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UCSD Scientific Python User Group presentation April 10th, 2013: Implementing design principles in matplotlib
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| { | |
| "metadata": { | |
| "name": "design_with_bokeh" | |
| }, | |
| "nbformat": 3, | |
| "nbformat_minor": 0, | |
| "worksheets": [ | |
| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# Implementation of typographic and design principles in `matplotlib` and iPython notebook\n", | |
| "\n", | |
| "UCSD Scientific Python User's Group, April 10th, 2013\n", | |
| "\n", | |
| "* Author: Olga Botvinnik\n", | |
| "* Email: obotvinn@ucsd.edu\n", | |
| "* Twitter: @olgabot\n", | |
| "* Blog: http://blog.olgabotvinnik.com\n", | |
| "\n", | |
| "## Outline\n", | |
| "\n", | |
| "1. Why should I care about design?\n", | |
| "2. Matplotlib\n", | |
| " 1. Default colors\n", | |
| " 1. Lines and scatterplots\n", | |
| " 2. Heatmaps\n", | |
| " 2. Default fonts\n", | |
| " 3. Removing \"chartjunk\"\n", | |
| " 4. Sparklines\n", | |
| "3. Final notes\n", | |
| " 1. iPython notebook\n", | |
| " 1. Default fonts and layouts via custom profiles\n", | |
| " 2. Bokeh plotting package: the future?" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Why should I care about design?\n", | |
| "\n", | |
| "Bad design = difficult interpretation, possible loss of information, and inability to recognize trends. I will use concepts from [_Visual Display of Quantitative Information_, 2nd Ed, by Edward Tufte, Graphics Press (2001)](http://www.amazon.com/Visual-Display-Quantitative-Information/dp/0961392142).\n", | |
| "\n", | |
| "Do not do [this bad example from the `matplotlib` gallery](http://matplotlib.org/examples/pylab_examples/hist2d_log_demo.html):\n", | |
| " \n", | |
| "\n", | |
| "\n", | |
| "Why is this so bad? The divergent 'rainbow' color scheme makes it difficult to compare. Humans are terrible at using different hues to discriminate between different values, but alright at using saturation, such as one color from very light to very dark.\n", | |
| "\n", | |
| "Or [this also terrible example from the gallery](http://matplotlib.org/examples/pylab_examples/demo_ribbon_box.html):\n", | |
| " \n", | |
| "\n", | |
| "\n", | |
| "Why is this so bad? The graphics of the box distract from the true information. It would be much more effective as a plain bar chart.\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "We will talk about how to" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Matplotlib\n", | |
| "\n", | |
| "### Default colors\n", | |
| "\n", | |
| "#### Lines and scatterplots\n", | |
| "\n", | |
| "The default colors in `matplotlib` are not pretty, nor are they conducive to easy comparison. They were meant to be familiar to `MATLAB` users but that's not a good reason for poor design choices." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "# For setting parameters, we will need to use matplotlib (mpl) directly\n", | |
| "import matplotlib as mpl\n", | |
| "\n", | |
| "# This is the usual invocation of pyplot\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "\n", | |
| "import numpy as np\n", | |
| "import pandas as pd\n", | |
| "\n", | |
| "# Set the random seed for consistency\n", | |
| "np.random.seed(12)\n", | |
| "\n", | |
| "# I happen to know that there are 7 default colors in matplotlib\n", | |
| "for i in range(7):\n", | |
| " plt.plot(np.random.randn(1000).cumsum())" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
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0b+uZVNkZLtvwbQPmRjPGSiPuce64jnSlI6+Dmo9q2J68nbbMNmxG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fAdpQVTBAbDz3I+znYoYK69Jiaso5UsXQr88Qdz1EhMZArB3Ly6l9f33zdQkLaDmVlvPHoUgxs3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\n", | |
| "text": [ | |
| "<matplotlib.figure.Figure at 0x10dfcb090>" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 1 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Ugh. It's an unfortunate mishmash of RGB+CYMK: Red, blue, green, and cyan, yellow, magenta and blac(k). But we already know that we can do better.\n", | |
| "\n", | |
| "In 2003, Cynthia Brewer and colleagues released guidelines for coloring maps with sequential, divergent, and qualitative colors, and these guidelines are now available through [http://colorbrewer2.org/](http://colorbrewer2.org/). These colors are included in an existing package in `R`, but only recently someone added these colors to Python through the package `brewer2mpl`, intended as being used in `matplotlib`.\n", | |
| "\n", | |
| "An example import is, (from the author's [blog post](http://penandpants.com/tag/colorbrewer/)):\n", | |
| "\n", | |
| " import brewer2mpl\n", | |
| " bmap = brewer2mpl.get_map('Set1', 'qualitative', 5)\n", | |
| " colors = bmap.mpl_colors\n", | |
| " \n", | |
| "So let's install this package." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "! sudo easy_install brewer2mpl" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "Password:" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "\r\n" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 18 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "(can't do interactive terminal stuff in iPython so I did this in my actual terminal)\n", | |
| " \n", | |
| "The output:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "raw", | |
| "metadata": {}, | |
| "source": [ | |
| "Searching for brewer2mpl\n", | |
| "Reading http://pypi.python.org/simple/brewer2mpl/\n", | |
| "Reading https://github.com/jiffyclub/brewer2mpl/wiki\n", | |
| "Best match: brewer2mpl 1.3.1\n", | |
| "Downloading http://pypi.python.org/packages/source/b/brewer2mpl/brewer2mpl-1.3.1.zip#md5=ae1e2cfc57e7e022e0208e2b5a994292\n", | |
| "Processing brewer2mpl-1.3.1.zip\n", | |
| "Writing /tmp/easy_install-9BiZaj/brewer2mpl-1.3.1/setup.cfg\n", | |
| "Running brewer2mpl-1.3.1/setup.py -q bdist_egg --dist-dir /tmp/easy_install-9BiZaj/brewer2mpl-1.3.1/egg-dist-tmp-UKWbyr\n", | |
| "zip_safe flag not set; analyzing archive contents...\n", | |
| "brewer2mpl.brewer2mpl: module references __file__\n", | |
| "Adding brewer2mpl 1.3.1 to easy-install.pth file\n", | |
| "\n", | |
| "Installed /Library/Frameworks/EPD64.framework/Versions/7.3/lib/python2.7/site-packages/brewer2mpl-1.3.1-py2.7.egg\n", | |
| "Processing dependencies for brewer2mpl\n", | |
| "Finished processing dependencies for brewer2mpl" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "! cat ~/.matplotlibrc | grep color_cycle" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "cat: /Users/olga/.matplotlibrc: No such file or directory\r\n" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 19 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "import brewer2mpl\n", | |
| "\n", | |
| "# brewer2mpl.get_map args: set name set type number of colors\n", | |
| "bmap = brewer2mpl.get_map('Set2', 'qualitative', 7)\n", | |
| "colors = bmap.mpl_colors\n", | |
| "print colors" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "[(0.4, 0.7607843137254902, 0.6470588235294118), (0.9882352941176471, 0.5529411764705883, 0.3843137254901961), (0.5529411764705883, 0.6274509803921569, 0.796078431372549), (0.9058823529411765, 0.5411764705882353, 0.7647058823529411), (0.6509803921568628, 0.8470588235294118, 0.32941176470588235), (1.0, 0.8509803921568627, 0.1843137254901961), (0.8980392156862745, 0.7686274509803922, 0.5803921568627451)]\n" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 28 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "We have a list of 3-tuples of RGB decimal values, from 0 to 1, as specified in the [`matplotlib` `colors` API](http://matplotlib.org/api/colors_api.html). You may be used to seeing RGB specifications in values between 0 and 255, and this is the same thing, except it's a fraction of 255.\n", | |
| "\n", | |
| "Now let's use these colors to plot. To do so, we'll have to change the default color cycle of matplotlib via the command,\n", | |
| "\n", | |
| " mpl.rcParams['axes.color_cycle'] = colors\n", | |
| " \n", | |
| "Now that `mpl` we imported earlier is coming in handy!" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "# Set the random seed for consistency\n", | |
| "np.random.seed(12)\n", | |
| "\n", | |
| "# Change the default colors\n", | |
| "mpl.rcParams['axes.color_cycle'] = colors\n", | |
| "\n", | |
| "# I happen to know that there are 7 default colors in matplotlib\n", | |
| "for i in range(7):\n", | |
| " plt.plot(np.random.randn(1000).cumsum())" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "png": 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pZk/voyT7r8m3Tmdu/vWIghGfdSrnFw33A8Sy3dSHXwY0p7OOzmRx1iiAwMFn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\n9dDzA+i/HwJPQnBkb9McMn7B+jHPAs9tYDxqyWpsEL0Nshb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AmLH2DYhVcmtXiCmNPtxO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\nQVpgFMuLd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| |
| "text": [ | |
| "<matplotlib.figure.Figure at 0x10dff9190>" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 34 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Now that looks much better! Here is a cheat sheet of the ColorBrewer colors (from the [cbrewer](http://www.mathworks.com/matlabcentral/fileexchange/34087-cbrewer-colorbrewer-schemes-for-matlab) page on Mathworks website)\n", | |
| "\n", | |
| "" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "As for scatterplots, I prefer to show them with a very thin, grey line around the circle. So instead of no outlines like this:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "# Set the random seed for consistency\n", | |
| "np.random.seed(12)\n", | |
| "\n", | |
| "# Change the default colors\n", | |
| "#mpl.rcParams['axes.color_cycle'] = \n", | |
| "colors = brewer2mpl.get_map('Set2', 'qualitative', 7).mpl_colors\n", | |
| "\n", | |
| "#matplotlib.image.cmap = brewer2mpl.get_map('Set2', 'qualitative', 7).mpl_colormap\n", | |
| "\n", | |
| "# I happen to know that there are 7 default colors in matplotlib\n", | |
| "for i, color in enumerate(colors):\n", | |
| " plt.scatter(np.random.randn(1000), np.random.randn(1000), \n", | |
| " color=color)" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "png": 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vPkvRSdAYXMXq8L1g2/giDeVMUruQwilmMILV6D7X2lZKkT63k2zvayAg0rKW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\nt5vlsSYSBbvQe1eJdsefpqlnC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| |
| "text": [ | |
| "<matplotlib.figure.Figure at 0x10e11c210>" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 60 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Or an overpowering black outline that speaks louder than the plot itself," | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "# Set the random seed for consistency\n", | |
| "np.random.seed(12)\n", | |
| "\n", | |
| "# Change the default colors\n", | |
| "#mpl.rcParams['axes.color_cycle'] = \n", | |
| "colors = brewer2mpl.get_map('Set2', 'qualitative', 7).mpl_colors\n", | |
| "\n", | |
| "#matplotlib.image.cmap = brewer2mpl.get_map('Set2', 'qualitative', 7).mpl_colormap\n", | |
| "\n", | |
| "# I happen to know that there are 7 default colors in matplotlib\n", | |
| "for i, color in enumerate(colors):\n", | |
| " plt.scatter(np.random.randn(1000), np.random.randn(1000), \n", | |
| " color=color, edgecolors='k')" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "png": 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yC/YQp4qjSR1TGvzYnTUXdzN831ySMuPYPLcYYU/SaFBdxYAu5uTlveL44aPU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tW7UmKfk1G77xpHd7e3LzLYR0fkFejgqZREbTwFqcib5Kx5Bm1Pauxq57J8BWgbRWAGH9OwOQ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\nmbglSZJKmP8HxIDXQQd7RBcAAAAASUVORK5CYII=\n", | |
| "text": [ | |
| "<matplotlib.figure.Figure at 0x10e108890>" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 58 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "A light grey, thin outline balances both visibility and aesthetics." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "# Set the random seed for consistency\n", | |
| "np.random.seed(12)\n", | |
| "\n", | |
| "# Change the default colors\n", | |
| "#mpl.rcParams['axes.color_cycle'] = \n", | |
| "colors = brewer2mpl.get_map('Set2', 'qualitative', 7).mpl_colors\n", | |
| "\n", | |
| "#matplotlib.image.cmap = brewer2mpl.get_map('Set2', 'qualitative', 7).mpl_colormap\n", | |
| "\n", | |
| "# I happen to know that there are 7 default colors in matplotlib\n", | |
| "for i, color in enumerate(colors):\n", | |
| " plt.scatter(np.random.randn(1000), np.random.randn(1000), \n", | |
| " color=color,\n", | |
| " edgecolors='grey',linewidths=0.1)" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "png": 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Hq7etpD0aZ+b59bS0HCFbvApNqSdmCjaGyOauJDd7FF3dQLrzK7w0+D1WFDoJ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\nkwaWrqx1ACZti8UJwSY9S59xL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MYO3UeudQunZ6doGeeZs0vcHl/CFtENyhnQeGa+ypBb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\nAABJRU5ErkJggg==\n", | |
| "text": [ | |
| "<matplotlib.figure.Figure at 0x10fc94290>" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 56 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Now to introduce 'Set2' as our default colors, we must change our `.matplotlibrc` file.\n", | |
| "\n", | |
| "Let's check where ours is." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "# For some reason, this doesn't work with mpl\n", | |
| "import matplotlib\n", | |
| "matplotlib.matplotlib_fname()" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 11, | |
| "text": [ | |
| "'/Library/Frameworks/EPD64.framework/Versions/7.3/lib/python2.7/site-packages/matplotlib/mpl-data/matplotlibrc'" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 11 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "According to the [`matplotlib` customization](http://matplotlib.org/users/customizing.html) information, the order in which the `matplotlibrc` files are looked at:\n", | |
