Package: ants Version: 1.9+svn532-4~maverick.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 36280 Depends: libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libinsighttoolkit3.18, libstdc++6 (>= 4.4.0) Suggests: fsl, gridengine-client Homepage: http://www.picsl.upenn.edu/ANTS/ Priority: extra Section: science Filename: pool/main/a/ants/ants_1.9+svn532-4~maverick.nd1_i386.deb Size: 11264466 SHA256: 19ae2ae26e2dbdc77965cfd13127593a61c44a12fd380197406a593204412f29 SHA1: 98c6aac582676dc1116ffc15ccd0f3ba0191b567 MD5sum: 0e23be8015dc59288f9238bf14deb877 Description: advanced normalization tools for brain and image analysis Advanced Normalization Tools (ANTS) is an ITK-based suite of normalization, segmentation and template-building tools for quantitative morphometric analysis. Many of the ANTS registration tools are diffeomorphic, but deformation (elastic and BSpline) transformations are available. Unique components of ANTS include multivariate similarity metrics, landmark guidance, the ability to use label images to guide the mapping and both greedy and space-time optimal implementations of diffeomorphisms. The symmetric normalization (SyN) strategy is a part of the ANTS toolkit as is directly manipulated free form deformation (DMFFD). Package: biosig-tools Source: biosig4c++ Version: 0.94.2+svn2552-1~pre1~nd10.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 648 Depends: libc6 (>= 2.7), libcholmod1.7.1 (>= 1:3.4.0), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.2.1), zlib1g (>= 1:1.1.4) Homepage: http://biosig.sf.net/ Priority: extra Section: science Filename: pool/main/b/biosig4c++/biosig-tools_0.94.2+svn2552-1~pre1~nd10.10+1_i386.deb Size: 250176 SHA256: f7a0589e97c538acca51bc9e1243d86cdb12a64132f737dfc138fc04fad05767 SHA1: c6c4a1bf528a55cceb7327c6c0b49222abf4dce0 MD5sum: 7db5b3835524b3bce3f15fcfe9da503c Description: format conversion tools for biomedical data formats Based on BioSig library, this package provides command line tools, such as * save2gdf: converter between different file formats, including but not limited to SCP-ECG(EN1064), HL7aECG (FDA-XML), GDF, EDF, BDF, CWFB. save2gdf can be also used to upload or retrieve data from a bscs server. TODO... Extend? ship client/server? Package: caret-data Version: 5.6~dfsg.1-1 Architecture: all Maintainer: Michael Hanke Installed-Size: 236780 Homepage: http://brainmap.wustl.edu/caret Priority: optional Section: science Filename: pool/main/c/caret-data/caret-data_5.6~dfsg.1-1_all.deb Size: 175205418 SHA256: 329a14cfd5547064496d4f6909db62578412857ad7d5f73e129335481f550b47 SHA1: 7d6cbdd77b04f258327d2bc9fcc4b0494fcf71bc MD5sum: e5f41497554088124975dfc27ba6378b Description: common data files for Caret This package provides online help, tutorials and atlas datasets for Caret. Package: cython Version: 0.13-1~nd10.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 3508 Depends: python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), python2.6, libc6 (>= 2.3.6-6~) Suggests: gcc Homepage: http://cython.org/ Priority: optional Section: python Filename: pool/main/c/cython/cython_0.13-1~nd10.10+1_i386.deb Size: 758174 SHA256: 0a4cb1be3390b6332fa7de4b6c1c984fdd35e5f497482d95aecf1e95d6905e69 SHA1: 01e95a582861a39eb767e24557d0f11e33a4fdca MD5sum: e76022cb6f8d25c29c869fab18fedc4e Description: C-Extensions for Python Cython is a language that makes writing C extensions for the Python language as easy as Python itself. Cython is based on the well-known Pyrex, but supports more cutting edge functionality and optimizations. . The Cython language is very close to the Python language, but Cython additionally supports calling C functions and declaring C types on variables and class attributes. This allows the compiler to generate very efficient C code from Cython code. . This makes Cython the ideal language for wrapping for external C libraries, and for fast C modules that speed up the execution of Python code. Python-Version: 2.6 Package: cython-dbg Source: cython Version: 0.13-1~nd10.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 3868 Depends: python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), libc6 (>= 2.3.6-6~), cython (= 0.13-1~nd10.10+1) Suggests: gcc Homepage: http://cython.org/ Priority: extra Section: debug Filename: pool/main/c/cython/cython-dbg_0.13-1~nd10.10+1_i386.deb Size: 1459994 SHA256: 4198c478a47494fd518453571479ce59f1263453507bceb4681e6e41f84bd32e SHA1: 477dabb507e76ba854bae394815d67e909a78dc2 MD5sum: c1eedd830558e8a8a55996ef70bf0982 Description: C-Extensions for Python (Debug Build of Cython) This package contains Cython libraries built against versions of Python configured with --pydebug. Python-Version: 2.6 Package: dicomnifti Version: 2.28.14-2~maverick.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 488 Depends: libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libnifti1 (>> 1.1.0-2), libstdc++6 (>= 4.4.0) Homepage: http://cbi.nyu.edu/software/dinifti.php Priority: optional Section: science Filename: pool/main/d/dicomnifti/dicomnifti_2.28.14-2~maverick.nd1_i386.deb Size: 147284 SHA256: 408383702dc93a854b0675506b660b4f769d97aa270a6115910ee62f5e63ec49 SHA1: 8787d9a7fbbe29c9ff679ceaf6bbf5ec871345cb MD5sum: 3f6038e143ce660149f1563c018a1cfb Description: converts DICOM files into the NIfTI format The dinifti program converts MRI images stored in DICOM format to NIfTI format. The NIfTI format is thought to be the new standard image format for medical imaging and can be used with for example with FSL, AFNI, SPM, Caret or Freesurfer. . dinifti converts single files, but also supports fully automatic batch conversions of complete dicomdirs. Additionally, converted NIfTI files can be properly named, using image series information from the DICOM files. Package: fslview Version: 3.1.8+4.1.6-2~maverick.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 3868 Depends: libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libnewmat10ldbl, libnifti1 (>> 1.1.0-2), libqt3-mt (>= 3:3.3.8-b), libqwt4c2, libstdc++6 (>= 4.4.0), libvtk5.4, libvtk5.4-qt3 Recommends: fslview-doc Suggests: fsl-atlases Conflicts: fsl-fslview Replaces: fsl-fslview Homepage: http://www.fmrib.ox.ac.uk/fsl/fslview Priority: optional Section: science Filename: pool/main/f/fslview/fslview_3.1.8+4.1.6-2~maverick.nd1_i386.deb Size: 1493370 SHA256: 731204fa6f615fe33b7de517619ce8fb38172ed0be35708aad8796d4bbd05837 SHA1: 24159fe931085d0b329f730ae37e89716583cf32 MD5sum: 1e85096a7518e4f12ea2c1856ef17639 Description: viewer for (f)MRI and DTI data This package provides a viewer for 3d and 4d MRI data as well as DTI images. FSLView is able to display ANALYZE and NIFTI files. The viewer supports multiple 2d viewing modes (orthogonal, lightbox or single slices), but also 3d volume rendering. Additionally FSLView is able to visualize timeseries and can overlay metrical and stereotaxic atlas data. . FSLView is part of FSL. Package: fslview-doc Source: fslview Version: 3.1.8+4.1.6-2~maverick.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 3216 Depends: qt3-assistant Homepage: http://www.fmrib.ox.ac.uk/fsl/fslview Priority: optional Section: doc Filename: pool/main/f/fslview/fslview-doc_3.1.8+4.1.6-2~maverick.nd1_all.deb Size: 2378930 SHA256: 0619f0704097efa80d21adb5e852e5ea97a00b84eee6a6c90cb5825b80b6b68e SHA1: e4dbfb09e83e7593d3036b3abd9af6ec4d93fa2c MD5sum: 963b9b6b17837cf2aced5b54ac1b22ca Description: Documentation for FSLView This package provides the online documentation for FSLView. . FSLView is part of FSL. Package: gdf-tools Source: libgdf Version: 0.1.0~svn31-1~nd10.