rhiever/Avida data munging.ipynb

## Avida data munging.ipynb
{
 "metadata": {
  "name": "Avida data munging"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cat data/average.dat"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "# Avida Average Data\r\n",
        "# Sun Nov 10 16:56:30 2013\r\n",
        "#  1: Update\r\n",
        "#  2: Merit\r\n",
        "#  3: Gestation Time\r\n",
        "#  4: Fitness\r\n",
        "#  5: Repro Rate?\r\n",
        "#  6: Size\r\n",
        "#  7: Copied Size\r\n",
        "#  8: Executed Size\r\n",
        "#  9: Abundance\r\n",
        "# 10: Proportion of organisms that gave birth in this update\r\n",
        "# 11: Proportion of Breed True Organisms\r\n",
        "# 12: Genotype Depth\r\n",
        "# 13: Generation\r\n",
        "# 14: Neutral Metric\r\n",
        "# 15: Lineage Label\r\n",
        "# 16: True Replication Rate (based on births/update, time-averaged)\r\n",
        "\r\n",
        "0 97 389 0 0 100 100 97 1 1 1 0 0 0 0 0 \r\n",
        "100 97.2812 388.5 0.250401 0 100.281 100.297 97.2812 2.66667 0 0 1.26562 7 -1.28436 0 0 \r\n",
        "200 97.0406 388.007 0.250049 0 100.699 100.594 97.0644 2.20526 0.00477327 0.00477327 2.87351 14.9737 -0.840766 0 0 \r\n",
        "300 97.5443 391.622 0.249262 0 102.587 101.49 97.7649 2.03309 0.0596745 0.045208 4.42134 22.7803 -0.757276 0 0 \r\n",
        "400 98.1893 394.576 0.248741 0 103.335 102.661 98.2583 1.89153 0.0620047 0.0405594 6.14779 30.428 -0.589098 0 0 \r\n",
        "500 98.2703 394.671 0.249051 0 103.449 102.817 98.4092 1.81281 0.0622568 0.0404071 7.52858 38.0931 -0.300366 0 0.166442 \r\n",
        "600 99.3254 399.501 0.24903 0 105.002 104.106 99.5449 1.73029 0.0519589 0.0347319 8.83162 45.678 -0.343048 0 0.16719 \r\n",
        "700 100.018 401.094 0.249646 0 106.057 104.923 100.181 1.68146 0.055 0.0361111 9.93889 53.3328 0.0792456 0 0.167527 \r\n",
        "800 99.5484 398.69 0.250317 0 105.936 104.521 99.8819 1.70038 0.0505837 0.0319622 10.7865 60.9361 0.735015 0 0.168154 \r\n",
        "900 99.4655 397.094 0.250977 0 105.397 104.438 99.6023 1.72071 0.0550306 0.036687 11.8185 68.6276 0.532398 0 0.16885 \r\n",
        "1000 100.847 401.426 0.252145 0 107.232 106.104 101.131 1.66898 0.0458333 0.0294444 13.095 76.3561 0.0197712 0 0.169275 \r\n",
        "1100 103.273 410.512 0.253747 0 109.973 108.552 103.685 1.67583 0.0469705 0.0305725 14.5384 84.132 -0.43407 0 0.169868 \r\n",
        "1200 102.119 401.684 0.256153 0 108.269 107.112 102.337 1.66728 0.0497499 0.0297387 15.5648 92.0456 -0.686364 0 0.170135 \r\n",
        "1300 102.573 401.536 0.257717 0 108.623 107.511 102.858 1.70085 0.0486246 0.0311198 16.7794 99.8908 -0.69664 0 0.170849 \r\n",
        "1400 101.46 397.033 0.258352 0 107.949 106.581 101.879 1.72614 0.0533482 0.0347319 17.4007 107.82 -0.375643 0 0.171537 \r\n",
        "1500 100.919 390.655 0.261473 0 107.118 105.993 101.193 1.72002 0.0527778 0.0325 18.2706 116.428 -0.399163 0 0.172579 \r\n",