| "\n", | |
| "1. `matplotlibrc` in the current working directory, usually used for specific customizations that you do not want to apply elsewhere.\n", | |
| "2. `.matplotlib/matplotlibrc`, for the user\u2019s default customizations. See `.matplotlib` directory location.\n", | |
| "3. `INSTALL/matplotlib/mpl-data/matplotlibrc`, where `INSTALL` is something like `/usr/lib/python2.5/site-packages` on Linux, and maybe `C:\\Python25\\Lib\\site-packages` on Windows. Every time you install matplotlib, this file will be overwritten, so if you want your customizations to be saved, please move this file to your `.matplotlib` directory.\n", | |
| "\n", | |
| "So that we can distinguish our custom `matplotlibrc` file, we'll make the `~/.matplotlib` directory and the `matplotlibrc` file within it. If you haven't created this directory and the file already, you will need to instantiate one. \n", | |
| "\n", | |
| "We will use a [sample `.matplotlibrc` file](http://matplotlib.org/_static/matplotlibrc) is available from the `matplotlib` website. " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "%%bash\n", | |
| "mkdir ~/.matplotlib\n", | |
| "cd ~/.matplotlib\n", | |
| "wget http://matplotlib.org/_static/matplotlibrc \n", | |
| "cat ~/.matplotlib/matplotlibrc" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "### MATPLOTLIBRC FORMAT\n", | |
| "\n", | |
| "# This is a sample matplotlib configuration file - you can find a copy\n", | |
| "# of it on your system in\n", | |
| "# site-packages/matplotlib/mpl-data/matplotlibrc. If you edit it\n", | |
| "# there, please note that it will be overwritten in your next install.\n", | |
| "# If you want to keep a permanent local copy that will not be\n", | |
| "# overwritten, place it in HOME/.matplotlib/matplotlibrc (unix/linux\n", | |
| "# like systems) and C:\\Documents and Settings\\yourname\\.matplotlib\n", | |
| "# (win32 systems).\n", | |
| "#\n", | |
| "# This file is best viewed in a editor which supports python mode\n", | |
| "# syntax highlighting. Blank lines, or lines starting with a comment\n", | |
| "# symbol, are ignored, as are trailing comments. Other lines must\n", | |
| "# have the format\n", | |
| "# key : val # optional comment\n", | |
| "#\n", | |
| "# Colors: for the color values below, you can either use - a\n", | |
| "# matplotlib color string, such as r, k, or b - an rgb tuple, such as\n", | |
| "# (1.0, 0.5, 0.0) - a hex string, such as ff00ff or #ff00ff - a scalar\n", | |
| "# grayscale intensity such as 0.75 - a legal html color name, eg red,\n", | |
| "# blue, darkslategray\n", | |
| "\n", | |
| "#### CONFIGURATION BEGINS HERE\n", | |
| "\n", | |
| "# the default backend; one of GTK GTKAgg GTKCairo GTK3Agg GTK3Cairo\n", | |
| "# CocoaAgg FltkAgg MacOSX QtAgg Qt4Agg TkAgg WX WXAgg Agg Cairo GDK PS\n", | |
| "# PDF SVG Template\n", | |
| "# You can also deploy your own backend outside of matplotlib by\n", | |
| "# referring to the module name (which must be in the PYTHONPATH) as\n", | |
| "# 'module://my_backend'\n", | |
| "backend : GTKAgg\n", | |
| "\n", | |
| "# If you are using the Qt4Agg backend, you can choose here\n", | |
| "# to use the PyQt4 bindings or the newer PySide bindings to\n", | |
| "# the underlying Qt4 toolkit.\n", | |
| "#backend.qt4 : PyQt4 # PyQt4 | PySide\n", | |
| "\n", | |
| "# Note that this can be overridden by the environment variable\n", | |
| "# QT_API used by Enthought Tool Suite (ETS); valid values are\n", | |
| "# \"pyqt\" and \"pyside\". The \"pyqt\" setting has the side effect of\n", | |
| "# forcing the use of Version 2 API for QString and QVariant.\n", | |
| "\n", | |
| "# if you are running pyplot inside a GUI and your backend choice\n", | |
| "# conflicts, we will automatically try to find a compatible one for\n", | |
| "# you if backend_fallback is True\n", | |
| "#backend_fallback: True\n", | |
| "\n", | |
| "#interactive : False\n", | |
| "#toolbar : toolbar2 # None | toolbar2 (\"classic\" is deprecated)\n", | |
| "#timezone : UTC # a pytz timezone string, eg US/Central or Europe/Paris\n", | |
| "\n", | |
| "# Where your matplotlib data lives if you installed to a non-default\n", | |
| "# location. This is where the matplotlib fonts, bitmaps, etc reside\n", | |
| "#datapath : /home/jdhunter/mpldata\n", | |
| "\n", | |
| "\n", | |
| "### LINES\n", | |
| "# See http://matplotlib.org/api/artist_api.html#module-matplotlib.lines for more\n", | |
| "# information on line properties.\n", | |
| "#lines.linewidth : 1.0 # line width in points\n", | |
| "#lines.linestyle : - # solid line\n", | |
| "#lines.color : blue # has no affect on plot(); see axes.color_cycle\n", | |
| "#lines.marker : None # the default marker\n", | |
| "#lines.markeredgewidth : 0.5 # the line width around the marker symbol\n", | |
| "#lines.markersize : 6 # markersize, in points\n", | |
| "#lines.dash_joinstyle : miter # miter|round|bevel\n", | |
| "#lines.dash_capstyle : butt # butt|round|projecting\n", | |
| "#lines.solid_joinstyle : miter # miter|round|bevel\n", | |
| "#lines.solid_capstyle : projecting # butt|round|projecting\n", | |
| "#lines.antialiased : True # render lines in antialised (no jaggies)\n", | |
| "\n", | |
| "### PATCHES\n", | |
| "# Patches are graphical objects that fill 2D space, like polygons or\n", | |
| "# circles. See\n", | |
| "# http://matplotlib.org/api/artist_api.html#module-matplotlib.patches\n", | |
| "# information on patch properties\n", | |
| "#patch.linewidth : 1.0 # edge width in points\n", | |
| "#patch.facecolor : blue\n", | |
| "#patch.edgecolor : black\n", | |
| "#patch.antialiased : True # render patches in antialised (no jaggies)\n", | |
| "\n", | |
| "### FONT\n", | |
| "#\n", | |
| "# font properties used by text.Text. See\n", | |
| "# http://matplotlib.org/api/font_manager_api.html for more\n", | |
| "# information on font properties. The 6 font properties used for font\n", | |
| "# matching are given below with their default values.\n", | |
| "#\n", | |
| "# The font.family property has five values: 'serif' (e.g. Times),\n", | |
| "# 'sans-serif' (e.g. Helvetica), 'cursive' (e.g. Zapf-Chancery),\n", | |
| "# 'fantasy' (e.g. Western), and 'monospace' (e.g. Courier). Each of\n", | |
| "# these font families has a default list of font names in decreasing\n", | |
| "# order of priority associated with them.\n", | |
| "#\n", | |
| "# The font.style property has three values: normal (or roman), italic\n", | |
| "# or oblique. The oblique style will be used for italic, if it is not\n", | |
| "# present.\n", | |
| "#\n", | |
| "# The font.variant property has two values: normal or small-caps. For\n", | |
| "# TrueType fonts, which are scalable fonts, small-caps is equivalent\n", | |
| "# to using a font size of 'smaller', or about 83% of the current font\n", | |
| "# size.\n", | |
| "#\n", | |
| "# The font.weight property has effectively 13 values: normal, bold,\n", | |
| "# bolder, lighter, 100, 200, 300, ..., 900. Normal is the same as\n", | |
| "# 400, and bold is 700. bolder and lighter are relative values with\n", | |
| "# respect to the current weight.\n", | |
| "#\n", | |
| "# The font.stretch property has 11 values: ultra-condensed,\n", | |
| "# extra-condensed, condensed, semi-condensed, normal, semi-expanded,\n", | |
| "# expanded, extra-expanded, ultra-expanded, wider, and narrower. This\n", | |
| "# property is not currently implemented.\n", | |
| "#\n", | |
| "# The font.size property is the default font size for text, given in pts.\n", | |
| "# 12pt is the standard value.\n", | |
| "#\n", | |
| "#font.family : sans-serif\n", | |
| "#font.style : normal\n", | |
| "#font.variant : normal\n", | |
| "#font.weight : medium\n", | |
| "#font.stretch : normal\n", | |
| "# note that font.size controls default text sizes. To configure\n", | |
| "# special text sizes tick labels, axes, labels, title, etc, see the rc\n", | |
| "# settings for axes and ticks. Special text sizes can be defined\n", | |
| "# relative to font.size, using the following values: xx-small, x-small,\n", | |
| "# small, medium, large, x-large, xx-large, larger, or smaller\n", | |
| "#font.size : 12.0\n", | |
| "#font.serif : Bitstream Vera Serif, New Century Schoolbook, Century Schoolbook L, Utopia, ITC Bookman, Bookman, Nimbus Roman No9 L, Times New Roman, Times, Palatino, Charter, serif\n", | |
| "#font.sans-serif : Bitstream Vera Sans, Lucida Grande, Verdana, Geneva, Lucid, Arial, Helvetica, Avant Garde, sans-serif\n", | |
| "#font.cursive : Apple Chancery, Textile, Zapf Chancery, Sand, cursive\n", | |
| "#font.fantasy : Comic Sans MS, Chicago, Charcoal, Impact, Western, fantasy\n", | |
| "#font.monospace : Bitstream Vera Sans Mono, Andale Mono, Nimbus Mono L, Courier New, Courier, Fixed, Terminal, monospace\n", | |
| "\n", | |
| "### TEXT\n", | |
| "# text properties used by text.Text. See\n", | |
| "# http://matplotlib.org/api/artist_api.html#module-matplotlib.text for more\n", | |
| "# information on text properties\n", | |
| "\n", | |
| "#text.color : black\n", | |
| "\n", | |
| "### LaTeX customizations. See http://www.scipy.org/Wiki/Cookbook/Matplotlib/UsingTex\n", | |
| "#text.usetex : False # use latex for all text handling. The following fonts\n", | |
| " # are supported through the usual rc parameter settings:\n", | |
| " # new century schoolbook, bookman, times, palatino,\n", | |
| " # zapf chancery, charter, serif, sans-serif, helvetica,\n", | |
| " # avant garde, courier, monospace, computer modern roman,\n", | |
| " # computer modern sans serif, computer modern typewriter\n", | |
| " # If another font is desired which can loaded using the\n", | |
| " # LaTeX \\usepackage command, please inquire at the\n", | |
| " # matplotlib mailing list\n", | |
| "#text.latex.unicode : False # use \"ucs\" and \"inputenc\" LaTeX packages for handling\n", | |
| " # unicode strings.\n", | |
| "#text.latex.preamble : # IMPROPER USE OF THIS FEATURE WILL LEAD TO LATEX FAILURES\n", | |
| " # AND IS THEREFORE UNSUPPORTED. PLEASE DO NOT ASK FOR HELP\n", | |
| " # IF THIS FEATURE DOES NOT DO WHAT YOU EXPECT IT TO.\n", | |
| " # preamble is a comma separated list of LaTeX statements\n", | |
| " # that are included in the LaTeX document preamble.\n", | |
| " # An example:\n", | |
| " # text.latex.preamble : \\usepackage{bm},\\usepackage{euler}\n", | |
| " # The following packages are always loaded with usetex, so\n", | |
| " # beware of package collisions: color, geometry, graphicx,\n", | |
| " # type1cm, textcomp. Adobe Postscript (PSSNFS) font packages\n", | |
| " # may also be loaded, depending on your font settings\n", | |
| "\n", | |
| "#text.dvipnghack : None # some versions of dvipng don't handle alpha\n", | |
| " # channel properly. Use True to correct\n", | |
| " # and flush ~/.matplotlib/tex.cache\n", | |
| " # before testing and False to force\n", | |
| " # correction off. None will try and\n", | |
| " # guess based on your dvipng version\n", | |
| "\n", | |
| "#text.hinting : 'auto' # May be one of the following:\n", | |
| " # 'none': Perform no hinting\n", | |
| " # 'auto': Use freetype's autohinter\n", | |
| " # 'native': Use the hinting information in the\n", | |
| " # font file, if available, and if your\n", | |
| " # freetype library supports it\n", | |
| " # 'either': Use the native hinting information,\n", | |
| " # or the autohinter if none is available.\n", | |
| " # For backward compatibility, this value may also be\n", | |
| " # True === 'auto' or False === 'none'.\n", | |
| "text.hinting_factor : 8 # Specifies the amount of softness for hinting in the\n", | |
| " # horizontal direction. A value of 1 will hint to full\n", | |
| " # pixels. A value of 2 will hint to half pixels etc.\n", | |
| "\n", | |
| "#text.antialiased : True # If True (default), the text will be antialiased.\n", | |
| " # This only affects the Agg backend.\n", | |
| "\n", | |
| "# The following settings allow you to select the fonts in math mode.\n", | |
| "# They map from a TeX font name to a fontconfig font pattern.\n", | |
| "# These settings are only used if mathtext.fontset is 'custom'.\n", | |
| "# Note that this \"custom\" mode is unsupported and may go away in the\n", | |
| "# future.\n", | |
| "#mathtext.cal : cursive\n", | |
| "#mathtext.rm : serif\n", | |
| "#mathtext.tt : monospace\n", | |
| "#mathtext.it : serif:italic\n", | |
| "#mathtext.bf : serif:bold\n", | |
| "#mathtext.sf : sans\n", | |
| "#mathtext.fontset : cm # Should be 'cm' (Computer Modern), 'stix',\n", | |
| " # 'stixsans' or 'custom'\n", | |
| "#mathtext.fallback_to_cm : True # When True, use symbols from the Computer Modern\n", | |
| " # fonts when a symbol can not be found in one of\n", | |
| " # the custom math fonts.\n", | |
| "\n", | |
| "#mathtext.default : it # The default font to use for math.\n", | |
| " # Can be any of the LaTeX font names, including\n", | |
| " # the special name \"regular\" for the same font\n", | |
| " # used in regular text.\n", | |
| "\n", | |
| "### AXES\n", | |
| "# default face and edge color, default tick sizes,\n", | |
| "# default fontsizes for ticklabels, and so on. See\n", | |
| "# http://matplotlib.org/api/axes_api.html#module-matplotlib.axes\n", | |
| "#axes.hold : True # whether to clear the axes by default on\n", | |
| "#axes.facecolor : white # axes background color\n", | |
| "#axes.edgecolor : black # axes edge color\n", | |
| "#axes.linewidth : 1.0 # edge linewidth\n", | |
| "#axes.grid : False # display grid or not\n", | |
| "#axes.titlesize : large # fontsize of the axes title\n", | |
| "#axes.labelsize : medium # fontsize of the x any y labels\n", | |
| "#axes.labelweight : normal # weight of the x and y labels\n", | |
| "#axes.labelcolor : black\n", | |
| "#axes.axisbelow : False # whether axis gridlines and ticks are below\n", | |
| " # the axes elements (lines, text, etc)\n", | |
