Skip to main content

A hierarchical data modeling framework for modern science data standards

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 objects.

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

Overall Health

https://github.com/hdmf-dev/hdmf/actions/workflows/run_coverage.yml/badge.svg https://github.com/hdmf-dev/hdmf/actions/workflows/run_tests.yml/badge.svg https://github.com/hdmf-dev/hdmf/actions/workflows/codespell.yml/badge.svg https://github.com/hdmf-dev/hdmf/actions/workflows/ruff.yml/badge.svg https://github.com/hdmf-dev/hdmf/actions/workflows/check_sphinx_links.yml/badge.svg https://github.com/hdmf-dev/hdmf/actions/workflows/run_pynwb_tests.yml/badge.svg https://github.com/hdmf-dev/hdmf/actions/workflows/run_hdmf_zarr_tests.yml/badge.svg https://github.com/hdmf-dev/hdmf/actions/workflows/run_all_tests.yml/badge.svg https://github.com/hdmf-dev/hdmf/actions/workflows/deploy_release.yml/badge.svg https://codecov.io/gh/hdmf-dev/hdmf/branch/dev/graph/badge.svg Documentation Status

Installation

See the HDMF documentation.

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

  • Manuscript:

@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},
  note={}}
  • RRID: (Hierarchical Data Modeling Framework, RRID:SCR_021303)

LICENSE

“hdmf” Copyright (c) 2017-2024, 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-3.14.5.tar.gz (15.8 MB view details)

Uploaded Source

Built Distribution

hdmf-3.14.5-py3-none-any.whl (338.5 kB view details)

Uploaded Python 3

File details

Details for the file hdmf-3.14.5.tar.gz.

File metadata

  • Download URL: hdmf-3.14.5.tar.gz
  • Upload date:
  • Size: 15.8 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.12.6

File hashes

Hashes for hdmf-3.14.5.tar.gz
Algorithm Hash digest
SHA256 e25223f382c0471d492ce0ef92c0a72cf8143ec188d55a7a9931b6ffecf80d1c
MD5 e61a0fb61e77d5896e89af21553ce359
BLAKE2b-256 0cd2a68806ae7ff20f5f577e360623becffd5ef89d2f8a19c258dae370c86329

See more details on using hashes here.

File details

Details for the file hdmf-3.14.5-py3-none-any.whl.

File metadata

  • Download URL: hdmf-3.14.5-py3-none-any.whl
  • Upload date:
  • Size: 338.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.12.6

File hashes

Hashes for hdmf-3.14.5-py3-none-any.whl
Algorithm Hash digest
SHA256 b85835aed69b25fbe110b8bea03ad8f3e2126cc75c46cc4f064a1338373e6ff1
MD5 74323079715d5aa7772682e21e7dd7ab
BLAKE2b-256 92c13e194b35b63cec35304f673afecae6c48a983beb5b949324ec8c7ab33db8

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