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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 <https://scikit-learn.org>_ Python toolbox
for multivariate statistics with applications
such as predictive modeling, 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 <https://docs.conda.io/en/latest/miniconda.html>_ for instance).
Either way, create and activate a new python environment.
With venv:
.. code-block:: bash
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.
With conda:
.. code-block:: bash
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:
.. code-block:: bash
python -m pip install -U nilearn
Please find all development setup instructions in the
contribution guide <https://nilearn.github.io/stable/development.html#setting-up-your-environment>_.
Try importing nilearn in a python / iPython session:
.. code-block:: python
import nilearn
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 <https://meet.jit.si/nilearn-drop-in-hours>_ 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 <https://nilearn.github.io/stable/development.html#how-to-get-help>_.
The required dependencies to use the software are listed
in the file pyproject.toml <https://github.com/nilearn/nilearn/blob/main/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 https://nilearn.github.io/stable/development.html
FAQs
Statistical learning for neuroimaging in Python
We found that nilearn demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 9 open source maintainers collaborating on the project.
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