{
 "metadata": {
  "name": "",
  "signature": "sha256:2bd39fe31e7465295478e64e24d9c795ec770a761acc20c63922fdd48f16d309"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": [],
     "source": [
      "> This is one of the 100 recipes of the [IPython Cookbook](http://ipython-books.github.io/), the definitive guide to high-performance scientific computing and data science in Python.\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# 6.1. Making nicer matplotlib figures with prettyplotlib"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "1. Let's first import NumPy and matplotlib."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "2. We first draw several curves with matplotlib."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize=(6,4));\n",
      "np.random.seed(12)\n",
      "for i in range(8):\n",
      "    x = np.arange(1000)\n",
      "    y = np.random.randn(1000).cumsum()\n",
      "    plt.plot(x, y, label=str(i))\n",
      "plt.legend();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAEACAYAAAC6d6FnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10bb00ed0>"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "3. Now, we create the exact same plot with prettplotlib. We can basically replace the `matplotlib.pyplot` namespace with `prettyplotlib`."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# You may have to run the following before you can continue\n",
      "# !pip install prettyplotlib"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Downloading/unpacking prettyplotlib\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB): \r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):   0%  4.1kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):   1%  8.2kB"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):   1%  12kB \r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):   2%  16kB"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):   2%  20kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):   3%  24kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):   4%  28kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):   4%  32kB"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):   5%  36kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):   5%  40kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):   6%  45kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):   7%  49kB"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):   7%  53kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):   8%  57kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):   8%  61kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):   9%  65kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):   9%  69kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  10%  73kB"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  11%  77kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  11%  81kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  12%  86kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  12%  90kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  13%  94kB"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  14%  98kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  14%  102kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  15%  106kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  15%  110kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  16%  114kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  17%  118kB"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  17%  122kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  18%  126kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  