Package: afni-atlases Source: afni-data Version: 0.20180120-1.1+nd18.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 109419 Homepage: http://afni.nimh.nih.gov Priority: extra Section: science Filename: pool/main/a/afni-data/afni-atlases_0.20180120-1.1+nd18.04+1_all.deb Size: 98214444 SHA256: 0856660b51d43e481685002dd8599f4d1bbae5522cf7a4e81024b7abc201dcc4 SHA1: d93085ad5d93ae6804848ee27214a2bb5966d82e MD5sum: 1017a599b41327936b4da772fdc25dce Description: standard space brain atlases for AFNI AFNI is an environment for processing and displaying functional MRI data. It provides a complete analysis toolchain, including 3D cortical surface models, and mapping of volumetric data (SUMA). . This package provide AFNI's standard space brain templates in HEAD/BRIK format. Package: asciidoctor Version: 1.5.7.1-1~nd~nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 283 Depends: neurodebian-popularity-contest, ruby | ruby-interpreter, ruby-asciidoctor Multi-Arch: foreign Homepage: http://asciidoctor.org Priority: optional Section: text Filename: pool/main/a/asciidoctor/asciidoctor_1.5.7.1-1~nd~nd18.04+1_amd64.deb Size: 64176 SHA256: 60a8a558a264729c06d272b9a57248ad3cfa47cbf9f7ad6bf8bdbaf874f7b610 SHA1: 575172d02a0dcceac9d0f06c8eb6f86dd9b427d0 MD5sum: 925d820a78f5ea22f707c001d5b7bb1a Description: AsciiDoc to HTML rendering for Ruby Asciidoctor is a pure Ruby processor for converting AsciiDoc source files and strings into HTML 5, DocBook 4.5, DocBook 5.0 and other formats. Package: asciidoctor-doc Source: asciidoctor Version: 1.5.7.1-1~nd~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3304 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: asciidoctor Homepage: http://asciidoctor.org Priority: optional Section: doc Filename: pool/main/a/asciidoctor/asciidoctor-doc_1.5.7.1-1~nd~nd18.04+1_all.deb Size: 345560 SHA256: 4c4a3b27251a4400bb20229d164a82948e89b368f98081d66b2ea0e63878854f SHA1: b26f6a5adb171d9a6e18b199900502fc84470416 MD5sum: 2c7a64d03fdcf8039d62345a9c0f5a8f Description: AsciiDoc to HTML rendering for Ruby (documentation) Asciidoctor is a pure Ruby processor for converting AsciiDoc source files and strings into HTML 5, DocBook 4.5, DocBook 5.0 and other formats. . This package contains the documentation for asciidoctor. Package: btrbk Version: 0.28.0-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 351 Depends: neurodebian-popularity-contest, perl, btrfs-progs (>= 4.12) | btrfs-tools (>= 4.12) Recommends: openssh-client, pv, mbuffer Suggests: openssl, python3 Homepage: https://digint.ch/btrbk/ Priority: optional Section: utils Filename: pool/main/b/btrbk/btrbk_0.28.0-1~nd18.04+1_all.deb Size: 93464 SHA256: c3360232d61434f15707890b44082e15d1d13927ef60a2434d956d6cdaf74ce9 SHA1: 3a889d99f997e47ca12dedb08301d4e545917bc5 MD5sum: 57d7c112353e6e32c0b6d8dc2adf31e0 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: cnrun-tools Source: cnrun Version: 2.1.0-1~nd90+1+nd17.04+1+nd17.10+1+nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 69 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libcnrun2 (>= 2.0.0), libgcc1 (>= 1:3.0), libgsl23, libstdc++6 (>= 5.2) Homepage: http://johnhommer.com/academic/code/cnrun Priority: optional Section: science Filename: pool/main/c/cnrun/cnrun-tools_2.1.0-1~nd90+1+nd17.04+1+nd17.10+1+nd18.04+1_amd64.deb Size: 18192 SHA256: 063b73cf6697082728cad4abec3fe5efc10f410d51e15ca51f036916689b73a0 SHA1: 70c7aae8cb40a7b90d370702df1a60eae4a49871 MD5sum: c86887d17050cad05130a48091502690 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: connectome-workbench Version: 1.3.2-2~nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 47216 Depends: neurodebian-popularity-contest, libc6 (>= 2.27), libftgl2 (>= 2.1.3~rc5), libgcc1 (>= 1:3.0), libgl1, libglu1-mesa | libglu1, libgomp1 (>= 4.9), libosmesa6 (>= 10.2~), libqt5core5a (>= 5.9.0~beta), libqt5gui5 (>= 5.2.0), libqt5network5 (>= 5.0.2), libqt5opengl5 (>= 5.0.2), libqt5printsupport5 (>= 5.0.2), libqt5widgets5 (>= 5.7.0), libqt5xml5 (>= 5.1.0), 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_1.3.2-2~nd18.04+1_amd64.deb Size: 20448312 SHA256: c9cbb09cd4c5d207f524d331a23d4ad6cdf11bdbc303d7e38772a71873be19f2 SHA1: 5f42dfd6c7ec8b2425c26acc21d0c248d6e2f93a MD5sum: 02822ffa2b4b3e1fb95055776e345f1a 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: 1.3.2-2~nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 199848 Depends: neurodebian-popularity-contest, connectome-workbench (= 1.3.2-2~nd18.04+1) Homepage: http://www.nitrc.org/projects/workbench/ Priority: extra Section: debug Filename: pool/main/c/connectome-workbench/connectome-workbench-dbg_1.3.2-2~nd18.04+1_amd64.deb Size: 197717140 SHA256: de97b7776e17c4946bd77ae471399c3cae33959b5a6d99d1623f72c2aaab3daf SHA1: d0ee3a7d0d379b9323bbe8354d70d85ffca6a78c MD5sum: 8748f4bfe38b6b203bf25e98a667645e 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: 25ae140e43eb7a9f3d2721e6657e15fedf4a7ea6 34c988b5fee63bd1a047dd51f3d1f57353452916 Package: convert3d Version: 0.0.20170606-1~pre1~nd17.04+1+nd17.10+1+nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 63101 Depends: neurodebian-popularity-contest, libc6 (>= 2.27), libfftw3-double3 (>= 3.3.5), libgcc1 (>= 1:4.0), libgdcm2.8, libinsighttoolkit4.12 (>= 4.12.0-dfsg1-2~), libqt5core5a (>= 5.9.0~beta), libqt5gui5 (>= 5.8.0), libqt5widgets5 (>= 5.7.0), libstdc++6 (>= 5.2) Homepage: https://sourceforge.net/projects/c3d/ Priority: optional Section: science Filename: pool/main/c/convert3d/convert3d_0.0.20170606-1~pre1~nd17.04+1+nd17.10+1+nd18.04+1_amd64.deb Size: 9739904 SHA256: f98f0d4a600c8dfb12a97fd9c6796b768541c1ecf1af5dab1e4767907aad9419 SHA1: 55f549cbe890cb23862760d74a9677f0320b0019 MD5sum: add49f3434d44b1fba4174ef91abb815 Description: tool(s) for converting 3D images between common file formats C3D is a (command-line and GUI) tool for converting 3D images between common file formats. The tool also includes a growing list of commands for image manipulation, such as thresholding and resampling. Package: copyq Version: 3.6.1-1~nd1~nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 4799 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:3.0), libqt5core5a (>= 5.9.0~beta), libqt5gui5 (>= 5.8.0), libqt5network5 (>= 5.0.2), libqt5script5 (>= 5.6.0~beta), libqt5svg5 (>= 5.6.0~beta), libqt5widgets5 (>= 5.7.0), libqt5x11extras5 (>= 5.6.0), libstdc++6 (>= 4.8), libx11-6, libxtst6 Recommends: copyq-plugins (= 3.6.1-1~nd1~nd18.04+1) Suggests: copyq-doc Homepage: https://hluk.github.io/CopyQ/ Priority: optional Section: utils Filename: pool/main/c/copyq/copyq_3.6.1-1~nd1~nd18.04+1_amd64.deb Size: 1283168 SHA256: 05443e1a4d852de0ac55350b39785265fe6a1576db9b8a2c1b74a41661430353 SHA1: c0179eb1a782bdb714bce80d4e18731e1be7fb48 MD5sum: 89e0db0bb6b5ee974d08a92d625f4dda Description: Advanced clipboard manager with editing and scripting features CopyQ monitors system clipboard and saves its content in customized tabs. Saved clipboard can be later copied and pasted directly into any application. . Items can be: * edited with internal editor or with preferred text editor, * moved to other tabs, * drag'n'dropped to applications, * marked with tag or a note, * passed to or changed by custom commands, * or simply removed. . Features: * Support for Linux, Windows and OS X 10.9+ * Store text, HTML, images or any other custom formats * Quickly browse and filter items in clipboard history * Sort, create, edit, remove, copy/paste, drag'n'drop items in tabs * Add notes or tags to items * System-wide shortcuts with customizable commands * Paste items with shortcut or from tray or main window * Fully customizable appearance * Advanced command-line interface and scripting * Ignore clipboard copied from some windows or containing some text * Support for simple Vim-like editor and shortcuts * Many more features Package: copyq-doc Source: copyq Version: 3.6.1-1~nd1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1601 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0), sphinx-rtd-theme-common Built-Using: sphinx (= 1.6.7-1ubuntu1) Multi-Arch: foreign Homepage: https://hluk.github.io/CopyQ/ Priority: optional Section: doc Filename: pool/main/c/copyq/copyq-doc_3.6.1-1~nd1~nd18.04+1_all.deb Size: 897676 SHA256: 32757da046e32b1b57b590c0cbd3ab49b57d1dd138c6a97218b93257176519cc SHA1: 8a9556d7c7d5cc536bf117cef478603557f509de MD5sum: e66f573f7ecec602cbe951309c4068e7 Description: Documentation and examples for CopyQ - HTML format CopyQ monitors system clipboard and saves its content in customized tabs. Saved clipboard can be later copied and pasted directly into any application. . This contains the documentation in HTML format. Package: copyq-plugins Source: copyq Version: 3.6.1-1~nd1~nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2576 Depends: neurodebian-popularity-contest, copyq (= 3.6.1-1~nd1~nd18.04+1), libc6 (>= 2.14), libgcc1 (>= 1:3.0), libqt5core5a (>= 5.9.0~beta), libqt5gui5 (>= 5.2.0), libqt5widgets5 (>= 5.7.0), libstdc++6 (>= 4.8) Homepage: https://hluk.github.io/CopyQ/ Priority: optional Section: utils Filename: pool/main/c/copyq/copyq-plugins_3.6.1-1~nd1~nd18.04+1_amd64.deb Size: 568316 SHA256: 804b81e60e5265fc6557e99fb6ae39cc46ef9458f38201a865b8d6c859e21be0 SHA1: bc03cf8343b6046a93ea987fbbc1bfaeb63fb1f0 MD5sum: 1577951ab2fe932a4da1f9a092b75bf9 Description: Plugins for CopyQ CopyQ monitors system clipboard and saves its content in customized tabs. Saved clipboard can be later copied and pasted directly into any application. . This package contains plugins that add various item types support and features to CopyQ, including: * Text with Highlighting * Images * Web Pages * Various Data * Notes * Encryption * FakeVim Editor * Synchronize Items to Disk * Item Tags * Pinned Items Package: datalad Version: 0.11.4-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 132 Depends: neurodebian-popularity-contest, python3-datalad (= 0.11.4-1~nd18.04+1), python3-argcomplete, python3:any Suggests: datalad-containers, datalad-crawler, datalad-neuroimaging Homepage: http://datalad.org Priority: optional Section: science Filename: pool/main/d/datalad/datalad_0.11.4-1~nd18.04+1_all.deb Size: 95588 SHA256: cb034dc2b908d7af4d5fbbe9588e2240335ff026988ead863eb81ee4903d037c SHA1: d6d35567cac5d31947bb031ec712eb76018046f4 MD5sum: 233fdc63a7a1271deca104171a0097a2 Description: data files management and distribution platform DataLad is a data management and distribution platform providing access to a wide range of data resources already available online. Using git-annex as its backend for data logistics it provides following facilities built-in or available through additional extensions . - command line and Python interfaces for manipulation of collections of datasets (install, uninstall, update, publish, save, etc.) and separate files/directories (add, get) - extract, aggregate, and search through various sources of metadata (xmp, EXIF, etc; install datalad-neuroimaging for DICOM, BIDS, NIfTI support) - crawl web sites to automatically prepare and update git-annex repositories with content from online websites, S3, etc (install datalad-crawler) . This package provides the command line tools. Install without Recommends if you need only core functionality. Package: datalad-container Version: 0.3.1-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 84 Depends: neurodebian-popularity-contest, datalad (>= 0.11.1~), python3-requests (>= 1.2), python3-chardet (>= 3.0.4), python3-datalad, python3:any (>= 3.3.2-2~) Recommends: singularity-container Suggests: docker.io Homepage: http://datalad-container.rtfd.org Priority: optional Section: science Filename: pool/main/d/datalad-container/datalad-container_0.3.1-1~nd18.04+1_all.deb Size: 17784 SHA256: 27020e2fb832f456aa7fa1c946e2e26002c55f2823c3c57cc2085a4c22f7e659 SHA1: ea0df3d9aedefc21742f3f93866e2abb850f2430 MD5sum: 8521de1d853ba2c6dcd6c25f25263376 Description: DataLad extension for working with containerized environments This extension enhances DataLad (http://datalad.org) for working with computational containers. Package: dcm2niix Version: 1:1.0.20190410-1~nd1~nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 763 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:3.0), libopenjp2-7 (>= 2.0.0), libstdc++6 (>= 5.2), libyaml-cpp0.5v5 Homepage: https://github.com/rordenlab/dcm2niix Priority: optional Section: science Filename: pool/main/d/dcm2niix/dcm2niix_1.0.20190410-1~nd1~nd18.04+1_amd64.deb Size: 194516 SHA256: f503a13fb9adc9fb97e0c143005ca0eb9cfe6aa596bcc30581fb6dc519245ecc SHA1: 2857e15369a8a203e0aeb8fd88ce33c427199f78 MD5sum: a446e2fae13e1e7be9b7f30bb912dba5 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: dh-octave Version: 0.6.0~bpo9+1+nd1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 61 Depends: neurodebian-popularity-contest, liboctave-dev (>= 4.2.1-5), debhelper (>= 11), cme, libparse-debcontrol-perl, libmime-tools-perl, dh-octave-autopkgtest, perl Priority: optional Section: devel Filename: pool/main/d/dh-octave/dh-octave_0.6.0~bpo9+1+nd1~nd18.04+1_all.deb Size: 20048 SHA256: eabfa763f6f4ff1a2a3d0f2f7ae692ec97d8b9e1d5aaa19db52fdc5920d50430 SHA1: 911d062df41aa32686c8c3da2dc6c075fb58b4cd MD5sum: 1c4d777fc219be1cb0f542d5d8dbd579 Description: Debhelper-based infrastructure for building Octave add-on packages Since version 3.0 of Octave (a numerical computation software), add-ons can be installed through the pkg.m system. This package provides the infrastructure for packaging such add-ons for Debian, based on debhelper. It replaces the deprecated octave-pkg-dev package. This package contains debhelper-like scripts for building, checking and cleaning the add-on package as well as for generating the substitution variables in debian/control. . This package is intended to be used by the Debian Octave Group and should be of little interest to general users. Package: dh-octave-autopkgtest Source: dh-octave Version: 0.6.0~bpo9+1+nd1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 23 Depends: neurodebian-popularity-contest, gnuplot-nox, fonts-freefont-otf Priority: optional Section: devel Filename: pool/main/d/dh-octave/dh-octave-autopkgtest_0.6.0~bpo9+1+nd1~nd18.04+1_all.deb Size: 8504 SHA256: 3ac9f32f9928b941fb71540cdc46ac707104454795149ec583b6fa044abe986e SHA1: 56de1b9fca49329a400a1169ae2d421d2a15b906 MD5sum: d40f224e24b9ddc68cbf73490e9a3bca Description: script for the automatic testing of Octave add-on packages This package contains the dh_octave_check script that runs the unit tests contained in all *.m and *.cc files available in the source tree from which it is launched. It is intended to be used by the support for Octave-Forge add-on packages, which is implemented in autodep8. Package: fsl-melview Source: melview Version: 1.0.1+git9-ge661e05~dfsg.1-1~nd90+1+nd17.04+1+nd17.10+1+nd18.04+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~nd90+1+nd17.04+1+nd17.10+1+nd18.04+1_all.deb Size: 14132 SHA256: dbb9a44857a6002fede9dfaf3790a25b49cfdc7c5177ab15482e5ec413be678b SHA1: fe682e7eaf4d362566f51f16269dc82dfd8f57bb MD5sum: 3d6113be820954219d515010846df847 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: fsleyes Version: 0.15.2-1~nd17.10+1+nd18.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 239791 Depends: neurodebian-popularity-contest, python-fsl, python-fsleyes-props, python-fsleyes-widgets, python-wxgtk3.0, python-six (>= 1.0~), python-jinja2, python-scipy, python-matplotlib, python-numpy, python-opengl (>= 3.1~), python-indexed-gzip, python-nibabel, python-pil, python-pyparsing, python:any (<< 2.8), python:any (>= 2.7.5-5~) Priority: optional Section: science Filename: pool/main/f/fsleyes/fsleyes_0.15.2-1~nd17.10+1+nd18.04+1_all.deb Size: 26726256 SHA256: e5c11fdb600c2e3350c948c17d4906c30b533e198a0c2f9a973806891cb0acb0 SHA1: dfbe97d8d56f9de39585d952c56e2beb743e579c MD5sum: aeeaef340b4aa3016b68f120d4b04430 Description: FSL image viewer Feature-rich viewer for volumetric (medical) images. Package: git-annex-remote-rclone Version: 0.5-1~ndall+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 23 Depends: neurodebian-popularity-contest, git-annex | git-annex-standalone, rclone Homepage: https://github.com/DanielDent/git-annex-remote-rclone Priority: optional Section: utils Filename: pool/main/g/git-annex-remote-rclone/git-annex-remote-rclone_0.5-1~ndall+1_all.deb Size: 7842 SHA256: 0b1d65c740ce1073ecdae6db121d304fe02c4bb95df552326894118a65b38319 SHA1: 34a2323c4387e61c4a69617150c463f9a7b772c5 MD5sum: 00c5a0407a998eba72d4f5eb0ad71189 Description: rclone-based git annex special remote This is a wrapper around rclone to make any destination supported by rclone usable with git-annex. . Cloud storage providers supported by rclone currently include: * Google Drive * Amazon S3 * Openstack Swift / Rackspace cloud files / Memset Memstore * Dropbox * Google Cloud Storage * Microsoft One Drive * Hubic * Backblaze B2 * Yandex Disk . Note: although Amazon Cloud Drive support is implemented, it is broken ATM see https://github.com/DanielDent/git-annex-remote-rclone/issues/22 . Package: git-annex-standalone Source: git-annex Version: 7.20190219+git191-g2d6a364d4-1~ndall+1 Architecture: amd64 Maintainer: Richard Hartmann Installed-Size: 184762 Depends: git, netbase, openssh-client Recommends: lsof, gnupg, bind9-host, youtube-dl, git-remote-gcrypt (>= 0.20130908-6), nocache, aria2 Suggests: xdot, bup, adb, tor, magic-wormhole, tahoe-lafs, libnss-mdns, uftp Conflicts: git-annex Breaks: datalad (<= 0.11.1~) Provides: git-annex Homepage: http://git-annex.branchable.com/ Priority: optional Section: utils Filename: pool/main/g/git-annex/git-annex-standalone_7.20190219+git191-g2d6a364d4-1~ndall+1_amd64.deb Size: 64643552 SHA256: 1d98216176c6a7873523bf97e7c2507eedac9d9c9df1de5bf61e0cc9c849ac89 SHA1: 0d04a53a587dca28162c99f8d335bb2a8fb63e37 MD5sum: 48159438b0649056fa3c3439e9b68832 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: golang-github-ncw-rclone-dev Source: rclone Version: 1.41-1~ndall0 Architecture: all Maintainer: Debian Go Packaging Team Installed-Size: 2492 Depends: golang-bazil-fuse-dev, golang-github-aws-aws-sdk-go-dev, golang-github-mreiferson-go-httpclient-dev, golang-github-ncw-go-acd-dev, golang-github-ncw-swift-dev, golang-github-pkg-errors-dev, golang-github-pkg-sftp-dev, golang-github-rfjakob-eme-dev, golang-github-skratchdot-open-golang-dev, golang-github-spf13-cobra-dev, golang-github-spf13-pflag-dev, golang-github-stacktic-dropbox-dev, golang-github-stretchr-testify-dev, golang-github-tsenart-tb-dev, golang-github-unknwon-goconfig-dev, golang-github-vividcortex-ewma-dev, golang-golang-x-crypto-dev, golang-golang-x-net-dev, golang-golang-x-oauth2-google-dev, golang-golang-x-sys-dev, golang-golang-x-text-dev, golang-google-api-dev