Package: aghermann Version: 1.0.6-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1664 Depends: neurodebian-popularity-contest, libatk1.0-0 (>= 1.12.4), libc6 (>= 2.14), libcairo-gobject2 (>= 1.10.0), libcairo2 (>= 1.2.4), libconfig++9, libfftw3-double3, libgcc1 (>= 1:4.1.1), libgdk-pixbuf2.0-0 (>= 2.22.0), libglib2.0-0 (>= 2.31.18), libgomp1 (>= 4.9), libgsl0ldbl (>= 1.9), libgtk-3-0 (>= 3.3.16), libitpp8, liblua5.2-0, libpango-1.0-0 (>= 1.14.0), libpangocairo-1.0-0 (>= 1.14.0), libsamplerate0 (>= 0.1.7), libstdc++6 (>= 4.9), libunique-3.0-0 (>= 2.90.1), libvte-2.91-0 Suggests: edfbrowser Homepage: http://johnhommer.com/academic/code/aghermann Priority: optional Section: science Filename: pool/main/a/aghermann/aghermann_1.0.6-1~nd80+1_amd64.deb Size: 551072 SHA256: 149aede7c827f9aa93bd7e385768b88237e364a730ad3fa8c91fbc4d0e7ce1ce SHA1: 7d57353c20be5e952a4746d507d5e1d88be150b5 MD5sum: 9b6c438100ac83dc4867587c9f6fb35f Description: Sleep-research experiment manager Aghermann is a program designed around a common workflow in sleep-research, complete with scoring facility; cairo subpixel drawing on screen or to file; conventional PSD and EEG Micrcontinuity profiles; Independent Component Analysis; artifact detection; and Process S simulation following Achermann et al, 1993. Package: ants Version: 2.1.0-2~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 179911 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libinsighttoolkit4.7, libstdc++6 (>= 4.9) Recommends: environment-modules Suggests: fsl, gridengine-client, r-base-core Conflicts: gpe-conf Homepage: http://www.picsl.upenn.edu/ANTS/ Priority: extra Section: science Filename: pool/main/a/ants/ants_2.1.0-2~nd80+1_amd64.deb Size: 25940004 SHA256: a446e41c94fd17e203d108658cc1ac5566c4146440683f7154bfa70180123deb SHA1: 8bce94307780b331b2e62c068a649feb4da3d300 MD5sum: cb7471d30900c50abaf4eb254f4d44a2 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). . This package provides environment-modules configuration. Use 'module load ants' to make all cmdline tools available in your shell. Package: bats Version: 0.4.0-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 24 Depends: neurodebian-popularity-contest Homepage: https://github.com/sstephenson/bats Priority: optional Section: shells Filename: pool/main/b/bats/bats_0.4.0-1~nd80+1_all.deb Size: 14438 SHA256: 91d349bc09ae54251276658bd62cedabe7f7c643e24a5d9724e6f1242f1d8cdd SHA1: 4c736d03d8060cd5a57df9eb00a9de5132401b5e MD5sum: 080515586f9ef3668b56722414198938 Description: bash automated testing system Bats is a TAP-compliant testing framework for Bash. It provides a simple way to verify that the UNIX programs you write behave as expected. Bats is most useful when testing software written in Bash, but you can use it to test any UNIX program. Package: biosig-tools Source: biosig4c++ Version: 1.4.1-2~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 664 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libcholmod2.1.2, 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_1.4.1-2~nd80+1_amd64.deb Size: 241292 SHA256: b33b1e43edf09415df2ac69e78bf3fc7ea259148265f8bf47a9368d67a0165f4 SHA1: 0bd913400789d6795eb674cf239c4d976a695890 MD5sum: 9c238e7eae3099d59e0487edc1a699f2 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: btrbk Version: 0.20.0-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 105 Depends: neurodebian-popularity-contest, perl, libdate-calc-perl, btrfs-tools (>= 3.14) Suggests: openssh-client Homepage: http://www.digint.ch/btrbk/ Priority: optional Section: utils Filename: pool/main/b/btrbk/btrbk_0.20.0-1~nd80+1_all.deb Size: 34656 SHA256: 5c65a06d75fd745e51a99b46b17531533ed7e72605b21464c0c95f897f1a06be SHA1: 1b4a4a5a1ed51ecb56e5bc2487936eb417cc8a29 MD5sum: 5d759fb65bc704874b40a9518858622f Description: backup tool for btrfs volumes Backup tool for btrfs volumes, using a configuration file, allows creation of backups from multiple sources to multiple destinations at once, with ssh and configurable retention support (daily/weekly/monthly). Package: btrfs-tools Version: 4.1.2-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 3390 Depends: neurodebian-popularity-contest, e2fslibs (>= 1.42), libblkid1 (>= 2.17.2), libc6 (>= 2.8), libcomerr2 (>= 1.01), liblzo2-2, libuuid1 (>= 2.16), zlib1g (>= 1:1.2.0) Homepage: http://btrfs.wiki.kernel.org/ Priority: optional Section: admin Filename: pool/main/b/btrfs-tools/btrfs-tools_4.1.2-1~nd80+1_amd64.deb Size: 492528 SHA256: 0dcf447620b06772fb4fbab2d49ca631f3ae8e5af80a72b0e09edc8224e6c95e SHA1: f84758211534d2207266e8fcaa08e0e8b2aec78d MD5sum: e2405acac1c15a6810b838606bd15079 Description: Checksumming Copy on Write Filesystem utilities Btrfs is a new copy on write filesystem for Linux aimed at implementing advanced features while focusing on fault tolerance, repair and easy administration. . This package contains utilities (mkfs, fsck) used to work with btrfs and an utility (btrfs-convert) to make a btrfs filesystem from an ext3. Package: btrfs-tools-dbg Source: btrfs-tools Version: 4.1.2-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 4936 Depends: neurodebian-popularity-contest, btrfs-tools (= 4.1.2-1~nd80+1) Homepage: http://btrfs.wiki.kernel.org/ Priority: extra Section: debug Filename: pool/main/b/btrfs-tools/btrfs-tools-dbg_4.1.2-1~nd80+1_amd64.deb Size: 3872172 SHA256: 14155abf70d0ccccdf38464d825af8a0d8d24cfb7c97061d1d5779f3d824e33b SHA1: f0e45e7afc603f1819ff894adede3c881746be69 MD5sum: 450fd079e6a92347a1fbb21141df1f72 Description: Checksumming Copy on Write Filesystem utilities (debug) Btrfs is a new copy on write filesystem for Linux aimed at implementing advanced features while focusing on fault tolerance, repair and easy administration. . This package contains the debugging symbols. Package: caret Version: 5.6.4~dfsg.1-2~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 18488 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), 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 (>= 4.6), libvtk5.8, zlib1g (>= 1:1.2.3.3) 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.1-2~nd70+1_amd64.deb Size: 7376026 SHA256: 18a7e2e54edf249436ed4f63dd73b3ca2642abf97e3f0a9581486abf65c8e32c SHA1: 23be5cb2697e9529f5b34e2e492dd578ba623d47 MD5sum: f484324687f5fde85147cfb3e892d3ca 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 information. Package: cde Version: 0.1+git9-g551e54d-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1015 Depends: neurodebian-popularity-contest, libc6 (>= 2.14) Homepage: http://www.pgbovine.net/cde.html Priority: optional Section: utils Filename: pool/main/c/cde/cde_0.1+git9-g551e54d-1~nd80+1_amd64.deb Size: 366176 SHA256: f595abe759eab4e44013e0d0bbd6ba19fce10069bcf42028ce30a1b803905a48 SHA1: 4062b87a4135dfe8160868e164485364e89731d6 MD5sum: 3e9b6599ece0e52734cebfced3b24215 Description: package everything required to execute a Linux command on another computer CDEpack (Code, Data, and Environment packaging) is a tool that automatically packages up everything required to execute a Linux command on another computer without any installation or configuration. A command can range from something as simple as a command-line utility to a sophisticated GUI application with 3D graphics. The only requirement is that the other computer have the same hardware architecture (e.g., x86) and major kernel version (e.g., 2.6.X) as yours. CDEpack allows you to easily run programs without the dependency hell that inevitably occurs when attempting to install software or libraries. . Typical use cases: 1. Quickly share prototype software 2. Try out software in non-native environments 3. Perform reproducible research 4. Instantly deploy applications to cluster or cloud computing 5. Submit executable bug reports 6. Package class programming assignments 7. Easily collaborate on coding projects Package: cgroup-bin Source: libcgroup Version: 0.37.1-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 162 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~nd70+1_amd64.deb Size: 66046 SHA256: 6943e3e514829289c63c84f6112178c4bf02a24b7b6a31c688839b83aa659a59 SHA1: 904415be370d6ab952e17e040fadd0ef13a611e0 MD5sum: f11926de39d36d36c044fb12e6eb2da0 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: cmtk Version: 3.2.2-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 25039 Depends: neurodebian-popularity-contest, libbz2-1.0, libc6 (>= 2.14), libdcmtk2 (>= 3.6.0), libfftw3-double3, libgcc1 (>= 1:4.1.1), libgomp1 (>= 4.9), libqtcore4 (>= 4:4.6.1), libqtgui4 (>= 4:4.5.3), libsqlite3-0 (>= 3.5.9), libstdc++6 (>= 4.6), zlib1g (>= 1:1.1.4) Recommends: sri24-atlas Suggests: numdiff Homepage: http://www.nitrc.org/projects/cmtk/ Priority: extra Section: science Filename: pool/main/c/cmtk/cmtk_3.2.2-1~nd80+1_amd64.deb Size: 3709690 SHA256: f38e77996407760123a556ffd6bab070aa9b542eaec8f64f458ce5cce13b45e5 SHA1: aed014de5e095d4c612fbc699a407946b4f9f6ec MD5sum: a8eb080e52f3de437561aa43311cc400 Description: Computational Morphometry Toolkit A software toolkit for computational morphometry of biomedical images, CMTK comprises a set of command line tools and a back-end general-purpose library for processing and I/O. . The command line tools primarily provide the following functionality: registration (affine and nonrigid; single and multi-channel; pairwise and groupwise), image correction (MR bias field estimation; interleaved image artifact correction), processing (filters; combination of segmentations via voting and STAPLE; shape-based averaging), statistics (t-tests; general linear regression). Package: cnrun Version: 1.1.14-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 331 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libgomp1 (>= 4.2.1), libgsl0ldbl (>= 1.9), libreadline6 (>= 6.0), libstdc++6 (>= 4.6), libxml2 (>= 2.6.27) Suggests: gnuplot Homepage: http://johnhommer.com/academic/code/cnrun Priority: optional Section: science Filename: pool/main/c/cnrun/cnrun_1.1.14-1~nd80+1_amd64.deb Size: 130178 SHA256: 07f3141ed0863070ee3b840bf3ea785713c5b7f853c0fe6589a61667a0f8eee6 SHA1: 8d6ba7d9c9fc807ac6d275b9fb4386e602c49a20 MD5sum: 7b5658b82c5069b75146996e87ba0c90 Description: NeuroML-capable neuronal network simulator CNrun is a neuronal network model simulator, similar in purpose to NEURON except that individual neurons are not compartmentalised. It can read NeuroML files (e.g., as generated by neuroConstruct); provides a Hodgkin-Huxley neuron (plus some varieties), a Rall and Alpha-Beta synapses, Poisson, Van der Pol, Colpitts oscillators and regular pulse generator; external inputs and logging state variables. Uses a 6-5 Runge-Kutta integration method. Basic scripting and (if run interactively) context-aware autocompletion. Package: cnrun-tools Source: cnrun Version: 2.0.1-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 52 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libcnrun2 (>= 2.0.0), libgcc1 (>= 1:4.1.1), libgomp1 (>= 4.2.1), libgsl0ldbl (>= 1.9), libstdc++6 (>= 4.4.0), libxml2 (>= 2.6.27) Homepage: http://johnhommer.com/academic/code/cnrun Priority: optional Section: science Filename: pool/main/c/cnrun/cnrun-tools_2.0.1-1~nd80+1_amd64.deb Size: 20780 SHA256: e093cce3ef6ca663cfbc886e2f5ee10ad52a88abda428eee81e692b06f0484c8 SHA1: 637f6b73e1a49f66d04564be38eb414fd1417836 MD5sum: 846d3fe5a20b5210b4fb857ada90fe44 Description: NeuroML-capable neuronal network simulator (tools) CNrun is a neuronal network simulator implemented as a Lua package. This package contains two standalone tools (hh-latency-estimator and spike2sdf) that may be of interest to CNrun users. . See lua-cnrun description for extended description. Package: condor Version: 8.4.0~dfsg.1-1~nd80+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 46 Depends: neurodebian-popularity-contest, htcondor Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: oldlibs Filename: pool/main/c/condor/condor_8.4.0~dfsg.1-1~nd80+1_all.deb Size: 15668 SHA256: 0b6941d86b0f5b089458a6be5777f86dfe46adecc9d42ad224cf82fa37e03a5d SHA1: 106f8296f219d6a03a9a759ac5e632e62bf43758 MD5sum: cf0887b22fef5d58dbf27a0627311e2a Description: transitional dummy package This package aids upgrades of existing Condor installations to the new project and package name "HTCondor". The package is empty and it can safely be removed. Package: condor-dbg Source: condor Version: 8.4.0~dfsg.1-1~nd80+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 46 Depends: neurodebian-popularity-contest, htcondor-dbg Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: oldlibs Filename: pool/main/c/condor/condor-dbg_8.4.0~dfsg.1-1~nd80+1_all.deb Size: 15678 SHA256: 4d050455a81f353f10402e81744955144345f36aa180fdce6c9b75e288b828b5 SHA1: 8035947e5512036db82771bdd4332f7b6a4fb38e MD5sum: c9260e29fbb39f677c0cb60d41112445 Description: transitional dummy package This package aids upgrades of existing Condor installations to the new project and package name "HTCondor". The package is empty and it can safely be removed. Package: condor-dev Source: condor Version: 8.4.0~dfsg.1-1~nd80+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 46 Depends: neurodebian-popularity-contest, htcondor-dev Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: oldlibs Filename: pool/main/c/condor/condor-dev_8.4.0~dfsg.1-1~nd80+1_all.deb Size: 15674 SHA256: dd60c852490fe6fc1b9c7378fa13db987ad16244f8de108374e81eb684b52471 SHA1: eea754b946b466a794c8c93907f30cae36384a04 MD5sum: 725b9d5126659801ca5ca98870b5765d Description: transitional dummy package This package aids upgrades of existing Condor installations to the new project and package name "HTCondor". The package is empty and it can safely be removed. Package: condor-doc Source: condor Version: 8.4.0~dfsg.1-1~nd80+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 46 Depends: neurodebian-popularity-contest, htcondor-doc Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: oldlibs Filename: pool/main/c/condor/condor-doc_8.4.0~dfsg.1-1~nd80+1_all.deb Size: 15674 SHA256: 23adf1811dec4762542137310ee530c61a60e3554210f7a88ef765a186a3f43b SHA1: a95e67371276f72acf2184885b20ff2e153b118b MD5sum: fd1ddb0ba0d854b9ba6da047a1e1a263 Description: transitional dummy package This package aids upgrades of existing Condor installations to the new project and package name "HTCondor". The package is empty and it can safely be removed. Package: connectome-workbench Version: 1.1.1-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 37938 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libftgl2 (>= 2.1.3~rc5), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, libgomp1 (>= 4.9), libosmesa6 (>= 10.2~), libqt4-network (>= 4:4.5.3), libqt4-opengl (>= 4:4.7.0~beta1), libqt4-xml (>= 4:4.5.3), libqtcore4 (>= 4:4.8.0), libqtgui4 (>= 4:4.8.0), libqtwebkit4 (>= 2.1.0~2011week13), libstdc++6 (>= 4.9), zlib1g (>= 1:1.2.3.4) Recommends: caret Suggests: ffmpeg Homepage: http://www.nitrc.org/projects/workbench/ Priority: extra Section: science Filename: pool/main/c/connectome-workbench/connectome-workbench_1.1.1-1~nd80+1_amd64.deb Size: 19326578 SHA256: fc67df3c05f093c1e03f8b59c5c545ef3a11e9e5808443b8580bfe781b7adf80 SHA1: b6daddc4ea54a4bf283edae1683985f62d51ae2c MD5sum: 2bc0c6d8571b70c2d83f356d948e411b Description: brain visualization, analysis and discovery tool Connectome Workbench is a brain visualization, analysis and discovery tool for fMRI and dMRI brain imaging data, including functional and structural connectivity data generated by the Human Connectome Project. . Package includes wb_command, a command-line program for performing a variety of analytical tasks for volume, surface, and CIFTI grayordinates data. Package: connectome-workbench-dbg Source: connectome-workbench Version: 1.1.1-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 110015 Depends: neurodebian-popularity-contest, connectome-workbench (= 1.1.1-1~nd80+1) Homepage: http://www.nitrc.org/projects/workbench/ Priority: extra Section: debug Filename: pool/main/c/connectome-workbench/connectome-workbench-dbg_1.1.1-1~nd80+1_amd64.deb Size: 108009302 SHA256: 881f3693427d79a1b33296921cee212c7099ad6734f91c4d5c4114e51d249222 SHA1: cd0cf2b8ad03bea016cd4f352a0c474b5dba5278 MD5sum: fa84a434d0ad738d04bb70507ef36c41 Description: brain visualization, analysis and discovery tool -- debug symbols Connectome Workbench is a brain visualization, analysis and discovery tool for fMRI and dMRI brain imaging data, including functional and structural connectivity data generated by the Human Connectome Project. . Package includes wb_command, a command-line program for performing a variety of analytical tasks for volume, surface, and CIFTI grayordinates data. . This package contains debug symbols for the binaries. Package: connectomeviewer Version: 2.1.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1578 Depends: neurodebian-popularity-contest, python (<< 2.8), 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 (>= 4.0.0), 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_2.1.0-1~nd70+1_all.deb Size: 1356156 SHA256: 84e3a8e4487cd67005eaf2c292b248e7e812057408ca7b7e012d71c3684298c2 SHA1: a20067603c1694d3c598d7e261e2bb64a98253df MD5sum: 4325ba9177d6224461c4520b1b7a41a0 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: 3.4.2-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 4143 Depends: neurodebian-popularity-contest, libc6 (>= 2.3.4), libfuse2 (>= 2.8.1), libglobus-common0 (>= 14), libglobus-gss-assist3 (>= 8), libglobus-gssapi-gsi4 (>= 10), libkrb5-3 (>= 1.6.dfsg.2), libmysqlclient16 (>= 5.1.50-1), libncurses5 (>= 5.5-5~), libopenmpi1.3, libreadline6 (>= 6.0), libstdc++6 (>= 4.1.1), libtinfo5, 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_3.4.2-1~nd70+1_amd64.deb Size: 1385724 SHA256: 3e3d109bc2270fb3aaa50af892e1758c0a833bc2b4142041d24b58f8ec47bb9f SHA1: 2f24ce46104eb364890a38d7b3fb230dfaeb2eac MD5sum: 1cb17a03918c8b97568dc66fb129be01 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: 3.4.2-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 956 Depends: neurodebian-popularity-contest Homepage: http://nd.edu/~ccl/software/ Priority: extra Section: libs Filename: pool/main/c/cctools/coop-computing-tools-dev_3.4.2-1~nd70+1_amd64.deb Size: 223124 SHA256: 966931cea9c2772f2f274e8f28f56862ea3bb2ce7e75b925db29948b5a327135 SHA1: 7823084ce78245027b30988d842a5c2059714e33 MD5sum: 73c8560240dd6698aceb86886f1abe28 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: 3.4.2-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2319 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_3.4.2-1~nd70+1_all.deb Size: 310890 SHA256: ca1fc4a117105875244c5c1a16994aa4e1c7496de9d177e96bbd351def1da0b5 SHA1: 154b372d4c5b7a25d5885e2ae8d79e64808671b2 MD5sum: c5f2ca94795a12217de0438befa22e8d 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: dcm2niix Version: 0.20150909.1+git1-g8914c07-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 219 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1) Homepage: http://www.mccauslandcenter.sc.edu/CRNL/tools/dcm2niix Priority: optional Section: science Filename: pool/main/d/dcm2niix/dcm2niix_0.20150909.1+git1-g8914c07-1~nd80+1_amd64.deb Size: 87562 SHA256: 20983d2d1c9ce07492aa3d26684819de0d2be61462005918070fb6a1dd14a25c SHA1: 0193752e38304ba32da6b857398519944b614375 MD5sum: 0aa8471b06057bea7a6694f1e5a8fe1f Description: converts DICOM and PAR/REC files into the NIfTI format This is the successor of the well-known dcm2nii program. it aims to provide same functionality albeit with much faster operation. This is a new tool that is not yet well tested, and does not handle ancient proprietary formats. Use with care. Package: debian-handbook Version: 6.0+20120509~nd+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 23215 Depends: neurodebian-popularity-contest Homepage: http://debian-handbook.info Priority: optional Section: doc Filename: pool/main/d/debian-handbook/debian-handbook_6.0+20120509~nd+1_all.deb Size: 21998670 SHA256: b33f038d8363175473cc056a5f98fc7af52386a466b45d4b2e42d2f25233a3ed SHA1: 7a0b369b4548a3f4fb61aa1ef9efa2ddf2b319e2 MD5sum: 3e3d2cf990fcc5ed1ed6bdbfb5c1c3dd Description: reference book for Debian users and system administrators Accessible to all, the Debian Administrator's Handbook teaches the essentials to anyone who wants to become an effective and independent Debian GNU/Linux administrator. . It covers all the topics that a competent Linux administrator should master, from the installation and the update of the system, up to the creation of packages and the compilation of the kernel, but also monitoring, backup and migration, without forgetting advanced topics like SELinux setup to secure services, automated installations, or virtualization with Xen, KVM or LXC. . The Debian Administrator's Handbook has been written by two Debian developers — Raphaël Hertzog and Roland Mas. . This package contains the English book covering Debian 6.0 “Squeeze”. Package: debruijn Version: 1.6-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 144 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libfftw3-3, libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1) Homepage: http://www.cfn.upenn.edu/aguirre/wiki/public:de_bruijn_software Priority: extra Section: science Filename: pool/main/d/debruijn/debruijn_1.6-1~nd70+1_amd64.deb Size: 47854 SHA256: 834ad1dd20d8b12c483e3e6abf0c21a05985bc74575b068ba1d17dcd7392967e SHA1: 1fb9fbf9c85b92721515bc835966e4411466044c MD5sum: 15ea946e8d25cb946d78d24ab573601a 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: dh-systemd Source: init-system-helpers Version: 1.18~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 28 Depends: neurodebian-popularity-contest, perl, debhelper Multi-Arch: foreign Priority: extra Section: admin Filename: pool/main/i/init-system-helpers/dh-systemd_1.18~nd80+1_all.deb Size: 13826 SHA256: fad199a538859c12433c2d796a5b7c257f4c68b5744b79438fc19cc69469ec6c SHA1: 18cf78f58846e1a30f3d91c930849f7e247e3bed MD5sum: 48d4a45db3f3d8a0ad1c378e5b7c66e7 Description: debhelper add-on to handle systemd unit files dh-systemd provides a debhelper sequence addon named 'systemd' and the dh_systemd_enable/dh_systemd_start commands. . The dh_systemd_enable command adds the appropriate code to the postinst, prerm and postrm maint scripts to properly enable/disable systemd service files. The dh_systemd_start command deals with start/stop/restart on upgrades for systemd-only service files. Package: dicomnifti Version: 2.32.1-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 518 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libnifti2, libstdc++6 (>= 4.9) Homepage: http://cbi.nyu.edu/software/dinifti.php Priority: optional Section: science Filename: pool/main/d/dicomnifti/dicomnifti_2.32.1-1~nd80+1_amd64.deb Size: 96212 SHA256: 437398b06da13e02390c51cfc49954f219592f04c476e5f7152b21f6bcb88fab SHA1: ab08ef602f3595654825456ac4eae0609d1b7964 MD5sum: 2c0a69f24d7b422b07bd9fb9383e4b3f 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: 2.3.1-6~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2711 Depends: neurodebian-popularity-contest, libc6 (>= 2.15), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.9) Homepage: http://dmtcp.sourceforge.net Priority: optional Section: utils Filename: pool/main/d/dmtcp/dmtcp_2.3.1-6~nd80+1_amd64.deb Size: 663368 SHA256: b10c095624c4d4ea05a2e05e46f9b5a812de6ec6227152e891d0db7290b8f711 SHA1: 1dadf781e57a3af6d9738ebfba78971e344bc660 MD5sum: dca168c2f708d7a7f04746bdb7986d07 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 Open MPI, 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-Window 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: 2.3.1-6~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 26290 Depends: neurodebian-popularity-contest, dmtcp Homepage: http://dmtcp.sourceforge.net Priority: extra Section: debug Filename: pool/main/d/dmtcp/dmtcp-dbg_2.3.1-6~nd80+1_amd64.deb Size: 4427068 SHA256: 1e20a69ddb5e531f5666970eb9331c3295b2044a6740b1b67e3fe4a6e6a8affb SHA1: 5f0ae46937ddd8660ea6786c5efaafef7b723546 MD5sum: 8b7a8630f693e539773af76361e46738 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 Open MPI, 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-Window applications, as long as they do not use extensions (e.g.: no OpenGL, no video). . This package contains debugging symbols for DMTCP. Package: edac-utils Version: 0.18-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 55 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libedac1, lsb-base (>= 3.0-6) Recommends: dmidecode Homepage: http://sourceforge.net/projects/edac-utils Priority: extra Section: admin Filename: pool/main/e/edac-utils/edac-utils_0.18-1~nd70+1_amd64.deb Size: 29430 SHA256: 988d46cb01ef63b5f373dd756b9e0c550cd90cd55aeec1b4f48c8ef4e09338b5 SHA1: e6f070ab8993ab02d714936df3c8d4f99361e541 MD5sum: 4adcfc104be6d115e60f64aa39e2689b Description: report kernel-detected PCI and ECC RAM errors This package contains the user-space utilities for use with the EDAC kernel subsystem. EDAC (Error Detection and Correction) is a set of Linux kernel modules for handling hardware-related errors. Currently its major focus is ECC memory error handling. However it also detects and reports PCI bus parity errors. . PCI parity errors are supported on all architectures (and are a mandatory part of the PCI specification). . Main memory ECC drivers are memory controller specific. At the time of writing, drivers exist for many x86-specific chipsets and CPUs, and some PowerPC, and MIPS systems. . This package provides command lines tools Package: eegdev-plugins-free Source: eegdev Version: 0.2-3~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 83 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libeegdev0 (= 0.2-3~nd70+1), libc6 (>= 2.3.4), libexpat1 (>= 2.0.1), libusb-1.0-0 (>= 2:1.0.8), libxdffileio0 (>= 0.0) Multi-Arch: same Homepage: http://cnbi.epfl.ch/software/eegdev.html Priority: extra Section: libs Filename: pool/main/e/eegdev/eegdev-plugins-free_0.2-3~nd70+1_amd64.deb Size: 28728 SHA256: 836e4c8f55206d8ba1f4440c05a7adbdf7582513bdc8561caac85ef475a7b618 SHA1: 3ac001b19c6df3cf304066cbd952a89cf90fd1fd MD5sum: f3264511bd8d6984c73c23c15014229e Description: Biosignal acquisition device library (free plugins) eegdev is a library that provides a unified interface for accessing various EEG (and other biosignals) acquisition systems. This interface has been designed to be both flexible and efficient. The device specific part is implemented by the mean of plugins which makes adding new device backend fairly easy even if the library does not support them yet officially. . The core library not only provides to users a unified and consistent interfaces to the acquisition device but it also provides many functionalities to the device backends (plugins) ranging from configuration to data casting and scaling making writing new device backend an easy task. . This library is particularly useful to handle the acquisition part of a Brain Computer Interface (BCI) or any realtime multi-electrode acquisition in neurophysiological research. . This package contains the devices plugins that depends only on free components. Package: eeglab11-sampledata Source: eeglab11 Version: 11.0.0.0~b~dfsg.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8109 Depends: neurodebian-popularity-contest Priority: extra Section: science Filename: pool/main/e/eeglab11/eeglab11-sampledata_11.0.0.0~b~dfsg.1-1~nd70+1_all.deb Size: 7224720 SHA256: a25c47daa7e5cabbab1e2864994d7ca0d5b207e5609c31fe0f62c32fae733590 SHA1: 6a5b78425b50d335c0f1e49bc20cd68aae0ab3fc MD5sum: fdcfc99b0c53436258c20f5eee125e50 Description: sample EEG data for EEGLAB tutorials EEGLAB is sofwware for processing continuous or event-related EEG or other physiological data. . This package provide some tutorial data files shipped with the EEGLAB distribution. Package: eegview Version: 0.0-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 22 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libeegdev0, libmcpanel0 (>= 0.0), libxdffileio0 (>= 0.0) Homepage: http://cnbi.epfl.ch/software/eegview.html Priority: extra Section: science Filename: pool/main/e/eegview/eegview_0.0-1~nd70+1_amd64.deb Size: 13090 SHA256: 0cd9b3391951f0b54841cc3f4cadf3acd9069f94518f9e9629f75d63340cc37b SHA1: f0450b1a88c93694bd1d23ce2bbb25a8d4a6ebd1 MD5sum: 55e8a7fe33d248c3d17c4d9332f1ffab Description: Software to display EEG data in realtime This software allows one to display EEG signal in realtime as well as record them. It is the minimal recording panel needed to do simple experiment. Package: environment-modules Source: modules Version: 3.2.10-8~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 185 Depends: neurodebian-popularity-contest, debhelper (>= 9), tcl8.6 (>= 8.6.0), libc6 (>= 2.14) Homepage: http://modules.sourceforge.net/ Priority: optional Section: devel Filename: pool/main/m/modules/environment-modules_3.2.10-8~nd80+1_amd64.deb Size: 102562 SHA256: 59e66a910dc707036352a3c1e966cbdc8800338b80f845779211bb94c7d858d7 SHA1: 3203b00386bc556746b1736262f90b6ba2cdc4c7 MD5sum: a0b77ee9064a87d48933fd6d76b0c4b4 Description: Modular system for handling environment variables The Modules package provides for the dynamic modification of a user's environment via modulefiles. Each modulefile contains the information needed to configure the shell for an application. Once the Modules package is initialized, the environment can be modified dynamically on a per-module basis using the module command which interprets modulefiles. Typically modulefiles instruct the module command to alter or set shell environment variables such as PATH, MANPATH, etc. modulefiles may be shared by many users on a system and users may have their own collection to supplement or replace the shared modulefiles. The modules environment is common on SGI/Crays and many workstation farms. Package: fail2ban Version: 0.8.13-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 563 Depends: neurodebian-popularity-contest, python:any (>= 2.6.6-7~), lsb-base (>= 2.0-7) Recommends: iptables, whois, python-pyinotify Suggests: python-gamin, mailx, system-log-daemon Homepage: http://www.fail2ban.org Priority: optional Section: net Filename: pool/main/f/fail2ban/fail2ban_0.8.13-1~nd80+1_all.deb Size: 165550 SHA256: f0a20ce5bb7483c9b9846f17fa5fff67d323fdb963040a7df0ca4a94ccc86eb5 SHA1: 3fbc5b5fbb0d604694839b0741d7ea7df70bc0e2 MD5sum: 7dcd8848ca2d1e233ad278dad141adc0 Description: ban hosts that cause multiple authentication errors Fail2ban monitors log files (e.g. /var/log/auth.log, /var/log/apache/access.log) and temporarily or persistently bans failure-prone addresses by updating existing firewall rules. Fail2ban allows easy specification of different actions to be taken such as to ban an IP using iptables or hostsdeny rules, or simply to send a notification email. . By default, it comes with filter expressions for various services (sshd, apache, qmail, proftpd, sasl etc.) but configuration can be easily extended for monitoring any other text file. All filters and actions are given in the config files, thus fail2ban can be adopted to be used with a variety of files and firewalls. Package: freeipmi Version: 1.4.9-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, freeipmi-common (= 1.4.9-1~nd80+1), freeipmi-tools, freeipmi-ipmidetect, freeipmi-bmc-watchdog Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: admin Filename: pool/main/f/freeipmi/freeipmi_1.4.9-1~nd80+1_all.deb Size: 1242 SHA256: 81953e99816f51e803c75315992e0fcab7cd8ac81e0d5937a3360713e938b503 SHA1: 9f7b5a3cdfc34aa01d416acc878026baea076911 MD5sum: ba1ef64b5d1a33a72f3fece83eb96937 Description: GNU implementation of the IPMI protocol FreeIPMI is a collection of Intelligent Platform Management IPMI system software. It provides in-band and out-of-band software and a development library conforming to the Intelligent Platform Management Interface (IPMI v1.5 and v2.0) standards. . This metapackage depends on all separate modules of freeipmi. Package: freeipmi-bmc-watchdog Source: freeipmi Version: 1.4.9-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 126 Pre-Depends: dpkg (>= 1.15.7.2~) Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libfreeipmi16 (>= 1.1.5), libgcrypt20 (>= 1.6.0), freeipmi-common (= 1.4.9-1~nd80+1), freeipmi-tools Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: admin Filename: pool/main/f/freeipmi/freeipmi-bmc-watchdog_1.4.9-1~nd80+1_amd64.deb Size: 45212 SHA256: b786796fdb9581e8599b5925e2d83ebbe832d25ba2280901d67d79c1ce349a51 SHA1: b3233a937bf636f34010eddfc69fa14514590fd2 MD5sum: 7c39cdea4e0fcb0f9705efcf35f5235c Description: GNU implementation of the IPMI protocol - BMC watchdog FreeIPMI is a collection of Intelligent Platform Management IPMI system software. It provides in-band and out-of-band software and a development library conforming to the Intelligent Platform Management Interface (IPMI v1.5 and v2.0) standards. . This package contains a watchdog daemon for hardware BMC watchdogs. Package: freeipmi-common Source: freeipmi Version: 1.4.9-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 451 Pre-Depends: dpkg (>= 1.15.7.2~) Depends: neurodebian-popularity-contest Suggests: freeipmi-tools Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: admin Filename: pool/main/f/freeipmi/freeipmi-common_1.4.9-1~nd80+1_all.deb Size: 339582 SHA256: 67a773733273133e1620a652e71f8249967df1b1a14a34ad282d802879029052 SHA1: 165b19841137eda849098cb403f536b45e378816 MD5sum: b61a80146d85c4aa4049e2bffb58671f Description: GNU implementation of the IPMI protocol - common files FreeIPMI is a collection of Intelligent Platform Management IPMI system software. It provides in-band and out-of-band software and a development library conforming to the Intelligent Platform Management Interface (IPMI v1.5 and v2.0) standards. . This package provides configuration used by the rest of FreeIPMI framework and generic documentation to orient the user. Package: freeipmi-ipmidetect Source: freeipmi Version: 1.4.9-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 106 Pre-Depends: dpkg (>= 1.15.7.2~) Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libfreeipmi16 (>= 1.1.5), libgcrypt20 (>= 1.6.0), libipmidetect0 (>= 1.1.5), freeipmi-common (= 1.4.9-1~nd80+1) Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: admin Filename: pool/main/f/freeipmi/freeipmi-ipmidetect_1.4.9-1~nd80+1_amd64.deb Size: 37888 SHA256: b83736b18557702936567ad1d92b027a8c883de0077270353b26684a3e1ad079 SHA1: d37d875ab58652f9ed2d34d576f2e4ef827d8ab8 MD5sum: ca7a538c2f3f4bc99814c70e440e2db1 Description: GNU IPMI - IPMI node detection tool FreeIPMI is a collection of Intelligent Platform Management IPMI system software. It provides in-band and out-of-band software and a development library conforming to the Intelligent Platform Management Interface (IPMI v1.5 and v2.0) standards. . This package contains a tool and a daemon for detecting IPMI nodes. Package: freeipmi-ipmiseld Source: freeipmi Version: 1.4.9-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 192 Pre-Depends: dpkg (>= 1.15.7.2~) Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libfreeipmi16 (>= 1.4.4), libgcrypt20 (>= 1.6.0), freeipmi-common (= 1.4.9-1~nd80+1), sysvinit-utils (>= 2.88dsf-50~) Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: admin Filename: pool/main/f/freeipmi/freeipmi-ipmiseld_1.4.9-1~nd80+1_amd64.deb Size: 79126 SHA256: b0f5c5a7d5c880afcecd21dcbc413717608967770791f72f686f9596641330f4 SHA1: 7d6fa76bcb2ed5b78f91dbbf71afcdf2817754f4 MD5sum: 2ca0cf029a85d0e11c111950e921b72e Description: GNU IPMI - IPMI node detection tool FreeIPMI is a collection of Intelligent Platform Management IPMI system software. It provides in-band and out-of-band software and a development library conforming to the Intelligent Platform Management Interface (IPMI v1.5 and v2.0) standards. . This package contains ipmiseld which takes the system event log from the BMC and imports it to syslog Package: freeipmi-tools Source: freeipmi Version: 1.4.9-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2839 Depends: neurodebian-popularity-contest, libc6 (>= 2.15), libfreeipmi16 (>= 1.4.9-1~nd80+1), libgcrypt20 (>= 1.6.0), libipmiconsole2 (>= 1.4.4), libipmidetect0 (>= 1.1.5), freeipmi-common (= 1.4.9-1~nd80+1) Suggests: freeipmi-ipmidetect, freeipmi-bmc-watchdog Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: admin Filename: pool/main/f/freeipmi/freeipmi-tools_1.4.9-1~nd80+1_amd64.deb Size: 601228 SHA256: 583f2ec3bb796b7d93aa12f375c58d4f6ee8ee3897d45fe7a3dad811118b74d5 SHA1: 12a176550cdafcf42f8cff6c90409a2fd2eef655 MD5sum: f46913a494ea862e2375049f3de0d050 Description: GNU implementation of the IPMI protocol - tools FreeIPMI is a collection of Intelligent Platform Management IPMI system software. It provides in-band and out-of-band software and a development library conforming to the Intelligent Platform Management Interface (IPMI v1.5 and v2.0) standards. . This package contains assorted IPMI-related tools: * bmc-config - configure BMC values * bmc-info - display BMC information * ipmi-chassis - IPMI chassis management utility * ipmi-fru - display FRU information * ipmi-locate - IPMI probing utility * ipmi-oem - IPMI OEM utility * ipmi-pet - decode Platform Event Traps * ipmi-raw - IPMI raw communication utility * ipmi-sel - display SEL entries * ipmi-sensors - display IPMI sensor information * ipmi-sensors-config - configure sensors * ipmiconsole - IPMI console utility * ipmiping - send IPMI Get Authentication Capabilitiy request * ipmipower - IPMI power control utility * pef-config - configure PEF values * rmcpping - send RMCP Ping to network hosts Package: freenect Source: libfreenect Version: 1:0.5.2+git6-g5455843+dfsg-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 12 Depends: neurodebian-popularity-contest, libfreenect-bin, libfreenect-dev, libfreenect0.5, libfreenect-doc Homepage: http://openkinect.org/ Priority: extra Section: libs Filename: pool/main/libf/libfreenect/freenect_0.5.2+git6-g5455843+dfsg-1~nd80+1_amd64.deb Size: 8486 SHA256: 8eb4e1df960cb26108053c0c6005f1c6f0a8c4662085e34658ce9deb7770724e SHA1: eeb59fe75edbc4a5c2425468ba74ada425890d1c MD5sum: 44d8cc019eb41341b693a2ce08a8b213 Description: library for accessing Kinect device -- metapackage libfreenect is a cross-platform library that provides the necessary interfaces to activate, initialize, and communicate data with the Kinect hardware. Currently, the library supports access to RGB and depth video streams, motors, accelerometer and LED and provide binding in different languages (C++, Python...) . This library is the low level component of the OpenKinect project which is an open community of people interested in making use of the Xbox Kinect hardware with PCs and other devices. . This is the metapackage to install all components of the project. Package: fsl-melview Source: melview Version: 1.0.1+git9-ge661e05~dfsg.1-1~nd80+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 108 Depends: neurodebian-popularity-contest, python, python-matplotlib, python-numpy, python-pkg-resources, python-scipy, python:any (<< 2.8), python:any (>= 2.7.5-5~), python-nibabel, python-pyface, python-traits, python-traitsui, python-enthoughtbase Suggests: fsl-core Homepage: http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Melview Priority: optional Section: science Filename: pool/main/m/melview/fsl-melview_1.0.1+git9-ge661e05~dfsg.1-1~nd80+1_all.deb Size: 14000 SHA256: 4037316faad3880de99e3311790b746bca35186d72eb6e4bd290f665dbc743e1 SHA1: dc2e658ebcb1dba0753da06b22d9220c46142be2 MD5sum: 0376de6719bbd4d79f744cc8cbb7e3f1 Description: viewer for the output of FSL's MELODIC This viewer can be used to facilitate manual inspection and classification of ICA components computed by MELODIC. As such, it is suited to generate hand-curated labels for FSL's ICA-based denoising tool FIX. Python-Version: 2.7 Package: fslview Version: 4.0.1-2~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 6632 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libnewmat10ldbl, libnifti2, libqt4-qt3support (>= 4:4.5.3), libqt4-xml (>= 4:4.5.3), libqtcore4 (>= 4:4.7.0~beta1), libqtgui4 (>= 4:4.7.0~beta1), libqwt5-qt4, libstdc++6 (>= 4.6), libvtk5.8, libvtk5.8-qt4 Recommends: fslview-doc, qt-assistant-compat 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_4.0.1-2~nd80+1_amd64.deb Size: 2386778 SHA256: eb8518df44aded4164db077ea76c5ecdaf2e6c6111f54a235f4d353de564effd SHA1: cb8b8cd1621648f7a8ad54efe8661ab82b108bcf MD5sum: 305011a86c72870553d109783b9de5b8 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: 4.0.1-2~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2874 Depends: neurodebian-popularity-contest Homepage: http://www.fmrib.ox.ac.uk/fsl/fslview Priority: optional Section: doc Filename: pool/main/f/fslview/fslview-doc_4.0.1-2~nd80+1_all.deb Size: 2346530 SHA256: b0bed3fb0fc52559ac29e758a2bf2f3dc97267fe19c94b50c941469e653d4bc3 SHA1: 727e80a9a735047a8ad647b7fb79099b78da2973 MD5sum: 7cd53240373654c7d4acb1180c577b2f Description: Documentation for FSLView This package provides the online documentation for FSLView. . FSLView is part of FSL. Package: gcalcli Version: 3.3.2-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1739 Depends: neurodebian-popularity-contest, python, python-dateutil, python-gflags, python-googleapi Recommends: gxmessage, python-parsedatetime, python-simplejson, python-vobject Homepage: https://github.com/insanum/gcalcli Priority: extra Section: utils Filename: pool/main/g/gcalcli/gcalcli_3.3.2-1~nd80+1_all.deb Size: 1669878 SHA256: 2496992e95fe65a8899a85d26a3fb6ecb76ef9db9463b398df45898008651e9c SHA1: 5f91bef0652b784baff1f842f08032e49e54be58 MD5sum: 6f29d72983195065cf0e533f84af25f6 Description: Google Calendar Command Line Interface gcalcli is a Python application that allows you to access your Google Calendar from a command line. It's easy to get your agenda, search for events, and quickly add new events. Additionally gcalcli can be used as a reminder service to execute any application you want. Package: gdf-tools Source: libgdf Version: 0.1.2-2~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 181 Depends: neurodebian-popularity-contest, libboost-filesystem1.49.0 (>= 1.49.0-1), libboost-program-options1.49.0 (>= 1.49.0-1), libboost-system1.49.0 (>= 1.49.0-1), libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libgdf0, libstdc++6 (>= 4.1.1) Homepage: http://sourceforge.net/projects/libgdf Priority: extra Section: utils Filename: pool/main/libg/libgdf/gdf-tools_0.1.2-2~nd70+1_amd64.deb Size: 55060 SHA256: 355f4069983473e95ee8ea106a75b681d8f6834ac6525f6ffc5a82348c0bae25 SHA1: f9c76ca93b63b84af5f9564c672ffa917f990fce MD5sum: 537eacc7f40107f5427640ef53cd7ef2 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: git-annex Version: 5.20150327+git27-g6af24b6-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 55466 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libffi6 (>= 3.0.4), libgmp10, libgnutls-deb0-28 (>= 3.3.0), libgsasl7 (>= 1.4), libicu52 (>= 52~m1-1~), libidn11 (>= 1.13), libsqlite3-0 (>= 3.5.9), libxml2 (>= 2.7.4), libyaml-0-2, zlib1g (>= 1:1.1.4), git (>= 1:2.0), rsync, wget, curl, openssh-client (>= 1:5.6p1) Recommends: lsof, gnupg, bind9-host, quvi, git-remote-gcrypt (>= 0.20130908-6), nocache, aria2, bittornado | bittorrent Suggests: graphviz, bup, tahoe-lafs, libnss-mdns Homepage: http://git-annex.branchable.com/ Priority: optional Section: utils Filename: pool/main/g/git-annex/git-annex_5.20150327+git27-g6af24b6-1~nd80+1_amd64.deb Size: 8433316 SHA256: 261c91747b08b3d5be7c47c5c33bb030e9dd3b584cc50455ee1715010fe1ab56 SHA1: 69b86c1fe598215263b8cbbb1cab016a99b763c7 MD5sum: 17a176e5b6dcd7965a16d29e804ab225 Description: manage files with git, without checking their contents into git git-annex allows managing files with git, without checking the file contents into git. While that may seem paradoxical, it is useful when dealing with files larger than git can currently easily handle, whether due to limitations in memory, time, or disk space. . It can store large files in many places, from local hard drives, to a large number of cloud storage services, including S3, WebDAV, and rsync, with a dozen cloud storage providers usable via plugins. Files can be stored encrypted with gpg, so that the cloud storage provider cannot see your data. git-annex keeps track of where each file is stored, so it knows how many copies are available, and has many facilities to ensure your data is preserved. . git-annex can also be used to keep a folder in sync between computers, noticing when files are changed, and automatically committing them to git and transferring them to other computers. The git-annex webapp makes it easy to set up and use git-annex this way. Package: git-annex-standalone Source: git-annex Version: 5.20151104+gitge9cdce6-1~ndall+1 Architecture: amd64 Maintainer: Richard Hartmann Installed-Size: 391408 Depends: git, openssh-client Recommends: lsof, gnupg, bind9-host, quvi, git-remote-gcrypt (>= 0.20130908-6), nocache, aria2 Suggests: graphviz, bup, tahoe-lafs, libnss-mdns Conflicts: git-annex Provides: git-annex Homepage: http://git-annex.branchable.com/ Priority: optional Section: utils Filename: pool/main/g/git-annex/git-annex-standalone_5.20151104+gitge9cdce6-1~ndall+1_amd64.deb Size: 26941516 SHA256: 16be5a5cc40b9281549c7ab93241845a4d566e6bac63cea12f7803084dac256d SHA1: 46ff12eb0d29351095257705f6f8fd116487556f MD5sum: ceef8eff4707c8f70f94641e9b618ff1 Description: manage files with git, without checking their contents into git -- standalone build git-annex allows managing files with git, without checking the file contents into git. While that may seem paradoxical, it is useful when dealing with files larger than git can currently easily handle, whether due to limitations in memory, time, or disk space. . It can store large files in many places, from local hard drives, to a large number of cloud storage services, including S3, WebDAV, and rsync, with a dozen cloud storage providers usable via plugins. Files can be stored encrypted with gpg, so that the cloud storage provider cannot see your data. git-annex keeps track of where each file is stored, so it knows how many copies are available, and has many facilities to ensure your data is preserved. . git-annex can also be used to keep a folder in sync between computers, noticing when files are changed, and automatically committing them to git and transferring them to other computers. The git-annex webapp makes it easy to set up and use git-annex this way. . This package provides a standalone bundle build of git-annex, which should be installable on any more or less recent Debian or Ubuntu release. Package: glew-utils Source: glew Version: 1.9.0-3~bnd1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 426 Depends: neurodebian-popularity-contest, libglew1.9 (= 1.9.0-3~bnd1~nd70+1), libc6 (>= 2.2.5), libgl1-mesa-glx | libgl1, libx11-6, libxext6, libxi6, libxmu6 Replaces: libglew1.6 (<< 1.7) Homepage: http://glew.sourceforge.net Priority: optional Section: utils Filename: pool/main/g/glew/glew-utils_1.9.0-3~bnd1~nd70+1_amd64.deb Size: 135510 SHA256: 0d75203ae8729dbdbc8fe4863cc2f4df1800c6ccb93a7b34da45d70d3caf1feb SHA1: 7c75495dff3565257df62d2449a9210083f6f861 MD5sum: 58c11432c32f4f0d68b579ffee66d604 Description: OpenGL Extension Wrangler - utilities For more information about GLEW please refer to the description of the libglew-dev package. . This package contains the utilities which can be used to query the supported OpenGL extensions. Package: gmsl Version: 1.1.5-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 78 Depends: neurodebian-popularity-contest, make Homepage: http://gmsl.sourceforge.net/ Priority: optional Section: devel Filename: pool/main/g/gmsl/gmsl_1.1.5-1~nd80+1_all.deb Size: 13862 SHA256: 65c6777ad3bf087edac18673d59547ca9499a8dacddd5f1dc63ebf832322d395 SHA1: c973b5e00f569c95b0342c03d07721e81baa5b60 MD5sum: 3cf6064e4bcf17b9c38d3ddc0ae6c848 Description: extra functions to extend functionality of GNU Makefiles The GNU Make Standard Library (GMSL) is a collection of functions implemented using native GNU Make functionality that provide list and string manipulation, integer arithmetic, associative arrays, stacks, and debugging facilities. . Note that despite the name of this project, this library is NOT standard and is NOT written or distributed by the GNU project. Package: guacamole Source: guacamole-client Version: 0.8.3-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 476 Depends: neurodebian-popularity-contest, guacd Recommends: libguac-client-vnc0 Suggests: tomcat6 | jetty Homepage: http://guac-dev.org/ Priority: extra Section: net Filename: pool/main/g/guacamole-client/guacamole_0.8.3-1~nd80+1_all.deb Size: 429968 SHA256: 199b137cea7084f7727a00d808af54469b264d084846b8fc978cd79e8e707285 SHA1: 4d02ff1226ee4ac1cf0779ddab99ff6c40ec4068 MD5sum: 913bae8021a50c12a6d3a237af4980b9 Description: HTML5 web application for accessing remote desktops Guacamole is an HTML5 web application that provides access to a desktop environment using remote desktop protocols. A centralized server acts as a tunnel and proxy, allowing access to multiple desktops through a web browser. No plugins are needed: the client requires nothing more than a web browser supporting HTML5 and AJAX. Package: guacamole-tomcat Source: guacamole-client Version: 0.8.3-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 11 Depends: neurodebian-popularity-contest, debconf, guacamole, tomcat6, libguac-client-vnc0, debconf (>= 0.5) | debconf-2.0 Homepage: http://guac-dev.org/ Priority: extra Section: net Filename: pool/main/g/guacamole-client/guacamole-tomcat_0.8.3-1~nd80+1_all.deb Size: 6944 SHA256: 66d24988666662a841348d8d797234cdb02aba88f5c281a42a50780916829e96 SHA1: dd91ac3b658536777037707e9f1ce61156b46fb6 MD5sum: c388254b7275c79d78921051bf63f0ec Description: Tomcat-based Guacamole install with VNC support Guacamole is an HTML5 web application that provides access to a desktop environment using remote desktop protocols. A centralized server acts as a tunnel and proxy, allowing access to multiple desktops through a web browser. No plugins are needed: the client requires nothing more than a web browser supporting HTML5 and AJAX. . This metapackage depends on Tomcat, Guacamole, and the VNC support plugin for guacamole. Guacamole is automatically installed and configured under Tomcat. Package: guacd Source: guacamole-server Version: 0.8.3-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 34 Depends: neurodebian-popularity-contest, lsb-base (>= 3.0-6), libc6 (>= 2.15), libguac5, libssl1.0.0 (>= 1.0.0) Homepage: http://guac-dev.org/ Priority: extra Section: net Filename: pool/main/g/guacamole-server/guacd_0.8.3-1~nd80+1_amd64.deb Size: 15914 SHA256: 31f8476f6810f5cd11bac51a8b239857c9b9d9e9554dabf87455162eb58d72be SHA1: 70c274cc08b78878f288dc0e04a2b3a4974a9380 MD5sum: a93da4efadc6834fbcfa0ac785c7a892 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: heudiconv Version: 0.1-1~nd80+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 79 Depends: neurodebian-popularity-contest, python, python-dcmstack, python-dicom, python-nibabel, python-numpy, python-nipype Recommends: mricron Homepage: https://github.com/nipy/heudiconv Priority: optional Section: science Filename: pool/main/h/heudiconv/heudiconv_0.1-1~nd80+1_all.deb Size: 10262 SHA256: b4029e49d1a562e0a24b75aa960215194025443dd379df56b72335d1f718ea41 SHA1: f7e10e6f4b5bfb3cc7f5a1dc66da916b615eb7dc MD5sum: faf7df3bed44fc7a5ac64ac75996b946 Description: DICOM converter with support for structure heuristics This is a flexible dicom converter for organizing brain imaging data into structured directory layouts. It allows for flexible directory layouts and naming schemes through customizable heuristics implementations. It only converts the necessary dicoms, not everything in a directory. It tracks the provenance of the conversion from dicom to nifti in w3c prov format. Package: htcondor Source: condor Version: 8.4.0~dfsg.1-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 13190 Depends: neurodebian-popularity-contest, adduser, debconf (>= 0.5) | debconf-2.0, libdate-manip-perl, python, libclassad7 (= 8.4.0~dfsg.1-1~nd80+1), perl, libc6 (>= 2.15), libcgroup1 (>= 0.37.1), libcomerr2 (>= 1.01), libcurl3 (>= 7.16.2), libexpat1 (>= 2.0.1), libgcc1 (>= 1:4.1.1), libglobus-callout0 (>= 3), libglobus-common0 (>= 15), libglobus-ftp-client2 (>= 7), libglobus-gass-transfer2 (>= 7), libglobus-gram-client3 (>= 12), libglobus-gram-protocol3 (>= 11), libglobus-gsi-callback0 (>= 4), libglobus-gsi-cert-utils0 (>= 8), libglobus-gsi-credential1 (>= 6), libglobus-gsi-openssl-error0 (>= 2), libglobus-gsi-proxy-core0 (>= 6), libglobus-gsi-proxy-ssl1 (>= 4), libglobus-gsi-sysconfig1 (>= 5), libglobus-gss-assist3 (>= 9), libglobus-gssapi-error2 (>= 4), libglobus-gssapi-gsi4 (>= 10), libglobus-io3 (>= 11), libglobus-openssl-module0 (>= 3), libglobus-rsl2 (>= 9), libglobus-xio0 (>= 3), libgsoap5, libgssapi-krb5-2 (>= 1.6.dfsg.2), libk5crypto3 (>= 1.6.dfsg.2), libkrb5-3 (>= 1.10+dfsg~), libkrb5support0 (>= 1.7dfsg~beta2), libldap-2.4-2 (>= 2.4.7), libltdl7 (>= 2.4.2), libpcre3 (>= 1:8.35), libssl1.0.0 (>= 1.0.0), libstdc++6 (>= 4.9), libuuid1 (>= 2.16), libvirt0 (>= 0.5.0), libvomsapi1, libx11-6, zlib1g (>= 1:1.1.4) Recommends: dmtcp, ecryptfs-utils Suggests: docker, coop-computing-tools Breaks: condor (<< 8.0.5~) Replaces: condor (<< 8.0.5~) Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: science Filename: pool/main/c/condor/htcondor_8.4.0~dfsg.1-1~nd80+1_amd64.deb Size: 3735692 SHA256: 3149ef8671d5dd50c9ce7db22185b8718e0ebd98fae8c47c68c5729bc58e5923 SHA1: 1062bf255586eb67781e221b940db382f5896ff8 MD5sum: 24d8453fa68989f60c05ef883109a54e Description: distributed workload management system Like other full-featured batch systems, HTCondor provides a job queueing mechanism, scheduling policy, priority scheme, resource monitoring, and resource management. Users submit their serial or parallel jobs to HTCondor; HTCondor places them into a queue. It chooses when and where to run the jobs based upon a policy, carefully monitors their progress, and ultimately informs the user upon completion. . Unlike more traditional batch queueing systems, HTCondor can also effectively harness wasted CPU power from otherwise idle desktop workstations. HTCondor does not require a shared file system across machines - if no shared file system is available, HTCondor can transfer the job's data files on behalf of the user. . This package can set up an appropriate initial configuration at install time for a machine intended either as a member of an existing HTCondor pool or as a "Personal" (single machine) HTCondor pool. Package: htcondor-dbg Source: condor Version: 8.4.0~dfsg.1-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 31645 Depends: neurodebian-popularity-contest, htcondor (= 8.4.0~dfsg.1-1~nd80+1) Breaks: condor-dbg (<< 8.0.5~) Replaces: condor-dbg (<< 8.0.5~) Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: debug Filename: pool/main/c/condor/htcondor-dbg_8.4.0~dfsg.1-1~nd80+1_amd64.deb Size: 29429180 SHA256: 26884c705eb68a8ce3db35d5fd564a60f321ee995949dd07b3c4a58f65db89bd SHA1: f5a334406c7ab3e6440a97abb1b4be77142972da MD5sum: 8bbb4ed26bf4063e0f53368e57d40a51 Description: distributed workload management system - debugging symbols Like other full-featured batch systems, HTCondor provides a job queueing mechanism, scheduling policy, priority scheme, resource monitoring, and resource management. Users submit their serial or parallel jobs to HTCondor; HTCondor places them into a queue. It chooses when and where to run the jobs based upon a policy, carefully monitors their progress, and ultimately informs the user upon completion. . Unlike more traditional batch queueing systems, HTCondor can also effectively harness wasted CPU power from otherwise idle desktop workstations. HTCondor does not require a shared file system across machines - if no shared file system is available, HTCondor can transfer the job's data files on behalf of the user. . This package provides the debugging symbols for HTCondor. Package: htcondor-dev Source: condor Version: 8.4.0~dfsg.1-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 1686 Depends: neurodebian-popularity-contest Breaks: condor-dev (<< 8.0.5~) Replaces: condor-dev (<< 8.0.5~) Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: libdevel Filename: pool/main/c/condor/htcondor-dev_8.4.0~dfsg.1-1~nd80+1_amd64.deb Size: 309346 SHA256: 39bec7ad5202a3d29dda606ca7bbbf2c537fbde76d556efc37259d602609f2be SHA1: 7c1ad463c1ba927720f441c1ff3850c05a008cb8 MD5sum: 096588940c33e6ac764a3d88b91c0306 Description: distributed workload management system - development files Like other full-featured batch systems, HTCondor provides a job queueing mechanism, scheduling policy, priority scheme, resource monitoring, and resource management. Users submit their serial or parallel jobs to HTCondor; HTCondor places them into a queue. It chooses when and where to run the jobs based upon a policy, carefully monitors their progress, and ultimately informs the user upon completion. . Unlike more traditional batch queueing systems, HTCondor can also effectively harness wasted CPU power from otherwise idle desktop workstations. HTCondor does not require a shared file system across machines - if no shared file system is available, HTCondor can transfer the job's data files on behalf of the user. . This package provides headers and libraries for development of HTCondor add-ons. Package: htcondor-doc Source: condor Version: 8.4.0~dfsg.1-1~nd80+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 5876 Depends: neurodebian-popularity-contest Breaks: condor-doc (<< 8.0.5~) Replaces: condor-doc (<< 8.0.5~) Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: doc Filename: pool/main/c/condor/htcondor-doc_8.4.0~dfsg.1-1~nd80+1_all.deb Size: 1063900 SHA256: dcd4e4a0fea3a08a51c44833ac75c27852b67fa6b098a98bc5a9aabc018e208d SHA1: e54a547a14daefad1158892b8b78f9d566994dd2 MD5sum: 4d7b3edbbc2fb27ba64c7bd98b523583 Description: distributed workload management system - documentation Like other full-featured batch systems, HTCondor provides a job queueing mechanism, scheduling policy, priority scheme, resource monitoring, and resource management. Users submit their serial or parallel jobs to HTCondor; HTCondor places them into a queue. It chooses when and where to run the jobs based upon a policy, carefully monitors their progress, and ultimately informs the user upon completion. . Unlike more traditional batch queueing systems, HTCondor can also effectively harness wasted CPU power from otherwise idle desktop workstations. HTCondor does not require a shared file system across machines - if no shared file system is available, HTCondor can transfer the job's data files on behalf of the user. . This package provides HTCondor's documentation in HTML and PDF format, as well as configuration and other examples. Package: impressive Version: 0.10.5-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 331 Depends: neurodebian-popularity-contest, python-opengl, python-pygame, python-imaging, poppler-utils | xpdf-utils (>= 3.02-2) Recommends: pdftk, perl Suggests: ghostscript, latex-beamer Conflicts: keyjnote (<< 0.10.2r-0) Replaces: keyjnote (<< 0.10.2r-0) Provides: keyjnote Homepage: http://impressive.sourceforge.net/ Priority: optional Section: x11 Filename: pool/main/i/impressive/impressive_0.10.5-1~nd80+1_all.deb Size: 151676 SHA256: a9a474d6944c7c520f2822c6f71668b83a094a814c745168201f2b54e6e8668a SHA1: 107b9f9e0a7cb7c45cd4d99b6d4f92dc7cad68f2 MD5sum: 70297dcb5e99a5c597771bb425fc2f54 Description: PDF presentation tool with eye candies Impressive is a program that displays presentation slides using OpenGL. Smooth alpha-blended slide transitions are provided for the sake of eye candy, but in addition to this, Impressive offers some unique tools that are really useful for presentations. Some of them are: * Overview screen * Highlight boxes * Spotlight effect * Presentation scripting and customization Package: incf-nidash-oneclick-clients Source: incf-nidash-oneclick Version: 2.