hdmf-zarr

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hdmf-zarr

The hdmf-zarr library implements a Zarr v2 backend for HDMF as well as convenience classes for integration of Zarr with PyNWB to support writing of NWB files to Zarr.

Status: The Zarr backend is under development and may still change. See the overiew page <https://hdmf-zarr.readthedocs.io/en/stable/overview.html>_ for an overview of the available features and known limitations of hdmf-zarr.

Support for Zarr v3 is planned. You can track progress of the support in https://github.com/hdmf-dev/hdmf-zarr/issues/202.

Documentation Status

Latest release:

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Dev branch:

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CI / Health Status

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If you use HDMF or hdmf_zarr in your research, please use the following citation:

• A. J. Tritt, O. Ruebel, B. Dichter, R. Ly, D. Kang, E. F. Chang, L. M. Frank, K. Bouchard, "HDMF: Hierarchical Data Modeling Framework for Modern Science Data Standards," 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, 2019, pp. 165-179, doi: 10.1109/BigData47090.2019.9005648.
• HDMF-Zarr, RRID:SCR_022709

Documentation

See the hdmf-zarr documentation for details: https://hdmf-zarr.readthedocs.io

Usage

The library is intended to be used in conjunction with HDMF. hdmf-zarr mainly provides with the ZarrIO class an alternative to the HDF5IO I/O backend that ships with HDMF. To support customization of I/O settings, hdmf-zarr provides ZarrDataIO (similar to H5DataIO in HDMF). Using ZarrIO and ZarrDataIO works much in the same way as HDF5IO. To ease integration with the NWB data standard and PyNWB, hdmf-zarr provides the NWBZarrIO class as alternative to pynwb.NWBHDF5IO. See the tutorials included with the documentation for more details.