theengineear/plotly and matplotlib

## plotly and matplotlib
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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plotly, matplotlib, and mpld3.\n",
      "==============================\n",
      "\n",
      "###Quickstart\n",
      "Matplotlylib lets you convert matplotlib figures to beautiful D3js graphs drawn online with Plotly's [Python API](http://plot.ly/api/python). It's this easy...\n",
      "\n",
      "```\n",
      ">>> ## make an mpl figure ##\n",
      ">>> import plotly.plotly as py\n",
      ">>> py.plot_mpl(fig) \n",
      "```\n",
      "\n",
      "For install:\n",
      "\n",
      "```bash\n",
      "$ pip install plotly\n",
      "```\n",
      "\n",
      "###Intro\n",
      "The mplexporter package is a recent development as a part of the [mpld3 project](http://mpld3.github.io/). The goal of mpld3 is to expand python's matplotlib use to the browser. By separating the matplotlib exporting and the actual graphic rendering, many renderers can now leverage the parsed matplotlib information. This makes the matplotlib-to-browser project easier for everyone. Nice.\n",
      "\n",
      "Now, enter Plotly. Like keeping your data around? Want to share your plots and/or data online? We allow users to easily create and share interactive, publication-quality visualizations, right in a web browser. While Plotly's [Python API](https://plot.ly/api/python) gives users access to plotly directly, users may already have high-quality plots that they want to get online and share. Standing on the shoulders of the mplexporter, plotly is developing matplotlylib, a package that converts mpl figures to plotly figures with a simple call to `plot_mpl` or `iplot_mpl` (for plotting in IPython) . This function creates a JSON dictionary and sends it to be rendered on plotly with an option to put the resulting figure back into an IPython notebook.\n",
      "\n",
      "If your data are important to you, they're important to us, and we'll help you share them with others. \n",
      "\n",
      "For more infomation on using Plotly, you can check out our other [IPython notebooks](http://nbviewer.ipython.org/github/plotly/IPython-plotly/tree/master/). Or, just take a look at some practical uses from [Wired Science](http://www.wired.com/wiredscience/2014/02/much-real-olympic-gold-medal-cost/ \"How Much Would a Real Olympic Gold Medal Cost?\") and [The Washinton Post](http://www.washingtonpost.com/blogs/wonkblog/wp/2013/06/14/do-low-taxes-on-the-rich-leave-the-middle-class-with-lower-wages/ \"Do low taxes on the rich leave the middle-class with lower wages?\").\n",
      "\n",
      "A quick rundown of what this notebook covers. This is what matplotlylib supports so far.\n",
      "\n",
      "* Imports\n",
      "* Strip Style and Resize\n",
      "* Line Plots\n",
      "* Subplots\n",
      "* Annotations\n",
      "* Bar Charts\n",
      "* Scatter Plots\n",
      "* The Nitty Gritty (fine print)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Imports\n",
      "Let's start by calling the IPython 'magic' function for matplotlib so that plots show up inline. Next, we'll import all of the matplotlib tools necessary for creating the figures in this notebook."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import matplotlib.pyplot as plt # so we don't have to look at mpl's backend\n",
      "import matplotlib.gridspec as gridspec # for subplots\n",
      "import matplotlib.cm as cm # for fun-with-colors\n",
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The only import required to render figures in plotly is the `iplot_mpl` function. We'll also fill in our creds here so that they're accessible later when we actually make the calls to plotly. You can use the `mpld3` username for now, or switch to your own Plotly username and api_key. Don't have a username? There's no better time than the [present](https://plot.ly/ \"plotly population +1?\")."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import plotly.plotly as py\n",
      "py.sign_in('mpld3','js5825bxqr')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Start Plotting!\n",
      "\n",
      "Simply make a figure and use the `fig_to_plotly` call."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots()\n",
      "ax.plot(np.random.rand(10), '-o')\n",
      "ax.set_title('hello world... wide web')\n",
      "py.iplot_mpl(fig, strip_style=True, filename='hello mpl!')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~mpld3/11\" height=\"525\" width=\"100%\"></iframe>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x108e6a410>"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Hover, zoom, pan--dang! That graph is live."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Strip style and resize\n",
      "Let's get this out of the way early. While it's certainly true that you can make darn-fine looking graphs in matplotlib, it often takes a bit of effort. \n",
      "\n",
      "Plotly's worked hard on its defaults, so why not give them a shot?\n",
      "\n",
      "```\n",
      "py.iplot(fig, strip_style=True)\n",
      "```\n",
      "\n",
      "Also, by default we resize mpl plots, but you can revert that:\n",
      "\n",
      "```\n",
      "py.iplot(fig, resize=False)\n",
      "```\n",
      "\n",
      "Let's see it in action.\n",
      "\n",
      "Better one?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig_strip = plt.figure()\n",
      "ax = fig_strip.add_subplot(111)\n",
      "ax.plot([1, 2, 3, 4], [10, 5, 9, 5], 'o')\n",
      "ax.plot([1, 2, 3, 4, 5], [5, 9, 5, 7, 4])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "[<matplotlib.lines.Line2D at 0x1099a58d0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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       "text": [
        "<matplotlib.figure.Figure at 0x1097a7210>"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "... better two."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "py.iplot_mpl(fig_strip, strip_style=True, filename='style-strip')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~mpld3/18\" height=\"525\" width=\"100%\"></iframe>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x10aa42c50>"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We'll almost always use `strip_style=True` in this notebook."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Line Plots\n",
      "Alright, let's get going!\n",
      "\n",
      "Here we instantiate a matplotlib figure object and fill it with stuff we want to plot! IPython's matplotlib magic function will catch this code block and pop it inline when you 'shift-enter'."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig1 = plt.figure()\n",
      "x = np.arange(0,1.0,0.01)\n",
      "y1 = np.sin(2*np.pi*x)\n",
      "y2 = np.cos(4*np.pi*x)\n",
      "ax = fig1.add_subplot(111)\n",
      "ax.plot(x, y1, 'b--', label='sin')\n",
      "ax.plot(x, y2, 'ro', label='cos')\n",
      "ax.set_title(\"It's a Sign\")\n",
      "ax.set_xlabel('time')\n",
      "ax.set_ylabel('amplitude')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 44,
       "text": [
        "<matplotlib.text.Text at 0x10aa9bf50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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374/69esDEHqnN998U9pICDGQ6dMBmRJIEyIrm53GrFmz4Orqinv37qGYt7GC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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10aa5a0d0>"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The following line takes the matplotlib figure object, extracts the data with mplexporter, renders a new plot with PlotlyRenderer, and sends it to Plotly. The plot shows up inline because of the `notebook=True` keyword argument. This causes the `fig_to_plotly` function to call `plotly.iplot` (IPython plot) instead of the normal `plotly.plot` function. Taking that keyword argument out defaults to the normal plot function and will open a new window in your browser with the plot.\n",
      "\n",
      "Note how you can zoom, pan, autoscale, and see data points by hovering. The data you plotted is right there. If you want to modify the dataset, style, layout, or add notes, you can click the 'data and graph' link below the plot.\n",
      "\n",
      "And again, if you have a [plotly profile](https://plot.ly/~RhettAllain/ \"Wired Science Blogger\"), you can save and share your awesome sinusoids with others.\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "py.iplot_mpl(fig1, strip_style=True, filename=\"a sign\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~mpld3/12\" height=\"525\" width=\"100%\"></iframe>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x1097e0850>"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Subplots\n",
      "But why have one axes object when you could have five!? Gridspec is a handy way to intuitively partition figure space into nice looking subplots. Here are just some simple line plots stretching over a 3x3 grid of plotting areas."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig2 = plt.figure() # matplotlib.figure.Figure obj\n",
      "gs = gridspec.GridSpec(3, 3)\n",
      "ax1 = fig2.add_subplot(gs[0,:])\n",
      "ax1.plot([1,2,3,4,5], [10,5,10,5,10], 'r-')\n",
      "ax2 = fig2.add_subplot(gs[1,:-1])\n",
