Package: arno-iptables-firewall Version: 1.9.2.k-3~karmic.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~karmic.nd1_all.deb Size: 132478 SHA256: baf52294b6f11e2d1d499799363b0c7428eb6c5519511130d5188a452356ef2e SHA1: 6e354743a73c7f42e4461003e1bf7f321ee2df79 MD5sum: c3441a52075f488db2dbb4d448f7deaf 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~nd09.10+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~nd09.10+1_all.deb Size: 73018 SHA256: d7ddfcad626dbb0f16b190d704a02cb4b73e51256fff26e999fd56dbd9eeeabf SHA1: 3b98c0a9b16ae6df62dbeb5ec7e1c11a60c211d1 MD5sum: f997e6eda1ebf85f67e25a05fffe8f2a 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~nd09.10+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~nd09.10+1_amd64.deb Size: 255758 SHA256: adaf79277a43883c7fe8bf5f3c8d5431c840463554e6a04cd56b4f0663f8f3af SHA1: 593dbc38c210cc2b10c56be3c1b7063deb27b1ad MD5sum: 977f0444a7d684c77dcf5da16a94a11b 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~nd09.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 18624 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, libminc2-1, libqt4-assistant (>= 4.5.1), libqt4-network (>= 4.5.1), libqt4-opengl (>= 4.5.1), libqt4-xml (>= 4.5.1), libqtcore4 (>= 4.5.1), libqtgui4 (>= 4.5.1), 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~nd09.10+1_amd64.deb Size: 7144260 SHA256: 0a0433d07bb3c411f8f8cc4633460c8d4587fb6996bae5de87acdc5b44a8493f SHA1: 4270f30840b49be2e9fb3434e04e943c9977b84a MD5sum: f30b15b88e960706b1bcef1524a695ef 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~nd09.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 124 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libclassad0 (= 1.0.9-2~nd09.10+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~nd09.10+1_amd64.deb Size: 36586 SHA256: 4b53b65d78929d0bea1207599c10c35936f45a3d49a66ada8316d51ae59152d5 SHA1: ebec15e4f16d58e1c1bc513cc4934329d5ca62f2 MD5sum: 81b618b74031f7d634f15c72c1a54f79 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~nd09.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 4932 Depends: python (<< 2.7), python (>= 2.5), 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~nd09.10+1_amd64.deb Size: 1322940 SHA256: 434b9ce34f516afcdc2196598e740b23cda23075a28992a9445500b846e9a1c1 SHA1: 0e4a47fbf3c2222c7dfbe22b4c82bc22d9e0b326 MD5sum: 2ba699fb37b1d4128dc8544bfcbe8ab4 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: 2.5, 2.6 Package: cython-dbg Source: cython Version: 0.13-1~nd09.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 10520 Depends: python (<< 2.7), python (>= 2.5), python-support (>= 0.90.0), libc6 (>= 2.3), cython (= 0.13-1~nd09.10+1) Suggests: gcc Homepage: http://cython.org/ Priority: extra Section: debug Filename: pool/main/c/cython/cython-dbg_0.13-1~nd09.10+1_amd64.deb Size: 3404228 SHA256: 5c431a276502dce63a0bbd117d5defb9433ce4151c526cd177bd9a6dcf4b07b5 SHA1: 9b930a4c0f01ee49bcc9c781dfa16a38fca0ab9a MD5sum: a171455ed5cc025cbc260dc89a67f061 Description: C-Extensions for Python (Debug Build of Cython) This package contains Cython libraries built against versions of Python configured with --pydebug. Python-Version: 2.5, 2.6 Package: dicomnifti Version: 2.28.14-2~karmic.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~karmic.nd1_amd64.deb Size: 159300 SHA256: b999b4bd3004c2553e5b417a157afcbf4fac4fb9a09cf3e0563400a795f5bb81 SHA1: 077c50d5a5a094ace5bd6affbf6e72e8c95bfcf0 MD5sum: 2c3b2014e97a78189056ad3def17cbad 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: epydoc-doc Source: epydoc Version: 3.0.1-4~karmic.nd1 Architecture: all Maintainer: Kenneth J. Pronovici Installed-Size: 15000 Recommends: iceweasel | www-browser Priority: optional Section: doc Filename: pool/main/e/epydoc/epydoc-doc_3.0.1-4~karmic.nd1_all.deb Size: 1544574 SHA256: 352b6477b048abd019d7aef1f0bda1ecd360cf48cd1bc89cbca010ebe7118666 SHA1: 009a69d67fc361fa30db68eed4b7955d1a8ac0ec MD5sum: 12c6f4a68bfd735d73d401cdfbd8f303 Description: official documentation for the Epydoc package Epydoc is a tool for generating API documentation for Python modules based on their docstrings. A lightweight markup language called epytext can be used to format docstrings and to add information about specific fields, such as parameters and instance variables. Epydoc also understands docstrings written in ReStructuredText, Javadoc, and plaintext. . This package contains the API reference and usage information for Epydoc, all available through the Debian documentation system (dhelp, dwww, doc-central, etc.) in the Devel section. Package: fslview Version: 3.1.8+4.1.6-2~karmic.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 4164 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~karmic.nd1_amd64.deb Size: 1525712 SHA256: 2918eba7fd74c4eec876c36eca05123141909ebee710952a721f1eb15835f5c7 SHA1: c4d1534d5894387ece0b5944ccd99de46679e2be MD5sum: 889714bc45ec6cdced99606c7f8c6428 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~karmic.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~karmic.nd1_all.deb Size: 2378972 SHA256: 5d4ed542a39f9204afbe6575f07ca17c2d200cecbf569f5518d36f7f852503a9 SHA1: 85ee2eae47a22442e7c39d46f92f510c6d1b6bd7 MD5sum: 665ea4e5860f4aa4e35963956ee99f1a Description: Documentation for FSLView This package provides the online documentation for FSLView. . FSLView is part of FSL. Package: gifti-bin Source: gifticlib Version: 1.0.9-1~karmic.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~karmic.nd1_amd64.deb Size: 29258 SHA256: 9386ce9f49711059cb62d73e41275c2c63aab8885b9ddaf77af264472e3a7568 SHA1: a64d46f0ee4d227c1394e19a216877bbbe6c5e81 MD5sum: 999d1985aa21572ebd6cc4b80119868d 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: hdf5-tools Source: hdf5 Version: 1.8.3-2.1~karmic.nd1 Architecture: amd64 Maintainer: Debian GIS Project Installed-Size: 1260 Depends: libc6 (>= 2.7), libhdf5-serial-1.8.3 | libhdf5-1.8.3, zlib1g (>= 1:1.1.4) Homepage: http://hdfgroup.org/HDF5/ Priority: optional Section: science Filename: pool/main/h/hdf5/hdf5-tools_1.8.3-2.1~karmic.nd1_amd64.deb Size: 398472 SHA256: d1540ded6a7b8f0c817af5cbc9b922d885b8e8038e7bc9548300bbf10e2a2ccb SHA1: 28c265baee5917c18a71108e65f5763f633790bc MD5sum: a749d91e0e9cf9ac7abb33081ebf6a23 Description: Hierarchical Data Format 5 (HDF5) - Runtime tools HDF5 is a file format and library for storing scientific data. HDF5 was designed and implemented to address the deficiencies of HDF4.x. It has a more powerful and flexible data model, supports files larger than 2 GB, and supports parallel I/O. . This package contains runtime tools for HDF5. Package: itksnap Version: 2.0.0-1~karmic.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 8492 Depends: libc6 (>= 2.4), libfltk1.1 (>= 1.1.8~rc1), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, libinsighttoolkit3.14, libstdc++6 (>= 4.4.0), libvtk5.2 Homepage: http://www.itksnap.org Priority: extra Section: science Filename: pool/main/i/itksnap/itksnap_2.0.0-1~karmic.nd1_amd64.deb Size: 3642802 SHA256: f9a6797a39525a65d526c091a153e4f4ca89df95626eb92ddc2608d8d6dbd9c0 SHA1: 6b3dd5a7d2b0d8dc9e6655b507f8c05e1755aee6 MD5sum: c881c8c97ec00c167c27934025284fb0 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~nd09.