Package: datalad Version: 0.8.1-1~nd100+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 100 Depends: neurodebian-popularity-contest, python-argcomplete, python-datalad (= 0.8.1-1~nd100+1), python:any Homepage: http://datalad.org Priority: optional Section: science Filename: pool/main/d/datalad/datalad_0.8.1-1~nd100+1_all.deb Size: 68198 SHA256: 52d452b812288b44465dcb687475df977138d498b1df570b2a157913059aafb6 SHA1: bf068b03083f6eb3b0b2a8075faad67a3f6c0874 MD5sum: 776e518ab088ac63aee03c770a11ebf6 Description: data files crawler and data distribution DataLad is a data distribution providing access to a wide range of data resources already available online (initially aiming at neuroscience domain). Using git-annex as its backend for data logistics it provides following facilities . - crawling of web sites to automatically prepare and update git-annex repositories with content from online websites, S3, etc - command line interface for manipulation of collections of datasets (install, uninstall, update, publish, save, etc.) and separate files/directories (add, get), as well as search within aggregated meta-data . This package provides the command line tool. Install without Recommends if you need only core functionality. Package: dcm2niix Version: 1:1.0.20170624+git8-g87d2142-1~nd100+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 547 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), libgcc1 (>= 1:3.0), libopenjp2-7 (>= 2.0.0), libstdc++6 (>= 5.2), libyaml-cpp0.5v5, zlib1g (>= 1:1.2.0) Homepage: https://github.com/rordenlab/dcm2niix Priority: optional Section: science Filename: pool/main/d/dcm2niix/dcm2niix_1.0.20170624+git8-g87d2142-1~nd100+1_i386.deb Size: 131388 SHA256: 087020a4ef2df725471eefd75036cc7c95b48157174da0c80079ab1fefb45663 SHA1: a6f033fb0b883621d5f4fef35be53f4ab0f75dfb MD5sum: 21b4752e7b2edc1c4780002b44d14305 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: dcm2niix-dbgsym Source: dcm2niix Version: 1:1.0.20170624+git8-g87d2142-1~nd100+1 Auto-Built-Package: debug-symbols Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 506 Depends: dcm2niix (= 1:1.0.20170624+git8-g87d2142-1~nd100+1) Homepage: https://github.com/rordenlab/dcm2niix Priority: extra Section: debug Filename: pool/main/d/dcm2niix/dcm2niix-dbgsym_1.0.20170624+git8-g87d2142-1~nd100+1_i386.deb Size: 477336 SHA256: 12d6f9ddd8ffcdff073970fdcbc60a5006990599110af041dfecbc9fda280718 SHA1: d93f1f3ce2c1234b3ed59f55f3e138a7a1174ccf MD5sum: 4d90ec7275d2943dcd992a17e99f4661 Description: Debug symbols for dcm2niix Build-Ids: 0694cd36870fbc51c659104eca617f2716e2cca7 d013655acd816047463b5ba93a0f7bc0851132db 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: 6.20170810+gitgff6f9e203-1~ndall+1 Architecture: i386 Maintainer: Richard Hartmann Installed-Size: 160061 Depends: git, openssh-client Recommends: lsof, gnupg, bind9-host, quvi, git-remote-gcrypt (>= 0.20130908-6), nocache, aria2 Suggests: xdot, bup, tor, magic-wormhole, tahoe-lafs, libnss-mdns, uftp Conflicts: git-annex Provides: git-annex Homepage: http://git-annex.branchable.com/ Priority: optional Section: utils Filename: pool/main/g/git-annex/git-annex-standalone_6.20170810+gitgff6f9e203-1~ndall+1_i386.deb Size: 35954240 SHA256: 4b1912553ff2582c6aad70a7bcbeb24794f8b58459ab5cc075d20718061571b4 SHA1: 824d62afdc6cf5f4b8bfa2fffb685cfc19cc187c MD5sum: caf7ffb911eecb33929107f87c0e0d44 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: 1.36-1~ndall0 Architecture: all Maintainer: Debian Go Packaging Team Installed-Size: 1198 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-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 