      "ax2.plot([1,2,3,4], [1,4,9,16], 'k-')\n",
      "ax3 = fig2.add_subplot(gs[1:, 2])\n",
      "ax3.plot([1,2,3,4], [1,10,100,1000], 'b-')\n",
      "ax4 = fig2.add_subplot(gs[2,0])\n",
      "ax4.plot([1,2,3,4], [0,0,1,1], 'g-')\n",
      "ax5 = fig2.add_subplot(gs[2,1])\n",
      "ax5.plot([1,2,3,4], [1,0,0,1], 'c-')\n",
      "gs.update(wspace=0.5, hspace=0.5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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tBtavNx29OzUt4+/vj6CgIJSVlSE8PBz79+/H4MGDRQzNGIEjc4ZRNp2bl0Qj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\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107be4b50>"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Again, if you're fond of matplotlib's syntax, or, you're just feeling like dipping your toes in the plotly pool for now, inserting a simpe `iplot_mpl` call sends the above figure to Plotly and it responds by sticking back into this notebook.\n",
      "\n",
      "By default, matplotlib subplots don't have common axis elements and so the rendered plotly graphic will reflect this. However, as we've mentioned, plotly's all about sharing. You can check out the possibilities for anchoring different subplots to the same axis in the [plotly docs](https://plot.ly/api/python/docs/subplots \"<3 subplots\") if you're trying to work through the API. Alternatively, you can use ploty's GUIs to change all of the plot's attributes--including axis sharing."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "py.iplot_mpl(fig2, strip_style=True, filename='simple subplots')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~mpld3/13\" height=\"525\" width=\"100%\"></iframe>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x109ae1750>"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Annotations\n",
      "The matplotlib package supports multiple ways of getting annotations onto figures. These too get sent to plotly when you call `fig_to_plotly`. Let's start with some simple annotations that mark plot boundaries."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig3 = plt.figure()\n",
      "ax = fig3.add_subplot(111)\n",
      "ax.plot([1,2,3], 'b-')\n",
      "ax.plot([3,2,1], 'b-')\n",
      "ax.text(0.001, 0.999, 'top-left', transform=ax.transAxes, va='top', ha='left')\n",
      "ax.text(0.001, 0.001, 'bottom-left', transform=ax.transAxes, va='baseline', ha='left')\n",
      "ax.text(0.999, 0.999, 'top-right', transform=ax.transAxes, va='top', ha='right')\n",
      "ax.text(0.999, 0.001, 'bottom-right', transform=ax.transAxes, va='baseline', ha='right')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "<matplotlib.text.Text at 0x1094bd150>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10aa3e490>"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plotly will import these and anchor them appropriately to the plot, not to the data. You can pan and zoom in the following figure to check this. If you decide you don't want them anchored to the 'page', you can later switch them via the 'Notes' tab in Plotly."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "py.iplot_mpl(fig3, filename='annotations')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~mpld3/14\" height=\"525\" width=\"100%\"></iframe>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x108ec8f90>"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's try a more interesting example. The following code creates a figure with a bunch of different line objects, each annotated with a text object that is anchored to data. Another annotation is anchored to the mpl axes objects so that it will remain in the top-left corner of the plot when you pan or zoom."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gs = gridspec.GridSpec(2, 1)\n",
      "fig4 = plt.figure()\n",
      "ax1 = fig4.add_subplot(gs[0,0])\n",
      "x = np.arange(-3, 3, 0.01)*2*np.pi\n",
      "sin_x = np.sin(x)\n",
      "ax1.plot(x, sin_x, 'k-', label='sin(x)')\n",
      "num_lines = 20\n",
      "colors = cm.rainbow(np.linspace(0, 1, num_lines))\n",
      "for iii in range(0, num_lines):\n",
      "    y = x\n",
      "    for jjj in range(1, iii+1):\n",
      "        sign = (-1)**jjj\n",
      "        deg = 1+2*jjj\n",
      "        y = np.add(y, sign*x**(deg)/np.math.factorial(deg))\n",
      "    ct_h = int(len(x)/2)\n",
      "    while abs(y[ct_h]) < 10:\n",
      "        ct_h += 1\n",
      "    ct_l = int(len(x)/2)\n",
      "    while abs(y[ct_l]) < 10:\n",
      "        ct_l -= 1\n",
      "    x_plot = x[ct_l:ct_h+1]\n",
      "    y_plot = y[ct_l:ct_h+1]\n",
      "    ax1.plot(x_plot, y_plot, color=colors[iii], label='L{}'.format(iii))\n",
