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: biosig-tools Source: biosig4c++ Version: 0.94.1+svn2521-1~pre0~karmic.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 640 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_0.94.1+svn2521-1~pre0~karmic.nd1_i386.deb Size: 246426 SHA256: a7bf300a1961bab5dec03eb71c6614f74f2eb8c0ae39f1c772f3b2b961b57d1a SHA1: 3e13c010f6f35e41e6597d51379d34ccf4d50f96 MD5sum: 12a70648131974124b34868001648e1f Description: format conversion tools for biomedical data formats Based on libbiosig4c++ 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.1-1~karmic.nd1 Architecture: i386 Maintainer: Michael Hanke Installed-Size: 18200 Depends: 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.1-1~karmic.nd1_i386.deb Size: 7062074 SHA256: d483e457a19befb6af6dd2d3610e32776bcbfe7ea7ee4c168dc9cfccaad42e9a SHA1: 86e58b728df85253471ce7f067b4a24532cf8eaa MD5sum: 7cfa08cdae56200f4314d6d3455f9ab4 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: cython Version: 0.13-1~nd09.10+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 4588 Depends: python (<< 2.7), python (>= 2.5), python-support (>= 0.90.0), python2.6, libc6 (>= 2.3.6-6~) Suggests: gcc Homepage: http://cython.org/ Priority: optional Section: python Filename: pool/main/c/cython/cython_0.13-1~nd09.10+1_i386.deb Size: 1111086 SHA256: f27157e33a152428b6f50dc43c2accb12b37e778e42d2d964881ebbddf45be9a SHA1: 639408aaf73a7298b9f68f00cfaa9f43ff7295b8 MD5sum: 3fbefd799515980a1cb1e243f2b1555c 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 7628 Depends: python (<< 2.7), python (>= 2.5), python-support (>= 0.90.0), libc6 (>= 2.3.6-6~), 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_i386.deb Size: 2892452 SHA256: c993c8bd1c493658fa9ef6074d71e10ea55ebe79d9b8f7ff799b30c54dddba54 SHA1: 34658d55a032778abb73fd3d45ac9614f7343af7 MD5sum: e063489f961af02a493eb213a68e2d62 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: i386 Maintainer: NeuroDebian Team Installed-Size: 488 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_i386.deb Size: 150834 SHA256: 980c4c3ace720ddccd755912bea1c077a78e96beebc75c3a9acaaf036c1810f4 SHA1: 9de8acbea943c1986f4b698c81b773f679e3b157 MD5sum: 090dd33f5be13d1d7b9e77f13e58aaed 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: i386 Maintainer: NeuroDebian Team Installed-Size: 3864 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_i386.deb Size: 1498060 SHA256: a21d1edb7d8219bc2264b6dd291562b181e0aef4e7b011bd5d3c044f2e253c73 SHA1: aceab9fb1490c40c052c3ee06725d20638018abe MD5sum: d4bc2972997cd57c880b480cb921d75e 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: i386 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_i386.deb Size: 28738 SHA256: bf0291f5696262d0460e052dd26123a473ffd339193c8f091c81aeb70797af34 SHA1: fb397865b70521be68301a3ec185072d8b5c9c89 MD5sum: ca56c7255d45b25cf6342b4cadd76f97 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: i386 Maintainer: Debian GIS Project Installed-Size: 1176 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_i386.deb Size: 365984 SHA256: a49e7b254cfb26c032ea50a5cc29873328c8b10b5f8a21a62b7d443b0ac1ec39 SHA1: 176111eae5e2bdf25f6fbfa11284749f8b9b0635 MD5sum: f4b9465a055970c2e630039101d753a3 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: i386 Maintainer: NeuroDebian Team Installed-Size: 8124 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_i386.deb Size: 3596096 SHA256: e37f08ddc796f2f0ed4dec133c10324b8688037fac662c10e2ee61aa353e2019 SHA1: 73469d7027b400bc092b59f5f6e3d1e97219da53 MD5sum: 9a44fae0e2f6cb76c5112a0b9ee22cbb 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: 0.94.1+svn2521-1~pre0~karmic.