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 28 Depends: neurodebian-popularity-contest, python (>= 2.5.0), python-dicom, dcmtk, python-httplib2 Homepage: http://xnat.incf.org/ Priority: extra Section: science Filename: pool/main/i/incf-nidash-oneclick/incf-nidash-oneclick-clients_2.0-1~nd70+1_all.deb Size: 9652 SHA256: fac3ad8fc2cf1126a2b7fd3a9497594c3372cf7ae5a006d552d0b18e97334a11 SHA1: 803b8e967a16602928187f76ba0a8813d6a68866 MD5sum: c70545ff21713e721dbd16f9a195cbde Description: utility for pushing DICOM data to the INCF datasharing server A command line utility for anonymizing and sending DICOM data to the XNAT image database at the International Neuroinformatics Coordinating Facility (INCF). This tool is maintained by the INCF NeuroImaging DataSharing (NIDASH) task force. Package: init-system-helpers Version: 1.18~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 29 Depends: neurodebian-popularity-contest, perl Breaks: systemd (<< 44-12) Multi-Arch: foreign Priority: extra Section: admin Filename: pool/main/i/init-system-helpers/init-system-helpers_1.18~nd80+1_all.deb Size: 13478 SHA256: 8815a6d00cd21f4d25405cfeaccfe0aab3ca44bf00e30ff9174db001f726a8ff SHA1: 5bf89dc5a7570c3b97e38d9b8b09b74c87a20718 MD5sum: f0dfe1bba896452c36f02a8b9df15ba9 Description: helper tools for all init systems This package contains helper tools that are necessary for switching between the various init systems that Debian contains (e.g. sysvinit, upstart, systemd). An example is deb-systemd-helper, a script that enables systemd unit files without depending on a running systemd. . While this package is maintained by pkg-systemd-maintainers, it is NOT specific to systemd at all. Maintainers of other init systems are welcome to include their helpers in this package. Package: insighttoolkit4-examples Source: insighttoolkit4 Version: 4.7.0-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2836 Depends: neurodebian-popularity-contest Suggests: libinsighttoolkit4-dev Conflicts: insighttoolkit-examples Replaces: insighttoolkit-examples Homepage: http://www.itk.org/ Priority: optional Section: devel Filename: pool/main/i/insighttoolkit4/insighttoolkit4-examples_4.7.0-1~nd80+1_all.deb Size: 2500506 SHA256: 1002fe3ccc4cb4fa7bc9f0048fb7379d509e433d59c7290164f37e5214a1bbbf SHA1: 4ecfd9fc1bdea9565c79f5340c7a24d2259dbcc4 MD5sum: a50c77e7b9105fa4f20ff5b28cc953c7 Description: Image processing toolkit for registration and segmentation - examples ITK is an open-source software toolkit for performing registration and segmentation. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Typically the sampled representation is an image acquired from such medical instrumentation as CT or MRI scanners. Registration is the task of aligning or developing correspondences between data. For example, in the medical environment, a CT scan may be aligned with a MRI scan in order to combine the information contained in both. . This package contains the source for example programs. Package: insighttoolkit4-python Source: insighttoolkit4 Version: 4.7.0-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 877731 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libfftw3-double3, libfftw3-single3, libgcc1 (>= 1:4.1.1), libgdcm2.4, libinsighttoolkit4.7, libpython2.7 (>= 2.7), libstdc++6 (>= 4.9) Conflicts: insighttoolkit-python Replaces: insighttoolkit-python Homepage: http://www.itk.org/ Priority: optional Section: python Filename: pool/main/i/insighttoolkit4/insighttoolkit4-python_4.7.0-1~nd80+1_amd64.deb Size: 74428892 SHA256: eef4d515bfdc6ed9d8fe78c47e9ac2b34463aece36e16f336e6ac5f3813e01bf SHA1: 83218f03a197513ff9684e8861e1ab44d29c9f9d MD5sum: 3ebfb89d01501ece215bfe1ac1d2c97e Description: Image processing toolkit for registration and segmentation - Python bindings ITK is an open-source software toolkit for performing registration and segmentation. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Typically the sampled representation is an image acquired from such medical instrumentation as CT or MRI scanners. Registration is the task of aligning or developing correspondences between data. For example, in the medical environment, a CT scan may be aligned with a MRI scan in order to combine the information contained in both. . This package contains the Python bindings. Package: ipython01x Version: 0.13.2-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4808 Depends: neurodebian-popularity-contest, python-argparse, python-configobj, python-decorator, python-pexpect, python-simplegeneric, python (>= 2.6.6-7~), python (<< 2.8) Recommends: python-tornado (>= 2.1.0~), python-pygments, python-qt4, python-zmq, python-matplotlib Suggests: ipython01x-doc, python-gobject, python-gtk2, python-numpy, python-profiler Conflicts: ipython-common, python2.3-ipython, python2.4-ipython Replaces: ipython-common, python2.3-ipython, python2.4-ipython Homepage: http://ipython.org/ Priority: optional Section: python Filename: pool/main/i/ipython01x/ipython01x_0.13.2-1~nd70+1_all.deb Size: 1306320 SHA256: d259e419c42ab2f29c62a358f1b70ac483246c60043a213cf2a0e2ebb27940b9 SHA1: f1da0836b718381b16709910018994a049da53cd MD5sum: 445c27ebd25688a209351c5432f11a9b Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This is a non-official, custom build of IPython post 0.11 with notebooks support. It provides IPython01X module thus not conflicting with system-wide installed IPython Package: ipython01x-doc Source: ipython01x Version: 0.13.2-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 16664 Depends: neurodebian-popularity-contest, libjs-jquery, ipython01x Homepage: http://ipython.org/ Priority: optional Section: doc Filename: pool/main/i/ipython01x/ipython01x-doc_0.13.2-1~nd70+1_all.deb Size: 7243134 SHA256: a34015da70830de42c97645c790f2fdc179da0b1b48848617dd8926b23b017e2 SHA1: 50455b67f63f0e2b7b95c4cda4c6f61feb14fa09 MD5sum: 4c412f1cfd211f9b4a81a0f7986b445f Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This package contains the documentation. . This is a non-official, custom build of IPython post 0.11 with workbooks support. It provides IPython01X module thus not conflicting with system-wide installed IPython Package: ipython01x-notebook Source: ipython01x Version: 0.13.2-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython01x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: python Filename: pool/main/i/ipython01x/ipython01x-notebook_0.13.2-1~nd70+1_all.deb Size: 896 SHA256: e6bf753904ea6c85c72689ffbe60b4f7b77243e38733c4c8a486c9b6fdeb69cd SHA1: 9226720c79cf6b2fecae5206e4a5af313318d950 MD5sum: 587920ae0a922c5a9ea5d60f75c52367 Description: enhanced interactive Python shell -- notebook dummy package This is a dummy package depending on ipython01x which ships notebook functionality inside. It is made so to stay in line to modularization of official ipython package in Debian. There is no real good reason to install this package. Package: ipython01x-parallel Source: ipython01x Version: 0.13.2-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython01x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: oldlibs Filename: pool/main/i/ipython01x/ipython01x-parallel_0.13.2-1~nd70+1_all.deb Size: 824 SHA256: 0097d83205fc332bebc5e9e178063ab3c6d740909a6c8ce7da2930d300556864 SHA1: ced489b459fa0edfd0a0414d2a0b4cac6cd7e9a8 MD5sum: 172195f46a65a28d182025cd62cd2503 Description: enhanced interactive Python shell This is a transitional package and can be safely removed after the installation is complete. Package: ipython01x-qtconsole Source: ipython01x Version: 0.13.2-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython01x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: python Filename: pool/main/i/ipython01x/ipython01x-qtconsole_0.13.2-1~nd70+1_all.deb Size: 910 SHA256: 009e2f9b28f70112713dfd1fa64bff7958a250fc2d5f622ef925c49d15afa5a1 SHA1: fb211e7d7981402a4329181ed727148ee38195d4 MD5sum: 9bb764488392203162c98cee5d3f794d Description: enhanced interactive Python shell -- notebook dummy package This is a dummy package depending on ipython01x which ships qt console functionality inside. It is made so to stay in line to modularization of the official ipython package in Debian. There is no real good reason to install this package. Package: ipython1x Version: 1.1.0+git7-gf5891e9-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 11748 Depends: neurodebian-popularity-contest, python-argparse, python-configobj, python-decorator, python-pexpect, python-simplegeneric, python (>= 2.7), python (<< 2.8) Recommends: python-tornado (>= 2.1.0~), python-pygments, python-qt4, python-zmq, python-matplotlib Suggests: ipython1x-doc, python-gobject, python-gtk2, python-numpy, python-profiler Conflicts: ipython-common, python2.3-ipython, python2.4-ipython Replaces: ipython-common, python2.3-ipython, python2.4-ipython Homepage: http://ipython.org/ Priority: optional Section: python Filename: pool/main/i/ipython1x/ipython1x_1.1.0+git7-gf5891e9-1~nd80+1_all.deb Size: 4486952 SHA256: e9d45addc339d1cf0f676fc909fdba1abded3926365b6d75e20048cc34534af9 SHA1: a4b3869c2d4c89a9c5c32963b640e58661281e7e MD5sum: 195c78b6458e2d20e628f65ea7aeaf43 Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This is a non-official, custom build of IPython post 0.11 with notebooks support. It provides IPython1X module thus not conflicting with system-wide installed IPython Package: ipython1x-doc Source: ipython1x Version: 1.1.0+git7-gf5891e9-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 10389 Depends: neurodebian-popularity-contest, libjs-jquery, ipython1x Homepage: http://ipython.org/ Priority: optional Section: doc Filename: pool/main/i/ipython1x/ipython1x-doc_1.1.0+git7-gf5891e9-1~nd80+1_all.deb Size: 4191008 SHA256: fb7147cecf4e6734e1067aaf4b19bfbce64a60321928701ab542ea12946ff881 SHA1: 88c14c6a1d3b44de677124d78de8c2de2ce9157b MD5sum: 55c38708127b4547a961f375d358b28c Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This package contains the documentation. . This is a non-official, custom build of IPython post 0.11 with workbooks support. It provides IPython1X module thus not conflicting with system-wide installed IPython Package: ipython1x-notebook Source: ipython1x Version: 1.1.0+git7-gf5891e9-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython1x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: python Filename: pool/main/i/ipython1x/ipython1x-notebook_1.1.0+git7-gf5891e9-1~nd80+1_all.deb Size: 912 SHA256: 83cf4506f1e3a9c416f0751cfc00550b1a2a5d4bd4c8e52235e9474437ba8a88 SHA1: 6a1b2f767b540568deeb5f271581f0cc76935dac MD5sum: c05c0e50e41602aa3ded0bec067c88a6 Description: enhanced interactive Python shell -- notebook dummy package This is a dummy package depending on ipython1x which ships notebook functionality inside. It is made so to stay in line to modularization of official ipython package in Debian. There is no real good reason to install this package. Package: ipython1x-parallel Source: ipython1x Version: 1.1.0+git7-gf5891e9-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython1x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: oldlibs Filename: pool/main/i/ipython1x/ipython1x-parallel_1.1.0+git7-gf5891e9-1~nd80+1_all.deb Size: 842 SHA256: 3f572f1bf658bbe07cc10028ef2bfe1fc914da829095a73b60fdf3112e8ab5c2 SHA1: 81706771e078d4b5bff517d7cad4727affaba86d MD5sum: 242d823a4b0fdde6b3dff81d717a55c1 Description: enhanced interactive Python shell This is a transitional package and can be safely removed after the installation is complete. Package: ipython1x-qtconsole Source: ipython1x Version: 1.1.0+git7-gf5891e9-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, ipython1x (>= 0.13.1~git33-gcfc5692-2~) Homepage: http://ipython.org/ Priority: extra Section: python Filename: pool/main/i/ipython1x/ipython1x-qtconsole_1.1.0+git7-gf5891e9-1~nd80+1_all.deb Size: 922 SHA256: f53d8dd6c84ccc9cdf71012761cbabdcb964dd53edd1e2bdb3dd44d80f6877a2 SHA1: cd845c332973a6f975d5c4e2efa8c98cdc16fa85 MD5sum: e636861be9e70a778292b9e9899912c9 Description: enhanced interactive Python shell -- notebook dummy package This is a dummy package depending on ipython1x which ships qt console functionality inside. It is made so to stay in line to modularization of the official ipython package in Debian. There is no real good reason to install this package. Package: ipython2x Version: 2.0.0+git8-gee204ae-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 12337 Depends: neurodebian-popularity-contest, python-argparse, python-configobj, python-decorator, python-pexpect, python-simplegeneric, python (>= 2.7), python (<< 2.8) Recommends: python-tornado (>= 3.1.0~), python-pygments, python-qt4, python-zmq, python-matplotlib, environment-modules Suggests: ipython2x-doc, python-gobject, python-gtk2, python-numpy, python-profiler Conflicts: ipython-common, python2.3-ipython, python2.4-ipython Replaces: ipython-common, python2.3-ipython, python2.4-ipython Homepage: http://ipython.org/ Priority: optional Section: python Filename: pool/main/i/ipython2x/ipython2x_2.0.0+git8-gee204ae-1~nd80+1_all.deb Size: 5617488 SHA256: 84a478317cfd861bd4b5e6242ff1b7fa78f2594feaa7d59faa68a13cd7c3ca5c SHA1: 391ffc73f76e69d5f85df1be03d0cf9806aae840 MD5sum: 5941abc6b41974ac86fb265676fec0b2 Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This is a non-official, custom build of IPython 2.x seres with all fresh goodness from the IPython team. It provides IPython2X module thus not conflicting with system-wide installed IPython Package: ipython2x-doc Source: ipython2x Version: 2.0.0+git8-gee204ae-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 12949 Depends: neurodebian-popularity-contest, libjs-jquery, ipython2x (= 2.0.0+git8-gee204ae-1~nd80+1) Homepage: http://ipython.org/ Priority: optional Section: doc Filename: pool/main/i/ipython2x/ipython2x-doc_2.0.0+git8-gee204ae-1~nd80+1_all.deb Size: 4678038 SHA256: 338c971db98ebb20925d28a851013f3b94db7c54ac3a03a3783292cdd7ac7432 SHA1: f55c39bce9db6c36d058987b4a6a2a84f77d1c8d MD5sum: 29828e6870cab2363d2e45d93cdea8bd Description: enhanced interactive Python shell IPython can be used as a replacement for the standard Python shell, or it can be used as a complete working environment for scientific computing (like Matlab or Mathematica) when paired with the standard Python scientific and numerical tools. It supports dynamic object introspections, numbered input/output prompts, a macro system, session logging, session restoring, complete system shell access, verbose and colored traceback reports, auto-parentheses, auto-quoting, and is embeddable in other Python programs. . This package contains the documentation. . This is a non-official, custom build of IPython 2.x. It provides IPython2X module thus not conflicting with system-wide installed IPython Package: isis-utils Source: isis Version: 0.4.7-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 941 Depends: neurodebian-popularity-contest, libisis-core0 (= 0.4.7-1~nd70+1), libboost-date-time1.48.0 (>= 1.48.0-1), libboost-filesystem1.48.0 (>= 1.48.0-1), libboost-regex1.48.0 (>= 1.48.0-1), libboost-system1.48.0 (>= 1.48.0-1), libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libmuparser0debian1, liboil0.3 (>= 0.3.1), libstdc++6 (>= 4.6) Homepage: https://github.com/isis-group Priority: extra Section: science Filename: pool/main/i/isis/isis-utils_0.4.7-1~nd70+1_amd64.deb Size: 271212 SHA256: 4017f4a3e56c998b5f276c39ed371cd04b8140907540be37d606ef40aea7376f SHA1: 5c685ea0a27a05eb1a29f22bfecece60e9ca6a2d MD5sum: 8c989e2f0ed79484796e7667374cfcd8 Description: utilities for the ISIS neuroimaging data I/O framework This framework aids access of and conversion between various established neuro-imaging data formats, like Nifti, Analyze, DICOM and VISTA. ISIS is extensible with plugins to add support for additional data formats. . This package provides a number of utilities to process neuroimaging data. This includes a multi-format converter and tools to inspect image meta data. Package: itksnap Version: 3.2.0-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 14511 Depends: neurodebian-popularity-contest, libc6 (>= 2.15), libgcc1 (>= 1:4.1.1), libgdcm2.4, libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, libinsighttoolkit4.6, libqt5core5a (>= 5.1.0), libqt5gui5 (>= 5.2.0), libqt5opengl5 (>= 5.0.2), libqt5qml5 (>= 5.0.2), libqt5widgets5 (>= 5.2.0), libstdc++6 (>= 4.9), libvtk6.1, zlib1g (>= 1:1.1.4) Homepage: http://www.itksnap.org Priority: optional Section: science Filename: pool/main/i/itksnap/itksnap_3.2.0-1~nd80+1_amd64.deb Size: 3343574 SHA256: 6e485abd7016509d64d6007694205109d2b9efd1571e71ff159fe108973b0c00 SHA1: ed228532501e8d2d1af6081174957765d356e486 MD5sum: 367d2fc26c88928c38261062dc29e952 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~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 92 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), 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~nd70+1_amd64.deb Size: 23078 SHA256: 408ce54522f5db716c99edea2089993436feb23cef17c2dfa7766beea8b76a68 SHA1: 0287982a5f922663215618f48730f7b209f7f7c9 MD5sum: 93df9b567b8a475c135145bc332a5656 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: 1.4.1-2~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1732 Depends: neurodebian-popularity-contest, libbiosig1 (= 1.4.1-2~nd80+1) Homepage: http://biosig.sf.net/ Priority: extra Section: libdevel Filename: pool/main/b/biosig4c++/libbiosig-dev_1.4.1-2~nd80+1_amd64.deb Size: 300848 SHA256: 8f70b538e887b00ea5d276101fd8c4ac9fa699b920d9f1e7cb6d7724b3698a63 SHA1: d9e261b2405bd2664c8282a81f6600e0edf5b44b MD5sum: 47e9cb9f4951aa995871c4c910b57505 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: libbiosig1 Source: biosig4c++ Version: 1.4.1-2~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 911 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libcholmod2.1.2, libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), zlib1g (>= 1:1.1.4) Homepage: http://biosig.sf.net/ Priority: extra Section: libs Filename: pool/main/b/biosig4c++/libbiosig1_1.4.1-2~nd80+1_amd64.deb Size: 277316 SHA256: 407173972610b74e14ab925d249eec84c584fe1715624f6444e0e1acf5eb21bb SHA1: 5a8b183c59e2f21fbb8de0d3ed2c0320782a6fc1 MD5sum: 36bea61c92ceb65bf80e2c8decaaf1d2 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: libbiosig1-dbg Source: biosig4c++ Version: 1.4.1-2~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 332 Depends: neurodebian-popularity-contest, libbiosig1 (= 1.4.1-2~nd80+1) Homepage: http://biosig.sf.net/ Priority: extra Section: debug Filename: pool/main/b/biosig4c++/libbiosig1-dbg_1.4.1-2~nd80+1_amd64.deb Size: 65386 SHA256: 1c55da076174e0953f38b1cff820ed6af6630cfecbe3905a40edd3e80e666ffe SHA1: 0794a1eb4cd5768808e82e5456f604560f703da0 MD5sum: fe795abb18d9ed17544f82f89c721f3c 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~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 51 Depends: neurodebian-popularity-contest, libcgroup1 (= 0.37.1-1~nd70+1) Homepage: http://sourceforge.net/projects/libcg/ Priority: extra Section: libdevel Filename: pool/main/libc/libcgroup/libcgroup-dev_0.37.1-1~nd70+1_amd64.deb Size: 17398 SHA256: 9b70596ab471f2ab23d987d531483f1af36055bc5d8d4772a5545aa8081811c7 SHA1: e324f022fe2b3482fbf00beddb40146569f9b644 MD5sum: 4a13cc06d0cde6bc9aed2b5c4c837380 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~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 78 Depends: neurodebian-popularity-contest, libc6 (>= 2.7) Homepage: http://sourceforge.net/projects/libcg/ Priority: extra Section: libs Filename: pool/main/libc/libcgroup/libcgroup1_0.37.1-1~nd70+1_amd64.deb Size: 38540 SHA256: 0bcf895f197ed19172cc3998452ef5d7c1b2fea61d68f385557d957a70e09047 SHA1: 3d877f31553b67cbac17086cb5ddc48aef394ad8 MD5sum: 8fefbb087322d3ca99c0d7bd5957c707 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: libclassad-dev Source: condor Version: 8.4.0~dfsg.1-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 1449 Depends: neurodebian-popularity-contest, libclassad7 (= 8.4.0~dfsg.1-1~nd80+1) Conflicts: libclassad0-dev Replaces: libclassad0-dev Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: libdevel Filename: pool/main/c/condor/libclassad-dev_8.4.0~dfsg.1-1~nd80+1_amd64.deb Size: 245344 SHA256: eab0a4362cac87c788b27d4fc288d3b1a1a131a61cf291e9cd700b6312451902 SHA1: 61f4aad6393908a0d363358a6bd9e0af6aff2c6f MD5sum: cd48c4fd6ee1c73cf1aec695d1260d25 Description: HTCondor classads expression language - development library Classified Advertisements (classads) are the lingua franca of HTCondor, used for describing jobs, workstations, and other resources. There is a protocol for evaluating whether two classads match, which is used by the HTCondor central manager to determine the compatibility of jobs, and workstations where they may be run. . This package provides the static library and header files. Package: libclassad3 Source: condor Version: 7.8.8~dfsg.1-2~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 912 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libpcre3 (>= 8.10), libstdc++6 (>= 4.6) Homepage: http://research.cs.wisc.edu/condor Priority: extra Section: science Filename: pool/main/c/condor/libclassad3_7.8.8~dfsg.1-2~nd80+1_amd64.deb Size: 275718 SHA256: dc5e5a1899754d0af0cf570861daf377fe26e070c2bf3fe83268df9c623d250f SHA1: a07644649a297971235be7f335f9049e5a003376 MD5sum: 0ffe26b6caa5a6fe633339639efbd47b Description: Condor classads expression language - runtime library Classified Advertisements (classads) are the lingua franca of Condor, used for describing jobs, workstations, and other resources. There is a protocol for evaluating whether two classads match, which is used by the Condor central manager to determine the compatibility of jobs, and workstations where they may be run. . This package provides the runtime library. Package: libclassad7 Source: condor Version: 8.4.0~dfsg.1-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 642 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libpcre3 (>= 1:8.35), libstdc++6 (>= 4.9) Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: libs Filename: pool/main/c/condor/libclassad7_8.4.0~dfsg.1-1~nd80+1_amd64.deb Size: 199976 SHA256: 0697feafb06e29eae11f7a3b6d12ae188d8382ed7a9e8d168f0e53bd51c9fce2 SHA1: 3a4a5379a9340726b574fea6316321864e08647d MD5sum: b4e2df1d5039dc6c2af666baa381395c Description: HTCondor classads expression language - runtime library Classified Advertisements (classads) are the lingua franca of HTCondor, used for describing jobs, workstations, and other resources. There is a protocol for evaluating whether two classads match, which is used by the HTCondor central manager to determine the compatibility of jobs, and workstations where they may be run. . This package provides the runtime library. Package: libcnrun2 Source: cnrun Version: 2.0.1-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 277 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libgomp1 (>= 4.2.1), libgsl0ldbl (>= 1.9), libstdc++6 (>= 4.6), libxml2 (>= 2.7.4) Homepage: http://johnhommer.com/academic/code/cnrun Priority: optional Section: science Filename: pool/main/c/cnrun/libcnrun2_2.0.1-1~nd80+1_amd64.deb Size: 83984 SHA256: c111730fdac90a3e11bb268e1cbc13aaeb68a5864df576d4fb9e6ecb7269051d SHA1: cbe067d902c6029cc308836735bc8acaba35a585 MD5sum: 196dc77c459a845f292dac2c58fad6be Description: NeuroML-capable neuronal network simulator (shared lib) CNrun is a neuronal network simulator implemented as a Lua package. This package contains shared libraries. . See lua-cnrun description for extended description. Package: libcnrun2-dev Source: cnrun Version: 2.0.1-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 114 Depends: neurodebian-popularity-contest, libcnrun2 (= 2.0.1-1~nd80+1) Suggests: pkg-config Homepage: http://johnhommer.com/academic/code/cnrun Priority: optional Section: libdevel Filename: pool/main/c/cnrun/libcnrun2-dev_2.0.1-1~nd80+1_amd64.deb Size: 24740 SHA256: 04575cec97ddc38b6133edccb678c36cecf4ba76b3f9930244fa2b8f2ef047fa SHA1: f4c8f785cc9e77b830fd3ce37e8333299ca3dab6 MD5sum: a1a87506067233ce1bf2bca7f670e34c Description: NeuroML-capable neuronal network simulator (development files) CNrun is a neuronal network simulator implemented as a Lua package. This package contains development files. . See lua-licnrun description for extended description. Package: libdmtcpaware-dev Source: dmtcp Version: 1.2.5-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 16 Depends: neurodebian-popularity-contest, libdmtcpaware1 (= 1.2.5-1~nd70+1) Homepage: http://dmtcp.sourceforge.net Priority: optional Section: libdevel Filename: pool/main/d/dmtcp/libdmtcpaware-dev_1.2.5-1~nd70+1_amd64.deb Size: 7116 SHA256: 1e11c37aa39b18b267ec0a22fc1558345abc35d68987d3d8eaffdbb1731577fd SHA1: edba98ee173c4443bad08c28fd68b11e79916dc6 MD5sum: 4539e55d0bc18e29412b313ac19ff803 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-Window 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: 1.2.5-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 15 Depends: neurodebian-popularity-contest, dmtcp, libc6 (>= 2.2.5) Homepage: http://dmtcp.sourceforge.net Priority: optional Section: libs Filename: pool/main/d/dmtcp/libdmtcpaware1_1.2.5-1~nd70+1_amd64.deb Size: 7178 SHA256: b1b684b8e5a00c74daf03b22562de8f62b848351dfdee21b6b570076d6aa35fe SHA1: 14cb91c8f0ed5205f37622adb930c108da3afb05 MD5sum: 3a98a1ec607005792cb722662ffbc0ef 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-Window 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: libdouble-conversion-dbg Source: double-conversion Version: 2.0.1-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 116 Depends: neurodebian-popularity-contest, libdouble-conversion1 (= 2.0.1-1~nd80+1) Multi-Arch: same Homepage: http://double-conversion.googlecode.com Priority: extra Section: debug Filename: pool/main/d/double-conversion/libdouble-conversion-dbg_2.0.1-1~nd80+1_amd64.deb Size: 101846 SHA256: 7336a00d667c94e5b4aa21afba861ef0da5d86cc4dd1bbde10a29cbccdd42b69 SHA1: 04477213052b92cd26c7fcf4bf84eb1f9c18294a MD5sum: 2925d4f4cbac97d1332355b70459e248 Description: routines to convert IEEE floats to and from strings (debugging symbols) This library provides routines to convert IEEE single and double floats to and from string representations. It offers at lot of flexibility with respect to the conversion format: shortest, fixed, precision or exponential representation; decimal, octal or hexadecimal basis; control over number of digits, leading/trailing zeros and spaces. . The library consists of efficient conversion routines that have been extracted from the V8 JavaScript engine. The code has been refactored and improved so that it can be used more easily in other projects. . This package contains the detached debugging symbols of the library. Package: libdouble-conversion-dev Source: double-conversion Version: 2.0.1-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 199 Depends: neurodebian-popularity-contest, libdouble-conversion1 (= 2.0.1-1~nd80+1) Homepage: http://double-conversion.googlecode.com Priority: extra Section: libdevel Filename: pool/main/d/double-conversion/libdouble-conversion-dev_2.0.1-1~nd80+1_amd64.deb Size: 48892 SHA256: 4c2859bdd45cb00469f7e7f6bdc27b7f57edd13472ad6ae1824f4cb32a970f36 SHA1: 4d0db9586b9f86c2a417fbb490719308f487dea9 MD5sum: afd971e1f713717186384f242defd32e Description: routines to convert IEEE floats to and from strings (development files) This library provides routines to convert IEEE single and double floats to and from string representations. It offers at lot of flexibility with respect to the conversion format: shortest, fixed, precision or exponential representation; decimal, octal or hexadecimal basis; control over number of digits, leading/trailing zeros and spaces. . The library consists of efficient conversion routines that have been extracted from the V8 JavaScript engine. The code has been refactored and improved so that it can be used more easily in other projects. . This package contains a static version of the library and development headers. Package: libdouble-conversion1 Source: double-conversion Version: 2.0.1-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 75 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1) Multi-Arch: same Homepage: http://double-conversion.googlecode.com Priority: extra Section: libs Filename: pool/main/d/double-conversion/libdouble-conversion1_2.0.1-1~nd80+1_amd64.deb Size: 32828 SHA256: 5efcbb0279524a44cd62e146bf96b6ebdf5e1130e22c7d52377bd073b5940a95 SHA1: aee3994945d4c22e1907b0ef0fe083f6f6ada152 MD5sum: 2bfab9330704e9155a3426bb9b039cbd Description: routines to convert IEEE floats to and from strings This library provides routines to convert IEEE single and double floats to and from string representations. It offers at lot of flexibility with respect to the conversion format: shortest, fixed, precision or exponential representation; decimal, octal or hexadecimal basis; control over number of digits, leading/trailing zeros and spaces. . The library consists of efficient conversion routines that have been extracted from the V8 JavaScript engine. The code has been refactored and improved so that it can be used more easily in other projects. . This package contains a shared version of the library. Package: libdrawtk-dev Source: drawtk Version: 2.0-2~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 69 Depends: neurodebian-popularity-contest, libdrawtk0 (= 2.0-2~nd70+1) Multi-Arch: same Homepage: http://cnbi.epfl.ch/software/drawtk.html Priority: extra Section: libdevel Filename: pool/main/d/drawtk/libdrawtk-dev_2.0-2~nd70+1_amd64.deb Size: 43592 SHA256: b9ed91474f944d36ce4c32a19c86395a334bbcb53810d2e8bf27e345d4d91a75 SHA1: 1d54cad3a594b00e71c63d0bc3b504877c64eb1b MD5sum: 2625bc1927e69418f5178569a2ed05a8 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 allowing 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: 2.0-2~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 66 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libfontconfig1 (>= 2.9.0), libfreeimage3, 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 (>= 1.2.11) Multi-Arch: same Homepage: http://cnbi.epfl.ch/software/drawtk.html Priority: extra Section: libs Filename: pool/main/d/drawtk/libdrawtk0_2.0-2~nd70+1_amd64.deb Size: 35678 SHA256: 4de278f86722f70c73e28e6de17b0994340367a5af09c8bfd369bd1c3c7d6c4d SHA1: 1011317660c481167f8fc3f2cd5f664e4e742c29 MD5sum: 11c462aea8bba5f7f3966e2bba5a6d04 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 allowing 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: 2.0-2~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 73 Depends: neurodebian-popularity-contest, libdrawtk0 (= 2.0-2~nd70+1) Multi-Arch: same Homepage: http://cnbi.epfl.ch/software/drawtk.html Priority: extra Section: debug Filename: pool/main/d/drawtk/libdrawtk0-dbg_2.0-2~nd70+1_amd64.deb Size: 64094 SHA256: a7a9aa3d793255265cbc76f50946f235c12ceec4f5aada9d9035e4fd760d2aac SHA1: 3a32c6290c276c60a4507509fdbde3db1de72c8b MD5sum: e1abd9cd8085b59a3294625b7845a4b9 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 allowing 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: libedac-dev Source: edac-utils Version: 0.18-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 29 Depends: neurodebian-popularity-contest, libedac1 (= 0.18-1~nd70+1) Homepage: http://sourceforge.net/projects/edac-utils Priority: extra Section: libdevel Filename: pool/main/e/edac-utils/libedac-dev_0.18-1~nd70+1_amd64.deb Size: 18998 SHA256: 0c6768e4e88205c8ac7bd3d32a571ab88ed0a2991131163134693e9ab0df048e SHA1: 173c8ae845418e377c6a14a4757024908c9da8e4 MD5sum: 99ff79c80af866929fe2461bf488ded8 Description: report kernel-detected PCI and ECC RAM errors This package contains the user-space utilities for use with the EDAC kernel subsystem. EDAC (Error Detection and Correction) is a set of Linux kernel modules for handling hardware-related errors. Currently its major focus is ECC memory error handling. However it also detects and reports PCI bus parity errors. . PCI parity errors are supported on all architectures (and are a mandatory part of the PCI specification). . Main memory ECC drivers are memory controller specific. At the time of writing, drivers exist for many x86-specific chipsets and CPUs, and some PowerPC, and MIPS systems. . This package contains development files for the library Package: libedac1 Source: edac-utils Version: 0.18-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 24 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libsysfs2 Provides: libedac Homepage: http://sourceforge.net/projects/edac-utils Priority: extra Section: libs Filename: pool/main/e/edac-utils/libedac1_0.18-1~nd70+1_amd64.deb Size: 14990 SHA256: 3ca056758eb752f400ce3e498b075b32b5560aeecb4bd8a6b5203287191c6106 SHA1: a9d8cf91b6055ff7ff7e44cd5a233b22ef7f1024 MD5sum: f11364b55608db2d0ce50e9a2d684d84 Description: report kernel-detected PCI and ECC RAM errors This package contains the user-space utilities for use with the EDAC kernel subsystem. EDAC (Error Detection and Correction) is a set of Linux kernel modules for handling hardware-related errors. Currently its major focus is ECC memory error handling. However it also detects and reports PCI bus parity errors. . PCI parity errors are supported on all architectures (and are a mandatory part of the PCI specification). . Main memory ECC drivers are memory controller specific. At the time of writing, drivers exist for many x86-specific chipsets and CPUs, and some PowerPC, and MIPS systems. . This package includes shared library Package: libedac1-dbg Source: edac-utils Version: 0.18-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 76 Depends: neurodebian-popularity-contest, libedac1 (= 0.18-1~nd70+1) Provides: libedac Homepage: http://sourceforge.net/projects/edac-utils Priority: extra Section: debug Filename: pool/main/e/edac-utils/libedac1-dbg_0.18-1~nd70+1_amd64.deb Size: 33438 SHA256: b2847b4ffec34d3a152113d7b94b95e963adfe51ea8ec0bf2a6ef4093b69b0d2 SHA1: defa9fd481f8f7082f3c761be3787f5ac322a100 MD5sum: be6a39e8861765ddd1d58aa781c961b1 Description: report kernel-detected PCI and ECC RAM errors This package contains the user-space utilities for use with the EDAC kernel subsystem. EDAC (Error Detection and Correction) is a set of Linux kernel modules for handling hardware-related errors. Currently its major focus is ECC memory error handling. However it also detects and reports PCI bus parity errors. . PCI parity errors are supported on all architectures (and are a mandatory part of the PCI specification). . Main memory ECC drivers are memory controller specific. At the time of writing, drivers exist for many x86-specific chipsets and CPUs, and some PowerPC, and MIPS systems. . This package includes shared library with debugging symbols not stripped Package: libeegdev-dev Source: eegdev Version: 0.2-3~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 47 Depends: neurodebian-popularity-contest, libeegdev0 (= 0.2-3~nd70+1) Multi-Arch: same Homepage: http://cnbi.epfl.ch/software/eegdev.html Priority: extra Section: libdevel Filename: pool/main/e/eegdev/libeegdev-dev_0.2-3~nd70+1_amd64.deb Size: 22434 SHA256: f7637b0e52661b8441133d6925c47def9eca334fe94b794a2d91bef9684e48ad SHA1: 2c9709328f5dfa1c55dd362c332c7638d3e87731 MD5sum: 5c9256c9af42dfdbf9d8c991795fbf68 Description: Biosignal acquisition device library (Developement files) eegdev is a library that provides a unified interface for accessing various EEG (and other biosignals) acquisition systems. This interface has been designed to be both flexible and efficient. The device specific part is implemented by the mean of plugins which makes adding new device backend fairly easy even if the library does not support them yet officially. . The core library not only provides to users a unified and consistent interfaces to the acquisition device but it also provides many functionalities to the device backends (plugins) ranging from configuration to data casting and scaling making writing new device backend an easy task. . This library is particularly useful to handle the acquisition part of a Brain Computer Interface (BCI) or any realtime multi-electrode acquisition in neurophysiological research. . This package contains the files needed to compile and link programs which use eegdev. Its provides also the headers neeeded to develop new device plugins. The manpages and examples are shipped in this package. Package: libeegdev0 Source: eegdev Version: 0.2-3~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 80 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.3.2) Recommends: eegdev-plugins-free Multi-Arch: same Homepage: http://cnbi.epfl.ch/software/eegdev.html Priority: extra Section: libs Filename: pool/main/e/eegdev/libeegdev0_0.2-3~nd70+1_amd64.deb Size: 45012 SHA256: 57ed4a80b199995c051f7ec75e4bd13a053b445714cc86cbbd46f94919bbf01d SHA1: c49e563b9ce3a5036db6733d17c5294187a37389 MD5sum: b2f1601746465bc19b1cb601dfde021b Description: Biosignal acquisition device library eegdev is a library that provides a unified interface for accessing various EEG (and other biosignals) acquisition systems. This interface has been designed to be both flexible and efficient. The device specific part is implemented by the mean of plugins which makes adding new device backend fairly easy even if the library does not support them yet officially. . The core library not only provides to users a unified and consistent interfaces to the acquisition device but it also provides many functionalities to the device backends (plugins) ranging from configuration to data casting and scaling making writing new device backend an easy task. . This library is particularly useful to handle the acquisition part of a Brain Computer Interface (BCI) or any realtime multi-electrode acquisition in neurophysiological research. . This package contains the core library Package: libeegdev0-dbg Source: eegdev Version: 0.2-3~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 167 Depends: neurodebian-popularity-contest, libeegdev0 (= 0.2-3~nd70+1) Multi-Arch: same Homepage: http://cnbi.epfl.ch/software/eegdev.html Priority: extra Section: debug Filename: pool/main/e/eegdev/libeegdev0-dbg_0.2-3~nd70+1_amd64.deb Size: 141626 SHA256: e468d27bac7ec9ce64199276604e9db4e26fb476f9b6a79d8332936abb941c5e SHA1: 60e5088b5a1919d66432776737a1958ad4d83efd MD5sum: e9bec57f87b66369c7b1cb71afd6ad26 Description: Biosignal acquisition device library (Debugging symbols) eegdev is a library that provides a unified interface for accessing various EEG (and other biosignals) acquisition systems. This interface has been designed to be both flexible and efficient. The device specific part is implemented by the mean of plugins which makes adding new device backend fairly easy even if the library does not support them yet officially. . The core library not only provides to users a unified and consistent interfaces to the acquisition device but it also provides many functionalities to the device backends (plugins) ranging from configuration to data casting and scaling making writing new device backend an easy task. . This library is particularly useful to handle the acquisition part of a Brain Computer Interface (BCI) or any realtime multi-electrode acquisition in neurophysiological research. . This package provides the debugging symbols for the library. Package: libeigen3-dev Source: eigen3 Version: 3.0.1-1.1~nd70+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~nd70+1_amd64.deb Size: 509864 SHA256: 9d12cf00824b926884b6259eea04aad3c85b603603b490a96d0ff4ce45064756 SHA1: fbce8e2f0c2bcb9306b10abf8d53c2fffbc36fca MD5sum: 28d181ca81e4c367057b63a98f8761d4 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~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 10344 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~nd70+1_all.deb Size: 2377384 SHA256: a49fd82e5f6a6d048154bd60d83245d840e38ec31ca1c90607c04479eaf6f04a SHA1: 551f098e9a8eae57dc8ac6baceb92ff5c87871e9 MD5sum: aefa7c3d5f3f5bfd5e3a481d932d7477 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: libfreeipmi-dev Source: freeipmi Version: 1.4.9-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 7703 Depends: neurodebian-popularity-contest, freeipmi-common (= 1.4.9-1~nd80+1), libfreeipmi16 (= 1.4.9-1~nd80+1) Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: libdevel Filename: pool/main/f/freeipmi/libfreeipmi-dev_1.4.9-1~nd80+1_amd64.deb Size: 916208 SHA256: a99d7eb5c5683dbdce513407fdd3685410db3034711b4cd844f202bacf9a01db SHA1: b6c282ffccc4d82b14e21ab9d0c2ebc59a76658d MD5sum: 213af8c654be30e8d9f923d8238225bf Description: GNU IPMI - development package FreeIPMI is a collection of Intelligent Platform Management IPMI system software. It provides in-band and out-of-band software and a development library conforming to the Intelligent Platform Management Interface (IPMI v1.5 and v2.0) standards. . This is the development package for libfreeipmi. Package: libfreeipmi12 Source: freeipmi Version: 1.1.5-3~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 4625 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcrypt11 (>= 1.4.5), freeipmi-common (= 1.1.5-3~nd70+1) Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: libs Filename: pool/main/f/freeipmi/libfreeipmi12_1.1.5-3~nd70+1_amd64.deb Size: 1101074 SHA256: bccd5585424f3f555842a785001a1d4cb81481bc327ba9c8eb8a415d6800263a SHA1: f0a8f731d1f7a3e374f741e651d998a4f27d3444 MD5sum: b56b0a15f23b98cab71767390e76ea16 Description: GNU IPMI - libraries FreeIPMI is a collection of Intelligent Platform Management IPMI system software. It provides in-band and out-of-band software and a development library conforming to the Intelligent Platform Management Interface (IPMI v1.5 and v2.0) standards. . OpenIPMI, KCS, SMIC, SSIF, LAN drivers, and an IPMI API in a C Library. Package: libfreeipmi16 Source: freeipmi Version: 1.4.9-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 5081 Depends: neurodebian-popularity-contest, libc6 (>= 2.15), libgcrypt20 (>= 1.6.1), freeipmi-common (= 1.4.9-1~nd80+1) Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: libs Filename: pool/main/f/freeipmi/libfreeipmi16_1.4.9-1~nd80+1_amd64.deb Size: 825404 SHA256: 2fe3025348f7d9bd48471d3c26c1f1022626c9e7c1d3cf39859e7c97c8229577 SHA1: c1a6a4a78e6f0c2cbb6cde8d7d6af03004adb7d9 MD5sum: 9a2ea745710c998157dacb504a66c135 Description: GNU IPMI - libraries FreeIPMI is a collection of Intelligent Platform Management IPMI system software. It provides in-band and out-of-band software and a development library conforming to the Intelligent Platform Management Interface (IPMI v1.5 and v2.0) standards. . OpenIPMI, KCS, SMIC, SSIF, LAN drivers, and an IPMI API in a C Library. Package: libfreenect-bin Source: libfreenect Version: 1:0.5.2+git6-g5455843+dfsg-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 248 Depends: neurodebian-popularity-contest, freeglut3, libc6 (>= 2.14), libfreenect0.5 (>= 1:0.5.2), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, libstdc++6 (>= 4.4.0) Breaks: libfreenect-demos (<< 1:0.1.2+dfsg-1) Replaces: libfreenect-demos (<< 1:0.1.2+dfsg-1) Homepage: http://openkinect.org/ Priority: extra Section: utils Filename: pool/main/libf/libfreenect/libfreenect-bin_0.5.2+git6-g5455843+dfsg-1~nd80+1_amd64.deb Size: 55074 SHA256: bdd4e072b622c3a592f842021c3f87f8826af6d25e48c82a07cc102bc6aa2969 SHA1: d47e3059e66cba9b4e0ebc8ef278d66005ba1eb5 MD5sum: c80529513f04da58b82155270a15f63b Description: library for accessing Kinect device -- utilities and samples libfreenect is a cross-platform library that provides the necessary interfaces to activate, initialize, and communicate data with the Kinect hardware. Currently, the library supports access to RGB and depth video streams, motors, accelerometer and LED and provide binding in different languages (C++, Python...) . This library is the low level component of the OpenKinect project which is an open community of people interested in making use of the Xbox Kinect hardware with PCs and other devices. . This package includes utilities and sample programs for kinect. Package: libfreenect-demos Source: libfreenect Version: 1:0.5.2+git6-g5455843+dfsg-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 12 Depends: neurodebian-popularity-contest, libfreenect-bin Homepage: http://openkinect.org/ Priority: extra Section: libdevel Filename: pool/main/libf/libfreenect/libfreenect-demos_0.5.2+git6-g5455843+dfsg-1~nd80+1_amd64.deb Size: 8530 SHA256: 95f896e7ba7e377d7e1718baabc636ebf64607b49ad6c491fd99b6edc0413884 SHA1: 52267490deaae829708a839cc6c16a4d3bd8f26c MD5sum: 437d6c21072ae78046c2e8d0f79bdf6e Description: library for accessing Kinect device -- dummy package libfreenect is a cross-platform library that provides the necessary interfaces to activate, initialize, and communicate data with the Kinect hardware. Currently, the library supports access to RGB and depth video streams, motors, accelerometer and LED and provide binding in different languages (C++, Python...) . This library is the low level component of the OpenKinect project which is an open community of people interested in making use of the Xbox Kinect hardware with PCs and other devices. . This package is a metapackage to do the transition from libfreenect-demos to libfreenect-bin. This package can be removed after installation. Package: libfreenect-dev Source: libfreenect Version: 1:0.5.2+git6-g5455843+dfsg-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 62 Depends: neurodebian-popularity-contest, libfreenect0.5 (= 1:0.5.2+git6-g5455843+dfsg-1~nd80+1), libusb-1.0-0-dev (>= 1.0.18~) Multi-Arch: same Homepage: http://openkinect.org/ Priority: extra Section: libdevel Filename: pool/main/libf/libfreenect/libfreenect-dev_0.5.2+git6-g5455843+dfsg-1~nd80+1_amd64.deb Size: 19288 SHA256: 469e2ce6be0b5df2395e5454cf8b4926371b0f0ca01d6a445d188636ff489143 SHA1: 52e62065abd0db63f8273f654070e8772dccf825 MD5sum: 751498e192a245db9a2d7ca353042e15 Description: library for accessing Kinect device -- development files libfreenect is a cross-platform library that provides the necessary interfaces to activate, initialize, and communicate data with the Kinect hardware. Currently, the library supports access to RGB and depth video streams, motors, accelerometer and LED and provide binding in different languages (C++, Python...) . This library is the low level component of the OpenKinect project which is an open community of people interested in making use of the Xbox Kinect hardware with PCs and other devices. . This is the development package containing the libraries and header for software development with libfreenect. Package: libfreenect-doc Source: libfreenect Version: 1:0.5.2+git6-g5455843+dfsg-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 647 Depends: neurodebian-popularity-contest Multi-Arch: foreign Homepage: http://openkinect.org/ Priority: extra Section: doc Filename: pool/main/libf/libfreenect/libfreenect-doc_0.5.2+git6-g5455843+dfsg-1~nd80+1_all.deb Size: 90094 SHA256: 07b97bea0018616a664021dcbe00c3b33948f445ffd21873f14dc7bb3a310784 SHA1: 482753c937bf65d5c29dc0dd0db67ebc259fc43b MD5sum: e2c15d0c5bb851aad639fda259414920 Description: library for accessing Kinect device -- documentation libfreenect is a cross-platform library that provides the necessary interfaces to activate, initialize, and communicate data with the Kinect hardware. Currently, the library supports access to RGB and depth video streams, motors, accelerometer and LED and provide binding in different languages (C++, Python...) . This library is the low level component of the OpenKinect project which is an open community of people interested in making use of the Xbox Kinect hardware with PCs and other devices. . This package contains the documentation of the API of libfreenect. Package: libfreenect0.1 Source: libfreenect Version: 1:0.1.2+dfsg-6~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 95 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.7), libusb-1.0-0 (>= 2:1.0.8) Conflicts: libfreenect Multi-Arch: same Homepage: http://openkinect.org/ Priority: extra Section: libs Filename: pool/main/libf/libfreenect/libfreenect0.1_0.1.2+dfsg-6~nd70+1_amd64.deb Size: 37326 SHA256: 9b9b4ab0e3b082ae8cad872a1f1b64d9dc7080bcebde303f77c8b21f7e4c55e5 SHA1: b98c27ea41b699387dc41b17d8cbbc39d1d4034f MD5sum: 16d2de34d4596452ab503d083fdeefc8 Description: library for accessing Kinect device libfreenect is a cross-platform library that provides the necessary interfaces to activate, initialize, and communicate data with the Kinect hardware. Currently, the library supports access to RGB and depth video streams, motors, accelerometer and LED and provide binding in different languages (C++, Python...) . This library is the low level component of the OpenKinect project which is an open community of people interested in making use of the Xbox Kinect hardware with PCs and other devices. . This package contains the shared library of libfreenect. Package: libfreenect0.5 Source: libfreenect Version: 1:0.5.2+git6-g5455843+dfsg-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 107 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libusb-1.0-0 (>= 2:1.0.12) Conflicts: libfreenect Multi-Arch: same Homepage: http://openkinect.org/ Priority: extra Section: libs Filename: pool/main/libf/libfreenect/libfreenect0.5_0.5.2+git6-g5455843+dfsg-1~nd80+1_amd64.deb Size: 42198 SHA256: c662ada397564f941c757d09c63c3cfb900cf2da668b9b2f1920cf8a16fe3122 SHA1: 988564248d4d840208a5d4b887dff191d34cb11d MD5sum: a234a0e2c6fddb41b8ff4d82f22daec0 Description: library for accessing Kinect device libfreenect is a cross-platform library that provides the necessary interfaces to activate, initialize, and communicate data with the Kinect hardware. Currently, the library supports access to RGB and depth video streams, motors, accelerometer and LED and provide binding in different languages (C++, Python...) . This library is the low level component of the OpenKinect project which is an open community of people interested in making use of the Xbox Kinect hardware with PCs and other devices. . This package contains the shared library of libfreenect. Package: libgdf-dev Source: libgdf Version: 0.1.2-2~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 97 Depends: neurodebian-popularity-contest, libgdf0 (= 0.1.2-2~nd70+1) Homepage: http://sourceforge.net/projects/libgdf Priority: extra Section: libdevel Filename: pool/main/libg/libgdf/libgdf-dev_0.1.2-2~nd70+1_amd64.deb Size: 19770 SHA256: 49e0f5a48d6a13bb15ea6429e7830b1b027b9487d5de5f77a29ef7218fc6b5da SHA1: ad0c3299900360cae38db590fe3b6b90dbb4d033 