02_plotting.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:fa195c239abd3864ce8446586a406ccfd7c70f8f60d969ff8ddc18b78b7740ab"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "X,Y Plots"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = np.linspace(1,10,200)\n",
      "I = np.exp(-d)\n",
      "plot(d,I)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "[<matplotlib.lines.Line2D at 0x108376b90>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x1081d0810>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = np.linspace(1,10,200)\n",
      "I = np.exp(-d)\n",
      "plot(d,I)\n",
      "ylabel('I')\n",
      "xlabel('d')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "<matplotlib.text.Text at 0x10832be50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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7lB6EW1wsIjykSSpbXeMOQKowxxHuybQ5u0wnWQ9Dkopm15NUWhm2TwwmCZU9\nE4VUWs8QHlrV1PV0Gg5oq8zZ9SSV1lzgfsJtwd8HXo43HEmSJEmSJEmSJEmSJEmSJEmSJEmSJJXE\n/wcc/Fwk8+PoiAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1083f5fd0>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = np.linspace(1,10,200)\n",
      "I = np.exp(-d)\n",
      "plot(d,I)\n",
      "ylabel(r'$I= \\exp(-d)$')\n",
      "xlabel('d')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "<matplotlib.text.Text at 0x10861df90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10865fc50>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = np.linspace(1,10,200)\n",
      "I = d**(-2)\n",
      "plot(d,I)\n",
      "ylabel(r'$I= 1 / d^2$')\n",
      "xlabel('d')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "<matplotlib.text.Text at 0x108672550>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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xXA4uuACOOQa+//3Y1UhSx+ySSoA33wzXynjgAZg0KXY1ktQ+u6QS4JBD4Lrr4JJLwgWX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\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108692b50>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = np.linspace(1,10,200)\n",
      "Iid2 = d**(-2)\n",
      "plot(d,Iid2)\n",
      "ylabel(r'$I$')\n",
      "Inex = np.exp(-d)\n",
      "plot(d,Inex)\n",
      "xlabel('d')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<matplotlib.text.Text at 0x1088b0310>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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rdb1XVllGhbeCCm8F5ZXlnCo/Rbm3/Mz6mfeqXqv13re7v2WXZ1fAa+Xecrw+\nL5XeSrw+75ml0ldrvZHv+/AFBEZdIRIdFU1UVBRRRFHyeQlzfjMH4MxrdT0CIXsv/+t8Pnr5ozrf\nq62uEAzWdjvW72DFGytCvt+GtmuKUPxkGAlkYSagAR4DvAROQM8BPJhhJoCtwFjO7jFsAy4MUp0i\nIpFqO9Db7iJqisEUlQ7EAuuB/rW2mQQs8j8fCawKVXEiImKP6zBHJm3D9BgApvmXKrP972cDl4a0\nOhERERERcbZXMfMNG+0upIY0YDnwDbAJeMjecgCIwxzuux7YDDxtbzkBXJiTFv9hdyF+O4ENmJrW\n2FvKGcnAu5hDuTdT97k8odYP8/+oaikmPP6uP4b5t7cReAtoa285ADyMqWeT/7kd6vqu7AgsBb4F\nlmD+nkWEK4GhhFcwdAOG+J8nYobKas+d2KGd/zEGM1czxsZaavox8FfgA7sL8duB+QcTTt4A7vE/\njwGSbKylLtHAfsyPIjulA3lUh8HfgLtsq8a4BPP9FIf5EbQUew6Uqeu78lngp/7nM4BnGmrEKZfY\n+hQotLuIWgowv8wBTmB+5Z1vXzlnnPQ/xmL+gh61sZYqPTAHGLxMeF2fK5xqScL8o37Vv16B+XUe\nTsZjDiTZ09CGQXYMKMf8CIrxP+bbWhFchOmtlwKVwCfAzTbUUdd3Zc0TiN8AvttQI04JhnCXjknp\n1TbXAebPdD2mO7kcMyRht+eA/8IcphwufMAyYC1wr821AGQAh4DXgK+Bl6ju/YWLH2CGbex2FPgt\nsBvYBxRh/izttAkT7B0xf27XY34QhYOaV5E44F+PGOmE11BSlUTMl0uDKRxiSZihJLfNddwAPO9/\n7iZ85hi6+x87Y4L0ShtrARiO+RV8mX/998CT9pVzllhMcHW2uxDMEM1moBOmx7AAuN3Wiox7MN8F\nnwAvYH4Q2SGdwO/K2j2IBkcR1GNomTbAfOAvwN9trqW2YmAh5gvHTldgurI7gLnA1cCfba3I2O9/\nPIT5YhlhYy1gLgOzF3ORSTCT0OF02PZ1wFeY/192Gw58DhzBDLm9h/l7ZrdXMbWNxfRicuwt54wD\nmDlRMD+IDjb0AQVD80UBr2B+ufze5lqqpFB9xEE8MIG6L18eSo9jJiszMEMR/wLutLUi09Vv73+e\nAFyL/b3RAszYfdWFosZjjroJF7digj0cbMUcsRWP+Xc4nvAYMu3if+wJfI/wGHYDc8BH1eT8XYTf\nj9hmm4sFAV77AAABKUlEQVQZSzyN+cdzt73lAOZoHy9mGKLqUL6J5/xE8A3EjE+vxxyK+V/2lnOW\nsYTHUUkZmP9H6zFjw4+de/OQGYzpMWRjfgWHy1FJCcBhqsM0HPyU6sNV38D03u22AlPTemCcTTVU\nfVeWUf1d2REzBxNxh6uKiIiIiIiIiIiIiIiIiIiIiIiIiIgjuewuQCTCZAHDgC9srkOk2XTms4i1\nfHYXINJSCgaRlvsZ5ro4n2JubCMiIq3YMMzlR+Iwl4zIxdyUSMSxYuwuQMThrsRc16jUv3xAeN0A\nSKTJNJQk0jI+AoNAoSCOp2AQaZkVmJs0VQ0l3YAmoMXhNJQk0jLrMDejz8bcAGWNveWIiIiIiIiI\niIiIiIiIiIiIiIiIiIiIiIiIiIiIONT/ARnd2y3+jeiXAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108bb5710>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = np.linspace(1,10,200)\n",