Homepage: https://github.com/ncw/rclone Priority: optional Section: devel Filename: pool/main/r/rclone/golang-github-ncw-rclone-dev_1.41-1~ndall0_all.deb Size: 399416 SHA256: 528b53f3312375d31d5cebb95472a57272cf242e14a92cfdf99c45be2ff5511d SHA1: 75f8871fd668e815023267a857b37ad60b9d1c2f MD5sum: a87865eafe10185420838e2e4ffd7b55 Description: go source code of rclone Rclone is a program to sync files and directories between the local file system and a variety of commercial cloud storage providers. . This package contains rclone's source code. Package: heudiconv Version: 0.5.3-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 958 Depends: neurodebian-popularity-contest, dcm2niix, python, python-dcmstack, python-dicom, python-nibabel, python-pathlib, python-numpy, python-nipype Recommends: python-pytest, python-datalad Homepage: https://github.com/nipy/heudiconv Priority: optional Section: science Filename: pool/main/h/heudiconv/heudiconv_0.5.3-1~nd18.04+1_all.deb Size: 246272 SHA256: feb21bb077f17e05c640f25bc22832dffea1c0f5677962fc87c192b2a224f6b3 SHA1: ceb4eac9080bea52bb51c862123dab56d7545789 MD5sum: 98873184e85eb43c0fed9dbbc2d61f0a 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: isympy-common Source: sympy Version: 1.3-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 211 Depends: neurodebian-popularity-contest, python:any | python:any Recommends: isympy | isympy3 Conflicts: isympy (<< 1.0-2) Breaks: python-sympy (<< 0.7.5-4) Homepage: http://sympy.org/ Priority: optional Section: python Filename: pool/main/s/sympy/isympy-common_1.3-1~nd18.04+1_all.deb Size: 186592 SHA256: 719a7be51dd8e09fa1d6d8379c4ff2c21a108a8791b6cac65700deffd6a96305 SHA1: 1a8166281cd3b191d504cfa2bd85d602d9ec367f MD5sum: c9508f8e42e0f783349cc72ff881fa74 Description: Python shell for SymPy SymPy is a Python library for symbolic mathematics (manipulation). It aims to become a full-featured computer algebra system (CAS) while keeping the code as simple as possible in order to be comprehensible and easily extensible. SymPy is written entirely in Python and does not require any external libraries, except optionally for plotting support. . This support package contains the common isympy python script, a wrapper for SymPy which can be invoked with either python2 or python3. . Install the isympy or isympy3 package to ensure all required dependencies are loaded. Package: isympy3 Source: sympy Version: 1.3-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 20 Depends: neurodebian-popularity-contest, isympy-common, python3-sympy Enhances: ipython3 Provides: isympy Homepage: http://sympy.org/ Priority: optional Section: python Filename: pool/main/s/sympy/isympy3_1.3-1~nd18.04+1_all.deb Size: 8360 SHA256: 337898618c71b253c7b95823f24cef38cd52fe8fd548338feb3d3df1f4e89e52 SHA1: eefa40a08b3395b8024c166d0212a77aaf81fd05 MD5sum: 8051e8a830b3444169117dca592d09be Description: Python3 shell for SymPy SymPy is a Python library for symbolic mathematics (manipulation). It aims to become a full-featured computer algebra system (CAS) while keeping the code as simple as possible in order to be comprehensible and easily extensible. SymPy is written entirely in Python and does not require any external libraries, except optionally for plotting support. . This package contains a Python3 shell (IPython shell if you have the ipython3 package installed) wrapper for SymPy. Package: jasp Version: 0.8.1.0~dfsg.1-1~nd90+1+nd17.04+1+nd17.10+1+nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Team Installed-Size: 40028 Depends: neurodebian-popularity-contest, libarchive13 (>= 3.0.4), libboost-filesystem1.65.1, libboost-system1.65.1, libc6 (>= 2.14), libgcc1 (>= 1:3.0), libqt5core5a (>= 5.9.0~beta), libqt5gui5 (>= 5.2.0), libqt5network5 (>= 5.0.2), libqt5printsupport5 (>= 5.0.2), libqt5webkit5 (>= 5.6.0~rc), libqt5widgets5 (>= 5.6.0~beta), libqt5xml5 (>= 5.1.0), libstdc++6 (>= 5.2), libjs-marked, r-base-core, r-cran-afex, r-cran-bayesfactor, r-cran-car, r-cran-effects, r-cran-hypergeo, r-cran-lme4, r-cran-logspline, r-cran-rjson Recommends: r-cran-ggplot2, r-cran-lsmeans, r-cran-plotrix, r-cran-rcpp, r-cran-rinside, r-cran-vcd, r-cran-vcdextra Homepage: https://jasp-stats.org Priority: optional Section: science Filename: pool/main/j/jasp/jasp_0.8.1.0~dfsg.1-1~nd90+1+nd17.04+1+nd17.10+1+nd18.04+1_amd64.deb Size: 33426752 SHA256: 948e191786802297ca430c7f72d0f8150e2fba9ca7dffd90e6e9b270eb7b9e8e SHA1: 2a3ba57ba436f88ed04a59e95d680fc5ae6cbf99 MD5sum: 9d0ccca3e4b407454f77378b87417610 Description: Bayesian statistics made accessible This is a statistics package with a graphical user interface. Its authors consider it "a low fat alternative to SPSS, a delicious alternative to R. Bayesian statistics made accessible." Package: libcnrun2 Source: cnrun Version: 2.1.0-1~nd90+1+nd17.04+1+nd17.10+1+nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 288 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:3.0), libgsl23, libgslcblas0, 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_2.1.0-1~nd90+1+nd17.04+1+nd17.10+1+nd18.04+1_amd64.deb Size: 82116 SHA256: 74b5d56986c7ff354b99d0cdde6d7acd3c0fafcedf5b1df02c48f2b71b88bb8a SHA1: ed07d68f3eba16657593a56ed9e5bd0a2470493c MD5sum: fce881d4cf0580a6d600c091e025c6c1 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: 2.1.0-1~nd90+1+nd17.04+1+nd17.10+1+nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 138 Depends: neurodebian-popularity-contest, libcnrun2 (= 2.1.0-1~nd90+1+nd17.04+1+nd17.10+1+nd18.04+1) Suggests: pkg-config Homepage: http://johnhommer.com/academic/code/cnrun Priority: optional Section: libdevel Filename: pool/main/c/cnrun/libcnrun2-dev_2.1.0-1~nd90+1+nd17.04+1+nd17.10+1+nd18.04+1_amd64.deb Size: 21632 SHA256: b7f1028419829a31662e4f8173de06748a79619b919caefa849846718c4fa0b1 SHA1: beab70664a78c819698525d34d08dab2798e01e4 MD5sum: 90c482833ae8f0fada708d46674eb108 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: libvw-dev Source: vowpal-wabbit Version: 8.6.1.dfsg1-1~nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 16015 Depends: neurodebian-popularity-contest, libvw0 (= 8.6.1.dfsg1-1~nd18.04+1) Homepage: http://hunch.net/~vw/ Priority: optional Section: libdevel Filename: pool/main/v/vowpal-wabbit/libvw-dev_8.6.1.dfsg1-1~nd18.04+1_amd64.deb Size: 1472624 SHA256: 9b785597bd57dc2d6449919553a7dced0faf56b566621422c7d9136e5bc9f084 SHA1: 60e6d4e277d1e5d80608f792fd3ca5d8ce4c065c MD5sum: cc30f34247a5849d9615ac21cc512433 Description: fast and scalable online machine learning algorithm - development files Vowpal Wabbit is a fast online machine learning algorithm. The core algorithm is specialist gradient descent (GD) on a loss function (several are available). VW features: - flexible input data specification - speedy learning - scalability (bounded memory footprint, suitable for distributed computation) - feature pairing . This package contains development files needed to compile and link programs which use vowpal-wabbit's libraries. Package: libvw0 Source: vowpal-wabbit Version: 8.6.1.dfsg1-1~nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2990 Depends: neurodebian-popularity-contest, libboost-program-options1.65.1, libc6 (>= 2.27), libgcc1 (>= 1:3.0), libstdc++6 (>= 5.2), zlib1g (>= 1:1.1.4) Homepage: http://hunch.net/~vw/ Priority: optional Section: libs Filename: pool/main/v/vowpal-wabbit/libvw0_8.6.1.dfsg1-1~nd18.04+1_amd64.deb Size: 809936 SHA256: 0dc64c3b6a34aa32f10e5a5ea30b8a1920e20f85c7d1d57e052b1d946b283d8f SHA1: a26c8fe6d48d5d54c861870add582e939b68abdb MD5sum: 40b0e8927ac335628552f4c4a7fbb014 Description: fast and scalable online machine learning algorithm - dynamic library Vowpal Wabbit is a fast online machine learning algorithm. The core algorithm is specialist gradient descent (GD) on a loss function (several are available). VW features: - flexible input data specification - speedy learning - scalability (bounded memory footprint, suitable for distributed computation) - feature pairing . This package contains vowpal-wabbit's dynamic libraries. Package: lua-cnrun Source: cnrun Version: 2.1.0-1~nd90+1+nd17.04+1+nd17.10+1+nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 118 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_2.1.0-1~nd90+1+nd17.04+1+nd17.10+1+nd18.04+1_amd64.deb Size: 38444 SHA256: 597fe96214e7dbe782c3d08cd20ac92bba61549d5cc374a13979980e2291f605 SHA1: 5b129c9e5e47c3e825f051af1a98b4951f7e0e1b MD5sum: db1b6ed0f8ee32dbb3d93c201720977c 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: mridefacer Version: 0.2-1~nd90+1+nd17.04+1+nd17.10+1+nd18.04+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~nd90+1+nd17.04+1+nd17.10+1+nd18.04+1_all.deb Size: 637452 SHA256: ab5a15de7c3ef796d813c5a2e14ae14dd36f338884bca80570f1ff4d6b5c3c8e SHA1: 760e57847e5005fd0a11a1a8d18ce42db9e541c9 MD5sum: f361b00ca73a41c0e14b72d59069bc29 Description: de-identification of MRI data This tool creates a de-face mask for volumetric images by aligning a template mask to the input. Such a mask can be used to remove image data from the vicinity of the facial surface, the auricles, and teeth in order to prevent a possible identification of a person based on these features. mrideface can process individual or series of images. In the latter case, the computed transformation between template image and input image will be updated incrementally for the next image in the series. This feature is most suitable for processing images that have been recorded in temporal succession. Package: mrtrix Version: 0.2.13-1~nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 9680 Depends: neurodebian-popularity-contest, libatkmm-1.6-1v5 (>= 2.24.0), libc6 (>= 2.27), libgcc1 (>= 1:3.0), libgl1, libglib2.0-0 (>= 2.12.0), libglibmm-2.4-1v5 (>= 2.54.0), libglu1-mesa | libglu1, libgsl23, libgslcblas0, libgtk2.0-0 (>= 2.8.0), libgtkglext1, libgtkmm-2.4-1v5 (>= 1:2.24.0), libsigc++-2.0-0v5 (>= 2.2.0), libstdc++6 (>= 5.2), zlib1g (>= 1:1.1.4) Suggests: mrtrix-doc, octave, matlab-support Homepage: http://www.brain.org.au/software/mrtrix Priority: extra Section: science Filename: pool/main/m/mrtrix/mrtrix_0.2.13-1~nd18.04+1_amd64.deb Size: 1342744 SHA256: 9f9cde2eb6d38306a6a0f644af4842b108c076f05f8fe65a698a8d258e943470 SHA1: 13e5930589198b5c550710479cd39aca321ee626 MD5sum: cab9ebbe7f64a5ca75199e74fa34720f Description: diffusion-weighted MRI white matter tractography Set of tools to perform diffusion-weighted MRI white matter tractography of the brain in the presence of crossing fibres, using Constrained Spherical Deconvolution, and a probabilisitic streamlines algorithm. Magnetic resonance images in DICOM, ANALYZE, or uncompressed NIfTI format are supported. Package: mrtrix-doc Source: mrtrix Version: 0.2.13-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3402 Depends: neurodebian-popularity-contest Homepage: http://www.brain.org.au/software/mrtrix Priority: extra Section: doc Filename: pool/main/m/mrtrix/mrtrix-doc_0.2.13-1~nd18.04+1_all.deb Size: 3180296 SHA256: b2b6e876e67bca20fb3d740d11841133b4a69ca24b821e73c40f81ea49043e50 SHA1: 6ebe3e5bbf8744e68868dc1dc23730d5fe6b1c47 MD5sum: ad4964b7183f6cf4d846400340a15319 Description: documentation for mrtrix Set of tools to perform diffusion-weighted MRI white matter tractography of the brain in the presence of crossing fibres, using Constrained Spherical Deconvolution, and a probabilisitic streamlines algorithm. Magnetic resonance images in DICOM, ANALYZE, or uncompressed NIfTI format are supported. . This package provides the documentation in HTML format. Package: mrtrix3 Version: 3.0~rc3+git135-g2b8e7d0c2-3~nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 47760 Depends: neurodebian-popularity-contest, libc6 (>= 2.27), libfftw3-double3 (>= 3.3.5), libgcc1 (>= 1:4.0), libgl1, libgomp1 (>= 4.9), libqt4-opengl (>= 4:4.7.0~beta1), libqtcore4 (>= 4:4.7.0~beta1), libqtgui4 (>= 4:4.8.0), libstdc++6 (>= 7), zlib1g (>= 1:1.1.4), python Recommends: python-numpy Suggests: mrtrix3-doc, octave, matlab-support Conflicts: mrtrix Homepage: http://www.mrtrix.org Priority: optional Section: science Filename: pool/main/m/mrtrix3/mrtrix3_3.0~rc3+git135-g2b8e7d0c2-3~nd18.04+1_amd64.deb Size: 7794760 SHA256: 264d7453202d64a460f93e3ada60f2990a64689f8c663110b4ecf73bd810a87e SHA1: 30dbf00ff10a749c5479683291cdf423f250732e MD5sum: aab576fb141a0f9146967c2e143e9f3b Description: diffusion-weighted MRI white matter tractography Set of tools to perform diffusion-weighted MRI white matter tractography of the brain in the presence of crossing fibres, using Constrained Spherical Deconvolution, and a probabilisitic streamlines algorithm. Magnetic resonance images in DICOM, ANALYZE, or uncompressed NIfTI format are supported. Package: mrtrix3-doc Source: mrtrix3 Version: 3.0~rc3+git135-g2b8e7d0c2-3~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 86 Depends: neurodebian-popularity-contest Conflicts: mrtrix-doc Homepage: http://www.mrtrix.org Priority: optional Section: doc Filename: pool/main/m/mrtrix3/mrtrix3-doc_3.0~rc3+git135-g2b8e7d0c2-3~nd18.04+1_all.deb Size: 47904 SHA256: 0fb497081d355830812ed8d525f4f6228552179853558599034a8fd565f5de8f SHA1: 61cca85976855905cd619e1fa54df308c54e75d7 MD5sum: f9cea66866cb74623fe2fdede05a452b Description: documentation for mrtrix3 Set of tools to perform diffusion-weighted MRI white matter tractography of the brain in the presence of crossing fibres, using Constrained Spherical Deconvolution, and a probabilisitic streamlines algorithm. Magnetic resonance images in DICOM, ANALYZE, or uncompressed NIfTI format are supported. . This package provides the documentation in HTML format. Package: neurodebian Version: 0.39.0~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 93 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_0.39.0~nd18.04+1_all.deb Size: 34176 SHA256: c3c738e890bec3045e000b08929746472dc2a0a7f7fe26900644c613b2a53466 SHA1: 6b989ae6ce8145b8be25e6c279a5aa5ee20c993a MD5sum: b4dbbc741eab8b82582dbf5f8ca1ba7f 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: 0.39.0~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 24 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_0.39.0~nd18.04+1_all.deb Size: 11168 SHA256: 8cbb7563f45b6226ea85ca6605db4af383f64edf6793767e0c8fd794b6e54385 SHA1: e78893598b8cee7ba4128eb5c0fc31400cb93296 MD5sum: 94647845356e4ab807d15e017ceee040 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: 0.39.0~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 190 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_0.39.0~nd18.04+1_all.deb Size: 117020 SHA256: 9542a63ad3130fdab1cea5bf46a5d1d8991c0ec9088accdda266d15baf3b8efe SHA1: a2046c264f4a757d5d3c9e1bb492348b9d9503e6 MD5sum: e9a5d96156d3f34cb18055d8a555f166 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: 0.39.0~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 112 Depends: devscripts, neurodebian-archive-keyring Recommends: ubuntu-keyring, python, zerofree, moreutils, time, debian-archive-keyring, apt-utils, cowbuilder, neurodebian-freeze Suggests: virtualbox-ose, virtualbox-ose-fuse, singularity-container Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-dev_0.39.0~nd18.04+1_all.deb Size: 34040 SHA256: a300ce51d6676cded6eeb2991fe06131c10064ca270e446630ee5555d77e18e5 SHA1: 02f822bd6a536ffd0b8cba257e567d21dbbb25ed MD5sum: 205410c4e21f6f5ef5270e59e49d9194 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-freeze Source: neurodebian Version: 0.39.0~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 37 Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-freeze_0.39.0~nd18.04+1_all.deb Size: 14988 SHA256: 19198ec681536d98107aa0d3ddd40adf61af3b669a276555e98a9d70eb6216d2 SHA1: 8463f679575bc43244d1a44fa420f122c73c6132 MD5sum: 6fdfd0243dbff01f9d0ed17bb849d576 Description: nd_freeze tool to freeze APT sources to use snapshots 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 minimalistic package provides nd_freeze script to be used in rich or minimalistic environments (such as Docker or Singularity recipes) to freeze their APT sources. Intended to assist making such images reproducible. Package: neurodebian-popularity-contest Source: neurodebian Version: 0.39.0~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 28 Depends: popularity-contest Homepage: http://neuro.debian.net Priority: optional Section: science Filename: pool/main/n/neurodebian/neurodebian-popularity-contest_0.39.0~nd18.04+1_all.deb Size: 13204 SHA256: 738eccf379f0dc8b388f674d66fe39d2761c6d3c149894c0aefe1cdfea55f465 SHA1: b25481e8cbf562d45707cc7fb848167166a6f92f MD5sum: a39edcec75eaa5b78b8da306f27a1d95 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: 0.6.3.1+ds-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7351 Depends: neurodebian-popularity-contest, gcc (>= 5.0) | g++ (>= 4.4) | clang (>= 3.0), scons (>= 2.0.0), python-appdirs | base-files (<< 7.2), python3-appdirs | base-files (<< 7.2), python-dev (>= 2.6.6-2), python3-dev, python3:any (>= 3.3.2-2~), python:any (<< 2.8), python:any (>= 2.7.5-5~) Recommends: python-lxml (>= 2.3), python-pyqt5, strace, chrpath Suggests: ccache Homepage: http://nuitka.net Priority: optional Section: python Filename: pool/main/n/nuitka/nuitka_0.6.3.1+ds-1~nd18.04+1_all.deb Size: 801760 SHA256: 0db77cbecae3d0ffc4c486994738f15cd92f8592346a3f570523c79ade38b04f SHA1: 66dc42a69bba954de5f6fe0a9578f0c69aeabe1d MD5sum: 873d880e995db2384e6b02e8c9cf9029 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: 3.0.15.20190401.dfsg1-1~nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 4583 Depends: neurodebian-popularity-contest, octave (>= 4.2.2), freeglut3, libasound2 (>= 1.0.16), libc6 (>= 2.14), libdc1394-22, libfreenect0.5 (>= 1:0.1.1), libgl1, libglib2.0-0 (>= 2.12.0), libglu1-mesa | libglu1, libgstreamer-plugins-base1.0-0 (>= 1.0.0), libgstreamer1.0-0 (>= 1.0.0), liboctave4, libopenal1 (>= 1.14), libpciaccess0 (>= 0.10.7), libportaudio2 (>= 19+svn20101113), libusb-1.0-0 (>= 2:1.0.9), libx11-6 (>= 2:1.2.99.901), libx11-xcb1, libxcb-dri3-0, libxcb1, libxext6, libxfixes3 (>= 1:5.0), libxi6 (>= 2:1.2.99.4), libxrandr2 (>= 2:1.4.0), libxxf86vm1, psychtoolbox-3-common (= 3.0.15.20190401.dfsg1-1~nd18.04+1), psychtoolbox-3-lib (= 3.0.15.20190401.dfsg1-1~nd18.04+1) Recommends: octave-audio, octave-image, octave-optim, octave-signal, octave-statistics, octave-pkg-dev Provides: psychtoolbox, psychtoolbox-3 Homepage: http://psychtoolbox.org Priority: optional Section: science Filename: pool/main/p/psychtoolbox-3/octave-psychtoolbox-3_3.0.15.20190401.dfsg1-1~nd18.04+1_amd64.deb Size: 900996 SHA256: 7ac31be0e92e766be7103a7c6737b5f0df4e6de541ad949d76af5815fb23a398 SHA1: 87165579409e5104eb5d0134b77164668f14139b MD5sum: f967905e00983a8924aa6f39f6d0e56a 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: patool Version: 1.12-3+nd1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 412 Depends: neurodebian-popularity-contest, python3:any (>= 3.4~), python:any (<< 2.8), python:any (>= 2.7.5-5~) Recommends: file Suggests: arj, bzip2 | lbzip2 | pbzip2, cabextract | lcab, ncompress, cpio | bsdcpio, lzop, p7zip-full, rar | unrar | zip | unzip, rpm2cpio, binutils, lha, unace | unace-nonfree | nomarch, unalz, lrzip (>= 0.551), tar (>= 1.26) | bsdtar, xdms, orange, lzip | plzip | clzip | pdlzip, sharutils, flac, archmage, genisoimage, python-argcomplete Homepage: http://wummel.github.io/patool/ Priority: optional Section: utils Filename: pool/main/p/patool/patool_1.12-3+nd1~nd18.04+1_all.deb Size: 37380 SHA256: cc05f4a0f45196a8cf73a3a2513d0c911d6a9021507f4243b998f8994e50bd2a SHA1: dd80f06ba3c5bc30bdff8834499830b99365be62 MD5sum: fdb26ed711739a63fa8fe8a8710386d3 Description: command line archive file manager Various archive formats can be created, extracted, tested, listed, compared, searched and repacked by patool. The archive format is determined with file and as a fallback by the archive file extension. . patool supports 7z (.7z), ACE (.ace), ADF (.adf), ALZIP (.alz), AR (.a), ARC (.arc), ARJ (.arj), BZIP2 (.bz2), CAB (.cab), compress (.Z), CPIO (.cpio), DEB (.deb), DMS (.dms), FLAC (.flac), GZIP (.gz), ISO (.iso), LZH (.lha, .lzh), LZIP (.lz), LZMA (.lzma), LZOP (.lzo), RAR (.rar), RPM (.rpm), RZIP (.rz), SHAR (.shar), SHN (.shn), TAR (.tar), XZ (.xz), ZIP (.zip, .jar) and ZOO (.zoo) formats. . It relies on helper applications to handle those archive formats (for example bzip2 for BZIP2 archives). . The archive formats TAR, ZIP, BZIP2 and GZIP are supported natively and do not require helper applications to be installed. Package: psychtoolbox-3-common Source: psychtoolbox-3 Version: 3.0.15.20190401.dfsg1-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 253612 Depends: neurodebian-popularity-contest Recommends: alsa-utils Suggests: gnuplot Homepage: http://psychtoolbox.org Priority: optional Section: science Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-common_3.0.15.20190401.dfsg1-1~nd18.04+1_all.deb Size: 23794256 SHA256: 9d12dac65a04527415a09fda814f4c294c3df50a1d75efb2f8cdb64486ba5d8d SHA1: b06f79c8de128536e06ceb0d1e7872bfae48af24 MD5sum: 5ea627193b02f034dd022a050af4854d 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: 3.0.15.20190401.dfsg1-1~nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 9492 Depends: neurodebian-popularity-contest, octave-psychtoolbox-3 (= 3.0.15.20190401.dfsg1-1~nd18.04+1) Homepage: http://psychtoolbox.org Priority: optional Section: debug Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-dbg_3.0.15.20190401.dfsg1-1~nd18.04+1_amd64.deb Size: 8278544 SHA256: 28d1d03b5b2bd263d1b765d9acdb5e56807f07e4eb1794f7d8c0f87f4b38ed51 SHA1: 9f457a21b12621c3553ff884366892760a84b20f MD5sum: bebed90eb53d3109cec854650dad1c10 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. Build-Ids: 2da6be5cde901b2240a8359e34043fde7a49aa7e 3031e47a437971ef05b7ffc4104862fa46aaec63 4b6239af636bb2afb01937a9fd71a2387fcaafd6 4ea0726553ec0b8111ecd56699eecd6d783fd23f 5fa78f93853a7c2207602d4418630eab5b50a9f0 963528c905cb38d2b24da228ee44b21511b86bdc 9e1d80239fb81b1bfa1cbe6ff960223573ed8e13 b3e3febb157155b3bfaeac66a7e2bfc607c13968 d292859c3450d4d40177c5738c7fd650df29a247 dff7313d5563fa75c49f9b8ad505cf1ae23c8980 fef897ae7e2564ad0ee5e65939bc093054c8cf6d Package: psychtoolbox-3-lib Source: psychtoolbox-3 Version: 3.0.15.20190401.dfsg1-1~nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 195 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libfontconfig1 (>= 2.12), libfreetype6 (>= 2.2.1), libgcc1 (>= 1:3.0), libgl1, libglu1-mesa | libglu1, libstdc++6 (>= 5) 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: optional Section: science Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-lib_3.0.15.20190401.dfsg1-1~nd18.04+1_amd64.deb Size: 74952 SHA256: 713a57491eb1952353be42cf0459ca716c71fea6fe95b4e4a2781367c798a053 SHA1: 637280ba8edb293ffed0ac0bcb55709008aa61a3 MD5sum: 012c40cc04cc9a5a17f4ab06b8c75705 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: pypy-hypothesis Source: python-hypothesis Version: 3.44.1-1~bpo9+1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 610 Depends: neurodebian-popularity-contest, pypy-enum34, pypy, pypy-attr, pypy-coverage Suggests: python-hypothesis-doc Homepage: https://github.com/DRMacIver/hypothesis Priority: optional Section: python Filename: pool/main/p/python-hypothesis/pypy-hypothesis_3.44.1-1~bpo9+1~nd18.04+1_all.deb Size: 120416 SHA256: 810b290223de0eb95f53aba1db7185b86f6db000dd201a5b67d5be30661c0c87 SHA1: 4a1d1f818b2be3d94ca58ebcef5cea1b2abe26a9 MD5sum: 3e73481f8a1476bc73106a27e65c0f63 Description: advanced Quickcheck style testing library for PyPy Hypothesis is a library for testing your Python code against a much larger range of examples than you would ever want to write by hand. It's based on the Haskell library, Quickcheck, and is designed to integrate seamlessly into your existing Python unit testing work flow. . Hypothesis is both extremely practical and also advances the state of the art of unit testing by some way. It's easy to use, stable, and extremely powerful. If you're not using Hypothesis to test your project then you're missing out. . This package contains the PyPy module. Package: python-boto Version: 2.44.0-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5140 Depends: neurodebian-popularity-contest, python-requests, python:any (<< 2.8), python:any (>= 2.7.5-5~), python-six Provides: python2.7-boto Homepage: https://github.com/boto/boto Priority: optional Section: python Filename: pool/main/p/python-boto/python-boto_2.44.0-1~nd18.04+1_all.deb Size: 739632 SHA256: 272bc22c063cc377b62b625271ec01c3a22a2d7030aa72be97b671c712c9620b SHA1: ae16c73682753c33148f744572fedf76f37fb8a1 MD5sum: 6259cefd0a2d8211dbfb5ec62ced59bd Description: Python interface to Amazon's Web Services - Python 2.x Boto is a Python interface to the infrastructure services available from Amazon. . Boto supports the following services: * Elastic Compute Cloud (EC2) * Elastic MapReduce * CloudFront * DynamoDB * SimpleDB * Relational Database Service (RDS) * Identity and Access Management (IAM) * Simple Queue Service (SQS) * CloudWatch * Route53 * Elastic Load Balancing (ELB) * Flexible Payment Service (FPS) * Simple Storage Service (S3) * Glacier * Elastic Block Store (EBS) * and many more... . This package provides the Python 2.x module. Package: python-bz2file Version: 0.98-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 35 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~) Provides: python2.7-bz2file Homepage: https://github.com/nvawda/bz2file Priority: optional Section: python Filename: pool/main/p/python-bz2file/python-bz2file_0.98-1~nd18.04+1_all.deb Size: 7920 SHA256: 8e05001a4c74dbb7b571230f657bf635b3ee63cdb99ba33a64600200bd7d4f4e SHA1: 2abb16321b37adfd36fb1c476543a26ef0aadc56 MD5sum: 1a1f71c61c7db903d9ba852fa6f3adc6 Description: Python library for reading and writing bzip2-compressed files Bz2file is a Python library for reading and writing bzip2-compressed files. . It contains a drop-in replacement for the file interface in the standard library's bz2 module, including features from the latest development version of CPython that are not available in older releases. . Bz2file for Python2. Package: python-dask-doc Source: dask Version: 0.17.5-2~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6964 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0), sphinx-rtd-theme-common, libjs-mathjax, libjs-bootstrap Built-Using: sphinx (= 1.6.7-1ubuntu1) Homepage: https://github.com/dask/dask Priority: optional Section: doc Filename: pool/main/d/dask/python-dask-doc_0.17.5-2~nd18.04+1_all.deb Size: 1768204 SHA256: e9532fdaf3677c39999cdc52e20cd5162442ecd8fa0ee5a00659a9abba997b14 SHA1: 1bcb678ef8953ce16ba0e2092a54aac913031d7f MD5sum: 7cc7adcc20210f486d54de0529e66e54 Description: Minimal task scheduling abstraction documentation Dask is a flexible parallel computing library for analytics, containing two components. . 1. Dynamic task scheduling optimized for computation. This is similar to Airflow, Luigi, Celery, or Make, but optimized for interactive computational workloads. 2. "Big Data" collections like parallel arrays, dataframes, and lists that extend common interfaces like NumPy, Pandas, or Python iterators to larger-than-memory or distributed environments. These parallel collections run on top of the dynamic task schedulers. . This contains the documentation Package: python-datalad Source: datalad Version: 0.11.4-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4396 Depends: neurodebian-popularity-contest, git-annex (>= 6.20180913~) | git-annex-standalone (>= 6.20180913~), patool, python-appdirs, python-fasteners, python-git (>= 2.1.6~), python-humanize, python-iso8601, python-keyrings.alt | python-keyring (<= 8), python-secretstorage, python-keyring, python-mock, python-msgpack, python-pil, python-requests, python-simplejson, python-six (>= 1.8.0), python-tqdm, python-wrapt, python-boto, python-chardet, python:any (<< 2.8), python:any (>= 2.7.5-5~) Recommends: python-exif, python-github, python-jsmin, python-html5lib, python-httpretty, python-libxmp, python-lzma, python-mutagen, python-nose, python-pyperclip, python-requests-ftp, python-vcr, python-whoosh Suggests: python-duecredit, python-bs4, python-numpy Provides: python2.7-datalad Homepage: http://datalad.org Priority: optional Section: python Filename: pool/main/d/datalad/python-datalad_0.11.4-1~nd18.04+1_all.deb Size: 916756 SHA256: 58d304f925b983496830484abf69fca2a21b9133cc1f372365f24bd370dc3667 SHA1: 1f3f263be1bbe35ed57c4e296e94f63191834de0 MD5sum: aeab5eaf43ebcddc814c39302ec4b677 Description: data files management and distribution platform DataLad is a data management and distribution platform providing access to a wide range of data resources already available online. Using git-annex as its backend for data logistics it provides following facilities built-in or available through additional extensions . - command line and Python interfaces for manipulation of collections of datasets (install, uninstall, update, publish, save, etc.) and separate files/directories (add, get) - extract, aggregate, and search through various sources of metadata (xmp, EXIF, etc; install datalad-neuroimaging for DICOM, BIDS, NIfTI support) - crawl web sites to automatically prepare and update git-annex repositories with content from online websites, S3, etc (install datalad-crawler) . This package installs the module for Python 2, and Recommends install all dependencies necessary for searching and managing datasets, publishing, and testing. If you need base functionality, install without Recommends. Package: python-dcmstack Source: dcmstack Version: 0.7-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 500 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: science Filename: pool/main/d/dcmstack/python-dcmstack_0.7-1~nd18.04+1_all.deb Size: 76032 SHA256: 76bd0483822282450e4bd05149b482eec0ec879ac67c7b7e32e25d286fee6e8f SHA1: b1321189a9ffae61015e706e35d6bf08c271acf5 MD5sum: 489f81c48497d714dc618152f9b3efcb 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, and command line tools (dcmstack, and nitool), as well as the documentation in HTML format. Package: python-dicom Source: pydicom Version: 1.2.1-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 13 Depends: neurodebian-popularity-contest, python-pydicom Homepage: https://pydicom.github.io Priority: optional Section: oldlibs Filename: pool/main/p/pydicom/python-dicom_1.2.1-1~nd18.04+1_all.deb Size: 4904 SHA256: 4b2d3fca8623d2bd20dc4d5035c27b483a0958cef6c569f43233c76b4d430f63 SHA1: c7887a909c187af788f8bed2424243ed7186eada MD5sum: 105a20129bb4526db82a347f73a324fe Description: transitional package for python-pydicom This is a transitional package. It can safely be removed. Package: python-dipy Source: dipy Version: 0.14.0-1~nd17.10+1+nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8631 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python:any (>= 2.6.6-7~), python-numpy (>= 1:1.7.1~), python-scipy, python-h5py, python-dipy-lib (>= 0.14.0-1~nd17.10+1+nd18.04+1) Recommends: python-matplotlib, python-vtk, python-nose, python-nibabel (>= 2.1.0) Suggests: ipython Provides: python2.7-dipy Homepage: http://dipy.org Priority: extra Section: python Filename: pool/main/d/dipy/python-dipy_0.14.0-1~nd17.10+1+nd18.04+1_all.deb Size: 3047844 SHA256: cd1f3c6f9c9b00d9d94af6b03f2852e625f4b8d532f386c336543ee708bf3f68 SHA1: 2138cdeb7977c44d51898122fc3d77fdea96fece MD5sum: 647d06f0f4453feaac6b40a1fc9b74af Description: Python library for the analysis of diffusion MRI datasets DIPY is a software project for computational neuroanatomy. It focuses on diffusion magnetic resonance imaging (dMRI) analysis and tractography but also contains implementations of other computational imaging methods such as denoising and registration that are applicable to the greater medical imaging and image processing communities. Additionally, DIPY is an international project which brings together scientists across labs and countries to share their state-of-the-art code and expertise in the same codebase, accelerating scientific research in medical imaging. . Here are some of the highlights: - Reconstruction algorithms: CSD, DSI, GQI, DTI, DKI, QBI, SHORE and MAPMRI - Fiber tracking algorithms: deterministic and probabilistic - Native linear and nonlinear registration of images - Fast operations on streamlines (selection, resampling, registration) - Tractography segmentation and clustering - Many image operations, e.g., reslicing or denoising with NLMEANS - Estimation of distances/correspondences between streamlines and connectivity matrices - Interactive visualization of streamlines in the space of images Python-Version: 2.7 Package: python-dipy-doc Source: dipy Version: 0.14.0-1~nd17.10+1+nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 16545 Depends: neurodebian-popularity-contest, libjs-jquery Suggests: python-dipy Homepage: http://dipy.org Priority: extra Section: doc Filename: pool/main/d/dipy/python-dipy-doc_0.14.0-1~nd17.10+1+nd18.04+1_all.deb Size: 12639612 SHA256: 0e598975843e08be440aa6953968e8215f93af0981b4875b444f886b8b9d5b1f SHA1: 8f130334485208428cb21c21036806274dd1d440 MD5sum: 7f8814cbb2032c5d97749fb0493a2776 Description: Python library for the analysis of diffusion MRI datasets -- documentation DIPY is a library for the analysis of diffusion magnetic resonance imaging data. . This package provides the documentation in HTML format. Package: python-dipy-lib Source: dipy Version: 0.14.0-1~nd17.10+1+nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 11786 Depends: neurodebian-popularity-contest, python-numpy (>= 1:1.13.1), python-numpy-abi9, python (<< 2.8), python (>= 2.7), libc6 (>= 2.14), libgomp1 (>= 4.9) Provides: python2.7-dipy-lib Homepage: http://dipy.org Priority: extra Section: python Filename: pool/main/d/dipy/python-dipy-lib_0.14.0-1~nd17.10+1+nd18.04+1_amd64.deb Size: 2125140 SHA256: c8d4e577e4389b65c9f39d9412fa90db8287e3188a4ea69a264c7530c002a789 SHA1: c806642ada09458df00c01c2a53eb5269fc6dfd2 MD5sum: 8bf2db0884ffe8a7d2be2fb88834a642 Description: Python library for the analysis of diffusion MRI datasets -- extensions DIPY is a library for the analysis of diffusion magnetic resonance imaging data. . This package provides architecture-dependent builds of the extensions. Python-Version: 2.7 Package: python-duecredit Source: duecredit Version: 0.7.0-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 246 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_0.7.0-1~nd18.04+1_all.deb Size: 53340 SHA256: dd74e7dc3783e4bca76abb759d938ca0ad5fa8f65dd99619c1e9497b20c1a420 SHA1: 561889fae7f4608dbb56fb407b0798a697a229f7 MD5sum: 4a30d49780d2cdad0c0f8b32c85afe92 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-exif Version: 2.1.2-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 130 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~) Provides: python2.7-exif Homepage: https://github.com/ianare/exif-py Priority: extra Section: python Filename: pool/main/p/python-exif/python-exif_2.1.2-1~nd18.04+1_all.deb Size: 27760 SHA256: f1744c010e29ac3c18594b06bee3aec8c7dae940ee7462816a886a2a433aa971 SHA1: a318f8898fc2489647c1dd4804f5b6d5df33a857 MD5sum: 227cf7e2094e182c0418f1cf4beef0be Description: Python library to extract Exif data from TIFF and JPEG files This is a Python library to extract Exif information from digital camera image files. It contains the EXIF.py script and the exifread library. . This package provides the Python 2.x module. Package: python-fsl Source: fslpy Version: 1.2.2-2~nd17.10+1+nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 404 Depends: neurodebian-popularity-contest, python-lxml, python-nibabel, python-six (>= 1.0~), python-deprecation, python-indexed-gzip, python-numpy, python:any (<< 2.8), python:any (>= 2.7.5-5~) Recommends: python-wxgtk3.0 Conflicts: fsl-melview (<= 1.0.1+git9-ge661e05~dfsg.1-1) Provides: python2.7-fsl Priority: optional Section: python Filename: pool/main/f/fslpy/python-fsl_1.2.2-2~nd17.10+1+nd18.04+1_all.deb Size: 84332 SHA256: 2afcd0dd580fe5131119664966196b3133b76a837413462b5b642e6ebae00830 SHA1: 30bed1680e6c061ecdb3f98dac5c429de105ee46 MD5sum: f5549bd48ab8dfd821766e80425c88bc Description: FSL Python library Support library for FSL. . This package provides the Python 2 module. Package: python-fsleyes-props Source: fsleyes-props Version: 1.2.1-3~nd17.10+1+nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 404 Depends: neurodebian-popularity-contest, python-wxgtk3.0, python-six (>= 1.0~), python-deprecation, python-matplotlib, python-numpy, python:any (<< 2.8), python:any (>= 2.7.5-5~) Conflicts: python-props (<= 1.0~) Replaces: python-props Provides: python-props, python2.7-fsleyes-props