Package: aghermann Version: 1.0.6-1~nd80+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1549 Depends: neurodebian-popularity-contest, libatk1.0-0 (>= 1.12.4), libc6 (>= 2.8), 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_i386.deb Size: 547986 SHA256: 6c5fed5ff010aa61b4621fe75ecb8641c1551d101a12c08a7e0f22c8216faeb0 SHA1: 3328191be79eb88d19980295110e15db9197f1f4 MD5sum: cff28c2192a7240e623cfffb44731ca8 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 167430 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), 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_i386.deb Size: 25175668 SHA256: 3fe482d3036f648cc199ac725328ae635301aeb6a275f3dff08b99e8a7df27cb SHA1: bcba81fd5e626c2028cf7ff318a8f3c5ec9b4f82 MD5sum: 8a5d6d896a0694fc8a2cc7ab7f24c38a 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 685 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), 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_i386.deb Size: 247956 SHA256: 55b1afcf376f66187ef1acab4925bdb216395de2428ed46568f093cbb77009d9 SHA1: cd5d3490839dcc0986055b41e5f0712e22c2277f MD5sum: a1e0ae5f3e4725dc35f560254bf435fe 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 3601 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_i386.deb Size: 523466 SHA256: e7b224fb1c2b7c7789d5bdf04af92109d753cc198282e7164873b58a80f85cf1 SHA1: 80982b8ed2ab6a9dd05755edc5a50cbee36aa93d MD5sum: 7f8701fac284bf1cf9cf9d121b7a39b6 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 4771 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_i386.deb Size: 3842056 SHA256: 9e952043be1409299f65d0d41ccb7c5a1c54906779a441d0e85e80773f46b23d SHA1: d88e9699419f78aacd6d594e5bb896fc92c3fcf3 MD5sum: 723fb255f131be6a9fbcfaad4a68314b 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 18495 Depends: neurodebian-popularity-contest, libc6 (>= 2.3.6-6~), 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_i386.deb Size: 7452648 SHA256: 3e01694737885e1a3ae5b06aed7320ab8edabdbc78510d8f2c3fbdd0325fb049 SHA1: d362d0c5de0f3751e56e495c412cfef616baaaf9 MD5sum: 600cf70c5e9193af0318fde18c123987 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 803 Depends: neurodebian-popularity-contest, libc6 (>= 2.1) Homepage: http://www.pgbovine.net/cde.html Priority: optional Section: utils Filename: pool/main/c/cde/cde_0.1+git9-g551e54d-1~nd80+1_i386.deb Size: 330694 SHA256: 9f81fdf7c35bd48dfaff78cd5feff19d868deabfb0e30b1fc32f3d358ea4cddf SHA1: 18f66b02ce5cd94ab5c784af51b7ff941207e492 MD5sum: 241a368b13f548415a31c720f36ced6e 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 137 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_i386.deb Size: 63608 SHA256: 9f5087592cb74bde00439f5d54b4388750a102289ce4ac9574231547d1657aea SHA1: 43342c8ebefccf39d1cfc066b8a1c4a451bdb67a MD5sum: 0354bc4d541b8a7b0d60df0c2e9f38de 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 22466 Depends: neurodebian-popularity-contest, libbz2-1.0, libc6 (>= 2.7), 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_i386.deb Size: 3713996 SHA256: 72a3766f3e9b5aa42c2d5d9fbaac6767a903dd101d17dbf25f330b4cc8d62cac SHA1: a97eb4fd5d696970e89933fc475e1615403b4101 MD5sum: 81da2bd4e94c5ad4075245a3745a2447 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 292 Depends: neurodebian-popularity-contest, libc6 (>= 2.8), 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_i386.deb Size: 127600 SHA256: 7098b138d4fd53d63438ccb6676e6c5a73be48e4257d5083300b35ab8a822225 SHA1: 80fa22f946095e323ffe1df3683f0320c38c7749 MD5sum: 4d7f718fddb44b8b55ed4e3ee019d5c2 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 46 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), 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_i386.deb Size: 20738 SHA256: 918585897dda56523930a2dab6775460464d4300d12819936c84b0fc8ee31357 SHA1: afae024176a20db6011cc4b3a8326feee1089e26 MD5sum: 4bcbd6dea762751cfa545f51543259f3 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: 8.4.2~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_8.4.2~dfsg.1-1~nd80+1_all.deb Size: 15764 SHA256: 6ce7f922635d8240a8868cf4637608ff3e134ad934219bc04b9f5a305801056b SHA1: 4a8ec53e20177a0a76f1d5bbbe92bd96c23874bc MD5sum: 3330559caeb2029a744fd7b9d9c7ce47 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: 8.4.2~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_8.4.2~dfsg.1-1~nd80+1_all.deb Size: 15780 SHA256: 2b2ed58946ad438ef788f90dc9981505d8920973248574f5d3dd61bf6d5a0fb5 SHA1: 2d04372fb89e4bad344d88a282d0d14dbe4f2342 MD5sum: 39c86b4132eba0dc9c663e81b09a91bf 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: 8.4.2~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_8.4.2~dfsg.1-1~nd80+1_all.deb Size: 15782 SHA256: 1cc261d0f08c2e53f083511cba8dc68ae3f999e346026a76a626256eab597196 SHA1: a99e4591b4f85cb4592651d4d715f1c704ab3d46 MD5sum: f2c2f8e1c8f95d39a955a862829f2a6b 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: 8.4.2~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_8.4.2~dfsg.1-1~nd80+1_all.deb Size: 15776 SHA256: 32afa7119db302898141bd967282e8ee1570dbf6914a9eba26596dbae5ac0042 SHA1: 8dd6e5fe3122a43ae7848e768bd1ac61fe57e6a2 MD5sum: 176bca849cb05720cfcf0068dca6018e 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 37902 Depends: neurodebian-popularity-contest, libc6 (>= 2.11), 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_i386.deb Size: 19559710 SHA256: 3e88150a24d95cd8762a290a02dd55c67fadd813996e722363f8c1771ae83b35 SHA1: 931bd63e753ddd8f674cf238c5e182ec66480bf1 MD5sum: e3aac163009517864c4b86c6b43ac33a 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 105798 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_i386.deb Size: 104352566 SHA256: 0654f20580632b3b30498a57fd3c297ed370e356883e4d9b63464f4ac3dda99b SHA1: 1d67f8e7422fe23fe73841eb0df626fa526a6960 MD5sum: 73ca8bb2dd5ff706a63152d63eb0afc6 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 4050 Depends: neurodebian-popularity-contest, libc6 (>= 2.3.6-6~), 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_i386.deb Size: 1398844 SHA256: 5551401b09a456076bd199fdf340bd2f0c09e0f079ec7658dbd10f05815eab79 SHA1: fc3721c9070e2815b1f48c9dfbd6cd9cd8530d62 MD5sum: fd3310d6fd930d51f45df09621526543 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 830 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_i386.deb Size: 222958 SHA256: c3bf9352beb6d6732566925c9f471078067880f33820445d42e6245f82b91024 SHA1: c97d96fea24d7be085de423fe7036ab213a99437 MD5sum: e2b1d5630029b2aa43f47254581acf85 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: i386 Maintainer: NeuroDebian Team Installed-Size: 226 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), 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_i386.deb Size: 88774 SHA256: ca5f98d725c1248f1de7e8641ef1e92edc00880c36850d96c541650ee3326d39 SHA1: 4bdb34bd232fb8f72aa127e753f2a51ddd884bbc MD5sum: 94f7e48dcd27a1081baa291f327b7fac 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 128 Depends: neurodebian-popularity-contest, libc6 (>= 2.1.3), 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_i386.deb Size: 45346 SHA256: 5359da2f63e045cb27969830711cb61285c4b34c7a7687a18db48a2e08ef4342 SHA1: fef5f10221fbfbb9edaaaa3875bc6ed506a5cbea MD5sum: df1baca3fc49684579ec2dad10cc9d82 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: i386 Maintainer: NeuroDebian Team Installed-Size: 507 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), 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_i386.deb Size: 97438 SHA256: bdf37a2c754921188b21592489a05fb148e80a4e8169336a04929d76139d747d SHA1: f4483e64a2714ba35198f649dfcf91d9a07af500 MD5sum: e6dae2f4f41655c9e8eaec0022157648 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 2769 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_i386.deb Size: 670516 SHA256: 26b9050a590be1d39b83da0027f2c276d93aa8c4aa9744f3ee4c9549316a6982 SHA1: da8f32332f4a9d9b420a5fa5de56233e580fac66 MD5sum: 6792e195335b9202a4bda73d9c7e6d19 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 20306 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_i386.deb Size: 4340920 SHA256: 4ab15e51b5bc12cb9dae84fb2ba1e5e2c2fa47bd383735a2911e66e2d42b7242 SHA1: 823dbb182b1f3832fd62a624fb686b686a4b38a9 MD5sum: 6a3acb0879c884d3187cab2df70602ef 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 54 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_i386.deb Size: 28796 SHA256: 7ec585f0e8766ff37c6107b440e14c2b54ed0ea8dd190061d6cd8d96ef6815dc SHA1: dbc2e8126d805d7bf0d06faa37c7ab5637ec5a0b MD5sum: b30f0907ff74af952b6b05139416d379 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 77 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libeegdev0 (= 0.2-3~nd70+1), libc6 (>= 2.3.6-6~), 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_i386.deb Size: 27524 SHA256: f1d6dec8b5cbc9e0ca00529f8b104166286cebc5fa1345866655843c50e742d2 SHA1: 0f2f02a659bc265f79ed1882ae345413865dcded MD5sum: b02fad1a1080734c5b44c9b2ec26cf5d 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 21 Depends: neurodebian-popularity-contest, libc6 (>= 2.3.6-6~), 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_i386.deb Size: 12716 SHA256: e04aed3b747363fe070da08a090a8fba64967aef974bff2389cc2e26cb7a3d52 SHA1: 51a6b4f282e219e5178f68c329aedd44c4d1d4bc MD5sum: 6cbfb8c0720e5aaf4232d664c7410c3e 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 187 Depends: neurodebian-popularity-contest, debhelper (>= 9), tcl8.6 (>= 8.6.0), libc6 (>= 2.4) Homepage: http://modules.sourceforge.net/ Priority: optional Section: devel Filename: pool/main/m/modules/environment-modules_3.2.10-8~nd80+1_i386.deb Size: 100126 SHA256: 91fbb1b0dc42b7792aa207e583de472b423b6f70d22fae44f577de567cb1111e SHA1: 9340120281479538d4b524a39eb06e21c4452bd1 MD5sum: 0d1535a516161281a60e0c9f331d6dfd 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 123 Pre-Depends: dpkg (>= 1.15.7.2~) Depends: neurodebian-popularity-contest, libc6 (>= 2.4), 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_i386.deb Size: 45400 SHA256: e660dad8766d9d84ea6eab10f42412dd6a34cc37f83db4978e5e49ebcc57aefb SHA1: d7942a7296d905ccee7466815941a34151bc8522 MD5sum: 5929279e4eda15cd34164a70c4adb648 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 108 Pre-Depends: dpkg (>= 1.15.7.2~) Depends: neurodebian-popularity-contest, libc6 (>= 2.4), 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_i386.deb Size: 39942 SHA256: 8aa947e26e7d6e817750d6785dbb4cb97e923a0ca67e361200b8d22098a64759 SHA1: 3442eece77250703d5f7b5c136df02ee5139fadd MD5sum: 5094ad6040b267a01edb3cdf04132968 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 202 Pre-Depends: dpkg (>= 1.15.7.2~) Depends: neurodebian-popularity-contest, libc6 (>= 2.4), 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_i386.deb Size: 82302 SHA256: c67352e01abfd5eece4ea8899007d64a6d1cc6ca7a29654ebb95959e3a15885a SHA1: df89fee3f43547d2a5d496a3e979118fa7d78c34 MD5sum: 0159d09b3bb8a48712df911fb7a8f8fd 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 3033 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_i386.deb Size: 623680 SHA256: ed7537f6d85694e0122549bbf4e40f18ef5627adf80504c17b259f947513701c SHA1: 5745a7cd69bf3e42852909ffd7e720dacfaa79e0 MD5sum: 280e2cc1e929d89dd531380763056872 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: i386 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_i386.deb Size: 8494 SHA256: 379a190889159605427a1257901356bf55f8ff38a2a525971eead3390fe8018a SHA1: ea9c3c97f7867c0c5df57c608181b1645b963f4d MD5sum: 14298b50875be203b5a42db6b2ece88c 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 6052 Depends: neurodebian-popularity-contest, libc6 (>= 2.3.6-6~), 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_i386.deb Size: 2332688 SHA256: 3cb88d1f6817c6a47ae3fe63162892c5d670cfc53213ac1afeadcb6c1f1fed74 SHA1: 84fcac3d2d7c76440b19df1d60e2f3ec9c00b795 MD5sum: 79a4edd2639238bd12ceb8b4857f19cb 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 168 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 (>= 2.3.6-6~), 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_i386.deb Size: 56122 SHA256: 6aa6489b99b1828d1c7957db5469c0ea99afb39c7b1172d9da6cfb3f5a4c53e4 SHA1: a47ee82829ac6fe83a477afd0b9435d626c11f91 MD5sum: a23a1a4453cc6ae9f0517748c081e2b1 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 40847 Depends: neurodebian-popularity-contest, libc6 (>= 2.11), 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_i386.deb Size: 8272490 SHA256: 34fb5511d61b20893406fbb53be89e85eea0fbcc811d8faa57cc851f22613465 SHA1: 115b391009ee51e6dcd20ce0c20aa71330fb6010 MD5sum: b5c9c0a3d5205a8be0747b4d591380cb 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: 5.20151116+gitg5416a1a-1~ndall+1 Architecture: i386 Maintainer: Richard Hartmann Installed-Size: 403433 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_5.20151116+gitg5416a1a-1~ndall+1_i386.deb Size: 29218670 SHA256: d8ae82a72312410149580c85865c4d1830f4da73bc62a01b4f6ee8693ef52757 SHA1: d362cbdbcb946cda5173cf4ae50074961e493cfe MD5sum: 79b8720bd21e298cc336c5bb3050d1c8 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 360 Depends: neurodebian-popularity-contest, libglew1.9 (= 1.9.0-3~bnd1~nd70+1), libc6 (>= 2.1), 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_i386.deb Size: 135728 SHA256: 9106f3d915f0509db8e4f30785cde816bc64a9ffde088922920a9b031c703d2e SHA1: 37ff0f57ba3c9280f983ea81172af8c36614f25b MD5sum: 7c2028c2984d8337dea75686e0bf63a2 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 29 