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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>Contributing — Matplotlib 3.4.1 documentation</title>

title="Search within Matplotlib 3.4.1 documentation"

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

</head><body>

<div id="unreleased-message"> You are reading an old version of the documentation (v3.4.1). For the latest version see <a href="https://matplotlib.org/stable/devel/contributing.html">https://matplotlib.org/stable/devel/contributing.html</a></div>

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<div style="float: left; position: absolute; width: 496px; bottom: 0; padding-bottom: 24px">Version 3.4.1</div>

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<ul>

<li><a href="../users/installing.html">Installation</a></li>

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

<li><a href="../gallery/index.html">Examples</a></li>

<li><a href="../tutorials/index.html">Tutorials</a></li>

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</nav>

<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="triage.html" title="Bug triaging and issue curation"

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

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

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<li class="nav-item nav-item-this"><a href="">Contributing</a></li>

</ul>

</div>

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

<ul>

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

<li><a class="reference internal" href="#contributor-incubator">Contributor Incubator</a><ul>

<li><a class="reference internal" href="#issues-for-new-contributors">Issues for New Contributors</a></li>

</ul>

</li>

<li><a class="reference internal" href="#submitting-a-bug-report">Submitting a bug report</a></li>

<li><a class="reference internal" href="#requesting-a-new-feature">Requesting a new feature</a></li>

<li><a class="reference internal" href="#contributing-code">Contributing code</a><ul>

<li><a class="reference internal" href="#how-to-contribute">How to contribute</a></li>

<li><a class="reference internal" href="#contributing-pull-requests">Contributing pull requests</a></li>

</ul>

</li>

<li><a class="reference internal" href="#contributing-documentation">Contributing documentation</a></li>

<li><a class="reference internal" href="#other-ways-to-contribute">Other ways to contribute</a></li>

<li><a class="reference internal" href="#coding-guidelines">Coding guidelines</a><ul>

<li><a class="reference internal" href="#api-changes">API changes</a></li>

<li><a class="reference internal" href="#adding-new-api">Adding new API</a></li>

<li><a class="reference internal" href="#new-modules-and-files-installation">New modules and files: installation</a></li>

<li><a class="reference internal" href="#c-c-extensions">C/C++ extensions</a></li>

<li><a class="reference internal" href="#keyword-argument-processing">Keyword argument processing</a></li>

<li><a class="reference internal" href="#using-logging-for-debug-messages">Using logging for debug messages</a><ul>

<li><a class="reference internal" href="#which-logging-level-to-use">Which logging level to use?</a></li>

</ul>

</li>

<li><a class="reference internal" href="#writing-examples">Writing examples</a></li>

</ul>

</li>

</ul>

</li>

</ul>

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<a href="../_sources/devel/contributing.rst.txt"

style="color: #c0c0c0" rel="nofollow">Show Page Source</a>

</div>

<h1>Contributing<a class="headerlink" href="#contributing" title="Permalink to this headline">¶</a></h1>

This project is a community effort, and everyone is welcome to

contribute. Everyone within the community

is expected to abide by our

<a class="reference external" href="https://github.com/matplotlib/matplotlib/blob/master/CODE_OF_CONDUCT.md">code of conduct</a>.

The project is hosted on

<a class="reference external" href="https://github.com/matplotlib/matplotlib">https://github.com/matplotlib/matplotlib</a>

<h2>Contributor Incubator<a class="headerlink" href="#contributor-incubator" title="Permalink to this headline">¶</a></h2>

If you are interested in becoming a regular contributor to Matplotlib, but

don't know where to start or feel insecure about it, you can join our non-public

communication channel for new contributors. To do so, please go to <a class="reference external" href="https://gitter.im/matplotlib/matplotlib">gitter</a> and ask to be added to '#incubator'.

This is a private gitter room moderated by core Matplotlib developers where you can

get guidance and support for your first few PRs. This is a place you can ask questions

about anything: how to use git, github, how our PR review process works, technical questions

about the code, what makes for good documentation or a blog post, how to get involved involved

in community work, or get "pre-review" on your PR.

