Oğuzhan_Sarıgöl_xG_Analysis.ipynb

{
  "cells": [
    {
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      "metadata": {
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      },
      "source": [
        "\n",
        "*   Probability\n",
        "*   Oğuzhan Sarıgöl & Asrınalp Şahin\n",
        "*   **Expected Goals xG**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "067142a7",
      "metadata": {
        "id": "067142a7"
      },
      "outputs": [],
      "source": [
        "# Libraries\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\"We will analyze in our code the expected goal locations of football players based on which areas of the field, at what times, with which feet, using which parts of their bodies, and from which angles and distances they are most likely to score.\""
      ],
      "metadata": {
        "id": "-EME6lQnU501"
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      "id": "-EME6lQnU501"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Verinin Okunması ve Sütunların Karşılıkları**\n",
        "\n",
        "1. **playerId**: Unique identifier for each player.\n",
        "2. **id**: Unique identifier for each shot event.\n",
        "3. **x_coordinates**: X coordinate of the shot on the field.\n",
        "4. **y_coordinates**: Y coordinate of the shot on the field.\n",
        "5. **goal**:Binary indicator whether the shot resulted in a goal (1 for yes, 0 for no).\n",
        "6. **time**: Time of the shot event in the game.\n",
        "7. **foot**: Indicator of which foot was used for the shot (1 for right foot, 0 for left foot).\n",
        "8. **bodypart**: Indicator of which part of the body was used for the shot (1 for foot, 2 for head, 3 for other).\n",
        "9. **preference**: Binary indicator whether the shot was taken with the player's preferred foot (1 for yes, 0 for no).\n",
        "10. **angles**: Angle at which the shot was taken relative to the goal.\n",
        "11. **distance**: Distance from which the shot was taken to the goal.\n",
        "12. **xG**: Expected goals value, a measure of the likelihood that a shot results in a goal.\n"
      ],
      "metadata": {
        "id": "0Q06fJr_R0Ha"
      },
      "id": "0Q06fJr_R0Ha"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7b8e61f1",
      "metadata": {
        "id": "7b8e61f1",
        "outputId": "1bc6db6e-2b47-4042-f0a4-b86abf70301f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   playerId         id  x_coordinates  y_coordinates  goal  time  foot  \\\n",
            "0     25413  177959212          92.40          27.88   1.0   2.0   1.0   \n",
            "1     26150  177959247          89.25          35.36   0.0   3.0   0.0   \n",
            "2     14763  177959280         100.80          35.36   1.0   4.0   1.0   \n",
            "3      7868  177959289          85.05          22.44   0.0   7.0   1.0   \n",
            "4      7868  177959429          78.75          20.40   0.0  14.0   1.0   \n",
            "\n",
            "   bodypart  preferrence  angles  distance      xG  \n",
            "0       1.0          1.0   26.77     14.01  0.1466  \n",
            "1       2.0          1.0   25.99     15.81  0.1106  \n",
            "2       3.0          1.0   78.79      4.41  0.4348  \n",
            "3       2.0          0.0   15.74     23.06  0.0331  \n",
            "4       1.0          1.0   12.59     29.56  0.0165  \n"
          ]
        }
      ],
      "source": [
        "file_path = '/allShots.csv'\n",
        "all_shots_df = pd.read_csv(\"/content/allShots.csv\")\n",
        "print(all_shots_df.head())\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "GIjuaq7AMYFC"
      },
      "id": "GIjuaq7AMYFC",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "77b65e4c",
      "metadata": {
        "id": "77b65e4c"
      },
      "outputs": [],
      "source": [
        "df=all_shots_df.copy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8bb14d9e",
      "metadata": {
        "id": "8bb14d9e"
      },
      "outputs": [],
      "source": [
        "df.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7bf053e8",
      "metadata": {
        "id": "7bf053e8"
      },
      "outputs": [],
      "source": [
        "df.tail(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**The following rows show the IDs and xG values of the top 5 players:**"
      ],
      "metadata": {
        "id": "_gHHrh50TLYH"
      },
      "id": "_gHHrh50TLYH"
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "print(df.iloc[:5, [0, 10]])"
      ],
      "metadata": {
        "id": "DlZkOn9BI099"
      },
      "id": "DlZkOn9BI099",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "be5a04b6",
      "metadata": {
        "id": "be5a04b6"
      },
      "outputs": [],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "70c68965",
      "metadata": {
        "id": "70c68965"
      },
      "outputs": [],
      "source": [
        "df.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe().T"
      ],
      "metadata": {
        "id": "DFAy62RmsVfs"
      },
      "id": "DFAy62RmsVfs",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ec5aec8f",
      "metadata": {
        "id": "ec5aec8f"
      },
      "outputs": [],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "86e7f34b",
      "metadata": {
        "id": "86e7f34b"
      },
      "outputs": [],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0f2dd266",
      "metadata": {
        "id": "0f2dd266"
      },
      "outputs": [],
      "source": [
        "df[\"xG\"].fillna(0, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a32914f1",
      "metadata": {
        "id": "a32914f1"
      },
      "outputs": [],
      "source": [
        "df['x_coordinates'].fillna(df['x_coordinates'].median(), inplace=True)\n",
        "df['y_coordinates'].fillna(df['y_coordinates'].median(), inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7b229065",
      "metadata": {
        "id": "7b229065"
      },
      "outputs": [],
      "source": [
        "df.xG.unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a4a38090",
      "metadata": {
        "id": "a4a38090"
      },
      "outputs": [],
      "source": [
        "df[\"xG\"].value_counts().count()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "667ebff6",
      "metadata": {
        "id": "667ebff6"
      },
      "outputs": [],
      "source": [
        "df[\"xG\"].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4510dbc7",
      "metadata": {
        "id": "4510dbc7"
      },
      "outputs": [],
      "source": [
        "#Bar plot graph of the top 100 players with the highest total expected goals.\n",
        "all_shots_df['xG_rounded'] = all_shots_df['xG']\n",
        "player_xg_totals = all_shots_df.groupby('playerId')['xG_rounded'].sum()\n",
        "top_100_players = player_xg_totals.nlargest(100)\n",
        "fig, ax = plt.subplots(figsize=(20, 10))\n",
        "top_100_players.plot.bar(ax=ax, width=0.8)\n",
        "ax.set_xticklabels(ax.get_xticklabels(), rotation=90, ha='center', )\n",
        "ax.set_xlabel('Player ID')\n",
        "ax.set_ylabel('Total xG')\n",
        "ax.set_title('Top 100 Players by Total xG')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Below, we calculated the count, mean, median, maximum, minimum, and standard deviation of the xG values.**"
      ],
      "metadata": {
        "id": "eL8AVjG7WhbO"
      },
      "id": "eL8AVjG7WhbO"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0a5ab6a6",
      "metadata": {
        "id": "0a5ab6a6"
      },
      "outputs": [],
      "source": [
        "print(\"Count: \", str(all_shots_df[\"xG\"].count()))\n",
        "print(\"Mean: \", str(all_shots_df[\"xG\"].mean()))\n",
        "print(\"Median: \", str(all_shots_df[\"xG\"].median()))\n",
        "print(\"Maximum: \", str(all_shots_df[\"xG\"].max()))\n",
        "print(\"Minimum: \", str(all_shots_df[\"xG\"].min()))\n",
        "print(\"Standard Deviation: \", str(all_shots_df[\"xG\"].std()))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Displaying the top 10 most frequent xG values using a bar plot graph.**\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "v5o0fo9zW6TG"
      },
      "id": "v5o0fo9zW6TG"
    },
    {
      "cell_type": "code",
      "source": [
        "all_shots_df['xG_rounded'] = all_shots_df['xG'].round(3)\n",
        "top_10_xg_values = all_shots_df['xG_rounded'].value_counts().nlargest(10)\n",
        "fig, ax = plt.subplots(figsize=(10, 6))\n",
        "top_10_xg_values.sort_index().plot.bar(ax=ax, width=0.8)\n",
        "ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='center')\n",
        "ax.set_xlabel('xG (Rounded to 3 Decimal Places)')\n",
        "ax.set_ylabel('Count')\n",
        "ax.set_title('Top 10 Most Frequent xG Values')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "IPpNeCcT-1Eu"
      },
      "id": "IPpNeCcT-1Eu",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Density plot of xG values**"
      ],
      "metadata": {
        "id": "W00rvI56Xchw"
      },
      "id": "W00rvI56Xchw"
    },
    {
      "cell_type": "code",
      "source": [
        "sns.displot(df.xG, kind=\"kde\", bw_adjust=1)\n"
      ],
      "metadata": {
        "id": "GOQ0K8fp_vOq"
      },
      "id": "GOQ0K8fp_vOq",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# xG Analysis Based on Shot Location\n",
        "**Below is a visual representation of where players have the highest expected goals (xG) by shooting towards the goal. As the x_coordinates value increases, the shot gets closer to the goal. As seen, players have higher expected goals when they are closer to the goal.**"
      ],
      "metadata": {
        "id": "j_LBFAWKXlCM"
      },
      "id": "j_LBFAWKXlCM"
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(data=df, x='x_coordinates', y='y_coordinates', hue='xG', palette='coolwarm')\n",
        "plt.title('Shot Locations by xG')\n",
        "plt.xlabel('x_coordinates')\n",
        "plt.ylabel('y_coordinates')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "kpigLSJfv_KY"
      },
      "id": "kpigLSJfv_KY",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Expected Goals Analysis with Angle and Distance Coloring\n",
        "\n",
        "Below is a graph analyzing the expected goals (xG) based on the angles and distances from which players take shots. It is observed that players have a higher expected goal value when they shoot closer to the goal and at approximately a 90-degree angle from the goal's perspective.\n",
        "\n",
        "## Graph: Angle, Distance, and Expected Goals (xG)\n",
        "\n",
        "This graph examines the angles (`angles`) and distances (`distance`) from which players take shots and their expected goals (`xG`). The color coding represents different xG values."
