, y_train)))\n",
        "\n",
        "print(\"accuracy score on test data for model 1 \" + str(clf.score(X_test, y_test)))\n",
        "print(\"accuracy score on test data for model 2 \" + str(clf2.score(X_test, y_test)))\n",
        "print(\"accuracy score on test data for model 3 \" + str(clf3.score(X_test, y_test)))"
      ],
      "metadata": {
        "id": "Hz09NqxsSU-X",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "98fd20c1-2e1f-462f-f6ed-89684e995ba7"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy score on training data for model 1 0.9958791208791209\n",
            "accuracy score on training data for model 2 0.9940476190476191\n",
            "accuracy score on training data for model 3 0.9917582417582418\n",
            "accuracy score on test data for model 1 0.8755803156917363\n",
            "accuracy score on test data for model 2 0.8792943361188487\n",
            "accuracy score on test data for model 3 0.8755803156917363\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#SC \n",
        "#plot pr curves for each model\n",
        "precision_1, recall_1, thresholds_1 = precision_recall_curve(y_test, y_pred_1[:,0], pos_label = [\"NPA\"])\n",
        "precision_2, recall_2, thresholds_2 = precision_recall_curve(y_test, y_pred_2[:,0], pos_label = [\"NPA\"])\n",
        "precision_3, recall_3, thresholds_3 = precision_recall_curve(y_test, y_pred_3[:,0], pos_label = [\"NPA\"])\n",
        "\n",
        "fig, ax = plt.subplots(1, figsize=(5, 5))\n",
        "\n",
        "ax.plot(recall_3, precision_3)\n",
        "ax.plot(recall_1, precision_1)\n",
        "ax.plot(recall_2, precision_2)\n",
        "ax.legend(['L1 Norm', 'L2 Norm', 'Elastic Net'])\n",
        "ax.set_title('Precision Recall Curves For 3 Loss Functions')\n",
        "ax.set_xlabel('Recall')\n",
        "ax.set_ylabel('Precision')\n",
        "\n",
        "plt.plot()"
      ],
      "metadata": {
        "id": "BH80h68FZlg-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "outputId": "c6f89e1c-4837-4893-befc-165a70da9a10"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#AL\n",
        "#Finally, we wanted to perform cross-validation for all 5 models, \n",
        "#with the specific parameters we settled upon during individual training and valuation \n",
        "\n",
        "X = merged_n[union_features].values\n",
        "y = classif\n",
        "\n",
        "#evaluation dict \n",
        "cross_val = {\"model\" : [],\n",
        "             \"accuracy_test\": [], \n",
        "             \"accuracy_train\": [], \n",
        "             \"precision\": [], \n",
        "             \"recall\" : [], \n",
        "             \"f1\" : []}\n",
        "\n",
        "# Create a KFold object with k=5\n",
        "kf = KFold(n_splits=5, shuffle=True, random_state=42)\n",
        "\n",
        "# Initialize a dictionary to store the models; parameters provided by Sarah and Muyoung for their models\n",
        "models = {\n",
        "    'KNeighbors': KNeighborsClassifier(n_neighbors=3),\n",
        "    'LogisticRegression': LogisticRegression(max_iter = 500, penalty = 'l1', solver = 'saga', C=0.029471),\n",
        "    'DecisionTree': DecisionTreeClassifier(max_depth=dt_depth, min_samples_leaf=dt_min_samples_leaf),\n",
        "    'RandomForest': RandomForestClassifier(n_estimators=RF_n_estimators, max_depth=RF_depth, min_samples_leaf=RF_min_samples_leaf),\n",
        "    'XGBoost': xgb.XGBClassifier(objective=\"binary:logistic\", n_estimators=XGB_n_estimators, max_depth=XGB_depth, random_state=42)\n",
        "}\n",
        "\n",
        "\n",
        "# Loop through each model and perform cross-validation\n",
        "for name, model in models.items():\n",
        "    accuracies = []\n",
        "    precision = []\n",
        "    recall = []\n",
        "    f1 = []\n",
        "    roc_auc = []\n",
        "\n",
        "    for train_index, test_index in kf.split(X):\n",
        "        X_train, X_test = X[train_index], X[test_index]\n",
        "        if name != \"XGBoost\":\n",
        "          y_train, y_test = y[train_index], y[test_index]\n",
        "        else:\n",
        "          y_train, y_test = y[train_index].replace(\"PA\", 1).replace(\"NPA\", 0), y[test_index].replace(\"PA\", 1).replace(\"NPA\", 0)\n",
        "\n",
        "        # Train the model on the training data\n",
        "        model.fit(X_train, y_train)\n",
        "\n",
        "        # Predict the labels of the test data\n",
        "        y_pred = model.predict(X_test)\n",
        "\n",
        "        # Compute the accuracy score and append it to the list\n",
        "        accuracy = accuracy_score(y_test, y_pred)\n",
        "        accuracy_train = model.score(X_train,y_train)\n",
        "        \n",
        "        if name != \"XGBoost\":\n",
        "          _pos_label = \"PA\"\n",
        "        else:\n",
        "          _pos_label = 1\n",
        "        \n",
        "        #calculate and print the precision\n",
        "        precision = (precision_score(y_test, y_pred, pos_label = _pos_label))\n",
        "\n",
        "        # calculate and print the recall\n",
        "        recall = (recall_score(y_test, y_pred, pos_label = _pos_label))\n",
        "\n",
        "        # calculate and print the F1 score\n",
        "        f1_sc = f1_score(y_test, y_pred, pos_label = _pos_label)\n",
        "        \n",
        "        cross_val[\"model\"].append(name)\n",
        "        cross_val[\"accuracy_test\"].append(accuracy)\n",
        "        cross_val[\"accuracy_train\"].append(accuracy_train)\n",
        "        cross_val[\"precision\"].append(precision) \n",
        "        cross_val[\"recall\"].append(recall)\n",
        "        cross_val[\"f1\"].append(f1_sc)\n",
        "\n",
        "    # Compute the mean and standard deviation of the accuracies\n",
        "    mean_accuracy = np.mean(accuracies)\n",
        "    std_accuracy = np.std(accuracies)\n",
        "\n",
        "cross_val = pd.DataFrame.from_dict(cross_val)\n",
        "cross_val"
      ],
      "metadata": {
        "id": "UnnTeF3f2via",
        "outputId": "c5d50374-21a5-4151-9cee-13947dac2ebc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 834
        }
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 model  accuracy_test  accuracy_train  precision    recall  \\\n",
              "0           KNeighbors       0.885145        0.945169   0.799127  0.863208   \n",
              "1           KNeighbors       0.883436        0.943273   0.800000  0.884956   \n",
              "2           KNeighbors       0.914110        0.941357   0.868313  0.897872   \n",
              "3           KNeighbors       0.895706        0.939824   0.855319  0.855319   \n",
              "4           KNeighbors       0.897239        0.942123   0.836653  0.889831   \n",
              "5   LogisticRegression       0.880551        0.920629   0.810185  0.825472   \n",
              "6   LogisticRegression       0.884969        0.913760   0.826840  0.845133   \n",
              "7   LogisticRegression       0.909509        0.912610   0.903670  0.838298   \n",
              "8   LogisticRegression       0.894172        0.916826   0.877273  0.821277   \n",
              "9   LogisticRegression       0.901840        0.914910   0.864407  0.864407   \n",
              "10        DecisionTree       0.848392        0.960506   0.751111  0.797170   \n",
              "11        DecisionTree       0.849693        0.957838   0.783186  0.783186   \n",
              "12        DecisionTree       0.842025        0.958988   0.789474  0.765957   \n",
              "13        DecisionTree       0.838957        0.954005   0.770833  0.787234   \n",
              "14        DecisionTree       0.845092        0.954005   0.780083  0.796610   \n",
              "15        RandomForest       0.885145        0.999617   0.834146  0.806604   \n",
              "16        RandomForest       0.901840        1.000000   0.855263  0.862832   \n",
              "17        RandomForest       0.911043        1.000000   0.896861  0.851064   \n",
              "18        RandomForest       0.903374        1.000000   0.880531  0.846809   \n",
              "19        RandomForest       0.906442        0.999617   0.872340  0.868644   \n",
              "20             XGBoost       0.905054        0.991948   0.850467  0.858491   \n",
              "21             XGBoost       0.907975        0.993101   0.860870  0.876106   \n",
              "22             XGBoost       0.918712        0.994251   0.895652  0.876596   \n",
              "23             XGBoost       0.915644        0.992334   0.891304  0.872340   \n",
              "24             XGBoost       0.909509        0.993101   0.864198  0.889831   \n",
              "\n",
              "          f1  \n",
              "0   0.829932  \n",
              "1   0.840336  \n",
              "2   0.882845  \n",
              "3   0.855319  \n",
              "4   0.862423  \n",
              "5   0.817757  \n",
              "6   0.835886  \n",
              "7   0.869757  \n",
              "8   0.848352  \n",
              "9   0.864407  \n",
              "10  0.773455  \n",
              "11  0.783186  \n",
              "12  0.777538  \n",
              "13  0.778947  \n",
              "14  0.788260  \n",
              "15  0.820144  \n",
              "16  0.859031  \n",
              "17  0.873362  \n",
              "18  0.863341  \n",
              "19  0.870488  \n",
              "20  0.854460  \n",
              "21  0.868421  \n",
              "22  0.886022  \n",
              "23  0.881720  \n",
              "24  0.876827  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fb70533b-3502-4dd1-8101-990e8e8bfa3d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>model</th>\n",
              "      <th>accuracy_test</th>\n",
              "      <th>accuracy_train</th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>f1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>KNeighbors</td>\n",
              "      <td>0.885145</td>\n",
              "      <td>0.945169</td>\n",
              "      <td>0.799127</td>\n",
              "      <td>0.863208</td>\n",
              "      <td>0.829932</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>KNeighbors</td>\n",
              "      <td>0.883436</td>\n",
              "      <td>0.943273</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.884956</td>\n",
              "      <td>0.840336</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>KNeighbors</td>\n",
              "      <td>0.914110</td>\n",
              "      <td>0.941357</td>\n",
              "      <td>0.868313</td>\n",
              "      <td>0.897872</td>\n",
              "      <td>0.882845</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>KNeighbors</td>\n",
              "      <td>0.895706</td>\n",
              "      <td>0.939824</td>\n",
              "      <td>0.855319</td>\n",
              "      <td>0.855319</td>\n",
              "      <td>0.855319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>KNeighbors</td>\n",
              "      <td>0.897239</td>\n",
              "      <td>0.942123</td>\n",
              "      <td>0.836653</td>\n",
              "      <td>0.889831</td>\n",
              "      <td>0.862423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>LogisticRegression</td>\n",
              "      <td>0.880551</td>\n",
              "      <td>0.920629</td>\n",
              "      <td>0.810185</td>\n",
              "      <td>0.825472</td>\n",
              "      <td>0.817757</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>LogisticRegression</td>\n",
              "      <td>0.884969</td>\n",
              "      <td>0.913760</td>\n",
              "      <td>0.826840</td>\n",
              "      <td>0.845133</td>\n",
              "      <td>0.835886</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>LogisticRegression</td>\n",
              "      <td>0.909509</td>\n",
              "      <td>0.912610</td>\n",
              "      <td>0.903670</td>\n",
              "      <td>0.838298</td>\n",
              "      <td>0.869757</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>LogisticRegression</td>\n",
              "      <td>0.894172</td>\n",
              "      <td>0.916826</td>\n",
              "      <td>0.877273</td>\n",
              "      <td>0.821277</td>\n",
              "      <td>0.848352</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>LogisticRegression</td>\n",
              "      <td>0.901840</td>\n",
              "      <td>0.914910</td>\n",
              "      <td>0.864407</td>\n",
              "      <td>0.864407</td>\n",
              "      <td>0.864407</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>DecisionTree</td>\n",
              "      <td>0.848392</td>\n",
              "      <td>0.960506</td>\n",
              "      <td>0.751111</td>\n",
              "      <td>0.797170</td>\n",
              "      <td>0.773455</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>DecisionTree</td>\n",
              "      <td>0.849693</td>\n",
              "      <td>0.957838</td>\n",
              "      <td>0.783186</td>\n",
              "      <td>0.783186</td>\n",
              "      <td>0.783186</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>DecisionTree</td>\n",
              "      <td>0.842025</td>\n",
              "      <td>0.958988</td>\n",
              "      <td>0.789474</td>\n",
              "      <td>0.765957</td>\n",
              "      <td>0.777538</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>DecisionTree</td>\n",
              "      <td>0.838957</td>\n",
              "      <td>0.954005</td>\n",
              "      <td>0.770833</td>\n",
              "      <td>0.787234</td>\n",
              "      <td>0.778947</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>DecisionTree</td>\n",
              "      <td>0.845092</td>\n",
              "      <td>0.954005</td>\n",
              "      <td>0.780083</td>\n",
              "      <td>0.796610</td>\n",
              "      <td>0.788260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>RandomForest</td>\n",
              "      <td>0.885145</td>\n",
              "      <td>0.999617</td>\n",
              "      <td>0.834146</td>\n",
              "      <td>0.806604</td>\n",
              "      <td>0.820144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>RandomForest</td>\n",
              "      <td>0.901840</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.855263</td>\n",
              "      <td>0.862832</td>\n",
              "      <td>0.859031</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>RandomForest</td>\n",
              "      <td>0.911043</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.896861</td>\n",
              "      <td>0.851064</td>\n",
              "      <td>0.873362</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>RandomForest</td>\n",
              "      <td>0.903374</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.880531</td>\n",
              "      <td>0.846809</td>\n",
              "      <td>0.863341</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>RandomForest</td>\n",
              "      <td>0.906442</td>\n",
              "      <td>0.999617</td>\n",
              "      <td>0.872340</td>\n",
              "      <td>0.868644</td>\n",
              "      <td>0.870488</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>XGBoost</td>\n",
              "      <td>0.905054</td>\n",
              "      <td>0.991948</td>\n",
              "      <td>0.850467</td>\n",
              "      <td>0.858491</td>\n",
              "      <td>0.854460</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>XGBoost</td>\n",
              "      <td>0.907975</td>\n",
              "      <td>0.993101</td>\n",
