xT.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "520cc62f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import rcParams\n",
    "rcParams['font.family'] = 'Century Gothic'\n",
    "rcParams['font.sans-serif'] = ['Tahoma']\n",
    "rcParams['font.weight'] = 'bold'\n",
    "rcParams['font.size'] = '13'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6b2c7378",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# import data\n",
    "df = pd.read_csv(\"modricevents.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9f31bd03",
   "metadata": {},
   "outputs": [
    {
     "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>Team</th>\n",
       "      <th>Player</th>\n",
       "      <th>Event</th>\n",
       "      <th>Mins</th>\n",
       "      <th>Secs</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>X2</th>\n",
       "      <th>Y2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Progressive Pass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>11</td>\n",
       "      <td>75</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Progressive Pass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>15</td>\n",
       "      <td>66</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Progressive Pass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>11</td>\n",
       "      <td>82</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Progressive Pass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>79</td>\n",
       "      <td>15</td>\n",
       "      <td>94</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Progressive Pass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>16</td>\n",
       "      <td>90</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Progressive Pass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>23</td>\n",
       "      <td>67</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Progressive Pass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>47</td>\n",
       "      <td>81</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Progressive Pass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>72</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Progressive Pass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>63</td>\n",
       "      <td>69</td>\n",
       "      <td>74</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Progressive Pass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>86</td>\n",
       "      <td>56</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Carries</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>35</td>\n",
       "      <td>43</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Carries</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>67</td>\n",
       "      <td>34</td>\n",
       "      <td>73</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Carries</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "      <td>56</td>\n",
       "      <td>76</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Carries</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>68</td>\n",
       "      <td>66</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Carries</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>20</td>\n",
       "      <td>90</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Carries</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>65</td>\n",
       "      <td>84</td>\n",
       "      <td>90</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Carries</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>91</td>\n",
       "      <td>82</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Carries</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>69</td>\n",
       "      <td>94</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Team  Player             Event  Mins  Secs   X   Y  X2  Y2\n",
       "0   Home  Modric  Progressive Pass     0     0  55  11  75  26\n",
       "1   Home  Modric  Progressive Pass     0     0  60  15  66  37\n",
       "2   Home  Modric  Progressive Pass     0     0  91  11  82  29\n",
       "3   Home  Modric  Progressive Pass     0     0  79  15  94  42\n",
       "4   Home  Modric  Progressive Pass     0     0  80  16  90  27\n",
       "5   Home  Modric  Progressive Pass     0     0  35  23  67  58\n",
       "6   Home  Modric  Progressive Pass     0     0  73  47  81  45\n",
       "7   Home  Modric  Progressive Pass     0     0  63  63  72  46\n",
       "8   Home  Modric  Progressive Pass     0     0  63  69  74  66\n",
       "9   Home  Modric  Progressive Pass     0     0  35  86  56  81\n",
       "10  Home  Modric           Carries     0     0  31  35  43  31\n",
       "11  Home  Modric           Carries     0     0  67  34  73  37\n",
       "12  Home  Modric           Carries     0     0  68  56  76  33\n",
       "13  Home  Modric           Carries     0     0  56  68  66  43\n",
       "14  Home  Modric           Carries     0     0  61  20  90  28\n",
       "15  Home  Modric           Carries     0     0  65  84  90  60\n",
       "16  Home  Modric           Carries     0     0  73  91  82  68\n",
       "17  Home  Modric           Carries     0     0  70  69  94  68"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "27254ee6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['X'] = df['X']*1.2\n",
