{
 "metadata": {
  "name": "5x-kmer-abundance-distributions-2013"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# configure paths etc.\n",
      "import sys\n",
      "import os\n",
      "os.environ['PYTHONPATH'] = '/usr/local/share/khmer/python'\n",
      "sys.path.insert(0, '/usr/local/share/khmer/python')\n",
      "\n",
      "import khmer\n",
      "import screed"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cd /mnt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/mnt\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# check out simulation code for making reads, from the diginorm paper repo on github\n",
      "!git clone git://github.com/ged-lab/2012-paper-diginorm.git\n",
      "!mkdir kmer"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "fatal: destination path '2012-paper-diginorm' already exists and is not an empty directory.\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "mkdir: cannot create directory `kmer': File exists\r\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cd kmer"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/mnt/kmer\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# build a random genome (8kb) with equal mixes of A/C/G/T\n",
      "import random\n",
      "random.seed(1)\n",
      "\n",
      "x = [\"A\"] + [\"G\"] + [\"C\"] + [\"T\"]\n",
      "x = x*2000\n",
      "random.shuffle(x)\n",
      "x = \"\".join(x)\n",
      "x"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "'CCACCCTTGGGTCCTTCCTGATGTGGCCACCCCAACCACCACATGTATTTACGAAGCTATCACCCTGGTTTAGGTAGCAATTTCCAACGGCCTAATAACCTTTCCTCGAGCATCGCCTTGGCTGTTACACACAGCGAGCAGCCTAGTCGTCCAGGTTTGGACGGTAGCGCGTTTGAATTTACCACAAAGAATCTGGTGAGCGATTTACTTAACATCATAGCGCTATGGACGCCTCATTCAGCCACAACCGAGTTTGACCAGGTCCACAGCTAGACCACCATTATCACATCGTTGTCTCACAAGTCTTGCAGCAACGTCAGTTGGGAAGTTGTAAGGTGAAGAAGACAAGCCGCCATGTGACACGGAGCTACTCTGTCAATCGTTGCGTCTATATAGACGAATTAGTAAGGAGTTCTTGTGGCGGGCTGTCTAGTTGCGGGGGACTGCTACCGGCGCGGCCTTCCCTCGAATGGGGATACTCTCCTCCTTTCTTGGTTACCCCTAACCCAAGACTGCGAGGCAGGCAATTCCACCTGCCCTCGTTGGAAGGTTAATTGGTAGGTATACCTGAGACAGATTGCACCGTATAGCACTTGTCATAGCAGGAGCGAGTAGGTAATATTCTTAAACCGTACGTACCAGAATGACATAACCCAATGTAGTGTGGCAGATTAAGCATGACACATCAGTGAGGGGCAATACGCACTTCATACTTGATTGTAGGGTTTTATTTGCCGGATTGGGATTAACTGAGTGTTACCACAACGCTAGTCCCGAAAAAAGGGAAGTGCACGTAGCCTCGTGTGCACCGATATTTTATAACCGAGGGAGGCAGCCTGTACATTGAGTCCAGCCTTGAAAAGGCTAGTTGAGTCCTTTCTACCGAATTTGTCTGACGGCGAAAAGGGCTCCTCTAACGCTCGTACGTATAGGAGTGCTGGTGATTCCAAAAACCAATCATTGTCACAACCGCCGTTGGCCTGCGCTGCGTTAGGCGTCATAACTCACTTCGCGGCGTGCTGTGTCATAGCCGAATCCTCCAATCTTTAAGAATGCGCGCTAATTTCCATCCCAGAGGGTGTTGGAGATGCACGAATAATAAAAGGTCGCGAGTCCAAACAAAGTTTACCGTTCGGTCGAGACGGTCAAGACTGCCACTAAAGAAGTCAATATTTCCTCGCCCTTCGACAATGGTTCGTGACAGGGCTATTGAGCAGTGGTGACCGCAGATTGATACAAGAAAACGCCACTCTAGCCAGACACGACCCGGTGCCCACCGTATGCAGGGCTGGGGGGAATGTTGAGGGATCGTGAAAGAAAATCACCTTGCCCCAGAACCGCAGTCAATTGTGCTCATTGAAACGTATCTGTCCAAAACTTAACGCCGACAGGGGTCGTCGCATAACGCCGCTAATAATCCGGGGCCACGGAGAGGCTGGGTCTGTATCTACCACCGACTAGAGAAGCGGGCATATGTGTCTTGTTTCGATCTTTCGTCGGTTATGTACTGTGAATTAAGCAAAAGCAAGCAGTAAGTGCGCGGAAGTGTATCCCCCGCCCCTGCTGTTACAACTCCTGATCTCGGTTCAGCATACGTATTCGCGGAAGTAAGCTACTAGGTTTACCATACGGGTTAGTGAAGGTACGTTTAATATATCCTTAATCGCTTGCAGCGAGTGGACACAGAAGCAAGGTCCGAGCATAACGGAGGATCAAGTATTAGAAGCTCAGGAAAAAAAGTACGCATCGATATATAGGGAAGGTGTCTTGCTTGCCGTGTTTCAGATTGCCAAGGAACCAGGAGTGCCCCATTTTCACCACTCTCTTGGAACTGTCAAGTCACTTATGCCGCCTTGCTCCCCGAAACGCGCATAAAAGGTGACAAAAGACGCGTGTAAAGGGCGCCAATGCACCAACGTTTTCTCCGGAGCTTGTCACCCATCGCCGAGGGCCAGTCGCCAACTCACCATCATCTTCATGAGGATTCACGCCCCGTACGGGGAGAGCGCACGGGAGCGGCCTGTTGCCGGTCTTACTCTTCACGATGTCGATTATAAGAAGGAGCTTGGAAGCTTGTGGCTTTGAGGTATGACAGACAATTTTGGATGCAAGTGTTAGCCATACAGGCTTTCAAGTGTTAATTCGTGTAGCGGGCAGTGAAAACGAAAGGTACGAGGGGTTATCTAGAATGAGTAGGGAAATTTGTATAAAGAGCAGTTGAAAGTTTGTGATGCTAATGTGACTGGGATCCGACCGAGCGGCTCATGGCAGAGAACGAATAAGTAAACTAAAGGGACCTGCATGACAGGAATGAGCAACTGACCAATCTTATGTTTAGCGTAATATTCTTTTGCCCCTGCATGAGATTCTGGTGTACTAGCACCGCTTATAGCCCGGTAGGACATCATCCATGGTCTGGCATCCAGGGAGGAGTCAACTACTCTAAGACCACCGCTCGGGCACCATCAGTATACTAATGTTGCACGAACAACAGTCGATCCCGGAACATGCACAGGGGTCTCCCTCGGTCCGTATCGGACATATTGAACGACTATCGGGGATGACCATTGCGTCCCAAGAGCACTATACCAGTTCCAATGTTGTCGGGGCACAGCCGCAGAACGGACTAGTAGCTTTCGGCAAAGAATCCCTAGACCTCACGACGCCTCCGACTGGGACCATTTGGCCTATAAAAAACATGTCAGCCACAATGGCTGCGACATGAATGGGTCGTAGAACTTTGCGTAGTAGCACATGGTAGTAGATCACACAGCCGGGTTCAGTGGCCCTATTTGACTGTTAACCCTCTGTTAAGTGTTCCCGAACACTAGTTCGGCTGTATTGACACTTGGGAATACGTTTGTAGGTTGAGCATTACGGTAGTTGTCCTTTACTGCCGGAGCAGAGGATCCTTCTATGTGTATCGTTCTGAATACTTGCTAAAAGATATAGACTTTTTCCTTTCACCGGGAGTGGTGCCTTGCAGCTAGCAGTGTGTCTTGGTCCTATGTGCAACTCTGCAAAGCCTCACGTGGCAGCAGGTATAGTCAGGAAAAGAGACAAGCACTGTCGGCACACCTACCTCGATATAAATGTACGCCAGTTGATGTGTGTCGCAGTGTAACAGGCAAAAGTACGTAAACGGCGTCGAACGGAGAAGCTGAGACCGCCTGTGTTTGATAGTACCTGCCGGCCTCTACCTGCTGAACTAGAAGGTAGCAACTACCTGTTCGTAAGCCCTCAGAGGTCCCAAACCGCCAAATCGTTAGTTTACAGCCCTATGCGCGGTTAGCCAGACGGGGACAGTGTGCTGTTTCCTTCCGCCATCTTTTTGCTTGATCGCCACGGGACCAAATCTTGGGGGCCTGGATCATATATACTTTACGGTACACAAACACTTGCATTCGGAGCATTAAACCAGAGTTGATCTGAAGTACAGGTGTACACTGTTTCCCGCTATTGCACCGGCGAGCGAGGCTGGCTCTCGGCCCGCATATTCATAAGTTTGGTCGAGGGCCCGTCGGTCATTCATCTTATTCTAGGTCCAAGAACGGGTAGCCACGGGGATTTCGCATATGTCTAGCATCGTTCCGTCGGCAAGCTCAAGGACCGCCTATAGTTCCCAAACATAACCTGAGTCGTTGCTCATAACATAAAGTAGAGATTGCGGTCTTACCATCTAATAACTTGTTTATCTTGACCTGGGGGACATGCCCAAAGTCCTTCAGGGTTTCGCCGACTGCTGATTACAAGCTATCCCATCCCAGTAAGACTAGATGCAGGTAGGAGTTTAAATCGGTGTCTTTTTCAACGAGAAGTCTGGGTCGTGCCTGCCGCCCCCGGATTTGATAAGCTCCTGTGCCTCAAAGGGGTTACTTGAGGCTACCTTATTTTGCACCGACCTTGAGCGTCCTAACGTGGCGGGTCAATATACTGAACGTTCTAATTCGCGACATAGTGTCAACCGACTGTAGTGCGGGAGTTTAGGACTTATACTCTAGCGCCATCGTGCATTGAAGCCAACTTAGCCATCTTCAAAAACTACCCCAAAGAGTTCGAGAGAAACGCCACGCCCTTAACCACTTCTAACGCAAAGGAGTCATTATCCCTCCCCTGTTCCGGCACTGGCTTCATTTTAAGCGCCTGTCTCTATCTGGGGTGCTCGTCCGGAGCAACATAAGGGTGTGTGACGAGAGTTAAGCCCCCTAACCTTGAGACAATGTATTTCAGCTCTACCGGTCCGGTAACCCTGTAGGATGGCACGCTGTGTGTCCGGGTGCCTTTTAAGAACTCGCGAGATTTCCTCTCACAGCTAGACTTGCAGATAATTGGTTGGCCCCGAGTCTGCGCATTTTGATAGGGTATGCTACTCTTAACTGCAAGGATTTACGAGTTCCGATACGTGCCCCTCTTTACTTGTTCAGGGGGCATACCCGTTGCTACGCCGCTAGAATTGCCCCTCAAGGAGAAGAATCCGCCAGTGCATATCGAGTTAGATTAAACGTAATCAGGGCACCGGAGGAACTATACGTGCTGCTCACACGGGCCGTCCGAGCGTATGCCGTTCAATGGTCCATCTTCTGATAACATGGCGCAATCTAGGATGGCGGCATCTGTCCTTCGGTTTACTAGCCAGGAGGCGCTGGAGACGTGTCGATCGAAAAAGCCTAGTATTCCCGCGGGGCCCTGGAATAAGAGGCTCCCTTGAGCCGTTAGGACGGTAAAACGGACGCCCCTTTCGACTCATAGTTGTAGCGGGGAGGCACGCTGGGCCGGAGTGCACGACTACTAAGGACAATAAGCTACCTGTTGGCGCAGTTTGAGCGTCGCTCTGCAAGGCTGATGAGCACCGTCCAGCGGTACCTCCGATTCGTCCAGTTTTGACCGAAATAACACCGATTCACTCAAGTACTCGGATTAAATTAGACTCTAACTTCCGAACATGCCCTCGGAGTTGCAAAACATCTTTGGATCCAATAAGGATTTGTAGGACCCGACCACTAGGACCATCTGAAAAACGAAATTGAGGAAGACCGCTCAGTCGGTAATCGTACCCACTAAAAATGTGGGCGACAGTTCAGCCCTCCGGTTTCCCTATCCTATGATCTGTTGCAAGTTGGTGTAGATTAATCCAGCAGGGACAGATTGCGTACCTCACGGTAGGCATGGAAAAGTGCTTCACTAAGGGGAAGTTTCCTGGATATTGAAGGCCGTATGGCAATAGTCGAAGTGAAGAGAAACCTCCCCGTCCACCTTAGCCACACCCGAAAGTGAGGGTCCTGAACTTGTCGTTTGCGGATTGGCTAGCAGACGACATAGGGCTGCAGTGACGGTCGTGTCGGGGCCAGCCGCGGGTTTTTTGGCTTGCCTCGGATAGTAGTGTCGTAGAAAGCGCACGAGCGGCTGTGGCTCCAAAGTAAGAAGGCAAAGCGGTATAACACAATCACACTATCACCATTAACAGGGACTTAAACCGAGAAAGTTCATTTGTCATGAACCCCAGTTTCAACGTTTCCTACCGTCTCCTATACCTCTTCTTGAGGCATGTCAGTGAGCGATTGACCTTTACCCTGGTGATTATTGTCACACGGTTTGGGGTACTTAACTTGCCACATATTACTTCGGAACTCTTCCAATCAGAAGGAAAGGTCACTTGGGTTCTAGATAGTAAGTCAACCAGTATTGGATGATGAGTCAAGATCTGGTAAGTCCGACCGAGCGGGTCGACCGCTGTAGGTATCCCTTACTTCCCGACAGTTTGGCACATGGTCCTCCCGATCTAACTCGGACTGCCCAGGGTTAAGCAAAATATCTGGATGGACAGAACATAGACGTACTGACACTTCGGGTCTAATTAACGTAGCGTATGTTGCTACTGTCGATACAGCAATCTGGCTAGTCTGGACTAGCGGACAGGGTACCAATCCACGGGTCGTAGCAAAACATTCTAACACAGAAAGATGATAGTCTTTGCTCGGGCTTTCAAGATGAGCGGATGCCATGCGAGCAAAAGAGGTAATAGCCAGAGTAGCCGTGAGAAAGAAAGGTTCTGCCGACGTGCATAGTGCATAGAAAGTTAGTACAACCGTATCCTTCGCCGCTCTGACCATGCTCCCAAAGGTAAGCTTACTCGGGCTACGCCGTAATAAGACACGCTTATTGTAAGCGTTTGTGGCAACTGTATCGGATAGCCGTTGTCTAGAGGGCTGCTACGCGGCTATTGACCGACCGCTAGGTATGGATTCCGGTATGGGGGCGAACAATAGATCCGTCCATAACCCTACCGCTACAATGGACACTTCGGATCGGTGGTCATGTTTCTGGCAGGTACATTGATCTGCGGAGGACAGTTGGGATTCGTAAAATACGAACCGTCGTCGCAGATCCTACATTATTCTCGAGCACGCGGAGACGACTAGCACCTAGATCCCACGCAGTCGGGCTGACCGATATTGCTCTTCAAGAGGGCCTGCGATCGTGTCCCTATCCTTCGGAGAGCGCTACGGGACAAGTGCCACAAGGTGAATCATTGGCCCGGTCTCACCACCGATGAAAATGGCACTCCCCCTATCCGGTATATTCATAGTACTTTTAGGCGTGGTAAGCAACACATCTCTAGAAGAAAGGTAGAACAGTGCTTTAGACCCTAGATTCTAGACCCCTGTATTGGGCACCCCGTGTGTAGAAAAGCGGTTACGTCAAAAACCCTTCTTTGACCATAATCCCCCGCTCCGACTCGATGCGCACCGCTACGGGAATGTCCCCTGCAAGTTGAGGCGACGGGGCAAATAAAGAGCTGCTTCGAGTAGTGTGAGCTGGTCGCCAGTATTGCCTCTGTTGGATATAGAAACCGGTCCCCTTCTAAATAATGTTCGCGATATCAGGGCGGGCCCTATAATTTAGCAACCACATCTGTACAGCCATCTACGTTAGTTTAAATAGTCAGAGCCATCTTGGAGTCTATAACATAGCACTCCGAGCGGTCACTATAACCTTACTGACAACATCGGACAAACCTGTCCACCGATTCAATGACGTTCGCTGTAGATTTAGAGGGATGTAAATGCGCAGTAATTGAGTGTCTTACAGCCCTTCTGTTGTCAAGGTGCCATAGACTTTATTTCGGCTGTTCTGTGCAACTCGTTCGCTGTACGAACGATCCACACCGGCCGGTCTATCGGACGGGACTCTGATGGTTCACCGTTATAAACATGATCTGGACCCGTTCCTCCCCGACTTACCCAGTGGTCGGTTATAACCAGCGGTTCTTTAACTAGTCTGACACCCCTGATTATATGGTCAAGCTCCCAGGAGGCGTTAATTCGTTTAATAGTCAATTCACACAACCCTGTTACAATATCCTCCGTAAACAACAGCAGTTTCTCAAATGTGCAATAGTGAGAGAATTGCTTGGCGCGCAAAGCATGAAACGAGGCGCATTCACGCCAGACTTCCCGTCTCAACGGTCCACAACTCGGACCGGAGGCTCTTCAAGTCTTACTCGCATCTCGACTTAAGAGGCCTTACTTCACCGCCGGTTCCTGGTATAATCGGTACTGGCTATCATTACGGCTCACTGGCAAATTGTTCCCCGTCCTGATGTAGGTCTGAAACTCCCCGGGTCGGCTTTTGTGTATGGGCCGTACGTTGCTAACCCCGGGTTCTCACTTATTAATTTACGTGGTTTTGTTACACAGCACCTCGTTGCCGCAGACTATGGCGTGTAGTTCTCCGAACGGCGCTCTTCTGTTGCGTATTATCATGTTTGATTAGCCCAGACAGCTGCGTACTCTGCGGTGGCGACTTTCGTCTAAAGGCATCGTTGATCCCCACATCCGGCGGATTGAGATCTGCACGTGGATTTCCATTCGGAGCTTCATCCCAAGCACATGTAAGCGATCTTTGCTGGCTCGACTCTGCTCGCAATACTCATGTGGAAGCAACTCAGCTGAGAGATCAATGTCTAGTTGACTTCTAATTATCCGAAGGGCCAAGGTACC'"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# write the genome to disk\n",
      "fp = open('genome.fa', 'w')\n",
      "fp.write('>genome\\n')\n",
      "fp.write(x)\n",
      "fp.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# make reads at 200x coverage, 1% error.  note, errors are indicated by lower case.\n",
      "!python /mnt/2012-paper-diginorm/pipeline/make-reads.py genome.fa > reads.fa\n",
      "!head reads.fa"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "10073 of 16000 reads mutated; 16150 total mutations\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        ">read0\r\n",
        "AATTTCCATCCCAGAGGGTGTTGGAGATGCACGAATAATAAAAGgTCGCGAGTCCAAACAAAGTTTACCGTTCGGTCGtGACGGTCAAGACTGCCACTAA\r\n",
        ">read1\r\n",
        "GGATGGCACGCTGTGTGTCCGGGTGCCTTTTAAGAACTCGCGAGATTTCCTCTCACAGCTAGACTTGCAGATAATTGGTTGGCCCCGaGTCTGCGCATTT\r\n",
        ">read2\r\n",
        "GCGACATGAATGGGTCGTAGAACTTTGCGTAGTAGCACATGGTAGTAGATCACACAGCCGGGTTCAGTGGCCCTATTTGACTGTTAACCCTCTGTTAAGT\r\n",
        ">read3\r\n",
        "GTTGTTCGTGCAACATTAGTATACTGATGGTGCCCGAGCGGTGGTCTTAGAGgAGTTGACTCCTCCCTGGATGCCAGACCATGGATGATGTCCTACCGGG\r\n",
        ">read4\r\n",
        "TGTTCCCCGTCCTGATGTcGGTCTGAAgCTCCCCGGGTCGGCTTcTGTGTATGGGCCGTACGTTGCTAACCCCGGGTTCTCACTTATTAATTTACGTGGT\r\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# build a database of all k-mer counts.  See http://khmer.readthedocs.org/en/latest/ for documentation\n",
      "!python /usr/local/share/khmer/scripts/load-into-counting.py -x 1e8 -N 4 -k 20 counts.kh reads.fa\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r\n",
        "PARAMETERS:\r\n",
        " - kmer size =    20 \t\t(-k)\r\n",
        " - n hashes =     4 \t\t(-N)\r\n",
        " - min hashsize = 1e+08 \t(-x)\r\n",
        "\r\n",
        "Estimated memory usage is 4e+08 bytes (n_hashes x min_hashsize)\r\n",
        "--------\r\n",
        "Saving hashtable to counts.kh\r\n",
        "Loading kmers from sequences in ['reads.fa']\r\n",
        "making hashtable\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "consuming input reads.fa\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "saving counts.kh\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "fp rate estimated to be 0.000\r\n",
        "DONE.\r\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# count all k-mers in the database and extract a k-mer distribution\n",
      "!python /usr/local/share/khmer/scripts/abundance-dist.py -s counts.kh reads.fa reads.dist\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "hashtable from counts.kh\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "K: 20\r\n",
        "HT sizes: [100000007, 100000037, 100000039, 100000049]\r\n",
        "outputting to reads.dist\r\n",
        "** squashing existing file reads.dist\r\n",
        "preparing hist...\r\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# in the dist file,\n",