Built-Using: go-md2man (= 1.0.6+ds-1), golang-1.7 (= 1.7.4-2), golang-bazil-fuse (= 0.0~git20160811.0.371fbbd-2), golang-blackfriday (= 1.4+git20161003.40.5f33e7b-1), golang-github-aws-aws-sdk-go (= 1.1.14+dfsg-2), golang-github-davecgh-go-spew (= 1.1.0-1), golang-github-go-ini-ini (= 1.8.6-2), golang-github-google-go-querystring (= 0.0~git20151028.0.2a60fc2-1), golang-github-jmespath-go-jmespath (= 0.2.2-2), golang-github-kr-fs (= 0.0~git20131111.0.2788f0d-2), golang-github-ncw-go-acd (= 0.0~git20161119.0.7954f1f-1), golang-github-ncw-swift (= 0.0~git20160617.0.b964f2c-2), golang-github-pkg-errors (= 0.8.0-1), golang-github-pkg-sftp (= 0.0~git20160930.0.4d0e916-1), golang-github-pmezard-go-difflib (= 1.0.0-1), golang-github-rfjakob-eme (= 1.0-2), golang-github-shurcool-sanitized-anchor-name (= 0.0~git20160918.0.1dba4b3-1), golang-github-skratchdot-open-golang (= 0.0~git20160302.0.75fb7ed-2), golang-github-spf13-cobra (= 0.0~git20161229.0.1dd5ff2-1), golang-github-spf13-pflag (= 0.0~git20161024.0.5ccb023-1), golang-github-stacktic-dropbox (= 0.0~git20160424.0.58f839b-2), golang-github-tsenart-tb (= 0.0~git20151208.0.19f4c3d-2), golang-github-unknwon-goconfig (= 0.0~git20160828.0.5aa4f8c-3), golang-github-vividcortex-ewma (= 0.0~git20160822.20.c595cd8-3), golang-go.crypto (= 1:0.0~git20170407.0.55a552f+REALLY.0.0~git20161012.0.5f31782-1), golang-golang-x-net-dev (= 1:0.0+git20161013.8b4af36+dfsg-3), golang-golang-x-oauth2 (= 0.0~git20161103.0.36bc617-4), golang-golang-x-sys (= 0.0~git20161122.0.30237cf-1), golang-google-api (= 0.0~git20161128.3cc2e59-2), golang-google-cloud (= 0.5.0-2), golang-testify (= 1.1.4+ds-1), golang-x-text (= 0.0~git20161013.0.c745997-2) Homepage: https://github.com/ncw/rclone Priority: extra Section: devel Filename: pool/main/r/rclone/golang-github-ncw-rclone-dev_1.36-1~ndall0_all.deb Size: 201776 SHA256: 88274c394a26a9f8f5f4766873acde54192537112e0f0e24ddfff1b9a5361f7c SHA1: fb44d37c3df4bdf641c85720b35bbfd835870a05 MD5sum: fdc9cfc7b0a9e18cdc83ed2fbce61433 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: nuitka Version: 0.5.27+ds-1~nd100+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3567 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), 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_0.5.27+ds-1~nd100+1_all.deb Size: 700318 SHA256: 15e18c072988e3c1dd24d415caa1a21c0a55efdb4be781883b06c92b5343a4b0 SHA1: afc3c44395da24d806c552b7d60e0fe1cb9090f0 MD5sum: 1634fcf2d06d74fca61289c2ac7098d6 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: python-datalad Source: datalad Version: 0.8.1-1~nd100+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3738 Depends: neurodebian-popularity-contest, git-annex (>= 6.20170525~) | git-annex-standalone (>= 6.20170525~), patool, python-appdirs, python-git (>= 2.1~), python-github, python-humanize, python-iso8601, python-keyrings.alt | python-keyring (<= 8), python-secretstorage | python-keyring (<< 9.2), python-keyring, python-mock, python-msgpack, python-pyld, python-requests, python-tqdm, python-simplejson, python-six (>= 1.8.0), python-wrapt, python-boto, python-jsmin, python:any (<< 2.8), python:any (>= 2.7.5-5~) Recommends: python-html5lib, python-httpretty, python-nose, python-numpy, python-requests-ftp, python-scrapy, python-vcr, python-yaml Suggests: python-bs4 Provides: python2.7-datalad Homepage: http://datalad.org Priority: optional Section: python Filename: pool/main/d/datalad/python-datalad_0.8.1-1~nd100+1_all.deb Size: 725996 SHA256: b1690cfbcbb609fa43998dfb9372f204399c2b60bfdd06a13d81cd30e7a7e17f SHA1: 9b6e14e4eca7648c105ff01fbeeed3a3781bc3f9 