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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"

"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">

<head>

<title>Customizing matplotlib — Matplotlib 2.0.0 documentation</title>

type="text/css" />

type="text/css" />

var DOCUMENTATION_OPTIONS = {

URL_ROOT: '../',

VERSION: '2.0.0',

COLLAPSE_INDEX: false,

FILE_SUFFIX: '.html',

HAS_SOURCE: true

};

</script>

title="Search within Matplotlib 2.0.0 documentation"

href="../_static/opensearch.xml"/>

</head>

<body>

</map>

</div>

<h3>Navigation</h3>

<ul>

<a href="../genindex.html" title="General Index"

accesskey="I">index</a></li>

<a href="../py-modindex.html" title="Python Module Index"

>modules</a> |</li>

<a href="interactive.html" title="Interactive plots"

accesskey="N">next</a> |</li>

<a href="colormapnorms.html" title="Colormap Normalization"

accesskey="P">previous</a> |</li>

<li><a href="../examples/index.html">examples</a>| </li>

<li><a href="../gallery.html">gallery</a>| </li>

<li><a href="../api/pyplot_summary.html">pyplot</a>| </li>

<li><a href="index.html" accesskey="U">User’s Guide</a> »</li>

</ul>

</div>

</a>

Travis-CI: <a href="https://travis-ci.org/matplotlib/matplotlib">

</a>

<h3><a href="../contents.html">Table Of Contents</a></h3>

<ul>

<li><a class="reference internal" href="#">Customizing matplotlib</a><ul>

<li><a class="reference internal" href="#using-style-sheets">Using style sheets</a><ul>

<li><a class="reference internal" href="#defining-your-own-style">Defining your own style</a></li>

<li><a class="reference internal" href="#composing-styles">Composing styles</a></li>

<li><a class="reference internal" href="#temporary-styling">Temporary styling</a></li>

</ul>

</li>

<li><a class="reference internal" href="#matplotlib-rcparams">matplotlib rcParams</a><ul>

<li><a class="reference internal" href="#dynamic-rc-settings">Dynamic rc settings</a></li>

<li><a class="reference internal" href="#the-matplotlibrc-file">The <code class="file docutils literal">matplotlibrc</code> file</a><ul>

<li><a class="reference internal" href="#a-sample-matplotlibrc-file">A sample matplotlibrc file</a></li>

</ul>

</li>

</ul>

</li>

</ul>

</li>

</ul>

<h3>Related Topics</h3>

<ul>

<li><a href="../contents.html">Documentation overview</a><ul>

<li><a href="index.html">User’s Guide</a><ul>

<li>Previous: <a href="colormapnorms.html" title="previous chapter">Colormap Normalization</a></li>

<li>Next: <a href="interactive.html" title="next chapter">Interactive plots</a></li>

</ul></li>

</ul>

</div>

<li><a href="../_sources/users/customizing.rst.txt"

rel="nofollow">Show Source</a></li>

</ul>

</div>

<h3>Quick search</h3>

</form>

</div>

</div>

<h1>Customizing matplotlib<a class="headerlink" href="#customizing-matplotlib" title="Permalink to this headline">¶</a></h1>

<h2>Using style sheets<a class="headerlink" href="#using-style-sheets" title="Permalink to this headline">¶</a></h2>

The <code class="docutils literal">style</code> package adds support for easy-to-switch plotting “styles” with

the same parameters as a <a class="reference external" href="http://matplotlib.org/users/customizing.html">matplotlibrc</a> file (which is read at startup to

configure matplotlib).

There are a number of pre-defined styles provided by matplotlib. For

example, there’s a pre-defined style called “ggplot”, which emulates the

aesthetics of <a class="reference external" href="http://ggplot2.org/">ggplot</a> (a popular plotting package for <a class="reference external" href="https://www.r-project.org/">R</a>). To use this style,

just add:

<div class="highlight-default"><div class="highlight"><pre>>>> import matplotlib.pyplot as plt

>>> plt.style.use('ggplot')

</pre></div>

</div>

To list all available styles, use:

<div class="highlight-default"><div class="highlight"><pre>>>> print(plt.style.available)

</pre></div>

</div>

<h3>Defining your own style<a class="headerlink" href="#defining-your-own-style" title="Permalink to this headline">¶</a></h3>

You can create custom styles and use them by calling <code class="docutils literal">style.use</code> with the

path or URL to the style sheet. Additionally, if you add your

<code class="docutils literal"><style-name>.mplstyle</code> file to <code class="docutils literal">mpl_configdir/stylelib</code>, you can reuse

your custom style sheet with a call to <code class="docutils literal">style.use(<style-name>)</code>. By default

<code class="docutils literal">mpl_configdir</code> should be <code class="docutils literal">~/.config/matplotlib</code>, but you can check where

yours is with <code class="docutils literal">matplotlib.get_configdir()</code>; you may need to create this

directory. You also can change the directory where matplotlib looks for

the stylelib/ folder by setting the MPLCONFIGDIR environment variable,

see <a class="reference internal" href="../faq/troubleshooting_faq.html#locating-matplotlib-config-dir">matplotlib configuration and cache directory locations</a>.