| "#axes.formatter.limits : -7, 7 # use scientific notation if log10\n", | |
| " # of the axis range is smaller than the\n", | |
| " # first or larger than the second\n", | |
| "#axes.formatter.use_locale : False # When True, format tick labels\n", | |
| " # according to the user's locale.\n", | |
| " # For example, use ',' as a decimal\n", | |
| " # separator in the fr_FR locale.\n", | |
| "#axes.formatter.use_mathtext : False # When True, use mathtext for scientific\n", | |
| " # notation.\n", | |
| "#axes.unicode_minus : True # use unicode for the minus symbol\n", | |
| " # rather than hyphen. See\n", | |
| " # http://en.wikipedia.org/wiki/Plus_and_minus_signs#Character_codes\n", | |
| "#axes.color_cycle : b, g, r, c, m, y, k # color cycle for plot lines\n", | |
| " # as list of string colorspecs:\n", | |
| " # single letter, long name, or\n", | |
| " # web-style hex\n", | |
| "\n", | |
| "#polaraxes.grid : True # display grid on polar axes\n", | |
| "#axes3d.grid : True # display grid on 3d axes\n", | |
| "\n", | |
| "### TICKS\n", | |
| "# see http://matplotlib.org/api/axis_api.html#matplotlib.axis.Tick\n", | |
| "#xtick.major.size : 4 # major tick size in points\n", | |
| "#xtick.minor.size : 2 # minor tick size in points\n", | |
| "#xtick.major.width : 0.5 # major tick width in points\n", | |
| "#xtick.minor.width : 0.5 # minor tick width in points\n", | |
| "#xtick.major.pad : 4 # distance to major tick label in points\n", | |
| "#xtick.minor.pad : 4 # distance to the minor tick label in points\n", | |
| "#xtick.color : k # color of the tick labels\n", | |
| "#xtick.labelsize : medium # fontsize of the tick labels\n", | |
| "#xtick.direction : in # direction: in, out, or inout\n", | |
| "\n", | |
| "#ytick.major.size : 4 # major tick size in points\n", | |
| "#ytick.minor.size : 2 # minor tick size in points\n", | |
| "#ytick.major.width : 0.5 # major tick width in points\n", | |
| "#ytick.minor.width : 0.5 # minor tick width in points\n", | |
| "#ytick.major.pad : 4 # distance to major tick label in points\n", | |
| "#ytick.minor.pad : 4 # distance to the minor tick label in points\n", | |
| "#ytick.color : k # color of the tick labels\n", | |
| "#ytick.labelsize : medium # fontsize of the tick labels\n", | |
| "#ytick.direction : in # direction: in, out, or inout\n", | |
| "\n", | |
| "\n", | |
| "### GRIDS\n", | |
| "#grid.color : black # grid color\n", | |
| "#grid.linestyle : : # dotted\n", | |
| "#grid.linewidth : 0.5 # in points\n", | |
| "#grid.alpha : 1.0 # transparency, between 0.0 and 1.0\n", | |
| "\n", | |
| "### Legend\n", | |
| "#legend.fancybox : False # if True, use a rounded box for the\n", | |
| " # legend, else a rectangle\n", | |
| "#legend.isaxes : True\n", | |
| "#legend.numpoints : 2 # the number of points in the legend line\n", | |
| "#legend.fontsize : large\n", | |
| "#legend.pad : 0.0 # deprecated; the fractional whitespace inside the legend border\n", | |
| "#legend.borderpad : 0.5 # border whitespace in fontsize units\n", | |
| "#legend.markerscale : 1.0 # the relative size of legend markers vs. original\n", | |
| "# the following dimensions are in axes coords\n", | |
| "#legend.labelsep : 0.010 # deprecated; the vertical space between the legend entries\n", | |
| "#legend.labelspacing : 0.5 # the vertical space between the legend entries in fraction of fontsize\n", | |
| "#legend.handlelen : 0.05 # deprecated; the length of the legend lines\n", | |
| "#legend.handlelength : 2. # the length of the legend lines in fraction of fontsize\n", | |
| "#legend.handleheight : 0.7 # the height of the legend handle in fraction of fontsize\n", | |
| "#legend.handletextsep : 0.02 # deprecated; the space between the legend line and legend text\n", | |
| "#legend.handletextpad : 0.8 # the space between the legend line and legend text in fraction of fontsize\n", | |
| "#legend.axespad : 0.02 # deprecated; the border between the axes and legend edge\n", | |
| "#legend.borderaxespad : 0.5 # the border between the axes and legend edge in fraction of fontsize\n", | |
| "#legend.columnspacing : 2. # the border between the axes and legend edge in fraction of fontsize\n", | |
| "#legend.shadow : False\n", | |
| "#legend.frameon : True # whether or not to draw a frame around legend\n", | |
| "\n", | |
| "### FIGURE\n", | |
| "# See http://matplotlib.org/api/figure_api.html#matplotlib.figure.Figure\n", | |
| "#figure.figsize : 8, 6 # figure size in inches\n", | |
| "#figure.dpi : 80 # figure dots per inch\n", | |
| "#figure.facecolor : 0.75 # figure facecolor; 0.75 is scalar gray\n", | |
| "#figure.edgecolor : white # figure edgecolor\n", | |
| "#figure.autolayout : False # When True, automatically adjust subplot\n", | |
| " # parameters to make the plot fit the figure\n", | |
| "\n", | |
| "# The figure subplot parameters. All dimensions are a fraction of the\n", | |
| "# figure width or height\n", | |
| "#figure.subplot.left : 0.125 # the left side of the subplots of the figure\n", | |
| "#figure.subplot.right : 0.9 # the right side of the subplots of the figure\n", | |
| "#figure.subplot.bottom : 0.1 # the bottom of the subplots of the figure\n", | |
| "#figure.subplot.top : 0.9 # the top of the subplots of the figure\n", | |
| "#figure.subplot.wspace : 0.2 # the amount of width reserved for blank space between subplots\n", | |
| "#figure.subplot.hspace : 0.2 # the amount of height reserved for white space between subplots\n", | |
| "\n", | |
| "### IMAGES\n", | |
| "#image.aspect : equal # equal | auto | a number\n", | |
| "#image.interpolation : bilinear # see help(imshow) for options\n", | |
| "#image.cmap : jet # gray | jet etc...\n", | |
| "#image.lut : 256 # the size of the colormap lookup table\n", | |
| "#image.origin : upper # lower | upper\n", | |
| "#image.resample : False\n", | |
| "\n", | |
| "### CONTOUR PLOTS\n", | |
| "#contour.negative_linestyle : dashed # dashed | solid\n", | |
| "\n", | |
| "### Agg rendering\n", | |
| "### Warning: experimental, 2008/10/10\n", | |
| "#agg.path.chunksize : 0 # 0 to disable; values in the range\n", | |
| " # 10000 to 100000 can improve speed slightly\n", | |
| " # and prevent an Agg rendering failure\n", | |
| " # when plotting very large data sets,\n", | |
| " # especially if they are very gappy.\n", | |
| " # It may cause minor artifacts, though.\n", | |
| " # A value of 20000 is probably a good\n", | |
| " # starting point.\n", | |
| "### SAVING FIGURES\n", | |
| "#path.simplify : True # When True, simplify paths by removing \"invisible\"\n", | |
| " # points to reduce file size and increase rendering\n", | |
| " # speed\n", | |
| "#path.simplify_threshold : 0.1 # The threshold of similarity below which\n", | |
| " # vertices will be removed in the simplification\n", | |
| " # process\n", | |
| "#path.snap : True # When True, rectilinear axis-aligned paths will be snapped to\n", | |
| " # the nearest pixel when certain criteria are met. When False,\n", | |
| " # paths will never be snapped.\n", | |
| "\n", | |
| "# the default savefig params can be different from the display params\n", | |
| "# Eg, you may want a higher resolution, or to make the figure\n", | |
| "# background white\n", | |
| "#savefig.dpi : 100 # figure dots per inch\n", | |
| "#savefig.facecolor : white # figure facecolor when saving\n", | |
| "#savefig.edgecolor : white # figure edgecolor when saving\n", | |
| "#savefig.format : png # png, ps, pdf, svg\n", | |
| "#savefig.bbox : standard # 'tight' or 'standard'.\n", | |
| "#savefig.pad_inches : 0.1 # Padding to be used when bbox is set to 'tight'\n", | |
| "\n", | |
| "# tk backend params\n", | |
| "#tk.window_focus : False # Maintain shell focus for TkAgg\n", | |
| "\n", | |
| "# ps backend params\n", | |
| "#ps.papersize : letter # auto, letter, legal, ledger, A0-A10, B0-B10\n", | |
| "#ps.useafm : False # use of afm fonts, results in small files\n", | |
| "#ps.usedistiller : False # can be: None, ghostscript or xpdf\n", | |
| " # Experimental: may produce smaller files.\n", | |
| " # xpdf intended for production of publication quality files,\n", | |
| " # but requires ghostscript, xpdf and ps2eps\n", | |
| "#ps.distiller.res : 6000 # dpi\n", | |
| "#ps.fonttype : 3 # Output Type 3 (Type3) or Type 42 (TrueType)\n", | |
| "\n", | |
| "# pdf backend params\n", | |
| "#pdf.compression : 6 # integer from 0 to 9\n", | |
| " # 0 disables compression (good for debugging)\n", | |
| "#pdf.fonttype : 3 # Output Type 3 (Type3) or Type 42 (TrueType)\n", | |
| "\n", | |
| "# svg backend params\n", | |
| "#svg.image_inline : True # write raster image data directly into the svg file\n", | |
| "#svg.image_noscale : False # suppress scaling of raster data embedded in SVG\n", | |
| "#svg.fonttype : 'path' # How to handle SVG fonts:\n", | |
| "# 'none': Assume fonts are installed on the machine where the SVG will be viewed.\n", | |
| "# 'path': Embed characters as paths -- supported by most SVG renderers\n", | |
| "# 'svgfont': Embed characters as SVG fonts -- supported only by Chrome,\n", | |
| "# Opera and Safari\n", | |
| "\n", | |
| "# docstring params\n", | |
| "#docstring.hardcopy = False # set this when you want to generate hardcopy docstring\n", | |
| "\n", | |
| "# Set the verbose flags. This controls how much information\n", | |
| "# matplotlib gives you at runtime and where it goes. The verbosity\n", | |
| "# levels are: silent, helpful, debug, debug-annoying. Any level is\n", | |
| "# inclusive of all the levels below it. If your setting is \"debug\",\n", | |
| "# you'll get all the debug and helpful messages. When submitting\n", | |
| "# problems to the mailing-list, please set verbose to \"helpful\" or \"debug\"\n", | |
| "# and paste the output into your report.\n", | |
| "#\n", | |
| "# The \"fileo\" gives the destination for any calls to verbose.report.\n", | |
| "# These objects can a filename, or a filehandle like sys.stdout.\n", | |
| "#\n", | |
| "# You can override the rc default verbosity from the command line by\n", | |
| "# giving the flags --verbose-LEVEL where LEVEL is one of the legal\n", | |
| "# levels, eg --verbose-helpful.\n", | |
| "#\n", | |
| "# You can access the verbose instance in your code\n", | |
| "# from matplotlib import verbose.\n", | |
| "#verbose.level : silent # one of silent, helpful, debug, debug-annoying\n", | |
| "#verbose.fileo : sys.stdout # a log filename, sys.stdout or sys.stderr\n", | |
| "\n", | |
| "# Event keys to interact with figures/plots via keyboard.\n", | |
| "# Customize these settings according to your needs.\n", | |
| "# Leave the field(s) empty if you don't need a key-map. (i.e., fullscreen : '')\n", | |
| "\n", | |
| "#keymap.fullscreen : f # toggling\n", | |
| "#keymap.home : h, r, home # home or reset mnemonic\n", | |
| "#keymap.back : left, c, backspace # forward / backward keys to enable\n", | |
| "#keymap.forward : right, v # left handed quick navigation\n", | |
| "#keymap.pan : p # pan mnemonic\n", | |
| "#keymap.zoom : o # zoom mnemonic\n", | |
| "#keymap.save : s # saving current figure\n", | |
| "#keymap.quit : ctrl+w # close the current figure\n", | |
| "#keymap.grid : g # switching on/off a grid in current axes\n", | |
| "#keymap.yscale : l # toggle scaling of y-axes ('log'/'linear')\n", | |
| "#keymap.xscale : L, k # toggle scaling of x-axes ('log'/'linear')\n", | |
| "#keymap.all_axes : a # enable all axes\n", | |
| "\n", | |
| "# Control location of examples data files\n", | |
| "#examples.directory : '' # directory to look in for custom installation\n", | |
| "\n", | |
| "###ANIMATION settings\n", | |
| "#animation.writer : ffmpeg # MovieWriter 'backend' to use\n", | |
| "#animation.codec : mp4 # Codec to use for writing movie\n", | |
| "#animation.bitrate: -1 # Controls size/quality tradeoff for movie.\n", | |
| " # -1 implies let utility auto-determine\n", | |
| "#animation.frame_format: 'png' # Controls frame format used by temp files\n", | |
| "#animation.ffmpeg_path: 'ffmpeg' # Path to ffmpeg binary. Without full path\n", | |
| " # $PATH is searched\n", | |
| "#animation.ffmpeg_args: '' # Additional arugments to pass to mencoder\n", | |
| "#animation.mencoder_path: 'ffmpeg' # Path to mencoder binary. Without full path\n", | |
| " # $PATH is searched\n", | |
| "#animation.mencoder_args: '' # Additional arugments to pass to mencoder\n" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stderr", | |
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| "HTTP request sent, awaiting response... 200 OK\n", | |
| "Length: 23428 (23K) [application/octet-stream]\n", | |
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| " 0K .......... .......... .. 100% 67.6K=0.3s\n", | |
| "\n", | |
| "2013-04-09 22:59:49 (67.6 KB/s) - \u2018matplotlibrc\u2019 saved [23428/23428]\n", | |
| "\n" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 15 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "You'll need to edit the `~/.matplotlib/matplotlibrc` file in a text editor on your own machine to change the colors. However, we can't just use that vector we created earlier, because we must use HEX colors. We can use the `mpl.colors.rgb2hex` function to convert the 3-tuples to HEX strings." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "for color in colors:\n", | |
| " print mpl.colors.rgb2hex(color)" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "#66c2a5" | |
| ] | |
| }, | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "\n", | |
| "#fc8d62\n", | |
| "#8da0cb\n", | |
| "#e78ac3\n", | |
| "#a6d854\n", | |
| "#ffd92f\n", | |
| "#e5c494\n" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 13 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Before I edit the file, let's see what the file looks like on the line we're going to edit, where it says `axes.color_cycle`," | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "! cat ~/.matplotlib/matplotlibrc | grep axes.color_cycle" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "#lines.color : blue # has no affect on plot(); see axes.color_cycle\r\n", | |
