comparison of runtime for single_rarefaction.py in QIIME 1.8.0 and 1.8.0-dev

180-dev.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:08ad309f1d4e7e35e9665af74d65e02e2bbf77ca41636083884f999a4e766136"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from __future__ import division\n",
      "from timeit import timeit\n",
      "from biom import load_table\n",
      "import pandas as pd\n",
      "from glob import glob\n",
      "\n",
      "t = load_table('table_mc5038.biom')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "(52663, 396)"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "axes = ['observation', 'sample']\n",
      "shape = t.shape\n",
      "\n",
      "for i, axis in enumerate(axes):\n",
      "    data[axis] = []\n",
      "    step_size = np.ceil(shape[i] / 10)\n",
      "    for n in arange(step_size, shape[i], step_size):\n",
      "        ids = t.ids(axis=axis)[:n]\n",
      "        subt = t.filter(ids, axis=axis, inplace=False)\n",
      "        out_path = 'test-tables/t.%d.%s.biom' % (n, axis)\n",
      "        subt.to_json('me', open(out_path, 'w'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = {'observation': [], 'sample': []}\n",
      "for fp in glob('test-tables/t.*.biom'):\n",
      "    fields = fp.split('.')\n",
      "    n = fields[1]\n",
      "    axis = fields[2]\n",
      "    def sr():\n",
      "            !single_rarefaction.py -i $fp -o x.biom -d 500\n",
      "    data[axis].append((int(n), timeit(sr, number=3) / 3))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.DataFrame(data['observation'], columns=['n', 'mean run time'], dtype=float)\n",
      "df.sort('n', inplace=True)\n",
      "df.plot(x='n', y='mean run time')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x117c26290>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAWwAAAEPCAYAAABm//5NAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nzv95XAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x118573890>"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.DataFrame(data['sample'], columns=['n', 'mean run time'], dtype=float)\n",
      "df.sort('n', inplace=True)\n",
      "df.plot(x='n', y='mean run time', legend=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10de7e5d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXUAAAEPCAYAAAC9RFRvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x114673a50>"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.plot?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}

180.ipynb