Note that a custom style sheet in <code class="docutils literal">mpl_configdir/stylelib</code> will

override a style sheet defined by matplotlib if the styles have the same name.

For example, you might want to create

<code class="docutils literal">mpl_configdir/stylelib/presentation.mplstyle</code> with the following:

<div class="highlight-default"><div class="highlight"><pre>axes.titlesize : 24

axes.labelsize : 20

lines.linewidth : 3

lines.markersize : 10

xtick.labelsize : 16

ytick.labelsize : 16

</pre></div>

</div>

Then, when you want to adapt a plot designed for a paper to one that looks

good in a presentation, you can just add:

</pre></div>

</div>

<h3>Composing styles<a class="headerlink" href="#composing-styles" title="Permalink to this headline">¶</a></h3>

Style sheets are designed to be composed together. So you can have a style

sheet that customizes colors and a separate style sheet that alters element

sizes for presentations. These styles can easily be combined by passing

a list of styles:

>>> plt.style.use(['dark_background', 'presentation'])

</pre></div>

</div>

Note that styles further to the right will overwrite values that are already

defined by styles on the left.

</div>

<h3>Temporary styling<a class="headerlink" href="#temporary-styling" title="Permalink to this headline">¶</a></h3>

If you only want to use a style for a specific block of code but don’t want

to change the global styling, the style package provides a context manager

for limiting your changes to a specific scope. To isolate your styling

changes, you can write something like the following:

<div class="highlight-default"><div class="highlight"><pre>>>> import numpy as np

>>> import matplotlib.pyplot as plt

>>>

>>> with plt.style.context(('dark_background')):

>>> plt.plot(np.sin(np.linspace(0, 2 * np.pi)), 'r-o')

>>>

>>> # Some plotting code with the default style

>>>

>>> plt.show()

</pre></div>

</div>

<h2>matplotlib rcParams<a class="headerlink" href="#matplotlib-rcparams" title="Permalink to this headline">¶</a></h2>

<h3>Dynamic rc settings<a class="headerlink" href="#dynamic-rc-settings" title="Permalink to this headline">¶</a></h3>

You can also dynamically change the default rc settings in a python script or

interactively from the python shell. All of the rc settings are stored in a

dictionary-like variable called <a class="reference internal" href="../api/matplotlib_configuration_api.html#matplotlib.rcParams" title="matplotlib.rcParams"><code class="xref py py-data docutils literal">matplotlib.rcParams</code></a>, which is global to

the matplotlib package. rcParams can be modified directly, for example:

<div class="highlight-default"><div class="highlight"><pre>import matplotlib as mpl

mpl.rcParams['lines.linewidth'] = 2

mpl.rcParams['lines.color'] = 'r'

</pre></div>

</div>

Matplotlib also provides a couple of convenience functions for modifying rc

settings. The <a class="reference internal" href="../api/matplotlib_configuration_api.html#matplotlib.rc" title="matplotlib.rc"><code class="xref py py-func docutils literal">matplotlib.rc()</code></a> command can be used to modify multiple

settings in a single group at once, using keyword arguments:

<div class="highlight-default"><div class="highlight"><pre>import matplotlib as mpl

mpl.rc('lines', linewidth=2, color='r')

</pre></div>

</div>

The <code class="xref py py-func docutils literal">matplotlib.rcdefaults()</code> command will restore the standard matplotlib

default settings.

There is some degree of validation when setting the values of rcParams, see

<a class="reference internal" href="../api/rcsetup_api.html#module-matplotlib.rcsetup" title="matplotlib.rcsetup"><code class="xref py py-mod docutils literal">matplotlib.rcsetup</code></a> for details.

</div>

<h3>The <code class="file docutils literal">matplotlibrc</code> file<a class="headerlink" href="#the-matplotlibrc-file" title="Permalink to this headline">¶</a></h3>

matplotlib uses <code class="file docutils literal">matplotlibrc</code> configuration files to customize all kinds

of properties, which we call <code class="xref py py-obj docutils literal">rc settings</code> or <code class="xref py py-obj docutils literal">rc parameters</code>. You can control

the defaults of almost every property in matplotlib: figure size and dpi, line

width, color and style, axes, axis and grid properties, text and font

properties and so on. matplotlib looks for <code class="file docutils literal">matplotlibrc</code> in four

locations, in the following order:

<li><code class="file docutils literal">matplotlibrc</code> in the current working directory, usually used for

specific customizations that you do not want to apply elsewhere.

</li>

<li><code class="file docutils literal">$MATPLOTLIBRC/matplotlibrc</code>.