MD5sum: 29075a8c88651e860995958623f0b1f4 Description: data files crawler and data distribution (Python 2) DataLad is a data distribution providing access to a wide range of data resources already available online (initially aiming at neuroscience domain). Using git-annex as its backend for data logistics it provides following facilities . - crawling of web sites to automatically prepare and update git-annex repositories with content from online websites, S3, etc - command line interface for manipulation of collections of datasets (install, uninstall, update, publish, save, etc.) and separate files/directories (add, get), as well as search within aggregated meta-data . This package installs the module for Python 2, and Recommends install all dependencies necessary for crawling, publishing, and testing. If you need base functionality, install without Recommends. Package: python-dipy Source: dipy Version: 0.12.0-1~nd100+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6929 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-dipy-lib (>= 0.12.0-1~nd100+1) Recommends: python-matplotlib, python-vtk, python-nose, python-nibabel, python-tables Suggests: ipython Provides: python2.7-dipy Homepage: http://dipy.org Priority: extra Section: python Filename: pool/main/d/dipy/python-dipy_0.12.0-1~nd100+1_all.deb Size: 3014488 SHA256: f5a083cc63eff24a2be6a46b7d8f5ffb731dfcdf2cc4443b9d0b6ba5495a2e2f SHA1: 3f073f686180dd8865046ffb8c3e80c20b944a3d MD5sum: 0363ce5e7c8d159445e068f372cdde01 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: 0.12.0-1~nd100+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 14215 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_0.12.0-1~nd100+1_all.deb Size: 10551850 SHA256: e422a518abf56346508fd94e8e0df481b9c7926aa6a8fbafcf9a54f6104fc61d SHA1: 16254163533e3554b325fd17c03563aa8c1952d4 MD5sum: 51b3af04733fb250180ae23bd4f4a4cb 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-nipy Source: nipy Version: 0.4.1-1~nd100+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3546 Depends: neurodebian-popularity-contest, python-numpy (>= 1:1.2), python (<< 2.8), python (>= 2.7), python:any (>= 2.6.6-7~), python-scipy, python-nibabel, python-nipy-lib (>= 0.4.1-1~nd100+1) Recommends: python-matplotlib, mayavi2, python-sympy Suggests: python-mvpa Provides: python2.7-nipy Homepage: http://neuroimaging.scipy.org Priority: extra Section: python Filename: pool/main/n/nipy/python-nipy_0.4.1-1~nd100+1_all.deb Size: 784144 SHA256: 74eb2ddba77e9d911fe83c757f789d6be7cf2329ea278013be1c2483adaf5bdb SHA1: d80903a582877e6a20e2d1aa2e5e069c9031cc6b MD5sum: 2869269c0210ea80dbc4d2c88fa36610 Description: Analysis of structural and functional neuroimaging data NiPy is a Python-based framework for the analysis of structural and functional neuroimaging data. It provides functionality for - General linear model (GLM) statistical analysis - Combined slice time correction and motion correction - General image registration routines with flexible cost functions, optimizers and re-sampling schemes - Image segmentation - Basic visualization of results in 2D and 3D - Basic time series diagnostics - Clustering and activation pattern analysis across subjects - Reproducibility analysis for group studies Python-Version: 2.7 Package: python-nipy-doc Source: nipy Version: 0.4.1-1~nd100+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 9677 Depends: neurodebian-popularity-contest, libjs-jquery, libjs-underscore Recommends: python-nipy Homepage: http://neuroimaging.scipy.org Priority: extra Section: doc Filename: pool/main/n/nipy/python-nipy-doc_0.4.1-1~nd100+1_all.deb