MD5sum: 0445434c686809a61771fae14c37878e 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: 0.1.2-2~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 802 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.6) Homepage: http://sourceforge.net/projects/libgdf Priority: extra Section: libs Filename: pool/main/libg/libgdf/libgdf0_0.1.2-2~nd70+1_amd64.deb Size: 215642 SHA256: 469da38ea2fe770668a4da33af5377aaa0754a41da1cc305753a3324e35c2c65 SHA1: 714853b4df5b9cab988ccb2db1bc0b586e5dfd01 MD5sum: 555bcb37c448b89f1f7fcf1da7412188 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: 0.1.2-2~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2169 Depends: neurodebian-popularity-contest, libgdf0 (= 0.1.2-2~nd70+1) Homepage: http://sourceforge.net/projects/libgdf Priority: extra Section: debug Filename: pool/main/libg/libgdf/libgdf0-dbg_0.1.2-2~nd70+1_amd64.deb Size: 533706 SHA256: 4744ae089d8c0517e9a30e06f321171223e329d195a4e855375e1374fba34bda SHA1: 76fc35326e58936043e6c29c7d6edc18b14d07a6 MD5sum: c76109515596cf0059c53cde25ff1bb7 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: libglew1.9 Source: glew Version: 1.9.0-3~bnd1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 650 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libgl1-mesa-glx | libgl1, libx11-6, libxext6, libxi6, libxmu6 Suggests: glew-utils Conflicts: libglew1 Multi-Arch: same Homepage: http://glew.sourceforge.net Priority: optional Section: libs Filename: pool/main/g/glew/libglew1.9_1.9.0-3~bnd1~nd70+1_amd64.deb Size: 183544 SHA256: 0d876aaa23812e4f9deef0c811a80da7c48aff1da4ad32bd892469e44a1957dc SHA1: bbf9a44448d0de7a762013201a56586c5c75cadf MD5sum: 53aa6d5ea36c16562c1a57cdb8842f44 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: libglew1.9-dbg Source: glew Version: 1.9.0-3~bnd1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 149 Depends: neurodebian-popularity-contest, libglew1.9 (= 1.9.0-3~bnd1~nd70+1) Homepage: http://glew.sourceforge.net Priority: extra Section: debug Filename: pool/main/g/glew/libglew1.9-dbg_1.9.0-3~bnd1~nd70+1_amd64.deb Size: 41050 SHA256: b42b58d0bf3125e0d092b13b6e8895959d3ca758a776ad68e1f1f75d191010b6 SHA1: 6601359f829eb6211ad83999be815b2f82e69da4 MD5sum: a01ac1e09bcc416e96959da36bf6e78a Description: OpenGL Extension Wrangler (debugging symbols) 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 (http://www.opengl.org/registry). . This package contains the debugging symbols for libglew1.9. Package: libglew1.9-dev Source: glew Version: 1.9.0-3~bnd1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1000 Depends: neurodebian-popularity-contest, libgl1-mesa-dev | libgl-dev, libglew1.9 (= 1.9.0-3~bnd1~nd70+1), libglu1-mesa-dev | libglu-dev Conflicts: libglew-dev, libglew1.6-dev Provides: libglew1.5-dev, libglew1.6-dev Multi-Arch: same Homepage: http://glew.sourceforge.net Priority: optional Section: libdevel Filename: pool/main/g/glew/libglew1.9-dev_1.9.0-3~bnd1~nd70+1_amd64.deb Size: 153184 SHA256: d858c48eabe2b6c73291897479415378a515d3fcd5fe3d3a5910f761bbe84531 SHA1: 7b47290310c401a1d5ccd2a06668234a0958905b MD5sum: 89cd0a5eea2cfda2152f3113c76b6f8f 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 (http://www.opengl.org/registry). . This package contains the development documentation as well as the required header files. Package: libglewmx1.9 Source: glew Version: 1.9.0-3~bnd1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 573 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libgl1-mesa-glx | libgl1, libx11-6, libxext6, libxi6, libxmu6 Conflicts: libglew1 Multi-Arch: same Homepage: http://glew.sourceforge.net Priority: optional Section: libs Filename: pool/main/g/glew/libglewmx1.9_1.9.0-3~bnd1~nd70+1_amd64.deb Size: 164558 SHA256: 512798738909e8061ec2ac8ed282074f2ff2eef5d37fcd34bb8a479eb006e326 SHA1: f670d8f8e974dd751c91476af5ad07e1e72e98bd MD5sum: b2a2202b632dac1665b39deed186d23d Description: OpenGL Extension Wrangler (Multiple Rendering Contexts) For more information about GLEW please refer to the description of the libglewmx-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: libglewmx1.9-dbg Source: glew Version: 1.9.0-3~bnd1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 122 Depends: neurodebian-popularity-contest, libglewmx1.9 (= 1.9.0-3~bnd1~nd70+1) Homepage: http://glew.sourceforge.net Priority: extra Section: debug Filename: pool/main/g/glew/libglewmx1.9-dbg_1.9.0-3~bnd1~nd70+1_amd64.deb Size: 33016 SHA256: 1546075ee0ac306518286d26e1b622a641e4e2944ba0949aab231f60bdcb20e1 SHA1: c796caf0a2f82518f7c2e959bd213b5ba595914d MD5sum: 74c5afad0329fed0c7c08649da17fa57 Description: OpenGL Extension Wrangler MX (debugging symbols) 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 (http://www.opengl.org/registry). . This package contains the debugging symbols for libglewmx1.9. Package: libglewmx1.9-dev Source: glew Version: 1.9.0-3~bnd1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 12 Depends: neurodebian-popularity-contest, libglew1.9-dev, libglewmx1.9 (= 1.9.0-3~bnd1~nd70+1) Conflicts: libglewmx-dev, libglewmx1.6-dev Provides: libglewmx1.5-dev, libglewmx1.6-dev Multi-Arch: same Homepage: http://glew.sourceforge.net Priority: optional Section: libdevel Filename: pool/main/g/glew/libglewmx1.9-dev_1.9.0-3~bnd1~nd70+1_amd64.deb Size: 8798 SHA256: d8b0d591b7f0cac49ae8bd93c8f5a7ca7849dacae294f05737462dc914d2a015 SHA1: 444dfdfad03400ec28214b4dd4b46e6cd726ae60 MD5sum: 0829a802b32c527758902532e8e24c5e Description: OpenGL Extension Wrangler MX - 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 (http://www.opengl.org/registry). . This package contains the development libraries compiled with GLEW_MX. Package: libguac-client-rdp0 Source: guacamole-server Version: 0.8.3-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 109 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.15), libcairo2 (>= 1.6.0), libfreerdp1 (>= 1.0.1), libguac5, libogg0 (>= 1.0rc3), libvorbis0a (>= 1.1.2), libvorbisenc2 (>= 1.1.2), ghostscript Recommends: libfreerdp-plugins-standard Multi-Arch: same Homepage: http://guac-dev.org/ Priority: extra Section: libs Filename: pool/main/g/guacamole-server/libguac-client-rdp0_0.8.3-1~nd80+1_amd64.deb Size: 36768 SHA256: f31f685c33f897986cbe5bac45b191cd2d56e7054773e549bbdee23a03163c5e SHA1: 63e8618ebcc3417e743b6661155679d5fa9ec126 MD5sum: 9cf04791a7f55f9973dbab483c214329 Description: RDP support plugin for Guacamole A plugin for the Guacamole proxy daemon (guacd) that provides support for the RDP protocol (Windows Remote Desktop). Package: libguac-client-ssh0 Source: guacamole-server Version: 0.8.3-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 56 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.15), libcairo2 (>= 1.2.4), libglib2.0-0 (>= 2.12.0), libguac5, libpango-1.0-0 (>= 1.22.0), libpangocairo-1.0-0 (>= 1.14.0), libssh-4 (>= 0.3.91) Multi-Arch: same Homepage: http://guac-dev.org/ Priority: extra Section: libs Filename: pool/main/g/guacamole-server/libguac-client-ssh0_0.8.3-1~nd80+1_amd64.deb Size: 26640 SHA256: 057af464772cbaaea88591467f6ebf7bdc57905be719d512c90086e10d46ad46 SHA1: 63fc40336f3da3a71a6b40f7d38fa8f11c3da30a MD5sum: 7b5422bd77d13b4b09d01f4b73599252 Description: SSH support plugin for Guacamole A plugin for the Guacamole proxy daemon (guacd) that provides support for the SSH protocol. Package: libguac-client-vnc0 Source: guacamole-server Version: 0.8.3-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 27 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libcairo2 (>= 1.6.0), libguac5, libpulse0 (>= 0.99.1), libvncserver0 Recommends: vnc4server Multi-Arch: same Homepage: http://guac-dev.org/ Priority: extra Section: libs Filename: pool/main/g/guacamole-server/libguac-client-vnc0_0.8.3-1~nd80+1_amd64.deb Size: 12472 SHA256: 59d67eb48a41f4fe66a71adae69ff4aeb0ac418d7684365c7503714d410cff1e SHA1: a7b882406e6aac20b2a3abf774e7ec2c314e6f6e MD5sum: ce291ced070b7ca72d9bdefe35540907 Description: VNC support plugin for Guacamole A plugin for the Guacamole proxy daemon (guacd) that provides support for the VNC protocol. Package: libguac-dev Source: guacamole-server Version: 0.8.3-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 214 Depends: neurodebian-popularity-contest, libguac5 (= 0.8.3-1~nd80+1) Replaces: libguac1-dev Multi-Arch: same Homepage: http://guac-dev.org/ Priority: extra Section: libdevel Filename: pool/main/g/guacamole-server/libguac-dev_0.8.3-1~nd80+1_amd64.deb Size: 45302 SHA256: c6b0883509fad8a858e5a9c9388f351532d82cfc4f0e34583085df0454011804 SHA1: e71ee69bbad84c4782d15eb79182a3823df9d48c MD5sum: 257ddb00d84699d6cf4c3276abdb7a22 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: libguac3 Source: libguac Version: 0.6.0-2~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 44 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libcairo2 (>= 1.2.4), libpng12-0 (>= 1.2.13-4) Homepage: http://guacamole.sourceforge.net/ Priority: extra Section: libs Filename: pool/main/libg/libguac/libguac3_0.6.0-2~nd70+1_amd64.deb Size: 18996 SHA256: ff66fd48c04b00670f263d5665f38a48abff7746fdecd4198a7ecf1d41fe5250 SHA1: c15b6001d1f4841b65dddc3073eaff902e2a5ed9 MD5sum: 8234d3926e3b325e0e6bddc639c6079a 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: libguac5 Source: guacamole-server Version: 0.8.3-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 64 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.17), libcairo2 (>= 1.2.4), libogg0 (>= 1.0rc3), libpng12-0 (>= 1.2.13-4), libvorbis0a (>= 1.1.2), libvorbisenc2 (>= 1.1.2) Multi-Arch: same Homepage: http://guac-dev.org/ Priority: extra Section: libs Filename: pool/main/g/guacamole-server/libguac5_0.8.3-1~nd80+1_amd64.deb Size: 26164 SHA256: 5df91153d4cdde128715fea2373654f2b92ab37469e80a4068e12b6570b31b47 SHA1: 2d4fc49a7eb81356f81c79ef5e76e0518fd2ddad MD5sum: a82ade3c9b3060e879fa92ab93c934cf 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: libinsighttoolkit4-dbg Source: insighttoolkit4 Version: 4.7.0-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 41926 Depends: neurodebian-popularity-contest, libinsighttoolkit4.7 (= 4.7.0-1~nd80+1) Homepage: http://www.itk.org/ Priority: extra Section: debug Filename: pool/main/i/insighttoolkit4/libinsighttoolkit4-dbg_4.7.0-1~nd80+1_amd64.deb Size: 37237366 SHA256: b65784d8324f8f86be1a10cde29f7dd358b117adbd4952b5413460a9d0263144 SHA1: 56f17f2e24e89109eabdbb4142855381af49b84d MD5sum: ce14e75331414e3251d26eeaf63fe28b Description: Debugging information for the Insight Toolkit ITK is an open-source software toolkit for performing registration and segmentation. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Typically the sampled representation is an image acquired from such medical instrumentation as CT or MRI scanners. Registration is the task of aligning or developing correspondences between data. For example, in the medical environment, a CT scan may be aligned with a MRI scan in order to combine the information contained in both. . This package contains the debug files of the libraries. Package: libinsighttoolkit4-dev Source: insighttoolkit4 Version: 4.7.0-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 25377 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libinsighttoolkit4.7 (= 4.7.0-1~nd80+1), libstdc++6 (>= 4.9), libgdcm2-dev, libdcmtk2-dev, libhdf5-dev Recommends: libfftw3-dev, uuid-dev Suggests: insighttoolkit4-examples Conflicts: libinsighttoolkit-dev, libinsighttoolkit3-dev Replaces: libinsighttoolkit-dev Homepage: http://www.itk.org/ Priority: optional Section: libdevel Filename: pool/main/i/insighttoolkit4/libinsighttoolkit4-dev_4.7.0-1~nd80+1_amd64.deb Size: 2958486 SHA256: 06a81ae388d34c76ed378135d6c46d9e3a5f156d94963ebd0403ffbe708c9813 SHA1: acf038b8a671ddecff82aaa08cfa8540c7b083cf MD5sum: ef75d4fa4b9de07b099d7e772356a477 Description: Image processing toolkit for registration and segmentation - development ITK is an open-source software toolkit for performing registration and segmentation. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Typically the sampled representation is an image acquired from such medical instrumentation as CT or MRI scanners. Registration is the task of aligning or developing correspondences between data. For example, in the medical environment, a CT scan may be aligned with a MRI scan in order to combine the information contained in both. . This package contains the development files needed to build your own ITK applications. Package: libinsighttoolkit4.2 Source: insighttoolkit4 Version: 4.2.1-2~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 22235 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), libgcc1 (>= 1:4.1.1), libgdcm2.2, libhdf5-7, libjpeg8 (>= 8c), libminc2-1, libnetcdfc7, libpng12-0 (>= 1.2.13-4), libstdc++6 (>= 4.6), zlib1g (>= 1:1.2.3.3) Homepage: http://www.itk.org/ Priority: optional Section: libs Filename: pool/main/i/insighttoolkit4/libinsighttoolkit4.2_4.2.1-2~nd70+1_amd64.deb Size: 7285294 SHA256: 9bb45e9cad5b6b8689ad5d4f081079321a62d7109dd0c6335ea25db4705136ac SHA1: a1e39e2e995bded3b3f1ecedd5cb18e8ba0f2104 MD5sum: 9632194c6a8271910e8e0daee2833fbe Description: Image processing toolkit for registration and segmentation - runtime ITK is an open-source software toolkit for performing registration and segmentation. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Typically the sampled representation is an image acquired from such medical instrumentation as CT or MRI scanners. Registration is the task of aligning or developing correspondences between data. For example, in the medical environment, a CT scan may be aligned with a MRI scan in order to combine the information contained in both. . This package contains the libraries needed to run ITK applications. Package: libinsighttoolkit4.5 Source: insighttoolkit4 Version: 4.5.0-3~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 21901 Depends: neurodebian-popularity-contest, libc6 (>= 2.15), libgcc1 (>= 1:4.1.1), libgdcm2.4, libjpeg8 (>= 8c), libpng12-0 (>= 1.2.13-4), libstdc++6 (>= 4.6), libtiff5 (>= 4.0.3), zlib1g (>= 1:1.2.3.4) Homepage: http://www.itk.org/ Priority: optional Section: libs Filename: pool/main/i/insighttoolkit4/libinsighttoolkit4.5_4.5.0-3~nd80+1_amd64.deb Size: 4708266 SHA256: f7ee32c0dd93e7e8c4b597efd65e25bc836abd091571fbe60a7020650ead300b SHA1: 7d868320e6691a6aae0ba0edd3df22ae0049b8a7 MD5sum: 9d13a53bfc3cbb071e8f860496bc33ab Description: Image processing toolkit for registration and segmentation - runtime ITK is an open-source software toolkit for performing registration and segmentation. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Typically the sampled representation is an image acquired from such medical instrumentation as CT or MRI scanners. Registration is the task of aligning or developing correspondences between data. For example, in the medical environment, a CT scan may be aligned with a MRI scan in order to combine the information contained in both. . This package contains the libraries needed to run ITK applications. Package: libinsighttoolkit4.7 Source: insighttoolkit4 Version: 4.7.0-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 23829 Depends: neurodebian-popularity-contest, libc6 (>= 2.15), libfftw3-double3, libfftw3-single3, libgcc1 (>= 1:4.1.1), libgdcm2.4, libhdf5-8, libhdf5-cpp-8 (>= 1.8.13), libjpeg62-turbo (>= 1.3.1), libpng12-0 (>= 1.2.13-4), libstdc++6 (>= 4.9), libtiff5 (>= 4.0.3), zlib1g (>= 1:1.2.3.4) Homepage: http://www.itk.org/ Priority: optional Section: libs Filename: pool/main/i/insighttoolkit4/libinsighttoolkit4.7_4.7.0-1~nd80+1_amd64.deb Size: 4693164 SHA256: 0a82cd6378ea24d3c1d6d736b6d1460576c4f879ace7f951628a4c08f034c9a3 SHA1: 83c2889cb58489e6f21ebd5265af62f4045225df MD5sum: caa0d0838e1ce1e720415747adcfc037 Description: Image processing toolkit for registration and segmentation - runtime ITK is an open-source software toolkit for performing registration and segmentation. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Typically the sampled representation is an image acquired from such medical instrumentation as CT or MRI scanners. Registration is the task of aligning or developing correspondences between data. For example, in the medical environment, a CT scan may be aligned with a MRI scan in order to combine the information contained in both. . This package contains the libraries needed to run ITK applications. Package: libipmiconsole-dev Source: freeipmi Version: 1.4.9-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 504 Depends: neurodebian-popularity-contest, freeipmi-common (= 1.4.9-1~nd80+1), libipmiconsole2 (= 1.4.9-1~nd80+1) Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: libdevel Filename: pool/main/f/freeipmi/libipmiconsole-dev_1.4.9-1~nd80+1_amd64.deb Size: 101852 SHA256: 4324ec635b7b710a9f61c6218802d0ccd372470ff49ae472c52c641e132ad9e5 SHA1: 38941dd05a4305e7eff6fed6a3a2c05e8c906f8d MD5sum: ebd0f920331cfbb2a90a2e9073bdece6 Description: GNU IPMI - ipmiconsole development package FreeIPMI is a collection of Intelligent Platform Management IPMI system software. It provides in-band and out-of-band software and a development library conforming to the Intelligent Platform Management Interface (IPMI v1.5 and v2.0) standards. . This is the development package for libipmiconsole. Package: libipmiconsole2 Source: freeipmi Version: 1.4.9-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 254 Depends: neurodebian-popularity-contest, libc6 (>= 2.15), libfreeipmi16 (>= 1.4.4), libgcrypt20 (>= 1.6.0), freeipmi-common (= 1.4.9-1~nd80+1) Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: libs Filename: pool/main/f/freeipmi/libipmiconsole2_1.4.9-1~nd80+1_amd64.deb Size: 86154 SHA256: 60375c7e86daf84d0135d653bee2f93e053d7c1e3613ca08525450e3456e8562 SHA1: 20425ccad2b7142beae8228397ba649dfe67d899 MD5sum: 6712a1bc82762c0298efbf9e6f31bcfc Description: GNU IPMI - Serial-over-Lan library FreeIPMI is a collection of Intelligent Platform Management IPMI system software. It provides in-band and out-of-band software and a development library conforming to the Intelligent Platform Management Interface (IPMI v1.5 and v2.0) standards. . A library for Serial-over-Lan (SOL). Package: libipmidetect-dev Source: freeipmi Version: 1.4.9-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 105 Depends: neurodebian-popularity-contest, freeipmi-common (= 1.4.9-1~nd80+1), libipmidetect0 (= 1.4.9-1~nd80+1) Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: libdevel Filename: pool/main/f/freeipmi/libipmidetect-dev_1.4.9-1~nd80+1_amd64.deb Size: 31664 SHA256: ab703ec7a62433a26fd69916927fc90aa63ad90d91746df251d0a1fcb9276c0b SHA1: e66a0891e6e6ef32f928fba0980bf39bba1e349a MD5sum: b240b13fe609c09dabcabb615bacd2f0 Description: GNU IPMI - ipmidetect development package FreeIPMI is a collection of Intelligent Platform Management IPMI system software. It provides in-band and out-of-band software and a development library conforming to the Intelligent Platform Management Interface (IPMI v1.5 and v2.0) standards. . This is the development package for libipmidetect. Package: libipmidetect0 Source: freeipmi Version: 1.4.9-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 64 Depends: neurodebian-popularity-contest, libc6 (>= 2.15), freeipmi-common (= 1.4.9-1~nd80+1) Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: libs Filename: pool/main/f/freeipmi/libipmidetect0_1.4.9-1~nd80+1_amd64.deb Size: 25736 SHA256: 84769daa5e8d9e2c11dd395aa69c0b71fa462c4bb84cbd6d881411594c606b0c SHA1: 90c8cf96410d60e395cff2c1dd2adc9060196d59 MD5sum: d93cba121fa567ed24e0d8fc27a128cb Description: GNU IPMI - IPMI node detection library FreeIPMI is a collection of Intelligent Platform Management IPMI system software. It provides in-band and out-of-band software and a development library conforming to the Intelligent Platform Management Interface (IPMI v1.5 and v2.0) standards. . A library for IPMI node detection. Package: libipmimonitoring-dev Source: freeipmi Version: 1.4.9-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 307 Depends: neurodebian-popularity-contest, freeipmi-common (= 1.4.9-1~nd80+1), libipmimonitoring5a (= 1.4.9-1~nd80+1) Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: libdevel Filename: pool/main/f/freeipmi/libipmimonitoring-dev_1.4.9-1~nd80+1_amd64.deb Size: 61644 SHA256: ee2e147a51dc9a31714568afd80c7e693f589af5b2c671097b6af277e10b8a8b SHA1: ea8511333f552122a0f60caa544fb69c7de80477 MD5sum: 21d28a54b35a014bea280f2ffab2157b Description: GNU IPMI - ipmimonitoring development package FreeIPMI is a collection of Intelligent Platform Management IPMI system software. It provides in-band and out-of-band software and a development library conforming to the Intelligent Platform Management Interface (IPMI v1.5 and v2.0) standards. . This is the development package for libipmimonitoring. Package: libipmimonitoring5 Source: freeipmi Version: 1.1.5-3~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 255 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libfreeipmi12 (>= 1.1.5), libgcrypt11 (>= 1.4.5) Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: libs Filename: pool/main/f/freeipmi/libipmimonitoring5_1.1.5-3~nd70+1_amd64.deb Size: 190190 SHA256: 94fabf06a59fccebc2393af51b00b535a22b6911d5dabbde4d807b0f5da9e045 SHA1: 8202e1460e43761586715f8cdb74b57dd7280685 MD5sum: c01c9ad2e377e8280799db669f92bdc5 Description: GNU IPMI - Sensor monitoring library FreeIPMI is a collection of Intelligent Platform Management IPMI system software. It provides in-band and out-of-band software and a development library conforming to the Intelligent Platform Management Interface (IPMI v1.5 and v2.0) standards. . A library for sensor monitoring. Package: libipmimonitoring5a Source: freeipmi Version: 1.4.9-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 119 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libfreeipmi16 (>= 1.4.4), libgcrypt20 (>= 1.6.0), freeipmi-common (= 1.4.9-1~nd80+1) Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: libs Filename: pool/main/f/freeipmi/libipmimonitoring5a_1.4.9-1~nd80+1_amd64.deb Size: 43540 SHA256: b85a01fa670f02baa6de7d111770533129205ec5918cd19f49fb00dbd03301aa SHA1: 8a870b26fe3a956a2a4a502df4a593cfc7a0181f MD5sum: 7bbcea998732624bd9836f3a507496a8 Description: GNU IPMI - Sensor monitoring library FreeIPMI is a collection of Intelligent Platform Management IPMI system software. It provides in-band and out-of-band software and a development library conforming to the Intelligent Platform Management Interface (IPMI v1.5 and v2.0) standards. . A library for sensor monitoring. Package: libisis-core-dev Source: isis Version: 0.4.7-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 260 Depends: neurodebian-popularity-contest, libisis-core0 (>= 0.4.7-1~nd70+1), libisis-core0 (<< 0.4.7-1~nd70+1.1~) Homepage: https://github.com/isis-group Priority: extra Section: libdevel Filename: pool/main/i/isis/libisis-core-dev_0.4.7-1~nd70+1_all.deb Size: 68948 SHA256: 71ba81e336312edd85331e45ad6c689d1133fe332506a79eb1d4e41946534675 SHA1: 7761d9efa1a6a2cadc67a0f2e546b165f088f855 MD5sum: cc18de68a3f8d8942ad55d38751a2d01 Description: I/O framework for neuroimaging data This framework aids access of and conversion between various established neuro-imaging data formats, like Nifti, Analyze, DICOM and VISTA. ISIS is extensible with plugins to add support for additional data formats. . This package provides headers and library to develop applications with ISIS. Package: libisis-core0 Source: isis Version: 0.4.7-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 9875 Depends: neurodebian-popularity-contest, libboost-date-time1.48.0 (>= 1.48.0-1), libboost-filesystem1.48.0 (>= 1.48.0-1), libboost-regex1.48.0 (>= 1.48.0-1), libboost-system1.48.0 (>= 1.48.0-1), libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), liboil0.3 (>= 0.3.10), libstdc++6 (>= 4.6) Recommends: libisis-ioplugins-common, libisis-ioplugins-dicom Homepage: https://github.com/isis-group Priority: extra Section: libs Filename: pool/main/i/isis/libisis-core0_0.4.7-1~nd70+1_amd64.deb Size: 2019594 SHA256: cdcafe66dd15c461a2729edeb8c576d48e2c7851ef89cfab489fd1ddfced9393 SHA1: b1f67caecec61424ed12e4ec992f7d1c03bb97f1 MD5sum: 43729e2dbee8c7de1dba835c87c60324 Description: I/O framework for neuroimaging data This framework aids access of and conversion between various established neuro-imaging data formats, like Nifti, Analyze, DICOM and VISTA. ISIS is extensible with plugins to add support for additional data formats. . This Package provides the core library needed by all applications that are build upon ISIS. Package: libisis-ioplugins-common Source: isis Version: 0.4.7-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 5602 Depends: neurodebian-popularity-contest, libisis-core0 (= 0.4.7-1~nd70+1), libboost-date-time1.48.0 (>= 1.48.0-1), libboost-filesystem1.48.0 (>= 1.48.0-1), libboost-iostreams1.48.0 (>= 1.48.0-1), libboost-regex1.48.0 (>= 1.48.0-1), libboost-system1.48.0 (>= 1.48.0-1), libbz2-1.0, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), liboil0.3 (>= 0.3.1), libpng12-0 (>= 1.2.13-4), libstdc++6 (>= 4.6), libvia2, zlib1g (>= 1:1.1.4) Homepage: https://github.com/isis-group Priority: extra Section: libs Filename: pool/main/i/isis/libisis-ioplugins-common_0.4.7-1~nd70+1_amd64.deb Size: 1459852 SHA256: 8c2acccca750c8f3eaf4fe0d731df4a4fdd413adfd3f1474f6dec1daba0f5de4 SHA1: b2ee68c5d832ecc73abc72e937c703518f23ee4d MD5sum: 4951b4b7f40ae7256669a41dedde5026 Description: data format plugins for the ISIS framework This framework aids access of and conversion between various established neuro-imaging data formats, like Nifti, Analyze, DICOM and VISTA. ISIS is extensible with plugins to add support for additional data formats. . This package provides plugins for data in NIfTI, PNG, VISTA format, raw-data access, as well as plugins for gzip-compression and tar-archive support. Package: libisis-ioplugins-dicom Source: isis Version: 0.4.7-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1414 Depends: neurodebian-popularity-contest, libisis-core0 (= 0.4.7-1~nd70+1), libboost-date-time1.48.0 (>= 1.48.0-1), libboost-filesystem1.48.0 (>= 1.48.0-1), libboost-regex1.48.0 (>= 1.48.0-1), libboost-system1.48.0 (>= 1.48.0-1), libc6 (>= 2.2.5), libdcmtk2 (>= 3.6.0), libgcc1 (>= 1:4.1.1), liboil0.3 (>= 0.3.1), libpng12-0 (>= 1.2.13-4), libstdc++6 (>= 4.6), libtiff4, zlib1g (>= 1:1.1.4) Homepage: https://github.com/isis-group Priority: extra Section: science Filename: pool/main/i/isis/libisis-ioplugins-dicom_0.4.7-1~nd70+1_amd64.deb Size: 371354 SHA256: bf0e86c60f5afb37972c9288c23f8bce8d840764fa8dac36955751a4a6516128 SHA1: 45780c7dc372670244e5190d46bf8bfcc9c5161f MD5sum: 7a94ddb3a274e1d32dfe1ca95fe9d563 Description: dicom io plugin for the ISIS framework This framework aids access of and conversion between various established neuro-imaging data formats, like Nifti, Analyze, DICOM and VISTA. ISIS is extensible with plugins to add support for additional data formats. . This package provides a plugin to read data from dicom datasets. It reads single files, or whole directories (a DICOMDIR is not needed). Package: libisis-qt4-0 Source: isis Version: 0.4.7-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 162 Depends: neurodebian-popularity-contest, libisis-core0 (= 0.4.7-1~nd70+1), libboost-date-time1.48.0 (>= 1.48.0-1), libboost-filesystem1.48.0 (>= 1.48.0-1), libboost-regex1.48.0 (>= 1.48.0-1), libboost-system1.48.0 (>= 1.48.0-1), libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), liboil0.3 (>= 0.3.1), libqtcore4 (>= 4:4.5.3), libqtgui4 (>= 4:4.5.3), libstdc++6 (>= 4.6) Conflicts: isis-qt4 Replaces: isis-qt4 Homepage: https://github.com/isis-group Priority: extra Section: libs Filename: pool/main/i/isis/libisis-qt4-0_0.4.7-1~nd70+1_amd64.deb Size: 48828 SHA256: c67b545cc531d7bda63c31b639d8e4d6489de01d0372cf8dfd45d22f31920933 SHA1: 6888db247f5fd474e1fb39f24f81c8c1e6c94ef1 MD5sum: c12584164ff4e2cf63435f53322009a3 Description: Qt4 bindings for ISIS data I/O framework This framework aids access of and conversion between various established neuro-imaging data formats, like Nifti, Analyze, DICOM and VISTA. ISIS is extensible with plugins to add support for additional data formats. Package: libisis-qt4-dev Source: isis Version: 0.4.7-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8 Depends: neurodebian-popularity-contest, libisis-qt4-0 (>= 0.4.7-1~nd70+1), libisis-qt4-0 (<< 0.4.7-1~nd70+1.1~), libqt4-dev Conflicts: isis-qt4-dev Homepage: https://github.com/isis-group Priority: extra Section: libdevel Filename: pool/main/i/isis/libisis-qt4-dev_0.4.7-1~nd70+1_all.deb Size: 5992 SHA256: f848c976204b1b3090c9bcba159204365ee5620986f0cadd15bc6a6b8a9dde80 SHA1: a9cc9f1a3bd89a7545ffe60b6ccc874c874986a6 MD5sum: 96ef7f5956383a9fe46cea8c8843d7cd Description: Qt4 bindings for ISIS data I/O framework (development headers) This framework aids access of and conversion between various established neuro-imaging data formats, like Nifti, Analyze, DICOM and VISTA. ISIS is extensible with plugins to add support for additional data formats. Package: libmcpanel-dev Source: mcpanel Version: 0.0-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 4 Depends: neurodebian-popularity-contest, libmcpanel0 (= 0.0-1~nd70+1) Homepage: http://cnbi.epfl.ch/software/mcpanel.html Priority: extra Section: libdevel Filename: pool/main/m/mcpanel/libmcpanel-dev_0.0-1~nd70+1_amd64.deb Size: 2392 SHA256: ae18b5e17e9b254156d3ffb73fdc17c8abe7e9da8d32a5408930a6c7dc7037a9 SHA1: 352b39d8b8b6a630b1dea50de1ec6d89bf41eb07 MD5sum: 30b0c24b2f9a2c1ac9b8557a5ab47712 Description: Library to display multichannel data in realtime (Developement files) This package provides a library written in C implementing a set of widgets designed to view in realtime multichannels signals. Despite it has been initially design to view signals coming from a BIOSEMI Activetwo EEG system, it is totally system agnostic and any user of other system might find it useful. . This package contains the files needed to compile and link programs which use mcpanel Package: libmcpanel0 Source: mcpanel Version: 0.0-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 145 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), libgdk-pixbuf2.0-0 (>= 2.22.0), libglib2.0-0 (>= 2.14.0), libgtk2.0-0 (>= 2.14.0), libpango1.0-0 (>= 1.14.0), librtfilter1 (>= 1.0) Homepage: http://cnbi.epfl.ch/software/mcpanel.html Priority: extra Section: libs Filename: pool/main/m/mcpanel/libmcpanel0_0.0-1~nd70+1_amd64.deb Size: 54376 SHA256: 25aa09e49b93c8e306d3cf6fe7a3e2fa376cb1a39304b1944b3129b7eee5f14d SHA1: 9bec70ba1a28eb6562c376f097085683f527fd28 MD5sum: 5cee97a95228cbc6b03adf60abe5be4f Description: Library to display multichannel data in realtime This package provides a library written in C implementing a set of widgets designed to view in realtime multichannels signals. Despite it has been initially design to view signals coming from a BIOSEMI Activetwo EEG system, it is totally system agnostic and any user of other system might find it useful. Package: libmcpanel0-dbg Source: mcpanel Version: 0.0-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 333 Depends: neurodebian-popularity-contest, libmcpanel0 (= 0.0-1~nd70+1) Homepage: http://cnbi.epfl.ch/software/mcpanel.html Priority: extra Section: debug Filename: pool/main/m/mcpanel/libmcpanel0-dbg_0.0-1~nd70+1_amd64.deb Size: 122690 SHA256: 76d45f284f3fd61530546a5cb9202c5043d150312bfc6542246b5850a9bedd03 SHA1: 7ddee15e9c74c06b3dcee67f03af9c4801a491e7 MD5sum: 1a04d1cab0226f858266d1b58d10baae Description: Library to display multichannel data in realtime (Debugging symbols) This package provides a library written in C implementing a set of widgets designed to view in realtime multichannels signals. Despite it has been initially design to view signals coming from a BIOSEMI Activetwo EEG system, it is totally system agnostic and any user of other system might find it useful. . This package contains the debugging information of the library. Package: libmia-2.0-8 Source: mia Version: 2.0.13-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 25189 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libboost-filesystem1.54.0, libboost-regex1.54.0, libboost-serialization1.54.0, libboost-system1.54.0, libboost-test1.54.0, libc6 (>= 2.14), libdcmtk2 (>= 3.6.0), libfftw3-single3, libgcc1 (>= 1:4.1.1), libglib2.0-0 (>= 2.12.0), libglibmm-2.4-1c2a (>= 2.36.2), libgsl0ldbl (>= 1.9), libgts-0.7-5 (>= 0.7.6), libitpp8, libjpeg8 (>= 8c), libnlopt0 (>= 2.3), libopenexr6 (>= 1.6.1), libpng12-0 (>= 1.2.13-4), libstdc++6 (>= 4.6), libtbb2, libtiff5 (>= 4.0.3), libvistaio14 (>= 1.2.14), libvtk5.8, libxml++2.6-2 (>= 2.36.0) Homepage: http://mia.sourceforge.net Priority: optional Section: libs Filename: pool/main/m/mia/libmia-2.0-8_2.0.13-1~nd80+1_amd64.deb Size: 3774978 SHA256: 62b58b864934a41926a871d935ce7f9b6340e42dbba4b966d21d28dd9a1fc225 SHA1: 53766cd61f48b1a8b61e53f20c262cd77dc81480 MD5sum: 27efbe187c35f2748bfdb048f4c89be6 Description: library for 2D and 3D gray scale image processing libmia comprises a set of libraries and plug-ins for general purpose 2D and 3D gray scale image processing and basic handling of triangular meshes. The libraries provide a basic infrastructure and generic algorithms, that can be specialized by specifying the apropriate plug-ins. Package: libmia-2.0-8-dbg Source: mia Version: 2.0.13-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 67823 Depends: neurodebian-popularity-contest, libmia-2.0-8 (= 2.0.13-1~nd80+1) Homepage: http://mia.sourceforge.net Priority: extra Section: debug Filename: pool/main/m/mia/libmia-2.0-8-dbg_2.0.13-1~nd80+1_amd64.deb Size: 61619930 SHA256: 1e0aa595610865545af17307274f144941ab127cac13b4069600c7c3303a923f SHA1: 9c7b42cd47b78d11fd9ee98f77afb776580410e4 MD5sum: 93c1b8459aa4301dd24133437992d2af Description: Debug information for the MIA library libmia comprises a set of libraries and plug.ins for general purpose 2D and 3D gray scale image processing and basic handling of triangular meshes. The libraries provide a basic infrastructure and generic algorithms, that can be specialized by specifying the apropriate plug-ins. libmia is library for general purpouse 2D and 3D gray scale image processing. This package provides the debug information of the library. Package: libmia-2.0-dev Source: mia Version: 2.0.13-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1093 Depends: neurodebian-popularity-contest, libmia-2.0-8 (= 2.0.13-1~nd80+1), libxml++2.6-dev (>= 2.34.1), libitpp-dev (>= 4.2), libtbb-dev, libgsl0-dev, libboost-all-dev (>= 1.46.1), libfftw3-dev, libblas-dev Recommends: libmia-2.0-doc Homepage: http://mia.sourceforge.net Priority: optional Section: libdevel Filename: pool/main/m/mia/libmia-2.0-dev_2.0.13-1~nd80+1_amd64.deb Size: 177506 SHA256: f3e4ae92dde95f0f6c585b22c726949fe88d5d7576d8f2e9303bbe41f71a2253 SHA1: c4c2c51605c23f30100177a94bffc3765ff57709 MD5sum: c1b025bf7aae0ea0aebe8dd3fbd34004 Description: library for 2D and 3D gray scale image processing, development files libmia comprises a set of libraries and plug-ins for general purpose 2D and 3D gray scale image processing and basic handling of triangular meshes. The libraries provide a basic infrastructure and generic algorithms, that can be specialized by specifying the apropriate plug-ins. This package provides the development files for the library. Package: libmia-2.0-doc Source: mia Version: 2.0.13-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 14011 Depends: neurodebian-popularity-contest, libjs-jquery Enhances: libmia-2.0-dev Homepage: http://mia.sourceforge.net Priority: optional Section: doc Filename: pool/main/m/mia/libmia-2.0-doc_2.0.13-1~nd80+1_all.deb Size: 835144 SHA256: 726e9838f111437a424373ad463485d6751d05795a34cc5181258f473f727569 SHA1: 0e4486b6c32aa80faba79762be75bcf2fd0c157a MD5sum: 2e0bb5ee8936e55c3e6dd40410b6be58 Description: library for 2D and 3D gray scale image processing, documentation libmia comprises a set of libraries and plug-ins for general purpose 2D and 3D gray scale image processing and basic handling of triangular meshes. The libraries provide a basic infrastructure and generic algorithms, that can be specialized by specifying the apropriate plug-ins. This package provides the Doxygen generated API reference. Package: libmialm-dev Source: libmialm Version: 1.0.7-2~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 421 Depends: neurodebian-popularity-contest, libmialm3 (= 1.0.7-2~nd80+1) Homepage: http://mia.sourceforge.net Priority: optional Section: libdevel Filename: pool/main/libm/libmialm/libmialm-dev_1.0.7-2~nd80+1_amd64.deb Size: 77886 SHA256: 435f95db17bd201a773ae5c9b3f50bfa92828dc66a9babfc10c7a69085e66f90 SHA1: 965bb0eb297b79c7c75f2a8806f3bac3699f1109 MD5sum: e4fd7bd277fe0cbd5ad9cdf15b8f3025 Description: Development files for the MIA landmark library This library implements handling for landmarks and 3D view positioning for optimal landmark visibility, and in-and output of these landmarks. This library is part of the MIA tool chain for medical image analysis. This package contains the development files - headers, shared libraries, and pkg-config files. Package: libmialm-doc Source: libmialm Version: 1.0.7-2~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 233 Depends: neurodebian-popularity-contest Suggests: devhelp Homepage: http://mia.sourceforge.net Priority: optional Section: doc Filename: pool/main/libm/libmialm/libmialm-doc_1.0.7-2~nd80+1_all.deb Size: 21192 SHA256: 24e19e72a14c464d4467399917a3ab462bc496381d678c9d0f9c5375089719b9 SHA1: 80e9e79f2a3b30f5a7544357bad7ebf79cfdcc80 MD5sum: 92d923fe54c36feea88f59f769431135 Description: Documentation for the MIA landmark library This library implements handling for landmarks and 3D view positioning for optimal landmark visibility, and in-and output of these landmarks. This library is part of the MIA tool chain for medical image analysis. This package contains the library documentation. Package: libmialm3 Source: libmialm Version: 1.0.7-2~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 57 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.7), libglib2.0-0 (>= 2.16.0), libxml2 (>= 2.7.4) Homepage: http://mia.sourceforge.net Priority: optional Section: libs Filename: pool/main/libm/libmialm/libmialm3_1.0.7-2~nd80+1_amd64.deb Size: 18380 SHA256: 97ad828449490937ba1fec2e878f6fcd207059197438a1115adb3b6e2ae0b59f SHA1: da6eee1cfac393765803d7110c3dd21cd12dd18e MD5sum: 1c0967f953593eccb5219c241e427c66 Description: Landmark handling for the MIA tool chain This library implements handling for landmarks and 3D view positioning for optimal landmark visibility, and in-and output of these landmarks. This library is part of the MIA tool chain for medical image analysis. Package: libmialm3-dbg Source: libmialm Version: 1.0.7-2~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 72 Depends: neurodebian-popularity-contest, libmialm3 (= 1.0.7-2~nd80+1) Homepage: http://mia.sourceforge.net Priority: extra Section: debug Filename: pool/main/libm/libmialm/libmialm3-dbg_1.0.7-2~nd80+1_amd64.deb Size: 59110 SHA256: d53f9ac5cb8cfad6fd59d1642ce5f33f9b9d460c47581e09c87c7f1bfd64a678 SHA1: ce12157841a481a419d85431b6c26e37b187bb10 MD5sum: bacb641af56d5d15ac921a99ed2ecfa5 Description: Debug information for the MIA landmark library This library implements handling for landmarks and 3D view positioning for optimal landmark visibility, and in-and output of these landmarks. This library is part of the MIA tool chain for medical image analysis. This package provides the debug information of the library. Package: libmtcp-dev Source: dmtcp Version: 1.2.5-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 12 Depends: neurodebian-popularity-contest, libmtcp1 (= 1.2.5-1~nd70+1) Homepage: http://dmtcp.sourceforge.net Priority: optional Section: libdevel Filename: pool/main/d/dmtcp/libmtcp-dev_1.2.5-1~nd70+1_amd64.deb Size: 5570 SHA256: 3e6a6093b56abd7f811c905b83c99d29c9ddff9b1e9c3d1a241f98cdf2f4d6e0 SHA1: 2608f9ee849370a6fc1e296c25c6a342721d39db MD5sum: d38ba8c402370b532a21ce89496b2add Description: Developer package for libmtcp 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-Window applications, as long as they do not use extensions (e.g.: no OpenGL, no video). . This package provides header files needed for building programs with libmtcp. Package: libmtcp1 Source: dmtcp Version: 1.2.5-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 110 Depends: neurodebian-popularity-contest, libc6 (>= 2.4) Homepage: http://dmtcp.sourceforge.net Priority: optional Section: libs Filename: pool/main/d/dmtcp/libmtcp1_1.2.5-1~nd70+1_amd64.deb Size: 45304 SHA256: cd44cdbb087c1c2b1461cbbfb63427d76b73d8b8dd270bbbb25ff55d95380e5a SHA1: 2f6323c122533988a850a78cc9e09495516dbc76 MD5sum: 64abe111633f6316aa32de5f03019d2c Description: DMTCP library needed for checkpointing a standalone process 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-Window applications, as long as they do not use extensions (e.g.: no OpenGL, no video). . This package provides libmtcp which is needed by DMTCP to checkpoint a single standalone process. Package: libnifti-dev Source: nifticlib Version: 2.0.0-2~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 592 Depends: neurodebian-popularity-contest, libnifti2 (= 2.0.0-2~nd80+1) Conflicts: libfslio-dev, libnifti0-dev, libnifti1-dev, libniftiio-dev Replaces: libnifti1-dev Homepage: http://niftilib.sourceforge.net Priority: optional Section: libdevel Filename: pool/main/n/nifticlib/libnifti-dev_2.0.0-2~nd80+1_amd64.deb Size: 138182 SHA256: 0248cb6aa2426df6a9f2852457357f7e2db7e4fc1d71d17560573981e9395d3d SHA1: ce5f57c5f21449ab30376357ce2e96f440f40abb MD5sum: d2a91de3cbc5bfe4108fca7a857c0525 Description: IO libraries for the NIfTI-1 data format Niftilib is a set of i/o libraries for reading and writing files in the NIfTI-1 data format. NIfTI-1 is a binary file format for storing medical image data, e.g. magnetic resonance image (MRI) and functional MRI (fMRI) brain images. . This package provides the header files and static libraries of libniftiio, znzlib and libnifticdf. Package: libnifti-doc Source: nifticlib Version: 2.0.0-2~nd80+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1691 Depends: neurodebian-popularity-contest, libjs-jquery Homepage: http://niftilib.sourceforge.net Priority: optional Section: doc Filename: pool/main/n/nifticlib/libnifti-doc_2.0.0-2~nd80+1_all.deb Size: 140034 SHA256: 4fbd25c6af906ed62a90acfd335c8e88a2d3a260541e49a92d13b83eb4f32642 SHA1: 9171a3f651808559076365b39dc057703456a36d MD5sum: 079405881f3344967f709385847bbf28 Description: NIfTI library API documentation Niftilib is a set of i/o libraries for reading and writing files in the NIfTI-1 data format. NIfTI-1 is a binary file format for storing medical image data, e.g. magnetic resonance image (MRI) and functional MRI (fMRI) brain images. . This package provides the library API reference documentation. Package: libnifti2 Source: nifticlib Version: 2.0.0-2~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 307 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), zlib1g (>= 1:1.1.4) Homepage: http://niftilib.sourceforge.net Priority: optional Section: libs Filename: pool/main/n/nifticlib/libnifti2_2.0.0-2~nd80+1_amd64.deb Size: 107464 SHA256: 47caec929f3368e4432d7120dd93765c1d208affc42d289770f9a5a17c86b0cc SHA1: 29e2ba986d14452f47a1d51c85e09336824bd3c7 MD5sum: 521e1586b3638e8d5419cfcc2823b9cc Description: IO libraries for the NIfTI-1 data format Niftilib is a set of i/o libraries for reading and writing files in the NIfTI-1 data format. NIfTI-1 is a binary file format for storing medical image data, e.g. magnetic resonance image (MRI) and functional MRI (fMRI) brain images. . This package contains the shared library of the low-level IO library niftiio, low-level IO library znzlib and the nifticdf shared library that provides functions to compute cumulative distributions and their inverses. Package: libnlopt-dev Source: nlopt Version: 2.4.1+dfsg-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 594 Depends: neurodebian-popularity-contest, libnlopt0 (= 2.4.1+dfsg-1~nd80+1) Homepage: http://ab-initio.mit.edu/wiki/index.php/NLopt Priority: optional Section: libdevel Filename: pool/main/n/nlopt/libnlopt-dev_2.4.1+dfsg-1~nd80+1_amd64.deb Size: 170170 SHA256: 68e31cf7e2290dbec0a847b9499c3aae3a56bcedcb94dfcb9ccff035bdcbe75f SHA1: d700530be75f5bcd10606c329b302e71d857c6c7 MD5sum: aa838fc6cb5e4945d54f5422d49098a0 Description: nonlinear optimization library -- development package NLopt is a free/open-source library for nonlinear optimization, providing a common interface for a number of different free optimization routines available online as well as original implementations of various other algorithms. Its features include: . * Callable from C, C++, Fortran, GNU Octave, Python, GNU Guile, GNU R. * A common interface for many different algorithms * Support for large-scale optimization. * Both global and local optimization algorithms. * Algorithms using function values only (derivative-free) and also algorithms exploiting user-supplied gradients. * Algorithms for unconstrained optimization, bound-constrained optimization, and general nonlinear inequality/equality constraints. . This package contains the header files, static libraries and symbolic links that developers using NLopt library will need. Package: libnlopt-guile0 Source: nlopt Version: 2.4.1+dfsg-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 130 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libnlopt0 (= 2.4.1+dfsg-1~nd80+1), libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), guile-1.8 Multi-Arch: same Homepage: http://ab-initio.mit.edu/wiki/index.php/NLopt Priority: optional Section: libs Filename: pool/main/n/nlopt/libnlopt-guile0_2.4.1+dfsg-1~nd80+1_amd64.deb Size: 37010 SHA256: 4eba2acdbcef218b4e723383a187d8f1c232c81b0edf0a7c04a71b6cc617f9ff SHA1: 72b30e6a8b5bce10826ee03af2f4baaa19d7245e MD5sum: 52ae0cac7c0256782c9e50e93ae4c9e8 Description: nonlinear optimization library -- Guile bindings NLopt is a free/open-source library for nonlinear optimization, providing a common interface for a number of different free optimization routines available online as well as original implementations of various other algorithms. Its features include: . * Callable from C, C++, Fortran, GNU Octave, Python, GNU Guile, GNU R. * A common interface for many different algorithms * Support for large-scale optimization. * Both global and local optimization algorithms. * Algorithms using function values only (derivative-free) and also algorithms exploiting user-supplied gradients. * Algorithms for unconstrained optimization, bound-constrained optimization, and general nonlinear inequality/equality constraints. . This package contains the bindings for GNU Guile. Package: libnlopt0 Source: nlopt Version: 2.4.1+dfsg-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 427 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1) Multi-Arch: same Homepage: http://ab-initio.mit.edu/wiki/index.php/NLopt Priority: optional Section: libs Filename: pool/main/n/nlopt/libnlopt0_2.4.1+dfsg-1~nd80+1_amd64.deb Size: 161656 SHA256: 356599dc7028876309616ad71f21f0ca72f7ef4f1fc20d194d02fe002efcdec3 SHA1: e4d115a51e321a46f83655b6b95275e6683651da MD5sum: 520e689681b5b3a1add0074a7044f890 Description: nonlinear optimization library NLopt is a free/open-source library for nonlinear optimization, providing a common interface for a number of different free optimization routines available online as well as original implementations of various other algorithms. Its features include: . * Callable from C, C++, Fortran, GNU Octave, Python, GNU Guile, GNU R. * A common interface for many different algorithms * Support for large-scale optimization. * Both global and local optimization algorithms. * Algorithms using function values only (derivative-free) and also algorithms exploiting user-supplied gradients. * Algorithms for unconstrained optimization, bound-constrained optimization, and general nonlinear inequality/equality constraints. . This package provides the shared libraries required to run programs compiled with NLopt. To compile your own programs you also need to install libnlopt-dev. Package: libodin-dev Source: odin Version: 1.8.8-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 25639 Depends: neurodebian-popularity-contest Homepage: http://od1n.sourceforge.net Priority: extra Section: libdevel Filename: pool/main/o/odin/libodin-dev_1.8.8-1~nd80+1_amd64.deb Size: 2635318 SHA256: c895be991bfbdf956ba75c9af0b4e42703787de4134505314268750959c54fcd SHA1: eee48bb25965c1e8731db83d9a616cbaaa124fee MD5sum: 3aeab49cb32940e62b464ff2000af226 Description: static libraries and header for ODIN sequences This package provides static libraries and headers of the ODIN libraries odindata, adinpara, odinqt, odinseq and tjutils. They are required for building magnetic resonance imaging (MRI) sequences with ODIN. Package: libopenmeeg-dev Source: openmeeg Version: 2.0.0.dfsg-4~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 197 Depends: neurodebian-popularity-contest Homepage: http://www-sop.inria.fr/odyssee/software/OpenMEEG/ Priority: extra Section: libdevel Filename: pool/main/o/openmeeg/libopenmeeg-dev_2.0.0.dfsg-4~nd70+1_amd64.deb Size: 42574 SHA256: edb2c179742df10e47c5529cd89af0948271294629a0a4763ae834bf0c18ed50 SHA1: 0cc9c3de65126a1ecf2b43174a5dd9aa98d07f61 MD5sum: 1faf21b27a88956f37f2dca7a422a096 Description: openmeeg library -- development files OpenMEEG consists of state-of-the art solvers for forward problems in the field of MEG and EEG. Solvers are based on the symmetric Boundary Element method [Kybic et al, 2005], providing excellent accuracy, particularly for superficial cortical sources. OpenMEEG can compute four types of lead fields (EEG, MEG, Internal Potential and Electrical Impedence Tomography). . This package provides static libraries and header files. Package: libopenmeeg1 Source: openmeeg Version: 2.0.0.dfsg-4~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1779 Depends: neurodebian-popularity-contest, libatlas3gf-base, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libmatio0, libstdc++6 (>= 4.6) Homepage: http://www-sop.inria.fr/odyssee/software/OpenMEEG/ Priority: extra Section: science Filename: pool/main/o/openmeeg/libopenmeeg1_2.0.0.dfsg-4~nd70+1_amd64.deb Size: 266080 SHA256: 8a691f73fca1e801ed17863649a177dd9f481ffe8dbb6534de298cd71ae925e4 SHA1: fb4d437654dd0c7e4f275af9de3d247c96092dae MD5sum: e6b4db0cbaa8df1103508dd63bab2fdf Description: library for solving EEG and MEG forward and inverse problems OpenMEEG consists of state-of-the art solvers for forward problems in the field of MEG and EEG. Solvers are based on the symmetric Boundary Element method [Kybic et al, 2005], providing excellent accuracy, particularly for superficial cortical sources. OpenMEEG can compute four types of lead fields (EEG, MEG, Internal Potential and Electrical Impedence Tomography). . This package provides dynamic libraries. Package: libopenwalnut1 Source: openwalnut Version: 1.4.0~rc1+hg3a3147463ee2-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 6519 Depends: neurodebian-popularity-contest, fonts-liberation (>= 1.0.0), libboost-date-time1.54.0, libboost-filesystem1.54.0, libboost-regex1.54.0, libboost-system1.54.0, libboost-thread1.54.0, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libopenscenegraph99, libstdc++6 (>= 4.6) Suggests: nvidia-glx | fglrx-glx Homepage: http://www.openwalnut.org Priority: extra Section: libs Filename: pool/main/o/openwalnut/libopenwalnut1_1.4.0~rc1+hg3a3147463ee2-1~nd80+1_amd64.deb Size: 1234884 SHA256: ace9b8111dc278facd6088b342f4f60ae400b1db14dfcb0cf24f7ebd31912d58 SHA1: 015bb2ef74c64737660fa661427dd4ba1ce25ed0 MD5sum: a34e10afb76bdfb26e9e19457c684463 Description: Framework for multi-modal medical and brain data visualization 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: 1.4.0~rc1+hg3a3147463ee2-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1997 Depends: neurodebian-popularity-contest, libopenwalnut1 (= 1.4.0~rc1+hg3a3147463ee2-1~nd80+1), libgl1-mesa-dev | libgl-dev, libopenscenegraph-dev (>= 3.0.0), libopenthreads-dev (>= 3.0.0), libboost-dev (>= 1.46.0), libboost-program-options-dev (>= 1.46.0), libboost-thread-dev (>= 1.46.0), libboost-filesystem-dev (>= 1.46.0), libboost-date-time-dev (>= 1.46.0), libboost-system-dev (>= 1.46.0), libboost-signals-dev (>= 1.46.0), libboost-regex-dev (>= 1.46.0), libeigen3-dev (>= 3.0.0) Homepage: http://www.openwalnut.org Priority: extra Section: libdevel Filename: pool/main/o/openwalnut/libopenwalnut1-dev_1.4.0~rc1+hg3a3147463ee2-1~nd80+1_amd64.deb Size: 249344 SHA256: f445911e4d254f15922482f0c0de576f2764faf2853b7d1f304c7f9edae1517e SHA1: 0d130509c17d899c3470a66cfccda95892be3f3b MD5sum: ad10ba9c2c6b2f78b1ac462f1683294e Description: Development files for the OpenWalnut visualization framework 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: 1.4.0~rc1+hg3a3147463ee2-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 48075 Depends: neurodebian-popularity-contest, libjs-jquery Homepage: http://www.openwalnut.org Priority: extra Section: doc Filename: pool/main/o/openwalnut/libopenwalnut1-doc_1.4.0~rc1+hg3a3147463ee2-1~nd80+1_all.deb Size: 2680842 SHA256: 6c7d3382ff3aa8841e1cdd634f1cbb0ff204ec60b5f06d0735d52efbaef62642 SHA1: 547bc361d9c6d67e0009ab096b884000472f90e9 MD5sum: ceb7c9397113c822666f9a54feaeec00 Description: Developer documentation for the OpenWalnut visualization framework 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~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 13 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~nd70+1_amd64.deb Size: 7792 SHA256: fd0540e8e09f8ec8b4f042032b107a65f506c52a9ec0e982c92be0f9affd3b66 SHA1: 6bf5ae5b92a7138626e6484e649f6d8357435f90 MD5sum: 2d86a22ab20cb9cb160e320a13e0daf9 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: librtfilter-dev Source: rtfilter Version: 1.1-4~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 20 Depends: neurodebian-popularity-contest, librtfilter1 (= 1.1-4~nd70+1) Multi-Arch: same Homepage: http://cnbi.epfl.ch/software/rtfilter.html Priority: extra Section: libdevel Filename: pool/main/r/rtfilter/librtfilter-dev_1.1-4~nd70+1_amd64.deb Size: 12600 SHA256: a12d14b8b6725556fe36db24d90cb0ca61525ad21dd14c0c5235e575b478d849 SHA1: 1461872afc86c1462cbe64684cffb3dd8d7e6813 MD5sum: bcc5f3b28fbffcc5450b5fc0cd43d365 Description: realtime digital filtering library (development files) rtfilter is a library that provides a set of routines implementing realtime digital filter for multichannel signals (i.e. filtering multiple signals with the same filter parameters). It implements FIR, IIR filters and downsampler for float and double data type (both for real and complex valued signal). Additional functions are also provided to design few usual filters: Butterworth, Chebyshev, windowed sinc, analytical filter... . One of the main differences from other libraries providing digital signal processing is that the filter functions have been specifically designed and optimized for multichannel signals (from few channels to several hundred). . This package contains the files needed to compile and link programs which use rtfilter. Package: librtfilter1 Source: rtfilter Version: 1.1-4~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 56 