      ],
      "metadata": {
        "id": "9tU_97gjYwfp"
      },
      "id": "9tU_97gjYwfp"
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(data=df, x='angles', y='distance', hue='xG', palette='viridis')\n",
        "plt.title('Angles vs Distance Colored by xG')\n",
        "plt.xlabel('Angles')\n",
        "plt.ylabel('Distance')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "-2t1vgYtxShE"
      },
      "id": "-2t1vgYtxShE",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Pair Plot Analysis: xG, Angles, and Distance\n",
        "\n",
        "This pair plot includes scatter plots for each pair of features and histograms showing the marginal distributions of each feature.\n",
        "\n",
        "* **xG and angles**: The scatter plot indicates a positive correlation between xG and angles. Generally, as angles increase, xG also tends to increase. xG appears higher for angles below 50. In the histograms, xG is concentrated between 0.0 and 0.2, while angles are concentrated between 0 and 50.\n",
        "\n",
        "* **xG and distance**: In this plot, there is a negative relationship between xG and distance. As distance increases, xG generally decreases. xG can have higher values when distance is below 20. The distance histogram shows concentration between 0 and 20.\n",
        "\n",
        "* **Angles and Distance**: There is a positive relationship between Angles and Distance. As distance increases, angles also tend to increase. However, this relationship is not as pronounced as between xG and angles. In the histograms, angles are mainly concentrated between 0 and 50, while distance is concentrated between 0 and 20."
      ],
      "metadata": {
        "id": "GUww2Pd7cmU8"
      },
      "id": "GUww2Pd7cmU8"
    },
    {
      "cell_type": "code",
      "source": [
        "sns.pairplot(df[['xG', 'angles', 'distance']])\n",
        "plt.suptitle('Pair Plot of xG, Angles, and Distance', y=1.02)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "hZaan-6f4CI7"
      },
      "id": "hZaan-6f4CI7",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Line Plot Analysis of xG and Distance\n",
        "\n",
        "* As distance decreases, an increase in expected goals (xG) is observed.\n",
        "* The highest density of expected goals is observed between 0 and 40 meters."
      ],
      "metadata": {
        "id": "s3ZLLnHMenSs"
      },
      "id": "s3ZLLnHMenSs"
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "sns.lineplot(x=\"xG\", y=\"distance\", data=df)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "RFQVgMQSJza3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "299cafe3-b252-4d8b-9da7-fda582c81ef8"
      },
      "id": "RFQVgMQSJza3",
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Analysis Based on Preferred Foot When Shooting\n",
        "\n",
        "This box plot visualizes the expected goals (xG) values based on which foot players prefer to use when shooting. The x-axis represents the feet used (0: Left, 1: Right), and the y-axis shows the xG values.\n",
        "\n",
        "A box plot displays the distribution, median, and outliers of xG values for each foot. The boxes represent the middle 50% of the xG values (interquartile range) and the median. The upper and lower lines of the boxes indicate the spread of the data and potential outliers.\n",
        "\n",
        "**Distribution of xG Based on Foot Used:**\n",
        "\n",
        "- When using the right foot (1), the xG values show a wider distribution compared to the left foot (0). This indicates a greater diversity in expected goal values for shots taken with the right foot.\n",
        "- When using the left foot (0), xG values show a narrower distribution, suggesting less variability in expected goal values for shots taken with the left foot.\n",
        "- Median values may be similar for both feet, but shots taken with the right foot have a wider range of outlier values."
      ],
      "metadata": {
        "id": "wjN7gu6SfL6G"
      },
      "id": "wjN7gu6SfL6G"
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(data=df, x='preferrence', y='xG')\n",
        "plt.title('xG by Foot Used')\n",
        "plt.xlabel('Foot (0: Left, 1: Right)')\n",
        "plt.ylabel('xG')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "Yt2OP-CIxc0H"
      },
      "id": "Yt2OP-CIxc0H",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Expected Goals Analysis Based on Match Minute\n",
        "\n",
        "This graph shows how expected goals (xG) values change over time during a match. The x-axis represents time (minutes in the match), and the y-axis shows xG values.\n",
        "\n",
        "A line plot illustrates the trend of xG values over time. This graph allows us to observe how xG values increase or decrease during specific time intervals. In essence, this graph helps us examine the trends of xG values over time and assess performance in specific time periods."
      ],
      "metadata": {
        "id": "hFoNff3jf3LV"
      },
      "id": "hFoNff3jf3LV"
    },
    {
      "cell_type": "code",
      "source": [
        "sns.lineplot(data=df, x='time', y='xG', errorbar=None)\n",
        "plt.title('xG over Time')\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('xG')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "q6WHOFjixpHY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "b630074d-a5ee-43b0-8793-f5a7a5975043"
      },
      "id": "q6WHOFjixpHY",
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# xG Analysis Based on Body Part Used\n",
        "1.0 - Foot, 2.0 - Head, 3.0 - Other body parts.\n",
        "\n",
        "This graph is used to compare expected goals (xG) values across different body parts used. The x-axis represents the body parts used, and the y-axis shows xG values.\n",
        "\n",
        "A violin plot displays the distribution and density of xG values for each body part. This allows us to see which body parts have higher or lower xG values and understand which body parts exhibit a wider range of xG values. This graph is highly useful for analyzing how effective players are using different body parts and determining which body part is more efficient.\n",
        "\n",
        "In the code below, we calculated how many shots were taken with each body part in our dataset."