    "df['Y'] = df['Y']*0.8\n",
    "df['X2'] = df['X2']*1.2\n",
    "df['Y2'] = df['Y2']*0.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "953c0d28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.00638303 0.00779616 0.00844854 0.00977659 0.01126267 0.01248344\n",
      "  0.01473596 0.0174506  0.02122129 0.02756312 0.03485072 0.0379259 ]\n",
      " [0.00750072 0.00878589 0.00942382 0.0105949  0.01214719 0.0138454\n",
      "  0.01611813 0.01870347 0.02401521 0.02953272 0.04066992 0.04647721]\n",
      " [0.0088799  0.00977745 0.01001304 0.01110462 0.01269174 0.01429128\n",
      "  0.01685596 0.01935132 0.0241224  0.02855202 0.05491138 0.06442595]\n",
      " [0.00941056 0.01082722 0.01016549 0.01132376 0.01262646 0.01484598\n",
      "  0.01689528 0.0199707  0.02385149 0.03511326 0.10805102 0.25745362]\n",
      " [0.00941056 0.01082722 0.01016549 0.01132376 0.01262646 0.01484598\n",
      "  0.01689528 0.0199707  0.02385149 0.03511326 0.10805102 0.25745362]\n",
      " [0.0088799  0.00977745 0.01001304 0.01110462 0.01269174 0.01429128\n",
      "  0.01685596 0.01935132 0.0241224  0.02855202 0.05491138 0.06442595]\n",
      " [0.00750072 0.00878589 0.00942382 0.0105949  0.01214719 0.0138454\n",
      "  0.01611813 0.01870347 0.02401521 0.02953272 0.04066992 0.04647721]\n",
      " [0.00638303 0.00779616 0.00844854 0.00977659 0.01126267 0.01248344\n",
      "  0.01473596 0.0174506  0.02122129 0.02756312 0.03485072 0.0379259 ]]\n",
      "8 12\n"
     ]
    }
   ],
   "source": [
    "xT = pd.read_csv(\"xT_Grid.csv\", header=None)\n",
    "xT = np.array(xT)\n",
    "xT_rows, xT_cols = xT.shape\n",
    "print(xT)\n",
    "print(xT_rows,xT_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "99c8758e",
   "metadata": {},
   "outputs": [
    {
     "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>Team</th>\n",
       "      <th>Player</th>\n",
       "      <th>Event</th>\n",
       "      <th>Mins</th>\n",
       "      <th>Secs</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>X2</th>\n",
       "      <th>Y2</th>\n",
       "      <th>x1_bin</th>\n",
       "      <th>y1_bin</th>\n",
       "      <th>x2_bin</th>\n",
       "      <th>y2_bin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Progressive Pass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>8.8</td>\n",
       "      <td>90.0</td>\n",
       "      <td>20.8</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Progressive Pass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>79.2</td>\n",
       "      <td>29.6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Progressive Pass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>109.2</td>\n",
       "      <td>8.8</td>\n",
       "      <td>98.4</td>\n",
       "      <td>23.2</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Progressive Pass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>94.8</td>\n",
       "      <td>12.0</td>\n",
       "      <td>112.8</td>\n",
       "      <td>33.6</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Progressive Pass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>12.8</td>\n",
       "      <td>108.0</td>\n",
       "      <td>21.6</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Progressive Pass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>18.4</td>\n",
       "      <td>80.4</td>\n",
       "      <td>46.4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Progressive Pass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>87.6</td>\n",
       "      <td>37.6</td>\n",
       "      <td>97.2</td>\n",
       "      <td>36.0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Progressive Pass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>75.6</td>\n",
       "      <td>50.4</td>\n",
       "      <td>86.4</td>\n",
       "      <td>36.8</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Progressive Pass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>75.6</td>\n",
       "      <td>55.2</td>\n",
       "      <td>88.8</td>\n",
       "      <td>52.8</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Progressive Pass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>68.8</td>\n",
       "      <td>67.2</td>\n",
       "      <td>64.8</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Carries</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>51.6</td>\n",
       "      <td>24.8</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Carries</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>80.4</td>\n",
       "      <td>27.2</td>\n",
       "      <td>87.6</td>\n",
       "      <td>29.6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Carries</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>81.6</td>\n",
       "      <td>44.8</td>\n",
       "      <td>91.2</td>\n",
       "      <td>26.4</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Carries</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>67.2</td>\n",
       "      <td>54.4</td>\n",
       "      <td>79.2</td>\n",
       "      <td>34.4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Carries</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>73.2</td>\n",
       "      <td>16.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>22.4</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Carries</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>67.2</td>\n",
