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.
In addition to the python package, we also provide an R package
that automatically generates and wraps a python environment with
ez_zarr
and all dependencies, available at https://github.com/fmicompbio/ezzarr.
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:
pip install ez-zarr
The current (development) ez_zarr
can be installed from github.com using:
pip install git+ssh://[email protected]/fmicompbio/ez_zarr.git
Alternatively, you can install ez-zarr
from the conda-forge
channel using:
conda install -c conda-forge --override-channels ez-zarr
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.
If you run into problems when using ez_zarr
, please first check whether the
answer is available in the help pages for the individual functions underneath 'Documentation' or in
the 'Getting started' vignette.
If not, please open an issue
and explain your problem. Try to provide a reproducible example, and always
include the code you used, that will make it much easier for us to help.
If you would like to contribute to ez_zarr
, you can do so by sending a pull
request to this repository. If the contribution involves changes in the
functionality provided by ez_zarr
, we encourage you to first open an issue
to discuss the intended contribution.