{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spatial Tournament Examples : Circle Topology\n",
    "\n",
    "In this notebook we will be performing a circle tournament. Ten random players\n",
    "are selected by the list of strategies in Axelrod. Afterwards these players are allocated\n",
    "into a circle network where each player competes with their two neughboors. Then we plot the results.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Python 3 \n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "\n",
    "# package for creation and visuliazation of networks\n",
    "import networkx as nx \n",
    "\n",
    "import axelrod as axl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "strategies = [s() for s in axl.ordinary_strategies]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "players = random.sample(strategies, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "G = nx.cycle_graph(len(players))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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07YCmaXuBp4G5N7rejiwlJcXgphBbW1smTJhgpogURWlI0zSjR/+5ubkmFYrp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbfda418cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos = nx.circular_layout(G)\n",
    "# for the nodes \n",
    "nx.draw_networkx_nodes(G,pos,\n",
    "                       node_color='r',\n",
    "                       node_size=100\n",
    "                       )\n",
    "# for the edges\n",
    "nx.draw_networkx_edges(G,pos,\n",
    "                       width=5, alpha = 0.5)\n",
    "# using labels\n",
    "labels={}\n",
    "for i in range(len(players)):\n",
    "    labels[i]= '%s' % (players[i])\n",
    "\n",
    "nx.draw_networkx_labels(G,pos,labels,font_size=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": []
    }
   ],
   "source": [
    "edges = G.edges()\n",
