Assignment13_SVM_anomaly_detection.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5r2msEl9py3R"
      },
      "source": [
        "Python program to implement Credit Card Fraud detection using Support Vector Machine classification.\n",
        "\n",
        "Link for the Dataset:\n",
        "\n",
        "https://www.kaggle.com/datasets/dhanushnarayananr/credit-card-fraud"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hi0nFuvoppT9"
      },
      "source": [
        "## The Code"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q1ABUPU7L6em"
      },
      "source": [
        "### Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import accuracy_score,precision_score,recall_score,f1_score,classification_report,confusion_matrix\n",
        "from IPython.display import display\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from time import time\n",
        "from sklearn.ensemble import VotingClassifier\n",
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "from warnings import filterwarnings\n",
        "filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EULYS1zCLF6l"
      },
      "source": [
        "### Load the dataset into a DataFrame"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "W6DsZ9qZvD-v"
      },
      "outputs": [],
      "source": [
        "# Download the csv from the kaggle website and Load the dataset into a DataFrame\n",
        "def load_dataset():\n",
        "    # TODO: Load the dataset\n",
        "    # Define the dataset as a Pandas DataFrame\n",
        "    # fetch dataset\n",
        "    df = pd.read_csv('card_transdata.csv')\n",
        "\n",
        "    # data (as pandas dataframes)\n",
        "    return df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zXJONO10fCBU"
      },
      "source": [
        "### Function for applying DQM Checks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "-BleLeI_fCBb"
      },
      "outputs": [],
      "source": [
        "def data_quality_checks(df):\n",
        "    # Handling missing values\n",
        "    print(f\"\\nAmount of missing data: {df.isnull().sum().sum()}\")\n",
        "    df.dropna(inplace=True)\n",
        "\n",
        "    # Check for duplicates\n",
        "    print(f\"\\nAmount of duplicate data: {df.duplicated().sum()}\\n\")\n",
        "    df.drop_duplicates(inplace=True)\n",
        "\n",
        "    # Check for unique values in categorical columns\n",
        "    for col in df.columns:\n",
        "        print(f\"Unique values in {col} : {df[col].nunique()}\")\n",
        "\n",
        "    # Check for outliers using z-score\n",
        "    numerical_columns = df.select_dtypes(exclude=[\"object\"]).columns\n",
        "    z_scores = np.abs((df[numerical_columns] - df[numerical_columns].mean()) / df[numerical_columns].std())\n",
        "    outliers = (z_scores > 3).any(axis=1)\n",
        "    print(f\"\\nOutliers Detected by Z-Score: {df[outliers].shape[0]}\\n\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TW1Bn5f99_o7"
      },
      "source": [
        "### Applying profiling and visualization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [],
      "source": [
        "def profiling_visualization(df):\n",
        "    # Display basic information about the dataset\n",
        "    df.info()\n",
        "    print()\n",
        "\n",
        "    # Descriptive statistics\n",
        "    display(df.describe())\n",
        "\n",
        "    # Categorical feature profiling\n",
        "    print(\"\\nCategorical Feature Profiling:\")\n",
        "    categorical_columns = df.select_dtypes(include=['object']).columns\n",
        "    for col in categorical_columns:\n",
        "        # take top 16 values only and plot them in a bar chart\n",
        "        plt.title(f\"{col} Distribution\", fontdict={'fontsize': 20})\n",
        "        df[col].value_counts().sort_values(ascending=False).head(20).plot(kind='bar')\n",
        "        plt.xticks(rotation=90)\n",
        "        plt.show()\n",
        "    print(\"-\"*50)\n",
        "\n",
        "    # Numerical feature profiling\n",
        "    print(\"\\nNumerical Feature Profiling:\")\n",
        "    numerical_columns = df.select_dtypes(include=['float64', 'int64']).columns\n",
        "    for col in numerical_columns:\n",
        "        fig , ax = plt.subplots(1,2, figsize=(13,5))\n",
        "        plt.suptitle(f\"{col} Distribution\", fontsize= 20)\n",
        "        data = df[col].sample(200, random_state=42)\n",
        "        ax[0].set_title(f\"{col} Histogram\", fontdict={'fontsize': 10})\n",
        "        sns.histplot(data, ax=ax[0])\n",
        "        ax[1].set_title(f\"{col} Boxplot\", fontdict={'fontsize': 10})\n",
        "        sns.boxplot(data, ax=ax[1], orient='h')\n",
        "        plt.show()\n",
        "    print(\"-\"*50)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1hYPzzv-zwh7"
      },
      "source": [
        "### Function for Feature engineering"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [],
      "source": [
        "def feature_engineering(df):\n",
        "    # Normalize the Numerical values\n",
        "    stdscaler = StandardScaler()\n",
        "    df.iloc[:,:3] = stdscaler.fit_transform(df.iloc[:,:3])\n",
        "\n",
        "    # Remove class imbalance from the target variable present in the dataset\n",
        "    X = df.drop('fraud', axis=1)  # Features\n",
        "    y = df['fraud']  # Labels\n",
        "\n",
        "    smote = SMOTE(random_state=42)\n",
        "    X_resampled, y_resampled = smote.fit_resample(X, y)\n",
        "\n",
        "    # Display the class distribution before and after SMOTE\n",
        "    print(\"Class distribution before SMOTE:\\n\", y.value_counts())\n",
        "    print(\"\\nClass distribution after SMOTE:\\n\", y_resampled.value_counts())\n",
        "\n",
        "    # creating a new DataFrame with the resampled data\n",
        "    df_resampled = pd.concat([pd.DataFrame(X_resampled, columns=X.columns), pd.Series(y_resampled, name='fraud')], axis=1)\n",
        "\n",
        "    df_resampled.iloc[:,3:] = df_resampled.iloc[:,3:].astype(\"int64\")\n",
        "\n",
        "    return df_resampled"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nzYbDoKJ0IJU"
      },
      "source": [
        "### Train test split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [],
      "source": [
        "def split_data(df):\n",
        "    # Split the data into train test ratio for further processing using sklearn\n",
        "    sample_df = df.groupby('fraud', group_keys=False).apply(lambda x: x.sample(min(len(x), 50000), random_state=42))\n",
        "    \n",
        "    X = sample_df.drop(\"fraud\", axis=1)\n",
        "    y = sample_df[\"fraud\"]\n",
        "    \n",
        "    X_train, X_test, y_train, t_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "    return X_train, X_test, y_train, t_test"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I7FjpJkA0njp"
      },
      "source": [
        "### Prediction using SVM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [],
      "source": [
        "def make_SVM_predict(X_train, X_test,y_train):\n",
        "    # Code to instantiate the SVM model with proper hyperparameters\n",
        "    # Define individual SVM models\n",
