{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Single Player Multi-game Analysis\n",
    "\n",
    "Look at a player's scoring over a range of games."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preliminaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "DBFILE = 'CarcassonneScore-sample.db'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "timefmt = \"%Y-%m-%dT%H:%M:%S\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "conn = sqlite3.connect(DBFILE)\n",
    "cur = conn.cursor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "playerID = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set up a dtype for storing data\n",
    "pinfodtype = [('gameID', float),\n",
    "              ('score', float)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get General Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "pname = cur.execute('SELECT name FROM players WHERE playerID={0:1.0f}'.format(playerID)).fetchall()[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "scoretypes = [x[0] for x in cur.execute('SELECT DISTINCT scoretype FROM scores WHERE playerID={0:1.0f}'.format(playerID)).fetchall()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "for stype in scoretypes:\n",
    "    pinfodtype.append(('N' + stype, float))\n",
    "    pinfodtype.append(('Score' + stype, float))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "pinfo = np.zeros(1, dtype=pinfodtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get a list of relevant games"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "gameIDs = [x[0] for x in cur.execute('SELECT DISTINCT gameID FROM turns WHERE playerID={0:1.0f}'.format(playerID)).fetchall()]\n",
    "for gameID in gameIDs:\n",
    "    curgame = np.zeros(1, dtype=pinfodtype)\n",
    "    curgame['gameID'] = gameID\n",
    "    scores = cur.execute('SELECT points,scoretype FROM scores WHERE gameID={0:1.0f} AND playerID={1:1.0f}'.format(gameID, playerID)).fetchall()\n",
    "    scores = np.array(scores,\n",
    "                      dtype=[('points', float), \n",
    "                             ('type', 'S20')])\n",
    "    for stype in scoretypes:\n",
    "        tscores = scores[scores['type']==stype.encode()]\n",
    "        curgame['N' + stype] = len(tscores)\n",
    "        curgame['Score' + stype] = np.nansum(tscores['points'])\n",
    "    pinfo = np.vstack((pinfo, curgame))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove bogus first entry\n",
    "pinfo = pinfo[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Scoring Habits for John Smith')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dFAJrgE/C1xaC++NPTU5YIiKSLPGe0d/g7p8BmNnRQB5wj7vfk7TIREQkKeK9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\nkuH+H3yi/QgeYxxpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc238a874e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 1,\n",
    "                       sharex=True, sharey=False)\n",
    "for stype in scoretypes:\n",
    "    ax[0].plot(pinfo['gameID'],\n",
    "               pinfo['N' + stype],\n",
    "               marker='o',\n",
    "               label=stype)\n",
    "    ax[1].plot(pinfo['gameID'],\n",
    "               pinfo['Score' + stype],\n",
    "               marker='o',\n",
    "               label=stype)\n",
    "\n",
    "fig.subplots_adjust(hspace=0)\n",
    "ax[0].set_ylabel('N Scores')\n",
    "ax[1].set_ylabel('Points per Type')\n",
    "ax[1].set_xlabel('Game Number')\n",
    "ax[0].legend(loc='best')\n",
    "ax[0].minorticks_on()\n",
    "ax[1].minorticks_on()\n",
    "ylim = ax[0].get_ylim()\n",
    "ax[0].set_ylim([0, ylim[1]])\n",
    "ylim = ax[1].get_ylim()\n",
    "ax[1].set_ylim([0, ylim[1]])\n",
    "xlim = ax[0].get_xlim()\n",
    "ax[0].set_xlim(-1, xlim[1]*1.5)\n",
    "ax[0].set_title('Scoring Habits for ' + pname)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Close things up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}