      "    ax1.text(x_plot[-15], y_plot[-15], '{}'.format(1+2*iii), ha='center', va='center')\n",
      "ax1.set_ylim(-10,10)\n",
      "ax1.set_xlabel('x')\n",
      "ax1.set_ylabel('y(x)')\n",
      "ax1.set_title('approximation to sin(x)', size=14)\n",
      "ax1.text(0.001, 0.999, 'num=deg', transform=ax1.transAxes, ha='left', va='top')\n",
      "\n",
      "ax2 = fig4.add_subplot(gs[1,0])\n",
      "x = np.arange(-5, 5, 0.01)\n",
      "ax2.plot(np.arange(0.01, 5, 0.01),\n",
      "         np.log(np.arange(0.01, 5, 0.01) + np.ones(len(np.arange(0.01, 5, 0.01)))),\n",
      "         'k-', label='ln(x + 1)')\n",
      "num_lines = 20\n",
      "colors = cm.rainbow(np.linspace(0, 1, num_lines))\n",
      "for iii in range(0, num_lines):\n",
      "    y = x\n",
      "    for jjj in range(1, iii+1):\n",
      "        sign = (-1)**jjj\n",
      "        deg = 1+jjj\n",
      "        y = np.add(y, sign*x**(deg)/deg)\n",
      "    ct_h = int(len(x)/2)\n",
      "    while abs(y[ct_h]) < 3:\n",
      "        ct_h += 1\n",
      "    ct_l = int(len(x)/2)\n",
      "    while abs(y[ct_l]) < 3:\n",
      "        ct_l -= 1\n",
      "    x_plot = x[ct_l:ct_h+1]\n",
      "    y_plot = y[ct_l:ct_h+1]\n",
      "    ax2.plot(x_plot, y_plot, color=colors[iii], label='L{}'.format(iii))\n",
      "    ax2.text(x_plot[-28], y_plot[-28], '{}'.format(1+iii), ha='center', va='center')\n",
      "ax2.set_ylim(-3,3)\n",
      "ax2.set_xlim(-2,4)\n",
      "ax2.set_xlabel('x')\n",
      "ax2.set_ylabel('y(x)')\n",
      "ax2.set_title('approximation to ln(1 + x)', size=14)\n",
      "ax2.text(0.001, 0.999, 'num=deg', transform=ax2.transAxes, ha='left', va='top')\n",
      "gs.update(wspace=0.2, hspace=0.5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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BggWYOXMmNmzYgJiYGGzcuBEzZ85stO2pU6cQFxeHKVOmID4+HiNHjoTJZHIs2uecXyUl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\njQetvL/Swk+P69Bq6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g4cyPDhwzl27BhpaWl8/PHH9jZJQaHSOHTTIQWF2sLcuXNxcnLi\nnnvuQZZlOnbsWKc6myncvCiehIKCgoJCuSg5CQUFBQWFclFEQkFBQUGhXBSRUFBQUFAoF0UkFBQU\nFBTKRREJBQUFBYVyUURCQUFBQaFcFJFQUFBQUCgXRSQUFBQUFMrl/wEGNvSRO3NrGgAAAABJRU5E\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1094161d0>"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Okay. You or a collaborator did all this work with matplotlib, but those labels for the second plot are really not showing up well. Rather than messing with that code, you could just shoot it over to plotly, drag the annotations interactively, save the figure, share it, and get feedback. To get a feel for the flexibility, run the `fig_to_plotly` function, click the 'data and graph' link, and then click the 'save and edit' link."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "py.iplot_mpl(fig4, filename='sin approx')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~mpld3/15\" height=\"525\" width=\"100%\"></iframe>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x108f6db10>"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Bars\n",
      "We like bar charts and histograms just as much as you do. Therefore, we've grabbed those for you too! Bars from bar charts are stored as 'patches' which are defined by paths. They are not, however, stored as a 'path_collection' since each bar is differently sized (or it should be unless you're making pretty boring bar charts). Plotly uses some educated guesses to decide whether rectangle patches that end up in each axes object are intended to be bar charts."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig7 = plt.figure()\n",
      "ax = fig7.add_subplot(111)\n",
      "left = [1, 2, 3, 4, 5, 6, 7]\n",
      "height = [5, 6, 1, 4, 3, 10, 15]\n",
      "width = 1\n",
      "ax.bar(left, height, width=width)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 52,
       "text": [
        "<Container object of 7 artists>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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m6KWXXvI6UtiOHz+uoqIiFRQUKDc3V4899pjXkVzp6elRYWGhiouLvY4SsQkTJmjq1Kkq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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109a0b610>"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "py.iplot_mpl(fig7, strip_style=True, filename='lemondade')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~mpld3/19\" height=\"525\" width=\"100%\"></iframe>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x10aa81d50>"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Using mpl, we can also get horizontal bars. Let's look at an example of making some horizontal histograms."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig8 = plt.figure()\n",
