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PyMVPA is a Python module intended to ease pattern classification analyses of large datasets. In the neuroimaging contexts such analysis techniques are also known as decoding or MVPA analysis. PyMVPA provides high-level abstraction of typical processing steps and a number of implementations of some popular algorithms. While it is not limited to the neuroimaging domain, it is eminently suited for such datasets. PyMVPA is truely free software (in every respect) and additionally requires nothing but free-software to run.

PyMVPA stands for MultiVariate Pattern Analysis (MVPA) in Python.

PyMVPA is developed inside the Debian Experimental Psychology Project. This website, the source code repository and download services are hosted on Alioth, a service that is kindly provided by the Debian project.

News

A complete fMRI example dataset is now available for download [22 Oct 2008]

PyMVPA 0.4.0 is out [21 Nov 2008]

Greatly enhanced documentation (glossary, references, examples), tons of bug-fixes and new mappers. This release also ensures compatibility with recent releases of the Shogun toolbox. See the changelog for details.

PyMVPA is also running on a cell phone! [15 Oct 2008]

Here is the proof.

Documentation

License

PyMVPA is free-software (beer and speech) and covered by the MIT License. This applies to all source code, documentation, examples and snippets inside the source distribution (including this website). Please see the appendix of the manual for the copyright statement and the full text of the license.

Download

Binary packages

Binary packages are available for:

If there are no binary packages for your particular operating system or platform, you need to compile your own. The manual contains instructions to build PyMVPA in various environments.

Source code

Source code tarballs of PyMVPA releases are available from the download area. Alternatively, one can also download a tarball of the latest development snapshot (i.e. the current state of the master branch of the PyMVPA source code repository).

To get access to both the full PyMVPA history and the latest development code, the PyMVPA Git repository is publicly available. To view the repository, please point your webbrowser to gitweb: http://git.debian.org/?p=pkg-exppsy/pymvpa.git

To clone (aka checkout) the PyMVPA repository simply do:

git clone git://git.debian.org/git/pkg-exppsy/pymvpa.git

After a short while you will have a pymvpa directory below your current working directory, that contains the PyMVPA repository.

More detailed instructions on installation requirements and on how to build PyMVPA from source are provided in the manual.

Support

If you have problems installing the software or questions about usage, documentation or something else related to PyMVPA, you can post to the PyMVPA mailing list (prefered) or contact the authors on IRC:

Mailing list:pkg-exppsy-pymvpa@lists.alioth.debian.org [subscription, archive]
IRC:#exppsy on OTFC/Freenode

All users should subscribe to the mailing list. PyMVPA is still a young project that is under heavy development. Significant modifications (hopefully improvements) are very likely to happen frequently. The mailing list is the prefered way to announce such changes. The mailing list archive can also be searched using the mailing list archive search located in the sidebar of the PyMVPA home page.

Publications

Below is a list of all publications about PyMVPA that have been published so far (in chronological order). If you use PyMVPA in your research please cite the one that matches best.

Hanke, M., Halchenko, Y. O., Sederberg, P. B., Hanson, S. J., Haxby, J. V. & Pollmann, S. (2008). PyMVPA: A Python toolbox for classifier-based data analysis.
First presentation of PyMVPA at the conference Psychologie und Gehirn [Psychology and Brain], Magdeburg, 2008. This poster received the poster prize of the German Society for Psychophysiology and its Application.
Hanke, M., Halchenko, Y. O., Sederberg, P. B., Hanson, S. J., Haxby, J. V. & Pollmann, S. (in press). PyMVPA: A Python toolbox for multivariate pattern analysis of fMRI data. Neuroinformatics.
First paper introducing fMRI data analysis with PyMVPA.
Hanke, M., Halchenko, Y. O., Sederberg, P. B., Hanson, S. J., Haxby, J. V. & Pollmann, S. (2008). PyMVPA: A Python toolbox for machine-learning based data analysis.
Poster emphasizing PyMVPA’s capabilities concerning multi-modal data analysis at the annual meeting of the Society for Neuroscience, Washington, 2008.

Authors & Contributers

The PyMVPA developers team currently consists of:

We are very grateful to the following people, who have contributed valueable advice, code or documentation to PyMVPA: