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

180-dev.ipynb

180.ipynb

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  {
   "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": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# t.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "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": 3
    },
    {
     "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": 4
    },
    {
     "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": 5,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x107c6e450>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Mevr6ype6+YLBYNQ/v3UrtGsXYPx4s/3yy1CxYoAKFeD4473P58W27fncbAcCAXJycgAK\n62UkpR2H3RXYAdwL+IHfgBqYM++6EX5ePWyRUti3zwzL697dXInYsyf85S9ep5JEK2sPuxqQ7tw/\nEmgBLAQmAHc5++8CPo5JSpEUNmWKmdtjxAiYMMFcmahiLeEOVbBrAF9gethzgYnAv4EsTPFeAVzq\nbCeFgrchtlI+d2zOV1y2JUvgqqvMRS69epmJmRo2TGg0wO5jB3bnS1S2Q/WwlwDnR9i/Cbg89nFE\nUsdvv0HXruZs+tln4YEHoFIlr1OJzTSXiEiC5eVB375mlry2beGZZyA9/dCPk9Sh+bBFPJafDzk5\nZumtpUth/nx4+WUVa4leyhVsm/tgoHxu2Zrviy/gjDMCDB4MY8eauT5OPdXrVPuz9dgVsDmfLT1s\nEXFh2TLo3NmcUd95pxmup3mopazUwxaJg/XroUcPGDMGunQx830ccYTXqSRZqIctkgA7d0JWlll1\n/PDDYflyePxxFWuJjZQr2Db3wUD53PIqX34+jBwJdeuaeahnz4ZXX4UTTvA+W7SUr+zUwxZJEjNm\nmLPoUAiGDYNLLvE6kZRX6mGLlNF//gNPPgkLFkDv3nDbbVAh5d6zSjyohy0SIxs3wqOPwt/+Bo0a\nmT717berWEv8pdyfmM19MFA+t+KZb9cuc4Vi3bpmea7vvzcjQI480vtssaB8ZacetoglQiFzsUuX\nLmb0x1dfmaW5RBJNPWyREsyZYz5QzMuD7GwzR7VIvKmHLVIKK1ea9RNvugnuuw+++UbFWryXcgXb\n5j4YKJ9bbvNt3gxPPGHmo65XD374ATIzoWJF77PFm/KVXaKypVzBFolkzx4YMADOPNMU7e++g27d\n4OijvU4mUkQ9bElpoRCMH28maDr1VDPd6bnnep1KUl1xPWyNEpGU9c030KkTbNgAr71mlukSsVnK\ntURs7oOB8rkVTb4ff4Q2baB1a3PBSzCYmGJdHo6dl2zOZ1MP+8/AdGAp8B3QwdnfA/gFs4r6QkDn\nJ2KtUAimToVWraBxY7Ma+Q8/mBEgh+l9piSJaHrY1Z1bEDgGWABcD9wMbAX6lfBY9bDFU9u2mQmZ\nBgww050+8oiZ8yPaqxNFvOCmh/2bcwPYBiwDahY8byzCicTaf/8Lb7xh1lD0++Gtt+Dii7XaiyS3\n0vawfUADYI6z3R5YBAwFkmIpUZv7YKB8boRC0LdvgOuug7/+1bQ6vv0Wxo0zU556XaxtPnagfG7Y\nOJfIMcBY4BHMmfZA4Dnne72AvsA9Bz4oMzMTn88HQHp6OhkZGfj9fqDol0zkdjAY9PT1lS/22xde\n6GfECMjKCrBjR5BevfyMGgXz5gVYuRJq17YjbzAY9PT1lc/e7UAgQE5ODkBhvYwk2nOOSsAkYDLw\naoTv+4CJQP0D9quHLXGzahW8+Sa88w40bWr6036/92fSIm65mUskDdPy+J79i3WNsPs3AEtc5BOJ\nSigEgQD8/e/m8vH8fJg/Hz7+GJo3V7GW8i2agt0EuANoTtEQvquBl4DFmB52M+CxOGWMqYK3IbZS\nvsh27IAhQ+C88+Chh+CKK8wZdna2uULR63zRsDkbKJ8bicoWTQ97JpEL++QYZxE5yOrVpu0xdKj5\nILFfPzNrns6kJRVpLhGxTigEM2eay8W/+ALuvBPatYM6dbxOJpIYxfWwVbDFGjt3wqhRplDn5UGH\nDqZYH3us18lEEksLGDhs7oNBauZbswaefRZq14YxY+DFF2HZMnNWXdpibfPxszkbKJ8bicqWcgVb\n7BAKwaxZZlWX+vXhjz9gxgz49FMzEVMF/WWKHEQtEUmoXbtg9GjT9ti8Gdq3h7Zt4bjjvE4mYg/1\nsMVTv/4KgwaZOT3OO8/0p6++WmfSIpGoh+2wuQ8G5S/f3LlmzumzzzYLBUyfDlOmwDXXxKdY23z8\nbM4GyueGTeOwRUpl924YOxb694fff4eHHzYz56UnxfRgIvZSS0RiZvNm05seNMisON6hgzmTjsWK\n4yKpRC0RiZt9+0xvum5dc7n41Knw+edmCS4Va5HYSbmCbXMfDJIv3/TpcP755oKXzz4zM+edc443\n2cDu42dzNlA+N9TDFqv997/wxBNmgYDsbDN7nub3EIkv9bClVLZuhd694e23oWNHc6tc2etUIuWL\netjiSn4+vPsunHmmGVO9eDE8/bSKtUgipVzBtrkPBnbm+/praNTInFV37RogJwdOPtnrVJHZePwK\n2JwNlM8NzSUinvvf/+C228x8Hx07msJ91llepxJJXephy0G2b4c+feD1181FL507w9FHe51KJHUU\n18PWKBEpFArByJHQpYtZ1HbhQqhVy+tUIlIg5VoiNvfBwLt88+dDkybwyivwwQdmXHWkYq3jV3Y2\nZwPlc8OmHvafgenAUuA7oIOzvyowDVgBTAU0U0QSWrsW7roLrrsO7rsP5s0zhVtE7BNND7u6cwsC\nxwALgOuBtsAGoA/wJFAF6HLAY9XDttTOnWZB2379TKF++mktxSViCzc97N+cG8A2YBlQE2gNNHP2\nvwcEOLhgi2VCIRg3zlyl2KCBOaP+y1+8TiUi0ShtD9sHNADmAicB65z965xt69ncB4P45gsGoXlz\n6NXLzPnx0UelL9apfPzcsjkbKJ8bNs4lcgwwDngE2HrA90LO7SCZmZn4fD4A0tPTycjIwO/3A0W/\nZCK3g8Ggp6/vRb569fw8+yyMGROgbVt4+WU/FSvak8/24xer7WAwaFUe5bNnOxAIkJOTA1BYLyOJ\ndhx2JWASMBl41dm3HPBj2iU1MB9M1j3gcephe2j3bjM/dVYW3HkndOumRQREkoGbuUTSgKHA9xQV\na4AJwF3O/buAj91FlFgJhWDiRLMs1/Tp5grFfv1UrEWSXTQFuwlwB9AcWOjcrgKygBaYYX2XOtvW\nK3gbYiu3+ZYuhSuvNFcnDhgAn3xiJmyKlfJ+/OLJ5mygfG4kKls0PeyZFF/YL49hFnFh40bo3h0+\n/BCefRYefBAqVfI6lYjEkuYSSXJ79sDAgfD883DzzdCzJ5xwgtepRMQNzSVSDk2ZAo89BjVrwhdf\neLs0l4jEn+YSsUw0+VasgFatzEx6WVlm0dtEFevycPy8YnM2UD43EpUt5Qp2Mtu8GR5/HBo3hmbN\n4LvvzMrkWktRJDWoh50E8vNhyBAzjvraa02/+qSkuK5URMpCPewk9b//Qdu2kJcHkyeb+T9EJDWl\nXEvE5j4YFOULhWDECGjYEC67DGbMsKNYJ8vxs5HN2UD53LBpHLYk2MaN8MAD8P33ZiSIDYVaRLyn\nHrZlJk+Ge++FW26B3r2hcmWvE4lIoqmHbbnt26FTJ/j0U9MKad7c60QiYhv1sC0wZ45pe2zfDm+8\nEbC6WNt4/MLZnM/mbKB8bmgcdgrYswe6doXrr4cXX4Rhw+CYY7xOJSK2Ug/bI8uWwR13QPXqZox1\njRpeJxIRW7iZD1tiKD8f+veHSy6Bf/4TJk1SsRaR6KRcwfayD7Z6NVxxBXzwAcyeDffff/Bl5Tb3\n6UD53LA5GyifG+phlyOhELz/PlxwgRn9MWMG