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1624 Depends: libbiosig0 (= 0.94.2+svn2552-1~pre1~nd09.10+1) Homepage: http://biosig.sf.net/ Priority: extra Section: libdevel Filename: pool/main/b/biosig4c++/libbiosig-dev_0.94.2+svn2552-1~pre1~nd09.10+1_amd64.deb Size: 382876 SHA256: 9d7e57ba199d32b6c590c3cc9e34e44620b29511ed5d77c156ea0117af158139 SHA1: 5d71865af10de322f675d3040a62c5c06afc021e MD5sum: 2e47940999d78ddf68abf739dc6d8231 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~nd09.10+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~nd09.10+1_amd64.deb Size: 305510 SHA256: 92bfc37744fb08da5bc851fb1ea00fcd0a12d59514d123fa87b1ddf932a97fb7 SHA1: 51d181da0caa48d2be0914f40b79bed815c563d3 MD5sum: 373d52973e88146bea5856a97c16d0a0 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~nd09.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 716 Depends: libbiosig0 (= 0.94.2+svn2552-1~pre1~nd09.10+1) Homepage: http://biosig.sf.net/ Priority: extra Section: debug Filename: pool/main/b/biosig4c++/libbiosig0-dbg_0.94.2+svn2552-1~pre1~nd09.10+1_amd64.deb Size: 174074 SHA256: 549a74a5fa29e938e9115c37f4965410666cf2cab241289be049963f621f1921 SHA1: eb2921d4c45e4672a97a70df591bbf74d138524f MD5sum: 061c83cad876e92f9f0183a4662f04ba 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~nd09.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2200 Depends: neurodebian-popularity-contest, libclassad0 (= 1.0.9-2~nd09.10+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~nd09.10+1_amd64.deb Size: 569496 SHA256: 08617c4efe872c2e074b91b071f7ec3c569ac0c5e1effe161d916766886a1179 SHA1: e5630507d55ff5bfc19445771eb5f26f5c23f796 MD5sum: 879bc8d107fe2c254459371066fda6de 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~nd09.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1064 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~nd09.10+1_amd64.deb Size: 425728 SHA256: f5a8948da1734819afd7525723384eec642a8e5b252647f86b3d3102360f5a3c SHA1: 1a14346fa82731ffb789f4d576766d562da0d7c6 MD5sum: af38d6eac8959bc57f614352109bcfff 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: libgiftiio-dev Source: gifticlib Version: 1.0.9-1~karmic.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 256 Depends: libgiftiio0 (= 1.0.9-1~karmic.nd1) Homepage: http://www.nitrc.org/projects/gifti Priority: optional Section: libdevel Filename: pool/main/g/gifticlib/libgiftiio-dev_1.0.9-1~karmic.nd1_amd64.deb Size: 64968 SHA256: 92811b29819978b618812c2fded3f8f6c5213e90fb6a4f9db90fd4fd6b942b09 SHA1: a7ba3802b827b6ac3d862278f2a5de93aad6bf6e MD5sum: 2aac17111b7d94a019b8d72d3f85193b 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~karmic.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~karmic.nd1_amd64.deb Size: 57548 SHA256: e7ccdd47b7ffe549d0306dd087b290484c2b056e20f507e3e65f9e0c0490c5c9 SHA1: 544aa73b8095df53ecb8f9f9cebb24cb3f403cde MD5sum: 7a902a7d8e683279a5803b86a0af82f0 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: libhdf5-doc Source: hdf5 Version: 1.8.3-2.1~karmic.nd1 Architecture: all Maintainer: Debian GIS Project Installed-Size: 180 Suggests: libhdf5-dev, www-browser, pdf-viewer, doc-base Homepage: http://hdfgroup.org/HDF5/ Priority: optional Section: doc Filename: pool/main/h/hdf5/libhdf5-doc_1.8.3-2.1~karmic.nd1_all.deb Size: 77692 SHA256: 2737bc11333c2f06d9860bbc72a4cab735eba9c8f817f73b174d1252e9ebb76c SHA1: 2a3e364b8aaa5b2bd16b9fd581606f7f0ff330ba MD5sum: 0c214ff584add34d707746b752cbd589 Description: Hierarchical Data Format 5 (HDF5) - Documentation HDF5 is a file format and library for storing scientific data. HDF5 was designed and implemented to address the deficiencies of HDF4.x. It has a more powerful and flexible data model, supports files larger than 2 GB, and supports parallel I/O. . This package contains documentation for HDF5. Package: libhdf5-lam-1.8.3 Source: hdf5 Version: 1.8.3-2.1~karmic.nd1 Architecture: amd64 Maintainer: Debian GIS Project Installed-Size: 4780 Depends: libc6 (>= 2.7), liblam4, zlib1g (>= 1:1.1.4) Conflicts: libhdf5-1.8.3 Provides: libhdf5-1.8.3 Homepage: http://hdfgroup.org/HDF5/ Priority: extra Section: libs Filename: pool/main/h/hdf5/libhdf5-lam-1.8.3_1.8.3-2.1~karmic.nd1_amd64.deb Size: 1484076 SHA256: 69ee07937cdc5b90e467eae4e04d93bbed388891d515bccdc1157dc83f24ab52 SHA1: ead26dcd8bb101145bb68b3f3a1212c57838f6ad MD5sum: d6a25784df847c09b36633dabf5ba5d2 Description: Hierarchical Data Format 5 (HDF5) - runtime files - LAM version HDF5 is a file format and library for storing scientific data. HDF5 was designed and implemented to address the deficiencies of HDF4.x. It has a more powerful and flexible data model, supports files larger than 2 GB, and supports parallel I/O. . This package contains runtime files for use with LAM. Package: libhdf5-lam-dev Source: hdf5 Version: 1.8.3-2.1~karmic.nd1 Architecture: amd64 Maintainer: Debian GIS Project Installed-Size: 9688 Depends: libhdf5-lam-1.8.3 (= 1.8.3-2.1~karmic.nd1), zlib1g-dev, libjpeg62-dev, lam4-dev Suggests: libhdf5-doc Conflicts: libhdf5-dev Provides: libhdf5-dev Homepage: http://hdfgroup.org/HDF5/ Priority: extra Section: libdevel Filename: pool/main/h/hdf5/libhdf5-lam-dev_1.8.3-2.1~karmic.nd1_amd64.deb Size: 1735260 SHA256: 490c0e110fa2736ac4fe8f4af4f4da536efcc90a36a3b1e7933b6a9888892fa3 SHA1: f95018b28e33fbe85f6fa036b0d0386eb42557b0 MD5sum: 5d364f4db083bf4a9539a472da9db1c6 Description: Hierarchical Data Format 5 (HDF5) - development files - LAM version HDF5 is a file format and library for storing scientific data. HDF5 was designed and implemented to address the deficiencies of HDF4.x. It has a more powerful and flexible data model, supports files larger than 2 GB, and supports parallel I/O. . This package contains development files for use with LAM. Package: libhdf5-mpich-1.8.3 Source: hdf5 Version: 1.8.3-2.1~karmic.nd1 Architecture: amd64 Maintainer: Debian GIS Project Installed-Size: 6004 Depends: libc6 (>= 2.7), libgcc1 (>= 1:4.1.1), libgfortran3 (>= 4.3), zlib1g (>= 1:1.1.4) Conflicts: libhdf5-1.8.3 Provides: libhdf5-1.8.3 Homepage: http://hdfgroup.org/HDF5/ Priority: extra Section: libs Filename: pool/main/h/hdf5/libhdf5-mpich-1.8.3_1.8.3-2.1~karmic.nd1_amd64.deb Size: 1960906 SHA256: 5366ea11a73b27569d1933091644a158f356c61d258010f53f56f8d82bd816fc SHA1: fdb1d130226753a653f4dd0b7ac7febd73c80be2 MD5sum: 579b091bb52e9f5b96547b1fe62a1941 Description: Hierarchical Data Format 5 (HDF5) - runtime files - MPICH version HDF5 is a file format and library for storing scientific data. HDF5 was designed and implemented to address the deficiencies of HDF4.x. It has a more powerful and flexible data model, supports files larger