              "      <td>0.860870</td>\n",
              "      <td>0.876106</td>\n",
              "      <td>0.868421</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>XGBoost</td>\n",
              "      <td>0.918712</td>\n",
              "      <td>0.994251</td>\n",
              "      <td>0.895652</td>\n",
              "      <td>0.876596</td>\n",
              "      <td>0.886022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>XGBoost</td>\n",
              "      <td>0.915644</td>\n",
              "      <td>0.992334</td>\n",
              "      <td>0.891304</td>\n",
              "      <td>0.872340</td>\n",
              "      <td>0.881720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>XGBoost</td>\n",
              "      <td>0.909509</td>\n",
              "      <td>0.993101</td>\n",
              "      <td>0.864198</td>\n",
              "      <td>0.889831</td>\n",
              "      <td>0.876827</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#SC and ML\n",
        "#fitting and extracting top features from XGBoost model\n",
        "X = merged[union_features]\n",
        "\n",
        "#fit\n",
        "XGB = xgb.XGBClassifier(objective=\"binary:logistic\", n_estimators=_n_estimators, max_depth=depth, random_state=42)\n",
        "XGB.fit(X, classif.replace({\"PA\": 1, \"NPA\": 0}))\n",
        "\n",
        "#predict\n",
        "XGB_Predict = XGB.predict_proba(X)\n",
        "\n",
        "#get coefficients and map to features\n",
        "XGB_imp = XGB.feature_importances_\n",
        "\n",
        "XGB_fea_imp = pd.concat([pd.DataFrame(union_features), pd.DataFrame(XGB_imp)], axis = 1)\n",
        "XGB_fea_imp.index = union_features\n",
        "XGB_fea_imp.columns = \"Features\", \"Importance\"\n",
        "\n",
        "print(XGB_fea_imp.nlargest(10, 'Importance')['Importance'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FPshdyFIMya3",
        "outputId": "3bd45e5e-4e3f-4194-e63e-6f8413ba281f"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "COG3386    0.118896\n",
            "COG3569    0.079657\n",
            "COG3131    0.059740\n",
            "COG0667    0.059123\n",
            "COG1609    0.044944\n",
            "COG0798    0.037420\n",
            "COG1744    0.036591\n",
            "COG0514    0.025736\n",
            "COG0297    0.024760\n",
            "COG1349    0.023005\n",
            "Name: Importance, dtype: float32\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#AL, SC and ML\n",
        "#applying XGB model to soil dataset\n",
        "\n",
        "#generate soil data\n",
        "#soil_dataset = pd.merge(soilcan, soilcog.drop(columns, axis = 1), on='2016_taxon_oid', how='left')\n",
        "\n",
        "#normalize\n",
        "scaler = StandardScaler()\n",
        "soil_norm = pd.DataFrame(scaler.fit_transform(soil_dataset.iloc[:,4:]), columns = soil_dataset.iloc[:,4:].columns)\n",
        "soil_norm.head()\n",
        "\n",
        "#extract union features\n",
        "X_soil = soil_norm[union_features]\n",
        "\n",
        "#make predictions\n",
        "XGBPredict = XGB.predict(X_soil)\n",
        "\n",
        "soil_dataset['XGBPredict'] = XGBPredict"
      ],
      "metadata": {
        "id": "9KvIsUsevYoJ"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "counts = soil_dataset.groupby(['Order','XGBPredict']).size().reset_index(name = 'count')\n",
        "\n",
        "counts[\"XGBPredict\"] = counts[\"XGBPredict\"].replace({1: \"PA\", 0: \"NPA\"})\n",
        "\n",
        "# create a stacked barplot using plotnine\n",
        "color_values = ['#542788', '#b2abd2','#fdae61']\n",
        "\n",
        "order_counts = ggplot(counts, aes(x='Order', y='count', fill='XGBPredict')) + \\\n",
        "    geom_bar(stat='identity') + \\\n",
        "    labs(x = 'Order', y='Count', fill='Classification') + \\\n",
        "    ggtitle('Classification of soil dataset')+ \\\n",
        "    coord_flip() + \\\n",
        "    scale_fill_manual(values = color_values) + \\\n",
        "    theme_classic()\n",
        "\n",
        "order_counts"
      ],
      "metadata": {
        "id": "4eRDcBCUv78f",
        "outputId": "eec201a8-42c5-48d4-a90f-8c168b46045b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        }
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (8755546207401)>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "counts[\"XGBPredict\"] = counts[\"XGBPredict\"].replace({1: \"PA\", 0: \"NPA\"})\n",
        "counts"
      ],
      "metadata": {
        "id": "CAOwBJ9jkfGC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "outputId": "164842cb-1835-4184-e7f2-acf15d6572b1"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Order XGBPredict  count\n",
              "0         Acinetobacter        NPA      4\n",
              "1        Actinobacteria        NPA     46\n",
              "2        Actinobacteria         PA     72\n",
              "3   Alphaproteobacteria        NPA     23\n",
              "4   Alphaproteobacteria         PA     20\n",
              "5            Bacillales        NPA    135\n",
              "6            Bacillales         PA     34\n",
              "7         Bacteroidetes        NPA     44\n",
              "8         Bacteroidetes         PA     16\n",
              "9       Burkholderiales        NPA     32\n",
              "10      Burkholderiales         PA     31\n",
              "11          Pseudomonas        NPA     17\n",
              "12          Pseudomonas         PA     26\n",
              "13     Xanthomonadaceae        NPA      6\n",
              "14     Xanthomonadaceae         PA      5"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a8130361-9eba-4bfb-ac98-816f658e0cb9\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Order</th>\n",
              "      <th>XGBPredict</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Acinetobacter</td>\n",
              "      <td>NPA</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Actinobacteria</td>\n",
              "      <td>NPA</td>\n",
              "      <td>46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Actinobacteria</td>\n",
              "      <td>PA</td>\n",
              "      <td>72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Alphaproteobacteria</td>\n",
              "      <td>NPA</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Alphaproteobacteria</td>\n",
              "      <td>PA</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Bacillales</td>\n",
              "      <td>NPA</td>\n",
              "      <td>135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Bacillales</td>\n",
              "      <td>PA</td>\n",
              "      <td>34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Bacteroidetes</td>\n",
              "      <td>NPA</td>\n",
              "      <td>44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Bacteroidetes</td>\n",
              "      <td>PA</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Burkholderiales</td>\n",
              "      <td>NPA</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Burkholderiales</td>\n",
              "      <td>PA</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Pseudomonas</td>\n",
              "      <td>NPA</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Pseudomonas</td>\n",
              "      <td>PA</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Xanthomonadaceae</td>\n",
              "      <td>NPA</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Xanthomonadaceae</td>\n",
              "      <td>PA</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a8130361-9eba-4bfb-ac98-816f658e0cb9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [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-a8130361-9eba-4bfb-ac98-816f658e0cb9 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-a8130361-9eba-4bfb-ac98-816f658e0cb9');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#SC\n",
        "\n",
        "# Visualizations and specifying some thigns\n",
        "from sklearn.inspection import PartialDependenceDisplay\n",
        "\n",
        "plt.rcParams['figure.figsize'] = [18, 5]\n",
        "\n",
        "X_train = pd.DataFrame(X_train)\n",
        "X_train.columns = union_features\n",
        "     "
      ],
      "metadata": {
        "id": "zuTochQBYj9_"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#SC and ML\n",
        "\n",
        "features = ['COG3386', 'COG3569', ('COG3386', 'COG3569')]\n",
        "PartialDependenceDisplay.from_estimator(XGB, X_train, features)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "z0zrV6NNaBZS",
        "outputId": "bd0c006b-9416-4fe2-9baf-704102d452e6"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.inspection._plot.partial_dependence.PartialDependenceDisplay at 0x7f68f3a41b80>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x500 with 4 Axes>"
            ],
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#SC and ML\n",
        "fig, ax = plt.subplots(3, 3, figsize =(24, 24))\n",
        "\n",
        "# Top three feature importances\n",
        "\n",
        "ax[0,0].set_title('1. Sugar lactone lactonase YvrE')\n",
        "PartialDependenceDisplay.from_estimator(XGB, X_train, ['COG3386'], kind='individual' , ax = ax[0,0])\n",
        "\n",
        "ax[0,1].set_title('2. DNA topoisomerase IB')\n",
        "PartialDependenceDisplay.from_estimator(XGB, X_train, ['COG3569'], kind='individual' , ax = ax[0,1])\n",
        "\n",
        "ax[0,2].set_title('3. Periplasmic glucan biosynthesis protein OpgG')\n",
        "PartialDependenceDisplay.from_estimator(XGB, X_train, ['COG3131'], kind='individual' , ax = ax[0,2])\n",
        "\n",
        "# Next top three \n",
        "ax[1,0].set_title('4. Pyridoxal reductase PdxI or related oxidoreductase, aldo/keto reductase family')\n",
        "PartialDependenceDisplay.from_estimator(XGB, X_train, ['COG0667'], kind='individual' , ax = ax[1,0])\n",
        "\n",
        "ax[1,1].set_title('5. DNA-binding transcriptional regulator, LacI/PurR family')\n",
        "PartialDependenceDisplay.from_estimator(XGB, X_train, ['COG1609'], kind='individual' , ax = ax[1,1])\n",
        "\n",
        "ax[1,2].set_title('6. Arsenite efflux pump ArsB, ACR3 family')\n",
        "PartialDependenceDisplay.from_estimator(XGB, X_train, ['COG0798'], kind='individual' , ax = ax[1,2])\n",
        "\n",
        "# Next top three\n",
        "\n",
        "ax[2,0].set_title('7. Lipoprotein Med, regulator of KinD/Spo0A, PBP1-ABC superfamily, includes NupN')\n",
        "PartialDependenceDisplay.from_estimator(XGB, X_train, ['COG1744'], kind='individual' , ax = ax[2,0])\n",
        "\n",
        "ax[2,1].set_title('8. Superfamily II DNA helicase RecQ')\n",
        "PartialDependenceDisplay.from_estimator(XGB, X_train, ['COG0514'], kind='individual' , ax = ax[2,1])\n",
        "\n",
        "ax[2,2].set_title('9. Glycogen synthase')\n",
        "PartialDependenceDisplay.from_estimator(XGB, X_train, ['COG0297'], kind='individual' , ax = ax[2,2])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "6dBzyqlecacH",
        "outputId": "825fc1f4-2d20-4c7f-fa09-775f08b5ac9c"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.inspection._plot.partial_dependence.PartialDependenceDisplay at 0x7f68e9441b20>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2400x2400 with 18 Axes>"
            ],
            "image/png": 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/iWOPPRavv/46pk2b1vp4HXbYYRgxYgT+8pe/QNM0LFu2DAsWLGi9HdM0cfjhh+Pjjz/GmWeeicGDB+OHH37AHXfcgSVLluDll1/e6mPYt29fXH/99bjssstw6qmn4t5774Wqqm32JZ8xYwYuv/xyPP/88+32pH7++ecxZcoU5Obmth7b2vPbMAzU1dW1y+F2u209m4uIiHoOIQSqq6sxdOjQLrn9Y445Br///e/xzjvv4IYbbmjzvX79+uGUU07BQw89hCuuuGKbs6hfffVVRCIRHH/88SgpKcEBBxyAZ555BieeeOJWrzdhwgRceOGF+Mc//oErr7wSgwcPBoDW/1533XW4/vrrMXnyZJxzzjn45ZdfcN999+HLL7/EggUL2sxcaWxsxKGHHooZM2bghBNOwPPPP49zzjkHTqezdeB3e+vOTZ199tmYN28ezj//fAwZMgT19fX4+OOP8fPPP2PPPfcEsP213CGHHIIJEybg73//O5555hmcf/758Hq9uOqqq/C73/0Ov/3tb3H//ffjlFNOwdixY9GvXz8AG2d6TJw4EevWrcNZZ52F3r1745NPPsHs2bOxYcOGNvUVsHHQPJFI4Mwzz4SmacjLy0MoFMLDDz+ME044AX/4wx8QDofxyCOPYOrUqfjiiy9at6J59913ccIJJ+Cggw7CzTffDAD4+eefsWDBgtbHa3vzdFY6nW6t5xKJBL799lvcfvvtmDBhQutjQURE1opGo4jH42hubsarr76Kt956C8cdd9xOvY+WJmV+fj4A4KmnnsLMmTMxdepU3HzzzYjFYrjvvvuw//7749tvv23TNNR1HVOnTsX++++PW2+9dZszJZ988kmEw2Gcd955SCQSuPPOOzFp0iT88MMPWx1T68z4WU1NDaZMmYLCwkJcccUVyMnJwcqVK/Hiiy+2u71nnnkGqVQKF1xwARoaGvD3v/8dM2bMwKRJk/Dhhx/iT3/6E5YtW4a77roLf/zjH/Hoo49uMZthGDjssMPw73//G8cffzwuuugihMNhvPvuu/jxxx9RWVm5xevOnj0bf//73zF9+nRMnToVCxcuxNSpU5FIJLb6OG6PzubrzPhWi48//hgvvvgizj33XPj9fvzjH//A0UcfjdWrV7c+j7bmhhtugCRJ+NOf/oSamhrMmTMHkydPxnfffQe3293hdYQQOPzww/HBBx/g97//PUaNGoX58+fjsssuw7p161rHQ7c1Bufz+XDUUUdh7ty5uP3226EoSut9PPfccxBCtJ5o0eKCCy5Abm4urr32WqxcuRJz5szB+eefj7lz5wIA5syZgwsuuAA+nw9XXXUVALR7Pm/rNoDO/+0dffTRWLRoES644AL07dsXNTU1ePfdd7F69eotbunz0EMP4cILL8QxxxyDiy66CIlEAt9//z0+//zzbX6m2J7fWUdjiZ35jFBYWIj77rsP55xzDo466ij89re/BQCMGDGi9fe6I2PRjY2NOPjgg/Hb3/4WM2bMwLx58/CnP/0Jw4cP3+rWSW+99RYMw8App5yyxcuccsop+OCDD/D222/jjDPOaD3+0UcfYe7cubjwwguhaRruvfdeHHzwwfjiiy9at5j69ttvcfDBB6O0tBTXX389DMPAX/7yl9Zm/eY5uJplNyOIqNv48ssvBQDx2GOP7dDt/Pjjj8LtdgsAYtSoUeKiiy4SL7/8sohGo+0uC0Bce+217Y736dNHzJw5s/XrCy64QEiSJL799tvWY/X19SIvL08AECtWrGg9HovF2t3eWWedJTwej0gkEq3HJk6cKACI+++/v1M/17XXXis2f9nq6L6mTp0q+vfv3/p1U1OT8Pv9YsyYMSIej7e5rGmarf8/bdo00adPn3a3N2fOHAFAPP30063HUqmUGDt2rPD5fCIUCgkhhFixYoUAIPLz80VDQ0PrZV955RUBQLz22mutxw466CAxfPjwNo+HaZpiv/32EwMHDtzWQ9Hu99bR4/Dpp58KAOLJJ59sPXbNNdcIAOLFF19sd/mWx2JLz8NUKiWKiorEsGHD2jyOr7/+ugAgrrnmmtZjM2fOFADEX/7ylza3sccee4jRo0e3ObZ59lQqJYYNGyYmTZrUeuyOO+4QAERtbW273C2eeuopIcuy+O9//9vm+P333y8AiAULFmzxui3S6bQYNWpU6/P6gQceaHeZsWPHtvsZvvjii3aP9dae3y3f6+jfWWedtc2cRERE3cFTTz0lAIhHHnkko+t/8MEHAoB44YUXtniZkSNHitzc3NavH3vsMQFAfPnll2L58uVCVVVx4YUXtn5/4sSJYujQoe1u57DDDhPjxo1r/frBBx8UqqqKmpqabeZ84YUXBADxwQcftDleU1MjnE6nmDJlijAMo/X43XffLQCIRx99tE0uAOK2225rPZZMJsWoUaNEUVGRSKVSQojO151CtK8Hg8GgOO+887b4c2RSy914442txxobG4Xb7RaSJIl//vOfrccXL17cLstf//pX4fV6xZIlS9pkuOKKK4SiKGL16tVCiP9fPwcCgXa/C13XRTKZbHOssbFRFBcXi9NPP7312EUXXSQCgYDQdX2LP3tn82xJR8+rPn36dFjLjRs3TtTV1W319oiIKHvOOuus1tdoWZbFMccc02bMZnu01C6PPvqoqK2tFevXrxdvvPGG6Nu3r5AkSXz55ZciHA6LnJwc8Yc//KHNdauqqkQwGGxzvOX99oorrmh3XzNnzmwzRtXynul2u8XatWtbj3/++ecCgLj44otbj2U6fvbSSy+11llb0pKjsLBQNDU1tR6fPXu2ACBGjhwp0ul06/ETTjhBOJ3OduOBEydObP360UcfFQDE7bff3u7+Nh2321xVVZVQVVUceeSRbY5fd911AkCbcc2W392m9dzmY587mq8z41tCbKzhnE6nWLZsWeuxhQsXCgDirrvu2uLPu+nPUV5e3qYufP755wUAceedd7Ye2/w59PLLLwsA4m9/+1ub2zzmmGOEJEmteTozBjd//nwBQLz11lttjo8YMaLNY9dSu0+ePLnNY3XxxRcLRVHaPIeGDh3a5rrbexud/dtrbGwUAMQtt9yyxZ9PiPbPgyOOOKLDzxnbsj2/sy2NJXb2M0Jtbe0Wx/o7Oxbd0d9KS65Nxz2TyaQoKSkRRx999FZ//lmzZgkAbXoKm/vmm28EAHHJJZe0Hmt53f7qq69aj61atUq4XC5x1FFHtR6bPn268Hg8Yt26da3Hli5dKlRVbfM6ePHFFwsA4rvvvmtz38lkUtTW1rb+Yx2fXVzim2gXNHToUHz33Xc46aSTsHLlStx555048sgjUVxcjIceeiij23z77bcxduzY1pkKAJCXl9fujDgAbc76CofDqKurw/jx4xGLxbB48eI2l9U0DaeddlpGmTa/r+bmZtTV1WHixIn49ddf0dzcDGDjrIpwOIwrrrii3R4amy71sSVvvvkmSkpKcMIJJ7QeczgcuPDCCxGJRPDRRx+1ufxxxx3XZhZtyyzkX3/9FQDQ0NCA999/HzNmzGh9fOrq6lBfX4+pU6di6dKl7ZZZ3J7HIZ1Oo76+HgMGDEBOTk6bZR3/9a9/YeTIkR2eFbetx+Krr75CTU0Nzj333DaP47Rp0zBo0CC88cYb7a5z9tlnt/l6/PjxrY9DR9kbGxvR3NyM8ePHt8ndMpvnlVde2eIyjC+88AIGDx6MQYMGtT6mdXV1rasIdLQc5OZUVcWDDz6IhoYG7LvvvvjDH/7Q7jLHHXccvv766zbLOM2dOxeapuGII45oc9mtPb/79u2Ld999t92/WbNmbTMnERGR1RYvXozzzjsPY8eOxcyZM7vsfnw+H8LhcIff69+/P04++WQ8+OCD2LBhwxZvo76+HvPnz29Tyx199NGQJAnPP/98xtnee+89pFIpzJo1C7L8/z9a/+EPf0AgEGhXG6mqirPOOqv1a6fTibPOOgs1NTX4+uuvAWx/3bmpnJwcfP7551i/fn2H38+kltt0BkNOTg523313eL1ezJgxo/X47rvvjpycnDY13gsvvIDx48cjNze3TV02efJkGIbRbmm+o48+ut0sB0VRWpeVN00TDQ0N0HUde+21V7s6MRqNbnW/uO3N01ljxoxpreFef/113HDDDVi0aBEOP/xwxOPxjG6TiIh2rlmzZuHdd9/FE088gUMOOQSGYSCVSu3QbZ5++ukoLCxEWVkZpk2bhmg0iieeeAJ77bUX3n33XTQ1NeGEE05o856jKArGjBnT4djEpqvMbcuRRx6J8vLy1q/32WcfjBkzBm+++eZWr9eZ8bOWsZfXX38d6XR6q7d37LHHIhgMtn49ZswYAMBJJ53UZpuLMWPGIJVKbXWc61//+hcKCgpwwQUXtPve1saq/v3vf0PX9XZL8nZ0Ozuis/k6M77VYvLkyW1mho8YMQKBQKDdmNmWnHLKKfD7/a1fH3PMMSgtLd3q8+DNN9+Eoii48MIL2xy/9NJLIYTAW2+9BaBzY3