Skip to main content

A lean and kind Open Microscopy Environment (OME) Next Generation File Format (NGFF) Zarr implementation.

Project description

ngff-zarr

PyPI - Version PyPI - Python Version Test DOI Documentation Status


A lean and kind Open Microscopy Environment (OME) Next Generation File Format (NGFF) Zarr implementation.

Features

  • Minimal dependencies
  • Work with arbitrary Zarr store types
  • Lazy, parallel, and web ready -- no local filesystem required
  • Process extremely large datasets
  • Multiple downscaling methods
  • Supports Python>=3.8
  • Implements version 0.4 of the OME-Zarr NGFF specification

Installation

To install the command line interface (CLI):

pip install 'ngff-zarr[cli]'

Documentation

More information an command line usage, the Python API, library features, and how to contribute can be found in our documentation.

See also

License

ngff-zarr is distributed under the terms of the MIT license.

Project details


Download files

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

Source Distribution

ngff_zarr-0.8.1.tar.gz (20.4 MB view details)

Uploaded Source

Built Distribution

ngff_zarr-0.8.1-py3-none-any.whl (38.5 kB view details)

Uploaded Python 3

File details

Details for the file ngff_zarr-0.8.1.tar.gz.

File metadata

  • Download URL: ngff_zarr-0.8.1.tar.gz
  • Upload date:
  • Size: 20.4 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.10.13

File hashes

Hashes for ngff_zarr-0.8.1.tar.gz
Algorithm Hash digest
SHA256 5bae124ab22ac110851767bdb6b5a9e1b82bd68fb10f5cf0026be65d6899c47b
MD5 dc084af7cf8b553e92f4777cd6d44d05
BLAKE2b-256 512c674abb3379803f8bb135af6a972e8cd882b3311b0076fb7362906e8da7fd

See more details on using hashes here.

Provenance

File details

Details for the file ngff_zarr-0.8.1-py3-none-any.whl.

File metadata

  • Download URL: ngff_zarr-0.8.1-py3-none-any.whl
  • Upload date:
  • Size: 38.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.10.13

File hashes

Hashes for ngff_zarr-0.8.1-py3-none-any.whl
Algorithm Hash digest
SHA256 804fdef45cc830866373c0eb4b46bf31d80b41d5922f47aafea8318480ea29c7
MD5 6ff886de2cc25e1538b642c34312a7a4
BLAKE2b-256 8a04f5bb0d15a14280107092b331722733ff85f5d8b5565e342dccd094854176

See more details on using hashes here.

Provenance

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