      ],
      "metadata": {
        "id": "DMX0wBNGgJe_"
      },
      "id": "DMX0wBNGgJe_"
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "sns.violinplot(data=df, x='bodypart', y='xG')\n",
        "plt.title('xG by Body Part Used')\n",
        "plt.xlabel('Body Part')\n",
        "plt.ylabel('xG')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "ePy_5kUByDUK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "82eca431-fd62-4ac1-cee6-8fbe5c7793ba"
      },
      "id": "ePy_5kUByDUK",
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=all_shots_df.copy()"
      ],
      "metadata": {
        "id": "cXmnV1Jo3nOF"
      },
      "id": "cXmnV1Jo3nOF",
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Hangi bodypart ile ne kadar şut çekildiğini anlamamıza yarayan kod.\n",
        "if 'bodypart' in df.columns and not df['bodypart'].isnull().all():\n",
        "    bodypart_counts = df['bodypart'].value_counts().reset_index()\n",
        "    bodypart_counts.columns = ['bodypart', 'count']\n",
        "    print(\"\\nCount of shots by each body part:\")\n",
        "    print(bodypart_counts)\n",
        "    if not bodypart_counts.empty:\n",
        "        most_frequent_bodypart = bodypart_counts.iloc[0]\n",
        "        print(f\"\\nThe most frequently used body part for shots is: {most_frequent_bodypart['bodypart']} with {most_frequent_bodypart['count']} shots\")\n",
        "    else:\n",
        "        print(\"\\nNo data available for body parts.\")\n",
        "else:\n",
        "    print(\"\\nThe 'bodypart' column is missing or contains no valid data.\")"
      ],
      "metadata": {
        "id": "V_cw5FUm3usT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8419926f-4c56-4497-a2c2-e13fa2aeae8c"
      },
      "id": "V_cw5FUm3usT",
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Count of shots by each body part:\n",
            "   bodypart  count\n",
            "0       1.0  21961\n",
            "1       2.0  14130\n",
            "2       3.0   6987\n",
            "\n",
            "The most frequently used body part for shots is: 1.0 with 21961.0 shots\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Correlation Heatmap for Understanding Relationships Between Columns\n",
        "\n",
        "This correlation heatmap visualizes the relationships between various columns in the 'allShots.csv' file. Correlation coefficients indicate the strength and direction of a linear relationship between two variables, ranging from -1 to 1, where:\n",
        "\n",
        "1: Perfect positive correlation (as one variable increases, the other also increases)\n",
        "0: No correlation (no linear relationship between variables)\n",
        "-1: Perfect negative correlation (as one variable increases, the other decreases)\n",
        "Colors in the heatmap represent these correlation coefficients:\n",
        "\n",
        "Dark red: Strong positive correlation\n",
        "Dark blue: Strong negative correlation\n",
        "White/gray shades: Weak or no correlation\n",
        "Let's examine some relationships based on examples:\n",
        "\n",
        "xG (Expected Goals) with Other Variables:\n",
        "\n",
        "There is a high positive correlation between xG and angles (\n",
        "𝑟\n",
        "=\n",
        "0.88\n",
        "). This indicates that as angles increase, the expected goal probability also increases.\n",
        "There is a strong negative correlation between xG and distance (\n",
        "𝑟\n",
        "=\n",
        "−\n",
        "0.69\n",
        "). This shows that as distance increases, the expected goal probability decreases.\n",
        "There is a moderate positive correlation between xG and x_coordinates (\n",
        "𝑟\n",
        "=\n",
        "0.60\n",
        "). This indicates that as x_coordinates increase, the expected goal probability also increases.\n",
        "x_coordinates with Other Variables:\n",
        "\n",
        "There is a very strong negative correlation between x_coordinates and distance (\n",
        "𝑟\n",
        "=\n",
        "−\n",
        "0.95\n",
        "). This indicates that as x_coordinates increase, distance decreases.\n",
        "Goal (Gol) with Other Variables:\n",
        "\n",
        "There is a positive correlation between goal and angles (\n",
        "𝑟\n",
        "=\n",
        "0.33\n",
        "). This suggests that as angles increase, the probability of scoring a goal also increases.\n",
        "This correlation heatmap allows you to quickly visualize relationships between variables in your dataset and understand which variables are strongly correlated with each other. Such analysis can be crucial, especially for modeling or prediction tasks."
      ],
      "metadata": {
        "id": "9FGdVuUv6zLl"
      },
      "id": "9FGdVuUv6zLl"
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "columns_of_interest = ['xG', 'x_coordinates', 'y_coordinates', 'goal', 'time', 'foot', 'bodypart', 'angles', 'distance']\n",
        "data_subset = df[columns_of_interest]\n",
        "corr_matrix = data_subset.corr()\n",
        "plt.figure(figsize=(12, 8))\n",
        "sns.heatmap(corr_matrix, annot=True, fmt=\".2f\", cmap='coolwarm', vmin=-1, vmax=1, linewidths=0.5)\n",
        "plt.title('Correlation Heatmap')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "FjqMt58h5B8m",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 781
        },
        "outputId": "7b647aa6-915d-4898-b9c9-40d6cf9df90b"
      },
      "id": "FjqMt58h5B8m",
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Identifying Outliers Using the IQR Method\n",
        "\n",
        "The analysis utilizes the Interquartile Range (IQR) method to measure how far 'xG' values deviate from the 1st quartile (Q1) and 3rd quartile (Q3) values. If a value is less than Q1 minus 1.5 times IQR or greater than Q3 plus 1.5 times IQR, it is considered an outlier. Outliers represent unusual values in the dataset. This analysis determines the total number of outliers and presents this count in a table format.\n",
        "\n",
        "A box plot has been created to visualize outliers. The box plot clearly displays the distribution of 'xG' values and highlights outliers. Within the plot, the line inside the box represents the median, while the edges of the box represent the first and third quartile (Q1 and Q3) of the data. Points outside the box indicate outliers, which are values that lie significantly beyond the majority of the data."
      ],
      "metadata": {
        "id": "YXaeN60PhbwP"
      },
      "id": "YXaeN60PhbwP"
    },
    {
      "cell_type": "code",
      "source": [
        "# Select the xG column\n",
        "xG = df['xG']\n",
        "\n",
        "# Determine outliers using the IQR method\n",
        "Q1 = xG.quantile(0.25)\n",
        "Q3 = xG.quantile(0.75)\n",
        "IQR = Q3 - Q1\n",
        "\n",
        "# Define the bounds for outliers\n",
        "lower_bound = Q1 - 1.5 * IQR\n",
        "upper_bound = Q3 + 1.5 * IQR\n",
        "\n",
        "# Identify outliers\n",
        "outliers = df[(xG < lower_bound) | (xG > upper_bound)]\n",
        "\n",
        "# Print the number of outliers\n",
        "print(f\"Number of outliers: {outliers.shape[0]}\")\n",
        "\n",
        "# Create a box plot of outliers\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.boxplot(x=xG)\n",
        "plt.title('Box Plot of xG Values Using IQR Method')\n",
        "plt.xlabel('xG')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "pL_KU7rB61SX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "outputId": "6301ddeb-8c8d-4a07-f5e4-fe5ab4dbb3b6"
      },
      "id": "pL_KU7rB61SX",
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of outliers: 2161\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Identifying Outliers Using the Z-score Method\n",
        "\n",
        "The Z-score method used in the analysis measures how far 'xG' values deviate from the mean and standard deviation. If a value is more than 3 standard deviations away from the mean (Z-score > 3 or Z-score < -3), it is considered an outlier. Outliers represent unusual values in the dataset. This analysis determines the total number of outliers and presents this count in a table format. A box plot has been created to visualize outliers. The box plot clearly displays the distribution of 'xG' values and highlights outliers. Within the plot, the line inside the box represents the median, while the edges of the box represent the first and third quartile (Q1 and Q3) of the data. Points outside the box indicate outliers, which are values that lie significantly beyond the majority of the data."