CTJ09GWVkZnnnmmdZjP/74I77//vsOZ6ieeeaZbR6r8ePHwzCMDrfQ25Jt3UZn//bcbjecTic+/PDD1m36OiMnJwdr167tcBvHzujs76yjscQd+YwA7JyxaJ/P1+Z363Q6sc8++2zzOdvyWW7Tn31zLd8LhUJtjo8dOxajR49u/bp379444ogjMH/+fBiGAcMw8N577+HII49ss6LWgAED2s3qbrntlhVTW7z55psoLCxs/dfRlqnUddigJtpF7bbbbnjqqadQV1eH77//HjfeeCNUVcWZZ56J9957b7tvb9WqVRgwYEC74x0dW7RoEY466igEg0EEAgEUFha2voG1FL0tysvL2+1nuD0WLFiAyZMnw+v1IicnB4WFha17c7TcV0sjsWXpj+21atUqDBw4sM3AI/D/l3bcvJjq3bt3m69bmtUtRc+yZcsghMDVV1/d5g2wsLAQ1157LYCNyyptj3g8jmuuuaZ1D5mCggIUFhaiqampzWO+fPnyHXocgI2DkJsbNGhQu8fB5XK1G2jMzc1tV/y9/vrr2HfffeFyuZCXl9e6JM2muY877jiMGzcOZ5xxBoqLi3H88cfj+eefb1MoL126FIsWLWr3mO62224AOv+YtuzBPXr06A4/CB177LGQZbl1CSEhBF544QUccsghCAQCbS67tee31+vF5MmT2/0bNGhQp3ISERFZpaqqCtOmTUMwGMS8efPaLOu3s0Uika0OZvz5z3+Grutb3Yt67ty5SKfT2GOPPbBs2TIsW7YMDQ0NGDNmTJtBte21pdrI6XSif//+7WqjsrKydtt4tNQpLfvdbW/duam///3v+PHHH9GrVy/ss88+uO6669oMGO2MWi4YDKKioqJdjRQMBtvUeEuXLsXbb7/dri6bPHkygPZ12ZaWw37iiScwYsQIuFwu5Ofno7CwEG+88UabOvHcc8/FbrvthkMOOQQVFRU4/fTT2+3huL15OqugoKC1hps2bRquvPJKPPzww/jkk0/w8MMPZ3SbRES0cw0aNAiTJ0/GKaecgtdffx2RSATTp09vt+fu9rjmmmvw7rvv4v3338f333+P9evX4+STTwaw8T0H2Lgv9ebvO++880679xxVVVFRUdHp+x44cGC7Y7vttlubvXM70pnxs4kTJ+Loo4/G9ddfj4KCAhxxxBF47LHHkEwm293e5mNfLc3qXr16dXh8a43A5cuXY/fdd2/T2O6Mltpl87HJvLy8NhNHdlRn83VmfKvF5o8f0PGY2ZZs/jyQJAkDBgzY6vNg1apVKCsra1dbb15ndmYMTpZl/O53v8PLL7+MWCwGYOOy7y6XC8cee+w2f97Nx0o7Y1u30dm/PU3TcPPNN+Ott95CcXFx63Y2VVVVW73/P/3pT/D5fNhnn30wcOBAnHfeeW22HtyWzv7OOhpL3JHPCMDOGYvu6DNAZ56zLc+3LZ10vOn3Nn9ubun1LhaLoba2FjU1NYjH453qWbTcdiQSaXN83LhxrSecTpkyZas/C+183IOaaBenKAqGDx+O4cOHY+zYsTjwwAPxzDPPtA7GbIlhGBndX1NTEyZOnIhAIIC//OUvqKyshMvlwjfffIM//elP7c6829K+KJ2xfPlyHHTQQRg0aBBuv/129OrVC06nE2+++SbuuOOOLZ7l19W2NEjb8uGnJdcf//hHTJ06tcPLdvTGujUXXHABHnvsMcyaNQtjx45FMBiEJEk4/vjju93jsKn//ve/OPzwwzFhwgTce++9KC0thcPhwGOPPYZnn3229XJutxv/+c9/8MEHH+CNN97A22+/jblz52LSpEl45513oCgKTNPE8OHDcfvtt3d4X5t/SMpUWVkZxo8fj+effx5XXnklPvvsM6xevbp1z8NN7cjzm4iIqDtqbm7GIYccgqamJvz3v//d5t7POyKdTmPJkiVbPbmuf//+OOmkk/Dggw/iiiuu6PAyLU3ocePGdfj9X3/9Ff3799/xwBabMWMGxo8fj5deegnvvPMObrnlFtx888148cUXt7ov3JZsqZbbVq0LbKx3f/Ob3+Dyyy/v8LItjfkWHdVMTz/9NE499VQceeSRuOyyy1BUVARFUXDTTTe1WcmmqKgI3333HebPn4+33noLb731Fh577DGccsopeOKJJzLKsyMOOuggAMB//vOfnT6Di4iIdtwxxxyDs846C0uWLOnwpK3OGD58+BbH1VrGYJ566imUlJS0+/7mTU5N09o1nXa2zo6fSZKEefPm4bPPPsNrr72G+fPn4/TTT8dtt92Gzz77rM3Mwx2pE7qLLc3ONgxju0/A7Oz4Vovu/Dh1ZgwO2Dgj+JZbbsHLL7+ME044Ac8++ywOO+ywNjPrW+yMn7ez462d+dubNWsWpk+fjpdffhnz58/H1VdfjZtuugnvv/8+9thjjw7vZ/Dgwfjll1/w+uuv4+2338a//vUv3Hvvvbjmmmtw/fXXd/rn2JauGEvcGWPRmf4OW5ro33//fZuVWTf1/fffAwCGDBmy1dvaES0Tgn788UeMHDmy9fimJ60+/fTTXXb/1DE2qIl6kL322gsA2iyBmJubi6ampjaXS6VS7ZZJ7NOnD5YtW9buNjc/9uGHH6K+vh4vvvgiJkyY0Hp8xYoVOxq/nddeew3JZBKvvvpqm7PoNl8uqWXJnB9//HGrb7ZbKkz79OmD77//HqZptvnQ0LJc+fYu/dEyAOpwOLZ5okBnzZs3DzNnzsRtt93WeiyRSLT73VZWVuLHH3/c6m1t7XEAgF9++aV12ewWv/zyS0ZLoPzrX/+Cy+XC/PnzoWla6/HHHnus3WVlWcZBBx2Egw46CLfffjtuvPFGXHXVVfjggw9al0ZauHAhDjrooE4t3b4jjjvuOJx77rn45ZdfMHfuXHg8HkyfPr1L75OIiMhqiUQC06dPx5IlS/Dee+916QACsLG+icfjWxxEafHnP/8ZTz/9dIcni61YsQKffPIJzj//fEycOLHN90zTxMknn4xnn30Wf/7zn7d4+52pjTZtcKdSKaxYsaJdnbd+/XpEo9E2s6iXLFkCYOPWHy23uSN1Z2lpKc4991yce+65qKmpwZ577okbbrgBhxxySJfUcltSWVmJSCSyQ7XuvHnz0L9/f7z44ottfgctszw25XQ6MX36dEyfPh2maeLcc8/FAw88gKuvvhoDBgzYKXk6S9d1AO1nZxARUffQsgVDR7Nad4aWMaiioqIued9pmSW6qSVLlrTWEh3p7PhZi3333Rf77rsvbrjhBjz77LP43e9+h3/+859ttv7YmSorK/H5558jnU7D4XB0+nottcuyZcvarMZSX1/fqZm5HY2JAhtno25a23Um3/aMb+0Mmz8PhBBYtmwZRowYscXr9OnTB++99x7C4XCbmaod1ZnbGoMDNq5Uuccee+CZZ55BRUUFVq9ejbvuuivjn2lHx/K292+vsrISl156KS699FIsXboUo0aNwm233bbVJqXX68Vxxx2H4447DqlUCr/97W9xww03YPbs2e22ldxcJr+zFp39jLClx7ArxqI765BDDoGiKHjqqadwyimndHiZJ598Eqqq4uCDD25zfEuvdx6Pp3WlJ5fL1ameRUuOZ555psMtS8kaXOKbyEY2bNiAxYsXb3MfmP/+978dXqZlT4tNzxCtrKxst+fagw8+2G4G9dSpU/Hpp5/iu+++az3W0NDQblnElrOpNj17KpVK4d57791q5kx0dF/Nzc3tir8pU6bA7/fjpptuQiKRaPO9Ta/r9Xo7/IBy6KGHoqqqqnVJZ2DjwNNdd90Fn8/XbrBzW4qKinDAAQfggQce6HC/xNra2u26PWDjY7H5GWt33XVXu9/j0UcfjYULF+Kll15qdxst128ZNN28SN9rr71QVFSE+++/v83yTm+99RZ+/vlnTJs2LaPckiS1ybly5Uq8/PLLbS7X0NDQ7rotZ921ZJkxYwbWrVvX4T7r8Xgc0Wh0u/NtydFHHw1FUfDcc8/hhRdewGGHHdZuyU4iIqJdiWEYOO644/Dpp5/ihRdewNixY7d42c7WrFuzcOFCzJo1C7m5uTjvvPO2etnKykqcdNJJeOCBB9otzddSq15++eU45phj2vybMWMGJk6cuM1lvrdUG02ePBlOpxP/+Mc/2tRhjzzyCJqbm9vVRrqu44EHHmj9OpVK4YEHHkBhYWHr3mqZ1p2GYbSrY4uKilBWVtZaK3VFLbclM2bMwKeffor58+e3+15TU1NrE3drOqr1P//8c3z66adtLldfX9/ma1mWWwf6Nq0TdzRPZ7322msA0GZmBhERZV9Hy9Wm02k8+eSTcLvdbU602xm1S4upU6ciEAjgxhtv7PD2Mhnz2dTLL7/cZq/YL774Ap9//vlWV0vp7PhZY2Nju7GlzcdeusLRRx+Nuro63H333e2+t7XZmQcddBBUVcV9993X5nhHt9ORyspKfPbZZ232JH/99dexZs2a7c7X2fGtneXJJ59ss2TyvHnzsGHDhq0+Dw499FAYhtHu57jjjjsgSVLrdTszBtfi5JNPxjvvvIM5c+YgPz8/o1V7Wni93g5PGOiszv7txWKxduPDlZWV8Pv9W32eb15zOp1ODBkyBEKITr12ZPI7a9HZzwgejwdA+88tXTEW3Vm9evXCaaedhvfee6/d3yoA3H///Xj//ffx+9//vt12B59++mmbPdzXrFmDV155BVOmTIGiKFAUBZMnT8bLL7+M9evXt15u2bJlrXuqt+jduzdOP/10vPXWW1t8jegOKxj0NJxBTdQN3H333Whqamp9IX3ttdewdu1aABuXbm5ZGmX27Nl44oknsGLFiq2eGXnzzTfj66+/xm9/+9vWwZlvvvkGTz75JPLy8jBr1qzWy55xxhk4++yzcfTRR+M3v/kNFi5ciPnz56OgoKDNbV5++eV4+umn8Zvf/AYXXHABvF4vHn74YfTu3RsNDQ2tZ2jtt99+yM3NxcyZM3HhhRdCkiQ89dRTXfICP2XKlNbZEmeddRYikQgeeughFBUVtXmzDQQCuOOOO3DGGWdg7733xoknnojc3FwsXLgQsVisdem/0aNHY+7cubjkkkuw9957w+fzYfr06TjzzDPxwAMP4NRTT8XXX3+Nvn37Yt68eViwYAHmzJmz1X0Rt+See+7B/vvvj+HDh+MPf/gD+vfvj+rqanz66adYu3YtFi5cuF23d9hhh+Gpp55CMBjEkCFD8Omnn+K9995Dfn5+m8tddtllmDdvHo499licfvrpGD16NBoaGvDqq6/i/vvvx8iRI1FZWYmcnBzcf//98Pv98Hq9GDNmDPr164ebb74Zp512GiZOnIgTTjgB1dXVuPPOO9G3b19cfPHF2/04TJs2DbfffjsOPvhgnHjiiaipqcE999yDAQMGtC7vAgB/+ctf8J///AfTpk1Dnz59UFNTg3vvvRcVFRXYf//9AWwsip9//nmcffbZ+OCDDzBu3DgYhoHFixfj+eefx/z581tXEdhRRUVFOPDAA3H77bcjHA7juOOO2+7baG5u3uJZmS17thMREXUXl156KV599VVMnz4dDQ0N7d7DNn3v6mzN2uK///0vEokEDMNAfX09FixYgFdffRXBYBAvvfRSh0v0be6qq67CU089hV9++QVDhw5tPf7MM89g1KhRW9zq4/DDD8cFF1yAb775BnvuuWeHlxk1ahQURcHNN9+M5uZmaJqGSZMmoaioCLNnz8b111+Pgw8+GIcffjh++eUX3Hvvvdh7773bvZ+XlZXh5ptvxsqVK7Hbbrth7ty5+O677/Dggw+2zsbJtO4Mh8OoqKjAMcccg5EjR8Ln8+G9997Dl19+2brCjsPh2Om13JZcdtllePXVV3HYYYfh1FNPxejRoxGNRvHDDz9g3rx5WLlyZbvPG5s77LDD8OKLL+Koo47CtGnTsGLFCtx///0YMmRIm9nJZ5xxBhoaGjBp0iRUVFRg1apVuOuuuzBq1KjWJQV3Rp6OrFu3rvVvIZVKYeHChXjggQdQUFDA5b2JiCx21llnIRQKYcKECSgvL0dVVRWeeeYZLF68GLfddlub5aq3t3bZmkAggPvuuw8nn3wy9txzTxx//PEoLCzE6tWr8cYbb2DcuHGdbqB2ZMCAAdh///1xzjnnIJlMtjYGt7SNBdD58bMnnngC9957L4466ihUVlYiHA7joYceQiAQwKGHHppx5m055ZRT8OSTT+KSSy7BF198gfHjxyMajeK9997DueeeiyOOOKLD6xUXF+Oiiy7CbbfdhsMPPxwHH3wwFi5ciLfeegsFBQXbnJF7xhlnYN68eTj44IMxY8YMLF++HE8//XTrTNztydfZ8a2dJS8vD/vvvz9OO+00VFdXY86cORgwYAD+8Ic/bPE606dPx4EHHoirrroKK1euxMiRI/HOO+/glVdewaxZs1p/7s6MwbU48cQTcfnll+Oll17COeecs10z4Dc3evRo3Hffffjb3/6GAQMGoKioqN2qP1vT2b+9JUuW4KCDDsKMGTMwZMgQqKqKl156CdXV1Tj++OO3ePtTpkxBSUkJxo0bh+LiYvz888+4++67MW3atE6NDWfyO2vR2c8ILSffzJ07F7vtthvy8vIwbNgwDBs2bKePRW+PO+64A4sXL8a5556Lt99+u3Wm9Pz58/HKK69g4sSJbVYFbTFs2DBMnToVF154ITRNa50At+mS6tdddx3eeecdjBs3Duecc07rSRjDhg1rM9EOAObMmYMVK1bgggsuwD//+U9Mnz4dRUVFqKurw4IFC/Daa69lvPUDZUgQkeX69OkjAHT4b8WKFa2XmzlzZrtjHVmwYIE477zzxLBhw0QwGBQOh0P07t1bnHrqqWL58uVtLmsYhvjTn/4kCgoKhMfjEVOnThXLli0Tffr0ETNnzmxz2W+//VaMHz9eaJomKioqxE033ST+8Y9/CACiqqqqzf3vu+++wu12i7KyMnH55ZeL+fPnCwDigw8+aL3cxIkTxdChQzv9OF177bVi85etV199VYwYMUK4XC7Rt29fcfPNN4tHH320w8fp1VdfFfvtt59wu90iEAiIffbZRzz33HOt349EIuLEE08UOTk5AoDo06dP6/eqq6vFaaedJgoKCoTT6RTDhw8Xjz32WJvbX7FihQAgbrnllnbZAYhrr722zbHly5eLU045RZSUlAiHwyHKy8vFYYcdJubNm7fNx2Lz22tsbGzN5/P5xNSpU8XixYs7/D3W19eL888/X5SXlwun0ykqKirEzJkzRV1dXetlXnnlFTFkyBChqqoA0OZnnTt3rthjjz2EpmkiLy9P/O53vxNr165tcx8zZ84UXq+3Xe6OfoePPPKIGDhwoNA0TQwaNEg89thj7S7373//WxxxxBGirKxMOJ1OUVZWJk444QSxZMmSNreVSqXEzTffLIYOHSo0TRO5ubli9OjR4vrrrxfNzc3bfFxbABDnnXfeVi/z0EMPCQDC7/eLeDze7vtbe35PnDhxi3/zfGsmIqLuaHveuzpbs37wwQdtbsPhcIjCwkIxYcIEccMNN4iampp213nssccEAPHll1+2+17L/ba8/3799dcCgLj66qu3mGHlypUCgLj44ou3mvWhhx4S/fv3F4qitKtp7777bjFo0CDhcDhEcXGxOOecc0RjY2Ob67fUBV999ZUYO3ascLlcok+fPuLuu+9ud1+dqTuFaFsPJpNJcdlll4mRI0cKv98vvF6vGDlypLj33nvbXW9Harkt1Td9+vQR06ZNa3MsHA6L2bNniwEDBgin0ykKCgrEfvvtJ2699VaRSqWEEFuvn03TFDfeeKPo06eP0DRN7LHHHuL1118XM2fObFOnz5s3T0yZMkUUFRUJp9MpevfuLc466yyxYcOG7c6zJR393Jt/hpNlWRQVFYkTTjhBLFu2bKu3R0REXe+5554TkydPFsXFxUJVVZGbmysmT54sXnnllXaX3d7a5YUXXtjm/X/wwQdi6tSpIhgMCpfLJSorK8Wpp54qvvrqqzb329H7bcv3Nn2/2/Q987bbbhO9evUSmqaJ8ePHi4ULF7a5bqbjZ99884044YQTRO/evYWmaaKoqEgcdthhbTJv6b17S49NR7XbxIkTxcSJE9tcLhaLiauuukr069dPOBwOUVJSIo455ph245ib03VdXH311aKkpES43W4xadIk8fPPP4v8/Hxx9tlnt8u3aQ0nhBC33XabKC8vF5qmiXHjxomvvvoq43ydGd8SYstjTh2N4W2u5ed47rnnxOzZs0VRUZFwu91i2rRpYtWqVW0uu/lzSIiN9dDFF18sysrKhMPhEAMHDhS33HKLME2z9TKdHYNrceihhwoA4pNPPmn3vS3V7h39PqqqqsS0adOE3+8XAFp/B9tzGy3Ht/a3V1dXJ8477zwxaNAg4fV6RTAYFGPGjBHPP/98m9vZ/HnwwAMPiAkTJoj8/HyhaZqorKwUl1122TbHG7fnd7a1scTOfkb45JNPxOjRo4XT6Ww3ftyZseiOHtct5eroObYlyWRS3HHHHWL06NHC6/UKj8cj9txzTzFnzpwOa/GWv5Onn3669e9qjz32aPf7FmLjc3aPPfYQTqdTVFZWiocfflhceumlwuVytbusruviscceE5MmTRJ5eXlCVVVRUFAgDjroIHH//fd3OMZLXUcSgvPWiShzs2bNwgMPPIBIJNK6ZBAREREREf1/BxxwAOrq6vDjjz9aHYWIiIhsaOXKlejXrx9uueUW/PGPf7Q6TrfW1NSE3Nxc/O1vf8NVV11ldZxd3lFHHYUffvihw32ACfjwww9x4IEH4oUXXsAxxxxjdRzbkCQJ5513XsYrThx55JFYtGhRh/tYU/fBPaiJqNPi8Xibr+vr6/HUU09h//33Z3OaiIiIiIiIiIiIsmbzsUpg4zK+wMYTBKlrbdiwAW+88QZOPvlkq6NQD7b568DSpUvx5ptv8jXABrgHNRF12tixY3HAAQdg8ODBqK6uxiOPPIJQKISrr77a6mhERERERERERETUg8ydOxePP/44Dj30UPh8Pnz88cd47rnnMGXKFIwbN87qeLusFStWYMGCBXj44YfhcDhw1llnWR2JerD+/fvj1FNPRf/+/bFq1Srcd999cDqduPzyy62ORtvABjURddqhhx6KefPm4cEHH4QkSdhzzz3xyCOPYMKECVZHIyIiIiIiIiIioh5kxIgRUFUVf//73xEKhVBcXIyLLroIf/vb36yOtkv76KOPcNppp6F379544oknUFJSYnUk6sEOPvhgPPfcc6iqqoKmaRg7dixuvPFGDBw40OpotA3cg5qIiIiIiIiIiIiIiIiIiLKCe1ATEREREREREREREREREVFWsEFNRERERERERERERERERERZwT2odxLTNLF+/Xr4/X5IkmR1HCIiIuphhBAIh8MoKyuDLPMcRDtg/UhERERWYv1oP6wfiYiIyGo7q4Zkg3onWb9+PXr16mV1DCIiIurh1qxZg4qKCqtjUCewfiQiIqLugPWjfbB+JCIiou5iR2tINqh3Er/fD2DjLyQQCFichoiIiHqaUCiEXr16tdYk1P2xfiQiIiIrsX60H9aPREREZLWdVUOyQb2TtCyrEwgEWCASERGRZbjUn32wfiQiIqLugPWjfbB+JCIiou5iR2tIbjBDRERERERERERERERERERZwQY1ERERERERERERERERERFlBRvURERERERERERERERERESUFWxQExERERERERERERERERFRVrBBTUREREREREREREREREREWcEGNRERERERERERERERERERZQUb1ERERERERERERERERERElBVsUBMRERERERERERERERERUVawQU1ERERERERERERERERERFnBBjUREREREREREREREREREWUFG9RERERERERERERERERERJQVbFATEREREREREREREREREVFWsEFNRERERERERERERERERERZwQY1ERERERERERERERERERFlBRvURERERERERERERERERESUFWxQExERERERERERERERERFRVrBBTUREREREREREREREREREWcEGNRERERERERERERERERERZcUu16D+z3/+g+nTp6OsrAySJOHll1/e5nU+/PBD7LnnntA0DQMGDMDjjz/e5TmJiIiIqPtgDUlERERE24P1IxEREVHmdrkGdTQaxciRI3HPPfd06vIrVqzAtGnTcOCBB+K7777DrFmzcMYZZ2D+/PldnJSIiIiIugvWkERERES0PVg/EhEREWVOtTrAznbIIYfgkEMO6fTl77//fvTr1w+33XYbAGDw4MH4+OOPcccdd2Dq1KlbvF4ymUQymWz9OhQKZR6aiIiIiCyVjRqS9SMRERHRroP1IxEREVHmdrkG9fb69NNPMXny5DbHpk6dilmzZm31ejfddBOuv/76LkzWXt8r3sjq/REREdHOc+QgDXNOnbztC5ItZFJDsn4kIiKi7bXy/6ZZHYF2ErvUjwBw2F3/QTShZ/1+dyWGAEzDhC4AwzBgAICwOhUREfUE/fM9mHfeeKtjbFOPb1BXVVWhuLi4zbHi4mKEQiHE43G43e4Orzd79mxccsklrV+HQiH06tWrS7MSERGRfYUTyW1fiGwjkxqS9SMRERFRz2WX+lHXdfy4Ltyl90FERERdpyFmjxVXenyDOlOapkHTNKtjEBERkU3s0avQ6ghkMSvqRxeARFbvkYiIiHYWyeoAZDkr6kdFUbJ6f0RERLRz2aWGlK0OYLWSkhJUV1e3OVZdXY1AILDF2dNERERE26swL8fqCLQT2aWGzPVygJGIiMiu3Hwb36XYpX40TdPqCERERLQD7LKjRI+fQT127Fi8+eabbY69++67GDt2rEWJtsznVGAIFomUJUJACCBtbPzSqQDCLq9sRETdUGVxjtURaCeySw1ZlutGQzxidQzqIYQAdAG0fGKR//dPssvp20RE3Uxx0Gl1BNqJ7FI/KoqCgEtBSucgUMaEgCJLEAAM04RpbtyTmoiIKBvy3PZo/doj5XaIRCJYtmxZ69crVqzAd999h7y8PPTu3RuzZ8/GunXr8OSTTwIAzj77bNx99924/PLLcfrpp+P999/H888/jzfeeMOqH2GLNBUwBUd3KDviSQHDBBwyoChAUgdcKqDIPX7hBSKijGicAdOt7ao1ZDipw+D5jZQlJgBFAryqBF0IpPWNn2FUmZ9hiIgyUeTvPrNqqb1dtX4UQkCRJCgSO6qZEpIEUwgYpoCJjc1pCfZZcpWIiOxNCMPqCJ2yyzWov/rqKxx44IGtX19yySUAgJkzZ+Lxxx/Hhg0bsHr16tbv9+vXD2+88QYuvvhi3HnnnaioqMDDDz+MqVOnZj37tghInMFKWeNyKHAogKwA4YQBtwq4NCeLaSKiDDmcu1zZtUvZVWvIvfoUIGHUWx2DeghhmoglDSTSBhyqggKfDFVRIXEKNRFRRnrnea2OQFuxq9aPAOBQZQjbLBDa/QgB6ObGVWRMU6BlroeqsCYiIqKuF/S6rI7QKbvcSOkBBxwAsZUu7uOPP97hdb799tsuTLVz5Lkd0NmhpiwxBFDgdaAxmoLL50ChX0OOxwmZM2CIiDJSYJPisKfaVWtISQb41k3ZYkqA5pRRketGnl/DuoYoNjSn4FD5JCQiykRVOG51BNqKXbV+3DiDGpAlrqCXKQFAkQzopoAsyxCmCUBiy5+IiLJCU+3xHr7LNah3Zcfu3Qu6yVKGsiOaSOONH6sgyzImDMhHUzSFuCG4hyARUab4AkoW6FfoxbpGDm5TdggAfk2F363il/Vh1EZS2Kd/LkqDXKKWiCgTw8v9VkegHqrY70I8zX1iMmUIE7GkDEneOJs6pRtwO1Tk+7ivPBERdb2++fZYhYcNahv5emUzUiaLQ8oGgdX1UZQENAwo9GLR+jBW1kXgczu4hyARUYYi8RQKuI8gZZlpCKQNnuBI2SFJgKJI+G51E9Y2JVCW40JVKIFI3B77XxERdTcBF4ftyBpBrxMuNqgzZpomgm5AN0ykDBPCVJHjdcLlsMeMNiIisrdBpfY4yZGVro2EkkkOMFLW5HqdSKQFFm2IoCacRMDjRN98LxSZxTQRUSY8msPqCNQDaQ4FHo0lP2VHJKHj4yW1SJsCw8uDyPc6EUnoUBXF6mhERLYUcLF+JGsYpoBusEGdKd0USKTSMCEhkkyjyKdhTUMceV7OoCYioq5XHUpYHaFTOFplI26HAlVmg5qyozmWhseloiGaQklAgyQB9ZEkNAcHGImIMpE2OIOQsq+qOYENzVzim7IjljIgAPTOdcPndGBVfQwNsRR8bjZYiIgyUdKoWR2BeigBAe4yuGMcDgUNkTTKcz2oCSXhUCVwUUIiIsoKYY83cTaobSSWNrgHNWWNJAnEkmkMLQ/A65Tx+sIqBNwKXGm+bBARZUI32aCm7IsmdcSSfO5RdqQMAxV5HjgVBcvqwogm03AqCjSOxhIRZURz8vWTsk+SJAwo8iOW1K2OYlsp3cSP60MYVBpALKVDlSVoqow87kFNRERZUBy0xxaD7DTZyL79CpDm8jqUFQIpw0TvPA8+W96AD36uRjRhojTggkPlDGoiokxwiwSywqheuYimWD9SdsiSQCxlQDdMNEZT0A2BEr8Dg0uDVkcjIrKlgUX22D+Qdi2maWJNfRQpbjOYsaRuYHCJDz5NxdLaJPK9TrgdKv+miYgoKypyPVZH6BQ2qG3EqcqQePIsZUk4qePrFQ34fl0ThAAqiz1wqDL83MeSiCgjqsTXT8q++mgC9dGk1TGohwgl0ijwOrGmMY60YcCrOSDLEmrDfA4SEWWiPpqyOgL1QLIsozjoQownOWZMAmAKgRV1USRTJnTThBBAYzxtdTQiIuoB1jXaY6s3jpTaSGM0xSW+KWvqwkl8ubIRummgOOBEUzSNKw4ZgFKbnH1DRNTdcDk3skKO24kcD/f/pSwRJlbUxeB2yHA7VFQWeeBwqOjF+pGIKCP9C3xWR6AeSJIkjCzPRTTNJb4zlUgZ+LUuAt0Ecr1OmKYJv8vBPaiJiCgrynO4xDftZINK/WxQU9asa4whz+uAU3WhOZaCLAP/+mYtVC5RS0SUkZuPHobiHJZelF3DeuVAc/J5R9lR1RTHU5+tAoSJPL8TG0IphBNRLK+JWB2NiMiWYikdBw8vszoG9TBCCJgQUNhNzZjjf6tgVhZ5EI4b8DoVrG2Kc9snIiLKir4F9jhJnKNVNvLJr/VIc/8XygYT+LUuiopcF9KmQCShI+B2IN+vIcfFGYBERJnQFA7wUPZ9saIOHy+ptzoG9RDNCR2yLLAhlIJDleFQVfg0Ffv2L7A6GhGRLQ0rC1odgXogSZLw36U1CCc5gzpTiYSJ9aEEVEUCTBMCQEMsjZKgPWa0ERGRvT30n19x54mjrY6xTWxQ28iq+ijShtUpqEcQQK5749JDK2pjyPE4kONxQJUlLkdERJQhSWbZRdkXTeoIcXCRssQ0THidKly5KmRZgmmaUACsrOUMaiKiTOR6WD9S9hmGgUhCR0rnJJlMmRJQWehFKJmGW5VRG04hxw30K/BaHY2IiHqAITY5yZGVro0cOrQMhmBxSNnxa00I7y+pRZ+ghvqEibUNcciSjJSHz0EiokwYJs8yo+zrk+/DwOKU1TGoh2iIJOGQJQwpDeBf365FLGmgyO9Cnl+zOhoRkS3levj6SdknyzLyvC5EeJJjxoQQ2BCKQ4ZA1AAaYknkuhwIuh1WRyMioh6gNpy0OkKnsEFtI0nDRNrg4DZlx+KqCLxOB2IGoKd1JE2BXLcKB181iIgyw5PMyAJlOW4MKvZbHYN6iJUOBTVNcaxqjMGpKAgZaeT7HBhUwucgEVEmynO5HDBZY+LuhUgbptUxbKsunMSKeicCLhXhRBrRZBoz9u6FfSsLrY5GREQ9gKbKVkfoFLaabCSWTCPFPagpS9KmifEDcvHGD9XwaDKQFli4thlejS8bRESZiCR15Fkdgnqcb1Y14j9Laq2OQT1ELGUgx60iZRhQFcAUAt+sacbqxpjV0YiIbGmP3rkYN5ANLcouSZKwrCaCJPcZzFg8bSCeTCORMrGyPgJVlRFK6Hjv52qroxERUQ8wuNiPwaUBq2NsEztNNtIQS0Fng5qyQAigT74XHy9rgFuVURp046d1zdA0FS6HPc6+ISLqbhSbnL1Iu5Y9euVAUSSrY1APURtOoiaUxLqmODRVRVmOCyvrN/4/ERFtP7fC10+yhsepgBVk5tKmCaeqAACa4zr6F3iwtjEOk6tqERFRFuxe5LU6Qqew0rWRPgVeGDqX16GuZwJ45bv1EELA73WitjkBh6rgoEFFGFDc/c+8ISLqjvLc3EOQss/ncqIkwOVBKTtiCQOr6huR0g0UepxoMk14nQomDMy3OhoRkS0NK8+xOgL1QEIIrGuKI5riDOqMmQLNiTTC8RTShgGXQ8FP65rRr9DDxj8REXW5dU1xqyN0ChvUNrK0OgyTM6gpCwSA8hwXflrfjHyvhrSswzQFYikDK+ujVscjIrKltGmCbULKNqcqwaspVsegHiLX64RfUyC5FBgAqkJJDO+VgxP37Wd1NCIiW+IWW2QFSZJwyti+SHKSTMbqwgl8u6YJX6yoR2WBD6P75GJVQwyj++QDElvURETUtfavtMcmg6x0bWRUr1wYJhvUlB0fLq6CT3Mg3+/AuuYEfC4H9htYgBE8g5uIKCNel9PqCNQDJXUToYRudQzqIXwuFZAkxFM6ZAlI6AYGFvmxiic4EhFlpDToRtDtsDoG9TCmaeLu95cikWaDOlO6YUKWBNY1xlGW48b7i2uQTBvYEEpYHY2IiHqABcvqMawi1+oY28QGtY2sro8hzRnUlCXrmhIY3S8PdeEENFWGQ5WwriGOxmja6mhERLY0Y7QGj4sDjJRdkaSO+nDS6hjUQyyuCqM5lsKG5gRM00Q8peOHdc0IxXmSBBFRJkb1CqJvgT32EKRdhyzLOH/SQM6g3gG6YeLR/y5Hkd+FgwYV46NfalCe64bM2dNERJQF4wbYY5stNqhtJJbUkeYMasoCIUzIkowCjwM/rQ3B5ZARcDuxT79cBN2cAUhElAmnKlsdgXqgX9Y349Nf662OQT1ENKGjV64LLlXF4uoQyoMujK3Mx8FDS6yORkRkS36e3EgWeXdRFUJJnmCWqeZ4Gos2hOFxyPi1NoKVDRH4XU6U5XBfbyIi6nqfLG/gDGraufoVeqGzQU1ZYJom1jcn8d3aZhjChBAmfC4F9bE0muIspomIMtE738vCi7Ju0pBiDLXBhxLaNSypCuE/S2tREtTw43qBgcUBVDUnsGAZT5IgIspEZZEPY3ya1TGohxFCwO1UoSg8wTZTkgBy3SqGlOegd54H369rRnlQw5i+rMuJiKjrDSr1Wx2hUzhOaiMuhwrd5PI61PUMU8aIigA++7UBcV2HLCQENAe+XtkIr4svG0REmdi3bw40h2J1DOph6iIpLKsJWx2DeoglNRHUhhMIJ1IwTBNLa8MIJXSkuEQoEVFGHKqMMf3tsUQj7TokSYLHKcNhcjnqTFU1G3A5VfTO92DxhhBcqoRY2kBNhFvvEBFR19td+KyO0CnsNNmI16lAN3n2ImVHOJmGbgqU+NxY1RDDiroIgh4HChw8e5uIKBOSzPdwyr7acBK/1katjkE9REM0CUNIWNuQQIHXgTyPht75bvTh/qlERBkpDbqsjkA9kBAC//6pBrE0V9DLVCiZRipl4JuVDfh+bQiQBByyjJ838MRRIiLqevXhJJf4pp3L51bBFb4pG4QQ+OzregjThOpQ4NdUCGFifUMcdWGe7UlElImj9yiDl/sIUpYpsgQH9z+nLHGpKoQp4NFUDC4Poqo5iZKACwMK7LG8GBFRd1Po5wnilH1CCAwo8UE3OAiZqZRuQpaA0b1zsL4pAY9TRq5Xw95c4puIiLJgdB97vN+wQW0jny6vh25weTzqeqYJ1EeTcKkKaiJJ5PucqIukoCoSgm42V4iIMqFwhTyyQO88L1g+UrbURhL4cX0j4