        "    svm_model1 = SVC(kernel='linear', C=5, probability=True)\n",
        "    svm_model2 = SVC(kernel='rbf', C=5, probability=True)\n",
        "    svm_model3 = SVC(kernel=\"poly\",degree=3, C=5, probability=True)\n",
        "    \n",
        "    # Create an ensemble using a voting classifier\n",
        "    ensemble_model = VotingClassifier(estimators=[('svm1', svm_model1), \n",
        "                                                  ('svm2', svm_model2), \n",
        "                                                  ('svm3', svm_model3)], voting='soft', n_jobs=-1)\n",
        "\n",
        "    # Train the ensemble model\n",
        "    start = time()\n",
        "    ensemble_model.fit(X_train, y_train)\n",
        "    print(f\"Training time: {time() - start} seconds\")\n",
        "\n",
        "    # Make predictions\n",
        "    start = time()\n",
        "    y_pred = ensemble_model.predict(X_test)\n",
        "    print(f\"Prediction time: {time() - start} seconds\")\n",
        "    \n",
        "    return y_pred"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YplPv9OA0na7"
      },
      "source": [
        "### Evaluate"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [],
      "source": [
        "def evaluate(y_pred, y_test):\n",
        "    # Use different metrics to find the score of predicted result\n",
        "    accuracy = accuracy_score(y_test, y_pred)\n",
        "    precision = precision_score(y_test, y_pred)\n",
        "    recall = recall_score(y_test, y_pred)\n",
        "    f1 = f1_score(y_test, y_pred)\n",
        "\n",
        "    print(f'Accuracy: {accuracy}')\n",
        "    print(f'Precision: {precision}')\n",
        "    print(f'Recall: {recall}')\n",
        "    print(f'F1 Score: {f1}\\n')\n",
        "    \n",
        "    print(classification_report(y_test, y_pred))\n",
        "\n",
        "    # Plot the confusion matrix\n",
        "    cm = confusion_matrix(y_test, y_pred)\n",
        "    sns.heatmap(cm, annot=True, fmt=\"d\")\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TYRZ-f3G6Tpj"
      },
      "source": [
        "### Main function to call the functions and to predict and evaluate the predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>distance_from_home</th>\n",
              "      <th>distance_from_last_transaction</th>\n",
              "      <th>ratio_to_median_purchase_price</th>\n",
              "      <th>repeat_retailer</th>\n",
              "      <th>used_chip</th>\n",
              "      <th>used_pin_number</th>\n",
              "      <th>online_order</th>\n",
              "      <th>fraud</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>57.877857</td>\n",
              "      <td>0.311140</td>\n",
              "      <td>1.945940</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10.829943</td>\n",
              "      <td>0.175592</td>\n",
              "      <td>1.294219</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5.091079</td>\n",
              "      <td>0.805153</td>\n",
              "      <td>0.427715</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.247564</td>\n",
              "      <td>5.600044</td>\n",
              "      <td>0.362663</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>44.190936</td>\n",
              "      <td>0.566486</td>\n",
              "      <td>2.222767</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   distance_from_home  distance_from_last_transaction  \\\n",
              "0           57.877857                        0.311140   \n",
              "1           10.829943                        0.175592   \n",
              "2            5.091079                        0.805153   \n",
              "3            2.247564                        5.600044   \n",
              "4           44.190936                        0.566486   \n",
              "\n",
              "   ratio_to_median_purchase_price  repeat_retailer  used_chip  \\\n",
              "0                        1.945940              1.0        1.0   \n",
              "1                        1.294219              1.0        0.0   \n",
              "2                        0.427715              1.0        0.0   \n",
              "3                        0.362663              1.0        1.0   \n",
              "4                        2.222767              1.0        1.0   \n",
              "\n",
              "   used_pin_number  online_order  fraud  \n",
              "0              0.0           0.0    0.0  \n",
              "1              0.0           0.0    0.0  \n",
              "2              0.0           1.0    0.0  \n",
              "3              0.0           1.0    0.0  \n",
              "4              0.0           1.0    0.0  "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df = load_dataset()\n",
        "display(df.head())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Amount of missing data: 0\n",
            "\n",
            "Amount of duplicate data: 0\n",
            "\n",
            "Unique values in distance_from_home : 1000000\n",
            "Unique values in distance_from_last_transaction : 1000000\n",
            "Unique values in ratio_to_median_purchase_price : 1000000\n",
            "Unique values in repeat_retailer : 2\n",
            "Unique values in used_chip : 2\n",
            "Unique values in used_pin_number : 2\n",
            "Unique values in online_order : 2\n",
            "Unique values in fraud : 2\n",
            "\n",
            "Outliers Detected by Z-Score: 104695\n",
            "\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1000000 entries, 0 to 999999\n",
            "Data columns (total 8 columns):\n",
            " #   Column                          Non-Null Count    Dtype  \n",
            "---  ------                          --------------    -----  \n",
            " 0   distance_from_home              1000000 non-null  float64\n",
            " 1   distance_from_last_transaction  1000000 non-null  float64\n",
            " 2   ratio_to_median_purchase_price  1000000 non-null  float64\n",
            " 3   repeat_retailer                 1000000 non-null  float64\n",
            " 4   used_chip                       1000000 non-null  float64\n",
            " 5   used_pin_number                 1000000 non-null  float64\n",
            " 6   online_order                    1000000 non-null  float64\n",
            " 7   fraud                           1000000 non-null  float64\n",
            "dtypes: float64(8)\n",
            "memory usage: 61.0 MB\n",
            "\n"
          ]
        },
        {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>distance_from_home</th>\n",
              "      <th>distance_from_last_transaction</th>\n",
              "      <th>ratio_to_median_purchase_price</th>\n",
              "      <th>repeat_retailer</th>\n",
              "      <th>used_chip</th>\n",
              "      <th>used_pin_number</th>\n",
              "      <th>online_order</th>\n",
              "      <th>fraud</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1000000.000000</td>\n",
              "      <td>1000000.000000</td>\n",
              "      <td>1000000.000000</td>\n",
              "      <td>1000000.000000</td>\n",
              "      <td>1000000.000000</td>\n",