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5) Multi-Arch: same Homepage: http://cnbi.epfl.ch/software/rtfilter.html Priority: extra Section: libs Filename: pool/main/r/rtfilter/librtfilter1_1.1-4~nd70+1_amd64.deb Size: 31184 SHA256: 5cb21d5d22780c438cb371f0287a238a330dd2cf54591b6559ae2f8b3213a192 SHA1: 9aa1d3347cd23c923b78a5b95354f0ff4b8dc6bc MD5sum: 6909e1084963448af461cd757243a098 Description: realtime digital filtering library rtfilter is a library that provides a set of routines implementing realtime digital filter for multichannel signals (i.e. filtering multiple signals with the same filter parameters). It implements FIR, IIR filters and downsampler for float and double data type (both for real and complex valued signal). Additional functions are also provided to design few usual filters: Butterworth, Chebyshev, windowed sinc, analytical filter... . One of the main differences from other libraries providing digital signal processing is that the filter functions have been specifically designed and optimized for multichannel signals (from few channels to several hundred). Package: librtfilter1-dbg Source: rtfilter Version: 1.1-4~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 41 Depends: neurodebian-popularity-contest, librtfilter1 (= 1.1-4~nd70+1) Multi-Arch: same Homepage: http://cnbi.epfl.ch/software/rtfilter.html Priority: extra Section: debug Filename: pool/main/r/rtfilter/librtfilter1-dbg_1.1-4~nd70+1_amd64.deb Size: 36504 SHA256: e734a37b8c13f0b21fde39054b56bbbe3e12d46a3e4038b5cbcf268b8abdac4b SHA1: f571aa39a5b5bb7258f3827e4c4de0cd4a1b7032 MD5sum: ce9f939e354e6927194126c8e97c56ce Description: realtime digital filtering library (debugging symbols) rtfilter is a library that provides a set of routines implementing realtime digital filter for multichannel signals (i.e. filtering multiple signals with the same filter parameters). It implements FIR, IIR filters and downsampler for float and double data type (both for real and complex valued signal). Additional functions are also provided to design few usual filters: Butterworth, Chebyshev, windowed sinc, analytical filter... . One of the main differences from other libraries providing digital signal processing is that the filter functions have been specifically designed and optimized for multichannel signals (from few channels to several hundred). . This package provides the debugging symbols of the library. Package: libshogun-dev Source: shogun Version: 1.1.0-6~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 16571 Depends: neurodebian-popularity-contest, libshogun11 (= 1.1.0-6~nd70+1) Homepage: http://www.shogun-toolbox.org Priority: optional Section: libdevel Filename: pool/main/s/shogun/libshogun-dev_1.1.0-6~nd70+1_amd64.deb Size: 2758284 SHA256: a7278b487e1a7f8be2c14b93575e4af06c59e48e171d6b39af8bb3dfc47d7fb0 SHA1: ca0eef589807b01c0399b4fde7bd38711f1ab89a MD5sum: 198b464ae98962a69da45ffe9918e6d6 Description: Large Scale Machine Learning Toolbox SHOGUN - is a new machine learning toolbox with focus on large scale kernel methods and especially on Support Vector Machines (SVM) with focus to bioinformatics. It provides a generic SVM object interfacing to several different SVM implementations. Each of the SVMs can be combined with a variety of the many kernels implemented. It can deal with weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain, where an optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Apart from SVM 2-class classification and regression problems, a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to train hidden markov models are implemented. The input feature-objects can be dense, sparse or strings and of type int/short/double/char and can be converted into different feature types. Chains of preprocessors (e.g. substracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing. . SHOGUN comes in different flavours, a stand-a-lone version and also with interfaces to Matlab(tm), R, Octave, Readline and Python. This package includes the developer files required to create stand-a-lone executables. Package: libshogun11 Source: shogun Version: 1.1.0-6~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 5456 Depends: neurodebian-popularity-contest, libarpack2 (>= 2.1), libatlas3-base, libc6 (>= 2.3.2), libgcc1 (>= 1:4.1.1), libglpk0 (>= 4.30), libhdf5-7, libjson0 (>= 0.10), liblzma5 (>= 5.1.1alpha+20120614), liblzo2-2, libstdc++6 (>= 4.6), libxml2 (>= 2.7.4), zlib1g (>= 1:1.1.4) Conflicts: libshogunui0, libshogunui1, libshogunui2, libshogunui3, libshogunui4, libshogunui5, libshogunui6 Homepage: http://www.shogun-toolbox.org Priority: optional Section: libs Filename: pool/main/s/shogun/libshogun11_1.1.0-6~nd70+1_amd64.deb Size: 1579418 SHA256: e312c308fedd2feade89316fa5221d7adf3370374bf5de523a988cd2b4b8b727 SHA1: 61bf9084866c2334fb40db46adaa023721d58e36 MD5sum: 2a22419cd27daa66f15efc778e600cd9 Description: Large Scale Machine Learning Toolbox SHOGUN - is a new machine learning toolbox with focus on large scale kernel methods and especially on Support Vector Machines (SVM) with focus to bioinformatics. It provides a generic SVM object interfacing to several different SVM implementations. Each of the SVMs can be combined with a variety of the many kernels implemented. It can deal with weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain, where an optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Apart from SVM 2-class classification and regression problems, a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to train hidden markov models are implemented. The input feature-objects can be dense, sparse or strings and of type int/short/double/char and can be converted into different feature types. Chains of preprocessors (e.g. substracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing. . SHOGUN comes in different flavours, a stand-a-lone version and also with interfaces to Matlab(tm), R, Octave, Readline and Python. This is the core library with the machine learning methods and ui helpers all interfaces are based on. Package: libvia-dev Source: via Version: 2.0.4-2~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 969 Depends: neurodebian-popularity-contest, libvia2 (= 2.0.4-2~nd70+1), x11proto-core-dev Conflicts: via-dev Homepage: http://www.cbs.mpg.de/institute/software/lipsia Priority: optional Section: libdevel Filename: pool/main/v/via/libvia-dev_2.0.4-2~nd70+1_amd64.deb Size: 212220 SHA256: 44b0d7f45533514a58d18da7477f8147320d23d1ca2e2da513ca0638c84372f0 SHA1: 279054de6615f664af82030520218d6bfd1d2d22 MD5sum: be7834dfbe616b25919944ea6968072e Description: library for volumetric image analysis VIA is a volumetric image analysis suite. The included libraries provide about 70 image analysis functions. . This package provides the header files and static libraries of vialib, vxlib and viaio. Package: libvia-doc Source: via Version: 2.0.4-2~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 903 Depends: neurodebian-popularity-contest Homepage: http://www.cbs.mpg.de/institute/software/lipsia Priority: optional Section: doc Filename: pool/main/v/via/libvia-doc_2.0.4-2~nd70+1_all.deb Size: 118466 SHA256: c508ad5f2de2d726a6ec321a5dda11ae53d8d1991ad9d407c85cfd9190a25184 SHA1: 20c0141728ccf9539a2a460c758d63970ddd85a2 MD5sum: 7094bbe0e4041f7c7ad8b07781132693 Description: VIA library API documentation VIA is a volumetric image analysis suite. The included libraries provide about 70 image analysis functions. . This package provides the library API reference documentation. Package: libvia2 Source: via Version: 2.0.4-2~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 568 Depends: neurodebian-popularity-contest, lesstif2 (>= 1:0.94.4), libatlas3gf-base, libc6 (>= 2.7), libgsl0ldbl (>= 1.9), libice6 (>= 1:1.0.0), libsm6, libx11-6, libxext6, libxmu6, libxt6 Homepage: http://www.cbs.mpg.de/institute/software/lipsia Priority: optional Section: libs Filename: pool/main/v/via/libvia2_2.0.4-2~nd70+1_amd64.deb Size: 172346 SHA256: 9006b22186b7f0e8cbc4e3914bd7bcb674379c3b8eaa01b69066eae7749a8415 SHA1: 69879a36111156fa87c5ff68e17d904d9487bd71 MD5sum: d2fc837ccadee5f7581304f29ad60a77 Description: library for volumetric image analysis VIA is a volumetric image analysis suite. The included libraries provide about 70 image analysis functions. . This package contains the shared libraries of vialib, vxlib and viaio. Package: libvistaio-dev Source: libvistaio Version: 1.2.16-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 173 Depends: neurodebian-popularity-contest, libvistaio14 (= 1.2.16-1~nd80+1) Homepage: http://mia.sourceforge.net Priority: optional Section: libdevel Filename: pool/main/libv/libvistaio/libvistaio-dev_1.2.16-1~nd80+1_amd64.deb Size: 108276 SHA256: 08867ac2b31dd2bb27950ff05462f5c651bdae3529988242d72fb1c9774d6e5b SHA1: 9f82bee831146469a94db116e28f354701ee0aff MD5sum: 3b8b7b8d8a1e467c4ea3b01f5eb5656b Description: Development files for the libvistaio library Vistaio is a library that handles loading and storing of data in a cross-platform manner. Its virtue is that the otherwise binary files provide an ascii header that makes it easy to get information about the contens of a file. It supports a variety of data types like images, vector fields and graphs. This is the development package containing the header files, and pkg-config script, and man pages. Package: libvistaio14 Source: libvistaio Version: 1.2.16-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 99 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.14) Homepage: http://mia.sourceforge.net Priority: optional Section: libs Filename: pool/main/libv/libvistaio/libvistaio14_1.2.16-1~nd80+1_amd64.deb Size: 37082 SHA256: 589673ca21aa41dadc0a4fe714f72e21dc9ad87157eb9004b1f78edcc5053551 SHA1: c419401c07ac65343aaf15c013ba8e86cca2d5d8 MD5sum: 78a43ca0185811552ff05b80a11e771b Description: Library for loading and storing various types of binary data Vistaio is a library that handles loading and storing of data in a cross-platform manner. Its virtue is that the otherwise binary files provide an ascii header that makes it easy to get information about the contens of a file. It supports a variety of data types like images, vector fields and graphs. Package: libvistaio14-dbg Source: libvistaio Version: 1.2.16-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 93 Depends: neurodebian-popularity-contest, libvistaio14 (= 1.2.16-1~nd80+1) Homepage: http://mia.sourceforge.net Priority: extra Section: debug Filename: pool/main/libv/libvistaio/libvistaio14-dbg_1.2.16-1~nd80+1_amd64.deb Size: 80574 SHA256: f7df7cce27cd6d0ad980f42812d915410e4d8797591890faf15bfae59c83c1ea SHA1: 7d165a4e2314db1c406d0fefa1848dfcc5313545 MD5sum: 2f0b5b77a5fb251b4806eb7e86fc7a4f Description: Debug information for the libvistaio library Vistaio is a library that handles loading and storing of data in a cross-platform manner. Its virtue is that the otherwise binary files provide an ascii header that makes it easy to get information about the contens of a file. It supports a variety of data types like images, vector fields and graphs. This is package containing the debug information. Package: libvrpn-dev Source: vrpn Version: 07.30+dfsg-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 702 Depends: neurodebian-popularity-contest, libvrpn0 (= 07.30+dfsg-1~nd70+1), libvrpnserver0 (= 07.30+dfsg-1~nd70+1) Homepage: http://www.cs.unc.edu/Research/vrpn/ Priority: extra Section: libdevel Filename: pool/main/v/vrpn/libvrpn-dev_07.30+dfsg-1~nd70+1_amd64.deb Size: 218312 SHA256: e2c64f9ab32628f52da98f7e959436435b8eeb050e6d7aba718c2f1fdf9ea038 SHA1: e212814878ef7949899e99aa05e51b5c93fe0b46 MD5sum: 4ba1b79a9a3682478198d778ea0ef38e Description: Virtual Reality Peripheral Network (development files) The Virtual-Reality Peripheral Network (VRPN) is a set of classes within a library and a set of servers that are designed to implement a network-transparent interface between application programs and the set of physical devices (tracker, etc.) used in a virtual-reality (VR) system. The idea is to have a PC or other host at each VR station that controls the peripherals (tracker, button device, haptic device, analog inputs, sound, etc). VRPN provides connections between the application and all of the devices using the appropriate class-of-service for each type of device sharing this link. The application remains unaware of the network topology. Note that it is possible to use VRPN with devices that are directly connected to the machine that the application is running on, either using separate control programs or running all as a single program. . This package contains the development files Package: libvrpn0 Source: vrpn Version: 07.30+dfsg-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 585 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1) Homepage: http://www.cs.unc.edu/Research/vrpn/ Priority: extra Section: libs Filename: pool/main/v/vrpn/libvrpn0_07.30+dfsg-1~nd70+1_amd64.deb Size: 232324 SHA256: 207a26dc84e865944ad5ede80d98f46629f66d1a2f4d2d5aa69fb8609fcaed49 SHA1: 262e5f1720d08bdf3bdb4ed7c4b3370e8a2c4d50 MD5sum: 7069f631935755d5e71f4e2693aee38c Description: Virtual Reality Peripheral Network (client library) The Virtual-Reality Peripheral Network (VRPN) is a set of classes within a library and a set of servers that are designed to implement a network-transparent interface between application programs and the set of physical devices (tracker, etc.) used in a virtual-reality (VR) system. The idea is to have a PC or other host at each VR station that controls the peripherals (tracker, button device, haptic device, analog inputs, sound, etc). VRPN provides connections between the application and all of the devices using the appropriate class-of-service for each type of device sharing this link. The application remains unaware of the network topology. Note that it is possible to use VRPN with devices that are directly connected to the machine that the application is running on, either using separate control programs or running all as a single program. . This package contains the client shared library Package: libvrpnserver0 Source: vrpn Version: 07.30+dfsg-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1422 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0) Homepage: http://www.cs.unc.edu/Research/vrpn/ Priority: extra Section: libs Filename: pool/main/v/vrpn/libvrpnserver0_07.30+dfsg-1~nd70+1_amd64.deb Size: 492400 SHA256: 158b3511b781eaf2b92acbff2451b966dea1824c104776a34027bedcd5c5ea3a SHA1: 098fa7e6edc42190c8170ccb7fa3c5e07d06fb7d MD5sum: a679028df66cf8f8fcd5be66573d0696 Description: Virtual Reality Peripheral Network (server library) The Virtual-Reality Peripheral Network (VRPN) is a set of classes within a library and a set of servers that are designed to implement a network-transparent interface between application programs and the set of physical devices (tracker, etc.) used in a virtual-reality (VR) system. The idea is to have a PC or other host at each VR station that controls the peripherals (tracker, button device, haptic device, analog inputs, sound, etc). VRPN provides connections between the application and all of the devices using the appropriate class-of-service for each type of device sharing this link. The application remains unaware of the network topology. Note that it is possible to use VRPN with devices that are directly connected to the machine that the application is running on, either using separate control programs or running all as a single program. . This package contains the shared library use in the VRPN server Package: libvtk-dicom-java Source: vtk-dicom Version: 0.5.5-2~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 265 Depends: neurodebian-popularity-contest, libvtk-java, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), libvtk-dicom0.5, libvtk5.8 Suggests: java-virtual-machine Homepage: http://github.com/dgobbi/vtk-dicom/ Priority: optional Section: java Filename: pool/main/v/vtk-dicom/libvtk-dicom-java_0.5.5-2~nd80+1_amd64.deb Size: 74594 SHA256: 7337b271c3b1590dcf0ce04414b03850f81daa98aa703c58c97a5b3cd0ead0e0 SHA1: 46e6680984c4d652d71738d1a37a1913f7c3ac0d MD5sum: 9b4442ff37a091e54ee218d4fab78685 Description: DICOM for VTK - java This package contains a set of classes for managing DICOM files and metadata from within VTK, and some utility programs for interrogating and converting DICOM files. . Java 1.5 bindings Package: libvtk-dicom0.5 Source: vtk-dicom Version: 0.5.5-2~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1861 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libgdcm2.4, libstdc++6 (>= 4.9), libvtk5.8, zlib1g (>= 1:1.2.3.4) Multi-Arch: same Homepage: http://github.com/dgobbi/vtk-dicom/ Priority: optional Section: libs Filename: pool/main/v/vtk-dicom/libvtk-dicom0.5_0.5.5-2~nd80+1_amd64.deb Size: 459288 SHA256: e80b61066a6627a340ea8bd83487a605ebe5e92a30a571562b638b16c9788507 SHA1: ce021422d8180ec4e0c4dceac17dd1b49a8637c1 MD5sum: 5d325f44abe29927396ad0e9701ac4d8 Description: DICOM for VTK - lib This package contains a set of classes for managing DICOM files and metadata from within VTK, and some utility programs for interrogating and converting DICOM files. . Libraries for runtime applications Package: libvtk-dicom0.5-dev Source: vtk-dicom Version: 0.5.5-2~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 531 Depends: neurodebian-popularity-contest, libvtk-dicom0.5 (= 0.5.5-2~nd80+1) Conflicts: libvtk-dicom0.4-dev Replaces: libvtk-dicom0.4-dev Provides: libvtk-dicom-dev Multi-Arch: same Homepage: http://github.com/dgobbi/vtk-dicom/ Priority: optional Section: libdevel Filename: pool/main/v/vtk-dicom/libvtk-dicom0.5-dev_0.5.5-2~nd80+1_amd64.deb Size: 80868 SHA256: 287c3e9056fa1c630f885c878fe8867f8ce522d7cff490637cbf8c369ca6c18e SHA1: 37c7f51ed79f08e56df52cb4b36fd72d25a41c31 MD5sum: cd522c8beeb127074ef3d086413551ca Description: DICOM for VTK - dev This package contains a set of classes for managing DICOM files and metadata from within VTK, and some utility programs for interrogating and converting DICOM files. . Development headers Package: libvtk-java Source: vtk Version: 5.8.0-7+b0~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 10911 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), libgcc1 (>= 1:4.1.1), libmysqlclient16 (>= 5.1.50-1), libpq5, libstdc++6 (>= 4.1.1), libvtk5.8, zlib1g (>= 1:1.1.4) Suggests: libvtk5-dev (= 5.8.0-7+b0~nd70+1), vtk-examples, vtk-doc, java-virtual-machine Homepage: http://www.vtk.org/ Priority: optional Section: java Filename: pool/main/v/vtk/libvtk-java_5.8.0-7+b0~nd70+1_amd64.deb Size: 4911660 SHA256: 68229a9bf87e3a1b9799f6ca833ed4bc50e6bff78f0b5459f2d5f37adaa965d4 SHA1: 63c1b0a65daaa2df22b45079c1a88bca460e4b78 MD5sum: d8c8e0aa2f5e0c6e630eca91a5dec181 Description: Visualization Toolkit - A high level 3D visualization library - java The Visualization Toolkit (VTK) is an object oriented, high level library that allows one to easily write C++ programs, Tcl, Python and Java scripts that do 3D visualization. . This package provides the VTK Java language support. Package: libvtk5-dev Source: vtk Version: 5.8.0-7+b0~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 12852 Depends: neurodebian-popularity-contest, libvtk5.8 (= 5.8.0-7+b0~nd70+1), libgl1-mesa-dev | libgl-dev, libglu1-mesa-dev | libglu-dev, libx11-dev, libxt-dev, x11proto-core-dev, libc6-dev, libxss-dev, libxft-dev, libexpat-dev, libjpeg-dev, libpng-dev, libtiff-dev, zlib1g-dev, tcl8.5-dev, tk8.5-dev, libavformat-dev, libavutil-dev, libavcodec-dev, libswscale-dev, libgl2ps-dev, libfreetype6-dev, libxml2-dev, libpq-dev, libnetcdf-dev, libmysqlclient-dev, mpi-default-dev, libqt4-dev Suggests: vtk-examples, vtk-doc Conflicts: libvtk-dev, libvtk32-dev, libvtk4-dev Replaces: libvtk-dev, libvtk32-dev, libvtk4-dev Homepage: http://www.vtk.org/ Priority: optional Section: libdevel Filename: pool/main/v/vtk/libvtk5-dev_5.8.0-7+b0~nd70+1_amd64.deb Size: 2565214 SHA256: 13c1ba8002c22cd09b24bbff4334db11310c4fe26eb8ce46c7c85eb1ae90bdf8 SHA1: 83441de1304fc5cb4136fd128b1f601274a215eb MD5sum: 2581309d3c9ac507f18bed2d8075bd29 Description: VTK header files for building C++ code The Visualization Toolkit (VTK) is an object oriented, high level library that allows one to easily write C++ programs, Tcl, Python and Java scripts that do 3D visualization. . This package provides the VTK header files required to compile C++ programs that use VTK to do 3D visualisation. Package: libvtk5-qt4-dev Source: vtk Version: 5.8.0-7+b0~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 549 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libqtcore4 (>= 4:4.7.0~beta1), libqtgui4 (>= 4:4.5.3), libstdc++6 (>= 4.1.1), libvtk5.8-qt4 (= 5.8.0-7+b0~nd70+1), libvtk5-dev (= 5.8.0-7+b0~nd70+1) Conflicts: libvtk5-qt3-dev Breaks: libvtk5-qt4 (<< 5.4.2-8) Replaces: libvtk5-qt4 (<< 5.4.2-8) Homepage: http://www.vtk.org/ Priority: optional Section: libdevel Filename: pool/main/v/vtk/libvtk5-qt4-dev_5.8.0-7+b0~nd70+1_amd64.deb Size: 109450 SHA256: f8a5d96cd72c4689d152150c988da0788fbf7a5f8f03642744d33b6a51b97cf5 SHA1: d4274d37e0fda0b58d5f54600cd1a248d2f8a7b6 MD5sum: 2918cf3fce1921511fbcac4acc8173f9 Description: Visualization Toolkit - A high level 3D visualization library - Qt devel The Visualization Toolkit (VTK) is an object oriented, high level library that allows one to easily write C++ programs, Tcl, Python and Java scripts that do 3D visualization. . This package provides the VTK header files required to compile C++ programs that use VTK GUI support for Qt4. Package: libvtk5.8 Source: vtk Version: 5.8.0-7+b0~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 47808 Depends: neurodebian-popularity-contest, libavcodec53 (>= 4:0.8-1~) | libavcodec-extra-53 (>= 4:0.8-1~), libavformat53 (>= 4:0.8-1~) | libavformat-extra-53 (>= 4:0.8-1~), libavutil51 (>= 4:0.8-1~) | libavutil-extra-51 (>= 4:0.8-1~), libc6 (>= 2.7), libexpat1 (>= 1.95.8), libfreetype6 (>= 2.2.1), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libgl2ps0, libjpeg8 (>= 8c), libmysqlclient16 (>= 5.1.50-1), libnetcdfc++5, libnetcdfc6, libopenmpi1.3, libpng12-0 (>= 1.2.13-4), libpq5, libqtcore4 (>= 4:4.6.1), libqtgui4 (>= 4:4.5.3), libsqlite3-0 (>= 3.5.9), libstdc++6 (>= 4.6), libswscale2 (>= 4:0.8-1~) | libswscale-extra-2 (>= 4:0.8-1~), libtiff4 (>= 3.9.5-2), libx11-6, libxml2 (>= 2.7.4), libxt6, zlib1g (>= 1:1.2.3.3) Suggests: openmpi-bin | lam-runtime, libvtk5-dev, vtk-examples, vtk-doc Conflicts: libvtk, libvtk32, libvtk4, libvtk4c2, libvtk4c2a, libvtk5, python-vtk (<< 4.4) Replaces: libvtk, libvtk32, libvtk4, libvtk4c2, libvtk4c2a, libvtk5 Homepage: http://www.vtk.org/ Priority: optional Section: libs Filename: pool/main/v/vtk/libvtk5.8_5.8.0-7+b0~nd70+1_amd64.deb Size: 15257918 SHA256: ed0323761444227f6664e788787b82249dc87fe12f4b55d6785bdc8fb259e472 SHA1: 71433fdf990dc407df7b0f4acdbcf6eeb3263897 MD5sum: 67a046cfe213ed96361d228646b5cdd0 Description: Visualization Toolkit - A high level 3D visualization library - runtime The Visualization Toolkit (VTK) is an object oriented, high level library that allows one to easily write C++ programs, Tcl, Python and Java scripts that do 3D visualization. . VTK enables users to concentrate on their work by providing a large number of excellent and feature packed high level functions that do visualization. The library needs OpenGL to render the graphics and for Linux machines Mesa is necessary. The terms/copyright can be read in /usr/share/doc/vtk/README and README.html. VTK-Linux-HOWTO has information about using vtk, getting documentataion or help and instructions on building VTK. . This package provides the shared libraries needed to run C++ programs that use VTK. . To compile C++ code that uses VTK you have to install libvtk5-dev. Package: libvtk5.8-qt4 Source: vtk Version: 5.8.0-7+b0~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1360 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libqt4-network (>= 4:4.5.3), libqt4-sql (>= 4:4.5.3), libqtcore4 (>= 4:4.7.0~beta1), libqtgui4 (>= 4:4.5.3), libstdc++6 (>= 4.4.0), libvtk5.8 Homepage: http://www.vtk.org/ Priority: optional Section: libs Filename: pool/main/v/vtk/libvtk5.8-qt4_5.8.0-7+b0~nd70+1_amd64.deb Size: 484074 SHA256: 422d8109b5f7b7b0af999c02a9568d0e03070c42e4202466d109bc76cee92c8a SHA1: 9d54d7d16173da33685bbb978b2bc229d343b656 MD5sum: 282bad631a4e495636f5ad980f6ca789 Description: Visualization Toolkit - A high level 3D visualization library - Qt runtime The Visualization Toolkit (VTK) is an object oriented, high level library that allows one to easily write C++ programs, Tcl, Python and Java scripts that do 3D visualization. . This package provides the VTK GUI support for Qt4. Package: libvw-dev Source: vowpal-wabbit Version: 7.3-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2576 Depends: neurodebian-popularity-contest, libvw0 (= 7.3-1~nd80+1) Homepage: http://hunch.net/~vw/ Priority: optional Section: libdevel Filename: pool/main/v/vowpal-wabbit/libvw-dev_7.3-1~nd80+1_amd64.deb Size: 579332 SHA256: 63b65584f34919b17e0c9f053990682e1ac39a1b86d6f001febc0f239b491c0b SHA1: d8a35bd36c45e39337393016d9ec4e90fd9f253c MD5sum: 9d96607e79183fad8ed8d9efbe2b7953 Description: fast and scalable online machine learning algorithm - development files Vowpal Wabbit is a fast online machine learning algorithm. The core algorithm is specialist gradient descent (GD) on a loss function (several are available). VW features: - flexible input data specification - speedy learning - scalability (bounded memory footprint, suitable for distributed computation) - feature pairing . This package contains development files needed to compile and link programs which use vowpal-wabbit's libraries. Package: libvw0 Source: vowpal-wabbit Version: 7.3-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 751 Depends: neurodebian-popularity-contest, libboost-program-options1.49.0 (>= 1.49.0-1), libc6 (>= 2.15), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0), zlib1g (>= 1:1.2.3.4) Homepage: http://hunch.net/~vw/ Priority: optional Section: libs Filename: pool/main/v/vowpal-wabbit/libvw0_7.3-1~nd80+1_amd64.deb Size: 310930 SHA256: 75b3c3bd444d0bfe7a6afd4eb892f4822b55af5623ec68ffc14b823c2e85651f SHA1: c8b0ed7b98d629f5e852d9952d62e87d37a01840 MD5sum: b31626586b559af900002b72c87ee2f6 Description: fast and scalable online machine learning algorithm - dynamic library Vowpal Wabbit is a fast online machine learning algorithm. The core algorithm is specialist gradient descent (GD) on a loss function (several are available). VW features: - flexible input data specification - speedy learning - scalability (bounded memory footprint, suitable for distributed computation) - feature pairing . This package contains vowpal-wabbit's dynamic libraries. Package: libxdffileio-dev Source: xdffileio Version: 0.3-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 32 Depends: neurodebian-popularity-contest, libxdffileio0 (= 0.3-1~nd70+1) Multi-Arch: same Homepage: http://cnbi.epfl.ch/software/xdffileio.html Priority: extra Section: libdevel Filename: pool/main/x/xdffileio/libxdffileio-dev_0.3-1~nd70+1_amd64.deb Size: 27776 SHA256: 12fa076aecf2cefc865ef21c27bfcd3701f6dcf432c82a8cc68507a08e3b73f6 SHA1: 9f6c4dd3b896bb19869a371c2c09adbcf1dad4bf MD5sum: f40cbfdc1f04fb44b430421e0af72b12 Description: Library to read/write EEG data file formats (development files) xdffileio is a library that provides a unified interface for writing and reading various biosignal file formats in realtime (i.e. streaming). It has been designed to provide a flexible, consistent and generic interface to all supported file formats while minimizing the overhead the function calls: the heaviest operations (type casting, scaling and formatting) are offloaded into a separated thread. This design makes its particularly suitable to be directly used in a data acquisition loop (like in electrophysiology recording or in Brain-Computer Interfaces (BCI)). . The genericity of the interface makes trivial various operations like transformation of a recorded file or its conversion to another file format. xdffileio currently supports EDF, BDF, GDF1 and GDF2 file formats and more will be added in future. . This package contains the files needed to compile and link programs which use xdffileio. Package: libxdffileio0 Source: xdffileio Version: 0.3-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 79 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.4) Multi-Arch: same Homepage: http://cnbi.epfl.ch/software/xdffileio.html Priority: extra Section: libs Filename: pool/main/x/xdffileio/libxdffileio0_0.3-1~nd70+1_amd64.deb Size: 42544 SHA256: bfc34faa9f7af89d20e60b8e371a2e1c6a7ee0a5725f232141f7d65d0cc1a553 SHA1: 27bac77b653abc13c477dff836950cca4f152fe8 MD5sum: 9f08c8270d74247e9d090ca341ee2adc Description: Library to read/write EEG data file formats xdffileio is a library that provides a unified interface for writing and reading various biosignal file formats in realtime (i.e. streaming). It has been designed to provide a flexible, consistent and generic interface to all supported file formats while minimizing the overhead of the function calls: the heaviest operations (type casting, scaling and formatting) are offloaded into a separated thread. This design makes its particularly suitable to be directly used in a data acquisition loop (like in electrophysiology recording or in Brain-Computer Interfaces (BCI)). . The genericity of the interface makes trivial various operations like transformation of a recorded file or its conversion to another file format. xdffileio currently supports EDF, BDF, GDF1 and GDF2 file formats and more will be added in future. Package: libxdffileio0-dbg Source: xdffileio Version: 0.3-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 71 Depends: neurodebian-popularity-contest, libxdffileio0 (= 0.3-1~nd70+1) Multi-Arch: same Homepage: http://cnbi.epfl.ch/software/xdffileio.html Priority: extra Section: debug Filename: pool/main/x/xdffileio/libxdffileio0-dbg_0.3-1~nd70+1_amd64.deb Size: 61922 SHA256: 9012007e5d7c465db911773f7fb8b4e465c884576555f0132ea94a3bdbcd5e25 SHA1: 4b9f9ff0afd69fd137d51301d1cebd5b74c3c443 MD5sum: 3d5eb6cd54918ea7762bed10926da8d2 Description: Library to read/write EEG data file formats (debugging symbols) xdffileio is a library that provides a unified interface for writing and reading various biosignal file formats in realtime (i.e. streaming). It has been designed to provide a flexible, consistent and generic interface to all supported file formats while minimizing the overhead the function calls: the heaviest operations (type casting, scaling and formatting) are offloaded into a separated thread. This design makes its particularly suitable to be directly used in a data acquisition loop (like in electrophysiology recording or in Brain-Computer Interfaces (BCI)). . The genericity of the interface makes trivial various operations like transformation of a recorded file or its conversion to another file format. xdffileio currently supports EDF, BDF, GDF1 and GDF2 file formats and more will be added in future. . This package provides the debugging symbols of the library. Package: lua-cnrun Source: cnrun Version: 2.0.1-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 109 Depends: neurodebian-popularity-contest, libcnrun2, lua5.1 | lua5.2 Suggests: gnuplot Homepage: http://johnhommer.com/academic/code/cnrun Priority: optional Section: science Filename: pool/main/c/cnrun/lua-cnrun_2.0.1-1~nd80+1_amd64.deb Size: 43130 SHA256: ad9e4fa18a9d20ac992acfd09616cc713f04f67a8a89b98ce58090d8cdd09046 SHA1: 6986cd644e5a3f831468a040417fe6d53b69879c MD5sum: a486b9f34698db758528c36741b76ad4 Description: NeuroML-capable neuronal network simulator (Lua package) CNrun is a neuronal network simulator, with these features: * a conductance- and rate-based Hodgkin-Huxley neurons, a Rall and Alpha-Beta synapses; * a 6-5 Runge-Kutta integration method: slow but precise, adjustable; * Poisson, Van der Pol, Colpitts oscillators and interface for external stimulation sources; * NeuroML network topology import/export; * logging state variables, spikes; * implemented as a Lua module, for scripting model behaviour (e.g., to enable plastic processes regulated by model state); * interaction (topology push/pull, async connections) with other cnrun models running elsewhere on a network, with interactions (planned). . Note that there is no `cnrun' executable, which existed in cnrun-1.*. Instead, you write a script for your simulation in Lua, and execute it as detailed in /usr/share/lua-cnrun/examples/example1.lua. Package: matlab-support-dev Source: matlab-support Version: 0.0.21~nd80+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 39 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_0.0.21~nd80+1_all.deb Size: 7574 SHA256: 49c1721689a944ad4d6dfa69f683055ac35ae36edf3f7b6ebc8ff0314f7a025e SHA1: c0f0d044147d29efed62e23575bbe3cccc06a7b8 MD5sum: 2ce67454b2165c70e323a8115d952268 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: mia-tools Source: mia Version: 2.0.13-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 8465 Depends: neurodebian-popularity-contest, libmia-2.0-8 (= 2.0.13-1~nd80+1), libboost-filesystem1.54.0, libboost-regex1.54.0, libboost-serialization1.54.0, libboost-system1.54.0, libboost-test1.54.0, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libglibmm-2.4-1c2a (>= 2.36.2), libgsl0ldbl (>= 1.9), libgts-0.7-5 (>= 0.7.6), libjpeg8 (>= 8c), libnlopt0 (>= 2.3), libopenexr6 (>= 1.6.1), libpng12-0 (>= 1.2.13-4), libstdc++6 (>= 4.6), libtbb2, libtiff5 (>= 4.0.3), libvistaio14 (>= 1.2.14), libvtk5.8, libxml++2.6-2 (>= 2.36.0) Recommends: mia-doc Homepage: http://mia.sourceforge.net Priority: optional Section: science Filename: pool/main/m/mia/mia-tools_2.0.13-1~nd80+1_amd64.deb Size: 1446330 SHA256: cb6b29994b6a7363e62fa3a5044914005bb1135c35cdbc8ef9d065d4e9431ddf SHA1: f7d9e1f72a31185eb18371cc23f42a71097dd0f7 MD5sum: a7fd35ec9589b29a2c3ff32a8a9612fd Description: Command line tools for gray scale image processing Command lines tools to run general purpose image processing tasks on 2D and 3D gray scale images, and basic operations on triangular meshes. Supported image processing algorithms are image filtering, combining, image registration, motion compensation for image series, and the estimation of various statiistics over images. Package: mia-tools-dbg Source: mia Version: 2.0.13-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 29935 Depends: neurodebian-popularity-contest, mia-tools (= 2.0.13-1~nd80+1) Homepage: http://mia.sourceforge.net Priority: extra Section: debug Filename: pool/main/m/mia/mia-tools-dbg_2.0.13-1~nd80+1_amd64.deb Size: 27664592 SHA256: 28b6ca35c294590b9b56c0734507a532fb01b6e50a3c73eba38c112e336dc848 SHA1: 1fbd7e4b20ff5b65ff4cd2c8d4a7e916d7ccd780 MD5sum: 35012d329e42902dc05d6aed712f652e Description: Debugging information for the MIA command line tools Debug information for the MIA command lines tools. These tools provide the means to run general purpose image processing tasks on 2D and 3D gray scale images, and basic operations on triangular meshes from the command line. Supported image processing algorithms are image filtering, combining, image registration, motion compensation for image series, and the estimation of various statiistics over images. Package: mia-tools-doc Source: mia Version: 2.0.13-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1145 Depends: neurodebian-popularity-contest Enhances: mia-tools Homepage: http://mia.sourceforge.net Priority: optional Section: doc Filename: pool/main/m/mia/mia-tools-doc_2.0.13-1~nd80+1_all.deb Size: 78560 SHA256: 0f790c9600f6ff7f5f71d22e58fb780e504e2d02f54df0fdc21265dad0b1c076 SHA1: 102958b49418e7dbaa2d028340ae3ca4ee2e513c MD5sum: adc0a1631aaaffdcb8dd96a5339fa97d Description: Cross-referenced documentation of the MIA command line tools Cross referenced documentation of the command line tools and plug-ins that are provided by the MIA gray scale image processing tool chain. These lines tools to provide the means to run general purpose image processing tasks on 2D and 3D gray scale images, and basic operations on triangular meshes interactively from the command line. Supported image processing algorithms are image filtering, combining, image registration, motion compensation for image series, and the estimation of various statistics over images. Package: mialmpick Version: 0.2.10-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 175 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgdk-pixbuf2.0-0 (>= 2.22.0), libgl1-mesa-glx | libgl1, libglade2-0 (>= 1:2.6.4-2~), libglib2.0-0 (>= 2.31.18), libglu1-mesa | libglu1, libgnomeui-0 (>= 2.22.0), libgtk2.0-0 (>= 2.20.0), libgtkglext1, libmialm3 (>= 1.0.7), libpng12-0 (>= 1.2.13-4), libpopt0 (>= 1.14), libvistaio14 (>= 1.2.14), libx11-6 Homepage: http://mia.sourceforge.net Priority: optional Section: science Filename: pool/main/m/mialmpick/mialmpick_0.2.10-1~nd80+1_amd64.deb Size: 68682 SHA256: ef65fb162334e73f23d2394eff365d5b8f54926ba4625058c4326113b6e645b4 SHA1: e853b4f2d06c110103d030676712f82099b5e494 MD5sum: 772bf4a60f86eadc38f84b1269abe2b0 Description: Tools for landmark picking in 3D volume data sets This tool provides a simple 3D renderer that can visualize surfaces directly from 3D volumes and can be used to set 3D landmarks. It is best suited for CT data sets. Package: mialmpick-dbg Source: mialmpick Version: 0.2.10-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 194 Depends: neurodebian-popularity-contest, mialmpick (= 0.2.10-1~nd80+1) Homepage: http://mia.sourceforge.net Priority: extra Section: debug Filename: pool/main/m/mialmpick/mialmpick-dbg_0.2.10-1~nd80+1_amd64.deb Size: 165568 SHA256: 54eeac09445926f9fabf49c55d58be886da28fa610bb48a1ef806ca43ad6cc53 SHA1: 350e6bda52dbea3ca41e6b7d80ee5b901e5bea8b MD5sum: a93abcbff8c6a2e231052b010fbfe08f Description: Debug information landmark picking tool mialmpick This tool provides a simple 3D renderer that can visualize surfaces directly from 3D volumes and can be used to set 3D landmarks. It is best suited for CT data sets. This package provides the debug information. Package: mitools Source: odin Version: 1.8.8-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 8286 Depends: neurodebian-popularity-contest, libblitz0ldbl, libc6 (>= 2.14), libdcmtk2 (>= 3.6.0), libgcc1 (>= 1:4.1.1), libgsl0ldbl (>= 1.9), liblapack3 | liblapack.so.3, libnifti2, libpng12-0 (>= 1.2.13-4), libqtcore4 (>= 4:4.7.0~beta1), libqtgui4 (>= 4:4.6.1), libqwt5-qt4, libstdc++6 (>= 4.9), libvtk5.8, zlib1g (>= 1:1.1.4), dcmtk Recommends: grace Homepage: http://od1n.sourceforge.net Priority: extra Section: science Filename: pool/main/o/odin/mitools_1.8.8-1~nd80+1_amd64.deb Size: 2030992 SHA256: 3c9c15e2e4045a7c3cd69020780482a6d73b649b357a11ecc46c7396d34e1ef8 SHA1: 7acb3d87790ca6d5aac611f24c28e26943e300a4 MD5sum: 33c15b9a7d20a8f8268174d74d41cd4d Description: view, convert and perform basic maths with medical image datasets The three contained tools micalc, miconv and miview are handy command-line utilities for converting, manipulating and viewing medical image data in various formats (DICOM, NIfTI, PNG, binary data, ...). Package: mriconvert Version: 1:2.0.8-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 5724 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.9), libwxbase3.0-0 (>= 3.0.2), libwxgtk3.0-0 (>= 3.0.2) Homepage: http://lcni.uoregon.edu/~jolinda/MRIConvert/ Priority: optional Section: science Filename: pool/main/m/mriconvert/mriconvert_2.0.8-1~nd80+1_amd64.deb Size: 848488 SHA256: 22ac5f87e38fae91ae3fb970317ab4cc401b7d21dcedbca062a5dc5b736c578f SHA1: a82834b7d3146744af49885b124207f154255b9f MD5sum: e578b63ddfdf83fd982d0bc1a354e393 Description: medical image file conversion utility MRIConvert is a medical image file conversion utility that converts DICOM files to NIfTI 1.1, Analyze 7.5, SPM99/Analyze, BrainVoyager, and MetaImage volume formats. Package: mricron Version: 0.20140804.1~dfsg.1-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 16525 Depends: neurodebian-popularity-contest, libatk1.0-0 (>= 1.12.4), libc6 (>= 2.2.5), libcairo2 (>= 1.2.4), libgdk-pixbuf2.0-0 (>= 2.22.0), libglib2.0-0 (>= 2.12.0), libgtk2.0-0 (>= 2.24.0), libpango-1.0-0 (>= 1.14.0), libx11-6, mricron-data Recommends: pigz Suggests: mricron-doc, fsl Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: science Filename: pool/main/m/mricron/mricron_0.20140804.1~dfsg.1-1~nd80+1_amd64.deb Size: 2269310 SHA256: 909a5ddcbfa4b5064059c73a7c220c25c1d7f175595534613010d0292a7bc13a SHA1: 785882ffa2b32e32b61cd4a4fe048e6345c81b1f MD5sum: a8ebd812111745bcb3b2d449d2890a60 Description: magnetic resonance image conversion, viewing and analysis This is a GUI-based visualization and analysis tool for (functional) magnetic resonance imaging. MRIcron can be used to create 2D or 3D renderings of statistical overlay maps on brain anatomy images. Moreover, it aids drawing anatomical regions-of-interest (ROI), or lesion mapping, as well as basic analysis of functional timeseries (e.g. creating plots of peristimulus signal-change). . In addition to 'mricron', this package also provides 'dcm2nii' that supports converting DICOM and PAR/REC images into the NIfTI format, and 'npm' for non-parametric data analysis. Package: mricron-data Source: mricron Version: 0.20140804.1~dfsg.1-1~nd80+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1710 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: science Filename: pool/main/m/mricron/mricron-data_0.20140804.1~dfsg.1-1~nd80+1_all.deb Size: 1661574 SHA256: 3b9c5a5f374f2748fc54cdfed11eefc122122bf464630a5b45691238bcbe6c8f SHA1: 89b2a2588d175c239fbd3045de0fedd936b2733e MD5sum: 77a6d98ac68ea62fa04088442162b6dd Description: data files for MRIcron This is a GUI-based visualization and analysis tool for (functional) magnetic resonance imaging. MRIcron can be used to create 2D or 3D renderings of statistical overlay maps on brain anatomy images. Moreover, it aids drawing anatomical regions-of-interest (ROI), or lesion mapping, as well as basic analysis of functional timeseries (e.g. creating plots of peristimulus signal-change). . This package provides data files for MRIcron, such as brain atlases, anatomy, and color schemes. Package: mricron-doc Source: mricron Version: 0.20140804.1~dfsg.1-1~nd80+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1022 Depends: neurodebian-popularity-contest Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: doc Filename: pool/main/m/mricron/mricron-doc_0.20140804.1~dfsg.1-1~nd80+1_all.deb Size: 580088 SHA256: 4fb92a2835d537023beec4273978b988762f78db9a529038f1a062f7680eb2f7 SHA1: 90d596df25560bcb9d70b5408ddde6a3a5cd1c0f MD5sum: a9977988ff8c3bfc6abb2aba1192eb8e Description: data files for MRIcron This is a GUI-based visualization and analysis tool for (functional) magnetic resonance imaging. MRIcron can be used to create 2D or 3D renderings of statistical overlay maps on brain anatomy images. Moreover, it aids drawing anatomical regions-of-interest (ROI), or lesion mapping, as well as basic analysis of functional timeseries (e.g. creating plots of peristimulus signal-change). . This package provides documentation for MRIcron in HTML format. Package: mridefacer Version: 0.1-1~nd80+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 673 Depends: neurodebian-popularity-contest, fsl-5.0-core | fsl-core Homepage: https://github.com/hanke/mridefacer Priority: optional Section: science Filename: pool/main/m/mridefacer/mridefacer_0.1-1~nd80+1_all.deb Size: 637030 SHA256: 2fb87e0e98d158b516178beb5467a0317bb334fcefc3b07ee081748b39332855 SHA1: dee2700eb2a2c1123d4de9433bab82af2dd60da0 MD5sum: 50d91dd719336c6c0f7da774fdbbab39 Description: de-identification of MRI data This tool creates a de-face mask for volumetric images by aligning a template mask to the input. Such a mask can be used to remove image data from the vicinity of the facial surface, the auricles, and teeth in order to prevent a possible identification of a person based on these features. mrideface can process individual or series of images. In the latter case, the computed transformation between template image and input image will be updated incrementally for the next image in the series. This feature is most suitable for processing images that have been recorded in temporal succession. Package: mrtrix Version: 0.2.12-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 9210 Depends: neurodebian-popularity-contest, libatk1.0-0 (>= 1.12.4), libatkmm-1.6-1 (>= 2.22.1), libc6 (>= 2.14), libcairo2 (>= 1.2.4), libcairomm-1.0-1 (>= 1.6.4), libfontconfig1 (>= 2.11), 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 (>= 2.36.2), 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), libpango-1.0-0 (>= 1.14.0), libpangocairo-1.0-0 (>= 1.14.0), libpangoft2-1.0-0 (>= 1.14.0), libpangomm-1.4-1 (>= 2.27.1), libpangox-1.0-0 (>= 0.0.2), libsigc++-2.0-0c2a (>= 2.2.0), libsm6, libstdc++6 (>= 4.6), libx11-6, libxmu6, libxt6, zlib1g (>= 1:1.1.4) Suggests: mrtrix-doc, octave, matlab-support Homepage: http://www.brain.org.au/software/mrtrix Priority: extra Section: science Filename: pool/main/m/mrtrix/mrtrix_0.2.12-1~nd80+1_amd64.deb Size: 1356600 SHA256: 2bfd87e755a2ae012e961e97024f66f480b1b5d1e859210ead3609528d278b4b SHA1: ebc883fe394040fbb7e70fd0673b089c28d236a6 MD5sum: 5301cb7155075645d0edc783d066ccd4 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: 0.2.12-1~nd80+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 3528 Depends: neurodebian-popularity-contest Homepage: http://www.brain.org.au/software/mrtrix Priority: extra Section: doc Filename: pool/main/m/mrtrix/mrtrix-doc_0.2.12-1~nd80+1_all.deb Size: 3199852 SHA256: 3c0583c1d09903cc9ab45bcc21b011cae73ee844d09bb1967142cc1fdb0e371d SHA1: d0a05758f0830b961b34e202a225f3dd912316cf MD5sum: 1efc4cd1c1661bd0318495ba7174046c 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: mwrap Version: 0.33-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 275 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1) Recommends: octave Homepage: http://www.cims.nyu.edu/~dbindel/mwrap/ Priority: extra Section: devel Filename: pool/main/m/mwrap/mwrap_0.33-1~nd70+1_amd64.deb Size: 218988 SHA256: c715af214c6837df83c0b9eb2558f57a8992e3c11a658e53836baaae9b34296c SHA1: 44f72ae8fa0c5bfbccc01f24fe5d3085415689b2 MD5sum: 3ed7bbc623b0fe797f1f990df999a281 Description: Octave/MATLAB mex generator MWrap is an interface generation system in the spirit of SWIG or matwrap. From a set of augmented Octave/MATLAB script files, MWrap will generate a MEX gateway to desired C/C++ function calls and Octave/MATLAB function files to access that gateway. The details of converting to and from Octave/MATLAB's data structures, and of allocating and freeing temporary storage, are hidden from the user. Package: netselect Version: 0.3.ds1-25~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 48 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), debconf (>= 0.5) | debconf-2.0 Suggests: netselect-apt Homepage: http://github.com/apenwarr/netselect Priority: optional Section: net Filename: pool/main/n/netselect/netselect_0.3.ds1-25~nd80+1_amd64.deb Size: 31392 SHA256: 1465690fafdf263eabff83dab9ff18960652faa28dcc78c3e442a97782f5ee60 SHA1: 9042f62d68bf83db59a92fbd34e3c2ffa7f47375 MD5sum: 2803feb1d16139c40aa800045f2427d6 Description: speed tester for choosing a fast network server This package provides a utility that can perform parallelized tests on distant servers using either UDP traceroutes or ICMP queries. . It can process a (possibly very long) list of servers, and choose the fastest/closest one automatically. Package: netselect-apt Source: netselect Version: 0.3.ds1-25~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 26 Depends: neurodebian-popularity-contest, wget, netselect (>= 0.3.ds1-17) Recommends: curl Suggests: dpkg-dev Enhances: apt Homepage: http://github.com/apenwarr/netselect Priority: optional Section: net Filename: pool/main/n/netselect/netselect-apt_0.3.ds1-25~nd80+1_all.deb Size: 16782 SHA256: 6220841f3f62894c5886f7c16c9ad5bd579c548179eb300c84b09c29037f175d SHA1: f1fa4c7a87af48462cbc61a15482999389aef69a MD5sum: 161a6391c1e53582a751d887a7b2e7d7 Description: speed tester for choosing a fast Debian mirror This package provides a utility that can choose the best Debian mirror by downloading the full mirror list and using netselect to find the fastest/closest one. . It can output a sources.list(5) file that can be used with package management tools such as apt or aptitude. Package: neurodebian Version: 0.37.2~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 71 Depends: python, wget, neurodebian-archive-keyring, debconf (>= 0.5) | debconf-2.0 Recommends: netselect Suggests: neurodebian-desktop, neurodebian-popularity-contest Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian_0.37.2~nd80+1_all.deb Size: 33426 SHA256: 9e142adefe37b3a1bf20512ccc7119a189d7fb1c67ef0fb540b2cd3cb03e3a97 SHA1: 06f177c1138649d3a77adc18ef19caf5919700e2 MD5sum: 50d3c5730f2ce204eb5fe1929cba15b4 Description: neuroscience-oriented distribution - repository configuration The NeuroDebian project integrates and maintains a variety of software projects within Debian that are useful for neuroscience (such as AFNI, FSL, PsychoPy, etc.) or generic computation (such as HTCondor, pandas, etc.). . This package enables the NeuroDebian repository on top of a standard Debian or Ubuntu system. Package: neurodebian-archive-keyring Source: neurodebian Version: 0.37.2~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 11 Breaks: neurodebian-keyring (<< 0.34~) Replaces: neurodebian-keyring (<< 0.34~) Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-archive-keyring_0.37.2~nd80+1_all.deb Size: 10076 SHA256: 36352b52194b3cc33adda577a5c8f35d41d43679a4d7ab5403a24eb2ab0dc91f SHA1: 0d2934816a7ad0693eee79eafe87e5df1ea828ef MD5sum: 388de4656c97ddc778c84a081cc3a4c7 Description: neuroscience-oriented distribution - GnuPG archive keys The NeuroDebian project integrates and maintains a variety of software projects within Debian that are useful for neuroscience (such as AFNI, FSL, PsychoPy, etc.) or generic computation (such as HTCondor, pandas, etc.). . The NeuroDebian project digitally signs its Release files. This package contains the archive keys used for that. Package: neurodebian-desktop Source: neurodebian Version: 0.37.2~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 147 Depends: ssh-askpass-gnome | ssh-askpass, desktop-base, adwaita-icon-theme | gnome-icon-theme, neurodebian-popularity-contest Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-desktop_0.37.2~nd80+1_all.deb Size: 116004 SHA256: 7e5a16ff3abb29b0962f5ba744c3f568809b3e233571694f95bf50ffb00a03ce SHA1: 0f87b5e617a6c49c1cd84eac537249955a110814 MD5sum: 7cb57e9463a4d2e2913c916df966f70b Description: neuroscience-oriented distribution - desktop integration The NeuroDebian project integrates and maintains a variety of software projects within Debian that are useful for neuroscience (such as AFNI, FSL, PsychoPy, etc.) or generic computation (such as HTCondor, pandas, etc.). . This package provides NeuroDebian artwork (icons, background image) and a NeuroDebian menu featuring the most popular neuroscience tools, which will be automatically installed upon initial invocation. Package: neurodebian-dev Source: neurodebian Version: 0.37.2~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 83 Depends: devscripts, neurodebian-archive-keyring Recommends: python, zerofree, moreutils, time, ubuntu-keyring, debian-archive-keyring, apt-utils, cowbuilder Suggests: virtualbox-ose, virtualbox-ose-fuse Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-dev_0.37.2~nd80+1_all.deb Size: 32262 SHA256: 60c1c593780bf33a09324228dac1ae66a1c0d1923e4e7cfe7abefb09e8606384 SHA1: 469ceb5ab261682216544bdb3b6e44cef5fc6d29 MD5sum: 2cbeb07c3d26f6c1b1b12d8148f625b6 Description: neuroscience-oriented distribution - development tools The NeuroDebian project integrates and maintains a variety of software projects within Debian that are useful for neuroscience (such as AFNI, FSL, PsychoPy, etc.) or generic computation (such as HTCondor, pandas, etc.). . This package provides sources and development tools used by NeuroDebian to provide backports for a range of Debian/Ubuntu releases. Package: neurodebian-guest-additions Source: neurodebian Version: 0.37.2~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 112 Depends: virtualbox-guest-utils, virtualbox-guest-x11, virtualbox-guest-dkms, sudo, neurodebian-desktop, lightdm | x-display-manager, zenity Recommends: chromium, update-notifier Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-guest-additions_0.37.2~nd80+1_all.deb Size: 16756 SHA256: 049c0ea0d2171b8f1514c7ba4fae03436e8e553b822ac085a6e2bcb342f1af97 SHA1: a2779e6b8281e6c33202830b0e90c39fb958b2c7 MD5sum: 7c3cd4f9a1e78209b1b609d5b8f53650 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: 0.32~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8 Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-keyring_0.32~nd80+1_all.deb Size: 7620 SHA256: 90f20210f2b397440a4eb3e88aeca5efcec09495bef47e69792278843659b13c SHA1: e17864bd1bff003ebd64fac14cd94002c241ce60 MD5sum: 5f140e898928627da96de7e170a58f7a 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: 0.37.2~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 14 Depends: popularity-contest Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-popularity-contest_0.37.2~nd80+1_all.deb Size: 12178 SHA256: 0cb13f0314d1ab5e327ae493329cf00bb58caa66dbfa75f49e9ed96224df7116 SHA1: c7323af6f627cc9aeb727f223932e056eb07652f MD5sum: 20eef9982dd414df7759de33e206dec6 Description: neuroscience-oriented distribution - popcon integration The NeuroDebian project integrates and maintains a variety of software projects within Debian that are useful for neuroscience (such as AFNI, FSL, PsychoPy, etc.) or generic computation (such as HTCondor, pandas, etc.). . 