| "#axes.color_cycle : b, g, r, c, m, y, k # color cycle for plot lines\r\n" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 16 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "I edited the `~/.matplotlib/matplotlibrc` file separately in a text editor." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "! cat ~/.matplotlib/matplotlibrc | grep axes.color_cycle" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "#lines.color : blue # has no affect on plot(); see axes.color_cycle\r\n", | |
| "axes.color_cycle : 66c2a5, fc8d62, 8da0cb, e78ac3, a6d854, ffd92f, e5c494 # color cycle for plot lines\r\n" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 17 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Now, in future instances (after we restart python and reload matplotlib) when we reset the color cycle to the defaults, we should get the correct 'Set2' colorbrewer colors. For now, we'll use the change we made to `mpl.rcParams` and to keep the colors the way they are.\n", | |
| "\n", | |
| "#### Heatmaps\n", | |
| "\n", | |
| "Let's use the same principles as before to improve this heatmap:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "from matplotlib.colors import LogNorm\n", | |
| "from pylab import *\n", | |
| "\n", | |
| "#normal distribution center at x=0 and y=5\n", | |
| "x = randn(100000)\n", | |
| "y = randn(100000)+5\n", | |
| "\n", | |
| "hist2d(x, y, bins=40, norm=LogNorm())\n", | |
| "colorbar()\n", | |
| "show()" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "png": 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| |
| "text": [ | |
| "<matplotlib.figure.Figure at 0x10dfd1ed0>" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 5 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "What's so bad about this? Well, it's using a rainbow of colors to indicate a single scale - increasing from zero. Let's use one of the _sequential_ colorbrewer palettes to improve this. I like green, so let's use that. We will tell `brewer2mpl` to give us a `matplotlib`-compatible colormap with the attribute `.mpl_colormap`, with the full call being,\n", | |
| "\n", | |
| " brewer2mpl.get_map('Greens', 'sequential', 8).mpl_colormap" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "from matplotlib.colors import LogNorm\n", | |
| "from pylab import *\n", | |
| "\n", | |
| "#normal distribution center at x=0 and y=5\n", | |
| "x = randn(100000)\n", | |
| "y = randn(100000)+5\n", | |
| "\n", | |
| "hist2d(x, y, bins=40, norm=LogNorm(), \n", | |
| " cmap=brewer2mpl.get_map('Greens', 'sequential', 8).mpl_colormap)\n", | |
| "colorbar()\n", | |
| "show()" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "png": 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| |
| "text": [ | |
| "<matplotlib.figure.Figure at 0x10ee32d50>" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 15 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "This is *much* easier to interpret, since we only have to distinguish an increase in saturation of the hue green, rather than be forced to think about multiple different hues and how their colors represent an increase in value.\n", | |
| "\n", | |
| "Though if you just have increases from 0 to larger numbers, it may be even simpler (and better) to just use grey. Maybe not as pretty, but very easy to interpret." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "from matplotlib.colors import LogNorm\n", | |
| "from pylab import *\n", | |
| "\n", | |
| "#normal distribution center at x=0 and y=5\n", | |
| "x = randn(100000)\n", | |
| "y = randn(100000)+5\n", | |
| "\n", | |
| "# norm=LogNorm() tells the function to use a logscale for the z-values\n", | |
| "hist2d(x, y, bins=40, norm=LogNorm(), \n", | |
| " cmap=brewer2mpl.get_map('Greys', 'sequential', 8).mpl_colormap)\n", | |
| "colorbar()\n", | |
| "show()" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "png": 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| |
| "text": [ | |
| "<matplotlib.figure.Figure at 0x10ee22fd0>" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 16 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "But what if your data has positive and negative values? Then you want to use a _divergent_ color map. I like blue-red (`RdBu` in reverse with these colormaps) because it has the natural interpretation of blue=cold, negative, and red=hot, positive.\n", | |
| "\n", | |
| "The below example is from [griddata_demo.py](http://matplotlib.org/examples/pylab_examples/griddata_demo.html) in the `matplotlib` gallery." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "from numpy.random import uniform, seed\n", | |
| "from matplotlib.mlab import griddata\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "import numpy as np\n", | |
| "# make up data.\n", | |
| "#npts = int(raw_input('enter # of random points to plot:'))\n", | |
| "seed(0)\n", | |
| "npts = 200\n", | |
| "x = uniform(-2,2,npts)\n", | |
| "y = uniform(-2,2,npts)\n", | |
| "z = x*np.exp(-x**2-y**2)\n", | |
| "# define grid.\n", | |
| "xi = np.linspace(-2.1,2.1,100)\n", | |
| "yi = np.linspace(-2.1,2.1,200)\n", | |
| "# grid the data.\n", | |
| "zi = griddata(x,y,z,xi,yi,interp='linear')\n", | |
| "# contour the gridded data, plotting dots at the nonuniform data points.\n", | |
| "CS = plt.contour(xi,yi,zi,15,linewidths=0.5,colors='k')\n", | |
| "CS = plt.contourf(xi,yi,zi,15,cmap=plt.cm.rainbow,\n", | |
| " vmax=abs(zi).max(), vmin=-abs(zi).max())\n", | |
| "plt.colorbar() # draw colorbar\n", | |
| "# plot data points.\n", | |
| "plt.scatter(x,y,marker='o',c='b',s=5,zorder=10)\n", | |
| "plt.xlim(-2,2)\n", | |
| "plt.ylim(-2,2)\n", | |
| "plt.title('griddata test (%d points)' % npts)" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 17, | |
| "text": [ | |
| "<matplotlib.text.Text at 0x10f546c50>" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "png": 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Bnp+7gzK7P4KyCWEHt5aqf7XMzLhYppuLc9Zz638ngVQG+X+LXaPS3zG8NBFJ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\n2aV7bN95C0dHSwb2b4BMVrr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mE7n45xnSDp+nXsvqBb6SZqZlEReXjrNTyd07BUFg97Jj\nSCUipverV+jrb6PqlRjb2QMLE5nG/cckZXD1USwqVcGfnUgk4sOetalfzZrf5+wjLS27WNfwXwwN\npXw6owP75+4gNTFdq76KQ0Wf0b6uvPFKvO7EXqQ10G4BLz+iDp4m6cY9svtM1l2fnkMJNyqm69un\ng+H7v+Hj7vDlKK0Uecb1q6Sd0Ty/SWFEV25JRngk2fGJ+ZZLzUxQZGSjKiAPS1oDb57fzj1Tf3D8\nNi/uGSFP382Bufu4fPgOLXoWvEh39ZcjDBlS8ll4ZqYc3y/30aFBFQa0qlbifgriaXQqNSYepP3n\nVxn/U9EBU63rOPD5oPqsWXAAuZ92dm0zU0NmfOLDni+2kplW9BuPtljampEaoxu/aD25eeOVeIRR\ntRK3TX/8jKSbeb8s4Rv2EHf2KiG+x4j5alGhZgNNKdFsWBAg8iEI8wBbuH0akuNLLEOEpQ9ZD+6T\neTf/3PDFRdlrAhGbC84L4dypMc9OXC+w3MBYhuqVe+vUqBoicRAy04nUaliF6g2dC5wZpydnkJiY\ngZNjyWbhMTGprPxyH5/0rUtTD80yLxaX6yFxKFWepGdt4cg1zRb+7K2M+em95uy5+ITr64qOfi0M\nS0tjPp7Sll2ztpCVod3sviicPOyR3gws1THeViqkElepVDw7eZ24u4+16kcbL5T489c423IMl7p9\nSshPG9Tnw1ZuQmZnw7MtfsifzCHlwCUyrxYvc5sgCCgTchb2tPI6EYlgyjKQjgBRMni2BHPtPG/i\nO80i9chB5E+faNUPgNTeHqdRBQf3ZDbtwPNztwsslxhIUGYr1MeVqtnz1YOlfHRsIrVbVKZFj4Jn\n4QG/HmXY0MYlkjvteCDbvjvBsvHeONmWXkh+Z6+q1HSMRibtwsJ3Nc/YKJGImT2kIcYyCRsWHiQ7\nu/hZJV9ia2vG5Emt2T1zCwp5yfspCicPB8If6d5tWE8F8xN/yeeff8WBldtAFU+fQ/NwbFf8VXlt\n3QgTLt9EyO6NoGxOzLFtVP9kFAAOvToSLq2Fges2sjNXgxCNtEpVjftVREeRsMYX8159eBzvXHSD\noug6HDoPgdgIUGSDSgna5IARiUjsPR/Rjq+RedbErEfvYueUEeRyUvbvQWxmjnR0weYhkVhcqI1Z\nkaXAwCi3fdmishUWla14sfccJuZG+bZLT84gNTWLKlWKtwuSIAic8/UnOjGT7yc203jneoAL++6y\n/W4klv/k9X5p/zU2kGBvKsPBTIZDY2ccrIywtzLG0ECCmbEB134p+UbSvZu7UNfFih/m7OWLwQ3I\nbF6j6Eb54GBvzuhR3vw0ehUXbyZhW9WaRTtGYm6tuweYraMVcc8TKTt/mLeHCqnEz50LQJE2DbHh\nCWJuBmusxFVKJTHXH5FSW/tIz6pDevLsr2nIEw7jMXuJ+ny4NGfG5Lj2N1JPHMewzkwMnIuOABQE\ngdSD+8kODSap73ySjHT3BfG8aw7UIsjqOvy1GNzqQvuBUNKoTKkBCX0X4Zjsj5Cdjcgof2VZEHHf\n/0jS3zeAOK5nTKbJ5IKDPBrPHFpgWWELYYWVXf31KMOGFC8fTnR0Clu/O8GQtm7qAB5NeBGfzq9r\nLlPb3pQfetTM81BKz1YSnZZNdGo2L6495WZqNtFp2WTKc8xEIhGIRSIG1LWnftfiJ4hzr2LB8vHe\nfLP1Fk2CYqg/qmT/+66uNgQ9yiLu+RyS4w5zettV+kxqX6K+8kMsFhe6eKun5FRIJb58+Ty6DR2J\naVVbao4Yq3G7yEv3yEpIBR0kSzR2qozPra25zr3qhSI2M8ei34B828ojwsm6fw+Tlq0Rm5qiiHxB\n4rq1mLTvQELtESUXShDghh8E34Ihn+B5K7cHi2diY2jamCDj8/DnQqjZBFr3LvHMPMKiHUQU35dc\nHh6JkNUSpKEoXrwotK7MIv+HWXZKeoFh2hnJ6ZiY5f9gUSpVJCRmULmy5rPw6+sucCcsnm9GN8FM\nw1B3QRDY9FcAQbHpfNHeDQuj/L9KJjIJ1WTGVLMuOORcrlSx7vpzNi87zfgmVfHoVFNj2QGMDaUs\nHt2EI9fCWTl7L9P61CG7Ze4572m/R3y77CrNvCuzYL5Pvm8ZrVpW5emzH5DLY3CrN7ZYMmiCfme1\n0uGNCva5sng9lpMmITEu3sxREzR1I1TExfKkaw9Q1kBWHZx3bCbzzi3CRQ3BWAtXvrsX4cJB8GqP\nh6yPRpFeQQZ+8PAa9P+g5OO+wktlLmRnI5IV7EYnD39G9JxFiC3Msf+/BdTyLPhfrKCgn+hrD7F4\nFkyDfnm31Ms8fpHM9GyadMr7tM46foOI50l07VK0jTkpKYO/vz3GO40c6dJYk8wvOTyPS2e57wX6\n1bGnvVvxsi0WRnq2kj+vRfA8JYupE1vgUIjiL4i0TDkrDtxHoRToMa0DZqY5WRO9W/xOYuKvmBjP\nZePVBZYAACAASURBVJVvK5o3y5umQKUSuHDxMampWQQGRtLr26E63dF+y/IjNP5yBNJiZlXUB/sU\nToWciZcURXqWThW4oFSS/iSC58aa55pQRISD0gwh4//IDuoFQLiJFsEfoXfgxFao1wKPZt8XK0zX\nU+4D7j4EoRtf4JcLsHbX1yA2McW8Z+986xk4OeO4frX6WJEWicRIVqykW4ZWZqTfzT/cPCUhHduq\n+Xud3LwVkUuBP3gYxedf+FG5shnff9dZrdTub77M2cAoZg9piI25ZulhX86+H8Wls/idGpjqOMWr\niUzClJYuJGbI+XXdFUxlEj58r0W+wUUFYWpkwGeDGhARm8b/lh7D09GCNpPa4eJsQ2bmWlRCLFUK\neEsRi0W0aZ2zaYurqw0HPt+qU0VepZotcaFRONTS/IGpp2jeGCWe9PgFFm663Snn8YqN2HVuDcWY\nEBnWa4BZl1aknxuLcsQ32gfSWNnj4b0ckbjkWQdfml2CGupGmcc0nojNsf9DmZCAxLroBeTY4+cw\ncXPGoqHmHhgW7lV59FdUvmXWDubEvUikhlfeheGMDDnGJv+aRBZ9fYF790cTHHKOXbtv06dXPbZ/\nd5zWtR1YNr7ojbNf8urse2QjzReyS4KVsQHzOlYnND6deT+coUlVC4aOalqsB7ijrSn/N7YpF+5F\nsW7+Qdas6sXJU0HUqTMYF5eiP7PatRwAOPjFNnouHaITRe7s6UDUjXtQAZR4cPUq5S2CzqiQLoYl\n4cmhS9A+bzrIkpJw6SYGluZEWjcpXkORCIdvF6NcHwDdtLB/k6N8PZ+5aaXA8/T30o7+9w8QeKnE\nfcXXHUrayWMa1U1wa0fClVv5lgmCkG/AT6EKq2VjQm6H51tUs6Y9Dx7868rm7m6JsfEexOKbVEtJ\n569vDvP5oAb0aaFZOlpBEFj/5xVW/RXA4ndq6NR8UhTuNiYs714TJ0sjpn1zkgv77ha7j1Z1HBjh\n486Wb4/Ru1c96tTWfKJTu5YD3brV4sDnW3P565cUJ08HngW9WYmwKgJvjBKv2q4hRlWLvy9jfsgT\nkoncd4KMd8YWq52gUBD37TcEP9A87FpN9DNIzUnjmkvZlgKet0zwqDkH4qNg9Tx4EVb8ThzdNfYl\nl9rbkxUZk2/ZreuJPNqWf5SosaUJ6Yl5TSpSmRRFAb7R5l0acfNWhPp4/rz2LF7kzE8/tOdmaDw/\nTGyusfkkwj+Y6UtOUs3amAWdquvcfKIpbapZ81PPmoQlZPDZ0lM8Pl30PqWvUtfVmhn96/HbnL0Y\nXw4uVts6tSvTvXttDn6xTWtFbmgsIytDrlUfevLyxihx2/q62YBZEASCv1uNaNSnOXtJHjvK41bt\niRg3CVVmZqHt4lf8jMXQEWBQDCUuz4b/Z++8w6so0zb+mzk1J70X0iuEIiV0hICAigr2uu5a11U/\ne3d1xYody+rqqrtrQ1exIKJSlKJIR2qAJBAgkEZ6TnL6me+PkHJy2sxJQFm5r4vr4sy878zklHue\ned7nue/P/g4/LiB7V/gxJe/uEESR3LDLyB7+FHz3PlQqb6wSQ0JxtPSulTo4J52G3eUe96WPzmH/\n2hKP+9RaFbZujUAdCIkw0NLS1Uau0aiYec4gGtaX8eAlQ9Co/X/lJUni/f+s562Nh3lqeg4TM46v\nkYIniKLA5UMTmX1aFp/vrOG5V3/CrsAQOTkmmOevHcWrC4uoWPCLonPnD0jgjNP7880DvSdypJMa\nKn2N/xkS7yu07TtI/NlTUB114qh96kUc9XMxb2mmbdUKr/OaPnwPw7gJHHR617V2w/5d8M4jMGYG\nudn3ImqPD4F3h6jWkTXgPvjJe3u8N4RddCmCSl50qk+Kw1zh3rEniKJX2QJLwRgObtrncV/WkGSv\nKZWe5XNWq4OWNhsJkf7f30M9om+Dtm9SWX0Fg1bFXaemc86AWG5/6ntqm7wHFj0RpFMz56oCdhxo\nYP07Pyk6b35+AqdPD4zID5XW8J9Hv2bLij3Ep0VTV3ayc7Mv0WsSX7VqFQMGDCAnJ4dXX33Vbf+K\nFSsIDw9n2LBhDBs2jCeeeMLvMZcsWcJHI+7mp3vkl/f0ldFDcFYademFna/1QwYjGB4DitHmeG4C\nMS7+BlVUNIeVuO189RZsWUXO6BfJrR2k+DqdNhMNP/+D2iWzqV32BHXLn8Xe4nkh0B9UQRFwlnKt\n57L6FESDvBtP1PgRmMorPO7ThARhbXFfdFXrtdjNnjU99JNHsmudvKeHxsXbGZ/vO9UmSRLv/ns9\n7xyNvk9Nl/99qmg2M39BEY+8v5lH3t/M3I+3Mu+Lnaz8bg9769owH4N29v6xwcw5PYdHXvmR+hb5\nAlYdioiCAGve+lHRObsTuZJo+r4z/8knc3OYfcl7JGRE07JMmUzFSfhGr5N8t912G2+++SZpaWmc\nfvrpXHbZZcTEuAoGTZo0ia+++kr2MS+//Drq6h6jseQxMmeNJGnC8bOT6lkPnvDCHNrWrEaTlo42\nLd1tvNNiwdHYyJGCPys70YSZ5JZnBHSNpgNrMO5aRNiIKwlZvhqn00py2p/Yb+iF9ngPr86+RmX4\ncHL6e7ZuSxidT/X63aSc5q51IqpVOOwOVGrXiDgkJowWmY43v+yr5/xx3u3bTBY7D724kvPy4/jT\ncPmVJ3tW7OX1vQ30D9UyJjqI8/qFIgJNNidVFjsVJjurV+7jkMmG5Wi3oiRBnE5NXqiW3LFppITr\nXZ4cms12qowWcqINfqtRQnVqnpqezYMvreKJ2ycSKTPXD3DZpCw+WrmXNW/9yNjr5at85ucnIEkS\nix74hLPmXCyrYsbcZgYpB1ATFR/G1hXFyPnm1xRXEBQZAvGyL+93iV6ReFNT+49y4sSJAEyfPp11\n69Zx1lmurudKc2DJyak0tn4OUiOGhONXDeAJgkZD8MRCr/tFnU4xgbfnvQMjcAB9ymgG105i79KX\n2FX0KWDAYq6jv/ZRSkcEpkaXu9XQZyWISmEcMAbH15953Jc0OJWKbQdIGe55zUOSJDci6fl9q2po\nIzHK+xPDk6/8yB3j00gOl9dj4HBKvP7pdsxOJ88PiUfTI30ToVURoVXR3wOpSpJErdXBnhYry74v\npdxkx3n0etvsTt4oM+MQtFxbEMvfZ/r/joTrNTwxLZuHXlrFU3dOIjxY/nrMZZOy+HjlPsVEPnBg\ne3meXCJ/9NOr+PSleYw960xScuVVxyx+ciGLn/oWQWXnnLVjGDRI+dPq7wW9IvENGzbQv39X7W9+\nfj5r1651IXFBEPj5558ZOnQoU6ZM4eabbyYrK8vtWLNnz+78/6OP3sPcQ8uJL5hBRLbvmtJNz31E\n6LXXIwZQENITgZg7KK0D7+3CZfamrj/UbmtCkhKBEKzWxs79gRJ5ICjdG9Jrizd1cBD9rz3L887x\n49k3/2uPJB6fFk31gXoS0ru8M5vqjESEuxb2+yKZHxdsZ0BciGwC371iL3/fW8+fMyIZInNOz2uJ\n1amJ1amZEOP6XZh/qJl/lGVitr/Gx5unyyJxgMggDY9PzebBF1fy9F2FhCp4Irt0UibzVuxl7Vs/\nMkYhkTslWPPsQsbdN9Pn2EHjshk0rkucKy4lirr9NUSne09xbf50KzbzW6j1H/L22293usWfhDuO\nec3U8OHDKS8vR6PR8O6773Lbbbfx9ddfu43rTuIA3zvlEaqlsRVtTR07734FXXQY+c/dhsqgvF3Z\naXevdPCHY2Ft1hPdI83uBA6QmXcHFvNfcTqt9B/08DG/Fm+QrBYQRARN31vJGeKjOFTl2VgiaMJQ\nSrfsdiFxy4rtDBjQ9fxtszlQe1EjtDuczN9RzUtn+dcq6Yi+LU4nc4ckuEXffYGpccEk6UvZ1zqW\nERGhLP92D5PPlKejEmXQMPu0LO5/YQXP3F0oWwMG4PLCLOat2Mu6t39i9HUTZM8bPCiR1av30VDT\nTGScfK2awadmU7R4LdE3eCf/6Q+exvt/vJyQ6BjuvfcVkpK60lyPPvqo7HP9HtCrhc2RI0eye3eX\nK/nOnTsZM8ZVejQ0NBSDwYBGo+Haa69lw4YNWCx94yTSVl1PcEIkRXe/wpHFGRz+tI6D//b8WO4L\nLbv2Uvnpt31yTZ5P0AD7diiKwiVJonnLx7T88hHZm7RuBA6g0YQxpOBVho56E62uax0ie5MW0wHl\njTy5Ww1Qp7wZw7xzJ20/rvQ7rq9s7DqgC9FjbnN96ti9u7qz2xCgrKyO/imeo7g331rLn0cm+00H\nlKzcy+3/3khhrIG7c2OOCYFDexqm+PREzOflsfjUfmxqNPHDt3tkz48N1jL7tCzue34FDQoWO6Gd\nyAUBxVUrM88ZRMn7yhZIkzJjqdxX63PMiIvH8mLbv3nswPMuBH4S7ugViYeHt/8oV61axf79+1m6\ndCmjR7uaEVdXV3fmKBcuXMiQIUPQ6eQvwPjCoRVbcIyciDYuAlGzG0EsRxut/LGr4uOvMY69SNZY\nR0MDrSuXK4vCP3+drGr3FJI32I01HPnmAdShiQyXrpJ/nm5wmBpo3vJf/wN74uu3FU85HDIB89Yt\nssY2bvbcdeiruigiOZqGQ3Vu250OJ6LK9Svc3GIhLKwrzVG3cg9DM93XVSrr26g32RgY7/tznL+g\niE8ONfPCkAQGB5A+UQpBEDpvEnfnxrCxwcSK75QR+RPTsvnby6so/UFZU9DlhVlYbA52f7RO9pyk\npHAqKj0vWHuDXAEr0Y/e/K8Jf1V5u3fvZuzYsej1el544YXO7eXl5UyePJmBAwdSWFjIvHnzen0t\nvS4xfOmll7jhhhuYOnUqN910EzExMbz55pu8+eabAMyfP5/BgwczdOhQ5s+f7/IH9RZ12/cROjCH\n/GduJe/R4Qx6+UqSLvGSW/WCtrJD6JLiEGXeWBrf/zeVYQoErdYthpxTUBmi/Y8FjEULaVz9GiOj\nZzOkcZr88/TA0NZZ2BvLsdbtVTYxIq69k1MJRBFUIpLVfy6+5puViu3sxLFjKPvZnchsbRb0fkyJ\nSyqayfZgDvHSW2u5fbz3ihWAHT+Ucths46/9Y9Eeo+jbH+7JjWZtvYlVCog8MkjD3Bl5vLXhEFu+\nLVJ0vqun5bJqRzV2u/zPKDYmhPpqZU1fJ3q9eEdV3rJly3jttdeorXV9soiOjubVV1/l7rvvdtmu\n0WiYO3cuO3fuZP78+Tz00EO0tLT06lp6TeKTJk1i165dlJaWcuuttwJwww03cMMNNwBw8803s2PH\nDrZs2cJ7773HkCEKmmF8QHI60ceEIwgCqiA96TdeQb9LzlJ85z7836+xnHalrLGW3btQxydCmMyK\nmeqDULyZ3OCL/Y8FTOUbQFAxJmYOGo0yVxpPKAi+m8afX8fR6vvRtTsyws+HtcpTS4Yx4zCt95/C\nCcnNoLVkv8d99bs8t/FHDUynssi9sUdXvJfopK4UjcVkRd9D11uSJLfmn+Wfb2NEUhihOu9LQpIk\n8c+yRm72EMUfTwiCwH250ayuM/HTYvmRtVolMmd6Dp/trGHTN8qI/IwR/Tj4hXfv056YNjWPvR8q\nS6kUTMun8rMViub8VtC9Ki8tLa2zKq87YmNjKSgoQNNjnSghIYGhQ9vNu2NiYhg4cCAbN/o3yfaF\nE7ZjUxBFht0uLwXiDZaaOgSVCjHYv8uOJEk0f/YJR4bJ0yHGaobPXiM7/yHZ1zO4ZjxD27x7UiqF\nKGoZFfsUtcuewGmRd7fXRKZCjedOSF84HDEZ08YNfsc1ZkygYZ1nMaxDP2ymtcrd6FlUqZA8uMLs\nL6ogPb8rX+pYtYO83K6KB0+P7Fabgy931XDRYN/Fx/O+LOLi5DCvi6LHE4IgcH9eNKtqW1mtgMhF\nUeCxqVl8sr1Kkd7KxEEJrNohf22kX79wKiqVReIxSRE0VPVOsuFYYN36A7zy91Wd/zzBW1WeUpSW\nlrJz505GjRoV8PXCCSBFKzmd/Pzguxz55SDjn72C2FO6SpV626Vpqa7Fce4Nsu5kxkULCTn9TJrk\namJXHYCLbkWskJem8bRw2RdQa0IpiH2UMmsbok5mQ09oJDTVQbi8FBAAKhW6/v6lZtX9kjF9d9jj\nvoQx+VT9vIOs8ye67RNEwa0m3GKyoQvqet927aqicFLX96Oiopm0ONec90cfbOK6gn4+n9iazDa2\nN5m5ItX/Qux/l5exx+4kRhSIEwUkwCJJmCSodUqYj95IOs6nBs4PUpM/wXcqpycEQeCBvBie2lOL\nsKSYcdO7uodbrXbMdifRHlJLgiDw+NRs7ltczNzJ8mzn1CoRQWiv7NFo5H3fY6KDqa9uJipe/hNk\nRFwoLUeaCY3t/VOnUpQkeq5Xj5qVwJmzutb1Xv27sicMuWhpaeGSSy5h7ty5BMsIIn3hNx+Jl329\nhm2vb6R82VQWX/FywMdp3rqbDRfeR+mz/+6M0MIG5yGGyCM2QaPhcOQU+SdMzSO3QrktrMPeRt2R\nVdisnsvqAoFOF4s6VH7bW0b0Je0VNQoRcob/9Qhf5NmYVUDtds9aKWqtGoef9vWq6hbi47s+z4bl\nuxia6Xoj2nOklcEJvj/zuZ/u4I4c3zcwm8NJzpcH+WO9E0FSc36QmjyNyACNyGititN1KqqdWhaY\ngxmrUzM7TMfsMB23hWhZb3Vw35JS3vh+H40KnOoFQeDBvBi+r2ll7ZL2yLqoxkjiU+tImrOWdzZ4\ntsLTqkX6xwazT0G0PHlIIuVfyhfKCiSl0n9UBpYV/p/efmuQU5XnCzabjQsuuIArr7ySWbNm9fp6\nfvMkro8OA6keUfMLQdFdd2ylUfjmP86mdulE9s1dRN3Kdu0GJSVvVdnKUjdyygk7Fvg6onBJkvh5\n+XQ2rr6dVUvH4LD3XQelkkhfE5kKycqd0+VW7MROn+BxcVNUq3HaPZOa3WpH1S0qbGtsJTjMtR9A\nklxvEjsONDAoratayWJzoPOjYrj1+1IS9CrivXhmduDWpXs55AjFykYeb7GQoBIZrFExSKMiR6Pi\nsFPi7dYQttpe5vJuhTWhosBVwVqeCdczXa/mHyv388CSUg7/fNDn+TogCAIP9Y9hSU0rm5eV8M2e\nWsz2C7A6/sM/N3i/8Z8/MJ7PP9sm6xxwfFIq6QMSObD7xNMXl1OV14GeKT1Jkrj22msZNGgQt99+\ne59cz2+exJPGD+aMj25lzGPRzPj83oCPowkLQVDtRpJa0IQf2yYdOQTutFuoXfIIKasbqKlchM3W\njNNpoaV5Iw7HF9iszZjNngWjAsWxStkoReTooQhenGK8iV4JguAaxa/dQubgrm5eh8PptoBpsTlc\nrM1+XriTCWneS1CdTol39jdyvR/p2aUr9zNSoyJYbCJYOItxHtQn41UiEk3omUeyF6XHSFFgm01H\nsV3P35rMVCgg8of7x/D+wSYmpkdi0HyGVvUn/m+s9+uODdZS1yZfy7t7SkUuYqKDaaiRT+T6YB3m\n1r7pGTne8FeVV1VVRUpKCnPnzuWJJ54gNTUVo9HI6tWr+eCDD/jhhx86RQG/++67Xl3Lbz4nDpBx\n9lgyzh4LQNO+CmytZshQ5rgz4tM5lP/nc8JPeZDwYQMVReHHojOzae2bhA65iFX/HIPTkYI+6FEm\nTl9HRu79HNhbQHzi+RiCfdeWH6laTH3tT8QnnUN45EhZlTkdbfmm/T8TlD6ur/4cRSipDCcn0XNt\ncfpZY7E2GVHrXatCnD20s/duO8TkS7rs1Q6X1pDpx3Vn9cFG7vKRi/7gy51cnhKGysf7aHVKLDLb\nmRuuY1aQxC57LaO0KsB1TqZaZGWsxAbrCi4KUuMpXnqy2clnpqlI6LnKsIgnmy3MsTkIk5GHFgSB\na9Mj2LHpENV/HYvF7iTMz9NDVJCGplarbH2VyUMSObTgFzIuLJA1ftrUPFZ9+BMFd8yQNR7o1Bf/\nrdaDe0NHVV53dFTkQXsVSnm5u07+hAkT+sQlqTt+85F4T7QcqGbvIeWi8vqEWHLuv4G4MyfhtNn6\nXpi+qQ7WL/EYhUuS5HI+e3Mlkt1KRkk4DrsVh+M1Wo3bkCQbAwbP5oxzjzBs9Jtev9imtnKKtt6F\n02nlDOOjmE2H2LPjAWw2eVFQ9iYtdmMNzVs+9jomUI2X3t7wmrJHYoj3X9bX0tBGeHTXudpW7XTp\n1GxsNLk5+JhtDoK8EGSDycbuFitjon3/3a//sI+bgjUIgkC0SmSCTo3Wy+dUoFVxY4iWGJXnn1mc\n6EQt7EVDMYkqicfCdPzth7JOQSx/yAvVUWK0olOLfgkcIClMR1WDSdaxASYNSmDldmUplcMVyhp/\n8grSaP12taI5J+GKE47EnTY7oqZ3DxB7n1PelegX3/+XTLt7E5ClZjd7n8yjdHYybWXtCz9NG/7N\ncP0tGIKz6Zd6MRrtLHIHPoUoU8VLrQljhulZxpefh0rQMuHQxZzd9jwDNsuvaR5muRjJbsW4209N\nuE25mFbL11/16U2ytbKOsATfnbhl++tI76ah0vxDkcuipt3hRO2FTAFemr+dO3N8v3+H2my0SZAr\ns2LDH+4KVfNyeCkvROzi4TCRaJXIVQYNc7/3vLjbExpRwOah9NIbmi12woPla6qojr5fShp/OqpU\n5GLUGYPYuFRZHftJuOKEI3FRqyFR5V5LrBRyHt/sNdU0z5fZut7WjDo41m1z8+aPcLZdjGR7jIZV\n7yA5nUgOK2pNKIIgMHjEXKads4/s/nfKOk32OjUDNkejFjyXLmavU5O9rusmZ7EcQZI85zVHCNdh\nqy2hbb/nSCh3qwG+fRdK5S+IAQgaNZad2xXN8QXdlo0kn5Le+dpmsaHu4bjjcEiouy1abi2rZ2i3\n9MqeQ03kxRgwWuxsPtyMrVt6ZvOyElKCNMT4aP4BeOXHA9wW0nfrCipB4PoQLTeGaNEc/T4O1apI\nUIl8u3J/n52nAw0mG5EhyiQvRuXGsmOn56oXT5g2NY998+TrrwiC0N69uf/E7d78tXHCkbgvtxe5\nkBslOmprESPkVsF4vikE50xBUP8HQf0YIYOmI9lNhA4JrEmpOznLGZu9To3FXMm+Pc97HVegvwvT\n3pVYqjxrmuSk3AKrvlBkplyVfBZtq3wLYhUf0FP5xRJZxzu8/SD9TunKZVftLCetf2Lna0+fZ32L\nxcUkYcuKUnJjguk/9xdO/ecBpv+rvUTM6ZT494FGrk73HemvWLWfkVoVwT0WT9ucUp+n5q4waFht\nsVO+2v9Cp1YUsMiMlC12JzqFTxEJkUHoPXTLekMgKZVxZw+h6jP/Amon4RknHImr9FocvSBxp92O\n4OOxujvsdbVUmZIDPheAIWsS6XetI+225YSPuBxRG8zAw8Nkz688NB9JkhQReHcM3z2ciKjRFO98\nBLvdXfdbEARGhf8NdUSKx/mCKJJzyuOw4E1okKmpog9GMpt8kpug1dGyQ14XocVoRhfSJT5lWbud\nzMFdn0tdZRPxcb5z8SV1bQhAg0lLm209K8sOI0kS8xbs5PKUcJ+LmXanxBcmOxcGuX4GdzRKhFS0\ncko1mPqYyB8M1fGq0f/3PDdUS3GtPIejQNBmsWPw84TSE9FRBkVVKlEJ4TTW9E4/5PeME47EQ9Pi\niTlFviJgT7SVHcKQLo+YHXV1EOmeIlEKTXg/tNGenWl8ob72Zxz2VnLW906ne3TZVFIyrmPPjr/S\nanR3jxcEkbyd3v9OQa0le8Qc+OhFMMqLsrQ5uVhL/Is29SR6c2UNdTtcvTN7pr5qyuuJT+tKlZhX\nbic/v6sDz2Kxo++RbpEkiSEJIYxN1SIKadwxIYc2m4PtTRbG+lnMfGd5GdcfXczsjn8YLUjsZZ8j\nik0KmnY8ocbhpKxbRG0QBfSC/6fGgWE6itbKL01UigajBeMA38YsPTFhfCbVCxT6aJ5YxSm/KZxw\nJK6PDCU8M3B94bbSAzSmjpU11lFfC+EySfzMPwV8Td5QXbGASVUytVr8YPD2DM4xv8z+0tdwODw7\npPuqIxe1BrKGPwYl8iRnq9Nm0rr8B59jgtL6YTrgWgsftH0NTluXQYe1pc0lCgf3krQ9e2pcNFMO\nHmxwUy6UpPba52XXDsD+xGm8MCOdt7/YyU2ZvtNljVYH5Q4ngzykIc4L0qNjCJFiLUN6sdi53uog\nvcpOfpXEG8auvz1FJXLI5NusJCdER7GMiB2U2yQC1ButhIcpM1np1y+cqiplkXVwWBCmpl/HHvBE\nxwlH4r1F3JmT0Ga4RsVOs5nGD96l5ZuvXb7oUlsbBMnUNYiW5x0oF1ZLLfqgxD6tnxUFNWeb56JS\nedfF9kXkqqBIGDZJ3smCw4i8+lqfQ5qSR9G81bXWtq5oP1H5Xflvw7ZNJA1O7XztdDrd3hOzxU5Q\nNycbaVOZm2ZK9ykd82stDtL81Ey/unI/t3hZzJwXJbEzQaIkQSSsF0JZS812rNIVmPk7H7V15fEH\nakR2bPCsM9MBrShgVVChohQNLRbCFeqot7VZXT4POUhIi+ara17nxzeX9X357/84fnck7glHHptD\n3XOrqPnrKxi/XdS5PfLGW1x//X0EOR6YdUdWEhUrkzAVQCVo/ObXszdpadn+OZLDvcNPSf24oPVd\nCSFGRGBrcE3POK12VLou0izfUkbysPTO1w0Ha4lLcS0F7Enqh+va6NctRWKzO9F4WAfxx7tlqw8S\nLLR3X3qCIAhkqUX0vfyOXGLQECHOQ8ufuTe06ykpX6OiSEbH5LHMRBjNdoIVmC8DNDWbFRP/on/9\nwuqvsvny7pVs/UJhKuZ3jpMkDthrapGsQ8GZiqO+S+hCkKtYqAC2hoOYK/yX7LUaixm6e2ifn78D\n/ohc128YtUse8ahxIpfI/TX+5OWrSTh3us8xxppmwuK7Kkd0RcUunpqeUNVgIiGy6xor69tICKA0\n8B9GKzf2YUmhN2SrRY4kqmnrp+esbhFshCjQJCPKVlKhEgiUPg02N5kJD1eWgnHYJSQpHNBjU2v+\nzgAAIABJREFUtyr3u/094ySJA3Gz7ydo/G5CZiQRduElx/RcTksztnr/bjvZ/R9AJfS98bBc5Jfl\nETb0MuqXz/H4eNtJ5M2B1+yr9Do0EV2Kgtb6xnbBs+7owR+1FY1EJ3WRent6xXWM3eFE061m/HBd\nG/3CXCPDFoudEB9iWGtW7WeoVkXQcWoHFwTBY4WMnLMfywqVQFIbTc1mmjKVLYbe9ca5pOcvZep9\ngxh+kbw1q95gb0icrH8nAk5IEt+3ILA23YZ1W/jpT8/RusJ1wU2TnEK/t18jfs5jiPpj66MoaIKQ\nbPJbn48lstepkSQHZSUve/yxDqwYgSFrMg0/veJxfu5WA+zeCEv8+wQ6LRaaPvoA43eLvBKDIKrI\nmOnb+q6+somYxC7dm+a6ViIjfD8ZVGwuJynMNbVT0WwhSe/5JilJEh+b7Fwa1LfSQq1OiaVmO3WO\nvo2a80PlVagcr1Rzc7OZED+fSU+ERYcw+aKBnPnQLDff1JPwjRPy3Tqyxb1Mzh+cdjvrZ95Cy5f5\nVN12J/YjR/r2ojYvl+VnKaj1SPbfjnJbznodEVGjKSuZ63H/4LpCtLF5tOxc4HF/ru5cUGtg9UKP\n+0v3huCor6Py5tuofXoZ1Q+8jHGR57EDku2EZyR63NcBi8mKPriLkOsqm1w0xD2hotlCv54kvvEQ\nSV70Rj5dsZ8LgtSIfRiFS5LEyBo4tzaZ/GonRhlpErnCULlHNVTkXocSBLKw3tRkJjRCWTqlrdlE\nsMI5J9GOXpO4P9dngAceeIDMzExGjBjhIqYeMAIMKSSHEzgaBcn8bmZlyiyVkpyyvCxFjR5nLyNx\nq7OF/9ZM5c3DSZS2fd6rYwGMLJ1AaNhADu57y+P+oa0zCRlwjtf5udF/ajdX3uLZzqqsIQ3b/oNI\n1hFI9kwctfJuoJ7KC3tCX7SfOD+NPnUmG9EG16i7wmynn5dI+yuTjb8bNfy3re9ys3Zgt91IG1/Q\n7NRwWEY03iBBpIzvqdwKlbgQLUeaPJeX9iWaW8wEhyuLxI2NbYQonHMS7eg1iftzfV6/fj0//vgj\nGzdu5O6773Zzfz5eENVq+l06ndDzi0l49SXUMf7rv60H9mNc+KW8EwSHU57gv6OxLyLxMvNC6mwS\nFuk1fmp6rFfH6sDYA2eh1oRTUf6Jx/05v/gm09zUW2HHz1DjLr8JEHbR2eiH/0LI6VGEXXyZrGtq\nrawjPNF3HXd1dQvxcd3y6laHSz4c2lvre0aUFWYbiR4icYfTyScmkU9ND3FVPRzoowVDjSDwt9AQ\nQoUCLjJI5PoxpwCocjhJkJlakBPW5MYYKF5ZKut4vYHD4UStsG7e2GQiWOFi6Em0o1ckLsf1ed26\ndVx44YVERUVx2WWXuWnwHk/o+8UT/9RjBE8slDVeFRqKo1lm+3BwOLT672YU1EEYsk/zO87ptHt9\n9I3VDAO2ohYeJFk3Qd71ycDEisux25owtXkmYn+mEtn9H/RqsqzNyib2r3eR8PxTiIb2iGv/6x/6\nPJ65tomQGN/+i7W1RmJiumr5jxwxkhjlGtF5yggY7U5CPRCNxSmhQgDqAAl1H65rzg4XaO6n4b0o\neWmKSodEgkreBcgZNSA2hKIa+QuggdZrBzLN2NhGY0LgTXy/Z/Rq5cab6/NZZ3V5La5fv54rr7yy\n83VsbCx79+4lK8u1dX727Nmd/y8sLAR3r9zjDjEkBKdRZjolJKydxP3oZQmiiD5pCPgRhttf+ipp\n0g3oBHcSi9IM4A/xm2h27Kef9lR51ycTU47cSGmm9zRCh6mEJ4gaPQzyXFkg2Wyg6daQI0nYGn3f\nIGObDqOP9J0qcTglFzPfmpoWkqLcH8v3N5i4+KN9aESBTy/PRPBCe0EqkT8aHGiFVzgnSKDfr7jI\nVuWUKCiQR2xyUtfJ4ToOKUintJhshAawuBsI+bc2mTAM9txYt2LFClasWKH4mL8XHHNnn56GCOA5\nCulO4gCfO1d4PWbGzPEoc/MLDIJWB3aZedGgUGhzF5gKFCqVHodkBjxHomHqNMLUyhzT5SJ7nZrS\n0d7/bslhw3z4F4JSR8k+ptPYghjSRciSyYQ6yDVFUzp/JdkXdjU4mVtMRKZ1pb3kLPQF76kgKsy9\nyWj2ssNsOnweAq08u2oV4V4SEIIgkKASeTT81ze9qnI4PaZ8AoXSRcoGo1WxdG2gMDa2ERnhSuL/\nJ17c/p/Co4HdUTz66KPH5ZpOFPQqzJDj+jx69GiKirpE348cOUJmpnIxqO6IG56reI7DZEaSnO0R\n4bGALgimycv1yoEo6o6S+LGHXTKxtP5GvjhyHo329pypr2ag7F8MtBbLk5HtQMj0M1HHdtXdOo1G\n1D28Tut2uJohmJtN6LvpdlhbLegMrimdnkFfc5uNsB5jBEFgcIIOvXoROvVSBsYd2zLSvkKzUyJU\nRu4cjk35YH2Lxc0dyR8qKpqIjVHu7mRsMmGIlClxcRIu6BWJy3F9Hj16NJ999hl1dXXMmzePAQMG\n9OaUAcFptfHTuKsoe+UbDl3+p2OjzSAIENF7xcMOiKog7MeJxIta/0VxWynllhyWN9zjd3wgZWeq\n8HAEddeNwdlqRB3q+0dramojqFvFgqmpzc3hvie0GhGrzXUx0umUuHNCMh9fGs78y+O4bmRSe5ej\nlwqR35Jyh9z3Wu5HEhGkptEob2G9wWghUmHH6vc/lJB5me9af09w2ByoeunY9XtFr9+1Dtdnm83G\nrbfe2un6DO3GoaNGjWLChAkUFBQQFRXFBx980OuLVoqWXaW07T8EzgewbH8YyWRCMMgrZ4q8+Tbq\n5bll9SlUqiDs0vFpCjKICQhCGSpJIkSV07ndZmtGkuxotfJt33K3Gig+xb8andPYgjrUd8RmbbWg\n7VYTbm5uI6hHZOiWqhuWzoGtBzkl011f5ZwBXU8CqUEayk12sj2QlAA4JalP68R/K8iMMlBWbWSY\njDRJg9GKbrSyp+aamhbGJPk22DiJvkWvV206XJ9LS0u59dZbgXby7u78/PTTT1NWVsamTZt+lUh8\n3wsfgzQdeA/9iDGd1RFycCxcuI1FX/sdo9FGUda/9zZ0cpAVdD7TI5/h1IjzmBz5Yuf2pvr1tDQp\ns1mTnE5Y68e3Exg4KYnoSb5z6k6nE1Hs8RXtESb3/HyyMqMpKm902RaqU9Nicc3xJ+jVVJo9p9YS\nVCLVx1AZsK9hc0qoZX5PQ7UqWkzyUooNRosiGVpjqwWDQrGsDpxobve96Y+RM1cJTsiOTaVw2u0I\nYiiiPpzQmWf/2peDucK/JndUzKlExfZt5Yk3CIJAtuEChoTcjFro+tHW160mImq0xzmiLhSH2X15\nWRBFWZ6cok6LKsh/brp7pB2dGU/lftc+hPAwPQ0NXZG/waClrQdhD4wPZme166JzxuhU9rV6JrNU\nlcDBYygo1deosdiJk+m+Y7Y70cus4W4wWgkLk79+sHbtAcaOSZc9vjtONPnZ3vTH+JurFCckie/9\nUr4RK8Cgl+6k3xVWMm4tIOyCwPwtjzd+7cjE5mwDSfKqPW7I9CWTG9gPMvvCQpfXYQkRNFc2dL7W\n6DTYehB0Tk4sJaW+fwSDCnPY3oPEMyOD2OulVT15aCIrLQ52yJCBPVawSBI6md+BSrNddhVLTauV\nuAh5xGw02TAY5IuwFe2qQj/12Clv/lbQm/4YOXOV4oQk8VqF2im6uGgGv3ofWXf+kTSdPCurgPD1\nO8fu2McZPyXOIzHlYq/79f2GotJ7acRpqoev3oKqA52bGt/7t99zRg/KcHmdmJ9C5U7X5qGeNzf1\n+HyKi12d0tWigL3bomVKbDDlja6LxKIoeL3VfFPZxsPNwYyqFvlSZupBCSRJ8qudUuWQiJfZ6FNl\ntpMwVF49eVWLhYRI/ymSw7WttJrtioIJh92JSt338s3HEyUrdvLNo592/vMEb/0x3bF+/Xry8/M7\nX3f0x8iZqxQn5nJwgFGqramFmkXLYYb8xRpFj3lNdeDZb9gFSn4YpaPtAZskBwpJkmht2U1K+lWK\n5zrMzbBkPggzYd7f4cPNoFLhaGr0P7kHWgcOpf6DL+g/bUjnNrVWhc1iQ6NrjxAj48M40kOGNTsp\njJKKZgaktC+wCYJnwtaJAmaHE71KxCFJzNndyGGTxD6jAys3AZ9yVX0li2LsjFdoFuwNFklibA1s\ntbVynSGEN72sGVc5nCTKdAuqNNs5RWYpoMnmRK/1/bd8tGIfV7+0EYfTQfiQTXz0yV7650Xy5ONT\nXBqrAIxGC8HBWhwOCZXMckhPON5PnsV4kZktjCO7cHLX68fmB3R8uf0xfYETMhIPtChWFxuFtbbB\n/8BuaHjtZfmDZV7Xbz3/VzLKRGKKd111X85ETosRnBLYLoC2BrAfHdvjby6pDPcw2xVB0eG01bum\nQVJy4jlU0hV5e/phhE3IYecB189ZFNpLDbtjaISeX45G6O8eaOSpPTG8vf80zJKEjmcRSKBJepJb\nG/vOGGK7zckeewhODvOOj+aw7XYn+SPlaXKXt9ncpHa9wSHju/fB8mostmewO27k+blr2bXrFr5b\nbOX7H4pdxt1x12JGjHqJiy+dT0VlE0mJviUSvMHpdB4TB61jhd70xxQUFPidqxQnJomLokfHGX8Q\nVKqjSoa/LkIGKFtc/TnNs3TrsYIoagmPGBbQXE14Epz/Z8h8AW57sb0JChBDw9x0aPbN9Z9icbu2\ngkEc2OWuWdD9xpiZEc3uQ646NplRBvY1uJZsjp6cxfqj20QEwAHYiNKquMIgoWcTwcKrjND0XW58\ngFokXmxBRw4z9d5LLEvtTtJlVHo4JQmbJKGSEbXXGC1EyfC+vP3cVLTquwjSvklOTix6/WdI0gES\nu+m4W60Ovl60Eaezll2769mzp4bQ0MCaqNqazRgCnPtroDf9MREREX7nKsUJmU5R6TQ4LVZZ1Q2/\nRej7DYUq+eMtpgo2ZP/IyNLjU63iCyXDLV51Rzpx9QNwtesmTWoatoMHUA0a3LnN3uZKqqXGSLJD\nejwpCa7t9jHZCWz8cJnLkLjYEI4cMRJ3VM1Qq1Vj6bEoOXRSNlvWlJHdzXszNlhLraV93JVp4Ryx\nNFBhXslD/cOxOMMIXrmfqfpyZvRh63uwKLArASocEukqCU/SVdUOJ7EyUykrj7QxMUZep+N/t1Vz\nyaX+b87ThvXj/ov6c95dUxFFgUXfFJGRcQ6nDOnKu2u1KiZMGMS6damkpESi16vR+knTeIOx0UTI\nCaZg2Jv+GE9ze4MTksQzzhpL3THwv/SE30LqIzn9GvbseIBtgxIZsiP7mJ7Ll2YKQN33TxIz9SGv\n+702+tjtbp6lgkpEcjhcthf951vyrzqz83VkSjQNB2uJOqqholKrcPQo/+uoUImL824OkZ8awYIF\nRiDeZXvHp6sSBO7Jc1Uvi1eJjNKqZNdgy4VOEMjwIY/471YbV05IlXWspTWtPH6lvKemQ81mkmUQ\nvtMpYbU7CTnaEHTRhZ4rTt7550wqKpuIjwvl8y+20e9c+Vo63dHa1KbYCejXRkd/THd0742B9v6Y\np59+Wtbc3uCETKdED8pA1AbmPxmUrsz7T9FixNnXKLwa+deQO/Ax9pe8zI4hx7C6xg+K0opQh/l2\n3vEGw6TJaHNcNW8M6cntnbTd0Fzm+oiSODCVih2epXE7oD51ICUlrkYTOo3KJRrXqEVsDvcbcoJO\nTaWXCpRbCtN5tNlC/XFs/FllsRMlCvSTkfYw2p0EqQRZqZQtFc2ckuDbAakDq4uqGZ/v319SFAWS\n+0Wg0ajYv7+e+DTfBtbecFJLvHc4IUm8N0i+fKayCaIKOvLvFhP87Rr40wTY9L37WAXaKb4WBz1e\nhqglb/BT7N3zNCZHnaK5crHzlMM+97ds/YSwUwIzkhb1ehftFICgtH6YDlT4nNc6cBhVu1yJXm/Q\nYuqm/xEaGUxjk2tqpn9yOLvLXfPinqhuyuRMvj/iWWM7TKNizpRMHm+2sNF67GvG11rsrLQ4uH5K\nhv/BwPxDzVzYT95i4pe7jnCejFQKwOLNh8m6sEDWWIDSvbVkZgZG4NCuYNiQqCy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WAAAg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\nO/PSyUU8OB1Dvf6GUaG0vXYRi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| |
| "text": [ | |
| "<matplotlib.figure.Figure at 0x10ee02750>" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 17 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "We'll improve on this example with a more natural, divergent colormap." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "from numpy.random import uniform, seed\n", | |
| "from matplotlib.mlab import griddata\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "import numpy as np\n", | |
| "# make up data.\n", | |
| "#npts = int(raw_input('enter # of random points to plot:'))\n", | |
| "seed(0)\n", | |
| "npts = 200\n", | |
| "x = uniform(-2,2,npts)\n", | |
| "y = uniform(-2,2,npts)\n", | |
| "z = x*np.exp(-x**2-y**2)\n", | |
| "# define grid.\n", | |
| "xi = np.linspace(-2.1,2.1,100)\n", | |
| "yi = np.linspace(-2.1,2.1,200)\n", | |
| "# grid the data.\n", | |
| "zi = griddata(x,y,z,xi,yi,interp='linear')\n", | |
| "# contour the gridded data, plotting dots at the nonuniform data points.\n", | |
| "CS = plt.contour(xi,yi,zi,15,linewidths=0.5,colors='k')\n", | |
| "\n", | |
| "# ---- This is the line we changed ---- #\n", | |
| "CS = plt.contourf(xi,yi,zi,15,\n", | |
| " cmap=brewer2mpl.get_map('RdBu', 'diverging', 8, reverse=True).mpl_colormap,\n", | |
| " vmax=abs(zi).max(), vmin=-abs(zi).max())\n", | |
| "\n", | |
| "plt.colorbar() # draw colorbar\n", | |
| "# plot data points.\n", | |
| "plt.scatter(x,y,marker='o',c='b',s=5,zorder=10)\n", | |
| "plt.xlim(-2,2)\n", | |
| "plt.ylim(-2,2)\n", | |
| "plt.title('griddata test (%d points)' % npts)\n" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 21, | |
| "text": [ | |
