Package: aghermann Version: 1.0.6-1~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1652 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libcairo2 (>= 1.2.4), libconfig++9, libfftw3-double3, libgcc1 (>= 1:4.1.1), 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), 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~nd15.04+1_amd64.deb Size: 542490 SHA256: cbf75ba444345ad0f64695571cb44d5b5a44a3ace869e92707db56a95e3e0b61 SHA1: d433cc6f548caa1a74c2884073ed979d59ca755b MD5sum: e33644b88dedf0f57a0033bef49c5cfc 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~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 180108 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libinsighttoolkit4.7, libstdc++6 (>= 4.9) Recommends: environment-modules Suggests: fsl, gridengine-client, r-base-core Conflicts: gpe-conf Homepage: http://www.picsl.upenn.edu/ANTS/ Priority: extra Section: science Filename: pool/main/a/ants/ants_2.1.0-2~nd15.04+1_amd64.deb Size: 26021846 SHA256: eaf3f5100811457c1d13fe31ba67013c76407aa56cd57c6d4fbe5d801ac7f8df SHA1: 8ea95f72d70dc95060946897ef2f8f802ac3c812 MD5sum: f7f30db1a4b374d787b9b3657e510de4 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~nd14.04+1+nd14.10+1+nd15.04+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~nd14.04+1+nd14.10+1+nd15.04+1_all.deb Size: 14510 SHA256: 02b32e81b73dd96324db85f9ed59e8f1a6cb15ba339dd6a2d14c2249679ff43c SHA1: 480cd9730f96ec53e75c9d3cfec8af43b04633ee MD5sum: a3121275ae8b1c2a28f6ed9ee07a0a02 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~nd14.10+1+nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 675 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libcholmod2.1.2, libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), zlib1g (>= 1:1.1.4) Homepage: http://biosig.sf.net/ Priority: extra Section: science Filename: pool/main/b/biosig4c++/biosig-tools_1.4.1-2~nd14.10+1+nd15.04+1_amd64.deb Size: 243630 SHA256: ffdf4d962e55cc2b5721cf57e206cf469640cb91a04fc8e5935d034514b317ed SHA1: b49c88da5e6af32440f0c0147d5a1e28d3248dc5 MD5sum: 9420242e46d56c1be44e9377c8420315 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~nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 104 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~nd15.04+1_all.deb Size: 32952 SHA256: d08cd56d23798f7eaf17596b76ed3733c712097a034876644bb12c346ce1603e SHA1: 9a3df1c297d6e9ce6de5e2d2948547ff9dcbe9e8 MD5sum: 62102aac8d548e19267fe4c92bfea714 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~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 3428 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~nd15.04+1_amd64.deb Size: 496976 SHA256: 566e4c70f2f79db1d16bcc6c2c16613a83aac82da12f966525a0c5ab58447ff5 SHA1: 80483300bb54c9d1a27210bfd32417112a76675d MD5sum: 3a918b9caa888fe3b2f94b58912e6c5d 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~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 5203 Depends: neurodebian-popularity-contest, btrfs-tools (= 4.1.2-1~nd15.04+1) Homepage: http://btrfs.wiki.kernel.org/ Priority: extra Section: debug Filename: pool/main/b/btrfs-tools/btrfs-tools-dbg_4.1.2-1~nd15.04+1_amd64.deb Size: 4204776 SHA256: 3a7ac41a376e282cdaf91b808d0285314cc74ce0f0012910120ddc0bdc79668b SHA1: d8a12033ebd9139a709da3946b7ee7847603b1da MD5sum: 3d70905b47319b86b668adeab5a4f546 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: cde Version: 0.1+git9-g551e54d-1~nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1030 Depends: neurodebian-popularity-contest, libc6 (>= 2.14) Homepage: http://www.pgbovine.net/cde.html Priority: optional Section: utils Filename: pool/main/c/cde/cde_0.1+git9-g551e54d-1~nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1_amd64.deb Size: 146380 SHA256: 8c888f628d666e5442b28c7725b09ad1e5352f039c830fc9eeb395abeb192073 SHA1: aeceffdbc0f20d34903da4a5835b7fe13769ac83 MD5sum: 97cc7e2ec1276d92ccda9e8b0338491b 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: cnrun-tools Source: cnrun Version: 2.0.3-1~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 84 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libcnrun2 (>= 2.0.0), libgcc1 (>= 1:4.1.1), libgsl0ldbl (>= 1.9), libstdc++6 (>= 4.4.0) Homepage: http://johnhommer.com/academic/code/cnrun Priority: optional Section: science Filename: pool/main/c/cnrun/cnrun-tools_2.0.3-1~nd15.04+1_amd64.deb Size: 17136 SHA256: 58c72fdd3c9381eaa91b44b236c19eee33a8c3b664acc9c7a10c1c084a67923e SHA1: b66a6e7eaf5891f2be59fe210330c345a48a75b4 MD5sum: 8238c472ec32c7dc015e3940d316d308 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~nd15.04+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~nd15.04+1_all.deb Size: 15766 SHA256: f3c66ffbe3c570cb2d8f34362f98423e3c933913a60c703a7303de48805fbccf SHA1: 2afdc94a82491b74c1ca2cd0ea32d3d52fd400cb MD5sum: 8e651f0c45af56b291f2f0b88bb6b46e 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~nd15.04+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~nd15.04+1_all.deb Size: 15774 SHA256: 2700785592c2fd53c3a9342aab6198c57b81115b6e320502719b991ee8ba8963 SHA1: 26c1beaac817a305b8d82f8caa81d04cbb99c8a1 MD5sum: ac5072d26da6fd0f974e61a556479546 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~nd15.04+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~nd15.04+1_all.deb Size: 15782 SHA256: 3f1372ef34fd88bbfe91ae1d87615a5858482f0f9bd350db91494eacc009ccf7 SHA1: 8230f603052185734f997e6e24a58d5693805103 MD5sum: b07e9237d6fc7824b311dde4081ca8ed 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~nd15.04+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~nd15.04+1_all.deb Size: 15776 SHA256: 2f71f6d7ed52413aaa46977f706f5b2c161fca8547307d8a7d234a3b41b68f76 SHA1: f4e4fbd2c67d609731af6b28065ae7b7bc8e8cb4 MD5sum: 1453352a802397ded77de5e340dbbb17 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~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 37946 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libftgl2 (>= 2.1.3~rc5), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, libgomp1 (>= 4.9), libosmesa6 (>= 10.2~), libqt4-network (>= 4:4.5.3), libqt4-opengl (>= 4:4.7.0~beta1), libqt4-xml (>= 4:4.5.3), libqtcore4 (>= 4:4.8.0), libqtgui4 (>= 4:4.8.0), libqtwebkit4, 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~nd15.04+1_amd64.deb Size: 19326606 SHA256: ebd9af164260e64c9c67380d5854daaf7bc6921c1cb7c7573add62e33447f2c0 SHA1: 65790676b77c5911325e2c281ada5872ba5248a2 MD5sum: 51c6b994466af07749b91c07dc97a1ff 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~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 109965 Depends: neurodebian-popularity-contest, connectome-workbench (= 1.1.1-1~nd15.04+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~nd15.04+1_amd64.deb Size: 107977156 SHA256: 0d93b288854dcb86961f1cc9c2cc2ae6d175846dd009b2e034e0dc64d8deb7b4 SHA1: db7e47fd26d83f6758bedfaecaf5a84b14c3e911 MD5sum: 38a6168ea68b7d1790b067bb63aa880d 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: dcm2niix Version: 0.20150909.1+git1-g8914c07-1~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 219 Depends: neurodebian-popularity-contest, libc6 (>= 2.14) 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~nd15.04+1_amd64.deb Size: 87442 SHA256: bb53a0b54b46d04872a561ec748b0328759cb735d97ed48498503be4c5b1705a SHA1: 6cd3822b0fccd818cc4ec49327624939745f80c1 MD5sum: dfe40a21e7c0c5b9df0819b0fb8a50b6 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: debruijn Version: 1.6-1~nd12.04+1+nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 151 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libfftw3-double3, 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~nd12.04+1+nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1_amd64.deb Size: 38570 SHA256: 8d7efbf6e537b29855897d05255e22621040aa476f07be8176d43be48bfda383 SHA1: 5620b2615aa5c33c0f1467230b3be94036162132 MD5sum: ee6d856eac0e3e4be84c8907a8536d60 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: eeglab11-sampledata Source: eeglab11 Version: 11.0.0.0~b~dfsg.1-1~nd11.10+1+nd12.04+1+nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+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~nd11.10+1+nd12.04+1+nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1_all.deb Size: 7072950 SHA256: 1cfbde4cf5fae7d0e475a8355234b5b271b7bc3da6963d893fdd4010b87d9924 SHA1: 43219f931dd34ac8fc6a1df9315de3fd18671d9f MD5sum: 384c0b575f5c78aade3abc699e1bb28c 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: freeipmi Version: 1.4.9-1~nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1 Depends: neurodebian-popularity-contest, freeipmi-common (= 1.4.9-1~nd15.04+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~nd15.04+1_all.deb Size: 1238 SHA256: 02868858b1a774420def3e1dd0c36f04d8e18e9f47eefca722c036a57c5b22ab SHA1: 7bdae04a803618668439829f16ba94f9c1d4a6d2 MD5sum: e7e5715fc60ca81deda28a01fafe4c88 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~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 126 Pre-Depends: dpkg (>= 1.15.7.2~) Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libfreeipmi16 (>= 1.1.5), freeipmi-common (= 1.4.9-1~nd15.04+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~nd15.04+1_amd64.deb Size: 45014 SHA256: 11118cecff885cf6980f5d0030914ab9c39f4fc9786244e1fcf779b7cfa6fb8f SHA1: 0b33e9f7ee66a6b7176b256b9e5fb584b3449f0c MD5sum: f1bc6e5d210352a307be4144962783ea 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~nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 305 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~nd15.04+1_all.deb Size: 189252 SHA256: 5b68a797164a829c0a195d58e107bd6e96b163c43e3601e25bd7cd946111999b SHA1: 8f205f8c86b09a8164bb6b2cad9fb5e90d32245b MD5sum: ee76bb2af4da77fa2b7cc7316d913e25 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~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 106 Pre-Depends: dpkg (>= 1.15.7.2~) Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libfreeipmi16 (>= 1.1.5), libipmidetect0 (>= 1.1.5), freeipmi-common (= 1.4.9-1~nd15.04+1) Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: admin Filename: pool/main/f/freeipmi/freeipmi-ipmidetect_1.4.9-1~nd15.04+1_amd64.deb Size: 37768 SHA256: a2d4097323712150f9c92ee4cea76a9606195d6f4df72d9f540710ae7868b157 SHA1: 86d26bed149e8cf123f2bd484998d11964709eab MD5sum: 7e97ebecd2f6b8bc3592ba0d97cafa46 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~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 192 Pre-Depends: dpkg (>= 1.15.7.2~) Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libfreeipmi16 (>= 1.4.4), freeipmi-common (= 1.4.9-1~nd15.04+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~nd15.04+1_amd64.deb Size: 79218 SHA256: 5b5e1fdb8d0cc062530359e890b80d789e7b94845df339586356bb64559a5424 SHA1: bbd45d165a9d8834396e8308190bcae869d0cb8a MD5sum: 54dadd213b50e3fe7856d4a2d367d07f 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~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2847 Depends: neurodebian-popularity-contest, libc6 (>= 2.15), libfreeipmi16 (>= 1.4.9-1~nd15.04+1), libipmiconsole2 (>= 1.4.4), libipmidetect0 (>= 1.1.5), freeipmi-common (= 1.4.9-1~nd15.04+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~nd15.04+1_amd64.deb Size: 600714 SHA256: 92c9660a69a375dd254f44932090a30eebb1c27e2703b20ef9ad3c8395db9962 SHA1: 45a4b8bdaf4bb059d718be7e7ed71e4607446ba2 MD5sum: 7e7cc0d9ec2b61c4106b2002042fd28a 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: 1:0.5.3-1~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 36 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_0.5.3-1~nd15.04+1_amd64.deb Size: 8708 SHA256: f26ca9b1e973201f7c8eee53ff01f1e877a5d287b6644c77ffb29bb7e333f1f3 SHA1: d39e7cc54546ea844698fdb25bf85745189ad580 MD5sum: 278086646a16d97a12ffb2e470eb6014 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~nd15.04+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~nd15.04+1_all.deb Size: 14056 SHA256: a88c7501dc04fb23357cef898821026a6fb6f8012305baf2d05ee9075848e39b SHA1: b7db1a2f9c523ad45b5b4ad48ae30cf0a4e13611 MD5sum: 2d85159ebbefc1fad913c2b6e03ae21b 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: gcalcli Version: 3.3.2-1~nd15.04+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~nd15.04+1_all.deb Size: 1669852 SHA256: b7a64e8275fdb8e438cc30f19d2bc74e37bf2f926c610f44ad44a1d65db3dc26 SHA1: 6baa05e70f3bbb019ab3a8d71ef1bf6383e23f91 MD5sum: 355003a4e6d67b7e6bb2d686a5c34926 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: git-annex-standalone Source: git-annex Version: 6.20160118+gitgdaf852e-1~ndall+1 Architecture: amd64 Maintainer: Richard Hartmann Installed-Size: 398324 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_6.20160118+gitgdaf852e-1~ndall+1_amd64.deb Size: 28581342 SHA256: c5c0c1cfbe542a826b3dfdce89ef90a3f6924111cdf60e2566e67aef85a45d60 SHA1: 34c7180aed531c41113ee6d30d3246e004269ce3 MD5sum: 5e213965ab0b9ec34f774c6b3ba64533 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: git-annex-standalone-dbgsym Source: git-annex Version: 6.20160118+gitgdaf852e-1~ndall+1 Architecture: amd64 Maintainer: Richard Hartmann Installed-Size: 45 Depends: git-annex-standalone (= 6.20160118+gitgdaf852e-1~ndall+1) Homepage: http://git-annex.branchable.com/ Priority: extra Section: debug Filename: pool/main/g/git-annex/git-annex-standalone-dbgsym_6.20160118+gitgdaf852e-1~ndall+1_amd64.deb Size: 9082 SHA256: ffc83c05f2226bc1626769dd6fbe71c25656c476f4bfbcaafaeb49359430881d SHA1: 2cb0c3d3467735212bde7dc845a0b4c770c5abf7 MD5sum: 32b56101742eb862e6ab3b6e859ef9ca Description: Debug symbols for git-annex-standalone Auto-Built-Package: debug-symbols Build-Ids: 75d11a3ee55319e6cebcf33286de8df9a39bcaea 75d11a3ee55319e6cebcf33286de8df9a39bcaea Package: heudiconv Version: 0.1-1~nd15.04+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~nd15.04+1_all.deb Size: 10282 SHA256: 0c40fa003e2a2647c77dec3258cbdf256d6642173f9f06e5d273f7d7180220e7 SHA1: 9c6b25a51ae3154c7a60c8870f37fd710891af62 MD5sum: 5ebd4b041958e21d8077e5303aa4e6cc Description: DICOM converter with support for structure heuristics This is a flexible dicom converter for organizing brain imaging data into structured directory layouts. It allows for flexible directory layouts and naming schemes through customizable heuristics implementations. It only converts the necessary dicoms, not everything in a directory. It tracks the provenance of the conversion from dicom to nifti in w3c prov format. Package: htcondor Source: condor Version: 8.4.2~dfsg.1-1~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 13024 Depends: neurodebian-popularity-contest, adduser, debconf (>= 0.5) | debconf-2.0, libdate-manip-perl, python, libclassad7 (= 8.4.2~dfsg.1-1~nd15.04+1), perl, libc6 (>= 2.15), libcgroup1 (>= 0.37.1), libcomerr2 (>= 1.01), libcurl3 (>= 7.16.2), libexpat1 (>= 2.0.1), libgcc1 (>= 1:4.1.1), libglobus-callout0 (>= 3), libglobus-common0 (>= 15), libglobus-ftp-client2 (>= 7), libglobus-gass-transfer2 (>= 7), libglobus-gram-client3 (>= 12), libglobus-gram-protocol3 (>= 11), libglobus-gsi-callback0 (>= 4), libglobus-gsi-cert-utils0 (>= 8), libglobus-gsi-credential1 (>= 6), libglobus-gsi-openssl-error0 (>= 2), libglobus-gsi-proxy-core0 (>= 6), libglobus-gsi-proxy-ssl1 (>= 4), libglobus-gsi-sysconfig1 (>= 5), libglobus-gss-assist3 (>= 9), libglobus-gssapi-error2 (>= 4), libglobus-gssapi-gsi4 (>= 10), libglobus-io3 (>= 11), libglobus-openssl-module0 (>= 3), libglobus-rsl2 (>= 9), libglobus-xio0 (>= 3), libgsoap5, libgssapi-krb5-2 (>= 1.6.dfsg.2), libk5crypto3 (>= 1.6.dfsg.2), libkrb5-3 (>= 1.10+dfsg~), libkrb5support0 (>= 1.7dfsg~beta2), libldap-2.4-2 (>= 2.4.7), libltdl7 (>= 2.4.2), libpcre3, libssl1.0.0 (>= 1.0.0), libstdc++6 (>= 4.9), libuuid1 (>= 2.16), libvirt0 (>= 0.5.0), libvomsapi1, libx11-6, zlib1g (>= 1:1.1.4) Recommends: dmtcp, ecryptfs-utils Suggests: docker, coop-computing-tools Breaks: condor (<< 8.0.5~) Replaces: condor (<< 8.0.5~) Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: science Filename: pool/main/c/condor/htcondor_8.4.2~dfsg.1-1~nd15.04+1_amd64.deb Size: 3751296 SHA256: 79d5c743c92038d2138aac31c78c386646e23470da5daf5d1c698effa35f1f9c SHA1: 245c997574ae448f9a864ee5306f99de9aa9b67a MD5sum: 2099bab030ddc50e87ca67d11e71c0d5 Description: distributed workload management system Like other full-featured batch systems, HTCondor provides a job queueing mechanism, scheduling policy, priority scheme, resource monitoring, and resource management. Users submit their serial or parallel jobs to HTCondor; HTCondor places them into a queue. It chooses when and where to run the jobs based upon a policy, carefully monitors their progress, and ultimately informs the user upon completion. . Unlike more traditional batch queueing systems, HTCondor can also effectively harness wasted CPU power from otherwise idle desktop workstations. HTCondor does not require a shared file system across machines - if no shared file system is available, HTCondor can transfer the job's data files on behalf of the user. . This package can set up an appropriate initial configuration at install time for a machine intended either as a member of an existing HTCondor pool or as a "Personal" (single machine) HTCondor pool. Package: htcondor-dbg Source: condor Version: 8.4.2~dfsg.1-1~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 31789 Depends: neurodebian-popularity-contest, htcondor (= 8.4.2~dfsg.1-1~nd15.04+1) Breaks: condor-dbg (<< 8.0.5~) Replaces: condor-dbg (<< 8.0.5~) Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: debug Filename: pool/main/c/condor/htcondor-dbg_8.4.2~dfsg.1-1~nd15.04+1_amd64.deb Size: 29532142 SHA256: 4c7a2ee9a32fd3700e52d285f05d1a4c4d406ab334409253ce5401dda95b856c SHA1: df28feb8b316efdc7d6d672c386591f4c395d6f4 MD5sum: 049a1a7733731b3e0f4a04799aa65a16 Description: distributed workload management system - debugging symbols Like other full-featured batch systems, HTCondor provides a job queueing mechanism, scheduling policy, priority scheme, resource monitoring, and resource management. Users submit their serial or parallel jobs to HTCondor; HTCondor places them into a queue. It chooses when and where to run the jobs based upon a policy, carefully monitors their progress, and ultimately informs the user upon completion. . Unlike more traditional batch queueing systems, HTCondor can also effectively harness wasted CPU power from otherwise idle desktop workstations. HTCondor does not require a shared file system across machines - if no shared file system is available, HTCondor can transfer the job's data files on behalf of the user. . This package provides the debugging symbols for HTCondor. Package: htcondor-dev Source: condor Version: 8.4.2~dfsg.1-1~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 1687 Depends: neurodebian-popularity-contest Breaks: condor-dev (<< 8.0.5~) Replaces: condor-dev (<< 8.0.5~) Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: libdevel Filename: pool/main/c/condor/htcondor-dev_8.4.2~dfsg.1-1~nd15.04+1_amd64.deb Size: 309900 SHA256: 12286059c11f3adcfaf993b41967f907cd74b357026dae72e0f2da8d59ad58e4 SHA1: 8c04fde9b9490527ef36056162e0e22ecdf8ee55 MD5sum: 08929fcdb812a39be6b93a31db22a3f5 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~nd15.04+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~nd15.04+1_all.deb Size: 1067820 SHA256: 72d27b3147c846645f51ee120b6695a1501b3a680c3aa0ca0b8ffd41d729a66c SHA1: 2647a41d81d4124b1f2f7f88b60398e38227145e MD5sum: 479d80027b991f170a3a0b35f8996b4d 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: 0.11.1-1~nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 465 Depends: neurodebian-popularity-contest, python, python-pygame, python-pil | python-imaging, poppler-utils | mupdf-tools | xpdf-utils (>= 3.02-2) Recommends: mplayer, pdftk, perl, xdg-utils 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_0.11.1-1~nd15.04+1_all.deb Size: 175700 SHA256: 3553cac32e97d151b5b98dae1a3126083cd4c44d57016a4eafaaab3ec1da6924 SHA1: e2a1290b5ea099abe219fa6c81cc880b2411bce1 MD5sum: fd4a66b05007877feefaca702f1d2488 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 * Support of movies presentation * Active hyperlinks within PDFs Package: incf-nidash-oneclick-clients Source: incf-nidash-oneclick Version: 2.0-1~nd12.04+1+nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+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~nd12.04+1+nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1_all.deb Size: 9296 SHA256: 57b3fa509741010f2e4dd7ae46a4ba6868713d095fbd3890f013381b8ba290a5 SHA1: 0ce4c9e82062cb35d32d318560d12ff4639238ba MD5sum: 01908128c6e1c439174825c0ade70f4a 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: insighttoolkit4-examples Source: insighttoolkit4 Version: 4.7.0-1~nd14.10+1+nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2837 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~nd14.10+1+nd15.04+1_all.deb Size: 2500624 SHA256: ee1b38aa0767758a463eaee5743228c7cecd39094da83e427662890b57530d3e SHA1: 10fcf8b43973e38825012028ff5d90c06082f430 MD5sum: d915c1023278e43df85eccca56780911 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~nd14.10+1+nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 875860 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libfftw3-double3, libfftw3-single3, libgcc1 (>= 1:4.1.1), libgdcm2.4, libinsighttoolkit4.7, libpython2.7 (>= 2.7), libstdc++6 (>= 4.9) Conflicts: insighttoolkit-python Replaces: insighttoolkit-python Homepage: http://www.itk.org/ Priority: optional Section: python Filename: pool/main/i/insighttoolkit4/insighttoolkit4-python_4.7.0-1~nd14.10+1+nd15.04+1_amd64.deb Size: 74717562 SHA256: c36787212baff9669b18c46d4adca46a438d6794c34c2a8317598e50cbfd8baa SHA1: 94cee32d786806cbccf7a85dc75d07c97830577a MD5sum: bd75c380e4269067ba47c8144e04d5af 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: ismrmrd-schema Source: ismrmrd Version: 1.3.2-1~nd15.04+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~nd15.04+1_all.deb Size: 5074 SHA256: f06f9047839d302dc10f85aaf2236da9c0a17622d707d3efa12dbd5677459c99 SHA1: 217ee012ed1a0c074afb8a1792d956988f053c8e MD5sum: 85264c74695d5b02fa454435884d6d4d 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~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 453 Depends: neurodebian-popularity-contest, ismrmrd-schema, libismrmrd1.3 (= 1.3.2-1~nd15.04+1), libboost-program-options1.55.0, libc6 (>= 2.14), 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~nd15.04+1_amd64.deb Size: 124864 SHA256: d650d7c7d8497b646cd270d7db49a6c53ff26d85514c2e613210f6f08b1cae85 SHA1: f42e02a9a8729eb414f26962ffe5fdd8b1cbcf5c MD5sum: 70d92010e2e3c9d74a153b7c3932b4f8 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: libbiosig-dev Source: biosig4c++ Version: 1.4.1-2~nd14.10+1+nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1751 Depends: neurodebian-popularity-contest, libbiosig1 (= 1.4.1-2~nd14.10+1+nd15.04+1) Homepage: http://biosig.sf.net/ Priority: extra Section: libdevel Filename: pool/main/b/biosig4c++/libbiosig-dev_1.4.1-2~nd14.10+1+nd15.04+1_amd64.deb Size: 303442 SHA256: 4dd1577fa51eef3fe3373ce4fec48fb9209db40afaf80a62a8b7885bfb36f435 SHA1: 3d0e4d15da9c14c1781a789518608af55c8ebdf0 MD5sum: 687f7341d39df5a018b572b72c6cf7a9 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~nd14.10+1+nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 926 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1) Homepage: http://biosig.sf.net/ Priority: extra Section: libs Filename: pool/main/b/biosig4c++/libbiosig1_1.4.1-2~nd14.10+1+nd15.04+1_amd64.deb Size: 280576 SHA256: 6795986429336312aca3a6b0a5b20e4cafe88af0643c5fdbb957403c44615c3e SHA1: 07587b9c81a495d02f6511ccbc1dc3ecf2c39c9a MD5sum: ab939b170a91ec7ecceef607ce869df0 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~nd14.10+1+nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 333 Depends: neurodebian-popularity-contest, libbiosig1 (= 1.4.1-2~nd14.10+1+nd15.04+1) Homepage: http://biosig.sf.net/ Priority: extra Section: debug Filename: pool/main/b/biosig4c++/libbiosig1-dbg_1.4.1-2~nd14.10+1+nd15.04+1_amd64.deb Size: 65566 SHA256: d90aed0edc08c489955d506954977d545bd70ec9c78cb5b0ff5b4f220bf3897a SHA1: f534c7e35bcb74e1704ec0a65045537ed5cf1a65 MD5sum: 71bb56fa386d8519e9545108d1b1920d 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: libclassad-dev Source: condor Version: 8.4.2~dfsg.1-1~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 1450 Depends: neurodebian-popularity-contest, libclassad7 (= 8.4.2~dfsg.1-1~nd15.04+1) Conflicts: libclassad0-dev Replaces: libclassad0-dev Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: libdevel Filename: pool/main/c/condor/libclassad-dev_8.4.2~dfsg.1-1~nd15.04+1_amd64.deb Size: 245174 SHA256: b94f2b0d2c137914730e0b24c528f32d3189639dea20e9d2c4176b90cef2375c SHA1: fa3beef5a741889af630d5924108d3437574850c MD5sum: 8c4256e73b9aa1efaaafd8eb1ed3f1d6 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: libclassad7 Source: condor Version: 8.4.2~dfsg.1-1~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 622 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libpcre3, libstdc++6 (>= 4.9) Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: libs Filename: pool/main/c/condor/libclassad7_8.4.2~dfsg.1-1~nd15.04+1_amd64.deb Size: 198170 SHA256: f0e07afcaa90e45b96962346fc97ab0af655270806c8495a25501cbd7cc59ad8 SHA1: d3d4b241d92871307ad7992e56602f771bd52550 MD5sum: e2eb7d3e959206e5c8636aa182d8618c 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: 2.0.3-1~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 300 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), 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_2.0.3-1~nd15.04+1_amd64.deb Size: 80312 SHA256: cc79e9290a412aadafefb277f39001783a2e2c51d677038769930414c942069a SHA1: 85a535cf75fca8475bd7548500fbd5fc1abf67b0 MD5sum: 02add97bbcaf7d183020414ba8c7ef0e 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: 2.0.3-1~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 167 Depends: neurodebian-popularity-contest, libcnrun2 (= 2.0.3-1~nd15.04+1) Suggests: pkg-config Homepage: http://johnhommer.com/academic/code/cnrun Priority: optional Section: libdevel Filename: pool/main/c/cnrun/libcnrun2-dev_2.0.3-1~nd15.04+1_amd64.deb Size: 21310 SHA256: fdfa2d1517b02024adc0b35311df678e7ba12d2bb03b6d831537e04bd34af4ce SHA1: b6c5341e99b3bdf65d820b17303aa90557800dab MD5sum: 71a408a4ebbeaca5522c2d7f44de9636 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: libfreeipmi-dev Source: freeipmi Version: 1.4.9-1~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 7704 Depends: neurodebian-popularity-contest, freeipmi-common (= 1.4.9-1~nd15.04+1), libfreeipmi16 (= 1.4.9-1~nd15.04+1) Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: libdevel Filename: pool/main/f/freeipmi/libfreeipmi-dev_1.4.9-1~nd15.04+1_amd64.deb Size: 916230 SHA256: 3aecc3c5dcafcdc783c0629ac6ebda0db035898bd110916ad76def38419480fc SHA1: 54e7c41332b0f7f65575b5d27c54401a11dba343 MD5sum: b86282eea2f5da51f56bf4f58680285d 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: libfreeipmi16 Source: freeipmi Version: 1.4.9-1~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 5057 Depends: neurodebian-popularity-contest, libc6 (>= 2.15), libgcrypt20 (>= 1.6.1), freeipmi-common (= 1.4.9-1~nd15.04+1) Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: libs Filename: pool/main/f/freeipmi/libfreeipmi16_1.4.9-1~nd15.04+1_amd64.deb Size: 822082 SHA256: 93504351f7a4a2c6c9260281febbc2bd456b861e2c3cc90c9ab079a545d7f105 SHA1: 2ce101a95d1a76f2fa14cf29c51a9bcf2e7bea55 MD5sum: b9457dcb241614c8295788f20cc4d5c9 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: 1:0.5.3-1~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 280 Depends: neurodebian-popularity-contest, freeglut3, libc6 (>= 2.14), libfreenect0.5 (>= 1:0.5.2), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libglu1-mesa | libglu1, libstdc++6 (>= 4.4.0) Breaks: libfreenect-demos (<< 1:0.1.2+dfsg-1) Replaces: libfreenect-demos (<< 1:0.1.2+dfsg-1) Homepage: http://openkinect.org/ Priority: extra Section: utils Filename: pool/main/libf/libfreenect/libfreenect-bin_0.5.3-1~nd15.04+1_amd64.deb Size: 55166 SHA256: 49b07d269fcc9cec1fbca22ab6a8e7f5953cfbd6dac79b6aa3eb259a3e5513c0 SHA1: ce32ce8126a6923c2517302d6314ff6189518eef MD5sum: e22a4c279137af85eeed193a466c32ab 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: 1:0.5.3-1~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 36 Depends: neurodebian-popularity-contest, libfreenect-bin Homepage: http://openkinect.org/ Priority: extra Section: libdevel Filename: pool/main/libf/libfreenect/libfreenect-demos_0.5.3-1~nd15.04+1_amd64.deb Size: 8738 SHA256: 9fd40840cf10c4a928ea44f932c82446993c08f1b1965565d6a3de8efd440400 SHA1: 719ff564cf17cc101219360a5e7d59f184d523f1 MD5sum: e5deec2ef891951fb8bc488635bfd664 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: 1:0.5.3-1~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 106 Depends: neurodebian-popularity-contest, libfreenect0.5 (= 1:0.5.3-1~nd15.04+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_0.5.3-1~nd15.04+1_amd64.deb Size: 19500 SHA256: 81889bfb041eafb445175a9ecbf0ba98aab759e7afbac392aa1be51f83bb8588 SHA1: 3e538ecea8376f4d24f8f061c3e498351efd2044 MD5sum: 988756378110e051aa213a0b08093e5b 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: 1:0.5.3-1~nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 814 Depends: neurodebian-popularity-contest Multi-Arch: foreign Homepage: http://openkinect.org/ Priority: extra Section: doc Filename: pool/main/libf/libfreenect/libfreenect-doc_0.5.3-1~nd15.04+1_all.deb Size: 134086 SHA256: 3580e993f39cb074587575f2e42ffcf32fe6cc9deb11d4554814c609bc36c8c1 SHA1: b7cf0fffd6e51243758bb6f21982392afd922e6a MD5sum: 100c61fe5fc6ec20f333ceb97aa2923f 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: libfreenect0.5 Source: libfreenect Version: 1:0.5.3-1~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 154 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libusb-1.0-0 (>= 2:1.0.12) Conflicts: libfreenect Multi-Arch: same Homepage: http://openkinect.org/ Priority: extra Section: libs Filename: pool/main/libf/libfreenect/libfreenect0.5_0.5.3-1~nd15.04+1_amd64.deb Size: 42510 SHA256: e905a4537f95b516f4f137a33ff0dc473458379985cfb7d84d6c4f912f06b8fc SHA1: 9442b3dd4685dc9f6b79294f8fd643fc5fa19de3 MD5sum: 5b0dc794422d455dd0e183ee63276c87 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: libinsighttoolkit4-dbg Source: insighttoolkit4 Version: 4.7.0-1~nd14.10+1+nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 41927 Depends: neurodebian-popularity-contest, libinsighttoolkit4.7 (= 4.7.0-1~nd14.10+1+nd15.04+1) Homepage: http://www.itk.org/ Priority: extra Section: debug Filename: pool/main/i/insighttoolkit4/libinsighttoolkit4-dbg_4.7.0-1~nd14.10+1+nd15.04+1_amd64.deb Size: 37251974 SHA256: 168a847fe67171bd677d8ac44d5bee1a0c4198ad0909f819e3f0b943bf03edc6 SHA1: 4cff85d53661d445f3c4d948a022684c88b6b457 MD5sum: f316d2355f638850d986801e3f2ef5ad 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~nd14.10+1+nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 25377 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libinsighttoolkit4.7 (= 4.7.0-1~nd14.10+1+nd15.04+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~nd14.10+1+nd15.04+1_amd64.deb Size: 2960266 SHA256: 79aabe32c12a4148994b6a19c84e33e556e7311a3b06704803e5f5ff24e9cf40 SHA1: 1d378d5375be2adb32175ee5ee3bffc9689df823 MD5sum: 0dd2be51b7389bdcc7eba7692c522f32 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: libinsighttoolkit4.7 Source: insighttoolkit4 Version: 4.7.0-1~nd14.10+1+nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 23440 Depends: neurodebian-popularity-contest, libc6 (>= 2.15), libfftw3-double3, libfftw3-single3, libgcc1 (>= 1:4.1.1), libgdcm2.4, libhdf5-8, libhdf5-cpp-8 (>= 1.8.13), libjpeg8 (>= 8c), 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~nd14.10+1+nd15.04+1_amd64.deb Size: 4677202 SHA256: 534ff8f7572c43e2c499b282c0a475813b928be2269a4d1cccea2fcf2ef9f5b8 SHA1: 9bdf1c48ce4bf1c6e98872620ea437ab51a10c31 MD5sum: a2b6458c92c776f4acf7dd37891424d2 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~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 504 Depends: neurodebian-popularity-contest, freeipmi-common (= 1.4.9-1~nd15.04+1), libipmiconsole2 (= 1.4.9-1~nd15.04+1) Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: libdevel Filename: pool/main/f/freeipmi/libipmiconsole-dev_1.4.9-1~nd15.04+1_amd64.deb Size: 101920 SHA256: b70b20b5fbaed7bb2520c9dc60c07c60e1c3294bfd4027fc6f0ee75ec96f945d SHA1: 97f6d08cb14b524b48b7cdbf072d89e7fc34641e MD5sum: bf5cdde89c64839cd883b8881b350fd5 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~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 250 Depends: neurodebian-popularity-contest, libc6 (>= 2.15), libfreeipmi16 (>= 1.4.4), freeipmi-common (= 1.4.9-1~nd15.04+1) Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: libs Filename: pool/main/f/freeipmi/libipmiconsole2_1.4.9-1~nd15.04+1_amd64.deb Size: 86136 SHA256: 7b14c370ea2e2b9fb44024f8682af333be146b99f0581b5c764c64e1a0123be0 SHA1: 03f464782fe362d0b70761c6ff90ea3f155a4183 MD5sum: 2eebd93bbac7d97bd0ff91a329ed2371 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~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 105 Depends: neurodebian-popularity-contest, freeipmi-common (= 1.4.9-1~nd15.04+1), libipmidetect0 (= 1.4.9-1~nd15.04+1) Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: libdevel Filename: pool/main/f/freeipmi/libipmidetect-dev_1.4.9-1~nd15.04+1_amd64.deb Size: 31660 SHA256: 7b3717b0f329e947f38c3ca6b92467b240e4ed86878c777b16cc94a15482ae0c SHA1: bcb16f3b737199cb3e478775171194fe433b9ee1 MD5sum: 9b086814c536b46faa040130238f1437 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~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 60 Depends: neurodebian-popularity-contest, libc6 (>= 2.15), freeipmi-common (= 1.4.9-1~nd15.04+1) Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: libs Filename: pool/main/f/freeipmi/libipmidetect0_1.4.9-1~nd15.04+1_amd64.deb Size: 25668 SHA256: 610afe9c149d3eda7e0220b5d66fb3067d3986c8f718d4ce0cb2e887157f2a39 SHA1: ee51185fb53bf0967a2d2876dfdff5ea155e4084 MD5sum: 112fc6875c71d3a8d9cee875e6cc32d1 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~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 308 Depends: neurodebian-popularity-contest, freeipmi-common (= 1.4.9-1~nd15.04+1), libipmimonitoring5a (= 1.4.9-1~nd15.04+1) Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: libdevel Filename: pool/main/f/freeipmi/libipmimonitoring-dev_1.4.9-1~nd15.04+1_amd64.deb Size: 61662 SHA256: 58c629fe26ff8e4078b1c00ede0642c483c9ef5b63a42fb56fe5e3cb7a6a2e95 SHA1: d9fc92022eb5ed06282eda27bf88914743733f0a MD5sum: 58c1c507a400ef753ac7e6c429df39ba 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: libipmimonitoring5a Source: freeipmi Version: 1.4.9-1~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 119 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libfreeipmi16 (>= 1.4.4), freeipmi-common (= 1.4.9-1~nd15.04+1) Homepage: http://www.gnu.org/software/freeipmi/ Priority: extra Section: libs Filename: pool/main/f/freeipmi/libipmimonitoring5a_1.4.9-1~nd15.04+1_amd64.deb Size: 43480 SHA256: 3f5dc00c937ea8ab2313b1ac86ec556ee54bae2497bc619e271f1ecd3f313836 SHA1: 0be98ab4bb975809ccb795a9b99908c0cf8ba841 MD5sum: 07ca1ff6dac3862217a443e698641a93 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: libismrmrd-dev Source: ismrmrd Version: 1.3.2-1~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 95 Depends: neurodebian-popularity-contest, ismrmrd-schema, libismrmrd1.3 (= 1.3.2-1~nd15.04+1) Suggests: libismrmrd-doc Homepage: http://ismrmrd.github.io/ Priority: optional Section: libdevel Filename: pool/main/i/ismrmrd/libismrmrd-dev_1.3.2-1~nd15.04+1_amd64.deb Size: 13864 SHA256: 8e0c6946b54a4180113833e539fdb14a6bd0214fcf962742d6502cca62c26070 SHA1: bcc352e97ca998acda89cab664fa291411472c09 MD5sum: 9d342e02574ccf6d6f6a5f3361d86725 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~nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1905 Depends: neurodebian-popularity-contest Homepage: http://ismrmrd.github.io/ Priority: optional Section: doc Filename: pool/main/i/ismrmrd/libismrmrd-doc_1.3.2-1~nd15.04+1_all.deb Size: 159234 SHA256: 44bfafd717b2b528bb093e7580194b142dd253fb20aba439a742c4a721c2c336 SHA1: 3585d20b7e6eb7c181da2b4dbaec2c13448f3c6e MD5sum: ea2a85b46b120a8ccab0a6f88fd30b33 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~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 373 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), 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~nd15.04+1_amd64.deb Size: 83210 SHA256: b6cb278efc135149d4624137ab22152bdc7dc00efdf94c3d06ce9516bd9d23b4 SHA1: b1b22de2a9ec5dad851df9e71662a205ef6ea34b MD5sum: 676f832e80f03e040b5cdb0e1f991078 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: libmia-2.0-8 Source: mia Version: 2.0.13-1~nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 25073 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libboost-filesystem1.55.0, libboost-regex1.55.0, libboost-serialization1.55.0, libboost-system1.55.0, libboost-test1.55.0, libc6 (>= 2.14), libdcmtk2 (>= 3.6.0), libfftw3-single3, libgcc1 (>= 1:4.1.1), libglib2.0-0 (>= 2.12.0), libglibmm-2.4-1c2a (>= 2.42.0), 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 (>= 4.9), 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~nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1_amd64.deb Size: 3820398 SHA256: f3acd07e544a5a5b3c3a7660897547f28a26b62d85fd8351bdaf3390869120db SHA1: d21980e8150f84b62376ff600cd54bf0d9d83b00 MD5sum: b477f4d5312e717f53ae8f35f34c92cd 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~nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 68562 Depends: neurodebian-popularity-contest, libmia-2.0-8 (= 2.0.13-1~nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1) Homepage: http://mia.sourceforge.net Priority: extra Section: debug Filename: pool/main/m/mia/libmia-2.0-8-dbg_2.0.13-1~nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1_amd64.deb Size: 62424812 SHA256: 7940969f4d3b45b50d928786004b049e36dc32facc8009bad74b7522cbb0825e SHA1: b5d95529e752e9a7a2ea8e8c876f5065f128bdcc MD5sum: fec7a45a727345f4cf4819bb4c2adcd4 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~nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1087 Depends: neurodebian-popularity-contest, libmia-2.0-8 (= 2.0.13-1~nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+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~nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1_amd64.deb Size: 171352 SHA256: 4f79aef0b2305372112fd6352bf5fc112e0c5b1495f0ed3cb69eb672131c7ec5 SHA1: 661a8a396636c1ded0418ad7795463efb8800349 MD5sum: 024f7c258fc480b1fd5a43ff8b133d24 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~nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 15009 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~nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1_all.deb Size: 1145952 SHA256: eec23fc789872924dfa1f6ad91b5f121ff036b3c5d6903edccf92b1c04cf6d2a SHA1: dc4ee452a72fc174049d605afe20fa0b55d94b2a MD5sum: 6702ad33379715aeeed5ab4cf930df61 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: libopenwalnut1 Source: openwalnut Version: 1.4.0~rc1+hg3a3147463ee2-1~nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 6819 Depends: neurodebian-popularity-contest, fonts-liberation (>= 1.0.0), libboost-date-time1.55.0, libboost-filesystem1.55.0, libboost-regex1.55.0, libboost-system1.55.0, libboost-thread1.55.0, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libgl1-mesa-glx | libgl1, libopenscenegraph100, libstdc++6 (>= 4.9) 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~nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1_amd64.deb Size: 1304164 SHA256: 88e4cc3fd81326777a606c5789301d33e27940ca3ba435066b70010f150b9d30 SHA1: 96abc8dc50e71debd1116ed25c0b1659cc07460d MD5sum: 253fa8c0611703300d6a5a4afff1a500 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~nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1997 Depends: neurodebian-popularity-contest, libopenwalnut1 (= 1.4.0~rc1+hg3a3147463ee2-1~nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+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~nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1_amd64.deb Size: 249550 SHA256: ab5a7094e7b69cbe9dcc4d7c04b3e74618f58a9552406d220c60b4dc9c439693 SHA1: 