</li>

<li>It next looks in a user-specific place, depending on your platform:

<li>On Linux, it looks in <code class="file docutils literal">.config/matplotlib/matplotlibrc</code> (or

<code class="xref py py-obj docutils literal">$XDG_CONFIG_HOME/matplotlib/matplotlibrc</code>) if you’ve customized

your environment.</li>

<li>On other platforms, it looks in <code class="file docutils literal">.matplotlib/matplotlibrc</code>.</li>

</ul>

See <a class="reference internal" href="../faq/troubleshooting_faq.html#locating-matplotlib-config-dir">matplotlib configuration and cache directory locations</a>.

</li>

<li><code class="file docutils literal">INSTALL/matplotlib/mpl-data/matplotlibrc</code>, where

<code class="file docutils literal">INSTALL</code> is something like

<code class="file docutils literal">/usr/lib/python3.5/site-packages</code> on Linux, and maybe

<code class="file docutils literal">C:\Python35\Lib\site-packages</code> 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

user-specific matplotlib directory.

</li>

</ol>

To display where the currently active <code class="file docutils literal">matplotlibrc</code> file was

loaded from, one can do the following:

<div class="highlight-default"><div class="highlight"><pre>>>> import matplotlib

>>> matplotlib.matplotlib_fname()

'/home/foo/.config/matplotlib/matplotlibrc'

</pre></div>

</div>

See below for a sample <a class="reference internal" href="#matplotlibrc-sample">matplotlibrc file</a>.

<h4>A sample matplotlibrc file<a class="headerlink" href="#a-sample-matplotlibrc-file" title="Permalink to this headline">¶</a></h4>

<a class="reference external" href="../_static/matplotlibrc">(download)</a><div class="highlight-default"><div class="highlight"><pre>### MATPLOTLIBRC FORMAT

# This is a sample matplotlib configuration file - you can find a copy

# of it on your system in

# site-packages/matplotlib/mpl-data/matplotlibrc. If you edit it

# there, please note that it will be overwritten in your next install.

# If you want to keep a permanent local copy that will not be

# overwritten, place it in the following location:

# unix/linux:

# $HOME/.config/matplotlib/matplotlibrc or

# $XDG_CONFIG_HOME/matplotlib/matplotlibrc (if $XDG_CONFIG_HOME is set)

# other platforms:

# $HOME/.matplotlib/matplotlibrc

#

# See http://matplotlib.org/users/customizing.html#the-matplotlibrc-file for

# more details on the paths which are checked for the configuration file.

#

# This file is best viewed in a editor which supports python mode

# syntax highlighting. Blank lines, or lines starting with a comment

# symbol, are ignored, as are trailing comments. Other lines must

# have the format

# key : val # optional comment

#

# Colors: for the color values below, you can either use - a

# matplotlib color string, such as r, k, or b - an rgb tuple, such as

# (1.0, 0.5, 0.0) - a hex string, such as ff00ff - a scalar

# grayscale intensity such as 0.75 - a legal html color name, e.g., red,

# blue, darkslategray

#### CONFIGURATION BEGINS HERE

# The default backend; one of GTK GTKAgg GTKCairo GTK3Agg GTK3Cairo

# MacOSX Qt4Agg Qt5Agg TkAgg WX WXAgg Agg Cairo GDK PS PDF SVG

# Template.

# You can also deploy your own backend outside of matplotlib by

# referring to the module name (which must be in the PYTHONPATH) as

# 'module://my_backend'.

backend : qt5agg

# If you are using the Qt4Agg backend, you can choose here

# to use the PyQt4 bindings or the newer PySide bindings to

# the underlying Qt4 toolkit.

#backend.qt4 : PyQt4 # PyQt4 | PySide

# Note that this can be overridden by the environment variable

# QT_API used by Enthought Tool Suite (ETS); valid values are

# "pyqt" and "pyside". The "pyqt" setting has the side effect of

# forcing the use of Version 2 API for QString and QVariant.

# The port to use for the web server in the WebAgg backend.

# webagg.port : 8888

# If webagg.port is unavailable, a number of other random ports will

# be tried until one that is available is found.

# webagg.port_retries : 50

# When True, open the webbrowser to the plot that is shown

# webagg.open_in_browser : True

# When True, the figures rendered in the nbagg backend are created with

# a transparent background.

# nbagg.transparent : False

# if you are running pyplot inside a GUI and your backend choice

# conflicts, we will automatically try to find a compatible one for

# you if backend_fallback is True

#backend_fallback: True

#interactive : False

#toolbar : toolbar2 # None | toolbar2 ("classic" is deprecated)

