Package: ants Version: 1.9.2+svn680.dfsg-2~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 38568 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libinsighttoolkit3.18, libstdc++6 (>= 4.5) Suggests: fsl, gridengine-client Homepage: http://www.picsl.upenn.edu/ANTS/ Priority: extra Section: science Filename: pool/main/a/ants/ants_1.9.2+svn680.dfsg-2~nd11.04+1_amd64.deb Size: 11576724 SHA256: 6f72962ad451eb63e52681193f9e77438780e5e8fa49b342fb0001189f942622 SHA1: 42276d75c445a6f31d1be759c663b6bb37e09b3d MD5sum: 07b081bf54b787477f91b7757b539137 Description: advanced normalization tools for brain and image analysis Advanced Normalization Tools (ANTS) is an ITK-based suite of normalization, segmentation and template-building tools for quantitative morphometric analysis. Many of the ANTS registration tools are diffeomorphic, but deformation (elastic and BSpline) transformations are available. Unique components of ANTS include multivariate similarity metrics, landmark guidance, the ability to use label images to guide the mapping and both greedy and space-time optimal implementations of diffeomorphisms. The symmetric normalization (SyN) strategy is a part of the ANTS toolkit as is directly manipulated free form deformation (DMFFD). Package: biosig-tools Source: biosig4c++ Version: 0.96.3+svn2677-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 48 Depends: neurodebian-popularity-contest, libbiosig0, libc6 (>= 2.4), libcholmod1.7.1 (>= 1:3.4.0), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), zlib1g (>= 1:1.1.4) Homepage: http://biosig.sf.net/ Priority: extra Section: science Filename: pool/main/b/biosig4c++/biosig-tools_0.96.3+svn2677-1~nd11.04+1_amd64.deb Size: 13820 SHA256: 0ff2c17b42a59193774b5a476563ba374fdc2a8ed50d95414e2bf0fa91b6fe72 SHA1: f3a7dc754a6923831a971bc8808171c6f40b736a MD5sum: 7be85a6a2c3b4133c5ea28792023987b 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. Package: caret Version: 5.6.4~dfsg.1-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 18852 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, libgomp1 (>= 4.2.1), libminc2-1, libqt4-network (>= 4:4.5.3), libqt4-opengl (>= 4:4.5.3), libqt4-xml (>= 4:4.5.3), libqtcore4 (>= 4:4.7.0~beta1), libqtgui4 (>= 4:4.5.3), libqwt5-qt4, libstdc++6 (>= 4.5), libvtk5.4, zlib1g (>= 1:1.2.3.3.dfsg) Recommends: qt-assistant-compat Suggests: caret-data (>= 5.6.2~dfsg.1~) Homepage: http://brainvis.wustl.edu/wiki/index.php/Caret:About Priority: optional Section: science Filename: pool/main/c/caret/caret_5.6.4~dfsg.1-1~nd11.04+1_amd64.deb Size: 7431450 SHA256: f112da9fe9e64b7737f5f72d79501afeb9e3e2377e91511a49bbeb07abd7388e SHA1: d876a9279265cccae950f5fb131f8f83972d673e MD5sum: ad15e79a85d3d6f24086b5ea920e65be 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. . Some functionality of Caret is only available when additional data files, provided by the caret-data package, are available. This includes: . - Map volumes to surface via PALS atlas - Multi-resolution morphing - Projection of foci via PALS atlas - Surface-based registration - Surface flattening . Currently the caret-data package is only available from the NeuroDebian repository. Please see http://neuro.debian.net for more imformation. Package: cgroup-bin Source: libcgroup Version: 0.37.1-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 288 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), libcgroup1 Homepage: http://sourceforge.net/projects/libcg/ Priority: extra Section: admin Filename: pool/main/libc/libcgroup/cgroup-bin_0.37.1-1~nd11.04+1_amd64.deb Size: 66206 SHA256: 7a3e45925089bff4c8796d67f19f3934266187039176b886336293d201e6bf99 SHA1: bb17b41cc4a6e4f301f42dc91bd92cac87ce92ce MD5sum: cbe8b915eb52f78adc2c5d90ac8f11c6 Description: Tools to control and monitor control groups Control Groups provide a mechanism for aggregating/partitioning sets of tasks, and all their future children, into hierarchical groups with specialized behaviour. . These tools help manipulate, control, administrate and monitor control groups and the associated controllers. Package: connectomeviewer Version: 2.0.0-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1884 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-cfflib (>= 2.0.5), python-networkx (>= 1.4), python-nibabel, python-numpy (>= 1.3.0), python-scipy, python-chaco, mayavi2, ipython Recommends: python-nipype, python-dipy, python-matplotlib, python-qscintilla2 Suggests: nipy-suite Homepage: http://www.connectomeviewer.org Priority: extra Section: python Filename: pool/main/c/connectomeviewer/connectomeviewer_2.0.0-1~nd11.04+1_all.deb Size: 1354976 SHA256: 6bf1ba4771e3120929c8f6a2283d07400ebf050ffc763407a99bfed5c535854c SHA1: d1b18a49d580160e87d0929f5146697aedd6a6aa MD5sum: 21910506bcbee95f3964e5ec8c959808 Description: Interactive Analysis and Visualization for MR Connectomics The Connectome Viewer is a extensible, scriptable, pythonic research environment for visualization and (network) analysis in neuroimaging and connectomics. . Employing the Connectome File Format, diverse data types such as networks, surfaces, volumes, tracks and metadata are handled and integrated. The Connectome Viewer is part of the MR Connectome Toolkit. Package: coop-computing-tools Source: cctools Version: 3.4.0-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 3424 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libfuse2 (>= 2.8.1), libglobus-common0, libglobus-gss-assist3, libglobus-gssapi-gsi4, libkrb5-3 (>= 1.6.dfsg.2), libmysqlclient16 (>= 5.1.21-1), libncurses5 (>= 5.5-5~), libopenmpi1.3, libreadline6 (>= 6.0), libstdc++6 (>= 4.1.1), zlib1g (>= 1:1.1.4), python Suggests: coop-computing-tools-doc, condor, gridengine-client Homepage: http://nd.edu/~ccl/software/ Priority: extra Section: utils Filename: pool/main/c/cctools/coop-computing-tools_3.4.0-1~nd11.04+1_amd64.deb Size: 1396862 SHA256: 4c456eb330a72014b70538df981c0ae4bef065f82ddf0228df0ca345f453bf69 SHA1: 01be913e100f6292b4088d173a22c101ccd4ece8 MD5sum: 9443dbb3133fe0f647ef811c8b4d458d Description: cooperative computing tools This is a collection of software that help users to share resources in a complex, heterogeneous, and unreliable computing environment. This includes: . * Chirp: A personal filesystem and I/O protocol that allows unprivileged users to share space securely, efficiently, and conveniently. When combined with Parrot, Chirp allows users to create custom wide-area distributed filesystems. * Parrot: A transparent user-level virtual filesystem that allows any ordinary program to be attached to a remote storage device such as an FTP server or a Chirp server. * Makeflow: A workflow system for parallel and distributed computing that uses a language very similar to Make. * Work Queue: A system and API for building master-worker style programs that scale up to thousands of processors. * All Pairs: A computational abstraction for running very large Cartesian products. * Wavefront: A computational asbtraction for running very large dynamic programming problems. * The Fault Tolerant Shell: A high-level programming language that allows users to combine the ease of shell scripting, the power of distributed programming, and the precision of compiled languages. Basically, parallel programming and exception handling for scripts. Package: coop-computing-tools-dev Source: cctools Version: 3.4.0-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 832 Depends: neurodebian-popularity-contest Homepage: http://nd.edu/~ccl/software/ Priority: extra Section: libs Filename: pool/main/c/cctools/coop-computing-tools-dev_3.4.0-1~nd11.04+1_amd64.deb Size: 208126 SHA256: 2a974a34163a1848196e94f4385093813e9b1291f06d69a9b0f2f3d33ef4decb SHA1: d7805e5ef6013cb6ab6d38733748b75224b7e8f5 MD5sum: bb36fb746df26cb04f9b6e10e980a9c4 Description: libraries and header files for coop-computing-tools These tools are a collection of software that help users to share resources in a complex, heterogeneous, and unreliable computing environment. . This package provides static libraries and header files for development. Package: coop-computing-tools-doc Source: cctools Version: 3.4.0-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2656 Depends: neurodebian-popularity-contest, libjs-jquery Homepage: http://nd.edu/~ccl/software/ Priority: extra Section: doc Filename: pool/main/c/cctools/coop-computing-tools-doc_3.4.0-1~nd11.04+1_all.deb Size: 301190 SHA256: 636e12bd44337e52022356d426bebb9684137ac5882fba34766b21dae2ba3bb2 SHA1: 69a98238c6d1ad5689716a5787944011b570295a MD5sum: a0410a35a81f4dad9227032a43bba8a5 Description: documentation for coop-computing-tools These tools are a collection of software that help users to share resources in a complex, heterogeneous, and unreliable computing environment. . This package provides the documentation (manual and API reference) in HTML format. Package: debruijn Version: 1.5-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 88 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libfftw3-3, libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0) Homepage: http://www.cfn.upenn.edu/aguirre/wiki/public:de_bruijn_software Priority: extra Section: science Filename: pool/main/d/debruijn/debruijn_1.5-1~nd11.04+1_amd64.deb Size: 39246 SHA256: ddb4ec25aae0d941f0eb73466e5014e561f7abaea2c88794b08497622c6d5d2a SHA1: 1c2e8a1aebb663dec4a6c12f0c334590d6e15e94 MD5sum: f3a472d394231dd2f239d48ef3deb2d4 Description: De Bruijn cycle generator Stimulus counter-balance is important for many experimental designs. This command-line software creates De Bruijn cycles, which are pseudo-random sequences with arbitrary levels of counterbalance. "Path-guided" de Bruijn cycles may also be created. These sequences encode a hypothesized neural modulation at specified temporal frequencies, and have enhanced detection power for BOLD fMRI experiments. Package: dicomnifti Version: 2.29.1-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 496 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libnifti2, libstdc++6 (>= 4.5) Homepage: http://cbi.nyu.edu/software/dinifti.php Priority: optional Section: science Filename: pool/main/d/dicomnifti/dicomnifti_2.29.1-1~nd11.04+1_amd64.deb Size: 163276 SHA256: 41864ef15e3f11eade0e175f9c4246884a20d539472851e24b530346465719fc SHA1: 9a51a11ee40cd3e876d2b65644ca9bf93021f6d0 MD5sum: e986dd1ddfc96f019225237e987637e6 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: dmtcp Version: 1.2.1-2~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 3732 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.5) Homepage: http://dmtcp.sourceforge.net Priority: optional Section: utils Filename: pool/main/d/dmtcp/dmtcp_1.2.1-2~nd11.04+1_amd64.deb Size: 1602634 SHA256: a9572d575559b140ac0cb09c1128b90ef56587e937a74ca65a3193814f25925a SHA1: fc87e0a64ec857849ab26dd6ffe064d6b5e6d8b3 MD5sum: fe668a827eb62926f615900f81f91acf Description: Checkpoint/Restart functionality for Linux processes DMTCP (Distributed MultiThreaded Checkpointing) is a tool to transparently checkpointing the state of an arbitrary group of programs including multi-threaded and distributed computations. It operates directly on the user binary executable, with no Linux kernel modules or other kernel mods. . Among the applications supported by DMTCP are OpenMPI, MATLAB, Python, Perl, and many programming languages and shell scripting languages. DMTCP also supports GNU screen sessions, including vim/cscope and emacs. With the use of TightVNC, it can also checkpoint and restart X-Windows applications, as long as they do not use extensions (e.g.: no OpenGL, no video). . This package contains DMTCP binaries. Package: dmtcp-dbg Source: dmtcp Version: 1.2.1-2~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 33040 Depends: neurodebian-popularity-contest, dmtcp Homepage: http://dmtcp.sourceforge.net Priority: extra Section: debug Filename: pool/main/d/dmtcp/dmtcp-dbg_1.2.1-2~nd11.04+1_amd64.deb Size: 8198672 SHA256: 9237cdc9f78d9318943f5e9173153c08f6897b8378836e3f5f5f268e7789796c SHA1: 90a903afa0235d8d1ce462afae219564bcbf2925 MD5sum: 8c8d57673afc6cb1c16ce67a24e62677 Description: Debug package for dmtcp DMTCP (Distributed MultiThreaded Checkpointing) is a tool to transparently checkpointing the state of an arbitrary group of programs including multi-threaded and distributed computations. It operates directly on the user binary executable, with no Linux kernel modules or other kernel mods. . Among the applications supported by DMTCP are OpenMPI, MATLAB, Python, Perl, and many programming languages and shell scripting languages. DMTCP also supports GNU screen sessions, including vim/cscope and emacs. With the use of TightVNC, it can also checkpoint and restart X-Windows applications, as long as they do not use extensions (e.g.: no OpenGL, no video). . This package contains debugging symbols for DMTCP. Package: freenect Source: libfreenect Version: 1:0.0.1+20101211+2-2~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 12 Depends: neurodebian-popularity-contest, libfreenect-demos, libfreenect-dev, libfreenect0.0 Homepage: http://openkinect.org/ Priority: extra Section: libs Filename: pool/main/libf/libfreenect/freenect_0.0.1+20101211+2-2~nd11.04+1_amd64.deb Size: 2536 SHA256: ef8428bce20be7a11bdad1860407c6f0a0ec32d38c2653444aaa01996f5d4d1b SHA1: 588837f36bff3e83c8f3dabb58a10c51933d9c84 MD5sum: 6042927b6887c231c3b6f8ee61512c9d Description: library for accessing Kinect USB camera -- meta package libfreenect is the core library for accessing the Microsoft Kinect USB camera. . This is the meta package to install all components of the project. Package: fslview Version: 3.1.8+4.1.9-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 4004 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libmysqlclient16 (>= 5.1.21-1), libnewmat10ldbl, libnifti2, libqt3-mt (>= 3:3.3.8-b), libqwt4c2, libstdc++6 (>= 4.5), libvtk5.4, libvtk5.4-qt3 Recommends: fslview-doc Suggests: fsl-atlases Conflicts: fsl-fslview Replaces: fsl-fslview Homepage: http://www.fmrib.ox.ac.uk/fsl/fslview Priority: optional Section: science Filename: pool/main/f/fslview/fslview_3.1.8+4.1.9-1~nd11.04+1_amd64.deb Size: 1494414 SHA256: b567b4ca01b7e4b76434ad160a244c3c08b3df0862b374e984d17515b6a116bd SHA1: 6a7790a6bfce93436f3493a19aa8578e747aa8c6 MD5sum: c0afa4f91717fbb1afdcf70dfeaae05e 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+4.1.9-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3124 Depends: neurodebian-popularity-contest, 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+4.1.9-1~nd11.04+1_all.deb Size: 2351300 SHA256: ee299b52a2e4b33885ac8eb912b6269326ce22efe96c0bb916915a4225b32e87 SHA1: 45a21cdafd50282403dc18b4e975721b6bd82a93 MD5sum: 333fd1c27598f73f5d44e5746616a79e Description: Documentation for FSLView This package provides the online documentation for FSLView. . FSLView is part of FSL. Package: gdf-tools Source: libgdf Version: 0.1.1-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 124 Depends: neurodebian-popularity-contest, libboost-filesystem1.42.0 (>= 1.42.0-1), libboost-program-options1.42.0 (>= 1.42.0-1), libboost-system1.42.0 (>= 1.42.0-1), libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libgdf0, libstdc++6 (>= 4.4.0) Homepage: http://sourceforge.net/projects/libgdf Priority: extra Section: utils Filename: pool/main/libg/libgdf/gdf-tools_0.1.1-1~nd11.04+1_amd64.deb Size: 41594 SHA256: 867e6deb6e588548668115d6e3be811e52526968f16f03808e91323b0b94782c SHA1: 98737092d002998651d4600e8e64cf20ac06c3f3 MD5sum: 134f9656178d832de7bf27289fd0e74d Description: IO library for the GDF -- helper tools GDF (General Dataformat for Biosignals) is intended to provide a generic storage for biosignals, such as EEG, ECG, MEG etc. . This package provides the tool shipped with the library (gdf_merger). Package: glew-utils Source: glew Version: 1.6.0-2~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 384 Depends: neurodebian-popularity-contest, libglew1.6 (= 1.6.0-2~nd11.04+1), libc6 (>= 2.4), libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, libx11-6, libxext6, libxi6, libxmu6 Replaces: libglew1.4 (<< 1.5) Homepage: http://glew.sourceforge.net/ Priority: optional Section: utils Filename: pool/main/g/glew/glew-utils_1.6.0-2~nd11.04+1_amd64.deb Size: 114440 SHA256: 8cdbfe081aedd5ef93097f7e89914803a970096810a2b8e21e29f4e86781c8af SHA1: 64683eb81ee70c84ced53704bb7b48b7f3b5352c MD5sum: e860b582f7858a473d9626b2fad70f50 Description: OpenGL Extension Wrangler - utilities For more information about GLEW please refer to the description of the libglew1.6-dev package. . This package contains the utilities which can be used to query the supported openGL extensions. Package: guacamole Version: 0.4.0-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 388 Depends: neurodebian-popularity-contest, guacd (>= 0.4), guacd (<< 0.5) Recommends: libguac-client-vnc0 Suggests: tomcat6 | jetty Homepage: http://guacamole.sourceforge.net/ Priority: extra Section: net Filename: pool/main/g/guacamole/guacamole_0.4.0-1~nd11.04+1_all.deb Size: 209776 SHA256: 6d17f05e022c088af5008699adcd2cfd84f6388d86c1357daa508524343551a2 SHA1: fd23ee53ad8f28eaba7ae19783e43e0e91d1a5a2 MD5sum: f7eeb91bff33d213c443691ef5a00bed Description: HTML5 web application for accessing remote desktops Guacamole is an HTML5 web application that provides access to your desktop using remote desktop protocols. A centralized server acts as a tunnel and proxy, allowing access to multiple desktops through a web browser; no plugins needed. The client requires nothing more than a web browser supporting HTML5 and AJAX. Package: guacamole-tomcat Source: guacamole Version: 0.4.0-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 72 Depends: neurodebian-popularity-contest, debconf, guacamole, tomcat6, libguac-client-vnc0, debconf (>= 0.5) | debconf-2.0 Homepage: http://guacamole.sourceforge.net/ Priority: extra Section: net Filename: pool/main/g/guacamole/guacamole-tomcat_0.4.0-1~nd11.04+1_all.deb Size: 2370 SHA256: 2267caea2362594c0873a25b8c13ae15a3771b03fd02013775abf054b46f72f6 SHA1: eb64e7ae2c8b5b9be514e68a7e5e9d85df1b2c45 MD5sum: 2baa559fad29c3993dc2869961a4e1af Description: Tomcat-based Guacamole install with VNC support This metapackage depends on Tomcat, Guacamole, and the VNC support plugin for guacamole. Guacamole is automatically installed and configured under Tomcat. Package: guacd Version: 0.4.0-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 88 Depends: neurodebian-popularity-contest, lsb-base (>= 3.0-6), libc6 (>= 2.4), libguac1 Homepage: http://guacamole.sourceforge.net/ Priority: extra Section: net Filename: pool/main/g/guacd/guacd_0.4.0-1~nd11.04+1_amd64.deb Size: 8414 SHA256: 96e05a852d7ab22a05162f34565dd9b8bc445a5d8679ae92850f65f3ade11466 SHA1: 4889464de6f5f4aace818ab1a1089cf76742b2a8 MD5sum: 845694c91026d63625a65cd9557e35de Description: Guacamole proxy daemon The Guacamole proxy daemon, guacd, translates between remote desktop protocols (like VNC) and the Guacamole protocol using protocol plugins. Once a user is authenticated with the Guacamole web application, a tunnel is established through the web application to guacd, allowing the JavaScript client to communicate to an arbitrary remote desktop server through guacd. Package: itksnap Version: 2.2.0-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 8656 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libfltk1.1 (>= 1.1.8~rc1), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, libinsighttoolkit3.18, libmysqlclient16 (>= 5.1.21-1), libstdc++6 (>= 4.5), libvtk5.4 Homepage: http://www.itksnap.org Priority: extra Section: science Filename: pool/main/i/itksnap/itksnap_2.2.0-1~nd11.04+1_amd64.deb Size: 3702022 SHA256: 83e35e240a98bf11e9101da23583f64914f377e9a8d90759c2fbaefe0a5e895e SHA1: 4eb46d9711aff7249a1221b43951b5047b954f50 MD5sum: 2a927c1c7af47cd7f13bbd74e8cf8b0c 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: klustakwik Version: 2.0.1-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 80 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1) Homepage: http://sourceforge.net/projects/klustakwik/ Priority: extra Section: science Filename: pool/main/k/klustakwik/klustakwik_2.0.1-1~nd11.04+1_amd64.deb Size: 22946 SHA256: 7b1c678e143a7244aedff53ca5064d8b6113ad4bd6c2c71232272474c96204c7 SHA1: b20487c1e162725ee846d1d9ebf2ebb2e8727e06 MD5sum: 83b91d26ee9a956103127078090401a0 Description: automatic sorting of the samples (spikes) into clusters KlustaKwik is a program for automatic clustering of continuous data into a mixture of Gaussians. The program was originally developed for sorting of neuronal action potentials, but can be applied to any sort of data. Package: libbiosig-dev Source: biosig4c++ Version: 0.96.3+svn2677-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1604 Depends: neurodebian-popularity-contest, libbiosig0 (= 0.96.3+svn2677-1~nd11.04+1) Homepage: http://biosig.sf.net/ Priority: extra Section: libdevel Filename: pool/main/b/biosig4c++/libbiosig-dev_0.96.3+svn2677-1~nd11.04+1_amd64.deb Size: 391474 SHA256: 3f19f1e667cec394dd7fe337e2d6de002cfad2f53f03590331e4622db053efe7 SHA1: 55b75908cb9c9c1b34209e0ae1b736b6c0a8428f MD5sum: 632f8674cd89d2738dd9e3307230b40f 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://pub.ist.ac.at/~schloegl/biosig/TESTED . . This package provides header files and static library. Package: libbiosig0 Source: biosig4c++ Version: 0.96.3+svn2677-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 880 Depends: neurodebian-popularity-contest, 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_0.96.3+svn2677-1~nd11.04+1_amd64.deb Size: 309408 SHA256: 7ebbf2a0fea0cd938702538d19d65f8c84abdf9a6cdfc89c6e66b8fdb6967118 SHA1: 1d9c94a1e5d42de635a12d80a0775e5a5e858a2d MD5sum: 3fde135092a7e5fa19b057df7dee58a2 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://pub.ist.ac.at/~schloegl/biosig/TESTED . . This package provides dynamic library. Package: libbiosig0-dbg Source: biosig4c++ Version: 0.96.3+svn2677-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 240 Depends: neurodebian-popularity-contest, libbiosig0 (= 0.96.3+svn2677-1~nd11.04+1) Homepage: http://biosig.sf.net/ Priority: extra Section: debug Filename: pool/main/b/biosig4c++/libbiosig0-dbg_0.96.3+svn2677-1~nd11.04+1_amd64.deb Size: 69618 SHA256: e538ba7febcb07a03d109549682295e03eeb6e634bbfc4899e53ccfc61d01b87 SHA1: 98d10c152bebd870d24d8bad25ad79373e7b6a96 MD5sum: 3240d0746ac767de4642587801edebc3 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://pub.ist.ac.at/~schloegl/biosig/TESTED . . This package provides debug symbols. Package: libcgroup-dev Source: libcgroup Version: 0.37.1-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 76 Depends: neurodebian-popularity-contest, libcgroup1 (= 0.37.1-1~nd11.04+1) Homepage: http://sourceforge.net/projects/libcg/ Priority: extra Section: libdevel Filename: pool/main/libc/libcgroup/libcgroup-dev_0.37.1-1~nd11.04+1_amd64.deb Size: 17386 SHA256: 9106e536c8523085d3fd9a92ea763d2294f1c8e71d22aa6b57eb7d6fae7a89e5 SHA1: 16e41ffe1ff849a9ad392dd2ce68dc1c1afa1799 MD5sum: 7248041de45809e9fc2cd634d9f466b7 Description: Development libraries to develop applications that utilize control groups Control Groups provide a mechanism for aggregating/partitioning sets of tasks, and all their future children, into hierarchical groups with specialized behaviour. . It provides API to create/delete and modify cgroup nodes. It will also in the future allow creation of persistent configuration for control groups and provide scripts to manage that configuration. Package: libcgroup1 Source: libcgroup Version: 0.37.1-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 96 Depends: neurodebian-popularity-contest, libc6 (>= 2.8) Homepage: http://sourceforge.net/projects/libcg/ Priority: extra Section: libs Filename: pool/main/libc/libcgroup/libcgroup1_0.37.1-1~nd11.04+1_amd64.deb Size: 38548 SHA256: ae1242aa1bb9d62a5c11c979b4495cb760350afc3463152b137bc0d0009ee969 SHA1: 2fadc831bd7cb861ce5808ba8c71a4e87c025a81 MD5sum: 2b99963bfc2079608fd6273f462b4f82 Description: Library to control and monitor control groups Control Groups provide a mechanism for aggregating/partitioning sets of tasks, and all their future children, into hierarchical groups with specialized behaviour. . This library allows applications to manipulate, control, administrate and monitor control groups and the associated controllers. Package: libdmtcpaware-dev Source: dmtcp Version: 1.2.1-2~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 36 Depends: neurodebian-popularity-contest, libdmtcpaware1 (= 1.2.1-2~nd11.04+1) Homepage: http://dmtcp.sourceforge.net Priority: optional Section: libdevel Filename: pool/main/d/dmtcp/libdmtcpaware-dev_1.2.1-2~nd11.04+1_amd64.deb Size: 6570 SHA256: 38c5e9bdc382cb2952092ef5dfc310d0497b1cdb2009611f8ad7e9a32a404d0f SHA1: 284289b65c96663f42b2f303c0dd0e376ecea448 MD5sum: d0a4c9fe644afbb31ce9a8ce2602e871 Description: DMTCP programming interface -- developer package DMTCP (Distributed MultiThreaded Checkpointing) is a tool to transparently checkpointing the state of an arbitrary group of programs including multi-threaded and distributed computations. It operates directly on the user binary executable, with no Linux kernel modules or other kernel mods. . Among the applications supported by DMTCP are OpenMPI, MATLAB, Python, Perl, and many programming languages and shell scripting languages. DMTCP also supports GNU screen sessions, including vim/cscope and emacs. With the use of TightVNC, it can also checkpoint and restart X-Windows applications, as long as they do not use extensions (e.g.: no OpenGL, no video). . This package provides libraries for developing applications that need to interact with dmtcp. Package: libdmtcpaware1 Source: dmtcp Version: 1.2.1-2~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 48 Depends: neurodebian-popularity-contest, dmtcp, libc6 (>= 2.2.5) Homepage: http://dmtcp.sourceforge.net Priority: optional Section: libs Filename: pool/main/d/dmtcp/libdmtcpaware1_1.2.1-2~nd11.04+1_amd64.deb Size: 6370 SHA256: 02187560538c74f0dfcfe4d404494eb9a6e2f8a0f8f5526fd7add68119b897a5 SHA1: 5471876be9fbc1a8c0043a51576166393f156fac MD5sum: 56971b7fe7dbabd3cbc0fb8f0403f02e Description: DMTCP programming interface DMTCP (Distributed MultiThreaded Checkpointing) is a tool to transparently checkpointing the state of an arbitrary group of programs including multi-threaded and distributed computations. It operates directly on the user binary executable, with no Linux kernel modules or other kernel mods. . Among the applications supported by DMTCP are OpenMPI, MATLAB, Python, Perl, and many programming languages and shell scripting languages. DMTCP also supports GNU screen sessions, including vim/cscope and emacs. With the use of TightVNC, it can also checkpoint and restart X-Windows applications, as long as they do not use extensions (e.g.: no OpenGL, no video). . This package provides a programming interface to allow checkpointed applications to interact with dmtcp. Package: libdrawtk-dev Source: drawtk Version: 1.0b-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 192 Depends: neurodebian-popularity-contest, libdrawtk0 (= 1.0b-1~nd11.04+1) Homepage: http://cnbi.epfl.ch/software/drawtk.html Priority: extra Section: libdevel Filename: pool/main/d/drawtk/libdrawtk-dev_1.0b-1~nd11.04+1_amd64.deb Size: 44308 SHA256: 65f8d279fd81c73b62ce58847189c8f089cab997f7cb7f3900ad9a61ed25c171 SHA1: 215cf098463b09a1da739443628efaf32c533e13 MD5sum: ddb31ac9afdd86dac1e0075d3bbd7d40 Description: Library to simple and efficient 2D drawings (development files) This package provides an C library to perform efficient 2D drawings. The drawing is done by OpenGL which allow us fast and nice rendering of basic shapes, text, images and videos. It has been implemented as a thin layer that hides the complexity of the OpenGL library. . This package contains the files needed to compile and link programs which use drawtk. Package: libdrawtk0 Source: drawtk Version: 1.0b-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 72 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libfontconfig1 (>= 2.8.0), libfreeimage3 (>= 3.13.1), libfreetype6 (>= 2.2.1), libgl1-mesa-glx | libgl1, libglib2.0-0 (>= 2.12.0), libgstreamer-plugins-base0.10-0 (>= 0.10.23), libgstreamer0.10-0 (>= 0.10.25), libsdl1.2debian (>= 1.2.10-1) Homepage: http://cnbi.epfl.ch/software/drawtk.html Priority: extra Section: libs Filename: pool/main/d/drawtk/libdrawtk0_1.0b-1~nd11.04+1_amd64.deb Size: 23992 SHA256: 6bc788b9f94e08e0778e5ffcc48747d6a0e9021377dae87a2e78f724067f304f SHA1: 4f53a83c8739642a99108eee91a89cbf6061532a MD5sum: 9915241a656f4543d558f8132eddda81 Description: Library to simple and efficient 2D drawings This package provides an C library to perform efficient 2D drawings. The drawing is done by OpenGL which allow us fast and nice rendering of basic shapes, text, images and videos. It has been implemented as a thin layer that hides the complexity of the OpenGL library. Package: libdrawtk0-dbg Source: drawtk Version: 1.0b-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 176 Depends: neurodebian-popularity-contest, libdrawtk0 (= 1.0b-1~nd11.04+1) Homepage: http://cnbi.epfl.ch/software/drawtk.html Priority: extra Section: debug Filename: pool/main/d/drawtk/libdrawtk0-dbg_1.0b-1~nd11.04+1_amd64.deb Size: 70086 SHA256: c5620b8f66d23c2e721ecaeb6fb3d36eb2769bc1c19b08ba5a2d439fd99875bd SHA1: 93a16ac635446e597bb9a65e9a9f3c057d9b940a MD5sum: e3a9d7ec7f603ab957b3a7f67f6dd4e7 Description: Library to simple and efficient 2D drawings (debugging symbols) This package provides an C library to perform efficient 2D drawings. The drawing is done by OpenGL which allow us fast and nice rendering of basic shapes, text, images and videos. It has been implemented as a thin layer that hides the complexity of the OpenGL library. . This package provides the debugging symbols for the library. Package: libeigen3-dev Source: eigen3 Version: 3.0.1-1.1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 3412 Depends: neurodebian-popularity-contest Suggests: libeigen3-doc Homepage: http://eigen.tuxfamily.org Priority: extra Section: libdevel Filename: pool/main/e/eigen3/libeigen3-dev_3.0.1-1.1~nd11.04+1_amd64.deb Size: 509838 SHA256: 7166106a7a86868990bd5c4c30d4553bcc610bce4ee6be57c4ad03b097dfe267 SHA1: b40911ac04f2764ac6df6519e9a23da1c5263113 MD5sum: 16f2824a3ec5616b7b1e5e5f8a5227f2 Description: lightweight C++ template library for linear algebra Eigen 3 is a lightweight C++ template library for vector and matrix math, a.k.a. linear algebra. . Unlike most other linear algebra libraries, Eigen 3 focuses on the simple mathematical needs of applications: games and other OpenGL apps, spreadsheets and other office apps, etc. Eigen 3 is dedicated to providing optimal speed with GCC. A lot of improvements since 2-nd version of Eigen. Package: libeigen3-doc Source: eigen3 Version: 3.0.1-1.1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 10268 Depends: neurodebian-popularity-contest, ttf-freefont, libjs-jquery Suggests: libeigen3-dev Homepage: http://eigen.tuxfamily.org Priority: extra Section: doc Filename: pool/main/e/eigen3/libeigen3-doc_3.0.1-1.1~nd11.04+1_all.deb Size: 2313118 SHA256: 65749da7a405cc76f859f243a134f73ea38dc26f9b389138c22f9c241ac0cda6 SHA1: 3aa2cc66e967ab82e54d3fe148e1c72ec3cf2908 MD5sum: b77c8cacfc03c883ce2cdcf8ebd2ff09 Description: eigen3 API docmentation Eigen 3 is a lightweight C++ template library for vector and matrix math, a.k.a. linear algebra. . This package provides the complete eigen3 API documentation in HTML format. Package: libfreenect-demos Source: libfreenect Version: 1:0.0.1+20101211+2-2~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 92 Depends: neurodebian-popularity-contest, freeglut3, libc6 (>= 2.3.4), libfreenect0.0, libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, libice6 (>= 1:1.0.0), libsm6, libstdc++6 (>= 4.4.0), libusb-1.0-0 (>= 2:1.0.8), libx11-6, libxext6, libxi6, libxmu6, libglut3 Homepage: http://openkinect.org/ Priority: extra Section: libdevel Filename: pool/main/libf/libfreenect/libfreenect-demos_0.0.1+20101211+2-2~nd11.04+1_amd64.deb Size: 25082 SHA256: 3d601d33e7a6bbbf9a7b6e98550d40be2616e59a9b86e19d33422307ffc62f6f SHA1: ebb1ba4176a2f82c0d4741b4aaf40f3e7cdd22f1 MD5sum: 2286fbea705056c879930a6fa0efda5b Description: library for accessing Kinect USB camera -- demonstrations libfreenect is the core library for accessing the Microsoft Kinect USB camera. . This package includes some example programs for kinect. All programs start with a freenec- prefix. Package: libfreenect-dev Source: libfreenect Version: 1:0.0.1+20101211+2-2~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 80 Depends: neurodebian-popularity-contest, libusb-1.0-0-dev, libfreenect0.0 (= 1:0.0.1+20101211+2-2~nd11.04+1) Homepage: http://openkinect.org/ Priority: extra Section: libdevel Filename: pool/main/libf/libfreenect/libfreenect-dev_0.0.1+20101211+2-2~nd11.04+1_amd64.deb Size: 18586 SHA256: 7741676f69ea9ddd8144d68ca73cc611d2da1d0c3a3c063c2be21383956362a6 SHA1: 7a1247d4714c7255b6686c917f6d59e8c8d76d27 MD5sum: 05eebf6793cc2d72230bbba4718c9fe7 Description: library for accessing Kinect USB camera -- development files libfreenect is the core library for accessing the Microsoft Kinect USB camera. . This is the development package containing the libraries and header for software development with libfreenect. Package: libfreenect0.0 Source: libfreenect Version: 1:0.0.1+20101211+2-2~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 96 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), libusb-1.0-0 (>= 2:1.0.8), udev Conflicts: libfreenect Homepage: http://openkinect.org/ Priority: extra Section: libs Filename: pool/main/libf/libfreenect/libfreenect0.0_0.0.1+20101211+2-2~nd11.04+1_amd64.deb Size: 27904 SHA256: 4842a210efb66c7ec414a67dc17d22b3054ae8c28af4059f56e939005cd8b724 SHA1: 938196a891a80e95bdfa84784c4cecfdd15bc66a MD5sum: 22b9aa708ca2362a5fab4d86c05f5ed5 Description: library for accessing Kinect USB camera libfreenect is the core library for accessing the Microsoft Kinect USB camera. Currently, the library supports access to: - RGB and Depth Images - Motors - Accelerometer - LED Package: libgdf-dev Source: libgdf Version: 0.1.1-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 140 Depends: neurodebian-popularity-contest, libgdf0 (= 0.1.1-1~nd11.04+1) Homepage: http://sourceforge.net/projects/libgdf Priority: extra Section: libdevel Filename: pool/main/libg/libgdf/libgdf-dev_0.1.1-1~nd11.04+1_amd64.deb Size: 18538 SHA256: c23e932e513adeac57badc00d8a1313467c4d9396b948655bd673e38d9aa4a0e SHA1: 41252235face22f096eb5d94a9fd3835b53cbc62 MD5sum: 73a53868b3cb67298dc105d41832bfaa Description: IO library for the GDF -- development library GDF (General Dataformat for Biosignals) is intended to provide a generic storage for biosignals, such as EEG, ECG, MEG etc. . This package provides the header files and static library. Package: libgdf0 Source: libgdf Version: 0.1.1-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 328 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.5) Homepage: http://sourceforge.net/projects/libgdf Priority: extra Section: libs Filename: pool/main/libg/libgdf/libgdf0_0.1.1-1~nd11.04+1_amd64.deb Size: 103624 SHA256: b28cdc12327f9f5a04fef6ba973cbd4e1eac44d89ff34f62b724b538a4332738 SHA1: 3c806d811ef44dcb071d445a699b20d8a3afbb27 MD5sum: 75dcc4b4afbc3d4c56e5d93d5c8155df Description: IO library for the GDF (general dataformat for biosignals) GDF (General Dataformat for Biosignals) is intended to provide a generic storage for biosignals, such as EEG, ECG, MEG etc. . This package contains the shared library. Package: libgdf0-dbg Source: libgdf Version: 0.1.1-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 5428 Depends: neurodebian-popularity-contest, libgdf0 (= 0.1.1-1~nd11.04+1) Homepage: http://sourceforge.net/projects/libgdf Priority: extra Section: debug Filename: pool/main/libg/libgdf/libgdf0-dbg_0.1.1-1~nd11.04+1_amd64.deb Size: 1380036 SHA256: 54c41ae587b88ed81ed90f1d327b8605727cf262eed5bcaa48f20b27dc68f07b SHA1: a6dbdab5963b4236b4e65cb64745120d078cdcd0 MD5sum: 98161f98b7c726c5df76e37c1fa74521 Description: IO library for the GDF -- debug symbols GDF (General Dataformat for Biosignals) is intended to provide a generic storage for biosignals, such as EEG, ECG, MEG etc. . This package provides debug symbols. Package: libglew1.6 Source: glew Version: 1.6.0-2~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 432 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, libx11-6, libxext6, libxi6, libxmu6 Suggests: glew-utils Conflicts: libglew1 Homepage: http://glew.sourceforge.net/ Priority: optional Section: libs Filename: pool/main/g/glew/libglew1.6_1.6.0-2~nd11.04+1_amd64.deb Size: 116836 SHA256: 38224d033d8e5a4a5bbaec7e9b22cc40a872449800276a4920e68c2573fd63ec SHA1: 154d3d6c7d1041d6cc9e03280c1a223192950141 MD5sum: ea0e656a878f3de0a639038b1b2d8e90 Description: OpenGL Extension Wrangler - runtime environment For more information about GLEW please refer to the description of the libglew-dev package. . This package contains the runtime support files. Package: libglew1.6-dev Source: glew Version: 1.6.0-2~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1512 Depends: neurodebian-popularity-contest, libglew1.6 (= 1.6.0-2~nd11.04+1), libgl1-mesa-dev | libgl-dev, libglu1-mesa-dev | libglu-dev Conflicts: libglew-dev, libglew1.5-dev Provides: libglew-dev, libglew1.5-dev Homepage: http://glew.sourceforge.net/ Priority: optional Section: libdevel Filename: pool/main/g/glew/libglew1.6-dev_1.6.0-2~nd11.04+1_amd64.deb Size: 245000 SHA256: 2926ca30a5aad8f933d782e4fa57a8d43c186b9928e04198df9665a66b9eabac SHA1: 20fb3a70d88020f5bd67c8afb864a0af66e3a189 MD5sum: 8b6f40311f443d0173d0735df08679b0 Description: OpenGL Extension Wrangler - development environment The OpenGL Extension Wrangler, GLEW for short, is a library that handles initialization of OpenGL extensions in a portable and simple way. Once the program initializes the library and checks the availability of extensions, it can safely call the entry points defined by the extension. Currently GLEW supports almost all the extensions found in the OpenGL extension registry[1]. . This package contains the development documentation as well as the required header files. Package: libglewmx1.6 Source: glew Version: 1.6.0-2~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 380 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, libx11-6, libxext6, libxi6, libxmu6 Conflicts: libglew1 Homepage: http://glew.sourceforge.net/ Priority: optional Section: libs Filename: pool/main/g/glew/libglewmx1.6_1.6.0-2~nd11.04+1_amd64.deb Size: 104048 SHA256: bc3bd7b13e08366dd71434d561f6a1723e559f14b5e9a52d8a1604e9ed4214df SHA1: 14ee76a641b25c031e0672d3a1fa65e0b0b466e3 MD5sum: 6ddea543b4682fc265693ef3fc17da29 Description: OpenGL Extension Wrangler - runtime environment For more information about GLEW please refer to the description of the libglew-dev package. . This package contains the runtime support files, built with GLEW_MX option, adding support for thread-safe usage of multiple rendering contexts. Package: libglewmx1.6-dev Source: glew Version: 1.6.0-2~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 524 Depends: neurodebian-popularity-contest, libglew-dev, libglewmx1.6 (= 1.6.0-2~nd11.04+1) Conflicts: libglewmx-dev, libglewmx1.5-dev Provides: libglewmx-dev, libglewmx1.5-dev Homepage: http://glew.sourceforge.net/ Priority: optional Section: libdevel Filename: pool/main/g/glew/libglewmx1.6-dev_1.6.0-2~nd11.04+1_amd64.deb Size: 101998 SHA256: 0a9cd9334773acee1b2aa7914da6b6fa3ebec6166cd91e84a699cc571d8cd8fb SHA1: f812b2491e7e7c704b048c9a9b862a090afe9616 MD5sum: a5e0aedf0131ac3a1edc1344cc18cbe1 Description: OpenGL Extension Wrangler - development environment The OpenGL Extension Wrangler, GLEW for short, is a library that handles initialization of OpenGL extensions in a portable and simple way. Once the program initializes the library and checks the availability of extensions, it can safely call the entry points defined by the extension. Currently GLEW supports almost all the extensions found in the OpenGL extension registry[1]. . This package contains the development libraries compiled with GLEW_MX Package: libguac-client-vnc0 Source: libguac-client-vnc Version: 0.4.0-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 88 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libcairo2 (>= 1.6.0), libguac1, libvncserver0 Recommends: vnc4server Homepage: http://guacamole.sourceforge.net/ Priority: extra Section: libs Filename: pool/main/libg/libguac-client-vnc/libguac-client-vnc0_0.4.0-1~nd11.04+1_amd64.deb Size: 9400 SHA256: 2f9e2b5a22e9c4bbf9171f0a95c599017a186cc025fc672c6de4f3500b9a4f40 SHA1: 8e97cf2bab993abaca62ca31850ca1c4d5e6fba8 MD5sum: a71d22dd77c435db65f9deac84248532 Description: VNC client plugin for Guacamole A plugin for the Guacamole proxy daemon (guacd) that provides support for the VNC protocol. Package: libguac1 Source: libguac Version: 0.4.0-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 72 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libcairo2 (>= 1.2.4) Homepage: http://guacamole.sourceforge.net/ Priority: extra Section: libs Filename: pool/main/libg/libguac/libguac1_0.4.0-1~nd11.04+1_amd64.deb Size: 11646 SHA256: 2b766d89fe1dd54469460cbccfae55f883d1556e2d5caa2260603a0b93ce1f14 SHA1: 0fa44a593e7ae305f920911c44a49027aec830cf MD5sum: 6802fd3b444f51cede480cea4bc8cf23 Description: Core Guacamole library used by guacd and client plugins The core Guacamole library which both guacd and client plugins depend on to provide low-level I/O and protocol support. Package: libguac1-dev Source: libguac Version: 0.4.0-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 124 Depends: neurodebian-popularity-contest, libguac1 (= 0.4.0-1~nd11.04+1) Homepage: http://guacamole.sourceforge.net/ Priority: extra Section: libdevel Filename: pool/main/libg/libguac/libguac1-dev_0.4.0-1~nd11.04+1_amd64.deb Size: 19026 SHA256: 2e31623de55d88cb988b9a40989f2692348cb8ad66f38426dc82690fd003e0ae SHA1: a93e9aba6e8e13396d6af7a458810aa5d4ee5a93 MD5sum: bb49a4c725e03df3ebdf79941b315594 Description: Development headers for the core Guacamole library The development headers for the core Guacamole library used by guacd and all client plugins. This package is required for development of new client plugins, or for building existing plugins and guacd. Package: libopenwalnut1 Source: openwalnut Version: 1.2.5-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 5344 Depends: neurodebian-popularity-contest, ttf-liberation (>= 1.0.0), libboost-date-time1.42.0 (>= 1.42.0-1), libboost-filesystem1.42.0 (>= 1.42.0-1), libboost-regex1.42.0 (>= 1.42.0-1), libboost-signals1.42.0 (>= 1.42.0-1), libboost-system1.42.0 (>= 1.42.0-1), libboost-thread1.42.0 (>= 1.42.0-1), libc6 (>= 2.7), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libopenscenegraph65 (>= 2.8.3), libstdc++6 (>= 4.5) Suggests: nvidia-glx | fglrx-glx Homepage: http://www.openwalnut.org Priority: extra Section: libs Filename: pool/main/o/openwalnut/libopenwalnut1_1.2.5-1~nd11.04+1_amd64.deb Size: 1640260 SHA256: b908510a6f8b036b74a5722db1c1e77e60ce69e5072a046ab61fa88ee37d1435 SHA1: 5a6d2362d913b499a0a728997b01c2e461e4d3cd MD5sum: aea9b530b133076df73162837eb37224 Description: Multi-modal medical and brain data visualization tool. OpenWalnut is a tool for multi-modal medical and brain data visualization. Its universality allows it to be easily extended and used in a large variety of application cases. It is both, a tool for the scientific user and a powerful framework for the visualization researcher. Besides others, it is able to load NIfTI data, VTK line data and RIFF-format CNT/AVR-files. OpenWalnut provides many standard visualization tools like line integral convolution (LIC), isosurface-extraction, glyph-rendering or interactive fiber-data exploration. The powerful framework of OpenWalnut allows researchers and power-users to easily extend the functionality to their specific needs. . This package contains the core API of OpenWalnut. Package: libopenwalnut1-dev Source: openwalnut Version: 1.2.5-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2092 Depends: neurodebian-popularity-contest, libopenwalnut1 (= 1.2.5-1~nd11.04+1), libgl1-mesa-dev | libgl-dev, libopenscenegraph-dev (>= 2.8.1), libopenthreads-dev (>= 2.8.1), libboost-dev (>= 1.42.0), libboost-program-options-dev (>= 1.42.0), libboost-thread-dev (>= 1.42.0), libboost-filesystem-dev (>= 1.42.0), libboost-date-time-dev (>= 1.42.0), libboost-system-dev (>= 1.42.0), libboost-signals-dev (>= 1.42.0), libboost-regex-dev (>= 1.42.0), libeigen3-dev (>= 3.0.0) Homepage: http://www.openwalnut.org Priority: extra Section: libdevel Filename: pool/main/o/openwalnut/libopenwalnut1-dev_1.2.5-1~nd11.04+1_amd64.deb Size: 262300 SHA256: 4621a890f03a5d3ce52f220990253d90346873d25c463172b4e6873ab92d31de SHA1: 5b4ee76585a8f90da1c3d80fdd60c9a7ed73e743 MD5sum: 8acef867028fb45bf716a4ff03319e97 Description: Multi-modal medical and brain data visualization tool. OpenWalnut is a tool for multi-modal medical and brain data visualization. Its universality allows it to be easily extended and used in a large variety of application cases. It is both, a tool for the scientific user and a powerful framework for the visualization researcher. Besides others, it is able to load NIfTI data, VTK line data and RIFF-format CNT/AVR-files. OpenWalnut provides many standard visualization tools like line integral convolution (LIC), isosurface-extraction, glyph-rendering or interactive fiber-data exploration. The powerful framework of OpenWalnut allows researchers and power-users to easily extend the functionality to their specific needs. . This package contains the headers for the core API of OpenWalnut. Package: libopenwalnut1-doc Source: openwalnut Version: 1.2.5-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 41708 Depends: neurodebian-popularity-contest, libjs-jquery Homepage: http://www.openwalnut.org Priority: extra Section: doc Filename: pool/main/o/openwalnut/libopenwalnut1-doc_1.2.5-1~nd11.04+1_all.deb Size: 4338428 SHA256: d11fa9cae44ed33c6775245ebd0a1d071e22da98a62e819a33d9cc079a2d7178 SHA1: 1c940ecddf427f36b31485c22dfb323dbe0b3159 MD5sum: 45e6151cc9c943d75bed541e20adf2eb Description: Multi-modal medical and brain data visualization tool. OpenWalnut is a tool for multi-modal medical and brain data visualization. Its universality allows it to be easily extended and used in a large variety of application cases. It is both, a tool for the scientific user and a powerful framework for the visualization researcher. Besides others, it is able to load NIfTI data, VTK line data and RIFF-format CNT/AVR-files. OpenWalnut provides many standard visualization tools like line integral convolution (LIC), isosurface-extraction, glyph-rendering or interactive fiber-data exploration. The powerful framework of OpenWalnut allows researchers and power-users to easily extend the functionality to their specific needs. . This package contains the core API documentation of OpenWalnut. Package: libpam-cgroup Source: libcgroup Version: 0.37.1-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 20 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libcgroup1, libpam0g (>= 0.99.7.1) Homepage: http://sourceforge.net/projects/libcg/ Priority: extra Section: admin Filename: pool/main/libc/libcgroup/libpam-cgroup_0.37.1-1~nd11.04+1_amd64.deb Size: 7740 SHA256: 86b332dd5d16060b4e75aa649ab734c038652ab0905551004d43f09a81f1b52e SHA1: 04ef101ca9dc21a6f9d94f3f8a6b9ea87a2c043e MD5sum: e3d55268bca2eba292f322a19ffa8abf Description: PAM module to move a user session into a cgroup Control Groups provide a mechanism for aggregating/partitioning sets of tasks, and all their future children, into hierarchical groups with specialized behaviour. . This PAM module will move a user session into an existing cgroup by attempting to match uid and gid against the defined cgroup rules configuration. Package: matlab-support-dev Source: matlab-support Version: 0.0.17~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 16 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_0.0.17~nd11.04+1_all.deb Size: 6722 SHA256: e2cab6d6b90463921cec7f005868f397a8c0d32185b915bddf1211f1c0cf6975 SHA1: 6458b01adf37055ffa425a898975343fd687c31c MD5sum: afc3c0bf78d1d01af711a6216077823d Description: helpers for packages building MATLAB toolboxes This package provides a Makefile snippet (analogous to the one used for Octave) that configures the locations for architecture independent M-files, binary MEX-extensions, and their corresponding sources. This package can be used as a build-dependency by other packages shipping MATLAB toolboxes. Package: mriconvert Version: 2.0.217-3~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2328 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0), libwxbase2.8-0 (>= 2.8.11.0), libwxgtk2.8-0 (>= 2.8.11.0) Homepage: http://lcni.uoregon.edu/~jolinda/MRIConvert/ Priority: optional Section: science Filename: pool/main/m/mriconvert/mriconvert_2.0.217-3~nd11.04+1_amd64.deb Size: 813362 SHA256: c30f963f3af9acde3b7b005b2893d0e5b392d34f37ad0280f51e59e9497098ae SHA1: c5e20fefabb2b2a1644974bcb8eabda5bb43cd72 MD5sum: 4bd696b4becdbf9d445b5dc8338e9aed Description: medical image file conversion utility MRIConvert is a medical image file conversion utility that converts DICOM files to NIfTI 1.1, Analyze 7.5, SPM99/Analyze, BrainVoyager, and MetaImage volume formats. Package: mrtrix Version: 0.2.9-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 7276 Depends: neurodebian-popularity-contest, libatk1.0-0 (>= 1.12.4), libatkmm-1.6-1 (>= 2.22.1), libc6 (>= 2.4), libcairo2 (>= 1.2.4), libcairomm-1.0-1 (>= 1.6.4), libfontconfig1 (>= 2.8.0), libfreetype6 (>= 2.2.1), libgcc1 (>= 1:4.1.1), libgdk-pixbuf2.0-0 (>= 2.22.0), libgl1-mesa-glx | libgl1, libglib2.0-0 (>= 2.12.0), libglibmm-2.4-1c2a (>= 2.28.0), libglu1-mesa | libglu1, libgsl0ldbl (>= 1.9), libgtk2.0-0 (>= 2.8.0), libgtkglext1, libgtkmm-2.4-1c2a (>= 1:2.24.0), libice6 (>= 1:1.0.0), libpango1.0-0 (>= 1.14.0), libpangomm-1.4-1 (>= 2.27.1), libsigc++-2.0-0c2a (>= 2.0.2), libsm6, libstdc++6 (>= 4.5), 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_0.2.9-1~nd11.04+1_amd64.deb Size: 2274206 SHA256: 2c00b7c6bfb4e7b9aa6a6124889b5d8920ec488e0773ba5a875a98cf47e1f0d5 SHA1: d1a025d951a3e19a093cc17243031c5f038ba774 MD5sum: b6bf23d5c0a9744e70ef4962f68e4d08 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. Magnetic resonance images in DICOM, ANALYZE, or uncompressed NIfTI format are supported. Package: mrtrix-doc Source: mrtrix Version: 0.2.9-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3304 Depends: neurodebian-popularity-contest Homepage: http://www.brain.org.au/software/mrtrix Priority: extra Section: doc Filename: pool/main/m/mrtrix/mrtrix-doc_0.2.9-1~nd11.04+1_all.deb Size: 2939868 SHA256: c372ef0d9b75a343192754d1e0a18055619b8961121b1745292516b9fba8db4b SHA1: 8a16ccc8e4835905d508be2c6d749598764d22b7 MD5sum: 633683d632e7d2a92debea37015f9ec5 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. Magnetic resonance images in DICOM, ANALYZE, or uncompressed NIfTI format are supported. . This package provides the documentation in HTML format. Package: neurodebian-desktop Source: neurodebian Version: 0.27~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 268 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_0.27~nd11.04+1_all.deb Size: 113964 SHA256: e147604f508c0f3aa54421b1752c624907a58c59565e745c10d0ea639ed940ad SHA1: ebc4d0d85d80f07d997787d0ab160c97b5562ee9 MD5sum: 0afc16db65bd6f61c238ce5e9039b7f4 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: 0.27~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5860 Depends: devscripts, cowbuilder, neurodebian-keyring Recommends: python, zerofree, moreutils, time, ubuntu-keyring Suggests: virtualbox-ose, virtualbox-ose-fuse Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-dev_0.27~nd11.04+1_all.deb Size: 5087698 SHA256: ed1ada81e8bc9fa931505107e891185cd40b98bbfa164ce44c6e6b90fc5fdc7d SHA1: 1da4901290cc07f135b0c07cfe161f756005d524 MD5sum: c495a949d9bbd325c48b456e67c1d0f8 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: 0.27~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 148 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_0.27~nd11.04+1_all.deb Size: 13444 SHA256: 2b923b58f01f3c0093a7d85df13128652d8d669f34d9e5b783e4bebbea19a401 SHA1: ef393338851752ab67f1ccee2c8c71badd766ada MD5sum: 6a43d013396ac5db540619a75ae4a374 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: 0.27~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 20 Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-keyring_0.27~nd11.04+1_all.deb Size: 6390 SHA256: 99b7b5dfc7ea295bb97fb9f90a88fda7304c93afc3fb099e67c382ea29079c23 SHA1: 8b3e9ab6b5adc20698f5ac14c7f001d20e527930 MD5sum: ed09f132cba7d0573aa9b98fc3f422b0 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: 0.27~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 16 Depends: popularity-contest Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-popularity-contest_0.27~nd11.04+1_all.deb Size: 5558 SHA256: 5647db12c8e3248f58dca9983711ea86045d9074ebbf18b2fa9d0b53ec31cdfa SHA1: 1f0e03a74f26209fb8d37e899593d7695248dc00 MD5sum: 96c2827933e4b9425f6f0db66b4eca7c 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: nipy-suite Version: 0.1.0-2 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 36 Depends: python-nibabel (>= 1.0.0), python-nipy (>= 0.1.2+20110114), python-dipy (>= 0.5.0), python-nipype (>= 0.3.3), python-nitime (>= 0.2) Suggests: python-mvpa, psychopy Homepage: http://www.nipy.org Priority: extra Section: python Filename: pool/main/n/nipy-suite/nipy-suite_0.1.0-2_all.deb Size: 3898 SHA256: 882c8580ebd2d458a92f8d851d1ec9291fecf05f6ed98a8b754eb831c95368c8 SHA1: 6501d1d201160520f5aad29d0f9007c17b7d9778 MD5sum: eb090e568264d2f439892bcb98485b8c Description: Neuroimaging in Python NiPy is a comprehensive suite of Python modules to perform analysis of Neuroimaging data in Python. nipy-suite is a metapackage depending on the projects developed under NiPy project umbrella, such as - nibabel: bindings to various neuroimaging data formats - nipy: analysis of structural and functional neuroimaging data - nitime: timeseries analysis - dipy: analysis of MR diffusion imaging data - nipype: pipelines and worfklows Package: nipy-suite-doc Source: nipy-suite Version: 0.1.0-2 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 32 Depends: python-nibabel-doc (>= 1.0.0), python-nipy-doc (>= 0.1.2+20110114), python-dipy-doc (>= 0.5.0), python-nipype-doc (>= 0.3.3), python-nitime-doc (>= 0.2) Suggests: python-mvpa-doc Homepage: http://www.nipy.org Priority: extra Section: doc Filename: pool/main/n/nipy-suite/nipy-suite-doc_0.1.0-2_all.deb Size: 2250 SHA256: 54985bd9d6eaa352608b357f2deeb066bd2ac12d3c2e463082f5d9178701bbad SHA1: 5d2f5e94ff6b7ff737fe966f4a2e5ff67df93cca MD5sum: 37d2f8b6b6d203edf208afb0cdb56fa3 Description: Neuroimaging in Python -- documentation NiPy is a comprehensive suite of Python modules to perform analysis of Neuroimaging data in Python. . nipy-suite-doc is a metapackage depending on the documentation packages for NiPy projects. Package: octave-biosig Source: biosig4c++ Version: 0.96.3+svn2677-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 56 Depends: neurodebian-popularity-contest, libbiosig0, libblas3gf | libblas.so.3gf | libatlas3gf-base, libc6 (>= 2.4), libcholmod1.7.1 (>= 1:3.4.0), libfftw3-3, libgcc1 (>= 1:4.1.1), libgfortran3 (>= 4.3), libhdf5-serial-1.8.4 | libhdf5-1.8.4, liblapack3gf | liblapack.so.3gf | libatlas3gf-base, libncurses5 (>= 5.5-5~), libreadline6 (>= 6.0), libstdc++6 (>= 4.1.1), zlib1g (>= 1:1.1.4) Homepage: http://biosig.sf.net/ Priority: extra Section: science Filename: pool/main/b/biosig4c++/octave-biosig_0.96.3+svn2677-1~nd11.04+1_amd64.deb Size: 17918 SHA256: 34ef3971d43361b3f8b2ffb863a216537243b1cd13cec4fb3dfebf4686c32c5f SHA1: 5d4c8b2b221f1859dbb156519ba396cfbc2a2470 MD5sum: c954df00099a1c7371b77d6b2a3b8b93 Description: Octave bindings for BioSig library This package provides Octave bindings for BioSig library. Primary goal -- I/O interface to variety of biomedical file formats, including but not limited to SCP-ECG(EN1064), HL7aECG (FDA-XML), GDF, EDF. Package: octave-gdf Source: libgdf Version: 0.1.1-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 288 Depends: neurodebian-popularity-contest, octave3.2 (>= 3.2.4), libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libgdf0, libstdc++6 (>= 4.4.0) Homepage: http://sourceforge.net/projects/libgdf Priority: extra Section: science Filename: pool/main/libg/libgdf/octave-gdf_0.1.1-1~nd11.04+1_amd64.deb Size: 116976 SHA256: 6da7a993a5f9de01ac477ae81d0cba1addaa365a562cc164b88d326eba933a1c SHA1: 3185a8012ce6c636521ed918662a3f53af963878 MD5sum: 0f45a86114fded6f2840b941f5439919 Description: IO library for the GDF -- Octave interface GDF (General Dataformat for Biosignals) is intended to provide a generic storage for biosignals, such as EEG, ECG, MEG etc. . This package provides Octave bindings for libgdf. Package: octave-psychtoolbox-3 Source: psychtoolbox-3 Version: 3.0.9+svn2265.dfsg1-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2328 Depends: neurodebian-popularity-contest, octave3.2 (>= 3.2.4), freeglut3, libasound2 (>> 1.0.24.1), libc6 (>= 2.7), libdc1394-22, libfreenect0.0, libgl1-mesa-glx | libgl1, libglew1.6 (>= 1.6.0), libglib2.0-0 (>= 2.12.0), libglu1-mesa | libglu1, libgstreamer-plugins-base0.10-0 (>= 0.10.23), libgstreamer0.10-0 (>= 0.10.24), libopenal1, libpciaccess0 (>= 0.8.0+git20071002), libraw1394-11, libusb-1.0-0 (>= 2:1.0.8), libx11-6 (>= 2:1.2.99.901), libxext6, libxi6 (>= 2:1.2.99.4), libxml2 (>= 2.6.27), libxxf86vm1, psychtoolbox-3-common (= 3.0.9+svn2265.dfsg1-1~nd11.04+1), psychtoolbox-3-lib (= 3.0.9+svn2265.dfsg1-1~nd11.04+1) Recommends: octave-audio, octave-image, octave-optim, octave-signal, octave-statistics Provides: psychtoolbox, psychtoolbox-3 Homepage: http://psychtoolbox.org Priority: extra Section: science Filename: pool/main/p/psychtoolbox-3/octave-psychtoolbox-3_3.0.9+svn2265.dfsg1-1~nd11.04+1_amd64.deb Size: 748156 SHA256: 9673baac032d6d6c1c0bdc857b60158fec19b2649ee600099985b695aa903acb SHA1: e8f93b1e26c63c0d469854668fecc40ddd4bf8d5 MD5sum: c4cfd30fea51e9c204c8f21302786147 Description: toolbox for vision research -- Octave bindings Psychophysics Toolbox Version 3 (PTB-3) is a free set of Matlab and GNU/Octave functions for vision research. It makes it easy to synthesize and show accurately controlled visual and auditory stimuli and interact with the observer. . The Psychophysics Toolbox interfaces between Matlab or Octave and the computer hardware. The Psychtoolbox's core routines provide access to the display frame buffer and color lookup table, allow synchronization with the vertical retrace, support millisecond timing, allow access to OpenGL commands, and facilitate the collection of observer responses. Ancillary routines support common needs like color space transformations and the QUEST threshold seeking algorithm. . This package contains bindings for Octave. Package: opensesame Version: 0.24-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5540 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-qt4, python-pygame (>= 1.8.1~), python-numpy (>= 1.3.0~), python-tk Recommends: python-serial (>= 2.3~), psychopy (>= 1.64.0), python-pyaudio (>= 0.2.4), python-imaging (>= 1.1.7), python-opengl (>= 3.0.1) Homepage: http://www.cogsci.nl/software/opensesame Priority: extra Section: science Filename: pool/main/o/opensesame/opensesame_0.24-1~nd11.04+1_all.deb Size: 3577522 SHA256: 4efbae53de6c75593c483707eab5e7afe91c398b4979a29b8244d152c8a66898 SHA1: faa71eba75dcd110c8cb9960de947c7550238cad MD5sum: 0e2eea06d428a012eb8e39623d290f18 Description: graphical experiment builder for the social sciences This graphical environment provides an easy to use, point-and-click interface for creating psychological experiments. In addition to a powerful sketchpad for creating visual stimuli, OpenSesame features a sampler and synthesizer for sound playback. For more complex tasks, OpenSesame supports Python scripting using the built-in editor with syntax highlighting. Python-Version: 2.6, 2.7 Package: openwalnut-modules Source: openwalnut Version: 1.2.5-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 14564 Depends: neurodebian-popularity-contest, libbiosig0, libboost-filesystem1.42.0 (>= 1.42.0-1), libboost-regex1.42.0 (>= 1.42.0-1), libboost-signals1.42.0 (>= 1.42.0-1), libboost-system1.42.0 (>= 1.42.0-1), libboost-thread1.42.0 (>= 1.42.0-1), libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libnifti2, libopenscenegraph65 (>= 2.8.3), libopenwalnut1, libstdc++6 (>= 4.5) Homepage: http://www.openwalnut.org Priority: extra Section: science Filename: pool/main/o/openwalnut/openwalnut-modules_1.2.5-1~nd11.04+1_amd64.deb Size: 4477082 SHA256: 69f2e89f7518808d43bcb5e66cfb98126f6337031e5e3cd726ce6e6922e1cdbf SHA1: 9b06ecd97d8112be05322cace9b0d06367f0a44c MD5sum: db9745248a65ce4b3e6f3c5a01e7bb82 Description: Multi-modal medical and brain data visualization tool. OpenWalnut is a tool for multi-modal medical and brain data visualization. Its universality allows it to be easily extended and used in a large variety of application cases. It is both, a tool for the scientific user and a powerful framework for the visualization researcher. Besides others, it is able to load NIfTI data, VTK line data and RIFF-format CNT/AVR-files. OpenWalnut provides many standard visualization tools like line integral convolution (LIC), isosurface-extraction, glyph-rendering or interactive fiber-data exploration. The powerful framework of OpenWalnut allows researchers and power-users to easily extend the functionality to their specific needs. . This package contains the currently available modules for OpenWalnut. Package: openwalnut-qt4 Source: openwalnut Version: 1.2.5-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1804 Depends: neurodebian-popularity-contest, libboost-filesystem1.42.0 (>= 1.42.0-1), libboost-program-options1.42.0 (>= 1.42.0-1), libboost-regex1.42.0 (>= 1.42.0-1), libboost-system1.42.0 (>= 1.42.0-1), libboost-thread1.42.0 (>= 1.42.0-1), libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libopenscenegraph65 (>= 2.8.3), libopenwalnut1, libqt4-opengl (>= 4:4.6.0), libqtcore4 (>= 4:4.7.0~beta1), libqtgui4 (>= 4:4.6.0), libqtwebkit4 (>= 2.1~really2.0.2), libstdc++6 (>= 4.5) Recommends: openwalnut-modules (= 1.2.5-1~nd11.04+1) Homepage: http://www.openwalnut.org Priority: extra Section: science Filename: pool/main/o/openwalnut/openwalnut-qt4_1.2.5-1~nd11.04+1_amd64.deb Size: 601034 SHA256: 6682f9b0bd457817b3f26b34615450fc6fa305c0fec4b1313a49657350622bde SHA1: cf36c00ef428bced757f1d6cf78f3cc3e665926e MD5sum: 15e9ae2a6a33af287c67ea689c60853d Description: Multi-modal medical and brain data visualization tool. OpenWalnut