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Version 3.1.0.post1496+gb03370a04
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Table of Contents

Contributing

This project is a community effort, and everyone is welcome to contribute. We follow the Python Software Foundation Code of Conduct in everything we do.

The project is hosted on https://github.com/matplotlib/matplotlib

Submitting a bug report

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

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

  2. 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.

  3. The actual outcome of the code snippet.

  4. The expected outcome of the code snippet.

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

    >>> import matplotlib
    >>> matplotlib.__version__
    '1.5.3'
    >>> import platform
    >>> platform.python_version()
    '2.7.12'
    

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.

Retrieving and installing the latest version of the code

When developing Matplotlib, sources must be downloaded, built, and installed into a local environment on your machine.

Follow the instructions detailed here to set up your environment to build Matplotlib from source.

Warning

When working on Matplotlib sources, having multiple versions installed by different methods into the same environment may not always work as expected.

To work on Matplotlib sources, it is strongly recommended to set up an alternative development environment, using the something like virtual environments in python, or a conda environment.

If you choose to use an already existing environment, and not a clean virtual or conda environment, uninstall the current version of Matplotlib in that environment using the same method used to install it.

If working on Matplotlib documentation only, the above steps are not absolutely necessary.

We use Git for version control and GitHub for hosting our main repository.

You can check out the latest sources with the command (see Set up your fork for more details):

git clone https://github.com/matplotlib/matplotlib.git

and navigate to the matplotlib directory. If you have the proper privileges, you can use git@ instead of https://, which works through the ssh protocol and might be easier to use if you are using 2-factor authentication.

Building Matplotlib for image comparison tests

Matplotlib's test suite makes heavy use of image comparison tests, meaning the result of a plot is compared against a known good result. Unfortunately, different versions of FreeType produce differently formed characters, causing these image comparisons to fail. To make them reproducible, Matplotlib can be built with a special local copy of FreeType. This is recommended for all Matplotlib developers.

Prior to compiling the C-extensions, copy setup.cfg.template to setup.cfg and edit it to contain:

[test]
local_freetype = True
tests = True

or set the MPLLOCALFREETYPE environmental variable to any true value. If you have previously built Matplotlib with a different version of Freetype, you will also need to remove the c/c++ build products. Do this is to delete the build folder or git clean -xfd. If you are going to be regularly working on Matplotlib, consider putting

export MPLLOCALFREETYPE=1

in your shell start up files.

Installing Matplotlib in developer mode

To install Matplotlib (and compile the C-extensions) run the following command from the top-level directory

python -mpip install -ve .

This installs Matplotlib in 'editable/develop mode', i.e., builds everything and places the correct link entries in the install directory so that python will be able to import Matplotlib from the source directory. Thus, any changes to the *.py files will be reflected the next time you import the library. If you change the C-extension source (which might happen if you change branches) you will need to run

python setup.py build

or re-run python -mpip install -ve ..

Alternatively, if you do

python -mpip install -v .

all of the files will be copied to the installation directory however, you will have to rerun this command every time the source is changed. Additionally you will need to copy setup.cfg.template to setup.cfg and edit it to contain

[test]
local_freetype = True
tests = True

In either case you can then run the tests to check your work environment is set up properly:

pytest

Note

Additional dependencies for testing: pytest (version 3.6 or later), Ghostscript, Inkscape

Contributing 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).

The best practices for using GitHub to make PRs to Matplotlib are documented in the Development workflow section.

A brief overview is:

  1. Create an account on GitHub if you do not already have one.

  2. Fork the project repository: 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.

  3. Clone this copy to your local disk:

    $ git clone https://github.com/YourLogin/matplotlib.git
    
  4. Create a branch to hold your changes:

    $ git checkout -b my-feature origin/master
    

    and start making changes. Never work in the master branch!

  5. Work on this copy, on your computer, using Git to do the version control. 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 GitHub with:

    $ git push -u origin my-feature
    

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. You may want to consider sending an email to the mailing list for more visibility.