      "Iid2 = d**(-2)\n",
      "plot(d,Iid2, label = r'$I = d^{-2}$')\n",
      "ylabel(r'$I$')\n",
      "Inex = np.exp(-d)\n",
      "plot(d,Inex, label = r'$I = \\exp(-d)$')\n",
      "xlabel('d')\n",
      "legend()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<matplotlib.legend.Legend at 0x108ddb710>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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3r0kSpcuPP0JUlJkuPC7OvPbubeZ20hBZEc9QYvBjTqeTNfvX8L+b/5d5afPo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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108cc8f10>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = np.linspace(1,10,200)\n",
      "Iid2 = d**(-2)\n",
      "plot(d,Iid2, 'r-', label = r'$I = d^{-2}$')\n",
      "ylabel(r'$I$')\n",
      "Inex = np.exp(-d)\n",
      "plot(d,Inex, 'g--',label = r'$I = \\exp(-d)$')\n",
      "xlabel('d')\n",
      "legend()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "<matplotlib.legend.Legend at 0x10901d710>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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nt4MHKz8/eNAsGVI2UbRqBS1bVt7i4iq/dsYZmpElAaHEIA32c/7PrMtex7q9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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108e2bd10>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = np.linspace(1,10,200)\n",
      "Iid = d**(-1)\n",
      "plot(d,Iid, 'b-.', label = r'$I = d^{-1}$')\n",
      "Iid2 = d**(-2)\n",
      "plot(d,Iid2, 'r-', label = r'$I = d^{-2}$')\n",
      "ylabel(r'$I$')\n",
      "Inex = np.exp(-d)\n",
      "plot(d,Inex, 'g--',label = r'$I = \\exp(-d)$')\n",
      "xlabel('d')\n",
      "legend()\n",
      "title('Distance Decay')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "<matplotlib.text.Text at 0x1090ae290>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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dQZkYAB668CE2HtzI3C1zvV/4s8+aOQ3r13u/bJu64QY45RSroxARbwjKpqQC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\nC2u4XAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10906ca10>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Scatter Plot"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.arange(100)\n",
      "y = 0.5 + np.random.normal(size=(100,))\n",
      "scatter(x,y)\n",
      "xlabel('x')\n",
      "ylabel('y')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "<matplotlib.text.Text at 0x1092bbed0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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V62pp2/a2i/bDPyUlJUXWrVsXciOIpqSkSPnyNSQs7CWBFWKz9ZMaNRrJyZMn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\noqCUUiqTmcNcqBCUnp7O4sWL8Xg8NG/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       "text": [
        "<matplotlib.figure.Figure at 0x108bc9450>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "scatter(x,y)\n",
      "vlines(x.mean(),y.min(),y.max())\n",
      "hlines(x.min(),x.max(),y.mean())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "<matplotlib.collections.LineCollection at 0x1094ba8d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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mjbngMqtXr6Zjxxtp3vwqRo58Ha/Xm691n4vT6SAsLPv7fRDDcASs/UD5+eef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\nRK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1093e67d0>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Distributions and Densities"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "\n",