      "#    column 1 is the k-mer count\n",
      "#    column 2 is the number of k-mers with that count\n",
      "#    column 3 is the number of k-mers with less than or equal to that count\n",
      "#    column 4 is the fraction of total k-mers with less than or equal to that count\n",
      "!head reads.dist\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0 0 0 0.0\r\n",
        "1 131282 131282 0.805\r\n",
        "2 21148 152430 0.934\r\n",
        "3 2552 154982 0.95\r\n",
        "4 210 155192 0.951\r\n",
        "5 14 155206 0.951\r\n",
        "6 1 155207 0.951\r\n",
        "7 1 155208 0.951\r\n",
        "8 1 155209 0.951\r\n",
        "9 3 155212 0.951\r\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# read in the numbers\n",
      "dist = numpy.loadtxt('reads.dist')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# plot the first two columns\n",
      "plot(dist[:,0], dist[:,1])\n",
      "xlabel('k-mer abundance')\n",
      "ylabel('# of k-mers with that abundance')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<matplotlib.text.Text at 0x412fb50>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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Az89Pjtgcit1oRETyueNDndevX8exY8fqzPzp6+uL7du3Ozouh2NlQ0QknztW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\nyYaIiByOyYaIiByOyYaIiBzu/wFjgiT45DbS3gAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x41187d0>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# this plot is biased towards 1, due to the large number of errors.\n",
      "# what does it look like up close?\n",
      "plot(dist[:,0], dist[:,1])\n",
      "axis(xmax=10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "(0.0, 10, 0.0, 140000.0)"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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NzixEZKRFdVjU1wf/7XLvX1ToDIvInm0SkUgS1WHROQTVwz2AEe2WWyA+Pjhx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\nAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x414d850>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# but out by ~200 you'll see a higher coverage peak.\n",
      "plot(dist[:,0], dist[:,1])\n",
      "axis(ymax=1000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "(0.0, 200.0, 0.0, 1000)"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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4hYWFLFmyBOjg96flALm5udbrr79ubd++3fL5fNaePXsSXaSkMmjQIOujjz46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\nEREXUwiIiLjY/wORfQJ3mkufKQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x42917d0>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# what happens if we trim out all the unique k-mers? => reads.fa.abundfilt\n",
      "!python /usr/local/share/khmer/scripts/filter-abund.py -C 2 counts.kh reads.fa"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "file with ht: counts.kh\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "loading hashtable\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "K: 20\r\n",
        "filtering reads.fa\r\n",
        "starting threads\r\n",
        "starting writer\r\n",
        "loading...\r\n",
        "... filtering 0\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "done loading in sequences\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DONE writing.\r\n",
        "processed 16000 / wrote 14368 / removed 1632\r\n",
        "processed 1600000 bp / wrote 1199434 bp / removed 400566 bp\r\n",
        "discarded 25.0%\r\n",
        "output in reads.fa.abundfilt\r\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# now let's look at the k-mer abundance distribution of the filtered reads\n",
      "!python /usr/local/share/khmer/scripts/load-into-counting.py -x 1e8 -N 4 -k 20 counts-filt.kh reads.fa.abundfilt\n",
      "!python /usr/local/share/khmer/scripts/abundance-dist.py -s counts-filt.kh reads.fa.abundfilt reads-filt.dist\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r\n",
        "PARAMETERS:"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r\n",
        " - kmer size =    20 \t\t(-k)\r\n",