#timezone : UTC # a pytz timezone string, e.g., US/Central or Europe/Paris

# Where your matplotlib data lives if you installed to a non-default

# location. This is where the matplotlib fonts, bitmaps, etc reside

#datapath : /home/jdhunter/mpldata

### LINES

# See http://matplotlib.org/api/artist_api.html#module-matplotlib.lines for more

# information on line properties.

#lines.linewidth : 1.5 # line width in points

#lines.linestyle : - # solid line

#lines.color : C0 # has no affect on plot(); see axes.prop_cycle

#lines.marker : None # the default marker

#lines.markeredgewidth : 1.0 # the line width around the marker symbol

#lines.markersize : 6 # markersize, in points

#lines.dash_joinstyle : miter # miter|round|bevel

#lines.dash_capstyle : butt # butt|round|projecting

#lines.solid_joinstyle : miter # miter|round|bevel

#lines.solid_capstyle : projecting # butt|round|projecting

#lines.antialiased : True # render lines in antialiased (no jaggies)

# The three standard dash patterns. These are scaled by the linewidth.

#lines.dashed_pattern : 2.8, 1.2

#lines.dashdot_pattern : 4.8, 1.2, 0.8, 1.2

#lines.dotted_pattern : 1.1, 1.1

#lines.scale_dashes : True

### PATCHES

# Patches are graphical objects that fill 2D space, like polygons or

# circles. See

# http://matplotlib.org/api/artist_api.html#module-matplotlib.patches

# information on patch properties

#patch.linewidth : 1 # edge width in points.

#patch.facecolor : C0

#patch.edgecolor : black # if forced, or patch is not filled

#patch.force_edgecolor : False # True to always use edgecolor

#patch.antialiased : True # render patches in antialiased (no jaggies)

### HATCHES

#hatch.color : k

#hatch.linewidth : 1.0

### Boxplot

#boxplot.notch : False

#boxplot.vertical : True

#boxplot.whiskers : 1.5

#boxplot.bootstrap : None

#boxplot.patchartist : False

#boxplot.showmeans : False

#boxplot.showcaps : True

#boxplot.showbox : True

#boxplot.showfliers : True

#boxplot.meanline : False

#boxplot.flierprops.color : 'k'

#boxplot.flierprops.marker : 'o'

#boxplot.flierprops.markerfacecolor : 'none'

#boxplot.flierprops.markeredgecolor : 'k'

#boxplot.flierprops.markersize : 6

#boxplot.flierprops.linestyle : 'none'

#boxplot.flierprops.linewidth : 1.0

#boxplot.boxprops.color : 'k'

#boxplot.boxprops.linewidth : 1.0

#boxplot.boxprops.linestyle : '-'

#boxplot.whiskerprops.color : 'k'

#boxplot.whiskerprops.linewidth : 1.0

#boxplot.whiskerprops.linestyle : '-'

#boxplot.capprops.color : 'k'

#boxplot.capprops.linewidth : 1.0

#boxplot.capprops.linestyle : '-'

#boxplot.medianprops.color : 'C1'

#boxplot.medianprops.linewidth : 1.0

#boxplot.medianprops.linestyle : '-'

#boxplot.meanprops.color : 'C2'

#boxplot.meanprops.marker : '^'

#boxplot.meanprops.markerfacecolor : 'C2'

#boxplot.meanprops.markeredgecolor : 'C2'

#boxplot.meanprops.markersize : 6

#boxplot.meanprops.linestyle : 'none'

#boxplot.meanprops.linewidth : 1.0

### FONT

#

# font properties used by text.Text. See

# http://matplotlib.org/api/font_manager_api.html for more

# information on font properties. The 6 font properties used for font

# matching are given below with their default values.

#

# The font.family property has five values: 'serif' (e.g., Times),

# 'sans-serif' (e.g., Helvetica), 'cursive' (e.g., Zapf-Chancery),

# 'fantasy' (e.g., Western), and 'monospace' (e.g., Courier). Each of

# these font families has a default list of font names in decreasing

# order of priority associated with them. When text.usetex is False,

# font.family may also be one or more concrete font names.

#

# The font.style property has three values: normal (or roman), italic

# or oblique. The oblique style will be used for italic, if it is not

# present.

#

# The font.variant property has two values: normal or small-caps. For

# TrueType fonts, which are scalable fonts, small-caps is equivalent

# to using a font size of 'smaller', or about 83%% of the current font

# size.

#

# The font.weight property has effectively 13 values: normal, bold,

# bolder, lighter, 100, 200, 300, ..., 900. Normal is the same as

# 400, and bold is 700. bolder and lighter are relative values with

# respect to the current weight.

#

# The font.stretch property has 11 values: ultra-condensed,

# extra-condensed, condensed, semi-condensed, normal, semi-expanded,

# expanded, extra-expanded, ultra-expanded, wider, and narrower. This

# property is not currently implemented.

#

# The font.size property is the default font size for text, given in pts.

# 10 pt is the standard value.

#

#font.family : sans-serif

#font.style : normal

#font.variant : normal

#font.weight : medium

#font.stretch : normal

# note that font.size controls default text sizes. To configure

# special text sizes tick labels, axes, labels, title, etc, see the rc

# settings for axes and ticks. Special text sizes can be defined

# relative to font.size, using the following values: xx-small, x-small,

# small, medium, large, x-large, xx-large, larger, or smaller

#font.size : 10.0

#font.serif : DejaVu 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

#font.sans-serif : DejaVu Sans, Bitstream Vera Sans, Lucida Grande, Verdana, Geneva, Lucid, Arial, Helvetica, Avant Garde, sans-serif