      "x = np.random.multivariate_normal([100,70],[[2.,1.7],[1.7,2.]], (100))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "array([[ 101.25687009,   72.0864128 ],\n",
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        "       [ 101.60313777,   71.89045127],\n",
        "       [  97.29642463,   66.96814402],\n",
        "       [  99.710477  ,   70.67942464],\n",
        "       [  99.20251741,   68.36746903],\n",
        "       [  99.74291523,   70.49835976],\n",
        "       [  98.71829781,   69.9684653 ],\n",
        "       [ 100.10614563,   69.48053717],\n",
        "       [ 101.08042067,   70.62572621],\n",
        "       [ 100.54246638,   69.87304497],\n",
        "       [  98.44787869,   69.67503032],\n",
        "       [ 100.37123134,   70.28670545],\n",
        "       [  98.80953644,   69.79612966],\n",
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        "       [  99.45731781,   69.9341221 ],\n",
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        "       [  99.932413  ,   70.15658834],\n",
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        "       [  99.56044582,   68.79651243],\n",
        "       [  98.92062124,   69.41659565],\n",
        "       [  99.39100685,   68.96541354],\n",
        "       [  99.96971412,   69.9903117 ],\n",
        "       [ 100.29742149,   70.68311755],\n",
        "       [  95.97574597,   66.84270312]])"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(x)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "[<matplotlib.lines.Line2D at 0x1095c5c50>,\n",
        " <matplotlib.lines.Line2D at 0x1095c5ed0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Qwn0k2AshhBeQYC+EEF5Agr0QQngBCfZCCOEFJNgLIYQXkGAvhBBeQIK9EEJ4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\nf4zQwfPoRmLcAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10901dc50>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(x[:,0], x[:,1], \"o\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "[<matplotlib.lines.Line2D at 0x1096e6810>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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M7/+nmLk3YfES50LKf/M7gCVhBXmRFeBU4CQruAywGrg1rCBccIrzY9btd1nP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\nRmbvAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10940a8d0>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x0h = hist(x[:,0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Y75r8d0h/Ad3WdLtvAr807HBN1tJdzmmbKX9MHXrLeT5pt+xfKSXZbGvp7fmc9jDw/tM9\ncJEbWn42/3khcB1pTXJN0/XXMT9KvTnnJ4GvANuBD+XzLyZ9oerH5Uebpd3zCfBh0l46368iVIvW\n5/I+0gfi9Afkh0hb86vW7rmc3hi+iLTB+e4KcrWzg1SI5D+3N83/bdJr8yLSNoEnSk/X0CnntPmy\nLa1TzuXA10kbJb9VQa5WnXKupXE4l58j/b9/p6xQj5LGfPbR2D3on0i74Tydh2w3nlW2djkXA18m\nfejsAWqVJJutXU6Ae4HfryTRqdplvJy01ruP9GZZX020WdrlvJm0lvZt0tp7Fe4nfTifJB0g73rS\n2P9DtN+l8bOkD80DNPYwmo85D5NWiqZI21YumYc5Pwe8TmN3wb3AufMw5++Stv/sJX2Iz4fdwiVJ\nkiRJkiRJkiRJkiRJkiRJkiQtRP8PH8JT340IH4QAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1092f8dd0>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x1h = hist(x[:,1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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SJEmSRuz/ntiNLusPwQ0AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1095e8390>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n,bins,bars = hist(x[:,1])\n",
      "\n",
      "y = normpdf(bins, mean(x[:,1]), std(x[:,1]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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SJEmSRuz/ntiNLusPwQ0AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109ac2c50>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(bins,y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "[<matplotlib.lines.Line2D at 0x109decfd0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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C8+fDtGkq9CLJqFYtmDMHNmyAdu2Sf118FfsQBgywGXjTp0Plyq7TiEi8HHss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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10972acd0>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(bins,y)\n",