than 2 GB, and supports parallel I/O. . This package contains runtime files for use with MPICH. Warning: the C++ interface is not provided for this version. Package: libhdf5-mpich-dev Source: hdf5 Version: 1.8.3-2.1~karmic.nd1 Architecture: amd64 Maintainer: Debian GIS Project Installed-Size: 17920 Depends: libhdf5-mpich-1.8.3 (= 1.8.3-2.1~karmic.nd1), zlib1g-dev, libjpeg62-dev, libmpich1.0-dev Suggests: libhdf5-doc Conflicts: libhdf5-dev Provides: libhdf5-dev Homepage: http://hdfgroup.org/HDF5/ Priority: extra Section: libdevel Filename: pool/main/h/hdf5/libhdf5-mpich-dev_1.8.3-2.1~karmic.nd1_amd64.deb Size: 2430844 SHA256: 9838450959aa42c9120c62233b181f7157df293a95a7028ff32b94aa7659435e SHA1: 58a41088e98ca89f48f333e4fe87c7213bf480a1 MD5sum: 16b2671d9da76b6a3f0414f229737442 Description: Hierarchical Data Format 5 (HDF5) - development files - MPICH version HDF5 is a file format and library for storing scientific data. HDF5 was designed and implemented to address the deficiencies of HDF4.x. It has a more powerful and flexible data model, supports files larger than 2 GB, and supports parallel I/O. . This package contains development files for use with MPICH. Warning: the C++ interface is not provided for this version. Package: libhdf5-openmpi-1.8.3 Source: hdf5 Version: 1.8.3-2.1~karmic.nd1 Architecture: amd64 Maintainer: Debian GIS Project Installed-Size: 5096 Depends: libc6 (>= 2.7), libgcc1 (>= 1:4.1.1), libgfortran3 (>= 4.3), libopenmpi1.3, zlib1g (>= 1:1.1.4) Conflicts: libhdf5-1.8.3 Provides: libhdf5-1.8.3 Homepage: http://hdfgroup.org/HDF5/ Priority: extra Section: libs Filename: pool/main/h/hdf5/libhdf5-openmpi-1.8.3_1.8.3-2.1~karmic.nd1_amd64.deb Size: 1570940 SHA256: 6bfa0d7154c1fadb7d8239c3684d44dca85e526bf1c68613a2cf94a404ea060d SHA1: 8f1a61c0b11efaa34698adfb4a1d645771bcef43 MD5sum: bad9a876769c2a2f29361f9b37a81a5a Description: Hierarchical Data Format 5 (HDF5) - runtime files - OpenMPI version HDF5 is a file format and library for storing scientific data. HDF5 was designed and implemented to address the deficiencies of HDF4.x. It has a more powerful and flexible data model, supports files larger than 2 GB, and supports parallel I/O. . This package contains runtime files for use with OpenMPI. Package: libhdf5-openmpi-dev Source: hdf5 Version: 1.8.3-2.1~karmic.nd1 Architecture: amd64 Maintainer: Debian GIS Project Installed-Size: 17924 Depends: libhdf5-openmpi-1.8.3 (= 1.8.3-2.1~karmic.nd1), zlib1g-dev, libjpeg62-dev, libopenmpi-dev Suggests: libhdf5-doc Conflicts: libhdf5-dev Provides: libhdf5-dev Homepage: http://hdfgroup.org/HDF5/ Priority: extra Section: libdevel Filename: pool/main/h/hdf5/libhdf5-openmpi-dev_1.8.3-2.1~karmic.nd1_amd64.deb Size: 2431580 SHA256: 388522887edc7ed35c428a3475fafe4278ffd0b6b1868309a5172ce447906491 SHA1: 68545e0041a0521c7dd079b46472a8f38e3997a4 MD5sum: 5f29b8c77cc7bb93b78431660d2b3e21 Description: Hierarchical Data Format 5 (HDF5) - development files - OpenMPI version HDF5 is a file format and library for storing scientific data. HDF5 was designed and implemented to address the deficiencies of HDF4.x. It has a more powerful and flexible data model, supports files larger than 2 GB, and supports parallel I/O. . This package contains development files for use with OpenMPI. Package: libhdf5-serial-1.8.3 Source: hdf5 Version: 1.8.3-2.1~karmic.nd1 Architecture: amd64 Maintainer: Debian GIS Project Installed-Size: 5404 Depends: libc6 (>= 2.7), libgcc1 (>= 1:4.1.1), libgfortran3 (>= 4.3), libstdc++6 (>= 4.4.0), zlib1g (>= 1:1.1.4) Conflicts: libhdf5-1.8.3 Provides: libhdf5-1.8.3 Homepage: http://hdfgroup.org/HDF5/ Priority: optional Section: libs Filename: pool/main/h/hdf5/libhdf5-serial-1.8.3_1.8.3-2.1~karmic.nd1_amd64.deb Size: 1667100 SHA256: 9a6066816310953a373bc77ceb1a98e3c60762fabb11f251fd3d8f8d12f17bb8 SHA1: 4f5e078e069c4386a07d690e0e9c97bce893ae79 MD5sum: ab172716de40353db9a94b955448c8d0 Description: Hierarchical Data Format 5 (HDF5) - runtime files - serial version HDF5 is a file format and library for storing scientific data. HDF5 was designed and implemented to address the deficiencies of HDF4.x. It has a more powerful and flexible data model, supports files larger than 2 GB, and supports parallel I/O. . This package contains runtime files for serial platforms. Package: libhdf5-serial-dev Source: hdf5 Version: 1.8.3-2.1~karmic.nd1 Architecture: amd64 Maintainer: Debian GIS Project Installed-Size: 18444 Depends: libhdf5-serial-1.8.3 (= 1.8.3-2.1~karmic.nd1), zlib1g-dev, libjpeg-dev Suggests: libhdf5-doc Conflicts: libhdf5-dev Provides: libhdf5-dev Homepage: http://hdfgroup.org/HDF5/ Priority: optional Section: libdevel Filename: pool/main/h/hdf5/libhdf5-serial-dev_1.8.3-2.1~karmic.nd1_amd64.deb Size: 2550600 SHA256: 87486105c3c75868c6d3bac0e1a4ebcb0ae3dbc2f89cd101d6512320db79fc6d SHA1: 4f369b5372d7605dca3002f176bf997328a0d412 MD5sum: 9dba166efb0cc2c084ac2ae19538dee6 Description: Hierarchical Data Format 5 (HDF5) - development files - serial version HDF5 is a file format and library for storing scientific data. HDF5 was designed and implemented to address the deficiencies of HDF4.x. It has a more powerful and flexible data model, supports files larger than 2 GB, and supports parallel I/O. . This package contains development files for serial platforms. Package: libnifti-dev Source: nifticlib Version: 2.0.0-1~karmic.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 624 Depends: libnifti2 (= 2.0.0-1~karmic.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~karmic.nd1_amd64.deb Size: 171336 SHA256: 81a72b14cf5141e0498e7ce2592556efe56c44e02f1b26988d46e623b779fe82 SHA1: 3337fa60869eda03776425bf854ce0de3a5237fb MD5sum: 76421715e6350ee8fd5c2eb4925f94bd 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~karmic.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1936 Homepage: http://niftilib.sourceforge.net Priority: optional Section: doc Filename: pool/main/n/nifticlib/libnifti-doc_2.0.0-1~karmic.nd1_all.deb Size: 248616 SHA256: 70fcdfd2c371ab527325dbec01e9ce5fe1d34e3dc931473e2c4960ef548deadb SHA1: 41895f75d06ec3f194458113aa48000a3f0143e2 MD5sum: 4328fe117b4decbbc34896aca931475d 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~karmic.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~karmic.nd1_amd64.deb Size: 123046 SHA256: 341ce26fb9c872c85f0c70a36b42869f8962366e1d531c54dc0bbf0b9098f703 SHA1: 82a41ed5dfc4dc61b8c90bf2b5b8cf6b466d9a63 MD5sum: ae232590b6647e8f987f11fd9686351e Description: IO libraries for the NIfTI-1 data format Niftilib is a set of i/o libraries for reading and writing files in the NIfTI-1 data format. NIfTI-1 is a binary file format for storing medical image data, e.g. magnetic resonance image (MRI) and functional MRI (fMRI) brain images. . This package contains the shared library of the low-level IO library niftiio, low-level IO library znzlib and the nifticdf shared library that provides functions to compute cumulative distributions and their inverses. Package: libopenmeeg-dev Source: openmeeg Version: 2.0.0.dfsg-2~karmic.