Contributing guide#

Configuring git#

To get set up for contributing, make sure you have git installed on your local computer:

• On Linux, the command sudo apt install git is usually sufficient; see the official Linux instructions for more options.

• On MacOS, download the .dmg installer; Atlassian also provides more detailed instructions and alternatives such as using MacPorts or Homebrew.

• On Windows, download and install git for Windows. With Git BASH it provides its own shell that includes many Linux-equivalent command line programs that are useful for development.

  Windows 10 also offers the Windows subsystem for Linux that offers similar functionality to git BASH, but has not been widely tested by MNE-Python developers yet and may still pose problems with graphical output (e.g. building the documentation)

Once git is installed, the only absolutely necessary configuration step is identifying yourself and your contact info:

$ git config --global user.name "Your Name"
$ git config --global user.email you@yourdomain.example.com

Make sure that the same email address is associated with your GitHub account and with your local git configuration. It is possible to associate multiple emails with a GitHub account, so if you initially set them up with different emails, you can add the local email to the GitHub account.

Sooner or later, git is going to ask you what text editor you want it to use when writing commit messages, so you might as well configure that now too:

$ git config --global core.editor emacs    # or vim, or nano, or subl, or...

There are many other ways to customize git’s behavior; see configuring git for more information.

GNU Make#

We use GNU Make to organize commands or short scripts that are often needed in development. These are stored in files with the name Makefile. MNE-Python has two Makefiles, one in the package’s root directory (containing mainly testing commands) and one in doc/ (containing recipes for building our documentation pages in different ways).

To check if make is already installed type

$ make

into a terminal and you should see

make: *** No targets specified and no makefile found.  Stop.

If you don’t see this or something similar, you may not have make installed.

bash: make: command not found

bash: conda: command not found

Forking the MNE-Python repository#

Once you have git installed and configured, and before creating your local copy of the codebase, go to the MNE-Python GitHub page and create a fork into your GitHub user account.

This will create a copy of the MNE-Python codebase inside your GitHub user account (this is called “your fork”). Changes you make to MNE-Python will eventually get “pushed” to your fork, and will be incorporated into the official version of MNE-Python (often called the “upstream version”) through a “pull request”. This process will be described in detail below; a summary of how that structure is set up is given here:

Creating the virtual environment#

These instructions will set up a Python environment that is separated from your system-level Python and any other managed Python environments on your computer. This lets you switch between different versions of Python (MNE-Python requires version 3.7 or higher) and also switch between the stable and development versions of MNE-Python (so you can, for example, use the same computer to analyze your data with the stable release, and also work with the latest development version to fix bugs or add new features). Even if you’ve already followed the installation instructions for the stable version of MNE-Python, you should now repeat that process to create a new, separate environment for MNE-Python development (here we’ll give it the name mnedev):

$ curl --remote-name https://raw.githubusercontent.com/mne-tools/mne-python/main/environment.yml
$ conda env create --file environment.yml --name mnedev
$ conda activate mnedev

Now you’ll have two MNE-Python environments: mne (or whatever custom name you used when installing the stable version of MNE-Python) and mnedev that we just created. At this point mnedev also has the stable version of MNE-Python (that’s what the environment.yml file installs), but we’re about to remove the stable version from mnedev and replace it with the development version. To do that, we’ll clone the MNE-Python repository from your remote fork, and also connect the local copy to the upstream version of the codebase, so you can stay up-to-date with changes from other contributors. First, edit these two variables for your situation:

$ GITHUB_USERNAME="insert_your_actual_GitHub_username_here"
$ # pick where to put your local copy of MNE-Python development version:
$ INSTALL_LOCATION="/opt"

Then make a local clone of your remote fork (origin):

$ cd $INSTALL_LOCATION
$ git clone https://github.com/$GITHUB_USERNAME/mne-python.git

Finally, set up a link between your local clone and the official repository (upstream):

$ cd mne-python
$ git remote add upstream https://github.com/mne-tools/mne-python.git
$ git fetch --all

Now we’ll remove the stable version of MNE-Python and replace it with the development version (the clone we just created with git). Make sure you’re in the correct environment first (conda activate mnedev), and then do:

$ cd $INSTALL_LOCATION/mne-python    # make sure we're in the right folder
$ conda remove --force mne  # the --force avoids dependency checking
$ pip install -e .

The command pip install -e . installs a python module into the current environment by creating a link to the source code directory (instead of copying the code to pip’s site_packages directory, which is what normally happens). This means that any edits you make to the MNE-Python source code will be reflected the next time you open a Python interpreter and import mne (the -e flag of pip stands for an “editable” installation).

