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      "metadata": {
        "id": "view-in-github",
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      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hadisotudeh/cfg-datascience-task/blob/main/CFG_Data_Scientist_Task_HadiSotudeh.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dRFH97wMDeM9"
      },
      "source": [
        "<center> <h1><b>CFG Data Science Interview Task - Hadi Sotudeh</b></h1> </center>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gqfFFS3ZoiQw"
      },
      "source": [
        "I make use of `scikit-learn` library in Python.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JT85GTCBcKE0"
      },
      "source": [
        "First, I need to import the relevant libraries, download the dataset, and have them available in Google Colab.\n",
        "</br>\n",
        "</br>\n",
        "It is highly recommended to enable `GPU` on this notebook for quicker computations."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kfnWg1RicYz9"
      },
      "source": [
        "## **Install & import libraries and download datasets**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HIKbTfGddmxQ"
      },
      "source": [
        "%%capture\n",
        "# Remove previous files\n",
        "! rm ShotData.csv *.joblib\n",
        "! rm *.py && rm -rf sample_data && rm -rf sample-data\n",
        "\n",
        "# Download the datasets (ShotData.csv and metrica matches)\n",
        "! wget https://www.dropbox.com/s/zpijv8epzrbzifr/ShotData.csv\n",
        "! git clone https://github.com/metrica-sports/sample-data\n",
        "\n",
        "# Clone required functions to read metrica data from LaurieOnTracking repository (Friends of Tracking)\n",
        "! git clone https://github.com/Friends-of-Tracking-Data-FoTD/LaurieOnTracking\n",
        "! mv LaurieOnTracking/Metrica_IO.py ./Metrica_IO.py && mv LaurieOnTracking/Metrica_Viz.py ./Metrica_Viz.py\n",
        "! rm -rf LaurieOnTracking"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6c3DZyhUcfr6"
      },
      "source": [
        "%%capture\n",
        "\n",
        "# Import Libraries\n",
        "# General libraries\n",
        "from tqdm import tqdm_notebook as tqdm\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import matplotlib\n",
        "import numpy as np\n",
        "import itertools\n",
        "import warnings\n",
        "import math\n",
        "import os\n",
        "\n",
        "# Machine learning libraries\n",
        "from sklearn.metrics import plot_roc_curve, roc_auc_score, brier_score_loss\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.calibration import calibration_curve\n",
        "from sklearn.dummy import DummyClassifier\n",
        "from sklearn.pipeline import Pipeline\n",
        "from lightgbm import LGBMClassifier\n",
        "from xgboost import XGBClassifier\n",
        "import joblib\n",
        "\n",
        "# Model interpretation library\n",
        "from sklearn.inspection import plot_partial_dependence\n",
        "\n",
        "# Metrica functions\n",
        "import Metrica_IO as mio\n",
        "import Metrica_Viz as mviz"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FhUa2YHViI1-"
      },
      "source": [
        "## **Set Global Parameters**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "laPcAlQuiMgi"
      },
      "source": [
        "# Show plots inside the jupyter notebook\n",
        "%matplotlib inline\n",
        "\n",
        "# Pandas settings to show more columns are rows in the jupyter notebook\n",
        "pd.set_option('display.max_rows', 500)\n",
        "pd.set_option('display.max_columns', 50000)\n",
        "\n",
        "# Increase font size of the plots \n",
        "plt.rcParams.update({'font.size': 18})\n",
        "\n",
        "# Don't show warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "# Dataset file path\n",
        "dataset = \"ShotData.csv\"\n",
        "\n",
        "# Target variable to predict\n",
        "dep_var = \"outcome\"\n",
        "\n",
        "# Hyper-paramter tuning variables\n",
        "cv = 5\n",
        "seed = 42\n",
        "scoring = 'roc_auc'\n",
        "\n",
        "######### This part is copied from https://github.com/Friends-of-Tracking-Data-FoTD/LaurieOnTracking/blob/master/Tutorial1_GettingStarted.py\n",
        "# Set up initial path to data\n",
        "DATADIR = './sample-data/data/'\n",
        "game_id = 2 # let's look at sample match 2\n",
        "\n",
        "home_team = \"Home\"\n",
        "away_team =  \"Away\"\n",
        "\n",
        "pitch_x = 105.\n",
        "pitch_y = 68.\n",
        "\n",
        "meters_per_yard = 0.9144 # unit conversion from yards to meters\n",
        "goal_line_width = 8*meters_per_yard"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O_UF_1q3b0Nr"
      },
      "source": [
        "#**Step 1 - Build an Expected Goal (xG) model**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5SfzBxqRDeNK"
      },
      "source": [
        "## Load ShotData and Perform Data Cleaning & Feature Engineering"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "Tsmg37-mDeNL",
        "scrolled": true,
        "outputId": "950e6dc5-956e-4871-87f7-aa7b26b12433"
      },
      "source": [
        "# Keep only relevant features and the label (dep_var)\n",
        "cols_to_keep = [\"position_x\",\"position_y\",\"play_type\",\"BodyPart\",\"Number_Intervening_Opponents\",\"Number_Intervening_Teammates\",\"outcome\"]\n",
        "\n",
        "# Read the train file\n",
        "df = pd.read_csv(dataset)[cols_to_keep]\n",
        "\n",
        "# Column preprocessing\n",
        "# Binarize dep_var (goal and non-goal), binarize body type (foot and header)\n",
        "df[\"BodyPart\"] = df[\"BodyPart\"].apply(lambda r: \"Foot\" if r in [\"Left\", \"Right\"] else r) # goal = 1 and else 0\n",
        "\n",
        "# Increase data quality, e.g., play_type has typo (Direct corner and Direct Corner) that need to be mapped to the same value\n",
        "df[\"play_type\"] = df[\"play_type\"].apply(lambda r: \"Direct corner\" if r in [\"Direct corner\", \"Direct Corner\"] else r)\n",
        "\n",
        "\n",
        "# Calculate extra relevant features such as distance and angle\n",
        "\n",
        "# Euclidean distance to center of the goal sqrt(x^2+y^2)\n",
        "df[\"distance\"] = np.sqrt(df[\"position_x\"]**2 + df[\"position_y\"]**2)\n",
        "\n",
        "# Shot angle ot the goal\n",
        "# The angle is arctan(goal_line_width*x/(x^2+y^2-(goal_line_width*/2)^2)), which is in radius and I convert it into degrees next\n",
        "df[\"angle\"] = np.arctan(goal_line_width*df[\"position_x\"]/(df[\"position_x\"]**2+df[\"position_y\"]**2-(goal_line_width/2)**2))\n",
        "df[\"angle\"] = np.rad2deg(df[\"angle\"].apply(lambda alpha: np.pi + alpha if alpha < 0 else alpha))\n",
        "\n",
        "# Rounding the dataframe (2 decimals)\n",
        "df = df.round(2)\n",
        "\n",
        "# Perform one-hot encoding without multicollinearity\n",
        "columns_to_encode = [\"play_type\",\"BodyPart\"]\n",
        "for col in columns_to_encode:\n",
        "  # Prevent multicollinearity\n",
        "  one_hot = pd.get_dummies(df[col], drop_first=True)\n",
        "  # Drop the column as it is now encoded\n",
        "  df = df.drop(col,axis = 1)\n",
        "  # Join the encoded df\n",
        "  df = df.join(one_hot)\n",
        "\n",
        "# Move outcome to the last column\n",
        "features = [x for x in df.columns if x not in [\"position_x\",\"position_y\",dep_var]]\n",
        "df = df[features + [dep_var]]\n",
        "\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "    }\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>Number_Intervening_Opponents</th>\n",
              "      <th>Number_Intervening_Teammates</th>\n",
              "      <th>distance</th>\n",
              "      <th>angle</th>\n",
              "      <th>Direct freekick</th>\n",
              "      <th>Open Play</th>\n",
              "      <th>Penalty</th>\n",
              "      <th>Head</th>\n",
              "      <th>Other</th>\n",
              "      <th>outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.33</td>\n",
              "      <td>95.59</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Goal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>24.95</td>\n",
              "      <td>16.67</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Missed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.77</td>\n",
              "      <td>88.20</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Missed</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>23.07</td>\n",
              "      <td>16.83</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Blocked</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>11.38</td>\n",
              "      <td>32.79</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Missed</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Number_Intervening_Opponents  Number_Intervening_Teammates  distance  \\\n",
              "0                             1                             0      3.33   \n",
              "1                             3                             0     24.95   \n",
              "2                             1                             0      3.77   \n",
              "3                             4                             2     23.07   \n",
              "4                             1                             0     11.38   \n",
              "\n",
              "   angle  Direct freekick  Open Play  Penalty  Head  Other  outcome  \n",
              "0  95.59                0          1        0     1      0     Goal  \n",
              "1  16.67                0          1        0     0      0   Missed  \n",
              "2  88.20                0          1        0     0      0   Missed  \n",
              "3  16.83                1          0        0     0      0  Blocked  \n",
