Package: ants Version: 1.9.2+svn680.dfsg-1~nd10.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 38536 Depends: libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libinsighttoolkit3.16, 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_1.9.2+svn680.dfsg-1~nd10.04+1_amd64.deb Size: 11483142 SHA256: d5ac307675e078824a39385b3ef3ca562505952140b77178b83a0bc34601eaa6 SHA1: dd17d0523482fc456afe418341d6e3657be5cfe4 MD5sum: 979230d5541da84daee2d35fa098f807 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: arno-iptables-firewall Version: 1.9.2.k-3~lucid.nd1 Architecture: all Maintainer: Michael Hanke Installed-Size: 844 Depends: iptables (>= 1.2.11), gawk, debconf (>= 1.3.22) | cdebconf (>= 0.43), debconf (>= 0.5) | debconf-2.0, iproute Recommends: lynx, dnsutils Homepage: http://rocky.eld.leidenuniv.nl/ Priority: optional Section: net Filename: pool/main/a/arno-iptables-firewall/arno-iptables-firewall_1.9.2.k-3~lucid.nd1_all.deb Size: 132470 SHA256: 2e22eebc483d94eeac7b8dede959f75add3268e432e7a878c5d39e61a129c58a SHA1: 51c20f3f9a968af47adc9bc6dff312a0ad1abd2a MD5sum: b24e68173b3ce290b28f85c017f3a7b6 Description: single- and multi-homed firewall script with DSL/ADSL support Unlike other lean iptables frontends in Debian, arno-iptables-firewall will setup and load a secure, restrictive firewall by just asking a few question. This includes configuring internal networks for internet access via NAT and potential network services (e.g. http or ssh). . However, it is in no way restricted to this simple setup. Some catch words of additional features, that can be enabled in the well documented configuration file are: DSL/ADSL, Port forwarding, DMZ's, portscan detection, MAC address filtering. Package: autotools-dev Version: 20100122.1~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 216 Depends: neurodebian-popularity-contest Enhances: cdbs, debhelper Homepage: http://savannah.gnu.org/projects/config/ Priority: optional Section: devel Filename: pool/main/a/autotools-dev/autotools-dev_20100122.1~nd10.04+1_all.deb Size: 72976 SHA256: 766a04a0c3afdee01758bfb82390e4b8dda93cc48112d6326bb5b221693a1efd SHA1: 5549c54091c9eb5526b080ca8368a98573da91a3 MD5sum: a91ae7c928212a4367d8b2d60ed7e7fc Description: Update infrastructure for config.{guess,sub} files This package installs an up-to-date version of config.guess and config.sub, used by the automake and libtool packages. It provides the canonical copy of those files for other packages as well. . It also documents in /usr/share/doc/autotools-dev/README.Debian.gz best practices and guidelines for using autoconf, automake and friends on Debian packages. This is a must-read for any developers packaging software that uses the GNU autotools, or GNU gettext. . Additionally this package provides seamless integration into Debhelper or CDBS, allowing maintainers to easily update config.{guess,sub} files in their packages. Package: biosig-tools Source: biosig4c++ Version: 0.94.2+svn2552-1~pre1~nd10.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 664 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~nd10.04+1_amd64.deb Size: 255476 SHA256: 57a6edf6ee6a68d1a25d671b6b0cf10d9b8b44b95963000402b18bad2f6cf0be SHA1: 667fa5669e8fa729301ace8c5709106ba64df06a MD5sum: ecaffa14432888408a2aca711d6c2167 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 Version: 5.6.1.3~dfsg.1-4~nd10.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 18648 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, libminc2-1, libqt4-assistant (>= 4:4.5.3), libqt4-network (>= 4:4.5.3), libqt4-opengl (>= 4:4.5.3), libqt4-xml (>= 4:4.5.3), libqtcore4 (>= 4:4.6.1), libqtgui4 (>= 4:4.5.3), libqwt5-qt4, libstdc++6 (>= 4.4.0), libvtk5.2, zlib1g (>= 1:1.2.3.3.dfsg) Suggests: caret-data (>= 5.6~dfsg.1) Homepage: http://brainvis.wustl.edu/wiki/index.php/Caret:About Priority: optional Section: science Filename: pool/main/c/caret/caret_5.6.1.3~dfsg.1-4~nd10.04+1_amd64.deb Size: 7153514 SHA256: 88818527b5c6d287db758ef44fefbfda72fb7955be7834d123c4d2d612e3f943 SHA1: 5af05b66747176d66ccab5daf777dd656856d185 MD5sum: fc34d2a16957380cc2def2cf0590dab2 Description: Computerized Anatomical Reconstruction and Editing Toolkit This software allows for creating, viewing and manipulating surface reconstructions of the cerebral and cerebellar cortex, viewing volumes and for displaying experimental data on the surfaces and volumes. While Caret is primarily a GUI application with 'caret_command' there is also a versatile command line tool, that allows access to a substantial proportion of Caret's functionality. . Caret can download and use stereotaxic atlases (human, monkey, mouse and rat) from an open online database. . Reference: . Van Essen, D.C., Dickson, J., Harwell, J., Hanlon, D., Anderson, C.H. and Drury, H.A. 2001. An Integrated Software System for Surface-based Analyses of Cerebral Cortex. Journal of American Medical Informatics Association, 8(5), 443-459. 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: classads Version: 1.0.9-2~nd10.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 124 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libclassad0 (= 1.0.9-2~nd10.04+1), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0) Homepage: http://www.cs.wisc.edu/condor/classad Priority: extra Section: misc Filename: pool/main/c/classads/classads_1.0.9-2~nd10.04+1_amd64.deb Size: 36722 SHA256: 3815b9e7fba8763e9d3c9af6b9497b2b41405aebdedc99ff9717ee424be8a3a8 SHA1: 5af0957b32b3c836632311c5dfa2c3192846512a MD5sum: 309a6a8b4b3a8629c9720f1aab836e62 Description: Condor's classad utilities A classad (classified ad) is a mapping from attribute names to expressions. In the simplest cases, the expressions are simple constants (integer, floating point, or string), thus a form of property list. Attribute expressions can also be more complicated. There is a protocol for evaluating an attribute expression of a classad vis a vis another ad. Two classads match if each ad has attribute requirements that evaluate to true in the context of the other ad. Classad matching is used by the Condor central manager to determine the compatibility of jobs and workstations where they may be run. . This package provides command line tools to manipulate, test and evaluate classads. Package: cython Version: 0.13-1~nd10.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 3684 Depends: python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), python2.6, libc6 (>= 2.3) Suggests: gcc Homepage: http://cython.org/ Priority: optional Section: python Filename: pool/main/c/cython/cython_0.13-1~nd10.04+1_amd64.deb Size: 866936 SHA256: aa00f08c2a1fa909529d5d46f8b3cc31c73c1a593565fb445c0dbc7a16568247 SHA1: b9c103f6398143c11c6d020f5a5534f70fc4f104 MD5sum: d81d5e6398e53a8c67d5aa976f659b45 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~nd10.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 5324 Depends: python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), libc6 (>= 2.3), cython (= 0.13-1~nd10.04+1) Suggests: gcc Homepage: http://cython.org/ Priority: extra Section: debug Filename: pool/main/c/cython/cython-dbg_0.13-1~nd10.04+1_amd64.deb Size: 1713010 SHA256: cc5b8c1a0b8bd1a50a76d4bc87872b8dd790b2a8baefbb23dc3b0eab1138bf53 SHA1: 36a3184c96b97382b9d883a9736e52a644c764ef MD5sum: 72cc71be36199bb7768913e8be390681 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~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 524 Depends: libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libnifti2, 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~lucid.nd1_amd64.deb Size: 158658 SHA256: b2005d847469a33bcd594ba4a3202e373a41ceb7327f72dc222f8e09934c9656 SHA1: 045409c68a634ae0e86619f76ba5ff3afdf2386c MD5sum: b0ee895556cce5f212e0d20bc00568c5 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: freenect Source: libfreenect Version: 1:0.0.1+20101211+1-2~nd10.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 32 Depends: neurodebian-popularity-contest, libfreenect-demos, libfreenect-dev, libfreenect0.0 Homepage: http://openkinect.org/ Priority: extra Section: libs Filename: pool/main/libf/libfreenect/freenect_0.0.1+20101211+1-2~nd10.04+1_amd64.deb Size: 1584 SHA256: 43f8c79d7bf9c436c554eab3cc0607253f45c489302c9dc9bdad60b418f27112 SHA1: 4298ad5fb364bfbbaa602fb35201349c796fd91a MD5sum: 1fd52e08d7bdcde25350ac1e5b1687ef Description: library for accessing Kinect USB camera -- meta package libfreenect is the core library for accessing the Microsoft Kinect USB camera. . This is the meta package to install all components of the project. Package: fslview Version: 3.1.8+4.1.6-2~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 4168 Depends: libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libnewmat10ldbl, libnifti2, libqt3-mt (>= 3:3.3.8-b), libqwt4c2, libstdc++6 (>= 4.4.0), libvtk5.2, libvtk5.2-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~lucid.nd1_amd64.deb Size: 1524282 SHA256: bd1c21144d8fb58994970e16b6a35e888f4302d8051a261c20118b0f33253c4a SHA1: ad70cfd2d70edbf4e365c429a0d314ccfaddbd1c MD5sum: ce50c0711e8d662f355f65b23914bd4c 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~lucid.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~lucid.nd1_all.deb Size: 2378978 SHA256: e281d1abfbaced29fa7c68ed41607acf19ebbd20e5e0a77a024717153dcb658c SHA1: 2f911edd963e4a484195d720ad3fa669eab86a48 MD5sum: 221a08bcb9ac0db48525a6b7e6e4bed2 Description: Documentation for FSLView This package provides the online documentation for FSLView. . FSLView is part of FSL. Package: gdf-tools Source: libgdf Version: 0.1.1~svn62-1~nd10.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 156 Depends: neurodebian-popularity-contest, libboost-filesystem1.40.0 (>= 1.40.0-1), libboost-program-options1.40.0 (>= 1.40.0-1), libboost-system1.40.0 (>= 1.40.0-1), 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_0.1.1~svn62-1~nd10.04+1_amd64.deb Size: 38798 SHA256: a0e69708c495e9e2e14582af318e164a4303ace23f4f90d28944343cf2d94563 SHA1: c33ac8946471bd259d8097c12d432bc8f89f49b0 MD5sum: 0f1cf1f026e2b5d776f2cf297e7897cc 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~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 128 Depends: libc6 (>= 2.3.4), libexpat1 (>= 1.95.8), libgiftiio0, libnifti2, 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~lucid.nd1_amd64.deb Size: 29244 SHA256: 46bf711ca88f18e5ad2d2e175bb3cec6925c901aeb45eca7243a84c2820921d1 SHA1: 76d719b5e81cdd6aeba7fafc4762209f1e9a7daa MD5sum: 939e433013601359b181258a85041993 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~nd10.