       "      <td>108.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Carries</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>87.6</td>\n",
       "      <td>72.8</td>\n",
       "      <td>98.4</td>\n",
       "      <td>54.4</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Carries</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>55.2</td>\n",
       "      <td>112.8</td>\n",
       "      <td>54.4</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Team  Player             Event  Mins  Secs      X     Y     X2    Y2  \\\n",
       "0   Home  Modric  Progressive Pass     0     0   66.0   8.8   90.0  20.8   \n",
       "1   Home  Modric  Progressive Pass     0     0   72.0  12.0   79.2  29.6   \n",
       "2   Home  Modric  Progressive Pass     0     0  109.2   8.8   98.4  23.2   \n",
       "3   Home  Modric  Progressive Pass     0     0   94.8  12.0  112.8  33.6   \n",
       "4   Home  Modric  Progressive Pass     0     0   96.0  12.8  108.0  21.6   \n",
       "5   Home  Modric  Progressive Pass     0     0   42.0  18.4   80.4  46.4   \n",
       "6   Home  Modric  Progressive Pass     0     0   87.6  37.6   97.2  36.0   \n",
       "7   Home  Modric  Progressive Pass     0     0   75.6  50.4   86.4  36.8   \n",
       "8   Home  Modric  Progressive Pass     0     0   75.6  55.2   88.8  52.8   \n",
       "9   Home  Modric  Progressive Pass     0     0   42.0  68.8   67.2  64.8   \n",
       "10  Home  Modric           Carries     0     0   37.2  28.0   51.6  24.8   \n",
       "11  Home  Modric           Carries     0     0   80.4  27.2   87.6  29.6   \n",
       "12  Home  Modric           Carries     0     0   81.6  44.8   91.2  26.4   \n",
       "13  Home  Modric           Carries     0     0   67.2  54.4   79.2  34.4   \n",
       "14  Home  Modric           Carries     0     0   73.2  16.0  108.0  22.4   \n",
       "15  Home  Modric           Carries     0     0   78.0  67.2  108.0  48.0   \n",
       "16  Home  Modric           Carries     0     0   87.6  72.8   98.4  54.4   \n",
       "17  Home  Modric           Carries     0     0   84.0  55.2  112.8  54.4   \n",
       "\n",
       "    x1_bin  y1_bin  x2_bin  y2_bin  \n",
       "0        4       0       7       0  \n",
       "1        5       0       5       1  \n",
       "2       11       0       9       0  \n",
       "3        9       0      11       2  \n",
       "4        9       0      11       0  \n",
       "5        0       1       5       4  \n",
       "6        8       3       8       2  \n",
       "7        6       5       6       2  \n",
       "8        6       5       7       5  \n",
       "9        0       7       3       7  \n",
       "10       0       2       0       0  \n",
       "11       7       2       7       1  \n",
       "12       7       4       7       1  \n",
       "13       5       5       5       2  \n",
       "14       6       0      11       0  \n",
       "15       6       7      11       4  \n",
       "16       8       7       9       6  \n",
       "17       7       5      11       6  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['x1_bin'] = pd.cut(df['X'], bins=xT_cols, labels=False)\n",
    "df['y1_bin'] = pd.cut(df['Y'], bins=xT_rows, labels=False)\n",
    "df['x2_bin'] = pd.cut(df['X2'], bins=xT_cols, labels=False)\n",
    "df['y2_bin'] = pd.cut(df['Y2'], bins=xT_rows, labels=False)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3056922f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# allow the users to pass a function and apply it on every single value of the Pandas series\n",
    "df['start_zone_value'] = df[['x1_bin', 'y1_bin']].apply(lambda x: xT[x[1]][x[0]], axis=1)\n",
    "df['end_zone_value'] = df[['x2_bin', 'y2_bin']].apply(lambda x: xT[x[1]][x[0]], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1524b74a",
   "metadata": {},
   "outputs": [
    {
     "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>Team</th>\n",
       "      <th>Player</th>\n",
       "      <th>Event</th>\n",
       "      <th>Mins</th>\n",
       "      <th>Secs</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>X2</th>\n",
       "      <th>Y2</th>\n",
       "      <th>x1_bin</th>\n",
       "      <th>y1_bin</th>\n",
       "      <th>x2_bin</th>\n",
       "      <th>y2_bin</th>\n",
       "      <th>start_zone_value</th>\n",
       "      <th>end_zone_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Progressive Pass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>8.8</td>\n",
       "      <td>90.0</td>\n",
       "      <td>20.8</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.011263</td>\n",
       "      <td>0.017451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Progressive Pass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>79.2</td>\n",
       "      <td>29.6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.012483</td>\n",
       "      <td>0.013845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Progressive Pass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>109.2</td>\n",
       "      <td>8.8</td>\n",
       "      <td>98.4</td>\n",
       "      <td>23.2</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0.037926</td>\n",
       "      <td>0.027563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Progressive Pass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>94.8</td>\n",