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 1232 Homepage: http://biosig.sf.net/ Priority: extra Section: libdevel Filename: pool/main/b/biosig4c++/libbiosig-dev_0.94.1+svn2521-1~pre0~karmic.nd1_i386.deb Size: 363182 SHA256: 9b2064f9070294be0df20be045f1cdedbcdfada33f5da931efb2c91fea78c6ba SHA1: 959345d6d4054fa4e42fbb301321cebd4f37ac00 MD5sum: c3453625a5f73872a9d2753d15475d51 Description: library for accessing files in biomedical data formats 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: 0.94.1+svn2521-1~pre0~karmic.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 772 Depends: libc6 (>= 2.7), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.2.1) Homepage: http://biosig.sf.net/ Priority: extra Section: libs Filename: pool/main/b/biosig4c++/libbiosig0_0.94.1+svn2521-1~pre0~karmic.nd1_i386.deb Size: 286848 SHA256: 8324e4054796a5074b73e06f9eb846a8f283633c72e3dce05c2599a2780ce059 SHA1: 2c2c34d3a353237a1cd0cd7aa3d59345ffa81d97 MD5sum: 51fad8369d2147a4dd15e4f5ebe69a12 Description: library for accessing files in biomedical data formats 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: libgiftiio-dev Source: gifticlib Version: 1.0.9-1~karmic.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 208 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_i386.deb Size: 62668 SHA256: b9c3952920cd7d058b4162abcf98fdf45fcc7ef596c31e01ece99e2eba513a24 SHA1: 3949868f921aeb6a8bbdd5949e2d739110446928 MD5sum: c37ce3cdbe126b2a9001002d296a1e04 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: i386 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_i386.deb Size: 57444 SHA256: ab60e9a9c8a05161152ee1b2d16dc446307c508627c66dd86aaed3e819e7c313 SHA1: 895b9edf40378f0e8d48859a9830f5921227bc9f MD5sum: 6ea63a23ce1ca074ff7c80ccbc5f48f0 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: i386 Maintainer: Debian GIS Project Installed-Size: 4612 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_i386.deb Size: 1035750 SHA256: 298d1601dbded6d02c9ac7b746456939fd09682d7eedae85bd3b73cee66abff6 SHA1: c728606df0667754ebd61bc7b7f5ba4b151ec0e8 MD5sum: de64c532d983f4759ef300e90737925c 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: i386 Maintainer: Debian GIS Project Installed-Size: 6552 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_i386.deb Size: 1483270 SHA256: 506d47ea334b316a3ef1a33ac2ec7ce510a2ae7faa65157aa853da2361e27f42 SHA1: f97dff298fddbacefaca88c1ddd7a3e8d55051b6 MD5sum: 60e80491074842d14fe92cba73263000 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: i386 Maintainer: Debian GIS Project Installed-Size: 5416 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_i386.deb Size: 1343432 SHA256: c3fb1efafad5697348cbf04428bc5eb48ce0533d33085b68bac4c792e4145b83 SHA1: 5eaa302e89ab557612de26a10467cad72aa0ebbb MD5sum: 796b90f180df70f8799dfa66ea40e9f4 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: i386 Maintainer: Debian GIS Project Installed-Size: 14648 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_i386.deb Size: 2157508 SHA256: 4d574b01ae58daf29ca766514490b613bb3235f9ce11e272e511d609bca624ea SHA1: 51c8496ce3d7694ef5212835ddcedea778baac8a MD5sum: 4851c3f046077e419e72861ec00c1ab1 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: i386 Maintainer: Debian GIS Project Installed-Size: 4876 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_i386.deb Size: 