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 8628 Depends: libc6 (>= 2.4), libfltk1.1 (>= 1.1.8~rc1), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, libinsighttoolkit3.16, libstdc++6 (>= 4.4.0), libvtk5.2 Homepage: http://www.itksnap.org Priority: extra Section: science Filename: pool/main/i/itksnap/itksnap_2.1.4-1~nd10.04+1_amd64.deb Size: 3701628 SHA256: a65f7bd93989b77d8e0e78555f1bab58e0b442097f113704523f7778ab936e3c SHA1: abf8f05dfcbe6900ab539059d22e867def220436 MD5sum: a4bfa5e51549c7d62ce4abdc9f83a8eb 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~nd10.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1624 Depends: libbiosig0 (= 0.94.2+svn2552-1~pre1~nd10.04+1) Homepage: http://biosig.sf.net/ Priority: extra Section: libdevel Filename: pool/main/b/biosig4c++/libbiosig-dev_0.94.2+svn2552-1~pre1~nd10.04+1_amd64.deb Size: 382234 SHA256: cc858ad1d460d58a2befdcff9e55c4aaadfdd7e6b3b5460d9450fc6eef6b759d SHA1: a14e5b039265b05b227a98a4f19fb824798cad45 MD5sum: d3944a12c07fcddde27ec72fb9aced1e 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~nd10.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 896 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~nd10.04+1_amd64.deb Size: 305224 SHA256: 6a94e1be8d2dbcba7c8d8c41373b4857f30b6ddf30fe5c45e90d715d29920adb SHA1: b6fcb996be1fae1c1e3826426e178abef0ffb833 MD5sum: 4ade1ad7eabbe7a092d9f97fa9fdb676 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~nd10.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 712 Depends: libbiosig0 (= 0.94.2+svn2552-1~pre1~nd10.04+1) Homepage: http://biosig.sf.net/ Priority: extra Section: debug Filename: pool/main/b/biosig4c++/libbiosig0-dbg_0.94.2+svn2552-1~pre1~nd10.04+1_amd64.deb Size: 173368 SHA256: 6abd103007b22c9e8dc5af341356a7b41e02d75e1547291c42cb27e365bcbc5b SHA1: f5aa7e7f1e1a7d31e559e91377632a26a936289c MD5sum: 4e9e047b77f63357476fe2ee0af10143 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: libclassad-dev Source: classads Version: 1.0.9-2~nd10.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2220 Depends: neurodebian-popularity-contest, libclassad0 (= 1.0.9-2~nd10.04+1) Conflicts: libclassad0-dev Replaces: libclassad0-dev Homepage: http://www.cs.wisc.edu/condor/classad Priority: extra Section: libdevel Filename: pool/main/c/classads/libclassad-dev_1.0.9-2~nd10.04+1_amd64.deb Size: 573802 SHA256: 0018fb2cdd27e047a57270dded4af3b4cf3e279cc311acc2969bcb39cb9bfa7f SHA1: 73b14abc84faa4152cb980e8332da4000d5429d0 MD5sum: 1b43e35a488d1298f4dfce70666b2d3a Description: library for Condor's classads expression language (development) A classad (classified ad) is a mapping from attribute names to expressions. In the simplest cases, the expressions are simple constants (integer, floating point, or string), thus a form of property list. Attribute expressions can also be more complicated. There is a protocol for evaluating an attribute expression of a classad vis a vis another ad. Two classads match if each ad has attribute requirements that evaluate to true in the context of the other ad. Classad matching is used by the Condor central manager to determine the compatibility of jobs and workstations where they may be run. . This package provides the static library and header files. Package: libclassad0 Source: classads Version: 1.0.9-2~nd10.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1076 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0) Homepage: http://www.cs.wisc.edu/condor/classad Priority: extra Section: libs Filename: pool/main/c/classads/libclassad0_1.0.9-2~nd10.04+1_amd64.deb Size: 429164 SHA256: 52413160647610e23c0136b4a4f508dced39cab02433200b6d72a403b542272e SHA1: 5108e0618d9d44707a8c3461caad4faaff43ba9c MD5sum: cc03dca02b9db84b0e639241222aa1b4 Description: library for Condor's classads expression language A classad (classified ad) is a mapping from attribute names to expressions. In the simplest cases, the expressions are simple constants (integer, floating point, or string), thus a form of property list. Attribute expressions can also be more complicated. There is a protocol for evaluating an attribute expression of a classad vis a vis another ad. Two classads match if each ad has attribute requirements that evaluate to true in the context of the other ad. Classad matching is used by the Condor central manager to determine the compatibility of jobs and workstations where they may be run. . This package provides the runtime library. Package: libfreenect-demos Source: libfreenect Version: 1:0.0.1+20101211+1-2~nd10.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 124 Depends: neurodebian-popularity-contest, freeglut3, libc6 (>= 2.3.4), libfreenect0.0, libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, libice6 (>= 1:1.0.0), libsm6, libstdc++6 (>= 4.4.0), libusb-1.0-0 (>= 2:1.0.6), libx11-6, libxext6, libxi6, libxmu6, libglut3 Homepage: http://openkinect.org/ Priority: extra Section: libdevel Filename: pool/main/libf/libfreenect/libfreenect-demos_0.0.1+20101211+1-2~nd10.04+1_amd64.deb Size: 24578 SHA256: f8ec89b1c155b6c3c93165f62f08f33eda6bf6f7030fb5ebc8c9e37f680ec951 SHA1: f1b43899802d2a3dd61984bc439c6e11ce9d56a7 MD5sum: 2fbd850289341d1afaacb99de1a5ec56 Description: library for accessing Kinect USB camera -- demonstrations libfreenect is the core library for accessing the Microsoft Kinect USB camera. . This package includes some example programs for kinect. All programs start with a freenec- prefix. Package: libfreenect-dev Source: libfreenect Version: 1:0.0.1+20101211+1-2~nd10.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 120 Depends: neurodebian-popularity-contest, libusb-1.0-0-dev, libfreenect0.0 (= 1:0.0.1+20101211+1-2~nd10.04+1) Homepage: http://openkinect.org/ Priority: extra Section: libdevel Filename: pool/main/libf/libfreenect/libfreenect-dev_0.0.1+20101211+1-2~nd10.04+1_amd64.deb Size: 21622 SHA256: 12f3cb916bd4a9492316a8004093af065d200709f5a4c6e615e653ec96f6c1e9 SHA1: bdf7cad7fd8e3959cd51a0123e803e0621a2826e MD5sum: 4420fd3ce2279287ec3c58f08b82905e Description: library for accessing Kinect USB camera -- development files libfreenect is the core library for accessing the Microsoft Kinect USB camera. . This is the development package containing the libraries and header for software development with libfreenect. Package: libfreenect0.0 Source: libfreenect Version: 1:0.0.1+20101211+1-2~nd10.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 136 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), libusb-1.0-0 (>= 2:1.0.6), udev Conflicts: libfreenect Homepage: http://openkinect.org/ Priority: extra Section: libs Filename: pool/main/libf/libfreenect/libfreenect0.0_0.0.1+20101211+1-2~nd10.04+1_amd64.deb Size: 26162 SHA256: daab140c6c2c0068c5231601921b21c5ad647d48857c6bad80272c78346829b3 SHA1: 873886473e6eed1b0ce18d31bc9b928a83985f69 MD5sum: 4a1b86b6bc5592c79695f46e173f4f71 Description: library for accessing Kinect USB camera libfreenect is the core library for accessing the Microsoft Kinect USB camera. Currently, the library supports access to: - RGB and Depth Images - Motors - Accelerometer - LED Package: libgdf-dev Source: libgdf Version: 0.1.1~svn62-1~nd10.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 172 Depends: neurodebian-popularity-contest, libgdf0 (= 0.1.1~svn62-1~nd10.04+1) Homepage: http://sourceforge.net/projects/libgdf Priority: extra Section: libdevel Filename: pool/main/libg/libgdf/libgdf-dev_0.1.1~svn62-1~nd10.04+1_amd64.deb Size: 18024 SHA256: cc5011feaae97cef713f3b1ae341853d12b32576596680eb99dd31945c6be1c0 SHA1: cfa7de99e13e33db12e6c94e28dfe3d0ef5392b0 MD5sum: 9cf8227f43c61698bef49a0616ff2c2c 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: 0.1.1~svn62-1~nd10.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 368 Depends: neurodebian-popularity-contest, 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_0.1.1~svn62-1~nd10.04+1_amd64.deb Size: 105942 SHA256: 9e83f7d20b39604c1c04cfddefad079e516a910d34060421124df79e589f0b74 SHA1: b15da4f7f1eb457352a3b79673c5ae4e2ab7a8fe MD5sum: 31f7769c667ce13a4689ae1f851b0e94 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: 0.1.1~svn62-1~nd10.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 4176 Depends: neurodebian-popularity-contest, libgdf0 (= 0.1.1~svn62-1~nd10.04+1) Homepage: http://sourceforge.net/projects/libgdf Priority: extra Section: debug Filename: pool/main/libg/libgdf/libgdf0-dbg_0.1.1~svn62-1~nd10.04+1_amd64.deb Size: 1112306 SHA256: 9ca075872b6cd8fa93ba705b73ae70735ddd6a904934da3651d108dd16841b87 SHA1: e0d07ab3b02d2a4c29517f799cb3584be0e8ef1b MD5sum: cc0501651e8c1a6976807708781adbc5 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~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 256 Depends: libgiftiio0 (= 1.0.9-1~lucid.nd1) Homepage: http://www.nitrc.org/projects/gifti Priority: optional Section: libdevel Filename: pool/main/g/gifticlib/libgiftiio-dev_1.0.9-1~lucid.nd1_amd64.deb Size: 64998 SHA256: 45f0db22721e17782f2d36f92ffd6b9ffd20a0f9613d56f0c2aaa846c4ca343f SHA1: 195febb32e6113c88617a7d78e8694e4881f7341 MD5sum: ae42e1bb953ebbe14e59986566635243 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~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 180 Depends: libc6 (>= 2.4), libexpat1 (>= 1.95.8), libnifti2, 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~lucid.nd1_amd64.deb Size: 57594 SHA256: f8b85858f70219645839d9e3109129208ad79a9a1546eb1a1e7f875845109718 SHA1: 00e59c2b2422da35b1a8c8e35e9714285deb93c0 MD5sum: 0ed5c2dde1ffd75079078e4c82424eac 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~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 624 Depends: libnifti2 (= 2.0.0-1~lucid.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~lucid.nd1_amd64.deb Size: 171290 SHA256: 113ba42ad361ebf6f7b7b2c429c02857738472be1f23bbcb2232b18cfc334d74 SHA1: 0d49b66ca760523e35331ceafa85d046a0d8d5d3 MD5sum: 9823b6daab84532d63e7dd88020e1a5a 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~lucid.