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~karmic.nd1_amd64.deb Size: 43858 SHA256: 2944d8415d7e237b047f9e4bbaaf553d04287bd4f7b9c2f6f2796ed4e243fdb8 SHA1: 165857530a71f8f20881ad61e6286e0657cdcc73 MD5sum: 17cc69f2eb159404c9d855feaf0f6652 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~karmic.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 968 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~karmic.nd1_amd64.deb Size: 255710 SHA256: ce8cca6bfce9304cb7a656bbac51fcd210d165c04a02b35011f90f122e8d956e SHA1: 0ab9e5a6dea766b5d8923c55b095c28351b0f183 MD5sum: c284c794e3f7ca7b55e4e8da94875968 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: matlab-support-dev Source: matlab-support Version: 0.0.14~nd09.10+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~nd09.10+1_all.deb Size: 5468 SHA256: afe3f33e4f3ec28e5ed8573884a68aafbc2554b2eeee822452896be8c936b0d1 SHA1: 4e147b3a507ecde588de7badb0f1950567c791ca MD5sum: f1c5850ed14cbdd5d78fdac79b8d066a 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: mrtrix Version: 0.2.8-1~karmic.nd1 Architecture: amd64 Maintainer: Michael Hanke Installed-Size: 7300 Depends: libatk1.0-0 (>= 1.20.0), libc6 (>= 2.4), libcairo2 (>= 1.2.4), libcairomm-1.0-1 (>= 1.6.4), libfontconfig1 (>= 2.4.0), libfreetype6 (>= 2.2.1), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglib2.0-0 (>= 2.12.0), libglibmm-2.4-1c2a (>= 2.22.0), libglu1-mesa | libglu1, libgsl0ldbl (>= 1.9), libgtk2.0-0 (>= 2.8.0), libgtkglext1, libgtkmm-2.4-1c2a (>= 1:2.18.0), libice6 (>= 1:1.0.0), libpango1.0-0 (>= 1.14.0), libpangomm-1.4-1 (>= 2.24.0), libsigc++-2.0-0c2a (>= 2.0.2), libsm6, libstdc++6 (>= 4.3), libx11-6, libxmu6, libxt6 Suggests: mrtrix-doc Homepage: http://www.brain.org.au/software/mrtrix Priority: extra Section: science Filename: pool/main/m/mrtrix/mrtrix_0.2.8-1~karmic.nd1_amd64.deb Size: 2261496 SHA256: 373702f5f89eefc4cc231a4638f7aa528d77636fcc236cfeafb334dc67a58a0d SHA1: 4a287126fd90192cd760c6275c08fd831ca47f72 MD5sum: 27c9f5a44ae89d066c077458dd6ea170 Description: diffusion-weighted MRI white matter tractography Set of tools to perform diffusion-weighted MRI white matter tractography of the brain in the presence of crossing fibres, using Constrained Spherical Deconvolution, and a probabilisitic streamlines algorithm. Magenetic resonance images in DICOM or ANALYZE format are supported. Package: mrtrix-doc Source: mrtrix Version: 0.2.8-1~karmic.nd1 Architecture: all Maintainer: Michael Hanke Installed-Size: 3416 Homepage: http://www.brain.org.au/software/mrtrix Priority: extra Section: doc Filename: pool/main/m/mrtrix/mrtrix-doc_0.2.8-1~karmic.nd1_all.deb Size: 2949218 SHA256: 106ead56716b48fb7e92c1ef9d6cf382965316eb069e8e55344d05fe083592c5 SHA1: 589ec0ceb6e31aa04495af0c8c0a1922a5c95643 MD5sum: bc023f9116be1a0d2b00f20a70353548 Description: documentation for mrtrix Set of tools to perform diffusion-weighted MRI white matter tractography of the brain in the presence of crossing fibres, using Constrained Spherical Deconvolution, and a probabilisitic streamlines algorithm. Magenetic resonance images in DICOM or ANALYZE format are supported. . This package provides the documentation in HTML format. Package: neurodebian-desktop Source: neurodebian Version: 0.22~nd09.10+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_0.22~nd09.10+1_all.deb Size: 113356 SHA256: 0fa420ee4ff9d02e94ae5d241447fe895a70e813f1fb984da77fafacea6fba8d SHA1: 45d35862ae6a7142c8afe977e35bc107a8d7e0ca MD5sum: 4c62a107db63b35fcf1d522f78e4351e 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: 0.22~nd09.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4372 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_0.22~nd09.10+1_all.deb Size: 3788386 SHA256: 8f04589e723427a2530a038740e08aba5fdb06d601d49ae98ef0f5bbf6333e9d SHA1: 4d91122ece9efc22778e1c1939ded582c6a265cc MD5sum: e5892658393e3878c69ce7bf8da65863 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: 0.22~nd09.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 232 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_0.22~nd09.10+1_all.deb Size: 10928 SHA256: 3d65856c3e1efa5f1869bff7aba4e23fb37c58b64b062afe8a072c46d509bce8 SHA1: a727b3fc5177a879281cb0ae5fbb473d0d1c378f MD5sum: 35e178e099bc758cef8f56666441e3b5 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: 0.22~nd09.10+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_0.22~nd09.10+1_all.deb Size: 4712 SHA256: eee15c2c05c47dd3623279515fa82ef0a98325cadc0b1c6cfa05f45af25d03c5 SHA1: 984cf8c36a1a3a0bf4cebf710fc52d2664b38afc MD5sum: db8717efa7b87ad29ec420968bb3ff7e 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: 0.22~nd09.10+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_0.22~nd09.10+1_all.deb Size: 3860 SHA256: f100b2fafb7c3e6f18008354ddfdc620c93aa1e8cf8b719325d74579f473549c SHA1: 4fc6ef7f614d3ecae3b6abdac14e695366419eb9 MD5sum: 2d608bcb1781a1b98cddb321ef31b99b 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~karmic.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~karmic.nd1_amd64.deb Size: 62292 SHA256: 3fae478b65c456ca6384188521d757b84f3307b49e639a662f151ac0d559c410 SHA1: c84e28d8853b23bf0871cd63f9a51bb62cedc218 MD5sum: 22013abf46cc88764bafabdd684af45f Description: tools shipped with the NIfTI library Niftilib is a set of i/o libraries for reading and writing files in the NIfTI-1 data format. NIfTI-1 is a binary file format for storing medical image data, e.g. magnetic resonance image (MRI) and functional MRI (fMRI) brain images. . This package provides the tools that are shipped with the library (nifti_tool, nifti_stats and nifti1_test). Package: octave-biosig Source: biosig4c++ Version: 0.94.2+svn2552-1~pre1~nd09.10+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-1.8.3 | libhdf5-1.8.3, liblapack3gf | liblapack.so.3gf | libatlas3gf-base, libncurses5 (>= 5.6+20071006-3), libreadline5 (>= 5.2), 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~nd09.10+1_amd64.deb Size: 593988 SHA256: 54529d0855959bf9804e41eac973647103d90c26c41ce62cf8ab884d74a7e2c0 SHA1: 98facfebc0155ee673f566eea9ddbee2a2588a6a MD5sum: bec8d664cda4c416972414878623bf62 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: openmeeg-tools Source: openmeeg Version: 2.0.0.dfsg-2~karmic.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 608 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~karmic.nd1_amd64.deb Size: 165538 SHA256: 77187d52c7a8ebb86fdcea49b6695b06c56d75acb6c46c0f5e0689f47b6fa6ce SHA1: 09fedc5faf4a0f7cfac9f8376db36d19a657384d MD5sum: b63aed663a3d7645d2905bdc60689f67 Description: tools for solving EEG and MEG forward and inverse problems OpenMEEG provides state-of-the art tools for processing EEG and MEG data. . The forward problem is implemented using the symmetric Boundary Element method [Kybic et al, 2005], providing excellent accuracy, particularly for superficial cortical sources. The source localization procedures implemented in OpenMEEG are based on a distributed source model, with three different types of regularization: the Minimum Norm, and the L2 and L1 norms of the surface gradient of the sources [Adde et al, 2005]. . This package provides command line tools. Package: psychopy Version: 1.63.01.dfsg-1~nd09.