LeeMendelowitz/lcps.ipynb

## lcps.ipynb
{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import common_prefix\n",
      "fname = 'lcp.V35.out'\n",
      "lcps = common_prefix.read(fname)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "lcps = np.array(lcps)\n",
      "\n",
      "num_sequences = len(lcps)+1\n",
      "print 'have %i sequences' % (num_sequences)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "have 1069684 sequences\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "min_lcp = 100\n",
      "\n",
      "# Break lcps into intervals such that all sequences in the interval\n",
      "# have an lcp of at least min_lcp bp.\n",
      "def getIntervals(min_lcp):\n",
      "    intervals = list(common_prefix.genIntervals(lcps, min_lcp))\n",
      "    intervals = [(s,e,e-s+1) for s,e in intervals] # Compute the number of sequences in the interval.\n",
      "    intervals = sorted(intervals, key = lambda t: t[2], reverse=True)\n",
      "    return intervals\n",
      "intervals = getIntervals(min_lcp)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Print intervals that have the most sequences\n",
      "for s,e,l in intervals[:10]:\n",
      "    print l"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "4686\n",
        "3007\n",
        "2449\n",
        "2411\n",
        "2390\n",
        "2220\n",
        "2052\n",
        "2025\n",
        "1960\n",
        "1842\n"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Ms = np.arange(5, 100, 10)\n",
      "m2intervals = {}\n",
      "for m in Ms:\n",
      "    m2intervals[m] = getIntervals(m)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = Ms\n",
      "y = [len(m2intervals[m]) for m in Ms]\n",
      "plt.plot(x,y)\n",
      "plt.title('Number of jobs vs minimum lcp')\n",
      "plt.xlabel('minimum lcp')\n",
      "plt.ylabel('number of jobs')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 41,
       "text": [
        "<matplotlib.text.Text at 0x10f648690>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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HKaWU3WjSUUopZTeadJRSStmNJh2llFJ2o0lHKaWU3WjSUUopZTeadJRSStmNJh2llFJ2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\nIDMBDnVTAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x115cd36d0>"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "random_lcps = common_prefix.read('lcp.random.out')\n",
      "# Break lcps into intervals such that all sequences in the interval\n",
      "# have an lcp of at least min_lcp bp.\n",
      "def getIntervals(lcps, min_lcp):\n",
      "    intervals = list(common_prefix.genIntervals(lcps, min_lcp))\n",
      "    intervals = [(s,e,e-s+1) for s,e in intervals] # Compute the number of sequences in the interval.\n",
      "    intervals = sorted(intervals, key = lambda t: t[2], reverse=True)\n",
      "    return intervals\n",
      "random_intervals = getIntervals(random_lcps, min_lcp)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print len(random_lcps)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "999999\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Ms = np.arange(5, 100, 10)\n",
      "m2intervals = {}\n",
      "for m in Ms:\n",
      "    m2intervals[m] = getIntervals(random_lcps, m)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = Ms\n",
      "y = [len(m2intervals[m]) for m in Ms]\n",
      "plt.plot(x,y)\n",
      "plt.title('Number of jobs vs minimum lcp for random sequences')\n",
      "plt.xlabel('minimum lcp')\n",
      "plt.ylabel('number of jobs')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<matplotlib.text.Text at 0x128d5ab90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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66HQ6bN269a72w1h4vYiIzJ600tWrV+Xq1autXfy25s+fLzExMSIiUllZKcXF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\niMjk/h/Yg0VOLPveswAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x102ec6dd0>"
       ]
      }
     ],
     "prompt_number": 12
    }
   ],
   "metadata": {}
  }
 ]
}