      "y = np.random.randn(10000) + 3\n",
      "x = np.random.randn(10000) - 3\n",
      "sum = x + y\n",
      "gs = gridspec.GridSpec(1,4)\n",
      "ax1 = fig8.add_subplot(gs[0,:2])\n",
      "ax1.set_xlabel('sum')\n",
      "ax2 = fig8.add_subplot(gs[0,2])\n",
      "ax2.set_xlabel('x')\n",
      "ax3 = fig8.add_subplot(gs[0,3])\n",
      "ax3.set_xlabel('y')\n",
      "gs.update(wspace=0.5)\n",
      "ax1.hist(sum, bins=100)\n",
      "histx = ax2.hist(x, bins=100, orientation='horizontal')\n",
      "histy = ax3.hist(y, bins=100, orientation='horizontal')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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AgIqKChw9ehQVFRUoLCzESy+9BK1Wy0/NRUK9emKeqUFY2FUPUlhDHRDvfT3s\ncb+hQYP9jBkzMHas4RUwPz8faWlpAIC0tDQcP34cAJCXl4fk5GQolUqo1WoEBASgtLSUh2qLh3r1\nxDxTvUilqDWSN+rRW2pYUy+bm5vh5eUFAPDy8kJzczMAoKGhASqVSn+cSqVCfX09B9UkxFYMNL2S\n8IN69JayeoBWoVCYnW8+0PcyMzP1X8fFxSEuLs7aqhA7xeV9Oq1n6v1OPXv+WD4bx1579DrDCvZe\nXl5oamqCt7c3Ghsb4enpCQDw8fFBbW2t/ri6ujr4+PiYPEffYE+kwja3TujfWcjKyhKvMiZnh9D6\nBf5YOhvHvmbemDKsNE5iYiIOHToEADh06BAWLlyo//8jR45Ao9GguroaVVVViImJ4a62hGe0dYL1\nKGcvLMrTW2rQnn1ycjLOnDmDlpYW+Pr64ve//z02b96MpKQk7N+/H2q1GseOHQMAhISEICkpCSEh\nIXBycsKePXtktKUAIZagnr2wBuvZ0/oGHdrPfogML17S3aPemmNt9bXRkdo9aOW8Upavtk5PT8en\nn34KT09PXL16dcCyB6cEYxpuKyciulMV4ZRukzdaU2AtpV3P/rCGqfU9plmy0RwBKNgTE3SbvFH+\nfjgo0HDB1Poe0yhXbynaG4cQTj1MLVCgEQLtRWQpCvaEcEoXYGiqnzAGHieRAy7XkNAA7RDZwwBt\n3+e2+DpJYYBWzoOyffHZ1jU1NViwYMEgA7T9y7bN96ylaICWEMmgjc+E1T9P70QTCwZAwZ4QTtEi\nKi4kJyfjySefRGVlJXx9fXHw4MEBjjSefUMTC0yjNM4QURpH+sRN49huuw2HmH/LtK5haGiAlhBO\nUc9eWMY5e7kGemtRGsdCuoVGhBApoX2ILEXB3kK6hUbyRx/2TNm9ezeCg4MRGhqKTZs2mTmSFlMJ\ni1bMWor+skk/3WJXQHKKi4uRn5+Pb775BkqlErdu3TJzNK3aFJbhp21q+4FRz56YRfvkAO+99x62\nbNkCpbI3RTB+/Hizx1POWEgP70BF+xCZR8GemEX75ABVVVU4e/Yspk2bhri4OFy6dMnM0TTPW1iU\np7cUpXEIAZCQkICmpiaj/9+2bRu6u7tx584dXLhwARcvXkRSUhKuX78+wJleQ1tbFjIzM2V5u01p\n3QISMJ4yTAZC8+wt1HcO9UPSmQ8vxLG28ppx/f6aO3cuNm/ejKeffhoAEBAQgK+++grjxo0zKheQ\n9zzv/sRKWFByAAAVeklEQVSfZ29b701r0XYJhPBo4cKF+PzzzwEAlZWV0Gg0RoG+L3sJ9NJAaRxL\nURqHkEGkp6cjPT0dYWFhcHZ2xuHDhwc8lmaDCI3SOJaiNI6FKI1jO6+ZmNsl2EobcUX8NA4gt1sP\nmkNpHMIT+uBHpI4WUlmKgj0xgxZYEamjnL2lqOs2AHd3D7S13aEcLCGSRjl7S1GwH8DDxUT0RiKW\nc3f3oNk4gqLtKSxl1QCtWq2Gu7s7HB0doVQqUVpaitu3b+PZZ5/FDz/8ALVajWPHjmHMmDGGhdrA\nQNbgA7L9n8v/WKm/Zjpi35bQVtqJC1IYoKX2toxVOXuFQoGSkhKUlZWhtLQUAJCTk4OEhARUVlZi\n1qxZyMnJsaYIIhm0DYAlqJcpNMrXW8rqAdr+V5n8/HykpaUBANLS0nD8+HFriyCS0G3X++NYilI4\nQqOZOJayumcfHx+P6Oho7Nu3DwDQ3NwMLy8vAICXlxeam5utryUhNoI+/QiLPklZzqoB2vPnz2PC\nhAm4desWEhISEBQUZPB9hUIx4N2dMjMz9V/LccMoIhwpbc7V1tYudhVko7CwEGvXrkVPTw9Wrlxp\n8qYx9EnKcpytoM3KyoKrqyv27duHkpISeHt7o7GxETNnzsT3339vWCgN0NrssVJ/3QAaoBUSX23d\n09ODxx9/HKdPn4aPjw+mTp2KDz74AMHBwQZl29Omc4BIA7SdnZ1oa2sDAHR0dODUqVMICwtDYmIi\nDh06BAA4dOgQFi5cONwiJIJmpxLLUVqBG6WlpQgICIBarYZSqcTSpUuRl5dndByNI1lu2JGsubkZ\nixYtAgB0d3dj2bJlmD17NqKjo5GUlIT9+/frp17aNlpFSixnT71MPtXX18PX11f/XKVS4auvvjI6\nji6ulht2sPfz80N5ebnR/3t4eOD06dNWVYpIlRMtGiKCGGisr79169box//kOPbH5XgU5SjIEND0\nSyIMHx8f1NbW6p/X1tZCpVIZHdd3oocc9b+AZWVlDftctBFaH3RzbUs4URsR3kVHR6Oqqgo1NTXQ\naDQ4evQoEhMTxa6WTaOefR+0H44lukFtNLC7d+9i9OjRYlfD5jk5OeHdd9/FnDlz0NPTg4yMDIOZ\nOGTo6OYlfTycbqmELqhJadqjtI5Vws3NTZL5ezGnXv7zn//ExIkTBS9bLGLvjSPFOMInunkJ52gG\nzuAof2+Ki4uL2FUgxCQK9oRwyNvbW+wqEGISBXtCCLEDFOyJVWgGEyG2gWbjEKvQDCZCbAP17Akh\nxA7YZbB3d/egtAMhxK7YZbBva7tjNG2Qgr+16LaFhEiZXQZ7U2jOuLVo3j0hUkbBnliBxvcJsRWy\nD/YDTw18mHag9MNw9V9pTJukESJVst8bp+/tBXVlPtwr2wnGAcsW9qWR/rFi7lki5t44tFeLfZQt\nFtobZ9h0m50RQoi82XmwJ4QQ+0DBnvCApmESIjV2Fex1g7WEb91oa2ujgE+IhNhVsNft40KE0Bvw\n+87OoU3TCBGPrCdKU1ARm+EtDGnTNELEw0vPvrCwEEFBQQgMDMSOHTv4KGJQ7u4e/VZ0yvq6JnH9\nc/i2l9MvLS1FTEwMoqKiMHXqVFy8eFHsKsnWhx9+iMmTJ8PR0RFXrlwRuzrywTjW3d3N/P39WXV1\nNdNoNCwiIoJVVFQYHMNDsUbQm6/5+cEG+Hqw5/Z0bDHPdXAyKofP155rTz/9NCssLGSMMXby5EkW\nFxc3rHKLi4sFOUaocvho6//7v/9j165dY3Fxcezy5cvDLluoNrDkGK7Ksaa9Oe/Zl5aWIiAgAGq1\nGkqlEkuXLkVeXh7XxZjUmxN2pkHYYSnh+fy6xWu6cnpX2yoUzjbRy58wYQLu3r0LAGhtbYWPj8+w\nzlNSUiLIMUKVw4egoCBMmjTJ6vMI2QZCvR7W4Dy3UV9fD19fX/1zlUqFr776yqKf/frrr3Ht2jVE\nRkYavNi6lIyb21jcu3fb5M8apm36r/Ak0qNb0Kb4eSDXGUCX2ddYTDk5OZg+fTo2bNgArVaLL7/8\nUuwqETIknAd7a3rVqakv4OrVi1iyZDk+/PCvAPoGcYa2NiXc3T30waD3e+0AujioORGPrtff+xor\nFM5wc3Pt9zr3Xux1+LggJCQkoKmpyej/t23bhl27dmHXrl1YtGgRPvzwQ6Snp6OoqIjzOtiLgdp6\n+/btWLBggQg1sgPDTgAN4Msvv2Rz5szRP9++fTvLyckxOMbf379fXpce9ODu4e/vz/Xbmrm5uem/\n1mq1zN3d3egYe3xf89HWOoPl7Km9h4bznn10dDSqqqpQU1ODX/ziFzh69Cg++OADg2P+8Y9/cF0s\nIbwKCAjAmTNn8PTTT+Pzzz83mVOm9zX3mJlNv6i9h4bzYO/k5IR3330Xc+bMQU9PDzIyMhAcHMx1\nMYQI6i9/+Qv+7d/+DQ8ePMDIkSPxl7/8RewqydbHH3+MNWvWoKWlBfPmzUNUVBQKCgrErpbNE2WL\nY0IIIcISbaXR7t27sWfPHjg6OmLevHm8Lr7auXMnNm7ciJaWFnh4cDvNb+PGjThx4gScnZ3h7++P\ngwcPYvTo0Zycu7CwEGvXrkVPTw9WrlyJTZs2cXLevmpra7FixQrcvHkTCoUCq1atwpo1azgvBwB6\nenoQHR0NlUqFTz75hJcyWltbsXLlSnz33XdQKBQ4cOAApk2bxktZfQ30WqnVari7u8PR0RFKpRKh\noaE4ceIEOjs78Ytf/AJqtRp79+7FqlWr8MMPP0CtVsPT0xNFRUXw9PRESkoKDhw4gDt37kChUECl\nUkGj0cDJyQnd3d1QKBTw8/PD999/DwBwc3NDe3s7vL29wRjD7du3DY7pex6tVov79+9j5MiR0Gg0\n8PT0RH19vcF5fH19cffuXXR3dxsc0/c8QO/A6ty5cwEA2dnZOHDgABwdHbFr1y7Mnj1blLYuLS3F\nsmXL8NFHHwEAnn76aRw7dgxarRbPPvssfvjhB3R0dKCrqwteXl64evUqsrOz8dZbb6GtrQ2PPfYY\n3Nzc8PLLL+PgwYO4efMmWlpaoNVqMW7cOPzxj3/E3r178Y9//AN3796Fl5cXHB0dsWrVKnR0dBic\nZ9SoUeK39bCz/Vb4/PPPWXx8PNNoNIwxxm7evMlbWTdu3GBz5sxharWa/fjjj5yf/9SpU6ynp4cx\nxtimTZvYpk2bODmvJYvTuNDY2MjKysoYY4y1tbWxSZMm8VIOY4zt3LmTpaSksAULFvByfsYYW7Fi\nBdu/fz9jjLGuri7W2trKW1k65l6r/u+7s2fPshUrVjBvb2/GGGM5OTksJiaG7dixQ/88JSWFXbly\nhQUGBrKIiAim0WjY2rVr2bhx41hPT4/Ba1ZaWsoeeeQR9vXXX7NVq1YxDw8P1tPTw1577TX23HPP\nGR3T9zyMMdbR0cEYY+zrr79mLi4urLi42OA8OTk5bN26dUbH9D+Pznfffaevc3V1NfP39zc6Rqi2\nZoyx5ORktmbNGhYaGspycnLYpk2b2MaNG/Xt/eKLL7LnnnuOhYaG6uv+xhtvsNdee01fd117f/fd\ndyw0NJQFBgayoqIiNmbMGJaTk8MaGxvZmjVr2KZNm1hbWxtTq9Xs8ccfNzqP2G0tykZo7733HrZs\n2QKlUgkAGD9+PG9lrVu3Dv/5n//J2/kTEhLg4NDbjLGxsairq+PkvEItTvP29kZkZCQAwNXVFcHB\nwWhoaOC8nLq6Opw8eRIrV67k7e5Cd+/exRdffIH09HQAveNHXH3KMmew16rv7ztjxgycO3cOY8aM\nAQCkpaWhvLwcaWlp+ueXL1/G2LFjce/ePSQnJ0OpVGLMmDF49NFHUVpaavCanT59Gv7+/rh58ybO\nnDmD8PBwlJaW4t///d/1awH6HtP3PADg4uICoDdP7uHhgfHjxxucJy0tDZ9++qnRMf3Po5OXl6ev\ns1qtRkBAgNExQrU1AFy5cgXLly/Xt+3x48eRn5+vb+/MzEycPXvWoO6Ojo4YO3asvu669s7Ly8Py\n5csREhICAOjq6kJYWBi8vb2xZcsWHD9+HK6urhg5ciSmT59udB6x21qUYF9VVYWzZ89i2rRpiIuL\nw6VLl3gpJy8vDyqVCuHh4bycv78DBw7g17/+NSfnMrU4Tfexjy81NTUoKytDbGws5+d++eWX8dZb\nb+kvjHyorq7G+PHj8fzzz+OJJ57ACy+8gM7OTt7K0zH3WikUCsTHxyM6Ohr79u0DANy6dQtOTr0Z\nVC8vL30aQfe8ubkZQG8w0X1015WzdOlSZGRkoLW1FQDw/fffo7m5GbGxsWhuboa/vz/q6+sNzqM7\nRpfO6nue27dvIzIyEtu2bUNERAQmT55sdJ6mpiajYwaqT0NDg0GduX7fDrWtm5ub9Z1JXZs0Nzcb\ntPetW7eM6r57925cvHgRr732msHv9sgjj+j/Rrq6uvDTTz8ZnLumpgZ1dXX45S9/aXQesduat7+8\nhIQEhIWFGT3y8/PR3d2NO3fu4MKFC3jrrbeQlJTESznZ2dnIysrSHzvcHuVAZfTNO2/btg3Ozs5I\nSUkZ9u/Sl9BbPrS3t2PJkiXIzc2Fq6srp+c+ceIEPD09ERUVxes9Q7u7u3HlyhW89NJLuHLlCkaN\nGoWcnBzeytMx91qdP38eZWVlKCgowH/913/hiy++MPuzvVtIGJ9v9erVSEpKws6dOzFhwgSsX78e\n7e3tKCgoQGpqKtzc3IzOoVAoDI5xdXU1Os/GjRtRXl6O5557Dt9//z2Ki4uNzuPg4GBwTElJicn6\nDKd9hsratrakvVevXo3q6mosXLgQY8eO1f9uGo0Gubm5yM3N1be37md1/y5ZsgQzZszAiBEjjM4j\ndlvzNkBrbnXhe++9h3/5l38BAEydOhUODg748ccfMW7cOM7K+fbbb1FdXY2IiAgAvWmEKVOmoLS0\nFJ6enpyUofP+++/j5MmT+Oyzz4Z0XnN8fHxQW1urf15bW2twFedSV1cXFi9ejOXLl2PhwoWcn/9/\n//d/kZ+fj5MnT+L+/fu4d+8eVqxYgcOHD3NajkqlgkqlwtSpUwH0/uEJEezNvVYTJkwA0JuqXLRo\nEUpLSzF+/Hh0d/euGm5sbIRSqURTUxO8vb3R2Niof38qlUr9eXWDdSqVClOmTMH8+fOxePFixMTE\nGPRS//nPf8LHxweNjY0YP3680TH9z6NbrTpx4kRUV1fj8uXLRufR1Ud3zKVLlxAXF2fyPP3boq6u\nbtj7CHHR1n177n1/l77t/eijjxqcW3dMfX09XnzxRWzduhVdXV0oLi7G5MmT9X8jSqUSI0aMAADc\nuHEDGo0Gy5cvx/379wc8j6htzcWgyVD9+c9/Zlu3bmWMMXbt2jXm6+vLe5l8DdAWFBSwkJAQduvW\nLU7P29XVxSZOnMiqq6vZgwcPeBug1Wq1LDU1la1du5bzc5tSUlLC5s+fz9v5Z8yYwa5du8YYY+zN\nN99kr7zyCm9l6Qz0WnV0dLB79+4xxhhrb29nTz75JPuf//kftmrVKubl5cUYYyw7O5vFxMToV5ln\nZ2ezTZs2serqav0A7YMHD9iFCxfYxIkTmVarZTt37mRqtZqtXbtWP0j34MED/WCfVqtl27dvZ5Mn\nTzY6pu95fv/737PFixczxhi7fPkyGzVqFCsoKDA4z+uvv85+97vfGR3z1Vdf6c/z9ttvs+TkZMYY\nMyjr+vXr+mPEauuNGzeyV155hYWGhurbduPGjQbt/a//+q8GA7Q1NTX6uu/cuZMtXbqUpaamstTU\nVIPfbfTo0Sw7O5tptVoWFRXFoqOjDdqg73mysrJEb2tR5tl3dXUhPT0d5eXlcHZ2xs6dOxEXF8dr\nmRMnTsSlS5c4n3oZGBgIjUajP+8vf/lL7Nmzh5NzFxQU6KeYZWRkYMuWLZyct69z587hqaeeQnh4\nuP4jYHZ2Np555hnOywKAM2fOYOfOncjPz+fl/F9//TVWrlwJjUbD+VRYc0y9VtXV1Vi0aBGA3hTT\nsmXL8M0336C4uBg3b96Eo6MjJk2ahBMnTuCFF17AjRs3oFar4ebmhi+//BItLS1wcXGBs7MzHjx4\nAA8PD7i7u8Pd3R3nz5/Xv2a63PyoUaPg6uqKjo4OjB49GpcvXzY4hjGGzs5O/XnGjRuHlpYWODg4\nQKvVwsfHB1VVVVAoFPrzjBs3Dp2dnVAoFAbH3Lp1S38ePz8/7N27V//pYfv27Thw4ACcnJyQm5uL\nOXPmiNLWW7ZsweLFi3HixAloNBo88sgj2LlzJ5KTk5GUlIQbN27g3r17UCgU+PHHH+Hl5YWpU6ei\nqKgIGo0GPj4+CA8PR3p6OhYuXIjw8HDcvHkTP/74Izw9PfHOO+/gz3/+M65du4a6ujqEhobqx2LC\nw8Px97//XX8etVotelvToipCCLEDdnUPWkIIsVcU7AkhxA5QsCeEEDtAwZ4QQuwABXtCCLEDFOwJ\nIcQOULAnhBA7QMGeEELsAAV7EXV0dGDevHmIjIxEWFgYjh07Bj8/P9y+fRsAcOnSJcycORNA71as\naWlpeOqpp6BWq/H3v/8dGzZsQHh4OObOnavfa4XYj4sXLyIiIgIPHjxAR0cHQkNDUVFRIXa1ZOvN\nN99Ebm6u/vlrr72GXbt2iVijoaFgL6LCwkL4+PigvLwcV69eHXSLgurqahQXFyM/Px/Lly9HQkIC\nvvnmG4wcOVK/DzaxH1OnTkViYiJef/11bNq0Campqfq91gn30tPT9Zv3abVaHD16FKmpqSLXynKi\n3ZaQ9O6fsWHDBmzevBnz58/H9OnTBzxWoVBg7ty5cHR0RGhoKLRarX4fjLCwMNTU1AhUayIlW7du\nRXR0NEaOHIndu3eLXR1Ze+yxxzBu3DiUl5ejqakJTzzxBMaOHSt2tSxGwV5EgYGBKCsrw6efforX\nX38dv/rVr+Dk5AStVgsAuH//vsHxzs7OAAAHBwf9Xb50zymNY59aWlrQ0dGBnp4e/PTTT/q7IRF+\nrFy5EgcPHkRzc7P+jmi2gtI4ImpsbMSIESOwbNkybNiwAWVlZfDz89Pfueu///u/9cfSfnXElBdf\nfBF//OMfkZKSwssN6YmhRYsWobCwEJcuXeJ8N0++Uc9eRFevXsXGjRvh4OAAZ2dnvPfee+js7ERG\nRgbc3d0RFxdncCecvnehMXXHHWJfDh8+jEceeQRLly6FVqvFk08+iZKSEt63C7dnSqUSv/rVrzB2\n7Fib+5ujLY4JIcRCWq0WU6ZMwUcffQR/f3+xqzMklMYhhBALVFRUIDAwEPHx8TYX6AHq2RNCiF2g\nnj0hhNgBCvaEEGIHKNgTQogdoGBPCCF2gII9IYTYAQr2hBBiB/4f/hhq/+rSeAwAAAAASUVORK5C\nYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108f6f850>"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "py.iplot_mpl(fig8, strip_style=True, filename='histograms')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~mpld3/20\" height=\"525\" width=\"100%\"></iframe>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x10a898c50>"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###Scatter\n",
