|Multivariate Pattern Analysis in Python|
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.
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.
Binary packages are available for:
RPM packages are provided through the OpenSUSE Build Service. The currently supported distributions include: CentOS 5, Fedora 9-12, RedHat Enterprise Linux 5, OpenSUSE 11.0 up to 11.2 (but also OpenSUSE Factory). The build service supports RPM-package repositories (SUSE-related and Fedora, Redhat and CentOS-related) and 1-click-installations.
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 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.
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:||email@example.com [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.
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.