Reproducing Cicular Coordinates

gistfile1.txt

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "n = 200\n",
    "r = np.random.uniform(0.8, 1.2, n)\n",
    "theta = np.random.uniform(0, 2 * np.pi, n)\n",
    "\n",
    "x = r * np.cos(theta)\n",
    "y = r * np.sin(theta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "IHIexVDYAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1043e57d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, y);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# x2 = list(x) + list(x + 2)\n",
    "# y2 = 2 * list(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# plt.scatter(x2, y2);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'x': x, 'y': y})\n",
    "# df = pd.DataFrame({'x': x2, 'y': y2})\n",
    "df.to_csv('points.txt', header=False, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7532927664359947,-0.3761290136723183\r\n",
      "0.6398059404258286,-0.6350591697398472\r\n",
      "0.3146185602816974,-0.7773728732884104\r\n",
      "0.23189992758629097,0.8679896463822555\r\n",
      "-0.6264740160583882,-0.621192421982833\r\n",
      "-0.8153561661519756,-0.40484741415368525\r\n",
      "-0.40036682563984366,-1.0604480298756678\r\n",
      "0.4974518042668474,-0.6825619405547722\r\n",
      "0.6157422087843355,0.9553982397215413\r\n",
      "0.9697205848295056,-0.6178861906673422\r\n"
     ]
    }
   ],
   "source": [
    "!head points.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Boundary matrix: test_boundary.txt\n",
      "Cocycles:        test_cocycle*.ccl\n",
      "Vertices:        test_vert.txt\n",
      "Diagram:         test_diagram.txt\n",
      "Simplex vector generated, size: 117291\n",
      "\n",
      "0%   10   20   30   40   50   60   70   80   90   100%\n",
      "|----|----|----|----|----|----|----|----|----|----|\n",
      "***************************************************\n",
      "Rips timer : Elapsed time [0.26] seconds\n",
      "Persistence timer : Elapsed time [0.17] seconds\n",
      "Total timer : Elapsed time [0.69] seconds\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "../../build/examples/cohomology/rips-pairwise-cohomology \\\n",
    "    -m 0.4 \\\n",
    "    -b test_boundary.txt \\\n",
    "    -c test_cocycle \\\n",
    "    -v test_vert.txt \\\n",
    "    -d test_diagram.txt \\\n",
    "    /Users/vale/Documents/Dionysus/examples/cohomology/points.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test_boundary.txt  test_cocycle-0.ccl test_cocycle-0.val test_diagram.txt   test_vert.txt\r\n"
     ]
    }
   ],
   "source": [
    "!ls test*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0 0.0348834\r\n",
      "0 0 0.0399901\r\n",
      "0 0 0.0412479\r\n",
      "0 0 0.0457209\r\n",
      "0 0 0.0469644\r\n",
      "0 0 0.056898\r\n",
      "0 0 0.0613569\r\n",
      "0 0 0.0628861\r\n",
      "0 0 0.0798663\r\n",
      "0 0 inf\r\n"
     ]
    }
   ],
   "source": [
    "!tail test_diagram.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "diagram = pd.read_table('test_diagram.txt', sep=' ', header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "JalFLH1JahFLX5JaxNKXpBb5P5qrUh3mI+6KAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a04df90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([0, 1], [0, 1])\n",
    "plt.scatter(diagram[1], diagram[2]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}