Contribute#

You've discovered a bug or something else you want to change in Matplotlib — excellent!

You've worked out a way to fix it — even better!

You want to tell us about it — best of all!

This project is a community effort, and everyone is welcome to contribute. Everyone within the community is expected to abide by our code of conduct.

Below, you can find a number of ways to contribute, and how to connect with the Matplotlib community.

Get started#

There is no pre-defined pathway for new contributors -- we recommend looking at existing issue and pull request discussions, and following the conversations during pull request reviews to get context. Or you can deep-dive into a subset of the code-base to understand what is going on.

There are a few typical new contributor profiles:

You are a Matplotlib user, and you see a bug, a potential improvement, or something that annoys you, and you can fix it.

You can search our issue tracker for an existing issue that describes your problem or open a new issue to inform us of the problem you observed and discuss the best approach to fix it. If your contributions would not be captured on GitHub (social media, communication, educational content), you can also reach out to us on gitter, Discourse or attend any of our community meetings.
You are not a regular Matplotlib user but a domain expert: you know about visualization, 3D plotting, design, technical writing, statistics, or some other field where Matplotlib could be improved.

Awesome -- you have a focus on a specific application and domain and can start there. In this case, maintainers can help you figure out the best implementation; open an issue or pull request with a starting point, and we'll be happy to discuss technical approaches.

If you prefer, you can use the GitHub functionality for "draft" pull requests and request early feedback on whatever you are working on, but you should be aware that maintainers may not review your contribution unless it has the "Ready to review" state on GitHub.
You are new to Matplotlib, both as a user and contributor, and want to start contributing but have yet to develop a particular interest.

Having some previous experience or relationship with the library can be very helpful when making open-source contributions. It helps you understand why things are the way they are and how they should be. Having first-hand experience and context is valuable both for what you can bring to the conversation (and given the breadth of Matplotlib's usage, there is a good chance it is a unique context in any given conversation) and make it easier to understand where other people are coming from.

Understanding the entire codebase is a long-term project, and nobody expects you to do this right away. If you are determined to get started with Matplotlib and want to learn, going through the basic functionality, choosing something to focus on (3d, testing, documentation, animations, etc.) and gaining context on this area by reading the issues and pull requests touching these subjects is a reasonable approach.

Get connected#

Do I really have something to contribute to Matplotlib?#

100% yes. There are so many ways to contribute to our community.

When in doubt, we recommend going together! Get connected with our community of active contributors, many of whom felt just like you when they started out and are happy to welcome you and support you as you get to know how we work, and where things are. Take a look at the next sections to learn more.

Contributor incubator#

The incubator is our non-public communication channel for new contributors. It is a private gitter (chat) room moderated by core Matplotlib developers where you can get guidance and support for your first few PRs. It's a place where 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 in community work, or get a "pre-review" on your PR.

To join, please go to our public community channel, and ask to be added to #incubator. One of our core developers will see your message and will add you.

New Contributors Meeting#

Once a month, we host a meeting to discuss topics that interest new contributors. Anyone can attend, present, or sit in and listen to the call. Among our attendees are fellow new contributors, as well as maintainers, and veteran contributors, who are keen to support onboarding of new folks and share their experience. You can find our community calendar link at the Scientific Python website, and you can browse previous meeting notes on GitHub. We recommend joining the meeting to clarify any doubts, or lingering questions you might have, and to get to know a few of the people behind the GitHub handles 😉. You can reach out to us on gitter for any clarifications or suggestions. We ❤ feedback!

Good first issues#

While any contributions are welcome, we have marked some issues as particularly suited for new contributors by the label good first issue. 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. Difficulty: Easy is suited for people with little Python experience. Difficulty: Medium and Difficulty: Hard require more programming experience. This could be for a variety of reasons, among them, though not necessarily all at the same time:

The issue is in areas of the code base which have more interdependencies, or legacy code.
It has less clearly defined tasks, which require some independent exploration, making suggestions, or follow-up discussions to clarify a good path to resolve the issue.
It involves Python features such as decorators and context managers, which have subtleties due to our implementation decisions.