        " - n hashes =     4 \t\t(-N)\r\n",
        " - min hashsize = 1e+08 \t(-x)\r\n",
        "\r\n",
        "Estimated memory usage is 4e+08 bytes (n_hashes x min_hashsize)\r\n",
        "--------\r\n",
        "Saving hashtable to counts-filt.kh\r\n",
        "Loading kmers from sequences in ['reads.fa.abundfilt']\r\n",
        "making hashtable\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "consuming input reads.fa.abundfilt\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "saving counts-filt.kh\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "fp rate estimated to be 0.000\r\n",
        "DONE.\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "hashtable from counts-filt.kh\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "K: 20\r\n",
        "HT sizes: [100000007, 100000037, 100000039, 100000049]\r\n",
        "outputting to reads-filt.dist\r\n",
        "** squashing existing file reads-filt.dist\r\n",
        "preparing hist...\r\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# load\n",
      "dist_filt = numpy.loadtxt('reads-filt.dist')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# plot reads v filtered reads k-mer abundance, overall.\n",
      "plot(dist[:,0], dist[:,1], label='reads')\n",
      "plot(dist_filt[:,0], dist_filt[:,1], label='filtered reads')\n",
      "xlabel('k-mer abundance')\n",
      "ylabel('# of k-mers with that abundance')\n",
      "legend()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "<matplotlib.legend.Legend at 0x428fbd0>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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CIDg4mLi4OE6ePEl8fDx2dnY4OjoC5d1t69evJysri9jY2ErJZs2aNeTn57Ny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\nRK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x4291250>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# now plot reads v filtered reads k-mer abundance\n",
      "plot(dist[:,0], dist[:,1], label='reads')\n",
      "plot(dist_filt[:,0], dist_filt[:,1], label='filtered reads')\n",
      "xlabel('k-mer abundance')\n",
      "ylabel('# of k-mers with that abundance')\n",
      "axis(ymax=1000)\n",
      "legend()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "<matplotlib.legend.Legend at 0x445c190>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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P3d2d6OhoevXqVaxr6ZABgEIIYSpGE0d52Lp1K3Xr1sXb25uIiAjD9pJ0BXvY\n4lGzZ882/B4YGIhOp0OVEocQQhhEREQU+vwtC7Mkjp9//pnNmzezbds2srKySE1NZfjw4fj6+hIT\nE4O3tzcxMTH4+voC4O/vz86dOw3HnzlzxvDcg9ybOACO7L6ILOQkhBB/CgwMJDAw0PB4zpw5pT6X\n0TaOe+Xm5nL58uUSX2TevHlcunSJ2NhYvvzyS4KCgvj888/x9/cnLCyMzMxMwsLC6NSpEwB+fn6E\nh4cTHx9PREQEOp0OR0fHYl9PkUkOhRDCZIwmjh49epCamkp2djaenp4EBwfzzjvvlOmid6udQkND\niY+Px93dnStXrvDyyy8D4OrqSmhoKEFBQYwbN47FixeX6Pw2Oh0o0sYhhBCmYHTKkXbt2nHs2DFW\nr17N6dOnmT9/Pp07d7aadToeNGz+o5/W8a9Pt5K5dp2FohJCCOtWlilHjJY4nJ2duXDhAp999hnD\nhg1DURQyMjJKdTFz0UocUlUlhBCmYDRxvPHGG4waNYquXbvStm1bfv/9d9zc3MwRW6lJG4cQQpjO\nQ3tV5efnc+LEiUJduFq0aMHXX39t6rjKRKZVF0II03loicPGxoZ169aRk5NjrnjKhU5mxxVCCJMx\nOo7jiSeeYOTIkTz//PM0aNDAsL1Dhw4mDawspMQhhBCmYzRx7N+/H0VReO+99wpt37Nnj8mCKiud\nokMGAAohhGkYTRzlNUTdnBRFkSlHhBDCRIz2qrp58ybz58+nf//+AJw+fZqVK1eaPLCysNHJJIdC\nCGEqRhPHm2++iaOjI3FxcQC4ubnxwQcfmDquMpGFnIQQwnSMJo5Dhw4xbtw4bGxsAKhSpYrhd2sl\nAwCFEMJ0jCaODh06cOnSJcPjb775ptQrAJqLgizkJIQQpmK0cXzKlCmMHz+euLg4WrZsSbNmzVi6\ndKk5Yis1nbRxCCGEyRhNHO7u7mzevJnExETy8/OpV6+eOeIqExtFFnISQghTMZo4VFXl8OHDHDhw\ngJycHFRVRVEUpk2bZo74SkXrVSWJQwghTMFo4hg3bhznzp3D398fOzs7c8RUZrJ0rBBCmI7RxBEZ\nGcmJEyceuua3tdEp0jguKrfsvGzsq9hbOgzxN2W0V9XTTz/NmjVryM7ONkc85UKbckQax0Xl1X5F\ne04nnrZ0GPfJz4ddu+C//4WEBEtHI0zFaOIICAhgwoQJODo6Gn6cnJzMEVup6WSSQ1GJ3cq6xZmk\nMxy+dtiYUzR5AAAgAElEQVTSodznk09g7Fh4/3346itLRyNMxWhVVWhoKFu3bqVz584Vpo3DRicj\nx0XFpaoql1Mv09i58QOfP3b9GAAnEk6YM6xi2boV3n4bkpMhOtrS0QhTMVriaNiwIR06dKgwSQOk\njUNUbGFHwmiyqAkBqwI4cu3Ifc8fvX6URk6NOHHDuhJHZiZERsJjj0H79nD0qKUjEqZitMTRqlUr\nAgIC6N+/P87OzgAVozuuIm0couLJzM1k9k+ziXwxkt2xu5nz0xy+HfxtoX2O3jjKUK+hrDuxzkJR\nPlhEBHh7g4sLeHlBTAzk5oKtraUjE+XNaImjfv36hISEYGNjQ3p6Ounp6aSlpZkjtlJTZD0OUUF9\nGP0hfg396KrvyiT/SeyJ20NyZnKhfY5eP0qIRwgpWSncyrploUjvt20bPPmk9nu1atCkCZw5Y9mY\nhGkYLXHMnj3b8Pu1a9eoX7++KeMpFzLJoagoVFUlJSuFWlVrcT39Ogt+XkDki5EAODs480SLJ9h4\naiNjO44FICc/hzNJZ2jr2hbPOp6cTDhJN303S74EAFRVSxybNv257W51lZeX5eISpmG0xHGvvn37\nmiqOcqWThZxEBfHp4U9ptrgZR64dYWr4VEZ7j8ajtofh+WFth/HFiS8Mj2MSY2jm0oxqttXwquvF\nyYSTlgj7PnFxcOdO4SQh7RyVV4kSh6pWjHYDKXGIiiAjN4M5P81hSqcp9FrTi6jLUbzZ481C+wS3\nDObszbPM2DGDqMtRbDu3jfb12gPgVdfLanpW7d0LPXrAveOEJXFUXkarqu41ZswYU8VRrmQFQGHN\n5u6dy4HLB9A76+nauCtzAufgXc+bejXqUc22WqF97WzsODD6AJ/8+gljt46lQC3g3wH/BqBN3TZs\nPL3REi/hPnv3Qvfuhbe1bw9HjkBODlSgTpmiGIyWOO5dJnbcuHEAvPrqq6aLqBzIAEBhzaKuRFHT\noSY/xf3E3KC5AIR4hNCpUacH7t+8ZnPe6f0OR18+yvHQ4wxuMxiATo06cSHlAlGXo4q81i+XfyE7\nr3xnfVBViPrLJR+UOFxdoWdPGDZMG1EuKg+jieN///sfa9euNTweP348iYmJJg2qrCRxCGt28dZF\nZnSZwenxp3n0kUdLfZ7qdtWZ12seU8KnFFmNPOh/g9h+fnupr/EgFy9Cp07w88/a42vXtAF/rVvf\nv+8XX2jPeXpCgwawZUu5hiIsxGji+Oabb/jss89Yv349I0aMoEqVKoSFhZkjtlLT6WQAoLBeF29f\npIlzk3I514h2I8gryOO9A+/dlzxuZ90m/nY8ey/uLZdr3XXoEFSvDnPmaI8jI6FbN9A94NPEwUFL\nFqtWwfDh2lgPUfEVmTiSk5NJTk4mMzOT//73v8yfPx8nJydmzZpFcnJyUYdZBRuZ5FBYqVtZt1BV\nFRcHl3I5n07Rsfbptaw7sY4nv3iS/x7+r2HywxMJJ6hmW80kiWPqVG2Mxvr18Pnn91dT3atqVejS\nRau2OnL/QHhRARWZODp06ICPjw8+Pj4EBgZy69Ytvv/+e3x8fOjYsaM5Yywx6VUlrNXFWxdp4tKk\nXJcpcK/tTtQ/o3jq0afYF7+PbmHdyC/I5/iN4zzT6hnOJJ0hNTu13K536JCWCN56C2bO1Noyhg83\nfpy3t5Y4VFX7954hYqKCKbJXVVxcnBnDKF+SOIS1Ks9qqnvZ2tgywW8CAF7LvDh09RAnEk7g28CX\n+Nvx/HzpZ4JbBpf5OqoKv/4KPj5Qty6MGFH8Y11dtRHlcXGwejV8+in8619atZeoWEo0jqOiUP6Y\n5LCCDDsRfyN3Sxym9Fjzx9h5YSfHbxynrWtbujfpTmR8ZLmc+/ffwdFRSxqlcbfUsWWLlki2l2+7\nvTCTSpk4tF5VqiQOYXVMVeK4V+/mvdlxYQcnE07i5epF9ybd+Snup3I596+/Qllqqjt0gHXrIC8P\n/u//4OuvtRHnM2Zos+sCHD4Mly6VS7jCRIpMHPv37wcgKyvLbMGUl7vdcSVxCGtjjsTRvUl3oi5H\n4WjnSK2qtejYoCPHbhwrl5kfDh0qW+Lw9taSRb9+EBKilTiGDoUlS/7sqjtqFEyZUuZQhQkVmTgm\nTZoEQOfOncvlQpcuXaJnz560bt2awMBA1q3TpoROS0tjwIAB6PV6QkJCSE9PNxyzZMkS3Nzc8PT0\nZN++fcW+liQOYa3MUVVVw64G/o388XLVJo5ycXDB3saehDulX8tVVbUutatXQ+/epY/N21v7t18/\nraqqfXtISoIPP9TGfBw9qo37iIyUmXWtWZGN466urowcOZIrV64wadKkQt9WFEVhyZIlJbqQra0t\nH3zwAe3btycpKQk/Pz/69evHsmXL0Ov1bNiwgenTp7N8+XJmzJhBQkICS5cuZdeuXcTGxjJp0iQO\nHy7eUpmSOIS1MkeJA+CZVs+QkZtheOz2iBvnks/hWsO1VOd76y343/9g505o1670cTVpAv/8JwQG\nao8//xycnLQ5rl55RVty9sUXwcYGFiyAeyauEFakyMTxzTffsGPHDvbs2YOPj899iaOk6tWrR716\n9QCoXbs2rVu35uDBg0RHRzNz5kzs7e0ZNWoUb7/9NgBRUVEEBwej1+vR6/WoqkpaWhqOjo5Gr6Wg\nNY7LIjLCmmTmZnI763apP7xLYpL/pEKPW9Zqyfnk86Wagv3QIfj4Y61Ru0GDssWlKFpvqrsa37M6\nbnCwlkjOn4eaNcHNTUsm7u5lu6Yof0UmDgcHB/r3749er6d9+/bletHz589z6tQp/Pz8ePHFF/Hw\n0KaR9vDwIPqPhYqjoqJo1aqV4Rh3d3eio6Pp1auX0fPrFB2KTiUjQ+v+J4Q1iL8dT2PnxlqJ2Mxa\n1tQSR0nl5WldbhctKnvSMOall7QVA1u00B6/9Ra88ALs2wdVSjQdqzA1o3+O+vXr8+qrr7Llj5ar\n/v37M23aNOqWsj9eWloagwYN4oMPPqBGjRolarArqqRz72JTgYGBePl5oSgFZGQ8cHchLOLz45/T\nzrUM9Txl0LJWS7b8VvKJonbt0rrfDh5sgqD+olcv7eeul1/WFoZ69134979Nf/3KLiIigohymvPF\naOJ45513qFu3ruGCYWFhvP3223zwwQclvlhubi7PPPMMw4cPZ8CAAQD4+voSExODt7c3MTEx+Pr6\nAuDv78/OnTsNx545c8bw3F/N/ssQ1JTMFNBJ4hDW48uTX7L2+Fqi/ln0TLam5PaIW6lKHF99pSWN\nchzoXmw6ndbG0aEDPPssPFr6+SAF2pfqwLuNS8Ccu5ONlYLRMvPu3bt57bXXqFu3LnXr1uWVV15h\n9+7dJb6QqqqMHj2aNm3aMOWevnb+/v6EhYWRmZlJWFgYnTppU0v7+fkRHh5OfHw8ERER6HS6YrVv\nwJ8DACVxCGuQlJHE+G3j+W7wd2Zp33iQlrVaci75HKqqkpSRRIFqfGaF7Gz49lv4xz/MEGAR9Hpt\nWpOXX0Y6u1gRo4kjMDCQBQsWcPPmTZKSkvjggw8KZa3i2r9/P2vXrmX37t14e3vj7e3NDz/8QGho\nKPHx8bi7u3PlyhVefvllQOvVFRoaSlBQEOPGjWPx4sXFf1GKDkVRDQOKhLCkj6I/YmCrgbSrZ5lq\nKoBaVWtho9hwOfUybZe15e3It40es2MHtGkDDRuaIcCHmDABbt/WGuiFdVBUI40MV69eZeHChWz/\nY26APn36MGPGDOrXr2+WAI1RFOW+dpL0nHRc5tZje0A6jz1mocCEQHsvNlvcjP2j9pdp7Y3y4Pep\nH63rtuZs0lnOJ5/nx+E/PjCZXbum9XzatEnrOjt+vAWC/Yvz57Wlad97zzztLX8HD/rsLPaxxhKH\ntXvQi8/IzcBpbm2+7pDBH00pQpjVrgu7GPrNUPTOepq4NGHjc5Zf4nXoN0NZd2Ide0fu5feU31l6\ncCnRY6Lv22/iRG2xpqefhuefB3t7CwT7ACdOaIMPH30Uxo7VVhYUpVeWxFEpO7kpKKhIG4ewnI2n\nNzKy/Ug863gSoA+wdDiA1s7RpXEXuum70blxZ8Z9P4607DQc7f9sOywogG++0XpT/dFL3mp4eUFs\nrBbbCy/AoEEyTstSKvEkh5I4hGWoqsrW37byYvsXGdFuBM1qNrN0SABM9p/Mhmc3oCgKVXRV8Kjt\nYVj06a6oKHBxsb6kcVe1atp0JS1aaOM7hGVU3sSBNI4Lyzhy/QjVbKvhXtu6hjzXqlqLhk5/tnR7\nuXpxIuFEoX3+9z+t66u169dP1i+3JKOJY+7cuYbfK8pMuTpFJ1VVwmK2nN1CP/d+lg7DKK+6XpxM\nOGl4rKrazLXPPGPBoIrpbuKo2C20FVeRieOdd97h559/ZuPGPxv1unTpYpagykqn6FClqkpYyJbf\ntvCU21OWDsOoNnXbFCpxHDgADg5aW4K1a99eW7/j7FlLR/L3VGTi8PDwYOPGjcTGxtKtWzfGjBlD\nUlISZyrAXMd3pya5kyFfR4R5ZeRmcDrxdKkmEzQ3r7penLjxZ+JYuVJbC8MSo8RLSlGgb19ZQdBS\nikwcLi4uvP3227Ro0YKIiAgmTZqEoijMnz+/3NboMCUFhQxJHMLMTiee5tFHHsXWxvq7+zRwbEBe\nQR4JdxJIT9d6U5VkDXFL69pVa8wX5ldk4ggPD6dv3778/vvvTJ8+nejoaKpVq8aqVas4cOCAOWMs\nFQUddzLyLR2G+Js5ceMEbeq2sXQYxaIoilZddeMEGzZA9+7wx8oHFYKvL0TfPwxFmEGRiePtt99m\n165dNGvWjOHDh5OXl0dSUhJdu3alXz/rb/iz11UjLVsaOYR5nUg4gVfdCtBI8AcvVy+O3ThGWBiM\nHm3paErG3R1u3tRWEBTmZbRX1RNPPEHHjh0ZO3YsjRo1Yv/+/YSFhZkjtjKpZuNMavZtS4ch/mZO\nJpw0LNlaEfR168usPXOIatcR904XLB1Oieh04OOjLTSVlibdc83JaOJ49913Db+vXr0agDp16pgs\noPJSo4oT6bmplg5D/M2cSKg4VVUAfdz6MPZ2Em1rdibs+ApLh1Nivr5w8KC20NSAAdrvwvRKNACw\nXVkWGzYzRztn0vOkxCHMJykjiYzcDBo7NTa+s5XIzYUv1tgyu99Y1p9YX6zp1q2Jnx9ERMBHH2mL\nPY0dq61aKEyrUo4cB3C0cyIjT0ocwnxOJpykTd02Ra5UaY1++EGbvqOffxtqVa1F5MVIS4dUIr6+\nsHs3dOumLTVbsyZUgJr0Cq/SJg4XB2cyVSlxCPM5caNiNYwDfPklDB2q/T7UayhrT6y1bEAl1Lgx\ntGoF//d/2tiOiRNhwwZLR1X5VdrE4VzViSxVShzCfKKuRNHWta2lwyi27GzYtk2bPh3gea/n+Sbm\nG/6969/cyrpl2eCKSVG06dbvrirdu7c2tuO2fGc0qUqbOGpVcyYbefcI8zh38xw/nP+BQa0HWTqU\nYvvxR2jb9s+xGw2dGnJ07FEu3r5IyJchpV6rwdxsbP78vUYNrdoqPNxy8fwdVNrEUbOaEzmKlDiE\neczcM5NpnafxSLVHLB1KsT1oQsPGzo1ZE7KGlKwUvo752jKBldHdCRB//x1WrbJ0NJVTpU0ctWs4\nk1fltsyeKUzuyLUj7Ivfx2T/yZYOpVgGD4bgYPj2Wxg48P7nbXQ2LHpiETN2zCAzt+KtTfDUU7B5\nM3TpApMnw6VLlo6o8qm0icPZwQmbaqlUkJngRQW24OcFTOs0jep21S0dilHJyVq7xujR8Pbb0KjR\ng/fr2awnHep34MPoD80bYDnQ67UVAjdu1P795BNLR1T5VMqlYwGc7J3QVbtNRgZUrWrpaERldTn1\nMj+c/4FlfZdZOpRi2bEDAgPhueeM7zuv1zwCVgUwpsMYalatCcDNjJvUrFrzj8XSrNeSJdq/depA\nUBC88YZW8mjevGLM/mvtrPuvXwbODs7oHFJlTQ5hUh9Ff8TwdsNxdnC2dCjFsm0bPPlk8fb1qO3B\n0x5P887+dwDIzc/F779+rDhUcUaYt2oFnp7w6KPQsqU0mpeXSps4nOydQBKHMKHvznzHp4c/ZaLf\nREuHUiwFBdqAv+ImDoBZPWYRdiSMo9ePsvb4WjJyM1h9bLXJYjSFjz7Sqqs+/BD+mDVJlFGlrapy\ntndGtbstiUOYxOu7X+erk1+xadAmWtZqaelwiuXQIa3qpmnT4h/T0KkhS4KX8I+N/yCvII91A9cx\n9JuhnE48jWcdT5PFWp5atdJ+OnaEmTMhJUUbYS5Kr1KXOFQ7KXGI8vfbzd/45NdPiPpnFN2bdLd0\nOMVSUADz5pVuPfEhXkMIahZEi1ot6NmsJ8PbDeezY5+Vf5AmVqsWPPEEfPWVpSOp+Cpt4nB2cCa/\nym0yK15vQmHlZu6eybROFWvMxptvamtXvP566Y5f1ncZW4dsBWBku5F8dvQzkjOTyzFC8xg5EhYv\nhhs3LB1JxVZpE0fVKlVRdbncTs+xdCiiEtkdu5v9l/YzuVPFGLMB2lTjq1drS8Pa25fuHIqiYF9F\nO7hVnVY87/U8z218jtz83PIL1AyCg7UeZb6+cOSIpaOpuCpt4lAUBdt8Z26my+hxUT62ndvG4P8N\nZk3IGqrZVrN0OMX2/vswbZrWvlFeFjy2AIcqDkzbMa38TmoGigL/+Q8sWKANFJTBgaVTaRvHAexU\nJ1IyUoHalg5FVGBhR8JY+PNCUrJS2DxkM50adbJ0SMV26ZLWBXX58vI9r43OhnUD19Hhkw5013fn\nudbFGBhiRQYNgvh46N8f9u+HahXne4BVqLQlDgB7nEjOkIkORemlZqcyY8cMlj+1nMtTL1eopFFQ\noH2zHjECnE0wzMTZwZkNz25g/LbxnEw4Wf4XMLEZM7RR5ssqxthNq1KpE0dVnTOpWVJVJUpv5eGV\nPNbiMbo36Y6Nzsb4AVZi505wd9dWx5tmwtoknwY+LHlyCUGfBbEpZpPpLmQCigJz52rJ9c4dS0dT\nsVTyxOHE7WwpcYjSyS/IZ0n0EqZ2mmrpUErk9m1tjqb33oNjx7Rv1aY0uM1gtg3dxvht49l8drNp\nL1bOvLwgIEBKHSVVqds4qtk4k5opJQ5ROssPLad+jfoVqnoKtC63fftq9ffm0rFBRzYN2kS/9f3Y\nNWIXXq4VZyXEWbOgRw9IStJ6XSUkaFOzyxx3RavUJY4aVZxIz5XEIUpuw6kN/L/I/8fqkNWWDsWo\npCQ4cADOnYMpU2DTJnjnHfPH4d/In/efeJ9nNz5LRm7FGXnbpg0cParNHPz669q9mzFDe+6112Dc\nOMvGZ40qdYnD0c6ZxDypqhLFdzXtKrMjZrP57GbCh4Xz6COPWjqkh/rtN3j8cW0KjRs34Nln4fBh\nbZS0JQxrO4zt57fz2q7XWBy82DJBlELjxn9Ov37rlrYyYk4O7N0L+fnw3XcwYIBlY7QmlTpxONk7\nEZ8vJQ5RPMmZyXQN68qzns8SMz7GMJW4tUlPh6lTta62R49qU4mMGmXpqP700ZMf0XZ5W5ztnXnJ\n5yUaORWx6IeVcnGBlSth+HAtcSQkaIMGVVVbltbBQUsuFy9qpRVT9FizdlZdVbV3715atWqFm5sb\nH35Y8gVlalZz5laWlDhKIiIiwtIhlFhufi47ft/BnZzSd41RVZUXv3uRAe4DWPDYgnJJGuV5L48e\nhf/7P23092OPQV6eVi21Y4d1JQ2AmlVrsmvELpIykmi3vB1fn75/CVpVVUnLTivRec353nzsMbh6\nVZuOvVs3WLgQli7VpmavV08beT5lijZh5JgxEBdnttCsglUnjsmTJ7NixQp27tzJxx9/TFJSUomO\n923rxO2sVCrgZ6HFVLTEUaAW8OJ3LzJ261j0i/T8Y+M/eH3365xOPP3A/eNvxzMrYhZ91/XlqXVP\nseHUBnLzc5m4fSLX06/z7mPvllts5XEv8/O1kc6PP679vmwZ9OoFYWFaQ27btmWP0xQefeRRlvZd\nys7hO5mwfQJDvh6C58eezIqYRYFawKjNo3jk3UcY+NVAlh1cxu7Y3ahG1nk293tTd8+n49ChWpK+\ndUsr8V27pk3lcuYM1K+vzbz7xBPaaPT//ldrdxo9WnuckHD/uSv6ktZWW1V1+7ZWUujeXZt99PHH\nHycqKoq+ffsW+xwedZujezScYYuXsbN1b2xtdDR0akh+tgORkVqdcBXbAhr6HCONy3g5d+POreok\nZydQt2oDXJx1uLo+/BrpOemkZKZQ37E+NzNuEnUlCp96fthm16OqcxrX06+jU3Q0dWmKTrEhKUmb\nL8jJ6c9zHD2qTUDXpQvE3DrMpjObGOgxEO/63oWulZGh1bu6uPy5LTMTLl/Wfm/SBOzsICUzhcy8\nTOrVqEfqnRxi4hNo3cSVGtVsuZ5+nWq21XC0c+TAxV+5mpTOwA6BVLHR/pdk52WTlJFE7Wq1UVWV\nS6mXuHkrm7ysqrRrXh90+cTfjufE5d9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       "text": [