<h3>Issues for New Contributors<a class="headerlink" href="#issues-for-new-contributors" title="Permalink to this headline">¶</a></h3>

While any contributions are welcome, we have marked some issues as

particularly suited for new contributors by the label

<a class="reference external" href="https://github.com/matplotlib/matplotlib/labels/good%20first%20issue">good first issue</a>

These are well documented issues, that do not require a deep understanding of

the internals of Matplotlib. The issues may additionally be tagged with a

difficulty. <code class="docutils literal notranslate">Difficulty: Easy</code> is suited for people with little Python experience.

<code class="docutils literal notranslate">Difficulty: Medium</code> and <code class="docutils literal notranslate">Difficulty: Hard</code> are not trivial to solve and

require more thought and programming experience.

</div>

<h2>Submitting a bug report<a class="headerlink" href="#submitting-a-bug-report" title="Permalink to this headline">¶</a></h2>

If you find a bug in the code or documentation, do not hesitate to submit a

ticket to the

<a class="reference external" href="https://github.com/matplotlib/matplotlib/issues">Issue Tracker</a>. You are

also welcome to post feature requests or pull requests.

If you are reporting a bug, please do your best to include the following:

<li>A short, top-level summary of the bug. In most cases, this should be 1-2

sentences.

</li>

<li>A short, self-contained code snippet to reproduce the bug, ideally allowing

a simple copy and paste to reproduce. Please do your best to reduce the code

snippet to the minimum required.

</li>

<li>The actual outcome of the code snippet.

</li>

<li>The expected outcome of the code snippet.

</li>

<li>The Matplotlib version, Python version and platform that you are using. You

can grab the version with the following commands:

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

>>> matplotlib.__version__

'3.4.1'

>>> import platform

>>> platform.python_version()

'3.9.2'

</pre></div>

</div>

</li>

</ol>

We have preloaded the issue creation page with a Markdown template that you can

use to organize this information.

Thank you for your help in keeping bug reports complete, targeted and descriptive.

</div>

<h2>Requesting a new feature<a class="headerlink" href="#requesting-a-new-feature" title="Permalink to this headline">¶</a></h2>

Please post feature requests to the

<a class="reference external" href="https://github.com/matplotlib/matplotlib/issues">Issue Tracker</a>.

The Matplotlib developers will give feedback on the feature proposal. Since

Matplotlib is an open source project with limited resources, we encourage

users to then also

<a class="reference internal" href="#contributing-code">participate in the implementation</a>.

</div>

<h2>Contributing code<a class="headerlink" href="#contributing-code" title="Permalink to this headline">¶</a></h2>

<h3>How to contribute<a class="headerlink" href="#how-to-contribute" title="Permalink to this headline">¶</a></h3>

The preferred way to contribute to Matplotlib is to fork the <a class="reference external" href="https://github.com/matplotlib/matplotlib/">main

repository</a> on GitHub,

then submit a "pull request" (PR).

A brief overview is:

<li><a class="reference external" href="https://github.com/join">Create an account</a> on GitHub if you do not

already have one.

</li>

<li>Fork the <a class="reference external" href="https://github.com/matplotlib/matplotlib">project repository</a>:

click on the 'Fork' button near the top of the page. This creates a copy of

the code under your account on the GitHub server.

</li>

<li>Clone this copy to your local disk:

<div class="highlight-default notranslate"><div class="highlight"><pre>git clone https://github.com/<YOUR GITHUB USERNAME>/matplotlib.git

</pre></div>

</div>

</li>

<li>Enter the directory and install the local version of Matplotlib.

See ref`<installing_for_devs>` for instructions

</li>

<li>Create a branch to hold your changes:

<div class="highlight-default notranslate"><div class="highlight"><pre>git checkout -b my-feature origin/master

</pre></div>

</div>

and start making changes. Never work in the <code class="docutils literal notranslate">master</code> branch!

</li>

<li>Work on this copy, on your computer, using Git to do the version control.

