rhiever/Plot average and SEM of multiple data files.ipynb

## Plot average and SEM of multiple data files.ipynb
{
 "metadata": {
  "name": "Plot average and SEM of multiple data files"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ls"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Homework 7.ipynb                                                     output_data20.csv\r\n",
        "hw7 answers.txt                                                      output_data21.csv\r\n",
        "hw7-Animal Foraging and the Evolution of Goal-Directed Cognition.py  output_data22.csv\r\n",
        "output_data0.csv                                                     output_data23.csv\r\n",
        "output_data1.csv                                                     output_data24.csv\r\n",
        "output_data10.csv                                                    output_data25.csv\r\n",
        "output_data11.csv                                                    output_data26.csv\r\n",
        "output_data12.csv                                                    output_data27.csv\r\n",
        "output_data13.csv                                                    output_data3.csv\r\n",
        "output_data14.csv                                                    output_data4.csv\r\n",
        "output_data15.csv                                                    output_data5.csv\r\n",
        "output_data16.csv                                                    output_data6.csv\r\n",
        "output_data17.csv                                                    output_data7.csv\r\n",
        "output_data18.csv                                                    output_data8.csv\r\n",
        "output_data19.csv                                                    output_data9.csv\r\n",
        "output_data2.csv\r\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!cat output_data0.csv"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0,7.96666666667,0.0,0.0,0.0\r",
        "\r\n",
        "1,7.2,0.0,6.53333333333,0.133333333333\r",
        "\r\n",
        "2,8.63333333333,0.0,2.26666666667,4.53333333333\r",
        "\r\n",
        "3,23.8333333333,0.0,12.9333333333,26.1333333333\r",
        "\r\n",
        "4,35.9666666667,0.266666666667,47.2,98.1333333333\r",
        "\r\n",
        "5,27.5333333333,0.533333333333,60.8,123.733333333\r",
        "\r\n",
        "6,35.8666666667,4.0,63.6,125.9\r",
        "\r\n",
        "7,53.0,4.36666666667,66.9333333333,128.066666667\r",
        "\r\n",
        "8,46.5,5.93333333333,65.7333333333,128.3\r",
        "\r\n",
        "9,55.9666666667,6.7,66.5666666667,126.733333333\r",
        "\r\n",
        "10,50.4666666667,5.6,78.1,129.7\r",
        "\r\n",
        "11,51.3333333333,10.9333333333,68.5,129.5\r",
        "\r\n",
        "12,50.6333333333,8.8,77.0333333333,129.7\r",
        "\r\n",
        "13,44.4333333333,3.73333333333,79.2333333333,131.066666667\r",
        "\r\n",
        "14,45.0333333333,4.26666666667,76.7666666667,133.8\r",
        "\r\n",
        "15,62.8,6.86666666667,75.3666666667,130.9\r",
        "\r\n",
        "16,63.1333333333,9.0,91.5,131.066666667\r",
        "\r\n",
        "17,47.5333333333,4.73333333333,88.9666666667,130.8\r",
        "\r\n",
        "18,44.0333333333,2.33333333333,109.033333333,128.4\r",
        "\r\n",
        "19,68.7666666667,9.2,118.066666667,128.4\r",
        "\r\n",
        "20,56.1333333333,7.73333333333,137.7,128.066666667\r",
        "\r\n",
        "21,59.8333333333,5.73333333333,118.066666667,130.133333333\r",
        "\r\n",
        "22,57.0666666667,3.6,126.1,130.166666667\r",
        "\r\n",
        "23,58.7666666667,3.46666666667,138.833333333,128.033333333\r",
        "\r\n",
        "24,58.5333333333,3.53333333333,142.566666667,128.333333333\r",
        "\r\n",
        "25,54.9333333333,5.16666666667,132.6,129.1\r",
        "\r\n",
        "26,62.6333333333,4.96666666667,126.766666667,130.133333333\r",
        "\r\n",
        "27,57.4666666667,5.26666666667,112.633333333,130.133333333\r",
        "\r\n",
        "28,60.8333333333,5.23333333333,123.166666667,132.266666667\r",
        "\r\n",
        "29,54.3666666667,5.66666666667,127.3,130.133333333\r",
        "\r\n",
        "30,59.5666666667,8.1,123.466666667,128.0\r",
        "\r\n",
        "31,64.9333333333,7.76666666667,88.4666666667,106.666666667\r",
        "\r\n",
