Package: aghermann Version: 1.1.1-1~nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1493 Depends: neurodebian-popularity-contest, libc6 (>= 2.8), libcairo2 (>= 1.2.4), libconfig++9v5, libfftw3-double3, libgcc1 (>= 1:3.0), libglib2.0-0 (>= 2.31.18), libgomp1 (>= 4.9), libgsl2, libgtk-3-0 (>= 3.3.16), libitpp8v5, liblua5.2-0, libpango-1.0-0 (>= 1.14.0), libsamplerate0 (>= 0.1.7), libstdc++6 (>= 5.2), libvte-2.91-0 Suggests: edfbrowser Homepage: http://johnhommer.com/academic/code/aghermann Priority: optional Section: science Filename: pool/main/a/aghermann/aghermann_1.1.1-1~nd16.04+1_i386.deb Size: 522984 SHA256: aafa593b279ab0456cc61c315e19ae70d5f63c69b227c58b729b50b1c20baa84 SHA1: f405f3b2b2afe18a2a5c8646c74687d6dc453c4f MD5sum: ff406c0bd8994f12eb16b581b0dd4ac8 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: btrbk Version: 0.23.3-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 255 Depends: neurodebian-popularity-contest, perl, btrfs-progs (>= 3.18.2) | btrfs-tools (>= 3.18.2) Recommends: openssh-client, pv Homepage: http://digint.ch/btrbk/ Priority: optional Section: utils Filename: pool/main/b/btrbk/btrbk_0.23.3-1~nd16.04+1_all.deb Size: 70918 SHA256: 7a613ae50cc61b506f1bf531528fa2d7c677e2abb1206d7c334db906f76b4858 SHA1: 08a2ae8cc13dad61bcec586faec577b0c4616024 MD5sum: e9565fdc7f21b8f3dd449c357c1e823e Description: backup tool for btrfs subvolumes Backup tool for btrfs subvolumes, using a configuration file, allows creation of backups from multiple sources to multiple destinations, with ssh and flexible retention policy support (hourly, daily, weekly, monthly). Package: cde Version: 0.1+git9-g551e54d-1~nd+1+nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 879 Depends: neurodebian-popularity-contest, libc6 (>= 2.1) Homepage: http://www.pgbovine.net/cde.html Priority: optional Section: utils Filename: pool/main/c/cde/cde_0.1+git9-g551e54d-1~nd+1+nd16.04+1_i386.deb Size: 150248 SHA256: 40e746aacc7d7090573c096de2a1373b1b5d9d379e6f2f6919e412c93de3e752 SHA1: ea236b848f09a501e08ebd7d7600ccfb683e058a MD5sum: 0ad739c044e3ec87b2e6f0c10093eab6 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: cmtk Version: 3.3.1-1~nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 25053 Depends: neurodebian-popularity-contest, libbz2-1.0, libc6 (>= 2.7), libdcmtk5, libfftw3-double3, libgcc1 (>= 1:4.2), libgomp1 (>= 4.9), libmxml1, libqtcore4 (>= 4:4.6.1), libqtgui4 (>= 4:4.5.3), libsqlite3-0 (>= 3.5.9), libstdc++6 (>= 5.2), zlib1g (>= 1:1.1.4) Recommends: sri24-atlas Suggests: numdiff Homepage: http://www.nitrc.org/projects/cmtk/ Priority: extra Section: science Filename: pool/main/c/cmtk/cmtk_3.3.1-1~nd16.04+1_i386.deb Size: 3903356 SHA256: c8105c8995fa781116dbc33f1691603577019eaaf4dbf7dede0fa79e437d315a SHA1: e3f0deded089d990dcea4d1d11f22bf668e819cc MD5sum: 6cc895849e8a39efd42755eff764f171 Description: Computational Morphometry Toolkit A software toolkit for computational morphometry of biomedical images, CMTK comprises a set of command line tools and a back-end general-purpose library for processing and I/O. . The command line tools primarily provide the following functionality: registration (affine and nonrigid; single and multi-channel; pairwise and groupwise), image correction (MR bias field estimation; interleaved image artifact correction), processing (filters; combination of segmentations via voting and STAPLE; shape-based averaging), statistics (t-tests; general linear regression). Package: cnrun-tools Source: cnrun Version: 2.0.3-1~nd+1+nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 54 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libcnrun2 (>= 2.0.0), libgcc1 (>= 1:3.0), libgsl2, libstdc++6 (>= 5.2) Homepage: http://johnhommer.com/academic/code/cnrun Priority: optional Section: science Filename: pool/main/c/cnrun/cnrun-tools_2.0.3-1~nd+1+nd16.04+1_i386.deb Size: 17740 SHA256: 80d6064624fb178ee53b97fb21ccba2e47c60032377439470c6e49e563879084 SHA1: 3e010972e51950474cc1d968077ebba237cc5f3e MD5sum: a2c05b65ba79286335282aadac1e288e 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: 8.4.8~dfsg.1-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 29 Depends: neurodebian-popularity-contest, htcondor Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: oldlibs Filename: pool/main/c/condor/condor_8.4.8~dfsg.1-1~nd16.04+1_all.deb Size: 15960 SHA256: 8f9fdc2299f71baeda62203c32b7abdecbd9ed90d81b5bf1810d5c036d6be10f SHA1: 2f25fedc7d338b6ac505bf37b20294d18cdaf448 MD5sum: 12a764600f8becc8159d49421c08490e 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: 8.4.8~dfsg.1-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 29 Depends: neurodebian-popularity-contest, htcondor-dbg Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: oldlibs Filename: pool/main/c/condor/condor-dbg_8.4.8~dfsg.1-1~nd16.04+1_all.deb Size: 15980 SHA256: b35477ab5681e576db73ce3fdf5093762fb128dd6511cb4499c367e11c557288 SHA1: 9f26c3eaacd66b615db090cda63b379a137c0fa5 MD5sum: 46d1e7c92c305c20a556af8428f987a4 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: 8.4.8~dfsg.1-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 29 Depends: neurodebian-popularity-contest, htcondor-dev Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: oldlibs Filename: pool/main/c/condor/condor-dev_8.4.8~dfsg.1-1~nd16.04+1_all.deb Size: 15976 SHA256: f6b45d7a6dcdccad0a6214cc873f03c40ab1427ef1806ce4225f16447792fe79 SHA1: ed72c557c1c57e4ef34ce34b463fd9dfe0975639 MD5sum: 142569b4ca9efe2dc2b9aa85d8122f7f 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: 8.4.8~dfsg.1-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 29 Depends: neurodebian-popularity-contest, htcondor-doc Homepage: http://research.cs.wisc.edu/htcondor Priority: extra Section: oldlibs Filename: pool/main/c/condor/condor-doc_8.4.8~dfsg.1-1~nd16.04+1_all.deb Size: 15976 SHA256: 95f0827daa97e5dd26304469310c4e57918107faa53b5d4b7e38168a86502dd0 SHA1: c6440cf5cee830313f0b2ca4366ff44f07c3aa5d MD5sum: ec7044d995a7aa6636c49b6b4aae80ca 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: 1.2.3-1~nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 52438 Depends: neurodebian-popularity-contest, libc6 (>= 2.11), libftgl2 (>= 2.1.3~rc5), libgcc1 (>= 1:4.2), 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), libstdc++6 (>= 5.2), 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_1.2.3-1~nd16.04+1_i386.deb Size: 25612082 SHA256: 4a1771aa30099c2e7bb635cfc83eb57aafd06489a2eb1ce1c8119f37faaf9c29 SHA1: dcaefe2cdb7ce25c623350219dcb62f56f4f774b MD5sum: 0b49c3261e25efcd634a44ad655a54b3 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: 1.2.3-1~nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 120722 Depends: neurodebian-popularity-contest, connectome-workbench (= 1.2.3-1~nd16.04+1) Homepage: http://www.nitrc.org/projects/workbench/ Priority: extra Section: debug Filename: pool/main/c/connectome-workbench/connectome-workbench-dbg_1.2.3-1~nd16.04+1_i386.deb Size: 118887276 SHA256: c644144f2e146e9edd76c87f4fa9a7781a42af3b017a57facd45229378100a22 SHA1: 721af4f01034bb26658e2a32c9ae605338f6a166 MD5sum: c130e05d0343e6cdb6a8fdd7ec22a816 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. Build-Ids: b149adfaad88aa680bf240ec81fe43293051fd55 b6677dc3b6035fa2f04d8eb8e36403e18df64f32 Package: datalad Version: 0.3-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 73 Depends: neurodebian-popularity-contest, python-argcomplete, python-datalad (= 0.3-1~nd16.04+1), python:any Homepage: http://datalad.org Priority: optional Section: science Filename: pool/main/d/datalad/datalad_0.3-1~nd16.04+1_all.deb Size: 41408 SHA256: fbce2580c21a1aa40bb9acd45ab1238fdd35a343450191b93031fa08b897bf2a SHA1: 990095eb14a313ef76e5ef4f8547648b76e732a7 MD5sum: 01ffeba6cff49c65e91eb69f27d537f6 Description: data files crawler and data distribution DataLad is a data distribution providing access to a wide range of data resources already available online (initially aiming at neuroscience domain). Using git-annex as its backend for data logistics it provides following facilities . - crawling of web sites to automatically prepare and update git-annex repositories with content from online websites, S3, etc - command line interface for manipulation of collections of datasets (install, uninstall, update, publish, save, etc.) and separate files/directories (add, get), as well as search within aggregated meta-data . This package provides the command line tool. Install without Recommends if you need only core functionality. Package: dcm2niix Version: 20160921-1~nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 227 Depends: neurodebian-popularity-contest, libc6 (>= 2.4) Homepage: http://www.mccauslandcenter.sc.edu/CRNL/tools/dcm2niix Priority: optional Section: science Filename: pool/main/d/dcm2niix/dcm2niix_20160921-1~nd16.04+1_i386.deb Size: 96962 SHA256: 938285c864c9784c4816ed9a19511b4cff5846e5e7d95a085cf7ffaf0ec14ba4 SHA1: 07252d364bb0ee89843cb741acae88de09b43528 MD5sum: 01d0ce2481f3b36d0243c1e47c5032d8 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~nd+1+nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 146 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libfftw3-double3, libgcc1 (>= 1:3.0), libstdc++6 (>= 5.2) 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~nd+1+nd16.04+1_i386.deb Size: 37108 SHA256: b24472a15422a78b99a56ac627ada323efe14988934465ac44850c3334b0e2f6 SHA1: 8ebbf01f1bf74e9265cc370a92c6e5e966568db2 MD5sum: f6906cde7bcd7fe06514372f39ce123d 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~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8117 Depends: neurodebian-popularity-contest Priority: extra Section: science Filename: pool/main/e/eeglab11/eeglab11-sampledata_11.0.0.0~b~dfsg.1-1~nd+1+nd16.04+1_all.deb Size: 7059424 SHA256: 3f090fdf3072e4e5b6ac524be1fff62f6d940c0e49f39e0843ba76d43e5b7d2b SHA1: 3f7081f142023ddee661a2980ff92df8a18bf7fe MD5sum: b9da13b2959435f2ec53d8cfda008794 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: fail2ban Version: 0.9.5-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1227 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~), init-system-helpers (>= 1.18~), lsb-base (>= 2.0-7) Recommends: python, iptables, whois, python3-pyinotify, python3-systemd Suggests: mailx, system-log-daemon, monit Homepage: http://www.fail2ban.org Priority: optional Section: net Filename: pool/main/f/fail2ban/fail2ban_0.9.5-1~nd16.04+1_all.deb Size: 254442 SHA256: b83f33f7969f1feae691a3aeb054895815d472875924cdeeb16b60fc3599d5f4 SHA1: 775cc82ef91031342db82c6465ddddb512c1dc2f MD5sum: d98ca12821d7188561bdd66f2300d676 Description: ban hosts that cause multiple authentication errors Fail2ban monitors log files (e.g. /var/log/auth.log, /var/log/apache/access.log) and temporarily or persistently bans failure-prone addresses by updating existing firewall rules. Fail2ban allows easy specification of different actions to be taken such as to ban an IP using iptables or hostsdeny rules, or simply to send a notification email. . By default, it comes with filter expressions for various services (sshd, apache, qmail, proftpd, sasl etc.) but configuration can be easily extended for monitoring any other text file. All filters and actions are given in the config files, thus fail2ban can be adopted to be used with a variety of files and firewalls. Following recommends are listed: . - iptables -- default installation uses iptables for banning. You most probably need it - whois -- used by a number of *mail-whois* actions to send notification emails with whois information about attacker hosts. Unless you will use those you don't need whois - python3-pyinotify -- unless you monitor services logs via systemd, you need pyinotify for efficient monitoring for log files changes Package: fsl-melview Source: melview Version: 1.0.1+git9-ge661e05~dfsg.1-1~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 73 Depends: neurodebian-popularity-contest, python-matplotlib, python-nibabel, python-numpy, python-pkg-resources, python-scipy, python:any (<< 2.8), python:any (>= 2.7.5-5~), 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~nd+1+nd16.04+1_all.deb Size: 13946 SHA256: 98fec451744471ae6b47ee84ac52821b45ffd63401f9f29586a9aa6be46a8702 SHA1: 0fb8488befc01e715be4378c0affa5f4d8bce34c MD5sum: 7b7b1bf6546af8b8bcfa435171ebfc89 Description: viewer for the output of FSL's MELODIC This viewer can be used to facilitate manual inspection and classification of ICA components computed by MELODIC. As such, it is suited to generate hand-curated labels for FSL's ICA-based denoising tool FIX. Python-Version: 2.7 Package: fslview Version: 4.0.1-6~nd+1+nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 6333 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.0), libnewmat10ldbl, libnifti2, libqt4-qt3support (>= 4:4.5.3), libqt4-xml (>= 4:4.5.3), libqtcore4 (>= 4:4.7.0~beta1), libqtgui4 (>= 4:4.7.0~beta1), libqwt5-qt4, libstdc++6 (>= 5.2), libvtk5.10, libvtk5.10-qt4 Recommends: fslview-doc, qt-assistant-compat Suggests: fsl-atlases Conflicts: fsl-fslview Replaces: fsl-fslview Homepage: http://www.fmrib.ox.ac.uk/fsl/fslview Priority: optional Section: science Filename: pool/main/f/fslview/fslview_4.0.1-6~nd+1+nd16.04+1_i386.deb Size: 1340522 SHA256: f1efe1f594e7e5907c478561377ef86986171accd2e610ee7d2ab20b7cbd36b3 SHA1: ab1f070237e1b16a8c6294530043348fd9fd2d6d MD5sum: 8ce5634a8b111dbe779f98d02a56b92a Description: viewer for (f)MRI and DTI data This package provides a viewer for 3d and 4d MRI data as well as DTI images. FSLView is able to display ANALYZE and NIFTI files. The viewer supports multiple 2d viewing modes (orthogonal, lightbox or single slices), but also 3d volume rendering. Additionally FSLView is able to visualize timeseries and can overlay metrical and stereotaxic atlas data. . FSLView is part of FSL. Package: fslview-doc Source: fslview Version: 4.0.1-6~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2930 Depends: neurodebian-popularity-contest Homepage: http://www.fmrib.ox.ac.uk/fsl/fslview Priority: optional Section: doc Filename: pool/main/f/fslview/fslview-doc_4.0.1-6~nd+1+nd16.04+1_all.deb Size: 2227648 SHA256: d85f59cee9b040e9680c9817ee0e831d88dca517d2880029a7e9674aead08469 SHA1: 326b580203ce06c723591e460854d73845119293 MD5sum: 9b50fc95856220ca1a5876e4772eff2f Description: Documentation for FSLView This package provides the online documentation for FSLView. . FSLView is part of FSL. Package: gcalcli Version: 3.4.0-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1766 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_3.4.0-1~nd16.04+1_all.deb Size: 1669680 SHA256: 4a58773ebb4576609bd4161572178d94854997e74ff3145fb33e74ab97a987ff SHA1: e62edfdb9467c933ae868eb3630a0b9229c77b7e MD5sum: 2446d0cab9c54b99c87e2fee70e5cd85 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: 6.20160923+gitgd1dabb3-1~ndall+1 Architecture: i386 Maintainer: Richard Hartmann Installed-Size: 422532 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_6.20160923+gitgd1dabb3-1~ndall+1_i386.deb Size: 29796568 SHA256: adf379c8d71b63214d4d73ff4b4d116e65c596a6ce6a4dadfb220d7879810bfc SHA1: 336792efc24f7aecd8870ca0800707dd1770b057 MD5sum: 5507160c7310d2907e319d89ce0051da 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: heudiconv Version: 0.1-1~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 49 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~nd+1+nd16.04+1_all.deb Size: 10286 SHA256: b869501f708fea57cfd20fd4325c042865373309c23439a3fc68da089e2821f3 SHA1: 1d3abeac3233e632cf971f22e066c6764272f9a3 MD5sum: 2981f738670efec821e5b4daeb773c86 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-doc Source: condor Version: 8.4.8~dfsg.1-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 6090 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_8.4.8~dfsg.1-1~nd16.04+1_all.deb Size: 1064646 SHA256: 83ab19817caa44838f51ba745b85b2ae17dc0f120e88aee23a67c3d5a94f4d95 SHA1: 7bdf6ad489e6da31343d35cba8dcbe09b6a8302b MD5sum: 831996d4c80b945fa61c3cf5e0aa04f9 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: incf-nidash-oneclick-clients Source: incf-nidash-oneclick Version: 2.0-1~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 39 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~nd+1+nd16.04+1_all.deb Size: 9092 SHA256: 3768f288d0fb7988e227131b3e41a31c00436992b06d2b18e8113e0db08835c0 SHA1: 65c2eeeeb2ec075ec99aecec567a613b9ed2343c MD5sum: 4ff78d0c6fe0960cbf435512258152c0 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: ismrmrd-schema Source: ismrmrd Version: 1.3.2-1~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 25 Depends: neurodebian-popularity-contest Homepage: http://ismrmrd.github.io/ Priority: optional Section: science Filename: pool/main/i/ismrmrd/ismrmrd-schema_1.3.2-1~nd+1+nd16.04+1_all.deb Size: 5088 SHA256: 5336188e50c28a623d14ce0305ec77a4efef641cdef4c53c5e52157daf080def SHA1: 3ec074921fdde47d6c380e07226bacd940068f41 MD5sum: 349af0ebe53b5ed02810a3874940a35c 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~nd+1+nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 400 Depends: neurodebian-popularity-contest, ismrmrd-schema, libismrmrd1.3 (= 1.3.2-1~nd+1+nd16.04+1), libboost-program-options1.58.0, libc6 (>= 2.4), libfftw3-single3, libgcc1 (>= 1:4.0), libstdc++6 (>= 5.2) Homepage: http://ismrmrd.github.io/ Priority: optional Section: science Filename: pool/main/i/ismrmrd/ismrmrd-tools_1.3.2-1~nd+1+nd16.04+1_i386.deb Size: 126932 SHA256: fe1a5bb2bcd51a09980f77fc42e5d524b87f76258ba1c74ad83c989606b64ea2 SHA1: b5f96ee9ac1b15623d6c0cb8fb1ccd4964ccd2f0 MD5sum: 78a77bb541fdacd9951cdc841a435955 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: libcnrun2 Source: cnrun Version: 2.0.3-1~nd+1+nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 257 Depends: neurodebian-popularity-contest, libc6 (>= 2.8), libgcc1 (>= 1:3.0), libgsl2, libstdc++6 (>= 5.2), 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~nd+1+nd16.04+1_i386.deb Size: 79662 SHA256: 8307584781fc1f14179b8bb0183c6e9c5a279120a7411aff373e884175ca96c6 SHA1: 112ddd39bc3f8a506f21f4a1a6a2795a4e1f0b84 MD5sum: 04b48afa51f449d9516fc7409b1a8068 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~nd+1+nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 137 Depends: neurodebian-popularity-contest, libcnrun2 (= 2.0.3-1~nd+1+nd16.04+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~nd+1+nd16.04+1_i386.deb Size: 21288 SHA256: 346533d9195e7bb4dd2540c3392ef1c3221291c6334ba3f58bc6a8df4d908902 SHA1: 9a2ac0102af29550a0d5716b25e08ce55f07c560 MD5sum: 7b353912745a6b0c847915e6909f0cf8 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: libdrawtk-dev Source: drawtk Version: 2.0-2~nd+1+nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 111 Depends: neurodebian-popularity-contest, libdrawtk0 (= 2.0-2~nd+1+nd16.04+1) Multi-Arch: same Homepage: http://cnbi.epfl.ch/software/drawtk.html Priority: extra Section: libdevel Filename: pool/main/d/drawtk/libdrawtk-dev_2.0-2~nd+1+nd16.04+1_i386.deb Size: 40844 SHA256: ab507cf52c7ba253e7d1b237ce70f3a9f0c13151508d5349d3832f6be3dd8c92 SHA1: a88012ac8f72f2993bc7290a8f0d8a73f05d8ce7 MD5sum: cb719ea3b2efbde0ea4a6f59bba05604 Description: Library to simple and efficient 2D drawings (development files) This package provides an C library to perform efficient 2D drawings. The drawing is done by OpenGL allowing fast and nice rendering of basic shapes, text, images and videos. It has been implemented as a thin layer that hides the complexity of the OpenGL library. . This package contains the files needed to compile and link programs which use drawtk. Package: libdrawtk0 Source: drawtk Version: 2.0-2~nd+1+nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 76 Depends: neurodebian-popularity-contest, libc6 (>= 2.17), libfontconfig1 (>= 2.11.94), libfreeimage3, libfreetype6 (>= 2.2.1), libgl1-mesa-glx | libgl1, libglib2.0-0 (>= 2.12.0), libgstreamer-plugins-base0.10-0 (>= 0.10.23), libgstreamer0.10-0 (>= 0.10.25), libsdl1.2debian (>= 1.2.11) Multi-Arch: same Homepage: http://cnbi.epfl.ch/software/drawtk.html Priority: extra Section: libs Filename: pool/main/d/drawtk/libdrawtk0_2.0-2~nd+1+nd16.04+1_i386.deb Size: 23784 SHA256: 9b9250169ad37f99ab5e0b108a3216f42eeaab97ce34ebbda49d42c195c198e1 SHA1: 3dec4b445fec15078cb2f306100b5f26d1b98632 MD5sum: 0404e7543471ba79bc8d07d003aa57c7 Description: Library to simple and efficient 2D drawings This package provides an C library to perform efficient 2D drawings. The drawing is done by OpenGL allowing fast and nice rendering of basic shapes, text, images and videos. It has been implemented as a thin layer that hides the complexity of the OpenGL library. Package: libdrawtk0-dbg Source: drawtk Version: 2.0-2~nd+1+nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 86 Depends: neurodebian-popularity-contest, libdrawtk0 (= 2.0-2~nd+1+nd16.04+1) Multi-Arch: same Homepage: http://cnbi.epfl.ch/software/drawtk.html Priority: extra Section: debug Filename: pool/main/d/drawtk/libdrawtk0-dbg_2.0-2~nd+1+nd16.04+1_i386.deb Size: 70360 SHA256: f050c1d671034ec5c3f4fa42ae6bc3250cbf24ffc7ce2003003a408e4ee8ad57 SHA1: 81e1e426e4488107140225fd037c7722348a70ed MD5sum: a06259ca15c8809cdda8b07583a2f12a Description: Library to simple and efficient 2D drawings (debugging symbols) This package provides an C library to perform efficient 2D drawings. The drawing is done by OpenGL allowing fast and nice rendering of basic shapes, text, images and videos. It has been implemented as a thin layer that hides the complexity of the OpenGL library. . This package provides the debugging symbols for the library. Build-Ids: c103a83e59b04eee0f72d82d1a4bee8aaf609c78 Package: libismrmrd-dev Source: ismrmrd Version: 1.3.2-1~nd+1+nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 67 Depends: neurodebian-popularity-contest, ismrmrd-schema, libismrmrd1.3 (= 1.3.2-1~nd+1+nd16.04+1) Suggests: libismrmrd-doc Homepage: http://ismrmrd.github.io/ Priority: optional Section: libdevel Filename: pool/main/i/ismrmrd/libismrmrd-dev_1.3.2-1~nd+1+nd16.04+1_i386.deb Size: 13858 SHA256: dcea28270e5a40f38043edec0361ec292f8375a786daf1fb2a45089e612de2b9 SHA1: 17d19a5c842f578a545edd7f0d79af3add7400ad MD5sum: 669d69c9af48b3884ee34c97d9a14af2 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~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1961 Depends: neurodebian-popularity-contest Homepage: http://ismrmrd.github.io/ Priority: optional Section: doc Filename: pool/main/i/ismrmrd/libismrmrd-doc_1.3.2-1~nd+1+nd16.04+1_all.deb Size: 157932 SHA256: 2f24afbb9b3936557b75c9f69827bad728cd057e3a34aac31466f47d948cb216 SHA1: accfe2ca334b0b97c931424db47de6e435fae5f7 MD5sum: f81b7692ba25e05413fe88a248cc60a4 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~nd+1+nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 348 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:3.0), libhdf5-10, libpugixml1v5 (>= 1.4), libstdc++6 (>= 5.2) Homepage: http://ismrmrd.github.io/ Priority: optional Section: science Filename: pool/main/i/ismrmrd/libismrmrd1.3_1.3.2-1~nd+1+nd16.04+1_i386.deb Size: 84820 SHA256: 16351d9ca61424eb182345452a905ba9a6daf576ef6e975dfd22bfe637c3818e SHA1: 8e6bf178067663c2b925285e4fada006956b0c08 MD5sum: 47b42af1be62067996d7603b7aa20c02 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: libvrpn-dev Source: vrpn Version: 07.30+dfsg-1~nd+1+nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 719 Depends: neurodebian-popularity-contest, libvrpn0 (= 07.30+dfsg-1~nd+1+nd16.04+1), libvrpnserver0 (= 07.30+dfsg-1~nd+1+nd16.04+1) Homepage: http://www.cs.unc.edu/Research/vrpn/ Priority: extra Section: libdevel Filename: pool/main/v/vrpn/libvrpn-dev_07.30+dfsg-1~nd+1+nd16.04+1_i386.deb Size: 141876 SHA256: 4cafee002201c1f182afa1b27a77dbebfef6462181f7ead535dc8c1faf2d1f51 SHA1: 8821c26453866aae7657ec2a3fd9ad45d3efd7ad MD5sum: 8d1516ffa614eac8dd24e40dd2600414 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~nd+1+nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 516 Depends: neurodebian-popularity-contest, libc6 (>= 2.15), libgcc1 (>= 1:3.0), 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~nd+1+nd16.04+1_i386.deb Size: 148488 SHA256: e79b75b9fc6fe573a616d4c9851d5020d8315ec722ccaefeacd84424803d0b9e SHA1: 2618892136aaa2c0331b8f9e560b5d3e5d334bfd MD5sum: c3a8a3bc78f801fd22ea969c36d205bc 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~nd+1+nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1189 Depends: neurodebian-popularity-contest, libc6 (>= 2.15), libgcc1 (>= 1:3.0), libstdc++6 (>= 5.2) Homepage: http://www.cs.unc.edu/Research/vrpn/ Priority: extra Section: libs Filename: pool/main/v/vrpn/libvrpnserver0_07.30+dfsg-1~nd+1+nd16.04+1_i386.deb Size: 337494 SHA256: acaa0ad372978a81b0acd4c97e50b537eb145dbc0c25c3addd1e3c816a54dc7d SHA1: 4885283e09e537515e77afa992c8c4078ff38612 MD5sum: eb7f192d1cc4080ff3cc97f8f71c6a8c 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~nd+1+nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 276 Depends: neurodebian-popularity-contest, libvtk-java, libc6 (>= 2.4), libgcc1 (>= 1:3.0), libstdc++6 (>= 4.1.1), libvtk-dicom0.5, libvtk5.10 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~nd+1+nd16.04+1_i386.deb Size: 73776 SHA256: f54af141b368592475dfc0b26e61792812e0e13ba4e8f559bce1693c11daf143 SHA1: dc653fb028a9b03920f19ad82a28c890efac2e6c MD5sum: 11be562bef7f81529eccf9c7f309897b 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~nd+1+nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1449 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.2), libgdcm2.6, libstdc++6 (>= 5.2), libvtk5.10, 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~nd+1+nd16.04+1_i386.deb Size: 431544 SHA256: e06d70a2078f2f31d0d7a9f04a595bda09ba7cf6099c7d92eee95b32a422cc55 SHA1: e0ea58fecfa30a15245a44e6f9777af492369ad8 MD5sum: 2b13af0fa2cf32257904482cd6b6018a 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~nd+1+nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 565 Depends: neurodebian-popularity-contest, libvtk-dicom0.5 (= 0.5.5-2~nd+1+nd16.04+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~nd+1+nd16.04+1_i386.deb Size: 80982 SHA256: cf0cfe94f2fea068d6f94721aac46744faa907a08d93cc0ac263a5af5260dd68 SHA1: 5ead5441cc59ea3872cff6ab97ab9848698421c2 MD5sum: 6c116100d42f34050048e5e2b2130e97 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~nd+1+nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 107 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~nd+1+nd16.04+1_i386.deb Size: 38220 SHA256: fe1c47db351badb555c25c3657f7f00dfb402907343ee3b3db09cfc8e96d1c25 SHA1: ebd5c65d56ad1137182928a60048a57527d0759a MD5sum: bdafb3988637791978ab72f8ef5f741d 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: mialmpick Version: 0.2.10-1~nd+1+nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 199 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgdk-pixbuf2.0-0 (>= 2.22.0), libgl1-mesa-glx | libgl1, libglade2-0 (>= 1:2.6.4-2~), libglib2.0-0 (>= 2.41.1), libglu1-mesa | libglu1, libgnomeui-0 (>= 2.22.0), libgtk2.0-0 (>= 2.20.0), libgtkglext1, libmialm3 (>= 1.0.7), libpng12-0 (>= 1.2.13-4), libpopt0 (>= 1.14), libvistaio14 (>= 1.2.14), libx11-6 Homepage: http://mia.sourceforge.net Priority: optional Section: science Filename: pool/main/m/mialmpick/mialmpick_0.2.10-1~nd+1+nd16.04+1_i386.deb Size: 71042 SHA256: 6f82fe2431d077ff80c383b1ad61c89b12106a7c2de05c84cd1b67b911ac3dee SHA1: 044943d05e433d8132e83d6ff379e2323e894557 MD5sum: 62f8a67e65ab139d7e71da1e0883f13d Description: Tools for landmark picking in 3D volume data sets This tool provides a simple 3D renderer that can visualize surfaces directly from 3D volumes and can be used to set 3D landmarks. It is best suited for CT data sets. Package: mialmpick-dbg Source: mialmpick Version: 0.2.10-1~nd+1+nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 171 Depends: neurodebian-popularity-contest, mialmpick (= 0.2.10-1~nd+1+nd16.04+1) Homepage: http://mia.sourceforge.net Priority: extra Section: debug Filename: pool/main/m/mialmpick/mialmpick-dbg_0.2.10-1~nd+1+nd16.04+1_i386.deb