Contributing guide#

Thanks for taking the time to contribute! MNE-Python is an open-source project sustained mostly by volunteer effort. We welcome contributions from anyone as long as they abide by our Code of Conduct.

There are lots of ways to contribute, such as:

Use the software, and when you find bugs, tell us about them! We can only fix the bugs we know about.
Answer questions on our user forum.
Tell us about parts of the documentation that you find confusing or unclear.
Tell us about things you wish MNE-Python could do, or things it can do but you wish they were easier.
Improve the accessibility of our website.
Fix bugs.
Fix mistakes in our function documentation strings.
Implement new features.
Improve existing tutorials or write new ones.
Contribute to one of the many Python packages that MNE-Python depends on.

To report bugs, request new features, or ask about confusing documentation, it’s usually best to open a new issue on our user forum first; you’ll probably get help fastest that way, and it helps keep our GitHub issue tracker focused on things that we know will require changes to our software (as opposed to problems that can be fixed in the user’s code). We may ultimately ask you to open an issue on GitHub too, but starting on the forum helps us keep things organized. For fastest results, be sure to include information about your operating system and MNE-Python version, and (if applicable) include a reproducible code sample that is as short as possible and ideally uses one of our example datasets.

If you want to fix bugs, add new features, or improve our docstrings/tutorials/website, those kinds of contributions are made through our GitHub repository. The rest of this page explains how to set up your workflow to make contributing via GitHub as easy as possible.

Overview of contribution process#

Note

Reminder: all contributors are expected to follow our code of conduct.

Changes to MNE-Python are typically made by forking the MNE-Python repository, making changes to your fork (usually by cloning it to your personal computer, making the changes locally, and then pushing the local changes up to your fork on GitHub), and finally creating a pull request to incorporate your changes back into the shared “upstream” version of the codebase.

In general you’ll be working with three different copies of the MNE-Python codebase: the official remote copy at mne-tools/mne-python (usually called upstream), your remote fork of the upstream repository (similar URL, but with your username in place of mne-tools, and usually called origin), and the local copy of the codebase on your computer. The typical contribution process is to:

synchronize your local copy with upstream
make changes to your local copy
push your changes to origin (your remote fork of the upstream)
submit a pull request from your fork into upstream

The sections Basic git commands and GitHub workflow (below) describe this process in more detail.

Setting up your local development environment#

Configuring git#

Git GUI alternative

GitHub desktop is a GUI alternative to command line git that some users appreciate; it is available for Windows and MacOS.

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.

Linux

Get make for Linux

macOS

Get make for macOS

Windows

If you see:

bash: make: command not found

Install make for git BASH (which comes with git for Windows):

Download make-newest.version-without-guile-w32-bin.zip from ezwinports
Extract zip-folder
Copy the contents into path_to_git\mingw64\ (e.g. by merging the folders with the equivalent ones already inside)
For the first time using git BASH, you need to run once (to be able to activate your mnedev environment):
```
$ conda init bash
```

If instead you see an error like:

bash: conda: command not found

at the top of your git BASH window, you need to add

path_to_Anaconda
path_to_Anaconda\Scripts

to Windows-PATH first.

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#

Supported Python environments

We strongly recommend the Anaconda or Miniconda environment managers for Python. Other setups are possible but are not officially supported by the MNE-Python development team; see discussion here. These instructions use conda where possible; experts may replace those lines with some combination of git and pip.

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 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"

Note

On Windows, add set before the variable names (set GITHUB_USERNAME=..., etc.).

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) and set up git diff to work properly:

$ cd mne-python
$ git remote add upstream https://github.com/mne-tools/mne-python.git
$ git fetch --all
$ git config --local blame.ignoreRevsFile .git-blame-ignore-revs

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 -e ".[test]"

And for building our documentation:

$ pip install -e ".[doc]"
$ conda install graphviz

Note

On Windows, if you installed graphviz using the conda command above but still get an error like this:

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

try adding the graphviz folder to path:

$ 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.

