Package: datalad Version: 0.11.8-1~nd110+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 136 Depends: neurodebian-popularity-contest, python3-datalad (= 0.11.8-1~nd110+1), python3-argcomplete, python3:any Suggests: datalad-containers, datalad-crawler, datalad-neuroimaging Homepage: http://datalad.org Priority: optional Section: science Filename: pool/main/d/datalad/datalad_0.11.8-1~nd110+1_all.deb Size: 99536 SHA256: fd73d5aa3e1ab445b79cf6774182b52302db2d911f946c81f1819fdfa4556129 SHA1: 1348efd436113ed46f9aade60e4788997ad04111 MD5sum: cc7594e3721f573b93e8d3ffc8c311aa Description: data files management and distribution platform DataLad is a data management and distribution platform providing access to a wide range of data resources already available online. Using git-annex as its backend for data logistics it provides following facilities built-in or available through additional extensions . - command line and Python interfaces for manipulation of collections of datasets (install, uninstall, update, publish, save, etc.) and separate files/directories (add, get) - extract, aggregate, and search through various sources of metadata (xmp, EXIF, etc; install datalad-neuroimaging for DICOM, BIDS, NIfTI support) - crawl web sites to automatically prepare and update git-annex repositories with content from online websites, S3, etc (install datalad-crawler) . This package provides the command line tools. Install without Recommends if you need only core functionality. Package: psychtoolbox-3-common Source: psychtoolbox-3 Version: 3.0.15.20190725.dfsg1-1~nd110+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 253689 Depends: neurodebian-popularity-contest Recommends: alsa-utils, libgamemode0 Suggests: gnuplot Homepage: http://psychtoolbox.org Priority: optional Section: science Filename: pool/main/p/psychtoolbox-3/psychtoolbox-3-common_3.0.15.20190725.dfsg1-1~nd110+1_all.deb Size: 23936956 SHA256: 29e2f27b2049fa2cb86ec915eff212d90eeb80a575dadbcb911002aeb565ab01 SHA1: 8bf646abaf5fc75b3a0fa452bb08faa1340ca607 MD5sum: da5cef5d8aed50e4afc529ed4e656be4 Description: toolbox for vision research -- arch/interpreter independent part Psychophysics Toolbox Version 3 (PTB-3) is a free set of Matlab and GNU/Octave functions for vision research. It makes it easy to synthesize and show accurately controlled visual and auditory stimuli and interact with the observer. . The Psychophysics Toolbox interfaces between Matlab or Octave and the computer hardware. The Psychtoolbox's core routines provide access to the display frame buffer and color lookup table, allow synchronization with the vertical retrace, support millisecond timing, allow access to OpenGL commands, and facilitate the collection of observer responses. Ancillary routines support common needs like color space transformations and the QUEST threshold seeking algorithm. . This package contains architecture independent files (such as .m scripts) Package: python3-datalad Source: datalad Version: 0.11.8-1~nd110+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 4573 Depends: neurodebian-popularity-contest, git-annex (>= 6.20180913~) | git-annex-standalone (>= 6.20180913~), patool, python3-appdirs, python3-distutils | libpython3-stdlib (<= 3.6.4~rc1-2), python3-fasteners, python3-git (>= 2.1.6~), python3-humanize, python3-iso8601, python3-keyrings.alt | python3-keyring (<= 8), python3-secretstorage, python3-keyring, python3-mock, python3-msgpack, python3-pil, python3-requests, python3-simplejson, python3-six (>= 1.8.0), python3-tqdm, python3-wrapt, python3-boto, python3-chardet, python3:any Recommends: python3-exif, python3-github, python3-jsmin, python3-html5lib, python3-httpretty, python3-libxmp, python3-lzma, python3-mutagen, python3-nose, python3-pyperclip, python3-requests-ftp, python3-vcr, python3-whoosh Suggests: python3-duecredit, python3-bs4, python3-numpy, datalad-containers, datalad-crawler, datalad-neuroimaging Homepage: http://datalad.org Priority: optional Section: python Filename: pool/main/d/datalad/python3-datalad_0.11.8-1~nd110+1_all.deb Size: 950208 SHA256: 5fb4ffa2870104b6c8b1c28fa7326f249d031f584601a64de9e7a2c406cf8adb SHA1: b3bf1b1158420775bc3b8a20b83f5f63da63834d MD5sum: 541528ee6f7510582994877349520cf5 Description: data files management and distribution platform DataLad is a data management and distribution platform providing access to a wide range of data resources already available online. Using git-annex as its backend for data logistics it provides following facilities built-in or available through additional extensions . - command line and Python interfaces for manipulation of collections of datasets (install, uninstall, update, publish, save, etc.) and separate files/directories (add, get) - extract, aggregate, and search through various sources of metadata (xmp, EXIF, etc; install datalad-neuroimaging for DICOM, BIDS, NIfTI support) - crawl web sites to automatically prepare and update git-annex repositories with content from online websites, S3, etc (install datalad-crawler) . This package installs the module for Python 3, and Recommends install all dependencies necessary for searching and managing datasets, publishing, and testing. If you need base functionality, install without Recommends. Package: python3-diskcache Source: diskcache Version: 