       "      <td>12.0</td>\n",
       "      <td>112.8</td>\n",
       "      <td>33.6</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>0.027563</td>\n",
       "      <td>0.064426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Progressive Pass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>12.8</td>\n",
       "      <td>108.0</td>\n",
       "      <td>21.6</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0.027563</td>\n",
       "      <td>0.037926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Progressive Pass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>18.4</td>\n",
       "      <td>80.4</td>\n",
       "      <td>46.4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0.007501</td>\n",
       "      <td>0.014846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Progressive Pass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>87.6</td>\n",
       "      <td>37.6</td>\n",
       "      <td>97.2</td>\n",
       "      <td>36.0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0.023851</td>\n",
       "      <td>0.024122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Progressive Pass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>75.6</td>\n",
       "      <td>50.4</td>\n",
       "      <td>86.4</td>\n",
       "      <td>36.8</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0.016856</td>\n",
       "      <td>0.016856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Progressive Pass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>75.6</td>\n",
       "      <td>55.2</td>\n",
       "      <td>88.8</td>\n",
       "      <td>52.8</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>0.016856</td>\n",
       "      <td>0.019351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Progressive Pass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>68.8</td>\n",
       "      <td>67.2</td>\n",
       "      <td>64.8</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>0.006383</td>\n",
       "      <td>0.009777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Carries</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>51.6</td>\n",
       "      <td>24.8</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.008880</td>\n",
       "      <td>0.006383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Carries</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>80.4</td>\n",
       "      <td>27.2</td>\n",
       "      <td>87.6</td>\n",
       "      <td>29.6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0.019351</td>\n",
       "      <td>0.018703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Carries</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>81.6</td>\n",
       "      <td>44.8</td>\n",
       "      <td>91.2</td>\n",
       "      <td>26.4</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0.019971</td>\n",
       "      <td>0.018703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Carries</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>67.2</td>\n",
       "      <td>54.4</td>\n",
       "      <td>79.2</td>\n",
       "      <td>34.4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.014291</td>\n",
       "      <td>0.014291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Carries</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>73.2</td>\n",
       "      <td>16.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>22.4</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0.014736</td>\n",
       "      <td>0.037926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Carries</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>67.2</td>\n",
       "      <td>108.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>0.014736</td>\n",
       "      <td>0.257454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Carries</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>87.6</td>\n",
       "      <td>72.8</td>\n",
       "      <td>98.4</td>\n",
       "      <td>54.4</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>0.021221</td>\n",
       "      <td>0.029533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Carries</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>55.2</td>\n",
       "      <td>112.8</td>\n",
       "      <td>54.4</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>0.019351</td>\n",
       "      <td>0.046477</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Team  Player             Event  Mins  Secs      X     Y     X2    Y2  \\\n",
       "0   Home  Modric  Progressive Pass     0     0   66.0   8.8   90.0  20.8   \n",
       "1   Home  Modric  Progressive Pass     0     0   72.0  12.0   79.2  29.6   \n",
       "2   Home  Modric  Progressive Pass     0     0  109.2   8.8   98.4  23.2   \n",
       "3   Home  Modric  Progressive Pass     0     0   94.8  12.0  112.8  33.6   \n",
       "4   Home  Modric  Progressive Pass     0     0   96.0  12.8  108.0  21.6   \n",
       "5   Home  Modric  Progressive Pass     0     0   42.0  18.4   80.4  46.4   \n",
       "6   Home  Modric  Progressive Pass     0     0   87.6  37.6   97.2  36.0   \n",
       "7   Home  Modric  Progressive Pass     0     0   75.6  50.4   86.4  36.8   \n",
       "8   Home  Modric  Progressive Pass     0     0   75.6  55.2   88.8  52.8   \n",
