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Showing content from https://matplotlib.org/devdocs/devel/../install/../devel/../api/matplotlib_configuration_api.html below:

matplotlib — Matplotlib 3.11.0.dev1079+g1663e9af1b documentation

matplotlib#

An object-oriented plotting library.

A procedural interface is provided by the companion pyplot module, which may be imported directly, e.g.:

import matplotlib.pyplot as plt

or using ipython:

at your terminal, followed by:

In [1]: %matplotlib
In [2]: import matplotlib.pyplot as plt

at the ipython shell prompt.

For the most part, direct use of the explicit object-oriented library is encouraged when programming; the implicit pyplot interface is primarily for working interactively. The exceptions to this suggestion are the pyplot functions pyplot.figure, pyplot.subplot, pyplot.subplots, and pyplot.savefig, which can greatly simplify scripting. See Matplotlib Application Interfaces (APIs) for an explanation of the tradeoffs between the implicit and explicit interfaces.

Modules include:

matplotlib.axes

The Axes class. Most pyplot functions are wrappers for Axes methods. The axes module is the highest level of OO access to the library.

matplotlib.figure

The Figure class.

matplotlib.artist

The Artist base class for all classes that draw things.

matplotlib.lines

The Line2D class for drawing lines and markers.

matplotlib.patches

Classes for drawing polygons.

matplotlib.text

The Text and Annotation classes.

matplotlib.image

The AxesImage and FigureImage classes.

matplotlib.collections

Classes for efficient drawing of groups of lines or polygons.

matplotlib.colors

Color specifications and making colormaps.

matplotlib.cm

Colormaps, and the ScalarMappable mixin class for providing color mapping functionality to other classes.

matplotlib.ticker

Calculation of tick mark locations and formatting of tick labels.

matplotlib.backends

A subpackage with modules for various GUI libraries and output formats.

The base matplotlib namespace includes:

rcParams

Default configuration settings; their defaults may be overridden using a matplotlibrc file.

use

Setting the Matplotlib backend. This should be called before any figure is created, because it is not possible to switch between different GUI backends after that.

The following environment variables can be used to customize the behavior:

MPLBACKEND

This optional variable can be set to choose the Matplotlib backend. See What is a backend?.

MPLCONFIGDIR

This is the directory used to store user customizations to Matplotlib, as well as some caches to improve performance. If MPLCONFIGDIR is not defined, HOME/.config/matplotlib and HOME/.cache/matplotlib are used on Linux, and HOME/.matplotlib on other platforms, if they are writable. Otherwise, the Python standard library's tempfile.gettempdir is used to find a base directory in which the matplotlib subdirectory is created.

Matplotlib was initially written by John D. Hunter (1968-2012) and is now developed and maintained by a host of others.

Occasionally the internal documentation (python docstrings) will refer to MATLAB®, a registered trademark of The MathWorks, Inc.

Backend management#
matplotlib.use(backend, *, force=True)[source]#

Select the backend used for rendering and GUI integration.

If pyplot is already imported, switch_backend is used and if the new backend is different than the current backend, all Figures will be closed.

Parameters:
backendstr

The backend to switch to. This can either be one of the standard backend names, which are case-insensitive:

  • interactive backends: GTK3Agg, GTK3Cairo, GTK4Agg, GTK4Cairo, MacOSX, nbAgg, notebook, QtAgg, QtCairo, TkAgg, TkCairo, WebAgg, WX, WXAgg, WXCairo, Qt5Agg, Qt5Cairo

  • non-interactive backends: agg, cairo, pdf, pgf, ps, svg, template

or a string of the form: module://my.module.name.

notebook is a synonym for nbAgg.

Switching to an interactive backend is not possible if an unrelated event loop has already been started (e.g., switching to GTK3Agg if a TkAgg window has already been opened). Switching to a non-interactive backend is always possible.

forcebool, default: True

If True (the default), raise an ImportError if the backend cannot be set up (either because it fails to import, or because an incompatible GUI interactive framework is already running); if False, silently ignore the failure.

matplotlib.get_backend(*, auto_select=True)[source]#

Return the name of the current backend.

