:orphan:

.. _contributing:

Contributing to MNE-Python
==========================

.. contents:: Page contents
   :local:
   :depth: 2

.. highlight:: console

.. NOTE: this first section (up until "overview of contribution process") is
   basically a copy/paste of CONTRIBUTING.rst from the repository root, with
   one sentence deleted to avoid self-referential linking. Changes made here
   should be mirrored there, and vice-versa.

MNE-Python is maintained by a community of scientists and research labs, and
accepts contributions in the form of bug reports, fixes, feature additions, and
documentation improvements (even just typo corrections). The best way to start
contributing is by `opening an issue`_ on our GitHub page to tell us about
errors you are encountering or to discuss your ideas for changes. For general
troubleshooting, you can also write to the `MNE mailing list`_ or chat with
developers on the `MNE gitter channel`_.

Changes 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, and then `pushing`_ the local changes up to your fork), and
finally creating a `pull request`_ to incorporate your changes back into the
shared "upstream" version of the codebase.

Users and contributors to MNE-Python are expected to follow our `code of
conduct`_.

.. collapse:: |rocket| Want an example to work through?
   :class: success

   A great way to learn to contribute is to work through an actual example
   We recommend that you take a look at the `GitHub issues marked "easy"`_,
   pick one that looks interesting, and work through it while reading this
   guide!

.. _`opening an issue`: https://github.com/mne-tools/mne-python/issues/new/choose
.. _`MNE mailing list`: http://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis
.. _`MNE gitter channel`: https://gitter.im/mne-tools/mne-python

.. _`forking`: https://help.github.com/en/articles/fork-a-repo
.. _`cloning`: https://help.github.com/en/articles/cloning-a-repository
.. _`pushing`: https://help.github.com/en/articles/pushing-to-a-remote
.. _`pull request`: https://help.github.com/en/articles/creating-a-pull-request-from-a-fork

.. _`code of conduct`: https://github.com/mne-tools/mne-python/blob/master/CODE_OF_CONDUCT.md
.. _`GitHub issues marked "easy"`:

Overview of contribution process
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

In general you'll be working with three different copies of the MNE-Python
codebase: the official remote copy at https://github.com/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:

1. synchronize your local copy with ``upstream``

2. make changes to your local copy

3. `push`_ your changes to ``origin`` (your remote fork of the upstream)

4. submit a `pull request`_ from your fork into ``upstream``

The sections :ref:`basic-git` and :ref:`github-workflow` (below) describe this
process in more detail.