Work on an issue#

In general, the Matplotlib project does not assign issues. Issues are "assigned" or "claimed" by opening a PR; there is no other assignment mechanism. If you have opened such a PR, please comment on the issue thread to avoid duplication of work. Please check if there is an existing PR for the issue you are addressing. If there is, try to work with the author by submitting reviews of their code or commenting on the PR rather than opening a new PR; duplicate PRs are subject to being closed. However, if the existing PR is an outline, unlikely to work, or stalled, and the original author is unresponsive, feel free to open a new PR referencing the old one.

Submit a bug report#

If you find a bug in the code or documentation, do not hesitate to submit a ticket to the Issue Tracker. 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:

A short, top-level summary of the bug. In most cases, this should be 1-2 sentences.
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.
The actual outcome of the code snippet.
The expected outcome of the code snippet.

The Matplotlib version, Python version and platform that you are using. You can grab the version with the following commands:

>>> import matplotlib
>>> matplotlib.__version__
'3.4.1'
>>> import platform
>>> platform.python_version()
'3.9.2'

We have preloaded the issue creation page with a Markdown form that you can use to organize this information.

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

Request a new feature#

Please post feature requests to the Issue Tracker.

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 participate in the implementation.

Contribute code#

How to contribute#

The preferred way to contribute to Matplotlib is to fork the main repository on GitHub, then submit a "pull request" (PR). You can do this by cloning a copy of the Maplotlib repository to your own computer, or alternatively using GitHub Codespaces, a cloud-based in-browser development environment that comes with the appropriated setup to contribute to Matplotlib.

Workflow overview#

A brief overview of the workflow is as follows.

Create an account on GitHub if you do not already have one.
Fork the project repository by clicking on the Fork button near the top of the page. This creates a copy of the code under your account on the GitHub server.
Set up a development environment:
Local development
Clone this copy to your local disk:
git clone https://github.com/<YOUR GITHUB USERNAME>/matplotlib.git
Using GitHub Codespaces
Check out the Matplotlib repository and activate your development environment:
1. Open codespaces on your fork by clicking on the green "Code" button on the GitHub web interface and selecting the "Codespaces" tab.
2. Next, click on "Open codespaces on <your branch name>". You will be able to change branches later, so you can select the default main branch.
3. After the codespace is created, you will be taken to a new browser tab where you can use the terminal to activate a pre-defined conda environment called mpl-dev:
  conda activate mpl-dev
Install the local version of Matplotlib with:
```
python -m pip install -e .
```
See Setting up Matplotlib for development for detailed instructions.
Create a branch to hold your changes:
```
git checkout -b my-feature origin/main
```
and start making changes. Never work in the main branch!
Work on this task using Git to do the version control. Codespaces persist for some time (check the documentation for details) and can be managed on codespaces. When you're done editing e.g., lib/matplotlib/collections.py, do:
```
git add lib/matplotlib/collections.py
git commit
```
to record your changes in Git, then push them to your GitHub fork with:
```
git push -u origin my-feature
```

Open a pull request on Matplotlib#

Finally, go to the web page of your fork of the Matplotlib repo, and click Compare & pull request to send your changes to the maintainers for review. The base repository is matplotlib/matplotlib and the base branch is generally main. For more guidance, see GitHub's pull request tutorial.

For more detailed instructions on how to set up Matplotlib for development and best practices for contribution, see Setting up Matplotlib for development.

GitHub Codespaces workflows#

If you need to open a GUI window with Matplotlib output on Codespaces, our configuration includes a light-weight Fluxbox-based desktop. You can use it by connecting to this desktop via your web browser. To do this:
1. Press F1 or Ctrl/Cmd+Shift+P and select Ports: Focus on Ports View in the VSCode session to bring it into focus. Open the ports view in your tool, select the noVNC port, and click the Globe icon.
2. In the browser that appears, click the Connect button and enter the desktop password (vscode by default).
Check the GitHub instructions for more details on connecting to the desktop.
If you also built the documentation pages, you can view them using Codespaces. Use the "Extensions" icon in the activity bar to install the "Live Server" extension. Locate the doc/build/html folder in the Explorer, right click the file you want to open and select "Open with Live Server."

Contribute documentation#

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:

Fix a typo
Clarify a docstring
Write or update an example plot
Write or update a comprehensive tutorial

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 How to contribute.

If you have trouble getting started, you may instead open an issue describing the intended improvement.

Other ways to contribute#

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 Citing Matplotlib guidelines.