Finally, we’ll add a few dependencies that are not needed for running MNE-Python, but are needed for locally running our test suite:

$ pip install -r requirements_testing.txt

And for building our documentation:

$ pip install -r requirements_doc.txt
$ conda install graphviz

WARNING: dot command 'dot' cannot be run (needed for graphviz output), check the graphviz_dot setting

$ PATH=$CONDA_PREFIX\\Library\\bin\\graphviz:$PATH

To build documentation, you will also require optipng:

• On Linux, use the command sudo apt install optipng.

• On MacOS, optipng can be installed using Homebrew.

• On Windows, unzip optipng.exe from the optipng for Windows archive into the doc/ folder. This step is optional for Windows users.

You can also choose to install some optional linters for reStructuredText:

$ conda install -c conda-forge sphinx-autobuild doc8

Basic git commands#

Learning to work with git can take a long time, because it is a complex and powerful tool for managing versions of files across multiple users, each of whom have multiple copies of the codebase. We’ve already seen in the setup commands above a few of the basic git commands useful to an MNE-Python developer:

• git clone <URL_OF_REMOTE_REPO> (make a local copy of a repository)

• git remote add <NICKNAME_OF_REMOTE> <URL_OF_REMOTE_REPO> (connect a local copy to an additional remote)

• git fetch --all (get the current state of connected remote repos)

Other commands that you will undoubtedly need relate to branches. Branches represent multiple copies of the codebase within a local clone or remote repo. Branches are typically used to experiment with new features while still keeping a clean, working copy of the original codebase that you can switch back to at any time. The default branch of any repo is called main, and it is recommended that you reserve the main branch to be that clean copy of the working upstream codebase. Therefore, if you want to add a new feature, you should first synchronize your local main branch with the upstream repository, then create a new branch based off of main and check it out so that any changes you make will exist on that new branch (instead of on main):

$ git checkout main            # switch to local main branch
$ git fetch upstream             # get the current state of the remote upstream repo
$ git merge upstream/main      # synchronize local main branch with remote upstream main branch
$ git checkout -b new-feature-x  # create local branch "new-feature-x" and check it out

Now that you’re on a new branch, you can fix a bug or add a new feature, add a test, update the documentation, etc. When you’re done, it’s time to organize your changes into a series of commits. Commits are like snapshots of the repository — actually, more like a description of what has to change to get from the most recent snapshot to the current snapshot.

Git knows that people often work on multiple changes in multiple files all at once, but that ultimately they should separate those changes into sets of related changes that are grouped together based on common goals (so that it’s easier for their colleagues to understand and review the changes). For example, you might want to group all the code changes together in one commit, put new unit tests in another commit, and changes to the documentation in a third commit. Git makes this possible with something called the stage (or staging area). After you’ve made some changes to the codebase, you’ll have what git calls “unstaged changes”, which will show up with the status command:

$ git status    # see what state the local copy of the codebase is in

Those unstaged changes can be added to the stage one by one, by either adding a whole file’s worth of changes, or by adding only certain lines interactively:

$ git add mne/some_file.py      # add all the changes you made to this file
$ git add mne/some_new_file.py  # add a completely new file in its entirety
$ # enter interactive staging mode, to add only portions of a file:
$ git add -p mne/viz/some_other_file.py

Once you’ve collected all the related changes together on the stage, the git status command will now refer to them as “changes staged for commit”. You can commit them to the current branch with the commit command. If you just type git commit by itself, git will open the text editor you configured it to use so that you can write a commit message — a short description of the changes you’ve grouped together in this commit. You can bypass the text editor by passing a commit message on the command line with the -m flag. For example, if your first commit adds a new feature, your commit message might be:

$ git commit -m 'ENH: adds feature X to the Epochs class'

Once you’ve made the commit, the stage is now empty, and you can repeat the cycle, adding the unit tests and documentation changes:

$ git add mne/tests/some_testing_file.py
$ git commit -m 'add test of new feature X of the Epochs class'
$ git add -p mne/some_file.py mne/viz/some_other_file.py
$ git commit -m 'DOC: update Epochs and BaseEpochs docstrings'
$ git add tutorials/new_tutorial_file.py
$ git commit -m 'DOC: adds new tutorial about feature X'

When you’re done, it’s time to run the test suite to make sure your changes haven’t broken any existing functionality, and to make sure your new test covers the lines of code you’ve added (see Running Tests and Building the documentation, below). Once everything looks good, it’s time to push your changes to your fork:

$ # push local changes to remote branch origin/new-feature-x
$ # (this will create the remote branch if it doesn't already exist)
$ git push origin new-feature-x

Finally, go to the MNE-Python GitHub page, click on the pull requests tab, click the “new pull request” button, and choose “compare across forks” to select your new branch (new-feature-x) as the “head repository”. See the GitHub help page on creating a PR from a fork for more information about opening pull requests.