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_i386.deb Size: 15600 SHA256: 1262d3360603d63dfe042f927ece7a63b51026fddf2ffcf815dffc0c8c0de7f8 SHA1: 618de60eccd1b947215d5af310f6da882cdb0867 MD5sum: e91e23374f84a58eaf3abf6db78bc154 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: 8.2.3~dfsg.1-5~nd80+1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 15089 Depends: neurodebian-popularity-contest, debconf (>= 0.5) | debconf-2.0, libc6 (>= 2.15), libcgroup1 (>= 0.37.1), libclassad7, 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 (>= 9), 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 (>= 8.10), libssl1.0.0 (>= 1.0.0), libstdc++6 (>= 4.9), libuuid1 (>= 2.16), libvirt0 (>= 0.5.0), libx11-6, zlib1g (>= 1:1.1.4), python, perl, adduser, libdate-manip-perl Recommends: dmtcp Suggests: 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_8.2.3~dfsg.1-5~nd80+1_i386.deb Size: 4118626 SHA256: 527306912dda0784ffd88e1a3a23926256040c28487c7cb706df7df384214f4a SHA1: 965f56614aab2cdd45558bf160977f0ddbbb2882 MD5sum: 8a26a4c97b054e5797b2ec070b88d59f 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: 8.2.3~dfsg.1-5~nd80+1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 33221 Depends: neurodebian-popularity-contest, htcondor (= 8.2.3~dfsg.1-5~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_8.2.3~dfsg.1-5~nd80+1_i386.deb Size: 31197764 SHA256: e7ab8869fbf9712f62c5376c42811b8eef0e585746d490fa24c31d6c380c0e9b SHA1: 4ac6c39a3c68dbcb974e6d07c6892e09daec6e03 MD5sum: 91c14e7f535b102ded71228860f83567 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: 8.2.3~dfsg.1-5~nd80+1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 1248 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: devel Filename: pool/main/c/condor/htcondor-dev_8.2.3~dfsg.1-5~nd80+1_i386.deb Size: 286650 SHA256: 5ea4e3141cfbe86ac6a191d8a99e86c7a9400bad8148e2a10d47fcd49139d91b SHA1: 1b38f4cd60de13b0770f0ea981e619dedc69c2dc MD5sum: e73e726f4f525e7044b7c17eadfa34ad 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: 8.4.2~dfsg.1-1~nd80+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 5911 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_8.4.2~dfsg.1-1~nd80+1_all.deb Size: 1068172 SHA256: 4b99af92fdf70e54175f17a31bca2031c2def66e412de59aa1f79f52f42b65ff SHA1: 9d85bb4e64b461b95540a06d35aa561f50d0b0af MD5sum: ba65c654cfed6dfbbefd1c84f83b0722 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 727458 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), 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_i386.deb Size: 65512820 SHA256: 034dcf2c3ffc057605ae5dedbad641e190d43d744c2ec09bcc893c8aa37c85a1 SHA1: 0c7cecfda06775ec5ebce26bd8f5093df3b9add2 MD5sum: ee6b1f371d3838fbae9b7bf540b9c5a6 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 885 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 (>= 2.3.6-6~), 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_i386.deb Size: 275184 SHA256: 5427b06b38ffc47c02f41c6b9d42c8523cb504abb6ad139f9861c212215d44e0 SHA1: 20aa8e36c9cfa2227afad2f9ef72faecf8e83441 MD5sum: 4a892a1378c0d7f1322eb53062562e7e 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: ismrmrd-schema Source: ismrmrd Version: 1.3.2-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 48 Depends: neurodebian-popularity-contest Homepage: http://ismrmrd.github.io/ Priority: optional Section: science Filename: pool/main/i/ismrmrd/ismrmrd-schema_1.3.2-1~nd80+1_all.deb Size: 5074 SHA256: c3b942e60a1a47d87d7c1967dc02529b9727a6e3099be037038a63ba8e3fec88 SHA1: abf6186723033c9fdf7d715c2ed7501eb9007376 MD5sum: 128249a55f15734da319037a1bd681d6 Description: ISMRM Raw Data format (ISMRMRD) - XML schema The ISMRMRD format combines a mix of flexible data structures (XML header) and fixed structures (equivalent to C-structs) to represent MRI data. . In addition, the ISMRMRD format also specifies an image header for storing reconstructed images and the accompanying C++ library provides a convenient way of writing such images into HDF5 files along with generic arrays for storing less well defined data structures, e.g. coil sensitivity maps or other calibration data. . This package provides the XML schema. Package: ismrmrd-tools Source: ismrmrd Version: 1.3.2-1~nd80+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 430 Depends: neurodebian-popularity-contest, ismrmrd-schema, libismrmrd1.3 (= 1.3.2-1~nd80+1), libboost-program-options1.55.0, libc6 (>= 2.4), libfftw3-single3, libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.4.0) Homepage: http://ismrmrd.github.io/ Priority: optional Section: science Filename: pool/main/i/ismrmrd/ismrmrd-tools_1.3.2-1~nd80+1_i386.deb Size: 128726 SHA256: a51e5e81fe1a767e56af0444bab5fa86a7608e1513e5c32897199887240209f9 SHA1: 93452b2b8b73c98ef05fdb20c1d478362ef5ac22 MD5sum: 03cb0303e4496f22b092029ced9524eb Description: ISMRM Raw Data format (ISMRMRD) - binaries The ISMRMRD format combines a mix of flexible data structures (XML header) and fixed structures (equivalent to C-structs) to represent MRI data. . In addition, the ISMRMRD format also specifies an image header for storing reconstructed images and the accompanying C++ library provides a convenient way of writing such images into HDF5 files along with generic arrays for storing less well defined data structures, e.g. coil sensitivity maps or other calibration data. . This package provides the binaries. Package: klustakwik Version: 2.0.1-1~nd70+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 88 Depends: neurodebian-popularity-contest, libc6 (>= 2.1.3), 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_i386.deb Size: 22258 SHA256: 5321fec361cb2ff2ae813693f8becd384c9b8faf595d7fe8707f520b1acf85df SHA1: 4ac78b37a834698a88bc89041c2d61276a727caa MD5sum: a1a16d24ce0cf8b981f2f379191839d2 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1359 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_i386.deb Size: 311936 SHA256: 0bae3e48a07bad91ae5f3cd76682123a9eaf98b6e4096da9c052355f6d5c39ba SHA1: 164d13c03b7e47b689effccfd8a16526a4f8e32d MD5sum: 777428e6e0de8543d7b3fed93d1a7c52 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 823 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), 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_i386.deb Size: 278454 SHA256: c786be2bff11c798ebcece79c1734c13a326ed44601b319ae860e98d910debc8 SHA1: f860867dae1ee78d783a3a9164327225691eab9c MD5sum: 3b3ce267abaec70b4c45a0b7fb74e093 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 298 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_i386.deb Size: 70596 SHA256: 82c20aeb3f5ba97bf7f36dee3cee4b7f90d7371d3bbe473ebb6196dabad56f21 SHA1: f9a7d023e55428795ebb1b879fc137c6dc79f9a7 MD5sum: 1d950d9c6a5b3b4cec6ece8bbd7d82d9 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: i386 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_i386.deb Size: 17400 SHA256: a510e8e72379490b0e5987512003957f636cda90d0c6b657ad1f17f85b146c0c SHA1: ff36c43e4f97fe7cf5f0a1963a6bd31b4916f0cb MD5sum: 7eaa7fa2534375bc6aaed8312972adc4 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 75 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_i386.deb Size: 37268 SHA256: 0bae64fba1ea1def702205889dfed7b5eafd61e86962493e952ef50cc2133277 SHA1: 5b6dd81e2ef694a149e3d1745862f819ffc137ea MD5sum: 9174141fd966b2d81a3137c6ee928ed2 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: 8.2.3~dfsg.1-5~nd80+1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 1194 Depends: neurodebian-popularity-contest, libclassad7 (= 8.2.3~dfsg.1-5~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_8.2.3~dfsg.1-5~nd80+1_i386.deb Size: 261108 SHA256: 397a9af81fadc228e12045730afd0aacdcf1854b4eb36eeadd39e13483f00e92 SHA1: 2d255e7b599a47b1a7f39222d575eb2413a7c197 MD5sum: 85c57d986553cf16b952dc86e7eb606b 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: i386 Maintainer: NeuroDebian Team Installed-Size: 826 Depends: neurodebian-popularity-contest, libc6 (>= 2.1.3), 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_i386.deb Size: 272968 SHA256: 1f4a0ed6216cf187646aa9729ba8923602942f27664e5f929af4efdaac75e06a SHA1: edfe470ae0ea05d402ab5c78007b2f7e733e2b47 MD5sum: c5ecbbec1cbf5f5874eb1d2434a866ee 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: 8.2.3~dfsg.1-5~nd80+1 Architecture: i386 Maintainer: NeuroDebian Team Installed-Size: 617 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libpcre3 (>= 8.10), libstdc++6 (>= 4.9) Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: libs Filename: pool/main/c/condor/libclassad7_8.2.3~dfsg.1-5~nd80+1_i386.deb Size: 205658 SHA256: f79f647627a2a81e386e6b6fb11ae4420ff0be1a272f022e4f8b204a345b54d8 SHA1: 8be44e2f7c2978ec2fdc6426a18a5ddbd6045a1a MD5sum: b488c3c84f6bf5bd36ffc2abdbdcfc48 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 250 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.8), 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_i386.deb Size: 84730 SHA256: 88c524e9888049bce7a4219cb40e820ed34bd55c84c132d5e7336347a67b8f7b SHA1: c59f8269a466c05378a1b8bbe55f988585a4db2e MD5sum: 13ca342c79186414b7f84c0c7f274af5 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 113 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_i386.deb Size: 24728 SHA256: 67a23e65156a2f0d2f44b029f218d722d6b6878eca2c5869a0a20fa693227d6e SHA1: 63452beffc11f59e8471903ddd7320e31c061397 MD5sum: 7dec76aba40a994522975d6eefc9b3cf 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: i386 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_i386.deb Size: 7296 SHA256: 7fb4852effe002dfb2a1570c6d6fca891bcf3d9c6f625a4a400eb9dbbbb9ca68 SHA1: 9ea31fa84e67acb904130d9b1f3a7a7e3fba226c MD5sum: aae725245bcbf2410b4b65d6ca04285a 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 15 Depends: neurodebian-popularity-contest, dmtcp, libc6 (>= 2.1.3) Homepage: http://dmtcp.sourceforge.net Priority: optional Section: libs Filename: pool/main/d/dmtcp/libdmtcpaware1_1.2.5-1~nd70+1_i386.deb Size: 7214 SHA256: 49b14f45f6148cdac462ebe533316370cb496e63643128018b1c8b477005ac9b SHA1: a7436f2da646739f43d36ecf912922dc9a64006f MD5sum: 14298b1b5be0b2cdc1699072be473a52 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 107 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_i386.deb Size: 96472 SHA256: 58b0969bb2a6874ee0e7b6edf1a5e822308e4ef7d38f2b1b62b6e8d81d4104b9 SHA1: 5ea9b370dc72adb35157145b6917064791cf6d6c MD5sum: 245061048498ab0e47d6ed299779ce94 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 181 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_i386.deb Size: 49976 SHA256: 79a439aa9bcc02ff1d31883ee80d8798c74a26eb84f35284e6e0c82193a642c5 SHA1: 9e0475fbd7ca7cdf6720308eb80d06d67b52b438 MD5sum: f5ddaff69dfe597273cf17fbbc5a0c0a 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 78 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.4), 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_i386.deb Size: 34546 SHA256: 52f36c71fe8a4c8b09686615e920ecf0b34c3b4a83afed6bebb0431e7bfdf57e SHA1: 0b94e24776821a5100d999b93555bad696270032 MD5sum: c1485a0b2f6004846b4adf6e855d84f7 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: i386 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_i386.deb Size: 43580 SHA256: 469dae99619f15969da4e43b80c3ef83704ee8c5900bd37fb0439ea22e770a18 SHA1: 96cebf3665096e5b0aac3e3519e8fd8011b828f7 MD5sum: 0d7fa300dd54149f6aa61aefa929cb2d 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 60 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_i386.deb Size: 35586 SHA256: e003207a440be631fb2b65ce7eb9b4976754f2a3bd5996cb78713fa2474894b7 SHA1: e4782d433cd491b2713386714ab475e5dab54a5b MD5sum: 7ceeb946636a6d2704a1d6c404eaa2a8 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 67 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_i386.deb Size: 61412 SHA256: e3363ee8de955c8a0843f66f55af926c2cbd5111f9cf24bb14b4fced6334f42b SHA1: e523a40ce1d1892433122c2f7209c9e5764e98b8 MD5sum: 45fbfcf89622003af23553f045c2593c 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 27 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_i386.deb Size: 18658 SHA256: 541f4525513b772dbb242ba44b22566cff46d1c0de1b75f53d9273fef0e1ac30 SHA1: d384a32e9db061d58e6d9a5fc0adccbe655548c7 MD5sum: 1fc0d99d746284bdbb726d21f2883e9e 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 23 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_i386.deb Size: 15030 SHA256: 162dd7d8bacdc0d5ba9ed03fb31d41fd0fb92ff9515e6921a3974a7d7ed3c0aa SHA1: c5f7e2cf9a3ce35a10d81bbbbe4d98f41a5e239c MD5sum: 5ee7257f13541d8a9ccbf5113d5c4a75 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 58 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_i386.deb Size: 31282 SHA256: 32298e876f31ea503e568b06dfc2a3bc2dbb0d89233226ce142a45f92732ea8e SHA1: 273187c50f6ce3ef75ba80854f09605f12d0fbf8 MD5sum: 0f3e16437aefd319327895e6cfef2a3c 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: i386 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_i386.deb Size: 22432 SHA256: a855297f19d4a7419bfcce8f7ca9d7b24255ede952bb0da899b9eeda7fa55ba1 SHA1: 1ecb05ffcdca1550e8b17bb0d507266a73f1ae16 MD5sum: 9fbec4ae05d8141fd5ec99b85fa92505 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 75 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.3.6-6~) 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_i386.deb Size: 45432 SHA256: 02b662f733b417bcae9a270d7f0d46b19b97003faf92fbe442dca9ff9dd9ce27 SHA1: bade1fdbb40f80cbab24e0d2fbd7dad61b04ded3 MD5sum: 047181a8fbc5e330b30c4af1a8982e9f 