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: 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: 1.3.1-1~nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 48607 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_1.3.1-1~nd18.04+1_amd64.deb Size: 22099592 SHA256: d6cad56a81c1562de1bbeaa37610e0d104774938f8f7bcd566340753a17be35c SHA1: 9eef5506deafcf6e0d5261ef2b0d291871ce7f9d MD5sum: 5f85640dea299e46fe367835e3d072f3 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: 1.3.1-1~nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 197630 Depends: neurodebian-popularity-contest, connectome-workbench (= 1.3.1-1~nd18.04+1) Homepage: http://www.nitrc.org/projects/workbench/ Priority: extra Section: debug Filename: pool/main/c/connectome-workbench/connectome-workbench-dbg_1.3.1-1~nd18.04+1_amd64.deb Size: 195543288 SHA256: 8f99f663c741919e6e46295702b0412bbe3da747f300081705c457d1bcf0664c SHA1: fb664d1f18bb04fcef0d76b88106eb8362e2a3c7 MD5sum: 97d6c07d5a79426a1cd6a08c28a6cd40 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: 627dc071fa40ddde4493511afe5ff4f11886abc8 e21867efda74d045af864d74bfdee48b56ff9c9a 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: datalad Version: 0.10.2-2~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 121 Depends: neurodebian-popularity-contest, python3-datalad (= 0.10.2-2~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_0.10.2-2~nd18.04+1_all.deb Size: 85496 SHA256: b60ac4cf0b4faae38490f4336716390d2362e571214969c2a69800786c287843 SHA1: 61af0d3b469c3aff7818d059610f80c072a2d76f MD5sum: b0abc281d42940dd3d27620c4fc42188 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: dcm2niix Version: 1:1.0.20180622-1~nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 633 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:3.0), libstdc++6 (>= 5.2), libyaml-cpp0.5v5 Homepage: https://github.com/rordenlab/dcm2niix Priority: optional Section: science Filename: pool/main/d/dcm2niix/dcm2niix_1.0.20180622-1~nd18.04+1_amd64.deb Size: 162824 SHA256: 36fb472beaf41956b13ce5fde7a1bd56fdbfbd200b381694f6f10034132ddbb9 SHA1: f87aba65c71d374614164bb02d5432d687531d23 MD5sum: abc2dc19c2f5b5e28a804c676caed96f 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: 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: 6.20180807+git63-gbafc55c4a-1~ndall+1 Architecture: amd64 Maintainer: Richard Hartmann Installed-Size: 184959 Depends: git, 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 (<= 0.9.1) Provides: git-annex Homepage: http://git-annex.branchable.com/ Priority: optional Section: utils Filename: pool/main/g/git-annex/git-annex-standalone_6.20180807+git63-gbafc55c4a-1~ndall+1_amd64.deb Size: 64740234 SHA256: f7771f6b2cc29202a81547723cbe6c8c41fa38572eae5759897c381aad2d0cde SHA1: 995088a74c9d330bfc7280b64a10c0d1110a1552 MD5sum: ccbc570c7fda6fcf45ce006a16bda2cd 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: 0.5-1~nd17.10+1+nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 199 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_0.5-1~nd17.10+1+nd18.04+1_all.deb Size: 48012 SHA256: bacd504c620e64fe71700f55314f77996d6995180f67436cffb2a75ee9602062 SHA1: 5fab45da0cb5f0b2db19af75ea5efd5382d4d9d5 MD5sum: dcbe4c2c753acf8976cc9f48fa491648 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: 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: nuitka Version: 0.5.32.5+ds-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7172 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_0.5.32.5+ds-1~nd18.04+1_all.deb Size: 750268 SHA256: a9644e766dc38bf25589c2357c21db9148f9d3d8175ee0e7a4b352348463a9c0 SHA1: 64c481e06ddeb451f9f5fd92d7515a43dd8e9ff1 MD5sum: 8252c87f18f3c52cac4a292b6c2cd111 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: 3.0.14.20180526.dfsg1-1~nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 4755 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), 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 (= 3.0.14.20180526.dfsg1-1~nd18.04+1), psychtoolbox-3-lib (= 3.0.14.20180526.