Machine learning for NeuroImaging in Python
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doc
nilearn
.gitignore
.travis.yml
AUTHORS.rst
COPYING
Makefile
README.rst
README.txt
plot_adhd_covariance.py
plot_canica_resting_state.py
plot_connect_comparison.py
plot_haxby_anova_svm.py
plot_haxby_different_estimators.py
plot_haxby_full_analysis.py
plot_haxby_grid_search.py
plot_haxby_masks.py
plot_haxby_mass_univariate.py
plot_haxby_multiclass.py
plot_haxby_searchlight.py
plot_haxby_simple.py
plot_haxby_stimuli.py
plot_ica_resting_state.py
plot_localizer_mass_univariate.py
plot_mask_computation.py
plot_miyawaki_reconstruction.py
plot_nifti_simple.py
plot_python_101.py
plot_rest_clustering.py
plot_roi_extraction.py
plot_simulated_data.py
plot_visualization.py
setup.cfg
setup.py

README.rst

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

Dependencies

The required dependencies to sue the software are Python >= 2.6, setuptools, Numpy >= 1.3, SciPy >= 0.7, Scikit-learn >= 0.12.1 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

This package uses distutils, which is the default way of installing python modules. To install in your home directory, use:

python setup.py install --user

To install for all users on Unix/Linux:

python setup.py build
sudo python setup.py install

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