When you're done editing e.g., <code class="docutils literal notranslate">lib/matplotlib/collections.py</code>, do:

<div class="highlight-default notranslate"><div class="highlight"><pre>git add lib/matplotlib/collections.py

git commit

</pre></div>

</div>

to record your changes in Git, then push them to GitHub with:

<div class="highlight-default notranslate"><div class="highlight"><pre>git push -u origin my-feature

</pre></div>

</div>

</li>

</ol>

Finally, go to the web page of your fork of the Matplotlib repo, and click

'Pull request' to send your changes to the maintainers for review.

See also

<li><a class="reference external" href="https://git-scm.com/documentation">Git documentation</a></li>

<li><a class="reference external" href="https://git-scm.com/book/en/v2/GitHub-Contributing-to-a-Project">Git-Contributing to a Project</a></li>

<li><a class="reference external" href="https://lab.github.com/githubtraining/introduction-to-github">Introduction to GitHub</a></li>

<li><a class="reference internal" href="gitwash/development_workflow.html#development-workflow">Development workflow</a> for best practices for Matplotlib</li>

<li><a class="reference internal" href="gitwash/index.html#using-git">Working with Matplotlib source code</a></li>

</ul>

</div>

<h3>Contributing pull requests<a class="headerlink" href="#contributing-pull-requests" title="Permalink to this headline">¶</a></h3>

It is recommended to check that your contribution complies with the following

rules before submitting a pull request:

<ul>

<li>If your pull request addresses an issue, please use the title to describe the

issue and mention the issue number in the pull request description to ensure

that a link is created to the original issue.

</li>

<li>All public methods should have informative docstrings with sample usage when

appropriate. Use the <a class="reference external" href="https://numpydoc.readthedocs.io/en/latest/format.html">numpy docstring standard</a>.

</li>

<li>Formatting should follow the recommendations of <a class="reference external" href="https://www.python.org/dev/peps/pep-0008/">PEP8</a>. You should consider

installing/enabling automatic PEP8 checking in your editor. Part of the test

suite is checking PEP8 compliance, things go smoother if the code is mostly

PEP8 compliant to begin with.

</li>

<li>Each high-level plotting function should have a simple example in the

<code class="docutils literal notranslate">Example</code> section of the docstring. This should be as simple as possible

to demonstrate the method. More complex examples should go in the

<code class="docutils literal notranslate">examples</code> tree.

</li>

<li>Changes (both new features and bugfixes) should be tested. See <a class="reference internal" href="testing.html#testing">Testing</a>

for more details.

</li>

<li>Import the following modules using the standard scipy conventions:

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

import numpy.ma as ma

import matplotlib as mpl

import matplotlib.pyplot as plt

import matplotlib.cbook as cbook

import matplotlib.patches as mpatches

</pre></div>

</div>

In general, Matplotlib modules should not import <a class="reference internal" href="../api/matplotlib_configuration_api.html#matplotlib.rcParams" title="matplotlib.rcParams"><code class="xref py py-obj docutils literal notranslate">rcParams</code></a> using <code class="docutils literal notranslate">from

matplotlib import rcParams</code>, but rather access it as <code class="docutils literal notranslate">mpl.rcParams</code>. This

is because some modules are imported very early, before the <a class="reference internal" href="../api/matplotlib_configuration_api.html#matplotlib.rcParams" title="matplotlib.rcParams"><code class="xref py py-obj docutils literal notranslate">rcParams</code></a>

singleton is constructed.

</li>

<li>If your change is a major new feature, add an entry to the <code class="docutils literal notranslate">What's new</code>

section by adding a new file in <code class="docutils literal notranslate">doc/users/next_whats_new</code> (see

<code class="file docutils literal notranslate">doc/users/next_whats_new/README.rst</code> for more information).

</li>

<li>If you change the API in a backward-incompatible way, please document it in

<code class="file docutils literal notranslate">doc/api/next_api_changes/behavior</code>, by adding a new file with the

naming convention <code class="docutils literal notranslate">99999-ABC.rst</code> where the pull request number is followed

by the contributor's initials. (see <code class="file docutils literal notranslate">doc/api/api_changes.rst</code> for more

information)

</li>

<li>See below for additional points about <a class="reference internal" href="#keyword-argument-processing">Keyword argument processing</a>, if

applicable for your pull request.

