{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "laurie_DIR = ''\n",
    "sys.path.append(laurie_DIR + '/LaurieOnTracking-master')\n",
    "import Metrica_Viz as mviz\n",
    "\n",
    "convert_DIR = ''\n",
    "sys.path.append(convert_DIR + '/lastrow_to_friendsoftracking-master')\n",
    "import lastrow_to_friendsoftracking as lrfot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lastrow_DIR = ''\n",
    "last_row = pd.read_csv(lastrow_DIR + '/Last-Row-master/datasets/positional_data/liverpool_2019.csv', index_col=('play', 'frame'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convert to Friends of Tracking wide format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:/Users/Ciaran/OneDrive/Documents/Last Man Analytics/20200428 - FoT and Seattle Competition/lastrow_to_friendsoftracking-master\\lastrow_to_friendsoftracking.py:70: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data_attack['Time [s]'] = data_attack['frame'] / 20\n",
      "C:/Users/Ciaran/OneDrive/Documents/Last Man Analytics/20200428 - FoT and Seattle Competition/lastrow_to_friendsoftracking-master\\lastrow_to_friendsoftracking.py:71: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data_defence['Time [s]'] = data_defence['frame'] / 20\n"
     ]
    }
   ],
   "source": [
    "data_attack, data_defence = lrfot.lastrow_to_friendsoftracking(last_row)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convert to Metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_attack = lrfot.lastrow_to_metric_coordinates(data_attack)\n",
    "data_defence = lrfot.lastrow_to_metric_coordinates(data_defence)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Liverpool Shoot Right to Left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_attack, data_defence = lrfot.lastrow_to_single_playing_direction(data_attack, data_defence)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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THx3SuWxHGvOHnE8kGuGPZS8ZHUfOQqw7XsymDZz4S3Xldum79nvLea1iG3az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\nYIz/A1HtX/oHoflWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Single Frame\n",
    "play = 'Bayern 0 - [1] Liverpool'\n",
    "frame = 10\n",
    "fig,ax = mviz.plot_frame( data_attack.loc[(play, frame)], data_defence.loc[(play, frame)],annotate=True )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Video of Goal\n",
    "PLOTDIR = ''\n",
    "mviz.save_match_clip(tracking_liverpool_attack.loc[m],tracking_liverpool_defense.loc[m],PLOTDIR,fname=name,include_player_velocities=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# All Videos of Goals\n",
    "PLOTDIR = ''\n",
    "for m in tracking_liverpool_goals.index.get_level_values('play').unique():\n",
    "    name = m.split(\" \")[0][:3].lower() + m.split(\" \")[1] + m.split(\" \")[-2] + m.split(\" \")[-1][:3].lower()\n",
    "    mviz.save_match_clip(tracking_liverpool_attack.loc[m],tracking_liverpool_defense.loc[m],PLOTDIR,fname=name,include_player_velocities=False)"
   ]
  }
 ],
 "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}