{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook shows how to build a basic tournament and view the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import axelrod as axl\n",
    "import random\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Playing matches: 100%|██████████| 78/78 [00:01<00:00, 52.49it/s]\n",
      "Analysing: 100%|██████████| 25/25 [00:00<00:00, 160.99it/s]\n"
     ]
    }
   ],
   "source": [
    "strategies = [s() for s in axl.basic_strategies]\n",
    "strategies.append(axl.Random())\n",
    "tournament = axl.Tournament(strategies)\n",
    "results = tournament.play()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Grudger',\n",
       " 'Defector',\n",
       " 'Tit For Tat',\n",
       " 'Win-Stay Lose-Shift: C',\n",
       " 'Bully',\n",
       " 'Alternator',\n",
       " 'Cycler DC',\n",
       " 'Random: 0.5',\n",
       " 'Suspicious Tit For Tat',\n",
       " 'Win-Shift Lose-Stay: D',\n",
       " 'Anti Tit For Tat',\n",
       " 'Cooperator']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.ranked_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualising the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HDt1zz080ONiq5uZqNTUtUHNzhSoq6jRvXji1Kq++XpXX3HhQ618U/e+79EL1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\njzo2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108b175c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = axl.Plot(results)\n",
    "plot.boxplot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many more strategies as well as outputs that the library can create: [take a look at the documentation for further details](http://axelrod.readthedocs.org/en/latest/index.html)."
   ]
  }
 ],
 "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}