{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import os\n",
    "from pandas.io.json import json_normalize\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Arc, Rectangle, ConnectionPatch\n",
    "from matplotlib.offsetbox import  OffsetImage\n",
    "#import squarify\n",
    "from functools import reduce\n",
    "path = \"\"\"C:\\\\Users\\\\Koushik\\\\Downloads\\\\open-data-master\\\\open-data-master\\\\data\\\\my_events\\\\\"\"\"\n",
    "Xg_req = pd.DataFrame(data=None)\n",
    "for filename in (os.listdir(path)):\n",
    "    #print(filename)\n",
    "    \n",
    "    with open(\"%s\" % path + filename,encoding=\"utf8\") as data_file:    \n",
    "        data = json.load(data_file)\n",
    "        df = pd.DataFrame(data=None)\n",
    "        \n",
    "        df = json_normalize(data, sep = \"_\")\n",
    "        \n",
    "        #df =  df[(df['type_name'] == \"Shot\")]\n",
    "        #df = df.loc[:,['location','shot_body_part_id','shot_end_location','shot_one_on_one','shot_technique_id','shot_type_id','under_pressure','shot_outcome_id']]\n",
    "        #print(df.shape)\n",
    "    Xg_req = Xg_req.append(df,ignore_index=True,sort=False)\n",
    "        #df.drop(df.index, inplace=True)\n",
    "        \n",
    "print(\"done\")\n",
    "df = Xg_req"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0               NaN\n",
       "1               NaN\n",
       "2               NaN\n",
       "3               NaN\n",
       "4               NaN\n",
       "5               NaN\n",
       "6               NaN\n",
       "7               NaN\n",
       "8               NaN\n",
       "9               NaN\n",
       "10              NaN\n",
       "11              NaN\n",
       "12              NaN\n",
       "13              NaN\n",
       "14              NaN\n",
       "15              NaN\n",
       "16              NaN\n",
       "17              NaN\n",
       "18              NaN\n",
       "19              NaN\n",
       "20              NaN\n",
       "21              NaN\n",
       "22              NaN\n",
       "23              NaN\n",
       "24              NaN\n",
       "25              NaN\n",
       "26              NaN\n",
       "27              NaN\n",
       "28              NaN\n",
       "29              NaN\n",
       "            ...    \n",
       "179346          NaN\n",
       "179347          NaN\n",
       "179348          NaN\n",
       "179349          NaN\n",
       "179350          NaN\n",
       "179351          NaN\n",
       "179352          NaN\n",
       "179353          NaN\n",
       "179354          NaN\n",
       "179355          NaN\n",
       "179356          NaN\n",
       "179357          NaN\n",
       "179358          NaN\n",
       "179359          NaN\n",
       "179360          NaN\n",
       "179361          NaN\n",
       "179362          NaN\n",
       "179363          NaN\n",
       "179364          NaN\n",
       "179365          NaN\n",
       "179366          NaN\n",
       "179367          NaN\n",
       "179368          NaN\n",
       "179369          NaN\n",
       "179370          NaN\n",
       "179371          NaN\n",
       "179372    Free Kick\n",
       "179373          NaN\n",
       "179374          NaN\n",
       "179375          NaN\n",
       "Name: shot_type_name, Length: 179376, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "through_ball = df.query('pass_through_ball == True')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "assist = df.query('pass_goal_assist == True')\n",
    "print(len(assist.index))\n",
    "goal = assist.query('shot_outcome_id == 97')\n",
    "#assist = df.query('pass_goal_assist == True')\n",
    "print(len(goal.index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>id</th>\n",
       "      <th>period</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>location</th>\n",
       "      <th>pass_end_location</th>\n",
       "      <th>pass_recipient_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>95ffe924-77f3-40e2-afc6-23e5fed8865f</td>\n",
       "      <td>1</td>\n",
       "      <td>00:00:38.660</td>\n",
       "      <td>[44.0, 17.0]</td>\n",
       "      <td>[95.0, 23.0]</td>\n",
       "      <td>Francesca Kirby</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>ce817472-51c1-4b9a-89d5-2b929f422b2e</td>\n",
       "      <td>1</td>\n",
       "      <td>00:08:03.753</td>\n",
       "      <td>[89.0, 65.0]</td>\n",
       "      <td>[116.0, 55.0]</td>\n",