Setting up your local environment for MNE-Python development
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Configuring git
~~~~~~~~~~~~~~~

.. sidebar:: Git GUI alternative

    `GitHub desktop`_ is a GUI alternative to command line git that some users
    appreciate; it is available for |windows| Windows and |apple| 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, we recommend `git Bash`_ rather than the `official Windows
  version of git`_, because git Bash 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.


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, just 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. 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.


Setting up the development environment
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. sidebar:: 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 :ref:`here
    <other-py-distros>`. 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 (MNE-Python requires
version 3.5 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). If you've already
followed the :ref:`installation instructions <install-python>` for the stable
version of MNE-Python, MNE-Python will be installed in the environment ``base``
(unless you provided a custom environment name during installation). Before
continuing, you should create a new, separate environment for MNE-Python
development (here we'll call it ``mnedev``)::

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

Now you'll have *two* MNE-Python environments: ``base`` (or whatever you called
it) for the stable version and ``mnedev`` for the development version. Next
`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::

    $ INSTALL_LOCATION="/opt"
    $ GITHUB_USERNAME="new_mne_contributor"

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

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

.. sidebar:: Remote URLs in git

    Here we use ``git://`` instead of ``https://`` in the URL for the
    ``upstream`` remote repository. ``git://`` URLs are read-only, so you can
    *pull* changes from ``upstream`` into your local copy (to stay up-to-date
    with changes from other contributors) but you cannot *push* changes from
    your computer into the ``upstream`` remote. Instead, you must push your
    changes to your own remote fork (``origin``) first, and then create a pull
    request from your remote into the upstream remote. In :ref:`a later section
    <github-ssh>` you'll see a third kind of remote URL for connecting to
    GitHub using SSH.

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

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

When you created the ``mnedev`` environment using the environment file, it
installed the *stable* version of MNE-Python, so next we'll remove that and
replace it with the *development* version (the clone we just created with git).
Make sure you're in the correct environment first (:samp:`conda activate
mnedev`), and then do::

    $ cd $INSTALL_LOCATION/mne-python    # make sure we're in the right folder
    $ pip uninstall -y mne
    $ 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 or building our
documentation::

    $ pip install sphinx sphinx-gallery sphinx_bootstrap_theme sphinx_fontawesome memory_profiler
    $ conda install sphinx-autobuild doc8  # linter packages for reStructuredText (optional)


.. _basic-git:

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:

- :samp:`git clone {<URL_OF_REMOTE_REPO>}` (make a local copy of a repository)

- :samp:`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 always called ``master``, and
it is recommended that you reserve the ``master`` 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 ``master`` branch with the
``upstream`` repository, then create a new branch based off of ``master`` and
`check it out`_ so that any changes you make will exist on that new branch
(instead of on ``master``)::

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

.. sidebar:: Alternative

    You can save some typing by using ``git pull upstream/master`` 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 easy(ish) 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 :ref:`run-tests` and
:ref:`build-docs`, 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`_.


.. _github-ssh:

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 :class:`mne.Evoked` method in :file:`mne/evoked.py` should
have a corresponding test in :file:`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 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 :ref:`the
deprecation section <deprecating>` and :class:`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 master reference;
classes, functions, methods, and attributes cannot be cross-referenced unless
they are included in the :doc:`python_reference`
(:file:`doc/python_reference.rst`).


.. _deprecating:

Deprecate with a decorator or a warning
---------------------------------------

MNE-Python has a :func:`~mne.utils.deprecated` decorator for classes and
functions that will be removed in a future version:

.. code-block:: python

    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 :func:`mne.utils.warn`. For example,
to rename a parameter from ``old_param`` to ``new_param`` you can do something
like this:

.. code-block:: python

    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'

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:

.. code-block:: python

    # 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)

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
:doc:`changelog <whats_new>` (:file:`doc/whats_new.rst`; this can be skipped
for very minor changes like correcting typos in the documentation). Note that
there are sections of the changelog for each release, and separate subsections
for bugfixes, new features, and changes to the public API. It is usually best
to wait to add a line to the changelog until your PR is finalized, to avoid
merge conflicts (since the changelog is updated with almost every PR).


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; :ref:`run the tests locally <run-tests>`
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., :class:`~mne.io.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 pep8_, pyflakes_, and flake8_ to check code style
automatically. From the :file:`mne-python` root directory, you can check for
style violations by running::

    $ make flake

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
:samp:`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 :term:`comprehension <list
comprehension>` or :ref:`generator <tut-generators>`.


Follow NumPy style for docstrings
---------------------------------

In most cases imitating existing docstrings will be sufficient, but consult the
`Numpy docstring style guidelines`_ for more complicated formatting such as
embedding example code, citing references, or including rendered mathematics.
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 docstyle


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 directives 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 :file:`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 :mod:`NumPy <numpy>`, :mod:`SciPy <scipy>`, etc. See the Sphinx
configuration file (: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


Other style guidance
--------------------

- Use single quotes whenever possible.

- Prefer :ref:`generators <tut-generators>` or
  :term:`comprehensions <list comprehension>` over :func:`filter`, :func:`map`
  and other functional idioms.

- Use explicit functional constructors for builtin containers to improve
  readability (e.g., :ref:`list() <func-list>`, :ref:`dict() <func-dict>`,
  :ref:`set() <func-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:

1. Python built-in (``os``, ``copy``, ``functools``, etc)
2. standard scientific (``numpy as np``, ``scipy.signal``, etc)
3. others
4. 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`` and optional modules (``sklearn``, ``pandas``, etc.) should
be nested (i.e., within a function or method, not at the top of a file).


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.


Vizualization
-------------

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

All visualization functions must accept a boolean ``show`` parameter and
typically return a :class:`matplotlib.figure.Figure` (or a list of
:class:`~matplotlib.figure.Figure` objects). 3D visualization functions return
a :class:`mayavi.core.api.Scene`, :class:`surfer.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.


.. _run_tests:

Running the test suite
~~~~~~~~~~~~~~~~~~~~~~

Running the full test suite is as simple as running ::

    $ make test

.. sidebar:: pytest flags

    The ``-x`` flag exits the pytest run as soon as the first test fails; this
    can save some time if you are running an entire file's or module's worth of
    tests instead of selecting just a single test as shown here.

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

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, e.g.::

    $ 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:

.. code-block:: python

    >>> mne.datasets.testing.data_path(verbose=True)  # doctest: +SKIP


.. _build-docs:

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
:file:`doc/Makefile`. From within the :file:`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
:samp:`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).

