matplotlib-201/matplotlib-201.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Understanding just enough of matplotlib...\n",
    "\n",
    "### (or, Why a State Machine within a State Machine is a Shitty Idea)\n",
    "\n",
    "<br>\n",
    "\n",
    "So, you want to plot something in Python. Perhaps you've typed \n",
    "\n",
    "```python \n",
    "import matplotlib.pyplot as plt\n",
    "```\n",
    "\n",
    "a few times...maybe you even managed to format your axis ticklabels for a special plot. Do you have a few examples lying around where you once used matplotlib to do something, but you don't quite remember _why_ it worked?\n",
    "\n",
    "This tutorial is for you. We will assume that you can follow the [simple plotting tutorials](http://matplotlib.org/users/pyplot_tutorial.html). Instead, we will try to understand the ideas _behind_ matplotlib's structure, but only enough to develop intuition for efficiently switching between simple, high-level commands and all the glorious guts beneath. \n",
    "\n",
    "This intuition requires an understanding of the different roles of **pyplot**, **backends**, and the **matplotlib API**. We'll start by understanding backends, why they're confusing, and how we can stop thinking about them. We'll then see how convenient pylot is, and what its limitations are. We'll end with a dive into the matplotlib object space. Expect some bouncing back-and-forth between concepts and explicit examples.\n",
    "\n",
    "Requirements:\n",
    "\n",
    "`matplotlib`, `numpy`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A Simple Example\n",
    "\n",
    "Let's start with an example from the tutorials."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x112ddd7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.plot([1,2,3,4])\n",
    "plt.ylabel('some numbers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations:\n",
    "* we've used an ipython \"magic\" function to do something to matplotlib\n",
    "* we've imported the pyplot module and used some functions defined in it\n",
    "* there's nothing object-oriented here: we called 3 stand-alone functions, sequentially\n",
    "* yet the `show` function caused the plot to render in a jupyter notebook.\n",
    "\n",
    "Lots of magic here..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Some background\n",
    "\n",
    "Matplotlib consists of 3 groups of Python objects:\n",
    "1. pyplot: a set of command-style functions to provide MATLAB-style interface\n",
    "2. Matplotlib API / frontend: classes that do the heavy lifting, creating and managing figures, text, lines, plots and so on. \n",
    "3. Backend: device dependent resources for turning abstract graphical objects into pixels\n",
    "\n",
    "**Efficient, effective use of matplotlib involves: configuring the backend correctly, using pyplot when possible, and dropping into the API when necessary.**\n",
    "\n",
    "Foundational ideas: (http://matplotlib.org/faq/usage_faq.html)\n",
    "\n",
    "Environmental hierarchy:\n",
    "* Pyplot state machine: it knows about what you’ve plotted (for purely programmatic use, this may be dropped as irrelevant [or unhelpful!])\n",
    "* pyplot functions that look like: `matplotlib.pyplot.FUNCTION`\n",
    "* Figure - associates Axes objects and the Canvas (a lower-level object)\n",
    "* Axes - a plot, plus a few things like a title\n",
    "* Axis - number-line-like: ticks, ticklabels, etc.\n",
    "* Plotting input is always one or more numpy.array objects\n",
    "* Backends combine a rendering engine (PNG,SVG,etc) with an environment awareness\n",
    "\n",
    "<img src=\"files/fig_map.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Peeling away the magic...\n",
    "\n",
    "The cell below is same example as above, but more verbose: the pyplot state machine maintains knowledge \n",
    " of what was plotted with `plot`, such that a call to `show` renders the figure, and the\n",
    " `%matplotlib` magic command handles the backend (object to pixel mapping, display, inline figure embedding). We've also made the namespaces really explicit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x112c2bcf8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pOIupqh6vqn297WeAbwPHrzrsfODG3jF7gKOTHNuwfnWEqUHqniZjECsD0u8EPl1Vtyf5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\nUOcEDwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11251f9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot\n",
    "%matplotlib inline\n",
    "\n",
    "matplotlib.pyplot.plot([1,2,3,4])\n",
    "matplotlib.pyplot.ylabel('some numbers')\n",
    "#matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It turns out that we don't even need the call to `show` because of what the `%matplotlib` call does.\n",
    "\n",
    "We can remove the magic function if we specify the backend with an explicit 'write' call. This is a good example of how pyplot and the backend manage separate concerns.\n",