      ],
      "metadata": {
        "id": "_sI5QiJJhv12"
      },
      "id": "_sI5QiJJhv12"
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Select the xG column\n",
        "xG = df['xG']\n",
        "\n",
        "# Calculate mean and standard deviation using Z-score method\n",
        "mean_xG = np.mean(xG)\n",
        "std_xG = np.std(xG)\n",
        "\n",
        "# Calculate Z-scores\n",
        "z_scores = (xG - mean_xG) / std_xG\n",
        "\n",
        "# Identify outliers (Z-score > 3 or Z-score < -3)\n",
        "outliers = df[(z_scores > 3) | (z_scores < -3)]\n",
        "\n",
        "# Print the number of outliers\n",
        "print(f\"Number of outliers: {outliers.shape[0]}\")\n",
        "\n",
        "# Create a box plot of outliers\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.boxplot(x=xG)\n",
        "plt.title('Box Plot of xG Values (Z-score Method)')\n",
        "plt.xlabel('xG')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "f4Gt4cSz8La7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "outputId": "a3cd60bc-9530-4da5-cb8b-e7e0ef521a52"
      },
      "id": "f4Gt4cSz8La7",
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of outliers: 970\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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16lTt3btXderUyfcy1qxZI0k6c+aM9u7dm+ngKSd169Yt1MfVFlc5baPq1atn+2VorVq1kqQcA9mFGjdurOjoaC1atEhPPvmkFi1aJGOMx1Ounn32WT399NPq27evxo0bp7CwMHl5eenBBx9Uenp6jst3uVxZHvBf+ICB9PR0hYeHa8GCBVkux33/jcvl0pIlS/T111/rgw8+0Jo1a9S3b19NmjRJX3/9dZZnhNzKlCmT6eZ1t7Nnz+quu+5SSkqKFi9enOVy9u/fn2m8fvrpp2rdurW6deumli1batmyZfroo480ceJEvfDCC1q6dOklfVJWYGCgx+/p6elyuVxatWpVlvdR5bR9stKjRw8NGDBABw8eVIcOHbJ9alte919eZHf/V07BsbC4x0fZsmUveV8ACg/hA7iCpaamSvrfJ++VKlXSunXrlJSU5HH2Y9euXc7rbtu2bdMzzzyje++9V1u2bFH//v21ffv2bC+BuFjuvnfv3p3ptV27dqls2bLOp8j5+aQz43LdT9xy2717t8c651du26hkyZJq3bq1PvvsM/3xxx+66qqrCtyXW0xMjJ5++mlt27ZNCxcuVI0aNdS0aVPn9SVLlqhNmzZ64403POb7888/cz1ICw0NzfILDzOeEZOkatWqad26dWrRokWmA+qs/POf/9Q///lPTZgwQQsXLlRMTIwWL16s/v37ZztPdHR0tk9Ge+SRR7R582ZNnTpVDRs2zLJN+fLlPZ7+JEn169d3/j8yMlJDhw7V0KFDdfjwYTVq1EgTJkxQhw4dnO+N+PHHH7MN0PkZr9mpVq2ajDGqUqWKatasmWPbvLjttts0aNAgff3113r77bdz7Dev++9izypk3E4Zz2CkpKQoPj7e2b7udnv37vV4n547d07x8fEe+84tPj5eXl5ehbLtANjDPR/AFercuXP66KOP5Ofn51xW1bFjR6WlpWnatGkebV9++WW5XC7nU99z586pT58+ioqK0tSpUzV79mwdOnRIDz300CWrNzIyUg0aNNCcOXM8nvLz448/6qOPPlLHjh2dae6Durw8DahJkyYKDw/XzJkzdfbsWWf6qlWrtHPnTt18880Fqjev22jUqFFKS0vTPffck+XlV/n9hNh9lmPUqFHasmVLpu/28Pb2zrTMuLg4j8cKZ6datWratWuXx+VKW7du1VdffeXRrlu3bkpLS9O4ceMyLSM1NdXZLydOnMhUS4MGDSTJY19kpXnz5vrxxx8ztVu2bJmmTZumLl265HgPUkBAgNq2bevxExoaqrS0NCUmJnq0DQ8PV1RUlNNXo0aNVKVKFU2ZMiXTGHOvT37Ga3Zuv/12eXt7a+zYsZm2kzEm399WX6pUKc2YMUNjxoxR586ds22X1/0nnX+v5fWpW1lp27at/Pz89J///MdjHd944w0lJiY6778mTZqoXLlymjlzplJSUpx2s2fPzrb/77//XnXq1LlkH4gAuDQ48wFcIVatWuWcwTh8+LAWLlyovXv36vHHH1dwcLAkqXPnzmrTpo1Gjhypffv2qX79+vroo4+0fPlyPfjgg84nvuPHj9eWLVv08ccfKygoSPXq1dOoUaP01FNP6c4778zTgVVBTJw4UR06dFDz5s3Vr18/JScn65VXXlFISIjGjBnjtGvcuLGk84/Hvfvuu+Xr66vOnTtn+Umzr6+vXnjhBd17771q1aqVunfvrkOHDmnq1KmqXLlygQNVXrdRy5YtNW3aNN1///2qUaOG8w3nKSkp2rNnjxYsWCA/Pz+VL18+T/1WqVJF1113nfP9BheGj06dOjlnY6677jpt375dCxYsyPa6+Yz69u2ryZMn66abblK/fv10+PBhzZw5U3Xq1PF4nGmrVq00aNAgPffcc9qyZYvat28vX19f7d27V3FxcZo6daruvPNOzZkzR7GxsbrttttUrVo1JSUladasWQoODs51DN1yyy0aN26cPvvsM7Vv317S+Zu8+/XrJ29vb/3rX//S/Pnzs5y3WrVqat68eZavJSUlqUKFCrrzzjtVv359lSpVSuvWrdN3333nfKGhl5eXZsyYoc6dO6tBgwa69957FRkZqV27dumnn35yLrXL63jNTrVq1TR+/Hg98cQT2rdvn2699VYFBQUpPj5ey5Yt08CBA/N8P5Cb+7tqcpLX/Sedf6/NmDFD48ePV/Xq1RUeHp7pDGJOypUrpyeeeEJjx47Vv//9b3Xp0kW7d+9WbGysmjZt6jwK3NfXV+PHj9egQYN044036q677lJ8fLzeeuutLMfuuXPn9Nlnn2no0KF5rgVAMVEET9gCUIiyetRuQECAadCggZkxY4bHYzCNMSYpKck89NBDJioqyvj6+poaNWqYiRMnOu2+//574+PjY+6//36P+VJTU03Tpk1NVFRUpsePZuR+1G5cXFyOdWf1qFdjjFm3bp1p0aKFCQwMNMHBwaZz585mx44dmeYfN26cueqqq4yXl1eeHrv79ttvm4YNGxp/f38TFhZmYmJizIEDBzza5PVRuwXZRps3bza9evUyV199tfHz8zMlS5Y09erVMw8//LDHI47zYvr06UaSadasWabXzpw5Yx5++GETGRlpAgMDTYsWLczGjRszPUY3u+0/f/58U7VqVePn52caNGhg1qxZk+lRu26vvfaaady4sQkMDDRBQUGmbt265tFHHzUJCQnGGGN++OEH0717