              "4  32.79                0          1        0     1      0   Missed  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9vZzT4vmh1F-"
      },
      "source": [
        "Check basic dataset statistics:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FrfeoLHFh0It",
        "outputId": "f05c2d75-3540-4282-bb93-a289e472475a"
      },
      "source": [
        "is_empty = \"No\"\n",
        "\n",
        "if df.isnull().sum().all():\n",
        "  is_empty = \"Yes\"\n",
        "\n",
        "print(f\"Is there any empty values in the dataset? {is_empty}\\n\")\n",
        "print(f\"There are {df.shape[0]} instances in the dataset.\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Is there any empty values in the dataset? No\n",
            "\n",
            "There are 10925 instances in the dataset.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZT28w-qCM6tl"
      },
      "source": [
        "Binarize outcome (goals vs. non-goals) and name it `goal`(0 or 1) and print the binarized distribution."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zjFsz6rkE1LG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e991f96b-f70b-4f0a-9656-df7b4d8bfeac"
      },
      "source": [
        "df[\"outcome\"] = df[\"outcome\"].apply(lambda r: 1 if r in [\"Goal\", \"owngoal\"] else 0) # goal = 1 and else 0\n",
        "\n",
        "# Change column name from outcome to goal\n",
        "dep_var = \"goal\"\n",
        "df = df.rename(columns={\"outcome\": \"goal\"})\n",
        "\n",
        "distributions = df[dep_var].value_counts(normalize=True)\n",
        "print(f\"Non-Goal percentage is {round(100*distributions[0],2)}% and the Goal percentage is {round(100*distributions[1],2)}%\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Non-Goal percentage is 87.42% and the Goal percentage is 12.58%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DxDr7RSnT6b0"
      },
      "source": [
        "Create Train and validation sets with (80%, 20%)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TlqXhMSrgnDI"
      },
      "source": [
        "# Selecting correspondnig columns for training and test stes\n",
        "\n",
        "X = df[features].values\n",
        "y = df[dep_var].values\n",
        "\n",
        "# Spliting train and test sets. 80% for the training and 20% for the test set.\n",
        "xs, valid_xs, y, valid_y = train_test_split(X, y, test_size=0.20, random_state=seed, shuffle=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hc2qlsHtsFsN"
      },
      "source": [
        "## Define the evaluation metric, train ML Models, and choose the best one"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lHeUZFHLqa-M"
      },
      "source": [
        "Define [AUC ROC](https://en.wikipedia.org/wiki/Receiver_operating_characteristic) as the evaluation metric because the dataset is imbalanced."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4rmV7EzUGEDl"
      },
      "source": [
        "def calc_auc_roc(y, prob_pred):\n",
        "  return roc_auc_score(y, prob_pred)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-bKrmHFWRawF"
      },
      "source": [
        "Now, it is time to do the hyper-parameter tuning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ukIXAHSARVc_"
      },
      "source": [
        "## Hyperparameters\n",
        "\n",
        "lr_hyperparameters = {\n",
        "    'lr__C': [0.01, 0.1, 1.0, 10, 100]\n",
        "}\n",
        "\n",
        "rf_hyperparameters = {\n",
        "    'rf__n_estimators': np.arange(20,100,10),\n",
        "    'rf__max_features': np.arange(0.5,1.0,0.1),\n",
        "    'rf__max_depth': np.arange(1,20,5),\n",
        "}\n",
        "\n",
        "xgb_hyperparameters = {\n",
        "    'xgb__max_depth': np.arange(2,12,2),  # the maximum depth of each tree\n",
        "    'xgb__learning_rate': [0.1,0.3],  # the training step for each iteration\n",
        "    'xgb__n_estimators': np.arange(1,80,10),\n",
        "}\n",
        "\n",
        "lgbm_hyperparameters = {\n",
        "    'lgbm__n_estimators': np.arange(10,140,20),\n",
        "    'lgbm__min_data_in_leaf': np.arange(100,1000,100),\n",
        "    'lgbm__max_depth': np.arange(2,10,2),\n",
        "\n",
        "}\n",
        "\n",
        "hyperparameters = {\n",
        "    'lr': lr_hyperparameters,\n",
        "    'rf': rf_hyperparameters,\n",
        "    'lgbm': lgbm_hyperparameters,\n",
        "    'xgb': xgb_hyperparameters,\n",
        "}\n",
        "\n",
        "# Pipeline of ML classiferis' pipielines \n",
        "pipelines = {\n",
        "    'bl': Pipeline([('bl', DummyClassifier(strategy='most_frequent'))]), # base line\n",
        "    'lr': Pipeline([('lr',LogisticRegression(random_state=seed, n_jobs=-1, penalty='l2'))]),\n",
        "    'rf': Pipeline([('rf', RandomForestClassifier(random_state=seed, n_jobs=-1, oob_score=True))]),\n",
        "    'xgb': Pipeline([('xgb', XGBClassifier(random_state=seed, n_jobs=-1))]),\n",
        "    'lgbm': Pipeline([('lgbm', LGBMClassifier(random_state=seed, n_jobs=-1))]),\n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 805,
          "referenced_widgets": [
            "ada4885d9cdb4daa914587df7f8adb78",
            "63ce3ee08b744465bf99607e82999d81",
            "7c9b720b270443479180962560af8908",
            "cf34e68469ed4ef0994027fba37d8dd2",
            "d7114151261149bca0f1130495ed7a3a",
            "76584213b87d4dd8b66a2502cbf7b482",
            "97309818842b4a458dc80deb68d69992",
            "7a0979181932401a87a2e0a6449545eb"
          ]
        },
        "id": "EXEEEu2feNXX",
        "outputId": "8a09bebe-8a7b-437f-f404-b72c73370837"
      },
      "source": [
        "# Start the training process\n",
        "\n",
        "results = []\n",
        "model_names = {\"bl\":\"Baseline\", \"lr\": \"Logistic Regression\" , \"rf\":\"Random Forest\", \n",
        "               \"xgb\": \"XGBoost\", \"lgbm\": \"Light Gradient Boosting\"}\n",
        "\n",
        "fig, ax =  plt.subplots(figsize=(8, 8))\n",
        "ax.plot([0, 1], [0, 1], \"k:\", label=\"Perfectly calibrated\")\n",
        "\n",
        "for key, pipeline in tqdm(pipelines.items()):\n",
        "  if key == 'bl':\n",
        "      model = pipeline\n",
        "  else:\n",
        "      model = GridSearchCV(pipeline, hyperparameters[key], cv=cv, scoring=scoring, n_jobs=-1)\n",
        "\n",
        "  model.fit(xs,y)\n",
        "\n",
        "  if hasattr(model,'best_estimator_'):\n",
        "      best = model.best_estimator_.named_steps[key]\n",
        "  else:\n",
        "      best = model\n",
        "\n",
        "  result = {}\n",
        "\n",
        "  result[\"model\"] = model_names[key]\n",
        "\n",
        "  train_prob_pred = best.predict_proba(xs)[:,1]\n",
        "\n",
        "  result[\"training (auc roc)\"] = calc_auc_roc(y, train_prob_pred)\n",
        "  \n",
        "  validation_prob_pred = best.predict_proba(valid_xs)[:,1]\n",
        "\n",
        "  result[\"validation (auc roc)\"]  = calc_auc_roc(valid_y, validation_prob_pred)\n",
        "\n",
        "\n",
        "  fraction_of_positives, mean_predicted_value = calibration_curve(valid_y, validation_prob_pred, n_bins=10)\n",
        "\n",
        "  result[\"Brier score\"] = brier_score_loss(valid_y, validation_prob_pred)\n",
        "\n",
        "  results.append(result)\n",
        "\n",
        "  # Do not log calibration information for baseline fitter, as I already have done it.\n",
        "  if key == 'bl':\n",
        "    continue\n",
        "\n",
        "  # Plot the calibration plot  \n",
        "  ax.plot(mean_predicted_value, fraction_of_positives, \"s-\", label=\"%s\" % (model_names[key], ))\n",
        "    \n",
        "  # Save the model\n",
        "  joblib.dump(best, f'{model_names[key]}.joblib')\n",
        "\n",
        "results_df = pd.DataFrame(results).round(3)\n",
        "display(results_df)\n",
        "\n",
        "ax.set_xlabel(\"Mean predicted value\")\n",
        "ax.set_ylabel(\"Fraction of positives\")\n",
        "ax.set_ylim([-0.05, 1.05])\n",
        "ax.legend(loc=\"lower right\")\n",
        "ax.set_title('Calibration plot  (reliability curve)')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.savefig('calibartion_plot.png', bbox_inches='tight')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ada4885d9cdb4daa914587df7f8adb78",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=5.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>model</th>\n",
              "      <th>training (auc roc)</th>\n",
              "      <th>validation (auc roc)</th>\n",
              "      <th>Brier score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Baseline</td>\n",
              "      <td>0.500</td>\n",
              "      <td>0.500</td>\n",
              "      <td>0.129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>0.793</td>\n",
              "      <td>0.802</td>\n",
              "      <td>0.090</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>0.836</td>\n",
              "      <td>0.803</td>\n",
              "      <td>0.089</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>XGBoost</td>\n",
              "      <td>0.808</td>\n",
              "      <td>0.798</td>\n",
              "      <td>0.090</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Light Gradient Boosting</td>\n",
              "      <td>0.808</td>\n",
              "      <td>0.802</td>\n",
              "      <td>0.091</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     model  training (auc roc)  validation (auc roc)  \\\n",
              "0                 Baseline               0.500                 0.500   \n",
              "1      Logistic Regression               0.793                 0.802   \n",
              "2            Random Forest               0.836                 0.803   \n",
              "3                  XGBoost               0.808                 0.798   \n",
              "4  Light Gradient Boosting               0.808                 0.802   \n",
              "\n",
              "   Brier score  \n",
              "0        0.129  \n",