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4404 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 Suggests: python-iolabs, python-pyepl Homepage: http://www.psychopy.org Priority: optional Section: science Filename: pool/main/p/psychopy/psychopy_1.63.01.dfsg-1~nd09.10+1_all.deb Size: 2361376 SHA256: 06b14f3decf50da3eca11bb0dcc89dae9541ec6bc0aa8714eb0129d8a0c57c0a SHA1: ac1aab87d3fa11b550193707da46360edb716717 MD5sum: 47499fd14627f5241a571eee03c1594b Description: environment for creating psychology stimuli in Python PsychoPy provides an environment for creating psychology stimuli using Python scripting language. It combines the graphical strengths of OpenGL with easy Python syntax to give psychophysics a free and simple stimulus presentation and control package. . The goal is to provide, for the busy scientist, tools to control timing and windowing and a simple set of pre-packaged stimuli and methods. PsychoPy features - High-level powerful scripting language (Python) - Simple syntax - Use of hardware-accelerated graphics (OpenGL) - Integration with Spectrascan PR650 for easy monitor calibration - Simple routines for staircase and constant stimuli experimental methods as well as curve-fitting and bootstrapping - Simple (or complex) GUIs via wxPython - Easy interfaces to joysticks, mice, sound cards etc. via PyGame - Video playback (MPG, DivX, AVI, QuickTime, etc.) as stimuli Python-Version: 2.5, 2.6 Package: python-biosig Source: biosig4c++ Version: 0.94.2+svn2552-1~pre1~nd09.10+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~nd09.10+1_amd64.deb Size: 337966 SHA256: 0ed460fc103795e5a7688f951dab69c9790bf474ce1c034cf11aec719a57ada2 SHA1: b115acdcfff248c64f416d8cff53d2527f4e1c39 MD5sum: 3b106677e207b26ad424217d572fd1dc Description: Python bindings for BioSig library This package provides Python bindings for BioSig library. Primary goal -- I/O interface to variety of biomedical file formats, including but not limited to SCP-ECG(EN1064), HL7aECG (FDA-XML), GDF, EDF. Package: python-brian Source: brian Version: 1.2.2~svn2229-1~pre1~nd09.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1736 Depends: python (>= 2.6), python-support (>= 0.90.0), python-brian-lib (>= 1.2.2~svn2229-1~pre1~nd09.10+1), python-matplotlib (>= 0.90.1), python-numpy (>= 1.3.0), python-scipy (>= 0.7.0) Recommends: python-sympy Suggests: python-brian-doc, python-nose Homepage: http://www.briansimulator.org/ Priority: extra Section: python Filename: pool/main/b/brian/python-brian_1.2.2~svn2229-1~pre1~nd09.10+1_all.deb Size: 296640 SHA256: d24ac37021a97f807fd1122a4c9694678a87fcf5c58ede0f517dd77a5568ec21 SHA1: c24495cf5a103bd0cb8fe26774fdeff4d149da39 MD5sum: d61a8ba6a43071d0d6efbce8596d58d0 Description: simulator for spiking neural networks Brian is a clock-driven simulator for spiking neural networks. It is designed with an emphasis on flexibility and extensibility, for rapid development and refinement of neural models. Neuron models are specified by sets of user-specified differential equations, threshold conditions and reset conditions (given as strings). The focus is primarily on networks of single compartment neuron models (e.g. leaky integrate-and-fire or Hodgkin-Huxley type neurons). Features include: - a system for specifying quantities with physical dimensions - exact numerical integration for linear differential equations - Euler, Runge-Kutta and exponential Euler integration for nonlinear differential equations - synaptic connections with delays - short-term and long-term plasticity (spike-timing dependent plasticity) - a library of standard model components, including integrate-and-fire equations, synapses and ionic currents - a toolbox for automatically fitting spiking neuron models to electrophysiological recordings Package: python-brian-doc Source: brian Version: 1.2.2~svn2229-1~pre1~nd09.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3984 Depends: libjs-jquery Suggests: python-brian Homepage: http://www.briansimulator.org/ Priority: extra Section: doc Filename: pool/main/b/brian/python-brian-doc_1.2.2~svn2229-1~pre1~nd09.10+1_all.deb Size: 856104 SHA256: 16b4cfc5d35b284e9c375532b6c8c8ca16d35fa20706c6d5a81292fb21428b0c SHA1: e6afc161422675fef33e596786d42177f7d5d57f MD5sum: f49fee2362e04a571c6d3682b8eddca4 Description: simulator for spiking neural networks - documentation Brian is a clock-driven simulator for spiking neural networks. . This package provides user's manual (in HTML and PDF formats), examples and demos. Package: python-brian-lib Source: brian Version: 1.2.2~svn2229-1~pre1~nd09.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 220 Depends: python (<< 2.7), python (>= 2.6), python-support (>= 0.90.0), libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0) Homepage: http://www.briansimulator.org/ Priority: extra Section: python Filename: pool/main/b/brian/python-brian-lib_1.2.2~svn2229-1~pre1~nd09.10+1_amd64.deb Size: 55218 SHA256: 2ecf88c10635389caa9dbc0290d5e1cb779615a889b914f49c16ff6e4dfc1ecb SHA1: 2cca43d1d600dc840b65388c242f212fd9c78eb9 MD5sum: ce2b12a7c29cdb780e936f5f7b54cf54 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-epydoc Source: epydoc Version: 3.0.1-4~karmic.nd1 Architecture: all Maintainer: Kenneth J. Pronovici Installed-Size: 1216 Depends: python (>= 2.1), python-support (>= 0.90.0) Recommends: gs-common, python-tk, python-docutils, texlive-latex-base, texlive-latex-extra, texlive-latex-recommended, texlive-fonts-recommended, graphviz Suggests: epydoc-doc, python-profiler Conflicts: python2.1-epydoc (<< 2.0-2), python2.2-epydoc (<< 2.0-2), python2.3-epydoc (<< 2.0-2) Replaces: python2.1-epydoc (<< 2.0-2), python2.2-epydoc (<< 2.0-2), python2.3-epydoc (<< 2.0-2) Priority: optional Section: python Filename: pool/main/e/epydoc/python-epydoc_3.0.1-4~karmic.nd1_all.deb Size: 267136 SHA256: 13e41553f4f85dfeb727bd64f5d41db7b740a6a2a1f11c9d13daf4e2514773cf SHA1: dbfd01805efa7940210f3c61c5f55abf718a3612 MD5sum: 6d547d3365ce2106f59f7722771fff68 Description: tool for generating Python API documentation Epydoc is a tool for generating API documentation for Python modules based on their docstrings. A lightweight markup language called epytext can be used to format docstrings and to add information about specific fields, such as parameters and instance variables. Epydoc also understands docstrings written in ReStructuredText, Javadoc, and plaintext. . This package contains the epydoc and epydocgui commands, their manpages, and their associated Python modules. Package: python-joblib Source: joblib Version: 0.4.6-1~nd09.10+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~nd09.10+1_all.deb Size: 38594 SHA256: c66bb7e714028c238b54cc1be8c93323bdfa97117ac78747ddd9c6f8829ea439 SHA1: 60a3833b774c0762595ebb03a5409f9ec6e205ed MD5sum: b419b08e4ce961bb35c9950e4a5eb1b2 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-mdp Source: mdp Version: 2.6-1~karmic.nd1 Architecture: all Maintainer: Yaroslav Halchenko Installed-Size: 1556 Depends: python (>= 2.4), python-support (>= 0.90.0), python-numpy Recommends: python-scipy, python-shogun-modular, python-libsvm Suggests: python-pp Enhances: python-mvpa Homepage: http://mdp-toolkit.sourceforge.net/ Priority: optional Section: python Filename: pool/main/m/mdp/python-mdp_2.6-1~karmic.nd1_all.deb Size: 294390 SHA256: a2da2ed423fc3d2066b7edbfea753071bbcfb48d666103f8f57c72ac45b90f30 SHA1: a7274dce2d5f3206fcb94ccef10ff40cc45182bc MD5sum: 52caed843ece81e8a7cb8ce4e6016cc1 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~karmic.