mkDAbeGPvk+fPBLLdY28QRHIqJMjOoVxO4lAatjUA8jyzIO2K2IW3TsgMZYCjXhJBZXR6AqEkwA0bSOpnga/FhIRERdTQh7nGTGBrWNVBb4uAc1ZYVpCqxrjCGh69gQSsCrKUgaJnYrCcDH2X9ERBlxqiy7KPuW14TxxcpGq2NQD1ETSmB9UwIFPidC8SQMQ4dpCvTJ81gdjYjIlor9XOKbsk8IgfXNcTaod0B1KIndir34dnUDXA4VujDgcagYP7DQ6mhERNQDyDbZZpAjpTby4ZJapNmgpmwQQDytQ5Fl5LoVrK6PoDjoRdow4eSrBhFRhuxx9iLtWnrneSAkPvcoOz5aXAunIqFPngfleR6oQkK9JwWXQ7E6GhGRLWkOewwu0q5FkiT4NBVphWOQmQoWO+B2KqiLpODXVESSBjwOBUuquQc1ERF1vWFl9liBh60mG5k+shSGTabmk70ZhomH/7sSDllH0gB8mhOmMBFLGUjqfA4SEWVE4gAjZd+i9WF8srzO6hjUQ6yoDUGSZJTmeLCuPo5ISsewsgD2G1BgdTQiIlvK9XIFM7LGwcNKrY5gewuW1sHtUJHjccCpyuid78E41kRERJQFZUF7rMLDBrWN1EVS0A02B6nrCQiUBzX8XBVCLGWi2K9hVK8AivxO5HqcVscjIrIlmZuNkQWKgxr6FnitjkE9RDytw+1UIEkSynLdWFUfRf9CP+Jpw+poRES25Dc4bEdkV4vWN8HlkJDSTSiyhHWNCbz94wbuQU1ERF1u5tg+UJTuv5IZK10biSR0pLjEN2WBMEyoioS0YUJAwKspCKcEVFXhArVEREQ2Ek8ZiCTTVsegHqK6OYHKIj965bpRH0miKa5jaFkQisKhWCKiTKh8/SSypXAijeW1EeR6NERTaaiSDEkCTh/Xz+poRETUA0iSPWpINqhtJJpMI8UZ1JQFQgj8WhuFKktwKgoKA25UNcVx0j59MLQiaHU8IiJb0rgHK1lAlQCPk889yo6h5UEU+jVEEwbcmopclwMr6qMo8mlWRyMisiUX96Ami3y1sgFJnZNkMlUfTmB5TRSFfg2hRBqaKsELJxaubbY6GhER9QCaKmNwafffh5oNahuRJAmyxOKQskACSoIuLFqfQv9CN4oDDoTiaaysj6IhlrI6HRGRLU0eXMwmNWVdSa4bsTTrR8qOilwDP64LYXCpH8uqQ/C7FSxa34yywcVWRyMisiVFYoOask8IgYZYCmk2qDNWG0kBEmCaAqoiwYSMAi+3zCMiItoUG9Q2MmlwEQzWhpQlS6siCHhUaA4FqqygLMeNkqALDpXNFSKiTHAParJC33wfynM8VsegHmJFbQQN0SRMASyujmJM3zxUhxNYVR+zOhoRkS3JsoTyXLfVMaiHkSQJvXJZP+6IukgSZUEXXA4FHlMBJAn9C71WxyIioh5iUInf6gidwga1jaxtjEPnEt+UJbWRJPK9LowoD6AumkYiZSCaNODkc5CIKCMmXz7JAo2xFDY0J6yOQT3E+qY4ZEnG+qY4VFmCgIAsSRjJLWKIiDLid3HYjrJPCIEfuBT1Dokk0ijwaWiKpSHLEoTYOK7bnEhbHY2IiHqA4WV+KEr3n2jIStdGKnJcMDmDmrIgoRtwO1SYpsDIXrn4dk0TqkNx1IUTyPVyD0EiokwIwQ41ZV84kUZdOGl1DOoh1jbEoMqAS5VgQiDP58RuJX54NH7sJCLKhJPbw5AFJElCTTiBcEK3OoptraiPoC6URDSlQ5ZkCGHCEMA+/fKsjkZERD3AIx+vxJkTK62OsU0cKbCRz5Y3Ii0Mq2NQD2CagBAmyvI8aI6nkdAN5Lg0xNImFJ7tSUSUETaoyQoBlwNlOS6rY1AP4VAk/FIVRnUohVEVOYglDQQLHQhxgJuIKCOyLMEeCzTSrsQwDNSFk9A5SyZjeR4nXKqCRMqALG8cZ2tOpHHQ4GJw5yciIupqxQF7jAOxQW0jbpcMhyFbHYN6AGEKBNwODCsLIpIwEE8a8LsVjOmfi4DLaXU8IiJbcqicAUPZF4qnsb4pbnUM6iHSugmfS0WywUBlQRDrmpNYXR/jFgdERBlyKjLgszoF9TSKouCa6UOtjmFrSd3ATW/+jJRTgabK0A0Bt6Zi0qBiq6MRERF1G2xQ28ievfJgcvYVZYEpBH5Y14SKXBd+2hCBbphQZRUfL61Dgc8eZ98QEXU35UEXVIUnmlF2BTwOFKfdVsegHmJlfRRBjwPhuI48n4YNoSTSpsAevXOtjkZEZEuyxLmWZI1fqsNI6pxBnal1DTGsb05AlgBF3vi3rMoyvlvTZHU0IiLqATRVxuDSgNUxtokNahtZ3xSDwdqQsiBlmPBqCqIJE4CALAGxlI5j96pEWY7H6nhERETUSQ5Zhkfj7H3KjlhShwQBl1NGLG1ClSX4NBU8x5aIKDMCAuCCwJRlQggosgSPkzVkpmJpHcUBJyRJBoSAEAKDSwMY1SvH6mhERETdBhvUNlKR64HB0R3KklAshdpIEqosI2GYyHE5sao+jppwyupoRES2NKwswBnUlHWRpI6q5oTVMaiHMITA16saIEsSFq1rRjJtAJKEj5fWWR2NiMiWKvLc2K2Yu1BTdkmShBrWjzsknNAhSxJkSULKEEimDbhUlTOoiYgoK0ZWBCHZYCUeNqhtZBSXxqMsEULgo19qocgSCnxOfL9aR/98LxpjKXicXCaUiIjILnyaipIgt+eg7JAlCZ8tr8dBQwrQGNNR6Hch1+PEyF5Bq6MREdmSmzNYySLFrB93iNMhY01jHBIAWQISaROSJKDK3b9ZQERElC1sUNvIN6sauQc1ZUVSN9AUS8GjqQiYKlxOBQ5VRko3rI5GRERE28GjKVAUzeoY1EMUep1oTqTRK9eLukgT0oYJn6bCoXL1CCKiTHAParKCEAK6yfHHHRFLGhCmgOaUYZoydAGEkgbyPA5bzGgjIiLKBjaobWRdUwy6wQKRup4QQL9CD+IpgVUNMXg0GbousGfvXPTO91odj4iIiDpJUxVoKmdfUXasqA1DU2Sk9I37T5sCWN8Ug+Zkg5qIKBPFfg298vgZnLJLkiSkdBNJTlLIWE0kgYZoCo6EBEkCIsk0vl/bBLdD5q7yRETU5YaVBzG0PMfqGNvEBrWNFAVcMNigpiwQ2Hi2rCIZWNtowqOpMACUBLm8NxERERF17Pt1zSgOulAdSsDjVBBLmWiIpbCvn8uEEhFlwu/isB1Zw6XKcHEFlIzphkBx0AVZAlyqBFmWoCkKJg0uhsQWNRERdbECn9PqCJ3CStdGCn0auMIOZUtaN7GmMYZkSkdprgeqJKE6lLA6FhGRbZXluKFwzzHKsv8uqcHXq5qsjkE9xPLaMHrnuvHVqkYU+J2IJnTURVIwxXqroxER2dLQsgAm7FZkdQzqgd5dVI0YZ1BnLJLUsaouAsMEFFmCgAlAwoe/1FodjYiIegCfU8WhI0qtjrFNbFDbSP9Cn9URqAf5dHkdivxuJIwG5HqcCCXSePm7tQi67HH2DRFRd3PMnuXwuBxWx6AeprLIB4/Gkp+yw6cpGFERxDs/VaNvrhfV0STWNMRRWcjlaYmIMlFkk9kvtGsRQqAi34OUYVodxbZiKR3heBrxtAFIAvleN5oTOmJJ3epoRETUAwwt9VsdoVM4WmUj361uBEtDygZTCISTOvK9JtK6gFOVEImnMWVYKUb3ybU6HhGRLakKl8ij7FMkGQ6Zzz3KjkFlAeR4NMiSjKZ4ChsaYlAVCbleNliIiDLBkxvJCpIkYfcSP5I6RyEzVRuO46d1EpI6EEmaKPID8aSOogC3PSEioq4nSfZYwZENahtxORWYXOObsmR4RRDLq8OQZcAwBZKGicZYCl+tbLQ6GhGRLe3ZOwdOh2J1DOph8nxO+N0s+Sk7fq2NImUYcDtkLK+LAkKC26GgJOC2OhoRkS1xD2qySu88j9URbC0US6FPvheRlI6VdVHke11wyioOHd79l1slIiLKFla6NjKoJGB1BOpBPvu1DvWRFAIuFc2JNNKmgCQB+VxijIgoIzL3nyYLhOJp1EdTVsegHsKrqVhSFULKMCFLEmojCQDAC1+tsTgZEZE9DS8P4jdDS6yOQT2MEAIbmuNWx7C1hlgKJkzkeRxYBSCaSCPo4YoIREREm2KDmojaSRsmIgkD9bEUgm4H9DQQTqThlBVEU4bV8YiIiKiTNFVGkDOoKUviKQNfr2pAjtuBdY1xGIaAYQrM2LuX1dGIiGzJr7GhRdZYXhu1OoKtJXUTaR3wOWX4XA5E0wa8bge+W9NkdTQiIuoBRlYEbbHMN0erbOSHtc0wBJf4pq5nCgFTmIgm03A5JHhcCjRFRlFQQ2mQSzQSEWVCtkFhSLuenzaEsJADYZQlQgDrmhJoiqZhmCZqwgnoAnjs4xVWRyMisqURvYI4YlSF1TGoh5EkCQGXyj2od0ChX8OCpXVodKvQDRPVoRSSKQO/BEJWRyMioh7AqUgYUha0OsY2sUFtI8Mruv8TinYNQgi8s6gKqbRASgdMU0CVJfz75xpU5LBBTUSUiUOGlcCtsfSi7BpSFkTvPK/VMaiHWNcUQ3Mijd2KfFhVF8HaxhgK/S4cuQebK0REmSjwc4stskaOx8kG9Q4I/W+rvGjSQNCjoD4CNCd1yJJkixltRERE2cBRUht584cNME3OoKauZwqBpkgKkABNlRBO6qhqjmNDcwIrgi6r4xER2dIBu+WzQU2W4BgYZUsibaBvnhdJ3UR9LI1oUsfIXm40xrgPOhFRJjxOxeoIRJSBcFyHQ5HQFE+jLMcFIAZhbmz8ExER0UYcJbWRAq/GJb4pSwQWLKuDYZjI9WgIJ9JY05jAPv3ysUfvHKvDERHZklvjYARln26YiCZ1q2NQDxFO6PBrMn5YH0ZTTIfH6UBjLIV42rA6GhGRLaUNzmCl7BNC4IPFNYjx/TtjTdEUkmkTteEkqjwORJMGTOhYXR+BBJ49SkREXSulG1zim3auXvlucAI1ZYNpCjTHdESTOvwuFWsaokjoBipyNZRwiW8ioowoEt/EKftcDgX5Ps3qGNRDlMTT+HZ1I5riaeR5HFgJQDcE8rw8QYeIKBMejTOoKfskScK5Bw6wOoatfba8HmsaYvjglxrsVhqALAOGCYzum291NCIi6gFG2mS7YDaobUSVZQhwcJuyQJHgVCWkdANBl4I1DXHIkBB0a3A7+AGZiCgzPFOesm91QwzLayJWx6AeoioUR0M0ieHlASxc0wwBE/G0AR+3NyAiyohL5edvyj4hBOYvqkJK5xhkpn5Y1wSvQ0IibWJpTQSNUR0CJoQQ3IOaiIi63OKqMAaXBqyOsU0cKbCRhkgCXN2JssEQAqYQ8Lqc+KUqjFA8BVkBwsk0mmJpq+MREdkSh3fICpGEjtpw0uoY1EM0xdLoX+SHQ5aR1A2osgwAcDlki5MREdmTLLORRdZYVR/jEvM7QgikTBlBt4pESocQgNupsjlNRES0CTaobeTdn6qhc41vygLDNCFJAuU5biyvjSBlCDgkGYNKAuhb4LU6HhGRLakKGzSUfaN65WBkrxyrY1APMX/RBsSTBkxJIJE2kDIMhONprG+KWx2NiMiWSoNAAbfqoCyTJAnDygNI6WxQZ0qChHcWVSHX48CapjgETARc3PKEiIhoU2xQ28jIXrncg5qyIm2Y+HZNE3YvDmBVfRRp3YAA0BxPoy6SsjoeEZEt9ckzochcppGyqy6aRHUzZ1BTdnicKqqbk/BpCpoTaUSTJgYWeeHTHFZHIyKyJafKExzJGnleDUk2qHdIfSyFIaV+rKyPQQiBHC8b1ERERJtig9pGnKoMQ7BDTV1PVWQ4FAVVzQmkDRMOVUYibWLcwAKUBNxWxyMiIqJOKvK7UOR3WR2Deojq5gR+Wt+MWDKN2uYE3E4FHqeMpTUhq6MREdmSIfyoyPVYHYN6IDvsW9md6YYJhyxjSGkA65riiCV1jKgIotDPFRGIiKjrlQXtMQ7EBrWN9C/0QXAHS8oGAZQEXVjXFIcqS1AlCS5VwcraGKo4C4uIKCPDygJc5puIdmm6uXFp7x/XhRBwa0imdey/eyEOGVZmdTQiIiKirImlDQgIRNMGHLICw0xjQ3MCLkfY6mhERNQDlAY0SJJkdYxtYoPaRprjaYANasqCaNKAYQiYAjCEQMow0b/IhxX1EThkNleIiDIxqNjHBjUR7dLqwgksq43C45DQHDPgdSnI9XA5SyIiIupZakMJOBQJ4VgaiiJBlmSkdRPjKgvQ/dsFRERkd7JNejhsUNvIuqYYDIMNaup6hhAwhAGfS4EZN5HUDfTK8+LA3YtRYpPlIYiIiAj4eX0IS6s5U4Oy49s1TSjwOLC+OYmA2wFDAHlsUBMREdlOOJG2OoKtLamOoMCnoSGWhiILKLKEplgaP20IwQYT2oiIyOaGlwc5g5p2rlTahM49qCkbBJBKCxR4nYglDQjI8DlV+Fx8ySAiIrKToEdFWa7b6hjUQwgAC9c2QFMlRFOA26HA7WT9SEREZCdCCFSHuL3bjmiIplDk19AQTUMWMhyqBEmSMLJXjtXRiIiIug2OFthIIm3AYIOassAwTTQn0uid70FCjyDgdqA6nMC/f66Gy6FYHY+IyJYO3K0QTr6GUpY1RNNYURe1Ogb1ECndQKHfhbWNCUBI8LtUuJ183SMiIrITSZKwtjGKlM4xyEwtrgqhyK9hbWMMAJDUDTgVBV+tbLA4GRER9QReTcXg0oDVMbaJDWobaY6nYZgsDqnrJU0TXqcKCCCRNlHgc6AxmsJP65u5xDcRUYZ0w2SDmrJuWHkQw8qDVsegHiKpG3j84xWIpwzIEpDn0yBj4+cYIiLafpoq8yRxyjrTNPHp8gbopml1FNtKGyaCbhW6aUKWJDhVCdGUjlUNMe5BTUREXW5Qid/qCJ3CBrWN5Pk0NqgpK4QQ8LkV1EeTkCUJPs0BUwjIEmDw8wkRUUYEV0Ehol1cNGkgYZgwTBMGJBT6nPh4WS10foYhIspIvwIv9uidZ3UM6mFkWcbsQwdbHcPWfq2NoKo5gcXVUcA0EXA7IMsSjt6zwupoRERE3QYb1DbSK9fDJb4pa/LWa1jbGIciCQASEikdJUE3euV5rI5GRGRLqiJbHYGIqEtFEjriSQOGABQZKAq48drC9RhZkWN1NCIiW8rzaFZHoB5ICIH7PlyOWNqwOop9CYFwXMfK2ggSuoEcjwNORcHzX62xOhkREfUAPqeKQ0eUWh1jm9igtpEcj8PqCNRD6KZAwKUiltKR1E2kDQMmJBy9Rzl8bqfV8YiIiKiTasIJ1ISSVsegHmJpdQRrGqKIJFJQFQX1kSQaYynsN6DA6mhERLZU5GeDmrJPkiSce+AAq2PY3j+/WAWHKqMukkDfAi+KfC7M2KuX1bGIiIi6DTaobeSjJbVc4puywhQC8ZQJpyJDkiTUR5Pom+9jc5qIiMhmDFMgwdkvlCV5Xgd8bhW53o0NlT55bqiyBKfKFSSIiDLBFXiI7KsxmoIsSRAADEMg4FZZlxMRUVa4HIrVETqFDWobOXGf3lZHoB5CCIHb310CIQBZEgjFdAwbEcTPG0LgKvNERJnZrdjHQUbKutKgG6VBt9UxqIdYWh2GYQjIkCBJwE/rQ6jIdWNYedDqaERERERZkzZMRFIGVAWQICGlm4ilDPxaG4UkWZ2OiIh2dYNK/JBs8IbDBrWNfLWqAaZpdQrqCXRToMDvxJcr03DICgyhw+1UkO91Isil5omIMqLI3b8wJCLaEfWRJNKGgKpISOkCuhAo8GuoCSWsjkZEZEtupwK/i5/Biewmnjag6yZUVYIiSzBMoCmWQnUobnU0IiLqAXYv9rFBTTtXgU+DyemrlCWK5MebC9dDFwJuh4JIUoffpUJT7bE8BBEREW1sGNZHU1bHoB7i17ooAMAwTaiKDN0QkAAY/AxDRJQR7vJGZE/heBq6aUIyFbhUGbowURVK4tARZZBt0DAgIiJ7s0NzGmCD2lb6F/qsjkA9yMI1jVAVGY3hBAaXBuF2yHj3p2rbvLgREXU3U4YUQ7PJHjC069BUGUHOvKIs8TpVGMJEKGFiWJkHjbE0di/1c5l5IiIi6lEaYimYQoIkBDRVhglAliRU5HqsjkZERNRtsEFNRB1a3RBH2jBhQkJxUEOeR0Np0I1e+SymiYgy4VB4gg9ln24KRFO61TGohzBNgcZIGj5NRq5Xw7KaCEr9LiTShtXRiIhsSZElOBTZ6hjUwwgh8Mp365EyuM9gpmpDCdRHkhAQcKkykrqJWMrE3C9Xc+IHERF1uWFlAQwpC1odY5vYoCaidnTDRHUogaRuItetIpYyMKQsiNUNMdSGk1bHIyKypRy3AxxeJKJd2cqGKDxOGYaQUBxw49vVTagOJbFgWZ3V0YiIbKlXrge7lfitjkE9jCRJ2L3Ej6TOBnWmZAlYVhtBPGnA73YgkTYBGdit2M8GNRERdTm7vNewQW0jd7+/DGmevUhZYJoCK+siSBsmynN9KPC6EEvpGFwaQKFfszoeERERddLKuih+rgpbHYN6iGTagMepIpTUke9RkdJNhJM69uyda3U0IiJb0hw8vZGyTwiBpdURjkHugPWNcaiyBN0UcMgSZAVwQbZNw4CIiCgb2KC2kYMGFcIUVqegniCeNvDGDxvgkAUCLgWj+uRgRV0U5Tlc3puIiMhOhpUFsTtnXlGW/LguhCc+WQGXqsDnckAXJuJpHbURrsBDRJSJHI8DHieH7ii7JEnCYSNKrY5hawvXNuGDxTWAACpyPXCrMpqTBoQQbFITEVGXG2STcSBWuTby6a8NMNihpiwQQkCVTUiQkTaAykIfflrfjPcWVyHH7bQ6HhGRLY0fUACnQ7E6BvUwoaSOejYHKUtW1UchSxJcLgXxtAEZQFM0jRj3QSciyojHydqRsk8IgeW1Eatj2Nr65gQcsgQTQNoQEBLgkCWM6pXDBjUREdH/sEFtIyMrgjAFl9ehrpc0BNJfCOR6FAgI5PucACRUNSWRTPE5SESUCd0w2aCmrPNpKjSVy4NSdqyoi8AQAjkuBxqiaTjUja95g0oCFicjIrInRWYji7JPkiRu77aDYikdv9aEIYRA2jAAIcGnqRv3oiYiIupibpuc5MgGtY1EkwZ0k4UMZYckKSgJaEjoJrxOFSv+tyd1n4Icq6MREdmSqrBJSNnnVGU42aCmLKkNJ6EbBvL9TtSFE3AqMkwIrKiLWh2NiMiW8r1OFAVcVsegHkYIgbWNcatj2JphAgYENIeCeNqEKQTiCR3frmkETzshIqKuNqZfHmS5+48FsUFtI/WRJNJsUFMWpAwTOW4VAhIMQ0A3TDREUhhTmYdxAwqsjkdERESdFEvqiKbSVsegHmJdUxym2NhQ+aUqDK9Dhm4I9CvwWh2NiMiWOIOarCBJEhRZQlLnGGSm6iNJVDUmEUnqiKUMpNI6UoZAJKFziW8iIupyi6vCGFIWtDrGNrFBbSMJw+Ae1JQVacPE0DI/muI6CnwOfLemGZGkgTwPl3giIiKyk+/XNWHhmmarY1AP4ddUQEjI8WpojNXD6ZDhdihwcXsDIiIiWxlcyu05dsSi9c3I9TpRHNAQSuqIJmV4nAqmDC2xOhoREVG3wQa1jew/oBCC/WnKgmTaQDShQ1GSCGgOLKuJIJbW4VJlRJO61fGIiGzJ41R4tjxl3b79C7Bvf65+QtnxS1UIX61qgF9T0Rw3kO9R4XF2/2XFiIiIqK2fN4Q4g3oHfL+2CeuaYsj1ONAYS6M5loLHqeL5r9ZYHY2IiHqAYWUBzqCmnasplobJDjVlQcowoZsCHqeKshwP8rwO1IQS+KU6jGiaH1CIiDKxR68cOFQ2qIlo17WhOQFNUeBxKtBNEwYEYmkD361psjoaEZEtFfk1lOW4rY5BPYwQAuEEJyfsCE2Vket2oLLAh7XNcQACfs2BY0dX8KRlIiKi/2GD2kaW1URgcA9qygJdCMTTOhyygt1L/KgLx9EYTSOeNqE5WEgTEWWCr55khepQAlXNCatjUA9RG07AoylQZAlCAM0xAzCBn9ZzmXkiokwkC31sUJMlgm6H1RFsbY/eOfhudRMUVYIqSTBNIJbaeNKezAY1ERF1sREVQVucEMUGtY0MKPLB4AxqypKa5iRSpoE8nxPrGmNQFWBJVQguJ/cQJCLKxO6Ffqgql7ql7CoOuFAccFkdg3qIFXURBNwOxJI6ZADhRAoBtxsBNz92EhFlwuvk6ydlnyRJ2L3Eb3UMW4unDJhCQJiAU5VhmAIeTcaoXjm2aBgQERFlwy5b6d5zzz245ZZbUFVVhZEjR+Kuu+7CPvvss8XLz5kzB/fddx9Wr16NgoICHHPMMbjpppvgcnWfAb3hFd1/zXjadSxa14wVtVE4VRmqqkCVgd2K/eidy7O3iYgyIcsciOjudsX60TQFeHojZcuaxhjyvBo2hOIwhAlIMioL/ThsRLnV0YiIiLrMrlhDcg/qHdMYSaIuksTK+iiiSR1N8RSSuoGXvl1ndTQiIuoBBpX4uQe1VebOnYtLLrkE999/P8aMGYM5c+Zg6tSp+OWXX1BUVNTu8s8++yyuuOIKPProo9hvv/2wZMkSnHrqqZAkCbfffrsFP0HHNjTHwQnUlC1J3YAhTKiShFA8BVNIWFEfhdPB2X9ERJkYVhoAHFyForvaVevHlQ1RrKyNWR2DeojaUBJDywJYVh2FpsiQJAFDmPhyZb3V0YiIbKk44ELvPK/VMWgrdtUacnBpwOoItra4KoRiv4ZCv4aAS0VzPA1IQK88D7d+IiKiLmeX9/FdskF9++234w9/+ANOO+00AMD999+PN954A48++iiuuOKKdpf/5JNPMG7cOJx44okAgL59++KEE07A559/ntXc26LIEjgFhrIhZZhY0xhDoV9DYzyNSMqALEkQQqDQp1kdj4jIljiDunvbVetHhyzB7eTJZZQdBX4NmkPC2sYYHKoMRUioak7C7+I+lkREmdBUntzY3e2qNeSb329AJKVbHcO2llSFsKoxhtpIChKA9c0JJHUDHy2uAfi5kIiIulh1KIFpI8qsjrFNu1yDOpVK4euvv8bs2bNbj8myjMmTJ+PTTz/t8Dr77bcfnn76aXzxxRfYZ5998Ouvv+LNN9/EySefvMX7SSaTSCaTrV+HQqGd90NsQXVzkntQU1YIIaCpCgIuJ+ojSUQSaaQNE3leDQX+7rPkFBGRnSgKm4Td1a5cPxb4NPhcu1zJT91UVXMcTTEDkZSBEp8DqxuTKA+6UBrkFjFERJnQVNaP3Vk2akgr6kchBMq4vdsOSesGNFWGBAmSBKgKEE8JXDp1d+5BTURE9D+73GhVXV0dDMNAcXFxm+PFxcVYvHhxh9c58cQTUVdXh/333x9CCOi6jrPPPhtXXnnlFu/npptuwvXXX79Ts28L96CmbPp6ZQOiKR25bgdCsTTiaRN+twLd5EkSRES0a9mV68f1zXH8WhPN6n1Sz1UXSSPX60AsqUP4HXAowK/1MXy8tNbqaEREttQ334uh5RwL6q6yUUNaUT9KkgRNlbkH9Q6Ip0xEkzoa4mnIkNAc02ECWLi22epoRETUA2iqbItlvne5BnUmPvzwQ9x444249957MWbMGCxbtgwXXXQR/vrXv+Lqq6/u8DqzZ8/GJZdc0vp1KBRCr169shWZqEulDXPjTD/ThMMpoz6WRtowsE/fPAwqzbE6HhERkeXsUj86FBlejSU/ZcewsgDWNsUhyUBjVEciZWD8bkFbLC1GRESUDdtbQ1pRPwoh2JzeQX63ioEOP1Y0xJBOG0imVbg1FaN65VgdjYiIqNvY5UarCgoKoCgKqqur2xyvrq5GSUlJh9e5+uqrcfLJJ+OMM84AAAwfPhzRaBRnnnkmrrrqKshy+yWVNE2DpmV3L976SJJbUFNWxJMGJAAlATeWV0cAYcAwBMpyvFZHIyIi2ul25fox6HZCyuMygpQd65ri6J3nQTxlwq0IyIqMHLcTP3C2EBFRRgr8Tm6T0I1lo4a0on4EAFaPOybX40Q4rkOVJYR183+Pp8SaiIiIsmJYecAWW0rscg1qp9OJ0aNH49///jeOPPJIAIBpmvj3v/+N888/v8PrxGKxdgWgoigANp412F18u7oJpuAZjNT1DHPjnpX5PieW1kQASFAUCU7uf0VERLugXbl+dCoyPE7F6hjUQxR4nagNxSEBqI0k0bfAg7pogs9BIqIMqQrYoO7GdtUaUpI2jv9wFnXmIgkda5tjqI8kURdJIprQ4dcFkroB2QYNAyIisrfFVWEu8W2VSy65BDNnzsRee+2FffbZB3PmzEE0GsVpp50GADjllFNQXl6Om266CQAwffp03H777dhjjz1al9e5+uqrMX369NYisTsoy3XBZG1IWSCEwJLqMFwOBaosoTaUhFdzQDcE4LA6HRER0c63q9aPbqcCN5uDlCXN8TQWbQhBhoDHKSOeMiBBwkguZ0lElBG+h3d/u2oNWRNOIsUGdcbqo0mYhglVkiFMAd0U8GgKVIUTP4iIiFrskg3q4447DrW1tbjmmmtQVVWFUaNG4e2330ZxcTEAYPXq1W3OVvzzn/8MSZLw5z//GevWrUNhYSGmT5+OG264waofoUNDSoNWR6Ae5Ie1zXAoMpyqjPpYEoV+F5bWhG2xNAQRUXc0rCzAAYlubFetH0OJNELxtNUxqIdYtCGESFIHZBmKLEGWgMpCH4oCLqujERERdYldtYYcXOq3OoKtlQQ0/LQhBK+mIppKwzBNVBZ4uQc1ERHRJiTRXdaPsblQKIRgMIjm5mYEAt1/6jzRtrzx/Xr4XSqW10bw/Fdr0DvPiwdO3svqWEREtAWsRewnG7+z9Y1xrGuKd8ltE22uMZbEv75eC5+mYnVjDIOKAzhij3Ls1TfP6mhERNQB1o/2w9+ZPSyrieA/S2sgDIGFa5vREEtg0u4lOH18f6ujERER7bCdVY/skjOoiWjHuRwKmuM6GiMpaIoMR/dZaYqIiIg6yaupKA5y9iplR9owoZsmDFPApcoIeFS4WUQSERHZihAbm6qUOcMUSKYNOBUZsgQIU4LfxWF4IiKiTfGdkYjaMU0B3RDwORWsb4rDoSgIupwIJ9LwOvmyQUSUCVnmFgmUfTWRBFbURq2OQT2EQ5EhIMEEkOvR4FIVaGxQExER2YokSVyKeif475JaOBQJAgKmEHA7FdSGk1bHIiKiHqDA57TFVq3sNBFROynDhCQJaKqCpngaigz0znNjRR0HuImIMjWklHtQU/YNLPJjYBH3EKTsSOoG5rz3C8qDGgwBOGQZmsrXPSIiIrv5eUMISd20OoatrWqIwutU0BRLI5IysLgqBIeqoPu3C4iIyO4qct0YUha0OsY2sUFtI79UhWGY3DKcup5umoAkoTaShKbKCCcBt+ZA2jChyBxkJCIisov6SBJ1kZTVMaiHaIqlIATg0lSk0iYcqoRXF66DYoMzt4mIuqMhZQFM2K3I6hhEtJ3ShglTAClDQIYECYDboaI4wK13iIio69lh9jTABrWt9C3wWB2BehDDFPhkQwglQRfWhxIwTRNlOW7kepxWRyMisiWFS3yTBSQJUBU+9yg70oYBv8uBWNKAW1UQSehoiKVw0OASq6MREdlSeY7b6gjUQw0uDVgdwdYaIkmU5bigGwKGacIAsHtpgEunExERbYINahvRVO7fRtkjSRJqIinke5xQINAUS+PjpbXwag6roxER2dKk3QvhcrL0ouxyORT4XVyBh7KjXpURdDuQ0k24HTI2NCegORSEE2mroxER2VJC5wniRHbUnNChSjIiqSRMATgUCbGUjuW1EaujERFRD9C/wGuLWdQcJSWiDsVTOhJpA5IEeBwq6iMp7D+wEMPKu//eBURE3RH3YSUreJwqPDwxgrLkm1UN8LsUJNImNjQnUVngxS/VYTgUvv4REWWCC/AQ2VNzLA2nKiNlCgix8bNgQyQFl0MBqyIiIupqdmhOA2xQE1EHhBCoDSfhdiqIpw1oDhkJ3UChX4PLwZn8RERERNTeusY4/E4FzfE0EroBl1OBbgjsXuK3OhoRkS0FXFzBjMiOGqIJaA4ZwhQwTAMORUJ9JIl4Sgdgj6YBERHZlxDCFk1qNqiJqJ20IbCmIYoivws1oSQMSCjwOVEXScHNBjURUUYKfBpkToMhol1YVSgJt1NFQzSNXnluuJ0KVEWCT+PHTiKiTDi5Ag9ZQAiB79c2Wx3D1tY1JuB1yjABpHSBHI8KRZYxoIgn7REREbXgSIGNNMfSEOAegtT1EmkTteEUhlfkYG1TDLphYkChD6oMRJK61fGIiGwp3+sEz5anbKsJJ1ATSlodg3qI6lAC5QENEgAJEnxOBeGEjp82hKyORkRkS+U5bvQv9Fkdg3oghSfW7pA8n4ZIIgXTEEjrBlwOFxwKH1MiIqJNsUFtI1+taoBhskFNXc8UApIsIeBWIAlANwXcThXNCR0uB182iIgyYoOldWjXE46nURWKWx2DeghVlrCuOQGfS0Wu14l42gAA7NE71+JkRET2xIYWWUGSJAzi9hw7RFNlfLGyAbIkQUCCx6lAUbgiAhER0abYabKR0X1yOX+assI0BZbVRiAJCYYQG2fBSBKaYinugUVElCEhBDiDmrJtbWMM36/hEo2UHV5NQSwFOHSBPI8ToZgOQwh8u6rR6mhERLZUnssZ1GSNhG5aHcHWQok0DNMEZMCUAKeiwKnysyAREdGm2KC2kfpo6n+D20RdL6A5kDAMGKaAQ5ERTabh1Rz/W6KWiIi2l8QZ1GSBilwP0hxfpCzRdRPv/lSFoqALDkVGbSSJtG6id77H6mhERLbEE8TJCkIIfLKsFik2qTMWTxtYVRdFQyyJUDSJ2nAcdZEUnv9qjdXRiIioBxhWFsCQsqDVMbaJDWobqeRZs5QlKd3EN85GxNMGkmkDPpeK2nAKuV4NHqdidTwiIltie5qs0L/Qh34FrCEpO1bVR/Hmj+vhdchwOCSsaYyhNKihT77X6mhERETUSZIkYcrQUqtj2FpNKIHaSBKRpAlDB3rleWGYAsfsWQGZ+3sTEREBYIPaVn5Y2wyDM6gpCwzThKYqaI6loJsCuV4nGmMp9CvwIt+nWR2PiIiIOumn9SH8XBWyOgb1EFXNCbhUGbIsQZUkNMdS8Gkq5n3N2UJERJkYUOjDqN65VsegHujnDSEkOYM6Y9WhBH6tiSCcMNCcSKE6lIQkS/h+HbfeISKirqepMgaXBqyOsU1sUNtIZZEX7E9TNiR1E7GUgZV1UQghkOfVUBtOoqo5gVBCtzoeEZEtDSsLQFVkq2NQDzO0PIih5d1/WSfaNbyzaAOWVYchICEU1xFP6ijPcWPSoCKroxER2ZJL5QpmZA07DGp3Z8tqIvjS44RT0ZHQdZTmuBBwOzGqV47V0YiIiLoNNqhtZH1TgntQU9YEXA6EU2mokgyPQ0Y0qaMmlEBfLjVPRERERB1I6SbSpomErqPIr6EpoaM06EYozhMciYgy4pbg4SJmlGVCCCxcy5m+OyKZNpDSDRgmoMoyICSkdR2/cGUjIiLKgt2K/ZCk7r+lBBvUNjKgiI1Byp7v1zYhkTThVCVIsgTD3HhyhNvBM7iJiDJhh8KQdj3N8TRC8bTVMaiHaI6nAUgIx3TEvQaEEBhWHkTfAu5BTUREZBeSJHGm7w4yTYFXFq6DKQScigzdMLGsJoHSoBsS96AmIqIutlux3+oIncIGtY2sqo/C5ARqypLq5jh004SqKkjrJnTTRL5fQ+88j9XRiIhsieMQZAWnIsPj5MlllB1luW4k0gZy3E6sbYxBVWT0yWdzmoiIyG64B/WOSeoGqkMJmAaQFgKrm2LY0BTHmP75kDm2S0REXeznDSEMKev+272xQW0jOR4nwCKGskBAYNH6EJyqBF0H0roJUwj0yvXAzUFuIiIi23A7Fb53U9aYpol42kA4kUZRQIOmKMj1Oq2ORURERNuJe1DvmKZoCvleJ5K6CVUC8r1OGIaJaSPKrI5GRETUbbBBbSNBt8PqCNRDpA0TzXEdLlVFcyoFRQbSusDSmjAaYimr4xER2dKI8iBURbY6BvUw8ZSBWIr7/1J2LK+JwqnIiKYNBF0qVFWCLAnUhhJWRyMisiWPpsCrcSyIyG4aYynIkJBKm/D7nBAm4FR50igREdGm2KC2kR/WNsMQnEJNXc8wBQQEHLKMhG5Ac8hIGyaiSQPFAa5RS0REZBcpw0QsZVgdg3qIRNpENKmjT74XXk2BQ5Ex96u1cDk4IEtElIkhpQGMH1hodQwi2k4NsRRkSYIhBNwOGZDAk5WJiIg2wwa1jQwr5/I6lB2GKfDOog2AJCGpCwhsXF3eNE3ug05ERGQj0aSOas5epSyJpHQYQqA4oCGaMqH877zGsyZUWhuMiIiIKIuaYmnIEmAKAbdDRcowAWGihnU5ERFlQVHAZXWETmGD2kYWrQ/BYHeQsijf78CP6wzEUyYggHWNcbicfNkgIsrE4BI/uKobZduquii+WdNodQzqIaqbE/A5FRQHXVhVH4MECc2xNF76Zq3V0YiIbKmy0IcRvXKsjkE90Iq6qNURbG19UxySBAgTcDtVRCIJLN4Qxj0fLgPnURMRUVe76tBBUNXu38fp/gmplW6a4ArflA1CCKR0EzkuJxK6geZ4CoYwkefTMKI8aHU8IiJbUrhDAllgzz65GMr3bsqSF75ai+/XNMDjVFHVHIdXk5FIGzh0eKnV0YiIbEmRWUCSNerCSSR10+oYttUcT6MpriOU1FETSqA6HEd9NI3DhpVCUdmiJiKirrW0NobBpd1/RWY2qG2kqinBGdSUFYm0gdpwEvG0AUWSEE0YiCZ1/LS+mc9BIqIMHb1nOVROoaYsW90Yw6+1nAFD2VEbjiPgUqFIEurCSeS4nQhoDtSEk1ZHIyKyJb9LRY7HaXUM6oEK/JrVEWxtVX0ERX4NsVQavfPdSBkmAu4k9u6fb3U0IiKiboMNahvJ9TkhePIiZYFumCgNulAdSsDtVBBK6kjoAiMqgpg8pMTqeEREtuTmFglkgWK/Cz4+9yhLEmkDC1c3IhRPI5EyEMxXkTRNeJw8OYeIKBMOhTMtKfuEEPh0eT3SBgchMxVOpFAbTqA5pqOqOYHaSBIpw8R3a5qsjkZERD2ApsqcQU07l8ehwuAa35QFhiljjz65mP9DFTRFRiieBoTAgCI/8n08i5aIiMgudFMgktStjkE9RK7HCUWWUBdNIGEYcMoqvA4FaxrjVkcjIrKlIr8Gr8ahO8ouSZKwR+8cLvG9AzY0x7FoXWjjPtSQWrfS+3plPSBx6X4iIupa+/bLszpCp7DKtZHhFdw/kLKnIeLGc5+vhtspw4AAINAUS/FsTyKiDA0rC0DlLBjKsjyvE3leLg1K2ZE2BKJpAy5VgSkkaA4Zuing5QxqIqKMcAY1WcUOs666M6ciIcfrACBQEnSjOZ7C2sY4fjO0BKrMv2siIupapUGX1RE6hQ1qIupQLG1CUSTEUyZkSNBNoH+hD/0KfVZHIyIiok5qjqfRHEtbHYN6iNpwAqm0gaiiwzBMBDwqVtRFURfhHtRERJlwKBIKuRcwWWB1fczqCLZWG05CmIAiyzAMA4m0DglANGkAMKyOR0REuzg2qInI1uojKeS4HVhVF4XTocKhSPC7HVbHIiIiou1gmgJpk8szUnYYpti4FK0sAbKEquYE+uZ7MbaywOpoREREtB3WN8eR4hLfGYskDdREkjBMgdWNcVQ1JhBN6fh5QwgSuMQ3ERF1LSEEhpR1/xWZ2aAmog7VR1JwO2QkdBOqYkJTZPy8IYQC7kFNRJSRgUU+LvFNWSfLEhxcRpCyxOVUIEsyook0nJKEpngKe/XJxYZm7kFNRJQJr6Yi4OKJ4v+PvTuPs/Sq68T/OefZ7l57VVfv6XSWTkIWiAnrKBgME3+iiL8RiSwZzA9RRsY4EhgHGJhRRF5qFNSACIqjQIbJCAjCQAQnGGRJSDqQTifpJL3Wvtz92c45vz8qxG4SqOrqqnuep+/n/Vf37Vo+6VTf+73ne873UO8ttmK04tR2jNxabEVYasVwXYGZeojFMEUnUk80qImIiDaX7wg2qIkov6JUIYo1PEciVRqB7+DwfBv1LseEEhGtxzkjJTaoqedKvgPP4TIY9caB6TrKgYNGN0ZqgKGSjzA1kII/g0RE68FnT7LlnLEyIp6gXrdHhUDJlxiuBCh6Lgr1DnxP4PnnjbIuIiKiTZeXQ4ZsUOfI1w7NQxtjOwb1AaUNUq3RThTKBRcLrRCDJR8XbxvAzuGS7XhERLnku47tCNSHGt0EC+3YdgzqE40nNjIaGBhjUCv4KAcOJmr5uP+KiIiIVuzg2s8ZqXdjDJR87BguQgqJw4suWpHCjqESG9RERLTpdo3k43WcDeoc2TpYBNvT1AvGGEw9uoBI6ZUd20bAFQKjlQAjOdl9Q0RERMAIX7uph+45vISBoot7Hg8xXPaRaoOCx7ecREREeWKMwTSv5zgjxqz8PRZcF2GikCrAcwT2jFVsRyMiIsoMrhbkyK6Rsu0I1Ee++MA00lRD6JX7BJXS+M7xOmYaoe1oRES5dNn2AXg8RU09dnihjSOLHdsxqE88PNvERZM1tMIUO0ZLSJRC0ePVBkRERHkihMDe8artGLnWjlJoAIEvESYKUao4UYuIiOj7sEFNRE9La6CbaJR9B0Uj0UkUHpppQnHMPBHRujzDDNiOQH1Im5WrO4h64ZyRCo4tdlAIJIquA62Bbz2+iIMzTdvRiIhyad9kDS84b8x2DCI6TY0whTCA70hoGMSpRuBI3Ht02XY0IiLqA5dtH4DIwZUSbFAT0dNKUo1OnGJyIMCJ5RRaGSRKYYxjQomI1kVmvy6ks9C2wSLGq3ztpt4oBXV8+UAHQkgUfRcQBrPNCK96zm7b0YiIcqno88Ql2fGtxxcRK207Rm59+8gSFjoJ5hshTix30YoSiMCBNoZ3UBMR0aZ7cLqJfZM12zFWxQZ1jhycboAHYKgXtDZYDhMkymCw4ONQ0kYlcLDcSTDXimzHIyLKJc0XcbJgrhXixBKv56De2H+0jvMmKvj6YwvwXIFUA3GqsNRJbEcjIsonAVQLnu0U1GeMMRgs8ufuTOwYKmG47GH3aAWdxMBzJMarBewd5x3URES0+Wo5qR/ZoM6RTqw4opF6wsCg0U2gtUYxcGBg4DoSZd8FJ3wTEa0Xd8pT7wWug2qRJT/1xrbhImoFF0oZOAKAlFhsR1juxLajERHlUtGTtiNQn/ruVAMJT1Cv20IrRrOT4vBCGzONLrqxwnw7wV/fdRg8QE1ERJvtR88fxcXbBm3HWBVXq3Lkip1DtiNQn1Da4BPfOAqIlVHfgECiDBwpUC3waYOIaD24EEE2KG0QJlxcpN4YLPqIlYIGIKWA0gZCCFy9Z8R2NCIiIlojIQR++vJttmPk2ndP1HH/sWVsGSyhEaYIXIlqwcP/92/OgSO58YSIiDaXzMk9g+w05Ug7SsHDq9QL7TCFNgbaCGgYCGOgtcYFW6r4sQvGbccjIiKiNZqoFTBRK9iOQX0iURr/eGAGgIExBkYZeK7AUpsnqImI1iPwJEo+l+6I8qYVpnAciVQpwABKA74jECkDKGU7HhERneUqQT7qx3ykJADAvUeXoQ1PwNDmi1MDzxFwBJA++SMnMFrhAjcREVGeKG14RQz1TDtKsdSJoTUQxgb1bozji1189v4TtqMREeXS+RNVXHUOp1BQ7x1d7NiOkGvTjS58R0BpIFEpFAyUMTix3EVODrUREVGOnTtWgcjBKEc2qHNk32QNhhcAU498+r5jKPsOunEKAwEjgKGyZzsWERERnYbFdozZZmg7BvWJZjfF0YUOhBAIE4XlMMZIJcC/vWTSdjQiolwq+o7tCNSnSvzZOyODBQ9Fz0GqNTqJggBQcB2cN56PhgEREVEvsEGdI8Nl33YE6hPGGDQ6KQbLHhbbCQqugFIG1SKfMoiIiPJEG4NEcYMj9UaYKAS+g4GCi2LgIK0DWwYC27GIiHKLZxTIlpEKX7/PRGIMir6LJDUIYwNAoOBLNqeJiIhOwm5Tjtz58BxSLjBSDyRKI1EaQyUPU/UI1aKPhXaECu++IiIiyhXeQU299H++O42ldoRy4KIdpyubHrspvnuibjsaEVEubR0sYe94xXYM6jPGGNx3jK/dZ2KhGaPgSaTGIE4VJAykkHhktmk7GhER9QGO+KYNt3esAgM2qKk3akUPruMgSjVGyj4WhEDB41MGERERET09IYCFToxa0UUzVAhThcmhEp61a9h2NCKiXHKd7C8s0tlHCIHLdwzajpFri60IS50I7VhD6ZXVXCkMxmsFODloGBARUb7loTkNsEGdK4Mljvim3ki1htIaUjgQMNDGoOg4eGCqjuEyxzwREa3HzuESHJmPApGIaD2MAVphivFqgIIj0eoqbKsVUA74tpOIiIj6RytMEXgOWpFCrA2EEAg8F7WCZzsaERFRZnClIEeOL3egeYCaesAYAyEkAldCSoFWrFDwJO58aA5DbFATEa3L//vMbShxQYJ6bKYRYqoe2o5BfSLRGs1QYaIKCCkAAYxUucmWiIgoT4wx+NS9JxArbTtKbn33RB0lz8F8O8ZSO0GSanSiFPceXbYdjYiI+kDgSuybrNmOsSo2qHNk73jVdgTqE3GqEKcGBU/CQMAYA+lIXHvxFuzbOmA7HhEREa0R76CmXpocCPC+Ox7GaCXAo7NtSAgsd1J87dC87WhERLk0OVDA7lHeQU29JYTAs3YN2Y6Ra64UgAGcuRZ8V6DgSuwcKXN0OhER0UnYoCaip+gmClGaQIoSfFfCdSS6icJsM0bM3Z5EROty8dYaXEfajkF9phkmaISp7RjUJ5bbITpxioLnolhwoWGw1A4xVuUmCSKi9ahw+g5Zcu/RJSSKYxzXa64ZIVEKJ5a7aIQJYq3R6MQ8QU1ERD3BE9S04Q5MNaANi0PafPVOgjg1UFqj5Dsw2iBONbYNFXDuGHdvExGthxC8f5p6z5ECnsOfPeqN75xoQmigHSUYLPhIUo3BYoCRMsd8ExGtR+BxcyPZcfWeEdsRcq0TK+w/toxWmMB3JBwYTA4WUStwKZ6IiDbfOaNl2xHWhK+KOXJ4oQ1e/0K9ECUKjpBItEbFd7DUieFKgUdmmujEynY8IqJcumiSJ6ip95baMY4td23HoD4xtdxFCsARAt00RqQ0Hplr4jP7j9uORkSUS+ePV/Hsc0dtx6A+NM7pJ2dkvhUhjjWUBpQBHEdCa4Phsg8puXmUiIg2V14OybBBnSNXnTMMHqCmXtDG4P88MI04NXAcQGmgFDhoRimU5g8hERFRXhQ8B4NFnl6l3nCEQJIqeK6DTpjCdwSKnoufvmyb7WhERLnku9zcSJRHidKIlYYyBkoZBAUHxcDFYIl1ORER0fewQZ0jx5dCKHaoqQe0MXCkRKoNpBDQ2qDouXj2uSPYMZSP8RBEREQEjFQCjFQC2zGoT3z+/hMo+h6qRQeJBjzXwdahAga4GEtERJQrn9s/hVac2o6RWwutGA9O1bHQDNEMU8Ra48BUA7d966jtaERE1AcqvovrLp20HWNVbFDnyDO2D9iOQH3kL+Vj0NoAkDAwEBB4fL6DhVZiOxoRUS5dspUjvqn3Zhohpuuh7RjUJ44vd1EMBGbqEaTQcKXA8eUQn7t/ynY0IqJc2jVSwsVbuRZEvZeHRe0se2S2icCVOLzQxuMLHQxVA/zo+WP4mSu2245GRESUGWxQE9FTxKlCmCiUfAfaaDhSIlYadz0yj3PGKrbjERHl0oUTFTaoqecGih58/txRjxR8iZLrYrYZIU6AWtFFGCk8by/vTyUiWo+AI77JAmMMHplt2Y6Ra4vtGEprxCqFhoEnBDwpECbKdjQiIuoDBc+xHWFN2KDOkUNzLRiO+KYe6MQKSq/cI9iOVu4PTFKNK3cP4cf3bbEdj4iIiNYoSjSaIcczUo9owHMkEqMRKY2i58JxBGoFvu0kIiLKCyEEtg0VbcfItW2DRXz7yCI6kYIQEr4rMdOK8Nh8G1II2/GIiOgsd/5EBSIHrzdcKciRLbUC2J6mXmh0YlQDCW0MOrFCpeCi3k7Q7KZ44ETDdjwiolw6nyeoyYJYabQiNqipN0KlIASQJAaOAKQUaIYp9h+r245GRJRLY9UAWwfZKKTeO7zQQZRq2zFyK1EKhxe7mG3ESBKNdpxgph4iTFQuGgZERJRvD043sW+yZjvGqtigzpE7Dswg1WxR0+Zrxyk8R0ID6CYKQ6UAqYlRKbgYLvu24xER5RKXIcgG35WoFPIx2onyT0KgHDgIEwUpBIQRqAYSXIclIiLKlzwsamdZK0oxUZvHTL0L3wOGSwXsHqvgGdt4pzwREW2+vByQYYM6R5597jBPUFNPdGOFYwttNKIUaWrgwEBpjVQbxNxBS0S0PuzQkAXLnQhHFrq2Y1CfaEYpKoGLWBnUOylcabBtuArNTbZEROvCa97IlgNTDZ6gPgONMMHUcojFTowwBcI4xXeOLcOT3LhHRESbb9+WKi7amv1NUWxQ58j+ow2kmsUhbT6tDZa7CQLXQao1wlTDlw72jlWwc6RkOx4RERGtUTfRqIeJ7RjUJ4ZKPjpJCs8IRGkKx3exY6iIy3cO2Y5GREREp4EnqM/MTCPE3QMFHF5oIXAlRmsBtg2W8PJnbuOIbyIioiewQZ0j11w0YTsC9YlEafyfB2YQOAZSCnRjhYIvUQr4lEFERJQnu0fK2FIr2I5BfeLwQhv1dowT9RDtWKHkOzAGuO/osu1oRES5xDuoifIpTBQAIEkNJAQ86cB1BJvTREREJ2G3iYieIlUG2hh04hS+I9GOU4xWfDw638RsM7Idj4goly7ZWsvNHTB09qh3E0zXQ9sxqE/UuwmGyx6OLXegtEHRd1EtuLhsx6DtaEREREQ9040VAIFYa0hhUPAkXIfNaSIiopOxQZ0jx5e7vL+NeqITKzgCWO4mKPoOFjsJPM/BruEydgyXbccjIiKiNTJmZdMZUS+MlgNIAbRCBWOAsu+i6PMtJxERUd7w/vMz0wxTGK2htYF0BBxHwJeO7VhERESZwtWCHGmHCXgDNfVCnGoMljwcr3dRCzwkKkLgCoxWOCKUiIgoT6QQkBwlSD2yY7iER+daiFMNRwKOI7DQjjnim4honTjim2wwxmD/sbrtGLnWCBMYrKyvOQA8KeF7rMmJiIhOxgZ1jix0Ymh2qKlHfNeBSjVEwQAG8B0HRZ+7PYmIiPJkvFbAOO+gph6RQuDrj85DC4Oi70AYDUeAI76JiIhyRAjB1+4z9MhsE998bBEKQOA6cCVQcLmmRkREdDI2qHPkgokqOGGHeiFKNW6/O4KQAkobSAFIKfCtxxd5fyoR0TrxDmqyYaYRYop3UFOPxKlCI1QQxsBzJDRWro7hCWoiovXhCWqifOpEChoGMAICgCMlygGX4YmIiE7GV8YceXi2xTuoqSca3RRLnQSeBDqxhuuujCPaNVLGWDWwHY+IiIjWSGmDVHEED/XGYjvBQjtEGGukxmCxneLQXBt7xyu2oxER5VLBlQAb1ES5000UtDJQWkFKF44UmK6H+Mx9x21HIyKiPvCTz5iElNk/JMMGdY60oxSKDWrqASmBWsHFcieGIzUEHASeRMV3uEmCiGidpOSdY9R7niNR4hUd1CM7h4uoFTxoBaRaY7waIPAdXLp90HY0IqJc4jVbZMsDJ+qIUm5yXK/7T9Qx1eiiGyt4jsR8O8L+43VcMFHlVC0iItp0e0bLuHjboO0Yq2KDOkdedOGE7QjUR77+6DwOTDcwVPKglYIjBe47XkfZ59MGEdF67JvkYgT13lDJQ7XA127qjRPLXZR8F3PNEK4UcATgS8l70ImIiHLEGINUGzjcYLtuWweKeNR34LsStYKHbYMlLHcSvPXf7oPr8j0hERERwAY1ET2NVGmUXA9RouFCwMCg5LsrRfUQx4sREa0HF3jIhuVugvlWZDsG9Ykk1SgHDlJt4DsrbzWjJMV3jtctJyMiyqfRSoAtA9zkQ70lhODd52coTjVCvfJ36bsODABHgM1pIiKik7BBnSPfenwRmtN1qAc6SYpCIKCNQaI1hFhpUp+od1HwWEwTEa1HJXDhsEdNPTZaCTBaCWzHoD4x34wghESqDaQwSJRGM9JodBPb0YiIcqnMEd9kyXeOc8T3mYhThanlDuJUoxMlmKl30QhT3Ht02XY0IiLqA4ErsW+yZjvGqtigzpHBkgde/0u9MAgPi60QriMRKo2iJ5GkGtsHCtg+VLIdj4gol3iAmojOdu0oXZm9owEDoBkmgJQYKHm2oxER5VKBDWqy5McuGLcdIdfqnQSf+vYJBG6I4WqALQNFtGONy3cM2o5GRESUGWxQ58je8artCNRHvvHYAnwp0IkUxisBljoxDi92oWwHIyLKqQsmqnB5hJqIzmLL3RhSChgAqdaYa6W4KAe7tomIskoK1o5EeZRojVhpGGPgSQkpBBwOJCQiIjoFG9RE9LRm6l34noNWmMD3HXRjjThVSBSP8RMRERHRUzVDBd8R0DDoxgoawI6RMi7eOmA7GhEREVHPJEojTlMIIeA5AqlS8NmhJiIiOkVmGtRpmuIrX/kKDh06hFe+8pWoVqs4ceIEarUaKpWK7XiZoDjfm3roRD1EwRXoSEBoA8BgoRNjG0d8ExGti+HL+KZgDUmUHfVuDE9KCAM0QoXtQwUstiPet0hEtE7j1QBbB4u2Y5x1WD/SZuvGCqkyEALwHIk4VShxZD8REdEpMtGgPnz4MF7ykpfgyJEjiKIIL37xi1GtVvGe97wHURTh1ltvtR0xEx6cbnBxm3pCaYPjyx0UXQnXcRA98YM314xweKFtOR0RUT5dNFmFB+6a30isIYmypRmmgAC0NkhThaLvYLgU8L5FIiLKDNaP1AuNboJEGUgI+K5ArAzKgWc7FhERUaZkokH9pje9CVdeeSXuu+8+jIyMPPn4y172Mtx4440Wk2ULR+NRLy13UmwfLMCRKaQBokThqt3D+NELxm1HIyIiAsAakihrWmECpYFuolEKXLTCFFsGClhoRbajERHlUsFzUA4ysXR31mD9uDZ87T4zs60QygBCAp50ECuDUZ+blYmIiE6WiSr3zjvvxF133QXf9095fPfu3Th+/LilVET9K1UaShloAMIAge+gHSs8MttEN1G24xER5dILLxhH4HGs20ZiDbm67x6v44Gphu0Y1CeaYYpWlCDRGiOBD62BIwttDBR5YoiIaD22DRWxd7xqO8ZZhfXj2uw/VkestO0YuXV8MUSrG0MZheUwgUo1olTjtm8dhbAdjoiIznoXb63hohwceM1Eg1prDaWe2vQ6duwYqlUW4t9zfKkDTvimXmg9MZ4xjBWEEBgu+Xh4tonlToLzt2TiaYOIKHckVyI2HGvI1e2dqGDHSMl2DOoT9x1dwl/830PwHIFOrJC6QD1MeOciEdE6+Q4LyI3G+nFttgwUEKVsUK/XfDOCAuBKF64EYm1gjMFkrQDBN4ZERLTJhMjHa00mOk0/8RM/gVtuuQUf/OAHAaz85bVaLbzjHe/AddddZzlddjy20IHSLA5p88WpRsV3ECUaUhj4rkCSaoxWfDx/76jteEREuZSX4jBPWEOurhMpLHZi2zGoTxxe6GKuGWOw6CPVGgYGZd+D53CkJRHRejiSz58bjfXj2uybrNmOkGsLrRAFx4HvCWwbLGK2GaESeJgcLHLjMhERbbpzRsu2I6xJJhrUv//7v49rr70WF110EcIwxCtf+Uo8/PDDGB0dxcc+9jHb8TJjoOBBGZ6hps0XJwoFz3linLeA5ziQUmBquYs7H563HY+IKJeevWcEvstFxo3EGnJ1i+0Ij853bMegPrHQjuC5DkqBC6UUtBFohQmaUWo7GhFRLlWLmVi2O6uwfqReaMcKqdEoSg+OkNAaKPgS546VuXGZiIjoCZmodLdv34777rsPn/jEJ3Dfffeh1Wrhda97Ha6//noUi0Xb8TLj8GIbSrNBTZuvE6VwHQknVYiUgSMFXClgAAQcMUZEtD7cZLbhWEOurlb0sHWwYDsG9YnDi22UfAkYAwGJwBOYaUbYOcR/j0RE61EtZGLZ7qzC+pF6oR0paGMgBeA4AsoARc9hc5qIiOgkmal0XdfF9ddfj+uvv952lMx61q4haDaoqQcWWiH+fv8JSCmR6hSVYOXeQFdKxIo/g0RElB2sIX+4Zphiuh7ajkF9InAljBEoFSS68cqibDNMsdzlCWoiovXwXMd2hLMS68cfzhiD//H1I0gUrxlcr+8cXUKYaISJwlwzwnwzxEjJxb1Hl21HIyKiPhC4MhfXdWSiQf3ud78bExMT+Pf//t+f8viHP/xhzM3N4eabb7aULFu+e7wBzdNX1AOx0ii4DqJEAQIoei5SLXD1nmG84Pwx2/GIiIgAsIZciz1jFewZq9iOQX3iW4cX8YlvHMVgqYDBosRyGCNOEtuxiIiInsT6cXVCCAwXPcSaDer12jJQhADgOhIwgNIGBY8bToiIiE6WiQb1Bz7wAfzt3/7tUx6/+OKL8YpXvILF4RN2jpTYoKaeSFKDatHDQjuEKyUSpZEqhYVOjPuP1W3HIyLKpX2T1ZUFCtowrCFXZ4zhdHnqmflGCAkg1UAlkHAjgURKXLZj0HY0IiIiAKwf12rf1uyfusqyoivx2funMFTyMDlUwHwrwt6JGi5nTURERPSkTDSop6enMTk5+ZTHx8bGMDU1ZSFRNu0cLtmOQH3CGIOiK5EooOhJpNogUQbnjFZwMd+kEBGti5S8b2yjsYZc3cGZJg5ON23HoD4x34qhoVEOJKQjYQzgO3zuIyKi7GD9uDZRqhGlPEG9XlP1EHGq0A4TTC93Ue8mOL7c5YhvIiLqCY74Pg07duzAP//zP+Occ8455fF//ud/xtatW9f1Nf/kT/4E733vezE9PY3LLrsM73vf+3DVVVf9wI9fXl7Gb/3Wb+H222/H4uIidu3ahVtuuQXXXXfdur7/ZphphOAV1NQLqdKIlIaAQeBJJFojURo7hkpssBARUWZsdA15NtaP541Xcc5o2XYM6hPHFjv4xDcOo+i6gAC+t6z92Hzbai4iorwaKHoYLvu2Y5xVuAa5NnlY1M6yowttBK7EcLWAiVoR860YF26p8gQ1ERHRSTLRoL7xxhvxH//jf0SSJHjRi14EALjjjjvw5je/Gb/xG79x2l/vE5/4BG666SbceuutuPrqq3HLLbfg2muvxcGDBzE+Pv6Uj4/jGC9+8YsxPj6OT37yk9i2bRsOHz6MwcHBM/1P21CDJR9gg5p6INEaGgaOEPAcibLnQhvDN8ZERJQpG1lDnq31oyMFHMn77qg3jix24LsCRgAOBFJlIKTAcIk1JBHRegQer4fZaFyDpF5YascwEPClgJQrtdGJ5S6++sgcBHjwg4iINtdzzx2BENl/vclEg/o3f/M3sbCwgF/5lV9BHMcAgEKhgJtvvhlvfetbT/vr/cEf/AFuvPFG3HDDDQCAW2+9FZ/97Gfx4Q9/GG95y1ue8vEf/vCHsbi4iLvuugue5wEAdu/evf7/oE0yUPRsR6A+0Y0V2qGC6wC+K+F7EtqIXDypERFR/9jIGvJsrR9nGiGm6qHtGNQnHptvo+h7MAYwAmhHKXaNlDFQ4vsYIiLKBq5BUi8sdRNIAbjOE5tMDDDbjPDcvaNwOZmQiIgIQEYa1EIIvOc978Hb3vY2HDhwAMViEeeddx6CIDjtrxXHMe6+++5TikopJa655hp87Wtfe9rP+fSnP43nPOc5+NVf/VV86lOfwtjYGF75ylfi5ptvhuM8/YmTKIoQRdGTv280Gqed9XTd9cg8FGd8Uw90E4VGmEBAwHck2nEKl4eviIgoYzaqhjyb68eJWgETtcKmfx8iAHhsrglPAgIGqTKIUo2RCpvTRESUHXlbg7RRP9KZW+4mEABcx0GUKnhSwhHAeeMVHv4gIiJ6QiYa1N9TqVTwIz/yI2f0Nebn56GUwsTExCmPT0xM4MEHH3zaz3n00Ufxj//4j7j++uvxuc99Do888gh+5Vd+BUmS4B3veMfTfs673/1uvPOd7zyjrKfLdyUb1NQbwiBJNYwRGK/6mF0KUfIkvv7oAgKPnWoiovW4ZGvtX3fQ04Y60xrybK4feYKaemmuFcKREgYC3UTBkwLlIFNvOYmIiADkZw3SRv2otcYH/unRnn7Ps83RhTYcBwhcgTDR8DwB15VsThMREZ0kE6sF7XYbv/u7v4s77rgDs7Oz0Fqf8uePPrq5RZHWGuPj4/jgBz8Ix3HwrGc9C8ePH8d73/veH7jA+Na3vhU33XTTk79vNBrYsWPHpubsJgpKsUFNm0/plVMvUgBbh0r42qF5VAseji93sHO4bDseEVEuGcPX8I1ms4bMS/3IE9TUS4dmmwhcCWUMulECxxUoeRKdOLUdjYgol1wp4bvc4LiR8rYGaaN+lFLi0h0Dm/o9znblwMXHvtGB70jEiYIrJTzJf8tEREQny0SD+pd+6ZfwT//0T3jVq16FycnJM9pNNjo6CsdxMDMzc8rjMzMz2LJly9N+zuTkJDzPO2WUzr59+zA9PY04juH7/lM+JwiCdY3/ORN7Rsvg0jb1QidKESqFoutg+2ARS+0EtYKHgufAdbjbk4iIsmGjasizuX4k6qWFVgxXSmitIZ+YGFHvpnh0rmU5GRFRPo1WAmwZKNqOcVbJ2xqkrfrxeXvHev49zyZhouBIAdcRiFIDVwh4LtfTiIiITpaJBvU//MM/4LOf/Sye97znnfHX8n0fz3rWs3DHHXfgZ37mZwCs7E6844478MY3vvFpP+d5z3se/vZv/3ZlIeWJ3WwPPfQQJicnn3Zx0ZbHFzpIv29nJ9FmMAZIUoOCa1D0HcSphoZGO1RIeYqfiIgyYqNqyLO5fiTqpaVuDNcBwkSjHLgw9ZUTRHOt2HY0IqJcClwHW3iQdUNxDZJ6YakdQ0oJ35HoxAlcR8ARAq2IU2WIiGjzVXJy1VYmUg4NDWF4eHjDvt5NN92E17zmNbjyyitx1VVX4ZZbbkG73cYNN9wAAHj1q1+Nbdu24d3vfjcA4A1veAPe//73401vehP+w3/4D3j44YfxO7/zO/i1X/u1Dcu0EXaPljkelHoiVQqAgTFAlGhAGISJhu8JDJY82/GIiHJJcqTbhtvIGvJsrR+Jemm5k8D3JMJEI3AltFnZZDtU4eQAIqL1KHnO6h9Ep4VrkNQLC+0IEgKuI6G0gedI1LsxTix3IXmQmoiINtm5Y5UzmhLTK5loUP+3//bf8Pa3vx1/9Vd/hVKpdMZf7+d//ucxNzeHt7/97Ziensbll1+Oz3/+85iYmAAAHDly5JRF4h07duALX/gCfv3Xfx2XXnoptm3bhje96U24+eabzzjLRpprhOD5aeqF5U6EVBu4UmCuFcGVElGisWe0gr3jVdvxiIiIAGxsDXm21o8nlrqYanRtx6A+sdCMEHgCJc+FMAap1lhoR7hggvUjEdF6TNQKtiOcdbgGuTaPzbdtR8i1mXoMRwJSAMoYSAH4jsR54/loGBAREfWCMBk4knvFFVfg0KFDMMZg9+7d8LxTT2jec889lpKtXaPRwMDAAOr1Omq12qZ8j4994zDHK1NPJKnG//j64xgpB3j2uSP46sPzEAB+/99djnPGKrbjERHR0+hFLZI1ea8he/H/7MBUHQ9N8/5f6o17Di9iuhlhohYgSTXuPryE3aNlfPDVP2I7GhERPQ3Wj6wfn44xBh/7xhHEisdk1uvbh5fw8HQTl+0awomlDmpFFyOVIn7mim22oxERUR8IXIl9k5tX221UPZKJE9Tfu6eFfrgX75uwHYH6xHQjxMe+eRieKzG11IGAQdFzwO0RRESUJawhV7dvcgD7Jnl5JfXG0aUOlrsxHACdVKPgSigNNMPEdjQiolzyHIkCx3xvKNaPqxNC4IqdQ4hSNqjX6/hiB4/OteAKrIz4lhKew5PTREREJ8tEg/od73iH7Qi58L+/fQKpZnFIm08bg1rBQ+BINCIFpYFSwcNMvYvFdmw7HhFRLl22fQCeywXGjcQacnXfOV7HA1MN2zGoTxxbaqPiewAE2nGCUiDRjhPMNiPb0YiIcmmw6LFBvcFYP67OGIOD002eoD4DC60YUgAGgDIrTX/Pkat+HhERUT/JRIMaAJaXl/HJT34Shw4dwm/+5m9ieHgY99xzDyYmJrBtG8efAMCN/2aP7QjUJ7Q2ODjVRCtKEcYpolQhcCUcx0HR55tjIqL14F1jm4M15A83Vg1wvub9v9QbSht85/gyQqUAI6CMwFDBxbm8IoaIiDKE9eMPJ4TAlbuHbMfItaV2jKUwxuRAAfOtCNuGitg1WsHlOwZtRyMiIsqMTDSo9+/fj2uuuQYDAwN4/PHHceONN2J4eBi33347jhw5go9+9KO2I2bCI7NNaM5Yph5IUo1i4KAVpVDKIFYGniMRpQq1YiaeNoiIiFhDrsFErYCJWsF2DOoTSaqx/8giWrFCwXeQNgzGawXce3TZdjQiolwarwbYOli0HeOswvpxbeY4/eSMeI6AKwQMBAABrYACT1ATERGdIhOdpptuugmvfe1r8Xu/93uoVv/1hMd1112HV77ylRaTZcu2wZLtCNQnOlGKkZKP5U6MbmygtIJSGgMFD7tHyrbjERHlkiN5gnqjsYYkypZ2lEBBIFEGWhv4rsSu4RL2jPJ9DBHRenAk8MZj/bg2u7j2c0bmmyG+JSVgAAEDBYNiwH/PREREJ8tEg/qb3/wmPvCBDzzl8W3btmF6etpComx6fKENbXiEmjZfJ1LwHAclz8WCjmE0ECmNsWrA+6+IiCgzWEOubq4ZYbYZ2o5BfWKxE6ObKASOxEInQiWQeGCqgc/eP2U7GhFRLp03XsWVu4dtxzirsH5cnTEGdx2aR5zyDur1eny+hWaUYrrRxWInRVAPMbUccaoMERH1ROBK7Jus2Y6xqkw0qIMgQKPReMrjDz30EMbGxiwkyqa947y7jXqjHaV4YGoZcy0HcaIgBBAnGoMlz3Y0IiKiJ7GGXN1w2efrN/WMUgaf3X8Cw6UAcaoxUPDRTTWev5f/HomI1qNayMSy3VmF9ePqhBDQxiBKlO0ouaWUQSdSWGhGaEUJFtsSs80I3kzTdjQiIuoDFd9lg3qtXvrSl+Jd73oXbrvtNgArhdCRI0dw88034+Uvf7nldNnx9/dNIdXcvUibL1EKS60EgSuh8L03J8BsI4SUHElERLQeWweLHPO9wVhDrs6RAg74c0e9MdsI4TsOOrGC6wjESmNLrYQdwxzxTURE2cD6cW1eetk22xFy7c6Dc5hvRZgcLkJIgcmBAp69ZxhFPxNL8UREdJa7bPuA7QhrkolXxd///d/Hz/3cz2F8fBzdbhc/+qM/iunpaTznOc/Bb//2b9uOlxkveyaLQ+qNMEnxp185hMCVkFjZOWugMd+OeQc1EdE6sUW48VhDrm62GWKmHtmOQX3i4bkmagUHc60EwgiEscZo1bcdi4iI6EmsH6kX6lEMz3URxRqeIwAhECYKV58zAslNy0RERAAy0qAeGBjAF7/4RXz1q1/F/v370Wq18MxnPhPXXHON7WiZ8uhcC5pXUFMPJEojVSsnXzxHQhvAlRLfPryEQ3Nt2/GIiHLppZdOosAd8xuKNeTqHCHgu5x+Qr0xUg7w6FwLBU+i0U1QK7gYKHLEPBERZQfrx9UZY/DFB2YQK05xXK9vH15CoxOh0QWaUQogxmNzbQyV67ajERFRH+Ad1Ovw/Oc/H89//vNtx8is8VoBxrBDTb0xUPQw14hQ9CS0MQAErtw9gst3DtqORkREdArWkD9YpeCyQU09s9wp4csHZ1ANfDyedDBS8TFcDmzHIiIiegrWjz+YEALPOXfEdoxcMwZ44EQd7ShFMXAxVPRx3pYqLt8xaDsaERFRZlhrUP/xH//xmj/2137t1zYxSX5UgkztJ6CznDECzXClkAYEyr7EiXoXOGo7GRFRPl2ytQbXYaPwTLGGPD2B6yBwHdsxqE80uylcKSEAaGMgBfDYfBuf3X/CdjQiolzaPVLGxdvycYdglrF+PH3VAiegnAmlNRwpoIyBJwWEMEiUxkwjtB2NiIj6wHg1gBDZv1LCWsfzD//wD0/5/dzcHDqdDgYHBwEAy8vLKJVKGB8fZ3FIZEGiDdqxQuAIOFIgcFdGNI5XeQqGiGg9ZA4KwzxgDXl6mmGCZpjajkF94qHZJgaLHuqdGL4jkWqgm6R4wfljtqMREeWSz82NG4L14+k7sdy1HSHX5lsxXCmgtIHvSDhSYrYR4oItNTh8X0hERATAYoP6sccee/LXf/u3f4s//dM/xV/8xV/gggsuAAAcPHgQN954I17/+tfbipg5C60IHPBNvZAqAxhAaQPPFXAdiWrBQTVw4DospImIyB7WkKcnTBQaYWI7BvUJKQQ8B4i1QeBJdBOF8WoBNZ7CIiIii1g/nh5jDA5ON2zHyDUpBHzHgVIajifhOQ4SZeA7Ihcn2oiIiHohEzOj3/a2t+GTn/zkk4UhAFxwwQX4wz/8Q/zcz/0crr/+eovpsiNMNbRmi5p6o1Z0oI1GnACOEKiWfLRjhU6kbEcjIsolU7ad4OzDGnJ1geeg7Gei5Kc+MFYJ8Ph8CwXXgSckwkTBdyQen2/bjkZElEu1oofhsm87xlmF9ePqhBB44YUTtmPk2sjRZfzzI/PQABwh4UhgsROjG2uwP01ERJsuJ2uQmVitmpqaQpo+dfSgUgozMzMWEmXTtsGi7QjURwwEDATCOIXnCOwcLmKkEmCoxDfHRETrwXWIjccacnW1gsfTq9Qz3z6yhFakMFD0oEwXgSsx3Qix3OEpfiKi9fAcAbBBvaFYP67NgakGolTbjpFb3z1Rx4nlLuaaEbQ2iLVGogwW2hFPUBMR0aZrRSn2TdZsx1hVJhrUP/7jP47Xv/71+NCHPoRnPvOZAIC7774bb3jDG3DNNddYTpcd860IhgeoqUeW2xFgVu6i9hyByVoJj8+3sdiObEcjIsqlZ+0cgpSO7RhnFdaQq+vEKdqcfkI9MtcIIYWAgUGsNCoFH4fn23holmNCiYjWIzVlbBsq2Y5xVmH9uDpjDJvTZ2i45GP7cBGL7RiTQ0UMlwKEicIVO4dsRyMiIsqMTDSoP/zhD+M1r3kNrrzySnjeygmPNE1x7bXX4kMf+pDldNkRp5p3UFNPaG1wZKELKQygDXxnZRxRteBipBTYjkdElEtSStsRzjqsIVe31IkxXefmMuqNdqJQChx04hSeBLpxikoQ4EfPG7MdjYgolwoeNzduNNaPqxNCIHAlm9RnQEqB40tdzLdDlAsOotigHaX40gM8pU9ERJtv62ABF20dsB1jVZloUI+NjeFzn/scHnroITz44IMAgAsvvBDnn3++5WTZspUjvqlHjDFoRilcsXKC2vcchKnG3vEKtnP3NhERZQRryNUNlXz4Dhe3qTcOD5UwvdzFUidFOfAw1Yhw0WQNTZ7iJyJaF8fhBseNxvpxbfIwFjTLHp1r4dBsE3PNEBO1IkYqPo4udHDJtgFITvgmIqJNNlbNxyHDTDSov+f8889nQUiUAaleOasfK0AZg5Gii06s4EiBKOUCIxHReviO5H1jm4Q15A9W8l2U/EyV/HQWEwA0gDBWqPgCBsCWgQL2jlcsJyMiIjoV60faTGGioM1KbaS0hhSAAVAOuHGUiIg2X17WHzOxWqWUwl/+5V/ijjvuwOzsLLQ+dYTMP/7jP1pKRtSfklTDlUAjUkiUwWStiBPLHZR8B+PVgu14RES5dP5EBa6TjwIxL1hDrm6mEWKmEdqOQX1ioR2jE6VwHYGFToLxcoCCz4VYIiLKDtaPqzPG4NBcy3aMXJttRnCkgBECxgASK6eqb/vWUUjwPSEREW2uVz9nJ5wcTNPLRIP6TW96E/7yL/8SP/mTP4lLLrkkN939XntwugGteQs1bb5mmMJ1JFIDJFpj10gJs40IY3sC7BrhiG8iovVwOMttw7GGXJ0xgGL9SD3yjG0DuOvQAowBurHGuWMBPEcgTDiBh4hoPRwp4HHM94Zi/bg6IQS2DPCawTNRDlw8NNOEMgbaABIGUaLw0ksn4bnZbxgQEVG+5aE5DWSkQf3xj38ct912G6677jrbUTKtGytowwVG2nyuIzBY8jDfipGkBlsGCnh4pokji234Lt8cExGtx56xCniAemOxhlzdloECtgxw+gn1xsMzTfiOwFQ7RiVw4EmB+WaMxxfatqMREeXScNnnFLMNxvpxbf7vwTm04tR2jNxqhQnufnwRJ5ZDdGOFuUaA2VaMLz80bzsaERH1gYrv4rpLJ23HWFUmGtS+72Pv3r22Y2TeFTuHbEegPnL7PcdQ9AQ0gIGCBykdVAsezpuo2o5GREQEgDXkWiy0Iiy0Y9sxqE8cnG6gUnBRDxPsGK7B9yRKgYsLt9RsRyMiIgLA+nGt8rConWXT9RBLrQgF18FINcA5oyUstCP8uyt32I5GRESUGZloUP/Gb/wG/uiP/gjvf//7OVqHKAOUNujECkXPgVYG1aKHgicwtRzivqPLtuMREeXSxVtrcDmicUOxhlxdreih5Gei5Kc+0IlTRFEKAQFHSniu5IhvIqIzwBHfG4/1I/VCohRipeG6Eo4QiJXhe0EiIqLvk4nVqq9+9av48pe/jH/4h3/AxRdfDM/zTvnz22+/3VIyov6Uao12lKLkOZACcIWA5zi4YucQ9k5UbMcjIiICwBpyLTxHwsvH1UN0FohTjelmhFrRBWAAI7DQ4ohvIqL14ojvjcf6kXqhG2uk2sAVgBArNZLHK/OIiIhOkYkG9eDgIF72spfZjkFET0iVRjfWCBwJ35MIUwVHAr7HYpqIiLKDNSRRthxf7CBKNcqBA2UADYOi53DENxERZQbrR+qFMFVItIHrOpBCIEoUfDaoiYiITpGJBvVHPvIR2xFy4ZHZFowxtmNQH6h3E8RKwZEuAs9FM0wgIRCwmCYiogxhDbm6xXaMhVZkOwb1ia8/uoixSoBOogADaG1Q4BF+IiLKENaPqzPG4JHZlu0YuTbXjKCUgSNXRvW34hRl1kRERESnyESDGgDSNMVXvvIVHDp0CK985StRrVZx4sQJ1Go1VCocKQwArSiB1rZTUD+IUgVPCAghUPQklrsppGSDmoiIsoc15A93/7FlfOOxRdsxqE/MtSJM1AIcW+pAP7GxtuizfiQiomxh/fjDCSGwdbBoO0aueY7AHQ8AQkq4UmC5o1Ereqt/IhERUR/JRIP68OHDeMlLXoIjR44giiK8+MUvRrVaxXve8x5EUYRbb73VdsRM2DfJ0XjUG41uAteVgAAKrgNjVsZ+F9igJiKiDGENubrn7R3F1XuGbcegPvGF707hW48tQwhAOoAxQMnPxFtOIiIiAKwf1+rIE9d20PpMLXcx3QhRKziIU4WppRADJQ+3feuo7WhERNQHLtlaw0VbB2zHWFUmVgve9KY34corr8R9992HkZGRJx9/2ctehhtvvNFismz5+DeOQmmO+KbN141TGKMhIOE7EgXfQZhquA4b1ERElB2sIVfnOpKv39QzcWqQKAVHCjiQMDCoBDwtRERE2cH6cW0em2ujFae2Y+TW9HIXS50YSruIFLDYidBJNI7MtyGksB2PiIjOcmXfYYN6re68807cdddd8H3/lMd3796N48ePW0qVPc/eM8wR39QTYapwz5ElKK1RDCQ8IRErDYdFNBERZQhryNWFiUI3VrZjUJ9ohCmiVMOVDiAAAYFygfctEhFRdrB+XJtzxso8QX2GyoGL0WqAcuBhsR1joOjgRRdNQAqurRER0ebKy1WtmWhQa62h1FMXzo4dO4ZqtWohUTYdXew+eZcb0WZS2kBrjTjVGCx5SLSGKwW0ARzW0URElBGsIVcXpRrNkKdfqDcKroNYaXgSkFJCCKDosUFNRETZwfpxbXjN4JmJEoWBoottgyUMFD0cW2xj62AJz9w5ZDsaERFRZmSiQf0TP/ETuOWWW/DBD34QACCEQKvVwjve8Q5cd911ltNlx4suHLcdgfpEoxvjtm8eQZQqFLwiUm1QDlwstCIMlvzVvwARET2F5wgI7pbfUKwhVzdQ9DBQ5Ihl6g3/MYEo1Sg5Aq4UAATKvIOaiIgyhPXj2hxd7NiOkGtLnRjKGAgBOFIgUQZFPx+n2YiIiHolE6sFv//7v49rr70WF110EcIwxCtf+Uo8/PDDGB0dxcc+9jHb8TLjSwdmkPIOauqBTpRiqOTjeL2DciChtMFA0cN8K8ZcK7Idj4goly6YqMLlGIoNxRpydTONEFP10HYM6hNRapCkBo7vPvF8Z1DwuBhLRETZwfpxbUo+J6Ccia2DRThCPtmkVsag6GViGZ6IiCgzMvHKuH37dtx33334+Mc/jv3796PVauF1r3sdrr/+ehSLRdvxMqNa8KDYoKYeqAYuLtlWwyNzTZR9Z+Uuas/BRVs54omIiLKDNeTqCq6DQZ6gph4ZqwZItYYUAlIICEgUeYKaiIgyhPXj2oxUAtsRcm2pk8AYA60BRwJK89oTIiKi75eZ1QLXdfGLv/iLtmNk2nPOHbEdgfrIUjuC0oDvODAAXMnTL0RElD2sIX+4wJMYLLFBTb2xZ7SMVBukMCg6Ep4r4DusIYmIKFtYP9JmS7WGNgZGG8AIQBueSiciIvo+mWlQHzx4EO973/tw4MABAMC+ffvwxje+ERdeeKHlZET9qRVruBJI1EpBLQDcc2QJkvenEhGtyyVba3DZqNlwrCF/uILnoMDTGtQj9U4CpQyUWqkjPSlwx4EZdBJlOxoRUS6dN17BFTuHbMc467B+/OGMMdh/rG47Rq7FSkMIAQUDCEALjQqnGhEREZ0iEw3q//W//hde8YpX4Morr8RznvMcAMC//Mu/4BnPeAY+/vGP4+Uvf7nlhNlw/7E6lOGIb+qNY8sdlAMXzTDFWC2AATBeDbB9qGQ7GhEREQDWkERZ000UDFbGWEop4ToSnUTh3125w3Y0IiIiAKwf10IIAd+ViFJtO0puNTsxmt0U0/UQwgDtUOHYQhdffXjedjQiIuoDIxUf+yazf11rJhrUb37zm/HWt74V73rXu055/B3veAfe/OY3szh8ghCAA55epc2njcHh+RYqgYeuSqGNQaIMDkw1MN+KbccjIsolnqDeeKwhibKlHaeQYqWWBAwCl895RESULawf1yYPi9pZ9tB0A+MDAYZLPrYOFVAt+qgWXUwOFjiZkIiINt3ukXwcMsxEg3pqagqvfvWrn/L4L/7iL+K9732vhUTZdBGLQ+oRZQwaUYqq70IC6MQKqdLYPjSIfZMDtuMREREBYA1JlDWNbgJHChgIaAMUfQdL7RgHphq2oxER5dJI2cd4rWA7xlmF9SP1Qr2bwHUkpBSIEg1HAK4U2DNahmCDmoiICEBGGtQ/9mM/hjvvvBN79+495fGvfvWreMELXmApVfZIyQKGekNCAEYgURoDRR/LnRiuI3BkoYso5Zh5IqL14AnqjccacnUzjRBT9dB2DOoTc40IjiMBY6C1hu9IzDW7qBUy8baTiCh3OIli47F+pF5odBN4AnCEQKIMhAASpXF0sWs7GhER9YEdw8VcbIjKxErBS1/6Utx88824++678exnPxvAyv0v//N//k+8853vxKc//elTPrZfPTLbemJcHtHm0tpAipWT06NVHw9Nt1AKHGwbLuKSrTxBTURE2cAacnUTtQImePKKeqQbJXAFoGFgIAAYNLopNN/CEBGtC58+Nx7rx7U5MNXgHdRn4Dsn6ljqpAgTg1nRRSdReOBEHUNlH9x2QkREm60VJbgoB30cYYz9jqeUa3tpFkJAKbXJadan0WhgYGAA9XodtdrmjOLuxOmmfF2i79eJFH79E99GlCjc8Pxz8NVHFlDwBF773D3YMZyP+wuIiPpNL2qRrMl7DdmP/8/o7Pbpe4/h7759HJ7rYKIaYPtwEY2uwn+69gLb0YiI6Gn0Yy3C+pF64X/8y+N4dLaFUuCiE6U4stzBtsESXve8PcjBgTYiIsq57UObe4J6o+qRTJyg1po78tbisfk2+FdFvZBoDSkFhBAwxsARK+OJqhzPSEREGcIakihbFtoxpJSQAjDGoBUqjFcD27GIiIiexPqReqEVKkgJ+I6DuShEQUpIIXIzcpWIiKgXMtdtCsMQhQLHED6dC7dwZyT1RpJqDBQ8KK0x34oxUHAx2wxxbKmL2WZkOx4RUS6dO1aBI7kYsVlYQxLZt9hK4AsAAlDawHMENzgSEVFmsX6kzdKJU0ghIAUQKQXHlfAcweY0ERHRSTKxWqCUwu/8zu/g1ltvxczMDB566CHs2bMHb3vb27B792687nWvsx0xE7ioTb1Sj1OUAxfNbozFdoyC76IdKxRcia1DRdvxiIhyiS/jG481JFG2LHdiuK6EEAKtOEW14KBS8GzHIiIiehLrx9VprfHZ+6dsx8i1E8tdTNQCeK5ElBqUPAHf4e3TREREJ8tEg/q3f/u38Vd/9Vf4vd/7Pdx4441PPn7JJZfglltuYXH4hPuP1aHsXxlOfWC5E6FadDHbEFDGwFEGwggYAALssBARUTawhlzdTCPEVD20HYP6RD1MIB0BCSBKDBzHQcXPxFtOIiIiAKwf12q5k9iOkGsXTtaeOEUNJMrALTjwXTaoiYiITpaJ1YKPfvSj+OAHP4gf//Efxy//8i8/+fhll12GBx980GKybGmGCRvU1BOJAkZKPo64Dkqei9lGiHLgoJsoHFns2I5HRJRLe8crcLjHZ0OxhlzdQNFDwMUw6pGS7yJOFCKlUAxcKGVQKTq2YxERET2J9ePqpJS4cvcwopT3da+XMQa3fesoSp6DxVYERwr4roN7jy7bjkZERH0gcCX2TWb/yuBMNKiPHz+OvXv3PuVxrTWShDv2vmf7UAkGbFBTb7TCBEXPQa3gYf/xZQyXfQSug1oxE08bRES5w970xmMNuboo0ah3+XdBvSEAwGgkqcZYxUGUKggDTNW7tqMREeVSOXBR41UJG4r149rkYVE7yxKl8ZWDcxguuTg408CWaoAtgwVM1ALb0YiIqA9sqRVsR1iTTHSaLrroItx5553YtWvXKY9/8pOfxBVXXGEpVfbsHCnZjkB9ZP+xZQSeg8ATWO7EuHhLDUMlvjEmIqLsYA25usVOhMPznH5CvaGNQSc1KAQOfM9FFGvMtUKM5+TNMRFR1nCD48Zj/Ui9ECcaSilI4UFpAwigEykAgCP4L5uIiAjISIP67W9/O17zmtfg+PHj0Frj9ttvx8GDB/HRj34Uf//3f287HlHf0dogUQZCAL7rIEo0aiWXi4tERJQprCFXN1oJ4LscsUy9cXixg//znSnsrVXgOUAjSrFzuMIakoiIMoP1I/VCmCooAwghYDQgBNAIY0zXQwg2qImIaJONVfMxsSMTDeqf/umfxmc+8xm8613vQrlcxtvf/nY885nPxGc+8xm8+MUvth2PqO/ESsMYAwmB4bIHrQ2MYQFNRETZwhpydZ1YYa4Z2Y5BfSJwBQwAT0r4roP5ZohqwcVMI7QdjYgol0q+gypHfG8o1o9rc2CqwTuoz8BcI8J0PYSBQT1MsNCOAC0AA45GICKiTffgdDMX13VkokENAC94wQvwxS9+0XaMTLv/WB3K8A5q2nxKGyij4bkCg6UAiTEIU4VH51q8BZ2IaJ12j5ThSK5GbDTWkD9cmCg0Q96nSL3hSIHAk0iNgdEGrgMcW+picpAnqImI1oPvvzcH68fV5WFRO8sOzTYxVi1gx3ARlcDDeLUIV0pcsWvIdjQiIqLMyEyDmlb3jO0DtiNQH5la7iBwHQgAWmkYDXRjhR28C52IaF3YmyYbBos+DNfBqEe2DBTwxQdmoLVBmKQYKHqQUmByoGg7GhEREZ0GnqA+M4/OtTDd6EJAox2nmG1EKPoO7j26bDsaERH1gcCVudhsZq1BPTQ0tOY7NxYXFzc5DRF9v26iUfAkwkTBcxyUgpXxjLUix4sREa1HxXfB68bOHGvI0zNQ8jBQ4ms39cZ8K4IxK9N4hJAo+i7q3QRHFzu2oxER5VK14GKw5NuOkXusH09fHha1s0xpjYlqgImBEqqFOkYrAcYqHi7fMWg7GhERUWZYa1DfcsstT/56YWEB//2//3dce+21eM5zngMA+NrXvoYvfOELeNvb3mYpIVF/60QpCp6LuXaEYuCgWnDQilKcWOrajkZElEsTlQC+79iOkXusIYmya7EVQQBoRSnOGStDaYOZeohLtnKRm4hoPTxH2o5wVmD9SL0WJhpSCMRKwYGA0hqBz0GmREREJxPG2L/U+OUvfzle+MIX4o1vfOMpj7///e/Hl770Jfzd3/2dnWCnodFoYGBgAPV6HbXa5izApIqjdah3PvhPj6Ba9OBIia88OIPnnTcGAWCozN3bRETr8eJ9W1DYxAZ1L2qRrMl7DdmP/8/o7PaNRxfw4TsPAY7EC88fRSc2qBU9vPxZ221HIyKip9GPtQjrx7XhGuSZ+crBOdx9eBEFV+Ibjy9h92gJl2wdwC9cvct2NCIiojO2UfVIJrZufeELX8B73vOepzz+kpe8BG95y1ssJMqmgzNN2N9OQP1AaYMT9S6uHCrh6EIb1aKHVpji/IkKLtvBiyyJiNbDczjfe6OxhiTKlqVujMQANVdCConANfBcPvcREVF2sH5cnTEGD043bcfItURpuFIgjFN4jkCiNCqFTCzDExERZUYmXhlHRkbwqU99Cr/xG79xyuOf+tSnMDIyYilV9ly8dcB2BOojH7rzUVQLLpa7CtWCi1hpHJpro1LgPZZEROsxWByCwwnfG4o1JFG2NLopYqVQKRSQGsDzBDzJ8bRERJQdrB9XJ4TA5EDBdoxcC5MUUgi0YgXfldAGqASZWIYnIiLKjEy8Mr7zne/EL/3SL+ErX/kKrr76agDA17/+dXz+85/Hn//5n1tOR9SflDEIXAetMMGWgQJggN2jZWwfKtmORkSUSw7vENxwrCGJsmWhGcF3JAQEtFLwXBcFj899RESUHawf16YTK9sRcq0aeHAdgU6s4DsSMECRd1ATERGdIhOvjK997Wuxb98+/PEf/zFuv/12AMC+ffvw1a9+9clikYh6R2sDCaAdJQjTFEMlH8eXu7hgSxU7htmgJiKibGANubpWlKIVprZjUJ84stRBtehBCCBRBq4rUfQ4OoKIiLKD9ePacO3nzMy3IriOXKmHpIQUQOBy0x4REdHJMtGgBoCrr74af/M3f2M7BhFh5fS0IwUanQSJNhgoeXh0oY3hcmA7GhER0SlYQ/5wc80Ij821bcegPqEMUHAdABKJNnCE4GkhIiLKHNaPtNnCWEEKgSjVqBZcCAFu2iMiIvo+XC0goqdIlYHWBloIJKlBJXCgjWExTURElDO7R0rYPcITMNQb3z2xhJl6DM8FokRDCoHpehfGdjAiopwarwbYOli0HYOITlOYKHhSIlUajjQwkChwTY2IiOgUbFAT0VOkWkMZwJMSUapQCTwICPgcR0RERJQrUaoRJrxDkHqjESq4DuBLCQODTqwwVPZx+Y5B29GIiIiIeqabKkgpkGgNV0ikABvURERE34cN6hz51LePI9E8f0C9kSqNwaKHbqzgugJSAN8+sgQhhO1oRES5dMnWGlyHG32ot757vI77jtVtx6A+IQQgpYTnOtAGmGmEuHLXkO1YREREdJoOTDUQpdp2jNx64EQDx5Y6WO4kKHkutDF4eLaB6UZoOxoREfWBwJXYN1mzHWNVbFDnyP9z2VbbEahPpErjc/tPwPckEqWRpBpCAFsHi5ioFWzHIyIiojW6ZPsAzt9StR2D+sRj823873uOIXAF2rGCBDBcCWzHIiIiIuqpMEnhSkAZQErAGIGAUwmJiIhOwQZ1jjiSJ1epN8Lkeyf1DRxHYqETw5UC1QKfMoiIiPIkcB0ELscJUu9oszLCcroRYWy0DI+TI4iIiHInD6eusmxquYPAFSh7EuO1AmAMdg1X0AgT29GIiKgP7B2v2I6wJta6TT/7sz+75o+9/fbbNzFJfnz63uPghG/qhTBVgADqnQRFz8HRhQ5cKXD/sToC3plDRLQuHPG9MVhDEmVXmCgoreEIgShRKPkOPIebbImIyC7Wj6fHGIPvHG/YjpFrs40IrhTQMHCkgBQSs80Iu0dLkLw6j4iINllermm11qAeGBiw9a1z69qLt9iOQH2iHaX4xwdmsNRJUPYdzDUjDBQ9bB8uYdtg0XY8IiLqY6whibKrFaaAEIhShYGSBwiRmzfGRER09mL9ePoSzfunz8T24RJmGuFKHfTEeO8oVZyOSUREdBJrDeqPfOQjtr51btXDBOAJauqBbqIgJbDUSRC4Eu0oxWStwPtyiIjIOtaQRNnVilIIAM0wxXDJhyME6t0EUaJsRyMiyqWi76Ba8GzHyD3Wj6dHCIFn7hyyHSPXZpshTnynCwFAaY3Ac3FksYNKweUJaiIi2nTnjVdysVmcF8rmiNaAYYeaekAKg5FKAa1uAt+ViFMD15VsUBMRERHRD9QKFdJUQUoPJc+B5wo8OtfCloGC7WhERLnEa96I8klpgzBRkEJAwaDou1Da4IKJai4aBkRERL2QmQb1Jz/5Sdx22204cuQI4jg+5c/uueceS6mypR2nMIbvTmjzNcMEWwcLOLbUhQGQaoNunOJz908hcHkHNRHRerzk4gkU/MyUXmcN1pA/3NHFDo4tdWzHoD6x0ArRTgwGCj4CT8KTEkIITA7wihgiIsoO1o+02VJlkCgDKQW0Mij5DurdBAemmrajERFRH9g3mY8NUZlYJf3jP/5j/NZv/RZe+9rX4lOf+hRuuOEGHDp0CN/85jfxq7/6q7bjZcaWWoHnp6knCp6Dc0bLeGS2ubLb02h0Y4PdIyVczjFPRETr4jucQrHRWEOurhK4mKjx9Cr1xo+cM4wDU034roQrJQqeRPbfEhMRUT9h/Ui9kCiNbhLDdQQSpVH2JY4vdTFS8Tjim4iINl0emtNARhrUf/qnf4oPfvCD+IVf+AX85V/+Jd785jdjz549ePvb347FxUXb8TKjHGTifxf1gVaYouA5UNog8CWUNnAcwHMkT1ATEVFmsIZc3VDZx1DZtx2D+sSJ5S5KvkSiDKoFgQKvhyEiooxh/Ui9kGqDdqjhCgFjVkZ8L3ZitCPFBjUREW260YrJRZM6Ex3PI0eO4LnPfS4AoFgsotlcGXfyqle9Cs9+9rPx/ve/32a8zPjf9xxHqrXtGNQHtDG4aOsAYABHSsAAriOw2Elw39Fl2/GIiHLp4q01uDxFvaFYQ65uphFiuh7ajkF94sRyB8XAgTIarpTwXAcen/eIiChDWD+uzhiDT993AoniGuR6dSKFo0sddGOFeRPjyGIXC60IjTC1HY2IiPpAd7qJfZM12zFWlYkG9ZYtW7C4uIhdu3Zh586d+Jd/+RdcdtlleOyxx3jn8kle9sxttiNQH9HawEAgShRcRyBwHVy+YxCjlcB2NCIiIgCsIddiolbgiG/qmWaY4MGZFqLEQIqVqw18N/u7tomIqH+wflydEAI/delW2zFyzRiDe44uIfAkCp6L88cqOLzYweU7Bm1HIyIiyoxMNKhf9KIX4dOf/jSuuOIK3HDDDfj1X/91fPKTn8S3vvUt/OzP/qzteER9KUo1BIA0NXCFQOBKeJInYIiIKDtYQxJlSzNK4UkJpTWMEHAdgUaY4LH5tu1oRES5NFD0MMyrOjYU68e1kZIbzM5EnBrEqUbgSsAYGAFIATTCxHY0IiLqA7WCZzvCmmSiQf3BD34Q+onR1b/6q7+KkZER3HXXXXjpS1+K17/+9ZbTEfWnbqIAYRBroOJLCACOwzcoRESUHawhV9eJU7QijhKk3mh0E/iugAaQKg0Yg4VmjHNHq7ajERHlUuBxk/hGY/1IvZBqjSTVKPkeVv4VG7TCFLONMBd3ghIRUb5VAzcXrzeZaFBLKSFPOpn5ile8Aq94xSssJiKiZpjAFQKJ0XCkhDIGLnfQEhFRhrCGXJ3SBoniuErqjYGih5mGhNIr18XMtiLsGC5hoJSP3dtERHT2Y/1IvZAog1RpuBLQWiBVBr6UOHeskouGARERUS9Ya1Dv378fl1xyCaSU2L9//w/92EsvvbRHqYjoexrdBFIKaG3gOhKGDWoiIsoA1pCnp1rwUM3JaCfKv4PTDRRdB/UohQaw1Elw+Y4h27GIiKjPsX6kXktShUQZSCEBAURKIfAlm9NEREQnsdagvvzyyzE9PY3x8XFcfvnlEELAmKee7hBCQCllISFRf1tsxyuFNAQGii4MBKJUw3U4YoyIiOxhDUmUXa0ohedJuInAUifGuaMVGBiOmSciWifPEQhcx3aM3GP9SL0WphrKmJXx3g7QjRVKPv8tExERncxag/qxxx7D2NjYk78momxZaMUwxsAYg/FqARACB6YaKHgsqImI1uPCLVVu8tkArCFPz0wjxFQ9tB2D+kS7m8KVAoErsdRJMFjycHCqidFqwXY0IqJcGix5CCp8D36mWD9Sr3VjBWMMhABcKdBJNMpcTyMiIjqFtQb1rl27nvz14cOH8dznPheue2qcNE1x1113nfKxRNQb9TCC0hpCAKPVApphist3DIK3WBIRrY/DaxI2BGvI0zNRK2CixuYg9UYzTHB0oQOlDQquxHInwbnjFewYLtmORkREfYz1I/VaJ0qh9MpVeVJKdGOFMq/dISIiOoW1BvXJXvjCF2Jqagrj4+OnPF6v1/HCF76Q43WILGiFGgaAIwRqBQ+pMghTjUqQiacNIiIi1pBEGdOOUkgBtLop9kxUMNMM8fzzRm3HIiIiehLrR+qFVpRCG0AIIPActKIEu4bLtmMRERFlSiY6TSsjT556qmhhYQHlMl+8iWzoxinME8V0yXeQGhd3PTKPwOV4WiKi9XjOnhH4HOu2oVhDrm6uGWG2yRHf1BuL7RhGawhh4AoBx5U4sRxiuRvbjkZElEtj1QCTA0XbMc4qrB/Xpt5NbEfItUaYQgoDDQHfFWiHKSq+i0Rp29GIiKgPeDm5YtBqg/pnf/ZnAQBCCLz2ta9FEARP/plSCvv378dzn/tcW/GI+pYxBt1EwQgD33XgSgFXCAxWA4xUgtW/ABERPQXvn944rCHXbrjsY6DIcYLUG81uiq8/toDRaoBmmOCCySoOTDewbZDNFSKi9eAFMRuH9ePpWWhFtiPkmpSAFBKpMih6DuabMZa7CQ5ON21HIyKiPnDx1trTbsjLGqsN6oGBAQArzbBqtYpi8V8XLnzfx7Of/WzceOONtuIR9a0o1YhSDaUMamUHrrPyZDZSCXiHIBERWccacu0cKXj/OfVMlCo0wgRbaj5iZeBKiVaY4ryJiu1oRES55ORgYTEvWD+enu+tC9H6LLRidOIUs80Q5cDF1HIXo5UA52+p5qJhQERE+fbgdBP7Jmu2Y6zKaoP6Ix/5CIwxAID3ve99qFQ2buHiT/7kT/De974X09PTuOyyy/C+970PV1111aqf9/GPfxy/8Au/gJ/+6Z/G3/3d321Yno3wwIkG9BN/X0SbKUk1tDHoJBrnlQO4UgAiP6MhiIjo7LZZNeTZWD8S9dJcM0LgSjRDjd2jZSy2Y4zXAgQurzcgIiK7uAZ5evKwqJ1lC60QtaKHwZKLC7ZUMNMIccGWCq7YOWQ7GhERUWZYv4PaGIO/+Zu/wX/+z/8Z55133oZ8zU984hO46aabcOutt+Lqq6/GLbfcgmuvvRYHDx7E+Pj4D/y8xx9/HP/pP/0nvOAFL9iQHBvtfJ48oB4JE4Wi5yBOUgxVfDhSIowUvnZonmNCiYjW6fl7R3kH9Qba6BrybK0fv3uizlGC1DOPzrdRcCW6SYrBkovjSyHvTiUioszgGuTaHZhq8AT1Gdh/tI5urDDXiDFTD7HQjnBsuYt7jy7bjkZERH0gcGUuNptZb1BLKXHeeedhYWFhw4rDP/iDP8CNN96IG264AQBw66234rOf/Sw+/OEP4y1vecvTfo5SCtdffz3e+c534s4778Ty8vKGZNlIB6aaUDxBTT2QKA3flegmBkNFH44UOLrcRbOboraNDWoiIrJvo2vIs7V+3Lelhgu3ZP9NCZ0dHpxq4O/uPY6dwyXUwxRjNR++ywk8RESUDVyDXBtjDA5ONxErNqjXa7ETw3MktFm5cscYTiUkIiL6ftYb1ADwu7/7u/jN3/xN/Nmf/RkuueSSM/pacRzj7rvvxlvf+tYnH5NS4pprrsHXvva1H/h573rXuzA+Po7Xve51uPPOO1f9PlEUIYqiJ3/faDTOKPda7B7l3b/UG60wxWjFR5wq1IoepFwZ2fjsc0bwby4Ysx2PiIgIwMbVkGdz/Sh5/zT1kDIGzTBFNXBR78TYPjkAYwyaYWI7GhFRLnmORIETeDZU3tYgbdSPQgj8zBXbNv37nNWMxmI7hu8I7B2r4IGpJi6YqOHyHYO2kxEREWVGJhrUr371q9HpdHDZZZfB930Ui6eOgVtcXFzz15qfn4dSChMTE6c8PjExgQcffPBpP+erX/0q/uIv/gL33nvvmr/Pu9/9brzzne9c88dvhH9+eAHKcPcibb5UGwyVAwACBc+BhECjk2Co5KETp7bjERHlUtFzIASbhRtpo2rIs7l+nGmEmK6HPf2e1L/mWxFcKdCMU0zWioAADs224PLEEBHRugyWPDaoN1je1iBt1I/GGOw/Vu/p9zzbLLQTuFLAkYARAAxQ8FgPERERnSwTDepbbrnF2vduNpt41atehT//8z/H6Ojomj/vrW99K2666aYnf99oNLBjx47NiPiklzxjy6Z+faKT/cP9J+A6YmUUEYBmHGO6GSLw+eaYiGg9zh2rwGF/ekPZqiHzVD96jkQ54Gs39UaiPJQLLhbbMS7bPoiFdoytg0XsHa/YjkZERAQgf2uQNupHIQQu40nfM/LofBOPTAsIIaA1IOTKhmUiIiL6V5loUL/mNa/ZsK81OjoKx3EwMzNzyuMzMzPYsuWpDd5Dhw7h8ccfx0/91E89+ZjWK6eUXdfFwYMHce655z7l84IgQBAEG5abKGvmmxEKngPXkah3ExgtcP54FbtGy7ajERERAdi4GvJsrh8rgcs7gKlnJioF3HFg9ol7FgXmmhGed+7aN3EQERFttrytQdpafzww1UCUcorjej1woonlMIaBwIMzDTS6Ce4+soQDU03b0YiIqA9csKWCi7YO2I6xqkw0qE8WhiHiOD7lsVqttubP930fz3rWs3DHHXfgZ37mZwCsFHt33HEH3vjGNz7l4y+88ELcf//9pzz2X/7Lf0Gz2cQf/dEfbfquRKKsWmhHKD1xWnq6EcJzBAZKnuVURERET+9MasizuX70XckGNfVMoxujG6eYHKhguZ2i5LsocPoOERFlFNcgf7BzeDjhjNz1iI+xSgADge2DJUzXQ4xXC3jBeaPgrU9ERLTZAjcf78Mz0aBut9u4+eabcdttt2FhYeEpf66UOq2vd9NNN+E1r3kNrrzySlx11VW45ZZb0G63ccMNNwBYuW9m27ZtePe7341CoYBLLrnklM8fHBwEgKc8TtRPltoJyr4DVwrMNkKUfId3XxERUaZsZA3J+pHozDXDFNoYlAMXJxpd7Bkt4+hiBzFPYBERrctYNcDWweLqH0hrxjXI1Rlj8PhC23aMXItSBSkFtAHCVKHoOjix3MWBqQYEO9RERLTJLts+kIvXm0w0qN/85jfjy1/+Mv7sz/4Mr3rVq/Anf/InOH78OD7wgQ/gd3/3d0/76/38z/885ubm8Pa3vx3T09O4/PLL8fnPfx4TExMAgCNHjkBKniQh+kGMMWhGT5x68SRmGxEqBQ+ew383RESUHRtZQ56t9aPWBtoY2zGoTxyaa2GkHMDAQEAgVhqew3ssiYgoO7gGuTohBC7csvaT5PRUX3tkHvOtEK6Q6EYpfE9CaY3LdwzmomFARETUC8IY+ytWO3fuxEc/+lH82I/9GGq1Gu655x7s3bsXf/3Xf42Pfexj+NznPmc74qoajQYGBgZQr9dPaxzQ6Xhsvs0FRuqJVBnc8sUHsW24jFddvQsf/drjGK34eMMLz7MdjYiIfoBe1CJZk/cashf/z2abIWYb0aZ8baLv98hsE81OjMQA50/UsNCO8KILx1Et8JoYIqIsYv3I+vHpaK3xkX9+fFO+dr+YaXTRjTU8V2Cw6EMIgVrRw2ueu9t2NCIiojO2UfVIJk5QLy4uYs+ePQBW7npZXFwEADz/+c/HG97wBpvRMmW04tuOQP1ECAwUXKRawxiDop+JpwsiIqInsYZc3Xi1gPFqwXYM6hNLnRjtKIWOFQqeRJRqNqeJiChTWD+uTkqJf/uMSdsxcq0dpfjbrz+OwHPRjlOMVHw4kieniYiITpaJjtOePXvw2GOPYefOnbjwwgtx22234aqrrsJnPvOZJ+9iIXBxh3pGa4MwUagEHhrdFK7joBxk4umCiIjoSawhibKlHaZoRSm2DBSw1I4xXglsRyIiIjoF68e14d3nZ2a60YWBQOAILEUaBceBl69J70RERJsuEx2nG264Affddx9+9Ed/FG95y1vwUz/1U3j/+9+PJEnwB3/wB7bjZUa9k8CAI75p88WpRhRp1AoOlroxSr6E77KSJiKibGENSZQtrShBO0oxUgkw04iwd6xsOxIREdEpWD9SL0SJhlIaBc9BlIZwHIFEGTw+37YdjYiI+sCukRKEyP7kjkw0qH/913/9yV9fc801ePDBB3H33Xdj7969uPTSSy0my5Z6N+Ed1NQTzTDBchSjFLhYbMeoFTy4HEVEREQZwxqSKFtmGhEGCh46UYqRkg/fdVDvJOgmynY0IqJcKgUOapymt6FYP67N5/ZPoRWntmPk1tRyFwdnmmhGCkcW2kiUQjcxSLRBDvoFRESUc+OVAq67NPvXdVhtUGut8d73vhef/vSnEccxfvzHfxzveMc7sGvXLuzatctmtEwqcBYM9UiUKmgNlHwHU/UQ5cCF5/Dnj4iIsoE15NodXmjj6GLXdgzqE8eWO3j2OcNY7iR4xrYypAC+eXiRzRUionWaqAV8Dt0grB9PTx4WtbPsvmPLOLbUwVXnjEAbg6t3j6EVp7jheefYjkZERJQZVhvUv/3bv43/+l//K6655hoUi0X80R/9EWZnZ/HhD3/YZqzMemCqAaV5gpp6o+Q7aEcplDYoeA5HfBMRUWawhly7KNFohontGNQnLpocQDvSGKn4kAKYbUSQAC6YqNqORkSUSwEPKmwY1o+n58BUA1GqbcfIre+eqGO2GWK2GWGxHePxxRaaUYr//e3jtqMREVEfuGCigou2DtiOsSqrDeqPfvSj+NM//VO8/vWvBwB86Utfwk/+5E/iQx/6EKRkEf79fuyCcdsRqE+kSuOv/+VxHFsO4TsSnisQsEFNREQZwRpy7c7fUsX5W9gcpN747ok6vvTADC7eWoMxBo8vtPFvL9mCgRJP/xERkV2sH0/Pvsma7Qi5Vu8kGCwFOG+sjIPTDWwbLOLhuSa2DgQc8U1ERJsuL6/jVhvUR44cwXXXXffk76+55hoIIXDixAls377dYrJsShV3LlJv1LsJfFdiqRPj3NEKJAQKrmM7FhEREQDWkKdDawNtOIGHeqMZphAC8FwHs60QgSsxWi3YjkVERMT68TRxDfLMdOMUQhgAAniiFp+pRyh4LrgfgoiIaIXVBnWapigUTl2w8DwPScIxhE/na4cWoLjASD0QJgolz0GSroz3To3haDEiIsoM1pBrd3ixjcMLHdsxqE80wwSDJR8GBg/PtPDcc0dsRyIiIgLA+vF0GGNw79Fl2zFyrZ2kcABoYSAgoLSBFAKXbh+A4BFqIiIiAJYb1MYYvPa1r0UQBE8+FoYhfvmXfxnlcvnJx26//XYb8TJnz1gZ7E9TL4SJxmhlCY4QMDDQGih4PEFNRETZwBpy7caqBRQ9qyU/9RFHCnz54CyW2hGiVGH3SHn1TyIiIuoB1o+np1bk9Rxn4rzxKu56ZAEqVZCOQKI1fFeyOU1ERHQSq6tVr3nNa57y2C/+4i9aSJIPjTCF0uxQU2/4roAnJVJlYACeoCYiosxgDbl2lcBFJWCDmnojURqOEHh4to3twyUEnoOHZ5pox8p2NCKiXBqvBtg6WLQd46zA+nHthBA4f6JqO0auLbVjCAjE6UpjOk4MPIfNaSIiopNZXa36yEc+YvPb505eLjan/NPa4NP3HsdQyUOl4GK2EfEOaiIiygzWkETZlCoDDY3lToJnbBuAlALtWOHyHYO2oxERUZ9j/Ui91I5SSGEQK4OCKxGnCq6QWGzHtqMREVEfGCp5uZjaweMURPQUqTZYDmNcMFnFaCXAwzNN+C5PUBMRERHRDxalCtPLIbYOBHCdldpRaYMw4QlqIqL1cKSA5/C9OFHeNKMUUgh0Ew3fkYhShShVaHQTyBw0DIiIKN+GSvm4qoMNaiJ6ijjVaHdTTNYKKAcuOpGCK/mmmIiIiIh+sGaYYLkd4+o9owBW7vs8NNdCOeAkHiKi9Rgu+RivFWzHIKLT1IlSSCnRjVMEnoNurOC7DnaPllf/ZCIioj7BBnWOLHUiGF5BTT2w3EnQjRW2DBSRKANIgShVmGmE4I8gEdH67BwuwZHcLU+9FacaidK2Y1CfeOBEAyOVAGXfQaoNEmXgSoELt/CqIiIiIuofrSiF7wp0Eo1y4GCpHaEScBmeiIjoZHxlzJH/+9A8lGZ7kDZfO0oRa41a0cN0PUQ1cPHYXAtSSgyXfNvxiIhyyeiVDT9EvTTXjHBsqWM7BvWJI4td7J2oQgjAdQRipeE7fN4jIiLKmwNTDUQpNzmu1wMnGljuJFhoRRitFDBdjzBaAe49umw7GhER9YHAldg3mf2N4mxQ50jRc9igpp4wRqMaeKgEKz9z1YKLh2fb2DZURK2Yj/sLiIiyRrA5TRbUii62i5LtGNQnljoJhksehMDKfYuJwnenGuD6NhHR+uwZK+PS7YO2Y1AfWmxHiPkCvm6NMIHnCnS7Go4UUFqjxCtPiIiITsEGdY5MDhZhOOObeiBOA5y/pYsw0Ui1ge86aHRjbB0Ywjm8L4eIiCg3qgUP1QI3l1FvPDLThONIAAKeI7HQijDXjPCiC4u2oxER5dJgia/hZMfu0YrtCLlW7ySo1h08OtfBzuEiZhodXDhZxeU7Bm1HIyIiygw2qHPEdyQ0G9TUA0obnDNaxmwzgtIGUgClwMViJ8ZQJ7Ydj4gol2oFD5KnqInoLNZNFEbKPmAAz5H47ok6Bos+fmT3sO1oREREdBq2DXJz2ZnY70kUfRdJqiFgkCpAQOCR2abtaERE1AfOHatAiOyvQbJBnSOtKIHmdB3qAWUMrtgxhIVODKUNlNYYKHh4ZKaF2UZkOx4RUS696MJxBJJj3ai3Zhohpuuh7RjUJzqxgpQCK1tqDeqdGLUi33ISERHliTEG+4/VbcfItblmhJLvQAMQQkIpDaUNJmoFyBw0DIiIKN/y0JwG2KDOlWft4skD6q1k1uCRehOxMtDG4MpzhnHuGMc8ERER5cVErYCJWsF2DOoTriNgDCCEwWwjRuA6CGOF+44u245GRJRLY9UAW3mSlSzg/MYzM1zxobWBMQbaAKkBSr7Lq3eIiIhOwgY1Ef1A47UAXzs0/8QpaoNh3n9FRESUK3GqkXIED/VIlGp4UkBrg26ioIyBkMD5E1Xb0YiIcsl18nH6hc4uQgjelXyGBoouDkw1ISCgtIY2BvVuhP97cM52NCIi6gPPP28EUkrbMVbFBjUR/UBl30U7SlHwJBJtUA7YoCYiIsqTVpRiocXrOag3hko+6t0Ec40YF2+r4cBUA61I4chix3Y0IqJcGip7GK9yEgpR3oSxgjAGEIA2gCME7j1Sx0ilAEdy4wkRERHABjUR/RCJ0nClQKoNokTh/mPLcJ3s77whIsqii7fW+BxKPTdc9jFc9m3HoD7y7cNLWGhHmBwooBUmKPsOLtjCE9RERETUP8JUIzUGAkCiNRxHYLGbYNtgEZINaiIi2mS8g5qIck9pA9cRaMcKBddg50gZY9XAdiwiIiIiyqiFTozhsg8hBFqRwlCJGySIiIiov4SJQpxq+K5Ekmg4QkAYg8t2DOamaUBERLTZeIyHiH6gVBsoZZCmBhpArcg9LURERET09IwxmG9GGK0EUNogjBUqBdaPRERE1F/CRCNRCr4nEKYKjly5U57NaSIion/F1YIcMcbYjkB9RAiBVGm0YoVS4EIbg8B1bMciIiIiooyaaUQYLPmQUiBONbqpwlDJsx2LiIiIToMxBo/MtmzHyLXFdoQoBQLHQSfW8BwJpbXtWERERJnCBnWOfPdEA0qzSU2bzwCoBC5qBRdxqrFt0MNsK8JsM7QdjYgot0bLAe8bo56baYSYqvP1m3qjHSXYUgvgSoEoUYhSjcBzMFXv2o5GRJRL5cBFrcCNPtRbQggcW+ogUmyorlcnVnhsrol6N0U7TpEqhVQL3Ht02XY0IiLqA4ErsW+yZjvGqtigzpFLtg3YjkB95J4jS3AloI1BNXBwZFFhthFhuMx7BImIiPJiolbARK1gOwb1iUaY4MhiB74j0UkUUqXRCFNIjrMkIloXPnuSLXvHq7Yj5JrcJrDYjjFY8nB8uQutXfiexOU7Bm1HIyIiygw2qInoadUKHk4shzAwEEKgHDioFVxsHSzajkZEREREGZSkGsIAniPRilJ0E4U9o2VukiAiIsqZHcMl2xFyrxOnGCj6SJQGIDBQ5IEPIiKik7FBTURPa6IW4BuPLQBmZeT3WDXAo/NtNMLUdjQioly6cEsVriNtxyAi2jSJWrmOyHclFloRImW4wE1ERJRDB6YaiFKO+D4TRxa7GKsozDYjOEKgVnA54puIiHqCI76JKNeqBQ/zrRjaAIHnQEqBH9k9hMB1bEcjIsolNqeJ6GyXKA0DwHMEDs01UfEdLHdiTC3zHnQiovUYrfqYHOAUM+q9PCxqZ101cLBruITZZgTXEZwoQ0REPXPhlnxc1cEGdY4cX+7CGGM7BvWJoZIPzwG6SYqC5yBKNcqBZzsWEREREWVUrDQAA1cInFgOUSu4aHZTXMr7FomI1oV3UBPlV6wMpBRIlYIUAr4rcdn2AQjBf9lEREQAG9S5ErgS7E9TLxgYPDjVRNFz0IkVSp6DdsTR3kRERET0gyVKwxhgvhXDkQIl34WQAo7kQiwRERH1D6UNUm0ghECqAVcDtYLH5jQREdFJ2KDOkdFKYDsC9ZEHjjfgSgllAM8BXC4sEhEREdEPEacaWhssdWJIAAXP4ek/IiIi6jsrm/YMtAE0DFItUAk8dGIe/iAios1X8vPR+s1HSiLqucCXWO4kKPsuGt0UxZw8qREREdG/mmmEmK7z/l/qjSjVaHQTXDhZxZcfVCgFEp4jbcciIiIi6qk4XZkqo7SGIwQSbdAIYxxb6nLzHhERbbq945VcTO1gx4mInpYnJZa7MUbKPo7XQ1y4pWo7EhEREZ2miVoBE7WC7RjUJ4wx+MQ3j2KyNoZ2lGK4XILvskFNRESUNwemGohSbTtGbs23ItS7MY4udtAKU6Ta4EQ9xL5Y2Y5GRER94MHpJvZN1mzHWBUb1ET0tIRY2fE5OeBhuROj5Du2IxERERFRhs02IwyWfKTGIEwUKgUHARvUREREuZOHRe0sOzTXxGDJx3g1wNSyh1hrDJV8AOAJaiIi2nR5OWzIBnWO3PHADFJjbMegPpAqDQGg4EpIKSCFgNb82SMiIsqbmUaIKY74ph4arwaIU40o1agELhvURERE1Hda3RSuA3RTBccRkEbg6GIHl+8YhJSsjYiIiAA2qHPlil1DMGxQUw8YY/DVR+bhuSunpoueg0aUWk5FREREp4sjvqmXUqVxYKqJTpxCGYPAc/DYfJubJIiI1mmsGmDrYNF2DCI6TfUwReA4iGIFVwqkQkAZ4IqdQ7m4E5SIiKgX2KDOkeGybzsC9REpBLQxqBZcxKlBqg2MMSykiYiIiOhpJcrAdyU6kYbBynUxD003McZNEkRE6+I7kg1qohyqd2P4jkQnVZAScKVEK0pw79FlrqsREdGmu2z7QC5eb9igJqKn5XsS3VhhS62A75xooOg5+NKBGQyWuFGCiGg9Lt8+8ORkCiKis1GsNDxHoB2lMNpgoRXDcYCrzhm2HY2IKJcKHkcBkx1xqm1HyLVGN0XBl1jupFAKCDwJAeDyHYO5aBgQERH1AhvUOZIoFofUG6kykBAwwmCiGuBfwhQwBjP1CG2O+iYiWpdnbK3Bsx2CiGgTJUrDcyTqYQhlDAaKHhJtOAmKiIgoR4wxeHi2aTtGrrWiFL7jIFYxtAY8T8BzHDaniYiITsIGdY48MtuC0ryDmnrDdyVqgYdOrBC4AlP1COeOl7F9sGQ7GhFRLvk8PU1EZ7k41QhcicV2jDjV2FIr4ATvnyYiIsoVIQQu3FKzHSPX7j9WRydKVg4bCQPXFRDC4OEZNv6JiGjz7R2v5GJTFBvUObJvksUh9c5XDs5ipOJjuhlislbEo/NNbB0sYYgjvomI1kVrAymzXxwSEa1XojSKnocTyx1UCy5So1H0OZ6WiIgoT4wxODDVsB0j19pRCs+RUEoDkBCQ0MZgcrAIviMkIqLNlofmNMAGNRH9AInS8B0HnVChWnTwwFSI7UMlVIscUEtEtB5sThPR2S5RGp4rcWyxg9FKgCgxqHdT3Hd02XY0IqJcGqsG2DpYtB2D+owQApdsG7AdI9cemW1iakkj1QbSpHCED99zUAm4FE9ERPQ9fFXMkePLXWiO+KYeUNrAlRIGQMl30AxTaGOwY6iIbXxzTERERERPI0o1WmGKVBsMll00whgzjRAXcRIUERFRbhhjsP9Y3XaMXGt0E7gOkGoDVwpoGHiOxNHFju1oRETUB7YPFXNxipoN6hwpuCsNQ6JemBwo4PhyF4WCxGOLEQqei/lWjHt5AoaIaF0u2VqD63DULfVWnGrEStuOQX0iTDSm6l0ICJR8FwenW3jm7kFctmPQdjQiIiI6DVx/PDND5QDT9S6MAbTWAATa3RQzzZAjvomIaNNtGyywQU0ba6QS2I5AfWSxHQMwMFrAaAMhgDJHEREREeVKK0qx2I5sx6A+4TkCgSuRKAXAoBmm2DFQwlS9azsaEVEulXwXA7xmi3pMCIHLubnsjAwWPUwtdyEAKCOQpgYGGnvHKshBv4CIiHJOynwckGG3iYieVqoMpBDoRClqRR/HlrrYM1bBKDdKEBER5UaiNFqRsh2D+kSqNHYOlxCmBvPNGBM1H4vdCKNV1o9EROsh2cgiyqUwUUi1hhQCCgaJVmhHKf7xwVmeoCYiok3305dvzUWTmg1qInpasVLwpEQjTDBQ9NBNFXduExER5cxErYCJWsF2DOoji+0I7SiGqQYYKvso+R7G+TNIRESUK/90cBax4qDv9Zpa7uLYYgetKEFqVg6BJEpjx1AxFw0DIiLKt4MzLeybrNmOsSo2qInoaSXKQDoCy22FWtFbuQOd702IiIiI6IdoRSmWOimuGavAwMDhHEsiIqLcuWjrgO0IubZ3vIy5ZoihZoREK/jSheMAP3LOiO1oREREmcEGdY4cmGpAaXYIqTcW2zGMNogShVQZjFUL+OID0yh4ju1oRES59ILzxuC73C1PRGe3Y4sdaG2wdaiI40sddGKFo4sd27GIiHKpWnAxWPJtx6A+NMbrOc6IFEAnSSGkgGNcKGgUPf5bJiIiOhkb1Dmyd7xiOwL1CW0MvvX4Ioq+A6UN0lRjsORhz1gZw3xzTES0LuxNE1E/uPvwEgbKK9N3tAHuPbqEOOU96ERE67F9uMgGNVlxYKqBKNW2Y+TWYjvC8cUQ9U4MKQW6cQpHSNx7dNl2NCIi6gOBKznimzaW53Blm3pHCKARJpAS6KQpip6Lc0YrPEFNRERERE9rqt5FvZtgsOgjSQ1cKdEKE57CIiJap7Lv2Y5AfSoPi9pZdnSxg0rgQmmNou9goS1wzlgZl+8YtB2NiIgoM9igJqKnVSu4eOBEE8NlH4k28B2D+4/XuVGCiGidLtlag8vnUOqxY0sdHF3s2o5BfWKxE2O45KEVpghTBUDjgRMN7BrhJCgiovXYO17BloGC7RjUh3iC+sycWO5iphmim6SItYt6N0E3Vvj2kSXb0YiIqA8UPCcXm83YoCaip9DawHUk6mGCLQMBYAAhJHYMlfjmmIiIKEdKvovRCkeDUm9sGSjg2EITRd9FojRmmxFSrTFQ5NtOIqL1KHKCGVmSh0XtLPMdgbLvwnUEhooewkQj1RpfO7QAYTscERGd9f6/f3OO7QhrwpUCInqKVBuk2sAVAmGiUfAkir7DN8dEREQ5M1z2MVxmg5p6pxmt1I7tOMVyOwEAXHXOsOVURET55Lt8D069Z4zBfcfqtmPk2mIrQqIVtDYADCQEGmGKX7hqB6TgVC0iItpcjpOPGpINaiJ6CqUN4kShXHBQ7yYI3AICV2K+FaERJrbjERHl0rbBIqTkfnkiOrs1ugnGqwFOLIeIUo2JagFDZd5BTURElBdCCN6VfIZmmyH01wSE0YB0ABgUPYc1ERER0UnYoM6RhVYEYzsE9YVurNAIU/iORJwaGBikSiNWGoMlz3Y8IiIiIsogrQ06cQohClhohUi0gpQC9x9bth2NiCiXRioBtg4WbccgotPUjRVSoyEMoJUGBFDy83GajYiIqFfYoM6RMNUwhi1q2nxCAInS8F0HRQ+IEoU4NTg010K9yxPURETr8aydQzxBTT030wgxXQ9tx6A+EaUa040IniPRSTQcABCGm2yJiNaLT6BEuRTGGlobCGOgYSDMyrruvUeXbUcjIqI+cNn2AQiR/TVINqhzZBt3zVIPtaIUtcCD70k0whQzzS5GKwWMVzmOiIhoPdibJhsmagVM1Aq2Y1CfqHdj+I7AWK0AaQSO1TvYOVzCpdsHbUcjIiIi6pl2nAAC0EZAa8AIwJciNw0DIiKiXmCDOkd4epp6JdUG7SjFaNmH0gbVwMFCO8a/OW8ce8YqtuMRERERUQa1I4XmE9fEVAIXD04nGK34SJS2HY2IKJekEHC4y5Eod9pRCikALYE4VRAAioGDmDURERH1QODm41oJNqhz5LsnGlCaTWrqDa0B33XQjRXGBwo4ONPkCSwiIiIi+oGOLbWhDTA5UEArStFNNVphiodmmrajERHl0mgl4PtwohxqRQoCAhIGiTIABMJY4ZHZFgS46YSIiDbXvslqLiZ2sEGdI5dsG7AdgfrIx795BCVfItUGQgg0uwk+9vXDCFxpOxr9/+zdd3Qc5fn28Wtmq3ov7h3jApgOpphuegs1hRIIhBIgJCTAL2BKCDWEhBCTCgmYkIQAKRDTTS8O1bjiXiVbsupqtWXmef9wrBdhG8uytaPRfj/n6IBXs7vXjmZn75175nkA+NKZew1Sfm7Y6xjIMrXN7VrDHNTIkMXrYgrYlkpyQ0qkXLXEUxpYnKuinJDX0QDAl6I+ufoFfYsxRh+vbPI6hq/VxxKyZCRLSjmubFsKBQMa159juwAAbESDGsAmXNconnQUDARUmGNrfSypxnhKs1c3a9dBxV7HAwAAXVQQDSpg53gdA1ni4xUNKs4JKe1KKddV0jGqa0to0VquoAaA7uhXnKOiXE7yQeYFGVp+u0SDtizLUtCy1ZpOKxKwlBcOqC2Z9joaACAL5Ib90fr1R0pIkmatbJLDPNTIAPd/21ki7aqqMKLl69uUdlztO6JEew4u8zgdAPhTNELZhcxL/++kMyATUmmjwtygkmlHa5vbFQ1ZmrWiyTdfjgGgtymM0pxG5lmWJWOMEmnmS+6ueMpRc1taoZCl5nha0VBAHy9v1D3PzRe9fwBATztl9wEaN6DY6xhbxZECH9llIMPAIDPSjqtoyFY86agsL6z2VFrxhKPGWFrzmUMQALplREWeggGmSUBmpR1XiTQNamRGIpVWTjCg1kRabYmUjCsV54V02p6DvI4GAAC2wS4Di72O4GsNsaQKcoLKCQfkOkY54ZBSbloH71Qh2wdzggIA/G1Mv0KvI3QJDWoAm0ikHMlsmCfHkhRrT8uVtP/IMu09lCuoAQDwi1jCUV1L0usYyBLr4ylFQwGtaojLNVJuxFZehKv/AABAdmluT8s1riwrKNeyZFlGKVcqzg17HQ0AkAUsn5wMRYPaR/787jKlXYb4Rs9rak8q6bgKBCw1xR01taeVGwmosiDqdTQAALANyvLDioYCXsdAlnhujpFlSZYt1belVJQTljFGc9c0ex0NAHypLC+sykK+hyPz3lpYpyRDfHfbxysa1NTmyHGTakukZRupJeXIGOObpgEAwL/m1bT44ipqGtQ+UpIXZg5qZERRTkgNrSkFLEv1sYRa4knlhYP6YFmDFq9r9ToeAPjSASPKFaZRiAxLu4YhvpExOUFbjfGUoqGQLEmhYECWpGHleV5HAwBfCjJZLTwyuCzX6wi+Nq+2WQtrW1ScF1YybVSYG1IqRsMfAJAZO1cXeB2hS2hQ+8jR4/t5HQFZoj3laF1LUomUo6Z4Um0pV4XRkIpyQtrZB2feAEBvxPzT8EJeOKgcToxAhrQlHeWHgmpPpuW6RpGwrcJoiKv4AQDwmSqu3N8utizJkowrSUZp15WMrZwwNREAABvRoAawiWTaVXl+WPNqmhVJ2ApsqKtVlh9SiAYLAAC+Mb+mWfNqWryOgSzR3J5WaX5YJTkhLa6LqSTPUsC2tKCWbRAAuqM4J8QQ38g4Y4zeXVzvdQxfa46nZMyGdSlJrmsUtI2GlOaJEb4BAD3NL9NJ0KD2kac+WKWUy3Aw6HnGGI2syNcnKxtV29yu/GhItmWpOe6oNI9hQgGgWxglDx4oy49oeAVTxCAz1jW3a01Tu3LDQSVSjuLJtFY0tGlMW9LraADgS5EgJ4jDG7Ekx362x8DSXM1cWi/HGFmS0o5RwLY1n5P2AAAZsNvAIl80qWlQ+8gpewzwOgKySFM8qYLPgpq1qlFDSnOVdKTqoqgGldJhAQDAL+paE1q0ttXrGMgSi+tiGlCco4BtKW2kVY0JDSvP1+rGdq+jAYAvMU0HvGBZliaPq/Y6hq8tq4vpHx+tUiptZAdsJdKOLNuW67qybU48AQBAokENYAta29PKCQfVmkgrYFvKC1rKZa4cAAB8pX9RDge3kTHL18ckGTmOlEg5CgctFeaENXlcldfRAMCXAnbvv/IFwKZiybQsScn/jYSZSrsqyg1pjyGl3gYDAKAXoUENYLPqW5OKJ9LKCQUVTzoKBQP650erVV4Q8ToaAPjSCbv2UzRM6YXMCtgWw4MiYwaV5mrpupja7JRa21PqXxxVynW1oJar+AGgOyoKIupfnON1DGShWCLtdQRfa25PybKkpOPKtiwlXUsluUGlHKZuBAD0vFDAH8eBOEoKYLPWNMfVGE+pLD+khGOUctOaMLhYO1UVeB0NAHwpHOQqVmRe2jWKp5hDEJkxqCRHi9e1akFNs8LBgAKBgEZW5Gu3QcVeRwMAAF1kjNGaprjXMXwtmXYVsCTHNQoGbKWctGRZml/DHNQAgJ43rn8hc1AD8K/apoSa2pIqiAQVsG0tqWvVu4vrtWJ9m9fRAMCXjt+ln3KjIa9jIMuU5oVVmhf2OgayRH4kpHiyVqsa25UTshQN2hpclud1LAAAsA0sy9LISi5O2B4NsYRs25LrGiloZFyjcMCW7YNmAQAAmUKDGsBmNcaTSrhGeSFb5bkR/XdpgwIBS2X5DPENAN0R8MnwOgDQXQFLWlrfKmMsBYOW+hUzBzoAAMg+9a1JWbIkY8m2LBlZaks5ak2kfHFFGwAAmUCD2kdWNcY3nHkH9DDXGDXEUooEbIWsgFxLcozRPsPKtO+wMq/jAQAAoBda3RhXIuUo6TgqyokoLxJUwJbiSYaZB4DuCAYs38whCOD/W9eakCxLlmVkWZJrpKBla++hpTSoAQD4HxrUPhIN2qI9jUxwXaPW9qTyIwGFgraa2hKKBG01xhJaUMt8OQDQHSMq8hWwORiBzPp0VZPmrGn2OgayxKqGNuVFQqprTaokL6TccEALalpUkscIPADQHaX5IVUX5ngdA1no01WNSqRcr2P41pw1LWqJJ9WaSCvtukqmHSUcRx+vbPI6GgAgC0SCtsb0K/Q6xlbRoPaRKMPjIUPaU44a2lIaVRVRMBDQ6sa48qIBpVyj9bGk1/EAwJeGlbkK2HyWI7N2qirQsHLmAEZmzFy6Xp/Vtsq2JUu2EmlHrjFyGAUKALrFsPuEB4wxkixFOA7ZbfnhgIpyQko6rqLBgHLDroZX5GnCoGKvowEA0GvQoPaRj1c2/q9IBHpWOr1hCCJjLEUDAcUSrvLDQY3tV6ThFflexwMAAF3UmkhrfSzhdQxkieZ4SjkhS8ZIiXRa+ZGgCnLC2mVgkdfRAABAF1mWpfED+OzeHi/MrpFrJEuWXGMky6g0lxFlAAD4PBrUPrJobStXHyAj0q6r6sKoArbkuK6STlpluWEVRNllAADgJ2ua4pq3huk5kBkNbQlVF+Vo1qpmNcbTGlaWJyY2AADAX1zX1e/eWOJ1DF9bVh/bcJGRZZRMGwUtqbKQBjUAAJ9Ht8lHvrbvEK8jIEu4rtFvXl+keNJR3HHkulJuJKgFtS1a3djudTwA8KVx/QsVDNhex0CWGduvUGOqe/+8Q+gbapvj+uvM5XIco7BtKRoOMk0RAAA+Y1mWdq4q8DqGr1UXRPX3D1bItgJqd1MKBWyuoAYA4AtoUPvI7NXNchjiGxlgjJHrSkFbak8YpY1R0LaUHwmpLD/sdTwA8CXb4jpCZJ5lWWLTQ6a0JV21p1wZGYVDQYVtS5GgrZTjeh0NAHzJtiwFbD7IkVmWZenAURVex/C15vaUHp+5TLaMUmlX+TlhNSeS+u/S9dTmAIAet8fgElk++MChQe0jzN2GTHrjszqlHCPbllzHqD3tqqY5rnjS8ToaAPhSVUFUNgcYAfRhLe1prW5sV244oFBAkiV9trZFrYm019EAwJfK8yOqLop6HQNZiO8t26c97SjpODKWpZTrKhKwtXhtTKm0aFADAHrc7oOKaVB76YEHHtDdd9+tmpoa7bbbbrr//vu1zz77bHbZ3/72t/rTn/6kTz/9VJK055576ic/+ckWlwf6ulTaVSLtqCWRVkV+RGljFE85clwKaQDoPkZB6e36Yv3ouoYtDxmzsrFNaddVTjgg27a1fH2bmtvTamhLeR0NAHxpVGU+DWof6Is1JLZPPOnIcaSAbclxpWg4oFgyrcnjqnzRMAAA+Jtt+2OKwT7ZoP7LX/6iq6++Wg8++KD23Xdf3XfffZo8ebLmz5+vysrKTZafMWOGzj77bE2cOFHRaFR33nmnjjrqKM2ePVsDBgzw4BVs3qyVTQzxjYxIOa7yI0GtaohrUEmOjDFqTzqKBm3mEQSAbuJARO/WV+vHulhCa5sTXsdAlqhtapdtWQoHA3Jdo/rWpAaW5urYXfp5HQ0AgB7RV2tIbJ+G1oTSrlHANrIsKRqw1NCW0pL6mCzxvRAA0LPG9CvwxXFIy5i+1/Hcd999tffee+uXv/ylJMl1XQ0aNEjf+c53dO211271/o7jqKSkRL/85S91zjnndOk5m5ubVVRUpKamJhUWFm5X/i2pa02o7/210BvFU2lNn7VGc9a0aFRlnv750SoNLsvX9ceOURVnbwNAt+SEAj1aHGaiFunL+mr9mEy7SrvM/4vM+PfHazT909VyXKNAwNYho8o1IzdJsgABAABJREFUtn+Rxg0o9joaAPhSwLYUDvbcFTDUj9sv0zUkfzN/eHFOjX72wgLlRQNavT6uPYeWyDWWLjhouNfRAABZYLeBRb44BtnnrqBOJpN6//33dd1113XcZtu2jjjiCL399ttdeoy2tjalUimVlpZucZlEIqFE4v9fjdLc3Nz90F30yQquoEZmtKccLV/fprxwQHUtCaVdqTgnpJWNcaVdtkEA6I7hFfkK9P6TF7NSX64fl9S1akFta48/DyBJti2lXFeuI+WELZUVRLWiIa7C3LDX0QDAl4pzQ6os4CTx3ioTNaQX9aMxRnPXtPT48/Rly+tjsmSUTrsbrqAOBhRLuj3eMAAAwE/6XIO6rq5OjuOoqqqq0+1VVVWaN29elx7jhz/8ofr3768jjjhii8vcfvvtuvnmm7cr67aqKIjQoEZGuMYoYNkqyA3qs9oWWdqw/RXlhFTMAUYA6BYOQ/Refbl+HF1dqNHVXF2DzGhqS+nP7y6TLKMRhflaXh/T4LI81bcyzDwAdEc4SAXZm2WihvSifrQsSyMr8zP6nH3NorXNMkZyXCNLUjBgKT/asyNqAQDgN32uQb297rjjDj3++OOaMWOGotEtn6V63XXX6eqrr+74d3NzswYNGtSj2XYZWNSjjw9sZIzR9FmrZctSTVO7IiFLAVuKJdOKpxyv4wEA0Kv05voRyKS2ZEqt7WlVFIQVCdhqSaQ1v7ZFoyoLvI4GAL5UEOWwXV/WlRrSq/qxJ4eWzwYt7Wk5kgLGSLYly7JUFAl5HQsAgF6lz1W65eXlCgQCqq2t7XR7bW2tqqurv/S+99xzj+644w69+OKL2nXXXb902Ugkokgkst15t8WslU1yuYIaGeAaI9dYKsoPqj6W1PDyPLUkHMUTjpraUl7HAwBf6l8Ulc111L1SX64fY4m0Ysl0Rp8T2eujFU1yjaXCaEixpKPCgK3GWFJcLAQA3cUOtDfLRA3pRf1ojNHHK5sy+px9zbrWhGxJKccoZFtyjFF+1NbSupjX0QAAWWBIWa4vRu3ocw3qcDisPffcUy+99JJOPvlkSZLrunrppZd0+eWXb/F+d911l2677TY999xz2muvvTKUdtuMqmJ4HWRGIu0oPxJQWW5YsURa5QURrW9J6K3F9cqP9LndBgBkxLCyHOXnME1Cb9SX68dk2lFrOw1qZEZtU0w5IVvtjlEkaKswGlR9a1KTdqrwOhoA+FIowFWsvVlfrSEty9KIijyvY/jaf5dsOHaWdo1CAVvtSVeN8ZSWN7SJdzUAoKcNKcv1OkKX9MlO09VXX61zzz1Xe+21l/bZZx/dd999isViOv/88yVJ55xzjgYMGKDbb79dknTnnXfqxhtv1GOPPaahQ4eqpqZGkpSfn6/8/N7TFH51wTqlHdfrGMgCadeoKDekpraUjKSgbWlNLKkDd6rQ8Ire854AAD+JhAJeR8CX6Kv1o2OkRJr6EZnRGE8rGJCClqWKgqjak2kVRIMqiDKkJQCgb+qLNaTrunp7Ub3XMXytoS0p25KSrqvicEjJtKPcUK4OHsVJewAAbNQnG9Rnnnmm1q1bpxtvvFE1NTWaMGGCpk+frqqqKknS8uXLZdv//3y1qVOnKplM6rTTTuv0OFOmTNFNN92Uyehf6tDRlV5HQBZ5/bN1enHOGuWHA6ppaZdxXQ0vz1O/4hyvowGALwVszpXvzfpq/egaI8dlihhkRjzpKu0a5YQD6lcc1YfL1qu6iNoRANB39cUa0rZt7TWkxOsYvtbantRrCyylUq6iBUE5RirM6ZOH4QEA6DbLGCY13hGam5tVVFSkpqYmFRYW9shzzKtplssFMMgAI6M5q5r15IcrFE+klTJSSU5QYwcUqbqAg4wA0B1n7T1IuT14FWEmahHsWJn4m9U2tWttS3uPPDbwRY+9s1Rza5p1wMhKjR9QpOmfrtHO1YWaOLLc62gA4EsVBRH178GTxKkf/ScTfzNjjBatY67k7fHfJfV6bOZyrWtp1+jKAoWCAe03olSH7/zlc5MDALAj9PQc1DuqHuHULR+pzI94HQFZoj3tanlDmxpiSVUVRFTT3C7HBHX0+H7aY3Cp1/EAAEAX5UeDsqyo1zGQJeIpV7nhkMoLwgoFbMUSabUkUuKcaADoHvaf8IJlWRpZ2TuGG/er95fVS8bIOEbhcEDptKuldW167bO1XkcDAGSBr5cO6dEG9Y5Cg9pHmtrTcvlyggwJ2ZYSaUfGsqT/7cyGluV5nAoAAGyLz9a2aM7qZq9jIEusj6WUF