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: extra Section: science Filename: pool/main/p/psychtoolbox-3/octave-psychtoolbox-3_3.0.14.20180526.dfsg1-1~nd18.04+1_amd64.deb Size: 938076 SHA256: 5245f60da24b19cf506690b7a1079d616eafb9dc0a4588ea00bf71ab68b563e6 SHA1: f5ccba62669cd0bb50c18b241f8308f2adabf8f6 MD5sum: 2dd67a66d567657a1f057d6175ce3e15 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: 3.0.14.20180526.dfsg1-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 253835 Depends: neurodebian-popularity-contest Recommends: alsa-utils Suggests: gnuplot Homepage: http://psychtoolbox.org Priority: extra Section: science Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-common_3.0.14.20180526.dfsg1-1~nd18.04+1_all.deb Size: 24194328 SHA256: efbfcc47427c5d62e44760ad37a61053e754cbc8b15d0ec0879d039b20ae371a SHA1: 33db20ba0203c375342eebf33c2622c07b5f15c6 MD5sum: 8e0c0c94a44d3aaf496830ef780155b3 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: 3.0.14.20180526.dfsg1-1~nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 18692 Depends: neurodebian-popularity-contest, octave-psychtoolbox-3 (= 3.0.14.20180526.dfsg1-1~nd18.04+1) Homepage: http://psychtoolbox.org Priority: extra Section: debug Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-dbg_3.0.14.20180526.dfsg1-1~nd18.04+1_amd64.deb Size: 1265588 SHA256: 69b2d48a8da8eee8b0f0a323bf61185589fdc0f3423c025e6aedcebfe2e27371 SHA1: 79b8740ede2e97c4101d4242714335aea8b8eec0 MD5sum: 99a0286f71a943587bbe1a26966e21b8 Description: toolbox for vision research -- debug symbols for binaries Psychophysics Toolbox Version 3 (PTB-3) is a free set of Matlab and GNU/Octave functions for vision research. It makes it easy to synthesize and show accurately controlled visual and auditory stimuli and interact with the observer. . The Psychophysics Toolbox interfaces between Matlab or Octave and the computer hardware. The Psychtoolbox's core routines provide access to the display frame buffer and color lookup table, allow synchronization with the vertical retrace, support millisecond timing, allow access to OpenGL commands, and facilitate the collection of observer responses. Ancillary routines support common needs like color space transformations and the QUEST threshold seeking algorithm. . To ease debugging and troubleshooting this package contains debug symbols for Octave bindings and other binaries. Package: psychtoolbox-3-lib Source: psychtoolbox-3 Version: 3.0.14.20180526.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: extra Section: science Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-lib_3.0.14.20180526.dfsg1-1~nd18.04+1_amd64.deb Size: 74376 SHA256: f54a89ae5ff5e3137add838df91945748e0e23bdb056f8815f721555a5974227 SHA1: 66074d51f51360c73e9ed4a0529f13cc2271171b MD5sum: bc6cfefec36d533e0d201707306e3081 Description: toolbox for vision research -- arch-specific parts Psychophysics Toolbox Version 3 (PTB-3) is a free set of Matlab and GNU/Octave functions for vision research. It makes it easy to synthesize and show accurately controlled visual and auditory stimuli and interact with the observer. . The Psychophysics Toolbox interfaces between Matlab or Octave and the computer hardware. The Psychtoolbox's core routines provide access to the display frame buffer and color lookup table, allow synchronization with the vertical retrace, support millisecond timing, allow access to OpenGL commands, and facilitate the collection of observer responses. Ancillary routines support common needs like color space transformations and the QUEST threshold seeking algorithm. . This package contains additional binaries (tools/dynamic libraries) used by both Octave and Matlab frontends. Package: python-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-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: 0.10.2-2~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4176 Depends: neurodebian-popularity-contest, git-annex (>= 6.20180509~) | git-annex-standalone (>= 6.20180509~), 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_0.10.2-2~nd18.04+1_all.deb Size: 865976 SHA256: 9708d75187475c80b2c596d20e9471efb9dfc3d742f60a75d0bd75b07b9a6d54 SHA1: d662ef133621f795034fc096a9b2a463a0e40cab MD5sum: c53b230ed5e9d963982a9cc32544083e 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: 0.6.2+git36-gc12d27d-1~nd17.10+1+nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 496 Depends: neurodebian-popularity-contest, python-dicom (>= 0.9.7~), python-nibabel (>= 2.0~), python-numpy, python:any (<< 2.8), python:any (>= 2.7.5-5~), libjs-sphinxdoc (>= 1.0) Provides: python2.7-dcmstack Homepage: https://github.com/moloney/dcmstack Priority: optional