is a tool for multi-modal medical and brain data visualization. Its universality allows it to be easily extended and used in a large variety of application cases. It is both, a tool for the scientific user and a powerful framework for the visualization researcher. Besides others, it is able to load NIfTI data, VTK line data and RIFF-format CNT/AVR-files. OpenWalnut provides many standard visualization tools like line integral convolution (LIC), isosurface-extraction, glyph-rendering or interactive fiber-data exploration. The powerful framework of OpenWalnut allows researchers and power-users to easily extend the functionality to their specific needs. . This package contains the QT4 GUI for OpenWalnut. Package: packaging-tutorial Version: 0.2~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 836 Depends: neurodebian-popularity-contest Priority: extra Section: doc Filename: pool/main/p/packaging-tutorial/packaging-tutorial_0.2~nd+1_all.deb Size: 680180 SHA256: 4f1c39bc3f108a98284df69fc1734c3eb97957be6e441928596ccbf1ba0d1292 SHA1: 1117c3ff4a15d620a0d8fc62edcc1bfcf45e6329 MD5sum: a3c8023d51d265c6a801496c98620528 Description: introduction to Debian packaging This tutorial is an introduction to Debian packaging. It teaches prospective developers how to modify existing packages, how to create their own packages, and how to interact with the Debian community. In addition to the main tutorial, it includes three practical sessions on modifying the 'grep' package, and packaging the 'gnujump' game and a Java library. Package: psychopy Version: 1.71.01.dfsg-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5016 Depends: neurodebian-popularity-contest, python (>= 2.4), python-support (>= 0.90.0), python-pyglet | python-pygame, python-opengl, python-numpy, python-scipy, python-matplotlib, python-lxml, python-configobj Recommends: python-wxgtk2.8, python-pyglet, python-pygame, python-openpyxl, python-imaging, python-serial, libavbin0, ipython Suggests: python-iolabs, python-pyxid Homepage: http://www.psychopy.org Priority: optional Section: science Filename: pool/main/p/psychopy/psychopy_1.71.01.dfsg-1~nd11.04+1_all.deb Size: 2659098 SHA256: a64ce52f3cee0b1008c5e600e66f4504504ac6bf2be4b61fe9443bbdaa48b8c4 SHA1: 091060ba1ef021f90646029b83196e25527b9b47 MD5sum: fff51a7560bfaa3142b452ba3bfb709d Description: environment for creating psychology stimuli in Python PsychoPy provides an environment for creating psychology stimuli using Python scripting language. It combines the graphical strengths of OpenGL with easy Python syntax to give psychophysics a free and simple stimulus presentation and control package. . The goal is to provide, for the busy scientist, tools to control timing and windowing and a simple set of pre-packaged stimuli and methods. PsychoPy features . - IDE GUI for coding in a powerful scripting language (Python) - Builder GUI for rapid development of stimulation sequences - Use of hardware-accelerated graphics (OpenGL) - Integration with Spectrascan PR650 for easy monitor calibration - Simple routines for staircase and constant stimuli experimental methods as well as curve-fitting and bootstrapping - Simple (or complex) GUIs via wxPython - Easy interfaces to joysticks, mice, sound cards etc. via PyGame - Video playback (MPG, DivX, AVI, QuickTime, etc.) as stimuli Python-Version: 2.6, 2.7 Package: psychtoolbox-3-common Source: psychtoolbox-3 Version: 3.0.9+svn2265.dfsg1-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 54300 Depends: neurodebian-popularity-contest Homepage: http://psychtoolbox.org Priority: extra Section: science Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-common_3.0.9+svn2265.dfsg1-1~nd11.04+1_all.deb Size: 19675056 SHA256: 84562afc25a0c5370a73aefb9bcf09a92bb8e544dfc12d02e9424c026e82ca77 SHA1: 54768718601fcb063c01073756cb0f6e2d83f0e9 MD5sum: bb6a9b37e04de3dae978e443a943fee8 Description: toolbox for vision research -- arch/interpreter independent part Psychophysics Toolbox Version 3 (PTB-3) is a free set of Matlab and GNU/Octave functions for vision research. It makes it easy to synthesize and show accurately controlled visual and auditory stimuli and interact with the observer. . The Psychophysics Toolbox interfaces between Matlab or Octave and the computer hardware. The Psychtoolbox's core routines provide access to the display frame buffer and color lookup table, allow synchronization with the vertical retrace, support millisecond timing, allow access to OpenGL commands, and facilitate the collection of observer responses. Ancillary routines support common needs like color space transformations and the QUEST threshold seeking algorithm. . This package contains architecture independent files (such as .m scripts) Package: psychtoolbox-3-dbg Source: psychtoolbox-3 Version: 3.0.9+svn2265.dfsg1-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2656 Depends: neurodebian-popularity-contest, octave-psychtoolbox-3 (= 3.0.9+svn2265.dfsg1-1~nd11.04+1) Homepage: http://psychtoolbox.org Priority: extra Section: debug Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-dbg_3.0.9+svn2265.dfsg1-1~nd11.04+1_amd64.deb Size: 802458 SHA256: a2e02b7ba1f03be97dd1df0794daa08776a8b062f19e7f09c2a3ebe022a2abbe SHA1: 3199e755945d404ef35143f9450d25b9f16b6954 MD5sum: 0d56c890a7683e24725eec941ab2526a Description: toolbox for vision research -- debug symbols for binaries Psychophysics Toolbox Version 3 (PTB-3) is a free set of Matlab and GNU/Octave functions for vision research. It makes it easy to synthesize and show accurately controlled visual and auditory stimuli and interact with the observer. . The Psychophysics Toolbox interfaces between Matlab or Octave and the computer hardware. The Psychtoolbox's core routines provide access to the display frame buffer and color lookup table, allow synchronization with the vertical retrace, support millisecond timing, allow access to OpenGL commands, and facilitate the collection of observer responses. Ancillary routines support common needs like color space transformations and the QUEST threshold seeking algorithm. . To ease debugging and troubleshooting this package contains debug symbols for Octave bindings and other binaries. Package: psychtoolbox-3-lib Source: psychtoolbox-3 Version: 3.0.9+svn2265.dfsg1-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 204 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libfontconfig1 (>= 2.8.0), libfreetype6 (>= 2.2.1), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, libstdc++6 (>= 4.5) Recommends: gstreamer0.10-plugins-base, gstreamer0.10-plugins-good Suggests: gstreamer0.10-plugins-bad, gstreamer0.10-plugins-ugly Homepage: http://psychtoolbox.org Priority: extra Section: science Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-lib_3.0.9+svn2265.dfsg1-1~nd11.04+1_amd64.deb Size: 60440 SHA256: 6c0b40dfb8bd84438814d71635aef10cabeee8153f33362063b41f4dea6287a7 SHA1: 76fd77325a57ff5071cda7145fb7516f49c78c45 MD5sum: 5902e0da8a70b47969636c9c960810c4 Description: toolbox for vision research -- arch-specific parts Psychophysics Toolbox Version 3 (PTB-3) is a free set of Matlab and GNU/Octave functions for vision research. It makes it easy to synthesize and show accurately controlled visual and auditory stimuli and interact with the observer. . The Psychophysics Toolbox interfaces between Matlab or Octave and the computer hardware. The Psychtoolbox's core routines provide access to the display frame buffer and color lookup table, allow synchronization with the vertical retrace, support millisecond timing, allow access to OpenGL commands, and facilitate the collection of observer responses. Ancillary routines support common needs like color space transformations and the QUEST threshold seeking algorithm. . This package contains additional binaries (tools/dynamic libraries) used by both Octave and Matlab frontends. Package: python-biosig Source: biosig4c++ Version: 0.96.3+svn2677-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 224 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python-support (>= 0.90.0), libbiosig0, libc6 (>= 2.4), libcholmod1.7.1 (>= 1:3.4.0), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), zlib1g (>= 1:1.1.4) Homepage: http://biosig.sf.net/ Priority: extra Section: python Filename: pool/main/b/biosig4c++/python-biosig_0.96.3+svn2677-1~nd11.04+1_amd64.deb Size: 52818 SHA256: 32491caa8ddf652124b486d9403758b8e7f50e5c7533d5ceeb36a5c973d7f64c SHA1: a747a4e4a493cdc9939171ccd023eecb227077da MD5sum: 814be39bc6ba592d07481e3c98ab00f0 Description: Python bindings for BioSig library This package provides Python bindings for BioSig library. Primary goal -- I/O interface to variety of biomedical file formats, including but not limited to SCP-ECG(EN1064), HL7aECG (FDA-XML), GDF, EDF. Package: python-brian Source: brian Version: 1.3.0-2~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1692 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-brian-lib (>= 1.3.0-2~nd11.04+1), python-matplotlib (>= 0.90.1), python-numpy (>= 1.3.0), python-scipy (>= 0.7.0) Recommends: python-sympy Suggests: python-brian-doc, python-nose, python-cherrypy Homepage: http://www.briansimulator.org/ Priority: extra Section: python Filename: pool/main/b/brian/python-brian_1.3.0-2~nd11.04+1_all.deb Size: 314082 SHA256: 2d9f572c910a8070e76d96f916b1858e7a3cfcb3d7750b8a21e75054dae76297 SHA1: fba506e6b9b94367e05eb793dbbce2c4f05d4f78 MD5sum: 916374ef38059036d8a2576acb1b07d3 Description: simulator for spiking neural networks Brian is a clock-driven simulator for spiking neural networks. It is designed with an emphasis on flexibility and extensibility, for rapid development and refinement of neural models. Neuron models are specified by sets of user-specified differential equations, threshold conditions and reset conditions (given as strings). The focus is primarily on networks of single compartment neuron models (e.g. leaky integrate-and-fire or Hodgkin-Huxley type neurons). Features include: - a system for specifying quantities with physical dimensions - exact numerical integration for linear differential equations - Euler, Runge-Kutta and exponential Euler integration for nonlinear differential equations - synaptic connections with delays - short-term and long-term plasticity (spike-timing dependent plasticity) - a library of standard model components, including integrate-and-fire equations, synapses and ionic currents - a toolbox for automatically fitting spiking neuron models to electrophysiological recordings Package: python-brian-doc Source: brian Version: 1.3.0-2~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5312 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-brian Homepage: http://www.briansimulator.org/ Priority: extra Section: doc Filename: pool/main/b/brian/python-brian-doc_1.3.0-2~nd11.04+1_all.deb Size: 1648998 SHA256: 0c3359089d96e80707830438cee12482972a6a61d9e5323ab90a915238058edf SHA1: 4ac1e6d3d78fd29c1f8527a665a5d46b3961c38b MD5sum: 1d481855def66843a0934bf99b4b9be6 Description: simulator for spiking neural networks - documentation Brian is a clock-driven simulator for spiking neural networks. . This package provides user's manual (in HTML format), examples and demos. Package: python-brian-lib Source: brian Version: 1.3.0-2~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 292 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-numpy (<< 1:1.6), python-numpy (>= 1:1.5.1), python-support (>= 0.90.0), libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0) Homepage: http://www.briansimulator.org/ Priority: extra Section: python Filename: pool/main/b/brian/python-brian-lib_1.3.0-2~nd11.04+1_amd64.deb Size: 105666 SHA256: d470b61aef5a3c7420bb456a7303ed374a7ef77a13afbba5d6787b4147516a43 SHA1: 28ad51104f7c324080d78017aab4a5d3a7ab440c MD5sum: 947bb40a899f8d3b2a03c50cffda8bd6 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-cfflib Source: cfflib Version: 2.0.5-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 768 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-lxml, python-numpy, python-networkx (>= 1.4), python-nibabel (>= 1.1.0) Recommends: python-nose, python-sphinx, python-tables, python-h5py Provides: python2.6-cfflib, python2.7-cfflib Homepage: http://cmtk.org/cfflib Priority: extra Section: python Filename: pool/main/c/cfflib/python-cfflib_2.0.5-1~nd11.04+1_all.deb Size: 217710 SHA256: f43ce8ebf52ffc74d10c86f356c53d6bdbf60d8ac8afdc330c789831b212b8d2 SHA1: c8b78aac188af4bb0a489bd6af44e00aad07af59 MD5sum: 6c16047dfeecab6c850d99dd75191b8b Description: Multi-modal connectome and metadata management and integration The Connectome File Format Library (cfflib) is a Python module for multi-modal neuroimaging connectome data and metadata management and integration. . It enables single subject and multi-subject data integration for a variety of modalities, such as networks, surfaces, volumes, fiber tracks, timeseries, scripts, arbitrary data objects such as homogeneous arrays or CSV/JSON files. It relies on existing Python modules and the standard library for basic data I/O, and adds a layer of metadata annotation as tags or with structured properties to individual data objects. Package: python-dipy Source: dipy Version: 0.5.0-2~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2068 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy, python-dipy-lib (>= 0.5.0-2~nd11.04+1) Recommends: python-matplotlib, python-vtk, python-nose, python-nibabel, python-tables Suggests: ipython Provides: python2.6-dipy, python2.7-dipy Homepage: http://nipy.org/dipy Priority: extra Section: python Filename: pool/main/d/dipy/python-dipy_0.5.0-2~nd11.04+1_all.deb Size: 1456854 SHA256: 52e7de9385053ebfd35fe6b01dd956c987e3498826420cd954ed71023b608158 SHA1: 3bc033430a4df3eae4f9a5a987aa58d17c274990 MD5sum: 0b908d7d0a09480199c69244e0a564b0 Description: toolbox for analysis of MR diffusion imaging data Dipy is a toolbox for the analysis of diffusion magnetic resonance imaging data. It features: - Reconstruction algorithms, e.g. GQI, DTI - Tractography generation algorithms, e.g. EuDX - Intelligent downsampling of tracks - Ultra fast tractography clustering - Resampling datasets with anisotropic voxels to isotropic - Visualizing multiple brains simultaneously - Finding track correspondence between different brains - Warping tractographies into another space, e.g. MNI space - Reading many different file formats, e.g. Trackvis or NIfTI - Dealing with huge tractographies without memory restrictions - Playing with datasets interactively without storing Python-Version: 2.6, 2.7 Package: python-dipy-doc Source: dipy Version: 0.5.0-2~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3212 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-dipy Homepage: http://nipy.org/dipy Priority: extra Section: doc Filename: pool/main/d/dipy/python-dipy-doc_0.5.0-2~nd11.04+1_all.deb Size: 1942128 SHA256: fe7e7da4a00561a8dea8d7e9dacde560b1e3465e9f74603b1cce5b4c99185e1c SHA1: def40767671dbb176b0d8241f592cca3e7654577 MD5sum: 9d3354418c37070a3452509b6469f72d Description: toolbox for analysis of MR diffusion imaging data -- documentation Dipy is a toolbox for the analysis of diffusion magnetic resonance imaging data. . This package provides the documentation in HTML format. Package: python-dipy-lib Source: dipy Version: 0.5.0-2~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1080 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0), libc6 (>= 2.4) Provides: python2.6-dipy-lib, python2.7-dipy-lib Homepage: http://nipy.org/dipy Priority: extra Section: python Filename: pool/main/d/dipy/python-dipy-lib_0.5.0-2~nd11.04+1_amd64.deb Size: 416520 SHA256: 7f307b8dc334edde446facabdc48968e837ac7512a667b91ab39d905f188073f SHA1: c780cfa33b1176f5fc57c8240877834db44cdbcd MD5sum: 