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: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 521 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), 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_amd64.deb Size: 129550 SHA256: 2afa7480898262c0d73e0ed4e3d9f16a14442551ffa42c8c3005dfedc48a200c SHA1: f8f7f790bb733a042b40a8eecf5349e0b4b4f558 MD5sum: f63d45a7625ec0b7b249753a49e5ce09 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: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 552 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_amd64.deb Size: 514016 SHA256: ab0d5cbd5a715e08d083eb959092801ab10fbdaa207c7dcd4c1f9a91917eb5c7 SHA1: 4b2aea1ed1fc076e33fd24c590012db28cd8bc92 MD5sum: 4200381110e62c0b06da37c177fe6e46 Description: Debug symbols for dcm2niix Build-Ids: b37c3cad0cdc8a0fd48c576db9c99e571d91e9cc bcb3f4d86d4f4b5ad1b9455f52916af615e0cb2d 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: 6.20170815+gitg22da64d0f-1~ndall+1 Architecture: amd64 Maintainer: Richard Hartmann Installed-Size: 164681 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_6.20170815+gitg22da64d0f-1~ndall+1_amd64.deb Size: 34678238 SHA256: dbe47a070910f4799ab8f5a74f19c077f2ed273f2e5cdcb846cbc6efcf3e39c7 SHA1: 7f171771a7248bd6bc9e5581a056abbd82e37382 MD5sum: 5720867472e09eff33e097caa691b75a 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-dipy-lib Source: dipy Version: 0.12.0-1~nd100+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 9425 Depends: neurodebian-popularity-contest, python-numpy (>= 1:1.10.0~b1), 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_0.12.0-1~nd100+1_amd64.deb Size: 1838284 SHA256: 2fd1f4e4b21eb4b1533534bcf22bf1e06d2d86d67d504b70bfe4576269cfec89 SHA1: 3f6bbcd4544e2c0fcace89c6562b4b8807df9798 MD5sum: c45ce7906f502ddd4f7b1ddc1dd61c0e 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-dipy-lib-dbgsym Source: dipy Version: 0.12.0-1~nd100+1 Auto-Built-Package: debug-symbols Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 8938 Depends: python-dipy-lib (= 0.12.0-1~nd100+1) Homepage: http://dipy.org Priority: extra Section: debug Filename: pool/main/d/dipy/python-dipy-lib-dbgsym_0.12.0-1~nd100+1_amd64.deb Size: 7875654 SHA256: 571110b0ad69355a796fcbe1f95bdda9a8655f08070d41e9e107287f0085dd36 SHA1: 6a5a1d3d479e5ecb7b640f1842ba69ce8293cb21 MD5sum: 6f4969776ac0a94b94aeab618c4a4d34 Description: Debug symbols for python-dipy-lib Build-Ids: 084921106b5b8faa27f343faaf140c16ec8678f1 19cabc86cae473b4cc6f27e35ad4955a99e0e120 2654b16b4a107f245efa95d91c55c3b0f0c23f50 330e3502253124f857db1449331885d0b58922d9 347dff78a7ad89732a21d04cc3b99527c8ce6615 41367730e19a41a37e3e3fb044c20bb459b63544 41a4205e8579b8f22efa5b4d05b8f67b3e376e7a 4e0df7106ad82088ee09bb10bc2cb95bb8ae6bb8 4f8375b9bdea7d2499c3100239cccf7d3dc0e037 5d5af17fa12f0f43afc59d51784168ac1486aef2 5e219c292f3c12b8ddccbcdb0f07a266b57f0b69 6c841d39a3ebf6a5be8da507becfa20832f96a16 73ec41e2b5980d6ae84a2f889f21d085eed2ee4a 