#font.cursive : Apple Chancery, Textile, Zapf Chancery, Sand, Script MT, Felipa, cursive

#font.fantasy : Comic Sans MS, Chicago, Charcoal, Impact, Western, Humor Sans, xkcd, fantasy

#font.monospace : DejaVu Sans Mono, Bitstream Vera Sans Mono, Andale Mono, Nimbus Mono L, Courier New, Courier, Fixed, Terminal, monospace

### TEXT

# text properties used by text.Text. See

# http://matplotlib.org/api/artist_api.html#module-matplotlib.text for more

# information on text properties

#text.color : black

### LaTeX customizations. See http://wiki.scipy.org/Cookbook/Matplotlib/UsingTex

#text.usetex : False # use latex for all text handling. The following fonts

# are supported through the usual rc parameter settings:

# new century schoolbook, bookman, times, palatino,

# zapf chancery, charter, serif, sans-serif, helvetica,

# avant garde, courier, monospace, computer modern roman,

# computer modern sans serif, computer modern typewriter

# If another font is desired which can loaded using the

# LaTeX \usepackage command, please inquire at the

# matplotlib mailing list

#text.latex.unicode : False # use "ucs" and "inputenc" LaTeX packages for handling

# unicode strings.

#text.latex.preamble : # IMPROPER USE OF THIS FEATURE WILL LEAD TO LATEX FAILURES

# AND IS THEREFORE UNSUPPORTED. PLEASE DO NOT ASK FOR HELP

# IF THIS FEATURE DOES NOT DO WHAT YOU EXPECT IT TO.

# preamble is a comma separated list of LaTeX statements

# that are included in the LaTeX document preamble.

# An example:

# text.latex.preamble : \usepackage{bm},\usepackage{euler}

# The following packages are always loaded with usetex, so

# beware of package collisions: color, geometry, graphicx,

# type1cm, textcomp. Adobe Postscript (PSSNFS) font packages

# may also be loaded, depending on your font settings

#text.dvipnghack : None # some versions of dvipng don't handle alpha

# channel properly. Use True to correct

# and flush ~/.matplotlib/tex.cache

# before testing and False to force

# correction off. None will try and

# guess based on your dvipng version

#text.hinting : auto # May be one of the following:

# 'none': Perform no hinting

# 'auto': Use FreeType's autohinter

# 'native': Use the hinting information in the

# font file, if available, and if your

# FreeType library supports it

# 'either': Use the native hinting information,

# or the autohinter if none is available.

# For backward compatibility, this value may also be

# True === 'auto' or False === 'none'.

#text.hinting_factor : 8 # Specifies the amount of softness for hinting in the

# horizontal direction. A value of 1 will hint to full

# pixels. A value of 2 will hint to half pixels etc.

#text.antialiased : True # If True (default), the text will be antialiased.

# This only affects the Agg backend.

# The following settings allow you to select the fonts in math mode.

# They map from a TeX font name to a fontconfig font pattern.

# These settings are only used if mathtext.fontset is 'custom'.

# Note that this "custom" mode is unsupported and may go away in the

# future.

#mathtext.cal : cursive

#mathtext.rm : serif

#mathtext.tt : monospace

#mathtext.it : serif:italic

#mathtext.bf : serif:bold

#mathtext.sf : sans

#mathtext.fontset : dejavusans # Should be 'dejavusans' (default),

# 'dejavuserif', 'cm' (Computer Modern), 'stix',

# 'stixsans' or 'custom'

#mathtext.fallback_to_cm : True # When True, use symbols from the Computer Modern

# fonts when a symbol can not be found in one of

# the custom math fonts.

#mathtext.default : it # The default font to use for math.