There are additional optional dependencies needed to run various tests, such as scikit-learn for decoding tests, or nibabel for MRI tests. If you want to run all the tests, consider using our MNE installers (which provide these dependencies) or pay attention to the skips that pytest reports and install the relevant libraries. For example, this traceback:

SKIPPED [2] mne/io/eyelink/tests/test_eyelink.py:14: could not import 'pandas': No module named 'pandas'

indicates that pandas needs to be installed in order to run the Eyelink tests.

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

Tip

You can save some typing by using git pull upstream/main to replace the fetch and merge lines above.

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 the test suite 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 skills 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

MNE-Python coding conventions#

General requirements#

All new functionality must have test coverage#

For example, a new mne.Evoked method in mne/evoked.py should have a corresponding test in mne/tests/test_evoked.py.

All new functionality must be documented#

This includes thorough docstring descriptions for all public API changes, as well as how-to examples or longer tutorials for major contributions. Docstrings for private functions may be more sparse, but should usually not be omitted.

Avoid API changes when possible#

Changes to the public API (e.g., class/function/method names and signatures) should not be made lightly, as they can break existing user scripts. Changes to the API require a deprecation cycle (with warnings) so that users have time to adapt their code before API changes become default behavior. See the deprecation section and mne.utils.deprecated for instructions. Bug fixes (when something isn’t doing what it says it will do) do not require a deprecation cycle.

Note that any new API elements should be added to the main reference; classes, functions, methods, and attributes cannot be cross-referenced unless they are included in the Python API Reference (doc/python_reference.rst).

Deprecate with a decorator or a warning#

MNE-Python has a deprecated() decorator for classes and functions that will be removed in a future version:

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'

If you need to deprecate a parameter, use mne.utils.warn(). For example, to rename a parameter from old_param to new_param you can do something like this:

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, FutureWarning)
        else:
            warn(depr_message + ' Since you passed values for both '
                 'old_param and new_param, old_param will be ignored.',
                 FutureWarning)
    # Do whatever you have to do with new_param
    return 'foo'

When deprecating, you should also add corresponding test(s) to the relevant test file(s), to make sure that the warning(s) are being issued in the conditions you expect:

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

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

You should also search the codebase for any cases where the deprecated function or parameter are being used internally, and update them immediately (don’t wait to the end of the deprecation cycle to do this). Later, at the end of the deprecation period when the stated release is being prepared:

delete the deprecated functions
remove the deprecated parameters (along with the conditional branches of my_other_function that handle the presence of old_param)
remove the deprecation tests
double-check for any other tests that relied on the deprecated test or parameter, and (if found) update them to use the new function / parameter.

Describe your changes in the changelog#

Include in your changeset a brief description of the change in the changelog using towncrier format, which aggregates small, properly-named .rst files to create a changelog. This can be skipped for very minor changes like correcting typos in the documentation.

There are six separate sections for changes, based on change type. To add a changelog entry to a given section, name it as doc/changes/devel/<PR-number>.<type>.rst. The types are:

notable: For overarching changes, e.g., adding type hints package-wide. These are rare.
dependency: For changes to dependencies, e.g., adding a new dependency or changing the minimum version of an existing dependency.
bugfix: For bug fixes. Can change code behavior with no deprecation period.
apichange: Code behavior changes that require a deprecation period.
newfeature: For new features.
other: For changes that don’t fit into any of the above categories, e.g., internal refactorings.