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 154 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_i386.deb Size: 136716 SHA256: 56d71de8349b19ea7ece753cae740a71561b00f69f4835acaad778287831c26a SHA1: b6dae3bc4e62b3b9d6990c0174f2afe7388a9b01 MD5sum: 6f6e4f7d9aa3d3b7b4b564072f3a175b 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: i386 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_i386.deb Size: 509876 SHA256: ff86b3cfc5828d83864e0294226170c314c36b995883869520f2eb0e95136666 SHA1: 1d8b0679d5d0936eef23f1e8e03ed5ffc4a640d1 MD5sum: 01e5c31cf474862b8a332fcd83367025 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 6468 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_i386.deb Size: 904614 SHA256: e0465273aef80645c647c499b31567e8c7a9021307285180a312c3a927a662c3 SHA1: f449d29af5a2941bbec222b5eb665762a6ef209e MD5sum: 93784ada45cd704cd660f9e0f1989864 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 3813 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_i386.deb Size: 987676 SHA256: 99c581e3037fd3edd83bd7b3a1b9357be84d2d699c9ccf69467ce90588c49399 SHA1: 1baa817c707b7519882efb498afd7441adaa8392 MD5sum: 0421ebd383d43d2576cda65e58ef8c87 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 4245 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_i386.deb Size: 750596 SHA256: 7435772c5f8880ae65f2e0eed8498e84c6f08aaccbd3b662b56d58af87a16c5c SHA1: eda52ad9a95f35bc17070f091341f8a2b332e9fd MD5sum: 5b07073c659b2c1f32beb591e7464091 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 205 Depends: neurodebian-popularity-contest, freeglut3, libc6 (>= 2.4), 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_i386.deb Size: 54286 SHA256: 9f4e02591b7d1a36d3ecbe4d4f10de231b45a37ff7cd1d34bc7fcc28380cb531 SHA1: 0436eb49f3de991b7c12398431d42938d57773de MD5sum: 0c4f9432d823c1bc012828d260b30f7e 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: i386 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_i386.deb Size: 8534 SHA256: a89f5af02065cdf0535f3b0c1e10bbc4a9ab32f8381aa7002d03799c5b74021a SHA1: 34f37e88d9d6a5092b9eb9b1df9cb69babf10ee4 MD5sum: 2160f0dd7c4c5f40e34f15843acaddb7 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: i386 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_i386.deb Size: 19274 SHA256: 367ba9119d9266d86fbf70d9edaa1cf00bbfb6b9c76cd129fb8750a0d8b286de SHA1: 3ef7569ddd215032cc5c9bf231f79515cb7d2dcc MD5sum: 9e3444b16a2dfacd14e0d705844e02c5 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 89 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_i386.deb Size: 36974 SHA256: 1cf5f09c66bc91f5630c8d4b4801beec660a8f0e67fb9a1135f1235271fc6d26 SHA1: 60259a06537808bf5965131c7caaa652959ade6e MD5sum: 718fdc4d88e5944822c245deeebc29a0 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 112 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.7), 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_i386.deb Size: 45092 SHA256: 5947de9052c90bbfaa264e2da72d495435b058d80dcd64213d2f80db82537d21 SHA1: 8aa78b615c198834fee989087acb53e082a83140 MD5sum: 22aade92e3efac829e11fc768c788344 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: i386 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_i386.deb Size: 19766 SHA256: 967417ba81d31914995db73727469a23daf9f89b7b028a599a8a0a9da9606373 SHA1: 95cfbc768b60d5b669016c4ac5fbff216b0aa41b MD5sum: 2ecf861db67718ae557fb8582576100a 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 702 Depends: neurodebian-popularity-contest, libc6 (>= 2.1.3), 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_i386.deb Size: 220094 SHA256: f495574c8600a1088f3011f4500edf7830c6156e3d4ee04aa78bfb7e9cdf9d0a SHA1: f68bcd5b9494f9d6b7c9f76fc5daed6f490e0bc0 MD5sum: 1a9a84823ace804039433f0a2251177f 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1581 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_i386.deb Size: 454420 SHA256: 34413648a35b99d1282cc806a1ef11359eda6e7b57a26bd68e77dc3e709e0582 SHA1: 9717750a0803b01393770a65f3f7181710ded220 MD5sum: a8c3bbe0572415e8315922e5ad23c068 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 542 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.1.3), 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_i386.deb Size: 166944 SHA256: 9ba1f32ddcd73af1eae30856f37bb069a0a6f3cd010a04013f06e99c692fbd80 SHA1: 4273dcd06636cba15c13e58bd16c7a454d59ae91 MD5sum: 369e2974e50d56c322dfe6dd1e23eb3d 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 127 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_i386.deb Size: 40120 SHA256: ba5505edf03251d0fc413c07c87188adcd4d4e30ed128af99f8885d82cf2cd44 SHA1: b7089ee5e4e564f261899c5d3863222f38bf180b MD5sum: d459fb5e5d1beced1f7e5080437cbcec 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: i386 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_i386.deb Size: 153180 SHA256: 833750d235946b0bab716fcd03cc5d8a128f1d284360574ddbc0409b294e0aaf SHA1: e6ed1b004d78f341becb9273525a529a19eb3462 MD5sum: 249a1d61b82e2dbe729e91f461b7ae21 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 482 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.1.3), 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_i386.deb Size: 148618 SHA256: e3b4b19d5a6ea0bf1eeda6abd84e398cacbacbe40db7c37aff2fee2c6950b5a9 SHA1: b26bb616ab69464fd5ac96315f158c5d4ab5f3cc MD5sum: 3ed3def0b1017d985f262201eb962e6c 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 104 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_i386.deb Size: 32292 SHA256: 67efe54fdb9194f2e41ec9bbaa005b4235ffa6dd617355ad9f86709f0244b125 SHA1: 7769288a7a169c44f888bc823fb20e012378cdb4 MD5sum: 9ebadedc1781f9c4153a4494bfee314f 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: i386 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_i386.deb Size: 8796 SHA256: 0b6d87917568f73061c152f72b67dfee512779b7b7f190aba3afe3764fa0b03b SHA1: 8381c2a6027c6148fcd970d85d41b4d8d094f681 MD5sum: e6c2bde87dca6caed35d4da96ac98938 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 90 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_i386.deb Size: 35758 SHA256: 1671e4632c78900fd8558f90e80356798b1e3d4cf35b5d5c1759ba8ca3a88ebd SHA1: 80dec91bb7f1cb36ab2dce02f88b78eb30f3bfa9 MD5sum: 1fc1ea2f8b1af59bc4e7c764cf1391fa 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 55 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_i386.deb Size: 26368 SHA256: b3afd19f857263679f2be828851a5e0f5a0554fe7ba9e4b5422f12244ccaf91d SHA1: e09d3e3ab7603e1753c1c46e6b0d19ce1d9e43aa MD5sum: ee66720ad6786eda0ce090a12445ef78 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 22 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_i386.deb Size: 12070 SHA256: 80dfeef08317445fab8bd624c6d6549dc7944c21d7db1da8ecff3c592ba917d1 SHA1: 2961725d0a5eb2153b1d91a4e86656785d3838bf MD5sum: 6910aa045c0f7c1b0e97569d0ef7c47e 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 189 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_i386.deb Size: 43502 SHA256: 7f940a3c457e73ec76285b58db49d9e408935dac2a7120ebf2b2038cae58925e SHA1: 08aa3d1d2a09c83dfd21918eea8c66e1144ad319 MD5sum: 9816cd988f78cbd52572b6ac0c3f18d2 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 41 Depends: neurodebian-popularity-contest, libc6 (>= 2.3.6-6~), 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_i386.deb Size: 18898 SHA256: 8ece96e808e6233682ecb92eee85f0ca5eb4a70bc93ddc637a04e2863d349c3c SHA1: 38307957a82a93873e9d4b4218f1a7e5c5ee3d77 MD5sum: 3ccf0c81208ae1ccd8778f89f49f299d 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 59 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_i386.deb Size: 26158 SHA256: bbc15de5c29b7fb89f6d2997964513728d05c681f49f17c4d0cbf8ee5c72053b SHA1: 74a38cc9b35cbd64dd2c4225746abd668cb99089 MD5sum: e77428048e7185a13085b4c393ed88cc 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 38849 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_i386.deb Size: 34858546 SHA256: 85753e8fde1b3000a8ff30c13c5a87ff0150dc7fc2b96dfb0caa5b6fa07407f4 SHA1: 4539a207cad48807a4cd2443c6eee23cc448971b MD5sum: 417bfcd4d0176d556535c2f7f3bf831e 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 25366 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_i386.deb Size: 2961508 SHA256: 2b6d034840966cf3b29ba9894a12dcbe8412efcfc65b3028dbe2f58ad2f795d7 SHA1: f53c0df29e7580c0d9743c1dde8d946c02acc115 MD5sum: f0406cb160f01003ed63ed875f6f0193 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 20738 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_i386.deb Size: 6987116 SHA256: 112fcf13c312f3c5a5d2413ef3d0689163a03f439c98cad396ac150a61e333ab SHA1: 8225d12caed33d7bd12776e316a5ac950aec95ab MD5sum: 626934b716b761fe516c58452eb39bb1 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 21316 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_i386.deb Size: 4587918 SHA256: 2e9f9b1b8b3939088990221c1bf26378f5373a0187357a6db8f03a9bd5c01570 SHA1: 071ae869f2fc67562998488a9736f28facf96dab MD5sum: 18b1398611f70d3db98336bac67ee28b 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 23142 Depends: neurodebian-popularity-contest, libc6 (>= 2.15), 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_i386.deb Size: 4730606 SHA256: 044297958ca6e0e842d065cd5c1a5d5505702c4ee5a558eca1bd707b4339f9f6 SHA1: 0935fe281e356287c893c6fafea51fa47785818a MD5sum: 83d94c02f017f3b4e4e8ecc89ac70ad3 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 434 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_i386.deb Size: 105726 SHA256: d65129d8b18751dbcc724153b61d43c9ed3ba165e4f2932c12df8f632acafbe7 SHA1: ab8d422c35688f732972efa5ec76edb67d366009 MD5sum: d7453ea689983ee3cf2bdfc28faa6064 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 301 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_i386.deb Size: 88430 SHA256: 8b3777e3b9ca207a523ba157372a31819a0a219076e4a6777b8fe14a5293cf7b SHA1: 98bd618b048853df58aa8c5e60247f6ac1cb9b50 MD5sum: b3c013b1623bd7af26b58fd45dab3d87 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 96 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_i386.deb Size: 33498 SHA256: dd15393c214028be6465b762694c7529c6837881609c1c2d8280e42b58d8da6a SHA1: 29c92e70d987fe9eead1de0d1bfe89a36e8d90be MD5sum: 0f22aa474aa3245181741e52d5504f74 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 67 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_i386.deb Size: 27392 SHA256: 0aeba92352a1d75113c21ca6f406a5af713b01de30f6df8eb23993c3c8c90e42 SHA1: ed8b24f1b3b2fae0811cbe9f9e5c6c19d833a904 MD5sum: 609164056588e982dbad28653154b665 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 283 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_i386.deb Size: 63654 SHA256: 1a9c6821c1c1b35a1af14d880e5bfd3db2f6099989089676316c66bcb635e474 SHA1: cc9a022007183061b78c20ec374c01e2f3aae6da MD5sum: 7fffb3995e665e23edd152cf871b921c 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 254 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_i386.deb Size: 190826 SHA256: 95338a6b36f630737109ef1a8a4246e693fa93560469729c15111e1e748856d9 SHA1: 535cd5bb636b8e4309787576623118ec0a08c213 MD5sum: 7439cfa506ad66ba7567f273fee18b5f 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 130 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), 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_i386.deb Size: 45610 SHA256: 1397127ddb3fda5f4c4ef43dc09af02a8da3b470569aca32715bfe66fdaa2af9 SHA1: dcb338676db0290c128857c7db48cfb1cd363da6 MD5sum: b265d40d4cf9e2fe3265f411ec093a24 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 8962 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 (>= 2.1.3), 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_i386.deb Size: 2055198 SHA256: cecbe1ff06f1eeff1050c45d32a8f6ea82459aa37da0b0d313e5c335b3636b61 SHA1: 0b8f949b2057c3d7cfaa1fb54e5424dadc7cdff5 MD5sum: d86509b6afccd2632a5c2dd28fa0cbfe 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 4950 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 (>= 2.1.3), 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_i386.deb Size: 1464592 SHA256: 93291909e6e93c8efaad9caf7b33b2322b7566dd846e39263c0555d6bf0b6d3c SHA1: 974109f3afcdb077023dabd05c24f6e3b808beb5 MD5sum: 9ffa35473fe1f4b7a1e54e202ee1cfa2 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1267 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 (>= 2.1.3), 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_i386.deb Size: 377378 SHA256: c2b91596b9ba07db2e45f47fd964cbac0df38c4562dbcfed28071785f80420d4 SHA1: 231ba57c10adbb38a54be6aea4e56ffcc7c28cf1 MD5sum: 2db20b5164d2025054fa04db533c7eb6 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 146 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 (>= 2.1.3), 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_i386.deb Size: 49604 SHA256: f24cb7f02e6a68b174f3826d722027d2755344f04a803abbefaea0c15553ea63 SHA1: c8d349600e2847daa45c0960b9f4abaf92ff8f88 MD5sum: 2a6767e35d2c327115c42de7cdc05011 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: libismrmrd-dev Source: ismrmrd Version: 1.3.2-1~nd80+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 95 Depends: neurodebian-popularity-contest, ismrmrd-schema, libismrmrd1.3 (= 1.3.2-1~nd80+1) Suggests: libismrmrd-doc Homepage: http://ismrmrd.github.io/ Priority: optional Section: libdevel Filename: pool/main/i/ismrmrd/libismrmrd-dev_1.3.2-1~nd80+1_i386.deb Size: 13854 SHA256: 87945be8a08f04fc023fd671eb21ef7f0557bcb98719896517aee1735517d38a SHA1: 2596f7c08cab7ca94b117cf6adf49373cf66182d MD5sum: 41ea7770c69ad5b3bd45c85324e5311f