       "      <td>Nikita Parris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>623</th>\n",
       "      <td>cf2ed5ec-1d2f-4746-a482-4ff7a1823adf</td>\n",
       "      <td>1</td>\n",
       "      <td>00:19:30.020</td>\n",
       "      <td>[39.0, 38.0]</td>\n",
       "      <td>[96.0, 42.0]</td>\n",
       "      <td>Ramona Bachmann</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1574</th>\n",
       "      <td>898b1dba-d2e6-431c-b830-4d897c946ad2</td>\n",
       "      <td>2</td>\n",
       "      <td>00:01:56.558</td>\n",
       "      <td>[41.0, 79.0]</td>\n",
       "      <td>[101.0, 47.0]</td>\n",
       "      <td>Ramona Bachmann</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3292</th>\n",
       "      <td>7799fb29-209f-4b3a-a73e-2c35f9d83c51</td>\n",
       "      <td>1</td>\n",
       "      <td>00:09:35.360</td>\n",
       "      <td>[74.0, 48.0]</td>\n",
       "      <td>[98.0, 32.0]</td>\n",
       "      <td>Mallory Weber</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        id  period     timestamp  \\\n",
       "27    95ffe924-77f3-40e2-afc6-23e5fed8865f       1  00:00:38.660   \n",
       "257   ce817472-51c1-4b9a-89d5-2b929f422b2e       1  00:08:03.753   \n",
       "623   cf2ed5ec-1d2f-4746-a482-4ff7a1823adf       1  00:19:30.020   \n",
       "1574  898b1dba-d2e6-431c-b830-4d897c946ad2       2  00:01:56.558   \n",
       "3292  7799fb29-209f-4b3a-a73e-2c35f9d83c51       1  00:09:35.360   \n",
       "\n",
       "          location pass_end_location pass_recipient_name  \n",
       "27    [44.0, 17.0]      [95.0, 23.0]     Francesca Kirby  \n",
       "257   [89.0, 65.0]     [116.0, 55.0]       Nikita Parris  \n",
       "623   [39.0, 38.0]      [96.0, 42.0]     Ramona Bachmann  \n",
       "1574  [41.0, 79.0]     [101.0, 47.0]     Ramona Bachmann  \n",
       "3292  [74.0, 48.0]      [98.0, 32.0]       Mallory Weber  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "through_ball= through_ball[[\"id\", \"period\", \"timestamp\", \"location\", \"pass_end_location\", \"pass_recipient_name\"]]\n",
    "through_ball.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>id</th>\n",
       "      <th>period</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>location</th>\n",
       "      <th>pass_end_location</th>\n",
       "      <th>pass_recipient_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>c547c736-4bf5-484f-9360-463abb19e465</td>\n",
       "      <td>1</td>\n",
       "      <td>00:05:45.020</td>\n",
       "      <td>[104.0, 36.0]</td>\n",
       "      <td>[109.0, 29.0]</td>\n",
       "      <td>Millie Bright</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>746</th>\n",
       "      <td>e3eca08d-7c54-4c65-a19a-faa9708225b5</td>\n",
       "      <td>1</td>\n",
       "      <td>00:23:20.340</td>\n",
       "      <td>[111.0, 24.0]</td>\n",
       "      <td>[111.0, 35.0]</td>\n",
       "      <td>So-yun Ji</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1649</th>\n",
       "      <td>d48d70cf-3464-40c9-80c0-f81d7ce46b25</td>\n",
       "      <td>2</td>\n",
       "      <td>00:03:39.570</td>\n",
       "      <td>[108.0, 5.0]</td>\n",
       "      <td>[115.0, 43.0]</td>\n",
       "      <td>Nikita Parris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2739</th>\n",
       "      <td>7f1fe749-1914-4f5d-8f0b-62c7c9f79196</td>\n",
       "      <td>2</td>\n",
       "      <td>00:40:33.251</td>\n",
       "      <td>[109.0, 48.0]</td>\n",
       "      <td>[97.0, 46.0]</td>\n",
       "      <td>Georgia Stanway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63663</th>\n",
       "      <td>59c55828-1423-4cf5-aef4-58f0cab1d9fe</td>\n",
       "      <td>2</td>\n",
       "      <td>00:06:57.202</td>\n",
       "      <td>[81.0, 28.0]</td>\n",
       "      <td>[87.0, 36.0]</td>\n",
       "      <td>Hayley Raso</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         id  period     timestamp  \\\n",
       "199    c547c736-4bf5-484f-9360-463abb19e465       1  00:05:45.020   \n",
       "746    e3eca08d-7c54-4c65-a19a-faa9708225b5       1  00:23:20.340   \n",
       "1649   d48d70cf-3464-40c9-80c0-f81d7ce46b25       2  00:03:39.570   \n",
       "2739   7f1fe749-1914-4f5d-8f0b-62c7c9f79196       2  00:40:33.251   \n",
       "63663  59c55828-1423-4cf5-aef4-58f0cab1d9fe       2  00:06:57.202   \n",
       "\n",
       "            location pass_end_location pass_recipient_name  \n",