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~lucid.nd1_all.deb Size: 245482 SHA256: 8532e280818991cfdd6fe8e883f0bb5b3d0ca9bb86360cd2fcb98a2750f01720 SHA1: 39744b584b030525f62bd876863ebfadcac7e9ff MD5sum: cb7b7605b2710de9700586340deca337 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~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 336 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~lucid.nd1_amd64.deb Size: 123024 SHA256: ba53d9dd6ebe7abd552f39127d2bc06896d367be47525ec0e29867fc833b54b0 SHA1: 33b02302b67518c8136e29fb7a52c677f78514ff MD5sum: 010aa718f728757323244a44e422706c 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: libodin-dev Source: odin Version: 1.8.1-3~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 21020 Homepage: http://od1n.sourceforge.net Priority: extra Section: libdevel Filename: pool/main/o/odin/libodin-dev_1.8.1-3~lucid.nd1_amd64.deb Size: 4200000 SHA256: 4d0d631eaa5d7de777cd874a0eecc53b9ec6eaaad7780e6edd1920ca0ae9f423 SHA1: 95586553e269981ec43c4d5d8023425ce3a75209 MD5sum: 0f18c7171b948f54416b1a66f594a64e Description: static libraries and header for ODIN sequences This package provides static libraries and headers of the ODIN libraries odindata, adinpara, odinqt, odinseq and tjutils. They are required for building magnetic resonance imaging (MRI) sequences with ODIN. Package: libopenmeeg-dev Source: openmeeg Version: 2.0.0.dfsg-2~lucid.nd1 Architecture: amd64 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~lucid.nd1_amd64.deb Size: 43856 SHA256: b0f360865c523d9912f14db82537e8202c02a4b64387b5e14f30231ce05f242d SHA1: 232d51205a9618c0020027427b543e87dfb1eeea MD5sum: f4b6ec1254a767e780bf41c692522849 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~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 960 Depends: libatlas3gf-base | libatlas.so.3gf, libblas3gf | libblas.so.3gf | libatlas3gf-base, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), liblapack3gf | liblapack.so.3gf | libatlas3gf-base, 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~lucid.nd1_amd64.deb Size: 252508 SHA256: 9b38eae709217b949c4fb23666f10060d501153b4bd3e46fa79a8bb577a9c6a8 SHA1: dd48a79df4a904587ae6609d51b4761d8bc9f648 MD5sum: 94b5eb327f14882834730ceb33299fe0 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: libsvm-dev Source: libsvm Version: 3.0-1~nd10.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 148 Depends: neurodebian-popularity-contest, libsvm3 (= 3.0-1~nd10.04+1) Homepage: http://www.csie.ntu.edu.tw/~cjlin/libsvm/ Priority: optional Section: libdevel Filename: pool/main/libs/libsvm/libsvm-dev_3.0-1~nd10.04+1_amd64.deb Size: 28204 SHA256: 953855956a95ce124cc864adb37306e0608321cb4cf6eef5704f6d043e70db4f SHA1: b64dd54d8aedda6a1d03f291842a843ea065607e MD5sum: 534d104b9b4a6fa1433e9ae34d98eff0 Description: The LIBSVM header files These are the header files for LIBSVM, a machine-learning library. Package: libsvm-java Source: libsvm Version: 3.0-1~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 48 Depends: neurodebian-popularity-contest, libsvm3-java Homepage: http://www.csie.ntu.edu.tw/~cjlin/libsvm/ Priority: optional Section: java Filename: pool/main/libs/libsvm/libsvm-java_3.0-1~nd10.04+1_all.deb Size: 2070 SHA256: cd2a86db92cc66fbb16350a64ab624f2511dcc74206606d4f824709995a9f60c SHA1: 48795086f73056edc3a91a03dfa0a68c01d54ab6 MD5sum: 30257da2669fedaca525b0b2394ff957 Description: Java API to support vector machine library The functionality of the libsvm are offered in a single jar file. It includes one-class, two-class, multiclass, regression-mode, and probablistic output functionality. . This package solely provides a symbolic link from svm.jar svm3.jar. Package: libsvm-tools Source: libsvm Version: 3.0-1~nd10.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 344 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), python, gnuplot Homepage: http://www.csie.ntu.edu.tw/~cjlin/libsvm/ Priority: optional Section: devel Filename: pool/main/libs/libsvm/libsvm-tools_3.0-1~nd10.04+1_amd64.deb Size: 120204 SHA256: 464671f5c09dc8a7e137ecfaf788f173fa84bcaa6d4290abc12fca1af8b7ef2a SHA1: fb0ea223a6243b609fa0cd3135664037ca55f5f8 MD5sum: 255992a55d4f12a17d5211c264593817 Description: The LIBSVM binary tools LIBSVM is an easy-to-use package for support vector classification, regression and one-class SVM. It supports multi-class classification, probability outputs, and parameter selection. LIBSVM homepage: http://www.csie.ntu.edu.tw/~cjlin/libsvm Package: libsvm3 Source: libsvm Version: 3.0-1~nd10.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 132 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1) Homepage: http://www.csie.ntu.edu.tw/~cjlin/libsvm/ Priority: optional Section: libs Filename: pool/main/libs/libsvm/libsvm3_3.0-1~nd10.04+1_amd64.deb Size: 46550 SHA256: f72d0f8057bc018f8e696bd38c8eb42477bf5da34a2e40fb55cf15dc0f339b4c SHA1: d47a59cf387be789999cbd10fda712704f5cee47 MD5sum: 7c92282c7ebd15008a7019183aecf58f Description: library implementing support vector machines The LIBSVM library is used to calculate Support Vector Machine optimizations with a great variety of powerful options. It includes one-class, two-class, multiclass, regression-mode, and probablistic output functionality. It is primarily of interest to machine-learning researchers and artificial intelligence application developers. Package: libsvm3-java Source: libsvm Version: 3.0-1~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 104 Depends: neurodebian-popularity-contest, gij | java-gcj-compat | java1-runtime | java2-runtime Suggests: java-virtual-machine Conflicts: libsvm2-java (<= 2.91-2) Homepage: http://www.csie.ntu.edu.tw/~cjlin/libsvm/ Priority: optional Section: java Filename: pool/main/libs/libsvm/libsvm3-java_3.0-1~nd10.04+1_all.deb Size: 60476 SHA256: e764602df3fd3e56e22b684e094974116883ad0e0ba3fd85d6eb650a59ab33e9 SHA1: c4532f5c5c7defa06c81962d2bd23db9164b85de MD5sum: de7bd41053daf8de4d5bebb3578df435 Description: Java API to support vector machine library The functionality of the libsvm are offered in a single jar file. It includes one-class, two-class, multiclass, regression-mode, and probablistic output functionality. Package: lipsia Version: 1.6.0-4~lucid.nd1 Architecture: amd64 Maintainer: Michael Hanke Installed-Size: 3940 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), libnifti2, 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~lucid.nd1_amd64.deb Size: 1350678 SHA256: e58b7a05472046790c2f66fc0f171f874baeee44138732e2afd4660f7a6e29ce SHA1: 2dcd43247f3fb012a68ea1a66d4d324608ceba1a MD5sum: f8464286082303e2b11f90212c5321bc 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~lucid.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~lucid.nd1_all.deb Size: 5539278 SHA256: fe6e759a40698cc35033edebd496733ddbb082eeeda66fcddb091906f0f1238e SHA1: 435a391bce4fdc0bbb017bf0214f4f42e18bec55 MD5sum: 2dddb5c30d3c0cbdfaa56523cc87dd21 Description: documentation for LIPSIA Leipzig Image Processing and Statistical Inference Algorithms (LIPSIA) . This package provides the LIPSIA documentation in HTML format. Package: matlab-support-dev Source: matlab-support Version: 0.0.14~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 48 Depends: neurodebian-popularity-contest Conflicts: matlab-dev (<= 0.0.14~) Replaces: matlab-dev (<= 0.0.14~) Priority: optional Section: devel Filename: pool/main/m/matlab-support/matlab-support-dev_0.0.14~nd10.04+1_all.deb Size: 5466 SHA256: 641583c5142535133ed0135cbf83a2cf845ded44d63cf8ba5fab07a0d1c3b5ba SHA1: 613a29a7f1a2f2f91687163a92bbf722ccf3ba58 MD5sum: efba16058b109d81e623e3a5a5b1cc13 Description: helpers for packages building Matlab toolboxes Analogous to Octave a Makefile snippet is provided that configures the locations for architecture independent M-files, binary MEX-extensions, and there corresponding sources. This package can be used as a build-dependency by other packages shipping Matlab toolboxes. Package: mitools Source: odin Version: 1.8.1-3~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 6980 Depends: libblitz0ldbl (>= 0.9), libc6 (>= 2.11), libdcmtk1 (>= 3.5.4), libgcc1 (>= 1:4.1.1), libgsl0ldbl (>= 1.9), libnifti2, liboil0.3 (>= 0.3.10), libpng12-0 (>= 1.2.13-4), libqtcore4 (>= 4:4.6.1), libqtgui4 (>= 4:4.5.3), libqwt5-qt4, libstdc++6 (>= 4.4.0), libvia0, libvtk5.2, zlib1g (>= 1:1.1.4), dcmtk Recommends: grace Homepage: http://od1n.sourceforge.net Priority: extra Section: science Filename: pool/main/o/odin/mitools_1.8.1-3~lucid.nd1_amd64.deb Size: 2453670 SHA256: 40cc6bc31ecb80983c8f89860d92a63c083f18450efe48242565ed7d157bc9ec SHA1: c3c5a30d5978224bba135229ed48cf0cfa2cb8c5 MD5sum: 88ca85cfc6dcd978837f1d025b8851a8 Description: view, convert and perform basic maths with medical image datasets The three contained tools micalc, miconv and miview are handy command-line utilities for converting, manipulating and viewing medical image data in various formats (DICOM, NIfTI, PNG, binary data, ...). Package: mriconvert Version: 2.0-2~nd10.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2172 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0), libwxbase2.8-0 (>= 2.8.10.1), libwxgtk2.8-0 (>= 2.8.10.1) Homepage: http://lcni.uoregon.edu/~jolinda/MRIConvert/ Priority: optional Section: science Filename: pool/main/m/mriconvert/mriconvert_2.0-2~nd10.04+1_amd64.deb Size: 764476 SHA256: 435cfa06f87c557b944a6fe60dae4397efeaae89531ae6929ddb104604e65667 SHA1: 3c2096d9914cd24758500bfb32b21c72f74fd95a MD5sum: 5e4bc7d158cd9bfb674f02c13b33517f Description: medical image file conversion utility MRIConvert is a medical image file conversion utility that converts DICOM files to NIfTI 1.1, Analyze 7.5 , SPM99/Analyze, BrainVoyager, and MetaImage volume formats. Package: mricron Version: 0.20101102.1~dfsg.1-2~nd10.