76bbbbc5c25b227b4e5c6ae777eb35ab61be4aff 7c39b355f84f49d1c67528212c4cf827911749d7 7da43111a75e62068b773d04ecd9bb5e4f8ac3fe 7dafefece304e62c4d50cb909094cbfdda0578af 813c24638068f0584a97a878cedb62bd962b9b13 9112b3d28b6ae015c6e8c5c97b87a6ef39c5c36d 9541459a691bea8b001a6e740c8c1d4fec94e70a a713d0637a78c95d6ae827ca72119df680ee1491 afb427fc8cd4430e464a9ad634d8ccfc97b1cc16 b294d72781713ab0f20209b5cebf650a9856a9a6 b92235e71b78b179ba72a602c30fbb56235218ea ba3aecaeaa30b64e1cf9d9b4645f267286621c75 c63bfb8576e0058d7ee41c14c8180cfd972af216 d1d1caba67eab461b55a14803bca2925a09fd559 d1e1b4e2970084ff9fef0cc72a340b8f3239e5ae d23c02b2c0addf054292dc5a25ea6d77d2b2b615 dc3702d6c1107142e59b51846f2b14233e5add29 f4039adc4cc7fcb205316719cd7efa5254d7822d fedfe03eeda56918e5da926b323b89d29ae335bb Python-Version: 2.7 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: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2791 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), 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_amd64.deb Size: 670352 SHA256: 62a313621ac6e5dabf4f35e03e0a7724831c98d9415ec19d6a9fb163e0c86d97 SHA1: 53dfaef2001df466919245b538d55ce4121ffa6e MD5sum: 3e6b31a7641b8931d24eac8f0502b01a 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: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 3905 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), 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_amd64.deb Size: 682802 SHA256: 6f2d5e287898154fb0d7862878539d17319e776473326c4ba73db4715755b6e5 SHA1: 4247fbf7c6e85b44fdab1739beae9658d3ca67ad MD5sum: 0f46c93226229f496f33c5f54861a238 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: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1329 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_amd64.deb Size: 834228 SHA256: 699a17d636f27f246ff0f92b8adb000ec1112266fa6eb47594d09f52ad31d536 SHA1: 8c7aac974e1ca98d3f572e4b96210f1fcfb4d47f MD5sum: d15d90001d2859d2ec4c52a0d5395da0 Description: Debug symbols for python-nipy-lib-dbg Build-Ids: 09ba45fdcd86f08f96796f97dac0b9e287857a64 1b5fcd38768e7c5215929fe19ee5e443f2272b3f 1c610b73af32d90ac2125d7bd0c1f0fbf48510bd 2e84bfbf4d856fc5441c30dee09e935ab7994297 463ed27cd424511494e9c777ab1e83dc3b491324 4be5d6af8f018ae952002735c424dc2e3dca58db 4d2b6640c10a89c8c0f6d31f66fa16016437a0b7 5c64b716c33964f4d449b9e9ccbb6514b02015e6 679019872d069a8bd534ffc43bb4d6fb9b4380c5 8cd6898b978209594897b0c8eb43ec8168ddb134 9c75bc0f0dd6fbf0f8601a0d1c13857eb8a3c26a b8601e3a3c388464032595f2d8fd1a981174cc68 cafec2befcb390d436fe6f65da313754e98360f2 e7ab642246d5829b4641bff0e257bba977fef142 Python-Version: 2.7 Package: python-nipy-lib-dbgsym Source: nipy Version: 0.4.1-1~nd100+1 Auto-Built-Package: debug-symbols Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2470 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_amd64.deb Size: 2095602 SHA256: 7c00c8b7a4afff53bcd76f749a199b86dfc4f9866e647020eeffc98c92e2f405 SHA1: c3cac1770f1af32f2010c27fa27c2a42167268ef MD5sum: 804c04f61407092975b46779d9a0778a Description: Debug symbols for python-nipy-lib Build-Ids: 2ea6685e522c5ff4e975db65545efd9420286efa 317057c71b20b67f0d62e856fd6ba2715eb577b3 44ca14db2031e149d9d76aeeaa04b5b3e88ea25d 4b0d5e5a0c19b9cb0f52fec5d73f6d8f4919b67d 4ed6bc6b06c2be904bde1a300ff72bca2e482950 8817cb680f8b050e5fbc58f79fc00ab457e54983 8c2f2c11caca1f1f76e4c37dbaa474949849df04 a4fa83aae262319509626623cf360b5b3854e4db a69d2fd1d4b37d674bb9e9c56cd71c34c784f3a6 b69156b84f7fe91b70705570fc217babd0577bfa c44406a2c3853f703062ebf858951918c3a02a0c eb2a5c8aeafb547acf4151000a562791bb615e75 eeb6d29459bcc489e733d9bdb3223e33d43c3d56 fb758e06d453fa33605c667f14bcc975d5aa1d32 Python-Version: 2.7 Package: python-numexpr Source: numexpr Version: 2.6.2-1~nd100+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 421 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 (>= 2.14), libgcc1 (>= 1:3.0), 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_amd64.deb Size: 143358 SHA256: 203e3fd0408e66e840d654515710b7bfbdb1c38e5c1cfd553bd8a7e58d704047 SHA1: e6e6b174e4bc9cb69beccf254c134f19c8595fb2 MD5sum: fbf0ef8b1a6712e110286f462b075702 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: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 235 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_amd64.deb Size: 217418 SHA256: ed25053fd51dc9a0e5535c1c028ea3114d2a5529a23d4350b02e51f29b721b6d SHA1: 7461f89e0c11f9bde45210e97bc87855a918a7eb MD5sum: 1bfdcd26fd4c1526198e7b72e9063c45 Description: Debug symbols for python-numexpr Build-Ids: 5fcd2deeca3d2d50130bd5d47b663a56b5d51537 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-sklearn-lib Source: scikit-learn Version: 0.19.0-1~nd100+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 6664 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:3.0), libstdc++6 (>= 5.2), python-numpy (>= 1:1.10.0~b1), 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_0.19.0-1~nd100+1_amd64.deb Size: 1557390 SHA256: 7ff49b03c49693eb038b16ef3e2110810c8464f8658f71fa490872c0dc81c1a5 SHA1: 13a950c9f57ac12c3870feda6ab207fbc4a06e9c MD5sum: 79cdf6c7df4df556a833bea1566e19fc 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-sklearn-lib-dbgsym Source: scikit-learn Version: 0.19.0-1~nd100+1 Auto-Built-Package: debug-symbols Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 6905 Depends: python-sklearn-lib (= 0.19.0-1~nd100+1) Priority: extra Section: debug Filename: pool/main/s/scikit-learn/python-sklearn-lib-dbgsym_0.19.0-1~nd100+1_amd64.deb Size: 5968578 SHA256: 8fddbee7109cb31b80a07906903513ea2a2e471647d4327c951ed2cce451ceab SHA1: e53582a26f12c3647d7711f6023055759cee39ee MD5sum: f4a76caea64fd2f2546495e0572d722f Description: Debug symbols for python-sklearn-lib Build-Ids: 04fe7dc3ce213206723ed3ec6596095f0a07959a 0fe17c4eb437e20edb736c2641c6f0c242ea79b7 1194d734fb3865ce5550a8afa315fa5d86cfa259 135e791ef84785b19207773418132b6cc2ab20e7 2d30503ae54c56e0014cea3b9b740a7275de43fa 