              "1        0.090  \n",
              "2        0.089  \n",
              "3        0.090  \n",
              "4        0.091  "
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iYpANxeBacG9"
      },
      "source": [
        "Select the best model and print its tunned parameters."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R5oUxCB9yH4j",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8928d4f1-335e-494b-f6db-c6d092e87e54"
      },
      "source": [
        "best_model_name = \"Logistic Regression\"\n",
        "selected_model = joblib.load(f\"{best_model_name}.joblib\")\n",
        "print(f\"selected model is {best_model_name}.\\n\")\n",
        "print(\"Its parameters are:\")\n",
        "selected_model.get_params()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "selected model is Logistic Regression.\n",
            "\n",
            "Its parameters are:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'C': 100,\n",
              " 'class_weight': None,\n",
              " 'dual': False,\n",
              " 'fit_intercept': True,\n",
              " 'intercept_scaling': 1,\n",
              " 'l1_ratio': None,\n",
              " 'max_iter': 100,\n",
              " 'multi_class': 'auto',\n",
              " 'n_jobs': -1,\n",
              " 'penalty': 'l2',\n",
              " 'random_state': 42,\n",
              " 'solver': 'lbfgs',\n",
              " 'tol': 0.0001,\n",
              " 'verbose': 0,\n",
              " 'warm_start': False}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 0
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "fTwuX8hSGJot",
        "outputId": "31d683a7-2e3a-4073-c9d5-f1afc5464d63"
      },
      "source": [
        "fig, ax =  plt.subplots(figsize=(7, 7))\n",
        "ax.set_title('AUC ROC Curve of the Logistic Regression Model')\n",
        "plot_roc_curve(selected_model, valid_xs, valid_y, ax=ax);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7qSyx17eDeNP"
      },
      "source": [
        "## Interpret the model for knowledge discovery"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AQLlEvcSwQC-"
      },
      "source": [
        "Print the coefficients and intercept of the logistic regression model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kcJBs3S56WsO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "outputId": "454122c4-c93c-45f7-e0fa-c495bbc108dc"
      },
      "source": [
        "intercept = round(selected_model.intercept_[0],2)\n",
        "print(f\"The model intercept is {intercept}\\n\")\n",
        "\n",
        "coefficients = [round(c,2) for c in selected_model.coef_[0]]\n",
        "\n",
        "print(\"The model coefficients are:\")\n",
        "pd.DataFrame(coefficients, features, columns=['coef']).sort_values(by='coef', ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The model intercept is 0.68\n",
            "\n",
            "The model coefficients are:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>coef</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Penalty</th>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Direct freekick</th>\n",
              "      <td>0.64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Number_Intervening_Teammates</th>\n",
              "      <td>0.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>angle</th>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>distance</th>\n",
              "      <td>-0.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Other</th>\n",
              "      <td>-0.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Number_Intervening_Opponents</th>\n",
              "      <td>-0.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Head</th>\n",
              "      <td>-0.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Open Play</th>\n",
              "      <td>-1.12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                              coef\n",
              "Penalty                       1.00\n",
              "Direct freekick               0.64\n",
              "Number_Intervening_Teammates  0.18\n",
              "angle                         0.02\n",
              "distance                     -0.09\n",
              "Other                        -0.34\n",
              "Number_Intervening_Opponents -0.39\n",
              "Head                         -0.95\n",
              "Open Play                    -1.12"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 0
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fZ1AtKzFK6m1"
      },
      "source": [
        "1-dimensional partial dependence plots"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j8wrJ-qJDeNS",
        "scrolled": false,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "a71311fb-00ed-468d-805c-1aae63f88d3e"
      },
      "source": [
        "explore_cols = [\"angle\", \"distance\", \"Number_Intervening_Opponents\",\"Number_Intervening_Teammates\"]\n",
        "\n",
        "valid_xs_df = pd.DataFrame(valid_xs, columns = features)\n",
        "\n",
        "for index, col in enumerate(explore_cols):\n",
        "    fig,ax = plt.subplots(figsize=(12, 4))\n",
        "    plot_partial_dependence(selected_model, valid_xs_df, [col], grid_resolution=20, ax=ax);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wHCNTkK1d0fT"
      },
      "source": [
        "2-dimensional partial dependence plots\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vOXfxf0vDeNS",
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "a8e80d52-f27f-4cc2-8d0e-87410106a801"
      },
      "source": [
        "paired_features = [\n",
        "                  (\"angle\",\"distance\"),\n",
        "                  (\"Number_Intervening_Opponents\",\"Number_Intervening_Teammates\"),\n",
        "                  (\"angle\",\"Number_Intervening_Opponents\"),\n",
        "                  (\"distance\",\"Number_Intervening_Opponents\"), \n",
        "                  ]\n",
        "\n",
        "for index, pair in enumerate(paired_features):\n",
        "  fig,ax = plt.subplots(figsize=(8, 8))\n",
        "  plot_partial_dependence(selected_model, valid_xs_df, [pair], grid_resolution=20, ax=ax);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mT_uV7eAeqPR"
      },
      "source": [
        "# **Step 2**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t_w_cWkFhkkb"
      },
      "source": [
        "## Functions needed to enrich the Metrica Shots based on xG model features (Step 1)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KBVJ8GynfP-r"
      },
      "source": [
        "def calc_distance(ball, event_team):\n",
        "  \"\"\"Euclidean distance to center of the target goal\"\"\"\n",
        "  ball_x, ball_y = ball\n",
        "  if event_team == home_team:\n",
        "    return np.sqrt((ball_x - (-pitch_x/2))**2 + (ball_y)**2)\n",
        "  else:\n",
        "    return np.sqrt((ball_x - (pitch_x/2))**2 + (ball_y)**2)\n",
        "\n",
        "def calc_angle(ball, event_team):\n",
        "  \"\"\"Shot angle to the goalposts in degrees\"\"\"\n",
        "  ball_x, ball_y = ball\n",
        "\n",
        "  x_distance_to_goal = abs(pitch_x/2 - ball_x)\n",
        "  y_distance_to_goal = abs(ball_y)\n",
        "  \n",
        "  if event_team == home_team:\n",
        "    x_distance_to_goal = abs(ball_x + pitch_x/2)\n",
        "  angle =  np.arctan(goal_line_width*x_distance_to_goal/(x_distance_to_goal**2+y_distance_to_goal**2-(goal_line_width/2)**2))\n",
        "  if angle < 0:\n",
        "    angle = np.pi + angle\n",
        "  return np.rad2deg(angle)\n",
        "\n",
        "def calc_playType(event_pre_Type, event_pre_Subtype):\n",
        "  '''Calculate the play type of a shot'''\n",
        "  # Possible playTypes from xg model ['Open Play', 'Direct freekick', 'Penalty', 'Direct corner']\n",
        "  play_type = \"Open Play\"\n",
        "  if event_pre_Type == \"SET PIECE\":\n",
        "    if event_pre_Subtype in [\"FREE KICK\",\"FREE KICK-RETAKEN\"]:\n",
        "      play_type = \"Direct freekick\"\n",
        "    elif event_pre_Subtype == \"CORNER KICK\":\n",
        "      play_type = \"Direct corner\"\n",
        "    elif event_pre_Subtype == \"PENALTY\":\n",
        "      play_type = \"Penalty\"\n",
        "  return play_type\n",
        "\n",
        "def calc_BodyPart(event_Subtype):\n",
        "  '''Calculate the body type involved in a shot'''\n",
        "  # Assuming all shots are with either head or foot\n",
        "  if \"HEAD\" in event_Subtype:\n",
        "    return \"Head\"\n",
        "  else:\n",
        "    return \"Foot\"\n",
        "\n",
        "def calc_trianlge_area(point1, point2, point3):\n",
        "  \"\"\"Calculate a triangle area with three given points (x,y)\"\"\"\n",
        "\n",
        "  x1, y1 = point1\n",
        "  x2, y2 = point2\n",
        "  x3, y3 = point3\n",
        "\n",
        "  return abs((x1*(y2-y3)+(x2*(y3-y1))+(x3*(y1-y2)))/2)\n",
        "\n",
        "def calc_Number_Intervening(ball, event_player, event_team, frame, frame_team_name):\n",
        "  \"\"\"Calculate whether a player lies inside the triangle between the shooter and the goalposts\"\"\"\n",
        "\n",
        "  number_intervening_players = 0\n",
        "  \n",
        "  goalpost1 = ()\n",
        "  goalpost2 = ()\n",
        "  \n",
        "  if event_team == home_team:\n",
        "    goalpost1 = (-pitch_x/2, goal_line_width/2)\n",
        "    goalpost2 = (-pitch_x/2, -goal_line_width/2)\n",
        "  else:\n",
        "    goalpost1 = (pitch_x/2, goal_line_width/2)\n",
        "    goalpost2 = (pitch_x/2, -goal_line_width/2)\n",
        "\n",
        "  shooter_triangle_area = calc_trianlge_area(ball, goalpost1, goalpost2)\n",
        "\n",
        "  id_range = range(1,15)\n",
        "  if frame_team_name == away_team:\n",
        "    id_range = range(15,27)\n",
        "  \n",
        "  cols = [[f\"{frame_team_name}_{id}_x\",f\"{frame_team_name}_{id}_y\"] for id in id_range]\n",
        "\n",