              "      <td>1000000.000000</td>\n",
              "      <td>1000000.000000</td>\n",
              "      <td>1000000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>26.628792</td>\n",
              "      <td>5.036519</td>\n",
              "      <td>1.824182</td>\n",
              "      <td>0.881536</td>\n",
              "      <td>0.350399</td>\n",
              "      <td>0.100608</td>\n",
              "      <td>0.650552</td>\n",
              "      <td>0.087403</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>65.390784</td>\n",
              "      <td>25.843093</td>\n",
              "      <td>2.799589</td>\n",
              "      <td>0.323157</td>\n",
              "      <td>0.477095</td>\n",
              "      <td>0.300809</td>\n",
              "      <td>0.476796</td>\n",
              "      <td>0.282425</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.004874</td>\n",
              "      <td>0.000118</td>\n",
              "      <td>0.004399</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.878008</td>\n",
              "      <td>0.296671</td>\n",
              "      <td>0.475673</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>9.967760</td>\n",
              "      <td>0.998650</td>\n",
              "      <td>0.997717</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>25.743985</td>\n",
              "      <td>3.355748</td>\n",
              "      <td>2.096370</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>10632.723672</td>\n",
              "      <td>11851.104565</td>\n",
              "      <td>267.802942</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       distance_from_home  distance_from_last_transaction  \\\n",
              "count      1000000.000000                  1000000.000000   \n",
              "mean            26.628792                        5.036519   \n",
              "std             65.390784                       25.843093   \n",
              "min              0.004874                        0.000118   \n",
              "25%              3.878008                        0.296671   \n",
              "50%              9.967760                        0.998650   \n",
              "75%             25.743985                        3.355748   \n",
              "max          10632.723672                    11851.104565   \n",
              "\n",
              "       ratio_to_median_purchase_price  repeat_retailer       used_chip  \\\n",
              "count                  1000000.000000   1000000.000000  1000000.000000   \n",
              "mean                         1.824182         0.881536        0.350399   \n",
              "std                          2.799589         0.323157        0.477095   \n",
              "min                          0.004399         0.000000        0.000000   \n",
              "25%                          0.475673         1.000000        0.000000   \n",
              "50%                          0.997717         1.000000        0.000000   \n",
              "75%                          2.096370         1.000000        1.000000   \n",
              "max                        267.802942         1.000000        1.000000   \n",
              "\n",
              "       used_pin_number    online_order           fraud  \n",
              "count   1000000.000000  1000000.000000  1000000.000000  \n",
              "mean          0.100608        0.650552        0.087403  \n",
              "std           0.300809        0.476796        0.282425  \n",
              "min           0.000000        0.000000        0.000000  \n",
              "25%           0.000000        0.000000        0.000000  \n",
              "50%           0.000000        1.000000        0.000000  \n",
              "75%           0.000000        1.000000        0.000000  \n",
              "max           1.000000        1.000000        1.000000  "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Categorical Feature Profiling:\n",
            "--------------------------------------------------\n",
            "\n",
            "Numerical Feature Profiling:\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1300x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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Vq1dPr7/+eroyrq6u+uKLL27pzoHY2FhJ0t13351lOT8/vzxvQ7rx3HNWcVasWFHDhg2TpHRjG2Tkk08+yfBuhjZt2lhX+lLvWLl5ucuXL0uSZsyYkeEbAFLdfHVs7dq11tgTs2fPVpkyZTJcrmPHjnrkkUckya6vFKxZs2aWYz6kXlUrW7asPv300wyf537rrbd01113Sbpx5T3ts+w3GzJkSIZXWR944AEFBQVJujFOyvjx41WtWrV05Xr37m1dNc/otysot7pvph5DNWvWlL+/f6br8fHxyfB1mUXV8OHDs7yaKt0YRyErvXv3VoMGDSQp3RXzmwUGBuqTTz7JcD8cOnSo9f837xup7S9Jbdu2zXT9xYoVk7e3d7rp2dUhLCxMDz74oKSM6/Dll19ad9+9++67WY5RkN9X3NOaNm2aUlJSJN14NbWvr2+6Mh07dtRzzz0nSfrjjz+0efPmTNfXuHFjvfHGG+mmOzk56dVXX5V0466Ym++0KWilS5e2/v/8+fM5Xi51PylVqlSW531PT095enrmPcA0sjsH59aMGTMyvLvumWeeUadOnSRJ8+fPtzkmiqI7ZV8FkHO3T+8IQL7YunWr1ZHr2bNnpgOLVaxYUR06dMjzdlJvSf/uu++sJEBhSEhIUExMjHbv3q1du3Zp165dKl68uM28zPj6+uqBBx7IdH7jxo0lSf/880+6eQsXLpQkVa1a1foHTE798ssvkqRatWpZtzRnJvUfXZs3b87XwUpz4/HHH5eLi0uG844fP649e/ZIkh577DHrLQc3K1asmDXA5fnz57N8A8ITTzyR6bx69epJutH5fPzxxzMs4+npaf3DM6PfrrDkdt9MPYb++usv/fHHH4Ueb0Hp0aNHrsobYxQbG6u9e/da7bZr1y4rKfnnn39mufwjjzwid3f3DOfVqlXL+kfezftG2sdqIiIichVzRk6fPq19+/bZ1KFs2bKSMq5D6jmlRIkS6tu37y1vP6+WLVsmSapTp06Wj3akjTF1mYw89dRTmf7dST3HSoV/rKb9x/6FCxdyvFzqfnL+/Pl0j5QVlKzOwbkVGhpq0+43S00MJCUlZfvImL3dKfsqgJxjzA7gDpN2TIamTZtmWbZZs2b69ddf87Sdnj17avz48dqwYYOCg4P16KOP6r777lObNm2sDn5+OXTokD744AMtWLBAhw4dyrLsmTNnVLVq1Qzn1ahRI8sr5Kl3odzcEb5+/bo1vkmbNm2yHZn+Zlu2bJEkRUdH53jZ69ev69y5c7c09kJepSYYMpJ2nJfsnnlPO3/Xrl1q2bJlhuVq1qyZ6TpSr9yVKVMm0+fb05bLzT9i8sOt7JtPPvmkJkyYoGvXrql169bq2LGjHnjgAbVp00Z16tTJ9X5WFHh5eWV6/N3s119/1bRp07RmzZosf7czZ85kuZ7UO4gyU6pUKV28eDHdNtq0aaOqVavqn3/+0ZAhQ/TNN9/ooYceUtu2bdW0adMcjf2yfv16TZkyRcuWLcvyzQ0Z1SH1DUKNGze2kmKF7dq1a9YYQ9kdzw0bNpSrq6vN+TAjWf0eae/0K+xjNe32MrpTJzMPPvigfH19FRcXp4ceekjt27dXly5d1LZtWzVo0CDfkhJpZXUOzq2c9ANS7dy5M8vksz3dSfsqgJzjzg7gDpO2w53dP5SzunU+O6NHj9Zzzz0nJycnnTp1SlOnTtXDDz+scuXKqW7dunrzzTd18uTJPK8/1W+//abatWvr008/zfYfk5J05cqVTOdl9w+K1ERI6m2yqc6dO2c9gpD2anBOnTp1KtfLSCrUO2bSyiqpkJv9KyAgIMPlbpbV75L6m+T0t0tOTs6yXH661X3zrrvu0ty5c1WqVCklJSVp4cKFGjBggEJDQ1WuXDk988wzhfpYTn7I6Lbymxlj9Pzzz6tz58769ddfs/2HRFbHtJT3fcPV1VULFiywBo7dvHmz3njjDbVp00a+vr7q2LGj5syZk+k+NXbsWLVp00bz5s3L9hWVGdUhNQGSl3NKfkn7OEd2x7Orq6v1KMitHs9S4R6rkm3CKTePV5YuXVq//PKLKlSoIGOMVq5cqVdffVVNmjSRn5+fHn74YesunfyS1Tk4t3LTDyjKr1q9k/ZVADlHsgO4gxXkleHUcT927dqlUaNGqVWrVtaV0N27d2vcuHGqXr36Ld32e+bMGT311FO6fPmyvLy8NHbsWEVGRurUqVO6du2ajDEyxmj58uXWMtmN2WEPqR2l+vXra+fOnTn+ZDS2SGHI6ZXK2/HOg/ySX/tm9+7dFRMTo//+9796+OGHrbuizpw5o6+//lpt27ZVr1690iXgiqqc7DszZ860xhVq0KCBZs2apT179ighIUFJSUlW2z3zzDOSCvaYrl27tnbu3Kn58+frueeeU/Xq1SXdSE4sWbJEPXr0UPPmzdMlLJcvX6633npL0o1H2z777DPt2LFDcXFxun79ulWH0aNHF1js+c3Rj+fUO2mkG4835cbdd9+t/fv36+uvv9ZTTz2lihUrSrrxeNr8+fPVpUsXdezYMd8S1Pl5t4gj/q6OWCcAecNjLMAdJu0VoZMnT2b5iEB+3HlRu3ZtjR8/XuPHj9fVq1e1bt06zZkzR19++aUuXryoJ598UgcOHMjT1cv/9//+n+Li4iTdGDwtLCwsw3IFfTXKz89Pzs7OSklJ0YkTJ3K9fOoVposXL2Y5wN3tIO0V0ez2n7SD3d3qQLVFTX7umz4+PurXr5/69esnSdqzZ49+/vlnffLJJzp+/Lhmz56thg0b6uWXX863+O3p888/lyRVr15dGzZsyHRQx8K6yuzi4qJu3bqpW7dukm68onrx4sWaOnWqtm7dqq1bt6p///42r6BNrUOpUqW0cePGTB/dy6oOZcqU0dGjR/N0TskvN/+9yEpSUpLOnj0r6fY7ni9fvmwNEu3l5WUNfpsbHh4e6tGjhzUmTUxMjH799Vd98skn2rt3r5YsWaJ///vf+vDDD/Mz9FuW3e+adv7Nv2vauxtSUlIyfQz00qVLtxBhztwp+yqA3OHODuAOk3YAzKxGIc/J/Nzy8PBQWFiYZs6cqYkTJ0q6cYX05lt8