6acd5148ae518cd0322ab547a4ce4bd0 Description: toolbox for analysis of MR diffusion imaging data -- extensions Dipy is a toolbox for the analysis of diffusion magnetic resonance imaging data. . This package provides architecture-dependent builds of the extensions. Python-Version: 2.6, 2.7 Package: python-freenect Source: libfreenect Version: 1:0.0.1+20101211+2-2~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 128 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libfreenect0.0, libusb-1.0-0 (>= 2:1.0.8) Suggests: python-numpy, python-matplotlib, python-opencv Homepage: http://openkinect.org/ Priority: extra Section: python Filename: pool/main/libf/libfreenect/python-freenect_0.0.1+20101211+2-2~nd11.04+1_amd64.deb Size: 33236 SHA256: 33fe05de82fc63d2b93244f574d0c142c31dd9c4223ee4a441fe1f4634748b2a SHA1: e1649047394fc850ceabf28fd2c6c85fe45de5d6 MD5sum: 5dce76c8621282462ed2c987235ba7d0 Description: library for accessing Kinect USB camera -- Python bindings libfreenect is the core library for accessing the Microsoft Kinect USB camera. . This package provides freenect extension to use libfreenect functionality from Python and includes some demo scripts. Package: python-joblib Source: joblib Version: 0.5.4-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 228 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0) Recommends: python-numpy, python-nose, python-simplejson Homepage: http://packages.python.org/joblib/ Priority: optional Section: python Filename: pool/main/j/joblib/python-joblib_0.5.4-1~nd11.04+1_all.deb Size: 44452 SHA256: 1cac0427122634237309e88a3ed9cf5040fd8c83e654a633f5f330e001a02a2b SHA1: 7862e06bf808d79414f43bbfd81963aa161a9157 MD5sum: 239a887510b67adab8ba45665c3dabd1 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-mpi4py Source: mpi4py Version: 1.2.2-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2208 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libopenmpi1.3, python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0) Recommends: openmpi-bin Suggests: python-numpy Homepage: http://code.google.com/p/mpi4py/ Priority: extra Section: python Filename: pool/main/m/mpi4py/python-mpi4py_1.2.2-1~nd11.04+1_amd64.deb Size: 738576 SHA256: 238c0e96a33cc14efb60e61d65a397717fb20982c583ee8d3faff5ea9248f11b SHA1: 6a06b014b097a1a91519cb6fc90f03ca89458dab MD5sum: 1465fd2442b9866dc4898e74e4061eb4 Description: bindings of the Message Passing Interface (MPI) standard MPI for Python (mpi4py) provides bindings of the Message Passing Interface (MPI) standard for the Python programming language, allowing any Python program to exploit multiple processors. . mpi4py is constructed on top of the MPI-1/MPI-2 specification and provides an object oriented interface which closely follows MPI-2 C++ bindings. It supports point-to-point (sends, receives) and collective (broadcasts, scatters, gathers) communications of any picklable Python object as well as optimized communications of Python object exposing the single-segment buffer interface (NumPy arrays, builtin bytes/string/array objects). Package: python-mpi4py-dbg Source: mpi4py Version: 1.2.2-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 5768 Depends: neurodebian-popularity-contest, python-mpi4py (= 1.2.2-1~nd11.04+1) Homepage: http://code.google.com/p/mpi4py/ Priority: extra Section: debug Filename: pool/main/m/mpi4py/python-mpi4py-dbg_1.2.2-1~nd11.04+1_amd64.deb Size: 1410212 SHA256: 6f0f6e2ae58e2b02f8974cc87da0ec2747ffffecd3f99b81415a7191c0111f30 SHA1: 029f08f65d7b7479bb6dae2e013df0878954814d MD5sum: 0422f8afe0410f280a74172e2b04f785 Description: bindings of the MPI standard -- debug symbols MPI for Python (mpi4py) provides bindings of the Message Passing Interface (MPI) standard for the Python programming language, allowing any Python program to exploit multiple processors. . mpi4py is constructed on top of the MPI-1/MPI-2 specification and provides an object oriented interface which closely follows MPI-2 C++ bindings. It supports point-to-point (sends, receives) and collective (broadcasts, scatters, gathers) communications of any picklable Python object as well as optimized communications of Python object exposing the single-segment buffer interface (NumPy arrays, builtin bytes/string/array objects). . This package provides debug symbols. Package: python-mpi4py-doc Source: mpi4py Version: 1.2.2-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 268 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-mpi4py Homepage: http://code.google.com/p/mpi4py/ Priority: extra Section: doc Filename: pool/main/m/mpi4py/python-mpi4py-doc_1.2.2-1~nd11.04+1_all.deb Size: 54626 SHA256: 3af585c5346cf72f64c7e3e655ba755e619a214fc943a806bd22c0f710154b50 SHA1: c671da5c6347ab81372616ead5b27b78eba29fd9 MD5sum: f041744082cb75b57427cb5b3d5e7a2e Description: bindings of the MPI standard -- documentation MPI for Python (mpi4py) provides bindings of the Message Passing Interface (MPI) standard for the Python programming language, allowing any Python program to exploit multiple processors. . mpi4py is constructed on top of the MPI-1/MPI-2 specification and provides an object oriented interface which closely follows MPI-2 C++ bindings. It supports point-to-point (sends, receives) and collective (broadcasts, scatters, gathers) communications of any picklable Python object as well as optimized communications of Python object exposing the single-segment buffer interface (NumPy arrays, builtin bytes/string/array objects). . This package provides HTML rendering of the user's manual. Package: python-mvpa-snapshot Source: pymvpa-snapshot Version: 0.6.0~rc4-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4596 Depends: neurodebian-popularity-contest, python (>= 2.4), python-support (>= 0.90.0), python-numpy, python-mvpa-snapshot-lib (>= 0.6.0~rc4-1~nd11.04+1) Recommends: python-nibabel, python-psyco, python-mdp, python-scipy, shogun-python-modular, python-pywt, python-matplotlib, python-reportlab, python-h5py Suggests: fslview, fsl, python-nose, python-lxml, python-openopt, python-rpy2, python-mvpa-doc, python-scikits-learn Conflicts: python-mvpa Provides: python2.6-mvpa-snapshot, python2.7-mvpa-snapshot Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa-snapshot/python-mvpa-snapshot_0.6.0~rc4-1~nd11.04+1_all.deb Size: 2314444 SHA256: 4088e986b047486230d993c17a2ddbf9b1e6dba2e7be7950e08d1ecd18ec8e6b SHA1: b7b54cf030f31d0a55537017bc73224f1f4ad672 MD5sum: 57faf9c7100871e174add5cbb5787786 Description: multivariate pattern analysis with Python PyMVPA eases pattern classification analyses of large datasets, with an accent on neuroimaging. 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 snapshot. The latest released version is provided by the python-mvpa package. Python-Version: 2.6, 2.7 Package: python-mvpa-snapshot-lib Source: pymvpa-snapshot Version: 0.6.0~rc4-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 224 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), libsvm2, python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0), python-numpy Conflicts: python-mvpa-lib Provides: python2.6-mvpa-snapshot-lib, python2.7-mvpa-snapshot-lib Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa-snapshot/python-mvpa-snapshot-lib_0.6.0~rc4-1~nd11.04+1_amd64.deb Size: 72096 SHA256: ed864f1ed0a958a3f3bc3da1ed503d9c3296573aa2a28aeb8a9e8edae7eb4941 SHA1: 4e9e419880a5716ebad1ea758467807dad5bb89a MD5sum: 5a123ae8458e1e6ab0d93f478129b819 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 snapshot. The latest released version is provided by the python-mvpa-lib package. Python-Version: 2.6, 2.7 Package: python-mvpa2 Source: pymvpa2 Version: 2.0.0~rc5-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4588 Depends: neurodebian-popularity-contest, python (>= 2.4), python-support (>= 0.90.0), python-numpy, python-mvpa2-lib (>= 2.0.0~rc5-1~nd11.04+1) Recommends: python-nibabel, python-psyco, python-mdp, python-scipy, shogun-python-modular, python-pywt, python-matplotlib, python-reportlab, python-h5py, python-psutil Suggests: fslview, fsl, python-nose, python-lxml, python-openopt, python-rpy2, python-mvpa2-doc, python-sklearn Provides: python2.6-mvpa2, python2.7-mvpa2 Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa2/python-mvpa2_2.0.0~rc5-1~nd11.04+1_all.deb Size: 2309358 SHA256: 49f7a9dba4238be95d12181e682e368ac88ad49d326a2bfd5eafacf8d7cea81f SHA1: 7f781b68f323fda176e573ab5a099c332acb93fe MD5sum: 11686da3c11c4f77cdd9b3226a71b5b5 Description: multivariate pattern analysis with Python PyMVPA eases pattern classification analyses of large datasets, with an accent on neuroimaging. 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 PyMVPA v.2. Previously released stable version is provided by the python-mvpa package. Python-Version: 2.6, 2.7 Package: python-mvpa2-lib Source: pymvpa2 Version: 2.0.0~rc5-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 224 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), libsvm2, python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0), python-numpy Conflicts: python-mvpa-lib Provides: python2.6-mvpa2-lib, python2.7-mvpa2-lib Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa2/python-mvpa2-lib_2.0.0~rc5-1~nd11.04+1_amd64.deb Size: 72446 SHA256: d41f14af95cbc42c9db68f0658994b731e52e40c6497682c672e11be457d74c6 SHA1: d118689cb1dd049da7bd26d96c3def3cb057eb1c MD5sum: ea9ce643a6c37c0bd5961089b119b377 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 snapshot. The latest released version is provided by the python-mvpa-lib package. Python-Version: 2.6, 2.7 Package: python-networkx Version: 1.4-2~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2672 Depends: neurodebian-popularity-contest, python (>= 2.6), 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_1.4-2~nd11.04+1_all.deb Size: 647290 SHA256: 46af0162d46d69b38d76984d10577b337ec8cbeb61b064ebca55500dd92ffd1c SHA1: eb58edd4c3c571c35d6a796e0d6c547be617d113 MD5sum: 721176531d2dc53a9eeb81d80861ffcc 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 it's meant a simple undirected graph, i.e. no self-loops and no multiple edges are allowed. By a network it's usually meant 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-networkx-doc Source: python-networkx Version: 1.4-2~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 15812 Depends: neurodebian-popularity-contest Homepage: http://networkx.lanl.gov/ Priority: optional Section: doc Filename: pool/main/p/python-networkx/python-networkx-doc_1.4-2~nd11.04+1_all.deb Size: 6229030 SHA256: 3f9a60dbe16c43365090f53f01ecabb8de486a1c785e8d57e547e606111a4636 SHA1: 5f4e62733f3342daf0a873ea56dac67bbdd5b121 MD5sum: d721409534a58309f5f63c1bedeb1371 Description: tool to create, manipulate and study complex networks - documentation 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 it's meant a simple undirected graph, i.e. no self-loops and no multiple edges are allowed. By a network it's usually meant a graph with weights (fields, properties) on nodes and/or edges. . The potential audience for NetworkX includes: mathematicians, physicists, biologists, computer scientists, social scientists. . This package contains documentation for NetworkX. Package: python-nibabel Source: nibabel Version: 1.1.0-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3608 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy Recommends: python-dicom Suggests: python-nibabel-doc Provides: python2.6-nibabel, python2.7-nibabel Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: python Filename: pool/main/n/nibabel/python-nibabel_1.1.0-1~nd11.04+1_all.deb Size: 1665814 SHA256: 634a9433263713d05172cc51911e48e6618840b4d39d1825e12330b4c93fab33 SHA1: 613177876f214ce3472783355072cca467927204 MD5sum: fc7082234d76848b6d934ea3a61b32d2 Description: Python bindings to various neuroimaging data formats NiBabel provides read and write access to some common medical and neuroimaging file formats, including: ANALYZE (plain, SPM99, SPM2), GIFTI, NIfTI1, MINC, as well as PAR/REC. The various image format classes give full or selective access to header (meta) information and access to the image data is made available via NumPy arrays. NiBabel is the successor of PyNIfTI. . This package also provides a commandline tool for conversion of PAR/REC to NIfTI images. Python-Version: 2.6, 2.7 Package: python-nibabel-doc Source: nibabel Version: 1.1.0-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2736 Depends: neurodebian-popularity-contest, libjs-jquery Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: doc Filename: pool/main/n/nibabel/python-nibabel-doc_1.1.0-1~nd11.04+1_all.deb Size: 404626 SHA256: 6312bef49e81bfa61b3e87a7e31f54c26039f705216667f2b4e506fabbe755f6 SHA1: ef837b8232c7835b69c1ae7401dbb13b9abab005 MD5sum: 40f31d2eaaeb0440bceb41627a05f96d Description: documentation for NiBabel NiBabel provides read and write access to some common medical and neuroimaging file formats, including: ANALYZE (plain, SPM99, SPM2), GIFTI, NIfTI1, MINC, as well as PAR/REC. The various image format classes give full or selective access to header (meta) information and access to the image data is made available via NumPy arrays. NiBabel is the successor of PyNIfTI. . This package provides the documentation in HTML format. Package: python-nifti Source: pynifti Version: 0.20100607.1-4~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1472 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libnifti2, python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0), python2.7, python-numpy, libjs-jquery Provides: python2.6-nifti, python2.7-nifti Homepage: http://niftilib.sourceforge.net/pynifti/ Priority: optional Section: python Filename: pool/main/p/pynifti/python-nifti_0.20100607.1-4~nd11.04+1_amd64.deb Size: 365468 SHA256: 0202f6f164552ea3e089b70427b3876341c7f8d1dc8d410e1e0940dd287929ec SHA1: 600af5e625f240e383b2a7f2d3b29bd1ad9cf76d MD5sum: 3c6da350f7b6cac541cc2b646c55e3e6 Description: Python interface to the NIfTI I/O libraries Using PyNIfTI one can easily read and write NIfTI and ANALYZE images from within Python. The NiftiImage class provides Python-style access to the full header information. Image data is made available via NumPy arrays. Python-Version: 2.6, 2.7 Package: python-nipy Source: nipy Version: 0.1.2+201100720-2~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3408 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-numpy (<< 1:1.6), python-numpy (>= 1:1.5.1), python-support (>= 0.90.0), python-scipy, python-nibabel, python-nipy-lib (>= 0.1.2+201100720-2~nd11.04+1) Recommends: python-matplotlib, mayavi2 Suggests: python-mvpa Provides: python2.6-nipy, python2.7-nipy Homepage: http://neuroimaging.scipy.org Priority: extra Section: python Filename: pool/main/n/nipy/python-nipy_0.1.2+201100720-2~nd11.04+1_all.deb Size: 703194 SHA256: 1f6743ba8ee6cbc5796dda944b97b99e30c3833c054d127f2af1e86dd338d67f SHA1: ebd851060a280679f0c8aadd515d0c68143f2e17 MD5sum: d6613e9f73313ce73a8c5b89c08331f7 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: 2.6, 2.7 Package: python-nipy-doc Source: nipy Version: 0.1.2+201100720-2~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9416 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+201100720-2~nd11.04+1_all.deb Size: 2511622 SHA256: 633acae8abe5d86d4bb69b80b57e93d1ae6254ed02b19fcef6ec306336ccd437 SHA1: 909d95f8c731a4d2f2a3cf594482a9f5146007eb MD5sum: 9000cda1e8256b8155700f92fb8f7f5f Description: documentation 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+201100720-2~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2664 Depends: neurodebian-popularity-contest, libblas3gf | libblas.so.3gf | libatlas3gf-base, libc6 (>= 2.4), liblapack3gf | liblapack.so.3gf | libatlas3gf-base, python (<< 2.8), python (>= 2.6), python-numpy (<< 1:1.6), python-numpy (>= 