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 15636 Depends: neurodebian-popularity-contest, libatk1.0-0 (>= 1.29.3), libc6 (>= 2.2.5), libcairo2 (>= 1.2.4), libglib2.0-0 (>= 2.12.0), libgtk2.0-0 (>= 2.8.0), libpango1.0-0 (>= 1.14.0), libx11-6 (>= 0), mricron-data Suggests: mricron-doc, fsl Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: science Filename: pool/main/m/mricron/mricron_0.20101102.1~dfsg.1-2~nd10.04+1_amd64.deb Size: 4866876 SHA256: 4ce88d103374b0c63ab83ff28aed48df5175a1b532f68d9e815e04facd0a5284 SHA1: 71c167a5aff6ad833d4ab1beca5446d2f4921623 MD5sum: 16fe39b805c3e04f795591b28f709d35 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.1-2~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1852 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: science Filename: pool/main/m/mricron/mricron-data_0.20101102.1~dfsg.1-2~nd10.04+1_all.deb Size: 1666052 SHA256: 63840c337c0f3f305ee8c92d73a22bef37241c9870608cf66b15c6fc57a743e8 SHA1: e0b610f7b6fb65ed69d01e33cc272d7c782a4211 MD5sum: b23b8ec4eabbeb5caf80313b79730ba3 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.1-2~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1220 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: doc Filename: pool/main/m/mricron/mricron-doc_0.20101102.1~dfsg.1-2~nd10.04+1_all.deb Size: 737630 SHA256: 2ffc6eb4cb8152f4a56b2f15ba5553c326167ac72fba8586081f5f0ff1e20a89 SHA1: ccdacef76ca6fdf0bf5020e63f28bec02cbf4cce MD5sum: c63bb65dd41cac7333b7a074dcde302d 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: neurodebian-desktop Source: neurodebian Version: 0.24~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 332 Depends: ssh-askpass-gnome | ssh-askpass, desktop-base, gnome-icon-theme, neurodebian-popularity-contest Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-desktop_0.24~nd10.04+1_all.deb Size: 113108 SHA256: 05c12694e9aa2f5616c92836d0a2e8ba9d9fee34460ec860ae48669f8ac0434a SHA1: 8e1b1f4849e3f7b28f53ff7c4abf3a9366e22353 MD5sum: 0fa5e9187a88b0220fffdeacd4f13de5 Description: neuroscience research environment This package contains NeuroDebian artwork (icons, background image) and a NeuroDebian menu featuring most popular neuroscience tools automatically installed upon initial invocation. Package: neurodebian-dev Source: neurodebian Version: 0.24~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4400 Depends: devscripts, cowbuilder, python, neurodebian-keyring Recommends: virtualbox-ose, virtualbox-ose-fuse, zerofree Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-dev_0.24~nd10.04+1_all.deb Size: 3794960 SHA256: 8abc7a3c0efbcb3c25f4c314d25975dd7809642cb1a1f8bc0d69818a7428d0b5 SHA1: 6c328dbe84d26de40984a3ace502ce996c69b15a MD5sum: 829a5965246f8c39283e64e936044688 Description: NeuroDebian development tools neuro.debian.net sphinx website sources and development tools used by NeuroDebian to provide backports for a range of Debian/Ubuntu releases. Package: neurodebian-guest-additions Source: neurodebian Version: 0.24~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 236 Pre-Depends: virtualbox-ose-guest-utils, virtualbox-ose-guest-x11, virtualbox-ose-guest-dkms Depends: sudo, neurodebian-desktop, gdm, update-manager-gnome, update-notifier Recommends: chromium-browser Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-guest-additions_0.24~nd10.04+1_all.deb Size: 12716 SHA256: 12a17f18cc3587ffd01f81f069e05404633137edb89b2193d050221a6436ea98 SHA1: db57d6d179b8bc64baabd055c91d9a7e2265074f MD5sum: bb25f7caab80317dabc72cf7ad57c469 Description: NeuroDebian guest additions (DO NOT INSTALL OUTSIDE VIRTUALBOX) This package configures a Debian installation as a guest operating system in a VirtualBox-based virtual machine for NeuroDebian. . DO NOT install this package unless you know what you are doing! For example, installation of this package relaxes several security mechanisms. Package: neurodebian-keyring Source: neurodebian Version: 0.24~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 56 Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-keyring_0.24~nd10.04+1_all.deb Size: 5792 SHA256: 7e3ceee3f206df7f6bcb38e89ef1fe5e7a2a3b235110ed7410bbce2b61ca3088 SHA1: dcb292882dfca40bb9bbe26b0928b201b65ef0e7 MD5sum: 7dadf4cf7401d18eff9ac77742c71d86 Description: GnuPG archive keys of the NeuroDebian archive The NeuroDebian project digitally signs its Release files. This package contains the archive keys used for that. Package: neurodebian-popularity-contest Source: neurodebian Version: 0.24~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 40 Depends: popularity-contest Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-popularity-contest_0.24~nd10.04+1_all.deb Size: 4960 SHA256: c79cfa943ecd859440bb21d7b99e5d596141cb4f6590fceb0f68e34d34df4e7a SHA1: 736bc5c9fb3e60e4f30412830d54ccd069206101 MD5sum: 31a9687ee595ed93e529bc04859c0b54 Description: Helper for NeuroDebian popularity contest submissions This package is a complement to the generic popularity-contest package to enable anonymous submission of usage statistics to NeuroDebian in addition to the popcon submissions to the underlying distribution (e.g. Debian or Ubuntu) popcon server. . Your participation in popcon is important for following reasons: - Popular packages receive more attention from developers, bugs are fixed faster and updates are provided quicker. - Assure that we do not drop support for a previous release of Debian or Ubuntu while are active users. - User statistics could be used by upstream research software developers to acquire funding for continued development. . It has an effect only if you have decided to participate in the Popularity Contest of your distribution, i.e. Debian or Ubuntu. You can always enable or disable your participation in popcon by running 'dpkg-reconfigure popularity-contest' as root. Package: nifti-bin Source: nifticlib Version: 2.0.0-1~lucid.nd1 Architecture: amd64 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~lucid.nd1_amd64.deb Size: 62296 SHA256: 1355d318a693371abf40cf3a424a4e9a2040e5ef66c7993f0597bc3506d4991b SHA1: dfcc4f30646e93220e188ad57c8831b397e15e23 MD5sum: e611321eba41d61af7443e9c1c9cc3db 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: nipy-suite Version: 0.1.0-2 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 36 Depends: python-nibabel (>= 1.0.0), python-nipy (>= 0.1.2+20110114), python-dipy (>= 0.5.0), python-nipype (>= 0.3.3), python-nitime (>= 0.2) Suggests: python-mvpa, psychopy Homepage: http://www.nipy.org Priority: extra Section: python Filename: pool/main/n/nipy-suite/nipy-suite_0.1.0-2_all.deb Size: 3898 SHA256: 882c8580ebd2d458a92f8d851d1ec9291fecf05f6ed98a8b754eb831c95368c8 SHA1: 6501d1d201160520f5aad29d0f9007c17b7d9778 MD5sum: eb090e568264d2f439892bcb98485b8c Description: Neuroimaging in Python NiPy is a comprehensive suite of Python modules to perform analysis of Neuroimaging data in Python. nipy-suite is a metapackage depending on the projects developed under NiPy project umbrella, such as - nibabel: bindings to various neuroimaging data formats - nipy: analysis of structural and functional neuroimaging data - nitime: timeseries analysis - dipy: analysis of MR diffusion imaging data - nipype: pipelines and worfklows Package: nipy-suite-doc Source: nipy-suite Version: 0.1.0-2 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 32 Depends: python-nibabel-doc (>= 1.0.0), python-nipy-doc (>= 0.1.2+20110114), python-dipy-doc (>= 0.5.0), python-nipype-doc (>= 0.3.3), python-nitime-doc (>= 0.2) Suggests: python-mvpa-doc Homepage: http://www.nipy.org Priority: extra Section: doc Filename: pool/main/n/nipy-suite/nipy-suite-doc_0.1.0-2_all.deb Size: 2250 SHA256: 54985bd9d6eaa352608b357f2deeb066bd2ac12d3c2e463082f5d9178701bbad SHA1: 5d2f5e94ff6b7ff737fe966f4a2e5ff67df93cca MD5sum: 37d2f8b6b6d203edf208afb0cdb56fa3 Description: Neuroimaging in Python -- documentation NiPy is a comprehensive suite of Python modules to perform analysis of Neuroimaging data in Python. . nipy-suite-doc is a metapackage depending on the documentation packages for NiPy projects. Package: octave-biosig Source: biosig4c++ Version: 0.94.2+svn2552-1~pre1~nd10.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1628 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 (>= 5.6+20071006-3), 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~nd10.04+1_amd64.deb Size: 593660 SHA256: 3b7f1fea32faa445d9545c253211ac260fcbf58639b20501eb986ecd7ce90d38 SHA1: 3de944c1e38c0fefff7d8798cbb4131cbd8b751b MD5sum: 369eac6ae6d2ccfbe10a15f2759f8de0 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: 0.1.1~svn62-1~nd10.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 388 Depends: neurodebian-popularity-contest, octave3.2 (>= 3.2.3), 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_0.1.1~svn62-1~nd10.04+1_amd64.deb Size: 132182 SHA256: ea1db6a446caa1560233b8104fc1275f755cb6d62528e2eb48efc672f8df3e93 SHA1: ccad6cbb2d432a94b87bb797d763ddb68b433415 MD5sum: b421b7c393a8eac74b04e804de1d3a7f 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: octave-psychtoolbox-3 Source: psychtoolbox-3 Version: 3.0.8+svn1934.dfsg1-1~pre2~nd10.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2564 Depends: neurodebian-popularity-contest, octave3.2 (>= 3.2.3), freeglut3, libasound2 (>> 1.0.22), libc6 (>= 2.7), libdc1394-22, libfreenect0.0, libgl1-mesa-glx | libgl1, libglew1.5 (>= 1.5.2), libglib2.0-0 (>= 2.12.0), libglu1-mesa | libglu1, libgstreamer-plugins-base0.10-0 (>= 0.10.23), libgstreamer0.10-0 (>= 0.10.24), libpciaccess0 (>= 0.8.0+git20071002), libraw1394-11, libstdc++6 (>= 4.1.1), libusb-1.0-0 (>= 2:1.0.6), libx11-6 (>= 0), libxext6, libxml2 (>= 2.6.27), libxxf86vm1, psychtoolbox-3-common (= 3.0.8+svn1934.dfsg1-1~pre2~nd10.04+1), psychtoolbox-3-lib (= 3.0.8+svn1934.dfsg1-1~pre2~nd10.04+1) Provides: psychtoolbox, psychtoolbox-3 Homepage: http://psychtoolbox.org Priority: extra Section: science Filename: pool/main/p/psychtoolbox-3/octave-psychtoolbox-3_3.0.8+svn1934.dfsg1-1~pre2~nd10.04+1_amd64.deb Size: 749072 SHA256: 09e22e0912d017bb3facf91c76fe1e33e20d6866d7dbd0a03ec3300d741806ec SHA1: 0a0b37ea68ba41b04460f1b187898d1aadf50180 MD5sum: f633031a48886349ffb57b9d3cbad8f8 Description: toolbox for vision research -- Octave bindings Psychophysics Toolbox Version 3 (PTB-3) is a free set of Matlab and GNU/Octave functions for vision research. It makes it easy to synthesize and show accurately controlled visual and auditory stimuli and interact with the observer. . The Psychophysics Toolbox interfaces between Matlab or Octave and the computer hardware. The Psychtoolbox's core routines provide access to the display frame buffer and color lookup table, allow synchronization with the vertical retrace, support millisecond timing, allow access to OpenGL commands, and facilitate the collection of observer responses. Ancillary routines support common needs like color space transformations and the QUEST threshold seeking algorithm. . This package contains bindings for Octave. . =========================== BIG FAT WARNING ============================ . This packaged version of Psychtoolbox-3 for Debian has been neither extensively tested nor officially released. It is known not to be fully compatible with 64-bit systems (yet). Please do not use it for conducting real experiments, and please report any detected problems to team@neuro.debian.net so we could assure future stable performance of PTB-3 on your systems. . ======================================================================== Package: odin Version: 1.8.1-3~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 4136 Depends: libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libqtcore4 (>= 4:4.5.3), libqtgui4 (>= 4:4.5.3), libstdc++6 (>= 4.4.0), libvtk5.2, mitools (= 1.8.1-3~lucid.nd1), libodin-dev, libgsl0-dev, libc6-dev | libc-dev, g++, libblas-dev | libatlas-base-dev, xterm | x-terminal-emulator, gdb Recommends: liboil0.3-dev | liboil-dev, libdcmtk1-dev Homepage: http://od1n.sourceforge.net Priority: extra Section: science Filename: pool/main/o/odin/odin_1.8.1-3~lucid.nd1_amd64.deb Size: 1573324 SHA256: 657bd81a759b23fe5890886d4752f8c7d446a4abccd8e8f3afd599988ed28099 SHA1: 4e538aeab5bbb3d132027c534ccf057ec2fd4650 MD5sum: 674745b2fece68298da5f034ff8fc191 Description: develop, simulate and run magnetic resonance sequences ODIN is a framework for magnetic resonance imaging (MRI). It covers the whole toolchain of MRI, from low-level data acquisition to image reconstruction. In particular, it aims at rapid prototyping of MRI sequences. The sequences can be programmed using a high-level, object oriented, C++ programming interface. It provides advanced sequence analysis tools, such as interactive plotting of k-space trajectories, a user interface for a fast compile-link-test cycle and a powerful MRI simulator which supports different virtual samples. For fast and flexible image reconstruction, ODIN contains a highly customizable, multi-threaded data-processing framework. Package: openmeeg-tools Source: openmeeg Version: 2.0.0.dfsg-2~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 600 Depends: libatlas3gf-base | libatlas.so.3gf, libblas3gf | libblas.so.3gf | libatlas3gf-base, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), liblapack3gf | liblapack.so.3gf | libatlas3gf-base, 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~lucid.nd1_amd64.deb Size: 162662 SHA256: fce9ef3c245aceea7d087c0bd02346b105ecfea7dfc9501076d3d46841f38916 SHA1: cbf5ec9b4f3dac5718b946b6631eb5980a49da6d MD5sum: f13cd918f20ef00389618b13b43585f1 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: opensesame Version: 0.22+git9-g8633c14-1~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2504 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-qt4, python-pygame (>= 1.8.1~), python-numpy (>= 1.3.0~) Recommends: python-serial (>= 2.3~) Homepage: http://www.cogsci.nl/software/opensesame Priority: extra Section: science Filename: pool/main/o/opensesame/opensesame_0.22+git9-g8633c14-1~nd10.04+1_all.deb Size: 711708 SHA256: 389e57e0e2b876f118a0a0b5dd097d379896c1d2daf73e87aec7b6e701915270 SHA1: c4e52ed50c4f04a5c66ae5dd95b2439e1c3e390b MD5sum: 191ce55b7d8693d795acc9a56dab7b71 Description: graphical experiment builder for the social sciences This graphical environment provides an easy to use, point-and-click interface for creating psychological experiments. In addition to a powerful sketchpad for creating visual stimuli, OpenSesame features a sampler and synthesizer for sound playback. For more complex tasks, OpenSesame supports Python scripting using the built-in editor with syntax highlighting. Python-Version: 2.6 Package: psychopy Version: 1.63.04.dfsg-1~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4480 Depends: neurodebian-popularity-contest, 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, ipython Suggests: python-iolabs Homepage: http://www.psychopy.org Priority: optional Section: science Filename: pool/main/p/psychopy/psychopy_1.63.04.dfsg-1~nd10.04+1_all.deb Size: 2370876 SHA256: 733875ea4433513a66dc21aaf1550bd8b5363b63a44b720a5548cc63daa78707 SHA1: 77338ed55e2c6d9cfacafc484a2edae4f5ed5b11 MD5sum: 10c59b4e5c734978f746543124a1d948 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 - IDE GUI for coding in a powerful scripting language (Python) - Builder GUI for rapid development of stimulation sequences - 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: psychtoolbox-3-common Source: psychtoolbox-3 Version: 3.0.8+svn1934.dfsg1-1~pre2~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 31372 Depends: neurodebian-popularity-contest Homepage: http://psychtoolbox.org Priority: extra Section: science Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-common_3.0.8+svn1934.dfsg1-1~pre2~nd10.04+1_all.deb Size: 13338554 SHA256: 3a967f5b05e1c71ca04130ce6e6339a5ee2dda2f1549586a0b159fbcaa02781d SHA1: d65ea1b0706f1b49fcb1ffbf02e14fe1bef15b52 MD5sum: 5c228598b53ea1c6b9f845463f9f7cc7 Description: toolbox for vision research -- arch/interpreter independent part Psychophysics Toolbox Version 3 (PTB-3) is a free set of Matlab and GNU/Octave functions for vision research. It makes it easy to synthesize and show accurately controlled visual and auditory stimuli and interact with the observer. . The Psychophysics Toolbox interfaces between Matlab or Octave and the computer hardware. The Psychtoolbox's core routines provide access to the display frame buffer and color lookup table, allow synchronization with the vertical retrace, support millisecond timing, allow access to OpenGL commands, and facilitate the collection of observer responses. Ancillary routines support common needs like color space transformations and the QUEST threshold seeking algorithm. . This package contains architecture independent files (such as .m scripts) . =========================== BIG FAT WARNING ============================ . This packaged version of Psychtoolbox-3 for Debian has been neither extensively tested nor officially released. It is known not to be fully compatible with 64-bit systems (yet). Please do not use it for conducting real experiments, and please report any detected problems to team@neuro.debian.net so we could assure future stable performance of PTB-3 on your systems. . ======================================================================== Package: psychtoolbox-3-dbg Source: psychtoolbox-3 Version: 3.0.8+svn1934.dfsg1-1~pre2~nd10.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2648 Depends: neurodebian-popularity-contest, octave-psychtoolbox-3 (= 3.0.8+svn1934.dfsg1-1~pre2~nd10.04+1) Homepage: http://psychtoolbox.org Priority: extra Section: debug Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-dbg_3.0.8+svn1934.dfsg1-1~pre2~nd10.04+1_amd64.deb Size: 776682 SHA256: e57d32895962c9f2e9e68e00e7dff6f82d0537e6d487d218c22a11b3505e996c SHA1: 5cfe97218fd706b08bd46c62f057748ba49a90ab MD5sum: 0c3227137e7be501f3d74d240a500665 Description: toolbox for vision research -- debug symbols for binaries Psychophysics Toolbox Version 3 (PTB-3) is a free set of Matlab and GNU/Octave functions for vision research. It makes it easy to synthesize and show accurately controlled visual and auditory stimuli and interact with the observer. . The Psychophysics Toolbox interfaces between Matlab or Octave and the computer hardware. The Psychtoolbox's core routines provide access to the display frame buffer and color lookup table, allow synchronization with the vertical retrace, support millisecond timing, allow access to OpenGL commands, and facilitate the collection of observer responses. Ancillary routines support common needs like color space transformations and the QUEST threshold seeking algorithm. . To ease debugging and troubleshooting this package contains debug symbols for Octave bindings and other binaries. . =========================== BIG FAT WARNING ============================ . This packaged version of Psychtoolbox-3 for Debian has been neither extensively tested nor officially released. It is known not to be fully compatible with 64-bit systems (yet). Please do not use it for conducting real experiments, and please report any detected problems to team@neuro.debian.net so we could assure future stable performance of PTB-3 on your systems. . ======================================================================== Package: psychtoolbox-3-lib Source: psychtoolbox-3 Version: 3.0.8+svn1934.dfsg1-1~pre2~nd10.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 272 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libfontconfig1 (>= 2.8.0), libfreetype6 (>= 2.2.1), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, libstdc++6 (>= 4.1.1) Homepage: http://psychtoolbox.org Priority: extra Section: science Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-lib_3.0.8+svn1934.dfsg1-1~pre2~nd10.04+1_amd64.deb Size: 61608 SHA256: 29a4082a3b209a081a5e253d1033024a6074e92135c33152ce246252a6650166 SHA1: 65bb4941272031123106ce5adb8a5012437e49c2 MD5sum: ef26b15c2d833c25144590b54e913311 Description: toolbox for vision research -- arch-specific parts Psychophysics Toolbox Version 3 (PTB-3) is a free set of Matlab and GNU/Octave functions for vision research. It makes it easy to synthesize and show accurately controlled visual and auditory stimuli and interact with the observer. . The Psychophysics Toolbox interfaces between Matlab or Octave and the computer hardware. The Psychtoolbox's core routines provide access to the display frame buffer and color lookup table, allow synchronization with the vertical retrace, support millisecond timing, allow access to OpenGL commands, and facilitate the collection of observer responses. Ancillary routines support common needs like color space transformations and the QUEST threshold seeking algorithm. . This package contains additional binaries (tools/dynamic libraries) used by both Octave and Matlab frontends. . =========================== BIG FAT WARNING ============================ . This packaged version of Psychtoolbox-3 for Debian has been neither extensively tested nor officially released. It is known not to be fully compatible with 64-bit systems (yet). Please do not use it for conducting real experiments, and please report any detected problems to team@neuro.debian.net so we could assure future stable performance of PTB-3 on your systems. . ======================================================================== Package: python-biosig Source: biosig4c++ Version: 0.94.2+svn2552-1~pre1~nd10.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1024 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~nd10.04+1_amd64.deb Size: 338056 SHA256: 8917ddd017bdb363b15733adc0de2ee5f2d43ffe118444f4ce7dc88b65559b97 SHA1: 8ce786ace84548d105392758fc393a898c5588d8 MD5sum: 655998f877f2e88b6f886d1e9ea1ad1d 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: 1.3.0-1~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1788 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-brian-lib (>= 1.3.0-1~nd10.04+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, python-cherrypy Homepage: http://www.briansimulator.org/ Priority: extra Section: python Filename: pool/main/b/brian/python-brian_1.3.0-1~nd10.04+1_all.deb Size: 313352 SHA256: 775a8320a0aed28a7acb225165f48f7f5b0b46cc8f1f7d72d16fdc9e839cbf4d SHA1: ff24f43981502a155ddd1328d8fa6cc6870c488f MD5sum: 62b89312a2fb57f85b240dc666eae136 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: 1.3.0-1~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5444 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-brian Homepage: http://www.briansimulator.org/ Priority: extra Section: doc Filename: pool/main/b/brian/python-brian-doc_1.3.0-1~nd10.04+1_all.deb Size: 1688130 SHA256: 1f13ac4840384206a181a343e4bbd474b5253ea73f85059bc444cefdd9563692 SHA1: 0444f5c1b5a7f520e736c41935cf4740e960af5d MD5sum: 1a08205bd0a61edffa9c2e3c6fb3b840 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 format), examples and demos. Package: python-brian-lib Source: brian Version: 1.3.0-1~nd10.