1110706 SHA256: 3ab2c0e4f40ff0f77ac209d76a2635da9935235fc2bd2d672fb5a56fd9a7aaf6 SHA1: 087850d11d1feb1a4f1683117880c726904270a4 MD5sum: 63aad1b646b9552c83d71c580289d4f6 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: i386 Maintainer: Debian GIS Project Installed-Size: 14648 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_i386.deb Size: 2158316 SHA256: 4a016f4d43bc2ad3bc69583dee19b03782a2c6ba871acc52ae3096b9d782748f SHA1: ff3881934a8f44fe8099bf42e54706723173b9d9 MD5sum: 00fdd47988731ffb5054337cae7e155f 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: i386 Maintainer: Debian GIS Project Installed-Size: 5176 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_i386.deb Size: 1212626 SHA256: 007ec637afa2072994c906212b5adedc4577b76d41082b73a563ac7bb0ed7614 SHA1: 34e075852b6d5f007c533fe3afac32bcd2233391 MD5sum: 40edaacb1df66afca414d4e65b4b673f 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: i386 Maintainer: Debian GIS Project Installed-Size: 14972 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_i386.deb Size: 2261026 SHA256: 3697ca2cdf7ba7b4577e4fddc946c6012cf9d636fce53297081eeb879e6693d8 SHA1: 8d45d0a14b8e0a5a9eadc35e73cb7c89eccd1c78 MD5sum: cf6faa39f1fb36969439a3f9ed602f09 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: i386 Maintainer: NeuroDebian Team Installed-Size: 464 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_i386.deb Size: 151636 SHA256: 189a9fcf6801263b46fa37264eff33da4f90cbb20a56fa695e3bd38bb817e570 SHA1: 1438cec40dc92e4dd875621fb4d15a6f4bae3bbc MD5sum: 5e9fc441119710428886e7fb4fb604a7 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: i386 Maintainer: NeuroDebian Team Installed-Size: 304 Depends: libc6 (>= 2.7), zlib1g (>= 1:1.1.4) Homepage: http://niftilib.sourceforge.net Priority: optional Section: libs Filename: pool/main/n/nifticlib/libnifti2_2.0.0-1~karmic.nd1_i386.deb Size: 107460 SHA256: 50294fb966b0f8ffd999ca381ebd893fe84333bf8fedd0b2abb9a204178feca6 SHA1: c88a8fe2ebe028e70726ada2623a003cce599e37 MD5sum: d7aa1664676d5c04c011b366910a3b0a 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: i386 Maintainer: NeuroDebian Team Installed-Size: 276 Homepage: http://www-sop.inria.fr/odyssee/software/OpenMEEG/ Priority: extra Section: libdevel Filename: pool/main/o/openmeeg/libopenmeeg-dev_2.0.0.dfsg-2~karmic.nd1_i386.deb Size: 43858 SHA256: 667349b6764e5bc806ad67123020a8a2f70d0bd502e9ed15a34f047d89d7b599 SHA1: 8fd768c0da68a90da089c1b7dc2e582abc5fe263 MD5sum: af608f79cc306d8836bbbe78692de276 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: i386 Maintainer: NeuroDebian Team Installed-Size: 828 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_i386.deb Size: 232422 SHA256: 4c40211e577b85c607a35c933f919abd8913bb8d63e9c58bee6a5ca9b1452598 SHA1: f6662020ea4c741445ce6658a047a1773622ec1a MD5sum: b159bd0dd433b5916c62439293e63517 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: mrtrix Version: 0.2.8-1~karmic.nd1 Architecture: i386 Maintainer: Michael Hanke Installed-Size: 6588 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_i386.deb Size: 2257994 SHA256: bb2ff411c71a871dd8f97763affcda314c2f2933fc438414e52b4c97dcb21a05 SHA1: 3bcbe900aa4d691affd9138eaca8ccf7ddcc1037 MD5sum: 7e0be6d1162d0200e6b7f5dc5300d606 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: nifti-bin Source: nifticlib Version: 2.0.0-1~karmic.