</li>

</ul>

In addition, you can check for common programming errors with the following

tools:

<ul>

<li>Code with a good unittest coverage (at least 70%, better 100%), check with:

<div class="highlight-default notranslate"><div class="highlight"><pre>python -m pip install coverage

python -m pytest --cov=matplotlib --showlocals -v

</pre></div>

</div>

</li>

<li>No pyflakes warnings, check with:

pyflakes path/to/module.py

</pre></div>

</div>

</li>

</ul>

Note

The current state of the Matplotlib code base is not compliant with all

of those guidelines, but we expect that enforcing those constraints on all

new contributions will move the overall code base quality in the right

direction.

</div>

See also

<li><a class="reference internal" href="#coding-guidelines">Coding guidelines</a></li>

<li><a class="reference internal" href="testing.html#testing">Testing</a></li>

<li><a class="reference internal" href="documenting_mpl.html#documenting-matplotlib">Writing documentation</a></li>

</ul>

</div>

<h2>Contributing documentation<a class="headerlink" href="#contributing-documentation" title="Permalink to this headline">¶</a></h2>

You as an end-user of Matplotlib can make a valuable contribution because you

more clearly see the potential for improvement than a core developer. For example, you can:

<li>Clarify a docstring</li>

<li>Write or update an <a class="reference internal" href="../gallery/index.html#gallery">example plot</a></li>

<li>Write or update a comprehensive <a class="reference internal" href="../tutorials/index.html#tutorials">tutorial</a></li>

</ul>

The documentation source files live in the same GitHub repository as the code.

Contributions are proposed and accepted through the pull request process.

For details see <a class="reference internal" href="#how-to-contribute">How to contribute</a>.

If you have trouble getting started, you may instead open an <a class="reference external" href="https://github.com/matplotlib/matplotlib/issues">issue</a>

describing the intended improvement.

See also

<li><a class="reference internal" href="documenting_mpl.html#documenting-matplotlib">Writing documentation</a></li>

</ul>

</div>

<h2>Other ways to contribute<a class="headerlink" href="#other-ways-to-contribute" title="Permalink to this headline">¶</a></h2>

It also helps us if you spread the word: reference the project from your blog

and articles or link to it from your website! If Matplotlib contributes to a

project that leads to a scientific publication, please follow the

<a class="reference internal" href="../citing.html">Citing Matplotlib</a> guidelines.

</div>

<h2>Coding guidelines<a class="headerlink" href="#coding-guidelines" title="Permalink to this headline">¶</a></h2>

<h3>API changes<a class="headerlink" href="#api-changes" title="Permalink to this headline">¶</a></h3>

Changes to the public API must follow a standard deprecation procedure to

prevent unexpected breaking of code that uses Matplotlib.

<li>Deprecations must be announced via a new file in

a new file in <code class="file docutils literal notranslate">doc/api/next_api_changes/deprecations/</code> with

naming convention <code class="docutils literal notranslate">99999-ABC.rst</code> where <code class="docutils literal notranslate">99999</code> is the pull request

number and <code class="docutils literal notranslate">ABC</code> are the contributor's initials.</li>

<li>Deprecations are targeted at the next point-release (i.e. 3.x.0).</li>

<li>The deprecated API should, to the maximum extent possible, remain fully

functional during the deprecation period. In cases where this is not

possible, the deprecation must never make a given piece of code do something

different than it was before; at least an exception should be raised.</li>

<li>If possible, usage of an deprecated API should emit a

<a class="reference internal" href="../api/_api_api.html#matplotlib._api.deprecation.MatplotlibDeprecationWarning" title="matplotlib._api.deprecation.MatplotlibDeprecationWarning"><code class="xref py py-obj docutils literal notranslate">MatplotlibDeprecationWarning</code></a>. There are a number of helper tools for this:<ul>

<li>Use <code class="docutils literal notranslate">cbook.warn_deprecated()</code> for general deprecation warnings.</li>