For example, for an enhancement PR with number 12345, the changelog entry should be added as a new file doc/changes/devel/12345.enhancement.rst. The file should contain:

A brief description of the change, typically in a single line of one or two sentences.
reST links to public API endpoints like functions (:func:), classes (:class:), and methods (:meth:). If changes are only internal to private functions/attributes, mention internal refactoring rather than name the private attributes changed.
Author credit. If you are a new contributor (we’re very happy to have you here! 🤗), you should using the :newcontrib: reST role, whereas previous contributors should use a standard reST link to their name. For example, a new contributor could write:
```
Short description of the changes, by :newcontrib:`Firstname Lastname`.
```
And an previous contributor could write:
```
Short description of the changes, by `Firstname Lastname`_.
```

Make sure that your name is included in the list of authors in doc/changes/names.inc, otherwise the documentation build will fail. To add an author name, append a line with the following pattern (note how the syntax is different from that used in the changelog):

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

Many contributors opt to link to their GitHub profile that way. Have a look at the existing entries in the file to get some inspiration.

Sometimes, changes that shall appear as a single changelog entry are spread out across multiple PRs. In this case, edit the existing towncrier file for the relevant change, and append additional PR numbers in parentheticals with the :gh: role like:

Short description of the changes, by `Firstname Lastname`_. (:gh:`12346`)

Test locally before opening pull requests (PRs)#

MNE-Python uses continuous integration (CI) to ensure code quality and test across multiple installation targets. However, the CIs are often slower than testing locally, especially when other contributors also have open PRs (which is basically always the case). Therefore, do not rely on the CIs to catch bugs and style errors for you; run the tests locally instead before opening a new PR and before each time you push additional changes to an already-open PR.

Make tests fast and thorough#

Whenever possible, use the testing dataset rather than one of the sample datasets when writing tests; it includes small versions of most MNE-Python objects (e.g., Raw objects with short durations and few channels). You can also check which lines are missed by the tests, then modify existing tests (or write new ones) to target the missed lines. Here’s an example that reports which lines within mne.viz are missed when running test_evoked.py and test_topo.py:

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

You can also use pytest --durations=5 to ensure new or modified tests will not slow down the test suite too much.

Code style#

Adhere to standard Python style guidelines#

All contributions to MNE-Python are checked against style guidelines described in PEP 8. We also check for common coding errors (such as variables that are defined but never used). We allow very few exceptions to these guidelines, and use tools such as ruff to check code style automatically. From the mne-python root directory, you can check for style violations by first installing our pre-commit hook:

$ pip install pre-commit
$ pre-commit install --install-hooks

Then running:

$ make ruff  # alias for `pre-commit run -a`

in the shell. Several text editors or IDEs also have Python style checking, which can highlight style errors while you code (and train you to make those errors less frequently). This functionality is built-in to the Spyder IDE, but most editors have plug-ins that provide similar functionality. Search for python linter <name of your favorite editor> to learn more.

Use consistent variable naming#

Classes should be named using CamelCase. Functions and instances/variables should use snake_case (n_samples rather than nsamples). Avoid single-character variable names, unless inside a comprehension or generator.

We (mostly) follow NumPy style for docstrings#

In most cases you can look at existing MNE-Python docstrings to figure out how yours should be formatted. If you can’t find a relevant example, consult the Numpy docstring style guidelines for examples of more complicated formatting such as embedding example code, citing references, or including rendered mathematics. Note that we diverge from the NumPy docstring standard in a few ways:

We use a module called sphinxcontrib-bibtex to render citations. Search our source code (git grep footcite and git grep footbibliography) to see examples of how to add in-text citations and formatted references to your docstrings, examples, or tutorials. The structured bibliographic data lives in doc/references.bib; please follow the existing key scheme when adding new references (e.g., Singleauthor2019, AuthoroneAuthortwo2020, FirstauthorEtAl2021a, FirstauthorEtAl2021b).
We don’t explicitly say “optional” for optional keyword parameters (because it’s clear from the function or method signature which parameters have default values).
For parameters that may take multiple types, we use pipe characters instead of the word “or”, like this: param_name : str | None.
We don’t include a Raises or Warns section describing errors/warnings that might occur.