Coding guidelines#

While the current state of the Matplotlib code base is not compliant with all of these guidelines, our goal in enforcing these constraints on new contributions is that it improves the readability and consistency of the code base going forward.

PEP8, as enforced by flake8#

Formatting should follow the recommendations of PEP8, as enforced by flake8. Matplotlib modifies PEP8 to extend the maximum line length to 88 characters. You can check flake8 compliance from the command line with

python -m pip install flake8
flake8 /path/to/module.py

or your editor may provide integration with it. Note that Matplotlib intentionally does not use the black auto-formatter (1), in particular due to its inability to understand the semantics of mathematical expressions (2, 3).

Package imports#

Import the following modules using the standard scipy conventions:

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

In general, Matplotlib modules should not import rcParams using from matplotlib import rcParams, but rather access it as mpl.rcParams. This is because some modules are imported very early, before the rcParams singleton is constructed.

Variable names#

When feasible, please use our internal variable naming convention for objects of a given class and objects of any child class:

base class	variable	multiples
`FigureBase`	`fig`
`Axes`	`ax`
`Transform`	`trans`	`trans_<source>_<target>` `trans_<source>` when target is screen
`Transform`	`trans`

Generally, denote more than one instance of the same class by adding suffixes to the variable names. If a format isn't specified in the table, use numbers or letters as appropriate.

Type hints#

If you add new public API or change public API, update or add the corresponding mypy type hints. We generally use stub files (*.pyi) to store the type information; for example colors.pyi contains the type information for colors.py. A notable exception is pyplot.py, which is type hinted inline.

Type hints are checked by the mypy pre-commit hook and can often be verified using tools\stubtest.py and occasionally may require the use of tools\check_typehints.py.

API changes and new features#

API consistency and stability are of great value; Therefore, API changes (e.g. signature changes, behavior changes, removals) will only be conducted if the added benefit is worth the effort of adapting existing code.

Because we are a visualization library, our primary output is the final visualization the user sees; therefore, the appearance of the figure is part of the API and any changes, either semantic or esthetic, are backwards-incompatible API changes.

Announce changes, deprecations, and new features#

When adding or changing the API in a backward in-compatible way, please add the appropriate versioning directive and document it for the release notes and add the entry to the appropriate folder:

addition	versioning directive	announcement folder
new feature	`.. versionadded:: 3.N`	`doc/users/next_whats_new/`
API change	`.. versionchanged:: 3.N`	`doc/api/next_api_changes/[kind]`

API deprecations are first introduced and then expired. During the introduction period, users are warned that the API will change in the future. During the expiration period, code is changed as described in the notice posted during the introductory period.

stage	required changes	announcement folder
introduce	introduce deprecation	`doc/api/next_api_changes/deprecation`
expire	expire deprecation	`doc/api/next_api_changes/[kind]`

For both change notes and what's new, please avoid using references in section titles, as it causes links to be confusing in the table of contents. Instead, ensure that a reference is included in the descriptive text.

API Change Notes#

API change notes for future releases are collected in next_api_changes. They are divided into four subdirectories:

Deprecations: Announcements of future changes. Typically, these will raise a deprecation warning and users of this API should change their code to stay compatible with future releases of Matplotlib. If possible, state what should be used instead.
Removals: Parts of the API that got removed. If possible, state what should be used instead.
Behaviour changes: API that stays valid but will yield a different result.
Development changes: Changes to the build process, dependencies, etc.

Please place new entries in these directories with a new file named 99999-ABC.rst, where 99999 would be the PR number, and ABC the author's initials. Typically, each change will get its own file, but you may also amend existing files when suitable. The overall goal is a comprehensible documentation of the changes.

A typical entry could look like this:

Locators
~~~~~~~~
The unused `Locator.autoscale()` method is deprecated (pass the axis
limits to `Locator.view_limits()` instead).

What's new#

Please place new portions of whats_new.rst in the next_whats_new directory.

When adding an entry please look at the currently existing files to see if you can extend any of them. If you create a file, name it something like cool_new_feature.rst if you have added a brand new feature or something like updated_feature.rst for extensions of existing features.

Include contents of the form:

Section title for feature
-------------------------

A bunch of text about how awesome the new feature is and examples of how
to use it.

A sub-section
~~~~~~~~~~~~~

Deprecation#

API changes in Matplotlib have to be performed following the deprecation process below, except in very rare circumstances as deemed necessary by the development team. This ensures that users are notified before the change will take effect and thus prevents unexpected breaking of code.