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 200 Depends: libc6 (>= 2.7), libnifti2 Homepage: http://niftilib.sourceforge.net Priority: optional Section: utils Filename: pool/main/n/nifticlib/nifti-bin_2.0.0-1~karmic.nd1_i386.deb Size: 59280 SHA256: 38ec93cf9d37196ee1227b302e7283558c731c105172b33304022d11a8cdb444 SHA1: e5b4d0e03ab788b6d892c2039e1cbb6ee337dcb4 MD5sum: 1ab9b505c624d37856cca2269b12e4c8 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: 0.94.1+svn2521-1~pre0~karmic.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 1424 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_0.94.1+svn2521-1~pre0~karmic.nd1_i386.deb Size: 552188 SHA256: 888b7ef5268d9bbcebf1717460be647e04976c18548b5e4c608d9be6083d3654 SHA1: a9e86405989a18352c4779acae879cd43fa70174 MD5sum: edac7ace22170160c9a86f91db8d4c41 Description: Octave bindings for BioSig4C++ 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, EDlibbiosig4c++. Package: openmeeg-tools Source: openmeeg Version: 2.0.0.dfsg-2~karmic.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 560 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_i386.deb Size: 153952 SHA256: 1f73ffcebe029d9e908781ad073af14ec462a5873f224ca4b5f25ef71fd0cd28 SHA1: c24408da1e46fea57fac47054ed10cb8f6b65db6 MD5sum: 9e33be1e654357401ab404726c45359d Description: tools for solving EEG and MEG forward and inverse problems OpenMEEG provides state-of-the art tools for processing EEG and MEG data. . The forward problem is implemented using the symmetric Boundary Element method [Kybic et al, 2005], providing excellent accuracy, particularly for superficial cortical sources. The source localization procedures implemented in OpenMEEG are based on a distributed source model, with three different types of regularization: the Minimum Norm, and the L2 and L1 norms of the surface gradient of the sources [Adde et al, 2005]. . This package provides command line tools. Package: psychopy Version: 1.62.02.dfsg-1~nd09.10+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3108 Depends: python (>= 2.4), python-support (>= 0.90.0), python-pyglet | python-pygame, python-opengl, python-numpy, python-matplotlib, python-lxml, python-configobj Recommends: python-wxgtk2.8, python-pyglet, python-pygame, python-openpyxl, python-imaging, python-serial, python-scipy, libavbin0 Suggests: python-iolabs, python-pyepl Homepage: http://www.psychopy.org Priority: optional Section: science Filename: pool/main/p/psychopy/psychopy_1.62.02.dfsg-1~nd09.10+1_all.deb Size: 1397592 SHA256: 843e2b22a4f5f651b3b2566a3c47fef8eef33bafa44c601591c1767ea5a9aeab SHA1: e62b4ab55b05c38397f946239af80540d03f407f MD5sum: a01b201a1c53483323a54b66328c7bb0 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: 0.94.1+svn2521-1~pre0~karmic.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 900 Depends: libc6 (>= 2.7), libcholmod1.7.1 (>= 1:3.4.0), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.2.1), zlib1g (>= 1:1.1.4) Homepage: http://biosig.sf.net/ Priority: extra Section: python Filename: pool/main/b/biosig4c++/python-biosig_0.94.1+svn2521-1~pre0~karmic.nd1_i386.deb Size: 319228 SHA256: a5ae432bb91621e27bfa6829774b20b02eaea6be6643b47b8991544682b0b3f7 SHA1: ffe8ae1a76349c4419363e009c1895bd2b43a788 MD5sum: 45324f148756233418d1612573a2472f Description: Python bindings for BioSig4C++ 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, EDlibbiosig4c++. 