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 148 Depends: libboost-filesystem1.42.0 (>= 1.42.0-1), libboost-program-options1.42.0 (>= 1.42.0-1), libboost-system1.42.0 (>= 1.42.0-1), libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libgdf0, libstdc++6 (>= 4.4.0) Homepage: http://sourceforge.net/projects/libgdf Priority: extra Section: utils Filename: pool/main/libg/libgdf/gdf-tools_0.1.0~svn31-1~nd10.10+1_i386.deb Size: 37198 SHA256: 63a9204c5954c16f6a3e29a1c21d01dfaa0fcec35efee4ad1af083a3f5d193d5 SHA1: 12622303e1acd666d792750f6f967796d4d5ba18 MD5sum: 909eb56ec3bc15921668219db0468e60 Description: IO library for the GDF -- helper tools GDF (General Dataformat for Biosignals) is intended to provide a generic storage for biosignals, such as EEG, ECG, MEG etc. . This package provides the tool shipped with the library (gdf_merger). Package: gifti-bin Source: gifticlib Version: 1.0.9-1~maverick.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 128 Depends: libc6 (>= 2.3.4), libexpat1 (>= 1.95.8), libgiftiio0, libnifti1 (>> 1.1.0-2), zlib1g (>= 1:1.1.4) Homepage: http://www.nitrc.org/projects/gifti Priority: optional Section: utils Filename: pool/main/g/gifticlib/gifti-bin_1.0.9-1~maverick.nd1_i386.deb Size: 28706 SHA256: ad377655a84bb88f5cc20c052cb55d7a7d274b4e88cdfccd76963fd084054207 SHA1: d8467b3f02d7abcea154d08cec72d8a8ea386569 MD5sum: 3762794dc8512d528b5f035b0d57eba2 Description: tools shipped with the GIFTI library GIFTI is an XML-based file format for cortical surface data. This reference IO implementation is developed by the Neuroimaging Informatics Technology Initiative (NIfTI). . This package provides the tools that are shipped with the library (gifti_tool and gifti_test). Package: itksnap Version: 2.1.4-1~nd10.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 8216 Depends: libc6 (>= 2.4), libfltk1.1 (>= 1.1.8~rc1), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, libinsighttoolkit3.18, libstdc++6 (>= 4.4.0), libvtk5.4 Homepage: http://www.itksnap.org Priority: extra Section: science Filename: pool/main/i/itksnap/itksnap_2.1.4-1~nd10.10+1_i386.deb Size: 3635140 SHA256: a59494541e75ac749dfdae3f3d8b02cb8ec802e729f01bc7fc484a2c8b7703d9 SHA1: 389fd4e28650416edda6292eb8ebe3f17e755001 MD5sum: 58609e66051ed7932cf1aa4269341830 Description: semi-automatic segmentation of structures in 3D images SNAP provides semi-automatic segmentation of structures in medical images (e.g. magnetic resonance images of the brain) using active contour methods, as well as manual delineation and image navigation. Noteworthy features are: . * Linked cursor for seamless 3D navigation * Manual segmentation in three orthogonal planes at once * Support for many different 3D image formats, including NIfTI * Support for concurrent, linked viewing and segmentation of multiple images * Limited support for color images (e.g., diffusion tensor maps) * 3D cut-plane tool for fast post-processing of segmentation results Package: libbiosig-dev Source: biosig4c++ Version: 0.94.2+svn2552-1~pre1~nd10.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1248 Depends: libbiosig0 (= 0.94.2+svn2552-1~pre1~nd10.10+1) Homepage: http://biosig.sf.net/ Priority: extra Section: libdevel Filename: pool/main/b/biosig4c++/libbiosig-dev_0.94.2+svn2552-1~pre1~nd10.10+1_i386.deb Size: 368662 SHA256: b5a56cdf94422f6c5974610352397d7cc6639f059a427b945ee015d645fbcdf4 SHA1: d5e9d46a4b1949a94e5f41d1691e534591c5e435 MD5sum: 1467ee825abaf72e8d7cee2f37c8896d Description: I/O library for biomedical data - development files BioSig is a library for accessing files in several biomedical data formats (including EDF, BDF, GDF, BrainVision, BCI2000, CFWB, HL7aECG, SCP_ECG (EN1064), MFER, ACQ, CNT(Neuroscan), DEMG, EGI, EEG1100, FAMOS, SigmaPLpro, TMS32). The complete list of supported file formats is available at http://hci.tugraz.at/schloegl/biosig/TESTED . . This package provides header files and static library. Package: libbiosig0 Source: biosig4c++ Version: 0.94.2+svn2552-1~pre1~nd10.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 772 Depends: libc6 (>= 2.7), libcholmod1.7.1 (>= 1:3.4.0), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.2.1), zlib1g (>= 1:1.1.4) Homepage: http://biosig.sf.net/ Priority: extra Section: libs Filename: pool/main/b/biosig4c++/libbiosig0_0.94.2+svn2552-1~pre1~nd10.10+1_i386.deb Size: 287020 SHA256: 7abacda54a7ade7e1c4c8befec7a930bf7f7a571530a257e6c45771178ea2a7a SHA1: ad03ea88fbc7e2282dddac909bf69554d34ab199 MD5sum: b28c8b2b36f5dddac77dd43330a813cd Description: I/O library for biomedical data - dynamic library BioSig is a library for accessing files in several biomedical data formats (including EDF, BDF, GDF, BrainVision, BCI2000, CFWB, HL7aECG, SCP_ECG (EN1064), MFER, ACQ, CNT(Neuroscan), DEMG, EGI, EEG1100, FAMOS, SigmaPLpro, TMS32). The complete list of supported file formats is available at http://hci.tugraz.at/schloegl/biosig/TESTED . . This package provides dynamic library. Package: libbiosig0-dbg Source: biosig4c++ Version: 0.94.2+svn2552-1~pre1~nd10.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 640 Depends: libbiosig0 (= 0.94.2+svn2552-1~pre1~nd10.10+1) Homepage: http://biosig.sf.net/ Priority: extra Section: debug Filename: pool/main/b/biosig4c++/libbiosig0-dbg_0.94.2+svn2552-1~pre1~nd10.10+1_i386.deb Size: 179014 SHA256: fe4d789053c849e1880dffc4879cdbb8d64d6a8ee5bad07ce9791be30907f397 SHA1: 3c9e550b7c3b6b381528e8cf4db48f98c9497618 MD5sum: 45f934a90b7e3535efab8222d7129b8b Description: I/O library for biomedical data - debug symbols BioSig is a library for accessing files in several biomedical data formats (including EDF, BDF, GDF, BrainVision, BCI2000, CFWB, HL7aECG, SCP_ECG (EN1064), MFER, ACQ, CNT(Neuroscan), DEMG, EGI, EEG1100, FAMOS, SigmaPLpro, TMS32). The complete list of supported file formats is available at http://hci.tugraz.at/schloegl/biosig/TESTED . . This package provides debug symbols. Package: libgdf-dev Source: libgdf Version: 0.1.0~svn31-1~nd10.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 164 Depends: libgdf0 (= 0.1.0~svn31-1~nd10.10+1) Homepage: http://sourceforge.net/projects/libgdf Priority: extra Section: libdevel Filename: pool/main/libg/libgdf/libgdf-dev_0.1.0~svn31-1~nd10.10+1_i386.deb Size: 17224 SHA256: 5af8812a2019002bb80b63ecbaa14fd700379ad92d4663afe16cf26bb1a409cb SHA1: cc57c55b19bffc9450ade065b958c2c42c15676d MD5sum: afbce83f7246a3c624a69ba9c9bf24a5 Description: IO library for the GDF -- development library GDF (General Dataformat for Biosignals) is intended to provide a generic storage for biosignals, such as EEG, ECG, MEG etc. . This package provides the header files and static library. Package: libgdf0 Source: libgdf Version: 0.1.0~svn31-1~nd10.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 320 Depends: libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0) Homepage: http://sourceforge.net/projects/libgdf Priority: extra Section: libs Filename: pool/main/libg/libgdf/libgdf0_0.1.0~svn31-1~nd10.10+1_i386.deb Size: 99246 SHA256: 630f341a0048992b697da36808c3bf610b44762856ca7b5521d15ef026233152 SHA1: a4f1d5bbc754c6a1a141105810358855cbc0b5b9 MD5sum: 4e9d733bad8060ab9bfe1e6ec12cea0d Description: IO library for the GDF (general dataformat for biosignals) GDF (General Dataformat for Biosignals) is intended to provide a generic storage for biosignals, such as EEG, ECG, MEG etc. . This package contains the shared library. Package: libgdf0-dbg Source: libgdf Version: 0.1.0~svn31-1~nd10.