       "9   Home  Modric  Progressive Pass     0     0   42.0  68.8   67.2  64.8   \n",
       "10  Home  Modric           Carries     0     0   37.2  28.0   51.6  24.8   \n",
       "11  Home  Modric           Carries     0     0   80.4  27.2   87.6  29.6   \n",
       "12  Home  Modric           Carries     0     0   81.6  44.8   91.2  26.4   \n",
       "13  Home  Modric           Carries     0     0   67.2  54.4   79.2  34.4   \n",
       "14  Home  Modric           Carries     0     0   73.2  16.0  108.0  22.4   \n",
       "15  Home  Modric           Carries     0     0   78.0  67.2  108.0  48.0   \n",
       "16  Home  Modric           Carries     0     0   87.6  72.8   98.4  54.4   \n",
       "17  Home  Modric           Carries     0     0   84.0  55.2  112.8  54.4   \n",
       "\n",
       "    x1_bin  y1_bin  x2_bin  y2_bin  start_zone_value  end_zone_value  \n",
       "0        4       0       7       0          0.011263        0.017451  \n",
       "1        5       0       5       1          0.012483        0.013845  \n",
       "2       11       0       9       0          0.037926        0.027563  \n",
       "3        9       0      11       2          0.027563        0.064426  \n",
       "4        9       0      11       0          0.027563        0.037926  \n",
       "5        0       1       5       4          0.007501        0.014846  \n",
       "6        8       3       8       2          0.023851        0.024122  \n",
       "7        6       5       6       2          0.016856        0.016856  \n",
       "8        6       5       7       5          0.016856        0.019351  \n",
       "9        0       7       3       7          0.006383        0.009777  \n",
       "10       0       2       0       0          0.008880        0.006383  \n",
       "11       7       2       7       1          0.019351        0.018703  \n",
       "12       7       4       7       1          0.019971        0.018703  \n",
       "13       5       5       5       2          0.014291        0.014291  \n",
       "14       6       0      11       0          0.014736        0.037926  \n",
       "15       6       7      11       4          0.014736        0.257454  \n",
       "16       8       7       9       6          0.021221        0.029533  \n",
       "17       7       5      11       6          0.019351        0.046477  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5e99f72d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['xT'] = df['end_zone_value'] - df['start_zone_value']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1b88df96",
   "metadata": {},
   "outputs": [
    {
     "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>Team</th>\n",
       "      <th>Player</th>\n",
       "      <th>Event</th>\n",
       "      <th>Mins</th>\n",
       "      <th>Secs</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>X2</th>\n",
       "      <th>Y2</th>\n",
       "      <th>x1_bin</th>\n",
       "      <th>y1_bin</th>\n",
       "      <th>x2_bin</th>\n",
       "      <th>y2_bin</th>\n",
       "      <th>start_zone_value</th>\n",
       "      <th>end_zone_value</th>\n",
       "      <th>xT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Progressive Pass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>8.8</td>\n",
       "      <td>90.0</td>\n",
       "      <td>20.8</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0.011263</td>\n",
       "      <td>0.017451</td>\n",
       "      <td>0.006188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Progressive Pass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>79.2</td>\n",
       "      <td>29.6</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0.012483</td>\n",
       "      <td>0.013845</td>\n",
       "      <td>0.001362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Progressive Pass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>109.2</td>\n",
       "      <td>8.8</td>\n",
       "      <td>98.4</td>\n",
       "      <td>23.2</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0.037926</td>\n",
       "      <td>0.027563</td>\n",
       "      <td>-0.010363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Progressive Pass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>94.8</td>\n",
       "      <td>12.0</td>\n",
       "      <td>112.8</td>\n",
       "      <td>33.6</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>0.027563</td>\n",
       "      <td>0.064426</td>\n",
       "      <td>0.036863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Progressive Pass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>12.8</td>\n",
       "      <td>108.0</td>\n",
       "      <td>21.6</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0.027563</td>\n",
       "      <td>0.037926</td>\n",
       "      <td>0.010363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Progressive Pass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>18.4</td>\n",
       "      <td>80.4</td>\n",
       "      <td>46.4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0.007501</td>\n",
       "      <td>0.014846</td>\n",
       "      <td>0.007345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Progressive Pass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>87.6</td>\n",
       "      <td>37.6</td>\n",