Description: ISMRM Raw Data format (ISMRMRD) - development files The ISMRMRD format combines a mix of flexible data structures (XML header) and fixed structures (equivalent to C-structs) to represent MRI data. . In addition, the ISMRMRD format also specifies an image header for storing reconstructed images and the accompanying C++ library provides a convenient way of writing such images into HDF5 files along with generic arrays for storing less well defined data structures, e.g. coil sensitivity maps or other calibration data. . This package provides the development files. Package: libismrmrd-doc Source: ismrmrd Version: 1.3.2-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2011 Depends: neurodebian-popularity-contest Homepage: http://ismrmrd.github.io/ Priority: optional Section: doc Filename: pool/main/i/ismrmrd/libismrmrd-doc_1.3.2-1~nd80+1_all.deb Size: 148536 SHA256: b9c447e8d6ab7f8ab5b576f1970883777a0f6964b47bff7489c48a9bf185334f SHA1: 2b0a8458aabd724724cc5d240d9c753ce27a2197 MD5sum: 4ff94a5328b28ddf3c82389b444791b7 Description: ISMRM Raw Data format (ISMRMRD) - documentation The ISMRMRD format combines a mix of flexible data structures (XML header) and fixed structures (equivalent to C-structs) to represent MRI data. . In addition, the ISMRMRD format also specifies an image header for storing reconstructed images and the accompanying C++ library provides a convenient way of writing such images into HDF5 files along with generic arrays for storing less well defined data structures, e.g. coil sensitivity maps or other calibration data. . This package provides the documentation. Package: libismrmrd1.3 Source: ismrmrd Version: 1.3.2-1~nd80+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 381 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libhdf5-8, libpugixml1 (>= 1.4), libstdc++6 (>= 4.4.0) Homepage: http://ismrmrd.github.io/ Priority: optional Section: science Filename: pool/main/i/ismrmrd/libismrmrd1.3_1.3.2-1~nd80+1_i386.deb Size: 86640 SHA256: 08c760f2c594ee541aa1c40807a40847a699260e694a9703b07792e6ee351be7 SHA1: 085a8ebb845af38aaf049137bfb2764e56c02db7 MD5sum: 6be49f693c2ccbd92aa739906029eab0 Description: ISMRM Raw Data format (ISMRMRD) - shared library The ISMRMRD format combines a mix of flexible data structures (XML header) and fixed structures (equivalent to C-structs) to represent MRI data. . In addition, the ISMRMRD format also specifies an image header for storing reconstructed images and the accompanying C++ library provides a convenient way of writing such images into HDF5 files along with generic arrays for storing less well defined data structures, e.g. coil sensitivity maps or other calibration data. . This package provides the shared library. Package: libmcpanel-dev Source: mcpanel Version: 0.0-1~nd70+1 Architecture: i386 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_i386.deb Size: 2400 SHA256: 59eaff9f071cc4479b916da38622dc7b737d7512304167dc943cd9604a88fc07 SHA1: 3272017794c376eb4df5c62b441706757b7fccfb MD5sum: 38ede0b381de8202d37494388ad4515c 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 144 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_i386.deb Size: 54116 SHA256: e06fd86a81cd02baadcb485648d6b032c9d11366e565d6509042ee1f2162a254 SHA1: b7f93d99947cac8c581c2d2efdc5e47d1dc7636b MD5sum: 78e6c3e38a85c5593e686c3ef50abfec 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 279 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_i386.deb Size: 117516 SHA256: d7778f90e28ffc7628343940231d5c2711ff1ae624e535897f8c8de1af2b6e12 SHA1: 32f83467828f89c3f878c04b017e313a1b3274a5 MD5sum: 957b4b1c88b8444e04476ce4225a82f2 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 21048 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 (>= 2.4), 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_i386.deb Size: 3315146 SHA256: f16b78d0de260f675aaa264cda883cd36b48ba433212cc5f4939e1db49f83d61 SHA1: 6fb2153099f0282cbd306429da386b622c586388 MD5sum: 1ffc8e7fcba2e5c20f3432da89282c53 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 60640 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_i386.deb Size: 56022284 SHA256: 43629030b01151a52d82d45bee3778cd68a61acd1cbe3bcf6f54296cccbc1077 SHA1: 6ed918afd45ecf5bd4eca6cf3c64edfeef0814ad MD5sum: 3a8e6a503494a1554bb779c338584049 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: i386 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_i386.deb Size: 177440 SHA256: 12383fc2e6b73ea20188726a2892f8a80020d70875affee6f9087cc09aa5fa04 SHA1: 602c1d3a19e66774cb46ed2d2155665dee09b65b MD5sum: 3fd9ecfbc0ca6265c9ef620f2617aedf 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 268 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_i386.deb Size: 70546 SHA256: 1cc06469c5bc3147aaeb267618a09bceedc265f831a67203c4857b2092b72a26 SHA1: 06b1b6ba9e75429c8e371eb3785d65bd7fb0cbc5 MD5sum: 74b7826fdef203b882243205f393887c 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 52 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_i386.deb Size: 18014 SHA256: ed28595da04dc285174a0c66d1f396ee228f8654862760633e66463f1b61decc SHA1: 85505a3d66162c0d26f35810c21d789bfa8bedfe MD5sum: 1fa4dacbb74bbbd4335be225be07a358 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 62 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_i386.deb Size: 52428 SHA256: 3458b80fdbbe34826e968f11292c811f44af870018cd3c93db4b473e4b4ad65f SHA1: 07e53b4b3d361f34a37aa66f57fbbe6cf0443a38 MD5sum: cd88732024056217351a54d0e4205a14 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: i386 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_i386.deb Size: 5562 SHA256: 272dd7e21fd4a7b12df8e30b02b124462865c9d5c52aa7a6a6d37019e018f626 SHA1: 1b345aec4c9a1234e1d5e6d5c0405468c8c535ea MD5sum: 4e37350dfa1afc8e106e006be188b94b 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 104 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_i386.deb Size: 40744 SHA256: 7c0d0c39e91866daa0cddd51343b03ed366d17b14a63735ae0fe771f5614c21f SHA1: 97bfd4d56a98eae8e2b2a19fe62324b92de3d5de MD5sum: 59cbd18177e671c4f4f99b394bd32254 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: i386 Maintainer: NeuroDebian Team Installed-Size: 458 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_i386.deb Size: 133690 SHA256: f40e93c0ea60436724d132f45f188c0a659683b8539254a204775d3772165e2c SHA1: a302949c30e0f45b3db470eb0b50c96b9061e89d MD5sum: a539ae645c8704e21c489c345b75497f 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: i386 Maintainer: NeuroDebian Team Installed-Size: 304 Depends: neurodebian-popularity-contest, libc6 (>= 2.7), zlib1g (>= 1:1.1.4) Homepage: http://niftilib.sourceforge.net Priority: optional Section: libs Filename: pool/main/n/nifticlib/libnifti2_2.0.0-2~nd80+1_i386.deb Size: 101652 SHA256: 19ae4feab4a952dd40a3a001133891c206cbd508a4fd616a128f1ed90706ea03 SHA1: a561e0b4a23e53a3d001a4306d6ddf464381abc4 MD5sum: a846fd5005f6d4d16be88af90fd9442e 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 522 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_i386.deb Size: 159154 SHA256: 56d44148e19fe96e2312656aa2a8d67e461db6d1ae4b6e85c4cbb562947e41ab SHA1: cb71c810baf81dd9f6a45664286570319450879a MD5sum: 4c649101d86affb1a294f0875871254a 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 132 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libnlopt0 (= 2.4.1+dfsg-1~nd80+1), libc6 (>= 2.1.3), 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_i386.deb Size: 34596 SHA256: 1bc7152ef1441dae0e1daaa1b9bbe2c5746f99e2747dbbc0df0ce507a2521427 SHA1: 6db96091cb776df53ee4eae8a82b49e46733db81 MD5sum: 08ce5e74127c546d8be695e0130d43c5 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 409 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.3.4), 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_i386.deb Size: 150522 SHA256: b07eb1aa3e9cbc67d25123c1c4622c539be04aa4ce12f630a432231bb0ac9ac3 SHA1: bb812b2b8f4a97bf8c09a891ff603e7c84043eeb MD5sum: 3e2c093a9a080ee93cef290259cae8e3 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: 1.8.5-1~nd70+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 15881 Depends: neurodebian-popularity-contest Homepage: http://od1n.sourceforge.net Priority: extra Section: libdevel Filename: pool/main/o/odin/libodin-dev_1.8.5-1~nd70+1_i386.deb Size: 4259704 SHA256: 18d1bd79cb5b723961e56976280138b1cf7e7b9c4a260cfe1885d9921aa15181 SHA1: 2c7379b426d1f0ed1ec2fb4f7a6dfb00bc0c6cbf MD5sum: 2f5bd48c71b828bf4450c1e362071ed7 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: i386 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_i386.deb Size: 42584 SHA256: 1b138256f37bacdb0e0ee52c85b109d94fb74ab9fd373d0c768d7f54940b320e SHA1: 5b7fc51b5f0f712c1ff7c72099d0d255d9f44646 MD5sum: ca8bad6ee7da2cd8fa95cd049c5f6c21 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1276 Depends: neurodebian-popularity-contest, libatlas3gf-base, libc6 (>= 2.1.3), 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_i386.deb Size: 259942 SHA256: 59afb52c5b48857db66b5dc041d762c15f0d8a86bac8471ff5ec0be5fdf01edc SHA1: 37e92d6e4dc204ad4097e3964db474773f155c0a MD5sum: c949750ac5614883f7b6a79918ecd878 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 6212 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 (>= 2.7), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libopenscenegraph99, libopenthreads14, 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_i386.deb Size: 1238854 SHA256: 43a081f8bbb6acb392941140aec28bd3618aaa07fb308fab31cbcf65a6f4c644 SHA1: 0e882c6ea7d50935e80b9c3c69e62ebb08636844 MD5sum: 242988e88460db56cd090d73115bacfc 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: i386 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_i386.deb Size: 249372 SHA256: 2336592570a4b1b0e84bc709732e2f3a75243f7f15453cbefa77b575783b1d25 SHA1: 6e7360e777718b55f0ef0518f1a9f7eda30cb1f1 MD5sum: 5acf03a0f3b8adee049189fe46baa3fc 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 12 Depends: neurodebian-popularity-contest, libc6 (>= 2.1.3), 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_i386.deb Size: 7736 SHA256: 43edd9f78ecd184519e27e823e8ed79372ec88de80c8e30faf2b7f08682bdef3 SHA1: 870b32d375fb1f355eedd9b48cbd6aeda1ad2c10 MD5sum: fb6eabcb8ea493d52a1fe096b0c0d7f3 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: i386 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_i386.deb Size: 12594 SHA256: 0091bc1405858774f7ed04ca1bf63d9afae550bc9395790cedecdeb78ac7b72f SHA1: 15700373cbb733ebec71e28dd400579cb3ab7908 MD5sum: 328e00e1b0fd0b142f67f3c31abbc400 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 52 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.2) 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_i386.deb Size: 28264 SHA256: 4b1e14387b3533fa1fd36882a1fa3ec01483b5923b6d1e47d110c2d608bbc8fa SHA1: c1785da4fb64746b19b7439677826b800afdc936 MD5sum: 4955cf072ce3df42382185c3f22ce567 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 34 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_i386.deb Size: 31870 SHA256: 690416197701c661edd03fc695c767dd4a5a88184d932a63442c21d44b94f860 SHA1: 36dbe92a53af4f4e96bc7822c53392b8316c66c6 MD5sum: 0a2e684a32cc512d6d1c805a8234db7e 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 13269 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_i386.deb Size: 2697136 SHA256: d60463fb3bdd20532eb9d40510fcbff83c0e4250167fc3e675347c388ff27d7e SHA1: 147cbdd5fe749d023c8777114e737ac436d5a7f5 MD5sum: ceb379e733e1ed8262aaf10acf903dbe 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 5217 Depends: neurodebian-popularity-contest, libarpack2 (>= 2.1), libatlas3-base, libc6 (>= 2.3.6-6~), 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_i386.deb Size: 1559954 SHA256: cbd98c3cc6e672375456fe96fd10379a1aeb3ac37a12629274d31b3ac3c87f01 SHA1: 006115e334eb9bcb47052c5683400fca431cc69e MD5sum: 4ae2b2998ebc2c818cc33b9c2fe5dcb1 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 715 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_i386.deb Size: 189806 SHA256: 56917504d3260063837d25b74fc16b6c01b1a92d83877fd258c68d4485fe7978 SHA1: d63b2a90c4533d53b284e31c4aae2a83ceaa4f1c MD5sum: 43d9a788ebe3e545add5a4506c9f2370 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 477 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_i386.deb Size: 155636 SHA256: 0c8833f1a723876f521fe85dbe323bf90ffd4090ed1ca1ecce3dbb4b40bd0c27 SHA1: d21db7fdb806ad20e1edf544353fd74d6d83a334 MD5sum: a3535443735035b0356991e7ef14b767 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: i386 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_i386.deb Size: 108260 SHA256: e047f24613b7e8fd13e3640e9049c9e8c9bae110aa0e5a33c29fc4a69c405878 SHA1: c309753e9ed3c25e56043cf8d6e095ffdcd1a623 MD5sum: eaa57b0fabdf563a1fd2f7366cb012f5 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 93 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.7) Homepage: http://mia.sourceforge.net Priority: optional Section: libs Filename: pool/main/libv/libvistaio/libvistaio14_1.2.16-1~nd80+1_i386.deb Size: 35976 SHA256: b05d20cb51185fee0741bbcd57b6a56366f7fa5af34ea0c6c1e9c5722bed5310 SHA1: ce98c01dbdb308b532e54365e5e751b1902784c9 MD5sum: f5b3fff0a8023a01316eb2f0fa17c77b 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 81 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_i386.deb Size: 71764 SHA256: c29cafa7b0b0c48881f97bdb80628051bb23c4e8d14f6b6504f96b44d4e27cc3 SHA1: 3a5ff022785b2443d5881932a51aff605c6a3801 MD5sum: cef90e183b245d75ba8b83b60f659a20 