To help you get started, we've collected the most common ways that nilearn is being used within popular public projects.

ME-ICA / tedana / tedana / io.py View on Github

raise ValueError('Number of echos must be specified. '
                         'Confirm that TE times are provided with the `-e` argument.')

    if isinstance(data, list):
        if len(data) == 1:  # a z-concatenated file was provided
            data = data[0]
        elif len(data) == 2:  # inviable -- need more than 2 echos
            raise ValueError('Cannot run `tedana` with only two echos: '
                             '{}'.format(data))
        else:  # individual echo files were provided (surface or volumetric)
            fdata = np.stack([utils.load_image(f) for f in data], axis=1)
            ref_img = check_niimg(data[0])
            ref_img.header.extensions = []
            return np.atleast_3d(fdata), ref_img

    img = check_niimg(data)
    (nx, ny), nz = img.shape[:2], img.shape[2] // n_echos
    fdata = utils.load_image(img.get_data().reshape(nx, ny, nz, n_echos, -1, order='F'))
    # create reference image
    ref_img = img.__class__(np.zeros((nx, ny, nz, 1)), affine=img.affine,
                            header=img.header, extra=img.extra)
    ref_img.header.extensions = []
    ref_img.header.set_sform(ref_img.header.get_sform(), code=1)

    return fdata, ref_img

View more ways to use nilearn

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 modeling, classification, decoding, or connectivity analysis.

Important links

• Official source code repo:
• HTML documentation (stable release):

Install

Latest release

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.

With venv:

python3 -m venv /
source //bin/activate

Windows users should change the last line to \\Scripts\activate.bat in order to activate their virtual environment.

With conda:

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

Development version

Please find all development setup instructions in the contribution guide.

Check installation

Try importing nilearn in a python / iPython session:

import nilearn

If no error is raised, you have installed nilearn correctly.

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 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.

Development

Detailed instructions on how to contribute are available at