Size: 140578 SHA256: 58174408e8cc80e4edb466d413a53c28e1ce6f4902af36ee0beec0ea3194de4f SHA1: 52757e64d2d8c847bec8f99316355487f73d742b MD5sum: 9d80dc6754bcc21652fbb28b0fbac8bd Description: Debug information landmark picking tool mialmpick This tool provides a simple 3D renderer that can visualize surfaces directly from 3D volumes and can be used to set 3D landmarks. It is best suited for CT data sets. This package provides the debug information. Build-Ids: 79bbd18e4ebde8ef0917a9b9544f4cf8b16925bb Package: mridefacer Version: 0.2-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 647 Depends: neurodebian-popularity-contest, num-utils, fsl-5.0-core | fsl-core Homepage: https://github.com/hanke/mridefacer Priority: optional Section: science Filename: pool/main/m/mridefacer/mridefacer_0.2-1~nd16.04+1_all.deb Size: 637352 SHA256: 77d8497006f219516a5d682f4dcbc432cafef37779349576a55adc80daea7509 SHA1: f5e58b7c8b273455ab745b500c367c0747692c56 MD5sum: 11a7610ad8e894687feae0d8b5a6c618 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~nd+1+nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 62 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~nd+1+nd16.04+1_i386.deb Size: 30920 SHA256: 0e6bc391cea86953dfeed426e80e83894bf0a1bc7f5cbd772b9ea342969007c1 SHA1: 3ea2fb828f44ab169a9efa5579a415b4fb42644e MD5sum: de569043411de8db808339df5b4722b9 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~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 38 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~nd+1+nd16.04+1_all.deb Size: 16814 SHA256: 22e5cc29f87c08ceb667084f2af3810aaefd1b5fc0828b2f0b0e29cb2a4dc46e SHA1: 90467c75aaba4e464a72ac39884410acc60cfc49 MD5sum: 60f499833563a70a8dc586c029092380 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: 0.37.5~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 91 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_0.37.5~nd16.04+1_all.deb Size: 34692 SHA256: a782eeb34e4e6c636517778c4ff979c12e9a230fbcedb23c122e7002655b6451 SHA1: 9315d5405e9604f9b7d20f83240b5d9b4097c9c1 MD5sum: 918d375cf39b03b83676ed89631ea9dd 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: 0.37.5~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 23 Depends: gnupg2 | gnupg, dirmngr 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_0.37.5~nd16.04+1_all.deb Size: 10378 SHA256: 43d94888c0bbb446b9f2a2d796441b71067126e5152efa50ae52bf6f0d70b829 SHA1: d8c7b1b10beaf2c54d021faddc31b0db56812875 MD5sum: e0c8f57ee231061ca4f5cbe445371af8 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: 0.37.5~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 189 Depends: ssh-askpass-gnome | ssh-askpass, desktop-base, adwaita-icon-theme | gnome-icon-theme, neurodebian-popularity-contest Recommends: reportbug-ng Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-desktop_0.37.5~nd16.04+1_all.deb Size: 116402 SHA256: 338f1476f9040ead6f5c01c581e3a3b8ed847592f4fdd441d358f7a9188bcc79 SHA1: 3f92954e7fcb8dd6435b6a99da191290c314b689 MD5sum: c732398e4e41daae5a5a16883ef5c9d0 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: 0.37.5~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 109 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_0.37.5~nd16.04+1_all.deb Size: 32768 SHA256: b607a59ddd213529a6b7074dcb368280f9f0f04a19e42dab4cdb5cbc919f0d58 SHA1: a04beb5d390f0d731f625291fec9831e24fe4931 MD5sum: 7de749bf47072b733c9696f9e5110c3e 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: 0.37.5~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 27 Depends: popularity-contest Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-popularity-contest_0.37.5~nd16.04+1_all.deb Size: 12380 SHA256: 9206f3a429df0b39a73d49bea5cb2979721e3a00ab38ebe53e0cc08ffe5a0fef SHA1: da69b2b40f9dccd606151011bdf9680ac1fa9a8e MD5sum: c7ff04f1195fe0fe3d15bc072dce04f8 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: 0.5.22+ds-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2945 Depends: neurodebian-popularity-contest, g++-6 | g++-5 | 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 (>= 2.7.5-5~) 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_0.5.22+ds-1~nd16.04+1_all.deb Size: 616568 SHA256: 5264a836996f105cb4d63f51adb80ea525b9495acfe8ddca1c58d60920f6d575 SHA1: 169a049a8cab8471fe9bd390df1b5c069e97111f MD5sum: 46811ecb14d524ecf0dcca2cfa5338c7 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-psychtoolbox-3 Source: psychtoolbox-3 Version: 3.0.12.20160514.dfsg1-1~nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 3433 Depends: neurodebian-popularity-contest, octave (>= 3.4.3-1~), freeglut3, libasound2 (>= 1.0.16), libc6 (>= 2.7), libdc1394-22, libfreenect0.5 (>= 1:0.1.1), libgcc1 (>= 1:3.0), libgl1-mesa-glx | libgl1, libglew1.13 (>= 1.12.0), libglib2.0-0 (>= 2.12.0), libglu1-mesa | libglu1, libgstreamer-plugins-base1.0-0 (>= 1.0.0), libgstreamer1.0-0 (>= 1.4.0), liboctave3, 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 (= 3.0.12.20160514.dfsg1-1~nd16.04+1), psychtoolbox-3-lib (= 3.0.12.20160514.dfsg1-1~nd16.04+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_3.0.12.20160514.dfsg1-1~nd16.04+1_i386.deb Size: 729154 SHA256: 6d2cc21d4b2199afbb0a9a47afb61fb2a99e75843d4271f86f1c045d0e897b9d SHA1: 5c02d660364c3f84f6fbcdfe14dd0129f5422d7b MD5sum: 7558bc9a95cf7776487fbf60faa4f076 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: openstack-pkg-tools Version: 52~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 191 Depends: neurodebian-popularity-contest, autopkgtest, libxml-xpath-perl, madison-lite, pristine-tar Priority: extra Section: devel Filename: pool/main/o/openstack-pkg-tools/openstack-pkg-tools_52~nd16.04+1_all.deb Size: 52268 SHA256: ce9a5f309792b3a87d6f8c00259b993da4eae392c3cd59c2d63acb6640964cc5 SHA1: 9b2978d36e9fe7878444813081916d17fc5178d1 MD5sum: 421e6c2753576c0ad60b3b768f92362c Description: Tools and scripts for building Openstack packages in Debian This package contains some useful shell scripts and helpers for building the Openstack packages in Debian, including: . * shared code for maintainer scripts (.config, .postinst, ...). * init script templates to automatically generate init scripts for sysv-rc, systemd and upstart. * tools to build backports using sbuild and/or Jenkins based on gbp workflow. * utility to maintain git packaging (to be included in a debian/rules). . Even if this package is maintained in order to build OpenStack packages, it is of a general purpose, and it can be used for building any package. Package: p7zip Version: 16.02+dfsg-1~nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 924 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:4.2), libstdc++6 (>= 4.1.1) Suggests: p7zip-full Homepage: http://p7zip.sourceforge.net/ Priority: optional Section: utils Filename: pool/main/p/p7zip/p7zip_16.02+dfsg-1~nd16.04+1_i386.deb Size: 372310 SHA256: 18e167f0049e39df9291720ecd22cb5226b98a59da52527c7626234e8f3810a1 SHA1: 700459cb47abe49df6afdcbbbe7c8b4801084f3e MD5sum: 81c38ca4a550ecd224373a033cc5e9d7 Description: 7zr file archiver with high compression ratio p7zip is the Unix command-line port of 7-Zip, a file archiver that handles the 7z format which features very high compression ratios. . p7zip provides: - /usr/bin/7zr a standalone minimal version of the 7-zip tool that only handles 7z, LZMA and XZ archives. 7z compression is 30-50% better than ZIP compression. - /usr/bin/p7zip a gzip-like wrapper around 7zr. . p7zip can be used with popular compression interfaces (such as File Roller or Nautilus). . Another package, p7zip-full, provides 7z and 7za which support more compression formats. Package: p7zip-full Source: p7zip Version: 16.02+dfsg-1~nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 4510 Pre-Depends: dpkg (>= 1.17.13) Depends: neurodebian-popularity-contest, p7zip (= 16.02+dfsg-1~nd16.04+1), libc6 (>= 2.4), libgcc1 (>= 1:4.2), libstdc++6 (>= 4.1.1) Suggests: p7zip-rar Breaks: p7zip (<< 15.09+dfsg-3~) Replaces: p7zip (<< 15.09+dfsg-3~) Homepage: http://p7zip.sourceforge.net/ Priority: optional Section: utils Filename: pool/main/p/p7zip/p7zip-full_16.02+dfsg-1~nd16.04+1_i386.deb Size: 1350846 SHA256: 8dc9ee3e577b960cdaffc7777bc40d0c01e4e40fdbebfbcd04de33f6d6fb3b5a SHA1: a89d357fab39bcb7d269942ec830e87c7ad5893d MD5sum: 2973ccbc1f4acc8d2872524a55092cae Description: 7z and 7za file archivers with high compression ratio p7zip is the Unix command-line port of 7-Zip, a file archiver that handles the 7z format which features very high compression ratios. . p7zip-full provides utilities to pack and unpack 7z archives within a shell or using a GUI (such as Ark, File Roller or Nautilus). . Installing p7zip-full allows File Roller to use the very efficient 7z compression format for packing and unpacking files and directories. Additionally, it provides the 7z and 7za commands. . List of supported formats: - Packing / unpacking: 7z, ZIP, GZIP, BZIP2, XZ and TAR - Unpacking only: APM, ARJ, CAB, CHM, CPIO, CramFS, DEB, DMG, FAT, HFS, ISO, LZH, LZMA, LZMA2, MBR, MSI, MSLZ, NSIS, NTFS, RAR (only if non-free p7zip-rar package is installed), RPM, SquashFS, UDF, VHD, WIM, XAR and Z. . The dependent package, p7zip, provides 7zr, a light version of 7za, and p7zip, a gzip-like wrapper around 7zr. Package: prov-tools Source: python-prov Version: 1.4.0-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 24 Depends: neurodebian-popularity-contest, python3:any (>= 3.3~), python3-prov (= 1.4.0-1~nd16.04+1) Homepage: https://github.com/trungdong/prov Priority: optional Section: python Filename: pool/main/p/python-prov/prov-tools_1.4.0-1~nd16.04+1_all.deb Size: 6446 SHA256: bdbee846a60a80a04461221ce243e1d122e6487696020fee2bc8a9eb4daa20bf SHA1: d4ca2cf1ddc0c1064c702378baec913c7a0be031 MD5sum: 6d7dbfa568d26547c324f3bcff31e0d9 Description: tools for prov A library for W3C Provenance Data Model supporting PROV-JSON and PROV- XML import/export. . Features: - An implementation of the W3C PROV Data Model in Python. - In-memory classes for PROV assertions, which can then be output as PROV-N. - Serialization and deserializtion support: PROV-JSON and PROV-XML. - Exporting PROV documents into various graphical formats (e.g. PDF, PNG, SVG). . This package provides the command-line tools for the prov library. Package: psychopy Version: 1.83.04.dfsg-2~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 15088 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-opencv, python-imaging, python-serial, python-pyo, python-psutil, python-gevent, python-msgpack, python-yaml, python-xlib, python-pandas, libxxf86vm1, ipython Suggests: python-iolabs, python-pyxid, libavbin0 Conflicts: libavbin0 (= 7-4+b1) Homepage: http://www.psychopy.org Priority: optional Section: science Filename: pool/main/p/psychopy/psychopy_1.83.04.dfsg-2~nd+1+nd16.04+1_all.deb Size: 6133828 SHA256: 7bc5b24a1849eaf939d573157788037c0f1f6a6a54bc84c45b025780d09b207f SHA1: 60bb27e236f204f1f43c10b98e20170e50ac4f60 MD5sum: 269a0abc067dfa3a1d79234abf54295b 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: 3.0.12.20160514.dfsg1-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 253492 Depends: neurodebian-popularity-contest Recommends: alsa-utils Suggests: gnuplot Homepage: http://psychtoolbox.org Priority: extra Section: science Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-common_3.0.12.20160514.dfsg1-1~nd16.04+1_all.deb Size: 24206914 SHA256: 7de703988c4bc3fceaa3d42d246ec6d7df83a85cbcdcf3e40e660e90a109d03b SHA1: 8be50f23cd6155917c3971481150c211030f1fe8 MD5sum: 9ef1e9fa7bd98831ea0926609c441dfe 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: 3.0.12.20160514.dfsg1-1~nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 2766 Depends: neurodebian-popularity-contest, octave-psychtoolbox-3 (= 3.0.12.20160514.dfsg1-1~nd16.04+1) Homepage: http://psychtoolbox.org Priority: extra Section: debug Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-dbg_3.0.12.20160514.dfsg1-1~nd16.04+1_i386.deb Size: 593826 SHA256: a8d159d17727f7b1e90a1bd5d6e8ab1efab1645e98fec898228fc199df0499b5 SHA1: a6b47c1befd584b71de8503d44de7eb1cd99690f MD5sum: 119f6e25a3d76c35fa400d59978ce81c 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: 3.0.12.20160514.dfsg1-1~nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 180 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libfontconfig1 (>= 2.11.94), libfreetype6 (>= 2.2.1), libgcc1 (>= 1:3.0), 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_3.0.12.20160514.dfsg1-1~nd16.04+1_i386.deb Size: 74410 SHA256: d7439e411d470dc0cf40d8ae11c0472240415e3b2056e86620770edba5b0516b SHA1: 02bbafe83e48ed866e99579b406e31b3c5e77c80 MD5sum: 262426c6ec352fc58d0fb1f82cf619d8 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-argcomplete Version: 1.0.0-1~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 111 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~) Priority: optional Section: python Filename: pool/main/p/python-argcomplete/python-argcomplete_1.0.0-1~nd+1+nd16.04+1_all.deb Size: 24608 SHA256: 156b7dbfd8a99d398fe740c822ba51762781af630c9ce000cdebb5622fe4cd2a SHA1: 937a26dd82a75e4b14bb613ae8f6dd187ee9e10b MD5sum: 3f721e38ee6f5930587547ae87875163 Description: bash tab completion for argparse Argcomplete provides easy, extensible command line tab completion of arguments for your Python script. . It makes two assumptions: . * You're using bash as your shell * You're using argparse to manage your command line arguments/options . Argcomplete is particularly useful if your program has lots of options or subparsers, and if your program can dynamically suggest completions for your argument/option values (for example, if the user is browsing resources over the network). . This package provides the module for Python 2.x. Package: python-boto3 Version: 1.2.2-2~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 720 Depends: neurodebian-popularity-contest, python-botocore, python-concurrent.futures, python-jmespath, python:any (<< 2.8), python:any (>= 2.7.5-5~), python-requests, python-six Homepage: https://github.com/boto/boto3 Priority: optional Section: python Filename: pool/main/p/python-boto3/python-boto3_1.2.2-2~nd16.04+1_all.deb Size: 58388 SHA256: 7b073c2e5bedfe1fb511389140c06e6bc7d763b8506c840f4bfeff826468834f SHA1: d557ed14ac3dcfc1d3e3185437b8a28c852a6442 MD5sum: 07ba3320ddb3a4001a9efaf1405a73f5 Description: Python interface to Amazon's Web Services - Python 2.x Boto is the Amazon Web Services interface for Python. It allows developers to write software that makes use of Amazon services like S3 and EC2. Boto provides an easy to use, object-oriented API as well as low-level direct service access. Package: python-datalad Source: datalad Version: 0.3-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2925 Depends: neurodebian-popularity-contest, git-annex (>= 6.20160808~) | git-annex-standalone (>= 6.20160808~), patool, python-appdirs, python-git (>= 2.0.3~), python-humanize, python-iso8601, python-keyrings.alt | python-keyring (<= 8), python-keyring, python-mock, python-msgpack, python-pyld, python-requests, python-tqdm, python-simplejson, python-six (>= 1.8.0), python-boto, python-jsmin, python:any (<< 2.8), python:any (>= 2.7.5-5~) Recommends: python-html5lib, python-httpretty, python-nose, python-numpy, python-requests-ftp, python-scrapy, python-vcr, python-yaml Suggests: python-bs4 Provides: python2.7-datalad Homepage: http://datalad.org Priority: optional Section: python Filename: pool/main/d/datalad/python-datalad_0.3-1~nd16.04+1_all.deb Size: 569372 SHA256: d93d5ff4a6f364e73344894b56477f8ee6fd325086e3815a5039de6b1cba36c6 SHA1: 687e381ae0f8ee7cf4e0e97a48347d7695197963 MD5sum: 250ee73caa2d5eac83883ed7e1678918 Description: data files crawler and data distribution (Python 2) DataLad is a data distribution providing access to a wide range of data resources already available online (initially aiming at neuroscience domain). Using git-annex as its backend for data logistics it provides following facilities . - crawling of web sites to automatically prepare and update git-annex repositories with content from online websites, S3, etc - command line interface for manipulation of collections of datasets (install, uninstall, update, publish, save, etc.) and separate files/directories (add, get), as well as search within aggregated meta-data . This package installs the module for Python 2, and Recommends install all dependencies necessary for crawling, publishing, and testing. If you need base functionality, install without Recommends. Package: python-dcmstack Source: dcmstack Version: 0.6.2+git33-gb43919a.1-1~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 505 Depends: neurodebian-popularity-contest, python-dicom (>= 0.9.7~), python-nibabel (>= 2.0~), python-numpy, 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~nd+1+nd16.04+1_all.deb Size: 77560 SHA256: fb442364f1761b5203c259ef22654f0f4bb883a8eac5dba645ca88b0ec327125 SHA1: 809e0e779921d515682e8f3b543df9321b89650c MD5sum: 307b067807c7a48930ebcc46f0121e7a 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-duecredit Source: duecredit Version: 0.5.0-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 234 Depends: neurodebian-popularity-contest, python-citeproc, python-requests, python-six, python:any (<< 2.8), python:any (>= 2.7.5-5~) Homepage: https://github.com/duecredit/duecredit Priority: optional Section: python Filename: pool/main/d/duecredit/python-duecredit_0.5.0-1~nd16.04+1_all.deb Size: 49836 SHA256: 8f6b727151ec14685e5ec685c741f6a80c6e392914e444d2c8c625be85b00468 SHA1: 7f3ad805ef2aa096fd0348549186326edd60215b MD5sum: af07180f2cd5a642d3a14d34cc7fc61f Description: Publications (and donations) tracer - Python 2.X duecredit is being conceived to address the problem of inadequate citation of scientific software and methods, and limited visibility of donation requests for open-source software. . It provides a simple framework (at the moment for Python only) to embed publication or other references in the original code so they are automatically collected and reported to the user at the necessary level of reference detail, i.e. only references for actually used functionality will be presented back if software provides multiple citeable implementations. . To get a sense of what duecredit is about, simply run or your analysis script with `-m duecredit`, e.g. . python -m duecredit examples/example_scipy.py Python-Egg-Name: duecredit Package: python-funcsigs Version: 0.4-2~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 63 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~) Suggests: python-funcsigs-doc Homepage: http://funcsigs.readthedocs.org Priority: optional Section: python Filename: pool/main/p/python-funcsigs/python-funcsigs_0.4-2~nd16.04+1_all.deb Size: 12746 SHA256: aa1594275088c19766b1ff28be4990783ecc4aad4b170a9a15fdab129d82765e SHA1: 7e6d04894fa13b3be7c6498bcc832877611b799b MD5sum: 2c202fea13d1545151b20b6b94dbd917 Description: function signatures from PEP362 - Python 2.7 funcsigs is a backport of the PEP 362 function signature features from Python 3.3's inspect module. The backport is compatible with Python 2.6, 2.7 as well as 3.2 and up. . This package contains the Python 2.7 module. Package: python-funcsigs-doc Source: python-funcsigs Version: 0.4-2~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 121 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Homepage: http://funcsigs.readthedocs.org Priority: optional Section: doc Filename: pool/main/p/python-funcsigs/python-funcsigs-doc_0.4-2~nd16.04+1_all.deb Size: 23504 SHA256: 0faa043d8d03c5cec5db82b53c9aa38070bf5468f05d153f2052a4a25698ca67 SHA1: 964ef9081f948d5efd2bffb9ad20f032b7ea69ba MD5sum: 49d0850dc20522e3d5f6fa56aa05c6ae Description: function signatures from PEP362 - doc funcsigs is a backport of the PEP 362 function signature features from Python 3.3's inspect module. The backport is compatible with Python 2.6, 2.7 as well as 3.2 and up. . This package contains the documentation. Package: python-git Version: 2.0.8-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1585 Depends: neurodebian-popularity-contest, git (>= 1:1.7) | git-core (>= 1:1.5.3.7), python-gitdb (>= 0.6.4), python:any (<< 2.8), python:any (>= 2.7.5-5~) Suggests: python-smmap, python-git-doc Homepage: https://github.com/gitpython-developers/GitPython Priority: optional Section: python Filename: pool/main/p/python-git/python-git_2.0.8-1~nd16.04+1_all.deb Size: 291704 SHA256: 6e875280f56bfe59663ccb647b1280eaf76bf625d8a0a5db27ac7cf7ada222a3 SHA1: 5cf6c16b45d20874388e7154a2e4819658101dbc MD5sum: a4e4f40541710db667ace57376f10132 Description: Python library to interact with Git repositories - Python 2.7 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. . This package provides the Python 2.7 module. Python-Version: 2.7 Package: python-git-doc Source: python-git Version: 2.0.8-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 951 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Homepage: https://github.com/gitpython-developers/GitPython Priority: optional Section: doc Filename: pool/main/p/python-git/python-git-doc_2.0.8-1~nd16.04+1_all.deb Size: 121648 SHA256: e858d75e557c2ffcf1f15864e3b38845a41bd0a6d8178d362d9d39b3dc683f4b SHA1: 255211244bd87b39ac2a0453936dc8e66b7a8d5c MD5sum: eabcecfb34291c17ea4bf349dbcbca69 Description: Python library to interact with Git repositories - docs 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. . This package provides the documentation. Package: python-joblib Source: joblib Version: 0.10.2-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 492 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_0.10.2-1~nd16.04+1_all.deb Size: 114540 SHA256: f0a9fa6afc7f7fa18d5607a26b1e8e15c7225bfffc2b72da0a14f2f3fc4226f6 SHA1: ecbabfcc62b150015dc8aacd5b5bf0f0c12d36d8 MD5sum: c25d627e10dad3db296292131f7bc59f 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-jsmin Version: 2.2.1-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 68 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~) Homepage: https://github.com/tikitu/jsmin Priority: optional Section: python Filename: pool/main/p/python-jsmin/python-jsmin_2.2.1-1~nd16.04+1_all.deb Size: 21544 SHA256: 0246adb0afaf4dbbe0ce01017668a5d702a13ffcd2b07f05cdfb3613ceef5fa2 SHA1: 9e01dde8a81ad77de521112cb737bc8962975904 MD5sum: a855dba74b7e95f5a8f60b6e1ebaf677 Description: JavaScript minifier written in Python - Python 2.x Python-jsmin is a JavaScript minifier, it is written in pure Python and actively maintained. . This package provides the Python 2.x module. Package: python-lda Source: lda Version: 1.0.2-9~nd+1+nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1319 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), python-numpy Homepage: https://pythonhosted.org/lda/ Priority: optional Section: python Filename: pool/main/l/lda/python-lda_1.0.2-9~nd+1+nd16.04+1_i386.deb Size: 239644 SHA256: daf3122845169280e8e30633326244a8b5ef2c9d1e0fb5199c76abd3cf75c0ae SHA1: fa34f456c580b97672e21a1e27083c52d439a151 MD5sum: 936f2ff623a4f4ef8defe59f53a00e37 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: 0.12+dfsg-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9400 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, python-pycuda, ipython Provides: python2.7-mne Homepage: http://martinos.org/mne Priority: optional Section: python Filename: pool/main/p/python-mne/python-mne_0.12+dfsg-1~nd16.04+1_all.deb Size: 4429294 SHA256: c47f48baf80955067036ff7bf38ea74cba7d331dc1d2190eab733bddb5ce1cb9 SHA1: 88551144c408775c27da88fbc52421344da97bd3 MD5sum: 0f008fb18cc33ad9c47443a2e68e9ea3 Description: Python modules for MEG and EEG data analysis This package is designed for sensor- and source-space analysis of MEG and EEG data, including frequency-domain and time-frequency analyses and non-parametric statistics. Package: python-mpi4py Source: mpi4py Version: 2.0.0-1~nd+1+nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1560 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libopenmpi1.10, python (>= 2.7), python (<< 2.8), mpi-default-bin Suggests: python-numpy Homepage: http://code.google.com/p/mpi4py/ Priority: extra Section: python Filename: pool/main/m/mpi4py/python-mpi4py_2.0.0-1~nd+1+nd16.04+1_i386.deb Size: 326584 SHA256: bb3b907c15968ed6ce3e17e553c6ae4f040e442449438d0d2df99278dd3bda49 SHA1: a6a2a895f4dd8804ddaf0fedbd4c0b730cab41dd MD5sum: a4e4ac735c9934d73ea6f2cb31be8979 Description: bindings of the Message Passing Interface (MPI) standard MPI for Python (mpi4py) provides bindings of the Message Passing Interface (MPI) standard for the Python programming language, allowing any Python program to exploit multiple processors. . mpi4py is constructed on top of the MPI-1/MPI-2 specification and provides an object oriented interface which closely follows MPI-2 C++ bindings. It supports point-to-point (sends, receives) and collective (broadcasts, scatters, gathers) communications of any picklable Python object as well as optimized communications of Python object exposing the single-segment buffer interface (NumPy arrays, builtin bytes/string/array objects). Package: python-mpi4py-dbg Source: mpi4py Version: 2.0.0-1~nd+1+nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 3596 Depends: neurodebian-popularity-contest, python-mpi4py (= 2.0.0-1~nd+1+nd16.04+1) Homepage: http://code.google.com/p/mpi4py/ Priority: extra Section: debug Filename: pool/main/m/mpi4py/python-mpi4py-dbg_2.0.0-1~nd+1+nd16.04+1_i386.deb Size: 931836 SHA256: f49d56acb738fdd1f0d3f1a1da03d8444c30af43bc179292578e3a4572e32f2e SHA1: 6bfc08b16ce9dbe1307c5cdd690bc916f5180979 MD5sum: bf2a7759882982b2d9e9c72ed6c81dd9 Description: bindings of the MPI standard -- debug symbols MPI for Python (mpi4py) provides bindings of the Message Passing Interface (MPI) standard for the Python programming language, allowing any Python program to exploit multiple processors. . mpi4py is constructed on top of the MPI-1/MPI-2 specification and provides an object oriented interface which closely follows MPI-2 C++ bindings. It supports point-to-point (sends, receives) and collective (broadcasts, scatters, gathers) communications of any picklable Python object as well as optimized communications of Python object exposing the single-segment buffer interface (NumPy arrays, builtin bytes/string/array objects). . This package provides debug symbols. Package: python-mvpa2 Source: pymvpa2 Version: 2.6.0-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8535 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python-numpy, python-mvpa2-lib (>= 2.6.0-1~nd16.04+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, python-duecredit 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_2.6.0-1~nd16.04+1_all.deb Size: 5096012 SHA256: e9ae674abfd1f5928fa03a2c886a6260fed5a595f770c3e30161bd8e017bab9d SHA1: ca01cfecb96da2a10e093cca483ea770aa921e80 MD5sum: fffd32508252d0d3c7d244461fbe5bee 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: 2.6.0-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 36117 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_2.6.0-1~nd16.04+1_all.deb Size: 4639730 SHA256: 083163aec79f87f85e68030d79f538f8467cd1417268a67e8f7f5bf1392436e3 SHA1: 291eaa2435bca16ae6a1b63840a9ee63104e0702 MD5sum: eea2f554f0ead75807f559354e4c6b1a 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: 2.6.0-1~nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 137 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:3.0), libstdc++6 (>= 4.1.1), libsvm3, python (<< 2.8), python (>= 2.7), python-numpy (>= 1:1.10.0~b1), 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_2.6.0-1~nd16.04+1_i386.deb Size: 51272 SHA256: 341fbfbeb747cfa00089d305b367bc278fe9f2265b04b3a5b3728bc678cfc262 SHA1: e63591a50d57c53d994781becb2793a673419ebb MD5sum: 3a25713cd05fe89ea3241f0bfbd16efd 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-neurosynth Source: neurosynth Version: 0.3-1~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 115 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy, python-scipy, python-nibabel, python-ply Recommends: python-nose, fsl-mni152-templates Suggests: python-testkraut Homepage: http://neurosynth.org Priority: extra Section: python Filename: pool/main/n/neurosynth/python-neurosynth_0.3-1~nd+1+nd16.04+1_all.deb Size: 28850 SHA256: 35e58bca96796988f7c3097c71ba0e078b6063215ce782dfb2461f30da939f7a SHA1: 37df2d9b9330ef7948d4bb86173b30a9eccaa0ca MD5sum: 6fbc209e0a5e48cd60f07cdbfc7aba56 Description: large-scale synthesis of functional neuroimaging data NeuroSynth is a platform for large-scale, automated synthesis of functional magnetic resonance imaging (fMRI) data extracted from published articles. This Python module at the moment provides functionality for processing the database of collected terms and spatial coordinates to generate associated spatial statistical maps. Package: python-nibabel Source: nibabel Version: 2.1.0-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 64211 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy, python-scipy Recommends: python-dicom, python-fuse, python-mock Suggests: python-nibabel-doc Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: python Filename: pool/main/n/nibabel/python-nibabel_2.1.0-1~nd16.04+1_all.deb Size: 2161982 SHA256: e1c42772ae8c2c60f721d9002f9be42cc734ffdd2a0af7982ff72286cb0ccef3 SHA1: b3b4f34c4995b562befdf801cede8ca5e39cd4ac MD5sum: d7ec21b39d1014be8930de0cbdd8ae9f 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: 2.1.0-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 20346 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_2.1.0-1~nd16.04+1_all.deb Size: 2686026 SHA256: 6f0f3b598a2f8d45f9ef7266c1644d5fc094b7f6f3222bbc6810d6ceb90b6209 SHA1: bd2001cf891ed8b707ea486ef3f1ecb1756a8e04 MD5sum: 3943c2901476d4803c7916457420dd2d 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: 0.2.5~dfsg.1-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2422 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_0.2.5~dfsg.1-1~nd16.04+1_all.deb Size: 731264 SHA256: 556fa551b0a378975a60af21259067980996ec5baef83b02c1c10d8ad461f800 SHA1: e6db7f5f06afd7c6986c6b9d7756cfb63ac3fae0 MD5sum: 604a86c38f7b85a5c13f8f103b4a8d63 Description: fast and easy statistical learning on neuroimaging data (Python 2) This Python module leverages the scikit-learn toolbox for multivariate statistics with applications such as predictive modelling, classification, decoding, or connectivity analysis. . This package provides the Python 2 version. Python-Version: 2.7 Package: python-nipy Source: nipy Version: 0.4.0+git26-gf8d3149-2~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3277 Depends: neurodebian-popularity-contest, python-numpy (>= 1:1.2), python (>= 2.7), python (<< 2.8), python-scipy, python-nibabel, python-nipy-lib (>= 0.4.0+git26-gf8d3149-2~nd16.04+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_0.4.0+git26-gf8d3149-2~nd16.04+1_all.deb Size: 738188 SHA256: 5055e9ccf0463f9edc78aec5246bd68ef7bf8395b02f31df1726997a3df0a320 SHA1: 91ed2b61cb376ac857927a25816ebe01e92b37a6 MD5sum: b9c693770043bde500570a9e4ea5f280 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: 0.4.0+git26-gf8d3149-2~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 10710 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_0.4.0+git26-gf8d3149-2~nd16.04+1_all.deb Size: 2649996 SHA256: 7996ac0e44000d3b24df81a48a9daf9e69197da02d5086d15501641f3b6c6965 SHA1: cb1976b8dd55b0763b9decb0cd7f2f3237b6a9ef MD5sum: d5e5ce94783fd8a0cda52318f12019bc 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: 0.4.0+git26-gf8d3149-2~nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 2726 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), python-numpy (>= 1:1.10.0~b1), 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_0.4.0+git26-gf8d3149-2~nd16.04+1_i386.deb Size: 572114 SHA256: 17cb54310e3faab20627a8647eec0c796711f1e713e2e0c504228a380a166d58 SHA1: afc4b51687051a87d0028055b901abe3ec9cb30f MD5sum: 9a48cd8f4ee1d94f1fbf93d67b89d790 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: 0.4.0+git26-gf8d3149-2~nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 3157 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), python-numpy (>= 1:1.10.0~b1), python-numpy-abi9, python-dbg (>= 2.7), python-dbg (<< 2.8), python-nipy-lib (= 0.4.0+git26-gf8d3149-2~nd16.04+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_0.4.0+git26-gf8d3149-2~nd16.04+1_i386.deb Size: 446934 SHA256: b1871a4cab09b86e100f97404b293a339c996c6d0b4d1023c8aa9c3beb158997 SHA1: 7d72e41a7107913e06780edf6581068bbd0a4758 MD5sum: 81c61029f3d6736200e2d090d743609f 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: 0.12.1+git4-gbc3a0b5-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9364 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, python-funcsigs, python-future, python-prov, python-psutil Recommends: ipython, python-nose, graphviz, python-xvfbwrapper, mayavi2, python-mock 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_0.12.1+git4-gbc3a0b5-1~nd16.04+1_all.deb Size: 1534790 SHA256: d6d24f37e41d190609c5ab1d72c262ae412c6a41ec3e85160a0fec6e1705709d SHA1: 7648c0b0acf90c6e78db46d33f8690733d1d809d MD5sum: 2ae984321bf33797a772dc1c962b9d5d 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: 0.12.1+git4-gbc3a0b5-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 30429 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_0.12.1+git4-gbc3a0b5-1~nd16.04+1_all.deb Size: 12830732 SHA256: 3829a209775209162000cb503489a73e85eb6c8a9170db3dde0e6e933856da9d SHA1: cbeb8ee31ae76ee8ca5b1784c4e150a336ae35ae MD5sum: fa1d56284d197454a4567cfdb52bbcb4 Description: Neuroimaging data analysis pipelines in Python -- documentation Nipype interfaces Python to other neuroimaging packages and creates an API for specifying a full analysis pipeline in Python. Currently, it has interfaces for SPM, FSL, AFNI, Freesurfer, but could be extended for other packages (such as lipsia). . This package contains Nipype examples and documentation in various formats. Package: python-nitime Source: nitime Version: 0.6+git15-g4951606-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9379 Depends: neurodebian-popularity-contest, python-matplotlib, python-numpy, python-scipy, python:any (<< 2.8), python:any (>= 2.7.5-5~) Recommends: python-nose, python-nibabel, python-networkx Homepage: http://nipy.org/nitime Priority: extra Section: python Filename: pool/main/n/nitime/python-nitime_0.6+git15-g4951606-1~nd16.04+1_all.deb Size: 2561756 SHA256: 7f012618442886ea647bebc2aac5c82d8625a0ed26994c75faf107b1988b076b SHA1: 4b5b8dd71f38879a0c275a7a7904ac62d8feb4cf MD5sum: 0e7dd979ffa6fd12bc9de38fa27dd523 Description: timeseries analysis for neuroscience data (nitime) Nitime is a Python module for time-series analysis of data from neuroscience experiments. It contains a core of numerical algorithms for time-series analysis both in the time and spectral domains, a set of container objects to represent time-series, and auxiliary objects that expose a high level interface to the numerical machinery and make common analysis tasks easy to express with compact and semantically clear code. Package: python-nitime-doc Source: nitime Version: 0.6+git15-g4951606-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7886 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Suggests: python-nitime Homepage: http://nipy.org/nitime Priority: extra Section: doc Filename: pool/main/n/nitime/python-nitime-doc_0.6+git15-g4951606-1~nd16.04+1_all.deb Size: 5786798 SHA256: 3fda61355e6ad37f5c0e0623a9bb626ce4a89758572ae9901680ed42aa66c36e SHA1: b6f90982dab3879568ac0f1c336f672820f65d41 MD5sum: abfa0c95a1941d1a066b418511e0b1c7 Description: timeseries analysis for neuroscience data (nitime) -- documentation Nitime is a Python module for time-series analysis of data from neuroscience experiments. . This package provides the documentation in HTML format. Package: python-openpyxl Source: openpyxl Version: 2.3.0-2~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1323 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-jdcal, python-lxml (>= 3.3.4) | python-et-xmlfile Recommends: python-pytest, python-pil, python-imaging Homepage: http://bitbucket.org/openpyxl/openpyxl/ Priority: optional Section: python Filename: pool/main/o/openpyxl/python-openpyxl_2.3.0-2~nd16.04+1_all.deb Size: 199220 SHA256: b70affd1599e94a0a0930fb4497a75e7466ed0e7b4f1e4ad1be006c6327e75b2 SHA1: bc62307b04fb297c5e98a1e2a55ff2339524aae8 MD5sum: 90c38c6c2346aab02e8a77c47243a274 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: 0.18.1-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 24213 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-dateutil, python-tz, python-numpy (>= 1:1.7~), python-pandas-lib (>= 0.18.1-1~nd16.04+1), python-pkg-resources, 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_0.18.1-1~nd16.04+1_all.deb Size: 2524450 SHA256: d06ba376921415206abbc86af56c6890fd65175a97750151f06c74de0be914d7 SHA1: a02e1e084db886727461f6321602a7ef477441d6 MD5sum: 57216dca4b6fbc858fd3a30f02ee2ba0 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: 0.18.1-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 57093 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_0.18.1-1~nd16.04+1_all.deb Size: 10956556 SHA256: a9b62ffe0e5b98a50fcab2fcff789648af728e92469506bc63b76cc7b6ea55be SHA1: 67cbc1cee9fa4cf338ac1932576b5b1b40f467da MD5sum: 61ff1479e902d9b2f8c6b048b275c29c Description: documentation and examples for pandas This package contains documentation and example scripts for python-pandas. Package: python-pandas-lib Source: pandas Version: 0.18.1-1~nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 6749 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), python-numpy (>= 1:1.10.0~b1), 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_0.18.1-1~nd16.04+1_i386.deb Size: 1564196 SHA256: 66f1fb21d41b7c2ab42c0ed1be1e8d2f6951c4d5fa3a9c1b94e183389ed8e3a8 SHA1: 985a9ad6007424450691fbfa4015c25f27960206 MD5sum: 583ad5f9e437bed58b69205b4f589ab2 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-prov Version: 1.4.0-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1235 Depends: neurodebian-popularity-contest, python-dateutil, python-lxml, python-networkx, python-six (>= 1.9.0), python:any (<< 2.8), python:any (>= 2.7.5-5~) Suggests: python-prov-doc, python-pydotplus Homepage: https://github.com/trungdong/prov Priority: optional Section: python Filename: pool/main/p/python-prov/python-prov_1.4.0-1~nd16.04+1_all.deb Size: 72412 SHA256: 3e144e0a0760856a48b24d284fd4d6108a499f63cd99d83bcc09ea17613c93d8 SHA1: 6340eb932f0fdfa50fabb48723db8f775d781cbf MD5sum: 8d6d9dfb535595c3d219c87e24bc4bd3 Description: W3C Provenance Data Model (Python 2) A library for W3C Provenance Data Model supporting PROV-JSON and PROV- XML import/export. . Features: - An implementation of the W3C PROV Data Model in Python. - In-memory classes for PROV assertions, which can then be output as PROV-N. - Serialization and deserializtion support: PROV-JSON and PROV-XML. - Exporting PROV documents into various graphical formats (e.g. PDF, PNG, SVG). . This package provides the prov library for Python 2. Package: python-prov-doc Source: python-prov Version: 1.4.0-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 816 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Homepage: https://github.com/trungdong/prov Priority: optional Section: doc Filename: pool/main/p/python-prov/python-prov-doc_1.4.0-1~nd16.04+1_all.deb Size: 69138 SHA256: 046640dd0ce708f60592fe7049baaac44b3f4e9a5de273733d29e6359cceccfb SHA1: 44692dd8ccb838fda977b247aeb22965be531459 MD5sum: 0b63a3dbece70949384995f2644cf462 Description: documentation for prov A library for W3C Provenance Data Model supporting PROV-JSON and PROV- XML import/export. . Features: - An implementation of the W3C PROV Data Model in Python. - In-memory classes for PROV assertions, which can then be output as PROV-N. - Serialization and deserializtion support: PROV-JSON and PROV-XML. - Exporting PROV documents into various graphical formats (e.g. PDF, PNG, SVG). . This package provides the documentation for the prov library. Package: python-pydotplus Version: 2.0.2-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 104 Depends: neurodebian-popularity-contest, graphviz, python-pyparsing (>= 2.0.1), python:any (<< 2.8), python:any (>= 2.7.5-5~) Suggests: python-pydotplus-doc Homepage: http://pydotplus.readthedocs.org/ Priority: optional Section: python Filename: pool/main/p/python-pydotplus/python-pydotplus_2.0.2-1~nd16.04+1_all.deb Size: 20240 SHA256: 2f2cbd16ebdf9303b3f63c0dabc1d0cb2eae3d15bb106c9cf7909b260c9560b4 SHA1: fd723ed2ffdf2a961a70954a6dbdf77c3cdea09c MD5sum: 7d94224fbe37b88a456bc0034ce397b2 Description: interface to Graphviz's Dot language - Python 2.7 PyDotPlus is an improved version of the old pydot project that provides a Python Interface to Graphviz's Dot language. . Differences with pydot: * Compatible with PyParsing 2.0+. * Python 2.7 - Python 3 compatible. * Well documented. * CI Tested. . This package contains the Python 2.7 module. Package: python-pydotplus-doc Source: python-pydotplus Version: 2.0.2-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 525 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0), sphinx-rtd-theme-common Homepage: http://pydotplus.readthedocs.org/ Priority: optional Section: doc Filename: pool/main/p/python-pydotplus/python-pydotplus-doc_2.0.2-1~nd16.04+1_all.deb Size: 46688 SHA256: 084d0a9e4882149dbbef0d18600ebc885f8cc725aa2aa72bca43a8d3ce414f76 SHA1: 1eed1e9afc09ba215f373b5a58ca248d95693529 MD5sum: 0b6ff044fc8b77c4f2fb1e7b09f0bb6f Description: interface to Graphviz's Dot language - doc PyDotPlus is an improved version of the old pydot project that provides a Python Interface to Graphviz's Dot language. . Differences with pydot: * Compatible with PyParsing 2.0+. * Python 2.7 - Python 3 compatible. * Well documented. * CI Tested. . This package contains the documentation. Package: python-pyepl Source: pyepl Version: 1.1.0+git12-g365f8e3-2~nd+1+nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1444 