        "<matplotlib.figure.Figure at 0x446cd50>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## question:\n",
      "\n",
      "Why is the green abundance distribution shifted left?? didn't we just eliminate abundance-1 k-mers?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## DIGRESSION - back to presentation to talk about digital normalization"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# run digital normalization\n",
      "!python /usr/local/share/khmer/scripts/normalize-by-median.py -x 2e8 -N 4 -k 20 -C 20 reads.fa"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r\n",
        "PARAMETERS:\r\n",
        " - kmer size =    20 \t\t(-k)\r\n",
        " - n hashes =     4 \t\t(-N)\r\n",
        " - min hashsize = 2e+08 \t(-x)\r\n",
        " - paired =\t      False \t\t(-p)\r\n",
        "\r\n",
        "Estimated memory usage is 8e+08 bytes (n_hashes x min_hashsize)\r\n",
        "--------\r\n",
        "making hashtable\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "DONE with reads.fa ; kept 3044 of 16000 or 19 %\r\n",
        "output in reads.fa.keep\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "fp rate estimated to be 0.000\r\n"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# let's look at the abundance distribution post-diginorm\n",
      "!python /usr/local/share/khmer/scripts/load-into-counting.py -x 1e8 -N 4 -k 20 counts-dn.kh reads.fa.keep\n",
      "!python /usr/local/share/khmer/scripts/abundance-dist.py -s counts-dn.kh reads.fa.keep reads-dn.dist\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r\n",
        "PARAMETERS:\r\n",
        " - kmer size =    20 \t\t(-k)\r\n",
        " - n hashes =     4 \t\t(-N)\r\n",
        " - min hashsize = 1e+08 \t(-x)\r\n",
        "\r\n",
        "Estimated memory usage is 4e+08 bytes (n_hashes x min_hashsize)\r\n",
        "--------\r\n",
        "Saving hashtable to counts-dn.kh\r\n",
        "Loading kmers from sequences in ['reads.fa.keep']\r\n",
        "making hashtable\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "consuming input reads.fa.keep\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "saving counts-dn.kh\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "fp rate estimated to be 0.000\r\n",
        "DONE.\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "hashtable from counts-dn.kh\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "K: 20\r\n",
        "HT sizes: [100000007, 100000037, 100000039, 100000049]\r\n",
        "outputting to reads-dn.dist\r\n",
        "** squashing existing file reads-dn.dist\r\n",
        "preparing hist...\r\n"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dist_dn = numpy.loadtxt('reads-dn.dist')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(dist[:,0], dist[:,1], label='reads')\n",
      "plot(dist_filt[:,0], dist_filt[:,1], label='filtered reads')\n",
      "plot(dist_dn[:,0], dist_dn[:,1], label='diginorm reads')\n",
      "xlabel('k-mer abundance')\n",
      "ylabel('# of k-mers with that abundance')\n",
      "axis(ymax=1000)\n",
      "legend()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "<matplotlib.legend.Legend at 0x46c8c10>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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s/v37jBgxgieffBJfX1+io6NJT0+nf//+qNVqBgwYQEZGhub+lStX4uXlha+v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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x446d490>"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Note, after running digital normalization, you have a LOT less data:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!ls -la reads.fa*"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "-rw-r--r-- 1 root root 1780890 2013-06-19 13:56 reads.fa\r\n",
        "-rw-r--r-- 1 root root 1361833 2013-06-19 13:56 reads.fa.abundfilt\r\n",
        "-rw-r--r-- 1 root root  336948 2013-06-19 13:56 reads.fa.keep\r\n"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    }
   ],
   "metadata": {}
  }
 ]
}