materials/matplotlib.ipynb

{
 "metadata": {
  "name": "matplotlib"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## An Introduction to Graphing in Python\n",
      "The core plotting library in Python is [matplotlib](http://matplotlib.sourceforge.net/). The [matplotlib gallery](http://matplotlib.sourceforge.net/gallery.html) is a great way to figure out how to make the kind of plots you want. Just look for a plot of the right basic style and click on the image to see the code that generated it.\n",
      "\n",
      "Two things that may differ a bit from other graphing programs that you are used to:\n",
      "\n",
      "* Making a plot will return a plot object. If you assign this to something you can use it to modify the figure later. This is why in some of the examples below you will see some output before the figure.\n",
      "* In some Python interpreters you will need to explicitly tell Python to display the figures. This is done using the ``show()`` function. Several examples of using ``show()`` are included below."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Basic plots of two variables\n",
      "----------------------------\n",
      "Generating basic bivariate plots is done using the plot() function."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "import numpy as np\n",
      "\n",
      "#generate some data\n",
      "x = np.array(range(20))\n",
      "y = 3 + 0.5 * x + np.random.randn(20)\n",
      "\n",
      "#plot the data\n",
      "plt.plot(x, y, 'bo')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can also scale any of the axes logarithmically."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.loglog(x, y, 'rs')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEJCAYAAAB4yveGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noQ+QiNAHSEToAyQi9AESEfoAifwfkQ1CAuEnlFQAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.semilogx(x, y, 'g^')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Histograms\n",
      "----------\n",
      "Histograms are made using the hist() function."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#generate some random numbers from a normal distribution\n",
      "data = 100 + np.random.randn(500)\n",
      "\n",
      "#make a histogram with 20 bins\n",
      "plt.hist(data, 20)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Labels\n",
      "------\n",
      "We can add axis labels to a figure using the xlabel() and ylabel() functions."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.hist(data, 20)\n",
      "plt.xlabel('Body Mass (g)', fontsize=20)\n",
      "plt.ylabel('Number of Individuals', fontsize= 20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "<matplotlib.text.Text at 0x2bba450>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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DBw6gf//+GDNmDFxdXXkLLBFRPadzS6J58+bw8/PDwYMHZRnplS0Jy8aWBLNb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\nAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Axis Limits\n",
      "-----------\n",
      "Axis limits are changed using the ``axis([xmin, xmax, ymin, ymax])`` function."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.hist(data, 20)\n",
      "plt.axis([90, 110, 0, 100])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "[90, 110, 0, 100]"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plotting multiple sets of data together\n",
      "---------------------------------------\n",
      "To plot multiple datasets together we tell Python not to overwrite the previous data using ``hold(True)``. Running ``hold(False)`` will cause Python to start overwriting the figure again."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.array(range(20))\n",
      "y = 3 + 0.5 * x + np.random.randn(20)\n",
      "z = 2 + 0.9 * x + np.random.randn(20)\n",
      "\n",
      "#plot the data\n",
      "plt.plot(x, y, 'bo')\n",
      "plt.hold(True)\n",
      "plt.plot(x, z, 'r^')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Subplots\n",
      "--------\n",
      "Subplots are generated using ``subplot(#ofRows, #ofCols, Position)``."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.subplot(1, 2, 1)\n",
      "plt.plot(x, y, 'rs')\n",
      "plt.subplot(1, 2, 2)\n",
      "plt.hist(data, 10)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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MjEhISHBMR+vp6RHPPPOMiIuLE4899pj49NNPZY8phBDPPfeceOeddxSt9Z//\n/KdITU0VCQkJIi0tTZw8eVKRcbdu3SoiIiJERESEePnll4c1jqu9v2HDBhEaGioiIyMdM3Xkjjl1\n6lQRFBQkxo8fL6ZMmSLOnj0re9z169eLBx980NGfiYmJ4ptvvpE97u7du0VsbKxjqvVwpg3rhPCC\nqR9ERCTbiLpiERGRljHQiYg0goFORKQRDHQiIo1goBMRaQQDnYhII/4fZpxAdhKmQB8AAAAASUVO\nRK5CYII=\n"
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "New Figures\n",
      "-----------\n",
      "Plotting multiple figures in the same script requires that we create new figures, which is done using ``figure()``. In this example the two figures are different figures rather than subplots of a single figure."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(z, x, 'go')\n",
      "plt.figure()\n",
      "plt.plot(z, y, 'rs')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "[<matplotlib.lines.Line2D at 0x3eb4710>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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       "output_type": "display_data",
       "png": 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      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Want to know more?\n",
      "------------------\n",
      "Matplotlib is very powerful and there are lots of functions and arugments to create different kinds of figures and modify them as much as you would like.\n",
      "Check out the [website](http://matplotlib.sourceforge.net/) or the [gallery](http://matplotlib.sourceforge.net/gallery.html) to learn more."
     ]
    }
   ],
   "metadata": {}
  }
 ]
}