testbed/matplotlib__matplotlib/galleries/users_explain/customizing.py

"""
.. redirect-from:: /users/customizing
.. redirect-from:: /tutorials/introductory/customizing

.. _customizing:

=====================================================
Customizing Matplotlib with style sheets and rcParams
=====================================================

Tips for customizing the properties and default styles of Matplotlib.

There are three ways to customize Matplotlib:

1. :ref:`Setting rcParams at runtime<customizing-with-dynamic-rc-settings>`.
2. :ref:`Using style sheets<customizing-with-style-sheets>`.
3. :ref:`Changing your matplotlibrc file<customizing-with-matplotlibrc-files>`.

Setting rcParams at runtime takes precedence over style sheets, style
sheets take precedence over :file:`matplotlibrc` files.

.. _customizing-with-dynamic-rc-settings:

Runtime rc settings
===================

You can dynamically change the default rc (runtime configuration)
settings in a python script or interactively from the python shell. All
rc settings are stored in a dictionary-like variable called
:data:`matplotlib.rcParams`, which is global to the matplotlib package.
See `matplotlib.rcParams` for a full list of configurable rcParams.
rcParams can be modified directly, for example:
"""

from cycler import cycler

import matplotlib.pyplot as plt
import numpy as np

import matplotlib as mpl

mpl.rcParams['lines.linewidth'] = 2
mpl.rcParams['lines.linestyle'] = '--'
data = np.random.randn(50)
plt.plot(data)

# %%
# Note, that in order to change the usual `~.Axes.plot` color you have to
# change the *prop_cycle* property of *axes*:

mpl.rcParams['axes.prop_cycle'] = cycler(color=['r', 'g', 'b', 'y'])
plt.plot(data)  # first color is red

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

mpl.rc('lines', linewidth=4, linestyle='-.')
plt.plot(data)

# %%
# Temporary rc settings
# ---------------------
#
# The :data:`matplotlib.rcParams` object can also be changed temporarily using
# the `matplotlib.rc_context` context manager:

with mpl.rc_context({'lines.linewidth': 2, 'lines.linestyle': ':'}):
    plt.plot(data)

# %%
# `matplotlib.rc_context` can also be used as a decorator to modify the
# defaults within a function:


@mpl.rc_context({'lines.linewidth': 3, 'lines.linestyle': '-'})
def plotting_function():
    plt.plot(data)

plotting_function()

# %%
# `matplotlib.rcdefaults` 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.

# %%
# .. _customizing-with-style-sheets:
#
# Using style sheets
# ==================
#
# Another way to change the visual appearance of plots is to set the
# rcParams in a so-called style sheet and import that style sheet with
# `matplotlib.style.use`. In this way you can switch easily between
# different styles by simply changing the imported style sheet. A style
# sheets looks the same as a :ref:`matplotlibrc<matplotlibrc-sample>`
# file, but in a style sheet you can only set rcParams that are related
# to the actual style of a plot. Other rcParams, like *backend*, will be
# ignored. :file:`matplotlibrc` files support all rcParams. The
# rationale behind this is to make style sheets portable between
# different machines without having to worry about dependencies which
# might or might not be installed on another machine. For a full list of
# rcParams see `matplotlib.rcParams`. For a list of rcParams that are
# ignored in style sheets see `matplotlib.style.use`.
#
# There are a number of pre-defined styles :doc:`provided by Matplotlib
# </gallery/style_sheets/style_sheets_reference>`. 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, add:

plt.style.use('ggplot')

# %%
# To list all available styles, use:

print(plt.style.available)

# %%
# Defining your own style
# -----------------------
#
# You can create custom styles and use them by calling `.style.use` with
# the path or URL to the style sheet.
#
# For example, you might want to create
# ``./images/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('./images/presentation.mplstyle')
#
#
# Distributing styles
# -------------------
#
# You can include style sheets into standard importable Python packages (which
# can be e.g. distributed on PyPI).  If your package is importable as
# ``import mypackage``, with a ``mypackage/__init__.py`` module, and you add
# a ``mypackage/presentation.mplstyle`` style sheet, then it can be used as
# ``plt.style.use("mypackage.presentation")``.  Subpackages (e.g.
# ``dotted.package.name``) are also supported.
#
# Alternatively, you can make your style known to Matplotlib by placing
# your ``<style-name>.mplstyle`` file into ``mpl_configdir/stylelib``.  You
# can then load 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 :envvar:`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.
#
# Once your ``<style-name>.mplstyle`` file is in the appropriate
# ``mpl_configdir`` you can specify your style with::
#
#    >>> import matplotlib.pyplot as plt
#    >>> plt.style.use(<style-name>)
#
#
# Composing styles
# ----------------
#
# 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.
#
#
# Temporary styling
# -----------------
#
# 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:

with plt.style.context('dark_background'):
    plt.plot(np.sin(np.linspace(0, 2 * np.pi)), 'r-o')
plt.show()

# %%
# .. _customizing-with-matplotlibrc-files:
#
# The :file:`matplotlibrc` file
# =============================
#
# 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. The :file:`matplotlibrc` is read at startup to
# configure Matplotlib. 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 and FreeBSD, it looks in
#      :file:`.config/matplotlib/matplotlibrc` (or
#      :file:`$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/python3.9/site-packages` on Linux, and maybe
#    :file:`C:\\Python39\\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.
#
# Once a :file:`matplotlibrc` file has been found, it will *not* search
# any of the other paths. When a
# :ref:`style sheet<customizing-with-style-sheets>` is given with
# ``style.use('<path>/<style-name>.mplstyle')``, settings specified in
# the style sheet take precedence over settings in the
# :file:`matplotlibrc` file.
#
# 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>`
# and see `matplotlib.rcParams` for a full list of configurable rcParams.
#
# .. _matplotlibrc-sample:
#
# The default :file:`matplotlibrc` file
# -------------------------------------
#
# .. literalinclude:: ../../../lib/matplotlib/mpl-data/matplotlibrc
#
#
# .. _ggplot: https://ggplot2.tidyverse.org/
# .. _R: https://www.r-project.org/