c3pVZvfu3ZJudFYy+8ek9H9jYhQUV1dXK0mxdu3aXF9pbtiwoaQbg5wV9dHus5M2WbNp06Ysy6YddPN2T/LcrCD3zZCQEL3++uvauHGjSpQoIUmaN2+eTZnCuLJZUNtIbbsHH3ww00SHMSbLQW0LUmBgoHr37q3IyEg1atRI0o3BRNM+ipJah3vuuSfLMYqy+v1T171ly5Y83RGQH7+Pu7u7Nbhvdsfz9u3bdf36dUm33/EcERGh+Ph4SVLnzp1VrNitXwsMDg7W4MGDtXnzZutOj5uP06IgN/2Am3/XtANQZ/UGm71792a5DfZVAAWFZAdwh2ncuLF1BeSrr77K9B/mx44d0++//15gcdx3333W/988OF/qa0IlZflK0tS3kVy9ejXT2/gvX76sr7766lZCzZEuXbpIunE1L7eP5qS+vcUYo48//jjfYytM5cuXt8Y4mDdvni5evJhhueTkZOv1uaVKlbL+YecoCmPfrFSpknVnVl6PoVtRUNtIbbusrgb//PPPdr3jQbqR5GzXrp2kGzGn3smT+l3Kug7bt2/P8h9lqeeUy5cva8aMGbmOL/X3udXfJjVZt3v37izfCvS///0v3TK3g3379mnkyJHW94xex34rvL29rUFAMxqINr9+p7zauXOnzSM8N5s5c6akG3c43fz64+DgYOv/s0rcffvtt1nGwL4KoKCQ7ADuMO7u7tYrP6Oioqw7LNJKSkpS3759lZiYmKdtnDt3TgsWLMjyDoe0iZS0HSbJdkC+AwcOZLqO1Ks4ly9fzvCKWXJysp5//nkdP348x7Hn1eDBg62r7P37989yhPejR4/afO/QoYM14v3EiROzvfq3c+dOLViw4BYjLjiDBg2SdONVmy+99FKGZd566y399ddfkm68BjCzV4PervJj3/zpp59s/gF9syNHjujvv/+WlP4YKl26tDVGTlbH0K3I6XGaW6ltt2DBggwf8zhw4IC1jxWktWvXav/+/ZnOT0xM1OrVqyXdePQh7R0cqXVYt25dhus4ffq0NeZIZp5++mlrXJ5///vf1rYycvM5Rfq/3+fUqVO39LaIAQMGWI8n9OvXTwkJCenK/P7779Y4K82aNcv2DR9FxcKFC9WqVSurfUaOHKn69evnah1LlizJMvEWHx9v/cP75uNU+r/fqaCO05zo169fhom5OXPmaNGiRZKkbt26pXvctFWrVtZdMB9++GGGf/MnTpyY7auz2VcBFBTG7ADuQGPGjNG8efN09OhRjRgxQlFRUXr22WdVrlw57d27V5MnT9bmzZvVpEmTPN1mn5CQoAcffFBVqlTRww8/rObNmysoKEjFihXTiRMntGDBAuvKSoUKFdS5c2eb5Vu1amX9/yuvvKJ///vfCgwMtG51rVKliooVK6bHHntMb7zxhq5du6bevXsrKipK999/v3x8fLR792598skn2rp1q1q3bq3169ffQotlLyAgQNOmTdOzzz6rU6dOqVmzZurbt686deqkgIAAXbx4Ubt27dIvv/yi6OjodB3bOXPmqFmzZjp37pwef/xxff3113r88cdVo0YNubi46NSpU9q+fbsWLFigjRs36rXXXrOu/BY1L7zwgr755htFRkYqIiJChw4d0sCBAxUcHKwTJ05o5syZ+vHHHyVJ1apVu60Gacyp/Ng3P/roI/Xo0UMPPPCA7r33XoWEhMjHx0fnz5/Xli1b9Mknn1iPTrzwwgs2yxYrVkxNmzbV+vXrNXPmTDVs2FANGjSQq6urpBuP19zqs+oNGzaUh4eHrl69qtGjR8vV1VVBQUHWPzYqVKiQ6WMoWXn22Wc1bNgwHT9+XC1bttSIESNUt25dXb16VStWrNBHH32ka9euqVGjRgX6KMvy5cs1fvx43X333XrggQdUr149lS1bVleuXNHevXs1ffp0a/t9+vSxefTh2Wef1YIFC3Tp0iW1a9dOr7/+uho3bixJ2rBhgyZPnqzY2Fi1bNlSkZGRGW7fw8NDX331lTp06KDLly8rLCxMzzzzjLp166aKFSvq2rVr+vvvv7Vo0SL98ssv6a6Kp55HU1JS9MILL+jFF19UmTJlrPmpg61mJzQ0VK+99pomTpyoP//8U40aNdKIESPUsGFDXbp0SQsWLNCUKVOUnJwsNzc3/fe//815IxewmJgYq87GGCUkJOj06dPasmWLFixYoB07dlhl+/Xrp3feeSfX25g7d666dOmi+++/Xx06dFDdunXl5+enCxcuaNeuXfr000917NgxSemPU+nG7xQTE6NffvlF//3vf9W6dWvrTgdvb+8Cf7146t/5Jk2aaMSIEQoNDVV8fLz+3//7f9ZvWbJkSX3wwQfpli1XrpweffRRzZ07V0uWLNGDDz6oQYMGyd/fX4cPH9ZXX32lH374Qa1atbLGRMkI+yqAAmMA3JF27dplAgICjKQMP7169TIRERHW95iYmHTraNeunZFk2rVrZzM9JiYm0/Wm/QQGBpotW7ZkGN9jjz2W6XJpY5k5c6ZxdnbOtOzjjz9uli1bZn1fuXJljutxszfffNNaT2ZmzZplPD09s6x3UFBQhstGR0ebunXr5qjt3nrrrSxjzY2c1GvlypVZtuHNzp49a1q3bp1lHUJCQszBgwfzHJMxxvTs2TPLNk2V0984N7I7Pm5130yNOauPs7OzGT9+fIbxLVy40Dg5OWW43JtvvpkvbTB8+PBMY0tbn5z+TsYYk5iYaDp06JDpej09Pc28efOyXGfac1BERESW2wsKCjKSTM+ePW2mp90Hs/p07drVXL58Od16e/funekyLi4u5qOPPsrRfr548WJTqlSpbOO4WXJysmnRokWOymd3fCQnJ5uBAwdmuX0fHx+zZMmSDJfPzfnjVvfRnP5uqZ/atWubH374Ict1ZhV/6n6Y3eeFF14wycnJ6da9fft24+7unuEyaffJ3J6Dszvm0rZzVm3m7e1tVq1alel2YmNjTY0aNTJd/oknnsj2b/Cduq8CKHg8xgLcoerUqaPdu3dr+PDhqlGjhtzd3VWmTBndc889mjNnjiIiIvK87qCgIP3xxx8aO3asOnTooFq1asnX11fFihVTmTJl1LZtW02cOFF///23dbXzZl9//bXef/99NWvWTD4+PpmO8t67d2+tXbtW3bp1U9myZeXq6qrAwEB17NhR3333nb799tt8fU1fdnr27KkDBw7o3//+txo3bixfX1+5uLioVKlSatGihd544w0tXrw4w2Vr1qypqKgozZkzR927d1flypXl6ekpNzc3BQYGqn379ho1apS2bt2qMWPGFFqd8sLPz09r1qzRl19+qY4dO8rf31+urq4qXbq02rdvr08//VRRUVEKCgqyd6gF5lb3zblz52rGjBl66qmn1KBBAwUEBKhYsWLy8vJSnTp1NGDAAG3fvl2jRo3KcPkHHnhAy5cvV9euXVW+fHnrro789N577+nzzz/X3XffLT8/v3w51lxdXfXrr79qypQpatKkiYoXLy5PT09Vr15dL7zwgrZt26ZHH300H6LP2tChQ/XDDz9owIABatGihSpXriwPDw95eHioSpUqeuyxx7Rw4UL99NNPGd7BMnPmTH311Ve6++67VbJkSbm7uysoKEjPPPOMNmzYkOO354SHh+uff/7Ru+++q1atWql06dJycXGRt7e3GjVqpCFDhmT4mICzs7N+//13jRo1SvXr15eXl1eeB4J0dnbW1KlTtWbNGvXo0UOVK1eWu7u7vL291aBBA73xxhvat2+fOnTokKf1F4bU80/VqlXVqVMnjR49WmvXrtXu3bv18MMP53m9H374ob7++ms999xzatKkiSpUqCA3Nzd5enqqZs2a6tmzp9auXatp06Zl+HesQYMGioyM1JNPPmm1a2EbO3asFi9erAceeED+/v5yc3NTlSpVNHDgQO3evdsamyYj/v7+2rRpk0aMGGH1Jfz8/NS2bVt9/fXXmjt3brbnBfZVAAXFyZgCfEE9AAAAAABAIePODgAAAAAA4FBIdgAAAAAAAIdCsgMAAAAAADgUXj0LALexY8eO6fz587lerkSJEgoODi6AiG4P169fV3R0dJ6WDQ4OVokSJfI5osJ1p9cfAAA4PgYoBYDbWK9evTR79uxcL9euXTutWrUq/wO6TRw8eDDPyZ6VK1eqffv2+RtQIbvT6w8AABwfj7EAAAAAAACHwp0dAAAAAADAoXBnBwAAAAAAcCgkO1Ao2rdvryFDhljfq1Spoo8++shu8RSGy5cvq3v37vL29paTk5Pi4uLsHZJl1qxZ8vX1tXcYDu/m/d7e7oTjDgDyE/0X+i93oqLWfylo7FeOi2QH7GLz5s3q169fjsrerh2L2bNna+3atdqwYYNOnDghHx8fe4dUYHL7R3HVqlVFrgN1KzKrz48//qjx48cX6LYPHjwoJycnRUVFpZt38+9yJxx3AFCQ7oTzKP2XzNF/yT+p/ZfUj5ubm6pXr663335bt8MoC05OTvrpp5/sHQaywatnYRdly5a1dwgF7sCBAwoJCVHdunUzLZOYmCg3N7dCjOr2cru3j5+fn71DsFFUj7vr16/L1dXV3mEAQLaK6nk0P9F/uXW3e/sUZv9l2bJlqlOnjq5du6Z169bp+eefV2BgoPr06VNoMcBxcWcH8t2lS5f07LPPysvLS4GBgZo0aVK6MmmvdhhjNHbsWFWuXFnu7u4qX768XnrpJUk3Mu6HDh3SK6+8YmV+Jens2bN68sknVaFCBRUvXlyhoaGaO3euzTbat2+vl156ScOHD5efn58CAgI0duxYmzJxcXHq37+//P395eHhobp162rhwoXW/HXr1unuu++Wp6enKlWqpJdeekmXLl3Ktg3at2+vSZMmac2aNXJycrJe01ilShWNHz9ezz77rLy9va2rQz/88IPq1Kkjd3d3ValSJV2bValSRW+//bbVrkFBQfrll190+vRpde3aVV5eXqpXr562bNmSbWwZOXDggLp27Sp/f395eXmpadOmWrZsmU2Zzz77TDVq1JCHh4f8/f31yCOPSLrx6tPVq1fr448/tn6jgwcPZrqtgwcP6p577pEklSpVSk5OTurVq5fVboMHD9aQIUNUpkwZhYeHS5ImT56s0NBQlShRQpUqVdLAgQN18eJFa52ptx8uWbJEISEh8vLyUseOHXXixAmrzKpVq9SsWTOVKFFCvr6+at26tQ4dOpTj+l+7dk0jRoxQpUqV5O7ururVq+uLL77Itj5prxidP39ezz77rEqVKqXixYurU6dO2rdvX67qcStu9biTst9XT5w4oQceeECenp4KDg7WnDlz0l3ddHJy0rRp0/Tggw+qRIkSeuedd5ScnKw+ffooODhYnp6eqlWrlj7++GObdffq1UvdunXTu+++K39/f/n6+mrcuHFKSkrSsGHD5Ofnp4oVKyoiIiJf2gvAnYX+C/0X+i//V5/C6r+ULl1aAQEBCgoKUo8ePdS6dWtt27bNmp+SkqJx48apYsWKcnd3V4MGDbR48WJJN47BsLAwhYeHW3eDnDt3ThUrVtSYMWOs9nNyctKvv/6qevXqycPDQy1atNCuXbuyjGvatGmqVq2a3NzcVKtWLX311VfWvCpVqkiSHnroITk5OVnfUQQZIJ8NGDDAVK5c2Sxbtszs2LHDdO7c2ZQsWdK8/PLLVpmgoCDz4YcfGmOM+f777423t7dZtGiROXTokNm0aZOZMWOGMcaYs2fPmooVK5px48aZEydOmBMnThhjjDl69KiZOHGi2b59uzlw4ICZMmWKcXFxMZs2bbK20a5dO+Pt7W3Gjh1r9u7da2bPnm2cnJzM77//bowxJjk52bRo0cLUqVPH/P777+bAgQNmwYIFZtGiRcYYY/bv329KlChhPvzwQ7N3716zfv1607BhQ9OrV69s2+Ds2bOmb9++pmXLlubEiRPm7NmzVr29vb3NBx98YPbv32/2799vtmzZYpydnc24ceNMdHS0iYiIMJ6eniYiIsKmvfz8/Mz06dPN3r17zYABA4y3t7fp2LGjmTdvnomOjjbdunUzISEhJiUlJdv4IiIijI+Pj/U9KirKTJ8+3ezcudPs3bvXjBo1ynh4eJhDhw4ZY4zZvHmzcXFxMXPmzDEHDx4027ZtMx9//LExxpi4uDjTsmVL07dvX+s3SkpKynTbSUlJ5ocffjCSTHR0tDlx4oSJi4uzfjMvLy8zbNgw8/fff5u///7bGGPMhx9+aFasWGFiYmLM8uXLTa1atcyAAQNs6uPq6mrCwsLM5s2bzdatW01ISIh56qmnjDHGXL9+3fj4+JihQ4ea/fv3m7/++svMmjXLql929TfGmMcee8xUqlTJ/Pjjj+bAgQNm2bJl5ttvv822Pmn3+wcffNCEhISYNWvWmKioKBMeHm6qV69uEhMTc1SPjMTExBhJZvv27enm3bz9Wz3ucrKvhoWFmQYNGpiNGzearVu3mnbt2hlPT09ru8YYI8mUK1fOzJw50xw4cMAcOnTIJCYmmjFjxpjNmzebf/75x3z99demePHi5rvvvrOW69mzpylZsqQZNGiQ+fvvv80XX3xhJJnw8HDzzjvvmL1795rx48cbV1dXc+TIkUzbDAAyQv+F/gv9F/v2XzZv3mx8fX3N7NmzrWmTJ0823t7eZu7cuebvv/82w4cPN66urmbv3r3GmBvHVKlSpcxHH31kjDHm0UcfNc2aNTPXr183xhizcuVKI8mEhISY33//3Tq2q1SpYhN/2v3qxx9/NK6urmbq1KkmOjraTJo0ybi4uJgVK1YYY4w5deqUkWQiIiLMiRMnzKlTpzKtJ+yLZAfy1YULF4ybm5uZN2+eNe3s2bPG09Mz087CpEmTTM2aNa0Tzs3Sls3KAw88YF577TXre7t27UybNm1syjRt2tSMGDHCGGPMkiVLjLOzs4mOjs5wfX369DH9+vWzmbZ27Vrj7Oxsrly5km08L7/8smnXrl26unTr1s1m2lNPPWXuv/9+m2nDhg0ztWvXtlnu6aeftr6fOHHCSDKjR4+2pkVGRhpJVocqKzef1DNSp04d88knnxhjjPnhhx+Mt7e3SUhIyLDszX8Us5P6h+f8+fPp1tOwYcNsl//+++9N6dKlre8RERFGktm/f781berUqcbf398Yc2MflGRWrVqV4xjT1j86OtpIMkuXLs11fVLbZe/evUaSWb9+vTX/zJkzxtPT0zpesqtHRlI7C56enqZEiRI2H2dn53w97rLbV/fs2WMkmc2bN1vz9+3bZySlS3YMGTIk0zqlGjRokOnevbv1vWfPniYoKMgkJydb02rVqmXuvvtu63tSUpIpUaKEmTt3brbrB4BU9F/+D/2XzNF/uaEg+i+urq5GUrp9t3z58uadd96xmda0aVMzcOBA6/u8efOMh4eHef31102JEiWsREjaOn777bfWtNRjO/WCys37VatWrUzfvn1ttvnoo4+af/3rX9Z3SWb+/PmZ1g9FA4+xIF8dOHBAiYmJat68uTXNz89PtWrVynSZRx99VFeuXFHVqlXVt29fzZ8/X0lJSVluJzk5WePHj1doaKj8/Pzk5eWlJUuW6PDhwzbl6tWrZ/M9MDBQp06dkiRFRUWpYsWKqlmzZobb+PPPPzVr1ix5eXlZn/DwcKWkpCgmJibL+LLSpEkTm+979uxR69atbaa1bt1a+/btU3JycoZ18ff3lySFhoamm5Zav9y4ePGihg4dqpCQEPn6+srLy0t79uyx2vP+++9XUFCQqlatqmeeeUbffPONLl++nOvt5ETjxo3TTVu2bJnuu+8+VahQQSVLltQzzzyjs2fP2sRQvHhxVatWzfqe9rf28/NTr169FB4eri5duujjjz+2ubUyu/pHRUXJxcVF7dq1y3O99uzZo2LFitkcG6VLl1atWrW0Z8+eHNUjK999952ioqJsPjfva2nl5bjLbl+Njo5WsWLF1KhRI2t+9erVVapUqXTryii2qVOnqnHjxipbtqy8vLw0Y8aMdMd0nTp15Oz8f3+6/P39bY4DFxcXlS5dOk/HAYA7F/2X7NF/yRr9l1vrv/z555+aN2+efv75Z73++uuSpISEBB0/fjzD/Sztth999FE99NBDeu+99/TBBx+oRo0a6bbTsmVL6/9Tj+2067i5ztltE7cHkh2wu0qVKik6OlqfffaZPD09NXDgQLVt21bXr1/PdJmJEyfq448/1ogRI7Ry5UpFRUUpPDxciYmJNuVuHvTQyclJKSkpkiRPT88s47p48aL69+9v84/HP//8U/v27bM5medWiRIl8rRc2rqkPvub0bTU+uXG0KFDNX/+fL377rtau3atoqKiFBoaarVnyZIltW3bNs2dO1eBgYEaM2aM6tevXyCjkd/cPgcPHlTnzp1Vr149/fDDD9q6daumTp0qSTa/d0a/tUkzmndERIQiIyPVqlUrfffdd6pZs6Y2btyYo/pnt6/kp+zqkZlKlSqpevXqNp+s4s7LcZefbv6dv/32Ww0dOlR9+vTR77//rqioKPXu3TtHx3RWxzkAFBT6LzlD/4X+S1ZS+y8hISF69NFHNWTIEE2aNElXr17N8bYvX76srVu3ysXFxWYsEYBkB/JVtWrV5Orqqk2bNlnTzp8/r71792a5nKenp7p06aIpU6Zo1apVioyM1M6dOyVJbm5uNlcIJGn9+vXq2rWrnn76adWvX19Vq1bNdhs3q1evno4ePZrpco0aNdJff/2V7h+Q1atXz9cRtkNCQrR+/XqbaevXr1fNmjXl4uKSb9vJyvr169WrVy899NBDCg0NVUBAQLpBuooVK6awsDC9//772rFjhw4ePKgVK1ZIyvg3ykpq++Vkma1btyolJUWTJk1SixYtVLNmTR0/fjznlUujYcOGGjlypDZs2KC6detqzpw5krKvf2hoqFJSUrR69eo81yckJERJSUk2x8bZs2cVHR2t2rVr56k+tyq3x112+2qtWrWUlJSk7du3W/P379+v8+fPZxvL+vXr1apVKw0cOFANGzZU9erVdeDAgXyoJQBkj/5L7tF/yRr9l7xxcXFRUlKSEhMT5e3trfLly2e4n6Xd9muvvSZnZ2f99ttvmjJlivX7ppWaIJL+79gOCQnJMIbM9u2023R1dc3VvgP7INmBfOXl5aU+ffpo2LBhWrFihXbt2qVevXrZ3HZ+s1mzZumLL77Qrl279M8//+jrr7+Wp6engoKCJN0Y8XjNmjU6duyYzpw5I0mqUaOGli5dqg0bNmjPnj3q37+/Tp48matY27Vrp7Zt26p79+5aunSpYmJi9Ntvv1kjPI8YMUIbNmzQ4MGDFRUVpX379unnn3/W4MGD89g6GXvttde0fPlyjR8/Xnv37tXs2bP16aefaujQofm6nazUqFFDP/74o3X156mnnrK5wrJw4UJNmTJFUVFROnTokL788kulpKRYt/dWqVJFmzZt0sGDB3XmzJlsr84EBQXJyclJCxcu1OnTp21GJr9Z9erVdf36dX3yySf6559/9NVXX2n69Om5ql9MTIxGjhypyMhIHTp0SL///rv27dtn/ZHLrv5VqlRRz5499dxzz+mnn35STEyMVq1apXnz5uW4PjVq1FDXrl3Vt29frVu3Tn/++aeefvppVahQQV27ds1VffJDXo677PbVu+66S2FhYerXr5/++OMPbd++Xf369ZOnp6fNG10yUqNGDW3ZskVLlizR3r17NXr0aG3evLlgGwEA/n/0X3KP/gv9l/zov5w9e1axsbE6evSofvvtN3388ce655575O3tLUkaNmyY/vOf/+i7775TdHS0Xn/9dUVFRenll1+WJP3666+aOXOmvvnmG91///0aNmyYevbsme5Cy7hx47R8+XLr2C5Tpoy6deuWYUzDhg3TrFmzNG3aNO3bt0+TJ0/Wjz/+aLNvV6lSRcuXL1dsbGyOLurAPkh2IN9NnDhRd999t7p06aKwsDC1adMmw+cYU/n6+urzzz9X69atVa9ePS1btkwLFixQ6dKlJd04OR08eFDVqlWz3m8/atQoNWrUSOHh4Wrfvr0CAgIyPWFl5YcfflDTpk315JNPqnbt2ho+fLiVpa1Xr55Wr16tvXv36u6771bDhg01ZswYlS9fPveNkoVGjRpp3rx5+vbbb1W3bl2NGTNG48aNs17/VRgmT56sUqVKqVWrVurSpYvCw8Ntxl3w9fXVjz/+qHvvvVchISGaPn265s6dqzp16ki6cRuli4uLateurbJly6Z79vhmFSpU0FtvvaXXX39d/v7+WXbA6tevr8mTJ+s///mP6tatq2+++UYTJkzIVf2KFy+uv//+W927d1fNmjXVr18/DRo0SP37989R/aUbryB75JFHNHDgQN11113q27ev9Rq/nNYnIiJCjRs3VufOndWyZUsZY7Ro0aJ0t34WhrwcdznZV7/88kv5+/urbdu2euihh9S3b1+VLFlSHh4eWcbTv39/Pfzww3r88cfVvHlznT17VgMHDiyw+gPAzei/5A79F/ov+dF/CQsLU2BgoKpUqaJ+/frpX//6l7777jtr/ksvvaRXX31Vr732mkJDQ7V48WL98ssvqlGjhk6fPq0+ffpo7NixVr3feust+fv764UXXrDZznvvvaeXX35ZjRs3VmxsrBYsWJDpnU7dunXTxx9/rA8++EB16tTRf//7X0VERFivYpakSZMmaenSpapUqZIaNmx4y+2AguFkcvIwFQAAeXD06FFVqlTJGqQNAACgsKxatUr33HOPzp8/L19fX3uHg0JWzN4BAAAcx4oVK3Tx4kWFhobqxIkTGj58uKpUqaK2bdvaOzQAAADcQXiMBciDtWvX2rzS7eaPvXXq1CnT2N59990C3/4LL7yQ6fZvvq0QjuX69et64403VKdOHT300EMqW7asVq1aZZdHdQAAtui/ZI3+C+BYeIwFyIMrV67o2LFjmc6vXr16IUaT3rFjx3TlypUM5/n5+cnPz69At3/q1CklJCRkOM/b21vlypUr0O0DAID06L9kjf4L4FhIdgAAAAAAAIfCYywAAAAAAMChkOwAAAAAAAAOhbexSEpJSdHx48dVsmRJOTk52TscAACKFGOMLly4oPLly8vZmeskBYk+CQAAmctNn4Rkh6Tjx4+rUqVK9g4DAIAi7ciRI6pYsaK9w3Bo9EkAAMheTvokJDsklSxZUtKNBvP29rZzNAAAFC0JCQmqVKmS9fcSBYc+CQAAmctNn4Rkh2TdJurt7U3HAgCATPBYRcGjTwIAQPZy0ifhwVsAAAAAAOBQSHYAAAAAAACHQrIDAAAAAAA4FJIdAAAAAADAoZDsAAAAAAAADoVkBwAAAAAAcCgkOwAAAAAAgEOxa7JjzZo16tKli8qXLy8nJyf99NNPNvONMRozZowCAwPl6empsLAw7du3z6bMuXPn1KNHD3l7e8vX11d9+vTRxYsXC7EWAAAAAACgKLFrsuPSpUuqX7++pk6dmuH8999/X1OmTNH06dO1adMmlShRQuHh4bp69apVpkePHtq9e7eWLl2qhQsXas2aNerXr19hVQEAAAAAABQxTsYYY+8gJMnJyUnz589Xt27dJN24q6N8+fJ67bXXNHToUElSfHy8/P39NWvWLD3xxBPas2ePateurc2bN6tJkyaSpMWLF+tf//qXjh49qvLly+do2wkJCfLx8VF8fLy8vb0LpH4AANyu+DtZeGhrAAAyl5u/k0V2zI6YmBjFxsYqLCzMmubj46PmzZsrMjJSkhQZGSlfX18r0SFJYWFhcnZ21qZNmzJd97Vr15SQkGDzAQAAAAAAjqHIJjtiY2MlSf7+/jbT/f39rXmxsbEqV66czfxixYrJz8/PKpORCRMmyMfHx/pUqlQpn6MHAAAAAAD2UmSTHQVp5MiRio+Ptz5Hjhyxd0gAAAAAACCfFNlkR0BAgCTp5MmTNtNPnjxpzQsICNCpU6ds5iclJencuXNWmYy4u7vL29vb5gMAAAAAABxDkU12BAcHKyAgQMuXL7emJSQkaNOmTWrZsqUkqWXLloqLi9PWrVutMitWrFBKSoqaN29e6DGnMsYoLi5OcXFxKiLjvwIAAAAAcMcoZs+NX7x4Ufv377e+x8TEKCoqSn5+fqpcubKGDBmit99+WzVq1FBwcLBGjx6t8uXLW29sCQkJUceOHdW3b19Nnz5d169f1+DBg/XEE0/k+E0sBSE+Pl7PTFshSfpqwL3y9fW1WywAAAAAANxp7Jrs2LJli+655x7r+6uvvipJ6tmzp2bNmqXhw4fr0qVL6tevn+Li4tSmTRstXrxYHh4e1jLffPONBg8erPvuu0/Ozs7q3r27pkyZUuh1uZmrp5e9QwAAAAAA4I5k12RH+/bts3zMw8nJSePGjdO4ceMyLePn56c5c+YURHgAAAAAAOA2VGTH7AAAAAAAAMgLkh0AAAAAAMChkOwAAAAAAAAOhWQHAAAAAABwKCQ7AAAAAACAQyHZAQAAAAAAHArJDgAAAAAA4FBIdgAAAAAAAIdCsgMAAAAAADgUkh0AAAAAAMChkOwAAAAAAAAOhWQHAAAAAABwKCQ7AAAAAACAQyHZAQAAAAAAHArJDgAAAAAA4FBIdgAAAAAAAIdSzN4BAAAAoGCdPHlS8fHx1ncfHx/5+/vbMSIAAAoWyQ4AAAAHdvLkSfV4+hklXU+0prm6uevrr74k4QEAcFg8xgIAAODA4uPjrUTHleC2ulK1na4nXrO50wMAAEfDnR0AAAB3iBRPX3uHAABAoeDODgAAAAAA4FBIdgAAAAAAAIdCsgMAAAAAADgUkh0AAAAAAMChkOwAAAAAAAAOhWQHAAAAAABwKCQ7AAAAAACAQyHZAQAAAAAAHArJDgAAAAAA4FBIdgAAAAAAAIdCsgMAAAAAADgUkh0AAAAAAMChkOwAAAAAAAAOhWQHAAAAAABwKCQ7AAAAAACAQyHZAQAAAAAAHArJDgAAAAAA4FBIdgAAAAAAAIdCsgMAAAAAADgUkh0AAAAAAMChkOwAAAAAAAAOhWQHAAAAAABwKCQ7AAAAAACAQyHZAQAAAAAAHArJDgAAAAAA4FBIdgAAAAAAAIdCsgMAAAAAADgUkh0AAAAAAMChkOwAAAAAAAAOhWQHAAAAAABwKCQ7AAAAAACAQyHZAQAAAAAAHArJDgAAAAAA4FBIdgAAAAAAAIdCsgMAAAAAADgUkh0AAAAAAMChkOwAAAAAAAAOhWQHAAAAAABwKCQ7AAAAAACAQyHZAQAAAAAAHArJDgAAAAAA4FBIdgAAAAAAAIdCsgMAAAAAADiUIp3sSE5O1ujRoxUcHCxPT09Vq1ZN48ePlzHGKmOM0ZgxYxQYGChPT0+FhYVp3759dowaAAAAAADYU5FOdvznP//RtGnT9Omnn2rPnj36z3/+o/fff1+ffPKJVeb999/XlClTNH36dG3atEklSpRQeHi4rl69asfIAQAAAACAvRSzdwBZ2bBhg7p27aoHHnhAklSlShXNnTtXf/zxh6Qbd3V89NFHGjVqlLp27SpJ+vLLL+Xv76+ffvpJTzzxhN1iBwAAAAAA9lGk7+xo1aqVli9frr1790qS/vzzT61bt06dOnWSJMXExCg2NlZhYWHWMj4+PmrevLkiIyMzXe+1a9eUkJBg8wEAAAAAAI6hSN/Z8frrryshIUF33XWXXFxclJycrHfeeUc9evSQJMXGxkqS/P39bZbz9/e35mVkwoQJeuuttwoucAAAAAAAYDdF+s6OefPm6ZtvvtGcOXO0bds2zZ49Wx988IFmz559S+sdOXKk4uPjrc+RI0fyKWIAAAAAAGBvRfrOjmHDhun111+3xt4IDQ3VoUOHNGHCBPXs2VMBAQGSpJMnTyowMNBa7uTJk2rQoEGm63V3d5e7u3uBxg4AAAAAAOyjSN/ZcfnyZTk724bo4uKilJQUSVJwcLACAgK0fPlya35CQoI2bdqkli1bFmqsAAAAAACgaCjSd3Z06dJF77zzjipXrqw6depo+/btmjx5sp577jlJkpOTk4YMGaK3335bNWrUUHBwsEaPHq3y5curW7du9g0eAAAAAADYRZFOdnzyyScaPXq0Bg4cqFOnTql8+fLq37+/xowZY5UZPny4Ll26pH79+ikuLk5t2rTR4sWL5eHhYcfIAQAAAACAvRTpZEfJkiX10Ucf6aOPPsq0jJOTk8aNG6dx48YVXmAAAAAAAKDIKtJjdgAAAAAAAOQWyQ4AAAAAAOBQSHYAAAAAAACHQrIDAAAAAAA4FJIdAAAAAADAoZDsAAAAAAAADoVkBwAAAAAAcCgkOwAAAAAAgEMh2QEAAAAAABwKyQ4AAAAAAOBQSHYAAAAAAACHQrIDAAAAAAA4FJIdAAAAAADAoZDsAAAAAAAADoVkBwAAAAAAcCgkOwAAAAAAgEMh2QEAAAAAABwKyQ4AAAAAAOBQSHYAAAAAAACHQrIDAADAgV27di3bMlevXtXevXt19erVQogIAICCR7IDAADAgcXGxmZb5vDhw+rXr58OHz5cCBEBAFDwSHYAAAAAAACHQrIDAAAAAAA4FJIdAAAAAADAoZDsAAAAAAAADoVkBwAAAAAAcCgkOwAAAAAAgEMh2QEAAAAAABwKyQ4AAAAAAOBQSHYAAAAAAACHQrIDAAAAAAA4FJIdAAAAAADAoZDsAAAAAAAADoVkBwAAAAAAcCgkOwAAAAAAgEMh2QEAAAAAABwKyQ4AAAAAAOBQSHYAAAAAAACHQrIDAAAAAAA4FJIdAAAAAADAoZDsAAAAAAAADoVkBwAAAAAAcCgkOwAAAAAAgEMh2QEAAAAAABwKyQ4AAAAAAOBQSHYAAAAAAACHQrIDAAAAAAA4FJIdAAAAAADAoZDsAAAAAAAADoVkBwAAAAAAcCgkOwAAAAAAgEMh2QEAAAAAABwKyQ4AAAAAAOBQSHYAAAAAAACHQrIDAAAAAAA4FJIdAAAAAADAoZDsAAAAAAAADoVkBwAAAAAAcCgkOwAAAAAAgEMh2QEAAAAAABwKyQ4AAAAAAOBQSHYAAAAAAACHQrIDAAAAAAA4lCKf7Dh27JiefvpplS5dWp6engoNDdWWLVus+cYYjRkzRoGBgfL09FRYWJj27dtnx4gBAAAAAIA9Felkx/nz59W6dWu5urrqt99+019//aVJkyapVKlSVpn3339fU6ZM0fTp07Vp0yaVKFFC4eHhunr1qh0jBwAAAAAA9lLM3gFk5T//+Y8qVaqkiIgIa1pwcLD1/8YYffTRRxo1apS6du0qSfryyy/l7++vn376SU888UShxwwAAAAAAOyrSN/Z8csvv6hJkyZ69NFHVa5cOTVs2FCff/65NT8mJkaxsbEKCwuzpvn4+Kh58+aKjIzMdL3Xrl1TQkKCzQcAAAAAADiGIp3s+OeffzRt2jTVqFFDS5Ys0YABA/TSSy9p9uzZkqTY2FhJkr+/v81y/v7+1ryMTJgwQT4+PtanUqVKBVcJAAAAAABQqIp0siMlJUWNGjXSu+++q4YNG6pfv37q27evpk+ffkvrHTlypOLj463PkSNH8iliAAAAAABgb0U62REYGKjatWvbTAsJCdHhw4clSQEBAZKkkydP2pQ5efKkNS8j7u7u8vb2tvkAAAAAAADHUKSTHa1bt1Z0dLTNtL179yooKEjSjcFKAwICtHz5cmt+QkKCNm3apJYtWxZqrAAAAAAAoGgo0m9jeeWVV9SqVSu9++67euyxx/THH39oxowZmjFjhiTJyclJQ4YM0dtvv60aNWooODhYo0ePVvny5dWtWzf7Bg8AAAAAAOyiSCc7mjZtqvnz52vkyJEaN26cgoOD9dFHH6lHjx5WmeHDh+vSpUvq16+f4uLi1KZNGy1evFgeHh52jBwAAAAAANhLkU52SFLnzp3VuXPnTOc7OTlp3LhxGjduXCFGBQAAAAAAiqoiPWYHAAAAAABAbpHsAAAAAAAADoVkBwAAAAAAcCgkOwAAAAAAgEMh2QEAAAAAABxKnpIdVatW1dmzZ9NNj4uLU9WqVW85KAAAAAAAgLzKU7Lj4MGDSk5OTjf92rVrOnbs2C0HBQAAAAAAkFfFclP4l19+sf5/yZIl8vHxsb4nJydr+fLlqlKlSr4FBwAAAAAAkFu5SnZ069ZNkuTk5KSePXvazHN1dVWVKlU0adKkfAsOAAAAAAAgt3KV7EhJSZEkBQcHa/PmzSpTpkyBBAUAAAAAAJBXuUp2pIqJicnvOAAAAAAAAPJFnpIdkrR8+XItX75cp06dsu74SDVz5sxbDgwAAAAAACAv8pTseOuttzRu3Dg1adJEgYGBcnJyyu+4AAAAAAAA8iRPyY7p06dr1qxZeuaZZ/I7HgAAAAAAgFvinJeFEhMT1apVq/yOBQAAAAAA4JblKdnx/PPPa86cOfkdCwAAAAAAwC3L02MsV69e1YwZM7Rs2TLVq1dPrq6uNvMnT56cL8EBAAAAAADkVp6SHTt27FCDBg0kSbt27bKZx2ClAAAAAADAnvKU7Fi5cmV+xwEAAAA7SUlJkST973//0+XLl+Xm5qZatWqpUaNGcnZ21pkzZ7Rnzx5JUoUKFdS5c2ft2rVLv/32m3bv3q0LFy7IGKNy5copPDxcjzzyiNzc3JScnKwdO3bo3Llz8vPzU7169eTi4pJu+6nlzpw5o7i4OPn6+qpMmTKqU6eOdu7cqaioKElSgwYN1KBBgwzXkZXM1p9ZPPkhp3W/XbcHAFkpCuekPCU7AAAA4BjWrFmjd999V5L0xx9/WNO3bdumuXPnZrjM1KlTM5x+6NAhzZgxQzNmzFDr1q114MABxcbGWvMDAgI0cOBAtW3b1mb7n332mU25VM7OzlYiRpK++uor+fr66tVXX7VZR3b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",
            "text/plain": [
              "<Figure size 1300x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1300x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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FAAAAAAAAuBJhBwAAAAAAsBTCDgAAAAAAYCmEHQAAAAAAwFIIOwAAAAAAgKUQdgAAAAAAAEsh7AAAAAAAAJZC2AEAAAAAAFyqSpUqatCggapUqeKW7Xu5ZasAAAAAAMCy7rzzTk2dOtVt2+fMDgAAAAAAYCmEHQAAAAAAwFIIOwAAAAAAgKUQdgAAAAAAAEsh7AAAAAAAAJZC2AEAAAAAACyFsAMAAAAAAFgKYQcAAAAAALAUwg4AAAAAAGAphB0AAAAAAMBSCDsAAAAAAIClEHYAAAAAAABLIewAAAAAAACWQtgBAAAAAAAshbADAAAAAABYCmEHAAAAAACwFMIOAAAAAABgKYQdAAAAAADAUgg7AAAAAACApRB2AAAAAAAASyHsAAAAAAAAlkLYAQAAAAAALIWwAwAAAAAAWAphBwAAAAAAsBTCDgAAAAAAYCmEHQAAAAAAwFIIOwAAAAAAgKUQdgAAAAAAAEsh7AAAAAAAAJZC2AEAAAAAACyFsAMAAAAAAFgKYQcAAAAAALAUwg4AAAAAAGAplgk7pk+froiICPn4+Kh58+batGmTu0sCAAAAAABuYImw44svvlB0dLTGjBmjbdu2qUGDBurYsaNOnjzp7tIAAAAAAEAhs0TYMXnyZPXv3199+/ZVnTp1NGPGDPn6+urjjz92d2kAAAAAAKCQebm7gFt15coVbd26VaNHj3ZM8/DwUPv27bV+/fosl0lJSVFKSorj/fnz5yVJiYmJLqkpKSlJknT28D6lplxyyToLS+LxI5KkrVu3OvajuPDw8FB6erq7y8iz4lq3VHxrp+7CVVzr3rdvn6Ti97084/t4UlKSy36uZazHGOOS9SF7GX3sqs8OAAArycuYpNiHHadPn1ZaWpoqVqzoNL1ixYrau3dvlsvExMRo3LhxmaaHhYW5tLat/57o0vUVpgEDBri7BAAoEorr9/LWrVu7fJ0XLlxQ6dKlXb5e/M+FCxckuX5MAgCAleRmTFLsw478GD16tKKjox3v09PTdfbsWZUtW1Y2m+2W15+YmKiwsDD99ttvCggIuOX13e7oT9ehL12HvnQt+tN1CqIvjTG6cOGCQkNDXbI+ZC80NFS//fab/P39XTImkfj/5Ur0pWvRn65DX7oOfek67h6TFPuwo1y5cvL09NSJEyecpp84cULBwcFZLmO322W3252mBQYGury2gIAA/oO4EP3pOvSl69CXrkV/uo6r+5IzOgqHh4eH7rjjjgJZN/+/XIe+dC3603XoS9ehL13HXWOSYn+DUm9vbzVp0kRLly51TEtPT9fSpUvVsmVLN1YGAAAAAADcodif2SFJ0dHRioyMVNOmTdWsWTNNmTJFycnJ6tu3r7tLAwAAAAAAhcwSYcdTTz2lU6dO6dVXX9Xx48fVsGFDLVq0KNNNSwuL3W7XmDFjMl0qg/yhP12HvnQd+tK16E/XoS9xI44J16EvXYv+dB360nXoS9dxd1/aDM+RAwAAAAAAFlLs79kBAAAAAABwPcIOAAAAAABgKYQdAAAAAADAUgg7AAAAAACApRB25NP06dMVEREhHx8fNW/eXJs2bcqx/ZdffqlatWrJx8dH9erV08KFCwup0uIhL/05c+ZM3XfffSpTpozKlCmj9u3b37T/byd5PTYzzJ07VzabTd27dy/YAouRvPbluXPnNHjwYIWEhMhut6tGjRr8X/8/ee3LKVOmqGbNmipZsqTCwsIUFRWly5cvF1K1RdeqVavUtWtXhYaGymaz6ZtvvrnpMitWrFDjxo1lt9t15513avbs2QVeJwof4xLXYUziOoxJXItxieswLnGNIj8uMcizuXPnGm9vb/Pxxx+bn3/+2fTv398EBgaaEydOZNl+7dq1xtPT07zxxhvml19+MS+//LIpUaKE2bVrVyFXXjTltT+feeYZM336dLN9+3azZ88e06dPH1O6dGnz+++/F3LlRU9e+zJDfHy8qVSpkrnvvvtMt27dCqfYIi6vfZmSkmKaNm1qunTpYtasWWPi4+PNihUrTGxsbCFXXvTktS8//fRTY7fbzaeffmri4+PNTz/9ZEJCQkxUVFQhV170LFy40Pz97383CxYsMJLM119/nWP7X3/91fj6+pro6Gjzyy+/mGnTphlPT0+zaNGiwikYhYJxieswJnEdxiSuxbjEdRiXuE5RH5cQduRDs2bNzODBgx3v09LSTGhoqImJicmy/ZNPPmkeeughp2nNmzc3AwcOLNA6i4u89ueNUlNTjb+/v5kzZ05BlVhs5KcvU1NTzT333GM+/PBDExkZycDi/+S1L99//31TtWpVc+XKlcIqsdjIa18OHjzYtGvXzmladHS0uffeewu0zuImN4OKv/3tb+auu+5ymvbUU0+Zjh07FmBlKGyMS1yHMYnrMCZxLcYlrsO4pGAUxXEJl7Hk0ZUrV7R161a1b9/eMc3Dw0Pt27fX+vXrs1xm/fr1Tu0lqWPHjtm2v53kpz9vdPHiRV29elVBQUEFVWaxkN++HD9+vCpUqKB+/foVRpnFQn768j//+Y9atmypwYMHq2LFiqpbt65ef/11paWlFVbZRVJ++vKee+7R1q1bHaeU/vrrr1q4cKG6dOlSKDVbCT9/rI9xieswJnEdxiSuxbjEdRiXuFdh//zxKpC1Wtjp06eVlpamihUrOk2vWLGi9u7dm+Uyx48fz7L98ePHC6zO4iI//XmjkSNHKjQ0NNN/nNtNfvpyzZo1+uijjxQbG1sIFRYf+enLX3/9VcuWLVPPnj21cOFCxcXF6c9//rOuXr2qMWPGFEbZRVJ++vKZZ57R6dOn1apVKxljlJqaqkGDBumll14qjJItJbufP4mJibp06ZJKlizppsrgKoxLXIcxieswJnEtxiWuw7jEvQp7XMKZHSjWJk6cqLlz5+rrr7+Wj4+Pu8spVi5cuKBevXpp5syZKleunLvLKfbS09NVoUIFffDBB2rSpImeeuop/f3vf9eMGTPcXVqxs2LFCr3++ut67733tG3bNi1YsEA//PCDJkyY4O7SACBbjEnyjzGJ6zEucR3GJcUXZ3bkUbly5eTp6akTJ044TT9x4oSCg4OzXCY4ODhP7W8n+enPDG+++aYmTpyoJUuWqH79+gVZZrGQ1748ePCgDh06pK5duzqmpaenS5K8vLy0b98+VatWrWCLLqLyc1yGhISoRIkS8vT0dEyrXbu2jh8/ritXrsjb27tAay6q8tOXr7zyinr16qXnn39eklSvXj0lJydrwIAB+vvf/y4PD3L63Mru509AQABndVgE4xLXYUziOoxJXItxieswLnGvwh6X8Mnkkbe3t5o0aaKlS5c6pqWnp2vp0qVq2bJllsu0bNnSqb0kLV68ONv2t5P89KckvfHGG5owYYIWLVqkpk2bFkapRV5e+7JWrVratWuXYmNjHa9HHnlEbdu2VWxsrMLCwgqz/CIlP8flvffeq7i4OMfgTJL279+vkJCQ23ZAIeWvLy9evJhp4JAxWLt2/yvkFj9/rI9xieswJnEdxiSuxbjEdRiXuFeh//wpkNueWtzcuXON3W43s2fPNr/88osZMGCACQwMNMePHzfGGNOrVy8zatQoR/u1a9caLy8v8+abb5o9e/aYMWPG8Ii36+S1PydOnGi8vb3NV199ZRISEhyvCxcuuGsXioy89uWNuPP5/+S1L48cOWL8/f3NkCFDzL59+8z3339vKlSoYF577TV37UKRkde+HDNmjPH39zeff/65+fXXX81///tfU61aNfPkk0+6axeKjAsXLpjt27eb7du3G0lm8uTJZvv27ebw4cPGGGNGjRplevXq5Wif8Yi3v/71r2bPnj1m+vTpPHrWghiXuA5jEtdhTOJajEtch3GJ6xT1cQlhRz5NmzbNVK5c2Xh7e5tmzZqZDRs2OOa1bt3aREZGOrWfN2+eqVGjhvH29jZ33XWX+eGHHwq54qItL/0ZHh5uJGV6jRkzpvALL4Lyemxej4GFs7z25bp160zz5s2N3W43VatWNf/4xz9MampqIVddNOWlL69evWrGjh1rqlWrZnx8fExYWJj585//bP7444/CL7yIWb58eZbf/zL6LzIy0rRu3TrTMg0bNjTe3t6matWqZtasWYVeNwoe4xLXYUziOoxJXItxieswLnGNoj4usRnDuTcAAAAAAMA6uGcHAAAAAACwFMIOAAAAAABgKYQdAAAAAADAUgg7AAAAAACApRB2AAAAAAAASyHsAAAAAAAAlkLYAQAAAAAALIWwAwD+z6FDh2Sz2RQbGytJWrFihWw2m86dO+fWugAAwO2FMQlw6wg7ABSaNm3aaPjw4QW+nfwOCMLCwpSQkKC6desWTGEAAKBIYEwCWB9hB2BxV65ccXcJLmOMUWpqaoGt39PTU8HBwfLy8nLZOq3U/wAA3Aor/UxkTAIUfYQdgMW0adNGQ4YM0fDhw1WuXDl17NhRu3fvVufOnVWqVClVrFhRvXr10unTpzMtM2TIEJUuXVrlypXTK6+8ImOMo01KSopGjBihSpUqyc/PT82bN9eKFSsc88+cOaMePXqoUqVK8vX1Vb169fT555875vfp00crV67U1KlTZbPZZLPZdOjQoRz3JeOvIT/++KOaNGkiu92uNWvWKD09XTExMapSpYpKliypBg0a6KuvvpJ07bTPtm3bSpLKlCkjm82mPn36SJIWLVqkVq1aKTAwUGXLltXDDz+sgwcPOrZ34ymjWVmzZo3uu+8+lSxZUmFhYXrxxReVnJzsmB8REaEJEyaod+/eCggI0IABA3LcRwAArIoxCWMSwJ0IOwALmjNnjry9vbV27VpNnDhR7dq1U6NGjbRlyxYtWrRIJ06c0JNPPplpGS8vL23atElTp07V5MmT9eGHHzrmDxkyROvXr9fcuXO1c+dOPfHEE+rUqZMOHDggSbp8+bKaNGmiH374Qbt379aAAQPUq1cvbdq0SZI0depUtWzZUv3791dCQoISEhIUFhaWq/0ZNWqUJk6