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 3568 Depends: libgdf0 (= 0.1.0~svn31-1~nd10.10+1) Homepage: http://sourceforge.net/projects/libgdf Priority: extra Section: debug Filename: pool/main/libg/libgdf/libgdf0-dbg_0.1.0~svn31-1~nd10.10+1_i386.deb Size: 1052046 SHA256: 0798e714bbded492906e0714f1b9065f02279d712259fec20910712df40b1266 SHA1: e432c0a67b7037b2dd90a94352abaa288e701efb MD5sum: c0402af254904796239de6c010b53091 Description: IO library for the GDF -- debug symbols GDF (General Dataformat for Biosignals) is intended to provide a generic storage for biosignals, such as EEG, ECG, MEG etc. . This package provides debug symbols. Package: libgiftiio-dev Source: gifticlib Version: 1.0.9-1~maverick.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 208 Depends: libgiftiio0 (= 1.0.9-1~maverick.nd1) Homepage: http://www.nitrc.org/projects/gifti Priority: optional Section: libdevel Filename: pool/main/g/gifticlib/libgiftiio-dev_1.0.9-1~maverick.nd1_i386.deb Size: 62362 SHA256: e7645b4762143dc3e4a6c6ecb6c88e72dc822a57921f504fb1e2ca8c9fb44698 SHA1: 6e3a1cab0bf1605f1d09636a81882c4908f9ce7e MD5sum: 5ce79f3b3df0df7d1d29590785b1a9da Description: IO library for the GIFTI cortical surface data format GIFTI is an XML-based file format for cortical surface data. This reference IO implementation is developed by the Neuroimaging Informatics Technology Initiative (NIfTI). . This package provides the header files and static library. Package: libgiftiio0 Source: gifticlib Version: 1.0.9-1~maverick.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 176 Depends: libc6 (>= 2.4), libexpat1 (>= 1.95.8), libnifti1 (>> 1.1.0-2), zlib1g (>= 1:1.1.4) Homepage: http://www.nitrc.org/projects/gifti Priority: optional Section: libs Filename: pool/main/g/gifticlib/libgiftiio0_1.0.9-1~maverick.nd1_i386.deb Size: 57236 SHA256: 44cf7e5acd2cb6d43f5bffbfc5e1110b5868b1a8e0ea110eed8b968574af5d49 SHA1: 34fc692fa9d906630488a5a3b8def1b36d5ba8c2 MD5sum: f14e2a0f09e5fba43c32c81d4596fb73 Description: IO library for the GIFTI cortical surface data format GIFTI is an XML-based file format for cortical surface data. This reference IO implementation is developed by the Neuroimaging Informatics Technology Initiative (NIfTI). . This package contains the shared library. Package: libnifti-dev Source: nifticlib Version: 2.0.0-1~maverick.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 460 Depends: libnifti2 (= 2.0.0-1~maverick.nd1) Conflicts: libfslio-dev, libnifti0-dev, libnifti1-dev, libniftiio-dev Replaces: libnifti1-dev Homepage: http://niftilib.sourceforge.net Priority: optional Section: libdevel Filename: pool/main/n/nifticlib/libnifti-dev_2.0.0-1~maverick.nd1_i386.deb Size: 151424 SHA256: 02dd63229792f2671cff56a1d0e0f670961084cd3ec39eeeaa0e438398ce3096 SHA1: b09d8a64ea93c72bd083452d2af14a696aeee2c1 MD5sum: 0e4089404ec474109398e654a5118c53 Description: IO libraries for the NIfTI-1 data format Niftilib is a set of i/o libraries for reading and writing files in the NIfTI-1 data format. NIfTI-1 is a binary file format for storing medical image data, e.g. magnetic resonance image (MRI) and functional MRI (fMRI) brain images. . This package provides the header files and static libraries of libniftiio, znzlib and libnifticdf. Package: libnifti-doc Source: nifticlib Version: 2.0.0-1~maverick.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1896 Homepage: http://niftilib.sourceforge.net Priority: optional Section: doc Filename: pool/main/n/nifticlib/libnifti-doc_2.0.0-1~maverick.nd1_all.deb Size: 245398 SHA256: ee170c249d6406abcf28bf82179b9327507e20bc341314191e9339ddbdf725f5 SHA1: 255d82f6d5115000ec47d28dd096cea340261cfc MD5sum: 6293a28d6b4ab6f3e08ee6f766fdd757 Description: NIfTI library API documentation Niftilib is a set of i/o libraries for reading and writing files in the NIfTI-1 data format. NIfTI-1 is a binary file format for storing medical image data, e.g. magnetic resonance image (MRI) and functional MRI (fMRI) brain images. . This package provides the library API reference documentation. Package: libnifti2 Source: nifticlib Version: 2.0.0-1~maverick.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 304 Depends: libc6 (>= 2.7), zlib1g (>= 1:1.1.4) Homepage: http://niftilib.sourceforge.net Priority: optional Section: libs Filename: pool/main/n/nifticlib/libnifti2_2.0.0-1~maverick.nd1_i386.deb Size: 107384 SHA256: 150bca33f426cc89796db8958d1b03730f80e4ae1d347039ba70ebfe2eabcd82 SHA1: a3a6f9736f7a0e218254e5cac55a3f2be26cdcf6 MD5sum: cea5d13d0539b1fdb36067594a636348 Description: IO libraries for the NIfTI-1 data format Niftilib is a set of i/o libraries for reading and writing files in the NIfTI-1 data format. NIfTI-1 is a binary file format for storing medical image data, e.g. magnetic resonance image (MRI) and functional MRI (fMRI) brain images. . This package contains the shared library of the low-level IO library niftiio, low-level IO library znzlib and the nifticdf shared library that provides functions to compute cumulative distributions and their inverses. Package: libopenmeeg-dev Source: openmeeg Version: 2.0.0.dfsg-2~maverick.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 276 Homepage: http://www-sop.inria.fr/odyssee/software/OpenMEEG/ Priority: extra Section: libdevel Filename: pool/main/o/openmeeg/libopenmeeg-dev_2.0.0.dfsg-2~maverick.nd1_i386.deb Size: 43856 SHA256: c92c21a2772e96d232289dadea0671887abf2b868ae988f209cd5b6c9eb574f1 SHA1: 4c7d482ca1198fd762853f13a387d0abd6a10bcd MD5sum: f51b11e44be747a9ba4f7c8d048907d2 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: libopenmeeg1 Source: openmeeg Version: 2.0.0.dfsg-2~maverick.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 816 Depends: libatlas3gf-base, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libmatio0, libstdc++6 (>= 4.4.0) Homepage: http://www-sop.inria.fr/odyssee/software/OpenMEEG/ Priority: extra Section: science Filename: pool/main/o/openmeeg/libopenmeeg1_2.0.0.dfsg-2~maverick.nd1_i386.deb Size: 233088 SHA256: aaa12f77bef369fae972b97534252d484e564f16a03d1834e28c9da7c81249d1 SHA1: 31d24420ada6c4e1ab753fb7ceef7e2dede0f0fb MD5sum: 395f4db513ea6d5f2eafc4b001ed1920 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: lipsia Version: 1.6.0-4~maverick.nd1 Architecture: i386 Maintainer: Michael Hanke Installed-Size: 3616 Depends: libc6 (>= 2.7), libdcmtk1 (>= 3.5.4), libfftw3-3, libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, libgsl0ldbl (>= 1.9), libice6 (>= 1:1.0.0), libnifti1 (>> 1.1.0-2), libqt3-mt (>= 3:3.3.8-b), libsm6, libstdc++6 (>= 4.4.0), libvia0, libx11-6, libxext6, zlib1g (>= 1:1.1.4), via-bin Recommends: dcmtk, lipsia-doc Homepage: http://www.cbs.mpg.de/institute/software/lipsia Priority: optional Section: science Filename: pool/main/l/lipsia/lipsia_1.6.0-4~maverick.nd1_i386.deb Size: 1271960 SHA256: a05ad0462cbf1c0dcdc33fee836640ec263e390a3c25ee24e19381606e6f5b02 SHA1: 26976df4dd314ad7144d4d1922ceb49066dafde9 MD5sum: acab84d96e8d07cad4dbfc55f5c89c7b Description: analysis suite for MRI and fMRI data Leipzig Image Processing and Statistical Inference Algorithms (LIPSIA) . This is a software package for the data processing and evaluation of functional magnetic resonance images. The analysis of fMRI data comprises various aspects including filtering, spatial transformation, statistical evaluation as well as segmentation and visualization. All these aspects are covered by LIPSIA. For the statistical evaluation, a number of well established and peer-reviewed algorithms were implemented in LIPSIA that allow an efficient and user-friendly processing of fMRI data sets. As the amount of data that must be handled is enormous, an important aspect in the development of LIPSIA was the efficiency of the software implementation. . LIPSIA operates exclusively on data in the VISTA data format. However, the package contains converters for medical image data in iBruker, ANALYZE and NIfTI format -- converting VISTA images into NIfTI files is also supported. Package: lipsia-doc Source: lipsia Version: 1.6.0-4~maverick.nd1 Architecture: all Maintainer: Michael Hanke Installed-Size: 7004 Homepage: http://www.cbs.mpg.de/institute/software/lipsia Priority: optional Section: doc Filename: pool/main/l/lipsia/lipsia-doc_1.6.0-4~maverick.nd1_all.deb Size: 5539260 SHA256: 82338bf6b7642c81e3d25791b543695be0cfdce051003635d2635dd36680651e SHA1: 4605c3847915a816361fb32d35551861a05db84d MD5sum: 405aaaf745427fdfe430d25bb7e8b238 Description: documentation for LIPSIA Leipzig Image Processing and Statistical Inference Algorithms (LIPSIA) . This package provides the LIPSIA documentation in HTML format. Package: mricron Version: 0.20101102.1~dfsg.1-1~nd10.