    "tournament = axl.Tournament(players, edges=G.edges(), repetitions=1)\n",
    "results = tournament.play(processes=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Inverse Punisher',\n",
       " 'Hard Go By Majority: 20',\n",
       " 'Once Bitten',\n",
       " 'Meta Minority',\n",
       " 'Calculator',\n",
       " 'ZD-Extort-4',\n",
       " 'Math Constant Hunter',\n",
       " 'Cycler CCD',\n",
       " 'Firm But Fair',\n",
       " 'Fortress3']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.ranked_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5FT7nNvicNclpEpIkSWqWYViSJEnNMgzP3tFDFzAAn/P4tfZ8wefcCp9zG3zOuplzhiVJ\nktQsR4YlSZLULMPwFKXzrKHrkCStmf7v9l5D1yFpOIbhKapuzsmjh65jXpJskeTFST6W5PQkH+2P\nbz90bbOW5FFJPp7kjCRnJTlz6JpmrX++T0my+dC1zEuSOw9dg2ar/7t98NB1zEOSTZIckuS0/m/X\nZ5M8L8kmQ9c2S/0bng8OXYc2XM4ZnrI+FC0FLgaK7m/tM4atajaSnAIcD5wB/Ay4A7An8IyqeuyQ\ntc1aki8Bf1JVPxi6lnnpg+GfA48Ffgr8A/CZqrph0MJmKMlnq2rvoesYSpJdq+rcoeuYtSQnA7cG\nvgTcBFBVrx60qBlI8jpgOfDmqlqe5NbAS4FNx/h8JyV5C/Bx4CJW/BvfNGhRM5Tk8cDLgK8CZwKv\nonudPryqPjlkbRsiw/CUJbn74nNVNcpd95L8E7Dr5B+UJLcCzq2qhw1X2ewlORp4eVX9ZOha5i3J\nw4AXAtvS7WZ0RlW9fdiqZiPJ+4FLueUL6CjvAvRB6RangKcAJzYQlHZbfK6qzh6illlKckZV7bGm\n58ckyVmLTlVV7T5IMXOQ5F+ARwK3A74C/AFwHXBmVe0yZG0boiVDFzBCBbwC2AJ4JrA/8N5BK5qd\ndwJfSHIJ8HNgS+C+wN8PWtV8bA+c328xvnAH4OED1zRTSQ4HHgCcS/dG4D/6858GRhmG6baP3wxY\neHNXdKMsY7QD3XN9F93If4BHAacPWdScfIVuqsQdgVcCY70bsKrRr9GPilXVI4euYc6uqapfAr9M\n8smquhogyfKB69ogOTI8ZUlOB54HvKuqdh/7O+4kS4DfA7aiuwVz2Zhvm7ds8S3zJPeqqsuGrGke\nkvwmcA/gO2OfFpNke+AFwBXAEcAHqmr06yCSfIruzf0rqurhSU6vqj2HrmvakvwUuGTxaWD7qtpq\ngJJmLskRVXVIknNZEfrDyAcwkrwMeFtV3Thx7jbAEVXVxBz5teHI8PRtUlXfSLJwPNpFiume5F7A\njcDnFqZLJHlcVZ08aHEzluRRdHPtfgO4P/B/q+r5w1Y1c68FJm8rvgF40kC1zEWSl9DdarwYeECS\nM6vqrQOXNTNV9VXguUl+HziMLjS0YPOq+mySl/bHo3zeYw28v05VHdJ/3nXoWuapqt6yktM70k1x\n0yKG4ek7M8m7gW2SHAF8fuiCZuh44Dt0CzJemeSAqroUOAQYdRgGXk0Xkj5XVTcmue/QBc1Kkv2B\nZwPbJzmHflQF+N6ghc3Hn06+iCY5DxhtGF5QVZcm