1:1.5.1), python-support (>= 0.90.0) Provides: python2.6-nipy-lib, python2.7-nipy-lib Homepage: http://neuroimaging.scipy.org Priority: extra Section: python Filename: pool/main/n/nipy/python-nipy-lib_0.1.2+201100720-2~nd11.04+1_amd64.deb Size: 1045860 SHA256: 61180b06008190d15e0f1c3a25ae60da4a7d2c72a7d2d8016d39b9cfbc701f82 SHA1: f03fc41f23a1a8b775c90b6f88f0f6908ffbea02 MD5sum: 693abb8b3de591eb075fc4e83615b7b5 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: 2.6, 2.7 Package: python-nipy-lib-dbg Source: nipy Version: 0.1.2+201100720-2~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2920 Depends: neurodebian-popularity-contest, libblas3gf | libblas.so.3gf | libatlas3gf-base, libc6 (>= 2.4), liblapack3gf | liblapack.so.3gf | libatlas3gf-base, python (<< 2.8), python (>= 2.6), python-numpy (<< 1:1.6), python-numpy (>= 1:1.5.1), python-support (>= 0.90.0), python-nipy-lib (= 0.1.2+201100720-2~nd11.04+1) Provides: python2.6-nipy-lib-dbg, python2.7-nipy-lib-dbg Homepage: http://neuroimaging.scipy.org Priority: extra Section: debug Filename: pool/main/n/nipy/python-nipy-lib-dbg_0.1.2+201100720-2~nd11.04+1_amd64.deb Size: 1186984 SHA256: 81f621cf1f0d7d1965ddb6a9b2306e16fbdabcd8435c71a55d5506c2b6e553a7 SHA1: ba2131d0887ade76a6f9cefdf397953dae27228c MD5sum: 2b4063bb989a5a7c9399f490429c2ff3 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 debugging symbols for architecture-dependent builds of the libraries. Python-Version: 2.6, 2.7 Package: python-nipype Source: nipype Version: 0.4.1-2~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2152 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-scipy, python-simplejson, python-traits (>= 4.0) | python-traits4, python-nibabel (>= 1.0.0~), python-networkx (>= 1.3), python-cfflib Recommends: ipython, python-nose, graphviz Suggests: fsl, afni, python-nipy, slicer, matlab-spm8, python-pyxnat Provides: python2.6-nipype, python2.7-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: python Filename: pool/main/n/nipype/python-nipype_0.4.1-2~nd11.04+1_all.deb Size: 384650 SHA256: e136f4a8263f50207e45395a9d25ee09c4dbcc7698a89a9dfbf1291a4e8c2d83 SHA1: 60214753067a6c18c0bf1526ced8d2f16196505c MD5sum: f05d3b2f0a3a38fba3a542cd2be83654 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: 0.4.1-2~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4176 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: doc Filename: pool/main/n/nipype/python-nipype-doc_0.4.1-2~nd11.04+1_all.deb Size: 988204 SHA256: 034260a7cc350b52d75a348a1340ab10b2f86d1d55098d245a24ac77a2cd342b SHA1: e66c27e77ab80d83d937c340965282b38615d6f3 MD5sum: 7adccd6a3585cdde9ff650f91a2aaf60 Description: Neuroimaging data analysis pipelines in Python -- documentation Nipype interfaces Python to other neuroimaging packages and creates an API for specifying a full analysis pipeline in Python. Currently, it has interfaces for SPM, FSL, AFNI, Freesurfer, but could be extended for other packages (such as lipsia). . This package contains Nipype examples and documentation in various formats. Package: python-nitime Source: nitime Version: 0.3.1-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9404 Depends: neurodebian-popularity-contest, python-numpy, python-scipy Recommends: python-matplotlib, python-nose, python-nibabel, python-networkx Homepage: http://nipy.org/nitime Priority: extra Section: python Filename: pool/main/n/nitime/python-nitime_0.3.1-1~nd11.04+1_all.deb Size: 3902358 SHA256: cb8fb98e6a1b36f8958c6dbabfe9f65de7fdc452026cc3de8e00fa8cb32838f4 SHA1: b75d3685d89af63cadf195df8da0f371ff6e02f3 MD5sum: 05169dc02a0515a22040c9414bee4d91 Description: timeseries analysis for neuroscience data (nitime) Nitime is a Python module for time-series analysis of data from neuroscience experiments. It contains a core of numerical algorithms for time-series analysis both in the time and spectral domains, a set of container objects to represent time-series, and auxiliary objects that expose a high level interface to the numerical machinery and make common analysis tasks easy to express with compact and semantically clear code. Package: python-nitime-doc Source: nitime Version: 0.3.1-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6960 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-nitime Homepage: http://nipy.org/nitime Priority: extra Section: doc Filename: pool/main/n/nitime/python-nitime-doc_0.3.1-1~nd11.04+1_all.deb Size: 5207026 SHA256: cecde9bd9dc5b2be44624aec77a3a791b699f7adc606220322c84b206ee6d2c5 SHA1: 3d76e3e78942ef77278855a5f1d927c54f43fa5f MD5sum: f1230db07b4bc4d5a1fe43d401044887 Description: timeseries analysis for neuroscience data (nitime) -- documentation Nitime is a Python module for time-series analysis of data from neuroscience experiments. . This package provides the documentation in HTML format. Package: python-openopt Source: openopt Version: 0.34+svn1146-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1448 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-numpy Recommends: python-scipy, python-cvxopt, python-matplotlib Suggests: lp-solve Conflicts: python-scikits-openopt Replaces: python-scikits-openopt Provides: python2.6-openopt, python2.7-openopt Homepage: http://www.openopt.org Priority: extra Section: python Filename: pool/main/o/openopt/python-openopt_0.34+svn1146-1~nd11.04+1_all.deb Size: 206392 SHA256: 4b6de4dce6337dd70ee31852c15b59a235caef7f6cd7bc982802ed7e98316eb5 SHA1: d89601333d99555bade49637bdb136d286d6d23a MD5sum: 301c9393d777482326fd3e488f78b19c Description: Python module for numerical optimization Numerical optimization framework developed in Python which provides connections to lots of solvers with easy and unified OpenOpt syntax. Problems which can be tackled with OpenOpt * Linear Problem (LP) * Mixed-Integer Linear Problem (MILP) * Quadratic Problem (QP) * Non-Linear Problem (NLP) * Non-Smooth Problem (NSP) * Non-Linear Solve Problem (NLSP) * Least Squares Problem (LSP) * Linear Least Squares Problem (LLSP) * Mini-Max Problem (MMP) * Global Problem (GLP) . A variety of solvers is available (e.g. IPOPT, ALGENCAN). Python-Version: 2.6, 2.7 Package: python-openpyxl Source: openpyxl Version: 1.5.5-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 456 Depends: neurodebian-popularity-contest, python (>= 2.6), 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_1.5.5-1~nd11.04+1_all.deb Size: 65578 SHA256: e2f338eae1574c73f1064bcb09df61b937d751b887413b69eea87adc0929a18d SHA1: dd0e291dfc26e52a4a2c9fecd89b91e582ea4527 MD5sum: eabf7e4e7666190ca0a1bd0267d18fe4 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-pandas Source: pandas Version: 0.4.3-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1372 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-numpy, python-dateutil, python-pandas-lib (>= 0.4.3-1~nd11.04+1) Recommends: python-scipy, python-matplotlib, python-tables, python-tz, python-xlrd, python-scikits.statsmodels Suggests: python-pandas-doc Provides: python2.6-pandas, python2.7-pandas Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python-pandas_0.4.3-1~nd11.04+1_all.deb Size: 261598 SHA256: 2d93e558a98ae8f7fc0732d16389e667ccf71b63856228a420ce0a3dd32b5246 SHA1: 70e9b53051e39be03af62cb037ccff02846b4c35 MD5sum: 145959d46c0887a69bb110cf1c9284ee Description: data structures for "relational" or "labeled" data pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. pandas is well suited for many different kinds of data: . - Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet - Ordered and unordered (not necessarily fixed-frequency) time series data. - Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels - Any other form of observational / statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure Package: python-pandas-lib Source: pandas Version: 0.4.3-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1724 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), python (<< 2.8), python (>= 2.6), python-numpy (<< 1:1.6), python-numpy (>= 1:1.5.1), python-support (>= 0.90.0) Provides: python2.6-pandas-lib, python2.7-pandas-lib Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python-pandas-lib_0.4.3-1~nd11.04+1_amd64.deb Size: 637716 SHA256: 30dac79b8dac5004850ef7014283f1d2ada7999e8be0080fddb2fcba6487f665 SHA1: 0e9d30e056096d3a0e812c692ff0b3f259abaa32 MD5sum: 3dcc0cc40e842d9a4779e29d74ab8eac Description: low-level implementations and bindings for pandas This is an add-on package for python-pandas providing architecture-dependent extensions. Python-Version: 2.6, 2.7 Package: python-pypsignifit Source: psignifit Version: 3.0~beta.20111109.1-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2496 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.5), python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-matplotlib, python-scipy Homepage: http://psignifit.sourceforge.net Priority: extra Section: python Filename: pool/main/p/psignifit/python-pypsignifit_3.0~beta.20111109.1-1~nd11.04+1_amd64.deb Size: 676566 SHA256: 2bfceb0500f8113f36f386df82b39b1ec8201a7f0cdbaab78ffbd2565f7fb7bc SHA1: 1df5db9e1ee306ccdd3a72b3fd61b3364be76965 MD5sum: b18a0937182d47f7e6888b8506894a7d Description: psychometric analysis of psychophysics data in Python Psignifit allows fitting of psychometric functions to datasets while maintaining full control over a large number of parameters. Psignifit performs the calculation of confidence intervals as well as goodness-of-fit tests. In addition it offers: . * full Bayesian treatment of psychometric functions including Bayesian model selection and goodness of fit assessment * identification of influential observations and outlier detection * flexible shape definition of the psychometric function . This package provides the Python bindings. Package: python-pyxid Source: pyxid Version: 1.0-1~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 80 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0) Homepage: https://github.com/cedrus-opensource/pyxid Priority: optional Section: python Filename: pool/main/p/pyxid/python-pyxid_1.0-1~nd+1_all.deb Size: 11020 SHA256: 1031c0d69dd73cb38f3e0b826193211706a94bfd04da4287288418b257e54249 SHA1: 0f0d0524354e5d07eb89efcb11779d9acd9d57e2 MD5sum: 1f2a9bc07952b1f5c6b65fc5c092f75c Description: interface for Cedrus XID and StimTracker devices pyxid is a Python library for interfacing with Cedrus XID (eXperiment Interface Device) and StimTracker devices. XID devices are used in software such as SuperLab, Presentation, and ePrime for receiving input as part of stimulus/response testing experiments. . pyxid handles all of the low level device handling for XID devices in Python projects. Package: python-scikits-learn Source: scikit-learn Version: 0.9.0.dfsg-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 84 Depends: neurodebian-popularity-contest, python-sklearn Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: oldlibs Filename: pool/main/s/scikit-learn/python-scikits-learn_0.9.0.dfsg-1~nd11.04+1_all.deb Size: 17102 SHA256: 6e7025a18a8cbcd4221f6e04906f13524eca6cc902b4c265c37edd4218a9ec37 SHA1: fcb7cbf861a2853a664378cb29a636311a7836d7 MD5sum: 997baf94a4c947b07a5cf56eb12dfbaa Description: transitional compatibility package for scikits.learn -> sklearn migration Provides old namespace (scikits.learn) and could be removed if dependent code migrated to use sklearn for clarity of the namespace. Package: python-scikits-learn-doc Source: scikit-learn Version: 0.8.1.dfsg-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 14604 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-scikits-learn Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: doc Filename: pool/main/s/scikit-learn/python-scikits-learn-doc_0.8.1.dfsg-1~nd11.04+1_all.deb Size: 9036084 SHA256: a1c13fad228674a644ddc6efec0a40293bf1e0b9d566d92c5a8ef0c8991dcdad SHA1: 91a5c10515892e10c2be79d3eaf1dfcbf8eb7a4d MD5sum: 6c06d3d0886a793db556ff704bcd1953 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: 0.8.1.dfsg-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2720 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.2.1), python (<< 2.8), python (>= 2.6), python-numpy (<< 1:1.6), python-numpy (>= 1:1.5.1), python-support (>= 0.90.0) Provides: python2.6-scikits-learn-lib, python2.7-scikits-learn-lib Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python-scikits-learn-lib_0.8.1.dfsg-1~nd11.04+1_amd64.deb Size: 1030416 SHA256: da7d49304012f997abb279ba915b466d657106c050f94e191c50814b1d8ddd7d SHA1: 9718ba149a4b96239afaf6d238affd425b9ee9bb MD5sum: 29dd3842eb6c4d0cac72d95ce89593c2 Description: low-level implementations and bindings for scikits-learn This is an add-on package for python-scikits-learn. It provides low-level implementations and custom Python bindings for the LIBSVM library. Python-Version: 2.6, 2.7 Package: python-scikits.statsmodels Source: statsmodels Version: 0.3.1-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 13280 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0), python-numpy, python-scipy Recommends: python-matplotlib, python-nose, python-rpy Conflicts: python-scikits-statsmodels Replaces: python-scikits-statsmodels Provides: python2.6-scikits.statsmodels, python2.7-scikits.statsmodels Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: python Filename: pool/main/s/statsmodels/python-scikits.statsmodels_0.3.1-1~nd11.04+1_all.deb Size: 3099420 SHA256: 000801a2eeba39ac4615918cccea8b44df0921c8032ef4a02fe02650e266bad9 SHA1: f66374451b7ce04ef15374e179047f7fbfef91f6 MD5sum: 6c6b5ac3f4b84c82ea85f536603b5b25 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 available for each estimation problem. Python-Version: 2.6, 2.7 Package: python-scikits.statsmodels-doc Source: statsmodels Version: 0.3.1-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 18740 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-scikits.statsmodels Conflicts: python-scikits-statsmodels-doc Replaces: python-scikits-statsmodels-doc Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: doc Filename: pool/main/s/statsmodels/python-scikits.statsmodels-doc_0.3.1-1~nd11.04+1_all.deb Size: 1876834 SHA256: 5c9faa84e49f622a760f2566d3d57a5065956828992f75321fae406389993124 SHA1: 30d1f412f4edd81f32fa791b1e4cf913af122391 MD5sum: c35ec65248bea671df9394fe37a23a6b Description: documentation and examples for python-scikits.statsmodels This package contains HTML documentation and example scripts for python-scikits.statsmodels. Package: python-simplegeneric Source: simplegeneric Version: 0.7-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 52 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0) Provides: python2.6-simplegeneric, python2.7-simplegeneric Homepage: http://pypi.python.org/pypi/simplegeneric Priority: extra Section: python Filename: pool/main/s/simplegeneric/python-simplegeneric_0.7-1~nd11.04+1_all.deb Size: 9810 SHA256: 8410abe659c8cc5a3710a0ecd3159d045b1549c5941cc7dd597e81b2a8785e2e SHA1: 9e4847ea8e12c7d1b798c81e88d58ec8d4f78fbf MD5sum: 77f08535f4631ba7375e1ac59ed76988 Description: Simple generic functions for Python The simplegeneric module lets you define simple single-dispatch generic functions, akin to Python's built-in generic functions like len(), iter() and so on. However, instead of using specially-named methods, these generic functions use simple lookup tables, akin to those used by e.g. pickle.dump() and other generic functions found in the Python standard library. Package: python-sklearn Source: scikit-learn Version: 0.9.0.dfsg-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2452 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-numpy, python-scipy, python-sklearn-lib (>= 0.9.0.dfsg-1~nd11.04+1) Recommends: python-nose, python-psyco, python-matplotlib, python-joblib (>= 0.4.5) Suggests: python-dap, python-scikits-optimization, python-sklearn-doc, ipython