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 10700 Depends: libatk1.0-0 (>= 1.29.3), libc6 (>= 2.3.6-6~), libcairo2 (>= 1.2.4), libgdk-pixbuf2.0-0 (>= 2.21.6), libglib2.0-0 (>= 2.12.0), libgtk2.0-0 (>= 2.8.0), libpango1.0-0 (>= 1.14.0), libx11-6, mricron-data Suggests: mricron-doc Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: science Filename: pool/main/m/mricron/mricron_0.20101102.1~dfsg.1-1~nd10.10+1_i386.deb Size: 4041374 SHA256: 0be87541823431e35e3b057e9b96ee0777e6fb9dfe085d209277f0427a960361 SHA1: 91cd6adc7b946b4e523d7e80563484b1c5231242 MD5sum: 0ea1e3dc61f049f6142f1b8b5f534c62 Description: magnetic resonance image conversion, viewing and analysis This is a GUI-based visualization and analysis tool for (functional) magnetic resonance imaging. MRIcron can be used to create 2D or 3D renderings of statistical overlay maps on brain anatomy images. Moreover, it aids drawing anatomical regions-of-interest (ROI), or lesion mapping, as well as basic analysis of functional timeseries (e.g. creating plots of peristimulus signal-change). . In addition to 'mricron', this package also provides 'dcm2nii' that supports converting DICOM and PAR/REC images into the NIfTI format, and 'npm' for non-parametric data analysis. Package: mricron-data Source: mricron Version: 0.20101102.1~dfsg.1-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1848 Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: science Filename: pool/main/m/mricron/mricron-data_0.20101102.1~dfsg.1-1~nd10.10+1_all.deb Size: 1663088 SHA256: c8f9015ba35861ad34658639a7201cfb09c2c460e7cc23384eb9945ab9210598 SHA1: 787b175f9c1c11e177a3e0c25a80f29fa09af56c MD5sum: aab159e30bbb74d90209ecbfc18db03e Description: data files for MRIcron This is a GUI-based visualization and analysis tool for (functional) magnetic resonance imaging. MRIcron can be used to create 2D or 3D renderings of statistical overlay maps on brain anatomy images. Moreover, it aids drawing anatomical regions-of-interest (ROI), or lesion mapping, as well as basic analysis of functional timeseries (e.g. creating plots of peristimulus signal-change). . This package provides data files for MRIcron, such as brain atlases, anatomy, and color schemes. Package: mricron-doc Source: mricron Version: 0.20101102.1~dfsg.1-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1216 Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: doc Filename: pool/main/m/mricron/mricron-doc_0.20101102.1~dfsg.1-1~nd10.10+1_all.deb Size: 734652 SHA256: bf812d97fcf4dd0c7414e1b88f84bb8225c1cb961689da3668765bd6aa735284 SHA1: 45c3dfece61f33cd75a3bb8f19c6fbc39fdfc7b0 MD5sum: 1b0a33ebc649787d7bd5d5c186024c39 Description: data files for MRIcron This is a GUI-based visualization and analysis tool for (functional) magnetic resonance imaging. MRIcron can be used to create 2D or 3D renderings of statistical overlay maps on brain anatomy images. Moreover, it aids drawing anatomical regions-of-interest (ROI), or lesion mapping, as well as basic analysis of functional timeseries (e.g. creating plots of peristimulus signal-change). . This package provides documentation for MRIcron in HTML format. Package: nifti-bin Source: nifticlib Version: 2.0.0-1~maverick.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 200 Depends: libc6 (>= 2.7), libnifti2 Homepage: http://niftilib.sourceforge.net Priority: optional Section: utils Filename: pool/main/n/nifticlib/nifti-bin_2.0.0-1~maverick.nd1_i386.deb Size: 59308 SHA256: 61bda2c52b962de9e33a75688f65ad0dd0df6c958007eafe8f5922e19e7163af SHA1: 71ff48ea63b49aa5cc07fad0e80347a6ed0d725d MD5sum: 3cb3c3ffaaa7a470ecfe6c83ef6ca79f Description: tools shipped with the NIfTI library Niftilib is a set of i/o libraries for reading and writing files in the NIfTI-1 data format. NIfTI-1 is a binary file format for storing medical image data, e.g. magnetic resonance image (MRI) and functional MRI (fMRI) brain images. . This package provides the tools that are shipped with the library (nifti_tool, nifti_stats and nifti1_test). Package: octave-biosig Source: biosig4c++ Version: 0.94.2+svn2552-1~pre1~nd10.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1420 Depends: libblas3gf | libblas.so.3gf | libatlas3gf-base, libc6 (>= 2.7), libcholmod1.7.1 (>= 1:3.4.0), libfftw3-3, libgcc1 (>= 1:4.1.1), libgfortran3 (>= 4.3), libhdf5-serial-1.8.4 | libhdf5-1.8.4, liblapack3gf | liblapack.so.3gf | libatlas3gf-base, libncurses5 (>= 5.7+20100313), libreadline6 (>= 6.0), libstdc++6 (>= 4.2.1), zlib1g (>= 1:1.1.4) Homepage: http://biosig.sf.net/ Priority: extra Section: science Filename: pool/main/b/biosig4c++/octave-biosig_0.94.2+svn2552-1~pre1~nd10.10+1_i386.deb Size: 553596 SHA256: ef4a5e8bdcd8a42b2afe9822ad25e108e5e6510e8356c10d6117bc2752a79498 SHA1: dbec3704fc6bce298885001e66106f2f66b2ef64 MD5sum: 98d5887e64648e128b60a19df20471bf Description: Octave bindings for BioSig library This package provides Octave bindings for BioSig library. Primary goal -- I/O interface to variety of biomedical file formats, including but not limited to SCP-ECG(EN1064), HL7aECG (FDA-XML), GDF, EDF. Package: octave-gdf Source: libgdf Version: 0.1.0~svn31-1~nd10.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 388 Depends: octave3.2 (>= 3.2.4), libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libgdf0, libstdc++6 (>= 4.4.0) Homepage: http://sourceforge.net/projects/libgdf Priority: extra Section: science Filename: pool/main/libg/libgdf/octave-gdf_0.1.0~svn31-1~nd10.10+1_i386.deb Size: 130816 SHA256: 81a8422d99c4a0321873a34961dc7e9edff07143cc6146e001239e17aafb24a6 SHA1: d64a6006859c05a879618468b422e8555de45377 MD5sum: 3417ad39ebdb39c67a4873123912af3e Description: IO library for the GDF -- Octave interface GDF (General Dataformat for Biosignals) is intended to provide a generic storage for biosignals, such as EEG, ECG, MEG etc. . This package provides Octave bindings for libgdf. . HIGHLY EXPERIMENTAL -- USE AT YOUR OWN RISK. Package: openmeeg-tools Source: openmeeg Version: 2.0.0.dfsg-2~maverick.