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nilearn

Nilearn enables approachable and versatile analyses of brain volumes. 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 modelling, 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

First make sure you have installed all the dependencies listed below. Then you can install nilearn by running the following command in a command prompt:

pip install -U --user nilearn

More detailed instructions are available at https://nilearn.github.io/stable/introduction.html#installation.

Office Hours

The nilearn office hours will not be held over the summer and will restart in September. See section How to get help for ways you can still engage with the core-developer team.

Dependencies

The required dependencies to use the software are listed in the file nilearn/setup.cfg.

If you are using nilearn plotting functionalities or running the examples, matplotlib >= 3.0 is required.

Some plotting functions in Nilearn support 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.

If you want to run the tests, you need pytest >= 3.9 and pytest-cov for coverage reporting.

Development

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