       "      <td>97.2</td>\n",
       "      <td>36.0</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>0.023851</td>\n",
       "      <td>0.024122</td>\n",
       "      <td>0.000271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Progressive Pass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>75.6</td>\n",
       "      <td>50.4</td>\n",
       "      <td>86.4</td>\n",
       "      <td>36.8</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0.016856</td>\n",
       "      <td>0.016856</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Progressive Pass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>75.6</td>\n",
       "      <td>55.2</td>\n",
       "      <td>88.8</td>\n",
       "      <td>52.8</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>0.016856</td>\n",
       "      <td>0.019351</td>\n",
       "      <td>0.002495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Progressive Pass</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>68.8</td>\n",
       "      <td>67.2</td>\n",
       "      <td>64.8</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>0.006383</td>\n",
       "      <td>0.009777</td>\n",
       "      <td>0.003394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Carries</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37.2</td>\n",
       "      <td>28.0</td>\n",
       "      <td>51.6</td>\n",
       "      <td>24.8</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.008880</td>\n",
       "      <td>0.006383</td>\n",
       "      <td>-0.002497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Carries</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>80.4</td>\n",
       "      <td>27.2</td>\n",
       "      <td>87.6</td>\n",
       "      <td>29.6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0.019351</td>\n",
       "      <td>0.018703</td>\n",
       "      <td>-0.000648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Carries</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>81.6</td>\n",
       "      <td>44.8</td>\n",
       "      <td>91.2</td>\n",
       "      <td>26.4</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0.019971</td>\n",
       "      <td>0.018703</td>\n",
       "      <td>-0.001267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Carries</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>67.2</td>\n",
       "      <td>54.4</td>\n",
       "      <td>79.2</td>\n",
       "      <td>34.4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0.014291</td>\n",
       "      <td>0.014291</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Carries</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>73.2</td>\n",
       "      <td>16.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>22.4</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0.014736</td>\n",
       "      <td>0.037926</td>\n",
       "      <td>0.023190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Carries</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>67.2</td>\n",
       "      <td>108.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>0.014736</td>\n",
       "      <td>0.257454</td>\n",
       "      <td>0.242718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Carries</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>87.6</td>\n",
       "      <td>72.8</td>\n",
       "      <td>98.4</td>\n",
       "      <td>54.4</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>0.021221</td>\n",
       "      <td>0.029533</td>\n",
       "      <td>0.008311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Home</td>\n",
       "      <td>Modric</td>\n",
       "      <td>Carries</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>55.2</td>\n",
       "      <td>112.8</td>\n",
       "      <td>54.4</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>0.019351</td>\n",
       "      <td>0.046477</td>\n",
       "      <td>0.027126</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Team  Player             Event  Mins  Secs      X     Y     X2    Y2  \\\n",
       "0   Home  Modric  Progressive Pass     0     0   66.0   8.8   90.0  20.8   \n",
       "1   Home  Modric  Progressive Pass     0     0   72.0  12.0   79.2  29.6   \n",
       "2   Home  Modric  Progressive Pass     0     0  109.2   8.8   98.4  23.2   \n",
       "3   Home  Modric  Progressive Pass     0     0   94.8  12.0  112.8  33.6   \n",
       "4   Home  Modric  Progressive Pass     0     0   96.0  12.8  108.0  21.6   \n",
       "5   Home  Modric  Progressive Pass     0     0   42.0  18.4   80.4  46.4   \n",
       "6   Home  Modric  Progressive Pass     0     0   87.6  37.6   97.2  36.0   \n",
       "7   Home  Modric  Progressive Pass     0     0   75.6  50.4   86.4  36.8   \n",
       "8   Home  Modric  Progressive Pass     0     0   75.6  55.2   88.8  52.8   \n",
       "9   Home  Modric  Progressive Pass     0     0   42.0  68.8   67.2  64.8   \n",
       "10  Home  Modric           Carries     0     0   37.2  28.0   51.6  24.8   \n",
       "11  Home  Modric           Carries     0     0   80.4  27.2   87.6  29.6   \n",
       "12  Home  Modric           Carries     0     0   81.6  44.8   91.2  26.4   \n",
       "13  Home  Modric           Carries     0     0   67.2  54.4   79.2  34.4   \n",
       "14  Home  Modric           Carries     0     0   73.2  16.0  108.0  22.4   \n",