        "  # If calculating teammates obscuring the goal, remove the action taker player from the list\n",
        "  if event_team == frame_team_name:\n",
        "    cols.remove([f\"{frame_team_name}_{event_player}_x\",f\"{frame_team_name}_{event_player}_y\"])\n",
        "\n",
        "  for col in cols:\n",
        "    playerPoint = tuple(frame[col])\n",
        "    if np.isnan(playerPoint[0]):\n",
        "      continue\n",
        "    player_triangles_areas = calc_trianlge_area(playerPoint, goalpost1, goalpost2) + calc_trianlge_area(playerPoint, goalpost1, ball) + calc_trianlge_area(playerPoint, goalpost2, ball)\n",
        "    if round(shooter_triangle_area) == round(player_triangles_areas):\n",
        "      number_intervening_players = number_intervening_players + 1\n",
        "  return number_intervening_players\n",
        "\n",
        "def calc_goal(event_Subtype):\n",
        "  '''Calculate whether the outcome is goal or not'''\n",
        "  if 'GOAL' in event_Subtype:\n",
        "    return 1\n",
        "  else:\n",
        "    return 0\n",
        "\n",
        "def calc_xG(instance):\n",
        "  '''Predict the goal-scoring probability of a shot by applying the trained model'''\n",
        "  x = instance[features].values.reshape(1,-1)\n",
        "  return round(selected_model.predict_proba(x)[:,1][0],2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jfq2yU0Dhshf"
      },
      "source": [
        "## Find the shot frames and enrich them with defined functions to predict xG values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z4WOXi9JfTf6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "outputId": "e84ac166-0b59-442b-88b0-13391bb4f017"
      },
      "source": [
        "# Read the event data\n",
        "events = mio.read_event_data(DATADIR,game_id)\n",
        "\n",
        "# Bit of housekeeping: unit conversion from metric data units to meters\n",
        "events = mio.to_metric_coordinates(events)\n",
        "\n",
        "#### TRACKING DATA ####\n",
        "\n",
        "# READING IN TRACKING DATA\n",
        "tracking_home = mio.tracking_data(DATADIR,game_id,'Home')\n",
        "tracking_away = mio.tracking_data(DATADIR,game_id,'Away')\n",
        "\n",
        "# Convert positions from metrica units to meters \n",
        "tracking_home = mio.to_metric_coordinates(tracking_home)\n",
        "tracking_away = mio.to_metric_coordinates(tracking_away)\n",
        "\n",
        "# Reverse direction of play in the second half \n",
        "# So that home team is always attacking from right->left\n",
        "# And the away team is always attacking from left->right\n",
        "tracking_home,tracking_away,events = mio.to_single_playing_direction(tracking_home,tracking_away,events)\n",
        "\n",
        "# Add a column event 'Minute' to the data frame\n",
        "events['Minute'] = events['Start Time [s]']/60.\n",
        "\n",
        "# Add information abou the previous action\n",
        "events[\"pre_Type\"] = events[\"Type\"].shift(1)\n",
        "events[\"pre_Subtype\"] = events[\"Subtype\"].shift(1)\n",
        "\n",
        "# Create the dataframe required by the xG model\n",
        "xg_features = ['Number_Intervening_Opponents', 'Number_Intervening_Teammates', 'distance', 'angle',\n",
        "               'Direct freekick', 'Open Play', 'Penalty', 'Head', 'Other']\n",
        "\n",
        "instances = []\n",
        "for index, event in events[events.Type==\"SHOT\"].iterrows():\n",
        "  # Extract required information\n",
        "  event_team = event.Team\n",
        "  event_player = event.From.replace(\"Player\",\"\").strip()\n",
        "  event_time_seconds = event[\"Start Time [s]\"]\n",
        "  event_time_minutes = event.Minute\n",
        "  event_Subtype = event.Subtype\n",
        "  event_pre_Type = event.pre_Type\n",
        "  event_pre_Subtype = event.pre_Subtype\n",
        "\n",
        "  frame_number = event[\"Start Frame\"] # get the event frame number\n",
        "  frame_home = tracking_home.loc[frame_number]\n",
        "  frame_away = tracking_away.loc[frame_number]\n",
        "\n",
        "  ball = (frame_home[\"ball_x\"], frame_home[\"ball_y\"])\n",
        "\n",
        "  instance = {}\n",
        "  instance[\"Frame\"] = frame_number\n",
        "  instance[\"team\"] = event_team\n",
        "  instance[\"Start Time [s]\"] =  event_time_seconds\n",
        "  instance[\"Minute\"] = event_time_minutes\n",
        "\n",
        "  if event_team == home_team:\n",
        "    instance[\"Number_Intervening_Opponents\"] = calc_Number_Intervening(ball, event_player, event_team, frame_away, away_team)\n",
        "    instance[\"Number_Intervening_Teammates\"] = calc_Number_Intervening(ball, event_player, event_team, frame_home, home_team)\n",
        "  elif event_team == away_team:\n",
        "    instance[\"Number_Intervening_Opponents\"] = calc_Number_Intervening(ball, event_player, event_team, frame_home, home_team)\n",
        "    instance[\"Number_Intervening_Teammates\"] = calc_Number_Intervening(ball, event_player, event_team, frame_away, away_team)\n",
        "\n",
        "  instance[\"distance\"] = calc_distance(ball, event_team)\n",
        "  instance[\"angle\"] = calc_angle(ball, event_team)\n",
        "  instance[\"BodyPart\"] = calc_BodyPart(event_Subtype)  \n",
        "  instance[\"play_type\"] = calc_playType(event_pre_Type, event_pre_Subtype)\n",
        "  instance[dep_var] = calc_goal(event_Subtype)\n",
        "  instances.append(instance)\n",
        "\n",
        "instances_df = pd.DataFrame(instances)\n",
        "instances_df = instances_df.round(2)\n",
        "\n",
        "# Perform one-hot encoding\n",
        "columns_to_encode = [\"play_type\",\"BodyPart\"]\n",
        "for col in columns_to_encode:\n",
        "  # Prevent multicollinearity\n",
        "  one_hot = pd.get_dummies(instances_df[col])\n",
        "  # Drop the column as it is now encoded\n",
        "  instances_df = instances_df.drop(col,axis = 1)\n",
        "  # Join the encoded df\n",
        "  instances_df = instances_df.join(one_hot)\n",
        "\n",
        "# There us no 'Other' in metrica body part data\n",
        "instances_df[\"Other\"] = 0\n",
        "# Move outcome to the last column\n",
        "instances_df = instances_df[[x for x in instances_df.columns if x not in [dep_var]] + [dep_var]]\n",
        "\n",
        "## Output Expected Goals\n",
        "instances_df['xG'] = instances_df.apply(lambda instance: calc_xG(instance), axis=1)\n",
        "\n",
        "display(instances_df.head())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Reading team: home\n",
            "Reading team: away\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Frame</th>\n",
              "      <th>team</th>\n",
              "      <th>Start Time [s]</th>\n",
              "      <th>Minute</th>\n",
              "      <th>Number_Intervening_Opponents</th>\n",
              "      <th>Number_Intervening_Teammates</th>\n",
              "      <th>distance</th>\n",
              "      <th>angle</th>\n",
              "      <th>Direct freekick</th>\n",
              "      <th>Open Play</th>\n",
              "      <th>Penalty</th>\n",
              "      <th>Foot</th>\n",
              "      <th>Head</th>\n",
              "      <th>Other</th>\n",
              "      <th>goal</th>\n",
              "      <th>xG</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4419</td>\n",
              "      <td>Home</td>\n",
              "      <td>176.76</td>\n",
              "      <td>2.95</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>22.60</td>\n",
              "      <td>10.82</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12202</td>\n",
              "      <td>Home</td>\n",
              "      <td>488.08</td>\n",
              "      <td>8.13</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>6.07</td>\n",
              "      <td>58.85</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>16484</td>\n",
              "      <td>Home</td>\n",
              "      <td>659.36</td>\n",
              "      <td>10.99</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>15.76</td>\n",
              "      <td>18.88</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>18515</td>\n",
              "      <td>Away</td>\n",
              "      <td>740.60</td>\n",
              "      <td>12.34</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>23.57</td>\n",
              "      <td>15.98</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>27345</td>\n",
              "      <td>Home</td>\n",
              "      <td>1093.80</td>\n",
              "      <td>18.23</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>10.45</td>\n",
              "      <td>33.51</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Frame  team  Start Time [s]  Minute  Number_Intervening_Opponents  \\\n",
              "0   4419  Home          176.76    2.95                             3   \n",
              "1  12202  Home          488.08    8.13                             1   \n",
              "2  16484  Home          659.36   10.99                             2   \n",
              "3  18515  Away          740.60   12.34                             1   \n",
              "4  27345  Home         1093.80   18.23                             1   \n",
              "\n",
              "   Number_Intervening_Teammates  distance  angle  Direct freekick  Open Play  \\\n",
              "0                             0     22.60  10.82                0          1   \n",
              "1                             0      6.07  58.85                0          1   \n",
              "2                             0     15.76  18.88                0          1   \n",
              "3                             1     23.57  15.98                0          1   \n",
              "4                             0     10.45  33.51                0          1   \n",
              "\n",
              "   Penalty  Foot  Head  Other  goal    xG  \n",
              "0        0     1     0      0     0  0.03  \n",
