schwarty/nilearn

Machine learning for NeuroImaging in Python

nilearn

NiLearn is a Python module for fast and easy statistical learning on NeuroImaging data.

It leverages the scikit-learn Python toolbox for multivariate statistics with applications such as predictive modelling, classification, decoding, or connectivity analysis.

This work is made available by the INRIA Parietal Project Team and the scikit-learn folks, among which P. Gervais, A. Abraham, V. Michel, A. Gramfort, G. Varoquaux, F. Pedregosa, B. Thirion, M. Eickenberg, C. F. Gorgolewski, D. Bzdok and L. Estève.

Important links

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

Dependencies

The required dependencies to use the software are:

• Python >= 2.6,
• setuptools
• Numpy >= 1.3
• SciPy >= 0.7
• Scikit-learn >= 0.12.1
• Nibabel >= 1.1.0.

This configuration almost matches the Ubuntu 10.04 LTS release from April 2010, except for scikit-learn, which must be installed separately.

Running the examples requires matplotlib >= 0.99.1

If you want to run the tests, you need nose >= 1.2.1 and coverage >= 3.6.

Install

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

pip install -U --pre --user nilearn

Note that nilearn has been released as a beta so you need to use the --pre command-line parameter only if your pip version is greater than 1.4.

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

Development

Build Status

Code

GIT

You can check the latest sources with the command:

git clone git://github.com/nilearn/nilearn

or if you have write privileges:

git clone [email protected]:nilearn/nilearn