<li>Use the decorator <code class="docutils literal notranslate">@cbook.deprecated</code> to deprecate classes, functions,

methods, or properties.</li>

<li>To warn on changes of the function signature, use the decorators

<code class="docutils literal notranslate">@cbook._delete_parameter</code>, <code class="docutils literal notranslate">@cbook._rename_parameter</code>, and

<code class="docutils literal notranslate">@cbook._make_keyword_only</code>.</li>

</ul>

</li>

<li>Deprecated API may be removed two point-releases after they were deprecated.</li>

</ul>

</div>

<h3>Adding new API<a class="headerlink" href="#adding-new-api" title="Permalink to this headline">¶</a></h3>

Every new function, parameter and attribute that is not explicitly marked as

private (i.e., starts with an underscore) becomes part of Matplotlib's public

API. As discussed above, changing the existing API is cumbersome. Therefore,

take particular care when adding new API:

<li>Mark helper functions and internal attributes as private by prefixing them

with an underscore.</li>

<li>Carefully think about good names for your functions and variables.</li>

<li>Try to adopt patterns and naming conventions from existing parts of the

Matplotlib API.</li>

<li>Consider making as many arguments keyword-only as possible. See also

<a class="reference external" href="https://emptysqua.re/blog/api-evolution-the-right-way/#adding-parameters">API Evolution the Right Way -- Add Parameters Compatibly</a>.</li>

</ul>

</div>

<h3>New modules and files: installation<a class="headerlink" href="#new-modules-and-files-installation" title="Permalink to this headline">¶</a></h3>

<li>If you have added new files or directories, or reorganized existing

ones, make sure the new files are included in the match patterns in

<code class="file docutils literal notranslate">MANIFEST.in</code>, and/or in package_data in <code class="file docutils literal notranslate">setup.py</code>.</li>

</ul>

</div>

<h3>C/C++ extensions<a class="headerlink" href="#c-c-extensions" title="Permalink to this headline">¶</a></h3>

<li>Extensions may be written in C or C++.</li>

<li>Code style should conform to PEP7 (understanding that PEP7 doesn't

address C++, but most of its admonitions still apply).</li>

<li>Python/C interface code should be kept separate from the core C/C++

code. The interface code should be named <code class="file docutils literal notranslate">FOO_wrap.cpp</code> or

<code class="file docutils literal notranslate">FOO_wrapper.cpp</code>.</li>

<li>Header file documentation (aka docstrings) should be in Numpydoc

format. We don't plan on using automated tools for these

docstrings, and the Numpydoc format is well understood in the

scientific Python community.</li>

</ul>

</div>

<h3>Keyword argument processing<a class="headerlink" href="#keyword-argument-processing" title="Permalink to this headline">¶</a></h3>

Matplotlib makes extensive use of <code class="docutils literal notranslate">**kwargs</code> for pass-through customizations

from one function to another. A typical example is in <a class="reference internal" href="../api/_as_gen/matplotlib.pyplot.text.html#matplotlib.pyplot.text" title="matplotlib.pyplot.text"><code class="xref py py-obj docutils literal notranslate">matplotlib.pyplot.text</code></a>.

The definition of the pylab text function is a simple pass-through to

<a class="reference internal" href="../api/_as_gen/matplotlib.axes.Axes.text.html#matplotlib.axes.Axes.text" title="matplotlib.axes.Axes.text"><code class="xref py py-obj docutils literal notranslate">matplotlib.axes.Axes.text</code></a>:

<div class="highlight-default notranslate"><div class="highlight"><pre># in pylab.py

def text(*args, **kwargs):

ret = gca().text(*args, **kwargs)

draw_if_interactive()

return ret

</pre></div>

</div>

<a class="reference internal" href="../api/_as_gen/matplotlib.axes.Axes.text.html#matplotlib.axes.Axes.text" title="matplotlib.axes.Axes.text"><code class="xref py py-obj docutils literal notranslate">text</code></a> in simplified form looks like this, i.e., it just

passes all <code class="docutils literal notranslate">args</code> and <code class="docutils literal notranslate">kwargs</code> on to <code class="docutils literal notranslate">matplotlib.text.Text.__init__</code>:

<div class="highlight-default notranslate"><div class="highlight"><pre># in axes/_axes.py

def text(self, x, y, s, fontdict=None, withdash=False, **kwargs):

t = Text(x=x, y=y, text=s, **kwargs)

</pre></div>

</div>

and <code class="docutils literal notranslate">matplotlib.text.Text.__init__</code> (again with liberties for illustration)

just passes them on to the <a class="reference internal" href="../api/_as_gen/matplotlib.artist.Artist.update.html#matplotlib.artist.Artist.update" title="matplotlib.artist.Artist.update"><code class="xref py py-obj docutils literal notranslate">matplotlib.artist.Artist.update</code></a> method:

<div class="highlight-default notranslate"><div class="highlight"><pre># in text.py

def __init__(self, x=0, y=0, text='', **kwargs):

super().__init__()

self.update(kwargs)

</pre></div>

</div>

<code class="docutils literal notranslate">update</code> does the work looking for methods named like

<code class="docutils literal notranslate">set_property</code> if <code class="docutils literal notranslate">property</code> is a keyword argument. i.e., no one

looks at the keywords, they just get passed through the API to the

artist constructor which looks for suitably named methods and calls

them with the value.

As a general rule, the use of <code class="docutils literal notranslate">**kwargs</code> should be reserved for

pass-through keyword arguments, as in the example above. If all the

keyword args are to be used in the function, and not passed

on, use the key/value keyword args in the function definition rather

than the <code class="docutils literal notranslate">**kwargs</code> idiom.

In some cases, you may want to consume some keys in the local

function, and let others pass through. Instead of popping arguments to

use off <code class="docutils literal notranslate">**kwargs</code>, specify them as keyword-only arguments to the local

function. This makes it obvious at a glance which arguments will be

consumed in the function. For example, in

<a class="reference internal" href="../api/_as_gen/matplotlib.axes.Axes.plot.html#matplotlib.axes.Axes.plot" title="matplotlib.axes.Axes.plot"><code class="xref py py-meth docutils literal notranslate">plot()</code></a>, <code class="docutils literal notranslate">scalex</code> and <code class="docutils literal notranslate">scaley</code> are

local arguments and the rest are passed on as

<a class="reference internal" href="../api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D" title="matplotlib.lines.Line2D"><code class="xref py py-meth docutils literal notranslate">Line2D()</code></a> keyword arguments:

<div class="highlight-default notranslate"><div class="highlight"><pre># in axes/_axes.py

def plot(self, *args, scalex=True, scaley=True, **kwargs):

lines = []

for line in self._get_lines(*args, **kwargs):

self.add_line(line)

lines.append(line)

</pre></div>

</div>

<h3>Using logging for debug messages<a class="headerlink" href="#using-logging-for-debug-messages" title="Permalink to this headline">¶</a></h3>

Matplotlib uses the standard Python <a class="reference external" href="https://docs.python.org/3/library/logging.html#module-logging" title="(in Python v3.9)"><code class="xref py py-obj docutils literal notranslate">logging</code></a> library to write verbose

warnings, information, and debug messages. Please use it! In all those places

you write <a class="reference external" href="https://docs.python.org/3/library/functions.html#print" title="(in Python v3.9)"><code class="xref py py-obj docutils literal notranslate">print</code></a> calls to do your debugging, try using <a class="reference external" href="https://docs.python.org/3/library/logging.html#logging.debug" title="(in Python v3.9)"><code class="xref py py-obj docutils literal notranslate">logging.debug</code></a>

instead!

To include <a class="reference external" href="https://docs.python.org/3/library/logging.html#module-logging" title="(in Python v3.9)"><code class="xref py py-obj docutils literal notranslate">logging</code></a> in your module, at the top of the module, you need to

<code class="docutils literal notranslate">import logging</code>. Then calls in your code like:

<div class="highlight-default notranslate"><div class="highlight"><pre>_log = logging.getLogger(__name__) # right after the imports

# code

# more code

_log.info('Here is some information')

_log.debug('Here is some more detailed information')

</pre></div>

</div>

will log to a logger named <code class="docutils literal notranslate">matplotlib.yourmodulename</code>.