| "<matplotlib.text.Text at 0x10ee3c6d0>" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "png": 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b6dChAzdv3iQxMZHHjx8zf/58mjZtytChQ/Hz86NPnz4qr5OnSjwsLAxbW1tE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\n6dMHCwsLFi9eLHMuJ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3/q1auHt7c3qampvHv3jrNnz3LixAmGDx/OqVOncp1L46G+YM1v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\nNGjQUIzROHENGjRoKMb8P1/V/JdbHFKiAAAAAElFTkSuQmCC\n", | |
| "text": [ | |
| "<matplotlib.figure.Figure at 0x10ee18e90>" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 21 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "We can do other colormaps just for fun, too. What does purple and green look like?" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "from numpy.random import uniform, seed\n", | |
| "from matplotlib.mlab import griddata\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "import numpy as np\n", | |
| "# make up data.\n", | |
| "#npts = int(raw_input('enter # of random points to plot:'))\n", | |
| "seed(0)\n", | |
| "npts = 200\n", | |
| "x = uniform(-2,2,npts)\n", | |
| "y = uniform(-2,2,npts)\n", | |
| "z = x*np.exp(-x**2-y**2)\n", | |
| "# define grid.\n", | |
| "xi = np.linspace(-2.1,2.1,100)\n", | |
| "yi = np.linspace(-2.1,2.1,200)\n", | |
| "# grid the data.\n", | |
| "zi = griddata(x,y,z,xi,yi,interp='linear')\n", | |
| "# contour the gridded data, plotting dots at the nonuniform data points.\n", | |
| "CS = plt.contour(xi,yi,zi,15,linewidths=0.5,colors='k')\n", | |
| "\n", | |
| "# ---- This is the line we changed ---- #\n", | |
| "CS = plt.contourf(xi,yi,zi,15,\n", | |
| " cmap=brewer2mpl.get_map('PRGn', 'diverging', 8, reverse=True).mpl_colormap,\n", | |
| " vmax=abs(zi).max(), vmin=-abs(zi).max())\n", | |
| "\n", | |
| "plt.colorbar() # draw colorbar\n", | |
| "# plot data points.\n", | |
| "plt.scatter(x,y,marker='o',c='b',s=5,zorder=10)\n", | |
| "plt.xlim(-2,2)\n", | |
| "plt.ylim(-2,2)\n", | |
| "plt.title('griddata test (%d points)' % npts)" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "pyout", | |
| "prompt_number": 23, | |
| "text": [ | |
| "<matplotlib.text.Text at 0x10f9eedd0>" | |
| ] | |
| }, | |
| { | |
| "output_type": "display_data", | |
| "png": 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d3Rk6dGi+xylUJR4WFoapqSkikYhTp05Rp06dXBU4wG2ff0hJ7Y5UUp8rF04U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\nNpHBYTw58DDtR1muX7+Oq2tqK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| |
| "text": [ | |
| "<matplotlib.figure.Figure at 0x10cf60c50>" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 23 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Default fonts\n", | |
| "\n", | |
| "The default font shipped with `matplotlib` is Bitsream Vera Sans, and it's not that pretty. I much prefer Helvetica, and I wrote a [tutorial](http://blog.olgabotvinnik.com/post/35807476900/how-to-set-helvetica-as-the-default-sans-serif-font-in) on how to set Helvetica as the default sans-serif font in `matplotlib`. It was originally wrote for Mac OSX users, but the concepts can be used on any system. The basic idea is that you need to either obtain a set of `Helvetica*.tff` files, or extract them from Mac OS X's `Helvetica.dfont` file. Unfortuantely, it's fairly involved, and I will leave the reader to follow the link and use the tutorial.\n", | |
| "\n", | |
| "Here are the before and after plots. Before:\n", | |
| " \n", | |
| "\n", | |
| "\n", | |
| "After:\n", | |
| " \n", | |
| "\n", | |
| "\n", | |
| "Much nicer! Unfortunately, I performed this change on my old computer and didn't have time to change the defaults on this one, so we will have to suffer through Bitstream Vera Sans together." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Removing 'chartjunk'\n", | |
| "\n", | |
| "'Chartjunk' is a term coined by Edward Tufte to describe any uninformative aspects of a graph. You can also think about the 'data-ink ratio' with the question, _How is this patch of ink contributing to the interpretation of these data?_\n", | |
| "\n", | |
| "For example, this bar graph has an extraordinarily low 'data-ink ratio', and this [unfortunate example](http://matplotlib.org/examples/pylab_examples/demo_ribbon_box.html) is also from the `matplotlib` gallery." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "import matplotlib.pyplot as plt\n", | |
| "import numpy as np\n", | |
| "from matplotlib.image import BboxImage\n", | |
| "\n", | |
| "from matplotlib._png import read_png\n", | |
| "import matplotlib.colors\n", | |
| "from matplotlib.cbook import get_sample_data\n", | |
| "\n", | |
| "class RibbonBox(object):\n", | |
| "\n", | |
| " original_image = read_png(get_sample_data(\"Minduka_Present_Blue_Pack.png\",\n", | |
| " asfileobj=False))\n", | |
| " cut_location = 70\n", | |
| " b_and_h = original_image[:,:,2]\n", | |
| " color = original_image[:,:,2] - original_image[:,:,0]\n", | |
| " alpha = original_image[:,:,3]\n", | |
| " nx = original_image.shape[1]\n", | |
| "\n", | |
| " def __init__(self, color):\n", | |
| " rgb = matplotlib.colors.colorConverter.to_rgb(color)\n", | |
| "\n", | |
| " im = np.empty(self.original_image.shape,\n", | |
| " self.original_image.dtype)\n", | |
| "\n", | |
| "\n", | |
| " im[:,:,:3] = self.b_and_h[:,:,np.newaxis]\n", | |
| " im[:,:,:3] -= self.color[:,:,np.newaxis]*(1.-np.array(rgb))\n", | |
| " im[:,:,3] = self.alpha\n", | |
| "\n", | |
| " self.im = im\n", | |
| "\n", | |
| "\n", | |
| " def get_stretched_image(self, stretch_factor):\n", | |
| " stretch_factor = max(stretch_factor, 1)\n", | |
| " ny, nx, nch = self.im.shape\n", | |
| " ny2 = int(ny*stretch_factor)\n", | |
| "\n", | |
| " stretched_image = np.empty((ny2, nx, nch),\n", | |
| " self.im.dtype)\n", | |
| " cut = self.im[self.cut_location,:,:]\n", | |
| " stretched_image[:,:,:] = cut\n", | |
| " stretched_image[:self.cut_location,:,:] = \\\n", | |
| " self.im[:self.cut_location,:,:]\n", | |
| " stretched_image[-(ny-self.cut_location):,:,:] = \\\n", | |
| " self.im[-(ny-self.cut_location):,:,:]\n", | |
| "\n", | |
| " self._cached_im = stretched_image\n", | |
| " return stretched_image\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "class RibbonBoxImage(BboxImage):\n", | |
| " zorder = 1\n", | |
| "\n", | |
| " def __init__(self, bbox, color,\n", | |
| " cmap = None,\n", | |
| " norm = None,\n", | |
| " interpolation=None,\n", | |
| " origin=None,\n", | |
| " filternorm=1,\n", | |
| " filterrad=4.0,\n", | |
| " resample = False,\n", | |
| " **kwargs\n", | |
| " ):\n", | |
| "\n", | |
| " BboxImage.__init__(self, bbox,\n", | |
| " cmap = cmap,\n", | |
| " norm = norm,\n", | |
| " interpolation=interpolation,\n", | |
| " origin=origin,\n", | |
| " filternorm=filternorm,\n", | |
| " filterrad=filterrad,\n", | |
| " resample = resample,\n", | |
| " **kwargs\n", | |
| " )\n", | |
| "\n", | |
| " self._ribbonbox = RibbonBox(color)\n", | |
| " self._cached_ny = None\n", | |
| "\n", | |
| "\n", | |
| " def draw(self, renderer, *args, **kwargs):\n", | |
| "\n", | |
| " bbox = self.get_window_extent(renderer)\n", | |
| " stretch_factor = bbox.height / bbox.width\n", | |
| "\n", | |
| " ny = int(stretch_factor*self._ribbonbox.nx)\n", | |
| " if self._cached_ny != ny:\n", | |
| " arr = self._ribbonbox.get_stretched_image(stretch_factor)\n", | |
| " self.set_array(arr)\n", | |
| " self._cached_ny = ny\n", | |
| "\n", | |
| " BboxImage.draw(self, renderer, *args, **kwargs)\n", | |
| "\n", | |
| "\n", | |
| "if 1:\n", | |
| " from matplotlib.transforms import Bbox, TransformedBbox\n", | |
| " from matplotlib.ticker import ScalarFormatter\n", | |
| "\n", | |
| " fig = plt.gcf()\n", | |
| " fig.clf()\n", | |
| " ax = plt.subplot(111)\n", | |
| "\n", | |
| " years = np.arange(2004, 2009)\n", | |
| " box_colors = [(0.8, 0.2, 0.2),\n", | |
| " (0.2, 0.8, 0.2),\n", | |
| " (0.2, 0.2, 0.8),\n", | |
| " (0.7, 0.5, 0.8),\n", | |
| " (0.3, 0.8, 0.7),\n", | |
| " ]\n", | |
| " heights = np.random.random(years.shape) * 7000 + 3000\n", | |
| "\n", | |
| " fmt = ScalarFormatter(useOffset=False)\n", | |
| " ax.xaxis.set_major_formatter(fmt)\n", | |
| "\n", | |
| " for year, h, bc in zip(years, heights, box_colors):\n", | |
| " bbox0 = Bbox.from_extents(year-0.4, 0., year+0.4, h)\n", | |
| " bbox = TransformedBbox(bbox0, ax.transData)\n", | |
| " rb_patch = RibbonBoxImage(bbox, bc, interpolation=\"bicubic\")\n", | |
| "\n", | |
| " ax.add_artist(rb_patch)\n", | |
| "\n", | |
| " ax.annotate(r\"%d\" % (int(h/100.)*100),\n", | |
| " (year, h), va=\"bottom\", ha=\"center\")\n", | |
| "\n", | |
| " patch_gradient = BboxImage(ax.bbox,\n", | |
| " interpolation=\"bicubic\",\n", | |
| " zorder=0.1,\n", | |
| " )\n", | |
| " gradient = np.zeros((2, 2, 4), dtype=np.float)\n", | |
| " gradient[:,:,:3] = [1, 1, 0.]\n", | |
| " gradient[:,:,3] = [[0.1, 0.3],[0.3, 0.5]] # alpha channel\n", | |
| " patch_gradient.set_array(gradient)\n", | |
| " ax.add_artist(patch_gradient)\n", | |
| "\n", | |
| "\n", | |
| " ax.set_xlim(years[0]-0.5, years[-1]+0.5)\n", | |
| " ax.set_ylim(0, 10000)\n", | |
| "\n", | |
| " fig.savefig('ribbon_box.png')\n", | |
| " plt.show()" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "png": 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vZRjjLxzE1v9soLm5mZEjR7JkyRJMJhMul5vj5TWUbioj1ZlHv/5ZTPnuwE7E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\nOAfx/wO5sBDrhttFlwAAAABJRU5ErkJggg==\n", | |
| "text": [ | |
| "<matplotlib.figure.Figure at 0x10f9e8890>" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 61 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Why is this so bad? We have these superfluous present boxes to represent five numbers. However, one thing that this figure does correctly is put the value the bar graph represents just above the bar. First, let's get rid of this silly and uninformative gradient by commenting it out." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "import matplotlib.pyplot as plt\n", | |
| "import numpy as np\n", | |
| "from matplotlib.image import BboxImage\n", | |
| "\n", | |
| "from matplotlib._png import read_png\n", | |
| "import matplotlib.colors\n", | |
| "from matplotlib.cbook import get_sample_data\n", | |
| "\n", | |
| "class RibbonBox(object):\n", | |
| "\n", | |
| " original_image = read_png(get_sample_data(\"Minduka_Present_Blue_Pack.png\",\n", | |
| " asfileobj=False))\n", | |
| " cut_location = 70\n", | |
| " b_and_h = original_image[:,:,2]\n", | |
| " color = original_image[:,:,2] - original_image[:,:,0]\n", | |
| " alpha = original_image[:,:,3]\n", | |
| " nx = original_image.shape[1]\n", | |
| "\n", | |
| " def __init__(self, color):\n", | |
| " rgb = matplotlib.colors.colorConverter.to_rgb(color)\n", | |
| "\n", | |
| " im = np.empty(self.original_image.shape,\n", | |
| " self.original_image.dtype)\n", | |
| "\n", | |
| "\n", | |
| " im[:,:,:3] = self.b_and_h[:,:,np.newaxis]\n", | |
| " im[:,:,:3] -= self.color[:,:,np.newaxis]*(1.-np.array(rgb))\n", | |
| " im[:,:,3] = self.alpha\n", | |
| "\n", | |
| " self.im = im\n", | |
| "\n", | |
| "\n", | |
| " def get_stretched_image(self, stretch_factor):\n", | |
| " stretch_factor = max(stretch_factor, 1)\n", | |
| " ny, nx, nch = self.im.shape\n", | |
| " ny2 = int(ny*stretch_factor)\n", | |
| "\n", | |
| " stretched_image = np.empty((ny2, nx, nch),\n", | |
| " self.im.dtype)\n", | |
| " cut = self.im[self.cut_location,:,:]\n", | |
| " stretched_image[:,:,:] = cut\n", | |
| " stretched_image[:self.cut_location,:,:] = \\\n", | |
| " self.im[:self.cut_location,:,:]\n", | |
| " stretched_image[-(ny-self.cut_location):,:,:] = \\\n", | |
| " self.im[-(ny-self.cut_location):,:,:]\n", | |
| "\n", | |
| " self._cached_im = stretched_image\n", | |
| " return stretched_image\n", | |
| "\n", | |
| "\n", | |
| "\n", | |
| "class RibbonBoxImage(BboxImage):\n", | |
| " zorder = 1\n", | |
| "\n", | |
| " def __init__(self, bbox, color,\n", | |
| " cmap = None,\n", | |
| " norm = None,\n", | |
| " interpolation=None,\n", | |
| " origin=None,\n", | |
| " filternorm=1,\n", | |
| " filterrad=4.0,\n", | |
| " resample = False,\n", | |
| " **kwargs\n", | |
| " ):\n", | |
| "\n", | |
| " BboxImage.__init__(self, bbox,\n", | |
| " cmap = cmap,\n", | |
| " norm = norm,\n", | |
| " interpolation=interpolation,\n", | |
| " origin=origin,\n", | |
| " filternorm=filternorm,\n", | |
| " filterrad=filterrad,\n", | |
| " resample = resample,\n", | |
| " **kwargs\n", | |
| " )\n", | |
| "\n", | |
| " self._ribbonbox = RibbonBox(color)\n", | |
| " self._cached_ny = None\n", | |
| "\n", | |
| "\n", | |
| " def draw(self, renderer, *args, **kwargs):\n", | |
| "\n", | |
| " bbox = self.get_window_extent(renderer)\n", | |
| " stretch_factor = bbox.height / bbox.width\n", | |
| "\n", | |
| " ny = int(stretch_factor*self._ribbonbox.nx)\n", | |
| " if self._cached_ny != ny:\n", | |
| " arr = self._ribbonbox.get_stretched_image(stretch_factor)\n", | |
| " self.set_array(arr)\n", | |
| " self._cached_ny = ny\n", | |
| "\n", | |
| " BboxImage.draw(self, renderer, *args, **kwargs)\n", | |
| "\n", | |
| "\n", | |
| "if 1:\n", | |
| " from matplotlib.transforms import Bbox, TransformedBbox\n", | |
| " from matplotlib.ticker import ScalarFormatter\n", | |
| "\n", | |
| " fig = plt.gcf()\n", | |
| " fig.clf()\n", | |
| " ax = plt.subplot(111)\n", | |
| "\n", | |
| " years = np.arange(2004, 2009)\n", | |
| " box_colors = [(0.8, 0.2, 0.2),\n", | |
| " (0.2, 0.8, 0.2),\n", | |
| " (0.2, 0.2, 0.8),\n", | |
| " (0.7, 0.5, 0.8),\n", | |
| " (0.3, 0.8, 0.7),\n", | |
| " ]\n", | |
| " heights = np.random.random(years.shape) * 7000 + 3000\n", | |
| "\n", | |
| " fmt = ScalarFormatter(useOffset=False)\n", | |
| " ax.xaxis.set_major_formatter(fmt)\n", | |
| "\n", | |
| " for year, h, bc in zip(years, heights, box_colors):\n", | |
| " bbox0 = Bbox.from_extents(year-0.4, 0., year+0.4, h)\n", | |
| " bbox = TransformedBbox(bbox0, ax.transData)\n", | |
| " rb_patch = RibbonBoxImage(bbox, bc, interpolation=\"bicubic\")\n", | |
| "\n", | |
| " ax.add_artist(rb_patch)\n", | |
| "\n", | |