              "1        0     1     0      0     1  0.46  \n",
              "2        0     1     0      0     0  0.10  \n",
              "3        0     1     0      0     0  0.09  \n",
              "4        0     0     1      0     0  0.12  "
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "SyYsjSAobaSq",
        "outputId": "4b0b05e5-4204-4234-c549-a3166e7c3f41"
      },
      "source": [
        "instances_df.groupby(\"team\")[dep_var, \"xG\"].sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>goal</th>\n",
              "      <th>xG</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>team</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Away</th>\n",
              "      <td>2</td>\n",
              "      <td>2.76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Home</th>\n",
              "      <td>3</td>\n",
              "      <td>2.07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      goal    xG\n",
              "team            \n",
              "Away     2  2.76\n",
              "Home     3  2.07"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 0
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L4bg7DXKfpo0",
        "outputId": "000d8458-485a-454f-d78e-b9aea16d9157"
      },
      "source": [
        "# Binning xG\n",
        "\n",
        "def calc_xg_quality(xg):\n",
        "  if xg < 0.07:\n",
        "    return \"poor\"\n",
        "  elif 0.07 <= xg < 0.15:\n",
        "    return \"fair\"\n",
        "  elif 0.15 <= xg < 0.30:\n",
        "    return \"good\"\n",
        "  elif 0.30 <= xg:\n",
        "    return \"very good\"\n",
        "\n",
        "instances_df[\"xG_quality\"] = instances_df[\"xG\"].apply(lambda xg: calc_xg_quality(xg))\n",
        "instances_df.groupby(\"team\")[\"xG_quality\"].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "team  xG_quality\n",
              "Away  very good     4\n",
              "      good          3\n",
              "      fair          2\n",
              "      poor          2\n",
              "Home  poor          5\n",
              "      fair          4\n",
              "      very good     4\n",
              "Name: xG_quality, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 0
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HckicCs5LEyr"
      },
      "source": [
        "# inspird from plot_events in Metrica_Viz\n",
        "def plot_xG(events, MarkerSize, color, figax=None, field_dimen = (105.0,68.0)):\n",
        "    \"\"\" plot_events( events )\n",
        "    \n",
        "    Plots xG shots on a football pitch. event data can be a single or several rows of a data frame. All distances should be in meters.\n",
        "    \n",
        "    Parameters\n",
        "    -----------\n",
        "        events: row (i.e. instant) of the home team tracking data frame\n",
        "        MarkerSize: marker size\n",
        "        color: color of indicator\n",
        "        fig,ax: Can be used to pass in the (fig,ax) objects of a previously generated pitch. Set to (fig,ax) to use an existing figure, or None (the default) to generate a new pitch plot, \n",
        "        field_dimen: tuple containing the length and width of the pitch in meters. Default is (105,68)\n",
        "        \n",
        "    Returrns\n",
        "    -----------\n",
        "       fig,ax : figure and aixs objects (so that other data can be plotted onto the pitch)\n",
        "\n",
        "    \"\"\"\n",
        "\n",
        "    if figax is None: # create new pitch \n",
        "        fig,ax = mviz.plot_pitch( field_dimen = field_dimen )\n",
        "    else: # overlay on a previously generated pitch\n",
        "        fig,ax = figax \n",
        "    for i,row in events.iterrows():\n",
        "          ax.plot( row['Start X'], row['Start Y'], color+'o', alpha=0.5, MarkerSize = MarkerSize)\n",
        "    return fig,ax"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8WDunl7v4wZ_"
      },
      "source": [
        "Plot the shots with their xG values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "WHL5t34jICIF",
        "outputId": "9895f8fe-1721-4165-c3f5-e97020feaabc"
      },
      "source": [
        "figax = None\n",
        "for index, instance in instances_df.iterrows():\n",
        "  frame_id = instance[\"Frame\"]\n",
        "  team = instance[\"team\"]\n",
        "  xG = instance[\"xG\"]\n",
        "  color = 'r'\n",
        "  if team == away_team:\n",
        "    color = 'b'\n",
        "  MarkerSize= math.ceil(xG*20) + 10\n",
        "  fig,ax = plot_xG(events[(events.Type==\"SHOT\")&(events[\"Start Frame\"]==frame_id)].iloc[-1:], MarkerSize=MarkerSize, color = color, figax=figax)\n",
        "  figax = (fig,ax)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KFtx-9lsiqup"
      },
      "source": [
        "## Explore Cases"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 847
        },
        "id": "XDt_VIHdmLtd",
        "outputId": "063b9559-e908-44c9-899e-3a2bd82bab69"
      },
      "source": [
        "instances_df.sort_values(by=[\"xG\"])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Frame</th>\n",
              "      <th>team</th>\n",
              "      <th>Start Time [s]</th>\n",
              "      <th>Minute</th>\n",
              "      <th>Number_Intervening_Opponents</th>\n",
              "      <th>Number_Intervening_Teammates</th>\n",
              "      <th>distance</th>\n",
              "      <th>angle</th>\n",
              "      <th>Direct freekick</th>\n",
              "      <th>Open Play</th>\n",
              "      <th>Penalty</th>\n",
              "      <th>Foot</th>\n",
              "      <th>Head</th>\n",
              "      <th>Other</th>\n",
              "      <th>goal</th>\n",
              "      <th>xG</th>\n",
              "      <th>xG_quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4419</td>\n",
              "      <td>Home</td>\n",
              "      <td>176.76</td>\n",
              "      <td>2.95</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>22.60</td>\n",
              "      <td>10.82</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.03</td>\n",
              "      <td>poor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>29754</td>\n",
              "      <td>Home</td>\n",
              "      <td>1190.16</td>\n",
              "      <td>19.84</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>25.39</td>\n",
              "      <td>13.83</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.03</td>\n",
              "      <td>poor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>64772</td>\n",
              "      <td>Away</td>\n",
              "      <td>2590.88</td>\n",
              "      <td>43.18</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>24.21</td>\n",
              "      <td>16.99</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.04</td>\n",
              "      <td>poor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>121027</td>\n",
              "      <td>Home</td>\n",
              "      <td>4841.08</td>\n",
              "      <td>80.68</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>22.93</td>\n",
              "      <td>17.66</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.05</td>\n",
              "      <td>poor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>117218</td>\n",
              "      <td>Home</td>\n",
              "      <td>4688.72</td>\n",
              "      <td>78.15</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>14.01</td>\n",
              "      <td>23.45</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.05</td>\n",
              "      <td>poor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>69887</td>\n",
              "      <td>Away</td>\n",
              "      <td>2795.48</td>\n",
              "      <td>46.59</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>24.58</td>\n",
              "      <td>15.25</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.05</td>\n",
              "      <td>poor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>56079</td>\n",
              "      <td>Home</td>\n",
              "      <td>2243.16</td>\n",
              "      <td>37.39</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>12.55</td>\n",
              "      <td>28.47</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.06</td>\n",
              "      <td>poor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>67067</td>\n",
              "      <td>Home</td>\n",
              "      <td>2682.68</td>\n",
              "      <td>44.71</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>22.64</td>\n",
              "      <td>11.07</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.07</td>\n",
              "      <td>fair</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>139891</td>\n",
              "      <td>Home</td>\n",
              "      <td>5595.64</td>\n",
              "      <td>93.26</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>31.11</td>\n",
              "      <td>13.40</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.09</td>\n",
              "      <td>fair</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>18515</td>\n",
              "      <td>Away</td>\n",
              "      <td>740.60</td>\n",
              "      <td>12.34</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>23.57</td>\n",
              "      <td>15.98</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.09</td>\n",
              "      <td>fair</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>16484</td>\n",
              "      <td>Home</td>\n",
              "      <td>659.36</td>\n",
              "      <td>10.99</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>15.76</td>\n",
              "      <td>18.88</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.10</td>\n",
              "      <td>fair</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>86191</td>\n",