18%  131kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  19%  135kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  19%  139kB"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  20%  143kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  21%  147kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  21%  151kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  22%  155kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  22%  159kB"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  23%  163kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  24%  167kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  24%  172kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  25%  176kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  25%  180kB"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  26%  184kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  27%  188kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  27%  192kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  28%  196kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  28%  200kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  29%  204kB"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  29%  208kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  30%  212kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  31%  217kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  31%  221kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  32%  225kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  32%  229kB"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  33%  233kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  34%  237kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  34%  241kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  35%  245kB"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  35%  249kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  36%  253kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  37%  258kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  37%  262kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  38%  266kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  38%  270kB"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  39%  274kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  39%  278kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  40%  282kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  41%  286kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  41%  290kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  42%  294kB"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  42%  299kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  43%  303kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  44%  307kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  44%  311kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  45%  315kB"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  45%  319kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  46%  323kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  47%  327kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  47%  331kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  48%  335kB"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  48%  339kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  49%  344kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  49%  348kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  50%  352kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  51%  356kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  51%  360kB"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  52%  364kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  52%  368kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  53%  372kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  54%  376kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  54%  380kB"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  55%  385kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  55%  389kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  56%  393kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  56%  397kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  57%  401kB"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  58%  405kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  58%  409kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  59%  413kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  59%  417kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  60%  421kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  61%  425kB"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  61%  430kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  62%  434kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  62%  438kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  63%  442kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  64%  446kB"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  64%  450kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  65%  454kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  65%  458kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  66%  462kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  66%  466kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  67%  471kB"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  68%  475kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  68%  479kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  69%  483kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  69%  487kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  70%  491kB"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  71%  495kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  71%  499kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  72%  503kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  72%  507kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  73%  512kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  74%  516kB"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  74%  520kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  75%  524kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  75%  528kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  76%  532kB"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  76%  536kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  77%  540kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  78%  544kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  78%  548kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  79%  552kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  79%  557kB"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  80%  561kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  81%  565kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  81%  569kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  82%  573kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  82%  577kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  83%  581kB"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  84%  585kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  84%  589kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  85%  593kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  