The aim of ez_zarr
is to provide easy, high-level access
to OME-Zarr filesets (high content screening microscopy data, stored
according to the NGFF
specifications in OME-Zarr with additional metadata fields, for
example the ones generated by the Fractal platform).
The goal is that users can write simple scripts working with plates, wells and fields of view, without having to understand how these are represented within an OME-Zarr fileset.
You can use ez_zarr
from the command line to get information about an OME-Zarr fileset:
ez_zarr tests/example_data/plate_ones.zarr
or from within python to get access to all its functionality:
## import module
from ez_zarr import ome_zarr
## open an Image
img = ome_zarr.Image('tests/example_data/plate_ones_mip.zarr/B/03/0')
img
# Image 0
# path: tests/example_data/plate_ones_mip.zarr/B/03/0
# n_channels: 2 (some-label-1, some-label-2)
# n_pyramid_levels: 3
# pyramid_zyx_scalefactor: [1. 2. 2.]
# full_resolution_zyx_spacing (micrometer): [1.0, 0.1625, 0.1625]
# segmentations: organoids
# tables (measurements): FOV_ROI_table
## legacy objects from `hcs_wrappers`
from ez_zarr import hcs_wrappers
plate_3d = hcs_wrappers.FractalZarr('tests/example_data/plate_ones.zarr')
plate_3d
# FractalZarr plate_ones.zarr
# path: tests/example_data/plate_ones.zarr
# n_wells: 1
# n_channels: 2 (some-label-1, some-label-2)
# n_pyramid_levels: 3
# pyramid_zyx_scalefactor: {'0': array([1. 2. 2.])}
# full_resolution_zyx_spacing: [1.0, 0.1625, 0.1625]
# segmentations:
# tables (measurements): FOV_ROI_table
A more extensive example is available from here, also available as an ipynb notebook.
The release version of ez_zarr
can be installed using:
pip install ez-zarr
The current (development) ez_zarr
can be installed from github.com using:
pip install git+ssh://git@github.com/fmicompbio/ez_zarr.git
ez_zarr
is released under the MIT License, and the copyright
is with the Friedrich Miescher Insitute for Biomedical Research
(see LICENSE).
ez_zarr
is being developed at the Friedrich Miescher Institute for
Biomedical Research by @silvbarb, @csoneson and @mbstadler.