3a75ed746a1498f0a819a7da74b96f9733f6edd2 3af181f8430a5f4496271905b3a3f2a6678c8615 3bd070a57a8599ff8205c068efb4c9a6de7dece2 3c71eb33e3724f1ef1bbc8b0ccc994da238cc124 3cf7ec9d92606e3895bc0c030e24c40835470cf5 411394459f530fd90af913e26070e3fd975b4784 45b4b3293e2760058b66abe4562540c8781f57aa 460e61f16d730ddffa3f939a884ba3d7ed1c7c3e 49f522a1782df1c69d2c3aa7e57c2238938a8c88 4c49d657de772dc7071522f038094955a4c0731d 4fea68d8b5e6fed91f274141a4a4f08553605494 69391190ba1fe258146fdea323f689b6d9110bd0 6c2a8054fef82315336af7bdfb8660646e81637d 74786debc7410a6f16fbcdaf25ed7857680485fd 74a0504983d6cadbffae498e5bd2e045a61d915c 7cce80d7fe19505ee82d4614d4478ebe01a286f4 8cdc4d5126b79e697cadd88ec3be373a1a4e0c86 923933ea21a4452ee8b85af8b846fc66fe87b26e 93b96cfab1bd3590de4e5071c44c7c58477272a1 a26553efc58a7692ae0a46c0acfa6f47e1575473 a35368f7925d2aa2c5859a8bc989c34131ac6a3f a408d9834de577c115f6701d94bfc11ae4095075 a966e4416f2a777692d3c3d6de64443dfa0086fe aaef13cdbae4d998bd3255a07230aefbb9b143e2 c9f734ca39dc858b16e1bae3300e5559507757a4 cc9f3efe45dc7dd02dd006ee9815637da7b2b39d d7f87eed5f2243fc1f3d00f9a723f284ebaa0b1c e41127fc5c0ccb1a9bbf5062a718cda5bd46dbd1 e4178deca77e6dd6623f68372c0d68142d2f3e75 e750c6b2bdc8a6563f5a285d6745ad18a7e7dbb9 ea0d843423329713bbe3f7048f92d736b7f63fee ea9eab403c244f9064d3208eccebc39f6abac1e8 eefd5825e27c3f5dc2a0ba2d0091fe1ae0b0389d f0d32fab760860d78c0ec58e4d3574404b6b8b2c f19487f5586fc41899db2880115a9decb73931e1 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: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 668 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 (>= 2.14), libgcc1 (>= 1:3.0), 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_amd64.deb Size: 138624 SHA256: 64769e58aae94ac0938f2c6845f2beff00e18abcdbd9e6e090a0ea4b95bef395 SHA1: 73c12ebd17f4cf014121071c16b59eca183cd467 MD5sum: 9b813eeac0b56e75ab0b8e77dbecd3e6 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: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 468 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_amd64.deb Size: 355020 SHA256: cd21f46bb867d567dba14068f248d2c5fd0d795f5828d070e3f8ac2748f5f445 SHA1: 114360c008c9a077b13f657a2bf4420f72791549 MD5sum: c7e9feb7ca04bacbc616659cbf0f60e6 Description: Debug symbols for python3-numexpr Build-Ids: 2a94e986f625abe46e09b434b24c0fb324b60777 ee327011dff36590c0763f731b7f92269acea872 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-sklearn-lib Source: scikit-learn Version: 0.19.0-1~nd100+1 Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 12297 Depends: neurodebian-popularity-contest, libc6 (>= 2.14), libgcc1 (>= 1:3.0), libstdc++6 (>= 5.2), python3-numpy (>= 1:1.10.0~b1), python3-numpy-abi9, python3 (<< 3.7), python3 (>= 3.5~) Homepage: http://scikit-learn.sourceforge.net Priority: optional Section: python Filename: pool/main/s/scikit-learn/python3-sklearn-lib_0.19.0-1~nd100+1_amd64.deb Size: 2142392 SHA256: f5e480816b49652e45b51f7aab84b8c7af0808fd1c386f159ab22bfee4397967 