advanced/python/matplotlib_master.ipynb

{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib\n",
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import brewer2mpl"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "brewer2mpl.print_all_maps()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Sequential\n",
        "Blues     :  {3, 4, 5, 6, 7, 8, 9}\n",
        "BuGn      :  {3, 4, 5, 6, 7, 8, 9}\n",
        "BuPu      :  {3, 4, 5, 6, 7, 8, 9}\n",
        "GnBu      :  {3, 4, 5, 6, 7, 8, 9}\n",
        "Greens    :  {3, 4, 5, 6, 7, 8, 9}\n",
        "Greys     :  {3, 4, 5, 6, 7, 8, 9}\n",
        "OrRd      :  {3, 4, 5, 6, 7, 8, 9}\n",
        "Oranges   :  {3, 4, 5, 6, 7, 8, 9}\n",
        "PuBu      :  {3, 4, 5, 6, 7, 8, 9}\n",
        "PuBuGn    :  {3, 4, 5, 6, 7, 8, 9}\n",
        "PuRd      :  {3, 4, 5, 6, 7, 8, 9}\n",
        "Purples   :  {3, 4, 5, 6, 7, 8, 9}\n",
        "RdPu      :  {3, 4, 5, 6, 7, 8, 9}\n",
        "Reds      :  {3, 4, 5, 6, 7, 8, 9}\n",
        "YlGn      :  {3, 4, 5, 6, 7, 8, 9}\n",
        "YlGnBu    :  {3, 4, 5, 6, 7, 8, 9}\n",
        "YlOrBr    :  {3, 4, 5, 6, 7, 8, 9}\n",
        "YlOrRd    :  {3, 4, 5, 6, 7, 8, 9}\n",
        "Diverging\n",
        "BrBG      :  {3, 4, 5, 6, 7, 8, 9, 10, 11}\n",
        "PRGn      :  {3, 4, 5, 6, 7, 8, 9, 10, 11}\n",
        "PiYG      :  {3, 4, 5, 6, 7, 8, 9, 10, 11}\n",
        "PuOr      :  {3, 4, 5, 6, 7, 8, 9, 10, 11}\n",
        "RdBu      :  {3, 4, 5, 6, 7, 8, 9, 10, 11}\n",
        "RdGy      :  {3, 4, 5, 6, 7, 8, 9, 10, 11}\n",
        "RdYlBu    :  {3, 4, 5, 6, 7, 8, 9, 10, 11}\n",
        "RdYlGn    :  {3, 4, 5, 6, 7, 8, 9, 10, 11}\n",
        "Spectral  :  {3, 4, 5, 6, 7, 8, 9, 10, 11}\n",
        "Qualitative\n",
        "Accent    :  {3, 4, 5, 6, 7, 8}\n",
        "Dark2     :  {3, 4, 5, 6, 7, 8}\n",
        "Paired    :  {3, 4, 5, 6, 7, 8, 9, 10, 11, 12}\n",
        "Pastel1   :  {3, 4, 5, 6, 7, 8, 9}\n",
        "Pastel2   :  {3, 4, 5, 6, 7, 8}\n",
        "Set1      :  {3, 4, 5, 6, 7, 8, 9}\n",
        "Set2      :  {3, 4, 5, 6, 7, 8}\n",
        "Set3      :  {3, 4, 5, 6, 7, 8, 9, 10, 11, 12}\n"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bmap = brewer2mpl.get_map('Greens', 'sequential', 5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "colors = bmap.mpl_colors"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot([0,1,2,3,4], [1,2,1,2,1], marker = 'o', c=colors)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10ba3e550>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0x10b326890>"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.scatter([0,1,2,3,4], [1,2,1,2,1], s=150, marker = 'o', c=colors)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "<matplotlib.collections.PathCollection at 0x10c113d10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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RUYczubm5rR/rdDrodDp77Z6IqEfQ6/XQ6/WdeoxKCCFsDZWXlyMtLQ0XLlxod/v58+fx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\ncv8HwsLWyLAI7rYAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10ba6e490>"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.scatter?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "matplotlib.backends?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Using matplotlib backend: agg\n",
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###The most basic plot you can make"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "\n",
      "\n",
      "x = np.arange(10)\n",
      "y = np.sin(x)\n",
      "\n",
      "pyplot.plot(x, y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "[<matplotlib.lines.Line2D at 0x1092c5310>]"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You can modify the linestyle"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pyplot.plot(x, y, linestyle = '--')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10929b0d0>]"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You can modify the symbol"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pyplot.plot(x, y, marker = 'o')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "[<matplotlib.lines.Line2D at 0x1092bf910>]"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "You can modify the color"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pyplot.plot(x, y, color = 'salmon')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10b49e690>]"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Label your plot"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pyplot.xlabel('x')\n",
      "pyplot.ylabel('sin(x)')\n",
      "pyplot.title('Sine curve')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<matplotlib.text.Text at 0x1098fec50>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Object oriented plotting"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = pyplot.figure()\n",
      "ax = fig.add_subplot(1, 1, 1)\n",
      "ax.plot(x, y, marker = 's', linestyle = ':', color = 'lime')\n",
      "ax.set_xlabel('x')\n",
      "ax.set_ylabel('sin(x)')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "<matplotlib.text.Text at 0x10b4b3310>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}