Rules#

Deprecations are targeted at the next point.release (e.g. 3.x)
Deprecated API is generally removed two point-releases after introduction of the deprecation. Longer deprecations can be imposed by core developers on a case-by-case basis to give more time for the transition
The old API must remain fully functional during the deprecation period
If alternatives to the deprecated API exist, they should be available during the deprecation period
If in doubt, decisions about API changes are finally made by the API consistency lead developer

Introduce deprecation#

Create deprecation notice
If possible, issue a MatplotlibDeprecationWarning when the deprecated API is used. There are a number of helper tools for this:
- Use _api.warn_deprecated() for general deprecation warnings
- Use the decorator @_api.deprecated to deprecate classes, functions, methods, or properties
- Use @_api.deprecate_privatize_attribute to annotate deprecation of attributes while keeping the internal private version.
- To warn on changes of the function signature, use the decorators @_api.delete_parameter, @_api.rename_parameter, and @_api.make_keyword_only
All these helpers take a first parameter since, which should be set to the next point release, e.g. "3.x".

You can use standard rst cross references in alternative.
Make appropriate changes to the type hints in the associated .pyi file. The general guideline is to match runtime reported behavior.
- Items marked with @_api.deprecated or @_api.deprecate_privatize_attribute are generally kept during the expiry period, and thus no changes are needed on introduction.
- Items decorated with @_api.rename_parameter or @_api.make_keyword_only report the new (post deprecation) signature at runtime, and thus should be updated on introduction.
- Items decorated with @_api.delete_parameter should include a default value hint for the deleted parameter, even if it did not previously have one (e.g. param: <type> = ...). Even so, the decorator changes the default value to a sentinel value which should not be included in the type stub. Thus, Mypy Stubtest needs to be informed of the inconsistency by placing the method into ci/mypy-stubtest-allowlist.txt under a heading indicating the deprecation version number.

Expire deprecation#

Create deprecation announcement. For the content, you can usually copy the deprecation notice and adapt it slightly.
Change the code functionality and remove any related deprecation warnings.
Make appropriate changes to the type hints in the associated .pyi file.
- Items marked with @_api.deprecated or @_api.deprecate_privatize_attribute are to be removed on expiry.
- Items decorated with @_api.rename_parameter or @_api.make_keyword_only will have been updated at introduction, and require no change now.
- Items decorated with @_api.delete_parameter will need to be updated to the final signature, in the same way as the .py file signature is updated. The entry in ci/mypy-stubtest-allowlist.txt should be removed.
- Any other entries in ci/mypy-stubtest-allowlist.txt under a version's deprecations should be double checked, as only delete_parameter should normally require that mechanism for deprecation. For removed items that were not in the stub file, only deleting from the allowlist is required.

Adding new API and features#

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:

Mark helper functions and internal attributes as private by prefixing them with an underscore.
Carefully think about good names for your functions and variables.
Try to adopt patterns and naming conventions from existing parts of the Matplotlib API.
Consider making as many arguments keyword-only as possible. See also API Evolution the Right Way -- Add Parameters Compatibly.

Versioning directives#

When making a backward incompatible change, please add a versioning directive in the docstring. The directives should be placed at the end of a description block. For example:

class Foo:
    """
    This is the summary.

    Followed by a longer description block.

    Consisting of multiple lines and paragraphs.

    .. versionadded:: 3.5

    Parameters
    ----------
    a : int
        The first parameter.
    b: bool, default: False
        This was added later.

        .. versionadded:: 3.6
    """

    def set_b(b):
        """
        Set b.

        .. versionadded:: 3.6

        Parameters
        ----------
        b: bool

For classes and functions, the directive should be placed before the Parameters section. For parameters, the directive should be placed at the end of the parameter description. The patch release version is omitted and the directive should not be added to entire modules.

New modules and files: installation#

If you have added new files or directories, or reorganized existing ones, make sure the new files are included in the match patterns in in package_data in setupext.py.
New modules may be typed inline or using parallel stub file like existing modules.

