Quickstart¶

nilearn¶

Nilearn enables approachable and versatile analyses of brain volumes and surfaces. It provides statistical and machine-learning tools, with instructive documentation & friendly community.

It supports general linear model (GLM) based analysis and leverages the scikit-learn Python toolbox for multivariate statistics with applications such as predictive modeling, classification, decoding, or connectivity analysis.

Important links¶

Official source code repo: https://github.com/nilearn/nilearn/
HTML documentation (stable release): https://nilearn.github.io/

Install¶

Latest release¶

The easiest way to install Nilearn is using pip. Execute the following command in the command prompt / terminal in the proper python environment:

python -m pip install nilearn

Please find all installation instructions on our install page.

Development version¶

Please find all development setup instructions in the contribution guide.

Drop-in Hours¶

The Nilearn team organizes regular online drop-in hours to answer questions, discuss feature requests, or have any Nilearn-related discussions. Nilearn drop-in hours occur every Wednesday from 4pm to 5pm UTC, and we make sure that at least one member of the core-developer team is available. These events are held on Jitsi Meet and are fully open, anyone is welcome to join! For more information and ways to engage with the Nilearn team see How to get help.

Dependencies¶

The required dependencies to use Nilearn are listed in the file pyproject.toml.

If you are using Nilearn plotting functionalities or running the examples, matplotlib is required.

Some plotting functions in Nilearn require both matplotlib and plotly as plotting engines. In order to use the plotly engine in these functions, you will need to install both plotly and kaleido, which can both be installed with pip and anaconda.

Please find all installation instructions on our install page.

If you want to run the tests, extra dependencies (such as pytest and pytest-cov) are required. See our development setup instructions to know how to install them.

Development¶

Detailed instructions on how to contribute are available at https://nilearn.github.io/stable/development.html