7ZVnBOWZUnxpKPGtpT4BgMAgH8YY/TJyiavY/jayoY2ybJk2bZCtiXHsrSsLqb9hpfJUu9vGAAAkAk0qH1kWDnNQWROYzypgG2rMZZQbiioyoIcLVkX0+pGhgkFgO7YubpAwQDzUCOzhpTmqSgn7HUMZInPaltV29ymnHBAAdtSynW0oLZZL82t9ToaAPjSrgOLNaAk1+sYyDKWZWm3QcVex/C1j1Y0yBgjI1dB21LQtrWuLaGCnBDtaQBAj/PD1dMSDWpfmbWyiSuokRGOMaptbldJblirm+LaqSqqQcURrW1NeB0NAHxrZEUeDWpk3NqWdn1W2+p1DGSJUMBSwLIVDthy0q7Wx1IqiIR05FjmWwSA7qgoYKo3ZJ4xRovrmIN6e9Q2tSvluLK0oTkdkCPLtnTwqAqvowEA0GvQoPaR3EiA+YeQEY5r5LpG0VBA7UlHJbkhJRxp5fq4RlczDxEAdIftj5MX0cekHaN4yvE6BrJEYTSoNZYUCtqavapR7cm0+hfnaE1T3OtoAOBL4YCt/sU5XsdAFqptYvS87WFbkutqw4gyaSMjo/xwwOtYAAD0KjSofWR1Y1yOQ4MaPS/tGuWEA6ptbFc4YCtk20o6jvYeVqwJg0q9jgcAALpobP9Cje1f6HUMZIlp7yxTwLaUTDmauaReZQVR1bcmtISrsACgW6IhW2PE5zgyyxij+hgj6G2PQCAgx2wY3jvhOHJdV+WFTLsDAMDn0aD2kX5FUbn0p5EhueGAmpJp5YcDsmxLqbSr2uaEPlrR6HU0APCl8f0LGeIbGdfQllI9U3QgQxrbUgpalt5YWKe8cFB1LQmFg0GV5nFAFgC6oyAa8joCspBt2zp8TJXXMXytuqBJ//hwhcLBgFzXKJlyNbA0olWNjCoDAOh5/YuivpiHmga1j4ysLPA6ArKEMUaNbWklko4qC6OKBG01xpLqX5Sj8nzmwAKA7uj9ZSH6ovxIUNEQJ0YgM/oVRzRz6XrlRIIKBYOSLIUD0j7DyryOBgC+VJjDYTt4Y1l9mxJp1+sYvvXxyka1Jh0V2ZYa2lJqTaZUEc/Rgppmr6MBALJAU1tSY/sXeR1jq6h0AWwimXa1tiUuy7ZUlh+R6xqtb0tp1uom9Stk/isA6I6y/IiYdQyZFrAtSTSokRlrGtu1PpbQcbtW69lZNXJcV3Utaf3ypQVeRwMAX9pjSKm+tt8Qr2MA2EaN8ZQcx1U0aCvtuko7RhWFEZXkcdEHAKDn+eHqaYkGta98VtsixzDGN3peczylxra0ggFL1QURJdKu8iMBNbQmVBBmtwEA3eG6rmgUItNmrWzS7NVNXsdAlnhvaYOGlueroS2tRMqVK0tG0iWHjPQ6GgD4UiFDfMMjY/ox9/n2eHPhWgVtS2X5UVmWlHKM9h1eql0H9P6r2QAA/mfbNKixgz3/6RolaVAjA5JJR67rKmjbCgVtpV2jhOOqrjWl0ry01/EAwJdcl89wZF5O2FJRLge3kRkb5rkysi2jpOuoOZ5Sv+KoPl7JSRIA0B0jKvJUWRT1OgayjOM4+u5fPlLCYYjv7qprSaot6WhtS1xGlpriaT03u1YzlzQw9xMAoMeN7Veo43bt73WMraJB7SO2bSlIbYgMMEFboWBAti3F02lFw0HFU66O2aWaOQQBAPCRWLuj+tak1zGQJfLCQdm2tD6WVEs8KVmWxvUr0YjKfK+jAYAvVRYwHDAyLxAI6Pav7OZ1DF+bubhO1/ztYw0qyZVrpEgwqWHleTpgZLksOtQAgB42pl+B1xG6hAa1jzS1p5V2uPoKmTGsLFcrGuKKJxyV5UblpF3VNLXrjc/WeR0NAHxpn2FlCgcZ4huZNaKqQOUFXHmFzPg4r0Hza5q1eG1MAdtWJOhqcHmOJgwq9joaAADYBtFQwOsIvraupV2yLVmWpVTaUW44qIAljevPEN8AAGxEg9pHvnngMIn+NDLAdY1+/vJ8LV8fl2MsJdOujCy1tKfUj+HFAKB7GOIbHljX3K6F61q9joEssbSuTSHbUkM8+b8D25bK88Na29zudTQA8KWccEAFzEONDDPGaO6aZq9j+Nrq5nbZliXXGKVcV4XRsGzLUi01EQAgAyoLIrKs3j9iBw1qH5kxb51c5qBGBrjGqKktpWDAkpN2tb4tKcmoKCeswhy+HANAd9hcPA0PVBVFlRvhChhkRjhoaca8OklGQdtSwLY0a2Wj1jHMPAB0y87VBTpgZIXXMZBlLMtSynGVSDPPYHetbGhX2jVaH0upLZlSJBDQkro2fbKyST7oFwAAfK5/UVRjfTBqBw1qHzlibJUMDWpkyLw1LVrZ0K5gUIqnHCXSrpbVxxRieFoA6JZh5czBisxb1RDXgrUtXsdAllixPq6ygrCW1AWUSKYUDdha15rSwTux/wOA7uhXyAhmyDxjjPIiQeUxBXq3hWxLeWFb+dGA0q7R0LJ89S/N0dCyXK+jAQCywMhKf3wHp0HtI6V5Ya8jIIu0JVNKOa5K8yNaui6mtOOoPD+sKr4gA0C32JwpDw8U5YY0oDjH6xjIEq3taeWGgnp7YZ1SrlFBTkANsaQczrEFgG5hhhh4wbIsDSjm2M/2aEs5CgcDSjuW0q6r/JyA0o6rfsU54mshAKCn+WF4b4kGNYAtqGtNqTAaUlvCkWM2zEG9U3WhJgwq9joaAADoooBl/W8uYKDn7T64WCvWt8lxpXjKVXmepWDQ0p5DSryOBgC+FAowghkyz3Ec3fTP2XI4w6zbFtS2yhhXze1JxRKO6loSKsmTFq5t9ToaACALRIK2xvQr9DrGVtGg9pE1TXHOnkVGpNOOYolUx3zTRrYc46q6kPGdAADwk8rCqCoZ/QQZ0hRPad6aFlkySqYdybJVlhtRc3va62gA4Ev5kSAnmiHjbNvWibv19zqGr82Yv04fr2hQJBRQ2jEqy4vKMUbPz6nxOhoAIAtcffhIryN0CQ1qH2mJp+R6HQJZIZZIK2DZsixLlYURzV7TrJxgUC/PXaecCF+OAaA7jh1XrWiE0guZ1diW1PpY0usYyBJNbSk1xBKyLMk1Ri2JtMb3jygU8MfwYgDQ2wSYIwYesCxLjislHY5CdldbKq14ylV72lXKcbWioU2rGuP6+v5D5JNRVwEAPrZgbUxj+xd5HWOrOErqIx8sb1CaS6iRAUnHVcA2siUZIzmuo5xwUCX5QfUrZB5LAOiOAEM0wgPRUEBl+YyAgsxYUNOiulhSjuvKNVKsPaXDx1WpsoCr+AEA8JOCHA4Zb4/CaEhFOUEl0q4Ko0GVF0SUdFydsvtAr6MBANBrUG34yLDyfDmGBjUy4/lZNSrODak95Shg20q70l5DyhgmFAAAH4mGAgwNioyJBG3VtSTUntrwnSUaCiqeSOujFY3eBgMAn6osiKh/MSeJI/PqWxNKprmCurua4im1JhzFk2kV54ZV09Smtc0J3fvCfK+jAQCywOSxVRo3oNjrGFtFg9pHcsNBGtTICMc1iiXTCgUsxZOu0o5RVUFEz366RsU5Ya/jAYAvHT2uStEwpRcyq741oXqG+EaGtKcd1bcm1NqeVGE0pEDAViQU1IRBxV5HAwAAXWSMkW3ZioYYAaq7SvMiKogE5LqOBpbkyLYtleVLVx42SjZD9wMAepjlk/kkOErqI8Mq8mRoUCMD4sm0EmlXjjFqjafkGqOS3JCOHV+taJCrsACgOyJcxQoPFOaElBNm20NmREKWVr8YVzBgKxqyFA1KKxvaOBALAN3EFdTwgmVZOnBUudcxfK2lPaUPljUo5Uq54YAa2tKKhix9sqrJ62gAgCwwYVCxL5rUNKh95LF3ljEHNTIimXaVEwrIdaU1ze0K2JaMMXrk7WXKi4S8jgcAvvSN/QYrL8o+FJnV0p5WfWvC6xjIEovWtcpIco3+Vz9aGlmZr7H9i7yOBgAAusgYo+mf1ijlMMR3d81a1aTmeFKxRFqtibRqmtrVmkjqly8v9DoaACALXHnYcO06uMzrGFtFg9pHvrH/UBnRoEbPa0uk9daiOhkjNcXTClpSu2N03K79Nag01+t4AOBLuVzFCg/kR4IMz4iMaYonFbAtJR1XsXZHIwbna0AxtSMAAH5TXhDxOoKvJdKu5qxqUso1Ki+Iak1TuxJpRyfu2s8XV7QBAPxtl0GlXkfoEhrUPvLWojo5XEGNDEimXVUWRJRIu0ql00rJVk7Q0k5VBQzRCACAj6xubNPSujavYyBLfLq6QZakVNrIzrE0uDRPTfGk1rW2ex0NAHypKBpSRWHU6xjIMpZlqX8R2932yAnZelpGActS2jFKpV2lXUsDynJlieNqAABINKh9ZVz/Qq8jIEvEEmm9t7he69sSCgRsJVOOCqJhvbOk3utoAOBbew8pVSjIlazIrNL8iIIBtjtkxqermhRrT8uyLUUCASXTrt5dul6DuIoaALolGLBU4XUIZKVE2lUizRDf3bVkXaua4o4cuVrdGFN9a0LptKvX5q/1OhoAIAtEAtL4gSVex9gqGtQ+8sHyRq6gRkYkUo7WxxNqT7mS2XD2bCLtaHl9TGX5Ya/jAYAvuY4r0aBGhgUsS2G2O2RIQ1tSKSPlhG05rqu2pKtd+heqOJdhQgGgO3IjTBEDb4zpx0Uy22N1Q0zhgFFhNKz8SFiWbakt5WheTQvXTwMAetxlk4Z5HaFLaFD7yODSXBrUyIi0a/TWonVaHYsrYBkFw0G1JR1VF+aoOC/kdTwA8KUAV7HCA2nHVSLF1S/IjJb2tFzXkZu2lQ4Z7T64WHPXNGtIWb7X0QDAl8oLwiqM8h0c8JtVTe1KuVJuTlCxRFqptCvHuDpz78E0qAEAPS4Y9Efr1x8pIYkhvpFZj79nK5E2SrpSSSSgwaW5aoqnlHA4yA0A3TG2mpPMkHlL6mKavbrZ6xjIEolUWilHclyjsSVRtaccvbZgrfoXt3odDQB8aZcBRRpcmud1DGQZY4zeWcwUb9tjaV2bjOsqNxRUY1tS7SlXQcsS1x0BADLBsvxxOhQNah9pTaRFHYNMSKddxRIphQKW0mlXgYClnEhAJXlhleUxxDcAdIc/SkP0NQHbUjjA1ofMKM2LqD3tqjQakG0HlEg7KsuP6IrDd/I6GgD4UpDPcHjAGKO61qTXMXytIBpQ2pXCtqW2pKuU6yovGlK/oqjX0QAAWYAGNXa4NxfWyzVcvYqeF0um1dSWluMaBYOWinPCCsjSO4vrVJrHHIIA0B3DynIVCjGPIDIrEgooj6FBkSHxZFrhgKWkkWRJew4pUdqV5te2eB0NAHypsiCi/sU5XsdAlrEsS4NKc72O4WuNbQnJshQNB5VMbRjiuyQa0ujqAq+jAQCyAA1q7HCzVjQpTYMaGZB2XTW3p2TMhiEaB5fmKpZK6+TdB2inKoaaBwDALyrzI1xBjYx5aU6NZFlKOY52G1SseNLVhEHF2mVAkdfRAADANqB63D7FOWFZliXXGLUm0nKMNLq6QKGA7XU0AAB6DRrUPrJTVZ7ShkG+0fMSaUcvz12raMCS0YYietaqJkWDAbUm0l7HAwBfygsHfHMGI/qOebXN+mRlk9cxkCVWNLTJklFpXlQDS3K1oLZFxTkhtaccr6MBgC9VFUQ0pDzf6xjIMpZlKRy0lUhzkUx31ba0K5V2tKI+puZ4SsaV8qJB/e71RV5HAwBkgf2Hl2ncgGKvY2wVDWofSbpGaZcGNTLASOGAreZkUgHbkpGloG3psXeXKy/KbgMAuuObE4cy1DIyLpFy1BxPeR0DWcI1RvnRkErzQsoNB/Xe4no1xVPi+DYAdM+eQ0p0Hg1qeKApnlKSD/BuW7K2VcGApYRjlDZGliW5rqWhvJ8BABlg2/4YsYNOk4+0pxylHBrU6Hlp11VlYVjNtUkFLUuNbQnVtSZUnGcUDvpj5wYAvY1hFBR4YLdBJRpSxoEwZMaK9TE1xdMqL4iqprFNaxrjcmTp9D0Heh0NAHxpUAnzTyPzjDH6dFWT0g4N6u6qj6WVE7LVnkorkXYVsCVZUmMbJ44CAHpe/6Ko1xG6hAa1j+xcXSiHg9vIANcYfbqqUZGwrbBta/HaVtW1JnXpYaM0rj9zCAIA4BfL6mOas6bZ6xjIEuvbUnLctAojQX22rlVJx6iiMKLSvLDX0QDAl3IjHLZD5lmWpQsPGu51DF+749m5iqfTampLSkYqLYioIBrkpBMAQEaM6VfodYQuodL1kX5FUdGeRiakXVfJtKtIMKD8cED18aRaEym1J9P6eEWj1/EAwJfG9S9UMMAoFMg0Sy4XvyBDokFbqZRR0LbUlnBVnBtSJGCpKZ70OhoA+FJBJOB1BGQhY4xemFPjdQxfW7S2RWFbaosn5RipIjespnhKhTkcigcAYCM+FX1k2fo2uYYjjOh5ibSjgcW5WrSuVYU5YYUtW5ZsvbmoXuX5Ea/jAYAv7VSZR4MaGZd2XDl0qJEpRkq6RgNKc9UST2t+bbvmrGlWe4rTbAGgO3YfXKSd+xd7HQNZxhijj5Y3eR3D1yLhgIKWFHeNjJEqCyOKBAMK+GROUAAAMoEGtY8URkNyGeIbGWAkRYKW0mlXgYClYNBSTsTWrJVNNKgBoJuOHV+lnEjI6xjIMo1tSa1uavc6BrJEICgVRsM6aFSFnvpgpda1tCs3HNA5E4d4HQ0AfInv3/CCZVk6ZOcKr2P4Wmt7sZ76YJXak44kKT8aUm7IVhFXUAMAMsCyLK8jdAmfij7Srzgq+tPIBNcY1cVScoxRWX5YrfENBfUBI0s1vNwf8xcAQG+TG6Y5jcwbWp4vV/74YgL/q2tt19DyHDW0JZV2HMWTjsoLor75cgwAvQ37T3jBsiztM6zM6xi+tqCmWc2JtFJpV5YlOa7RuuaEHn9vhdfRAABZ4LJDhisY7P3t396fEB2SaZcrqJERiZSjRWtb1J5ytevAYv1nVo3yw0EV5UbUnna8jgcAALqoX3FUhTmcHIHMeGVerXIjITnGyJKtlnZXJ0wo05h+nOAIAN1BexpeqW9NeB3B15atb1N7Mq32tKvckOS4UlM8oa/tP0S2GOYbANCz/NCclmhQ+8q7i9crxRyCyAQjxVKOHCPlhWwlUo4qinM1YVCx8iLsNgCgO5h/Gl54f+l6fbiCOQSRGbNXN+uY8ZVa09iu9fGEwkFp0k4VmrO62etoAOBL5QVh9SvK8ToGslBbkosTtofrGsWSG46rFedGlEintT6W1DtL1nPiCQCgx+0ysECBQMDrGFtFp8lHDt6pXFw/jUxwXaMHXvlMkaCtVxfUqTQ3qIST1k+fn6+cMLsNAOiO648erZL8qNcxkGWi4YAKOLkMGTKyPFe54ZBiibSSaVf5kZD6FeXIZohaAOiWaKj3H1hE31Scywg826M97ao1npTjSmW5YbWlHFmyNKa6wOtoAIAs4JdpYjha5SMfrWiU49KiRs9LOa6M66o0L6xl9TENLMnVsvo2leeH1a+I5goAdEfQ4gpqZF4q5ao1mfI6BrJEv+IcjetfpE9XN2ltc0LDK/L06DvL1a+Y+hEAumNkZb72HFLqdQxkGdd19faieq9j+FpNU7vi6Q2jYJbkR9TUllBLe1ozl6z3OBkAIBscPKrc6whdQoPaR1oTaaUdGtToefGkI0eWKvLCSqSNmhMprWiIaf8RZRpZxdmeANAdwYA/zl5E3zJxVIUmjqrwOgayxOrGuGLJlBpjSTW3p7RvSYleW7BWwyvzvY4GAL7kOC4NamScbduaNJr6cXuEbEuJlCtLkm1baml3ZCxpTP9CWQzyDQCAJBrUvjKsPE/G0KBGz3NcV7ZllHalnarz9MbCejmOUV44qIZY0ut4AOBLLqOgAOjjWtrTiicdrY8l1druyHWM6mMp7RpmmFAA6I5ImEYWMs8Yo7lrWryO4WtzaprUnnIUDEiukVqTjhxj9OZndb4ZdhUA4F+Tx1YwBzV2LEuWXK9DICskUq7a00YVhRGFAwGta21XXiSoeNpRTqT379gAoDeiPQ0v1Da3q6ap3esYyBJp19XK9W36eGWD8sIBWQFblQUhrsICgG4awBQJ8Agt1O0Ta08r5UhFUUvxpKOU4yoYsHXAyDKJBjUAoIfZtj+mGaRB7SNvL6pTmquvkAFtiZRsGfUviurdJfWyjKWSvLBqmxJqjDGPJQB0R8rlNDNkXjQUUGle2OsYyBIt7Wm9tahOacdo/IAivbNkvQKW9OFy5lsEgO5Ipou0U3Wh1zGQZSzL0pK6VsXa015H8a2PVzXJkWQHbC1viKkpnlJRTlDL1se9jgYAyALTP63Vcbv19zrGVtGg9pHGeEpph4Pb6HmOa1SWH1FOJKDF61oVCdnKjwa1y8AiVRdyBjcAdEcOQ9zCA41tSS2rb/M6BrKEJaOqghx9kG5QTiSg+WtaVFUY1ocrGr2OBgC+FLCkyeP7eR0DWSadTuu52bVcJLMdWtoTkqSSvJwNF4FYUkluRMvWt3F1OgCgxx2yU7nXEbqEBrWPnH/AMBkGCEUGtCcdfbi8Qe8uqpdjpMKArZBt6+3F9SrOocECAN2x37ASRcNMk4DMGlKWpyFleV7HQJaoa2nX9E/XqC6W1FufrVXQtjRxRJn2GFrmdTQA8KUR5fleR0AWCgaD+uHRO3sdw9ceeGWBZq2MqaogpEXxhIIBW1UFEX3nsJFeRwMAZIFBJbleR+gSGtQ+Ek+lZehPIwMa40nFUo7iKUeWpHA4qFgirZ37FWhYeYHX8QDAlyIhyi5kXkMsoYY2pudAZny4vEGvLahTPJHWyPJCFecarWhIKC/a6nU0APClSDCg3QaXeB0DWWhVI0NRb4+V9THZktKO1J52ZUtqTqQ07e1lXkcDAGSBaybvpGCw9x+H7P0J0eG+F+YzvA4yIu0YxdrTsixX+eGgIiFbScfoK3sMUlURQ3wDQHdEQrbXEZCF/vHRKr26oM7rGMgSqxvjakkktdfQEq1oaFcoKA3JzdWAEupHAOiOslxGMEPmua6rZz5Z7XUMX1tcH1coILUlU0o5RrkhW+1JV6X5Ya+jAQCygGX5Y0IJGtQ+YozECN/IBNuSdqrM05xVzcrPCSo3aMuVVNucUEu743U8APCl3QcXyaZHjQwbVJqjnfsx+gkyY3hZrl6cl5YxlpriCQ0tzVV+NKwcRpAAgG4JcYIjPGBZlpraU3LSHITsLtcYRYJSfSypZNpVTtCWI6PiXBrUAICeN6+mReMGFHsdY6s4UuAj/YpzlHYoDtHzHNdozqpGubYUtCXZUsgO6PXP1qmQOagBoFvG9itQKMgc1MisUZWFivhgWCf0De8tqVdDLKWhJUbhUEiRcEj9i3IU5OwcAOiWoE3tCG8M8cnclb3Vp6sbVRDJUSyRlC0pGt4wB3Xacb2OBgDIAqOr8r2O0CUcrfKR0dUFcqljkCHza5plW5bsgK3GtpQqCgOaPL5KBVHO9gSA7oiGOcCIzGuMp7SaOQSRIXNWNSoStLWqOaGqgpBGVeYr5boqzuVrJwB0R26E+hGZ5ziOZq9u9jqGr+VHQgoHjOpajIJBS66x1BBLaeG6Vq+jAQCygDH+uNCVIwU+YsyGIWKAnmaM1JZ0VJQTVnM8IcnWXiV5mru6RdEQX5ABoDuGluYqGOAqQmTWzCX1eo05qJEhK9e3qbIgrNakq5GFOSqKBhW0LOVF+NoJAN0RpnaEBwKBgE6YMMDrGL62Yn1ML86pUcrdMLy3MZJlWzpkp0qvowEAsoDtk1HMOFLgIyW5IaUY4hsZYMyGYb7DQamuVaoqCCqWTGvXQUU0VwCgm0JB9p/IvF0GFnsdAVnk37NSGlIS0YcrW+RKmrWqSWnXaPaaJq+jAYAv7TmkVBceNMLrGMgylmWppT2leNLxOopvrWtpV21TQglHCthGTjKlYNzSv2et9joaACALtLSndPxuvf9kMxrUPjJ3TascxvhGBjhGKs4Nan5tq3LDAQUDAVUXRfXWonrlR5iDGgC6Y0R5Pif5IOOSaVexJPUjMiNo21rXkpJxXC1b36ZoKKD+xVHtOqDE62gA4EsjK/wxfyD6nt04yXG7vLrAkW0bBSTlRoIKWdJuA0v0zQOGex0NAJAFdq4u8DpCl9Cg9pEz9h7km7Hj4X+vL1ir1nhSQ8ry1b84qkEluSrMDSkaYrcBAN0RsLxOgGy0cn2bFtYyhyAyo19BWK8tqFM0YmlYYZ5yI0HFEmmtamzzOhoA+FJZHieIwxvza5qV5iKZbqtpbldLe1qSlEwbGdtSWzKpf3/MFdQAgJ7nutUaN6DY6xhbRafJR95dXCfHpUGNnhdPOVpSF1MoYCsSsjV+QJGWr29TvCatUIA5qAGgO/YdWqqI1yGQdYryQqooiHodA1libUu7HGNUlhtRRUFUa5vbNGd1TLEEB7gBoDvyInz/RuYZY2RZFsd/tkPItuW6UiQohWypKCek/GhIuw4q9joaACALjOlX6HWELqFB7SPPzqpR2mX+F/Q8xzEKWJYC4YDCwYCKc8J6e1G9JgwuVlkeB7kBoDsCFieZIfP2GFyqfkW5XsdAlnjh01WKhmzlhIJauK5VqxraVF0U1XG79vM6GgD4kl+GZ0TfYlmWRlUxvPz2mLemUY6kqGXJWLYKc0Mqy48wdDoAICMsyx/DONKg9pFRlflKM8Q3MiDtuHpvyXoNKY2qICek+WuaJSONrixUOMT8qQDQHQGuQIAH1jS2ac6aFq9jIEu8u2S9HNdofTyt/oURWbK077BSDSzhJAkA6I6SvLDXEZCFjDH6z6waOQzx3W3vLWmQ40qOMTLJlFLpsNa3JfXgqwu9jgYAyAKn7TFA4weWeB1jq2hQ+0hTe0qpNA1q9DzHOIoGbYVCIeWFA5pb06LdBhWpMDeogEWDGgC6wx/nLqKveXn+Wr2xoM7rGMgSKxvaVV0YUUVhVCsa2uQao6X1cVn2eq+jAYAvJdPFGtOvyOsYyELrYwk59Ke7rS25Yf5pWZai4ZBCQVuLaltUWZjjbTAAAHoRGtQ+kkq7SjMHNTIg5RgVRINy045a2lPKjwY1aacK7TaoVEW5Ia/jAQCALtp7SInCNlfvIzNWNbVp2bqY1rYktbY5qZ2r8nTyHgM0aXSl19EAwJfCAU4QhzeKOfazXQK2FAlItiyV5gZVmhPSvNpW7TOs3OtoAIAswBzU2OGCtiXa08gEy7KVSLuS7arANTp2fD9JlubXNnsdDQB8a/dBJQoFOciIzAoHg2x3yJj1rUm1O64KI7ZCAVvl+RHNW9Osz2oYZh4AumP8gCIdNb6f1zGQZYwxWh9Leh3D1xxXsi1JllF+NKia5naFbUth6nIAADrQoPaRaDiooON4HQNZwLhGAduSLUtVhVFFQgE9++katSUdUUsDQPeMKs9VSQFDuiGz4sm0WtrTXsdAliiIBhS0o5q7pkkF0aASaVf//HiNBpay7wOAbrEsGtTIONd11Zbg+OP2yI0EZSRFggFFggEtWx9XWW5YjuHSIwBAzzM++byhQe0jrYmU6E8jExJpVwW5YVUVRDWyIk9PvL9CliwdObZKeRF2GwDQHTkhhslD5g2ryJfN8KDIkIW1Ib0+f53qYymNrMjRwnVtCgYsDS7J9ToaAPjSgJKo1xGQhQKBgPYfUeZ1DF974zOjpetalRcJKp50lHZcRcO22hIpr6MBALIADWrscHsOKZFLgxoZkkynVJIX0VuL1is3ZKshltI7S9YzBxYAdNNhoysUpfRChjXHU6ppjHsdA1li8dqYlqxvVcCyFAoE1NAW04jyfKUc1+toAOBLyRT7T2SeZVmavbpJadcfB7d7oxUNbUq7kiWjZfWtkpGW1MWUSLFOAQA9b/aqRk0YWuF1jK3iKKmPHD6m2jdnPsDfXNfoj28vUWM8Lcmoqd1o14FFcowl27K8jgcAvsT+E15YWhfTRyuavI6BLFHTHFddc1LVRVE1taeVFw6qJZlWA/NYAkC3NMfZfyLzHMdRW5IrZLZHMGApGpQKc8Ja15pSTthW2jEa07/Q62gAgCwwfmCJ1xG6hAa1j1z12PtKOTSo0fPSxqi+NancSEDtKVcTBhRpVWO7hpbnqSg37HU8APClAA1qeGBASY7GD+BAGDKjOR6XkauA7WppXUJleWGVF0RVmMPXTgDojvwo37+RebZta2z/Iq9j+FpDLKH6cEj1re0KWFJLPK0BxTnKZ9o8AAA68KnoI5ZlyWJ0ZWRA0Ejl+SG1JdLaZUCRHEn50ZAmjihXJMRGCADdEQkHvI6ALPTagrV6bUGd1zGQJZbWxWRbllraHRXkBNWvOKIDRpZr90HFXkcDAF/qX5TjdQRkoXQ6rTc/Wyeukem++taELNuorjWtgC25ZsP3QZtzlgEAGTCvpsUXV1HToPaReNoV07chE4wxsmxbeaGAEo7RgLyQ/rusQR+vbBJTUANA9/zmnD00uLTA6xjIMvUt7VofS3gdA1kiELDUryii1c1pDS3J1YCSPD03a41enFPrdTQA8KW9hpRoyonjvY6BLGPbtnYf3PsPavdmyxtimrWiSbLSSqWlkryg9hpcqlP2GOR1NABAFhg3wB8jodCg9pF9h5cqzemLyJCPVzSoqjCqsG3rD28tU05Qys8JyRg61ADQHS5nmcEDk3fpr+qiXK9jIEssbWjTf5fWK2hLriXNq2nWiPJ8HTmuyutoAOBLw8vzvY6ALGRZloJBLvXdHrZlaX2sXdb/DuNWF+aooS2p37++2NtgAICscM9p4xUO9/6pYmhQ+8jw8nw5Lg1q9DxjpPZkWh8ub9Rnta1yXUcFOVEdOrpKBTkhr+MBgC8VRym7kHlrWxNasj7udQxkiYbWhNY2J1WaG5bjuEqmjCoKIpqzutnraADgSwHL1q6DuJIVmWVZlupakkqmOcG2u9Y2JxRLSq6k3LClZNrRW4vrVJ4f8ToaACAL/OfTGp20x2CvY2wVR0p95PUFdUq5FIfIACO9v6xBSceRLVd7DCzS8qaE/vnJagWYMAcAuuWY8ZUqZoRvZFhLW1pNbe1ex0CWmF/bItuSinICamxLadcBRXphbq0izBEDAN3SGEvphAkDvI6BLOM4jta3MkXM9qhtapdtSbaRRpTnqyWeUsCSCqO9/2o2AID/He2TUcxoUPvIJ6vWK8UQ38iQVDqlsvyo4iFp9tqYiqNBfW3vQYpGuIIaALqjX2GO1xGQheaubtS8Na1ex0CWiCcd5YYDamhLq19xRJ+sbpZljErzOBgLAN1Rms/+E5lnWZbWNDECz/ZIOa7SRiqOBmSMq/q2pIpyQmpPOV5HAwCg16BB7SP7DC1TmiG+kQHGSMvqW/XpqhblhC0NLIpoQEme5q+NiQuoAaB7ErsPUJ7XIZB1yvIjKqY5iAzZUCc6si1pZUNc61tTKogGtKKRg9wA0B2la1u8joAsZIxRYZSLE7bH+lhSAUmV+WGtaGiXJSmVcpQXDngdDQCAXoMGtY+8umCdUjSokSGxeErFOQHlhMMK2QGtb0sqEgwoSIcaALrFcZimA5lX0xxXfQtDNCIz4qm0WhKOQpaUdKTiXFvxpKMgc1gCQLc0xJJeR0AWSqVSmlPLyRHboy2eUjQsNcRTSqUdGSOFgrYa21JeRwMAZAFj/NFHpEHtI3sNLWWIb2TMrJUNco0rx3XlWrbWNLWrMBKSRX8aALolZWjQIPNcY+RwgiMyxHWN+heEVRdLqV9hQHXxtBxHigS5WggAuiMctL2OgCwUCoVUmRfxOoavtQZtpZ201relJSMFA1IimdZ6u93raACALOC6/jgGSYPaRz5Z2SiXBjUywEhqiiclYykckuJpV4ePrlB1SY5s0aEGgO4oZDg3eGDvIaVKM9UdMmR9LKWZS9crJ2RpZXNa0oYvnPFU2ttgAOBTjXGuoEbmWZalXQYWeR3D12qa2vTO4gY5rhS0pFRaCgYtRYMcigcA9DyuoMYO19qeVJoGNTIkkTLKjwRUmhuUJekfH6+RZYkrqAGgmw4YUapxeTlex0CWee2z9XpvWb3XMZAl2lOuLCM1JTZ8Z7EllRcEVVHAvg8AumPPISVeR0AWchxH7y9r8DqGrzXGk0r97+K1tNlQE+WFbJXmcigeANDzgj45IcofKSFJGlSSyxCNyAhXUk1Tu9Kuq7rWlBxjFAlIIYZnBIBuizBEIzxgy1WA8hEZErSM2j63vW24etrRqoa4Z5kAwM+KI3wHR+a5rqu6Voai3h7JtCsjydKGUQqDllTf5mh9W6vHyQAA2SAWiykS6f3TddCg9pEFa1vl0qBGhrhGCliWjNJKpV25RgrarhjhGwC6py3FEI3IvP8urdO6NupHZF5AkiOpqd1IYohvAOiOubU0s5B5xhi1tvtj7sreauMMO+Z/P3HKcQBABv3u9SX6wUmlXsfYKhrUPpJKO3KoD5EhaWfDMESWpKAtRUOWgrZhjG8A6KaAxRXUyLxYgqNhyDxL///ArC3JpnwEgG4pjHIFNTLPGCMqyB0vaksleSGvYwAAssCRuw7yOkKX0KD2kRYuvIJHHFdKdBzg5msKAHTHktomjRtY7nUMZJmBJWEtqKOIRGZsrlp0tWFkHgDAtqtrSXgdAVmK62N2rJA2jCezvi3ldRQAQBZYWNus3YdVeh1jq2hQAwAAZEAwwBUwyLzaliSnlsFzjB8BAN2TF+GwHTKvqanJ6wh9SkAbGv5hWwoFqIoAAD1vWHme1xG6pM9Wug888IDuvvtu1dTUaLfddtP999+vffbZZ4vL/+1vf9MNN9ygpUuXatSoUbrzzjt17LHHZjAxAADoy1Y1tHkdAVvRF+vHZi68Qi/ASRIA0D1MkeAPfa2GLC4u9jpCn7Jx2pN4WoqnuTYdANDz5ixfp71G9vM6xlb1ydO2/vKXv+jqq6/WlClT9MEHH2i33XbT5MmTtXbt2s0u/9Zbb+nss8/WBRdcoA8//FAnn3yyTj75ZH366acZTr55S5YskWVZWnbn8Uo21ngdp09zk+1adufxWnbn8XKT7b3+cXvbc/ZWrIvMYD37G3+/nvfH55Z4HQFfoq/Vj5I0c+ZMLfnf+zq+eoHXcZDFDD/b9eMk27X0zuO19M7j5STbPc/Da+M1s74y97OqJS30bn2thnzuueeUm5urZXcer9ii972OA2yCYxfINmzz6I59+oe9jtAlfbJBfe+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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# SC and ML\n",
        "\n",
        "plt.rcParams['figure.figsize'] = [10, 5]\n",
        "importance = XGB.feature_importances_\n",
        "\n",
        "x_tick_labels = union_features[0::8]\n",
        "x_tick_label_idx = np.linspace(0,len(union_features),len(x_tick_labels))\n",
        "\n",
        "fig, ax = plt.subplots(1)\n",
        "ax.bar([x for x in range(len(importance))], importance)\n",
        "plt.xticks(x_tick_label_idx, x_tick_labels, size='small', rotation = 90)\n",
        "ax.set_ylabel('Feature importance')\n",
        "ax.set_title('Feature Importance of XGBoost')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 510
        },
        "id": "TBkAoIPjfO-h",
        "outputId": "399403f7-9a88-468d-ee86-644010e3f94b"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}