PyMVPA is a Python package intended to ease statistical learning analyses of
large datasets. It offers an extensible framework with a high-level interface
to a broad range of algorithms for classification, regression, feature
selection, data import and export. It is designed to integrate well with
related software packages, such as scikit-learn, and MDP. While it is not
limited to the neuroimaging domain, it is eminently suited for such datasets.
PyMVPA is free software and requires nothing but free-software to run.
PyMVPA stands for MultiVariate Pattern Analysis
(MVPA) in Python.
Contributing
We welcome all kinds of contributions, and you do not need to be a
programmer to contribute! If you have some feature in mind that is missing,
some example use case that you want to share, you spotted a typo in the
documentation, or you have an idea how to improve the user experience all
together – do not hesitate and contact us. We will then
figure out how your contribution can be best incorporated. Any contributor will
be acknowledged and will appear in the list of people who have helped to
develop PyMVPA on the front-page of the pymvpa.org.
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.
How to cite PyMVPA
Below is a list of 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, and email use the reference so
we could add it to our Who Is Using It? page.
Peer-reviewed publications
- 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.
- 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., Haxby, J. V., and Pollmann, S. (2010) Statistical learning analysis in neuroscience: aiming for transparency. Frontiers in Neuroscience. 4,1: 38-43
- Focused review article emphasizing the role of transparency to facilitate
adoption and evaluation of statistical learning techniques in neuroimaging
research.
- Haxby, J. V., Guntupalli, J. S., Connolly, A. C., Halchenko, Y. O., Conroy, B. R., Gobbini, M. I., Hanke, M. & Ramadge, P. J. (2011). A Common, High-Dimensional Model of the Representational Space in Human Ventral Temporal Cortex. Neuron, 72, 404–416
- The Hyperalignment paper
demonstrating its application to fMRI data in rich perceptual (movie) and
categorization (monkey-dog) experiments.
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.
Authors and 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:
- Florian Baumgartner, University of Magdeburg, Germany
- Sven Buchholz, University of Magdeburg, Germany
- Andrew C. Connolly, Dartmouth College, USA
- Michael W. Cole, Washington University in St. Louis, USA
- Ceyhun Çakar
- Reka Daniel, Princeton University, USA
- Greg Detre, Princeton University, USA
- Matthias Ekman, Donders Institute, Netherlands
- Ingo Fründ, TU Berlin, Germany
- Christoph Gohlke, University of California, Irvine, USA
- Scott Gorlin, MIT, USA
- Satrajit Ghosh, MIT, USA
- Jyothi Swaroop Guntupalli, Dartmouth College, USA
- Valentin Haenel, TU Berlin, Germany
- Stephen José Hanson, Rutgers University, USA
- James V. Haxby, Dartmouth College, USA
- James M. Hughes, Dartmouth College, USA
- James Kyle, UCLA, USA
- Emanuele Olivetti, Fondazione Bruno Kessler, Italy
- Russell Poldrack, University of Texas, USA
- Stefan Pollmann, University of Magdeburg, Germany
- Geethapriya Raghavan, University of Texas Austin, USA
- Rajeev Raizada, Dartmouth College, USA
- Per B. Sederberg, Princeton University, USA
- Tiziano Zito, BCCN, Germany
Acknowledgements
We are greatful to the developers and contributers of NumPy, SciPy and
IPython for providing an excellent Python-based computing environment.
Additionally, as PyMVPA makes use of a lot of external software
packages (e.g. classifier implementations), we want to acknowledge
the authors of the respective tools and libraries (e.g. LIBSVM, MDP,
scikit-learn, Shogun) and thank them for developing their packages
as free and open source software.
Finally, we would like to express our acknowledgements to the Debian
project for providing us with hosting facilities for mailing lists
and source code repositories. But most of all for developing the
universal operating system.
Grant support
PyMVPA development was supported, in part, by the following research grants.
This list includes grants funding development of specific algorithm
implementations in PyMVPA, as well as grants supporting individuals to work on
PyMVPA:
- German Federal Ministry of Education and Research
-
- German federal state of Saxony-Anhalt
- Project: Center for Behavioral Brain Sciences
- German Academic Exchange Service
-
McDonnel Foundation
- US National Institutes of Mental Health
- 5R01MH075706
- F32MH085433-01A1
- US National Science Foundation
-