Source: openmeeg
Section: science
Priority: extra
Maintainer: NeuroDebian Team <team@neuro.debian.net>
Uploaders: Yaroslav Halchenko <debian@onerussian.com>, Michael Hanke <michael.hanke@gmail.com>
Build-Depends: cdbs, debhelper (>= 7), cmake, doxygen, python-numpy, swig, python-all-dev (>= 2.4), python-support (>= 0.6), libatlas-base-dev, libvtk5-dev, libtiff4-dev | libtiff-dev, libmatio-dev
Standards-Version: 3.8.4
Homepage: http://www-sop.inria.fr/odyssee/software/OpenMEEG/
Vcs-Browser: http://git.debian.org/?p=pkg-exppsy/openmeeg.git
Vcs-Git: git://git.debian.org/git/pkg-exppsy/openmeeg.git
XS-DM-Upload-Allowed: yes


Package: libopenmeeg1
Architecture: any
Depends: ${shlibs:Depends}, ${misc:Depends}
Description: library for solving EEG and MEG forward and inverse problems
 OpenMEEG provides state-of-the art tools for processing EEG and MEG
 data.
 .
 The forward problem is implemented using the symmetric Boundary
 Element method [Kybic et al, 2005], providing excellent accuracy,
 particularly for superficial cortical sources.  The source
 localization procedures implemented in OpenMEEG are based on a
 distributed source model, with three different types of
 regularization: the Minimum Norm, and the L2 and L1
 norms of the surface gradient of the sources [Adde et al, 2005].


Package: libopenmeeg-dev
Section: libdevel
Architecture: any
Depends: ${shlibs:Depends}, ${misc:Depends}
Description: library for solving EEG and MEG forward and inverse problems
 OpenMEEG provides state-of-the art tools for processing EEG and MEG
 data.
 .
 The forward problem is implemented using the symmetric Boundary
 Element method [Kybic et al, 2005], providing excellent accuracy,
 particularly for superficial cortical sources.  The source
 localization procedures implemented in OpenMEEG are based on a
 distributed source model, with three different types of
 regularization: the Minimum Norm, and the L2 and L1
 norms of the surface gradient of the sources [Adde et al, 2005].
 .
 This package provides static libraries and header files.


Package: openmeeg-tools
Architecture: any
Depends: ${shlibs:Depends}, ${misc:Depends}
Description: tools for solving EEG and MEG forward and inverse problems
 OpenMEEG provides state-of-the art tools for processing EEG and MEG
 data.
 .
 The forward problem is implemented using the symmetric Boundary
 Element method [Kybic et al, 2005], providing excellent accuracy,
 particularly for superficial cortical sources.  The source
 localization procedures implemented in OpenMEEG are based on a
 distributed source model, with three different types of
 regularization: the Minimum Norm, and the L2 and L1
 norms of the surface gradient of the sources [Adde et al, 2005].
 .
 This package provides command line tools.


Package: python-openmeeg
Section: python
Architecture: any
Depends: ${shlibs:Depends}, ${misc:Depends}, ${python:Depends}, python-numpy
Provides: ${python:Provides}
XB-Python-Version: ${python:Versions}
Description: Python bindings for openmeeg library
 OpenMEEG provides state-of-the art tools for processing EEG and MEG
 data.
 .
 The forward problem is implemented using the symmetric Boundary
 Element method [Kybic et al, 2005], providing excellent accuracy,
 particularly for superficial cortical sources.  The source
 localization procedures implemented in OpenMEEG are based on a
 distributed source model, with three different types of
 regularization: the Minimum Norm, and the L2 and L1
 norms of the surface gradient of the sources [Adde et al, 2005].
 .
 This package provides Python bindings for OpenMEEG library.