    "\n",
    "**Restart kernel here if you want to prove it actually works.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x119257898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot\n",
    "\n",
    "matplotlib.pyplot.plot([1,2,3,4])\n",
    "matplotlib.pyplot.ylabel('some numbers')\n",
    "matplotlib.pyplot.savefig('test.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look for `test.png` in your local directory. \n",
    "\n",
    "What have we done? There's still the pyplot state machine (running in the ipython state machine!), but we've made explicit some of the magic the `%matplotlib inline` call was performing. There are other arguments for `%matplotlib` including `notebook`, which provides a more interactive inline plot, see http://ipython.readthedocs.io/en/stable/interactive/magics.html?highlight=magic#magic-matplotlib\n",
    "\n",
    "[OPTIONAL]\n",
    "\n",
    "We can also set the backend explicitly; see http://matplotlib.org/faq/usage_faq.html#what-is-a-backend. You'll need to restart your kernel again and uncomment the code in the cell below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#import matplotlib\n",
    "#matplotlib.use('TkAgg')\n",
    "\n",
    "#import matplotlib.pyplot\n",
    "\n",
    "#matplotlib.pyplot.plot([1,2,3,4])\n",
    "#matplotlib.pyplot.ylabel('some numbers')\n",
    "#matplotlib.pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's all we'll say about backends. You have options for how and where to render your plots: you can make use of the magic `%matplotlib` function, which sets an appropriate backend and does the inline plotting, or you can write your plots explicitly to files. We'll do the former here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Moving between pyplot and the matplotlib API\n",
    "\n",
    "You can find lots of neat plotting examples at http://matplotlib.org/examples/index.html. Most of them involve both `pyplot` function calls and operations on matplotlib API objects. The pattern is simple:\n",
    "\n",
    "```python\n",
    "    matplotlib.OBJECT() = matplotlib.pyplot.FUNCTION()\n",
    "    \n",
    "```\n",
    "\n",
    "Let's go back to our example, in which we were discarding the return values of the pyplot functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pOIupqh6vqn297WeAbwPHrzrsfODG3jF7gKOTHNuwfnWEqUHqniZjECsD0u8EPl1Vtyf5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\nUOcEDwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112bf7588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# call to 'plot' returns a tuple of Line2D objects\n",
    "line, = plt.plot([1,2,3,4])\n",
    "# call to 'ylabel' returns a Text object\n",
    "text = plt.ylabel('some numbers')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`line` is an object representing the line on the chart and all of its properties. Because it's an object, we can use its methods to modify the line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vDrrT9k5JsxFxnaTdkvbleuazZ6U77pAWFlbq/bvflWZmcv1oXXW73aqnUCrOr7nafG5S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\nA7vabSsGYbPyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e331c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here's an example of a setter method\n",
    "line, = plt.plot([1,2,3,4])\n",
    "line.set_color('r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vDrrT9k5JsxFxnaTdkvbleuazZ6U77pAWFlbq/bvflWZmcv1oXXW73aqnUCrOr7nafG5S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\nA7vabSsGYbPyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1119d96d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# and here's the pyplot way of doing the same thing:\n",
    "_ = plt.plot([1,2,3,4],'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6KyKesH3b+Ob4o4j4hO2bbH9R0v9IKubDafPMT9LbbZ+S9IKk/5X08+lGvLj2w4MjSd9r\n+yuS7pD0MlWwfrPmpvLX7gZJt0h6rH3tOSS9X+N3v9WwfjPnp3LX8Psl/Zlta/yz5SMRsb/qz04+\ndAYAyPtXWAMA+sFmAABgMwAAsBkAAMRmAAAQmwEAQGwGAACxGQAAJP0/elkS98eMcPAAAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1194bfbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# an example of some getter methods\n",
    "line, = plt.plot([1,2,3,4])\n",
    "axes = line.axes\n",
    "y_axis = axes.get_yaxis()\n",
    "\n",
    "# And another setter (and getter) call\n",
    "labels = y_axis.set_ticklabels([1,'',2,'','three','',4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6KyKesH3b+Ob4o4j4hO2bbH9R0v9IKubDafPMT9LbbZ+S9IKk/5X08+lGvLj2w4MjSd9r\n+yuS7pD0MlWwfrPmpvLX7gZJt0h6rH3tOSS9X+N3v9WwfjPnp3LX8Psl/Zlta/yz5SMRsb/qz04+\ndAYAyPtXWAMA+sFmAABgMwAAsBkAAMRmAAAQmwEAQGwGAACxGQAAJP0/elkS98eMcPAAAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e326d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# and the pyplot version\n",
    "import numpy as np\n",
    "plt.plot([1,2,3,4])\n",