4a204be77839b8f5393ffc2c02731904ed1e5c68 MD5sum: 6cfa1f824b936b624d005de49c9716a5 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~nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 48161 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~nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1_all.deb Size: 2689446 SHA256: 239e6402859ac1d87461dc8cee6fe3ca7e393e5b01fb9b37d1e9dc02c616ac7f SHA1: 44960c6c33539e8aeb9e4d89eef0d45de7ded0e3 MD5sum: 8ba9f709c27cf22714f22b95dbd409de 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: libvrpn-dev Source: vrpn Version: 07.30+dfsg-1~nd12.04+1+nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 659 Depends: neurodebian-popularity-contest, libvrpn0 (= 07.30+dfsg-1~nd12.04+1+nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1), libvrpnserver0 (= 07.30+dfsg-1~nd12.04+1+nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1) Homepage: http://www.cs.unc.edu/Research/vrpn/ Priority: extra Section: libdevel Filename: pool/main/v/vrpn/libvrpn-dev_07.30+dfsg-1~nd12.04+1+nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1_amd64.deb Size: 141360 SHA256: 972b3363ffd0a9c0d906b8d559fb18617905dd77cd735cae548dbfded382f2c6 SHA1: 6d06eb329d593ff50cd90c01c6aabd3f6aa74c7c MD5sum: 970cfb8fe78ce2015e2dbbf007f95111 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~nd12.04+1+nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 515 Depends: neurodebian-popularity-contest, libc6 (>= 2.15), 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~nd12.04+1+nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1_amd64.deb Size: 142046 SHA256: 579ae5186ead36252c7d52b2c32957fd05e290e42a918e191d57f49ad98151a6 SHA1: e3ae0f5c28e49a6cc0e593196149e871583b6dc0 MD5sum: 1bbc56b2c29a5c98b6b2e113ed5d0f8d 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~nd12.04+1+nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1287 Depends: neurodebian-popularity-contest, libc6 (>= 2.15), 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~nd12.04+1+nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1_amd64.deb Size: 328328 SHA256: a8feda6aebf04d1f437d9bfb264a494c53a42add7cd226910f2166a39fcb5030 SHA1: b86646a6fb112ef1a16e7b4e4ebdb20712d3e1e3 MD5sum: b0956b2b1cc6401c1e8ff345d551213e 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~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 265 Depends: neurodebian-popularity-contest, libvtk-java, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), libvtk-dicom0.5, libvtk5.8 Suggests: java-virtual-machine Homepage: http://github.com/dgobbi/vtk-dicom/ Priority: optional Section: java Filename: pool/main/v/vtk-dicom/libvtk-dicom-java_0.5.5-2~nd15.04+1_amd64.deb Size: 74924 SHA256: 89d7500666097fdf55c249c477b3d8a7c4bee6e63c65ebe50e697531c79a71cd SHA1: 78c4d91659cecdb977b95a5419b122ac862abfb5 MD5sum: bd4346d54404687a8baddbfef4fb59f3 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~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1846 Pre-Depends: multiarch-support Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libgdcm2.4, libstdc++6 (>= 4.9), libvtk5.8, zlib1g (>= 1:1.2.3.4) Multi-Arch: same Homepage: http://github.com/dgobbi/vtk-dicom/ Priority: optional Section: libs Filename: pool/main/v/vtk-dicom/libvtk-dicom0.5_0.5.5-2~nd15.04+1_amd64.deb Size: 454898 SHA256: c1c4245ab36615fdf24cf95a6d1ff830fc4c2b99e36f31f6d30d21280acedfcb SHA1: a45eff1daa2616aeb2d1f26d826d7b37d290b48a MD5sum: fb5914315b1255439c68e4560f900683 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~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 531 Depends: neurodebian-popularity-contest, libvtk-dicom0.5 (= 0.5.5-2~nd15.04+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~nd15.04+1_amd64.deb Size: 80828 SHA256: 5cf7b47b17d3ecd5f25b841f2cb91b784caf74a4344c3b5116f988dc77409241 SHA1: 481f2b94b9b919ad510d8a72e889d6ec77a85f95 MD5sum: 73ef42118c9e7c6f24e204111a02e833 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: lua-cnrun Source: cnrun Version: 2.0.3-1~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 157 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_2.0.3-1~nd15.04+1_amd64.deb Size: 39320 SHA256: 5abb6ad60571f374dbf38ca3c2d5e6e026357874837c49c6dfb4a900089d5579 SHA1: 1f6c2c4ca116a0e174b012aa4d40d72dd8ecc6d6 MD5sum: 1f06089cc6e7d10a67cababef1da33d0 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~nd15.04+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~nd15.04+1_all.deb Size: 7590 SHA256: 6a0f25f10a9072a0efe1bcebf7deababf8430d2420a807362a7a3e67ef409139 SHA1: f2705787305a11817d05b7b9fcef7f06de6a9668 MD5sum: 0f6fe1c1f34a343f405f67eefe6b8de8 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~nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 8649 Depends: neurodebian-popularity-contest, libmia-2.0-8 (= 2.0.13-1~nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1), libboost-filesystem1.55.0, libboost-regex1.55.0, libboost-serialization1.55.0, libboost-system1.55.0, libboost-test1.55.0, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libglibmm-2.4-1c2a (>= 2.42.0), 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 (>= 4.9), 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~nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1_amd64.deb Size: 1462566 SHA256: e54ffebf6a79051eb208ecb72e5e39cb4627121540933c3b8e791da4e2710fe3 SHA1: 3bc6600d21d25cf3aef9837c947c5a49902e88d2 MD5sum: 3c464e8cfe4d2881ab086d42078df160 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~nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 29262 Depends: neurodebian-popularity-contest, mia-tools (= 2.0.13-1~nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1) Homepage: http://mia.sourceforge.net Priority: extra Section: debug Filename: pool/main/m/mia/mia-tools-dbg_2.0.13-1~nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1_amd64.deb Size: 27011008 SHA256: f44f7bdb6b8b85b66540892fd6ce15f375b18a38accf7bf82aad79397be12339 SHA1: 93c35305f07982fe1adefdd4abe7b97e33231713 MD5sum: 44bc80e56e81d6c8530b010d377ab4aa 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~nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1138 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~nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1_all.deb Size: 72292 SHA256: 92cce88a1b27d71f7eb663c7e558a6e54dccd1f27cb8eeb1e1935a8a90fe4a68 SHA1: 9b3a267bf5c8f73ca78c1a83ce87c33b772ceda2 MD5sum: 4616bc37fbc9cf8c7a50b75196370c31 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: mriconvert Version: 1:2.0.8-1~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 5728 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.9), libwxbase3.0-0 (>= 3.0.2), libwxgtk3.0-0 (>= 3.0.2) Homepage: http://lcni.uoregon.edu/~jolinda/MRIConvert/ Priority: optional Section: science Filename: pool/main/m/mriconvert/mriconvert_2.0.8-1~nd15.04+1_amd64.deb Size: 849308 SHA256: f1b8805c1b42d7796e51b40d3ce3fbf9cf384c42a4d7bc72a62e0117f70da92b SHA1: e5222f654f1d8733dc3929461755ad9e68784e33 MD5sum: 434c1ba77982f13b67acdd665c99fa4e 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~nd14.04+1+nd14.10+1+nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 16459 Depends: neurodebian-popularity-contest, libatk1.0-0 (>= 1.12.4), libc6 (>= 2.2.5), libcairo2 (>= 1.2.4), libgdk-pixbuf2.0-0 (>= 2.22.0), libglib2.0-0 (>= 2.12.0), libgtk2.0-0 (>= 2.24.0), libpango-1.0-0 (>= 1.14.0), libx11-6, mricron-data Recommends: pigz Suggests: mricron-doc, fsl Homepage: http://www.cabiatl.com/mricro/mricron/index.html Priority: extra Section: science Filename: pool/main/m/mricron/mricron_0.20140804.1~dfsg.1-1~nd14.04+1+nd14.10+1+nd15.04+1_amd64.deb Size: 2246596 SHA256: d2e9e93c09b772c6f515036b24f40273d4ca65edaa421d589c21ba3a89fb6aa3 SHA1: 46bfbbcdd3a5de8dff191519e77cfb5dc55dda6e MD5sum: 3d1b1b7a2865dbc6fe07a26eb7c57d21 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~nd14.04+1+nd14.10+1+nd15.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 1676 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~nd14.04+1+nd14.10+1+nd15.04+1_all.deb Size: 1658370 SHA256: 3a0cc08f9c273a3eae09ac5d843094184d4756c0f14d7dbcec6eb34d399cf31e SHA1: e711e9eb9bbbdad7eb9fc866ace4a9bda25f9b41 MD5sum: 3624ac3b3d2ed52978d33bb1e7369fad 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~nd14.04+1+nd14.10+1+nd15.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 980 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~nd14.04+1+nd14.10+1+nd15.04+1_all.deb Size: 577378 SHA256: b6a1b67836ca1995b1523a5bfb18d45e1dbac66bd0fb56affc9d0630dbbaea51 SHA1: fe8f8d7e11063bdc1449c7ecff244746bcb4afe8 MD5sum: 64a09dcdc2147a3a86804a1df22f3399 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~nd15.04+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~nd15.04+1_all.deb Size: 637032 SHA256: bb4de0cb9846e4237cf8e0e2dd0b14c2769cf97383c6c01aee94ae431922048f SHA1: 12137ee6ef7c6b33c584d8b3d5e4a15a3291282c MD5sum: 8982f934373003078c67d1308fec9ba1 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: netselect Version: 0.3.ds1-25~nd14.04+1+nd14.10+1+nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 51 Depends: neurodebian-popularity-contest, libc6 (>= 2.15), 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~nd14.04+1+nd14.10+1+nd15.04+1_amd64.deb Size: 31638 SHA256: 1ce837ef8255a49a0b838949822161c04c7c1e3663273aaa8934589b704f5594 SHA1: 7baac35134cdadb6d9f27568f2252602e78cc4c4 MD5sum: 32ca16f51525647dc1ee25d77039ef0b 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~nd14.04+1+nd14.10+1+nd15.04+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~nd14.04+1+nd14.10+1+nd15.04+1_all.deb Size: 16868 SHA256: e81cf65d7d6444d0b0436350b5b05675d3272864df42b4e8004382b1c9619ab0 SHA1: 517a1b86da9da2a81ea9772568f318a449b7601d MD5sum: 596b0832af2d83d7970f6912c074838a 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~nd15.04+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~nd15.04+1_all.deb Size: 33408 SHA256: 1c36f207e5ae6a9f93eea4406333b2983094a906040a616cf5cc0bc05a0e8fe6 SHA1: 20c55e48b5b37bdf7b3e95fabad4afa563814d73 MD5sum: eb53c57dfa768dc1bb53f90da5fad09e 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~nd15.04+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~nd15.04+1_all.deb Size: 10068 SHA256: a8ebb76655ed20d69c63642ade6d68357000fd86b5a71e9de326d10498d6b057 SHA1: 38eea502322918a885e8594e32e7c58b600a7251 MD5sum: 21c6d6392227e3e72729dbaf37e595e9 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~nd15.04+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~nd15.04+1_all.deb Size: 115958 SHA256: 16243167de3bcfb692e790d2cb07166228a3eb20a38c4892f03580b75773bd7c SHA1: b8c360286b94a46c0abff1a5235bcf9d733c099c MD5sum: e38fd9952e1ae7bea51e97d182545682 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~nd15.04+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~nd15.04+1_all.deb Size: 32252 SHA256: 36f566cf7881e509bac03d7fd4c9767e0c73d5e232486a6a48a1537f606df16a SHA1: ddb9c7876b4852637eb21d19f3cc857241f9b0b3 MD5sum: ac7c245734d42a3f95e5bfdf86c7e076 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-popularity-contest Source: neurodebian Version: 0.37.2~nd15.04+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~nd15.04+1_all.deb Size: 12164 SHA256: 22b1dd731133cc766f8cbde861d7accd538aafe8f8626aae58927d81df444459 SHA1: 2775eb4bb97ad87c5ae35e07c98afc0120f072b8 MD5sum: 32612af4d4d644ccc5a09b0cd53bfc6d 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: nuitka Version: 0.5.17+ds-1~nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2816 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_0.5.17+ds-1~nd15.04+1_all.deb Size: 598304 SHA256: 30dd95b28bae2aeafb10f223f6f41e57cc3034eb7a775919e216515af1203676 SHA1: 33e3d65ea15f4a25319b0e7b794c483fb4f5dcce MD5sum: 923360d365be4daa2651af6759355851 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: octave-biosig Source: biosig4c++ Version: 1.4.1-2~nd14.10+1+nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 69 Depends: neurodebian-popularity-contest, octave (>= 3.4.3-1~), libbiosig1, libc6 (>= 2.4), liboctave2 Homepage: http://biosig.sf.net/ Priority: extra Section: science Filename: pool/main/b/biosig4c++/octave-biosig_1.4.1-2~nd14.10+1+nd15.04+1_amd64.deb Size: 19862 SHA256: f6c973646edb4b4d25c08a5b4a146c59372177f2b0d239e2a5ac49cc5a16ac21 SHA1: aa7972bb4225eb5c1fb069cbda59051d06032f92 MD5sum: fec25223cc7ce609b7b6f4122609e876 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-psychtoolbox-3 Source: psychtoolbox-3 Version: 3.0.12.20150725.dfgs1-1~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 4530 Depends: neurodebian-popularity-contest, octave (>= 3.4.3-1~), freeglut3, libasound2 (>= 1.0.16), libc6 (>= 2.14), libdc1394-22, libfreenect0.5 (>= 1:0.1.1), 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), 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), libxrandr2 (>= 2:1.2.99.3), libxxf86vm1, psychtoolbox-3-common (= 3.0.12.20150725.dfgs1-1~nd15.04+1), psychtoolbox-3-lib (= 3.0.12.20150725.dfgs1-1~nd15.04+1) Recommends: octave-audio, octave-image, octave-optim, octave-signal, octave-statistics Provides: psychtoolbox, psychtoolbox-3 Homepage: http://psychtoolbox.org Priority: extra Section: science Filename: pool/main/p/psychtoolbox-3/octave-psychtoolbox-3_3.0.12.20150725.dfgs1-1~nd15.04+1_amd64.deb Size: 882034 SHA256: a7bf8e5896945d381c398b6a09ab679cdf273c3812cff5b6faf691ba56fdecf3 SHA1: eace0b503834d0b974c17575544ad960f0e99386 MD5sum: 13003c967376d85d3a4d12852349ef99 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: openwalnut-modules Source: openwalnut Version: 1.4.0~rc1+hg3a3147463ee2-1~nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 21488 Depends: neurodebian-popularity-contest, libbiosig1, libboost-filesystem1.55.0, libboost-regex1.55.0, libboost-system1.55.0, libboost-thread1.55.0, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libnifti2, libopenscenegraph100, libopenwalnut1, libstdc++6 (>= 4.9) Homepage: http://www.openwalnut.org Priority: extra Section: science Filename: pool/main/o/openwalnut/openwalnut-modules_1.4.0~rc1+hg3a3147463ee2-1~nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1_amd64.deb Size: 3619696 SHA256: adb2b1b13f93f829416204e642a7d4bd232228d2eabdf278fa0b6d3f954f5357 SHA1: becb765f50f12a307bdbe96015013dc4eae26e7b MD5sum: e5ca38b1d1fc308fe045b2bb4c921c6e 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~nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2111 Depends: neurodebian-popularity-contest, libboost-filesystem1.55.0, libboost-program-options1.55.0, libboost-regex1.55.0, libboost-system1.55.0, libboost-thread1.55.0, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libopenscenegraph100, libopenwalnut1, libqt4-opengl (>= 4:4.6.0), libqtcore4 (>= 4:4.7.0~beta1), libqtgui4 (>= 4:4.8.0), libqtwebkit4, libstdc++6 (>= 4.9), libx11-6 Recommends: openwalnut-modules (= 1.4.0~rc1+hg3a3147463ee2-1~nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1) Homepage: http://www.openwalnut.org Priority: extra Section: science Filename: pool/main/o/openwalnut/openwalnut-qt4_1.4.0~rc1+hg3a3147463ee2-1~nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1_amd64.deb Size: 757334 SHA256: 669e497c1e602f440e916b33dccbc9b6af1c4d9fdef04143fdb1060beb8ee3de SHA1: 30c5721ca662c642734577e91cda7b4398fbfcf8 MD5sum: 05b727e3a944e796024b157efea14998 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: psychopy Version: 1.82.02.dfsg-1~nd15.04+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~nd15.04+1_all.deb Size: 6063402 SHA256: 17782789402ea78ba5095281d77de2c226e893e0672c4d551451c170e7e928f9 SHA1: 6b7be7599fbaad06f071d6c91839d6ac1023610e MD5sum: c41e21b4016cf42c9bfc298679ab7094 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~nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 233281 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~nd15.04+1_all.deb Size: 23787558 SHA256: 5b1512ff5f82e4c6718dd86383ff9525bc8d01aeffd33019ed6c5f439a84f815 SHA1: 70e03d3d70c36cecc6c7a9f822562d1511a6549e MD5sum: a2ff9bafe21c8ef059f0e44dea0e98b3 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~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 3719 Depends: neurodebian-popularity-contest, octave-psychtoolbox-3 (= 3.0.12.20150725.dfgs1-1~nd15.04+1) Homepage: http://psychtoolbox.org Priority: extra Section: debug Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-dbg_3.0.12.20150725.dfgs1-1~nd15.04+1_amd64.deb Size: 707828 SHA256: 046a3b28f1af480861ee865ac9612d84dd83585fdd48900e3c52eb29a1acff52 SHA1: f1e7e7dc3b790a9add5aa4e9779ae0a76664a74d MD5sum: 7ca7ec5d4d5186df9914a6045e2afa0f 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~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 162 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libfontconfig1 (>= 2.9.0), 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~nd15.04+1_amd64.deb Size: 56250 SHA256: 0d2f56c96d3cb9556c7e2846cc8a0767bd4e507c93c1517b76e85e86a60fd8cb SHA1: 23aacc3121446affb5a1dfa5509569c2ee8a4c94 MD5sum: 980d2304fefdc15e43b197ab3664019e 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~nd14.10+1+nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 214 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python-numpy (>= 1:1.8.0), python-numpy-abi9, python-support (>= 0.90.0), libbiosig1, libc6 (>= 2.14), libcholmod2.1.2, libgcc1 (>= 1:4.1.1), libpython2.7 (>= 2.7), libstdc++6 (>= 4.1.1), zlib1g (>= 1:1.1.4) Homepage: http://biosig.sf.net/ Priority: extra Section: python Filename: pool/main/b/biosig4c++/python-biosig_1.4.1-2~nd14.10+1+nd15.04+1_amd64.deb Size: 43652 SHA256: d7351195c54b2beed7a8758df6baab844374c71a37690ee80facc879c5e6dbdb SHA1: d5ac4b81cd74182f3c1243dc01f2a266f5b48388 MD5sum: 13ce53f1faf5e29f747c70d1eb0527dc 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-citeproc Source: citeproc-py Version: 0.3.0-1~nd15.04+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~nd15.04+1_all.deb Size: 80538 SHA256: e78c5d6b033c60fb1eb954d02a9213c98a0d4c3f5b922406133a43570db326c5 SHA1: e6b0aec260ad4942e8891d4a4ca20a38aa87c1af MD5sum: f9330d678d300a68ff73013a381d5bd3 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-contextlib2 Source: contextlib2 Version: 0.4.0-3~nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 55 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~) Homepage: http://contextlib2.readthedocs.org/ Priority: optional Section: python Filename: pool/main/c/contextlib2/python-contextlib2_0.4.0-3~nd15.04+1_all.deb Size: 8752 SHA256: 8fb9114427a19da8509f91fe08bfcd1099e2a6dd6d203cdc9c4ac8d7aaf6acec SHA1: 31f5b7eca19643a9b44bd3823be459e4c267d7e4 MD5sum: eb7c12cd39802fd000c28986818f4f20 Description: Backport and enhancements for the contextlib module - Python 2.7 contextlib2 is a backport of the standard library's contextlib module to earlier Python versions. . It also serves as a real world proving ground for possible future enhancements to the standard library version. . This package contains the Python 2.7 module. Package: python-dcmstack Source: dcmstack Version: 0.6.2+git33-gb43919a.1-1~nd15.04+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~nd15.04+1_all.deb Size: 78364 SHA256: 76ce758554a0d3ef2b9923d4bc53eaa13e8eb9190cc3c739402983d33c5950bf SHA1: 61851ee716e4311427dbc8d3f87cca4364f8e987 MD5sum: 84cb19726c92df822ddbee954a5fb863 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~nd14.04+1+nd14.10+1+nd15.04+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~nd14.04+1+nd14.10+1+nd15.04+1_all.deb Size: 357850 SHA256: 464e7732ae7a71154b8bc2b4b9c0ecb78de2cff7b6264b10e9ef8a552381281c SHA1: f381c431cc2550b40cc7826952b7568cb7d1db10 MD5sum: 65216a1227d059aed3b67923110da4ed 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~nd14.10+1+nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4609 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy, python-scipy, python-dipy-lib (>= 0.9.2-1~nd14.10+1+nd15.04+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~nd14.10+1+nd15.04+1_all.deb Size: 2338482 SHA256: af60e09e289d61c286ca64ed5caab8b7f39739d1a77c8b68d8d3f358a0a8fe53 SHA1: c8eb040015cc00375d96dece09867a3612b8bc98 MD5sum: c244c81ba67847bf36cf0022a693c718 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~nd14.10+1+nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 12500 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~nd14.10+1+nd15.04+1_all.deb Size: 10229524 SHA256: 67636668a73b266f95714464d5aeec2ce1fb88dce069c396fcb3927eda99c7a4 SHA1: d5cf3fda3cf51161522a34eb2cf83947c260f885 MD5sum: 8bc1cc163594cc4fcdebce9bf9a543bf 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~nd14.10+1+nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 5433 Depends: neurodebian-popularity-contest, python-numpy (>= 1:1.8.0), python-numpy-abi9, python (>= 2.7), python (<< 2.8), libc6 (>= 2.14), libgomp1 (>= 4.9) Provides: python2.7-dipy-lib Homepage: http://nipy.org/dipy Priority: optional Section: python Filename: pool/main/d/dipy/python-dipy-lib_0.9.2-1~nd14.10+1+nd15.04+1_amd64.deb Size: 974232 SHA256: 17e607b0616b440eb4c33c45db4dfe80b30104df2a21cb247787bb932d4f64fd SHA1: 0d0137701465a1326d93a919ac673126953dbf4f MD5sum: 6fba83c3d78321d35a9c78f9b3079ebd 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~nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2389 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~nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1_all.deb Size: 697408 SHA256: f74dc892bec012e1c5f95ddca15a82f6fed4a9429a3f169f01e0c4f307627130 SHA1: a5db64adb2e4e61043b162b718ed250b0ffe35a5 MD5sum: 23b727e97cb4f65e8f22843ec447f48d 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: 1:0.5.3-1~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 180 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python-numpy (>= 1:1.8.0), python-numpy-abi9, libc6 (>= 2.4), libfreenect0.5 (= 1:0.5.3-1~nd15.04+1) Suggests: python-matplotlib, python-opencv Provides: python2.7-freenect Homepage: http://openkinect.org/ Priority: extra Section: python Filename: pool/main/libf/libfreenect/python-freenect_0.5.3-1~nd15.04+1_amd64.deb Size: 47856 SHA256: f579c7e813199635e0bda12f6a1833a906e5d2b1852cd6a4d517ef289230d645 SHA1: f5b8ad75477b7f79731f5a4800f4adc3f3cb3f1e MD5sum: 2b7d9173d8cf3c7345fdf4ad77f5367f 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~nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1498 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~nd15.04+1_all.deb Size: 304728 SHA256: 4ed65e6932df3ae455597d310eab791ab24dcb3888103aa46d52caeb08cb305a SHA1: 9779e7d69612678db90ba4058652e9d823300aa0 MD5sum: a03ef4f481735c68772a1a86c80f43d4 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~nd14.10+1+nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 211 Depends: neurodebian-popularity-contest, python-smmap, python (>= 2.7), python (<< 2.8), libc6 (>= 2.14) Provides: python2.7-gitdb Homepage: https://github.com/gitpython-developers/gitdb Priority: extra Section: python Filename: pool/main/p/python-gitdb/python-gitdb_0.6.4-1~nd14.10+1+nd15.04+1_amd64.deb Size: 55320 SHA256: ae31efc467e3938dfa3c399d38b553b9924c51a998d00a54920e6c3f0c950723 SHA1: 012ff89e8cfcc285cde08caab571865e5d3a76d5 MD5sum: d70cccd765f06051c53edeeb8c35a494 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-humanize Version: 0.5.1-2~nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 117 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~) Homepage: http://github.com/jmoiron/humanize Priority: optional Section: python Filename: pool/main/p/python-humanize/python-humanize_0.5.1-2~nd15.04+1_all.deb Size: 13050 SHA256: 8e102dd936b503194d6c28dd694768580172f5af5d577357ab047d54b2687d80 SHA1: a6c99fc90d27a9f75e92f7a61e0c80b850b46031 MD5sum: ce1b21ab6971c535050bad3591f551d3 Description: Python Humanize library (Python 2) This library proposes various common humanization utilities, like turning a number into a fuzzy human readable duration ('3 minutes ago') or into a human readable size or throughput. . This is the Python 2 version of the package. Package: python-joblib Source: joblib Version: 0.9.3-1~nd15.04+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~nd15.04+1_all.deb Size: 78080 SHA256: 24ecb1f84ab29c369a03199ed49811888fc703c94a19b4e1c13cb4f068a1c1a9 SHA1: f2d2fc415c2813c840e39c4bb27002a18989c2a5 MD5sum: 93c9b2066a5c673a976caade6fe602cd 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-lda Source: lda Version: 1.0.2-9~nd14.10+1+nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1242 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), python-numpy Homepage: https://pythonhosted.org/lda/ Priority: optional Section: python Filename: pool/main/l/lda/python-lda_1.0.2-9~nd14.10+1+nd15.04+1_amd64.deb Size: 234920 SHA256: ec525071a960e1e5b714fce57671af98bd48d61e22b132363cad95e0c9dcbc7e SHA1: 17a57a7dbad62a6bf6c37f05cc8c57b4d3f5f3d6 MD5sum: 5d5c949606cac56e5cb7e054f558e34c 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-mne Version: 0.11+dfsg-1~nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9098 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_0.11+dfsg-1~nd15.04+1_all.deb Size: 4362032 SHA256: 64901cf896c7a7b37bf68ac63fd7f3c568b3544a2b25afa35c68459018bf4865 SHA1: 5c3df095bdace8810f2babe6fc8ffa34aa6a5605 MD5sum: 6cf2e2e910c0259a47af77bda5e1b944 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-mvpa2 Source: pymvpa2 Version: 2.4.1-1~nd15.04+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~nd15.04+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~nd15.04+1_all.deb Size: 5054478 SHA256: ee442cf064d20a41cb3cfd5c087700718dbc71ce9a1286a99f7ae5deb7313718 SHA1: 97fe588967fe6cc4f1b29945a29d4cbe51874c2e MD5sum: 161f0dc006c45d2a13971111f3c56bb4 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~nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 29664 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~nd15.04+1_all.deb Size: 4767868 SHA256: 97616b34f5734f3fef014853a8349d593afe8e78e2b3ccb5429a1e978d7d7273 SHA1: 72a0f9a918e785d54a5c0986ca6ed7a6f65d7ca7 MD5sum: 2495b69a019b9856838e185cd5f89cdd 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~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 172 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), libsvm3, python (<< 2.8), python (>= 2.7), python-numpy (>= 1:1.8.0), python-numpy-abi9 Provides: python2.7-mvpa2-lib Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa2/python-mvpa2-lib_2.4.1-1~nd15.04+1_amd64.deb Size: 47354 SHA256: a95258fcd337fa2c5861c99e5dc4abd7bb1d653f227d94e5c97d3ff8114c58dc SHA1: e652a48fe917ffa94404f27f74fe274fc62d7cc7 MD5sum: ddf61729ac7fcfa0bb297e9912aa4cc0 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-nibabel Source: nibabel Version: 2.0.2-1~nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 63313 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~nd15.04+1_all.deb Size: 1963356 SHA256: 4db8140f1e96db650aca1f459a9b75e309d54440a1c6edda6a289c5930effa15 SHA1: ef681f024a0b4d0892f0e0c94c0afc5c6dd56706 MD5sum: b4aa337c09070e1dafd6163a8cce194b 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~nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5572 