Contributing pull requests

It is recommended to check that your contribution complies with the following rules before submitting a pull request:

  • 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.

  • All public methods should have informative docstrings with sample usage when appropriate. Use the numpy docstring standard.

  • Formatting should follow the recommendations of PEP8. 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.

  • Each high-level plotting function should have a simple example in the Example section of the docstring. This should be as simple as possible to demonstrate the method. More complex examples should go in the examples tree.

  • Changes (both new features and bugfixes) should be tested. See Developer's tips for testing for more details.

  • 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
    
  • If your change is a major new feature, add an entry to the What's new section by adding a new file in doc/users/next_whats_new (see doc/users/next_whats_new/README.rst for more information).

  • If you change the API in a backward-incompatible way, please document it in doc/api/api_changes, by adding a new file describing your changes (see doc/api/api_changes/README.rst for more information)

  • See below for additional points about Keyword argument processing, if applicable for your pull request.

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

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

    python -mpip install coverage
    pytest --cov=matplotlib --showlocals -v
    
  • No pyflakes warnings, check with:

    python -mpip install pyflakes
    pyflakes path/to/module.py
    

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.

Issues for New Contributors

New contributors should look for the following tags when looking for issues. We strongly recommend that new contributors tackle issues labeled good first issue as they are easy, well documented issues, that do not require an understanding of the different submodules of Matplotlib. This helps the contributor become familiar with the contribution workflow, and for the core devs to become acquainted with the contributor; besides which, we frequently underestimate how easy an issue is to solve!

Other ways to contribute

Code is not the only way to contribute to Matplotlib. For instance, documentation is also a very important part of the project and often doesn't get as much attention as it deserves. If you find a typo in the documentation, or have made improvements, do not hesitate to send an email to the mailing list or submit a GitHub pull request. Full documentation can be found under the doc/ directory.

It also helps us if you spread the word: reference the project from your blog and articles or link to it from your website!

Coding guidelines

API changes

Changes to the public API must follow a standard deprecation procedure to prevent unexpected breaking of code that uses Matplotlib.

  • Deprecations must be announced via an entry in doc/api/next_api_changes.
  • Deprecations are targeted at the next point-release (i.e. 3.x.0).
  • 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.
  • If possible, usage of an deprecated API should emit a MatplotlibDeprecationWarning. There are a number of helper tools for this:
    • Use cbook.warn_deprecated() for general deprecation warnings.
    • Use the decorator @cbook.deprecated to deprecate classes, functions, methods, or properties.
    • To warn on changes of the function signature, use the decorators @cbook._delete_parameter, @cbook._rename_parameter, and @cbook._make_keyword_only.
  • Deprecated API may be removed two point-releases after they were deprecated.

Adding new API

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.

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 MANIFEST.in, and/or in package_data in setup.py.

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.

Keyword argument processing

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

# in pylab.py
def text(*args, **kwargs):
    ret =  gca().text(*args, **kwargs)
    draw_if_interactive()
    return ret

text() in simplified form looks like this, i.e., it just passes all args and kwargs on to matplotlib.text.Text.__init__():

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

and __init__() (again with liberties for illustration) just passes them on to the matplotlib.artist.Artist.update() method:

# in text.py
def __init__(self, x=0, y=0, text='', **kwargs):
    Artist.__init__(self)
    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 poping 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() statements to do your debugging, try using log.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 logger.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

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 cbook._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 cbook._warn_external (which uses warnings.warn) is that cbook._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 cbook._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 cbook._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 cbook._warn_external:

from matplotlib import cbook

def set_range(bottom, top):
    if bottom == top:
        cbook._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

Writing examples

We have hundreds of examples in subdirectories of matplotlib/examples, and these are automatically generated when the website is built to show up in the examples section of the website.

Any sample data that the example uses should be kept small and distributed with Matplotlib in the lib/matplotlib/mpl-data/sample_data/ directory. Then in your example code you can load it into a file handle with:

import matplotlib.cbook as cbook
fh = cbook.get_sample_data('mydata.dat')