C/C++ extensions#

Extensions may be written in C or C++.
Code style should conform to PEP7 (understanding that PEP7 doesn't address C++, but most of its admonitions still apply).
Python/C interface code should be kept separate from the core C/C++ code. The interface code should be named FOO_wrap.cpp or FOO_wrapper.cpp.
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.
C/C++ code in the extern/ directory is vendored, and should be kept close to upstream whenever possible. It can be modified to fix bugs or implement new features only if the required changes cannot be made elsewhere in the codebase. In particular, avoid making style fixes to it.

Keyword argument processing#

Matplotlib makes extensive use of **kwargs for pass-through customizations from one function to another. A typical example is text. The definition of matplotlib.pyplot.text is a simple pass-through to matplotlib.axes.Axes.text:

# in pyplot.py
def text(x, y, s, fontdict=None, **kwargs):
    return gca().text(x, y, s, fontdict=fontdict, **kwargs)

matplotlib.axes.Axes.text (simplified for illustration) just passes all args and kwargs on to matplotlib.text.Text.__init__:

# in axes/_axes.py
def text(self, x, y, s, fontdict=None, **kwargs):
    t = Text(x=x, y=y, text=s, **kwargs)

and matplotlib.text.Text.__init__ (again, simplified) just passes them on to the matplotlib.artist.Artist.update method:

# in text.py
def __init__(self, x=0, y=0, text='', **kwargs):
    super().__init__()
    self.update(kwargs)

update does the work looking for methods named like set_property if property 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 **kwargs 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 **kwargs 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 **kwargs, 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 plot(), scalex and scaley are local arguments and the rest are passed on as Line2D() keyword arguments:

# 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)

Using logging for debug messages#

Matplotlib uses the standard Python logging library to write verbose warnings, information, and debug messages. Please use it! In all those places you write print calls to do your debugging, try using logging.debug instead!

To include logging in your module, at the top of the module, you need to import logging. Then calls in your code like:

_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')

will log to a logger named matplotlib.yourmodulename.

If an end-user of Matplotlib sets up logging to display at levels more verbose than logging.WARNING in their code with the Matplotlib-provided helper:

plt.set_loglevel("debug")

or manually with

import logging
logging.basicConfig(level=logging.DEBUG)
import matplotlib.pyplot as plt

Then they will receive messages like

DEBUG:matplotlib.backends:backend MacOSX version unknown
DEBUG:matplotlib.yourmodulename:Here is some information
DEBUG:matplotlib.yourmodulename:Here is some more detailed information

Avoid using pre-computed strings (f-strings, str.format,etc.) for logging because of security and performance issues, and because they interfere with style handlers. For example, use _log.error('hello %s', 'world') rather than _log.error('hello {}'.format('world')) or _log.error(f'hello {s}').

Which logging level to use?#

There are five levels at which you can emit messages.

logging.critical and logging.error are really only there for errors that will end the use of the library but not kill the interpreter.
logging.warning and _api.warn_external are used to warn the user, see below.
logging.info 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 NaN, that can usually be ignored, but a mystified user could call logging.basicConfig(level=logging.INFO) and get an error message that says why.
logging.debug 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.

By default, logging displays all log messages at levels higher than logging.WARNING to sys.stderr.

The logging tutorial suggests that the difference between logging.warning and _api.warn_external (which uses warnings.warn) is that _api.warn_external should be used for things the user must change to stop the warning (typically in the source), whereas logging.warning can be more persistent. Moreover, note that _api.warn_external will by default only emit a given warning once for each line of user code, whereas logging.warning will display the message every time it is called.

By default, warnings.warn displays the line of code that has the warn call. This usually isn't more informative than the warning message itself. Therefore, Matplotlib uses _api.warn_external which uses warnings.warn, but goes up the stack and displays the first line of code outside of Matplotlib. For example, for the module:

# in my_matplotlib_module.py
import warnings

def set_range(bottom, top):
    if bottom == top:
        warnings.warn('Attempting to set identical bottom==top')

running the script:

from matplotlib import my_matplotlib_module
my_matplotlib_module.set_range(0, 0)  # set range

will display

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

Modifying the module to use _api.warn_external:

from matplotlib import _api

def set_range(bottom, top):
    if bottom == top:
        _api.warn_external('Attempting to set identical bottom==top')

and running the same script will display

UserWarning: Attempting to set identical bottom==top
my_matplotlib_module.set_range(0, 0)  # set range