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~nd15.04+1_all.deb Size: 2684448 SHA256: c1f733db940f0524de666e6c36cc5915eb38c7093b2a276961545861772be165 SHA1: 51e7054d510bad436081553596641960a81c0bb9 MD5sum: 3c95b6f3fd45c64c4dc2c57dcad58a53 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-nilearn Source: nilearn Version: 0.1.4+git3-g60d2a1b~dfsg.1-1~nd15.04+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~nd15.04+1_all.deb Size: 635418 SHA256: fe2c53c9e8d70942a8547f9b7d0648b1d4b801ba07cb239e4da4c92597625377 SHA1: 70dc1bdcb98a1c269e950cf10f92401ff7374562 MD5sum: 68f76c0ca78b5090471ad7392303c759 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~nd14.04+1+nd14.10+1+nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2953 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~nd14.04+1+nd14.10+1+nd15.04+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~nd14.04+1+nd14.10+1+nd15.04+1_all.deb Size: 725092 SHA256: 160c214858539837cfbdc74fa8865aa863f2c28f6cfa337ac0219ca90323c8f3 SHA1: c8e7ad67f471fdf7f4408164775cd59cfb2f051b MD5sum: 63f71570305f645de43550c5b4c7e455 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~nd14.04+1+nd14.10+1+nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 10533 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~nd14.04+1+nd14.10+1+nd15.04+1_all.deb Size: 3212362 SHA256: 7671102c3b5a569976d2dbbd6169ce0c7357e6b7acd462e630d8f9eff3cdec47 SHA1: db431521c7138a32b72fea4e58ab210e55870180 MD5sum: cf3178ecda72225350d12fefb716ccc8 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~nd14.04+1+nd14.10+1+nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2608 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), 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~nd14.04+1+nd14.10+1+nd15.04+1_amd64.deb Size: 616654 SHA256: f7e2899a11c9a34f5ece9c0eb3ddce9d07fbb0bb38b10092a173d2592476955f SHA1: 826924c60735bc388092291c6233b4721f692385 MD5sum: 39fcd22223095205294ea33f97a1a280 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~nd14.04+1+nd14.10+1+nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 3732 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), 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~nd14.04+1+nd14.10+1+nd15.04+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~nd14.04+1+nd14.10+1+nd15.04+1_amd64.deb Size: 623948 SHA256: 440a19d529abf8ff443443f1a8c07435c6e085ba920da7ef63a230a40fe01a80 SHA1: 5a46504b2eff3057165b2ca18cd20cd9f84f6f10 MD5sum: 88264f55ae8c1f5536e1583ec015262e 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~nd15.04+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~nd15.04+1_all.deb Size: 1430640 SHA256: dca236dea7e2aa6e791b65b4f81305181e946b3793310e62d210d9c21d245d25 SHA1: 26b85d07cb88b558ea7222df4b99834db458d361 MD5sum: caeeaade8e3b6ca8c2a0dc57f0e2607f 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~nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 23034 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~nd15.04+1_all.deb Size: 9028004 SHA256: 777cbee43b259aa9cb1c60fe8f0ce030e7d25090e9984a233c2305cf31729b96 SHA1: 64913047566ddcb2337476ac2791f38345631205 MD5sum: e86b51c5fcc57a83d4f1d0a612e72fa2 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-numexpr Source: numexpr Version: 2.4.3-1~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 380 Depends: neurodebian-popularity-contest, python-numpy (>= 1:1.8.0), python-numpy-abi9, python (<< 2.8), python (>= 2.7~), python:any (>= 2.7.5-5~), libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1) Homepage: http://code.google.com/p/numexpr/ Priority: optional Section: python Filename: pool/main/n/numexpr/python-numexpr_2.4.3-1~nd15.04+1_amd64.deb Size: 136050 SHA256: 11f1dcb0d1849ef2404f43afa61fa06354b167786e58b4cfc29203fdc262697e SHA1: ea9f0865a31429eed9973022f6135fd47212fb2f MD5sum: 42d9aef1d225ca0ad63acff8eb826946 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~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 258 Depends: neurodebian-popularity-contest, python-numpy (>= 1:1.8.0), python-numpy-abi9, python-dbg (<< 2.8), python-dbg (>= 2.7~), libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), python-numexpr (= 2.4.3-1~nd15.04+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~nd15.04+1_amd64.deb Size: 105798 SHA256: 74357f519d054e5e456006de6aebc360f817c2877fdb729926892240c3e53e48 SHA1: fede03f524578fbe9b742fbf5834a3eefd9aa3be MD5sum: 2b77c02b28e71ab8d2b94295e928ac32 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-openpyxl Source: openpyxl Version: 2.3.0~b2-1~nd15.04+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~nd15.04+1_all.deb Size: 192318 SHA256: 5da04ffa0d29174014bb7123e71cfc407ed5ccfe0a7fa14c833e6e0c34bc283e SHA1: fb8e16db00086311e9b98355c5e47390d004a0ac MD5sum: a4e546abf5c3e283fc6734b52ccc35c5 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~nd15.04+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~nd15.04+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~nd15.04+1_all.deb Size: 2403890 SHA256: 3666a940ad43a80c803d4c310f69bbfc1287b40e6a85a88dda823256304514aa SHA1: 18e3068eb92088ecee0a2644b0c957bc5df87ded MD5sum: d1c9bbb7833f87e8c9eb4389a962b357 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~nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 50104 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~nd15.04+1_all.deb Size: 10899320 SHA256: 7754346654d0c588dd25f3334d36a1e1f83962730598194b0049cd6865f3dba8 SHA1: ea93ae64564b0a17874e8429936dfb34c77a438d MD5sum: c37b590462e0713d5961f5a85e00298e 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~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 6135 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), 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~nd15.04+1_amd64.deb Size: 1615694 SHA256: 8839d4c517f34c5ff6cdac6d4a0fcd19bc9b71be469f9f54b01ef1df6a758f1d SHA1: 18a955c8135db8ec96d20b9c57413228d309cb32 MD5sum: 60c21f410dd7493940782d980e34a809 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~nd15.04+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~nd15.04+1_all.deb Size: 171908 SHA256: 1cda9c9e5ce9adca51ac94b068289918bf441f5f4608dff813a4d5497ef8b723 SHA1: 30178c6e1aab16e64976df63c49c73e4d1b601c5 MD5sum: ee285a9ffbf103a1277fd75c210968d5 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~nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1365 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~nd15.04+1_all.deb Size: 359064 SHA256: 9f8c7b3beb2c948c5510b6dedb186be30f8e97a7c1aded5fd4f8c6e92ba78213 SHA1: 26891c46e2788a9c62496054eb8338801fa920d1 MD5sum: 9e8b8e4fe78be8367ba515ad99983b4b Description: documentation and examples for patsy This package contains documentation and example scripts for python-patsy. Package: python-pprocess Source: pprocess Version: 0.5-1+nd0~nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+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~nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1_all.deb Size: 81970 SHA256: fbe3ab4eeebf2473f3e02e4cba721c67a6bdda5eaa26e5b47edb89804eede189 SHA1: f9d6c5adc7aef904c0da62f1027e4f24864a650d MD5sum: d0325feda6310adeed396c83a80bc49a 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-py Version: 1.4.30-1~nd15.04+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~nd15.04+1_all.deb Size: 66772 SHA256: 35f32e6f04896c734cf36cf563efa5d114fe8d958c9fa233a12b3b065234079c SHA1: 7de1445007ac1441d3b0596184d6ea425e899e57 MD5sum: f910e581ad941b0518e9553e7bb8d039 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-pyepl Source: pyepl Version: 1.1.0+git12-g365f8e3-2~nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1388 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-pyepl-common (= 1.1.0+git12-g365f8e3-2~nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1), python-numpy, python-imaging, python-pygame, python-pyode, python-opengl, ttf-dejavu, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libode1, libsamplerate0 (>= 0.1.7), libsndfile1 (>= 1.0.20), libstdc++6 (>= 4.4.0) Conflicts: python2.3-pyepl, python2.4-pyepl Replaces: python2.3-pyepl, python2.4-pyepl Provides: python2.7-pyepl Homepage: http://pyepl.sourceforge.net/ Priority: optional Section: python Filename: pool/main/p/pyepl/python-pyepl_1.1.0+git12-g365f8e3-2~nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1_amd64.deb Size: 284220 SHA256: 8cdbb9690a0a1b05f9b085851fcbfa05fbc0aad237632d81aa9eced05c6c7d6c SHA1: 79037f6fa790c38816eca28863789694b6dcf7c1 MD5sum: fa323ebf1e51230c09218f677ec963e0 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~nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+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~nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1_all.deb Size: 819470 SHA256: 4141b03991c1ae1733f1455890317059d1aa0b87522f81432f83775b99616c27 SHA1: 15bccebc6144753728e68c52ebb178bd2df0a480 MD5sum: 369149b435a66cd12d0d26065d4a0c36 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~nd14.04+1+nd14.10+1+nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2751 Depends: neurodebian-popularity-contest, python-numpy (>= 1:1.8.0), python-numpy-abi9, libblas3 | libblas.so.3, libc6 (>= 2.14), libgfortran3 (>= 4.6), liblapack3 | liblapack.so.3, 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~nd14.04+1+nd14.10+1+nd15.04+1_amd64.deb Size: 591014 SHA256: ea3175b046a9666f58637745757a63484a6d55ca2bde7dd113c6c967fdccf54c SHA1: b2a3c15e4e033dbb82cde54fb73a63453e4ef71b MD5sum: 99da41f6f8c0d4108a41b5536537a92e 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~nd14.04+1+nd14.10+1+nd15.04+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~nd14.04+1+nd14.10+1+nd15.04+1_all.deb Size: 839998 SHA256: 212a988a1ef94aff793ad65bdc635fac3dfc440aecf7efdd5f38cbb390acdf34 SHA1: 2e7990989ae789fa2f86732f0f9fd295990de72d MD5sum: 5ac30fa505fb269d8838ce6a8c51d154 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~nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 763 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~nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1_all.deb Size: 123226 SHA256: d409c53ce5df4066f077a76a10d198361c04acd5dd5dbb4ecff4e57887830fd0 SHA1: b4d3fcbacf3b4866b6b93b5351e74beb3e9f1c2b MD5sum: 6f1abf7b60095f3ea2d0b6252095dded 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~nd12.04+1+nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1647 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.9), python (<< 2.8), python (>= 2.7), 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~nd12.04+1+nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1_amd64.deb Size: 346398 SHA256: 683281c99a7224ecb55ce44f432edc25e7c473dc981bfff5a49a666e62015f89 SHA1: 7822b14485ecb92b28b417a6bdd1a43725c5c701 MD5sum: 0e37d8ebaba80e286bb74587b46a20d9 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~nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 424 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~nd15.04+1_all.deb Size: 102416 SHA256: 23f6d7792f555272a65266461601998d8e9281415111f8fdd75aaf92c9da67b9 SHA1: ad1262d1fa416f15245ed386823fe6c14b8b8bef MD5sum: 45719b33df858703d60f41b024993501 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~nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2878 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~nd15.04+1_all.deb Size: 401812 SHA256: b0fcc8b66c86bb5789a9dfac2c443269785a487c69e47e19b7dc5faa0b2326cf SHA1: c4bfef534ce999e00302bf5e2b09a2348690927a MD5sum: 1cd5b95e5bc83e57f57f32840afac210 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~nd15.04+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~nd15.04+1_all.deb Size: 19310 SHA256: b4839faa96df335abff71a17762604afe7d71f2edb6f6a8ab86b94baa549da3c SHA1: 7c11dc58047e5f367b59d76c00610d94f2e06362 MD5sum: 538400b3e80490327fe8cea90e40709b 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~nd15.04+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~nd15.04+1_all.deb Size: 5754 SHA256: 822f8211e91ba25e945a05a904f9f827bfe9a7285c3ba2af3e80f556e130a514 SHA1: e3fd32da52114cbd92f05fcd5636f25247b4b179 MD5sum: d482112963438d293bf9f18c27bf77ba 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-scikits-learn Source: scikit-learn Version: 0.17.0-3~nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 81 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_0.17.0-3~nd15.04+1_all.deb Size: 55554 SHA256: a01005bf1ed784f8e14b62f2cc21beb4e2d11beb6272ece1ca9fec366a902f06 SHA1: 24d5fde8bb223080809b99396a1f8deb3a008505 MD5sum: 36fc26c7587cf4a74b665354cc456b78 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-scrapy Version: 1.0.0-1~nd15.04+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~nd15.04+1_all.deb Size: 175052 SHA256: 0614c784915bd351babc6467c0daceacf2a1feda70b595c491707fbcffe5b307 SHA1: e15b3e6fb22a2a333e091f84db6890ba9c1091a5 MD5sum: 832715218483c9a595334f1727a26041 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~nd15.04+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~nd15.04+1_all.deb Size: 1520578 SHA256: d96313d26ed726ede0bcef27ad840d10568b6b98745ba14480438b43e75e30eb SHA1: c5725c15631ec7997efcc7864ce54410c0969af8 MD5sum: 0d7e95e51682ca39999a2f611f8afb66 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~nd15.04+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~nd15.04+1_all.deb Size: 117816 SHA256: 9b36c9a29beef1e724318efa364acfc897e0baa45db1ce198b9e46d5ab56aaa8 SHA1: 7e099883223770641c2ee02b25d8f433af2430c6 MD5sum: e598e548d3d3bc0850742135f8b435e3 