+VhVfW7oWubksn4k7U5JXgj829AFzUL/5u5f\nquqciXO7An9UVYcPV9nsJdkC2AfYhv7NTlUtnic/Ogt3p5McBtwFuCvwJwOXtcEZ7ajlUKrqMLrb\nbX8DvK+q3jhwSbN016p6VVW9lu6PzJFJ/nTooubkxv6j+hHyUY4kAVTVsX0gfElVPbyqdu0/P23o\n2ubg+iQPTbJ5Hxpamm/310MXMC9V9b/pAvCHgG9V1QsGLmlW/nQyCAP0U59a+Lv90f7zE4DvA789\nYC3ztJDztquqg+jW9mgRw/CUJdkGeATdys3HJxnzKuxbLfQUrqrvA48B/gx40KBVzcdb6VrK3Qc4\njRGPFvYjwwDbJnnd5Meghc3Hs4Cn0rVkejIwyjaJq/CloQuYlyTHV9Wnq+rwqjqlv6s3Rjeu5fkx\n2bSq3gdcXVXvBe42dEFz8t0knwdO69f4tPBvvdacJjF9JwN/y68uUhijZzDxhqqqrgee1U8TGbvv\n0M2f3ZquvdjvDVrNbC2Eoha6Ciz25Kp63sJBkgOBowesZ+aSHF5VL6mqF/fHr6qq1w9d1ywkeQDw\nQGCnJM/uTy+h65oyRhckeTnw9qq6vl9Q9WLggoHrmocrkmwGfLVvmTj6zaEAquqZSZZU1Q1JlgKP\nG7qmDZHdJKYsyUer6olD16HZ6hdS7T5x/JGqGu1isn4qyGda2YAiyVbAnYET6EaGA2xC1yXmfw1Z\n26wk2Q74HeBtwIv600uAV1bVr/ThHYMkO9ItgD0QeA/dv/Ny4J+r6ttD1jYLfR/4A+nu4G0KXEt3\n1+OYMW9AMan//+D+wKVV9Yuh65m1JB+uqqf0c+H3Bn401o3A1ocjw1OS5Hi6RUV3SXIR8PX+0mh3\noGtRq4vJqqqS/EeSfbjlBhSjCwy93YDHA9vRzf9fCEljvutxd2AXujaJu7DiOb98yKJmqaoupusU\nclzfVm4ruud9V2CMv9tbAp+sqpt/j/vWgVsCVw1W1QwleV9VPad//PyqOpKGpgDRLZoDeGBV7ZXk\n/EGr2UA5MjwlK9t5bsFYd6BrWZKXTrbYSrJVVf10yJpmLcmxi05VVT17pV88EknuVlWjfqMzqb8D\n8LaqetFqv3hE+o1j/ptuYRV0v9ujmxOf5GPAy6rqmxPnfodui94/G66y2Zm8i7f4jl4L+t/tK+i2\nZf6/wDmttZlbE44MT8lC4E2yf1Udm+TBwGuAY+l2sRqtJC+vqjet6nik9uaWi+aOpltgNVpVtX+S\nTehGGq6YbOY+Yr+f5L10I4U3AT8d8wtJfwdgsyS/NfYNRhZJv9J+7O48GYQBqupbSe40VEFzcKck\nu9ON+C88Bsa7tfoiT6Dr/PSf/RzxUQ9grCvD8PQ9nS4APx84APgUK1q6jNX7Fh2/f5Aq5iDJE+jm\n290nyQf700uAOwxX1XwkeSrd7/V/AvdMclRVnTBwWbP2RmBP4BS6hSdvGLacuXggcE6SH9HIVuPA\nNUneAXyNfpeyqhrj37FKctt+m17g5v67Y/YJYNeVPB7z1uqTHgm8NMnCBlEvoPs7rgmG4em7XZKH\n07Vv+UGS0U/Qp9tw48Sq+meAqvrR0AXN0Jl0882+yy0X3PxwyKLm5HnArv2q5FsD59AtMBuzX1TV\nz5MU3WKjBw9d0KxV1c5D1zCATw1dwJy8Hji1fyP/A7peu08DRjclZEHfB79lizeIus/QBW2IDMPT\nt7Bi87C+jcvHB65nHt4P7JPk9cC/Aif227qO0UOr6jNJvk03YjhpjCNJk24CfotuseBv9cdjd1z/\n3/Hf0YX/Tw9cz8z1C8neSHe342fAq6pq7K0izxq6gHmoqjOSXAw8GtiBLhA/qap+PGxlmqHFG0S5\nv8RKuIBuSpI8oKq+PDkfaUEj85JIcjfg7XS3oS6h24HvH4atarqS7FVVpyV55uJrVXXcEDXNS5Id\ngMOAO9KtPD+0qr4ybFWatiTnAk+vqsv7dmsnVNUuw1Y1W/3i0KILCvcFrqyqvYatStOU5M5V9ZOJ\n49EvegZI8id0vaTvTfe6/I6q+sywVW14HBmenh2AL7NiPtKC0c9LSvKXwB/TrVg9BngK3fSBzwOj\nCsNVdVr/+Ti4+Q3AViMeCb9ZPzrYRMP2PhAWK7bZXnjcwvzZJaxoFfhfdP2VR62q9p88TnLSULVo\nZj5Ct1HSgtEveu5Hgm9gYoOocgR0pQzDU7IQjqrqtUnuQNe3Mb/+u0bjCuCJVXUtrHjHneTPB65r\n6pI8D9gPuI4V2zFfnWR5VR08aHEzluRv6G6vXrNwbqxtisbcMWINHEW3U9l36HoPv3PYcmZv0R29\nbeg2HxmlPiA9s6o+MHQt89Dyoue+O8zB/SDOsqHr2ZAZhqcsydHAtnT9Khc2ZBh7K5ODq+ojE8dH\n021jO8Ym7k+rqock2RT4WlX9HkCSsweuax4e1driqonb5zcbe29l4CvAzsBSuhfQ3x22nLmY7DDw\nM7p2VKPUB6RHAx8YupY5aXnRM3Tvf06l+/9gYbOkVw9b0obHMDx99xjrdq2LNfqO+xqAqrouyeRm\nDC303P1aksdxy/ZTY9yla9Jr+s8BtgfGPkUC4Mh+xP9HAEneAIxyq/Ek9+wfHr/o0q3nXcucbZ3k\nq3S77y20zxvlTqlV9TPgZ0kuAx6x6PIHf/U7RudvFx07TWIlDMPT9/0kh3DLwDDWOcMtvuPeZmIb\n5qUTj7cetqy52Ixui+LH98ejv+uxaPfI7yR5yWDFzNhKthpfMOYd+P6GFfPB9wI+Sxt39PZf/ZeM\nUljxxnZr2gjDB1TVfgsHSY6g64yjCXaTmLIkhy4+N9Y+h0n+uG8z9hx+9Vby2NuMqQGLFtJtCpxa\nVa8ZtKgZS/Lcqjpm6DrmLclZVfXIoeuYhyTbAq8AtgCeCexfVe8dtqr5SvLpqnrM0HXMSpIH0G2g\n82Lg8P70ErpOMS3c4VorjgxP2ViD7yos9Jm9YdAqNFNJjqiqQyaCITTSWaHRhXT3TrJZVV3bb9P7\n9sXdFkaqpZGh99NtovOufiOGfYFRh+Ekh7Hi33gbxt9v9ya61+YrWTGN71rgWUMVtCEzDE9ZkrNY\nMZJ0L+D7VTXKXav6frsB9hjrfDNBVR3Sf24uGCZ5X1U9p38c4JiqOmDgsmbts3S7lJ1Gt7HMyweu\nZ2YmAlLothi/eSe2kS8y2qSqvtH9SgPjD4YAp/efFxZJjnojmaq6OMklwJ5j74E/DYbhKZu8zdZ3\nHHjPgOXMXL8y+QdJ/pBbrlYd9e5kSTahW1S0FHgX8MCq+uKwVc1GklVOeWmgs8LCAquF3/XRttya\n8DXgamAP4N/647E6fRWPx+7MJO+mWwNxBF1P+NFKsiPwvar6dpKn020c9F26zYNGq/+b9d+tvT6v\nC8PwlE2sToZuy9odh6pljv6w/1hQ3LK5+RidAJwN7FtVRyZ5E7+6PfNY3IWuQ8g/9h9XD1vOXP04\nyQHA+XTtxn6ymq8fg2OB/1NV/55kb7p/8z0GrmkmqqqFloi/oqoO67fdPgO4dMzbbSc5jm56wO2T\nLAU+SReCTwT2HrK2OXlI/7GghdfnteYCuilb1Jf0p8DxVfXlAUvSDCQ5var2THJmVe2+cDx0XbOS\nZAvgT+h2GrwN3e/16Lf0THJb4EC6KU/fAN5bVb8ctqrZSnKryZGjJFtU1f8MWZOmI8kq7+SMddFz\nki9U1SP6xxdV1YP6x2eOddMgrb0W5gnNRZLbJHkq3QvmKcBzquqvWgjCSXZP8k9Jzk5yXpLRhsIJ\nVyTZB9i877f8g6ELmqU+DP0n3fPcgm56yGgluWd/l+c36f57fhvw6f54lJJ8FLpbqEmOmrj0iYFK\nmpskeyw6fthQtczYjav4GPMi6M2S3KOf4rR84vHmQxc2D42+Pq81R4anJMkn6G6lXkzXmH/Lqnr+\nsFXNR5Lzgb2q6up+K+rTxr5TWZLNgAOAe9O9ATpmYTvqsennFN4N+CJdMLps4dpY5571d3hWpsY6\nT3qytdjkqFkLLccWjxIm+XhV/dmQNc1Skl2B8/o5pQF2qapzh65rFn7Nf8u00CWlxdfndeGc4enZ\nsqoWevl9LskZg1YzX2FFm7Wb+uOxezDwzskXE2CULybADv3nR/UfC6vvRzv3rIUXyZXpf5dv1T9c\neDxaK9loZOH3+l8HLWz2XlNVe8DNi6wOZaRrHlr9b3lCi6/Pa82R4SlJ8h1uuZvNMxaOR96ih/62\ny6GsCEmvq6qxr04+Y+HFpD8e9ZzhVvW/2y8B7kr3QnLVWHsrT7SFhBWhcKGf9Cjf9CxI8tiq+tTQ\ndcxLkguA3arq+r7r0dlV9UdD16XpSXL7fjR4D7rXZ2jk9XldODI8Pc9cdNzMyHBVnU5bbYkAbpvk\nNhMvJlsMXZBm4o10I2anAI8D3jBsObOzeCpEkt+oqh8NVc+cXZfk48CW9CNnI38D8BbgvH4QZ1vg\nTcOWoxk4Gdi9qs5I8rSxTu+aFsPwlLTYomcVO5NB92LyS+DDVbXK+VobuYUXk8vp5tO+eeB6NBu/\nqKqfJym69kx/uLpvGJETGek0mJV4M/CYqvr+0IXMyafowtJSYFk1cIs4yebAXnQ9hhfe8Iyyg8ZK\nbDd0ARs6w7DW2a/bmawfLT2Brmfp6FTVJ5OcDGwN/LiFFxOAfhX2Nqx4MTln2IpmI8kdgU2B4/rF\nkn8HfBU4ftDC5muUiyNX4ULguqGLmKPTgG8CJ1bVFUMXMyenAacy8s4/ExZ2VGxtd8V14pzhGUiy\nBFhaVU38R9f3oN2HW4ak1/3ab9rIJXkU8Be0c1uVvt3WlsDD6Dqn3HasK+6TfAx4WVV9c+Lc7wJv\nHetzXtASVMAXAAAMJUlEQVQvnGvtTd4FwJ2AhWBYY50bviDJvYCn0C3+/Srw/6rqomGrmp0kp1XV\nXkPXMS9JdlvVtRbvZK+OI8NTluSZwNOBuyR5EN077ycNXNasfRT4CPAEuq2JHzxsOXPR2m1VgB2q\n6uF9q62n93Msx+rOk0EYoKq+meROQxU0a0nuAvw93W6Dy4ClSa4C/rKqlg1a3Iw12mrqBroewzf1\nn5+c5MVVte+wZc3MsX0L1K/RT+sb8wipgXftGIan74Cq2rUPDDckufPQBc3BplX1viTPqKr3Jhn1\nyFmvtduqADckuRXw0yTPAH536IJmqJLcdnK3uf4OyJgdDhxVVWctnOhHl95O1x1ntJLcFTiIW97d\nGu2CoySfA74DfAh448IdgCSvHbKuGXsp3e94SwMYWkOG4em7oX/RrH7Cfgu3Ga/o51V+tW9wfvuh\nC5qD7YHzkyyj+zce/W1V4Gl0fWcPBp4K7DdsOTP1euDUJB+km2P423TPf8zTf7aZDMLQjS4ledVQ\nBc3Rh+j+bQ8DXg08ethyZqOfC38burta1/fnfiPJ9VV1VVUd+uv/FzZq36qqE4cuQhsm5wxPWZI/\npPujuiPwJbrm5l8ctqr56EcN7w9cWlW/GLoeTVeS46tqv4njIxYWUY5Rkq3pQtFv0QXiU6vqx8NW\nNTtJzgT24JZN+QN8voH58GdV1SOTnNNPBRpl3/BVzIX/HeDwBubCnwPcDvg6K6ZJjPqOh9acI8NT\nVlX/CuwN3Ry8Ma/UTbKq+VaPYaQjaP3iogOBx9ONsCwHPgkcPeKtiR8APBDYKcnCreMlwAOGq2r2\n+uD7wdV+4XhsQdcffSEML2y6cbfBKpqfi/u7W2f0m49cPXRBM7KyufDfGvNc+Al/RTcXXvoVhuEp\nS/LhqnpKkv8D/HGSH4343eeTgF8AnwD+eeBa5uVwuj+oj6+q6/oWci8E3kb3x3aMbqJbbHNl/zl0\nPXefNWBNmr4d6RaGHlZVNyyc7MPh2B1TVdcCr01yJHCXoQuakRbnwi94fVXtPXQR2jCNet/5gSz8\nEX1Q38ZltIuMqmp7VswbfQHw53SbFIx5FesDq+otVXUdQFVdV1VvZsSjpFV1cVUdB+wGXE/XbeAf\ngBYWh7bkArrWYmcnuf/E+Rbm0h258KCqrqSbOzxGC3Phn53kj5McQLcBxyjv5C3y/SQvS7Jnkt2T\njHrqj9aOYXj6fpnk/cCX+lvqNw5d0CxV1X8AHwA+B+xAF5jGbEl/O/Vm/ULJTQaqZ56OB7YC9u1H\nDt3CdVxuqqp30rWGfEuSw/qe6aOVZP9+B80HJjknybn93NLrh65tFqrqDOCJdHd4dqB7nk+qqjMH\nLWw+Lgc2o+uTvitdf2UJcAHdVPXh97HA16rq20luA9y9D4yjk+SvgD8CfkS3tedZVTXq8J9kL+AV\ndHNJv0/XZWA/4E1V9dkha5u1hUVFSc6sqt3HusioVQuLyCaOD6b73d66qu41XGWzl+S5VXXM0HVo\n+pKkqqpf4H0LY13nobXnyPAU9b0an11V3+6Prx9rEO49j64v5/2BQ4GzJkZWRqmqTgP+jG7h3MLI\nyhPHHoR7VyTZB9g8yRNoZ1vTVvy/yYOq+nu6FnqfH6acubozQJK9k/xLkhcMXZCm5u395zOA0/uP\nhccS4Mjw1CU5Gbg1XVu1m2Dcu9yoHf30kAOAewPfYMWiI2mjluSMqtojyfHAs4Hzq6qFnTRHL8nt\nq2qs3UE0JaOeDzaQvx26AGmakmw7cXhK/wHdYtHvzr8iaeo273dVvKKqlie5ZuiCNDUnA7sDJHlf\nVT1n4Hq0ATIMT99X6HbouiPwSvqew9JG7DvAF4F/mzgXui4Do92yVk3ZD3g4cGjfLvGogevRbNxj\n6AK0YTIMT98JwDuBV1TVDX2/4U8PXNNMJXkhcFJVuef7OD2EbpOR+wL/Dnysqi4ctiRpqq6h20b+\nhdxyBz5t/O6Z5HV0/64LjwGnMGoFw/D0bV5Vn03y0v64hT+s3wbemmRLup6VH+17dWoE+u3EvwiQ\n5CHAMUkurKrnDluZNDUn001xu3joQjR1z5x47KI5rZQL6KYsybvo+hnuCxwH3KOqmliZ3G/p+W66\nqSGnAUdU1XnDVqX1lWRr4HHAHnQ7z50KnDq5i5W0MUvy0ap64tB1SBqGYXgGkjyGbsX9pVV1yuq+\nfmOX5NHAPnTzpD8FfIRuRPzkqnr4kLVp/SVZDnwZOIuuldzNfzS8zagx6NtB3g74en+qquoZA5Yk\naY6cJjFlSY4CPgq8rdp5p3E/4FVV9b3Jk0m8jT4Obqyhsdtv9V8iaawcGZ6yJPcD/pxuZfJldPNn\nzxi2qtlJsiNwdb/j3tOAOwEnVNVVA5cmSWskye3pugD9DvAt4N1V9fNhq5I0L4bhGUlyZ+ClwF9W\n1RZD1zMLSY6jm0N6e2Ap8EngZ8DTq8qWcpI2CklOAT4MXATsBOxbVY8ZtipJ8+I0iSlL8hfAY+hC\n4snAbw9b0UzdvaoeAZDkoqp6Z//Y3rOSNiZbVtXCdtSXJjlo0GokzZVhePp+BuxTVb8YupA52CzJ\nPYBbAcsnHm8+bFmStFa+kuQY4Et0I8OXDFyPpDlymsSU9XNonwFsRd9juKpGOVKa5NhVXauq/edZ\niyStjyQ70c8ZdlMZqS2G4SlL8mXgL4H/XjhXVZcPV5EkaWWS7Af852Q/9CS70PWHP364yiTNk2F4\nypJ8CHhWVS0fuhZJ0qolObeqdl3J+fOqapchapI0f84Znr7tge8m+WZ/XG48IUkbpBtWcd7BDKkh\nhuEpq6odhq5h3uzRKWkjdVmSfavqxIUTSZ5K1yNeUiOcJjElSQ5jYpvaSWPfstYenZI2Rkk2Bw4F\nHsqKv9/nA6+rqmsGK0zSXBmGpyTJbqu6VlVnz7OWeUtydlXtNnF8jlNDJEnSxsAwrPWW5Ajgtqzo\n0XlNVT1v2KokSZJWzzCsqZjo0fnNqrpo6HokSZLWhAvotM6S3Augqi6jm3N3R+D3kyyvKndwkrRR\nSHJbYA9uuVnSBwctStLc3GroArRR+1vgyv7xvsBZwD8BbxisIklae6cB96N7TUz/IakRjgxrfWxR\nVT/uH/9DVZ0DkOSvB6xJktbWVVX1pqGLkDQMw7DWS5JNqurGqnpHf3wbYNOBy5Kk1ZpoibkkySeA\nr/XHo2+JKWkFw7DWx5HAx5L8HfAD4LeB5wNHDVqVJK2Z0/vPZyw678pyqSF2k9B6SXJv4InANsD3\ngY9V1deHrUqS1lyS46tqv4njI6rqkCFrkjQ/jgxrvVTVvwOHDV2HJK2tJPcHHgTslOTZ/eklwAOG\nq0rSvBmGJUmtKuAGuq44N/bnrgWeNVRBkubPaRKSpOYl2RS4Cyv6DH932IokzYsjw1pnSY5lFQtN\nqurZKzsvSRuavh3ko4A/AL4FXAfsOWhRkubGTTe0Pl4DvBa4Hvg88Ga65vX/M2BNkrS2HldVuwOX\nVdWuwE+GLkjS/DgyrHVWVZdD11Giqv6iP31Zkv89YFmStLau6z//MsnDgfsMWYyk+TIMaxq+kORU\n4BJge+CcgeuRpLVxSD9n+EXAwcCLB65H0hy5gE7rJUnoAvAVwN2By6vqh8NWJUmrl2TbVV1zAZ3U\nDsOw1luST1XVY4euQ5LWRpKbgC8C/zZ5GigXAUvtMAxrvSX5MHAVcBFwE0BVvX/QoiRpNZI8GHg8\ncF/g3+l20Lxw2KokzZtzhjUNnxm6AElaW1X1RbqRYZI8BDgmyYVV9dxhK5M0T44MaypsWC9pY5Nk\na+BxwB50O8+dCpxaVb8ctDBJc2UY1npbWcP6qrJhvaQNWpLlwJeBs+j6pd/8glhVrx6qLknz5TQJ\nTcPjqmrnJF+oqkckOWnogiRpDfimXZJhWFNhw3pJG52qOnvoGiQNz2kSWm9JdgS+AdyTrmH9P1bV\nacNWJUmStHqGYa2zJPdc1bWq+vY8a5EkSVoXhmGtsyTH0i04CbAX8FlsWC9JkjYihmFNRZKzquqR\nQ9chSZK0Nm41dAEaDd9VSZKkjY7dJLTOkhzGimkS90zyuoVr9uiUJEkbA6dJaJ0l2W1V12xZJEmS\nNgaGYUmSJDXLOcOSJElqlmFYkiRJzTIMS5IkqVmGYUmSJDXLMCxJkqRm/X8XHWapbkHL5gAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbfda34f5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = axl.Plot(results)\n",
    "plot.boxplot();"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
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