| " ax.annotate(r\"%d\" % (int(h/100.)*100),\n", | |
| " (year, h), va=\"bottom\", ha=\"center\")\n", | |
| "\n", | |
| "# patch_gradient = BboxImage(ax.bbox,\n", | |
| "# interpolation=\"bicubic\",\n", | |
| "# zorder=0.1,\n", | |
| "# )\n", | |
| "# gradient = np.zeros((2, 2, 4), dtype=np.float)\n", | |
| "# gradient[:,:,:3] = [1, 1, 0.]\n", | |
| "# gradient[:,:,3] = [[0.1, 0.3],[0.3, 0.5]] # alpha channel\n", | |
| "# patch_gradient.set_array(gradient)\n", | |
| "# ax.add_artist(patch_gradient)\n", | |
| "\n", | |
| "\n", | |
| " ax.set_xlim(years[0]-0.5, years[-1]+0.5)\n", | |
| " ax.set_ylim(0, 10000)\n", | |
| "\n", | |
| " fig.savefig('ribbon_box.png')\n", | |
| " plt.show()" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "png": 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BgLy8PObMmcN5552HaZo8+eSTqfd6+OGHmTVrFnfddRfXXHONvNh7HOtsADpL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\n+xhGcj4gGd7OmPtPm//8In8xNdUxQ3WKxxMFaCNpA8+M/hW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| |
| "text": [ | |
| "<matplotlib.figure.Figure at 0x10fbd5610>" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 63 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "That was easy, we just removed the call to the gradient. Next, let's get rid of these boxes and replace them with simple bars. I'm going to cut out the gradient and the box code, and add the line,\n", | |
| "\n", | |
| " ax.bar(year, h, color=bc)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "import matplotlib.pyplot as plt\n", | |
| "import numpy as np\n", | |
| "from matplotlib.image import BboxImage\n", | |
| "\n", | |
| "from matplotlib._png import read_png\n", | |
| "import matplotlib.colors\n", | |
| "from matplotlib.cbook import get_sample_data\n", | |
| "\n", | |
| "if 1:\n", | |
| " from matplotlib.transforms import Bbox, TransformedBbox\n", | |
| " from matplotlib.ticker import ScalarFormatter\n", | |
| "\n", | |
| " fig = plt.gcf()\n", | |
| " fig.clf()\n", | |
| " ax = plt.subplot(111)\n", | |
| "\n", | |
| " years = np.arange(2004, 2009)\n", | |
| " box_colors = [(0.8, 0.2, 0.2),\n", | |
| " (0.2, 0.8, 0.2),\n", | |
| " (0.2, 0.2, 0.8),\n", | |
| " (0.7, 0.5, 0.8),\n", | |
| " (0.3, 0.8, 0.7),\n", | |
| " ]\n", | |
| " heights = np.random.random(years.shape) * 7000 + 3000\n", | |
| "\n", | |
| " fmt = ScalarFormatter(useOffset=False)\n", | |
| " ax.xaxis.set_major_formatter(fmt)\n", | |
| "\n", | |
| " for year, h, bc in zip(years, heights, box_colors):\n", | |
| "# bbox0 = Bbox.from_extents(year-0.4, 0., year+0.4, h)\n", | |
| "# bbox = TransformedBbox(bbox0, ax.transData)\n", | |
| "# rb_patch = BboxImage(bbox, interpolation='bicubic')\n", | |
| "# rb_ptch = RibbonBoxImage(bbox, bc, interpolation=\"bicubic\")\n", | |
| "\n", | |
| "# ax.add_artist(rb_patch)\n", | |
| "# ax.add_artist(bbox)\n", | |
| "\n", | |
| " # --- this is the line we changed --- #\n", | |
| " ax.bar(year, h, color=bc)\n", | |
| "\n", | |
| " ax.annotate(r\"%d\" % (int(h/100.)*100),\n", | |
| " (year, h), va=\"bottom\", ha=\"center\")\n", | |
| "\n", | |
| "\n", | |
| " ax.set_xlim(years[0]-0.5, years[-1]+0.5)\n", | |
| " ax.set_ylim(0, 10000)\n", | |
| " \n", | |
| " fig.savefig('ribbon_box_no_ribbons.png')\n", | |
| " plt.show()" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "png": 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CgoJrOOLeSR3+Fy5cwP3334958+bBZDIB6HjuwOFwAAAcDgcMBgMAwGg0wm63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\nAABJRU5ErkJggg==\n", | |
| "text": [ | |
| "<matplotlib.figure.Figure at 0x10f2fc590>" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 85 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "But this is offset to the right. Let's move it to the left using `year-0.04`as the previous graph. Also lets change from these hideous colors to 'Set1', another qualitative colorbrewer scheme." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "import matplotlib.pyplot as plt\n", | |
| "import numpy as np\n", | |
| "from matplotlib.image import BboxImage\n", | |
| "\n", | |
| "from matplotlib._png import read_png\n", | |
| "import matplotlib.colors\n", | |
| "from matplotlib.cbook import get_sample_data\n", | |
| "\n", | |
| "if 1:\n", | |
| " from matplotlib.transforms import Bbox, TransformedBbox\n", | |
| " from matplotlib.ticker import ScalarFormatter\n", | |
| "\n", | |
| " fig = plt.gcf()\n", | |
| " fig.clf()\n", | |
| " ax = plt.subplot(111)\n", | |
| "\n", | |
| " years = np.arange(2004, 2009)\n", | |
| " box_colors = brewer2mpl.get_map('Set1', 'qualitative', 5).mpl_colors\n", | |
| "# box_colors = [(0.8, 0.2, 0.2),\n", | |
| "# (0.2, 0.8, 0.2),\n", | |
| "# (0.2, 0.2, 0.8),\n", | |
| "# (0.7, 0.5, 0.8),\n", | |
| "# (0.3, 0.8, 0.7),\n", | |
| "# ]\n", | |
| " heights = np.random.random(years.shape) * 7000 + 3000\n", | |
| "\n", | |
| " fmt = ScalarFormatter(useOffset=False)\n", | |
| " ax.xaxis.set_major_formatter(fmt)\n", | |
| "\n", | |
| " for year, h, bc in zip(years, heights, box_colors):\n", | |
| " # --- this is the line we changed --- #\n", | |
| " ax.bar(year-0.4, h, color =bc)\n", | |
| "\n", | |
| " ax.annotate(r\"%d\" % (int(h/100.)*100),\n", | |
| " (year, h), va=\"bottom\", ha=\"center\")\n", | |
| "\n", | |
| "\n", | |
| " ax.set_xlim(years[0]-0.5, years[-1]+0.5)\n", | |
| " ax.set_ylim(0, 10000)\n", | |
| " \n", | |
| " fig.savefig('ribbon_box_no_ribbons.png')\n", | |
| " plt.show()" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "png": 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bcwEAU6ZMAQBERUVh+fLlKCws9OUwvMIbczF27FjMmTMHDz/8MEaPHo0HH3wQ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\n", | |
| "text": [ | |
| "<matplotlib.figure.Figure at 0x10f9df910>" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 77 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Let's move the number up a little." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "import matplotlib.pyplot as plt\n", | |
| "import numpy as np\n", | |
| "from matplotlib.image import BboxImage\n", | |
| "\n", | |
| "from matplotlib._png import read_png\n", | |
| "import matplotlib.colors\n", | |
| "from matplotlib.cbook import get_sample_data\n", | |
| "\n", | |
| "if 1:\n", | |
| " from matplotlib.transforms import Bbox, TransformedBbox\n", | |
| " from matplotlib.ticker import ScalarFormatter\n", | |
| "\n", | |
| " fig = plt.gcf()\n", | |
| " fig.clf()\n", | |
| " ax = plt.subplot(111)\n", | |
| "\n", | |
| " years = np.arange(2004, 2009)\n", | |
| " box_colors = brewer2mpl.get_map('Set1', 'qualitative', 5).mpl_colors\n", | |
| "# box_colors = [(0.8, 0.2, 0.2),\n", | |
| "# (0.2, 0.8, 0.2),\n", | |
| "# (0.2, 0.2, 0.8),\n", | |
| "# (0.7, 0.5, 0.8),\n", | |
| "# (0.3, 0.8, 0.7),\n", | |
| "# ]\n", | |
| " heights = np.random.random(years.shape) * 7000 + 3000\n", | |
| "\n", | |
| " fmt = ScalarFormatter(useOffset=False)\n", | |
| " ax.xaxis.set_major_formatter(fmt)\n", | |
| "\n", | |
| " for year, h, bc in zip(years, heights, box_colors):\n", | |
| " # --- this is the line we changed --- #\n", | |
| " ax.bar(year-0.4, h, color =bc)\n", | |
| "\n", | |
| " ax.annotate(r\"%d\" % (int(h/100.)*100),\n", | |
| " (year, h), va=\"bottom\", ha=\"center\")\n", | |
| "\n", | |
| "\n", | |
| " ax.set_xlim(years[0]-0.5, years[-1]+0.5)\n", | |
| " ax.set_ylim(0, 10000)\n", | |
| " \n", | |
| " fig.savefig('ribbon_box_no_ribbons.png')\n", | |
| " plt.show()" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "png": 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CX0TEQAp/EREDKfxFRAwUdvh/9dVXuN1uJk2aBEAoFCIzMxOn08mUKVNoaGiw665cuZL4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\nMsRKTEy03nrrrTO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| |
| "text": [ | |
| "<matplotlib.figure.Figure at 0x10e118f50>" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 80 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "import matplotlib.pyplot as plt\n", | |
| "import numpy as np\n", | |
| "from matplotlib.image import BboxImage\n", | |
| "\n", | |
| "from matplotlib._png import read_png\n", | |
| "import matplotlib.colors\n", | |
| "from matplotlib.cbook import get_sample_data\n", | |
| "\n", | |
| "if 1:\n", | |
| " from matplotlib.transforms import Bbox, TransformedBbox\n", | |
| " from matplotlib.ticker import ScalarFormatter\n", | |
| "\n", | |
| " fig = plt.gcf()\n", | |
| " fig.clf()\n", | |
| " ax = plt.subplot(111)\n", | |
| "\n", | |
| " years = np.arange(2004, 2009)\n", | |
| " box_colors = brewer2mpl.get_map('Set1', 'qualitative', 5).mpl_colors\n", | |
| "# box_colors = [(0.8, 0.2, 0.2),\n", | |
| "# (0.2, 0.8, 0.2),\n", | |
| "# (0.2, 0.2, 0.8),\n", | |
| "# (0.7, 0.5, 0.8),\n", | |
| "# (0.3, 0.8, 0.7),\n", | |
| "# ]\n", | |
| " heights = np.random.random(years.shape) * 7000 + 3000\n", | |
| "\n", | |
| " fmt = ScalarFormatter(useOffset=False)\n", | |
| " ax.xaxis.set_major_formatter(fmt)\n", | |
| "\n", | |
| " for year, h, bc in zip(years, heights, box_colors):\n", | |
| " # --- this is the line we changed --- #\n", | |
| " ax.bar(year-0.4, h, color =bc)\n", | |
| "\n", | |
| " ax.annotate(r\"%d\" % (int(h/100.)*100),\n", | |
| " (year, h+100), va=\"bottom\", ha=\"center\")\n", | |
| "\n", | |
| "\n", | |
| " ax.set_xlim(years[0]-0.5, years[-1]+0.5)\n", | |
| " ax.set_ylim(0, 10000)\n", | |
| " \n", | |
| " fig.savefig('ribbon_box_no_ribbons.png')\n", | |
| " plt.show()" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "png": 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| |
| "text": [ | |
| "<matplotlib.figure.Figure at 0x10ec87d90>" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 82 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Let's think some more about this data-ink ratio. What do the right and top axes really tell us? They just make a box around the plot. It looks much cleaner without them. We'll remove them with,\n", | |
| "\n", | |
| "\tax.spines['top'].set_visible(False)\n", | |
| "\tax.spines['right'].set_visible(False)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "import matplotlib.pyplot as plt\n", | |
| "import numpy as np\n", | |
| "from matplotlib.image import BboxImage\n", | |
| "\n", | |
| "from matplotlib._png import read_png\n", | |
| "import matplotlib.colors\n", | |
| "from matplotlib.cbook import get_sample_data\n", | |
| "\n", | |
| "if 1:\n", | |
| " from matplotlib.transforms import Bbox, TransformedBbox\n", | |
| " from matplotlib.ticker import ScalarFormatter\n", | |
| "\n", | |
| " fig = plt.gcf()\n", | |
| " fig.clf()\n", | |
| " ax = plt.subplot(111)\n", | |
| "\n", | |
| " years = np.arange(2004, 2009)\n", | |
| " \n", | |
| " # --- changed this line --- #\n", | |
| " box_colors = brewer2mpl.get_map('Set1', 'qualitative', 5).mpl_colors\n", | |
| " \n", | |
| " heights = np.random.random(years.shape) * 7000 + 3000\n", | |
| "\n", | |
| " fmt = ScalarFormatter(useOffset=False)\n", | |
| " ax.xaxis.set_major_formatter(fmt)\n", | |
| "\n", | |
| " for year, h, bc in zip(years, heights, box_colors):\n", | |
| " # --- this is the line we changed --- #\n", | |
| " ax.bar(year-0.4, h, color =bc)\n", | |
| "\n", | |
| " ax.annotate(r\"%d\" % (int(h/100.)*100),\n", | |
| " (year, h+100), va=\"bottom\", ha=\"center\")\n", | |
| "\n", | |
| "\n", | |
| " ax.set_xlim(years[0]-0.5, years[-1]+0.5)\n", | |
| " ax.set_ylim(0, 10000)\n", | |
| " \n", | |
| " # --- Added this line --- #\n", | |
| " ax.spines['top'].set_visible(False)\n", | |
| " ax.spines['right'].set_visible(False)\n", | |
| " \n", | |
| " fig.savefig('ribbon_box_no_ribbons.png')\n", | |
| " plt.show()" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "png": 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ga8xY1NbWsn37dnJzcxkyZAjPPfccR4+23KkgGvs3ckFBQQHp6elNV/h10Ni/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\n", | |
| "text": [ | |
| "<matplotlib.figure.Figure at 0x10fbd31d0>" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 83 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Well that removed the axis, but the ticks remain. We'll remove them with \n", | |
| "\n", | |
| " ax.yaxis.set_ticks_position('left')\n", | |
| " ax.xaxis.set_ticks_position('bottom')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "import matplotlib.pyplot as plt\n", | |
| "import numpy as np\n", | |
| "from matplotlib.image import BboxImage\n", | |
| "\n", | |
| "from matplotlib._png import read_png\n", | |
| "import matplotlib.colors\n", | |
| "from matplotlib.cbook import get_sample_data\n", | |
| "\n", | |
| "if 1:\n", | |
| " from matplotlib.transforms import Bbox, TransformedBbox\n", | |
| " from matplotlib.ticker import ScalarFormatter\n", | |
| "\n", | |
| " fig = plt.gcf()\n", | |
| " fig.clf()\n", | |
| " ax = plt.subplot(111)\n", | |
| "\n", | |
| " years = np.arange(2004, 2009)\n", | |
| " \n", | |
| " # --- changed this line --- #\n", | |
| " box_colors = brewer2mpl.get_map('Set1', 'qualitative', 5).mpl_colors\n", | |
| " \n", | |
| " heights = np.random.random(years.shape) * 7000 + 3000\n", | |
| "\n", | |
| " fmt = ScalarFormatter(useOffset=False)\n", | |
| " ax.xaxis.set_major_formatter(fmt)\n", | |
| "\n", | |
| " for year, h, bc in zip(years, heights, box_colors):\n", | |
| " # --- this is the line we changed --- #\n", | |
| " ax.bar(year-0.4, h, color =bc)\n", | |
| "\n", | |
| " ax.annotate(r\"%d\" % (int(h/100.)*100),\n", | |
| " (year, h+100), va=\"bottom\", ha=\"center\")\n", | |
| "\n", | |
| "\n", | |
| " ax.set_xlim(years[0]-0.5, years[-1]+0.5)\n", | |
| " ax.set_ylim(0, 10000)\n", | |
| " \n", | |
| " # --- Added this line --- #\n", | |
| " ax.spines['top'].set_visible(False)\n", | |
| " ax.spines['right'].set_visible(False)\n", | |
| " \n", | |
| " # --- Added this line --- #\n", | |
| " ax.yaxis.set_ticks_position('left')\n", | |
| " ax.xaxis.set_ticks_position('bottom')\n", | |
| " \n", | |
| " fig.savefig('ribbon_box_no_ribbons.png')\n", | |