Parameters:
auto_selectbool, default: True

Whether to trigger backend resolution if no backend has been selected so far. If True, this ensures that a valid backend is returned. If False, this returns None if no backend has been selected so far.

Added in version 3.10.

Provisional

The auto_select flag is provisional. It may be changed or removed without prior warning.

matplotlib.interactive(b)[source]#

Set whether to redraw after every plotting command (e.g. pyplot.xlabel).

matplotlib.is_interactive()[source]#

Return whether to redraw after every plotting command.

Note

This function is only intended for use in backends. End users should use pyplot.isinteractive instead.

Default values and styling#
matplotlib.rcParams[source]#

An instance of RcParams for handling default Matplotlib values.

class matplotlib.RcParams(*args, **kwargs)[source]#

A dict-like key-value store for config parameters, including validation.

Validating functions are defined and associated with rc parameters in matplotlib.rcsetup.

The list of rcParams is:

find_all(pattern)[source]#

Return the subset of this RcParams dictionary whose keys match, using re.search(), the given pattern.

Note

Changes to the returned dictionary are not propagated to the parent RcParams dictionary.

copy()[source]#

Copy this RcParams instance.

matplotlib.rc_context(rc=None, fname=None)[source]#

Return a context manager for temporarily changing rcParams.

The rcParams["backend"] will not be reset by the context manager.

rcParams changed both through the context manager invocation and in the body of the context will be reset on context exit.

Parameters:
rcdict

The rcParams to temporarily set.

fnamestr or path-like

A file with Matplotlib rc settings. If both fname and rc are given, settings from rc take precedence.

Examples

Passing explicit values via a dict:

with mpl.rc_context({'interactive': False}):
    fig, ax = plt.subplots()
    ax.plot(range(3), range(3))
    fig.savefig('example.png')
    plt.close(fig)

Loading settings from a file:

with mpl.rc_context(fname='print.rc'):
    plt.plot(x, y)  # uses 'print.rc'

Setting in the context body:

with mpl.rc_context():
    # will be reset
    mpl.rcParams['lines.linewidth'] = 5
    plt.plot(x, y)
matplotlib.rc(group, **kwargs)[source]#

Set the current rcParams. group is the grouping for the rc, e.g., for lines.linewidth the group is lines, for axes.facecolor, the group is axes, and so on. Group may also be a list or tuple of group names, e.g., (xtick, ytick). kwargs is a dictionary attribute name/value pairs, e.g.,:

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

sets the current rcParams and is equivalent to:

rcParams['lines.linewidth'] = 2
rcParams['lines.color'] = 'r'

The following aliases are available to save typing for interactive users:

Thus you could abbreviate the above call as:

Note you can use python's kwargs dictionary facility to store dictionaries of default parameters. e.g., you can customize the font rc as follows:

font = {'family' : 'monospace',
        'weight' : 'bold',
        'size'   : 'larger'}
rc('font', **font)  # pass in the font dict as kwargs

This enables you to easily switch between several configurations. Use matplotlib.style.use('default') or rcdefaults() to restore the default rcParams after changes.

Notes

Similar functionality is available by using the normal dict interface, i.e. rcParams.update({"lines.linewidth": 2, ...}) (but rcParams.update does not support abbreviations or grouping).

matplotlib.rcdefaults()[source]#

Restore the rcParams from Matplotlib's internal default style.

Style-blacklisted rcParams (defined in matplotlib.style.core.STYLE_BLACKLIST) are not updated.

matplotlib.rc_file_defaults()[source]#

Restore the rcParams from the original rc file loaded by Matplotlib.

Style-blacklisted rcParams (defined in matplotlib.style.core.STYLE_BLACKLIST) are not updated.

matplotlib.rc_file(fname, *, use_default_template=True)[source]#

Update rcParams from file.