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~karmic.nd1) Suggests: python-mvpa Provides: python2.5-mlpy, python2.6-mlpy Homepage: https://mlpy.fbk.eu/ Priority: optional Section: python Filename: pool/main/m/mlpy/python-mlpy_2.2.0~dfsg1-1~karmic.nd1_all.deb Size: 55824 SHA256: ca0d69d554ee8905fd2a75d63b72eabb3d67042c7f885963932a10023a73bd9b SHA1: 7fc5ed77711f394cb2a64c03bd9388c4bb802a8c MD5sum: f6aca6dbe7bbaf5f8262372a476a1d31 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: 2.5, 2.6 Package: python-mlpy-doc Source: mlpy Version: 2.2.0~dfsg1-1~karmic.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1116 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~karmic.nd1_all.deb Size: 459648 SHA256: c925a4b0101ed4932c4be88fe0980143a66feedcc5cb5bbcdf4633863b0e3db9 SHA1: 8510d76b9f02db769439a7a6c6ad4825ce470a98 MD5sum: 12a947c4a44e6b6ea96abbf90642f9b9 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~karmic.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 564 Depends: libc6 (>= 2.3.4), libgsl0ldbl (>= 1.9), python (<< 2.7), python (>= 2.5), python-support (>= 0.90.0), python-numpy Provides: python2.5-mlpy-lib, 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~karmic.nd1_amd64.deb Size: 138296 SHA256: 97b0d282ede07f6ad84153098d6669be7b74e0ac53a1921b846a420fb2753c7e SHA1: 049682ff6929f386ff99b62113397baed0b7cba2 MD5sum: e4576ab1348e3eb490af48100f7d64c2 Description: low-level implementations and bindings for mlpy This is an add-on package for the mlpy providing compiled core functionality. Python-Version: 2.5, 2.6 Package: python-mvpa Source: pymvpa Version: 0.4.5-1~nd09.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4140 Depends: python (>= 2.5), python-support (>= 0.90.0), python2.6, python-numpy, python-mvpa-lib (>= 0.4.5-1~nd09.10+1) Recommends: python-nifti, python-psyco, python-mdp, python-scipy, shogun-python-modular, python-pywt, python-matplotlib, python-reportlab Suggests: fslview, fsl, python-nose, python-lxml, python-openopt, python-rpy, python-mvpa-doc Provides: python2.5-mvpa, python2.6-mvpa Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa/python-mvpa_0.4.5-1~nd09.10+1_all.deb Size: 2156620 SHA256: ce287f46a3406d5df6699c30833794d1765b11de4f34c3c2bb3feebea42a8d59 SHA1: a385fae0eadc60c3b5b44cb2fe30e4b756f3e292 MD5sum: 74780ff538695b1922857d77eaab83ab 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: 2.5, 2.6 Package: python-mvpa-doc Source: pymvpa Version: 0.4.5-1~nd09.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 40984 Depends: libjs-jquery Suggests: python-mvpa Homepage: http://www.pymvpa.org Priority: optional Section: doc Filename: pool/main/p/pymvpa/python-mvpa-doc_0.4.5-1~nd09.10+1_all.deb Size: 9054262 SHA256: a851b9b61cb3f7a0e6ab78b079399a5d1fadf4df4728f29b15e8fde8a255ae80 SHA1: 024867be31de5104ad317c27282a67542ed68274 MD5sum: 39660c22e22808fc2e77594b1b10124d Description: documentation and examples for PyMVPA PyMVPA documentation in various formats (HTML, TXT) including * User manual * Developer guidelines * API documentation * BibTeX references file . Additionally, all example scripts shipped with the PyMVPA sources are included. Package: python-mvpa-lib Source: pymvpa Version: 0.4.5-1~nd09.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 300 Depends: libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), libsvm2, python (<< 2.7), python (>= 2.5), python-support (>= 0.90.0), python-numpy Provides: python2.5-mvpa-lib, python2.6-mvpa-lib Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa/python-mvpa-lib_0.4.5-1~nd09.10+1_amd64.deb Size: 62194 SHA256: de3e2bbbe6330f1d9c6d12113902f16c1522787674e80006b4fff80f635c72e3 SHA1: 346ef6012a3375721713fe1f24251a2792373cd3 MD5sum: e46b2fcbe57146fad65116d471f48885 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: 2.5, 2.6 Package: python-mvpa-snapshot Source: pymvpa-snapshot Version: 0.5.0.dev+783+gde39-1~karmic.nd1 Architecture: all Maintainer: Experimental Psychology Maintainers Installed-Size: 4420 Depends: python (>= 2.4), python-support (>= 0.90.0), python2.6, python-numpy, python-mvpa-snapshot-lib (>= 0.5.0.dev+783+gde39-1~karmic.nd1) 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-scikits-openopt, python-rpy, python-mvpa-doc Conflicts: python-mvpa Provides: python2.5-mvpa-snapshot, python2.6-mvpa-snapshot Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa-snapshot/python-mvpa-snapshot_0.5.0.dev+783+gde39-1~karmic.nd1_all.deb Size: 2215394 SHA256: e984192ba7133d5be41938da8cc04a319aebaf2a4575df4aeca345642b696316 SHA1: 4a236f2879eb5da14c7e2bf147c5afb5effaac23 MD5sum: 5f91883019b4bdb36f3dc1264ddd6e6c 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: 2.5, 2.6 Package: python-mvpa-snapshot-lib Source: pymvpa-snapshot Version: 0.5.0.dev+783+gde39-1~karmic.nd1 Architecture: amd64 Maintainer: Experimental Psychology Maintainers Installed-Size: 296 Depends: libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), libsvm2, python (<< 2.7), python (>= 2.5), python-support (>= 0.90.0), python-numpy Conflicts: python-mvpa-lib Provides: python2.5-mvpa-snapshot-lib, python2.6-mvpa-snapshot-lib Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa-snapshot/python-mvpa-snapshot-lib_0.5.0.dev+783+gde39-1~karmic.nd1_amd64.deb Size: 60530 SHA256: 3e98351f30eb1c3dc3720cff3ecc9fa96f82e704d055ab8067c9ce8ad9036630 SHA1: 04563db4174ce087fb88d1aee144dc4fe2a70018 MD5sum: df7d86274197d593ee7c2f5afe112437 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: 2.5, 2.6 Package: python-networkx Version: 1.1-2~karmic.