| " plt.show()" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "png": 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ra1FeXo5BgwYhKCgIixcvxpkzZ3Dy5ElUVlZi/Pjx+Pzzz/01LK/46lj8/PPP\nyrt7mpubUVRUhCeffNIvY/KWr44FAMTHx2PXrl0AgF27dmHatGm9P6Ae8OWxAIAdO3Zg3rx5vT4O\nX/DVsdDpdDh37hxOnTqFn3/+GcXFxXjiiSf8Nigp7d+/X6hUKjFp0iQRHR0toqOjRUlJiWhubhYz\nZswQo0ePFhaLRXg8HqXPxo0bxbhx44ROpxMVFRXXPefJkyf75bt9fHUsWlpaxOTJk8WvfvUrMWXK\nFLF27Vp/DclrvjwvfvjhB/HrX/9aREVFiUWLFokffvjBH0Pymq9fI2PHjhU1NTW9PQyf8OWxKC4u\nFtOnTxcGg0EsW7bsmj69ye83diMiot4n7bQPEZHMGP5ERBJi+BMRSYjhT0QkIYY/EZGEGP5ERBL6\n/+2vN1IIZrm6AAAAAElFTkSuQmCC\n", | |
| "text": [ | |
| "<matplotlib.figure.Figure at 0x10ee36c90>" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 84 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Even better, let's remove the left axis and replace it with a white overlapping grid. This way, the reader doesn't have to move their eye back and forth to the left axis and back to see what value corresponds to what height. We will aslo remove the ticks on the x-axis, since the year name labels the position, and we don't need a tick there.\n", | |
| "\n", | |
| " ax.spines['left'].set_visible(False)\n", | |
| " ...\n", | |
| " ax.xaxis.set_ticks_position('none')\n", | |
| " ax.yaxis.set_ticks_position('none')\n", | |
| " ...\n", | |
| " ax.grid(axis = 'y', color ='white', linestyle='-')" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "import matplotlib.pyplot as plt\n", | |
| "import numpy as np\n", | |
| "from matplotlib.image import BboxImage\n", | |
| "\n", | |
| "from matplotlib._png import read_png\n", | |
| "import matplotlib.colors\n", | |
| "from matplotlib.cbook import get_sample_data\n", | |
| "\n", | |
| "if 1:\n", | |
| " from matplotlib.transforms import Bbox, TransformedBbox\n", | |
| " from matplotlib.ticker import ScalarFormatter\n", | |
| "\n", | |
| " fig = plt.gcf()\n", | |
| " fig.clf()\n", | |
| " ax = plt.subplot(111)\n", | |
| "\n", | |
| " years = np.arange(2004, 2009)\n", | |
| " \n", | |
| " # --- changed this line --- #\n", | |
| " box_colors = brewer2mpl.get_map('Set1', 'qualitative', 5).mpl_colors\n", | |
| " \n", | |
| " heights = np.random.random(years.shape) * 7000 + 3000\n", | |
| "\n", | |
| " fmt = ScalarFormatter(useOffset=False)\n", | |
| " ax.xaxis.set_major_formatter(fmt)\n", | |
| "\n", | |
| " for year, h, bc in zip(years, heights, box_colors):\n", | |
| " # --- this is the line we changed --- #\n", | |
| " ax.bar(year-0.4, h, color =bc)\n", | |
| "\n", | |
| " ax.annotate(r\"%d\" % (int(h/100.)*100),\n", | |
| " (year, h+100), va=\"bottom\", ha=\"center\")\n", | |
| "\n", | |
| "\n", | |
| " ax.set_xlim(years[0]-0.5, years[-1]+0.5)\n", | |
| " ax.set_ylim(0, 10000)\n", | |
| " \n", | |
| " # --- Added this line --- #\n", | |
| " ax.spines['top'].set_visible(False)\n", | |
| " ax.spines['right'].set_visible(False)\n", | |
| " ax.spines['left'].set_visible(False)\n", | |
| " \n", | |
| " # --- Added this line --- #\n", | |
| " ax.yaxis.set_ticks_position('none')\n", | |
| " ax.xaxis.set_ticks_position('none')\n", | |
| " \n", | |
| " ax.grid(axis = 'y', color ='white', linestyle='-')\n", | |
| " \n", | |
| " fig.savefig('ribbon_box_no_ribbons.png')\n", | |
| " plt.show()" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "png": 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| |
| "text": [ | |
| "<matplotlib.figure.Figure at 0x10f31f110>" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 96 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "It would look even nicer without the black lines around the bars. We will adjust the `ax.bar` line to set `linewidth=0`,\n", | |
| "\n", | |
| " ax.bar(year-0.4, h, color=bc, linewidth=0)" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "import matplotlib.pyplot as plt\n", | |
| "import numpy as np\n", | |
| "from matplotlib.image import BboxImage\n", | |
| "\n", | |
| "from matplotlib._png import read_png\n", | |
| "import matplotlib.colors\n", | |
| "from matplotlib.cbook import get_sample_data\n", | |
| "\n", | |
| "if 1:\n", | |
| " from matplotlib.transforms import Bbox, TransformedBbox\n", | |
| " from matplotlib.ticker import ScalarFormatter\n", | |
| "\n", | |
| " fig = plt.gcf()\n", | |
| " fig.clf()\n", | |
| " ax = plt.subplot(111)\n", | |
| "\n", | |
| " years = np.arange(2004, 2009)\n", | |
| " \n", | |
| " # --- changed this line --- #\n", | |
| " box_colors = brewer2mpl.get_map('Set1', 'qualitative', 5).mpl_colors\n", | |
| " \n", | |
| " heights = np.random.random(years.shape) * 7000 + 3000\n", | |
| "\n", | |
| " fmt = ScalarFormatter(useOffset=False)\n", | |
| " ax.xaxis.set_major_formatter(fmt)\n", | |
| "\n", | |
| " for year, h, bc in zip(years, heights, box_colors):\n", | |
| " # --- this is the line we changed --- #\n", | |
| " ax.bar(year-0.4, h, color=bc, linewidth=0)\n", | |
| "\n", | |
| " ax.annotate(r\"%d\" % (int(h/100.)*100),\n", | |
| " (year, h+100), va=\"bottom\", ha=\"center\")\n", | |
| "\n", | |
| "\n", | |
| " ax.set_xlim(years[0]-0.5, years[-1]+0.5)\n", | |
| " ax.set_ylim(0, 10000)\n", | |
| " \n", | |
| " # --- Added this line --- #\n", | |
| " ax.spines['top'].set_visible(False)\n", | |
| " ax.spines['right'].set_visible(False)\n", | |
| " ax.spines['left'].set_visible(False)\n", | |
| " \n", | |
| " # --- Added this line --- #\n", | |
| " ax.yaxis.set_ticks_position('none')\n", | |
| " ax.xaxis.set_ticks_position('none')\n", | |
| " \n", | |
| " ax.grid(axis = 'y', color ='white', linestyle='-')\n", | |
| " \n", | |
| " fig.savefig('ribbon_box_no_ribbons.png')\n", | |
| " plt.show()" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "display_data", | |
| "png": 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| |
| "text": [ | |
| "<matplotlib.figure.Figure at 0x10f144690>" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 97 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "So now we have a very nice looking bar graph! All we did was keep 'erasing' chart items that weren't informative. You can use these concepts in your own graphs.\n", | |
| "\n", | |
| "So far we've talked about things you can do with the existing `matplotlib` package. Now we'll talk about packages that implement other design principles." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "### Sparklines\n", | |
| "\n", | |
| "Recently Tufte has introduced the idea of 'Sparklines', or a 'data-word', is an intense, word-sized graphic. The following examples use [sparkplot](http://agile.unisonis.com/proj/sparkplot/sparkplot.py) and its introductory [blog post](http://agiletesting.blogspot.com/2005/04/sparkplot-creating-sparklines-with.html). For example, if you visualize the wins (red, up) and losses (blue, down) by the Lakers' 2002 season where they won the NBA championships, it is easy to see streaks of wins and losses, . It is also easy to compare to their 2005 performance, where they did not win the championship, . This is a very nice way to visualize binary data.\n", | |
| " \n", | |
| " \n", | |
| " " | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "Additionally, sparklines can be used to visualize a series of information. For example, this shows the number of messages sent on the message list `comp.lang.py` in 1994, , and you see that the minimum is zero and the maximum is 518. Compare this to the messages sent in 2004, .\n", | |
| "\n", | |
| "But you may not just be interested in the min and max, but maybe in deviations from the norm. The southern oscillation is a good indicator of El Nino, and values less than -1 usually define an El Nino weather pattern,  [data: Tahiti, 1955-1992]\n", | |
| "\n", | |
| "If you have some series data or binary data you'd like to incorporate into a sentence, Sparklines are great." | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## iPython Notebook\n", | |
| "\n", | |
| "To change your default fonts in iPython notebook, you will need to create a custom profile and create a custom CSS file, which is described thorougly [in this tutorial](http://nbviewer.ipython.org/urls/raw.github.com/Carreau/posts/master/Blog1.ipynb). If you like what you see in my iPython notebook, which includes [`Consolas`](http://en.wikipedia.org/wiki/Consolas) as the default code font, approximately 80-character column width, and centered cells, you may use my `custom.css` file:" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "# Find where my iPython directory is\n", | |
| "! ipython locate" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "/Users/olga/.ipython\r\n" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 1 | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "# Show the contents of my custom.css file, which I created using the above tutorial\n", | |
| "! cat /Users/olga/.ipython/profile_customcss/static/css/custom.css" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "/**write your css in here**/\r\n", | |
| "/* like */\r\n", | |
| "\r\n", | |
| "<style>\r\n", | |
| " .CodeMirror{\r\n", | |
| " font-family: \"Consolas\", sans-serif;\r\n", | |
| " }\r\n", | |
| " \r\n", | |
| "pre, code, kbd, samp {\r\n", | |
| " font-family: Consolas, monospace;\r\n", | |
| "}\r\n", | |
| "\r\n", | |
| "\tdiv.input{\r\n", | |
| "\twidth: 105ex;\r\n", | |
| "}\r\n", | |
| "\r\n", | |
| "div.text_cell{\r\n", | |
| "\twidth: 105ex;\r\n", | |
| "}\r\n", | |
| "\r\n", | |
| "div.text_cell_render{\r\n", | |
| "\twidth: 105ex;\r\n", | |
| "}\r\n", | |
| "\r\n", | |
| " div.cell{\r\n", | |
| " max-width:750px;\r\n", | |
| " margin-left:auto;\r\n", | |
| " margin-right:auto;\r\n", | |
| " }\r\n", | |
| "\r\n", | |
| " h1 {\r\n", | |
| " text-align:left;\r\n", | |
| " }\r\n", | |
| "</style>" | |
| ] | |
| } | |
| ], | |
| "prompt_number": 4 | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## Bokeh\n", | |
| "\n", | |
| "[Bokeh](https://github.com/continuumio/bokeh) (photography term for the aesthetic quality of a blurred background which focuses attention on the foreground, definition from the Bokeh Github readme) is a new package (started in March 2012, compared to `matplotlib` which started in 2002) which aims to have beautiful, interactive visualizations within the iPython framework. It uses the powerful [Data Driven Documents (d3)](http://d3js.org/) javascript library to render lovely vector-based graphics using the HTML5 canvas in the browser.\n", | |
| "\n", | |
| "I downloaded the package but couldn't get the examples to work, so I will show you the example notebook they provided. It will definitely be a package to watch! The underlying data structures in Bokeh are [`pandas`](http://pandas.pydata.org/) `DataFrame`s, so you can expect further integration with it and iPython in the future." | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "collapsed": false, | |
| "input": [ | |
| "from bokeh.mpl import PlotClient\n", | |
| "p = PlotClient(username='defaultuser', serverloc=\"http://portcon:5006\",userapikey=\"nokey\")\n", | |
| "p.use_doc('example')\n", | |
| "p.notebooksources()\n" | |
| ], | |
| "language": "python", | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "stream": "stdout", | |
| "text": [ | |
| "got read write apikey\n" | |
| ] | |
| }, | |
| { | |
| "html": [ | |
| "<div>\n", | |
| " \n", | |
| " <style>\n", | |
| " \n", | |
| " /* BEGIN /usr/lib64/python2.7/site-packages/bokeh/server/static/vendor/bokehjs/css/bokeh.css */\n", | |
| " svg {\n", | |
| " font: 12px sans-serif;\n", | |
| " margin: 0;\n", | |
| "}\n", | |
| "\n", | |
| ".ui-dialog-titlebar {\n", | |
| " height : 16px \n", | |
| "}\n", | |
| "\n", | |
| ".button_bar{\n", | |
| " width : 200px;\n", | |
| " overflow : hidden;\n", | |
| "}\n", | |
| ".button_bar > button{\n", | |
| " float : left;\n", | |
| "}\n", | |
| "\n", | |
| ".all_can_wrapper {position:relative; }\n", | |
| ".can_wrapper {position:absolute; }\n", | |
| ".main_ can_wrapper {}\n", | |
| ".main_can {}\n", | |
| ".x_can_wrapper { }\n", | |
| ".y_can_wrapper {}\n", | |
| ".y_can {}\n", | |
| ".shading {display:block; border:1px dashed green; position: absolute; z-index:100;}\n", | |
| ".gridplot_container {position: relative;}\n", | |
| ".gridplot_container .gp_plotwrapper { position:absolute; } \n", | |
| "button.active { border:1px solid blue }\n", | |
| ".table_wrap { }\n", | |
| ".table_wrap table { margin:5px; height:300px; display:block; overflow-y:scroll }\n", | |
| "/*\n", | |
| " .table_outer thead { fon t-size:15px; display:block; heig ht: 100px; overflow:auto; outline:1px solid green}\n", | |
| "\n", | |
| " .table_outer tbody { font -size:10px; display:table; height: 200px; overflow-y:scroll; outline:1px solid blue}\n", | |
| " .table_outer table td { border-left:1px solid black; border-right:1px solid black; }\n", | |
| "*/\n", | |
| "\n", | |
| "\n", | |
| ".plot_wrap .button_bar {height:30px}\n", | |
| ".plot_wrap .button_bar * {display:none}\n", | |
| ".plot_wrap:hover .button_bar * {display:inherit}\n", | |
| "\n", | |
| ".maximize {\n", | |
| " display : none;\n", | |
| " float : right;\n", | |
| "}\n", | |
| "\n", | |
| ".jsp:hover > .maximize{\n", | |
| " display : inline;\n", | |
| " float : right;\n", | |
| " vertical-align : top;\n", | |
| "}\n", | |
| "\n", | |
| ".plotclose {\n", | |
| " display : none;\n", | |
| " float : right;\n", | |
| "}\n", | |
| "\n", | |
| ".jsp:hover > .plotclose{\n", | |
| " display : inline;\n", | |
| " float : right;\n", | |
| " vertical-align : top;\n", | |
| "}\n", | |
| "\n", | |
| ".plottitle{\n", | |
| " height: 1em;\n", | |
| " border : none;\n", | |
| " resize : none;\n", | |
| " width : 200;\n", | |
| " overflow : hidden;\n", | |
| "}\n", | |
| "\n", | |
| ".plotsidebar{\n", | |
| " overflow-x : auto;\n", | |
| " width : 350;\n", | |
| " float : left;\n", | |
| "}\n", | |
| "\n", | |
| ".maxplot{\n", | |
| " float : left;\n", | |
| "}\n", | |
| "\n", | |
| "/* pandas */\n", | |
| "\n", | |
| ".bokehtable{ \n", | |
| " overflow : auto;\n", | |
| "}\n", | |
| ".pandassize{ |
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Hi, you wrote iPython instead of IPython in many places, if you ever update this one day...