       "15  Home  Modric           Carries     0     0   78.0  67.2  108.0  48.0   \n",
       "16  Home  Modric           Carries     0     0   87.6  72.8   98.4  54.4   \n",
       "17  Home  Modric           Carries     0     0   84.0  55.2  112.8  54.4   \n",
       "\n",
       "    x1_bin  y1_bin  x2_bin  y2_bin  start_zone_value  end_zone_value        xT  \n",
       "0        4       0       7       0          0.011263        0.017451  0.006188  \n",
       "1        5       0       5       1          0.012483        0.013845  0.001362  \n",
       "2       11       0       9       0          0.037926        0.027563 -0.010363  \n",
       "3        9       0      11       2          0.027563        0.064426  0.036863  \n",
       "4        9       0      11       0          0.027563        0.037926  0.010363  \n",
       "5        0       1       5       4          0.007501        0.014846  0.007345  \n",
       "6        8       3       8       2          0.023851        0.024122  0.000271  \n",
       "7        6       5       6       2          0.016856        0.016856  0.000000  \n",
       "8        6       5       7       5          0.016856        0.019351  0.002495  \n",
       "9        0       7       3       7          0.006383        0.009777  0.003394  \n",
       "10       0       2       0       0          0.008880        0.006383 -0.002497  \n",
       "11       7       2       7       1          0.019351        0.018703 -0.000648  \n",
       "12       7       4       7       1          0.019971        0.018703 -0.001267  \n",
       "13       5       5       5       2          0.014291        0.014291  0.000000  \n",
       "14       6       0      11       0          0.014736        0.037926  0.023190  \n",
       "15       6       7      11       4          0.014736        0.257454  0.242718  \n",
       "16       8       7       9       6          0.021221        0.029533  0.008311  \n",
       "17       7       5      11       6          0.019351        0.046477  0.027126  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e213ca1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from mplsoccer.pitch import Pitch,VerticalPitch\n",
    "from matplotlib.colors import LinearSegmentedColormap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "51ea0e09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 744.115x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "LINEWIDTH = 5  # starting linewidth\n",
    "DIFF_LINEWIDTH = 1.2 # amount the glow linewidth increases each loop\n",
    "NUM_GLOW_LINES = 10  # the amount of loops, if you increase the glow will be wider\n",
    "\n",
    "# in each loop, for the glow, we plot the alpha divided by the num_glow_lines\n",
    "# I have a lower alpha_pass_line value as there is a slight overlap in\n",
    "# the pass comet lines when using capstyle='round'\n",
    "ALPHA_PITCH_LINE = 0.3\n",
    "ALPHA_PASS_LINE = 0.15\n",
    "\n",
    "PP = '#FF5E5B'\n",
    "PC = '#00CECB'\n",
    "LINE_COLOR = '#FFFFEA'\n",
    "\n",
    "# plot as initial pitch and the lines with alpha=1\n",
    "# I have used grid to get a title and endnote axis automatically, but you could you pitch.draw()\n",
    "pitch = Pitch(line_color=LINE_COLOR, pitch_color='black', linewidth=3,\n",
    "              line_alpha=1, goal_alpha=1, goal_type='box')\n",
    "fig, ax = pitch.grid(grid_height=0.75, title_height=0, axis=False,\n",
    "                     endnote_height=0, title_space=0, endnote_space=0)\n",
    "ppXT=0\n",
    "pcXT =0\n",
    "for i in range(len(df['Event'])):\n",
    "    if df['Event'][i] == 'Progressive Pass':\n",
    "        pitch.lines(df['X'][i], df['Y'][i],\n",
    "                df['X2'][i],df['Y2'][i],\n",
    "            capstyle='round',  # cut-off the line at the end-location.\n",
    "            linewidth=LINEWIDTH, color=PP, comet=True,zorder =1, ax=ax['pitch'])\n",
    "        ppXT = ppXT + df['xT'][i]\n",
    "    if df['Event'][i] == 'Carries':\n",
    "        pitch.lines(df['X'][i], df['Y'][i],\n",
    "                df['X2'][i],df['Y2'][i],\n",
    "            capstyle='round',  # cut-off the line at the end-location.\n",
    "            linewidth=LINEWIDTH, color=PC, comet=True, ax=ax['pitch'])\n",
    "        pcXT = pcXT + df['xT'][i]\n",
    "\n",
    "ax['pitch'].text(30,61.5,'xT from Progressive Passes = '+\"{:.3f}\".format(ppXT),c=PP,fontsize=15,rotation=-90)\n",
    "ax['pitch'].text(25,61.5,'xT from Progressive Carries = '+\"{:.3f}\".format(pcXT),c=PC,fontsize=15,rotation=-90)\n",
    "# plotting the glow effect. it is essentially a loop that plots the line with\n",
    "# a low alpha (transparency) value and gradually increases the linewidth.\n",
    "# This way the center will have more color than the outer area.\n",
    "# you could break this up into two loops if you wanted the pitch lines to have wider glow\n",
    "for i in range(1, NUM_GLOW_LINES + 1):\n",
    "#     pitch = Pitch(line_color=LINE_COLOR, pitch_color='#fff',\n",
    "#                   linewidth=2 + (DIFF_LINEWIDTH * i),\n",
    "#                   line_alpha=ALPHA_PITCH_LINE / NUM_GLOW_LINES,\n",
    "#                   goal_alpha=ALPHA_PITCH_LINE / NUM_GLOW_LINES,\n",
    "#                   goal_type='box')\n",