cqD179qh+/fqKiYnRJ598ohkzZujnn39WVFSUnn32Wa1cuVJhYWGaP3++JGnfvn1KSEjQ1KlTJUnJycmKjo7Wli1btHTpUnl4eOjRRx9Venp6ruo4ePCgOnXqpMcff1w7d+7UF198oTVr1mjIkCFO7d588001aNBA27dv1yuvvJKrdQMAYEWMSRiTAG5jAFhK69atTaNGjRzvJ0yYYDp06ODU5rfffjOSzL59+xzL1K5d26SnpzvajBw50tSuXdsYY8zhw4eNp6enOXr0qNN6HnjgATN69Ohsa3nooYfMX/7yF6fahg0blut9Wb58uZFkvvnmG8e0y5cvG19fX7Nu3Tqntv369TM9evRwWu6PP/7Icf2nTp0yksyuXbuMMcbEx8cbSWb79u1Zrqdfv35mwIABTutYvXq18fDwMJcuXTLGGBMeHm66d++e630EAMCqGJMwJgHcyXUXgQEoMpo0aeL4eseOHVq+fLlKlSqVqd3BgwdVo0YNSVKLFi1ks9kc81q2bKm33npLaWlp2rVrl9LS0hxtM6SkpKhs2bKSpLS0NL3++uuaN2+ejh49qitXriglJUW+vr63vD9NmzZ1fB0XF6eLFy/qwQcfdGpz5coVNWrUKMf1HDhwQK+++qo2btyo06dPO/56cuTIkVzdAGzHjh3auXOnPv30U8c0Y4zS09MVHx+v2rVrZ6oXAIDbGWOSrDEmAQoeYQdgQX5+fo6vk5KS1LVrV/3zn//M1C4kJCRX60tKSpKnp6e2bt0qT09Pp3kZA5ZJkyZp6tSpmjJliurVqyc/Pz8NHz7cJTfDunF/JOmHH35QpUqVnNrZ7fYc19O1a1eFh4dr5syZCg0NVXp6uurWrZvrGpOSkjRw4EC9+OKLmeZVrlw5y3oBALidMSbJGmMSoOARdgAW17hxY82fP18RERE53tF748aNTu83bNig6tWry9PTU40aNVJaWppOnjyp++67L8vl165dq27duunZZ5+VJKWnp2v//v2qU6eOo423t7fS0tJuaX/q1Kkju92uI0eOqHXr1lm28fb2liSnbZ05c0b79u3TzJkzHfuwZs2aPG27cePG+uWXX3TnnXfms3oAAG5fjEmuYUwCFA5uUApY3ODBg3X27Fn16NFDmzdv1sGDB/XTTz+pb9++Tj94jxw5oujoaO3bt0+ff/65pk2bpmHDhkmSatSooZ49e6p3795asGCB4uPjtWnTJsXExOiHH36QJFWvXl2LFy/WunXrtGfPHg0cOFAnTpxwqiUiIkIbN27UoUOHnE7ZzAt/f3+NGDFCUVFRmjNnjg4ePKht27Zp2rRpmjNnjiQpPDxcNptN33//vU6dOqWkpCSVKVNGZcuW1QcffKC4uDgtW7ZM0dHRedr2yJEjtW7dOg0ZMkSxsbE6cOCAvv3220w3AwMAAJkxJmFMAhQmwg7A4kJDQ7V27VqlpaWpQ4cOqlevnoYPH67AwEB5ePzvW0Dv3r116dIlNWvWTIMHD9awYcOcHlE2a9Ys9e7dW3/5y19Us2ZNde/eXZs3b3acKvnyyy+rcePG6tixo9q0aaPg4GB1797dqZYRI0bI09NTderUUfny5XXkyJF87dOECRP0yiuvKCYmRrVr11anTp30ww8/qEqVKpKkSpUqady4cRo1apQqVqyoIUOGyMPDQ3PnztXWrVtVt25dRUVFadKkSXnabv369bVy5Urt379f9913nxo1aqRXX31VoaGh+doPAABuJ4xJGJMAhclmzHUPrQZwW2rTpo0aNmyoKVOmuLsUAABwG2NMAsBVOLMDAAAAAABYCmEHALcZNGiQSpUqleVr0KBB7i4PAADcJhiTANbDZSwA3ObkyZNKTEzMcl5AQIAqVKhQyBUBAIDbEWMSwHoIOwAAAAAAgKVwGQsAAAAAALAUwg4AAAAAAGAphB0AAAAAAMBSCDsAAAAAAIClEHYAAAAAAABLIewAAAAAAACWQtgBAAAAAAAshbADAAAAAABYyv8HK7hsQ8l/4XcAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 1300x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1300x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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fS7K56GhuRxxk8vf3L9T28utG23d2draGATdzsdTcjl6R/jnFIVN6enqht1GYbWfdflFsu7C12KO/fHx8VKZMmTy3l/n+uv61Lcz7/PLly7nOy/zD0VEyQ0cp51PTclO2bFktXLjQepTN1q1b9fLLL6tly5by9vZWhw4dNHv2bLu+d+zZV8X1+S4OmfXdaJ8kWU/byWufStLvBQAoDbgbCwCUIgMHDtQjjzyi2bNna9myZVq/fr3OnTuno0eP6pNPPtGnn36ql19+2eYuClkHvp988omaN2+er23l9geMvc7LLyxHbx9F52Ze28z3eceOHTVx4sSbruVGoUtR27Fjh/XnWrVqFWjdO++8U7t379bChQu1cOFCrV27Vvv379fly5e1dOlSLV26VO+9954WL16crz/Eb8SefWXGz7cZ9wkASgPCDgAoBpnfuGWeTpKbixcv3rAtPz8/vfDCC3rhhReUkZGhhIQEzZ8/X1OnTtXZs2f1xhtv6K677rKe9571QoceHh6qW7dugevPGnycOHEiz2VvNP9m3aj9a9euWb8dLcg34maR9SiKjIwMm+dZ5ee9VtxOnz6t9PT0PP94znz9r39tK1WqpGPHjiktLa1Q7/GSxDAMm2s3tGzZssBtlClTRl26dFGXLl0k/XPdnfj4eE2bNk3btm3Ttm3b9Oyzz2a7Ba2j3czn+/r3fm6K673v4+Oj5OTkfP1OzDyl6lb8nQUARYXTWACgGGRevO/s2bN5Lvf7778XqF0nJyc1bNhQr7/+ulasWGGd/s0331h/rl+/vvWbxfXr1xeo/Uzh4eHWn7du3Zrnsjeaf7MSEhJ07dq1XOfv3LnTem2C0v5Hb2FkvVBkXnfxKOh7rTikpaXleCpWpmvXrikhIUFS9tc281oeP//8c5Fdm6K4LF68WPv27ZMkNW3aNMc7kxRUYGCgnnrqKW3cuFENGzaUJC1atCjbqTyOPgrhZj7f9nrv26sPMuvbvn17nvt08uRJHTp0yGYdAMDNI+wAgGJQtWpVSf9cOC4xMTHHZdLS0vTdd98VehsNGza0HoGR9Xx/X19fNW3aVJI0e/Zs/fXXXwVuOygoyHoNgG+//TbXax1cvHjRJmgpCmfOnNHChQtznT9jxgzrz+3atSvSWkqizPeapDxvzTl37tziKKfArr+lbFbz58+3/hF7/Wv74IMPSpLOnTtXbHchKQqnTp1Sv379rM8zb0tqL2XLllWrVq0k/RMeXR/Aurm5SZJSU1Ptut38upnPd37f+3PmzMmzhsw+kG6uHzLrO3v2rObNm5frcp9//rkMw7BZBwBw8wg7AKAYZP5xIUnvvvtujssMHTpUR48ezbWNr7/+Os8LKv7888/WPwSzDvolaeTIkZL+uQ3tv//97zyPMElNTdW0adN05coVm+n9+/eX9M/h1i+++GKO6w4ZMqTQF0QtiKFDh+Z4aPiaNWv06aefSvrn9rA3unOMGTVv3tx6N4tJkyZZ/4jK6u2339aWLVuKu7R8+eijj/TTTz9lm378+HENGzZM0j+nY0VHR9vMj46OVnBwsCRp2LBhWrt2bZ7b+emnn7RmzRo7VW0fGzZsUJMmTXTkyBFJ/9w9KTPEya9169bleYemtLQ0636XL18+2216M29dW5DbW9tbYT/fdevWtZ4GMnXq1ByDim+++UbffvttntvPevvem+mHp556ynpR0RdffDHH3+87d+7Um2++KUmqUqWK9bQjAMDN45odAFAMGjRooGbNmmnjxo2aPn260tLSFB0drQoVKmjfvn369NNPtXLlSjVv3lwbNmzIsY3hw4erX79+6ty5s+655x7VrFlT5cqV0+nTp/XTTz/pgw8+kPTPufrPPPOMzbpRUVF6/vnnNWXKFK1du1a1a9dWv3791LJlS1WqVEkXL17U/v37tW7dOs2bN09///13tj8m+/fvr9jYWO3YsUMfffSRkpKS1K9fPwUHB+vPP//Uhx9+qB9//FGNGzfO81vVm1WvXj39+uuvatSokWJiYtSkSROlpqZq8eLFmjRpkq5duyZnZ2dNmzatyGooyfz8/PTII49ozpw5Wrp0qR588EENHDhQ/v7+Onz4sL788kt99913eb7XHMXX11ceHh7617/+pSFDhigqKkqurq7asmWL3nzzTR07dkyS9Prrr2e7sKarq6u++eYbtW7dWhcuXNC9996r7t27q0uXLqpataoyMjKUnJysbdu2af78+dq9e7c++OADmyCyqB09etTm1qLnz5/X6dOntWPHDsXHx9u8Hg888IDNUQz5tWLFCr3++uu6++67df/99ysiIkK+vr66fPmyfv/9d3388cfavn27JKl3797ZbvPavHlzrVq1Slu3btWECRPUsWNHlStXTpLk7u6uKlWqFGbX8+1mPt/Ozs569tlnNX78eO3Zs0f33nuvXnrpJYWEhOjEiRP69ttvFRcXd8P3ftaLOA8ZMkSvvPKKAgMDrae3hIWF5Xl73Ey+vr56++23NXDgQB05ckSNGjXSiBEj1Lx5c127dk3Lly/X22+/rQsXLshisejTTz/N8xa1AIACMgAAxWLv3r2Gn5+fISnHx7Bhw4zY2Fjr86SkJJv1Q0NDc1038+Hq6mrExsbmuP2MjAxjzJgxhrOz8w3bKVeunHHp0qVsbRw9etSoVatWruvdd999xtKlS63PV61aZbf+y9z/6OhoY/r06bnuh4uLizFnzpwc20hKSrIul1M/jRo1yjo/L6tWrSqSfcxsc9SoUXku16pVK0OS0apVqxznHz9+3KhRo0aur1P37t2N5cuX57kPN9pGpqyvS2H3LTo62pBkhIaGGlu3bjUqV66ca+2DBw/OczsbN240goODb/gel2TMnDmzQHUWRtbPdH4eoaGhxscff5xnm3m9j7O+h/N6dO7cOcfP+JEjRwwfH58c18n6XrjRZ+l6N/ps2ePzbRiGcfHiRaNp06a57nfr1q2NPXv23LD2bt265dpG1t/NWd+7uXnjjTcMJyenPH9v5/ReNIyC9XN+P4sAcKvgNBYAKCZ33HGHtm/frv79+ys0NFQuLi7y9fVVhw4d9MMPP+jtt9/Oc/1Vq1ZpypQp6tq1q8LDw+Xr6ytnZ2d5eXmpQYMGGjZsmH799Vf16tUrx/UtFotee+01/f7773rppZfUuHFj+fj4qEyZMvL09NSdd96pHj16aObMmUpOTpa7u3u2NoKCgrRjxw6NGzdOdevWlbu7u7y9vdW0aVN9+OGHWrJkiVxcXOzRXXl65plntG7dOnXr1k1BQUFycXFRlSpV1LNnT+3YsUPdu3cv8hpKMn9/f23evFnDhw9XjRo15OrqKh8fH91zzz366quvNGfOHIffWjU3jRs31vbt2zV48GBVr15dbm5uqlSpkjp06KDFixdrypQpea7ftGlT7du3Tx9//LHuv/9+6/vDzc1NwcHBuu+++/TGG2/ot99+U8+ePYtpr7JzdnZWxYoVFRYWpnvvvVfDhw/XkiVL9Mcff+jZZ58tdLvDhg3Td999p/79+6tp06YKCQmRm5ub3NzcFBYWpm7dumnRokVasGBBjp/xKlWqaMuWLerdu7duv/12m+tXFJeb+Xx7eHho5cqVeuONNxQeHi53d3d5eXnprrvu0tSpU7V8+XLrkSp5+eqrrzRx4kQ1adJEFSpUyPWuRvnx8ssva8eOHerTp4+qV68ud3d3lStXTrVr19bzzz/v8PciAJiVxTByOJkXAIASJiwsTIcOHVJ0dLTi4uIcXQ4AAABKMI7sAAAAAAAApkLYAQAAAAAATIWwAwAAAAAAmAq3ngUAFLmTJ0/q5MmTBV7PxcVFNWvWLIKK7O/o0aP6+++/C7xeuXLlVLVq1SKoCAAA4NZF2AEAKHIffvihxowZU+D1QkNDdfDgQfsXVAReeeUVzZw5s8DrtWrVSqtXr7Z/QQAAALcwwg4AQKlQWkIPAAAAOB63ngUAAAAAAKbCBUoBAAAAAICpEHYADhYXFydvb2+7tderVy916dLFbu05ksVi0YIFCxxdho2DBw/KYrEoISHB0aUAAOAQjF1yVxLHLsXBTK8hzIOwAzCZKVOmKC4uztFllDp5DdyyDlyCg4OVnJysunXr3rBNghEAAG6MsUvhxMXFyWKxWB/ly5dXo0aNNG/ePEeXdkOrV6+WxWLR2bNnHV0KTIywAzCZChUq2PXbFrNJS0u7qfXLlCmjgIAAOTuXrOs7G4aha9euOboMAAAKjLFL3vIau3h5eSk5OVnJycnasWOH2rdvr27duikxMbEYKwRKJsIOIIuwsDBNnjzZZlr9+vU1evRoSf/8QTl69GiFhITI1dVVQUFBGjx4sHXZ1NRUDRs2TFWqVFG5cuUUGRmZ7ZaScXFxCgkJkYeHhx566CGdPn063/WNHj1a9evX1yeffKLg4GB5eHioW7duOnfunHWZ6w8jbN26tQYPHqyXXnpJPj4+CggIsO5PflgsFn322Wd66KGH5OHhoRo1auj777+32Z/rBygLFiyQxWLJVveMGTMUEhKi8uXLa8CAAUpPT9fEiRMVEBAgPz8/vfHGG9m2n5ycrI4dO8rd3V3VqlXTf//7X5v5f/75p7p16yZvb2/5+Pioc+fONnftyOyPN954Q0FBQapVq1a+9z0n1x+t8ffff6tHjx7y9fWVu7u7atSoodjYWElS1apVJUkNGjSQxWJR69atJUkZGRkaO3asbrvtNrm6uqp+/fqKj4+32c6GDRtUv359ubm5qXHjxtY+zdxu5jciS5YsUaNGjeTq6qqffvpJBw4cUOfOneXv76/y5cvrrrvu0vLly23aDgsL07hx49SzZ0+VL19eoaGh+v777/XXX3+pc+fOKl++vCIiIvTzzz/fVF8BAIoeY5fsbqWxi8ViUUBAgAICAlSjRg2NGzdOTk5O2rVrl3WZv//+Wz179lTFihXl4eGhjh07at++fZKkv/76SwEBAXrzzTety2/YsEEuLi5asWKFTV/k9RpeLzU1VYMHD5afn5/c3NzUsmVLbd26VdI/Y6k2bdpIkipWrCiLxaJevXrl2hZQWIQdQAF89913mjRpkj755BPt27dPCxYsUHh4uHX+oEGDtHHjRs2dO1e7du3SI488og4dOlj/Q9m8ebN69+6tQYMGKSEhQW3atNG4ceMKVMP+/fv1zTffaOHChYqPj9eOHTs0YMCAPNeZOXOmypUrp82bN2vixIkaO3asli1blu9tjhkzRt26ddOuXbsUFRWlHj166MyZMwWq+8CBA1qyZIni4+M1Z84cff7557r//vt15MgRrVmzRm+99ZZGjhypzZs326z36quvqmvXrtq5c6d69Oih7t27a+/evZKkq1evqn379vL09NS6deu0fv16lS9fXh06dLD5FmTFihVKTEzUsmXLtGjRogLVfSOvvvqqfv31Vy1ZskR79+7VRx99pMqVK0uStmzZIklavny5kpOTrYeVTpkyRe+++67eeecd7dq1S+3bt9eDDz5ofZ+kpKSoU6dOCg8P1/bt2/X6669r+PDhOW5/xIgRmjBhgvbu3auIiAhduHBBUVFRWrFihXbs2KEOHTqoU6dOOnz4sM16kyZNUosWLbRjxw7df//9evLJJ9WzZ0898cQT2r59u6pXr66ePXuKG3YBQOnG2OXWGbukp6dr5syZkqSGDRtap/fq1Us///yzvv/+e23cuFGGYSgqKkpXr16Vr6+vZsyYodGjR+vnn3/W+fPn9eSTT2rQoEFq27attY2CvoYvvfSSvvvuO82cOVPbt2/X7bffrvbt2+vMmTMKDg7Wd999J0lKTExUcnKypkyZkq99BArEAGAVGhpqTJo0yWZavXr1jFGjRhmGYRjvvvuuUbNmTSMtLS3buocOHTLKlCljHD161GZ627ZtjZiYGMMwDOOxxx4zoqKibOY/+uijRoUKFfJV36hRo4wyZcoYR44csU5bsmSJ4eTkZCQnJxuGYRjR0dFG586drfNbtWpltGzZ0qadu+66yxg+fHi+tinJGDlypPX5hQsXDEnGkiVLDMMwjNjY2Gz1z58/38j662XUqFGGh4eHkZKSYp3Wvn17IywszEhPT7dOq1WrljF+/Hibbffr18+m7cjISKN///6GYRjGl19+adSqVcvIyMiwzk9NTTXc3d2NpUuXWvvD39/fSE1NzXM/Y2NjDUlGuXLlsj0kGfPnzzcMwzCSkpIMScaOHTsMwzCMTp06GU899VSObV6/bKagoCDjjTfesJl21113GQMGDDAMwzA++ugjo1KlSsbly5et86dPn27T1qpVqwxJxoIFC/LcL8MwjDp16hgffPCB9XloaKjxxBNPWJ8nJycbkoxXX33VOm3jxo2GJOv7CgBQMjF2ye5WHbs4OTkZrq6uRmxsrHWZ33//3ZBkrF+/3jrt1KlThru7u/HNN99Ypw0YMMCoWbOm8fjjjxvh4eHGlStXbPqiIK/hhQsXjLJlyxqzZs2yLp+WlmYEBQUZEydONAzj/8Yxf//9d577CNyMknXSOVDCPfLII5o8ebKqVaumDh06KCoqSp06dZKzs7N2796t9PR01axZ02ad1NRUVapUSZK0d+9ePfTQQzbzmzVrlu0UhryEhISoSpUqNutnZGQoMTFRAQEBOa4TERFh8zwwMFAnT57M9zazrl+uXDl5eXkVaH3pn8NsPT09rc/9/f1VpkwZOTk52Uy7vt1mzZple555KsfOnTu1f/9+m3Yl6cqVKzpw4ID1eXh4uFxcXG5Yo6enp7Zv355teo0aNXJdp3///uratau2b9+u++67T126dFHz5s1zXT4lJUXHjh1TixYtbKa3aNFCO3fulPTPtxwRERFyc3Ozzm/SpEmO7TVu3Njm+YULFzR69Gj98MMPSk5O1rVr13T58uVsR3ZkfU39/f0lyeabvsxpJ0+ezPV9BQAo+Ri73Dpjl0uXLmn58uXq16+fKlWqpE6dOmnv3r1ydnZWZGSkdZ1KlSqpVq1a1qNNJOmdd95R3bp19e2332rbtm1ydXW12U5BXsMDBw7o6tWrNmOdsmXLqkmTJjbbBIoaYQeQhZOTU7bD9q9evWr9OTg4WImJiVq+fLmWLVumAQMG6O2339aaNWt04cIFlSlTRtu2bVOZMmVs2ihfvnyx1J+bsmXL2jy3WCzKyMiwy/o36rO82rjZui5cuKBGjRpp1qxZ2eb5+vpafy5Xrly+2nNyctLtt9+e7+1LUseOHXXo0CEtXrxYy5YtU9u2bTVw4EC98847BWqnsK7ft2HDhmnZsmV65513dPvtt8vd3V3//ve/s13cLGvfZ56jnNO0grweAIDix9il4OubeewSERGhH3/8UW+99ZY6deqU77oOHDigY8eOKSMjQwcPHrT5AgQorbhmB5CFr6+vkpOTrc9TUlKUlJRks4y7u7s6deqk999/X6tXr9bGjRu1e/duNWjQQOnp6Tp58qRuv/12m0dm4l27du1s53Vu2rSpQDUePnxYx44ds1nfycnppi+8WVi+vr46f/68Ll68aJ1mz1utXt8/mzZtUu3atSX9cz7qvn375Ofnl63PK1SoYLcabsTX11fR0dH66quvNHnyZH366aeSZP1GJj093bqsl5eXgoKCtH79eps21q9frzvvvFOSVKtWLe3evVupqanW+ZkX9bqR9evXq1evXnrooYcUHh6ugIAAm4ueAQDMhbFLwZl97FKmTBldvnxZ0j+v37Vr12xew9OnTysxMdE67khLS9MTTzyhRx99VK+//rqeeeaZbEerFOQ1rF69ulxcXGzGOlevXtXWrVut28xpjATYG2EHkMW9996rL7/8UuvWrdPu3bsVHR1t801HXFycPv/8c+3Zs0d//PGHvvrqK7m7uys0NFQ1a9ZUjx491LNnT82bN09JSUnasmWLxo8frx9++EGSNHjwYMXHx+udd97Rvn37NHXq1AIdBipJbm5uio6O1s6dO7Vu3ToNHjxY3bp1c9ipBpGRkfLw8NDLL7+sAwcOaPbs2YqLi7Nb+99++61mzJih33//XaNGjdKWLVs0aNAgSVKPHj1UuXJlde7cWevWrVNSUpJWr16twYMH68iRI3arIS+vvfaa/ve//2n//v365ZdftGjRIuuAxs/PT+7u7oqPj9eJEyesVy3/z3/+o7feektff/21EhMTNWLECCUkJOj555+XJD3++OPKyMhQ3759tXfvXi1dutR6pEjWK8XnpEaNGpo3b54SEhK0c+dOa1sAAHNi7FJwZhq7GIah48eP6/jx40pKStKnn36qpUuXqnPnzpL+GRd07txZffr00U8//aSdO3fqiSeeUJUqVazLvPLKKzp37pzef/99DR8+XDVr1tTTTz9ts52CvIblypVT//799Z///Efx8fH69ddf1adPH126dEm9e/eWJIWGhspisWjRokX666+/dOHChQLvO3AjhB1AFjExMWrVqpUeeOAB3X///erSpYuqV69une/t7a3p06erRYsWioiI0PLly7Vw4ULrea2xsbHq2bOnXnzxRdWqVUtdunTR1q1bFRISIklq2rSppk+frilTpqhevXr68ccfNXLkyALVePvtt+vhhx9WVFSU7rvvPkVEROjDDz+0XycUkI+Pj7766istXrxY4eHhmjNnToFuD3cjY8aM0dy5cxUREaEvvvhCc+bMsX4r4OHhobVr1yokJEQPP/ywateurd69e+vKlSvy8vKyWw15cXFxUUxMjCIiInTPPfeoTJkymjt3riTJ2dlZ77//vj755BMFBQVZBxWDBw/W0KFD9eKLLyo8PFzx8fH6/vvvrdcG8fLy0sKFC5WQkKD69evrlVde0WuvvSZJNtfxyMl7772nihUrqnnz5urUqZPat29vc0V2AIC5MHYpODONXVJSUhQYGKjAwEDVrl1b7777rsaOHatXXnnFukxsbKwaNWqkBx54QM2aNZNhGFq8eLHKli2r1atXa/Lkyfryyy/l5eUlJycna3j20UcfWdso6Gs4YcIEde3aVU8++aQaNmyo/fv3a+nSpapYsaIkqUqVKhozZoxGjBghf39/axgE2JPFuP6ENQAl1ujRo7VgwQK7HmqJ0mHWrFl66qmndO7cObm7uzu6HAAA8oWxS+nHa4jSiguUAkAJ9MUXX6hatWqqUqWKdu7cqeHDh6tbt24EHQAAAEA+cBoLUILUqVNH5cuXz/GR01W7b9asWbNy3V6dOnXsvj3k3/Hjx/XEE0+odu3aGjJkiB555BHrhU8BACgpGLsAKKk4jQUoQQ4dOpTjrc+kf+7jfv092W/W+fPndeLEiRznlS1bVqGhoXbdHgAAMBfGLgBKKsIOAAAAAABgKpzGAgAAAAAATIWwAwAAAAAAmAp3Y5GUkZGhY8eOydPTUxaLxdHlAABQohiGofPnzysoKEhOTnxPUpQYkwAAkLuCjEkIOyQdO3ZMwcHBji4DAIAS7c8//9Rtt93m6DJMjTEJAAA3lp8xCWGHZL1K9J9//ikvLy8HVwMAQMmSkpKi4OBgu99VAdkxJgEAIHcFGZMQdkjWw0S9vLwYWAAAkAtOqyh6jEkAALix/IxJOPEWAAAAAACYCmEHAAAAAAAwFcIOAAAAAABgKoQdAAAAAADAVAg7AAAAAACAqRB2AAAAAAAAUyHsAAAAAAAApkLYAQAAAAAATIWwAwAAAAAAmAphBwAAAAAAMBXCDgAAAAAAYCqEHQAAAAAAwFQIOwAAAAAAgKk4NOxYu3atOnXqpKCgIFksFi1YsMBmvsViyfHx9ttvW5cJCwvLNn/ChAnFvCcAAAAAAKCkcHbkxi9evKh69erp6aef1sMPP5xtfnJyss3zJUuWqHfv3uratavN9LFjx6pPnz7W556enkVTcAEcPnxYp06dcnQZhVK5cmWFhIQ4ugwAAAAAAArFoWFHx44d1bFjx1znBwQE2Dz/3//+pzZt2qhatWo20z09PbMt60iHDx/WHXfU1uXLlxxdSqG4u3v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",
            "text/plain": [
              "<Figure size 1300x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1300x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1300x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--------------------------------------------------\n"
          ]
        }
      ],
      "source": [
        "data_quality_checks(df)\n",
        "profiling_visualization(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Class distribution before SMOTE:\n",
            " fraud\n",
            "0.0    912597\n",
            "1.0     87403\n",
            "Name: count, dtype: int64\n",
            "\n",
            "Class distribution after SMOTE:\n",
            " fraud\n",
            "0.0    912597\n",
            "1.0    912597\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "df = feature_engineering(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Shape of X_train: (80000, 7), y_train: (80000,), X_test: (20000, 7), y_test: (20000,)\n"
          ]
        }
      ],
      "source": [
        "X_train, X_test ,y_train, y_test = split_data(df)\n",
        "print(f\"\\nShape of X_train: {X_train.shape}, y_train: {y_train.shape}, X_test: {X_test.shape}, y_test: {y_test.shape}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Training the model...\n",
            "\n",
            "Training time: 626.1017932891846 seconds\n",
            "Prediction time: 13.295952320098877 seconds\n"
          ]
        }
      ],
      "source": [
        "print(\"\\nTraining the model...\\n\")\n",
        "y_pred = make_SVM_predict(X_train, X_test, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Evaluating the model...\n",
            "\n",
            "Accuracy: 0.9864\n",
            "Precision: 0.986645245506577\n",
            "Recall: 0.9860511791269443\n",
            "F1 Score: 0.9863481228668942\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.99      0.99      0.99     10035\n",
            "         1.0       0.99      0.99      0.99      9965\n",
            "\n",
            "    accuracy                           0.99     20000\n",
            "   macro avg       0.99      0.99      0.99     20000\n",
            "weighted avg       0.99      0.99      0.99     20000\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "print(\"\\nEvaluating the model...\\n\")   \n",
        "evaluate(y_pred, y_test)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.5"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}