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-pyepl-common (= 1.1.0+git12-g365f8e3-2~nd+1+nd16.04+1), python-numpy, python-imaging, python-pygame, python-pyode, python-opengl, ttf-dejavu, libc6 (>= 2.4), libgcc1 (>= 1:3.0), libode4, libsamplerate0 (>= 0.1.7), libsndfile1 (>= 1.0.20), libstdc++6 (>= 5.2) 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~nd+1+nd16.04+1_i386.deb Size: 278894 SHA256: 923ebba439458ed2c1ee0bf80d5ed1851bd98b9e63752ea6eb07a2af3f30d151 SHA1: af2184490807cf11c558e341d951ef0015c64336 MD5sum: 22b36e8ca2235e328bb2f21863d1fd75 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~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 822 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~nd+1+nd16.04+1_all.deb Size: 819350 SHA256: 2e955547503f670039815376e4ac8860679cb376009788bc07c133ad3cf0dc02 SHA1: 349b67ae265dd0cbb576d6f2cc991cb1b9582a46 MD5sum: d7b4b656eb6f9dacf1c258cfa9df35bb 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-pygraphviz Version: 1.3.1-1~nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 390 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7~), python:any (>= 2.7.5-5~), libc6 (>= 2.4), libcgraph6, graphviz (>= 2.16) Suggests: python-pygraphviz-doc Homepage: https://pygraphviz.github.io/ Priority: optional Section: python Filename: pool/main/p/python-pygraphviz/python-pygraphviz_1.3.1-1~nd16.04+1_i386.deb Size: 74872 SHA256: 541730ea83e282ea8cddaa7039d2498f4f8e015b18d28e8bfedd803d758737ba SHA1: a7ace86de19639acc916820d6d30444175450d1d MD5sum: 1e7e25c5b02003cce9b61bbf17407b63 Description: Python interface to the Graphviz graph layout and visualization package Pygraphviz is a Python interface to the Graphviz graph layout and visualization package. . With Pygraphviz you can create, edit, read, write, and draw graphs using Python to access the Graphviz graph data structure and layout algorithms. Package: python-pygraphviz-dbg Source: python-pygraphviz Version: 1.3.1-1~nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 243 Depends: neurodebian-popularity-contest, python-pygraphviz (= 1.3.1-1~nd16.04+1), python-dbg, libc6 (>= 2.4), libcgraph6 Homepage: https://pygraphviz.github.io/ Priority: extra Section: debug Filename: pool/main/p/python-pygraphviz/python-pygraphviz-dbg_1.3.1-1~nd16.04+1_i386.deb Size: 103418 SHA256: 7444b88f9733c85c62018a7223a88979f26a05c3f6bec2a6d9dc870cf23d549a SHA1: 6b4accda0d2075542f58d52457b2d5ccca209ded MD5sum: 4b091025d0a92420e55553a50078ee75 Description: Python interface to the Graphviz graph layout and visualization package (debug extension) Pygraphviz is a Python interface to the Graphviz graph layout and visualization package. . With Pygraphviz you can create, edit, read, write, and draw graphs using Python to access the Graphviz graph data structure and layout algorithms. . This package contains the debug extension for python-pygraphviz. Build-Ids: 7dab8fd2dec1c75e17c386072fd825a0c0cc0206 Package: python-pygraphviz-doc Source: python-pygraphviz Version: 1.3.1-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 312 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Homepage: https://pygraphviz.github.io/ Priority: optional Section: doc Filename: pool/main/p/python-pygraphviz/python-pygraphviz-doc_1.3.1-1~nd16.04+1_all.deb Size: 67408 SHA256: c1224a2179507867f56f20c63434a263faa02bc206dc14e3aed3e1f7acd7076e SHA1: 2d1f19d174498015d80ca43a7a1a2b805a6532e7 MD5sum: 44c786b37a5b2e55cd781dd78c953f93 Description: Python interface to the Graphviz graph layout and visualization package (doc) Pygraphviz is a Python interface to the Graphviz graph layout and visualization package. . With Pygraphviz you can create, edit, read, write, and draw graphs using Python to access the Graphviz graph data structure and layout algorithms. . This package contains documentation for python-pygraphviz. Package: python-scikits-learn Source: scikit-learn Version: 0.17.1-1~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 65 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_0.17.1-1~nd+1+nd16.04+1_all.deb Size: 56050 SHA256: 45adc827b4aa2474390176c25d13dc3446faa326e86319af3f008f496006af30 SHA1: 6338a6ab54c6a7239cdf79ba9748675bea42906d MD5sum: dfbe4583cca67c94614c0005eee8e8b7 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-seaborn Source: seaborn Version: 0.7.1-2~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 766 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_0.7.1-2~nd16.04+1_all.deb Size: 128300 SHA256: 86a2a9f8c05f612d9894e9eb0a762e46af1ff746b26c0d50e6a6bcad4c0ebce4 SHA1: c3fa00cdc23d36db72e059d45d99d4149f1b8313 MD5sum: af65ebef49f9b4c1aca6f40e72a3838f 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~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 49 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~nd+1+nd16.04+1_all.deb Size: 11102 SHA256: e53e9566f3e3305d5ec1a8b3ed6a12f61a5f0d3279112666903e47ce68c4498c SHA1: e2e8f56e31d6f67994297cf8ef87bd10077f81b0 MD5sum: 19367b5b0032f4ee89cd3f543ed27e5d 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~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 21 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~nd+1+nd16.04+1_all.deb Size: 13318 SHA256: c8c2f26a64c9b1993f231546b6d5d3f4d0f8a4e96f0653c53060c6dde6b36d59 SHA1: 3abcec44fa903592ff069a138e1f57a4ddc7a93a MD5sum: ce48e44e76d54ab53124a9eb02b58690 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: 0.17.1-1~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5283 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~), python-numpy, python-scipy, python-sklearn-lib (>= 0.17.1-1~nd+1+nd16.04+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_0.17.1-1~nd+1+nd16.04+1_all.deb Size: 1223586 SHA256: e3fc7a163df7f54a06719b94aaa8ae8a4fc13dd993db77e4d940c5885e90272c SHA1: c0aa990dd46f5fd4f68f524715831ca16b93c2f9 MD5sum: 1e29d4471ec9083dbe27a0022a2a961c 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: 0.17.1-1~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 24874 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_0.17.1-1~nd+1+nd16.04+1_all.deb Size: 4018952 SHA256: 2267e9c87f930981ff8e6dbaeb57c3dc0162b9c64ab2a7f2f8616ba16686f6cb SHA1: 5b8788a060ca872bf02e4b506c47f46f289384c9 MD5sum: 892cb3b87ccf35037b122e6e74effe21 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: 0.17.1-1~nd+1+nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 5116 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:3.0), libstdc++6 (>= 5.2), python-numpy (>= 1:1.10.0~b1), 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_0.17.1-1~nd+1+nd16.04+1_i386.deb Size: 1086944 SHA256: fd56d69c1af0a9a4facb232a7e76eecba254e13ecc4173b51755bece90d57869 SHA1: 14a5cc40f17316b4cfaf7d36a48ffece395b428a MD5sum: c338d76018429bb5fb6751d463dfb2d9 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: 0.9.0-3~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 90 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_0.9.0-3~nd+1+nd16.04+1_all.deb Size: 20198 SHA256: 37f7091dea7505df24c110b49803dc180666fb029226d920c1ed081d0bc5063c SHA1: 1c047a11bc08e73e4ea2c266b23a77b4b01d3b96 MD5sum: 6c69e2e20aaa72a6d5e0acd5c2483789 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. . This package for Python 2. Package: python-statsmodels Source: statsmodels Version: 0.8.0~rc1+git43-g1ac3f11-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 15861 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), python-numpy, python-scipy, python-statsmodels-lib (>= 0.8.0~rc1+git43-g1ac3f11-1~nd16.04+1), python-patsy Recommends: python-pandas, python-matplotlib, python-nose, python-joblib, python-cvxopt Suggests: python-statsmodels-doc Conflicts: python-scikits-statsmodels, python-scikits.statsmodels (<< 0.4) Replaces: python-scikits-statsmodels, python-scikits.statsmodels (<< 0.4) Provides: python2.7-statsmodels Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: python Filename: pool/main/s/statsmodels/python-statsmodels_0.8.0~rc1+git43-g1ac3f11-1~nd16.04+1_all.deb Size: 3333028 SHA256: 6628c6fa2d79a8f67e81522215303ba26e8d4a41cf5d9bf476694c3cd63a0b4c SHA1: aa25a52facf4acb543035bf24745bc30382593db MD5sum: ad05cc8bbd25dba38d302387719a8034 Description: Python module for the estimation of statistical models statsmodels Python module provides classes and functions for the estimation of several categories of statistical models. These currently include linear regression models, OLS, GLS, WLS and GLS with AR(p) errors, generalized linear models for six distribution families and M-estimators for robust linear models. An extensive list of result statistics are available for each estimation problem. Package: python-statsmodels-doc Source: statsmodels Version: 0.8.0~rc1+git43-g1ac3f11-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 53299 Depends: neurodebian-popularity-contest, libjs-jquery Recommends: libjs-mathjax Suggests: python-statsmodels Conflicts: python-scikits-statsmodels-doc, python-scikits.statsmodels-doc Replaces: python-scikits-statsmodels-doc, python-scikits.statsmodels-doc Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: doc Filename: pool/main/s/statsmodels/python-statsmodels-doc_0.8.0~rc1+git43-g1ac3f11-1~nd16.04+1_all.deb Size: 8399220 SHA256: d23868b7318a0fde44661eb36fbd892d2efc90a2404c26c8d5fcb9652eb5d8bd SHA1: 911b87e251cd8908061c97a3549220fe9ff6a4a1 MD5sum: 8c65e5a7792e9c66f054118bfff8c5d9 Description: documentation and examples for statsmodels This package contains HTML documentation and example scripts for python-statsmodels. Package: python-statsmodels-lib Source: statsmodels Version: 0.8.0~rc1+git43-g1ac3f11-1~nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1534 Depends: neurodebian-popularity-contest, python-numpy (>= 1:1.10.0~b1), python-numpy-abi9, python (>= 2.7), python (<< 2.8), libc6 (>= 2.4) Conflicts: python-scikits-statsmodels, python-scikits.statsmodels (<< 0.4) Replaces: python-scikits-statsmodels, python-scikits.statsmodels (<< 0.4) Homepage: http://statsmodels.sourceforge.net/ Priority: extra Section: python Filename: pool/main/s/statsmodels/python-statsmodels-lib_0.8.0~rc1+git43-g1ac3f11-1~nd16.04+1_i386.deb Size: 218358 SHA256: c42782c45f260cf76d83dbb5267719056e65431b3e0f2a5806aa9bec7ed5f686 SHA1: 254b7aa3703e59bf32ef0afff83b583362ec8b2a MD5sum: df630388f15267629bef5314d6a7ed10 Description: low-level implementations and bindings for statsmodels This package contains architecture dependent extensions for python-statsmodels. Package: python-stfio Source: stimfit Version: 0.15.3-1~nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1431 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python-numpy (>= 1:1.10.0~b1), python-numpy-abi9, libblas3 | libblas.so.3, libc6 (>= 2.7), libcholmod3.0.6, libfftw3-double3, libgcc1 (>= 1:3.0), libhdf5-10, liblapack3 | liblapack.so.3, libpython2.7 (>= 2.7), libstdc++6 (>= 5.2), zlib1g (>= 1:1.1.4), libsuitesparse-dev, zlib1g-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_0.15.3-1~nd16.04+1_i386.deb Size: 506322 SHA256: 7e099fb62d9009f06a3d2feea4252587359dae1df74a545a208618b24372837b SHA1: 791ff8f541ae4c7496ce3b4641ec9393418ccb49 MD5sum: e245eada8ca1951d05f0e2a933aff1f2 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-tqdm Source: tqdm Version: 4.4.1-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 138 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~) Homepage: https://github.com/tqdm/tqdm Priority: optional Section: python Filename: pool/main/t/tqdm/python-tqdm_4.4.1-1~nd16.04+1_all.deb Size: 35338 SHA256: c81195d58760b621ba8a1797aee0cc1293669b9dd399e9d94511375e2cc70af1 SHA1: 5ea5d39162710c9dfe3c08946fcd732c7bb27f19 MD5sum: 97fbf3728ac99ede9c09e66d44157f82 Description: fast, extensible progress bar for Python 2 tqdm (read taqadum, تقدّم) means “progress” in Arabic. tqdm instantly makes your loops show a smart progress meter, just by wrapping any iterable with "tqdm(iterable)". . This package contains the Python 2 version of tqdm . Package: python-vcr Source: vcr.py Version: 1.7.3-1.0.1~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 157 Depends: neurodebian-popularity-contest, python-contextlib2, python-mock, python-six (>= 1.5), python-wrapt, python-yaml, python:any (<< 2.8), python:any (>= 2.7.5-5~) Homepage: https://github.com/kevin1024/vcrpy/ Priority: optional Section: python Filename: pool/main/v/vcr.py/python-vcr_1.7.3-1.0.1~nd+1+nd16.04+1_all.deb Size: 43580 SHA256: d13dfc1adc96024f35330d4c72825e3f09b9767177330c804cc3575079e11498 SHA1: 38af2662eef11399c694c53bdca1e4159e830bc8 MD5sum: 212bc7388c199867c3f9d80be6d829c2 Description: record and replay HTML interactions (Python library) vcr.py records all interactions that take place through the HTML libraries it supports and writes them to flat files, called cassettes (YAML format by default). These cassettes could be replayed then for fast, deterministic and accurate HTML testing. . vcr.py supports the following Python HTTP libraries: - urllib2 (stdlib) - urllib3 - http.client (Python3 stdlib) - Requests - httplib2 - Boto (interface to Amazon Web Services) - Tornado's HTTP client . This package contains the modules for Python 2. Package: python-vtk-dicom Source: vtk-dicom Version: 0.5.5-2~nd+1+nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 488 Depends: neurodebian-popularity-contest, python (>= 2.7), python (<< 2.8), libc6 (>= 2.4), libgcc1 (>= 1:3.0), libpython2.7 (>= 2.7), libstdc++6 (>= 4.1.1), libvtk-dicom0.5, libvtk5.10, 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~nd+1+nd16.04+1_i386.deb Size: 87080 SHA256: 4760c6af91321baabaeb4158174964740a65941bdbedfb52447372c60ee201e2 SHA1: 97cdff78c4c2d4dc247c243c1f90c8667c8842b2 MD5sum: 358aaca9d9889e941d7ed99cfadc997d 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: python3-argcomplete Source: python-argcomplete Version: 1.0.0-1~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 96 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~) Priority: optional Section: python Filename: pool/main/p/python-argcomplete/python3-argcomplete_1.0.0-1~nd+1+nd16.04+1_all.deb