CSE891 2012 week4 solutions

solutions-week4-fitting.ipynb

{
 "metadata": {
  "name": "solutions-week4-quartet-fitting"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import csv\n\ndef load_csv(filename):\n    fp = open(filename, 'rb')\n    r = csv.reader(fp)\n    \n    xx = []\n    yy = []\n    for x, y in r:\n        xx.append(float(x))\n        yy.append(float(y))\n    \n    return xx, yy\n\nx1, y1 = load_csv('/usr/local/notebooks/quartet/q1.csv')\nx2, y2 = load_csv('/usr/local/notebooks/quartet/q2.csv')\nx3, y3 = load_csv('/usr/local/notebooks/quartet/q3.csv')\nx4, y4 = load_csv('/usr/local/notebooks/quartet/q4.csv')",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "from scipy import stats\n\nslope1, intercept1, r_value1, p_value1, std_err1 = stats.linregress(x1,y1)\nslope2, intercept2, r_value2, p_value2, std_err2 = stats.linregress(x2,y2)\nslope3, intercept3, r_value3, p_value3, std_err3 = stats.linregress(x3,y3)\nslope4, intercept4, r_value4, p_value4, std_err4 = stats.linregress(x4,y4)",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "# be sneaky -- turn things into numpy arrays\nimport numpy\n\nx1, y1 = numpy.array(x1), numpy.array(y1)\nx2, y2 = numpy.array(x2), numpy.array(y2)\nx3, y3 = numpy.array(x3), numpy.array(y3)\nx4, y4 = numpy.array(x4), numpy.array(y4)\n\n# ... so that I can multiply arrays (x1, etc.) by numbers, add to them, etc.\nyfit1 = x1 * slope1 + intercept1\nyfit2 = x2 * slope2 + intercept2\nyfit3 = x3 * slope3 + intercept3\nyfit4 = x4 * slope4 + intercept4\n\n## note, WITHOUT using numpy arrays, you could do\nyfitFOO = []\nfor x in x1:\n    yfitFOO.append(x * slope1 + intercept1)\n\n# see? they're the same:\nprint yfitFOO\nprint yfit1",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "[8.0010000000000012, 7.0008181818181834, 9.5012727272727293, 7.5009090909090927, 8.5010909090909106, 10.001363636363639, 6.0006363636363655, 5.0004545454545468, 9.0011818181818199, 6.500727272727274, 5.5005454545454562]\n[  8.001        7.00081818   9.50127273   7.50090909   8.50109091\n  10.00136364   6.00063636   5.00045455   9.00118182   6.50072727\n   5.50054545]\n"
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "plot(x1, y1, 'r.')\nplot(x1, yfit1, 'r-')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 10,
       "text": "[<matplotlib.lines.Line2D at 0x30af050>]"
      },
      {
       "output_type": "display_data",
       "png": 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       "text": "<matplotlib.figure.Figure at 0x3068dd0>"
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "plot(x2, y2, 'r.')\nplot(x2, yfit2, 'r-')\n",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 11,
       "text": "[<matplotlib.lines.Line2D at 0x31d3850>]"
      },
      {
       "output_type": "display_data",
       "png": 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I3csp3d3SggVc+hWAbbmzE2dQCcAh3NWJM6gE4DDO78QZVAJwMOeGeH29NHdu6DYGlQAc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\nrkJggg==\n",
       "text": "<matplotlib.figure.Figure at 0x31d3a10>"
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "plot(x3, y3, 'r.')\nplot(x3, yfit3, 'r-')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 12,
       "text": "[<matplotlib.lines.Line2D at 0x31f7e90>]"
      },
      {
       "output_type": "display_data",
       "png": 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       "text": "<matplotlib.figure.Figure at 0x30af590>"
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "plot(x4, y4, 'r.')\nplot(x4, yfit4, 'r-')",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 13,
       "text": "[<matplotlib.lines.Line2D at 0x3593c90>]"
      },
      {
       "output_type": "display_data",
       "png": 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APIyIA4CHEXEA8DDLEe/p6dGPf/xj3XLLLfrGN76ht956y865AACTMMXqAx977DGlp6fr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\nRsQBwMP+D+SsUFnexXTSAAAAAElFTkSuQmCC\n",
       "text": "<matplotlib.figure.Figure at 0x3593450>"
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "",
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}

solutions-week4-monty-hall.ipynb

solutions-week4-penny.ipynb