Priority: optional Section: python Filename: pool/main/f/fsleyes-props/python-fsleyes-props_1.2.1-3~nd17.10+1+nd18.04+1_all.deb Size: 75444 SHA256: 5efe94d355bf5fe20c4cc664e6bca8d5b4cbba4afb9fc4b651a958413bdfe3eb SHA1: 9cafb04e34afaa8ccaaaa5acea5b703b010eb21f MD5sum: 263ef61bed98a7aa5b5907359ea5a166 Description: Python descriptor framework Event programming framework with the ability for automatic CLI generation, and automatic GUI generation (with wxPython). . This package provides the Python 2 module. Package: python-fsleyes-widgets Source: fsleyes-widgets Version: 0.2.0-2~nd17.10+1+nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 374 Depends: neurodebian-popularity-contest, python-wxgtk3.0, python-six (>= 1.0~), python-deprecation, python-matplotlib, python-numpy, python:any (<< 2.8), python:any (>= 2.7.5-5~) Provides: python2.7-fsleyes-widgets Priority: optional Section: python Filename: pool/main/f/fsleyes-widgets/python-fsleyes-widgets_0.2.0-2~nd17.10+1+nd18.04+1_all.deb Size: 68972 SHA256: 954027d626199276d859fa932271ecdd6caeb001955eb0acef186e603d498d5a SHA1: fbe3723d7e15e2a81cbaec8d931475c21dbf621d MD5sum: b5570aecdb7d967443e30dcc4aa0145c Description: Python descriptor framework A collection of GUI widgets and utilities, based on wxPython. . This package provides the Python 2 module. Package: python-httpretty Version: 0.8.14-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 98 Depends: neurodebian-popularity-contest, python-urllib3, python:any (<< 2.8), python:any (>= 2.7.5-5~) Homepage: https://github.com/gabrielfalcao/httpretty Priority: optional Section: python Filename: pool/main/p/python-httpretty/python-httpretty_0.8.14-1~nd18.04+1_all.deb Size: 21032 SHA256: 41332aef69212fba4bfb6e0bbef56bf7607726eef270c187a981049441024137 SHA1: d380f040887108c0f01baaef8d6eb8485154a30f MD5sum: d5cab5ede9fc2875b8c9bdcb0b9fc088 Description: HTTP client mock - Python 2.x Once upon a time a Python developer wanted to use a RESTful API, everything was fine but until the day he needed to test the code that hits the RESTful API: what if the API server is down? What if its content has changed ? . Don't worry, HTTPretty is here for you. . This package provides the Python 2.x module. Package: python-hypothesis Version: 3.44.1-1~bpo9+1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 610 Depends: neurodebian-popularity-contest, python-enum34, python-attr, python-coverage, python:any (<< 2.8), python:any (>= 2.7.5-5~) Suggests: python-hypothesis-doc Homepage: https://github.com/DRMacIver/hypothesis Priority: optional Section: python Filename: pool/main/p/python-hypothesis/python-hypothesis_3.44.1-1~bpo9+1~nd18.04+1_all.deb Size: 120320 SHA256: 0d5d1e690c7eacbaa85530239f2cdaa4d533a9fa64b40263b9b630d6d2c0b100 SHA1: 8d55c6e7fc450927e5218797e7b5c4adc94c7987 MD5sum: 1e2b575ff76fc4070c695b74d3189798 Description: advanced Quickcheck style testing library for Python 2 Hypothesis is a library for testing your Python code against a much larger range of examples than you would ever want to write by hand. It's based on the Haskell library, Quickcheck, and is designed to integrate seamlessly into your existing Python unit testing work flow. . Hypothesis is both extremely practical and also advances the state of the art of unit testing by some way. It's easy to use, stable, and extremely powerful. If you're not using Hypothesis to test your project then you're missing out. . This package contains the Python 2 module. Package: python-hypothesis-doc Source: python-hypothesis Version: 3.44.1-1~bpo9+1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1079 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0), sphinx-rtd-theme-common Homepage: https://github.com/DRMacIver/hypothesis Priority: optional Section: doc Filename: pool/main/p/python-hypothesis/python-hypothesis-doc_3.44.1-1~bpo9+1~nd18.04+1_all.deb Size: 143692 SHA256: 2f4b40f4aacb486cfc890fd16b9f9a43ab162b3fe2ca54b098184935548f6984 SHA1: c5b906e561b1d4268ff4526fd6a0436e6de5f74b MD5sum: eef5642ed0212fa77e389b81ab77a8cf Description: advanced Quickcheck style testing library (documentation) Hypothesis is a library for testing your Python code against a much larger range of examples than you would ever want to write by hand. It's based on the Haskell library, Quickcheck, and is designed to integrate seamlessly into your existing Python unit testing work flow. . Hypothesis is both extremely practical and also advances the state of the art of unit testing by some way. It's easy to use, stable, and extremely powerful. If you're not using Hypothesis to test your project then you're missing out. . This package contains the documentation for Hypothesis. Package: python-indexed-gzip Source: indexed-gzip Version: 0.8.6-1~nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1163 Depends: neurodebian-popularity-contest, cython, python-numpy (>= 1:1.13.1), libc6 (>= 2.14), zlib1g (>= 1:1.2.2.4), python-numpy-abi9, python (<< 2.8), python (>= 2.7~), python:any (<< 2.8), python:any (>= 2.7.5-5~) Provides: python2.7-indexed-gzip Homepage: https://github.com/pauldmccarthy/indexed_gzip Priority: optional Section: python Filename: pool/main/i/indexed-gzip/python-indexed-gzip_0.8.6-1~nd18.04+1_amd64.deb Size: 337356 SHA256: f85b413fd4b9ce8dee5fe1cbfeb2acecec50981e50db64fcae4b4f6afe6eb049 SHA1: 9d06045cb75f9f69500f4379cb10d7007ed4bcad MD5sum: 12ecde132a247c49dcb816e0d4d310d0 Description: fast random access of gzip files in Python Drop-in replacement `IndexedGzipFile` for the built-in Python `gzip.GzipFile` class that does not need to start decompressing from the beginning of the file when for every `seek()`. It gets around this performance limitation by building an index, which contains *seek points*, mappings between corresponding locations in the compressed and uncompressed data streams. Each seek point is accompanied by a chunk (32KB) of uncompressed data which is used to initialise the decompression algorithm, allowing to start reading from any seek point. If the index is built with a seek point spacing of 1MB, only 512KB (on average) of data have to be decompressed to read from any location in the file. . This package provides the Python 2 module. Package: python-joblib Source: joblib Version: 0.13.2-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 875 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python:any (>= 2.6.6-7~), procps Recommends: python-numpy, python-pytest, python-simplejson, python-lz4, python-psutil Homepage: http://packages.python.org/joblib/ Priority: optional Section: python Filename: pool/main/j/joblib/python-joblib_0.13.2-1~nd18.04+1_all.deb Size: 192460 SHA256: 4fbaad2ec40cd2bcdac3af94b1f189c7dc20f872c01801357b79ecd341806773 SHA1: 2a46c2cd9f0925bc739421e95cc707b90b0f278d MD5sum: e87876810133566a161571a0399b5b2e 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-libxmp Source: python-xmp-toolkit Version: 2.0.1+git20140309.5437b0a-4~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 145 Depends: neurodebian-popularity-contest, libexempi3, python-tz, python:any (<< 2.8), python:any (>= 2.7.5-5~) Suggests: python-libxmp-doc Homepage: http://python-xmp-toolkit.readthedocs.org/ Priority: optional Section: python Filename: pool/main/p/python-xmp-toolkit/python-libxmp_2.0.1+git20140309.5437b0a-4~nd18.04+1_all.deb Size: 23376 SHA256: 03bc5e25132cdda4b7188eddc804b1c02d5086281416b5c371e3ffc0f642b323 SHA1: 5c40af82cb44589374df6b1210dcf31cdbd9a8f5 MD5sum: 55c68c2de45bbde1be94b1f402d626ef Description: Python library for XMP metadata Python XMP Toolkit is a library for working with XMP metadata, as well as reading/writing XMP metadata stored in many different file formats. . XMP (Extensible Metadata Platform) facilitates embedding metadata in files using a subset of RDF. Most notably XMP supports embedding metadata in PDF and many image formats, though it is designed to support nearly any file type. . This package provides Python bindings. Package: python-libxmp-doc Source: python-xmp-toolkit Version: 2.0.1+git20140309.5437b0a-4~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 237 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Recommends: python-libxmp, python3-libxmp Homepage: http://python-xmp-toolkit.readthedocs.org/ Priority: optional Section: doc Filename: pool/main/p/python-xmp-toolkit/python-libxmp-doc_2.0.1+git20140309.5437b0a-4~nd18.04+1_all.deb Size: 37176 SHA256: 04d289c82048bfec89269cc5a34b2223b07151f6c9862305d15447077e4d8b57 SHA1: 6492a907a187125002598188955f6ea18728a5fe MD5sum: 0c6aee0c62f81246fc5d013de40d0a33 Description: Python library for XMP metadata - documentation Python XMP Toolkit is a library for working with XMP metadata, as well as reading/writing XMP metadata stored in many different file formats. . XMP (Extensible Metadata Platform) facilitates embedding metadata in files using a subset of RDF. Most notably XMP supports embedding metadata in PDF and many image formats, though it is designed to support nearly any file type. . This package contains the documentation. Package: python-msgpack Source: msgpack-python Version: 0.4.8-1~nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 232 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7~), python:any (<< 2.8), python:any (>= 2.7.5-5~), libc6 (>= 2.14) Provides: msgpack-python Homepage: https://pypi.python.org/pypi/msgpack-python/ Priority: optional Section: python Filename: pool/main/m/msgpack-python/python-msgpack_0.4.8-1~nd18.04+1_amd64.deb Size: 67060 SHA256: c06da924745d259beac8cfcc3bd76995f20eac85c4f14dd27f6777d5ec5c950b SHA1: 1bd2dd729991ed8d70587680fa8f79bf5d43ca77 MD5sum: 99f4c5170886297f9e4f4f2b8b4648d7 Description: Python implementation of MessagePack format MessagePack is a binary-based efficient object serialization format. It enables the exchange of structured objects between many languages like JSON. But unlike JSON, it is very fast and small. . This package contains a Python extension module implementing the MessagePack format. Package: python-mvpa2 Source: pymvpa2 Version: 2.6.5-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 8602 Depends: neurodebian-popularity-contest, python-numpy, python:any (<< 2.8), python:any (>= 2.7.5-5~), python-mvpa2-lib (>= 2.6.5-1~nd18.04+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, python-mock 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_2.6.5-1~nd18.04+1_all.deb Size: 5106356 SHA256: a859f9b209a5e056239bb53dd3ed2ce770459bfaeac7cfb0ff9dc04a7636a100 SHA1: 5589ca034730bf44863d1711c5bb530ab10818ee MD5sum: 5e04676a31cc1ef914b06b70aecf132c 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: 2.6.5-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 20441 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_2.6.5-1~nd18.04+1_all.deb Size: 4260928 SHA256: 872c0eccce4cb62d4fc15822d80d0ae2288d1c6e3dc77ccdeaa2c53a3170f95d SHA1: 7b4f46f487f07312f0b0e64c2d056e1ef610f559 MD5sum: 498f26718581a15f2366e011c6d64d09 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: 2.6.5-1~nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 141 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:3.0), libstdc++6 (>= 5), libsvm3, python (<< 2.8), python (>= 2.7~), python-numpy (>= 1:1.13.1), python-numpy-abi9, python:any (<< 2.8), python:any (>= 2.7~) Provides: python2.7-mvpa2-lib Homepage: http://www.pymvpa.org Priority: optional Section: python Filename: pool/main/p/pymvpa2/python-mvpa2-lib_2.6.5-1~nd18.04+1_amd64.deb Size: 51648 SHA256: 0a567c988312ca10bdbafde8e2f6c4f984dd8ba2936529b352709b574d09b375 SHA1: 88a3f65c0be7bcb5341d047442b34c08349837bb MD5sum: c4f4459309a10675dfe7ac7a14097d4b Description: low-level implementations and bindings for PyMVPA v. 2 This is an add-on package for the PyMVPA framework. It provides a low-level implementation of an SMLR classifier and custom Python bindings for the LIBSVM library. . This is a package of a development snapshot. The latest released version is provided by the python-mvpa-lib package. Python-Version: 2.7 Package: python-nibabel Source: nibabel Version: 2.4.0-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 65816 Depends: neurodebian-popularity-contest, python-numpy, python-six (>= 1.3), python:any (<< 2.8), python:any (>= 2.7.5-5~), python-scipy, python-bz2file Recommends: python-dicom, python-fuse Suggests: python-nibabel-doc, python-mock Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: python Filename: pool/main/n/nibabel/python-nibabel_2.4.0-1~nd18.04+1_all.deb Size: 2645768 SHA256: 9a4f248def1ee61141da376aea053ef3af92e014aae9a56f5727c97a697bd68c SHA1: 7dde1ed2ee7c61ee49b26bd8690fab44d47f250b MD5sum: 46615d4266415eda23be77a6968ec323 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: 2.4.0-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9209 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_2.4.0-1~nd18.04+1_all.deb Size: 1482196 SHA256: ff943ffb555ccb13d6021b642da5c22c72cb31619a429378de8222b5cf54117c SHA1: a64ea0896ee3dc1e8c8c2e94cf00c8dc0b131ca9 MD5sum: 0abcecb72baf4f138d084c1ef176bd83 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-nipy Source: nipy Version: 0.4.2-2~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3547 Depends: neurodebian-popularity-contest, python-numpy (>= 1:1.2), python (<< 2.8), python (>= 2.7), python:any (>= 2.6.6-7~), python-scipy, python-nibabel, python-nipy-lib (>= 0.4.2-2~nd18.04+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_0.4.2-2~nd18.04+1_all.deb Size: 783300 SHA256: 617f00aa81de8fc80887429b3f7ed7171e06ff6cfe2097a1c73625bfea91423f SHA1: 02583b2b03e380b004071b13620162708cc6e9ac MD5sum: 712efe8584f061298c471009e1c9922b 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: 0.4.2-2~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9415 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_0.4.2-2~nd18.04+1_all.deb Size: 1903248 SHA256: cb615a6500d769246c4e381651f1d4838829e1a4549de1e2a3ac42e0fb3bb26f SHA1: e6d7a67914aa9019e1521f19737db722c0da1153 MD5sum: 634cdd3de1c7bfa1e77c17457d58019c 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: 0.4.2-2~nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2670 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), python-numpy (>= 1:1.13.1), python-numpy-abi9, python (<< 2.8), python (>= 2.7) Provides: python2.7-nipy-lib Homepage: http://neuroimaging.scipy.org Priority: extra Section: python Filename: pool/main/n/nipy/python-nipy-lib_0.4.2-2~nd18.04+1_amd64.deb Size: 650012 SHA256: 8685aabfe1f394009e57054449777cb56eac74b7b3a0aec1180331ad4d191d97 SHA1: a8b297e43f1c59e8a0392feb0fcbda1528887dc5 MD5sum: d68fae3cb5e65599246f4753025084df 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: 0.4.2-2~nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2878 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), python-numpy (>= 1:1.13.1), python-numpy-abi9, python-dbg (<< 2.8), python-dbg (>= 2.7), python-nipy-lib (= 0.4.2-2~nd18.04+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_0.4.2-2~nd18.04+1_amd64.deb Size: 725828 SHA256: 5b4fabf3b2955d6d3c19eb7d80b9fc6324e6fd1273e3dcf8717d8b63071ded4f SHA1: 485ddcbc0fd46ddc7833c5e238e14204fef5a314 MD5sum: 8f895efc463c024ddfe13772c975f622 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: 1.1.9-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 10996 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python-click, python-concurrent.futures, python-configparser, python-dateutil (>= 2.2), python-funcsigs, python-future, python-networkx (>= 1.3), python-nibabel (>= 1.0.0~), python-numpy, python-packaging, python-prov, python-scipy, python-simplejson, python-traits, python:any (>= 2.6.6-7~), python-traits (>= 4.5.0) | python-traits4 (>= 4.5.0), python-psutil Recommends: ipython, python-pytest, graphviz, python-xvfbwrapper, mayavi2, python-mock, python-pydotplus, python-pydot, python-cfflib Suggests: fsl, afni, python-nipy, slicer, matlab-spm8, python-pyxnat, mne-python, elastix, ants, python-pytest-xdist, python-bids Provides: python2.7-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: python Filename: pool/main/n/nipype/python-nipype_1.1.9-1~nd18.04+1_all.deb Size: 1891272 SHA256: a275f985637f3b008fe586c6b745fc0dd725120bc642a25eaad25c05dcfdf346 SHA1: 35a35778ec3bbde6ac6087f9a3b0223c738625e7 MD5sum: ac4a487df88365e0251d8005a620a022 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: 1.1.9-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 38457 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_1.1.9-1~nd18.04+1_all.deb Size: 17418076 SHA256: 2896652af664af5f5a5944c69422b3d09494df0bb5f054fcd30d6b4d6fb5249e SHA1: d9d1898af1aa316560877336bf8b06811af0027d MD5sum: 998674b9700fc39418e43ebe73815264 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-pandas Source: pandas Version: 0.23.3-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 12452 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python-dateutil, python-numpy (>= 1:1.7~), python-tz, python:any (>= 2.6.6-7~), python-pandas-lib (>= 0.23.3-1~nd18.04+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: https://pandas.pydata.org/ Priority: optional Section: python Filename: pool/main/p/pandas/python-pandas_0.23.3-1~nd18.04+1_all.deb Size: 1743236 SHA256: 51f619068360de414a3f7487eb9b1dc9b6982e5876d91341c978c1883f4ea7f1 SHA1: 1cb5048e40fa73a88966fb656462cb458e3cb35d MD5sum: c8b5476836e10455bd2c6a48aca19080 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: 0.23.3-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 36 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-mathjax Suggests: python-pandas Homepage: https://pandas.pydata.org/ Priority: optional Section: doc Filename: pool/main/p/pandas/python-pandas-doc_0.23.3-1~nd18.04+1_all.deb Size: 18236 SHA256: 22e2bb53b59b52d2fdee550a52feb79fbba12cf34ef043880645c9cdaf7c698b SHA1: 9472e41e18721d039692eb7fb072bb491a0e3b59 MD5sum: fb1aca469ba7c9e1d8c7650332fe0c9e Description: documentation and examples for pandas This package contains documentation and example scripts for python-pandas. Package: python-pandas-lib Source: pandas Version: 0.23.3-1~nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 16107 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:3.0), libstdc++6 (>= 5.2), python-numpy (>= 1:1.13.1), python-numpy-abi9, python (<< 2.8), python (>= 2.7) Provides: python2.7-pandas-lib Homepage: https://pandas.pydata.org/ Priority: optional Section: python Filename: pool/main/p/pandas/python-pandas-lib_0.23.3-1~nd18.04+1_amd64.deb Size: 3449072 SHA256: 