Size: 1841564 SHA256: 9c6baf890a3db8c481d993773c3eafbc8181123578d3e4cc6ed5f70fc4c2043d SHA1: 2edd3b0bffbf23be5f20ac5bcbadbbbe28e1ce45 MD5sum: 48f02e986c9db62cdc14f73ed2386398 Description: documentation and examples for NiPy This package contains NiPy documentation in various formats (HTML, TXT) including * User manual * Developer guidelines * API documentation Package: python-nipy-lib Source: nipy Version: 0.4.1-1~nd100+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 2910 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), python-numpy (>= 1:1.10.0~b1), python-numpy-abi9, python (<< 2.8), python (>= 2.7) Provides: python2.7-nipy-lib Homepage: http://neuroimaging.scipy.org Priority: extra Section: python Filename: pool/main/n/nipy/python-nipy-lib_0.4.1-1~nd100+1_i386.deb Size: 594262 SHA256: 5acc9d1047a13e2abba2b842aecc0fbfe3b288ea97a386531b1b5dc10d583be3 SHA1: 8b6e519df29ee3a24252cee7c920d6d400610af1 MD5sum: 3b8c31fca1e6db3f545088be4db3f1b1 Description: Analysis of structural and functional neuroimaging data NiPy is a Python-based framework for the analysis of structural and functional neuroimaging data. . This package provides architecture-dependent builds of the libraries. Python-Version: 2.7 Package: python-nipy-lib-dbg Source: nipy Version: 0.4.1-1~nd100+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 3296 Depends: neurodebian-popularity-contest, libc6 (>= 2.4), python-numpy (>= 1:1.10.0~b1), python-numpy-abi9, python-dbg (<< 2.8), python-dbg (>= 2.7), python-nipy-lib (= 0.4.1-1~nd100+1) Provides: python2.7-nipy-lib-dbg Homepage: http://neuroimaging.scipy.org Priority: extra Section: debug Filename: pool/main/n/nipy/python-nipy-lib-dbg_0.4.1-1~nd100+1_i386.deb Size: 462998 SHA256: 0ed5afb52f5ef96121c6de519fdc3887ae6aacdf841ded3e7f240b0b6e03e259 SHA1: 53598503103dfd6d31394dcbfd7296ee8e4ab8a7 MD5sum: ebaff18f80621b01c9deedfce2e8fce5 Description: Analysis of structural and functional neuroimaging data NiPy is a Python-based framework for the analysis of structural and functional neuroimaging data. . This package provides debugging symbols for architecture-dependent builds of the libraries. Python-Version: 2.7 Package: python-nipy-lib-dbg-dbgsym Source: nipy Version: 0.4.1-1~nd100+1 Auto-Built-Package: debug-symbols Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1225 Depends: python-nipy-lib-dbg (= 0.4.1-1~nd100+1) Homepage: http://neuroimaging.scipy.org Priority: extra Section: debug Filename: pool/main/n/nipy/python-nipy-lib-dbg-dbgsym_0.4.1-1~nd100+1_i386.deb Size: 818350 SHA256: 44bdefc8eacaa6457d646d827f95cde247212c3fd2198cf7e0ff914c32e81ec6 SHA1: fff9b5f3e82a2f1fe37d611d374dd702b2d4d4ee MD5sum: 338f41d6fa837a7de8a8534fd59f7860 Description: Debug symbols for python-nipy-lib-dbg Build-Ids: 27016059ddb559b960178ec0808013af6a5f0a52 30d24c16b7be8ce821410f047e2bd756d6116c98 4563f302b3ce8b64285523ef67eb25c64eb6802b 49f2de3f2c330f3c4127a1260e9768abce3380f8 5acb45f4023fdade691db0a5de34427433d47e2a 612987df10ea814d4d8ae97a2912c7b9672857a7 867b7e65fb5d01f818bdc189316390431f2163d0 92d475fcdc2c08d93351af7fecde185bb7008ccf a770cb5c4311cbc09961a0682b3ad6323a05ca2a ab354c876670beab7225d81a583dac2e82909ecf c708ec926e759ab5ad2337d9530f6fa39cda27ec e73f8612faa6770bb2a4df3644d9cbaa68b87542 f6a43ab135978def1dad1d0cb20012329caf554a fdb533f6e9c57c26f152007b16205fcb7c27b049 Python-Version: 2.7 Package: python-nipy-lib-dbgsym Source: nipy Version: 0.4.1-1~nd100+1 Auto-Built-Package: debug-symbols Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 2141 Depends: python-nipy-lib (= 0.4.1-1~nd100+1) Homepage: http://neuroimaging.scipy.org Priority: extra Section: debug Filename: pool/main/n/nipy/python-nipy-lib-dbgsym_0.4.1-1~nd100+1_i386.deb Size: 1821688 SHA256: eb5977ee9b7b7c1d45267b1e3d3cd3f98a8743f5335a0adcc7a815393ad41b1f SHA1: 9a074a6f95ae994521635baceceea71a65fabe9b MD5sum: 146d2b29de93ac093312a4c914d4be5a Description: Debug symbols for python-nipy-lib Build-Ids: 06dbe929a8c1f92bc2dbd6b1297a2b5bb7ef89c3 080bb869691c3c372bb86d825326e4c826b28496 3bffc6809b73b965d63fa763e460e867edcdbf30 5bce68e79adb045f1cb6e0c934078e738e8fd138 86155691b9378cb469492ddfde9194c89a18fde7 8624a683a9defbda7758f42ce836a0944872e87c 8e75c5054a62d0f41047721cd7ca98a506d0690d a0810bdde65564cb541a138dc9ecda729872b8c8 bc18e3b6a10c8dd970d89a3b5eef0feab31dcfc7 e1acfee45cd8a410461f2ed5f450b9b0f57a088b f233557454b82c0423e50f7a4a57f7c86666ed64 f33369af9275db979e236be11496c9d46369e709 fa0593cb035f8d6d57b0eb8e02d1b25e1abf45bd fc44d8c11f6bf948b6cbe598c378f07c9cd06c9a Python-Version: 2.7 Package: python-numexpr Source: numexpr Version: 2.6.2-1~nd100+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 476 Depends: neurodebian-popularity-contest, python (<< 2.8), python (>= 2.7~), python-numpy (>= 1:1.10.0~b1), python-numpy-abi9, python:any (<< 2.8), python:any (>= 2.7.5-5~), libc6 (>= 2.4), libgcc1 (>= 1:4.2), libstdc++6 (>= 4.1.1), python-pkg-resources Homepage: https://github.com/pydata/numexpr Priority: optional Section: python Filename: pool/main/n/numexpr/python-numexpr_2.6.2-1~nd100+1_i386.deb Size: 130082 SHA256: ac03ef0809b182524a469b565c321058d3077e13693f0522bf521c79cc1b7660 SHA1: 64f00f39af5ab46af6649fefa3d38d6d0b73408e MD5sum: 0a44a2285fee7ecf86622efa700f4caa Description: Fast numerical array expression evaluator for Python and NumPy Numexpr package evaluates multiple-operator array expressions many times faster than NumPy can. It accepts the expression as a string, analyzes it, rewrites it more efficiently, and compiles it to faster Python code on the fly. It's the next best thing to writing the expression in C and compiling it with a specialized just-in-time (JIT) compiler, i.e. it does not require a compiler at runtime. . This is the Python 2 version of the package. Package: python-numexpr-dbgsym Source: numexpr Version: 2.6.2-1~nd100+1 Auto-Built-Package: debug-symbols Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 240 Depends: python-numexpr (= 2.6.2-1~nd100+1) Priority: extra Section: debug Filename: pool/main/n/numexpr/python-numexpr-dbgsym_2.6.2-1~nd100+1_i386.deb Size: 212170 SHA256: 6407430feb94ae984707692a76cee2ba772d333b6acc397c466fe2432d24e003 SHA1: 59ccde14a7cd407a21219e4e6f5c4ff6a1295abc MD5sum: e553aa6302189926f4428d0e412de6ef Description: Debug symbols for python-numexpr Build-Ids: 0fc2bb738ecec6d778fd42ac40b5b398c086d225 Package: python-scikits-learn Source: scikit-learn Version: 0.19.0-1~nd100+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 93 Depends: neurodebian-popularity-contest, python-sklearn Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: oldlibs Filename: pool/main/s/scikit-learn/python-scikits-learn_0.19.0-1~nd100+1_all.deb Size: 83658 SHA256: 4fcd6a5cbd435ed70f15c835e3767557a75380c7238643283929de30ea8552d1 SHA1: 728be67ed95534591d1140196e9ad7cebbb46f7e MD5sum: b933eada79f575d9441ada97cdd496a4 Description: transitional compatibility package for scikits.learn -> sklearn migration Provides old namespace (scikits.learn) and could be removed if dependent code migrated to use sklearn for clarity of the namespace. Package: python-sklearn Source: scikit-learn Version: 0.19.0-1~nd100+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6995 