      "hist(x[:,1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "(array([  5.,   6.,   8.,  15.,  21.,  19.,   8.,   9.,   4.,   5.]),\n",
        " array([ 66.84270312,  67.47464115,  68.10657918,  68.73851721,\n",
        "         69.37045523,  70.00239326,  70.63433129,  71.26626932,\n",
        "         71.89820734,  72.53014537,  73.1620834 ]),\n",
        " <a list of 10 Patch objects>)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x109e2c850>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(bins,y)\n",
      "hist(x[:,1],normed=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "(array([ 0.07912168,  0.09494602,  0.12659469,  0.23736505,  0.33231107,\n",
        "         0.30066239,  0.12659469,  0.14241903,  0.06329735,  0.07912168]),\n",
        " array([ 66.84270312,  67.47464115,  68.10657918,  68.73851721,\n",
        "         69.37045523,  70.00239326,  70.63433129,  71.26626932,\n",
        "         71.89820734,  72.53014537,  73.1620834 ]),\n",
        " <a list of 10 Patch objects>)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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U063T8lEoetB2Mit0ZB+q/DH0e84zY+hV6EUSwznfmdE6h0rD2CXwq+8kAKlB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\nRERERERERERERERErPp/3BTFWMgHTlgAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109f7dc90>"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Saving to a file"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(bins,y)\n",
      "hist(x[:,1],normed=1)\n",
      "savefig(\"hist.png\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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U063T8lEoetB2Mit0ZB+q/DH0e84zY+hV6EUSwznfmdE6h0rD2CXwq+8kAKlB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\nRERERERERERERERErPp/3BTFWMgHTlgAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109ed0b50>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!ls *.png"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "hist.png\r\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.stats import gaussian_kde"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y = x[:,1]\n",
      "density = gaussian_kde(y)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "xs = np.linspace(y.min(),y.max(),30)\n",
      "plot(xs, density(xs))\n",
      "xlabel('y')\n",
      "ylabel('f(y)')\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "<matplotlib.text.Text at 0x10b291f90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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RDBlim/vNnAnHHec6jYik06ZNtuvCOefAyJH+3vbDT9Njs8rzz9smYtOnq0iI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\niIiIiIiIiIiIiIjI/v4f/IBSxCML7ngAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b26cbd0>"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "xs = np.linspace(y.min()*.8,y.max()*1.2,30)\n",
      "plot(xs, density(xs))\n",
      "xlabel('y')\n",
      "ylabel('f(y)')\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "<matplotlib.text.Text at 0x10b55f590>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10b590dd0>"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There is a lot more to matplotlib. One can visit the [gallery](http://matplotlib.org/gallery.html) and pull examples in to get a sense of what is possible, and how to adapt examples for your own purposes.\n",
      "\n",
      "\n",
      "For now we move on to generating choropleth maps."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}