    "_ = plt.yticks(np.arange(1,4.5,0.5),(1,'',2,'','three','',4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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i/9ZYn5ToHtpeZ3tR0hVJCxFxrm/I2HvXhh9JhXRG0taIuG57n6R/kvTJhueE0WSxd7Zv\nl/RtSY8W31FnZcj6kt3DiPiZpM/Y/pCkU7Z3R8Qr07xmW0rhsqStpc83F/f1j9kyZEwbDV1bRLxz\nIwMj4juSPmj7w/VNsXKp7t1QOeyd7VvU+4L5bEQ8v8qQpPdv2Ppy2MOI+G9JL0r6zb6Hxt67thwU\nXpV0p+1tttdLul/Syb4xJyUdkiTbOyVdi4ir9U5zIkPXVj7HZ3uHej8q/JN6pzm1td6EmOre3TBw\nbZns3TclnYuIrw94PPX9W3N9qe6h7Y/Y3lDcvk3SZ9X7QZaysfeuFaePIuJ9249IOqXegerpiDhv\n++Hew3E8Il6yvd/2JUnvSkrijW6jrE3SH9r+U0nvSfofSZ9rbsbjK96EOCfpl23/WNIRSeuV+N5J\nw9em9Pdul6TPS3qtODcdkh5X76flcti/oetTunv4MUnP2LZ6X1uejYjvTvt1kzevAQBWtOX0EQCg\nBTgoAABWcFAAAKzgoAAAWMFBAQCwgoMCAGAFBwUAwAoOCgCAFf8PkfrdAovjkRYAAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112da1128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# now for something really crazy\n",
    "import matplotlib.ticker as ticker\n",
    "\n",
    "# plot and get the y-axis\n",
    "line, = plt.plot([1,2,3,4])\n",
    "axes = line.axes\n",
    "y_axis = axes.get_yaxis()\n",
    "\n",
    "# I want major tick marks at integer y-values (meh, just use pyplot)\n",
    "_ = plt.yticks(range(1,5))\n",
    "\n",
    "# I want a single, un-labeled, minor tick at y = 2.75\n",
    "y_axis.set_minor_locator( ticker.FixedLocator([2.75]))\n",
    "\n",
    "# I want proportional precision in my y-labels\n",
    "def func(x,pos): \n",
    "    \"\"\" \n",
    "    return a string representation of `x` with floating point precision `pos`\n",
    "    \"\"\"\n",
    "    return_str = '{0:.' + str(pos) + 'f}'\n",
    "    return return_str.format(x)\n",
    "\n",
    "tick_formatter = ticker.FuncFormatter( func )\n",
    "labels = y_axis.set_major_formatter(tick_formatter)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What do we observe?\n",
    "\n",
    "* pyplot functions are convenient, even when dealing with API objects\n",
    "* the pyplot namespace is flat, while the API namespace is hiearchical\n",
    "* somethings you just _can't_ do with pyplot\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The matplotlib API\n",
    "\n",
    "We've seen how pyplot functions interact with maplotlib API by returning objects. Now we'll briefly note the 3 layers of classes that make the API work.\n",
    "\n",
    "* Canvas - the area onto which the figure is drawn\n",
    "\n",
    "Inherits from `matplotlib.backend_bases.FigureCanvas`\n",
    "\n",
    "* Renderer - the object which knows how to draw on the Canvas\n",
    "\n",
    "Inherits from `matplotlib.backend_bases.Renderer`\n",
    "\n",
    "* Artists - the object that knows how to use a renderer to paint onto the canvas\n",
    "\n",
    "Inherits from `matplotlib.artist.Artist`\n",
    "\n",
    "`Artist` objects handle all the high level constructs like representing and laying out the figure, text, and lines. Almost all objects interacted with are Artists, including container-like objects such as `Figure`, `Axes`, `Axis`, and graphical primitives such as `Rectangle`, `Line2D`, and `Text`. \n",
    "\n",
    "<img src=\"files/fig_map.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Axes` objects are one of the most important API components, because they are the containers that hold and reference most other objects. The `Axes` class provides helpful interface methods like `plot` and `hist` which create primitive `Artist` instances, like `Line2D`, from input numpy arrays and strings.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAACGCAYAAAA4sPpFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112b2b198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# use pyplot to get a figure\n",
    "fig = plt.figure()\n",
    "# create an array of Axes objects and get the specified one\n",
    "ax = fig.add_subplot(2,1,1) # two rows, one column, first plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note above that there is a `Subplot` class, and that inherits from `Axes`.\n",
    "\n",
    "A `Figure` can place an `Axes` at an arbitrary location with the `add_axes` method. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVgAAAB3CAYAAABG4VgXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1194bf860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2 = plt.figure()\n",
    "ax2 = fig2.add_axes([0.15, 0.1, 0.7, 0.3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make some dummy data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6DQPdutWaFCdPrlgCiJYnAeeqwOWXw9SpULu2dRz37Ak//xx0VC5RjRpl5fN37IBbbrHf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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a6c19b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# have to recreate the figure and subplots because inline plotting is opaque.\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(2,1,1)\n",
    "\n",
    "t = np.arange(0.0, 1.0, 0.01)\n",
    "s = np.sin(2*np.pi*t)\n",
