CfL alpha in Q4.ipynb

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    "1 -9 10\n",
    "1 -9 -1\n",
    "1 8 9\n",
    "1 -8 -9\n",
    "1 -8 -6\n",
    "1 -8 -16\n",
    "1 8 13\n",
    "1 -8 -13\n",
    "1 -8 -12\n",
    "1 -7 -8\n",
    "1 -7 14\n",
    "1 7 -12\n",
    "1 7 12\n",
    "1 7 10\n",
    "1 -7 -10\n",
    "1 6 16\n",
    "1 -6 -16\n",
    "1 -6 -15\n",
    "1 -6 -12\n",
    "1 6 11\n",
    "1 5 15\n",
    "1 4 14\n",
    "1 -4 -13\n",
    "1 3 -15\n",
    "1 -3 -13\n",
    "1 -3 -12\n",
    "1 3 11\n",
    "1 2 -15\n",
    "1 -2 -14\n",
    "1 2 13\n",
    "1 -2 -13\n",
    "1 -16 -9\n",
    "1 -16 -8\n",
    "1 -16 -6\n",
    "1 -16 4\n",
    "1 -16 3\n",
    "1 16 -12\n",
    "1 15 9\n",
    "1 -15 -8\n",
    "1 -15 4\n",
    "1 -15 3\n",
    "1 -15 2\n",
    "1 15 -13\n",
    "1 15 12\n",
    "1 15 11\n",
    "1 15 -10\n",
    "1 14 -9\n",
    "1 -14 -8\n",
    "1 14 7\n",
    "1 14 6\n",
    "1 -14 -6\n",
    "1 -14 6\n",
    "1 -14 5\n",
    "1 14 4\n",
    "1 -14 4\n",
    "1 14 -16\n",
    "1 -14 -16\n",
    "1 14 -15\n",
    "1 -14 15\n",
    "1 -14 12\n",
    "1 -14 0\n",
    "1 13 9\n",
    "1 -13 4\n",
    "1 -13 -2\n",
    "1 13 -16\n",
    "1 13 16\n",
    "1 13 15\n",
    "1 -13 15\n",
    "1 13 -14\n",
    "1 -13 14\n",
    "1 -13 12\n",
    "1 13 10\n",
    "1 12 9\n",
    "1 -12 -7\n",
    "1 12 6\n",
    "1 -12 4\n",
    "1 -12 3\n",
    "1 -12 -16\n",
    "1 12 15\n",
    "1 -12 15\n",
    "1 12 -11\n",
    "1 12 11\n",
    "1 12 -10\n",
    "1 11 7\n",
    "1 -11 -5\n",
    "1 -1 -15\n",
    "1 -1 -13\n",
    "1 -11 -16\n",
    "1 11 15\n",
    "1 11 -13\n",
    "1 11 -10\n",
    "1 11 10\n",
    "1 -11 10\n",
    "1 -11 -1\n",
    "1 -11 0\n",
    "1 10 -9\n",
    "1 10 8\n",
    "1 -10 -8\n",
    "1 10 16\n",
    "1 10 15\n",
    "1 10 14\n",
    "1 10 -13\n",
    "1 -10 13\n",
    "1 10 -11\n",
    "1 10 10\n",
    "1 0 15\n",
    "1 0 13\n",
    "1 0 -11\n",
    "1 0 -10\"\"\"\n",
    "tuples = [tuple(map(int, line.split())) for line in summary.splitlines()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MRdwWBgDAGX+KAwCAM8IVAABnJfMz17cfsv3d4IgzTzXP0b23+eiDDlLWOPDuNx9UOdr2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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ac7bda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "grid = np.zeros((33,33))\n",
    "for v, i, j in tuples:\n",
    "    grid[i+16, j+16] = v\n",
    "grid[16,16] = 0\n",
    "plt.axis('off')\n",
    "plt.title('log count for alpha in Q3')\n",
    "sns.heatmap(np.log2(grid[::-1].clip(1)),cbar=False,square=True, mask=grid[::-1]<=1);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}