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: i386 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_i386.deb Size: 218318 SHA256: bad2b52596dab124b81aeecfda196792a53db5eaccdc6923e4e0812abdc4278a SHA1: 7e10a6e4b056a65cbd4e78c6ba66b9069e08ad84 MD5sum: a03dd59c1dcf7a1b444dc91ba97acb6e 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 560 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_i386.deb Size: 234300 SHA256: 99984854a957d8ea9b12f78f52a46004f1ba2f88ff8d452a1942eca270bf1f0a SHA1: 21c4848bef48468722c5ad64d4e10cd8416671a9 MD5sum: c586ad152d4f6dd5ff6f5132dc89a5d9 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1281 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_i386.deb Size: 479352 SHA256: 3ef7edde055153945a0595fbaaece3cd54c0cb1ea9a66c0d104c58e53e4f87b0 SHA1: d8f7a02da2f201d47031b52806cbbd172dda6a06 MD5sum: e7fcf08d42ca6512ea1edef0f162c47e 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 264 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_i386.deb Size: 73552 SHA256: b296036283f658be14dad2f450c9cd7d6a0b74e9847094e7a011042c9e797155 SHA1: 9b95ef57f03fb1f432bb56447464439599568dde MD5sum: 41ea65c939a69fa646a8513884109d09 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1493 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.4), 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_i386.deb Size: 436938 SHA256: e1fb7f310f1640125cf2ac36f8603bd1203d1b498bb182235128e74747e44ec1 SHA1: baa904c241bfaccb3bdedfcd4021645511e0dd98 MD5sum: 34871f46f26a10c249ce69994d53105c 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: i386 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_i386.deb Size: 80806 SHA256: 6e42c9e287ebcefcfaeae77d87025ac8b54ddb0afe53f123ba3079f8ee0e0cb0 SHA1: 64f5d2a1437eea1f24e4308a4781acb718950a83 MD5sum: 914bb3f2fb0511b130ab42a0fe63cd01 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 11334 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_i386.deb Size: 5114824 SHA256: 3cff83fc76452905b2145f2d567169ca2ad9826c48a465d6475fb7ddb0697c55 SHA1: 41471ccea4cc893d326f7e2e847dc324e7c6e5f1 MD5sum: 6e9783ed43bf4f59379875ad28acbe93 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 12834 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_i386.deb Size: 2557364 SHA256: 81300df0471fd7a17d11b410e1fef6c330879bcefff5f863e31acecdc5bcf49c SHA1: 468a20cdeb68191cf823aaf78a26948b465c4076 MD5sum: 198407ed22d818f753f563356db16fbb 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 537 Depends: neurodebian-popularity-contest, libc6 (>= 2.1.3), 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_i386.deb Size: 108490 SHA256: 36f68fb320dfab9cd977d16f4b9aac5dac5310e8b69a05e02b434d53405d497e SHA1: f36fe016d9e114b6dbe4e51c7af9a44cf492ed18 MD5sum: c7e1ac6d56bc1a939a62495f7141b008 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 45213 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_i386.deb Size: 15082554 SHA256: 9c829ad7f9f50216c1a7411b815e8cc661a2cc84e7a1f692e5aed66b7cf3ad5b SHA1: 664b76b9535327b2108087fb2c60c7da3290cc3e MD5sum: a527f99783e410446feaf8549504d602 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1262 Depends: neurodebian-popularity-contest, libc6 (>= 2.1.3), 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_i386.deb Size: 500040 SHA256: 704b23fd6cd57820989ddce5b9cdb3b3453b5fa328eeb03be2fbd7fd4ad8145b SHA1: 2912165e6d1eb451e56e9b2d723e98cf0c37d456 MD5sum: 56b31c3653351aeffec9be754c8953a5 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1898 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_i386.deb Size: 535248 SHA256: f92f5050c3b13ca359bb41a516c6341f4b81abe37650eeca93ca602adb245e02 SHA1: ad756ae09bb2f6996021b3ac0f67d5d2e6bb192f MD5sum: fd91b608ac445fd5e0242ca24bc434dd 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 710 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_i386.deb Size: 301216 SHA256: 790cb00bcdc5cf402afb48ae43db006ee9723f6ad1d6d1555e191b9bb4e29728 SHA1: 0d35f978e360b63289c43e98c17454cc6aae37e6 MD5sum: 6ead67a85a7b53d996ebecdadc85ffbf 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: i386 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_i386.deb Size: 27768 SHA256: 00dfeb55310cf224fd6c1678ef2fa74af694e0f691aba93422fb0d8c20b8b97b SHA1: 25e87725d92be7a09cadf7e4c77b6410355e2bf5 MD5sum: bc00e7a4b45fdac86acea168cff9eaaf 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 82 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_i386.deb Size: 45470 SHA256: be2f239b1e916bd7b92f256fcbe3ad146611186124a1b985a4ba370221fe0177 SHA1: 6f5dfe360e47bce362e1210fb51b98b75fe993b6 MD5sum: a9250c821f7040a2698e085add1a7f53 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 66 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_i386.deb Size: 60314 SHA256: 4e7bcedf458d900cf67582b264058bee7bc1506808593c0972798eb7fe6e0a4d SHA1: fb592be35ac62d039ce614d0147f58c4e16a37f6 MD5sum: a9bab68580d0e6c0233d0eeac6fad862 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 100 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_i386.deb Size: 42544 SHA256: 9889b3e0e58ddac251b810fce6138cd80ccea4624f39d536367395309388cf43 SHA1: bd7dfd6ddebec4abf5ae8d9321f9ed4ea880eec9 MD5sum: 45ec8edafa907dae0c167c6cd15f5a6d 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 7409 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 (>= 2.4), 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_i386.deb Size: 1370164 SHA256: 7303de4741c16ee849c69a724dcbf126ca4e4267a3cc69145b626e326879bf61 SHA1: b873c9499e4e0e5ece43efa0dc5aeabb11d31f48 MD5sum: 83089d7b5235f74e451c9018566fcebe 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 27943 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_i386.deb Size: 26202128 SHA256: e6ad72f4349fba230343dcf08cf0b90e0041205470e2871ea1af5abe3cb6c5ba SHA1: da517924e458b5cfdfc96d9ef6339727b07d824a MD5sum: eceb8d82f6147c9267465ae74a4c1b67 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 168 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), 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_i386.deb Size: 68276 SHA256: 4bd8457dc460d5c8dd775e03628d7214fd16fc5278af8711dc875b66ebc901a6 SHA1: ae43a0411b5cbc58f5fd9c9c660cc074cd3ee955 MD5sum: e751342cd33c254791d44c5bbed01462 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 177 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_i386.deb Size: 155908 SHA256: 804536e776a08168769801da370d7ab2be2639913a9ab37bd61fc79fc434662c SHA1: 4d5b3da7cf895f0978ac557cac3b35e71e553a79 MD5sum: 1cf467d28568d7ce1d76dec917f523cb 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: 1.8.5-1~nd70+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 6745 Depends: neurodebian-popularity-contest, libatlas3gf-base, libblitz0ldbl, libc6 (>= 2.3.6-6~), libdcmtk2 (>= 3.6.0), libgcc1 (>= 1:4.1.1), libgsl0ldbl (>= 1.9), libnifti2, libpng12-0 (>= 1.2.13-4), libqtcore4 (>= 4:4.7.0~beta1), libqtgui4 (>= 4:4.5.3), libqwt5-qt4, libstdc++6 (>= 4.6), 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_1.8.5-1~nd70+1_i386.deb Size: 2648412 SHA256: 81bfabd5c6fa7160ad6f45d0d096a32196f3272b553ca6ea81a5c782ab2be974 SHA1: 0a37b62cc0d25c1d1f205cfac5c44b5774081288 MD5sum: 36e7734a02871b83c7ca4590dae73d6d 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 5368 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), 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_i386.deb Size: 832102 SHA256: c8b8780abdabe955d8465ae57aa88f52f6e2ed573ac882779476622858551de6 SHA1: 0f55ba8a21b19d3f16b2cea148e9856b8ce38920 MD5sum: 224791fe6273062f5b8d4723a8e92aa9 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: i386 Maintainer: NeuroDebian Team Installed-Size: 12557 Depends: neurodebian-popularity-contest, libatk1.0-0 (>= 1.12.4), libc6 (>= 2.3.6-6~), 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_i386.deb Size: 2082262 SHA256: 963899407ef9f73216aceac66d20db2d7c233c184f154dabc7eebcb705cea695 SHA1: 408fdfd01f9c1d8720eb3a27cfb9d66835557ebf MD5sum: 17cfcc22471a23aa5139defcc3237312 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: i386 Maintainer: NeuroDebian Team Installed-Size: 8563 Depends: neurodebian-popularity-contest, libatk1.0-0 (>= 1.12.4), libatkmm-1.6-1 (>= 2.22.1), libc6 (>= 2.3.6-6~), 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_i386.deb Size: 1447192 SHA256: 56601a3753c6f457baebb2219015203afb57d7c13d75284da00f36ce5a78566f SHA1: 78ae5dc2748cfe6f7459c9ccdb4221d356a077fa MD5sum: 6e8970f3db1b48befb0c67cf815cf918 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 274 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_i386.deb Size: 218300 SHA256: 84e97d28ee1c712f121d4a89924819c33e9886ac869b4d242bfb27fcfef737ba SHA1: bcf5b838cb9bb28386d4b9d96d2a3ba271885c7d MD5sum: 298901fcaccae4b65b8cc5c42efd3202 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 46 Depends: neurodebian-popularity-contest, libc6 (>= 2.1), 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_i386.deb Size: 31146 SHA256: 0499405947dd5a8f4d37ae8e34f4257f614e450d2d3e55ebba99d3f8dc6ff9aa SHA1: 708ed8afc29422ca7536cedd11a59a5db9a2c056 MD5sum: a28b696dea8efd6b12f02b1b353a01cf 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: i386 Maintainer: NeuroDebian Team Installed-Size: 178 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_i386.deb Size: 56076 SHA256: 1e6b71a0699ba1b327b73ce34158625df761b22e5ebfa0311c18b1c8198ea074 SHA1: 5330f2ff0864d24082424d7fe8f8b6f6334e4aa7 MD5sum: 010f1601edaa08e237ed2c31df98ebbb 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 2338 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), 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_i386.deb Size: 363934 SHA256: 7f42d628f7aef6cdb0b958f5eb1dea2c8c2ddb27a9d20e4c619dfa17dbf476fb SHA1: 476cbc656ed371708528e7fac86cbc065e57accc MD5sum: 2a858e85e0d4dcfb2ba9d4b5fbe8a8f6 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: 0.5.16.1+ds-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2726 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_0.5.16.1+ds-1~nd80+1_all.deb Size: 591218 SHA256: 2a29f6eb952a4c8f140046a70d3b14d794f358abeba5c311ba920b727e6f404a SHA1: 460c4a5adae29de94749cab859b4eb7f09c907f3 MD5sum: 75de7e46cb81c9fc1626a2b38c4ac30c 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 836 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_i386.deb Size: 592190 SHA256: a104da4bb8a63844aadde4c4f2f67fe3148699c53d138d8103196bd0cf0e3804 SHA1: 48e006134b5a035a2dedbaba33e93a52917451ce MD5sum: 32f4f46357193453a439256bc64cc71c 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 67 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_i386.deb Size: 20556 SHA256: 0f5e0d58236638e6605b91fa32ef49cbb2d5bbad7aa7c1fb485c31177c81967b SHA1: 77a6e2a091faa9b1f2fe6b9e53bab3b0fed342a2 MD5sum: ea9dca386c1534e60549c10adf95542e 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 287 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_i386.deb Size: 121594 SHA256: b34ec80e9dc94b7fe0b9655fdd5cd4201f1f5b0a9d4083634001ed1d7e92a6cb SHA1: 8cb47dca9e06723a80985ee04b71620f5c06b0ad MD5sum: 1fe9b1c81062100727476ef465ad700f 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 83 Depends: neurodebian-popularity-contest, libnlopt0 (= 2.4.1+dfsg-1~nd80+1), libc6 (>= 2.3.4), 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_i386.deb Size: 25110 SHA256: 9270b80b4e0482b3100f41221c9b26851bc616cdfe29de101799dc54a1295b66 SHA1: 67d69d6d55b26dd65b6fdb2c627f0f8f24250133 MD5sum: 4d11f4e72bba8c679426a464fa4b4fbd 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 4096 Depends: neurodebian-popularity-contest, octave (>= 3.4.3-1~), freeglut3, libasound2 (>= 1.0.16), libc6 (>= 2.7), 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_i386.deb Size: 804406 SHA256: 7ff2b71776a4635f230da00f0286f4fa79e8f40b7388f18d79017eed32c9097b SHA1: 4d491270b9763ca384d5fa004794bba1f1bf1544 MD5sum: efa51d0b6af73aa2cec3b64f3147d818 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: 1.8.5-1~nd70+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 4061 Depends: neurodebian-popularity-contest, libc6 (>= 2.3.6-6~), libdcmtk2 (>= 3.6.0), libgcc1 (>= 1:4.1.1), libqtcore4 (>= 4:4.5.3), libqtgui4 (>= 4:4.5.3), libstdc++6 (>= 4.6), libvtk5.8, mitools (= 1.8.5-1~nd70+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_1.8.5-1~nd70+1_i386.deb Size: 1649310 SHA256: 531260e45bdc6453fa1f14d57634cf5641cbbb932b8f513e4df21c9cbf1a4a3b SHA1: 5c13518235ccaf26d5d379b44ebecda3dce4f442 MD5sum: 5d1a6e7af2d6021bc125ef0e512b74f8 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 544 Depends: neurodebian-popularity-contest, libatlas3gf-base, libc6 (>= 2.3.6-6~), 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_i386.deb Size: 184400 SHA256: 25fc04997cd71a3c7e041f1fc1f77e2afc173d5e7d89a6a1860e04adcf17d55e SHA1: e0ac6dc8ecc0694e0e912f28e859420cea33f313 MD5sum: 4512fa93a8486a08228ea800861ba254 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1182 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_i386.deb Size: 451510 SHA256: e528dda72ed2e161e6e9bec3ae970f580f418cf4b2de07502d901a64fad20c91 SHA1: 7a9f32d06cc62296036e2902448c891975aad66e MD5sum: b9427052e27ad19baff7ad7874f7537d 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: i386 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_i386.deb Size: 100670 SHA256: 7bd5e4580de5f4c009dcb41983adad7bc29c8218ce6bf92af909bcd80a296940 SHA1: 81206fea13ee6f25ee8be1eb8588d49bb16423e3 MD5sum: 8aada141b8aa36e29b50340d3febcfe4 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 