If an end-user of Matplotlib sets up <a class="reference external" href="https://docs.python.org/3/library/logging.html#module-logging" title="(in Python v3.9)"><code class="xref py py-obj docutils literal notranslate">logging</code></a> to display at levels more

verbose than <code class="docutils literal notranslate">logging.WARNING</code> in their code with the Matplotlib-provided

helper:

<div class="highlight-default notranslate"><div class="highlight"><pre>plt.set_loglevel("debug")

</pre></div>

</div>

or manually with

<div class="highlight-default notranslate"><div class="highlight"><pre>import logging

logging.basicConfig(level=logging.DEBUG)

import matplotlib.pyplot as plt

</pre></div>

</div>

Then they will receive messages like:

<div class="highlight-default notranslate"><div class="highlight"><pre>DEBUG:matplotlib.backends:backend MacOSX version unknown

DEBUG:matplotlib.yourmodulename:Here is some information

</pre></div>

</div>

<h4>Which logging level to use?<a class="headerlink" href="#which-logging-level-to-use" title="Permalink to this headline">¶</a></h4>

There are five levels at which you can emit messages.

<li><a class="reference external" href="https://docs.python.org/3/library/logging.html#logging.critical" title="(in Python v3.9)"><code class="xref py py-obj docutils literal notranslate">logging.critical</code></a> and <a class="reference external" href="https://docs.python.org/3/library/logging.html#logging.error" title="(in Python v3.9)"><code class="xref py py-obj docutils literal notranslate">logging.error</code></a> are really only there for errors that

will end the use of the library but not kill the interpreter.</li>

<li><a class="reference external" href="https://docs.python.org/3/library/logging.html#logging.warning" title="(in Python v3.9)"><code class="xref py py-obj docutils literal notranslate">logging.warning</code></a> and <a class="reference internal" href="../api/_api_api.html#matplotlib._api.warn_external" title="matplotlib._api.warn_external"><code class="xref py py-obj docutils literal notranslate">_api.warn_external</code></a> are used to warn the user,

see below.</li>

<li><a class="reference external" href="https://docs.python.org/3/library/logging.html#logging.info" title="(in Python v3.9)"><code class="xref py py-obj docutils literal notranslate">logging.info</code></a> is for information that the user may want to know if the

program behaves oddly. They are not displayed by default. For instance, if

an object isn't drawn because its position is <code class="docutils literal notranslate">NaN</code>, that can usually

be ignored, but a mystified user could call

<code class="docutils literal notranslate">logging.basicConfig(level=logging.INFO)</code> and get an error message that

says why.</li>

<li><a class="reference external" href="https://docs.python.org/3/library/logging.html#logging.debug" title="(in Python v3.9)"><code class="xref py py-obj docutils literal notranslate">logging.debug</code></a> is the least likely to be displayed, and hence can be the

most verbose. "Expected" code paths (e.g., reporting normal intermediate

steps of layouting or rendering) should only log at this level.</li>

</ul>

By default, <a class="reference external" href="https://docs.python.org/3/library/logging.html#module-logging" title="(in Python v3.9)"><code class="xref py py-obj docutils literal notranslate">logging</code></a> displays all log messages at levels higher than

<code class="docutils literal notranslate">logging.WARNING</code> to <a class="reference external" href="https://docs.python.org/3/library/sys.html#sys.stderr" title="(in Python v3.9)"><code class="xref py py-obj docutils literal notranslate">sys.stderr</code></a>.