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 (either Debian or Ubuntu) popcon server. . Participating in popcon is important for the following reasons: * Popular packages receive more attention from developers; bugs are fixed faster and updates are provided quicker. * It ensures that support is not dropped for a previous release of Debian or Ubuntu while there are active users. * User statistics may be useful for upstream research software developers seeking funding for continued development. . This requires that popcon is activated for the underlying distribution (Debian or Ubuntu), which can be achieved by running "dpkg-reconfigure popularity-contest" as root. Package: nifti-bin Source: nifticlib Version: 2.0.0-2~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 176 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), libnifti2 Homepage: http://niftilib.sourceforge.net Priority: optional Section: utils Filename: pool/main/n/nifticlib/nifti-bin_2.0.0-2~nd80+1_amd64.deb Size: 55256 SHA256: 7747c3bb66583dcb8e55ef702e181c0d4a14b7c334d2495b4b5b1656d2f07926 SHA1: 7cbf4859609d800a547affc7ac9a4a05e59b00ae MD5sum: dd122885124c269306c9bd94e5e6a203 Description: tools shipped with the NIfTI library Niftilib is a set of i/o libraries for reading and writing files in the NIfTI-1 data format. NIfTI-1 is a binary file format for storing medical image data, e.g. magnetic resonance image (MRI) and functional MRI (fMRI) brain images. . This package provides the tools that are shipped with the library (nifti_tool, nifti_stats and nifti1_test). Package: nifti2dicom Version: 0.4.9-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2444 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libgdcm2.4, libinsighttoolkit4.6, libstdc++6 (>= 4.9), nifti2dicom-data (= 0.4.9-1~nd80+1) Homepage: https://github.com/biolab-unige/nifti2dicom Priority: optional Section: science Filename: pool/main/n/nifti2dicom/nifti2dicom_0.4.9-1~nd80+1_amd64.deb Size: 364000 SHA256: 9e2566f8c2d099f5ecf932a884baffdebd4916ed129aadcdb70b45dc33183849 SHA1: 512c627efc631befc6fef77052402159215fa697 MD5sum: de6b6ddb8ce52de068d633366aba39af Description: convert 3D medical images to DICOM 2D series Nifti2Dicom is a convertion tool that converts 3D NIfTI files (and other formats supported by ITK, including Analyze, MetaImage Nrrd and VTK) to DICOM. Unlike other conversion tools, it can import a DICOM file that is used to import the patient and study DICOM tags, and allows you to edit the accession number and other DICOM tags, in order to create a valid DICOM that can be imported in a PACS. . This package includes the command line tools. Package: nifti2dicom-data Source: nifti2dicom Version: 0.4.9-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 606 Depends: neurodebian-popularity-contest Homepage: https://github.com/biolab-unige/nifti2dicom Priority: optional Section: science Filename: pool/main/n/nifti2dicom/nifti2dicom-data_0.4.9-1~nd80+1_all.deb Size: 615964 SHA256: e71ffc179fdeb735f54b8f41aa25e6d613b35d4f74c5e87880dad39c8fa429b1 SHA1: 98011391b6bcbd103afdd9d45efeea83b24e6971 MD5sum: 52234def9eb3b04ffebbcc1fdad846fe Description: data files for nifti2dicom This package contains architecture-independent supporting data files required for use with nifti2dicom, such as such as documentation, icons, and translations. 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: nuitka Version: 0.5.15+ds-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2675 Depends: neurodebian-popularity-contest, g++-4.9 | g++-4.8 | g++-4.7 | g++-4.6 (>= 4.6.1) | g++-4.5 | g++-4.4 | clang (>= 3.0), scons (>= 2.0.0), python-dev (>= 2.6.6-2), python:any (>= 2.6.6-7~) Recommends: python-lxml (>= 2.3), python-qt4 (>= 4.8.6), strace Suggests: ccache Homepage: http://nuitka.net Priority: optional Section: python Filename: pool/main/n/nuitka/nuitka_0.5.15+ds-1~nd80+1_all.deb Size: 586002 SHA256: 558624d12097386a9f26955c9484efbd9e04f20f5f70c8ee7e99afa1ca5e2a61 SHA1: a65e22dc1af4c660ce9aa50f9d39c8e345f9da5f MD5sum: 79bc029a8364efbc25cd0a67129dae96 Description: Python compiler with full language support and CPython compatibility This Python compiler achieves full language compatibility and compiles Python code into compiled objects that are not second class at all. Instead they can be used in the same way as pure Python objects. Package: numdiff Version: 5.6.0-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 857 Depends: neurodebian-popularity-contest, libc6 (>= 2.3), dpkg (>= 1.15.4) | install-info Homepage: http://nongnu.org/numdiff/ Priority: extra Section: science Filename: pool/main/n/numdiff/numdiff_5.6.0-1~nd70+1_amd64.deb Size: 597436 SHA256: 9a87c4bc5f50898003ea1e5b7d878a07272dd746fb2a09c23f1fe7932e35c076 SHA1: a1273fa841c9e27b2c8b0f308efa768c553375e9 MD5sum: f984f122c6cb98339f6b6f76433650b4 Description: Compare similar files with numeric fields. Numdiff is a console application that can be used to compare putatively similar files line by line and field by field, ignoring small numeric differences or/and different numeric formats. It is similar diff or wdiff, but it is aware of floating point numbers including complex and multi-precision numbers. Numdiff is useful to compare text files containing numerical fields, when testing or doing quality control in scientific computing or in numerical analysis. Package: octave-biosig Source: biosig4c++ Version: 1.4.1-2~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 69 Depends: neurodebian-popularity-contest, octave (>= 3.4.3-1~), libbiosig1, libc6 (>= 2.4), libcholmod2.1.2, libgcc1 (>= 1:4.1.1), liboctave2, 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_1.4.1-2~nd80+1_amd64.deb Size: 19858 SHA256: 00d1d6d031885ea85d19b285ef3cfb1ea36a139b45d407dc0fa968de6f63cbc2 SHA1: 590a9bce674049ba74c2ff10d36032aa263ffb9c MD5sum: 4c06cc354aba5a3434951600ae1bde96 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: 0.1.2-2~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 293 Depends: neurodebian-popularity-contest, octave (>= 3.4.3-1~), libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libgdf0, liboctave1, libstdc++6 (>= 4.6) Homepage: http://sourceforge.net/projects/libgdf Priority: extra Section: science Filename: pool/main/libg/libgdf/octave-gdf_0.1.2-2~nd70+1_amd64.deb Size: 122574 SHA256: e0a171f30504237fcd1dd96a87ad19db327264a180cc5d656d51954bb18f3b78 SHA1: a2116add360c1b38634f378faffa4c8b6d4e7025 MD5sum: 792d9caa7f43dcf8e8b52bd59616ae32 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-nlopt Source: nlopt Version: 2.4.1+dfsg-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 88 Depends: neurodebian-popularity-contest, libnlopt0 (= 2.4.1+dfsg-1~nd80+1), libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), liboctave1 (>= 3.6.2), libstdc++6 (>= 4.1.1) Homepage: http://ab-initio.mit.edu/wiki/index.php/NLopt Priority: optional Section: math Filename: pool/main/n/nlopt/octave-nlopt_2.4.1+dfsg-1~nd80+1_amd64.deb Size: 25680 SHA256: 4565f700367d3bacc9f5d6ec5cca2d2c2330fb3e4592655372907f6b132ba36a SHA1: bab5abab662a2ab2e166409c9b0706350a5b8d40 MD5sum: 05b29d778f64c3948f09308c773d3fff Description: nonlinear optimization library -- GNU Octave package NLopt is a free/open-source library for nonlinear optimization, providing a common interface for a number of different free optimization routines available online as well as original implementations of various other algorithms. Its features include: . * Callable from C, C++, Fortran, GNU Octave, Python, GNU Guile, GNU R. * A common interface for many different algorithms * Support for large-scale optimization. * Both global and local optimization algorithms. * Algorithms using function values only (derivative-free) and also algorithms exploiting user-supplied gradients. * Algorithms for unconstrained optimization, bound-constrained optimization, and general nonlinear inequality/equality constraints. . This package contains the module for the GNU Octave. Package: octave-psychtoolbox-3 Source: psychtoolbox-3 Version: 3.0.12.20150725.dfgs1-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 4286 Depends: neurodebian-popularity-contest, octave (>= 3.4.3-1~), freeglut3, libasound2 (>= 1.0.16), libc6 (>= 2.14), libdc1394-22, libfreenect0.5 (>= 1:0.1.1), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglib2.0-0 (>= 2.12.0), libglu1-mesa | libglu1, libgstreamer-plugins-base1.0-0 (>= 1.0.0), libgstreamer1.0-0 (>= 1.4.0), liboctave2, libopenal1 (>= 1.14), libpciaccess0 (>= 0.8.0+git20071002), libstdc++6 (>= 4.1.1), libusb-1.0-0 (>= 2:1.0.9), libx11-6 (>= 2:1.2.99.901), libx11-xcb1, libxcb-dri3-0, libxcb1, libxext6, libxfixes3, libxi6 (>= 2:1.2.99.4), libxml2 (>= 2.6.27), libxrandr2 (>= 2:1.2.99.3), libxxf86vm1, psychtoolbox-3-common (= 3.0.12.20150725.dfgs1-1~nd80+1), psychtoolbox-3-lib (= 3.0.12.20150725.dfgs1-1~nd80+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_3.0.12.20150725.dfgs1-1~nd80+1_amd64.deb Size: 841162 SHA256: 85840f3ddb91abad3129f5ce48a4028a56235c44e788a7a76c0eafa981232f50 SHA1: 9f91b6701e5c4f47082ba66e21dabed29a34e50c MD5sum: 596517d34067d1e394e9c58dbcb3832c 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. . See also http://www.psychtoolbox.org/UsingPsychtoolboxOnUbuntu for additional information about systems tune-up and initial configuration. . This package contains bindings for Octave. Package: odin Version: 1.8.8-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 4451 Depends: neurodebian-popularity-contest, libblitz0ldbl, libc6 (>= 2.14), libdcmtk2 (>= 3.6.0), libgcc1 (>= 1:4.1.1), liblapack3 | liblapack.so.3, libnifti2, libpng12-0 (>= 1.2.13-4), libqtcore4 (>= 4:4.5.3), libqtgui4 (>= 4:4.5.3), libstdc++6 (>= 4.6), libvtk5.8, mitools (= 1.8.8-1~nd80+1), zlib1g (>= 1:1.1.4), libodin-dev, libgsl0-dev, libc6-dev | libc-dev, g++, libblas-dev | libatlas-base-dev, xterm | x-terminal-emulator, gdb Recommends: liboil0.3-dev | liboil-dev, libdcmtk1-dev Homepage: http://od1n.sourceforge.net Priority: extra Section: science Filename: pool/main/o/odin/odin_1.8.8-1~nd80+1_amd64.deb Size: 1279944 SHA256: 7d63b318af85e071fc3ed136fa237b181bd77f4c83cb567407a05257ec70d057 SHA1: 9779151a66abef4079c4f569ebb8d39f3d4b8782 MD5sum: b6137c614f12b4125a402a87bb121120 Description: develop, simulate and run magnetic resonance sequences ODIN is a framework for magnetic resonance imaging (MRI). It covers the whole toolchain of MRI, from low-level data acquisition to image reconstruction. In particular, it aims at rapid prototyping of MRI sequences. The sequences can be programmed using a high-level, object oriented, C++ programming interface. It provides advanced sequence analysis tools, such as interactive plotting of k-space trajectories, a user interface for a fast compile-link-test cycle and a powerful MRI simulator which supports different virtual samples. For fast and flexible image reconstruction, ODIN contains a highly customizable, multi-threaded data-processing framework. Package: openmeeg-tools Source: openmeeg Version: 2.0.0.dfsg-4~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 637 Depends: neurodebian-popularity-contest, libatlas3gf-base, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libmatio0, libopenmeeg1, libstdc++6 (>= 4.1.1) Homepage: http://www-sop.inria.fr/odyssee/software/OpenMEEG/ Priority: extra Section: science Filename: pool/main/o/openmeeg/openmeeg-tools_2.0.0.dfsg-4~nd70+1_amd64.deb Size: 197344 SHA256: c09fab2d3cefd845d022b5a8d13fe3adeb087133a9e39c2cb7c1d10babad7ff0 SHA1: 665303acb9e0a173b4286b8326eeb5f6395f7136 MD5sum: 32f370fe5c7f4c3cd0f823ca0f99da65 Description: openmeeg library -- command line tools OpenMEEG consists of state-of-the art solvers for forward problems in the field of MEG and EEG. Solvers are based on the symmetric Boundary Element method [Kybic et al, 2005], providing excellent accuracy, particularly for superficial cortical sources. OpenMEEG can compute four types of lead fields (EEG, MEG, Internal Potential and Electrical Impedence Tomography). . This package provides command line interface to openmeeg functionality. Package: opensesame Version: 0.27.4-2~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 26639 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-qscintilla2, gnome-icon-theme Recommends: python-serial (>= 2.3~), psychopy (>= 1.64.0), python-pyaudio (>= 0.2.4), python-imaging (>= 1.1.7), python-opengl (>= 3.0.1), expyriment (>= 0.5.2), ipython-qtconsole (>= 0.12), python-markdown Homepage: http://www.cogsci.nl/software/opensesame Priority: extra Section: science Filename: pool/main/o/opensesame/opensesame_0.27.4-2~nd80+1_all.deb Size: 25359240 SHA256: d72a73498e799a77b82b925a103fa427ae624eddaedbadda65943ae7c9310984 SHA1: a8c259ff741277768ae078fb3d32b797510ed93d MD5sum: ddf77ad74ed6b51d22af4ae31f5701ab 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. Package: openvibe-bin Source: openvibe Version: 0.14.3+dfsg2-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1205 Depends: neurodebian-popularity-contest, openvibe-libs (= 0.14.3+dfsg2-1~nd70+1), openvibe-data (= 0.14.3+dfsg2-1~nd70+1), libboost-regex1.49.0 (>= 1.49.0-1), libboost-thread1.49.0 (>= 1.49.0-1), libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libgdk-pixbuf2.0-0 (>= 2.22.0), libglib2.0-0 (>= 2.12.0), libgtk2.0-0 (>= 2.18.0), libopenal1, libpango1.0-0 (>= 1.14.0), libstdc++6 (>= 4.6), zlib1g (>= 1:1.1.4) Homepage: http://openvibe.inria.fr Priority: extra Section: science Filename: pool/main/o/openvibe/openvibe-bin_0.14.3+dfsg2-1~nd70+1_amd64.deb Size: 447952 SHA256: 7bb74eef7ae4ad1230b11b7bb4ac5cef5e5127f1d4de8271eb5cc1e070710096 SHA1: 21e444958d84d29880355f971c2bf0ae609c3017 MD5sum: 285d647cb96b4a366e8f8a9c514ac0fb Description: Software platform for BCI (tools and demos) OpenViBE enables to design, test and use Brain-Computer Interfaces (BCI). OpenViBE is a software for real-time neurosciences (that is, for real-time processing of brain signals). It can be used to acquire, filter, process, classify and visualize brain signals in real time. . The graphical user interface of OpenViBE is simple to access and very easy to use for creating BCI scenarios and saving them for later use. In the designer, the available functions are listed in the right-hand window. The user simply drags and drops the selected functions in the left-hand window. He can then connect boxes together to add processing steps to the scenario being created. Lastly, the application is started by pressing the Play button to run the BCI. . OpenViBE is a library of functions written in C++ which can be integrated and applied quickly and easily using modules. The platform's main advantages are modularity, portability, availability of different tools for different types of user, including programmers and non-programmers, superior code performance and compatibility with virtual reality technologies. . The software also offers many 2D and 3D visualization tools to represent brain activity in real time. It is compatible with many EEG- and MEG-type machines because of its generic acquisition server. . OpenViBE offers many pre-configured scenarios for different applications including mental imagery, neurofeedback, P300 signals, etc... . This package contains differents executable including acquisition server, tools and demos. Package: openvibe-data Source: openvibe Version: 0.14.3+dfsg2-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9328 Depends: neurodebian-popularity-contest Homepage: http://openvibe.inria.fr Priority: extra Section: science Filename: pool/main/o/openvibe/openvibe-data_0.14.3+dfsg2-1~nd70+1_all.deb Size: 2024456 SHA256: 7b72cf2a61f9764f3d6d4b8c632db691ffb517dcdb6d500c521b8a1eec381302 SHA1: 107a4c5c7588594034039a389571a77eb3914d1d MD5sum: b10cbfaf7110dfa2a5582c30cbe29212 Description: Software platform for BCI (Data files) OpenViBE enables to design, test and use Brain-Computer Interfaces (BCI). OpenViBE is a software for real-time neurosciences (that is, for real-time processing of brain signals). It can be used to acquire, filter, process, classify and visualize brain signals in real time. . The graphical user interface of OpenViBE is simple to access and very easy to use for creating BCI scenarios and saving them for later use. In the designer, the available functions are listed in the right-hand window. The user simply drags and drops the selected functions in the left-hand window. He can then connect boxes together to add processing steps to the scenario being created. Lastly, the application is started by pressing the Play button to run the BCI. . OpenViBE is a library of functions written in C++ which can be integrated and applied quickly and easily using modules. The platform's main advantages are modularity, portability, availability of different tools for different types of user, including programmers and non-programmers, superior code performance and compatibility with virtual reality technologies. . The software also offers many 2D and 3D visualization tools to represent brain activity in real time. It is compatible with many EEG- and MEG-type machines because of its generic acquisition server. . OpenViBE offers many pre-configured scenarios for different applications including mental imagery, neurofeedback, P300 signals, etc... . This package contains the data files. Package: openvibe-dev Source: openvibe Version: 0.14.3+dfsg2-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 715 Depends: neurodebian-popularity-contest, openvibe-libs (= 0.14.3+dfsg2-1~nd70+1) Homepage: http://openvibe.inria.fr Priority: extra Section: libdevel Filename: pool/main/o/openvibe/openvibe-dev_0.14.3+dfsg2-1~nd70+1_amd64.deb Size: 100674 SHA256: fadf4332d3b6faef5d7642e1badbae0e6ce1b6525b794f006f08f6459339e96b SHA1: 1fffeb81109d43bfbffee2228c75357a92d3abde MD5sum: af83e6ed54ee2191fb2b54808a6b6af0 Description: Software platform for BCI (development files) OpenViBE enables to design, test and use Brain-Computer Interfaces (BCI). OpenViBE is a software for real-time neurosciences (that is, for real-time processing of brain signals). It can be used to acquire, filter, process, classify and visualize brain signals in real time. . The graphical user interface of OpenViBE is simple to access and very easy to use for creating BCI scenarios and saving them for later use. In the designer, the available functions are listed in the right-hand window. The user simply drags and drops the selected functions in the left-hand window. He can then connect boxes together to add processing steps to the scenario being created. Lastly, the application is started by pressing the Play button to run the BCI. . OpenViBE is a library of functions written in C++ which can be integrated and applied quickly and easily using modules. The platform's main advantages are modularity, portability, availability of different tools for different types of user, including programmers and non-programmers, superior code performance and compatibility with virtual reality technologies. . The software also offers many 2D and 3D visualization tools to represent brain activity in real time. It is compatible with many EEG- and MEG-type machines because of its generic acquisition server. . OpenViBE offers many pre-configured scenarios for different applications including mental imagery, neurofeedback, P300 signals, etc... . This package contains the development files. Package: openvibe-libs Source: openvibe Version: 0.14.3+dfsg2-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2229 Depends: neurodebian-popularity-contest, openvibe-data (= 0.14.3+dfsg2-1~nd70+1), libc6 (>= 2.4), libexpat1 (>= 2.0.1), libgcc1 (>= 1:4.1.1), libglib2.0-0 (>= 2.12.0), libgtk2.0-0 (>= 2.24.0), libogre-1.7.4, libstdc++6 (>= 4.6), libx11-6 Homepage: http://openvibe.inria.fr Priority: extra Section: libs Filename: pool/main/o/openvibe/openvibe-libs_0.14.3+dfsg2-1~nd70+1_amd64.deb Size: 638288 SHA256: 42738ee9281785e614122f66d7b611292264fac53bb8300bc3c8cf62979726f2 SHA1: bb5689098455a366380f9bfc482f20ff639dd9ac MD5sum: 0c5ae2f984ccd4c9ed50796cb34c61e4 Description: Software platform for BCI (shared libraries) OpenViBE enables to design, test and use Brain-Computer Interfaces (BCI). OpenViBE is a software for real-time neurosciences (that is, for real-time processing of brain signals). It can be used to acquire, filter, process, classify and visualize brain signals in real time. . The graphical user interface of OpenViBE is simple to access and very easy to use for creating BCI scenarios and saving them for later use. In the designer, the available functions are listed in the right-hand window. The user simply drags and drops the selected functions in the left-hand window. He can then connect boxes together to add processing steps to the scenario being created. Lastly, the application is started by pressing the Play button to run the BCI. . OpenViBE is a library of functions written in C++ which can be integrated and applied quickly and easily using modules. The platform's main advantages are modularity, portability, availability of different tools for different types of user, including programmers and non-programmers, superior code performance and compatibility with virtual reality technologies. . The software also offers many 2D and 3D visualization tools to represent brain activity in real time. It is compatible with many EEG- and MEG-type machines because of its generic acquisition server. . OpenViBE offers many pre-configured scenarios for different applications including mental imagery, neurofeedback, P300 signals, etc... . This package contains the shared libraries. Package: openvibe-plugins Source: openvibe Version: 0.14.3+dfsg2-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 5663 Depends: neurodebian-popularity-contest, openvibe-libs (= 0.14.3+dfsg2-1~nd70+1), openvibe-data (= 0.14.3+dfsg2-1~nd70+1), libalut0 (>= 1.0.1), libboost-regex1.49.0 (>= 1.49.0-1), libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libgdk-pixbuf2.0-0 (>= 2.22.0), libglib2.0-0 (>= 2.12.0), libgtk2.0-0 (>= 2.18.0), libitpp7, liblapack3 | liblapack.so.3 | libatlas3-base, libopenal1, libpango1.0-0 (>= 1.14.0), libstdc++6 (>= 4.6), libvorbisfile3 (>= 1.1.2), libvrpnserver0, libx11-6 Homepage: http://openvibe.inria.fr Priority: extra Section: libs Filename: pool/main/o/openvibe/openvibe-plugins_0.14.3+dfsg2-1~nd70+1_amd64.deb Size: 1663650 SHA256: ede595c32587f73c53b6a31f0c96726678e9ebe8c738e0d5b9b6d49203136ef7 SHA1: 45db90bb059f137ca063d059fd2f163189adb5c7 MD5sum: 8ef62059722ec22c84bf35314e146861 Description: Software platform for BCI (plugins) OpenViBE enables to design, test and use Brain-Computer Interfaces (BCI). OpenViBE is a software for real-time neurosciences (that is, for real-time processing of brain signals). It can be used to acquire, filter, process, classify and visualize brain signals in real time. . The graphical user interface of OpenViBE is simple to access and very easy to use for creating BCI scenarios and saving them for later use. In the designer, the available functions are listed in the right-hand window. The user simply drags and drops the selected functions in the left-hand window. He can then connect boxes together to add processing steps to the scenario being created. Lastly, the application is started by pressing the Play button to run the BCI. . OpenViBE is a library of functions written in C++ which can be integrated and applied quickly and easily using modules. The platform's main advantages are modularity, portability, availability of different tools for different types of user, including programmers and non-programmers, superior code performance and compatibility with virtual reality technologies. . The software also offers many 2D and 3D visualization tools to represent brain activity in real time. It is compatible with many EEG- and MEG-type machines because of its generic acquisition server. . OpenViBE offers many pre-configured scenarios for different applications including mental imagery, neurofeedback, P300 signals, etc... . This package contains the plugins. Package: openwalnut-modules Source: openwalnut Version: 1.4.0~rc1+hg3a3147463ee2-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 19847 Depends: neurodebian-popularity-contest, libbiosig1, libboost-filesystem1.54.0, libboost-regex1.54.0, libboost-system1.54.0, libboost-thread1.54.0, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libnifti2, libopenscenegraph99, libopenwalnut1, libstdc++6 (>= 4.6) Homepage: http://www.openwalnut.org Priority: extra Section: science Filename: pool/main/o/openwalnut/openwalnut-modules_1.4.0~rc1+hg3a3147463ee2-1~nd80+1_amd64.deb Size: 3322322 SHA256: 2d339e7455fab7d24654d790147932863e0e1114d8630a03fcc067c566199729 SHA1: 371b72658a854d042ff6fb307c1b6f20afabe32f MD5sum: 105e64ee6a8723b7115a32820f73521c Description: Loaders, algorithms and visualization modules for OpenWalnut 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: 1.4.0~rc1+hg3a3147463ee2-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1955 Depends: neurodebian-popularity-contest, libboost-filesystem1.54.0, libboost-program-options1.54.0, libboost-regex1.54.0, libboost-system1.54.0, libboost-thread1.54.0, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libopenscenegraph99, libopenwalnut1, libqt4-opengl (>= 4:4.6.0), libqtcore4 (>= 4:4.7.0~beta1), libqtgui4 (>= 4:4.8.0), libqtwebkit4 (>= 2.1.0~2011week13), libstdc++6 (>= 4.6), libx11-6 Recommends: openwalnut-modules (= 1.4.0~rc1+hg3a3147463ee2-1~nd80+1) Homepage: http://www.openwalnut.org Priority: extra Section: science Filename: pool/main/o/openwalnut/openwalnut-qt4_1.4.0~rc1+hg3a3147463ee2-1~nd80+1_amd64.deb Size: 734128 SHA256: 8076eb0e04378a5f666a7aeaa6976e887d6b842bf8d172a695053db04559265b SHA1: 8011ad4559d75586910381a0de627f23270fac56 MD5sum: cd8ac7e27bb88a539eb576aa409a5659 Description: Qt based user interface for OpenWalnut 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: 0.8~nd0 Architecture: all Maintainer: Lucas Nussbaum Installed-Size: 1550 Priority: extra Section: doc Filename: pool/main/p/packaging-tutorial/packaging-tutorial_0.8~nd0_all.deb Size: 1488332 SHA256: 491bc5917f698fee06888998e8a295a6caac2950148bb160b457aff72437eadb SHA1: c5d75d04b01f681ead660ce8d8fe068ab887fba0 MD5sum: 8fbf7c362fd4091a78c50404eb694402 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: 1.82.02.dfsg-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 14481 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-pyglet | python-pygame, python-opengl, python-numpy, python-scipy, python-matplotlib, python-lxml, python-configobj Recommends: python-wxgtk3.0, python-wxgtk2.8, python-pyglet, python-pygame, python-openpyxl, python-imaging, python-serial, python-pyo, python-psutil, python-gevent, python-msgpack, python-yaml, python-xlib, python-pandas, libavbin0, libxxf86vm1, ipython Suggests: python-iolabs, python-pyxid Conflicts: libavbin0 (= 7-4+b1) Homepage: http://www.psychopy.org Priority: optional Section: science Filename: pool/main/p/psychopy/psychopy_1.82.02.dfsg-1~nd80+1_all.deb Size: 6063380 SHA256: a66feb2b2c12e768da158a24196ee1a8c3d1fd771e51353bdf7602ee4dab6e5c SHA1: 06743835073190e930c26d853f7c61ad954b3515 MD5sum: 78de8bebf976b6afbf1eb41868865445 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: 2.7 Package: psychtoolbox-3-common Source: psychtoolbox-3 Version: 3.0.12.20150725.dfgs1-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 233285 Depends: neurodebian-popularity-contest Recommends: subversion Suggests: gnuplot Homepage: http://psychtoolbox.org Priority: extra Section: science Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-common_3.0.12.20150725.dfgs1-1~nd80+1_all.deb Size: 23797100 SHA256: b82fa01cd664f78b9cb5270719fade500007b08dc69da27a1f2ad5da98c6b6d2 SHA1: 35b676b01c01923090a248a1757679131d7b7201 MD5sum: c46e70afeffe0f15ddecd5109e4a7af7 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: 3.0.12.20150725.dfgs1-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 3712 Depends: neurodebian-popularity-contest, octave-psychtoolbox-3 (= 3.0.12.20150725.dfgs1-1~nd80+1) Homepage: http://psychtoolbox.org Priority: extra Section: debug Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-dbg_3.0.12.20150725.dfgs1-1~nd80+1_amd64.deb Size: 706288 SHA256: 03c40ec755335e5c1b357df67e56dbcffd4bbc51a47c8ff5ed4cc2c2eefd4061 SHA1: 6a94be1230750516fcc9509891ad887cca94242a MD5sum: f1660a5b7ef543d3fa19b560c0f3df61 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: 3.0.12.20150725.dfgs1-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 153 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libfontconfig1 (>= 2.11), libfreetype6 (>= 2.2.1), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, libstdc++6 (>= 4.6) Recommends: gstreamer1.0-plugins-base, gstreamer1.0-plugins-good, gstreamer1.0-plugins-bad, gstreamer1.0-plugins-ugly, gstreamer1.0-libav Homepage: http://psychtoolbox.org Priority: extra Section: science Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-lib_3.0.12.20150725.dfgs1-1~nd80+1_amd64.deb Size: 55380 SHA256: 0f893de50f2fa478677d313c08c00c890a8241b83fb4e6ad5d6910355960cea1 SHA1: d24f8c987b7ea869e4643beb3a631ae073f60552 MD5sum: 6c3059fab295d21890f0de8990c9c983 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: 1.4.1-2~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 203 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python-numpy (>= 1:1.8.0), python-numpy-abi9, python-support (>= 0.90.0), libbiosig1, libc6 (>= 2.14), libcholmod2.1.2, libgcc1 (>= 1:4.1.1), libpython2.7 (>= 2.7), 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_1.4.1-2~nd80+1_amd64.deb Size: 42692 SHA256: 7f530394301b61753a2a0486641fff859958606507f91bcaa664485278675c4e SHA1: b697d2baf8dd105a98b5dc6ec97cc31d2c00d340 MD5sum: 3ac3fac18705758003fdf294bc52c775 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: 1.4.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2336 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-brian-lib (>= 1.4.1-1~nd70+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_1.4.1-1~nd70+1_all.deb Size: 549130 SHA256: 7c9586033503713d95ee640005799fd631ebc23b9857fa54739f713c13945ddc SHA1: 8df0debf188bacd59bf8e48470edc079ea401c5b MD5sum: 4d38a81ea37270a2ac871681b3c124b6 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: 1.4.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6798 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_1.4.1-1~nd70+1_all.deb Size: 2245550 SHA256: bbff81c2bf503166de3140452772e3684fcef15a172b12d230b1199a7333719d SHA1: 141fc07a2f6c5c65177d701a0612d7e1ba06f65d MD5sum: 99633747e0f9db0d78f345974120c115 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: 1.4.1-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 249 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.6), python-numpy (>= 1:1.6.1), python-numpy-abi9, python-support (>= 0.90.0), libc6 (>= 2.2.5), 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_1.4.1-1~nd70+1_amd64.deb Size: 95922 SHA256: d71134f96e4ce240a5a547af41c69ed5b621f90e30c670833255b57f3a4a7d17 SHA1: 43491ae5e0e436252cbe6638d9b1c6c9d860f52f MD5sum: d3ca3eb38666c9097fa1c94cc9dd27b3 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~nd70+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~nd70+1_all.deb Size: 217682 SHA256: 315d0c9976626dc452d7a4f03c9ff782c4caa12e182713db2c33d71233777b37 SHA1: 2f09d150c91742140a16fba4f03eceb4ad364e04 MD5sum: 34ba30e9fe7f1e59a608e67b241ca26c 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-citeproc Source: citeproc-py Version: 0.3.0-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 727 Depends: neurodebian-popularity-contest, python-lxml, python:any (<< 2.8), python:any (>= 2.7.5-5~) Homepage: https://github.com/brechtm/citeproc-py Priority: optional Section: python Filename: pool/main/c/citeproc-py/python-citeproc_0.3.0-1~nd80+1_all.deb Size: 80526 SHA256: 9749bd6ba6c54bc5a68d92a2b881253387ec4d40d0392f0ddc184f19c0f6fcdf SHA1: 54ae3ccf83f78d3ac1dbab78afd3a481f3efb13f MD5sum: 4a8eee1074304e53d7cf5d886adfc431 Description: Citation Style Language (CSL) processor for Python Citeproc-py is a library that produces formatted bibliographies and citations from bibliographic databases following formatting instructions provided by XML style files written in the Citation Style Language (CSL). . Currently, BibTeX and JSON are supported as input database formats, and plain text, reStructuredText and HTML as output format. . This package contains the Python modules. Package: python-dcmstack Source: dcmstack Version: 0.6.2+git33-gb43919a.1-1~nd80+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 516 Depends: neurodebian-popularity-contest, python-dicom (>= 0.9.7~), python-nibabel (>= 2.0~), python-numpy, python, python:any (<< 2.8), python:any (>= 2.7.5-5~), libjs-sphinxdoc (>= 1.0) Provides: python2.7-dcmstack Homepage: https://github.com/moloney/dcmstack Priority: optional Section: python Filename: pool/main/d/dcmstack/python-dcmstack_0.6.2+git33-gb43919a.1-1~nd80+1_all.deb Size: 78446 SHA256: 96e1cca9b93b8110036e4a6a0ce53224ef625bdb5be8273b6c0508cf9e52b0e0 SHA1: 1f11138d91f9dff16cc8ecb7a1e2b8b528ab4cfd MD5sum: fbd7bdaef09b55897e08e14b7a73eae3 Description: DICOM to Nifti conversion DICOM to Nifti conversion with the added ability to extract and summarize meta data from the source DICOMs. The meta data can be injected into a Nifti header extension or written out as a JSON formatted text file. . This package provides the Python package, command line tools (dcmstack, and nitool), as well as the documentation in HTML format. Package: python-dicom Source: pydicom Version: 0.9.9-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1522 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8) Recommends: python-numpy, python-imaging Suggests: python-matplotlib Homepage: http://pydicom.org/ Priority: optional Section: python Filename: pool/main/p/pydicom/python-dicom_0.9.9-1~nd80+1_all.deb Size: 357826 SHA256: ecaa9246e830055f4a49c45ef3dc438f6857dade9893876ab7d2e9cb1be7d7e1 SHA1: 46aa7757be5009a94e82e6692cdc4b9521f2909a MD5sum: 3f7d86d5e2caf4c396d952d65dc14f4b Description: DICOM medical file reading and writing pydicom is a pure Python module for parsing DICOM files. DICOM is a standard (http://medical.nema.org) for communicating medical images and related information such as reports and radiotherapy objects. . pydicom makes it easy to read DICOM files into natural pythonic structures for easy manipulation. Modified datasets can be written again to DICOM format files. Package: python-dipy Source: dipy Version: 0.9.2-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4611 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy, python-scipy, python-dipy-lib (>= 0.9.2-1~nd80+1) Recommends: python-matplotlib, python-vtk, python-nose, python-nibabel, python-tables Suggests: ipython Provides: python2.7-dipy Homepage: http://nipy.org/dipy Priority: optional Section: python Filename: pool/main/d/dipy/python-dipy_0.9.2-1~nd80+1_all.deb Size: 2341604 SHA256: d771713738d7856cb7b056d32951396c8c5ad45673a93feaaddff2016e8ad463 SHA1: 2addd5936a0f113d3a4dbd88c55fc6aba13be15c MD5sum: f78a99c633778727a3fe904140471c5e 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: 2.7 Package: python-dipy-doc Source: dipy Version: 0.9.2-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 12502 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-dipy Homepage: http://nipy.org/dipy Priority: optional Section: doc Filename: pool/main/d/dipy/python-dipy-doc_0.9.2-1~nd80+1_all.deb Size: 10231608 SHA256: 1ed51cc51c0e24ed7ea06706b826a5f797aca067e8c3ee2e15d1f160ada7e421 SHA1: 98caa02cb3b3e83556ac0485ca05da54d1217404 MD5sum: 2c5b51eb6e9d4cb31464622946ae5165 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: 0.9.2-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 5431 Depends: neurodebian-popularity-contest, python-numpy (>= 1:1.8.0), python-numpy-abi9, python (>= 2.7), python (<< 2.8), libc6 (>= 2.14), libgomp1 (>= 4.9) Provides: python2.7-dipy-lib Homepage: http://nipy.org/dipy Priority: optional Section: python Filename: pool/main/d/dipy/python-dipy-lib_0.9.2-1~nd80+1_amd64.deb Size: 977300 SHA256: 52f3671907dd4c40f6f72bc7c914c75722a3274e201850c61cb993ace316d2cf SHA1: af5facdd41f1453b5ea4b94c8e786598bbaf0eb3 MD5sum: c2fca1a3afd49323c7afb77228a7991f 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: 2.7 Package: python-expyriment Version: 0.7.0+git34-g55a4e7e-3~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2419 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-support (>= 0.90.0), python-pygame (>= 1.9.1~), python-opengl (>= 3.0.0), ttf-freefont, libjs-jquery, libjs-underscore Recommends: python-serial (>= 2.5~), python-numpy (>= 1.3.0~) Suggests: python-parallel (>= 0.2), python-pyxid Homepage: http://www.expyriment.org Priority: optional Section: science Filename: pool/main/p/python-expyriment/python-expyriment_0.7.0+git34-g55a4e7e-3~nd80+1_all.deb Size: 698700 SHA256: 0d4ff189c1c2bc6c7ac5142936cd588b752f4da68686778bd4750d18c41ef31d SHA1: f64222c7f86a213c127132cedd996adb328dcc45 MD5sum: 98e1885aba4d13ea35911b0e274872de Description: Python library for cognitive and neuroscientific experiments Expyriment is a light-weight Python library for designing and conducting timing-critical behavioural and neuroimaging experiments. The major goal is to provide a well-structured Python library for a script-based experiment development with a high priority on the readability of the resulting programme code. Due to the availability of an Android runtime environment, Expyriment is also suitable for the development of experiments running on tablet PCs or smart-phones. Package: python-freenect Source: libfreenect Version: 1:0.5.2+git6-g5455843+dfsg-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 144 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python-numpy (>= 1:1.8.0), python-numpy-abi9, libc6 (>= 2.4), libfreenect0.5 (= 1:0.5.2+git6-g5455843+dfsg-1~nd80+1), libpython2.7 (>= 2.7) Suggests: python-matplotlib, python-opencv Provides: python2.7-freenect Homepage: http://openkinect.org/ Priority: extra Section: python Filename: pool/main/libf/libfreenect/python-freenect_0.5.2+git6-g5455843+dfsg-1~nd80+1_amd64.deb Size: 47422 SHA256: cace8a4fc6089bb12679a128fea3f6b5ab1357b8c757d3f7a62c032774f08736 SHA1: 6ab918abf2abf110aa531bcf443ea39275fc8393 MD5sum: 1111442b8aedab7121f577c82686060d Description: library for accessing Kinect device -- Python bindings libfreenect is a cross-platform library that provides the necessary interfaces to activate, initialize, and communicate data with the Kinect hardware. Currently, the library supports access to RGB and depth video streams, motors, accelerometer and LED and provide binding in different languages (C++, Python...) . This library is the low level component of the OpenKinect project which is an open community of people interested in making use of the Xbox Kinect hardware with PCs and other devices. . This package provides freenect extension to use libfreenect functionality from Python and includes some demo scripts. Package: python-git Version: 1.0.1+git137-gc8b8379-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1499 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-gitdb (>= 0.6.4), git (>= 1:1.7) | git-core (>= 1:1.5.3.7), libjs-jquery Suggests: python-smmap Homepage: https://github.com/gitpython-developers/GitPython Priority: optional Section: python Filename: pool/main/p/python-git/python-git_1.0.1+git137-gc8b8379-1~nd80+1_all.deb Size: 304818 SHA256: 3f5ca058c6f32559aebbdafff104813ab6720f6d8d210e3dad91559b3281da70 SHA1: 16342f2edb2dfb8793119becd8391f3b9a96f366 MD5sum: 34b0ea85e0f942e026d9abdc8169f5fe Description: Python library to interact with Git repositories python-git provides object model access to a Git repository, so Python can be used to manipulate it. Repository objects can be opened or created, which can then be traversed to find parent commit(s), trees, blobs, etc. Python-Version: 2.7 Package: python-gitdb Version: 0.6.4-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 210 Depends: neurodebian-popularity-contest, python-smmap, python (>= 2.7), python (<< 2.8), libc6 (>= 2.14) Provides: python2.7-gitdb Homepage: https://github.com/gitpython-developers/gitdb Priority: extra Section: python Filename: pool/main/p/python-gitdb/python-gitdb_0.6.4-1~nd80+1_amd64.deb Size: 55254 SHA256: 19d95dc5c1bd67945bc60a76c31c81b39ae567a353f1558fcde826bd0c5c4446 SHA1: 9c8b50e4a66de4fe7fe441df2ef9f23a2ba9b763 MD5sum: 4aea41e5ac11f996111041f420e4ff44 Description: pure-Python git object database The GitDB project implements interfaces to allow read and write access to git repositories. In its core lies the db package, which contains all database types necessary to read a complete git repository. These are the LooseObjectDB, the PackedDB and the ReferenceDB which are combined into the GitDB to combine every aspect of the git database. Package: python-isis Source: isis Version: 0.4.7-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 11805 Depends: neurodebian-popularity-contest, libboost-date-time1.48.0 (>= 1.48.0-1), libboost-filesystem1.48.0 (>= 1.48.0-1), libboost-python1.48.0 (>= 1.48.0-1), libboost-regex1.48.0 (>= 1.48.0-1), libboost-system1.48.0 (>= 1.48.0-1), libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libisis-core0, liboil0.3 (>= 0.3.1), libpython2.7 (>= 2.7), libstdc++6 (>= 4.6) Conflicts: isis-python Replaces: isis-python Homepage: https://github.com/isis-group Priority: extra Section: python Filename: pool/main/i/isis/python-isis_0.4.7-1~nd70+1_amd64.deb Size: 2456274 SHA256: 68944d6d3f77b7bd01128a055e9dee329e7aa3d449caebfb458bc2043811879e SHA1: 2b64501a36ba03af8a5946f3a93746233f45a216 MD5sum: eaa08181943340e25bedbc39e53f68be Description: Python bindings for ISIS data I/O framework (development headers) This framework aids access of and conversion between various established neuro-imaging data formats, like Nifti, Analyze, DICOM and VISTA. ISIS is extensible with plugins to add support for additional data formats. Package: python-jdcal Source: jdcal Version: 1.0-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 27 Depends: neurodebian-popularity-contest Homepage: https://github.com/phn/jdcal Priority: optional Section: python Filename: pool/main/j/jdcal/python-jdcal_1.0-1~nd80+1_all.deb Size: 7768 SHA256: fee8ec32ed79b8754b089b2816902be653f2b3f33a5b2eb5ab4c7c5f24da6573 SHA1: abe2a943520a1560d52fd7ddfa705e99a909e6c7 MD5sum: 3dadf005d0a31af047ecdf77fef481d8 Description: Julian dates from proleptic Gregorian and Julian calendars This module contains functions for converting between Julian dates and calendar dates. . Different regions of the world switched to Gregorian calendar from Julian calendar on different dates. Having separate functions for Julian and Gregorian calendars allow maximum flexibility in choosing the relevant calendar. Package: python-joblib Source: joblib Version: 0.9.2-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 345 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8) Recommends: python-numpy, python-nose, python-simplejson Homepage: http://packages.python.org/joblib/ Priority: optional Section: python Filename: pool/main/j/joblib/python-joblib_0.9.2-1~nd80+1_all.deb Size: 77908 SHA256: 1730b68525d5eae91abfa30433e9e288e49baf254d160bd98609b3a0cbf5d69b SHA1: a9b24bc60d288f473bcadfdaf1744c131f1a7c36 MD5sum: f80725f4546c1120151ba6c6d7255968 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. . This package contains the Python 2 version. Package: python-lazyarray Source: lazyarray Version: 0.1.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 19 Depends: neurodebian-popularity-contest, python2.7 | python2.6, python (>= 2.6.6-7~), python (<< 2.8), python-numpy Homepage: http://bitbucket.org/apdavison/lazyarray/ Priority: optional Section: python Filename: pool/main/l/lazyarray/python-lazyarray_0.1.0-1~nd70+1_all.deb Size: 7334 SHA256: 72dadd7fab4a8d37309793af8b50d73a7ea93f6c223509fe58ad502936fa852d SHA1: 3a45ca7b469e524691c3ed6ec708b24bd59391a8 MD5sum: 80d3117e7a8b1fa74d6551c6f2f306ed Description: Python module providing a NumPy-compatible lazily-evaluated array The 'larray' class is a NumPy-compatible numerical array where operations on the array (potentially including array construction) are not performed immediately, but are delayed until evaluation is specifically requested. Evaluation of only parts of the array is also possible. Consequently, use of an 'larray' can potentially save considerable computation time and memory in cases where arrays are used conditionally, or only parts of an array are used (for example in distributed computation, in which each MPI node operates on a subset of the elements of the array). Package: python-lda Source: lda Version: 1.0.2-9~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1242 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), python-numpy Homepage: https://pythonhosted.org/lda/ Priority: optional Section: python Filename: pool/main/l/lda/python-lda_1.0.2-9~nd80+1_amd64.deb Size: 234928 SHA256: 3d2347cf0a556127a05400fe62dc0d6fab7802bc3159ec2eef60dc812e825cc6 SHA1: 3b72486946f08514c3904cb2c38a811ce65c4073 MD5sum: 8a37b54a06e95d1226d9ff8b953d70ac Description: Topic modeling with latent Dirichlet allocation for Python 3 lda implements latent Dirichlet allocation (LDA) using collapsed Gibbs sampling. . This package contains the Python 2.7 module. Package: python-mdp Source: mdp Version: 3.3+git19-g4ec2f29-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1486 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy Recommends: python-scipy, python-libsvm, python-joblib, python-scikits-learn | python-sklearn, python-pp Suggests: python-py, shogun-python-modular Enhances: python-mvpa Homepage: http://mdp-toolkit.sourceforge.net/ Priority: optional Section: python Filename: pool/main/m/mdp/python-mdp_3.3+git19-g4ec2f29-1~nd80+1_all.deb Size: 428168 SHA256: 02708b2fe6721395055041ef74cc9cea2e06560f6e460990c7de3c6d8ff53db2 SHA1: 2674dbf17a99e2a01c9dd04b97473c31b22b0822 MD5sum: 94ab516623977584aacc973e9de84582 Description: Modular toolkit for Data Processing Python data processing framework for building complex data processing software by combining widely used machine learning algorithms into pipelines and networks. Implemented algorithms include: Principal Component Analysis (PCA), Independent Component Analysis (ICA), Slow Feature Analysis (SFA), Independent Slow Feature Analysis (ISFA), Growing Neural Gas (GNG), Factor Analysis, Fisher Discriminant Analysis (FDA), and Gaussian Classifiers. . This package contains MDP for Python 2. Package: python-mne Version: 0.8.6+dfsg-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7181 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy, python-scipy, python-sklearn, python-matplotlib, python-joblib (>= 0.4.5), xvfb, xauth, libgl1-mesa-dri, help2man, libjs-jquery, libjs-jquery-ui Recommends: python-nose, mayavi2 Suggests: python-dap, ipython Provides: python2.7-mne Homepage: http://martinos.org/mne Priority: optional Section: python Filename: pool/main/p/python-mne/python-mne_0.8.6+dfsg-1~nd80+1_all.deb Size: 4022564 SHA256: 5bd87ebe18a246c323ae0cea0c277889dd23e06dd638e37736ce77b72bb266cc SHA1: 0d954c6c151c10f4526c8231b3f89122066f08a4 MD5sum: 66c304a383b758a98195306cc8d2a77d Description: Python modules for MEG and EEG data analysis This package is designed for sensor- and source-space analysis of MEG and EEG data, including frequency-domain and time-frequency analyses and non-parametric statistics. Package: python-mpi4py Source: mpi4py Version: 1.3.1+hg20131106-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1275 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libopenmpi1.3, python (>= 2.7), python (<< 2.8), mpi-default-bin Suggests: python-numpy Homepage: http://code.google.com/p/mpi4py/ Priority: extra Section: python Filename: pool/main/m/mpi4py/python-mpi4py_1.3.1+hg20131106-1~nd80+1_amd64.deb Size: 410908 SHA256: 629078eefa687720deb5cb144d3193f23525da07a167cc89053d3053c58bbf95 SHA1: e2d4987ab3f80a71c72d40320a6e217bee0fe936 MD5sum: 51322aea9c225dfa5ff240dac9eac23a 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: 1.3.1+hg20131106-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 5366 Depends: neurodebian-popularity-contest, python-mpi4py (= 1.3.1+hg20131106-1~nd80+1) Homepage: http://code.google.com/p/mpi4py/ Priority: extra Section: debug Filename: pool/main/m/mpi4py/python-mpi4py-dbg_1.3.1+hg20131106-1~nd80+1_amd64.deb Size: 1299800 SHA256: 8e41f0faae468afe4661956b5d69b9da98a95e9f8dbb73dea3b085a156040cae SHA1: 2320f49ce5c071fd7e6c90e939abff455e02b493 MD5sum: d8ffd8a8d50b2fd5957bd35de87bac36 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: 1.3.1+hg20131106-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 256 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Suggests: python-mpi4py Homepage: http://code.google.com/p/mpi4py/ Priority: extra Section: doc Filename: pool/main/m/mpi4py/python-mpi4py-doc_1.3.1+hg20131106-1~nd80+1_all.deb Size: 73304 SHA256: ae5fd24ec3ce5afd1e854a7d7d7e01a4027c3d3e82eb4a031047fb0b9e736eaa SHA1: e8f91092f057870dc9a87d3d0ffc6f6e453a2b7c MD5sum: 02bcf26a670a88be0e826782ac2c953c 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-msgpack Source: msgpack-python Version: 0.4.2-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 179 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7~), python:any (>= 2.7.5-5~), libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1) Breaks: msgpack-python (<< 0.3.0-1) Replaces: msgpack-python (<< 0.3.0-1) Provides: msgpack-python Homepage: http://pypi.python.org/pypi/msgpack-python/ Priority: optional Section: python Filename: pool/main/m/msgpack-python/python-msgpack_0.4.2-1~nd80+1_amd64.deb Size: 55934 SHA256: 8358ba4cdc87dd807d534466d069c648b14eedea81a1dfa2de0e798608775188 SHA1: a4b532ea8dfc5b07d942170b688d86ebfaa335d1 MD5sum: 5922958be6bfb347a05b408ca99533f9 Description: Python implementation of MessagePack format MessagePack is a binary-based efficient object serialization format. It enables the exchange of structured objects between many languages like JSON. But unlike JSON, it is very fast and small. . This package contains a Python extension module implementing the MessagePack format. Package: python-mvpa Source: pymvpa Version: 0.4.8-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3547 Depends: neurodebian-popularity-contest, python (>= 2.5), python-numpy, python-support (>= 0.90.0), python2.7, python-mvpa-lib (>= 0.4.8-1~nd70+1) Recommends: python-nifti, python-psyco, python-mdp, python-scipy, shogun-python-modular, python-pywt, python-matplotlib, python-reportlab Suggests: fslview, fsl, python-nose, python-lxml, python-openopt, python-rpy, python-mvpa-doc Provides: python2.6-mvpa, python2.7-mvpa Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa/python-mvpa_0.4.8-1~nd70+1_all.deb Size: 2204982 SHA256: d11d2301a31c5906b71d199f1d0c084f8b9cf9ac33bb537e24ab2b469b9099a4 SHA1: b362bf026b65424993dc7e63229b8670b55f487c MD5sum: e1bcf9e0206de77156760bbd52d0452f 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, GNB, Ridge Regressions, Sparse Multinomial Logistic Regression), and bindings to external machine learning libraries (libsvm, shogun). . While it is not limited to neuroimaging data (e.g. fMRI, or EEG) it is eminently suited for such datasets. Python-Version: 2.6, 2.7 Package: python-mvpa-doc Source: pymvpa Version: 0.4.8-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 37572 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-mvpa Homepage: http://www.pymvpa.org Priority: optional Section: doc Filename: pool/main/p/pymvpa/python-mvpa-doc_0.4.8-1~nd70+1_all.deb Size: 8475162 SHA256: 650e2c780f78250bf58fada5c40a799f5b05cc59c640faac1f210075f4dc4102 SHA1: 01df95b2235666e3922f97ccfc582d42fa04e77d MD5sum: 6f013cc65b4edae93e4b62095cf568eb Description: documentation and examples for PyMVPA PyMVPA documentation in various formats (HTML, TXT) including * User manual * Developer guidelines * API documentation * BibTeX references file . Additionally, all example scripts shipped with the PyMVPA sources are included. Package: python-mvpa-lib Source: pymvpa Version: 0.4.8-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 193 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), libsvm3, python (<< 2.8), python (>= 2.6), python-numpy (>= 1:1.4.1), python-numpy-abi9, python-support (>= 0.90.0) Provides: python2.6-mvpa-lib, python2.7-mvpa-lib Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa/python-mvpa-lib_0.4.8-1~nd70+1_amd64.deb Size: 72850 SHA256: 7ce7fe100b0fe479eeea64791a083f8aaa1d22742ff1e3bdcd7187c2746bd4f2 SHA1: b54c74f54e8b986dd7652e9eb3008d4fdcb40d5d MD5sum: 723c80e0cb3e08805a5014a09ebf9799 Description: low-level implementations and bindings for PyMVPA This is an add-on package for the PyMVPA framework. It provides a low-level implementation of an SMLR classifier and custom Python bindings for the LIBSVM library. Python-Version: 2.6, 2.7 Package: python-mvpa2 Source: pymvpa2 Version: 2.4.0.1-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8237 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python-numpy, python-mvpa2-lib (>= 2.4.0.1-1~nd80+1) Recommends: python-h5py, python-lxml, python-matplotlib, python-mdp, python-nibabel, python-nipy, python-psutil, python-psyco, python-pywt, python-reportlab, python-scipy, python-sklearn, python-shogun, liblapack-dev, python-pprocess, python-statsmodels, python-joblib Suggests: fslview, fsl, python-mvpa2-doc, python-nose, python-openopt, python-rpy2 Provides: python2.7-mvpa2 Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa2/python-mvpa2_2.4.0.1-1~nd80+1_all.deb Size: 5051300 SHA256: fea3b96414fbaa841b3100b24dcb47ffb2af93e4359e7ffb8c8485565a860e0b SHA1: 650bb8d46313c27f4cac5942aa6581c7f0834480 MD5sum: 201ca036df34b161f48e73e5bed2a411 Description: multivariate pattern analysis with Python v. 2 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: 2.7 Package: python-mvpa2-doc Source: pymvpa2 Version: 2.4.0.1-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 29543 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Suggests: python-mvpa2, python-mvpa2-tutorialdata, ipython-notebook Homepage: http://www.pymvpa.org Priority: optional Section: doc Filename: pool/main/p/pymvpa2/python-mvpa2-doc_2.4.0.1-1~nd80+1_all.deb Size: 4729640 SHA256: 9efcfdc95fd523c0a60f7ce880ecaa776dbd1e3169729bf954c1233534a40287 SHA1: de0ab71300367d70d1e7d931892b063f63773d9e MD5sum: b812bf83a97db23af87d0fecd88d93f8 Description: documentation and examples for PyMVPA v. 2 This is an add-on package for the PyMVPA framework. It provides a HTML documentation (tutorial, FAQ etc.), and example scripts. In addition the PyMVPA tutorial is also provided as IPython notebooks. Package: python-mvpa2-lib Source: pymvpa2 Version: 2.4.0.1-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 171 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), libsvm3, python (<< 2.8), python (>= 2.7), python-numpy (>= 1:1.8.0), python-numpy-abi9 Provides: python2.7-mvpa2-lib Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa2/python-mvpa2-lib_2.4.0.1-1~nd80+1_amd64.deb Size: 47028 SHA256: 951f95277b43bf8ce41ef4a786a5e80d356150a4b734339faebf71e910559442 SHA1: d521c7a33840ab4718ff333cbfcdc8c78a60660c MD5sum: f877322d58c6e0e41f146bbab36889f9 Description: low-level implementations and bindings for PyMVPA v. 2 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: 2.7 Package: python-neo Source: neo Version: 0.3.3-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2915 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy (>= 1:1.3~), python-quantities (>= 0.9.0~) Recommends: python-scipy (>= 0.8~), python-tables (>= 2.2~), libjs-jquery, libjs-underscore Suggests: python-nose Homepage: http://neuralensemble.org/trac/neo Priority: extra Section: python Filename: pool/main/n/neo/python-neo_0.3.3-1~nd80+1_all.deb Size: 1384774 SHA256: 1429887b9cc9c30c4c5c00029c1a087ece80e596090d5651684dadad27c0d2df SHA1: add5352d94444ae633c3d824e73a6bd840034902 MD5sum: 976bec4075e3a0d80b0bd8f6c042be9b Description: Python IO library for electrophysiological data formats NEO stands for Neural Ensemble Objects and is a project to provide common classes and concepts for dealing with electro-physiological (in vivo and/or simulated) data to facilitate collaborative software/algorithm development. In particular Neo provides: a set a classes for data representation with precise definitions, an IO module with a simple API, documentation, and a set of examples. . NEO offers support for reading data from numerous proprietary file formats (e.g. Spike2, Plexon, AlphaOmega, BlackRock, Axon), read/write support for various open formats (e.g. KlustaKwik, Elan, WinEdr, WinWcp, PyNN), as well as support common file formats, such as HDF5 