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 556 Depends: libatlas3gf-base, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libmatio0, libopenmeeg1, libstdc++6 (>= 4.4.0) Homepage: http://www-sop.inria.fr/odyssee/software/OpenMEEG/ Priority: extra Section: science Filename: pool/main/o/openmeeg/openmeeg-tools_2.0.0.dfsg-2~maverick.nd1_i386.deb Size: 152402 SHA256: 783356064d0921be805bb9c99ef62c6f48a210e4b39c6501a9fa3002eff59a64 SHA1: 5e22f941bd62323ab4d94da7db34036895e71df0 MD5sum: 1913d1041bc1eadff878a7839c546dd3 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: psychopy Version: 1.62.02.dfsg-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3108 Depends: python (>= 2.4), python-support (>= 0.90.0), python-pyglet | python-pygame, python-opengl, python-numpy, python-matplotlib, python-lxml, python-configobj Recommends: python-wxgtk2.8, python-pyglet, python-pygame, python-openpyxl, python-imaging, python-serial, python-scipy, libavbin0 Suggests: python-iolabs, python-pyepl Homepage: http://www.psychopy.org Priority: optional Section: science Filename: pool/main/p/psychopy/psychopy_1.62.02.dfsg-1~nd10.10+1_all.deb Size: 1397562 SHA256: 3d49ba33fcb8db91932ad932784ad32c6a88183ef8fd1f61453d4ce79abe4ef2 SHA1: 7fad3efee6b4f1a0a1a7dba1c152382f6682ab28 MD5sum: f490d965aff7a7cb0dde18f66528b984 Description: environment for creating psychology stimuli in Python PsychoPy provides an environment for creating psychology stimuli using Python scripting language. It combines the graphical strengths of OpenGL with easy Python syntax to give psychophysics a free and simple stimulus presentation and control package. . The goal is to provide, for the busy scientist, tools to control timing and windowing and a simple set of pre-packaged stimuli and methods. PsychoPy features - High-level powerful scripting language (Python) - Simple syntax - Use of hardware-accelerated graphics (OpenGL) - Integration with Spectrascan PR650 for easy monitor calibration - Simple routines for staircase and constant stimuli experimental methods as well as curve-fitting and bootstrapping - Simple (or complex) GUIs via wxPython - Easy interfaces to joysticks, mice, sound cards etc. via PyGame - Video playback (MPG, DivX, AVI, QuickTime, etc.) as stimuli Python-Version: 2.6 Package: python-biosig Source: biosig4c++ Version: 0.94.2+svn2552-1~pre1~nd10.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 900 Depends: libc6 (>= 2.7), libcholmod1.7.1 (>= 1:3.4.0), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.2.1), zlib1g (>= 1:1.1.4) Homepage: http://biosig.sf.net/ Priority: extra Section: python Filename: pool/main/b/biosig4c++/python-biosig_0.94.2+svn2552-1~pre1~nd10.10+1_i386.deb Size: 319632 SHA256: 6cfd5cf41459e652e81487bd3b047494fad2f176be56f5cb275bd9eeec731b7f SHA1: 8cf5e5ceb7cd5d293f32fcbe2c93632a3081c38e MD5sum: a7c92c53bba6d8de6d60c64ef0a1e96e Description: Python bindings for BioSig library This package provides Python bindings for BioSig library. Primary goal -- I/O interface to variety of biomedical file formats, including but not limited to SCP-ECG(EN1064), HL7aECG (FDA-XML), GDF, EDF. Package: python-brian Source: brian Version: 1.2.2~svn2229-1~pre1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1736 Depends: python (>= 2.6), python-support (>= 0.90.0), python-brian-lib (>= 1.2.2~svn2229-1~pre1~nd10.10+1), python-matplotlib (>= 0.90.1), python-numpy (>= 1.3.0), python-scipy (>= 0.7.0) Recommends: python-sympy Suggests: python-brian-doc, python-nose Homepage: http://www.briansimulator.org/ Priority: extra Section: python Filename: pool/main/b/brian/python-brian_1.2.2~svn2229-1~pre1~nd10.10+1_all.deb Size: 296622 SHA256: 0eb10161d7cdb8ea735962a89d977c7dd85dbc7ff6874b61d2dbb69ef8f22f7f SHA1: abff994408f18d5bea5c4576b1a0e9dc0dbff555 MD5sum: 67eb7b4194b77423cc706bf1f60a27b0 Description: simulator for spiking neural networks Brian is a clock-driven simulator for spiking neural networks. It is designed with an emphasis on flexibility and extensibility, for rapid development and refinement of neural models. Neuron models are specified by sets of user-specified differential equations, threshold conditions and reset conditions (given as strings). The focus is primarily on networks of single compartment neuron models (e.g. leaky integrate-and-fire or Hodgkin-Huxley type neurons). Features include: - a system for specifying quantities with physical dimensions - exact numerical integration for linear differential equations - Euler, Runge-Kutta and exponential Euler integration for nonlinear differential equations - synaptic connections with delays - short-term and long-term plasticity (spike-timing dependent plasticity) - a library of standard model components, including integrate-and-fire equations, synapses and ionic currents - a toolbox for automatically fitting spiking neuron models to electrophysiological recordings Package: python-brian-doc Source: brian Version: 1.2.2~svn2229-1~pre1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4004 Depends: libjs-jquery Suggests: python-brian Homepage: http://www.briansimulator.org/ Priority: extra Section: doc Filename: pool/main/b/brian/python-brian-doc_1.2.2~svn2229-1~pre1~nd10.10+1_all.deb Size: 860490 SHA256: 82e24868fb4b7d44576842e376ee5d641cb3fbf697e39cffc865dff38273bdbf SHA1: 627055882f14388348ae5d43b1ab5e9b8a205a63 MD5sum: 8c0526915891e659a953c38023211294 Description: simulator for spiking neural networks - documentation Brian is a clock-driven simulator for spiking neural networks. . This package provides user's manual (in HTML and PDF formats), examples and demos. Package: python-brian-lib Source: brian Version: 1.2.2~svn2229-1~pre1~nd10.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 204 Depends: python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0) Homepage: http://www.briansimulator.org/ Priority: extra Section: python Filename: pool/main/b/brian/python-brian-lib_1.2.2~svn2229-1~pre1~nd10.10+1_i386.deb Size: 51760 SHA256: 98f9464171038f8d063411a56516771c604d8cdf09d346364883addadba0c360 SHA1: 634f6014c4ba25556e631d085055402c9b399f05 MD5sum: 0d7b8febbc05b166b7a048c8e44950cb Description: simulator for spiking neural networks -- extensions Brian is a clock-driven simulator for spiking neural networks. . This package provides Python binary extensions. Package: python-dicom Source: pydicom Version: 0.9.4.1-1~maverick.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1804 Depends: python (>= 2.5), python-support (>= 0.90.0) Recommends: python-numpy, python-imaging Suggests: python-matplotlib Homepage: http://code.google.com/p/pydicom/ Priority: optional Section: python Filename: pool/main/p/pydicom/python-dicom_0.9.4.1-1~maverick.nd1_all.deb Size: 360124 SHA256: e11a5ca1d87ad166eefa550498743db961ae2727733b6f01d40d177c7d9c53dd SHA1: 3f767eb7480ef44c6c7a56a839f01e99f8cf849d MD5sum: 0adfc3a91a5b7419208ef94bb7a699a9 Description: DICOM medical file reading and writing pydicom is a pure Python module for parsing DICOM files. DICOM is a standard (http://medical.nema.org) for communicating medical images and related information such as reports and radiotherapy objects. . pydicom makes it easy to read DICOM files into natural pythonic structures for easy manipulation. Modified datasets can be written again to DICOM format files. Package: python-joblib Source: joblib Version: 0.4.5-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 244 Depends: python, python-support (>= 0.90.0) Recommends: python-numpy, python-nose, python-simplejson Homepage: http://packages.python.org/joblib/ Priority: optional Section: python Filename: pool/main/j/joblib/python-joblib_0.4.5-1~nd10.10+1_all.deb Size: 37914 SHA256: fcbd55071f7b03be1c33bb6bf43a241c910a815ce169d3c65de6c760c9c00e59 SHA1: 83d571b5097cf2f8055b65ae6d787fbcf2d1ac45 MD5sum: bfd5b17a888e1396e8ebcf93257a2431 Description: tools to provide lightweight pipelining in Python Joblib is a set of tools to provide lightweight pipelining in Python. In particular, joblib offers: - transparent disk-caching of the output values and lazy re-evaluation (memoize pattern) - easy simple parallel computing - logging and tracing of the execution . Joblib is optimized to be fast and robust in particular on large, long-running functions and has specific optimizations for numpy arrays. Package: python-mlpy Source: mlpy Version: 2.2.0~dfsg1-1~maverick.