              "      <td>Away</td>\n",
              "      <td>3447.64</td>\n",
              "      <td>57.46</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>17.32</td>\n",
              "      <td>22.95</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.11</td>\n",
              "      <td>fair</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>27345</td>\n",
              "      <td>Home</td>\n",
              "      <td>1093.80</td>\n",
              "      <td>18.23</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>10.45</td>\n",
              "      <td>33.51</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.12</td>\n",
              "      <td>fair</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>90165</td>\n",
              "      <td>Away</td>\n",
              "      <td>3606.60</td>\n",
              "      <td>60.11</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>12.81</td>\n",
              "      <td>15.70</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.17</td>\n",
              "      <td>good</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>111758</td>\n",
              "      <td>Away</td>\n",
              "      <td>4470.32</td>\n",
              "      <td>74.51</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>11.42</td>\n",
              "      <td>28.46</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.19</td>\n",
              "      <td>good</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>136060</td>\n",
              "      <td>Away</td>\n",
              "      <td>5442.40</td>\n",
              "      <td>90.71</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5.59</td>\n",
              "      <td>63.39</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.27</td>\n",
              "      <td>good</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>63362</td>\n",
              "      <td>Away</td>\n",
              "      <td>2534.48</td>\n",
              "      <td>42.24</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6.76</td>\n",
              "      <td>56.76</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.31</td>\n",
              "      <td>very good</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>124336</td>\n",
              "      <td>Home</td>\n",
              "      <td>4973.44</td>\n",
              "      <td>82.89</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6.70</td>\n",
              "      <td>57.21</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.31</td>\n",
              "      <td>very good</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>73983</td>\n",
              "      <td>Home</td>\n",
              "      <td>2959.32</td>\n",
              "      <td>49.32</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5.18</td>\n",
              "      <td>70.28</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.31</td>\n",
              "      <td>very good</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>53049</td>\n",
              "      <td>Away</td>\n",
              "      <td>2121.96</td>\n",
              "      <td>35.37</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>8.48</td>\n",
              "      <td>44.71</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.34</td>\n",
              "      <td>very good</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>98880</td>\n",
              "      <td>Home</td>\n",
              "      <td>3955.20</td>\n",
              "      <td>65.92</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>6.23</td>\n",
              "      <td>45.75</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.39</td>\n",
              "      <td>very good</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12202</td>\n",
              "      <td>Home</td>\n",
              "      <td>488.08</td>\n",
              "      <td>8.13</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>6.07</td>\n",
              "      <td>58.85</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.46</td>\n",
              "      <td>very good</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>132570</td>\n",
              "      <td>Away</td>\n",
              "      <td>5302.80</td>\n",
              "      <td>88.38</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>20.48</td>\n",
              "      <td>18.66</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.47</td>\n",
              "      <td>very good</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>115009</td>\n",
              "      <td>Away</td>\n",
              "      <td>4600.36</td>\n",
              "      <td>76.67</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>11.89</td>\n",
              "      <td>34.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.72</td>\n",
              "      <td>very good</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Frame  team  Start Time [s]  Minute  Number_Intervening_Opponents  \\\n",
              "0     4419  Home          176.76    2.95                             3   \n",
              "5    29754  Home         1190.16   19.84                             3   \n",
              "9    64772  Away         2590.88   43.18                             3   \n",
              "19  121027  Home         4841.08   80.68                             2   \n",
              "18  117218  Home         4688.72   78.15                             2   \n",
              "11   69887  Away         2795.48   46.59                             2   \n",
              "7    56079  Home         2243.16   37.39                             2   \n",
              "10   67067  Home         2682.68   44.71                             1   \n",
              "23  139891  Home         5595.64   93.26                             3   \n",
              "3    18515  Away          740.60   12.34                             1   \n",
              "2    16484  Home          659.36   10.99                             2   \n",
              "13   86191  Away         3447.64   57.46                             2   \n",
              "4    27345  Home         1093.80   18.23                             1   \n",
              "14   90165  Away         3606.60   60.11                             1   \n",
              "16  111758  Away         4470.32   74.51                             2   \n",
              "22  136060  Away         5442.40   90.71                             1   \n",
              "8    63362  Away         2534.48   42.24                             0   \n",
              "20  124336  Home         4973.44   82.89                             0   \n",
              "12   73983  Home         2959.32   49.32                             1   \n",
              "6    53049  Away         2121.96   35.37                             1   \n",
              "15   98880  Home         3955.20   65.92                             1   \n",
              "1    12202  Home          488.08    8.13                             1   \n",
              "21  132570  Away         5302.80   88.38                             2   \n",
              "17  115009  Away         4600.36   76.67                             1   \n",
              "\n",
              "    Number_Intervening_Teammates  distance  angle  Direct freekick  Open Play  \\\n",
              "0                              0     22.60  10.82                0          1   \n",
              "5                              0     25.39  13.83                0          1   \n",
              "9                              1     24.21  16.99                0          1   \n",
              "19                             0     22.93  17.66                0          1   \n",
              "18                             0     14.01  23.45                0          1   \n",
              "11                             0     24.58  15.25                0          1   \n",
              "7                              0     12.55  28.47                0          1   \n",
              "10                             0     22.64  11.07                0          1   \n",
              "23                             0     31.11  13.40                1          0   \n",
              "3                              1     23.57  15.98                0          1   \n",
              "2                              0     15.76  18.88                0          1   \n",
              "13                             1     17.32  22.95                0          1   \n",
              "4                              0     10.45  33.51                0          1   \n",
              "14                             0     12.81  15.70                0          1   \n",
              "16                             1     11.42  28.46                0          1   \n",
              "22                             0      5.59  63.39                0          1   \n",
              "8                              0      6.76  56.76                0          1   \n",
              "20                             0      6.70  57.21                0          1   \n",
              "12                             0      5.18  70.28                0          1   \n",
              "6                              0      8.48  44.71                0          1   \n",
              "15                             0      6.23  45.75                0          1   \n",
              "1                              0      6.07  58.85                0          1   \n",
              "21                             4     20.48  18.66                1          0   \n",
              "17                             0     11.89  34.00                0          0   \n",
              "\n",
              "    Penalty  Foot  Head  Other  goal    xG xG_quality  \n",
              "0         0     1     0      0     0  0.03       poor  \n",
              "5         0     1     0      0     0  0.03       poor  \n",
              "9         0     1     0      0     0  0.04       poor  \n",
              "19        0     1     0      0     1  0.05       poor  \n",
