msund/MPL from SWC

## MPL from SWC
{
 "metadata": {
  "name": "SWC"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": "Interactive matplotlib plots"
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "A blend of Plotly and matplotlib plotting for interactive plots, drawn from the [SWC repo](http://nbviewer.ipython.org/github/swcarpentry/notebooks/blob/master/matplotlib.ipynb)."
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "%matplotlib inline\nimport matplotlib.pyplot as plt # side-stepping mpl backend\nimport matplotlib.gridspec as gridspec # subplots\nimport numpy as np",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "from matplotlylib import fig_to_plotly\nusername = 'IPython.Demo'\napi_key = '1fw3zw2o13'",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig1 = plt.figure()\n\n#generate some data\nx = np.array(range(20))\ny = 3 + 0.5 * x + np.random.randn(20)\n\n#plot the data\nplt.plot(x, y, 'bo')\nplt.show()\n\nfig_to_plotly(fig1, username, api_key, notebook = True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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4mTNnNHPmTE2YMEEul0u7du2ypKCqqhI5nWu6HaOxBACEF/V+4ufOndO5c+eUn5+vCxcu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\nwf4PF4ZdRrgXJoEAAAAASUVORK5CYII=\n",
       "text": "<matplotlib.figure.Figure at 0x10ba34ed0>"
      },
      {
       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/2992/600/450\" width=\"650\"></iframe>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": "<IPython.core.display.HTML at 0x10ba49b90>"
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig2 = plt.figure()\n\n#generate some random numbers from a normal distribution\ndata = 100 + np.random.randn(500)\n\n#make a histogram with 20 bins\nplt.hist(data, 20)\nplt.show()\n\nfig_to_plotly(fig2, username, api_key, notebook = True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": "<matplotlib.figure.Figure at 0x10ba49410>"
      },
      {
       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/2995/600/450\" width=\"650\"></iframe>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": "<IPython.core.display.HTML at 0x10c2bf410>"
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig3 = plt.figure()\n\nplt.hist(data, 20)\nplt.xlabel('Body Mass (g)', fontsize=20)\nplt.ylabel('Number of Individuals', fontsize= 20)\nplt.show()\n\nfig_to_plotly(fig3, username, api_key, notebook = True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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cXd2tHTyZgEE3902ePBn79u3DwIED0bNnTwQGBkKlUmHPnj346aef8Morr2Dt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\n",
       "text": "<matplotlib.figure.Figure at 0x10baaca90>"
      },
      {
       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/2997/600/450\" width=\"650\"></iframe>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": "<IPython.core.display.HTML at 0x10c26ab90>"
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig4 = plt.figure()\n\nplt.hist(data, 20)\nplt.axis([90, 110, 0, 100])\nplt.show()\n\nfig_to_plotly(fig4, username, api_key, notebook = True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": "<matplotlib.figure.Figure at 0x10c8be910>"
      },
      {
       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/2998/600/450\" width=\"650\"></iframe>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": "<IPython.core.display.HTML at 0x10c8ba690>"
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig5 = plt.figure()\n\nx = np.array(range(20))\ny = 3 + 0.5 * x + np.random.randn(20)\nz = 2 + 0.9 * x + np.random.randn(20)\n\n#plot the data\nplt.plot(x, y, 'bo')\nplt.hold(True)\nplt.plot(x, z, 'r^')\nplt.show()\n\nfig_to_plotly(fig5, username, api_key, notebook = True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": "<matplotlib.figure.Figure at 0x10c1b3750>"
      },
      {
       "html": "<iframe height=\"500\" id=\"igraph\" scrolling=\"no\" seamless=\"seamless\" src=\"https://plot.ly/~IPython.Demo/2999/600/450\" width=\"650\"></iframe>",
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": "<IPython.core.display.HTML at 0x10c1b1a50>"
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "fig6 = plt.figure()\n\nplt.subplot(1, 2, 1)\nplt.plot(x, y, 'rs')\nplt.subplot(1, 2, 2)\nplt.hist(data, 10)\nplt.show()\n\nfig_to_plotly(fig6, username, api_key, notebook = True)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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