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 424 Depends: python (>= 2.4), python-support (>= 0.90.0), python2.6, python-numpy, python-mlpy-lib (>= 2.2.0~dfsg1-1~maverick.nd1) Suggests: python-mvpa Provides: python2.6-mlpy Homepage: https://mlpy.fbk.eu/ Priority: optional Section: python Filename: pool/main/m/mlpy/python-mlpy_2.2.0~dfsg1-1~maverick.nd1_all.deb Size: 55840 SHA256: 8283e4ed0f79671d42b030290729eda5c540214d36125e1b06fd0b5b3c9a21c7 SHA1: 4bd22103a057fe6eecf855acf12e7e077d38fc19 MD5sum: b0171fc77f895c31946c6d7aed80768d Description: high-performance Python package for predictive modeling mlpy provides high level procedures that support, with few lines of code, the design of rich Data Analysis Protocols (DAPs) for preprocessing, clustering, predictive classification and feature selection. Methods are available for feature weighting and ranking, data resampling, error evaluation and experiment landscaping. . mlpy includes: SVM (Support Vector Machine), KNN (K Nearest Neighbor), FDA, SRDA, PDA, DLDA (Fisher, Spectral Regression, Penalized, Diagonal Linear Discriminant Analysis) for classification and feature weighting, I-RELIEF, DWT and FSSun for feature weighting, *RFE (Recursive Feature Elimination) and RFS (Recursive Forward Selection) for feature ranking, DWT, UWT, CWT (Discrete, Undecimated, Continuous Wavelet Transform), KNN imputing, DTW (Dynamic Time Warping), Hierarchical Clustering, k-medoids, Resampling Methods, Metric Functions, Canberra indicators. Python-Version: 2.6 Package: python-mlpy-doc Source: mlpy Version: 2.2.0~dfsg1-1~maverick.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1132 Depends: libjs-jquery Suggests: python-mlpy Homepage: https://mlpy.fbk.eu/ Priority: optional Section: doc Filename: pool/main/m/mlpy/python-mlpy-doc_2.2.0~dfsg1-1~maverick.nd1_all.deb Size: 478862 SHA256: d4a1ca68b49b3b1fa7a35abdc1e263f00970d4a6cfde9fd5388508b5c3273ce9 SHA1: 3a203bde333a49868ea27467bb04fa9eb6870af6 MD5sum: da3c2ef1ca5363c2774440218a32fb73 Description: documention and examples for mlpy mlpy provides high level procedures that support, with few lines of code, the design of rich Data Analysis Protocols (DAPs) for preprocessing, clustering, predictive classification and feature selection. Methods are available for feature weighting and ranking, data resampling, error evaluation and experiment landscaping. . This package provides user documentation for mlpy in various formats (HTML, PDF). Package: python-mlpy-lib Source: mlpy Version: 2.2.0~dfsg1-1~maverick.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 288 Depends: libc6 (>= 2.3.6-6~), libgsl0ldbl (>= 1.9), python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), python-numpy Provides: python2.6-mlpy-lib Homepage: https://mlpy.fbk.eu/ Priority: optional Section: python Filename: pool/main/m/mlpy/python-mlpy-lib_2.2.0~dfsg1-1~maverick.nd1_i386.deb Size: 61960 SHA256: 69fd245c477437e07fad88bfc8448283b7dac70035a9d4036cb72122d3355f27 SHA1: e1cf93b4d9c32c486d07079351bab5336b9d572c MD5sum: 76626ea954c78fb83a4782061996ff41 Description: low-level implementations and bindings for mlpy This is an add-on package for the mlpy providing compiled core functionality. Python-Version: 2.6 Package: python-mvpa Source: pymvpa Version: 0.4.5-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4144 Depends: python (>= 2.5), python-support (>= 0.90.0), python2.6, python-numpy, python-mvpa-lib (>= 0.4.5-1~nd10.10+1) Recommends: python-nifti, python-psyco, python-mdp, python-scipy, shogun-python-modular, python-pywt, python-matplotlib, python-reportlab Suggests: fslview, fsl, python-nose, python-lxml, python-openopt, python-rpy, python-mvpa-doc Provides: python2.6-mvpa Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa/python-mvpa_0.4.5-1~nd10.10+1_all.deb Size: 2156652 SHA256: 0d228449c2d9bda679310a534b862c74f86e0ee7a3659e067e77dc9559d1a363 SHA1: 3bc906f2b7eed67b9788f3adbf41e724430515eb MD5sum: 8020da8cb6a4df47fd81669bffe7ee2c Description: multivariate pattern analysis with Python Python module to ease pattern classification analyses of large datasets. It provides high-level abstraction of typical processing steps (e.g. data preparation, classification, feature selection, generalization testing), a number of implementations of some popular algorithms (e.g. kNN, GNB, Ridge Regressions, Sparse Multinomial Logistic Regression), and bindings to external machine learning libraries (libsvm, shogun). . While it is not limited to neuroimaging data (e.g. fMRI, or EEG) it is eminently suited for such datasets. Python-Version: 2.6 Package: python-mvpa-doc Source: pymvpa Version: 0.4.5-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 41004 Depends: libjs-jquery Suggests: python-mvpa Homepage: http://www.pymvpa.org Priority: optional Section: doc Filename: pool/main/p/pymvpa/python-mvpa-doc_0.4.5-1~nd10.10+1_all.deb Size: 9012602 SHA256: 402a7262245b46a4a32abe12b7659382d1644c210cc199e713da6ac36ff16864 SHA1: 426b48e5d94d41837567eca8fd7957313c13eec9 MD5sum: 3ef659e7f06589db1fccabe36d7c9e6d Description: documentation and examples for PyMVPA PyMVPA documentation in various formats (HTML, TXT) including * User manual * Developer guidelines * API documentation * BibTeX references file . Additionally, all example scripts shipped with the PyMVPA sources are included. Package: python-mvpa-lib Source: pymvpa Version: 0.4.5-1~nd10.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 176 Depends: libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), libsvm2, python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), python-numpy Provides: python2.6-mvpa-lib Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa/python-mvpa-lib_0.4.5-1~nd10.10+1_i386.deb Size: 33656 SHA256: 17d56342aea03e1d1345c81314137984a8b09cb81c4fa2fcb8060da99e30415e SHA1: 6b2cb6c3a882bd9bd6e837fe92c966c5fd6cbe3c MD5sum: e57bd6458322de2c3887ae39ab5501d5 Description: low-level implementations and bindings for PyMVPA This is an add-on package for the PyMVPA framework. It provides a low-level implementation of an SMLR classifier and custom Python bindings for the LIBSVM library. Python-Version: 2.6 Package: python-networkx Version: 1.1-2~maverick.nd1 Architecture: all Maintainer: Debian Python Modules Team Installed-Size: 2628 Depends: python (>= 2.5), python-support (>= 0.90.0) Recommends: python-numpy, python-scipy, python-pygraphviz | python-pydot, python-pkg-resources, python-matplotlib, python-yaml Homepage: http://networkx.lanl.gov/ Priority: optional Section: python Filename: pool/main/p/python-networkx/python-networkx_1.1-2~maverick.nd1_all.deb Size: 679658 SHA256: 1b57041f1c6383b2c67aee7c5adced6e17c27769de5768b2a5625146b12aefed SHA1: 7dea67b649381618bd855eeea17892721e71ec39 MD5sum: ffbdff4561f896997795d265f14d2263 Description: tool to create, manipulate and study complex networks NetworkX is a Python-based package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. . The structure of a graph or network is encoded in the edges (connections, links, ties, arcs, bonds) between nodes (vertices, sites, actors). If unqualified, by graph we mean a simple undirected graph, i.e. no self-loops and no multiple edges are allowed. By a network we usually mean a graph with weights (fields, properties) on nodes and/or edges. . The potential audience for NetworkX includes: mathematicians, physicists, biologists, computer scientists, social scientists. Package: python-nibabel Source: nibabel Version: 1.0.0-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2776 Depends: python (>= 2.5), python-support (>= 0.90.0), python-numpy, python-scipy Recommends: python-dicom Suggests: python-nibabel-doc Provides: python2.6-nibabel Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: python Filename: pool/main/n/nibabel/python-nibabel_1.0.0-1~nd10.10+1_all.deb Size: 1056526 SHA256: 3b11c09b2c115daf761f1963f87bfdb5c5eb28c5cec63f91d437c1bcbd9a1c1a