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-six Source: six Version: 1.9.0-3~bpo8+1~nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 34 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~nd15.04+1_all.deb Size: 11116 SHA256: fc7e55a9aeb35b9915d17997bbaac2285366d8988e79a4563a7e0d1278d4ecbf SHA1: bf898ae83cc9862f0601efc88bdf43f2c2ad8f78 MD5sum: d809a46ec4bdf9c7a510ae028a400b77 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~nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 13 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~nd15.04+1_all.deb Size: 13320 SHA256: 1b7f71fec73b08228f4570ea39513d466b8715046a0cd9d4e61c6c701a44d120 SHA1: 019d89a8d8d80a06846cd57bff82236d18882701 MD5sum: 4d3d73be56dba394305fb07066765c21 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-sklearn Source: scikit-learn Version: 0.17.0-3~nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5274 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~), python-numpy, python-scipy, python-sklearn-lib (>= 0.17.0-3~nd15.04+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_0.17.0-3~nd15.04+1_all.deb Size: 1223328 SHA256: 6f635baa24a12a00c3aceb8f3eb4c02c0af424efa5d4f6158fae8f7dd57b1301 SHA1: d51cebb72c0d12c24853490dc90335bdab6a9d70 MD5sum: 933bcc558e22bf949955a56cf97c1b39 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: 0.17.0-3~nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 23762 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_0.17.0-3~nd15.04+1_all.deb Size: 4069236 SHA256: ac686c2b11aff6922ec82f49cd3570d51f1bd423edd6423b56e3351c5738f694 SHA1: 8cb1943242c7c990b10680f4a59d013784443bb0 MD5sum: 53458e0543fd1a12d43bc87284aad136 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: 0.17.0-3~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 5107 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), python-numpy (>= 1:1.8.0), python-numpy-abi9, python (<< 2.8), python (>= 2.7~) Conflicts: python-scikits-learn-lib Replaces: python-scikits-learn-lib Provides: python2.7-sklearn-lib Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python-sklearn-lib_0.17.0-3~nd15.04+1_amd64.deb Size: 1160656 SHA256: 7770d08a0caa07a5d7d3f2042b6ab6ba47680eeaaeed3de7289f38ade8413906 SHA1: 4e23fede0b973d29c4d5a2242b4d73f05d563bb2 MD5sum: 45ba5ef1425509970019e3be002719c7 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~nd15.04+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~nd15.04+1_all.deb Size: 20086 SHA256: 8056d44a58027c554907b9ed773444af0c95dc73608bb458100b9cb65d265856 SHA1: b9b63e32255c98f631f72b3f8f394d9a68209dc4 MD5sum: 3eccd6dd18f185194c6ef539dc09a0a5 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-rtd-theme Source: sphinx-rtd-theme Version: 0.1.8-1~nd15.04+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~nd15.04+1_all.deb Size: 117238 SHA256: 241167342a48b4a3e2abf8cc4a03c286b9532965eb218d1e86753035295e27f6 SHA1: 6a6e4de3b3ccd3c7b1b5dcd9c7e6b50a7140f180 MD5sum: 1c029e3a01effa7c5994ec68488b1ecc 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-spykeutils Source: spykeutils Version: 0.4.2-1~nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2089 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_0.4.2-1~nd15.04+1_all.deb Size: 309712 SHA256: f6fdf11386deb4564528eb8517eb491f0aa81a5344e9fe5c52b7ba0d72e0cce8 SHA1: 23ff0cac7cc506c84c0257d03d835739dc526692 MD5sum: b3043522c2d46971c952dc0460b4db71 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-stfio Source: stimfit Version: 0.14.11-1~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 959 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python-numpy (>= 1:1.8.0), python-numpy-abi9, libbiosig1, libblas3 | libblas.so.3, libc6 (>= 2.14), libcholmod2.1.2, libfftw3-double3, libgcc1 (>= 1:4.1.1), libhdf5-8, liblapack3 | liblapack.so.3, libpython2.7 (>= 2.7), libstdc++6 (>= 4.9), libbiosig-dev, libsuitesparse-dev Recommends: python-matplotlib, python-scipy, python-pandas Provides: python2.7-stfio Homepage: http://www.stimfit.org Priority: optional Section: python Filename: pool/main/s/stimfit/python-stfio_0.14.11-1~nd15.04+1_amd64.deb Size: 311174 SHA256: f83b91ef811d367ad1f769bbe8781f7c839cee748937dd4cb22a077d8378c126 SHA1: f1b45b9b9a95fe678345b1968a56c02bbec1bb4c MD5sum: 6453b404b57ee06a743662f95b98bf32 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: 0.6-1~nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 226 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-nibabel, python-numpy, python-scipy, 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_0.6-1~nd15.04+1_all.deb Size: 41738 SHA256: 5924ccf7c65883818fd9b7f994d5b351c7c20239dcfc5afee0e44f840e12c4d8 SHA1: dd9a6ab2677b927b8a2a8a21f273469cd5eff1e1 MD5sum: a37edb908d08b61b65ddb9457261ecf1 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~nd15.04+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~nd15.04+1), python-tables-lib (<< 3.2.1-1~nd15.04+1.1~), python-tables-data (= 3.2.1-1~nd15.04+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~nd15.04+1_all.deb Size: 345168 SHA256: 23e0c4c81d83c6dbcda67ce645ffdd9e1476619aaf6429f19939ea9259778604 SHA1: d814f96ec983770f900eaef05a65acd3ddeaf5db MD5sum: ff36dc7b5212d2e309f27edceaba3011 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~nd15.04+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~nd15.04+1_all.deb Size: 51418 SHA256: 6f6947dc5cb0036aa0a19a8629f4915470c7848e01777e0869909cbe01ea68f8 SHA1: 35d18073d42d66cf67ff2b33b16dbf4f21e5db0c MD5sum: 503e9bb7fbb3fd58bb09d479b11281af 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~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1596 Depends: neurodebian-popularity-contest, python-numpy (>= 1:1.8.0), python-numpy-abi9, python-dbg (<< 2.8), python-dbg (>= 2.7~), libbz2-1.0, libc6 (>= 2.14), libhdf5-8, liblz4-1 (>= 0.0~r113), liblzo2-2, libsnappy1, zlib1g (>= 1:1.1.4), python-tables (= 3.2.1-1~nd15.04+1), python-tables-lib (= 3.2.1-1~nd15.04+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~nd15.04+1_amd64.deb Size: 478204 SHA256: 04e5278f510b2714b8276a7ce3cd27d711a8b1d7b87849257e6bbcca0d898b79 SHA1: e3c57c1d809e29d7a0eb2ea420c18258e3e10a6a MD5sum: c3df1a329e813b1fe81177e02f6fd893 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~nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8786 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~nd15.04+1_all.deb Size: 4247860 SHA256: 29ff1735304d19050bb129ee2d92f35475137e7fce21a4b91906bad4670e2bc8 SHA1: 6bd5d23a1547c3f4844ff0dd3d310d24eec6fcd9 MD5sum: 221e9c82df65b409582dae82fe8e30b1 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~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1360 Depends: neurodebian-popularity-contest, python-numpy (>= 1:1.8.0), python-numpy-abi9, python (<< 2.8), python (>= 2.7~), libbz2-1.0, libc6 (>= 2.14), libhdf5-8, liblz4-1 (>= 0.0~r113), liblzo2-2, libsnappy1, zlib1g (>= 1:1.1.4) Recommends: python-tables (= 3.2.1-1~nd15.04+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~nd15.04+1_amd64.deb Size: 381440 SHA256: ffca059b0b471b82a67ddf48e03d2184c34ae6cd1d1ba2b492bc810b4df2e980 SHA1: dd3b4a99437e24bff9bcd3c48025a293a6f84dda MD5sum: 50a07bcd9b9a0032901625725471f5c4 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-vtk-dicom Source: vtk-dicom Version: 0.5.5-2~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 495 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~nd15.04+1_amd64.deb Size: 96618 SHA256: c9bfdf2c501919b9ada7d0226037cff4d23c84a7999d7cf963648dd8c26c387e SHA1: 68582b45e65c3ba1e8918e12ddb70c9310bf3851 MD5sum: a82f06940a156bfd7da1397994a3cc43 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~nd15.04+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~nd15.04+1_all.deb Size: 14200 SHA256: 466ff41a2f0cef0dd0f474e9793f8a5c87372a06350e24de8b90b0ddf8fbc036 SHA1: 42eafdde8f0f7b6c76188862c28ad03720592be4 MD5sum: 9ec87e9b559013ff08911b3060225c9b 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~nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 727 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~nd15.04+1_all.deb Size: 163416 SHA256: 64183383fd2a66d1f2b49a53444d2c55813b04f12cf30a92a48efadb08ef6a58 SHA1: b94fcb6411c480646e9725797d0a6e3abf7d7708 MD5sum: 74e208601582d9138f57efa85a142e97 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~nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2559 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~nd15.04+1_all.deb Size: 880206 SHA256: a42f0a23dfba7bfbb552f59b52ccd9b6476b3ec4fe82e45ffc7a35a5de0bcfd0 SHA1: 40f8b6e41e39915463a63b2cd6b439878719d24d MD5sum: 9882068e7050e3da0ff440a83e0d3218 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: python3-citeproc Source: citeproc-py Version: 0.3.0-1~nd15.04+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~nd15.04+1_all.deb Size: 82014 SHA256: 58b7e715fe22c7ca7582fef12c2edd4b96f9de5a130d918c457bcd9bb5a3edb8 SHA1: 409f20d9dbdc0a9377728cc98a61e937e57f0b92 MD5sum: fa1423d015f429068fee440bcfcc53f8 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-contextlib2 Source: contextlib2 Version: 0.4.0-3~nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 55 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~) Homepage: http://contextlib2.readthedocs.org/ Priority: optional Section: python Filename: pool/main/c/contextlib2/python3-contextlib2_0.4.0-3~nd15.04+1_all.deb Size: 8820 SHA256: 3b52de236d6d107bf61dfe47cc3589717782f321456a14f6cd2c4cc2fd6ae47b SHA1: a87a9836dafc312f11f0f490348defcf8cdee1f6 MD5sum: 749d9d79388b8cf378a1a8379db5e2b7 Description: Backport and enhancements for the contextlib module - Python 3.x contextlib2 is a backport of the standard library's contextlib module to earlier Python versions. . It also serves as a real world proving ground for possible future enhancements to the standard library version. . This package contains the Python 3.x module. Package: python3-humanize Source: python-humanize Version: 0.5.1-2~nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 114 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~) Homepage: http://github.com/jmoiron/humanize Priority: optional Section: python Filename: pool/main/p/python-humanize/python3-humanize_0.5.1-2~nd15.04+1_all.deb Size: 12780 SHA256: c890195acad6f2ced1a4d1caf801e4c4e04bbb21e34c435742948566ff073d93 SHA1: f1328ce16c32a2793ff7c8ffdbf626fbcad7adbe MD5sum: d86f479895a015202bd33893f9029796 Description: Python Humanize library (Python 3) This library proposes various common humanization utilities, like turning a number into a fuzzy human readable duration ('3 minutes ago') or into a human readable size or throughput. . This is the Python 3 version of the package. Package: python3-joblib Source: joblib Version: 0.9.3-1~nd15.04+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~nd15.04+1_all.deb Size: 74986 SHA256: b6c2932ca378bb9094eab293e2a1af106bdf12f94de83064b05e4ddb5f46ecf9 SHA1: 3dc0fd6b945f328d58b98379c06d42a8c2aa54ea MD5sum: 02630b57a3a58f86f88e3251e84ae7c0 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~nd14.10+1+nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1242 Depends: neurodebian-popularity-contest, python3 (<< 3.5), python3 (>= 3.4~), python3-numpy, python3-pbr, libc6 (>= 2.14) Homepage: https://pythonhosted.org/lda/ Priority: optional Section: python Filename: pool/main/l/lda/python3-lda_1.0.2-9~nd14.10+1+nd15.04+1_amd64.deb Size: 234278 SHA256: 7dadc0e3296a538a958d89be3efea93c7d5dcf040a7e90ac4e227fdbf2301446 SHA1: aca20fe65a84d516d15a23cc9f9cabf4d71d44ee MD5sum: ab355a23ed9ddc67f8d56f7329a014ec 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-nibabel Source: nibabel Version: 2.0.2-1~nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 63272 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~nd15.04+1_all.deb Size: 1953668 SHA256: e1890266d03a9e59fbf7d79d681b090ca34bfe9151fe3981a7a3dc9129bb9ac2 SHA1: b0326ff60b8ce4e1169d81636d8b943d4f95ec8b MD5sum: 6ca88242008c353fb443bd536555eea4 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~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 369 Depends: neurodebian-popularity-contest, python3-numpy (>= 1:1.8.0), python3-numpy-abi9, python3 (<< 3.5), python3 (>= 3.4~), libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1) Homepage: http://code.google.com/p/numexpr/ Priority: optional Section: python Filename: pool/main/n/numexpr/python3-numexpr_2.4.3-1~nd15.04+1_amd64.deb Size: 130508 SHA256: e841c78fc06c719bbefa1a564884f423aa0e2e23336cf0d1befa7c41ea2767ba SHA1: f007a76026f61fa98555d5195f095ab929dec24a MD5sum: e6552462ced15ef442e405fb54bc7867 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~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 258 Depends: neurodebian-popularity-contest, python3-numpy (>= 1:1.8.0), python3-numpy-abi9, python3-dbg (<< 3.5), python3-dbg (>= 3.4~), libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), python3-numexpr (= 2.4.3-1~nd15.04+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~nd15.04+1_amd64.deb Size: 105978 SHA256: 7384f79ed57e308f4035351aadafd618df423a0720f69d94f9867a54ba728fea SHA1: cd32c2755da7cd5dab32e6e832ad0711c89830fa MD5sum: 7a200132875d61c1d7fd3dfeeed673bf 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~nd15.04+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~nd15.04+1_all.deb Size: 190944 SHA256: 33ac18ccdd62d354aa313b233b0b108050090d091f68eaa4a0cc0dd6e3838a79 SHA1: 2ba1be50ba5bd1de2937e78f9530c9fe5e503444 MD5sum: cf8252814f053d9941fe888ce3236d16 