If any of the tests failed before you pushed your changes, try to fix them, then add and commit the changes that fixed the tests, and push to your fork. If you’re stuck and can’t figure out how to fix the tests, go ahead and push your commits to your fork anyway and open a pull request (as described above), then in the pull request you should describe how the tests are failing and ask for advice about how to fix them.

To learn more about git, check out the GitHub help website, the GitHub Learning Lab tutorial series, and the pro git book.

Connecting to GitHub with SSH (optional)#

One easy way to speed up development is to reduce the number of times you have to type your password. SSH (secure shell) allows authentication with pre-shared key pairs. The private half of your key pair is kept secret on your computer, while the public half of your key pair is added to your GitHub account; when you connect to GitHub from your computer, the local git client checks the remote (public) key against your local (private) key, and grants access your account only if the keys fit. GitHub has several help pages that guide you through the process.

Once you have set up GitHub to use SSH authentication, you should change the addresses of your MNE-Python GitHub remotes, from https:// addresses to git@ addresses, so that git knows to connect via SSH instead of HTTPS. For example:

$ git remote -v  # show existing remote addresses
$ git remote set-url origin git@github.com:$GITHUB_USERNAME/mne-python.git
$ git remote set-url upstream git@github.com:mne-tools/mne-python.git

General requirements#

from mne.utils import deprecated

@deprecated('my_function is deprecated and will be removed in 0.XX; please '
            'use my_new_function instead.')
def my_function():
   return 'foo'

from mne.utils import warn

def my_other_function(new_param=None, old_param=None):
    if old_param is not None:
        depr_message = ('old_param is deprecated and will be replaced by '
                        'new_param in 0.XX.')
        if new_param is None:
            new_param = old_param
            warn(depr_message, DeprecationWarning)
        else:
            warn(depr_message + ' Since you passed values for both '
                 'old_param and new_param, old_param will be ignored.',
                 DeprecationWarning)
    # Do whatever you have to do with new_param
    return 'foo'

# test deprecation warning for function
with pytest.warns(DeprecationWarning, match='my_function is deprecated'):
    my_function()

# test deprecation warning for parameter
with pytest.warns(DeprecationWarning, match='values for both old_param'):
    my_other_function(new_param=1, old_param=2)
with pytest.warns(DeprecationWarning, match='old_param is deprecated and'):
    my_other_function(old_param=2)

Bugs
----

- Short description of the changes (:gh:`0000` by :newcontrib:`Firstname Lastname`)

- ...

.. _Your Name: https://www.your-website.com/

- Short description of the changes from one contributor (:gh:`0000` by `Contributor Name`_)
- Short description of the changes from several contributors (:gh:`0000` by `Contributor Name`_, `Second Contributor`_, and `Third Contributor`_)

- Short description of the changes from one contributor in multiple PRs (:gh:`0000`, :gh:`1111` by `Contributor Name`_)
- Short description of the changes from several contributors in multiple PRs (:gh:`0000`, :gh:`1111` by `Contributor Name`_, `Second Contributor`_, and `Third Contributor`_)

$ pytest --cov=mne.viz --cov-report=term-missing mne/viz/tests/test_evoked.py mne/viz/tests/test_topo.py

Code style#

$ make flake

$ pytest mne/tests/test_docstring_parameters.py
$ make docstyle

$ python -m sphinx.ext.intersphinx https://docs.python.org/3/objects.inv > python.txt

Code organization#

Running the test suite#

The full test suite can be run by calling make test from the mne-python root folder. Testing the entire module can be quite slow, however, so to run individual tests while working on a new feature, you can run the following line:

$ pytest mne/tests/test_evoked.py::test_io_evoked --verbose

Or alternatively:

$ pytest mne/tests/test_evoked.py -k test_io_evoked --verbose

Make sure you have the testing dataset, which you can get by running this in a Python interpreter:

>>> mne.datasets.testing.data_path(verbose=True)  

Building the documentation#

Our documentation (including docstrings in code files) is in reStructuredText format and is built using Sphinx and Sphinx-Gallery. The easiest way to ensure that your contributions to the documentation are properly formatted is to follow the style guidelines on this page, imitate existing documentation examples, refer to the Sphinx and Sphinx-Gallery reference materials when unsure how to format your contributions, and build the docs locally to confirm that everything looks correct before submitting the changes in a pull request.