SHA1: 4b99d8369f4e768f954b8875fba2ece9971dacd6 MD5sum: 8d566a6c283d548c35ba2b09728d2d86 Description: Python bindings to various neuroimaging data formats This package provides read and write access to some common medical and neuroimaging file formats, including: ANALYZE (plain, SPM99, SPM2), GIFTI, NIfTI1, MINC, as well as PAR/REC. The various image format classes give full or selective access to header (meta) information and access to the image data is made available via NumPy arrays. NiBabel is the successor of PyNIfTI. . This package also provides a commandline tool for conversion of PAR/REC to NIfTI images. Python-Version: 2.6 Package: python-nibabel-doc Source: nibabel Version: 1.0.0-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2716 Depends: libjs-jquery Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: doc Filename: pool/main/n/nibabel/python-nibabel-doc_1.0.0-1~nd10.10+1_all.deb Size: 398618 SHA256: d66b8ac32caaeaad1ab1314ba94df2f0cb7d1cf1396f95916534790f0bb932c5 SHA1: d965fd80013ee6b53bbb48fef2e7f59cc53b415f MD5sum: b66cadb2152267e718c84f17a3af396e Description: documentation for NiBabel NiBabel provides read and write access to some common medical and neuroimaging file formats, including: ANALYZE (plain, SPM99, SPM2), GIFTI, NIfTI1, MINC, as well as PAR/REC. The various image format classes give full or selective access to header (meta) information and access to the image data is made available via NumPy arrays. NiBabel is the successor of PyNIfTI. . This package provides the documentation in HTML format. Package: python-nifti Source: pynifti Version: 0.20100607.1-1~maverick.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 1132 Depends: libc6 (>= 2.4), libnifti1 (>> 1.1.0-2), python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), python2.6, python-numpy, python-numpy-ext, libjs-jquery Provides: python2.6-nifti Homepage: http://niftilib.sourceforge.net/pynifti/ Priority: optional Section: python Filename: pool/main/p/pynifti/python-nifti_0.20100607.1-1~maverick.nd1_i386.deb Size: 272608 SHA256: a63dbee756c7c65f5cb09b3793c2716a58da19ffd89c2812cffc93dfd37c93c7 SHA1: 2c28423dd5a9a4596cd7bc9ee0028f7536c2ad95 MD5sum: 7a117a3483d766faedee550e080d9ffc Description: Python interface to the NIfTI I/O libraries Using PyNIfTI one can easily read and write NIfTI and ANALYZE images from within Python. The NiftiImage class provides Python-style access to the full header information. Image data is made available via NumPy arrays. Python-Version: 2.6 Package: python-nipype Source: nipype Version: 0.3.2-1~maverick.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1716 Depends: python (>= 2.5), python-support (>= 0.90.0), python-scipy, python-simplejson, python-traits Recommends: python-nifti, ipython, python-nose, python-networkx Suggests: fsl, afni, lipsia, python-nipy Provides: python2.6-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: python Filename: pool/main/n/nipype/python-nipype_0.3.2-1~maverick.nd1_all.deb Size: 269190 SHA256: 34d1e732c2f6b613f9cb99cd2c3961a6283349484beef4a8265369f1961f10b7 SHA1: 811f18c612a1a546f438410fae8a234b9cbedefa MD5sum: aeec13b279567a2dca79648b3828f8e7 Description: Neuroimaging data analysis pipelines in Python Nipype interfaces Python to other neuroimaging packages and creates an API for specifying a full analysis pipeline in Python. Currently, it has interfaces for SPM, FSL, AFNI, Freesurfer, but could be extended for other packages (such as lipsia). Package: python-nipype-doc Source: nipype Version: 0.3.2-1~maverick.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 3224 Depends: libjs-jquery Suggests: python-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: doc Filename: pool/main/n/nipype/python-nipype-doc_0.3.2-1~maverick.nd1_all.deb Size: 774716 SHA256: f64d2d2fefd479b9392f117205223ff24c117c26cdc3af6fe29b77466f650d15 SHA1: fd53363af0225c7bb44a539b22a13f9f009a9a13 MD5sum: 21bbda904e514b4c334c7e723347f666 Description: Neuroimaging data analysis pipelines in Python -- documentation Nipype interfaces Python to other neuroimaging packages and creates an API for specifying a full analysis pipeline in Python. Currently, it has interfaces for SPM, FSL, AFNI, Freesurfer, but could be extended for other packages (such as lipsia). . This package contains Nipype documentation in various formats. Package: python-nitime Source: nitime Version: 0.2-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 752 Depends: python-numpy, python-scipy Recommends: python-matplotlib, python-nose, python-nibabel, python-networkx Homepage: http://nipy.org/nitime Priority: extra Section: python Filename: pool/main/n/nitime/python-nitime_0.2-1~nd10.10+1_all.deb Size: 312900 SHA256: a597d5285ed2764312a046981666d2fa05fe4e643eecaa260b7c0aec0eb27449 SHA1: 3396ad0f1d125996d6abd2bdc9840d03fe9733ec MD5sum: f121a3106a95ecadd3bad854468df318 Description: timeseries analysis for neuroscience data (nitime) Nitime is a Python module for time-series analysis of data from neuroscience experiments. It contains a core of numerical algorithms for time-series analysis both in the time and spectral domains, a set of container objects to represent time-series, and auxiliary objects that expose a high level interface to the numerical machinery and make common analysis tasks easy to express with compact and semantically clear code. Package: python-nitime-doc Source: nitime Version: 0.2-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4096 Depends: libjs-jquery Suggests: python-nitime Homepage: http://nipy.org/nitime Priority: extra Section: doc Filename: pool/main/n/nitime/python-nitime-doc_0.2-1~nd10.10+1_all.deb Size: 2663834 SHA256: ad266363c0d0170244078baec756d2712034342903a5a6546ea73d52200fcea9 SHA1: b308c4bd273ba626d076fe7e83544668773ab7d9 MD5sum: 1226038c73f2b8fbeabd32bf60f09ad7 Description: timeseries analysis for neuroscience data (nitime) -- documentation Nitime is a Python module for time-series analysis of data from neuroscience experiments. . This package provides the documentation in HTML format. Package: python-openmeeg Source: openmeeg Version: 2.0.0.dfsg-2~maverick.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 560 Depends: libatlas3gf-base, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libmatio0, libopenmeeg1, libpython2.6 (>= 2.6), libstdc++6 (>= 4.4.0), python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), python-numpy Provides: python2.6-openmeeg Homepage: http://www-sop.inria.fr/odyssee/software/OpenMEEG/ Priority: extra Section: python Filename: pool/main/o/openmeeg/python-openmeeg_2.0.0.dfsg-2~maverick.nd1_i386.deb Size: 146574 SHA256: ba0240b2c518087ddee3eb74e28f819a5d9d27d616cf33bdf2f654faf9837a71 SHA1: ab0544329617edd43b16af2b4ace8c56a18eac30 MD5sum: d7ccd32f13a73b2938663c511cdbad2a 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. Python-Version: 2.6 Package: python-openpyxl Source: openpyxl Version: 1.1.0-1~maverick.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 448 Depends: python, python-support (>= 0.90.0) Recommends: python-nose Homepage: http://bitbucket.org/ericgazoni/openpyxl/ Priority: optional Section: python Filename: pool/main/o/openpyxl/python-openpyxl_1.1.0-1~maverick.nd1_all.deb Size: 49184 SHA256: 5cdbef28c90ba9a004c40dcd1a7dbd46a47e8d071510627d43dba4a94aee6b57 SHA1: e7c9e1bcf4345c507210870199e01a8fcc514fdd MD5sum: e5783f04682ec139db7681ef9f707d32 Description: module to read/write OpenXML xlsx/xlsm files Openpyxl is a pure Python module to read/write Excel 2007 (OpenXML) xlsx/xlsm files. Package: python-pyepl Source: pyepl Version: 1.1.0-3~maverick.