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: 0.4.5-1~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_0.4.5-1~nd09.10+1_all.deb Size: 37890 SHA256: 2d22c0d20f8c28622b3dc7c8a8736799fff4286dd420751c55a37c4b92f8840f SHA1: 13a43635cb734ee5df48013a41d78c802e57137c MD5sum: 51f63d0f3662b0c88d5ebf77ed7d757f 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: i386 Maintainer: NeuroDebian Team Installed-Size: 524 Depends: libc6 (>= 2.3.6-6~), 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_i386.deb Size: 120316 SHA256: 3d21677896139b66c5d1aad7478ecd922a256f089a67d86fa034029c42ba94cb SHA1: c35aad187f7a7f1fab43d2cbe2f18cad70a4d15b MD5sum: 9be15ff94f16e2c9583426642cf2b925 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 284 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_i386.deb Size: 60222 SHA256: 913dafac6101ad527287ef691e87b84dcdfcb660b14d5a537f0444e912792c6b SHA1: c5cccbc4e2c057401b5c1390d1466bf015d95b40 MD5sum: 2f4604587c2f6e685a88032ffb84f37d 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: i386 Maintainer: Experimental Psychology Maintainers Installed-Size: 280 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_i386.deb Size: 58480 SHA256: 344086c4bf8c66253e4a122ccd28199c3079908b532c4a74a682a2bcde4257ee SHA1: c382fca743f4ce7fe84ca77e2b9a33fbe968baf2 MD5sum: e0eab9eaed32d6a5fb0948064b7d176c 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: i386 Maintainer: NeuroDebian Team Installed-Size: 1380 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_i386.deb Size: 340212 SHA256: 25b2fce94ecfb4df5bb7e3cf4051c3055e9b04c3ebd8d420fedd3a6a347877be SHA1: 662449351efd34d45e10e7d5e183d5b12c6084c3 MD5sum: 189489b038ef324dd1666621b3c0b703 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-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: i386 Maintainer: NeuroDebian Team Installed-Size: 560 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_i386.deb Size: 151860 SHA256: af1818d6cefb5cd4f59426b6020801e8380db6fc53377163b650caa57f8109aa SHA1: 71d4a6e050881a401644fbaa0bc2c30830c2c485 MD5sum: d69cb3052702b89596f628885bae7744 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: i386 Maintainer: NeuroDebian Team Installed-Size: 2212 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_i386.deb Size: 539592 SHA256: c4b71b149f040f995f856c02b64c99c48d4db525f408b7d90853d043caadee9e SHA1: dbad6c80a9fdcc6b4e9f52eb6632d1f8909af97b MD5sum: 8b103c6a78e93135b2d83efbc1b32880 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-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: i386 Maintainer: NeuroDebian Team Installed-Size: 908 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_i386.deb Size: 309374 SHA256: e3efd1c19c97ddd21df1542dd31f928865a238f03a8c1dc92712d8a9e92730b7 SHA1: a5412669ff1be44f33279b2b6b96ca392f39568c MD5sum: 15dd2489a7fdb81cfaf900dbfa4e6fc7 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: svgtune Version: 0.1.0-1~karmic.nd1 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_0.1.0-1~karmic.nd1_all.deb Size: 6764 SHA256: 747da3956aba0860a3d06f351522bcf32c5cda19f172412999856833011cd561 SHA1: 5402f3e3a744f48b89a44a7d270ffa5d6c06d860 MD5sum: 5aab9bdf7440c8b6656831fae69784db 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~svn1172-1~karmic.nd1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 9280 Depends: libc6 (>= 2.7), libfontconfig1 (>= 2.4.0), 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 Homepage: http://www.voxbo.org Priority: extra Section: science Filename: pool/main/v/voxbo/voxbo_1.8.5~svn1172-1~karmic.nd1_i386.deb Size: 3493294 SHA256: 8237e234ed437fe3e80a7de2685831e497fdb2dd85cf4e09f4cf169d4b989274 SHA1: 527aaf5a3a820d265ac47eafdc6fa1a9ad012d12 MD5sum: 71f6cf3540a415b091d71696cdebecf7 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.