2084 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_i386.deb Size: 642118 SHA256: 18260d8a12d0bbb7c02edfae43305fc2a3d87b2d0b9a9618f5f64b2407da7f2d SHA1: d9da2889afa8500acc8aa72164412a6132f526b0 MD5sum: 7eb3018f66b715ecd65c2fbe2722bde2 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 5367 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_i386.deb Size: 1664640 SHA256: 83edf8e9f79f1da4ef9f8f284e60264df78e0598b7e49b1674f304457cc81c99 SHA1: 653d0756c7a07ac463f03bfa6800731e0f0a2534 MD5sum: b34326af5a15862cddfc8738839994f4 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 17721 Depends: neurodebian-popularity-contest, libbiosig1, libboost-filesystem1.54.0, libboost-regex1.54.0, libboost-system1.54.0, libboost-thread1.54.0, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libnifti2, libopenscenegraph99, libopenthreads14, 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_i386.deb Size: 3282284 SHA256: ca7139c82b050ee47c7e21ca525400f2c543f962f963d0efa886fc3133a5606d SHA1: 5dcf89c3acc2fedd23b8e294950c478587cd6b63 MD5sum: 4adbb6e2c8a4b6358077c60afc9a1709 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1924 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 (>= 2.4), libgcc1 (>= 1:4.1.1), libopenscenegraph99, libopenthreads14, 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_i386.deb Size: 739150 SHA256: e368f235d677ceceb95fc90f866be7808cc8878ea8f9b52de24ace4ef50db4ff SHA1: 0ab745de0171ea8b3379eb91284679a4c8608e10 MD5sum: ce6b50f9c7c31b65be111596d9319aa3 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 3468 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_i386.deb Size: 689396 SHA256: 3c4f0783e541eac07c99fcc74d94ed7976b79cbbd66d3cd878c10849b9d977dc SHA1: d0b1d0dc0480f888a4eec39b70fd6634c684be11 MD5sum: bb522818a2ab6ed8a5a1c20c77bb2957 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 137 Depends: neurodebian-popularity-contest, libc6 (>= 2.1.3), 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_i386.deb Size: 54840 SHA256: 2ec8ec46fabfb3469b93a7917e7fdab11142ae056bab994be430bd2867c3fb32 SHA1: 418d8a720c20cbddbb322ca9aed0d10d8328f512 MD5sum: 0debbaf98f8afa8e34a62557d0493eaf 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 201 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 (>= 2.3.6-6~), 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_i386.deb Size: 43308 SHA256: ef2e093a96d198d5901bad55ce3cf3fbb1da39e9127f752c73f063eb22b5318d SHA1: 23c1ec819cf088d4cff3c0ea27ff0bda422d8775 MD5sum: 20aa89c1edaaec7f72da01e246093725 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 224 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 (>= 2.3.6-6~), 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_i386.deb Size: 92804 SHA256: 261e0fa45f855317185177cec1c7eb114c99414b02988e8cd79ca1fa9bff6e3c SHA1: 0b4d361b8d5065c57a4348a20aef5a18e96cd966 MD5sum: 799aa6719d866c2f1902c9fb694fa2fc 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 5760 Depends: neurodebian-popularity-contest, python-numpy (>= 1:1.8.0), python-numpy-abi9, python (>= 2.7), python (<< 2.8), libc6 (>= 2.4), 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_i386.deb Size: 939338 SHA256: 70e5c3d81cc7f635a131976e19f286c100bc933252542ab57f2a44fa86e2bdb6 SHA1: 364b6a90918a671a48ae9131d648ed95b1c89431 MD5sum: 3307d8c0dab5b95028530dc37d895767 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 147 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_i386.deb Size: 47030 SHA256: 779fc2a9e86564a8aa6babdb186fd6eeaa74408847a0832be477f47d67a3d32c SHA1: 1c24dc5f2815ef8f8407ab2fce9e89ac6f15fd6c MD5sum: 811b8904c04138c323b119034a8bafff 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 209 Depends: neurodebian-popularity-contest, python-smmap, python (>= 2.7), python (<< 2.8), libc6 (>= 2.4) 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_i386.deb Size: 56274 SHA256: 06fa2b29a7e1f8363c9c11c46dd780907dee965f58ef583cb4fc166dfdd0aa33 SHA1: 7098cef5cc5785bdadf9fde7d0e7c8219ef71e63 MD5sum: 0484bdb281b2df1ac1dc94e1b337ce14 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 10713 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 (>= 2.1.3), 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_i386.deb Size: 2514564 SHA256: 78c53aa5fd1ff2cbf3829ae40378a3556e8ba5ff8b11f491c6a579721ce65c13 SHA1: 6bce29c49ccbe7fdf3715eed6f55d5aa3817cf89 MD5sum: fd2b4724800ae17da1160bda3cc36455 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: 0.9.3-1~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_0.9.3-1~nd80+1_all.deb Size: 78090 SHA256: 0caaf4e5dc90de5c3f5f9e77aab9bc054e3843dfafa0f2bf036d9039079fa295 SHA1: 4bb8f12130a8ce336a4409ef9daf33469393e1a9 MD5sum: d29096839c02ba6fa1e76d64b951282b 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1245 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), python-numpy Homepage: https://pythonhosted.org/lda/ Priority: optional Section: python Filename: pool/main/l/lda/python-lda_1.0.2-9~nd80+1_i386.deb Size: 236570 SHA256: 89a711af383008f00e51c1f2272292c29dd808905ea04be0cf56b55c64b1ce9c SHA1: 553436b31309fc0e537d04b8a1316ba7f45134a1 MD5sum: 7312bd67f65857a0d0a8c9e599c8a434 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: 0.10.1+dfsg-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8878 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_0.10.1+dfsg-1~nd80+1_all.deb Size: 4321966 SHA256: 1c6dc5ee473688cb7f3cc5d868fad1759bc1cdb95c5c6d01f95d132249bcd154 SHA1: 2bfccfb2a75a64aab1c8a5bacaa5578d75ab8d68 MD5sum: 663a8261405a9b1f5d151c239f0b4af7 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1473 Depends: neurodebian-popularity-contest, libc6 (>= 2.3.6-6~), 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_i386.deb Size: 476214 SHA256: 3591d02579afe49104c4170ee3b7cde53499c74c4fdd4291a45d32703c54c579 SHA1: 535ba70343496fb99cb5f70105b6731e9cad57ee MD5sum: 5069e439e92f236300e4f1fa56133e5c 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 3510 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_i386.deb Size: 1201494 SHA256: 58930f270fdf68d6b1a8f243441360d8514ba940f687b1c198f35aaa337b3163 SHA1: b73786c10ce1240d92bdc13badeaec18befbdcfc MD5sum: 5e4c9eae5a65609d3af20402eb47a85b 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 187 Depends: neurodebian-popularity-contest, python (<< 2.8), python:any (>= 2.7.5-5~), python (>= 2.7~), libc6 (>= 2.4), 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_i386.deb Size: 57420 SHA256: 5e829cf51dd15fef331f85f3179f1e75913487e058f67dabc3409e7af60ac994 SHA1: 0555fa2b47688d0a82cf15800cf71fa1817ee205 MD5sum: 81384a44e561e8ac60405101795272f7 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 173 Depends: neurodebian-popularity-contest, libc6 (>= 2.3.6-6~), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), libsvm3, python (<< 2.8), python (>= 2.6), python-support (>= 0.90.0), python-numpy 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_i386.deb Size: 71336 SHA256: f5339913330bd1f9f210a2b3c45425f4f99ab2331e110d7df77a6bcae79d864f SHA1: cc74c2c796228a2df90718eb6262be917391e7a3 MD5sum: fdc698f978bc80c4b5f0a2b56289d8a1 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: 2.4.1-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8245 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python-numpy, python-mvpa2-lib (>= 2.4.1-1~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_2.4.1-1~nd80+1_all.deb Size: 5054488 SHA256: dc26770f8acd6c98e3852a2c6ca0edc7b546826f4a22b8eaa6d2843e2b669be8 SHA1: 0c3d41831f68510012578e4a4378c34054c8c9ee MD5sum: 04db69c7c3bc2789400f3ea862d27a28 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: 2.4.1-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 29632 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_2.4.1-1~nd80+1_all.deb Size: 4734884 SHA256: df87de98a1b733dbbef728fad51057722aca82ca209c71f883f816e075ab2706 SHA1: 77090d2025fb8340092354154f4b2bd6f5823724 MD5sum: ac6f346782ca8cfaba5f92ea02090de6 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: 2.4.1-1~nd80+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 167 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), 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_2.4.1-1~nd80+1_i386.deb Size: 48062 SHA256: 5363327e39c773cf784b0cbee71a90bb5edfa65e1544a9c5fbbb597b3f5c3196 SHA1: a33986f75c7c193ce82692b4335799ca4b7ab509 MD5sum: e14624746dc266f11e1d161196f15682 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: i386 Maintainer: NeuroDebian Team Installed-Size: 105 Depends: neurodebian-popularity-contest, libc6 (>= 2.3.6-6~), 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_i386.deb Size: 20650 SHA256: 3466108aa22955d880d0ad934bebfb6cf987e2d8eba79bbcf621b5df1099567b SHA1: 5cb8d7dde8d0e6f5b7c63884fcb1152082309b7b MD5sum: e997f09ba1145b0356ded6b9fa205376 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: 2.0.2-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 63322 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_2.0.2-1~nd80+1_all.deb Size: 1972524 SHA256: eafb8b47cf8441eeb64849a129f9205de3da7ff3d2444c2dac1871056d8980d5 SHA1: f7a3b3a5397bf80dcaf7e6523f846e863288cf3c MD5sum: e55301bdea1315ebe13fb6698d751467 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: 2.0.2-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5578 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_2.0.2-1~nd80+1_all.deb Size: 2692580 SHA256: 1220e8924af25fd4216fe3988fbbeb0b634656cfc640081d0295232bf9f12a79 SHA1: b6ea4eba8260dfd6dd6d8714e7c4ca202291dba8 MD5sum: 0ad4eef31f9eb60a3d275a1e95086b4b 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1424 Depends: neurodebian-popularity-contest, libc6 (>= 2.3.6-6~), 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_i386.deb Size: 376566 SHA256: c7f0a800b13969aa5c9fea44746c75bc1b625782c1f0ed4b038f032a3c7f61a6 SHA1: ad8a836ac5e240cdd905c5da53a93b6b67bb1245 MD5sum: c25a388a86f23b154944c5e2cdc83391 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 2656 Depends: neurodebian-popularity-contest, libblas3 | libblas.so.3, libc6 (>= 2.4), 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_i386.deb Size: 550202 SHA256: c245eed3e49e218709a5bbf961498c2f348b005a106ea0c795f7417b2418096d SHA1: a8dc530162e8b8da5501feeb13a88573baa56911 MD5sum: 38285f63c42aa724f4a72e3f6c55f839 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 3054 Depends: neurodebian-popularity-contest, libblas3 | libblas.so.3, libc6 (>= 2.4), 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_i386.deb Size: 431066 SHA256: 43d54226e9e8ebe06ca3ee2429643710cee4fc8a7d21cbe89564c2549e01a611 SHA1: 181ff439742a2b1f48b0890d194e9f5a02ef2f0e MD5sum: a6778fd459391c6dec03219066718c2f 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 247 Depends: neurodebian-popularity-contest, libnlopt0 (= 2.4.1+dfsg-1~nd80+1), libc6 (>= 2.4), 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_i386.deb Size: 65638 SHA256: 57c599a8f581d992c6406dfef1408ff931aeecad5dc7c302bb97b399451d22a9 SHA1: 583790b1ab5f4b290f5e7b007106ef19a02c9d12 MD5sum: 221ecdc4d855c1451164829613926344 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 446 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 (>= 2.4), 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_i386.deb Size: 122924 SHA256: ffb53390fe5d4390b86b877f0defda14183039754b5808805ef2f1fa641f0362 SHA1: f30d34b1e885deac6a9c077b744b397cea9c732d MD5sum: 4b002f88c5bf00264d80a4494db3c92c 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 324 Depends: neurodebian-popularity-contest, python-numpy (>= 1:1.8.0), python-numpy-abi9, python-dbg (<< 2.8), python-dbg (>= 2.7~), libc6 (>= 2.4), 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_i386.deb Size: 93510 SHA256: bdd0504ad3a9f1387bd50cd31da988253e4ceeb6bf0c870c76fe940ba8645166 SHA1: 7a904ee83baadf3767dbf2899085d6dd88d56ac6 MD5sum: fd672066b70e85114ccddaf769eca8d0 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 596 Depends: neurodebian-popularity-contest, libatlas3gf-base, libc6 (>= 2.1.3), 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_i386.deb Size: 161580 SHA256: b513fa782f6433090860e42a0fb32134dd7918543a418dcd6519fd547f88ec01 SHA1: 8963619574323765ab2afc7ea54f59216f56c2cc MD5sum: c38a280ffc8e6a5b33626e40ff2ac382 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: 0.17.1-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 20021 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-dateutil, python-tz, python-numpy (>= 1:1.7~), python-pandas-lib (>= 0.17.1-1~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_0.17.1-1~nd80+1_all.deb Size: 2404816 SHA256: 4279c51719976eed3a344cb4be256cd27ba4b8b983ed547eacbc9e8092aaec13 SHA1: 1f76fff111932aae6ec6fadee78e328c6299edae MD5sum: ee3cb28a4685a80c99c2e19e57b674c6 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: 0.17.1-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 50142 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_0.17.1-1~nd80+1_all.deb Size: 10926660 SHA256: 32c271d60ff1868e08ab7e046b8f43ee62752d54ab53d84fd28127313f5212a0 SHA1: c51a26acd0dfd67e799ec960570df722223b34c7 MD5sum: d7e85d49ee35eb77bb3a4df6752d27f8 Description: documentation and examples for pandas This package contains documentation and example scripts for python-pandas. Package: python-pandas-lib Source: pandas Version: 0.17.1-1~nd80+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 6764 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), 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_0.17.1-1~nd80+1_i386.deb