Skip to main content

A package for standardizing hierarchical object data

Project description

The Hierarchical Data Modeling Framework, or 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.

Documentation of HDMF can be found at https://hdmf.readthedocs.io

Latest Release

https://badge.fury.io/py/hdmf.svg https://anaconda.org/conda-forge/hdmf/badges/version.svg

Build Status

Linux

Windows and macOS

https://circleci.com/gh/hdmf-dev/hdmf.svg?style=shield https://dev.azure.com/hdmf-dev/hdmf/_apis/build/status/hdmf-dev.hdmf?branchName=dev

Conda

https://circleci.com/gh/conda-forge/hdmf-feedstock.svg?style=shield

Overall Health

https://github.com/hdmf-dev/hdmf/workflows/Run%20coverage/badge.svg https://codecov.io/gh/hdmf-dev/hdmf/branch/dev/graph/badge.svg Requirements Status Documentation Status

Installation

See the HDMF documentation for details http://hdmf.readthedocs.io/en/latest/getting_started.html#installation

Code of Conduct

This project and everyone participating in it is governed by our code of conduct guidelines. By participating, you are expected to uphold this code.

Contributing

For details on how to contribute to HDMF see our contribution guidelines.

Citing HDMF

@INPROCEEDINGS{9005648,
  author={A. J. {Tritt} and O. {Rübel} and B. {Dichter} and R. {Ly} and D. {Kang} and E. F. {Chang} and L. M. {Frank} and K. {Bouchard}},
  booktitle={2019 IEEE International Conference on Big Data (Big Data)},
  title={HDMF: Hierarchical Data Modeling Framework for Modern Science Data Standards},
  year={2019},
  volume={},
  number={},
  pages={165-179},
  doi={10.1109/BigData47090.2019.9005648}}

LICENSE

“hdmf” Copyright (c) 2017-2021, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy). All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

  1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

  2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

  3. Neither the name of the University of California, Lawrence Berkeley National Laboratory, U.S. Dept. of Energy nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

You are under no obligation whatsoever to provide any bug fixes, patches, or upgrades to the features, functionality or performance of the source code (“Enhancements”) to anyone; however, if you choose to make your Enhancements available either publicly, or directly to Lawrence Berkeley National Laboratory, without imposing a separate written license agreement for such Enhancements, then you hereby grant the following license: a non-exclusive, royalty-free perpetual license to install, use, modify, prepare derivative works, incorporate into other computer software, distribute, and sublicense such enhancements or derivative works thereof, in binary and source code form.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

hdmf-2.5.1.tar.gz (270.7 kB view details)

Uploaded Source

Built Distribution

hdmf-2.5.1-py2.py3-none-any.whl (163.2 kB view details)

Uploaded Python 2 Python 3

File details

Details for the file hdmf-2.5.1.tar.gz.

File metadata

  • Download URL: hdmf-2.5.1.tar.gz
  • Upload date:
  • Size: 270.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.8.8

File hashes

Hashes for hdmf-2.5.1.tar.gz
Algorithm Hash digest
SHA256 a4163bbab7029a2df6012e52fc9156afe1303f7b7d880faa89b6db1fe9fc9823
MD5 f3c39c14e7059316162ae9a5d66e30b5
BLAKE2b-256 f82bcedfccb322153ade0d03b10feae5d3a48e383eea751680da434edea677cc

See more details on using hashes here.

File details

Details for the file hdmf-2.5.1-py2.py3-none-any.whl.

File metadata

  • Download URL: hdmf-2.5.1-py2.py3-none-any.whl
  • Upload date:
  • Size: 163.2 kB
  • Tags: Python 2, Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.60.0 CPython/3.8.8

File hashes

Hashes for hdmf-2.5.1-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 1b3c6ced67fbdd7893b66e474ea8b9ae74a84b4e1edcc59089052b4c5d403a9c
MD5 f3c87277d7600c5be18154c417ab02af
BLAKE2b-256 0a7a58bc4a8d395632dd8b3a49a432f738f8e4259fee9ae30c0dbec88096825d

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page