You can build the documentation locally using GNU Make with doc/Makefile. From within the doc directory, you can test formatting and linking by running:

$ make html_dev-noplot

This will build the documentation except it will format (but not execute) the tutorial and example files. If you have created or modified an example or tutorial, you should instead run PATTERN=<REGEX_TO_SELECT_MY_TUTORIAL> make html_dev-pattern to render all the documentation and additionally execute just your example or tutorial (so you can make sure it runs successfully and generates the output / figures you expect).

> set PATTERN=<REGEX_TO_SELECT_MY_TUTORIAL>
> make html_dev-pattern

After either of these commands completes, make show will open the locally-rendered documentation site in your browser. Additional make recipes are available; run make help from the doc directory or consult the Sphinx-Gallery documentation for additional details.

Modifying command-line tools#

MNE-Python provides support for a limited set of Command line tools using Python. These are typically used with a call like:

$ mne browse_raw ~/mne_data/MNE-sample-data/MEG/sample/sample_audvis_raw.fif

These are generally available for convenience, and can be useful for quick debugging (in this case, for mne.io.Raw.plot).

If a given command-line function fails, they can also be executed as part of the mne module with python -m. For example:

$ python -i -m mne browse_raw ...

Because this was launched with python -i, once the script completes it will drop to a Python terminal. This is useful when there are errors, because then you can drop into a post-mortem debugger:

>>> import pdb; pdb.pm()  

GitHub workflow#

Nearly everyone in the community of MNE-Python contributors and maintainers is a working scientist, engineer, or student who contributes to MNE-Python in their spare time. For that reason, a set of best practices have been adopted to streamline the collaboration and review process. Most of these practices are common to many open-source software projects, so learning to follow them while working on MNE-Python will bear fruit when you contribute to other projects down the road. Here are the guidelines:

• Search the GitHub issues page (both open and closed issues) in case someone else has already started work on the same bugfix or feature. If you don’t find anything, open a new issue to discuss changes with maintainers before starting work on your proposed changes.

• Implement only one new feature or bugfix per pull request (PR). Occasionally it may make sense to fix a few related bugs at once, but this makes PRs harder to review and test, so check with MNE-Python maintainers first before doing this. Avoid purely cosmetic changes to the code; they make PRs harder to review.

• It is usually better to make PRs from branches other than your main branch, so that you can use your main branch to easily get back to a working state of the code if needed (e.g., if you’re working on multiple changes at once, or need to pull in recent changes from someone else to get your new feature to work properly).

• In most cases you should make PRs into the upstream’s main branch, unless you are specifically asked by a maintainer to PR into another branch (e.g., for backports or maintenance bugfixes to the current stable version).

• Don’t forget to include in your PR a brief description of the change in the changelog (doc/whats_new.rst).

• Our community uses the following commit tags and conventions:

  • Work-in-progress PRs should be created as draft PRs and the PR title should begin with WIP.

  • When you believe a PR is ready to be reviewed and merged, convert it from a draft PR to a normal PR, change its title to begin with MRG, and add a comment to the PR asking for reviews (changing the title does not automatically notify maintainers).

  • PRs that only affect documentation should additionally be labelled DOC, bugfixes should be labelled FIX, and new features should be labelled ENH (for “enhancement”). STY is used for style changes (i.e., improving docstring consistency or formatting without changing its content).

  • the following commit tags are used to interact with our continuous integration (CI) providers. Use them judiciously; do not skip tests simply because they are failing:

    • [skip circle] Skip CircleCI, which tests successful building of our documentation.

    • [skip actions] Skip our GitHub Actions, which test installation and execution on Linux and macOS systems.

    • [skip azp] Skip azure which tests installation and execution on Windows systems.

    • [ci skip] is an alias for [skip actions][skip azp][skip circle]. Notice that [skip ci] is not a valid tag.

    • [circle full] triggers a “full” documentation build, i.e., all code in tutorials and how-to examples will be executed (instead of just nicely formatted) and the resulting output and figures will be rendered as part of the tutorial/example.

This sample pull request exemplifies many of the conventions listed above: it addresses only one problem; it started with an issue to discuss the problem and some possible solutions; it is a PR from the user’s non-main branch into the upstream main branch; it separates different kinds of changes into separate commits and uses labels like DOC, FIX, and STY to make it easier for maintainers to review the changeset; etc. If you are new to GitHub it can serve as a useful example of what to expect from the PR review process.