    "#     pitch.draw(ax=ax['pitch'])  # we plot on-top of our previous axis from pitch.grid\n",
    "    for j in range(len(df['Event'])):\n",
    "        if df['Event'][j] == 'Progressive Pass':\n",
    "            pitch.lines(df['X'][j], df['Y'][j],\n",
    "                df['X2'][j],df['Y2'][j],\n",
    "            linewidth=LINEWIDTH + (DIFF_LINEWIDTH * i),\n",
    "                capstyle='round',  # capstyle round so the glow extends past the line\n",
    "                alpha=ALPHA_PASS_LINE / NUM_GLOW_LINES,\n",
    "                color=PP, comet=True, zorder =1,ax=ax['pitch'])\n",
    "        if df['Event'][j] == 'Carries':\n",
    "            pitch.lines(df['X'][j], df['Y'][j],\n",
    "                df['X2'][j],df['Y2'][j],\n",
    "             linewidth=LINEWIDTH + (DIFF_LINEWIDTH * i),\n",
    "                capstyle='round',  # capstyle round so the glow extends past the line\n",
    "                alpha=ALPHA_PASS_LINE / NUM_GLOW_LINES,\n",
    "                color=PC, comet=True, ax=ax['pitch'])\n",
    "            \n",
    "plt.savefig('Modric.png', bbox_inches='tight',dpi=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1e75b46a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 744.115x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "LINEWIDTH = 5  # starting linewidth\n",
    "DIFF_LINEWIDTH = 1.2 # amount the glow linewidth increases each loop\n",
    "NUM_GLOW_LINES = 10  # the amount of loops, if you increase the glow will be wider\n",
    "\n",
    "# in each loop, for the glow, we plot the alpha divided by the num_glow_lines\n",
    "# I have a lower alpha_pass_line value as there is a slight overlap in\n",
    "# the pass comet lines when using capstyle='round'\n",
    "ALPHA_PITCH_LINE = 0.3\n",
    "ALPHA_PASS_LINE = 0.15\n",
    "\n",
    "PP = '#FF5E5B'\n",
    "PC = '#00CECB'\n",
    "LINE_COLOR = 'grey'\n",
    "\n",
    "# plot as initial pitch and the lines with alpha=1\n",
    "# I have used grid to get a title and endnote axis automatically, but you could you pitch.draw()\n",
    "pitch = Pitch(line_color=LINE_COLOR, linewidth=3,\n",
    "              line_alpha=1, goal_alpha=1, goal_type='box')\n",
    "fig, ax = pitch.grid(grid_height=0.75, title_height=0, axis=False,\n",
    "                     endnote_height=0, title_space=0, endnote_space=0)\n",
    "ppXT=0\n",
    "pcXT =0\n",
    "for i in range(len(df['Event'])):\n",
    "    if df['Event'][i] == 'Progressive Pass':\n",
    "        pitch.lines(df['X'][i], df['Y'][i],\n",
    "                df['X2'][i],df['Y2'][i],\n",
    "            capstyle='round',  # cut-off the line at the end-location.\n",
    "            linewidth=LINEWIDTH, color=PP, comet=True,zorder =1, ax=ax['pitch'])\n",
    "        ppXT = ppXT + df['xT'][i]\n",
    "    if df['Event'][i] == 'Carries':\n",
    "        pitch.lines(df['X'][i], df['Y'][i],\n",
    "                df['X2'][i],df['Y2'][i],\n",
    "            capstyle='round',  # cut-off the line at the end-location.\n",
    "            linewidth=LINEWIDTH, color=PC, comet=True, ax=ax['pitch'])\n",
    "        pcXT = pcXT + df['xT'][i]\n",
    "\n",
    "ax['pitch'].text(30,68,'xT from Progressive Passes = '+\"{:.3f}\".format(ppXT),c=PP,fontsize=20,rotation=-90)\n",
    "ax['pitch'].text(25,68,'xT from Progressive Carries = '+\"{:.3f}\".format(pcXT),c=PC,fontsize=20,rotation=-90)\n",
    "# plotting the glow effect. it is essentially a loop that plots the line with\n",
    "# a low alpha (transparency) value and gradually increases the linewidth.\n",
    "# This way the center will have more color than the outer area.\n",
    "# you could break this up into two loops if you wanted the pitch lines to have wider glow\n",
    "for i in range(1, NUM_GLOW_LINES + 1):\n",
    "#     pitch = Pitch(line_color=LINE_COLOR, pitch_color='#fff',\n",
    "#                   linewidth=2 + (DIFF_LINEWIDTH * i),\n",
    "#                   line_alpha=ALPHA_PITCH_LINE / NUM_GLOW_LINES,\n",
    "#                   goal_alpha=ALPHA_PITCH_LINE / NUM_GLOW_LINES,\n",
    "#                   goal_type='box')\n",
    "#     pitch.draw(ax=ax['pitch'])  # we plot on-top of our previous axis from pitch.grid\n",
    "    for j in range(len(df['Event'])):\n",
    "        if df['Event'][j] == 'Progressive Pass':\n",
    "            pitch.lines(df['X'][j], df['Y'][j],\n",
    "                df['X2'][j],df['Y2'][j],\n",
    "            linewidth=LINEWIDTH + (DIFF_LINEWIDTH * i),\n",
    "                capstyle='round',  # capstyle round so the glow extends past the line\n",
    "                alpha=ALPHA_PASS_LINE / NUM_GLOW_LINES,\n",
    "                color=PP, comet=True, zorder =1,ax=ax['pitch'])\n",
    "        if df['Event'][j] == 'Carries':\n",
    "            pitch.lines(df['X'][j], df['Y'][j],\n",
    "                df['X2'][j],df['Y2'][j],\n",
    "             linewidth=LINEWIDTH + (DIFF_LINEWIDTH * i),\n",
    "                capstyle='round',  # capstyle round so the glow extends past the line\n",
    "                alpha=ALPHA_PASS_LINE / NUM_GLOW_LINES,\n",
    "                color=PC, comet=True, ax=ax['pitch'])\n",
    "            \n",
    "plt.savefig('ModricW.png', bbox_inches='tight',dpi=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "265fb2af",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}