Statistical learning for neuroimaging in Python
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.
1. Setup a virtual environment
We recommend that you install nilearn in a virtual Python environment, either managed with the standard library venv or with conda (see miniconda for instance). Either way, create and activate a new python environment.
python3 -m venv /<path_to_new_env> source /<path_to_new_env>/bin/activate
Windows users should change the last line to \<path_to_new_env>\Scripts\activate.bat in order to activate their virtual environment.
conda create -n nilearn python=3.9 conda activate nilearn
2. Install nilearn with pip
Execute the following command in the command prompt / terminal in the proper python environment:
python -m pip install -U nilearn
Please find all development setup instructions in the contribution guide.
Try importing nilearn in a python / iPython session:
If no error is raised, you have installed nilearn correctly.
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.
The required dependencies to use the software are listed in the file pyproject.toml.
If you are using nilearn plotting functionalities or running the examples, matplotlib >= 3.3.0 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 >= 6.0.0 and pytest-cov for coverage reporting.
Detailed instructions on how to contribute are available at http://nilearn.github.io/stable/development.html
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