      "Scatter plots are a pretty common usage for matplotlib. Information about scatter plots is stored in something called a 'path_collection' which we needn't get into. The actual markers for scatter plots are stored as Bezier curves. The PlotlyRenderer will attempt to figure out what marker you've entered and import that into plotly. 'Nuff said.\n",
      "\n",
      "Here's an example."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig9 = plt.figure()\n",
      "ax = fig9.add_subplot(111)\n",
      "num = 1000\n",
      "x1 = np.linspace(-0.5,1,num) + (0.5 - np.random.rand(num))\n",
      "y1 = np.linspace(-5,5,num) + (0.5 - np.random.rand(num))\n",
      "x2 = np.linspace(-0.5,1,num) + (0.5 - np.random.rand(num))\n",
      "y2 = np.linspace(5,-5,num) + (0.5 - np.random.rand(num))\n",
      "x3 = np.linspace(-0.5,1,num) + (0.5 - np.random.rand(num))\n",
      "y3 = (0.5 - np.random.rand(num))\n",
      "ax.scatter(x1, y1)\n",
      "ax.scatter(x2, y2)\n",
      "ax.scatter(x3, y3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "<matplotlib.collections.PathCollection at 0x111aeaf50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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mIVKFvCDkj4i4mDdvHpMmTWLp0qWULFkdVdWvEHlFvjZgGEE+/fRT+vcfjNPp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\n1zREC4BFycsr4bzzLuDjjz+uco6vvvrKfE9GIzIXx6nNzJmHr4Gfsp9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m+yRUFCJpRCLN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9fS5p6H7hjWorvTm1E6MDUPzVM210MQaY/P56Yb7ovINAKBIjzKzj3nLFSIbEefjTqIvITu0PJo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\nbqj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waSoqKigpKYnCwsIoOTmZ/vjjjwbbuZL/m+PLhQsXUmhoKJnNZjp69Gg7W9g0t7N/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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x111ac2dd0>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "py.iplot_mpl(fig9, strip_style=True, filename='scatter')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~mpld3/21\" height=\"525\" width=\"100%\"></iframe>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x111abe990>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print py.get_figure('mpld3', 21).get_data()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[{'y': [-5.03006452087552, -5.036045221712857, -5.290399372442822, -4.482998437312267, -4.7491264833718745, -4.520645553989646, -4.49318337293355, -4.7237576936224945, -5.087620199201376, -4.434718171208473, -4.567072540306583, -4.853527134227404, -5.240045103596636, -4.380594028296839, -5.067150354216137, -4.869529361963287, -4.521353734221693, -5.029803873147144, -4.477407749168734, -4.978159767777407, -4.429814743877793, -5.042178543770352, -4.666717513781173, -4.994870248646861, -4.435253641842587, -4.619952785836999, -5.075326367752013, -4.475348211239511, -5.023482794445645, -5.1961113051859416, -5.148274178122428, -4.244329514719324, -4.491575896466524, -4.26373692996891, -5.114160233239405, -4.183059311903151, -4.301795376235267, -4.4941103919965535, -4.223392108630992, -4.534375276285611, -4.432245828479358, -4.484137860650778, -4.255168326150005, -4.3759586217946245, -4.252956232506688, -4.874487155373729, -4.183036031816236, -4.274041130928878, -4.546838922225571, 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       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "###The Nitty Gritty\n",
      "Out of curiosity or for the sake of troubleshooting, you might find yourself needing to inspect the dictionaries and possibly even the mplexporter. The following shows how you go about running the PlotlyRenderer and making a manual call to plotly."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig12 = plt.figure()\n",
      "ax = fig12.add_subplot(111)\n",
      "ax.plot([10,20,30],[100,200,300], 'bo')\n",
      "ax.set_xlim(5, 35)\n",
      "ax.set_ylim(90, 310)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 58,
       "text": [