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 220 Depends: neurodebian-popularity-contest, 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_1.3.0-1~nd10.04+1_amd64.deb Size: 55220 SHA256: 85dc0281b03a75f246eb86d7f95c122f55b8337fafdc3e4f86f79d0650e3118f SHA1: 8ad8051c83bc111b6fcb73fc553491e9ed964519 MD5sum: d5b1efa95c78dda1aa1ee6a02f4e9f67 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: 0.9.5~rc1-1~lucid.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1864 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_0.9.5~rc1-1~lucid.nd1_all.deb Size: 372938 SHA256: f69403f36f3840a1b00833c53b9d5423ad12355073192a8245781a34954569bd SHA1: bda525b6678d316b2c50a3f9d3de8517ab999de9 MD5sum: 0c83a634b771ca0cc6de5bf844aabec6 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-dipy Source: dipy Version: 0.5.0-1~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2204 Depends: neurodebian-popularity-contest, python-numpy, python-scipy, python-dipy-lib (>= 0.5.0-1~nd10.04+1) Recommends: python-matplotlib, python-vtk, python-nose, python-nibabel, python-tables Suggests: ipython Provides: python2.6-dipy Homepage: http://nipy.org/dipy Priority: extra Section: python Filename: pool/main/d/dipy/python-dipy_0.5.0-1~nd10.04+1_all.deb Size: 1459144 SHA256: 71ede7a2041ae54687ce69c3baf61754170bd199b8cc0f22a93aad6362d81254 SHA1: af1ea2a6a4bcf1f79fb8294fd39508e8709ba109 MD5sum: db8e6eb41cd922c18ebecba281e3b801 Description: toolbox for analysis of MR diffusion imaging data Dipy is a toolbox for the analysis of diffusion magnetic resonance imaging data. It features: - Reconstruction algorithms, e.g. GQI, DTI - Tractography generation algorithms, e.g. EuDX - Intelligent downsampling of tracks - Ultra fast tractography clustering - Resampling datasets with anisotropic voxels to isotropic - Visualizing multiple brains simultaneously - Finding track correspondence between different brains - Warping tractographies into another space, e.g. MNI space - Reading many different file formats, e.g. Trackvis or NIfTI - Dealing with huge tractographies without memory restrictions - Playing with datasets interactively without storing Python-Version: 2.6 Package: python-dipy-doc Source: dipy Version: 0.5.0-1~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3292 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-dipy Homepage: http://nipy.org/dipy Priority: extra Section: doc Filename: pool/main/d/dipy/python-dipy-doc_0.5.0-1~nd10.04+1_all.deb Size: 1950834 SHA256: b519b7814dcc308e808503ae39bbccf72a896ef70f5f466a8df862e049b980fc SHA1: 35fe0fd21b24c7b4757e1baeae09c226e68dba29 MD5sum: 46ad25359b198ee642fd02fc610b8f06 Description: toolbox for analysis of MR diffusion imaging data -- documentation Dipy is a toolbox for the analysis of diffusion magnetic resonance imaging data. . This package provides the documentation in HTML format. Package: python-dipy-lib Source: dipy Version: 0.5.0-1~nd10.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 660 Depends: neurodebian-popularity-contest, libc6 (>= 2.4) Provides: python2.6-dipy-lib Homepage: http://nipy.org/dipy Priority: extra Section: python Filename: pool/main/d/dipy/python-dipy-lib_0.5.0-1~nd10.04+1_amd64.deb Size: 222540 SHA256: b3df2be55cd5456980f9e63c08c36f031f25703d4fbb3e8b30257707194c0442 SHA1: d0e2956075ab0cdb7213a0478a4b2b3fea68421b MD5sum: f664a5aba5d2d6cd9abb0c650de11e1f Description: toolbox for analysis of MR diffusion imaging data -- extensions Dipy is a toolbox for the analysis of diffusion magnetic resonance imaging data. . This package provides architecture-dependent builds of the extensions. Python-Version: 2.6 Package: python-freenect Source: libfreenect Version: 1:0.0.1+20101211+1-2~nd10.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 164 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libfreenect0.0, libusb-1.0-0 (>= 2:1.0.6) Suggests: python-numpy, python-matplotlib, python-opencv Homepage: http://openkinect.org/ Priority: extra Section: python Filename: pool/main/libf/libfreenect/python-freenect_0.0.1+20101211+1-2~nd10.04+1_amd64.deb Size: 32686 SHA256: 7cda5a250cf0b1d1ea36099c5d777c3bf8a400d643f0dfcd1427936e55cb4d19 SHA1: f6d314e49530078932edfebc46e878d78c8ae48f MD5sum: a2a4c42d897d8e700e3a7f38c6e5962a Description: library for accessing Kinect USB camera -- Python bindings libfreenect is the core library for accessing the Microsoft Kinect USB camera. . This package provides freenect extension to use libfreenect functionality from Python and includes some demo scripts. Package: python-joblib Source: joblib Version: 0.4.6-1~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 244 Depends: 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_0.4.6-1~nd10.04+1_all.deb Size: 38680 SHA256: cc7b595677e9f715d1806fba4cf07b1871dc48815f77a74d45aa2bdd8360bbf5 SHA1: 15366b84f0a3688be5c3e2ae9fcb08a8f994eb42 MD5sum: 2403f989196c8eace8098be253a93413 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-libsvm Source: libsvm Version: 3.0-1~nd10.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 76 Depends: neurodebian-popularity-contest, libsvm3 (= 3.0-1~nd10.04+1), python-support (>= 0.90.0) Provides: python2.6-libsvm Homepage: http://www.csie.ntu.edu.tw/~cjlin/libsvm/ Priority: optional Section: python Filename: pool/main/libs/libsvm/python-libsvm_3.0-1~nd10.04+1_amd64.deb Size: 7606 SHA256: 0829f6b39b71a98b7ec5aafb97df942099e53786220c863c61ec43fde70109d7 SHA1: 4e8fc49364c84f3e22066858f5cb43947c1a5f10 MD5sum: 4ba71f768c2600517bd6310e6e9d8ed8 Description: Python interface for support vector machine library Python interface for the LIBSVM library using ctypes. This new python interface is provided since 2.91, and it is incompatible with the old one. Package: python-mdp Source: mdp Version: 3.0+git8-g921253a-1~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1872 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-numpy Recommends: python-scipy, shogun-python-modular, python-libsvm, python-scikits-learn, python-joblib Suggests: python-pp, python-py Enhances: python-mvpa Homepage: http://mdp-toolkit.sourceforge.net/ Priority: optional Section: python Filename: pool/main/m/mdp/python-mdp_3.0+git8-g921253a-1~nd10.04+1_all.deb Size: 450952 SHA256: f0b8ac85f8de699d0b0824c40a7275dd905b7473238d691406a9a49ec369fc2a SHA1: 4b581210dc160e498ad97c3034178d9433e334d7 MD5sum: cef3f48e247b16b24910e695d7e51b63 Description: Modular toolkit for Data Processing Python data processing framework. Implemented algorithms include: Principal Component Analysis (PCA), Independent Component Analysis (ICA), Slow Feature Analysis (SFA), Independent Slow Feature Analysis (ISFA), Growing Neural Gas (GNG), Factor Analysis, Fisher Discriminant Analysis (FDA), and Gaussian Classifiers. Package: python-mlpy Source: mlpy Version: 2.2.0~dfsg1-1~lucid.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~lucid.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~lucid.nd1_all.deb Size: 55840 SHA256: baccc7f46670565245f2d9aa65acaea015872d8bb7bf398642a714a9fea52a2f SHA1: 38f57e733f49c401922c25d85a89b5044e68181b MD5sum: cd3699a47eb5ad404aa7864cf95d69ea 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~lucid.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~lucid.nd1_all.deb Size: 478768 SHA256: 57a934ed3182a9624128bea20def7710fd79b49a10356d8a04a243aeb6ca6a7c SHA1: d9e6be19ce66c9a83358c704eed4daa666588a1d MD5sum: 24528b12939bcd8156611770fc0162e7 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~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 312 Depends: libc6 (>= 2.3.4), 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~lucid.nd1_amd64.deb Size: 70842 SHA256: 973a999bedf71bf13119caaef348bdf5156967d0a792c02d8171d6f691fa4397 SHA1: 551d017b25a89cc23feafca4e43a941a513ad880 MD5sum: f2cbf5b76110123e0b15aaaac0b94e1d 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: 0.4.6-1~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4172 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python2.6, python-numpy, python-mvpa-lib (>= 0.4.6-1~nd10.04+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_0.4.6-1~nd10.04+1_all.deb Size: 2187772 SHA256: 72c0d307b53d564335ed5accc336d0e6630a76152ab9db7bf39f4f3d31fc1ac3 SHA1: 04e5ea5e3d3b82abadd33824cf0486448fd8e32e MD5sum: 00c637c0cc71103b02cccde2945ee4c9 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: 0.4.6-1~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 41116 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-mvpa Homepage: http://www.pymvpa.org Priority: optional Section: doc Filename: pool/main/p/pymvpa/python-mvpa-doc_0.4.6-1~nd10.04+1_all.deb Size: 9089848 SHA256: 0b2ff7808816c2fc52dbc43bc2e92d4b0d08afeb3742ff6f22be09e7bece65a6 SHA1: ee24c86581da36880469117e4cf01cce1b8b6dae MD5sum: 815b4903cf592e461498db65342b3af3 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: 0.4.6-1~nd10.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 184 Depends: neurodebian-popularity-contest, 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_0.4.6-1~nd10.04+1_amd64.deb Size: 35318 SHA256: f4ee8e9cc253fd739c265098650ac63ce36c6dc3b5fbbfc4576d659fd874bf25 SHA1: bcf627d2eef021b4e270b52750f0bfba3f487d27 MD5sum: 882c88b4fdf3a341382baba667ebaac8 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-mvpa-snapshot Source: pymvpa-snapshot Version: 0.6.0~rc2-1~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4644 Depends: neurodebian-popularity-contest, python (>= 2.4), python-support (>= 0.90.0), python-numpy, python-mvpa-snapshot-lib (>= 0.6.0~rc2-1~nd10.04+1) Recommends: python-nibabel, python-psyco, python-mdp, python-scipy, shogun-python-modular, python-pywt, python-matplotlib, python-reportlab, python-h5py Suggests: fslview, fsl, python-nose, python-lxml, python-scikits-openopt, python-rpy, python-mvpa-doc Conflicts: python-mvpa Provides: python2.6-mvpa-snapshot Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa-snapshot/python-mvpa-snapshot_0.6.0~rc2-1~nd10.04+1_all.deb Size: 2286982 SHA256: 03208f26e7faa988c800da98ef7a5d408530a8c6b013da515bb52dd279d95780 SHA1: 7db4fa026e37569c21116ff9631386e0b507205d MD5sum: 98bb3ec7f73f975091c73c8cde8c484e 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, 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. . This is a package of a development snaphot. The latest released version is provided by the python-mvpa package. Python-Version: 2.6 Package: python-mvpa-snapshot-lib Source: pymvpa-snapshot Version: 0.6.0~rc2-1~nd10.