SHA1: a63786d77911857324139d61e1f3cbac84757656 MD5sum: bcdc964e83a7a5d7668683abf262816a 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-sklearn-lib-dbgsym Source: scikit-learn Version: 0.19.0-1~nd100+1 Auto-Built-Package: debug-symbols Architecture: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 14182 Depends: python3-sklearn-lib (= 0.19.0-1~nd100+1) Priority: extra Section: debug Filename: pool/main/s/scikit-learn/python3-sklearn-lib-dbgsym_0.19.0-1~nd100+1_amd64.deb Size: 12219144 SHA256: 3c5ae86f382b747800dd9b4b1ff95fa331eafedc59813ceb638414eaa3322049 SHA1: cfe74fecff26be7f1439bbc00e16591b3caea092 MD5sum: e77c910fdb4f3cfee90a9d69a8e89da2 Description: Debug symbols for python3-sklearn-lib Build-Ids: 02eda2cc78155a66f5af4f15f4a19a2ffa46e131 055d94f5509de5295c0454ec149b3f775119e432 08b30e77abdf1dd2626e569a8f6f8cd145cbda87 11a62da21ae9832fa101debf590c528c9a553fe8 1606fd8609c5bf868202c8e3c721a1e9481c9a13 1cf848670265779685fedaa7397fbfe7c708ddf8 22f5f039d346d9146cdfd6f08dcc094797de540b 23f7b5d6de083345acea1324929181e7a835e16d 2534a9bb2208ba004d85b57f4a3cb0eba3bdd05a 29b061bee812d477b8906cfaddaedd860f4614c3 2cf3789c6e6d34e34e2c1f5ff6abc5e3243b8414 2ea5164218deca8079f4449cb0892ce8042c2f06 30b63b167906adf835dd2372a0a944783a5cf97b 347a614a0d157cf91a5cc0dc43b644627c5b8fcc 3711659e422eda2273cbf04626249564dd25dde2 3a2ff46c9fc217818eb9a73e6f7e403f80d0f2dd 3c9f5d1b6969e4bb134b9a69c2f34da7732a0642 3d5c8577928243438a34635c98f2a71276b0b632 3fe34675c5761638dad466b0b659e6f4acd0423e 4813530166c4e74177c7ae6ba6ce709faad4b7b2 487cce61436bf277a2cbef10e7cdecca1eda9d39 4a2ebccf46cefd3a8a8013e7f188f955b78cac4e 4b9e8afbb82ebdebab5c10994d6ca32ddf623a30 4e49909e0dee30db0ddfddf90b9fd9a106333106 573d99fbd62b51ebdfe55b0904849e091c2e60b8 58ed759756fc8b837dcfbbb81412e452c7d3cdc1 5c14d0d29b7cb17688193962ee668182db6a4503 5e71021aa45125bcce156d9048cb9490cfd5a408 62d67f686c7d23e9eee09e055413afae28f890ce 62e918e9a902cedfeacfff2ac3f7c88228362736 62ffde5a440a976041d748b8b3ab75d646243996 6589ea54feb57bb092f4d1476279a8419eab4238 678d3994a95793f19484c457b0eff67e011697c7 685162149a1c79e30f72edcf45e10a2744ef9e13 696c46f3b545ecf3635a9fd5d4fe97ad60efb0e2 6d39b07c2b973b3839e528ffe69baded526a4d12 6fbeffa7978a9848042040ed0c5d26f29461a3fb 72c4c08e4fd4280851b4e7821c074c2dda3806e8 7423406e9b3da6dd516785175b1633f6a380af99 762d42d37f8f31011c4026f048ec0a195844be9f 769e373e17417c28909d81df8201ad5cae3d1554 7b35924f2060c67614742b4dcb53079a19845323 7bf92aca1788287f2f33952a5f9ca4396fe921ed 8053e6baad62fc1fb1c98f127676df55996fe2d2 80c2db14713743826163bc4d62a615eaf47645da 8550ebf64d5851ebdb07c8d68124e87ce8e5f3a0 86db95973265701ab7a36506f99303a2e91b9e65 8e38831343e8bd76783f4f16723de49a26085aab 8f831ac4a761c352dff3b208aa5f9e48e153fd2f 917b2abccfb2a86bb1eaa98239967e958701e12f 95c78331e7e93837d34677faa2420db474f74a9c 9dea1939f3788e7bcdc941da86b370f518411602 a2eccd45aef125631ba0660812ae7509d72898ca a7a13ac19afa6f0cff237fe37c4a0d79c2b76055 