Enhances: python-mdp, python-mvpa2 Breaks: python-scikits-learn (<< 0.9~) Replaces: python-scikits-learn (<< 0.9~) Provides: python2.6-sklearn, python2.7-sklearn Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python-sklearn_0.9.0.dfsg-1~nd11.04+1_all.deb Size: 770578 SHA256: b7ce551535c2f813c9aaa698c395118964d93b64dd473e41118de690dd382c54 SHA1: 165fc7725141a64c28a077387815a048d7719e36 MD5sum: 4d090bdb7159404d711b72c16ffe6400 Description: Python modules for machine learning and data mining scikit-learn is a collection of Python modules relevant to machine/statistical learning and data mining. Non-exhaustive list of included functionality: - Gaussian Mixture Models - Manifold learning - kNN - SVM (via LIBSVM) Python-Version: 2.6, 2.7 Package: python-sklearn-doc Source: scikit-learn Version: 0.9.0.dfsg-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 21640 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-sklearn Conflicts: python-scikits-learn-doc Replaces: python-scikits-learn-doc Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: doc Filename: pool/main/s/scikit-learn/python-sklearn-doc_0.9.0.dfsg-1~nd11.04+1_all.deb Size: 12885594 SHA256: d44756b572e479e750adbbc0152a3244268f22fc6b624e04ee33cee01a9ef457 SHA1: b9a4abad9c46d99255212ad837f0f3c239717969 MD5sum: 4beae70d51f785f6886560e344b2ce85 Description: documentation and examples for scikit-learn This package contains documentation and example scripts for python-sklearn. Package: python-sklearn-lib Source: scikit-learn Version: 0.9.0.dfsg-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2976 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), python (<< 2.8), python (>= 2.6), python-numpy (<< 1:1.6), python-numpy (>= 1:1.5.1), python-support (>= 0.90.0) Conflicts: python-scikits-learn-lib Replaces: python-scikits-learn-lib Provides: python2.6-sklearn-lib, python2.7-sklearn-lib Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python-sklearn-lib_0.9.0.dfsg-1~nd11.04+1_amd64.deb Size: 1083926 SHA256: e23726ec5b9d1a8ff1de53ec55d275e5ba5530ee44987e81bd292cafc8731c42 SHA1: 0d1b1099097eac539485c0e838bf1cec178aab03 MD5sum: 8e2643bfca18c2c23d8e3c785ccd0a88 Description: low-level implementations and bindings for scikit-learn This is an add-on package for python-sklearn. It provides low-level implementations and custom Python bindings for the LIBSVM library. Python-Version: 2.6, 2.7 Package: python-sphinx Source: sphinx Version: 1.0.7-2~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4188 Depends: neurodebian-popularity-contest, 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_1.0.7-2~nd11.04+1_all.deb Size: 1260262 SHA256: a83da5c5e441ebd3cc4e0edfe0ebfe288603963b02fbcf9d391d5c3f8bc8a24c SHA1: 7c545c400b92ddfef48ad178b5d27b69debd8fd3 MD5sum: cabb0d6fe118ff07f7cd35ae3d00eb1d Description: tool for producing documentation for Python projects Sphinx is a tool for producing documentation for Python projects, using reStructuredText as markup language. . Sphinx features: * HTML, CHM, LaTeX output, * Cross-referencing source code, * Automatic indices, * Code highlighting, using Pygments, * Extensibility. Existing extensions: - automatic testing of code snippets, - including doctrings from Python modules. Package: python-stfio Source: stimfit Version: 0.10.18-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 476 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libhdf5-serial-1.8.4 | libhdf5-1.8.4, libpython2.7 (>= 2.7), libstdc++6 (>= 4.4.0), python-numpy Recommends: python-matplotlib, python-scipy Homepage: http://www.stimfit.org Priority: optional Section: python Filename: pool/main/s/stimfit/python-stfio_0.10.18-1~nd11.04+1_amd64.deb Size: 201250 SHA256: 5e5530e5547e30d549547c74aa1397904b5bb9666ca1f309765d7eed907f040d SHA1: c3dedbff80e80ca67f8cba778d0addeb0e818207 MD5sum: 79860223eb79620f203517e515c7a3c0 Description: A Python module to read common electrophysiology file formats. The stfio module allows you to read common electrophysiology file formats from Python. Axon binaries (abf), Axon text (atf), HEKA (dat), CFS (dat/cfs), Axograph (axgd/axgx) are currently supported. Package: python-surfer Source: pysurfer Version: 0.1+git21-g55debc4-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 124 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0), python-numpy, python-scipy, python-nibabel, python-imaging, mayavi2, python-argparse, ipython Homepage: http://pysurfer.github.com Priority: extra Section: python Filename: pool/main/p/pysurfer/python-surfer_0.1+git21-g55debc4-1~nd11.04+1_all.deb Size: 21898 SHA256: 40e6f89252941289a2586d4319ce3894f6a36a4709406d9afe046e6a47afc0f7 SHA1: 12ef66a9207e2ece3bd8a5e8d00a1a03aaf7f0e3 MD5sum: 38b625c514f9fed0205b732251adcc5d Description: visualize Freesurfer's data in Python This is a Python package for visualization and interaction with cortical surface representations of neuroimaging data from Freesurfer. It extends Mayavi’s powerful visualization engine with a high-level interface for working with MRI and MEG data. . PySurfer offers both a command-line interface designed to broadly replicate Freesurfer’s Tksurfer program as well as a Python library for writing scripts to efficiently explore complex datasets. Python-Version: 2.6, 2.7 Package: python-tornado Version: 2.1.0-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 948 Depends: neurodebian-popularity-contest, python2.7 | python2.6, python (>= 2.7.1-0ubuntu2), python-pycurl, ca-certificates Recommends: python-mysqldb Breaks: python (>= 2.8), python (<< 2.6) Homepage: http://www.tornadoweb.org/ Priority: optional Section: python Filename: pool/main/p/python-tornado/python-tornado_2.1.0-1~nd11.04+1_amd64.deb Size: 223380 SHA256: af12fa62f3761ee98fd80b31dc71419c42e813cea05ad7be74a0e6a8dab24ab6 SHA1: b715c833fe58897711aa5f7cca698227f5f22644 MD5sum: 7da6080f8fc2c1e350f5bd74ca06bb97 Description: scalable, non-blocking web server and tools Tornado is an open source version of the scalable, non-blocking web server and tools that power FriendFeed. The FriendFeed application is written using a web framework that looks a bit like web.py or Google's webapp, but with additional tools and optimizations to take advantage of the underlying non-blocking infrastructure. Package: python-traits4 Source: python-traits Version: 4.0.0-1~cbp1~nd11.04+1 Architecture: amd64 Bugs: mailto:bugs@neuro.debian.net Maintainer: NeuroDebian Team Installed-Size: 2256 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0) Suggests: python-traitsui Conflicts: python-traits (>= 4.0~) Homepage: http://pypi.python.org/pypi/traits Priority: optional Section: python Filename: pool/main/p/python-traits/python-traits4_4.0.0-1~cbp1~nd11.04+1_amd64.deb Size: 393982 SHA256: 57c2f40cbb982e96c90967925e1dd72c15464cca081b057f392f5097557a630f SHA1: 0a69bcc91d9878acf7c3952a45a5b5fb7352aae5 MD5sum: af60f0d7d26e8a0d138d5e27770f191a Description: Manifest typing and reactive programming for Python The traits package provides a metaclass with special attributes that are called traits. A trait is a type definition that can be used for normal Python object attributes, giving the attributes some additional characteristics: * Initialization: A trait attribute can have a default value * Validation: A trait attribute is manifestly typed. * Delegation: The value of a trait attribute can be contained in another object * Notification: Setting the value of a trait attribute can fired callbacks * Visualization: With the TraitsUI package, GUIs can be generated automatically from traited objects. Uploaders: Yaroslav Halchenko , Michael Hanke Vcs-Browser: http://git.debian.org/?p=pkg-exppsy/python-traits4.git Vcs-Git: git://git.debian.org/git/pkg-exppsy/python-traits4.git Package: python-tz Version: 2011h-0.1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 176 Depends: neurodebian-popularity-contest, tzdata, python, python-central (>= 0.6.11) Homepage: http://pypi.python.org/pypi/pytz/ Priority: optional Section: python Filename: pool/main/p/python-tz/python-tz_2011h-0.1~nd11.04+1_all.deb Size: 45696 SHA256: dec58324c02dfafb1a90c148546b7757fb790fbae06e9bbf764af43ece6b1fbe SHA1: 090be051cd3b814376dabb3c44c5f84dbd6c77c4 MD5sum: 8c4e781ebf1aeae18fc742c1cbdd02de Description: Python version of the Olson timezone database python-tz brings the Olson tz database into Python. This library allows accurate and cross platform timezone calculations using Python 2.3 or higher. It also solves the issue of ambiguous times at the end of daylight savings, which you can read more about in the Python Library Reference (datetime.tzinfo). Python-Version: all Package: python-workqueue Source: cctools Version: 3.4.0-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 412 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), python2.7 | python2.6, python (>= 2.7.1-0ubuntu2), python (<< 2.8) Homepage: http://nd.edu/~ccl/software/ Priority: extra Section: python Filename: pool/main/c/cctools/python-workqueue_3.4.0-1~nd11.04+1_amd64.deb Size: 133696 SHA256: 0937d47e6ee5b0050da493b15309bc0340e1c8e117d72711b54cd1783e3b2555 SHA1: 09ef70e031badfdfeca85fcd0e4409b6d9ab4c9a MD5sum: 37b7ad7ab9fcd31f14f83787aa9c6b3c Description: cooperative computing tools work queue Python bindings CCTools's Work Queue is a system and API for building master-worker style programs that scale up to thousands of processors. This package provides bindings to access this system from Python. Package: sigviewer Version: 0.5.1+svn556-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 992 Depends: neurodebian-popularity-contest, libbiosig0, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libqt4-xml (>= 4:4.5.3), libqtcore4 (>= 4:4.7.0~beta1), libqtgui4 (>= 4:4.6.1), libstdc++6 (>= 4.5) Homepage: http://sigviewer.sourceforge.net Priority: extra Section: science Filename: pool/main/s/sigviewer/sigviewer_0.5.1+svn556-1~nd11.04+1_amd64.deb Size: 422992 SHA256: 504147e2c6535a690bc9325c990db0590f39098009da63d7065043e125810318 SHA1: 0f5e2da422edbbe0ad83186e77699eaa551355eb MD5sum: 064ff4751aa19d285d8e251e249cc3e1 Description: GUI viewer for biosignals such as EEG, EMG, and ECG SigViewer is a viewing and scoring software for biomedical signal data. It relies on biosig4c++ library which supports a number of data formats (including EDF, BDF, GDF, BrainVision, BCI2000, CFWB, HL7aECG, SCP_ECG (EN1064), MFER, ACQ, CNT(Neuroscan), DEMG, EGI, EEG1100, FAMOS, SigmaPLpro, TMS32). The complete list of supported file formats is available at http://pub.ist.ac.at/~schloegl/biosig/TESTED . . Besides displaying biosignals, SigViewer supports creating annotations to select artifacts or specific events. Package: spm8-common Source: spm8 Version: 8.4290~dfsg.1-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 22192 Depends: neurodebian-popularity-contest Recommends: spm8-data, spm8-doc Priority: extra Section: science Filename: pool/main/s/spm8/spm8-common_8.4290~dfsg.1-1~nd11.04+1_all.deb Size: 10547232 SHA256: 311aef085f7e4020abe98273e633d85d054b79eeec6b20638906c766424d10f1 SHA1: 94df1ab77472264814457881c5af62927f308821 MD5sum: 197fd2735629c7274ea3783181fc9bdc 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: 8.4290~dfsg.1-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 73084 Depends: neurodebian-popularity-contest Priority: extra Section: science Filename: pool/main/s/spm8/spm8-data_8.4290~dfsg.1-1~nd11.04+1_all.deb Size: 52167568 SHA256: de99254176288f13c810031edd9076d7144137fcf2a47188bb847a35a7d70158 SHA1: 8e92a60804c5cc98514fae95e12f45c7dd6f688a MD5sum: 2b5e078263f961028a278528d21aa864 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: 8.4290~dfsg.1-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9380 Depends: neurodebian-popularity-contest Priority: extra Section: doc Filename: pool/main/s/spm8/spm8-doc_8.4290~dfsg.1-1~nd11.04+1_all.deb Size: 8648816 SHA256: 1246975b745587320cfa229ea7b4475a5133e9ecbbb472d6fa6b08468d7e0554 SHA1: 7ed14c87012cef9a2d7371c45ee738e6590940b3 MD5sum: a6b73e1f5d718a59ef15d3604e3af435 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: stabilitycalc Version: 0.1-1~nd11.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 148 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0), python-numpy, python-matplotlib, python-scipy, python-nifti Recommends: python-dicom Homepage: https://github.com/bbfrederick/stabilitycalc Priority: extra Section: science Filename: pool/main/s/stabilitycalc/stabilitycalc_0.1-1~nd11.04+1_all.deb Size: 28604 SHA256: 7115735e58fac8410d4bfc6d85302a8f304f999a972890839d0414a522f4616a SHA1: 52aa4733dc53968bf6bf7e7577c9b9b742e842cc MD5sum: a701387e4074e29fc3769e2b4cc47294 Description: evaluate fMRI scanner stability Command-line tools to calculate numerous fMRI scanner stability metrics, based on the FBIRN quality assurance test protocal. Any 4D volumetric timeseries image in NIfTI format is support input. Output is a rich HTML report. Python-Version: 2.6, 2.7 Package: stimfit Version: 0.10.18-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2120 Depends: neurodebian-popularity-contest, libblas3gf | libblas.so.3gf | libatlas3gf-base, libc6 (>= 2.4), libfftw3-3, libgcc1 (>= 1:4.1.1), libhdf5-serial-1.8.4 | libhdf5-1.8.4, liblapack3gf | liblapack.so.3gf | libatlas3gf-base, libpython2.7 (>= 2.7), libstdc++6 (>= 4.4.0), libwxbase2.8-0 (>= 2.8.11.0), libwxgtk2.8-0 (>= 2.8.11.0), python-wxgtk2.8 (>= 2.8.9), python-numpy, python-matplotlib Recommends: python-scipy Homepage: http://www.stimfit.org Priority: optional Section: science Filename: pool/main/s/stimfit/stimfit_0.10.18-1~nd11.04+1_amd64.deb Size: 753266 SHA256: d3c8bb2a3f34f2f154de4eb982bf2094cb0b3983f3dbc80c4225c55c00fc4351 SHA1: a1aed36c5df7850c3550082649f087a59b2a0970 MD5sum: 43b79a80afbe9eacb118be1609b2ee7a Description: A program for viewing and analyzing electrophysiological data Stimfit is a free, fast and simple program for viewing and analyzing electrophysiological data. It features an embedded Python shell that allows you to extend the program functionality by using numerical libraries such as NumPy and SciPy. Package: stimfit-dbg Source: stimfit Version: 0.10.18-1~nd11.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 24000 Depends: neurodebian-popularity-contest, stimfit Recommends: python-matplotlib, python-scipy, python-stfio Homepage: http://www.stimfit.org Priority: extra Section: debug Filename: pool/main/s/stimfit/stimfit-dbg_0.10.18-1~nd11.04+1_amd64.deb Size: 6423362 SHA256: 4eabcc6158da0a26284fcc7f7eb9eb9f979ffb759c52921fa195f0cad5b28f36 SHA1: 57a6efad146b55565743e167ee47715a69ecfa33 MD5sum: 88f8157a61338505674a4061b12c66a2 Description: Debug symbols for stimfit Stimfit is a free, fast and simple program for viewing and analyzing electrophysiological data. It features an embedded Python shell that allows you to extend the program functionality by using numerical libraries such as NumPy and SciPy. Package: svgtune Version: 0.1.0-2 Architecture: all Maintainer: Yaroslav Halchenko Installed-Size: 64 Depends: python, python-lxml Suggests: inkscape Homepage: http://github.com/yarikoptic/svgtune Priority: optional Section: graphics Filename: pool/main/s/svgtune/svgtune_0.1.0-2_all.deb Size: 6680 SHA256: 69b4df1e0b4c247673265c7f5bb2b2ffe2209d783617bf7f6eadce86633f80e1 SHA1: bec339e4453c35a05a616deef6769a6f2ad2d00d MD5sum: ef6797498477a73f930ad9bc0db3ba73 Description: tool to generate a set of .svg files out of a single .svg file svgtune is just a little helper to generate a set of .svg files out of a single .svg file, by tuning respective groups/layers visibility, transparency or anything else. . It might come very handy for generation of incremental figures to be embedded into the presentation in any format which inkscape could render using original .svg file (e.g. pdf, png).