The <a class="reference external" href="https://docs.python.org/3/howto/logging.html#logging-basic-tutorial">logging tutorial</a> suggests that the difference between <a class="reference external" href="https://docs.python.org/3/library/logging.html#logging.warning" title="(in Python v3.9)"><code class="xref py py-obj docutils literal notranslate">logging.warning</code></a>

and <a class="reference internal" href="../api/_api_api.html#matplotlib._api.warn_external" title="matplotlib._api.warn_external"><code class="xref py py-obj docutils literal notranslate">_api.warn_external</code></a> (which uses <a class="reference external" href="https://docs.python.org/3/library/warnings.html#warnings.warn" title="(in Python v3.9)"><code class="xref py py-obj docutils literal notranslate">warnings.warn</code></a>) is that

<a class="reference internal" href="../api/_api_api.html#matplotlib._api.warn_external" title="matplotlib._api.warn_external"><code class="xref py py-obj docutils literal notranslate">_api.warn_external</code></a> should be used for things the user must change to stop

the warning (typically in the source), whereas <a class="reference external" href="https://docs.python.org/3/library/logging.html#logging.warning" title="(in Python v3.9)"><code class="xref py py-obj docutils literal notranslate">logging.warning</code></a> can be more

persistent. Moreover, note that <a class="reference internal" href="../api/_api_api.html#matplotlib._api.warn_external" title="matplotlib._api.warn_external"><code class="xref py py-obj docutils literal notranslate">_api.warn_external</code></a> will by default only

emit a given warning once for each line of user code, whereas

<a class="reference external" href="https://docs.python.org/3/library/logging.html#logging.warning" title="(in Python v3.9)"><code class="xref py py-obj docutils literal notranslate">logging.warning</code></a> will display the message every time it is called.

By default, <a class="reference external" href="https://docs.python.org/3/library/warnings.html#warnings.warn" title="(in Python v3.9)"><code class="xref py py-obj docutils literal notranslate">warnings.warn</code></a> displays the line of code that has the <code class="docutils literal notranslate">warn</code>

call. This usually isn't more informative than the warning message itself.

Therefore, Matplotlib uses <a class="reference internal" href="../api/_api_api.html#matplotlib._api.warn_external" title="matplotlib._api.warn_external"><code class="xref py py-obj docutils literal notranslate">_api.warn_external</code></a> which uses <a class="reference external" href="https://docs.python.org/3/library/warnings.html#warnings.warn" title="(in Python v3.9)"><code class="xref py py-obj docutils literal notranslate">warnings.warn</code></a>,

but goes up the stack and displays the first line of code outside of

Matplotlib. For example, for the module:

<div class="highlight-default notranslate"><div class="highlight"><pre># in my_matplotlib_module.py

import warnings

def set_range(bottom, top):

if bottom == top:

warnings.warn('Attempting to set identical bottom==top')

</pre></div>

</div>

running the script:

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

my_matplotlib_module.set_range(0, 0) #set range

</pre></div>

</div>

will display:

<div class="highlight-default notranslate"><div class="highlight"><pre>UserWarning: Attempting to set identical bottom==top

warnings.warn('Attempting to set identical bottom==top')

</pre></div>

</div>

Modifying the module to use <a class="reference internal" href="../api/_api_api.html#matplotlib._api.warn_external" title="matplotlib._api.warn_external"><code class="xref py py-obj docutils literal notranslate">_api.warn_external</code></a>:

def set_range(bottom, top):

if bottom == top:

_api.warn_external('Attempting to set identical bottom==top')

</pre></div>

</div>

and running the same script will display:

</pre></div>

</div>

<h3>Writing examples<a class="headerlink" href="#writing-examples" title="Permalink to this headline">¶</a></h3>

We have hundreds of examples in subdirectories of <code class="file docutils literal notranslate">matplotlib/examples</code>,

and these are automatically generated when the website is built to show up in

the <a class="reference internal" href="../gallery/index.html#gallery">examples</a> section of the website.

Any sample data that the example uses should be kept small and

distributed with Matplotlib in the

<code class="file docutils literal notranslate">lib/matplotlib/mpl-data/sample_data/</code> directory. Then in your

example code you can load it into a file handle with:

<div class="highlight-default notranslate"><div class="highlight"><pre>import matplotlib.cbook as cbook

fh = cbook.get_sample_data('mydata.dat')

</pre></div>

</div>

</div>

Last updated on Mar 31, 2021.

Created using

<a href="http://sphinx-doc.org/">Sphinx</a> 3.2.1.

Doc version v3.4.1-3-gc6d08c6a8b-dirty.

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