    "line, = ax.plot(t, s, color='blue', lw=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's compare the 'line' object, a `Line2D` instance, to the graphical primitives that the `Axes` knows about."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x11a6c4c88>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.lines[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x11a6c4c88>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "line"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected (hoped for?), they are the same object. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Helper libraries\n",
    "\n",
    "In addition to modules that define common Artists, the API contains a bunch of modules that provide helper classes around a particular topic. You saw an example earlier when we imported `matplotlib.ticker` and used its Locator and Formatter classes. \n",
    "\n",
    "Other examples are (http://matplotlib.org/api/index.html):\n",
    "\n",
    "* dates\n",
    "* units\n",
    "* finance\n",
    "* legend\n",
    "* colors\n",
    "* animation\n",
    "\n",
    "Most of what is implemented in these modules are additional Artists, which can delegate their rendering to the same classes used by other Artists. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Example\n",
    "\n",
    "Let's dig into an example that uses the `colors` module. There are patterns of use here that are typical across the API. See documentation and examples at http://matplotlib.org/examples/color/colormaps_reference.html.\n",
    "\n",
    "Goal: make a dynamic set of curves with different colors, and label the curves by their RGB values.\n",
    "\n",
    "Why? Suppose you have an unknown number of curves that will display on a dashboard. To distinguish them, you'll need to _dynamically_ assign _distinguishable_ colors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qIYQQZ86cEQ4ODqKp+Q9bYWGhmDJlivDx8RFRUVFi8+bNOvsvvPCCmDFjRoc+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\nRvLTqKGkpYXxI0fZouZ3a7FDUQO89jgc+E+4SrGukInubgoaG9k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cRizdXMHUEAW5NbQp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1Z+NjiWJE8NnBlqRERuY4C3ks/bdl8JwJvuvZd999/PWGdn/J7/pZ/BM/fDTX+CzNh+CQIm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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1194b75c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as colors\n",
    "import matplotlib.cm as cmx\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# some random data:\n",
    "NCURVES = 6\n",
    "np.random.seed(101)\n",
    "# each curve consists of 20 points with random values in [0,1]\n",
    "curves = [np.random.random(20) for i in range(NCURVES)]\n",
    "\n",
    "# make one Subplot/Axes object in the figure,\n",
    "# and make the figure a bit bigger\n",
    "fig = plt.figure(figsize=(14,8))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "# use pyplot to get a color map\n",
    "jet = plt.get_cmap('jet') \n",
    "\n",
    "# make an object that represent a normalization onto a range\n",
    "color_normalizer = colors.Normalize(vmin=0, vmax=len(curves)-1)\n",
    "\n",
    "# an object to map scalar data in the range specified by the normalizer\n",
    "# to a color on the specified color map\n",
    "scalar_map = cmx.ScalarMappable(norm=color_normalizer,cmap=jet)\n",
    "\n",
    "for idx in range(len(curves)):\n",
    "    # get the np.array\n",
    "    line = curves[idx]\n",
    "    # map it to a color value\n",
    "    color_val = scalar_map.to_rgba(idx)\n",
    "\n",
    "    # make the legend text\n",
    "    color_text = (\n",
    "        'color {0}: ({1:4.2f},{2:4.2f},{3:4.2f})'.format(idx,color_val[0],color_val[1],color_val[2])\n",
    "        )\n",
    "    \n",
    "    # use Axes.plot to plot it\n",
    "    _ = ax.plot(line,\n",
    "                color=color_val,\n",
    "                label=color_text)\n",
    "\n",
    "# legend stuff\n",
    "handles,labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles, labels, loc='upper right')\n",
    "ax.grid()\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Things\n",
    "\n",
    "* Use pyplot when possible and to get access to underlying API objects\n",
    "    * there are lots of convenience functions, e.g. `plt.tight_layout()`\n",
    "    * its flat namespace is convenient for simple things, but can only do so much\n",
    "* `Artist` objects include graphical primitives (lines, text), container objects (axes), and helper classes defined in auxiliary modules (like `DateFormatter` in `matplotlib.dates`).\n",
    "    * they typically contain property getter and setter methods, as well a functions that return associated objects.\n",
    "* There are _lots_ of matplotlib/pyplot examples and tutorials."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extra Credit\n",
    "\n",
    "Modify the script above to color the line by the curve's maximum value. Use a color map that makes the ordering obvious."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}