       "199    [104.0, 36.0]     [109.0, 29.0]       Millie Bright  \n",
       "746    [111.0, 24.0]     [111.0, 35.0]           So-yun Ji  \n",
       "1649    [108.0, 5.0]     [115.0, 43.0]       Nikita Parris  \n",
       "2739   [109.0, 48.0]      [97.0, 46.0]     Georgia Stanway  \n",
       "63663   [81.0, 28.0]      [87.0, 36.0]         Hayley Raso  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "assist= assist[[\"id\", \"period\", \"timestamp\", \"location\", \"pass_end_location\", \"pass_recipient_name\"]]\n",
    "assist.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_pitch(ax):\n",
    "    # focus on only half of the pitch\n",
    "    #Pitch Outline & Centre Line\n",
    "    Pitch = Rectangle([0,0], width = 120, height = 80, fill = False)\n",
    "    #Left, Right Penalty Area and midline\n",
    "    LeftPenalty = Rectangle([0,22.3], width = 14.6, height = 35.3, fill = False)\n",
    "    RightPenalty = Rectangle([105.4,22.3], width = 14.6, height = 35.3, fill = False)\n",
    "    midline = ConnectionPatch([60,0], [60,80], \"data\", \"data\")\n",
    "\n",
    "    #Left, Right 6-yard Box\n",
    "    LeftSixYard = Rectangle([0,32], width = 4.9, height = 16, fill = False)\n",
    "    RightSixYard = Rectangle([115.1,32], width = 4.9, height = 16, fill = False)\n",
    "\n",
    "\n",
    "    #Prepare Circles\n",
    "    centreCircle = plt.Circle((60,40),8.1,color=\"black\", fill = False)\n",
    "    centreSpot = plt.Circle((60,40),0.71,color=\"black\")\n",
    "    #Penalty spots and Arcs around penalty boxes\n",
    "    leftPenSpot = plt.Circle((9.7,40),0.71,color=\"black\")\n",
    "    rightPenSpot = plt.Circle((110.3,40),0.71,color=\"black\")\n",
    "    leftArc = Arc((9.7,40),height=16.2,width=16.2,angle=0,theta1=310,theta2=50,color=\"black\")\n",
    "    rightArc = Arc((110.3,40),height=16.2,width=16.2,angle=0,theta1=130,theta2=230,color=\"black\")\n",
    "    \n",
    "    element = [Pitch, LeftPenalty, RightPenalty, midline, LeftSixYard, RightSixYard, centreCircle, \n",
    "               centreSpot, rightPenSpot, leftPenSpot, leftArc, rightArc]\n",
    "    for i in element:\n",
    "        ax.add_patch(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure() #set up the figures\n",
    "fig.set_size_inches(7, 5)\n",
    "ax=fig.add_subplot(1,1,1)\n",
    "draw_pitch(ax) #overlay our different objects on the pitch\n",
    "plt.ylim(-2, 82)\n",
    "plt.xlim(-2, 122)\n",
    "#plt.plot(x_axis,y_axis,'ro')\n",
    "#plt.plot(x,y,'bo')\n",
    "#plt.axis('off')\n",
    "\n",
    "for i in range(len(through_ball)):\n",
    "    # annotate draw an arrow from a current position to pass_end_location\n",
    "    ax.annotate(\"\", xy = (through_ball.iloc[i]['pass_end_location'][0], through_ball.iloc[i]['pass_end_location'][1]), xycoords = 'data',\n",
    "               xytext = (through_ball.iloc[i]['location'][0], through_ball.iloc[i]['location'][1]), textcoords = 'data',\n",
    "               arrowprops=dict(arrowstyle=\"->\",connectionstyle=\"arc3\", color = \"blue\"),)\n",
    "\"\"\"\n",
    "for i in range(len(assist)):\n",
    "    # annotate draw an arrow from a current position to pass_end_location\n",
    "    ax.annotate(\"\", xy = (assist.iloc[i]['pass_end_location'][0], assist.iloc[i]['pass_end_location'][1]), xycoords = 'data',\n",
    "               xytext = (assist.iloc[i]['location'][0], assist.iloc[i]['location'][1]), textcoords = 'data',\n",
    "               arrowprops=dict(arrowstyle=\"->\",connectionstyle=\"arc3\", color = \"red\"),)\n",
    "\"\"\"\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure() #set up the figures\n",
    "fig.set_size_inches(7, 5)\n",
    "ax=fig.add_subplot(1,1,1)\n",
    "draw_pitch(ax) #overlay our different objects on the pitch\n",
    "plt.ylim(-2, 82)\n",
    "plt.xlim(-2, 122)\n",
    "#plt.plot(x_axis,y_axis,'ro')\n",
    "#plt.plot(x,y,'bo')\n",
    "#plt.axis('off')\n",
    "\n",
    "for i in range(len(assist)):\n",
    "    # annotate draw an arrow from a current position to pass_end_location\n",
    "    ax.annotate(\"\", xy = (assist.iloc[i]['pass_end_location'][0], assist.iloc[i]['pass_end_location'][1]), xycoords = 'data',\n",
    "               xytext = (assist.iloc[i]['location'][0], assist.iloc[i]['location'][1]), textcoords = 'data',\n",
    "               arrowprops=dict(arrowstyle=\"->\",connectionstyle=\"arc3\", color = \"red\"),)\n",
    "\n",
    "plt.show()    "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}