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~), python-numpy, python-scipy, python-sklearn-lib (>= 0.19.0-1~nd100+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_0.19.0-1~nd100+1_all.deb Size: 1450596 SHA256: a44826a47fad72420d3e16fb8ae501abdcd81142b0357df0e6eb285f5440869e SHA1: a78ab162999c4c9f4141be7993921b57f9f1ed7d MD5sum: 2576b421d209bf3f852f51f0ca433370 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: 0.19.0-1~nd100+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 33262 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_0.19.0-1~nd100+1_all.deb Size: 5108238 SHA256: 82685287f88320aefe2019b539fe2d9847acb11239cd6eda00baf35b6ecc1d99 SHA1: 70caa72474464c35ba444d7a9c6240cd2ad42fe8 MD5sum: 495ee36fcf5a9d7535620fe75ca9fb60 Description: documentation and examples for scikit-learn This package contains documentation and example scripts for python-sklearn. Package: python-whoosh Version: 2.7.4+git6-g9134ad92-1~nd100+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1745 Depends: neurodebian-popularity-contest, python:any (<< 2.8), python:any (>= 2.7.5-5~) Suggests: python-whoosh-doc Homepage: http://bitbucket.org/mchaput/whoosh/ Priority: optional Section: python Filename: pool/main/p/python-whoosh/python-whoosh_2.7.4+git6-g9134ad92-1~nd100+1_all.deb Size: 290690 SHA256: 5cc2d7491e8ab52563aa8a32cdd3a223b78899713b74fbce565c022bba475819 SHA1: 745ce72af3b29abb3e44f83ee80a8fe450908bd9 MD5sum: a4a92bdf70ca01e924e0381d347635b4 Description: pure-Python full-text indexing, search, and spell checking library (Python 2) Whoosh is a fast, pure-Python indexing and search library. Programmers can use it to easily add search functionality to their applications and websites. As Whoosh is pure Python, you don't have to compile or install a binary support library and/or make Python work with a JVM, yet indexing and searching is still very fast. Whoosh is designed to be modular, so every part can be extended or replaced to meet your needs exactly. . This package contains the python2 library Package: python-whoosh-doc Source: python-whoosh Version: 2.7.4+git6-g9134ad92-1~nd100+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 2213 Pre-Depends: dpkg (>= 1.17.14) Depends: neurodebian-popularity-contest, libjs-sphinxdoc (>= 1.0) Replaces: python-whoosh (<< 2.1.0) Homepage: http://bitbucket.org/mchaput/whoosh/ Priority: extra Section: doc Filename: pool/main/p/python-whoosh/python-whoosh-doc_2.7.4+git6-g9134ad92-1~nd100+1_all.deb Size: 241300 SHA256: c28cc15134ac59dfd4c7384e30f72d97f7b263f0e27d7074afb29d953e785352 SHA1: 23d3044711e4074d5e1be14cf7b4cf5d644c2b2e MD5sum: 5d4ab18c7fe2b916a8aea674872b4224 Description: full-text indexing, search, and spell checking library (doc) Whoosh is a fast, pure-Python indexing and search library. Programmers can use it to easily add search functionality to their applications and websites. As Whoosh is pure Python, you don't have to compile or install a binary support library and/or make Python work with a JVM, yet indexing and searching is still very fast. Whoosh is designed to be modular, so every part can be extended or replaced to meet your needs exactly. . This package contains the library documentation for python-whoosh. Package: python3-numexpr Source: numexpr Version: 2.6.2-1~nd100+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 778 Depends: neurodebian-popularity-contest, python3 (<< 3.7), python3 (>= 3.5~), python3-numpy (>= 1:1.10.0~b1), python3-numpy-abi9, python3:any (>= 3.3.2-2~), libc6 (>= 2.4), libgcc1 (>= 1:4.2), libstdc++6 (>= 4.1.1), python3-pkg-resources Homepage: https://github.com/pydata/numexpr