d3P11Vcbf39/Ex4ebjp16mQ2bdqUp/WsV6+e6devn/O7e2zl9tO7d+9sl3n27FkzYsQIU79+fRMUFGRKlixp6tevb2JjYzO1/fLLL027du2cdvXq1TOvvPKKR5u8jFf3o3aPHDmSZU3vvvuuuf76603JkiVNyZIlTXR0tBk2bJjZvXt3jtsnr+P1wkftuuW2/4wx5uDBg+bmm282QUFBHo+8za5v9z769NNPPaZPmzbNREdHG19fXxMREWGGDBmS5d+R2NhYU6VKFePv72+aNGliPv/88ywfAb1q1SojyezduzfHdQdQ/LiMsXBXGAAA+TRv3jwNGzZMv//+e7Y3T+Pv6dZbb5XL5dKyZcuKuhQA+UT4AAAUS+np6apXr566d++e6Zu18fe1c+dO1a1bV1u2bMnxccgAiifCBwAAAAAreNoVAAAAACsIHwAAAACsIHwAAAAAsILwAQAAAMCKAn/JYHp6uhISEhQUFCSXy1WYNQEAAAC4jBhjlJSUpKioKHl5ZX9+o8DhIyEhQRUrVizo7AAAAACuMPv371eFChWyfb3A4SMoKMjpIDg4uKCLAQAAAHCZO3nypCpWrOhkhOwUOHy4L7UKDg4mfAAAAADI9XYMbjgHAAAAYAXhAwAAAIAVhA8AAAAAVhA+AAAAAFhB+AAAAABgBeEDAAAAgBWEDwAAAABWED4AAAAAWEH4AAAAAGAF4QMAAACAFYQPAAAAAFYQPgAAAABYQfgAAAAAYAXhAwAAAIAVhA8AAAAAVhA+AAAAAFhB+AAAAABgBeEDAAAAgBWEDwAAAABWED4AAAAAWEH4AAAAAGAF4QMAAACAFYQPAAAAAFYQPgAAAABYQfgAAAAAYAXhAwAAAIAVhA8AAAAAVhA+AAAAAFhB+AAAAABgBeEDAAAAgBWEDwAAAABWED4AAAAAWEH4AAAAAGAF4QMAAACAFYQPAAAAAFYQPgAAAABYQfgAAAAAYAXhAwAAAIAVhA8AAAAAVhA+AAAAAFhB+AAAAABgBeEDAAAAgBU+RV1AUTp06JASExOLuowrTkhIiCIiIoq6DAAAABQzf9vwcejQId3Ts5fOpZwt6lKuOL5+/po/by4BBAAAAB7+tuEjMTFR51LOKrlqK6UHhFjv3yv5TwXGf67kKjcoPbC09f4vFa8zidKvnykxMZHwAQAAAA9/2/Dhlh4QovSSZYuu/8DSRdo/AAAAYAs3nAMAAACwgvABAAAAwArCBwAAAAArCB8AAAAArCB8AAAAALCC8AEAAADACsIHAAAAACsIHwAAAACsIHwAAAAAsILwAQAAAMAKwgcAAAAAKwgfAAAAAKwgfAAAAACwgvABAAAAwArCBwAAAAArCB8AAAAArCB8AAAAALCC8AEAAADACsIHAAAAACsIHwAAAACsIHwAAAAAsILwAQAAAMAKwgcAAAAAKwgfAAAAAKwgfAAAAACwgvABAAAAwArCBwAAAAArCB8AAAAArCB8AAAAALCC8AEAAADACsIHAAAAACsIHwAAAACsIHwAAAAAsILwAQAAAMAKwgcAAAAAKwgfAAAAAKwgfAAAAACwgvABAAAAwArCBwAAAAArCB8AAAAArCB8AAAAALCC8AEAAADACsIHAAAAACsIHwAAAACsIHwAAAAAsILwAQAAAMAKwgcAAAAAKwgfAAAAAKwgfAAAAACwgvABAAAAwArCBwAAAAArCB8AAAAArCB8AAAAALCC8AEAAADACsIHAAAAACsIHwAAAACsIHwAAAAAsILwAQAAAMAKwgcAAAAAKwgfAAAAAKwgfAAAAACwgvABAAAAwArCBwAAAAArCB8AAAAArCB8AAAAALCC8AEAAADACsIHAAAAACsIHwAAAACsIHwAAAAAsILwAQAAAMAKwgcAAAAAKwgfAAAAAKwgfAAAAACwgvABAAAAwArCBwAAAAArCB8AAAAArCB8AAAAALCC8AEAAADACsIHAAAAACsIHwAAAACsIHwAAAAAsILwAQAAAMAKwgcAAAAAKwgfAAAAAKwgfAAAAACwgvABAAAAwArCBwAAAAArCB8AAAAArLgiwseZM2e0Z88enTlzpqhLASDekwAAIGtXRPj4/fffNXDgQP3+++9FXQoA8Z4EAABZuyLCBwAAAIDij/ABAAAAwArCBwAAAAArCB8AAAAArCB8AAAAALCC8AEAAADACsIHAAAAACsIHwAAAACsIHwAAAAAsILwAQAAAMAKwgcAAAAAKwgfAAAAAKwgfAAAAACwgvABAAAAwArCBwAAAAArCB8AAAAArCB8AAAAALCC8AEAAADACsIHAAAAACsIHwAAAACsIHwAAAAAsILwAQAAAMAKwgcAAAAAKwgfAAAAAKwgfAAAAACwgvABAAAAwArCBwAAAAArCB8AAAAArCB8AAAAALCC8AEAAADACsIHAAAAACsIHwAAAACsIHwAAAAAsILwAQAAAMAKwgcAAAAAKwgfAAAAAKwgfAAAAACwgvABAAAAwArCBwAAAAArCB8AAAAArCB8AAAAALCC8AEAAADACsIHAAAAACsIHwAAAACsIHwAAAAAsILwAQAAAMAKwgcAAAAAKwgfAAAAAKwgfAAAAACwgvABAAAAwArCBwAAAAArCB8AAAAArCB8AAAAALCC8AEAAADACsIHAAAAACsIHwAAAACsIHwAAAAAsILwAQAAAMAKwgcAAAAAKwgfAAAAAKwgfAAAAACwgvABAAAAwArCBwAAAAArCB8AAAAArCB8AAAAALCC8AEAAADACsIHAAAAACsIHwAAAACsIHwAAAAAsILwAQAAAMAKwgcAAAAAKwgfAAAAAKwgfAAAAACwgvABAAAAwArCBwAAAAArCB8AAAAArCB8AAAAALCC8AEAAADACsIHAAAAACsIHwAAAACsIHwAAAAAsILwAQAAAMAKwgcAAAAAKwgfAAAAAKwgfAAAAACwgvABAAAAwArCBwAAAAArfIq6AABXlk2bNumRRx6RJA0cOLCIq7GjatWqqlChgj7//PNc2/r4+Khhw4aqW7eutm/frq1btyolJSXX+f7xj39ox44dzu8lSpTQ6dOnM7Xz9vZWQECA7rzzTlWvXl1z587VsWPHVKZMGfXt21fNmjVTfHy8BgwYIGOMJCkoKEje3t6SpKioKNWqVUvLli3LtpYuXbrIGKMDBw4oISFBLpdLpUqVUpkyZXTq1Cnt3r1bxhilp6erRIkSSklJyXIdy5Ur5/HaNddco3r16mnXrl06c+aMfHx89M033zjtmzRpomuvvVYtW7bUY489pgMHDigtLc1Z7/79+2vWrFlKT0935nn99ddVvXp1SVJycrJeeOEFrV+/PtftnR8ul0sBAQEKDQ1VUlKSAgMDdfjwYY82Xl5eHnXlxsvLS/3791eZMmU0ceJEpaamysfHR+Hh4Tpx4oQCAwOd7RcWFqYffvjB2Z8Z6woKCtK5c+cUFhamU6dO6c8///RoM2bMGLVu3Vr79u1Tv379nO0ZGBioBg0aKCYmRvfdd5/HPBUqVNAff/zh9Oft7a3w8HAdPHhQxhj5+vrq0Ucf1Y033uiMq8TERI0cOVIHDx5UWlqaKlWqpMjISO3bt09Hjx5VRESEHn/8cT333HM6cOCAgoKC1L9/fx06dEj//e9/JUm1a9dWeHi46tWrJ29vb/3111967rnnlJCQoKioKD3xxBMqVapUpm2ZnJysV155RevXr3feM/7+/goLC9OJEyfk4+Oja665Rp07d9auXbt07tw57dmzR1u3blV6erq8vLxUvnx5Va5cOcs+UlJStHz5cu3Zs0effPKJ0tLS5O/vr4CAACUnJystLU0lSpRQSEiI+vfvrwMHDmjdunVKSUlRcnKyvL29Vb58eU2YMEEhISFZjofjx49r+PDhznt58uTJ8vb21siRI3Xo0CFFREQ48//888/O+9vlcmnWrFnOeyCjhIQEDRw4UKdOnZKfn59GjBihNm3aOPssOykpKVq8eLGWLVum5ORklS5dWj4+Pjpz5kyW65GWlqZt27bp+PHjCgsLU7169ZSSkqJXX31VBw4cUIUKFTRo0CAFBgZ6bM+EhARFRETIGKPDhw8rKipKt9xyi/z8/HKsL6d+c1u3jOvoriGv/br7O3z4sHbu3ClJuuqqq3KdN2Nf5cuXV9WqVXXy5MlMNWfcX15eXrrrrrvUs2dP7dq1q0DrWJRc5sK/Vnl08uRJhYSEKDExUcHBwYVdV77s2bNHAwcO1GuvvaaaNWvma55T/+ii9JJlL3GFmXmdOqqSO94vsv4vFfd65Wdf4MrRunXroi4ByFKLFi301VdfFXUZfzvPPPOMXn31Vf3xxx+Ftszy5cvL29s7y2VGR0dr5syZzu8jR44s9P2esY+ZM2cqLi7OCW0X66qrrtKCBQs8pt1+++06fvz4RS87Y+hu166dzp07l2W7Z555RjfccEOWr82cOVOLFy/OtS/3enz++eeKjY3VwYMHndf8/f119uzZTPO0aNFCFStWzHF7ent7q2vXrho8eHCO/WfVb/ny5TV06NBs180tq32aW79Z9ZeXeXMbP+6ax40bl+3+yqp9but4qeQ1G3DZFYBCQfAovmrXrq0yZcoUdRlFiuCRdy6Xq9CWNWrUKI+Q4OXlJS+vnA89svqUuGXLlipdurQk6ejRo84y27dvr9dff13t27eXy+XSrl27nIO8SxE8JDl9uA/ELyZ4XLit//jjD8XExDi/Zwwe//jHPzRp0iT5+PzvohUfHx9NmzZNzZo181iOt7e3evbs6fEpuPtvdMbg4evrq549eyo0NNRpN2rUqCzP4l4YPHIaJ3/88Yduv/12jR49WlWrVtX06dO1cuVK1atXzwkeLVu21Pz589WjRw/5+vrqq6++0uLFixUcHKymTZtKOn+Gt0SJEpLOn/kMDg7W4sWLPQLmhT7//PNM/U6fPl1Vq1bV6NGjczxD7V7H4OBgPfLII3r33Xf1yCOP5Nivuz/3uC5durR69OihJk2aSJICAgKynDdjX126dJHL5VK1atUUFBQkSWrbtq2qVq2qUaNGeQSPDh06ONvE7Y477sjzOhYHhA8AF23Tpk1FXQJysHPnTo0fPz7b14tzMHEf4GQ84Moou0/X3nrrLTVq1OiS1SUp0+VIGY0YMaLQ+/P19S20Zc2dOzfL6atXr9asWbMKrZ+MfH199fbbb2d7+VnDhg0lnb8MJSQkxGNc7tmzR2+//bZCQkKUmpoqSQoPD9djjz2m6tWr68knn9SqVaucAHLkyJFCCR5+fn6KiIjINH3Xrl165513PC7ByumAOCuhoaFavXq1E6rc/vjjDyUmJur48eNO8FixYoViY2NVvXp1Z/0lKTU1VVFRUZkucV26dKn69eunjz/+WPPnz3emf/vtt86BbKNGjbRmzRr169dPy5Yt09KlS512U6ZM8QhV7kut3Bo1auRcdvf+++97XGZVrlw5SecvFWvSpInGjx+vOnXqyOVyadu2bfLx8dE///lP/fzzz4qMjNTAgQM9LvN8/fXX9cMPPyg0NFTvv/++3n//fYWGhmrz5s1asGCBQkNDFRcXl+WlnGlpaYqNjVXz5s2dfkuUKKE6depo/Pjxat68uWbMmJFlYExJSVFcXJyz/E6dOqlMmTLq1KmTx/SM/br7u/baa3Xw4EGFhoZqyZIlGjhwoF588UVdd911CgoKUunSpT3mzdjX4sWL9e2336p58+aaNWuWli1bptDQUH366ace+zUwMFDr1q1zwlDGv2/vvvuuatSokes6Fhd5vufj7NmzHqfJTp48eUkKuhi//fbbJWmL/GP7/r247/FA0QkKClJSUlK2rw8ZMiTL6U2aNLno8Ojt7X3J/qFr27at1q5d63HAlVF2/xb1799f//nPf/TDDz9ckrokadq0adm+NnHixELvz33QmN39Ixmn53aPyahRo7KcHhAQoAEDBlxUndldVtO1a1eNHTtWktSsWTMZY/Tdd985rwcFBalixYrav3+/fHx8dOzYMdWuXVs7d+7UoUOHtGPHDpUtW1aJiYlOu23btjmhJSAgwBkvFwbDC98flSpV8vh3Kqvt1bRpU3333Xc6dOiQbrzxRn3yySeSzoeoc+fOKT09XWfOnJF0PgSPHj3a43XpfGjObuyeOHFCO3bsUL9+/TRp0iRJ5w/cjxw5opEjR+qvv/6SdP6MhzvkjBw50tl+SUlJ2rlzp4YPH57p39yRI0c647NChQrOe/TRRx912vTr18/jLFRYWJjCwsKc0JNx2y5fvtxj+e57h5o1a6bg4GD179/fWYcjR444++fYsWNOH6+++qokqVu3bmrRooWGDRvm9LFq1Spn2aNHj1ZaWpr69evnfOjQt29fTZo0SR9++KHz/8uXL1fXrl096tq2bZsOHjyop59+OtMZNi8vL8XExHj0m9Hy5csz9evm4+OTZb/u/lq0aKH09HSPeTP2161bN73zzjvOvBn72rFjh0fNXl5eTl8Z34udO3eWj4+PNm/e7LTft2+fE1Ddy85pHYuLPIeP5557zvmjUVxNmDChqEvA/2FfAHa1bNlSK1euzPd8DRs2zFP4yHhAdaH83EidX926ddPatWvzPV9aWpqqVKlyCSoqerfffruWLFmSaXr16tW1Z88eSdKdd96pd955J9tlHDt2LNvXcrsVNCAgwDnozs/8HTt2dPZlr169lJ6e7hE+kpKSVKlSJe3fv98JL3379nXOIh0/ftz55Lhy5crav39/pnshunbtqrVr1yoxMdFjenR0tEdfAwYM0FNPPeX8ntUY7tWrlzNPdHS0Ez4ytnUHiwEDBmj69OmSzn9C7X6v1KxZ0+NBERc6fvy4mjdv7vweFhamI0eO6NChQ8427tevn/P6oUOHnNpOnz6tRx99VMeOHXO2ebt27bR27VqnXcbtcuG9Glm9P/r27auXXnrJqc0tISHBo5079Pfq1UuSPNZB+t/+yfjhwIEDBySdHwdhYWEefWRcvrv2jMt0/39CQoJzWdqFNWVcXnbvfff0rO6hcS/vwnXJqoYL+3OPywvndfcXFRXlMW/GvrZs2ZKpZvdyMv7NdS8j4zpm3F/uZea0jsVFnsPHE088oeHDhzu/nzx5UhUrVrwkRRXUyJEjValSpTy1/e233zhAvoTysy9w+fu7PNWqOPviiy8KNN/mzZvz1C6nmx29vLwu2ZmPnA6gc+Lt7a34+PhCrqZ4yHh5TEY///yz8/9ZhZOMypQpk+2ZMpfLlWMAySl4uOfPysqVKxUREaEjR45o7ty5mfoICgpy9pm/v7/++usvvfnmm87rYWFhzv0g+/btc6ZlFBcXJ0kKCQnxqHPXrl0e7S68tCyrMx8ZL03LOH/G8e4+szFr1iynz+TkZKetOwxmJywsTBs3bnR+dx8wRkRE6K+//lJSUpLeeOMNNW7c2Jnu3n7u/VemTBn99ddfMsY44e7CS8Xc2yWj+Ph41alTx2PahdvbzX3g6xYcHKyjR49q7ty5evHFFz3WQfrf/sl4WWSFChW0adMmrVy5Ui1atPDoI+Py3eu4ceNGderUSZKc5UdFRXn8/4Xcy8tq3dzTL1y3C9cxY78ZZdWveznucXnhvO7+3MHAPW/Gvq666qpMNbv7yvihj3sZGdcx4/5yLzOndSwueNoVT7sqVDzt6u8p4+N1UTzNmDEj20uvypQpk+Mn4UXJfSCc3eUrwcHBWV569dZbb+mVV165pJdd3XfffdleejVixIhCv/Qqp7NP+TV37lznU+uMVq9erQMHDqh///6F0k9Gvr6+WrhwYaZLZdwaNmzohOGQkBDn0ivp/AHp3Llz1a1bN+esRnh4uBYtWuTcVH3mzBl16NBBxhjFxcVl209++Pn5KTQ0NNOZBOl8CClRooRzedTMmTNzfQpTRu7r/e+6665Mj0B2X5Zz++23Szp/z0epUqWUmJioW265xaPt0qVLdfz4cY99tnz5cuc+jAMHDuiee+6RJL344ovOpVeNGjXSSy+95FyedPz4cae/sLAwxcXFOds2JSVF