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# Contributing

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

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 working on the Matplotlib source, setting up a virtual environment or a conda environment is recommended.

Warning

If you already have a version of Matplotlib installed, use an virtual environment or uninstall using the same method you used to install it. Installing multiple versions of Matplotlib via different methods into the same environment may not always work as expected.

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.

To make sure the tests run locally you must build against the correct version of freetype. To configure the build system to fetch and build it either export the env `MPLLOCALFREETYPE` as:

    export MPLLOCALFREETYPE=1

or copy `setup.cfg.template` to `setup.cfg` and edit it to contain

    [test]
    local_freetype = True

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

    pip install -v -e ./

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 `pip install -v -e ./`.

Alternatively, if you do

    pip 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:

    python tests.py

Note

Additional dependencies for testing: pytest (version 3.0 or later), mock (if python < 3.3), Ghostscript, Inkscape

See also

- Developer’s tips for testing

## 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):

> 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.

See also

- Git documentation
- Development workflow.
- Working with Matplotlib source code

### 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 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 PEP8 recommendation. 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/whats_new` (see `doc/users/whats_new/README` 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` for more information)

> - See below for additional points about Keyword argument processing, if code in your pull request does that.

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:

>       pip install coverage
>       python tests.py --with-coverage

> - No pyflakes warnings, check with:

>       pip 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.

See also

- Coding guidelines
- Developer’s tips for testing
- Developer’s tips for documenting matplotlib

### Issues for New Contributors

New contributors should look for the following tags when looking for issues. We strongly recommend that new contributors tackle new-contributor-friendly issues (easy, well documented issues, that do not require an understanding of the different submodules of Matplotlib) and Easy-fix issues. 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

### 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. You can `pop` the ones to be used locally and pass on the rest. 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, **kwargs):
        scalex = kwargs.pop('scalex', True)
        scaley = kwargs.pop('scaley', True)
        if not self._hold: self.cla()
        lines = []
        for line in self._get_lines(*args, **kwargs):
            self.add_line(line)
            lines.append(line)

Note: there is a use case when `kwargs` are meant to be used locally in the function (not passed on), but you still need the `**kwargs` idiom. That is when you want to use `*args` to allow variable numbers of non-keyword args. In this case, python will not allow you to use named keyword args after the `*args` usage, so you will be forced to use `**kwargs`. An example is `matplotlib.contour.ContourLabeler.clabel()`:

    # in contour.py
    def clabel(self, *args, **kwargs):
        fontsize = kwargs.get('fontsize', None)
        inline = kwargs.get('inline', 1)
        self.fmt = kwargs.get('fmt', '%1.3f')
        colors = kwargs.get('colors', None)
        if len(args) == 0:
            levels = self.levels
            indices = range(len(self.levels))
        elif len(args) == 1:
           ...etc...

### Developing a new backend

If you are working on a custom backend, the backend setting in `matplotlibrc` (Customizing matplotlib) supports an external backend via the `module` directive. If `my_backend.py` is a Matplotlib backend in your `PYTHONPATH`, you can set it on one of several ways

- in `matplotlibrc`:

      backend : module://my_backend

- with the `MPLBACKEND` environment variable:

      > export MPLBACKEND="module://my_backend"
      > python simple_plot.py

- with the use directive in your script:

      import matplotlib
      matplotlib.use('module://my_backend')

### 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')