Section: python Filename: pool/main/d/dcmstack/python-dcmstack_0.6.2+git36-gc12d27d-1~nd17.10+1+nd18.04+1_all.deb Size: 75196 SHA256: 22e26aba387d5dd6f1f841867bad16a4666896cd99c32baefc06c1a9bcab349b SHA1: 7a7f5f7c35a91954d1be25b8c6a3cc68bab2d563 MD5sum: 630fe3b11639e0520e70a2fd685dd00c 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: 1.1.0-2~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_1.1.0-2~nd18.04+1_all.deb Size: 4760 SHA256: 012f4e82cacaae93f2f70638f53491ba4c1ecd9e95bb03b1cff766ca4a068cf4 SHA1: 122de81a2ce73e190ee74154162675b481132143 MD5sum: 4b3db276675607d58668d4e8fe81f731 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: 0.6.4-1~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_0.6.4-1~nd18.04+1_all.deb Size: 52976 SHA256: ed4ddb4429acf841ae32bb21cd2710db9892846d2f3f378f39a94f947bb7f0d9 SHA1: de2dd2b13e7e07b60d0be23855878585c64cf8d6 MD5sum: 0b23b6c7c9f10074823093ee2b79c779 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-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: 0.12.1-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 806 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 Homepage: http://packages.python.org/joblib/ Priority: optional Section: python Filename: pool/main/j/joblib/python-joblib_0.12.1-1~nd18.04+1_all.deb Size: 178020 SHA256: 12d086f96422ca57a022222d1c65308b7c5ea2920dc337b17447b564989cdf16 SHA1: 76dccd20855f886451974ec81ff0142da6792080 MD5sum: 577c5054986c4825789a2ecd9038e09b 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-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: 2.3.0-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 65171 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python:any (>= 2.6.6-7~), python-numpy, python-scipy, python-six Recommends: python-dicom, python-fuse, python-mock Suggests: python-nibabel-doc Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: python Filename: pool/main/n/nibabel/python-nibabel_2.3.0-1~nd18.04+1_all.deb Size: 2585412 SHA256: 3d66de4cb470cd90a2149b99a38a685ef1485adf6f7691cf255885abf370e4b5 SHA1: 4bd5575b5b649a27e52373a49edd4975d5d1d319 MD5sum: 93c8760b0c6984484fda0e68cff4f82f 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: 2.3.0-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9088 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_2.3.0-1~nd18.04+1_all.deb Size: 1470968 SHA256: 95721b85e8a6fd451802808bea2014bad926105d0389d7bf7bccca164c993f9e SHA1: e9df3c34a681ac133d8634197e818865e0039443 MD5sum: 66096fbd6b0768e14acd0c02419e3c91 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-nipype Source: nipype Version: 1.1.1-2~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 10890 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-mock, python-networkx (>= 1.3), python-nibabel (>= 1.0.0~), python-numpy, python-packaging, python-prov, python-pytest, python-pytest-xdist, 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, graphviz, python-xvfbwrapper, mayavi2, python-pydotplus, python-pydot, python-cfflib Suggests: fsl, afni, python-nipy, slicer, matlab-spm8, python-pyxnat, mne-python, elastix, ants Provides: python2.7-nipype Homepage: http://nipy.sourceforge.net/nipype/ Priority: optional Section: python Filename: pool/main/n/nipype/python-nipype_1.1.1-2~nd18.04+1_all.deb Size: 1877716 SHA256: 5ebc9dc0b6dbeb0ab1b5a6fda744af4d67f2f159652cd3395d3bbb0ffd9aec2b SHA1: 9b741385c1699b33b1a9beadc28b0be057d729d5 MD5sum: 1f55c82504544c7323665d2d5bfafcd6 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: 1.1.1-2~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 46794 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_1.1.1-2~nd18.04+1_all.deb Size: 19919456 SHA256: 436189d153d13a741bdece72102dc0a90ca674def88d498ac6cd116508c5d498 SHA1: bdfd13cd120eeda82e605117b15935debc11a8b7 MD5sum: 5b8ec5181a2dc922356e158e08191dc4 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: 0.4.1+git34-ga5b54c2-1~nd90+1+nd17.04+1+nd17.10+1+nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 779 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_0.4.1+git34-ga5b54c2-1~nd90+1+nd17.04+1+nd17.10+1+nd18.04+1_all.deb Size: 168312 SHA256: 0f04222e8ee44213a66a04c54ce4a61b58a212007b9dbc3c5f77cb252ed73c0a SHA1: 952c2cd48431f64ce8507fce19b5564c25c043cd MD5sum: 76d42cff69fb3479b35a4a6804061a9d Description: statistical models in Python using symbolic