        "(90, 310)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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cnIRfo66uLvY4FAopFArNZCjPjLt3E/+rHBtbPMcjAWBFOBxWOBye8fEzCnxFRYVOnDih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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109409110>"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "First, we will need access to the Exporter and PlotlyRenderer classes, so we import them! The Exporter crawls through the matplotlib figure, judiciously grabs pertinent information, and invokes the renderer with which it was instantiated. The PlotlyRenderer accepts calls from the Exporter and creates an appropriate JSON dictionary for use with plotly."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from plotly.matplotlylib import Exporter, PlotlyRenderer\n",
      "renderer = PlotlyRenderer()\n",
      "exporter = Exporter(renderer)\n",
      "exporter.run(fig12)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 59
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can look at the resulting list of data and layout dictionaries by looking at the renderer's data and layout attributes."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "renderer.plotly_fig"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 60,
       "text": [
        "{'data': [{'marker': {'color': '#0000FF',\n",
        "    'line': {'color': '#000000', 'width': 0.5},\n",
        "    'opacity': 1,\n",
        "    'size': 6,\n",
        "    'symbol': 'dot'},\n",
        "   'mode': 'markers',\n",
        "   'name': '_line0',\n",
        "   'type': 'scatter',\n",
        "   'x': [10.0, 20.0, 30.0],\n",
        "   'y': [100.0, 200.0, 300.0]}],\n",
        " 'layout': {'autosize': False,\n",
        "  'height': 320,\n",
        "  'hovermode': 'closest',\n",
        "  'margin': {'b': 40, 'l': 60, 'pad': 0, 'r': 47, 't': 31},\n",
        "  'showlegend': False,\n",
        "  'width': 480,\n",
        "  'xaxis': {'anchor': 'y',\n",
        "   'domain': [0.0, 1.0],\n",
        "   'mirror': True,\n",
        "   'range': (5.0, 35.0),\n",
        "   'showgrid': False,\n",
        "   'showline': True,\n",
        "   'ticks': 'inside',\n",
        "   'zeroline': False},\n",
        "  'yaxis': {'anchor': 'x',\n",
        "   'domain': [0.0, 1.0],\n",
        "   'mirror': True,\n",
        "   'range': (90.0, 310.0),\n",
        "   'showgrid': False,\n",
        "   'showline': True,\n",
        "   'ticks': 'inside',\n",
        "   'zeroline': False}}}"
       ]
      }
     ],
     "prompt_number": 60
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Finally, to create the figure in plotly, we can call `plotly.iplot`. Again, we'll need a couple more tools from plotly, so we'll import them. We instantiate our plotly class and call it `py` by entereing our username and api_key. Then we use plotly's iplot method to drop our plot back into this IPython notebook. For more information about plotly and IPython, Plotly's [IPython docs](https://plot.ly/api/ipython) have you covered."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "py.iplot(renderer.plotly_fig, filename='nitty-gritty', fileopt='overwrite')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~mpld3/24\" height=\"525\" width=\"100%\"></iframe>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x10a8dc890>"
       ]
      }
     ],
     "prompt_number": 61
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Additionally, you can use this opportunity to add to or modify the data and layout dictionaries. Just as an example, we can change the plot and paper background colors. You might find this procedure helpful if you've snooped around the JSON drop-down in plotly on some of your favorite plots from the plotly [gallery](https://plot.ly/api/python/gallery). If you see a feature you want, just add it!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "renderer.resize()\n",
      "renderer.plotly_fig['layout']['plot_bgcolor'] = '#80adb0'\n",
      "renderer.plotly_fig['layout']['paper_bgcolor'] = '#fefafd'\n",
      "renderer.plotly_fig['layout']['showgridlines'] = False\n",
      "py.iplot(renderer.plotly_fig)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~mpld3/41\" height=\"525\" width=\"100%\"></iframe>"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0x10a8bf5d0>"
       ]
      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Follow and/or contribute to the matplotlylib progress [here](https://github.com/mpld3/matplotlylib).\n",
      "\n",
      "Analyze and visualize data, together, with plotly [here](https://plot.ly/)."
     ]
    }
   ],
   "metadata": {}
  }
 ]
}