# Can be any of the LaTeX font names, including

# the special name "regular" for the same font

# used in regular text.

### AXES

# default face and edge color, default tick sizes,

# default fontsizes for ticklabels, and so on. See

# http://matplotlib.org/api/axes_api.html#module-matplotlib.axes

#axes.facecolor : white # axes background color

#axes.edgecolor : black # axes edge color

#axes.linewidth : 0.8 # edge linewidth

#axes.grid : False # display grid or not

#axes.titlesize : large # fontsize of the axes title

#axes.titlepad : 6.0 # pad between axes and title in points

#axes.labelsize : medium # fontsize of the x any y labels

#axes.labelpad : 4.0 # space between label and axis

#axes.labelweight : normal # weight of the x and y labels

#axes.labelcolor : black

#axes.axisbelow : 'line' # draw axis gridlines and ticks below

# patches (True); above patches but below

# lines ('line'); or above all (False)

#axes.formatter.limits : -7, 7 # use scientific notation if log10

# of the axis range is smaller than the

# first or larger than the second

#axes.formatter.use_locale : False # When True, format tick labels

# according to the user's locale.

# For example, use ',' as a decimal

# separator in the fr_FR locale.

#axes.formatter.use_mathtext : False # When True, use mathtext for scientific

# notation.

#axes.formatter.useoffset : True # If True, the tick label formatter

# will default to labeling ticks relative

# to an offset when the data range is

# small compared to the minimum absolute

# value of the data.

#axes.formatter.offset_threshold : 4 # When useoffset is True, the offset

# will be used when it can remove

# at least this number of significant

# digits from tick labels.

# axes.spines.left : True # display axis spines

# axes.spines.bottom : True

# axes.spines.top : True

# axes.spines.right : True

#axes.unicode_minus : True # use unicode for the minus symbol

# rather than hyphen. See

# http://en.wikipedia.org/wiki/Plus_and_minus_signs#Character_codes

#axes.prop_cycle : cycler('color',

# ['1f77b4', 'ff7f0e', '2ca02c', 'd62728',

# '9467bd', '8c564b', 'e377c2', '7f7f7f',

# 'bcbd22', '17becf'])

# color cycle for plot lines

# as list of string colorspecs:

# single letter, long name, or

# web-style hex

#axes.autolimit_mode : data # How to scale axes limits to the data.

