95% confidence intervals.ipynb

95% confidence intervals.ipynb

{
 "metadata": {
  "name": "95% confidence intervals"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from TSStickleback import TSStickleback\n",
      "\n",
      "lake = []\n",
      "\n",
      "for sb_count in range(5000):\n",
      "    sb = TSStickleback()\n",
      "    lake.append(sb)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.stats import sem\n",
      "from random import randint\n",
      "%pylab inline\n",
      "\n",
      "samples = []\n",
      "\n",
      "for sample_count in range(300):\n",
      "    captured_fish = lake[randint(0, len(lake))]\n",
      "    samples.append(captured_fish.redness)\n",
      "    \n",
      "sample_mean = mean(samples)\n",
      "sample_95ci = 1.96 * sem(samples)\n",
      "sample_min = sample_mean - sample_95ci\n",
      "sample_max = sample_mean + sample_95ci\n",
      "\n",
      "figure(figsize=(15, 7))\n",
      "plot(sample_mean, \"bo\")\n",
      "plot(sample_min, \"ro\")\n",
      "plot(sample_max, \"ro\")\n",
      "ylim(0, 10)"
     ],
     "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": 19,
       "text": [
        "(0, 10)"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10e796ed0>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.stats import sem\n",
      "from random import randint\n",
      "%pylab inline\n",
      "\n",
      "samples_over_time = []\n",
      "\n",
      "for time_point in range(50):\n",
      "    samples = []\n",
      "\n",
      "    for sample_count in range(30):\n",
      "        captured_fish = lake[randint(0, len(lake))]\n",
      "        samples.append(captured_fish.redness)\n",
      "        \n",
      "    samples_over_time.append(samples)\n",
      "    \n",
      "mean_over_time = []\n",
      "sem_over_time = []\n",
      "\n",
      "for time_point_samples in samples_over_time:\n",
      "    mean_over_time.append(mean(time_point_samples))\n",
      "    sem_over_time.append(sem(time_point_samples))\n",
      "    \n",
      "figure(figsize=(15, 7))\n",
      "errorbar(x = range(len(mean_over_time)), y = mean_over_time, yerr = sem_over_time)"
     ],
     "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": 20,
       "text": [
        "<Container object of 3 artists>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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42DDg9tuB558HrrwSaN6czxM3uf3cnD2bExKbNPFuNtqKYK11a5Z77tplzZqs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\nAICFCGsAAAAAYKH/ARkshYmPGWrIAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107d309d0>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}