Size: 21058 SHA256: c93bdaf1b23b65d220a8c08b7043df874b281287d7145de0427dd1c2234f151d SHA1: 16ffed2622ee3e42555797e3f02591e8097e9419 MD5sum: 9fb059ae75b85aec11d96ed5c6c686ed Description: bash tab completion for argparse (for Python 3) Argcomplete provides easy, extensible command line tab completion of arguments for your Python script. . It makes two assumptions: . * You're using bash as your shell * You're using argparse to manage your command line arguments/options . Argcomplete is particularly useful if your program has lots of options or subparsers, and if your program can dynamically suggest completions for your argument/option values (for example, if the user is browsing resources over the network). . This package provides the module for Python 3.x. Package: python3-boto3 Source: python-boto3 Version: 1.2.2-2~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 716 Depends: neurodebian-popularity-contest, python3-botocore, python3-jmespath, python3:any (>= 3.3.2-2~), python3-requests, python3-six Homepage: https://github.com/boto/boto3 Priority: optional Section: python Filename: pool/main/p/python-boto3/python3-boto3_1.2.2-2~nd16.04+1_all.deb Size: 58080 SHA256: 413d967eb1e92a29223c47517db82b3c216e2ccee47c0df766800113ea6bb482 SHA1: d1c429e14196e1d8fdac8a053e714cd6431a8cd3 MD5sum: 8fc1b3425641b60db58bf864bea336c9 Description: Python interface to Amazon's Web Services - Python 3.x Boto is the Amazon Web Services interface for Python. It allows developers to write software that makes use of Amazon services like S3 and EC2. Boto provides an easy to use, object-oriented API as well as low-level direct service access. Package: python3-duecredit Source: duecredit Version: 0.5.0-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 236 Depends: neurodebian-popularity-contest, python3-citeproc, python3-requests, python3-six, python3:any (>= 3.3.2-2~) Homepage: https://github.com/duecredit/duecredit Priority: optional Section: python Filename: pool/main/d/duecredit/python3-duecredit_0.5.0-1~nd16.04+1_all.deb Size: 50062 SHA256: 234ba4405e3239bd4e409a9a04f55bdd571e7eebad9e5961f87c86795b09aa71 SHA1: f040a55ba952a28624501c3a51e4c2d57dd88832 MD5sum: abaa1e044235cb9607cd608816e8e41c Description: Publications (and donations) tracer duecredit is being conceived to address the problem of inadequate citation of scientific software and methods, and limited visibility of donation requests for open-source software. . It provides a simple framework (at the moment for Python only) to embed publication or other references in the original code so they are automatically collected and reported to the user at the necessary level of reference detail, i.e. only references for actually used functionality will be presented back if software provides multiple citeable implementations. . To get a sense of what duecredit is about, simply run or your analysis script with `-m duecredit`, e.g. . python3 -m duecredit examples/example_scipy.py Python-Egg-Name: duecredit Package: python3-funcsigs Source: python-funcsigs Version: 0.4-2~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 63 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~) Suggests: python-funcsigs-doc Homepage: http://funcsigs.readthedocs.org Priority: optional Section: python Filename: pool/main/p/python-funcsigs/python3-funcsigs_0.4-2~nd16.04+1_all.deb Size: 12840 SHA256: 30ac14fecd0e1891912e0b3e81201fc8ed42978cfe3262adc77afb8698bc1997 SHA1: c10eb7d912d55099dbfe4d9652a1d23faf3226b3 MD5sum: cbd2d39553eb4ca7a3b1295af66d310a Description: function signatures from PEP362 - Python 3.x funcsigs is a backport of the PEP 362 function signature features from Python 3.3's inspect module. The backport is compatible with Python 2.6, 2.7 as well as 3.2 and up. . This package contains the Python 3.x module. Package: python3-git Source: python-git Version: 2.0.8-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1583 Depends: neurodebian-popularity-contest, git (>= 1:1.7) | git-core (>= 1:1.5.3.7), python3-gitdb (>= 0.6.4), python3:any (>= 3.3.2-2~) Suggests: python-git-doc Homepage: https://github.com/gitpython-developers/GitPython Priority: optional Section: python Filename: pool/main/p/python-git/python3-git_2.0.8-1~nd16.04+1_all.deb Size: 291746 SHA256: 71dfe86896957bcd160eb9230adf10022fed0fcedc8c6b4deadc31bf72696d6b SHA1: faa3b57200225e585f71e4b7cc02da53b4b03f51 MD5sum: 69a37bff2ab44898ccd187eb74225223 Description: Python library to interact with Git repositories - Python 3.x 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. . This package provides the Python 3.x module. Package: python3-joblib Source: joblib Version: 0.10.2-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 487 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_0.10.2-1~nd16.04+1_all.deb Size: 111798 SHA256: a20e7089c53cdb988b0b7c6ac41aec804784efe7a211bf6ba7966d00ef5474c1 SHA1: 18f1531bc06df57e76b5c4d4aca86a26c763a613 MD5sum: 16c413253b35e723a2069db9656dcc75 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-jsmin Source: python-jsmin Version: 2.2.1-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 68 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~) Homepage: https://github.com/tikitu/jsmin Priority: optional Section: python Filename: pool/main/p/python-jsmin/python3-jsmin_2.2.1-1~nd16.04+1_all.deb Size: 21612 SHA256: 3344d1c8edcf36faf774fdeafdbb2669d3c6df114603d9352a397bf7ed2d160d SHA1: db767406e3300c3f557df9c3feb51516e06c482e MD5sum: 8afeb5e7e7b9db0b819a30de472482a6 Description: JavaScript minifier written in Python - Python 3.x Python-jsmin is a JavaScript minifier, it is written in pure Python and actively maintained. . This package provides the Python 3.x module. Package: python3-lda Source: lda Version: 1.0.2-9~nd+1+nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1319 Depends: neurodebian-popularity-contest, python3 (<< 3.6), python3 (>= 3.5~), python3-numpy, python3-pbr, libc6 (>= 2.4) Homepage: https://pythonhosted.org/lda/ Priority: optional Section: python Filename: pool/main/l/lda/python3-lda_1.0.2-9~nd+1+nd16.04+1_i386.deb Size: 239982 SHA256: d8f3a5a4bb3e05d1049996a5ed93046361b8765175f6eaec598823e7258a8ccd SHA1: 50fc85fa7e348d469afaabd98f941778af35a401 MD5sum: 33a5928c9ef2868abc96237486045bf2 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-mpi4py Source: mpi4py Version: 2.0.0-1~nd+1+nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1513 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libopenmpi1.10, python3 (<< 3.6), python3 (>= 3.5~) Recommends: mpi-default-bin Suggests: python3-numpy Homepage: http://code.google.com/p/mpi4py/ Priority: extra Section: python Filename: pool/main/m/mpi4py/python3-mpi4py_2.0.0-1~nd+1+nd16.04+1_i386.deb Size: 322630 SHA256: 6669d53787a4e5083971862f0f2b89b50b95be6550f1472eed19c9e0c4024993 SHA1: eb93185bd8a85a54d65a37e56a343794cd7e8b8a MD5sum: 5a65d21ac8529e13e227d277839ad5c0 Description: bindings of the Message Passing Interface (MPI) standard MPI for Python (mpi4py) provides bindings of the Message Passing Interface (MPI) standard for the Python programming language, allowing any Python program to exploit multiple processors. . mpi4py is constructed on top of the MPI-1/MPI-2 specification and provides an object oriented interface which closely follows MPI-2 C++ bindings. It supports point-to-point (sends, receives) and collective (broadcasts, scatters, gathers) communications of any picklable Python object as well as optimized communications of Python object exposing the single-segment buffer interface (NumPy arrays, builtin bytes/string/array objects). Package: python3-mpi4py-dbg Source: mpi4py Version: 2.0.0-1~nd+1+nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 4137 Depends: neurodebian-popularity-contest, python3-mpi4py (= 2.0.0-1~nd+1+nd16.04+1) Homepage: http://code.google.com/p/mpi4py/ Priority: extra Section: debug Filename: pool/main/m/mpi4py/python3-mpi4py-dbg_2.0.0-1~nd+1+nd16.04+1_i386.deb Size: 1067992 SHA256: bfe992781bd7defc416e7638cb44cc8c5f68ba705af1c2a0780669a38111b43d SHA1: 492bb2ab63f57c77b1be7b6b0886fb353ba65f9d MD5sum: 2b36e6a86e7c561c8e993553289ece42 Description: bindings of the MPI standard -- debug symbols MPI for Python (mpi4py) provides bindings of the Message Passing Interface (MPI) standard for the Python programming language, allowing any Python program to exploit multiple processors. . mpi4py is constructed on top of the MPI-1/MPI-2 specification and provides an object oriented interface which closely follows MPI-2 C++ bindings. It supports point-to-point (sends, receives) and collective (broadcasts, scatters, gathers) communications of any picklable Python object as well as optimized communications of Python object exposing the single-segment buffer interface (NumPy arrays, builtin bytes/string/array objects). . This package provides debug symbols. Package: python3-nibabel Source: nibabel Version: 2.1.0-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 64183 Depends: neurodebian-popularity-contest, python3-numpy, python3-scipy Suggests: python-nibabel-doc, python3-dicom, python3-fuse, python3-mock Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: python Filename: pool/main/n/nibabel/python3-nibabel_2.1.0-1~nd16.04+1_all.deb Size: 2154656 SHA256: 9c760991a0a887cdd65153791a3722534203cdf66dc16029f99c2ef9fccec0cd SHA1: 5df7ed77d30ce807373999d8294e95f46bfa053c MD5sum: 2fa45514b87aa0c0e6e6c3252b76f3a3 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-nilearn Source: nilearn Version: 0.2.5~dfsg.1-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2158 Depends: neurodebian-popularity-contest, python3-nibabel (>= 1.1.0), python3:any (>= 3.3.2-2~), python3-numpy (>= 1:1.6), python3-scipy (>= 0.9), python3-sklearn (>= 0.12.1) Recommends: python-matplotlib Homepage: https://nilearn.github.io Priority: extra Section: python Filename: pool/main/n/nilearn/python3-nilearn_0.2.5~dfsg.1-1~nd16.04+1_all.deb Size: 685224 SHA256: 50d5820df0e96c898a62291baf032c83862b57bdcd5b144618143bd9ef79a719 SHA1: 11e003ff9cbe8bab038c64bbb46277793027021d MD5sum: e477063ef5c537c9b2cf5e48ce3e8981 Description: fast and easy statistical learning on neuroimaging data (Python 3) This Python module leverages the scikit-learn toolbox for multivariate statistics with applications such as predictive modelling, classification, decoding, or connectivity analysis. . This package provides the Python 3 version. Package: python3-openpyxl Source: openpyxl Version: 2.3.0-2~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1319 Depends: neurodebian-popularity-contest, python3-jdcal, python3:any (>= 3.3.2-2~), python3-lxml (>= 3.3.4) | python3-et-xmlfile Recommends: python3-pytest, python3-pil Homepage: http://bitbucket.org/openpyxl/openpyxl/ Priority: optional Section: python Filename: pool/main/o/openpyxl/python3-openpyxl_2.3.0-2~nd16.04+1_all.deb Size: 198366 SHA256: fdbf672443fd6a1fef2972081a62e73df077d6df43362d6bb9c4103d82bb2bff SHA1: 7da58edb8f3231d5a648b5ea1f8d82c778ec35b9 MD5sum: bd9936151cca80596c5a727f5cf04c02 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: 0.18.1-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 24211 Depends: neurodebian-popularity-contest, python3-dateutil, python3-numpy (>= 1:1.7~), python3-tz, python3:any (>= 3.3.2-2~), python3-pandas-lib (>= 0.18.1-1~nd16.04+1), python3-pkg-resources, python3-six Recommends: python3-scipy, python3-matplotlib, python3-numexpr, python3-tables, python3-bs4, python3-html5lib, python3-lxml Suggests: python-pandas-doc Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python3-pandas_0.18.1-1~nd16.04+1_all.deb Size: 2522862 SHA256: 8a569b547006390075ff16d384fd64e9daa16b0275414c31fa8b01556a7fc82a SHA1: 5c99460d43646cee9e247eacc6c89a96ce3b69a2 MD5sum: c6d843506a50b22be8d16e14c9934baf 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: 0.18.1-1~nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 6618 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), python3-numpy (>= 1:1.10.0~b1), python3-numpy-abi9, python3 (<< 3.6), python3 (>= 3.5~) Homepage: http://pandas.sourceforge.net Priority: optional Section: python Filename: pool/main/p/pandas/python3-pandas-lib_0.18.1-1~nd16.04+1_i386.deb Size: 1541892 SHA256: e28ae49e4c055d09fefee35cdd7dcfd02eceebb82d80d1bb96ebe2c8a9727fbd SHA1: 1ae71f4b376bf5b4c12ea355b42dd3b19de40b86 MD5sum: b40c043ba8a9bc2f5e598ee83f926792 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-prov Source: python-prov Version: 1.4.0-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1235 Depends: neurodebian-popularity-contest, python3-dateutil, python3-lxml, python3-networkx, python3-six (>= 1.9.0), python3:any (>= 3.3.2-2~) Suggests: python-prov-doc, python3-pydotplus Homepage: https://github.com/trungdong/prov Priority: optional Section: python Filename: pool/main/p/python-prov/python3-prov_1.4.0-1~nd16.04+1_all.deb Size: 72570 SHA256: 6e2836dd7da34d0a68513ac450fb3cd6a13be3aa79e9f284b3ccd16596d97ed4 SHA1: 77cd01fd8a21b7f58be5e975f7f32f36742d4dd9 MD5sum: ca78f5a4c2b8d6093898c99c5a2e2043 Description: W3C Provenance Data Model (Python 3) A library for W3C Provenance Data Model supporting PROV-JSON and PROV- XML import/export. . Features: - An implementation of the W3C PROV Data Model in Python. - In-memory classes for PROV assertions, which can then be output as PROV-N. - Serialization and deserializtion support: PROV-JSON and PROV-XML. - Exporting PROV documents into various graphical formats (e.g. PDF, PNG, SVG). . This package provides the prov library for Python 3. Package: python3-pydotplus Source: python-pydotplus Version: 2.0.2-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 104 Depends: neurodebian-popularity-contest, graphviz, python3-pyparsing (>= 2.0.1), python3:any (>= 3.3.2-2~) Suggests: python-pydotplus-doc Homepage: http://pydotplus.readthedocs.org/ Priority: optional Section: python Filename: pool/main/p/python-pydotplus/python3-pydotplus_2.0.2-1~nd16.04+1_all.deb Size: 20322 SHA256: b19df8771f49c2b313d2a1acb1ecd2771576485c6ad9156596b50714764bdfef