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~nd15.04+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~nd15.04+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~nd15.04+1_all.deb Size: 2400826 SHA256: c79dbf4cb02be3a767fe918da693bbd7e7f89bffa795b95aa54981694745c4ff SHA1: f10ad946a1cfb0a8959891b73789bcb6c22167ac MD5sum: cc152473b71fbbba1c23204e80c6d24e 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~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 5964 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), 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~nd15.04+1_amd64.deb Size: 1573304 SHA256: d4d02c908e26ea9857b6b34263045d96852634e323fe5d4c40e08ebe9a98423d SHA1: 2b14f72fee040574628a3eb06d9e6bbc7b4d4325 MD5sum: de1587c642b0879b51b44724b165e800 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~nd15.04+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~nd15.04+1_all.deb Size: 170618 SHA256: f88d672501715f85702d1211e410c402714591cfb70550ba1afea7722a080b23 SHA1: e2cf13d3458626f1368afe82ae936beaa3714aa4 MD5sum: cc20c9ff6aeb07e835272b44d688c5e2 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-py Source: python-py Version: 1.4.30-1~nd15.04+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~nd15.04+1_all.deb Size: 66838 SHA256: 4e214febd0caf31121ded7dae39868ce386ef8ba3ddb08a820a0b84a54b48dfc SHA1: b109dcac1c0b64d42f409ba35142c96a0f65e3c6 MD5sum: 2f45a639062f4fa28996390cad086a5b 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~nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 424 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~nd15.04+1_all.deb Size: 102554 SHA256: ef5be096433403b409d7337c2738bdf618c693ecdc219137d703db0bf0e7c782 SHA1: 5795f4eb32561bf8736c2d78c5ae4a6342b00fc3 MD5sum: 4e4696b222b9a886ff3d6319cc6024ed 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~nd15.04+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~nd15.04+1_all.deb Size: 19384 SHA256: d506e0097b754cfc2b5209ec864ec8dc65e4917ee5b3bf868a24eaca2939b363 SHA1: 6f4eb8678d23365142097cdb20bac10286ece3f4 MD5sum: 72bbb569c0e4facb25a9867cc74ea9f4 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~nd15.04+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~nd15.04+1_all.deb Size: 5830 SHA256: 24a9fb9b036f4557135e30cd451889d1c8ddc4416812d9deadef6a921cdeded4 SHA1: 80efabadf804d1c61de37879f5c2dade1e8ec453 MD5sum: 23e6cc34e05a10fb41a04ea016c8f0db 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~nd15.04+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~nd15.04+1_all.deb Size: 117852 SHA256: 96eeeb5abd95e910808fde1f3d5cd4cbe39dce8d7be2afc83850e430bf9cc0b2 SHA1: 7006d7ee583567cf4d6bd4a8b3b1a61fb35df3c6 MD5sum: cd28467849d3f7b026327d0f114762c7 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~nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 35 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~nd15.04+1_all.deb Size: 11200 SHA256: 2448500407de73e001990702acb803c5eda1b9225b1e5908b64acf50fe981823 SHA1: 3de9903634937b862d142cc83064f49575e2dfcc MD5sum: aed70178b10a218d513f2d1e9691836a 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-sklearn Source: scikit-learn Version: 0.17.0-3~nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5274 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~), python3-numpy, python3-scipy, python3-sklearn-lib (>= 0.17.0-3~nd15.04+1), python3-joblib (>= 0.9.2) Recommends: python3-nose, python3-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_0.17.0-3~nd15.04+1_all.deb Size: 1222592 SHA256: 15452554213fec95011c886425932f248ad8bfc95b7d27fb194de38e5c79ae9b SHA1: 4ff7df52bf6b9a2a75d18d2f37f4fe3a5898eed8 MD5sum: 3e9a893f7e018ebcdc6f464a5a661816 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: 0.17.0-3~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 4698 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), python3-numpy (>= 1:1.8.0), python3-numpy-abi9, python3 (<< 3.5), python3 (>= 3.4~) Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python3-sklearn-lib_0.17.0-3~nd15.04+1_amd64.deb Size: 1079374 SHA256: 90555374dacecdb506bc6548039b1cd4ecd97e84f0266e2b6222678633996e69 SHA1: 5b8eab4fbfc68521bdad2d77b5e5e28a8b908388 MD5sum: 616ce4bb5d9d7d5820672573d284032d 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~nd15.04+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~nd15.04+1_all.deb Size: 117294 SHA256: 365d13896e9229c8d9ffa6d5701d126d657eeaf951022fe48ab07c55bed2e56b SHA1: b52e873a588f29f913b6925673776c9c3bfce477 MD5sum: 2190713250e854576a47d53ee4aa8675 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~nd15.04+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~nd15.04+1), python3-tables-lib (<< 3.2.1-1~nd15.04+1.1~), python-tables-data (= 3.2.1-1~nd15.04+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~nd15.04+1_all.deb Size: 334886 SHA256: b72060567e46b8ba586d32055e7f9c2bfc8a345e448db6015c620565750763f5 SHA1: 90183f0ae5df3773cdc043c508cb3ae1215346e8 MD5sum: e678b5a92757a187e4906ac8e81c0d8b 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~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1558 Depends: neurodebian-popularity-contest, python3-numpy (>= 1:1.8.0), python3-numpy-abi9, python3-dbg (<< 3.5), python3-dbg (>= 3.4~), libbz2-1.0, libc6 (>= 2.14), libhdf5-8, liblz4-1 (>= 0.0~r113), liblzo2-2, libsnappy1, zlib1g (>= 1:1.1.4), python3-tables (= 3.2.1-1~nd15.04+1), python3-tables-lib (= 3.2.1-1~nd15.04+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~nd15.04+1_amd64.deb Size: 468002 SHA256: 462d0dad1275ceae0c76b89b3dfe61315672d19c58b6dff07711494c7fdae475 SHA1: fdf262449f5fb037eccbcd1a85bbcc4fc5c8e30d MD5sum: 21f5564cfc103432c0c165465010d5a6 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~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1313 Depends: neurodebian-popularity-contest, python3-numpy (>= 1:1.8.0), python3-numpy-abi9, python3 (<< 3.5), python3 (>= 3.4~), libbz2-1.0, libc6 (>= 2.14), libhdf5-8, liblz4-1 (>= 0.0~r113), liblzo2-2, libsnappy1, zlib1g (>= 1:1.1.4) Recommends: python3-tables (= 3.2.1-1~nd15.04+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~nd15.04+1_amd64.deb Size: 368060 SHA256: f4b4ad8b23978543231ed173bc4555fdca89e335082d36ee37073a250e49076f SHA1: 2f5dfb67cf13367288cd5e0106f5e679b351c353 MD5sum: 1dcf6486c77f3a0ee4d958a93a5f3ad9 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-w3lib Source: python-w3lib Version: 1.11.0-1~nd15.04+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~nd15.04+1_all.deb Size: 14286 SHA256: 5234acf308d716e4e992ce1e32de3913eadd7ff7d814e8e7b919077233b1c8b8 SHA1: 53b9ed7790ff55cd7726d049c32c08e3a95e5f79 MD5sum: 8538c2b6a93fb76fb7a853db35cd533a 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~nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 727 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~nd15.04+1_all.deb Size: 163370 SHA256: 236d305d47a66dd096ee55dc15a342d198b1323c22fb99526c08aa742388d889 SHA1: c94963eccdeb3332ba008b76e068ce7d85f968ba MD5sum: a05c4cfadba72ce7e82a9e80b89cd1ce 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: spm8-common Source: spm8 Version: 8.5236~dfsg.1-1~nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 18499 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~nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1_all.deb Size: 9749522 SHA256: bee3a8a8d0af681ba91432ac463ec34332831ecdadbfaa1e0c109a56f8a238b8 SHA1: 3c3872cd974ef2eb3c96fc7b06cffb46ada71913 MD5sum: d6409b2fa71161921cae88d7463c1a37 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~nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 72987 Depends: neurodebian-popularity-contest Priority: extra Section: science Filename: pool/main/s/spm8/spm8-data_8.5236~dfsg.1-1~nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1_all.deb Size: 45490194 SHA256: 7cfaaff742fc76511c629cee8e2e378f48d3f2fc89a8cc08990ad1b42b5e3936 SHA1: 6e1accea5582cbb34454b3c4540caad106ca7fcb MD5sum: aee12a1a58cd615510e6a960405dfb3a 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~nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 9242 Depends: neurodebian-popularity-contest Priority: extra Section: doc Filename: pool/main/s/spm8/spm8-doc_8.5236~dfsg.1-1~nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1_all.deb Size: 8935228 SHA256: 7fff13109743814047c5484270313ba9ccab6f45f94254af4c7868caa9b843e3 SHA1: 755c714e5b684c1436b121dfe5022f9d4bf804d4 MD5sum: 42740eef8ef7c2e6f319320784a3738a Description: manual for SPM8 Statistical Parametric Mapping (SPM) refers to the construction and assessment of spatially extended statistical processes used to test hypotheses about functional brain imaging data. These ideas have been instantiated in software that is called SPM. It is designed for the analysis of fMRI, PET, SPECT, EEG and MEG data. . This package provides the SPM manual in PDF format. Package: stabilitycalc Version: 0.1-1~nd11.04+1+nd11.10+1+nd12.04+1+nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 119 Depends: neurodebian-popularity-contest, python, python-support (>= 0.90.0), python-numpy, python-matplotlib, python-scipy, python-nifti Recommends: python-dicom Homepage: https://github.com/bbfrederick/stabilitycalc Priority: extra Section: science Filename: pool/main/s/stabilitycalc/stabilitycalc_0.1-1~nd11.04+1+nd11.10+1+nd12.04+1+nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1_all.deb Size: 22822 SHA256: 03f99830143fac4d44e7d19f3da19a63f78215aab4e427efe765acd31e6fe223 SHA1: 230b6f40d4e07188d5324e9698ebd9dda213a20b MD5sum: 676750a675b5dbe7a0937e0a88d2511c 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: 2.7 Package: stimfit Version: 0.14.11-1~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2933 Depends: neurodebian-popularity-contest, libbiosig1, libblas3 | libblas.so.3, libc6 (>= 2.14), libcholmod2.1.2, libfftw3-double3, libgcc1 (>= 1:4.1.1), libhdf5-8, liblapack3 | liblapack.so.3, libpython2.7 (>= 2.7), libstdc++6 (>= 4.9), libwxbase3.0-0 (>= 3.0.2), libwxgtk3.0-0 (>= 3.0.2), python-numpy (>= 1:1.8.0), python-numpy-abi9, python2.7, python:any (>= 2.6.6-7~), libbiosig-dev, libsuitesparse-dev, python-wxgtk3.0 | python-wxgtk2.8 (>= 2.8.9), python-matplotlib Recommends: python-scipy Homepage: http://www.stimfit.org Priority: optional Section: science Filename: pool/main/s/stimfit/stimfit_0.14.11-1~nd15.04+1_amd64.deb Size: 761214 SHA256: 6a60050ee132e984fe24b5a2db844c5dd1c36102b07d9442e18dc2d3b17b71c3 SHA1: 69052f4ac204a8a591a45e66dd22d16ff705f3ef MD5sum: ce46d004d8a46d0bed9cd4f06462b72d 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~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 32406 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~nd15.04+1_amd64.deb Size: 6128426 SHA256: 3d07fa6e22b5fb21ef406d723d8eeaca32358b0c36a3be860ca87bcfa4cd5653 SHA1: 7016646e20dd934e5f08e5316fc6e7adc47121aa MD5sum: 6ae34f89fc27d30584f956c3ce68fd77 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: testkraut Version: 0.0.1-1~nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 359 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~nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1_all.deb Size: 86220 SHA256: 5b1e47fba04d313b1eca80cf698d2aa4b9cd5d24e0428450b2bc56a3ed809f62 SHA1: ea19ee270d5348f70033fabb34c77e3dbb164b25 MD5sum: 13eb82ea4aadd1d37019e0a52499d2ae 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: 2.7 Package: vrpn Version: 07.30+dfsg-1~nd12.04+1+nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 323 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.1.1), libvrpn0 (= 07.30+dfsg-1~nd12.04+1+nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1), libvrpnserver0 (= 07.30+dfsg-1~nd12.04+1+nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1) Homepage: http://www.cs.unc.edu/Research/vrpn/ Priority: extra Section: utils Filename: pool/main/v/vrpn/vrpn_07.30+dfsg-1~nd12.04+1+nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1_amd64.deb Size: 48486 SHA256: d3d395e86091b725db85ec9f6fc4041b3c48f558b221795326bd1760443731fc SHA1: 1d7a3c0fbb97b13c57f5bf558c276a7db4cf5e36 MD5sum: 2ab3105e25fc7bc4631ec6aa75be7481 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~nd12.04+1+nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 5034 Depends: neurodebian-popularity-contest, libvrpn0 (= 07.30+dfsg-1~nd12.04+1+nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1), libvrpnserver0 (= 07.30+dfsg-1~nd12.04+1+nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1), vrpn (= 07.30+dfsg-1~nd12.04+1+nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1) Homepage: http://www.cs.unc.edu/Research/vrpn/ Priority: extra Section: debug Filename: pool/main/v/vrpn/vrpn-dbg_07.30+dfsg-1~nd12.04+1+nd12.10+1+nd13.04+1+nd13.10+1+nd14.04+1+nd14.10+1+nd15.04+1_amd64.deb Size: 1057648 SHA256: 48dede6c64bab2a5a98f418dedc9e32d30854f8e2b8f68b300e1e47e9cd96757 SHA1: 238d5f960ea84cc476948eca6d48c5501db1b6a3 MD5sum: 301a7b465c209f7a39bf3be02c7f07f0 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~nd15.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 267 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.1.1), libstdc++6 (>= 4.9), libvtk-dicom0.5, libvtk5.8 Homepage: http://github.com/dgobbi/vtk-dicom/ Priority: optional Section: utils Filename: pool/main/v/vtk-dicom/vtk-dicom-tools_0.5.5-2~nd15.04+1_amd64.deb Size: 72858 SHA256: dcfc8c7d91a0928dc0aff8fc1227caf381ef0c6c73ac4d21d983f447cae32b58 SHA1: 1f11e5f3b3dd567c22d17a36314b8d9ff91dcd50 MD5sum: 58e3306f24926da26ecf2820b17a0bd1 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