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 172 Depends: neurodebian-popularity-contest, 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 Conflicts: python-mvpa-lib Provides: python2.6-mvpa-snapshot-lib Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa-snapshot/python-mvpa-snapshot-lib_0.6.0~rc2-1~nd10.04+1_amd64.deb Size: 33532 SHA256: e2d144186bbe39a8a01ed6e7bf5350438b383dd6765f10abf96dacb53f772292 SHA1: fd993a8a8da5193ca2055ee667f8f8f6b4d97149 MD5sum: 43d5d486073839addc54d5b775d75c6f 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. . This is a package of a development snaphot. The latest released version is provided by the python-mvpa-lib package. Python-Version: 2.6 Package: python-networkx Version: 1.1-2~lucid.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~lucid.nd1_all.deb Size: 679700 SHA256: 3b794d495a6f1402468c60983b75a41ace947be4219d2708c30469d700ae4f86 SHA1: e3e60ed53545f966ce1b2bef6b05b7cbb35d65b7 MD5sum: b12742ab70af33463e848ecf7cf0d6b4 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~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2784 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~nd10.04+1_all.deb Size: 1061204 SHA256: a1860b967241be2e82da0866817497c2eae53fce4b7651b73cd3b382614b85be SHA1: 1f398f07b9a9e6a33191a0c20b26d319448d3945 MD5sum: 006d54fdec6d45901107fa9446a30510 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~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2724 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~nd10.04+1_all.deb Size: 404032 SHA256: f44ba0faea0b3c14b57d41fa7610576d32d0e91c08935439b7359290bb7be12d SHA1: bc11bc63614912287cfeb039e1d193dae04130b5 MD5sum: de39e6259816aa6bedbc506ca02180b3 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: 0.20100607.1-2~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 1184 Depends: libc6 (>= 2.4), libnifti2, python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), python2.6, python-numpy, libjs-jquery Provides: python2.6-nifti Homepage: http://niftilib.sourceforge.net/pynifti/ Priority: optional Section: python Filename: pool/main/p/pynifti/python-nifti_0.20100607.1-2~lucid.nd1_amd64.deb Size: 285356 SHA256: 9c412d06a6ba605eb308f2296e8af953e5bd50828b27e2c273ea17b338490624 SHA1: acf880288dca88acbc4b241ff17759c6b2d91918 MD5sum: a2a78580df1e53a2b19f6043dba7385c 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: 0.3.3-1~lucid.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1752 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_0.3.3-1~lucid.nd1_all.deb Size: 277548 SHA256: e4a925dc3e2675271763c1b8a4248d73a2d38b238961a111ed182bd3c5d84020 SHA1: fec3ab250c45a1d74b28bb0a37cd7908d2224faa MD5sum: 0a4bee8815bdcc05d9c60d7739b81b9e 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: 0.3.3-1~lucid.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 3640 Depends: libjs-jquery Suggests: python-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: doc Filename: pool/main/n/nipype/python-nipype-doc_0.3.3-1~lucid.nd1_all.deb Size: 840644 SHA256: 465c043ca61af61479dbdd011fd350442d20a2a8b08212bf7c8adc709756dad3 SHA1: eba0a4d1ca01aab7eb6d0aed1161df8d86c6ed2d MD5sum: a283b2cea992bd74f4c8017b7b23b62d 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 examples and documentation in various formats. Package: python-nitime Source: nitime Version: 0.2-1~nd10.04+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~nd10.04+1_all.deb Size: 312890 SHA256: 08bc683cc3e1dd08eeafbeabd00aca2a78a894457bc95b8d9c74a33a4c96851d SHA1: 045ae2bb6f71f0c9097094ee9c63322fd20263cb MD5sum: d434630b1977ec754c48b15baaa2cc9b 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~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4064 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~nd10.04+1_all.deb Size: 2629310 SHA256: e2795895b936d7e1f4fddb76dd840a81b4ab752a09a3e3bf4fb24abbe8ee8753 SHA1: 01c630f63f90db5afe9f99488fe91325a3545ad3 MD5sum: 4d2e87cd2816fb4df284f472efff57ea 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~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 600 Depends: libatlas3gf-base | libatlas.so.3gf, libblas3gf | libblas.so.3gf | libatlas3gf-base, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), liblapack3gf | liblapack.so.3gf | libatlas3gf-base, 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~lucid.nd1_amd64.deb Size: 157588 SHA256: 5d21094ca71038b88adacdff2df3805515f25627746e7e84d841de003ed14ef5 SHA1: 209bc128ce2ca80e74506bff570acb4d3e9cd5cc MD5sum: 03abf5e8ea6fe312c37d33b3a2b1fe55 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~lucid.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 448 Depends: 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~lucid.nd1_all.deb Size: 49172 SHA256: 9fb810f9f2c088e4eeb1b5a017928da93c3d27c46b4426aa9d21a5f8f7cd13d6 SHA1: 02381c55c25c230f8b8edd8f5160dc727d6688d8 MD5sum: b834dd72b36f8c3918c17734ffda076f 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~lucid.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 1696 Depends: python (<< 2.7), python (>= 2.6), python-central (>= 0.6.11), python-pyepl-common (= 1.1.0-3~lucid.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~lucid.nd1_amd64.deb Size: 381458 SHA256: 4addcaafc82fe9d2405811a06fde30266fdf9fbf4c785353b56cbb6d7d1d59dc SHA1: e97c7a1c5e1e4ab1dccbe6ade4a221c85742fc9f MD5sum: ce299feeb3dec63353cb835e3e526e97 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~lucid.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~lucid.nd1_all.deb Size: 817818 SHA256: 774bf05fe607359b31db378294947bcb52fcc9389f4401c967a172845766d9b3 SHA1: b50b0ce486acdaa088a42413d55ea9b1fbe99cb2 MD5sum: 4f523a841595f99e74aad775107bfbc7 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-pyglet Source: pyglet Version: 1.1.4.dfsg-1~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4356 Depends: neurodebian-popularity-contest, python (>= 2.4), python-support (>= 0.90.0), python-ctypes | python (>= 2.5), libgtk2.0-0, libgl1 | libgl1-mesa-swx11, libglu1 | libglu1-mesa Recommends: libasound2 | libopenal1 Provides: python2.6-pyglet Homepage: http://www.pyglet.org Priority: optional Section: python Filename: pool/main/p/pyglet/python-pyglet_1.1.4.dfsg-1~nd10.04+1_all.deb Size: 972204 SHA256: c0a0710fc32967b41456e40df7770178d8442555aaa2037bab4a3f437d5da8f7 SHA1: d6b2bc7d9dc785cc5f9cef1c258ea48249921728 MD5sum: 4bf7272cae665691f702fa30fc9b9f79 Description: cross-platform windowing and multimedia library This library provides an object-oriented programming interface for developing games and other visually-rich applications with Python. pyglet has virtually no external dependencies. For most applications and game requirements, pyglet needs nothing else besides Python, simplifying distribution and installation. It also handles multiple windows and fully aware of multi-monitor setups. . pyglet might be seen as an alternative to PyGame. Package: python-pyoptical Source: pyoptical Version: 0.2-1~lucid.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 72 Depends: python-serial Enhances: psychopy, python-pyepl Homepage: http://github.com/esc/pyoptical Priority: extra Section: python Filename: pool/main/p/pyoptical/python-pyoptical_0.2-1~lucid.nd1_all.deb Size: 6938 SHA256: 19de2c227b5d42bf669a9f17fe2b57a673389f897f5cfc38c36c5561f4a8d688 SHA1: 3a8e1f8a435a5f8c8c73e694284096d4df474f02 MD5sum: 72a4517f5c3bc2eb5154b247fc4e12c2 Description: python interface to the CRS 'OptiCAL' photometer The 'OptiCAL' is a photometer that is produced by Cambridge Research Systems (CRS). This device is a standard tool for gamma-calibration of display devices in vision research. This package provides a free-software replacement for the Windows-software distributed by the manufacturer that allows querying an OptiCAL via a serial connection. pyoptical can be used as a library for third-party applications or as a standalone command line tool. Python-Version: 2.6 Package: python-scikits-learn Source: scikit-learn Version: 0.6.0.dfsg-1~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1308 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-numpy, python-scipy, python-scikits-learn-lib (>= 0.6.0.dfsg-1~nd10.04+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_0.6.0.dfsg-1~nd10.04+1_all.deb Size: 254822 SHA256: 721cc819fe8c7d59af64721343680b310280453dc1cadbe1f6dd2fbf52040375 SHA1: 68fcbfcacb3ff530134a45ce612f52f9edfa033b MD5sum: c7f179a5abeaf9e77f763fcc686aec25 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: 0.6.0.dfsg-1~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8856 Depends: neurodebian-popularity-contest, 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_0.6.0.dfsg-1~nd10.04+1_all.deb Size: 4471554 SHA256: acad8ba7bcc0cb11e024d65145d0ee262d6dad2470a0fca1a9c1cc46177600a2 SHA1: a6207ded617c0986b3156f424e1bb78aa2d7c2ce MD5sum: 2ebf6cc43e4a3b8ee8f519347e77ec25 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: 0.6.0.dfsg-1~nd10.