1KnjdSoRSTbqYcfZpk1mMYElS8xwvfPP9zqRiNhO\nPWwPfPYZnHsunHwyLFigYi0i7qRcwU5Er2n7dmjXzvSohw2DV16BI4+M7rE29+lA+dywORsonxs2\n9bDfAdYBS8L29QB+Yf9FeQWYO9dcBPPHH7BoEVx6qdeJRKS8iKaHfTGwDRgG1Hf2dQe2Av0O8diU\n6WHv2WPmqR40CN54A266yetEIpKs3MwlMgPwRXpOd5HKj+XLoU0bqFYNgkGNqxaR+HDTw24PLAKG\nAumxiRN/sew15efDa69B06Zwzz1m4ia3xdrmPh0onxs2ZwPlc8P2+bAHAs8593sBfYF7Iv1gZmYm\nPp8PgPT0dDIyMvD7/UDRL5nI7WAwGJPn++UXuO66ADt2wOzZfk4/3a588dpWvrJvB4NBq/Ionz3b\ngUCAnJwcgMJ6GUm0bQ0fMJGiHna03yuXPexRo+CRR6BDB+jSBQ7TJLUiEkOxng+7BvCrc/8G9h9B\nUm5t2gQPPWRGf0yebK5cFBFJlGh62KOAWcCZwGrgbuAlYDGmh90MeCxeAWOt4G1IaU2ZYi6CqV4d\nvv02fsW6rPkSRfnKzuZsoHxuJCpbNGfYt0XY906sg9gqLw86d4YJEyAnBy6/3OtEIpKqNJdICebP\nN8P1GjaE11+H9KQZCyMiyUxrOpbCnj3wwgswcCAMGAA33+x1IhERzSVykB9+gCZNzDqLCxcmvljb\n3KcD5XPD5mygfG4kKlvKFezihEKm7dGkCWRmmlEgJ5/sdSoRkSLqYQNr1sDdd8PmzTB8OJxxhteJ\nRCSVaT7sYnz4oZmnukkT+PprFWsRsVfKFeyCXtPu3fDww/DMM2Yh3G7d7Lhi0eY+HSifGzZnA+Vz\nw6Zx2OXOmjXwj3/An/5khu5puJ6IJIOU62F/+SXcdps5u+7SBSqk3HsMEbFdyo/DDoXMUl19+phl\nu664wutEIiKlkxLnl9u2wa23wsiR8OqrAauLtc19OlA+N2zOBsrnhsZhx8jy5dCoERx7LMycaSZv\nEhFJRuW6h/3RR2bl8hdfhHvv9SyGiEippFQPe+9eM1xv9GizbNeFF3qdSETEvXLXElm/Hq680swD\n8s03Bxdrm/tgoHxu2ZzP5mygfG6oh10Gc+eaqVAvusjMBVKtmteJRERip1z0sEMhGDwYunaFt9+G\n666L+0uKiMRNue1h79hh1lmcP9+MAtFcICJSXiV1S2TlSmjcGHbuNO2QaIq1zX0wUD63bM5nczZQ\nPjds6mG/A6xj/5XRqwLTgBXAVCDhs3FMnmx61W3bmgtijj460QlERBIrmh72xcA2YBhQ39nXB9jg\nfH0SqAJ0ifDYmPew8/Ph+efhrbfMsL2mTWP69CIiniuuhx3th44+YCJFBXs50Axz5l0dCAB1Izwu\npgU7N9csirtli5nHukaNmD21iIg1Yr2AwUmYYo3z9aQyPk/UFi0yQ/bq1IEvvih7sba5DwbK55bN\n+WzOBsrnRjLNhx1ybhFlZmbi8/kASE9PJyMjA7/fDxT9kofaXr3aT8eO8MADAS67DCpVKt3jw7eD\nwWCpXz+R28pXfvMFg0Gr8iifPduBQICcnByAwnoZiZuWiB/4DagBTCcOLZHdu+Gxx2DaNDMvyDnn\nlPmpRESSRqxbIhOAu5z7dwEfl/F5irVmDTRrZr7On69iLSISTcEeBcwCzgRWA22BLKAFZljfpc52\nzAQCZg6Q1q3NmfXxx8fyuQOxe7I4UD53bM5nczZQPjcSlS2aHvZtxey/PJZBwFxi3q8fvPwyDB8O\nLVrE+hVERJKXNXOJbN0Kd98Nq1bB2LFQu3Z8g4mI2CrWPeyYKlgVJj0dZsxQsRYRicTzgj1uHFx8\nMTz+uJlpr3Ll+L6ezX0wUD63bM5nczZQPjds6mHHxd698PTT5orFyZPNRTEiIlI8T3rY69ebVcwP\nO8xM3KSFBkREiljTwy5YFaZxY60KIyJSGgkr2KEQDBoE114Lr79uZtyrWDFRr17E5j4YKJ9bNuez\nORsonxvlqoe9Ywc8+CAsWABffw2nn56IVxURKV/i3sP+6acQN94I9eqZdRe10ICISMnczoddVqET\nTwzx7LPw8MOQFu9XExEpBzz70HHcOGjf3p5ibXMfDJTPLZvz2ZwNlM+NRGWLe8HWEl4iIrFhzVwi\nIiJiWDMOW0REyiblCrbNfTBQPrdszmdzNlA+N8pND1tERGJDPWwREcuohy0ikuTcFuxVwGJgITDP\ndZoEsLkPBsrnls35bM4GyudGsvSwQ4AfaAA0cp0mAYLBoNcRSqR87ticz+ZsoHxuJCpbLFoillzD\nGJ3Nmzd7HaFEyueOzflszgbK50aissXiDPtz4BvgPvdxRESkOG6nV20C/Ar8CZgGLAdmuA0VT6tW\nrfI6QomUzx2b89mcDZTPjURli2U7ozuwDegbti8InBfD1xARSQWLgIxYPuFRwLHO/aOBr4ErYvkC\nIiJSxE1L5CTgX2HP8z4w1XUiERERERFJvHeAdcCSsH1VMR98rsCc6aeHfe8p4D+YD0XD2zYXOM/x\nH6B/2P4dE0pJAAADpElEQVQjgNHO/jlA7Rjk6wH8grnIaCFwtYf5/gxMB5YC3wEdnP02HMPisvXA\njuNXGZiL+Xzme+BFZ78Nx66kfD2w4/gBVHQyTHS2bTl2xeXrgT3HLildjLlYJ7wg9gE6O/efBLKc\n+/Uwf7yVAB/wI0Ufts6j6IKfT4GrnPsPAW86928BPohBvu5Axwg/60W+6hR9oHEM8ANwFnYcw+Ky\n2XT8jnK+Hob5n64pdhy7kvLZdPw6YlqoE5xtm45dpHw2Hbuk5WP/grgc01cH8z/9cuf+U5g/ggKf\nARcBNYBlYftvBQaF/cxfnfuHAb/HIF934PEIP+dVvnAfA5dj3zEMz2bj8TsKmA+cjZ3HLjyfLcfv\nFMy1G80pOoO16dhFytcDO45duZr86SRMGwLna8EfwMmYtzMFfgFqRti/xtmP83W1c38vsAXzts2t\n9pjhOkMpetvndT4f5t3AXOw7hgXZ5jjbthy/Cpgzq3UUtW9sOnaR8oEdx+8V4AkgP2yfTccuUr4Q\ndhy7clWww4Wcm00GAqdi3u7/yv7j1b1yDDAOeATYesD3vD6GxwBjMdm2Ydfxy3dynAJcgjkbC+f1\nsTswnx87jl8rYD2mD1zcNSBeHrvi8tlw7IDyVbDXYd5OgXlLst65vwbzQVaBUzD/+q1x7h+4v+Ax\ntZz7hwHHA5tc5ltP0R/jEIr6W17lq4Qp1sMxbQew5xgWZBsRls224wfm7OgTzAdMthy7SPkaYsfx\nawy0BlYCo4BLMX9/thy7SPmGYcexS3o+Dv7QsaCf1IWDP7g4HPOv5E8U/es5F9NPSuPgDwYGOvdv\npWwfDByYr0bY/ceAkR7mS8P8Ib5ywH4bjmFx2Ww5ftUoekt8JPAVcBl2HLuS8lUP+xmv//4AmlHU\nI7bl2BWXz5a/vaQ1ClgL7Mb0g9pi+kCfE3lo0NOYT3CXA1eG7S8YevMj8FrY/iOADykaeuNzme9u\nTBFajOmDfUxRn86LfE0xb5uDFA1Vugo7jmGkbFdjz/GrD3zr5FuM6XeCHceupHy2HL8CzSgahWHL\nsQvnD8s3HLuOnYiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIJDMfZurLwZgFEqZgJv0XERHL+IA9\nwLnO9mjgds/SiJRCeZqtTyRaKzFzQwAsQPM5SJJQwZZUtCvs/j7MNJci1lPBFhFJEirYkooOXNHE\nttWJRERERERERERERERERERERERERERERERERERSx/8D6Xctcs6Z7jsAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107c6e050>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "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": 6,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x107cbd810>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x107c68fd0>"
       ]
      }
     ],
     "prompt_number": 6
    }
   ],
   "metadata": {}
  }
 ]
}