        "32,64.8333333333,7.63333333333,80.0666666667,109.933333333\r",
        "\r\n",
        "33,78.4333333333,7.16666666667,84.0666666667,125.866666667\r",
        "\r\n",
        "34,63.1333333333,7.0,74.2,131.2\r",
        "\r\n",
        "35,73.5333333333,7.06666666667,79.3333333333,130.233333333\r",
        "\r\n",
        "36,67.5666666667,7.26666666667,84.7,131.433333333\r",
        "\r\n",
        "37,68.6333333333,7.46666666667,81.4333333333,133.433333333\r",
        "\r\n",
        "38,59.7,8.36666666667,70.9666666667,129.766666667\r",
        "\r\n",
        "39,66.0666666667,8.76666666667,70.0,128.833333333\r",
        "\r\n",
        "40,61.6,8.86666666667,71.2333333333,130.0\r",
        "\r\n",
        "41,60.2,10.3,74.1666666667,125.9\r",
        "\r\n",
        "42,64.8666666667,7.86666666667,68.9666666667,118.1\r",
        "\r\n",
        "43,59.1666666667,8.0,69.9333333333,121.333333333\r",
        "\r\n",
        "44,71.0333333333,10.2666666667,73.5,120.5\r",
        "\r\n",
        "45,62.0666666667,10.2666666667,75.4666666667,125.866666667\r",
        "\r\n",
        "46,65.4333333333,10.4666666667,70.4333333333,124.833333333\r",
        "\r\n",
        "47,62.8,13.1,78.6666666667,133.9\r",
        "\r\n",
        "48,55.5333333333,15.5666666667,70.1,135.6\r",
        "\r\n",
        "49,69.2333333333,11.0,71.6333333333,131.566666667\r",
        "\r\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import glob\n",
      "from numpy import mean\n",
      "from scipy.stats import sem\n",
      "\n",
      "list_of_mean_fitness = []\n",
      "list_of_stderr_fitness = []\n",
      "list_of_mean_trait1 = []\n",
      "list_of_stderr_trait1 = []\n",
      "list_of_mean_trait2 = []\n",
      "list_of_stderr_trait2 = []\n",
      "list_of_mean_trait3 = []\n",
      "list_of_stderr_trait3 = []\n",
      "\n",
      "for gen in range(50):\n",
      "    \n",
      "    list_of_fitness = []\n",
      "    list_of_trait1 = []\n",
      "    list_of_trait2 = []\n",
      "    list_of_trait3 = []\n",
      "    \n",
      "    for file_name in glob.glob(\"output_data*\"):\n",
      "        with open(file_name) as in_file:\n",
      "            for line in in_file:\n",
      "                split_line = line.split(\",\")\n",
      "                if int(split_line[0]) == gen:\n",
      "                    list_of_fitness.append(float(split_line[1]))\n",
      "                    list_of_trait1.append(float(split_line[2]))\n",
      "                    list_of_trait2.append(float(split_line[3]))\n",
      "                    list_of_trait3.append(float(split_line[4]))\n",
      "                    break\n",
      "                    \n",
      "    mean_fitness = mean(list_of_fitness)\n",
      "    mean_trait1 = mean(list_of_trait1)\n",
      "    mean_trait2 = mean(list_of_trait2)\n",
      "    mean_trait3 = mean(list_of_trait3)\n",
      "    \n",
      "    stderr_fitness = 1.96*sem(list_of_fitness)\n",
      "    stderr_trait1 = 1.96*sem(list_of_trait1)\n",
      "    stderr_trait2 = 1.96*sem(list_of_trait2)\n",
      "    stderr_trait3 = 1.96*sem(list_of_trait3)\n",
      "    \n",
      "    list_of_mean_fitness.append(mean_fitness)\n",
      "    list_of_stderr_fitness.append(stderr_fitness)\n",
      "    list_of_mean_trait1.append(mean_trait1)\n",
      "    list_of_stderr_trait1.append(stderr_trait1)\n",
      "    list_of_mean_trait2.append(mean_trait2)\n",
      "    list_of_stderr_trait2.append(stderr_trait2)\n",
      "    list_of_mean_trait3.append(mean_trait3)\n",
      "    list_of_stderr_trait3.append(stderr_trait3)\n",
      "    \n",
      "errorbar(x=range(len(list_of_mean_fitness)),\n",
      "            y=list_of_mean_fitness,\n",
      "            yerr=list_of_stderr_fitness)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "<Container object of 3 artists>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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++w5r1qzB8OHD8fbbbyMuLg4mkwllZWUwmUyIj49vqvY67dAh4OpV4KabvN0S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\nSOP+H1VwcnKRYfoFAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10fd65590>"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "1.96 * array([1, 2, 3])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "array([ 1.96,  3.92,  5.88])"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}