              "18        0     0     1      0     0  0.05       poor  \n",
              "11        0     1     0      0     0  0.05       poor  \n",
              "7         0     0     1      0     0  0.06       poor  \n",
              "10        0     1     0      0     0  0.07       fair  \n",
              "23        0     1     0      0     0  0.09       fair  \n",
              "3         0     1     0      0     0  0.09       fair  \n",
              "2         0     1     0      0     0  0.10       fair  \n",
              "13        0     1     0      0     0  0.11       fair  \n",
              "4         0     0     1      0     0  0.12       fair  \n",
              "14        0     1     0      0     0  0.17       good  \n",
              "16        0     1     0      0     0  0.19       good  \n",
              "22        0     0     1      0     0  0.27       good  \n",
              "8         0     0     1      0     0  0.31  very good  \n",
              "20        0     0     1      0     0  0.31  very good  \n",
              "12        0     0     1      0     1  0.31  very good  \n",
              "6         0     1     0      0     1  0.34  very good  \n",
              "15        0     1     0      0     0  0.39  very good  \n",
              "1         0     1     0      0     1  0.46  very good  \n",
              "21        0     1     0      0     0  0.47  very good  \n",
              "17        1     1     0      0     1  0.72  very good  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 0
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BL35iGP8fWLH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "outputId": "27e9095c-d763-42aa-e53f-282c4f83e752"
      },
      "source": [
        "# Plot a shot taken by the Home team\n",
        "frame_id = 12202\n",
        "fig,ax = mviz.plot_events(events[(events.Type==\"SHOT\")&(events[\"Start Frame\"]==frame_id)].iloc[-1:], indicators = ['Marker','Arrow'], annotate=False, field_dimen = (105.0,68.0))\n",
        "fig,ax = mviz.plot_frame(tracking_home.loc[frame_id], tracking_away.loc[frame_id], field_dimen = (105.0,68.0), figax = (fig,ax) )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "CexyXI2Jm6EB",
        "outputId": "6af8d4c3-c5b3-4f2a-b560-42db141975a2"
      },
      "source": [
        "# Plot a shot taken by the Away team\n",
        "frame_id = 115009\n",
        "fig,ax = mviz.plot_events(events[(events.Type==\"SHOT\")&(events[\"Start Frame\"]==frame_id)].iloc[-1:], indicators = ['Marker','Arrow'], annotate=False, field_dimen = (105.0,68.0))\n",
        "fig,ax = mviz.plot_frame(tracking_home.loc[frame_id], tracking_away.loc[frame_id], field_dimen = (105.0,68.0), figax = (fig,ax) )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jLzS7rH4RqD6gaSseVMXiDZJsPYuzbKpYvEHTVjzIBgUWdWQvV1dJRHZ3Rjab5PFIcyeUqaqsQA5XVs4L/qqcu0BNFXOUy9l1s+ch/Ufgo/rA23/SeVsPaM7WA6pojehXf3ehQsGA2Q8L/YRpA1Bd+159fuPPde+cj2pyYaW+d8GdWvHaL3WID35YxEcnLtatE6+QJP337j/rf/atNbmioSeZTevb236nA9EW/f3kq/SPU69X0F2oX7212uzSzpo72KWqpevp5ZpHjtfL1eNw6dyisfI43Ip7k9rX1KZSR6PqnkpKcuir3/6+7vzpapV0dP9M2ulQe5FHKbdTH330b2ov9mlndYVJjwj9iZFXSOputfP5V3+uNzsPqNIb1HcvuFOjvWVmlwWc1B2Tl+jWiVfIyBr67vbfE1xP4n/2rdV3t/9eRtbQrROv0B2Trza7JOAY7+7l6nW4Nb14nDwOtyKZuN5of1tZb+SdHr6Njbr90b+qeUShNs8co80zx+iNcyt1sKpULeWFMhw23bXyRZWFIyY9IvQnwit6dWXiWvHaL7W57S2VFxTpW3M/rgpPqdllASf0sUlX6ebxlymTNXTv1kf0p8ZNZpdkCX9q3KT7tj6iTM98949NusrskoCjZKJe2VzdrbA8DpemFY+V2+5SZzqm7R37lckZsrkyMqLdXQccTz4pV8ZQ3Hv8dRtxr1vujMFGBXmC8IqjxIykvrb5V9ravlcjPMX61tyPqdTNPCEMPTePu0y3TFgoI2vom288qtrW7WaXZCnrW7fr3954VEbW0C0TFuqD4y41uySg1+Ferm67U+cWvRNc3+zcL6OnK86RPXydTzyhiK/vBceHNyqA9RFecYxkNq2vbf517xSCe2d/hC4EGFKuqJijO6qXKJvL6lvbfqu/tGwzuyRLerllm7617XeSpH+ovkZXVLCNKoaGsvl1ysa8mlo0uneqwJudB2Tkcr3HHNXDt6NDaZejz9+Zdjnkjyb7PAbWQHjFcXWPwP5aDbGQzikarX+aflPe75MOa5hRPF5fmvZ+SdLKXc9obfNWkyuytrXNdfrZrqclSV+adqNmFI83uSJAGlFTp3HFpcolvEoYqZ7g+k4f8mN6+BYXy5U2+vydbFSQPwivOKGOdFT/svlX6krHNX/EdH1k4iKzS8IwV+Yu1Fdn3iKX3anH96/X4wdqzS4pLzy+f70e379eLrtT/zLzFtrlwXR3XPAe/fwHTiXTWb2+N6xkwtZnD9/MDTcoEDt2o4ojFcZSWkuv17xAeEWfGuIhffONR2Xksrp14hWaFzzH7JIwTDlsdn111ocULCjU6217tLL+2ZP/EE7Zyvpntbltj8oKCvW1WR+Sw8bHA8wxL3iObp14hWafZ2jWit+odFGtTtbD17j+eqWdDnnjxw+wbFSQX+jzipPaFK7Xr/es1t9PvkpfmX6TPvW3Hymcot0IBtdtExdpevE4tSQ69M2tj7CVcT/L5rL6162P6sfv+bSmF4/TrROuyIsesBi6ysIRLait14L19QpEk4r4C/TqZTO07LMflyT9as9qvemoU9VSnbyHb2WlHlh+ue5a+aKC7TF1+dxKuxxypQ0VxrqD6wPLL2ejgjzBn9Y4JY/sW6eN4XqVuP36/LnvN7scDDMzi8frlvELehdodaRjZpeUlzrSUX1r22+VzWV1y4SFzH/FgJlS36Sv3/+Urlm9TVmbFCr1KWuTPvzS2xrx8eV688Un9ei+daf1O3dWV+juFUv19OLpsksKtsdkl/T04um6e8VSNijII4RXnJKccvrutv9VVzqui8qn6urK880uCcOE2+7UF6e9X3abXY/te0l17XvNLimv1bXv1WP7XpLDZtcXp71fbjsX6NC/ysIR3bXyRRkOm9pKfEq7nZLNppKiMvlHj5dht6vgn/5JwXDXaf/uUDCgVUvP0xfuu0nLv3+rvnDfTVq19DxGXPMM4RWnLJTq0o93/lGStLz6GhW7/CZXhOHg1glXaLSvXPsiTfrNWy+YXc6w8Ju3XtC+aLPG+Mp164QrzC4HeWZBbf0xGwq47E6N942UJL1li8iWSrGhAE6I8IrTsqZps14N7VKhy6c7q68xuxzkubG+ct007lJlc1l9f8cqpXN9t8JB/0jnDH1/++PK5rK6adylGuMrN7sk5JEF6+uP2VBgnG+EnHaH2lNRtSY72VAAfSK84rT9eOcflM5mdFXlXE0tHG12Ochjy6uvldPu0HONm7S9c7/Z5Qwr2zv367nGTXLaHVpefa3Z5SCPBKLJozYUCDg9GuEpVjaX095okyQ2FEDfCK84bQ3xsB7f373y8w5GXzFA5pZO1oXlUxXNJPTfu/9sdjnD0n/v/rOimYQuKp+q80onmV0O8kTEX3DUhgLj/N3TBQ4lwkoY3a2u2FAAfRm0mfirrrxHZZ6i3u9fuu57kqRQolPLnr9nsMpAP3lk3zpdWzVPc0on6oJgtTaGubyD/nX7pCslSY/te0nt6ajJ1QxP7emoHtv3kj42+SrdPulKvb5xpdklIQ+sm1+ta1ZvU5vbqWK3X0Uun9JZQwdjod5jCmMpPb14uolV4nQMdsYbtJHXIx/UqdyOoS2aSfS2MWFBB/rbvOA5mlY8Vu2piFaxi5apVh2oVXsqqunF49ikBP1iXU1174YCY7zd86kb46He7V/ZUMB6BjvjMW0AZ+wPB/+mznRMM0rGa1bJBLPLQR65efwCSdJv33659zIizJEwUvrd2y9Jkm4ef5nJ1SAfhIIBPbD8chXZClQY7lQ6HtOheJtcqYyC7TE5jBwbCqBPhFecsYSR0hMHXpEk3Th2vsnVIF+cU1ilOaUTFc0k9PTBDWaXA0lPHdygaCahOaWTdE5hldnlIA/srK5Q43/+RJnbblNrvE0lbRE2FMApo/s0zsofD/xVfzd+gS4uP1cVnlI1JdrMLgkW977RF0mSnml4VTGD1cZDQcxI6pmGV3XTuEt13eiL9P0dj5tdEiyuwlOqudMvUXxajT45PawO5rXjNDDyirPSno7qpeatstvsWsKuWzhLfqdHCytmSZKeOvg3k6vBkZ7qGQW/vGKW/E6PydXA6q6pukB2m13rmrcSXHHaBi28hhKdp3U7rOOZhlclSVdVzpVNNpOrgZUtGDlTHodbr4V3qyEeNrscHKEhHtJr4d3yONxaMHKm2eXAwmyy6apRcyW98/kBazvTjOeNpVSQSJ/2+QZt2sDhVgnPLbpPkrTkha8O1qkxwLa279OheJtGeUs1s2Q8e8/jjC2qmCNJev7Q6yZXYm2pcKFaa2crtH6WMlGvnP64yubXqbxmi9zB098v/rDVh17X3OBkXVExh9CBMzarZIJGeIp1KN6mrXxe5IUzzXg1r+7RhZv26fVZY7R5xhg1VhRJtpMPgjFtAGctp5zWNtdJki4bMcPkamBVpe6AZpdOVMpIa33LNrPLsaxI/Rhtv/92Na+eJ9lycpV2SracmlfP0/b7b1ekfswZ/+6/tGxTykhrVsl4lbpZCY4zc9nI7s+JF5u2mFwJBpMjYygQSag8FNHYA2FNqW+SP5aSI5vTrG0H9fePvKI7f/0XjWzuOOnvYsEW+sVfmt/Q341foPkjpusnu54yuxxY0EVlUyVJm9p2s1DrDKXChdq9cplsDkPOQLz3dps7I7s7IiPu1u6VyzRtxYNnNAIbM5J6rW23Lio/VxeWTdVzjRv7s3wMEzXl0yR1/zEEC8vl5MoY8iQy8sZT8ibTsm/cKHV16eoXtmlkS5eCHTE5M1m5MoYcRvbYXyHJmTHUVuxVaUdMnQGPYr6T76xGeEW/2NnVoFCyUyM8xZrgr+jdnxo4VReVd4fXV1p3mFyJdbXWzlYu4zgquB7J4U0p1R5Qa+0sVS1df0bnqG3doYvKz9XF5YRXnL6J/gqN8BQrlOzUzq6DZpeDd8vl5E4Z8iVS8iTS8iTS8iXS8keTGtEa0cjWLhV3JeTMGHJlDNmzuXd+VJLz5Yxks+m8loNKO+xKuZ1qGlWk5hEBhUr9SnhcintcinvdShQ4VXWoQx/57d/kymT1u+vm6s1zKk5p2gDhFf0ip5w2hut1deX5uiBYTXjFabHb7JpTOkmS9Gpol8nVWFdo/Sw5fIk+j3H6EgrVzj7j8Hp4K+jZJRNlt9mVzR07mgKcyPnB7l2zrLil+EDNJR9otmxOnmR3EPUk0/LFu/8t6kyooqVT5eGoAtGknBlDzkxWtlx3ID0yQuZsNmUcdqWdDsW9LrVUlai5vFAdxd7uMOpxK+Fx6vIl90o+n/597d2nVNuhkTk9s2i6Ns0Zp4THdcqPifCKfrO5bY+urjxfs0om6H/3/8XscmAh1YFK+Z0eHYy1qiV58vlOOL5M1Ns9x7UPNldGmfbCMz5Hc6JdDbGQqnxlqg5UMnqG4zpR0Kv6eHd4fb1tj8kVnp5I/RjtXrlMuYxDDl9CrtJO5dJONa+ep5a1czV5+SoFqg8MWj12IytPMi1vovs/TyIte8FfZGtt1QfWvKYRoYi8iZScmWx3IFV3IM0d8TtyNpsyTocyTrsi/gI1lwfUUl6ozkKP4h634h5nTyh1Kel2Kmc/hW5Chaf3/5bOIq/WXzT5tH5GIryiH9W175MkzSgZb3IlsJppxWMlSW90vG1yJdbm9MeVSztlc2dOeEwu7ZTD3/fo7Mls7dinKl+Zzi0eS3jFMfoKet99vUqjv5PR1vhes8s8ZUfOJTcSv1Vs+w+VTR2S3T1KntGfk7PgxrOaS36YI2N0B9FkzxzSRFreeErBtpgqWrtUHorIlTZOOH/U+dec5HBoQktIGadD4RJ/dyAtK1Q0UKB4gbPnsr1bcY9LKbfjlC7RD0WEV/SbpkSb2lMRlbgD7LaF0zK1qHsF/I6O/SZXYm1l8+vUvHqe7O7ICY/JxDyqWHx22+6+2XlAV1eer3OLxujJs/pNyDd9LRr0eZMqcNn1hS87ZPuUIXfQxEJPxx8mqawpIVvqYe2Nfke5XPeC0myqUbF998g3XrI5bzl2LvlxFjR542n5YimVhyPHLGiyG9ljOqXnJBkOe+8IafOIQrWUF6ol6FfM5z4qjF5+7Tclt1vfHcRWpGXhiBbU1mvB+noFokkVjNmkzA03qKwsolBw4DqSEF7Rr+q7GjSvbIqqCysJrzhlkwKjJHW/fnDmymu2qGXtXBlxtxze1DH3G3G37E5D5TV1Z3WeXZ3do60Te5434LC+Fg36HB4FAlLzwbTSZ7FocDBNeqtFm5+fokJnh3bE/0O5XFIeSX5JAUn+bEJF+7+tyrFlGv3HQ5rbsV5FXckTLmg6zHA6TrygyevumUfqUsLjkuE4xa6mbnc/PvKTm1LfpLtWvihXxlDE51ao1Kdz7HY5H3pIX4816oHll2tndcWAnJvwin71VqRJ88qmaLx/JG1QcEqcNofG+kZIkvZGm02uxtrcwS5NXr5Ku1cuU6o9IKcvIZsro1zaqUzMI7vT0OTlq856ccm+WIskaayvXA6bXQaLttCjr0WDXmd3uHL44zp0FosGB9PC2nq9lCtWkbNV9xtNKpWO3UcyE5Kr849KpXwq6kgo4XHpQGWJmkZ0L2iKed1KFLiU8DgV87iVLHAqe6qBdIgqC0d018oXZThsigR879zh8Ugej4z9h3TXyhd194qlAzICS3hFv9ofa5UkjfaWmVwJrKLCUyKn3aFD8TYls6e/TSCOFqg+oGkrHlRr7SyFamcr014ohz+hisUbVF5T1y+rohNGSk2JdlV4SlThKVVDPNQPlSMf9LVo0OvoDq8ZR0JG1DOYZZ2xiuZOeQqiSmfdeslWrGSuQ2FJ0Z7/IpLirgo5Rt8v2eya9amfmVvwIFlQW9894npkcD1C3OtWsD2my2rrtWrpef1+fsIr+tWhnv3oR3mtMpkJZqvwlkoS00z6kTvYpaql6wd0ZOtQvE0VnhKN8pQQXtGrr0WDBfbuVkjRRPasFw0OlqaRRbp45J+1pmGZNhbfpQMd/94751WSZPfIN+YLysW9Zz2X3EoWrK9XxNf3NIUun1sLByi8WnvcGkPO4TZH5QVFJlcCqxhRUCxJakq0m1wJTsfhPzZGeEpMrgRDSdn8Ohmx44+quu3d40iuzDIAACAASURBVGWxTofKaqyxNezammotLnpWBUrK57pB4wq/LLd9pCSb7O5K+cbfI6f/xn6ZS24lgWhSaZejz2PSLof80YHZLZHwin4VSnZfkgyy7zlOUYnbL0lqT514hTyGnvZUVJJU3PP8AVL3okGb05ARP3ZUzmV3KhKRss6UZYLenokj9Kdbxuvm9/xQjpRkc35YZdXPq2RenQqnrZHNeYtyhqNf5pJbScRfIFfa6PMYV9pQ1H/yrV7PBOEV/SqZTStlpOV2uHovEQF9KXZ1z5nqSMdMrgSnoyPdHV5LXMef84bh6fCiwZzhUKo9oGzKqVxOsqVdamm2K5XOaeKdj1sq6O2ZOEJrPhtU4IEnZb95m2Legp6NPmyqWLxB01Y8OKgbFAwF6+ZXKxA7tqPJkQpjKa2tqR6Q8zPnFf0uaiTldrjkcxYomWIBDvrmdXT/ZR7PDMzlJQyMhNH9weVxDMzICqzreIsGPYG0brsto4XXdulz9dYMeoMxl9wq1tVUa/HaN+WNpxT3HjvK7o2nlHI69BLhNX/YZdPN4xdoWvFYbe/Yr0f3rVPuqA5w1pY0ugOrm5FXnIICR/frJB87DdiPaKpzy/iFefVefye88j7Hsd4d9EZ5SvWJ+V/SoXhSqje5uH6W75/pxxMKBvTA8st118oXFWyPqcvn7p4Dm0hIHR1yGDk9sPzyAduogPBqgpvHL9DHJl8lSbq4/FxJ0iP71ppZUr8yct3zYBw2ZqXg5A4HvHzsFXrz+AW9Xx9+z+fLe/3w82U/tuslcAx7z+fB4c+HfJLvn+knsrO6QnevWKrLauu1sLZewfaYFMgpc9tturtsOzts5ZvD+7if6Hurs/FhBkjK9/c673Ocvnz8fMjv93nfQsGAVi09r7cd1nOL7uu+fYC3qCW8mmB7x/7ev84Of59PXD3tUNLZY/v8Ae+W6RnBc9r6brtiRfn8XnfZu5+vdB6OpKH/ZXpeJ86e100qXKjW2tkKrZ+lTNQrpz+usvl1Kq/ZYqnFXFJ+v8+HKsKrCR7dt06Sjpofk08Oz2FM5eEcRvS/ZO/cycHdl3swPLpvXe/lxF/u/nNevdc9PXPaD89xB/py+H1eYHcpUj9Gu1cuUy7jkMOXkKu0U7m0U82r56ll7VxNXr7KUqv38/0zfSgivJogp1zezoexySa/s7tBdSRjjR1UYK6Y0d1l4PDrJp8cuWgj397zh5+vw88f0JdoTzeRSKtXu1cuk81hyBmI995vc2dkd0dkxN3avXKZpq140DIjsPn8mT5UEV7RrwJOjxw2u6KZhCkLcPLpUtRw0dvsnn6hllLM5hI4DZmcoVgmqT/+wSdbxiXHEcH1SA5vSqn2gFprZ9GSCifEcnD0q2BBoSQpnBz8oBipH6Pt99+u5tXzJFtOrtJOyZZT8+p52n7/7YrUjxn0mnBybT3h5/BrB9ZQ2rOL3uE/PoCTCSU79cQTTvkK+14P4fQlFKqdPUhVwYoIr+hXI3v2OW9JdgzqeVPhwt5LUa6SiOzujGw2ye7OyFUSkc1haPfKZUqFCUhDTXOiXZJU4Sk1uRKcjlE9z1dTz/MHnExLskMdHZLH03fHAZsrIyOaf9OI0H8Ir+hXVd6gJKkxHh7U87bWzu6e/O89/nZ1Dm9K2YxDrbWzBrUunNyhRJskqbLntQNrGNXzfDX1PH/AyTTG21RcLDmNvoNpLu2Uw8+aCZwY4RX9arx/pCRpf7RlUM8bWj9LDl/f/7PjUtTQ1JrsVCyTVInbr2KX3+xycAqKXX6VuP2KZhJqTXaaXQ4sYn+sRTfckFE22vf89kzMo7KaLYNUFayI8Ip+NcFfIUnaF20e1PNmol7ZXH3Po+JS1NC1N9okSZoQqDC5EpyKiQFz3uewtn2RJl1/vaFCr1NG/Pit8Yy4W3anofKaukGuDlZCeEW/sdvsmlxYKUna1dUwqOd2+uPKpftunsGlqKGrvuf1MrVwtMmV4FRMKepe/Fg/yO9zWFt9pFGVldL3vmNIWadS7QFlU07lclI21f19znBo8vJVdIdBnwiv6DeT/BXyONxqiIfVlTl+G5SBUja/Tkas71FVLkUNXW92djckn1pERwgrOLfnedrRaZ1G8jBfZzqmxnhYF89zaMk3nlHF4g2SbMq0F0qyqWLxBk1b8aClNiiAOejzin4zs2SCJGlr+95BP3d5zRa1rJ0rI+4+7qItLkUNbW90vC1JmlUyQTbZjmruj6HFJptm9bzXt/U8b8BhZeGIFtTWa8H6egWiSUX8BVo3v1rraqoVCgZU175Xld6gLpo8Qo0F6+nlijPCyCv6zfnByZKkLW1vDfq53cEuTV6+SjnDwaUoC2qMh9WUaFex2987nxJD06TAKBW5fDoUbxv0riIY2qbUN+nr9z+la1ZvU9YmhUp9ytqka1Zv09fvf0pT6pt6Px/m9nxeAGeC8Ip+4bI7Nad0kiRpY7jelBoC1Qc0bcWDXIqyqE09r5sLy6aaXAn6cmHZFEnSa227Ta4EQ0lZOKK7Vr4ow2FTW4lPabdTstmUdjvVVuKT4bDprpUv6q1dr0mS5pRMlMvOxV+cGV456Bfnl06Wx+HWrq4GhVPmjW66g12qWsqlKCv6a+sOXVs1TzXl57JP+BBWM2KaJOmV1h0mV4KhZEFtvVwZQ5HA8dtgxb1uBdtjmrnude1a1KBzCqs0t3SS/hbaOciVIh8w8op+cenIGZKk9S3bTK4EVrUpvFsJI6Vzi8f27tSGoaXCU6KpRWOUMFJ6LczIK96xYH29Ir7jt786rMvn1sLaetW2bJckXTZy5mCUhjxEeMVZc9udumTEdEnS2iYWROHMJLNp1faM5l1ewWYSQ9Hh56W2ZbuS2bTJ1WAoCUSTSrscfR6Tdjnkjya1trn7c+KSEdPlZuoAzsCgvWpWXXmPyjxFvd+/dN33JEmhRKeWPX/PYJWBAXDJiBnyOz16s/OADsZDZpcDC1tzaLOuqJitq0adp8f2rTO7HLzLVaPmSpLWNNFyDkeL+AvkShvdc11PwJU2FPUX6ECsVW92HtDUojG6ZMR0Xk95YLAz3qCNvB75oE7ldljHe6vmSZKebdhociWwulfDu9Sa7NQ4/8jedkwYGmaXTNRY/wi1Jju1IbzL7HIwxKybX61A7Ng2hUcqjKW0tqZakvRcz+fFtVXvGfDaMPAGO+MxbQBnZVJglGaXTlQsk9SL/PWMs2Tksnq24VVJ0vVjLja5Ghzp+jEXSZKeaXhV2VzW5Gow1KyrqVba6ZA3fvwA642nlHI69FJPeF3TtEWxTFJzSidqop/2eDg9hFeclRvHXiJJeq5xo2JG0uRqkA+eOrhB6WxGl4yYrkpv0OxyIKnKG9QlI6Yrnc3o6YMbzC4HQ1AoGNADyy+Xw8gp2B6TK5WRcjm5UhkF22NyGDk9sPxyhYIBSVLMSOpPjZskSTeOu8TM0mFBhFecsQpPiRZVzJaRNbRqf63Z5SBPhFNdWtO0RQ6bXR8cd6nZ5UDSTeMuld1m1wuHNpvaCg9D287qCt29YqmeXjxddknB9pjskp5ePF13r1iqndVHj7A+vn+9jKyhRRVz6DCC08IyP5yxD024XA67Q883vqZDiTazy0EeeWzfS7py1Hm6uvJ8PbrvJTXx+jJNhadUSyovkJHL6rdvv2x2ORjiQsGAVi09T6uWnnfSYw8l2rSmuU5XjjpPH55wuX6wY9UgVIh8MGgjr6FE52ndjqFtjK9cV4+aKyNr6OG9L5pdDvLM/liLXmjaIpfdqdsmXmF2OcPabROvkNPu0JpDm7U/1mJ2OcgzD7+1RkbW0NWj5mqMr9zscnCGBjvjDdrI6+FWCc8tuk+StOSFrw7WqTEA/mHyEjnsDj19cAPtsTAgHnrrBS0cOVNXjjpPTxx4RfVdDWaXNOycU1ilK0edp3Q2o9/sXWN2OchDB+Mh/enQa7q2ap7+YfIS3VP3kNkl4QwMdsZjzitO2wXBatWMmKZYJqlfvbXa7HKQpxriYT1x4BXZbXZ96pylZpczLH3ynKWy2+xatb9WjfGw2eUgT/1qz/OKZZKqGTFN5werzS4HFkB4xWkpsLv02anXS5Ie3rtGbamIyRUhnz30VvdrbGbJeF1TeYHZ5Qwr11bN08yS8WpLRZgahAEVTkX0cM/I/j9OeR+7buGkCK84LR+ZuEhV3qD2RA7p9/vXm10O8lzMSOpnu56WJN1ZfY2C7kKTKxoegu5C/cPkJZKkn+58ijZ4GHC/379eb0UOqcpXpo9MXGx2ORjiCK84ZTOLx+sD4y6RkcvqB9sfl0GjcgyCF5u26K+tOxRwefXFae+XTTazS8prNtn0pWnvV8Dl1SutO3r3oQcGkpHL6vs7VsnIZXXTuEs0o3i82SVhCCO84pQEnB59ZfpNstvsenTfOr3ZddDskjCM/GDHE+pMx/Sesim6gZ23BtSyMRdrXtkUdaSi+uGOJ8wuB8PIm50H9Ni+dbLb7Pqn6Tcp4PSYXRKGKMIrTsmXpt2oUd5Svdl5QA+9xapjDK5wqkvf2/64JOmO6iWaWjTG5Iry09SiMfp4dfd0ge/veJwNCTDofvPWGr3ZeUCjvKX64rQbzS4HQxSzonFSH5pwueaPmK5IOq5/3fqoMjnD7JIwDNW2btcT+2t1w9gafW3mh/TZDT9Rezpqdll5o9Qd0Ndmfkhuu1NP7K9VbesOs0vCMJTJGfrXrY/qJ+/5tC7JlOq+9VH5n3pGgWhSEX+B1s2v1rqaaoWCAaXChWqtna33fqdAHR3SW9lPqmx+ncprtsgd5A+vfMbIK/o0v3ya/n7Slcrmsrp/22/Z6QimWln/rLa279UIT7H+z6wPsyq5n7jtTv2fWR/WCE+x6tr36uf1z5hdEoaxpkSbHvr9d1TwkY/o0j9tVFFBQKFSn7I26ZrV2/T1+59ScI1X2++/Xc2r58lul0aNkmTLqXn1PG2//3ZF6rk6k88Irzih6UVjtWLGzZKkX+7+szaEdppcEYa7TM7QfVsfUXOiXTNKxuufp39QdhZwnRW7bPrn6R/U9OJxak6061+3PsJiTJiqLBzRom//So3pTmnkSE0oG6dCl09pt1NtJT4dylbo7Z/eIHcuJVdJRB6PZLNJdndGrpKIbA5Du1cuUypMd5J8RXjFcU30V+gbcz6iAodLTx3coMfefsnskgBJUlsqoq9u/pW60nFdOnKGPjPlOrNLsrTPTn2fLh05Q13puP7l9Qfp3QzTLaitlytjaJ8tpqZEuxw2u6YUjZHPUSBJei5+nTJZl0bGj38l0OFNKZtxqLV21mCWjUFEeMUxxvpG6P65H1Ohy6falu36vzv/YHZJwFH2RZv19bqHlDLSum7MRfo0O3Cdkc9MuU5LR1+olJHWPXUP6e1Yi9klAVqwvl4Rn1uStDfSpHAqIpfdoWnFY+V1FGhd2xL5XF0qa+uZ855IyLZvn2bsaNScNw5qxo5GVcVa1LluhomPAgOJ8IqjTAqM0nfOv0Ml7oA2hHbqX7c+oiyXEDEE1bXv1dfrHlYqm9ENY2v06XOW0gP2FNlk02emXKfrx1zcG1y3tu81uyxAkhSIJpV2OSRJOeVU33lQ7amoXHanphWPVSxbLIc9LWcmq0A0Kfvrr8l28IBkk1Iuh2STKtvaNHJPSlPqm0x+NBgIhFf0mlE8Xt+e2x1cXw3t1DfqHlaazgIYwl4N79I3jgiw/zzjg3LZHGaXNaS5bA6tmPHB7uCazejrdQ9rY7je7LKAXhF/gVzpdz57ssppZ1d3gHXbnaooSCuXK1DWbtPEt0PdE17dBcrauv94zdpsijk88js6ddfKF1UWZipMviG8QpJ0ecVs3T/3Ywq4vHqpeavu2fKQUtmM2WUBJ7UhtFNf2/wrRTMJXVExW/fO+aj8NDc/roDTo3vnfFSXV8xWNJPQVzf/Sq+Gd5ldFnCUdfOrFYiljrotm8tqZ+cBhZNdurFivTJGieTxyJbLSY5ju45EjCJdVvYnuTOGLqvlj7N8Q3iFPjpxsf6/GTfLbXfqyQOv6JtvPMaIKyzl9bY9+sqm/1Q42aW5wcn6v/M+pfH+kWaXNaRM8FfoR/M+pbnByQolu/TlTf+hzW17zC4LOMa6mmqlnQ554+8KsMppV1eDFhY8rgKHIVvSJ5er4Jifjxk+uewpLSj9k7p8bi0kvOYdwit068QrZOSy+unOp/TjnX9kjissaXekUZ/f+HPVdzWoylemB+Z9UgtHzjS7rCFh4ciZ+uG8T6jKV6b6rgZ9YePPtSdyyOyygOMKBQN6YPnlchg5BdtjcqUyUi4nVyqj0vaocsntWvLPrylj2NWeHaWE4VI2J6WyboXT5TLk1OfG3atyd4vSLof80aTZDwn9jA7fw1SZ+53+d+2piP7tjcf0OqMwsLimRLu+uPEXuuvcG3TlqPP0/8+8RfMaN+mnO59SzBh+H2A+R4E+PeU6XVU5V5L0fONreuDNJ5XMpk2uDEPF4V2qQutnKRP1yumPD4ldqnZWV+juFUt1WW29FtbWK9geU9RfoKcXT9dLNdUKBbfo36r/Q5viH9CT4cvVnCqVzdaq95b/TgtK/6Ryd3fnDFfaUNR/7OgsrI3wOkyFjtiz/DMbfqLWZKeJ1QD9J5lN69vbfqftHW9refW1urryfM0pnaTvbvtfbW5/y+zyBs2c0kn68rQbNdJTooSR0i/qn9UfD/7N7LIwhETqx2j3ymXKZRxy+BJylXYql3aqefU8taydq8nLVylQfcC0+kLBgFYtPU+rlp533Ps3zHfrfS98R5+98M+SpFeOs6VxYSylpxdPH9A6MfiYNgCCK/LSHw/+TZ/Z8GO92XlAFZ4S/fv5d+jL0z6goDtgdmkDKugu1FemfUD/PvfjGukp0Y6O/frMhp8QXHGUVLhQu1cuk81hyFUSkd2dsdwuVetqqhVzSIoc203AnTY09mCbxhxs0/XPbNH3/+V3ev9Tr9N5IE8QXgHkrf2xVn1+40o9uOd5pbIZXVU5V/958Rd087jL8q6llsvm0M3jLtN/Xvx5XVk5VykjrQf3PK8vbPqFDsRazS4PQ0xr7ezuEVdv6rj3W2GXqsNzY5XJSE1NvXNji9tjmr6jUeXhqPaNLlXziEJlbdI1q7fp6/c/Re/XPEB4BZDXsrmsHt77ou585Yf6S8s2+ZwFuqN6iX5x8ef03qr3yGW39uwpl92ppVXv0S8u/pzuqF4in7NAf2nZpjv/+oAe3vsiCzBxXKH1s+TwJfo8xulLKFQ7e5AqOjM7qyuU/PWvlbntNtkljWyNaPzBNoVKfdo6dZQ6SnySzaa026m2Ep8Mh43er3nA2v/XBoBTdCjRpm/UPazzSyfrk1OWarx/pD537g26beIi/f7tl/VUwwbFjeOPQg1FXodbS0dfqBvHXqKygu5Lu/siTfrZrqe1qW23ydVhqMtEvXKV9j1lzObKKNM+dKcN9KqslPGJT+gL5+zX+596Xdes3qa2Et9xD4173Qq2x3RZbf0J59Ji6CO8AhhWNrXt1if/+iNdOnKGbhm/UJMLK3XnOdfqlgkL9fyh1/XCoc3a2XXQ7DJPaGrhaF0xao6uHDVXhS6vJKm+q0GP7lunl5vfUFY5kyuEFTj9ceXSTtncJ96MJpd2yuHve3R2qFmwvl4Rn7vPYw73fiW8WhfhFcCwk1VO65q3al3zVs0LnqMPTViomSUT9P6x8/X+sfN1MNaqNU1b9GLTFu0fAvNFx/rKdUXFHF1RMVtVvrLe2+va9+qRvWvZJQunrWx+nZpXz5PdfeLL55mYRxWLNwxiVWcvEE0qVHr8UdfD0i6Hgu2xQaoIA4HwCmBYezW8S6+Gd2lK4WhdUTFbCytma7SvXLdNXKTbJi7Srs6D2tS2WwdjIR2Mh9QQCymcGpj+lzbZFHQHVOUrU5W3TKN9ZTo/WK1zCqt6jwklu7S2aYteaNqsXV0NA1IH8l95zRa1rJ0rI+4+7qItI+6W3WmovKbOhOrO3F73WK1uWKI1Xe9VxChSwNGpBaXPaWHpc/R+zSOEVwCQtLProHZ2HdQv6p/VnNJJuqJiti4dOUPnFI3WOUWjjzo2YaTUEA+rIRbq/jce6v2+NdmpXB+X7m2yqbygqCegBlXl7fm353uP49hLntFMQi83v6E1TVu0uW0PUwNw1tzBLk1evqq7HVZ7QE5fQjZXRrm0U5mYR3anocnLV5m6UcHpitSP0afa/kuBNkMed0RBZ7PSObeeaf2Ang+/T58bd6/O9W+l92seILwCwBGyyum1tt16rW23frTzD5oXPEeTAqOOCpslbr8mBUZpUmDUMT+fMtJqTLSpIRZS9Ihdve6ZdauqfGWq9JTK7XCd8PztqehRYXhP5JBeDe9SOnviuYnAmQhUH9C0FQ+qtXaWQrWzlWkvlMOfUMXiDSqvqbNUcG1slHavXCZ3SUrjIvuVk2TY7HLbUgraWxUzfPrh21/Tt0d/QgFnUi/VVJtdMs6CaeH1uUX3mXVqADgl6WxGta3bVdu6/ajb/U6PKr1BjT48ctoTbCu9ZSorKNR4/0iN94886mdqRkzr/TqU7FLjEQG1IR7SwXhYjfGwohlrLZCBtbmDXapaul5VS9ebXcpZefJJh3IZh7Ilht4aV6aJb4fkzhjKOOzK2mwK2KNqS5ZpbfsSNa14W6Fg/m5WMhzyFSOvAHCaopmE6rsaVH+cOaceh7tnhDao95RN1TVVF0iS7q17uGeKQVgJC7XkAqzgiSecvX1rI/4CvVk9UsG2mMraonKnDWWcdiXLDf2i+KM6t/oXJleLs2VaeF3ywlfNOjUADJiEkdKeyCHtiRzSyy3besPryy3bTK4MyF8dHd19aQ9LuZw6NLJIh0YW9d6Wy0mZdr8Z5Q2KoZCrBmvUl5FXAABgXY2NKom4NKUxJF8uoYzDrnDQr1CpTynXOzHHin1rcXxsDwsAACxpSn2TCj7yEd2YfFhd2SKlXA7JJo1o7dLU+mYFou8smszEPCqr2WJitegvhFcAAGA5ZeGI7lr5ouR06v3VdXI60ooZPmVtNqWdDuVs6l64lc5Ytm8tjo/wCgAALGdBbb1cGUMKBFTpadPnxt0rQ06F0+VKZd3K2BxKGy65DhYoZzgs17cWJ0Z4BQAAlrNgfb0ivnc29TjXv1X3Tv6s3lv+O+VkVzhTrrTDqQ+7f61pKx5UoPqAidWiP7FgCwAAWE4gmlSo1HfUbeXuFt1Y8ZBurHio+4ZcTsH2mNYHbzWhQgwURl4BAIDlRPwFcqWNPo9xpQ1F/QWDVBEGC+EVAABYzrr51QrE+t7wozCW0lq2gs07hFcAAGA562qqlXY6pEjkuPd74ymlnA69RHjNO8x5BQAAlhMKBvTA8su18jdvyrZ3r2aGm+XIZmXY7Yr73AqX+PTdTy9WKBgwu1T0M0ZeAQCA9dmknM3W/a+6t4NFfiK8AgAAy+ndpMDnU27mTG2dVqUtM0Zr67Qq7ZlQrqTHqbtWvqiy8PGnFcC6CK8AAMByjtyk4HjiXrfcGUOX1dYPcmUYaIRXAABgOe/epOB4unxuLSS85h3CKwAAsJxANKm0y9HnMWmXQ/5ocpAqwmAhvAIAAMthk4Lhi/AKAAAsh00Khi/CKwAAsBw2KRi+CK8AAMByDm9SoExGamqSK5WRcjm5UhkF22NyGDk9sPxyNinIQ4RXAABgSTurK5T89a+Vue022SUF22OyS3p68XTdvWKpdlZXmF0iBgDbwwIAAOuqrJTxiU/oC+fsN7sSDBJGXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGUQXgEAAGAZhFcAAABYBuEVAAAAlkF4BQAAgGU4zTrxc4vuM+vUAAAgz5Arhg9GXgEAAGAZpo28Lnnhq2adGgAA5AnyxNAxWKPfjLwCAADAMgivAAAAsAzCKwAAACyD8AoAAADLILwCAADAMgivAAAAsAzCKwAAACyD8AoAAADLILwCAADAMgivAAAAsAzCKwAAACyD8AoAAADLILwCAADAMgivAAAAsAzCKwAAACyD8Arg/7Vv/65VnXEcx7/npuUaSBQuiag0BtIOLo6FKAShItkqLkGXQnHr5B+QsZs4dnDon6BDN0E61yFkDJIugRLBH4FUolVbTrcbsUH80XtuP+X1mp57zuV+n+nw5uEeAIghXgEAiCFeAQCIIV4BAIghXgEAiCFeAQCIIV4BAIghXgEAiCFeAQCIIV4BAIghXgEAiCFeAQCIIV4BAIghXgEAiCFeAQCIIV4BAIghXgEAiCFeAQCIIV4BAIghXgEAiCFeAQCIIV4BAIghXgEAiCFeAQCIIV4BAIghXgEAiCFeAQCIIV4BAIghXgEAiCFeAQCIIV4BAIghXgEAiCFeAQCIIV4BAIghXgEAiCFeAQCIIV4BAIghXgEAiCFeAQCIIV4BAIghXgEAiCFeAQCIIV4BAIghXgEAiCFeAQCIIV4BAIghXgEAiCFeAQCIIV4BAIghXgEAiCFeAQCIIV4BAIghXgEAiCFeAQCIIV4BAIghXgEAiCFeAQCIIV4BAIjRabzO9o8M15fnz9XxyUGX4wEA+JcdnxzU5flzw88z/cMjnffJSH/9DbOH9uP1288v1DcL5+un336pH3+9U6/av7rcCgAAH+HTZqKufrFcX3+2fWH4YQAAAaJJREFUWBPN/nno0f6Revzi95HN7fTkdWvv4XB998F6tW1bl+bO1urpK9WrpsutAADwgXrV1OrpK3Vp7my1bVt3H6wP7209ezTi2R3a+/OP4fr6xq26tnazdl/u1eLMqbo4d6bLrQAA8IEuzp2pxZlTtftyr66t3azrG7eG917vvVEY6wtbm0+368bG7aqqWjm5VL3G+2MAAP9lvaZXKyeXqqrqxsbt2ny63e38Tqcd4N6T+7X9fKcG/elamDo27u0AAPAWC1PHatCfru3nO3Xvyf3O53f6wtbr7nz1/T+u/fDld2PYCcDoHfTMA0h2YnIwlmfb2E9eAQDgXXV+8rr882rXIwEA+J9w8goAQAzxCgBADPEKAEAM8QoAQAzxCgBADPEKAEAM8QoAQAzxCgBADPEKAEAM8QoAQAzxCgBADPEKAEAM8QoAQAzxCgBADPEKAECMpm3bd/9y0zyqqq3RbQcAAGq+bdvZg268V7wCAMA4+dsAAAAxxCsAADHEKwAAMcQrAAAxxCsAADHEKwAAMcQrAAAxxCsAADHEKwAAMf4GFPTm8q8stcIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VnFO4ziNsm1M"
      },
      "source": [
        "## THE END!"
      ]
    }
  ]
}