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 :file:`doc` directory or
consult the `Sphinx-Gallery`_ documentation for additional details.


Modifying command-line tools
~~~~~~~~~~~~~~~~~~~~~~~~~~~~

MNE-Python provides support for a limited set of :ref:`python_commands`.
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 :class:`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 :func:`post-mortem debugger <python:pdb.pm>`:

.. code-block:: python

    >>> import pdb; pdb.pm()  # doctest:+SKIP


.. _`github-workflow`:

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 `MNE-Python 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 master
  branch, so that you can use your master 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 master 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
  :doc:`changelog <whats_new>` (:file:`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 `circle`_, which tests successful building of our
      documentation.

    - ``[skip travis]`` Skip `travis`_, which tests 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 travis][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-master branch into
the upstream master 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.


.. MNE

.. _MNE-Python GitHub: https://github.com/mne-tools/mne-python
.. _MNE-Python issues page: https://github.com/mne-tools/mne-python/issues
.. _open a new issue: https://github.com/mne-tools/mne-python/issues/new/choose
.. _This sample pull request: https://github.com/mne-tools/mne-python/pull/6230

.. git installation

.. _the .dmg installer: https://git-scm.com/download/mac
.. _official Windows version of git: https://git-scm.com/download/win
.. _official Linux instructions: https://git-scm.com/download/linux
.. _more detailed instructions and alternatives: https://www.atlassian.com/git/tutorials/install-git
.. _Windows subsystem for Linux: https://docs.microsoft.com/en-us/windows/wsl/about
.. _git bash: https://gitforwindows.org/
.. _GitHub desktop: https://desktop.github.com/

.. github help pages

.. _GitHub Help: https://help.github.com
.. _GitHub learning lab: https://lab.github.com/
.. _fork: https://help.github.com/en/articles/fork-a-repo
.. _clone: https://help.github.com/en/articles/cloning-a-repository
.. _push: https://help.github.com/en/articles/pushing-to-a-remote
.. _branches: https://help.github.com/en/articles/about-branches
.. _several help pages: https://help.github.com/en/articles/connecting-to-github-with-ssh
.. _draft PRs: https://help.github.com/en/articles/about-pull-requests#draft-pull-requests
.. _convert it from a draft PR to a normal PR: https://help.github.com/en/articles/changing-the-stage-of-a-pull-request
.. _pull request: https://help.github.com/en/articles/creating-a-pull-request-from-a-fork
.. _creating a PR from a fork: https://help.github.com/en/articles/creating-a-pull-request-from-a-fork

.. git docs

.. _check it out: https://git-scm.com/docs/git-checkout
.. _added: https://git-scm.com/docs/git-add
.. _commits: https://git-scm.com/docs/git-commit
.. _commit: https://git-scm.com/docs/git-commit
.. _status: https://git-scm.com/docs/git-status

.. git book

.. _pro git book: https://git-scm.com/book/
.. _stage: https://git-scm.com/book/en/v2/Git-Tools-Interactive-Staging
.. _configuring git: https://www.git-scm.com/book/en/v2/Customizing-Git-Git-Configuration

.. sphinx

.. _sphinx: http://www.sphinx-doc.org
.. _sphinx-gallery: https://sphinx-gallery.github.io
.. _reStructuredText: http://sphinx-doc.org/rest.html
.. _intersphinx: http://www.sphinx-doc.org/en/master/usage/extensions/intersphinx.html
.. _sphobjinv: https://sphobjinv.readthedocs.io/en/latest/

.. linting

.. _NumPy docstring style guidelines: https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt
.. _PEP 8: https://www.python.org/dev/peps/pep-0008/
.. _pep8: https://pypi.org/project/pep8
.. _pyflakes: https://pypi.org/project/pyflakes
.. _Flake8: http://flake8.pycqa.org/

.. misc

.. _anaconda: https://www.anaconda.com/distribution/
.. _miniconda: https://conda.io/en/latest/miniconda.html
.. _Spyder: https://www.spyder-ide.org/
.. _GNU Make: https://www.gnu.org/software/make/
.. _continuous integration: https://en.wikipedia.org/wiki/Continuous_integration
.. _matplotlib: https://matplotlib.org/
.. _travis: https://travis-ci.org/mne-tools/mne-python/branches
.. _azure: https://dev.azure.com/mne-tools/mne-python/_build/latest?definitionId=1&branchName=master
.. _circle: https://circleci.com/gh/mne-tools/mne-python