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~karmic.nd1_all.deb Size: 679700 SHA256: 20006cf6cb411bc1ead33ecd9ae40565e1d028f4831e0d34065d33aa850bb7b8 SHA1: 2f91b854f12ef9e7b6dbe71cc5f05814f9daad7c MD5sum: 1a81a8905ee51f5b2d59b8d587aae3f6 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-snapshot Source: nibabel-snapshot Version: 1.0.0.dev+137+gf1c6-1~karmic.nd1 Architecture: all Maintainer: Michael Hanke Installed-Size: 964 Depends: python (>= 2.5), python-support (>= 0.90.0), python-numpy, libjs-jquery, python-scipy Conflicts: python-nibabel Provides: python2.5-nibabel-snapshot, python2.6-nibabel-snapshot Homepage: http://nipy.sourceforge.net/nibabel Priority: optional Section: python Filename: pool/main/n/nibabel-snapshot/python-nibabel-snapshot_1.0.0.dev+137+gf1c6-1~karmic.nd1_all.deb Size: 469796 SHA256: 806db3453ecc94ec1c0083c32cd29bf5eea6fb3b931e930e8f709c43adb51f80 SHA1: 6c791fad36c9735b47ea8a945ba4e887b8609174 MD5sum: 3387700332b502442b780ad2f36a3600 Description: Python bindings to various neuroimaging data formats Currently supported formats are: . * ANALYZE (including SPM2 and SPM99 variants) * MINC * NIfTI * PAR/REC . This package also provides a commandline tool for conversion of PAR/REC to NIfTI images. Python-Version: 2.5, 2.6 Package: python-nifti Source: pynifti Version: 0.20100607.1-2~karmic.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 1496 Depends: libc6 (>= 2.4), libnifti2, python (<< 2.7), python (>= 2.5), python-support (>= 0.90.0), python2.6, python-numpy, libjs-jquery Provides: python2.5-nifti, python2.6-nifti Homepage: http://niftilib.sourceforge.net/pynifti/ Priority: optional Section: python Filename: pool/main/p/pynifti/python-nifti_0.20100607.1-2~karmic.nd1_amd64.deb Size: 361618 SHA256: b29fbc23a21bce4d7ff0bb77f9ad38a10a5f5ff0b67b5e3efd3ba8bbc7545a62 SHA1: e21820c0dfdd84222cda2ed1a5bfd91c577411f4 MD5sum: 450396611d496a8bcf523a99af8250d2 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: 2.5, 2.6 Package: python-nipy Source: nipy Version: 0.1.2+20110114-1~nd09.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4588 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python2.5, python-scipy, python-numpy (>= 1.2), python-nifti (>> 0.20090302), python-nipy-lib (>= 0.1.2+20110114-1~nd09.10+1) Recommends: python-matplotlib, mayavi2 Suggests: python-mvpa Provides: python2.5-nipy, python2.6-nipy Homepage: http://neuroimaging.scipy.org Priority: extra Section: python Filename: pool/main/n/nipy/python-nipy_0.1.2+20110114-1~nd09.10+1_all.deb Size: 1165282 SHA256: 38a60b2bc61690a5de16576f86e19da30077db4aa3b0099be8b46dadc3ec652d SHA1: 6ae4415b649235371a1e974b80960a75dee19f64 MD5sum: 631b0d135dcd39311528ca53b569b0c2 Description: Analysis of structural and functional neuroimaging data NiPy is a Python-based framework for the analysis of structural and functional neuroimaging data. It currently has a full system for general linear modeling of functional magnetic resonance imaging (fMRI). Python-Version: 2.5, 2.6 Package: python-nipy-doc Source: nipy Version: 0.1.2+20110114-1~nd09.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 11000 Depends: neurodebian-popularity-contest, libjs-jquery Recommends: python-nipy Homepage: http://neuroimaging.scipy.org Priority: extra Section: doc Filename: pool/main/n/nipy/python-nipy-doc_0.1.2+20110114-1~nd09.10+1_all.deb Size: 2753886 SHA256: 26112eb1f6f9efd745d0cc459301be924db46273277e24b635125f7a38b7abff SHA1: 31188e9203155991266fa67950dcf8fe906ceabb MD5sum: 8985a9a320f9a4ef46397129c9db895b Description: documention and examples for NiPy This package contains NiPy documentation in various formats (HTML, TXT) including * User manual * Developer guidelines * API documentation Package: python-nipy-lib Source: nipy Version: 0.1.2+20110114-1~nd09.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 3364 Depends: neurodebian-popularity-contest, libatlas3gf-base | libatlas.so.3gf, libc6 (>= 2.4), liblapack3gf | liblapack.so.3gf | libatlas3gf-base, python (<< 2.7), python (>= 2.5), python-support (>= 0.90.0) Provides: python2.5-nipy-lib, python2.6-nipy-lib Homepage: http://neuroimaging.scipy.org Priority: extra Section: python Filename: pool/main/n/nipy/python-nipy-lib_0.1.2+20110114-1~nd09.10+1_amd64.deb Size: 1233468 SHA256: 7ddc06e9987b8f120b13f8a6e90e1dbe9fe28bf729f00a4cfa59997f6af4dd8d SHA1: 8622414a230a8952c69aae5b00b01dddf530e29d MD5sum: b0287681034f39fbadedd866ee9f1937 Description: Analysis of structural and functional neuroimaging data NiPy is a Python-based framework for the analysis of structural and functional neuroimaging data. It currently has a full system for general linear modeling of functional magnetic resonance imaging (fMRI). . This package provides architecture-dependent builds of the libraries. Python-Version: 2.5, 2.6 Package: python-nipype Source: nipype Version: 0.3.3-1~karmic.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: python2.5-nipype, python2.6-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: python Filename: pool/main/n/nipype/python-nipype_0.3.3-1~karmic.nd1_all.deb Size: 277562 SHA256: 22693d2484e104e0a30f54175331b7539c23edf3d6a2e8af330f78612524cdd2 SHA1: 32f7f24bf1b5da14275f2d08d3f8f4182eeca46c MD5sum: c3a23a5fdcc7efdfe9b430f756451043 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~karmic.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~karmic.nd1_all.deb Size: 840494 SHA256: c9a78de04fb849e111493d90273e1e84eace1be20c7d8fdc80e26450631fa598 SHA1: 89eae0d223fda3a96d2c76928fa3e4c332d64d72 MD5sum: ed4627d885cc60de192b880b67532b87 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-openmeeg Source: openmeeg Version: 2.0.0.dfsg-2~karmic.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~karmic.nd1_amd64.deb Size: 160818 SHA256: 0af284e745b3c12574a251aeb04f86bc1dc15ae49e77276c9f27a72769766611 SHA1: f47411bd7665ad94a020e580e18ac30720adf01c MD5sum: 49e7fdec481ca98597dbe142e80a32c8 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~karmic.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~karmic.nd1_all.deb Size: 49168 SHA256: c07ee2a79603285af575a7b545af9533cf2d8d163fbcef1f4cf88b5da4fe3bd0 SHA1: 28c272b539a31bc972cb8578abc6f620805e9eb3 MD5sum: 0162a7035c3faf8794a41a447acd6bf9 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~karmic.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 2440 Depends: python (<< 2.7), python (>= 2.5), python-central (>= 0.6.11), python-pyepl-common (= 1.1.0-3~karmic.nd1), python-numpy, python-imaging, python-pygame, python-pyode, python-opengl, ttf-dejavu, libasound2 (>> 1.0.18), libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libode1, libsamplerate0, libsndfile1, libstdc++6 (>= 4.4.0) Conflicts: python2.3-pyepl, python2.4-pyepl Replaces: python2.3-pyepl, python2.4-pyepl Provides: python2.5-pyepl, python2.6-pyepl Homepage: http://pyepl.sourceforge.net/ Priority: optional Section: python Filename: pool/main/p/pyepl/python-pyepl_1.1.0-3~karmic.nd1_amd64.deb Size: 605798 SHA256: 0f29a6a9d2e3535a26cc6ad89a28ec6b2fd4608390f411fedcbe9c3365d45cda SHA1: 2618144b0350d6161a625718cd3687b29f63d2a5 MD5sum: edb24c6a2096bcb415d062a747be8845 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: 2.5, 2.6 Package: python-pyepl-common Source: pyepl Version: 1.1.0-3~karmic.