ae72506c2de3730a36df2482a1bc5e35efb62da3 b39e20200fa5df81af7877a7c1bef9f333f50d36 b65483064ed336d4da41a48c2ccb62c57fa706b1 b706bce496c9f9d8639b9b6924d046eb05998934 ba165eaf35604369de59771abbf420fb02e23bee bcfa515f3f262faf1bf7b196b73a4316e3aca138 bd36c58ba4d2759e2a8ee2fbc631fcbefe43dd27 bec49b559b4b316c7c70e9e6e07a3541bb36b5a4 c653f2559744fffb06c4a1a57439ec30156dcd0f c8cb0b384f6785ba44424fd151927b7a3a309a89 c9f8a05bac118a1f9dee777b324c6710c2a7befb cc9f8bc9dae8b4ad783c40e419ce65d9f04dab2b d30d5f770cd1f8522622ee903aa202a5d5a8bd3c d44e30ad9c3e423fc7ac200a26a0c0045abe39ed d461162dd7557204bb2d702e7493cdce86268511 d5c2c17f6be6185e92478cedb4148e4cbafdba58 d5fe047d9ebb7891724e24638cfe0a537292f659 d917e6ca7cd060b402192e51cd46fbdaf13ef53c e8cc1d10460b3dc8b8c2e11919e81f5b33eb4bdd e917b4bcb0f20710ca66e4c972e8ba1043a3d37d eaf09f69418b8f1b0f3adcec6449f917bcb35221 ed5d8301fe1785fce1cbb3cb8debe0b5715f90ae f65602efc5ed26be0b6c80880f0fc752cd81a018 f6dbc48c1a374532e1a96712aaf66fad5e172511 f75e888165c07bc4389cab43d5557bb49f520d66 fe8623379283e711aaa1d381b91f58074eacca24 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: amd64 Maintainer: Debian Go Packaging Team Installed-Size: 11599 Depends: libc6 (>= 2.3.2) 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_amd64.deb Size: 3007896 SHA256: 411f7f77ac00f6acb4d56085b8b7885c67dd40c559eec7ea5878124056caaf65 SHA1: 750dd2b0df62b311b2127a6360f29cc381cf514c MD5sum: a67712b0ea9da6855adea4cbd071999e 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: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 2344 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_amd64.deb Size: 287884 SHA256: f9556a761214ced542d3643055001c3e8c17d1c4607b952651b9df4309e8fdbf SHA1: c058ebfe0df8206a85c9f01324c1a0853b45f7ab MD5sum: 0ba0d25171895df9b60b912174663183 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: amd64 Maintainer: NeuroDebian Maintainers Installed-Size: 1292 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_amd64.deb Size: 978708 SHA256: cdea1a576f60bdbb1be38b76c12cd286c0f5321af334a48d6aacee5edede0c14 SHA1: 21781858bf5b7e18ba0b2df4df4c47149d117384 MD5sum: 3a14f7d084d83adbef84c5d5acce2955 Description: Debug symbols for singularity-container Build-Ids: 12eb41ad9114b19ae29630848b7b941d55db2d79 18d70a84518384c92360b54a8afda353857dc431 22328b8f314890a229f5b02ff816b7ce1fc63915 3b941ee4d30ac2d22febe3e9d815f47c6ba4ec84 41696d55bd3c34f40baefcaff5dda566db60fddb 48afac9ba230c633a93ce0c6582f5dbcb0c191c7 503f60097851a0e2aa8179fa10e270bda9367704 775da4242304774e57066052d162cad39d1c00fa 7841fe57730e427c1fb277bbda9718798f087ecd 78fd3d81ddfdaea2fc2d24d0fe34fa63ec0e863b 841ee7d8c6d54a608dce536fc1b703fb09f0b99e 9b25563dcaeeacc6f47c534cab2a0e9fe94b6c24 9fe90f611d1a3d17c520824feb97f0700a2012e4 a6f400baa6bb03a0b7b80088fd2ff2803fd865a2 c4a18c1df737848e8ca669aef2985ca5c8cbee24 d8db5e74cda99a83825e0238819d7b8947de9d9e d9f2150cff2b8a0f8b2191fdf5fc4f0210e2fc2c fde93bd4564848e1c1689afaaa41ffe7a67c03d4