7du3d5bfqFEj5731/vvvq2fPns5+cV86Jp2/dO2FF16Ql5eXkpOT1aFDB/n4+KhJkyb67bffNH/+fOexye6D9nfffVfdunVTcHCwE5q6du2qkydP6oMPPlBMTIxOnjypVatWZXo4QVpammJiYlS1alWNHz/e49Kr9PR0PfXUU4qPj3f6zSglJUUdOnRw+s146VVqaqpTQ8Z+3f1VrlxZ3377rUJCQpx53f39+uuvOnPmjJKSkpx5M/a1ePFi9e7d26k5PT3d6euNN95Qnz59JJ0/o/bBBx/I5XIpJiZGUVFRHn/fPvroI/n4+OS4jpdaXrMB4YPwUagIH39fPO2q+KpVq5aOHDlSrE/Do/jI7czHxXAfDOZ0qZ774Cyj66+/Xj/++KP+/PNPjyDarl07de3aVXFxcVq3bp2MMc6jcC/V066k85dhNWjQIE+Pnc1JVts64+N2Mz7tqnbt2rr33nv15JNPOuvv4+OjyZMna968eR6Xlbkf73rhY1zXr1/v8bQrHx8fde3aVatXr9aJEyecdlk9bjerp13lNE7c36PSvHlzxcTEqEqVKnrssce0fft2Sef36aBBg7Ry5UotWbLEqSk0NFTVqlXTpk2bnKc6nT59Wk2aNNEvv/yiEydO6O67787xsbejR4/26Dc+Pl4LFizQxo0bNXbs2FwfJRwaGqq+ffuqefPm2rhxo958881s+3X3FxkZqYSEBJUuXVodOnTQ3r17tWnTJpUsWVKnTp3KNG/Gvlq2bKkPPvhAVapU0ZEjR5SUlKS2bdvq9OnT2rBhg0d/7dq101dffeXxPU933HGHbrzxxjyt46VE+MjjPISPwkX4+HsjgKC44ns+isal+J6PyMhIeXl58T0f+fR3/56PyMhIDRky5LL6ng93zXn9no+8ruOlQvjI4zyEj8JF+MDf8RIsvuGcbzjnG875hnO+4ZxvOP+7f8M54SOP8xA+ChfhA1LB3pMAAODyxTecAwAAAChWCB8AAAAArCB8AAAAALCC8AEAAADACsIHAAAAACsIHwAAAACsIHwAAAAAsILwAQAAAMAKwgcAAAAAKwgfAAAAAKwgfAAAAACwgvABAAAAwArCBwAAAAArCB8AAAAArCB8AAAAALCC8AEAAADACsIHAAAAACsIHwAAAACsIHwAAAAAsILwAQAAAMAKwgcAAAAAKwgfAAAAAKwgfAAAAACwgvABAAAAwArCBwAAAAArCB8AAAAArCB8AAAAALCC8AEAAADACsIHAAAAACsIHwAAAACsIHwAAAAAsILwAQAAAMAKwgcAAAAAKwgfAAAAAKwgfAAAAACwgvABAAAAwArCBwAAAAArCB8AAAAArCB8AAAAALCC8AEAAADACsIHAAAAACsIHwAAAACsIHwAAAAAsILwAQAAAMAKwgcAAAAAKwgfAAAAAKwgfAAAAACwgvABAAAAwArCBwAAAAArCB8AAAAArCB8AAAAALCC8AEAAADACsIHAAAAACsIHwAAAACsIHwAAAAAsILwAQAAAMAKwgcAAAAAKwgfAAAAAKwgfAAAAACwgvABAAAAwArCBwAAAAArCB8AAAAArCB8AAAAALCC8AEAAADACsIHAAAAACsIHwAAAACsIHwAAAAAsILwAQAAAMAKwgcAAAAAKwgfAAAAAKwgfAAAAACwgvABAAAAwArCBwAAAAArCB8AAAAArCB8AAAAALCC8AEAAADACsIHAAAAACsIHwAAAACsIHwAAAAAsILwAQAAAMAKwgcAAAAAKwgfAAAAAKwgfAAAAACwgvABAAAAwArCBwAAAAArCB8AAAAArLgiwsfVV1+t1157TVdffXVRlwJAvCcBAEDWfIq6gMIQEBCgmjVrFnUZAP4P70kAAJCVK+LMBwAAAIDij/ABAAAAwArCBwAAAAArCB8AAAAArCB8AAAAALCC8AEAAADACsIHAAAAACsIHwAAAACsIHwAAAAAsILwAQAAAMAKwgcAAAAAKwgfAAAAAKwgfAAAAACwgvABAAAAwArCBwAAAAArCB8AAAAArCB8AAAAALCC8AEAAADACsIHAAAAACsIHwAAAACsIHwAAAAAsILwAQAAAMAKwgcAAAAAKwgfAAAAAKwgfAAAAACwgvABAAAAwArCBwAAAAArCB8AAAAArCB8AAAAALCC8AEAAADACsIHAAAAACsIHwAAAACsIHwAAAAAsILwAQAAAMAKwgcAAAAAKwgfAAAAAKwgfAAAAACwgvABAAAAwArCBwAAAAArCB8AAAAArCB8AAAAALCC8AEAAADACsIHAAAAACsIHwAAAACsIHwAAAAAsILwAQAAAMAKwgcAAAAAKwgfAAAAAKwgfAAAAACwgvABAAAAwArCBwAAAAArCB8AAAAArCB8AAAAALCC8AEAAADACsIHAAAAACsIHwAAAACsIHwAAAAAsILwAQAAAMAKwgcAAAAAKwgfAAAAAKwgfAAAAACwgvABAAAAwArCBwAAAAArCB8AAAAArCB8AAAAALCC8AEAAADACsIHAAAAACsIHwAAAACsIHwAAAAAsILwAQAAAMAKwgcAAAAAKwgfAAAAAKwgfAAAAACwgvABAAAAwArCBwAAAAArCB8AAAAArCB8AAAAALCC8AEAAADACsIHAAAAACsIHwAAAACsIHwAAAAAsILwAQAAAMAKwgcAAAAAKwgfAAAAAKwgfAAAAACwgvABAAAAwArCBwAAAAArCB8AAAAArCB8AAAAALDCp6gLKGpeZxKLpt/kPz3+e6Uoqu0JAACA4u9vGz5CQkLk6+cv/fpZkdYRGP95kfZ/Kfj6+SskJKSoywAAAEAx87cNHxEREZo/b64SE/mkvrCFhIQoIiKiqMsAAABAMfO3DR/S+QDCQTIAAABgBzecAwAAALCC8AEAAADACsIHAAAAACsIHwAAAACsIHwAAAAAsILwAQAAAMAKwgcAAAAAKwgfAAAAAKwgfAAAAACwgvABAAAAwArCBwAAAAArCB8AAAAArCB8AAAAALCC8AEAAADACsIHAAAAACsIHwAAAACsIHwAAAAAsILwAQAAAMAKwgcAAAAAKwgfAAAAAKwgfAAAAACwgvABAAAAwArCBwAAAAArCB8AAAAArCB8AAAAALCC8AEAAADACsIHAAAAACsIHwAAAACsIHwAAAAAsILwAQAAAMAKwgcAAAAAKwgfAAAAAKwgfAAAAACwgvABAAAAwArCBwAAAAArCB8AAAAArCB8AAAAALCC8AEAAADACsIHAAAAACsIHwAAAACsIHwAAAAAsILwAQAAAMAKn4LOaIyRJJ08ebLQigEAAABw+XFnAndGyE6Bw0dSUpIkqWLFigVdBAAAAIArSFJSkkJCQrJ93WVyiyfZSE9PV0JCgoKCguRyuQpcYF6dPHlSFStW1P79+xUcHHzJ+8OViXGEwsA4QmFgHKEwMI5QWC52LBljlJSUpKioKHl5ZX9nR4HPfHh5ealChQoFnb3AgoODeXPhojGOUBgYRygMjCMUBsYRCsvFjKWczni4ccM5AAAAACsIHwAAAACsuGzCh7+/v0aPHi1/f/+iLgWXMcYRCgPjCIWBcYTCwDhCYbE1lgp8wzkAAAAA5Mdlc+YDAAAAwOWN8AEAAADACsIHAAAAACsIHwAAAACsKFbhY/r06apcubICAgJ07bXX6ttvv82xfVxcnKKjoxUQEKC6detq5cqVlipFcZafcTRr1iy1bNlSoaGhCg0NVdu2bXMdd/h7yO/fI7fFixfL5XLp1ltvvbQF4rKQ33H0559/atiwYYqMjJS/v79q1qzJv23I9ziaMmWKatWqpcDAQFWsWFEPPfSQzpw5Y6laFEeff/65OnfurKioKLlcLr333nu5zrN+/Xo1atRI/v7+ql69umbPnl04xZhiYvHixcbPz8+8+eab5qeffjIDBgwwpUuXNocOHcqy/VdffWW8vb3Niy++aHbs2GGeeuop4+vra7Zv3265chQn+R1HPXr0MNOnTzebN282O3fuNH369DEhISHmwIEDlitHcZLfceQWHx9vrrrqKtOyZUtzyy232CkWxVZ+x9HZs2dNkyZNTMeOHc2XX35p4uPjzfr1682WLVssV47iJL/jaMGCBcbf398sWLDAxMfHmzVr1pjIyEjz0EMPWa4cxcnKlSvNyJEjzdKlS40ks2zZshzb//rrr6ZEiRJm+PDhZseOHeaVV14x3t7eZvXq1RddS7EJH82aNTPDhg1zfk9LSzNRUVHmueeey7J9t27dzM033+wx7dprrzWDBg26pHWieMvvOLpQamqqCQoKMnPmzLlUJeIyUJBxlJqaaq677jrz+uuvm969exM+kO9xNGPGDFO1alWTkpJiq0RcBvI7joYNG2ZuvPFGj2nDhw83LVq0uKR14vKRl/Dx6KOPmjp16nhMu+uuu8xNN9100f0Xi8uuUlJS9P3336tt27bONC8vL7Vt21YbN27Mcp6NGzd6tJekm266Kdv2uPIVZBxd6PTp0zp37pzCwsIuVZko5go6jp555hmFh4erX79+NspEMVeQcfT++++refPmGjZsmCIiInTNNdfo2WefVVpamq2yUcwUZBxdd911+v77751Ls3799VetXLlSHTt2tFIzrgyX8jjb56KXUAiOHj2qtLQ0RUREeEyPiIjQrl27spzn4MGDWbY/ePDgJasTxVtBxtGFHnvsMUVFRWV6w+HvoyDj6Msvv9Qbb7yhLVu2WKgQl4OCjKNff/1Vn3zyiWJiYrRy5Ur9/PPPGjp0qM6dO6fRo0fbKBvFTEHGUY8ePXT06FFdf/31MsYoNTVVgwcP1pNPPmmjZFwhsjvOPnnypJKTkxUYGFjgZReLMx9AcfD8889r8eLFWrZsmQICAoq6HFwmkpKS1LNnT82aNUtly5Yt6nJwGUtPT1d4eLhee+01NW7cWHfddZdGjhypmTNnFnVpuIysX79ezz77rGJjY/XDDz9o6dKl+vDDDzVu3LiiLg2QVEzOfJQtW1be3t46dOiQx/RDhw6pfPnyWc5Tvnz5fLXHla8g48jtpZde0vPPP69169apXr16l7JMFHP5HUe//PKL9u3bp86dOzvT0tPTJUk+Pj7avXu3qlWrdmmLRrFTkL9HkZGR8vX1lbe3tzOtdu3aOnjwoFJSUuTn53dJa0bxU5Bx9PTTT6tnz57q37+/JKlu3bo6deqUBg4cqJEjR8rLi8+dkbvsjrODg4Mv6qyHVEzOfPj5+alx48b6+OOPnWnp6en6+OOP1bx58yznad68uUd7SVq7dm227XHlK8g4kqQXX3xR48aN0+rVq9WkSRMbpaIYy+84io6O1vbt27Vlyxbnp0uXLmrTpo22bNmiihUr2iwfxURB/h61aNFCP//8sxNeJWnPnj2KjIwkePxNFWQcnT59OlPAcAfa8/caA7m7pMfZF33LeiFZvHix8ff3N7NnzzY7duwwAwcONKVLlzYHDx40xhjTs2dP8/jjjzvtv/rqK+Pj42Neeukls3PnTjN69GgetYt8j6Pnn3/e+Pn5mSVLlpj//ve/zk9SUlJRrQKKgfyOowvxtCsYk/9x9Pvvv5ugoCBz3333md27d5sVK1aY8PBwM378+KJaBRQD+R1Ho0ePNkFBQWbRokXm119/NR999JGpVq2a6datW1GtAoqBpKQks3nzZrN582YjyUyePNls3rzZ/Pbbb8YYYx5//HHTs2dPp737UbsjRowwO3fuNNOnT7/yHrVrjDGvvPKKufrqq42fn59p1qyZ+frrr53XWrVqZXr37u3R/p133jE1a9Y0fn5+pk6dOubDDz+0XDGKo/yMo0qVKhlJmX5Gjx5tv3AUK/n9e5QR4QNu+R1HGzZsMNdee63x9/c3VatWNRMmTDCpqamWq0Zxk59xdO7cOTNmzBhTrVo1ExAQYCpWrGiGDh1qTpw4Yb9wFBuffvpplsc77rHTu3dv06pVq0zzNGjQwPj5+ZmqVauat956q1BqcRnDOTgAAAAAl16xuOcDAAAAwJWP8AEAAADACsIHAAAAACsIHwAAAACsIHwAAAAAsILwAQAAAMAKwgcAAAAAKwgfAAAAAKwgfAAAAACwgvABALgoKSkpmjhxoho1aqSSJUsqJCRE9evX11NPPaWEhISiLg8AUIy4jDGmqIsAAFyezp49q/bt22vbtm0aO3asWrRooXLlyik+Pl6LFi1SaGionnvuuaIuEwBQTBA+AADZOnLkiOrWrasHHnhATz75pCRpw4YNat26tVatWqXvvvtOI0eO1KZNm9SwYcNM8xtj5HK5bJcNACimCB8AgBytXLlSt956qzZs2KBatWqpQYMGuuWWWzR58mTVr19fkZGRWr16dVGXCQC4DHDPBwAgRx07dtSAAQMUExOjwYMHq2TJks6lVHv27FGtWrU82t92220qVaqUSpUqpeuuu64oSgYAFFOEDwBArl566SWlpqYqLi5OCxYskL+/f7ZtY2NjtWXLFvXt21enT5+2WCUAoLgjfAAAcvXLL78oISFB6enp2rdvnzO9Ro0a2r17t0fbyMhIVa9eXWFhYZarBAAUd4QPAECOUlJSdM899+iuu+7SuHHj1L9/fx0+fFiS1L17d61du1abN28u4ioBAJcDbjgHAORoxIgRWrJkibZu3apSpUqpVatWCgkJ0YoVK3TmzBm1bdtWO3bs0OjRo9WyZUuFhoZqz549GjVqlFJTU/X9998X9SoAAIoJwgcAIFvr169Xu3bt9Omnn+r666+XJO3bt0/169fX888/ryFDhujs2bOaMmWKFi1apD179ig9PV1VqlRRhw4d9NBDD6lixYpFvBYAgOKC8AEAAADACu75AAAAAGAF4QMAAACAFYQPAAAAAFYQPgAAAABYQfgAAAAAYAXhAwAAAIAVhA8AAAAAVhA+AAAAAFhB+AAAAABgBeEDAAAAgBWEDwAAAABW/H/QOERmH7SUsgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Convert ipynb to HTML in Colab\n",
        "# Upload ipynb\n",
        "from google.colab import files\n",
        "f = files.upload()\n",
        "\n",
        "# Convert ipynb to html\n",
        "import subprocess\n",
        "file0 = list(f.keys())[0]\n",
        "_ = subprocess.run([\"pip\", \"install\", \"nbconvert\"])\n",
        "_ = subprocess.run([\"jupyter\", \"nbconvert\", file0, \"--to\", \"html\"])\n",
        "\n",
        "# download the html\n",
        "files.download(file0[:-5]+\"html\")\n"
      ],
      "metadata": {
        "id": "XKPjmSrQhLAc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 38
        },
        "outputId": "5a7dd6bf-7a89-4126-d5eb-b24cfdabd8be"
      },
      "id": "XKPjmSrQhLAc",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-bf37791f-dcb9-45cc-915e-6028bce2eb6c\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-bf37791f-dcb9-45cc-915e-6028bce2eb6c\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
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