Size: 1603672 SHA256: a68baa10a740a0b2d8b314b20b813632c85814c414b38fb2b896b91a3d47b16b SHA1: 04096ddca6301a7ca8789550bfe3c45c709de32f MD5sum: b51cb61edd5086941d4112afffad22de 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: 0.4.1-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 795 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_0.4.1-1~nd80+1_all.deb Size: 171744 SHA256: d6e6c69b7aaf573e8b92345cae378ffa5e288b5d4f62cd01c1f394e384ddf64c SHA1: 078c45a0cfdd3ef740650d04cac3d8b0fbec2c2e MD5sum: 94eb4df75e894deebae70cdfb439df28 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: 0.4.1-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1371 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_0.4.1-1~nd80+1_all.deb Size: 361314 SHA256: ddf14eefd490d06a00af54ec2a075bcb71807585415a202458d918b732979e96 SHA1: 24f81c9ea6963749fdd37f712cb7146d4ec05f21 MD5sum: a45040349f70d90b1ee51d807e95b73c 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 539 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_i386.deb Size: 116700 SHA256: 5f817d41d14b6f53a1825d433649d6f91eac035e80a45da514cfdfa7388382b7 SHA1: c77610df7493553f4f350a8a24779bd53395695a MD5sum: 9f1dbd98bd02fabd5c8b734d8259bce4 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1314 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 (>= 2.3.6-6~), 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_i386.deb Size: 353634 SHA256: c1f35778f3196b4ad98b194ef85aa2c103b91765071c962937133341835884e6 SHA1: 01995d9d7ea288a726eb29d3a4ad3126b7402a83 MD5sum: c6dd0eb97c228a0bab9d3f87f9299ea0 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1815 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 (>= 2.11), 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_i386.deb Size: 375192 SHA256: 59c308cf620ebabdea0274bc8b54ad01f3e5da788a859f61c65c189612618c8e SHA1: 4198d98c59f5689e044b7711e76e1448ac425aaf MD5sum: 9a898701075e7f540287629d1921e0cf 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 2304 Depends: neurodebian-popularity-contest, libc6 (>= 2.3.6-6~), 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_i386.deb Size: 676608 SHA256: c5ebf23ac6bacd1ef9e8f8f55f60983a55c68c8cf486900ff34a7715751f71f0 SHA1: 3e0b1f8c24844ffabca8c1c659068101720af1a0 MD5sum: 5466a1c8a89486dc8c86e434a1ec3488 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: 0.17.0-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 80 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_0.17.0-1~nd80+1_all.deb Size: 55034 SHA256: 48492ee816e5913aa2ac535f2a5434d7b93f8757bdbde50c127d652a8ae2f23f SHA1: 1049c55c4559ef5f1c039ff4b169e73fd066c61a MD5sum: 1dd962c4bd5ffbde659f187a1a93bc02 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 7411 Depends: neurodebian-popularity-contest, python-numpy (>= 1:1.8.0), python-numpy-abi9, python (>= 2.7), python (<< 2.8), libc6 (>= 2.4) 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_i386.deb Size: 983230 SHA256: b60f20b767514656734edfa2c3e8e882f21dc90a15a5634e76996db879b430df SHA1: 505b27e9f9ede4d3dd150504908b977b86730592 MD5sum: 23586efc61034b9d30d24ae3058fed84 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: 0.17.0-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5273 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~), python-numpy, python-scipy, python-sklearn-lib (>= 0.17.0-1~nd80+1), python-joblib (>= 0.9.2) 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_0.17.0-1~nd80+1_all.deb Size: 1223516 SHA256: 685bad09404dc60e6d92da4c5ad37d8acdb95666ed55bf023f263ed6e1c530aa SHA1: d3cc2b6b3969c32e14e7a17955cb55dcb90fd697 MD5sum: a11f87ea4052a0623b0f21d574ba085c 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: 0.17.0-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 23761 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore 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_0.17.0-1~nd80+1_all.deb Size: 4400628 SHA256: 5a6a7d48f6cf5a69eca9c69325b77d03e1239581c9ff7c2c99b641fa4ae04550 SHA1: 24495cbac4cc1c55d640e2dfc8be5c5cf2fd645e MD5sum: 089f2e68badd0e39117c38a203842ffc 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: 0.17.0-1~nd80+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 5350 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), 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_0.17.0-1~nd80+1_i386.deb Size: 1125692 SHA256: ce99dce2a69b975db4604de3a64856707fc6aa621ab9d9699347bb1cad6d8263 SHA1: 935492c6266bf2d854894b7958bc5692ae02e8d5 MD5sum: 81277a3ab0af1d7737930af48371ab1f 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 418 Depends: neurodebian-popularity-contest, python-numpy (>= 1:1.8.0), python-numpy-abi9, python (>= 2.7), python (<< 2.8), libc6 (>= 2.4) 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_i386.deb Size: 84556 SHA256: cf63d82f3123382da9bd44bba559a5f0f0c1911f53ac77053e1461ec875dc93d SHA1: 5722d84fa3a775255a051addd353e692c9820cf8 MD5sum: 9a14a4a83a4c954cd7420e9bad12975b Description: low-level implementations and bindings for statsmodels This package contains architecture dependent extensions for python-statsmodels. Package: python-stfio Source: stimfit Version: 0.14.11-1~nd80+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 956 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 (>= 2.4), 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_0.14.11-1~nd80+1_i386.deb Size: 315652 SHA256: f721f4eba4aed96eb61fdd28411813cda1c3519adfde5a8929e75d008309ec4c SHA1: cac53b75dcaff6cd45dc273070db12bead1487dd MD5sum: ee4c23f492794cb934b2336f00e10e3c 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1790 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 (>= 2.4), 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_i386.deb Size: 491028 SHA256: c333c38d20f4d456281103671bb9e16b82bb6a4b23954d506e5b26c26d05d8fe SHA1: 4dde5575b5b029c98cc825c457f080fbde057f65 MD5sum: 5fe87d50cd14a2e4e732b66f86bf3416 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1462 Depends: neurodebian-popularity-contest, python-numpy (>= 1:1.8.0), python-numpy-abi9, python (<< 2.8), python (>= 2.7~), libbz2-1.0, libc6 (>= 2.4), 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_i386.deb Size: 391556 SHA256: 76b599e74cca28115aca94f3cc851333967f7aa8d823c67bfaf4db140a354058 SHA1: be1e208dfad33112fabe99b7ed075fd2ac54afdd MD5sum: b9263b6b6b5d16d94a8ca9ead1044a1f 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: i386 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_i386.deb Size: 223258 SHA256: 05a2da61d06c5539b61fff62e2355a39d407963418a33727578acc8058d005c1 SHA1: db9ba05e2fda6dd2cd50a5ae17cd48c025d32b82 MD5sum: 9db167fb4a1d563aa24741863f66d64a 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1787 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_i386.deb Size: 687716 SHA256: 08f7cde9c9c64985f353f01a64d71e5296c1e0416367bdaec8e81b0b20a37a88 SHA1: 80c740a2027b751d17a34a0bcc04679ab89fbc2c MD5sum: fabdacdc99bd0979c23413044e15e244 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 28222 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_i386.deb Size: 6785548 SHA256: 1ba181f0fcbdd9f4770dc969ba35cf925277022b22c04aa83a26e57ecc64b715 SHA1: fecd82192e44c2d68061a99aeb8c524165c99b7e MD5sum: e505844b62d4d167adab024912249116 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 486 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_i386.deb Size: 91648 SHA256: 549114de2b7bc91027359611f01ea30576e8aa2ec0a111e2ff22383f0914c156 SHA1: c94971fe8a0a6c26036a8e6dae1cc88dfd77b0cd MD5sum: 7fa17e62f04429e454a6a7a63606a83c 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 400 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_i386.deb Size: 137504 SHA256: e04c3b75609a700aed12e82c8a5aa3f595b86e84a0aaaaecc91e272019b93103 SHA1: b5d98774de8efce2c14caa63e810ad3ba1c13831 MD5sum: d46b70a88e0acf0f53a57a362d76e743 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: 0.9.3-1~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_0.9.3-1~nd80+1_all.deb Size: 74970 SHA256: f9366777250923fd3efd978a13036bdbe6f99897bda5e332d715cd322a60c51b SHA1: 4c3526b44478c034549e0a832dc2fec73c8828f5 MD5sum: 19b866ea1d64796a2185c391570a56a3 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1242 Depends: neurodebian-popularity-contest, python3 (<< 3.5), python3 (>= 3.4~), python3-numpy, python3-pbr, libc6 (>= 2.4) Homepage: https://pythonhosted.org/lda/ Priority: optional Section: python Filename: pool/main/l/lda/python3-lda_1.0.2-9~nd80+1_i386.deb Size: 235774 SHA256: 2e8464fb6b7824ad4ff6b8c6e8da3e33c6526e2ce84a053315bddb7c2b9a25e9 SHA1: d7ad7e9f55113159a9f6c43afc5acd1eea696b9c MD5sum: 9c0ffb1409aa2909c127d3302b630c2c 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1439 Depends: neurodebian-popularity-contest, libc6 (>= 2.3.6-6~), libopenmpi1.3, python3 (>= 3.3), python3:any (>= 3.3.2-2~), 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_i386.deb Size: 465942 SHA256: 2b642dc4a5cf7b69858c7aae96455188ca92cdf358b8ca3ff3d1c3ae91207216 SHA1: 5d9f2a8021da526b63b22ccd6761b1da56ceb095 MD5sum: c25cc552fabe207e6aaff21abbce664e 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 3511 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_i386.deb Size: 1196910 SHA256: 12c3980669ed48414bf59d190e2b050178b04283ec33be4a2b2fd3c6be121bc2 SHA1: cc225869c86fe1543f808855f17392db4e90aee7 MD5sum: f4b815276781deaf971fe361bd208106 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 184 Depends: neurodebian-popularity-contest, python3 (>= 3.4~), python3 (<< 3.5), libc6 (>= 2.4), 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_i386.deb Size: 54362 SHA256: 7fc18378541294eb533ef03dbe1be2a2fdf52c1f9fff3d89a852ab87ebea1ad0 SHA1: c568a1441d7396d35a91a39aabf84be4a4f5846b MD5sum: fc941c42fb94f6ad26ecbad1d2d836c0 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: 2.0.2-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 63281 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_2.0.2-1~nd80+1_all.deb Size: 1963092 SHA256: 9d22ffea57f969b5e9809070ee8de8c92dcec7d9a9ed6529c0e39b453eb6d38f SHA1: 63aa64fce88c0be1e189719edc0cdaef6cd7cbb2 MD5sum: f1677863a6765ab9bd204bcf7f071e05 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 432 Depends: neurodebian-popularity-contest, python3-numpy (>= 1:1.8.0), python3-numpy-abi9, python3 (<< 3.5), python3 (>= 3.4~), libc6 (>= 2.4), 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_i386.deb Size: 117136 SHA256: 0415a206cdfe7d874753623400a779f4347681ca9d43e20203d4f5a4dc297996 SHA1: dcb5122093bddeccb8c8ac8b0e7f7459dd1c21ad MD5sum: b94e69dcd257965d93687a0f2367fde1 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 324 Depends: neurodebian-popularity-contest, python3-numpy (>= 1:1.8.0), python3-numpy-abi9, python3-dbg (<< 3.5), python3-dbg (>= 3.4~), libc6 (>= 2.4), 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_i386.deb Size: 93686 SHA256: 13386c85339a1769b2d1e0bc238c49431a661808330b59b54bca693a3660d9c6 SHA1: d1603041cc1ae8853c1a2b71096a78f9282806ce MD5sum: 372fe22a00454140a195613d04f6e643 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: 0.17.1-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 20000 Depends: neurodebian-popularity-contest, python3-dateutil, python3-numpy (>= 1:1.7~), python3-tz, python3:any (>= 3.3.2-2~), python3-pandas-lib (>= 0.17.1-1~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_0.17.1-1~nd80+1_all.deb Size: 2401648 SHA256: 368c511fabda1bf961643e0df4e97555cc9c184d30a18e32707fff0222edde9a SHA1: 2bdc0ee938c9cf15d3f25bd2ea9b1cea3605de38 MD5sum: 010e67fa50aced2dc6a3fc199e3e8b8f 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: 0.17.1-1~nd80+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 6636 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), 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_0.17.1-1~nd80+1_i386.deb Size: 1569772 SHA256: da8dccac734992229614fec9037ead4d4c6331d1e203b218a7e2727504c38df7 SHA1: e6f46b3cae929a44ac925ef4279f65e74f91102d MD5sum: b73b5468996db3e338ed06f1f13be04b 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: 0.4.1-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 793 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_0.4.1-1~nd80+1_all.deb Size: 171226 SHA256: 51f6929601b879120e47f9791862525dab7a5197060238b8764226dda9ee82da SHA1: 7ce6e824ee0a89801cf648b314495b67d2d15ade MD5sum: 87ce4d366b2d35210a9ee70125194a98 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 242 Depends: neurodebian-popularity-contest, libc6 (>= 2.13), python3 (<< 3.5), python3 (>= 3.4~) 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_i386.deb Size: 60310 SHA256: 179e61d712d00b2bb273038dbf203babb1f4d92a9848fbe37f5a00f2aa823996 SHA1: 66e49a4f35c0111cdcbfa37e45aed0e5b5ad2dee MD5sum: 8edd6fee938ef48ecd85838370c634cd 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 6883 Depends: neurodebian-popularity-contest, python3-numpy (>= 1:1.8.0), python3-numpy-abi9, python3 (>= 3.4~), python3 (<< 3.5), libc6 (>= 2.4) 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_i386.deb Size: 905760 SHA256: 84b0fb18feb5c13c8665b2fa3198d11dbc667a3f0cc161b291ca5436205711a7 SHA1: 591a5e50db179e12262b0cf921817e515719e93a MD5sum: 49b6350d5515fd8d85153e4f8011769c 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-sklearn Source: scikit-learn Version: 0.17.0-1~nd80+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5273 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~), python3-numpy, python3-scipy, python3-sklearn-lib (>= 0.17.0-1~nd80+1), python3-joblib (>= 0.9.2) Recommends: python-nose, python-matplotlib Suggests: python3-dap, python-sklearn-doc, ipython3 Enhances: python3-mdp, python3-mvpa2 Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python3-sklearn_0.17.0-1~nd80+1_all.deb Size: 1223156 SHA256: 18b1cbfb789de444fe3b4fbf3ecd92699102cf2d70083b66daf38b3c4c8b84fc SHA1: 76a9479e82d4637f14723756152fe83211beac2c MD5sum: 5b28e29b9bb8ea77f9f16ae5a5a85826 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) . This package contains the Python 3 version. Package: python3-sklearn-lib Source: scikit-learn Version: 0.17.0-1~nd80+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 4871 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), 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://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python3-sklearn-lib_0.17.0-1~nd80+1_i386.deb Size: 1038792 SHA256: cd932298ebe211bc414d63db049e72dab93b05dd91f303b5da146e3fa5b9d449 SHA1: 1578ac1e951deb97ad4928d5598fed2a79532319 MD5sum: 22bf4fc42b0c6c2f2a693d0e83faf924 Description: low-level implementations and bindings for scikit-learn - Python 3 This is an add-on package for python-sklearn. It provides low-level implementations and custom Python bindings for the LIBSVM library. . This package contains the Python 3 version. 