Package: datalad Version: 0.8.0-1~nd100+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 100 Depends: neurodebian-popularity-contest, python-argcomplete, python-datalad (= 0.8.0-1~nd100+1), python:any Homepage: http://datalad.org Priority: optional Section: science Filename: pool/main/d/datalad/datalad_0.8.0-1~nd100+1_all.deb Size: 67728 SHA256: 08ede52d59f90e6a813a7d21439937528918355a048b6084a8c9552c3e8e804a SHA1: ed80131d6d985ccd2e43e2e5e9440ff4237ab399 MD5sum: 5c4ff8d71a0099423187e0618f15dea5 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: 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: 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: 0.8.0-1~nd100+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3700 Depends: neurodebian-popularity-contest, git-annex (>= 6.20160808~) | git-annex-standalone (>= 6.20160808~), 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_0.8.0-1~nd100+1_all.deb Size: 716930 SHA256: 043e2018114e41723b4a9ee71c827149d9fecd543ff4e459c2eccb5b5c0dd704 SHA1: 31cd5d16e4fb5ab74f52be0d5e17d9508ae2c0ce MD5sum: c88cb88c398723695ab825ef9f2c7dcd 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-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-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