1548ee72ced9b4095902a0c5fa61e7e9d25d3a3834eba79f4b5b9b84b414c500 SHA1: 5d652bd447202d2b1a569c224381e08ccf1996b1 MD5sum: 3424fd909232c87b269019595dd4142c Description: low-level implementations and bindings for pandas This is an add-on package for python-pandas providing architecture-dependent extensions. . This package contains the Python 2 version. Python-Version: 2.7 Package: python-patsy Source: patsy Version: 0.5.0+git13-g54dcf7b-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 780 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python-numpy, python-six, python:any (>= 2.6.6-7~) Recommends: python-pandas, python-openpyxl Suggests: python-patsy-doc Homepage: http://github.com/pydata/patsy Priority: optional Section: python Filename: pool/main/p/patsy/python-patsy_0.5.0+git13-g54dcf7b-1~nd18.04+1_all.deb Size: 168356 SHA256: 8efeef8c7b7ddf5e1c83e399b4fa22a2402900029fd3f44e4f586e8d1cd8e070 SHA1: 76bf15bf4717b77eac9c8832e3101fdaf3c99205 MD5sum: 079b4d1820538b630c0cc492c5b0000a Description: statistical models in Python using symbolic formulas patsy is a Python library for describing statistical models (especially linear models, or models that have a linear component) and building design matrices. . This package contains the Python 2 version. Package: python-patsy-doc Source: patsy Version: 0.5.0+git13-g54dcf7b-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1188 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Suggests: python-patsy Homepage: http://github.com/pydata/patsy Priority: optional Section: doc Filename: pool/main/p/patsy/python-patsy-doc_0.5.0+git13-g54dcf7b-1~nd18.04+1_all.deb Size: 343700 SHA256: 322973a22358b5e11f495deedd89a33e328bdd9dd5d240233f2d1a0e3b43f6ac SHA1: 558594330c09672372aa4a715fefe178c1a2d9dd MD5sum: db50469e0ef1a50614badf52d152918d Description: documentation and examples for patsy This package contains documentation and example scripts for python-patsy. Package: python-pprocess Source: pprocess Version: 0.5-2~nd17.04+1+nd17.10+1+nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 762 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~) Provides: python2.7-pprocess Homepage: http://www.boddie.org.uk/python/pprocess.html Priority: optional Section: python Filename: pool/main/p/pprocess/python-pprocess_0.5-2~nd17.04+1+nd17.10+1+nd18.04+1_all.deb Size: 83108 SHA256: 8ae099383d8c157ae1300aaf8cf62bc0b9e23cb636813b168f781b2c8379df59 SHA1: d0073bb3d9099579f73c5f3bc814e88812cbc34d MD5sum: ac9d0604892092eb645835244e5eb2ba Description: elementary parallel programming for Python The pprocess module provides elementary support for parallel programming in Python using a fork-based process creation model in conjunction with a channel-based communications model implemented using socketpair and poll. On systems with multiple CPUs or multicore CPUs, processes should take advantage of as many CPUs or cores as the operating system permits. Package: python-props Source: props Version: 0.10.1-1~nd90+1+nd17.04+1+nd17.10+1+nd18.04+1 Architecture: all Maintainer: NeuroDebian Team Installed-Size: 651 Depends: neurodebian-popularity-contest, python-wxgtk3.0, python-matplotlib, python-numpy, python-six, python:any (<< 2.8), python:any (>= 2.7.5-5~) Provides: python2.7-props Priority: optional Section: python Filename: pool/main/p/props/python-props_0.10.1-1~nd90+1+nd17.04+1+nd17.10+1+nd18.04+1_all.deb Size: 117480 SHA256: 25eda699c2fb413e4a54006ba2fe99acaf7e444d2b8377cf0d3ca8d6fb7c4ff0 SHA1: 13e829aa2775b53b096e8c0f075e79d4fac3a4bf MD5sum: 05009edec10f60423d2ce747b3b0e056 Description: Python descriptor framework Event programming framework with the ability for automatic CLI generation, and automatic GUI generation (with wxPython). . This package provides the Python 2 module. Package: python-pydicom Source: pydicom Version: 1.2.1-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 14179 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python:any (>= 2.6.6-7~) Recommends: python-numpy, python-pil Suggests: python-matplotlib, python-gdcm Breaks: python-dicom (<< 1~) Replaces: python-dicom (<< 1~) Provides: python-dicom Homepage: https://pydicom.github.io Priority: optional Section: python Filename: pool/main/p/pydicom/python-pydicom_1.2.1-1~nd18.04+1_all.deb Size: 4396928 SHA256: b00193d6e31148d148bd759287447789d2143367655b573a410fcbc4e81cbf13 SHA1: 804ee27d0a16234a28580023d930a6664225d39a MD5sum: fd62acd6f02cf16443b10c2c1117ad47 Description: DICOM medical file reading and writing (Python 2) pydicom is a pure Python module for parsing DICOM files. DICOM is a standard (http://medical.nema.org) for communicating medical images and related information such as reports and radiotherapy objects. . pydicom makes it easy to read DICOM files into natural pythonic structures for easy manipulation. Modified datasets can be written again to DICOM format files. . This package installs the module for Python 2. Package: python-pydicom-doc Source: pydicom Version: 1.2.1-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2791 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0), sphinx-rtd-theme-common Breaks: python-dicom-doc (<< 1~) Replaces: python-dicom-doc (<< 1~) Provides: python-dicom-doc Homepage: https://pydicom.github.io Priority: optional Section: doc Filename: pool/main/p/pydicom/python-pydicom-doc_1.2.1-1~nd18.04+1_all.deb Size: 345884 SHA256: 339d8c5436f3dd776a669ef38952452a8f2522460cb009c89cc6e928b52fe823 SHA1: 1dc24a38862a3de40bbab2b2a896f1bae70fa4be MD5sum: da130241d4d5fab244f7f59dd1165f7a Description: DICOM medical file reading and writing (documentation) pydicom is a pure Python module for parsing DICOM files. DICOM is a standard (http://medical.nema.org) for communicating medical images and related information such as reports and radiotherapy objects. . pydicom makes it easy to read DICOM files into natural pythonic structures for easy manipulation. Modified datasets can be written again to DICOM format files. . This package contains the documentation. Package: python-pydot Source: pydot Version: 1.2.3-1.1~nd17.10+1+nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 90 Depends: neurodebian-popularity-contest, python-pyparsing (>= 2.0.1+dfsg1-1), python:any (<< 2.8), python:any (>= 2.7.5-5~), graphviz Homepage: https://github.com/erocarrera/pydot Priority: optional Section: python Filename: pool/main/p/pydot/python-pydot_1.2.3-1.1~nd17.10+1+nd18.04+1_all.deb Size: 20560 SHA256: 46529b3c6a0f2a0a924f39aa1875a0fba4a636eb76d614d02e90647918da9a5c SHA1: 7f1a688ad4549db5d372360eff00c339da287c3d MD5sum: ac2c61010959580243595a75c8732436 Description: Python interface to Graphviz's dot pydot allows one to easily create both directed and non directed graphs from Python. Currently all attributes implemented in the Dot language are supported. . Output can be inlined in Postscript into interactive scientific environments like TeXmacs, or output in any of the format's supported by the Graphviz tools dot, neato, twopi. Package: python-pynwb Source: pynwb Version: 0.5.1-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 687 Depends: neurodebian-popularity-contest, python-dateutil, python-h5py, python-numpy, python-pandas, python-requests, python-ruamel.yaml, python-six, python:any (<< 2.8), python:any (>= 2.7.5-5~) Homepage: https://github.com/NeurodataWithoutBorders/pynwb Priority: optional Section: python Filename: pool/main/p/pynwb/python-pynwb_0.5.1-1~nd18.04+1_all.deb Size: 98156 SHA256: 3cc7b8a3da93334f4a3fb44e42f2bb425b6192db4cc2ffaaeb236ff6d03004a9 SHA1: d506818c38749c6da37f057c5b59b2d429d0d0d0 MD5sum: 2f4be911f2b80dbec3ec8e62f56a9c39 Description: Python library for working with Neurodata in the NWB format PyNWB is a Python package for working with NWB files. It provides a high-level API for efficiently working with Neurodata stored in the NWB format. . Neurodata Without Borders: Neurophysiology (NWB:N) is a project to develop a unified data format for cellular-based neurophysiology data, focused on the dynamics of groups of neurons measured under a large range of experimental conditions. Package: python-pyperclip Version: 1.6.0-2~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 42 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python:any (>= 2.6.6-7~), xclip | xsel | python-gi | python-qt4 Homepage: https://github.com/asweigart/pyperclip Priority: optional Section: python Filename: pool/main/p/python-pyperclip/python-pyperclip_1.6.0-2~nd18.04+1_all.deb Size: 9516 SHA256: 03fbfab1fdeb0edce08c2bc8305ca1001062bac41998c1390e14d1d06fe669db SHA1: 09073d95bf783414de0c303f45e07793007c3e72 MD5sum: 4ba75f1dfbda8d071a9a5a3ad886694d Description: Cross-platform clipboard module for Python This module is a cross-platform Python module for copy and paste clipboard functions. . It currently only handles plaintext. Package: python-requests Source: requests Version: 2.8.1-1~bpo8+1~nd90+1+nd17.04+1+nd17.10+1+nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 243 Depends: neurodebian-popularity-contest, python-urllib3 (>= 1.12), python:any (<< 2.8), python:any (>= 2.7.5-5~), ca-certificates, python-chardet Suggests: python-ndg-httpsclient, python-openssl, python-pyasn1 Breaks: httpie (<< 0.9.2) Homepage: http://python-requests.org Priority: optional Section: python Filename: pool/main/r/requests/python-requests_2.8.1-1~bpo8+1~nd90+1+nd17.04+1+nd17.10+1+nd18.04+1_all.deb Size: 68300 SHA256: 00ea179e2885183c035a39cf0e9b3662275f88dc2a7f656a35ea1187b0fa8532 SHA1: 7346cfc18d6164c02a85a6d571cfa61835e41a00 MD5sum: 1c2977f809255bfdc23281e18af95632 Description: elegant and simple HTTP library for Python2, built for human beings Requests allow you to send HTTP/1.1 requests. You can add headers, form data, multipart files, and parameters with simple Python dictionaries, and access the response data in the same way. It's powered by httplib and urllib3, but it does all the hard work and crazy hacks for you. . Features . - International Domains and URLs - Keep-Alive & Connection Pooling - Sessions with Cookie Persistence - Browser-style SSL Verification - Basic/Digest Authentication - Elegant Key/Value Cookies - Automatic Decompression - Unicode Response Bodies - Multipart File Uploads - Connection Timeouts Package: python-requests-whl Source: requests Version: 2.8.1-1~bpo8+1~nd90+1+nd17.04+1+nd17.10+1+nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 353 Depends: neurodebian-popularity-contest, ca-certificates, python-urllib3-whl Homepage: http://python-requests.org Priority: optional Section: python Filename: pool/main/r/requests/python-requests-whl_2.8.1-1~bpo8+1~nd90+1+nd17.04+1+nd17.10+1+nd18.04+1_all.deb Size: 319100 SHA256: 835245b149dd24dc4ecfce54c285f7416a95421b1f3a28a04980e0bb80f2ac01 SHA1: 0db33718d54f707e368a8f7da749bfef07816373 MD5sum: 257c2a4ec35a3f87da20f2e62feb3609 Description: elegant and simple HTTP library for Python, built for human beings Requests allow you to send HTTP/1.1 requests. You can add headers, form data, multipart files, and parameters with simple Python dictionaries, and access the response data in the same way. It's powered by httplib and urllib3, but it does all the hard work and crazy hacks for you. . Features . - International Domains and URLs - Keep-Alive & Connection Pooling - Sessions with Cookie Persistence - Browser-style SSL Verification - Basic/Digest Authentication - Elegant Key/Value Cookies - Automatic Decompression - Unicode Response Bodies - Multipart File Uploads - Connection Timeouts . This package provides the universal wheel. Package: python-skimage Source: skimage Version: 0.14.0-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 26057 Depends: neurodebian-popularity-contest, python-matplotlib (>= 1.3.1), python-networkx (>= 1.8), python-numpy, python-pil, python-scipy, python-six (>= 1.10.0), python-skimage-lib (>= 0.14.0-1~nd18.04+1), python (<< 2.8), python (>= 2.7), python-cloudpickle, python-pywt, python:any (>= 2.6.6-7~) Recommends: python-pytest, python-qt4 Suggests: python-opencv, python-skimage-doc Homepage: http://scikit-image.org Priority: optional Section: python Filename: pool/main/s/skimage/python-skimage_0.14.0-1~nd18.04+1_all.deb Size: 19923008 SHA256: 2c0b7c597ab364af409dc9efaa5c41004b06435b1cd46764a65b974d7a0692bb SHA1: a526b8db5e19b6270e7997a4bbaec3d17fe2672b MD5sum: 62ca557896e419274a78e77b594f9d00 Description: Python modules for image processing scikit-image is a collection of image processing algorithms for Python. It performs tasks such as image loading, filtering, morphology, segmentation, color conversions, and transformations. . This package provides the Python 2 module. Package: python-skimage-doc Source: skimage Version: 0.14.0-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1672 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Suggests: python-skimage Multi-Arch: foreign Homepage: http://scikit-image.org Priority: optional Section: doc Filename: pool/main/s/skimage/python-skimage-doc_0.14.0-1~nd18.04+1_all.deb Size: 786844 SHA256: ecef6924626f3f9db373e7fda51a01c63452b412abe8638ba4b93623952c3fce SHA1: 0d94c3d3766f64f7fab928517c1cff1b66eb2385 MD5sum: e458c9f936989048694957f96a0a8dc9 Description: Documentation and examples for scikit-image scikit-image is a collection of image processing algorithms for Python. It performs tasks such as image loading, filtering, morphology, segmentation, color conversions, and transformations. . This package contains documentation and example scripts for python-skimage. Package: python-skimage-lib Source: skimage Version: 0.14.0-1~nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 11229 Depends: neurodebian-popularity-contest, python-numpy (>= 1:1.13.1), python-numpy-abi9, python (<< 2.8), python (>= 2.7), libc6 (>= 2.14), libgcc1 (>= 1:3.0), libstdc++6 (>= 5.2) Recommends: python-skimage Provides: python2.7-skimage-lib Homepage: http://scikit-image.org Priority: optional Section: python Filename: pool/main/s/skimage/python-skimage-lib_0.14.0-1~nd18.04+1_amd64.deb Size: 1780524 SHA256: 29c6e089a7d1ffa8104f47f6cc6436c2d7eac8b9f074b28d404792099ed1f6d3 SHA1: 8d9687f5e603135eec56f182b73585c7a42cd8ec MD5sum: f76cf1264faf711d7e8e4a683fd40fa5 Description: Optimized low-level algorithms for scikit-image This is an add-on package for python-skimage. It provides optimized, low-level implementations of algorithms. . This package provides the Python 2 libraries. Package: python-sklearn Source: scikit-learn Version: 0.20.0+dfsg-2~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7540 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~), python-numpy, python-scipy, python-sklearn-lib (>= 0.20.0+dfsg-2~nd18.04+1), python-joblib (>= 0.9.2) Recommends: python-nose, python-pytest, 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_0.20.0+dfsg-2~nd18.04+1_all.deb Size: 1517588 SHA256: fdc84dda0f0af1815d9e8670491673b141b0c43a0a70e2d7a10522be449be827 SHA1: b8e91bcdf6dd4f68349c8876731bbc16c5aaff89 MD5sum: 2636a70f83786c3205f466cf4fbf7afc 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: 0.20.0+dfsg-2~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 87370 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0), libjs-bootstrap, libjs-mathjax Suggests: python-sklearn Conflicts: python-scikits-learn-doc Replaces: python-scikits-learn-doc Built-Using: sphinx (= 1.6.7-1ubuntu1) Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: doc Filename: pool/main/s/scikit-learn/python-sklearn-doc_0.20.0+dfsg-2~nd18.04+1_all.deb Size: 39843940 SHA256: e4434800a6ec6ee767ee7432119e2f0ca89c8b5c8699244191152da33e9b95ac SHA1: 7bf40186dd0aad80494be2cf8f43f1c680b5be16 MD5sum: 0f56f8817338ece186bb3d3febeafcc5 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: 0.20.0+dfsg-2~nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 7314 Depends: neurodebian-popularity-contest, libatlas3-base, libc6 (>= 2.14), libgcc1 (>= 1:3.0), libstdc++6 (>= 5.2), python-numpy (>= 1:1.13.1), python-numpy-abi9, python (<< 2.8), python (>= 2.7~), python:any (<< 2.8), python:any (>= 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_0.20.0+dfsg-2~nd18.04+1_amd64.deb Size: 1583556 SHA256: b45209e6008f83d9f8a199021294267af592bd420afbd521f3a412325b8aaa7b SHA1: 1081e6beff3a59e5baedf0f037d3d415dd2ec737 MD5sum: 81ff028f6d70ff2b1f3cdef69bb4b118 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-sphinx-gallery Source: sphinx-gallery Version: 0.2.0-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 217 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~), python-matplotlib, python-pil, python-sphinx Suggests: python-sphinx-gallery-doc, mayavi2, python-seaborn Homepage: https://sphinx-gallery.github.io/ Priority: optional Section: python Filename: pool/main/s/sphinx-gallery/python-sphinx-gallery_0.2.0-1~nd18.04+1_all.deb Size: 71888 SHA256: 187c96a7dc694bace9c60a3ecf33e40955a57f94161b4e7943a3f5bdd04217f6 SHA1: 0b44b91d42e5a2a2a37730e1616068faf3e4adc3 MD5sum: 2ea0bf7c48acd64c4f87b3dc054b1a90 Description: extension that builds an HTML gallery of examples from Python scripts * Simple examples that run out of the box are the best way to learn a library * Pleasing, organized, visual layouts * Links, searching, backlinks throughout examples and documentation . This package contains the Python 2 version of sphinx-gallery. Package: python-sphinx-gallery-doc Source: sphinx-gallery Version: 0.2.0-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2597 Depends: neurodebian-popularity-contest Homepage: https://sphinx-gallery.github.io/ Priority: optional Section: doc Filename: pool/main/s/sphinx-gallery/python-sphinx-gallery-doc_0.2.0-1~nd18.04+1_all.deb Size: 1328876 SHA256: f63a6b2046fcfa3abc217e3e837fdc05ecf1c76724300ca9e0eb9e55adec1c1d SHA1: e58fcdbdc4f33f153840530042dc86a64d17f56d MD5sum: 19613e357bb27018a73b292c95469c2c Description: extension that builds an HTML gallery of examples from Python scripts (Doc) * Simple examples that run out of the box are the best way to learn a library * Pleasing, organized, visual layouts * Links, searching, backlinks throughout examples and documentation . This package contains documentation for sphinx-gallery. Package: python-stfio Source: stimfit Version: 0.15.8-1~nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1428 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7~), python-numpy (>= 1:1.13.1), python-numpy-abi9, python2.7, python:any (<< 2.8), python:any (>= 2.7.5-5~), libblas3 | libblas.so.3, libc6 (>= 2.14), libfftw3-double3 (>= 3.3.5), libgcc1 (>= 1:3.0), libhdf5-100, liblapack3 | liblapack.so.3, libpython2.7 (>= 2.7), libstdc++6 (>= 5.2), 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_0.15.8-1~nd18.04+1_amd64.deb Size: 485144 SHA256: 22a1c2664a520d41d6010e58a328f3bd804cfdeefe742612e8b9e28618abb42f SHA1: 9697aae468a6b060269481d3dc3012bcdceb08b9 MD5sum: b206b8291b705d7013804c182ddf2ef3 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-sympy Source: sympy Version: 1.3-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 26373 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~), python-mpmath Recommends: python-pil, python-ctypes, ipython, python-numpy, python-pyglet, python-gmpy Suggests: texlive-fonts-extra, dvipng, python-sympy-doc Enhances: isympy Homepage: http://sympy.org/ Priority: optional Section: python Filename: pool/main/s/sympy/python-sympy_1.3-1~nd18.04+1_all.deb Size: 3499420 SHA256: 77e3ecb1c95afd35e9b2562192d734f82e50b29c67142bfa49931bf0da739b99 SHA1: 8fe0199bf603abc853ec8db4cfda2c3a32b572b2 MD5sum: 00b59c837d3c01b249245ba023cef078 Description: Computer Algebra System (CAS) in Python SymPy is a Python library for symbolic mathematics (manipulation). It aims to become a full-featured computer algebra system (CAS) while keeping the code as simple as possible in order to be comprehensible and easily extensible. SymPy is written entirely in Python and does not require any external libraries, except optionally for plotting support. Package: python-sympy-doc Source: sympy Version: 1.3-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 58949 Depends: neurodebian-popularity-contest Suggests: python-sympy Homepage: http://sympy.org/ Priority: optional Section: doc Filename: pool/main/s/sympy/python-sympy-doc_1.3-1~nd18.04+1_all.deb Size: 7248216 SHA256: dc49d55e563905f985e295e8df67ecc02838547c8ad46d3fb8de229bdec557e5 SHA1: e3da47048666277ac57d28174f2d4aa45f5fc16a MD5sum: 726cd27aba9a4edd8e96303b10dab7d1 Description: Computer Algebra System (CAS) in Python - Documentation SymPy is a Python library for symbolic mathematics (manipulation). It aims to become a full-featured computer algebra system (CAS) while keeping the code as simple as possible in order to be comprehensible and easily extensible. SymPy is written entirely in Python and does not require any external libraries, except optionally for plotting support. . This package contains the documentation for sympy Python module. Package: python-urllib3 Version: 1.12-1~bpo8+1~nd90+1+nd17.04+1+nd17.10+1+nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 254 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~), python-six Recommends: ca-certificates, python-ndg-httpsclient, python-openssl, python-pyasn1 Suggests: python-ntlm Homepage: http://urllib3.readthedocs.org Priority: optional Section: python Filename: pool/main/p/python-urllib3/python-urllib3_1.12-1~bpo8+1~nd90+1+nd17.04+1+nd17.10+1+nd18.04+1_all.deb Size: 65532 SHA256: c145793413b31e82702be3241a1e66132d8893276ac9e49337753667c31be299 SHA1: 2829979657aa7e8c9a03a35c4680aa8f102af336 MD5sum: d43700c36d4af637d0e59926e4156dcb Description: HTTP library with thread-safe connection pooling for Python urllib3 supports features left out of urllib and urllib2 libraries. . - Re-use the same socket connection for multiple requests (HTTPConnectionPool and HTTPSConnectionPool) (with optional client-side certificate verification). - File posting (encode_multipart_formdata). - Built-in redirection and retries (optional). - Supports gzip and deflate decoding. - Thread-safe and sanity-safe. - Small and easy to understand codebase perfect for extending and building upon. Package: python-urllib3-whl Source: python-urllib3 Version: 1.12-1~bpo8+1~nd90+1+nd17.04+1+nd17.10+1+nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 102 Depends: neurodebian-popularity-contest, python-six-whl Recommends: ca-certificates Homepage: http://urllib3.readthedocs.org Priority: optional Section: python Filename: pool/main/p/python-urllib3/python-urllib3-whl_1.12-1~bpo8+1~nd90+1+nd17.04+1+nd17.10+1+nd18.04+1_all.deb Size: 93052 SHA256: 658e64c416fbafd9bc070f9a20c9d0ddab39668df2bf08b2f8e36468b798cfdd SHA1: 30caaaaf95c7b1227cce9e7c1cecef270b129e5c MD5sum: dce299ce3ade22de0085cded08bedab0 Description: HTTP library with thread-safe connection pooling urllib3 supports features left out of urllib and urllib2 libraries. . - Re-use the same socket connection for multiple requests (HTTPConnectionPool and HTTPSConnectionPool) (with optional client-side certificate verification). - File posting (encode_multipart_formdata). - Built-in redirection and retries (optional). - Supports gzip and deflate decoding. - Thread-safe and sanity-safe. - Small and easy to understand codebase perfect for extending and building upon. . This package contains the universal wheel. Package: python-wrapt Version: 1.9.0-4~nd0~nd17.04+1+nd17.10+1+nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 121 Depends: neurodebian-popularity-contest, python-six, python (<< 2.8), python (>= 2.7~), python:any (<< 2.8), python:any (>= 2.7.5-5~), libc6 (>= 2.4) Homepage: https://github.com/GrahamDumpleton/wrapt Priority: optional Section: python Filename: pool/main/p/python-wrapt/python-wrapt_1.9.0-4~nd0~nd17.04+1+nd17.10+1+nd18.04+1_amd64.deb Size: 27860 SHA256: dfd57f9afc98eda3447fe3d4de30c311efb9b2a09e715f59478de45ae6d8c8a7 SHA1: fedff294d2758758602c77386ca5f7979c80163f MD5sum: e829aa0607cbce58da2f5b4ad8f6c23e Description: decorators, wrappers and monkey patching. - Python 2.x The aim of the wrapt module is to provide a transparent object proxy for Python, which can be used as the basis for the construction of function wrappers and decorator functions. . The wrapt module focuses very much on correctness. It therefore goes way beyond existing mechanisms such as functools.wraps() to ensure that decorators preserve introspectability, signatures, type checking abilities etc. The decorators that can be constructed using this module will work in far more scenarios than typical decorators and provide more predictable and consistent behaviour. . To ensure that the overhead is as minimal as possible, a C extension module is used for performance critical components. An automatic fallback to a pure Python implementation is also provided where a target system does not have a compiler to allow the C extension to be compiled. . This package contains the Python 2.x module. Package: python-wrapt-doc Source: python-wrapt Version: 1.9.0-4~nd0~nd17.04+1+nd17.10+1+nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 461 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0), sphinx-rtd-theme-common Homepage: https://github.com/GrahamDumpleton/wrapt Priority: optional Section: doc Filename: pool/main/p/python-wrapt/python-wrapt-doc_1.9.0-4~nd0~nd17.04+1+nd17.10+1+nd18.04+1_all.deb Size: 49736 SHA256: 5defb7d4bef5f2b569c3b7b2dce81c4afe346bdb9b7d5d971b6909743e328477 SHA1: dc2cd4e998317d4dbf8da1d58926798629b350cb MD5sum: 073fb58d36ceee0a4dadfee6a852098a Description: decorators, wrappers and monkey patching. - doc The aim of the wrapt module is to provide a transparent object proxy for Python, which can be used as the basis for the construction of function wrappers and decorator functions. . The wrapt module focuses very much on correctness. It therefore goes way beyond existing mechanisms such as functools.wraps() to ensure that decorators preserve introspectability, signatures, type checking abilities etc. The decorators that can be constructed using this module will work in far more scenarios than typical decorators and provide more predictable and consistent behaviour. . To ensure that the overhead is as minimal as possible, a C extension module is used for performance critical components. An automatic fallback to a pure Python implementation is also provided where a target system does not have a compiler to allow the C extension to be compiled. . This package contains the documentation. Package: python-xlwt Source: xlwt Version: 1.3.0-2~nd0~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 445 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~) Suggests: python-xlrd, python-xlrt-doc Homepage: https://pypi.python.org/pypi/xlwt Priority: optional Section: python Filename: pool/main/x/xlwt/python-xlwt_1.3.0-2~nd0~nd18.04+1_all.deb Size: 83872 SHA256: 5c953000522ed0a40179c07ea8dc0ea4ac2ae31e1b074081efae316f1562d48c SHA1: 4a06974461b0e57c4412136b0a720966037a8d89 MD5sum: f5e1690584d1b6246262282b0b2a478f Description: module for writing Microsoft Excel spreadsheet files - Python 2.7 This package provides a pure Python module for writing spreadsheet files readable by Excel 97/2000/XP/2003, OpenOffice.org Calc, and Gnumeric. Excel spreadsheets can be generated on any platform without needing Excel or a COM server. . Xlwt is a fork of the unmaintained pyExcelerator module with several bugfixes and enhancements. For the functionality previously provided by the parse_xls function, see the python-xlrd package. . This package provides the Python 2.7 module. Package: python-xlwt-doc Source: xlwt Version: 1.3.0-2~nd0~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 246 Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Breaks: python-xlwt (<< 1.3.0) Replaces: python-xlwt (<< 1.3.0) Homepage: https://pypi.python.org/pypi/xlwt Priority: optional Section: doc Filename: pool/main/x/xlwt/python-xlwt-doc_1.3.0-2~nd0~nd18.04+1_all.deb Size: 52708 SHA256: ad2a10c48e05f4c94e60d9147e9bd4fa52e5c7b133d6ca325578bee0142023b0 SHA1: 4c4e7768dafda6758505b255ae8942dd9be7b389 MD5sum: 16b3706edf48716c2a3563d1a2089932 Description: module for writing Microsoft Excel spreadsheet files - doc This package provides a pure Python module for writing spreadsheet files readable by Excel 97/2000/XP/2003, OpenOffice.org Calc, and Gnumeric. Excel spreadsheets can be generated on any platform without needing Excel or a COM server. . Xlwt is a fork of the unmaintained pyExcelerator module with several bugfixes and enhancements. For the functionality previously provided by the parse_xls function, see the python-xlrd package. . This package provides the documentation. Package: python3-boto Source: python-boto Version: 2.44.0-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 5140 Depends: neurodebian-popularity-contest, python3-requests, python3:any (>= 3.3.2-2~), python3-six Homepage: https://github.com/boto/boto Priority: optional Section: python Filename: pool/main/p/python-boto/python3-boto_2.44.0-1~nd18.04+1_all.deb Size: 739796 SHA256: d595e83ff222c6241630fd26fa46fb04a76e75e63cf614e22a54114fc858df27 SHA1: 031953dfdf305ffb24a442a94b31cfecec9136ab MD5sum: 86dd962c7e54994f10498e0a331f50ea Description: Python interface to Amazon's Web Services - Python 3.x Boto is a Python interface to the infrastructure services available from Amazon. . Boto supports the following services: * Elastic Compute Cloud (EC2) * Elastic MapReduce * CloudFront * DynamoDB * SimpleDB * Relational Database Service (RDS) * Identity and Access Management (IAM) * Simple Queue Service (SQS) * CloudWatch * Route53 * Elastic Load Balancing (ELB) * Flexible Payment Service (FPS) * Simple Storage Service (S3) * Glacier * Elastic Block Store (EBS) * and many more... . This package provides the Python 3.x module. Package: python3-bz2file Source: python-bz2file Version: 0.98-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 35 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~) Homepage: https://github.com/nvawda/bz2file Priority: optional Section: python Filename: pool/main/p/python-bz2file/python3-bz2file_0.98-1~nd18.04+1_all.deb Size: 8008 SHA256: 6da2d779a7143e865f5d2f04f7ef0eb0535651c5ceabe7f4c36f419e74ed4bd0 SHA1: bb2994e294d8f7e89d1fb08a5f6a191a04f2e04e MD5sum: 94de06b99b7d760c0e358da174bc84d1 Description: Python3 library for reading and writing bzip2-compressed files Bz2file is a Python library for reading and writing bzip2-compressed files. . It contains a drop-in replacement for the file interface in the standard library's bz2 module, including features from the latest development version of CPython that are not available in older releases. . Bz2file for Python3. Package: python3-dask Source: dask Version: 0.17.5-2~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2406 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~), python3-toolz Recommends: python3-cloudpickle, python3-partd, python3-numpy, python3-pandas, python3-requests Suggests: ipython, python-dask-doc, python3-bcolz, python3-blosc, python3-boto, python3-distributed (>= 1.21), python3-graphviz, python3-h5py, python3-psutil, python3-scipy, python3-sqlalchemy, python3-skimage, python3-sklearn, python3-tables Homepage: https://github.com/dask/dask Priority: optional Section: python Filename: pool/main/d/dask/python3-dask_0.17.5-2~nd18.04+1_all.deb Size: 429444 SHA256: e4e2e028704c221cf2f15ac486f0c9c07925e3b7c5029cd193d6062dcfe5858a SHA1: 8edbd6f93fcff30019dcd54dac803c7d2d6820b7 MD5sum: eb29c80b93c5f47a5e0b37932ddf7a06 Description: Minimal task scheduling abstraction for Python 3 Dask is a flexible parallel computing library for analytics, containing two components. . 1. Dynamic task scheduling optimized for computation. This is similar to Airflow, Luigi, Celery, or Make, but optimized for interactive computational workloads. 2. "Big Data" collections like parallel arrays, dataframes, and lists that extend common interfaces like NumPy, Pandas, or Python iterators to larger-than-memory or distributed environments. These parallel collections run on top of the dynamic task schedulers. . This contains the Python 3 version. Package: python3-datalad Source: datalad Version: 0.11.4-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4396 Depends: neurodebian-popularity-contest, git-annex (>= 6.20180913~) | git-annex-standalone (>= 6.20180913~), patool, python3-appdirs, python3-distutils | libpython3-stdlib (<= 3.6.4~rc1-2), python3-fasteners, python3-git (>= 2.1.6~), python3-humanize, python3-iso8601, python3-keyrings.alt | python3-keyring (<= 8), python3-secretstorage, python3-keyring, python3-mock, python3-msgpack, python3-pil, python3-requests, python3-simplejson, python3-six (>= 1.8.0), python3-tqdm, python3-wrapt, python3-boto, python3-chardet, python3:any (>= 3.3.2-2~) Recommends: python3-exif, python3-github, python3-jsmin, python3-html5lib, python3-httpretty, python3-libxmp, python3-lzma, python3-mutagen, python3-nose, python3-pyperclip, python3-requests-ftp, python3-vcr, python3-whoosh Suggests: python3-duecredit, python3-bs4, python3-numpy, datalad-containers, datalad-crawler, datalad-neuroimaging Homepage: http://datalad.org Priority: optional Section: python Filename: pool/main/d/datalad/python3-datalad_0.11.4-1~nd18.04+1_all.deb Size: 916980 SHA256: a717103e202e21cd35452ab4a96bc03df292c4a3e6ef88de64212e23a37cdbc3 SHA1: 1a5df09ff7e94d50fee1ca8ec60d05906219ecef MD5sum: 2f1a25589ea600bf43bfb53ec1e6b5cd Description: data files management and distribution platform DataLad is a data management and distribution platform providing access to a wide range of data resources already available online. Using git-annex as its backend for data logistics it provides following facilities built-in or available through additional extensions . - command line and Python interfaces for manipulation of collections of datasets (install, uninstall, update, publish, save, etc.) and separate files/directories (add, get) - extract, aggregate, and search through various sources of metadata (xmp, EXIF, etc; install datalad-neuroimaging for DICOM, BIDS, NIfTI support) - crawl web sites to automatically prepare and update git-annex repositories with content from online websites, S3, etc (install datalad-crawler) . This package installs the module for Python 3, and Recommends install all dependencies necessary for searching and managing datasets, publishing, and testing. If you need base functionality, install without Recommends. Package: python3-dcmstack Source: dcmstack Version: 0.7-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 186 Depends: neurodebian-popularity-contest, python3-dicom (>= 0.9.7~), python3-nibabel (>= 2.0~), python3-numpy, python3:any (>= 3.3.2-2~) Homepage: https://github.com/moloney/dcmstack Priority: optional Section: science Filename: pool/main/d/dcmstack/python3-dcmstack_0.7-1~nd18.04+1_all.deb Size: 35404 SHA256: 19625d707897416a8ccf524dcb795f22be674801039e418d368e951a39c3dbff SHA1: ec876269a5d192d958fdaa21b1c9f44099ba60ff MD5sum: 811308c223ac29cd4631c5af7979be21 Description: DICOM to NIfTI conversion - python3 package 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 Python3 package. Package: python3-dicom Source: pydicom Version: 1.2.1-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 13 Depends: neurodebian-popularity-contest, python3-pydicom Homepage: https://pydicom.github.io Priority: optional Section: oldlibs Filename: pool/main/p/pydicom/python3-dicom_1.2.1-1~nd18.04+1_all.deb Size: 4904 SHA256: 76809ba6969e5f95059bc2fa9fc1ee3cc97a9d6a42ed7db074384ba2c51ca876 SHA1: cf3365611e9873603f994d216b7ac378b554dd86 MD5sum: a45830603c52e65f94e4ab885c90371e Description: transitional package for python3-pydicom This is a transitional package. It can safely be removed. Package: python3-duecredit Source: duecredit Version: 0.7.0-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 248 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_0.7.0-1~nd18.04+1_all.deb Size: 53596 SHA256: ea210435d9296ad4a5ba29f089d28f37cda923b9e0060c879c52aa40ae362a7e SHA1: c6a7e9008fd07b67499a4512516dbbc2c992ba88 MD5sum: dca5fcf7e5a9987f0f663738d00630ec 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-exif Source: python-exif Version: 2.1.2-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 130 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~) Homepage: https://github.com/ianare/exif-py Priority: extra Section: python Filename: pool/main/p/python-exif/python3-exif_2.1.2-1~nd18.04+1_all.deb Size: 27832 SHA256: f8473c8061ec37db7bde4c804ace1b7e23038d9d5c646717d9f57a13b4bc8146 SHA1: 172c0124ababbcdc38099521895391b8ca5ef0a6 MD5sum: b30728754a0fc1c9264f95aa7147a4f0 Description: Python library to extract Exif data from TIFF and JPEG files This is a Python library to extract Exif information from digital camera image files. It contains the EXIF.py script and the exifread library. . This package provides the Python 3.x module. Package: python3-fsl Source: fslpy Version: 1.2.2-2~nd17.10+1+nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 403 Depends: neurodebian-popularity-contest, python3-lxml, python3-nibabel, python3-six (>= 1.0~), python3-deprecation, python3-indexed-gzip, python3-numpy, python3-wxgtk4.0, python3:any (>= 3.3.2-2~) Priority: optional Section: python Filename: pool/main/f/fslpy/python3-fsl_1.2.2-2~nd17.10+1+nd18.04+1_all.deb Size: 84304 SHA256: 95d890bf94a09fc1ac1e3f0abec4704ea5efaec82c2a5aeca14835d0b9db2afc SHA1: a2d18cbd7f8b605aeb9cd831c56f349c1c2a6e25 MD5sum: 3eae59a019fbac0edf3f0124a0c5ea2f Description: FSL Python library Support library for FSL. . This package provides the Python 3 module. Package: python3-httpretty Source: python-httpretty Version: 0.8.14-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 98 Depends: neurodebian-popularity-contest, python3-urllib3, python3:any (>= 3.3.2-2~) Homepage: https://github.com/gabrielfalcao/httpretty Priority: optional Section: python Filename: pool/main/p/python-httpretty/python3-httpretty_0.8.14-1~nd18.04+1_all.deb Size: 21112 SHA256: 7871162f68154a7628fc9f5a51e82f1d6d89a357aecab26b74c2e2ad92a95ca1 SHA1: 76dd2f110a39d72c816f669ca2f1b92f04275b95 MD5sum: fe6acc47e8a7926eb3d23bd915e5ad53 Description: HTTP client mock - Python 3.x Once upon a time a Python developer wanted to use a RESTful API, everything was fine but until the day he needed to test the code that hits the RESTful API: what if the API server is down? What if its content has changed ? . Don't worry, HTTPretty is here for you. . This package provides the Python 3.x module. Package: python3-hypothesis Source: python-hypothesis Version: 3.44.1-1~bpo9+1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 610 Depends: neurodebian-popularity-contest, python3-attr, python3-coverage, python3:any (>= 3.3.2-2~) Suggests: python-hypothesis-doc Homepage: https://github.com/DRMacIver/hypothesis Priority: optional Section: python Filename: pool/main/p/python-hypothesis/python3-hypothesis_3.44.1-1~bpo9+1~nd18.04+1_all.deb Size: 120360 SHA256: 8483c480f0969af1f62f0c01476b3d0469371d0e5f2da39cc3c87526e288d6bf SHA1: 4120650bbf83f92f51f5a42096b542a58700be37 MD5sum: f957686ef39d10f908828d48a71c3b4a Description: advanced Quickcheck style testing library for Python 3 Hypothesis is a library for testing your Python code against a much larger range of examples than you would ever want to write by hand. It's based on the Haskell library, Quickcheck, and is designed to integrate seamlessly into your existing Python unit testing work flow. . Hypothesis is both extremely practical and also advances the state of the art of unit testing by some way. It's easy to use, stable, and extremely powerful. If you're not using Hypothesis to test your project then you're missing out. . This package contains the Python 3 module. Package: python3-indexed-gzip Source: indexed-gzip Version: 0.8.6-1~nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1183 Depends: neurodebian-popularity-contest, cython3, python3-numpy, libc6 (>= 2.14), zlib1g (>= 1:1.2.2.4), python3 (<< 3.7), python3 (>= 3.6~), python3:any (>= 3.3.2-2~) Provides: python3.6-indexed-gzip Homepage: https://github.com/pauldmccarthy/indexed_gzip Priority: optional Section: python Filename: pool/main/i/indexed-gzip/python3-indexed-gzip_0.8.6-1~nd18.04+1_amd64.deb Size: 352884 SHA256: e5d96736bb592d83b3ed5d38bf9d18ca39a6b234694152ac64bdd9918c5de383 SHA1: 3c6a46947e66237a697e5ac746d4dc1a9a1255f1 MD5sum: a6ef0ab59f9b7b04930069c5fe92ccfd Description: fast random access of gzip files in Python Drop-in replacement `IndexedGzipFile` for the built-in Python `gzip.GzipFile` class that does not need to start decompressing from the beginning of the file when for every `seek()`. It gets around this performance limitation by building an index, which contains *seek points*, mappings between corresponding locations in the compressed and uncompressed data streams. Each seek point is accompanied by a chunk (32KB) of uncompressed data which is used to initialise the decompression algorithm, allowing to start reading from any seek point. If the index is built with a seek point spacing of 1MB, only 512KB (on average) of data have to be decompressed to read from any location in the file. . This package provides the Python 3 module. Package: python3-joblib Source: joblib Version: 0.13.2-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 870 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~), procps Recommends: python3-numpy, python3-pytest, python3-simplejson, python3-psutil Homepage: http://packages.python.org/joblib/ Priority: optional Section: python Filename: pool/main/j/joblib/python3-joblib_0.13.2-1~nd18.04+1_all.deb Size: 189368 SHA256: 1fd7d7373865e97e53f5966c5c764d66f13339e7c7c931fac7a5fb06cee83da0 SHA1: 6c62bafff8e95330a090c766636523e1ed4f1c5a MD5sum: 942a38bdaf0982b7938ec9b679987dc4 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-libxmp Source: python-xmp-toolkit Version: 2.0.1+git20140309.5437b0a-4~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 145 Depends: neurodebian-popularity-contest, libexempi3, python3-tz, python3:any (>= 3.3.2-2~) Suggests: python-libxmp-doc Homepage: http://python-xmp-toolkit.readthedocs.org/ Priority: optional Section: python Filename: pool/main/p/python-xmp-toolkit/python3-libxmp_2.0.1+git20140309.5437b0a-4~nd18.04+1_all.deb Size: 23480 SHA256: 470301d9e17950b473864ca92376c79cbbf33b13e30964696087c838666e6b2a SHA1: 4fc2129798bcfd362848d68735e4070eb9691a98 MD5sum: a3b6a28dab3c32495fcd43036b49a5ad Description: Python3 library for XMP metadata Python XMP Toolkit is a library for working with XMP metadata, as well as reading/writing XMP metadata stored in many different file formats. . XMP (Extensible Metadata Platform) facilitates embedding metadata in files using a subset of RDF. Most notably XMP supports embedding metadata in PDF and many image formats, though it is designed to support nearly any file type. . This package provides Python3 bindings. Package: python3-msgpack Source: msgpack-python Version: 0.4.8-1~nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 229 Depends: neurodebian-popularity-contest, python3 (<< 3.7), python3 (>= 3.6~), python3:any (>= 3.3.2-2~), libc6 (>= 2.14) Homepage: https://pypi.python.org/pypi/msgpack-python/ Priority: optional Section: python Filename: pool/main/m/msgpack-python/python3-msgpack_0.4.8-1~nd18.04+1_amd64.deb Size: 67196 SHA256: 08baed9b6858b0d8d0c15c3a4d16ccad8a6c39864da102bed08eaa2c2f6fd139 SHA1: dc930f6c9fe845c781d5102784980a621df96315 MD5sum: eb853d8de644fb8004b7dfc793174ca0 Description: Python 3 implementation of MessagePack format MessagePack is a binary-based efficient object serialization format. It enables the exchange of structured objects between many languages like JSON. But unlike JSON, it is very fast and small. . This package contains a Python 3 extension module implementing the MessagePack format. Package: python3-nibabel Source: nibabel Version: 2.4.0-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 65801 Depends: neurodebian-popularity-contest, python3-numpy (>= 1.2), python3-six (>= 1.3), python3:any (>= 3.3.2-2~), python3-scipy Recommends: python3-dicom, python3-fuse Suggests: python-nibabel-doc, python3-mock Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: python Filename: pool/main/n/nibabel/python3-nibabel_2.4.0-1~nd18.04+1_all.deb Size: 2641876 SHA256: 54dadf95c8b95ce41341c72ea5a03348d2e960bbd2d27b980f05b5cf08268104 SHA1: 5592da3cc6e0e072bf9b41b72f1759b9842954a6 MD5sum: 0ce7f9ed7e0322652e1e597a4d93726f 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-pandas Source: pandas Version: 0.23.3-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 12450 Depends: neurodebian-popularity-contest, python3-dateutil, python3-numpy (>= 1:1.7~), python3-tz, python3:any (>= 3.3.2-2~), python3-pandas-lib (>= 0.23.3-1~nd18.04+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: https://pandas.pydata.org/ Priority: optional Section: python Filename: pool/main/p/pandas/python3-pandas_0.23.3-1~nd18.04+1_all.deb Size: 1743280 SHA256: 70ae0e91c6a422281c2d7a1688bfe73d8634fa423c5300b5afe84ee4625a6397 SHA1: 0dba4911012c5e4bdd229a88904fbad8c10492c4 MD5sum: 4ae88fae1397a00675baaa297c6f78fb 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: 0.23.3-1~nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 15816 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:3.0), libstdc++6 (>= 5.2), python3-numpy (>= 1:1.13.1), python3-numpy-abi9, python3 (<< 3.7), python3 (>= 3.6~), python3:any (>= 3.2~) Homepage: https://pandas.pydata.org/ Priority: optional Section: python Filename: pool/main/p/pandas/python3-pandas-lib_0.23.3-1~nd18.04+1_amd64.deb Size: 3388624 SHA256: ac954f5184a5cc94d0d23f0284fc5b1aed581fe4be329cbb9cac561e098e4b23 SHA1: 089b2b22f4d026bba55e11479eeca219e68c6e83 MD5sum: ef1f713881dab21dde955344848ad86c Description: low-level implementations and bindings for pandas - Python 3 This is an add-on package for python-pandas providing architecture-dependent extensions. . This package contains the Python 3 version. Package: python3-patsy Source: patsy Version: 0.5.0+git13-g54dcf7b-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 779 Depends: neurodebian-popularity-contest, python3-numpy, python3-six, python3:any (>= 3.3.2-2~) Recommends: python3-pandas Suggests: python-patsy-doc Homepage: http://github.com/pydata/patsy Priority: optional Section: python Filename: pool/main/p/patsy/python3-patsy_0.5.0+git13-g54dcf7b-1~nd18.04+1_all.deb Size: 168412 SHA256: 82234dc094bd4ef8eaf4369b9442d29b18697e9fac8d88fda8e5cce95a44ef5c SHA1: 31806f281b4c7bb04c5cbc377709640166b27d47 MD5sum: 610eab0fd127c02273d2f7758d298054 Description: statistical models in Python using symbolic formulas patsy is a Python library for describing statistical models (especially linear models, or models that have a linear component) and building design matrices. . This package contains the Python 3 version. Package: python3-pydicom Source: pydicom Version: 1.2.1-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 14179 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~) Recommends: python3-numpy, python3-pil Suggests: python3-matplotlib, python3-gdcm Breaks: python3-dicom (<< 1~) Replaces: python3-dicom (<< 1~) Provides: python3-dicom Homepage: https://pydicom.github.io Priority: optional Section: python Filename: pool/main/p/pydicom/python3-pydicom_1.2.1-1~nd18.04+1_all.deb Size: 4396728 SHA256: 8fb4a6fe72f83f2568ef06c2f69bfd975a16aa98bf2409c88e3eb0ca6dcedd06 SHA1: 8901ca70e7f7437d9a260e9ffe2e8d67fa48099b MD5sum: 247b967c1d58cb70a812a3e16745df5a Description: DICOM medical file reading and writing (Python 3) pydicom is a pure Python module for parsing DICOM files. DICOM is a standard (http://medical.nema.org) for communicating medical images and related information such as reports and radiotherapy objects. . pydicom makes it easy to read DICOM files into natural pythonic structures for easy manipulation. Modified datasets can be written again to DICOM format files. . This package installs the module for Python 3. Package: python3-pydot Source: pydot Version: 1.2.3-1.1~nd17.10+1+nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 88 Depends: neurodebian-popularity-contest, python3-pyparsing (>= 2.0.1+dfsg1-1), python3:any (>= 3.3.2-2~), graphviz Homepage: https://github.com/erocarrera/pydot Priority: optional Section: python Filename: pool/main/p/pydot/python3-pydot_1.2.3-1.1~nd17.10+1+nd18.04+1_all.deb Size: 20008 SHA256: 41f4ca06d766dc94272217a3b5417db93a060b2c6164512af010f283ee97e6c1 SHA1: 066fd17c8ac2e53eaea3a917f229654a0a4d7a15 MD5sum: 45e40d63dca62c0694420a038d81522b Description: Python interface to Graphviz's dot (Python 3) pydot allows one to easily create both directed and non directed graphs from Python. Currently all attributes implemented in the Dot language are supported. . Output can be inlined in Postscript into interactive scientific environments like TeXmacs, or output in any of the format's supported by the Graphviz tools dot, neato, twopi. . This package contains pydot for Python 3. Package: python3-pynwb Source: pynwb Version: 0.5.1-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 687 Depends: neurodebian-popularity-contest, python3-dateutil, python3-h5py (>= 2.7.1), python3-numpy, python3-pandas, python3-requests, python3-ruamel.yaml, python3-six, python3:any (>= 3.3.2-2~) Homepage: https://github.com/NeurodataWithoutBorders/pynwb Priority: optional Section: python Filename: pool/main/p/pynwb/python3-pynwb_0.5.1-1~nd18.04+1_all.deb Size: 98164 SHA256: f612651e9723beb1a54208a033319cb3c4fe68725262b35c885917ee5d3edbbb SHA1: 2319c3d71cd8df7a2bbc4167efb5a17aedd78b45 MD5sum: e27b2e9778290c716aad3cf9be977ea8 Description: Python library for working with Neurodata in the NWB format PyNWB is a Python package for working with NWB files. It provides a high-level API for efficiently working with Neurodata stored in the NWB format. . Neurodata Without Borders: Neurophysiology (NWB:N) is a project to develop a unified data format for cellular-based neurophysiology data, focused on the dynamics of groups of neurons measured under a large range of experimental conditions. Package: python3-pyperclip Source: python-pyperclip Version: 1.6.0-2~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 42 Depends: neurodebian-popularity-contest, python3:any (>= 3.4~), xclip | xsel | python3-gi | python3-pyqt4 Homepage: https://github.com/asweigart/pyperclip Priority: optional Section: python Filename: pool/main/p/python-pyperclip/python3-pyperclip_1.6.0-2~nd18.04+1_all.deb Size: 9624 SHA256: dbb4e17d7cc1047cff9a6d699f629cac709a491bdbe2a129e3cfe37c2be9fc34 SHA1: 9706c13d57ad0cfa3357062a7a72aa6b8422bbc9 MD5sum: c75563d873d66a10f3a2f823d87bb26f Description: Cross-platform clipboard module for Python3 This module is a cross-platform Python3 module for copy and paste clipboard functions. . It currently only handles plaintext. . This is the Python 3 version of the package. Package: python3-reprozip Source: reprozip Version: 1.0.14-1~nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 176 Depends: neurodebian-popularity-contest, python3 (<< 3.7), python3 (>= 3.6~), python3-requests, python3-rpaths, python3-usagestats, python3-yaml, python3:any (>= 3.3.2-2~), libc6 (>= 2.14), libsqlite3-0 (>= 3.5.9) Multi-Arch: same Homepage: https://www.reprozip.org Priority: optional Section: python Filename: pool/main/r/reprozip/python3-reprozip_1.0.14-1~nd18.04+1_amd64.deb Size: 46492 SHA256: d7f7e3fac35da5455e294b70baf7e606c966c30e0f35602a3b8d0484265f8f30 SHA1: 08d2ba9697cd42ec32ea28911096a4a1fccb2b27 MD5sum: 802a7588940260f0a7e894600b272f0a Description: modules for the ReproZip packer ReproZip is a tool aimed at simplifying the process of creating reproducible experiments from command-line executions, a frequently-used common denominator in computational science. . It tracks operating system calls and creates a package that contains all the binaries, files and dependencies required to run a given command on the author’s computational environment (packing step). A reviewer can then extract the experiment in his environment to reproduce the results (unpacking step). . This package provides the modules for Python 3. Package: python3-reprozip-dbg Source: reprozip Version: 1.0.14-1~nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 265 Depends: neurodebian-popularity-contest, python3-dbg (<< 3.7), python3-dbg (>= 3.6~), python3-dbg:any (>= 3.3~), libc6 (>= 2.14), libsqlite3-0 (>= 3.5.9), python3-reprozip (= 1.0.14-1~nd18.04+1) Multi-Arch: same Homepage: https://www.reprozip.org Priority: optional Section: debug Filename: pool/main/r/reprozip/python3-reprozip-dbg_1.0.14-1~nd18.04+1_amd64.deb Size: 112384 SHA256: df0914bc3d44d27181d1b9c89e7e08c0a1e470395e8dfc085cf644c369a2cb75 SHA1: 26ac9cd563b8d86d345ee93c5769184de4bc8775 MD5sum: edd38fe53735988ef782c33a8c7ffc88 Description: debug extensions for the ReproZip packer ReproZip is a tool aimed at simplifying the process of creating reproducible experiments from command-line executions, a frequently-used common denominator in computational science. . It tracks operating system calls and creates a package that contains all the binaries, files and dependencies required to run a given command on the author’s computational environment (packing step). A reviewer can then extract the experiment in his environment to reproduce the results (unpacking step). . This package provides the debug extensions for Python 3. Build-Ids: 4cf35cca243fa8f71f55a000a946bbf16b5a4ffd Package: python3-requests Source: requests Version: 2.8.1-1~bpo8+1~nd90+1+nd17.04+1+nd17.10+1+nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 240 Depends: neurodebian-popularity-contest, python3-urllib3 (>= 1.12), python3:any (>= 3.3.2-2~), ca-certificates, python3-chardet Suggests: python3-ndg-httpsclient, python3-openssl, python3-pyasn1 Homepage: http://python-requests.org Priority: optional Section: python Filename: pool/main/r/requests/python3-requests_2.8.1-1~bpo8+1~nd90+1+nd17.04+1+nd17.10+1+nd18.04+1_all.deb Size: 68096 SHA256: 294ae65338c8ed82338ad1634a1144f89129762c124744dc2977382cfeb036c4 SHA1: e4119f62c9c97e7a16db21879c881f349261a1a2 MD5sum: 89ca22e1cd036102bac29575c7c5cc8a Description: elegant and simple HTTP library for Python3, built for human beings Requests allow you to send HTTP/1.1 requests. You can add headers, form data, multipart files, and parameters with simple Python dictionaries, and access the response data in the same way. It's powered by httplib and urllib3, but it does all the hard work and crazy hacks for you. . Features . - International Domains and URLs - Keep-Alive & Connection Pooling - Sessions with Cookie Persistence - Browser-style SSL Verification - Basic/Digest Authentication - Elegant Key/Value Cookies - Automatic Decompression - Unicode Response Bodies - Multipart File Uploads - Connection Timeouts . This package contains the Python 3 version of the library. Package: python3-skimage Source: skimage Version: 0.14.0-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 26053 Depends: neurodebian-popularity-contest, python3-matplotlib, python3-networkx, python3-numpy, python3-pil, python3-scipy, python3-six (>= 1.10.0), python3-skimage-lib (>= 0.14.0-1~nd18.04+1), python3-cloudpickle, python3-pywt, python3:any (>= 3.3.2-2~) Recommends: python3-pytest, python3-dask Suggests: python-skimage-doc Homepage: http://scikit-image.org Priority: optional Section: python Filename: pool/main/s/skimage/python3-skimage_0.14.0-1~nd18.04+1_all.deb