4.0.0-1~nd110+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 209 Depends: neurodebian-popularity-contest, python3:any Homepage: http://www.grantjenks.com/docs/diskcache/ Priority: optional Section: python Filename: pool/main/d/diskcache/python3-diskcache_4.0.0-1~nd110+1_all.deb Size: 33652 SHA256: 4064bff48742469ce85a20859ec0e3fa9a2220c83fa7458356ca03543743e37d SHA1: 11acb0ba5f0c5588d333a90ad131485016397e58 MD5sum: 496229e62a516f908815a054921c14ae Description: Python module for Disk and file backed persistent cache DiskCache is an Apache2 licensed disk and file backed cache library, written in pure-Python. Its features include . - Django compatible API - Thread-safe and process-safe - Supports multiple eviction policies (LRU and LFU included) - Keys support “tag” metadata and eviction Package: python3-hdmf Source: hdmf Version: 1.1.2-1~nd110+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 392 Depends: neurodebian-popularity-contest, python3-dateutil, python3-h5py, python3-numpy, python3-pandas, python3-requests, python3-ruamel.yaml, python3-six, python3:any, python3-certifi, python3-chardet, python3-idna, python3-urllib3 Homepage: https://github.com/hdmf-dev/hdmf Priority: optional Section: python Filename: pool/main/h/hdmf/python3-hdmf_1.1.2-1~nd110+1_all.deb Size: 63672 SHA256: ae1c5f98e5e75b469c43f879721ab57561b69b14086575b455bebafff545caf4 SHA1: b0629cd131b4672d0f7c2d072f9e7215a462ca62 MD5sum: 64e5974e6b2358f9b349259f637437e5 Description: Hierarchical Data Modeling Framework The Hierarchical Data Modeling Framework (HDMF) is a Python package for working with hierarchical data. It provides APIs for specifying data models, reading and writing data to different storage backends, and representing data with Python object. Package: python3-neo Source: neo Version: 0.7.2-1~nd110+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 3485 Depends: neurodebian-popularity-contest, python3-numpy, python3-quantities, python3:any Recommends: python3-scipy (>= 0.8~), python3-tables (>= 2.2~), libjs-jquery, libjs-underscore Suggests: python3-nose Homepage: http://neuralensemble.org/trac/neo Priority: optional Section: python Filename: pool/main/n/neo/python3-neo_0.7.2-1~nd110+1_all.deb Size: 1309360 SHA256: a648f225bf07f9678c0d5a16f333d1bf2a7dddda7b5b0906a0ffe4929cee3bd7 SHA1: 44734afa69009303db86e70925e6f9cc8cc0daad MD5sum: 66958ad49e2f13d1d97955f8f1ec887e Description: Python IO library for electrophysiological data formats NEO stands for Neural Ensemble Objects and is a project to provide common classes and concepts for dealing with electro-physiological (in vivo and/or simulated) data to facilitate collaborative software/algorithm development. In particular Neo provides: a set a classes for data representation with precise definitions, an IO module with a simple API, documentation, and a set of examples. . NEO offers support for reading data from numerous proprietary file formats (e.g. Spike2, Plexon, AlphaOmega, BlackRock, Axon), read/write support for various open formats (e.g. KlustaKwik, Elan, WinEdr, WinWcp, PyNN), as well as support common file formats, such as HDF5 with Neo-structured content (NeoHDF5, NeoMatlab). . Neo's IO facilities can be seen as a pure-Python and open-source Neuroshare replacement. Package: python3-pynwb Source: pynwb Version: 1.0.3-1~nd110+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 424 Depends: neurodebian-popularity-contest, python3-chardet, python3-dateutil, python3-h5py (>= 2.7.1), python3-hdmf, python3-numpy, python3-pandas, python3-ruamel.yaml, python3-six, python3:any Homepage: https://github.com/NeurodataWithoutBorders/pynwb Priority: optional Section: python Filename: pool/main/p/pynwb/python3-pynwb_1.0.3-1~nd110+1_all.deb Size: 60480 SHA256: 0bfbc0e279e515a0ce05da671d3f5c2cbebddcc4f26d91f2b5d31c5d6eb55c75 SHA1: c2a15401e2a757cd6d9f802532f7ed53af6df4bf MD5sum: f124f2fbebd81f8c8c69dca1a32a1d95 Description: Python library for working with Neurodata in the NWB format PyNWB is a Python package for working with NWB files. It provides a high-level API for efficiently working with Neurodata stored in the NWB format. . Neurodata Without Borders: Neurophysiology (NWB:N) is a project to develop a unified data format for cellular-based neurophysiology data, focused on the dynamics of groups of neurons measured under a large range of experimental conditions. Package: python3-quantities Source: python-quantities Version: 0.12.3-1~nd110+1 Architecture: all Maintainer: NeuroDebian Maintainers Installed-Size: 354 Depends: neurodebian-popularity-contest, python3-numpy, python3:any Suggests: python3-unittest2 Homepage: http://packages.python.org/quantities/ Priority: optional Section: python Filename: pool/main/p/python-quantities/python3-quantities_0.12.3-1~nd110+1_all.deb Size: 57624 SHA256: 04af7004229d545ef71e61da555608447eb34882c890c120be6c0877321cad03 SHA1: e2461fd99e38f6dddbc79963a0844cecd49e3e35 MD5sum: 3d4cee6b7093dac06d5a3e2cee06a000 Description: Library for computation of physical quantities with units, based on numpy Quantities is designed to handle arithmetic and conversions of physical quantities, which have a magnitude, dimensionality specified by various units, and possibly an uncertainty. Quantities builds on the popular numpy library and is designed to work with numpy ufuncs, many of which are already supported.