Package: datalad Version: 0.7.0-2~nd100+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 98 Depends: neurodebian-popularity-contest, python-argcomplete, python-datalad (= 0.7.0-2~nd100+1), python:any Homepage: http://datalad.org Priority: optional Section: science Filename: pool/main/d/datalad/datalad_0.7.0-2~nd100+1_all.deb Size: 66286 SHA256: 4b5babb074a4688a19c483255b2bca4655041c7097037f865b1faa051a36fe85 SHA1: 6c9587036437c32d1d64d04025cfe0045b1d0830 MD5sum: 6b8979d628b568fdcbb1f17a8d9f49ea 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: nuitka Version: 0.5.26.4+ds-1~nd100+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3461 Depends: neurodebian-popularity-contest, gcc (>= 5.0) | g++ (>= 4.4) | clang (>= 3.0), scons (>= 2.0.0), 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_0.5.26.4+ds-1~nd100+1_all.deb Size: 683878 SHA256: 975a385b58daec07bee9c2782221b94a245474647ea72a763999ff36e54f5e2d SHA1: 7f68313131ebb516d9814a69c7046a77bca1cc76 MD5sum: 4c75d70c942c58d3092c74e2b515881b 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: 0.7.0-2~nd100+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3650 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_0.7.0-2~nd100+1_all.deb Size: 707520 SHA256: afc01141c5d52675a8e324df201745562f90a7c0bc60d06b06ef90346638491b SHA1: 5e5d058b091b8324853ff26f630f8a31b31b4393 MD5sum: 9135212fc9154b6cf61180b027419ae6 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-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-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