Size: 19903844 SHA256: 4508b078b875aa138fd329c28cb513793e411df268fb51d49c5696433a55f4ce SHA1: 76db0817158359758e9b038f83d8af6fa41dc94f MD5sum: ec9253f15b08a3a6e46e1cfd3b7c1b6b Description: Python 3 modules for image processing scikit-image is a collection of image processing algorithms for Python. It performs tasks such as image loading, filtering, morphology, segmentation, color conversions, and transformations. . This package provides the Python 3 module. Package: python3-skimage-lib Source: skimage Version: 0.14.0-1~nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 10834 Depends: neurodebian-popularity-contest, python3-numpy (>= 1:1.13.1), python3-numpy-abi9, python3 (<< 3.7), python3 (>= 3.6~), python3:any (>= 3.2~), libc6 (>= 2.14), libgcc1 (>= 1:3.0), libstdc++6 (>= 5.2) Recommends: python3-skimage Multi-Arch: same Homepage: http://scikit-image.org Priority: optional Section: python Filename: pool/main/s/skimage/python3-skimage-lib_0.14.0-1~nd18.04+1_amd64.deb Size: 1724376 SHA256: ac8419d2cefbc87d8835a3c09fc7bf33f3545acdc428232ef85b2247138621cd SHA1: 19d762e09832411e7d69fb1d710cc227821741e3 MD5sum: c7aab635c55c760361a2e3008b85821a Description: Optimized low-level algorithms for Python 3 scikit-image This is an add-on package for python-skimage. It provides optimized, low-level implementations of algorithms. . This package provides the Python 3 libraries. Package: python3-sklearn Source: scikit-learn Version: 0.20.0+dfsg-2~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7539 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~), python3-numpy, python3-scipy, python3-sklearn-lib (>= 0.20.0+dfsg-2~nd18.04+1), python3-joblib (>= 0.9.2) Recommends: python3-nose, python3-pytest, 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_0.20.0+dfsg-2~nd18.04+1_all.deb Size: 1517464 SHA256: 62578f769485fd99e9dd08863fb5c6eb336d0ddc98d3518cb836238c04d4a261 SHA1: 5e893a9dee32cdfe348b5eac185cb8633d818a6f MD5sum: 87fc62b92b92e7a70096642e6a14b612 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: 0.20.0+dfsg-2~nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 6933 Depends: neurodebian-popularity-contest, libatlas3-base, libc6 (>= 2.14), libgcc1 (>= 1:3.0), libstdc++6 (>= 5.2), python3-numpy (>= 1:1.13.1), python3-numpy-abi9, python3 (<< 3.7), python3 (>= 3.6~) Provides: python3.6-sklearn-lib Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python3-sklearn-lib_0.20.0+dfsg-2~nd18.04+1_amd64.deb Size: 1503232 SHA256: 092ed687475c21488d9584ad65df5cac4086ea1af916e0637474d1457e832923 SHA1: 09cd6d0fc4765543c82e1808fdbfff2abf5fca65 MD5sum: bfc897fb96c2a6f709158a7446d76201 Description: low-level implementations and bindings for scikit-learn - Python 3 This is an add-on package for python-sklearn. It provides low-level implementations and custom Python bindings for the LIBSVM library. . This package contains the Python 3 version. Package: python3-sphinx-gallery Source: sphinx-gallery Version: 0.2.0-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 217 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~), python3-matplotlib, python3-pil, python3-sphinx Suggests: python-sphinx-gallery-doc, python3-seaborn Homepage: https://sphinx-gallery.github.io/ Priority: optional Section: python Filename: pool/main/s/sphinx-gallery/python3-sphinx-gallery_0.2.0-1~nd18.04+1_all.deb Size: 71960 SHA256: feb786342f3e479c2184f7e628e2d0c4ee07c785394e20f08511d7883510706c SHA1: 4182e67ceabd0173bd72efb60d9c23881be7bc8b MD5sum: a37099ba5668d6a9ffb6d67cf42e004a Description: extension that builds an HTML gallery of examples from Python scripts (Python 3) * Simple examples that run out of the box are the best way to learn a library * Pleasing, organized, visual layouts * Links, searching, backlinks throughout examples and documentation . This package contains the Python 3 version of sphinx-gallery. Package: python3-sympy Source: sympy Version: 1.3-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 26373 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~), python3-mpmath Recommends: python3-pil, ipython3, python3-numpy Suggests: texlive-fonts-extra, dvipng, python-sympy-doc Enhances: isympy3 Homepage: http://sympy.org/ Priority: optional Section: python Filename: pool/main/s/sympy/python3-sympy_1.3-1~nd18.04+1_all.deb Size: 3499860 SHA256: 53cb14c2b1c4c1c9d6fbbba3fbc6264a8e87e2be45b3123d418614d89710a1c6 SHA1: 9ee3c3940df9a2dd9aa8aa853cc8346afcdd550b MD5sum: 278de7715c7f0137bf7e5615cea211e1 Description: Computer Algebra System (CAS) in Python (Python3) SymPy is a Python library for symbolic mathematics (manipulation). It aims to become a full-featured computer algebra system (CAS) while keeping the code as simple as possible in order to be comprehensible and easily extensible. SymPy is written entirely in Python and does not require any external libraries, except optionally for plotting support. . This package contains the Python 3 version of sympy. Package: python3-urllib3 Source: python-urllib3 Version: 1.12-1~bpo8+1~nd90+1+nd17.04+1+nd17.10+1+nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 254 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~), python3-six Recommends: ca-certificates Suggests: python3-ndg-httpsclient, python3-openssl, python3-pyasn1 Homepage: http://urllib3.readthedocs.org Priority: optional Section: python Filename: pool/main/p/python-urllib3/python3-urllib3_1.12-1~bpo8+1~nd90+1+nd17.04+1+nd17.10+1+nd18.04+1_all.deb Size: 65652 SHA256: 2f09fcb63c34fe4fefab358337681b21f4a9370fa8dcb95b0b6af97327eaaba7 SHA1: 7902c9e8853b1d0d853496b318e8a07c2d54fe79 MD5sum: 45fb1be596dafa3b069af7cebd2df35f Description: HTTP library with thread-safe connection pooling for Python3 urllib3 supports features left out of urllib and urllib2 libraries. . - Re-use the same socket connection for multiple requests (HTTPConnectionPool and HTTPSConnectionPool) (with optional client-side certificate verification). - File posting (encode_multipart_formdata). - Built-in redirection and retries (optional). - Supports gzip and deflate decoding. - Thread-safe and sanity-safe. - Small and easy to understand codebase perfect for extending and building upon. . This package contains the Python 3 version of the library. Package: python3-wrapt Source: python-wrapt Version: 1.9.0-4~nd0~nd17.04+1+nd17.10+1+nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 121 Depends: neurodebian-popularity-contest, python3-six, python3 (<< 3.7), python3 (>= 3.6~), python3:any (>= 3.3.2-2~), libc6 (>= 2.4) Homepage: https://github.com/GrahamDumpleton/wrapt Priority: optional Section: python Filename: pool/main/p/python-wrapt/python3-wrapt_1.9.0-4~nd0~nd17.04+1+nd17.10+1+nd18.04+1_amd64.deb Size: 27824 SHA256: 04db28dcf79c3e15931313a343905595d06e0103ca0a16c2e8f36acc9cac0261 SHA1: 4543a1762bb0f513d0402d2f8f5b57cf1b9de84e MD5sum: 83abda97b55ffb2acceee7f205d955f3 Description: decorators, wrappers and monkey patching. - Python 3.x The aim of the wrapt module is to provide a transparent object proxy for Python, which can be used as the basis for the construction of function wrappers and decorator functions. . The wrapt module focuses very much on correctness. It therefore goes way beyond existing mechanisms such as functools.wraps() to ensure that decorators preserve introspectability, signatures, type checking abilities etc. The decorators that can be constructed using this module will work in far more scenarios than typical decorators and provide more predictable and consistent behaviour. . To ensure that the overhead is as minimal as possible, a C extension module is used for performance critical components. An automatic fallback to a pure Python implementation is also provided where a target system does not have a compiler to allow the C extension to be compiled. . This package contains the Python 3.x module. Package: python3-xlwt Source: xlwt Version: 1.3.0-2~nd0~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 445 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~) Suggests: python3-xlrd, python-xlrt-doc Homepage: https://pypi.python.org/pypi/xlwt Priority: optional Section: python Filename: pool/main/x/xlwt/python3-xlwt_1.3.0-2~nd0~nd18.04+1_all.deb Size: 83968 SHA256: 6d0022cfbde172d085b49b28144d97d9bb1536d00b3f36d2de16a669d49435f0 SHA1: da74706e07d2d85bf7218d34788b11d7d9710a10 MD5sum: ae10770e07bc32bca6ed431a1c02a884 Description: module for writing Microsoft Excel spreadsheet files - Python 3.x This package provides a pure Python module for writing spreadsheet files readable by Excel 97/2000/XP/2003, OpenOffice.org Calc, and Gnumeric. Excel spreadsheets can be generated on any platform without needing Excel or a COM server. . Xlwt is a fork of the unmaintained pyExcelerator module with several bugfixes and enhancements. For the functionality previously provided by the parse_xls function, see the python-xlrd package. . This package provides the Python 3.x module. Package: rclone Version: 1.41-1~ndall0 Architecture: amd64 Maintainer: Debian Go Packaging Team Installed-Size: 19633 Depends: libc6 (>= 2.3.2) Built-Using: go-md2man (= 1.0.8+ds-1), golang-1.10 (= 1.10.3-1), golang-bazil-fuse (= 0.0~git20160811.0.371fbbd-2), golang-github-a8m-tree (= 0.0~git20171213.cf42b1e-1), golang-github-abbot-go-http-auth (= 0.0~git20150714.0.46b9627-2), golang-github-aws-aws-sdk-go (= 1.12.79+dfsg-1), golang-github-azure-azure-sdk-for-go (= 10.3.0~beta-1), golang-github-azure-go-autorest (= 8.3.1-1), golang-github-coreos-bbolt (= 1.3.1-coreos.5-1), golang-github-davecgh-go-spew (= 1.1.0-4), golang-github-dgrijalva-jwt-go-v3 (= 3.1.0-2), golang-github-djherbis-times (= 1.0.1+git20170215.d25002f-1), golang-github-dropbox-dropbox-sdk-go-unofficial (= 4.1.0-1), golang-github-go-ini-ini (= 1.32.0-2), golang-github-google-go-querystring (= 0.0~git20170111.0.53e6ce1-4), golang-github-jlaffaye-ftp (= 0.0~git20170707.0.a05056b-1), golang-github-jmespath-go-jmespath (= 0.2.2-2), golang-github-kardianos-osext (= 0.0~git20170510.0.ae77be6-5), golang-github-kr-fs (= 0.0~git20131111.0.2788f0d-2), golang-github-mattn-go-runewidth (= 0.0.2+git20170510.3.97311d9-1), golang-github-ncw-go-acd (= 0.0~git20171120.887eb06-1), golang-github-unknwon-goconfig (= 0.0~git20160828.0.5aa4f8c-3), golang-github-vividcortex-ewma (= 0.0~git20160822.20.c595cd8-3), golang-google-cloud (= 0.9.0-5), golang-goprotobuf (= 0.0~git20170808.0.1909bc2-2) Homepage: https://github.com/ncw/rclone Priority: optional Section: net Filename: pool/main/r/rclone/rclone_1.41-1~ndall0_amd64.deb Size: 4810068 SHA256: b62160db730a2285a36444f0eb30a9f4c6a67957e03fff27de9cc3f8a7ecd689 SHA1: 68368135f21e5fa81d2904f9391054208697d5b3 MD5sum: f58523511ec0a1334697803e366f753e Description: rsync for commercial cloud storage Rclone is a program to sync files and directories between the local file system and a variety of commercial cloud storage providers: . - Google Drive - Amazon S3 - Openstack Swift / Rackspace cloud files / Memset Memstore - Dropbox - Google Cloud Storage - Amazon Drive - Microsoft One Drive - Hubic - Backblaze B2 - Yandex Disk Package: reprozip Version: 1.0.14-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 14 Depends: neurodebian-popularity-contest, python3:any (>= 3.3~), python3-reprozip Multi-Arch: foreign Homepage: https://www.reprozip.org Priority: optional Section: utils Filename: pool/main/r/reprozip/reprozip_1.0.14-1~nd18.04+1_all.deb Size: 3640 SHA256: 33f8657ad4be0be129ec7be7db007bdaab9312625b805e19452e43e6cdf15a36 SHA1: e10ca304374f2e6adadc2d0192250d15d13fb97a MD5sum: 0b8aa86e68b8ce07042ba89843ee6bc6 Description: tool for reproducing scientific experiments (packer) ReproZip is a tool aimed at simplifying the process of creating reproducible experiments from command-line executions, a frequently-used common denominator in computational science. . It tracks operating system calls and creates a package that contains all the binaries, files and dependencies required to run a given command on the author’s computational environment (packing step). A reviewer can then extract the experiment in his environment to reproduce the results (unpacking step). . This package provides the ReproZip packer. Package: ruby-asciidoctor Source: asciidoctor Version: 1.5.7.1-1~nd~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 817 Depends: neurodebian-popularity-contest, ruby | ruby-interpreter Breaks: asciidoctor (<< 1.5.6.2-1) Replaces: asciidoctor (<< 1.5.6.2-1) Homepage: http://asciidoctor.org Priority: optional Section: ruby Filename: pool/main/a/asciidoctor/ruby-asciidoctor_1.5.7.1-1~nd~nd18.04+1_all.deb Size: 183652 SHA256: 1bd60cf37c603d692317b3795a8d1d9554a325b7b68f77ac88a0f9dc1d557d09 SHA1: 4ff52a84a8161595393378756b12f27b02b74e23 MD5sum: 861e21d8cd6d63083ac44df52ffcf992 Description: AsciiDoc to HTML rendering for Ruby (core libraries) Asciidoctor is a pure Ruby processor for converting AsciiDoc source files and strings into HTML 5, DocBook 4.5, DocBook 5.0 and other formats. . This package contains the library files used by the asciidoctor package. Ruby-Versions: all Package: singularity-container Version: 2.6.1-2~nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2508 Depends: neurodebian-popularity-contest, python, squashfs-tools, ca-certificates, libarchive13 (>= 3.0.4), libc6 (>= 2.27) Recommends: e2fsprogs Homepage: http://www.sylabs.io Priority: optional Section: admin Filename: pool/main/s/singularity-container/singularity-container_2.6.1-2~nd18.04+1_amd64.deb Size: 352904 SHA256: 7aa374c949802003d7bef0255b6f398707058bff431f5bdc9517b70f3aa135ee SHA1: 32d29f0788cc282140cab434df0da1c885b1fdca MD5sum: 66ff8456a627f2f290c9a821feeab1ea 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: stimfit Version: 0.15.8-1~nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 3242 Depends: neurodebian-popularity-contest, libblas3 | libblas.so.3, libc6 (>= 2.14), libfftw3-double3 (>= 3.3.5), libgcc1 (>= 1:3.0), libhdf5-100, liblapack3 | liblapack.so.3, libpython2.7 (>= 2.7), libstdc++6 (>= 5.2), libwxbase3.0-0v5 (>= 3.0.4+dfsg), libwxgtk3.0-gtk3-0v5 (>= 3.0.4+dfsg), python-numpy (>= 1:1.13.1), python-numpy-abi9, python2.7, python:any (>= 2.7.5-5~), 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_0.15.8-1~nd18.04+1_amd64.deb Size: 901828 SHA256: 0c39bb81f58fb058d2b116072c0b8b360fa55e3ef94501794ca337437f905934 SHA1: 2aafc16b5c6abe6d6eca94a96f7d8336d74b46da MD5sum: db9b78728fe11937fb5947d9806fed97 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: 0.15.8-1~nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 8534 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_0.15.8-1~nd18.04+1_amd64.deb Size: 8292804 SHA256: ed830c176858e021fdd033207a972dd7c0fc5118b38ba753f9e36b4174b44c04 SHA1: 7745f9e4ec958bf39bd4166a8fe2fbf27a867c18 MD5sum: 5988d16cf6fa769a1205f1dbc4fa3ee3 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. Build-Ids: 1477d586cf868d076e5ff1fe248d35eef86877e3 47457d4f6c8d9c98112db060c76c75e5babc5d26 4c0c0dac69dce2c9572f6570419672c3ecec2581 5ee73f012c8ed9a76b97d15e236e30a5358c9874 c0c26338820a9ee9c2e3c5b4fdb1bef03f2574c7 da2fde50bddb1a901b88243293b06ef868d60bb1 e74967bedda569784436501fb7a48e58f604e665 Package: vowpal-wabbit Version: 8.6.1.dfsg1-1~nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 234 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:3.0), libstdc++6 (>= 7), libvw0 (= 8.6.1.dfsg1-1~nd18.04+1) Suggests: vowpal-wabbit-doc Homepage: http://hunch.net/~vw/ Priority: optional Section: science Filename: pool/main/v/vowpal-wabbit/vowpal-wabbit_8.6.1.dfsg1-1~nd18.04+1_amd64.deb Size: 59032 SHA256: 0d184f96842946c74d556dd1ead5969d76daf7cc8d7fdfe0a409f205efec0de9 SHA1: fe731742332682368c820c709ff9d98821cf2324 MD5sum: 51010e2c6bc03551684e862d42a029ed Description: fast and scalable online machine learning algorithm Vowpal Wabbit is a fast online machine learning algorithm. The core algorithm is specialist gradient descent (GD) on a loss function (several are available). VW features: - flexible input data specification - speedy learning - scalability (bounded memory footprint, suitable for distributed computation) - feature pairing Package: vowpal-wabbit-dbg Source: vowpal-wabbit Version: 8.6.1.dfsg1-1~nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 603 Depends: neurodebian-popularity-contest, vowpal-wabbit (= 8.6.1.dfsg1-1~nd18.04+1) Homepage: http://hunch.net/~vw/ Priority: optional Section: debug Filename: pool/main/v/vowpal-wabbit/vowpal-wabbit-dbg_8.6.1.dfsg1-1~nd18.04+1_amd64.deb Size: 89044 SHA256: 8951c91bf0d7d6ce89dddef45b0f9122404e788105ec29187f074eeba0fa570f SHA1: a74bd112cd6daffb06541aaddfebb475f224fa82 MD5sum: 763275a8224486fa3f887629dc1eba83 Description: fast and scalable online machine learning algorithm - debug files Vowpal Wabbit is a fast online machine learning algorithm. The core algorithm is specialist gradient descent (GD) on a loss function (several are available). VW features: - flexible input data specification - speedy learning - scalability (bounded memory footprint, suitable for distributed computation) - feature pairing . This package contains debug symbols for the binaries shipped by vowpal-wabbit packages. Build-Ids: 40461077bc2fd7b9d55158f97b293ccb0204da31 812912d904f8081bcd7649cb934a2a806f0155ac 86b4310ac9d2f34572c1b6fd510f8480e438bffd 8d766c95edcaa284aac24605df13cb23d5a1ca82 8edaa581c59b70bf86b2eb592cdef2828b4e2d03 b0da4782dc4b57d9d0b91b1c1efd576db005f75e db8adc82838b47450105aabd3df0db648df98811 eb88d5447d5af5e32857cb27e6fbda8317406cf2 Package: vowpal-wabbit-doc Source: vowpal-wabbit Version: 8.6.1.dfsg1-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 26318 Depends: neurodebian-popularity-contest Recommends: vowpal-wabbit Homepage: http://hunch.net/~vw/ Priority: optional Section: doc Filename: pool/main/v/vowpal-wabbit/vowpal-wabbit-doc_8.6.1.dfsg1-1~nd18.04+1_all.deb Size: 18693472 SHA256: 3924e0119fdad74b1b824f02e0fc6164d441ceccdace719147b230e040094c9e SHA1: f07e3653b997fcc203628a6cc5de088e49b02a45 MD5sum: 5ab092b1a3bbcbf35a22251fb68aefa2 Description: fast and scalable online machine learning algorithm - documentation Vowpal Wabbit is a fast online machine learning algorithm. The core algorithm is specialist gradient descent (GD) on a loss function (several are available). VW features: - flexible input data specification - speedy learning - scalability (bounded memory footprint, suitable for distributed computation) - feature pairing . This package contains examples (tests) for vowpal-wabbit.