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~karmic.nd1_all.deb Size: 817826 SHA256: af092b3f9d0c2d780438753bf914eb30ff4fe60993098f6187b82821040e1526 SHA1: 982c801e2e3bcfee1084c3289535f08343814ec5 MD5sum: 2ddece677353b6d2194715660c1a7120 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~nd09.10+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: python2.5-pyglet, python2.6-pyglet Homepage: http://www.pyglet.org Priority: optional Section: python Filename: pool/main/p/pyglet/python-pyglet_1.1.4.dfsg-1~nd09.10+1_all.deb Size: 972186 SHA256: 05ffe8699bc8e69fb00ae20a01c8c269a18030fab58dab84fd7a895e36b67681 SHA1: 1dc3bd704d69a0bc64079c4d2c3de667fc4d1373 MD5sum: 71db49074636da298f2b6ee5c93a60af 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~karmic.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~karmic.nd1_all.deb Size: 6932 SHA256: f9f2c9a7770e3c0e6d0e2dfa1ddc06beb3f5ad09be3fdbfd09a3eef5a70a7c04 SHA1: 91ed70d1b6c3e7d24ae43314e04318afcfd6643b MD5sum: c1f6b1fadc5bdf8b71eea01444c41234 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: 2.5, 2.6 Package: python-scikits-learn Source: scikit-learn Version: 0.4-2~karmic.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 524 Depends: python (<< 2.7), python (>= 2.5), python-support (>= 0.90.0), python-numpy, python-scipy, python-scikits-learn-lib (>= 0.4-2~karmic.nd1) Recommends: python-nose, python-psyco, python-matplotlib Suggests: python-dap, python-scikits-optimization, python-scikits-learn-doc Provides: python2.5-scikits-learn, python2.6-scikits-learn Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python-scikits-learn_0.4-2~karmic.nd1_all.deb Size: 123920 SHA256: 9d074285209ef1aef28cd7f642fc7a6022464932643a6b9dc98bfe8bbefe2ac7 SHA1: 158497addcb00e4b7604a4e2962701a49ac5cc9b MD5sum: c86e83355d91b1c2ec031c82f0dc0af5 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: 2.5, 2.6 Package: python-scikits-learn-doc Source: scikit-learn Version: 0.4-2~karmic.nd1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1132 Depends: libjs-jquery Suggests: python-scikits-learn Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: doc Filename: pool/main/s/scikit-learn/python-scikits-learn-doc_0.4-2~karmic.nd1_all.deb Size: 107826 SHA256: 6cb2bc178ef4282a6737bf6bc438ffd164d0042e33389fb1afc1fc9620d7d32c SHA1: 48b4d1d721aa1f5f342a47a24062755690ab1cb3 MD5sum: 8c5b114a770ecb97ffc902f8fe2b60f2 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: 0.4-2~karmic.nd1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 1040 Depends: libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.2.1), libsvm2, python (<< 2.7), python (>= 2.5), python-support (>= 0.90.0), python-numpy Provides: python2.5-scikits-learn-lib, 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_0.4-2~karmic.nd1_amd64.deb Size: 370768 SHA256: d3fac2ac929c1ba304d932024f92bc6027076a539384dd6d5bd86569a99dbed3 SHA1: ac5311e10e3439ec09aaa3d0b29cc3a96f8aa4e5 MD5sum: 969b679953069dda500e431f0ca44ebb 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: 2.5, 2.6 Package: python-scikits-statsmodels Source: statsmodels Version: 0.2.0+bzr1990-1~karmic.nd1 Architecture: all Maintainer: Experimental Psychology Maintainers Installed-Size: 9660 Depends: python (<< 2.7), python (>= 2.5), python-support (>= 0.90.0), python-numpy, python-scipy Recommends: python-matplotlib, python-nose, python-rpy Provides: python2.5-scikits-statsmodels, 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~karmic.nd1_all.deb Size: 1874616 SHA256: e32c4dee0c8a85900b5fdc393da58f91a05734bcdd23094f026dbe69471a100d SHA1: 770124df665eec0d3a1a5d463321a0f40eaaec74 MD5sum: 7aca46b5889f83bf61f9520cab6c987b 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: 2.5, 2.6 Package: python-scikits-statsmodels-doc Source: statsmodels Version: 0.2.0+bzr1990-1~karmic.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~karmic.nd1_all.deb Size: 307420 SHA256: 55c4001d153835c75a39f780885be99133f19fa77658f35d9129b1943c2f3329 SHA1: c37233a396c7414a315d9f0485ce5bd8ea4c21c1 MD5sum: 5de75b1ff06ab92dcf4a5da3f01909ad 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~nd09.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4580 Depends: python (>= 2.4), python-support (>= 0.90.0), python-docutils (>= 0.5), python-pygments (>= 0.8), python-jinja2 (>= 2.2), libjs-jquery Recommends: python (>= 2.6) | python-simplejson, python-imaging Suggests: jsmath Homepage: http://sphinx.pocoo.org/ Priority: optional Section: python Filename: pool/main/s/sphinx/python-sphinx_1.0.1-1~nd09.10+1_all.deb Size: 1233542 SHA256: e0becd6bcd4ed3ab22d58bd8481754c6b2d49b9b8041a5ee2ad1bdcaf2efaa86 SHA1: e178f2430e59bbb5bb504614ac2505580516cc8c MD5sum: 80482ad42d9c992274e5d882115d900e 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-sympy Source: sympy Version: 0.6.7-1.1~nd09.10+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~nd09.10+1_all.deb Size: 1696254 SHA256: e0c154de2f019fe2dd8030c2c6a20770e4f635aa82c468e024e8d2c3443b904d SHA1: 14dac42d65b3b01d57256425263a9b0067b15053 MD5sum: dd0528f3afca28d783d728a9175033d3 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: spm8-common Source: spm8 Version: 8.4010~dfsg.1-4~nd09.10+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~nd09.10+1_all.deb Size: 10087692 SHA256: 8063fa9e6267199ad818fcdc475dc4c9a39894aff28b049b6d47e48d7888197b SHA1: 155e5b6863babc181ff94542e65f7eb6bbd00692 MD5sum: 232c74af292bc8f92b10824f6a31a232 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~nd09.10+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~nd09.10+1_all.deb Size: 52168456 SHA256: 5bb1b364712fbdebefc0e8c5e8e62001756bfaf80a2b445fd882e4e921ea001a SHA1: 402c19cc1d03f6af1e3d75d2129c3c38ef2054a7 MD5sum: 7a8abd93784c25070698f9cc54bee88c 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~nd09.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 11732 Depends: neurodebian-popularity-contest Priority: extra Section: doc Filename: pool/main/s/spm8/spm8-doc_8.4010~dfsg.1-4~nd09.10+1_all.deb Size: 10842938 SHA256: f6ae25f6c4baf785112c2928a8761a87f65318408bd51c803f81150aa062d38d SHA1: f169494a8d6c26778305001dddc2b5071ee1df1a MD5sum: d0e4f55242154983899a6ca0afed798f 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: 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~svn1222-1~nd09.10+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 10000 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), libfontconfig1 (>= 2.4.0), libfreetype6 (>= 2.2.1), libgcc1 (>= 1:4.1.1), libgsl0ldbl (>= 1.9), libpng12-0 (>= 1.2.13-4), libqt4-network (>= 4.5.1), libqt4-qt3support (>= 4.5.1), libqtcore4 (>= 4.5.1), libqtgui4 (>= 4.5.1), 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~svn1222-1~nd09.10+1_amd64.deb Size: 3639032 SHA256: c34471016a5dc2031042b7a40526cecbe4a6aa239242e77b56e075111589f0c7 SHA1: 5523d4ad1138a723035d6c4a88feaa93c1660779 MD5sum: 7bb00e93f59d6c347ca51a0a3a1a5771 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.