SHA1: 6410b7286d956011e3f522ce1db8d460294c4743 MD5sum: bacc9756268bd576896ea738f9e31359 Description: interface to Graphviz's Dot language - Python 3.x PyDotPlus is an improved version of the old pydot project that provides a Python Interface to Graphviz's Dot language. . Differences with pydot: * Compatible with PyParsing 2.0+. * Python 2.7 - Python 3 compatible. * Well documented. * CI Tested. . This package contains the Python 3.x module. Package: python3-pygraphviz Source: python-pygraphviz Version: 1.3.1-1~nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 386 Depends: neurodebian-popularity-contest, python3 (<< 3.6), python3 (>= 3.5~), libc6 (>= 2.4), libcgraph6, graphviz (>= 2.16) Suggests: python-pygraphviz-doc Homepage: https://pygraphviz.github.io/ Priority: optional Section: python Filename: pool/main/p/python-pygraphviz/python3-pygraphviz_1.3.1-1~nd16.04+1_i386.deb Size: 74786 SHA256: b703919cff169d89f239bf8c774756472bb48283571bb672417544cb3a087600 SHA1: 9ef45c2528df844bebb25d196d809680d0bb1d20 MD5sum: 9d190c98c43724e6d3a7f3852accf5b0 Description: Python interface to the Graphviz graph layout and visualization package (Python 3) Pygraphviz is a Python interface to the Graphviz graph layout and visualization package. . With Pygraphviz you can create, edit, read, write, and draw graphs using Python to access the Graphviz graph data structure and layout algorithms. . This package contains the Python 3 version of python-pygraphviz. Package: python3-pygraphviz-dbg Source: python-pygraphviz Version: 1.3.1-1~nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 244 Depends: neurodebian-popularity-contest, python3-pygraphviz (= 1.3.1-1~nd16.04+1), python3-dbg, libc6 (>= 2.4), libcgraph6 Homepage: https://pygraphviz.github.io/ Priority: extra Section: debug Filename: pool/main/p/python-pygraphviz/python3-pygraphviz-dbg_1.3.1-1~nd16.04+1_i386.deb Size: 103342 SHA256: f354506db47d2c178a6463db75634d74baede880863474d4920b497044db613a SHA1: df661eece00fefea2530505e8885a37ce283ec62 MD5sum: eba99d2d6ca3e3b10535ea37169c1b04 Description: Python interface to the Graphviz graph layout and visualization package (py3k debug extension) Pygraphviz is a Python interface to the Graphviz graph layout and visualization package. . With Pygraphviz you can create, edit, read, write, and draw graphs using Python to access the Graphviz graph data structure and layout algorithms. . This package contains the debug extension for python3-pygraphviz. Build-Ids: 3dc438e0c3b0f9f0b459a9385574c62030113ba6 Package: python3-seaborn Source: seaborn Version: 0.7.1-2~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 766 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_0.7.1-2~nd16.04+1_all.deb Size: 128352 SHA256: 66bf9c26034f0925ba87e88ec57da2182501b5e6122faf5ffaf7f3dc37aed194 SHA1: f178a36166ba2524b7b5d4d373264263bf6a1d01 MD5sum: ea11b84936961fce0ee1045b416c1038 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~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 49 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~nd+1+nd16.04+1_all.deb Size: 11162 SHA256: de89c30fb266478195674d595d12a1513db7e2956c8af953ce1f2720e45d4ad9 SHA1: 4c90cc7b7fc37bedacd44c403965b35de5a1751b MD5sum: ab68168bc1f5b17416eb4c2f602f33a3 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: 0.17.1-1~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5282 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~), python3-numpy, python3-scipy, python3-sklearn-lib (>= 0.17.1-1~nd+1+nd16.04+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_0.17.1-1~nd+1+nd16.04+1_all.deb Size: 1223126 SHA256: 3b28b162208fa5b5f7235a0fbc97bed0f84e4275c4cb26bc8ed0565b40c7937b SHA1: 406b42aa243cabebfdb495f02e24f0871fa9c5df MD5sum: eeb95f2492c4d2fea8c7c02d99d5b94b 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: 0.17.1-1~nd+1+nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 4641 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:3.0), libstdc++6 (>= 5.2), python3-numpy (>= 1:1.10.0~b1), python3-numpy-abi9, python3 (<< 3.6), python3 (>= 3.5~) Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python3-sklearn-lib_0.17.1-1~nd+1+nd16.04+1_i386.deb Size: 1019242 SHA256: d4f01985fc771f79438b443931ed4e80c052541f896470b53a2773de1a2977c5 SHA1: 89100f2f83326f86e6c515b3a2c383b541dfc13e MD5sum: b2a37e19595b09107ff3d6cfae63c25d 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-smmap Source: python-smmap Version: 0.9.0-3~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 90 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~) Suggests: python3-nose Homepage: https://github.com/Byron/smmap Priority: extra Section: python Filename: pool/main/p/python-smmap/python3-smmap_0.9.0-3~nd+1+nd16.04+1_all.deb Size: 20284 SHA256: bfa892cc23a05905952f05183485a112e9aed4c5b0386ebf8c08e72cf9e87a06 SHA1: 9e20c8d297d9788e3b08ec4aa7e00e980e05606c MD5sum: f14ccea8516ee46232a99dac3d53ca3d 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. . This package for Python 3. Package: python3-tqdm Source: tqdm Version: 4.4.1-1~nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 140 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~) Homepage: https://github.com/tqdm/tqdm Priority: optional Section: python Filename: pool/main/t/tqdm/python3-tqdm_4.4.1-1~nd16.04+1_all.deb Size: 35604 SHA256: e03a2ce506171570fbfe625a7b4829bca73ccf0293faa675c768c917e67aa611 SHA1: 81b511da03a2f4a286d24f5c5a08f23dda06099a MD5sum: 690d8637c9663d70b0a6a898c73b418c Description: fast, extensible progress bar for Python 3 and CLI tool tqdm (read taqadum, تقدّم) means “progress” in Arabic. tqdm instantly makes your loops show a smart progress meter, just by wrapping any iterable with "tqdm(iterable)". . This package contains the Python 3 version of tqdm and its command-line tool. Package: python3-vcr Source: vcr.py Version: 1.7.3-1.0.1~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 157 Depends: neurodebian-popularity-contest, python3-six (>= 1.5), python3-wrapt, python3-yaml, python3:any (>= 3.3.2-2~) Homepage: https://github.com/kevin1024/vcrpy/ Priority: optional Section: python Filename: pool/main/v/vcr.py/python3-vcr_1.7.3-1.0.1~nd+1+nd16.04+1_all.deb Size: 43654 SHA256: 18be5074cff6cd48dd691e1921cc15a96ad10de55944bad110958f0842ca8628 SHA1: 4de90dcf566f64fceaaa00a00af8fe588e105295 MD5sum: 4bb489a1b4d0759306109fcd31ec7e58 Description: record and replay HTML interactions (Python3 library) vcr.py records all interactions that take place through the HTML libraries it supports and writes them to flat files, called cassettes (YAML format by default). These cassettes could be replayed then for fast, deterministic and accurate HTML testing. . vcr.py supports the following Python HTTP libraries: - urllib2 (stdlib) - urllib3 - http.client (Python3 stdlib) - Requests - httplib2 - Boto (interface to Amazon Web Services) - Tornado's HTTP client . This package contains the modules for Python 3. Package: remake Version: 4.1+dbg1.1+dfsg-1~nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 326 Depends: neurodebian-popularity-contest, guile-2.0-libs, libc6 (>= 2.17), libreadline6 (>= 6.0) Homepage: http://bashdb.sourceforge.net/remake Priority: extra Section: devel Filename: pool/main/r/remake/remake_4.1+dbg1.1+dfsg-1~nd16.04+1_i386.deb Size: 155238 SHA256: 28624e41b64478f10f2d76619506df6ac128ada3a9bacc0afd2d34c7db06ef1d SHA1: 5d42d2d225f14f9bb8182d00108508a9321a38fe MD5sum: 44b2cf467609f2a0db0e18e6143cac73 Description: GNU make fork with improved error reporting and debugging Modernized version of GNU make utility that adds improved error reporting, the ability to trace execution in a comprehensible way, and a debugger. Some of the features of the debugger are: * see the target call stack * set breakpoints on targets * show and set variables * execute arbitrary "make" code * issue shell commands while stopped in the middle of execution * inspect target descriptions * write a file with the commands of the target expanded Package: singularity-container Version: 2.1.2-1~nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 302 Depends: neurodebian-popularity-contest, libc6 (>= 2.14) Homepage: http://gmkurtzer.github.io/singularity Priority: optional Section: admin Filename: pool/main/s/singularity-container/singularity-container_2.1.2-1~nd16.04+1_i386.deb Size: 53636 SHA256: 339505667f004788b55c1a9ad0a181578f8b695c01ddbc25d65c716a573f1917 SHA1: 2c315c89626e0ca2165f73cd7708ef261f299d82 MD5sum: 2587bce141b3cb96730a2ceefb0743d0 Description: container platform focused on supporting "Mobility of Compute" Mobility of Compute encapsulates the development to compute model where developers can work in an environment of their choosing and creation and when the developer needs additional compute resources, this environment can easily be copied and executed on other platforms. Additionally as the primary use case for Singularity is targeted towards computational portability, many of the barriers to entry of other container solutions do not apply to Singularity making it an ideal solution for users (both computational and non-computational) and HPC centers. Package: spm8-common Source: spm8 Version: 8.5236~dfsg.1-1~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 19186 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~nd+1+nd16.04+1_all.deb Size: 9781970 SHA256: 30cac74b9ad9db32f093eece5bae13d28ce4e1222878358f3afc6e6a67b55b67 SHA1: 4899f3ba6a1ff156b67ed1b324ae4a83ffbbab9e MD5sum: 093fd447d8b43ebafc21585625545b37 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~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 73019 Depends: neurodebian-popularity-contest Priority: extra Section: science Filename: pool/main/s/spm8/spm8-data_8.5236~dfsg.1-1~nd+1+nd16.04+1_all.deb Size: 45497774 SHA256: bde3c93b9c1168ff6fa5b3269782006cf5613ba3f2b7b49abd5249d07600335b SHA1: af6dce85c4ee9079c720d544dfacfe2fc2cffa67 MD5sum: 494ca73671489f3feaf525e4295caff3 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~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 9251 Depends: neurodebian-popularity-contest Priority: extra Section: doc Filename: pool/main/s/spm8/spm8-doc_8.5236~dfsg.1-1~nd+1+nd16.04+1_all.deb Size: 8934936 SHA256: 7f6f08608f31115b8e51d149f8560a2cfe399bd44562ff34bf2aeb24a9632bc9 SHA1: 31e802efc7a2237d47eb5a13d1d6f6a6f9ed7324 MD5sum: c96ec4b1e942cf1f8f8eb55b327d7c40 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: stimfit Version: 0.15.3-1~nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 3070 Depends: neurodebian-popularity-contest, libblas3 | libblas.so.3, libc6 (>= 2.7), libcholmod3.0.6, libfftw3-double3, libgcc1 (>= 1:3.0), libhdf5-10, liblapack3 | liblapack.so.3, libpython2.7 (>= 2.7), libstdc++6 (>= 5.2), libwxbase3.0-0v5 (>= 3.0.2+dfsg), libwxgtk3.0-0v5 (>= 3.0.2+dfsg), zlib1g (>= 1:1.1.4), python-numpy (>= 1:1.10.0~b1), python-numpy-abi9, python2.7, python:any (>= 2.6.6-7~), libsuitesparse-dev, zlib1g-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_0.15.3-1~nd16.04+1_i386.deb Size: 942172 SHA256: b3b9c4d1b18378e35f23ed786bca4f122c8d884382c8c61c20950319fa78ba24 SHA1: fd6102b33f195cbbd645ff6b51940d3fd42d0047 MD5sum: 881de9247d912d5302724f76de23d5ab 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: 0.15.3-1~nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 23634 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_0.15.3-1~nd16.04+1_i386.deb Size: 6188594 SHA256: 6f2e92ac2bd437dd71ce9cc22265cd043c9ff6d9a9cc07b81d7ee0bbdc1ddccf SHA1: 40746cc5ef74d5cd213d0e84eb2202b54a875187 MD5sum: 5cbfda330f3574f35130f3851d418bfc 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: ubuntu-keyring Version: 2010.+09.30~nd+1+nd16.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 24 Recommends: gpgv Priority: important Section: misc Filename: pool/main/u/ubuntu-keyring/ubuntu-keyring_2010.+09.30~nd+1+nd16.04+1_all.deb Size: 11702 SHA256: e6052ad683b7b3eac5152f3790fcf69f3340f2526d81e9e394a6c4b11fbb26c0 SHA1: 288fe25a2be199481113954438cd17bc01060293 MD5sum: f432078db30b3298f5dbac78eaad7c06 Description: GnuPG keys of the Ubuntu archive The Ubuntu project digitally signs its Release files. This package contains the archive keys used for that. Package: vrpn Version: 07.30+dfsg-1~nd+1+nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 304 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:3.0), libstdc++6 (>= 4.2.1), libvrpn0 (= 07.30+dfsg-1~nd+1+nd16.04+1), libvrpnserver0 (= 07.30+dfsg-1~nd+1+nd16.04+1) Homepage: http://www.cs.unc.edu/Research/vrpn/ Priority: extra Section: utils Filename: pool/main/v/vrpn/vrpn_07.30+dfsg-1~nd+1+nd16.04+1_i386.deb Size: 50080 SHA256: 52adb68e9d82018dea964fc2379dbdebd7a5542c0d0f73716a69dd757e7d964b SHA1: 0c9153900f900b5bc52f1ec1033266288fd23099 MD5sum: bfc32a2243716481c3713ddfebf6b217 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~nd+1+nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 3519 Depends: neurodebian-popularity-contest, libvrpn0 (= 07.30+dfsg-1~nd+1+nd16.04+1), libvrpnserver0 (= 07.30+dfsg-1~nd+1+nd16.04+1), vrpn (= 07.30+dfsg-1~nd+1+nd16.04+1) Homepage: http://www.cs.unc.edu/Research/vrpn/ Priority: extra Section: debug Filename: pool/main/v/vrpn/vrpn-dbg_07.30+dfsg-1~nd+1+nd16.04+1_i386.deb Size: 889098 SHA256: 8a17d17e7f305b6cf58d3d0b95ad602c8160d2357182b4c00824f16b92fbf0a1 SHA1: d98f0672bc8091a9222d3bba3fc4f2e6104653f1 MD5sum: 2cfb9bf52fab2f412f784a1894952fdf 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~nd+1+nd16.04+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 261 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:3.0), libstdc++6 (>= 5.2), libvtk-dicom0.5, libvtk5.10 Homepage: http://github.com/dgobbi/vtk-dicom/ Priority: optional Section: utils Filename: pool/main/v/vtk-dicom/vtk-dicom-tools_0.5.5-2~nd+1+nd16.04+1_i386.deb Size: 74396 SHA256: edf5a859a468884f5f47ec9e9707b16a531ccfbaa0d6b6322e01698d1ac72f61 SHA1: 3d222700d951633a6c194e0cbdca19e4da7be398 MD5sum: 858bf00e216c7481083d12cf5a4cc2c1 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