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1763 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 (>= 2.4), 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_i386.deb Size: 482020 SHA256: bf5fb97f9f8458bd6ba87b25c0a8bea77bec42146c04fd02cd8084cc68d472fb SHA1: 6fbb8e3991d5ed4d456d4f111a8af026b6a56b9a MD5sum: 03b0646b1dee95f67362f8fdfaa522e8 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1419 Depends: neurodebian-popularity-contest, python3-numpy (>= 1:1.8.0), python3-numpy-abi9, python3 (<< 3.5), python3 (>= 3.4~), libbz2-1.0, libc6 (>= 2.4), 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_i386.deb Size: 379542 SHA256: 755a19f51ea04402f07db46b492e41bc64ecb4de6ecfc732d091cbd4a95af249 SHA1: 1dcbc53a70c2f3836355422a1e8e26cb11abed9e MD5sum: 88355e5bce21c1c0422548dca206b480 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 3272 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), 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_i386.deb Size: 485762 SHA256: 7c25bb2a9a967f09833a4d09a3c25cec109f046f5fafad1f796da9239895c079 SHA1: 78d44d2c9b5a61289ee9a13dcca86e821b9c72ca MD5sum: 748158d397be5dcf72b07939ad57f2bd 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 285 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_i386.deb Size: 174220 SHA256: 16dc2cb50299e87ab0d77c7410ae6cb37cbe2fb55368224d77cf62c6c80378df SHA1: 33ba0ff05c142b82a6cc32747e3ed6d4f79f5c1f MD5sum: 293eeed71510b5cc618f2475eb777f5e 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 139 Depends: neurodebian-popularity-contest, libarpack2 (>= 2.1), libatlas3-base, libc6 (>= 2.3.6-6~), 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_i386.deb Size: 44436 SHA256: 2f66f11dd4f28724d347186cff4e521c53515fa2a5b4ff24aa9938affe26ef84 SHA1: bdf4d1ba7412f575fa7738f62b02d428bebd5c56 MD5sum: f1a8f4de11ed8a927002f042fce7ebd7 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 7564 Depends: neurodebian-popularity-contest, libarpack2 (>= 2.1), libatlas3-base, libc6 (>= 2.3.6-6~), 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_i386.deb Size: 1610202 SHA256: ecb779a20f581bc99789a5a957ddbba5b382f37322d7c8f49075cbae514a1176 SHA1: 4de58df824c725bd35aa941a9c72abb313103365 MD5sum: 62d3113cfa50c6f968d0486b226402f9 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 67467 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_i386.deb Size: 15946416 SHA256: 52ab8b93ed54edbd436b4fb6599632be4a5a2af8b85dce43e406867095553e01 SHA1: e36d49c92c4bcc609a5728c6523a6e9a597b826f MD5sum: fc183071c99dff11f6cebbf3ea1c77f0 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 203 Depends: neurodebian-popularity-contest, libarpack2 (>= 2.1), libatlas3-base, libc6 (>= 2.3.6-6~), 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_i386.deb Size: 59994 SHA256: 39303c945b5d825b6801e810fd93dec9e56537da08e14edf788806a32b115835 SHA1: f156bb65304c0d74c3261cb0e57d280940ed4d1b MD5sum: 64f68e82b41d23c6e71a6a02c19d5d34 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 8016 Depends: neurodebian-popularity-contest, libarpack2 (>= 2.1), libatlas3-base, libc6 (>= 2.3.6-6~), 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_i386.deb Size: 2376476 SHA256: 929e165f834c1e8c0fb3db53ac79f5db398dd3777139c429511ea7f155f75087 SHA1: 14007d2254bfb577fb5ebb81dd536cf838cceea9 MD5sum: 917f8c82dd311e585ea0292e0f8d2d30 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 12760 Depends: neurodebian-popularity-contest, libarpack2 (>= 2.1), libatlas3-base, libc6 (>= 2.3.6-6~), 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_i386.deb Size: 2514456 SHA256: adb2e2b86217572730a45d189187401e6235208d5dbd0377d6fb38e8899aaf2a SHA1: d0b7097f3b30e33b73e9724f993220e91ab3192d MD5sum: 22c1efe3e06637e4213cc6e87b733d29 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 26876 Depends: neurodebian-popularity-contest, libarpack2 (>= 2.1), libatlas3-base, libc6 (>= 2.3.6-6~), 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_i386.deb Size: 6022350 SHA256: acbf195a7a7de514b8ce1c0d9967ef8d398174802a8c1ca615557384d4f59f68 SHA1: 57735f39db18c23ac3d836cb2910a490d0d554fc MD5sum: 5e10388f2b1106c2b07543469272e975 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 229 Depends: neurodebian-popularity-contest, libarpack2 (>= 2.1), libatlas3-base, libc6 (>= 2.3.6-6~), 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_i386.deb Size: 64668 SHA256: 206ca944624e937002b2d7c2ba8e724435fd2750998821875b473abab6d98603 SHA1: 41875c4eab9ff34bbe49bac7e46998dac62390bd MD5sum: 5b7deea53ffa72e332e59b135670cd92 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 256 Depends: neurodebian-popularity-contest, libarpack2 (>= 2.1), libatlas3-base, libc6 (>= 2.3.6-6~), 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_i386.deb Size: 65066 SHA256: e4b2980fb21ca6e8cb36f7d1274826bac6f5e02951d7e126951b0ec3d2a30a5c SHA1: d71b2a5ef9be9e47b0bfeb2049b6411b1c33af1f MD5sum: d8c5233ea02c74c2450220bc434ec2fa 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 9935 Depends: neurodebian-popularity-contest, libarpack2 (>= 2.1), libatlas3-base, libc6 (>= 2.3.6-6~), 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_i386.deb Size: 1878032 SHA256: 01faccb19ae22776ac59e369e8f8b29b2eb03e78294a2213fa44991360a2b020 SHA1: 2a97dc00483ba3b0e0522f452cf22cedf3a821e5 MD5sum: c18f04c77508a07b2058c29e13732422 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 944 Depends: neurodebian-popularity-contest, libbiosig1, libc6 (>= 2.3.6-6~), 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_i386.deb Size: 338736 SHA256: 6dd69336d4187dec1d779f0c557d03a8db64fdec2f9a78c028c423a45db3b6d3 SHA1: 33b515e2381e23724ba5bafb2bae2dd0fd4787e3 MD5sum: a05aa2ef327fefe61cc8f910337556d9 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: 0.14.11-1~nd80+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 2680 Depends: neurodebian-popularity-contest, libbiosig1, libblas3 | libblas.so.3, libc6 (>= 2.4), 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_0.14.11-1~nd80+1_i386.deb Size: 764800 SHA256: 5c71d32f2ab0b91ccb5d253757216f08e5a98f4f109b40a1c1cfbc647474a6a0 SHA1: 97c1e01bef064b8bc38221fe718ce9c709e668af MD5sum: 4d99b5bd6a802be3259d614c09c0a37d 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: 0.14.11-1~nd80+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 24160 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_0.14.11-1~nd80+1_i386.deb Size: 4452118 SHA256: 9cbe635acc7363cb5e004b6d8e217fa93a6a82d1aad71fcbbc9f37f31407f36c SHA1: 23a0ca9b2b999c44040c11820c4fd2874cdf216b MD5sum: c6ffd00f4fe35b2160259245ba56efab 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 17913 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_i386.deb Size: 5575448 SHA256: db6fc0818630854c57cf93dc16f9f04174a8146603d7b852e94766239d31a23e SHA1: 50dbeb7114b117248c70470d0637409d90b4732b MD5sum: 6644fea58558302edd2bd120c6a7732e 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 244 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_i386.deb Size: 80342 SHA256: ae13fb5ae47b03f1a5ba38c25bcc5674bb89c73a469a0c9472e2095e0a6828a8 SHA1: 43c290b569a2e7e3d3c5c445d597d67178b83cd3 MD5sum: ae8c226e817175733dbe1e1da45ebcdd 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 580 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_i386.deb Size: 229136 SHA256: f14e54f4d29882dff5efe9b4de1e8c0e1477f881d1194ccf5acbfc9fe5ac19d9 SHA1: e5de45d70367e23fe8bb1489adf5952ee1dbd7d6 MD5sum: 7fab58d11b929387a892607ca9d7561b 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 2635 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_i386.deb Size: 1167920 SHA256: 7b79c6f51557612c554c6dcb7289aaf57fdb80c501b2de057105d34889beb7e7 SHA1: a5bd862821fafd6f8a36001e1f684ed96b214bc8 MD5sum: e32441d6bd7711411a4136f101ea757c 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1104 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_i386.deb Size: 506614 SHA256: 7a8a5a9bce1b6849900f08c2d2367a0f9b231e4b85ab5207629e270e7d323755 SHA1: 4c00cc41f63ec2ce00e57894e6cf7c15b812293a MD5sum: d9fc92b1b0cfb4b24fe21886933fa88c 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 787 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_i386.deb Size: 289736 SHA256: 9cd0f86c17862f44b1e02f67c1bc7dbaf1eb3a2e546abb641d085a90ace2099b SHA1: b0eecce9003acc408b5502526fb476724bf34e9e MD5sum: 3c706b4a0128e78fce5d015935d644fb 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 18720 Depends: neurodebian-popularity-contest, libboost-python1.55.0, libboost-system1.55.0, libboost-thread1.55.0, libc6 (>= 2.11), 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_i386.deb Size: 5271666 SHA256: 06f9c196626027d2ecf069a607f7dcd5c221c0349ceae4387d7e3560359b7515 SHA1: ae5207a1b6a9c1661e2a39d70748e798da9b96fb MD5sum: 8b50a516ef599ca87c268a6c2cf533cc 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 44193 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_i386.deb Size: 43263814 SHA256: f826cb8aeff8b0ebb6ab55ce75e1d970f4226ef25a48ba1f4466e2b44e60e45f SHA1: ecfc3fded689961dfd9b61d8ff570b953614b7c0 MD5sum: ff4d1f5b01541767f45875c34178b381 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 500 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_i386.deb Size: 169608 SHA256: 6f0f72c3f1a29e2eacab8761769bb352224352c8696a1f7193218587f96149db SHA1: ba36ad1b6eb3b45c96ab51db724f0292e60c79e2 MD5sum: 675c7b8e6fe2c1336d76bc5dac7c21bc 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 45 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_i386.deb Size: 20728 SHA256: 56fb89d372290e4df8ff3a1388c6c748e1423f34c861c8f0d5c3eca7f56e058f SHA1: 16a4c46fa91c6080038f62252fa52ee7f46277d4 MD5sum: 4a4b855cfa3ce3c2d980da7bdfa679e4 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 5610 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_i386.deb Size: 2168278 SHA256: 8988128932725bd7ba9711b88244bfd606030a816c8765b12e49bb8fec5befa6 SHA1: cfc489d65dfe03158f85a777790115042c92a6db MD5sum: 3a9c4c88c7978aee4949a362791b8574 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 9696 Depends: neurodebian-popularity-contest, libc6 (>= 2.3.6-6~), 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_i386.deb Size: 3704676 SHA256: e287d12a4f8562cc6ed2f8e64d64938cfa33a64e2a0edaf34fd1a52d7da63e78 SHA1: f361d60af81addd6abc74b53da16da063985c7e3 MD5sum: 5f54ecfba6b9c661369ce81d661a53db 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 327 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_i386.deb Size: 111150 SHA256: d7b89bc0906c4a5ce0388808e2eddbca9bf1a84f0576ba2e2e6713782562dbdf SHA1: 8a2654c34b0b6e266bf03916f3cda75a844bcb46 MD5sum: 8c6a69010853c891c6da4b1bc99c8b60 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 4203 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_i386.deb Size: 1635436 SHA256: a788357d1644ac712d488a6e6c3879efd17f77a4591817dce2991d30df15b8f1 SHA1: 22985accf28c8f2ed701dfaaf7083384ff82c2d5 MD5sum: e1c58e3802bf7e285b688af4b098a7b4 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 245 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_i386.deb Size: 73338 SHA256: 1fbe463d0bd334f4f8da8a765b666decdd570049e4dd9e2a8087a44b78c016db SHA1: 17848cc0775f1422c667af881e0bdc219b17bb88 MD5sum: 9bc068d5d8099c4e8f18f16f46f323b3 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 473 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_i386.deb Size: 249314 SHA256: e41759a2b9cce7cceb13753d1b270ce73534e52630e33c70d06ddb2b86ca01c8 SHA1: 0d9e8ded6b3c931b81d133ff0decba994d6b0a03 MD5sum: a6d6383ede5c3b85ed68c4dca377ad83 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 830 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_i386.deb Size: 341378 SHA256: 865d7001778b4af7b01c703e55529bb636bb386353e4aced800d36facfb58b88 SHA1: 76755ed34a06b8e663f40d48cb488906d14b8a96 MD5sum: d8e32052d4db5a715aa6f020ea86430f 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: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 5804 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_i386.deb Size: 4142704 SHA256: 66687a822868b877cc2db25953618d498b4dbc157014881a3ef84b07215abdba SHA1: 061882bed8bcbcd4b6d8ac42209d1fc37ef5a331 MD5sum: f8a0b357dbb31ae1b6483b034b59c8d9 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.