formulas patsy is a Python library for describing statistical models (especially linear models, or models that have a linear component) and building design matrices. . This package contains the Python 2 version. Package: python-patsy-doc Source: patsy Version: 0.4.1+git34-ga5b54c2-1~nd90+1+nd17.04+1+nd17.10+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_0.4.1+git34-ga5b54c2-1~nd90+1+nd17.04+1+nd17.10+1+nd18.04+1_all.deb Size: 343612 SHA256: 4ff7df2df8b881ae9403a4c4d5b1b9893216a9c2ab083139805c3b7670ece97f SHA1: 4886fa72049deb4101eb3e018b09b6f454236787 MD5sum: 562489b71ac0d3faf662d31934b99053 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: 1.1.0-2~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 10042 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7), python:any (>= 2.6.6-7~) Recommends: python-numpy, python-pil, python-gdcm Suggests: python-matplotlib 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_1.1.0-2~nd18.04+1_all.deb Size: 4317496 SHA256: 613cce196ac6b0b0d1c2ed62aa848af9dc2bd6516b082aab0a7eb530cf0a34d9 SHA1: 42f6a4eb17cd1223120a9796a7f85203da31e3d2 MD5sum: 25100e92cf1ce49f583818094cb54d48 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: 1.1.0-2~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2789 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_1.1.0-2~nd18.04+1_all.deb Size: 323424 SHA256: 8818371a38b9082372c2f66ebf76cf2bf2e7d58099b0475623b02c108819da9b SHA1: ab9134d83ec5f241399fecdb9bdbdd688c14c230 MD5sum: 4e35a472e970e8e41259b2a7f6626deb 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-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: 0.19.2-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7024 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~), python-numpy, python-scipy, python-sklearn-lib (>= 0.19.2-1~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_0.19.2-1~nd18.04+1_all.deb Size: 1456104 SHA256: d49356d96b2ce6ed3adcadd09a24e79d1f33d047d027f3d2d632a85556992e77 SHA1: 8ba490755e547317b10461b59ca0240d4d17eb10 MD5sum: f7684df86bbd5612b94624818ebf9ace 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: 0.19.2-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 33573 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Suggests: python-sklearn Conflicts: python-scikits-learn-doc Replaces: python-scikits-learn-doc Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: doc Filename: pool/main/s/scikit-learn/python-sklearn-doc_0.19.2-1~nd18.04+1_all.deb Size: 5102952 SHA256: 456934917c26aabcb2949d2094749ce20b4e4e549e2b0a9d3ce38c71a47fda6d SHA1: 9f07bbcfa89f0997753b3bc90c09eba02608167e MD5sum: 80d42ff9086953132fa3c5db41ad5274 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: 0.19.2-1~nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 6972 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~), 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_0.19.2-1~nd18.04+1_amd64.deb Size: 1598288 SHA256: 5d89c5a0634b8eb78095da5e2ba39e21ac84185be3ebc268cf5043d661aa758a SHA1: 53f08ddbcb6f3a76b616ebfebaee082f6f9818b8 MD5sum: 50e7707f3e6c77d47ff71c411084825b 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-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-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-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: 0.10.2-2~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4176 Depends: neurodebian-popularity-contest, git-annex (>= 6.20180509~) | git-annex-standalone (>= 6.20180509~), patool, python3-appdirs, python3-distutils, 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_0.10.2-2~nd18.04+1_all.deb Size: 866212 SHA256: 3a005351bcbe8ecad1ddc3739a1735e585b35b487f95b262ac5d2a808346a148 SHA1: ac9ab7a28bb8df26fdfb2ed3390bc9956a40b8f0 MD5sum: 187a2b9a97e7a806bb858c2eeac115d2 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-dicom Source: pydicom Version: 1.1.0-2~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_1.1.0-2~nd18.04+1_all.deb Size: 4764 SHA256: 797013c852a449581bd6b308cd873afce8a9b4c5d5ad5369350a6ca2c8c57e51 SHA1: 746422e47f2ba6fa39e3c54c957c3e9c9df22d00 MD5sum: 138f5e7475fec14f3bdfb91135054e6a Description: transitional package for python3-pydicom This is a transitional package. It can safely be removed. Package: python3-duecredit Source: duecredit Version: 0.6.4-1~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_0.6.4-1~nd18.04+1_all.deb