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 1584 Depends: python (<< 2.7), python (>= 2.6), python-central (>= 0.6.11), python-pyepl-common (= 1.1.0-3~maverick.nd1), python-numpy, python-imaging, python-pygame, python-pyode, python-opengl, ttf-dejavu, libasound2 (>> 1.0.22), libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libode1, libsamplerate0, libsndfile1 (>= 1.0.20), libstdc++6 (>= 4.4.0) Conflicts: python2.3-pyepl, python2.4-pyepl Replaces: python2.3-pyepl, python2.4-pyepl Provides: python2.6-pyepl Homepage: http://pyepl.sourceforge.net/ Priority: optional Section: python Filename: pool/main/p/pyepl/python-pyepl_1.1.0-3~maverick.nd1_i386.deb Size: 348724 SHA256: e492fe000c55e4d56cf178e68c68c458b42b21fbe092d83db7f6465b38ea4b8b SHA1: 02d7343f7b494907b0739c48bf3a73e600238180 MD5sum: 06ea149b83d3e816ef5ecbe05339dc96 Description: module for coding psychology experiments in Python PyEPL is a stimuli delivery and response registration toolkit to be used for generating psychology (as well as neuroscience, marketing research, and other) experiments. . It provides - presentation: both visual and auditory stimuli - responses registration: both manual (keyboard/joystick) and sound (microphone) time-stamped - sync-pulsing: synchronizing your behavioral task with external acquisition hardware - flexibility of encoding various experiments due to the use of Python as a description language - fast execution of critical points due to the calls to linked compiled libraries . This toolbox is here to be an alternative for a widely used commercial product E'(E-Prime) . This package provides PyEPL for supported versions of Python. Python-Version: 2.6 Package: python-pyepl-common Source: pyepl Version: 1.1.0-3~maverick.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 852 Depends: python Homepage: http://pyepl.sourceforge.net/ Priority: optional Section: python Filename: pool/main/p/pyepl/python-pyepl-common_1.1.0-3~maverick.nd1_all.deb Size: 817816 SHA256: c48e4f818f9d5f08f12d59d6c1131b7f163f29c8801d3373889a7c20795d2d9f SHA1: 82228fe2202df811391f45df27ceaabcc7abca89 MD5sum: f4abbefb9d51c38b9370f48229577970 Description: module for coding psychology experiments in Python PyEPL is a stimuli delivery and response registration toolkit to be used for generating psychology (as well as neuroscience, marketing research, and other) experiments. . It provides - presentation: both visual and auditory stimuli - responses registration: both manual (keyboard/joystick) and sound (microphone) time-stamped - sync-pulsing: synchronizing your behavioral task with external acquisition hardware - flexibility of encoding various experiments due to the use of Python as a description language - fast execution of critical points due to the calls to linked compiled libraries . This toolbox is here to be an alternative for a widely used commercial product E'(E-Prime) . This package provides common files such as images. Package: python-scikits-learn Source: scikit-learn Version: 0.5-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1112 Depends: python (>= 2.5), python-support (>= 0.90.0), python-numpy, python-scipy, python-scikits-learn-lib (>= 0.5-1~nd10.10+1) Recommends: python-nose, python-psyco, python-matplotlib, python-joblib (>= 0.4.5) Suggests: python-dap, python-scikits-optimization, python-scikits-learn-doc Provides: python2.6-scikits-learn Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python-scikits-learn_0.5-1~nd10.10+1_all.deb Size: 203450 SHA256: 6994b428f50c6e174c8ebbe53f047cb901a1939b2091d60695ac531cf1667950 SHA1: 2176a051eb3867a1dda96219faca01ce14dbff39 MD5sum: 83105b0472d6fc78e6df85d2644152b6 Description: Python modules for machine learning and data mining scikit-learn is a collection of Python modules relevant to machine/statistical learning and data mining. Non-exhaustive list of included functionality: - Gaussian Mixture Models - Manifold learning - kNN - SVM (via LIBSVM) Python-Version: 2.6 Package: python-scikits-learn-doc Source: scikit-learn Version: 0.5-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6396 Depends: libjs-jquery Suggests: python-scikits-learn Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: doc Filename: pool/main/s/scikit-learn/python-scikits-learn-doc_0.5-1~nd10.10+1_all.deb Size: 3205264 SHA256: a2e80602b72ecc68962477e09293a8f23d0913c9a5f547dba91dc7ae67eac546 SHA1: 7beceb08670ad9e809920541b2787742fbaddc69 MD5sum: 0cbb673608d0b1850cb64043b4a45e82 Description: documentation and examples for scikit-learn This package contains documentation and example scripts for python-scikits-learn. Package: python-scikits-learn-lib Source: scikit-learn Version: 0.5-1~nd10.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 656 Depends: libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.2.1), libsvm2, python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), python-numpy Provides: python2.6-scikits-learn-lib Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python-scikits-learn-lib_0.5-1~nd10.10+1_i386.deb Size: 210078 SHA256: a2fcbe8ad64563153e047221cbf225adecead1c5da183c899a4080bba879c899 SHA1: 660b19f642e20b86c1ef1f8a9ad24d760a4d9899 MD5sum: 6819e46e35ca80f76b032d0ff4338e08 Description: low-level implementations and bindings for scikits-learn This is an add-on package for python-scikits-learn. It provides low-level implementations and custom Python bindings for the LIBSVM library. Python-Version: 2.6 Package: python-sphinx Source: sphinx Version: 1.0.1-1~nd10.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4580 Depends: python (>= 2.4), python-support (>= 0.90.0), python-docutils (>= 0.5), python-pygments (>= 0.8), python-jinja2 (>= 2.2), libjs-jquery Recommends: python (>= 2.6) | python-simplejson, python-imaging Suggests: jsmath Homepage: http://sphinx.pocoo.org/ Priority: optional Section: python Filename: pool/main/s/sphinx/python-sphinx_1.0.1-1~nd10.10+1_all.deb Size: 1233508 SHA256: e5d101c4574a093267d4a65cc4838ddf81741164e8c6e498e42717ed804ad40f SHA1: e88a483cdc8e67acf308b87956dee74b4e6e92c3 MD5sum: 8b215a4eef0990392885ae8c7b64aa93 Description: tool for producing documentation for Python projects Sphinx is a tool for producing documentation for Python projects, using reStructuredText as markup language. . Sphinx features: * HTML, CHM, LaTeX output, * Cross-referencing source code, * Automatic indices, * Code highlighting, using Pygments, * Extensibility. Existing extensions: - automatic testing of code snippets, - including doctrings from Python modules. Package: sigviewer Version: 0.4.3+svn511-1~nd10.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1004 Depends: libbiosig0, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libgdf0, libqt4-xml (>= 4:4.5.3), libqtcore4 (>= 4:4.7.0~beta1), libqtgui4 (>= 4:4.6.1), libstdc++6 (>= 4.4.0) Homepage: http://sigviewer.sourceforge.net Priority: extra Section: science Filename: pool/main/s/sigviewer/sigviewer_0.4.3+svn511-1~nd10.10+1_i386.deb Size: 423164 SHA256: cd66b5ef9ee9265f447e9a458bb46fb25edabf4be10f3fb588f364efa39cc471 SHA1: 95381727b77831abcf576bc7a2abeb3ccc1611e3 MD5sum: a821a91fe1ac253d18670bb57501803e Description: GUI viewer for biosignals such as EEG, EMG, and ECG SigViewer is a viewing and scoring software for biomedical signal data. It relies on biosig4c++ library which supports a number of data formats (including EDF, BDF, GDF, BrainVision, BCI2000, CFWB, HL7aECG, SCP_ECG (EN1064), MFER, ACQ, CNT(Neuroscan), DEMG, EGI, EEG1100, FAMOS, SigmaPLpro, TMS32). The complete list of supported file formats is available at http://hci.tugraz.at/schloegl/biosig/TESTED . . Besides displaying biosignals, SigViewer supports creating annotations to select artifacts or specific events.