with Neo-structured content (NeoHDF5, NeoMatlab). . Neo's IO facilities can be seen as a pure-Python and open-source Neuroshare replacement. Package: python-networkx Version: 1.4-2~nd70+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~nd70+1_all.deb Size: 647240 SHA256: d330d947a368e24c1c211bb38680d39b541734610380b2eae4295581dc4cd792 SHA1: b2038a2f713e9b53f792369bacc2b37b26f406e1 MD5sum: 80ada5a82a23d92f2ce8d69d952d4f7f 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~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 15840 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~nd70+1_all.deb Size: 6234176 SHA256: 8a284c712351861f561505f6f7a85a6d6b86732f9020951066fca67be022c7a9 SHA1: d7da2a947abc8026e87191c4ff5893cdbd013adb MD5sum: d0470a135f7b7ae6fbb4252e2b688f86 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-neuroshare Version: 0.9.2-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 106 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), python (<< 2.8), python (>= 2.7~), python-numpy (>= 1:1.8.0), python-numpy-abi9, python:any (>= 2.7.5-5~), python-h5py Homepage: http://www.g-node.org/neuroshare-tools Priority: extra Section: python Filename: pool/main/p/python-neuroshare/python-neuroshare_0.9.2-1~nd80+1_amd64.deb Size: 20306 SHA256: 77c1b8cb92d11ac7fbfd4ef03b0cc4e841e643b65c5c8b4e241ff3ea19c3bce6 SHA1: abf0543ce8fccab0b5e03ac240765d03813180d2 MD5sum: 2afcdd99ce2a1e38b711a7373b643f5d Description: Python interface and tools for Neuroshare The Neuroshare API is a standardized interface to access electrophysiology data stored in various different file formats. To do so, it uses format- specific shared libraries. . This package provides a high-level Python interface to the Neuroshare API that focuses on convenience for the user and enables access to all available metadata and data. The data is returned in NumPy arrays, which provides a quick route to further examination and analysis. . In addition, this package contains the ns2hdf converter tool that converts neuroshare-compatible files into the HDF5 (Hierarchical Data Format, ver. 5) file format. Package: python-neuroshare-doc Source: python-neuroshare Version: 0.9.2-1~nd80+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 284 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Homepage: http://www.g-node.org/neuroshare-tools Priority: extra Section: doc Filename: pool/main/p/python-neuroshare/python-neuroshare-doc_0.9.2-1~nd80+1_all.deb Size: 95644 SHA256: f4b4a7e1cc2e299a2674c8c0dc5eb01908e8fccedb7d42b6bc1526b8ea55f5e4 SHA1: 76b2500a21c9e7dea6985ba1f75eaedfaa9dc2cc MD5sum: 32f1b4bf3064548a3da8bffc2f950c14 Description: Python interface and tools for Neuroshare The Neuroshare API is a standardized interface to access electrophysiology data stored in various different file formats. To do so, it uses format- specific shared libraries. . This package provides a high-level Python interface to the Neuroshare API that focuses on convenience for the user and enables access to all available metadata and data. The data is returned in NumPy arrays, which provides a quick route to further examination and analysis. . In addition, this package contains the ns2hdf converter tool that converts neuroshare-compatible files into the HDF5 (Hierarchical Data Format, ver. 5) file format. . This package contains HTML documentation files for python-neuroshare. Package: python-neurosynth Source: neurosynth Version: 0.3-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 83 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy, python-scipy, python-nibabel, python-ply Recommends: python-nose, fsl-mni152-templates Suggests: python-testkraut Homepage: http://neurosynth.org Priority: extra Section: python Filename: pool/main/n/neurosynth/python-neurosynth_0.3-1~nd80+1_all.deb Size: 32502 SHA256: e5a90ab22d96f24f5ef426b81d5c62bfcad9e07b2aeafb9bc8d79d304ff81da1 SHA1: 2a85d328353b89a0e54ffa994a06aef761e5cdcd MD5sum: a4d179f353ed5b498bd6c08300037e66 Description: large-scale synthesis of functional neuroimaging data NeuroSynth is a platform for large-scale, automated synthesis of functional magnetic resonance imaging (fMRI) data extracted from published articles. This Python module at the moment provides functionality for processing the database of collected terms and spatial coordinates to generate associated spatial statistical maps. Package: python-nibabel Source: nibabel Version: 2.0.1-3~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 63354 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy, python-scipy Recommends: python-dicom, python-fuse Suggests: python-nibabel-doc Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: python Filename: pool/main/n/nibabel/python-nibabel_2.0.1-3~nd80+1_all.deb Size: 1978884 SHA256: b83b9dbc91664f45b44749d76f23b682111c156b05993fbd9436a291b69720af SHA1: 6a9d8ea9255f30d63bc87aeee8e4720ef1309cf2 MD5sum: 14d154accf54a3d9250bf7a174d20170 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 tools: . - dicomfs - FUSE filesystem on top of a directory with DICOMs - nib-ls - 'ls' for neuroimaging files - parrec2nii - for conversion of PAR/REC to NIfTI images Package: python-nibabel-doc Source: nibabel Version: 2.0.1-3~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5562 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-mathjax Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: doc Filename: pool/main/n/nibabel/python-nibabel-doc_2.0.1-3~nd80+1_all.deb Size: 2690384 SHA256: 48d1c1580a0a6228c11cd1fe3f28411abed8f9f8a08f20ed7db8d3835c21f3d6 SHA1: 6a6503ab5ef65846440689e9b5b8f890d3213302 MD5sum: 60cc26c1b29126b84dae639a8c1e52bd 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~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1480 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), 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~nd70+1_amd64.deb Size: 375728 SHA256: 7a4ac791f54109e24feabe3d4afc0be1d6dc52e7d1c78541d41bb515cca6a6f0 SHA1: 488170931671b03655c6e5db5a53e9fc9409ebd3 MD5sum: bc525d5d55673a048e1bcfb70e30c514 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-nilearn Source: nilearn Version: 0.1.4+git3-g60d2a1b~dfsg.1-1~nd80+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1861 Depends: neurodebian-popularity-contest, python-numpy (>= 1:1.6), python-nibabel (>= 1.1.0), python:any (<< 2.8), python:any (>= 2.7.5-5~), python-scipy (>= 0.9), python-sklearn (>= 0.12.1) Recommends: python-matplotlib Provides: python2.7-nilearn Homepage: https://nilearn.github.io Priority: extra Section: python Filename: pool/main/n/nilearn/python-nilearn_0.1.4+git3-g60d2a1b~dfsg.1-1~nd80+1_all.deb Size: 635472 SHA256: 0e9421efe714e3790bc5497a29bd7bf1a8306ef6a03697bc817b1b47e8794564 SHA1: adbe884cd544df4c832b8249742a561e6d9344b9 MD5sum: afd62684f453a9ca88b51ae0572a06da Description: fast and easy statistical learning on neuroimaging data This Python module leverages the scikit-learn toolbox for multivariate statistics with applications such as predictive modelling, classification, decoding, or connectivity analysis. Python-Version: 2.7 Package: python-nipy Source: nipy Version: 0.3.0+git262-gbb838d7-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2954 Depends: neurodebian-popularity-contest, python-numpy (>= 1:1.2), python (>= 2.7), python (<< 2.8), python-scipy, python-nibabel, python-nipy-lib (>= 0.3.0+git262-gbb838d7-1~nd80+1) Recommends: python-matplotlib, mayavi2, python-sympy Suggests: python-mvpa Provides: python2.7-nipy Homepage: http://neuroimaging.scipy.org Priority: extra Section: python Filename: pool/main/n/nipy/python-nipy_0.3.0+git262-gbb838d7-1~nd80+1_all.deb Size: 726362 SHA256: 513bed50864e978d4ba248f20792bb3041941370f821a360e34b5f9ba259638e SHA1: c21881ef75b8411d28e19e0b35a84fe727ea69b7 MD5sum: f25d702984c13ef6096ef41ee6853497 Description: Analysis of structural and functional neuroimaging data NiPy is a Python-based framework for the analysis of structural and functional neuroimaging data. It provides functionality for - General linear model (GLM) statistical analysis - Combined slice time correction and motion correction - General image registration routines with flexible cost functions, optimizers and re-sampling schemes - Image segmentation - Basic visualization of results in 2D and 3D - Basic time series diagnostics - Clustering and activation pattern analysis across subjects - Reproducibility analysis for group studies Python-Version: 2.7 Package: python-nipy-doc Source: nipy Version: 0.3.0+git262-gbb838d7-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8011 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Recommends: python-nipy Homepage: http://neuroimaging.scipy.org Priority: extra Section: doc Filename: pool/main/n/nipy/python-nipy-doc_0.3.0+git262-gbb838d7-1~nd80+1_all.deb Size: 1148552 SHA256: 4b4ce2122dd79451d5a593c1cc5c38101226fbd78aa00be98f4dc0920e7d5025 SHA1: 9c0db9401da6e37519eb0eee813947f9b8419d22 MD5sum: 20f6b08d79f3a5039f6af4e1bdbba718 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: 0.3.0+git262-gbb838d7-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2530 Depends: neurodebian-popularity-contest, libblas3 | libblas.so.3, libc6 (>= 2.14), liblapack3 | liblapack.so.3, python-numpy (>= 1:1.8.0), python-numpy-abi9, python (>= 2.7), python (<< 2.8) Provides: python2.7-nipy-lib Homepage: http://neuroimaging.scipy.org Priority: extra Section: python Filename: pool/main/n/nipy/python-nipy-lib_0.3.0+git262-gbb838d7-1~nd80+1_amd64.deb Size: 594706 SHA256: 0b2bc261d787fdadcd206351a7c30d2a782393b99693bda3db2b7f4921546cf0 SHA1: f7a314cfebe818b0c470cb45ded9194510ee4240 MD5sum: 52864dc97c3b96afdb86e0904d06c192 Description: Analysis of structural and functional neuroimaging data NiPy is a Python-based framework for the analysis of structural and functional neuroimaging data. . This package provides architecture-dependent builds of the libraries. Python-Version: 2.7 Package: python-nipy-lib-dbg Source: nipy Version: 0.3.0+git262-gbb838d7-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 3637 Depends: neurodebian-popularity-contest, libblas3 | libblas.so.3, libc6 (>= 2.14), liblapack3 | liblapack.so.3, python-numpy (>= 1:1.8.0), python-numpy-abi9, python-dbg (>= 2.7), python-dbg (<< 2.8), python-nipy-lib (= 0.3.0+git262-gbb838d7-1~nd80+1) Provides: python2.7-nipy-lib-dbg Homepage: http://neuroimaging.scipy.org Priority: extra Section: debug Filename: pool/main/n/nipy/python-nipy-lib-dbg_0.3.0+git262-gbb838d7-1~nd80+1_amd64.deb Size: 612076 SHA256: b3b4936efd371f9d0f555a3fa25d3a51004832bee38ff73261362ba50d300ef3 SHA1: eb51f19eeda08c448ea871f5f349819499650800 MD5sum: 9572fe67942f84c2e29e782695ca7499 Description: Analysis of structural and functional neuroimaging data NiPy is a Python-based framework for the analysis of structural and functional neuroimaging data. . This package provides debugging symbols for architecture-dependent builds of the libraries. Python-Version: 2.7 Package: python-nipype Source: nipype Version: 0.11.0-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7986 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), 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, python-xvfbwrapper, mayavi2 Suggests: fsl, afni, python-nipy, slicer, matlab-spm8, python-pyxnat, mne-python, elastix, ants Provides: python2.7-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: python Filename: pool/main/n/nipype/python-nipype_0.11.0-1~nd80+1_all.deb Size: 1415398 SHA256: 909c6362dec628df877e1d635389314699185c21b2814c9a5503622ce6dd5d7c SHA1: 35c5602746f16d7b709e0d5105057e12d54d3f54 MD5sum: 9dfadd0fc39bd0364df0c96f34ac6ee0 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: 0.11.0-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 22922 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Suggests: python-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: doc Filename: pool/main/n/nipype/python-nipype-doc_0.11.0-1~nd80+1_all.deb Size: 8969500 SHA256: 285361fbae42e6e74d8e337370a29687619f96eeb6d651cbee65e579ba579a99 SHA1: 7b4b8c223258f287fabb25207e3b4e7c62fcc743 MD5sum: 149d10c03fecab06f15dcf89d46d0807 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: 0.5-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9348 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), 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_0.5-1~nd80+1_all.deb Size: 2542792 SHA256: 4c9ba32a90637503214f2c4bad85b210d4a2700bd1425b768b66b9160a7e72f8 SHA1: b63fd23528602cd94e660646cbc4436d76ee8d84 MD5sum: 208acdfb8ce6aed66bd4706b992f1dce 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: 0.5-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7731 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_0.5-1~nd80+1_all.deb Size: 5758564 SHA256: fff54e7a75bb1395dbd6b3665b215081e3bb9049c235cb5b0bfd5c3a3e0e6fd1 SHA1: 7511cbfeef2407965d8bbb25b77d2f640eea1e02 MD5sum: 170a607760eafcfbce8e124c2a1f8475 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-nlopt Source: nlopt Version: 2.4.1+dfsg-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 261 Depends: neurodebian-popularity-contest, libnlopt0 (= 2.4.1+dfsg-1~nd80+1), libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), python-numpy (>= 1:1.7-0~b1), python-numpy-abi9, python (>= 2.7), python (<< 2.8) Provides: python2.7-nlopt Homepage: http://ab-initio.mit.edu/wiki/index.php/NLopt Priority: optional Section: python Filename: pool/main/n/nlopt/python-nlopt_2.4.1+dfsg-1~nd80+1_amd64.deb Size: 67752 SHA256: 28d544d13193d4f923f4bc69f67ccbe88b4fe72fcbdd1b1c90f7cd1fdc8fa077 SHA1: dd106e81a3d8f77c26fa9bd0c1af783d4b65f653 MD5sum: 7ae5642e06ebfc006e8dce665d177068 Description: nonlinear optimization library -- Python bindings NLopt is a free/open-source library for nonlinear optimization, providing a common interface for a number of different free optimization routines available online as well as original implementations of various other algorithms. Its features include: . * Callable from C, C++, Fortran, GNU Octave, Python, GNU Guile, GNU R. * A common interface for many different algorithms * Support for large-scale optimization. * Both global and local optimization algorithms. * Algorithms using function values only (derivative-free) and also algorithms exploiting user-supplied gradients. * Algorithms for unconstrained optimization, bound-constrained optimization, and general nonlinear inequality/equality constraints. . This package contains the Python bindings. Package: python-numexpr Source: numexpr Version: 2.4.3-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 380 Depends: neurodebian-popularity-contest, python-numpy (>= 1:1.8.0), python-numpy-abi9, python (<< 2.8), python (>= 2.7~), python:any (>= 2.7.5-5~), libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1) Homepage: http://code.google.com/p/numexpr/ Priority: optional Section: python Filename: pool/main/n/numexpr/python-numexpr_2.4.3-1~nd80+1_amd64.deb Size: 136082 SHA256: ae0ecce266a2472ad390ca010d180c3c6938711bce1b085ae1f074f8c739804e SHA1: 7593f373619f430125bc981f7efa2209f6a10c00 MD5sum: fcb77696a4f2ad7992025ab189d15aa2 Description: Fast numerical array expression evaluator for Python and NumPy Numexpr package evaluates multiple-operator array expressions many times faster than NumPy can. It accepts the expression as a string, analyzes it, rewrites it more efficiently, and compiles it to faster Python code on the fly. It's the next best thing to writing the expression in C and compiling it with a specialized just-in-time (JIT) compiler, i.e. it does not require a compiler at runtime. . This is the Python 2 version of the package. Package: python-numexpr-dbg Source: numexpr Version: 2.4.3-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 258 Depends: neurodebian-popularity-contest, python-numpy (>= 1:1.8.0), python-numpy-abi9, python-dbg (<< 2.8), python-dbg (>= 2.7~), libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), python-numexpr (= 2.4.3-1~nd80+1), python-numpy-dbg Homepage: http://code.google.com/p/numexpr/ Priority: extra Section: debug Filename: pool/main/n/numexpr/python-numexpr-dbg_2.4.3-1~nd80+1_amd64.deb Size: 105696 SHA256: 9dbc042a2ef77fa86a39e37c9102f12cc9ae9e618135b160890a8069acc3f9a6 SHA1: c781cff50ecf94675198fa1e45552053a286ebf8 MD5sum: 1f39535bce7104c686f372760528e33a Description: Fast numerical array expression evaluator for Python and NumPy (debug ext) Numexpr package evaluates multiple-operator array expressions many times faster than NumPy can. It accepts the expression as a string, analyzes it, rewrites it more efficiently, and compiles it to faster Python code on the fly. It's the next best thing to writing the expression in C and compiling it with a specialized just-in-time (JIT) compiler, i.e. it does not require a compiler at runtime. . This package contains the extension built for the Python 2 debug interpreter. Package: python-openmeeg Source: openmeeg Version: 2.0.0.dfsg-4~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 652 Depends: neurodebian-popularity-contest, libatlas3gf-base, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libmatio0, libopenmeeg1, libpython2.7 (>= 2.7), libstdc++6 (>= 4.1.1), python (<< 2.8), python (>= 2.7), python-numpy (>= 1:1.6.1), python-numpy-abi9, python-support (>= 0.90.0) Provides: python2.7-openmeeg Homepage: http://www-sop.inria.fr/odyssee/software/OpenMEEG/ Priority: extra Section: python Filename: pool/main/o/openmeeg/python-openmeeg_2.0.0.dfsg-4~nd70+1_amd64.deb Size: 166148 SHA256: 807f97c09c018dcd54c1965c2bc176468f0d2f18c9c7a574481d12c4333796f1 SHA1: ace23af7d56c818dd85677ea140858ceddc05acc MD5sum: f91e2544ae3fe175e90491435ebe54f2 Description: openmeeg library -- Python bindings OpenMEEG consists of state-of-the art solvers for forward problems in the field of MEG and EEG. Solvers are based on the symmetric Boundary Element method [Kybic et al, 2005], providing excellent accuracy, particularly for superficial cortical sources. OpenMEEG can compute four types of lead fields (EEG, MEG, Internal Potential and Electrical Impedence Tomography). . This package provides Python bindings for OpenMEEG library. Python-Version: 2.7 Package: python-openopt Source: openopt Version: 0.38+svn1589-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 954 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0), python-numpy Recommends: python-scipy, python-cvxopt, python-matplotlib, python-setproctitle 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_0.38+svn1589-1~nd70+1_all.deb Size: 245060 SHA256: 19a135e4be8de62b737ca038370ef26c98892482f2291ec50c700b1ca2a5c996 SHA1: 847bd52591836b097723a48e910c63f5abb60272 MD5sum: f4ba9ac3e1c8940039fdb02678385adb 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: 2.3.0~b2-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1121 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-jdcal Recommends: python-pytest, python-pil, python-imaging, python-lxml Homepage: http://bitbucket.org/openpyxl/openpyxl/ Priority: optional Section: python Filename: pool/main/o/openpyxl/python-openpyxl_2.3.0~b2-1~nd80+1_all.deb Size: 192448 SHA256: 6826f8df6d279470b64212f9fbc4afa3fd7bafa04226f049c3c1c412e2c95496 SHA1: 6f704dc1dd8609c4805737db69ab61502979378a MD5sum: 424560ee9da2b383988145c5f54e83f4 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: 0.17.0+git8-gcac4ad2-2~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 19815 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-dateutil, python-tz, python-numpy (>= 1:1.7~), python-pandas-lib (>= 0.17.0+git8-gcac4ad2-2~nd80+1), python-six Recommends: python-scipy, python-matplotlib, python-tables, python-numexpr, python-xlrd, python-statsmodels, python-openpyxl, python-xlwt, python-bs4, python-html5lib, python-lxml Suggests: python-pandas-doc Provides: python2.7-pandas Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python-pandas_0.17.0+git8-gcac4ad2-2~nd80+1_all.deb Size: 2347566 SHA256: 6e70357fb6ccc02ff7bffe7d0ccf4070438684181d275c9ddc0d0eb2e53e37d8 SHA1: 8cea4f1b550ca86f55c2644108036a3123b9ba39 MD5sum: 108ce868b9b2ae11cf1cd0badf4d52c6 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 . This package contains the Python 2 version. Package: python-pandas-doc Source: pandas Version: 0.17.0+git8-gcac4ad2-2~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 49123 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-pandas Homepage: http://pandas.sourceforge.net Priority: optional Section: doc Filename: pool/main/p/pandas/python-pandas-doc_0.17.0+git8-gcac4ad2-2~nd80+1_all.deb Size: 10983574 SHA256: 3881bcad86e9de77c7039f81a13ee4def9b4578edba93a644001c082d6efc804 SHA1: 79f38dc3b267ccf17f6bb8f62e498d505aaf1d4d MD5sum: 1ae53d4641dfccdde8e1e7d7e91ea534 Description: documentation and examples for pandas This package contains documentation and example scripts for python-pandas. Package: python-pandas-lib Source: pandas Version: 0.17.0+git8-gcac4ad2-2~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 6080 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), python-numpy (>= 1:1.8.0), python-numpy-abi9, python (>= 2.7), python (<< 2.8) Provides: python2.7-pandas-lib Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python-pandas-lib_0.17.0+git8-gcac4ad2-2~nd80+1_amd64.deb Size: 1616730 SHA256: 0f6da0ed68619b570127ea06fe6bb2e1bf9a650c27d18f3102fe02abd684d61a SHA1: 77fbc2e5f27d406636d23eab9176d94dc0b5320e MD5sum: b0ee8ac8d72231ed6b42ad3dd6021217 Description: low-level implementations and bindings for pandas This is an add-on package for python-pandas providing architecture-dependent extensions. . This package contains the Python 2 version. Python-Version: 2.7 Package: python-patsy Source: patsy Version: 0.4.0-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 753 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-six, python-numpy Recommends: python-pandas, python-openpyxl Suggests: python-patsy-doc Homepage: http://github.com/pydata/patsy Priority: optional Section: python Filename: pool/main/p/patsy/python-patsy_0.4.0-1~nd80+1_all.deb Size: 168774 SHA256: c802b4617ee4446ce2f8a5903557fdb43828bb755823e651439351da6af4518c SHA1: b5a1853290ad241eb866400039802f166837291c MD5sum: 477ebe1b954813f723608b0bd3d06185 Description: statistical models in Python using symbolic formulas patsy is a Python library for describing statistical models (especially linear models, or models that have a linear component) and building design matrices. . This package contains the Python 2 version. Package: python-patsy-doc Source: patsy Version: 0.4.0-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1334 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Suggests: python-patsy Homepage: http://github.com/pydata/patsy Priority: optional Section: doc Filename: pool/main/p/patsy/python-patsy-doc_0.4.0-1~nd80+1_all.deb Size: 358816 SHA256: 74719ea41d4c303e8bdd00b5d745077b5bc068e10fa4d9428be59792e4b211f8 SHA1: 8c9aeb7c3dfa246c21b629b15c87c1ff531ad5bb MD5sum: 6764e83666e3ae00cad8943395583e13 Description: documentation and examples for patsy This package contains documentation and example scripts for python-patsy. Package: python-pp Source: parallelpython Version: 1.6.2-2~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 119 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0) Homepage: http://www.parallelpython.com/ Priority: optional Section: python Filename: pool/main/p/parallelpython/python-pp_1.6.2-2~nd70+1_all.deb Size: 34272 SHA256: 076297344fdb2aad569d128266cbb592689458ac0e2ec4d78a5e8ca14bf8d5b7 SHA1: 910e6bf6e2bb4575f1e378cb1af24d0f91b2bd44 MD5sum: ed9536ef265e9d7e3cd7356d561e2f60 Description: parallel and distributed programming toolkit for Python Parallel Python module (pp) provides an easy and efficient way to create parallel-enabled applications for SMP computers and clusters. pp module features cross-platform portability and dynamic load balancing. Thus application written with PP will parallelize efficiently even on heterogeneous and multi-platform clusters (including clusters running other application with variable CPU loads). Python-Version: 2.6, 2.7 Package: python-pprocess Source: pprocess Version: 0.5-1+nd0~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 716 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0) Homepage: http://www.boddie.org.uk/python/pprocess.html Priority: optional Section: python Filename: pool/main/p/pprocess/python-pprocess_0.5-1+nd0~nd80+1_all.deb Size: 108518 SHA256: 5ae3166fbed14cd80ec51447d49b56fc418aff6ee0f153ff2c32f045d0ee28e7 SHA1: 27ceb41bf9906a6b3f3c73804a62f076106d3f2e MD5sum: faa99656def87072448248dd904523b9 Description: elementary parallel programming for Python The pprocess module provides elementary support for parallel programming in Python using a fork-based process creation model in conjunction with a channel-based communications model implemented using socketpair and poll. On systems with multiple CPUs or multicore CPUs, processes should take advantage of as many CPUs or cores as the operating system permits. Python-Version: 2.7 Package: python-psutil Version: 2.1.1-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 541 Depends: neurodebian-popularity-contest, libc6 (>= 2.13), python (>= 2.7~), python (<< 2.8), python:any (>= 2.7.5-5~) Homepage: http://code.google.com/p/psutil/ Priority: optional Section: python Filename: pool/main/p/python-psutil/python-psutil_2.1.1-1~nd80+1_amd64.deb Size: 116542 SHA256: bb31f310510776ab725fa903bd5f5a625d4d639737ef5e7e57c4a46eb89d8e4e SHA1: 3db628df14aa36cede79761789ecf0405cf43346 MD5sum: 6f79e3c21d55ad29465ce6ab40e75ef4 Description: module providing convenience functions for managing processes psutil is a module providing an interface for retrieving information on running processes and system utilization (CPU, memory) in a portable way by using Python, implementing many functionalities offered by tools like ps, top and Windows task manager. . It currently supports Linux, OS X, FreeBSD and Windows. Package: python-py Version: 1.4.30-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 269 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~), python-pkg-resources Suggests: subversion, python-pytest, python-pytest-xdist Homepage: https://bitbucket.org/pytest-dev/py Priority: optional Section: python Filename: pool/main/p/python-py/python-py_1.4.30-1~nd80+1_all.deb Size: 66880 SHA256: 29878d0f00d086b16a5bbb3a2d090b42563053543f35447bd5b487f6e2c0c6fa SHA1: 424c91c1ef682d5a3acfbc1755855946bff9d2bb MD5sum: 9f61dc34827814788b13c9b55565b399 Description: Advanced Python development support library (Python 2) The Codespeak py lib aims at supporting a decent Python development process addressing deployment, versioning and documentation perspectives. It includes: . * py.path: path abstractions over local and Subversion files * py.code: dynamic code compile and traceback printing support . This package provides the Python 2 modules. Package: python-pyentropy Source: pyentropy Version: 0.4.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 73 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0), python-numpy (>= 1.3) Recommends: python-scipy Suggests: python-nose Provides: python2.6-pyentropy, python2.7-pyentropy Homepage: http://code.google.com/p/pyentropy Priority: extra Section: python Filename: pool/main/p/pyentropy/python-pyentropy_0.4.1-1~nd70+1_all.deb Size: 21330 SHA256: af5c1ea7542c31abb491d792b1bfaef5d5a74aef7402c4659297bec687394d72 SHA1: d0b06b12f69cf46fc8a2db6c3ec5cdc548da2fe0 MD5sum: fbbf7aeb5538f3b546599d3eb9e9a81b Description: Python module for estimation information theoretic quantities A Python module for estimation of entropy and information theoretic quantities using cutting edge bias correction methods, such as * Panzeri-Treves (PT) * Quadratic Extrapolation (QE) * Nemenman-Shafee-Bialek (NSB) Python-Version: 2.6, 2.7 Package: python-pyepl Source: pyepl Version: 1.1.0+git12-g365f8e3-2~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1374 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-pyepl-common (= 1.1.0+git12-g365f8e3-2~nd80+1), python-numpy, python-imaging, python-pygame, python-pyode, python-opengl, ttf-dejavu, libasound2 (>= 1.0.16), libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libode1, libsamplerate0 (>= 0.1.7), libsndfile1 (>= 1.0.20), libstdc++6 (>= 4.4.0) Conflicts: python2.3-pyepl, python2.4-pyepl Replaces: python2.3-pyepl, python2.4-pyepl Provides: python2.7-pyepl Homepage: http://pyepl.sourceforge.net/ Priority: optional Section: python Filename: pool/main/p/pyepl/python-pyepl_1.1.0+git12-g365f8e3-2~nd80+1_amd64.deb Size: 382578 SHA256: ce35ffd9425fae2dd5cf0341acc9ab5f98f61b1f66c32f2b3d8e1f9b4faa26a2 SHA1: ef84aa91e70bd4cd5c9db0eae5ec8efb6552d6bf MD5sum: e1f207c519f565d31807de4d5dd80a40 Description: module for coding psychology experiments in Python PyEPL is a stimuli delivery and response registration toolkit to be used for generating psychology (as well as neuroscience, marketing research, and other) experiments. . It provides - presentation: both visual and auditory stimuli - responses registration: both manual (keyboard/joystick) and sound (microphone) time-stamped - sync-pulsing: synchronizing your behavioral task with external acquisition hardware - flexibility of encoding various experiments due to the use of Python as a description language - fast execution of critical points due to the calls to linked compiled libraries . This toolbox is here to be an alternative for a widely used commercial product E'(E-Prime) . This package provides PyEPL for supported versions of Python. Package: python-pyepl-common Source: pyepl Version: 1.1.0+git12-g365f8e3-2~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 813 Depends: neurodebian-popularity-contest, python Homepage: http://pyepl.sourceforge.net/ Priority: optional Section: python Filename: pool/main/p/pyepl/python-pyepl-common_1.1.0+git12-g365f8e3-2~nd80+1_all.deb Size: 818240 SHA256: 0cf560e52f9fef943e9bd03a42f4fb21e0099745231112cd26a2b2cd6be23c64 SHA1: 19d42a99a170557d5c278c99cd6d5b75c6719d41 MD5sum: 3a377cf1d67b89be927ddb6c1348ed3b Description: module for coding psychology experiments in Python PyEPL is a stimuli delivery and response registration toolkit to be used for generating psychology (as well as neuroscience, marketing research, and other) experiments. . It provides - presentation: both visual and auditory stimuli - responses registration: both manual (keyboard/joystick) and sound (microphone) time-stamped - sync-pulsing: synchronizing your behavioral task with external acquisition hardware - flexibility of encoding various experiments due to the use of Python as a description language - fast execution of critical points due to the calls to linked compiled libraries . This toolbox is here to be an alternative for a widely used commercial product E'(E-Prime) . This package provides common files such as images. Package: python-pymc Source: pymc Version: 2.3.4+ds-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1937 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python-numpy (>= 1:1.8.0), python-numpy-abi9, python-support (>= 0.90.0), libblas3 | libblas.so.3, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libgfortran3 (>= 4.6), liblapack3 | liblapack.so.3, libquadmath0 (>= 4.6), python-scipy, python-matplotlib, python-nose Recommends: python-tables Suggests: python-pydot, ipython Homepage: http://pymc-devs.github.com/pymc/ Priority: extra Section: python Filename: pool/main/p/pymc/python-pymc_2.3.4+ds-1~nd80+1_amd64.deb Size: 439608 SHA256: 84b35e8c4c2f6732b88f51475c31f576341ee8d49d837e5d2aed327c3408e9a3 SHA1: 38d13c05d18a0c4f547033a64365e52aac493bc5 MD5sum: 4fc4efd27b95346bf3a69932ea46101b Description: Bayesian statistical models and fitting algorithms PyMC is a Python module that implements Bayesian statistical models and fitting algorithms, including Markov chain Monte Carlo. Its flexibility and extensibility make it applicable to a large suite of problems. Along with core sampling functionality, PyMC includes methods for summarizing output, plotting, goodness-of-fit and convergence diagnostics. Package: python-pymc-doc Source: pymc Version: 2.3.4+ds-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1860 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Homepage: http://pymc-devs.github.com/pymc/ Priority: extra Section: doc Filename: pool/main/p/pymc/python-pymc-doc_2.3.4+ds-1~nd80+1_all.deb Size: 839918 SHA256: 35314024bcd121be1d6fca0a1ac3b2e6e68205046e637f871680b01e7579905f SHA1: e4df27dfdc90ff9803444fab5e1b9f06205adf80 MD5sum: 4ac5c9146b53b484c358d3681c82355f Description: Bayesian statistical models and fitting algorithms PyMC is a Python module that implements Bayesian statistical models and fitting algorithms, including Markov chain Monte Carlo. Its flexibility and extensibility make it applicable to a large suite of problems. Along with core sampling functionality, PyMC includes methods for summarizing output, plotting, goodness-of-fit and convergence diagnostics. . This package provides the documentation in HTML format. Package: python-pynn Source: pynn Version: 0.7.5-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 777 Depends: neurodebian-popularity-contest, python (>= 2.5), python-support (>= 0.90.0) Recommends: python-jinja2, python-cheetah Suggests: python-neuron, python-brian, python-csa Homepage: http://neuralensemble.org/trac/PyNN Priority: extra Section: python Filename: pool/main/p/pynn/python-pynn_0.7.5-1~nd70+1_all.deb Size: 192128 SHA256: 3ed89b456870d6b6530e6662b034a3906298a8b612109135b96518fc3837c8bc SHA1: fa36b5bb19a5cf7b87a4fe9d12d43fccd90b1844 MD5sum: fc397ee0c6e5376bda371cc680f0c56a Description: simulator-independent specification of neuronal network models PyNN allows for coding a model once and run it without modification on any simulator that PyNN supports (currently NEURON, NEST, PCSIM and Brian). PyNN translates standard cell-model names and parameter names into simulator-specific names. Package: python-pypsignifit Source: psignifit3 Version: 3.0~beta.20120611.1-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2456 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.6), 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/psignifit3/python-pypsignifit_3.0~beta.20120611.1-1~nd70+1_amd64.deb Size: 690242 SHA256: a022c8575548aa7a9c33b971c2bea24fa24c495b3163922565e917278d4362ed SHA1: d6ffaffae5443e84d90207f0b89e643276c86143 MD5sum: e4c1525ece15a0887f723bbb0019470e 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-pytest Source: pytest Version: 2.7.2-2~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 453 Depends: neurodebian-popularity-contest, python-pkg-resources, python-py (>= 1.4.29), python, python:any (<< 2.8), python:any (>= 2.7.5-5~) Suggests: python-mock (>= 1.0.1) Homepage: http://pytest.org/ Priority: optional Section: python Filename: pool/main/p/pytest/python-pytest_2.7.2-2~nd80+1_all.deb Size: 132496 SHA256: 49a31f3ed0dad09e171dd63111a19d44396d42afaa2389056b799fd4b82994d5 SHA1: 9a2087ee296a958872e1f4d539afa53768809da8 MD5sum: 04b74df1970eb24c1166cfbdddb5b64b Description: Simple, powerful testing in Python This testing tool has for objective to allow the developers to limit the boilerplate code around the tests, promoting the use of built-in mechanisms such as the `assert` keyword. Package: python-pytest-doc Source: pytest Version: 2.7.2-2~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2907 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Recommends: python-pytest | python3-pytest Homepage: http://pytest.org/ Priority: optional Section: doc Filename: pool/main/p/pytest/python-pytest-doc_2.7.2-2~nd80+1_all.deb Size: 431850 SHA256: 32ec6bbedd96054a0a31e2f1636785a654f8d4bce556d8d04e66ca025bb5da08 SHA1: 386a870c0dd697337e5fe0046aba91fc0c97abb5 MD5sum: b9579ff3b24f8711b862b4abf0c058f0 Description: Simple, powerful testing in Python - Documentation This testing tool has for objective to allow the developers to limit the boilerplate code around the tests, promoting the use of built-in mechanisms such as the `assert` keyword. . This package contains the documentation for pytest. Package: python-pytest-localserver Source: pytest-localserver Version: 0.3.4-2~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 82 Depends: neurodebian-popularity-contest, python-pytest, python-werkzeug (>= 0.10), python:any (<< 2.8), python:any (>= 2.7.5-5~) Homepage: https://bitbucket.org/pytest-dev/pytest-localserver/ Priority: optional Section: python Filename: pool/main/p/pytest-localserver/python-pytest-localserver_0.3.4-2~nd80+1_all.deb Size: 19300 SHA256: 0d0a0c0dcbbc09d147fb31f4fe2d3afe7c86c14f2fcd2015d1e2dbec3c139988 SHA1: 5dd26bc5fbd73c66605b5c459f5e2256f9f1a1a7 MD5sum: 412322b0f12504eb1048e346a14312c5 Description: py.test plugin to test server connections locally (Python 2) pytest-localserver is a plugin for the Pytest testing framework which enables to test server connections locally. . This package contains the modules for Python 2. Package: python-pytest-tornado Source: pytest-tornado Version: 0.4.4-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 13 Depends: neurodebian-popularity-contest, python-pytest, python-tornado, python:any (<< 2.8), python:any (>= 2.7.5-5~) Homepage: https://github.com/eugeniy/pytest-tornado Priority: optional Section: python Filename: pool/main/p/pytest-tornado/python-pytest-tornado_0.4.4-1~nd80+1_all.deb Size: 5752 SHA256: e7271a46c37b485751e1cad68f08dda8952b48adc25df5b28d7d046716544f58 SHA1: 37321ddd1b9e0fcd22c5aae93ca7ea74ff587e3c MD5sum: 39d7ef3d9081e1d566ba4eb46aebe226 Description: py.test plugin to test Tornado applications pytest-tornado is a plugin for the Pytest testing framework which provides fixtures and markers to simplify testing of Tornado applications (Python web framework and ansynchronous networking library). . This package contains the plugin for Python 2 code. 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-pyxnat Source: pyxnat Version: 0.9.1+git39-g96bf069-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1722 Depends: neurodebian-popularity-contest, python-lxml, python-simplejson, python-httplib2 (>= 0.7.0) Recommends: python-networkx, python-matplotlib Homepage: http://packages.python.org/pyxnat/ Priority: extra Section: python Filename: pool/main/p/pyxnat/python-pyxnat_0.9.1+git39-g96bf069-1~nd70+1_all.deb Size: 376574 SHA256: f3143d606791308341d10dd7752b4f8a89d4d962ddc1bfdfb43324c11b19e0fb SHA1: b35f0b369867653fb22853d37c7b2e56825267ae MD5sum: c172162c217fd132f93dfebf701445c5 Description: Interface to access neuroimaging data on XNAT servers pyxnat is a simple Python library that relies on the REST API provided by the XNAT platform since its 1.4 version. XNAT is an extensible database for neuroimaging data. The main objective is to ease communications with an XNAT server to plug-in external tools or Python scripts to process the data. It features: . - resources browsing capabilities - read and write access to resources - complex searches - disk-caching of requested files and resources Package: python-quantities Version: 0.10.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 323 Depends: neurodebian-popularity-contest, python2.7 | python2.6, python (>= 2.6.6-7~), python (<< 2.8), python-numpy (>= 1.4) Homepage: http://packages.python.org/quantities/ Priority: extra Section: python Filename: pool/main/p/python-quantities/python-quantities_0.10.1-1~nd70+1_all.deb Size: 62650 SHA256: 7105f0be0bad6a6896943c81ffc4f7ebd4e7ce36829bf3747f8fbb603246e059 SHA1: c36035905534efefa681ab02a9b30a297c46c3fc MD5sum: 370baf01ebbe89b0e73e46b3b3dee9e2 Description: Library for computation of physical quantities with units, based on numpy Quantities is designed to handle arithmetic and conversions of physical quantities, which have a magnitude, dimensionality specified by various units, and possibly an uncertainty. Quantities builds on the popular numpy library and is designed to work with numpy ufuncs, many of which are already supported. Package: python-scikits-learn Source: scikit-learn Version: 0.16.1-2~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 51 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_0.16.1-2~nd80+1_all.deb Size: 47870 SHA256: 7cbdb132b6474e8c014694e4400f8dca1b7a592d8e36067d2929c54160dbc366 SHA1: 5bdd490e58b67aa76732f6fae1c614c0ee15e536 MD5sum: b455f9f68f1e0165045a270ca5cb1acf 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.statsmodels Source: statsmodels Version: 0.6.1-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9 Depends: neurodebian-popularity-contest, python-statsmodels Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: oldlibs Filename: pool/main/s/statsmodels/python-scikits.statsmodels_0.6.1-1~nd80+1_all.deb Size: 5914 SHA256: 25d0fd1a8d63c6a7f4da4a359138520abac3874a972eca5b6f4981356ba26595 SHA1: e222d5e9fbdbe7d5f483c206e61302ae788c9633 MD5sum: 018aaf49b3643c2b6c844d04653005e8 Description: transitional compatibility package for statsmodels migration Provides old namespace (scikits.statsmodels) and could be removed if dependent code migrated to use statsmodels for clarity of the namespace. Package: python-scrapy Version: 1.0.0-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 808 Depends: neurodebian-popularity-contest, python-boto, python-cssselect, python-libxml2, python-queuelib, python-twisted-conch, python-twisted-core, python-twisted-mail, python-twisted-web, python-w3lib (>= 1.8), python (>= 2.7), python (<< 2.8) Recommends: ipython, python-django, python-guppy, python-imaging, python-lxml, python-mysqldb, python-pygments Suggests: python-openssl Provides: python2.7-scrapy Homepage: http://scrapy.org/ Priority: optional Section: python Filename: pool/main/p/python-scrapy/python-scrapy_1.0.0-1~nd80+1_all.deb Size: 175066 SHA256: efd903cb9101fcd3634cfeaac9ac3a51c4dc588ba821157fa7e666035b105821 SHA1: 3eacc9ae632270a266d7743ebdd5e745ff2a4ba9 MD5sum: 4938e7ff717b85b9728a472a662e0acb Description: Python web scraping and crawling framework Scrapy is a fast high-level screen scraping and web crawling framework, used to crawl websites and extract structured data from their pages. It can be used for a wide range of purposes, from data mining to monitoring and automated testing. . This package provides the python-scrapy script and modules. Package: python-scrapy-doc Source: python-scrapy Version: 1.0.0-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7013 Depends: neurodebian-popularity-contest Recommends: libjs-jquery, libjs-underscore Homepage: http://scrapy.org/ Priority: optional Section: doc Filename: pool/main/p/python-scrapy/python-scrapy-doc_1.0.0-1~nd80+1_all.deb Size: 1520684 SHA256: de9f83dc30a7e382c1e4131c595061ef47993ae5383c819d0faead822bbe4a5f SHA1: e04f754faa68da025441e3ca1e5e17cba5d99b0d MD5sum: 5a2535482e7b792b256eee9263fb73d9 Description: Python web scraping and crawling framework documentation Scrapy is a fast high-level screen scraping and web crawling framework, used to crawl websites and extract structured data from their pages. It can be used for a wide range of purposes, from data mining to monitoring and automated testing. . This package provides the python-scrapy documentation in HTML format. Package: python-seaborn Source: seaborn Version: 0.6.0-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 676 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~), python-numpy, python-scipy, python-pandas, python-matplotlib Recommends: python-statsmodels, python-patsy Homepage: https://github.com/mwaskom/seaborn Priority: optional Section: python Filename: pool/main/s/seaborn/python-seaborn_0.6.0-1~nd80+1_all.deb Size: 117806 SHA256: 9b977a16638be250f1b996ea5605ae557143f1fc54899745db7ebb8a19b0e966 SHA1: 745804c985c9640713257f44f173e5186691afbb MD5sum: b92647a9c18491cda26cbe462e447835 Description: statistical visualization library Seaborn is a library for making attractive and informative statistical graphics in Python. It is built on top of matplotlib and tightly integrated with the PyData stack, including support for numpy and pandas data structures and statistical routines from scipy and statsmodels. . Some of the features that seaborn offers are . - Several built-in themes that improve on the default matplotlib aesthetics - Tools for choosing color palettes to make beautiful plots that reveal patterns in your data - Functions for visualizing univariate and bivariate distributions or for comparing them between subsets of data - Tools that fit and visualize linear regression models for different kinds of independent and dependent variables - A function to plot statistical timeseries data with flexible estimation and representation of uncertainty around the estimate - High-level abstractions for structuring grids of plots that let you easily build complex visualizations . This is the Python 2 version of the package. Package: python-simplegeneric Source: simplegeneric Version: 0.7-1~nd70+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~nd70+1_all.deb Size: 9810 SHA256: c0bf53d256b2a9520f7c40efd3af9d01c92802949256bdc3ddcbe6f8c809ba45 SHA1: b9a5abab569c8269207372b91c7e89a7230efc84 MD5sum: 46e1c70528d4fd5c5636ec720f54787f 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-six Source: six Version: 1.9.0-3~bpo8+1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 37 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~) Multi-Arch: foreign Homepage: http://pythonhosted.org/six/ Priority: optional Section: python Filename: pool/main/s/six/python-six_1.9.0-3~bpo8+1~nd80+1_all.deb Size: 13886 SHA256: 76839e25ab6a96b8edbe40469030e50a971fc2e166203e541171751bfc1b9e79 SHA1: c716422f9d64f8839f09a0fffcbb781a44d0af5e MD5sum: 2a84968d381a4cc123e580e3f9d547e5 Description: Python 2 and 3 compatibility library (Python 2 interface) Six is a Python 2 and 3 compatibility library. It provides utility functions for smoothing over the differences between the Python versions with the goal of writing Python code that is compatible on both Python versions. . This package provides Six on the Python 2 module path. It is complemented by python3-six. Package: python-six-whl Source: six Version: 1.9.0-3~bpo8+1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 16 Depends: neurodebian-popularity-contest Multi-Arch: foreign Homepage: http://pythonhosted.org/six/ Priority: optional Section: python Filename: pool/main/s/six/python-six-whl_1.9.0-3~bpo8+1~nd80+1_all.deb Size: 16132 SHA256: 5e83e9f2d8d0d9c96deed851fe207694277d3a2c7036cc00a18824477fc3486b SHA1: f560ab106f08fd248b25379282dc81773fd91858 MD5sum: a36cf02f2a9ec4de800b5ee818681461 Description: Python 2 and 3 compatibility library (universal wheel) Six is a Python 2 and 3 compatibility library. It provides utility functions for smoothing over the differences between the Python versions with the goal of writing Python code that is compatible on both Python versions. . This package provides Six as a universal wheel. Package: python-skimage Source: skimage Version: 0.10.1-2~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 15134 Depends: neurodebian-popularity-contest, libfreeimage3, python-numpy, python-scipy (>= 0.10), python-six (>= 1.3.0), python-skimage-lib (>= 0.10.1-2~nd80+1), python (>= 2.7), python (<< 2.8) Recommends: python-imaging, python-matplotlib (>= 1.0), python-nose, python-pil, python-qt4 Suggests: python-opencv, python-skimage-doc Homepage: http://scikit-image.org Priority: optional Section: python Filename: pool/main/s/skimage/python-skimage_0.10.1-2~nd80+1_all.deb Size: 11937826 SHA256: 0304c98d3834908d099f9f5ff18ce79019677ffa7a418fd146272fd616ac5d5c SHA1: 663e58c0492ddb34e2a655d9b0d3df1b9d208b5c MD5sum: 47ff7e090f264311e5fcfb12cb8fd615 Description: Python modules for image processing scikit-image is a collection of image processing algorithms for Python. It performs tasks such as image loading, filtering, morphology, segmentation, color conversions, and transformations. . This package provides the Python 2 module. Package: python-skimage-doc Source: skimage Version: 0.10.1-2~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 21907 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Suggests: python-skimage Homepage: http://scikit-image.org Priority: optional Section: doc Filename: pool/main/s/skimage/python-skimage-doc_0.10.1-2~nd80+1_all.deb Size: 17244012 SHA256: 0d9ce7aa9709bb519fb1d223cbde7ff44c19eaf683642ea315abaf1f25c3661b SHA1: ab3d7951aad3cae4252147f4f11d63afa4bb0fa3 MD5sum: 1ee5a98e90a2810ad4c3df9ba11b7970 Description: Documentation and examples for scikit-image This package contains documentation and example scripts for python-skimage. Package: python-skimage-lib Source: skimage Version: 0.10.1-2~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 6924 Depends: neurodebian-popularity-contest, python-numpy (>= 1:1.8.0), python-numpy-abi9, python (>= 2.7), python (<< 2.8), libc6 (>= 2.14) Recommends: python-skimage Provides: python2.7-skimage-lib Homepage: http://scikit-image.org Priority: optional Section: python Filename: pool/main/s/skimage/python-skimage-lib_0.10.1-2~nd80+1_amd64.deb Size: 1056420 SHA256: c047d4eb6eeb9be5a6633d5d4d2a408d5af143766948ceaa23e84201329899e6 SHA1: 93f64dc56cc1848064578a0166d418482a217e29 MD5sum: 07e8fb778315cc682539ad7bb411f551 Description: Optimized low-level algorithms for scikit-image This is an add-on package for python-skimage. It provides optimized, low-level implementations of algorithms. . This package provides the Python 2 libraries. Python-Version: 2.7 Package: python-sklearn Source: scikit-learn Version: 0.16.1-2~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4588 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~), python-numpy, python-scipy, python-sklearn-lib (>= 0.16.1-2~nd80+1), python-joblib (>= 0.4.5) Recommends: python-nose, python-matplotlib 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: python2.7-sklearn Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python-sklearn_0.16.1-2~nd80+1_all.deb Size: 1112730 SHA256: beacc1a6dbf9b3db1b6c2357582ee38e3f13c91ebfb896b7b43128c45fc91f19 SHA1: 590657de781b4fec5621fdc3ec305510d3b14401 MD5sum: c506b0130ee97005dd7395bf57526127 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) Package: python-sklearn-doc Source: scikit-learn Version: 0.16.1-2~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 23597 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_0.16.1-2~nd80+1_all.deb Size: 3968696 SHA256: 151c604901c9b9b40adc1ee7f7fb11a674a50ca70ff635c0e5da7d56e9549760 SHA1: 5b42094d43917f1c14792a7ea83580f48bb57b95 MD5sum: 17abe995542472499fe3f33234600552 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: 0.16.1-2~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 4646 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), python-numpy (>= 1:1.8.0), python-numpy-abi9, python (<< 2.8), python (>= 2.7~) Conflicts: python-scikits-learn-lib Replaces: python-scikits-learn-lib Provides: python2.7-sklearn-lib Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python-sklearn-lib_0.16.1-2~nd80+1_amd64.deb Size: 1061134 SHA256: 02da4429daa8b78e29dd3abef8187a1d878eb8fc0aa859491432dc9e10b68e3b SHA1: 0112e772cb3a300ddc1e010019cb35db5174d903 MD5sum: 6ecd26b48baf5ad51e0d321fc7fe8780 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. Package: python-smmap Version: 0.9.0-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 68 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8) Suggests: python-nose Provides: python2.7-smmap Homepage: https://github.com/Byron/smmap Priority: extra Section: python Filename: pool/main/p/python-smmap/python-smmap_0.9.0-1~nd80+1_all.deb Size: 20072 SHA256: 1b64c9fcde895a7f59ab09d033bd9343fe882fd9409c5a118ffc727f3ba96114 SHA1: 32af70b566081f2e5f254dd8a3cc552071562387 MD5sum: 0e65363b6b9868a3fa2b5bf526e6e533 Description: pure Python implementation of a sliding window memory map manager Smmap wraps an interface around mmap and tracks the mapped files as well as the amount of clients who use it. If the system runs out of resources, or if a memory limit is reached, it will automatically unload unused maps to allow continued operation. Package: python-sphinx Source: sphinx Version: 1.0.7-2~nd70+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~nd70+1_all.deb Size: 1260232 SHA256: 648244da9a934daaee709edb7cd2d109551e93e215ebd43730a5a0bff017a035 SHA1: a878bb9a26d7085fd2ec3e02fa606ae3a44a9528 MD5sum: 9be86574fc484fd49d5be81bd6deba03 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-sphinx-rtd-theme Source: sphinx-rtd-theme Version: 0.1.8-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 332 Depends: neurodebian-popularity-contest, fonts-font-awesome, fonts-lato, libjs-modernizr, python:any (<< 2.8), python:any (>= 2.7.5-5~) Recommends: python-sphinx Homepage: https://github.com/snide/sphinx_rtd_theme Priority: optional Section: python Filename: pool/main/s/sphinx-rtd-theme/python-sphinx-rtd-theme_0.1.8-1~nd80+1_all.deb Size: 117260 SHA256: 1c49214be8ee9b556a957137c84111204898dc32dd86409cf0d78dc4636fd066 SHA1: 570867c5c572f28cfe75317c0af59076b146b0c6 MD5sum: 612f293a40d4f21f31cd6aafbef331f6 Description: sphinx theme from readthedocs.org (Python 2) This mobile-friendly sphinx theme was initially created for readthedocs.org, but can be incorporated in any project. . Among other things, it features a left panel with a browseable table of contents, and a search bar. . This is the Python 2 version of the package. Package: python-spyderlib Source: spyder Version: 2.2.5+dfsg-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4028 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), libjs-sphinxdoc (>= 1.0), libjs-jquery, libjs-mathjax, python-qt4 Recommends: ipython-qtconsole, pep8, pyflakes (>= 0.5.0), pylint, python-matplotlib, python-numpy, python-psutil (>= 0.3.0), python-rope, python-scipy, python-sphinx Suggests: tortoisehg, gitk Breaks: spyder (<< 2.0.12-1) Replaces: spyder (<< 2.0.12-1) Provides: python2.7-spyderlib Homepage: http://code.google.com/p/spyderlib/ Priority: extra Section: python Filename: pool/main/s/spyder/python-spyderlib_2.2.5+dfsg-1~nd80+1_all.deb Size: 1869444 SHA256: 614b5a0866095bc25acec27e9705a55a4174e6f380d94531699e6a05cc20b7b3 SHA1: 4cf601d4fd567cf0ee458363ab021f83f72c6d6b MD5sum: 84956cefcbd8bef0e60b6a517eb1752e Description: python IDE for scientists Originally written to design Spyder (the Scientific PYthon Development EnviRonment), the spyderlib Python library provides ready-to-use pure-Python widgets: source code editor with syntax highlighting and code introspection/analysis features, NumPy array editor, dictionary editor, Python console, etc. It's based on the Qt Python binding module PyQt4 (and is compatible with PySide since v2.2). Package: python-spykeutils Source: spykeutils Version: 0.4.1-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2020 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-scipy, python-quantities, python-neo (>= 0.2.1), python-nose, python-sphinx Recommends: python-guidata, python-guiqwt, python-tables, libjs-jquery, libjs-underscore, python-sklearn (>= 0.11), python-joblib (>= 0.4.5) Provides: python2.7-spykeutils Homepage: https://github.com/rproepp/spykeutils Priority: extra Section: python Filename: pool/main/s/spykeutils/python-spykeutils_0.4.1-1~nd80+1_all.deb Size: 307860 SHA256: 77ac38b8d163f77ceb0e8f4ca12805e5aaa897e1dc5ed4ed9501a69882ffab33 SHA1: 2bcff73fb55ffd25a122fa5353ffaed1fddd154d MD5sum: 25e326a0505672f9f48a21322286e042 Description: utilities for analyzing electrophysiological data spykeutils is a Python library for analyzing and plotting data from neurophysiological recordings. It can be used by itself or in conjunction with Spyke Viewer, a multi-platform GUI application for navigating electrophysiological datasets. Package: python-statsmodels Source: statsmodels Version: 0.6.1-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 12724 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy, python-scipy, python-statsmodels-lib (>= 0.6.1-1~nd80+1), python-patsy Recommends: python-pandas, python-matplotlib, python-nose, python-joblib, python-cvxopt Suggests: python-statsmodels-doc Conflicts: python-scikits-statsmodels, python-scikits.statsmodels (<< 0.4) Replaces: python-scikits-statsmodels, python-scikits.statsmodels (<< 0.4) Provides: python2.7-statsmodels Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: python Filename: pool/main/s/statsmodels/python-statsmodels_0.6.1-1~nd80+1_all.deb Size: 2570500 SHA256: 65efa6a329fe9d9410ce3b5cfc2d3aee8c353261eead226f38490a32589dec70 SHA1: c48bf92d4d70f26bc4dd26d5826c6e3ed852eb1a MD5sum: f911c59157ae596da2fc2cfcd2188277 Description: Python module for the estimation of statistical models statsmodels Python module 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. Package: python-statsmodels-doc Source: statsmodels Version: 0.6.1-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 44357 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-statsmodels Conflicts: python-scikits-statsmodels-doc, python-scikits.statsmodels-doc Replaces: python-scikits-statsmodels-doc, python-scikits.statsmodels-doc Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: doc Filename: pool/main/s/statsmodels/python-statsmodels-doc_0.6.1-1~nd80+1_all.deb Size: 11938158 SHA256: f7821da7ad53760dc63738b43317307cabd29db9455b842af21ba8f9f7b0dd04 SHA1: 6ef3b7c8d913ea4e1e8c5fe60b958745bd33dd7d MD5sum: 918a724520702412c244a0b47c7af411 Description: documentation and examples for statsmodels This package contains HTML documentation and example scripts for python-statsmodels. Package: python-statsmodels-lib Source: statsmodels Version: 0.6.1-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 500 Depends: neurodebian-popularity-contest, python-numpy (>= 1:1.8.0), python-numpy-abi9, python (>= 2.7), python (<< 2.8), libc6 (>= 2.14) Conflicts: python-scikits-statsmodels, python-scikits.statsmodels (<< 0.4) Replaces: python-scikits-statsmodels, python-scikits.statsmodels (<< 0.4) Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: python Filename: pool/main/s/statsmodels/python-statsmodels-lib_0.6.1-1~nd80+1_amd64.deb Size: 96636 SHA256: ec408303f14d8d0f226ad7dbaf201e698099a26b69231db2bc48b5385c20da46 SHA1: 403d0b00092778b06b4fe6b1cd880fd66772cc58 MD5sum: 82ca6b129c1496dea3c197fdc1f76b04 Description: low-level implementations and bindings for statsmodels This package contains architecture dependent extensions for python-statsmodels. Package: python-stfio Source: stimfit Version: 0.14.10-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 934 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python-numpy (>= 1:1.8.0), python-numpy-abi9, libbiosig1, libblas3 | libblas.so.3, libc6 (>= 2.14), libcholmod2.1.2, libfftw3-double3, libgcc1 (>= 1:4.1.1), libhdf5-8, liblapack3 | liblapack.so.3, libpython2.7 (>= 2.7), libstdc++6 (>= 4.9), libbiosig-dev, libsuitesparse-dev Recommends: python-matplotlib, python-scipy, python-pandas Provides: python2.7-stfio Homepage: http://www.stimfit.org Priority: optional Section: python Filename: pool/main/s/stimfit/python-stfio_0.14.10-1~nd80+1_amd64.deb Size: 313260 SHA256: 741851380c54e623dd26cdac230f92c3326c75ffbe66f9c37c6642d5c933ca3f SHA1: 27873d78b43083a9eeb6398df176a5daf91c344a MD5sum: 7412381136dc73c453d59df6e737b174 Description: 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: 0.5-1~nd80+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 214 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy, python-scipy, python-nibabel, python-pil | python-imaging, mayavi2, python-argparse Recommends: mencoder Homepage: http://pysurfer.github.com Priority: extra Section: python Filename: pool/main/p/pysurfer/python-surfer_0.5-1~nd80+1_all.deb Size: 39582 SHA256: 458dd38845d11ff3262648a02a952861c530e60fc9a6dcd4885ac0780d6c12bd SHA1: 8a9f89c4f3fb74195eb6593a7e6e979ad0c6a7d0 MD5sum: c0c778dc58d70375e9f247743a5de7fe 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: 2.7 Package: python-tables Source: pytables Version: 3.2.1-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2711 Depends: neurodebian-popularity-contest, python-numpy, python, python-numexpr, python:any (<< 2.8), python:any (>= 2.7.5-5~), python-tables-lib (>= 3.2.1-1~nd80+1), python-tables-lib (<< 3.2.1-1~nd80+1.1~), python-tables-data (= 3.2.1-1~nd80+1) Suggests: python-tables-doc, python-netcdf, vitables Homepage: http://www.pytables.org Priority: optional Section: python Filename: pool/main/p/pytables/python-tables_3.2.1-1~nd80+1_all.deb Size: 345096 SHA256: bb2808ebf25b62248bcdcfbd80dd4157b1de943d734d9021861951f9da940309 SHA1: 714aafb78a193688d6dba6743ea56eb2723c2979 MD5sum: 7200d7b31098cfc7bd80e1ecdae9727a Description: hierarchical database for Python based on HDF5 PyTables is a hierarchical database package designed to efficiently manage very large amounts of data. PyTables is built on top of the HDF5 library and the NumPy package. It features an object-oriented interface that, combined with natural naming and C-code generated from Pyrex sources, makes it a fast, yet extremely easy to use tool for interactively save and retrieve large amounts of data. . - Compound types (records) can be used entirely from Python (i.e. it is not necessary to use C for taking advantage of them). - The tables are both enlargeable and compressible. - I/O is buffered, so you can get very fast I/O, specially with large tables. - Very easy to select data through the use of iterators over the rows in tables. Extended slicing is supported as well. - It supports the complete set of NumPy, Numeric and numarray objects. . This is the Python 2 version of the package. Package: python-tables-data Source: pytables Version: 3.2.1-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 922 Depends: neurodebian-popularity-contest Homepage: http://www.pytables.org Priority: optional Section: python Filename: pool/main/p/pytables/python-tables-data_3.2.1-1~nd80+1_all.deb Size: 51356 SHA256: 10cc17d336d13ebcf24cbe2098290b3e87f0e41a49d7258f8c213abd7bd1c2c7 SHA1: fc23d9596b213ed887a8b9f3730912960c1c53e1 MD5sum: c0c9510ca6c59a677ed6e9841910cb8a Description: hierarchical database for Python based on HDF5 - test data PyTables is a hierarchical database package designed to efficiently manage very large amounts of data. PyTables is built on top of the HDF5 library and the NumPy package. It features an object-oriented interface that, combined with natural naming and C-code generated from Pyrex sources, makes it a fast, yet extremely easy to use tool for interactively save and retrieve large amounts of data. . This package includes daya fils used for unit testing. Package: python-tables-dbg Source: pytables Version: 3.2.1-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1588 Depends: neurodebian-popularity-contest, python-numpy (>= 1:1.8.0), python-numpy-abi9, python-dbg (<< 2.8), python-dbg (>= 2.7~), libbz2-1.0, libc6 (>= 2.14), libhdf5-8, liblz4-1 (>= 0.0~r113), liblzo2-2, libsnappy1, zlib1g (>= 1:1.1.4), python-tables (= 3.2.1-1~nd80+1), python-tables-lib (= 3.2.1-1~nd80+1), python-numpy-dbg, python-numexpr-dbg Suggests: python-tables-doc, python-netcdf Homepage: http://www.pytables.org Priority: extra Section: debug Filename: pool/main/p/pytables/python-tables-dbg_3.2.1-1~nd80+1_amd64.deb Size: 477294 SHA256: 5ed51ca6c646e2303e516a908a77b8d9e70d03b5c79d82a6c64bd6487455dba3 SHA1: 67bf2138c6e1b4557f5a29f0d43a23ee82d6cb06 MD5sum: 640c0d3c8623bda010ca2a0855eb1e1e Description: hierarchical database for Python based on HDF5 (debug extension) PyTables is a hierarchical database package designed to efficiently manage very large amounts of data. PyTables is built on top of the HDF5 library and the NumPy package. It features an object-oriented interface that, combined with natural naming and C-code generated from Pyrex sources, makes it a fast, yet extremely easy to use tool for interactively save and retrieve large amounts of data. . - Compound types (records) can be used entirely from Python (i.e. it is not necessary to use C for taking advantage of them). - The tables are both enlargeable and compressible. - I/O is buffered, so you can get very fast I/O, specially with large tables. - Very easy to select data through the use of iterators over the rows in tables. Extended slicing is supported as well. - It supports the complete set of NumPy, Numeric and numarray objects. . This package contains the extension built for the Python 2 debug interpreter. Package: python-tables-doc Source: pytables Version: 3.2.1-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8795 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0), libjs-jquery-cookie Suggests: xpdf | pdf-viewer, www-browser Homepage: http://www.pytables.org Priority: optional Section: doc Filename: pool/main/p/pytables/python-tables-doc_3.2.1-1~nd80+1_all.deb Size: 4250226 SHA256: 81061bb79948504897a0011d1fc2038bb17a9ef3df5831a8d7861063a3555d2e SHA1: e84a06f500ae0f4fb4c991dc9c393550599c349a MD5sum: 0d45062a3e71c10e410b6b24ae1be405 Description: hierarchical database for Python based on HDF5 - documentation PyTables is a hierarchical database package designed to efficiently manage very large amounts of data. PyTables is built on top of the HDF5 library and the NumPy package. It features an object-oriented interface that, combined with natural naming and C-code generated from Pyrex sources, makes it a fast, yet extremely easy to use tool for interactively save and retrieve large amounts of data. . This package includes the manual in PDF and HTML formats. Package: python-tables-lib Source: pytables Version: 3.2.1-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1356 Depends: neurodebian-popularity-contest, python-numpy (>= 1:1.8.0), python-numpy-abi9, python (<< 2.8), python (>= 2.7~), libbz2-1.0, libc6 (>= 2.14), libhdf5-8, liblz4-1 (>= 0.0~r113), liblzo2-2, libsnappy1, zlib1g (>= 1:1.1.4) Recommends: python-tables (= 3.2.1-1~nd80+1) Breaks: python-tables (<< 3.0.0-3) Replaces: python-tables (<< 3.0.0-3) Homepage: http://www.pytables.org Priority: optional Section: python Filename: pool/main/p/pytables/python-tables-lib_3.2.1-1~nd80+1_amd64.deb Size: 380510 SHA256: dd15d206f727fcb9071ad26f28e9fc089fdb598f7780134fc7341f99f0d93745 SHA1: 8af9d712b508e7c1392f14d7d2694d71ec888134 MD5sum: 59f51f0aa10bcac70883fa0274473d15 Description: hierarchical database for Python based on HDF5 (extension) PyTables is a hierarchical database package designed to efficiently manage very large amounts of data. PyTables is built on top of the HDF5 library and the NumPy package. It features an object-oriented interface that, combined with natural naming and C-code generated from Pyrex sources, makes it a fast, yet extremely easy to use tool for interactively save and retrieve large amounts of data. . - Compound types (records) can be used entirely from Python (i.e. it is not necessary to use C for taking advantage of them). - The tables are both enlargeable and compressible. - I/O is buffered, so you can get very fast I/O, specially with large tables. - Very easy to select data through the use of iterators over the rows in tables. Extended slicing is supported as well. - It supports the complete set of NumPy, Numeric and numarray objects. . This package contains the extension built for the Python 2 interpreter. Package: python-tornado Version: 2.1.0-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 944 Depends: neurodebian-popularity-contest, python2.6 | python2.7, python (>= 2.6.6-7~), python (<< 2.8), python-pycurl, ca-certificates Recommends: python-mysqldb Homepage: http://www.tornadoweb.org/ Priority: optional Section: python Filename: pool/main/p/python-tornado/python-tornado_2.1.0-1~nd70+1_amd64.deb Size: 223278 SHA256: 9062ef84ccd1c826e50113258d5a09ab715beb06e7e2300fb9db501d5c98b824 SHA1: 26ef56d367e2c3cd3faf2346df6e6d57e8231eb6 MD5sum: 362566030893f1c38e209b2abd9ad48b 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-tz Version: 2012c-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 138 Depends: neurodebian-popularity-contest, tzdata, python2.7 | python2.6, python (>= 2.6.6-7~), python (<< 2.8) Homepage: http://pypi.python.org/pypi/pytz/ Priority: optional Section: python Filename: pool/main/p/python-tz/python-tz_2012c-1~nd70+1_all.deb Size: 39000 SHA256: 4d99b0c0de79ceca4b307484afb320bed4f244d51252ae87a29f931d16f93959 SHA1: 67aa4d3871f125fa3f04b2f0fddee56d9bcdb8db MD5sum: 7766a106c9f3ea0f29222f96da871952 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). Package: python-visionegg Source: visionegg Version: 1.2.1-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1811 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgl1-mesa-glx, python-numpy (>= 1:1.6.1), python-numpy-abi9, python (>= 2.6.6-7~), python (<< 2.8) Homepage: http://www.visionegg.org Priority: optional Section: python Filename: pool/main/v/visionegg/python-visionegg_1.2.1-1~nd80+1_amd64.deb Size: 689524 SHA256: 09de82645afd4a4c0ea675863456c7a61f5c586eebfb14cc9312562a340ae4a2 SHA1: 6213935e74054454f8a977d4de5b1779f005b93c MD5sum: d24b683266426907c1c97a8af797d7a6 Description: Python library for 2D/3D visual stimulus generation The Vision Egg is a programming library that uses standard, inexpensive computer graphics cards to produce visual stimuli for vision research experiments. Package: python-vtk Source: vtk Version: 5.8.0-7+b0~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 29686 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python-support (>= 0.90.0), libavcodec53 (>= 4:0.8-1~) | libavcodec-extra-53 (>= 4:0.8-1~), libavformat53 (>= 4:0.8-1~) | libavformat-extra-53 (>= 4:0.8-1~), libavutil51 (>= 4:0.8-1~) | libavutil-extra-51 (>= 4:0.8-1~), libc6 (>= 2.7), libgcc1 (>= 1:4.1.1), libmysqlclient16 (>= 5.1.50-1), libopenmpi1.3, libpq5, libpython2.7 (>= 2.7), libstdc++6 (>= 4.2.1), libswscale2 (>= 4:0.8-1~) | libswscale-extra-2 (>= 4:0.8-1~), libvtk5.8, libx11-6, tcl-vtk, tcl8.5 (>= 8.5.0), tk8.5 (>= 8.5.0), zlib1g (>= 1:1.1.4) Suggests: vtk-examples, vtk-doc, mayavi2 Homepage: http://www.vtk.org/ Priority: optional Section: python Filename: pool/main/v/vtk/python-vtk_5.8.0-7+b0~nd70+1_amd64.deb Size: 7300084 SHA256: 79cccf123b81910b7404da2470228da7f730901303fce6e8f1df9d8142398e79 SHA1: 670e7945161349fb336d6eeb74a71065dbaa92d2 MD5sum: 2c6b5f97194d746ffe6b6df8abf74895 Description: Python bindings for VTK The Visualization Toolkit (VTK) is an object oriented, high level library that allows one to easily write C++ programs, Tcl, Python and Java scripts that do 3D visualization. . This provides the shared libraries that enable one to use VTK from Python scripts. You will need Python and vtk installed to use this. Some useful information may be available in /usr/share/doc/python-vtk/. Python-Version: 2.7 Package: python-vtk-dicom Source: vtk-dicom Version: 0.5.5-2~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 499 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libpython2.7 (>= 2.7), libstdc++6 (>= 4.1.1), libvtk-dicom0.5, libvtk5.8, python-vtk Provides: python2.7-vtk-dicom Homepage: http://github.com/dgobbi/vtk-dicom/ Priority: optional Section: python Filename: pool/main/v/vtk-dicom/python-vtk-dicom_0.5.5-2~nd80+1_amd64.deb Size: 96052 SHA256: f6d1c63bdf8168da4edf4c1d01fdc4e39af2be698627bf2a419b5a10693024e7 SHA1: 277cf0c0ba10109a0fc18a5d728cf97948e463a6 MD5sum: d610e94e1b63ef2d6d243731c8eb3a0a Description: DICOM for VTK - python This package contains a set of classes for managing DICOM files and metadata from within VTK, and some utility programs for interrogating and converting DICOM files. . Python 2.x bindings Package: python-w3lib Version: 1.11.0-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 40 Depends: neurodebian-popularity-contest, python-six (>= 1.6.1), python:any (<< 2.8), python:any (>= 2.7.5-5~) Homepage: http://pypi.python.org/pypi/w3lib Priority: optional Section: python Filename: pool/main/p/python-w3lib/python-w3lib_1.11.0-1~nd80+1_all.deb Size: 14186 SHA256: 838417f8c75b96930c36e419f4ea4db9a3813be172958fe409f06b3130815a97 SHA1: 90830b45cf0671d9361e88823b25ce6acd15557f MD5sum: e11f0b97bbfc89fd624ecec80ec8f763 Description: Collection of web-related functions for Python (Python 2) Python module with simple, reusable functions to work with URLs, HTML, forms, and HTTP, that aren’t found in the Python standard library. . This module is used to, for example: - remove comments, or tags from HTML snippets - extract base url from HTML snippets - translate entites on HTML strings - encoding mulitpart/form-data - convert raw HTTP headers to dicts and vice-versa - construct HTTP auth header - RFC-compliant url joining - sanitize urls (like browsers do) - extract arguments from urls . The code of w3lib was originally part of the Scrapy framework but was later stripped out of Scrapy, with the aim of make it more reusable and to provide a useful library of web functions without depending on Scrapy. . This is the Python 2 version of the package. Package: python-werkzeug Version: 0.10.4+dfsg1-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 741 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~), libjs-jquery Recommends: python-simplejson | python (>= 2.6), python-openssl, python-pyinotify Suggests: ipython, python-genshi, python-pkg-resources, python-lxml, python-greenlet, python-redis, python-pylibmc | python-memcache, python-werkzeug-doc Homepage: http://werkzeug.pocoo.org/ Priority: optional Section: python Filename: pool/main/p/python-werkzeug/python-werkzeug_0.10.4+dfsg1-1~nd80+1_all.deb Size: 177722 SHA256: a78a238de557e4ea963e3c8be431bdbf61fec6bfcbb0e79f8f56c6367ba01e03 SHA1: f2862b3e5b0fede532059b4bdf351f49984a63d5 MD5sum: d977ef1a2322d05619bed5624f888227 Description: collection of utilities for WSGI applications The Web Server Gateway Interface (WSGI) is a standard interface between web server software and web applications written in Python. . Werkzeug is a lightweight library for interfacing with WSGI. It features request and response objects, an interactive debugging system and a powerful URI dispatcher. Combine with your choice of third party libraries and middleware to easily create a custom application framework. Package: python-werkzeug-doc Source: python-werkzeug Version: 0.10.4+dfsg1-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2573 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Conflicts: python-werkzeug (<< 0.9.3+dfsg-2) Replaces: python-werkzeug (<< 0.9.3+dfsg-2) Homepage: http://werkzeug.pocoo.org/ Priority: extra Section: doc Filename: pool/main/p/python-werkzeug/python-werkzeug-doc_0.10.4+dfsg1-1~nd80+1_all.deb Size: 894542 SHA256: e480b0ee9754b43c966f0457f4d66b186f28bc0da47bd1141fb61812d348b858 SHA1: 8a46eb8650f1ab6f5937eba4c925af7dbb69c47a MD5sum: 3aa7e9385ec06c6b4f8bbeb3b0b01daf Description: documentation for the werkzeug Python library Werkzeug is a lightweight library for interfacing with WSGI. It features request and response objects, an interactive debugging system and a powerful URI dispatcher. Combine with your choice of third party libraries and middleware to easily create a custom application framework. Package: python-workqueue Source: cctools Version: 3.4.2-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 423 Depends: neurodebian-popularity-contest, libc6 (>= 2.3.4), python2.7 | python2.6, python (>= 2.6.6-7~), python (<< 2.8) Homepage: http://nd.edu/~ccl/software/ Priority: extra Section: python Filename: pool/main/c/cctools/python-workqueue_3.4.2-1~nd70+1_amd64.deb Size: 135890 SHA256: 15167347dea5799e30bf991316e6bdb5f4a670a3c82be15c2e60c9d75f7fdbd7 SHA1: c28021009479d583b02107bd3aeea1171a22709d MD5sum: 3d7f7820c55367c46d2858966d0a731c 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: python3-citeproc Source: citeproc-py Version: 0.3.0-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 731 Depends: neurodebian-popularity-contest, python3, python3-lxml, python3:any (>= 3.3.2-2~) Homepage: https://github.com/brechtm/citeproc-py Priority: optional Section: python Filename: pool/main/c/citeproc-py/python3-citeproc_0.3.0-1~nd80+1_all.deb Size: 81994 SHA256: 34e22341d1720c6e083863838ba4d8183bc4aec4afd7b0ff26a646ffb3aebc85 SHA1: c075e0408d7a2f63e6ca882686290d54f4acf305 MD5sum: dff2cdad4ee4afb80cde61e2e9297c82 Description: Citation Style Language (CSL) processor for Python3 Citeproc-py is a library that produces formatted bibliographies and citations from bibliographic databases following formatting instructions provided by XML style files written in the Citation Style Language (CSL). . Currently, BibTeX and JSON are supported as input database formats, and plain text, reStructuredText and HTML as output format. . This package contains the Python 3 modules and the CLI tool csl_unsorted. Package: python3-jdcal Source: jdcal Version: 1.0-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 23 Depends: neurodebian-popularity-contest Homepage: https://github.com/phn/jdcal Priority: optional Section: python Filename: pool/main/j/jdcal/python3-jdcal_1.0-1~nd80+1_all.deb Size: 7562 SHA256: c5ad702b69998664e0755bd7fcce5d13371926824e61867301c6ba514b2acf9e SHA1: 5b328197620c02da79b12469c47206dbbddae70b MD5sum: e6d8b97b557b4ccf40242ab4fc0fc945 Description: Julian dates from proleptic Gregorian and Julian calendars This module contains functions for converting between Julian dates and calendar dates. . Different regions of the world switched to Gregorian calendar from Julian calendar on different dates. Having separate functions for Julian and Gregorian calendars allow maximum flexibility in choosing the relevant calendar. Package: python3-joblib Source: joblib Version: 0.9.2-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 339 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~) Recommends: python3-numpy, python3-nose, python3-simplejson Homepage: http://packages.python.org/joblib/ Priority: optional Section: python Filename: pool/main/j/joblib/python3-joblib_0.9.2-1~nd80+1_all.deb Size: 74802 SHA256: 6022a0563f981281ec4d80f7164209e41a7a278070fd76d78104f3ff61f8443a SHA1: b5702ec72c883693676dd966717b674e4af959f1 MD5sum: 95408f91569cb2bfdd952e3f7b4af8e8 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. . This package contains the Python 3 version. Package: python3-lda Source: lda Version: 1.0.2-9~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1238 Depends: neurodebian-popularity-contest, python3 (<< 3.5), python3 (>= 3.4~), python3-numpy, python3-pbr, libc6 (>= 2.14) Homepage: https://pythonhosted.org/lda/ Priority: optional Section: python Filename: pool/main/l/lda/python3-lda_1.0.2-9~nd80+1_amd64.deb Size: 234480 SHA256: e1c152a6a400fb9956c14e6c69a6b8625b4858266166ad3e77d3175e936dca31 SHA1: d73efffc85477c0d3cb6fe4320d3141c45035c62 MD5sum: 2379e99da6857b95346f464972275ce9 Description: Topic modeling with latent Dirichlet allocation lda implements latent Dirichlet allocation (LDA) using collapsed Gibbs sampling. . This package contains the Python 3.x module. Package: python3-mdp Source: mdp Version: 3.3+git19-g4ec2f29-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1482 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~), python3-numpy Homepage: http://mdp-toolkit.sourceforge.net/ Priority: optional Section: python Filename: pool/main/m/mdp/python3-mdp_3.3+git19-g4ec2f29-1~nd80+1_all.deb Size: 426716 SHA256: 7909c0d3a30a2e22252308ac909b10605cc4aaa652575019fef179c3ce35ce34 SHA1: 0874b19f60a3ac1b8241fb0809fb04627d502f92 MD5sum: d9a6b5d66841cc62719a024b2c48eefc Description: Modular toolkit for Data Processing Python data processing framework for building complex data processing software by combining widely used machine learning algorithms into pipelines and networks. Implemented algorithms include: Principal Component Analysis (PCA), Independent Component Analysis (ICA), Slow Feature Analysis (SFA), Independent Slow Feature Analysis (ISFA), Growing Neural Gas (GNG), Factor Analysis, Fisher Discriminant Analysis (FDA), and Gaussian Classifiers. . This package contains MDP for Python 3. Package: python3-mpi4py Source: mpi4py Version: 1.3.1+hg20131106-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1230 Depends: neurodebian-popularity-contest, libc6 (>= 2.2.5), libopenmpi1.3, python3:any (>= 3.3.2-2~), python3 (>= 3.3), python3 (<< 3.4) Recommends: mpi-default-bin Suggests: python3-numpy Homepage: http://code.google.com/p/mpi4py/ Priority: extra Section: python Filename: pool/main/m/mpi4py/python3-mpi4py_1.3.1+hg20131106-1~nd80+1_amd64.deb Size: 402640 SHA256: 2d5193929310694acc3b2b164e07c6c6cbb2ce39d0a637cf8c40407393e5eaad SHA1: 683af99e81361ede3454028cc302e2b63f989e7d MD5sum: ae8f76668391fa206006455ef7bea5b9 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: python3-mpi4py-dbg Source: mpi4py Version: 1.3.1+hg20131106-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 5379 Depends: neurodebian-popularity-contest, python3-mpi4py (= 1.3.1+hg20131106-1~nd80+1) Homepage: http://code.google.com/p/mpi4py/ Priority: extra Section: debug Filename: pool/main/m/mpi4py/python3-mpi4py-dbg_1.3.1+hg20131106-1~nd80+1_amd64.deb Size: 1295638 SHA256: 233aefda7cb7bfa2a9e355b4366380250393ec47c7b3e62bac2726e090ed9eb2 SHA1: 451891e6ed8691e2f1f0efb92baf7922aaec531a MD5sum: a92a368118b3749c3ca5e01e41655977 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: python3-msgpack Source: msgpack-python Version: 0.4.2-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 181 Depends: neurodebian-popularity-contest, python3 (>= 3.4~), python3 (<< 3.5), libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1) Homepage: http://pypi.python.org/pypi/msgpack-python/ Priority: optional Section: python Filename: pool/main/m/msgpack-python/python3-msgpack_0.4.2-1~nd80+1_amd64.deb Size: 52948 SHA256: bc30e8b8a33c63f7a300d3d76a83a002820a1c2f9c3a49334dc00cb1b4296356 SHA1: 86c1b2e666e13457914eb0e296f7fc7712ae90e4 MD5sum: edbafae4a5434726e8f04c0fc9fddb0f Description: Python 3 implementation of MessagePack format MessagePack is a binary-based efficient object serialization format. It enables the exchange of structured objects between many languages like JSON. But unlike JSON, it is very fast and small. . This package contains a Python 3 extension module implementing the MessagePack format. Package: python3-nibabel Source: nibabel Version: 2.0.1-3~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 63313 Depends: neurodebian-popularity-contest, python3-numpy, python3-scipy Suggests: python-nibabel-doc, python3-dicom, python3-fuse Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: python Filename: pool/main/n/nibabel/python3-nibabel_2.0.1-3~nd80+1_all.deb Size: 1969248 SHA256: 26c500c109d6158f7ed92e26f1fac264c80710290381829b3662fab7b19ea065 SHA1: d615cb6a6e19806785a615de6612bfa54834b99a MD5sum: 53b9c4583e0c7c431d9ebcfcbfb822d2 Description: Python3 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. Package: python3-numexpr Source: numexpr Version: 2.4.3-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 369 Depends: neurodebian-popularity-contest, python3-numpy (>= 1:1.8.0), python3-numpy-abi9, python3 (<< 3.5), python3 (>= 3.4~), libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1) Homepage: http://code.google.com/p/numexpr/ Priority: optional Section: python Filename: pool/main/n/numexpr/python3-numexpr_2.4.3-1~nd80+1_amd64.deb Size: 130578 SHA256: 92e17707a2ceb363c286388f8299e47bb7d3ed09873eaacb81434d48250b3c01 SHA1: 07c7c1a80ea7c96f905ff4755a33f7e373715d1f MD5sum: 88e4a65ef4f4047822d1b38420f6d483 Description: Fast numerical array expression evaluator for Python 3 and NumPy Numexpr package evaluates multiple-operator array expressions many times faster than NumPy can. It accepts the expression as a string, analyzes it, rewrites it more efficiently, and compiles it to faster Python code on the fly. It's the next best thing to writing the expression in C and compiling it with a specialized just-in-time (JIT) compiler, i.e. it does not require a compiler at runtime. . This package contains numexpr for Python 3. Package: python3-numexpr-dbg Source: numexpr Version: 2.4.3-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 258 Depends: neurodebian-popularity-contest, python3-numpy (>= 1:1.8.0), python3-numpy-abi9, python3-dbg (<< 3.5), python3-dbg (>= 3.4~), libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), python3-numexpr (= 2.4.3-1~nd80+1), python3-numpy-dbg Homepage: http://code.google.com/p/numexpr/ Priority: extra Section: debug Filename: pool/main/n/numexpr/python3-numexpr-dbg_2.4.3-1~nd80+1_amd64.deb Size: 105904 SHA256: 7a12202ae511ce547e684614cfed98ccbb6c2fcdc98d2252798d44913cb9115b SHA1: 7af1e9271228022774f7f5a38ca9291fb3d0562b MD5sum: f6e9c34fe631220fe49fe43342edde7b Description: Fast numerical array expression evaluator for Python 3 and NumPy (debug ext) Numexpr package evaluates multiple-operator array expressions many times faster than NumPy can. It accepts the expression as a string, analyzes it, rewrites it more efficiently, and compiles it to faster Python code on the fly. It's the next best thing to writing the expression in C and compiling it with a specialized just-in-time (JIT) compiler, i.e. it does not require a compiler at runtime. . This package contains the extension built for the Python 3 debug interpreter. Package: python3-openpyxl Source: openpyxl Version: 2.3.0~b2-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1115 Depends: neurodebian-popularity-contest, python3-jdcal, python3:any (>= 3.3.2-2~) Recommends: python3-pytest, python3-pil, python3-lxml Homepage: http://bitbucket.org/openpyxl/openpyxl/ Priority: optional Section: python Filename: pool/main/o/openpyxl/python3-openpyxl_2.3.0~b2-1~nd80+1_all.deb Size: 190958 SHA256: 6edeaa69938d649a0d37e0a7e2a73eccf244a5f4a86041e7fa2b586251039fa8 SHA1: abbda5a9b5c1390d107738759e9add83294b8486 MD5sum: 09cd6c4caf5e7a471cd6278281e9562b 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: python3-pandas Source: pandas Version: 0.17.0+git8-gcac4ad2-2~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 19794 Depends: neurodebian-popularity-contest, python3-dateutil, python3-numpy (>= 1:1.7~), python3-tz, python3:any (>= 3.3.2-2~), python3-pandas-lib (>= 0.17.0+git8-gcac4ad2-2~nd80+1) Recommends: python3-scipy, python3-matplotlib, python3-numexpr, python3-tables, python3-bs4, python3-html5lib, python3-six, python3-lxml Suggests: python-pandas-doc Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python3-pandas_0.17.0+git8-gcac4ad2-2~nd80+1_all.deb Size: 2345058 SHA256: e499a0f551fc8a7f1a03f41ea945fa78a4fc2cc3d7ed32c3481cd13d148cc018 SHA1: 47e716751f0d902b5e5974c6e1288b3186dd6193 MD5sum: 6c12bf86e2f7785a4d24413ede28012a Description: data structures for "relational" or "labeled" data - Python 3 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 . This package contains the Python 3 version. Package: python3-pandas-lib Source: pandas Version: 0.17.0+git8-gcac4ad2-2~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 5905 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), python3-numpy (>= 1:1.8.0), python3-numpy-abi9, python3 (<< 3.5), python3 (>= 3.4~) Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python3-pandas-lib_0.17.0+git8-gcac4ad2-2~nd80+1_amd64.deb Size: 1566570 SHA256: 285a0d77f3d75003a63630244d989590ba54dd1ec332c9b311db3ed614f91231 SHA1: 9dbcd190c14061221929e5a02c9be3c42bfc73cb MD5sum: fb18d85076689583d08c887b08cadedb Description: low-level implementations and bindings for pandas - Python 3 This is an add-on package for python-pandas providing architecture-dependent extensions. . This package contains the Python 3 version. Package: python3-patsy Source: patsy Version: 0.4.0-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 751 Depends: neurodebian-popularity-contest, python3-numpy, python3-six, python3:any (>= 3.3.2-2~) Recommends: python3-pandas Suggests: python-patsy-doc Homepage: http://github.com/pydata/patsy Priority: optional Section: python Filename: pool/main/p/patsy/python3-patsy_0.4.0-1~nd80+1_all.deb Size: 168278 SHA256: a61f7e3e732f2c4b2c1c5892193e4662b26de998bf610221d8cf196649da35dc SHA1: 308f8684305ea523984d324969e420f02d95c18a MD5sum: 08d947c828706569e0386cd13f392df5 Description: statistical models in Python using symbolic formulas patsy is a Python library for describing statistical models (especially linear models, or models that have a linear component) and building design matrices. . This package contains the Python 3 version. Package: python3-psutil Source: python-psutil Version: 2.1.1-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 248 Depends: neurodebian-popularity-contest, libc6 (>= 2.13), python3 (>= 3.4~), python3 (<< 3.5) Homepage: http://code.google.com/p/psutil/ Priority: optional Section: python Filename: pool/main/p/python-psutil/python3-psutil_2.1.1-1~nd80+1_amd64.deb Size: 60044 SHA256: cf5ae8506a888ed09caf9def74ca17c49b96a2d2494633a93663f9a48b9b9872 SHA1: a3a46ea9f38ab07d5ef7298b4354ac4c1e59b2c3 MD5sum: d52813c4601bb97235ca9513e8cbc32c Description: module providing convenience functions for managing processes (Python3) psutil is a module providing an interface for retrieving information on running processes and system utilization (CPU, memory) in a portable way by using Python, implementing many functionalities offered by tools like ps, top and Windows task manager. . It currently supports Linux, OS X, FreeBSD and Windows. . This package contains the Python 3 version of psutil. Package: python3-py Source: python-py Version: 1.4.30-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 270 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~), python3-pkg-resources Suggests: subversion, python3-pytest Homepage: https://bitbucket.org/pytest-dev/py Priority: optional Section: python Filename: pool/main/p/python-py/python3-py_1.4.30-1~nd80+1_all.deb Size: 66964 SHA256: 0be0b6c86615a0cb47fee21527795fca5d5ae2f3ba519b20baa6ada199f8406b SHA1: 3f54ef0fe86d1fb56a44c34ba9edb595013f25c3 MD5sum: 5d1d9cd752555fc0d5145707ddf7d733 Description: Advanced Python development support library (Python 3) The Codespeak py lib aims at supporting a decent Python development process addressing deployment, versioning and documentation perspectives. It includes: . * py.path: path abstractions over local and Subversion files * py.code: dynamic code compile and traceback printing support . This package provides the Python 3 modules. Package: python3-pytest Source: pytest Version: 2.7.2-2~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 453 Depends: neurodebian-popularity-contest, python3-pkg-resources, python3-py (>= 1.4.29), python3, python3:any (>= 3.3.2-2~) Homepage: http://pytest.org/ Priority: optional Section: python Filename: pool/main/p/pytest/python3-pytest_2.7.2-2~nd80+1_all.deb Size: 132628 SHA256: 5968b6baf94eda70fbe5f7ff3f5271cb1a05389218c4c418d5ea7a1d03fd6b12 SHA1: 2f9c7f663b22d299557f1d0b2346453242bed341 MD5sum: 5307d9b36baa6d53e6b510613415bc14 Description: Simple, powerful testing in Python3 This testing tool has for objective to allow the developers to limit the boilerplate code around the tests, promoting the use of built-in mechanisms such as the `assert` keyword. . This package provides the Python 3 module and the py3.test script. Package: python3-pytest-localserver Source: pytest-localserver Version: 0.3.4-2~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 82 Depends: neurodebian-popularity-contest, python3-pytest, python3-werkzeug (>= 0.10), python3:any (>= 3.3.2-2~) Homepage: https://bitbucket.org/pytest-dev/pytest-localserver/ Priority: optional Section: python Filename: pool/main/p/pytest-localserver/python3-pytest-localserver_0.3.4-2~nd80+1_all.deb Size: 19372 SHA256: 0d1da2077153dbf6ebb88de5d844398a8269dfcdd2fbb9befa0455c355e3b90f SHA1: 088576c064cb11bf4c775e2355c3c4405e17a479 MD5sum: 31950d7929b8f5bc47ef7fe64122f5f9 Description: py.test plugin to test server connections locally (Python 3) pytest-localserver is a plugin for the Pytest testing framework which enables to test server connections locally. . This package contains the modules for Python 3. Package: python3-pytest-tornado Source: pytest-tornado Version: 0.4.4-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 13 Depends: neurodebian-popularity-contest, python3-pytest, python3-tornado, python3:any (>= 3.3.2-2~) Homepage: https://github.com/eugeniy/pytest-tornado Priority: optional Section: python Filename: pool/main/p/pytest-tornado/python3-pytest-tornado_0.4.4-1~nd80+1_all.deb Size: 5820 SHA256: c36e4bc43604c1953c95f9be7994446aaf1ae336689672d74f0a128ca85c31f3 SHA1: 80565a6fbf372412f1dee739dcbf193a78a71078 MD5sum: c712cade12fba7f339899dec108da5f1 Description: py.test plugin to test Tornado applications (Python 3) pytest-tornado is a plugin for the Pytest testing framework which provides fixtures and markers to simplify testing of Tornado applications (Python web framework and ansynchronous networking library). . This package contains the plugin for Python 3 code. Package: python3-seaborn Source: seaborn Version: 0.6.0-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 676 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~), python3-numpy, python3-scipy, python3-pandas, python3-matplotlib Recommends: python3-patsy Homepage: https://github.com/mwaskom/seaborn Priority: optional Section: python Filename: pool/main/s/seaborn/python3-seaborn_0.6.0-1~nd80+1_all.deb Size: 117862 SHA256: 86f029ebae665ab95afb7503fe4317714413fd7d4948986ed5b0ffdaeb9041c6 SHA1: 260d89cbc26fe5a659c1141e2315a91d2946b1d1 MD5sum: 77a7de8c0bdb41afc53e558144b1722c Description: statistical visualization library Seaborn is a library for making attractive and informative statistical graphics in Python. It is built on top of matplotlib and tightly integrated with the PyData stack, including support for numpy and pandas data structures and statistical routines from scipy and statsmodels. . Some of the features that seaborn offers are . - Several built-in themes that improve on the default matplotlib aesthetics - Tools for choosing color palettes to make beautiful plots that reveal patterns in your data - Functions for visualizing univariate and bivariate distributions or for comparing them between subsets of data - Tools that fit and visualize linear regression models for different kinds of independent and dependent variables - A function to plot statistical timeseries data with flexible estimation and representation of uncertainty around the estimate - High-level abstractions for structuring grids of plots that let you easily build complex visualizations . This is the Python 3 version of the package. Package: python3-six Source: six Version: 1.9.0-3~bpo8+1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 37 Depends: neurodebian-popularity-contest, python3:any (>= 3.4~) Multi-Arch: foreign Homepage: http://pythonhosted.org/six/ Priority: optional Section: python Filename: pool/main/s/six/python3-six_1.9.0-3~bpo8+1~nd80+1_all.deb Size: 13958 SHA256: 6a1c30e151bee7856baeabcd9fc769ee28b9a3fbef1fb71c62463f8b47186c52 SHA1: b2be01f790da51c41656003f907a7045f9d85db2 MD5sum: 18328bce7f3a0371989e2ba799a0c128 Description: Python 2 and 3 compatibility library (Python 3 interface) Six is a Python 2 and 3 compatibility library. It provides utility functions for smoothing over the differences between the Python versions with the goal of writing Python code that is compatible on both Python versions. . This package provides Six on the Python 3 module path. It is complemented by python-six. Package: python3-skimage Source: skimage Version: 0.10.1-2~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 15115 Depends: neurodebian-popularity-contest, libfreeimage3, python3-numpy, python3-scipy (>= 0.10), python3-six (>= 1.3.0), python3-skimage-lib (>= 0.10.1-2~nd80+1), python3:any (>= 3.3.2-2~) Recommends: python3-imaging, python3-matplotlib (>= 1.0), python3-nose, python3-pil Suggests: python-skimage-doc Homepage: http://scikit-image.org Priority: optional Section: python Filename: pool/main/s/skimage/python3-skimage_0.10.1-2~nd80+1_all.deb Size: 11920326 SHA256: cfe6d867e84116350ed6632814cc00697c1380d6ea9003e9e669b22357cbd8f6 SHA1: b85790519fe2ef301b9c297c9022a89c446cd617 MD5sum: ff3a41580728434802c9c85d83e77aba Description: Python 3 modules for image processing scikit-image is a collection of image processing algorithms for Python. It performs tasks such as image loading, filtering, morphology, segmentation, color conversions, and transformations. . This package provides the Python 3 module. Package: python3-skimage-lib Source: skimage Version: 0.10.1-2~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 6497 Depends: neurodebian-popularity-contest, python3-numpy (>= 1:1.8.0), python3-numpy-abi9, python3 (<< 3.5), python3 (>= 3.4~), libc6 (>= 2.14) Recommends: python3-skimage Homepage: http://scikit-image.org Priority: optional Section: python Filename: pool/main/s/skimage/python3-skimage-lib_0.10.1-2~nd80+1_amd64.deb Size: 998878 SHA256: 7316ad55ea0313de4be563267a6e9b774159d88ddce280f94e1e62de41d8a0ab SHA1: 4842d45d9d5213d344a542772109b893d029b7ad MD5sum: 5f76b3e1628963420ecc5903c3f666d0 Description: Optimized low-level algorithms for Python 3 scikit-image This is an add-on package for python-skimage. It provides optimized, low-level implementations of algorithms. . This package provides the Python 3 libraries. Package: python3-sphinx-rtd-theme Source: sphinx-rtd-theme Version: 0.1.8-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 332 Depends: neurodebian-popularity-contest, fonts-font-awesome, fonts-lato, libjs-modernizr, python3:any (>= 3.3.2-2~) Recommends: python3-sphinx Homepage: https://github.com/snide/sphinx_rtd_theme Priority: optional Section: python Filename: pool/main/s/sphinx-rtd-theme/python3-sphinx-rtd-theme_0.1.8-1~nd80+1_all.deb Size: 117290 SHA256: e7f9c0fcb733cfa82c2469b5199d964ed19b72c6eac1a7eb4750355ebf1ca209 SHA1: c1463134ab7491e73ce10e0485708b68e0e0c8d2 MD5sum: 3542b33f788075743ec29bea198ccbb8 Description: sphinx theme from readthedocs.org (Python 3) This mobile-friendly sphinx theme was initially created for readthedocs.org, but can be incorporated in any project. . Among other things, it features a left panel with a browseable table of contents, and a search bar. . This is the Python 3 version of the package. Package: python3-tables Source: pytables Version: 3.2.1-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2694 Depends: neurodebian-popularity-contest, python3-numpy, python3-numexpr, python3:any (>= 3.3.2-2~), python3-tables-lib (>= 3.2.1-1~nd80+1), python3-tables-lib (<< 3.2.1-1~nd80+1.1~), python-tables-data (= 3.2.1-1~nd80+1) Suggests: python-tables-doc, python-netcdf, vitables Homepage: http://www.pytables.org Priority: optional Section: python Filename: pool/main/p/pytables/python3-tables_3.2.1-1~nd80+1_all.deb Size: 334836 SHA256: 8b9547d9b908b372325a3aa7c44435028da84921e5ab5d154426a8f6d8a7c563 SHA1: a4c5847201f32150799adbfa0c64c1ccd69d89a2 MD5sum: b5a397d791e2860b2a25947712e7cb45 Description: hierarchical database for Python3 based on HDF5 PyTables is a hierarchical database package designed to efficiently manage very large amounts of data. PyTables is built on top of the HDF5 library and the NumPy package. It features an object-oriented interface that, combined with natural naming and C-code generated from Pyrex sources, makes it a fast, yet extremely easy to use tool for interactively save and retrieve large amounts of data. . - Compound types (records) can be used entirely from Python (i.e. it is not necessary to use C for taking advantage of them). - The tables are both enlargeable and compressible. - I/O is buffered, so you can get very fast I/O, specially with large tables. - Very easy to select data through the use of iterators over the rows in tables. Extended slicing is supported as well. - It supports the complete set of NumPy, Numeric and numarray objects. . This is the Python 3 version of the package. Package: python3-tables-dbg Source: pytables Version: 3.2.1-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1558 Depends: neurodebian-popularity-contest, python3-numpy (>= 1:1.8.0), python3-numpy-abi9, python3-dbg (<< 3.5), python3-dbg (>= 3.4~), libbz2-1.0, libc6 (>= 2.14), libhdf5-8, liblz4-1 (>= 0.0~r113), liblzo2-2, libsnappy1, zlib1g (>= 1:1.1.4), python3-tables (= 3.2.1-1~nd80+1), python3-tables-lib (= 3.2.1-1~nd80+1), python3-numpy-dbg, python3-numexpr-dbg Suggests: python-tables-doc, python-netcdf Homepage: http://www.pytables.org Priority: extra Section: debug Filename: pool/main/p/pytables/python3-tables-dbg_3.2.1-1~nd80+1_amd64.deb Size: 467240 SHA256: 6816e180378e10806b8a9a55f818b302aa884876a624999d2816e5565ee4bf52 SHA1: 38efeb686d33d31257a7c6e1e12dfca81c6f0725 MD5sum: 253eeed3e1c60077ebc22ee2dbfde7d3 Description: hierarchical database for Python 3 based on HDF5 (debug extension) PyTables is a hierarchical database package designed to efficiently manage very large amounts of data. PyTables is built on top of the HDF5 library and the NumPy package. It features an object-oriented interface that, combined with natural naming and C-code generated from Pyrex sources, makes it a fast, yet extremely easy to use tool for interactively save and retrieve large amounts of data. . - Compound types (records) can be used entirely from Python (i.e. it is not necessary to use C for taking advantage of them). - The tables are both enlargeable and compressible. - I/O is buffered, so you can get very fast I/O, specially with large tables. - Very easy to select data through the use of iterators over the rows in tables. Extended slicing is supported as well. - It supports the complete set of NumPy, Numeric and numarray objects. . This package contains the extension built for the Python 3 debug interpreter. Package: python3-tables-lib Source: pytables Version: 3.2.1-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1313 Depends: neurodebian-popularity-contest, python3-numpy (>= 1:1.8.0), python3-numpy-abi9, python3 (<< 3.5), python3 (>= 3.4~), libbz2-1.0, libc6 (>= 2.14), libhdf5-8, liblz4-1 (>= 0.0~r113), liblzo2-2, libsnappy1, zlib1g (>= 1:1.1.4) Recommends: python3-tables (= 3.2.1-1~nd80+1) Breaks: python3-tables (<< 3.0.0-3) Replaces: python3-tables (<< 3.0.0-3) Homepage: http://www.pytables.org Priority: optional Section: python Filename: pool/main/p/pytables/python3-tables-lib_3.2.1-1~nd80+1_amd64.deb Size: 368220 SHA256: 6ee9b3618e7392b62f7c06f03be40b4bb4789628d858e7805b1bac7c6ae41be6 SHA1: afe45a4248214ba58924a9e21bb4db77af8467e9 MD5sum: e095853fe8b2ed8bc0da37ad719e4eb4 Description: hierarchical database for Python3 based on HDF5 (extension) PyTables is a hierarchical database package designed to efficiently manage very large amounts of data. PyTables is built on top of the HDF5 library and the NumPy package. It features an object-oriented interface that, combined with natural naming and C-code generated from Pyrex sources, makes it a fast, yet extremely easy to use tool for interactively save and retrieve large amounts of data. . - Compound types (records) can be used entirely from Python (i.e. it is not necessary to use C for taking advantage of them). - The tables are both enlargeable and compressible. - I/O is buffered, so you can get very fast I/O, specially with large tables. - Very easy to select data through the use of iterators over the rows in tables. Extended slicing is supported as well. - It supports the complete set of NumPy, Numeric and numarray objects. . This package contains the extension built for the Python 3 interpreter. Package: python3-tz Source: python-tz Version: 2012c-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 108 Depends: neurodebian-popularity-contest, tzdata, python3 (>= 3.2.3-3~) Homepage: http://pypi.python.org/pypi/pytz/ Priority: optional Section: python Filename: pool/main/p/python-tz/python3-tz_2012c-1~nd70+1_all.deb Size: 31954 SHA256: 3e97caf66172c67dea29b32d60a6a976e032f2e3cb18dfea5ec7bb0c1a7618af SHA1: 4c06117f76e0b1ad499102b3844bd8cf2357cb7a MD5sum: 464ec516d7b9cbcf1f82127ecd56ebb7 Description: Python3 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). . This package contains the Python 3 version of the library. Package: python3-w3lib Source: python-w3lib Version: 1.11.0-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 40 Depends: neurodebian-popularity-contest, python3-six (>= 1.6.1), python3:any (>= 3.3.2-2~) Homepage: http://pypi.python.org/pypi/w3lib Priority: optional Section: python Filename: pool/main/p/python-w3lib/python3-w3lib_1.11.0-1~nd80+1_all.deb Size: 14280 SHA256: 00e2b52d04fc8cf94f107476c5ef9f2922a647e7e5da347db417d14ce637d31e SHA1: 52b1c77e79ff6b74cef50a1743a042b20eca92c7 MD5sum: 04f656027fef03362c2612f0990f5800 Description: Collection of web-related functions for Python (Python 3) Python module with simple, reusable functions to work with URLs, HTML, forms, and HTTP, that aren’t found in the Python standard library. . This module is used to, for example: - remove comments, or tags from HTML snippets - extract base url from HTML snippets - translate entites on HTML strings - encoding mulitpart/form-data - convert raw HTTP headers to dicts and vice-versa - construct HTTP auth header - RFC-compliant url joining - sanitize urls (like browsers do) - extract arguments from urls . The code of w3lib was originally part of the Scrapy framework but was later stripped out of Scrapy, with the aim of make it more reusable and to provide a useful library of web functions without depending on Scrapy. . This is the Python 3 version of the package. Package: python3-werkzeug Source: python-werkzeug Version: 0.10.4+dfsg1-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 741 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~), libjs-jquery Recommends: python3-simplejson | python3, python3-openssl, python3-pyinotify Suggests: ipython3, python3-pkg-resources, python3-lxml, python-werkzeug-doc Homepage: http://werkzeug.pocoo.org/ Priority: optional Section: python Filename: pool/main/p/python-werkzeug/python3-werkzeug_0.10.4+dfsg1-1~nd80+1_all.deb Size: 177650 SHA256: cec6d58f65e9e93d8c3d0d408975abbf643d04b343c5c661ed65f3b58d8b4a91 SHA1: 4c2b728b3a25150fb584cd487f023204a8a1ade6 MD5sum: 034ea42f458f359dabd8e01c4e839728 Description: collection of utilities for WSGI applications The Web Server Gateway Interface (WSGI) is a standard interface between web server software and web applications written in Python. . Werkzeug is a lightweight library for interfacing with WSGI. It features request and response objects, an interactive debugging system and a powerful URI dispatcher. Combine with your choice of third party libraries and middleware to easily create a custom application framework. Package: qnifti2dicom Source: nifti2dicom Version: 0.4.9-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 3272 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libgdcm2.4, libinsighttoolkit4.6, libqt5core5a (>= 5.0.2), libqt5gui5 (>= 5.0.2), libqt5widgets5 (>= 5.0.2), libstdc++6 (>= 4.9), libvtk6.1, nifti2dicom (= 0.4.9-1~nd80+1), nifti2dicom-data (= 0.4.9-1~nd80+1) Homepage: https://github.com/biolab-unige/nifti2dicom Priority: optional Section: science Filename: pool/main/n/nifti2dicom/qnifti2dicom_0.4.9-1~nd80+1_amd64.deb Size: 491910 SHA256: a364296a61a628f2ccbb0cb0155bcca840e172949b2b86d8a581b5564b2c9909 SHA1: 3371d448a1ba41ffea4b52459b9a5269ef50b203 MD5sum: 81216c143418b36fe6a1c672410ec836 Description: convert 3D medical images to DICOM 2D series (gui) Nifti2Dicom is a convertion tool that converts 3D NIfTI files (and other formats supported by ITK, including Analyze, MetaImage Nrrd and VTK) to DICOM. Unlike other conversion tools, it can import a DICOM file that is used to import the patient and study DICOM tags, and allows you to edit the accession number and other DICOM tags, in order to create a valid DICOM that can be imported in a PACS. . This package contains the Qt4 GUI. Package: remake Version: 3.82+dbg0.9+dfsg-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 293 Depends: neurodebian-popularity-contest, libc6 (>= 2.11), libreadline6 (>= 6.0) Homepage: http://bashdb.sourceforge.net/remake Priority: extra Section: devel Filename: pool/main/r/remake/remake_3.82+dbg0.9+dfsg-1~nd70+1_amd64.deb Size: 178652 SHA256: 71beb753e52dc4268a8cb1360128f9681ab4ac6b778303e42d069d9dcc393ee4 SHA1: a2f4e3bc0fa94c32886eac2b76ebd673217c6b0d MD5sum: 1a5b16e71b674ed5c6fb4b1bfc643231 Description: GNU make fork with improved error reporting and debugging Modernized version of GNU make utility that adds improved error reporting, the ability to trace execution in a comprehensible way, and a debugger. Some of the features of the debugger are: * see the target call stack * set breakpoints on targets * show and set variables * execute arbitrary "make" code * issue shell commands while stopped in the middle of execution * inspect target descriptions * write a file with the commands of the target expanded Package: shogun-cmdline-static Source: shogun Version: 1.1.0-6~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 143 Depends: neurodebian-popularity-contest, libarpack2 (>= 2.1), libatlas3-base, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libglpk0 (>= 4.30), libhdf5-7, libjson0 (>= 0.10), liblzma5 (>= 5.1.1alpha+20110809), liblzo2-2, libreadline6 (>= 6.0), libshogun11 (= 1.1.0-6~nd70+1), libstdc++6 (>= 4.1.1), libxml2 (>= 2.6.27), zlib1g (>= 1:1.1.4) Conflicts: shogun-cmdline Replaces: shogun-cmdline Homepage: http://www.shogun-toolbox.org Priority: optional Section: science Filename: pool/main/s/shogun/shogun-cmdline-static_1.1.0-6~nd70+1_amd64.deb Size: 45708 SHA256: 99e614a2f65d6b8db989989349e05b04a7cb6f4ef3dc08ea24ccce6f29f4644b SHA1: a1acfde2d2d54d9ba11824e222de4e769565102e MD5sum: 7857dcf2000949af764ca9e68c7bcc9e Description: Large Scale Machine Learning Toolbox SHOGUN - is a new machine learning toolbox with focus on large scale kernel methods and especially on Support Vector Machines (SVM) with focus to bioinformatics. It provides a generic SVM object interfacing to several different SVM implementations. Each of the SVMs can be combined with a variety of the many kernels implemented. It can deal with weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain, where an optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Apart from SVM 2-class classification and regression problems, a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to train hidden markov models are implemented. The input feature-objects can be dense, sparse or strings and of type int/short/double/char and can be converted into different feature types. Chains of preprocessors (e.g. substracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing. . SHOGUN comes in different flavours, a stand-a-lone version and also with interfaces to Matlab(tm), R, Octave, Readline and Python. This is the Readline package. Package: shogun-csharp-modular Source: shogun Version: 1.1.0-6~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 7355 Depends: neurodebian-popularity-contest, libarpack2 (>= 2.1), libatlas3-base, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libglpk0 (>= 4.30), libhdf5-7, libjson0 (>= 0.10), liblzma5 (>= 5.1.1alpha+20110809), liblzo2-2, libshogun11, libstdc++6 (>= 4.1.1), libxml2 (>= 2.6.27), zlib1g (>= 1:1.1.4), libmono-corlib4.0-cil (>= 2.10.1) Homepage: http://www.shogun-toolbox.org Priority: optional Section: science Filename: pool/main/s/shogun/shogun-csharp-modular_1.1.0-6~nd70+1_amd64.deb Size: 1512808 SHA256: 92802ee1447e9c260a7f77bc230e5aee023e72116cf0ebed656a9fa8e878ae97 SHA1: b70fd8e43f8dcff15e08a18730d68434580f7608 MD5sum: 3392c27ad6895e3bb25258a901845b83 Description: Large Scale Machine Learning Toolbox SHOGUN - is a new machine learning toolbox with focus on large scale kernel methods and especially on Support Vector Machines (SVM) with focus to bioinformatics. It provides a generic SVM object interfacing to several different SVM implementations. Each of the SVMs can be combined with a variety of the many kernels implemented. It can deal with weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain, where an optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Apart from SVM 2-class classification and regression problems, a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to train hidden markov models are implemented. The input feature-objects can be dense, sparse or strings and of type int/short/double/char and can be converted into different feature types. Chains of preprocessors (e.g. substracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing. . SHOGUN comes in different flavours, a stand-a-lone version and also with interfaces to Matlab(tm), R, Octave, Readline and Python. This is the modular csharp package employing swig. Package: shogun-dbg Source: shogun Version: 1.1.0-6~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 72645 Depends: neurodebian-popularity-contest, libshogun11 (= 1.1.0-6~nd70+1) Homepage: http://www.shogun-toolbox.org Priority: extra Section: debug Filename: pool/main/s/shogun/shogun-dbg_1.1.0-6~nd70+1_amd64.deb Size: 16120910 SHA256: 19a2e3a2a01f472d99226761eadeee7f0590b0d87ccea1178e1c42196e7c1e09 SHA1: 1a803d0f28f806e131670da91633c1ae9fc38075 MD5sum: 757deabee3fe99b4c69b0de93680717c Description: Large Scale Machine Learning Toolbox SHOGUN - is a new machine learning toolbox with focus on large scale kernel methods and especially on Support Vector Machines (SVM) with focus to bioinformatics. It provides a generic SVM object interfacing to several different SVM implementations. Each of the SVMs can be combined with a variety of the many kernels implemented. It can deal with weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain, where an optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Apart from SVM 2-class classification and regression problems, a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to train hidden markov models are implemented. The input feature-objects can be dense, sparse or strings and of type int/short/double/char and can be converted into different feature types. Chains of preprocessors (e.g. substracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing. . SHOGUN comes in different flavours, a stand-a-lone version and also with interfaces to Matlab(tm), R, Octave, Readline and Python. This package contains debug symbols for all interfaces. Package: shogun-doc-cn Source: shogun Version: 1.1.0-6~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1545 Depends: neurodebian-popularity-contest Recommends: shogun-python-modular, libshogun-dev Homepage: http://www.shogun-toolbox.org Priority: optional Section: doc Filename: pool/main/s/shogun/shogun-doc-cn_1.1.0-6~nd70+1_all.deb Size: 556068 SHA256: f8376758069c8e22fedb758202fea6063d95aa3aa4400f084c4f8e10b9118796 SHA1: 3f5b5ae50cc2dcf41c120bb995369dcda3e5cddd MD5sum: 44dcec822faa27167037f325ff2be792 Description: Large Scale Machine Learning Toolbox SHOGUN - is a new machine learning toolbox with focus on large scale kernel methods and especially on Support Vector Machines (SVM) with focus to bioinformatics. It provides a generic SVM object interfacing to several different SVM implementations. Each of the SVMs can be combined with a variety of the many kernels implemented. It can deal with weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain, where an optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Apart from SVM 2-class classification and regression problems, a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to train hidden markov models are implemented. The input feature-objects can be dense, sparse or strings and of type int/short/double/char and can be converted into different feature types. Chains of preprocessors (e.g. substracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing. . SHOGUN comes in different flavours, a stand-a-lone version and also with interfaces to Matlab(tm), R, Octave, Readline and Python. This is the Chinese user and developer documentation. Package: shogun-doc-en Source: shogun Version: 1.1.0-6~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 85407 Depends: neurodebian-popularity-contest Recommends: shogun-python-modular, libshogun-dev Conflicts: shogun-doc Replaces: shogun-doc Homepage: http://www.shogun-toolbox.org Priority: optional Section: doc Filename: pool/main/s/shogun/shogun-doc-en_1.1.0-6~nd70+1_all.deb Size: 17119184 SHA256: 3f07ea2441ab9f83d787f60ddb9cd08f4fc9394f062ac584ffe7e2a14e9b437f SHA1: d0333cc59cb4433eefd2ba5123fe7384b6430041 MD5sum: 4462916c2cb8bd9f994d83f46f465022 Description: Large Scale Machine Learning Toolbox SHOGUN - is a new machine learning toolbox with focus on large scale kernel methods and especially on Support Vector Machines (SVM) with focus to bioinformatics. It provides a generic SVM object interfacing to several different SVM implementations. Each of the SVMs can be combined with a variety of the many kernels implemented. It can deal with weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain, where an optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Apart from SVM 2-class classification and regression problems, a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to train hidden markov models are implemented. The input feature-objects can be dense, sparse or strings and of type int/short/double/char and can be converted into different feature types. Chains of preprocessors (e.g. substracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing. . SHOGUN comes in different flavours, a stand-a-lone version and also with interfaces to Matlab(tm), R, Octave, Readline and Python. This is the English user and developer documentation. Package: shogun-elwms-static Source: shogun Version: 1.1.0-6~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 208 Depends: neurodebian-popularity-contest, libarpack2 (>= 2.1), libatlas3-base, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libglpk0 (>= 4.30), libhdf5-7, libjson0 (>= 0.10), liblzma5 (>= 5.1.1alpha+20110809), liblzo2-2, libpython2.7 (>= 2.7), libshogun11 (= 1.1.0-6~nd70+1), libstdc++6 (>= 4.1.1), libxml2 (>= 2.6.27), zlib1g (>= 1:1.1.4), r-base-core, python (<< 2.8), python (>= 2.7), python-numpy (>= 1:1.6.1), python-numpy-abi9 Conflicts: shogun-elwms Replaces: shogun-elwms Homepage: http://www.shogun-toolbox.org Priority: optional Section: science Filename: pool/main/s/shogun/shogun-elwms-static_1.1.0-6~nd70+1_amd64.deb Size: 61254 SHA256: b3dc6f206322cb919513ad828846d34fc623478bf505f7af0e6de1afad1c313b SHA1: 9d5e39a6f9e3d104d13eea20481345775156f99c MD5sum: 15644852e4cb04ee9b119de1ec567231 Description: Large Scale Machine Learning Toolbox SHOGUN - is a new machine learning toolbox with focus on large scale kernel methods and especially on Support Vector Machines (SVM) with focus to bioinformatics. It provides a generic SVM object interfacing to several different SVM implementations. Each of the SVMs can be combined with a variety of the many kernels implemented. It can deal with weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain, where an optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Apart from SVM 2-class classification and regression problems, a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to train hidden markov models are implemented. The input feature-objects can be dense, sparse or strings and of type int/short/double/char and can be converted into different feature types. Chains of preprocessors (e.g. substracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing. . SHOGUN comes in different flavours, a stand-a-lone version and also with interfaces to Matlab(tm), R, Octave, Readline and Python. This is the eierlegendewollmilchsau package, providing interfaces and interoperability commands to R, Octave and Python all at once. Package: shogun-java-modular Source: shogun Version: 1.1.0-6~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 7574 Depends: neurodebian-popularity-contest, libarpack2 (>= 2.1), libatlas3-base, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libglpk0 (>= 4.30), libhdf5-7, libjson0 (>= 0.10), liblzma5 (>= 5.1.1alpha+20110809), liblzo2-2, libshogun11 (= 1.1.0-6~nd70+1), libstdc++6 (>= 4.1.1), libxml2 (>= 2.6.27), zlib1g (>= 1:1.1.4) Homepage: http://www.shogun-toolbox.org Priority: optional Section: science Filename: pool/main/s/shogun/shogun-java-modular_1.1.0-6~nd70+1_amd64.deb Size: 2247632 SHA256: ac5f4180ff5657214665e003533eb11923dd90ec0a631b15eb532eae8de1ad8b SHA1: c7da3ec3d6947e9d88dcaad075055948b87d45f6 MD5sum: 22a2d1c24fe1100268930ef759db2928 Description: Large Scale Machine Learning Toolbox SHOGUN - is a new machine learning toolbox with focus on large scale kernel methods and especially on Support Vector Machines (SVM) with focus to bioinformatics. It provides a generic SVM object interfacing to several different SVM implementations. Each of the SVMs can be combined with a variety of the many kernels implemented. It can deal with weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain, where an optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Apart from SVM 2-class classification and regression problems, a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to train hidden markov models are implemented. The input feature-objects can be dense, sparse or strings and of type int/short/double/char and can be converted into different feature types. Chains of preprocessors (e.g. substracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing. . SHOGUN comes in different flavours, a stand-a-lone version and also with interfaces to Matlab(tm), R, Octave, Readline and Python. This is the modular java package employing swig. Package: shogun-lua-modular Source: shogun Version: 1.1.0-6~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 11227 Depends: neurodebian-popularity-contest, libarpack2 (>= 2.1), libatlas3-base, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libglpk0 (>= 4.30), libhdf5-7, libjson0 (>= 0.10), liblua5.1-0, liblzma5 (>= 5.1.1alpha+20110809), liblzo2-2, libshogun11 (= 1.1.0-6~nd70+1), libstdc++6 (>= 4.1.1), libxml2 (>= 2.6.27), zlib1g (>= 1:1.1.4) Homepage: http://www.shogun-toolbox.org Priority: optional Section: science Filename: pool/main/s/shogun/shogun-lua-modular_1.1.0-6~nd70+1_amd64.deb Size: 2614016 SHA256: 6438b82eae5c423aca41f2fa03df2472bedc3eac92290071dd1c47acde91565d SHA1: a1728276cb4624e1514879c2cf5a98381527c8d5 MD5sum: 3d992952901b9fb93d31c6bcc9cef9ba Description: Large Scale Machine Learning Toolbox SHOGUN - is a new machine learning toolbox with focus on large scale kernel methods and especially on Support Vector Machines (SVM) with focus to bioinformatics. It provides a generic SVM object interfacing to several different SVM implementations. Each of the SVMs can be combined with a variety of the many kernels implemented. It can deal with weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain, where an optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Apart from SVM 2-class classification and regression problems, a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to train hidden markov models are implemented. The input feature-objects can be dense, sparse or strings and of type int/short/double/char and can be converted into different feature types. Chains of preprocessors (e.g. substracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing. . SHOGUN comes in different flavours, a stand-a-lone version and also with interfaces to Matlab(tm), R, Octave, Readline and Python. This is the modular lua package employing swig. Package: shogun-python-modular Source: shogun Version: 1.1.0-6~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 26343 Depends: neurodebian-popularity-contest, libarpack2 (>= 2.1), libatlas3-base, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libglpk0 (>= 4.30), libhdf5-7, libjson0 (>= 0.10), liblzma5 (>= 5.1.1alpha+20110809), liblzo2-2, libpython2.6 (>= 2.6), libpython2.7 (>= 2.7), libshogun11 (= 1.1.0-6~nd70+1), libstdc++6 (>= 4.1.1), libxml2 (>= 2.6.27), zlib1g (>= 1:1.1.4), python (<< 2.8), python (>= 2.6.6-7~), python-numpy (>= 1:1.6.1), python-numpy-abi9 Recommends: python-matplotlib, python-scipy Homepage: http://www.shogun-toolbox.org Priority: optional Section: science Filename: pool/main/s/shogun/shogun-python-modular_1.1.0-6~nd70+1_amd64.deb Size: 5923306 SHA256: 7d316f9cec1676fda6f0df9864e44078052df9755481d040bfdcf43535c7c5ff SHA1: aa44f7118f66b7baac0e0747e3a93b7cee09fb44 MD5sum: dc82087d5a260cb68e405261ce9c7468 Description: Large Scale Machine Learning Toolbox SHOGUN - is a new machine learning toolbox with focus on large scale kernel methods and especially on Support Vector Machines (SVM) with focus to bioinformatics. It provides a generic SVM object interfacing to several different SVM implementations. Each of the SVMs can be combined with a variety of the many kernels implemented. It can deal with weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain, where an optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Apart from SVM 2-class classification and regression problems, a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to train hidden markov models are implemented. The input feature-objects can be dense, sparse or strings and of type int/short/double/char and can be converted into different feature types. Chains of preprocessors (e.g. substracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing. . SHOGUN comes in different flavours, a stand-a-lone version and also with interfaces to Matlab(tm), R, Octave, Readline and Python. This is the modular Python package employing swig. Package: shogun-python-static Source: shogun Version: 1.1.0-6~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 232 Depends: neurodebian-popularity-contest, libarpack2 (>= 2.1), libatlas3-base, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libglpk0 (>= 4.30), libhdf5-7, libjson0 (>= 0.10), liblzma5 (>= 5.1.1alpha+20110809), liblzo2-2, libpython2.6 (>= 2.6), libpython2.7 (>= 2.7), libshogun11 (= 1.1.0-6~nd70+1), libstdc++6 (>= 4.1.1), libxml2 (>= 2.6.27), zlib1g (>= 1:1.1.4), python (<< 2.8), python (>= 2.6), python-numpy (>= 1:1.6.1), python-numpy-abi9 Recommends: python-matplotlib Conflicts: shogun-python Replaces: shogun-python Homepage: http://www.shogun-toolbox.org Priority: optional Section: science Filename: pool/main/s/shogun/shogun-python-static_1.1.0-6~nd70+1_amd64.deb Size: 66208 SHA256: 6caf382be4e16049d9cef7483afa01729d1b646a656fd2554ed6aa0d595a9138 SHA1: 4f107dd0b29b8a5057b6009429421b644d960a64 MD5sum: 4a1dfdedd327b095c24fb3911997ff3a Description: Large Scale Machine Learning Toolbox SHOGUN - is a new machine learning toolbox with focus on large scale kernel methods and especially on Support Vector Machines (SVM) with focus to bioinformatics. It provides a generic SVM object interfacing to several different SVM implementations. Each of the SVMs can be combined with a variety of the many kernels implemented. It can deal with weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain, where an optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Apart from SVM 2-class classification and regression problems, a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to train hidden markov models are implemented. The input feature-objects can be dense, sparse or strings and of type int/short/double/char and can be converted into different feature types. Chains of preprocessors (e.g. substracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing. . SHOGUN comes in different flavours, a stand-a-lone version and also with interfaces to Matlab(tm), R, Octave, Readline and Python. This is the static Python package without using swig. Package: shogun-r-static Source: shogun Version: 1.1.0-6~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 260 Depends: neurodebian-popularity-contest, libarpack2 (>= 2.1), libatlas3-base, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libglpk0 (>= 4.30), libhdf5-7, libjson0 (>= 0.10), liblzma5 (>= 5.1.1alpha+20110809), liblzo2-2, libshogun11 (= 1.1.0-6~nd70+1), libstdc++6 (>= 4.1.1), libxml2 (>= 2.6.27), zlib1g (>= 1:1.1.4), r-base-core Conflicts: shogun-r Replaces: shogun-r Homepage: http://www.shogun-toolbox.org Priority: optional Section: science Filename: pool/main/s/shogun/shogun-r-static_1.1.0-6~nd70+1_amd64.deb Size: 65462 SHA256: 59962bed7a822cd2aed8ce5405cb2a679b35230c14a25deb54ed1a3686931ae5 SHA1: 13f03c6874369d5a42daa5914444ade604959dfa MD5sum: 64a9036ac53c3914593c1fe35acaca7a Description: Large Scale Machine Learning Toolbox SHOGUN - is a new machine learning toolbox with focus on large scale kernel methods and especially on Support Vector Machines (SVM) with focus to bioinformatics. It provides a generic SVM object interfacing to several different SVM implementations. Each of the SVMs can be combined with a variety of the many kernels implemented. It can deal with weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain, where an optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Apart from SVM 2-class classification and regression problems, a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to train hidden markov models are implemented. The input feature-objects can be dense, sparse or strings and of type int/short/double/char and can be converted into different feature types. Chains of preprocessors (e.g. substracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing. . SHOGUN comes in different flavours, a stand-a-lone version and also with interfaces to Matlab(tm), R, Octave, Readline and Python. This is the R package. Package: shogun-ruby-modular Source: shogun Version: 1.1.0-6~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 8969 Depends: neurodebian-popularity-contest, libarpack2 (>= 2.1), libatlas3-base, libc6 (>= 2.2.5), libgcc1 (>= 1:4.1.1), libglpk0 (>= 4.30), libhdf5-7, libjson0 (>= 0.10), liblzma5 (>= 5.1.1alpha+20110809), liblzo2-2, libruby1.9.1 (>= 1.9.2.0), libshogun11 (= 1.1.0-6~nd70+1), libstdc++6 (>= 4.1.1), libxml2 (>= 2.6.27), zlib1g (>= 1:1.1.4) Homepage: http://www.shogun-toolbox.org Priority: optional Section: science Filename: pool/main/s/shogun/shogun-ruby-modular_1.1.0-6~nd70+1_amd64.deb Size: 1971980 SHA256: 9b844b1f15b54779af1c14764dad3ee0799b456ec6347aa9494fd636cfd18d9a SHA1: 172a04c97937f5690926c314630ade366641a9f5 MD5sum: 3315adbba945a37febe4ee8d693633e5 Description: Large Scale Machine Learning Toolbox SHOGUN - is a new machine learning toolbox with focus on large scale kernel methods and especially on Support Vector Machines (SVM) with focus to bioinformatics. It provides a generic SVM object interfacing to several different SVM implementations. Each of the SVMs can be combined with a variety of the many kernels implemented. It can deal with weighted linear combination of a number of sub-kernels, each of which not necessarily working on the same domain, where an optimal sub-kernel weighting can be learned using Multiple Kernel Learning. Apart from SVM 2-class classification and regression problems, a number of linear methods like Linear Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel) Perceptrons and also algorithms to train hidden markov models are implemented. The input feature-objects can be dense, sparse or strings and of type int/short/double/char and can be converted into different feature types. Chains of preprocessors (e.g. substracting the mean) can be attached to each feature object allowing for on-the-fly pre-processing. . SHOGUN comes in different flavours, a stand-a-lone version and also with interfaces to Matlab(tm), R, Octave, Readline and Python. This is the modular ruby package employing swig. Package: sigviewer Version: 0.5.1+svn556-3~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 972 Depends: neurodebian-popularity-contest, libbiosig1, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libqt4-xml (>= 4:4.5.3), libqtcore4 (>= 4:4.8.0), libqtgui4 (>= 4:4.6.1), libstdc++6 (>= 4.6) Homepage: http://sigviewer.sourceforge.net Priority: extra Section: science Filename: pool/main/s/sigviewer/sigviewer_0.5.1+svn556-3~nd80+1_amd64.deb Size: 331784 SHA256: 1f92d3471571a849d168fdbaf30cc1496891286658fbed63d4ecc06b2cb7c4eb SHA1: ef2ac60c5e79af04e2a06259de6c884ca3f51666 MD5sum: 588acc5945ea9ae5afdb70c785112f95 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: 8.5236~dfsg.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 18626 Depends: neurodebian-popularity-contest Recommends: spm8-data, spm8-doc Priority: extra Section: science Filename: pool/main/s/spm8/spm8-common_8.5236~dfsg.1-1~nd70+1_all.deb Size: 10751106 SHA256: 4b0892096fb3e6c5ba1254a3c3a218a92ae151e1a37fb8fc29dadbac8b624a6d SHA1: 0397da1f5bbd5171f4ef11c705679bd2a2915530 MD5sum: 283cc17b8f9c34af894c68533fe70a57 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: 8.5236~dfsg.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 73046 Depends: neurodebian-popularity-contest Priority: extra Section: science Filename: pool/main/s/spm8/spm8-data_8.5236~dfsg.1-1~nd70+1_all.deb Size: 52177460 SHA256: 51fc6055c99b93fcf82446d3357a9b8143dee566714de2921103a58a61eef981 SHA1: 11a2d79617c8c0883acdfc4e3689baf240bcdb79 MD5sum: e3fb3e6df0f60a562696f6ad2a91b292 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: 8.5236~dfsg.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 9273 Depends: neurodebian-popularity-contest Priority: extra Section: doc Filename: pool/main/s/spm8/spm8-doc_8.5236~dfsg.1-1~nd70+1_all.deb Size: 8991102 SHA256: e203c8227771f56005d1e04f7fbec1a7bfc58c5ba9dde1da5aa8bc32f434f9c2 SHA1: a984401fdd20fa64f68b76ec1fc06d73e6ed6b4c MD5sum: 3c6e980cbe8ec3bc7f268fcb98d177bf 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: spyder Version: 2.2.5+dfsg-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 113 Depends: neurodebian-popularity-contest, python:any, python-spyderlib (= 2.2.5+dfsg-1~nd80+1) Homepage: http://code.google.com/p/spyderlib/ Priority: extra Section: devel Filename: pool/main/s/spyder/spyder_2.2.5+dfsg-1~nd80+1_all.deb Size: 56608 SHA256: a8de9d9b4f9c988d157dc58f29cc63483fb3c9d27e132578f450c2b19915d715 SHA1: 99f8f5034cd15eb4ad529ddc15c72eef97ac72ec MD5sum: 7203c15127b6d591fc6c676a129e191b Description: python IDE for scientists Spyder (previously known as Pydee) is a free open-source Python development environment providing MATLAB-like features in a simple and light-weighted software Package: spykeviewer Version: 0.4.2-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1122 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-guidata, python-guiqwt (>= 2.1.4), python-spyderlib, python-spykeutils (>= 0.4.0), python-neo (>= 0.2.1), python-matplotlib, python-scipy, python-nose, python-sphinx, python-tables Recommends: libjs-jquery, libjs-underscore, ipython-qtconsole (>= 0.12) Homepage: http://www.ni.tu-berlin.de/software/spykeviewer Priority: extra Section: python Filename: pool/main/s/spykeviewer/spykeviewer_0.4.2-1~nd80+1_all.deb Size: 536902 SHA256: cefd4c243b25adc50dafa98768200950122261f6b20abff46178455be692294c SHA1: 0dea19ebc724acabc44920060874371c34f03673 MD5sum: 4fa1028deeb1ddcf20d6964a20d62a39 Description: graphical utility for analyzing electrophysiological data Spyke Viewer is a multi-platform GUI application for navigating, analyzing and visualizing electrophysiological datasets. Based on the Neo framework, it works with a wide variety of data formats. Spyke Viewer includes an integrated Python console and a plugin system for custom analyses and plots. Package: stabilitycalc Version: 0.1-1~nd70+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~nd70+1_all.deb Size: 28600 SHA256: d06a1ee5b6de6404f66db07820f084ca9699bfcef21015bb34c9cd64e1900e74 SHA1: 6515b207f33e7ef2ea59d0db40bb2b35d39355b8 MD5sum: 365f3a53daff4820e153393bb90a269c 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: 0.14.10-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2932 Depends: neurodebian-popularity-contest, libbiosig1, libblas3 | libblas.so.3, libc6 (>= 2.14), libcholmod2.1.2, libfftw3-double3, libgcc1 (>= 1:4.1.1), libhdf5-8, liblapack3 | liblapack.so.3, libpython2.7 (>= 2.7), libstdc++6 (>= 4.9), libwxbase3.0-0 (>= 3.0.2), libwxgtk3.0-0 (>= 3.0.2), python-numpy (>= 1:1.8.0), python-numpy-abi9, python2.7, python:any (>= 2.6.6-7~), libbiosig-dev, libsuitesparse-dev, python-wxgtk3.0 | python-wxgtk2.8 (>= 2.8.9), python-matplotlib Recommends: python-scipy Homepage: http://www.stimfit.org Priority: optional Section: science Filename: pool/main/s/stimfit/stimfit_0.14.10-1~nd80+1_amd64.deb Size: 757850 SHA256: 54154645be0e7eb5540d3d1a060366560fbc4a86468e15310d6d730cfd0ecc06 SHA1: 0ea66324252e8ca275e193dccd629f030c2a1422 MD5sum: d9709cd26400bfdab61a6272970dd3cd Description: 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: 0.14.10-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 32224 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_0.14.10-1~nd80+1_amd64.deb Size: 6140610 SHA256: 1a1da492c52f8f1c4ae488a3de34ae2bc7574e42ce683a603c841a6819d6c8de SHA1: a8b5039bf1745e1f7ee0588822247dfe9cbd6e23 MD5sum: 206946e03725f217347e2207d83a86ce 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. This package contains the debug symbols for Stimfit. Package: svgtune Version: 0.1.0-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 13 Depends: neurodebian-popularity-contest, python, python-lxml Suggests: inkscape Homepage: http://github.com/yarikoptic/svgtune Priority: optional Section: graphics Filename: pool/main/s/svgtune/svgtune_0.1.0-1~nd70+1_all.deb Size: 6828 SHA256: 664347bc9decb736aec4f14819a9eef0c8afedf8aae82d45087ff30facae72af SHA1: c2ca191c7b3cd09c05d737e60ed14c298dd3190e MD5sum: ac63ca302b7db2272aced98a86d44a08 Description: tool to generate a set of .svg files out of a single .svg file svgtune is just a little helper to generate a set of .svg files out of a single .svg file, by tuning respective groups/layers visibility, transparency or anything else. . It might come very handy for generation of incremental figures to be embedded into the presentation in any format which inkscape could render using original .svg file (e.g. pdf, png). Package: tcl-vtk Source: vtk Version: 5.8.0-7+b0~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 16157 Depends: neurodebian-popularity-contest, libvtk5.8 (= 5.8.0-7+b0~nd70+1), libc6 (>= 2.7), libgcc1 (>= 1:4.1.1), libmysqlclient16 (>= 5.1.50-1), libpq5, libstdc++6 (>= 4.1.1), libx11-6, tcl8.5 (>= 8.5.0), tk8.5 (>= 8.5.0), zlib1g (>= 1:1.1.4) Suggests: vtk-examples, vtk-doc Replaces: vtk, vtk-tcl Homepage: http://www.vtk.org/ Priority: optional Section: interpreters Filename: pool/main/v/vtk/tcl-vtk_5.8.0-7+b0~nd70+1_amd64.deb Size: 5322644 SHA256: 2f787363443fa6563ed9ad98b9e2e570a354106c758c5652b4ebf23aca8e4fec SHA1: eec67a1fadd025ba3069f8bf73fb323f5c466bd0 MD5sum: 93c1f13de7e12e1be1c105180edbd3d5 Description: Tcl bindings for VTK The Visualization Toolkit (VTK) is an object oriented, high level library that allows one to easily write C++ programs, Tcl, Python and Java scripts that do 3D visualization. . This provides the shared libraries and executable that enable one to use VTK from Tcl/Tk scripts. You will need Tcl/Tk and vtk installed to use this. Package: testkraut Version: 0.0.1-1~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 358 Depends: neurodebian-popularity-contest, python (>= 2.6), python-support (>= 0.90.0), python-numpy, libjs-underscore, libjs-jquery, python-argparse Recommends: strace, python-scipy, python-colorama, python-apt Homepage: https://github.com/neurodebian/testkraut Priority: extra Section: python Filename: pool/main/t/testkraut/testkraut_0.0.1-1~nd70+1_all.deb Size: 100034 SHA256: 569f799af355429d7939adc34742caadb6f3eb108bb1a32b35cc5cabdb8336ca SHA1: e4a40dab2d773f92b8a810ba078d96d218775dcb MD5sum: 1a32c11b522abfa6f8b658c890f2cbe4 Description: test and evaluate heterogeneous data processing pipelines This is a framework for software testing. That being said, testkraut tries to minimize the overlap with the scopes of unit testing, regression testing, and continuous integration testing. Instead, it aims to complement these kinds of testing, and is able to re-use them, or can be integrated with them. . In a nutshell testkraut helps to facilitate statistical analysis of test results. In particular, it focuses on two main scenarios: . * Comparing results of a single (test) implementation across different or changing computational environments (think: different operating systems, different hardware, or the same machine before an after a software upgrade). * Comparing results of different (test) implementations generating similar output from identical input (think: performance of various signal detection algorithms). . While such things can be done using other available tools as well, testkraut aims to provide a lightweight, yet comprehensive description of a test run. Such a description allows for decoupling test result generation and analysis – opening up the opportunity to “crowd-source” software testing efforts, and aggregate results beyond the scope of a single project, lab, company, or site. Python-Version: 2.6, 2.7 Package: tigervnc-common Source: tigervnc Version: 1.2.0+X1.12.4-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 258 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libgnutls26 (>= 2.12.17-0), libice6 (>= 1:1.0.0), libjpeg8 (>= 8c), libpam0g (>= 0.99.7.1), libsm6, libstdc++6 (>= 4.6), libwrap0 (>= 7.6-4~), libx11-6 (>= 2:1.4.99.1), libxext6, zlib1g (>= 1:1.1.4) Conflicts: tigervnc-server (<< 1.1.90), tigervnc-viewer (<< 1.1.90) Homepage: http://www.tigervnc.org Priority: optional Section: x11 Filename: pool/main/t/tigervnc/tigervnc-common_1.2.0+X1.12.4-1~nd70+1_amd64.deb Size: 82548 SHA256: 505ea20351136af2e78ca5874b7ba7f71dede7c08fafd7e5d7ad1a6673f63783 SHA1: 3230862b1e04a912b263cca1ba9c63d93e9b45b0 MD5sum: c4df729b6bfa12d296a3fa039eb7158d Description: Virtual network computing; Common software needed by clients and servers VNC stands for Virtual Network Computing. It is, in essence, a remote display system which allows you to view a computing `desktop' environment not only on the machine where it is running, but from anywhere on the Internet and from a wide variety of machine architectures. . It is implemented in a client/server model. This package provides the common software for both client and server. Package: tigervnc-scraping-server Source: tigervnc Version: 1.2.0+X1.12.4-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 590 Depends: neurodebian-popularity-contest, xserver-common (>= 2:1.7.7), keyboard-configuration, udev (>= 149), tigervnc-common, libc6 (>= 2.11), libgcc1 (>= 1:4.1.1), libgnutls26 (>= 2.12.17-0), libice6 (>= 1:1.0.0), libjpeg8 (>= 8c), libpam0g (>= 0.99.7.1), libsm6, libstdc++6 (>= 4.6), libwrap0 (>= 7.6-4~), libx11-6, libxext6, libxtst6, zlib1g (>= 1:1.1.4) Recommends: libgl1-mesa-dri (>= 7.1~rc1) Suggests: xfonts-100dpi | xfonts-75dpi, xfonts-scalable Breaks: tigervnc-server (<< 1.1.90) Replaces: tigervnc-server (<< 1.1.90) Provides: vnc-server Homepage: http://www.tigervnc.org Priority: optional Section: x11 Filename: pool/main/t/tigervnc/tigervnc-scraping-server_1.2.0+X1.12.4-1~nd70+1_amd64.deb Size: 224032 SHA256: cfe13eebecb61f5f73e90295940e05d8d2966810d7369ca3c400cf4551582952 SHA1: 16fd139d1f61395765c1e4f326dccf21f2c96923 MD5sum: 1bad73138d9c24c66d856e1e3d3a4978 Description: Virtual network computing server software VNC stands for Virtual Network Computing. It is, in essence, a remote display system which allows you to view a computing `desktop' environment not only on the machine where it is running, but from anywhere on the Internet and from a wide variety of machine architectures. . This package provides a vncserver which uses screen scraping of an already running X server to provide its VNC desktop. The VNC desktop can be viewed by any vncviewer even on other operating systems. . Note: If you only want to scrap your local X11 server, you should consider the tigervnc-xorg-extension package. This package provides the vnc extension for your local X11 server. The usage of this extension is more efficient than a scraping vnc server. Package: tigervnc-standalone-server Source: tigervnc Version: 1.2.0+X1.12.4-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2608 Depends: neurodebian-popularity-contest, xserver-common (>= 2:1.7.7), keyboard-configuration, udev (>= 149), tigervnc-common, libaudit0 (>= 1.7.13), libc6 (>= 2.11), libgcc1 (>= 1:4.1.1), libgcrypt11 (>= 1.4.5), libgnutls26 (>= 2.12.17-0), libjpeg8 (>= 8c), libpam0g (>= 0.99.7.1), libpixman-1-0 (>= 0.21.6), libselinux1 (>= 2.0.82), libstdc++6 (>= 4.6), libwrap0 (>= 7.6-4~), libx11-6, libxau6, libxdmcp6, libxfont1 (>= 1:1.4.2), zlib1g (>= 1:1.1.4), perl Recommends: libgl1-mesa-dri (>= 7.1~rc1) Suggests: xfonts-100dpi | xfonts-75dpi, xfonts-scalable Breaks: tigervnc-server (<< 1.1.90) Replaces: tigervnc-server (<< 1.1.90) Provides: vnc-server Homepage: http://www.tigervnc.org Priority: optional Section: x11 Filename: pool/main/t/tigervnc/tigervnc-standalone-server_1.2.0+X1.12.4-1~nd70+1_amd64.deb Size: 1176434 SHA256: 6de0a44cda867df8c32be1727d5a65fbee5a3e9fb78809f62ff6d154ee133f36 SHA1: 493c21441192a995fb574f4dadea412c576cbf05 MD5sum: 5ae8af197561408f96fccc8c46a2fcb6 Description: Virtual network computing server software VNC stands for Virtual Network Computing. It is, in essence, a remote display system which allows you to view a computing `desktop' environment not only on the machine where it is running, but from anywhere on the Internet and from a wide variety of machine architectures. . This package provides a standalone vncserver to which X clients can connect. The server generates a display that can be viewed with a vncviewer. . Note: This server does not need a display. You need a vncviewer to see something. This viewer may also be on a computer running other operating systems. Package: tigervnc-viewer Source: tigervnc Version: 1.2.0+X1.12.4-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1121 Depends: neurodebian-popularity-contest, xserver-common (>= 2:1.7.7), tigervnc-common, libc6 (>= 2.11), libfontconfig1 (>= 2.9.0), libgcc1 (>= 1:4.1.1), libgnutls26 (>= 2.12.17-0), libice6 (>= 1:1.0.0), libjpeg8 (>= 8c), libpam0g (>= 0.99.7.1), libsm6, libstdc++6 (>= 4.6), libwrap0 (>= 7.6-4~), libx11-6, libxcursor1 (>> 1.1.2), libxext6, libxfixes3, libxft2 (>> 2.1.1), libxinerama1, zlib1g (>= 1:1.1.4) Provides: vnc-viewer Homepage: http://www.tigervnc.org Priority: optional Section: x11 Filename: pool/main/t/tigervnc/tigervnc-viewer_1.2.0+X1.12.4-1~nd70+1_amd64.deb Size: 514712 SHA256: c5945488c72556a475075d4751dab111fda3e416665b10340b934efa7ad82b5c SHA1: fb36d4fe2cf5d5e588faae88b362e64c076b3203 MD5sum: 0df0484e1129d68d970638c051c62534 Description: Virtual network computing client software for X VNC stands for Virtual Network Computing. It is, in essence, a remote display system which allows you to view a computing `desktop' environment not only on the machine where it is running, but from anywhere on the Internet and from a wide variety of machine architectures. . It is implemented in a client/server model. This package provides a vncclient for X, with this you can connect to a vncserver somewhere in the network and display its content in a window. There are vncservers available for other operating systems. Package: tigervnc-xorg-extension Source: tigervnc Version: 1.2.0+X1.12.4-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 792 Depends: neurodebian-popularity-contest, xserver-common (>= 2:1.7.7), keyboard-configuration, udev (>= 149), tigervnc-common, libaudit0 (>= 1.7.13), libc6 (>= 2.11), libgcc1 (>= 1:4.1.1), libgnutls26 (>= 2.12.17-0), libjpeg8 (>= 8c), libpam0g (>= 0.99.7.1), libstdc++6 (>= 4.6), libwrap0 (>= 7.6-4~), zlib1g (>= 1:1.1.4) Recommends: libgl1-mesa-dri (>= 7.1~rc1) Suggests: xfonts-100dpi | xfonts-75dpi, xfonts-scalable Breaks: tigervnc-server (<< 1.1.90) Replaces: tigervnc-server (<< 1.1.90) Provides: vnc-server, vnc-xorg-extension Homepage: http://www.tigervnc.org Priority: optional Section: x11 Filename: pool/main/t/tigervnc/tigervnc-xorg-extension_1.2.0+X1.12.4-1~nd70+1_amd64.deb Size: 288610 SHA256: 39f2cdd1912dc038a5f42b85ffb59417d4743e20dcba876f4505e59492352526 SHA1: 3c5e8d96beea3d24bfd14b5c84bf4ec9a38635a2 MD5sum: a1ff87dc29126eba83bd5e1faec862bc Description: Virtual network computing server software VNC stands for Virtual Network Computing. It is, in essence, a remote display system which allows you to view a computing `desktop' environment not only on the machine where it is running, but from anywhere on the Internet and from a wide variety of machine architectures. . It contains an X server connector so clients can connect to your local X desktop directly. Package: ubuntu-keyring Version: 2010.+09.30~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 13 Recommends: gpgv Priority: important Section: misc Filename: pool/main/u/ubuntu-keyring/ubuntu-keyring_2010.+09.30~nd70+1_all.deb Size: 11794 SHA256: c326d77f59c53ce386ed48a4f622087920af9c2d0a9b826e734680500b0cd3a0 SHA1: a46c68a0539f105919576423f0daeb6709e6a10a MD5sum: 8bed9b239d848186981a2e04eec03bb1 Description: GnuPG keys of the Ubuntu archive The Ubuntu project digitally signs its Release files. This package contains the archive keys used for that. Package: utopia-documents Version: 2.4.4-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 19941 Depends: neurodebian-popularity-contest, libboost-python1.55.0, libboost-system1.55.0, libboost-thread1.55.0, libc6 (>= 2.14), libexpat1 (>= 2.0.1), libfreetype6 (>= 2.2.1), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglew1.10 (>= 1.10.0), libglu1-mesa | libglu1, libice6 (>= 1:1.0.0), libpcre3 (>= 8.10), libpcrecpp0 (>= 7.7), libpython2.7 (>= 2.7), libqglviewer2, libqjson0 (>= 0.7.1), libqt4-network (>= 4:4.7.0~beta1), libqt4-opengl (>= 4:4.5.3), libqt4-script (>= 4:4.5.3), libqt4-svg (>= 4:4.5.3), libqt4-xml (>= 4:4.5.3), libqt4-xmlpatterns (>= 4:4.5.3), libqtcore4 (>= 4:4.8.0), libqtgui4 (>= 4:4.8.0), libqtwebkit4 (>= 2.1.0~2011week13), libraptor1 (>= 1.4.21-3), libsm6, libssl1.0.0 (>= 1.0.0), libstdc++6 (>= 4.9), libx11-6, libxext6, zlib1g (>= 1:1.1.4), python:any (>= 2.6.6-7~), python2.7, python-imaging, python-lxml (<< 3.0.0) | python-cssselect, python-lxml, xdg-utils, python-suds Homepage: http://utopiadocs.com Priority: optional Section: science Filename: pool/main/u/utopia-documents/utopia-documents_2.4.4-1~nd80+1_amd64.deb Size: 5240584 SHA256: 5f17cae58f53963d989b67f810b86c6bb2a86ae4e1cf3a8a6b1b0bbf734973b0 SHA1: d550ae847bc38a93ab0b46fc0f839587563ea6e9 MD5sum: ad9c942dead1b74b9c54d107b1da6f80 Description: PDF reader that displays interactive annotations on scientific articles. Utopia Documents is a free PDF reader that connects the static content of scientific articles to the dynamic world of online content. It makes it easy to explore an article's content and claims, and investigate other recent articles that discuss the same or similar topics. . Get immediate access to an article's metadata and browse the relationship it has with the world at large. Generate a formatted citation for use in your own work, follow bibliographic links to cited articles, or get a document's related data at the click of a button. . Various extensions provide links to blogs, online data sources and to social media sites so you can see what other researchers have been saying about not only the article you're reading but its subject matter too. Package: utopia-documents-dbg Source: utopia-documents Version: 2.4.4-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 45796 Depends: neurodebian-popularity-contest, utopia-documents (= 2.4.4-1~nd80+1) Homepage: http://utopiadocs.com Priority: extra Section: debug Filename: pool/main/u/utopia-documents/utopia-documents-dbg_2.4.4-1~nd80+1_amd64.deb Size: 44543122 SHA256: ae8af33fd93a0dbefb1b259175926f0e7c53322a87d1bc57f309be5c6a3731b4 SHA1: c679b42720263ef8079366acf3ab7c76d773d5e3 MD5sum: 93b563768d9f4103bfa9d5d97fc8babe Description: debugging symbols for utopia-documents Utopia Documents is a free PDF reader that connects the static content of scientific articles to the dynamic world of online content. . This package contains the debugging symbols for utopia-documents. Package: via-bin Source: via Version: 2.0.4-2~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 628 Depends: neurodebian-popularity-contest, lesstif2 (>= 1:0.94.4), libatlas3gf-base, libc6 (>= 2.7), libgsl0ldbl (>= 1.9), libice6 (>= 1:1.0.0), libpng12-0 (>= 1.2.13-4), libsm6, libvia2, libx11-6, libxext6, libxmu6, libxt6 Recommends: libvia-doc Conflicts: via, via-utils Replaces: via-utils Homepage: http://www.cbs.mpg.de/institute/software/lipsia Priority: optional Section: science Filename: pool/main/v/via/via-bin_2.0.4-2~nd70+1_amd64.deb Size: 180770 SHA256: 24267304fa1bc850db435571d21e5a6377fc6a46fc2ab6651aea60829f51f6a1 SHA1: 4d805d92442f6d4bc1596aacd2565e1eb4171e5d MD5sum: b11732d6d0c3028be0668f041559164a Description: tools for volumetric image analysis VIA is a volumetric image analysis suite for functional and structural (medical) images. The suite consists of different tools ranging from simple data handling over viewers to complex image transformation. . All tools operate on data in VISTA format. The package contains several converters from e.g. PNG, PGM or PNM to this data format and back. Package: vowpal-wabbit Version: 7.3-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 51 Depends: neurodebian-popularity-contest, libboost-program-options1.49.0 (>= 1.49.0-1), libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0), libvw0 (= 7.3-1~nd80+1), zlib1g (>= 1:1.1.4) Suggests: vowpal-wabbit-doc Homepage: http://hunch.net/~vw/ Priority: optional Section: science Filename: pool/main/v/vowpal-wabbit/vowpal-wabbit_7.3-1~nd80+1_amd64.deb Size: 20796 SHA256: 1303783bc3922b503f96350e78b9042a6c9149790787943bc89d4193664755bd SHA1: 7e0d025f0a1a64c9d8280606f81dc569e8c2566e MD5sum: b8668a725de6d4bc2d9a92c1754e370d Description: fast and scalable online machine learning algorithm Vowpal Wabbit is a fast online machine learning algorithm. The core algorithm is specialist gradient descent (GD) on a loss function (several are available). VW features: - flexible input data specification - speedy learning - scalability (bounded memory footprint, suitable for distributed computation) - feature pairing Package: vowpal-wabbit-dbg Source: vowpal-wabbit Version: 7.3-1~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 8203 Depends: neurodebian-popularity-contest, vowpal-wabbit (= 7.3-1~nd80+1) Homepage: http://hunch.net/~vw/ Priority: extra Section: debug Filename: pool/main/v/vowpal-wabbit/vowpal-wabbit-dbg_7.3-1~nd80+1_amd64.deb Size: 2398204 SHA256: a1c081f5999cf7e05578dca6d52af703b50bbfd748f298b950f1d8c579616a76 SHA1: 08f96ff7a33f28ef5da53e9032bbc64beb20ff13 MD5sum: 0b74ec818866b7bbb20324e530df0cca Description: fast and scalable online machine learning algorithm - debug files Vowpal Wabbit is a fast online machine learning algorithm. The core algorithm is specialist gradient descent (GD) on a loss function (several are available). VW features: - flexible input data specification - speedy learning - scalability (bounded memory footprint, suitable for distributed computation) - feature pairing . This package contains debug symbols for the binaries shipped by vowpal-wabbit packages. Package: vowpal-wabbit-doc Source: vowpal-wabbit Version: 7.3-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 70918 Depends: neurodebian-popularity-contest Recommends: vowpal-wabbit Homepage: http://hunch.net/~vw/ Priority: optional Section: doc Filename: pool/main/v/vowpal-wabbit/vowpal-wabbit-doc_7.3-1~nd80+1_all.deb Size: 50202356 SHA256: 8bb47b480801ecdf84afcda31516a3767c49785f8429e4780cbdc72f5f8d4de5 SHA1: f7f651df80ebf16745bc64017e5ad98b92f8d33d MD5sum: 8da37fb5c8b657db003767ec656bb302 Description: fast and scalable online machine learning algorithm - documentation Vowpal Wabbit is a fast online machine learning algorithm. The core algorithm is specialist gradient descent (GD) on a loss function (several are available). VW features: - flexible input data specification - speedy learning - scalability (bounded memory footprint, suitable for distributed computation) - feature pairing . This package contains examples (tests) for vowpal-wabbit. Package: voxbo Version: 1.8.5~svn1246-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 10232 Depends: neurodebian-popularity-contest, libc6 (>= 2.3.4), libfontconfig1 (>= 2.8.0), libfreetype6 (>= 2.2.1), libgcc1 (>= 1:4.1.1), libgsl0ldbl (>= 1.9), libpng12-0 (>= 1.2.13-4), libqt4-network (>= 4:4.5.3), libqt4-qt3support (>= 4:4.5.3), libqtcore4 (>= 4:4.6.1), libqtgui4 (>= 4:4.6.1), libstdc++6 (>= 4.4.0), libx11-6, libxext6, libxi6, libxrender1, zlib1g (>= 1:1.1.4) Suggests: mni-colin27-nifti, matlab-spm8 Homepage: http://www.voxbo.org Priority: extra Section: science Filename: pool/main/v/voxbo/voxbo_1.8.5~svn1246-1~nd70+1_amd64.deb Size: 3755282 SHA256: 6bdf208f5be29c38d1a289781dc115bd8ced8ccca4666749bee93844550f6005 SHA1: 72dec9297a356f210fdea023341c40156a40fa0c MD5sum: da38303fc1cedf6c540fa94536d1a418 Description: processing, statistical analysis, and display of brain imaging data This is a toolkit for analysis of functional neuroimaging (chiefly fMRI) experiments and voxel-based lesion-behavior mapping. VoxBo supports the modified GLM (for autocorrelated data), as well as the standard GLM for non-autocorrelated data. The toolkit is designed to be interoperable with AFNI, FSL, SPM and others. Package: vrpn Version: 07.30+dfsg-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 386 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), libvrpn0 (= 07.30+dfsg-1~nd70+1), libvrpnserver0 (= 07.30+dfsg-1~nd70+1) Homepage: http://www.cs.unc.edu/Research/vrpn/ Priority: extra Section: utils Filename: pool/main/v/vrpn/vrpn_07.30+dfsg-1~nd70+1_amd64.deb Size: 113446 SHA256: 30c10d6d3bb885fc839c049206fd74a92891b53e3a1dec479b4cd1beaeab96e3 SHA1: 9a7b23fc31f9c78fe54ea0dfcfe6e62f899cd5a5 MD5sum: 41c5739a785c4f86691d9bb9f130cafd Description: Virtual Reality Peripheral Network (executables) The Virtual-Reality Peripheral Network (VRPN) is a set of classes within a library and a set of servers that are designed to implement a network-transparent interface between application programs and the set of physical devices (tracker, etc.) used in a virtual-reality (VR) system. The idea is to have a PC or other host at each VR station that controls the peripherals (tracker, button device, haptic device, analog inputs, sound, etc). VRPN provides connections between the application and all of the devices using the appropriate class-of-service for each type of device sharing this link. The application remains unaware of the network topology. Note that it is possible to use VRPN with devices that are directly connected to the machine that the application is running on, either using separate control programs or running all as a single program. . This package contains the executables like the VRPN server. Package: vrpn-dbg Source: vrpn Version: 07.30+dfsg-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 5779 Depends: neurodebian-popularity-contest, libvrpn0 (= 07.30+dfsg-1~nd70+1), libvrpnserver0 (= 07.30+dfsg-1~nd70+1), vrpn (= 07.30+dfsg-1~nd70+1) Homepage: http://www.cs.unc.edu/Research/vrpn/ Priority: extra Section: debug Filename: pool/main/v/vrpn/vrpn-dbg_07.30+dfsg-1~nd70+1_amd64.deb Size: 1811102 SHA256: 12728df36d1115de7ad9b7937afa94a364869f221a24422d6b7a36af30513149 SHA1: 927f5bc1061857f298fccd6eac170612468f3be9 MD5sum: 9a06cfd29e23ff071f489c5253cd7552 Description: Virtual Reality Peripheral Network (debugging symbols) The Virtual-Reality Peripheral Network (VRPN) is a set of classes within a library and a set of servers that are designed to implement a network-transparent interface between application programs and the set of physical devices (tracker, etc.) used in a virtual-reality (VR) system. The idea is to have a PC or other host at each VR station that controls the peripherals (tracker, button device, haptic device, analog inputs, sound, etc). VRPN provides connections between the application and all of the devices using the appropriate class-of-service for each type of device sharing this link. The application remains unaware of the network topology. Note that it is possible to use VRPN with devices that are directly connected to the machine that the application is running on, either using separate control programs or running all as a single program. . This package contains the debugging symbols of the libraries and executables. Package: vtk-dicom-tools Source: vtk-dicom Version: 0.5.5-2~nd80+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 267 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.9), libvtk-dicom0.5, libvtk5.8 Homepage: http://github.com/dgobbi/vtk-dicom/ Priority: optional Section: utils Filename: pool/main/v/vtk-dicom/vtk-dicom-tools_0.5.5-2~nd80+1_amd64.deb Size: 72962 SHA256: 0ee264a3c4ff9e9a3dd06c28f38f1002be7da2e0b37f4062f5043b574b4ac875 SHA1: 9f618954c9501fa2c5b5654ccebaee296c1b9496 MD5sum: f6bd602590b18c2ac647cc84144b06b6 Description: DICOM for VTK - tools This package contains a set of classes for managing DICOM files and metadata from within VTK, and some utility programs for interrogating and converting DICOM files. . Command line tools Package: vtk-doc Source: vtk Version: 5.8.0-7+b0~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 342007 Depends: neurodebian-popularity-contest, doc-base Suggests: libvtk5-dev, vtk-examples, vtkdata Homepage: http://www.vtk.org/ Priority: optional Section: doc Filename: pool/main/v/vtk/vtk-doc_5.8.0-7+b0~nd70+1_all.deb Size: 66709864 SHA256: 1a71117b4f7574428e9da98482fb0c2cb41581e0ca6d2e931ea639a5da51263c SHA1: 74a40de489c5a44161b4aa468547d356da0bf911 MD5sum: 4c9a59935cca888f4c608d56f7eb3213 Description: VTK class reference documentation The Visualization Toolkit (VTK) is an object oriented, high level library that allows one to easily write C++ programs, Tcl, Python and Java scripts that do 3D visualization. . This package contains exhaustive HTML documentation for the all the documented VTK C++ classes. The documentation was generated using doxygen and some excellent perl scripts from Sebastien Barre et. al. Please read the README.docs in /usr/share/doc/vtk-doc/ for details. The documentation is available under /usr/share/doc/vtk/html. Package: vtk-examples Source: vtk Version: 5.8.0-7+b0~nd70+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2521 Depends: neurodebian-popularity-contest Suggests: libvtk5-dev, tcl-vtk, python-vtk, vtk-doc, python, tclsh, libqt4-dev Homepage: http://www.vtk.org/ Priority: optional Section: graphics Filename: pool/main/v/vtk/vtk-examples_5.8.0-7+b0~nd70+1_all.deb Size: 578898 SHA256: d070189a36ffd5bed00de02b3c794d0fa8f8bb2765fbc36f0f99c1634cda5ac7 SHA1: 132096d02c71c2f969d9baff0842e4afcdbb501c MD5sum: c018d4c1cace1b218dec239a1cf5e39e Description: C++, Tcl and Python example programs/scripts for VTK The Visualization Toolkit (VTK) is an object oriented, high level library that allows one to easily write C++ programs, Tcl, Python and Java scripts that do 3D visualization. . This package contains examples from the VTK source. To compile the C++ examples you will need to install the vtk-dev package as well. Some of them require the libqt4-dev package. . The Python and Tcl examples can be run with the corresponding packages (python-vtk, tcl-vtk). Package: xmhtml1 Source: xmhtml Version: 1.1.7-17~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 528 Depends: neurodebian-popularity-contest, lesstif2 (>= 1:0.94.4), libc6 (>= 2.7), libjpeg8 (>= 8c), libpng12-0 (>= 1.2.13-4), libxpm4 Priority: optional Section: libs Filename: pool/main/x/xmhtml/xmhtml1_1.1.7-17~nd70+1_amd64.deb Size: 256342 SHA256: 3ec43406d65e048e526712069eb321c6ffb44f9ed828b3648d9d0e7cb5bdcbbc SHA1: add62d428312df8cbfb9fbbc287535e6d70ebd27 MD5sum: 25c341b0b6a71c758df0050d3376436b Description: A Motif widget for display HTML 3.2 XmHTML is a high performance Motif Widget capable of displaying HTML 3.2 confirming text. Graphics support, lesstif compatibility and extensive documentation are amongst its many features. . This package provides the runtime shared library. The xmhtml-dev package provides the header files, and the static library. Package: xmhtml1-dev Source: xmhtml Version: 1.1.7-17~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 985 Depends: neurodebian-popularity-contest, xmhtml1, lesstif2-dev | libmotif-dev, libc6-dev Conflicts: xmhtml-dev Provides: xmhtml-dev Priority: optional Section: devel Filename: pool/main/x/xmhtml/xmhtml1-dev_1.1.7-17~nd70+1_amd64.deb Size: 345494 SHA256: fe8bf3f19ee02df02bdfedba5e22a5ae424ae89495ec24491a5f19d3e0cd843a SHA1: fc34586925edb9638c175287d3e9a856dd1ab4a6 MD5sum: d34d4ffca25f74e83a4bd4ee1d6f11db Description: A Motif widget for display HTML 3.2 XmHTML is a high performance Motif Widget capable of displaying HTML 3.2 confirming text. Graphics support, lesstif compatibility and extensive documentation are amongst its many features. . This is the development kit, containing static libraries and header files necessary to build programs that use xmhtml. The runtime library is provided by the xmhtml package. Package: xppaut Version: 6.11b+1.dfsg-1~nd70+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 5809 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), libx11-6 Homepage: http://www.math.pitt.edu/~bard/xpp/xpp.html Priority: optional Section: science Filename: pool/main/x/xppaut/xppaut_6.11b+1.dfsg-1~nd70+1_amd64.deb Size: 4187354 SHA256: 5fc94705f843f8719cc00b08626bfe7800b55fd64b26d944313e5a09984da942 SHA1: 2b7c0b88eab5942584ce1c79755424e71b8363fb MD5sum: f5b23e13b63b29d071c016e1176a554c Description: Phase Plane Plus Auto: Solves many kinds of equations XPPAUT is a tool for solving * differential equations, * difference equations, * delay equations, * functional equations, * boundary value problems, and * stochastic equations. . The code brings together a number of useful algorithms and is extremely portable. All the graphics and interface are written completely in Xlib which explains the somewhat idiosyncratic and primitive widgets interface.