lecture-week03-plotting.ipynb

{
 "metadata": {
  "name": "lecture-week03-plotting"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "xs = range(0, 100)\n",
      "ys = []\n",
      "for x in xs:\n",
      "    ys.append(x**2 - 10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print xs"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(xs, ys)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "[<matplotlib.lines.Line2D at 0x255ab10>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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bTysq4O9/h4svtnMSkpPtTObjW69eGlgWkbbRYUIiNTWVhQsX0rdvX2688UY2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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2498c90>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(xs, ys, 'b-.')\n",
      "plot(xs, ys, 'rx') # you can use *, ., +, x, and lots of other things\n",
      "legend(['first line', 'second line'])\n",
      "axis(xmin=0, xmax=10, ymin=-20, ymax=150)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "(0, 10, -20, 150)"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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h5OTk1EdTUldXeRCJzEwqK+H06eqH3HOPfSWlX39Vghdxd/WS5PPy8ggLC3O8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\n8iIiXuz/A5jIBfvAWFRZAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2c819d0>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import csv\n",
      "fp = open('/usr/local/notebooks/quartet/q1.csv', 'rb')\n",
      "r = csv.reader(fp)\n",
      "\n",
      "xx = []\n",
      "yy = []\n",
      "\n",
      "for item in r:\n",
      "    x, y = item\n",
      "    x = float(x)\n",
      "    y = float(y)\n",
      "    xx.append(x)\n",
      "    yy.append(y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "xx"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "[10.0, 8.0, 13.0, 9.0, 11.0, 14.0, 6.0, 4.0, 12.0, 7.0, 5.0]"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "yy"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "[8.04, 6.95, 7.58, 8.81, 8.33, 9.96, 7.24, 4.26, 10.84, 4.82, 5.68]"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(xx, yy, 'r.')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "[<matplotlib.lines.Line2D at 0x2c77890>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x2e98b50>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}