Priority: optional Section: python Filename: pool/main/n/numexpr/python3-numexpr_2.6.2-1~nd100+1_i386.deb Size: 124610 SHA256: 2ebaa0c84d7520cf41ad732ca3e37b40c33c2342a6fe36ef6c06fa6570773f4b SHA1: 5dc16c5a2ce0bd11871cfa9b863d0ec9af348b06 MD5sum: bc39ef599f93843ac086f2e91b10cd45 Description: Fast numerical array expression evaluator for Python 3 and NumPy Numexpr package evaluates multiple-operator array expressions many times faster than NumPy can. It accepts the expression as a string, analyzes it, rewrites it more efficiently, and compiles it to faster Python code on the fly. It's the next best thing to writing the expression in C and compiling it with a specialized just-in-time (JIT) compiler, i.e. it does not require a compiler at runtime. . This package contains numexpr for Python 3. Package: python3-numexpr-dbgsym Source: numexpr Version: 2.6.2-1~nd100+1 Auto-Built-Package: debug-symbols Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 486 Depends: python3-numexpr (= 2.6.2-1~nd100+1) Priority: extra Section: debug Filename: pool/main/n/numexpr/python3-numexpr-dbgsym_2.6.2-1~nd100+1_i386.deb Size: 357502 SHA256: 888c266a26508f4d07c4d12f757cec5da0df1595fbd04dfc080ab2930699729d SHA1: 4e284d3cd9953b243595de9fab5e5ec4e3bfadd2 MD5sum: 813fd1f4fb9ef0a7a6646e248e825956 Description: Debug symbols for python3-numexpr Build-Ids: ea00f9c6634ed273044612ee01220667f2de8569 fe7535b0aee9ba9db5c7eac11f56af75cff0a4d7 Package: python3-sklearn Source: scikit-learn Version: 0.19.0-1~nd100+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 6994 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~), python3-numpy, python3-scipy, python3-sklearn-lib (>= 0.19.0-1~nd100+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_0.19.0-1~nd100+1_all.deb Size: 1450252 SHA256: 0c0d58b03c1f813f5264b9c659e46fe2711e2519c83187a093c5ce9ff424ec43 SHA1: ce8a2ebef6fcc48426072576b7df336450df8d72 MD5sum: 2d971920ad9bb003b842d77a2bcaa0c7 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-whoosh Source: python-whoosh Version: 2.7.4+git6-g9134ad92-1~nd100+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 1745 Depends: neurodebian-popularity-contest, python3:any (>= 3.3.2-2~) Suggests: python-whoosh-doc Homepage: http://bitbucket.org/mchaput/whoosh/ Priority: optional Section: python Filename: pool/main/p/python-whoosh/python3-whoosh_2.7.4+git6-g9134ad92-1~nd100+1_all.deb Size: 290826 SHA256: 571725d4546678cbe70e525f38fc66853aa7fda32e27b56dfaac8ce5b674210b SHA1: fb6fa2b9a76d4ed9fec61aeb4972ce88487d5e7f MD5sum: b46e377dd534da6136e6d9d087ca69a2 Description: pure-Python full-text indexing, search, and spell checking library (Python 3) Whoosh is a fast, pure-Python indexing and search library. Programmers can use it to easily add search functionality to their applications and websites. As Whoosh is pure Python, you don't have to compile or install a binary support library and/or make Python work with a JVM, yet indexing and searching is still very fast. Whoosh is designed to be modular, so every part can be extended or replaced to meet your needs exactly. . This package contains the python3 library Package: rclone Version: 1.36-1~ndall0 Architecture: i386 Maintainer: Debian Go Packaging Team Installed-Size: 10498 Depends: libc6 (>= 2.3.6-6~) Built-Using: go-md2man (= 1.0.6+ds-1), golang-1.7 (= 1.7.4-2), golang-bazil-fuse (= 0.0~git20160811.0.371fbbd-2), golang-blackfriday (= 1.4+git20161003.40.5f33e7b-1), golang-github-aws-aws-sdk-go (= 1.1.14+dfsg-2), golang-github-davecgh-go-spew (= 1.1.0-1), golang-github-go-ini-ini (= 1.8.6-2), golang-github-google-go-querystring (= 0.0~git20151028.0.2a60fc2-1), golang-github-jmespath-go-jmespath (= 0.2.2-2), golang-github-kr-fs (= 0.0~git20131111.0.2788f0d-2), golang-github-ncw-go-acd (= 0.0~git20161119.0.7954f1f-1), golang-github-ncw-swift (= 0.0~git20160617.0.b964f2c-2), golang-github-pkg-errors (= 0.8.0-1), golang-github-pkg-sftp (= 0.0~git20160930.0.4d0e916-1), golang-github-pmezard-go-difflib (= 1.0.0-1), golang-github-rfjakob-eme (= 1.0-2), golang-github-shurcool-sanitized-anchor-name (= 0.0~git20160918.0.1dba4b3-1), golang-github-skratchdot-open-golang (= 0.0~git20160302.0.75fb7ed-2), golang-github-spf13-cobra (= 0.0~git20161229.0.1dd5ff2-1), golang-github-spf13-pflag (= 0.0~git20161024.0.5ccb023-1), golang-github-stacktic-dropbox (= 0.0~git20160424.0.58f839b-2), golang-github-tsenart-tb (= 0.0~git20151208.0.19f4c3d-2), golang-github-unknwon-goconfig (= 0.0~git20160828.0.5aa4f8c-3), golang-github-vividcortex-ewma (= 0.0~git20160822.20.c595cd8-3), golang-go.crypto (= 1:0.0~git20170407.0.55a552f+REALLY.0.0~git20161012.0.5f31782-1), golang-golang-x-net-dev (= 1:0.0+git20161013.8b4af36+dfsg-3), golang-golang-x-oauth2 (= 0.0~git20161103.0.36bc617-4), golang-golang-x-sys (= 0.0~git20161122.0.30237cf-1), golang-google-api (= 0.0~git20161128.3cc2e59-2), golang-google-cloud (= 0.5.0-2), golang-testify (= 1.1.4+ds-1), golang-x-text (= 0.0~git20161013.0.c745997-2) Homepage: https://github.com/ncw/rclone Priority: optional Section: net Filename: pool/main/r/rclone/rclone_1.36-1~ndall0_i386.deb Size: 2843042 SHA256: 66e28221c2624e00ed7486aea2001e9ee3642772f21f224fc153a93a2fc1e5ba SHA1: 63fa3d3cc336295d45b921981dcd235fe487ac63 MD5sum: edd5dedd27f7c1e243034b1e4abdfeb0 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: 2.3.1-2~nd100+1 Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 2356 Depends: neurodebian-popularity-contest, libc6 (>= 2.16), python Homepage: http://gmkurtzer.github.io/singularity Priority: optional Section: admin Filename: pool/main/s/singularity-container/singularity-container_2.3.1-2~nd100+1_i386.deb Size: 265964 SHA256: 50cad9a5a7fb707f1ec4e8ead282841a3162d85774f9d42ce45252e5e94accec SHA1: a9295a571b23ee04ed70902b2914f74aff12041b MD5sum: 4bcc9c3c7feed18bf2530cff49e666b4 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: singularity-container-dbgsym Source: singularity-container Version: 2.3.1-2~nd100+1 Auto-Built-Package: debug-symbols Architecture: i386 Maintainer: NeuroDebian Maintainers Installed-Size: 1002 Depends: singularity-container (= 2.3.1-2~nd100+1) Priority: extra Section: debug Filename: pool/main/s/singularity-container/singularity-container-dbgsym_2.3.1-2~nd100+1_i386.deb Size: 759638 SHA256: 9be7531f06759e1472f841954f50c0e40713405a3bc2061fd0978ccb44233dea SHA1: 1703a5e2c1ce9343b91fd49fc03adbe73b0f1f38 MD5sum: 137a6a16d58d1650676ab46319fb6f9f Description: Debug symbols for singularity-container Build-Ids: 06a430b2159c7e4c6d1f0200b8b902928a472cbb 14fe9b187712435eb9c3e061fcc006381bf0cffd 236030a3a062e2391b8de7ce4d3c0803ae8fdf7b 47a535937505cb1c5037a2a406946332809968b3 5f0267c4019dd3444d2257282211696fe8b2b7e9 79a198e48c58c0fbce7c596332f10aae7761b216 7c70472c9a814f3da16fb569df9178b27ca728e3 9d0da52ab5080e834e836aa929302b2e5b99fdad a3aa923760e367281aaa9cfcea3325f75791fd0b accf33d0a9927615a6ab031a0213215f74019fb1 b045893d52ff775cbf4c01e63842d12710d404a1 c04bd628c54a4f437b46e9161aa0aa891fe1fe12 c1499d94d2d635c8df0a874f3b27df92edb77285 c2a41d2d026f11f821bf1e6d77a5c0f765bc7308 cc6aefc60db498a789167a962922d169b93310ff ed087c90e21e1ee76e490f05a27527b5e16960bd f80e923c39e0fa4713370ed4c489a24a71b34181 fc7087a5a332ed43c9d2a7ea0552a08eafaddfd3