# Use "data" to use data limits, plus some margin

# Use "round_number" move to the nearest "round" number

#axes.xmargin : .05 # x margin. See `axes.Axes.margins`

#axes.ymargin : .05 # y margin See `axes.Axes.margins`

#polaraxes.grid : True # display grid on polar axes

#axes3d.grid : True # display grid on 3d axes

### DATES

# These control the default format strings used in AutoDateFormatter.

# Any valid format datetime format string can be used (see the python

# `datetime` for details). For example using '%%x' will use the locale date representation

# '%%X' will use the locale time representation and '%%c' will use the full locale datetime

# representation.

# These values map to the scales:

# {'year': 365, 'month': 30, 'day': 1, 'hour': 1/24, 'minute': 1 / (24 * 60)}

# date.autoformatter.year : %Y

# date.autoformatter.month : %Y-%m

# date.autoformatter.day : %Y-%m-%d

# date.autoformatter.hour : %m-%d %H

# date.autoformatter.minute : %d %H:%M

# date.autoformatter.second : %H:%M:%S

# date.autoformatter.microsecond : %M:%S.%f

### TICKS

# see http://matplotlib.org/api/axis_api.html#matplotlib.axis.Tick

#xtick.top : False # draw ticks on the top side

#xtick.bottom : True # draw ticks on the bottom side

#xtick.major.size : 3.5 # major tick size in points

#xtick.minor.size : 2 # minor tick size in points

#xtick.major.width : 0.8 # major tick width in points

#xtick.minor.width : 0.6 # minor tick width in points

#xtick.major.pad : 3.5 # distance to major tick label in points

#xtick.minor.pad : 3.4 # distance to the minor tick label in points

#xtick.color : k # color of the tick labels

#xtick.labelsize : medium # fontsize of the tick labels

#xtick.direction : out # direction: in, out, or inout

#xtick.minor.visible : False # visibility of minor ticks on x-axis

#xtick.major.top : True # draw x axis top major ticks

#xtick.major.bottom : True # draw x axis bottom major ticks

#xtick.minor.top : True # draw x axis top minor ticks

#xtick.minor.bottom : True # draw x axis bottom minor ticks

#ytick.left : True # draw ticks on the left side

#ytick.right : False # draw ticks on the right side

#ytick.major.size : 3.5 # major tick size in points

#ytick.minor.size : 2 # minor tick size in points

#ytick.major.width : 0.8 # major tick width in points

#ytick.minor.width : 0.6 # minor tick width in points

#ytick.major.pad : 3.5 # distance to major tick label in points

#ytick.minor.pad : 3.4 # distance to the minor tick label in points

#ytick.color : k # color of the tick labels

#ytick.labelsize : medium # fontsize of the tick labels

#ytick.direction : out # direction: in, out, or inout

#ytick.minor.visible : False # visibility of minor ticks on y-axis

#xtick.major.left : True # draw y axis left major ticks

#xtick.major.right : True # draw y axis right major ticks

#xtick.minor.left : True # draw y axis left minor ticks

#xtick.minor.right : True # draw y axis right minor ticks

### GRIDS

#grid.color : b0b0b0 # grid color

#grid.linestyle : - # solid

#grid.linewidth : 0.8 # in points

#grid.alpha : 1.0 # transparency, between 0.0 and 1.0

### Legend

#legend.loc : best

#legend.frameon : True # if True, draw the legend on a background patch

#legend.framealpha : 0.8 # legend patch transparency

#legend.facecolor : inherit # inherit from axes.facecolor; or color spec

#legend.edgecolor : 0.8 # background patch boundary color

#legend.fancybox : True # if True, use a rounded box for the

# legend background, else a rectangle

#legend.shadow : False # if True, give background a shadow effect

#legend.numpoints : 1 # the number of marker points in the legend line

#legend.scatterpoints : 1 # number of scatter points

#legend.markerscale : 1.0 # the relative size of legend markers vs. original

#legend.fontsize : medium

# Dimensions as fraction of fontsize:

#legend.borderpad : 0.4 # border whitespace

#legend.labelspacing : 0.5 # the vertical space between the legend entries

#legend.handlelength : 2.0 # the length of the legend lines

#legend.handleheight : 0.7 # the height of the legend handle

#legend.handletextpad : 0.8 # the space between the legend line and legend text

#legend.borderaxespad : 0.5 # the border between the axes and legend edge

#legend.columnspacing : 2.0 # column separation

### FIGURE

# See http://matplotlib.org/api/figure_api.html#matplotlib.figure.Figure

#figure.titlesize : large # size of the figure title (Figure.suptitle())

#figure.titleweight : normal # weight of the figure title

#figure.figsize : 6.4, 4.8 # figure size in inches

#figure.dpi : 100 # figure dots per inch

#figure.facecolor : white # figure facecolor; 0.75 is scalar gray

#figure.edgecolor : white # figure edgecolor

#figure.autolayout : False # When True, automatically adjust subplot

# parameters to make the plot fit the figure

#figure.max_open_warning : 20 # The maximum number of figures to open through

# the pyplot interface before emitting a warning.

# If less than one this feature is disabled.

# The figure subplot parameters. All dimensions are a fraction of the

#figure.subplot.left : 0.125 # the left side of the subplots of the figure

#figure.subplot.right : 0.9 # the right side of the subplots of the figure

#figure.subplot.bottom : 0.11 # the bottom of the subplots of the figure

#figure.subplot.top : 0.88 # the top of the subplots of the figure

#figure.subplot.wspace : 0.2 # the amount of width reserved for blank space between subplots,

# expressed as a fraction of the average axis width

#figure.subplot.hspace : 0.2 # the amount of height reserved for white space between subplots,

# expressed as a fraction of the average axis height

### IMAGES

#image.aspect : equal # equal | auto | a number

#image.interpolation : nearest # see help(imshow) for options

#image.cmap : viridis # A colormap name, gray etc...

#image.lut : 256 # the size of the colormap lookup table

#image.origin : upper # lower | upper

#image.resample : True

#image.composite_image : True # When True, all the images on a set of axes are

# combined into a single composite image before

# saving a figure as a vector graphics file,

# such as a PDF.

### CONTOUR PLOTS

#contour.negative_linestyle : dashed # dashed | solid

#contour.corner_mask : True # True | False | legacy

### ERRORBAR PLOTS

#errorbar.capsize : 0 # length of end cap on error bars in pixels

### HISTOGRAM PLOTS

#hist.bins : 10 # The default number of histogram bins.

# If Numpy 1.11 or later is

# installed, may also be `auto`

### SCATTER PLOTS

#scatter.marker : o # The default marker type for scatter plots.

### Agg rendering

### Warning: experimental, 2008/10/10

#agg.path.chunksize : 0 # 0 to disable; values in the range

# 10000 to 100000 can improve speed slightly

# and prevent an Agg rendering failure

# when plotting very large data sets,

# especially if they are very gappy.

# It may cause minor artifacts, though.

# A value of 20000 is probably a good

# starting point.

### SAVING FIGURES

#path.simplify : True # When True, simplify paths by removing "invisible"

# points to reduce file size and increase rendering

# speed

#path.simplify_threshold : 0.1 # The threshold of similarity below which

# vertices will be removed in the simplification

# process

#path.snap : True # When True, rectilinear axis-aligned paths will be snapped to

# the nearest pixel when certain criteria are met. When False,

# paths will never be snapped.

#path.sketch : None # May be none, or a 3-tuple of the form (scale, length,

# randomness).

# *scale* is the amplitude of the wiggle

# perpendicular to the line (in pixels). *length*

# is the length of the wiggle along the line (in

# pixels). *randomness* is the factor by which

# the length is randomly scaled.

# the default savefig params can be different from the display params

# e.g., you may want a higher resolution, or to make the figure

# background white

#savefig.dpi : figure # figure dots per inch or 'figure'

#savefig.facecolor : white # figure facecolor when saving

#savefig.edgecolor : white # figure edgecolor when saving

#savefig.format : png # png, ps, pdf, svg

#savefig.bbox : standard # 'tight' or 'standard'.