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1336 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.2.1), 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_0.6.0.dfsg-1~nd10.04+1_amd64.deb Size: 460314 SHA256: f7fbd62ccabe8f257023569652e76791a2fa42a5e1c00bf3b5ae191298ba82c6 SHA1: 64cbad4969f7e0dd57b3c3242fdacd6f7bb574cc MD5sum: 03bed261dc179f36ea3841c980724b8f 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-scikits-statsmodels Source: statsmodels Version: 0.2.0+bzr1990-1~lucid.nd1 Architecture: all Maintainer: Experimental Psychology Maintainers Installed-Size: 9624 Depends: python (>= 2.5), python-support (>= 0.90.0), python-numpy, python-scipy Recommends: python-matplotlib, python-nose, python-rpy Provides: python2.6-scikits-statsmodels Homepage: http://statsmodels.sourceforge.net/ Priority: optional Section: python Filename: pool/main/s/statsmodels/python-scikits-statsmodels_0.2.0+bzr1990-1~lucid.nd1_all.deb Size: 1874514 SHA256: d2c2c1010f11027fbdece0919ff27d9b733377f5426cd44b1518aa2a9ec43e58 SHA1: cb3452ecc1bb014bdf5b694178f935c375621d5b MD5sum: 45560609f527ffd385fca31ae084523c Description: classes and functions for the estimation of statistical models scikits.statsmodels is a pure Python package that provides classes and functions for the estimation of several categories of statistical models. These currently include linear regression models, OLS, GLS, WLS and GLS with AR(p) errors, generalized linear models for six distribution families and M-estimators for robust linear models. An extensive list of result statistics are avalable for each estimation problem. Python-Version: 2.6 Package: python-scikits-statsmodels-doc Source: statsmodels Version: 0.2.0+bzr1990-1~lucid.nd1 Architecture: all Maintainer: Experimental Psychology Maintainers Installed-Size: 2184 Depends: libjs-jquery Suggests: python-scikits-statsmodels Homepage: http://statsmodels.sourceforge.net/ Priority: optional Section: doc Filename: pool/main/s/statsmodels/python-scikits-statsmodels-doc_0.2.0+bzr1990-1~lucid.nd1_all.deb Size: 307516 SHA256: a21a5c3f213685a246aec2728f360ae20eaf22942a867581bf339b744aa254c5 SHA1: ee3ea925e350a73677a9a588a48bf4891c989ee5 MD5sum: 6bb2b036b77d4fd62c57f4211845349e Description: documentation and examples for python-scikits-statsmodels This package contains HTML documentation and example scripts for python-scikits-statsmodels. Package: python-sphinx Source: sphinx Version: 1.0.1-1~nd10.04+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~nd10.04+1_all.deb Size: 1233544 SHA256: 97e57b8c6f13e92a6b7aaf44a27ad0654c55621ec7f619e0d38e2cc2e95305ba SHA1: a2b3b5c5ca8f34a8bfddbeb8e41e1d327fd24402 MD5sum: 7f832b31572752fb3cea33c7f391e965 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: python-stfio Source: stimfit Version: 0.10.12-2~nd10.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 520 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libhdf5-serial-1.8.4 | libhdf5-1.8.4, libpython2.6 (>= 2.6), libstdc++6 (>= 4.4.0), python-numpy Recommends: python-matplotlib, python-scipy Homepage: http://www.stimfit.org Priority: optional Section: python Filename: pool/main/s/stimfit/python-stfio_0.10.12-2~nd10.04+1_amd64.deb Size: 212792 SHA256: 323379b4240a361335b99cfb0340816e746f8b03622074317b71a6d808cbbe6a SHA1: f82ceecf5ce8c86f90318a5fac170f2a4c253b22 MD5sum: 1b1ed6e718fb4a61e4638d32b07ca0ad Description: A Python module to read common electrophysiology file formats. The stfio module allows you to read common electrophysiology file formats from Python. Axon binaries (abf), Axon text (atf), HEKA (dat), CFS (dat/cfs), Axograph (axgd/axgx) are currently supported. Package: python-sympy Source: sympy Version: 0.6.7-1.1~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9268 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0) Recommends: python-imaging, python-ctypes, ipython Homepage: http://code.google.com/p/sympy/ Priority: optional Section: python Filename: pool/main/s/sympy/python-sympy_0.6.7-1.1~nd10.04+1_all.deb Size: 1696380 SHA256: 998b38bfe45696bbeced69358a5620c9dde141bdd5be2640797b384bff6ddd2f SHA1: 53543ef42b225f2b190593f7df8a5b869933069a MD5sum: 68e7f81588ef209c34224974fd3f1c63 Description: Computer Algebra System (CAS) in Python SymPy is a Python library for symbolic mathematics (manipulation). It aims to become a full-featured computer algebra system (CAS) while keeping the code as simple as possible in order to be comprehensible and easily extensible. SymPy is written entirely in Python and does not require any external libraries, except optionally for plotting support. Package: sigviewer Version: 0.5.0-1~nd10.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1112 Depends: neurodebian-popularity-contest, libbiosig0, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libgdf0, libqt4-xml (>= 4:4.5.3), libqtcore4 (>= 4:4.6.1), 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_0.5.0-1~nd10.04+1_amd64.deb Size: 450118 SHA256: 778c7d789716e79f5a82bd05aae0a9fa9279efd57b1f3603db2374997bb35a9a SHA1: e9fde7df57d90445a260f030e47df1b48dd0941e MD5sum: a93a31a4d32719210fe9fc0d731a3225 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. Package: spm8-common Source: spm8 Version: 8.4010~dfsg.1-4~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 21124 Depends: neurodebian-popularity-contest Recommends: spm8-data, spm8-doc Priority: extra Section: science Filename: pool/main/s/spm8/spm8-common_8.4010~dfsg.1-4~nd10.04+1_all.deb Size: 10087676 SHA256: 8221fbe2c1f5a71ce0598577f891029d65a75ac43146cbbbaa1c1810e4db3efc SHA1: 94bc3322091015e95f92485f5e3c9d375e835cc1 MD5sum: 4ee206e418f727fa199260ad4b70d2f0 Description: analysis of brain imaging data sequences Statistical Parametric Mapping (SPM) refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional brain imaging data. These ideas have been instantiated in software that is called SPM. It is designed for the analysis of fMRI, PET, SPECT, EEG and MEG data. . This package provides the platform-independent M-files. Package: spm8-data Source: spm8 Version: 8.4010~dfsg.1-4~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 73316 Depends: neurodebian-popularity-contest Priority: extra Section: science Filename: pool/main/s/spm8/spm8-data_8.4010~dfsg.1-4~nd10.04+1_all.deb Size: 52168464 SHA256: f2b2ba3632ed430ce701e1ba2435ae82ddf0c576186de08d8175f821edf4d2dd SHA1: 469d89d0b42063832c3b31022b70c06e92243be2 MD5sum: f203c4fce1900a495ba4d58bc1088624 Description: data files for SPM8 Statistical Parametric Mapping (SPM) refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional brain imaging data. These ideas have been instantiated in software that is called SPM. It is designed for the analysis of fMRI, PET, SPECT, EEG and MEG data. . This package provide the data files shipped with the SPM distribution, such as various stereotaxic brain space templates and EEG channel setups. Package: spm8-doc Source: spm8 Version: 8.4010~dfsg.1-4~nd10.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 11288 Depends: neurodebian-popularity-contest Priority: extra Section: doc Filename: pool/main/s/spm8/spm8-doc_8.4010~dfsg.1-4~nd10.04+1_all.deb Size: 10423804 SHA256: 7e0d823b7a24c125b8e9d621fe5d5d5773176d0b1282797ec7f11c8f3b7be62a SHA1: 8f707e17772ae35c2b611fc50d6d483d4da65c3a MD5sum: 6d0901bfa71f718569a21df4d7ad2fe2 Description: manual for SPM8 Statistical Parametric Mapping (SPM) refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional brain imaging data. These ideas have been instantiated in software that is called SPM. It is designed for the analysis of fMRI, PET, SPECT, EEG and MEG data. . This package provides the SPM manual in PDF format. Package: stimfit Version: 0.10.12-2~nd10.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2116 Depends: neurodebian-popularity-contest, libblas3gf | libblas.so.3gf | libatlas3gf-base, libc6 (>= 2.4), libfftw3-3, libgcc1 (>= 1:4.1.1), libhdf5-serial-1.8.4 | libhdf5-1.8.4, liblapack3gf | liblapack.so.3gf | libatlas3gf-base, libpython2.6 (>= 2.6), libstdc++6 (>= 4.4.0), libwxbase2.8-0 (>= 2.8.10.1), libwxgtk2.8-0 (>= 2.8.10.1), python-wxgtk2.8 (>= 2.8.9), python-numpy, python-matplotlib Recommends: python-scipy Homepage: http://www.stimfit.org Priority: optional Section: science Filename: pool/main/s/stimfit/stimfit_0.10.12-2~nd10.04+1_amd64.deb Size: 748770 SHA256: 4d94ac830026a19e6b4f2c4a224378f1303367e298a5c6df9c17d77a2c61cbf9 SHA1: 9298c1cbbad2d3cbf264c4172d9a6163f31cc9a3 MD5sum: 5fdb087825648155624f035ce35beee0 Description: A program for viewing and analyzing electrophysiological data Stimfit is a free, fast and simple program for viewing and analyzing electrophysiological data. It features an embedded Python shell that allows you to extend the program functionality by using numerical libraries such as NumPy and SciPy. Package: stimfit-dbg Source: stimfit Version: 0.10.12-2~nd10.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 14820 Depends: neurodebian-popularity-contest, stimfit Recommends: python-matplotlib, python-scipy, python-stfio Homepage: http://www.stimfit.org Priority: extra Section: debug Filename: pool/main/s/stimfit/stimfit-dbg_0.10.12-2~nd10.04+1_amd64.deb Size: 4970380 SHA256: 183a542992e27549b097e277c4130dcfb53e3cc1b5d66794910afb2bbf0fc213 SHA1: 611209effbf8f008e2241997cae8f78b2fa6a991 MD5sum: fd499d2318c773704da1ec3f2d8e9f0b Description: Debug symbols for stimfit Stimfit is a free, fast and simple program for viewing and analyzing electrophysiological data. It features an embedded Python shell that allows you to extend the program functionality by using numerical libraries such as NumPy and SciPy. Package: svgtune Version: 0.1.0-2 Architecture: all Maintainer: Yaroslav Halchenko Installed-Size: 64 Depends: python, python-lxml Suggests: inkscape Homepage: http://github.com/yarikoptic/svgtune Priority: optional Section: graphics Filename: pool/main/s/svgtune/svgtune_0.1.0-2_all.deb Size: 6680 SHA256: 69b4df1e0b4c247673265c7f5bb2b2ffe2209d783617bf7f6eadce86633f80e1 SHA1: bec339e4453c35a05a616deef6769a6f2ad2d00d MD5sum: ef6797498477a73f930ad9bc0db3ba73 Description: tool to generate a set of .svg files out of a single .svg file svgtune is just a little helper to generate a set of .svg files out of a single .svg file, by tuning respective groups/layers visibility, transparency or anything else. . It might come very handy for generation of incremental figures to be embedded into the presentation in any format which inkscape could render using original .svg file (e.g. pdf, png). Package: voxbo Version: 1.8.5~svn1246-1~nd10.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 10292 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), libfontconfig1 (>= 2.8.0), libfreetype6 (>= 2.2.1), libgcc1 (>= 1:4.1.1), libgsl0ldbl (>= 1.9), libpng12-0 (>= 1.2.13-4), libqt4-network (>= 4:4.5.3), libqt4-qt3support (>= 4:4.5.3), libqtcore4 (>= 4:4.6.1), libqtgui4 (>= 4:4.6.1), libstdc++6 (>= 4.4.0), libx11-6, libxext6, libxi6, libxrender1, zlib1g (>= 1:1.1.4) Suggests: mni-colin27-nifti, matlab-spm8 Homepage: http://www.voxbo.org Priority: extra Section: science Filename: pool/main/v/voxbo/voxbo_1.8.5~svn1246-1~nd10.04+1_amd64.deb Size: 3755792 SHA256: 6416312fc85af42d3067f4dface068c3068cc41855b03f76134a4b1577077643 SHA1: d2e75c31cd22de17b695cad7d0b74fece55e9487 MD5sum: dd75772f73c088902dea4b14abac8064 Description: processing, statistical analysis, and display of brain imaging data This is a toolkit for analysis of functional neuroimaging (chiefly fMRI) experiments and voxel-based lesion-behavior mapping. VoxBo supports the modified GLM (for autocorrelated data), as well as the standard GLM for non-autocorrelated data. The toolkit is designed to be interoperable with AFNI, FSL, SPM and others.