85%  598kB"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  86%  602kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  86%  606kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  87%  610kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  88%  614kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  88%  618kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  89%  622kB"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  89%  626kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  90%  630kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  91%  634kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  91%  638kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  92%  643kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  92%  647kB"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  93%  651kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  94%  655kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  94%  659kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  95%  663kB"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  95%  667kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  96%  671kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  96%  675kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  97%  679kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  98%  684kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  98%  688kB"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  99%  692kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):  99%  696kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB): 100%  697kB\r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB):            \r",
        "  Downloading prettyplotlib-0.1.7.tar.gz (697kB): 697kB downloaded\r\n",
        "  Running setup.py egg_info for package prettyplotlib\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "    \r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Requirement already satisfied (use --upgrade to upgrade): matplotlib>=1.2.1 in /Users/admin/Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages (from prettyplotlib)\r\n",
        "Downloading/unpacking brewer2mpl>=1.3.1 (from prettyplotlib)\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "  Downloading brewer2mpl-1.4.1.tar.gz\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "  Running setup.py egg_info for package brewer2mpl\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "    \r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Installing collected packages: prettyplotlib, brewer2mpl\r\n",
        "  Running setup.py install for prettyplotlib\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "    \r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "  Running setup.py install for brewer2mpl\r\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "    \r\n",
        "Successfully installed prettyplotlib brewer2mpl\r\n",
        "Cleaning up...\r\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import prettyplotlib as ppl\n",
      "plt.figure(figsize=(6,4));\n",
      "np.random.seed(12)\n",
      "for i in range(8):\n",
      "    x = np.arange(1000)\n",
      "    y = np.random.randn(1000).cumsum()\n",
      "    ppl.plot(x, y, label=str(i))\n",
      "ppl.legend();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAEACAYAAAC6d6FnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/xi3eAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10bb2a510>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The figure appears clearer, and the colors are more visually pleasant."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "4. Let's show another example with an image. We first use matplotlib's `pcolormesh` function to display a 2D array as an image."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize=(4,3));\n",