# 'tight' is incompatible with pipe-based animation

# backends but will workd with temporary file based ones:

# e.g. setting animation.writer to ffmpeg will not work,

# use ffmpeg_file instead

#savefig.pad_inches : 0.1 # Padding to be used when bbox is set to 'tight'

#savefig.jpeg_quality: 95 # when a jpeg is saved, the default quality parameter.

#savefig.directory : ~ # default directory in savefig dialog box,

# leave empty to always use current working directory

#savefig.transparent : False # setting that controls whether figures are saved with a

# transparent background by default

# tk backend params

#tk.window_focus : False # Maintain shell focus for TkAgg

# ps backend params

#ps.papersize : letter # auto, letter, legal, ledger, A0-A10, B0-B10

#ps.useafm : False # use of afm fonts, results in small files

#ps.usedistiller : False # can be: None, ghostscript or xpdf

# Experimental: may produce smaller files.

# xpdf intended for production of publication quality files,

# but requires ghostscript, xpdf and ps2eps

#ps.distiller.res : 6000 # dpi

#ps.fonttype : 3 # Output Type 3 (Type3) or Type 42 (TrueType)

# pdf backend params

#pdf.compression : 6 # integer from 0 to 9

# 0 disables compression (good for debugging)

#pdf.fonttype : 3 # Output Type 3 (Type3) or Type 42 (TrueType)

# svg backend params

#svg.image_inline : True # write raster image data directly into the svg file

#svg.fonttype : 'path' # How to handle SVG fonts:

# 'none': Assume fonts are installed on the machine where the SVG will be viewed.

# 'path': Embed characters as paths -- supported by most SVG renderers

# 'svgfont': Embed characters as SVG fonts -- supported only by Chrome,

# Opera and Safari

#svg.hashsalt : None # if not None, use this string as hash salt

# instead of uuid4

# docstring params

#docstring.hardcopy = False # set this when you want to generate hardcopy docstring

# Set the verbose flags. This controls how much information

# matplotlib gives you at runtime and where it goes. The verbosity

# levels are: silent, helpful, debug, debug-annoying. Any level is

# inclusive of all the levels below it. If your setting is "debug",

# you'll get all the debug and helpful messages. When submitting

# problems to the mailing-list, please set verbose to "helpful" or "debug"

# and paste the output into your report.

#

# The "fileo" gives the destination for any calls to verbose.report.

# These objects can a filename, or a filehandle like sys.stdout.

#

# You can override the rc default verbosity from the command line by

# giving the flags --verbose-LEVEL where LEVEL is one of the legal

# levels, e.g., --verbose-helpful.

#

# You can access the verbose instance in your code

# from matplotlib import verbose.

#verbose.level : silent # one of silent, helpful, debug, debug-annoying

#verbose.fileo : sys.stdout # a log filename, sys.stdout or sys.stderr

# Event keys to interact with figures/plots via keyboard.

# Customize these settings according to your needs.

# Leave the field(s) empty if you don't need a key-map. (i.e., fullscreen : '')

#keymap.fullscreen : f, ctrl+f # toggling

#keymap.home : h, r, home # home or reset mnemonic

#keymap.back : left, c, backspace # forward / backward keys to enable

#keymap.forward : right, v # left handed quick navigation

#keymap.pan : p # pan mnemonic

#keymap.zoom : o # zoom mnemonic

#keymap.save : s # saving current figure

#keymap.quit : ctrl+w, cmd+w # close the current figure

#keymap.grid : g # switching on/off a grid in current axes

#keymap.yscale : l # toggle scaling of y-axes ('log'/'linear')

#keymap.xscale : L, k # toggle scaling of x-axes ('log'/'linear')

#keymap.all_axes : a # enable all axes

# Control location of examples data files

#examples.directory : '' # directory to look in for custom installation

###ANIMATION settings

#animation.html : 'none' # How to display the animation as HTML in

# the IPython notebook. 'html5' uses

# HTML5 video tag.

#animation.writer : ffmpeg # MovieWriter 'backend' to use

#animation.codec : h264 # Codec to use for writing movie

#animation.bitrate: -1 # Controls size/quality tradeoff for movie.

# -1 implies let utility auto-determine

#animation.frame_format: 'png' # Controls frame format used by temp files

#animation.ffmpeg_path: 'ffmpeg' # Path to ffmpeg binary. Without full path

# $PATH is searched

#animation.ffmpeg_args: '' # Additional arguments to pass to ffmpeg

#animation.avconv_path: 'avconv' # Path to avconv binary. Without full path

# $PATH is searched

#animation.avconv_args: '' # Additional arguments to pass to avconv

#animation.mencoder_path: 'mencoder'

# Path to mencoder binary. Without full path

# $PATH is searched

#animation.mencoder_args: '' # Additional arguments to pass to mencoder

#animation.convert_path: 'convert' # Path to ImageMagick's convert binary.

# On Windows use the full path since convert

# is also the name of a system tool.

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