Statistical learning for neuroimaging in Python
Project description
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 git@github.com:nilearn/nilearn
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