The style package adds support for easy-to-switch plotting "styles" with the same parameters as a matplotlibrc 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 ggplot (a popular plotting package for R). To use this style, just add:

>>> import matplotlib.pyplot as plt
>>> plt.style.use('ggplot')

To list all available styles, use:

>>> print(plt.style.available)

You can create custom styles and use them by calling style.use with the path or URL to the style sheet. Additionally, if you add your <style-name>.mplstyle file to mpl_configdir/stylelib, you can reuse your custom style sheet with a call to style.use(<style-name>). By default mpl_configdir should be ~/.config/matplotlib, but you can check where yours is with matplotlib.get_configdir(); 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 :ref:`locating-matplotlib-config-dir`.

Note that a custom style sheet in mpl_configdir/stylelib will override a style sheet defined by matplotlib if the styles have the same name.

For example, you might want to create mpl_configdir/stylelib/presentation.mplstyle with the following:

axes.titlesize : 24
axes.labelsize : 20
lines.linewidth : 3
lines.markersize : 10
xtick.labelsize : 16
ytick.labelsize : 16

Then, when you want to adapt a plot designed for a paper to one that looks good in a presentation, you can just add:

>>> import matplotlib.pyplot as plt
>>> plt.style.use('presentation')

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:

>>> import matplotlib.pyplot as plt
>>> plt.style.use(['dark_background', 'presentation'])

Note that styles further to the right will overwrite values that are already defined by styles on the left.

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:

>>> 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()

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 :data:`matplotlib.rcParams`, which is global to the matplotlib package. rcParams can be modified directly, for example:

import matplotlib as mpl
mpl.rcParams['lines.linewidth'] = 2
mpl.rcParams['lines.color'] = 'r'

Matplotlib also provides a couple of convenience functions for modifying rc settings. The :func:`matplotlib.rc` command can be used to modify multiple settings in a single group at once, using keyword arguments:

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

The :func:`matplotlib.rcdefaults` command will restore the standard matplotlib default settings.

There is some degree of validation when setting the values of rcParams, see :mod:`matplotlib.rcsetup` for details.

matplotlib uses :file:`matplotlibrc` configuration files to customize all kinds of properties, which we call rc settings or rc parameters. 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 :file:`matplotlibrc` in four locations, in the following order:

1. :file:`matplotlibrc` in the current working directory, usually used for specific customizations that you do not want to apply elsewhere.

2. :file:`$MATPLOTLIBRC` if it is a file, else :file:`$MATPLOTLIBRC/matplotlibrc`.

3. It next looks in a user-specific place, depending on your platform:

   • On Linux, it looks in :file:`.config/matplotlib/matplotlibrc` (or $XDG_CONFIG_HOME/matplotlib/matplotlibrc) if you've customized your environment.
   • On other platforms, it looks in :file:`.matplotlib/matplotlibrc`.

   See :ref:`locating-matplotlib-config-dir`.

4. :file:`{INSTALL}/matplotlib/mpl-data/matplotlibrc`, where :file:`{INSTALL}` is something like :file:`/usr/lib/python3.5/site-packages` on Linux, and maybe :file:`C:\\Python35\\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 user-specific matplotlib directory.

To display where the currently active :file:`matplotlibrc` file was loaded from, one can do the following:

>>> import matplotlib
>>> matplotlib.matplotlib_fname()
'/home/foo/.config/matplotlib/matplotlibrc'

See below for a sample :ref:`matplotlibrc file<matplotlibrc-sample>`.

.. htmlonly::

    `(download) <../_static/matplotlibrc>`__

.. literalinclude:: ../_static/matplotlibrc