Project Organization#

This section describes how the project is organized.


Nilearn uses issues for tracking bugs, requesting potential features, and holding project discussions.


Labels are useful to quickly sort issues and easily find what you are looking for in the issue tracker.

When creating an issue, the user is responsible for a very basic labeling categorizing the issue:

  • Bug for bug reports.

  • Documentation for documentation related questions or requests.

  • Enhancement for feature requests.

First of all, the user might have mislabeled the issue, in which case a member of the Core developers team needs to correct the labels.

In addition to these basic labels, we have many more labels which describes in more detail a given issue. First, we try to describe the estimated amount of work required to solve each issue:

  • effort: high The issue is likely to require a serious amount of work (more than a couple of days).

  • effort: medium The issue is likely to require a decent amount of work (in between a few hours and a couple days).

  • effort: low The issue is likely to require a small amount of work (less than a few hours).

We also try to quantify the estimated impact of the proposed change on the project:

  • impact: high Solving this issue will have a high impact on the project.

  • impact: medium Solving this issue will have a decent impact on the project.

  • impact: low Solving this issue will have a small impact on the project.

Finally, we also indicate the priority level of the issue:

  • priority: high The task is urgent and needs to be addressed as soon as possible.

  • priority: medium The task is important but not urgent and should be addressed over the next few months.

  • priority: low The task is not urgent and can be delayed.

Some issues—particular those which are low effort and low to medium priority—can serve as good starting project for new contributors. We label these issues with the Good first issue label which can be seen as an equivalent to a “very low effort” label. Because of this, good first issues do not require a separate effort label.

Other labels can be used to describe further the topic of the issue:

  • API This issue is related to the Nilearn’s API.

  • code quality This issue tackles code quality (code refactoring, PEP8…).

  • Datasets This issue is related to datasets or the nilearn.datasets module.

  • Discussion This issue is used to hold a general discussion on a specific topic where community feedback is desired (no need to specify effort, priority, or impact here).

  • GLM This issue is related to the nilearn.glm module.

  • Infrastructure This issue describes a problem with the project’s infrastructure (CI/CD…).

  • Installation The issue describes a problem with the installation of Nilearn.

  • Maintenance This issue is related to maintenance work.

  • Plotting The issue is related to plotting functionalities.

  • Testing The issue is related to testing.

  • Usage This issue is a usage question and should have been posted on neurostars.

Finally, we use the following labels to indicate how the work on the issue is going:

  • in progress Can be used to indicate that this issue is currently being investigated.

  • next-release Commonly used for tagging PRs, this can be used to indicate that this issue should be solved before the next release.

  • stalled This issue is currently stalled and has no recent activity. Use this label before closing due to inactivity.

Closing policy#

Usually we expect the issue’s author to close the issue, but there are several possible reasons for a community member to close an issue:

  • The issue has been solved: kindly asked the author whether the issue can be closed. In the absence of reply, close the issue after two weeks.

  • The issue is a usage question: label the issue with Usage and kindly redirect the author to neurostars. Close the issue afterwards.

  • The issue has no recent activity (no messages in the last three months): ping the author to see if the issue is still relevant. In the absence of reply, label the issue with stalled and close it after 2 weeks.

Pull Requests#

We welcome pull requests from all community members, if they follow the Contribution Guidelines inspired from scikit learn conventions. (More details on their process are available here).

How to make a release?#

This section describes how to make a new release of Nilearn. It is targeted to the specific case of Nilearn although it contains generic steps for packaging and distributing projects. More detailed information can be found on

The packaging specification is contained in pyproject.toml. We use hatchling and hatch-vcs as described in these guidelines to build the sdist, wheel, and extract version number from the git tag.

We assume that we are in a clean state where all the Pull Requests (PR) that we wish to include in the new release have been merged.

Prepare code for the release#

The repository should be checked and updated in preparation for the release.

One thing that must be done before the release is made is to update all versionchanged and versionadded directives from the current [x.y.z].dev tag to the new version number.

Additionally, make sure all deprecations that are supposed to be removed with this new version have been addressed. If this new release comes with dependency version bumps (Python, Numpy…), make sure to implement and test these changes beforehand. Ideally, these would have been done before such as to update the code base if necessary. Finally, make sure the documentation can be built correctly.

Prepare the release#

Switch to a new branch locally:

git checkout -b REL-x.y.z

First we need to prepare the release by updating the file nilearn/doc/changes/latest.rst to make sure all the new features, enhancements, and bug fixes are included in their respective sections.

We also need to write a “Highlights” section promoting the most important additions that come with this new release. Finally, we need to change the title from to x.y.z:

.. currentmodule:: nilearn

.. include:: names.rst


**Released MONTH YEAR**


- Nilearn now includes functionality A
- ...

Once we have made all the necessary changes to nilearn/doc/changes/latest.rst, we should rename it into nilearn/doc/changes/x.y.z.rst, where x.y.z is the corresponding version number.

We then need to update nilearn/doc/changes/whats_new.rst and replace:

.. _latest:
.. include:: latest.rst


.. _vx.y.z:
.. include:: x.y.z.rst

Add these changes and submit a PR:

git add doc/whats_new.rst
git commit -m "REL x.y.z"
git push origin REL-x.y.z

Once the PR has been reviewed and merged, pull from master and tag the merge commit:

git checkout master
git pull upstream master
git tag x.y.z
git push upstream --tags


When building the distribution as described below, hatch-vcs, defined in pyproject.toml, extracts the version number using this tag and writes it to a file.

Build the distributions and upload them to Pypi#

First of all we should make sure we don’t include files that shouldn’t be present:

git checkout x.y.z

If the workspace contains a dist folder, make sure to clean it:

rm -r dist

In order to build the binary wheel files, we need to install build:

pip install build

And, in order to upload to Pypi, we will use twine that you can also install with pip:

pip install twine

Build the source and binary distributions:

python -m build

This should add two files to the dist subfolder:

  • one for the source distribution that should look like PACKAGENAME-VERSION.tar.gz

  • one for the built distribution that should look like PACKAGENAME-PACKAGEVERSION-PYTHONVERSION-PYTHONCVERSION-PLATFORM.whl

This will also update

Optionally, we can run some basic checks with twine:

twine check dist/*

We are now ready to upload to Pypi. Note that you will need to have an account on Pypi, and be added to the maintainers of Nilearn. If you satisfy these conditions, you should be able to run:

twine upload dist/*

Once the upload is completed, make sure everything looks good on Pypi. Otherwise you will probably have to fix the issue and start over a new release with the patch number incremented.

At this point, we need to upload the binaries to GitHub and link them to the tag. To do so, go to the Nilearn GitHub page under the “Releases” tab, and edit the x.y.z tag by providing a description, and upload the distributions we just created (you can just drag and drop the files).

Build and deploy the documentation#

Before building the documentation, make sure that the following LaTeX dependencies are installed on your system:

You can check if each package is installed by using command -v <command-name> as in:

command -v dvipng

If the package is installed, then the path to its location on your system will be returned. Otherwise, you can install using your system’s package manager or from source, for example:

cd dvipng
make install

See available linux distributions of texlive-latex-base and texlive-latex-extra:

We now need to update the documentation:

cd doc
make install

This will build the documentation (beware, this is time consuming…) and push it to the GitHub pages repo.


At this point, the release has been made.

We also need to create a new file doc/changes/latest.rst with a title and the usual New, Enhancements, Bug Fixes, and Changes sections for the version currently under development:

.. currentmodule:: nilearn

.. include:: names.rst





Finally, we need to include this new file in doc/changes/whats_new.rst:

.. _latest:
.. include:: latest.rst