      "np.random.seed(12)\n",
      "plt.pcolormesh(np.random.rand(16, 16));\n",
      "plt.colorbar();"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAPYAAADICAYAAADbYhwVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nw3Dg/wGjA61ZoKvrfwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10bb2a550>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The default *rainbow* color map is known to mislead data visualization."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "5. Now, we use prettyplotlib to display the exact same image."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize=(4,3));\n",
      "np.random.seed(12);\n",
      "ppl.pcolormesh(np.random.rand(16, 16));"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0x10bbad410>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAVcAAAEACAYAAAAHujVXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAGlZJREFUeJzt3XmYFOW1x/Fv94DsEBHcAooSVEARouFGjDAR14uKu+Ia\njcb4GGPcA/cKKiY3Stz3R8Ur3ojGEIOKoqCCIDuyGBTcAXdFJYggy8z94+1h2qarq6ZOVXdR+X18\n6oGeqTp1ZIbDO2+/9R4QERERERERERERERERERERERGRmI0EPgVeK/j4WcAbwCLg+nInJSISMa9a\nl+9/gHeBucAe1hseAPQquOGewHSgS+51e+tNREQqrFity9cbmAq0BQYBT0dx004FN7wcOCeKwCIi\nCdIJ7+J6IfC7vNfv+AXLhkjgENzodQ5wP9AtRAwRkS1Jb+D1vNefA51LXRCmuDbFDY0PAMYCd4SI\nISKyJcnkjny1pS5oFOImM4BJwBrgKeBeXMFdm3/ShruvLHljX81amC4HyB50ojlG7WfLzTE23DnC\ndH3jq28x57DxxiHmGDfeM9kc47Lhp5iuzxxynDmHif1PNcfo3qG1OUbLHzQ1Xd/6of8z51Dz/GPm\nGLWL3zDHaHzjmMLC1WBNoPa74KevAhryRZyJ+yn9udzr9rg3tzyFKa7TgcOBZ3BD5XcoKKwiIuX2\nHXA+rQKdezergp1YbyZwEzAKOBS3Wqokv+I6GugHbAMsB4bmgh+Cm39YDFzSwCRFRGKRzQQcAG/+\nc3VdrWuHq3XDgMa5z90LzMKtFpgDfAmc5ncLv+I6yOPjv/YLLCJSbmHeRMrxqnX5fp87AgkzLSAi\nkkjZoDO3tneEAlFxFZHUaBR0WqAMVFxFJDUM0wKRU3EVkdQIPC1QBiquIpIaGrmKiMQgozlXEZHo\naeQqIhIDzbmKiMRAS7FERGLwbzEtkNl7P9P1Lw+8wJxDo8y95hg/WzzHHOPrhbadtdpv3GDOYf0n\nX5pjXPbINeYYz57zR9P1A075rTmHpWvXm2P0v/Bkc4zsgceYrt949x/MOVRddJ05xvxhB5pjREXT\nAiIiMfi3GLmKiJRbdrP9rCtHxVVEUiNJ0wJJGkWLiJhkAx4e+uI2wX4L15CwUCvgRmA+rmlAyR5a\nGrmKSGoYl2LdCpwHLMW1cxkNfJH3+UG4DbR7AvsBNwCefYc0chWR1Mhmgh1FtMn9+jKuuD4P/EfB\nOQcC43K/nw78qGQuof8vREQSxjAt8BNc26o6rwM/LTjnOdzotRlwFLAXsItXLpoWEJHU8HpDa8mG\n9by50by++TGgAzAZWIKbm/VsOOs3ch0JfAq8VuRzlwI1QNtQaYqIRCxLpujRtdFWDGzSYtNRxGxg\nj7zX3YEZBed8CwzHdb0+H1gDfOSdS2kPAocV+XhH4GDc3ISISCIY5lxX5n7tC3TC1beZBee0AbYC\nmgODgQklc/HJdQrwVZGP3wRc4XOtiEhZZQIeHn6Ha6M9EbgLt1LgvNwB0A33U/wC3ABzWKlcwsy5\nDgQ+ABaGuFZEJDbGpViTga4FH8vfoGQ6sHvgXBp48+bAENyQuU6CnokQkX9nSXpCq6HFtTNuPmJB\n7nUHYC5ugvez/BM3jrLtSHXAfUNM1wPQspU5RM3yN8wxmrc35vHdGnMO782y7cwFsGi8fQelfXZs\n439SCTULp5hzOHvYSeYYNTMK3+sovw1LPzbHWNb/cHOMHrdebI7BCRHEIFlrSxtaXF8Dtst7/R6w\nD2Dfz05ExChBA1ffQj8amAbsBiwHzir4fG0cSYmIhJHNZAId5eA3ch3k8/ldo0pERMQqSSNXPaEl\nIqmxJc+5iogkVkYNCkVEopec0qriKiIpomkBEZEYJGhWQMVVRNIjk6CJARVXEUmN5JRWFVcRSZGq\nBFXXJM3/ioiYZAL+58Gv+2sz4CFgHm4HrYGlctHIVURSwzhw9ev+eiawGugF7Ay8CDyJxzYAsRXX\nlfNsTQpm/HW4OYdDjt7LHKOqU0dzjMbGXbGu3etQcw4fr9tojvFfPXY0x3j741Wm628bcIk5h5vf\nfMkco6q9/ftizu77mK7vsvvW5hx2mfC0Ocb6q35jjhEVw2qB/O6vUN/9dVzeOSuBVrj22m1xbV88\n91fRtICIpIahE0GQ7q+jgSrcaHYqcGqpXDQtICKpkfUonQvWfceC9Z6NWoP6DbAB2AHXVnscbnqg\nptjJKq4ikhpeswI9t2pCz62abHr98JpvCk+ZDYzIe90dGF9wTl/gAdx0wExc59fd+P6IdxNNC4hI\nasTc/fUF4Ehc3dwVN+9atLCCRq4ikiLGJ7Tqur82Bm6jvvsruY8/iusAOwf4HLioVDAVVxFJDWOD\nQr/uryvxKaj5VFxFJDUS9ICWiquIpEeSimuQN7RGAp/iOr/WGYF7TOxV4BbcY2EiIhVlfPw1UkGK\n64PAYQUfex63VGFfoAVwSsR5iYg0WCYT7CiHIMV1CvBVwccm4BbO1uCewe0XcV4iIg1WFfAohyjm\nXM8F7o8gjoiISZoaFA4FVgGPR5CLiIhJckqrrbj+AjgU6F/sk0uXbfZ4WYMcckwP0/UAjS4Zao5x\nTa8jzDFaV9kehNu5qf0HjGEfLzLHmNq1tzlG9bwXTdcv3n1/cw6rL/y1Ocbvxy8xx7j16uNM168c\nP8ucwyu9DjTH2G+UfQc7Rk6wxyAdxfUw4HLco2Jro0tHRCS8JE0LBBlSjQamAbsDy4GzgduBlsBE\n3K7cd8WVoIhIUIa9BSIXZOQ6qMjHRkadiIiIVaZclTMAPaElIqmRTdA+fyquIpIaW9qcq4jIFsH4\nhJZf99fLcO8xzcNtB7AB+IFXMBVXEUmNTCYT6PBQ1/31IOACoF3B5/+M6/zaCxgMTAK+9gqm4ioi\nqWEYueZ3f11KffdXL6fgVlJ5UnEVkdTIZjKBjiKCdH+t0xz3ANWYUrnoDS0RSY1seZZiHYlrre05\nJQAqriKSIhmPn8VnfruGmd+uKXVpkO6vdU7GZ0oAVFxFJEW83qz6aYvm/LRF802v71hRuIvq97q/\nLsN1f72mSKg2uXN897BWcRWR1DAuc/Xr/gpwNG4P65LDYIhxE5mvq/euNQXYWGPOoc2zk80xVp86\nwBzj1enLTNdP/Hq1OYdTd97GHKPLC0+aY9S8Nc8WYIb9a7ry6WnmGFuPecIco+bRO2wBtt3enMPg\nM683x4jCiI0ro6hFte906xLoxM6vvwUxb6KlkauIpEaCHtBScRWR9PBYZlURKq4ikhplWooViIqr\niKRGggauKq4ikh4qriIiMdBm2SIiMdDIVUQkBklaLeC3K9ZI4FPcxrB1WgFjcY+I/QPXqFBEpOKM\nm2VHyq+4Pohro53vfFxh7QJ8ANibwIuIRCCbzQQ6ypKLz+enAIU7HPQGHgC+w41sS20oKyJSNsZO\nBJEKM+eav6nsYlyxFRGpuARNuYYqrglKX0SkXpK6v4YprrOBrrgOiF1zrzfT8le+2x2Wtn0H2/XA\nhsFnmmNUNd/KHKPPpUeZrm9+yzhzDltv3cQc4+OjjzPH2OHRUabr55462JzDzju1Mseomf6MOQaN\nG5suv+j0P5lTuPX+i80xojDil8W2Tm04r82yKyFMKjOBs4FmuV9nRJqRiEhIxjlXv9ba4KZFZ+fO\nm1QqF7/iOhqYBuwGLAfOAu4GdgKWAD8E7vGJISJSHtlMsKM4v9baGdyb+INxP7UfXyoVv2mBQR4f\nH+hznYhI2WWyoecF8ltrQ31r7fw5uX2BhcDE3OsvSgVM0AyFiIhR+JFrkNbahwK1uCWqT+Vee9Lj\nryKSHh7zqVO+XMXUr1ZZozcFeuKmDZoDE4A98einpeIqIqnhtStW33at6duu9abXf3rvk8JTgrTW\nng40AeounoN7E+y5YvfUtICIpEf4zQXyW2t3wrXWnllwzgygH27U2hboBbzilYpGriKSGsb9XP1a\na6/A7bcyB/gcGAp84xVMxVVE0qPK9MP4ZNwSq3z3Fry+O3f4UnEVkdTY0h9/FRFJJrV5ERGJgUau\nIiLRS9LGLbGV+Q1P3F5ruf5fIwrnkRtuzdoN5hg7jC+6hK1Bap4w/r80aWrOIfPjA8wxsrv2NMdw\nD7iEt+rYg80ZtHzoMXOMmpfGmGMMPv2Pput7trTv2LZifY05xgUz/26OUbVXvyhqUe3q4/YPdGKL\nMa9AzNunauQqIqmh1toiInGwLcWKlIqriKSGlmKJiMRB0wIiIjHQyFVEJHqaFhARiYOmBUREopek\nkatl3cK5uOaFc4FboklHRMSgKhvsKM6v+2s1bt/Xebnjv0ulEnbk2hYYQn2Lg6dx/WTsjzOJiIRk\nfIigrvvrUlwtG83mTQgnA0cFCRZ25LoG9+hYG6AZbmfur0LGEhGJRvhOBPndX5dS3/11szsETcVS\nXM8H3sf1k3kFmBUylohINOLt/loL9AHmAzcBnUulEnZaoD1uN+5uuBHr48AAvt/jW0SkrLze0Jr8\n4Qomf7TCGv5VoCOwHjgTN41whGcuIW8yADgdODn3+nxcU68r6054sHU70/ZHZ0x8yHI5ALXPP2GO\nsWKsZ/+xwLbutbPp+myfPuYcpl1h32XsZzOeNseo/eoz2/UvjDXnMGWEPcYBj9h2tALI7tHbdP0N\nXezfF1csedkcY9XZZ5hjtHlhXiS7Yq2/YECgExvfOQ6+X//aAJNwTQcBbsd1f/UaMGZwP7XvBHxX\n7ISw0wJTgH1xb2w1AQ7HzVGIiFROvN1ft6O+IB8JLMSjsEL4aYF/AdcBT+DezBoPvBQylohINLKm\nXbH8ur8ej/spfQOusF5aKpjlIYL/zR0iIslgK65+3V/vzB2B6AktEUmPBD2hpeIqIumh4ioiEgMV\nVxGRGNjmXCOl4ioi6aHiKiISA00LiIjEQCNXEZEYaOQqIhIDFVcRkRgkqLjGlsk9Lbcx7Yr17UbT\n5QBc/MW75hif/9y+81DbQQebrv/22anmHGrWbTDHyDSqMsdo8ctBpuuz/U8w5/BCN/vX9I1vPffr\nCOyCl/9iC7B+nTmH2tmTzDFYu9YcotEVd0WyK9aGYWcGu981D0GM9Q80chWRNNEbWiIiMUjQtEBy\nyryIiFU2G+wozq/7a52f4LYdPLZUKhq5ikh62EauQbq/VgHX4/awLnkzjVxFJD3i7/56IfA34HO/\nVFRcRSQ9whfXIN1ffwgMxDVnBdcN1pOmBUQkPeJdLXAL8HtcUc3gMy2g4ioi6eFRXCe98yGT3/mw\n1JWzgRF5r7vj5lXz7QM8mvt9O1xj1vXAk8UCWoprC+AuYD/cO2dnAzMM8UREbDyKa3WXjlR36bjp\n9fAJswtPye/+ugzX/fWagnN2zfv9g8BTeBRWsBXXa3JJnIcrri0MsURE7GyrBfy6vzaIpbgehBu1\n1j37trLEuSIi8bMVV7/ur/nO8gsWdva3A9AU967ZTODK3GsRkcoJv1ogcmGLa1NgN2AMUI2b/D0x\nopxERMKxPaEVqbDTAm8DS3ATuuCeZDgDGFV3wnHdtzMl9uKSwgcjGm54213MMQZfPsAc46MHC990\nbJi/vuW7XtnXOfv+0ByjxTknmWNQs9F2+VTP9w8Ce2nlt+YY115n290LYHifk03Xd2u+lTmHH+/Q\n2hyj85w55hhccZc9BqRmb4G3cE8wZIEBwMRIMhIRCStbFewoA8sbWpfhRqpNcYX10dKni4jELJuc\nkauluL7J5o+HiYhUTiY5T/TrCS0RSY8EzbmquIpIeqgTgYhIDDRyFRGJgeZcRURiUFWeZVZBqLiK\nSHpoWkBEJAaaFhARiUGCHiJITpkXEbHKZIMdxfm11h4ILADmA+Nwfbc8aeQqIukRb2vticDY3O/7\nATfiCnJRsRXX2pJ9Ef0de3qxrrYNzGH9BnMM1q83hxj/7grT9ef22dmcQxRqF79uD7J6tenyUfdP\nMacwfOrD5hh/6HuGOcbQ5fNM139z+vHmHFr99RlzjA/62P+uRib8aoH81tpQ31p7XN45qwvOX0sJ\nmhYQkfSIt7U2wDHA+8BI4NxSqWhaQETSw2M+ddKit5m86O0o7vBE7jgJ+AfQy+tEFVcRSQ+P1QLV\ne3Wheq8um14Pf/y5wlOCtNbO9xiuiWEzYE3RVHyTFRHZUoRfLZDfWrsTrrX2zIJzOgN11fs/gbl4\nFFbQyFVE0iTe1trH4dpZrQfmAVeUCqbiKiLpYXtCy6+19g25IxAVVxFJD23cIiISgxRt3FIFzAE+\nAI60pyMiYpCiTgQX4RbbtoogFxERmwSNXC1lvgNuOcL91C9PEBGpHNvGLZGyjFxvBi4HWkeUi4iI\nTYJGrmGL6xHAZ7i1XtXFTti6es+QoZ1GQ24zXQ9Qu+Ybc4yFvfubYxy753am61v/fbOnSRps4/N/\nMcdgh53MIar2sG3ycdrKs8w51E580hzjqo8XmWOsPus40/UzX/3YnEP/VbZNhQB2uO5icwyO/LU9\nBqRizrUPcBRuWqApbvQ6CrfAVkSkMrLJWYoVtswPAToCuwAnAy+iwioilRZ+V6zIRbXO1bh7q4hI\nBFLWQ2ty7hARqawE9dDSE1oikh4pG7mKiCRDgpZiJafMi4hYxdv99VRc99cFwCPAbqVSUXEVkdTI\nZKsCHR7qur8eBFwAtCv4/Lu4Arw3rjvsVaVyUXEVkfQIP3LN7/66lPrur/mmU9+xYByuvbYnFVcR\nSY9sJtixuaDdX+v8CniqVCp6Q0tE0qM8qwUOAk7DPanqScVVRNLDY7XApLkLmTx3Yakrg3Z/7QHc\nAxwGfF0qoIqriKSHx8i1et+eVO/bc9Pra+97pPCU/O6vy3DdX68pOGcnYAxu1cDbfqnEVlwz3Wy7\nYl3StuQqh0CG9rHv4vT3T1f6n+Tj0HUbTdd/uUsPcw6HX3mMOUbtvNnmGBunvWC6/ncjZ5hzuGPB\nYHOMIdsW9rFruOFXn2C6/uf72HPYePvV5hiZjh3NMSJj27jFr/vrUKAtbuQKrgtsb69gGrmKSHrY\nHn/16/56Tu4IRMVVRNJDj7+KiMQgQY+/qriKSHpo5CoiEgONXEVEYqCRq4hI9DJVyemhpeIqIumR\noJGrJZOOwEvAImAScEoUCYmIhJaSBoXrgYuB+bh9D2fhdolZFUFeIiINl5KR6ye4wgruMbFFwL7m\njEREwkrJyDXfj3C7yMyKKJ6ISMNlkzNyjaK4tgIew00RrI4gnohIOCla59oYtwXXw8DY/E+se2aC\nKfANv6k2XQ8w67FXzTGGLXjGnkf1iabrj3j/n+Ycar9Ybo5Bo63MIWqmP2u6fnCPHe05jLrTHKNv\nm+bmGHTf23T5P3/7Z3MKzZrax1edr7L9f0TKtitWpCx/shngAeCfwC3RpCMiYpGckatlgmJ/XKuD\nA4F5ueOwKJISEQnF9oaWX2vtPXBNCtcCl/qlYhm5TkUNDkUkSWxzrnWttZfiWmePxq2EqrMCV3SP\nDhJMxVFEUiQT8NhMkNbanwNzcGv8fam4ikh6hJ8WaGhrbV/aW0BE0sNjVmDStJlMml7eZfgqriKS\nHl7dX/ffj+r999v0+tqbNluOF7S1dmCaFhCR9Ag/LZDfWrsTrrX2TK+7BElFI1cRSRHTagG/1trb\n40a4rYEa4CKgG/BNsWAqriKSHralWH6ttT/BbbUaiIqriKRIcp7QUnEVkfRI0cYtIiLJkaDNsmMr\n8zWLp9darp9/4EnmHHo8MNQcY8Mjj5hjNLnrcdP1N2/bxZxD8yr7l/q8DxaZY/xX+z1M11+35CVz\nDjULp5ljrL71PnOMW6ctNV3ftVljcw7Hf/imOca6C08wx2gyckIUtai29vNlgU7MtN8JYp5D0MhV\nRNJD0wIiInFQcRURiZ5GriIiMVBxFRGJg4qriEj0siquIiIxUHEVEYleguZcLY8z+DXzEhEpL1uD\nwkhZimtdM6+DgAuAdpFkJCISWugeWpELW1yDNPMSESmvFIxcI2/mJSJilqDiqje0RCQ9ErQrVlht\ngHl5r28HBlQoFxERgNoGHF9WKMdA5lHfzGsxekNLRCQS/XBLsd4GflvhXEREREREwqn0AwYdgZeA\nRcAk4JQK5JCvCjeN8lSF7t8CeAh4k8qu7DgXmAbMBW4p0z1HAp8Cr+V9rBUwFlgG/ANoWaE8RuD+\nnryK+/NoVoEc6lyKaxfdNuYcSuVxFu7PYxFwfRny2CLVzcfuTGXmY7cHeuZ+3w54F/cXqlIuAf4C\nPFmh+/8ZGA40xa0QaVP69Fi0Bd7DFfos8AxwaBnuewDQi+//Rb4C9yZsE+AO4LIK5XEw7s8iC9wH\n/LICOYAbjIzHfX3KUVyL5bEnMB2o62nUvgx5bHEKVxLcRuVXEjwF/LxC9+4ATMzdv1Ij1/nEPyry\n0wx4H9gRV2AnAb3LdO9OfP8v8t+o/8f3x4CtyVn4PPIdD4yqUA6PAz0oX3EtlsflwDllundZxLEo\nLGkPGPwI6A7MqtD9b8Z949RU6P4dcCPWu4GZwJW51+W2BjgfV2A/AV6hcl+T/O/RxZSvyJdyLpX5\nx3cg8AGwsAL3zncIbvQ6B7gf6FbZdOy2/BW3pbUCHgMuBlZX4P5HAJ/hRvKV2q6nKbAbMAaoxv1D\nc2IF8miPK/DdcKOW/ajcTzTJ2TrJGQqsonwj6DrNgSHAsLyPVfL7tC1uymAsbrpmixZHcZ0N5PdP\n7g7MiOE+fhrjCsrDuC9WJfQBjsL9uDUaOJDy/OiX721gCW5UtCaXx+FlzgHc6HBGLp8VuELStwJ5\ngPse7Zr7fdfc60r5BW7u+bQK3Lsz7h+6Bbjv0Q64Nxu3rUAuM3ADoTW479U9qMxPWIlX6QcMMrgi\ndlOZ71tKPyo35/okbmOdLG5EEPcbJ8W0xhXWtrg3kp4E+pfp3p0o/oZWM+BOyvOGVrE8DsO9M75N\nme5fLId8lZxzPRb3vZnBfa9OKVMeW5xKP2DwM9wc53xcoZ+H+0aupH5UbrXAbriRwXzcyoEWFcrj\nF8Bk3EhxOOWZlhoNfAR8ByzHLfepxFKsujzW5fI4G7dUcSn136N3lSmH/D+LfO9SnuJaLI8q4B5c\n3XgCNy8uIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiKSfP8PKAj2+LZF7BAAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10bbaead0>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This visualization is much clearer, in that high or low values are better brought out than with the rainbow color map."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "> You'll find all the explanations, figures, references, and much more in the book (to be released later this summer).\n",
      "\n",
      "> [IPython Cookbook](http://ipython-books.github.io/), by [Cyrille Rossant](http://cyrille.rossant.net), Packt Publishing, 2014 (500 pages)."
     ]
    }
   ],
   "metadata": {}
  }
 ]
}