Style-blacklisted rcParams (defined in matplotlib.style.core.STYLE_BLACKLIST) are not updated.

Parameters:
fnamestr or path-like

A file with Matplotlib rc settings.

use_default_templatebool

If True, initialize with default parameters before updating with those in the given file. If False, the current configuration persists and only the parameters specified in the file are updated.

matplotlib.rc_params(fail_on_error=False)[source]#

Construct a RcParams instance from the default Matplotlib rc file.

matplotlib.rc_params_from_file(fname, fail_on_error=False, use_default_template=True)[source]#

Construct a RcParams from file fname.

Parameters:
fnamestr or path-like

A file with Matplotlib rc settings.

fail_on_errorbool

If True, raise an error when the parser fails to convert a parameter.

use_default_templatebool

If True, initialize with default parameters before updating with those in the given file. If False, the configuration class only contains the parameters specified in the file. (Useful for updating dicts.)

matplotlib.get_configdir()[source]#

Return the string path of the configuration directory.

The directory is chosen as follows:

  1. If the MPLCONFIGDIR environment variable is supplied, choose that.

  2. On Linux, follow the XDG specification and look first in $XDG_CONFIG_HOME, if defined, or $HOME/.config. On other platforms, choose $HOME/.matplotlib.

  3. If the chosen directory exists and is writable, use that as the configuration directory.

  4. Else, create a temporary directory, and use it as the configuration directory.

matplotlib.matplotlib_fname()[source]#

Get the location of the config file.

The file location is determined in the following order

matplotlib.get_data_path()[source]#

Return the path to Matplotlib data.

Logging#
matplotlib.set_loglevel(level)[source]#

Configure Matplotlib's logging levels.

Matplotlib uses the standard library logging framework under the root logger 'matplotlib'. This is a helper function to:

Typically, one should call set_loglevel("info") or set_loglevel("debug") to get additional debugging information.

Users or applications that are installing their own logging handlers may want to directly manipulate logging.getLogger('matplotlib') rather than use this function.

Parameters:
level{"notset", "debug", "info", "warning", "error", "critical"}

The log level of the handler.

Notes

The first time this function is called, an additional handler is attached to Matplotlib's root handler; this handler is reused every time and this function simply manipulates the logger and handler's level.

Colormaps and color sequences#
matplotlib.colormaps[source]#

Container for colormaps that are known to Matplotlib by name.

The universal registry instance is matplotlib.colormaps. There should be no need for users to instantiate ColormapRegistry themselves.

Read access uses a dict-like interface mapping names to Colormaps:

import matplotlib as mpl
cmap = mpl.colormaps['viridis']

Returned Colormaps are copies, so that their modification does not change the global definition of the colormap.

Additional colormaps can be added via ColormapRegistry.register:

mpl.colormaps.register(my_colormap)

To get a list of all registered colormaps, you can do:

from matplotlib import colormaps
list(colormaps)
matplotlib.color_sequences[source]#

Container for sequences of colors that are known to Matplotlib by name.

The universal registry instance is matplotlib.color_sequences. There should be no need for users to instantiate ColorSequenceRegistry themselves.

Read access uses a dict-like interface mapping names to lists of colors:

import matplotlib as mpl
colors = mpl.color_sequences['tab10']

For a list of built in color sequences, see Named color sequences. The returned lists are copies, so that their modification does not change the global definition of the color sequence.

Additional color sequences can be added via ColorSequenceRegistry.register:

mpl.color_sequences.register('rgb', ['r', 'g', 'b'])
Miscellaneous#
class matplotlib.MatplotlibDeprecationWarning[source]#

A class for issuing deprecation warnings for Matplotlib users.

matplotlib.get_cachedir()[source]#

Return the string path of the cache directory.

The procedure used to find the directory is the same as for get_configdir, except using $XDG_CACHE_HOME/$HOME/.cache instead.

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