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This content refers to the previous stable release of PyMVPA. Please visit www.pymvpa.org for the most recent version of PyMVPA and its documentation.

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 truly 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

PyMVPA 0.4.8 is out [24 Apr 2012]
This release brings a set of bug fixes, including compatibility with recent shogun (>=1.0) and libsvm >= 3.10. See the changelog for details.
PyMVPA Extravaganza 2009 at Dartmouth College [30th Nov – 4th Dec]
Read more about the topics and achievements.
First publication from outside the PyMVPA team employing PyMVPA [19 Jul 2009]
Sun et al. (2009): Elucidating an MRI-Based Neuroanatomic Biomarker for Psychosis: Classification Analysis Using Probabilistic Brain Atlas and Machine Learning Algorithms.

Documentation

For users

For developers

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://github.com/PyMVPA/PyMVPA

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

git clone http://github.com/PyMVPA/PyMVPA

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 (preferred) 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 preferred 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. In addition there is also a list of studies done by other groups employing PyMVPA somewhere in the analysis.

Peer-reviewed publications

Hanke, M., Halchenko, Y. O., Haxby, J. V., and Pollmann, S. (accepted) Statistical learning analysis in neuroscience: aiming for transparency. Frontiers in Neuroscience.
Focused review article emphasizing the role of transparency to facilitate adoption and evaluation of statistical learning techniques in neuroimaging research.
Hanke, M., Halchenko, Y. O., Sederberg, P. B., Olivetti, E., Fründ, I., Rieger, J. W., Herrmann, C. S., Haxby, J. V., Hanson, S. J. and Pollmann, S. (2009) PyMVPA: a unifying approach to the analysis of neuroscientific data. Frontiers in Neuroinformatics, 3:3.
Demonstration of PyMVPA capabilities concerning multi-modal or modality-agnostic data analysis.
Hanke, M., Halchenko, Y. O., Sederberg, P. B., Hanson, S. J., Haxby, J. V. & Pollmann, S. (2009). PyMVPA: A Python toolbox for multivariate pattern analysis of fMRI data. Neuroinformatics, 7, 37-53.
First paper introducing fMRI data analysis with PyMVPA.

Posters

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.
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.

Studies employing PyMVPA

  • Sun et al. (2009): Elucidating an MRI-Based Neuroanatomic Biomarker for Psychosis: Classification Analysis Using Probabilistic Brain Atlas and Machine Learning Algorithms.
  • Manelis et al. (2010): Implicit memory for object locations depends on reactivation of encoding-related brain regions

Authors & Contributors

The PyMVPA developers team currently consists of:

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