        "1600 100.144 385.35 0.263644 0 106.35 105.418 100.257 1.76039 0.0561111 0.035 19.1522 125.037 -0.154035 0 0.173579 \r\n",
        "1700 99.5001 378.697 0.267176 0 105.355 104.709 99.6044 1.71613 0.0467056 0.0278009 19.9038 134.054 0.176485 0 0.174973 \r\n",
        "1800 98.9425 373.999 0.269835 0 105.126 104.147 99.0731 1.75475 0.0600167 0.0386218 20.7208 143.352 -0.313794 0 0.176027 \r\n",
        "1900 98.98 367.746 0.274942 0 104.711 103.823 99.0644 1.78394 0.0525 0.0366667 21.1625 153.13 -1.049 0 0.177886 \r\n",
        "2000 99.1242 365.064 0.278449 0 104.219 103.55 98.6424 1.75219 0.0600167 0.042234 22.1573 162.941 -1.05512 0 0.179802 \r\n",
        "2100 100.661 356.767 0.289598 0 104.249 103.388 98.5211 1.77427 0.0536111 0.0358333 23.0431 174.474 -1.45462 0 0.181909 \r\n",
        "2200 104.382 352.583 0.303919 0 104.121 103.406 98.6151 1.75341 0.0478043 0.0322401 24.01 186.047 -2.17415 0 0.185483 \r\n",
        "2300 112.165 343.177 0.332716 0 103.017 102.488 97.6253 1.7769 0.0586111 0.0361111 25.0956 198.929 -3.22455 0 0.191419 \r\n",
        "2400 144.224 336.954 0.425745 0 101.51 101.018 95.9878 1.8858 0.0469444 0.0330556 26.2378 213.01 -3.34896 0 0.203892 \r\n",
        "2500 228.99 338.639 0.651955 0 100.878 100.487 94.6994 1.87013 0.0638889 0.0480556 29.0725 230.326 -2.4245 0 0.236041 \r\n",
        "2600 338.468 344.714 0.943163 0 100.933 100.552 93.7769 1.81087 0.055 0.0361111 32.9364 252.888 -1.45485 0 0.306475 \r\n",
        "2700 489.667 351.033 1.34646 0 101.113 100.617 93.0367 1.89023 0.0414004 0.0313976 36.7763 277.786 0.964663 0 0.421674 \r\n",
        "2800 682.64 358.208 1.84592 0 101.467 100.888 92.7467 1.8358 0.0558333 0.0397222 40.5464 301.807 3.27106 0 0.592537 \r\n",
        "2900 1058.6 365.159 2.78909 0 101.614 101.284 92.8041 1.89321 0.0511253 0.034454 44.0486 325.701 6.94499 0 0.850113 \r\n",
        "3000 1709.15 376.768 4.29733 0 102.151 101.734 92.9422 1.89321 0.0519589 0.0355654 47.7399 352.08 10.1912 0 1.26188 \r\n",
        "3100 2476.55 391.839 6.0237 0 102.447 102.066 92.888 1.84324 0.0472485 0.0319622 51.6984 380.447 12.5344 0 1.85991 \r\n",
        "3200 3090.21 406.69 7.28693 0 102.502 102.142 93.0033 1.88825 0.0433454 0.0311198 54.6507 402.935 13.2531 0 2.62346 \r\n",
        "3300 3559.22 413.84 8.34925 0 102.454 102.011 93.0192 1.87013 0.0519444 0.0355556 56.8433 421.212 13.2422 0 3.4811 \r\n",
        "3400 3982.5 416.103 9.51062 0 102.282 101.711 92.9517 1.83861 0.0525 0.035 58.7142 439.144 12.4876 0 4.39534 \r\n",
        "3500 4452.65 411.278 11.0239 0 101.926 101.366 92.7396 1.91436 0.0466796 0.0311198 60.5488 457.61 11.8644 0 5.30486 \r\n",
        "3600 4751.93 401.415 12.3617 0 102.084 101.669 93.2268 1.86618 0.0547526 0.0375208 62.2474 475.41 10.8634 0 6.17845 \r\n",
        "3700 5063.32 387.598 13.9595 0 102.482 102.059 93.7929 1.91996 0.0522513 0.0358533 63.7332 492.184 10.1665 0 7.02551 \r\n",
        "3800 5231.84 366.823 15.3899 0 102.689 102.331 94.112 1.9062 0.0567139 0.0386433 64.896 509.035 10.2263 0 7.89598 \r\n",
        "3900 5410.58 339.447 17.1529 0 102.54 102.084 93.7647 1.95068 0.0605724 0.0433454 65.9942 527.616 11.167 0 8.80994 \r\n",
        "4000 5479.47 318.317 18.3247 0 102.826 102.273 94.0475 1.95174 0.0680745 0.0458461 66.8124 545.257 11.0613 0 9.70411 \r\n",
        "4100 5521.78 301.869 19.2499 0 102.159 101.806 93.6744 1.96507 0.0713889 0.0508333 67.3894 561.964 12.0331 0 10.5425 \r\n",
        "4200 5511.97 289.174 19.7646 0 101.853 101.399 93.2739 1.947 0.0713889 0.0480556 67.8869 577.549 13.6725 0 11.2712 \r\n",
        "4300 5516.36 280.514 20.1391 0 101.666 101.336 93.0436 2.0095 0.0711704 0.0472616 68.3361 591.58 14.2208 0 11.8683 \r\n",
        "4400 5450.89 282.02 19.9991 0 101.742 101.477 93.1634 1.95279 0.0677966 0.0427897 68.9875 604.86 14.3135 0 12.2798 \r\n",
        "4500 5533.47 279.979 20.334 0 101.843 101.395 93.2487 1.9753 0.0641845 0.0430675 69.6157 617.747 14.0924 0 12.5771 \r\n",
        "4600 5520.33 278.943 20.3069 0 102.068 101.504 93.4491 2.02704 0.0753196 0.0514175 70.1098 630.064 14.6446 0 12.7494 \r\n",
        "4700 5522.95 285.466 20.2111 0 102.038 101.607 93.6128 2.00111 0.0661111 0.0477778 70.685 642.293 14.6097 0 12.836 \r\n",
        "4800 5619.23 282.5 20.5209 0 102.456 101.823 93.9986 1.95016 0.0705556 0.0466667 71.5167 654.615 13.3069 0 12.8732 \r\n",
        "4900 5525.85 284.008 20.1096 0 102.687 102.383 94.7664 1.95016 0.0636111 0.0469444 72.4822 666.521 13.4999 0 12.926 \r\n",
        "5000 5546.46 279.817 20.4691 0 101.753 101.351 93.8372 2.03448 0.0808558 0.0602945 72.5477 678.7 14.9475 0 12.9343 \r\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline\n",
      "\n",
      "merits = []\n",
      "\n",
      "with open(\"data/average.dat\") as in_file:\n",
      "    for line in in_file:\n",
      "        if len(line.split()) == 16:\n",
      "            merits.append(line.split()[1])\n",
      "            \n",
      "plot(merits)\n",
      "xticks(arange(0, 51, 10), arange(0, 5001, 1000))\n",
      "xlabel(\"Update\")\n",
      "ylabel(\"Merit\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.kernel.zmq.pylab.backend_inline].\n",
        "For more information, type 'help(pylab)'.\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "<matplotlib.text.Text at 0x10de27bd0>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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LlsBttylExKQgEZHLYhjmleyTJ1tdiQQLBYmIXJZ33zXP0ho82OpKJFgoSETksixYAI88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\nItIsChIREWmW/wfZQEIbjkK01QAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10db48590>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print arange(0, 51, 10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 0 10 20 30 40 50]\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "arange(0, 5001, 1000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "array([   0, 1000, 2000, 3000, 4000, 5000])"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}