nilearn 0.1a1

Statistical learning for neuroimaging in Python

nilearn

This projects contains a tutorial on how to process functional Magnetic Resonance Imaging (fMRI) data with the scikit-learn.

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 and B. Thirion.

Important links

• Official source code repo: https://github.com/nilearn/nilearn/

• HTML documentation (stable release): http://nilearn.github.com/

Dependencies

The required dependencies to sue 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 recent python-coverage and python-nose. (resp. 3.6 and 1.2.1).

Install

The simplest is to use pip. Not that nilearn has been released as an alpha so you need to use the --pre command-line parameter:

pip install -U --pre --user nilearn

Development

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 [e-mail]:nilearn/nilearn