Size: 53248 SHA256: 5b384d03b626047f2f27ec3d1357db3f521fb0b2de9ac0ac2f7c988440e6291b SHA1: b0ddf3076f82f1cb6aa7e056745d2cc04e18a56f MD5sum: 574577a36c922d2e2a63b7952e498f05 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-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: 0.12.1-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 801 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~), procps Recommends: python3-numpy, python3-pytest, python3-simplejson Homepage: http://packages.python.org/joblib/ Priority: optional Section: python Filename: pool/main/j/joblib/python3-joblib_0.12.1-1~nd18.04+1_all.deb Size: 174980 SHA256: 8ffa187b68c31438977922e3623b5600c195e456c6e4e2d538a67d38eee5941b SHA1: 9729b02f1bf87f17dcee25b1a3d5e3c8b9e9e8eb MD5sum: 9a8700301ea56fbfd7af263882dc3145 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-nibabel Source: nibabel Version: 2.3.0-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 65157 Depends: neurodebian-popularity-contest, python3-numpy (>= 1.2), python3:any (>= 3.3.2-2~), python3-scipy, python3-six Suggests: python-nibabel-doc, python3-dicom, python3-fuse, python3-mock Homepage: http://nipy.sourceforge.net/nibabel Priority: extra Section: python Filename: pool/main/n/nibabel/python3-nibabel_2.3.0-1~nd18.04+1_all.deb Size: 2581316 SHA256: 52856b45ead0b59bdb4ecb0fa134861e87ef9e2ad2f1dfa1ecdfd577e98eed89 SHA1: 200aad9e2e681f6ab5fbcbfef8efc7dea0475ab0 MD5sum: 59feaeb7b86efa0db3e29f100453df53 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: 0.4.1+git34-ga5b54c2-1~nd90+1+nd17.04+1+nd17.10+1+nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 778 Depends: neurodebian-popularity-contest, python3-numpy, python3-six, python3:any (>= 3.3.2-2~) Recommends: python3-pandas Suggests: python-patsy-doc Homepage: http://github.com/pydata/patsy Priority: optional Section: python Filename: pool/main/p/patsy/python3-patsy_0.4.1+git34-ga5b54c2-1~nd90+1+nd17.04+1+nd17.10+1+nd18.04+1_all.deb Size: 168408 SHA256: 581d89afc72695123dd9a8dbfce88423252f72407574f4ec8bcb06d3639b5e4c SHA1: d3338426d8c783e1e7f2ccdb2129246c6cc5e6f2 MD5sum: 50030fb684ea40012c8b7cae0cd60b3c 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: 1.1.0-2~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 10042 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~) Recommends: python3-numpy, python3-pil, python3-gdcm Suggests: python3-matplotlib 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_1.1.0-2~nd18.04+1_all.deb Size: 4317828 SHA256: 93bc99eb8ac7e9d45882e7d58d96edf5fc1a34809cb974100f32edac0a72b1f5 SHA1: c82c9abb88649eedaa34371b7dd0a0189fe54071 MD5sum: ad52528cd6935f04f06704b1b6a7412e 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-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-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: 0.19.2-1~nd18.04+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 7023 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~), python3-numpy, python3-scipy, python3-sklearn-lib (>= 0.19.2-1~nd18.04+1), python3-joblib (>= 0.9.2) Recommends: python3-nose, python-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_0.19.2-1~nd18.04+1_all.deb Size: 1456044 SHA256: 95dc34dce51eda45e0772de26fa5079ae62ca35d9b79c7d9ce341ba588c0d3ce SHA1: 4508edc110afb3014fc4d43bd5f1e91c21345c8b MD5sum: 8bd4e14f9a2c528df96d55b755e34d80 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: 0.19.2-1~nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 6599 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~) Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python3-sklearn-lib_0.19.2-1~nd18.04+1_amd64.deb Size: 1507812 SHA256: 5a6ec160090d215a9e9fbf7d3a4859c192427d45acc3052bc004bda556df733a SHA1: c14d4748706e67106fcce320d11dc69f78e404b5 MD5sum: c6fefac3e742c5874df42bdfb1bbaa4d 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-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: singularity-container Version: 2.5.2-2~nd18.04+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2427 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_2.5.2-2~nd18.04+1_amd64.deb Size: 340136 SHA256: d691abf9dc08e61a51c67b3258cc65bd15b8c1d65cc1beab586c02a4a8a38a73 SHA1: 5353012b612aa05207f194af57848588695d4045 MD5sum: 69834f93d390034f394d29bb807e0816 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.