Private function/method docstrings may be brief for simple functions/methods, but complete docstrings are appropriate when private functions/methods are relatively complex. To run some basic tests on documentation, you can use:

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

Cross-reference everywhere#

Both the docstrings and dedicated documentation pages (tutorials, how-to examples, discussions, and glossary) should include cross-references to any mentioned module, class, function, method, attribute, or documentation page. There are sphinx roles for all of these (:mod:, :class:, :func:, :meth:, :attr:, :doc:) as well as a generic cross-reference directive (:ref:) for linking to specific sections of a documentation page.

Warning

Some API elements have multiple exposure points (for example, mne.set_config and mne.utils.set_config). For cross-references to work, they must match an entry in doc/python_reference.rst (thus :func:`mne.set_config` will work but :func:`mne.utils.set_config` will not).

MNE-Python also uses Intersphinx, so you can (and should) cross-reference to Python built-in classes and functions as well as API elements in NumPy, SciPy, etc. See the Sphinx configuration file (doc/conf.py) for the list of Intersphinx projects we link to. Their inventories can be examined using a tool like sphobjinv or dumped to file with commands like:

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

Note that anything surrounded by single backticks that is not preceded by one of the API roles (:class:, :func:, etc) will be assumed to be in the MNE-Python namespace. This can save some typing especially in tutorials; instead of see :func:`mne.io.Raw.plot_psd` for details you can instead type see `mne.io.Raw.plot_psd` for details.

Other style guidance#

Use single quotes whenever possible.
Prefer generators or comprehensions over filter(), map() and other functional idioms.
Use explicit functional constructors for builtin containers to improve readability (e.g., list(), dict(), set()).
Avoid nested functions or class methods if possible — use private functions instead.
Avoid *args and **kwargs in function/method signatures.

Code organization#

Importing#

Import modules in this order, preferably alphabetized within each subsection:

Python built-in (copy, functools, os, etc.)
NumPy (numpy as np) and, in test files, pytest (pytest)
MNE-Python imports (e.g., from .pick import pick_types)

When importing from other parts of MNE-Python, use relative imports in the main codebase and absolute imports in tests, tutorials, and how-to examples. Imports for matplotlib, scipy, and optional modules (sklearn, pandas, etc.) should be nested (i.e., within a function or method, not at the top of a file). This helps reduce import time and limit hard requirements for using MNE.

Return types#

Methods should modify inplace and return self, functions should return copies (where applicable). Docstrings should always give an informative name for the return value, even if the function or method’s return value is never stored under that name in the code.

Visualization#

Visualization capabilities should be made available in both function and method forms. Add public visualization functions to the mne.viz submodule, and call those functions from the corresponding object methods. For example, the method mne.Epochs.plot() internally calls the function mne.viz.plot_epochs().

All visualization functions must accept a boolean show parameter and typically return a matplotlib.figure.Figure (or a list of Figure objects). 3D visualization functions return a mne.viz.Figure3D, mne.viz.Brain, or other return type as appropriate.

Visualization functions should default to the colormap RdBu_r for signed data with a meaningful middle (zero-point) and Reds otherwise. This applies to both visualization functions and tutorials/examples.

Running the test suite#

pytest flags

The -x flag exits the pytest run when any test fails; this can speed up debugging when running all tests in a file or module.

The --pdb flag will automatically start the python debugger upon test failure.

The full test suite can be run by calling pytest -m "not ultraslowtest" mne 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-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 make html-pattern PATTERN=<REGEX_TO_SELECT_MY_TUTORIAL> 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).

After either of these commands completes, make show will open the locally-rendered documentation site in your browser. If you see many warnings that seem unrelated to your contributions, it might be that your output folder for the documentation build contains old, now irrelevant, files. Running make clean will clean those up. 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.
Examples and tutorials should execute as quickly and with as low memory usage as possible while still conveying necessary information. To see current execution times and memory usage, visit the sg_execution_times page. To see unused API entries, see the sg_api_usage page.

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.